Standards-based, tastefully opinionated, rock-solid financial model templates for forecasting and fundraising for any type of business. 2026-01-05T00:00:00Z https://foresight.is/ Taylor Davidson [email protected] 2014-07-22T00:00:00Z https://foresight.is/employee-equity/ View Employee Equity 101 on Slideshare In June 2014 I gave a talk at Venture for America at their summer training camp in Providence, RI. In the slides above (and on Slideshare), I covered the basics of understanding: How venture funding works and how employee equity is created How options are created and valued The philosophical and tactical reasons why employee equity exists Considerations around how to structure equity plans Data and resources to benchmark and value equity plans The topic of employee equity is personally important to me because it's one that is difficult for people to talk about, isn't discussed openly, and it's hard for people to learn about. I know I've made mistakes in structuring equity in the past, and I'd like to help others from doing the same. You can view the slides at Employee Equity 101, on Slideshare. Resources I introduced and leveraged in the talk: AngelList CrunchBase Visualization of dilution Fed Wilson’s Cap Table Template Mark Suster Cap Table and Valuation Anonymous Startup Salaries Wealthfront Buffer and Open Equity Formula Salary Negotiation: Make More Money, Be More Valued by Patrick McKenzie An Introduction To Stock & Options by David Weekly and video Questions, contact me anytime. 2015-07-30T00:00:00Z https://foresight.is/assumptions/ If you're like 90% of the entrepreneurs I talk to, your first question when you start building your financial model is "what do I input as my assumptions?" Building forward-looking projections for uncertain businesses is by nature difficult: how do we predict how people will adopt or use something that doesn't exist? And without good assumptions, isn't my model useless? There's a couple ways to address this: Research: Use the past performance of other companies as data points for your assumptions. Thankfully, there is more and more data coming to open circles that you can use to benchmark your assumptions: every startup post-mortem has data points, many Quora posts offer data points for comparison, countless blog posts discuss what to expect for a range of rates or metrics. Gather as much data as you can, understand how someone else's experience can inform yours based on difference in product, approach, stage, etc. (and how your experience will likely differ), and use them to benchmark your assumptions. Historical metrics: If you have past performance data, that adds potential data points to your forward-looking assumptions. It doesn't provide the exact answers, since future performance may not look like the past, but it's one data point to help you ground your assumptions. And it helps you understand what metrics in your business you'd need to change to make a big breakthrough in performance. Make range estimates, not point estimates: Instead of agonizing over whether your conversion rate will be 2% or 5%, focus on the possible range or conversion rates and evaluate the results based upon the range of estimates, not the point estimate of 2% or 5%. Creating a range helps you focus your thinking how the inputs influence the outputs, rather than focusing narrowly on justifying the inputs. How to use assumptions in a model # Use assumptions as variables: Don't hard-code assumptions into formulas. Instead, create your assumptions so that you can easily change an assumption in one place and all formulas and outputs will recalcuate automatically. Label assumptions clearly: Use descriptive labels so you can understand what assumptions mean. Add notes to your assumptions so you can clearly explain what they mean. Organize your assumptions together: There are multiple ways to approach this, but a general best practice is to organize your assumptions together on a single sheet, so that it's easy to see all the assumptions at the same time in a "control center" for your model. Show your work. Part of the reason it's best practice to structure assumptions as variables is so that it's easy to change assumptions later with a minimal amount of effort, but another reason is that it helps expose your thinking and structure. This carries itself into the rest of your model: show how your calculations work by breaking your thinking into multiple lines, rather than condense calculations into a single line. In the example below, you can see two approaches to a basic user and revenue buildup: I've laid out a couple assumptions and then calculated users and revenue in two ways. One exposes a lot more information that can lead to better intermediate decision-making, one condenses the calculations and is harder to see the independent impact of changing the assumptions. Note: you'll notice the calculated user and revenue numbers are different, that's because I rounded up the user numbers more granularly for option 2, and that led to a slightly different ending result. In the Standard Financial Model (screenshot below), I organize all of the assumptions for the model on a single sheet, grouping them together by section, label and provide details about each assumption, and use a formatting convention (blue text color) to denote all assumptions. My goal is to easily signify where the assumptions are in the model and what they mean, so that anyone can figure out what's going on as quickly as possible. [1] I also usually create a separate "Key Metrics" section where summarize key inputs and outputs in the model. I'll pull out key metrics that are calculated in other places in the models and report them in the Key Metrics section so that it's easy to see them in one place. I will also create a Key Inputs section next to the Key Metrics to pull out important assumptions from the Assumptions sheet and place them next to the metrics, so that I can easily change key inputs and see how the metrics change, instantly. In the Venture Fund Model (screenshot below) I have an assumptions page with all the assumptions in the model, but I've pulled out a couple key assumptions - fund size, check size, type of deal, etc. - and placed them next to the performance metrics so I can easily see how the inputs impact the outputs. That helps me understand how key assumptions impact the model and helps me quickly scan to see if a model is performing like I expect it to. Structurally creating assumptions in a model is easy, grounding and justifying assumptions is much harder. Start with a good, clean struture, but don't get hung up on grounding your assumptions perfectly when you start building a model. As you build your model you'll change what assumptions you need, add new assumptions, and find out new data to use. Just focus on continuing to build and understand, and come back to your assumptions once you have the outputs of the model ready to evaluate. You'll notice that I don't necessarily show my work in all places in this model. It's partly because I've tried to make the models a little lighter visually and use less lines for calculations, but it's also because many of the formulas use a mix of index and range functions for a lot of calculations, and it's cleaner for users to see the final result rather than the many intermediate calcs involved in those types of formulas. ↩︎ 2015-08-07T00:00:00Z https://foresight.is/advice/ Every day we're given advice, solicited and unsolicited, from everywhere and everyone: friends, advisors, mentors, bosses, colleagues, thought leaders, over coffee, blog posts, podcasts, panels: literally everywhere. Advice can be good or bad: or consistent, unusual, cliche, unexpected, insightful, biased, valuable, worthless. Great advice can come from unexpected sources, and sometimes great sources can give bad advice. The fact is, it's hard to give good advice. It's easy to talk, but hard to listen, ask the right questions, interpret what people say (and don't say), and synthesize everything you hear and know to give the best advice for a particular person, at a particular time, for a particular issue. It's not easy. In my work I spend a lot of time helping entrepreneurs with fundraising, pitching, product and strategy. There are countless blog posts on the topic of what startups should do and how they should do it; countless opinions on how to write a pitch deck, how much money to raise, how to find investors, how to convince investors to invest, how to structure deals. The advice in the area is endless, but it's also hard for an entrepreneur to understand and use, especially a new entrepreneur, because much of the advice is seemingly conflicting or confusing. It's hard to grasp the nuance, individual biases, or specific market conditions wrapped up in everything one reads and hears. If advice comes before a question, discount the value of it, instantly. A lot of what I do starts with listening and asking questions: what advice are you hearing, what is the market telling you, what do you think? Does the advice resonate with you? Does someone's suggestions fit how you want to pitch and position your business? What have you thought about but discarded, why did you reach this conclusion? And I'll be honest about what I know and what I don't know, being vigilant about being aware of my inherent biases or blindspots created by my experiences. Giving advice this way is far harder than simply telling someone what I think, but if I focus on listening and interpreting before talking, it's far more valuable in the long term: for me and for the entrepreneur. * * * I love seeing how an entrepreneur tells the story of their business. I love hearing how someone talks about their progress, their thought process and their plans. I love reading a deck and thinking about the questions I should ask, the questions others will ask, the questions that someone is scared to answer. Much like a financial model, the artifact of a deck isn't the most important thing: we can have different opinions on whether decks are unnecessary, critical, or somewhere in-between, but we can probably agree that coherently telling the story and strategy behind your business is mission critical for fundraising. And I think that a deck can be a vehicle, a teaching instrument, or an important element to helping structure and tell that story and be a valuable part of the fundraising process. When I was raising money for a early-stage startup in 2000 it was a different environment with different expectations. We had to write detailed business plans [1], comprehensive financial models, as well as a detailed pitch deck. It's different now: the audience is (mostly) more educated, there's much more information being shared by practicioners on the web, so much more information and opinion and how to raise money, and the document expectations are far different: no more business plans, no more detailed decks, more of an opportunity to have a product to show someone, and thus more conversations about what you're building and how it impacts people. There's tons of advice today on how to create a good deck and how to pitch your business, but at the end of the day that "something" that separates great pitches from good is something that you won't find in a blog post: it's something about you and your unique story or position that allows you to convince someone that you're going to succeed. And that has to come from you. Listen to the advice, but at the end of the day, listen to yourself. Much like the 40 page whitepapers of today's crypto businesses. ↩︎ 2016-02-29T00:00:00Z https://foresight.is/presenting-financials/ I've reviewed thousands, maybe tens of thousands of pitch decks, covering early, growth and late stage venture capital and private equity. There's a flow to presenting the argument of a business opportunity that can create a mind meld between an entrepreneur and an investor, where you're able to spend your time appropriately around the key insights, bets, and questions behind the product, business, and team. Good pitches aren't just pitches, but conversations. Yet far too often, I've seen a great flow break down when it comes time to discuss the financials. After slides full of crisp, clear illumination of the problem, solution, market, team and plan, the financials are often left to a header like "Financial Summary" and a screen capture of a five year projection of revenues, costs, and net income, and perhaps the ever-present hockey stick of growth of revenues, users, subscribers. Flow, killed. But why? My plan is conservative. # No, it's probably not, and even if it is, presenting a set of three or five year financial projections and then confidently calling it conservative misses the point. Why do investors, as is the common rule of thumb, take a look at a set of projections and cut it in half? The heuristics "it takes twice as long to get done" or "it takes twice as much money" are easy, simple ways to adjust a model that are often proved true when plans hit the real world, but the reason behind the dance is more nuanced. Convervative or not, the reason why an investor doesn't believe an entrepreneur's projections often isn't in the projections themselves but the way they are displayed and presented. How can you have a good conversation about a business with a slide like any of these? Don't make anyone figure out your key points. Tell them directly. # Neither a chart of projected growth nor a screen capture of a three to five year projected income statement works because it doesn't make the story clear. Most slides in a deck work to simplify the key insight and present data to construct the argument; but in these cases, all of the data is presented, usually without commentary, without making the key insights clear. It's left up to the reviewer - the investor - to make their insights, and usually, we simply don't. We focus on other areas of analysis because we can use our gut and our instincts around human and corporate behavior to ground our thoughts, but with this, we have no context, no bearing, no easy way to benchmark or understand the story. Highlight operational fundamentals, not just financial projections. # The thing is, a three to five year projection isn't how an entrepreneur thinks. When you're getting started, you're not thinking about what revenue will be in three years, we're focused on two fundamentals: Does the business work on a per-customer basis at any point in the business? Can the business grow? A chart obscures the instinctual analysis we do when we think about our businesses. It hides many conflicting trends happening in the business, and obscures the simple questions we ask ourselves. Does it solve a problem, will people pay for it, can we provide it for less than it costs us, and can we grow it. That's the better way to talk about and present the financials for a business: show how it works, show how it can grow, and show it's big. User economics # Per-user eonomics are key. This is whay SaaS businesses are obsessed with LTV (long-term value) and CAC (customer acquisition cost) and many other customer metrics, because they have to focus on how a business makes money on a per-customer basis. It's not as simple as LTV greater than CAC, though: time to breakeven matters, upsell and expansion opportunities matter, MRR (marginal recuring revenue) matters, and many other metrics matter because they tie back to fundamental user economics. Solid growth can obscure poor user economics, but only for so long. For help in creating and communicating your unit economics, download the free Unit Economics Forecasting for Excel and Google Sheets → Market size # There's a reason why we - entrepreneurs and investors - care about market size. Sizing a market is hard to reduce to a set of formulas in Excel, and while we can do all the data analysis possible to forecast one's market size, they are still data points, not the answer. Markets can change if you change what people do. And that's why presenting a market sizing isn't as simple as stating how many people bought something this past year, or what a research firm projects people will spend three years from now: they are data points to help make your argument, but you still have to tell the full story. If you've proven that the user economics can work and that the market is large enough, then you're left with one more question: What will it cost to find out? Prove that you understand your costs # Interestingly, cost budgets are much easier for us to discuss. An entrepreneur can (generally) control their costs, but will have far less control over adoption of their product, and thus their revenue projections will be far more variable than their cost projections. That's what a cost budget frames the conversation around: what will it cost to find out if the business can work? And this is key: the cost budget is important because it shows that you've thought about what it takes to actually build the business. It shows a plan for how you will spend the money you raise from investors. It shows you are thinking about who you need to hire, what you need to focus on, how you plan to balance your priorities over a specific timeframe in order to achieve key milestones for your business. Key milestones could include: X number of customers, or users Key product launches Key results from beta users Signals of "proof" that the business is worth investing further capital into The level of detail for a cost budget depends on the level of the business: the more unknowns in the business, the less detail the cost budget needs to have. Focus on hiring (what roles, when, and how much), acquisition costs (if you will be spending on acquisition), and major expenses related to building the product and the business. The key in creating a cost budget is to not get lost in the details and let the small items take your attention from the big issues. Don't worry about each individual line-item; focus on the big items, and be clear about how you will use the money you raise, what you'll be able to achieve with that money, and how that positions you for further success. For help in creating a cost budget, download the free Runway Budgeting Tool for Excel and Google Sheets → Financial projections are meaningless. Or are they? # Often times we think that because financial projections are wrong - that they can't accurately predict the future - that analyzing projections is a waste of time. I'd argue that's the wrong question. The question isn't whether they are right, but why or why not they won't be: not the numbers themselves, but the rationale behind them. I've written about this extensively in the past, but the crux is that we can't simply say that the projections are meaningless and thus we shouldn't care about them, but to reframe how we think of them. So, the recap: Don't depend on the screen capture of the income statement to tell the financial story of your business. Be careful in how you pitch the "hockey stick" growth curve. If it's actual results, then highlight it, but if it's purely a projection, it will be tough to support on its own. Simple slides that explain user economics and and market size can effectively communicate key financial insights. Crisply explain the costs to start and operate the business to achieve key milestones over a certain period of time that maps back to your funding ask. 2016-03-24T00:00:00Z https://foresight.is/how-to-choose-accelerator/ It’s accelerator season. As you may have seen, a number of accelerators are currently accepting applications for their next classes. YC. 500. TechStars. And a host of others, including AngelPad, ERA, Matter, The Brandery, AlphaLab, and many more. [1] If you’re thinking about doing an accelerator, here’s how you can start thinking about which programs could be a fit for you. Do your research and think of an accelerator as a long-term partner # Most accelerators will take an ownership stake in your company. All accelerators will have an impact in how you spend your time (during the accelerator's time period) building your company. Just as you would choose an investor or a business partner, choose an accelerator for the long-term, not the short-term. Ignore the rankings # Don’t pick an accelerator based on someone else’s algorithm, pick an accelerator based on your specific situation and needs. Rankings can help you create a decision set of potential accelerators, but it can’t help you choose which one. Regardless how good a ranking system’s algorithm is, it’s still unable to capture your own specific situation and need. Don’t simply choose your local accelerator # Let’s be honest: it can be very disruptive to you life to pick up and move temporarily for an accelerator in a different city or a foreign country. But starting a company is usually disruptive to your life, and often means you’ll have to set aside the rest of your life for a period of time anyway. The simple fact is that not all accelerators are equal, and if you are looking to optimize for the long-term success of your company, you owe it to yourself to be honest about the potential for your local accelerator to be the best choice to help you succeed. Based on your particular business or the realities of your life, your local accelerator could be the best fit. But you have to think beyond your city to be able to make that conscious choice. Fit matters # Industry fit matters: there are a few, very strong accelerators that are good for almost any type of “accelerator-ready” business, but there are also a number of accelerators that have a tight focus on specific industries or verticals and thus are good fits for those businesses. Matter for media and content businesses. HAXLR8R for hardware startups. Blueprint Health for healthcare startups. And many more. Stage fit matters: some accelerators are more focused on very early companies that have significant unknowns, some are focused on companies that will be able to leverage the accelerator program for growth, some have specific traction expectations for companies either coming in or exiting from the program. Be honest about where you are at with your business and your capacity to leverage an accelerator program. Do your research. Pay attention to how they describe themselves, the type of companies that have gone through it, how the program is set up, what mentors they have attracted, how they define success. How an accelerator describes their program and their values will say a lot about how they could be a fit for you. The program’s principals will define your experience # One of the major assets of an accelerator are the people you get the chance to worth with, meet, learn from, interact with, and leverage for advice, product and business feedback, and fundraising support. The accelerator staff will be your daily, regular, go-to people for help, and they are obviously a critical part of any accelerator experience. They will likely be the people you’ll work with the most through the program; they’ll set the priorities, pace, schedule, and interaction methods of the program. They will determine what matters by the choices they make in how they set up the program. So, pay attention to how an accelerator talks about their program. What do they look for in an entrepreneur? Are they clear in how the program is structured? What are their expectations or definitions of success? Are they clear about what makes their program different? How can they best help a company succeed? Are they defined and differentiated in how they talk about their program, or does it appear vague and pretty similar to other accelerators? Mentors matter, but not in the obvious way # Mentors matter, but be careful of the pages full of pictures of people with great titles. Why? The level of interaction of the mentors will vary widely, so don’t count on leveraging the program to meet a specific person. And once you start leveraging mentors and the accelerator’s network, you might be surprised by who turns out to be the best mentor for you: it may not be any of the names that attracted you to the program. Consider it more of a sample of the network that the accelerator has access to rather than a definitive list of the people that will help you. Judge the mentor list warily. [2] Talk to accelerator alumni and be careful about inherent biases # The world of startup advice is often full of simple heuristics like “I did this, my company succeeded, you should do this.” One person’s choices and journey can often not be extrapolated into the best choices for everyone else to make. So, as you talk to other people that have gone through your choice of accelerators (and yes, you should talk to founders that have been through the programs before), don’t just listen to what they say about the program but ask questions to help define specifically what made it a success - or a failure - for them. Dig deep and be honest about how you and your particular situation relates to their own situation. Will you have the same opportunities or challenges as they did? Don’t optimize simply for the investment terms. # We offer the same advice for companies raising investment rounds: don’t optimize for valuation, optimize for the factors that will do the best to help your company succeed. The same advice holds for accelerators. Great accelerators may offer terms that invest less and take more of an ownership of the company than others, but it it could because they help accelerate your company more than the rest. Like most things, you get what you pay for. You don’t need to do an accelerator to build a great business. Accelerators aren’t for everyone or all companies. Leveraged effectively, they can be great partners for your business and your life. Just make sure to evaluate carefully and chose wisely. Disclaimer: I have been or currently am a mentor for a number of accelerators, including TechStars, ERA, The Brandery, and AlphaLab, and I helped start Venture for America’s first accelerator. ↩︎ I led pulling together the mentor network for Venture for America’s first accelerator. I worked hard to create a set of mentors with the skillets and relationships to best help the specific range of entrepreneurs we had in the program, and to also provide the best opportunities for startups to best leverage their mentors. I've thought about this a lot, and suffice it to say, it's not easy to create a great mentoring experience. ↩︎ 2017-04-19T00:00:00Z https://foresight.is/fast-standard/ I started building financial models in 1998 at my first job out of college at a small private equity firm. I was an economics major at university, strong in math with some accounting classes under my belt, but not versed in using spreadsheets and Excel to understand businesses. As the only analyst or associate at the firm, I didn't have a class of colleagues to learn from, and we didn't have the Excel training that's prevalent at the major investment banks, so I learned from my boss and a lot of trial and error. I found some investment bank models and reviewed their structures, looked at the models we used at the firm, and tried to follow those conventions and create analyses that the firm's partners could understand and use. Everyone creates models in their own way - different layouts, conventions, structures, formatting, notes and more - and while that generally works for an individual for one-time analyses, over time it can create issues for anyone trying to use models over a long period of time, especially with multiple people. It can be hard enough to figure out what you've done in the past, looking through unnoted calculations, parsing out the flow and logic that you created to get to an answer, that then using those models with others can prove to be very difficult. I've seen thousands of excel models forecasting startups, yet still amazed at how different the logic processes and layouts are.— Taylor Davidson (@tdavidson) January 22, 2016 What's interesting about the FAST Standard # That's why the FAST Standard is interesting. The Standard was created by industry practitioners to provide guidance to spreadsheet-based financial modelers on how to design good models, promotes standardization in financial modeling and setting a set of shared goals for financial models. The Standard isn't focused on Excel or shortcut keys or the tools we use to create models, but about how to think about financial modeling. The Standard is based on four tenets, as explained by the Standard's overview: Flexible: To be effective, the structure and style of models require flexibility for both immediate usage and the long term. They should allow multiple users to run scenarios and sensitivities and to make modifications over an extended period as new information becomes available. This level of flexibility is achieved through maintaining the simplicity of the model, rather than attempting to incorporate complex devices with an option for every eventuality. Appropriate: Models must reflect key business assumptions directly and faithfully without being cluttered in unnecessary detail.The modeller must not lose sight of what a model is: a good representation of reality, rather than reality itself. Spurious precision is distracting, verging on dangerous, particularly when it is unbalanced. For example, highly specific tax assumptions may lead to an expectation that all elements of the model are equally certain, creating a false impression if the revenue forecast is essentially guesswork. An excessively detailed base case will drown the more important scenario-based risk analysis and may prevent the practical execution of Monte Carlo analysis. Structured: Rigorous consistency in layout and organisation is essential in retaining the model’s logical integrity over time, particularly as a model’s author may change. A consistent approach to structuring workbooks, worksheets, and formulas saves time when building, learning, or maintaining the model. Transparent: Effective models are founded upon simple, clear formulas that can be understood by other modellers and non-modellers alike. How I use the FAST Standard # While I sometimes fail to abide to the standard ... Actual excel formula used in a model update. (Update coming soon) pic.twitter.com/8syQ2A9pzF— Taylor Davidson (@tdavidson) April 29, 2016 ... it's a set of principles that resonates deeply with me. When I build a financial model I am looking to build structures that thousands of people can understand and use to make real, practical business decisions, even if they are not gifted at Excel. It's not easy: I have changed the fundamental structures behind the Foresight financial models numerous times over the years, always looking to make the core structure and design more powerful, more adaptable, easier to use, more practical, more accessible to a wider range of people and business types. While the Standard is most valuable to the professional financial modeler, the set of principles are powerful for anyone using Excel to do meaningful businesses analyses, especially shared analyses within teams and companies. Even if you choose to not strictly apply the FAST Standard, the modeling techniques and ethos behind it are still powerful and meaningful. I've been building models for years, and have lived years of my life inside Excel, but I still found learning the Standard - and learning to apply it - powerful for my own work, testing some of my conventions and making some of my fundamental beliefs about models clearer and crisper. Foresight is a sponsoring body of the FAST Standard and I passed my FAST certification in April 2017, and I'm excited to help spread the idea of FAST to more people. Why does this matter? Making the models clear and easy to use is powerful for everyone using a Foresight model. I have worked extensively to rewrite the core structure to make it easier to use, more transparent, and easier to edit over time (replacing single input assumptions with easier methods to change them over time, for example), and I am always working more of the principles of FAST into all of my models. Here's to better, easier, faster financial models. 2018-04-05T00:00:00Z https://foresight.is/when-hire-cfo/ When you first start your company, one of the founders will essentially be the chief financial offier (CFO). While they may not have the full financial background of a CFO, someone will be in charge of managing the numbers behind the business and taking care of generating insights and reports for the company to discuss, reports to investors, managing accountants, and handling state and federal financial filings. In the beginning, I'd recommend outsourcing payroll and bookkeeping to external firms from the beginning, and save your time for managing cash flow and budgets. Check out Pilot, Kick, and local bookkeepers as options for bookkeeping services. You'll still be in charge of understanding and using the financial reports, but someone else will be responsible for doing the accounting and creating the consolidated financial statements. Eventually you'll have to hire a CFO, but the answer for "when" isn't crystal clear. Often you will start with a VP of Finance because you won't need the level of seniority and experience of a CFO for a small company. You can also hire a CFO on a part-time basis; I once worked as a part-time CFO for a startup, and there are many people available to work on this basis. Nomad Financial, Propeller, and Decision CFO are three of many companies that specialize in providing outsourced part-time financial services and have noted experience with startups. Later on, you'll have to have a CFO full-time in house as a senior member of the management team. Exactly when can vary by the company and the other roles that you'd envision the CFO taking on and taking from the other senior executives. CFOs are often hired to solve a problem, to fix a company's critical financial management processes, rather than proactively to help create a company's culture of using numbers and analysis to guide business decisions, but it need not be that way. A strong CFO that understands strategy and your industry can be a great asset in other ways earlier on. Don't wait to bring in a CFO to clean up problems. 2018-04-08T00:00:00Z https://foresight.is/modeling-revenues/ Let's start modeling your revenues by estimating your market size. Why? Understanding your market size is critical (market beats all) and will be a core component of any of your fundraising conversations. For an idea, or a pre-seed or seed stage company, it's not necessarily important to have detailed financial projections. Instead, what you need is an understanding of the market size and dynamics, and how your product solves a problem in the market. The more evolved your business becomes, the more detailed projections you'll want to build. Let's start by creating a sketch of the market. First: Understand your market # How big is the market? (How much money is spent in the space?) Of the overall market, how much of it is "addressable", meaning how much of the market are you targeting and could reasonably count as a potential customer? Once you have the "addressable market", or sometimes called TAM (Total Addressable Market), now you have your market size. Create a short summary, or a chart or graph, that shows the total market and the total addressable market, and explain how you estimated the addressable market. If the market is changing, make sure to explain that. Is the overall market growing, or staying the same? Is the market shifting? Explain the dynamics and how you are building within those dynamics. Data might be hard to come by, but keep looking, and when you don't know, create the best estimates you can with the information you have, and note how you did it. Being able to explain how is more important than complete accuracy. Second: Understand your per-user economics # The important thing here is to be able to have a basic idea of how much money you can make per-item, per-customer, per-whatever metric matters. Focus on your direct production costs, which will be very different depending on the type of business you are. Outline your basic per-unit costs and what you can expect to be able to charge. That will give you an idea of how much margin you make from a single item, a single customer, etc. For level-setting and benchmarking, start thinking about your type of business. For a SaaS business, is your LTV (long-term value) > CAC (customer acquisition costs)? For a transaction business, how many customers / items do you have to sell before you can enough money from sales to pay for your fixed costs (people, office, etc.)? That will give you an idea of whether your cost structure makes sense, how much of the market you have to capture, and how big of a business you have to be (scale) to be profitable. This analysis will give you the top-down sketch you need for the early-stages of your business (and your financial model). If you're at the really early stages of your business and your idea, and even if you're fundraising from early-stage capital, this might be as far as you go. If you can convince someone that a) there's a large, available, willing market to sell into, and b) that your user economics work, then the question becomes less financial and more executional, meaning can you convince someone that you can execute the business to achieve those economics and capture that market. Market Share Revenue Forecast # The next step could be to create a top-down revenue forecast where you forecast out your addressable market, then assume a percentage of the market that you think you can capture, and by multiplying the two you've created a top-down revenue forecast based on market share. This typically sounds like: "We have a market size of $500 MM. If we capture 2% of the market, our revenues will be $10 MM." While easy to do, this type of revenue forecast doesn't provide much detail into what you have to do in order to capture that market share, and the lack of operational metrics - customers, prices, etc. - doesn't provide a) you much of a guide to work with or b) much of an indication to potential investors that you know what it takes to achieve that market share. It's a good starting point and benchmark, but the next step would be to create a bottoms-up revenue forecast, starting with modeling out the key operational metrics that drive revenues. User and Customer Estimates # Estimating users (customers, clients, et. al.) is really hard, and you'll be wrong no matter what you do; but that doesn't mean it's not valuable, because estimating users is a valuable way to benchmark your model. How many do I have to get to be profitable? How many months does someone have to be a SaaS customer? How many in-app purchases do I have to get someone to make? It's easy to get lost in the details, but start estimating users by using whatever data you have on performance to date, it's the best starting point. And then break out user acquisition into a couple channels: Paid marketing: Facebook, Google, Twitter, etc. The key here is to use the estimates of marketing spend you created earlier and attach some metrics to them, like CPC (cost-per-click) and CPM (cost-per-impression) to figure out ow much $$ in spend tranzincs to ads, and then click and conversion rates to estimate how many of the clicks turn into customers. Conversion rates from pageviews from ads are terribly difficult to estimate, but they'll be low. Sub-10%, sub-5%, sub 1%, most likely. PR, press, viral: I usually lump these together, but the important one is viral. You can model viral growth by estimating the # of people who invite others * the # of people that each person invites * the % of those invited that convert , which results in a viral coefficient (>1 means the product can self-sustainably grow virally). But more important than these estimates are thinking about how your product can grow virally: what viral hooks exist in the product? Where are things shared? How do people convert? How efficient is the process? What is your viral loop? Direct sales: B2B companies have another obvious set of channels, consisting of outside sales and inside sales staffs, with their own plans and teams and structures. The key here is leads, lead nurturing, lead conversion, and the staff you'll need to support sales. Once you have a good idea of user acquisition estimates, then you'll be able to understand your acquisition costs and estimate your customer acquisition cost (CAC), and important metric for understanding your business model. Revenue Estimates # Building on our user and customer estimates, next is to build a bottoms-up revenue estimate. A bottoms-up revenue estimate is created by taking a line-by-line approach to estimating customers and the applicable revenues. The approach is more detailed than a tops-down approach, and it's far more valuable as well. But it's not because it's "more accurate" or "correct" than a tops-down estimate: a bottoms-up approach is more valuable because it creates valuable insights around marketing, product, expected sales and adoption. What I typically do is think through each action and interaction that leads to revenue. Calculate your numbers and lay our your spreadsheet anyway you like, but make sure you think through how the product works and how it generates usage and revenue. If you do, you'll create a bottoms-up revenue estimate that can provide meaningful insights into how your business works. The Foresight models take two basic approaches to revenue forecasts: the Runway Budgeting Tool does not have any prebuilt revenue structures, so you are free to create whatever forecasting methodology you want, whether it's a top-down, simple or detailed bottoms-up. The Standard Financial Model has an extensive prebuilt bottoms-up approach to revenue forecasting that models multiple acquisition methods and models a two-step conversion funnel, creating detailed operational metrics behind the revenue forecasts. To learn more about that, see The Default Revenue Model. 2018-04-09T00:00:00Z https://foresight.is/modeling-costs/ If you're an early-stage company or idea (pre-seed or seed), costs are probably the only thing you'll be able to create with any degree of control or understanding. “You can’t predict your revenue with any kind of precision, but you should be able to manage your expenses exactly to plan.” Brad Feld and Jason Mendelson, Venture Deals Start with a hiring plan # Start with your employee costs, because your hiring plan is often the most important decision you have to make, and for most early-stage companies it's the biggest component of your costs. It's easy to start building your hiring plan. Use a free template like the Runway Budgeting Tool or create a new Excel workbook or Google Sheet. Start by creating a time scale for your forecasts, likely listing out the months you want to model in columns across the top, and then start writing down in the rows the people you'll need to hire. As a general rule, there's usually a simple, manual way to model things, and a more complicated, automated way. In my models, I tend to use the simpler method by using one row per employee hired, and then just typing in their monthly salary per month. I usually just type their monthly salary in the first month they are hired, then create a simple formula so that all future months use the same salary. [1] After starting your hiring plan, complete it by typing in all the different roles you think you'll hire for, and carry your estimates out for 12 to 36 months. Twelve months is probably the max you can grasp right now, but some people (investors and others) will often ask you what your 2, 3, 5, and 10 year plans are. If you're fundraising from experienced early-stage investors, they probably won't expect detailed financials or estimates for over 12-36 months. As always, depends on the person, but remember that what a person asks for says a lot about what they care about. [2] Forecast operating costs # After a hiring plan, move on to estimating your primary cost areas, using the same simple methodology of one row per cost, and type in the costs per month in the relevant months. This could include: Non-employee contractor costs, if you didn't include them in your hiring plan New employee costs like furniture, computers, cell phones. etc. Office rent, office utilities, business insurance Monthly infrastructure costs, like payroll management, accounting, any monthly SaaS services you use to run your business Marketing costs, if you're going to spend on direct advertising, email marketing, Facebook ads, etc. (remember this, for when we start estimating user acquisition) Lumpy costs like conferences, employee travel, etc. One-time costs like legal, company formation Outsourced costs, perhaps outsourced product development Purchasing of materials, supplies, or products for sale Payment processing (Stripe, Braintree, PayPal, etc.) I usually start by typing in all the costs, then working through the proper accounting treatment to segment the costs into Selling, General, and Administrative (SG&A) and Cost of Goods Sold (COGS). That way, we focus on cash without worrying about accounting statements yet, and we can segment them into proper accounting treatment when it comes to create financial statements. Where do you find benchmarks for costs? Finding good data is tough. Ask fellow founders and friends to start. I find Quora to be one of the best sources for benchmark data on the web. Joe Stump put together an outline a few years ago that's still relevant. Just remember that one person's experience will not necessarily be yours, and that the costs can vary drastically depending on your business, competition, goals, funding stage, what you need to prove to hit your next milestones, and many more factors. Sum up the expenses # At the end of this, you'll have a hiring plan and an outline of operating expenses. The next step would be to sum these up into SG&A and COGS expenses, so that you can understand the degree to which your expenses are fixed or variable, and start to think about the gross margin (revenues less cost of goods sold) and unit economics of the business. If you are fundraising, typically we would create a Sources and Uses chart at this stage. A Sources and Uses chart is typically a simple summary of the funds being raised (the sources) and the major cost areas where the funds will be spent (the uses), and it's often a table and a pie chart to give some detail and a quick overview. For more on sources and uses, see sources and uses › The big picture # For many early-stage financings, this will be the majority of your financial modeling, in the sense that your cost budget will be the most scrutinized aspect of your financial plan because it gives a look into how you are thinking about operationally growing the business and allocating investment capital. It's a plan you're laying out to an investor, and it's important that it reflects how you're truly looking to build the business. As hard as forecasting costs is, forecasting revenues is much harder. That said, it's often not as important. Depending on the background of the investor, convincing an early-stage investor about the potential for the business is often more about convincing them about the market size and your ability to capture a significant share of the market more than a specific revenue forecast. That's why I start out forecasting revenues by estimating market sizes. Read Modeling Revenues to learn more about that. In most of my templates, I add in formulas to automatically factor in an optional annual salary increase, and then layer in benefits, payroll taxes and other staffing-related costs. Additionally, I usually create a way to denote each person as an employee or contractor (for benefits-related costs and headcount reporting) and assign them to a category, like product, engineering, sales, marketing, and more, for summaries that help you understand and demonstrate what functional parts in the company are being invested in through hiring. And in the Standard Model and above, I have a number of optional sections that calculate additional hires automatically based on % of revenues spent on certain categories of hires or N number of customers / clients / users acquired. More about that here › ↩︎ For early-stage companies, 1 year or 3 year projections are likely sufficient, and it's often fine to do them on a monthly basis for the first year and quarterly or annually for years 2 and 3. For later-stage, more mature companies, 5 year projections are the default. Some investors will ask for 5 year forecasts even for early-stage companies, usually investors that come from more of a big company or Wall Street background instead of an early-stage, venture background. My Runway & Cash Budget Tool covers 3 years, done at a monthly, quarterly, and annual basis, while my Standard Financial Model and above cover 5 years, also done at a monthly, quarterly, and annual basis. ↩︎ 2018-04-10T00:00:00Z https://foresight.is/financial-statements/ Following up on the introduction to finance and accounting, here's a deep dive into the three statements of the consolidated financial statements. Income Statement # The income statement is usually the report that draws the most attention, and since each component of the statement shines light into different parts of the business's performance, it's worth explaining the details of the statement to understand what the different terms mean. Here's an overview of the basic structure of the income statement. Revenue # Revenues measure the money that is brought into a company from its business activities, often called sales. Revenues can be reported divided up into different business activities, or by different departments or regions, in the effort to provide more information and transparency into how a business operates. The calculation of revenues often distinguishes between Gross Revenues and Net Revenues, net revenues reflected the revenues after accounting for discounts, returns, chargebacks, affiliates, or other contra-revenues. Revenue can be recognized in different periods than the cash or payment for the revenues are received (under accural accounting), so it's important to distinguish in our forecasts when revenue is recognized and when cash or payments are received. If cash is received in advance of the revenues being recognized (for example, 12 months upfront payment for a SaaS service), then the balance goes to a balance sheet account for deferred revenue liabilities, and you decrease the revenue liability over time as you recognize the revenues. If cash is received after revenues are recognized, it's usually recorded as an accounts receivable. For modeling purposes, if the cash is generally received within the same time period as the recognition of revenues (say 30 days for monthly forecasting), you can generally assume that revenues equals cash receipts. The Foresight models - Standard and above - are prebuilt to handle a wide variety of revenue recognition and cash receipts scenarios, and will automatically handle the revenue recognition, cash, and balance sheet impacts of many different scenarios. Cost of Goods Sold (COGS) # Cost of goods sold (or cost of sales, or cost of revenues, or COGS) reflect the direct costs to produce the goods sold that earned the revenues during that period. The rules for what costs are included into COGS can vary by business and how revenues are earned, but always true back to that basic principle. Production does not involve distribution or sales, but can include support, if the support is a function that is involved in providing the product, rather than selling the product. For most costs it's fairly straightforward to figure out which costs are allocated to COGS and which ones to SG&A, but it can get a little complicated. GAAP and IFRS provides rules and guidance on how to handle certain costs, and for specific questions, best to consultant an accountant with experience with your types of businesses (or simply send me an email). The Foresight Standard Model handle the allocation of costs to COGS and SG&A in a very flexible way. You can specify a COGS margin or amount - relating to revenues - and additionally type in a number of costs into the Expenses section, and allocate them to COGS simply by selecting COGS in the dropdown, and the correct accounting treatment is handled automatically. This way, you have two methods to forecast COGS that provide flexibility to change it over time, and the edits required to shift a cost between COGS and SG&A is trivial. Gross Margin # Gross Margin reflects the amount of revenue that the company retains after incurring the direct costs associated with providing the products and services that earned the revenues. Or, more simply: Gross Margin = Revenue - Cost of Goods Sold And for gross margin %: Gross Margin % = ( Revenue - Cost of Goods Sold ) / Revenue Gross margin is an important concept since it is one measure of operations and profitability, and helps a company think about the costs related to produce the products and services it sells. It also helps a company think about how much money is left over to pay for the costs to operate the business. Selling, General and Administrative (SG&A) # Selling, general and administrative (SG&A) costs are the costs associated with operating the business, and not in producing the products or services. SG&A costs encompass many different types of costs and can be broken down into direct and indirect selling costs, fixed and variable overhead, operating expenses. Essentially, it's all the costs related to the business that are not COGS. The Foresight models handle the allocation of costs to SG&A in a very flexible way, and allow you to assign the costs to a set of dynamic categories to help in reporting the components of SG&A. Just input the costs in each row, select SG&A in the dropdown, select the reporting category in the dropdown, and the model handles the accounting treatment automatically. The reporting is not a formal part of the income statement but provides valuable additional insight into the major cost areas. The models use one repoting category to represent selling costs (e.g. acquisition costs, marketing costs), which is then used for calculating customer acquisition cost (CAC). It's not a formal part of the income statement but an important additional metric for business analysis that the models handle automatically. EBITDA - Earnings before interest, taxes, depreciation, and amortization # Fairly self-explanatory, EBITDA is a popular metric that can be used to understand the core operating earnings of a company, and it's often used for valuation ratios and evaluating the company without the impact of accounting choices. By definition, EBITDA is: EBITDA = Net income + interest + taxes + depreciation + amortization I usually report EBITDA on a forecasted income statement, although it's not necessary in formal corporate accounting, as it would usually be something that is calculated as an additional information point outside of the income statement. I do it that way because it's a fairly popular metric to help understand a company's profitability and it's easy to see the flow of the revenues and expenses that way. For the Foresight models, that means that EBITDA is calculated as: EBITDA = Gross Margin - SG&A By definition, that means that the SG&A does not include the "ITDA", which are reported separately below EBITDA on the income statement. The Foresight models let you input interest, depreciation, and amortization as expenses, but select the appropriate category (interest and depreciation/amortization) so that the model separates the expenses into the appropriate accounting treatment. Depreciation and Amortization # Depreciation is a difficult thing for most beginners to financial modeling to understand, but it need not be too hard: it is an accounting method for a company to spread the cost of purchasing an asset over it's useful life. It attempts to help correct for large one-time expenses by effectively allowing you to account for the expense over the time period where the asset will contribute to the business's operations (the useful life). It also holds with the general accounting goal of matching expenses to the time periods in which they contribute to earning revenues. Depreciation does not impact cash, which is often the trip up for beginning financial modelers. It is one of the ways in which the income statement deviates from the statement of cash flows, and which causes net income during a period to not equal net change in cash flows during the same period. Depreciation is recorded on the income statement, and also recorded on the balance sheet as it decreases the value of the asset on the balance sheet. When you "capitalize" an expense (recording it as a capital expenditure instead of an ordinary operating expense), you record the value of the expense on the balance sheet, and then in future periods depreciate the asset on the income statement, reduce the value of the asset on the balance sheet (typically through an accumulated depreciation account), and add it back to net income on the statement of cash flows under the cash flow from operations section. Amortization refers broadly to the spreading of expenses over a period of time, such as a mortgage, loan, or purchase of asset, but for the context of the income statement refers strictly to the spreading of costs of purchasing intangible assets over their useful life. The rationale and concept is similar to depreciation, but applies to the purchase of intangible assets such as patents or the goodwill created by purchasing another company for a premium over it's valuation For many companies, depreciation and amortization could be minor issues in financial models. If a company is not making significant investments in hard assets, or it not capitalizing software development costs, then depreciation may irrelevant or minor and is often ignored in very basic financial reports and analysis. Amortization will be nonexistent unless the company has purchased intangible assets. The Foresight models handle the depreciation for all capital expenditures - all expenses categorized as CAPEX in the expenses section - using a straight-line method, and using a time period that can be input in the Get Started or Settings sheets. Straight-line is the easiest method of depreciation, and effectively spreads the cost of purchasing the asset equally over a time period, which is intended to be the asset's useful life, or time period where the asset can be used productively in the business. Depreciation and it's impact on the assets accounts are automatically handled by the model on the income statement, balance sheet, and statement of cash flows. There are many different methods for depreciation than straight-line, and you may find yourself wanting to create depreciation schedules that depreciate different assets over different periods of time and using different depreciation methods. This is not prebuilt into the models, but can be added by expanding the depreciation schedule on the Forecast sheet. Interest # Interest expenses (and income) are generally recorded separately from revenues, COGS and SG&A (i.e. above EBITDA) as they generally are not associated with a company's core operations, and represent the costs (and earnings) from financing the company. For some companies, earning income from interest may be a core part of their revenues, and should be treated appropriately as revenue. The Standard Financial Model has automatic schedules that calculate funding from debt instruments - loans - and their payback schedules, including interest expenses, using a set of user inputs around loan terms and interest rates. If you input new debt into one of the months under the cashflow forecast, the models will automatically handle all accounting impacts, and you can enter new debt into any month with different terms and interest rates, each month's debt is handled separately. Other Income and Other Expenses # Other income and expenses are earnings or expenses that are not related to a company's core business operations, and thus are recorded separately from revenues and expenses. For most early-stage companies or users of the Foresight models, this will generally be insignificant and can be ignored and left as zeros, but the model structure allows you to input these into the income statement if they occur. EBT - Earnings before Taxes # EBT represents earnings before taxes, and is simply: EBT = Net Income + Taxes EBT may not be recorded on all income statements, but I break it out on the Foresight models so that corporate taxes may be calculated accurately. Thus, similar to EBITDA, I build down to EBT, thus EBT is calculated as: EBT = EBITDA - depreciation - amortization - interest expenses + interest income - other income + other expenses Taxes # Taxes reflect corporate taxes paid on the earnings from the business (EBT). Business taxes, excise taxes, and other non-income related taxes are operating expenses and not reflected here. In the Foresight models, the corporate tax rate is an input (for the % of EBT that is paid as tax). The calculations of tax look to model the tax situations for most companies. No tax is paid when there is a net loss (negative EBT, or more simply, expenses greater than revenues), and I track net losses over time to track the loss carryforward to apply the net losses against the net gains so that the company does not pay taxes until cumulative net profits are greater than cumulative net losses. This assumes that the losses can be carried forward in entirety over the three or five year time periods used in the models. All of this is done automatically, and the only user input required is the corporate tax rate, on the Get Started or Settings sheet. Consult a tax professional for any questions around specific tax treatment and rules around loss carryforwards applicable to your situation. A note about value-added taxes: I'm often asked about VAT (Value-Added Tax), for users outside of the USA. Technically, a business is collecting VAT and then disbursing it to governments, so it is not income, and the VAT you pay on expenses is recompensed by the government, so it is not an expense. Sales and expenses should be recorded net of VAT, and thus VAT does not show up on a company's income statement as revenues or expenses. VAT would be recorded on the balance sheet under VAT control accounts to track how much VAT has been collected and paid, and while this could have a balance sheet impact - and particularly an impact on cash balances - if there is a significant period between when VAT is collected and when it is paid, for the purposes of financial modeling the typical assumption is to assume that time period is within a month (or is consistent over time), and thus I generally ignore accounting for VAT in the Foresight models. Net Income # Typically the last item on an income statement is net income, the company's total earnings or profit. In the Foresight models, net income is: Net income = EBT - taxes I typically report the net income %, which is a measure of the ability of the company to turn revenues into profit: Net income % = Net Income / Revenues While net income often the thing that people look at first, I hope the explanation of the income statement helps explain that in understanding a business's performance, it's just one measure of operational success. When analayzing an income statement, often I'll look at gross margin %, EBITDA %, and net income % together to get a look at how successfully the business turns revenues into profits, to draw insights about the business model of a company, and compare it to other companies in its industry, to get a feel for whether the company is performing well or not. Different types of companies will have drastically different profitability profiles given the fundamental operations of the business and the realities of their industries. Statement of Cash Flows # The Statement of Cash Flows breaks down the cash flows of a business during a period into three main areas, separating out the changes in cash created from the operations of the business, investing in the business, and financing the business. Typically, the statement of cash flows records the cash on hand at the beginning of the period, the increase or decrease in cash flow resulting from operations, investing, and financing, and the cash on hand at the end of the period. In the Foresight models, I build a typical statement of cash flows and also generally include a fourth statement that provides an alternative look at the cashflows of a company, and is a core part of the automatic fundraising calculations. For more on that function, read about the funding round forecasting › Cash Flows from Operations # The cash flow from operations details how the operations of the business creates an increase or decrease in the amount of cash held by the company. The methodology is to start with net income, and then adjust it by adding back non-cash expenses (depreciation, amortization), and then adding (or subtracting) the changes in cash flows from working capital. Changes in working capital is the net change in many balance sheet accounts that reflect the differences in timing between then cash is received or disbursed and when the revenue or expense is recorded on the balance sheet. The impact of these timing differences are tracked on the balance sheets as assets and liabilties, and the net of the changes in these accounts during this time period is the net changes in working capital that is added or subtracted to net income to calculate cash flows from operations. For the exact details on which balance sheet accounts are used to calculate changes in working capital, consult How to changes in working capital capital affect a company's cash flow? or review the Foresight financial model for exactly how I do it. Cash Flows from Investing # The cash flows from investing detail the net cash spent to invest in the business, generally through capital expenditures, investments in the financial markets, and investing in subsidiary companies not consolidated into the company's statements. In the Foresight models, generally the only cash flow from investing are capital expenditures. Cash Flows from Financing # The cash flows from financing detail the aggregate changes in cash from financing the business. This will typically record equity investments, new debt, repayments of the principal on debt, dividends, changes in owner's capital, stock repurchases, and other financing-related impacts. For the users of Foresight models, it is typically new equity investments, new debt, and any debt repayments that are important in this section. The models do allow for dividends to investors, but it is not commonly used. This section fits most usecases for private companies, but if you have specific areas that require changes to this section, feel free to add them in or contact me for any questions. Balance Sheet # The balance sheet reflects the financial condition of the firm at a specific date in time, usually at the end of an income statement period. The balance sheet reports the company’s assets, liabilities, and shareholder’s equity, and a correctly calculated balance sheet (and consolidated financial statements) will hold true to a basic accounting premise: Assets = Liabilities + Sharedholder's Equity Thus, the balance sheet is divided into three section: Assets # Assets are resources that the company owns or controls with the expectation that they will derive future economic benefit from them. The most obvious asset is cash, but they include a variety of current (short-term) assets, fixed assets, investments, and intangible assets, and this section on the balance sheet is typically broken out into current and long-term assets. Assets are valued at historical cost (also called book value), and adjusted for aging, use, or improvements, through depreciation and other methods. For more about assets, Investopedia has a good summary. In the Foresight models the default assets section consists of: Current Assets Cash Accounts Receivable, or money due in the future for services or products already delivered Inventory (if applicable), the value of products currently available for sale Work in Progress (if appplicable, and only for the Standard models and above), the value of products currently in manufacturing process but not yet available to sell Prepaid Expenses Long Term Assets Property, Plant and Equipment, the value of the cumulative capital expenditures over time Accumulated Depreciation, the cumulative of the depreciation expense to date Other Assets Other assets, not calculated but left as an input to use if relevant for your company The user inputs are on the Get Started or Settings sheets, and differ depending on the forecast method. Accounts Receivable's input is for Days Accounts Receivable, the average number of days between delivery of a product or service and when cash is received. Prepaid expenses is for the % of non-salary SG&A expenses. Inventory (and if using a Standard Model or above, Work in Progress) is calculated based on a number of assumptions around inventory purchasing behavior and desired stock levels. Depreciation is handled using the depreciation schedule and an input for # of months to depreciate an asset purchase over (using the straight-line depreciation method). All of these assumptions are 0 by default and can in many cases be left at 0. The exact balance sheet accounts for your company may differ, for example if you hold some cash in short-term investments, or other things specific to your business, and the model can be edited to fit your exact balance sheet accounts. Liabilities # Liabilities are financial obligations that have resulted from the business's operations. Liabilities are typically broken out into current and noncurrent (long-term) liabilities, and include things like loans, accounts payable, credit cards payable, mortgages, accrued expenses, and deferred revenues. In the Foresight models, the typical balance sheet consists of: Current Liabilities Accounts Payable, bills that have been received but not yet paid. This input is a percentage of current period SG&A. Accrued Liabilties (or accrued expenses), expenses that have been incurred, not billed, and not yet paid. This input is a percentage of current period SG&A. Deferred Revenues Liability, a liability created when cash has been received for services not yet provided. This is explained in the context of revenues under the income statement description above. Inventory Accounts Payable, or payments due for inventory (and work in progress, if applicable) that have been received but not yet paid for. Bad Debt Allowance, an allowance for accounts receivable that may not be collected, based on the company's historical percentage of accounts receivable that is written off and not collected. This is an input as the percentage of accounts receivable expected to be bad debt. Noncurrent Liabilties Long-term debt, which represents any debt that has been taken out, and is reduced over time by any debt repayments. This is calculated from the debt fundraising forecast and the debt repayment schedule. Shareholder's Equity # The shareholder's equity represents the financial value of the firm, measured only by the assets and liabilities on the balance sheet. The accounts here will typically consist of any equity investments, retained earnings, current period net income (or loss), and any owner's equity, if applicable. The Foresight models use a fairly simple shareholder's equity section: Owner's Equity, value of cash or equity put into the company by the owner, with a default input of zero Equity Investment, which is calculated from the fundraising and cash forecast Retained earnings, the cumulative value of net income (loss) to date. Many companies reset retained earnings every year and use net income (loss) for year to date; for simplicity's sake I use current period net income and let retained earnings grow over time Current period net income (loss), which is calculated from the income statement As a note, I do not use "plugs" to force balance sheets to balance (meaning, to make assets = liabilities + shareholder's equity), and there is a check at the bottom of the balance sheet to make sure that the balance sheet balances according to that equation. If the result of that check is anything other than zero, then the user should look at their statements to see what is not being reflected appropriately. Often the balance sheet fails to balance on the initial period if there is a beginning cash balance and no other accounts are recorded for the opening balance sheet, since that means there are liabilities or shareholder's equity values not being reflected. Usually the edit is to increase the retained earnings for the value of the cash, if no other opening balance sheet information is supplied. Why build financial statements for your financial projections? # Financial statements are critical for running a business, but not terribly meaningful until you have a business to run. And even then, for early-stage startups, the problem with financial statements is that they surface the wrong metrics for startups. "Large companies need financial tools to monitor how well the are executing a known business model. Startups need metrics to evaluate how well their search for a business model is going." - Steve Blank But while you can build a basic understanding of your business without creating a set of statements, you'll need financial statements when you're raising capital or once have a business to run. Investors will often want to see your projected statements so they can understand the business deeper and make sure you know the impacts of business strategies on the financial condition of a company. The important thing to me about financial statements is not about creating them, but about using them, and knowing what they inform about a business and what they hide. For SaaS businesses, for example, the classic income statement is a start, but a deeper look at marginal recurring revenue (MRR) and its components are critical to understand the current state and future of the business. To me, financial statements are important because they are a standard and accepted way of presenting your projections to people, but you can't stop there. There are many metrics to use to explain how your business operates that don't show up on a set of financial statements. LTV, CAC, website traffic, average order size, churn, cohort performance, MRR, any many other metrics are important operational metrics that explain how a business is performing in way that aren't captured by financial statements. That's why I advocate a way of presenting your financials to investors that isn't just about a summarized income statement. Read more at How to pitch your financial projections › 2018-04-11T00:00:00Z https://foresight.is/finance-basics/ I build Foresight's templates so that they can be used with a minimal amount of knowledge about finance or accounting, and ideally, no need to edit the template other than using the inputs and paying attention to the instructions on how to use the model. I have an MBA in Finance and Accounting and 24 years experience in corporate finance and spreadsheet modeling, and I've worked to embed as much of that knowledge into building model templates that are accessible to beginners and powerful for experienced finance practitioners. If you have experience in finance and accounting, the templates should make sense to you immediately. If you're a beginner, some basic knowledge of spreadsheets [1] and finance allows you to unlock a lot of the functionality and power of the templates. With that in mind, here's the basic concepts about finance and accounting you need to know to be able to use the Foresight templates. [2] The "Accounting Equation" and Double-Entry Accounting # The double-entry accounting system is based on the idea that every financial transaction has an equal and opposite impact on at least two financial accounts, and is recorded as a debit to at least one account and a credit to at least one other account. The equation underlying this is: Assets = Liabilities + Shareholder's Equity In using the templates, you should never have to worry about this, as the process of forecasting doesn't involve making entries into an accounting system. That said, understanding the basic principle underlying accounting is still a good thing to understand. Everything that's forecasted - revenues, expenses, investments, etc. - impact the financial accounts in multiple ways, and the resulting financial reports - the consolidated financial statements - are tightly interlinked because of how double-entry accounting records financial transactions. Generally Acceptable Accounting Principles (GAAP) and International Financial Reporting Standards (IFRS) # What is GAAP? GAAP, or generally acceptable accounting principles, is a set of rules that set how accounting works. GAAP is the foundation for how the Financial Accounting Standard Board (FASB) sets the accounting standards for companies in the USA. There are no set of accounting rules throughout the world, however. The International Accounting Standards Board (IASB) sets the international financial reporting standards (IFRS) that are used in the UK and widely across Europe, as well as other countries. The basic difference between GAAP and IFRS is that GAAP is rules-based and IFRS is principles-based, and have a number of technical differences around inventory costing, revenue recognition, and other accounting methods. If you want to read about GAAP, start here, and more about the differences between GAAP and IFRS, start here, but for the purposes of general forecasting at the level of detail for most companies using these templates it isn't important. The templates are built to handle a wide variety of companies and the concepts applied are fairly universal. [3] Cash and Accrual Accounting # Fool.com explains this well: Companies can track their income and expenses on either a cash basis or an accrual basis. Under the cash method, revenue is recorded when it is actually received from customers, and expenses are recorded when cash is actually paid out. Under the accrual method, revenue is recorded when it is earned and expenses are recorded when they are incurred, regardless of when the cash is actually received or paid out. The Foresight templates are built to handle either cash or accrual accounting, it's up to you in terms of how you input your revenues and expenses and use the inputs around accounts receivable, inventory, and other accounts that are used to help handle the timing differences between revenues, expenses, and cash receipts and disbursements. The Standard Model has a lot more prebuilt functionality for accrual accounting, but any of the models can be used for cash or accrual-based accounting. Which should you use? C Corporations, companies that gross more than $5 mm in revenue, or companies that sell products to consumers and gross more than $1 mm in revenues must use accrual accounting. GAAP requires the use of accrual accounting, and public companies will have to use accrual accounting. If you are looking to raise investment capital will want to use accrual accounting. Cash accounting, however, is pretty straightforward and easier to maintain, and is used by many small companies. More on cash accounting here. The Matching Principle # The matching principle is a fundamental concept in accounting that ensures expenses are recognized in the same period as the revenues they generate. This principle is integral to accrual accounting, where transactions are recorded when they occur, rather than when cash changes hands. The core idea behind the matching principle is to provide a more accurate picture of a company's financial performance and profitability within a specific period. By aligning expenses with associated revenues, the matching principle ensures that financial statements reflect the true costs of generating income. This approach helps in avoiding significant fluctuations in profitability from one period to the next due to timing differences in recognizing revenues and expenses. For example, if a company incurs costs to produce goods in one period, but sells those goods in a later period, the costs associated with producing those goods are not recognized until the sales occur. This creates a clearer way of understanding the performance of a business. Consolidated Financial Statements # The financial report of a company usually consists of three different reports, that combined, are called the consolidated financial statements. Each of the three reports provides a different view of the company's performance, but they tie together to provide a "consolidated" view of the company's financial results and current position. Income Statement: Often called a profit and loss, or P&L, report, the income statement is the most commonly known and used financial report. The income statement summarizes revenues and income and expenses and losses for the period, and ends with net income or net earnings, the bottom-line profit for the period. Typically we build income statements to look at different periods, commonly for months, quarters, and years. More at Investopedia Balance Sheet: A summary of the financial condition at a date in time (usually at the end of an income statement period). As opposed to the income statement and statement of cash flows, which report results over a period of time, the balance sheet reports results at a specific date, and thus it can be thought of a snapshot of the company on a particular date. The balance sheet reports the company's assets, liabilities, and shareholder's equity. A proper balance sheet will result in assets = liabilties + shareholder's equity. More on the balance sheet, assets, liabilities and equity at Investopedia Statement of Cash Flows: A summary of the net cash increase or decrease during a financial reporting period (the same period as the income statement). The statement of cash flows typically consists of three sections to report the cash inflows and outflows from operating the business (cash flows from operations), from investing in property, plant and equipment, or capital expenditures (cash flows from investing), and from financing the business, including external investments, loans, dividends, and more (cash flows from financing). More at Investopedia When we're building projections for an early-stage company, sometimes we will only build the income statement and a simple statement of cash flows, because our first focus is on the company's revenues, costs, and cash position. So it's not uncommon to start there and build a balance sheet later, especially if the business is relatively simple, doesn't have significant timing gaps between when revenues are earned and cash is received, and doesn't have significant physical assets or capital expenditures. Not to say the balance sheet is not necessary, just that it's often something we'll add later. [4] So, why do we build financial statements? The key questions will vary based on the kind of business and its maturity, but the reports are designed to answer a few questions: Does the business earn a profit or incur a loss during a period? How did revenues change over time, and is the growth or declines in the business reasonable? Are the profit margins of the business reasonable? What is the cash flow resulting from the profit or loss during the period? What does the company own or owe (assets and liabilities)? Where does the business get the capital needed to operate the business, and is it being used appropriately? Does the business need more capital in the future? To help understand those questions and the financial statements deeper, read the article about Financial Statements › OpenVC's post on Accounting 101 for Founders is a great explainer of the tactics to managing accounting at a growing company, detailing the tools, roles, and responsibilities involved in building a company's capabilities in accounting. For a primer on spreadsheets, read these about Microsoft Excel and Google Sheets. ↩︎ If you have questions about any finance or accounting term that I don't explain, or want to understand it deeper, Investopedia is a great resource, just go there and search their reference materials. ↩︎ I have a better understanding of GAAP simply because I'm from the USA and did my MBA in Finance and Accounting in the USA, but there's nothing in the templates that won't fit companies that don't use GAAP. ↩︎ For the Venture Capital Model and models for venture funds, the terminologies for the consolidated statements are a bit different, but capture the same goals, and they add on an additional statement for the change in capital positions for the limited and general partners. They are different in the model because the standard industry reports for financial funds are a bit different. ↩︎ 2018-04-12T00:00:00Z https://foresight.is/byom/ Bring Your Own Model is a functionality that turns Foresight's Standard Model into a modular model, allowing you to bring or build your own revenue and expense forecasts into the Standard Model with a minimum of integration effort. How does it work? # The Standard Model contains many sheets, but is essentially separated into two integrated components: The financial core, consisting of the consolidated financial statements, operating costs, cash forecast, cap table, valuation, actuals reporting, summary, key reports, and funnel reporting The revenue forecast, consisting of the revenue model Get Started sheet and the revenue calculations on the Forecast sheet The revenue forecast feeds into the financial core through the Forecast sheet, which exposes the key integration points into the financial core of the model and allows you to feed any revenue forecast into the financial model without having to figure out all the integration points. Any revenue or expense items can be linked into the Revenues and Expenses section on Forecast. Any operational metrics (clients, customers, subscribers, etc.) can also be linked into the operating metrics on the Forecast sheet, as well as any actual historical financials you may want to use. This process turns the typically hours-long process of customizing a model into minutes. What does this mean? # Let's say you already have a revenue forecast you like, whether you built it or you're using a non-Foresight template. And let's say you like the revenue forecast, but don't think it does cash forecasting, or funding forecasts, or the cap table, or LTV, CAC, or other metrics reporting as well as Foresight. That's fine: simply duplicate the revenue forecast sheets from the non-Foresight model into the Standard Model, use your existing model for the revenue forecast, and link into the financial core through the Forecast sheet. Or build your own custom revenue forecast, by creating a brand new sheet or inserting rows at the top of Forecast, your choice, and link the revenues and necessary metrics into the Forecast sheet. You can still fully use all the financial and reporting components of the Standard Model, but you don't have to use my revenue forecast. Here's a practical example. Say you have four revenue streams that are all sold to different types of customers with different pricing mechanisms. The default growth and revenue structure in the Standard Model won't be a great solution for modeling that, but you can delete the revenue model sheet in the Standard Model, build your own revenue forecast in a new, blank sheet to model your growth and revenues exactly how you want, and feed them into the model through the Forecast sheet. All that is required for this functionality to work is to link your revenues into a line on the Forecast sheet and select a revenue category in the category dropdown. Optionally, you can link into new lines: Billings, if the billings schedule does not equal recognized revenue every month Acquisition costs, if modeled in your revenue model, including any sales staff, commissions, or growth-related expenses part of your forecast Customer metrics, if you want to use customer, client, or user-related metrics in any drivers to calculate expenses or other revenue streams directly in the Forecast sheet. How this solves a common problem with templates # The problem with financial model templates has always been that they do some things you need, but not everything, and it can be incredibly hard to modify them to do what you want. One of the reasons for that is because modeling the revenues for a business - the sales methodologies, the growth engine, what is sold and how it's sold, etc. - can be incredibly specific to each business, and it's very hard to build a model that can work for a wide variety of businesses. That's why I've worked to build a modular model - a standardized financial core with an easy methodology to integrate with any revenue forecast - to help solve the problem with financial model templates, and to make it as easy as possible for you to use a template and minimize the amount of time you have to spend to make it work for you. This feature is a default feature in the Standard Model. Learn more about the Standard Financial Model here › 2018-06-10T00:00:00Z https://foresight.is/operational-financial-models/ What is an "operational" model? A typical financial model consists of an analysis and forecast of the financials of a business, with the financial statements - income statement, balance sheet, and statement of cash flows - as the centerpiece of the model. With a business with a lot of historical information, the forecasting method may be to simply take the past and predict different rates of change for the components of costs and revenues to show how revenue, margins, and net income will change over time. Without historical data, the methods used to forecast can be thinner. In many financial models the forecasts start by having an input of what sales revenues are throughout the forecasting period, but without any thought into how the business creates those revenues. [1] That's a fundamentally limiting way to analyze and forecast a business. One of the reasons why financial models are always wrong is not because of the models themselves but because of our methods and our expectations. A good model is one that helps us think about a business and make operational decisions by understanding their financial impact. It's important to tie together metrics like advertising spend and customer acquisition cost (CAC) with user acquisition; it's important to tie together business development staffing and client contracts signed; it's important to tie together the forecasts with metrics so we can make sense of the business. In many financial models the forecasts start by having an input of what sales revenues are throughout the forecasting period, but without any thought into how the business creates those revenues. That's a fundamentally limiting way to analyze and forecast a business. Financial statements are created in all of my models, but they aren't really the centerpieces. The Revenue Model and Costs sheets - which combines users, customers, expenses, CAC, revenues, staffing, and more - and the key metrics analyses are the core of what I try to build in my models. If we're going to create useful models they have to reflect business thinking, not just financial thinking. This is one of the major differences between models built for private equity or investment banking transactions, and models built for early-stage venture capital and startup transactions. When your fundamental business premise is for the business to disrupt the market or create a new one, using the company's past financials simply won't work the way it does in forecasting mature businesses. ↩︎ 2018-07-20T00:00:00Z https://foresight.is/excel-key-functions/ If you are going to use spreadsheets to create financial projections, it's critical to learn how to use the built-in functions to build the formulas to create your forecasts. Investing the time to learn the key functions will not only help you use spreadsheets faster, but it will also open you up to better ways to lay out the structure of a spreadsheet, analyze data, and communicate your analyses. Everything here refers to Excel, but these same functions are also in available in Google Sheets and can be used the same way. I don't care about functions per se, but I care about how to use functions to streamline and improve the user experience for the users of the Foresight templates. Here's five functions that I've found to be important to understand for business modeling: IF # The IF function is fairly easy to understand, and it's a basic building block of many Excel formulas: = IF (test this, report this if true, report this if false) Nesting IFs - using IF functions within IF functions, creating a tree of outcomes based on different tests - is another common need when you have to sort through different conditions to figure out how to calculate a number or report a data point. The key, though, is to remember that a) creating extensive IF tests can be cumbersome and very difficult to understand and explain to users, and b) there are many different ways to do conditional tests to create calculations. MIN and MAX can replace many IF statements that involve simple tests of different conditions, and can help make formulas shorter in those scenarios. SUMIF, SUMIFS, COUNTIF, COUNTIFS, and even SUMPRODUCT are additional ways to use conditions to do tests for calculations. The key is to develop an understanding of what tool to use in different situations, and how to lay out the data and use the formulas to accomplish your goals. Spreadsheet design and data layout matters because it underlines what formulas you will have to use to do create your calculations and build your forecasts. I used to create extensive nested IF statements, sometimes creating formulas that worked, but were essentially incomprehensible to users because of the degree of the testing. Learning and mastering SUMPRODUCT, SUMIFS, COUNTIFS, INDEX and MATCH opened up new ways for me to use Excel, and changed how I approached spreadsheets and data analysis. INDEX # Index is a very useful fonction for returning the value at a given position. You can pass in a range or an array, and the row and column reference to use, and it will return the value in that cell. It's a fairly simple formula, but it's more powerful than the LOOKUP, HLOOKUP, and VLOOKUP functions that we often use for similar purposes. I use it often to help me use data based on timing inputs, especially in my structures that use monthly cohorts and where I may make repeating purchases, contract lengths, churn, or other behaviors dependent on time. But there are many other ways to use INDEX to find, lookup, and return data that you can use in your formulas; start by reading about Excel INDEX function › INDEX and MATCH # Combining the INDEX formula with MATCH changed how I built financial models. Instead of using VLOOKUP and HLOOKUP, INDEX and MATCH provides much more flexibility and power. Instead of me attempting to explain why, start with INDEX and MATCH at Exceljet, it's a great overview of the theory and practical application of the approach and gives practical examples about how you can use it. SUMIFS # Everybody knows the SUM formula, a simple formula that allows you to sum multiple values. The common usage is to sum a range: = SUM(A1:D1) or a list of values: = SUM(A1,B2,C3) but you often find yourself needing to do different types of sums, perhaps every nth row, the top n values, and a variety of conditional sums. To learn the Excel SUM function deeper, read Exceljet on Excel SUM function › If you're an intermediate user you likely already know SUMIF, a function where you can sum a range based on a condition for it to test, but SUMIFS extends that functionality to let you test multiple critera. Combined wth COUNTIFS (count across a range based on multiple conditions), it's a tremendously useful function to understand to help you create formulas that can be very consistent across ranges of data. One of the major ways in which I use SUMIFS is in the cost summaries on the Costs sheets in the Foresight templates. Structurally, I used to create the costs forecasts by laying out the different types of costs into groups - structured like a traditional accounting chart of accounts - which works well, but it also makes it harder to add lines or change the structure of the accounts. In my recent models, I moved to a flat input of costs - one cost each row - but with a number of dropdown selections next to the cost, so I could allocate the costs to different operating areas, to SG&A or COGS, or other characteristics. Then, in using SUMIFS, I'm able to easily create formulas that sum up the costs in different ways, without requiring a lot of SUM links directly to lines, making it simpler, easier to change, and more robust for analytical purposes. Learn more at Excel SUMIFS Function › SUMPRODUCT # SUMPRODUCT is an example of a fairly simple function that has a tremendous amount of versatility embedded in it. It's not just a different way to do a SUM; SUMPRODUCT multiples ranges or arrays together and returns the sum of the products, but doesn't work like a normal array function, and has a lot more power built into it. After INDEX and MATCH, SUMPRODUCT is the next function that unlocked for me a new way to think about builing the financial model templates. If you want to learn SUMPRODUCT deeper, start here: Excel SUMPRODUCT Function › 2020-12-15T00:00:00Z https://foresight.is/lambda-functions/ Microsoft recently announced the availability of LAMBDA functions, a new feature to Excel that allows users to create custom functions within Excel's formula language, without requiring extra code: LAMBDA allows you to define your own custom functions using Excel’s formula language. ... With LAMBDA, you can take any formula you’ve built in Excel and wrap it up in a LAMBDA function and give it a name (like “MYFUNCTION”). Then anywhere in your sheet, you can refer to MYFUNCTION, re-using that custom function throughout your sheet. Why reusable custom functions matter # Put simply, this allows us to build formulas that are much easier to compose, understand, and use. I'll give an example: when I create a monthly cohort-based calculation of churn, I often create a set of rows to map to each months' new acquisitions, and then calculate churn using one (or multiple) standard churn curves to reflect a logarithmic decline in churn, so that I can assume that a user's churn rate goes down as a function of how long someone is a user. I create the formula so that it applies in all cases that occur in the set of cohort calcs, meaning that the formula is created, then copied over each cell, and for 3 years of monthly cohorts, that means 36 columns (3 years * 12 months per per) and 36 rows. With LAMBDA, instead I would create one custom function (that is likely fairly long and complicated), then create a formula in those cells that merely calls the custom function and passes in the relevant parameters. Less code exposed to you, easier to understand, easier to audit. Here's a good walkthrough on how to use it: Great video from Leila Gharani on using Excel LAMBDA. Leila highlights the ability to do recursion. #Excel #ExcelIsAPlatform @msexcel https://t.co/Dho7QI1WbY— Brian Jones (@jones206) December 16, 2020 The process is straightforward: Use Name Manager to define a name using a LAMBDA formula as its definition Then use the custom function you created in your worksheet, passing in the parameters into the function You can also type LAMBDAs directly into your worksheet and call them in line (useful for testing), but to reuse them throughout your worksheet you will need to define them in Name Manager. Using recursion in LAMBDA functions # Another powerful benefit is recursion: If you create a LAMBDA called MYFUNCTION for example, you can call MYFUNCTION within the definition of MYFUNCTION. This is something that before, was only possible in Excel through script (like VBA/JavaScript). You may not have run across a situation that requires recursion, but I see it as a valuable way to reduce the use of helper functions and output and streamline outputs for complicated calculations. Details on use at Announcing LAMBDA. Details on setup, paramters, technical restrictions, and examples, consult the LAMBDA function support page on Microsoft. When will LAMBDA functions show up in Foresight models? # Personally, I'm looking forward to using LAMBDA and updating many of my models to simplify the formula construction. That said, users of Foresight models won't see them deployed until the capability is widely available. Currently, LAMBDA functions are only available to Microsoft Office 365 subscribers that are using the latest version of Microsoft Office, with access to the Beta channel. Here's how to get Beta channel access through Office Insider → Since I build models used by people across many versions of Excel, Google Sheets, and other spreadsheet programs, I try to minimize the use of constructions that are not available to users of different versions. Once LAMBDA functions are publicly released and available to all Office 365 subscribers, I'll consider how to deploy them in my models. Questions, ask anytime. 2022-04-29T00:00:00Z https://foresight.is/fund-resources/ Here's a list of posts and template models to use to model venture funds: How to Model a Venture Capital Fund. Post detailing how to model a venture capital fund, detailing all the components, methods, considerations, and links to template models to review, on OpenVC. Portfolio Construction for Dummies, by Hadley Harris (Eniac Ventures) Free Excel and Google Sheets Model and explanations on how to model a venture fund. Tactyc. Web app to build, manage and strategize venture portfolio models Venture Fund Model, by Craig Thomas. Free model in Causal for modeling portfolio construction. Open Source Venture Model (V1), by Sam Gerstenzang. Google Sheet, open source, helpful for understanding how follow-on dynamics work in modeling and planning for reserves. Airstream Alpha Fund Budget Template. Fund budget template from a fund CFO firm (submit email to access). Venture Capital Model, Overall Forecast, by Foresight. Free Google Sheet (can download to Excel) for forecasting a Simple, free model in Causal to show overall returns based on fund size, follow-on reserves, and fees. Venture Capital Model, Annual Forecast, by Foresight. Free Google Sheet (can download to Excel) and Causal model for forecasting a venture fund, including all fund performance metrics, showing annual cashflows over time. Venture Capital Model, Annual Forecast, Portfolio Model by Foresight. Free Google Sheet (can download to Excel) for forecasting a venture fund, including all fund performance metrics, showing annual cashflows over time, using a manual input method for creating a portfolio. Venture Capital Model, Annual Forecast, Detailed Portfolio Construction, by Foresight. $ Google Sheet or Excel model to use to forecast a venture fund, including all fund performance metrics, using annual cashflows over time and a detailed approach to portfolio construction. Venture Fund Model, Quarterly Forecast, by Foresight. $$ Excel and Google Sheets model to use in detailing cash flows and presenting to LPs in fundraising. Used by hundreds of emerging managers to raise funds. Includes financial statements for the fund, full forecast and statements for the management company, done on a quarterly timescale. Venture Fund Model, Quarterly, with Tracking by Foresight. $$$ Excel and Google Sheets model to use in detailing cash flows and presenting to LPs in fundraising. Used by hundreds of emerging managers to raise funds. Includes financial statements for the fund, full forecast and statements for the management company, done on a quarterly timescale. Adds in tracking to track actual investments, compare budgeted investment pacing and performance to actuals, and create rolling forecasts. Fund of Venture Funds. Free Google Sheet (can download to Excel) for modeling fund of funds, detailing new fund commitments and the resulting capital calls, investment schedules, and distributions of the funds invested in. Angel and Solo GP Portfolio Tracking, by Foresight. Free Google Sheet (can download to Excel) for angel investors to track and report performance metrics. Rolling Fund Template, by Foresight (with Angellist). Free Google Sheet (can download to Excel) for budgeting capital, fees and investments for Angellist rolling funds. Cap Table and Exit Waterfall Tool, by Foresight. Free Excel Model (can upload to Google Sheets) to create a cap table through multiple rounds of investments, and forecast how investment rounds impact ownership, dilution, valuations, and distribution of proceeds to entrepreneurs and investors through a detailed exit waterfall. VC Fund Performance Calculator, by Angellist. Percentile scores of venture funds across vintage years by combining performance data from hundreds of recent venture capital funds. Venture Fund Economics, by Fred Wilson of USV. An outline of fund economics, with examples based on Union Square Ventures (from 2008). Venture Capital Method (Valuation). Examples of how to use the Venture Capital Method for valuation, free Google Sheets. An LP take on VC portfolio construction covers how a limited partner thinks about portfolio construction. Pitchbook NVCA Venture Monitor, a report updated quarterly with the latest trends and figures in the venture capital industry CB Insights State of Venture research report, updated quarterly, with the latest metrics on the venture capital industry More tools and resources for building financial models for venture capital funds. To suggest additional resources, contact me → 2022-09-03T00:00:00Z https://foresight.is/budgeting-rolling-fund/ On Angellist, How to Make a Budget for a Rolling Fund: Managing a Rolling Fund isn’t all that different from operating a business. You provide a service (access to great deals) to customers (your investors) and charge money for it (with management fees and carried interest). And just like running any other business, you need to budget in order to manage your money—and your LPs’ money—responsibly. I’ve spent my career building financial models for startups. When I launched my Rolling Fund on AngelList, I created a budget to help forecast revenues and expenses for managing it. Here are some best practices I learned along the way. Read the rest of the post of budgeting for the management company of rolling funds on AngelList, and download the tool at Rolling Fund Budget Template. 2022-09-03T00:00:00Z https://foresight.is/how-build-fund-model/ Originally posted at VC Lab, How to Build a VC Fund Model Even quantitatively-minded first-time GPs can find modeling a challenge because venture capital models are often quite different from the models you may be familiar with. There are also some similarities and basic differences that we can leverage to get a running start. Build an Overall Forecast # Start with a simple budget including total capital, expenses, investments, proceeds and distributions. At the same time, factor in assumptions for management fees and carry that most closely relate to your situation. Once you have this framework in place, you can apply an assumption of gross return multiple on your invested capital to estimate returns. Building this model may seem simplistic, but it’s essential for closing your first round of capital. Not only does it give you a basic understanding of your business and the size of investments you can make from your fund, but a well-designed forecast shows prospective LPs that you know what you’re doing. Forecast Cash Flows Over Time # It’s imperative that you understand the long commitment you’re making into this fund, and how/when you’ll make investment decisions. A cash flow forecast provides the foundation for that analysis. Once you have an overall budget, you should estimate how you’ll deploy capital. Here are the basic steps: Create a budget for your expected management fees and fund expenses over time. Build a forecast of new investments over your new investment timeframe. Optionally, create a forecast of follow-ons based on your initial investments, follow-on reserve strategy and expected timing between rounds. Add your forecast of expenses to your forecast of investments to create a capital call schedule. Then, to fully understand and calculate performance metrics, build a forecast of proceeds from those investments and the resulting distributions from the fund to investors. This process will take your overall budget into an annual or quarterly budget of cash flows. Once you have this framework, you should estimate how you’ll deploy capital. This means when and how much you’re putting into companies at the first check, and potential follow-ons. If you then layer on a fees estimate, you’ll be able to forecast capital calls and net out the proceeds and distributions. Create a Portfolio Model By Detailing your Investment Strategy # Portfolio construction is the process of creating your portfolio strategy, check sizes, follow on reserves, and expectations around valuations, ownership, and dilution over time. To wrap your head around the portfolio model for your firm, you need to make some key assumptions about the companies you’re going to be investing in. For example: How big are the initial and follow-on checks? How much do you expect them to grow over time? When is the expected exit and at what multiples? Obviously, if this is your first fund it may be difficult to estimate these inputs, and you should only put the level of detail into the portfolio modeling that’s appropriate to your stage. For example, if you’re at the idea stage, a hypothetical may work best to test your assumptions. Therefore, you should keep your model as simple as possible. Conversely if you’re further along, you may need more detail, several different company scenarios, and example companies. Whenever possible, you can base this model on either your direct experience or comps if you have the data. Create Scenarios And Model Power Laws That Affect Performance # There are two basic ways to scenario-plan your fund’s performance: using discrete scenarios or probabilistic models. In both cases, the goal is to understand the power laws and events that most affect your performance. That is: what X-Factors can change the trajectory of your fund the most. In discrete scenario planning you’ll develop a base, best and worst case expectation. Choose several inputs as the starting point, and make intuitive assumptions about their application to your performance. If you’re comfortable with them, probabilistic models provide an added level of insight when assessing scenarios. By creating simulated timelines on a probabilistic basis, you can arrive at a range of possible results, and assign a degree of confidence to each. Though this produces less specific outcomes, it may provide a more realistic view. Sanity Checks # Before sharing your model with prospective investors, it’s critical that you sanity (and gut) check your expectations, performance and assumptions. Venture accelerators – like VC Lab’s free program – provide an excellent way to acquire and share benchmarks and insights from emerging managers and mentors. In particular, we suggest you pay close attention to a few “gotchas” that often bedevil first time managers. First, check that your strategy makes sense. That is, do other people think it’s logical that you can get the kind of deals you seek at your check size? What about the team – can you handle the volume you’re looking for with the people you’ve allocated? Comparisons can be really helpful here. Secondly, check to make sure you’re not forgetting crucial expenses. Doing so will leave your invested capital too high, and distort your returns. At the same time, ensure your follow on strategy makes sense. For example, will you get many pro-rata opportunities and will you have the capital necessary to make those follow-on calls? Lastly, ensure your returns assumptions are as realistic as possible without succumbing to overly conservative thought. That is, you’re looking for a targeted return that most LPs will consider reasonable and exciting. Go too far into the black, and you’ll trigger their disbelief engine. But if you undershoot, they may not get excited at all. Again, it’s helpful to check this with others who’ve been through the same time. 2022-09-03T00:00:00Z https://foresight.is/saas-modeling/ The Role Forward, Taylor Davidson on Building an Early-Stage SaaS Financial Model Taylor Davidson, financial modeling expert and founder of Foresight, discusses the core building blocks of an early-stage SaaS financial model and why flexibility and customization are vital elements of model creation as a business grows. Starting a company requires an idea and people to help bring it to reality. But a company requires more than a vision, even though it’s fundamental to its success — it needs a well-thought-out business model. For early-stage SaaS startups, creating and implementing a financial model is challenging for a variety of reasons. Which is why Taylor Davidson founded Foresight, a company that offers an extensive library of financial model templates for entrepreneurs. You can also listen to this in Mosaic's Role Forward podcast, available wherever you listen to podcasts. 2022-09-14T00:00:00Z https://foresight.is/model-venture-capital-fund/ Originally written for OpenVC, How to Model a Venture Capital Fund As a prospective or emerging fund manager, you will spend most of your time crafting your investment thesis, and detailing how and why founders and investors should work with you, towards creating a point of view on how you will create a successful venture fund. Creating a financial model for your fund allows you to detail your thesis and strategy in quantitative terms, to create some expectations for how you will spend your investors’ capital and generate returns on your investments. While creating a financial model for a venture capital fund is difficult, given the speculative nature of forecasting investment results, the act of creating a forecast is still valuable, as it allows you to: Outline to potential limited partners the fund’s expenses, your fees for operating the fund, and their expected capital calls Reflect your investment strategy, primarily in terms of check sizes, follow on reserves, and investment deployment timelines, and make sure that your strategy reflects reasonable benchmarks and expectations Create a forecast of capital reserves and operating budgets so you can plan capital deployment, hiring, and operational strategies Demonstrate to potential limited partners that you have a solid plan on how to deploy capital towards your investment thesis Create a point of view on how your thesis aligns with typical investments in your industry, and create a grounded range of expectations of how those investments will generate returns As the founder of Foresight, and an ex-venture capitalist, I’ve worked with over nine hundred emerging and prospective venture fund managers creating financial models for portfolio construction and operating cash flows for their first funds. I’ve worked with managers that have raised hundreds of millions of dollars from limited partners around the world, mentored at accelerators for prospective fund managers, and worked directly with angel investors and venture capitalists analyzing their investment portfolios and potential returns. My goal from this post is for you to come away with a solid idea of what it means to create a financial model for a venture capital fund, the various ways you can create a portfolio construction and operating cash flow model, and provide you with templates and examples that you can use for modeling your fund. Before you get started # Modeling a venture fund typically consists of two models: A model for the fund, the entity making the investments A model for the management company, the entity managing the fund (or multiple funds) Modeling the management company is similar to modeling any operating company, and typically consists of modeling revenues - primarily management fees from the fund(s) - and expenses - salaries, overhead, legal, accounting, travel, etc. - to create a forecast of cash flows and financials. This post will focus on creating a model for a fund, not for the management company. Budgeting the Management Company of a Venture Capital Fund covers modeling a management company in detail. Modeling a venture fund can vary from modeling other types of private equity investments. Private equity funds and real estate investment funds often have fund structures and expectations in how and when proceeds are received that differ substantially from venture capital funds. Similarly, hedge funds have different structures and expectations around investment and limited partner liquidity, making them substantially different from venture funds. Open-ended venture funds, evergreen funds, venture debt, revenue-sharing venture funds, and fund of funds share many similarities regarding capital budgeting and deployment, but often differ in the timing and nature of proceeds and recycling. For this post, we will focus on closed-ended venture funds making investments into equity or equity-like structures (convertible notes, SAFEs, SAFTs, and other token structures). Contact me if you have questions on modeling alternative venture structures. The best models for venture funds are built with the intent of reflecting their investment strategies. Extreme outcomes from a small number of investments usually drive overall portfolio returns, and the resulting power laws make it difficult to create deterministic forecasts of when and how these extreme outcomes will happen. I focus on creating models that help managers understand their strategies, using their expectations of their investment thesis to understand if their strategies “work” based on their assumptions. Tools to build a model # Spreadsheets - primarily Microsoft Excel and Google Sheets - are the most common tools used to build financial models for venture capital funds, because of the accessibility and familiarity that most emerging managers have with the tools. Many modelers will use add-ins to assist with scenario modeling to help understand the impact of variability in their assumptions, and a number of tools for Monte Carlo and scenario analysis are readily available to help understand portfolio construction assumptions and ranges of expected returns. A few prebuilt templates in Excel or Google Sheets include: Airstream Alpha's Fund Financial Budget Template Hadley Harris' Portfolio Construction for Dummies Sam Gerstenzang's Open Source Venture Model (V1) As well as models created by me: Foresight's Venture Capital Model (Annual Forecast) Foresight's Venture Capital Model (Quarterly Forecast) Articial Intelligence-enabled modeling tools like Shortcut and generalized LLM providers like ChatGPT, Claude, and Microsoft Copilot can be used to create spreadsheet financial models, but at this point with my tests their understanding of how venture funds works is not sufficient enough to create accurate, workable models, and thus at this point I cannot recommend using them to create models for venture capital funds. Even their understanding of basic cap table calculations is not to be trusted. Web-based financial modeling tools that can be used to build financial models for venture funds are few and far between. Carta Fund Forecasting (formerly Tactyc), is a platform for fund managers to create and manage venture portfolios, which includes a range of features to help fund managers understand portfolio construction and fund management. Using this platform requires a demo by Carta and a Carta subscription. The 10 components to building a venture fund model # The first consideration in building a model for a fund is whether you want to model the overall fund, or whether you want to add on modeling the cash flows over time. Modeling the total fund investments and returns, without considerations for when those investments and returns happen, is a simple exercise and can be useful for basic understanding of fund strategy. Adding the notion of timing of when significant events in your portfolio happen - write-offs, unrealized gains from markups in investments, proceeds from exits - adds in richer detail to understand the cash flows over time and develop an appreciation for the key metrics that managers are judged on. For your first attempt at building a model, start simple by modeling the total fund investments and returns, which would likely be a one or two sheet model. As you develop your competence in modeling and understanding of venture economics, add in more complexity and detail to your model. When it comes time to start making investments, you will want to have a model that captures cash flows over time so that you can properly budget and plan for your investments, reserve capital for follow-ons if that is part of your strategy, and start to plan for the future of your fund. While a fund model may not be the central focus of your fundraising process, it will typically be required in due diligence, and it can be a valuable way for you to demonstrate to potential limited partners that you have fully thought through what it takes to run a fund. Capital Budgeting # Capital budgeting is the process of determining how much money you can invest. At the simplest level, overall invested capital is typically: Committed capital - organizational fees (expenses incurred to set up the fund) - fund operational fees (operational expenses paid by the fund) - management fees (fees paid to the management company to operate the fund) + recycled proceeds (recycled proceeds back into the fund, often expressed as a percentage of management fees or a percentage of fund committed capital) = Invested capital The budget can be created for just the overall amount invested, or can be created to show the budget for multiple periods over time (per year, per quarter, per month) if you are modeling cash flows over time. The primary driver of your budget for capital to invest is your fund size, or total committed capital. Out of this, the fund entity will pay a number of expenses, and the remainder is available for the fund to invest. Organizational Expenses # Organizational expenses include out-of-pocket expenses paid to set up and organize the fund, which can include the legal costs to draft and execute the limited partnership agreement (LPA) as well as costs incurred by the general partners to source and close limited partners' commitments to the fund, up to a cap or maximum amount defined in the LPA. Organizational expenses will typically only incur at the very beginning of the fund, or only in combination with each close of new committed capital into the fund. Operational Expenses # Operational expenses, or partnership expenses or fund expenses as defined in many LPAs, are the expenses bourne by the fund to administer and maintain the fund entity, which will typically consist of fund administration expenses, taxes, audit, fund legal costs, and additional expenses outlined in the LPA related to the management company's administration. Managers of the fund have some discretion for what expenses to charge to the fund, typically up to an annual and/or total cap defined in the LPA, and managers typically aim to limit expenses paid by the fund. These expenses often scale by the size of the fund, due to the increased costs in managing larger funds with more limited partners (LPs) and often more regulatory burdens. Fund administration expenses are typically paid quarterly, while tax and audit are bunched around Q4 and Q1 to reflect the timing of fiscal year and tax filing responsibilities. Additional partnership expenses, including expenses related to portfolio company administration, oversight and administration to the fund and their limited partners, and perhaps investment marketing and due diligence, may be charged to or partially allocated to the fund and the management company and trued up quarterly based on the terms of the LPA. Operational expenses may also have a cap or maximum amount that can be charged annually or over the lifetime of the fund. Management Fees # Management Fees are typically defined as a fund expense, and are paid by the fund to the management company to pay the managers for executing on the fund's investment strategy. The managers of the fund use these management fees to pay for the expenses incurred by the manaagement company, including salaries, marketing, rent, travel, legal, and typical operational expenses. Venture capitalists often describe the fees paid by limited partners as a "2 and 20" model, reflecting the general standard of 2% management fees and 20% carried interest. Note that while management fees may be commonly thought of as 2%, that is an annual cost, and for a ten year fund the total management fees would be a total of 2% * 10 or 20%. Management fees are generally charged for the lifetime of the fund defined in the LPA, and generally not charged during any fund extension periods defined in the LPA or agreed to by limited partners. Management fees are typically paid quarterly by the fund to the management company. That said, there are variations from the typical structure. Fees may change over the fund lifetime. It is not unusual to charge higher percentages during the investment period, and then decrease during the later periods (often thought of as the harvesting period), and it may change often depending on how the managers and limited partners choose to structure it. The idea is for the higher fees in the early period to help the general partner pay for the higher operational costs associated with sourcing and supporting investments in the earlier years of the fund. A fund could be structured for a single stepdown or multiple stepdowns, depending on what the general and limited partners agree to. For example, a fund could pay 2.5% for the first five years and 1.5% for the next five years, resulting in the same total of 20%. Or a fund may change the fee every year, there are many potential ways to structure it. Different limited partners may pay different fees. For example, early committed or large limited partners, including "anchor" investors, may pay reduced management fees (and carried interest) as compensation for their role in helping provide positive signals to other investors. Philathropic capital may pay zero fees, or may not require any returns above return of capital, or cover early losses, as their role as a catalyst in helping support a fund's investment mandate. In addition, the capital invested by the general partners into the fund, the GP commit, typically will not pay management fees or carried interest. Fees may be calculated in different ways. The standard is to charge fees as a percentage of total committed capital, or fund size (e.g. 2% * total committed capital, charged every year). It could also be structured as a percentage of total called capital to date, or a percentage of assets under management. Assets under management could be defined as invested capital (average during a period or total at end of period, reflecting currently invested capital or cost basis of current portfolio companies, meaning that invested capital goes up as the fund makes investments and goes down with writeoffs or exits) or as the net asset value (NAV) of investments, meaning the value of the unrealized gains in the current portfolio may also be included. The basis for calculation may change over time. As one example, funds may structure fees to be calculated as a percentage of committed capital for the first five years, and a percentage of assets under management for the next five years. For funds that are holding multiple closes on their committed capital, it is important to note that holding multiple closes will not change the total fees paid by the fund over it's lifetime, as later investors will “catchup” on fees that they would have paid in earlier period, so that all limited partners have the same prorata cost basis and allocation of expenses for the fund. https://youtu.be/v2EUv5uJ0Qg To understand the size of organizational and operational fees, the best method is to talk to other fund managers or talk to the service providers directly for their quotes on expenses for your fund. Many providers - Carta, AngelList, Allocations, Anduin, Assure, and others - provide online pricing estimates. VC Lab has a good overview on managing fund expenses to understand what should be charged to the fund and the management company, what type of expenses to expect, and some help on how much to budget for fund operational expenses. They also provide a sample limited partnership agreement free to download, called the Cornerstone LPA. Portfolio Construction # Once you’ve created your capital budget and decided on the core timescale necessary for your current needs, the next consideration is how you want to approach portfolio construction. Portfolio construction is the process of creating your portfolio strategy, check sizes, follow on reserves, and expectations around valuations, ownership, and dilution over time. Portfolio construction at its core is making choices between trade offs, including choices such as: Should the fund be concentrated or diversified? Meaning, should the fund invest in a small or large number of companies, relatively? What check size should the fund target? What check size makes sense given the fund's perspective on targeted ownership percentage, stage of investment, and strategy to lead or follow? How many companies should the fund invest in? At a given fund size, larger check sizes means a smaller number of investments. Should the fund reserve for follow-on investments? Investing in follow-ons can be an important part of a fund's investment strategy, but also reduces the number of new investments that can be made. These choices matter, as funds have to consider their target portfolio size and whether the number of companies they are investing in gives them the opportunity to invest in a large exit or "winner". Small, concentrated portfolios gives funds less chances to be invested in a large exit, which are statistically a small percentage of the total companies founded every year, but large portfolios can create logistical and operational challenges in terms of sourcing and supporting a large number of companies that have to be balanced against the gains from diversification (the gains from diversification reduce after a certain protfolio size). At a given fund size, larger portfolios also mean smaller check sizes, which gives companies a smaller ownership in companies and potentially impacting the "return the fund" math underlying venture capital returns. High performing funds are typically driven by (a) a larger number of large exits and (b) the larger size of those exits. Smaller check sizes means lower initial ownership and a lower total return from large exits, meaning a fund has to be right on a larger number of exits in order for the fund to perform. Many investors will operate under the idea that every investment they make has to have the potential to return the fund - return capital to investors plus potentially generate additional returns for general partners - which has implications for target ownership size, initial check size, and follow-on strategy and approach to dilution. Should you build a concentrated fund? at Signature Block is a good discussion of the tradeoffs and perspectives on portfolio construction. There are multiple ways to build portfolio construction in a fund model: Specific investment. In this approach, instead of assuming an average investment, we create specific assumptions around each new and follow-on investment made (modeling the timing and check sizes) as well as the exits (modeling the size and timing of proceeds from those investments). This approach is also often used for portfolio tracking, to track portfolio performance or budget reserves for follow-ons and return expectations from the portfolio. See my free Excel model for this approach. Average investment. In this approach, you would assume the average new investment (check size) and the allocation of capital to new investments and follow-on investments, and assume an overall return multiple on invested capital. This is the simplest approach, and works well for initial analysis, if you are not pursuing a follow-on strategy, and if you do not need a lot of detail around your portfolio construction. See my free Excel model for this approach. Detailed cap table. In this approach, we use the same assumptions as above, but detail out the capital allocated, check size, and expected participation into each follow-on round of financing. As part of this, we will often detail out the expected future rounds to show how ownership changes over subsequent rounds, show our dilution, and estimate returns and changes in investment value over time. See my Excel model for this approach. There is no single right way to approach portfolio construction, and the “best” way to forecast a portfolio depends on your specific needs. More detail may sound appealing, but requires more assumptions and may not be necessary for your needs. If this is your first fund and you’re in the early planning stages, start with the first approach. If you have a set of investments you know you are making (or have already made), start with an approach that uses specific investments. If you are thinking about portfolio construction and allocation to follow-ons and what to think through the mechanics of dilution and returns, start with the average investment or detailed cap table approach. Ultimately the question to ask is how you plan on using your model, what decisions you will make with it, and your ability to create, use, and manipulate the important decisions in your model. https://youtu.be/M71rD_9VXbk Returns Expectations # The methodology to forecast return expectations is often determined by your approach to portfolio construction, but the simplest method is to assume a gross exit multiple for your invested capital. Gross exit multiple * invested capital = proceeds from investments Proceeds from investments are then distributed to the investors in the fund, typically defined as the fund’s limited partners. Distributions are capital paid back to the investors, and can vary from proceeds received depending on recycling provisions or other agreements with limited partners. A schedule of distributions is often called a waterfall, which depicts the flow of capital to investors based on the fund’s agreements with it’s investors. Typically limited partners are first paid back the capital they invested, perhaps with a preferred return or after accounting for a hurdle rate of performance, and then general partners start to receive carried interest, or a percentage of the distributions paid based on the performance of the investments. Fred Wilson has a foundational post on modeling fund economics at Venture Fund Economics, Gross and Net Returns In many models, a straightforward way to model return economics is to create an input to assume a gross exit multiple, then calculate gross proceeds using that multiple. You can get more detailed by assuming different gross proceeds multiples for different types of investments, different stages (new and follow-on, by stage), or just an overall blended multiple. A typical method to add more detail is to create a table of types of exits - zero, small, medium, large, etc. - and the gross exit multiple per type of exit, and the % of companies that achieve each exit, to calculate an overall blended proceeds multiple that provides more color to the investment strategy. This method abstracts away how the multiple occurs, and does not require one to model the underlying changes in the invested capital post-invested. Meaning, it does not model out ownership, dilution, the impact of additional rounds, time to exit, or the different stages and multiples that exits can occur. If desired, using the more detailed portfolio construction methods to show the rounds of financing that occur post-investment - on an average or specific-company basis - can help detail your expectations of how many companies raise additional capital, how large the rounds are, how much your ownership is diluted, when potential exits can happen, and the different exit points you expect to see in your investments. Creating an expectation of entry valuation, overall dilution, and average exit can be helpful to ground your gross exit multiple assumption. If worthwhile, creating a more detailed portfolio construction can help show how you expect to earn the gross exit multiple. Capital Deployment # Capital deployment modeling involves creating a schedule of investments, fees, and capital calls over time. The easiest way to model a fund is to not do this, meaning just model the overall investments and exits without accounting for when they happen, but if you decide that it’s important to create a time-based forecast, then you will have to create a capital deployment schedule. https://youtu.be/_6A08U--6DI Most managers will want to start this by assuming an investment timeframe, in terms of quarter or years, post-close of the fund where new investments will be made. Then you can average out the deployment of capital allocated to new investments over this timeframe. If you are investing in follow-ons, typically you will assume deployment of capital into follow-ons using an assumption of time from initial investment to each follow-on round that is part of your strategy. This will extend outside of your investment timeframe. With this forecast of invested capital per period (quarters, years), then you can create a forecast of management fees and operational fees per period, to create a schedule of called capital. Many funds will call capital according to investments plus fees per quarter, but some funds will call capital annually, or based on need for new investments and operating expenses, or if the fund is smaller, call a significant portion or all capital up front, upon fund close. Regardless, once you’ve forecasted investments and fees, then you can apply the logic for calling capital, and create a forecast of available fund capital: Capital Available, beginning of period + called capital - management fees - operational and organizational fees - invested capital = capital available, end of period Many funds will additionally want to create a forecast of reserves for follow-on capital, based on their schedule of new investments, to show how they are budgeting capital for potential follow-ons. For forecasting a new fund, this isn’t typically too important because your forecast of new and follow-on investments and called capital implicitly assumes this, but once you start tracking actual investments and can apply some logic to different expectations of follow-ons for specific companies, creating a capital reserves forecast can be very important to help for capital deployment budgeting. https://youtu.be/kQhRUOvw9SM Realized Cash Flows # If you are creating a time-based fund model, you will likely want to create a forecast of proceeds and write-offs (when the value of your investments in companies are marked down to zero, likely because the company went out of business). The typical method is to use your earlier assumption of gross exit multiple and an assumption of average hold period, and then calculate when proceeds happen and how large based on your capital deployment schedule. Proceeds from investments are then either recycled back into new investments or distributed to investors in the fund. Waterfall Distribution of Proceeds # A waterfall depicts how the proceeds are distributed to the investors and managers of the fund, according to the funds’ provisions. In a typical USA-based venture fund, a waterfall just involves modeling the return of capital to the limited partners, then any distributions above that multiplied by the fund’s carry to calculate carried interest. An additional level of detail that can come into play is if the fund is using a tiered carry approach, where the carried interest percentage increases as the fund hits certain return benchmarks as specified in the fund agreements. In many non-USA venture funds (and US private equity funds), there are additional structures that can complicate waterfall modeling. Preferred returns - commonly cited as a percentage return on invested capital - or a hurdle rate - a required rate of return on invested capital - serve to provide first returns to limited partners before general partners can share in the distributions, although terms such as a general partner catchup can allow general partners to earn back the carried interested they would have earned on the preferred return, if there are sufficient proceeds. For an overall portfolio model, this can be fairly simple to model, as you do not need to account for who receives distributions when, but for a time-based model, it gets more complicated as you will need to track the distributions of proceeds against called capital to show the return of capital to limited partners, potentially the distributions to pay for the preferred return and optionally GP catchup, and then the split of distributions between limited partners and the general partners based off the carried interest earned by the general partners on the proceeds. https://youtu.be/UByIN1JrNLQ Unrealized changes in the portfolio # An additional option in building your fund model is whether you want to show the changes in valuation in the underlying portfolio. Residual value is the current market value of the investments, which represents the sum of current invested capital (investments made, less any write-offs) plus unrealized gains from increases in value of the investments. Most funds will mark up their investments based on updated valuations of their companies, particularly when they raise new rounds at different valuations. Modeling these underlying changes in the portfolio is only necessary if doing a time-based model, since once all investments are exited, residual value is equal to zero. It can be complicated to add this to a model, but is necessary for understanding the value of a portfolio when the fund is still deploying capital or not fully exited. Personally, I think this is valuable to add for any investor managing other people’s money, so that they can provide the level of detail and communication around performance reporting that limited partners should demand. Mechanically, typically you will have to use a detailed portfolio construction approach to show the expected timing to write-offs, additional funding rounds, and exits to create a schedule of current invested capital and unrealized gains, increasing them when new investments are made or when investments are marked up, and decreasing them when investments are written off or exits occur. https://youtu.be/I-5AN8nXZGM Performance Metrics # Two primary metrics are used to evaluate all types of investments, IRR and return multiples. You will want to create a model to report two variations of these, by gross and net: Gross multiple = proceeds / invested capital Net multiple = distributions / paid in capital The key difference between these is that gross highlights the performance of the investments, net highlights the performance of the fund by including expenses (carried interest, management fees, and other operational and organizational expenses). You will also want to detail gross and net IRR, and typically the best way to do this is to create one line to show gross cash flows per period, and one for net cash flows per period - using the same definitions for gross and net above - and then use IRR or XIRR functions in spreadsheets to calculate IRR. If you are creating an overall portfolio model only, calculating multiples will be straightforward, you can estimate IRRs by using the total returns and an expectation of the fund’s lifetime. Using a time-based model will allow you to report IRRs more robustly. https://youtu.be/iJjGkQTCK18 By definition, these metrics only measure realized returns, so they only tell part of the story for a fund that is currently investing and not fully exited from their investments. Interim IRR is an additional metric that allows you to include the unrealized value of your investments in your “proceeds”. Mechanically, this requires building a per-period cohort approach to model out the cash flows and total residual value at the end of each period, and calculate Interim IRR per each forecasted period. Since Interim IRR will equal overall IRR at fund exit, this is only valuable to show the changes in value over time. Typically funds will forecast this to show their expected J curve, to show the negative IRRs early (since you will call capital, and your investments will be less than called capital because there are fees to operate the fund) and then rise over time as investments are marked up and exits occur. All Foresight models that model cash flows over time include a prebuilt section to model Interim IRR. Structurally, I typically model interim IRRs based on paid in capital, but do not include expected carried interest, so Interim IRRs usually rise and then decline over time to overall net IRR as residual values are taken off and distributions net of carried interest occur. More complicated approaches could factor expected carried interest into their calculations. For a deeper discussion of interim IRRs, I highly encourage you to read Allen Latta’s post on IRRs. He has a number of posts that dive deep into the mechanics of funds that are great resources for fund managers. When modeling funds over time, you will also want to include four key performance metrics to aid in benchmarking and quick communication of returns: Paid-in Capital, or portion of current cumulative paid-in capital to total committed capital Residual Value to Paid in Capital (RVPI), showing current residual value (less estimated carried interest) divided by current paid in capital Distributed Value to Paid in Capital (DPI), showing current cumulative distributions divided by paid in capital Total Value to Paid in Capital (TVPI), showing total value from distributions plus residual value, divided by paid in capital. TVPI = DPI + RVPI The key to these metrics is that they explain different things about the fund’s performance; RVPI highlights how much value in the fund is in the current investments, and DPI reports how much has been distributed to investors. At fund exit, RVPI = 0 and DPI = TVPI, so if you are creating an overall fund model, these metrics will not be as relevant as the multiple and IRR metrics above, but they can be valuable if creating a time-based fund model. More on metrics at Metrics for Venture Capital Funds GP and LP Economics # Many funds will want to show the return metrics separately for GPs and LPs, using the waterfall detailed above to forecast the cash flows to each party. Most fund models at this stage do not detail out the specific returns to each LP, except in cases when limited partners have different investment terms (potentially different carry, management fees, or ownership of a portion of the GP’s carry), which will require more detailed modeling to show the different returns to types of LPs. Most funds will not detail out the returns to each GP in their fund model, but often managers will want to create a separate sheet or model to show the potential returns to each GP based on their individual general partner commitment and share of the carry. Management Company Budgeting # So far, we have focused on modeling for the fund, but an additional concern can be in budgeting for the management company operating the fund. This can be valuable to detail the hiring plans and operational strategy for the management operational strategy, and important to show that the cash flows from the management fees can support the management company’s overhead. Modeling this is very similar to budgeting for any operating company, involving a forecast of hires and salaries, insurance, accounting, legal, and overhead costs, and using the fund’s forecast of management fees as the revenues for the management company. Funds commonly misinterpret what expenses are paid for by the fund and what are paid by the management company. VC Lab has a good explainer at Managing Fund Expenses for Venture Capitalists. Typically funds will model the management company through the entire fund life, but as the management fees from the fund expire, either (a) assume that they will raise another fund managed by the same management company, showing management fees from the new fund, or (b) show how the management company expenses will decrease over time. Typically this is not an important issue, as most fund managers will strive to raise another fund beyond the immediate one being forecasted, so any shortfall in revenues would be covered by the future fund. Budgeting the Management Company of a Venture Capital Fund covers modeling a management company in detail. For additional detail on budgeting for Rolling funds, I wrote a post on budgeting for Rolling funds with AngelList. Special Considerations in Venture Fund Modeling # Some special cases can create specific needs in creating fund models, I’ll cover each in brief but happy to answer additional questions by contacting me. Revenue-Share Funds # The prior discussion of modeling proceeds assumes that the primary method for earning returns are through exits from the companies. Revenue-sharing funds typically make investments into companies that earn them distributions over time, in advance of exits, as a share in their companies’ revenues. The basic structure for modeling the fund still holds, except you will need to create a schedule of proceeds that aligns with the different timing expectations of revenue-share agreements. The current suite of Foresight models work for forecasting the investments and all portfolio metrics for funds using revenue-sharing, but are not prebuilt to handle the calculation of proceeds from revenue-sharing over time. Fund managers can create a custom forecast of proceeds from revenue-sharing that can be linked into the core structure with minimal effort. For questions, contact Taylor. Venture Debt Funds # Similar to revenue-share funds, venture debt funds invest using different investment structures, and tend to have different size and distribution of returns for their investments. The basic structure of the fund model still holds, except you will need to create a debt amortization schedule to show the repayments of principal and interest, as well as the proceeds from any equity share or warrants, will be necessary to model a venture debt fund. The current suite of Foresight models work for forecasting the investments and all portfolio metrics for funds using venture debt, but are not prebuilt to handle the calculation of repayment of capital over time. Fund managers can create a custom forecast that can be linked into the core structure with minimal effort. For questions, contact Taylor. Fund of Funds # Fund of funds invest in venture capital funds. While similar to modeling venture funds, typically you will want to create a schedule of new fund commitments to create a forecast of expected capital calls (structurally similar to a forecast of new and follow-on capital, except there are many more “rounds” of capital calls than follow-ons into companies). Typically we model the expected underlying value of the funds to report total fund-of-fund economics in a similar method to fund modeling. Check out the free Fund of Venture Funds Model. Crypto Funds # Crypto funds, whether they are structured as traditional funds or DAOs, are typically modeled in much of the same way as other funds. Typically crypto funds will hold more investments that can be marked to market easier, since many tokens will be traded on crypto exchanges, and funds will have more liquidity options, and thus will need robust structures to track token prices, liquidity timings, and track residual and realized value, but the same structure and metrics still apply. The core Foresight models support crypto investments, often managers will want to customize the proceeds forecasts to capture the often earlier liquidity of these investments, and the typical phased selling of portions of their tokens. Once investing, fund managers will often want to use data tools to automatically pull in crypto asset prices for portfolio tracking purposes. Contact Taylor for any questions. European v American Waterfall # In a total fund (or “European”) waterfall approach, the fund is required to return all of the called capital to-date before being able to share in the distributions through carried interest payments. In a deal-by-deal (or “American”) waterfall approach, the fund is required to return the cost basis of an investment (invested capital plus imputed fund fees) before sharing in the distribution on that investment. A deal-by-deal waterfall allows fund managers to participate in distributions earlier than a total fund approach, but “clawbacks” are structured so that if later investments fail to return capital, then the carried interest paid earlier to GPs will be returned or "clawed back" to the fund. While the carried interest on each deal is different for an American waterfall, and the timing of carried interest is different, the total carry is the same for a European and American waterfall. European and American waterfalls are detailed at Fund Waterfalls For managers investing through a series of Special Purpose Vehicles (SPVs) and modeling the aggregate of those vehicles, the waterfall works a bit differently. The American waterfall is commonly referred to as deal-by-deal, but to be clear, that does not mean that each deal is separate; the fund still has to return on the total called capital for all investments, and mangers can be subject to clawbacks if the aggregate carry paid out in the earlier periods does not pay the full cost basis of later investments that do not return capital. SPVs work differently, and they are true deal-by-deal, meaning one investment does not have to pay back the cost basis of other investments. Each SPV will often have different LPs, and the terms for each deal can be different, so this is a natural occurance. Important to note that all things being equal in terms of investments and returns, the same investments made as individual SPVs instead of out of a single fund (European or American waterfall) will result in higher carry being paid to the GP. The current suite of Foresight models offer a dropdown select of European Waterfall (total fund to date), American Waterfall (deal by deal, with clawback), or SPVs Waterfall (no clawback). Additional details at Creating and Managing Investment SPVs. Carried Interest # Carried interest, or carry, the "20" of the common "2 and 20" fee model, compensates general partners for the performance of the capital invested by the fund. Carry is calculated as a percentage of the proceeds to the fund above the return of paid in capital (for a total fund waterfall) or a percentage of the proceeds from an investment above the invested capital in that investment plus the cost basis on that invested capital (imputed management fees, operational expenses, organizational fees). For example, under a total fund waterfall if a fund had $100 million in paid-in-capital and $150 million in proceeds and a 20% carry, the fund would return $100 million to investors and then charge carried interest on the $50 million gain on investments (150 - 100), or 20% * 50mm = $10 mm in carried interest. Total distributions to limited partners would be 100 + 50 - 10 = $140mm. There are additional considerations, of course. A fund could charge different carried interest to different limited partners, as detailed in the section on management fees. A fund could charge tiered carry, where one carried interest rate is charged on proceeds after return of capital and up to an agreed net return multiple or rate of return, and then a higher carried interest rate for proceeds above that net multiple or rate of return. If there is a tiered carry, there could also potentially be a "catchup" to allow the GP to earn what they would have earned at the higher carried interest rate on the proceeds they were paid at the lower carried interest rate. Rolling Funds # AngelList’s Rolling Funds add a special consideration in modeling, since typically the fees are collected differently than traditional funds, and the waterfall is done on a per-period basis since each period (quarter) is a separate fund. For more detail, read How to make a budget for a rolling fund, which I authored with AngelList. Foresight offers the model built for AngelList for free at Rolling Fund Budgeting template. General Partner Commit # Some funds will model their waterfall treating the general partner commitment - the investment by the general partners into the fund, commonly called the GP commit - as a limited partner interest, some will not. If the fund treats the GP commit as an LP interest, then it is fairly straightforward to show the economics to GPs and LPs, as the GPs will take distributions using the percentage of the fund size that they invested in (GP commit divided by committed capital) plus their carried interest. If the GP commit is not treated as an LP interest, then the waterfall is amended to show the return of LP capital first, then the split between LPs and GPs using the carried interest percentage, and the GP’s returns do not include a portion attributed to their commit. This is a small assumption that most funds will not need to consider, if necessary it is a checkbox option in most of my models. Note that management fees are typically not charged on funds invested into the fund via the general partner commitment, so the total management fees charged to the fund commonly will be less than you would expect if you just assumed fees to be a percentage of the total fund size. All Foresight models are prebuilt to handle treating the GP commit as an LP or interest or not, simply check the box as appropriate and all formulas change automatically. Recycling # Recycling allows investors to invest more capital. If your LPA allows you to recycle early proceeds from investments back into new investments (instead of distributing these proceeds to the investors in the fund) and to pay for fees, then many managers of funds will choose to recycle these proceeds, typically up to a percentage of committed capital or a percentage of the amount of the management fees charged to the fund, in order to boost their fund returns. There is extensive literature on the web about the incentives and reasons to recycle management fees; for capital budgeting purposes, we usually assume a percentage of management fees to be recycled (0 to 100%, or over 100% if managers are more aggressive) and add that back to increase invested capital. Recycling is commonly misunderstood by emerging managers and can be difficult for managers to execute. Even if it is expressed as "management fee recycling", recycling fees does not decrease the total fees managers get, as recycling comes out of proceeds, not management fees. Many managers overbudget for how much they will be able to recycle, as LPAs often define (a) when recycling can occur, limiting it to a certain period, often the investment period defined in the LPA, (b) what it can be recycled into (new investments, follow investments, etc.), and (c) how much can be recycled (total proceeds from an exit, only the original invested capital into the company that exited, etc.). All Foresight models are prebuilt to handle recycling with a couple options on how to use it. More details on recyling are covered at Recycling Management fees based on invested capital # Modeling management fees based on committed or called capital is straightforward, but modeling fees based on invested capital (also called assets under management) takes special structural considerations. Since the fees are typically a percentage of the total invested capital at the end of each period, calculating the fees in spreadsheets creates a natural circular reference that is not easy to manage, and can be complicated for spreadsheets to calculate without more detailed linear optimization approaches. In practice most managers use an iterative approach by varying their assumptions to get total called capital as close to total committed capital as possible, but it can take some attention to manage model updates when using management fees based on invested capital All Foresight models are prebuilt to handle modeling fees based on committed, called capital, and assets under management, simply select the appropriate option. Evergreen or Open-ended Funds # Evergreen funds typically recycle a large share of proceeds back into new investments, maintaining a base of capital that is constantly recycled over time. Open-ended funds typically allow investors to redeem invested capital based on agreed timelines, and pay distributions over time. Modeling redemptions and new investors coming creates a couple of issues, and often requires calculating a NAV (net asset value) to use so that new investors are investing at a higher cost basis than earlier investors. With the recent announcement of Sequoia’s change in fund structure, this has created new levels of interest in open-ended funds or permanent capital structures, although it is still not a common structure for emerging managers to use. LP side letters and varying terms # Some funds will create arrangements where different limited partners get different terms, namely either different fees (management fees or carried interest) or priority placement in the waterfall. Some funds will work with LPs to be first-loss capital, or may get a preferred return prior to other LPs, or they may get a portion of the GP carry. Each special term usually requires specific modeling for each consideration by breaking out the LPs separately, so the effect of each term can be clear. Modeling multiple funds and SPVs # Often managers will invest through multiple funds, managing their primary fund, an opportunity fund, or special-purpose investment vehicles (SPVs) to make special investments outside of the fund with their limited partners. The best approach is usually to model each of these separately, and then combine into one comprehensive model, to show the cash flows and impact of each strategy separately and as a whole. All Foresight models can support modeling multiple funds and separate investment vehicles. Users can duplicate the two core sheets creating the fund forecast, then link them into the Forecast sheet to create an aggregated view of the total investment strategy. Management Fees, Cashless Contributions, and Fee Waivers # Budgeting for managers to pay their GP commit can be an issue for many managers. Managers may fund their commit from their own personal wealth, or pay for their GP commit using their management fees (post-tax), therefore reducing the net profits from the management company. Management fee waivers are cashless contributions to the fund, where the fund manager chooses to satisfy some of their GP commit by waiving their management fees. The major rationale behind cashless contributions is to satisfy the GP commit without paying ordinary income taxes on the management fees waived to satisfy the commit, but that has significant tax implications and managers should consult experienced tax professionals before using this strategy. Using your Venture Model # Creating a forecast is easy, making sure it’s believable is hard. Data on fund performance is sparse and difficult to collect, and typically best known by experienced fund managers and limited partners that work across multiple funds. Data can be specific to a certain time period or vintage, and may not be relevant for your strategy, geographical focus, stage, or current market conditions. Even data from known industry data sources CB Insights and Pitchbook can be more directional than statistically significant. Three data sources that are regularly updated and available: Carta Data Desk is regularly updated with new research and analysis on the venture industry. Pitchbook – NVCA Venture Monitor. Updated quarterly with data and insights. CB Insights State of Venture. Updated quarterly with data and insights. Scenario Modeling # Creating scenarios is an important way to think about your returns. There are two primary ways to define scenarios: Range-based scenarios, to evaluate different inputs and impact on outputs Situation-based scenarios, to evaluate different strategies Range-based scenarios are typically done to show best, base, and worst case scenarios by varying the fund’s overall return multiples. They can be good for analyzing scenarios and discussing options with potential limited partners. Situation-based scenarios are typically better when you are considering how to structure the fund and evaluating different strategies, to show how the numbers reflect different fund strategies. All Foresight models allow managers to use tools for scenario modeling, including Monte Carlo simulation tools and other Excel and Google Sheets add-in simulation tools. You can also create scenario tables in additional sheets to create your own scenarios based on the inputs you want to evaluate. More at Scenarios. Tracking Actual Investments # Once you are actually making investments, it can be valuable to create a way to track your investments, create a specific schedule of capital reserves, and track your capital deployment pacing. Are you deploying capital faster or slower than expected? Are follow-ons coming in sooner than expected? Does your original budgeting strategy still hold? Usually I will create one model for tracking investments, useful to help report investment performance to limited partners and internal team members, and one model for forecast, and then create a model that combines the two to aid in budget variance analysis. The free model for venture portfolio tracking can provide a good structure to use for tracking investments in an angel or venture portfolio. The more advanced Foresight model combines tracking, forecasting, and variance analysis into a single model. Strategy drives model, model reflects strategy # Don’t lose sight of the big picture. A model is an analysis tool, and is only as good as the data used to create the assumptions. Be careful using the model for portfolio strategy optimization decisions, as the data for benchmarking venture funds can be difficult to find and use for your specific model. There are a number of additional resources that you can leverage to learn more. Join Slack groups for experienced or emerging managers, or groups like On Deck Venture Capital or On Deck Angels, of which I am an alum, or join accelerators like VC Lab, First Round Capital’s Angel Track, or Oper8r to get additional assistance in learning how to be an angel or venture capital investor. Learning how venture fund economics work is a valuable educational exercise for prospective angels and general partners, and an important part of demonstrating to potential investors the tactical operational knowledge of how investments generate proceeds and returns. Many emerging managers have learned the basics behind fund modeling on the job, realizing their gaps in knowledge once they had deployed significant capital, but today there are many resources and networks available to people that want to learn venture fund economics. Questions, ask anytime. 2022-10-17T00:00:00Z https://foresight.is/cap-table-scenario-modeling/ First, a disclaimer: I am not a lawyer or a financial advisor and am not offering financial or legal advice. Scenario modeling is a common usecase for the Cap Table and Exit Waterfall Tool, as it is easy to enter in details on rounds and evaluate the impact of future fundraising rounds on ownership and exit outcomes. It's also easy to use an export of a share register from an equity management tool, import it into the tool, and model fundraising scenarios and proceeds to shareholders at a variety of exit valuations. Cap Table and Exit Waterfall Tool 4.9 Download the tool used for the scenario analysis in this post. Download for Free > 1004 In the video above, I detail how to: Import an existing cap table Model out a new fundraising round to see dilution and impact on ownership Model out an exit and distribution of proceeds to shareholders at multiple exit valuations, using just the existing cap table as well as adding in a new fundraising round Calculate gross exit multiples and returns to shareholder classes and individual investors The first through third points took about 10 minutes to do, then I spent the rest of the time showing how to do the fourth point; I do not prebuild the returns (multiple, ROI, IRR) analysis into the model at the moment given a wide variety of edge cases that complicate it, but I'm always happy to answer questions and to help. 2022-10-17T00:00:00Z https://foresight.is/standard-model-comparison/ The new Standard Financial Model is live and available for download. What's changed? Quite a lot: Overall, a simpler structure with data passed through less sheets. Hooks, Pricing, Pipeline, Returns, Valuation and Fundraising have all been deprecated, although all the same core features still exist in the model, just in different ways (the automatic fundraising option has been deprecated, but it was a lot of complication for something the majority of people didn't use). The deprecation of Hooks is the biggest change; the input for actual financials was moved to the Forecast sheet where it can be used in-line with the forecasted financials, making the process of comparing actuals to the forecast easier. The prebuilt Revenue Model was rewritten, moving the revenue calculations to its own sheet, and streamlined by removing one of the customer/channel columns. While that was removed as a prebuilt option, it's actually easier to add additional customer bases and revenue streams than before. (Duplicate the prebuilt revenue model to do a heavy implementation of a new customer set, or use the drivers on the Forecast sheet to add in any custom revenue stream.) The Forecasting Drivers were rebuilt, moved inline with each line's calculations, with a new way to declare operational and financial metrics to use as drivers, enabling this to be the most flexible driver-based model I've ever created. The data entry for forecasting revenues and expenses was streamlined; while this default setup now groups everything together - revenues, expenses, fundraising, inventory purchases, accounts receivable collections, and much more - it's easy to insert blank rows and create section separators, and no formulas need to be adjusted. While the Pricing sheet was discarded, the previous methods used to calculate average revenue per user/customer/etc was pretty complicated, and it's now a lot easier to create a custom list of SKU prices or subscription tiers and use them in the model. The Ecommerce, SaaS, and Services Model variants of this model were temporarily deprecated, but will be back soon; at the moment, all purchasers of those models can download the default Standard Model. Previously there was no real difference between the Standard and the variants except for the selected preset information, and the new Standard Model offers the same functionality; soon there will be more differentiated revenue models tightly tuned to each business model. Not sure if the model is right for you? Contact me anytime. 2022-11-02T00:00:00Z https://foresight.is/benchmarks/ Benchmarking the assumptions and outputs of financial models is a difficult problem when modeling privately-held, growing companies where data can be sparse and hard to apply to your specific situation. Answering questions like "what should my gross margin be?", "how much should I spend on marketing?", "how much should I plan on revenues growing?" are difficult to answer without knowing the strategy (e.g. Saas or Ecommerce? growth or profit?), stage of development (e.g. pre- or post-market fit? Seed or Series B stage? $1mm in ARR or $20mm in ARR?), capital structure (e.g. venture-financed or bootstrapped?) and specific context behind the business. Early-stage companies looking to benchmark their projections need to pay attention to those concerns, and use benchmark and comparable company data as directional signs, not absolute answers. Meaning, if a data source says that the average CAC payback period for a SaaS business is N, you have to contextualize that data point to your own situation, and understand whether your metrics should be different or not. You have to find the right comparison set for where you are and for where you want to go. On the topic of using data in your assumptions, How to Create Assumptions Finding benchmark data can be hard, so I'll try to share links to data points as I find them. Here's the beginning of a list of data sources and reports for early-stage companies. [1] [2] KeyBanc Private SaaS Company Survey Results is a great resource for data points on private SaaS companies (also here's the 2021 survey results) Finmark's Metric Benchmarks Report 2022 shares benchmark data for VC-backed SaaS startups at different stages. Using data from Bessemer, KeyBanc, Openview, SaaS Capital, and others, it helps founders gauge how their metrics compare to similar-sized companies and contextualize the data with analysis and key findings to apply to your company. RevOps B2B SaaS Benchmark Report for more data on private SaaS companies Public Comps for metrics from public SaaS companies Meritech Capital's Benchmarking page for public company ratios and S-1 analyses Opex Engine for data and reports (paid) For modeling venture funds, I typically refer people to: Pitchbook – NVCA Venture Monitor, updated quarterly with data and insights CB Insights State of Venture, updated quarterly with data and insights Questions on assumptions or benchmarks for your specific situation, contact me anytime. Please contact me with links to more reports, I'd love to share more. ↩︎ Thank you to Brian Weisberg for contributing a number of resources. ↩︎ 2022-11-02T00:00:00Z https://foresight.is/new-courses/ I recently lanched a few new coursess to help people leverage models and apply finance to real-world decisions, here's a quick overview of the current course listing: The Cap Table and Exit Waterfall Masterclass is a live, 4 session (plus 2 office hours) masterclass over three weeks, covering how to model cap tables, equity fundraises, SAFE and convertible notes, options, and exit waterfalls and returns to shareholders. I've taught over 150 VCs, founders, CFOs, and lawyers through 5 cohorts this year, and it's rated a 9.1 out of 10. More here > A new "short course" on cap tables called Build a Cap Table from Scratch as a 3 hour workshop that focuses on the fundamentals on how to model issuing equity, converting SAFEs and convertible notes, and modeling changes in option pools. First session is December 8th, sign up here > How to Model Venture Funds is another 3 hour workshop, created to help VCs, angel investors and syndicate leads learn the mechanics to modeling venture funds. First session is November 16th, sign up here > Finance for Founders is a live, four class workshop over 2 weeks to help founders learn how to create and run a budgeting, forecasting, and reporting process at a startup, created with Christian Wattig of FP&A Prep. Second cohort starts December 5th, sign up here > All classes focus on teaching you how to build your own models and analyses, using examples in Excel and Google Sheets but with the goal for you to be able to create your own models from scratch. Here's what a few people have said about the courses: "The course was great for reinforcing my overall knowledge of cap tables and start-up financing options. Taylor did a very nice job." "Taylor's classes, videos and resources were very helpful as I further navigate cap tables. My understanding of dilution and waterfall modeling has greatly improved as a result of his class. Taylor was quick to respond to questions and provide updated resources based on class responses / questions. I would highly recommend this class" "I really like the step-by-step templates to help understand how the maths work in building out cap tables and scenarios." Questions, ask anytime. If you're curious about what the classes are like, here is a recent workshop about modeling SAFEs, and you can watch the replay, download the slides, and get the spreadsheet. 2022-11-22T00:00:00Z https://foresight.is/year-end/ As the fiscal year comes to a close, it's a particularly challenging time for founders and finance professionals as they work to close out forecasts for the current year and prepare for the year upcoming, especially given the macro situation many businesses find themselves in. Be nice to Finance people this time of year. We are getting bombarded with 'how are we going to end the year?' questions weekly in an uncertain time, trying to get v18 done of the budget where we are taking hard lines on cost, prepping for the audit, oh and holidays too.— MM PE Backed CFO (@cfo_mm) November 22, 2022 To help with that, I'll be posting content to help people through these typical challenges. To kick that off, here's a couple things happening right now: A free workshop on November 29th at 12 PM ET with Christian Wattig, Financial Storytelling with Chris and Taylor. We will cover how to communicate key insights about the financials of a business with stakeholders, focusing on how to tell a story with numbers. Sign up here, and if you can't make it live, register so you can watch the replay. A special 30% off discount using the code YE30 through Nov 28, 2022 for templates and courses; $25 USD off all model purchases using the code STARTNOW. As you're working on 2023 budgets, some frameworks and best practices to consider: Revenue-Led Planning by Taimur Abdaal at Causal is less about planning for revenues, more about creating budgeting processes to connect (a) operational and financial data and (b) operational and financial teams. How to Present an Operating Plan to a Board walks through a way to present an operating plan for two years for a SaaS business, breaking down the reporting and key metrics he proposes that are important to understand a SaaS business. Popular Planning Methods Compared, by Chris Wattig helps break down three popular ways to create budgets and helps you think about when to use each one. Heuristics of Forecasting, a podcast with Brian Weisberg, CFO at Tidelift, focusing on efficiently planning for headcount. More content and posts to come on year-end closing and forecasting for the new year. Questions, contact me anytime. 2022-12-02T00:00:00Z https://foresight.is/ai-spreadsheets/ Can artificial intelligence (AI) be used to build more accurate and efficient spreadsheet forecast models? As we've started to see in ... AI-powered image editing and generation (e.g. Dall-E [1], Facet), AI chatbots for writing, research and coding (ChatGPT, Jasper, CopyAI, Moonbeam, and scores of others), AI-powered video generation and editing (e.g. Synthesia), AI-enabled programming assistants (Github's Copilot), AI-powered legal document creation and reviews (Tome), ... and many other areas, the idea of using AI to help people do work is on the cusp of mass experimentation. Prompting AI engines (the idea of "prompt battles" is fire) might soon be the first thing we do to create a paragraph or a block of code. Instead of using Google search to dig through endless half-answers around the web and figure out how to apply them to our needs, why not just ask an AI chatbot to do it for us? [2] A decade ago I wrote a post about personal APIs, the idea of creating a way for someone to access people's knowledge and insights without direct interaction. Way too early then, but it's becoming possible today. It's possible today to use machine learning to train an AI model on a set of things you're previously written, and then create a prompt input box on a website for people to ask your AI model questions and get advice. The days of using AI in financial reporting are coming soon. Examples of AI around financial forecasting and spreadsheets are starting to bubble up: Microsoft Excel, Zoho, and Google Sheets are all adding AI-powered features to their platforms to help make it easier for users to use spreadsheets to accomplish common tasks. Function suggestions with pre-filled guesses based on highlighting a section of data, cleaning messy data, and creating reports are early examples of their efforts. Microsoft has released a preview of Copilot for Finance, to help automate the processes to get, aggregate, and reconcile data, as well as recommend insights, build charts and displays of data, and speed up the process of analyzing financial data. Formulabot translates text prompts into formulas, using AI to write Excel and Google Sheets spreadsheet formulas. Spreadsheets involve their own coding "language" and thought process, and making it easier to write spreadsheet formulas is analagous to making it easier to write programming code. Prophet is a forecasting procedure implemented in R and Python, released as open source software by Facebook's data science team. It's built to use historical time series data to create forecasts of operational and financial data. And there's likely countless others I'm missing. It makes sense that incorporating AI into the process of building a spreadsheet forecast model can enable companies to benefit from the enhanced data analysis and predictive capabilities of AI. Gather financial and operational data - historical financial information such as income statements, balance sheets, and cash flow statements, as well as data on expected changes in the company's operations, such as new products or services, changes in pricing, or expansion into new markets - and train an AI model to use for forecasting and data analysis. Take public market data to create a tool to query for benchmarks and comparisons for types of businesses at specific stages. Train a model on your own company's data to create a tool to help forecast future sales, inventories, cash position. Once we have an AI model, the results can then be incorporated into a spreadsheet forecast model, using cells as prompts to generate forward-looking projections or generate insights from historical data to inform the forecast assumptions and projections. In addition to improving the accuracy and efficiency of the forecast model, AI could be used to automate certain aspects of the model-building process. Automatically update the model with new data as it becomes available. Automatically generate reports and presentations based on AI-powered inferences on the actual and forecasted data in the model. Dump data on a company into an AI model for it to figure out which statistical and forecasting methods to use, and why. The days of using AI to build spreadsheet forecast models to make more accurate and informed financial projections is coming closer by the day. Does it replace the role of an analyst or a CFO? No, but that's not the point. Joel Shapiro at 4 Phases of Analytics Evolution: From Spreadsheets to AI Workbenches: ... if an AI model indicates that a certain customer segment is likely to increase in value, humans still need to decide what to do about it. Should they engage more with these customers to ensure a good outcome occurs or keep the status quo because these customers are likely to lead to a good outcome on their own? In other words, it takes human expertise, and AI provides valuable inputs to supplement that expertise. Models are about the analysis, not the artifact, and the more we can leverage technology to create the artifacts, the more time and energy we leave for people for analyses and decision-making. The evolution from spreadsheet jockey to prompt CFO is upon us. Header image credit, "painting about artificial intelligence and spreadsheets in the style of edward hopper" by DALL-E 2. ↩︎ Yes, this article started with a prompt to ChatGPT about how to use AI in spreadsheets, which kicked off some ideas to explore and a different post than I originally expected. ↩︎ 2022-12-02T00:00:00Z https://foresight.is/cashflow/ A thirteen-week cash flow forecast is a financial tool used to forecast and manage a company's cash inflows and outflows over a 13 week period. Given the focus on short-term cash flows, this type of model is valuable in situations when financial health is in question and managing cash is of utmost important (restructurings, turnarounds, and similar situations). The 13 week cashflow model uses the same key information that we see in consolidated financial statements - revenue from sales, costs of goods sold, operating expenses, and any planned investments or financing activities, and more - but is constructed a bit differently. The 13 week cashflow model is typically created using the direct method of forecasting cash flows, using cash receipts (inflows) and cash disbursements (outflows) broken down by a few key areas: Operating cash receipts (not recognized revenues using accrual-method accounting, but collected cash from operations) Operating cash disbursements (cash payments for payroll, materials, marketing, rent, other operating expenses) Non-operating disbursements (non-operating expenses, other expenses, interest, principal payments on debt) Net cash inflows (outflows) = operating cash receipts less operating cash disbursements less no-operating disbursements Wall Street Prep has a good graphic describing the 13 week cash flow model, as well as an Excel template to download, at 13 Week Cash Flow Model. Deeper technical considerations on building a model like this are explained at 13 week cash flow forecast. The goal is to use this to then forecast: Cash at the beginning of the week Net cash inflows (outflows) during the week Changes in debt from debtor-in-possession (DIP), revolving loan, or other short-term loan Cash at the end of the week To connect this to a company's consolidated financials, typically this model will have a section to show cash to EBITDA reconcilliation, detailing the balance sheet roll-forwards (e.g. forecasted changes in working capital from balance sheet accounts like accounts receivable, accounts payable, inventory, payroll) to check that the forecasted EBITDA matches the net cash flow from the 13 week cashflow model. Roll-forwards are prebuilt in the balance sheet forecast in the Standard Model and Runway Budgeting Tool. This short-term focus on liquidity and managing cash allows management and, if applicable, restructuring professionals, to build a clear view of cash needs of the business and helps determine the financing needed to support the business. If the business is deemed to not be a going concern, then it will go through liquidation or sale. Once the net cash flows have been calculated for each week, it is important to analyze the overall pattern of the cash flows. This will help identify any potential problems, such as a lack of cash on hand to meet financial obligations or an over-reliance on financing. Why not just build a weekly Statement of Cash Flows? While a statement of cash flows is typically an indirect model, where the changes in cash flows come from the income statement and changes in working capital accounts from the balance sheet, the detail of cash receipts from sales and customers and payments for expenses focuses the user of the model on the specific timing of when cash is received and disbursed. Rather than using recognized revenues and adjusting for the timing of when the revenues are billed and received, directly looking at cash sharpens one's view of how to manage through cash and liquidity issues. By regularly updating and reviewing the 13 week cashflow model, a company can stay on top of its short-term cash needs and make adjustments as necessary to ensure its financial stability. This can help the company avoid potential cash flow issues and make more informed business decisions. By regularly updating and reviewing the model, a company can make more informed business decisions and avoid potential cash flow issues. 2022-12-12T00:00:00Z https://foresight.is/cap-table-hygiene/ A clean cap table will not guarantee an easy fundraising process, but a messy one can cause problems: My view is that company formation and growth is hard enough - one has to deal with market risk, technology risk, team risk, downstream financing risk, etc; therefore deals that layer "bad organizational/legal hygiene" as an additional risk factor into the investment evaluation tend to fail to secure investment. Cap table hygiene refers to practice of keeping a cap table up to date, accurate, easy to use, and clean of the type of issues that would cause key stakeholders (e.g. investors, board of directors, potential partners, acquirers, executives, or lawyers) to question the future of the company. I group typical markers of cap table hygiene into two broad groups of issues, technical and practical. Technical Considerations # While the math behind cap tables is fairly simple in most cases - addition, division, simple algebra - the terminology can be opaque, difficult to understand from legal documents, and easily misapplied, leading to unclear structures and incorrect calculations. Here's a few technical things I commonly check in cap tables: Share price calculations. Easy to make errors with conversions and new option pools. SAFE and Convertible Note conversions. Easy to make errors on conversion methods, calculating share price, rounding of shares (or mistaken fractional shares), or calculating the share price using common conventions for the type of investment without reviewing the terms of the actual agreement. Cap tables that exclude share counts. I see this sometimes where a cap table cites ownership percentages but does not show share counts. That invites questions over the math, which is usually wrong. Practical Considerations # Practical considersations bundles potential narrative, business strategy, and executional issues that may be hidden in the capital structure, ownership incentives, or past decisions made by the company. Here's a few I look for: First and foremost, cap tables should be up-to-date and regularly maintained. Cap tables that aren't updated invite questions about business infrastructual management and record-keeping, and create lots of catchup work when it is necessary to work through changes in capital structure that impact shareholders. Too many founders. As Will Price points out, too many founders means too small ownership to sustain dilution over multiple rounds, potentially reducing founder ownership below what is required to keep the founders incented to maximize the value of the equity of the company. It also points to potential issues later down the line with founder dynamics, splintered decision-making, and unclear executional strategy with a lot of founders (greater than 2 or 3) guiding the company. Who's in charge? Too many shareholders. The issue with too many shareholders is that, not properly handled, it can create practical and logistical issues with executing decisions requiring shareholder consents. It is common to raise money from many institutional and angel investors (and beneficial if they are strategically leveraged), but too many can create fundraising narrative issues (e.g. why did the company need to get this many investors? were they unable to get committed investors to write larger checks? can they leverage their investors) and logistical issues with shareholder agreements. [1] Dead equity. The issue with "dead equity" is equity that is owned by former executives, advisors, and partners no longer actively involved in growing the business. Too much equity owned by people no longer involved in the day to day operations or active in growing the value of the equity can create create a damper on the people still involved in the business, as other are benefiting without doing the work. Founders with not enough equity. The issue with founders not owning enough equity is that it may damper the incentives of founders and key executives to grow the equity value of the business. What's "too little" varies by stage; at early stages they need to have enough to last through the dilution from expected later rounds. "Too little" also varies over time and by region; current expectations of ownership by founders and key executives are generally higher than they were 20 years ago, and expectations can also vary widely by geography and local venture funding markets. Nonstandard terms for preferred investors. Do the participation rights of preferred investors make sense for the stages raised? Are there dividends or preferred returns baked into their agreements? Are other terms standard for the stages raised? Liquidity preferences. Does the amount of capital raised to date, and the liquidity preferences on that capital, make sense given the current state of the business, future growth, and capital needs? Has the company used its capital efficiently? Does the current valuation make sense compared to the amount of capital raised? Nonstandard vesting schedules, or no vesting schedules at all. The key question is whether founders have vesting schedules, and are incented to stay and grow the equity valuation of the company. Nonstandard vesting schedules could point to inexperienced founders that did not know better, or experienced founders getting too much value for their contributions. Maintaining cap table hygiene is critical # It is important for you, someone at your company, or a trusted advisor (e.g. attorney) knows how to manage and maintain the cap table, and makes changes when needed. The cap table may be in a spreadsheet or a web service (e.g. Carta, Pulley, Shareworks, LTSE, or others); spreadsheets work for analysis and scenario planning, but web services have a number of advantages in terms of managing records, staying abreast of rules, handling complicated calculations, managing vesting schedules and option plans, and more. Regardless of what tool you use, it is important that you/team/advisor keeps the cap table up-to-date and accurate at all times. One way to get lots of investors but keep a clean cap table is through bundling investors into a single entity through special purpose vehicles (SPVs) and roll up vehicles (RUVs). ↩︎ 2022-12-12T00:00:00Z https://foresight.is/fpa-startups/ An early-stage startup has a very simple team and stack: a founder and a spreadsheet. A founder's first finance hire is themselves. But as the company grows, building out process to organize people and data (financial and operational) become key to maximizing the opportunity. “I wish I hired a CFO sooner”-Every startup ever— Jason Hershman (@JasonHershman) December 12, 2022 Let’s first understand the roles # The finance function at a ompany typically encompasses a range of executional and strategic roles covering accounting, AR/AP, analysis, forecasting, and exective-level strategy. Looking to hire for a finance role? Or looking to find a new finance role at a startup? Check out Hire with Foresight > Bookkeeper # Bookkeepers are responsible for recording and maintain financial transactions in general ledgers (accounting software). Bookkeepers lay the groundwork for accountants to prepare statements and analyses. AR/AP (Accounts Receivable / Accounts Payable) # The AR/AP function is typically tasked to pay invoices, send invoices, and follow up on invoices to collect payments (collections). Often this function is performed by bookkeepers in the beginning, but separated into full-time role(s) as the company scales. Accountant # At the most junior level - accountant clerk - up through staff and senior accountants, to controller (most senior accountant), accountants are responsible for a company's financial statements. At smaller companies accountants can be outsourced, or if in-house can have more expanded roles that encompass managing cash, AR/AP, payroll, and more. At larger companies the function becomes more specialized and companies will build out accounting teams to handle the financial complexity to the business. FP&A (Finance, Planning, and Analysis) # FP&A can range from FP&A Analysts, FP&A Manager, up to Head of Finance, VP of Finance, and CFO (Chief Financial Officer). The function typically encompasses budgeting, forecasting, and data analysis, reviewing actuals and budgets, understanding variances, reforecasting, and communicating with business heads to work through the financial impact of business decisions. CFO and Exec Team # The Chief Financial Officer (CFO) oversees all financial functions, working with executives and the board to translate strategy into budgets and forecasts, and works with business heads to execute on the strategy. Early-stage companies rarely have full-time CFOs, the responsibilities typically filled by CEO or COO, but part-time CFOs or finance coaches for executives can be huge value-adds to help executives understand and communicate the financial impact of business decisions. I use 5 sacred Planning & Reporting processes to drive financial performance.Here's the breakdown 👇(Warning: Contains controversial views about rolling forecasts)— The Secret CFO (@SecretCFO) September 22, 2022 Business Units # These are the typical operational roles we are familiar with - General Manager, Business Manager, Product Manager, Product Analyst, Data Analyst, and more - that will run the business and use operational data to drive product and business decisions. FP&A intersects with business units to mix operational and financial data, analysis, and reporting to the executive team. At smaller companies the roles can be quite broad, and often narrows as the company grows. One key thing to remember: being effective at FP&A involves partnering with business units to understand the business model outside the numbers. Akshay Kothari, Chief Operating Officer at Notion, in an interview with Compound: ... when I was running finance over the past 9 months, I met with all the best CFOs to understand what finance meant and what the best finance leaders did. One CFO I met had spent years as the CFO of one of the largest tech companies. He did a full 2 hour financial review with me. When we scheduled the meeting, I thought we we’re going to go through numbers. But he was instead focused on attracting and keeping the best talent, creating a path to becoming a platform, and having a strong business model. We didn’t talk about numbers much at all. What I learned is that these CFOs are business builders and team builders first and foremost. Startups don’t need all of these people at the beginning. # A startup's first finance hire is often an outsourced bookkeper to make sure their revenues and expenses are recorded accordingly, that invoices are going out and getting paid, and that cash balances on the books reconcile with the balances at your bank. As the company scales, the first additions are often at the strategic level - part-time CFO, for assistance in establishing processes, reporting for investors, interpretation of financials for strategy, assisting with important business decisions and financial impact - and the transactional level - staff accountant, AR/AP, analysts - to process the volume of activity and data you are creating. The route can be very different, though, depending on the skillsets of the people involved and the key needs of the company. Spendesk has additional pointers on hiring for FP&A at startups at FP&A best practices for startups, with good commentary on how a couple companies scaled their finance functions. 2023-01-03T00:00:00Z https://foresight.is/circulars/ What is a circular reference? # A circular reference refers to a specific formula construction in spreadsheets like Microsoft Excel and Google Sheets that occurs when a formula in a cell refers back to that same cell or refers to other cells that depend on the original cell. This creates a loop in the formula that cannot be immediately resolved. What Is Circular Reference in Excel? When an Excel formula refers back to its own cell, either directly or indirectly, it creates a circular reference. There are two types of circular references, direct and indirect: Direct circular references occur when a formula in a cell refers to its own cell. For example, you put the formula B1 + B2 in the cell B2, the formula is unable to calculate a result. This type of circular reference is a simple formula construction error and should always be fixed. Indirect circular references occur when a formula in a cell refers to a cell that is calculated based on the original cell. For example, you put the formula B1 + B2 in cell B3, and the formula in cell B2 is 1 + B3. This type of circular reference can be trickier to find and debug, sometimes can be resolved by using iterative calculations, often can be eliminated by creating a new formula using algebra, and sometimes is unavoidable. I've spent countless hours working through circular references; let's dive into those options for handling circular references a bit more to help make it easier for you to figure out what to do. Instead of counting sheep, I fall asleep by working through Excel circular references.— taylor (@tdavidson) October 27, 2017 How to find a circular reference # Microsoft Excel will identify a circular reference by a warning message popup that says: There are one or more circular references where a formula refers to its own cell either directly or indirectly. This might cause them to calculate incorrectly. Try removing or changing these references, or moving the formulas to different cells. Once you click ok, then a few things can happen, namely: Excel may display a zero, or the last calculated value, and it may stop recalculating the cell with the circular reference. Meaning, if you ignore the circular reference, you cannot trust the calculations because it will stop recalculating that cell and other cells that draw from that cell. Excel may show the warning message again if you continue to create new formulas referencing the original cell with a circular reference If you create additional formulas with circular references, Excel may not display the warning message again. The error message may continue to appear in other workbooks if a workbook with a circular reference is open. Excel has tools to help you track down the source of a circular reference to help you review and debug the formulas. The status bar in the bottom will report the cell reference where the circular is located, green arrows should appear on the screen to show you what cells the formula is referencing, and you can use trace precedents to trace through the formulas to figure out what's happening. From there, you can figure out if you want to change the formula or use iterative calculations. Google Sheets will identify a circular reference with an error message in the cell, saying: Circular dependency detected. To resolve with iterative calculation, see File > Spreadsheet Settings. Tracking down the source of circular references in Google Sheets is a bit tricker, as it does not have the same tools to identify the source of the circular or trace precedents and dependents as Excel, so you'll have to unravel the formula yourself. Of course, those warning messages won't appear if you have iterative calculations turned on, so let's discuss that next. Using iterative calculations # Both Microsoft Excel and Google Sheets can use iterative calculations to evaluate these types of formulas. This means that the spreadsheet program will attempt to calculate the formula multiple times, using an estimate for the final result each time, until the difference between the estimate and the actual result is small enough to be considered accurate. In Microsoft Excel, you can turn on iterative calculations by going through the menu: Excel > Preferences > Calculation > Enable iterative calculation (PC) or Use iterative calculation (Mac), check the box In Google Sheets, you can turn on iterative calculations by going through the menu: File > Settings > Calculation > Iterative Calculation (select on) In both programs you will be presented with a couple options to define the threshold and the max number of iterations. You can use them to define the parameters for how the program uses iterations, although I find in most cases you won't need to modify those settings. To understand how spreadsheet programs use iterative calculations, essentially the spreadsheet is attempting to calculate the formula, then running the calculations again, and again, etc., until eventually it (a) reaches the max number of iterations in the settings, or (b) reaches a solution to the formula. Circular references can be useful in some cases because they can simplify the formulas needed to solve some problems. There are good kinds of circular reference, if used appropriately. However, circular references can also cause problems if they are not used correctly. If the formula does not converge on a stable result, the spreadsheet program may continue to iterate indefinitely, causing the program to crash or slow down significantly (remember the spreadsheet is doing the calculations many times to find the answer, and if the result of that calculation is leveraged in other calculations, it can make the spreadsheet work significantly slower). Additionally, circular references can be difficult to troubleshoot because the formula depends on itself, making it harder to understand how the final result was calculated. In situations where circular references are not able to converge on a stable result, and rewriting the formulas into algebra are not possible, then we can use tools like Solver to help manually identify solutions, or leverage more advanced techniques like linear programming. Rewriting formulas using algebra # In many cases, circular references can be solved by rewriting the formulas to use algebraic techniques. This is why using circulars are often considered to be lazy or ignorant ways to create formulas. Let's go through a specific example. Circulars often come up in building cap tables because of the nature of what we are often trying to calculate, and a particular way I often see them is when calculating share prices or option pools. A common requirement is to assume an option pool of a certain size of the postmoney capitalization. Let's assume that we want 10% of the postmoney capitalization to be in an option pool. One way that people commonly first attempt to do this is to calculate the option pool by multiplying the option pool percentage by the number of existing shares. So if there are 10,000,000 shares currently, we would calculate 10,000,000 * 10% = 1,000,000 shares, resulting in 11,000,000 shares in total. But, 1,000,000/11,000,000 is 9.09%, not 10%, because we did not account for the dilutive impact of the new shares. shares prior to round + shares issued to new investors + new options created in the roun = total fully diluted shares after the round We could build the formulas to calculate the number of new options by multiplying the total fully diluted shares after the round * 10%, and turn on iterative calculations. And that would work (in my opinion, totally fine). Or we could calculate the new options as: ( shares prior to round + shares issued to new investors ) * ( 1 / ( 1 - 10%) - 1 ) Using that formula leveraging simple algebra, there's no circular reference involved, and thus no need for iterative calculations. And our goal in building models is to use this approach as much as possible, as detailed by the FAST Standard and other codifications of model building best practices. Learning to live with circulars # In some cases, though, trying to eliminate all circulars can be a fool's errand, or may create undesired usability issues for model users. In my latest cap table model, I use circulars instead of algebra to make the concepts easier to understand and the math easier to follow. Earlier iterations used algebra as much as possible, but that created (1) problems with stability when making complicated edits and additions, and (2) problems with comprehension by users. Replacing the algebra with circulars makes the model easier to understand, and is still industry-compliant with how funds, law firms, and founders build and use cap tables for financing rounds. Meaning, instead of using the algebra method above, I use the circular reference construction. I know that many may view that as wrong, but I think it depends on your vantage point and end goal. From an expert model building perspective, yes, I agree it's not the best way, but from a "how do I make this easy for thousands of people that don't want to understand the algebra" perspective, I think it makes the most sense. In the Cap Table and Exit Waterfall Tool, I use a dropdown to select to use formulas using circular references or not. That's a stylistic choice, so that I can distribute the model with circulars turned off to eliminate the circular warning message when people open the file, and then use a flag to tell people when to select the formulas that use iterations so that people can then turn on iterations proactively. Switching from using circulars to manual adjustments # I'll highlight another example of circulars, this one instead from my series of template models for venture capital funds. Management fees for funds are often calculated quarterly as a percentage of the total committed capital. Since the total committed capital of the closed-end fund is a fixed number and does not change over the life of the fund, calculating fees is usually straightforward: annual assumed percentage / 4 * the total committed capital We can then use that to budget fees over time and budget capital calls and cashflows so that we call the entire fund over time, and total called capital will equal total committed capital without any issues. Even changing the percentage charged per year (a common requirement) is simple to do. In some cases fund agreements will charge management fees based on assets under management, meaning that we calculate the management fee each quarter as: value of the total capital currently invested at the end of each period * the annual assume percentage / 4 Since that value of invested capital changes every period, and since the value of invested capital depends on how much of the committed capital we can actually invest after paying expenses (including the management fee), that creates a problem for a model builder. One way is to build the formulas and just turn on iterative calculations. Usually that will work without issues, but in some cases the spreadsheet program can fail to find a single, stable solution, often because either the computer or the web browser (in the case of Google Sheets) is not powerful enough to find a stable solution, or there are multiple potential answers so the model will find different answers every time it recalculates. [1] If iterative calculations do not work, then we either: Add a linear programming solution to the spreadsheet so that it creates constraints to help the spreadsheet software identify the right solution, or Change the calcs to remove the circular reference, which then means that it requires manual adjustment to get called capital close to committed capital Linear programming solutions can be difficult for users to explain and maintain, so instead, let's talk about how to change the calculation method and the trade-off it entails. Starting with invested capital # In my models I typically start with the fund size (total committed capital) as a key input, and everything flows from that assumption, figuring out an investment and called capital schedule based on that fixed number. But to use assets under management, the cleanest way without using iterations is to start from an assumption of how much is invested, and then calculate up to the total committed capital. Different approach to the same goal. I don't use that method in my template venture capital models because (1) committed capital is more common than assets under management, (2) iterative calculations usually work, and (3) the alternative approach is usually more difficult from someone to contextualize and input their assumptions. Most people think about their funds in terms of the fund size, and then use assumptions around expenses to figure out how much they have to invest, and it's easier to just assume fund size rather than alter multiple assumptions around ivnested capital and expenses to get to the fund size one wants to target. Cleanest math, but harder for practical use for most cases, in my opinion. Manually adjusting invested capital # Another approach is to estimate the expenses using committed capital, and then manually adjust up the invested capital amount (either using manual iterations or Goal Seek) to get called capital to equal committed capital. This manual adjustment allows for a fund manager to increase their budget to get them to equal, replacing a spreadsheet auto calc with a human adjustment to deal with the practical issue with circulars. While this works, it is also not particularly elegant and will require a manager to manually update this after changing any assumption regarding expenses or return profile, thus it is easy to make a mistake. Solving with algebra # For years I said that the algebra for calculating management fees using assets under management was too hard, but in mid-December 2024 I solved it with a new methodology. On the Forecast sheet there is a subcalculation routine that calculates assets under management as a percentage of invested capital given the capital deployment strategy and investment strategy (expectations of writeoffs and exits). The key is to separate out the forecast of management fees from the fees calculated using committed capital and the fees calculated using assets under management, do the algebra for the capital deployment and investment strategy to calculate the assets under management given $1 of invested capital, and then use the management fees incurred under the assets under management period on $1 of invested capital in the input for estimated total invested capital. It's still doing the same thing committed capital - expenses - management fees + recycled capital but now it's using a lot more subcalculations for management fees to make it work. This now allows a fund manager to select management fees to use committed capital at any point in the fund life, and the model will adjust, without requiring circulars. The option to use circulars still exists as a checkbox, the hope is that it no longer has to be used. This method will work for forecasts, but for manual inputs of investment strategy or actuals tracking it will not work, at least not at this time. People often forget that spreadsheets, in the context of modeling businesses, are better as tools for analysis than a finished, completed set of answers. The key for you in understanding how to address circular references is to understand the goals of your model, how you need to use it, and what you need to decide from it. This same dynamic often comes up when people wonder why IRRs in Excel are unstable or not calculating correctly. A subject for a later post. ↩︎ 2023-01-03T00:00:00Z https://foresight.is/why-teach/ The Cap Table Masterclass debuted in early 2022 and has evolved throughout its eight cohorts of students. Here's what I learned from teaching the course in 2022 and how it has evolved through 2023 and 2024. The first cohort in 2022 leveraged what I had learned from building, using, and iterating on the Cap Table and Exit Waterfall tool for the last 5+ years. My goal was to teach people how to do the math in that model for themselves, to understand how to issue equity, convert SAFEs and notes, issue option pools, and build exit waterfalls. I took a lot of time to build slides for each session and prepare for the class. But my second session didn't go as planned; building a model live during class from scratch, plus teaching the content, was a bit more challenging than I had expected. After that session, I then sat down and rebuilt new components and examples to cover the material for that class, and re-recorded a new session for people. And then I rebuilt the slides and model components and examples for each session. Overall, it worked, and the 8.8 (out of 10) course rating indicated I was on to something. The second session used the same course materials, and it worked ok, which the 8.5 rating indicated. But for the third cohort, I changed it up; I removed three sessions, condensing the material from two of them into other sessions, removing the session on modeling tokens (most people weren't as interested in it as I was, so I turned it into a post and free model), and created two office hours to give more space for people to ask questions, and the course felt better, which I think the 9.0 rating reflected. That's when I changed direction. Teaching the course, building new tools, and listening to questions forced me to reconsider my fundamental assumptions about the best way to teach the material. I rebuilt the base cap table and exit waterfall tool, reworked 50% of my slides, and redid all of my example tools to alter how I taught the concepts. Here's my thing: the terms we commonly use - e.g. premoney conversion, postmoney option pool - don't match up with how the legal documents describe what happens, and I think the process of translation creates confusion and makes it harder for a less experienced practitioner (for example, a founder that goes through the fundraising process a few times) to understand what's going on. Opaque, confusing, almost exclusionary. The math behind cap tables is not the hard part; the hard part is understanding the terminology and how to use it. I refocused the content to create more of a progression for students. Less algebra and complicated math, more focus on first principles, detailed use of the actual legal documents used in financings, and a nuanced shift in how to teach the common terminology. I can still dive into the complicated math if needed (and all the examples and explainer videos are still available), but they aren't the first thing I teach. And I also added an optional case study, a chance for people to work alongside the class to build their own model, much like I do for investors and founders all the time, and focused the office hours on the case study. I liked the flow of the course better, and I felt people understood things easier, and the ratings reflected it: cohorts 4 and 5 rated an overall 9.8 out of 10. For cohort 6, I redrafted the intro slides to the topic to cover cap table hygiene better, streamlined the case study, and edited the flow of two of the sessions. To create more opportunities for practice, I created a series of mini-cases or practice exercises with prompts to model and accompanying answer keys and videos discussing how to model each one. And based on chatter on Twitter, I added details on crowd safes and docs used in crowdfunding, to expand on different types of fundraising documents. In parallel with the masterclass, in late 2022 and early 2023 I also ran Build a Cap Table from Scratch, a one-day workshop created to be a short course on the topic, a condensed format to cover cap table modeling (excluding the exit waterfall component). The format has been well-received, but I also think it has some challenges in helping people learn the concept, as by nature of being a one-day, three hour workshop it doesn't provide a lot of space for people to reflect, review, and practice. Works for some, doesn't work for others. After running the same content for cohort 7 of the masterclass, in mid-2023 I tweaked the format for cohorts 8 and 9 in two main ways: Adding a new session on modeling investor returns, expanding on the exit waterfall material to show how to use waterfalls in valuations, portfolio management, and forecasting returns for investors across multiple classes of shares. The primary goal is to create a second class on waterfalls to give more time and review of the material while expanding on how to apply the concepts for practical goals. More flexibility to choose what to learn. The classes always provided flexibility in how people can learn (join live, watch on your own, practice on your own, live Q&A with me), for this cohort I added options in what people can choose to learn. The masterclass can now be taken in sections - Beginning Cap Tables, Intermediate Cap Tables, and Intermediate Exit Waterfalls - and you can take whatever combination of classes you want. Start with any section (prerequisites not required), register for additional sections, take them when you want, or upgrade to the Masterclass if you want. More flexibility in content to help people learn just what they want. For cohort 10, I changed the cadence of the classes to one per week to allow for more time to review the materials and work on the practice exercises between live sessions, improved the extra videos for those choosing to take the course self-paced, and added a new section of content to the exit waterfalls section about handling unconverted convertibles (SAFEs and convertible notes) in liquidity distribution calculations. Cohorts 11, 12, and 13 in 2024 followed the same cadence and content as cohort 10, with some changes to the first class around anti-dilution (to help make the topic more approachable), and the testing of an alternate time slot. Cohort 14 is the first cohort offered with the option for participants to earn continuing professional education (CPE) credits, after completing the NASBA accredidation process. Currently I am working on creating a better community for course participants and alumni, as well as easier access to new content for course alumni. A lot of changes over time, and to me this highlights why I teach: doing the work with thousands of people is how I learn, and every course is an opportunity for me to get better. I'm looking forward to seeing how the courses continues to evolve. If you'd like to join me for the Masterclass, you can register for the next course here, or learn about all of the cap table courses here. Question on whether a course is a fit for you, contact me. 2023-02-04T00:00:00Z https://foresight.is/spv/ What is a Special Purpose Vehicle (SPV)? # A Special Purpose Vehicle (SPV) is a legal entity created for the purpose of holding a specific asset or group of assets, such as a single company's equity or debt. SPVs are commonly used in private investments, including early-stage venture capital, to bundle a group of investors into a single investment into a company and to separate the risks and rewards from investments in one company from other companies How do SPVs work? # SPVs are highly flexible vehicles for making investments. Typically formed as limited liability companies (LLCs) or limited partnerships, both of which are pass-through entities, meaning that the income and expenses of the SPV are "passed through" to the owners, or members, of the entity in proportion of their ownership. SPVs are typically used to pool investors to make a single investment in a company, appearing as a single investor on the company's cap table. This structure offers simplicity to founders because it reduces the number of investors on a cap table and thus reduces administrative work and requirements. For investors, the structure means that the members of the SPV do not have direct voting or information rights, and have to depend on the general partner (GP) of the SPV to represent their interests. For many investors, this is an acceptable trade-off for access to investments sourced by GPs, often at lower minimum check sizes than would be required if they were investing directly into a company. Angellist also offers Roll Up Vehicles, an SPV led by the founder of a company to help make it easier for founders to bring in accredited investors at smaller check sizes. There are legal limits to how many investors can invest in an SPV depending on how much money the SPV raises, and the type of investment made by the SPV will determine the necessary investor accreditation of the SPV's members. What is an SPV? by Angellist is a great primer on how SPVs are structured and used. SPVs will have expenses to organize and maintain the SPV's entity, and can have management fees and carried interest costs, depending on the SPV's rules. How are SPVs created and managed? # A number of companies offer services to create and administer SPVs. Creating an SPV incurs the normal legal costs in creating LLCs or limited partnerships, as well as the annual filing and reporting requirements of the LLC and tax forms (K1s) to the limited partners of the SPV. Managing the legal requirements of an SPV can be difficult for a general partner that may create many SPVs over their investing career, and thus there are a number of platforms to handle the upfront and annual requirements. A number of companies that manage SPVs for investors are highlighted at Fund Management, but popular platforms include: [1] Sydecar Angellist Carta Allocations Canopy Odin Fund admins, legal firms, accountants, manual options with spreadsheets How are the economics of SPVs modeled? # Modeling the expected returns of a single SPV is straightforward, and can be done by modeling the amount invested and valuation of the investment, potential future investment rounds to understand dilution, graduation rates, and potential exit sizes to calculate expected value, gross and net multiple, and IRRs from the investment. Modeling the aggregate performance of a number of SPVs involves a bit more effort. For a general partner that leads many investments through SPVs from a network of potential limited partners, called a syndicate lead, the goal is to model out the aggregate or average value of a number of SPV investments to understand the aggreate realized and expected performance of their investments. Modeling the performance of the known or average future investment is straightforward, and involves the same process as modeling the expected returns of a single SPV. However, for understanding the potential returns to the syndicate's limited partners, understanding how the waterfall works takes a bit more thinking. In the Foresight venture capital fund models, the default assumption of the fund waterfall is a European "total fund" waterfall whereby the fund must return total called capital to-date before taking carried interest out of the fund's distributions to limited partners. However, the default waterfall behavior can be changed through a dropdown on the Forecast sheet to selected either the American "deal by deal" waterfall or the SPV "no clawback" options; the SPV no clawback option is used to model an aggregation of SPVs where each deal is independent, meaning that the GP only needs to return capital on an individual investment - not the overall fund - to earn carried interest. Therefore, for syndicate leads looking to use the Foresight venture capital fund models to model an aggregate of SPVs instead of a fund, simply change that waterfall option; and all other assumptions remain the same. This structure calls out one of the cons to a limited partner investing in a number of SPVs instead of investing into a fund; all else being equal, the limited partner will earn less because the GP manager does not need to use capital from winners to pay off the losers. As a simple math example, consider an LP that invests $50 into a fund (investing $10 per investment into 5 investments) v. an LP that invests into 5 SPVs at $10 per investment (total $50). All fees being equal, and assuming a 20% carry, if 3 of the investments return 5x gross and 2 of the investments return 0x, then both the fund and SPV will have total gross proceeds of 5 * 10 * 3 = $150, or 150 / 50 = 3x overall. The fund will charge 20% carry on (150 - 50), or the $100 gains after returning total invested capital, and LPs will receive 50 + 100 * (1 - 20%) = $130 in distributions, and GPs will earn $20 in carry. The SPV will charge 20% carry on (150 - 30), or the $120 gain on those investments, and LPs will receive 30 + 120 * (1 - 20%) = $126 in distributions, and GPs will earn (150 - 30) * 20% = $24 in carry. LPs in the fund earn a 130/50 or 2.6x net, whereas LPs in the SPVs earn 126/50, or 2.52x net. The difference in returns in this scenario comes only from the structure of the SPVs that separates the risks and rewards of each investment. Obviously that's a simple example, and "all else being equal" rarely applies in venture, and there are other pros and cons for funds v. SPVs. But it's important for LPs and GPS to understand this economic difference between a fund and SPVs. One additional complication is that any individual limited partner may have very different returns than the "average" limited partner across the syndicate's SPVs. Since investors will usually make different investment decisions on participating in the SPVs sponsored by the syndicate, the investments that limited partners make may vary widely, and thus the returns from their investments may vary widely. While all limited partners in a fund will see the same performance in their investments (on a relative basis, noting the absolute returns will vary based on the amount they invested in the fund), the limited partners participating in a syndicate across a number of SPVs can see very different performance based on what deals they decided to invest in. Consider the previous example, but alter the assumption so that 2 investments still returned 0x, 1 returned 10x, 1 returned 4x, and 1 returned 1x, for a total of 10 * 10 + 10 * 4 + 10 * 1 = $150 in total. If this was a fund, all investors would earn the same 2.6x net after carry. If this was a series of SPVs, the investor that invested in each deal would get the same 2.52x net as before; but consider an investor that invested in the 2 0x and 1 4x return, they would earn an overall 4 / 3 = 1.33x (before carry), but an investor that did 1 0x, 1 10x and 1 1x would earn an overall 11 / 3 = 3.67x (before carry). Varying the investments increases an investors' variability in investment performance, and while it can result in higher than average returns, it can also have the opposite effect on limited partner performance. Please conduct your own due diligence on these platforms, this is not a recommendation or advice on which platform you should use. ↩︎ 2023-10-27T00:00:00Z https://foresight.is/milestone/ Today I am celebrating a Foresight milestone: $1 million in sales from templates and tools. I started creating tools and templates for companies about 15 years ago, and starting selling them over ten years ago, iterating, refining, revamping, and learning how to make tools for entrepreneurs and venture capitalists to use to make business decisions. I'm just as proud about the over 39k people that have downloaded a template via Gumroad (a portion of the total downloads and customers I've had across different platforms). My original goal behind Foresight was to make building models and forecasts easier, better, faster, and cheaper, so that more people could understand how to build models on their own. Creating free tools and templates has always been core to my mission and helps drive an impact to all entrepreneurs of all stages, geographies, and paths. I am as energized as ever to continue to improve the tools and use the newest technologies in the space. Thank you for your ideas, comments, assistance, and support, appreciated as always. 2023-11-06T00:00:00Z https://foresight.is/defining-metrics/ How do you define the metrics to care about in your business? There are lists of metrics that work for types of businesses: For SaaS, there are many lists of metrics, here's one example from Stripe For Ecommerce, there are also many lists, here's one example from Shopify Here's an example set of metrics for marketplace businesses by A16Z but picking the right metrics for your business is more nuanced than just getting the data and calculating everything on a list. Watch the video above for 10 minutes on how to think about picking the right metrics to help you understand the performance of your business and make decisions. 2024-01-14T00:00:00Z https://foresight.is/lpa/ Decile Group (VC Lab) offers a free downloadable template limited partner agreement (LPA) called the Cornerstone LPA which is a great resource for emerging managers of venture capital funds. I highlight it because it is a great way for emerging managers to familiarize themselves with the terms and structures that define how venture funds operate, to see exactly how an American Waterfall is defined, or how management fees, operating expenses, and many other terms are built into fund operating agreements. Download the latest version of the Cornerstone LPA > 2024-01-24T00:00:00Z https://foresight.is/handbook/ My quick review of the Financial Modelling Handbook (buy here): technical, practical, accessible, valuable, immediately applicable. People with a familiarity with financial modeling but looking to improve their skills will benefit from the handbook, which combines a philosophy to financial modeling with highly practical demonstrations and examples to teach you how to apply that philosophy to build financial models that can be understood and used for small and big projects. stoked to be diving into this guidebook to financial modeling by @kenny_wj , the book I wish I had written :) pic.twitter.com/hIWJ2mhHm4— taylor (@tdavidson) October 6, 2023 Written by Kenny Whitelaw-Jones, the handbook starts with explaining why and how to use financial models, to set the stage for applying those insights to a practical example of a project finance financial model. The handbook provides detailed instructions on how to build common components of financial models, and along the way teaches the rationale behind many modeling conventions and how to apply them, such as why flags are valuable, typical sign conventions, standard ways for building corkscrews, how to structure inputs and calculations, how to build scenarios, how to create variance analyses, and many other components critical to building financial models, with examples in the book and downloadable practice models to use. The case study is particularly valuable: I picked up a number of ideas on balance sheet modeling, debt interest and principal payments, and tax calculations that I applied in my models. The screenshots in the book are useful to explain the structure, formatting, and rationale, and the accompanying downloadable Excel files provide concrete examples for you to inspect and use. I would recommend it to anyone looking to become a better financial modeller, whether you taught yourself or took trainings to learn financial modelling, because the handbook does such a good job of explaining why we do things and then showing you how to do it. I taught myself how to build models from thousands of hours of iterating on models, but guides like the Financial Modeling Handbook can help you shorten that learning curve. 2024-02-08T00:00:00Z https://foresight.is/section-174/ The One Big Beautiful Bill Act signed on July 4, 2025 reinstated expensing of US-based research and development (R&D) costs. The backstory to why this matters is below, updated with the changes implemented in the act. A change in tax laws in the USA has created a lot of chatter recently: A tax change rarely causes panic across the tech industry, but it’s happening in the US. If Section 174 tax changes stay, the US will be one of the least desirable countries to launch tech startups.A deepdive into this important topic in today's issue: https://t.co/cq0tfnHnWR pic.twitter.com/RN9ciidvLB— The Pragmatic Engineer (@Pragmatic_Eng) January 4, 2024 Ben Thompson at Stratechery explained it in April 2023, Startups and the R&D Tax Credit: For many years research and development costs have effectively counted as normal expenses, which means they decrease a company’s tax liability (because they reduce profits). Because the 2017 “Tax Cuts and Jobs Act” was passed via the reconciliation process (in order to avoid a filibuster), it had to be budget neutral after 10 years; one tactic used to accomplish this is to make future changes to the tax code that increase revenue, even though the bill’s drafters anticipate those changes will be rolled back before they are implemented. One of these changes was to change research and development costs from expenses that could be realized immediately to expenses that could only be amortized over a minimum of 5 years (and 15 years for international research and development) starting in 2022; this amortization schedule also starts on July 1 of the year in which the expenses are incurred. Why this matters # The reason why this has created some panic is that it significantly impacts corporate income taxes for companies where software development is a major expense; previously you could deduct the full cost of software development (primarily salaries for software engineers you employ) as an expense, but now you have to amortize it, over either 5 years for domestic expenses or 15 years for foreign expenses. Here's an example of the math: How it used to work: If a company had $1.5 million in revenue and $1 million in expenses (let's say it was entirely domestic R&D), it would pay taxes on its $500,000 profit. How it works now: In the same example, the company would have to amortize the $1 million in expenses over five years, so it would deduct only $200,000 (one fifth) and would pay taxes on $1.3 million in profit. The implications on this for technology companies investing in software development with revenues operating close to break-even on a cash flow basis are enormous, and the industry has been lobbying for Congress to amend the law. While the IRS has been providing updating guidance (Dec 22, 2023) providing limited relief, Congress has yet to pass legislation addressing the core concern of companies. The impact is real. The elimination of expensing research and development (R&D) disincentivizes companies to spend on software development (and software engineers). And while companies do get the benefit of amortizing the expenses in years 2-5 (meaning, that over time companies get the benefit from the amortization of previous years expenses), startups that are not around in five years will not see that benefit. There is hope; as part of the Tax Relief for American Families and Workers Act of 2024, there would be temporary reprieve by (a) delaying the change to Section 174 to Jan 1 2026 and (b) applying the reprieve retroactively, allowing companies to amend their 2022 tax filings. On January 31 the bill passed the US House of Representatives, but it is unknown if or when it could pass in the US Senate. The One Big Beautiful Bill Act # The uncertainty is now over. The One Big Beautiful Bill Act (OBBBA) signed July 5, 2025 addresses the issues raised above and reinstates expensing of US-based R&D costs in the period when the expenses occur, but foreign R&D must still be amortized over a 15-year period. While the change will require some adjustments for companies that have already filed quarterly reports, the act eliminates the issues raised above and provides clarity for companies in how to handle their research and development budgeting. Questions, ask anytime. If you're wondering how to handle this in a financial model, the Standard Model was amended in Dec 2023 to make it easier to categorize an expense for amortization, input the amortization period, and the model will automatically handle amortization and any associated impact to corporate income taxes and consolidated financial statements. 2024-03-05T00:00:00Z https://foresight.is/fund-to-firm/ Last week Graph Advisors hosted a session on how to go from fund to firm, focused on helping emerging venture capital managers think about how to go from running their first fund to building an enduring firm that can manage multiple funds. Watch the replay below: https://www.youtube.com/watch?v=0lJaULPtn-A Graph Advisors summed up the takeaways from the session, highlighting the key components of the roadmap to building a firm: Relationships - This is all about your relationship management, connections, and outreach. Again, its not about the software you choose its about communication, references, and asks from LPs, founders, and advisors. ... The Pitch - The story behind what you are doing is essential. Being able to distill down your thesis into a few sentences or a few words make the word-of-mouth narrative easy to pass along to investment committees, partners, and anyone else who needs a brief summary. The Team - Who brought you here may not be who can bring you there. It’s time to review, measure, and negotiate what your firm needs today. This means looking at full time employees and service providers. The Execution Plan - This is about building the machine to bring everything together. When do you want to do your 2nd first close. When are you going to work on this (your calendar is the best indicator). Who can help you do it. Why is this a priority. What does your 2nd first close look like? Read the full recap on Graph Advisor's blog. 👉 To me, the question for emerging managers is how do you take the excitement of a new fund - in yourself, from your investors, partners, and founders - into building more than just a fund, but an approach and system that can raise, deploy, manage, and deliver returns over multiple funds. What I'm most excited about is the new range of tools, information, resources, and support for venture fund management that we've seen emerge over the last five plus years to help new managers professionalize their investing approaches and practices. Onwards and upwards. 2024-04-03T00:00:00Z https://foresight.is/investing-ai/ Artifical Intelligence (AI) venture fundings have generated a lot of headlines over the past year. $1 billion invested by Microsoft into OpenAI (creator of ChatGPT) in January 2019, followed up later investments of up to $13 billion in 2023. Google invested $500 million in Anthropic in late 2022 (creator of Claude), with further investments of up to $1.5 billion to come over time. Amazon invested $1.25 billion into Anthropic in September 2022, and an additional $2.75 billion in March 2024. In 2023 alone, reports showed that big tech companies, including Amazon, Google, Microsoft, and Nvidia, backed 75+ AI startups, marking a 57% increase in AI deals compared to 2022. The total investment by these tech giants amounted to two-thirds of the $27 billion raised by fledgling AI companies in 2023, with Microsoft being a major player through its partnerships with OpenAI and Inflection. Techpedia, Amazon vs. OpenAI: AI Battle Intensifies with Anthropic Investment The money gets the headlines, but what's actually interesting is the why and the how behind these investments. Balance sheet business development # More than just an investment, these are business development deals to protect valuable business units: The likes of Anthropic and OpenAI require an immense amount of computing power to train their models and the cloud giants can help. These ‘strategic collaborations’ include contingencies that require the company to leverage the investor's cloud services instead of shopping around to other providers for the best product, price and service. The cloud providers are investing capital into a business, partnering in their future success, implementing the tooling across their suite of offerings, and are doing so by essentially taking cash out of their left pocket and placing it into their right pocket. Or in financial terms: using their balance sheet to drive the income statement. The outflow of cash in dollars may seem large, but those dollars, thanks to their strategic partnership, will eventually flow back into the business via cloud consumption. The merits of these revenues can be debated given it's cashless revenue, but if the large language models are successful, the low quality revenues today should eventually turn into high quality revenues down the road. Titan, The Weekly These deals are all announced not only as investments, but partnerships to work together on developing AI computing systems, with the AI companies announcing the cloud companies as their "preferred cloud provider" and the cloud companies announcing the intent to offer AI models and tools for developers to build into their own applications. And given the amount of capital needed to pay for the cloud services necessary to develop, deploy, and continually refine the LLM models used for AI services, deals like these are necessary to fund operating expenses, as few venture capital funds are large enough to consider writing these kind of check sizes out of their funds. AI companies (I would believe) get to pay less than rack rate through these partnerships, fund their growth at a lower cost of capital than venture capital, and the cloud companies get access to valuable technology for them and their cloud customers to build AI into their product suites (e.g. Microsoft Copilot and Amazon Bedrock, respectively). Drew Breunig dives into some of the implications of the cloud provider support of LLM builders in How to Build a Bigger Bubble Cash, credits, and convertibles # The billion dollar headlines also miss an important point: unlike investments from a venture capital or investment firm, these are not simple investments of dollars but highly structured deals that (seem to) involve a mixture of investment dollars for equity, cloud services credits valued as investment dollars, convertible notes, and revenue sharing. Here's a few examples of deal structures that have been rumoured: Microsoft’s infusion would be part of a complicated deal in which the company would get 75% of OpenAI’s profits until it recoups its investment, the people said. (It’s not clear whether money that OpenAI spends on Microsoft’s cloud-computing arm would count toward evening its account.) After that threshold is reached, it would revert to a structure that reflects ownership of OpenAI, with Microsoft having a 49% stake, other investors taking another 49% and OpenAI’s nonprofit parent getting 2%. There’s also a profit cap that varies for each set of investors — unusual for venture deals, which investors hope might return 20 or 30 times their money. Semafor, Microsoft eyes $10 billion bet on ChatGPT Google's investments have been more straightforward, with reports that their $500 million Anthropic investment was in a convertible note that converted in a later equity investment round led by Menlo Ventures (interestingly, done through an SPV rather than direct fund investments). Amazon’s investment, disclosed in a quarterly filing, included a $1.25 billion deployed in September in the form of a convertible note that converts in this round. Amazon also “has an agreement,” the company wrote, to invest up to $2.75 billion in a second note that would expire in Q1, 2024. Forbes, Inside AI unicorn Anthropic’s unusual US$750 million fundraise Many reports have noted that the publicly-announced investment amounts include a mixture of cash and credits for cloud services, which would point to the need for investment structures flexible enough to handle variable investment amounts. Convertible notes provide some additional return to investors in the form of interest rates; interest rates on notes have doubled from 5% in 2019 to just under 10% in late 2023, and combined with the average 20% discount rate, can be a flexible way to structure an investment with a business partner before they convert through an external investor-led equity investment round. The rwave of investments and partnerships in the AI industry points to a pragmatic approach towards funding innovation. While the headlines focus on the massive amounts of money being invested, the real story is perhaps more grounded. Shared goals, shared technology, shared data, and hopefully ground-breaking improvements in AI. 2024-08-13T00:00:00Z https://foresight.is/earn-cpe/ New in Fall 2024, participants in the Cap Table and Exit Waterfall Masterclass can now optionally earn continuing professional education (CPE) credits from taking the course. Here's the details on what CPE credits are, whether they apply to you, and what you need to do to earn them. What are CPE credits? # Many licensed professionals - in fields from information technology, accounting, and more - are required to take continuing professional education (CPE) courses to continue their education and keep up to date in their field in order to maintain their license. Requirements vary by field and organization, but in accounting, the American Institute of Certified Public Accountants (AICPA) requires certified public accountants to complete at least 120 hours of training in each three-year reporting period. In the United States, each state's Board of Accountancy can set their own requirements on the types of credits required, but in general, courses that have been certified by NASBA (National Association of State Boards of Accountancy) can count toward the CPE requirements. How to earn CPE credits with Foresight # New in Fall 2024, Foresight (Unstructured Ventures, LLC) is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors, and is offering the Cap Table and Exit Waterfall Masterclass for 9 CPE credits in the Finance field of Study at the Basic Program Level, offered through the Group Internet Based delivery method. The time and participation requirements to earn credits are detailed here. Taking the course for CPE credits is purely optional. If you are not interested in earning CPE credits, you can simply take the course as usual, and while the participation requirements are structured to enhance the course experience, you won't be required to fulfill them. The Cap Table and Exit Waterfall Masterclass is structured to help participants build new skills in venture capital, including how to build a cap table for a company, calculate share prices, the impact of new fundraising rounds on shareholders, convert SAFEs and convertible notes, create and expand option pools, and create liquidity distributions to calculate the returns to shareholders from an exit event. Over 300 people have joined the previous 13 cohorts of the course (here's how it has evolved over time), and if you're interested in learning more about the course and when it's offered next, check out all the details here. You are interested in the topic but not interested in a live class format, the Cap Table and Exit Waterfall Course is a self-paced option with recorded videos and access to the same materials as the masterclass. 2024-09-23T00:00:00Z https://foresight.is/revenue-models-types/ Building a solid financial model requires a solid understanding of key drivers of revenues and expenses underlying the company's business model. Foresight's financial model templates are built to be used for many different business models, based on my experience in building financial models for thousands of different businesses across a multitude of revenue models. Let's unpack the meaning of business models, revenue models, and how to model common business models. Revenue models v. Business models # Although the terms are often used interchangeably, there is a difference between a business model and a revenue model. A business model is the overall strategy behind a company - what it does, who it serves, how it is distributed, how it incurs expenses, and how it earns money - while a revenue model focuses on how value is exchanged between a business and its customers, including pricing, timing, and payment methods. Additionally, the term revenue stream is often used interchangeably with revenue model, but there is a difference. A revenue model is a framework for how a business generates money, a revenue stream is a specific source of income within a revenue model. A revenue model is a component of a business model, as it focuses on how a company makes money, while a business model includes the larger operational strategy behind the revenue model. More about business models at What is a Business Model? Modeling revenue models # From a financial modeling perspective, modeling the revenue model can mechanically be done in a few different ways: Top-Down Forecast # A top down forecast typically starts with a macroeconomic forecast for the market a company operates in, and the uses an assumption of market share capture to calculate the company's revenues. While typically better used for estimating or justifying the addressable market size for a business, it can be used to forecast revenues in the absence of past data and to provide a "check" on more detailed bottoms-up forecasts. All too often, a company builds a detailed bottoms-up forecast based on forecasting customer acquisition and retention, but fails to check to make sure that the implied percentage of the addressable market captured is reasonable. But the issue with depending on this as a primary forecast method is that it gives no insights into the operational drivers of growth and thus is not useful for making tactical deecisions about the business. Another tops-down way to forecast revenues is to start with the previous period's revenues and apply an assumed growth rate to calculate the current and future revenues. If the business is mature, the past is an indicator of the future, and you have historical data to use, this can be a good starting point to forecasting a business. Like a top-down forecast, this method does not capture key operational decisions to understand "why" a business is growing, and if you are forecasting an immature business with limited historical data, this method suffers from the same resulting issues. Bottoms-Up Forecast # This method involves building up a forecast based on the building blocks relevant for the company at that stage in time. Often this is based onmodeling customer-focused operational components of the business model, including: Growth - how the business generates new potential customers (marketing channels, sales channels, etc.) Conversion - how the business converts them into new customers (trials, contracts, etc.) Retention - how the business retains customers (customer support, product updates, etc.) Revenues - how the business generates revenue from customers (including pricing, timing, payment methods, etc.) The methods to forecast growth could include: A sales capacity (or quota capacity) forecast for a sales-driven business, which forecasts the hiring of new sales people, their sales ramp (the time it takes to become fully productive), and their lead generation success or sales quota to forecast the number of new customers acquired per period. A marketing channel forecast for marketing-driven business, which forecasts marketing spend, new leads, new customers, or some operational growth metric for the business. A product-led growth forecast for product-drive companies, which may focus on activating, engaging, and retaining customers through their interactions with the product. Customer acquisition is driven by inbound strategies to drive trials or free users, converting and retaining them to paid users, and building sharing and virality into how customers use the product to create a cycle of growth where the product itself is the primary driver of growth. Modeling a company's complete business model could encompass a mixture of all of these methods, and could be split up by business segments, customer segments, product features, or any level of grouping that can help deaverage a business and help build a better forecast. Modeling business models # A business model may consist of multiple revenue models, depending on how the business creates value for users and how the business chooses to monetize that value. In addition, modeling a business model often includes: Variable expenses - how the business incurs expenses that are variable based on growth and retention of customers (for example, marketing expenses) Fixed expenses - how the business incurs expenses that are fixed regardless of growth or retention of customers Semi-fixed expenses - how the business incurs expenses that are fixed over some time period or some number of customers, but will change, often in step changes, as the number of customers changes (for example, a hiring plan or rent) Cash flows - how the expenses are paid and how the business collects cash for earned revenues (for example, the timing of those payments) Modeling revenues and expenses is the core part of building a financial model for a business, and everything else - financial statements, key performance indicators, and more - are ways to view and analyze the performance of the business based on (a) the accounting rules the business is subject to and (b) the metrics, key drivers, and benchmarks for that specific business model. A common tool used to visualize, communicate, and design business models is the business model canvas, which breaks down business model into nine primary components for strategic analysis and exploration. FP&A models # Financial Planning and Analysis (FP&A) models encompass a wide range of financial models to support business decision making and strategic planning. They include the core aspects of revenue models and business models detailed above, but may include any analysis to understand the past, present, and future performance of a business. Mosaic's post on FP&A Modeling: 8 Models for Financial Forecasting is helpful for understanding different models in FP&A and includes a few examples of modeling revenue models and business models. Common revenue models # Below are are a list of a few common revenue models with considerations about how to model the revenue model and their related operational and expense components. Subscription # Subscription is a common revenue model where customers (or subscribers) pay a recurring fee for access to a product or service. The fee could be be paid monthly, annually, or some other time period, and the services or products delivered on a recurring (most typically available at all times each month) basis. Companies will often create different subscription tiers to target the features, functionality, access, or pricing to different customer segments. Software-as-a-service (SaaS) is a common business model for software companies leveraging a subscription revenue model. Subscription revenue models can often be used for physical products as well, such as subscription boxes, where the customer pays a monthly fee to get a new set of products each month. Key components to modeling a subscription revenue model include: Acquisition of new customers. This may be based on marketing spend, outbound sales, partnerships, and many other acquisition channels and methods a company decides to use. Simple approaches will just model the total new customers acquired per period, other approaches will model the growth activity and it's conversion into new customers, and more complex methods will model each acquisition channel separately and may even model retention and revenues per original acquisition channel. Retention and Churn. At every point where the customer's contract is up for renewal, the business must model the likelihood of the customer renewing or ending their subscription. Churn reflects customers ending their subscriptions, and there are multiple ways to model churn; many companies will model churn as a percentage of the total number of customers at the beginning of the period, while others will look to model the churn of customers who have been with the business for a certain period of time. The difference is that the latter approach requires a time-based cohort approach, which is more complicated, but can better reflect churn if the expectation of churn changes over a customer's lifetime. Bookings, Billings and Recognized Revenues. The concept of revenues is often misunderstood. The total value of a customer's contract when a new contract is signed (bookings), when customers are billed (billings), and when revenues are recognized (recognized revenues) may be different if the contract lengths are greater than one month and the amount paid upfront is greater than one month's revenues. Billings reflects when the customer is billed, and recognized revenues reflects when the business delivers the service or product and recognizes the revenues on the income statement. New billings each period are added to deferred revenue, which is reduced by the amount of recognized revenues each period. Note, billings does not represent cash received, as the timing of payments may be different from when the customer is billed. Modeling the related operational components of a subscription revenue model requires modeling the costs necessary to deliver the product or service. This could include product and material costs related to the products delivered that period, shipping, fulfillment, customer support or customer success costs, hosting, and more. Software-as-a-Service # The complexities in modeling SaaS businesses often come down to defining the customer segments and pricing tiers to model. If the company has different pricing tiers, some companies may choose to model each pricing tier separately, while others will just model the total number of subscribers using a weighted-average revenue per customer approach. The two approaches can be equivalent if you assume the same growth rates of customers in each pricing tier, and you can still break out the revenue contribution by pricing tier even if you use a weighted-average approach. But some will prefer the granularity of modeling each pricing tier separately if they want to make different growth, retention, or changes in pricing or revenues per period. In addition, Software-as-a-Service (SaaS) businesses are often best modeled using a time-based cohort approach to model the acquisition and retention of customers over time, allowing you to model a retention curve that changes as a customer ages (e.g. high churn early in a lifetime, lower churn as a customer's total time with the business increases). Meaning, instead of just using one retention curve to apply to all customers, you model the retention of each monthly cohort of new customers separately (usually each cohort is in it's own row), and sum up the total of all the cohorts to get the total number of customers at the end of each period. The detail in a cohort approach allows a modeler for more flexibility and granularity in modeling: A. Use a retention curve that reflect changing retention per period related to how long a customer has been a subscriber B. Use a revenue curve that may reflect changing revenues per customer per cohort, either to reflect grandfathered plans, land and expand strategies (revenue increases as a customer grows their business, usage, seats, etc.), upgrades to larger pricing tiers, changes in the mix of pricing tiers offered (if using a weighted-average approach to modeling multiple tiers), and other per-cohort revenue adjustments. There are innumerable places on the web to learn more about SaaS businesses, I suggest starting with The SaaS CFO as a good resource for the topics that matter. Product-as-a-Service # The Product-as-a-Service model (PaaS), also known as Product Service Systems, is a business model where a company bundles services with products. PaaS can usually be modeled using the same mechanics as a subscription revenue model. There are also a few revenue models that are not considered subscription revenue models, but can be modeled using the same or similar methods. Pay-per-seat # Pay per seat is a revenue model where a customer pays a recurring fee based on the number of users ("seats") they have in their organization. Modeling pay-per-seat is very similar to subscription revenue models, and the number of seats can either be an explicit part of the revenue calculations (e.g. organizations * seats * price per seat) or an implicit part of the revenue calculations (e.g. organizations * price per organization). Usage-based # Usage-based (or consumption) business models charge customers based on how much they use a product or services. Usage is monitored and billed on set time periods or based on usage limits per period. Usage-based business models can often be modeled using subscription revenue methods by calculating the average usage per period and the revenues per use to determine the average revenue per customer per period, and either using that for all customers or dividing the customers up into different segments or groups based on typical usage. The variation in monthly spend creates issues in reporting and forecasting, but subscription mechanics are still a good base for usage-based business models. Royalties # Royalties are often a subset of usage-based business models, where a distributor of a product will pay fees to the creator or owner of the product based on customer usage. Many royalty business models can be modeled with the same mechanics as usage-based business models. Advertising # Advertising-supported business models earn revenues from advertisers who pay to have their ads displayed to the company's users. Advertising revenue models can be modeled by using a demand-driven approach (number of advertisers, average price paid per advertisement), a supply-based approach (number of users, number of impressions, advertisements per impression, number of advertisements sold per impression or fill rate, price per ad), or a subscription-like approach (number of active users, advertising revenue per active user per period). Regardless of the modeling technique used, businesses will often simplify their understanding of the business down to users and revenues with metrics like advertising revenue per user (ARPU). The right approach will vary by the business and its needs at that point in time. Renting and Leasing # Renting (providing access to physical products, spaces, or fixed assets for periods up to a year) and leasing (typically longer-term contracts) business model scan also be modeled using the same mechanics as subscription revenue models, although often with different terminologies. Modeling licensing and renting can also involve more detailed accounting treatment of revenues and expenses, including amortization and deprecation of the assets being rented or leased. Freemium # Freemium is often described as a subscription revenue model, but it's actually an acquisition model. Freemium involves giving users access to a free version of the product or service and selling them access to premium features. The mechanics to modeling freemium businesses are the same as subscription revenue models, just noting that it is important to model the acquisition and retention of free users, their conversion to paid users, and the costs to providing the product or service to all users (not just the paid users). Product-led Growth # Product-led Growth (PLG) refers to an acquisition strategy that is based on leveraging product mechanics for outbound and inbound customer acquisition. This may lean on tactics to create virality or sharing of the byproducts of customer usage of the product or service. PLG doesn't define a revenue model, but it does indicate a focus on what a company is allocating resources to, and a company must treat it as a component of it's business model in order to succeed. Licensing # Licensing is a business model that often uses a subscription revenue model, where the customer pays a recurring fee for access to a product or service, but can also use a transactional revenue model where the customer pays a one-time fee for unlimited access for a particular time, or a one-time fee for a specific use, or a one-time fee for perpetual use, or other variations. Licensing is often used for intellectual property, trademarks, patents, among other types of products, and can also be used by software companies one-time sales of licenses for products. There are many different ways to model a subscription revenue model, and the right approach will depend on the stage of the business, the complexity necessary to model at that time, and the key questions for stakeholders and potential investors to address. Transaction # A transaction revenue model is where a customer pays a one-time fee to get a product or service in return. Many different types of business models leverage a transaction revenue model, including manufacturers, marketplaces, retail (both physical and ecommerce), wholesalers, affiliates, reselling, agency, brokerage, services, and more. A transaction-based revenue model is also related to other types of revenue models, such as donations, commissions, and more, and can come under names like markups, cost-plus, or other variations. Key components to modeling a transaction revenue model include: Transactions per period. This may be done by modeling customers, orders, or whatever the transaction is called, but the key is to model the level of transaction activity per period Average transaction value or average order value (AOV). This will reflect the gross merchandise value (GMV) or average order value (AOV) or some other metric that captures the average monetary value of a transaction. Gross transaction value (GT) or gross merchandise value (GMV). This is the total value of all transactions in a period. Revenues. This may be equal to gross transaction value if the business is selling something they made or purchased, or it may be a percentage of gross transaction value if they are operating a marketplace or other business model where they are facilitating transactions. Ecommerce # Ecommerce is a common business model for companies selling physical or digital products that uses a transactional revenue model. Companies produce goods that they sell to consumers, primarily using the internet to acquire customers, without requiring customers to step into a physical store to make a purchase. Modeling ecommerce businesses can be straightforward or complex, depending on the number of SKUs, warehouses, inventory management, materials purchasing, marketing channels, repeat customer behavior, and other factors, and one of the key things for a business to decide is what level of detail is valuable for forecasting and operating the business. I typically model ecommerce businesses using a time-based cohort approach to model new and repeat customers and calculate revenues as total orders multipled by average order value, or when available or applicable layer a cohort-based approach for the average order value or average transaction value of repeat purchases to then calculate revenues. A key component to modeling ecommerce businesses is modeling inventory management and the cash flows associated with purchasing materials or inventory. More details on how to model inventory at Inventory. Marketplace # A marketplace business model is one where the business brings together buyers and sellers and faciliates transactions between them. Marketplaces may charge fees to buyers and/or sellers, they may charge subscription fees to sell or buy, they may charge transaction fees on a fixed or semi-fixed amount per transaction or number of transactions per period, or transaction fees as a percentage of the transaction amount. Modeling a marketplace business model often utilizes a mix of subscription and transactional revenue models, and is fundamentally based on modeling the supply-side (sellers) and demand-side (buyers) of the marketplace and the resulting transactions the marketplace facilitates. Services # A services, consulting, or professional services business model is where the business provides a service to customers and charges a fee for the service. The services could be one-time or recurring over a period of time (a services contract, a consulting agreement, a project, a retainer, etc.), and priced as a one-time, recurring, or the contact culd specify a payment schedule (e.g. a percentage upfront, percentage upon completion or end of contract). Modeling a services business model can be harder because of the variations in the ways people forecast services businesses from a project and staffing perspective. You could model the number of projects, revenues, hours and staffing required per project, and create a staffing plan combining employees and contractors necessary to deliver the projects. You could also model the employees, their salaries, billing rates, and utilization per period to calculate revenues and costs. A demand-drivern forecast (projects) is usually a better approach because it forces one to think about how to drive projects and growth, but both approaches can work. Financial # Financial services firms often work a bit differently than other businesses. This is not exhaustive, but a summary a few common revenue models in this area. Interest # In its simplest form, an interest revenue model is where a business loans money to a customer and charges interest on the loan, which ia paid through a monthly loan payment comprised of principal and interest. Their costs are typically the cost of funds, meaning the expenses they pay on the money they lend out. A common revenue model for commercial banks, they accept deposits from customers and lend out money to individuals and businesses. Trading # Trading is a revenue model where a business buys and sells financial instruments, such as stocks, bonds, currencies, commodities, and more, and earns a profit from the difference in the price of the instrument when it is bought and sold. Businessess may do this with their own capital, or they may do it on the behalf of customers, charging subscription fees, a percentage of the assets managed, a percentage of the profits from trading, or other variations. Trades may be short-term (e.g. public equities, futures, options) or long-term (e.g. private equity, venture capital). Trading may also involve an arbitrage strategy, where the business buys and sells the same asset in different markets to profit from the difference in price. Modeling trading revenues models is typically similar to transactional revenue models, where you have to calculate the cost of the assets bought, and the proceeds from selling the assets, and the total value of the assets held by the business, and then from that you can calculate the revenues depending on which specific revenue stream the business is using. Insurance # Insurance is a revenue model where a business sells insurance policies to customers, and in exchange for a premium, the business agrees to pay for losses that the customer may incur. The revenues to the business are the regular premium payments from the customers. Modeling an insurance revenue model is similar to a subscription revenue model, calculating the acquisition and retention of customers and the regular premium payments. The payments to customers when they incur losses is an expense, and the business must model the liklihood of the timing and amount of those losses to forecast the projected expenses and cash flows. Summary # Modeling a business model can be hard, and it can be confusing to sort through the different types of revenue models and the pricing schemes that are used. Suggestions on business or revenue models to add above, or questions on modeling your business, ask anytime. 2024-10-12T00:00:00Z https://foresight.is/vibes/ Financial models do not have to be complicated to be effective. What they do have to do is reflect the key drivers of the business at a level of detail that helps operators make business decisions. As companies get more mature, larger, and complex, the scope of decisions increase and the range of business decisions expand, making complex financial reporting and analysis more valuable. But for companies raising early-stage investment capital, complicated financial models may distract from the key decisions that operators need to make and make it harder to communicate to potential investors how operational decisions will drive financial results. Which is why the frame of "vibes" v. Excel is interesting: Discussing vibe vs excel rounds with @jordihays https://t.co/U27um8HxTf pic.twitter.com/zVX4Z6BIeV— John Coogan (@johncoogan) October 4, 2024 As Jordi notes: ... does a company have product market fit or narrative market fit? And you actually get crazier multiple for narrative market fit. ... The real crazy multiples come when it's both. An "Excel round" is shorthand for a fundraising round where the business's numbers - some mixture of top-line revenue growth, gross margins, unit economics, operating metrics - are used to justify product market fit, and the valuation is justified by those numbers. A "vibe round" is something different, where the business's narrative - the story of the business, the vision for the future, the team, the market opportunity, the product, the go-to-market strategy, the competitive positioning, the traction - is used to justify the valuation. lolOr something like this where x = valuation pic.twitter.com/CamlH1XBDc— Matt Turck (@mattturck) September 20, 2024 The combination of the two - vibes and Excel, narrative and numbers - is the goal. Financial models help to the degree that they help operators understand, communicate, and leverage resources (e.g. investors, employees, customers) to drive the business forward. Far too often I see complicated financial models that are not helpful to founders and investors achieve those goals, but it's not as simple as "complicated is bad", because some businesses, operators, industries, and stages of growth require and value the nuance that a complicated financial model can capture. The key in those situations is to make the presentation of the numbers as clear as possible, focusing on the key numbers that demonstrate the business narrative. Or as I prefer to say it, party up front, business in the back. [1] The flip side to "business in front, party in the back". ↩︎ 2025-03-12T00:00:00Z https://foresight.is/franchise-taxes/ Corporate franchise taxes are an important part of a company's compliance requirements. Franchise taxes come under many names (business privilege tax, business franchise tax, business license tax, etc.) and vary widely in how they are calculated. The amount a company owes is typically based on one or more of the following factors: Net Worth: The value of the company's assets or capital stock. Share Capital: The number of authorized shares or total share value. This could be the Authorized Shares Method or Assumed Par Value Capital Method. For example,Delaware charges franchise tax based on two methods - the number of authorized shares, and the assumed par value capital method - allowing companies to choose the one that results in the lowest tax liability. Margin or Gross receipts: Some states use revenue or margin (revenue after deductions such as cost of goods sold or compensation)as part of the calculation. Fixed Fees: A flat fee regardless of size or structure. In addition, franchise taxes can be subject to minimums, maximums, or comparisons to multiple methods, and can vary for C Corporations, S Corporations, LLCs, LPs, and other business structures. A company incorporated in one state but doing business in others may face multiple tax liabilities based on foreign qualification requirements, and failure to meet annual reporting requirements can lead to penalties or the revocation of good standing. As always, be aware of franchise tax requirements when incorporating or expanding your business, and work with qualified lawyers and tax professionals to ensure your compliance requirements fit your business model and goals. Discern has detailed guides on franchise taxes for many states. 2025-08-12T00:00:00Z https://foresight.is/fractional-cfo/ In 2024 I joined Laconia Capital Group as their Chief Financial Officer on a fractional basis. It marked a significant step in my evolution from startup business development to an early-stage venture capital investor to an entrepreneur, consultant, and educator for venture capital funds and startups to a chief financial officer for venture capital funds, a twenty year journey that has taught me a lot of lessons. [1] I am available to add an additional venture capital firm that wants a fractional CFO to provide financial leadership, compliance oversight, and operational support for their investing activities. Leveraging my own experience as a venture investor and what I have learned from working with countless venture firms on forecasting, planning, and reporting (leveraging my template financial models), I am looking to play a deeper role in helping firms turn their forecasts into reality. As a fractional CFO, I can be responsible for: Managing the firm's service providers - fund administrators, auditors, accounting, tax, legal, payroll, insurance, valuation, compliance - as a chief financial officer, chief compliance officer, and chief administration officer, as needed Manage limited partner quarterly and annual reporting to limited partners, assisting with investor letter, investor updates, fund financial statements, and fair market valuations Lead the firm's investment deal operations, including deal funding, deal expense management, wire payments, and deal document execution Lead the firm's capital planning, modeling for capital calls, fund reserves and investment pacing, while leading the capital call operations Support in investor relations, deal execution, and portfolio management Manage the firm's compliance with federal and state registrations (Form ADV, Form PF, SEC registrations, etc.) Lead firm financial operations, including expense management, cashflow management, bill processing and payment, payroll processing, wire payments, banking, and financial reporting Create and manage operational budgets for the management company, including managing bookkeeping or directly maintaining the financials for the management company if needed Create liquidation waterfall and cap tables for investments and assist in deal financial analysis Lead the firm's portfolio management operations, working with the investment team on gathering portfolio company information and creating consolidated views of the portfolio by managing key performance indicator (KPI) reporting Manage data rooms for prospective limited partners and fund fundraising activities Additionally, leveraging my experience working with startups for the past twenty years, I can also assist as needed with portfolio companies on their own financial operations, including financial modeling, forecasting, fundraising planning, operational metrics and financial reporting. Contact me to discuss how I can help your firm. I wrote this over fifteen years ago, but think it's still relevant today. More directly applicable might be a post from ten years ago, 23 Things I've Learned as a VC. For more details on my professional background, here is my resume. ↩︎ 2025-08-13T00:00:00Z https://foresight.is/budget-forecast/ This one stopped me in my tracks: Stop using Budget and Forecast interchangeably. Please just stop. pic.twitter.com/YetoiJd9bs— Former MM PE Backed CFO (@cfo_mm) August 11, 2025 Wait, what's the difference? A budget is a target or plan for the future, typically created once per planning cycle and fixed for the year. For startups, a budget is more than just a spreadsheet: it is a fundraising tool, a hiring plan, and an outline of how the company plans to spend money to achieve the strategy laid out for investors. It sets expected revenues and expenses, serving as both a roadmap and a performance benchmark. Early-stage startups often focus on operating budgets (covering headcount, marketing spend, and core expenses), while also tracking cash budgets to ensure they can make payroll and cover burn until the next funding round. Some use zero-based budgeting, starting from scratch each year to justify every expense, which helps avoid bloat. Others take an incremental approach to budgeting, building off last year’s plan if the business is stable. A forecast is an updated projection of what will actually happen, refreshed monthly, quarterly, or as needed based on the most recent data and trends. For startups, forecasts are essential for updating runway expectations, adapting hiring plans, and reacting to customer adoption, retention, and usage. Forecasts can vary from short-term forecasts, which might focus on the next 3–6 months of cash flow (including the 13 week cashflow forecast), long-term forecasts, which help model future fundraising needs or expansion plans over the medium term, and rolling forecasts, which continually project forward for a set horizon (e.g., the next 12 months), shifting as new data comes in. While the budget is a fixed plan reflecting the startup’s goals and commitments (e.g. "we’ll grow ARR to $2 mm and keep burn under $100K per month"), the forecast is a flexible, reality-checked view that drives decisions (e.g. "based on Q2 churn, ARR is trending to $1.6 mm; we may need to delay the Series A raise or cut hiring plans to address cashflow"). The budget keeps the team aligned on the big picture, while the forecast ensures founders and investors have a current, realistic view of the company’s trajectory. 2025-09-03T00:00:00Z https://foresight.is/ai-cap-table/ Can today's AI tools calculate a cap table for a new equity round? Here's what I tested: can you calculate the price per share for a new financing, given this cap table, and tell me the main things to look out for? please identify any questions you have before you can make the calculations. Please round the shares to full shares and round the share prices to 4 significant digits. Please output a spreadsheet with the details. existing common shares: 10 million existing preferred shares: 2 million, bought at $1 per share unconverted premoney SAFE, $500k with a 20% discount and $8mm cap unconverted postmoney SAFE, $750k with a 20% discount and $10mm cap new equity round (SAFEs will convert) of $1.5mm at a $12.5 mm premoney valuation. premoney SAFE will convert using investor friendly method ( investor friendly refers to converting the premoney safe so that new investors do not bear the dilution associated with converting that safe) no existing option pool, no changes in the pool. A pretty simple example, much simpler than a typical scenario where a company may convert 10+ SAFEs in a round. Unfortunately, while the logic sounded reasonable, the math was wrong. Why? Basic structural errors in calculating the correct share counts to use for the share prices led to a series of miscalculated share prices and share counts. While the errors aren't huge, the impact is meaningful: wrong share prices, wrong share counts, and if implemented, it would have left founders confused, investors unhappy, and credibility lost. Watch the video above for the details from ChatGPT and Claude, compared to the default Cap Table and Exit Waterfall Tool. AI tools are better than your average blog post at explaining how investment structures work. But it can’t (yet) teach you to ask the right questions for your specific situation. Experienced human judgment still matters. 2025-11-30T00:00:00Z https://foresight.is/agents/ I recently updated the Tools section of the site with a refresh of the tools, companies, and technologies in finance today, and redid the screenshots and many of the blurbs on each company. One trend was clear: everyone was building AI into their value proposition, in terms of the primary value message, new features, core principles, or even shifts in strategy or product. The interest in AI is everywhere. Back in December 2022 I wrote How will we use AI to build spreadsheet forecast models?: It makes sense that incorporating AI into the process of building a spreadsheet forecast model can enable companies to benefit from the enhanced data analysis and predictive capabilities of AI. Gather financial and operational data - historical financial information such as income statements, balance sheets, and cash flow statements, as well as data on expected changes in the company's operations, such as new products or services, changes in pricing, or expansion into new markets - and train an AI model to use for forecasting and data analysis. Take public market data to create a tool to query for benchmarks and comparisons for types of businesses at specific stages. Train a model on your own company's data to create a tool to help forecast future sales, inventories, cash position. Once we have an AI model, the results can then be incorporated into a spreadsheet forecast model, using cells as prompts to generate forward-looking projections or generate insights from historical data to inform the forecast assumptions and projections. In addition to improving the accuracy and efficiency of the forecast model, AI could be used to automate certain aspects of the model-building process. Automatically update the model with new data as it becomes available. Automatically generate reports and presentations based on AI-powered inferences on the actual and forecasted data in the model. Dump data on a company into an AI model for it to figure out which statistical and forecasting methods to use, and why. We're starting to see spreadsheet modeling introduce AI into their tools, notably Shortcut, a model building AI tool or agentic tool for Excel, and Excel's own Agent Mode. These tools extend the idea of "vibe coding" into "vibe working", the idea that you iterate on the model through back-and-forth prompts with the tool to adjust and build your model and analysis. While the benchmark performance of 50-60% for these tools don't sound great, it's actually not that far off human modeling performance, which is notoriously (or perhaps surprisingly) often less-than-accurate. AI is coming into every finance tool, but what does that mean? The shift from building to iterating # The introduction of AI agents into financial modeling represents a fundamental shift in how we work with forecasts. Instead of starting from scratch or copying templates, we're moving toward an iterative, conversational approach to model building. You describe what you need, the AI builds it, you review and refine through prompts, and the cycle continues until you have something that works. This "vibe working" approach has real benefits. It can help less experienced modelers get started faster, reduce the time spent on repetitive formula writing, and enable faster experimentation with different assumptions and scenarios. For experienced modelers, it can accelerate the initial model setup, allowing more time for analysis and strategic thinking rather than mechanical spreadsheet construction. The accuracy question # The 50-60% accuracy rate for these tools might seem concerning, but it's worth putting that in context. Human-built financial models are often riddled with errors, from simple formula mistakes to structural logic problems, and these problems often go unnoticed until they cause significant problems. AI isn't perfect either. As I wrote in Don't trust your AI with your cap table, AI tools can generate models that sound authoritative but can still have fundamental subject knowledge and mathematical model errors. All errors matter: a misplaced decimal point, an incorrect reference, or a flawed assumption can cascade through a model and lead to poor decisions. The key is to treat AI as a powerful assistant, not a replacement for human judgment. Use it to accelerate your work, but always verify the logic, check the formulas, and validate the outputs against your understanding of the business. What this means for FP&A teams # For FP&A professionals, this shift means: Faster model creation: Less time building, more time analyzing Lower barriers to entry: Team members with less spreadsheet expertise can contribute more effectively More experimentation: Easier to test different scenarios and assumptions New skills required: Understanding how to prompt effectively and validate AI-generated work becomes critical The tools are getting better, but they're not perfect. The best approach is to use AI to handle the mechanical aspects of model building while you focus on the strategic thinking, business understanding, and validation that ensures your forecasts are both accurate and useful. The future of financial modeling isn't about AI replacing analysts, it's about AI augmenting their capabilities, allowing them to spend less time on formula writing and more time on the analysis and insights that drive better business decisions. Questions about AI in forecasting, ask anytime. 2025-12-10T00:00:00Z https://foresight.is/fund-extensions-secondaries/ Venture capital funds typically have a defined lifetime, usually 10 years with the option to extend for an additional 1-2 years. But what happens when the fund's investments haven't fully exited by the end of the fund's lifetime? And what options do limited partners have if they need liquidity before the fund's investments have fully exited? Fund managers have three primary mechanisms to address these scenarios: fund extensions, continuation funds, and secondaries. Understanding how each works and how to model them is important for fund managers planning for the end of their fund's lifecycle and for limited partners evaluating their investment options. Fund extensions # A fund extension is a formal extension of the fund's lifetime beyond the original term specified in the limited partnership agreement (LPA). Extensions are typically used when the fund still holds investments that haven't exited, and the general partners (GPs) need more time to realize value from those investments. When extensions happen # Fund extensions typically occur when the fund is approaching the end of its stated lifetime (typically 10 years) but still holds ownership in portfolio companies that are performing well but haven't reached an exit point. Market conditions might make it difficult to exit investments at reasonable valuations, or the fund may need additional time to work with portfolio companies to create exit opportunities. Most LPAs include provisions for fund extensions, typically requiring approval from a majority of limited partners (often 50-75% by committed capital) and sometimes requiring a vote of the advisory committee or board. Modeling extensions # When modeling a fund extension, you'll need to consider several factors. The extension period is typically 1-2 years, though some LPAs allow for multiple extensions. The extension period should be long enough to allow for meaningful progress on exits, but not so long that it becomes a de facto permanent fund. Most LPAs specify that management fees are not charged during extension periods, though some may allow for reduced fees (e.g., 1% instead of 2%) to cover basic operational expenses. This is an important consideration for budgeting the management company. The fund will still incur operational expenses during the extension period - fund administration, audit, tax, legal - which will reduce the capital available for distributions to LPs. During the extension period, the GP's focus shifts from making new investments to managing existing portfolio companies toward exits. This may involve more active work with portfolio companies, potential bridge financing, or strategic positioning for exits. In your fund model, you can add an extension period by extending the forecast timeline beyond the original fund lifetime, setting management fees to zero (or reduced rate) for the extension period, continuing to model operational expenses, modeling potential exits and write-offs during the extension period, and updating performance metrics (IRR, TVPI, DPI, RVPI) to reflect the extended timeline. The Venture Capital Model is prebuilt for 20 years, and by default the model automatically adds fund extension periods if the portfolio construction approach and timing of exits creates exits in years outside of the fund lifetime. Continuation funds # Instead of extending the existing fund, a fund manager may choose to create a continuation fund - a new fund vehicle that acquires the remaining portfolio companies from the original fund. This allows the original fund to close out cleanly while giving the portfolio companies more time to reach exit, all within a new fund structure. When to use continuation funds vs. extensions # Continuation funds are an alternative to fund extensions, and the choice between them depends on several factors. You might choose a continuation fund when the fund wants to close out the original fund cleanly rather than extend it, when there's demand from new LPs to invest in the remaining portfolio, when the GP wants to reset fund economics (management fees, carried interest) for the remaining assets, when the portfolio is strong enough to attract new capital at attractive valuations, or when LPs want optionality - some may roll into the continuation fund, others may take cash. On the other hand, you might choose a fund extension when the fund just needs a short additional period (1-2 years) to complete exits, when the portfolio doesn't warrant creating a new fund structure, when LPs prefer to keep everything in the original fund, or when the administrative complexity of a continuation fund is not justified. How continuation funds work # A continuation fund transaction typically works as follows. First, the GP selects which portfolio companies to transfer to the continuation fund, often the best-performing companies that need more time. The portfolio is then valued, typically by an independent third party, to establish a fair market value (FMV) for the assets. A new fund is created with new LPs (or existing LPs who choose to roll) who invest capital to purchase the portfolio assets. In recent years, continuation funds have gained significant traction - in 2024, continuation vehicles accounted for approximately 85% of GP-led secondary transaction volume. Existing LPs in the original fund typically have three options. They can roll over by transferring their interest into the continuation fund to maintain exposure. They can cash out by taking cash at the transaction price, realizing returns now, though potentially at a discount to eventual exit value. Or they can remain in the original fund with remaining assets, if any assets aren't transferred. The selected portfolio companies are then transferred from the original fund to the continuation fund at the established FMV. The continuation fund has its own economics: new management fees (typically 2% on invested capital or AUM), new carried interest (typically 20%), a new fund lifetime (typically 5-10 years), and a new operational structure. Modeling continuation funds # Modeling a continuation fund requires modeling both the original fund and the new continuation fund. For the original fund model, you'll model the fund through the continuation fund transaction date, calculate the value of assets being transferred to the continuation fund, model the transaction (assets transferred at FMV, cash proceeds to LPs who cash out, and any remaining assets that stay in the original fund), and close out the original fund or model remaining assets if some stay. For the continuation fund model, you'll model the new fund structure including fund size (the capital raised to purchase assets), management fees (either on invested capital or assets under management), carried interest structure, and fund lifetime. You'll model the acquired portfolio including starting value (the fair market value at acquisition), expected exits over the continuation fund's lifetime, and expected returns to continuation fund LPs. Finally, you'll model LP economics: for LPs who rolled, show their economics in the continuation fund; for LPs who cashed out, show their final returns from the original fund. The FMV used for the transaction is critical - it determines the price for LPs who cash out and the starting value for the continuation fund. Continuation funds are typically priced at or near NAV (unlike secondaries which are often at a discount), as the GP is facilitating the transaction. You'll want to model the economics for each LP option (roll over, cash out, remain) to help LPs make decisions. The GP may earn new management fees and carried interest in the continuation fund, which impacts management company budgeting. Continuation fund transactions typically take 6-12 months to complete, including due diligence, fund formation, and LP decisions. Advantages and disadvantages of continuation funds # Continuation funds offer advantages for both GPs and LPs. For GPs, they provide clean closure of the original fund, new management fees and carried interest on the continuation fund, the ability to bring in new LPs who want exposure to the portfolio, and more time to realize value from strong portfolio companies. For LPs, they provide optionality - the ability to roll, cash out, or remain, liquidity options for LPs who need it, the ability to maintain exposure to strong portfolio companies if rolling, and a fresh fund structure with new economics. However, continuation funds also have disadvantages. They're more complex than extensions - requiring fund formation, valuation, LP decisions, and asset transfer. They have higher transaction costs (legal, valuation, fund formation) compared to extensions. They take longer to execute (6-12 months) compared to extensions, which can be done more quickly. They require coordinating with LPs on their decisions (roll, cash out, remain). And there's valuation risk: if the FMV is set too high, continuation fund LPs may overpay; if too low, original fund LPs who cash out may leave money on the table. Example: modeling a continuation fund # Consider a concrete example: a $50M fund that's 10 years old with 5 remaining portfolio companies and a combined NAV of $75M (1.5x on invested capital). A continuation fund transaction establishes FMV at $75M, and the new fund raises $75M to purchase assets. LPs have options: 60% roll, 30% cash out, 10% remain. For the original fund, you'd transfer $75M of assets to the continuation fund, distribute $22.5M cash to LPs who cash out (30% × $75M), and the remaining LPs (60% roll, 10% remain) would have interests in the continuation fund or remaining assets. The continuation fund would start with $75M in assets, have a 5-year lifetime, charge 2% management fees with 20% carry, and you'd model exits over 5 years with returns to continuation fund LPs. Secondaries # A secondary transaction occurs when a limited partner sells their interest in the fund to another party before the fund has fully exited its investments. Secondaries provide liquidity to LPs who need to exit their investment early, while allowing the fund to continue operating normally. Types of secondary transactions # There are several types of secondary transactions. LP-to-LP secondaries occur when one limited partner sells their interest to another limited partner or a new investor. This is the most common type of secondary transaction and typically requires GP consent. GP-led secondaries occur when the general partner initiates a transaction to provide liquidity to LPs, often by creating a new fund vehicle to acquire the assets from the existing fund. This can be structured as a continuation fund, a tender offer where LPs can choose to roll their interests into the new fund or take cash, or a strip sale where a portion of the portfolio is sold to a new fund. Direct secondaries occur when an investor purchases direct interests in specific portfolio companies rather than fund interests. This is less common but can occur when an LP wants exposure to specific companies. Modeling secondaries # Modeling secondaries in a fund model can be complex because they represent a change in ownership structure rather than a change in the fund's operations. For LP-to-LP secondaries, these transactions don't typically impact the fund model itself, as the fund continues operating normally. However, you may want to track the percentage of the fund that has been sold, the pricing of secondary transactions (often at a discount to NAV), and the impact on remaining LPs' ownership percentages. Foresight's venture models are prebuilt to handle secondaries in a couple different ways, depending on the portfolio construction method. Details at Secondaries. For GP-led secondaries, these transactions can significantly impact the fund model. You'll need to model the transfer of portfolio companies from the original fund to the continuation fund, price the secondary transaction (typically at a discount to net asset value, often 10-30% depending on the quality of the portfolio and market conditions), model the choice LPs have to either take cash at the secondary price, roll their interest into the continuation fund, or remain in the original fund if allowed, and if creating a continuation fund, model the new fund's economics including management fees, carried interest, and expected returns. Secondary buyers will conduct due diligence on the portfolio and will price based on their assessment of the portfolio's value, not the fund's reported NAV. Secondary transactions can take 3-6 months to complete, including due diligence, negotiation, and legal documentation. GP-led secondaries can create new economics for the GP, including new management fees and carried interest in the continuation fund. Secondary transactions can have tax implications for both sellers and buyers, and the structure of the transaction matters. Secondary pricing # Secondary transactions are typically priced based on net asset value (NAV), which is the current reported value of the fund's investments. Secondary buyers typically require a discount, reflecting an illiquidity premium (the buyer is taking on the risk of future exits), portfolio quality (stronger portfolios command smaller discounts), market conditions (tighter markets mean smaller discounts), and time to exit (longer expected hold periods mean larger discounts). This discount typically ranges from 10-30%, depending on these factors. Choosing between extensions, continuation funds, and secondaries # Fund managers have three primary options when a fund approaches its end with remaining portfolio companies. They can extend the original fund's lifetime, which is simplest but provides limited time. They can create a new fund to acquire remaining assets, which is more complex but provides fresh structure and LP optionality. Or they can provide liquidity to LPs through a secondary sale, which provides liquidity but may be at a discount. The choice depends on several factors. Strong portfolios may warrant continuation funds or attract secondary buyers. If LPs need liquidity, secondaries or continuation funds with cash-out options may be preferred. Short extensions (1-2 years) may favor extensions; longer periods may favor continuation funds. GPs may prefer continuation funds to reset economics and bring in new LPs. Combining strategies # Fund managers may use multiple strategies. They might extend first, then create a continuation fund if more time is needed. They might extend the fund but offer secondary liquidity to LPs who need it. Or they might create a continuation fund where some LPs roll and others take cash (secondary). Modeling combined strategies requires extending the fund timeline if using an extension, modeling the continuation fund or secondary transaction, showing the economics to LPs under each option, and comparing scenarios to help LPs make decisions. Performance metrics impact # Extensions, continuation funds, and secondaries all impact key performance metrics differently. Extensions typically reduce internal rate of return (IRR) because they extend the time period over which returns are calculated, even if the total return multiple remains the same. Continuation funds reset the clock - LPs who roll start fresh in the new fund, while LPs who cash out realize their IRR at the transaction date. Secondaries can impact IRR depending on the pricing and timing. For DPI (distributed to paid-in capital), extensions delay DPI as distributions are pushed further into the future. Continuation funds accelerate DPI for LPs who cash out (at FMV), while LPs who roll maintain their exposure. Secondaries can accelerate DPI if LPs take cash, but at a discount to eventual exit value. For RVPI (residual value to paid-in capital), extensions maintain RVPI as investments remain in the portfolio. Continuation funds convert RVPI to DPI for LPs who cash out (at FMV), while LPs who roll transfer their RVPI to the continuation fund. Secondaries reduce RVPI for LPs who exit, but the pricing reflects the discount to NAV. For TVPI (total value to paid-in capital), extensions maintain TVPI (DPI + RVPI) but delay the conversion of RVPI to DPI. Continuation funds maintain TVPI for LPs who roll (transferred to continuation fund), while LPs who cash out realize TVPI at the transaction date. Secondaries convert RVPI to DPI at a discount, potentially reducing TVPI for exiting LPs. Best practices # When modeling extensions and secondaries, model different scenarios throughout the fund's life, and keep LPs informed about the fund's progress and potential need for extensions or secondary options. Create scenarios for no extension with all exits by year 10, a 1-year extension with partial exits, a 2-year extension with full exits, a continuation fund transaction, secondary transactions at various points, and combined strategies like an extension then a continuation fund. Understanding your LP base helps in planning for secondaries. Clearly document your assumptions about exit timing, continuation fund valuations, secondary pricing, and extension periods in your model. Model all three options (extension, continuation fund, secondary) to help make the best decision for your fund and LPs. Questions on modeling fund extensions, continuation funds, and secondaries, ask anytime. 2026-01-05T00:00:00Z https://foresight.is/ai-models/ Here is today's test of an AI modeling tool: Build a 10-year cash flow forecast for a venture capital fund, including standard VC performance metrics (IRR, TVPI, DPI, RVPI, J-curve visualization, capital calls, distributions, NAV, etc.) The idea is to see what today's AI tools can create, attempting to build something like Foresight's Venture Capital Model, Annual Forecast model, a fairly stripped down approach to building a simple fund forecast. For this example I used Shortcut, an Excel AI agent and spreadsheet modeling tool. Shortcut has raised over $30 million in venture capital, aiming to do more than Microsoft's Agent Mode and general LLMs in building a tool to help people create financial models and analyses. After some questions and modifications to the prompt to build out more details, agree on the sheet structure, assumptions layout, and implementation plan, Shortcut got to work. And the first draft looked solid. But as I started looking into the details, I started running into issues, both in terms of model structure and business logic. Overall, the structure we settled on makes sense. A sheet for assumptions, a sheet for annual cash flows, a sheet for metrics reporting, and a sheet for charts. Curiously, the first version of the model created the cash flow forecast using hard-coded numbers instead of linking to the assumptions, but that was easy for Shortcut to fix once I pointed it out. The basic structure for fund parameters, investment pacing and exit assumptions makes sense, and the first draft of the fund cash flows covered the basics. The issues came once I started digging into the model and auditing the results. Called capital was greater than total fund size because the model assumed that invested capital was equal to the fund size, and added management fees on top of that. In addition, it took management fees in year 0 before the fund had called any capital. Not too hard to fix once I pointed out the errors, took a couple prompts but that got fixed. TVPI (total value to paid-in capital) was greater than the gross exit multiple assumption, a foundational business logic error. TVPI is a net metric, meaning it will reflect performace on paid-in capital after fees, and will be thus be lower than the gross multiple, which reflects the performance on invested capital before the impact of fees (including management fees, fund operating expenses, carried interest). This one took a bit longer to fix, because (a) I had to remove the residual value that led to RVPI > 0 at fund exit (another logic error), thus inflating TVPI, which proved harder for Shortcut to fix because it didn't understand the source of unrealized gains and how to model the increases and decreases of them, and (b) fixing it exposed a model structure flaw in how it calculated gross proceeds from the invested capital (the calculated gross proceeds didn't result in the assumed gross exit multiple, meaning the model was not internally consistent). Even after multiple corrections, the logic for calculated unrealized gains was incorrect, resulting in inflated net asset value (NAV) in the early years and a mistaken J Curve. This one would be harder for a less-experienced modeler or fund manager to catch, but very important to get right. The waterfall calcs were incorrect. Fund waterfalls can be difficult to understand, and I've seen this one in many fund manager-created models (including a few AI-generated models managers have sent me), so I wasn't surprised by this. But again, unless you know how a waterfall should work and know how to apply it, this would be easy to miss. By default Shortcut built in a hurdle rate, which would not be the default assumption for US venture funds (but could be for other types of funds), and calculated the carried interest as a simple percentage of gross proceeds, without accounting for the return of capital. Once we fixed that, there was a misunderstanding of what waterfall approach for Shortcut to use. Fixing that one required a bit of explaining, but Shortcut was able to build a European waterfall pretty easily after that. I have a hunch an American waterfall would have been more challenging. The concept of a GP commit was incorrectly applied. I didn't include a GP commit, or the capital that a general partner (GP) has to invest in the fund along side limited partners (LP), in the initial prompt, but once I attempted to add it in, it took a few prompts to get management fees, proceeds, and cash flows adjusted correctly. Easy if you know what to look for, hard if you are not familiar with the implications of the GP commit. The Sensitivity table approach was at first practical but not obvious, then after attempting to fix it fundamentally flawed. This one is harder, and perhaps a bit more subjective, though. We only created sensitivity tables because Shortcut suggested it, and I agreed even though I knew tables like that are difficult to do in spreadsheets. Shortcut's first output was a table of fund metrics (IRR and TVPI) by Gross Exit Multiple, a table of IRR by gross exit multiple and exit timing, and a table of TVPI by gross exit multiple and timing. The issue is that the first output was entirely hard-coded, or to be more specific, I am assuming it was created by Shortcut by running the model for each combination of variables and outputing the value into the table. Here's the catch: if you change any of the assumptions, then the table is entirely wrong and needs to be regenerated. But that's an issue common to spreadsheet users, and one that can really only be solved with Excel's Data Table option, which takes careful structural thinking for an Excel user to implement. Shortcut doesn't support Data Tables at the moment, so after I chatted with Shortcut about options it created formula-based sensitivites by scaling the calcs, which sounds good but is a flawed approach as well. At the end of the day using Shortcut to generate the tables "manually" is likely the best approach, as it's far quicker to prompt an AI tool to create the table than for the modeler to generate it manually themselves. But again, if you didn't know that the table was created statically, you might not know you need to regerate it each time you changed anything in the model assumptions or structure. The charts did not provide the right insights. Shortcut and I agreed to two charts: one a J curve showing fund performance over time, and the second a chart of NAV compared to cumulative distributions. Shortcut created a J curve chart using cumulative net cash flows instead of IRR, which would be the J curve chart that users would be expecting, but the shape was correct. The NAV and cumulative distributions chart was close, but the use of a stacked chart created the wrong insights about cumulative distributions. Overall I still think Shortcut did fairly well, and if I gave it a 50-60 out of 100 grade, that would be on par with expectations for most human modelers. Do not interpret this as a takedown of Shortcut, or a negative outlook on AI modeling tools; I am impressed with what it was able to create from a fairly simple prompt and an hour of guidance. While I am pointing out above what it got wrong, I could write just as much about what it got right with barely any guidance from me: multiple-sheet structure, primary fund assumptions, layout and the initial assumptions for investment deployment and gross proceeds, IRR calculations, and much more. There's a lot to like. My warning is aimed at users: as we build AI into financial tools, it is important that we are able to analyze the outputs with a critical eye to the structure, calculations, and outputs. You still have to know the business well enough to interpret the model and catch potential errors. Just like my tests in using AI to build cap tables, you cannot simply trust the outputs, and it will take some fundamental knowledge outside of the LLM or modeling tool for you to be able to use AI to create models or analyze financial data. [1] I think the challenge here is that it's a bit different than coding, as financial modeling lacks the same generally understood and documented languages, frameworks, and tools as coding, and requires more fundamental and specific business knowledge that is less structured and documented. I love work like the Financial Modeling Handbook and FAST because they work to build and communicate general approaches and standards for model building, providing the educational base to help people build better models. AI will get better, but there is still ample room today for modelers to use domain expertise and spreadsheet ability to improve on the outputs of AI finance tools. As an example, I have had a number of users send me models that they created first from AI with requests for what they expected to be minor edits, only to have me point out fundamental flaws in the model logic that they had not caught or properly understood. The experiences have made me question everything I use AI to help create; if I'm not the subject matter expert in what AI creates, how do I know if it's right or wrong? ↩︎