Fama-French models, idiosyncratic volatility, event study

Codes to clean data and construct variables for empirical finance.

An introduction to database and data management in empirical finance

An introduction to popular databases in empirical finance research.

A toolkit for asset pricing research

A Python tool for extracting stock repurchase program data from SEC 10-Q and 10-K filings

An end-to-end Automated ML pipeline for empirical asset pricing & DJI forecasting. Bridges econometric rigor with modern AI using H2O AutoML. Features include advanced preprocessing (Winsorization, ADF), statistical validation via the Diebold-Mariano test, and model explainability using SHAP values. Optimized for reproducible quantitative research.

Quantile Local Projections linking DeFi liquidation shocks to ETH tail risk. Empirical evidence for endogenous market fragility (2021-2025)

End-to-End Python implementation of Mo et al.'s (2025) ACT-Tensor methodology; a tensor completion framework for financial dataset imputation. Implements cluster-based CP decomposition, HOSVD factor extraction, temporal smoothing (CMA/EMA/Kalman), and downstream asset pricing evaluation. Transforms sparse data into dense machine readable data.

Cross-sectional Transformer and FFN for stock return prediction and alpha generation. Implements GKX (2020) NN5 replication and MSRR loss (Kelly et al. 2025) for direct portfolio Sharpe optimization. Avg SDF Sharpe 2.05, significant alpha (t=5.34) unexplained by FF5+Momentum.

Who funds TGA rebuilds? Auction-schedule surprise identification of Treasury funding channels. MMFs + ON RRP.

Code for "Do CEO Voices Move Markets?" — vocal stress and analyst tone in earnings calls as predictors of high-frequency order flow. Princeton ORF Senior Thesis (2026).

Literature-driven index inclusion research toolkit with dashboard, event studies, and HS300 RDD workflows.

Empirical study of election-year effects on October VIX returns using regression analysis and bootstrap inference.

📈 Forecast daily log-returns of the Dow Jones Industrial Average using an Automated Machine Learning pipeline that combines economic data and computational techniques.