Calculates 103 firm characteristics from CRSP + Compustat directly in Python – no WRDS SAS cloud

Sample SAS programs that process WRDS data and facilitate econometric analysis

Data matching for corporate governance research

A python script to create a mapping table between I/B/E/S and Compustat

This GitHub repository shows data collection and analysis for “Regulatory Fragmentation” paper by Kalmenovitz, Lowry and Volkova, The Journal of Finance (Forthcoming)

Fuzzy match entity names (primarily persons and companies) across databases

Pipeline dealing with WRDS (Wharton Research Data Services) datasets including crsp, master, etc, in order to build mega-database for scaling in Market Microstructure research

US-equity quantitative research, backtest and paper-trading system (codename Plutus).

Replication code for "The Shape of Beta: Industry Factor Structure and Crisis Risk Premium" (Woo & Kim, 2026)

Classify CRSP-style delisting reasons from SEC EDGAR for quant pipelines

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.

Minimal PEAD (post-earnings announcement drift) backtest using Wharton Research Data Services (IBES + CRSP) — Python pipeline for research & plots.

Rolling-window XGBoost cross-sectional return prediction for US equities (1995-2024). Out-of-sample annualized Sharpe 1.03, monthly CAPM alpha +2.19% (t=6.08), market beta -0.43 over 300 months (2000-2024).

Academically rigorous implementation of the Fama-French (2015) five-factor model using WRDS (CRSP + Compustat) data.

按决策难度匹配 Agent 介入方式的智能数据准备系统 | Intelligent Data Preparation Agent

Point-in-time insider filing de-noising, ML scoring, and cross-sectional return tests.

Idiosyncratic volatility and abnormal returns during VIX spike events empirical study using a survivorship-bias-free CRSP universe (2005–2024)

Replication of Fama & French (1992) Tables 1-3: cross-sectional sort on size and book-to-market using 1.04M CRSP-Compustat observations. Factor model regression. VCU FIRE 691.

Time-series analysis of stock returns: autocorrelation, ARIMA, SARIMA, Holt-Winters forecasting, and out-of-sample R² evaluation on CRSP monthly data. VCU FIRE 691.

Testing stock return predictability using lagged dividend-price ratio (Cochrane / Goyal-Welch) on CRSP annual and monthly data (1927-2024). 5-year R² of 8.41%. VCU FIRE 691.