compustat

provide cik to cusip links using 13G and 13D filings

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 guide aims to be a full instruction on how to download and merge Refinitiv (formerly Thomson Reuters) Datastream Worldscope data into one comprehensive dataset of yearly stock quoted financial statements.

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

Stata .do files for dissertation examining corporate social responsibility, stakeholder management, and corporate financial performance

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.

Panel analysis of whether ChatGPT's introduction affected firm fundamentals. US Compustat data 2018-2024, firm fixed effects with clustered standard errors, testing cost efficiency, investment, revenue growth and profitability across resource, production and technology industries.

Stock Factor Data

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.

Point-in-time asset-pricing pipeline linking Compustat geographic segments to macro states, CRSP returns, and leakage-aware ML diagnostics.

Empirical analysis of ESG performance and financial returns using CRSP, Compustat, and Refinitiv data. Panel of 18K+ U.S. firm-years (2013–2023). Covers multi-database merging, OLS/panel regressions with fixed effects, and industry-level double materiality classification. Python · pandas · statsmodels · WRDS

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).

Does the OECD production network predict equity returns once country and sector are controlled for? An answer that depends on the numeraire, with a process log of the defects found auditing the study's own repairs.

End-to-end Python replication of 'The Value of Information: A Puzzle' (Kadan et. al, 2026). Estimates equilibrium dollar value of private information in US equity markets via discrete quadratic covariation of 1-min NYSE TAQ price changes & signed order flow. Implements CLNV trade signing, Amihud filtering, 2-way FE regressions, & SDF entropy bounds

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

Data availability and measured alpha in cross-sectional equity signals. 53 markets, 1995-2024, WRDS.