What problem does it solve?
It solves the problem of obtaining high-quality academic finance datasets from WRDS so you can build reproducible research inputs for accounting and returns analysis.
Core Features & Use Cases
- WRDS API access to core databases: Pull Compustat fundamentals (e.g.,
comp.funda), CRSP stock returns (e.g., crsp.msf), and TAQ intraday trades/quotes (e.g., taq.ct_YYYYMMDD) using the Python wrds package.
- Research-ready identifiers and merges: Support CRSP–Compustat linking via the CCM table (e.g.,
crsp.ccmxpf_lnkhist) to map gvkey to permno with date-valid constraints.
- Dataset construction for empirical finance: Enable downstream computation of accounting ratios, market equity, portfolio returns, and microstructure metrics using retrieved fields.
Use case: You need a research-grade panel dataset combining annual fundamentals with CRSP returns and (optionally) intraday TAQ data, including correct gvkey→permno matching for your sample period.
Quick Start
Use the wrds-data-access skill to download Compustat fundamentals, CRSP returns, and the CRSP-Compustat link table from WRDS via the Python API, then compute key ratios and prepare merged datasets for your study.
