Deep learning for forecasting company fundamental data

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

Functions to convert (WRDS) SAS data to PostgreSQL, parquet, and CSV

Sample SAS programs that process WRDS data and facilitate econometric analysis

Template for an Accounting or Finance Research Project

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.

A Julia package for downloading, merging, and using CRSP and Compustat data from the Wharton Research Data Services (WRDS)

A lightweight Claude Code project for exploring academic literature, brainstorming research ideas, and managing citations. Powered by Corbis MCP.

A simple SAS program that can extract TAQ data from WRDS cloud for a list of companies on particular dates during specific time periods.

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

Access TAQ from WRDS and tick data from LSEG Tick History

This R package provides simple functions for accessing data from WRDS. It includes credential management via the system keyring, database tools, and functions for downloading generic tables, Compustat fundamentals, and linking tables.

Calculation of stock realized variance based on trade data on WRDS cloud

Code that runs on WRDS cloud computer to combine daily stock files in the database into monthly stock files and export them

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

Multi-pillar L/S equity research on DoorDash (DASH): alt-data signals → GOV surprise forecast → revenue / contribution-margin / EBITDA chain → CAR event study. Pre-registered Q1 2026 prediction.

Research framework for optimal high-frequency market making with Avellaneda-Stoikov quoting, WRDS TAQ replay backtesting, queue-aware fills, volatility-adaptive spreads, and robust execution/P&L analysis.

Master workshop: introduction to WRDS

Research Project Template