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Global Factor, Stock, and Firm data

This repo contains Python code to generate the global dataset of factor returns, stock returns, and firm characteristics from “Is there a Replication Crisis in Finance?” by Jensen, Kelly, and Pedersen (Journal of Finance, 2023).

Instructions

Prerequisites

  • Obtain your WRDS credentials.
  • Ensure you have uv installed on your system.

Steps

  1. Clone the repo

    • Clone the folder to your local machine by running the following command from your terminal: 
      git clone https://github.com/bkelly-lab/jkp-data.git

  2. Input WRDS credentials

    • To save your WRDS credentials, navigate to the jkp-data/ folder and run:

      uv run python code/wrds_credentials.py

      Kindly follow the prompts.

      Note: If you need to change your password or credentials, run uv run python code/wrds_credentials.py --reset and then uv run python code/wrds_credentials.py

  3. Run the script

    • We run the code via a Slurm scheduler, but we also show how to run it in an interactive Python session.

    • Before running the following commands, make sure you are in jkp-data/

    • On a cluster with a Slurm scheduler, run:

      sbatch slurm/submit_job_som_hpc.slurm

      to create the factor returns, stock returns, and firm characteristics.

      In an interactive session, run:

      uv run python code/main.py

      to create the stock returns and firm characteristics, and

      uv run python code/portfolio.py

      to create the factor returns.

    IMPORTANT: When starting the code, you may be prompted to grant access to WRDS using two-factor authentication, for example via a Duo notification. You need to approve this request, as the program will otherwise fail. After a few seconds or minutes, you should see data being created in data/raw. If that is not the case, please check your internet connection or credentials.

When the code is finished, you can find the output in:

data/processed/

Please see the release notes (documentation/release_notes.html) for a description of the output files and a comparison between the output of the SAS/R codebase and the new Python codebase.

Notes

  • By default, the end date for the data in the code is 2024-12-31, which you can change by editing line 4 of the code/main.py file. For example, for May 6, 1992, use: end_date = pl.datetime(1992, 5, 6).

  • To run the code, we utilize a high performance computing cluster, where we request 450 GB RAM and 128 CPU cores. Running the routine takes about 6 hours.

  • To understand the data, please refer to our documentation.

  • We distribute the global factor returns generated from this codebase at jkpfactors.com and the stock returns and firm characteristics at wrds-www.wharton.upenn.edu/pages/get-data/contributed-data-forms/global-factor-data/.

  • The original SAS/R codebase is still available at github.com/bkelly-lab/ReplicationCrisis, but we recommend using this new Python codebase for future work.