Resources

This page contains a number of machine learning, big data and asset pricing resources I have found helpful. I have compiled this evolving set of resources in the hope it will also help others. You will find resources for both the TensorFlow and Pytorch implementations of neural networks in Python, although I have started leaning more and more towards TensorFlow. I have also gathered a consolidated (but in no way comprehensive) list of anomaly factors used in the published asset pricing literature in an effort to make utilizing these factors in new research easier.

• Anomaly Factors

  • Fama-French 3- and 5-factors: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

  • q5 factors of Hou, Mo, Xue and Zhang (2021): http://global-q.org/factors.html

  • Big data factors of Pelger (and associated code): https://mpelger.people.stanford.edu/data-and-code

  • Shrinking the Cross-Section factors (Kozak, Nagel and Santosh, 2020): https://www.serhiykozak.com/data

  • Empirical Asset Pricing via Machine Learning (Gu, Kelly and Xiu, 2020) factors - raw data for generating the factors: https://dachxiu.chicagobooth.edu/ 

  • Amit Goyal macro factors: http://www.hec.unil.ch/agoyal/

  • 205 factors recreated in Chen and Zimmerman (2021): https://www.openassetpricing.com/data/ 

  • 153 factors from 93 countries from Is There a Replication Crisis in Finance (Jensen, Kelly and Pedersen) - code for recreating the factors can be found in their Github repository: https://www.bryankellyacademic.org/

  • Downside risk bond factor (Bai, Bali and Wen, 2019): https://sites.google.com/a/georgetown.edu/turan-bali/data-working-papers?authuser=0

  • Option implied factors and associated Python code: https://www.vilkov.net/codedata.html

  • AQR Capital factors: https://www.aqr.com/Insights/Datasets 

  • McCracken and Ng (2015) macro factors: https://research.stlouisfed.org/econ/mccracken/fred-databases/