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.

Pytorch
- Attention
- Masked attention
- RProp
- Tutorials from the developers
- General documentation from the developers
- PyTorch implementation of the Dive into Deep Learning online book
- Example code annotated at each line so you can see what the code is doing
TensorFlow
- Attention
- Implemented models - a collection of completely implemented models using TensorFlow
- General documentation from the developers
Data Resources
- Uber's 3M time-series analysis
- Facebook via Social Science One
- Kaggle
Sentiment Analysis
- Modern sentiment analysis architecture
Miscellaneous
- Variational autoencoder
- Convolutional neural networks
- Stanford's computer science department has various open source resources
- Git resources
- Monte Carlo as a gradient estimation method
- Heebum Lee Github repository where he has compiled his own list of useful machine learning resources
- Open source machine learning code
- Apache Spark resources for distributive computing
- Maximilian Kasy website where he has compiled his own list of useful machine learning and computational resources
Machine Learning Theory
Textbooks
- Machine Learning - A Probabilistic Perspective: Kevin Murphy (2012)
- Pattern Recognition and Machine Learning: Christopher M. Bishop (2006)
- Elements of Statistical Learning: Hastie, Tibshiranni and Friedman (2017)
- Interpretable Machine Learning - Molnar (2024)

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/
- 272 high frequency (1-minute) factors created in Aleti (2022): https://www.sakethaleti.com/data

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