Deep Learning and Momentum Investing

Discover how to apply deep learning models to financial data in a disciplined and interpretable way

In this post I provide an overview of my new working paper on deep learning and momentum in U.S. equities. I begin with a short summary of the paper highlighting the research question and main results, after that I deliberately put on my practitioner’s hat (buy-side quant / PM, currently in transition between jobs and open to new opportunities, hint hint) and focus on the following practical aspects of disciplined quantitative research:

1. Motivated choice of features and features engineering
2. Systematic approach to selecting optimal network architectures and building ensembles
3. Interpretation of model’s predictions

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