Quant Radio: Revisiting Momentum with Deep Learning

Can deep learning outperform traditional quant strategies? In this episode, we explore how a simple neural network model was applied to momentum trading — and how it stacks up against the market.

Inspired by Richard Sutton’s Bitter Lesson, this study puts brute-force computation to the test in financial prediction. We walk through the data setup, model architecture, rolling validation process, and — most importantly — the results. Despite only achieving 52% classification accuracy, the model delivered an annualized return of 12.8% with strong risk-adjusted performance.

We also compare the results to the original 2013 study, dissect challenges in replicating quant research, and ask what this experiment reveals about the future of AI in finance.

Find the full research paper here: https://community.quantopian.com/c/community-forums/in-the-article-the-bitter-lesson-published-on

For more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.

Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.