Can machine learning really predict stock market returns with just 12 months of data? This episode explores a bold claim made by a prominent academic paper using Random Fourier Features (RFF) to forecast market movements with stunning accuracy — and the fascinating rebuttal that followed.Join us as we break down:The mechanics behind the KMZ RFF strategyWhy its seemingly impressive performance might just be mathematical coincidenceHow it unintentionally mimics a simple momentum strategy with built-in volatility timingWhat this means for the limits of learning in finance, especially with small dataThrough empirical results, intuitive analogies, and critical analysis, we unpack whether complexity in financial models is truly virtuous — or just cleverly disguised simplicity.💡 Perfect for anyone interested in quant finance, machine learning, or the truth behind flashy claims.Find the full research paper here: https://community.quantopian.com/c/community-forums/seemingly-virtuous-complexity-in-return-predictionFor 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.

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