Machine Learning and the Cross-Section of Expected Stock Returns

This paper develops a comprehensive machine learning framework for predicting stock returns. Using a large set of firm characteristics and macroeconomic variables, we demonstrate that neural network models significantly outperform traditional linear factor models in out-of-sample return prediction. The economic gains from ML-based trading strategies are substantial and robust to transaction costs.