Abstract

We combine insights from machine learning and finance research to build machine learn-ing algorithms for stock selection. Our study builds on weekly data for the historical constitu-ents of the S&P 500 over the period from January 1999 to March 2021 and includes typical equity factors as well as additional fundamental data, technical indicators, and historical re-turns. Deep neural networks (DNN), long short-term neural networks (LSTM), random forest, gradient boosting, and regularized logistic Regression models are trained on stock characteris-tics to predict whether a specific stock outperforms the market over the subsequent week. We analyze a trading strategy that picks stocks with the highest probability predictions to outper-form the market. Our empirical results show a substantial and significant outperformance of machine learning based stock selection models compared to a simple equally weighted bench-mark. Moreover, we find non-linear machine learning models such as neural networks and tree-based models to outperform more simple regularized logistic regression approaches. The re-sults are robust when applied to the STOXX Europe 600 as alternative asset universe. However, all analyzed machine learning strategies demonstrate a substantial portfolio turnover and trans-action costs have to be marginal to capitalize on the strategies.