Aktuelles IQAM Research (vormals: „IQ-Kap: Privates Institut für quantitative Kapitalmarktforschung der DekaBank GmbH“) Forschungsprojekt untersucht den Einsatz von Machine Learning zur Aktienselektion


Stock Picking with Machine Learning

Dominik Wolff

Deka Investment GmbH; Darmstadt University of Technology; Frankfurt University of Applied Sciences

Fabian Echterling

Deka Investment GmbH

Date Written: April 22, 2020


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.

Keywords: Investment Decisions, Equity Portfolio Management, Stock Selection, Stock Picking, Machine Learning, Neural Networks, Deep Learning, Long Short-Term Neural Networks (LSTM), Random Forest, Boosting

JEL Classification: G11, G17, C58, C63