Stock Picking with Machine Learning

39 Pages Posted: 23 Jun 2020

See all articles by Dominik Wolff

Dominik Wolff

Darmstadt University of Technology; Institute for quantitative Capital Market research at Deka Bank (IQ-KAP); Deka Investment GmbH; Frankfurt University of Applied Sciences

Fabian Echterling

Deka Investment GmbH

Date Written: April 22, 2020

Abstract

We combine insights from machine learning and finance research to build machine learning algorithms for stock selection. Our study builds on weekly data for the historical constituents of the S&P 500 over the period from 1999 to 2019 and includes typical equity factors as well as additional fundamental data, technical indicators, and historical returns. Deep Neural Networks (DNN), Long Short-Term Neural Networks (LSTM), Random Forest, Boosting, and Regularized Logistic Regression models are trained on stock characteristics 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 outperform the market. Our empirical results show a substantial and significant outperformance of machine learning based stock selection models compared to a simple equally weighted benchmark. 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 results 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 transaction 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

Suggested Citation

Wolff, Dominik and Echterling, Fabian, Stock Picking with Machine Learning (April 22, 2020). Available at SSRN: https://ssrn.com/abstract=3607845 or http://dx.doi.org/10.2139/ssrn.3607845

Dominik Wolff (Contact Author)

Darmstadt University of Technology

Hochschulstraße 1
S1|02 40
Darmstadt, Hessen D-64289
Germany

Institute for quantitative Capital Market research at Deka Bank (IQ-KAP) ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

HOME PAGE: http://www.iq-kap.de/en

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

Frankfurt University of Applied Sciences ( email )

Nibelungenplatz 1
Frankfurt / Main, 60318
Germany

Fabian Echterling

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

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