Stock Picking with Machine Learning

45 Pages Posted: 23 Jun 2020 Last revised: 25 Apr 2022

See all articles by Dominik Wolff

Dominik Wolff

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

Fabian Echterling

Deka Investment GmbH

Date Written: April 22, 2020

Abstract

We analyze 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 January 1999 to March 2021 and build on typical equity factors, additional firm fundamentals and technical indicators. A variety of machine learning models are trained on the binary classification task to predict whether a specific stock out- or underperforms the cross sectional median return over the sub-sequent week. We analyze weekly trading strategies that invest in stocks with the highest pre-dicted outperformance probability. Our empirical results show substantial and significant out-performance of machine learning based stock selection models compared to an equally weighted benchmark. Interestingly, we find more simplistic regularized logistic regression models to perform similarly well compared to more complex machine learning models. The results are robust when applied to the STOXX Europe 600 as alternative asset universe.

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 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)

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

Technical University of Darmstadt

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