Machine Learning Approaches for Equity Market Predictions

44 Pages Posted: 28 Nov 2018 Last revised: 29 Apr 2019

See all articles by Dominik Wolff

Dominik Wolff

Institute for quantitative Capital Market research at Deka Bank (IQ-KAP); Deka Investment GmbH; University of Giessen

Dr. Ulrich Neugebauer

Deka Investment GmbH

Date Written: April 25, 2019

Abstract

We empirically analyze equity premium predictions with ‘traditional’ linear regression models and tree-based machine learning approaches. Based on a commonly used dataset of equity market predictors extended by additional fundamental, macroeconomic, sentiment and risk indicators, we find mixed results for machine learning algorithms for equity market predictions. In contrast to sophisticated linear regression models such as penalized least squares or principal component regressions (PCR), the analyzed machine learning algorithms fail to significantly outperform the historical average benchmark forecast. However, an investment strategy that uses machine learning predictions in a market-timing strategy outperforms a passive buy-and-hold investment. Compared to sophisticated linear prediction models machine learning algorithms do not improve forecast accuracy in our problem set.

Keywords: Machine Learning, Equity Return Forecasts, Predictive Regression, Three-Pass Regression Filter, Random Forest, Boosting

JEL Classification: G17, G11, C53

Suggested Citation

Wolff, Dominik and Neugebauer, Ulrich, Machine Learning Approaches for Equity Market Predictions (April 25, 2019). Available at SSRN: https://ssrn.com/abstract=3265107 or http://dx.doi.org/10.2139/ssrn.3265107

Dominik Wolff (Contact Author)

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

University of Giessen ( email )

Goethestraße 58
Giessen, 35390
Germany

Ulrich Neugebauer

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

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