The Expected Returns on Machine-Learning Strategies

26 Pages Posted: 7 Feb 2024 Last revised: 21 Mar 2024

See all articles by Vitor Azevedo

Vitor Azevedo

Department of Financial Management - RPTU Kaiserslautern-Landau

Christopher Hoegner

Technische Universität München (TUM), Department of Financial Management and Capital Markets, Students

Mihail Velikov

Pennsylvania State University

Date Written: November 18, 2023

Abstract

This study assesses the expected returns of machine learning-based anomaly trading strategies, accounting for transaction costs, post-publication decay, and the post-decimalization era of high liquidity. Contrary to claims in prior literature, more sophisticated machine learning strategies are profitable, earning net out-of-sample monthly returns of up to 1.42%, despite having turnover rates exceeding 50% and selecting some difficult-to-arbitrage stocks. A trading strategy that employs a long short-term memory model to combine anomaly characteristics yields a six-factor generalized (net) alpha of 1.20% (t-stat of 3.46). While prevalent cost-mitigation techniques reduce turnover and costs, they do not improve net anomaly performance. Overall, we document return predictability from deep-learning models that cannot be explained by common risk factors or limits to arbitrage.

Keywords: Stock market anomalies, machine learning models, return prediction, transaction costs, asset pricing models.

JEL Classification: G11, G12, G14, C45, C58

Suggested Citation

Azevedo, Vitor and Hoegner, Christopher and Velikov, Mihail, The Expected Returns on Machine-Learning Strategies (November 18, 2023). Available at SSRN: https://ssrn.com/abstract=4702406 or http://dx.doi.org/10.2139/ssrn.4702406

Vitor Azevedo (Contact Author)

Department of Financial Management - RPTU Kaiserslautern-Landau ( email )

Kaiserslautern
Germany

Christopher Hoegner

Technische Universität München (TUM), Department of Financial Management and Capital Markets, Students ( email )

Munich, 80333
Germany

Mihail Velikov

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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