Machine Learning and the Stock Market

68 Pages Posted: 27 Aug 2018 Last revised: 16 Jun 2021

See all articles by Jonathan Brogaard

Jonathan Brogaard

University of Utah - David Eccles School of Business

Abalfazl Zareei

Stockholm University

Date Written: January 26, 2021

Abstract

Practitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by applying a diverse set of machine learning algorithms. The results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time. In addition, we find that the evolutionary genetic algorithm’s attitude in not shying away from erroneous predictions gives it an edge in building profitable strategies compared to the strict loss-minimization-focused machine learning algorithms.

Keywords: Technical trading, Machine learning, Big data analysis

JEL Classification: B26, G12, G14, C58, N20

Suggested Citation

Brogaard, Jonathan and Zareei, Abalfazl, Machine Learning and the Stock Market (January 26, 2021). Proceedings of Paris December 2020 Finance Meeting EUROFIDAI - ESSEC, Available at SSRN: https://ssrn.com/abstract=3233119 or http://dx.doi.org/10.2139/ssrn.3233119

Jonathan Brogaard (Contact Author)

University of Utah - David Eccles School of Business ( email )

1645 E Campus Center Dr
Salt Lake City, UT 84112-9303
United States

HOME PAGE: http://www.jonathanbrogaard.com

Abalfazl Zareei

Stockholm University ( email )

Universitetsvägen 10
Stockholm, Stockholm SE-106 91
Sweden

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