Machine Learning and the Stock Market

66 Pages Posted: 27 Aug 2018 Last revised: 26 Jan 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 designing a machine learning algorithm to search for profitable technical trading rules while controlling for data-snooping. Our 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.

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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