Machine Learning and the Stock Market

57 Pages Posted: 27 Aug 2018  

Jonathan Brogaard

University of Utah - David Eccles School of Business

Abalfazl Zareei

Stockholm University

Date Written: August 16, 2018

Abstract

Recent advances in machine learning methodologies have improved the usefulness of the technology. This paper examines whether machine learning using only past prices as the input can detect mispricings. Generally searching for mispricings is a slow process and can easily suffer from data-snooping. This paper provides a machine learning algorithm to search for mispricings while controlling for data-snooping. The process generates significant out-of-sample alpha. Overall, the results show that mispricings still exist, but have decreased over time, implying that markets have recently become more efficient.

Keywords: Asset Pricing, Anomalies, Mispricings, 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 (August 16, 2018). 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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