Intraday Stock Predictability Everywhere

61 Pages Posted: 5 Jul 2023

See all articles by Fred Liu

Fred Liu

University of Guelph; University of Western Ontario, Department of Economics

Lars Stentoft

Department of Economics, University of Western Ontario; Center for Interuniversity Research and Analysis on Organization (CIRANO); Aarhus University - CREATES

Date Written: June 30, 2023

Abstract

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons. While linear models have the strongest statistical predictive power, nonlinear models economically dominate them and machine learning intraday long-short portfolios based on their forecasts attain Sharpe ratios of 4 after transaction costs. Predictability is short-lived, highest in the middle of the day, and more pronounced for less liquid firms, which indicates that slow-moving capital is an economic source of mispricing.

Keywords: Machine Learning, Intraday Return Prediction, High-Frequency, Equity Market, Big Data, Lasso, Elastic Net, Random Forest, Gradient Boosting, Neural Networks, Deep Learning, Fintech

JEL Classification: C45, C52, C55, C58, G0, G1, G17

Suggested Citation

Liu, Fred and Stentoft, Lars, Intraday Stock Predictability Everywhere (June 30, 2023). Available at SSRN: https://ssrn.com/abstract=4496917 or http://dx.doi.org/10.2139/ssrn.4496917

Fred Liu (Contact Author)

University of Guelph ( email )

50 Stone Road East
Guelph, Ontario N1G 2W1
Canada

University of Western Ontario, Department of Economics ( email )

London, Ontario
Canada

Lars Stentoft

Department of Economics, University of Western Ontario ( email )

London, Ontario N6A 5B8
Canada

Center for Interuniversity Research and Analysis on Organization (CIRANO)

2020 rue University, 25th floor
Montreal H3C 3J7, Quebec
Canada

Aarhus University - CREATES

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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