Predicting High-Frequency Industry Returns: Machine Learners Meet News Watchers

43 Pages Posted: 8 Oct 2020

See all articles by Hao Jiang

Hao Jiang

Michigan State University

Sophia Zhengzi Li

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick

Peixuan Yuan

Renmin University of China - School of Finance

Date Written: September 26, 2020

Abstract

This paper uses machine learning-based as well as fundamental-driven, news-based approaches to uncover patterns of high-frequency return predictability for sector exchange-traded funds (ETFs). A LASSO predictor that aggregates high-frequency price movements of a broad universe of individual stocks predicts ETF returns out-of-sample. The news-driven return on ETF constituent firms positively predicts ETF returns, but the component of ETF returns orthogonal to the news return negatively predicts them. These different signals contain independent information, and have different strengths, with the LASSO predictor providing continuous flows of information most powerful during trading hours and the news return offering sporadic information particularly useful during market close. A composite signal combining all three signals with Gradient Boosted Regression Trees (GBRT) has very strong power to forecast ETF returns, especially during the COVID-19 pandemic.

Keywords: Industry Return, ETF, Return Predictability, LASSO, Boosted trees, News

JEL Classification: G10, G14, G40

Suggested Citation

Jiang, Hao and Li, Sophia Zhengzi and Yuan, Peixuan, Predicting High-Frequency Industry Returns: Machine Learners Meet News Watchers (September 26, 2020). Available at SSRN: https://ssrn.com/abstract=3700466 or http://dx.doi.org/10.2139/ssrn.3700466

Hao Jiang

Michigan State University ( email )

315 Eppley Center
Department of Finance
East Lansing, MI 48824
United States

HOME PAGE: http://sites.google.com/site/haojiangfinance/

Sophia Zhengzi Li (Contact Author)

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick ( email )

100 Rockafeller Rd
Piscataway, NJ 08854
United States

Peixuan Yuan

Renmin University of China - School of Finance ( email )

Ming De Main Building
Renmin University of China
Beijing, Beijing 100872
China

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