Granular Information and Sectoral Movements

75 Pages Posted: 8 Oct 2020 Last revised: 6 Oct 2022

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: October 5, 2022

Abstract

This paper shows a strong link between the granular information contained in individual stock prices and sectoral movements. Using machine learning algorithms, we find that a predictor aggregating the price movements of a broad cross section of individual stocks predicts sector ETF returns at intraday and lower frequencies. When we combine the information from structural models with machine learning, the resulting information signals have even stronger return predictability. A trading strategy that exploits the return predictability is profitable after trading costs. These results support theories of granular and network origins of aggregate shocks.

Keywords: Granular Information, Sectoral Movements, Exchange-Traded Funds, Machine Learning

JEL Classification: G10, G14, G40

Suggested Citation

Jiang, Hao and Li, Sophia Zhengzi and Yuan, Peixuan, Granular Information and Sectoral Movements (October 5, 2022). 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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