Cross-Predictability of Earnings and Stock Returns: A Latent Factor Approach

43 Pages Posted: 30 Apr 2018 Last revised: 28 Dec 2020

See all articles by Zhenping Wang

Zhenping Wang

State of Wisconsin Investment Board

Date Written: December 25, 2020

Abstract

I propose a new method, three-pass regression filter developed by Kelly and Pruitt (2015), to predict non-announcing firms’ earnings news using the cross-section of all available early announcers’ earnings news, the number of which can be as large as thousands. The method assumes a set of common latent factors driving the earnings news of non-announcing firms and early announcers and thus efficiently reduces the dimension of announced earnings. Empirical tests show that the extracted measure strongly predicts the earnings surprise and the earnings announcement return with both statistical and economic significance. A long-short trading strategy based on the extracted information realizes a 10% value-weighted alpha annually, indicating a delayed reaction of investors. The return predictability is stronger for small firms or firms with less investor attention. Controlling a series of documented information channels has little impact on the predictive power of this extracted measure.

Keywords: Earnings, Partial Least Squares, Investment, Cross-Predictability

JEL Classification: G14, G12

Suggested Citation

Wang, Zhenping, Cross-Predictability of Earnings and Stock Returns: A Latent Factor Approach (December 25, 2020). Available at SSRN: https://ssrn.com/abstract=3168829 or http://dx.doi.org/10.2139/ssrn.3168829

Zhenping Wang (Contact Author)

State of Wisconsin Investment Board ( email )

121 E Wilson St
Madison, WI 53703
United States

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