Learning about Risk-Factor Exposures from Earnings: Implications for Asset Pricing and Manipulation

50 Pages Posted: 9 Mar 2021 Last revised: 29 Mar 2021

See all articles by Anne Beyer

Anne Beyer

Stanford University - Graduate School of Business

Kevin Smith

Stanford University Graduate School of Business

Date Written: March 8, 2021

Abstract

When valuing a firm, investors must assess not only its expected future cash flows but also the systematic risk inherent in these cash flows. In this paper, we model the process by which investors may learn about firms' betas from earnings and how this learning process affects the relationship between earnings, announcement returns, and expected future returns. The model's main predictions are: (i) earnings response coefficients vary with macroeconomic conditions and are lower in upswings than downturns; (ii) earnings positively and negatively predict future returns in economic upswings and downturns, respectively, leading to return autocorrelation; and (iii) real earnings management rises in economic downturns and contributes to systematic risk in the economy. These predictions are directly attributable to investors' uncertainty regarding firms' exposures to systematic risk.

Keywords: Earnings betas, aggregate earnings, post-earnings announcement drift, earnings manipulation, return autocorrelation

JEL Classification: G11, G12, M40

Suggested Citation

Beyer, Anne and Smith, Kevin, Learning about Risk-Factor Exposures from Earnings: Implications for Asset Pricing and Manipulation (March 8, 2021). Journal of Accounting & Economics (JAE), Forthcoming, Stanford University Graduate School of Business Research Paper, Available at SSRN: https://ssrn.com/abstract=3800379

Anne Beyer

Stanford University - Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Kevin Smith (Contact Author)

Stanford University Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
217
Abstract Views
640
rank
182,997
PlumX Metrics