Learning about risk-factor exposures from earnings: Implications for asset pricing and manipulation*

• 成果类型: Article
• 署名作者: Beyer, Anne; Smith, Kevin C.
• 署名单位: Stanford University
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101
• DOI: 10.1016/j.jacceco.2021.101404
• 发表日期: 2021
• 关键词: business-cycle; MARKET; management; cost; INFORMATION; disclosure; momentum; offs

摘要

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. (c) 2021 Elsevier B.V. All rights reserved.