Investor learning, earnings signals, and stock returns

Peng-Chia Chiu () and Timothy D. Haight ()
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Peng-Chia Chiu: the Chinese University of Hong Kong
Timothy D. Haight: Loyola Marymount University

Review of Quantitative Finance and Accounting, 2020, vol. 54, issue 2, No 10, 698 pages

Abstract: Abstract Prior studies show that investor learning about earnings-based return predictors from academic research erodes return predictability. However, the signaling power of “bottom-line” earnings has declined over time, which complicates assessments of investor learning about profitability signals underlying earnings. We show that modified earnings variables with lower susceptibility to signal weakening exhibit rates of return attenuation that are 30–64% lower than rates for bottom-line earnings variables over our sample period. Notably, return gaps between bottom-line and less susceptible variables are widest in recent years, especially within non-overlapping samples and samples with weak bottom-line signals (e.g., special items, losses, fourth fiscal quarter). Our results hold after controlling for risk factors known to predict returns, they do not appear to be attributable to ex ante earnings volatility, and they are robust to alternative sample selection criteria, sub-period partitions, and portfolio holding windows. Overall, our results suggest that while investor learning is apparent in the data, learning efforts to date have been suboptimal at exploiting profitability signals within firms’ earnings streams.

Keywords: Investor learning; Earnings properties; Market efficiency; Stock returns (search for similar items in EconPapers)
JEL-codes: G11 G12 G13 G14 M41 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11156-019-00803-w

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