Investor Learning, Earnings Signals, and Stock Returns

Review of Quantitative Finance and Accounting, Forthcoming

46 Pages Posted: 1 Apr 2019

See all articles by Peng-Chia Chiu

Peng-Chia Chiu

The Chinese University of Hong Kong, Shenzhen

Timothy Haight

Loyola Marymount University

Date Written: March 7, 2019

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

JEL Classification: G11, G12, G13, G14, M41

Suggested Citation

Chiu, Peng-Chia and Haight, Timothy, Investor Learning, Earnings Signals, and Stock Returns (March 7, 2019). Review of Quantitative Finance and Accounting, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3348325

Peng-Chia Chiu (Contact Author)

The Chinese University of Hong Kong, Shenzhen ( email )

Timothy Haight

Loyola Marymount University ( email )

7900 Loyola Boulevard
Los Angeles, CA 90045
United States

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