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Investor learning, earnings signals, and stock returns

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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.

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Notes

  1. For example, McLean and Pontiff (2016) show gradual erosion in the profitability of 97 anomaly-based trading strategies (58% decline on average) in the post publication period. Other large-scale studies documenting return attenuation include Green et al. (2013) and Chordia et al. (2014).

  2. Studies that link investor learning to the elimination of prominent accounting-based return predictors include Johnson and Schwartz (2001), Richardson et al. (2010), Green et al. (2011), and Milian (2015).

  3. Studies that document significant changes to the properties of earnings over time include Givoly and Hayn (2000), Dichev and Tang (2008), and Bushman et al. (2016).

  4. For example, the degree of SUE-MSUE portfolio overlap decreases from an average of 61% (44% to 81% range) in the 1979–1989 subperiod to an average of 51% (39% to 65% range) in the 2000–2017 subperiod.

  5. We do not attempt to formally quantify the effect of signal weakening on return attenuation in relation to other documented drivers. We leave such analysis for future research.

  6. Green et al. (2013) report that 147 of their 330 are accounting-based return predictors.

  7. Bushman et al. (2016) speculate that the declining accrual/cash-flow relation over time may explain, in part or in full, the decline in the accrual anomaly in recent years (e.g., Green et al. 2011).

  8. The evidence in this section pertains to “bottom-line” earnings, which the literature typically defines as GAAP earnings before extraordinary items. Later we will argue that various subtotals of earnings (e.g., gross profit) are likely to be significantly less susceptible to the changes summarized in this section.

  9. Formal attempts to isolate and quantify the various sources of earnings-based return attenuation (e.g., investor learning, signal weakening, and declining trading frictions) go beyond the scope of our study, so we leave such attempts to future research.

  10. Novy-Marx (2013, pp. 2–3) describes gross profit as “the cleanest accounting measure of true economic profitability. The farther down the income statement one goes, the more polluted profitability measures become, and the less related they are to true economic profitability”.

  11. SUE’s return predictability, conventionally referred to as post earnings announcement drift (PEAD), dates back to the seminal work of Ball and Brown (1968) who were the first to document the phenomenon. For reviews of the PEAD literature, see Kothari (2001).

  12. Expected earnings are assumed to follow a seasonal random walk with drift. The drift term is measured as the average of quarterly earnings growth over the previous eight quarters.

  13. With regard to our portfolio tests, we implement the model with the most flexible design rather than the model with the maximum return. Results are qualitatively unchanged under various holding windows and portfolio formation dates. Section 4 further discusses portfolio test considerations.

  14. Results (untabulated) are qualitatively unchanged using univariate specifications.

  15. Our finding in Panel A.3 of an uptick (downtick) from sub-period two to three in SUE’s (MSUE3’s) incremental ability to predict next quarter’s SUE likely stems from two sources: (a) increased classification fluidity between recurring and non-recurring items and (b) signal weakening among non-operating items (included in MSUE3), consistent with evidence in Bushman et al. (2016).

  16. We also examine the higher moments of the portfolio returns in each subperiod (untabulated). Standard deviations increase from subperiod one to subperiod three for all four portfolios, though increases are not monotonic (standard deviations decrease from subperiod one to subperiod two for all portfolios except MSUE2). MSUE returns are positively skewed in most subperiods (MSUE3 returns are negatively skewed in subperiod two), while SUE returns are negatively skewed in subperiod two (skewness = -0.096) and subperiod three (skewness = − 1.460).

  17. Other return attenuation studies that use cumulative return graphs to depict temporal return patterns include Richardson et al. (2010) and Green et al. (2011).

  18. The decline of the non-overlapping MSUE1 portfolio return in Table 4 likely reflects investor learning about the return predictability of gross profit levels documented in Novy-Marx (2013).

  19. Black et al. (2000), Cready et al. (2010), and Cready et al. (2012) show that certain “non-recurring” charges (e.g., restructurings) have recurring effects on firms’ earnings streams, suggesting that not all special items are irrelevant for firm value. This possibility should bias against finding return disparities across our weak signal portfolios.

  20. In the full sample, we find (untabulated) that the percentage of firms reporting special items (losses) increases from 14.6% to 36.2% (24.7% to 30.9%) from sub-period one to three. Meanwhile, the percentage of firms reporting special items or losses in the fourth quarter increases from 39.3% to 60.51%.

  21. Dopuch et al. (2010) show that the accrual anomaly is significantly stronger after removing loss firms from the analysis, even in the post Sarbanes–Oxley Act of 2002 era. Such findings reinforce our key points because they suggest that rising loss frequencies over time can mechanically erode accruals’ return predictability.

  22. Balakrishnan et al. (2010), Novy-Marx (2013), and Ball et al. (2015) show that levels of both bottom line and less susceptible measures predict cross-sectional excess returns over long horizons.

  23. In other untabulated analysis, we examined temporal patterns in each news variable’s earnings response coefficient (ERC) by regressing cumulative market-adjusted stock returns over the 3-day earnings announcement window on each news variable (along with conventional control variables) within each subperiod examined in our paper (i.e., 1979–1989, 1990–1999, and 2000–2017). We found that ERCs increase over our sample period for all four news variables, with all three MSUE ERCs increasing at a higher rate (57.6% increase on average from the first subperiod to the third subperiod) than the SUE ERC (32.3% increase from the first subperiod to the third subperiod). These results suggest that the stronger declines of SUE’s return predictability that we document (relative to the declines of the three MSUE variables) are not driven by stronger earnings announcement window responses to SUE over time.

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Acknowledgements

We would like to thank Cheng-Few Lee (the Editor), two anonymous reviewers, Brad Barber, Novia Chen, Scott Delanty, Lucile Faurel, Laurel Franzen, Nicholas Guest, Marinilka Kimbro, Qin Li, Alex Nekrasov, Linda Myers, Morton Pincus, Terry Shevlin, Siew Hong Teoh, Hai Tran, Mitch Warachka, Crystal Xu, participants at the 2015 European Accounting Association Annual Congress, the 2015 CAAA Annual Conference, the 2016 American Accounting Association Annual Meeting, and workshop participants at Loyola Marymount University, the University of California, Irvine, National Taipei University, Singapore Management University, and Sun Yat-Sen University for their helpful comments and suggestions.

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Authors and Affiliations

  1. School of Accountancy, the Chinese University of Hong Kong, 12 Chak Cheung Street, Shatin, N.T., Hong Kong

    Peng-Chia Chiu

  2. College of Business Administration, Loyola Marymount University, 1 LMU Drive, Los Angeles, CA, 90045, USA

    Timothy D. Haight

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  1. Peng-Chia Chiu
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Correspondence to Timothy D. Haight.

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Appendix: variable definitions

Appendix: variable definitions

Variable name

Definition

MSUE1

Standardized unexpected gross profit, calculated as quarterly gross profit per share minus expected gross profit per share, scaled by the standard deviation of quarterly gross profit growth over the previous eight quarters. Gross profit is defined as revenue minus cost of goods sold. Expected gross profit follows a seasonal random walk with drift. The drift term is the average of quarterly gross profit growth over the previous eight quarters

MSUE2

Standardized unexpected operating profit, calculated as quarterly operating profit per share minus expected operating profit per share, scaled by the standard deviation of quarterly operating profit growth over the previous eight quarters. Operating profit is defined as revenue minus cost of goods sold and selling, general and administrative expense. Expected operating profit follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit growth over the previous eight quarters

MSUE3

Standardized unexpected earnings before one-time items, calculated as quarterly earnings per share adjusted for special items minus expected earnings per share adjusted for special items, scaled by the standard deviation of quarterly earnings growth adjusted for special items over the previous eight quarters. Expected earnings adjusted for special items follows a seasonal random walk with drift. The drift term is the average of quarterly earnings growth adjusted for special items over the previous eight quarters

SUE

Standardized unexpected earnings, calculated as quarterly earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth over the previous eight quarters

Size

Firm size, calculated as the natural log of the market capitalization as of the end of the most recent fiscal quarter for which data are available (in millions)

BM

Book-to-market ratio, calculated as book value of equity divided by market value of equity at the end of the most recent fiscal quarter for which data are available

MOM

The buy-and-hold 6-month stock return ending 1 month prior to the portfolio formation date

R_MSUE1

The decile ranking of MSUE1 based on the distribution for each calendar quarter

R_MSUE2

The decile ranking of MSUE2 based on the distribution for each calendar quarter

R_MSUE3

The decile ranking of MSUE3 based on the distribution for each calendar quarter

R_SUE

The decile ranking of SUE based on the distribution for each calendar quarter

R_Size

The decile ranking of Size based on the distribution for each calendar quarter

R_BM

The decile ranking of BM based on the distribution for each calendar quarter

R_MOM

The decile ranking of MOM based on the distribution for each calendar quarter

ADJ_RET

Size-adjusted return over the 3-month period beginning in the first month of the calendar quarter that is at least 3 months subsequent to fiscal quarter-end. The methodology to construct size-adjusted portfolios is based on Lyon et al. (1999)

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Chiu, PC., Haight, T.D. Investor learning, earnings signals, and stock returns. Rev Quant Finan Acc 54, 671–698 (2020). https://doi.org/10.1007/s11156-019-00803-w

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