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Review of Quantitative Finance and Accounting
Erschienen in: Review of Quantitative Finance and Accounting 2/2020

25.11.2019 | Original Research

Post-earnings announcement drift and parameter uncertainty: evidence from industry and market news

verfasst von: Claire Y. C. Liang, Rengong Zhang

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 2/2020

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Abstract

Post-earnings announcement drift (PEAD), one of the most prominent and robust return anomalies, is often attributed to investor naiveté or irrationality. A competing explanation is parameter uncertainty, which posits that PEAD may occur as rational investors encounter parameter uncertainty and must learn about the true values of a firm’s pricing parameters over time. This study extends the parameter uncertainty explanation for PEAD and hypothesizes that industry or market news arriving during the drift period affects drift strength. Consistent with our hypothesis, we find that the prices of high surprise firms show stronger responses to industry/market news in the drift period. Hence, the drift becomes stronger (weaker or reversed) when drift-period industry/market news agrees (disagrees) with a firm’s prior earnings news. The evidence helps to distinguish the parameter uncertainty theory from competing behavioral explanations based on investor naiveté or irrational biases, a task previous studies find difficult. Overall, our findings indicate that asset pricing anomalies need not imply investor irrationality—anomalies could arise from rational investors learning about pricing parameters over time.
Vorheriger Artikel Optimal portfolio and spending rules for endowment funds
Nächster Artikel Does the monitoring effect of Big 4 audit firms really prevail? Evidence from managerial expropriation of cash assets

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Fußnoten
1
See Bhattacharya et al. (2012) and Hwang and Rubesam (2015) for disappearing price momentum after the late 1990s, and Green et al. (2011) for the disappearing accruals anomaly after 2003. No study has documented the disappearance of PEAD, despite being weaker in recent years (Ayers et al. 2011; Chordia et al. 2014). In our sample, PEAD remains statistically significant after 2000 (see “Appendix 4”).
 
2
The parameter uncertainty theory (Lewellen and Shanken 2002) is also referred to as “rational structural uncertainty” (Brav and Heaton 2002), “rational learning” (Markov and Tamayo 2006; Francis et al. 2007), or simply “learning” (Pastor and Veronesi 2009) in the literature. In this paper, PEAD explanations motivated by investor naiveté or irrational biases are referred to as “behavioral” explanations to distinguish them from the parameter uncertainty theory or limits of arbitrage.
 
3
Although the stock market returns may reverse in the long term (De Bondt and Thaler 1985), there is no evidence showing autocorrelations at the quarterly frequency. We do not observe any autocorrelations in quarterly stock market returns during our sample period.
 
4
Although the results in Kovacs (2016) show coexistence of seeming underreaction and overreaction, Kovacs focuses the discussions on investor underreaction only. For example, in column (a) of Table 4 Panel B in Kovacs (2016), the loading on earnings surprise for disconfirming industry news is the sum of UE (4.9908) and UE*Infoday (− 10.5818), which is negative (− 5.5910). If the “positive” price response to confirming industry news indicates investor “underreaction,” then the “negative” response suggests “overreaction.” Hence, the evidence cannot be interpreted solely as underreaction.
 
5
A recent study by Du et al. (2011) challenges the finding by Kothari et al. (2006). However, the finding in Kothari et al. (2006) that on the whole aggregate prices do not underreact to aggregate earnings does not preclude the finding in Du et al. (2011), that is a subset of stocks may under- or over-react to market information.
 
6
Investors constantly update their beliefs as new information arrives. They infer market and industry conditions from firm earnings and vice versa (Eden and Loewenstein 1999).
 
7
In simple Bayesian updating models with unknown but fixed pricing parameters, investor uncertainty decreases over time as investors gradually learn about the pricing parameters. However, as Lewellen and Shanken (2002) point out, uncertainty persists in the real world because the economy evolves over time and parameters change constantly. Brav and Heaton (2002) and Markov and Tamayo (2006) also illustrate that parameter uncertainty models yield results resembling those of behavioral models the most when pricing parameters change over time. In this study, we do not examine the changes of parameter uncertainty but focus the tests on the implications of Bayesian updating.
 
8
As 1985:Q1 earnings are announced in Q2, UEP(t) starts in 1985:Q2.
 
9
The Fama–French three factors (RMRF, SMB, HML) are from Ken French’s website: http://​mba.​tuck.​dartmouth.​edu/​pages/​faculty/​ken.​french/​data_​library.​html. Following Jegadeesh and Titman (1993), we calculate the momentum factor WML by sorting firms into deciles based on the returns from the prior six months and then taking the difference between the returns from the top decile (winner) and the bottom decile (loser).
 
10
Although Doyle et al. (2006) first subtract market returns from the raw returns R(t + 1), it makes no difference in the Fama–MacBeth regressions except for the intercept.
 
11
We also estimate Eq. (4) using the size-BM adjusted abnormal return as the dependent variable, following Livnat and Mendenhall (2006) and Ayers et al. (2011), and obtain similar results (see “Appendix 6”).
 
12
Some people may wonder if stock market return reflects investor sentiment rather than news related to the fundamentals. In untabulated tests, we use the change in Baker–Wurgler sentiment (SENT(t + 1)−SENT(t); Baker and Wurgler (2006)) as the proxy for drift-period market news but do not find it to have a significant effect on PEAD. In contrast, if GDP change (GDP(t + 1)− GDP(t)) is used to proxy for market news, the effect of drift-period market news on PEAD again becomes observable.
 
13
Similar to industry news measures derived from stock returns, small (equity market value < $10 million) or low-price firms (price per share < $1) are excluded from the industry news calculations based on analyst forecast revisions. In every period, an industry is required to have at least five firms for the calculations.
 
14
Compared to DEP calculated with per share data, DEP estimated with a firm’s total earnings and stock market value is less susceptible to measurement errors caused by stock splits or share repurchases. In unreported tests, we obtain similar results using DEP calculated with per share data.
 
15
We also have estimated Eq. (4) using the standardized unexpected earnings (SUE(t); Bernard and Thomas 1990) as the proxy for earnings surprise. The results with SUE are less pronounced but remain consistent with our hypothesis (see “Appendix 5”).
 
16
Fama–MacBeth regressions do not work well in this setting when the standard errors correlate across time (Petersen 2009).
 
17
Petersen (2009) shows that clustering standard errors by time works as well as Fama–MacBeth regressions in addressing cross-firm correlations in the same period.
 
18
A recent study by Zhang et al. (2014) also shows that the risk associated with a PEAD strategy has been understated, and the PEAD portfolio’s loadings on systematic factors are greater than shown in prior studies.
 
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Metadaten
Titel
Post-earnings announcement drift and parameter uncertainty: evidence from industry and market news
verfasst von
Claire Y. C. Liang
Rengong Zhang
Publikationsdatum
25.11.2019
Verlag
Springer US
Erschienen in
Review of Quantitative Finance and Accounting / Ausgabe 2/2020
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI
https://doi.org/10.1007/s11156-019-00857-w

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