Recent studies suggest the apparent delay in the stock‐price response to earnings announcements (i.e., post‐earnings announcement drift) is caused by investors who underestimate the autocorrelation of seasonally‐differenced earnings (persistence). I present results that suggest: (1) a firm’s future persistence is predictable on the basis of its past persistence; (2) the immediate stock‐price response to earnings is positively related to historical persistence; (3) post‐earnings‐announcement drift is independent of historical persistence; and (4) consistent with (2) and (3), the difference between a firm’s current observed persistence and that implied in stock prices is independent of its historical persistence. These results extend prior research by demonstrating that investors are aware not only that seasonally‐differenced earnings are autocorrelated, but that investors recognize firm‐specific differences in the magnitude of the autocorrelation.

Analyst forecasts, Earnings forecast error persistence, Analyst forecast revisions

We examine the effect of option listing on the immediate stock-price response to earnings announcements. Contrary to prior studies using earlier data, we find firms initiating option trading after 1986 fail to exhibit a significant decline in earnings response. We then examine 420 firms initiating option trading during 1973–1993. In a series of tests controlling for market-wide effects and changing firm size we find some evidence that option listing may actually increase the stock-price response to earnings, but no evidence listing reduces the response. Both longitudinal and cross-sectional tests produce similar results.

In this study, we investigate whether the security market reaction to the announcement of lower than expected earnings (bad news earnings) is dependent on the fiscal quarter of the announcement (earlier quarters versus fourth quarter). Such dependence could arise from the provisions of generally accepted accounting principles which allow extensive use of managers' fiscal-year expectations when formulating interim cost estimates. These provisions provide managers with a potential means of delaying bad news earnings until the fourth-quarter earnings announcement. Support for the view that managers delay the release of bad news is provided by recent research on the timing of information releases (e.g., the release of bad earnings news, bad dividend news, and bad nonearnings news).' Since managers have the means (through generally accepted accounting principles for interim reporting) and the tendency (as suggested by the empirical evidence on the timing of information releases) to delay bad news, it seems plausible to hypothesize a larger security

We investigate the relation between the Value Line enigma and post-earnings-announcement drift. The ability of Value Line's ‘timeliness’ ranks to predict future abnormal returns is well-documented. However, we show that most rank changes occur within eight trading days of an earnings announcement. Once we control for post-earnings-announcement drift, differences in abnormal returns across Value Line timeliness ranks are no longer significant. Moreover, we find that timeliness ranks have no predictive power for firms with small earnings ‘surprises’. We conclude that the Value Line enigma is a manifestation of post-earnings-announcement drift.

Previous research has shown that analysts' forecasts of quarterly earnings per share (EPS) are more accurate than those of accepted time‐series models. In addition, some previous research suggests that, on average, analysts' forecasts tend to be optimistic (i.e., biased). Two explanations for analysts' superiority have been proposed: (1) analysts use more recent information than can time‐series models and (2) analysts use forecast‐relevant information not included in the time‐series of past earnings. This paper provides evidence on a third potential source of analyst superiority: the possibility that humans can use past earnings data to predict future earnings more accurately than can mechanical time‐series models. We find that human judges do no worse than accepted time‐series models when both use the same information set: namely, the series of past EPS figures. To date, little or no research has attempted to determine why analyst bias might exist. Still, some possible reasons have been forwarded. First, pessimistic forecasts or reports may hinder future efforts of the analyst or the analyst's employer to obtain information from the company being analyzed. Second, forecast data bases may suffer a selection bias if analysts tend to stop following those firms that they perceive as performing poorly. This study proposes, and provides evidence regarding, a third possible explanation for analyst bias: the use of judgmental heuristics by analysts. Many studies have shown that human predictions are often biased because of the use of such heuristics. We present evidence that suggests this may be the case for analysts' forecasts of earnings per share. Résumé. De précédents travaux de recherche ont démontré que les prévisions des analystes relatives au bénéfice par action (BPA) trimestriel sont plus exactes que celles que permettent d'obtenir les modèles reconnus basés sur les séries chronologiques. De plus, les résultats de certains travaux de recherche laissent croire qu'en moyenne, les prévisions des analystes tendent à être optimistes (c'est‐à‐dire biaisées). Deux explications à cette supériorité ont été proposées: 1) l'information que les analystes utilisent est plus récente que celles utilisées dans les modèles fondés sur les séries chronologiques et 2) les analystes utilisent de l'information pertinente aux prévisions qui ne figure pas dans les séries chronologiques relatives aux bénéfices passes. Les auteurs attribuent à un troisième facteur potentiel cette supériorité: la possibilité pour les humains d'utiliser les données relatives aux bénéfices passés pour prédire les bénéfices futurs de façon plus précise que ne le peuvent les modèles fondés sur les séries chronologiques. Ils en viennent à la conclusion que les humains obtiennent des résultats tout aussi efficaces que les modèles chronologiques reconnus lorsqu'ils utilisent un jeu de renseignements identique, soit les données historiques relatives au BPA. Jusqu'à maintenant, peu de chercheurs, sinon aucun, ont tenté de déterminer à quoi tiendrait l'existence d'un biais chez l'analyste. Malgré tout, certaines explications possibles ont été proposées. Premièrement, les prévisions ou les rapports pessimistes peuvent faire obstacle aux efforts futurs de l'analyste ou de son employeur pour obtenir de l'information de la société faisant l'objet de l'analyse. Deuxièmement, les bases de données servant à la prévision peuvent être entachées d'un biais de sélection si les analystes ont tendance à cesser de suivre les entreprises qui leur semblent afficher une piètre performance. Les auteurs proposent et attestent une troisième explication possible du biais de l'analyste: l'utilisation de méthodes heuristiques fondées sur le jugement. De nombreuses études ont démontré que les prédictions humaines sont souvent biaisées par suite de l'utilisation de ces méthodes heuristiques. Les auteurs apportent des arguments qui permettent de croire que ce pourrait être le cas des prévisions des analystes du bénéfice par action.

Post–earnings announcement drift is the tendency for a stock's cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks following an earnings announcement. We show that the drift is significantly larger when defining the earnings surprise using analysts' forecasts and actual earnings from I/B/E/S than when using a time series model based on Compustat earnings data. Neither Compustat's policy of restating earnings nor the inclusion of “special items” in reported earnings contribute significantly to the disparity in drift magnitudes. Rather, our results suggest that this disparity is attributable to differences between analyst forecasts and those of time-series models—or at least to factors correlated with these differences. Further, we document that analyst forecasts lead to return patterns around future earnings announcements that differ from those observed when using time-series models, suggesting that the two types of surprises may capture somewhat different forms of mispricing.

We show that the vast majority of investors ignore value-relevant accruals information when it is first released, but that investors who initiate trades of at least 5,000 shares tend to transact in the proper direction. These investors trade on accruals information only when the previously-announced earnings signal is non-negative. Unconditionally, those investors initiating the smallest trades appear to respond to accruals in the wrong direction, but further investigation suggests this behavior is explained by their attraction to attention-grabbing stocks. Finally, we find that those who trade on accruals information have insufficient market power to mitigate the accruals anomaly.