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E. Elton, M. Gruber, M. Gultekin (1984)
Professional Expectations: Accurary and Diagonosis of ErrorsJournal of Financial and Quantitative Analysis, 19
Russell Barefield, E. Comiskey (1975)
The accuracy of analysts' forecasts of earnings per shareJournal of Business Research, 3
E. Joyce, Gary Biddle (1981)
Are Auditors' Judgments Sufficiently Regressive?Journal of Accounting Research, 19
Foster Foster, Olsen Olsen, Shevlin Shevlin (October 1984)
Earnings Releases, Anomalies, and the Behavior of Security ReturnsThe Accounting Review
R. Lipe (1986)
The information contained in the components of earningsJournal of Accounting Research, 24
Bamber Bamber (July 1987)
Unexpected Earnings, Firm Size, and Trading Volume Around Quarterly Earnings AnnouncementsThe Accounting Review
L. Brown, R. Hagerman, P. Griffin, M. Zmijewski (1987)
Security Analyst Superiority Relative to Univariate Time-Series Models in Forecasting Quarterly EarningsUC Davis: Accounting (Topic)
D. Kahneman, A. Tversky (1979)
Prospect theory: An analysis of decision under risk Econometrica 47
Kenneth Lorek, Allen Bathke (1984)
The relationship between time-series models and the security market's expectation of quarterly earningsThe Accounting Review
Kahneman Kahneman, Tversky Tversky (March 1979)
Prospect Theory: An Analysis of Decision Under RiskEconometrica
McDonald McDonald (July 1973)
An Empirical Examination of the Reliability of Published Predictions of Future EarningsThe Accounting Review
L. Brown, R. Hagerman, P. Griffin, M. Zmijewski (2008)
An Evaluation of Alternative Proxies for the Market's Assessment of Unexpected EarningsCapital Markets: Market Efficiency eJournal
W. Collins, W. Hopwood, J. Mckeown (1984)
The Predictability Of Interim Earnings Over Alternative QuartersJournal of Accounting Research, 22
W. Beaver, R. Clarke, William Wright (1979)
ASSOCIATION BETWEEN UNSYSTEMATIC SECURITY RETURNS AND THE MAGNITUDE OF EARNINGS FORECAST ERRORSJournal of Accounting Research, 17
Abdolmohammadi (1987)
An Examination of the Effects of Experience and Task Complexity on Audit JudgementsThe Accounting Review
L. Brown, Michael Rozeff (1979)
Univariate Time-Series Models of Quarterly Accounting Earnings per Share: A Proposed ModelJournal of Accounting Research, 17
Tversky Tversky (September 1974)
Judgment under Uncertainty: Heuristics and BiasesScience
D. Bao, M. Lewis, W. Lin, J. Manegold (1983)
Applications of time‐series analysis in accounting: A reviewJournal of Forecasting, 2
R. Libby, R. Blashfield (1978)
Performance of a composite as a function of the number of judgesOrganizational Behavior and Human Performance, 21
Kinney Kinney, Uecker Uecker (January 1982)
Mitigating the Consequences of Anchoring in Audit JudgmentsThe Accounting Review
R. Ashton, Sandra Kramer (1980)
Students As Surrogates In Behavioral Accounting Research - Some EvidenceJournal of Accounting Research, 18
Dov Fried, Dan Givoly (1982)
Financial analysts' forecasts of earnings: A better surrogate for market expectationsJournal of Accounting and Economics, 4
O'Brien O'Brien (January 1988)
Analyst Forecasts as Earnings ExpectationsJournal of Accounting and Economics
A. Tversky, D. Kahneman (1973)
Availability: A heuristic for judging frequency and probabilityCognitive Psychology, 5
Foster Foster (January 1977)
Quarterly Accounting Data: Time‐Series Properties and Predictive‐Ability ResultsThe Accounting Review
Peter Mathers (1981)
Picking a LoserMeanjin, 40
P. Griffin (1977)
Time-Series Behavior Of Quarterly Earnings - Preliminary EvidenceJournal of Accounting Research, 15
Brown Brown (July 1987b)
An Evaluation of Alternative Proxies for the Market's Assessment of Unexpected EarningsJournal of Accounting and Economics
L. Pankoff, Robert Virgil (1970)
Some Preliminary Findings from a Laboratory Experiment on the Usefulness of Financial Accounting Information to Security AnalystsJournal of Accounting Research, 8
Abstract. 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.
Contemporary Accounting Research – Wiley
Published: Mar 1, 1990
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