Abstract, Previous research has shown that analysts' forecasts of quarterly eamings 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 forecastrelevant information not included in the time-series of past eamings. This paper provides evidence on a third potential source of analyst superiority: the possibility that humans can use past eamings data to predict future eamings 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 sedes 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 eamings per share.Resume, De precedents travaux de recherche ont demontre que les previsions des analystes relatives au benefice par action (BPA) trimestriel sont plus exactes que celles que permettent d'obtenir les modeles reconnus bases sur les series chronologiques. De plus, les resultats de certains travaux de recherche laissent croire qu'en moyenne, les previsions des analystes tendent a etre optimistes (c'est-a-dire biaisees). Deux explications a cette superiorite ont ete proposees: 1) I'information que les analystes utilisent est plus recente que celles utilisees dans les modeles fondes sur les series chronologiques et 2) les analystes utilisent de I'information pertinente aux previsions qui ne figure pas dans les series chronologiques relatives aux benefices passes. Les auteurs attribuent a un troisieme facteur potentiel cette superiorite: la possibilite pour les humains d'utiliser les donnees relatives aux benefices passes pour predire les benefices futurs de fa9on plus precise *
IntroductionResearch has shown that tinancial analysts' forecasts of corporate eamings per share (EPS) are more accurate than those produced by time-series models (Brown and Rozeff, 1979;Fried and Givoly, 1982;Collins, Hopwood, and McKeown, 1984;Brown, Griffin, Hagerman, and Zmijewski, 1987a) but that analysts also tend to overestimate future eamings (Fried and Givoly, 1982;O'Brien, 1988). Prior explanat...
