Experience, information asymmetry, and rational forecast bias |
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Authors: | April Knill Kristina Minnick Ali Nejadmalayeri |
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Institution: | 1. Florida State University, Room 143 RBB, 821 Academic Way, Tallahassee, FL, 32306-1110, USA 2. Bentley University, AAC 217, 175 Forest Street, Waltham, MA, 02452, USA 3. Oklahoma State University, North Hall Room 305, 700?N Greenwood Drive, Tulsa, OK, 74106, USA
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Abstract: | This study examines whether it is ever rational for analysts to post biased estimates and how information asymmetry and analyst experience factor into the decision. Using a construct where analysts wish to minimize their forecasting error, we model forecasted earnings when analysts combine private information with consensus estimates to determine the optimal forecast bias, i.e., the deviation from the consensus. We show that the analyst??s rational bias increases with information asymmetry, but is concavely related with experience. Novice analysts post estimates similar to the consensus but as they become more experienced and develop private information channels, their estimates become biased and deviated from the consensus. Highly seasoned analysts, who have superior analytical skills and valuable relationships, need not post biased forecasts. |
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