Depressive Realism and Analyst Forecast Accuracy
37 Pages Posted: 27 Jul 2020
Date Written: July 1, 2020
Whether a bad mood enhances or hinders problem-solving and financial decision making is an open question. Using the Gallup Analytics survey, we test the depressive realism hypothesis in the earnings forecasts provided by Estimize users. The depressive realism hypothesis states that mild forms of depression improve judgment tasks because of higher attention to detail and slower information processing. We find that a 1-standard-deviation increase in the segment of the U.S. population with depression leads to a 0.25\% increase in future forecast accuracy, supporting the hypothesis. This influence is comparable to other determinants of Estimize users' accuracy, like the geographic proximity of users to firms, users' experience, and their professional status. Our result is robust to using an IV analysis, different measures of forecast accuracy and mood, as well as alternative explanations.
Keywords: Depressive Realism; Estimize; Earnings Forecast Accuracy; Negative Mood
JEL Classification: G00, G24
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