Depressive Realism and Analyst Forecast Accuracy

37 Pages Posted: 27 Jul 2020

See all articles by Sima Jannati

Sima Jannati

University of Missouri-Columbia

Sarah Khalaf

Kuwait University

Du Nguyen

University of Missouri at Columbia - Robert J. Trulaske, Sr. College of Business

Date Written: July 1, 2020

Abstract

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

Suggested Citation

Jannati, Sima and Khalaf, Sarah and Nguyen, Du, Depressive Realism and Analyst Forecast Accuracy (July 1, 2020). Available at SSRN: https://ssrn.com/abstract=3640794 or http://dx.doi.org/10.2139/ssrn.3640794

Sima Jannati (Contact Author)

University of Missouri-Columbia ( email )

401 Cornell Hall
COLUMBIA, MO 65211
United States

Sarah Khalaf

Kuwait University ( email )

Safat, 13060
Kuwait

Du Nguyen

University of Missouri at Columbia - Robert J. Trulaske, Sr. College of Business ( email )

336 Cornell Hall
Columbia, MO 65211
United States

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