Probability Weighting and Analyst Bias: Theory and Evidence

41 Pages Posted: 17 Dec 2016

See all articles by Kathryn Brightbill

Kathryn Brightbill

University of Iowa, Henry B. Tippie College of Business, Department of Accounting

Cristi A. Gleason

University of Iowa - Department of Accounting

Mark Penno

University of Iowa

Date Written: December 16, 2016

Abstract

Analyst forecasting bias is frequently attributed to opportunism. We argue that opportunism is not a necessary condition for bias and propose a simple model, based on research in behavioral economics and psychology, of belief-based probability weighting. The model is used to develop benchmarks which we test empirically. Our model explains the change in the magnitude of forecast bias over the forecast horizon, the percentage of optimistic and pessimistic forecast errors at long horizons, and seemingly counter-intuitive findings around the implementation of Regulation Fair Disclosure. Our results may inform regulators as they attempt to reduce or eliminate analyst forecast bias; eliminating incentives to bias may not lead to statistically unbiased forecasts.

Keywords: Analyst, Bias, Forecast, Probability, Weighting, Opportunistic, archival, regulation fair disclosure, fair disclosure, financial

Suggested Citation

Brightbill, Kathryn and Gleason, Cristi A. and Penno, Mark C., Probability Weighting and Analyst Bias: Theory and Evidence (December 16, 2016). Available at SSRN: https://ssrn.com/abstract=2886643 or http://dx.doi.org/10.2139/ssrn.2886643

Kathryn Brightbill (Contact Author)

University of Iowa, Henry B. Tippie College of Business, Department of Accounting ( email )

108 Pappajohn Business Building
Iowa City, 52242-1000
United States

Cristi A. Gleason

University of Iowa - Department of Accounting ( email )

108 Pappajohn Business Building
Iowa City, IA 52242-1000
United States

Mark C. Penno

University of Iowa ( email )

W324 Pappajohn Business Building
21 East Market St
Iowa City, IA 52242-1000
United States

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