True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration

Journal of Behavioral Decision Making, Forthcoming

36 Pages Posted: 3 May 2005 Last revised: 26 Jun 2012

See all articles by Markus Glaser

Markus Glaser

Ludwig Maximilian University of Munich - Faculty of Business Administration (Munich School of Management)

Martin Weber

University of Mannheim - Department of Banking and Finance

Thomas Langer

University of Muenster - Finance Center

Date Written: June 25, 2012

Abstract

Overconfidence is often regarded as one of the most prevalent judgment biases. Several studies show that overconfidence can lead to suboptimal decisions of investors, managers, or politicians. Recent research, however, questions whether overconfidence should be regarded as a bias and shows that standard "overconfidence" findings can easily be explained by different degrees of knowledge of agents plus a random error in predictions. We contribute to the current literature and ongoing research by extensively analyzing interval estimates for knowledge questions, real financial time series, and for artificially generated charts. We thereby suggest a new method to measure overconfidence in interval estimates which is based on the implied probability mass behind a stated prediction interval. We document overconfidence patterns which are difficult to reconcile with rationality of agents and which cannot be explained by differences in knowledge as differences in knowledge do not exist in our task. Furthermore, we show that overconfidence measures are reliable in the sense that there exist stable individual differences in the degree of overconfidence in interval estimates, thereby testing an important assumption of behavioral economics and behavioral finance models: stable individual differences in the degree of overconfidence across people. We do this in a "field experiment," for different levels of expertise of subjects (students on the one hand and professional traders and investment bankers on the other hand), over time, by using different miscalibration metrics, and for tasks which avoid common weaknesses like a non-representative selection of trick questions.

Keywords: overconfidence, miscalibration, judgment biases, individual differences, reliability, expert judgment, interval estimates

JEL Classification: C9, G1

Suggested Citation

Glaser, Markus and Weber, Martin and Langer, Thomas, True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration (June 25, 2012). Journal of Behavioral Decision Making, Forthcoming. Available at SSRN: https://ssrn.com/abstract=712583 or http://dx.doi.org/10.2139/ssrn.712583

Markus Glaser (Contact Author)

Ludwig Maximilian University of Munich - Faculty of Business Administration (Munich School of Management) ( email )

Schackstra├če 4
Munich, 80539
Germany

Martin Weber

University of Mannheim - Department of Banking and Finance ( email )

D-68131 Mannheim
Germany
+49 621 181 1532 (Phone)
+49 621 181 1534 (Fax)

Thomas Langer

University of Muenster - Finance Center ( email )

Universitatsstr. 14-16
Muenster, 48143
Germany
+49 251 83 22033 (Phone)

Register to save articles to
your library

Register

Paper statistics

Downloads
1,897
rank
7,661
Abstract Views
5,666
PlumX Metrics