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

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, for 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 that avoid common weaknesses such as a non‐representative selection of trick questions. Copyright © 2012 John Wiley & Sons, Ltd.

Caution! Warnings as a Useless Countermeasure to Reduce Overconfidence? An Experimental Evaluation in Light of Enhanced and Dynamic Warning Designs

“…However, there is still an ongoing debate on what the true underlying causes are (Glaser et al, ; Klayman, Soll, González‐Vallejo, & Barlas, ; Soll & Klayman, ). One suggestion is that overly precise estimates (e.g.…”

“…Overconfidence is one of the most prevalent and robust findings in decision-making (De Bondt & Thaler, 1995;Glaser, Langer, & Weber, 2013;Lichtenstein, Fischhoff, & Phillips, 1982), and research finds overconfidence often leading to biased, i.e. suboptimal decisions (Glaser et al, 2013). According to Moore and Healy (2008), overconfidence can be classified into the categories of overestimation, overplacement, and overprecision.…”

“…The arguably most robust type of overconfidence is overprecision (Haran, Moore, & Morewedge, 2010), whereby people are overconfident about the accuracy of their estimates and beliefs. Following this categorization of overconfidence, this paper addresses overconfidence in terms of the definition of overprecision, sometimes also labeled miscalibration (Glaser et al, 2013).…”

“…(Glaser et al, 2013, p. 405). This elicitation method was used in recent studies (Fellner & Krügel, 2012;Glaser et al, 2013;Jain, Mukherjee, Bearden, & Gaba, 2013) and to some extent in the first overconfidence experiments (see Lichtenstein et al, 1982, for an overview). It has been applied to the tasks of forecasting stock values and stock indices (e.g.…”

Overconfidence and stock returns: a behavioural perspective

“…Barber and Odean (2001) and Chuang and Lee (2006) support the previous claim also arguing that individual overconfident investors underestimate risk and trade more in riskier securities. Glaser et al (2013) stress that the way researchers measure overconfidence drives the results in the direction of documenting overconfidence when there is in fact none. These studies show that overconfidence can easily be explained by different degrees of knowledge of agents plus a random error in predictions (Glaser et al, 2013).…”