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Debiasing Through Experience Sampling: The Case of Myopic Loss Aversion

Laura Hueber () and Rene Schwaiger ()

Working Papers from Faculty of Economics and Statistics, University of Innsbruck

Abstract: We introduce a training intervention based on a novel tool to mitigate behavior consistent with myopic loss aversion (MLA). We present the results of a large-scale online experiment with 894 student participants. The study featured a two-step debiasing training intervention based on experience sampling and a subsequent elicitation of MLA. We found that participants at baseline exhibit behavior consistent with MLA, which was not the case for decisionmakers who underwent the debiasing training intervention. Nonetheless, we found no statistically significant difference-in-difference effect of the training intervention on the magnitude of MLA. However, when we focused on the more attentive participants by excluding participants with the 10% longest and 10% shortest processing times on the task relevant instruction screens, the magnitude of the difference-in-difference effect of the training intervention increased strongly and became statistically significant when controlling for age, gender, education, field of study, investment experience, and financial risk preferences.

Keywords: online experiment; myopic loss aversion; debiasing; experience sampling (search for similar items in EconPapers)
JEL-codes: G11 G41 G51 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2021-01
New Economics Papers: this item is included in nep-cbe and nep-exp
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