Learning to be overprecise

Christoph Merkle; Philipp Schreiber

doi:10.1007/s11573-024-01203-w

Learning to be overprecise

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Abstract

We replicate and extend two studies on the dynamics of overconfidence among financial professionals. Using 20 years of data from the ZEW Financial Market Survey with over 40,000 individual forecasts of confidence intervals, we document that participants are overprecise during the entire time period with no evidence of learning on the aggregate. We confirm that professionals update in a Bayesian manner after hits and misses by contracting or expanding their confidence intervals, respectively. However, this updating is insufficient to reach proper calibration. We cannot confirm other predictions of a Bayesian model. An explanation based on self-attribution bias fits the data better.

Originalsprog	Engelsk
Tidsskrift	Journal of Business Economics
ISSN	0044-2372
DOI	https://doi.org/10.1007/s11573-024-01203-w
Status	Accepteret/In press - 2024

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keywords = "Bayesian learning, D03, D83, D84, Financial forecasting, G17, G41, Miscalibration, Overconfidence, Overprecision, Replication",

author = "Christoph Merkle and Philipp Schreiber",

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AU - Schreiber, Philipp

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N2 - We replicate and extend two studies on the dynamics of overconfidence among financial professionals. Using 20 years of data from the ZEW Financial Market Survey with over 40,000 individual forecasts of confidence intervals, we document that participants are overprecise during the entire time period with no evidence of learning on the aggregate. We confirm that professionals update in a Bayesian manner after hits and misses by contracting or expanding their confidence intervals, respectively. However, this updating is insufficient to reach proper calibration. We cannot confirm other predictions of a Bayesian model. An explanation based on self-attribution bias fits the data better.

AB - We replicate and extend two studies on the dynamics of overconfidence among financial professionals. Using 20 years of data from the ZEW Financial Market Survey with over 40,000 individual forecasts of confidence intervals, we document that participants are overprecise during the entire time period with no evidence of learning on the aggregate. We confirm that professionals update in a Bayesian manner after hits and misses by contracting or expanding their confidence intervals, respectively. However, this updating is insufficient to reach proper calibration. We cannot confirm other predictions of a Bayesian model. An explanation based on self-attribution bias fits the data better.

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KW - D83

KW - D84

KW - Financial forecasting

KW - G17

KW - G41

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KW - Overconfidence

KW - Overprecision

KW - Replication

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SN - 0044-2372

JO - Journal of Business Economics

JF - Journal of Business Economics

ER -

Learning to be overprecise

Abstract

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