Learning to be overprecise

Christoph Merkle; Philipp Schreiber

doi:10.1007/s11573-024-01203-w

Learning to be overprecise

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › 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.

Original language	English
Journal	Journal of Business Economics
ISSN	0044-2372
DOIs	https://doi.org/10.1007/s11573-024-01203-w
Publication status	Accepted/In press - 2024

Keywords

Bayesian learning
D03
D83
D84
Financial forecasting
G17
G41
Miscalibration
Overconfidence
Overprecision
Replication

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10.1007/s11573-024-01203-w

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title = "Learning to be overprecise",

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.",

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

author = "Christoph Merkle and Philipp Schreiber",

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doi = "10.1007/s11573-024-01203-w",

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AU - Schreiber, Philipp

N1 - Publisher Copyright: © The Author(s) 2024.

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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.

KW - Bayesian learning

KW - D03

KW - D83

KW - D84

KW - Financial forecasting

KW - G17

KW - G41

KW - Miscalibration

KW - Overconfidence

KW - Overprecision

KW - Replication

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AN - SCOPUS:85203427773

SN - 0044-2372

JO - Journal of Business Economics

JF - Journal of Business Economics

ER -

Learning to be overprecise

Abstract

Keywords

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