Coherence and Consistency of Investors' Probability Judgments

This study investigates the quality of direct probability judgments and quantile estimates with a focus on calibration and consistency. The two response modes use different measures of miscalibration, so it is difficult to directly compare their relative (in)accuracy. We employed a more refined with...

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Published inManagement science Vol. 53; no. 11; pp. 1731 - 1744
Main Authors Budescu, David V, Du, Ning
Format Journal Article
LanguageEnglish
Published Linthicum, MD INFORMS 01.11.2007
Institute for Operations Research and the Management Sciences
SeriesManagement Science
Subjects
Online AccessGet full text
ISSN0025-1909
1526-5501
DOI10.1287/mnsc.1070.0727

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Abstract This study investigates the quality of direct probability judgments and quantile estimates with a focus on calibration and consistency. The two response modes use different measures of miscalibration, so it is difficult to directly compare their relative (in)accuracy. We employed a more refined within-subject design in which decision makers (DMs) used both response modes to make judgments about a random sample of stocks accompanied by identical information to facilitate comparison between the two judgment methods. DMs judged the probabilities that the stocks will reach a certain threshold, provided lower and upper bounds of these forecasts, and estimated median, 50%, 70%, and 90% confidence intervals of their future prices. We found that the judgments were internally consistent and coherent, but in most cases they were slightly miscalibrated. We used several new methods of analysis that allow for more precise and reliable comparison between the two response modes. We inferred point probability estimates for the target events from the confidence intervals and analyzed them by the same methods applied to binary judgments. Interestingly, when we quantified miscalibration in identical fashion for both methods we did not find evidence of differential levels of miscalibration for the probability judgments and the confidence intervals. We discuss the theoretical and practical implications of these results.
AbstractList This study investigates the quality of direct probability judgments and quantile estimates with a focus on calibration and consistency. The two response modes use different measures of miscalibration, so it is difficult to directly compare their relative (in)accuracy. We employed a more refined within-subject design in which decision makers (DMs) used both response modes to make judgments about a random sample of stocks accompanied by identical information to facilitate comparison between the two judgment methods. DMs judged the probabilities that the stocks will reach a certain threshold, provided lower and upper bounds of these forecasts, and estimated median, 50%, 70%, and 90% confidence intervals of their future prices. We found that the judgments were internally consistent and coherent, but in most cases they were slightly miscalibrated. We used several new methods of analysis that allow for more precise and reliable comparison between the two response modes. We inferred point probability estimates for the target events from the confidence intervals and analyzed them by the same methods applied to binary judgments. Interestingly, when we quantified miscalibration in identical fashion for both methods we did not find evidence of differential levels of miscalibration for the probability judgments and the confidence intervals. We discuss the theoretical and practical implications of these results. Key words: overconfidence; calibration; confidence intervals; probability judgments; response modes; investment decision; forecasts
This study investigates the quality of direct probability judgments and quantile estimates with a focus on calibration and consistency. The two response modes use different measures of miscalibration, so it is difficult to directly compare their relative (in)accuracy. We employed a more refined within-subject design in which decision makers (DMs) used both response modes to make judgments about a random sample of stocks accompanied by identical information to facilitate comparison between the two judgment methods. DMs judged the probabilities that the stocks will reach a certain threshold, provided lower and upper bounds of these forecasts, and estimated median, 50%, 70%, and 90% confidence intervals of their future prices. We found that the judgments were internally consistent and coherent, but in most cases they were slightly miscalibrated. We used several new methods of analysis that allow for more precise and reliable comparison between the two response modes. We inferred point probability estimates for the target events from the confidence intervals and analyzed them by the same methods applied to binary judgments. Interestingly, when we quantified miscalibration in identical fashion for both methods we did not find evidence of differential levels of miscalibration for the probability judgments and the confidence intervals. We discuss the theoretical and practical implications of these results.
This study investigates the quality of direct probability judgments and quantile estimates with a focus on calibration and consistency. The two response modes use different measures of miscalibration, so it is difficult to directly compare their relative (in)accuracy. We employed a more refined within-subject design in which decision makers (DMs) used both response modes to make judgments about a random sample of stocks accompanied by identical information to facilitate comparison between the two judgment methods. DMs judged the probabilities that the stocks will reach a certain threshold, provided lower and upper bounds of these forecasts, and estimated median, 50%, 70%, and 90% confidence intervals of their future prices. We found that the judgments were internally consistent and coherent, but in most cases they were slightly miscalibrated. We used several new methods of analysis that allow for more precise and reliable comparison between the two response modes. We inferred point probability estimates for the target events from the confidence intervals and analyzed them by the same methods applied to binary judgments. Interestingly, when we quantified miscalibration in identical fashion for both methods we did not find evidence of differential levels of miscalibration for the probability judgments and the confidence intervals. We discuss the theoretical and practical implications of these results. [PUBLICATION ABSTRACT]
This study investigates the quality of direct probability judgements and quantile estimates with a focus on calibration and consistency. The two response modes use different measures of miscalibration, so it is difficult to directly compare their relative (in)accuracy. We employed a more refined within-subject design in which decision makers (DMs) used both response modes to make judgments about a random sample of stocks accompanied by identical information to facilitate comparison between the two judgment methods. DMs judged the probabilities that the stocks will reach a certain threshold, provided lower and upper bounds of these forecasts, and estimated median, 50%, 70%, and 90% confidence intervals of their future prices. We found that the judgments were internally consistent and coherent, but in most cases they were slightly miscalibrated. We used several new methods of analysis that allow for more precise and reliable comparison between the two response modes. We inferred point probability estimates for the target events from the confidence intervals and analyzed them by the same methods applied to binary judgments. Interestingly, when we quantified miscalibration in identical fashion for both methods we did not find evidence of differential levels of miscalibration for the probability judgments and the confidence intervals. We discuss the theoretical and practical implications of these results. Reprinted by permission of the Institute for Operations Research and Management Science (INFORMS)
Audience Trade
Academic
Author Budescu, David V
Du, Ning
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Issue 11
Keywords probability judgments
Probabilistic approach
Decision making
investment decision
Median
Emotion emotionality
Calibration
Forecasting
response modes
Confidence interval
overconfidence
Confidence
confidence intervals
Quantile
Investment
Judgment
forecasts
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Snippet This study investigates the quality of direct probability judgments and quantile estimates with a focus on calibration and consistency. The two response modes...
This study investigates the quality of direct probability judgements and quantile estimates with a focus on calibration and consistency. The two response modes...
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StartPage 1731
SubjectTerms Analysis
Applied sciences
Calibration
Coherence
Combinatorial probabilities
Confidence interval
Confidence intervals
Consistent estimators
Decision makers
Decision theory
Decision theory. Utility theory
Decision-making
Economic forecasts
Estimation methods
Evaluation
Exact sciences and technology
Expected values
forecasts
Geometric probabilities
Interval estimators
Investment analysis
investment decision
Investment strategies
Investors
Judgment
Judgmental probability
Management science
Operational research and scientific management
Operational research. Management science
overconfidence
Prices
Probabilities
Probability
Probability judgments
response modes
Significance tests
Stock prices
Stocks
Studies
Title Coherence and Consistency of Investors' Probability Judgments
URI http://mansci.journal.informs.org/cgi/content/abstract/53/11/1731
https://www.jstor.org/stable/20122330
http://econpapers.repec.org/article/inmormnsc/v_3a53_3ay_3a2007_3ai_3a11_3ap_3a1731-1744.htm
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