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 in | Management science Vol. 53; no. 11; pp. 1731 - 1744 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Linthicum, MD
INFORMS
01.11.2007
Institute for Operations Research and the Management Sciences |
| Series | Management Science |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0025-1909 1526-5501 |
| DOI | 10.1287/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. |
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| 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) |
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| Author | Budescu, David V Du, Ning |
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| 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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| 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 |
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