Human Judgement is Heavy Tailed: Empirical Evidence and Implications for the Aggregation of Estimates and Forecasts
36 Pages Posted: 13 Jul 2010
Date Written: July 12, 2010
How frequent are large disagreements in human judgment? The substantial literature relating to expert assessments of real-valued quantities and their aggregation almost universally assumes that errors follow a jointly normal distribution. We investigate this question empirically using 17 datasets that include over 20,000 estimates and forecasts. We findnd incontrovertible evidence for excess kurtosis, that is, of fat tails. Despite the diversity of the analyzed datasets as regards to the degree of uncertainty about the quantity being assessed and to the level of expertise and sophistication of those making the assessments, we find consistency in the frequency with which an expert is in large disagreement with the consensus. Fitting a generalized normal distribution to the data, we find values for the shape parameter ranging from 1 to 1.6 (where 1 is the double-exponential distribution, and 2 the normal distribution). This has important implications, in particular for the aggregation of expert estimates and forecasts. We describe optimal Bayesian aggregation with heavy tails, and propose a simple average-median average heuristic that performs well for the range of empirically observed distributions.
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Can Investors Profit from the Prophets? Consensus Analyst Recommendations and Stock Returns
By Brad M. Barber, Reuven Lehavy, ...
Security Analysts' Career Concerns and Herding of Earnings Forecasts
By Jeffrey D. Kubik, Amit Solomon, ...
The Relation between Analysts' Forecasts of Long-Term Earnings Growth and Stock Price Performance Following Equity Offerings
By Patricia Dechow, Amy P. Hutton, ...
Analyzing the Analysts: When Do Recommendations Add Value?
By Narasimhan Jegadeesh, Joonghyuk Kim, ...
Herding Among Investment Newsletters: Theory and Evidence
An Empirical Analysis of Analysts' Target Prices: Short Term Informativeness and Long Term Dynamics
By Alon Brav and Reuven Lehavy
How Do Analysts Use Their Earnings Forecasts in Generating Stock Recommendations?
Are Small Investors Naive About Incentives?
Are Investors Naive About Incentives?
Analyst Forecast Revisions and Market Price Formation