Probability Weighting and Analyst Bias : Theory and Evidence

Analyst forecasting bias is frequently attributed to opportunism. We argue that opportunism is not a necessary condition for bias and propose a simple model, based on research in behavioral economics and psychology, of belief-based probability weighting. The model is used to develop benchmarks which we test empirically. Our model explains the change in the magnitude of forecast bias over the forecast horizon, the percentage of optimistic and pessimistic forecast errors at long horizons, and seemingly counter-intuitive findings around the implementation of Regulation Fair Disclosure. Our results may inform regulators as they attempt to reduce or eliminate analyst forecast bias; eliminating incentives to bias may not lead to statistically unbiased forecasts