Analysts’ weighting of private and public information

Using both a linear regression method and a probability-based method, we find that on average, analysts place larger than efficient weights on (i.e., they overweight) their private information when they forecast corporate earnings. We also find that analysts overweight more when issuing forecasts more favorable than the consensus, and overweight less, and may even underweight, private information when issuing fore-casts less favorable than the consensus. Further, the deviation from efficient weight-ing increases when the benefits from doing so are high or when the costs of doing so are low. These results suggest that analysts ’ incentives play a larger role in misweight-ing than their behavioral biases. This article provides evidence on how analysts weight private and public information in forecasting corporate earnings. Specifically, we examine whether sell-side analysts misweight information, and if so, the potential explanations for such misweighting. We define misweighting as analysts weighting information differently from the efficient benchmark weights that minimize forecast errors (FE) (i.e., the optimal statistical weights i