Quantifying Cognitive Biases in Analyst Earnings Forecasts

Abstract

This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by cognitive biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts. The model enables us to test for cognitive biases, and to quantify their magnitude. We estimate the model and find strong evidence that analysts are overconfident about the precision of their own information and also subject to cognitive dissonance bias. But they are able to make corrections for bias in the forecasts of others. We show that our measure of overconfidence varies with book-to-market ratio in a way consistent with the findings of Daniel and Titman (1999). We also demonstrate the existence of these biases in international data.