Analysts’ Forecast Dispersion and Stock Returns: A Quantile Regression Approach

Abstract

Prior research has not provided conclusive evidence on the association between analysts’ forecast dispersion and subsequent stock returns. Since inferences from prior studies may be confounded by research design choices, we use the quantile regression (QR) approach and assess the hidden non-monotonic relations between dispersion and stock returns within a broader sample. The empirical results show that dispersion is negatively associated with subsequent stock returns when the latter is in lower quantiles. In contrast, when the stock returns are in high quantiles, dispersion is positively associated with subsequent stock returns. Moreover, the association between dispersion and stock returns is trivial when the mid-range return quantiles are concerned. These non-uniform connections between dispersion and stock returns reflect the different status of overpricing correction process. Our findings help to reconcile the mixed results reported by prior research concerning the relation between analysts’ forecast dispersion and subsequent stock returns.