Security price responses to unexpected earnings: a nonparametric investigation

The widely used linear model of the unexpected earnings/returns relationship has been challenged. In this article we propose a flexible nonparametric approach to study this relationship in which splines are used to approximate the unknown regression function. Spline confidence bands are constructed based on wild bootstrap to examine the adequacy of certain linear/nonlinear specifications. Monte Carlo results show that the proposed bands have excellent coverage of the true regression function with little computing load. These properties make the procedure highly recommended for extracting information from large and complicated datasets. The proposed approach has also been applied to the real world financial data from the unexpected earnings/returns study, and we find significant evidence of nonlinearity. The nonlinearity persists when we control the measurement errors of the earning surprises and firm size.