Abstract

A typical quantitative approach for analyzing and forecasting equity returns is to build a model based upon a set of factors and then estimate the model based upon a set of data and some type of least squares procedure. However, as the data in equity markets is usually far from well behaved and some standard statistical assumptions do not hold, this procedure can miss significant relationships. This paper uses the quantile regression technique to reveal effects that are missed by OLS. The empirical results using S&P500 data show dramatic improvement in performance using QR forecasts.

Event associated with this file can be found here.