PT - JOURNAL ARTICLE
AU - Pohlman, Lawrence
AU - Ma, Lingjie
TI - Return Forecasting by Quantile Regression
AID - 10.3905/joi.2010.19.4.116
DP - 2010 Nov 30
TA - The Journal of Investing
PG - 116--121
VI - 19
IP - 4
4099 - http://joi.pm-research.com/content/19/4/116.short
4100 - http://joi.pm-research.com/content/19/4/116.full
AB - A typical quantitative approach for analyzing and forecasting equity returns is to build a model based on a set of factors and then estimate the model based on a set of data and some type of least squares procedure. However, as the data in equity markets are usually far from well behaved and some standard statistical assumptions do not hold, this procedure can miss significant relationships. This article uses the quantile regression technique to reveal effects that are missed by OLS. The empirical results using S&P 500 Index data show dramatic improvement in performance using QR forecasts.