Robust Strategies for Quantitative Investment Management

Abstract

We show how quantile estimation combined with robust methods can be used in quantitative investment management. A portfolio manager uses a quantitative model to select securities. The objective is to outperform a benchmark portfolio, subject to risk constraints. Traditional stock selection models express expected returns as a function of factors where all parts of the return distribution are affected similarly. This is subsumed by the quantile approach in which a stock's entire return distribution is a conditional function of factors. Robust methods insure that our estimates do not depend on a small subset of the data. Regression quantile estimates then detect the potentially different impact of factors at the center and tails of the return distribution. This is illustrated in assessing a model's forecasting accuracy while controlling for return differences between economic sectors. We are thereby able to detect forecasting properties that would have been missed by a conventional analysis of the data. Mathematics Subject Classification (2000). Primary 9IB28; Secondary 62G35.