Minimum Distance Estimation of the Cross Section of Expected Stock Returns

Résumé du livre

"Lewellen (2015), building on the prior work of Haugen and Baker (1996) and Hanna and Ready (2005), showed that Fama-MacBeth regression slopes with many anomaly variables can be used to forecast returns out-of-sample. This paper proposes a different way to "combine" the anomaly variables, using the minimum distance estimator that is more efficient than the Fama-MacBeth. The method essentially weights period-by-period slopes by their estimated precisions. By substantially reducing the amount of noise in the estimates, this method allows a trading strategy that produces larger long-short portfolio spreads and alphas than those produced by the Fama-MacBeth method, when stocks are sorted by the resulting fitted values. In direct comparisons, it is also shown that such a strategy generates significant abnormal returns not spanned by the returns from the Fama- MacBeth strategy, and that the explanatory power of these return estimates is larger than that of the Fama-MacBeth estimates. The results are robust to different variable selections, time periods and rolling window lengths. The strategy also performs better while having lower transaction costs. In addition, I present an application that uses my method to generate the level, slope and curve factor model in the spirit of Clarke (2016), and show that such a three-factor model performs substantially better than his version of the factor model and also favorably to other leading factor models."--Page viii