Download or read book Minimum Distance Estimation of the Cross Section of Expected Stock Returns written by Hao Zou and published by . This book was released on 2017 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.