Return Predictability Revisited Using Weighted Least Squares

I show that important conclusions about time-series return predictability change when using least squares estimates weighted by ex-ante return variance (WLS-EV) instead of OLS. In small-sample simulations, WLS-EV results in large efficiency gains relative to OLS, fewer false negatives, and avoids the bias associated with ex-post weighting schemes. Empirically, traditional predictors such as the dividend-to-price ratio perform better inand out-of-sample using WLS-EV. Unlike OLS estimates, WLSEV estimates of the predictability afforded by the variance risk premium, politics, the weather, and the stars are not significant, suggesting their relations with future returns are spurious, nonlinear, or time-varying. ∗Thanks to Svetlana Bryzgalova, John Griffin, Michael Halling (Imperial discussant), Yufeng Han (SFA discussant), Bryan Kelly, Arthur Korteweg, Xiang (Nicole) Liu, Zack Liu, Rasmus Varneskov (AFA discussant) and seminar participants at the 2017 American Finance Association Meeting, the 11th Annual Hedge Fund Conference at Imperial College, the University of California San Diego, Massachusetts Institute of Technology, the 2015 Southern Finance Association Meeting, and The University of Texas at Austin for their helpful comments. I conducted part of this research while visiting the MIT Sloan School of Management. Previously circulated with the title “Weighted Least Squares Estimates of Return Predictability Regressions.” Data and code are available on my website, http://travislakejohnson.com. Send correspondences to [email protected] or Speedway Stop B6600, Austin, TX 78712. Return Predictability Revisited Using Weighted Least Squares 1