Return Predictability Revisited Using Weighted Least Squares

50 Pages Posted: 7 Feb 2015 Last revised: 12 Feb 2017

Travis L. Johnson

The University of Texas at Austin - Department of Finance

Date Written: February 11, 2017

Abstract

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 in- and out-of-sample using WLS-EV. Unlike OLS estimates, WLS-EV 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.

Keywords: Return predictability, weighted least squares, volatility, out-of-sample predictability, variance risk premium

JEL Classification: G10, G11, G12

Suggested Citation

Johnson, Travis L., Return Predictability Revisited Using Weighted Least Squares (February 11, 2017). Available at SSRN: https://ssrn.com/abstract=2561112 or http://dx.doi.org/10.2139/ssrn.2561112

Travis L. Johnson (Contact Author)

The University of Texas at Austin - Department of Finance ( email )

Red McCombs School of Business
Austin, TX 78712
United States

HOME PAGE: http://faculty.mccombs.utexas.edu/johnson

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