Predictability of Stock Returns based on the Partial Least Squares Methodology
Empirical evidence on the predictability of aggregate stock returns has shown that many commonly used predictor variables have little power to predict the market out-of-sample. However, a recent paper by Kelly and Pruitt (2013) find that predictors with strong out-of-sample performance can be constructed, using a partial least squares methodology, from the valuation ratios of portfolios. This paper shows that the statistical significance of this out-of-sample predictability is overstated for two reasons. Firstly, the analysis is conducted on gross returns rather than excess returns, and this raises the apparent predictability of the equity premium due to inclusion predictable movements of interest rates. Secondly, the bootstrap statistics used to assess out-of-sample significance do not account for small-sample bias in the estimated coefficients. This bias is well known to affect tests of in-sample significance (Stambaugh (1986)) and I show it is also important for out-of-sample tests of significance. Accounting for both these effects can radically change the conclusions; for example, the recursive out-of-sample R2 values for the sample period 1965-2010 are insignificant for the prediction of one-year excess returns, and one-month returns, except in the case of the book-to-market ratios of six size-and value-sorted portfolios, which is significant at the 10% level.
Research Institute : Finance and Banking Research Group (FiBRe)