Stock Returns and Dividend Yields Revisited: A New Way to Look at an Old Problem

Publisher Website
Google Scholar
Abstract
The problem of whether stock returns can be predicted from dividend yields is discussed. I apply a new statistical method for finding reliable confidence intervals for regression parameters in the context of dependent and possibly heteroscedastic data, called subsampling. The method works under very weak conditions and avoids the pitfalls of having to choose a structural model to fit to observed data. Appropriate simulation studies suggest that it has better small-sample properties than the generalized method of moments, which is also model free and works under weak conditions. Applying the subsampling method to three datasets, I do not find convincing evidence for the predictability of stock returns.