Recent empirical evidence suggests that the variance risk premium predicts aggregate stock market returns. We demonstrate that statistical finite sample biases cannot “explain” this apparent predictability. Further corroborating the existing evidence of the U.S., we show that country...

This paper examines the information content of two different measures of aggregate equity-market order flow for future macro fundamentals and expected stock market returns. The first measure, the cross-sectional average of individual stock order flows, predicts future growth rates for industrial...

We construct daily real-time indices capturing the public information on realized and anticipated economic activity. The one-month change in realized fundamentals predicts U.S. stock returns across horizons with strongest results between a month and a quarter. The information in anticipated...

This article documents how the changing composition of U.S. publicly traded firms has prompted a decline in the long-run mean of the aggregate dividend-price ratio, most notably since the 1970s. Adjusting the dividend–price ratio for such changes resolves several issues with respect to the...

Research examining the usefulness of non-linear models for stock market returns has almost reached an impasse. While there is general recognition of the superior ability of non-linear models to describe the data, there is less certainty about their ability to forecast the data. As such simple...

Using a simple and well-established model for predictive power this letter assess how much in-sample data is required to obtain good out-of-sample forecasts. Specifically using the present value predictive model for monthly stock returns we conduct a backward recursive exercise where the...

We compare several representative sophisticated model averaging and variable selection techniques of forecasting stock returns. When estimated traditionally, our results confirm that the simple combination of individual predictors is superior. However, sophisticated models improve dramatically...

We forecast quarterly US stock returns using eighteen predictor variables both individually and in multivariate regressions, with the former also used in forecast combinations. Using rolling and recursive approaches, we consider a range of statistical and economic evaluation measures. We...