Predicting the direction of US stock markets using industry returns

In this paper, we examine the directional predictability of US excess stock market returns by lagged excess returns from industry portfolios and a number of other commonly used variables by means of dynamic probit models. We focus on the directional component of the market returns because, for investment purposes, forecasting the direction of return correctly is presumably more relevant than the accuracy of point forecasts. Our findings suggest that only a small number of industries have predictive power for market returns, meaning that we find little evidence of stock markets reacting with a delay to information contained in industry returns. We also find that the binary response models outperform conventional predictive regressions in forecasting the direction of the market return. Finally, we test trading strategies and find that some of the industry portfolios do contain information that can be used to improve investment returns.