We examine the relation between monthly stock returns and lagged publicly available information. Our primary objective is to determine whether the variables proposed in the literature to predict the equity premium contain incremental information to an investor. We find that certain variables do...

Previous empirical studies have shown that predictive regressions in which model uncertainty is assessed and propagated generate desirable properties when predicting out-of-sample. However, it is still not clear (a) what the important conditioning variables for predicting stock returns...

We use a machine learning algorithm called Adaboost to find direction-of-change patterns for the S&P 500 index using daily prices from 1962 to 2004. The patterns are able to identify periods to take long and short positions in the index. This result, however, can largely be explained by...