Understanding and Forecasting Stock Price Changes
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 out-of-sample are, and (b) how composite weighted ensembles outperform model selection criteria. By comparing the unconditional accuracy of prediction regressions to the conditional accuracy conditioned on specific explanatory variables masked), we find that cross-sectional premium and term spread are robust predictors of future stock returns. Additionally, using the bias-variance decomposition for the 0/1 loss function, the analysis shows that lower bias, and not lower variance, is the fundamental difference between composite weighted ensembles and model selection criteria. This difference, nevertheless, does not necessarily imply that model averaging techniques improve our ability to describe monthly up-and-down movements' behavior in stock markets.
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