Abstract
We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls within the variable’s past 24-month high and low. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads to significantly larger forecast gains. We also show how a simple equal-weighted combination of our constrained forecasts leads to further improvements in forecast accuracy, generating forecasts that are more accurate than those obtained using current constrained methods. Further analysis confirms that these findings are robust to the presence of model instabilities and structural breaks.
•We propose a novel approach to impose constraints on predictive regressions.•Unlike previous methods, our constraints act directly on the predictor variable.•We set the predictor to zero whenever its value falls within past central tendency.•Out-of-sample evidence indicates that our approach forecasts well.