ScienceDirect Publication: Journal of Empirical Finance
Wed, 04 Dec 2019 00:03:18 GMT language
Publication date: Available online 2 December 2019
Source: Journal of Empirical Finance
Author(s): Zhiyuan Pan, Davide Pettenuzzo, Yudong Wang
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.