This research introduces a novel constraint-based model framework for predicting risk premiums, thoroughly examining the mechanism and limitations of existing models in the literature and leveraging advanced machine learning techniques. The proposed framework effectively captures the regime-dependent forecasting characteristics. It incorporates the information content of predictive regression, “naive” historical average model, and zero value model, significantly reducing model uncertainty and parameter instability across univariate and multivariate predictions. Empirical analysis demonstrates the superiority of our strategy in terms of out-of-sample forecasting performance over a variety of competing models and under different market conditions, highlighting the robustness of our results. We further substantiate the validity of considering the market regime as an economic state variable and justify the rationality of our constraint-based model in elucidating the source of the improved predictability. Our study holds significant implications for financial and economic research, as well as practical applications in portfolio management and risk assessment.
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