Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning

Abstract

We propose a new momentum-determined indicator-switching (N-MDIS) strategy, harnessing the power of machine learning to enhance the accuracy of equity premium prediction. Specifically, we re-examine the regime-dependent feature of univariate predictive regression relative to the benchmark. Furthermore, we investigate the prediction mechanism of the momentum-determined indicator-switching (MDIS) strategy and validate the significance of market regime information for the MDIS. Our findings demonstrate an overwhelmingly superior ex-post forecasting performance compared with the MDIS. More notably, our empirical results substantiate that machine learning greatly aids in momentum indicator selection. The results show that the N-MDIS with machine learning generates more accurate ex-ante equity premium forecasts than both MDIS strategy and N-MDIS strategy with logistic regression, yielding statistically and economically significant results. Moreover, our new approach exhibits robust forecasting performance across a series of robustness tests.