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

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.