Detecting Regimes of Predictability in the U.S. Equity Premium

We investigate the stability of predictive regression models for the U.S. equity premium. A new approach for detecting regimes of temporary predictability is proposed using se- quential implementations of standard (heteroskedasticity-robust) regression t-statistics for predictability applied over relatively short time periods. Critical values for each test in the sequence are provided using subsampling methods. Our primary focus is to develop a real-time monitoring procedure for the emergence of predictive regimes using tests based on end-of-sample data in the sequential procedure, although the procedure could be used for an historical analysis of predictability. Our proposed method is robust to both the degree of persistence and endogeneity of the regressors in the predictive regression and to certain forms of heteroskedasticity in the shocks. We discuss how the detection procedure can be designed such that the false positive rate is pre-set by the practitioner at the start of the monitoring period. We use our approach to investigate for the presence of regime changes in the predictability of the U.S. equity premium at the one-month horizon by traditional macroeconomic and financial variables, and by binary technical analysis indicators. Our results suggest that the one-month ahead equity premium has temporarily been predictable (displaying so-called ‘pockets of predictability’), and that these episodes of predictability could have been detected in real-time by practitioners using our proposed methodology.