Efficient Tests of Stock Return Predictability

Empirical studies have suggested that stock returns can be predicted by ï¬ nancial variables such as the dividend-price ratio. However, these studies typically ignore the high persistence of predictor variables, which can make ï¬ rst-order asymptotics a poor approximation in ï¬ nite samples. Using a more accurate asymptotic approximation, we propose two methods to deal with the persistence problem. First, we develop a pretest that determines when the conventional t-test for predictability is misleading. Second, we develop a new test of predictability that results in correct inference regardless of the degree of persistence and is efficient compared to existing methods. Applying our methods to US data, we ï¬ nd that the dividend-price ratio and the smoothed earningsprice ratio are sufficiently persistent for conventional inference to be highly misleading. However, we ï¬ nd some evidence for predictability using our test, particularly with the earnings-price ratio. We also ï¬ nd evidence for predictability with the short-term interest rate and the long-short yield spread, for which the conventional t-test leads to correct inference.