Title: Testing for episodic predictability in stock returns
Authors: Robert Taylor - University of Essex (United Kingdom) [presenting]
Paulo Rodrigues - Universidade Nova de Lisboa (Portugal)
Matei Demetrescu - University of Kiel (Germany)
Iliyan Georgiev - University of Bologna (Italy)
Abstract: Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in returns. Recent approaches based on subsamples have been considered, suggesting that predictability might exist only within so-called `pockets of predictability'. These methods are prone to the criticism that the sub-sample dates are endogenously determined. To avoid this we propose new tests based on the maximum of statistics from sequences of forward and backward recursive, rolling, and double-recursive predictive sub-sample regressions. We show that the limiting distributions of our proposed tests are robust to both the degree of persistence and endogeneity of the regressors in the predictive regression, but not to any heteroskedasticity present even if the sub-sample statistics are based on heteroskedasticity-robust standard errors. We therefore develop fixed regressor wild bootstrap implementations which we demonstrate to be first-order asymptotically valid. Finite sample behaviour against a variety of temporarily predictable processes is considered. An empirical application to US stock returns illustrates the usefulness of the new predictability testing methods we propose.