We evaluate predictive regressions that explicitly consider the time-variation of coefficients in a comprehensive Bayesian framework. For monthly returns of the S&P 500 index, we demonstrate statistical as well as economic evidence of out-of-sample predictability: relative to an investor using the historic mean, an investor using our methodology could have earned consistently positive utility gains (between 1.8% and 5.8% per year over different time periods). We also find that predictive models with constant coefficients are dominated by models with time-varying coefficients. Finally, we show a strong link between out-of-sample predictability and the business cycle.

Surprisingly little is known about the business cycle dynamics of leverage. The existing evidence documents that target leverage evolves pro-cyclically either for all firms or financially constrained ones. In contrast, we show that, on average, target leverage ratios evolve counter-cyclically once cyclicality is measured comprehensively, accounting for variation in explanatory variables and model parameters. These counter-cyclical dynamics are robust to different subsamples of firms, data samples, empirical models of leverage, and definitions of leverage. There is a fraction of 10–25% of firms with pro-cyclical dynamics whose characteristics are consistent with counter-cyclical dynamics for loss-given-default and probability of default.