Imperfect Predictability and Mutual Fund Dynamics: How Managers Use Predictors in Changing Systematic Risk

Suppose a fund manager uses predictors in changing portfolio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by observing how the benchmark returns are related to some set of imperfect predictors, and in part on the basis of their own information set. In this portfolio allocation process, managers care about the potential benefits arising from the market timing generated by benchmark predictors and by private information. In doing this, we impose a structure on fund returns, betas, and benchmark returns that help to analyze how managers really use predictors in changing investments over time. The main findings of our empirical work are that beta dynamics are significantly affected by economic variables, even though managers do not care about benchmark sensitivities towards the predictors in choosing their instrument exposure, and that persistence and leverage effects play a key role as well. Conditional market timing is virtually absent, if not negative, over the period 1990-2005. However such anomalous negative timing ability is offset by the leverage effect, which in turn leads to increase mutual fund extra performance.