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Imperfect predictability and mutual fund dynamics. How managers use predictors in changing systematic risk

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Author Info
Gianni Amisano () (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
Roberto Savona () (Department of Business Studies, University of Brescia. Address: Dipartimento di Economia Aziendale, Università degli Studi di Brescia, c/da S. Chiara n° 50, 25122 Brescia, Italy.)
Abstract

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 concern themselves with 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 analyse 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 an increase in mutual fund extra performance. JEL Classification: C11, C13, G12, G13.

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Paper provided by European Central Bank in its series Working Paper Series with number 881.

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Length: 53 pages
Date of creation: Mar 2008
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Handle: RePEc:ecb:ecbwps:20080881

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Keywords: Equity mutual funds conditional asset pricing models time-varying beta Bayesian analysis.

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