Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2008

Abstract

This article develops a Kalman filter model to track dynamic mutual fund factor loadings. It then uses the estimates to analyze whether managers with market-timing ability can be identified ex ante. The primary findings are as follows: (i) Ordinary least squares (OLS) timing models produce false positives (nonzero alphas) at too high a rate with either daily or monthly data. In contrast, the Kalman filter model produces them at approximately the correct rate with monthly data; (ii) In monthly data, though the OLS models fail to detect any timing among fund managers, the Kalman filter does; (iii) The alpha and beta forecasts from the Kalman model are more accurate than those from the OLS timing models; (iv) The Kalman filter model tracks most fund alphas and betas better than OLS models that employ macroeconomic variables in addition to fund returns.

Discipline

Finance | Finance and Financial Management

Research Areas

Finance

Publication

Review of Financial Studies

Volume

21

Issue

1

First Page

233

Last Page

264

ISSN

0893-9454

Identifier

10.1093/rfs/hhm049

Publisher

Oxford University Press (OUP): Policy F - Oxford Open Option D

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