Consider an economy in which the underlying security returns follow a linear factor model with constant coeffcients. While portfolios that invest in these securities will, in general, have a linear factor structure, it will be one with time-varying coeffcients. However, under certain assumptions regarding the portfolio's investment strategy, it is possible to estimate these time-varying alphas and betas. Importantly, this can be done without direct knowledge of either the portfolio manager's exact investment strategy or of the alphas and betas of the individual securities in which the portfolio invests. This paper develops and estimates a Kalman filter statistical model to track time-varying fund alphas and betas. Several tests indicate that relative to a rolling OLS model the Kalman filter model produces more accurate fund factor loadings both in and out of sample. This appears to be in large part due to the attempts of fund managers to time the market by varying their fund's risk exposure from period to period. Another advantage of the Kalman filter model is that the dynamic parameter estimates can be used to classify funds by their trading strategies and to determine the source of a fund's profits or losses. The tests in this paper indicate that the superior and inferior returns produced by some funds arise almost entirely from attempts at market timing rather than managerial selectionability. However, as other research in the area of mutual fund performance measurement have found, overall there appears to be little evidence that, inaggregate, fund investors earn superior returns.