Estimating the dynamics of mutual fund alphas and betas

• Main Authors: MAMAYSKY, Harry, SPIEGEL, Matthew, ZHANG, Hong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: Finance; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7051; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8050/viewcontent/hhm049.pdf
• Institution: Singapore Management University

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.