Abstract
We analyze the shortcomings of existing multi-factor models based on portfolio performance data in detecting investment portfolio dynamics such as gradual style drift or rapid changes in strategy. We then lay the groundwork for a new approach, which we call Dynamic Style Analysis (DSA), representing a time-series multi-factor portfolio analysis model. The major concepts of the new methodology are gradually introduced and applied to analyses of both model portfolios and well-known US equity mutual funds. By comparing publicly available holdings data with results obtained with DSA, we demonstrate both the superiority of the new model and its remarkable accuracy in detecting portfolio dynamics. We also address issues such as the computational complexity of DSA and its practical applications in the areas of risk management, performance measurement and investment research. One of the major applications of the new methodology lies in hedge fund due diligence and risk monitoring, where the importance of uncovering and controlling hidden factor dynamics is especially valuable given the limited transparency of hedge funds.
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