We review existing financial multi-factor models from the standpoint of their performance in
detecting hidden investment portfolio dynamics. Using practical examples we present and analyze
the shortcomings of these models in detecting both a gradual and rapid changes in investment
portfolio structure. We then lay the groundwork for a new approach, which we call Dynamic
Style Analysis (DSA), representing a true time-series multi-factor portfolio analysis model.
At the core of the methodology we present a new dynamic regression model, which we call Constrained
Flexible Least Squares (CFLS). One of the most important features of the DSA model is
that it is fully adaptive, i.e., all model parameters are determined from data. The major concepts
of the new methodology are gradually introduced and applied to analyses of both model portfolios
and well-known public US 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.
Paper Available at:
ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2004/2004-47.pdf
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