Content area
Full text
(ProQuest: ... denotes formulae omitted.)
There are two main approaches used in the selection of stocks in actively managed equity portfolios. One is the traditional approach based on fundamental analysis, where managers research and analyze the unique aspects of individual firms.1 The other is the quantitative approach, in which managers research models and then mechanically use those models to identify stocks. The popularity of the quantitative approach is attributed to the belief that it has the potential to be less susceptible to cognitive errors and biases, and given the empirical power of asset pricing theory, we expect quantitative management to do better than random chance.2
In a recent study on the challenges facing the investment community, Fabozzi, Focardi, and Jonas [2008] identify an investment process as "fundamental" if it is performed by human asset managers using information and judgment, while a "quantitative" process is one in which the value-added decisions are based on quantitative outputs generated by computer-driven models using fixed rules. Further, they refer to a process as being "hybrid" if a manager uses a combination of the two approaches.
Several recent studies examine the relative performance of these investment processes using mutual fund data. Zhao [2006] directly examines how the stock selection approach affects mutual fund performance and economies of scale. She characterizes the quantitative managers as "Quant Jocks" and the traditional managers as "Tire Kickers." She finds that there is no significant difference in their investment performance. Moreover, she finds that although managers can cheaply screen a large universe of stocks, the stocks that they invest in are smaller and less liquid, which results in higher transaction costs and limited scalability of quantitative investment strategies.
Wermers, Yao, and Zhao [2007] also examine the differences in performance between mutual fund managers who employ quantitative approaches and managers who use fundamental analysis in their selection process. They find that employing quantitative models designed to take advantage of known market anomalies does not produce above-average performance results.3 They then infer that any above-average performance obtained by skilled fund managers must have been generated by unique fundamental information on individual stocks.
Casey and Quark [2004] examine a group of active Quantitative funds and find that Quantitative managers outperformed Fundamental managers. Similarly, Ahmad and Nanda [2005]...