Estimating the number of mutual fund styles using the generalized style classification approach and the GAP statistic

A growing empirical literature has shown that funds managers can he classified by their investment style and that this classification is useful in predicting performance. Brown and Goetzmann (1997) created a generalized style classification (GSC) technology that has been used in various studies to decide what funds managers belong to what styles. The GSC technology relies on k-means clustering to group fund manager returns into styles. Since the GSC technology relies on k-means clustering, the number of styles must be known a priori. Conventional techniques can be used to estimate the number of groups/style but they are dependent on various unrealistic assumptions. A new method to estimate the number of styles, first proposed by Tibshirani, Walther, and Hastie, (2000), known as the Gap statistic is adapted to the GSC technology. The approach is used to verify the number of styles in Japanese mutual funds data.