Clustering financial time series: an application to mutual funds style analysis

Abstract

Classification can be useful in giving a synthetic and informative description of contexts characterized by high degrees of complexity. Different approaches could be adopted to tackle the classification problem: statistical tools may contribute to increase the degree of confidence in the classification scheme. A classification algorithm for mutual funds style analysis is proposed, which combines different statistical techniques and exploits information readily available at low cost. Objective, representative, consistent and empirically testable classification schemes are strongly sought for in this field in order to give reliable information to investors and fund managers who are interested in evaluating and comparing different financial products. Institutional classification schemes, when available, do not always provide consistent and representative peer groups of funds. A “return-based” classification scheme is proposed, which aims at identifying mutual funds’ styles by analysing time series of past returns. The proposed classification procedure consists of three basic steps: (a) a dimensionality reduction step based on principal component analysis, (b) a clustering step that exploits a robust evolutionary clustering methodology, and (c) a style identification step via a constrained regression model first proposed by William Sharpe. The algorithm is tested on a sample of Italian mutual funds and achieves satisfactory results with respect to (i) the agreement with the existing institutional classification and (ii) the explanatory power of out of sample variability in the cross-section of returns.