Double clustering for rating mutual funds


Due to the increasing proliferation of mutual funds, in-depth evaluationof the available products for portfolio selection purposes is a difficult task.Hence, classification schemes giving quick information about which fundsare worth to be monitored, are often provided. The aim of this work is toshow an application of clustering methods to the mutual funds historicaldata. Starting from the monthly time series of the Net Asset Values of aspecific style-based category, namely the Large Blend US mutual funds, weapply distance-based clustering methods twice on a set of return, risk andperformance measures: firstly, with the aim of reducing data dimension, andsecondly to cluster funds in homogeneous classes. The adopted procedureclaims the feature of producing a partition of funds that are readily inter-pretable from a financial point of view and it is further possible to rank theidentified groups, thus obtaining a rating of funds that turns out to accountfor different propensities toward the risk exposure.

DOI Code: 10.1285/i20705948v8n1p44

Keywords: cluster analysis; dimension reduction; mutual funds; perfomance measures; portfolio selection


Cheridito, P., Stadje, M., 2009. Time-inconsistency of var and time -consistent alternatives. Finance Research Letters 6, 40–46.

Cogneau, P., Hubner, G., 2009. The (more than) 100 Ways to Measure Portfolio Performance. Part 1: Standardized Risk-Adjusted Measures. Journal of Performance Measurement 13, 56–71.

Corduas, M., Piccolo, D., 2008. Time series clustering and classification by the autoregressive metric. Computational Statistics and Data Analysis 52, 1860–1872.

Das, N., 2005. A new approach to hedge fund classification. Indian Journal of Economics and Business 4.

Eling, M., 2008. Does the measure matter in the mutual fund industry?

Financial Analysts Journal 64, 54–66.

Elton, E., Gruber, M., Blake, C., 1996. Survivorship bias and mutual fund performance. Review of Financial Studies 9, 1097–1120.

Kim, M., Shukl, R., Tomas, M., 2000. Mutual fund objective misclassification. Journal of Economics and Business 52, 309–323.

Lajbcygier, P., Yahya, A., 2008. Soft Clustering for Funds Management Style Analysis: Out-of-Sample Predictability. Technical Report.

Lisi, F., Otranto, 2010. Clustering mutual funds by return and risk levels, in: Corazza, M., Pizzi C, E. (Eds.), Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, pp. 183–191.

Lonnbark, C., Holmberg, U., Br ̈nn ̈s, K., 2011. Value at risk and expected shortfall for large portfolios. Finance Research Letters 8, 59 – 68.

Lytkin, N., Kulikowski, C., Muchnik, I., 2008. Variance-based criteria for clustering and their application to the analysis of management styles of mutual funds based on time series of daily returns. DIMACS Tech. Rep .

Morningstar, I., 2007. The New Morningstar Rating Methodology. Morningstar Research Report.

Pattarin, F., Paterlini, S., Minerva, T., 2004. Clustering financial time series: an application to mutual funds style analysis. Computational Statistics and Data Analysis 47, 353–372.

Sidana, G., Acharya, D., 2007. Classifying mutual funds in India: Some results from clustering. Indian Journal of Economics and Business 6, 71–79

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