An Analysis on the Spanish Classification of Mutual Funds

In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which does not conform with the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds are misclassified. Then, we propose an alternative classification, based in a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from this alternative classification, in three to four cases, also attain better performances in terms of return/risk and include a smaller number of assets. Finally, we analyze whether the errors in the legal classification could be due to a gaming process of the smallest mutual funds and an inverse relation between mutual fund’s size and the rate of misclassifications is found.