Directional distance based diversification super-efficiency DEA models for mutual funds

Abstract

Current data envelopment analysis (DEA) models with diversification cannot discriminate the performance of efficient mutual funds. Based on the directional distance function and diversification DEA models, this paper proposes two diversification super-efficiency models for discriminating the performance of efficient mutual funds on financial market. The proposed diversification super-efficiency models as well as the corresponding diversification DEA models are feasible and can deal with negative values in risk measures, transaction costs and return measures. The proposed methods generate bounded super-efficiency scores for all the funds. Under the assumption of discrete return distributions, all the models in the proposed diversification super-efficiency methods can be transformed into linear programming (LP) problems by choosing proper risk and return measures. To demonstrate the validity and practicality of the proposed diversification super-efficiency methods, we apply them to evaluate the performance of mutual funds in the American market. The empirical results show that the proposed diversification super-efficiency models can distinguish efficient funds well and the linear combination of efficient funds might be inefficient. Moreover, the backtesting results show that the proposed diversification super-efficiency models generally have a good practice value for the actual portfolio selection.