Multiplier dynamic data envelopment analysis based on directional distance function: An application to mutual funds

This paper extends the multiplier dynamic data envelopment analysis (DEA) by using directional distance function (DDF). Based on the duality theory, a multiplier network DDF model is proposed for the dynamic system which consists of a sequence of periods linked by carryovers. The proposed multiplier dynamic model is non-oriented and is able to handle negative data that possibly exist in inputs, carryovers and outputs. The overall efficiency score calculated by the proposed multiplier dynamic model can be decomposed into a weighted average of period efficiency scores. The approach that determines a unique efficiency score for each period is also proposed. To demonstrate the validity and practicality of the proposed dynamic model, we apply it to evaluate the performance of mutual funds in the American market. The empirical results show that the proposed multiplier dynamic model has strong ability to discriminate performance and good practice value for the actual portfolio selection. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper extends the multiplier dynamic DEA by using directional distance function (DDF), proposing a non-oriented model able to handle negative data and calculate overall efficiency score as well as unique efficiency score for each period in a dynamic system. Empirical results show its strong ability to discriminate performance and good practice value in portfolio selection.