Academic journal article Journal of Business Economics and Management

The Efficiency Evaluation of Mutual Fund Managers Based on Dara, Cara, Iara

Academic journal article Journal of Business Economics and Management

The Efficiency Evaluation of Mutual Fund Managers Based on Dara, Cara, Iara

Article excerpt

1. Introduction

Mutual fund performance can be evaluated by either the parametric approach or nonparametric approach. The first approach has been frequently studied in the literature, while the second approach has been poorly considered in the performance evaluation models until now.

Earlier studies of fund performance evaluation are started with the models based on Jensen's alpha (i.e., Jensen 1968), and are then extended by adding more variables as explanatory factors (i.e., Carhart 1997) to improve the models. Most models are grounded on parametric models, in which they require a strong theoretical model and a benchmark to compute the outcome. Moreover, they only evaluate the funds performance in terms of the relationship between risk premium and return, without realizing the amount of resources that has been spent.

Data envelopment analysis is a non-parametric method used to evaluate the relative efficiency of decision-making units (DMU), which is first introduced by Charnes et al. (1978). DEA is employed for relative efficiency appraisal of DMUs. The efficiency evaluation of mutual fund managers in the DEA framework provides several advantages. First, unlike the parametric models, there is no necessity to run a theoretical model. Second, since the model evaluates the relative performance of funds, there is no need to assign a benchmark as well. Third, DEA does not require the assumptions of function forms relating inputs to outputs. Finally, DEA can incorporate factors needed into the model. Banker and Maindiratta (1986) explain that integral outcome of the DEA analysis is a set of inefficiency measure that identifies the source of the inefficiency and indicates the extent to which the various inputs need to be reduced or outputs need to be increased for making the inefficient DMUs efficient. The marginal share of each input or output can be clarified by this information.

Since research on the efficiency of mutual funds is scarce and only a few studies focus on this field (i.e., Murthi et al. 1997), we fill several gaps in the literature. First, we evaluate the full universe of more than 17,000 mutual funds in the Bloomberg database over the period 2005 to 2010. This large sample provides the possibility of overcoming the small-sample problems that plagued prior studies concerning the efficiency evaluation of mutual fund. Second, unlike earlier studies, we evaluate funds managers' efficiency in terms of management style. Third, we propose an optimal choice pattern to make decisions in selecting the funds by investors; in addition, we prioritize efficient managers in terms of their own efficiency scores. Fourth, unlike earlier studies that only evaluate the funds in terms of relative (technical) efficiency, we calculate two other efficiency measures, namely, management and scale efficiency. Fifth, we propose three models of DEA in the Decreasing Absolute Risk Aversion (DARA), Constant Absolute Risk Aversion (CARA), and Increasing Absolute Risk Aversion (IARA) framework to identify the best model in evaluating the efficiency of fund managers.

2. Background

2.1. Fund performance measurement

Fund managers use many techniques to know how funds would perform. The performance measures enable managers to distinguish funds in terms of their performance. Although there are some performance measures, none of them can accurately predict the fund performance. The existing methods are simplistic and based on two variables, return and risk. They often disregard the amount of resource consumption for increasing one unit of return. Due to the fact that researchers are trying to propose a top model for the comprehensive evaluation of the performance of funds, they have extended the models in the framework of two parametric (i.e., Carhart 1997) and non-parametric approaches.

2.2. Non-parametric approach

Non-parametric approaches try to assess the efficacy of DMUs with multiple input and output. …

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