Best-performing US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition

Premachandra, I. M.; Zhu, Joe; Watson, John; Galagedera, Don Upatissa Asoka

doi:10.1016/j.jbankfin.2012.07.018

Cited by 87 publications

(82 citation statements)

References 32 publications

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“…However, in reality, they may fall short due to reasons within and sometimes beyond their control. It is this notion of a shortfall of performance of some mutual funds relative to other funds in the sample that aligns with the concept of production inefficiency which is a measurable quantity.In the past 25 years, innovative approaches have been introduced to measure mutual fund performance at both the individual fund product level (Murthi et al 1997;McMullen and Strong 1998) and more recently at the fund family level (Premachandra et al 2012). In general, the findings support the assertion that fund managers should be concerned about inefficiencies not only in managing funds but also in their operations.…”

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confidence: 79%

“…Our modelling framework aligns with the network structure of Färe and Whittaker (1995). Although we consider only one output from the first stage and one output from the second stage in this particular application, the DEA model that we use here allows multiple inputs, outputs and intermediate measures (Premachandra et al 2012).Our model splits the overall process of a DMU into two stages and assesses the efficiencies of both stages simultaneously. Our two-stage network DEA model not only assesses the overall performance of the DMUs, but also decomposes the overall efficiency into two components associated with the performance in the two stages.…”

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confidence: 99%

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Mutual Fund Industry Performance: A Network Data Envelopment Analysis Approach

Premachandra

Zhu

Watson

et al. 2016

International Series in Operations Research &Amp; Management Science

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The objective of this chapter is twofold. First, we present a comprehensive review of the DEA literature that has evaluated mutual fund performance. Second, we present a two-stage DEA model that decomposes the overall efficiency of a decision-making unit into two components and demonstrate its applicability by assessing the relative performance of 66 large mutual fund families in the US over the period [1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008]. By decomposing the overall efficiency into operational management efficiency and portfolio management efficiency components, we reveal the best performers, the families that deteriorated in performance, and those that improved in their performance over the sample period. We also make frontier projections for poorly performing mutual fund families and highlight how the portfolio managers have managed their funds relative to the others during financial crisis periods. IntroductionThe mutual fund industry in the US is by far the largest such industry in the world, managing US$14.3 trillion in assets by the end of the calendar year 2012. Research on performance at the mutual fund family level is limited (Tower and Zheng 2008;Elton et al. 2007), possibly due to the complex nature of the analysis involved. Despite the limited existing research to date, an understanding of performance (absolute and relative) at the fund family level is important as investors tend to invest in funds within the same mutual fund family rather than across a number of families. The reasons for investing within one mutual fund family include convenience in searching for investment opportunities and recordkeeping (Kempf and Ruenzi 2008) and flexibility of switching funds without additional sales charges and restrictions imposed by the fund family (Elton et al. 2006(Elton et al. , 2007.Mutual fund performance receives substantial coverage in much of the US financial press due to the rapid growth of the mutual fund industry as well as the vital role it plays in the financial market. Investors and media commentators are keen to acquire an enhanced understanding of operational aspects at both the fund level and the fund family level given the recent turmoil experienced in the US financial market. This chapter gives an overview of the US mutual fund industry for open funds and respond to the line of criticism faced by the standard DEA-models by using a two-stage network DEA model that decomposes the overall efficiency of a fund family into two components; an operational management efficiency and portfolio management efficiency and thereby making a contribution to the mutual fund performance appraisal literature and mutual fund industry at large. We demonstrate the application of the proposed DEA model by examining the relative performance of 66 large mutual fund families in the US over the period 1993-2008. We conceptualise the activities of mutual fund management as a two-stage process as follows. In the first stage, we focus on the operatio...

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confidence: 79%

mentioning

confidence: 99%

Mutual Fund Industry Performance: A Network Data Envelopment Analysis Approach

Premachandra

Zhu

Watson

et al. 2016

International Series in Operations Research &Amp; Management Science

Self Cite

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“…They juxtaposed the traditional and two-stage DEA models. Premachandra et al (2012) analysed efficiency of mutual funds in the USA by employing the two-stage DEA methodology. Their approach was not only applicable to the constant return to scale and variable return to scale settings, but also allowed the introduction of new intermediate input or output measure in the profitability stage.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Indeed, two-stage network DEA has become omnipresent in the banking sector. It is important to note that some two-stage DEA studies do not include undesirable variables in their models (Fukuyama & Matousek 2011;Premachandra et al 2012), while others do; for example, two-stage network DEA with directional distance function was applied by Asmild and Matthews (2012). Indeed, one might arrive at biased efficiencies if undesirable variables related to the risk dimension remain ignored (Chen 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative efficiency assessment based on the dynamic slack-based network data envelopment analysis for commercial banks in Ghana

Zhou

Amowine

Huang

2018

SAJEMS

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Background: Gains in bank efficiency improvement have widely been regarded as one of the most effective and efficient means of ensuring sustainability of a financial system. Aim: This article proposes a relative dynamic two-stage network data envelopment analysis model for measurement of bank efficiency based on the slack-based measure. Setting: Twenty-seven banks in Ghana during the period of 2009–2014. Methods: By considering simultaneous processes within the framework of two-stage data envelopment analysis, the slack-based measure approach identifies the sources of inefficiency in the banks. Results: In the empirical analysis, non-performing loans are an undesirable output in one production process, which should also be treated as a carry-over factor; that is to say, some non-performing loans from the preceding year can be collected in the current year. The carry-over factors should be used to indicate the presence of performance gaps that exist in the banks. The proposed model was used to measure the efficiency of the 27 banks in Ghana during the period of 2009–2014. We also present useful suggestions for improvement in bank efficiency based on the empirical results. Conclusion: The 27 main commercial banks in Ghana are far from efficient. For all banks, the efficiency score in the second stage is much higher than that of the first stage. That means more attention should be paid to the first stage of production in order to increase the banks’ efficiency.

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“…The basic two-stage structure is the simplest network structure where all exogenous inputs are supplied to the first process to produce intermediate products for the second process to produce the final outputs (Chen and Zhu [42]). The general two-stage structure is a generalization of the basic two-stage structure, which allows both stages to consume exogenous inputs supplied from outside and to produce final outputs (Premachandra [48]). The series structure consists of a series of process connected in a sequence (Park et al [49]).…”

Section: Introductionmentioning

confidence: 99%

Economic and Technical Efficiency of the Biomass Industry in China: A Network Data Envelopment Analysis Model Involving Externalities

et al. 2017

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This paper proposes the network data envelopment analysis (DEA) model accounting for negative externalities and applies it for decomposition of profit inefficiency in the biomass-agriculture circular system (Bio-AG system). A circular structure of the Bio-AG system which is different from the previously applied network structures is assumed. Since the negative externalities (i.e., pollutant emissions from the biomass industry) occur in the Bio-AG system, the property rights are taken into consideration to model the externalities-adjusted profits. Therefore, the changes in profits due to changes in the property rights (assuming no property rights, allocating property rights to agricultural sector, and allocating property rights to biomass power generation sector) are quantified. Further, the decomposition shows that the biomass power generation sector is less affected by technical inefficiency if contrasted to allocative inefficiency in terms of the profit loss. The findings suggest that the biomass power generation technology influences the profits of the biomass industry. What is more, the inefficient allocation of resources is now the key factor undermining performance of the biomass industry. Therefore, the government should adopt measures to improve the allocation of resources and prevent excessive investments or development of less efficient technologies.

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Best-performing US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition

Cited by 87 publications

References 32 publications

Mutual Fund Industry Performance: A Network Data Envelopment Analysis Approach

Mutual Fund Industry Performance: A Network Data Envelopment Analysis Approach

Quantitative efficiency assessment based on the dynamic slack-based network data envelopment analysis for commercial banks in Ghana

Economic and Technical Efficiency of the Biomass Industry in China: A Network Data Envelopment Analysis Model Involving Externalities

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