DEA Performance Assessment of Mutual Funds

Abstract

). Data envelopment analysis (DEA) is a "data-oriented" approach for evaluating the performance of a set of entities called decision-making units (DMUs) whose performance is categorized by multiple metrics. These performance metrics are classified or termed as inputs and outputs under DEA. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations. In the circumstance of benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a "production frontier," but rather lead to a "best-practice frontier" (Cook, Tone, and Zhu, 2014).Over the years, we have seen a variety of DEA empirical applications. This handbook aims to compile state-of-the-art empirical studies and applications using DEA. It includes a collection of 18 chapters written by DEA experts.Chapter 1, by Chen, Gregoriou, and Rouah, examines the performance of chief executive officers (CEOs) of US banks and thrifts. The authors find evidence that best-practice CEOs who have a DEA efficiency score of one are rewarded with higher compensation compared to underperforming CEOs who have a DEA efficiency score greater than one. They also find DEA efficiency score to be a highly significant predictor of CEO compensation.Chapter 2, by Yu and Chen, is dedicated to describe the network operational structure of transportation organizations and the relative network data envelopment analysis model. Route-based performance evaluation, environmental factors, undesirable outputs, and multi-activity framework are incorporated into their application. v Chapter 3, by Hu and Chang, demonstrates how to use different types of DEA models to compute the total-factor energy efficiency scores with an application to energy efficiency.Chapter 4, by Growitsch, Jamasb, Müller, and Wissner, explores the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks.Chapter 5, by Volz, provides a brief review of previous applications of DEA to the professional baseball industry followed by two detailed applications to Major League Baseball.Chapter 6, by Cummins and Xie, examines efficiency and productivity of US property-liability (P-L) insurers using DEA. The authors estimate pure technical, scale, cost, revenue, and profit efficiency over the period 1993-2011. Insurers' adjacent year total-factor productivity changes, and their contributing factors are also investigated.Chapter 7, by Premachandra, Zhu, Watson, and Galagedera, presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components and demonstrates its applicability by assessing the relative performance of 66 large mutual fund families in the USA over the period 1993-2008. Chapter 8, by Basso and Funari, presents a comprehensive review of the literature of DEA models for the performance assessment o...

Data Envelopment Analysis and Multifactor Asset Pricing Models

“…A single-stage model meets this requisite with a relatively low level of complexity. This is important since the increasing sophistication of DEA models can be part of the explanation for the 'lack of visibility' of the methodology in the financial practice (Basso and Funari 2016).…”

“…Recent research on the financial applications of DEA largely focuses on suggesting ad hoc methods to sort mutual funds (McMullen and Strong 1998;Galagedera and Silvapulle 2002;Basso and Funari 2001;Basso and Funari 2005;Basso and Funari 2016;Zhao et al 2011;Babalos et al 2015;Premachandra et al 2016;Vidal-Garcia et al 2018;Andreu et al 2018), hedge funds (Gregoriou and Gueyie 2003;Gregoriou et al 2005), socially responsible mutual funds (Basso and Funari 2005;Basso and Funari 2007;Pérez-Gladish et al 2013;Abdelsalam et al 2014) and pension funds (Guillén 2011;Gökgöz and Çandarli 2011). However, as noted above, to our best knowledge, DEA has been rarely used to improve the performance of asset pricing models, with a few exceptions as Rubio et al (2018) for the US market.…”

Mutual fund performance: The decision quality and capital magnet efficiencies

“…Comparing evaluation tools such as ratio analysis and data envelopment analysis (DEA), the latter, which is a nonparametric frontier methodology suitable for evaluating the performance of decision‐making units (DMUs) from a multidimensional perspective, is thus a better alternative for evaluating mutual funds. It has the following several advantages as documented in Basso and Funari (). First, DEA generates relative efficiencies with a simultaneous scrutiny of inputs and outputs.…”

Introducing Weights Restrictions in Data Envelopment Analysis Models for Mutual Funds

“…In the last two decades, the data envelopment analysis (DEA) methodology has been applied in different ways to measure the performance of mutual funds, besides a great many applications on the more diverse fields of performance evaluation (more than 10,000 journal articles have been published on DEA; for a recent review, see [1]). By now, there is a wide selection of literature in the mutual funds field, counting more than a hundred papers; for a review, see [2]. In addition, there are many DEA applications to other financial services industry; the majority of studies focus on the banking sector, but several DEA applications to non-banking financial services are also found in the literature; see [3] for a general survey of DEA applications and [4] for recent DEA applications to financial services including mutual funds, banks, insurance, mergers and acquisitions (M&A), and stock selection.…”

“…The paper is organized as follows. In Section 2, we define the DEA model without restrictions adopted to evaluate the performance of mutual funds, which will be extended to include weight restrictions in the following sections; this is model DEA-V proposed in [2,7]. The different types of restrictions that can be set on the weights of DEA models are introduced and discussed in Section 3.…”