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

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.     

Ranking efficient units in DEA

Super-efficiency data envelopment analysis (DEA) model can be used in ranking the performance of efficient decision making units (DMUs). Because of the infeasibility problem associated with the super-efficiency DEA model, ranking has been restricted to the model where constant returns to scale and proportional changes in all inputs or all outputs are assumed. In fact, when super-efficiency is used as an efficiency stability measure, infeasibility means the highest super-efficiency. However, if super-efficiency is interpreted as input saving or output surplus achieved by a specific efficient DMU, infeasibility does not necessarily mean the highest super-efficiency. In order to obtain a complete ranking of efficient DMUs when the two assumptions are relaxed, a modified super-efficiency DEA model is proposed to overcome the infeasibility problem and to correctly capture the super-efficiency represented by the input saving or the output surplus. The current paper suggests using both input- and output-oriented super-efficiency models to characterize the super-efficiency when infeasibility occurs. As a result, we can rank the efficient DMUs if infeasibility occurs. The approach is applied to 20 largest Japanese companies and 15 US cities, respectively.     

Super-efficiency based on a modified directional distance function

The problem of infeasibility arises in conventional radial super-efficiency data envelopment analysis (DEA) models under variable returns to scale (VRS). To tackle this issue, a Nerlove–Luenberger (N–L) measure of super-efficiency is developed based on a directional distance function. Although this N–L super-efficiency model does not suffer infeasibility problem as in the conventional radial super-efficiency DEA models, it can produce an infeasible solution in two special situations. The current paper proposes to modify the directional distance function by selecting proper feasible reference bundles so that the resulting N–L measure of super-efficiency is always feasible. As a result, our modified VRS super-efficiency model successfully addresses the infeasibility issues occurring either in conventional VRS models or the N–L super-efficiency model. Numerical examples are used to demonstrate our approach and compare results obtained from various super-efficiency measures.     

Microfinance institutions and efficiency

Microfinance Institutions (MFIs) are special financial institutions. They have both a social nature and a for-profit nature. Their performance has been traditionally measured by means of financial ratios. The paper goes beyond simple financial ratios using a data envelopment analysis (DEA) approach to measure the efficiency of MFIs.     

Governance Structures,Multidimensional Efficiency and Firm Profitability

Using a panel data set of 361 German corporations for the period 1991–1996 we test the hypothesis that firms with more efficient governance structures have higher profitability. To determine efficiency we compare firms with respect to ownership concentration, the identity of owners, capital structure, investment and firm growth by a multi-input/multi-output Data Envelopment Analysis (DEA). This non-parametric linear programming technique considers both multiple in- and out- puts. Based on the concept of pareto efficiency, it computes an efficiency score where the associated weights of the inputs and outputs are determined endogenously. The DEA efficiency scores are then used as explanatory variables in panel data regressions of profitability. Our main finding is that the efficiency scores indeed contribute significantly to explaining profitability differences between firms, even after controlling for industry effects and unobserved systematic firm effects.     

超效率DEA模型的模糊扩展

在输入、输出数据为模糊数而效率值为准确值的模糊DEA模型中,会出现有两个以上有效单元,从而无法对所有决策单元进行全排序的情形。本文利用模糊数的基于α-截集的比较规则,建立了模糊条件下的超效率DEA模型,有效地解决了模糊决策单元的全排序问题。文末给出了一个实例。     

Integration and efficiency convergence in EU banking markets

Evidence of financial integration and convergence are considered of importance in assessing the outcome of EU deregulation policies aimed at improving the efficiency and performance of banking sectors. This paper evaluates the recent dynamics of bank cost efficiency by means of data envelopment analysis (DEA). Borrowing from the growth literature, we apply dynamic panel data models (GMM) to the concepts of β-convergence and σ-convergence to assess the speed at which banking markets are integrating. We also employ a partial adjustment model to evaluate convergence towards best practice. Results seem to provide supporting evidence of convergence of efficiency levels towards an EU average. Nevertheless, there is no evidence of an overall improvement of efficiency levels towards best practice.     

存在保证域的模糊超效率DEA模型

模糊DEA模型是用于解决存在模糊数据的决策单元(DMUs)效率评价问题的,然而现有的模糊DEA模型分辨率低,本文构建了存在保证域的模糊超效率DEA模型,并给出了一种基于截集的求解方法并进行了证明,该模型有效地解决了输入和输出全部或部分为模糊数的决策单元全排序问题。最后给出了一个银行效率评价的实例说明了方法的有效性。     

带权重约束的DEA超效率评价模型及其应用研究

针对产学研协同创新项目的相对效率比较问题,数据包络分析（DEA）法是比较有效的数量分析方法。但DEA法所构建的优化模型中,由于约束条件没有充分考虑评价指标体系内部指标之间客观存在的内在权重大小信息,从而导致模型过分强调了评价单元的投入产出特征差异,甚至出现了原本非常重要的指标其权重却为0的不合理现象。本文提出DEMATEL法和数据包络分析法有机结合的思想,将运用DEMATEL法获得的指标之间内在的权重大小信息,纳入DEA法非阿基米德无穷小量ε限制的CCR模型及其超效率模型的约束条件,创新模型的可行域,构建了权重约束型ε-CCR模型和超效率ε-CCR模型。并以美国2014-2015年度国家科学基金会产学研合作研究中心发布的年度报告数据,对美国71个以高校为主导的产学研协同创新中心的相对效率进行了实证分析。实证结果显示,权重约束型超效率ε-CCR模型比没有权重约束的超效率ε-CCR模型更有效,不仅能对多个评价单位进行全排序,而且能克服无权重约束的超效率ε-CCR模型存在的评价结果不合理现象。这些结果有助于进一步推动DEA法的理论创新和应用推广,并能为多投入多产出项目的决策提供新的方法,使实际问题决策更合理更科学。     

基于DEA和SFA评价的企业效率与企业价值之联结性研究——以中日电器行业上市公司为样本

以2000年至2006年间近300家中日两国电气器械行业上市公司为样本,分别使用DEA和SFA技术估算了两国电气器械行业上市公司的技术效率,并发现即使在市场竞争充分的电气器械行业中,中日两国都存在严重的低效率现象.然后,基于面板数据随机效应模型,在引入了企业经营业绩、企业规模、财务杠杆、股权结构以及产品市场等控制变量的前提下,检验了企业效率与企业价值的联结性.研究结果表明,在日本电气器械行业中,企业效率对企业价值有着显著的弹性贡献(约等于0.3);然而,在中国电气器械行业上市公司中,却未能发现这二者之间有显著的相关性.     

Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers

Previous applications of data envelopment analysis (DEA) and its subsequent Malmquist indices to efficiency and productivity measurements have been criticised for not providing statistical inferences regarding the significance of observed results. In this paper, DEA and a Malmquist index are combined with a bootstrap method in order to provide succinct statistical inferences that determine the performance of grain producers in Eastern Norway. The data cover the period between 1987 and 1997. Results reveal: (i) a significant degree of inefficiency (approximately 11%) and an average productivity progress of 38% over the period considered; (ii) the formidable productivity progress observed is primarily explained by technical efficiency changes that enabled producers to catch up with front runners; and (iii) environmental factors, such as weather conditions, impact both efficiency and productivity. Finally, the analysis reveals that using bootstrapping to make statistical inferences suggests that researchers should be careful in making performance comparisons based on conventional DEA methods, as any discovered differences may not be significant.     

R&D与广告对中日家电企业技术效率影响的比较研究

基于DEA和SFA模型,本文首先分析了2001-2005年间中日两国家电上市企业的市场势力效率和盈利效率这两类技术效率;其次,在构造DEA-TOBIT和Battese&Coelli(1995)SFA这两个计量模型的基础上,进一步研究了企业R&D与广告宣传这两种市场竞争策略对这两类技术效率的当期和累计影响效果。结果显示,非参数估计DEA和参数估计SFA这两个方法在中日家电企业技术效率评价方面取得了较为一致的结果;同时,所构造的DEA-TOBIT和SFA这两个计量模型也显示了在竞争性家电制造业中,R&D对企业市场势力效率和盈利效率的累积影响效果均为积极显著,但却未能证实长期内广告宣传对这两类技术效率的积极效果。     

Inventory leanness and the financial performance of firms

This paper examines the financial consequences that inventory leanness has on firm performance. We conduct an econometric analysis using 4324 publicly traded US manufacturing companies for the period 1980–2008. Using an instrumental variable fixed effects estimator we find a nonlinear relationship between inventory leanness and financial performance. However, we note that the maximum point of this inverted U-shaped relationship often lies at the extreme end of the investigated sample – suggesting a decreasing return from leanness rather than an optimal level. We show that the strength of this relationship is highly dependent on both the industry and inventory component (raw materials, work in process and finished goods) studied. The main novelty and direct implication of our findings is that most firms still have much potential to increase profitability by becoming leaner and they are unlikely to cross a threshold where profitability decreases with increased leanness. We display how much the average firm could gain by becoming leaner and show how this sensitivity changes by inventory component and industry. Finally, we highlight several new econometric aspects that we believe must be addressed when empirically investigating the inventory-performance link.     

DEA game cross-efficiency approach to Olympic rankings

A number of studies have used data envelopment analysis (DEA) to evaluate the performance of the countries in Olympic games. While competition exists among the countries in Olympic games/rankings, all these DEA studies do not model competition among peer decision making units (DMUs) or countries. These DEA studies find a set of weights/multipliers that keep the efficiency scores of all DMUs at or below unity. Although cross efficiency goes a further step by providing an efficiency measure in terms of the best multiplier bundle for the unit and all the other DMUs, it is not always unique. This paper presents a new and modified DEA game cross-efficiency model where each DMU is viewed as a competitor via non-cooperative game. For each competing DMU, a multiplier bundle is determined that optimizes the efficiency score for that DMU, with the additional constraint that the resulting score should be at or above that DMU 's estimated best performance. The problem, of course, arises that we will not know this best performance score for the DMU under evaluation until the best performances of all other DMUs are known. To combat this “chicken and egg” phenomenon, an iterative approach leading to the Nash equilibrium is presented. The current paper provides a modified variable returns to scale (VRS) model that yields non-negative cross-efficiency scores. The approach is applied to the last six Summer Olympic Games. Our results may indicate that our game cross-efficiency model implicitly incorporates the relative importance of gold, silver and bronze medals without the need for specifying the exact assurance regions.     

Efficiency of well-diversified portfolios: Evidence from data envelopment analysis

In this work, we evaluate eight exchange traded funds (ETFs) and their benchmark index (the KOSPI 200 Index), based on the Sharpe ratio and the Treynor ratio and find that the performance of these well-diversified portfolios are quite poor relative to individual stocks. Investors׳ preference to avoid the well-diversified portfolios would be related to this poor performance. However, we empirically show that ETFs and the KOSPI 200 Index are the most efficient investment instruments with respect to the new performance measure designed on the basis of the data envelopment analysis (DEA) methodology. Examining the panel data over the period between 2003 and 2014 indicates that well-diversified portfolios improve the efficiency by adjusting the input variables (σ and β). Furthermore, they do so more effectively as they mature.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.     

DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment

Using an additive super-efficiency data envelopment analysis (DEA) model, this paper develops a new assessment index based on two frontiers for predicting corporate failure and success. The proposed approach is applied to a random sample of 1001 firms, which is composed of 50 large US bankrupt firms randomly selected from Altman's bankruptcy database and 901 healthy matching firms. This sample represents the largest firms that went bankrupt over the period 1991–2004 and represents a full spectrum of industries. Our findings demonstrate that the DEA model is relatively weak in predicting corporate failures compared to healthy firm predictions, and the assessment index improves this weakness by giving the decision maker various options to achieve different precision levels of bankrupt, non-bankrupt, and total predictions.     

A method for planning long term strategy in basic industries

Standard market studies may provide a valid estimate of future demand, but they are unable to furnish objective data about future supply and selling prices. Consequently any profitability or feasibility study making use of standard methods involves a large amount of guess-work disguised as scientific data, with the resulting probability of error. This probability is most true and most dangerous in heavy industry.The method described here does not attempt to determine actual future supply, this is impossible. Instead, it strives to define potential supply by conducting a worldwide study of the opportunities for creating new production units able to operate at low cost.The method, makes it possible to select the soundest and most profitable of the possible projects, without concern for those of competitors, since these will already have been analyzed within the framework of the study. The author claims that this method can provide objective estimates of long term return on investment, by taking as an average selling price the cost of sales of the last competitor on the list of those whose output is needed to meet normal demand.     

Assessing the performance of a development bank

The performance of a development bank can be assessed on the basis of ‘financial function’ and ‘development function’ criteria. While the financial function measures its operational efficiency in terms of the profitability of its investment operations, the developmental function evaluates its allocational efficiency as reflected by its investment activities for the economic development of the country and the stimulation of the capital market. The objective of this paper is to appraise the performance of the Industrial Finance Corporation of India, in terms of its profitability. The profitability has been analysed with reference to its (i) operating earnings, (ii) cost of operations, (iii) gross and net profit margins on the loan portfolio and (iv) rate of return and capital appreciation on the securities portfolio. The paper concludes with suggested measures to improve its profitability in future.     

Efficiency decomposition in network data envelopment analysis with slacks-based measures

Conventional data envelopment analysis (DEA) treats the production system as a black box when measuring efficiency, ignoring its internal structure. By taking the operations of the component processes of the system into consideration, several network DEA models have been developed. Of these, the slacks-based measure (SBM) approach has attracted much attention for its ability to provide suitable efficiency measures, especially for weakly efficient production units. This paper proposes a general SBM model for network systems, and is able to decompose the system efficiency into a weighted average of the process efficiencies. This relationship holds for all types of network structure. An example shows that the network model has stronger discriminating power than the conventional black-box model, and the system efficiency is indeed a weighted average of the process efficiencies. The decomposition of the system efficiency helps identify key factors to improve the performance of a production unit.