Arithmetic returns for investment performance measurement

This paper introduces new money-weighted metrics for investment performance analysis, based on arithmetic means of holding period rates weighted by the investment’s market values. This approach generates rates of return which measure a fund’s or portfolio’s performance and a fund manager’s performance. It also enables to show that the Internal Rate of Return (IRR) is a weighted mean of holding period rates associated with interim values which differ from market values, so that value additivity is violated. The manager’s Arithmetic Internal Rate of Return (AIRR) is shown to be the true period equivalent of the cumulative Time Weighted Rate of Return (TWRR), whereas the period TWRR (a geometric return) provides a different ranking. The method is easily generalized for coping with varying benchmark rates. We also cope with the practical problem of estimating interim values whenever they are not available.     

Estimating returns to scale in DEA

This paper addresses issues of returns to scale in Data Envelopment Analysis. Starting with the model developed by Banker, but avoiding Banker's conclusions on returns-to-scale, the paper shows how two close variants (inputs and outputs oriented) of the Banker-Charnes-Cooper model can be used to provide precise estimates of returns to scale. The estimation of returns to scale for each unit is done by testing the existence of solutions in four regions defined in the neighborhood of the analyzed unit. Numerical examples and graphs are used to illustrate the proposed procedures.     

Non-parametric tests of returns to scale

This paper discusses various statistics for testing hypotheses regarding returns to scale in the context of non-parametric models of technical efficiency. In addition, the paper presents bootstrap estimation procedures which yield appropriate critical values for the test statistics. Evidence on the true sizes and power of the various proposed tests is obtained from Monte-Carlo experiments. This paper is an extension of earlier work in [Manage. Sci. 44 (1998) 49; J. Appl. Statist. 27 (2000b) 779].     

Loss of control and returns to scale in organizations

The paper considers the efficiency of organizations as a function of their size and mode of operation.     

A note on returns to scale in DEA

This brief note adds computational convenience and efficiency to the article by Banker and Thrall on returns to scale in DEA by modifying one of their suggestions to avoid the need for examining all alternate optima in order to reach a decision.     

Global sub-increasing and global sub-decreasing returns to scale in free disposal hull technologies: Definition,characterization and calculation

Global Returns to Scale (GRS) is an important notion in performance analysis under Free Disposal Hull (FDH) technologies. Ray Average Productivity (RAP) function has a crucial role in interpreting the GRS. We define two new notions, called Global Sub-Increasing RTS (G-SIRS) and Global Sub-Decreasing RTS (G-SDRS), invoking the behaviour of the RAP function at its maximizers. These notions are useful from a managerial standpoint, because they help the decision maker to decide about contracting or expanding the operation of the units under consideration. After presenting a motivating example and defining G-SIRS and G-SDRS, some theorems are proved to characterize these new notions. A polynomial-time algorithm is sketched which is able to determine the GRS of the Decision Making Units (DMUs) under evaluation. The second part of the paper is devoted to calculating a stability interval for GRS. It is done through some theorems and a polynomial-time algorithm. The results of the paper are illustrated by numerical examples as well as empirical studies with real-world data. Computational experiments are reported as well.     

Measurement of returns to scale in radial DEA models

A general approach is proposed in order to measure returns to scale and scale elasticity at projections points in the radial data envelopment analysis (DEA) models. In the first stage, a relative interior point belonging to the optimal face is found using a special, elaborated method. In previous work it was proved that any relative interior point of a face has the same returns to scale as any other interior point of this face. In the second stage, we propose to determine the returns to scale at the relative interior point found in the first stage.     

DEA cross-efficiency evaluation under variable returns to scale

Cross-efficiency evaluation in data envelopment analysis (DEA) has been developed under the assumption of constant returns to scale (CRS), and no valid attempts have been made to apply the cross-efficiency concept to the variable returns to scale (VRS) condition. This is due to the fact that negative VRS cross-efficiency arises for some decision-making units (DMUs). Since there exist many instances that require the use of the VRS DEA model, it is imperative to develop cross-efficiency measures under VRS. We show that negative VRS cross-efficiency is related to free production of outputs. We offer a geometric interpretation of the relationship between the CRS and VRS DEA models. We show that each DMU, via solving the VRS model, seeks an optimal bundle of weights with which its CRS-efficiency score, measured under a translated Cartesian coordinate system, is maximized. We propose that VRS cross-efficiency evaluation should be done via a series of CRS models under translated Cartesian coordinate systems. The current study offers a valid cross-efficiency approach under the assumption of VRS—one of the most common assumptions in performance evaluation done by DEA.     

On a basic definition of returns to scale

A concept that has sparked considerable interest in DEA is that of returns to scale (RTS). One of the basic and useful definitions of RTS in DEA models is based upon the PPS. In this work, a discussion about the PPS-based definition of RTS is given, leading to a modified definition of RTS which is suitable in the presence of multiple supporting hyperplanes passing through the unit under assessment. The definition provided is a simplification of a definition given by Tone.     

Duality in data envelopment analysis under constant returns to scale

This paper explores duality in models of data envelopment analysis(DEA) for assessing the productive efficiencies of organizationalunits where efficient production is characterized by constantreturns to scale. The paper identifies dualityof the spacesin which efficiency is measured and discusses the practicalimplications of duality in DEA.     

Inducing efficiency in oligopolistic markets with increasing returns to scale

We consider a Cournot oligopoly market of firms possessing increasing returns to scale technologies (which may not be identical). It is shown that an external regulating agency can increase total social welfare without running a deficit by offering to subsidize one firm an amount which depends on the output level of that firm and the market price. The firms bid for this contract, the regulator collects the highest bid upfront and subsidizes the highest bidding firm. It is shown that there exists a subsidy schedule such that (i) the regulator breaks even, (ii) the subsidized firm obtains zero net profit and charges a price equal to its average cost, (iii) every other firm willingly exit the market and (iv) market price decreases, consumers are better off and total welfare improves.     

Measurement of returns to scale using non-radial DEA models

There are some specific features of the non-radial data envelopment analysis (DEA) models which cause some problems for the returns to scale measurement. In the scientific literature on DEA, some methods were suggested to deal with the returns to scale measurement in the non-radial DEA models. These methods are based on using Strong Complementary Slackness Conditions from optimization theory. However, our investigation and computational experiments show that such methods increase computational complexity significantly and may generate as optimal, solutions contradicting optimization theory. In this paper, we propose and substantiate a direct method for the returns to scale measurement in the non-radial DEA models. Our computational experiments documented that the proposed method works reliably and efficiently on the real-life data sets.     

Pricing of decision-making units under non-constant returns to scale

In this article, we revisit a recent work on pricing decision-making units by Färe et al (2013) and extend it to allow for non-constant returns to scale technologies.     

Scale elasticity and returns to scale in the presence of alternative solutions

Scale elasticity (SE) and returns to scale (RTS) are important topics in performance analysis, which help managers to make decisions about the expansion or contraction of the operation of decision making units under assessment. In this paper, some new results about these topics in the presence of alternative solutions, regarding the concept of multifunction, are provided.At first, some properties of some multifunctions (functions), defined with respect to the optimal solutions of DEA models, are established which help us in what follows. In turn, the relationships between the considered multifunctions and the concept of RTS and SE are studied. Finally an approach for the estimation of the RTS classification of units is obtained, which leads to an important corollary as an interesting result which introduces a connection between two concepts, RTS and SE. This is important from an applied point of view. Also, from a technical point of view, the proofs of theorems which give this corollary use a main lemma of convex analysis literature and give a constructive proof about RTS.     

The measurement of returns to scale under a simultaneous occurrence of multiple solutions in a reference set and a supporting hyperplane

This research theoretically explores the measurement of RTS (Returns to Scale) under a possible occurrence of multiple solutions in DEA (Data Envelopment Analysis). In this study, the occurrence of multiple solutions is classified into Type I and Type II. Type I is an occurrence of multiple solutions in a reference set. Type II is an occurrence of multiple solutions on a supporting hyperplane passing on the reference set. Both Types I and II are very well known among DEA researchers, but previous research has not sufficiently explored a simultaneous occurrence of Type I and Type II in the RTS measurement. The two types of multiple solutions influence a degree of RTS in the DEA measurement. Such a quantitative issue on RTS is examined from the perspective of the Type I and Type II problems. To deal with such difficulties, a new linear programming approach is proposed to identify all efficient DMUs (Decision Making Units) that consist of a reference set, even if multiple solutions occur on the reference set. Based upon the research result, we can identify when multiple solutions of Type I and/or Type II occur on the RTS measurement and how to deal with such difficulties. Our research result makes it possible to measure a degree of scale economies (RTS) under the simultaneous occurrence of Type I and Type II.     

Stability of the classification of returns to scale in FDH models

This paper deals with the estimation of returns to scale (RTS) in free disposal hull (FDH) models and provides some stability intervals for preserving the RTS classification. It has been shown that the proposed stability intervals can be obtained via a polynomial-time algorithm based on the calculation of certain ratios of inputs and outputs, without solving any mathematical programming problem. The results of the study have been proved via some lemmas and theorems and have been illustrated by numerical examples and a real application.     

Improving envelopment in Data Envelopment Analysis under variable returns to scale

In a Data Envelopment Analysis model, some of the weights used to compute the efficiency of a unit can have zero or negligible value despite of the importance of the corresponding input or output. This paper offers an approach to preventing inputs and outputs from being ignored in the DEA assessment under the multiple input and output VRS environment, building on an approach introduced in Allen and Thanassoulis (2004) for single input multiple output CRS cases. The proposed method is based on the idea of introducing unobserved DMUs created by adjusting input and output levels of certain observed relatively efficient DMUs, in a manner which reflects a combination of technical information and the decision maker’s value judgements. In contrast to many alternative techniques used to constrain weights and/or improve envelopment in DEA, this approach allows one to impose local information on production trade-offs, which are in line with the general VRS technology. The suggested procedure is illustrated using real data.     

Improving the identification of returns to scale in data envelopment analysis

In order to assess the efficiencies of a set of production units, it is necessary to identify the nature of returns to scale which characterise efficient production. Some methods have been developed to test the nature of the scale elasticity across the full range of scale sizes. However these tests are heavily weighted by the majority of the units and may not identify small ranges of scale size where different returns to scale hold. This paper develops a procedure based on a combination of Data Envelopment Analysis and regression analysis to identify the ranges of scale size where the returns to scale may differ from those in other ranges for the single-output, multi-input case. We also develop a measure of scale size across different input mixes.     

Measurement of returns to scale using a non-radial DEA model

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