Using stochastic frontier analysis instead of data envelopment analysis in modelling investment performance

Open Access via the Springer Agreement Funding Information: No funds, grants, or other support was received. The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.Peer reviewe

Analyzing Banks' Performance During the Recent Breakdowns. What Were the Main Drivers?

We observe the main e±ciency drivers of European Banking Groups after the burst of the Global Financial Crisis. This analysis is a live issue within the studies in the field of intermediation. The observed period (2010–2021) is emblematic of the complexity of the financial market in the last two decades. The e±ciency levels derive from a stochastic frontier approach; a k-means cluster analysis distinguishes the units into three homogeneous groups, so that the main determinants of the higher level of efficiency can be identified. They are linked to a particular business model, specific managerial choices, costs rationalization and liquidity optimization

Lending activity efficiency. A comparison between fintech firms and the banking sector

The FinTech phenomenon is undoubtedly increasingly changing the morphology of the global financial system, as well as the existing competitive levers in particular sectors, including lending. The aim of this study is to offer a comparative analysis of the level of efficiency exhibited by FinTech firms operating in this sector with that of banks, which have traditionally carried out this activity. We measure efficiency levels by implementing the Stochastic Data Envelopment Analysis (SDEA). The study, referred to 2021, analyses a data set composed of all the Italian FinTech firms engaged in the lending business and all the Italian banks. We find higher efficiency levels for banks compared to FinTech firms. The results are certainly interesting both at corporate level and for regulatory purposes

Microfoundations for stochastic frontiers

The purpose of the paper is to propose microfoundations for stochastic frontier models. Previous work shows that a simple Bayesian learning model supports gamma distributions for technical inefficiency in stochastic frontier models. The conclusion depends on how the problem is formulated and what assumptions are made about the sampling process and the prior. After the new formulation of the problem it turns out that the distribution of the one-sided error component does not belong to a known family. Moreover, we find that without specifying a utility function or even the cost inefficiency function, the relative effectiveness of managerial input can be determined using only cost data and estimates of the returns to scale. The point of this construction is that features of the inefficiency function u(z) can be recovered from the data, based on the solid microfoundation of expected utility of profit maximization but the model does not make a prediction about the distribution

A Data Envelopment Analysis Toolbox for MATLAB

The Data Envelopment Analysis Toolbox is a new package for MATLAB that includes functions to calculate the main data envelopment analysis models. The package includes code for the standard radial input, output and additive measures, allowing for constant and variable returns to scale, as well as recent developments related to the directional distance function, and including both desirable and undesirable outputs when measuring efficiency and productivity; i.e., Malmquist and Malmquist-Luenberger indices. Bootstrapping to perform statistical analysis is also included. This paper describes the methodology and implementation of the functions, and reports numerical results using a reliable productivity database on US agriculture to illustrate their use

Assessment of the efficiency of spanish football teams through profiling

The aim of this paper is to assess the efficiency of Spanish football teams that participated in the Spanish First Division between 2011 and 2016. We started by specifying the production function of football teams using the production process as a basis. Considering all the moves that can be made during a match, ordering them in the logical sequence that usually links them together and considering ball possession and non-possession as different phases lead to disaggregating the match into eight subdivisions whose efficiency is calculated using the data envelopment analysis (DEA) variant known as profiling. The representative input and output variables considered in these eight subdivisions are moves made during the matches. However, the actions football teams perform, irrespective of their type, are not the result of a standardised procedure. This has two consequences on the number of moves in the field of play: firstly, a minimal variation in playing conditions (both the team's and its opponent's) can alter the number; and, secondly, it is very difficult to control and arrive at a figure possibly established in advance. Since these circumstances can be interpreted as data imprecision, one of the stochastic DEA proposals has also been used in this paper as a calculation tool to verify the robustness of the results. The results show the subdivisions in which the use of moves can be improved to increase the number of actions in the next stage. This knowledge could provide guidance for technical personnel for their training sessions

Performances management when modelling internal structure

The performances management is a key issue for public as well as private organizations. The core of the performances management in the DEA context are essentially the relative efficiency measurement for organizations considered as a “black box” that use inputs to produce two or more outputs. In reality, organizations/ production process are comprised of a number of divisions/stages which performs different functions/tasks interacting among them. For these reasons modelling internal structures of organizations/production process allow to discover the inefficiency of individual divisions/stages. In this paper we estimate the relative efficiency of a production process once modelling its internal structure with a network structure of three divisions/stages interrelated among them. To outline the differences in the performances management in the two cases (“black box” vs network structure) we compare they empirical cumulative distribution functions

Performances management when modelling internal structure

The performances management is a key issue for public as well as private organizations. The core of the performances management in the DEA context are essentially the relative efficiency measurement for organizations considered as a “black box” that use inputs to produce two or more outputs. In reality, organizations/ production process are comprised of a number of divisions/stages which performs different functions/tasks interacting among them. For these reasons modelling internal structures of organizations/production process allow to discover the inefficiency of individual divisions/stages. In this paper we estimate the relative efficiency of a production process once modelling its internal structure with a network structure of three divisions/stages interrelated among them. To outline the differences in the performances management in the two cases (“black box” vs network structure) we compare they empirical cumulative distribution functions

A Bayesian approach for correcting bias of data envelopment analysis estimators

The validity of data envelopment analysis (DEA) efficiency estimators depends on the robustness of the production frontier to measurement errors, specification errors and the dimension of the input-output space. It has been proven that DEA estimators, within the interval (0, 1], are overestimated when finite samples are used while asymptotically this bias reduces to zero. The non-parametric literature dealing with bias correction of efficiencies solely refers to estimators that do not exceed one. We prove that efficiency estimators, both lower and higher than one, are biased. A Bayesian DEA method is developed to correct bias of efficiency estimators. This is a two-stage procedure of super-efficiency DEA followed by a Bayesian approach relying on consistent efficiency estimators. This method is applicable to ‘small’ and ‘medium’ samples. The new Bayesian DEA method is applied to two data sets of 50 and 100 E.U. banks. The mean square error, root mean square error and mean absolute error of the new method reduce as the sample size increases

Dear-shiny: an interactive web app for data envelopment analysis

In this paper, we describe an interactive web application (deaR-shiny) to measure efficiency and productivity using data envelopment analysis (DEA). deaR-shiny aims to fill the gap that currently exists in the availability of online DEA software offering practitioners and researchers free access to a very wide variety of DEA models (both conventional and fuzzy models). We illustrate how to use the web app by replicating the main results obtained by Carlucci, Cirà and Coccorese in 2018, who investigate the efficiency and economic sustainability of Italian regional airport by using two conventional DEA models, and the results given by Kao and Liu in their papers published in 2000 and 2003, who calculate the efficiency scores of university libraries in Taiwan by using a fuzzy DEA model because they treat missing data as fuzzy numbers