Abstract
Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this paper, we fill this gap by proposing a framework for the out-of-sample evaluation of decision making units. We tested the performance of the proposed framework in risk assessment and bankruptcy prediction of companies listed on the London Stock Exchange. Numerical results demonstrate that the proposed out-of-sample evaluation framework for DEA is capable of delivering an outstanding performance and thus opens a new avenue for research and applications in risk modelling and analysis using DEA as a non-parametric frontier-based classifier and makes DEA a real contender in industry applications in banking and investment.
Highlights
Since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978, Data envelopment analysis (DEA) has become a well-established non-parametric methodology for performance evaluation and benchmarking
We tested the performance of the proposed framework in risk assessment and bankruptcy prediction of companies listed on the London Stock Exchange
We describe the main steps of the proposed out-of-sample evaluation framework for DEA: Input: data set of historical observations, say X, where each observation is a decision making units (DMUs) along with the corresponding available information and the observed risk or bankruptcy status Y ; Step 1: divide the “historical” sample X into an estimation set X E – hereafter referred to as training sample I—and a test set XT – hereafter referred to as test sample I
Summary
Since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978, Data envelopment analysis (DEA) has become a well-established non-parametric methodology for performance evaluation and benchmarking. We fill this gap by proposing a framework for the out-of-sample evaluation of decision making units. In this paper we shall focus on the first category of models to illustrate how outof-sample evaluation of companies could be performed. The most popular static bankruptcy prediction models are based on statistical methodologies (e.g., Altman 1968, 1983; Taffler 1984), stochastic methodologies (e.g., Theodossiou 1991; Ohlson 1980; Zmijewski 1984), and artificial intelligence methodologies (e.g., Kim and Han 2003; Li and Sun 2011; Zhang et al 1999; Shin et al 2005). The issue of out-of-sample evaluation remains to be addressed when DEA is used as a classifier
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