دانلود رایگان مقاله انگلیسی یک چارچوب ارزیابی خارج از نمونه برای DEA با کاربرد آن در پیش بینی ورشکستگی - اشپرینگر 2017

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.

4 Conclusions

Out-of-sample evaluation is commonly used for validating prediction models of both continuous and discrete variables and testing their performance. The counterpart of this evaluation framework is lacking in DEA. This paper fills this gap. In fact, we proposed a generic outof-sample evaluation framework for DEA and tested the performance of an instance of it in bankruptcy prediction. The accuracy of our framework, as suggested by our numerical results, suggests that this tool could prove valuable in industry implementations of DEA models in bankruptcy prediction and credit scoring. We also provided empirical evidence that DEA as a classifier is a real contender to Discriminant Analysis, which is one of the most commonly used classifiers by practitioners.