Production , Manufacturing and Logistics DEA as a tool for bankruptcy assessment : A comparative study with logistic regression technique
Production , Manufacturing and Logistics DEA as a tool for bankruptcy assessment : A comparative study with logistic regression technique
复制标题
DOI：
--
复制
发表时间：
2008
影响因子：
4.6
通讯作者：
I. M. Premachandra;G. S. Bhabra;Toshiyuki Sueyoshi
中科院分区：
文献类型：
--
作者：
I. M. Premachandra;G. S. Bhabra;Toshiyuki Sueyoshi
本文提出了数据包络分析（DEA）作为一种快速和简单的工具来评估公司破产。DEA是一种非参数方法，用于测量分类函数的权重估计（而不是参数估计），以区分违约和非违约公司。使用最近的一个样本，在美国的大型企业破产，我们研究的DEA在评估企业破产的能力，通过比较它与逻辑回归（LR）。我们发现，DEA优于LR评估破产样本外。DEA的这一特征对投资者具有吸引力和实际意义。DEA相对于LR的另一个优势是它没有与统计和计量经济学方法相关的假设。此外，DEA不需要大样本量的破产评估，通常需要这样的统计和计量经济学方法。在使用小样本进行投资决策时，需要如此大的样本量对从业者来说是一个重大的不利因素。DEA可以绕过与样本大小相关的困难。因此，DEA方法是一种实用的破产评估方法。2007 Elsevier B.V.保留所有权利。
This paper proposes data envelopment analysis (DEA) as a quick-and-easy tool for assessing corporate bankruptcy. DEA is a non-parametric method that measures weight estimates (not parameter estimates) of a classification function for separating default and non-default firms. Using a recent sample of large corporate failures in the United States, we examine the capability of DEA in assessing corporate bankruptcy by comparing it with logistic regression (LR). We find that DEA outperforms LR in evaluating bankruptcy out-of-sample. This feature of DEA is appealing and has practical relevance for investors. Another advantage of DEA over LR is that it does not have assumptions associated with statistical and econometric methods. Furthermore, DEA does not need a large sample size for bankruptcy evaluation, usually required by such statistical and econometric approaches. The need for such a large sample size is a significant disadvantage to practitioners when investment decisions are made using small samples. DEA can bypass such a difficulty related to a sample size. Thus, DEA is a practically appealing method for bankruptcy assessment. 2007 Elsevier B.V. All rights reserved.