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An out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy prediction

机译：基于TOPSIS的分类器的样本外框架及其在破产预测中的应用

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摘要

Since the publication of the seminal paper by Hwang and Yoon (1981) proposing Technique for Order Performance by the Similarity to Ideal Solution (TOPSIS), a substantial number of papers used this technique in a variety of applications requiring a ranking of alternatives. Very few papers use TOPSIS as a classifier (e.g. Wu and Olson, 2006; Abd-El Fattah et al., 2013) and report a good performance as in-sample classifiers. However, in practice, its use in predicting discrete variables such as risk class belonging is limited by the lack of an out-of-sample evaluation framework. In this paper, we fill this gap by proposing an integrated in-sample and out-of-sample framework for TOPSIS classifiers and test its performance on a UK dataset of bankrupt and non-bankrupt firms listed on the London Stock Exchange (LSE) during 2010-2014. Empirical results show an outstanding predictive performance both in-sample and out-of-sample and thus opens a new avenue for research and applications in risk modelling and analysis using TOPSIS as a non-parametric classifier and makes it a real contender in industry applications in banking and investment. In addition, the proposed framework is robust to a variety of implementation decisions.
机译：自从Hwang和Yoon（1981）发表了具有开创性的论文以来，该论文提出了“通过与理想解决方案的相似性来执行订单性能的技术”（TOPSIS），大量论文将这种技术用于需要对替代方案进行排名的各种应用中。很少有论文使用TOPSIS作为分类器（例如Wu和Olson，2006; Abd-El Fattah et al。，2013），并报告了作为样本内分类器的良好性能。但是，实际上，由于缺乏样本外评估框架，其在预测离散变量（例如风险类别归属）中的使用受到限制。在本文中，我们通过为TOPSIS分类器提出一个集成的样本内和样本外框架并在伦敦证券交易所（LSE）上市的英国破产和非破产公司数据集上测试其绩效，填补了这一空白。 2010-2014。实证结果表明，样本内和样本外均具有出色的预测性能，从而为使用TOPSIS作为非参数分类器的风险建模和分析研究和应用开辟了一条新途径，使其成为行业内应用的真正竞争者。银行和投资。另外，所提出的框架对于各种实施决策是鲁棒的。

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