理解银行倒闭:对遗传规划产生的规则的仔细检查

A. García-Almanza, Biliana Alexandrova-Kabadjova, Serafín Martínez-Jaramillo
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引用次数: 5

摘要

提出了一种基于遗传规划(GP)的破产预测新方法——演化决策规则(EDR)。为了获得分类机制的最佳参数,我们使用了从美国联邦存款保险公司(FDIC)获得的数据集。该集合由有限的金融机构数据组成,作为广泛用于检测银行倒闭的变量。其结果是一套可理解的决策规则,允许识别破产案例。此外,这些规则的可靠性是根据真阳性率和假阳性率来衡量的,在整个数据集上计算,并在接收工作特征(ROC)空间上绘制。为了检验该机构的精度性能,我们进行了两个实验:第一个实验旨在检验变量的有用程度,提供了定量和定性分析。第二个实验完成了超过1000个不同的重新采样案例,用来衡量该方法的性能。据我们所知,这是该领域第一个能够对该方法的预测结构提供有用见解的计算技术。这项工作的主要贡献有三点:首先,我们希望将一种具有竞争力的新颖方法引入破产预测领域,该方法在纯性能方面可与最近在类似作品中提出的最先进方法相媲美;其次,该方法提供了透明度的额外优势,因为生成的规则可以用简单的财务比率完全解释;第三,也是最后,所提出的方法包括处理高度不平衡样本的尖端技术;这在破产申请中很常见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming
This paper presents a novel method to predict bankruptcy, using a Genetic Programming (GP) based approach called Evolving Decision Rules (EDR). In order to obtain the optimum parameters of the classifying mechanism, we use a data set, obtained from the US Federal Deposit Insurance Corporation (FDIC). The set consists of limited financial institutions’ data, presented as variables widely used to detect bank failure. The outcome is a set of comprehensible decision rules, which allows to identify cases of bankruptcy. Further, the reliability of those rules is measured in terms of the true and false positive rate, calculated over the whole data set and plot over the Receiving Operating Characteristic (ROC) space. In order to test the accuracy performance of the mechanism, we elaborate two experiments: the first, aimed to test the degree of the variables’ usefulness, provides a quantitative and a qualitative analysis. The second experiment completed over 1000 different re-sampled cases is used to measure the performance of the approach. To our knowledge this is the first computational technique in this field able to give useful insights of the method’s predictive structure. The main contributions of this work are three: first, we want to bring to the arena of bankruptcy prediction a competitive novel method which in pure performance terms is comparable to state of the art methods recently proposed in similar works, second, this method provides the additional advantage of transparency as the generated rules are fully interpretable in terms of simple financial ratios, third and final, the proposed method includes cutting edge techniques to handle highly unbalanced samples, something that is very common in bankruptcy applications.
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