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首页> 外文期刊>Expert systems with applications >Predicting Bank Financial Failures Using Neural Networks, Supportvector Machines And Multivariate Statistical Methods:rna Comparative Analysis In The Sample Of Savings Deposit Insurancernfund (sdif) Transferred Banks In Turkey
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Predicting Bank Financial Failures Using Neural Networks, Supportvector Machines And Multivariate Statistical Methods:rna Comparative Analysis In The Sample Of Savings Deposit Insurancernfund (sdif) Transferred Banks In Turkey

机译：使用神经网络，支持向量机和多元统计方法预测银行的财务失败：在土耳其储蓄存款保险基金（sdif）转帐银行的样本中的rna比较分析

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摘要

Bank failures threaten the economic system as a whole. Therefore, predicting bank financial failures is crucial to prevent and/or lessen the incoming negative effects on the economic system. This is originally a classification problem to categorize banks as healthy or non-healthy ones. This study aims to apply various neural network techniques, support vector machines and multivariate statistical methods to the bank failure prediction problem in a Turkish case, and to present a comprehensive computational comparison of the classification performances of the techniques tested. Twenty financial ratios with six feature groups including capital adequacy, asset quality, management quality, earnings, liquidity and sensitivity to market risk (CAMELS) are selected as predictor variables in the study. Four different data sets with different characteristics are developed using official financial data to improve the prediction performance. Each data set is also divided into training and validation sets. In the category of neural networks, four different architectures namely multi-layer perceptron, competitive learning, self-organizing map and learning vector quantization are employed. The multivariate statistical methods; multivariate discriminant analysis, k-means cluster analysis and logistic regression analysis are tested. Experimental results are evaluated with respect to the correct accuracy performance of techniques. Results show that multi-layer perceptron and learning vector quantization can be considered as the most successful models in predicting the financial failure of banks.
机译：银行倒闭威胁着整个经济体系。因此，预测银行的财务失败对于防止和/或减轻对经济系统的负面影响至关重要。这最初是将银行归类为健康银行或不健康银行的分类问题。这项研究旨在将各种神经网络技术，支持向量机和多元统计方法应用于土耳其案例中的银行故障预测问题，并提出对所测试技术的分类性能的综合计算比较。在本研究中，选择具有六个特征组的二十个财务比率作为预测变量，其中包括资本充足率，资产质量，管理质量，收益，流动性和对市场风险的敏感性（CAMELS）。使用官方财务数据开发了具有不同特征的四个不同数据集，以提高预测性能。每个数据集也分为训练集和验证集。在神经网络的类别中，采用了四种不同的体系结构，即多层感知器，竞争性学习，自组织图和学习矢量量化。多元统计方法；测试了多元判别分析，k均值聚类分析和逻辑回归分析。关于技术的正确精度性能，评估了实验结果。结果表明，多层感知器和学习矢量量化可以被认为是预测银行财务失败的最成功模型。

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