Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
复制标题
基于k近邻原理的OR-CBR财务困境预测
DOI：
10.1016/j.eswa.2007.09.038
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发表时间：
2009
影响因子：
8.5
通讯作者：
Li, Hui
中科院分区：
文献类型：
--
作者：
Sun, Jie;Sun, Bo-Liang;Li, Hui
自20世纪60年代以来，包括破产预测在内的财务困境预测引起了广泛的关注。各种各样的技术已经在这一领域，从统计的，如多元判别分析（MDA），Logit等，机器学习的，如神经网络（NN），支持向量机（SVM）等。基于案例的推理（CBR），这是解决问题的关键方法之一，自1996年以来，没有赢得足够的关注，在财务困境预测。在该研究中，上级关系（OR），包括严格的差异，弱差异，和无差异的情况下，在每个功能，建立了一个新的基于特征的相似性度量机制的k-近邻的原则。与传统的基于距离的相似性机制以及基于神经网络、模糊集理论、决策树等的相似性机制不同，基于OR的CBR预测方法（简称OR-CBR）的准确性直接由差异参数、无差异参数、否决参数和邻域参数等四类参数决定。从OR-CBR模型的产生背景、具体模型的形式化描述以及相应算法的实现等方面对OR-CBR模型进行了详细的描述。以中国上市公司三年的真实数据为样本，在留一交叉验证和最大归一化处理下，实验结果表明OR-CBR在财务困境预测方面优于MDA、Logit、NN、SVM、DT、Basic CBR和Grey CBR。这意味着OR-CBR可以作为我国财务危机预警的首选模型。
Financial distress prediction including bankruptcy prediction has called broad attention since 1960s. Various techniques have been employed in this area, ranging from statistical ones such as multiple discriminate analysis (MDA), Logit, etc. to machine learning ones like neural networks (NN), support vector machine (SVM), etc. Case-based reasoning (CBR), which is one of the key methodologies for problem-solving, has not won enough focus in financial distress prediction since 1996. In this study, outranking relations (OR), including strict difference, weak difference, and indifference, between cases on each feature are introduced to build up a new feature-based similarity measure mechanism in the principle of k-nearest neighbors. It is different from traditional distance-based similarity mechanisms and those based on NN, fuzzy set theory, decision tree (DT), etc. Accuracy of the CBR prediction method based on OR, which is called as OR-CBR, is determined directly by such four types of parameters as difference parameter, indifference parameter, veto parameter, and neighbor parameter. It is described in detail that what the model of OR-CBR is from various aspects such as its developed background, formalization of the specific model, and implementation of corresponding algorithm. With three year’s real-world data from Chinese listed companies, experimental results indicate that OR-CBR outperforms MDA, Logit, NN, SVM, DT, Basic CBR, and Grey CBR in financial distress prediction, under the assessment of leave-one-out cross-validation and the process of Max normalization. It means that OR-CBR may be a preferred model dealing with financial distress prediction in China.
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DOI：
10.1007/3-540-60654-8_27
发表时间：
1995-01
期刊：
--
影响因子：
--
作者：
C. Price;I. Pegler
通讯作者：
C. Price;I. Pegler
影响因子：
--
作者：
ZADEH, LA
通讯作者：
ZADEH, LA
影响因子：
8
作者：
E. Altman
通讯作者：
E. Altman
DOI：
10.1017/s0269888900006585
发表时间：
1994-03
期刊：
The Knowledge Engineering Review
影响因子：
--
作者：
Ralph Bergmann;K. Althoff;Mirjam Minor;Meike Reichle;Kerstin Bach
通讯作者：
Ralph Bergmann;K. Althoff;Mirjam Minor;Meike Reichle;Kerstin Bach
影响因子：
8.5
作者：
Wu, Chih-Hung;Tzeng, Gwo-Hshiung;Fang, Wen-Chang
通讯作者：
Fang, Wen-Chang