Name
Title | ||
|---|---|---|
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2007.09.038 | Expert Syst. Appl. |
Keywords | Field | DocType |
outranking relations,financial distress prediction,neighbor parameter,indifference parameter,cbr prediction method,preferred model,k -nearest neighbors,grey cbr,bankruptcy prediction,difference parameter,k-nearest neighbor,case-based reasoning,veto parameter,basic cbr,support vector machine,neural network,multiple discriminant analysis,case based reasoning,k nearest neighbors,decision tree,case base reasoning,leave one out cross validation,machine learning,fuzzy set theory,k nearest neighbor | k-nearest neighbors algorithm,Decision tree,Data mining,Similarity measure,Computer science,Support vector machine,Fuzzy set,Bankruptcy prediction,Artificial intelligence,Linear discriminant analysis,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 1 | Expert Systems With Applications |
Citations | PageRank | References |
41 | 1.02 | 35 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Li | 1 | 472 | 15.82 |
| Jie Sun | 2 | 374 | 12.21 |
| Bo-Liang Sun | 3 | 48 | 1.50 |