Financial distress prediction based on similarity weighted voting CBR

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4093 LNAI, Issue , 2006, Pages 947-958

  Sun, Jie ^a   Hui, Xiao Feng ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; INFORMATION RETRIEVAL; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; DATA MINING; DISCRIMINANT ANALYSIS; FINANCE; FORECASTING; SUPPORT VECTOR MACHINES;

CASE RETRIEVAL; CASE-BASED REASONING (CBR); FINANCIAL DISTRESS PREDICTION; MULTI DISCRIMINANT ANALYSIS (MDA); CASE REPRESENTATION; CASEBASED REASONINGS (CBR); CHINESE LISTED COMPANIES; FINANCIAL DISTRESS; FINANCIAL DISTRESS PREDICTION MODEL; NEURAL NETWORKS (NNS); PREDICTIVE ABILITIES;

FORMAL LOGIC; CASE BASED REASONING;

EID: 33749376158     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11811305_103     Document Type: Conference Paper

References (15)

1. Beaver W.: Financial Ratios as Predictors of Failure. Journal of Accounting Research, Vol. 4. (1966)71-111
2. Altman E. I.: Financial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, Vol. 23. (1968) 589-609
3. Ohlson J. A.: Financial Ratios and Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, Vol. 18. (1980) 109-131
4. Odom M, Sharda R.: A Neural Networks Model for Bankruptcy Prediction. Proceedings of the IEEE International Conference on Neural Network (1990) 163-168
5. Shin K.-S., Lee T. S., Kim H.-J.: An Application of Support Vector Machines in Bankruptcy Prediction Model. Expert Systems with Applications, Vol. 28. (2005) 127-135
6. Min J. H., Lee Y.-C.: Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters. Expert Systems with Applications, Vol. 28. (2005) 128-134
7. Hui X.-F., Sun J.: An Application of Support Vector Machine to Companies' Financial Distress Prediction. Lecture Notes in Computer Science, Vol. 3885. Springer-Verlag, Berlin (2006) 274-282
8. Schank, R.: Dynamic Memory: A Theory of Learning in Computers and People. Cambridge University Press, New York (1982)
9. Sankar K. P., Simon C. K. S.: Foundations of Soft Cased-Based Reasoning. Wiley, New Jersey (2004)
10. Hansen J., McDonald J., Stice, J.: Artificial Intelligence and Generalized Qualitative Response Models: An Empirical Test on Two Audit Decision Making Domains. Decision Science, Vol. 23. (1992) 708-723
11. Kim S. H., Noh H. J.: Predictability of Interest Rates Using Data Mining Tools: A Comparative Analysis of Korea and the US. Expert Systems with Applications, Vol. 13. (1997) 85-95
12. Jo H., Han I.: Bankruptcy Prediction Using Case-Based Reasoning, Neural Networks, and Discriminant Analysis. Expert Systems with Applications, Vol. 13. (1997) 97-108
13. Xu X.- Z., Gao G.-A.: Research and Implement of Case - based Reasoning in an Multi criteria Evaluation IDSS. Computer Integrated Manufacture System (in Chinese), Vol. 7. (2001) 16-18
14. Ai W.-G., Li H., Sun J.: Case Based Reasoning Model in a Multi-Criteria Group Intelligent Decision Support System. Journal of Harbin Institute of Technology (in Chinese), Vol. 36. (2004) 805-807
15. Hsu C.-W., Chang C.-C., Lin C.-J.: A Practical Guide to Support Vector Classification. Technical Report, http://www.csie.ntu.edu.tw/~cjlin/papers/ guide/guide.pdf