AUTHORS: Worawat Choensawat and Piruna Polsiri
ABSTRACT: This paper introduces the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) into the area of finance for Thai firms. This study started with collecting financial data from 82 finance companies and 15 commercial banks operating in the period 1992-1997, before the East Asian economic crisis occurred. Financial data on failed and non-failed firms were then examined to develop fuzzy rules based on CAMEL variables. ANFIS is applied to the area of finance for Thai firms for constructing failure prediction models. These models show that prediction accuracy is greater than 90 percent for one to five years prior to failure, indicating the robustness of models over time. In experiments, models yield more accurate forecasting than a logistic model that has been used in the area of finance for Thai firms. The purpose of this study is to present that models using ANFIS are better suited for financial data sets with high nonlinearity than a logistic model.
Keywords: failure prediction models; financial sector fragility; early warning systems; adaptive neuro-fuzzy inference systems (ANFIS); East Asian economic crisis
LINK: http://mit.itu.bu.ac.th/publications/Bankruptcy_JACIII.pdf
REFERENCES:
| MLA | Choensawat, Worawat, and Piruna Polsiri. "Financial institution failure prediction using adaptive neuro-fuzzy inference systems: Evidence from the east Asian economic crisis." Journal of Advanced Computational Intelligence and Intelligent Informatics 17.1 (2013): 83-92. | |
| APA | Choensawat, W., & Polsiri, P. (2013). Financial institution failure prediction using adaptive neuro-fuzzy inference systems: Evidence from the east Asian economic crisis. Journal of Advanced Computational Intelligence and Intelligent Informatics, 17(1), 83-92. | |
| ISO 690 | CHOENSAWAT, Worawat; POLSIRI, Piruna. Financial institution failure prediction using adaptive neuro-fuzzy inference systems: Evidence from the east Asian economic crisis. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2013, 17.1: 83-92. | |