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Financial Institution Failure Prediction Using Adaptive Neuro-Fuzzy Inference Systems: Evidence from the East Asian Economic Crisis
Author(s) -
Worawat Choensawat,
Piruna Polsiri
Publication year - 2013
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2013.p0083
Subject(s) - adaptive neuro fuzzy inference system , robustness (evolution) , financial crisis , computer science , financial institution , logistic regression , inference system , finance , inference , econometrics , fuzzy logic , artificial intelligence , machine learning , economics , fuzzy control system , macroeconomics , biochemistry , chemistry , gene
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 thatmodels using ANFIS are better suited for financial data sets with high nonlinearity than a logistic model.

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