Financial Institution Failure Prediction Using ANFIS

Paper:

Financial Institution Failure Prediction Using AdaptiveNeuro-Fuzzy Inference Systems:

Evidence from the East Asian Economic CrisisWorawat Choensawat∗ and Piruna Polsiri∗∗

∗School of Science and Technology, Bangkok UniversityRama 4 Road, Klong-Toey, Bangkok 10110, Thailand

E-mail: worawat.c@bu.ac.th∗∗Faculty of Business Administration, Dhurakij Pundit University

110/1-4 Prachachuen Road, Laksi, Bangkok 10210, ThailandE-mail: piruna.poi@dpu.ac.th

[Received September 10, 2012; accepted November 23, 2012]

This paper introduces the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) into the area of fi-nance for Thai firms. This study started with collect-ing financial data from 82 finance companies and 15commercial banks operating in the period 1992-1997,before the East Asian economic crisis occurred. Fi-nancial data on failed and non-failed firms were thenexamined to develop fuzzy rules based on CAMELvariables. ANFIS is applied to the area of finance forThai firms for constructing failure prediction models.These models show that prediction accuracy is greaterthan 90 percent for one to five years prior to failure, in-dicating the robustness of models over time. In exper-iments, models yield more accurate forecasting than alogistic model that has been used in the area of financefor Thai firms. The purpose of this study is to presentthat models using ANFIS are better suited for financialdata sets with high nonlinearity than a logistic model.

Keywords: failure prediction models, financial sectorfragility, early warning systems, adaptive neuro-fuzzy in-ference systems (ANFIS), East Asian economic crisis

1. Introduction

Constructing sound prediction models for financial in-stitution failures could contribute positively and signifi-cantly to the economy. Early-warning systems developedfrom failure prediction models, for examples, have beenproven to reduce the chance that a financial institutionmay get into difficulty or even go bankrupt [1–8]. Thisshould in turn prevent the systemic collapse of a country’seconomy.

A lack of effective early warning systems may lead to acatastrophe of economic. The collapse of the Thai finan-cial and banking sector in 1997-1998 is a good example.Thailand was at the origin of the Asian financial crisis of1997. During the East Asian economic crisis, 70 out of91 finance companies closed in 1997-1998. In relation tobanking, of the 15 domestic banks operating in 1994, one

was closed, three merged with government-owned banks,two were taken over by the government and three becameforeign-owned during the crisis.

Even though the main origin of the East Asian financialcrisis was not a lack of sound early-warning systems, theadverse impact of the crisis might have been less if Thai-land had such effective systems. On the bright side, theeconomic crisis enabled us to develop failure predictionmodels for financial institutions in an emerging marketeconomy where little evidence has been provided.

Since the 1970s, models attempting to predict the dif-ficulty and failure of individual financial institutions, i.e.,early warning systems, have been developed [1, 5, 6, 9–11] that were mostly applied to banking and financialsectors in developed countries. These models emphasizeidentifying financial institutions early that are potentiallyfinancially troubled and may fail.

To date, several approaches based on Artificial NeuralNetworks (ANNs) have been proposed to predict finan-cial difficulty and bankruptcy [12–16]. Although ANNshave proven to be a powerful general technique for clas-sification tasks, however, the most significant shortcom-ing of ANNs is that a trained ANN is essentially a “blackbox.” ANNs cannot provide a comprehensive explanationof how to relate input attributes to output prediction. Aftertraining, neural networks are often very difficult to inter-pret [17].

This study also relates to literature on predicting diffi-culty and failure/bankruptcy of financial institutions dur-ing an economy-wide crisis. The objective of this paper,using Thailand as an example, is to develop a model ofpredictive failure which provide a comprehensive expla-nation of prediction results. The purpose is to employ thetechnique of an Adaptive Neuro-Fuzzy Inference System(ANFIS), which is applied in the area of finance to Thaifirms. The output of ANFIS is explained as rule-basedsystems. Although the crisis took place over a decade ago,its remaining impact is still felt in Thailand. Its implica-tions also continue to be widely debated in the literature.

Overall, the proposed models show accuracy rates ofmore than 90 percent, which is explained with a set of

Vol.17 No.1, 2013 Journal of Advanced Computational Intelligence 83and Intelligent Informatics

Choensawat, W. and Polsiri, P.

54 fuzzy rules at most. These results indicate that mod-els serve as efficient early warning systems. This papershows the development of a set of fuzzy rules based onCAMEL variables that have been widely used in priorstudies. CAMEL variables are based upon five criticalelements of a credit union’s operations, which are (1)Capital, (2) Asset Quality, (3) Management competency,(4) Earnings, and (5) Liability. In addition, this paper hasincluded firm size as another variable for examining the“too big to fail” effect. Last, a comparison is shown in ex-periments between the proposed prediction model and alogistic model [18, 19]. Comparison is based on the samevariables and our resulting accuracy is better than that us-ing the logistic model.

The paper is structured as follows. Section 2 describesrelated works on CAMEL and prediction models. Sec-tion 3 discusses data, variables, and methodology used inthis study. Section 4 examines empirical results of pro-posed models. Section 5 concludes with conclusions andbrief statement of future work.

2. Related Work

2.1. CAMELThe CAMEL ranking system, commonly used for

the evaluation of financial performance, uses some fi-nancial ratios to help evaluate a bank’s performance.Tarawneh [20] used CAMEL the ranking system to in-vestigate the financial performance of Omani’s commer-cial banks. He worked on different measurable relation-ships among bank size, asset, management, operationalefficiency and financial performance based on data for theperiod of 1999-2003.

Kouser et al. [21] investigated the financial perfor-mance of Islamic banks and compared this with conven-tional banks operating in Pakistan using results of theCAMEL method. In their study, ratios defined by theCAMEL method are analyzed by using ANOVA to inves-tigate significant differences.

This paper aims to develop financial institution predic-tion models that are sets of fuzzy rules based on CAMEL-type analysis. Criteria of CAMEL variables selection arethe availability of data and the statistical significance ofvariables.

2.2. Prediction ModelsMost previous research on the causes and origins of

the East Asian crisis and other economic crises has stud-ied macroeconomic factors that may help predict financialand/or currency crises [4, 22–26]. Although early warn-ing systems using macroeconomic variables were effec-tive in the timely detection of systemic crises, they didnot recognize the contribution of firm-level weakness tothe incidence of the crisis. In other words, macroeco-nomic analysis is “unlikely to be able to discriminate be-tween the view that distressed financial institutions werehit by exogenous shocks and the view that many weak-

nesses before the crisis may have led to systemic financialdistress” [27]. Hence, early warning systems using firm-level or microeconomic data are worth developing.

2.2.1. Linear and Nonlinear Regression ModelsAminian et al. [28] forecast economic data by com-

paring linear and nonlinear regression techniques. Forthe purpose of generality, the nonlinear regression tech-nique outperforms the linear regression technique sinceeconomic data analyzed often exhibit some nonlinearitythat cannot be captured by a linear model. Their workemploys neural networks to forecast macroeconomic be-havior based on financial data.

Zopounidis and Doumpos [29] presented the applica-tion of the UTilites Additives DIScriminantes (UTADIS)method in forecasting bankruptcy risk and business failureprediction. Their results showed that the UTADIS methodperformed better than linear discriminant analysis.

2.2.2. Soft Computing ModelsOlmeda [30] and Sookhanaphibarn et al. [31] compared

the accuracy of parametric and nonparametric classifiersin the problem of bankruptcy prediction. They proposeda combination of decision support systems as an optimalsystem for bankruptcy risk rating. Their hybrid classi-fier consists of a neural network and logit. For compari-son, their neural networks outperformed regression mod-els and showed good performance for modeling nonlinearsystems, but they suffered from an inability to explain thesteps used to make decisions and to incorporate rules intheir architecture.

Ravikumar and Ravi [32] developed a set of ensembleclassifiers using a simple majority voting scheme consist-ing of ANFIS, SVM, Linear RBF, semionline RBF1 andsemionline RBF2, Orthogonal RBF, and MLP. The au-thors conducted experiment on Spanish and US bank data.Models ANFIS, semionline RBF2 and MLP emerged asthe most important models because they figured in thebest ensemble combinations. The above study showedthat ANFIS is advantageous in applications to the area offinance. For comparison with neural networks, resultingrules of ANFIS can be further adjusted without retraininga new data set.

2.3. Studies of Finance in Thai FirmsAfter the East Asian economic crisis of 1997,

most studies of Thai firms investigated the impact ofbankruptcy as addressed by Reynolds et al. [33] andUrapeepatanapong et al. [34]. Tirapat and Nittayagaset-wat [35] focused on the analysis of financial variables inbankruptcy. Pongsatat et al. [36] examined the use ofOhlson’s logit model and Altman’s four-variance modelfor predicting the bankruptcy of large and small firmsin Thailand. Charumilind et al. [37] conducted empiri-cal analysis using univariate analysis by comparing pat-terns of financing structures and firms characteristics todiscover the important factors determining access to long-term bank debt prior to the East Asian economic crisis.

84 Journal of Advanced Computational Intelligence Vol.17 No.1, 2013and Intelligent Informatics