An Indonesian study of the use of CAMEL(S) ratios as predictors of bank failure

This study investigates whether CAMEL(S) ratios can be used to predict bank failure. Based on the literature review, the study used 13 variables representing CAMEL ratios, one representing sensitivity to market risk, and one representing bank size. Most of the analysis was done using multivariate logistic regression since it is more flexible and relatively free of restrictions. To evaluate for consistency, multiple discriminant analysis was also carried out. The results found that logistic regression in tandem with multiple discriminant analysis could function as an early warning system for identifying bank failure and as a complement to on-site examination. The results suggest that the variables ECTA (adequacy ratio), RORA (assets quality), ROA (management), OEOI (earnings), CBTD (liquidity), and LGBS (bank size) are statistically significant in explaining bank failure. Therefore, stakeholders should focus on these variables to identify and solve banking problems.

[1]  Quinn McNemar,et al.  Psychological statistics, 2nd ed. , 1955 .

[2]  Arturo Estrella,et al.  Capital Ratios as Predictors of Bank Failure , 2000 .

[3]  Anurag Agarwal,et al.  Predicting the outcome following bankruptcy filing: a three-state classification using neural networks , 1997, Intell. Syst. Account. Finance Manag..

[4]  H. Huizinga,et al.  Financial Structure and Bank Profitability , 2000 .

[5]  Jeffery W. Gunther.  Between a rock and a hard place: the CRA-safety and soundness pinch , 1999 .

[6]  Katherine Schipper,et al.  Application of Classification Techniques in Business, Banking and Finance. , 1983 .

[7]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[8]  J. Hair.  Multivariate data analysis , 1972 .

[9]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[10]  Brenda González-Hermosillo,et al.  Banking System Fragility: Likelihood Versus Timing of Failure: An Application to the Mexican Financial Crisis , 1996, SSRN Electronic Journal.

[11]  Linda M. Hooks.  Bank Asset Risk: Evidence from Early-Warning Models , 1995 .

[12]  Kathleen L. Henebry.  Do cash flow variables improve the predictive accuracy of a Cox proportional hazards model for bank failure , 1996 .

[13]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1997, Intell. Syst. Account. Finance Manag..

[14]  Robert W. Scapens,et al.  EXPLAINING CORPORATE FAILURE: A CATASTROPHE THEORY APPROACH , 1981 .

[15]  Thomas E. McKee.  Developing a bankruptcy prediction model via rough sets theory , 2000, Intell. Syst. Account. Finance Manag..

[16]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[17]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[18]  Edward I. Altman,et al.  Application of Classification Techniques in Business, Banking and Finance. , 1983 .

[19]  J. Sinkey.  A MULTIVARIATE STATISTICAL ANALYSIS OF THE CHARACTERISTICS OF PROBLEM BANKS , 1975 .

[20]  Edward Sallis,et al.  The Structure of Organisations , 1990 .

[21]  Timothy B. Bell,et al.  Neural nets or the logit model? A comparison of each model's ability to predict commercial bank failures , 1997, Intell. Syst. Account. Finance Manag..

[22]  Rolph E. Anderson,et al.  Nederlandse samenvatting en bewerking van 'Multivariate data analysis, 4th Edition, 1995' , 1998 .

[23]  N. L. Meade,et al.  Rank Transformations and the Prediction of Corporate Failure , 1998 .

[24]  Kathleen L. Henebry.  A TEST OF THE TEMPORAL STABILITY OF PROPORTIONAL HAZARDS MODELS FOR PREDICTING BANK FAILURE , 1997 .

[25]  Andrew P. Meyer,et al.  The role of supervisory screens and econometric models in off-site surveillance , 1999 .

[26]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[27]  Beverly Hirtle,et al.  Supervisory Information and the Frequency of Bank Examinations , 1999 .