Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case

The objective of this paper is to propose a methodological framework for constructing the integrated early warning system (IEWS) that can be used as a decision support tool in bank examination and supervision process for detection of banks that are experiencing serious problems. Sample and variable set of the study contains 40 privately owned Turkish commercial banks (21 banks failed during the period 1997–2003) and their financial ratios. Well known multivariate statistical technique (principal component analysis) was used to explore the basic financial characteristics of the banks, and discriminant, logit and probit models were estimated based on these characteristics to construct IEWS. Also, importance of early warning systems in bank examination was evaluated with respect to cost of failure. Results of the study show that, if IEWS was effectively employed in bank supervision, it can be possible to avoid from the bank restructuring costs at a significant amount of rate in the long run.