The prediction of bank failures has been researched in a large number of studies presented in literature. It is especially crucial because bank failures are a direct threat to the economy. However, bank insolvencies are not a new topic; they have become increasingly common since the late 1970s. Various methods have been used to construct prediction models in order to establish the probability of future bank failures. Researchers developed appropriate tools which would have enabled them to detect bank problems from past experience. This paper provides a review of such methodologies employed to find the indicators of a banking crisis and assesses their performance.
Firstly, the relatively more popular approach is to use logit or probit models. A representative study that uses the logit framework is by Logan (2001). This study investigates the balance sheet characteristics of the small and medium-sized UK banks in two time periods, specifically in the quarter prior to the announcement of the closure of Bank of Credit and Commerce International (BCCI) and before the recession of the early 1990s. It deals with finding the short-term predictors and the longer-term leading indicators of bank failures. The dependent variable in the logistic regression is a discrete variable that takes the value 1 for failure and the value 0 for non failure. According to Logan (2001) the results from logit and probit estimation techniques are fairly close.
As stated in Logan (2001) analysis, explanatory variables such as rapid credit growth, high levels of provisions as a percentage of total assets and high ratio of risk-weighted assets to unweighted assets constitute measures of credit risk and would be potential leading indicators of bank failure. Furthermore, illiquidity creates additional losses and it is proxied by the following variables: the non-marketable loans as a percentage of total assets, the proportion of bank's deposits placed by other UK banks and the liquidity mismatch between short term assets and liabilities. Moreover the excessive exposure to real estate industry or the high dependence on Net Interest Income increases the bank vulnerability. Another helpful variable in predicting failure is the size of the bank with the purpose to capture diversification opportunities. Also, capital adequacy and some earning variables like income to cost ratio, profit as a percentage of total assets, provisions and profitability net of tax or profitability pre tax display the ability of the bank to overcome unexpected losses. The main measure of capital is identified by the leverage ratio which expresses the assets as a percentage of the total net capital.
Interpreting the results of this academic work, low loan growth, low profitability, low short-term assets relative to liabilities, high dependence on net interest income and low leverage are found to be significant short term predictors of banking crises. As Logan (2001:24) points out, "banks that went to fail were already showing signs of fragility." In the short-term analysis, it is lower rather than higher credit growth which is associated with failure because vulnerable banks need to write off bad loans. Additionally, the more short-term liabilities exceed short-term assets, the greater the possibility of bankruptcy because the cost of serving the liabilities is bigger than the revenue from the assets. Furthermore, the earnings from traditional lending are more volatile. Consequently, when the dependence on net interest rate is high, the likelihood of failure will be increased. As expected, lower probability is also presented to be positively related to failure. Moreover, leverage ratio has a negative effect on failure, indicating that the probability of failure will be increased if the leverage ratio is de