Some insight can be provided by assessing whether, even in a simple environment, a complex predictive rule outperforms a simple one. To assess that,  BIS central bankers’ speeches  11  consider a wider range of potential predictors of bank failure than just capital. A natural starting point would be the well-known CAMEL (capital, asset quality, management, earnings and liquidity) indicators of bank strength. These are a commonly-used performance metric, including by bank supervisors. 16 Table 6 presents the results of a logit regression of US bank failure using the CAMEL indicators as predictors. All five CAMEL measures are correctly signed, although not all are statistically significant. The in-sample fit suggests an important explanatory role for the CAMEL measures. When fitting bank failure from the past, many indicators dominate the few. But how would this model have performed out-of-sample in situations where information was partial? As one way of exploring that question, consider estimating the model using a subset of the sample of banks and using that sub-set to predict bank failure for the remainder of the sample. This can be done for a sequence of random samples of varying sizes. 17 The size of these samples proxies the degree of information imperfection – the smaller the sample, the greater the model uncertainty. These random bank samples of varying sizes are used to re-estimate a set of models of bank failure.
Measures of risk-weighted capital are statistically insignificant, while the leverage ratio is significant at the 1% level. Contrary to the risk-sensitivity doctrine, less appears to have offered more. These results are robust to the inclusion of a broader set of macro factors, such as GDP growth and credit (Table 3). Using different methods and samples, other studies support the predictive superiority, or at least equivalence, of leverage over capital ratios (IMF (2009), Demirguc-Kunt et al (2010), Estrella et al (2000)). A second source of regulatory complexity is the definition of capital. Table 4 considers the predictive performance of a selection of measures of capital for a sub-set of banks. Two key conclusions emerge. First, simpler measures of accounting capital based on equity capital (core Tier 1) outperform broader, more complex, measures. Second, simple market-based measures of banks’ equity dominate accounting measures in their crisis-predictive performance. Simple, once again, beats complex. Put another way, consider a straight horse-race between the most complex measure of banks’ capital position (the Basel Tier 1 ratio) and the simplest (the market value of equity relative to unweighted assets). The explanatory power of the simple measure is about 10 times greater than the complex measure. However well they perform in theory or in sample, complex capital rules do not appear to have performed well in practice and out-of-sample. That is a sobering message for the architects of the Tower of Basel. (5)  Predicting bank failure – simple or complex?