DISTRIBUTIONS OF FINANCIAL RATIOS IN THE COMMERCIAL BANKING INDUSTRY

Much research in banking assumes that the data are normally distributed. There has been little empirical confirmation of this assumption. In this paper, the normality assumption is subjected to an extensive test using data for virtually all U.S. commercial banks for several years. The statistical characteristics of 11 common financial ratios are investigated. The findings reject any broad assumption of population normality.     

METHODOLOGICAL IMPLICATIONS OF NON-NORMALLY DISTRIBUTED FINANCIAL RATIOS

Empirical studies have shown distributions of financial ratios are skewed. An explanation for this is given and it is argued that in such circumstances comparison of a fmancial ratio with some norm (e.g. industry average) is likely to misinform. It is also shown that where financial ratios are used as inputs to statistical models normality is irrelevant but a method of transformation into a normal distribution is provided whereby original interrelationships are preserved. Finally, because of the inadequacies of financial ratios, it is shown how regression analysis may be used in financial statement analysis.     

COSTS OF FINANCIAL DISTRESS AND INTEREST COVERAGE RATIOS

Creditors routinely impose on a borrowing firm a minimum interest coverage ratio that the firm has to maintain. I show that nonlinear costs of financial distress provide a possible explanation of why firms find it optimal to have an interest coverage ratio covenant in their debt indenture, even in the absence of information asymmetries or agency costs.     

METHODOLOGICAL IMPLICATIONS OF NON-NORMALLY DISTRIBUTED FINANCIAL RATIOS: A REPLY

This reply extends the contention that fhancial ratios cannot be used comparatively with any safety. It is shown that the obstacles posed by Horrigan for residual analysis may easily be overcome.     

METHODOLOGICAL IMPLICATIONS OF NON-NORMALLY DISTRIBUTED FINANCIAL RATIOS: A COMMENT

This comment argues that proposals to replace financial ratio analysis with regression analysis of the separate ratio components are unnecessary and misguided. Financial ratios are used to predict other variables and not to predict their own components. These ratios also help deal with the size scale problem inherent in regular accounting data, but regression error terms would suffer from the same size effect as the regular data. The actual statistical distribution of financial ratios is an open, important question, but that question in itself does not call for an abandonment of fmancial ratios.