Maria M. Correia

Financial Statement Analysis and the Prediction of Financial Distress

Financial statement analysis has been used to assess a companys likelihood of financial distress — the probability that it will not be able to repay its debts. Financial statement analysis was used by credit suppliers to assess the credit worthiness of its borrowers. Today, financial statement analysis is ubiquitous and involves a wide variety of ratios and a wide variety of users, including trade suppliers, banks, creditrating agencies, investors and management, among others. Financial distress refers to the inability of a company to pay its financial obligations as they mature. Empirically, academic research in accounting and finance has focused on either bond default or bankruptcy. The basic issue is whether the probability of distress varies in a significant manner conditional upon the magnitude of the financial statement ratios. This monograph discusses the evolution of three main streams within the financial distress prediction literature: The set of dependent and explanatory variables used, the statistical methods of estimation, and the modeling of financial distress.

Have Changes in Financial Reporting Attributes Impaired the Ability of Financial Ratios to Assess Distress Risk

This study explores the effect of cross-sectional and time-series differences in financial reporting attributes on the predictive ability of financial ratios for bankruptcy. We identify proxies for discretion over financial reporting, the importance of intangible assets, and the effects of changing accounting standards over time. Our proxies include earnings restatements, discretionary accruals, research and development intensity, book-to-market ratios and incurrence of losses. We test the ability of financial ratios to predict bankruptcy for a sample of bankrupt and non-bankrupt firms listed on NYSE/AMEX or NASDAQ during the 1962-2002 period. Each of our proxies is associated with less informative financial ratios as reflected in predictive ability for bankruptcy. We compare the findings for the accounting model to those of a model based on market-related variables, and find that the market-related variables do not compensate for the lessened predictive ability of financial ratios. In our time-series analysis, descriptive statistics confirm that each of our proxies exhibits a trend consistent with decreasing informativeness over time. Our time-series tests reveal a decline in the predictive ability of financial ratios for bankruptcy, and document that this decline is associated with our financial reporting variables.

Does Fundamental Volatility Help Explain Credit Risk

We examine whether fundamental measures of volatility are incremental to market based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth, (ii) dispersion in analyst forecasts of future earnings, and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out of sample forecasts of bankruptcy and are useful in explaining cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.