Multi-period Bayesian Bankruptcy Prediction: Using financial ratios and the maturity schedule of long-term debt
by Leonid Philosophov of the Moscow Committee of Bankruptcy Affairs,
January 5, 2006
Abstract: This study investigates the multi-period prediction of a firm's bankruptcy as a multi-alternative problem of Statistical Decision Theory. This approach enables a simultaneous assessment to be made of the prediction of bankruptcy and the time horizon at which the bankruptcy could occur. To illustrate the approach, U.S. bankruptcy data is used to make a comparative statistical analysis of various financial variables with a view to identifying four relatively independent financial ratios that have the potential for multi-period bankruptcy forecasting. These ratios not only characterize the quantity and quality of debt, but also the firm's ability to repay the debt. The study also investigates new type of predictive information - the maturity schedule of a firm's long-term debt. We develop Bayesian-type forecasting rules that use both the financial ratios and maturity schedule factors. These rules noticeably enhance bankruptcy prediction compared with the familiar one-period (two-alternative) Z-score rules of Altman (1968) for bankruptcy within the first one, two or three years. Predictive factors derived from schedule information also enhance bankruptcy prediction at distant time horizons. This approach can be used to develop new types of models for credit risk assessment, valuing risky bonds and stocks, optimizing a firm's capital structure.
Keywords: Multi-period bankruptcy prediction, time to bankruptcy, schedule of paying off long term debt, Bayesian decision rules, forecast efficiency.