Assessing the time horizon of bankruptcy using financial ratios and the maturity schedule of long-term debt

Leonid V. Philosophov, Jonathan Batten, Vladimir L. Philosophov

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


This study investigates the multi-period prediction of firm 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, using U.S. bankruptcy data, a comparative statistical analysis of various financial variables is undertaken to identify four relatively independent financial ratios that have the potential for multi -period bankruptcy forecasting. These ratios characterize the quantity and quality of debt, as well as the firm's ability to repay the debt. The study also investigates a new type of predictive information - the maturity schedule of a firm's long-term debt. Bayesian-type forecasting rules are developed that jointly use the financial ratios and maturity schedule factors. The 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 additionally enhance bankruptcy prediction at distant time horizons.
Original languageEnglish
Title of host publicationProceedings of the European Finance Association 32nd Annual Meeting
EditorsW. Goetzmann
Place of PublicationBelgium
PublisherEuropean Financial Management Association
Number of pages39
Publication statusPublished - 2005
EventAnnual Meeting of the European Finance Association (32nd : 2005) - Moscow, Russia
Duration: 24 Aug 200527 Aug 2005


ConferenceAnnual Meeting of the European Finance Association (32nd : 2005)
CityMoscow, Russia


  • multi-period bankruptcy prediction
  • time to bankruptcy
  • schedule of paying off long-term debt
  • Bayesian decision rules
  • forecast efficiency


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