Loan defaults and hazard models for bankruptcy prediction

Purpose - Recent bankruptcy research uses hazard models and extensive samples of companies. The large samples used have precluded the inclusion of a variable related to companies' loan default status in the models. With a sample limited to financially distressed companies, the authors aim to examine if results differ when loan default status and/or audit opinion variables are omitted from hazard bankruptcy prediction models. Design/methodology/approach - The sampling frame is publicly traded US companies, consisting of 111 bankrupt and 310 matching companies from 2003 to 2007. The study applies logistic regression to choose variables for parsimonious bankruptcy prediction models to validate hypotheses. Loan default status and/or audit opinion variables are included as potential predictive variables along with variables included in previous hazard bankruptcy prediction models. Findings - Results reveal that loan default and audit opinion variables: improve the predictive accuracy for financially distressed samples with hazard model characteristics; and change the significance on some variables included in previous hazard models. Research limitations/implications - Auditors' propensity to issue going‐concern modifications varies over time. To allow manual collection of loan default status information, the authors' sample was limited. Consequently, their results may not be generalizable to other bankruptcy hazard models. Practical implications - Results from hazard models that do not include loan default status or auditor opinion variables should be interpreted with caution. Auditors might improve their going‐concern modification decisions by attributing more importance to loan default status. Also, the auditor's opinion adds incremental bankruptcy risk information to lenders and investors. Originality/value - Recent bankruptcy research uses hazard models and extensive samples of companies. However, these studies omit a potentially important variable available to financial statement users, loan default status. The authors demonstrate that including variables for loan default status and auditor's opinion improves bankruptcy prediction models and can change conclusions drawn about other variables.