A Bayesian Survival Model Approach for Business Distress Prediction

Abstract

The early warning signals of corporate distress and failure have been a major area of concern for shareholders, policy makers and academicians alike. Numerous approaches have been applied to examine firm insolvency ranging from the famous Altman’s Z-score, traditional econometrics, financial ratio analysis to the more contemporary tools of Artificial Intelligence and Machine Learning. The Cox proportional survival hazard model is a commonly applied technique not only in the field of medical sciences for estimating occurrences of a specific event but also in failure prediction of private firms. The study investigates distress prediction of firms in context of emerging nation like India where otherwise the application of Bayesian survival models is limited. A rich panel of firms spanning over ten years and representing varied sectors like manufacturing, services, mining and construction is compiled for the purpose. The study contributes by developing hazard (survival) modelling using Bayesian perspective. The advantage of Bayesian method lies in dealing effectively with censored and small samples over usual frequentist methods. Both standard Cox survival model for censored failure time and Bayesian estimation have been performed to assess and compare their performance. It is found that prediction accuracy of Bayesian Cox model is significantly higher than of the classical Cox model. The study contributes by providing useful insights in detecting early signs of distress in Indian corporate sector that is otherwise scant in literature.