A Quest for a Signal Forecasting Corporate Failure: The ‘KPP’ Model for Bankruptcy Prediction


  • Dev Prasad University of Massachusetts Lowell
  • Yash R. Puri University of Massachusetts Lowell
  • Rama Koundinya University of Massachusetts Boston


business, economics, Altman Z-score, analytical hierarchy, credit quality, expertise, financial distress, KPPZ-score, judgement, risk scoring


Financial distress leading to corporate or institutional failure result in significant losses of economic value, employment, personal income, and tax revenues. For almost a century, researchers have studied the problems and have proposed alternate models for bankruptcy prediction - traditional as well as nontraditional such as the use of neural networks. The motivation for the utilization of bankruptcy prediction models could be self- improvement, regulatory purposes, investment purposes, and so on. However, smaller business organizations and individual investors are not likely to have the resources and technology to utilize the more complex models. An analysis utilizing the KPP model presented in this study shows that the credit risk profiles generated by this model are excellent predictors of financial distress and bankruptcy risk. The KPP model also acts as an early warning signal since bankruptcy could be predicted as far back as five years before the date of bankruptcy.




How to Cite

Prasad, D., Puri, Y. R., & Koundinya, R. (2023). A Quest for a Signal Forecasting Corporate Failure: The ‘KPP’ Model for Bankruptcy Prediction. Journal of Applied Business and Economics, 24(4). Retrieved from https://articlearchives.co/index.php/JABE/article/view/5262