Estimation of Bankruptcy Probabilities Using Bayesian Approach  Yongdai Kim and Gunhee Lee Department of Statistics, Hankuk University of Foreign Studies, Yongin, Korea College of Business Administration, Sogang University, Seoul, Korea  Abstract Bankruptcy probabilities of private companies are estimated via Bayesian approach.
The proportional hazard models are used with time to bankruptcy as a dependent variable.
Several financial ratios are included as independent variables.
MCMC algorithm is used to obtain the posterior distribution.
Finally, bankruptcy probabilities of companies are predicted using the predictive probabilities.
Keywords: Proportional Hazard Model, Bayesian analysis, Markov Chain Monte Carlo, bankruptcy prediction model.

Estimation of Bankruptcy Probabilities Using Bayesian Approach

Estimation of Bankruptcy Probabilities Using Bayesian Approach Yongdai Kim and Gunhee Lee Department of Statistics, Hankuk University of Foreign Studies, Yongin, Korea College of Business Administration, Sogang University, Seoul, Korea Abstract Bankruptcy probabilities of private companies are estimated via Bayesian approach.
The proportional hazard models are used with time to bankruptcy as a dependent variable.
Several financial ratios are included as independent variables.
MCMC algorithm is used to obtain the posterior distribution.
Finally, bankruptcy probabilities of companies are predicted using the predictive probabilities.
Keywords: Proportional Hazard Model, Bayesian analysis, Markov Chain Monte Carlo, bankruptcy prediction model.
business administration mcmc algorithm used obtain independent variables studies yongin predicted using predictive finally keywords proportional hazard estimation bankruptcy probabilities sogang