Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies

Background:The suitability and performance of the bankruptcy prediction models is an empirical question.The aim of this paper is to develop a bankruptcy prediction model for Indian manufacturing companies on a sample of 208 companies consisting of an equal number of defaulted and non-defaulted firms.Out of 208 companies,130 are used for estimation sample,and 78 are holdout for model validation.The study reestimates the accounting based models such as Altman EI(Journal of Finance 23:19189-209,1968)Z-Score,Ohlson JA(Journal of Accounting Research 18:109-131,1980)Y-Score and Zmijewski ME(Journal of Accounting Research 22:59-82,1984)X-Score model.The paper compares original and re-estimated models to explore the sensitivity of these models towards the change in time periods and financial conditions.Methods:Multiple Discriminant Analysis(MDA)and Probit techniques are employed in the estimation of Z-Score and X-Score models,whereas Logit technique is employed in the estimation of Y-Score and the newly proposed models.The performance of all the original,re-estimated and new proposed models are assessed by predictive accuracy,significance of parameters,long-range accuracy,secondary sample and Receiver Operating Characteristic(ROC)tests.Results:The major findings of the study reveal that the overall predictive accuracy of all the three models improves on estimation and holdout sample when the coefficients are re-estimated.Amongst the contesting models,the new bankruptcy prediction model outperforms other models.Conclusions:The industry specific model should be developed with the new combinations of financial ratios to predict bankruptcy of the firms in a particular country.The study further suggests the coefficients of the models are sensitive to time periods and financial condition.Hence,researchers should be cautioned while choosing the models for bankruptcy prediction to recalculate the models by looking at the recent data in order to get higher predictive accuracy.

Understanding the indicative factors of university/college closings

Higher education has been in a financially precarious position for many years –facing either a total transformation or elimination. Tuition increases and fewercollege-age students from shifting demographics are primary reasons for thefinancial distress. Alternative financial stability models have assumed linearvariable relationships and improperly calculate the probability of default.Stakeholders have historically relied upon models such as those developed byEdmit and the Department of Education which are inadequate at separatingfinancially sound from unsound universities. We used an Automated MachineLearning approach utilizing multiple models to explain the relationship betweenmetrics and the probability of default/closure allowing for more informedmanagerial decisions. This research, although applied to the homogeneousgroup of small liberal arts universities, can be applied to online and stateuniversities and will allow the opportunity to take preventive steps to mitigatethe likelihood of closing due to financial distress.