The purpose of this paper is to improve the information quality of bankruptcy prediction models proposed in the literature by building prediction intervals around the point estimates generated by these models and to determine if the use of the prediction intervals in conjunction with the point estimated yields an improvement in predictive accuracy over traditional models. The authors calculated the point estimates and prediction intervals for a sample of firms from 1991 to 2008. The point estimates and prediction intervals were used in concert to classify firms as bankrupt or non-bankrupt. The accuracy of the tested technique was compared to that of a traditional bankruptcy prediction model. The results indicate that the use of upper and lower bounds in concert with the point estimates yield an improvement in the predictive ability of bankruptcy prediction models. The improvements in overall prediction accuracy and non-bankrupt firm prediction accuracy are statistically significant at the 0.01 level. The authors present a technique that (1) provides a more complete picture of the firm’s status, (2) is derived from multiple forms of evidence, (3) uses a predictive interval technique that is easily repeated, (4) can be generated in a timely manner, (5) can be applied to other bankruptcy prediction models in the literature, and (6) is statistically significantly more accurate than traditional point estimate techniques. The current research is the first known study to use the combination of point estimates and prediction intervals to in bankruptcy prediction.
The authors would like to thank workshop participants at Mississippi State University and Oregon State University, participants in the 2011 Decision Sciences Institute Annual Meeting, and participants in the 2011 INFORMS Annual Conference for their helpful comments and suggestions.
Lam, M. and Trinkle, B.S. (2014), "Using Prediction Intervals to Improve Information Quality of Bankruptcy Prediction Models", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 10), Emerald Group Publishing Limited, Bingley, pp. 37-52. https://doi.org/10.1108/S1477-407020140000010014
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