Given the dynamic nature of the characteristics of financially distressed/bankrupt firms over time, it is essential for regulators, practitioners, and academics, to periodically test and enhance the performance of financial distress/corporate default prediction models.
This is particularly important as the areas of application of such models have been broadened to include: the monitoring of the financial situation of institutions by regulators, the evaluation of the financial viability of corporations by auditing firms, the measurement of the riskiness of portfolios, the pricing of credit derivatives and other fixed-income securities, etc. Therefore, research on financial distress/default prediction models could be further enhanced by taking into account recent methodological developments in the field of econometrics and statistics as well as the current improvements of databases that now include qualitative information. With regard to the former, new longitudinal techniques could be applied to the financial distress field in order to test whether these technical refinements are capable of enhance the overall predictive accuracy of the models and/or provide new insights as to role of individual variables and the effect of particular types of variables on the probability of failing into the financial distress/corporate default category.
Improvements to longitudinal discrete choice methodologies have not been tested in this field and it would be useful to test whether a potential gain in performance of the model is able to compensate for the increase in the complexity of such novel techniques. In fact, these advances in discrete choice modelling have alleviated questionable assumptions such as the independently and identically distributed errors assumption and allowed for unobserved heterogeneity. If these models proved to enhance the performance of prediction models, an additional question would be whether they could be adopted by practitioners given the intensiveness of resources required for their estimation. Finally, taking into account corporate finance theory, other qualitative variables such as directors
characteristics could be incorporated to prediction models to test whether they enhance their performance.
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