Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model

Abstract

In our work, we compare the predictive power of different bankruptcy prediction models built on financial indicators calculable from businesses’ accounting data on the database of the first Hungarian bankruptcy model. For modelling, we use data-mining methods often applied in bankruptcy prediction: neural networks (NN), support vector machines (SVM) and the rough set theory (RST) capable of rule-based classification. The point of departure for our comparative analysis is the practical finding that black-box-type data-mining methods typically show better classification performance than models whose results are easy to interpret, i.e. there seems to be a kind of trade-off between the interpretability and predictive power of bankruptcy models. Empirical results lead us to conclude that the RST approach can be a competitive alternative to black-box-type SVM and NN models. In our research, we did not find any major trade-off between the interpretability and predictive performance of bankruptcy models on the database of the first Hungarian bankruptcy model.

Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries

“…The results were comparable to those obtained by Virág and Kristóf, i.e., models of neural networks proved to be better, and among them the one with two layers was exceptionally superior. In subsequent studies, using the same learning sample from the years 1990-1991, Virág and Kristóf (2014) built models using the techniques of support vector machines and rough set theory. Based on the validation sample it was found that both models generated efficiencies similar to the one of the model developed using artificial neural networks.…”

Modelling bankruptcy prediction models in Slovak companies

“…In addition, some authors have investigated the applicability of other artificial intelligence methods to bankruptcy prediction, for example, the principal component analysis [16,17], support vector machines [18,19], decision trees [20,21], rough sets, [12,22], data envelopment analysis [23,24], and others.…”

Stock és flow típusú számviteli adatok alkalmazása a csődelőrejelző modellekben

“…Néhány önké-nyesen kiragadott példa a teljesség igénye nélkül: Virág -Hajdu (2001), Virág -Kristóf (2005), , Bozsik (2010), Virág -Nyitrai (2013), Virág -Nyitrai (2014). Találhatók azonban olyan tanulmányok is, amelyek egyértelműen rögzítik, hogy a kérdéses mutatók esetén a stock adatokat átlagolva vették figyelembe.…”