Applicability of scoring models in firms' default prediction. The case of Slovakia

Abstract

Bankruptcy prediction has long been regarded as a critical topic within the academic and banking community. To the best of our knowledge, no previous study in the Slovak Republic has attempted to develop a bankruptcy prediction model putting together statistical and artificial intelligence approaches performed on a such an amount of data. This paper seeks to fill this gap. Our aim is to develop a hybrid bankruptcy prediction model using a genetic algorithm in the process of training a neural network (GA-NN). The research data set comprises a balanced sample of both healthy and bankrupt firms operating in Slovakia in the period from 2014 to 2017. Financial information regarding a firm's financial situation are acquired from the Finstat database, which stores annual reports. For the purpose of comparing the classification accuracy of the proposed GA-NN model, two more models are constructed, namely BP-NN (back-propagation neural network model) as well as MDA (multiple discrimination model). The results gained by utilizing these models suggest the superiority of the developed GA-NN model to both BP-NN and MDA models in terms of prediction performance.