Využitie skóringových modelov pri predikcii defaultu ekonomických subjektov v Slovenskej republike

Applicability of Scoring Models in Firms' Default Prediction. The Case of Slovakia

Matúš Mihalovič

Politická ekonomie, 2018, vol. 2018, issue 6, 689-708

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.

Keywords: bankruptcy prediction; genetic algorithms; hybrid classifier; neural networks; pre-diction performance; scoring model; GA-NN model; default; decision trees (search for similar items in EconPapers)
JEL-codes: C45 C53 G32 G33 M21 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.18267/j.polek.1226

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