Abstract
Bankruptcy prediction is a problem that is becoming more and more interesting. This problem concerns in particular financial and accounting researchers. Nevertheless, it is a field that gathers the focus of companies, creditors, investors and in general firms which are interested in investments or transactions. Because of a variety of parameters, such as multiple accounting ratios or many potential explanatory variables, the complexity of this problem is very high. For this reason, the probability for a company to go bankrupt or not is very difficult to be calculated. Moreover, the precise determination of the bankruptcy is a very important issue. All the above details constitute a complex problem and by taking into account the data that need to be processed, we conclude that machine learning techniques and reliable predictive models are necessary. In this paper, the effectiveness of a dense deep neural network in bankruptcy prediction relating to solvent Greek firms is tested. The experimental results showed that the provided scheme gives promising outcomes.
Supported by Hellenic State Scholarships Foundation (IKY).
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Acknowledgements
S.-A. N. Alexandropoulos is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKY).
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Alexandropoulos, SA.N., Aridas, C.K., Kotsiantis, S.B., Vrahatis, M.N. (2019). A Deep Dense Neural Network for Bankruptcy Prediction. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_37
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