Early Warning Against Insolvency of Enterprises Based on a Self-learning Artificial Neural Network of the SOM Type

Kamila Migdał-Najman (), Krzysztof Najman () and Paweł Antonowicz ()
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Kamila Migdał-Najman: University of Gdansk
Krzysztof Najman: University of Gdansk
Paweł Antonowicz: University of Gdansk

Chapter Chapter 12 in Effective Investments on Capital Markets, 2019, pp 165-176 from Springer

Abstract: Abstract The article describes the use of a self-learning neural network of the SOM type to forecast insolvency of enterprises in construction industry. The research was carried out on the basis of information regarding 578 enterprises that went into bankruptcy in the years 2007–2013. These entities constituted a sample singled out from a population of 4750 enterprises that went bankrupt in Poland during that time, for which it was possible to obtain financial statements in the form of balance sheets and profit-and-loss accounts for the period of 5 years prior to the bankruptcy. Twelve (12) variables in the form of financial analysis indicators have been assessed, which are most commonly used in the systems of early warning about insolvency. The network constructed allowed effective classification of nearly all entities as insolvent a year before the announcement of their bankruptcy.

Keywords: Bankruptcy; Insolvency; Artificial neural network; Forecasting (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-21274-2_12

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DOI: 10.1007/978-3-030-21274-2_12

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