Development of Bankruptcy Prediction Model for Latvian Companies
Arnis Staško, Ilze Birzniece, Ģirts Ķēbers

This paper addresses the financial performance prediction of Latvian companies. It is of critical importance to be able to provide timely warnings to management, investors, employees, stakeholders and other interested parties who wish to reduce their losses. The literature review structures previously made research in company performance prediction. Estimating the risk of bankruptcy of Latvian companies has been carried out by applying two commonly used approaches: Altman's Z-score estimation and experience-based machine learning approach using C4.5 Decision Tree. The results show that Altman's Z-score method predicts bankruptcy for a massive number of companies, while the ML method predicts bankruptcy for only a few. Each of the approaches has its drawbacks. We propose an extended company performance prediction model that considers other factors that influence distress risk, e.g. changes in regulation and other environmental factors. Expert opinion is of great value to estimate a company's future performance; therefore, an automated solution supporting experts in their decision-making is presented.

insolvency, bankruptcy, prediction, Altman's Z-score, machine learning

Staško, A., Birzniece, I., Ķēbers, Ģ. Development of Bankruptcy Prediction Model for Latvian Companies. Complex Systems Informatics and Modeling Quarterly, 2021, No. 27, pp.45-59. ISSN 2255-9922. Available from: doi:10.7250/csimq.2021-27.02

Publication language
English (en)
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