Development of Bankruptcy Prediction Model for Latvian Companies - Arnis Stasko; Ilze Birzniece; Girts Kebers (Complex Systems Informatics and Modeling Quarterly)
This article addresses the financial performance prediction for 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. There are literature review structures that previously made research into 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 an 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 these 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 in estimating a company’s future performance; therefore, an automated solution supporting experts in their decision-making is presented.