Insolvency Prediction of Georgian Construction Sector Companies - Dushuashvili, Tatuli (The Journal of Accounting and Management, 2024)
Insolvency prediction is one of the critical issues for a company’s financial health analysis. It allows revealing the factors affecting a company’s financial state and foresee conditions threatening its financial health. Consequently, numerous research was done in bankruptcy/insolvency analysis. However, no unique model predicts the insolvency of any type of firm. Models differ according to industry-related features and country-specific characteristics. The paper aims to evaluate the financial health of the construction companies operating in small and developing countries like Georgia and predict their future insolvency. The analysis of companies’ individual financial statements was conducted to determine financial health, based on which the insolvency prediction model of construction companies was created. The model applied logistic regression and used key financial ratios to predict insolvency probability. According to the results, the model accuracy reached 90%, meaning that the model predicts insolvency by 90%. The study is one of the first attempts to predict the insolvency of Georgian companies by using financial ratios, as the publicly accessible data on financial statements recently became available. The model developed in the paper will help researchers predict the insolvency of the construction sector in countries with similar characteristics to Georgia.