Insolvency Prediction of Georgian Construction Sector Companies
Keywords:
Financial Health; Financial Ratios; Insolvency AnalysisAbstract
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.
References
Abraham, F.; Cortina J.J. & Schmukler, S.L. (2020). Growth of Global Corporate Debt: Main Facts and Policy Challenges. World Bank Policy Research Working Paper, No. 9394.
Adya, M. & Collopy, F. (1998). How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting, 17(5-6), pp. 481-495.
Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), pp. 589-609.
Altman, E. (1971). Railroad Bankruptcy Propensity. The Journal of Finance, 26(2), pp. 333-345.
Altman, E.; Haldeman, R. & Narayanan, P. (1977). Zeta analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, pp. 29-54.
Aziz, M. & Dar, H. (2006). Predicting corporate bankruptcy: where we stand. Corporate Governance, 6(1), pp. 18-33.
Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, pp. 71-111.
Bellovary, J.L; Giacomino, D.E. & Akers, M.D. (2007). A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 33, pp. 1-42.
Chenghui, L. & Dilanchiev, A. (2023). The Nexus of Financial Deepening and Poverty: the Case of Black Sea Region Economies. The Singapore Economic Review, 68(4), pp. 1183-1205.
Chow, T.S.J. (2015). Stress Testing Corporate Balance Sheets in Emerging Economies. International Monetary Fund Working Paper, No. WP/15/216.
Giriūniene, G.; Giriūnas, L.; Morkunas, M. & Brucaite, L. (2019). A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania. Economies, 7, p. 82.
Hol, S. (2007). The influence of the business cycle on bankruptcy probability. International Transactions in Operational Research, 14(1), pp. 75-90.
Liu, J. (2004). Macroeconomic determinants of corporate failures: evidence from the UK. Applied Economics, 36(9), pp. 939-945.
Liu, Y.; Dilanchiev, A.; Kaifei, X. & Hajiyeva, A.M. (2022). Financing SMEs and business development as new post Covid-19 economic recovery determinants. Economic Analysis and Policy, 76, pp. 554-567.
Nuta, A.C.; Habib, A.M; Neslihanoglu, S.; Dalwai, T. & Rangu, C.M. (2024). Analyzing the market performance of Romanian firms: do the COVID-19 crisis and classification type matter? International Journal of Emerging Markets.
Odom, M. & Sharda, R. (1990, June 17-21). A neural network model for bankruptcy prediction. International Joint Conference on Neural Networks (IJCNN), San Diego, CA, United States.
Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 18(1), pp. 109-131.
Shi, Y. & Li, X. (2019). An overview of bankruptcy prediction models for corporate firms: a systematic literature review. Intangible Capital, 15(2), pp. 114-127.
Shin, K.S.; Lee, T.S. & Kim, H.J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), pp. 127-135.
Tam, K.Y. & Kiang, M.Y. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 38(7), pp. 913-1065.
Zhou, L.; Lai, K.K. & Yen, J. (2010, November 18-20). Bankruptcy Prediction Incorporating Macroeconomic Variables Using Neural Network. 15th Conference on Technologies and Applications of Artificial Intelligence, (TAAI), Hsinchu, Taiwan.
Zmijewski, M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, pp. 59-82.
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