Insolvency in the Republic of Croatia

The issue of insolvency is significantly present in countries undergoing transition. In Croatia, there has been significant research on the possibilities of using the existing models for determining the domestic companies’ ability to maintain solvency. Likewise, no model based on business enterprises’ financial data has yet been proposed, which leaves an open space for this research. The purpose of this paper is to calculate and analyse solvency indicators; total debt to assets ratio, total debt to equity ratio, Altman Z-score, and the Kralicek Quick Test. This paper analyses the financial data for the period 1996–2014 and provides evidence that the insolvency of Croatian companies increased with the global financial crisis. Multiple regression analysis is used in order to show the relation between total debt to assets ratio as the dependent variable, and current assets and liabilities ratio and dummy variables as independent variables. The conclusions and recommendations for mitigating the impact of insolvency in this paper would be useful for managers, public policymakers and all stakeholders in companies with financial problems, as well as for financially still-healthy companies

Insolvency prediction in the presence of data inconsistencies

In this paper we use data inconsistencies as an indicator of financial distress. Traditional models for insolvency prediction normally ignore inconsistent data, either by removing or replacing it. Instead of removing that information, we propose a new variable to capture it; using it together with traditional accounting variables (based on financial ratios) for the purpose of insolvency prediction. Computational tests use three datasets based on the financial results of 2033 Brazilian Health Maintenance Organizations over 7 years (2001 to 2007). Sixteen classification methods were used to evaluate whether or not the new variable impacted solvency prediction. Tests show a statistically significant improvement in classification accuracy – average results improve 1.3 (p = 0.003) and 1.8 (p = 0.006) percentage points, for 10-fold and leave-one-out cross-validations respectively. In addition, the analysis of false positives and false negatives shows that the new variable reduces the potentially harmful misclassification of false negatives (i.e. financially distressed companies being classified as financially healthy) and also reduces the estimated overall error rate. Regarding the extensibility of the results, even though this work uses data from Brazilian companies only, the calculation of the financial ratios variables, as well as the inconsistencies, could be extended to most companies worldwide subject to governmental accounting regulations aligned with the International Financial Reporting Standards