Researcher at Vilnius University Publishes New Study Findings on Risk Management (Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian …): Insurance – Risk Management

2023 JUN 13 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Policy and Law Daily -- Research findings on risk management are discussed in a new report. According to news reporting from Vilnius, Lithuania, by NewsRx journalists, research stated, “Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets.”

Funders for this research include Research Council of Lithuania.

The news reporters obtained a quote from the research from Vilnius University: “Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs.”

According to the news editors, the research concluded: “The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk.”

For more information on this research see: Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector. Risks, 2023,11(5). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks11050097.

Our news journalists report that more information may be obtained by contacting Rasa Kanapickiene, Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania. Additional authors for this research include Tomas Kanapickas, Audrius Neciunas.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the authors of this research: Rasa Kanapickiene (http://orcid.org/0000-0001-6083-2894), Tomas Kanapickas (http://orcid.org/0009-0006-3870-9287).

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