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Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

Author

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  • Katarina Valaskova

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

  • Tomas Kliestik

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

  • Lucia Svabova

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

  • Peter Adamko

    (Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia)

Abstract

The issue of the debt, bankruptcy or non-bankruptcy of a company is presented in this article as one of the ways of conceiving risk management. We use the Amadeus database to obtain the financial and accounting data of Slovak enterprises from 2015 and 2016 to calculate the most important financial ratios that may affect the financial health of the company. The main aim of the article is to reveal financial risks of Slovak entities and to form a prediction model, which is done by the identification of significant predictors having an impact on the health of Slovak companies and their future prosperity. Realizing the multiple regression analysis, we identified the significant predictors in conditions of the specific economic environment to estimate the corporate prosperity and profitability. The results gained in the research are extra important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the unhealthy or unfavorable financial situation of the company.

Suggested Citation

  • Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2144-:d:154028
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    15. Magdalena Tutak & Jarosław Brodny & Małgorzata Dobrowolska, 2020. "Assessment of Work Conditions in a Production Enterprise—A Case Study," Sustainability, MDPI, vol. 12(13), pages 1-35, July.
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    17. Andrea Senova & Alica Tobisova & Robert Rozenberg, 2023. "New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    18. Henry Okwo & Charity Ezenwakwelu & Anthony Igwe & Benedict Imhanrenialena, 2019. "Firm Size and Age mediating the Firm Survival-Hedging Effect: Hayes’ 3-Way Parallel Approach," Sustainability, MDPI, vol. 11(3), pages 1-17, February.
    19. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.

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