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Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

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  • Katarina Valaskova

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

  • Pavol Durana

    (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)

  • Jaroslav Jaros

    (Center for Technology Transfer, University of Zilina, University Science Park, Univerzitna 1, 010 26 Zilina, Slovakia)

Abstract

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

Suggested Citation

  • Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
  • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:5:p:92-:d:355077
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    5. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    6. Roman Vavrek & Ivana Kravčáková Vozárová & Rastislav Kotulič, 2021. "Evaluating the Financial Health of Agricultural Enterprises in the Conditions of the Slovak Republic Using Bankruptcy Models," Agriculture, MDPI, vol. 11(3), pages 1-19, March.
    7. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    8. Mário Santiago Céu & Raquel Medeiros Gaspar, 2022. "Vegetative cycle and bankruptcy predictors of agricultural firms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(12), pages 445-454.
    9. Abuselidze, George & Kotliarov, Valerii & Petrychuk, Svitlana & Danylevska-Zhugunisova, Olga & Mohylevska, Olga, 2022. "Study of structural imbalances in agricultural engineering," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 363, pages 1-10.
    10. Rafał Balina & Marta Idasz-Balina & Noer Azam Achsani, 2021. "Predicting Insolvency of the Construction Companies in the Creditworthiness Assessment Process—Empirical Evidence from Poland," JRFM, MDPI, vol. 14(10), pages 1-16, September.
    11. Hung Van Vu & Huong Ho & Quoc Hoi Le, 2020. "Impact of Farmers’ Associations on Household Income: Evidence from Tea Farms in Vietnam," Economies, MDPI, vol. 8(4), pages 1-16, October.
    12. Manuel Rico & Santiago Cantarero & Francisco Puig, 2021. "Regional Disparities and Spatial Dependence of Bankruptcy in Spain," Mathematics, MDPI, vol. 9(9), pages 1-20, April.
    13. Mário S. Céu & Raquel M. Gaspar, 2023. "Financial Distress in European Vineyards and Olive Groves," Working Papers REM 2023/0266, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    14. Dorohan-Pysarenko, Liudmyla & Rębilas, Rafał & Yehorova, Olena & Yasnolob, Ilona & Kononenko, Zhanna, 2021. "Methodological peculiarities of probability estimation of bankruptcy of agrarian enterprises in Ukraine," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 7(2), June.

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