Open Access
SHS Web Conf.
Volume 129, 2021
The 21st International Scientific Conference Globalization and its Socio-Economic Consequences 2021
Article Number 09002
Number of page(s) 12
Section Economic Sustainability and Economic Resilience
Published online 16 December 2021
  1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. [CrossRef] [Google Scholar]
  2. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. [CrossRef] [Google Scholar]
  3. Boďa, M., & Úradníček, V. (2019). Predicting financial distress of Slovak agricultural enterprises. Ekonomický Časopis, 67(4), 426–452. [Google Scholar]
  4. Boratyńska, K., & Grzegorzewska, E. (2018). Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches. Journal of Business Research, 89, 175–181. [CrossRef] [Google Scholar]
  5. Brewer, B. E., Wilson, C. A., Featherstone, A. M., Harris, J. M., Erickson, K., & Hallahan, C. (2012). Measuring the financial health of US production agriculture. Journal of ASFMRA, 178–193. [Google Scholar]
  6. Chrastinova, Z. (1998). Methods of Assessment of Economic Solvency and Prediction of Financial Situation of Agricultural Enterprises. Bratislava: VUEPP. [Google Scholar]
  7. Csikosova, A. Janoskova, M., & Culkova, K. (2019). Limitation of financial health prediction in companies from Post-Communist countries. Journal of Risk and Financial Management, 12(15), 1–14. [CrossRef] [Google Scholar]
  8. Gurcik, L. (2002). G-index-The financial situation prognosis method of agricultural enterprises. Agricultural Economics, 48(8), 373–378. [Google Scholar]
  9. Hampel, D., Vavrina, J., & Janová, J. (2012). Predicting bankruptcy of companies based on the production function parameters. In 30th international conference mathematical methods in economics. Karviná: Silesian University in Opava, School of Business Administration. [Google Scholar]
  10. Karas, M., Reznakova, M., & Pokorny, P. (2017). Predicting bankruptcy of agriculture companies: Validating selected models. Polish Journal of Management Studies, 15. [Google Scholar]
  11. Kiaupaite-Grushniene, V. (2016). Altman Z-score model for bankruptcy forecasting of the listed Lithuanian agricultural companies. In 5th International Conference on Accounting, Auditing, and Taxation (ICAAT 2016) (pp. 222-234). Atlantis Press. [Google Scholar]
  12. Klepac, V., & Hampel, D. (2017). Predicting financial distress of agricultural companies in EU. Agricultural Economics – Czech, 63, 347–355. [CrossRef] [Google Scholar]
  13. Lukason, O. (2014). Why and how agricultural firms fail: evidence from Estonia. Bulgarian Journal of Agricultural Science, 20(1), 5–11. [Google Scholar]
  14. Maczynska, E. (1994). Assessment of the condition of the enterprise. Simplified methods. Zycie Gospodarcze, 38, 42–45. [Google Scholar]
  15. Neumaierová, I., & Neumaier, I. (2013). Vypovídací schopnost Indexu IN05. In Ekonomika v pohybu: Sborník příspěvků z mezinárodní konference pořádané u příležitosti šedesátého výročí VŠE a fakulty [Economy in motion: Proceedings from the international conference organized on the occasion of the 60th anniversary of University of Economics and the faculty], Prague: Prague University of Economics and Business, 169–176. [Google Scholar]
  16. Pacáková, V., Labudová V., Sipková, Ľ., Šoltés, E., & Vojtková, M. (2009). Štatistické metódy pre ekonómov. 411. [Google Scholar]
  17. Popescu, A. (2014). Research regarding the use of discriminant analysis for assessing the bankruptcy risk of agricultural companies. Scientific Papers, Series Management, Economic Engineering in Agriculture and Rural Development, 14(4). [Google Scholar]
  18. Purves, N., Niblock S. J., & Sloan, K. (2015). On the relationship between financial and non-financial factors: A case study analysis of financial failure predictors of agribusiness firms in Australia. Agricultural Finance Review, 75(2), 282–300. [CrossRef] [Google Scholar]
  19. Rajin, D., Milenković, D., & Radojević, T. (2016). Bankruptcy prediction models in the Serbian agricultural sector. Economics of Agriculture, 63(1), 89–105. [Google Scholar]
  20. Septaningtiyas, I. E., Utami, E. S., & Sumani, S. (2020). Financial distress prediction on agricultural sector companies in Indonesia stock exchange. International Journal of Research Science and Management, 7(1), 155–159. [Google Scholar]
  21. Správa o poľnohospodárstve a potravinárstve v SR za rok 2019. file:///D:/prevzate%20subory/sprava_o_polnohospodarstve_a_potravinarstve_v_slovenskej_republike_za_rok_2019.pdf. [Google Scholar]
  22. Stehel, V., Horák, J., & Vochozka, M. (2019). Prediction of institutional sector development and analysis of enterprises active in agriculture. Business Administration and Management, 24(4), 103–118. [Google Scholar]
  23. Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model. Accounting and Business Research, 13(52), 295–358. [CrossRef] [Google Scholar]
  24. Valášková, K., Švábová, L., & Ďurica, M. (2017). Verification of prediction models in conditions of slovak agricultural resort. Economics, Management, Innovation, 9(3), 30–38. [Google Scholar]
  25. Vavřina, J., Hampel, D., & Janová, J. (2013). New approaches for the financial distress classification in agribusiness. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(4), 1177–1182. [CrossRef] [Google Scholar]

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