Altman Model Verification Using a Multi-Criteria Approach for Slovakian Agricultural Enterprises

dc.contributor.authorVavrek, Roman
dc.contributor.authorGundová, Petra
dc.contributor.authorKravčáková Vozárová, Ivana
dc.contributor.authorKotulič, Rastislav
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2021-03-16T10:51:27Z
dc.date.available2021-03-16T10:51:27Z
dc.description.abstractThe Altman model is still one of the most widely used predictive models in the 21st century, and it aims to highlight the differences between bankrupt and healthy enterprises. This model has been modified several times; its most well-known forms are from 1968, 1983 and 1995. However, the use of the Altman Z-score for Slovak enterprises is more than questionable. The unsuitability of the model for the conditions of Slovak companies has been confirmed by several empirical surveys. The objective of this study was to verify the validation of these three variants of the Altman model, depending on how an unprosperous company is identified, using a sample of 996 agricultural enterprises operating in the Slovak Republic. Four indicators were selected for the identification of an unprosperous enterprise – economic results, total liquidity, equity, and economic value added – and they were monitored over the last year or, as the case may be, over the last three years from 2014 to 2016. Using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Coefficient of variation (CV) methods as an objective method for weight determination, a combination of the Altman model from 1968 and the negative total liquidity in the last reference year was determined to be the best. One of our main findings is that the way in which an unprosperous enterprise is identified is a significant factor affecting the overall reliability of the Altman model. The Altman model from 1968 and 1983 confirmed the differences resulting from the natural conditions in which the enterprises operate. The economic results and economic value added (EVA) proved to be inappropriate as indicators for defining an unprosperous enterprise in the conditions of the Slovak Republic.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2021-1-010
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/159935
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonAlaka, H. A., Oyedele, L. M., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Application, 94, 164–184. https://doi.org/10.1016/j.eswa.2017.10.040
dc.relation.isbasedonAlmamy, J., Aston, J., & Ngwa, L. N. (2016). An evaluation of Altman’s Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK. Journal of Corporate Finance, 36, 278–285. https://doi.org/10.1016/j.jcorpfin.2015.12.009
dc.relation.isbasedonAltman, E. I. (2002). Bankruptcy, credit risk, and high yield junk bonds. Malden: Blackwell.
dc.relation.isbasedonAltman, E. I., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt. New York, NY: Wiley. http://doi.org/10.1002/9781118267806
dc.relation.isbasedonAltman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
dc.relation.isbasedonAltman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-score Model. Journal of International Financial Management & Accounting, 28(2), 131–171. https://doi.org/10.1111/jifm.12053
dc.relation.isbasedonAntunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modelling and visualization for bankruptcy prediction. Applied Soft Computing, 60, 831–843, https://doi.org/10.1016/j.asoc.2017.06.043
dc.relation.isbasedonBakeš, V., & Valášková, K. (2018). Application and verification of Slovak prediction models in conditions of national economy. Podniková ekonomika a manažment. Elektronický časopis o ekonomike, manažmente, marketingu a logistike podniku, 2, 3–15.
dc.relation.isbasedonBalcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63–93. http://doi.org/10.1016/j.bar.2005.09.001
dc.relation.isbasedonBank, V., Tarasquina, A., & Bank, S. (2006). Financial analysis. Moscow: Prospect.
dc.relation.isbasedonBelas, J., Cipovova, E., Novak, P., & Polach, J. (2012). Impacts of the Foundation Internal Ratings Based Approach Usage on Financial Performance of Commercial Bank. E&M Economics and Management, 15(3), 142–154.
dc.relation.isbasedonBieliková, T. (2016). Inovatívne možnosti diagnostikovania finančného zdravia výrobného podniku vo väzbe na jeho finančno-ekonomickú výkonnosť. Banská Bystrica: EF UMB.
dc.relation.isbasedonBieliková, T., Cút, S., & Úradníček, V. (2014). The influence of the definition of risky company on financial situation diagnostic models in Slovak dynamic economic environment. In M. Čulík (Ed.), Managing and modelling of financial risks: Proceedings from 7th international scientific conference (pp. 38–45). Ostrava: VSB – Technical University of Ostrava.
dc.relation.isbasedonBocharov, V. (2007). Financial analysis. Saint Petersburg: Peter Press.
dc.relation.isbasedonBoďa, M., & Úradníček, V. (2016). The portability of Altman’s Z-score model to predicting corporate financial distress of Slovak companies. Technological and Economic Development of Economy, 22(4), 532–553. https://doi.org/10.3846/20294913.2016.1197165
dc.relation.isbasedonBoďa, M., & Úradníček, V. (2019). Predicting Financial Distress of Slovak Agricultural Enterprises. Ekonomický časopis, 67(4), 426–452.
dc.relation.isbasedonBrealey, R. A., Myers, S. C., & Allen, F. (2011). Principles of Corporate Finance. New York, NY: McGraw-Hill/Irwin.
dc.relation.isbasedonČámská, D. (2016). Accuracy of Models Predicting Corporate Bankruptcy in a Selected Industry Branch. Ekonomický časopis, 64(4), 353–366.
dc.relation.isbasedonČámská, D., & Klecka, J. (2020). Comparison of Prediction Models Applied in Economic Recession and Expansion. Journal of Risk and Financial Management, 13(3), 1–16, https://doi.org/10.3390/jrfm13030052
dc.relation.isbasedonDamodaran, A. (2004). Valuation the Big Picture. Retrieved October 10, 2019, from http://people.stern.nyu.edu/adamodar/pdfiles/country/BrazilJune04.pdf
dc.relation.isbasedonDamodaran, A. (2014). Equity Risk Premiums: Looking Backwards and Forwards. Retrieved October 15, 2019, from http://people.stern.nyu.edu/adamodar/pdfiles/country/ERP.pdf
dc.relation.isbasedonDelina, R., & Packová, M. (2013). Prediction bankruptcy models validation in Slovak business environment. E&M Economics and Management, 16(3), 101–110.
dc.relation.isbasedonĎurica, M. (2018). Decision Tree Financial Distress Prediction Model for Slovak Companies. Podniková ekonomika a manažment, 2, 16–26.
dc.relation.isbasedonGavúrová, B., Packová, M., Mišanková, M., & Smrčka, Ľ. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156–1173. https://doi.org/10.3846/16111699.2017.1400461
dc.relation.isbasedonHosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Application, 117, 287–299.
dc.relation.isbasedonHwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making Methods and Applications: A State-of-the-Art Survey. Berlin, Heidelberg: Springer-Verlag.
dc.relation.isbasedonKabát, L. (2011a). Ako ďalej v aplikácii Altmanovho modelu? Finančný manažér, 11(1), 7–9.
dc.relation.isbasedonKabát, L. (2011b). Problémy aplikácie bonitných a predikčných modelov v podnikateľskom prostredí SR. Finančný manažér, 11(3), 89–90.
dc.relation.isbasedonKaras, M., & Řežnáková, M. (2012). Financial Ratios as Bankruptcy Predictors: The Czech Republic Case. In D. Pavelková, J. Strouhal, & M. Pasekova (Eds.), Proceeding of the 1st WSEAS International Conference on Finance, Accounting and Auditing (pp. 56–67). Athen: WSEAS.
dc.relation.isbasedonKeršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 1611–1699. https://doi.org/10.3846/jbem.2010.12
dc.relation.isbasedonKlepáč, V., & Hampel, D. (2017). Predicting financial distress of agriculture companies in EU. Agricultural Economics, 63(8), 347–355. https://doi.org/10.17221/374/2015-AGRICECON
dc.relation.isbasedonKlieštik, T., Kočišová, K., & Mišanková, M. (2015). Logit and Probit Model used for Prediction of Financial Health of Company. Procedia Economics and Finance, 23, 850–855.
dc.relation.isbasedonKlieštik, T., Mišanková, M., Valašková, K., & Svabová, L. (2018). Bankruptcy Prevention: New Effort to Reflect on Legal and Social Changes. Science and Engineering Ethics, 24(2), 791–803. http://doi.org/10.1007/s11948-017-9912-4
dc.relation.isbasedonKliestik, T., Valaskova, K., Lazaroiu, G., Kovacova, M., & Vrbka, J. (2020). Remaining Financially Healthy and Competitive: The Role of Financial Predictor. Journal of Competitiveness, 12(1), 74–92, https://doi.org/10.7441/joc.2020.01.05
dc.relation.isbasedonKo, Y. C., Fujita, H., & Li, T. (2017). An evidential analysis of Altman Z-score for financial predictions: case study on solar energy companies. Applied Soft Computing, 52, 748–759. https://doi.org/10.1016/j.asoc.2016.09.050
dc.relation.isbasedonKočišová, K., & Mišanková, M. (2014). Discriminant Analysis as a Tool for Forecasting Company’s Financial Health. Procesia – Social and Behavioral Sciences, 110, 1148–1157. https://doi.org/10.1016/j.sbspro.2013.12.961
dc.relation.isbasedonKotulič, R., Adamišin, P., Kravčáková Vozárová, I., & Vavrek, R. (2017). The impact of management skills of agricultural entities in relation to economic efficiency and Natural-Climatic conditions in Slovakia. Journal of Environmental Management and Tourism, 8(1), 92–99. https://doi.org/10.14505//jemt.v8.1(17).09
dc.relation.isbasedonKotulič, R., Király, P., & Rajčániová, M. (2018). Finančná analýza podniku. Bratislava: Wolters Kluwer SR.
dc.relation.isbasedonKováčová, M., & Kubala, P. (2018). Verification of Prediction Models Based on Discriminant Analysis in Conditions of Slovak Republic. Podniková ekonomika a manažment, 2, 52–64.
dc.relation.isbasedonKráľ, P., Fleischer, M., Stachová, M., & Nedelová, G. (2016). Corporate financial distress prediction of Slovak companies: Z-score models vs. alternatives. In M. Boďa, V. Mendelová (Eds.), AMSE 2016 – 19th Applications of Mathematics and Statistics in Economics Conference Proceedings (pp. 224–231). Banská Štiavnica: UMB.
dc.relation.isbasedonLe, T., Lee, M. Y., Park, J. R., & Baik, S. W. (2018). Oversampling techniques for bankruptcy prediction: Novel features from a transaction dataset. Symmetry-Basel, 10(4), 1–13. https://doi.org/10.3390/sym10040079
dc.relation.isbasedonLesáková, Ľ., Elexa, Ľ., & Gundová, P. (2015). Finančno-ekonomická analýza podniku 2. Banská Bystrica: UMB.
dc.relation.isbasedonLi, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Economics and Finance, 62, 1–19.
dc.relation.isbasedonLifschutz, S., & Jacobi, A. (2010). Predicting bankruptcy: evidence from Israel. International Journal of Business and Management, 5(4), 133–141. https://doi.org/10.5539/ijbm.v5n4p133
dc.relation.isbasedonMaňasová, Z. (2008). Úpadky podniků v České republice a možnosti jejich včasné predikce. Prague: Prague University of Economics and Business.
dc.relation.isbasedonMardani, A., Zavadskas, E. K., Govindan, K., Senin, A. A., & Jusoh, A. (2016). VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications. Sustainability, 8(1), 1–38. https://doi.org/10.3390/su8010037
dc.relation.isbasedonMařík, M., Čada, K., Dušek, D., Maříková, P., Rýdlová, B., & Rajdl, J. (2011). Metody oceňování podniku: proces ocenění – základní metody a postupy. Prague: Ekopress.
dc.relation.isbasedonMegginson, W. L., Meles, A., Sampagnaro, G., & Verdoliva, V. (2019). Financial Distress Risk in Initial Public Offerings: How Much Do Venture Capitalists Matter? Journal of Corporate Finance, 59, 10–30. https://doi.org/10.1016/j.jcorpfin.2016.09.007
dc.relation.isbasedonMendelová, V., & Bieliková, T. (2017). Diagnostikovanie finančného zdravia podnikov pomocou metódy DEA: Aplikácia na podniky v Slovenskej Republike. Politická ekonomie, 65(1), 26–44. https://www.doi.org/10.18267/j.polek.1125
dc.relation.isbasedonMihalovič, M. (2018). Využitie skóringových modelov při predikcii úpadku ekonomických subjektov v Slovenskej republike. Politická ekonomie, 66(6), 689–708. https://doi.org/10.18267/j.polek.1226
dc.relation.isbasedonMrkvička, J., & Kolář, P. (2006). Financial analysis. Prague: ASPI.
dc.relation.isbasedonNeumaierová, 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] (pp. 169–176). Prague: Prague University of Economics and Business.
dc.relation.isbasedonNeumaierová, I., & Neumaier, I. (2016). The Performance Ranking of Chosen Manufacturing Division. In J. Krajíček, J. Nešleha, & K. Urbanovský (Eds.), 13th International Scientific Conference of the European Financial Systems (pp. 502–507). Brno: Masaryk University.
dc.relation.isbasedonPereira, J., Basto, M., & da Silva, A. (2016). The Logistic Lasso and Ridge Regression in Predicting Corporate Failure. Procedia Economics and Finance, 39, 634–641.
dc.relation.isbasedonPitrová, K. (2011). Possibilities of the Altman Zeta Model Application to Czech Firms. E&M Economics and Management, 14(3), 66–76.
dc.relation.isbasedonRahman, A., Belas, J., Kliestik, T., & Tyll, L. (2017). Collateral requirements for SME loans: empirical evidence from the Visegrad countries. Journal of Business Economics and Management, 18(4), 650–675. https://www.doi.org/10.3846/16111699.2017.1357050
dc.relation.isbasedonRežnáková, M., & Karas, M. (2015). The prediction capabilities of bankruptcy models in a different environmnet: an example of the Altman Model under the conditions in the Visegrad group countries. Ekonomický časopis, 63(6), 617–633.
dc.relation.isbasedonSchönfeld, J., Kuděj, M., & Smrčka, L. (2018). Financial health of enterprises introducing safeguard procedure based on bankruptcy models. Journal of Business Economics and Management, 19(5), 692–705. https://doi.org/10.3846/jbem.2018.7063
dc.relation.isbasedonSingla, A., Sing Ahuja, I., & Sing Sethi, A. (2017). Comparative Analysis of Technology Push Strategies Influencing Sustainable Development in Manufacturing Industries Using Topsis and Vikor Technique. International Journal for Quality Research, 12(1), 129–146. https://doi.org/10.18421/IJQR12.01-08
dc.relation.isbasedonŠofránková, B., Kiseľáková, D., & Horváthová, J. (2017). Actual questions of risk management in models affecting enterprise performance. Ekonomický časopis, 65(7), 644–667.
dc.relation.isbasedonStreimikiene, D., Balezentis, T., Krisciukaitiene I., & Balezentis, A. (2012). Prioritizing sustainable electricity production technologies: MCDM approach. Renewable & Sustainable Energy Reviews, 16(5), 3302–3311. https://doi.org/10.1016/j.rser.2012.02.067
dc.relation.isbasedonSuder, A., & Kahraman, C. (2018). Multiattribute evaluation of organic and inorganic agricultural food investments using fuzzy TOPSIS. Technological and Economic Development of Economy, 24(3), 844–858. https://doi.org/10.3846/20294913.2016.1216905.
dc.relation.isbasedonSulub, S. A. (2014). Testing the predictive power of Altman’s revised Z-model: The case of 10 multinational companies. Research Journal of Finance and Accounting, 5(21), 174–184.
dc.relation.isbasedonSun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. http://doi.org/10.1016/j.knosys.2013.12.006
dc.relation.isbasedonSušický, J. (2011). Využitelnost bankrotních modelů a jejich aplikace v podmínkách České Republiky. Prague: Czech University of Life Sciences Prague.
dc.relation.isbasedonSůvová, H., & Knaifl, O. (2008). Finanční analýza I. Prague: Ambis College.
dc.relation.isbasedonValášková, K., Klieštik, T., & Kováčová, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105–121. https://doi.org/10.24136/oc.2018.006
dc.relation.isbasedonValáškova, K., Klieštik, T., Švabová, L., & Adamko, P. (2018). Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis. Sustainability, 10(7), 1–15. https://doi.org/10.3390/su10072144
dc.relation.isbasedonVavrek, R. (2019). Evaluation of the Impact of Selected Weighting Methods on the Results of the TOPSIS Technique. International Journal of Information Technology & Decision Making, 18(6), 1821–1843. https://doi.org/10.1142/S021962201950041X
dc.relation.isbasedonVavrek, R., & Chovancová, J. (2019). Assessment of economic and environmental energy performance of EU countries using CV-TOPSIS technique. Ecological Indicators, 106, 105519. https://doi.org/10.1016/j.ecolind.2019.105519
dc.relation.isbasedonVavrek, R., & Bečica, J. (2017). Capital City as a Factor of Multi-Criteria Decision Analysis – Application on Transport Companies in the Czech Republic. Mathematics, 8(10), 1765. http://doi.org/10.3390/math8101765
dc.relation.isbasedonYalcin, N., & Ünlü, U. (2018). A Multi-Criteria Performance Analysis of Initial Public Offering (IPO) Firms Using Critic and Vikor Methods. Technological and Economic Development of Economy, 24(2), 534–560. https://doi.org/10.3846/20294913.2016.1213201
dc.relation.isbasedonYi, W. (2012). Z-score Model on Financial Crisis Early Warning of Listed Real Estate Companies in China: A Financial Engineering Perspective. Systems Engineering Procedia, 3, 153–157. https://doi.org/10.1016/j.sepro.2011.11.021
dc.relation.isbasedonYoon, K. (1980). Systems selection by multiple attribute decision making. Manhattan, KS: Kansas State University.
dc.relation.isbasedonZalai, K. et al. (2013). Finančno-ekonomická analýza podniku. Bratislava: Sprint 2.
dc.relation.isbasedonZavadskas, E. K., Mardani, A., Turskis, Z., Jusoh A., & Nor, K. (2016). Development of TOPSIS Method to Solve Complicated Decision-Making Problems: An Overview on Developments. From 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 1–38. http://doi.org/10.1142/S0219622016300019
dc.relation.isbasedonZavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. https://doi.org/10.3846/20294913.2014.892037
dc.relation.isbasedonZelenkov, Y., Fedorova, E., & Chekrizov, D. (2017). Two-step classification method based on genetic algorithm for bankruptcy forecasting. Expert Systems with Applications, 88, 393–401. https://doi.org/10.1016/j.eswa.2017.07.025
dc.relation.isbasedonZwingli, J., & White, T. (2010). Can you trust your credit model? Credit and Financial Management Review, 16(2), 33–42.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectunprosperous enterpriseen
dc.subjectAltman modelen
dc.subjectTOPSIS techniqueen
dc.subjectcoefficient of variation methoden
dc.subject.classificationB23
dc.subject.classificationQ14
dc.titleAltman Model Verification Using a Multi-Criteria Approach for Slovakian Agricultural Enterprisesen
dc.typeArticleen
local.accessopen
local.citation.epage164
local.citation.spage146
local.facultyFaculty of Economics
local.filenameEM_1_2021_10
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue1
local.relation.volume24
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
EM_1_2021_10.pdf
Size:
1.31 MB
Format:
Adobe Portable Document Format
Description:
článek
Collections