Being an Outlier : a Company Non-prosperity Sign?

• Autorzy: Lucia Svabova , Marek Durica
• Języki publikacji: EN

Abstrakty

EN

Research background: The state of financial distress or imminent bankruptcy are very difficult situations that the management of every company wants to avoid. For these reasons, prediction of company bankruptcy or financial distress has been recently in a focus of economists and scientists in many countries over the world.
Purpose of the article: Various financial indicators, mostly financial ratios, are usually used to predict the financial distress. In order to create a strong prediction model and a statistically significant prediction of bankruptcy, it is advisable to use a deep statistical analysis of the data. In this paper, we analysed the real financial ratios of Slovak companies from the year 2017. In the phase of data preparation for further analysis, we checked the existence of outliers and found that there are some companies that are multivariate outliers because are significantly different from other companies in the database. Thus, we deeply focused on these outlying companies and analysed whether to be an outlier is a sign of financial distress.
Methods: We analysed whether there are much more non-prosperous companies in the set of outlier companies and if their financial indicators are significantly different from those of the prosperous companies. For these analyses, we used testing of the statistical hypotheses, such as the test for equality of means and chi-square test.
Findings & Value added: The ratio of non-prosperous companies between the outliers is significantly higher than 50% and the attributes of non-prosperity and being an outlier are dependent. The means of almost all financial ratios of prosperous and non-prosperous companies among outliers are significantly different. (original abstract)

Słowa kluczowe

EN

Bankruptcy; Enterprises bankruptcy forecasting; Financial indicators

PL

Bankructwo; Prognozowanie upadłości przedsiębiorstwa; Wskaźniki finansowe

• Czasopismo: Equilibrium
• Rocznik: 2019
• Tom: 14
• Numer: nr 2
• Strony: 359--375

Twórcy

autor

Lucia Svabova

• University of Zilina, Slovak Republic

autor

Marek Durica

• University of Zilina, Slovak Republic

Bibliografia

• Adamko, P., & Svabova, L. (2016). Prediction of the risk of bankruptcy of Slovak companies. In: M. Culik (Ed.). Proceedings of the 8th international scientific conference managing and modelling of financial risks. Ostrava.
• Ahmad, I., Olah, J., Popp, J., & Mate, D. (2018). Does business group affiliation matter for superior performance? Evidence from Pakistan. Sustainability, 10(9). doi: 10.3390/su10093060.
• Alrawashdeh, M. J., Radwan, T. R., & Abunawas, K. A. (2018). Performance of linear discriminant analysis using different robust methods. European Journal of Pure and Applied Mathematics, 11(1). doi: 10.29020/nybg.ejpam.v11i1.3176.
• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23. doi: 10.1111/j.1540-6261.1968.tb00843.x.
• Barkar, A. A. (2017). Development of instruments of financial diagnostics of crisis. St. Peterburg University Journal of Economic Studies, 33(4). doi: 10.21638/11701/spbu05.2017.408.
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4. doi: 10.2307/2490171.
• Bengamou, E., & Melot, V. (2018). Seven proofs of the Pearson chi-squared independence test and its graphical interpretation. SSRN, doi: 10.2139/ssrn.3239829.
• Bielikova, T., Banyiova, T., & Piterkova, A. (2014). Prediction techniques of agriculture enterprises failure. Procedia Economics and Finance, 12. doi: 10.1016/s2212-5671(14)00319-0.
• Brozyna, J., Mentel, G., & Pisula, T. (2016). Statistical methods of the bankruptcy prediction in the logistics sector in Poland and Slovakia. Transformations in Business & Economics, 15(1).
• Delina, R., & Packova, M (2013). Prediction bankruptcy models validation in Slovak business environment. E + M Ekonomie a management, 16(3).
• Dima, A. M., & Vasilache, S. (2016). Credit risk modeling for companies default prediction using neural networks. Romanian Journal of Economic Forecasting, 19(3).
• Eberhardt, R., & Flinger, A. (1977). Comparison of two tests for equality of two proportions. American Statistician, 31(4). doi: 10.2307/2683536.
• Figini, S., Bonelli, F., & Giovannini, E. (2017). Solvency prediction for small and medium enterprises in banking. Decision Support Systems, 102. doi: 10.1016/j.dss.2017.08.001.
• Gavliak, R. (2006). Bankruptcy prediction in Slovak companies using linear probability models. Forum Statisticum Slovacum, 2(5).
• Gavurova, B., Janke, F., Packova, M., & Pridavok, M. (2017). Analysis of impact of using the trend variables on bankruptcy prediction models performance. Ekonomicky casopis, 65(4).
• Harumova, A, & Janisova, M. (2014). Rating Slovak enterprises by scoring functions. Ekonomicky casopis, 62(5).
• Hoaglin, D. C., & Iglewicz, B. (1987). Fine-tuning some resistant rules for outlier labeling. Journal of the American Statistical Association, 82. doi: 10.2307/2289392.
• Jabeur, S. B., & Fahmi, Y. (2017). Forecasting financial distress for French firms: a comparative study. Empirical Economics, 54(3). doi: 10.1007/s00181-017-1246-1.
• Jing, Z., & Fang, Y. (2017). Predicting US bank failures: a comparison of logit and data mining models. Journal of Forecasting, 37(2). doi: 10.1002/for.2487.
• Kostrzewska, J., Kostrzewski, M., Pawelek, B., & Galuszka, K. (2016). The classical and Bayesian logistic regression in the research on the financial standing of enterprises after bankruptcy in Poland. In: M. Papiez & S. Smiech (Eds.). Proceedings of 10th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Cracow: Foundation of the Cracow University of Economics.
• Kovacova, M., & Kliestik, T. (2017). Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.40.
• Kral, P., Fleischer, M., Stachova, M., Nedelova, G., & Sobisek, L. (2016). Corporate finance distress prediction of Slovak companies: Z-score model vs. alternatives. In: M. Boda & V. Mendelova (Eds.). Proceedings of the applications of mathematics and statistics in economics AMSE 2016. Banska Bystrica: Financ.
• Kral, P., Janoskova, K., & Kliestik, T. (2018). Key determinants of the public transport user's satisfaction. Administratie si Management Public, 31. doi: 10.24818/amp/2018.31-03.
• Linares-Mustaros, S., Coenders, G., & Vives-Mestres, M. (2017). Financial performance and distress profiles. From classification according to financial ratios to compositional classification. Advances in Accounting, 40. doi: 10.1016/j.adiac.2017.10.003.
• Lohk, P., & Siimann, P. (2016). Predicting the risk of encountering financial difficulties by the example of Estonian municipalities. In Proceedings of the 5th international conference on accounting, auditing, and taxation (ICAAT 2016). doi: 10.2991/icaat-16.2016.30.
• Mihalovic, M. (2016). Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics & Sociology, 9(4). doi: 10.14254/2071-789x.2016/9-4/6.
• Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18. doi: 10.2307/2490395.
• Pawelek, B., Galuszka, K., Kostrzewska, J., & Kostrzewski, M. (2015). Classification methods in the research on the financial standing of construction enterprises after bankruptcy in Poland. In: F. Palumbo, A. Montanari, M. Vichi (Eds.). Data science: innovative developments in data analysis and clustering. Springer International Pulishing. doi: 10.1007/978-3-319-55723-6_3.
• Pawelek, B., Kostrzewska, J., & Lipieta, A. (2015). The problem of outliers in the research on the financial standing of construction enterprises in Poland. In: M. Papiez & S. Smiech (Eds.). Proceedings of 9th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Cracow: Foundation of the Cracow University of Economics.
• Siekelova, A., Spuchlakova, E., & Zvarikova, K. (2017). Selected predictive models of financial health based on the multiple discriminant analysis method and their application in selected enterprises. Podnikova ekonomika a manazment, 2.
• Szetela, B., Mentel, G., & Brozyna, J. (2016). In search of insolvency among European countries. Economic Research - Ekonomska Istraživanja, 29(1). doi: 10.1080/1331677x.2016.1237301.
• Tabachnick, B. G., & Fidell, L. S., (2012). Using multivariate statistics. Boston, USA: Pearson Education.
• Tsai, C. F., & Cheng, K. C. (2012). Simple instance selection for bankruptcy prediction. Knowledge-based Systems, 27. doi: 10.1016/j.knosys.2011.09.017.
• Valaskova, K., Svabova, L., & Durica, M. (2017). Verification of prediction models in conditions of the Slovak agricultural resort. Economics, Management, Innovation, 9(3).
• Wilks, S. S. (1946). Sample criteria for testing equality of means, equality of variances, and equality of covariances in a normal multivariate distribution. Annals of Mathematical Statistics, 17(3). doi: 10.1214/aoms/1177730940.
• Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22. doi: 10.2307/2490859.
• Zvarikova, K., Spuchlakova, E., & Sopkova, G., (2017). International comparison of the relevant variables in the chosen bankruptcy models used in the risk management. Oeconomia Copernicana, 8(1). pp. 143-155. doi: 10.24136/oc.v8i1.10.

Identyfikatory

DOI

10.24136/eq.2019.017