# Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Data and Methodology

#### 3.1. Data and Conditions of Classification

#### 3.2. Methods Used for Bankruptcy Prediction

#### 3.3. Metrics of the Prediction Models Comparison

## 4. Results and Discussion

^{2}. These findings correspondent with the results of Barboza et al. [71], who conducted intensive research evaluating bankruptcy using traditional statistic techniques and early artificial intelligence models and found that machine learning models show 10% better accuracy in relation to tradition models (LR and MDA).

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Fitzpatrick, P. A comparison of ratios of successful industrial enterprises with those of failed firms. Certif. Public Account.
**1932**, 2, 598–605. [Google Scholar] - Alaka, H.A.; Oyedele, L.-O.; Owolabi, H.A.; Kumar, V.; Ajayi, S.O.; Akinade, O.O.; Bilal, M. Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Syst. Appl.
**2018**, 94, 164–184. [Google Scholar] [CrossRef] - Kubickova, D.; Nulicek, V. Predictors of financial distress and bankruptcy model construction. Int. J. Manag. Sci. Bus. Adm.
**2016**, 2, 34–41. [Google Scholar] - Karas, M.; Reznakova, M. Creating a new bankruptcy prediction model: The grey zone problem. In Proceedings of the 24th IBIMA Conference: Crafting Global Competitive Economies: 2020 Vision Strategic Planning & Smart Implementation, Milan, Italy, 6–7 November 2014; Soliman, K.S., Ed.; International Business Information Management Assoc—IBIMA: Norristown, PA, USA, 2014; pp. 911–919. [Google Scholar]
- Gavurova, B.; Packova, M.; Misankova, M.; Smrcka, L. Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. J. Bus. Econ. Manag.
**2017**, 18, 1156–1173. [Google Scholar] [CrossRef] [Green Version] - Virag, M.; Hajdu, O. Pénzügyi mutatószámokon alapuló csõdmodellszámítások. Bankszemle XV
**1996**, 5, 42–53. [Google Scholar] - Neumaier, I.; Neumaierova, I. Try to count your Index IN 95. Terno
**1995**, 5, 7–10. [Google Scholar] - Neumaier, I.; Neumaierova, I. INFA Financial analysis—Application in energy sector. Sekt. A Odvetv. Anal. Asp. Energ.
**1999**, 4, 32–75. [Google Scholar] - Neumaier, I.; Neumaierova, I. Analysis of the value creation—Application of INFA financial analysis. Sekt. A Odvetv. Anal. Asp. Invest. Strojir.
**2001**, 8, 23–39. [Google Scholar] - Neumaier, I.; Neumaierova, I. Index IN 05. In Proceedings of the Conference European Financial Systems, Brno, Czech Republic, 21–23 June 2005; Masaryk University: Brno, Czech Republic, 2005; pp. 143–148. [Google Scholar]
- Maczynska, E. Assessment of the conditions of the enterprise. Simplified methods. Zycie Gospod.
**1994**, 38, 42–45. [Google Scholar] - Gajdka, J.; Stos, D. The Use of Discriminant Analysis in Assessing the Financial Condition of Enterprises; Wydawnictvo Akademii Ekonomicznej v Krakowie: Krakow, Poland, 1996. [Google Scholar]
- Hamrol, M.; Czajka, B.; Piechocki, M. Company bankruptcy—A discriminant analysis model. Przegląd Organ.
**2004**, 6, 35–39. [Google Scholar] [CrossRef] - Prusak, B. Nowoczesne Metody Prognozowania Zagrozenia Finansowego Predsiebiorst; DiFin: Krakow, Poland, 2005. [Google Scholar]
- Gruszczynski, M. Financial distress of companies in Poland. Int. Adv. Econ. Res.
**2004**, 10, 249–256. [Google Scholar] [CrossRef] - Chrastinova, Z. Methods of Assessment of Economic Solvency and Prediction of Financial Situation of Agricultural Enterprises; VUEPP: Bratislava, Slovakia, 1998. [Google Scholar]
- Gurcik, L. G-index—The financial situation prognosis method of agricultural enterprises. Agric. Econ.
**2002**, 48, 373–378. [Google Scholar] - SARIO. Automotive Sector in Slovakia; Slovak Investment and Trade Development Agency: Bratislava, Slovakia, 2020.
- Binkert, C.H. Fruherkennung von Unternehmenskrisen mit Hilfe Geeigneter Methoden im deutschen und Slowakischen Wirtschaftsraum. Ph.D. Thesis, University of Economics in Bratislava, Bratislava, Slovakia, 1999. [Google Scholar]
- Hurtosova, J. Development of Rating Model as a Tool to Assess the Enterprise Credibility. Ph.D. Thesis, University of Economics in Bratislava, Bratislava, Slovakia, 2009. [Google Scholar]
- Delina, R.; Packova, M. Prediction bankruptcy models validation in Slovak business environment. Ekon. Manag.
**2013**, 16, 101–112. [Google Scholar] - Rohacova, V.; Kral, P. Corporate failure prediction using DEA: An application to companies in the Slovak republic. In Proceedings of the Applications of Mathematics and Statistics in Economics, Jindrichuv Hradec, Czech Republic, 2–6 September 2015; University of Economics: Prague, Czech Republic, 2015; pp. 1–8. [Google Scholar]
- Gulka, M. The prediction model of financial distress of enterprises operating in conditions of SR. Biatec
**2016**, 24, 5–10. [Google Scholar] - Boda, M.; Uradnicek, V. Inclusion of weights and their uncertainty into quantification within a pyramid decomposition of a financial indicator. Ekon. Cas.
**2016**, 64, 70–92. [Google Scholar] - Svabova, L.; Durica, M. Being an outlier: A company non-prosperity sign? Equilib.—Q. J. Econ. Econ. Policy
**2019**, 14, 359–375. [Google Scholar] [CrossRef] - Belas, J.; Smrcka, L.; Gavurova, B.; Dvorsky, J. The impact of social and economic factors in the credit risk management of SME. Technol. Econ. Dev. Econ.
**2018**, 24, 1215–1230. [Google Scholar] [CrossRef] [Green Version] - Budiarto, D.S.; Rahmawati, B.; Prabowo, M.A. Accounting information system and non-financial performance in small firm: Empirical research based on ethnicity. J. Int. Stud.
**2019**, 12, 338–351. [Google Scholar] [CrossRef] - Bartosova, V.; Kral, P. Methodological framework of financial analysis results objectification in Slovak republic. J. Mod. Account. Audit.
**2017**, 13, 394–400. [Google Scholar] - Toth, Z.; Mura, L. Support for small and medium enterprises in the economic crisis in selected EU countries. In Proceedings of the 12th International Conference on Hradec Economic Days: Economic Development and Management of Regions, Hradec Kralove, Czech Republic, 4–5 February 2014. [Google Scholar]
- Beaver, W.H. Financial ratios as predictors of failure. J. Account. Res.
**1966**, 4, 71–111. [Google Scholar] [CrossRef] - Altman, E.I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ.
**1968**, 23, 589–609. [Google Scholar] [CrossRef] - Ohlson, J.A. Financial ratios and the probabilistic prediction of bankruptcy. J. Account. Res.
**1980**, 18, 109–131. [Google Scholar] [CrossRef] [Green Version] - Prusak, B. Review of research into enterprise bankruptcy prediction in selected central and European countries. Int. J. Financ. Stud.
**2018**, 6, 60. [Google Scholar] [CrossRef] [Green Version] - Kliestik, T.; Valaskova, K.; Kliestikova, J.; Kovacova, M.; Svabova, L. Prediction of Financial Health of Enterprises in Transition Economies; EDIS: Zilina, Slovakia, 2019.
- Antunes, F.; Ribiero, B.; Pereira, F. Probabilistic modeling and visualization for bankruptcy prediction. Appl. Soft Comput.
**2017**, 60, 831–843. [Google Scholar] [CrossRef] [Green Version] - Stefko, R.; Gavurova, B.; Rigelsky, M.; Ivankova, V. Evaluation of selected indicators of patient satisfaction and economic indices in OECD country. Econ. Sociol.
**2019**, 12, 149–165. [Google Scholar] [CrossRef] [PubMed] - Kliestik, T.; Misankova, M.; Valaskova, K.; Svabova, L. Bankruptcy prevention: New effort to reflect on legal and social changes. Sci. Eng. Ethics
**2018**, 24, 791–803. [Google Scholar] [CrossRef] - Chou, C.; Hsieh, S.; Qiu, C. Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Appl. Soft Comput.
**2017**, 56, 298–316. [Google Scholar] [CrossRef] - Sharifabadi, M.R.; Mirhaj, M.; Izadinia, N. The impact of financial ratios on the prediction of bankruptcy of small and medium companies. Quid
**2017**, 1, 164–173. [Google Scholar] - Tian, S.; Yu, Y.; Guo, H. Variable selection and corporate bankruptcy forecasts. J. Bank. Financ.
**2015**, 52, 89–100. [Google Scholar] [CrossRef] - Bellovary, J.; Giacomino, D.; Akers, M. A review of bankruptcy prediction studies: 1930 to present. J. Financ. Educ.
**2007**, 33, 1–43. [Google Scholar] - Kumar, P.P.; Ravi, V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques—A review. Eur. J. Oper. Res.
**2007**, 180, 1–28. [Google Scholar] [CrossRef] - Calderon, T.G.; Cheh, J.J. A roadmap for future neural networks research in auditing and risk assessment. Int. J. Account. Inf. Syst.
**2002**, 3, 203–236. [Google Scholar] [CrossRef] - Dimitras, A.I.; Zanakis, S.H.; Zopoundis, C. A survey of business failure with an emphasis on prediction method and industrial applications. Eur. J. Oper. Res.
**1996**, 90, 487–513. [Google Scholar] [CrossRef] - O’Leary, D.E. Using neural network to predict corporate failure. Int. J. Intell. Syst. Account. Financ. Manag.
**1998**, 7, 187–197. [Google Scholar] [CrossRef] - Scott, J. The probability of bankruptcy: A comparison of empirical predictions and theoretical models. J. Bank. Financ.
**1981**, 5, 317–344. [Google Scholar] [CrossRef] - Kovacova, M.; Kliestik, T.; Valaskova, K.; Durana, P.; Juhaszova, Z. Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oecon. Copernic.
**2019**, 10, 743–772. [Google Scholar] [CrossRef] [Green Version] - Jones, S. Corporate bankruptcy prediction: A high dimensional analysis. Rev. Account. Stud.
**2017**, 22, 1366–1422. [Google Scholar] [CrossRef] - Jacobson, T.; Linde, J.; Roszbach, K. Firm default and aggregate fluctuations. J. Eur. Econ. Assoc.
**2013**, 11, 945–972. [Google Scholar] [CrossRef] [Green Version] - Bruneau, C.; De Bandt, O.; El Amri, W. Macroeconomic fluctuations and corporate financial fragility. J. Financ. Stab.
**2012**, 8, 219–235. [Google Scholar] [CrossRef] [Green Version] - Nam, C.W.; Kim, T.S.; Park, N.J.; Lee, H.K. Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies. J. Forecast.
**2008**, 27, 493–506. [Google Scholar] [CrossRef] - Tomas Zikovic, I. Challenges in predicting financial distress in emerging economies: The case of Croatia. East. Eur. Econ.
**2018**, 56, 1–27. [Google Scholar] [CrossRef] - Tinoco, M.H.; Wilson, N. Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. Int. Rev. Financ. Anal.
**2013**, 30, 394–419. [Google Scholar] [CrossRef] - Giriuniene, G.; Giriunas, L.; Morkunas, M.; Brucaite, L. A comparison on leading methodologies for bankruptcy prediction: The case of the construction sector in Lithuania. Economies
**2019**, 7, 82. [Google Scholar] [CrossRef] [Green Version] - Filipe, S.F.; Grammatikos, T.; Michala, D. Forecasting distress in European SME portfolios. J. Bank. Financ.
**2016**, 64, 112–135. [Google Scholar] [CrossRef] - Kacer, M.; Ochotnicky, P.; Alexy, M. The Altman’s revised Z’-Score model, non-financial information and macroeconomic variables: Case of Slovak SMEs. Ekon. Cas.
**2019**, 67, 335–366. [Google Scholar] - Wilson, N.; Ochotnicky, P.; Kacer, M. Creation and destruction in transition economies: The SME sector in Slovakia. Int. Small Bus. J.—Res. Entrep.
**2016**, 34, 579–600. [Google Scholar] [CrossRef] - Du Jardin, P. Dynamics of firm financial evolution and bankruptcy prediction. Expert Syst. Appl.
**2017**, 75, 25–43. [Google Scholar] [CrossRef] - Tuffnell, C.; Kral, P.; Siekelova, A.; Horak, J. Cyber-physical smart manufacturing systems: Sustainable industrial networks, cognitive automation, and data-centric business models. Econ. Manag. Financ. Mark.
**2019**, 14, 58–63. [Google Scholar] - Mattsson, B.; Steinert, O. Corporate Bankruptcy Prediction Using Machine Learning Techniques. Bachelor’s Thesis, University of Gothenburg, Gothenburg, Sweden, 2017. [Google Scholar]
- Barbuta-Misu, N.; Madaleno, M. Assessment of bankruptcy risk of large companies: European countries evolution analysis. J. Risk Financ. Manag.
**2020**, 13, 58. [Google Scholar] [CrossRef] - Pisula, T. An ensemble classifier-based scoring model for predicting bankruptcy of Polish companies in the Podkapackie Voivodeship. J. Risk Financ. Manag.
**2020**, 13, 37. [Google Scholar] [CrossRef] [Green Version] - Oliveira, M.D.N.T.; Ferriera, F.A.F.; Peréz-Bustamante Ilander, B.O.; Jalali, M.S. Integrating cognitive mapping and MDCA for bankruptcy prediction in small-and medium-sized enterprises. J. Oper. Res. Soc.
**2017**, 68, 985–997. [Google Scholar] [CrossRef] - Tsai, C. Feature selection in bankruptcy prediction. Knowl. Based Syst.
**2009**, 22, 120–127. [Google Scholar] [CrossRef] - Le, T.; Le, H.S.; Vo, M.T.; Lee, M.Y.; Baik, S.W. A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset. Symmetry
**2018**, 10, 250. [Google Scholar] [CrossRef] [Green Version] - Le, T.; Lee, M.Y.; Park, J.R.; Baik, S.W. Oversampling technique for bankruptcy prediction: Novel features from a transaction dataset. Symmetry
**2018**, 10, 79. [Google Scholar] [CrossRef] [Green Version] - Le, T.; Vo, B.; Fujita, H.; Nguyen, N.; Baik, W.S. A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting. Inf. Sci.
**2019**, 494, 294–310. [Google Scholar] [CrossRef] - Wang, M.; Chen, H.; Li, H.; Cai, Z.; Zhao, X.; Tong, C.; Li, J.; Xu, X. Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction. Eng. Appl. Artif. Intell.
**2017**, 63, 54–68. [Google Scholar] [CrossRef] - Mai, F.; Shaonan, T.; Chihoon, L.; Ling, M. Deep learning models for bankruptcy prediction using textile disclosures. Eur. J. Oper. Res.
**2019**, 274, 743–758. [Google Scholar] [CrossRef] - Hosaka, T. Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Syst. Appl.
**2019**, 13, 287–299. [Google Scholar] [CrossRef] - Qu, Y.; Quan, P.; Lei, M.; Shi, Y. Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Comput. Sci.
**2019**, 162, 895–899. [Google Scholar] [CrossRef] - Kovacova, M.; Kliestik, T. Logit and probit application for the prediction of bankruptcy in Slovak companies. Equilib. Q. J. Econ. Econ. Policy
**2017**, 12, 775–791. [Google Scholar] [CrossRef] - Affes, Z.; Hentati-Kaffel, R. Predicting US banks bankruptcy: Logit versus canonical discriminant analysis. Comput. Econ.
**2019**, 54, 199–244. [Google Scholar] [CrossRef] [Green Version] - Barboza, F.; Kimura, H.; Altman, E. Machine learning models and bankruptcy prediction. Expert Syst. Appl.
**2017**, 83, 405–417. [Google Scholar] [CrossRef] - Mihalovic, M. Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Econ. Sociol.
**2016**, 9, 101–118. [Google Scholar] [CrossRef] [PubMed] - Cho, S.; Kim, J.; Bae, J.K. An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Syst. Appl.
**2009**, 36, 403–410. [Google Scholar] [CrossRef] - Van Gestel, T.; Baesens, B.; Martens, D. From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing
**2010**, 73, 2955–2970. [Google Scholar] [CrossRef] - Kim, S.Y. Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. Serv. Ind. J.
**2011**, 31, 441–468. [Google Scholar] [CrossRef] - Chen, M.Y. Comparing traditional statistics, decision tree classification and support vector machine technique for financial bankruptcy prediction. Intell. Autom. Soft Comput.
**2012**, 18, 65–73. [Google Scholar] [CrossRef] - Nyitrai, T.; Virag, M. The effect of handling outliers on the performance of bankruptcy prediction models. Socio-Econ. Plan. Sci.
**2019**, 67, 34–42. [Google Scholar] [CrossRef] - Altman, E.I.; Iwanicz-Drozdowska, M.; Laitinen, E.K.; Suvas, A. A race for long horizon bankruptcy prediction. Appl. Econ.
**2020**. early access. [Google Scholar] [CrossRef] - Ben Jabeur, S. Bankruptcy prediction using partial least squares logistic regression. J. Retail. Consum. Serv.
**2017**, 36, 197–202. [Google Scholar] [CrossRef] - Olson, D.L.; Delen, D.; Meng, Y. Comparative analysis of data mining methods for bankruptcy prediction. Decis. Support Syst.
**2012**, 52, 464–473. [Google Scholar] [CrossRef] - Klepac, V.; Hampel, D. Prediction of bankruptcy with SVM classifier among retail business companies in EU. Acta Univ.
**2016**, 64, 627–634. [Google Scholar] - Hudakova, M.; Masar, M.; Luskova, M.; Patak, M.R. The dependence of perceived business risks on the size of SMEs. J. Compet.
**2018**, 10, 54–69. [Google Scholar] [CrossRef] [Green Version] - Garcia, V.; Marques, A.I.; Sanchez, S.J. Exploring the synergetic effects of samples types in the performance of ensembles for credit risk and corporate bankruptcy prediction. Inf. Fusion
**2019**, 47, 88–101. [Google Scholar] [CrossRef] - Son, H.; Hyun, H.; Du Phan, H.; Hwang, H.J. Data analytical approach for bankruptcy prediction. Expert Syst. Appl.
**2019**, 138, 112816. [Google Scholar] [CrossRef] - Svabova, L.; Durica, M. A closer view of the statistical methods globally used in bankruptcy prediction of companies. In Proceeding of the 16th International Scientific Conference on Globalization and its Socio Economic Consequences, Rajecke Teplice, Slovakia, 5–6 October 2016; Kliestik, T., Ed.; University of Zilina: Zilina, Slovakia, 2016; pp. 2174–2181. [Google Scholar]
- Breiman, L. Bagging predictors. Mach. Learn.
**1996**, 24, 123–140. [Google Scholar] [CrossRef] [Green Version] - LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature
**2015**, 521, 436–444. [Google Scholar] [CrossRef] - Nielsen, M. Neural Networks and Deep Learning; Determination Press: New Haven, CT, USA, 2015. [Google Scholar]
- Svabova, L.; Kral, P. Selection of predictors in bankruptcy prediction models for Slovak enterprises. In Proceedings of the 10th International Days of Statistics and Economics, Prague, Czech Republic, 8–10 September 2016; Loster, T., Pavelka, T., Eds.; Melandrium: Slany, Czech Republic, 2016; pp. 1759–1768. [Google Scholar]
- Eysenck, G.; Kovalova, E.; Machova, V.; Konecny, V. Big data analytics processes in industrial internet of things systems: Sensing and computing technologies, machine learning techniques, and autonomous decision-making algorithms. J. Self-Gov. Manag. Econ.
**2019**, 7, 28–34. [Google Scholar] - Kral, P.; Kanderova, M.; Kascakova, A.; Nedelova, G.; Valencakova, V. Multivariate Statistical Methods Focused on the Solution of Problems of Economic Practice; Matej Bel University: Banska Bystrica, Slovakia, 2009. [Google Scholar]
- Das, S.; Chatterjee, S. Multicollinearity Problem—Root Cause, Diagnostics and Way Outs. SSRN Library. Available online: https://ssrn.com/abstract=1830043 (accessed on 11 March 2020).
- Hafezi, M.H.; Liu, L.; Millward, H. Learning daily activity sequences of population groups using random forest theory. Transp. Res. Rec.
**2018**, 47, 194–207. [Google Scholar] [CrossRef] - Komprdova, K. Decision Trees and Forests; IBA: Brno, Czech Republic, 2012. [Google Scholar]
- Choudhary, A.K.; Harding, J.A.; Tiwari, M.K. Data mining in manufacturing: A review based on the kind of knowledge. J. Intell. Manuf.
**2009**, 20, 501–521. [Google Scholar] [CrossRef] - Williams, G.J.; Simoff, S.J. Data Mining—Theory, Methodology, Techniques and Applications; Springer: Berlin, Germany, 2006. [Google Scholar]
- Klepac, V.; Hampel, D. Predicting bankruptcy of manufacturing companies in EU. Econ. Manag.
**2018**, 21, 159–174. [Google Scholar] - Lehman, E.L.; Casella, G. Theory of Point Estimation; Springer: New York, NY, USA, 1998. [Google Scholar]
- Hyndman, R.J.; Koehler, A.B. Another look at measured of forecast accuracy. Int. J. Forecast.
**2006**, 22, 679–688. [Google Scholar] [CrossRef] [Green Version] - Bien, J.; Friedman, J.; Hastie, T.; Simon, N.; Taylor, J.; Tibshirani, R.; Tibshirani, R.J. Strong rules for discarding predictors in lasso-type problems. J. R. Stat. Soc.
**2012**, 74, 1–22. [Google Scholar] - Naidu, P.; Govinda, I. Bankruptcy prediction using neural networks. In Proceedings of the 2nd International Conference on Inventive Systems and Control, Coimbatora, India, 19–20 January 2018. [Google Scholar]
- Alfaro, E.; Garcia, N.; Gamez, M.; Elizondo, D. Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decis. Support Syst.
**2008**, 45, 110–122. [Google Scholar] [CrossRef] - Lee, K.; Booth, D.; Alam, P. A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Syst. Appl.
**2005**, 29, 1–16. [Google Scholar] [CrossRef] - Bagheri, M.; Valipour, M.; Amin, V. The bankruptcy prediction in Tehran share holding using neural network and its comparison with logistic regression. J. Math. Comput. Sci.
**2012**, 5, 219–228. [Google Scholar] [CrossRef] - Karminsky, A.M.; Burekhin, R.N. Comparative analysis of methods for forecasting bankruptcies of Russian construction companies. Bizn. Inform.
**2019**, 13, 52–66. [Google Scholar] [CrossRef] - Chaudhuri, A.; De, K. Fuzzy support vector machine for bankruptcy prediction. Appl. Soft Comput.
**2011**, 11, 2472–2486. [Google Scholar] [CrossRef] - Chen, M.Y. Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches. Comput. Math. Appl.
**2011**, 62, 4514–4524. [Google Scholar] [CrossRef] [Green Version] - Lee, S.; Choi, W.S. A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Syst. Appl.
**2013**, 40, 2941–2946. [Google Scholar] [CrossRef]

Data | Total | Not in Crisis | In Crisis | Ratio |
---|---|---|---|---|

2016 | 47,414 | 41,266 | 6148 | 12.97% |

2016 training | 35,561 | 30,950 | 4611 | 12.97% |

2016 validation | 11,853 | 10,316 | 1537 | 12.97% |

2017 | 64,757 | 54,835 | 9922 | 15.32% |

2018 | 56,743 | 49,016 | 7727 | 13.62% |

Profitability ratios (P) | Algorithm | |

R1 | Return on capital (net) | EAT / total liabilities |

R2 | Return on capital (gross) | (EBIT + cost interests) / total liabilities |

R3 | Return on corporate revenues (net) | EAT / revenues |

Activity ratios (A) | Algorithm | |

A1 | Asset turnover | Revenues / total assets |

A2 | Current assets turnover | Revenues / current assets |

Liquidity ratios (L) | Algorithm | |

L1 | Cash ratio | Cash and cash equivalents / current liabilities |

L2 | Quick ratio | (Cash and cash equivalents + account receivables) / current liabilities |

L3 | Current ratio | Current assets / current liabilities |

L4 | Net working capital ratio | Net working capital / total assets |

Ratios of indebtedness and capital structure (Z) | Algorithm | |

Z1 | RE–TA ratio | Retained earnings / total assets |

Z2 | Debt ratio | (Current + non-current liabilities) / total assets |

Z3 | Current debt ratio | Current liabilities / total assets |

Z4 | Financial debt ratio | (Bank loans + issued bonds) / total assets |

Z5 | Debt–equity ratio | (Current + non-current liabilities) / equity |

L1 | L2 | L3 | L4 | R1 | R2 | R3 | A1 | A2 | Z1 | Z2 | Z3 | Z4 | Z5 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

St. dev. | 1.69 | 2.32 | 2.48 | 0.58 | 0.23 | 0.25 | 0.25 | 1.94 | 0.84 | 0.51 | 0.52 | 0.51 | 0.14 | 2.50 |

Var. | 2.85 | 5.39 | 6.15 | 0.34 | 0.05 | 0.06 | 0.06 | 3.77 | 0.71 | 0.26 | 0.27 | 0.26 | 0.02 | 6.27 |

Min | 0.00 | 0.00 | 0.00 | −4.87 | −1.61 | −1.61 | −1.87 | 0.00 | 0.00 | −3.76 | 0.05 | 0.00 | 0.00 | −0.82 |

1stQ | 0.06 | 0.43 | 0.69 | −0.22 | −0.02 | −0.02 | −0.02 | 0.39 | 0.25 | −0.08 | 0.44 | 0.32 | 0.00 | 0.05 |

Mean | 0.87 | 1.64 | 1.94 | 0.02 | 0.02 | 0.04 | 0.00 | 1.27 | 0.65 | −0.05 | 0.76 | 0.67 | 0.07 | 1.26 |

Median | 0.24 | 0.91 | 1.12 | 0.07 | 0.02 | 0.03 | 0.01 | 0.66 | 0.41 | 0.01 | 0.73 | 0.61 | 0.00 | 0.37 |

3rdQ | 0.84 | 1.71 | 2.03 | 0.38 | 0.09 | 0.12 | 0.06 | 1.22 | 0.71 | 0.16 | 0.95 | 0.89 | 0.07 | 1.30 |

Max | 13.02 | 22.19 | 23.68 | 2.14 | 1.63 | 1.76 | 1.81 | 19.10 | 9.62 | 1.61 | 5.42 | 5.40 | 0.72 | 20.96 |

LR | RF | NN | ||
---|---|---|---|---|

R1 | ROAeat | + | + | + |

R2 | ROAebit | + | + | − |

R3 | Net profit margin | − | + | − |

L1 | Cash ratio | − | − | + |

L2 | Quick ratio | + | + | + |

L3 | Current ratio | + | + | + |

L4 | Net working capital / Total assets | − | + | + |

Z1 | Retained earnings /Total assets | − | − | + |

Z2 | Debt Ratio | − | + | + |

Z3 | Current liability / Total assets | − | + | − |

Z4 | Credit indebtedness | − | − | − |

Z5 | Equity / Total liabilities | + | + | + |

A1 | Total Asset Turnover | − | − | − |

A2 | Current Asset Turnover | − | − | − |

2017 | 2018 | |||||||
---|---|---|---|---|---|---|---|---|

no | yes | error | no | yes | error | |||

LR | no | 44,716 | 10,119 | 0.185 | no | 40,024 | 8992 | 0.183 |

yes | 1852 | 8070 | 0.187 | yes | 1438 | 6289 | 0.186 | |

totals | 46,568 | 18,189 | 0.185 | totals | 41,462 | 15,281 | 0.184 | |

NN | no | 44,874 | 9961 | 0.182 | no | 40,202 | 8814 | 0.180 |

yes | 1798 | 8124 | 0.181 | yes | 1397 | 6330 | 0.181 | |

totals | 46,672 | 18,085 | 0.182 | totals | 41,599 | 15,144 | 0.180 | |

RF | no | 44,817 | 10,018 | 0.183 | no | 40,161 | 8855 | 0.181 |

yes | 1804 | 8118 | 0.182 | yes | 1399 | 6328 | 0.181 | |

totals | 46,621 | 18,136 | 0.183 | totals | 41,560 | 15,183 | 0.181 |

2017 | 2018 | ||||||
---|---|---|---|---|---|---|---|

NN | LR | RF | NN | LR | RF | ||

MSE | 0.088 | 0.096 | 0.090 | ↓ | 0.081 | 0.087 | 0.082 |

RMSE | 0.297 | 0.309 | 0.300 | ↓ | 0.284 | 0.296 | 0.287 |

Mean per class error | 0.205 | 0.212 | 0.199 | ↓ | 0.211 | 0.208 | 0.207 |

LogLoss | 0.331 | 0.405 | 0.339 | ↓ | 0.294 | 0.345 | 0.309 |

R^{2} | 0.322 | 0.263 | 0.306 | ↑ | 0.315 | 0.257 | 0.300 |

Gini | 0.758 | 0.742 | 0.755 | ↑ | 0.772 | 0.755 | 0.764 |

AUC | 0.879 | 0.871 | 0.877 | ↑ | 0.886 | 0.877 | 0.882 |

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## Share and Cite

**MDPI and ACS Style**

Gregova, E.; Valaskova, K.; Adamko, P.; Tumpach, M.; Jaros, J.
Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods. *Sustainability* **2020**, *12*, 3954.
https://doi.org/10.3390/su12103954

**AMA Style**

Gregova E, Valaskova K, Adamko P, Tumpach M, Jaros J.
Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods. *Sustainability*. 2020; 12(10):3954.
https://doi.org/10.3390/su12103954

**Chicago/Turabian Style**

Gregova, Elena, Katarina Valaskova, Peter Adamko, Milos Tumpach, and Jaroslav Jaros.
2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods" *Sustainability* 12, no. 10: 3954.
https://doi.org/10.3390/su12103954