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The effectiveness of discriminant models based on the example of the manufacturing sector

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  • Sebastian Klaudiusz Tomczak
  • Edward Radosiński

Abstract

The best models of bankruptcy prediction have been selected that can indicate the deteriorating situation of a company several years before bankruptcy occurs. There are a lot of methods for evaluating the financial statements of enterprises, but only a few can assess a company as a whole and recognise sufficiently early the deteriorating financial standing of a business. The matrix method was used to classify companies in order to assess the models. The correctness of the classification made by the models was tested based on data covering a period of five years before the bankruptcy of enterprises. To analyse the effectiveness of these discriminant models, the financial reports of manufacturing companies were used. Analysis of 33 models of bankruptcy prediction shows that only 5 models were characterized by sufficient predictive ability in the five years before the bankruptcy of enterprises. The results obtained show that so far a unique, accurate, optimal model, by which companies could be assessed with very high efficiency, has not been identified. That is why it is vital to continue research related to the construction of models enabling accurate evaluation of the financial condition of businesses.

Suggested Citation

  • Sebastian Klaudiusz Tomczak & Edward Radosiński, 2017. "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(3), pages 81-97.
  • Handle: RePEc:wut:journl:v:3:y:2017:p:81-97:id:1310
    DOI: 10.5277/ord170306
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    as
    1. Sinkey, Joseph F, Jr, 1975. "A Multivariate Statistical Analysis of the Characteristics of Problem Banks," Journal of Finance, American Finance Association, vol. 30(1), pages 21-36, March.
    2. Almamy, Jeehan & Aston, John & Ngwa, Leonard 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, Elsevier, vol. 36(C), pages 278-285.
    3. Edward I. Altman, 2013. "Predicting financial distress of companies: revisiting the Z-Score and ZETA® models," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 17, pages 428-456, Edward Elgar Publishing.
    4. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    5. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    6. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    7. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. Micha, Bernard, 1984. "Analysis of business failures in France," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 281-291, June.
    10. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    11. Pettway, Richard H & Sinkey, Joseph F, Jr, 1980. "Establishing On-Site Bank Examination Priorities: An Early-Warning System Using Accounting and Market Information," Journal of Finance, American Finance Association, vol. 35(1), pages 137-150, March.
    12. Tomczak, Sebastian, 2014. "Comparative analysis of liquidity ratios of bankrupt manufacturing companies," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 10(3), pages 1-14.
    13. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    14. Ruslan Druzin, 2013. "About Possibility Of Usage Methodological Approaches To Bankruptcy Prediction," Studies and Scientific Researches. Economics Edition, "Vasile Alecsandri" University of Bacau, Faculty of Economic Sciences, issue 18.
    15. Ketz, Je, 1978. "Effect Of General Price-Level Adjustments On The Predictive Ability Of Financial Ratios," Journal of Accounting Research, Wiley Blackwell, vol. 16, pages 273-284.
    16. Appetiti, Sandro, 1984. "Identifying unsound firms in Italy : An attempt to use trend variables," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 269-279, June.
    17. Goudie, A W & Meeks, G, 1991. "The Exchange Rate and Company Failure in a Macro-Micro Model of the UK Company Sector," Economic Journal, Royal Economic Society, vol. 101(406), pages 444-457, May.
    18. Bardos, Mireille, 1998. "Detecting the risk of company failure at the Banque de France," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1405-1419, October.
    19. Altman, Edward I & Loris, Bettina, 1976. "A Financial Early Warning System for Over-the-Counter Broker-Dealers," Journal of Finance, American Finance Association, vol. 31(4), pages 1201-1217, September.
    20. Sebastian Tomczak, 2014. "Comparative Analysis Of The Bankrupt Companies Of The Sector Of Animal Slaughtering And Processing," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(3), pages 59-86, September.
    21. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    22. Chun-Yu Ho & Patrick McCarthy & Yi Yang & Xuan Ye, 2013. "Bankruptcy in the pulp and paper industry: market’s reaction and prediction," Empirical Economics, Springer, vol. 45(3), pages 1205-1232, December.
    23. Magdalena Mosionek-Schweda, 2014. "The Use Of Discriminant Analysis To Predict The Bankruptcy Of Companies Listed On The Newconnect Market," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(3), pages 87-105, September.
    24. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    25. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    26. Libby, R, 1975. "Accounting Ratios And Prediction Of Failure - Some Behavioral Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 13(1), pages 150-161.
    27. Altman, Edward I. & Margaine, Michel & Schlosser, Michel & Vernimmen, Pierre, 1974. "Financial and Statistical Analysis for Commercial Loan Evaluation: A French Experience," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(2), pages 195-211, March.
    28. Przemysław Dominiak & Mariusz Mazurkiewicz, 2011. "Analysis of the risk of company's bankruptcy in Polish food and beverage production sector using the Cox regression," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 21(1), pages 19-31.
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    1. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    2. Sebastian Klaudiusz Tomczak, 2023. "General bankruptcy prediction models for the Visegrád Group. The stability over time," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(4), pages 171-187.
    3. Andrzej Geise & Magdalena Kuczmarska & Jarosław Pawlowski, 2021. "Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 99-116.
    4. Sebastian Klaudiusz Tomczak & Piotr Staszkiewicz, 2020. "Cross-Country Application of Manufacturing Failure Models," JRFM, MDPI, vol. 13(2), pages 1-10, February.
    5. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    6. Sebastian Klaudiusz Tomczak, 2020. "Multi-class Models for Assessing the Financial Condition of Manufacturing Enterprises," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 14(2), June.
    7. Sebastian Klaudiusz Tomczak, 2021. "Ratio Selection between Six Sectors in the Visegrad Group Using Parametric and Nonparametric ANOVA," Energies, MDPI, vol. 14(21), pages 1-20, November.

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