Towards Valuation Multidimensional Business Failure Risk for the Companies Listed on the Bucharest Stock Exchange

Conference paper
Part of the Eurasian Studies in Business and Economics book series (EBES, volume 3/2)

Abstract

Current research aims at developing a comprehensive financial instrument towards valuation business failure risk for a sample of 69 companies listed on the Bucharest Stock Exchange in 2013. There were considered several financial ratios such as liquidity ratios (e.g., current ratio, quick ratio, cash ratio), indebtedness ratios (e.g., general indebtedness ratio, financial stability ratio, global financial autonomy ratio, financial independence ratio, borrowing capacity ratio, long-term financial autonomy, leverage ratio, debt service coverage ratio), as well as solvency ratios (e.g., global solvency ratio and patrimonial solvency ratio). By taking into consideration the large number of selected ratios, we employed the principal component analysis as multidimensional analysis technique which ensures the non-redundant decomposition of the total variability out of the initial causal space through a lower number of components. Thereby, there were retained five principal components (being underlined liquidity, financial autonomy, financial independence, debt service coverage ratio, and solvency) which cumulate 90.5895 % of the initial information. Subsequently, based on the selected principal components we reported the aggregate business failure risk indicator.

Keywords

Business failure risk Principal component analysis Correlation matrix Eigenvectors Eigenvalues 

Notes

Acknowledgement

This work was cofinanced from the European Social Fund through Sectoral Operational Programme Human Resources Development 2007–2013, project number POSDRU/159/1.5/S/134197 “Performance and excellence in doctoral and postdoctoral research in Romanian economics science domain”.

References

  1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.CrossRefGoogle Scholar
  2. Bataille, E., Bruneau, C., & Michaud, F. (2007). Business cycle and corporate failure in France: Is there a link? Computational Economics, 29(2), 173–197.CrossRefGoogle Scholar
  3. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.CrossRefGoogle Scholar
  4. Boyacioglu, M. A., Kara, Y., & Baykan, O. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355–3366.CrossRefGoogle Scholar
  5. Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research, 166(2), 528–546.CrossRefGoogle Scholar
  6. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276.CrossRefGoogle Scholar
  7. Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167–179.CrossRefGoogle Scholar
  8. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). San Francisco: Morgan Kaufmann.Google Scholar
  9. Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT Press.Google Scholar
  10. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.CrossRefGoogle Scholar
  11. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441.CrossRefGoogle Scholar
  12. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3/4), 321–377.CrossRefGoogle Scholar
  13. Hu, Y.-C., & Ansell, J. (2009). Retail default prediction by using sequential minimal optimization technique. Journal of Forecasting, 28(8), 651–666.CrossRefGoogle Scholar
  14. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.Google Scholar
  15. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.CrossRefGoogle Scholar
  16. Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques—A review. European Journal of Operational Research, 180(1), 1–28.CrossRefGoogle Scholar
  17. Li, H., & Sun, J. (2011). Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. Expert Systems with Applications, 38(5), 6244–6253.CrossRefGoogle Scholar
  18. Libby, R. (1975). Accounting ratios and the prediction of failure: Some behavioral evidence. Journal of Accounting Research, 13(1), 150–161.CrossRefGoogle Scholar
  19. McGurr, P., & DeVaney, S. (1998). Predicting business failure of retail firms: An analysis using mixed industry models. Journal of Business Research, 43(3), 169–176.CrossRefGoogle Scholar
  20. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11), 559–572.CrossRefGoogle Scholar
  21. Tsai, C.-F. (2009). Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2), 120–127.CrossRefGoogle Scholar
  22. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco: Morgan Kaufmann.Google Scholar
  23. Zeytinoglu, E., & Akarim, Y. D. (2013). Financial failure prediction using financial ratios: An empirical application on Istanbul Stock Exchange. Journal of Applied Finance & Banking, 3(3), 107–116.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ştefan Cristian Gherghina
    • 1
  • Georgeta Vintilă
    • 1
  1. 1.Department of FinanceBucharest University of Economic StudiesBucharestRomania

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