Vol. 5 No. 4 (2017): BUSINESS & MANAGEMENT STUDIES: AN INTERNATIONAL JOURNAL
Articles

ESTIMATION OF BANKRUPTCY PROBABILITIES BY USING FUZZY LOGIC AND MERTON MODEL: AN APPLICATION ON USA COMPANIES

Çiğdem ÖZARİ
İstanbul Aydın University
Bio

Published 2018-01-07

How to Cite

ÖZARİ, Çiğdem. (2018). ESTIMATION OF BANKRUPTCY PROBABILITIES BY USING FUZZY LOGIC AND MERTON MODEL: AN APPLICATION ON USA COMPANIES. Business & Management Studies: An International Journal, 5(4), 211–234. https://doi.org/10.15295/bmij.v5i4.168

Abstract

In this study, we have worked on developing a brand-new index called Fuzzy-bankruptcy index. The aim of this index is to find out the default probability of any company X, independent from the sector it belongs. Fuzzy logic is used to state the financial ratiointerruption change related with time and inside different sectors, the new index is created to eliminate the number of the relativity of financial ratios. The four input variables inside the five main input variables used for the fuzzy process, are chosen from both factor analysis and clustering and the last input variable calculated from Merton Model. As we analyze in the past cases of the default history of companies, one could explore different reasons such as managerial arrogance, fraud and managerial mistakes, that are responsible for the very poor endings of prestigious companies like Enron, K-Mart. Because of these kind of situations, we try to design a model which one could be able to get a better view of a company’s financial position, and it couldbe prevent credit loan companies from investing in the wrong company and possibly from losing all investments using our Fuzzy-bankruptcy index.

Downloads

Download data is not yet available.

References

  1. Bandemer, H. and Gottwald, S. (1995). Fuzzy Sets, Fuzzy Logic, Fuzzy Methods with Applications, Wiley, New York, NY, USA.
  2. Benos A. and Papanastasopoulos G. (2005). Extending the Merton Model: A Hybrid Approach to Assessing Credit Quality. http://econwpa.repec.org/eps/fin/papers/0505/0505020.pdf
  3. Bharath S. T. and Shumway T. (2004). Forecasting Default with the KMV-Merton Model, Working Paper in University of Michigan.
  4. Black, F., and Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81, 637-659.
  5. Crouhy. M. Galai. D., Mark. R. (2000). A Comparative Analysis of Current Credit Risk Models. Journal of Banking and Finance.
  6. D'amico, G. (2008). A Convergence Result in the Estimation of Markov Chains with Application to Compound Options. Journal of Statistical Theory and Practice, 2(4), 693-705.
  7. Duffie D. and . Leandro A, Saita. Ke Wang. (2006). Multi-period Corporate Default Prediction with Stochastic Covariates, Journal of Financial Economics. 59-117.
  8. Duffie, D., & Singleton, K. J. (2012). Credit risk: pricing, measurement, and management. Princeton University Press.
  9. Chen, D. H., Chou, H. C., Wang, D., &Zaabar, R. (2009). the Predictive Performance of a Barrier Option Credit Risk Model in an Emerging Market. Working Paper.
  10. Groenen, P. J., Kaymak, U., & van Rosmalen, J. (2007). Fuzzy clustering with Minkowski distance functions. Fuzzy Clustering and its Applications, Wiley, 53-68.
  11. Haack, S. (1979). Do we need “fuzzy logic”?. International journal of man-machine studies, 11(4), 437-445.
  12. Hull J. C., Options (1997). Futures and Other Derivatives. Prentice Hall.
  13. Hull J., White A., Joseph L. Rotman. (2000). Valuing Credit Default Swaps I: No Counterparty Default Risk, April.
  14. Hull, J., Nelken, I., & White, A. (2004). Merton’s model, credit risk, and volatility skews. Journal of Credit Risk Volume, 1(1), 05.
  15. Miyake, M., & Inoue, H. (2009). A default probability estimation model: An application to Japanese companies. Journal of Uncertain Systems, 3(3), 210-220.
  16. Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.
  17. Lin, S., Ansell, J., &Andreeva, G. (2007). Merton models or credit scoring: modelling default of a small business. University of Edinburgh Management School
  18. Saretto, A. (2005). Predicting and pricing the probability of default.
  19. Shnaider, E., &Kandel, A. (1989). The use of fuzzy set theory for forecasting corporate tax revenues. Fuzzy Sets and Systems, 31(2), 187-204.
  20. Huschens, S., Vogl, K., &Wania, R. (2005). Estimation of default probabilities and default correlations. In Risk Management (pp. 239-258). Springer Berlin Heidelberg.
  21. Tudela, M., & Young, G. (2005). A Merton-model approach to assessing the default risk of UK public companies. International Journal of Theoretical and Applied Finance, 8(06), 737-761.
  22. Zadeh L.A. (1965). Fuzzy sets. Info. &Ctl, Vol. 8. 338-353.
  23. Zadeh L.A. (1968). Fuzzy Algorithms. Info. &Ctl, Vol. 12. 94-102.
  24. Zadeh, L. A. (1984). Making computers think like people [fuzzy set theory]. IEEE spectrum, 21(8), 26-32