Application of the Altman Z’’ Score Model in Forecasting the Financial Position of BIST Companies
https://doi.org/10.26794/2587-5671-2023-27-2-192-202
Abstract
For measuring financial performances of companies and identifying financial failure, there are a lot of models in the literature. Among these models, Z Score model is of the most used in terms of its being an accounting-based model and simple applicability. The purpose of this paper is found out whether the Z” Score model, which was revised by Altman, could be useful in making financial decisions about long-term firm value. For this purpose, panel cointegration analyzes were carried out among the variables, with the firm values of the publicly traded companies listed on the Turkish BIST (Istanbul Stock Exchange) as the independent variable and the Z” Score values as the dependent variable. Although the research is specific to Turkey, the results of the research are considered to be applicable globally, as Altman states that the Z” Score model can also be used by developing country companies. It has been proven that Altman Z” Score Model, applied in public company, has a high prediction power directed to financial success of the firms. According to the results of the analysis, 1 unit increase in the Z” Score values of the companies cause an increase of 0.353 units in the logarithmic return calculated over the firm value. Z” Score Model can be a precious indicator for heads of companies, accounting and financial managers, auditors, creditors, investors to make accurate decisions directed to assessing financial structures of companies in advance.
About the Authors
İ. E. Göktürk
Necmettin Erbakan University
Turkey
Competing Interests:
Turkey
İbrahim Emre Göktürk - Assist. Prof., Faculty of Health Science, Department of Social Service
Konya
Competing Interests:
The authors have no conflicts of interest to declare
H. S. Yalçinkaya
Necmettin Erbakan University
Turkey
Competing Interests:
Turkey
Hüseyin Serdar Yalçınkaya - Assist. Prof., Department of Accounting and Tax, Eregli Vocational School
Konya
Competing Interests:
The authors have no conflicts of interest to declare
References
1. Ahn B. S., Cho S. S., Kim C. Y. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications. 2000;18(2):65–74. DOI: 10.1016/S 0957–4174(99)00053–6
2. Moyer R. C. Forecasting financial failure: A re-examination. Financial Management. 1977;6(1):11–17. DOI: 10.2307/3665489
3. Petty J. W., Keown J. A., Scott D. F., Martin J. D. Basic financial management. 6th ed. Englewood Cliffs, NJ: Prentice-Hall; 1993. 684 p.
4. Brigham E. F., Gapenski L. C. Financial management theory and practice. 8th ed. Chicago, IL: The Dryden Press; 1997. 875 p.
5. Hunter J, Isachenkova N. Failure risk: A comparative study of UK and Russian firms. Journal of Policy Modeling. 2001;23(5):511–521. DOI: 10.1016/S 0161–8938(01)00064–3
6. Poston K. M., Harmon K., Gramlich J. D. A test of financial ratios as predictors of turnaround versus failure among financially distressed firms. Journal of Applied Business Research. 1994;10(1):41–56. DOI: 10.19030/jabr.v10i1.5962
7. McKee T. E. Rough sets bankruptcy prediction models versus auditor signalling rates. Journal of Forecasting. 2003;22(8):569–586. DOI: 10.1002/for.875
8. Meeks G., Meeks J. G. Self-fulfilling prophecies of failure: The endogenous balance sheets of distressed companies. Abacus. 2009;45(1):22–43. DOI: 10.1111/j.1467–6281.2009.00276.x
9. Chiarello T. C., Pletsch C. S., Da Silva A., Da Silva T. P. Financial performance, intangible assets and value creation in brazilian and chilean information technology companies. Revista Galega de Economia = Economic Review of Galicia. 2014;23(4):73–88. DOI: 10.15304/rge.23.4.2787
10. Lifschutz S., Jacobi A. Predicting bankruptcy: Evidence from Israel. International Journal of Business and Management. 2010;5(4):133–141. DOI: 10.5539/ijbm.v5n4p133
11. Andrade G., Kaplan S. N. How costly is financial (not economic) distress? Evidence from highly leveraged transactions that became distressed. The Journal of Finance. 1998;53(5):1443–1493. DOI: 10.1111/0022–1082.00062
12. Outecheva N. Corporate financial distress: An empirical analysis of distress risk. Doctor oeconomiae dissertation. St. Gallen: University of St. Gallen; 2007. 200 p. URL: https://www.e-helvetica.nb.admin.ch/api/download/urn%3Anbn%3Ach%3Abel‑130003%3Adis3430.pdf/dis3430.pdf
13. Schall L. D., Haley C. W. Introduction to financial management. 3rd ed. New York, NY: McGraw-Hill Book Company; 1983. 912 p.
14. Moyer R. C., McGuigan J.R., Kretlow W. J. Contemporary financial management. 4th ed. St. Paul, MN: West Publishing Company; 1992. 862 p.
15. Altman E. I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance. 1968;23(4):589–609. DOI: 10.1111/j.1540–6261.1968.tb00843.x
16. Altman E. I. Corporate financial distress: A complete guide to predicting, avoiding, and dealing with bankruptcy. New York, NY: John Wiley & Sons, Inc;. 1983. 368 p. (Frontiers in Finance Series).
17. Altman E. I. Corporate financial distress and bankruptcy. 2nd ed. New York, NY: John Wiley & Sons, Inc.; 1993. 384 p.
18. Zmijewski M. E. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research. 1984;22:59–82. DOI: 10.2307/2490859
19. Springate G. L. Predicting the possibility of failure in a Canadian firm. Unpublished doctoral dissrtation. Burnaby, BC: Simon Fraser University; 1978.
20. Ohlson J. A. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 1980;18(1):109–131. DOI: 10.2307/2490395
21. Fulmer J. G., Moon J. E., Gavin T. A., Erwin M. J. A bankruptcy classification model for small firms. Journal of Commercial Bank Lending. 1984;66(11):25–37.
22. Calandro J. Considering the utility of Altman’s Z"-score as a strategic assessment and performance management tool. Strategy & Leadership. 2007;35(5):37–43. DOI: 10.1108/ 10878570710819206
23. Hayes S. K., Hodge K. A., Hughes L. W. A study on the efficiency of Altman’s Z to predict of specialty retail firms doing business in contemporary times. Economics and Business Journal: Inquiries & Perspectives. 2010;3(1):122–134.
24. Beaver W. H. Financial ratios as predictors of failure. Journal of Accounting Research. 1966;4:71–111. DOI: 10.2307/2490171
25. Grice J. S., Ingram R. W. Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research. 2001;54(1):53–61. DOI: 10.1016/S 0148–2963(00)00126–0
26. Platt H. D., Platt M. B. Understanding differences between financial distress and bankruptcy. Review of Applied Economics. 2006;2(2):141–157.
27. Bemmann M. Improving the comparability of insolvency predictions. Dresden Discussion Paper Series in Economics. 2005;(8). DOI: 10.2139/ssrn.731644
28. Dichev I. D. Is the risk of bankruptcy a systematic risk? The Journal of Finance. 1998;53(3):1131–1147. DOI: 10.1111/0022–1082.00046
29. Francis J. Corporate compliance with debt covenants. Journal of Accounting Research. 1990;28(2):326–347. DOI: 10.2307/2491153
30. Miller W. Comparing models of corporate bankruptcy prediction: Distance to default vs. Z-score. SSRN Electronic Journal. 2009. DOI: 10.2139/ssrn.1461704
31. Takahashi M., Taques F., Basso L. Altman’s bankruptcy prediction model: Test on a wide out of business private companies sample. iBusiness. 2018;10(1):21–39. DOI: 10.4236/ib.2018.101002
32. Charalambous C., Charuitou A., Kaourou F. Comparative analysis of artificial neural network models: Application in bankruptcy prediction. Annals of Operations Research. 2000;99(1):403–425. DOI: 10.1023/A:1019292321322
33. Hanson R. O. A study of Altman’s revised four-variable Z"-score bankruptcy prediction model as it applies to the service industry. Doctor of business administration dissertation. Fort Lauderdale, FL: Nova Southeastern University; 2003. 153 p. URL: https://www.proquest.com/openview/d787fc069fb7c0c4b93122e3faba6174/1?pq-origsite=gscholar&cbl=18750&diss=y
34. Tamari M. Financial ratios as a means of forecasting bankruptcy. Management International Review. 1966;6(4):15–21.
35. Ramaratnam M. S., Jayaraman R. A study on measuring the financial soundness of select firms with special reference to Indian steel industry — An empirical view with Z score. Asian Journal of Management Research. 2010;1(1):724–735.
36. Ijaz M. S., Hunjra A. I., Hameed Z., Maqbool A., Azam R. Assessing the financial failure using Z-score and current ratio: A case of sugar sector listed companies of Karachi stock exchange. World Applied Sciences Journal. 2013;23(6):863–870. DOI: 10.5829/idosi.wasj.2013.23.06. 2243
37. Tyagi V. A study to measures the financial health of selected firms with special reference to Indian logistic industry: An application of Altman’s Z score. Industrial Engineering Letters. 2014;4(4):43–52. URL: https://iiste.org/Journals/index.php/IEL/article/view/12246/12599
38. Sansesh C. The analytical study of Altman Z score on NIFTY 50 companies. International Journal of Management & Social Sciences. 2016;3(3):433–438. DOI: 10.21013/jmss.v3.n3.p6
39. Shariq M. Bankruptcy prediction by using the Altman Z-score model in Oman: A case study of Raysut Cement Company SAOG and its subsidiaries. Australasian Accounting, Business and Finance Journal. 2016;10(4):70–80. DOI: 10.14453/aabfj.v10i4.6
40. Ko Y.-C., Fujita H., Li T. An evidential analysis of Alman’s Z-score for financial prediction: Case study on solar energy companies. Applied Soft Computing. 2017;52:748–759. DOI: 10.1016/j.asoc.2016.09.050
41. Milašinović M., Knežević S., Mitrović A. Bankruptcy forecasting of hotel companies in the Republic of Serbia using Altman’s Z-score model. Hotel and Tourism Management. 2019;7(2):87–95. DOI: 10.5937/menhottur1902087M
42. Muzır E., Çağlar N. The accuracy of financial distress prediction models in Turkey: A comparative investigation with simple model proposals. Anadolu Üniversitesi Sosyal Bilimler Dergisi = Anadolu University Journal of Social Sciences. 2009;9(2):15–48. URL: https://core.ac.uk/download/pdf/6827016.pdf
43. Yılmaz H., Yıldıran M. Borsada işlem gören işletmelerde mali başarısızlık tahmini: Altman modeli’nin BIST uygulaması. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi = Journal of Aksaray University Faculty of Economics and Administrative Sciences. 2015;7(3):43–49.
44. Özdemir F. S. Turkish uniform accounting system and applicability of Altman Z score models in the context of public and private companies. Ege Akademik Bakış = Ege Academic Review. 2014;14(1):147–161. (In Turk.).
45. Altman E. I. Predicting financial distress of companies: Revisiting the Z-score and ZETA models. 2000. URL: https://pages.stern.nyu.edu/~ealtman/Zscores.pdf
46. Yıldız A. Corporate ratings based on the corporate governance index and Altman z score applying with the logistic regression method. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi = Suleyman Demirel University. The Journal of Faculty of Economics and Administrative Sciences. 2014;19(3):71–89. (In Turk.).
47. Breusch T. S., Pagan A. R. The Lagrange multiplier test and its applications to model specification tests in econometrics. The Review of Economic Studies. 1980;47(1):239–253. DOI: 10.2307/2297111
48. Pesaran M. H. General diagnostic tests for cross section dependence in panels. Empirical Economics. 2021;60(1):13–50. DOI: 10.1007/s00181–020–01875–7
49. Altıntaş H., Mercan M. The relationship between research and development (R&D) expenditures: Panel cointegration analysis under cross sectional dependency on OECD countries. Ankara Üniversitesi SBF Dergisi = Ankara University SBF Journal. 2015;70(2):345–376. (In Turk.).
50. Pesaran M. H., Ullah A., Yamagata T. A bias-adjusted LM test of error cross-section independence. The Econometrics Journal. 2008;11(1):105–127. DOI: 10.1111/j.1368–423X.2007.00227.x
51. Tatoğlu Yerdelen F. Panel Zaman Serileri Analizi Stata Uygulamalı. 3rd ed. İstanbul: Beta Yayıncılık; 2017. 359 p.
52. Kar M., Ağır H., Türkmen S. Econometric estimation of the relationship between electricity consumption and economic growth in developing countries. In: Yardımcıoğlu F., Beşel F., Inan V., eds. Economic studies. Pesa Publications. 2018;2:305–321. (In Turk.).
53. Hsiao C. Analysis of panel data. New York, NY: Cambridge University Press; 2015. 562 p. (Econometric Society Monographs. No. 54).
54. Turgut E., Uçan O. Investigation of the relationship between corruption and tax rate in the sample of OECD countries. Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi = Niğde Ömer Halisdemir University. The Journal of Graduate School of Social Scienc es. 2019;1(3):1–17. (In Turk.).
55. Taylor M. P., Sarno L. The behavior of real exchange rates during the post-Bretton Woods period. Journal of International Economics. 1998;46(2):281–312. DOI: 10.1016/S 0022–1996(97)00054–8
56. Breuer J. B., McNown R., Wallace M. Series-specific unit root tests with panel data. Oxford Bulletin of Economics and Statistics. 2002;64(5):527–546. DOI: 10.1111/1468–0084.00276
57. Pesaran M. H. A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics. 2007;22(2):265–312. DOI: 10.1002/jae.951
58. Küçükaksoy İ., Akalın G. Testing of the Fisher hypothesis with a dynamic panel data analysis: An application on OECD countries. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi = Hacettepe University Journal of Economics and Administrative Sciences. 2017;35(1):19–40. (In Turk.). DOI: 10.17065/huniibf.303303
59. Çetin M., Doğan İ., Işık H. The impact of energy consumption on environmental pollution: A panel data analysis. International Anatolia Academic Online Journal. 2014;2(1):15–29. (In Turk.).
60. Kao C., Chiang M.-H. On the estimation and inference of a cointegrated regression in panel data. In: Baltagi B. H., Fomby T. B., Carter Hill R., eds. Nonstationary panels, panel cointegration, and dynamic panels. Bingley: Emerald Group Publishing Limited; 2001:179–222. (Advances in Econometrics. Vol. 15). DOI: 10.1016/S 0731–9053(00)15007–8
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For citations:
Göktürk İ.E., Yalçinkaya H.S. Application of the Altman Z’’ Score Model in Forecasting the Financial Position of BIST Companies. Finance: Theory and Practice. 2023;27(2):192-202. https://doi.org/10.26794/2587-5671-2023-27-2-192-202
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