Using Altman Z-Score Model in Comparing Firms’ Financial Performance Applied Research on Egyptian Stock Market

Authors

DOI:

https://doi.org/10.56830/WRBA11202105

Keywords:

Altman Z-score, Financial Distress, Prediction Models, Egyptian Stock Markets

Abstract

Financial performance has been of concern to management and other stakeholders since the 2008 financial crisis. The impact of financial distress and bankruptcy on firms cannot be taken for granted. Financial distress is detrimental to big organizations and the small organizations alike. This study was conducted with the purpose testing if Altman’s failure prediction model is good indicator in predicting financial distress of firm working in Egyptian stock market. The study took a sample of seven companies from firms working in Egyptian stock market during the period from 2016 to 2020. Data was extracted from secondary sources for a period of five years. Data extracted included working capital, total assets, retained earnings, market capitalization total liabilities and sales. The collected data was then analyzed using Microsoft excel software. The study established that the Altman’s Z-score model was good indicator for predicting financial distress of firm working in Egyptian stock market. The study recommends the adoption of Altman’s failure prediction model in predicting financial distress of firm working in Egyptian stock market by not only investors but also all other stakeholders. 

References

Al Zaabi, O. (2011). Potential for the Application of Emerging Market Z-Score in UAE Islamic Banks. International Journal of Islamic and Middle Eastern Finance and Management, 4(2), 158–173.

Alifiah, M. N. (2014). Prediction of Financial Distress Companies in the Trading and Services Sector in Malaysia using Macroeconomic Variables. ProcediaSocial and Behavioral Sciences, 129, 90–98.

Almamy, J., Aston, J., & Ngwa, L. N. (2015). 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, 36, 278–285.

Altameemi, A. F. (2021). The Relationship Between Financial Flexibility and Market Value Added: The Mediation Effect Role of the Corporate Size (A Practical Study on a Sample of Jordanian Industry Sector Firms). International

Journal of Economics and Finance, 13(1), 1–52. https://ideas.repec.org/a/ibn/ijefaa/v13y2021i1p52.html

Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589–609.

Altman, E. I. (2000). Predicting Financial Distress of Companies: Revisiting the Zscore and ZETA Models. In Handbook of Research Methods and Applications in Empirical Finance.

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model. Journal of International

Financial Management & Accounting, 28(2), 131–171. https://doi.org/10.1111/JIFM.12053

Assagaf, A., Murwaningsari, E., Gunawan, J., & Mayangsari, S. (2019). Estimates Model of Factors Affecting Financial Distress: Evidence from Indonesian State-owned Enterprises. Asian Journal of Economics, Business and Accounting, 1–19.

Calandro, J. (2007). Considering the Utility of Altman’s Z-score as a Strategic Assessment and Performance Management Tool. Strategy and Leadership, 35(5), 37–43.

Edward Altman. (2012). Enhancements to Assess Credit Risk in Global Environment--. Experience Stern. https://www.stern.nyu.edu/experiencestern/faculty-research/altman-launches-zscore-plus

Elviani, S., Simbolon, R., Riana, Z., Khairani, F., Dewi, S. P., & Fauzi, F. (2020). The Accuracy of the Altman, Ohlson, Springate and Zmejewski Models in Bankruptcy Predicting Trade Sector Companies in Indonesia. In Budapest

International Research and Critics Institute (BIRCI-Journal) (Vol. 3, pp. 334– 347).

Gunathilaka, C. (2014). Financial Distress Prediction: A Comparative Study of Solvency Test and Z-score Models with Reference to Sri Lanka. The IUP Journal of Financial Risk Management, 11(3), 39–51.

Habib, A., Bhuiyan, B. U., & Islam, A. (2013). Financial Distress, Earnings Management and Market Pricing of Accruals during the Global Financial Crisis. Managerial Finance.

Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2011). Predicting Corporate Financial Distress Based on Integration of Support Vector Machine and Logistic Regression. Expert Systems with Applications, 33(2), 434–440.

Imelda, E., & Alodia, I. (2017). The Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange. Indian-Pacific Journal of Accounting and Finance, 1(1), 51– 63.

Indriyanti, M. (2019). The Accuracy of Financial Distress Prediction Models: Empirical Study on the World’s 25 Biggest Tech Companies in 2015–2016 Forbes’s Version. KnE Social Sciences, 442–450.

Kashyap, S., & Bansal, R. (2019). Modeling Financial Distress Prediction of Indian Companies. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 1C2).

Khaliq, A., Altarturi, B. H. M., Thaker, H. M. T., Harun, M. Y., & Nahar, N. (2014). Identifying Financial Distress Firms: a Case Study of Malaysia’s Government Linked Companies (GLC). International Journal of Economics, Finance and Management, 3(3).

Khurshid, M. R. (2013). Determinants of Financial Distress Evidence from KSE 100 index. Business Review, 8(1), 7–19.

Kihooto, E., Omagwa, J., Wachira, M., & Emojong, R. (2016). Financial Distress in Commercial and Services Companies listed at Nairobi Securities Exchange, Kenya. European Journal of Business and Management, 8(27), 86–89.

Kordestani, G., Bakhtiari, M., & Biglari, V. (2011). Ability of Combinations of Cash Flow Components to Predict Financial Distress. Business: Theory and Practice, 12(3), 277–285.

Liao, Q., & Mehdian, S. (2016). Measuring Financial Distress and Predicting Corporate Bankruptcy: An index approach. Review of Economic and Business Studies, 9(1), 33–51.

Manaseer, S., & Al-Oshaibat, S. D. (2018). Validity of Altman Z-score Model to Predict Financial Failure: Evidence from Jordan. International Journal of Economics and Finance, 10(8).

Mohd Ali, M., & Mohd Nasir, N. (2018). Corporate Governance and Financial Distress: Malaysian Perspective. Asian Journal of Accounting Perspectives, 11(1), 108–128. https://doi.org/10.22452/AJAP.VOL11NO1.5

Mu-Yen Chen. (2014). A High-Order Fuzzy Time Series Forecasting Model for Internet Stock Trading. North-Holland, 37, 461–467.

Onyiri, S. (2014). Predicting Financial Distress using Altman’s Z-score and the Sustainable Growth Rate. In Northcentral University. ProQuest Dissertations Publishing.

Restianti, T., & Agustina, L. (2018). The Effect of Financial Ratios on Financial Distress Conditions in Sub Industrial Sector Company. Accounting Analysis Journal, 7(1), 25–33. https://doi.org/10.15294/AAJ.V7I1.18996

Richardson, S. (2006). Over-Investment of Free Cash Flow. Review of Accounting Studies, 11(2–3), 159–189. https://doi.org/10.1007/S11142-006-9012-1

Sandin, A. R., & Porporato, M. (2007). Corporate Bankruptcy Prediction Models Applied to Emerging Economies Evidence from Argentina in the years 19911998. International Journal of Commerce and Management, 17(4), 295–311.

Shahwan, T. M. (2015). The Effects of Corporate Governance on Financial Performance and Fnancial Distress: Evidence from Egypt. Corporate Governance, 15(5), 641–662. https://doi.org/10.1108/CG-11-2014-0140 Sulub, S. A. (2014). Testing the Predictive Power of Altman’s revised Z’ model: The Case of 10 multinational companies. Research Journal of Finance and Accounting, 5(21), 174–184.

Taffler, R. (1983). The Assessment of Company Solvency and Performance using a Statistical Model. Accounting and Business Research, 52. https://www.research.manchester.ac.uk/portal/en/publications/theassessment-of-company-solvency-and-performance-using-a-statisticalmodel(734e4dee-5f31-4f80-85df-7e8eaa64389c)/export.html

Taffler, R. J. (1982). Forecasting Company Failure in the UK Using Discriminant Analysis and Financial Ratio Data. Journal of the Royal Statistical Society. Series A (General), 145(3), 342. https://doi.org/10.2307/2981867

Tanjung, P. R. S. (2020). Comparative Analysis Of Altman Z-Score, Springate, Zmijewski And Ohlson Models In Predicting Financial Distress. EPRA International Journal of Multidisciplinary Research (IJMR, 126.

Ul Hassan, E., Zainuddin, Z., & Nordin, S. (2017). A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis. Indian-Pacific Journal of Accounting and Finance, 1(3), 13–23.

Vosoughi, M., Derakhshan, H., & Alipour, M. (2016). Investigating the Relationship Between Financial Distress and Investment Efficiency of Companies listed on the Tehran Stock Exchange. Accounting, 2(4), 167–176.

Waqas, H., & Md-Rus, R. (2018). Predicting financial distress: Applicability of Oscore and logit model for Pakistani firms. Business and Economic Horizons (BEH, 14(1232-2019–760), 389–401.

Zhang, Z., Xie, L., Lu, X., & Zhang, Z. (2014). Determinants of Financial Distress in U.S. Large Bank Holding Companies. SSRN Electronic Journal.

https://doi.org/10.2139/SSRN.2392892

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Published

2026-02-04