Predicting Financial Failure Using The Bankometer S-Score Model. A Study of A Sample of Iraqi Private Banks

Authors

  • Amina Abdelah Halbos Middle Technical University AL- Rusafa Management Institute, Baghdad, Iraq
  • Rami Abaas Hameed Middle Technical University AL- Rusafa Management Institute, Baghdad, Iraq
  • Mokhaled Fouad Shujaa Middle Technical University AL- Rusafa Management Institute, Baghdad, Iraq

Abstract the research aims to verify the suitability of the Bankometer S-Score model for predicting financial failure in Iraqi financial institutions. A number of private Iraqi banks that were placed under the tutelage of the Central Bank of Iraq were chosen to be the research sample, and the reason for selecting this sample was to verify the accuracy of the Bankometer S-Score model in predicting financial failure. As for the temporal limits of the research, it was between 2017 and 2018, a period that reflects the continued tutelage of the Central Bank of Iraq over these banks. The research concluded that the Bankometer model can be adopted to predict financial failure in Iraqi financial institutions because the S-Score of the research sample banks was within the standard limit of S < 50, which indicates the state of financial hardship and is identical to the financial problems experienced by the banks of the research sample that were the reason for placing them under Central bank tutelage. Here, the research hypothesis can be accepted.

Keywords:

financial failure, prediction of financial failure, Bankometer S-Score model, guardianship

[1] Chieng, J. (2013). Verifying the Validity of Altman’s Z” Score as a Predictor of Bank Failures in the Case of the Eurozone (Doctoral Dissertation, Dublin, National College of Ireland).

[2] Ghosn, F. (2019). Testing Z-Score Model on Lebanese Listed Banks. Research Journal of Finance and Accounting, 10 (12), 78-85.

[3] Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance September: 589-609.

[4] Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395

[5] Lennox, C. (1999). Identifying failing companies: A reevaluation of the logit, probit and DA approaches. Journal of Economics and Business 51: 347-364.

[6] Barniv, R., & J. B. McDonald. (1999). Review of categorical models for classification issues in accounting and finance. Review of Quantitative Finance and Accounting. . Journal of Finance September: 13: 39-62.

[7] Shirata, C. Y., Takeuchi, H., Ogino, S., & Watanabe, H. (2011). Extracting key phrases as predictors of corporate bankruptcy: Empirical analysis of annual reports by text mining. Journal of Emerging Technologies in Accounting, 8(1), 31–44. https://doi.org/10.2308/jeta-10182 .

[8] Cheng, K. F., Chu, C. K., & Hwang, R. C. (2010). Predicting bankruptcy using the discrete-time semiparametric hazard model. Quantitative Finance, 10(9), 1055–1066. https://doi.org/10.1080/14697680902814274.

[9] Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2013). Partial Least Square Discriminant Analysis for bankruptcy prediction. Decision Support Systems, 54(3), 1245-1255. doi:10.1016/j.dss.2012.11.015

[10] Elam, R. (1975). The Effect of Lease Data on the Predictive Ability of Financial Ratios. Accounting Review, 50(1), 25–43. Retrieved from http://libaccess.mcmaster.ca.libaccess.lib.mcmaster.ca/login.

[11] Gentry, J. A., Newbold, P., & Whitford, D. T. (1985). Predicting Bankruptcy: If Cash Flow’s Not the Bottom Line, What Is? Financial Analysts Journal, 41(5), 47–56. https://doi.org/10.2469/faj.v41.n5.47

[12] Arulvel, K. K., & Balaputhiran, S. (2013). Financial position of banking sector: A comparative study between state and private sector banks in Sri Lanka. ACADEMICIA: An International Multidisciplinary Research Journal. 3(2), 212-221.

[13] Anita Erari, A. E. (2013). Financial Performance Analysis of PT. Bank Papua: Application of Cael, Z-Score and Bankometer. IOSR Journal of Business and Management, 7(5), 8–16. https://doi.org/10.9790/487x-0750816

[14] Park, S. S., & Hancer, M. (2012). A comparative study of logit and artificial neural networks in predicting bankruptcy in the hospitality industry. Tourism Economics, 18(2), 311–338. https://doi.org/10.5367/te.2012.0113

[15] eaver, W. H. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71. https://doi.org/10.2307/2490171

[16] Olsen, M., Bellas, C., & Kish, L. V. (1983). Improving the prediction of restaurant failure through ratio analysis. International Journal of Hospitality Management, 2(4), 187–193. https://doi.org/10.1016/0278-4319(83)90019-1

[17] Cho, M. (1994). Prediction business failure in the hospitality industry. Unpublished Doctoral Dissertation. Virginia Polytechnic Institute and State University, Blacksburg, Virginia.

[18] Ross, A.S., Westerfield, W. R., & Jafee, J. (2002). Corporate Finance. McGraw Hill Companies, USA.Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131

[19] Janssen, R. (2011). The Altman (1968) model Z-score model: Is it useful for more recent time periods and all industries? Unpublished Master Dissertation, Tilburg School of Economics and Management, Tilburg.

[20] Fahlenbrach, R. et al. (2012), “This time is the same: using banking performance in 1998 to explain bank performance during the recent financial crisis”, Journal of Finance, Vol. 67, pp. 2139-2185.

[21] Beltratti and Stulz (2012) connected the impact of governance and regulation to the credit crisis and found that banks with better governance and more fragile financing had a worse performance during the crisis.

[22] Fahlenbrach, R. et al. (2012), “This time is the same: using banking performance in 1998 to explain bank performance during the recent financial crisis”, Journal of Finance, Vol. 67, pp. 2139-2185

[23] Aebi Lastly,. (2012), addressing an issue related to the corporate governance structure, found that when the Chief Risk Officer reports directly to the board of directors, banks have a better performance during credit crisis.

[24] Graham and Horner (1988) refer the influence of an adequate management and, following them>

[25] Thomson, B. (1991), “Predicting Bank Failures in the 1980s”, Economic Review, Federal Reserve Bank of Cleveland, Vol. 27, Nº1, pp. 9-20.

[26] Vilén, M. (2010), “Predicting Failures of Large U.S. Commercial Banks”, Master’s Thesis, Aalto University School of Economics.

[27] Poghosyan, T. and M. Cihák (2009), “Distress in European Banks: An Analysis Based on a New Dataset”, IMF Working Papers.

[28] Winny, H and Luciana, S.A. (2005). Analisis Rasio Camel terhadap Prediksi Kondisi Bermasalah pada Lembaga Perbankan Perioda 2000-2002. Jurnal Akuntasi and Keuangan, 7(2),131-147.

[29] Shar, Amir Hussain, Muneer Ali Shah and Hajan Jamali, (2010). Performance Evaluation of Banking Sector in Pakistan: An Application of Bankometer. International Journal of Business and Management. 5(9), 81-86

[30] Parveen Chauhan, Sushil Kumar (2019). Analysis of Scheduled Commercial banks: Bankometer Model . International Journal of 360 Management Review, Vol. 07, Issue 01.

[31] Levratto, N. (2013). From Failure to Corporate Bankruptcy: A Review. Journal of Innovation and Entrepreneurship, 2 (1), 20. Available: https://doi.org/10.1186/2192-5372-2-20

[32] Mohammed, A. A. E. and Kim-Soon, N. (2012). Using Altman’s Model and Current Ratio to Assess the Financial Status of Companies Quoted in the Malaysian Stock Exchange. International Journal of Scientific and Research Publications, 2 (7), 1-11

Predicting Financial Failure Using The Bankometer S-Score Model. A Study of A Sample of Iraqi Private Banks. (2023). Journal Port Science Research, 6(3), 184-193. https://doi.org/10.36371/port.2023.3.2

License

Copyright (c) 2023 Journal Port Science Research

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite

Predicting Financial Failure Using The Bankometer S-Score Model. A Study of A Sample of Iraqi Private Banks. (2023). Journal Port Science Research, 6(3), 184-193. https://doi.org/10.36371/port.2023.3.2