Increase the income based on partner selection to reduce bankruptcy risk by mathematical model and solve it by genetic algorithm

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, South Tehran Branch. Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

One of the goals of financial institutions is to strengthen the economic infrastructure in developing the financial sphere. In this regard, financial institutions should take the necessary planning to increase their incomes, and if they do not pay attention, the consequences can be predicted for this group of economic activists Increasing income and reducing the risk of bankruptcy are among the most important goals for financial institutions and enterprises. Therefore, considering the increase of income and the integration approach based on the selection of partners in the field of banking, this paper presents a mathematical model based on reducing the risk of bankruptcy. The multi-objective genetic algorithm method has been used to solve and optimize the model. The proposed method was implemented on real data related to ten Iranian banks and the results led to the formation of a financial firm with a combination of banks to maximize the income and minimize the bankruptcy risk.

Keywords

[1] A. Ahmadian, Design of rapid warning system for predicting bankruptcy exposure time, Quart. J. Appl. Econom. Theor. 4 (2015) 119–144.
[2] J. Alves and R. Meneses, Partner selection in coopetition: a three step model, J. Res. Market. Entrepreneurship 17(1) (2015) 23–35.
[3] R. Ayadi and R.H. Schmidt, Investigating Diversity in the Banking Sector in Europe: The Performance and Role of Savings Banks, CEPS, 2009.
[4] M. Azarbad, M. Ekhtiari, A. Sarfaraz and F. Abdi, A framework to select commercial bank partner using fuzzy BSC-DEA method, Manag. Sci. Lett. 1(4) (2011) 467–480.
[5] L. Becchetti, R. Ciciretti and A. Paolantoni, The participation bank difference before and after the global financial crisis, J. Int. Money Finance 69 (2016) 224–246.
[6] F. Betz, S. Oprica, T.A. Peltonen and P. Sarlin, Predicting Insolvency in European Banks, European Central Bank, 2013.
[7] P.E. Bierly and S. Gallagher. Explaining alliance partner selection: Fit, trust and strategic expediency, Long Range Plan. 40(2) (2007) 134–153.
[8] R.D. Brunner, Context-sensitive monitoring and evaluation for the World Bank, Policy Sci. 37(2) (2004) 103–136.
[9] L. Chiaramonte, E. Croci and F. Poli, Should we trust the Zscore? Evidence from the European Banking Industry, Global Finance J. 28 (2015) 111—131.
[10] S. Clerides, M.D. Delis and S. Kokas, A new Data Set on Competition in NationalBanking Markets”, Financ. Market. Institut. Instrum. 24(2-3) (2015) 267–311.
[11] J.L. Cummings and S.R. Holmberg, Best-fit alliance partners: the use of critical success factors in a comprehensive partner selection process, Long Range Plan. 45(2-3) (2012) 136–159.
[12] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evolution. Comput. 6(2) (2002) 182–197.
[13] A. Divandari. The Need to Reform the Banking System, Quart. J. Econom. News 149 (2016) 23–24.
[14] M. Farokhiani, Investigating the Relationship Between Equity Structure and Bank Risk Indicators, Iranian Institute of Banking. 2007.
[15] J.M. Geringer and L. Hebert, Measuring performance of international joint ventures. J. Int. Business Stud. 22(2) (1991) 249–263.
[16] P. Gogas, T. Papadimitriou and A. Agrapetidou, Forecastingbankfailures and stress testing: A machine learning approach, Int. J. Forecast. 34(3) (2018) 440-–455.
[17] I. Hatak and K. Hyslop, Cooperation between family businesses of different size: A case study, J. Cooperati. Organ. Manag. 3(2) (2015) 52–59.
[18] H. Hesse and M. Cih´ak, ˇ Cooperative banks and financial stability, IMF Working Papers 2007(2) (2007).
[19] R. Iyer and M. Puri, Understing Bank Runs: The Importance of Depositor-Bank Relationships & Networks, Cambridge, 2008.
[20] Xiaofei. Li, C. Escalante and J. Epperson, Agricultural banking and bank failures of the Late 2000s financial crisis: A Survival analysis using Cox proportional Hazard Model, Southern Agricultural Economics Association (SAEA) Annual Meeting, Dallas, Texas, 1-4 February 2014.
[21] Z . Lin and L. Wang, Multi-stage partner selection based on genetic-ant colony algorithm in agile supply chain network, IEEE Young Computer Scientists, The 9th Int. Conf. 2008.
[22] M. Moradi, Designing A Profit Quality Model in Tehran Stock Exchange with Emphasis on The Role of Accruals, Account. Audit. Res. 25 (2015) 76–99.
[23] M. Morshedzadeh, Development of A Model for Calculating Corporate Income Tax Inflationary Economy of Iran, PhD Thesis, University of Tehran, 2019.
[24] B.J, Nalebuff, A. Brandenburger and A. Maulana, Co-opetition, Harper Collins Business London, 1996.
[25] M.R. Nikbakht and M. Ghorbani, Identify the indicators of banks at bankruptcy risk based on the Theme method, Scientif. Quart. Experiment. Financ. Account. Stud. 66 (2020) 51–86.
[26] A. C. Okezie, Capital ratio as predictors of insolvency: A Case study of The Nigerian banking system. Global Journal of Human Social Science, (2011). 3-11.
[27] C. Prakash and M. Barua. A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry, Sustain. Product. Consump. 7 (2016) 66–78.
[28] M. Rezvanian, The effect of financing structure on the risk of bank insolvency, in Banking. Iranian Institute of Banking Education. 2013.
[29] G. Rihab, Ownership structure, deposits structure, income structure and insolvency risk in GCC Islamic banks, J. Islamic Account. Business Res. 7(2) (2016) 73–100.
[30] K. Ruihao, Predicting Financial Insolvency in Debt Contracting, University of California, Los Angeles, 2012.
[31] Ch. Seungho, K. Yong, P. Junho and Sh. Hojong, Bank partnership and liquidity crisis, J. Bank. Finance 120 (2020) 70–81.
[32] M. Sergio, P. Cortez and P. Rita, Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation, Expert Syst. Appl. 42 ( 2015) 42–52.
[33] S. Sheri and M.M. Naderi, Determining the relationship between macro-economic factors and bank credit risk,
Account. Audit. Res. 4(16) (2012) 102–119.
[34] V. Sundararajan and L. Errico, Islamic financial institutions and products in the global financial system: key issues in risk management and challenges ahead, Int. Monet. Fund. 2 (2002) 1–27.
[35] H. Turan, The weighting of factors affecting credit risk in banking, Procedia Econom. Finance 38 (2016) 49–53.
[36] T. Yong and J. Anchor, Does competition only impact on insolvency risk? New evidence from the Chinese banking industry, Int. J. Manager. Finance 13(3) (2017) 1–36.
[37] M. Zineldin, Co-opetition: the organization of the future, Market. Intell. Plan. 22(7) (2004) 780–790.
Volume 13, Issue 1
March 2022
Pages 2549-2560
  • Receive Date: 05 September 2021
  • Revise Date: 06 October 2021
  • Accept Date: 21 October 2021