International Journal of Nonlinear Analysis and Applications

Analysis of the modeling of indicators affecting bank runs in the banking industry

Document Type : Research Paper

Authors

1 Department of Management, Shahid Ashrafi Esfahani University, Isfahan, Iran

2 Department of Management, University of Isfahan, Isfahan, Iran

3 Department of Marketing, Business Management Faculty, University of Tehran, Tehran, Iran

Abstract

This article aims to identify indicators affecting bank runs for their modeling in the banking industry (to predict, prevent, prepare, restrain, recover, improve, learn, and increase accuracy in bank run processes). For this purpose, the data extracted by the researchers from their previous research, which was collected with the theme analysis method, was used to test the 20 main indicators of this model using Interpretive Structural Modeling (ISM). Economic crises, microeconomic problems, and tax laws are the most effective indicators of a bank run. The lack of management of the 8 triggers intensifies the political crisis. With neglect and lack of planning in curbing previous crises, internal technology disruptions and technology crises occur. Ultimately, the combined pressure of the three previous crises brings the final blow to the social structure, and a social crisis occurs. It is suggested that banks use the proposed model of this research by forming bank-run management in their organizational chart and considering the 3 actions of pre-bank run, during bank run, and after bank run, the process of preparing and managing any emergency or unexpected situation. Plan the banks' business, shareholders, employees, customers, and income. Bank-run management helps banks maintain their professional reputation with customers, competitors, and industry managers during and after a crisis, ultimately increasing productivity during and after a crisis.

Keywords

[1] E. Abounoori, N. Mehregan, and N. Safari, Identifying factors affecting the probability of banking system crises in selected countries of the world using logit panel model, Econ. Res. Policy Quart. 26 (2018), no. 88, 7–37.
[2] I. Adelopo, R. Lloydking and V. Tauringana, Determinants of bank profitability before, during, and after the financial crisis, Int. J. Manag. Financ. 14 (2018), no. 4, 378–398.
[3] G. Ahuja, C.M. Lampert, and V. Tandon, 1 moving beyond Schumpeter: management research on the determinants of technological innovation, Acad. Manage. Annal. 2 (2008), no. 1, 1–98.
[4] M.J. Asgharpour, Group Decision-Making and Game Theory with an ”Operations Research” Approach, Tehran: University of Tehran Press Institute, 2003.
[5] R. Attri, N. Dev and V. Sharma, Interpretive structural modelling (ISM) approach: An overview, Res. J. Manag. Sci. 2 (2013), no. 2, 3–8.
[6] R.J. Barro, Economic growth in East Asia before and after the financial crisis, NBER Working Paper, (2001), no. 8330.
[7] S. Bartoletto, B. Chiarini, E. Marzano, and P. Piselli, Banking crises and business cycle: Evidence for Italy (1861-2016), J. Financ. Econ. Policy 11 (2019), no. 1, 34–61.
[8] S. Brown, D. Demetriou, and P. Theodossiou, Banking crisis in Cyprus: Causes, consequences and recent developments, Multinat. Finance J. 22 (2018), no. 1/2, 63–118.
[9] K. Buehler, R. Burkhardt, M. Dietz, S. Khanna, M. Lemerle, A. Mehta, M.C. Nadeau, K. Rajgopal, J. Sengupta, M. Sieberer, and O. White, A Test of Resilience: Banking Through the Crisis, and Beyond, McKinsey Global Banking Annual Review, 2020.
[10] E.P. Davis and D. Karim, Comparing early warning systems for banking crises, J. Financ. Stab. 4 (2008), no. 2, 89–120.
[11] D.W. Diamond and P.H. Dybvig, Bank runs, deposit insurance, and liquidity, J. Politic. Econ. 91 (1983), no. 3, 401–419.
[12] I. Domac and M.S.M. Peria, Banking crises and exchange rate regimes: Is there a link?, J. Politic. Econ. 61 (2003), no. 1, 401–419.
[13] M.N. Faisal, D.K. Banwet, and R. Shankar, Supply chain risk mitigation: modelling the enablers, Bus. Process Manag. J. 12 (2006), no. 4), 535–552.
[14] M.H. Goh, A Manager’s Guide to Implement Your Crisis Management Plan, Business Continuity Management Specialist Series, Singapore: GMH Pte Ltd, 2016.
[15] N.N. Hartmann and B. Lussier, Managing the sales force through the unexpected exogenous COVID-19 crisis, Ind. Market. Manag. 88 (2020), 101–111.
[16] G. Kaminsky, S. Lizondo, and C.M. Reinhart, Leading indicators and currency crises, Staff Papers-Int. Monet. Fund. 35 (1998), no. 1, 1–48.
[17] L. Laeven and F. Valencia, Systemic banking crises database II, IMF Econ. Rev. 68 (2020), 307–361.
[18] G. Levieuge, Y. Lucotte and F. Pradines-Jobet, The cost of banking crises: does the policy framework matter?, J. Int. Money Finance 110 (2021), 102290.
[19] R. Levine, C. Lin, and W. Xie, Corporate resilience to banking crises: The roles of trust and trade credit, J. Financ. Quant. Anal. 53 (2018), no. 4, 1441–1477. [20] B. Levitt and J.G. March, Organizational learning, Ann. Rev. Soc. 14 (1988), 319–340.
[21] W. Lu and J. Swisher, A comparison of bank and credit union growth around the financial crisis, Amer. J. Bus. J. 35 (2019), no. 1, 25–44.
[22] N. Mahin Aslaninia, B. Salmani, F. Falahi, and H. Asgharpour, Investigating the effect of the currency crisis on the dynamics of GDP: A panel generalized quadratic approach, Quart. J. Appl. Econ. Theor. 7 (2020), no. 1, 185–210.
[23] T. Papadimitriou, P. Gogas, and A. Agrapetidou, The resilience of the US banking system, Int. J. Financ. Econ. 27 (2022), no. 3, 2819–2835.
[24] C.B. Pellegrini, L. Pellegrini and E. Sironi, Explaining systemic risk in Latin American banking industry over 2002-2015, Asia-Pacific Contemp. Finance Dev. 26 (2019), 287–309.
[25] C. Ruza, M. de la Cuesta-Gonzalez, and J. Paredes-Gazquez, Banking system resilience: An empirical appraisal, J. Econ. Stud. 46 (2019), no. 6, 1241–1257.
[26] J.N. Warfield, Societal systems: Planning, policy and complexity, Willy Interscience, New York, 1976.
[27] P. Wilms, J. Swank and J. de Haan, Determinants of the real impact of banking crises: A review and new evidence, North Amer. J. Econ. Finance 43 (2018), 54–70.
[28] M. Yazdani, H. Dargahi, and M. Nikzad, Evaluation of production losses caused by monetary crises and the role of central bank intervention in emerging economies, Shahid Beheshti Univ. Econ. Model. Quart. 8 (2017), no. 29, 43–64.
[29] Z. Zarei and A. Kamijani, Identifying and predicting banking crises in Iran, Economic Model. Quart. 99 (2015), no. 29, 1–23.
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 11
November 2024
Pages 51-63
  • Receive Date: 28 June 2023
  • Revise Date: 21 September 2023
  • Accept Date: 16 November 2023