Abstract.
In recent years, financial crises have occurred frequently in each region, and banks are facing harsh management environments. Bank runs of customers during a financial crisis are one of a bank’s most serious risks. This research aims to build a bank run model for financial crises, use that model to estimate the amount of deposit funds which flow out, and propose a framework for financial crisis risk management. The model proposed in this paper enables understanding of the factors which have the largest impacts on bank runs, providing valuable information for banks to cope with such risks. The model uses survey data, and clarified that bank runs have differences which depend on customer characteristics and branch location. We understood that during a financial crisis, an appropriate branch strategy must be adopted depending on the location and customer characteristics of each bank branch.
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Davies, G., Chun, R., Vinhas, R. S., Roper, S.: Corporate Reputation and Competitiveness. Routledge, 2003.
Pathan, S., Skully, M., Wickramanayake, J.: Reforms in Thai bank governance: The aftermath of the Asian financial crisis. International Review of Financial Analysis. 17(2), 345–362, 2008.
Choe, H., Lee, B. S.: Korean bank governance reform after the Asian financial crisis. Pacific-Basin Finance Journal. 11, 483–508, 2003.
Mitton, T.: A cross-firm analysis of the impact of corporate governance on the East Asian financial crisis. Journal of Financial Economics. 64, 215–241, 2002.
La Porta, R., Lopez de Silanes, F., Shleifer, A.: Corporate ownership around the world. Journal of Finance. 54, 471–517, 1999.
Johnson, S., Boone, P., Breach, A., Friedman, E.: Corporate governance in the Asian financial crisis. Journal of Financial Economics. 58, 141–186, 2000.
Anuchitworawong, C.: Deposit insurance, corporate governance, and discretionary behavior: Evidence from Thai financial institutions. Centre for Economic Institute Working Paper, 2004-15. Hitotsubashi University, 2004.
Lo, A. W., Repin, D. V.: The psychophysiology of real-time financial risk processing. Journal of Cognitive Neuroscience. 14, 323–339, 2002.
Witten, I. H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, 2005.
Bhattacharyya, S., Pendharker, P.C.: Inductive, evolutionary, and neural computing techniques for discrimination: a comparative study. Decision Science. 29(4), 871–899, 1998.
Quinlan. J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers. San Francisco, 1993.
Yada, K., Ip, E., Katoh, N.: Is this brand ephemeral? A multivariate tree-based decision analysis of new product sustainability. Decision Support Systems. 44(1), 223–234, 2007.
Mattsatsinis, N., Siskos, Y.: Intelligent Support Systems for Marketing Decisions. Springer, 2002.
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Yada, K., Washio, T., Ukai, Y. et al. Modeling Bank Runs in Financial Crises. Rev Socionetwork Strat 3, 19–31 (2009). https://doi.org/10.1007/s12626-008-0005-3
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DOI: https://doi.org/10.1007/s12626-008-0005-3