Early Warning System of Financial Distress Based On Credit Cycle Index
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Early Warning System of Financial Distress Based On Credit Cycle Index

Authors: Bi-Huei Tsai

Abstract:

Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightlydistressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the one-stage model has the lower misclassification error rate than the two-stage model. The one-stage model is more accurate than the two-stage model.

Keywords: Multinomial logit model, corporate governance, company failure, reorganization, bankruptcy.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100108

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References:


[1] F. J. L. Iturriaga and I. P. Sanz. “Bankruptcy Visualization and Prediction Using Neural Networks: A Study of U.S. Commercial Banks.” Expert Systems with Applications, vol. 42, no. 6, 2015, pp. 2857-2869.
[2] D. J. Philippe. “Bankruptcy Prediction Using Terminal Failure Processes.” European Journal of Operational Research, vol. 242, no. 1, 2015, pp. 286-303
[3] A. Vineet and T. Richard. “Comparing the Performance of Market-based and Accounting-based Bankruptcy Prediction Models.” Journal of Banking & Finance, vol. 32, no. 8, 2007, pp. 1541-1551.
[4] S. G. Hanson, M. H. Pesaran, and T. Schuermann. “Firm Heterogeneity and Credit Risk Diversification.” Journal of Empirical Finance, vol. 15, no. 4, 2008, pp.583-612.
[5] B.-H. Tsai, C.-F. Lee, and L. Sun, “The Impact of Auditors’ Opinions, Macroeconomic and Industry Factors on Financial Distress Prediction: An Empirical Investigation.” Review of Pacific Basin Financial Markets and Policies, vol. 12, no.3, 2009, pp. 417-454.
[6] S. Johnson, P. Boone, A. Breach, and E. Friedman, “Corporate Governance in the Asian Financial Crisis, Journal of Financial Economics, vol. 58, 2000, pp. 141-186.
[7] F. J. L. Iturriaga and V. L. Crisostomo “Do Leverage, Dividend Payout, and Ownership Concentration Influence Firms' Value Creation? An Analysis of Brazilian Firms.” Emerging Markets Finance and Trade, vol. 46, no. 3, 2010, pp. 80-94.
[8] M. C. Jensen and W. H. Meckling. “Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure,” Journal of Financial Economics, vol. 3, no. 4, 1976, pp. 305-360.
[9] R. La Porta, F. Lopez-de-Silanes, and Shieifer, A. Corporate Ownership around the world, Journal of Finance, vol. 54, 1999, pp. 471-517.
[10] S. Claessens, S. Djankov, and L. H. P. Lang, “The Separation of Ownership and Control in East Asian Corporation,” Journal of Financial Economics, vol. 58, pp. 81-112.
[11] S. K. Staikouras, “Multinational Banks, Credit Risk and Financial Crises.” Emerging Markets Finance and Trade, vol. 41, no. 2, 2005, pp.82-106.
[12] E. I. Altman, B. Brady, A. Reti., and A. Sironi. “The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications.” Journal of Business, vol. 78, no. 6, 2005, pp.2203-2227.
[13] B. Belkin and L. Forest. “The Effect of Systematic Credit Risk on Loan Portfolio: Value at Risk and on Loan Pricing.” Wagner Math Finance Report, 2007.
[14] J. Kim, “A Way to Condition the Transition Matrix on Wind.” unpublished paper, RiskMetrics Group, New York, NY, 1999.
[15] B. Belkin, S. J. Suchower, and L.R. Forest. “A One-Parameter Representation of Credit Risk and Transition Matrices.” CreditMetrics Monitor. 1998, pp. 46-56.
[16] B. Belkin, S. J. Suchower, and L. R. Forest. “The Effect of Systematic Credit Risk on Loan Portfolio Value at Risk and on Loan Pricing.” CreditMetrics Monitor. 1998, pp. 17-28.
[17] T. C. Wilson, “Portfolio Credit Risk, I .Risk Magazine.” 1997, pp. 111-117.
[18] T. C. Wilson, “Portfolio credit risk, II .Risk Magazine.” 1997, pp.56-61.
[19] W. Hopwood, J. C. McKeown, and J. F. Mutchler. 1994. A re-examination of auditor versus model accuracy within the context of the going-concern opinion decision. Contemporary Accounting Research, vol. 10, no.2, pp. 409-431
[20] W. H. Beaver, “Financial Ratio as Predictors of Failure. Empirical Research in Accounting: Selected Studies.” Journal of Accounting Research, vol. 4, no. Supplement, 1966, pp. 71-111.
[21] W. H. Beaver, “Market Prices, Financial Ratios, and the Prediction of Failure.” Journal of Accounting Research. vol. autumn, 1968, pp. 179-192.
[22] T. Lancaster, The Econometric Analysis of Transition Data. New York: Cambridge University Press, 1990.
[23] T. Shumway, “Forecasting Bankruptcy More Accurately: A Simple Hazard Model.” The Journal of Business, vol. 74, no. 1, 2001, pp. 101-124.
[24] J. Begley, J. Ming, and S. Watts. “Bankruptcy Classification Errors in the 1980s: An Empirical Analysis of Altman’s and Ohlson’s Models.” Review of Accounting Studies, vol. 1, no. 4, 1996: 267-284.