Early Warning System of Financial Distress Based On Credit Cycle Index

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.


Authors:



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.