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Application of Company Financial Crisis Early Warning Model- Use of “Financial Reference Database“

Authors: Chiung-ying Lee, Chia-hua Chang


In July 1, 2007, Taiwan Stock Exchange (TWSE) on market observation post system (MOPS) adds a new "Financial reference database" for investors to do investment reference. This database as a warning to public offering companies listed on the public financial information and it original within eight targets. In this paper, this database provided by the indicators for the application of company financial crisis early warning model verify that the database provided by the indicator forecast for the financial crisis, whether or not companies have a high accuracy rate as opposed to domestic and foreign scholars have positive results. There is use of Logistic Regression Model application of the financial early warning model, in which no joined back-conditions is the first model, joined it in is the second model, has been taken occurred in the financial crisis of companies to research samples and then business took place before the financial crisis point with T-1 and T-2 sample data to do positive analysis. The results show that this database provided the debt ratio and net per share for the best forecast variables.

Keywords: Financial reference database, Financial early warning model, Logistic Regression.

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[1] Altman, E. I., "Financial Ratios, Discriminant Analysis and the Predictionof Corporate Bankruptcy," Journal of Finance, vol.23, no.4, 1968, pp.589-609.
[2] Beaver, W. H., "Financial Ratios as Predictors of Failure," Journal of Accounting Research, vol.4, 1966, pp.71-111.
[3] Cox, D.R. and E. J. Snell. Analysis of binary data (2nd edition). London: Chapman & Hall, 1989.
[4] Deakin, E. B.. "A discriminant analysis of predictors of business failure." Journal of Accounting Research, vol.10, no1, 1972, pp.167- 179.
[5] Koh, H. C., Tan, S. S., "A Neural network approach to the prediction of going concern status," Accounting and Business Research, vol21, 1999, pp.211-216.
[6] Lau, H. L., "A Five-State Financial Distress Prediction Model," Journal of Accounting Research, vol.25, no.1, 1987, pp.127-138.
[7] Martin, D. "Early warning of banking failure." Journal of Banking and Finance, 1977, pp.249-276.
[8] Nagelkerke, N. J. D.. "A note on a general definition of the coefficient of determination." Biometrika, vol.78, no.3, 1991, pp.691-692. Covers the two measures of R-square for logistic regression which are found in SPSS output.
[9] Ohlson, J. A., "Financial Rations and the Probabilistic Prediction of Bankruptcy," Journal of Accounting Research, vol.18, 1980, pp.109- 131.
[10] Odom, M. D., Sharda, R., "A Neural Network Model for Bankruptcy Prediction," Proceedings of the IEEE International Conference on Neural Networks, 1990, pp.163-168.
[11] Retzlaff-Roberts, D. L.. "Relating discriminant analysis and DEA to one another." Computers and Operations Research, vol.23, no.4, 1996, pp.311-322.
[12] Theodossiou, P.T., "Predicting Shifts in the mean of a Multivariate Time Series Process: An Application in Predicting Business Failures," Journal of American Statistical Association, 1993, pp.441-449.
[13] Zeitun, R., Tian, G., Keen, S., "Default Probability for the Jordanian Companies: A Test of Cash Flow Theory," International Research Journal of Finance and Economics, vol.8, 2007, pp.147-162.
[14] Zmijewski, M. E. "Methodological issues related to the estimation of financial distress prediction models." Journal of Accounting Research, 1984, pp.59-86.