Abstract
We presents some empirical results of a study regarding financial ratios as predictors of Japanese corporate bankruptcy based on a large sample of bankrupt and non-bankrupt firms’ financial data. In this study, variable as predictors of bankruptcy had been selected by three AI-based data mining techniques and two conventional statistical methods, Logit analysis and Stepwise. After the selection of a set of variables for every method, discriminant power of each set was compared to verify the most suitable data mining technique to select financial variables. Finally, the study concludes that a set of variables selected by Logit analysis (with logit model) indicated the best discriminant power, more than 87% accuracy.
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References
Quinlan, J.R.: Introduction of decision trees. Machine Learning 1 (1986) 81–106.
Terano, T., Ishino Y.,: Interactive Generic Algorithm Based Feature Selection and its Application to Marketing Data Analysis. Feature Extraction, Construction and Selection: A Data Mining Perspective, Massachusetts (1998) 393–406.
Breimann, L., J.H. Frieman, R.A. Olshen, and C.J. Stone: Classification and Regression Trees. Chapman & Hall, London (1984).
Altaian, E., R. G. Haldeman and P. Narayanan: ZETA Analysis: A new model to indentify bankruptcy risk of corporations, Journal of Banking and Finance Vol. 1, June (1977) 35.
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© 2000 Springer-Verlag Berlin Heidelberg
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Shirata, C.Y., Terano, T. (2000). Extracting Predictors of Corporate Bankruptcy: Empirical Study on Data Mining Methods. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_25
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DOI: https://doi.org/10.1007/3-540-45571-X_25
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