Future Works

For the overall results obtained in this paper, some suggestions of further research are given as follows.

• The sample selected in this paper is limited in the population of listed companies. In order to promote the robustness of the XCSR model, it is suggested to extend the company type for a more complete population of corporation.

• There is a tendency of pay more and more attention on qualitative variables recently. For example, because of the Asia financial risk in 1997, corporate governance has become a quite important issue to maintain the going-concern of a company. Related legislation has made to request companies put the mechanism of corporate governance into practice.

Therefore, to incorporate the relevant variables into explanatory variables may assist in disclosing the hidden information and increase the predictive power.

• Though the obtained regularities in the XCSR model have been discussed in this paper, they could be further analyzed. For example, different industry of companies would be separated to obtain individual industrial regularities. To compare distinct industries may show the difference among them. Consequently, the analysis of regularities will provide stakeholders with much more knowledge for that company.

Reference

[1] Altman, E.I., “Financial Ratio, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, Journal of Finance, Vol. 23, No. 4, pp. 589-609, September 1968.

[2] Altman, E. I., Haldman, R. G., Narayan, P., “Zeta analysis, a new model to identify bankruptcy risk of corporations”, Journal of Banking and Finance, Vol. 1, pp. 29-54, 1977.

[3] Ohlson, J.A., “Financial Ratios and the Probability Prediction of Bankruptcy”, Journal of Accounting Research, Vol. 18, No. 1, pp. 109-131, 1980.

[4] Shih, S.P., “Financial distress predictive model and the financial characteristic of financial distress companies”, Soochow University, Master Thesis, 2000.

[5] Zmijewski, M.E., “Methodological Issues Related to the Estimation of Financial Distress Prediction Model”, Journal of Accounting Research, Vol. 22, pp. 59-82, 1984.

[6] Shin, K.S. and Lee, Y.J., “A genetic algorithm application in bankruptcy prediction modeling”, Expert Systems with Applications, Vol. 23, pp. 321-328, October 1, 2002.

[7] Altman, E.I., Marco, G., Varetto, F., “Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural network”, Journal of Banking and Finance, Vol. 18, pp.

505-529, 1994.

[8] Boritz, J. and Kennedy, D., “Effectiveness of Neural Network Types for Prediction of Business Failure”, Expert Systems with Applications, Vol. 9, pp. 503-512, 1995.

[9] Atiya, A.F., “Bankruptcy prediction for credit risk using neural networks: A survey and new results”, Neural Networks, Vol. 12, No. 4, pp. 929–935, IEEE Transactions on, July 2001.

[10] Beaver, R., “Financial ratios as predictors of failure”, Empirical Research in Accounting:

Selected Studies 1966, Journal of Accounting Research, vol. 4, pp. 71–111, 1966.

[11] Grice, J.S. and Dugan, M.T., “The limitation of bankruptcy prediction models: some cautions for the researcher”, Review of Quantitative Finance and Accounting, Vol. 17, pp.

151-166, 2001.

[12] Varetto, F., “Genetic algorithms applications in the analysis of insolvency risk”, Journal of Banking and Finance, Vol. 22, pp. 1421-1439, October 1998.

[13] Hopwood, Mckeown, W., J.C. , Mutchler, J.F. ,”A reexamination of Auditor versus Model Accuracy Within the context of the Going-Concern Opinion Decision”, Contemporary Accounting Research, Vol. 10, pp. 409-432, Spring 1994.

[14] Foster, B.P.,Ward,T.J., amd Woodroof, J., ”An analysis of the usefulness of Debt defaults and Going Concern Opinions in Bankruptcy Risk Assessment.,” Journal of Accounting Auditing and Finance , pp. 351-371, Summer 1998.

[15] Chen, H.L., “Examination and Comparison of New and Old TCRI Methods”, Money Watching and Credit Rating, Vol. 17, 1999.

[16] Hwang, W.L., “Make up and test the model of Financial Risk”, Soochow University, Master Thesis, 1993.

[17] Wu, S.P., “The Research on the Audit Opinion to Financial Distress Predictive Power”, National Chung Hsing University, Master Thesis, 1996.

[18] Cheng, P.Y., “A Study of Corporate Distress Prediction Model in Taiwan”, Chao Yang University of Technology, Master Thesis, 1997.

[19] Zeng, J.N., ”The Research on the Financial Analysis application of credit strategy in Bank”, National Dong Hwa University, Master Thesis, 1999.

[20] Kao, B.K., “Prediction of Corporation Financial Distress”, National Sun Yat-sen University, Master Thesis, 2000.

[21] Jang, D.C., “Application and Comparison of Corporate Distress Prediction models in Taiwan”, Quarterly Review of the Bank of Taiwan, Vol. 54, pp. 147-163, 2003.

distress model”, Fu Jen Catholic University, Master Thesis, 2002.

[23] Deakin, E.B., “A Discriminant Analysis of Predictors of Failure”, Journal of Accounting Research, pp. 167-179, spring, 1972.

[24] Chen, Z.R., “ The Empirical Study of applying Financial Ratios to Financial Distress Prediction”, National Cheng Chi University, Doctor Dissertation, 1983.

[25] Sharma, S., Applied Multivariate Techniques, J. Wiley, New York, 1996.

[26] Lo, A.W., “Logit versus Discriminat Analysis: A specification test and application to corporate bankruptcy”, Journal of Econometries, pp. 151 – 178, March 1986.

[27] Odom, M. and Sharda, R.,“A neural networks model for bankruptcy prediction“, Proceedings of the IEEE International Conference on Neural Network, Vol. 2, pp. 163–168, 1990.

[28] Tam, K. and Kiang, M., “Managerial applications of the neural networks: The case of bank failure predictions”, Management Science, vol. 38, pp. 416–430, 1992.

[29] Guo, Q.Y., “Application of Artificial Neural Network to Financial Distress Prediction Models”, Tam Kang University, Master Thesis, 1994.

[30] Tsai, C.T., “Business Failure Prediction using Neural Networks”, National Cheng Kung University, Master Thesis, 1995.

[31] Mossman, C.E., Bell G., Swartz, L.M., Turtle, H., “An empirical comparison of bankruptcy models”, The Financial Review, Vol. 33, pp. 35-54, May 1998.

[32] Lin, T.Y., Financial Statement Analysis, Hwa-Tai BookStore, Taipei, 2000.

[33] Holmes, J.H., Lanzi, P.L., Stolzmann, W., Wilson, S.W., “Learning classifier systems:

New models, successful applications”, Information Processing Letters, Vol. 82, pp. 23-30, 2002.

[34] Lanzi, P.L., Stolzmann, W., Wilson, S.W., Learning Classifier Systems: From Foundations to Applications, Lecture Notes in Artificial Intelligence, Vol. 1813, Springer, Berlin, 2000.

[35] Lanzi, P.L., Riolo, R.L., “A roadmap to the last decade of learning classifier system research (from 1989 to 1999)”, in: P.L. Lanzi, W. Stolzmann, S.W. Wilson (Eds.), Learning Classifier Systems: From Foundations to Applications, Lecture Notes in Artificial Intelligence, Vol. 1813, pp. 33–62, Springer, Berlin, 2000.

[36] Wilson, S.W., “Introduction to Learning Classifier Systems (mostly XCS)”, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-03), Chicago, Illinois, July 2003.

[37] Wilson, S.W., “State of XCS classifier system research”, in: P.L. Lanzi, W. Stolzmann, S.W. Wilson (Eds.), Learning Classifier Systems: From Foundations to Applications, Lecture Notes in Artificial Intelligence, Vol. 1813, pp. 63–82, Springer, Berlin, 2000.

[38] Wilson, S.W., “Classifier fitness based on accuracy”, Evolutionary Computation, Vol. 3, pp. 149–175, 1995.

[39] Wilson, S.W., “Generalization in the XCS Classifier System”, Proceedings of the Third Annual Genetic Programming Conference, Morgan Kaufmann, San Francisco, CA, pp.

665–674, 1998.

[40] Wilson, S.W., “Get real! XCS with continuous-valued inputs”, In: Lanzi PL, Stolzmann W, Wilson SW (Eds), Learning Classifier Systems: from Foundations to Applications, Lecture Notes in Artificial Intelligence, Vol. 1813, pp. 209-220 Springer, Berlin, 2000.

[41] Wilson, S.W., “ZCS: a zeroth order classifier system”, Evolutionary Computation, Vol. 2, pp. 1–18, 1994.

[42] Venturini, G., “Apprentissage Adaptatif et Apprentissage Supervisé par Algorithme Génétique”, Thèse de Docteur en Science (Informatique), Université de Paris-Sud, 1994.

[43] Lawrence, D., Handbook of genetic algorithms, New York: Van Nostrand Reinhold, 1991.

[44] Goldberg, D. E., Genetic algorithms in search, optimization and machine learning,

[45] Kovacs, T., “Steady state genetic algorithm deletion techniques”, Internal Report, School of Computer Science, University of Birminghm, 1997.

[46] Lou, Z.Q., Fundamental Analysis and Portfolio Management, TingMao pub co., Taipei, 2002.

[47] Gibson, C.H., Financial Statement Analysis: Using Financial Accounting Information, South-Western College Pub., 1998.

[48] Huberty, C.J., “Issues in the Use and Interpretation of Discriminant Analysis”, Psychological Bulletin, Vol. 95, pp. 156 – 171, 1984.

[49] Ott, R.L., An Introduction to Statistical Methods and Data Analysis, California:

Wadsworth, Publishing Inc., 1993.

[50] Menard, Scott., Applied Logistic Regression Analysis, Thousand Oaks, CA: SAGE Publications, Inc., 1995.