財務危機預測長久以來都是一個重要且常被廣泛討論的主題，發展出好的財務危機預警模型可以有效幫助，銀行決策。影響整個財務危機預警流程主要有兩個議題分別是特徵挑選(Feature selection)與分類器演算法(Classifier algorithm)，過去研究顯示，單純改良分類器演算法，準確率很難有顯著的提升。本論文將目標放在另一個議題特徵挑選，我們觀察到財務比率數量會隨著年代大幅成長，如何在大量的財務比率下挑選出重要的財務比率，變成很重要的議題，近幾年，研究顯示基因演算法應用於特徵挑選在單一特定的資料集下表現相當好，我們知道特徵集合成長速度相當的快速，如果只驗證基因演算法在單一特定的特徵集合的效果是不足夠的，本論文模擬了特徵集合越來越大的情況，觀察基因演算法表現情形，最後觀察出基因演算法在不加入公司治理的特徵情況下，當特徵集合越來越大，基因演算法挑選出來的特徵組合，準確率還是能夠穩定成長而且較其他特徵挑選方法穩定。

Financial distress problem has been important and widely studied topic, development of good financial analysis model can help bank to decisions. There are two major factors, namely feature selection and classifier algorithm, influencing financial distressed prediction. Previous researches show that the forecasting accuracy is very difficult to have significant improvement by improving classification algorithm only; therefore, our research focus on the feature selection issue. Over time，we observed financial ratio growing quickly, that mean feature selection become more important, In recent years, Previous researches have shown genetic algorithm applied to feature selection in unique feature set have good performance, but we know feature size growing quickly, it is not enough to prove genetic algorithm in unique feature set. In our research, we simulate ratio growing situation, consider genetic algorithm performance. Finally, if we exclude corporate governance, we discover genetic algorithm predict performance become well when feature size larger.

參考文獻
[1] W. H. Beaver, "Financial ratios as predictors of failure," Journal of accounting research, vol. 4, pp. 71-111, 1966.
[2] E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, vol. 23, pp. 589-609, 1968.
[3] J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of accounting research, vol. 18, pp. 109-131, 1980.
[4] K. Y. Tam and M. Y. Kiang, "Managerial applications of neural networks: the case of bank failure predictions," Management science, vol. 38, pp. 926-947, 1992.
[5] M. O. E. Nur Ozkan-Gunay, "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, vol. 8, pp. 465 - 480, 2007.
[6] D. K. Chandra, V. Ravi, and I. Bose, "Failure prediction of dotcom companies using hybrid intelligent techniques," Expert Systems with Applications, vol. 36, pp. 4830-4837, 2009.
[7] L. H. Chen and H. D. Hsiao, "Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study," Expert Systems with Applications, vol. 35, pp. 1145-1155, 2008.
[8] Y. Ding, X. Song, and Y. Zen, "Forecasting financial condition of Chinese listed companies based on support vector machine," Expert Systems with Applications, vol. 34, pp. 3081-3089, 2008.
[9] Z. Hua, Y. Wang, X. Xu, B. Zhang, and L. Liang, "Predicting corporate financial distress based on integration of support vector machine and logistic regression," Expert Systems with Applications, vol. 33, pp. 434-440, 2007.
[10] K.-S. Shin, T. S. Lee, and H.-j. Kim, "An application of support vector machines in bankruptcy prediction model," Expert Systems with Applications, vol. 28, pp. 127-135, 2005.
[11] C.-H. Wu, G.-H. Tzeng, Y.-J. Goo, and W.-C. Fang, "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert Systems with Applications, vol. 32, pp. 397-408, 2007.
[12] H. Jo and I. Han, "Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction," Expert Systems with Applications, vol. 11, pp. 415-422, 1996.
[13] H. Li and J. Sun, "Ranking-order case-based reasoning for financial distress prediction," Knowledge-Based Systems, vol. 21, pp. 868-878, 2008.
[14] S. Cho, J. Kim, and J. K. Bae, "An integrative model with subject weight based on neural network learning for bankruptcy prediction," Expert Systems with Applications, vol. 36, pp. 403-410, 2009.
[15] "台灣經濟新報. Available: http://www.tej.com.tw/twsite/."
[16] M. Blum, "Failing company discriminant analysis," Journal of Accounting Research, vol. 12, pp. 1-25, 1974.
[17] P. D. Chant, "On the predictability of corporate earnings per share behavior," The Journal of Finance, vol. 35, pp. 13-21, 1980.
[18] E. B. Deakin, "A discriminant analysis of predictors of business failure," Journal of Accounting Research, vol. 10, pp. 167-179, 1972.
[19] J. E. Ketz, "The effect of general price-level adjustments on the predictive ability of financial ratios," Journal of Accounting Research, vol. 16, pp. 273-284, 1978.
[20] C. Casey and N. Bartczak, "Using Operating Cash Flow Data to Predict Financial Distress: Some Extensions," Journal of Accounting Research, vol. 23, pp. 384-401, 1985.
[21] M. J. Gombola, M. E. Haskins, J. E. Ketz, and D. D. Williams, "Cash flow in bankruptcy prediction," Financial Management, vol. 16, pp. 55-65, 1987.
[22] C. H. Wu, G. H. Tzeng, Y. J. Goo, and W. C. Fang, "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert Systems with Applications, vol. 32, pp. 397-408, 2007.
[23] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
[24] B. Back, T. Laitinen, and K. Sere, "Neural networks and genetic algorithms for bankruptcy predictions," Expert Systems with Applications, vol. 11, pp. 407-413, 1996.
[25] P. W. Huang and C. L. Liu, "Using genetic algorithms for feature selection in predicting financial distresses with support vector machines," in Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on, 2006, pp. 4892-4897.
[26] S. H. Min, J. Lee, and I. Han, "Hybrid genetic algorithms and support vector machines for bankruptcy prediction," Expert Systems with Applications, vol. 31, pp. 652-660, 2006.
[27] R. S. Sexton, R. S. Sriram, and H. Etheridge, "Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach," Decision Sciences, vol. 34, pp. 421-442, 2003.
[28] "MBA智庫百科. Avaliable: http://wiki.mbalib.com/wiki/MBA."
[29] G. R. Iversen and H. Norpoth, Analysis of variance: Sage Publications, Inc, 1987.
[30] K. D. a. M. K, Logistic regression A self-learning text, 3 ed. New York Springer, 1994.
[31] J. Sun and H. Li, "Financial distress prediction based on serial combination of multiple classifiers," Expert Systems with Applications, vol. 36, pp. 8659-8666, 2009.
[32] A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artificial intelligence, vol. 97, pp. 245-271, 1997.
[33] R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial intelligence, vol. 97, pp. 273-324, 1997.
[34] Y. Saeys, I. Inza, and P. Larrañaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, pp. 2507-2517, 2007.
[35] H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," Knowledge and Data Engineering, IEEE Transactions on, vol. 17, pp. 491-502, 2005.
[36] J. Kittler, "Feature set search algorithms," Pattern recognition and signal processing, vol. 41, p. 60, 1978.
[37] J. H. Holland, Adaptation in natural and artificial systems: University of Michigan press, 1975.
[38] M. Srinivas and L. M. Patnaik, "Genetic algorithms: A survey," Computer, vol. 27, pp. 17-26, 1994.
[39] "公開觀測資訊站. Available: http://mops.twse.com.tw/mops/web/index."
[40] "Matlab ga tool box. Available: http://www.mathworks.com/help/toolbox/gads/gaoptimset.html."
[41] "SAS toolbox. Available: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect052.htm."
[42] I. G. Dambolena and S. J. Khoury, "Ratio Stability and Corporate Failure," The Journal of Finance, vol. 35, pp. 1017-1026, 1980.
[43] K. Y. Tam and M. Y. Kiang, "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, vol. 38, pp. 926-947, 1992.
[44] E. I. Altman, R. G. Haldeman, and P. Narayanan, "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, vol. 1, pp. 29-54, 1977.
[45] M. Blum, "Failing Company Discriminant Analysis," Journal of Accounting Research, vol. 12, pp. 1-25, 1974.
[46] M. J. Gombola, M. E. Haskins Jr, E. Ketz, and D. D. Williams, "Cash Flow in Bankruptcy Prediction," Financial Management vol. 16, pp. 55-65, Winter87 1987.
[47] C. L. N. a. R. E. Smith, "A Comparison of General Price Level and Historical Cost Financial Statements in the Prediction of Bankruptcy," The Accounting Review, vol. 54, pp. 72-87, 1979.
[48] H. Li and J. Sun, "Business failure prediction using hybrid2 case-based reasoning (H2CBR)," Computers & Operations Research, vol. 37, pp. 137-151, 2010.