博碩士論文 985205003 詳細資訊


姓名 吳昭慧(Chao-hui Wu)  查詢紙本館藏   畢業系所 軟體工程研究所
論文名稱 以組合專精型多分類器於財務危機預測之研究
(On Multiple Classifiers to Financial Distress Prediction)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 在企業經營管理或個人投資風險評估中,若能有效利用財務報表分析,結合資料探勘(Data Mining)與機器學習(Machine Learning)的方式,預測台灣上市櫃公司是否發生財務危機問題,將極具參考價值。
  運用資料探勘(Data Mining)技術與多分類器(Multiple Classifiers)組合機制建立財務預測(Financial Prediction)模型,針對台灣上市櫃公司是否發生財務危機做探討。培養在實務應用中,善用專業背景知識與實作技巧,以融合跨領域的研究內容。觀察到證交所定義的財務危機類型(紓困、跳票或經營疑慮等)各有其特殊意義,可依危機定義將危機類別切割成不同的子類別作探討,每個子類別對應於一種危機型態,其中紓困類與經營疑慮類為發生率最高者。初步以SVM (Support Vector Machine,支持向量機)演算法建構此兩專精型分類器(紓困型、繼續經營疑慮型),並給予兩分類器不同的訓練資料集與特徵集,目的為建構各專精型分類器並有效預測該類財務危機。由於專精型分類器用於處理特定危機類型,預測結果方透過本研究提出的OR組合機制對危機與非危機類別做判別。
  本論文貢獻在於提出新的多分類器OR組合機制用於財務預測領域,相較於傳統單一分類器,我們的多分類器架構除了能有效提升整體危機公司與非危機公司樣本的預測準確率,更能大幅降低危機公司誤判率,富有研究價值。
摘要(英) How to effectively predict financial distress is an important issue in corporate financial management. We use data mining and machine learning methodology to analysis financial statement or financial ratio. Traditional approaches usually formalize financial prediction problem as two-class problem, attempting to differentiate the financially distressed companies (the distressed class) from the normal companies (the non-distressed class). However, there are many factors contributing to a company’s financial crisis. Taiwan Stock Exchange Corporation (TWSE) defines several kinds of financial crisis which show distinct reason. This observation motivates us to further segment the distressed class into a few subclasses. Each subclass corresponds to one crisis type. We propose new methods to design multiple classifier system. Each classifier for a subclass gives a meaningful training set and feature set. It makes that each classifier is professional for each sub-problem. This model is different from the existing approaches that each classifier is not designed for the same pattern recognition problem. The prediction accuracy is superior to traditional approaches by using our prediction model.
關鍵字(中) ★ 支持向量機
★ 財務預測
★ 資料探勘
★ 機器學習
★ 多分類器組合
關鍵字(英) ★ Financial Prediction
★ Multiple Classifier
★ Data Mining
★ Machine Learning
論文目次 中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 3
1-3 論文架構 4
二、文獻探討 5
2-1 多分類器組合系統 5
2-2 分類器的使用 6
三、實驗架構與設計 7
3-1 實驗公司樣本 8
3-2 實驗資料前置處理(Data Preprocessing) 9
3-3 特徵集合與特徵挑選(Feature Selection) 10
3-4 SVM分類器的參數 11
3-5 挑選推薦特徵組合與參數的迭代方式 12
3-6 驗證模型的方式 14
3-7 前測理論 15
3-8 SVM多分類器組合 17
四、實驗結果以及比較分析 19
4-1 實驗結果 20
4-2 結果分析 24
4-3 延伸討論 26
五、結論與未來展望 29
5-1 結論與貢獻 29
5-2 未來展望 30
參考文獻 32
附錄一 台灣實驗公司樣本與特徵集 36
附錄二 台灣實驗數據總表 51
附錄三 各分類器挑選出的特徵整理 54
參考文獻 [1] Altman, E.I., “Financial ratio, discriminant analysis, and the prediction of corporate bankruptcy”, Journal of Finance, 23, 589-609, 1968.
[2] Beaver, W., “Financial ratios as predictors of failure. Empirical research in accounting: selected studies”, Journal of Accounting Research, 4, 71-111, 1966.
[3] Baykut, A. and Ercil, A., “Towards automated classifier combination for pattern recognition”, Multiple classifier systems, 94-105, 2003.
[4] Brunelli, R. and Falavigna, D., “Person identification using multiple cues”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 955–966, 1995.
[5] Benediktsson, J. A., Sveinsson, J. R., Ersoy, O. K. and Swain, P. H. “Parallel consensual neural networks”, IEEE Transactions on NeuralNetworks, 8, 540–564, 1997.
[6] Chen, L. and Hsiao, H., “Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study”, Expert Systems with Applications, 35, 2008.
[7] Cho, S., Kim, J. and Bae, J.K., “An integrative model with subject weight based on neural network learning for bankruptcy prediction”, Expert Systems with Applications, 36, 403-410, 2009.
[8] Chandra, D. K., Ravi, V. and Bose I., “Failure prediction of dotcom companies using hybrid intelligent techniques”, Expert Systems with Applications, 36, 4830–4837, 2009.
[9] Ding, Y., Song, X. and Zen, Y., “Forecasting financial condition of Chinese listed companies based on support vector machine”, Expert Systems with Applications, 34, 3081-3089, 2008.
[10] Ho, T.K., Hull, J.J. and Srihari, “Decision combination in multiple classifier systems”, IEEE Transactions on Pattern Analysis Machine Intelligence, 16, 1, 66-75, 1994.
[11] Huang, Y.S. and Suen C.Y., “The behavior knowledge space method for combination of multiple classifiers”, Proceedings IEEE Conference CVPR, 347-352, 1993.
[12] Huang, Y.S. and Suen C.Y., “A method of combining multiple experts for the recognition of unconstrained handwritten numerals”, IEEE Transaction Pattern Analysis Machine Intelligence, 17, 1, 90-94, 1995.
[13] Hua, Z., Wang Y., Xu X., Zhang B. and Liang L., “Predicting corporate financial distress based on integration of support vector machine and logistic regression”, Expert Systems with Applications, 33, 434–440, 2007.
[14] Jo, H. and Han, I., “Integration of case-based forecasting, neural network, and discriminate analysis for bankruptcy prediction”, Expert Systems with Applications, 11, 415-422, 1996.
[15] Kittler, J. et al, “On Combining Classifiers”, IEEE Transaction Pattern Analysis Machine Intelligence, 20, 3, 226-240, 1998.
[16] Kim, E., Kim, W. and Lee, Y., “Combination of multiple classifiers for the customer’s purchase behavior prediction”, Decision Support Systems, 34, 167-175, 2002.
[17] Kim, M.J., Min, P. and Han, I., “An evolutionary approach to the combination of multiple classifiers to predict a stock price index”, Expert Systems with Applications, 31, 241-247, 2006.
[18] Mazurov, V.D., Krivonogov, A.I., and Kazantsev, V.L., “Solving of optimization and identification problems by the committee methods”, Pattern Recognition, 20, 4, 371-378, 1987.
[19] Mandler, E. and Schuermann, J., “Combining the classification results of independent classifiers based on the Dempster/Shafer theory of evidences”, Pattern Recognition and Artificial Intelligence, Gelsema and Kannal, Eds. Amsterdam: Elsevier Science, 381-393, 1988.
[20] Neggu, D., Guo, G. and Wang, S., “An effective combination based on class-wise expertise of diverse classifiers for predictive toxicology data mining”, Lecture Notes in Artificial Intelligence, 4093, 165-172.
[21] Ohlson, J.A., “Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, 18, 109-31, 1980.
[22] Ozkan-Gunay, E.N. and Ozkan M., “Prediction of bank failures in emerging financial markets: an ANN approach”, The Journal of Risk Finance, 8, 5, 465-480, 2007.
[23] Press, S.J. and Wilson, S., “Choosing between logistic regression and discriminant analysis”, J. Amer. Statist. Ass., 73, 364, 699-705, 1978.
[24] Ruta, D. and Gabrys, B., “Classifier selection for majority voting”, Information Fusion, 6, 63-81, 2005.
[25] Shin, K. S., Lee, T. S., and Kim, H. J., “An application of support vector machines in bankruptcy prediction model”, Expert Systems with Application, 28, 127-135, 2005.
[26] Li, H. and Sun, J., “Ranking-order case-based reasoning for financial distress prediction”, Knowledge-Based Systems, 21, 868-878, 2008.
[27] Sun, J. and Li, H., “Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers”, Expert Systems with Applications, 35, 818-827, 2008.
[28] Li, H. and Sun, J., “Predicting financial distress using multiple case-based reasoning combine with support vector machine”, Expert Systems with Applications, 2009.
[29] Sun, J. and Li, H., “Financial distress prediction based on serial combination of multiple classifiers”, Expert Systems with Applications, 36, 8659-8666, 2009.
[30] Li, H., Sun, J. and Sun, B. L., “Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors”, Expert Systems with Applications, 36, 643-659, 2009.
[31] Tumer, K. and Ghosh, J., “Analysis of decision boundaries in linearly combined neural classifiers”, Pattern Recognition, 29, 2, 341-348, 1996.
[32] Tam, K. Y. and Kiang, M.Y., “Managerial applications of neural networks: the case of bank failure predictions”, Management Science, 38, 926-947, 1992.
[33] Tsai, C.-F. and Wu, J.-W., “Using neural network ensembles for bankruptcy prediction and credit scoring”, Expert Systems with Applications, 34, 2639-2649, 2008.
[34] Xu, L., Kvzyzak, A. and Suen, C.Y., “Method of combining multiple classifiers and their application to handwritten numeral recognition”, IEEE Transaction System, Man and Cybernation, 22, 3, 418-435, 1992.
[35] West, D., Dellana, S. and Qian, J., “Neural network ensemble strategies for financial decision applications”, Computers and Operations Research, 32, 2543-2559.
[36] Wu, C. H., Tzeng, G. H., Goo, Y. J. and Fang, W. C., “A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy”, Expert Systems with Applications, 32, 397-408, 2007.
[37] Zmijewski, M. E., “Methodological Issues Related to the Estimation of Financial Distress Prediction Models”, Journal of Accounting Research, 22, 59-82, 1984.
[38] Venter, G. and Sobieszczanski-Sobieski, J., “Particle Swarm Optimization”, AIAA Journal, 41, 8, 2003.
[39] Taiwan Economic Journal, available from: http://www.tej.com.tw/
[40] 陳恩加,「基於支持向量機多分類器於企業危機預測之研究」,國立海洋大學資訊工程碩士,2010。
[41] 蔡明勳,「基於財務能力分群的財務變數挑選流程:以中國和台灣上市上櫃公司為例」,國立海洋大學資訊工程碩士,2011。
指導教授 梁德容(De-ron Liang) 審核日期 2011-7-21
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡