多分類器組合應用於財務危機預測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：22

、訪客IP：3.145.47.221

姓名

楊順雄(YANG,SHUN-HSIUNG) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

多分類器組合應用於財務危機預測
(Financial distress prediction based on multiple Classifiers)

相關論文

★ 基於最大期望算法之分析陶瓷基板機器暗裂破片率	★ 基於時間序列預測的機器良率預測
★ 基於OpenPose特徵的行人分心偵測	★ 建構深度學習CNN模型以正確分類傳統AOI模型之偵測結果
★ 一種結合循序向後選擇法與回歸樹分析的瑕疵肇因關鍵因子擷取方法與系統－以紡織製程為例	★ 融合生成對抗網路及領域知識的分層式影像擴增
★ 針織布異常偵測方法研究	★ 基於工廠生產資料的異常機器維修預測
★ 萃取駕駛人在不同環境之駕駛行為方法	★ 基於刮痕瑕疵資料擴增的分割拼接影像生成
★ 應用卷積神經網路於航攝影像做基於坵塊的水稻判釋之研究	★ 採迴歸樹進行規則探勘以有效同時降低多種紡織瑕疵
★ 應用增量式學習於多種農作物判釋之研究	★ 應用自動化測試於異質環境機器學習管道之 MLOps 系統
★ 農業影像二元分類：坵塊分離的檢測	★ 應用遷移學習於胚布瑕疵檢測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
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。
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)

近年來，由於全球金融危機的爆發，許多經濟實體發生都會遭受巨大的損失，甚至導致破產，所以財務危機預測financial distress prediction（FDP）的問題一直以來都是被廣泛討論和持續研究的主題。在企業融資領域，要如何有效地預測財務危機，對於企業財務管理是一個很重要的問題。最近，使用多分類器組合來解決FDP問題越來越被重視。而本研究中就是在多分類器組合的基礎架構上提出了可提升FDP效果的方法。一般典型的財務預測基本上就是做二元分類（危機類和非危機類）的問題。然而，我們發現台灣證券交易所（TWSE）不僅定義了一間公司是否有發生財務危機，且還定義一間公司發生了哪種財務危機。因此，我們想試著建構各類財務危機的專精分類器再利用OR組合機制將所有危機種類分類器的預測結果做組合。我們會在本論文中會提出一個新的多分類器組合架構，並會在論文中試著證明我們所提出的預測模型相較以往傳統的預測模型有著更好的預測表現。

摘要(英)

In recent years, many economic entities have suffered great loss or even become bankrupt due to the breakout of global financial crisis. Financial distress prediction (FDP) has been widely studding. In the field of corporate finance, to predict financial distress effectively is an important issue in corporate financial management. Recently, some studies which take advantages of multiple classifiers combination to solve FDP problem have been noticed. This paper proposed a FDP method with increased efficiency based on parallel combination of multiple classifiers. Traditional approaches usually take financial prediction problem as Binary Classification (distressed class and non-distressed class) problem. However, we found that Taiwan Stock Exchange Corporation (TWSE) not only defined two classes for all companies but also defined subclasses for the distress companies. Hence, we wanted to construct specific classifier for each distress type and then combine all distress classifiers prediction results with the OR mechanism. We also proposed a new prediction model with multiple classifiers and showed that our method had outperformed the traditional approach.

關鍵字(中)

★ 財務危機預測
★ 多分類器
★ 資料探勘
★ 機器學習

關鍵字(英)

★ Financial Distress Prediction
★ Multiple Classifier
★ Data Mining
★ Machine Learning

論文目次

Abstract ii
誌謝 iii
圖目錄 v
表目錄 vi
一、緒論 1
1-1. 研究背景 1
1-2. 研究動機 2
1-3. 論文架構 3
二、文獻探討 4
2-1. 多分類器應用 4
三、實驗架構設計與結果 6
3-1. 資料來源 6
3-2. 資料前置處理 7
3-3. 實驗假設 7
3-4. Solution-1 【Parallel-OR】 8
3-4-1實驗設計 9
3-4-2實驗結果 14
3-4-3實驗討論 16
3-4-4延伸討論 -Degree of Divergence 17
3-5. Solution-2【Iterative Parallel-OR】 20
3-5-1實驗設計 20
3-5-2實驗結果與討論 21
四、結論及未來展望 23
4-1結論 23
4-2未來展望 24
參考文獻 27
附錄一 29
附錄二 35

參考文獻

[1] P. J. Fitzpartrick, "A comparison of ratios of successful industrial enterprises with those of failed companies," Journal of Accounting Research, pp. 598–605, 1932.
[2] W. H. Beaver, "Financial Ratios As Predictors of Failure," Journal of Accounting Research, vol. 4, pp. 71-111, 1966.
[3] E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, vol. 23, pp. 589-609, 1968.
[4] J. A. Ohlson, "Financial Ratios and the Probabilistic Prediction of Bankruptcy," Journal of Accounting Research, vol. 18, pp. 109-131, 1980.
[5] 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, July 1, 1992 1992.
[6] E. N. Ozkan-Gunay and M. Ozkan, "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, The, vol. 8, pp. 465-480, 2007.
[7] 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, Apr 2009.
[8] 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, Oct 2008.
[9] Z. S. Hua, Y. Wang, X. Y. 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, Aug 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, Jan 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, Feb 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] H. Li, J. Sun, and B. L. Sun, "Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors," Expert Systems with Applications, vol. 36, pp. 643-659, 2009.
[15] C. F. Tsai and J. W. Wu, "Using neural network ensembles for bankruptcy prediction and credit scoring," Expert Systems with Applications, vol. 34, pp. 2639-2649, 2008.
[16] E. Kim, W. Kim, and Y. Lee, "Combination of multiple classifiers for the customer’s purchase behavior prediction," Decision Support Systems, vol. 34, pp. 167-175, 2003.
[17] R. Brunelli and D. Falavigna, "Person identification using multiple cues," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, pp. 955-966, 1995.
[18] Y. S. Huang and C. Y. Suen, "A method of combining multiple experts for the recognition of unconstrained handwritten numerals," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, pp. 90-94, 1995.
[19] L. Xu, A. Krzyzak, and C. Y. Suen, "Methods of combining multiple classifiers and their applications to handwriting recognition," Systems, Man and Cybernetics, IEEE Transactions on, vol. 22, pp. 418-435, 1992.
[20] T. K. Ho, J. J. Hull, and S. N. Srihari, "Decision combination in multiple classifier systems," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, pp. 66-75, 1994.
[21] M. J. Kim, S. H. Min, and I. Han, "An evolutionary approach to the combination of multiple classifiers to predict a stock price index," Expert Systems with Applications, vol. 31, pp. 241-247, 2006.
[22] S. J. Press and S. Wilson, "Choosing between logistic regression and discriminant analysis," Journal of the American Statistical Association, vol. 73, pp. 699-705, 1978.
[23] D. Ruta and B. Gabrys, "Classifier selection for majority voting," Information fusion, vol. 6, pp. 63-81, 2005.
[24] 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.
[25] J. Sun and H. Li, "Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers," Expert Systems with Applications, vol. 35, pp. 818-827, 2008.
[26] 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.
[27] D. Neagu, G. Guo, and S. Wang, "An effective combination based on class-wise expertise of diverse classifiers for predictive toxicology data mining," Advanced Data Mining and Applications, pp. 165-172, 2006.
[28] D. West, S. Dellana, and J. Qian, "Neural network ensemble strategies for financial decision applications," Computers & operations research, vol. 32, pp. 2543-2559, 2005.
[29] P. Viola and M. J. Jones, "Robust real-time face detection," International journal of computer vision, vol. 57, pp. 137-154, 2004.
[30] J. H. Min and Y. C. Lee, "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters," Expert Systems with Applications, vol. 28, pp. 603-614, 2005.
[31] X. F. Hui and J. Sun, "An application of support vector machine to companies’ financial distress prediction," Modeling Decisions for Artificial Intelligence, pp. 274-282, 2006.
[32] Y. Tang, Y. Q. Zhang, N. V. Chawla, and S. Krasser, "SVMs modeling for highly imbalanced classification," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 39, pp. 281-288, 2009.

指導教授

梁德容(De-ron Liang)

審核日期

2012-10-19

推文