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研究生:何秉劼
研究生(外文):Ho, Ping-Chieh
論文名稱:運用支援向量機於企業財務危機之研究
論文名稱(外文):Applying Support Vector Machine to the Enterprise Financial Distress
指導教授:齊德彰齊德彰引用關係
指導教授(外文):Chi, Der-Jang
口試委員:齊德彰李慕萱葉清江
口試委員(外文):Chi, Der-JangLee, Mu-ShangYeh, Ching-Chiang
口試日期:2012-06-18
學位類別:碩士
校院名稱:中國文化大學
系所名稱:會計學系
學門:商業及管理學門
學類:會計學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:56
中文關鍵詞:財務危機支援向量機最小平方法支援向量機區別分析t檢定因素分析
外文關鍵詞:financial distressSupport Vector MachineLeast Squares Support Vector MachineDiscriminant Analysist-testFactor Analysis
相關次數:
  • 被引用被引用:1
  • 點閱點閱:210
  • 評分評分:
  • 下載下載:13
  • 收藏至我的研究室書目清單書目收藏:0
企業財務危機分類模型一直是熱門研究議題,從早期傳統統計方法來建立模型,時至今日已經有大量的人工智慧演算法所建立的模型出現。而「支援向量機」與「最小平方法支援向量機」是現今較新穎的人工智演算法,究竟兩種方法在企業財務危機分類的診斷上有何差異?本研究嘗試建立此兩種方法之企業財務危機分類模型並比較差異,另外再以區別分析、t檢定與因素分析來相結合,以建立一個二階段的財務危機分類模型,並探討結合上述變數篩選方法所建立的模型之分類績效差異。
此研究以2001年至2011年底台灣非金融業之上市及上櫃公司為樣本,取財務比率、智慧資本和公司治理指標資料來建立企業財務危機分類模型。
本研究發現,以t檢定先進行變數篩選後,將有助於提升模型的整體分類績效。另外,在「支援向量機」和「最小平方法支援向量機」所分別建立的財務危機分類模型中,各組模型的分類績效並無明顯差異。此研究結果希望能提供日後學者在建立財務危機分類模型時能當參考之用。

Many previous studies have examined classification models for enterprise financial distress. While earlier models were built using traditional statistical methods, other machine learning algorithms are being used for building models nowadays. Support Vector Machine (SVM) and Least Squares Support Vector Machine (LS-SVM) are relatively new machine learning algorithms. How are they different? This study builds two classification models for financial distress using these two methods and compares the differences between them. This study also combines discriminant analysis, t-test, and factor analysis with SVM and LS-SVM to build a two-step classification model of enterprise financial distress and discusses the predictive performance of the models built using the abovementioned feature selection methods.
The sample includes listed and OTC companies in Taiwan, which were observed during the period 2001–2011. The study uses financial ratios, index of intellectual capital, and corporate governance for building the models.
It was found that models based on feature selection by t-test could forecast enterprise financial distress more accurately, and both SVM and LS-SVM had similar classified ability for building prediction models of financial distress. These findings could be useful for future studies.

內容目錄
中文摘要 ........................ iii
英文摘要 ........................ iv
誌謝辭  ........................ v
內容目錄 ........................ vi
表目錄  ........................ viii
圖目錄  ........................ x
第一章  緒論...................... 1
  第一節  研究動機.................. 1
  第二節  研究背景.................. 2
第三節  研究目的.................... 3
第四節  論文架構與研究流程圖.............. 3
第二章  文獻回顧.................... 6
  第一節  企業財務危機診斷模型之相關文獻....... 6
  第二節  SVM與LS-SVM............... 15
  第三節  變數篩選方法................ 18
第三章  研究方法.................... 20
  第一節  財務危機操作型定義............. 20
  第二節  變數定義與衡量............... 22
第三節  樣本來源.................... 23
第四節  建模流程.................... 24
第四章  模型評估.................... 28
  第一節  SVM與LS-SVM組.............. 28
第二節  整合區別分析與SVM和LS-SVM組......... 29
第三節  整合t檢定與SVM和LS-SVM組.......... 31
第四節  整合因素分析與SVM和LS-SVM組.......... 34
第五節  各模型間比較.................. 37
第五章  結論與建議................... 42
  第一節  結論.................... 42
  第二節  研究限制與建議............... 43
參考文獻 ........................ 44
附錄A  ........................ 50
附錄B  ........................ 54

一、中文部分

李天行,唐筱菁 (2004),整合財務比率與智慧資本於企業危機診斷模式之建構-類神經網路與多元適應性雲形迴歸之應用,資訊管理學報,11(2),161-189。

林淑萍,黃劭彥,蔡昆霖(2007),企業危機分類模式之研究-DEA-DA、邏輯斯迴歸與類神經網路之應用,會計與公司治理,4(1),35-56。

林豐騰(2009),企業財務危機預測-整合財務指標、公司治理因素及智慧資本構面模型,績效與策略研究,6(2),59-72。

邱志洲,簡德年,高淩菁(2003),演化式類神經網路在企業危機診斷上之應用-智慧資本指標的考量,臺大管理論叢,14(2),1-22。

邱登裕,鍾典村,吳致遠,謝齊莊(2007),以GA-SVM法探討企業財務危機之研究,中華管理學報,8(4),61-85。

曾淑峰,江俊豪(2008),GA-SVM組合式信用風險財務危機模型之研究,台灣金融財務季刊,9(1),1-25。

葉宗翰(2010),運用支援向量機於按成本設計(DTC)預測系統之研究-以飛機結構系統研發為例,國防大學中正理工學院國防科學研究所未出版之博士論文,28。

賴鈺城,李崑進,李善玉(2010),公司治理下電子業之財務預警模型,華人前瞻研究,6(1),1-23。

羅淑娟,林晶璟,陳義方(2009),應用邏吉斯迴歸技術探討財務危機預警變數與資料長度之適用性研究-以台灣上市電子產業為例,臺北科技大學學報,42(2),83-106。

羅聖雅,(2010),應用分量迴歸模型於財務危機的探討,創新研發學刊,6(2),24-38。

二、英文部分

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Blum, M. (1974). Failing company discriminant analysis. Journal of Accounting Research, 12(1), 1-25.

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Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167-179.

Dash, M., Liu, H. (1997). Feature selection for classification. Intel-ligent Data Analysis, 1(1), 131-156.

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. J. Mach. Learn. Res., 3(1), 1157-1182.

Houghton, K., & Woodliff, D. (1987). Financial ratios: The prediction of corporate ‘success’ and failure. Journal of Business Finance & Accounting, 14(4), 537-554.

Mensah, Y. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study. Journal of Accounting Research, 22(1), 380-395.

Min, J. H. & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614.

Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. International Joint Conferenceon
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Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.

Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293-300.

Shah, J., & Murtaza, M. (2000). A neural network based clustering procedure for bankruptcy prediction. American Business Review, 18(2), 80-86.

Suykens, J. A. K., Vandewalle, J.,& De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14(1), 23-35.

Shin, K.S. Lee, Y.J. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23(3), 321-328.

Shin, K.S. Lee, T.S., & Kim, H.J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127-135.

Tan, P. N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. Boston, Massachusetts: PearsonAddison Wesley.

Tam, K., & Kiang, M. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38(7), 926-947.

Tsai, C.-F. (2009). Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2), 120-127.

Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York, New York: Springer.

Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: The case of Chinese listed companies. Quality and Quantity, 45(3), 671-686.

Yang, M., & Da-wei, X. (2010). The selection method for hyper-parameters of support vector classification by adaptive chaotic cultural algorithm. International Journal of Intelligent Computing and Cybernetics, 3(3), 449-462.

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