跳到主要內容

臺灣博碩士論文加值系統

(3.235.120.150) 您好!臺灣時間:2021/08/06 01:50
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:陳琇宜
研究生(外文):Show-Yi Chen
論文名稱:應用資料探勘於全額交割股恢復交易之探討
論文名稱(外文):Using data mining approach for prediction of resuming stocks requiring full delivery to normal trades
指導教授:胡念祖胡念祖引用關係
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:資訊管理研究所在職專班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:40
中文關鍵詞:全額交割資料探勘分類法則
外文關鍵詞:Stocks requiring full deliveryData miningClassification rule
相關次數:
  • 被引用被引用:3
  • 點閱點閱:283
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
2008年以來發生全球金融風暴至今,全世界許多企業面臨財務危機的窘境甚至面臨倒閉,加上近幾年經濟不景氣、原物料的上漲等因素之影響,使得很多危機造成國內企業因週轉不靈、跳票或申請重整等原因,被打入全額交割。然而並不是每間危機公司都能夠順利的從失敗中找到方法,重新找到企業的重生契機,究竟影響企業能否順利恢復正常交易的主要因素為何,實值得探討。 本研究住要目的乃是欲探勘出過去在台灣股市中,若干因財務危機而被打入全額交割的公司能成功恢復(櫃)的因素。 在此本文我們應用以決策樹為基礎之資料探勘方法來探索出因財務危機而被打入全額交割股的上市、上櫃公司是否能成功重新上市上櫃成功的分類法則。更進一步,利用以決策樹演算法為基礎之boosting ensemble 方法所建立之多重分類器模型被建立。由實驗數據顯示利用所建立之多重分類器模型使得分類準確率能成功被提升,且型二錯誤也能成功的被減小。此外、所探勘出之法則可以被發展成為判斷因財務危機之全額交割股的公司是否能成功重新上市上櫃之電腦決策模型如同建立專家系統一般。
In recent years, a number of financial crises have made prediction of resuming stocks requiring full delivery to normal trades to become a noticeable topic to both practices and academy. In order to make their decisions correctly in time, all of the creditors, analysts, investors and regulators wish to predict whether financially distressed firms will be able to emerge based on the information available at the time of the company’s stocks requiring full delivery. However, evaluating the feasibility of financial reorganization success is complex. In this research, we employed decision tree-based mining techniques to develop a prediction model. Besides, the multi-learner model constructed by boosting ensemble approach with decision tree algorithm is used to enhance the prediction accuracy rate. The empirical results show that the classification accuracy has been improved by using multi-leaner model in terms of less Type II errors. In particular, the extracted rules from the data mining approach can be developed as a computer model for the prediction and like expert systems.
目錄      
中文摘要.............................................. i
英文摘要.............................................. ii
誌 謝.......................................... iii
目 錄............................................... iv
表 目 錄.............................................. vi
圖 目 錄.............................................. vii
一、 緒論......................................... 1
1.1 研究背景..................................... 1
1.2 研究流程..................................... 6
二、 文獻探討..................................... 8
2.1 破產預測..................................... 8
2.2 破產恢復..................................... 8
2.3 資料探勘..................................... 9
2.4 決策樹和法則的探勘........................... 11
2.5 精確度的改善................................. 15
三、 研究方法與實驗結果........................... 18
3.1 研究程序..................................... 18
3.2 研究環境和方法............................... 18
3.3 研究資料來源................................. 18
3.4 變數......................................... 24
3.5 實驗結果..................................... 25
四、 結論......................................... 35
參考文獻.............................................. 37
[1]Altman, E. (1968), “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,” Journal of Finance, 23, pp. 589-609.
[2]Breiman, L (1996), “Bagging predictors,” Machine Learning, 24 (2), pp.123-140.
[3]Berry, A. J. M. and Limoff, Gordon. (1997), Data Mining Techniques, John Wiley & Sons, Canada.
[4]Becchetti, L., & Sierra, J. (2003), “Bankruptcy risk and productive efficiency in manufacturing firms,” Journal of Banking and Finance, 27(11), pp. 2099-2120.
[5]Berlin, M. (1996), “For Better and for Worse: Three Lending Relationships,” Business Review, pp.3-12.
[6]Bhargava, M., Dubelaar, C., and Scott, S. (1998), “Predicting bankruptcy in the retail sector: An examination of the validity of key measures of performance,” Journal of Retailing and Consumer Services, 5(2), pp. 105-117.
[7]Bryan, M. D., S. Tiras, and Wheatley, C. (2002), “The interaction of solvency with liquidity and its association with bankruptcy emergence,” Journal of Business Finance and Accounting, 29, pp. 935-965.
[8]Campbell, S. V. (1993). The significance of direct bankruptcy costs in determining the outcome of bankruptcy reorganization. Doctoral dissertation. University of Oregon.
[9]Campbell, S. (1996), “Predicting Bankruptcy Reorganization for Closely Held Firms,” Accounting Horizons, 10, pp. 12-25.
[10]Casey, C, McGee, V. and Stickney. C. (1986), “Discriminating between reorganized and liquidated firms in bankruptcy,” The Accounting Review, 61, pp. 249-262.
[11]Chang, C. L. (2007). A study of applying data mining to early intervention for developmentally-delayed children. Expert Systems with Applications, 33(2), pp. 407-412.
[12]Chen, K. and Wei, K. (1993), “Creditors'' Decisions to Waive Violations of Accounting-Based Debt Covenants,” The Accounting Review, 68, pp. 218-33.
[13]Cheung, K.-W., Kwok, J. T., Law, M. H., and Tsui, K. C. (2003), “Mining customer product rating for personalized marketing,” Decision Support Systems, 35, pp. 231-243.
[14]Donoher, W. J. (2004), “To File or Not to File? Systemic incentives, corporate control, and the bankruptcy decision,” Journal of Management, 30(2), pp. 239-262.
[15]Fabling, R., and Grimes, A. (2005), “Insolvency and economic development: Regional variation and adjustment,” Journal of Economics and Business, 57(4), pp. 339-359
[16]Fich, E.M. and Slezak, S.L. (2008) “Can corporate governance save distressed firms from bankruptcy? An empirical analysis,” Review of Quantitative Finance and Accounting, 30(2), pp. 225-52.
[17]Frawley, W.J., Piatetsky-Shapiro, G., and Matheus, C.J. (1991). Knowledge discovery in database: An overview. In Knowledge Discovery in Databases, pp. 1-27. Cambridge, MA: AAAI/MIT Press, Reprinted in AI Magazine, 13(3), 1992.
[18]Freund, Y., and Schapire R. E. (1997), “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, (55:1), pp. 119-139.
[19]Han, J., and Kamber, M. (2006). Data Mining: Concepts and Techniques (2nd ed.). Amsterdam: Morgan Kaufmann.
[20]Huang, C.L., and Wang, C.J. (2006), “A GA-based feature selection and parameters optimization for support vector machines,” Expert Systems with Applications, 31, pp. 231-240.
[21]Kane, G.D., Richardson, F., and Graybeal, P. (1996), “Recession-Induced Stress and the Prediction of Corporate Failure,” Contemporary Accounting Research, 13, pp. 631-50.
[22]Kane, G.D., Richardson, F. and Meade, N. (1998), “Rank Transformations and the Prediction of Corporate Failures,” Contemporary Accounting Research, 15, pp. 145-66.
[23]Kim, Y.S., and Street, W.N. (2004), “An intelligent system for customer targeting: A data mining approach,” Decision Support Systems, 37, pp. 215-228.
[24]Kim, Y.S., Street, W.N., and Menczer, F. (2006), “Optimal ensemble construction via meta-evolutionary ensembles,” Expert Systems with Applications, 30, pp. 705-714.
[25]Laitinen, E.K., and Laitinen, T. (2000), “Bankruptcy prediction: Application of the Taylor’s expansion in logistic regression,” International Review of Financial Analysis, 9(4), 327-349.
[26]Liu, C.-L. and Chen, T.-C. (March 2009), “A study of applying data mining approach to the information disclosure for Taiwan’s stock market investors,” Expert Systems with Applications, 36(2), Part 2, pp. 3536-3542.
[27]LoPucki, L.M. (1983), “The debtor in full control—systems failure under Chapter 11 of the Bankruptcy Code?” American Bankruptcy Law Journal, 57, 99-126.
[28]Ohlson, J. (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” Journal of Accounting Research, 18, 109-31.
[29]Parpinelli, R.S., Lopes, H. S., and Freitas, A.A. (2002). “Data mining with an ant colony optimization algorithm,” IEEE Transactions on Evolutionary Computing, 6(4), pp. 321-332.
[30]Peng, S., Xu, Q., Ling, X.B., Peng, X., Du, W., and Chen, L. (2003), “Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines,” FEBS Letters, 555, pp. 358-362.
[31]Roiger, R.J., and Geatz, M.W. (2003). Data Mining: A Tutorial-based Primer. Boston: Addison Wesley.
[32]Sharkey, A.J. (1996), “On combining artificial neural nets,” Connection Science, 8, pp. 299-314.
[33]Tsai, Y.C., Cheng, C.H., and Chang, J.R. (2006), “Entropy-based fuzzy rough classification approach for extracting classification rules,” Expert Systems with Applications, 31(2), pp. 436-443.
[34]Wei, Z.P., and Dong, H.S. (2000). Theory and Practices of E-commerce. Taipei: HwaTai Publications.
[35]White, M.J. (1983), “Bankruptcy costs and the new bankruptcy code,” Journal of Finance, 38, 477-487.
[36]White, M.J. (1989), “The corporate bankruptcy decision,” Journal of Economic Perspectives, 3 (1), 129-151.
[37]Yin, Chi-Lung. (1993). The effect of corporate governance on corporate reorganization. Master dissertation Fu-Jen Catholic University.
[38]李俊憲 (2006) ,影響全額交割股恢復交易之因素-公司治理與財務變數觀點, 碩士論文,長榮大學經營管理研究所。
[39]曾新穆、李建億(2004)譯,資料探勘 (R.J. Roiger, M.W. Geatz, Data Mining, A Tutorial-based Primer, Addision Wesly, 2003),東華書局出版。
[40]葉銀華 (2005),蒸發的股王-領先發現地雷危機,華泰書局。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊