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論文中文名稱:股票選取策略研究-分別以Vikor與流形學習結合分類迴歸樹之應用 [以論文名稱查詢館藏系統]
論文英文名稱:A study on stock selection strategy by using Cart, vikor and manifold learning method [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:商業自動化與管理研究所
畢業學年度:97
出版年度:98
中文姓名:李國偉
英文姓名:Guo-Wei Li
研究生學號:96488061
學位類別:碩士
語文別:中文
口試日期:2009-06-29
論文頁數:59
指導教授中文名:林鳳儀
口試委員中文名:趙莊敏;葉清江
中文關鍵詞:股票市場分類迴歸樹流形學習VIKOR排序法
英文關鍵詞:Stock selectionCARTmanifoldVIKOR
論文中文摘要:股票市場已逐漸成為台灣目前投資的主要市場之一,股票市場變動快速而且影響層面錯綜複雜,容易使得投資者因聽取小道消息而盲目投資,資料探勘技術於股市方面已經有許多學者進行研究,其中類神經網路和支援向量機都有不錯分類能力,但其產生出來之預測結果不容易解讀,使得投資者有理解上問題。因此,本研究選擇可提供預測規則之決策樹,從數個簡單財務及非財務比率即可了解模型之建構過程。
在2004年以來陸續爆發博達、太電等掏空弊案,2007年次級房貸危機爆發,故本研究認為影響股價報酬因素必須加入公司治理變數與系統風險變數來進行研究。而為了提高預測能力,本研究也分別以流形學習及VIKOR排序法結合決策樹來建構預測模型;最後研究結果發現公司治理逐漸成為股價報酬的重要因素,而在使用流行學習對變數降維時,其因財務數據多群集性質而不適用,在運用VIKOR排序法時所取得的樣本時,不僅得以更少的樣本來建構模型,並且得到更好的預測準確率。
論文英文摘要:The stock market has gradually become one of the major investment markets in Taiwan. The stock market is very complex, and changes too fast that too invest. There are many studies using data mining technology in the stock market. Using neural network and support vector machine could provide a precise classification, but the results generated by black-box interpretation is not easy to understand. Therefore, this study provides prediction rules by choose the decision tree, from several financial and non-financial ratios to understand the process of model construction.
Since 2004, The burst of financial scandal of PEWC and ProComp, and the second half of 2007, the outbreak of the subprime mortgage crisis in the U.S.A. This study is to investigate whether corporate governance variables and system risk has affected in the stock return. To the improve forecasting power, we take the methodology manifold learning and VIKOR together and combine with decision tree respectively to construct prediction models.
The results found that corporate governance has gradually become an important factor in stock return, and VIKOR can serves better forecasting model.
論文目次:摘 要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究範圍 3
1.4 論文結構 3
1.5 研究流程 4
第二章 文獻探討 5
2.1 股價報酬之相關研究 5
2.1.1 財務報表資訊 5
2.1.2 公司治理 7
2.1.3 系統風險變數 9
2.2 決策樹 11
2.2.1 分類迴歸樹 12
2.3 VIKOR排序法 13
2.4 流形學習 14
第三章 研究設計 17
3.1 研究架構 17
3.2 研究方法 18
3.2.1 分類迴歸樹(CART) 18
3.2.2 運用拉普拉斯特徵映射進行變數降維 19
3.2.3 運用VIKOR排序法挑選樣本 20
第四章 資料處理流程與研究結果 23
4.1 資料說明 23
4.1.1 資料來源和研究對象 23
4.1.2 變數說明 24
4.2 使用CART建立預測模型 27
4.2.1 以1990-2008年為研究期間 27
4.2.2 以2005-2008年為研究期間 29
4.2.3 跨期分析比較 31
4.3 拉普拉斯特徵映射結合CART建立預測模型 33
4.3.1以1990-2008年為研究期間 33
4.3.2 以2005-2008年為研究期間 36
4.4 以VIKOR篩選樣本並建立CART預測模型 37
4.4.1 以1990-2008年為研究期間 38
4.4.2 以2005-2008年為研究期間 45
第五章 結論 54
參考文獻 56
一、 中文部分 56
二、 英文部分 56
論文參考文獻:一、 中文部分

1. 丁秀儀(2004),上市公司公司治理,經營績效與機構投資人投資行為關聯性之研究,國立政治大學企業管理研究所,博士論文。
2. 林俊宏、曾國雄、任維廉(2005),利用VIKOR方法解決企業資源規劃系統評選問題,農業與經濟,第三十四卷34,第69-90頁。
3. 許美滿、吳壽山、鍾惠民與林怡群(2004),會計與公司治理,第68-91頁。
4. 葉銀華、柯承恩與李存修(2002),公司治理與評等系統,台北,商智文化事業股份有限公司。
5. 簡楨富(2005),決策分析與管理,雙葉畫廊有限公司,台北。
6. 鄭忠樑(2002),運用分類樹於股價報酬率預測之研究,元智大學資訊管理研究所碩士論文。

二、 英文部分

1. Agrawal, A. and G. N. Mandelker (1990), “Large Shareholders and the Monitoring of Managers: The Case of Antitakeover Charter Amendments,” The Journal of Financial and Quantitative Analysis, 25(2), pp.143-161.
2. Altman, E. (1993). Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankrupcy. New York, NY: Wiley.
3. Aman, H., Nguyen, P. (2008 )Do stock prices reflect the corporate governance quality of Japanese firms? Journal of the Japanese and International Economies 22 (4), pp.647-662
4. Ashbaugh, H., Collins, D. W. and R. LaFond. (2005). Corporate governance and the cost of equity capital. SSRN Working Paper.
5. Basu S., (1983), “The Relationship Between Earnings Yield, Market Value and Return for NYSE Common Stocks,” Journal of Financial Economics, 12(1),pp.129-156.
6. Beasley, M. S. (1996). An empirical analysis of the relation between the board of directors composition and financial statement fraud. The Accounting Review, 71(4), pp.443-465.
7. Belkin, M. and Niyougi, P. (2003), Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computations, vol. 15, pp.1373-1396.
8. Black, F. M. C., Jensen, M. C. and Scholes, M., (1972), ‘The capital asset pricing model: Some empirical tests’, Studied in the Theory of Capital Market, Praeger Publishers, New York.
9. Black, B., (2001),“The Corporate Governance Behavior and Market Value of Russian Firms” Emerging Markets Review, 2, pp.89-108.
10. Bortolotti, B., J. D’Souza, M. Fantini, & W. L. Megginson, (2002), Privatization and the sources of performance improvement in the global telecommunications industry, Telecommunications Policy, 26, pp.243-268.
11. Breiman, L., J. H. Friedman, R. A. Olshen & C. J. Stone (1984), Classification and Regression Trees, New York: Chapman & Hall.
12. Byrd, Parrino, Pritsch(1998),Stockholder-manager conflicts and firm value, Financial Analyst Journal, AIMR.
13. Cayton, L. (2008), Algorithms for manifold learning, UCSD Technical Report CS2008-0923.
14. Durnev, Art and Kim (2005), E. Hav ,To Steal or Not to Steal: Firm Attributes, Legal Environment, and Valuation , Journal of Finance,3,1461-1493,
15. Errity, A. and McKenna, J. (2006), An investigation of manifold learning for speech analysis, in Proc. of the Int. Conf. on Spoken Language Processing, Pittsburgh PA, USA.
16. Fama, F.E. and French R.F. (2006), “The Value Premium and CAPM,” Journal of
17. Finance, 61, 2163-2185.
18. Foreman, .D.(2003). “A logistic analysis of bankruptcy within the US local telecommunications industry,” Journal of Economics and Business, vol.55, no2, pp.135-166.
19. Francis, J., R. LaFond, P. Olsson and K. Schipper., (2003), Earnings Quality and the Pricing Effects of Earnings Patterns, FASB working paper, University of Wisconsin.
20. Grundy, K. and B. G. Malkiel, (1996), “Reports of beta’s death have been greatly exaggerated,” The Journal of Portfolio Management,pp.36-44.
21. Gompers, P., and A. Metrick. (2001). Institutional investors and equity prices. Quarterly Journal of Economics 116:pp.229-260
22. Hartigan, J. A., (1975), Clustering Algorithms, John Wiley, New York.
23. Hastie, T. ; R.Tibshirani, ; J. Friedman, (2001) "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Spring Series in Statistics.
24. Heston, S. L., K. G. Rouwenhorst, and R. E. Wessels, (1999), “The role of beta and size in the cross-section of European stock returns,” European Financial Management 5,pp.9-27.
25. Hopwood, W. S. and Schaefer, T. F., (1988), "Incremental information content of earnings and nonearnings-based financial ratios", Contemporary Accounting Research 5 ,pp.318-342
26. Javier Estrada, (2005), “Adjusting P/E ratios by growth and risk: the PERG ratio”, International Journal of Managerial Finance,Vol.1,pp.187-203.
27. Jeffery S. Abarbanell and Brian J. Bushee. )1998_. “Abnormal Returns to a Fundamental Analysis Strategy.” The Accounting Review, vol.73, no.1 (January) :pp.19-45.
28. Jensen and Ruback,(1983) “The market for corporate control: the scientific evidence”, Journal of Financial. Economics 11, 1983,pp.5-50
29. Jensen, Michael C. (1993), “The Modern Industrial Revolution, Exit, and the Failure of Internal Control Systems,” Journal of Finance, 48,pp.831-880.
30. Kao, Duen-Li & Robert D. Shumaker (1999), “Equity Style Time,” Financial Analysts Journal, January-February,pp.37-48.
31. Leland, H. E. and D. H. Pyle. (1977). Informational asymmetrics, financial structure, and financial intermediation. Journal of Finance, 32,pp.371-387.
32. Li, B., Zheng, C.-H. and Huang, D.-S. (2008), Locally linear discriminant embedding: An efficient method for face recognition, Pattern Recognition, Vol. 41,pp.3813-3821.
33. Luo, Z., Wu, X., Guo, S., and Ye, B. (2008), Diagnosis of breast cancer tumor based on manifold learning and support vector machine, Information and Automation, ICIA 2008. International Conference,pp.703-707.
34. Martinez , W. L. and Martinez, A. R. (2005), Exploratory data analysis with MATLAB /Wendy L. Martinez, Angel R. Martinez., Boca Raton, Fla. : Chapman & Hall/CRC.
35. Miller, G., AND J. Piotroski., (2000), Forward-Looking Earnings Statements: Determinants and Market Response. Working paper, University of Chicago.
36. Opricovic, S., (1998), Multicriteria Optimization of Civil Engineering System, Faculty of Civil Engineering, Belgrade
37. Ou, J. and S. Penman, (1989), “ Financial statement analysis and prediction of stock returns”,Journal of Accounting and Economics ,Vol.11(4),pp.295-329.
38. Easton, P. D., (1986), “Accounting Earnings and Security Valuation: Empirical Evidence of the Fundamental Links,” Journal of Accounting Research, Vol. 23,pp.54-77.
39. Pettengill, G. S. Sundaram, and I. Mathur(2002), “Payment for Risk: Constant Beta vs. Dual-Beta Models,” Financial Review, 37, No.2, pp123-36.
40. Quinlan, J. R., (1986), Induction of decision tree, Machine Learning, 1,pp.81-106.
41. Quinlan, J. R., (1996), Improved use of continuous attributes in C4.5, Journal of Artificial Intelligence Research, 4,pp.77-90.
42. Ross, S. A. (1976), "The arbitrage theory of capital asset pricing." Journal of Economic Theory 13,pp.341-360.
43. Ribeiro, B. and Vieira, A. and Neves, J. , (2008)"Supervised Isomap with Dissimilarity Measures in Embedding Learning", in Proc. of the Ibero-American Conference on Pattern Recognition, Progress in Pattern Recognition, Image Analysis and Applications, Lecture Notes in Computer Science (LNCS),Springer Berlin / Heidelberg, Vol. 5197, September. ,pp.389-396
44. Sorensen, Eric H., Joseph J. Mezrich & Keith L. Miller (1996), “Asset Allocation — The Cart Before the Bourse,” Salomon Brothers, June.
45. Sorensen, Eric H., Keith L. Miller & Chee K. Ooi (2000), “The Decision Tree Approach to Stock Selection,” Journal of Portfolio Management, 27(1),pp.42-52.
46. Sharpe,W.F. (1964), “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk”, Journal of Finance, Vol.19,pp.425-442.
47. Shleifer, Andrei, and Robert W. Vishny, (1997), A survey of corporate governance, Journal of Finance 52,pp.737-783.
48. Shleifer, A. and R. Vishny (1986), “Large Shareholders and Corporate Control,” Journal of Political Economy, 95,pp.461-488.
49. Tzeng, G.H. (2003), “Multiple Objective Decision Making in Past, Present, and Future,” Multi-Objective Programming and Goal-Programming: Theory and Applications, by Tanino, T., Tanaka, T. and Inuiguchi, M. (eds): Springer,pp.65-76.
50. Wu, C. H.*, Tseng, G. H., Goo, Y. J., and Fang, W. C. (2007), “A Real-Valued Genetic Algorithm to Optimize the Parameters of Support Vector Machine for Predicting Bankruptcy”, Expert Systems with Applications. 32(2), March, 2007,pp.397-408.
51. Yeh, Y., T. Lee and T. Woidtke (2001), “Family Control and Corporate Governance: Evidence from Taiwan,” International Review of Finance, 2 (1/2),pp.21-48.
52. Yu, P.L., (1973), “A class of solutions for group decision problems,” Management Science, 19 (8),pp.936–946.
53. Zeleny, M. (1982), Multiple Criteria Decision Making: McGraw-Hill, New York.
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