進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2306201118073200
論文名稱(中文) 以技術分析發展股票資訊預測方法
論文名稱(英文) A Technical Analysis-based Method for Stock Market Forecasting
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所碩博士班
系所名稱(英) Institue of Manufacturing Information and Systems
學年度 99
學期 2
出版年 100
研究生(中文) 曹湘庭
研究生(英文) Hsiang-Ting Tsao
學號 p96981116
學位類別 碩士
語文別 中文
論文頁數 52頁
口試委員 指導教授-陳裕民
共同指導教授-陳育仁
口試委員-李昇暾
口試委員-王素貞
中文關鍵字 預測  股票市場  技術分析  支援向量機  粒子群優化演算法 
英文關鍵字 Forecasting  stock market  technical analysis  support vector machine  particle swarm optimization 
學科別分類
中文摘要 由於股票市場的動態變化以及股票價格的影響因素眾多,使得股票價格評定困難度增加;再者,人類在處理資訊時,常會對於立即可用且切身相關的資訊加以放大其重要性,使得投資決策制定隨波逐流而無法客觀理性。因此,如何運用有效的資訊協助投資者進行股票投資決策已成為股票投資理財重要的課題之一。
本研究主要利用技術分析發展一股票預測方法,以預測出符合投資者偏好之個股,進而提昇股票投資者之決策支援品質與獲利能力。針對上述目的,本研究主要研究項目包括: (i) 技術分析為基之股票預測流程設計,(ii) 技術分析為基之股票預測方法發展以及(iii) 技術分析為基之股票預測方法驗證與比較。其中,技術分析為基之股票預測方法包括趨勢為基之個股分類方法、合適技術指標選定方法與交易訊號預測方法。
英文摘要 Dynamic changes in the stock market, and many factors influence stock prices, making the stock price to increase the difficulty of assessing. Furthermore, when human beings process information, often available for an immediate and vital information related to amplify the importance, making the decision-making of investment can not be objective and rational drift. Therefore, how to effectively use information to help investors make stock investment and also it has become one of the important issues in financial
decision-making.
In this study, the development of a stock using technical analysis forecasting method to predict the preferences of the investors out of stocks, and thus enhance the quality of the stock investors and decision-support profitability. For these purposes, the main research tasks include the study: (i) Design A Technical Analysis-based Process for Stock Market Forecasting, (ii) Development A Technical Analysis-based Method for Stock Market Forecasting, and (iii) Validation and Comparison of Technical Analysis-based Method for Stock Market Forecasting. Among them, the Technical Analysis-based Method for Stock Market Forecasting including Trend-based Stock Type Classification method, Adaptive Stock Market Indicator Selection method, and Stock Market Trading Signal Forecasting method.
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 研究背景................................1
1.2 研究動機...................................1
1.3 研究目的.......................................2
1.4 研究問題分析..................................2
1.5 研究項目與方法..........................3
1.6 研究發展程序..........................5
第二章 文獻探討.................................7
2.1 相關投資決策理論.......................7
2.2股票市場走勢的分析................8
2.2.1 效率市場假設...............8
2.2.2 基本分析.....................9
2.2.3 技術分析....................9
2.3股票投資的預測方法........................10
2.3.1 Apriori演算法................11
2.3.2 標準差..........................12
2.3.3 支援向量機(Support Vector Machine,SVM) ..12
2.3.4 粒子群優化演算法(Particle Swarm Optimization, PSO) .......................13
第三章 股票投資之決策支援模式設計...........................15
3.1 股票投資之決策支援模式.................15
3.2 股票投資之決策支援流程...................16
第四章 技術分析為基之股票預測方法發展.....................20
4.1 技術分析為基之股票預測程序.......................20
4.2 趨勢為基之個股分類方法....................21
4.3 合適技術指標選定方法.........................26
4.4 交易訊號預測方法................................30
第五章 技術分析為基之股票預測方法驗證與比較............35
5.1 方法可行性驗證...............................35
5.2 方法準確度評估與比較..................45
第六章 結論與未來研究方向...............................48
6.1 結果與貢獻...........................48
6.2 未來研究方向.........................49
參考文獻.........................................50
參考文獻 [1] http://www.dgbas.gov.tw/mp.asp?mp=1, Directorate-general of budget, Accounting and Statistics, Executive Yuan, R.O.C. (Taiwan).
[2] http://www.twse.com.tw/ch/index.php, Taiwan Stock Exchange.
[3] http://www.wikipedia.org/
[4] R. Agrawal and R. Srikant, Fast algorithms for mining generalized association rules. In: Proceedings of the 20th International Conference on Very Large Database (VLDB94), Santiago, Chile, pp. 487-499, 1994.
[5] E. Altay and M. H. Satman, Stock market forecasting: artificial neural networks and linear regression comparison in an emerging market, Journal of Financial Management and Analysis, Vol. 18, No. 2, pp. 18-33, 2005.
[6] K. Baker and J. Haslem, Information Needs of Individual Investors, The Journal of Accountancy, Vol. 136, No. 5, pp. 64-69, 1973.
[7] C. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167, 1998.
[8] P. C. Chang, C. H. Liu, J. L. Lin, C. Y. Fan and C. S. P. Ng, A neural network with a case based dynamic window for stock trading prediction, Expert Systems with Applications, Vol. 36, No. 3, pp. 6889-6898, 2009.
[9] T. Chavarnakul and D. Enke, Intelligent technical analysis based equivolume charting for stock trading using neural networks, Expert Systems with Applications, Vol. 34, No. 2, pp. 1004-1017, 2008.
[10] N. Darvas, How I made $2,000,000 in the stock market, Publisher: Lyle Stuart, 2001.
[11] A. I. Diler, Predicting direction of ISE national-100 index with back propagation trained neural network, Journal of Istanbul Stock Exchange, Vol. 7, No. 25-26, pp. 65-81, 2003.
[12] R. D. Edwards and J. Magee, Technical Analysis of Stock Trends, 8th Edition, Publisher: AMACOM, 1966.
[13] E. F. Fama, Efficient Capital Market: A Review of Theory and Empirical Work, The Journal of Finance, Vol. 25, No. 2, pp. 383-417, 1970.
[14] Y. M. Ha, P. Sanghyun, S. W. Kim, J. I. Won and J. H. Yoon, A stock recommendation system exploiting rule discovery in stock databases, Information and Software Technology, Vol. 51, No. 7, pp. 1140-1149, 2009.
[15] C. L. Huang and C. Y. Tsai, A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting, Expert System with Applications, Vol. 36, No. 2, pp. 1529-1539, 2009.
[16] W. Jiang and Y. Xu ,Y. Xu, A Novel Intrusions Detection Method Based on HMM Embedded Neural Network, Advances, Natural Computation,Lecture Notes in Computer Science,Vol. 3610, pp. 139-148, 2005.
[17] Y. Kara, M. A. Boyacioglu and Ö. K. Baykan, Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange, Expert Systems with Applications, Vol. 38, No. 5, pp. 5311-5319, 2011.
[18] J. Kennedy and R. Eberhart, Particle swarm optimization. In Proceedings of IEEE International on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
[19] J. M. Keynes, The General Theory of Employment, Interest and Money, Publisher: Atlantic Publishers & Dist, 1936.
[20] C. D. Kirkpatrick and J. R. Qahlquist, TECHNICAL ANALYSIS: The complete Resource for Financial Market Technicians, Vice President, Publisher: Tim Moore, 2010.
[21] R. K. Lai, C. Y. Fan, W. H. Huang and P. C. Chang, Evolving and clustering fuzzy decision tree for financial time series data forecasting, Expert Systems with Applications, Vol. 36, No, 2, pp. 3761-3773, 2009.
[22] T. P. Liang, Decision support systems and business intelligence, Publisher: BestWize, 2006.
[23] X. Liang, H. Zang, J. Xiao and Y. Chen, Improving option price forecasts with neural networks and support vector regressions, Neurocomputing, Vol. 72, No. 13-15, pp. 3055-3065, 2009.
[24] L. X. Liu, Y. Q. Zhuang and X. Y. Liu, Tax forecasting theory and model based on SVM optimized by PSO, Expert Systems with Applications, Vol. 38, No, 1, pp. 116-120, 2011.
[25] T. Y. Mieko and T. Seiji, Adaptive use of technical indicators for the prediction of intra-day stock prices, Physica A: Statistical Mechanics and its Applications, Vol. 383, No. 1, pp. 125-133, 2007.
[26] J. R. Nofsinger and R. W. Sias, Herding and Feedback Trading by Institutional and Individual Investors, The Journal of Finance, Vol. 54, No. 6, pp. 2263-2295, 1999.
[27] C. R. Radcliffe, Investment: Concepts, Analysis and Strategy, 5th Edition, Publisher: Addison Wesley, 1997.
[28] M. Ratner and R. P. C. Leal, Test of technical trading strategies in the emerging equity markets of Latin America and Asia, Journal of Banking and Finance,Vol 23, No. 12, pp. 1887-1905, 1999.
[29] P. Slovic, Psychological study of human judgment: implications for investment decision making, Journal of Finance, Vol. 27, No. 4, pp.779-799, 1972.
[30] H. A. Simon, Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations, 4th Edition, Publisher: Free Press, 1997.
[31] C. F. Tsai and Y. C. Hsiao, Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches, Decision Support Systems, Vol. 50, No. 1, pp 258-269, 2010.
[32] A. C. R. Van Riel and H. Ouwersloot, J.Lemmink, Antecedents of Effective Decision Making:A Cognitive Approach, The IUP Journal of Managerial Economics, Vol. 5, No. 4, pp. 7-28, 2006.
[33] L. Yu, S. Wang and K. K. Lai, Mining stock market tendency using GA-based support vector machines, Lecture Notes in Computer Science, Vol. 3828, pp. 336-345, 2005.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2016-07-04起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw