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系統識別號 U0002-2502201308404800
中文論文名稱 MA交易規則在股票市場之投資操作與應用的兩篇文章
英文論文名稱 Two Essays on MA Trading Rules: Stock Markets Applications
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 101
學期 1
出版年 102
研究生中文姓名 李仁在
研究生英文姓名 Jen-Tsai Lee
學號 897620141
學位類別 博士
語文別 英文
口試日期 2013-01-19
論文頁數 54頁
口試委員 指導教授-倪衍森
委員-邱建良
委員-李命志
委員-陳勝源
委員-邱哲修
委員-廖東亮
委員-張琬喻
委員-倪衍森
中文關鍵字 技術分析  黃金交叉  死亡交叉  VMA交易規則  成份股 
英文關鍵字 Technical analysis  Golden cross  Dead Cross  VMA trading rules  Constituent stocks 
學科別分類
中文摘要 大多數的投資人或多或少會採用技術分析來交易股票。此外,個別投資者也往往會依照技術指標所發出的訊號來決定交易的時機。雖然技術分析在實務上廣泛地被採用,然而技術分析在學術研究上卻相當地的有限。因此,我們努力探討更多有關於技術交易規則,特別針對在技術分析中廣泛被使用的Moving Average (MA)交易規則。

本論文為探討與MA有關的二篇研究文章,第一篇為採用MA交易規則,並以三個相當具有代表性指數之成份股為研究標的,即道瓊工業指數、富時100指數、上證50指數之成份股,來探討投資人當MA交易訊號發出時介入是否有利可圖。其結果顯示,投資人於上證50成份股發出死亡交叉訊號時購入股票較為有利,此現象可能是由於中國個人投資者的從眾行為所致。本研究亦發現,當投資人介入黃金交叉且當日的漲幅越高之富時100與道瓊30成分股,則介入後之週報酬之負值會越大,此說明黃金交叉且當日的股價漲幅似有過度反應的現象。
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第二篇乃是以巴西,俄羅斯,印度和中國(金磚四國)的股價指數為研究標的,並探討投資者使用Variable-length MA(VMA)交易訊號出現時介入的有效性。其研究結果顯示若以整個研究期間來探討時,則發現投資人在VMA買入訊號發出時介入較賣出訊號發出時介入有較高的投資報酬率。然而若將研究期間分為金融危機前、金融危機期間來探討,則發覺金融海嘯前與整個期間的實證結果相一致,但海嘯前與海嘯期間有顯著的差異。是以若以全期的實證結果為結論,則無法分辨金融危機前和金融危機時期存有顯著的差異。

總之,由於全球國際化,自由化,技術創新的結果,本研究發現全球的股票市場日益趨近弱勢效率市場,如本論文之第一篇與第二篇文章中顯示,顯著的樣本並不多。但吾等認為技術分析仍有值得探討的空間,因為若技術指標在實務上無任何舉足輕重的地位,則實務上不應出現如此多與技術分析有關的資訊。
英文摘要 Most of market participants, more or less, might trade stocks in accordance with the technical analysis. In addition, individual investors often decide the trading timing in accordance with the trading signals emitted by technical indicators. Even though technical analysis are wildly employed in the real world; however, the technical analysis issues investigated seem to be limited in the academic aspects. Therefore, we endeavor to explore more information released from technical trading rules, especially in the Moving Average (MA) trading rules, which would be regarded as one of the popular trading rules in technical analysis.
Essay one employ the MA trading rules for investigating whether investors are able to make profits for the constituent stocks of three representative indices including the DJ30, the FTSE100, and the SSE100. The results show that market participants had better buy the constituent stocks of SSE50 as the dead cross presented, the phenomena might be caused by the herding behaviors for individual investors in China. Moreover, we reveal that the negative 5-days mean returns would be enlarged as the higher daily returns are shown in the golden cross days, which are emitted by the MA(5,20) trading rule for the constituent stocks of FTSE100 and DJ30. We infer that the results might be resulted from overreaction phenomena appeared in the golden-cross day.
In essay two, while analyzing the data periods including the pre-financial and financial crisis periods by Variable-length MA(VMA) trading rules, the results show that investors might make profits as buying signals rather than as selling signals shown for the Brazil, Russia, India and China (BRIC) stock markets. However, investors may find it difficult to make profits in a financial crisis period, since the significant results shown during the full period might not reveal the differences between the pre-financial and financial crisis periods.
In sum, we infer that due to the Internationalization, Liberalization, technology innovation in the worldwide share markets; therefore, the share market are quite close to weak market efficiency, as shown the few significant results presented in the essay one and essay two. However, we still deem that technical analysis issues are worthwhile for investigation, since if all of the technical indicators do not matter in the real world, then it is impossible so many resources related to technical analysis appeared in the web.
論文目次 Contents
Contents …………………………………………………………....…………………I
List of Tables ……………………………………………………………………….III
List of Figures ……………………………………………………………………....IV
Chapter 1 Introduction ……………………………………………………………...1
Chapter 2 Do Moving Average Trading Rules with “Wide” and “In-depth” Concerns Really Matter? ……………………………………………….6
2.1 Introduction …………………………………………………..…………….…6
2.2 Literature Reviews ……………………………………………………………9
2.3 Data and Technical Trading Introduced ………………………………..…13
2.3.1 The Data ……………………………………………………………..13
2.3.2 Technical Trading Rules…………………………………….............14
2.4 Empirical Results and Analyses …………………………………………….16
2.4.1 Descriptive Statistics ………………………………………………..17
2.4.2 5-days Mean Returns for Trading Signals Emitted by MA ……...17
2.4.3 5-days Mean Returns for the MA with Different K Indicators ….18
2.4.4 5-days Mean returns for the MA with Different RSIs ……………20
2.4.5 5-days Mean Returns for MA Trading Signals with More Concerns……………………………………………………………22
2.5 Concluding Remarks …………………………………………………………...24
Chapter 3 Do VMA Trading Rules Matter: Evidence for the BRIC Countries ..26
3.1 Introduction ………………………………………………………………….26
3.2 Literature Reviews ……………………………………………………….….29
3.3 Data and Methodology ………………………………………………………31
3.4 Empirical Results ……………………………………………………………33
3.4.1 Descriptive Statistics …………………………………………..……33
3.4.2 The Results of Employing the VMA Trading Rules for
the Full Period …………………….…………….………..………...34
3.4.3 The VMA Trading Rules for Two Sub-periods …………..……….36
3.5 Concluding Remarks ………………………………………………..….……39
Chapter 4 Conclusion ……………………………………...….……………………41
References ………………………………………………..…………………………44
Appendix A: Introduction for the DJ30, FTSE100, and SSE50 Indices ………..50
a. Dow Jones Industrial Average ……...………………………………………50
b. Financial Times Stock Exchange 100 Index ……………………………….50
c. Shanghai Stock Exchange 50 Index ………………………………………...51
Appendix B: Introduction for BRIC countries ………………………………..….53
Appendix C: The Stock indices of the BRIC countries …………………………..54

List of Tables
Table 2.1 Descriptive statistics ……………………………………….……….…..17
Table 2.2 Mean returns for the MA for either golden crosses or dead
Crosses emitted …………………………………………………………18
Table 2.3 Results for the golden and dead crosses with different K indicators ...19
Table 2.4 Results for the golden and dead crosses with RSIs …………………...21
Table 2.5 Results for golden or dead crosses with more concerns ………………23
Table 3.1 Descriptive statistics …………………………………………………….33
Table 3.2 Results of the full period ………………………………………………..35
Table 3.3 Results for the pre-financial tsunami period ………………………….37
Table 3.4 Results for the financial tsunami period ………………………………38
Table A.1 Brief introduction for Shanghai Stock Exchange over 2005-2009 …..52
Table A.2 GDP proportion of the world with BRIC countries ………………….53

List of Figures
Figure 2.1: The stock indices of the DJ30, FTSE100, and SSE50 ………………14
Figure 3.1: The Stock indices of the BRIC countries …………………………….34
Figure C.1: Brazil Bovesp Index (Brazil) ………………………………………...54
Figure C.2: Russian RTS Stock Index (Russia) ………………………………….54
Figure C.3: BOMBAY 500 Stock Index (India) ………………………………….54
Figure C.4: Shanghai Synthesis Index (China) …………………………………..54
參考文獻 Alexander, Sindey. S., 1964. Price Movements in Speculative Markets: Trend or Random Walks. No.2 In P. Cootner (ed.), The Random Character of Stock Market Prices, Massachusetts Institute of Technology Process, Cambridge, MA.
Aloui, R., Aissa, M.S.B., and Nguyen, D.K., 2011. Global Financial Crisis, Extreme Interdependences, and Contagion Effects: the Role of Economic Structure? Journal of Banking & Finance 35(1): 130–141.
Baba, B., and Nin, K., 2007. Prediction of Golden Cross and Dead Cross by Neural Networks and Its Utilization. Knowledge-based Intelligent Information and Engineering Systems Lecture Notes in Computer Science 4693: 642-648
Baig T., and Goldfajn I., 1999. Financial Market Contagion in the Asian Crisis, IMF Staff Papers 46: 167-195.
Bail, M., 2005. The Stochastic Oscillator: Technical Tuesday with Mark Bail. http://pennysleuth.com/the-stochastic-oscillator-technical-tuesday-with-mark-bail. accessed 7 September 2010.
Barberis, N., Shleifer, A., and Vishny, R., 1998. A Model of Investor Sentiment. Journal of Financial Economics 49(3): 307-343.
Baytas, A., and Cakici, N., 1999. Do Markets Overreact: International Eevidence. Journal of Banking and Finance 23(7): 1121-1144.
Bessembinder, H., and Chan, K., 1995. The Profitability of Technical Trading Rules in the Asian Stock Markets. Pacific-Basin Finance Journal 3(2-3): 257–84.
Bessembinder, H., and Chan, K., 1998. Market Efficiency and the Returns to Technical Analysis. Financial Management 27(2): 5-17.
Bohan, J., 1981. Relative Strength: Further Positive Evidence. Journal of Portfolio Management. 8(1): 36-39.
Bowman, R.G., and Iverson, D., 1998. Short-run Overreaction in the New Zealand Stock Market. Pacific Basin Finance Journal 6(5): 475-491.
Brock, W., Lakonishok, J., and LeBaron, B., 1992. Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47(5): 1731-1764.
Caginalp, G., and Desantis, M., 2011. Stock Price Dynamics: Nonlinear Trend, Volume, Volatility, Resistance, and Money Supply. Quantitative Finance 11(6): 849-861.
Calvo, S., and Reinhart, C.M., 1996. Capital Flows to Latin America: Is There Evidence of Contagion Effects? in Private Capital Flows to Emerging Markets After the Mexican Crisis (Eds) : 151–71.
Chan, L.K.C., Jegadeesh, N., and Lakonishok, J., 1996. Momentum Strategies. Journal of Finance 51(5): 1681-1713.
Chang, Y.H., Metghalchi, M., and Chan, C.C., 2006. Technical Trading Strategies and Cross-national Information Linkage: The Case of Taiwan Stock Market. Applied Financial Economics 16(10): 731-743.
Chiang, T.C., Jeon, B.N., and Li, H., 2007. Dynamic Correlation Analysis of Financial Contagion: Evidence from the Asian Markets. Journal of International Money and Finance 26(7): 1206–1228.
Chiang, Y.C., Ke, M.C., Liao, T.L., and Wang, C.D., 2012. Are Technical Trading Strategies still Profitable? Evidence from the Taiwan Stock Index Futures Market. Applied Financial Economics 22(12): 955-965.
Chopra, N., Lakonishok, J., and Ritter, J. R., 1992. Measuring Abnormal Performance: Do Stocks Overreact. Journal of Financial Economics 31(2): 235–268.
Chuang, Wen-I, and Lee, Bong-Soo, 2006. An Empirical Evaluation of the Overconfidence Hypothesis. Journal of Banking and Finance 30(9): 2489-2515.
Clare, A. and Thomas, S., 1995. The Overreaction Hypothesis and The UK Stock Market. Journal of Business Finance Accounting 22(7): 961–73.
Coutts, J.A., and Cheung, K.C., 2000. Trading Rules and Stock Return: Some Preliminary Short Run Evidence From The Hang Seng 1985-1997. Applied Financial Economics 10(9): 579-586.
Daniel, K.D., Hirshleifer, D., and Subrahmanyam, A., 1998. Investor Psychology and Security Market Under- and Over-reaction. Journal of Finance 53(6): 1839-1885.
Day, T.E. and Wang, P., 2002. Dividends, Nonsynchronous Prices, and The Returns From Trading The Dow Jones Industrial Average. Journal of Empirical Finance 9(4): 431-454.
Delong, J.B., Shleifer, A., Summers, L.H., and Waldmann, R.J., 1990. Positive Feedback Investment Strategies and Destabilizing Rational Speculation. Journal of Finance 45(2): 379-395.
Demir, I., Muthuswamy, J., and Walter, T., 2004. Momentum Returns in Australian Equities: The Influences of Size, Risk, Liquidity and Return Computation. Pacific Basin Finance Journal 12(2): 143-158.
DeBondt, W.F.M., and Thaler, R.H., 1985. Does The Stock Market Overreact. Journal of Finance 40(3): 793–805.
DeBondt, W.F.M., and Thaler, R.H., 1987. Further Evidence of Investor Overreaction and Stock Market Seasonality. Journal of Finance 42(3): 557-581.
Fama, E.F., 1965. The Behavior of Stock Market Prices. Journal of Business 38(1): 34-105.
Fama, E.F., 1970. Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25(2): 383-417.
Fama, E.F. and Blume, M.E., 1966. Filter Rules and Stock Market Trading Profits. Journal of Business 39(1): 226-241.
Fang, Y., and Xu, D., 2003. The Predictability of Asset Returns: an Approach Combining Technical Analysis and Time Series Forecasts. International Journal of Forecasting 19(3): 369-385.
Fifield, S.G.M., Power, D.M., and Sinclair, C.D., 2005. An Analysis of Trading Strategies in Eleven European Stock Markets. European Journal of Finance 11(6): 531-548.
Gaunt, C., 2000. Overreaction in The Australian Equity Market: 1974-1997. Pacific Basin Finance Journal 8(3-4): 375-398.
Gencay, R., 1996. Non-Linear Prediction of Security Returns with Moving Average Rules. Journal of Forecasting 15(3): 165-174.
Gencay, R., 1998. The Predictability of Security Returns with Simple Technical Trading Rules. Journal of Empirical Finance 5(4): 347-359.
Gunasekarage, A., and Power, D.M., 2001. The Profitability of Moving Average Trading Rules in South Asian Stock Markets. Emerging Markets Review 2(1): 17–33.
Heng, F.T.K., Azizan, N.A., and Yeap, L.W., 2012. Technical Trading Systems as Crystal Balls in Reducing Risk: The Malaysian Stock Market. International Business Management 6(2): 140-146.
Hon M.T., Strauss J.K., and Yong, S.-K., 2007. Deconstructing the NASDAQ Bubble: A Look at Contagion Across International Stock Markets. Journal of International Financial Markets, Institutions and Money 17(3): 213–230.
Hong, H. and Stein, J.C., 1999. A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets. Journal of Finance 54(6): 2143-2184.
Huang, A.Y., 2012. Asymmetric Dynamics of Stock Price Continuation. Journal of Banking and Finance 36(6): 1839-1855.
Ito, A., 1999. Profits on Technical Trading Rules and Time-varying Expected Returns: Evidence from Pacific-Basin Equity Markets. Pacific-Basin Finance Journal 7(3-4): 283–330.
Jensen, M.C. and Bennington, G., 1970. Random Walks and Technical Theories: Some Additional Evidences. Journal of Finance 25(2): 469-482.
Kahneman, D., and Tversky, A., 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica 47(2): 263-292.
Kang, J., Liu, M., and Ni, X., 2002. Contrarian and Momentum Strategies in China’s Stock Market: 1993– 2000. Pacific-Basin Finance Journal 10(3): 243– 265.
Kenourgios, D., Samitas, A., and Paltalidis, N., 2011. Financial Crises and Stock Market Contagion in a Multivariate Time-varying Asymmetric Framework. Journal of International Financial Markets, Institutions and Money 21(1): 92–106.
King, M., and Wadhwani, S., 1990. Transmission of Volatility between Stock Markets. Review of Financial Studies 3(1): 5–33.
Kwon K.Y., and Kish, R.J., 2002. Technical Trading Strategies and Return Predictability: NYSE. Applied Financial Economics 12(9): 639–53.
Lane, G.C., 1984. Trading Strategies. Futures Symposium International.
Loh E.Y.L., 2007. An Alternative Test for Weak Form Efficiency Based on Technical Analysis. Applied Financial Economics 17(12): 1103-1012.
Mazouz, K. and Li, X., 2007. The Overreaction Hypothesis in the UK Market: Empirical Analysis. Applied Financial Economics 17(13): 1101-1111.
Michael, D.M., 2007. Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises. Emerging Markets Finance and Trade 43(4): 46-73.
Michayluk, D., and Neuhauser, K.L., 2006. Investor Overreaction During Market Declines: Evidence from the 1997 Asian Financial Crisis. Journal of Financial Research 29(2): 217-234.
Miwa, K., and Ueda, K., 2002. The Influence of Investor Sentiment on the Formation of Golden-cross and Dead-cross. AAAI(www.aaai.org) Technical Report WS-02-10: 54-59.
Naughton, T., Truong, C., and Veeraraghavan, M., 2008. Momentum Strategies and Stock Returns: Chinese Evidence. Pacific-Basin Finance Journal 16(4): 476-492.
Patel, A., 2000. Trading Online, 2nd ed., Pearson Education Ltd, London.
Pring, M.J., 1991. Technical Analysis Explained, 2nd ed., New York: McGraw- Hill Book Company.
Ranter M., and Leal R.P.C., 1999. Test of Technical Trading Strategies in the Emerging Equity Markets of Latin America and Asia. Journal of Banking and Finance 23(12): 1887-1905.
Rodriguez J.C., 2007. Measuring Financial Contagion: A Copula Approach. Journal of Empirical Finance 14(3): 401–423.
Rosillo, R., Fuente, D. de la, and Brugos, J.A.L., 2013. Technical Analysis and the Spanish Stock Exchange: Testing the RSI, MACD, Momentum and Stochastic Rules using Spanish Market Companies. Applied Economics 45(12): 1541-1550.
Tian, G.G., Wan, G.H., and Guo, M.Y., 2002. Market Efficiency and the Returns to Simple Technical Trading Rules: New Evidence from US Equity Market and Chinese Equity Markets. Asia-Pacific Financial Markets 9(3-4): 241–58.
Wang, J., Burton, B.M., and Power, D.M., 2004. Analysis of the Overreaction Effect in the Chinese Stock Market. Applied Economics Letters 11(7): 173-144.
Wang, Zi-Mei, Chao, Shin-Chiao, and Chang, Ya-Ting, 2012. Technical Analyses and Order Submission Behaviors: Evidence from an Emerging Market. International Review of Economics and Finance 24: 109–128
Wong, W.K., Manzur, M., and Chew, B.K., 2003. How Rewarding is Technical Analysis? Evidence from Singapore Stock Market. Applied Financial Economics 13(7): 543-551.
Wu, Y., 2011. Momentum Trading, Mean Reversal and Overreaction in Chinese Stock Market. Review of Quantitative Finance and Accounting 37(3): 301-323.
Zhang, X.F., 2006. Information Uncertainty and Stock Returns. Journal of Finance 61(1): 105-137.
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