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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 胡星陽 | |
| dc.contributor.author | Yu-Chun Cheng | en |
| dc.contributor.author | 鄭宇君 | zh_TW |
| dc.date.accessioned | 2021-06-08T04:24:12Z | - |
| dc.date.copyright | 2010-06-30 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-06-23 | |
| dc.identifier.citation | 1. Alexander, S. (1961), “Price Movement in Speculative Markets:Trends or Random Walks”, Industrial Management Review, 2,7-26.
2. Alexander, S. (1964), “Price movement in speculative markets: trend or random walks, No. 2”, The Random Character of Stock Market Prices, MIT Press, Cambridge, MA, Cootner, P. (ed.), 338-372. 3. Bessembinder, Hendrik and Chan (1995), “The Profitability of Technical Trading Rules in the Asian Stock Markets”, Pacific-Basin Financial Journal, 3, 257-284. 4. Bessembinder, Hendrik and Chan (1998), “Market Efficiency and the Returns to Technical Analysis?”, Financial Management 27(2), 5-17. 5. Black, F. (1986), “Noise, Journal of Finance, 41, 529-543. 6. Blume, Easley, and O’Hara (1994), “Market statistics and technical analysis: the role of volume”, Journal of Finance, 49, 153-181. 7. Brock, Lakonishok, and LeBaron (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns”, Journal of Finance, 47,1731-1764. 8. Brown, D.P. and Jennings, R.H. (1989), “On technical analysis”, Review of Financial Studies, 2, 527-551. 9. Chang, E. J., Lima, E. J. A. and Tabak, B. M.(2004), “Testing for Predictability in Emerging Equity Markets”, Emerging Markets Review,5, 295-316. 10. Clyde, W.C. and Osler, C.L. (1997), “Charting: chaos theory in disguise?”, Journal of Futures Markets, 17, 489–514. 11. De Long, J.B., Shleifer, A., Summers, L.H. and Waldmann, R.J. (1990), “Positive feedback investment strategies and destabilizing rational speculation”, Journal of Finance, 45, 379-395. 12. De Long, J.B., Shleifer, A., Summers, L.H. and Waldmann, R.J. (1991), “The survival of noise traders in financial markets”, Journal of Business, 64, 1-19. 13. Fama, E. and Blume, M. (1966), “Filter Rules and Stock Market Trading,” Journal of Business, 39, 226-241. 14. Fama, E. (1970), “Efficient Capital Market: A Review of Theory and Empirical Works”, Journal of Finance, 25, 383-417. 15. Froot, Scharfstein, and Stein (1992), “Herd on the street: informational inefficiencies in a market with short-term speculation”, Journal of Finance, 47, 1461-1484. 16. Grundy, B.D. and McNichols, M. (1989), “Trade and the revelation of information through prices and direct disclosure”, Review of Financial Studies, 2, 495-526. 17. Hellwig, M. (1982), “Rational expectations equilibrium with conditioning on past prices: a mean–variance example”, Journal of Economic Theory, 26, 279-312. 18. James, F. E. (1968), “Monthly Moving Averages-An Effective Investment Tool? ”, Journal of Financial and Quantitative Analysis, 3, 315-326. 19. Jensen, M. and Benington, G. (1970), “Random Walks and Technical Theories: Some Additional Evidence”, Journal of Finance, 25, 469-482. 20. Lee, C. F. and Rui, O. M. (2000), “Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets”, Review of Quantitative Finance and Accounting, 14(4), 341-360. 21. Lo, MacKinlay (1999), “A Non-Random Walk Down Wall Street”, New York, W.W. Norton. 22. Lo, Mamaysky, and Wang (2000), “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation”, Journal of Finance, 55, 1705-1765. 23. Park and Irwin (2007), “What do we know about the profitability of technical analysis?”, Journal of Economic Surveys, 21, 786-826. 24. 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, 23,1887-1905. 25. Schmidt, A.B. (2002), “Why technical trading may be successful? A lesson from the agent-based modeling”, Physica, A 303, 185-188. 26. Shleifer, A. and Summers, L.H. (1990), “The noise trader approach to finance”, Journal of Economic Perspectives, 4, 19-33. 27. Smidt, S. (1965), Amateur Speculators, Ithaca, NY, Graduate School of Business and Public Administration, Cornell University. 28. Stengos, T. (1996), “Nonparametric forecasts of gold rates of return”. In W.A. Barnett, A.P.Kirman and M. Salmon (eds), Nonlinear Dynamics and Economics: Proceedings of the Tenth International Symposium on Economic Theory and Econometrics (pp. 393–406), Cambridge, Cambridge University Press. 29. Sweeney, R.J. (1986), “Beating the Foreign Exchange Market,” Journal of Finance, 41, 163-182. 30. Sweeny, R.J. (1988), “Some new filter rule tests: methods and results”, Journal of Financial and Quantitative Analysis, 23, 285–300. 31. Van Horne, J.C. and Parker, G.G.C. (1968), “Technical trading rules: a comment”, Financial Analysts Journal, 24, 128-132. 32. 安芷誼,民94,技術分析對台灣股票市場投資績效之探討-移動平均線法,銘傳大學國際企業學系碩士在職專班碩士論文。 33. 林澤利,民95,從技術指標探討台灣股市效率之研究-以台灣五十指數成份股為例,東吳大學經濟學系碩士論文。 34. 陳賢達,民97,技術分析在股票市場產生超額報酬可能性之實證探討-以寶來台灣50 ETF為例,台灣科技大學管理學院碩士班碩士論文。 35. 黃光廷,民91,技術分析、基本分析與投資組合避險績效之研究,成功大學會計學研究所碩士論文。 36. 趙永昱,民91,技術分析交易法則在股市擇時之實證研究,中山大學財務管理學系碩士在職專班碩士論文。 37. 蘇明南,民90,移動平均線法則應用於台灣股市之實證研究,淡江大學財務金融學系碩士論文。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22673 | - |
| dc.description.abstract | 技術分析一直以來便在市場上廣為使用。學術界自西元1960年代就開始有許多技術分析的文獻出現。不過大部分的文獻都著重在整體市場的分析,較少有文獻針對個股來探討技術分析的有效性。因此本研究根據二篇理論論文,尋找台灣股票市場技術分析的獲利能力可能與哪些因素有關。
本研究使用二條移動平均線的操作策略,研究台灣股票市場894家上市公司的技術分析超額報酬率,是否與非法人交易比重以及資訊不對稱程度有關,而資訊不對稱的代理變數為公司市值、董監持股比例與股價淨值比。樣本期間為西元1990年1月1日至西元2009年12月31日,並劃分二個子樣本期間。 本研究所獲之實證結果如下: 1. 公司市值在整體樣本與第二個子樣本的顯著性較高,在第一子樣本顯著性較低。 2. 股價淨值比和移動平均超額報酬率之間的顯著性,在不同的樣本期間的情況十分一致。整體而言股價淨值比越高的公司,技術分析的效果越好。 3. 非法人成交比重與移動平均的超額報酬有顯著的相關性。非法人成交比重越大的公司,技術分析效果越好。 4. 如果不考慮非法人成交比重,則公司市值為影響技術分析有效性的主要因素。如果考慮非法人成交比重,則非法人成交比重與股價淨值比為影響技術分析有效性的主要因素。 5. 由於考慮非法人成交比重的四因子模型之R-square較高,因此在本研究之樣本範圍內,加入非法人成交比重的模型解釋能力較好。 本研究之結論為:非法人成交比重越高,或是股價淨值比越高的公司,技術分析的效果將會越好。 | zh_TW |
| dc.description.abstract | This thesis tries to find out some factors that may influent the effectiveness of technical analysis and chooses the simplest and most popular technical trading rule – moving average – by utilizing over 800 stocks from Taiwan Stock Exchange from 1990 to 2009. According to two theoretical papers, there are two theories that may explain the profitability of technical analysis. Therefore two hypotheses are tested: behavioral theory hypothesis and informational theory hypothesis. The proxy variable for behavioral theory is the percentage of non-institutional trading. Three proxy variables for informational theory are capitalization, percentage of insider holdings, and price to book ratio.
The empirical results indicate that the behavioral theory hypothesis is strongly support during the sample period. The percentage of non-institutional trading is statistically significant in regression model. For the other three variables, price to book ratio is also statistically significant, both is full and sub- sample period. Overall the results shows that moving average trading rule provides more excess returns for stocks with higher percentage of non-institutional trading or higher price to book ratio. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T04:24:12Z (GMT). No. of bitstreams: 1 ntu-99-R97723014-1.pdf: 381480 bytes, checksum: f7a237755e2f710916ae18a1369002e6 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii 英文摘要 iv 第一章 緒論 1 第二章 文獻探討 3 2.1 移動平均相關文獻 3 2.2 技術分析理論文獻 5 第三章 研究假設 7 3.1 行為理論假設 7 3.2 資訊理論假設 7 第四章 研究方法 9 4.1 自變數設定 9 4.2 移動平均線法 12 4.3 迴歸模型 17 4.4 資料來源 18 第五章 實證結果 20 5.1 超額報酬的檢定 20 5.2 變數相關性 23 5.3 迴歸模型結果 24 第六章 結論 28 6.1 結論 28 6.2 研究限制 30 6.3 往後研究建議 30 參考文獻 31 | |
| dc.language.iso | zh-TW | |
| dc.subject | 移動平均 | zh_TW |
| dc.subject | 技術分析 | zh_TW |
| dc.subject | technical analysis | en |
| dc.subject | moving average | en |
| dc.title | 技術分析的有效性:行為與資訊觀點 | zh_TW |
| dc.title | Effectiveness of Technical Analysis: From Behavioral and Informational views | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳明賢,蘇永成 | |
| dc.subject.keyword | 技術分析,移動平均, | zh_TW |
| dc.subject.keyword | technical analysis,moving average, | en |
| dc.relation.page | 33 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2010-06-23 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| 顯示於系所單位: | 財務金融學系 | |
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