跳到主要內容

臺灣博碩士論文加值系統

(3.229.142.104) 您好!臺灣時間:2021/07/28 13:12
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

: 
twitterline
研究生:鍾家慶
研究生(外文):Chung, Chia-Chin
論文名稱:交易量對移動平均法則的獲利研究
論文名稱(外文):The Effects of Trading Volume on the Profitability of Moving Average Rules
指導教授:張永和張永和引用關係
指導教授(外文):Chang, Yung-Ho
口試委員:詹家昌林丙輝李春安
口試委員(外文):Chan, Chia-ChungLin, Bing-HueiLi, Chun-An
口試日期:2012-06-28
學位類別:碩士
校院名稱:東海大學
系所名稱:財務金融學系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:40
中文關鍵詞:交易量移動平均線法則卓越能力分析資訊揭露
外文關鍵詞:Trading VolumeMoving Average RulesSuperior Predictive AbilityInformation Disclosure
相關次數:
  • 被引用被引用:6
  • 點閱點閱:255
  • 評分評分:
  • 下載下載:18
  • 收藏至我的研究室書目清單書目收藏:1
本研究主要探討在台灣的股票市場中,成交量高與成交量低的公司是否隱含著低交易量的公司存在著較嚴重的資訊不對稱問題,因此依據移動平均法則進行買賣不能打敗買入持有策略,獲取較低報酬。實證結果顯示,台灣2010年之所有上市公司存在著交易量愈大的公司,其交易策略與買進持有策略報酬差異愈大之特性。也就是說,交易量愈大的公司,隱含資訊不對稱問題較小,投資人可利用移動平均法則操作獲得顯著報酬。整體來看,交易策略績效以100天移動平均線為最佳。產業區隔下的交易策略實證結果顯示,電子產業與傳統產業的交易策略績效皆以100天移動平均線為最佳。最後,本研究採用Hansen (2005)卓越預測能力檢定的結果顯示,成交量中間組、最小組以150天移動平均線為最佳預測交易策略,成交量最大組則以100天移動平均線為最佳預測交易策略。產業區隔下最佳預測法則的檢定結果顯示無論成交量高低,電子產業以50天移動平均線為最佳預測交易策略;整體傳統產業以150天移動平均線為最佳預測交易策略,但傳統產業在成交量最大組為100天移動平均線為最佳預測交易策略。
In this paper, we mainly investigate whether companies with low trading volume imply the fact that having a serious problem of information asymmetry so that investors can't earn higher return by adopting moving average rules rather than buy-and-hold strategy in Taiwan stock market. Our empirical results show that companies with bigger trading volume have significantly higher return among all the three trading strategies, while the best performance of trading strategy is 100 days moving average rules. As for the industrial segment, empirical results show that the best performance for electronic ones and traditional ones is 100 days moving average rules.

Finally, we adopt Hansen (2005) Superior Predictive Ability to test the best realistic predictive rule, and we find among the companies with middle and low trading volume, whose best predictive trading rules are 150 days moving average rules;the biggest trading volume ones, whose best predictive trading rules are 100 days moving average rules. Moreover, regardless of the trading volume level, best predictive trading rules for electronic ones are 50 days moving average rules. For the traditional ones, their best predictive trading rules are 150 days moving average rules, but the biggest trading volume group whose best predictive trading rules is 100 days moving average rules.

摘要.............................................................................................................................................I
英文摘要....................................................................................................................................II
目錄...........................................................................................................................................III
表目錄..................................................................................................................................IV
圖目錄..................................................................................................................................V

第一章 緒論...............................................................................................................................1
第一節 研就背景與動機.....................................................................................................................1
第二節 研究目的.....................................................................................................................2
第二章 文獻探討. .....................................................................................................................5
第一節 交易量相關文獻...................................................................................................5
第二節 移動平均法則-國內、國外相關文獻...........................................................................................7
第三章 資料來源及研究方法.................................................................................................10
第一節 資料說明.....................................................................................................................10
第二節 研究方法與變數定義.....................................................................................................................11
第三節 卓越預測能力.....................................................................................................................12
第四章 實證分析.....................................................................................................................15
第一節 樣本敘述統計.........................................................................................................15
第二節 實證結果.............................................................................................................15
第五章 結論與建議.....................................................................................................................21
參考文獻...................................................................................................................................23

李淑惠(2006),「技術指標與股價漲跌幅非線性關係之獲利能力之探討」,台灣管理學刊,第6卷,第1期,頁129-156。

林良炤(1997),「 KD 技術指標應用在台灣股市之實證研究」,國立台灣大學商學研究所碩士論文。

洪美慧(1997),「技術分析應用於台灣股市之研究-移動平均線、乖離率指標與相對強弱指標之評估」,東海大學管理研究所碩士論文。

洪振虔(2011),「交易量和報酬之關係與交易策略」,中山管理評論,第19卷,第2 期,頁305-342。

高秀斌(1998),「技術分析下股票買賣獲利能力之實證研究」,國立中央大學企業管理研究所碩士論文。

陳建全(1998),「台灣股市技術分析之實證研究」,國立台灣大學商學研究所碩士論文。

郭壁菁(2003),「股市價量關係之研究:多國比較」,雲林科技大學財務金融研究所碩士論文。

莊珮玲、林信助和郭炳伸(2011),「技術交易策略在外匯市場無往不利?」,臺灣經濟預測與政策,第41卷,第2期,頁95-126。

傅英芬、劉海清(2010),「處分效果、紀律投資與股價趨勢,東吳經濟商學學報」,第69期,頁83-116。

程定國(2009),「短中期技術面整合策略之研究」,臺灣大學企業管理碩士專班學位論文。

楊家維(2000),「技術分析用於當沖之有效性研究 —台灣股市之實證分析」,國立台北大學經濟學研究所碩士論文。

劉映興、陳家彬(2002),「臺灣股票市場交易值、交易量與發行量加權股價指數關係之實證研究-光譜分析之應用」,農業經濟半年刊,第72期,頁65-85。

樓禎祺、何培基(2003),「股價移動平均線之理論與實證-以台灣股市模擬投資操作為例」,育達研究叢刊,第 5、6 期合刊,頁 27~52。

賴宏祺(1997),「技術分析有效性之研究」,國立中興大學企業管理研究所碩士論文。

魏嘉君(2008),「技術分析指標之獲利能力:已開發國家與開發中國家整合探討」,東海大學財務金融研究所碩士論文。

Brock W., J. Lakonishok, and B. LeBaron, 1992, Simple Technical Trading Rule and the Stochastic Properties of Stock Return, Journal of Finance, 41, 1731-1764.

Chen. G. M. Firth., and O. M. Rui, 2001, the Dynamic Relation between Stock Returns, Trading Volume, and Volatility, Financial Review 38, 153-174.

Chordia, Tarun, and Bhaskaran Swaminathan, 2000, Trading Volume and Cross-Autocorrelation in Stock Returns, Journal of Finance, 55, 913-935.

Cootner, Paul H, 1964, Stock Market Price: Random versus System Change, Industrial Management Review, 3, 24-25.

Coutts J. Andrew and Cheung Kwong-C, 2000, Trading Rules and Stock Returns: Some Preliminary Short Run Evidence from the Hang Seng 1985-1997, Applied Financial Economics, 579-586.

Fama, E, F., 1995, Size and Book-Market Factors in Earnings and Returns, Journal of Finance, 50(1),131-155.

Ferson, W., and C. Harvey, 1993, The Risk and Predictabilities of International Equity Returns, Review of Financial Studies, 6, 527-566.

George, T., G. Kaul, and M. Nimalendran, 1994, Trading Volume and Transaction Costs in Specialist Markets, The Journal of Finance, XLIX(4), 1489-1505.

Gervais, S., R. Kaniel, and D. Mingelgrin, 2001, The High Volume Return Premium, The Journal of Finance, LVI(3), .877-919.

Gunasekarage, A. and D. M. Power, 2001, The profitability of moving average trading rules in South Asian stock markets, Emerging Markets Review, 2, 17-33.

Hiemstra, C., and J. Jones. 1994, Testing for Linear and Nonliear Granger Causality in The Stock Price-Volume Relation, The Journal of Finance, XLIX(5), 1639-1664.

Hsu, P. and C. Kuan, 2005, Reexamining the Profitability of Technical Analysis with Data Snooping Checks, Journal of Financial Econometrics, 3, 606-628.

Hansen, P. R., 2005, A Test for Superior Predictive Ability., Journal of Business & Economic Statistics, American Statistical Association, 23, 364-380.

James Jr. F. E, 1968, Monthly Moving Averages-An Efficient Investment Tool, Journal of Financial and Quantitative Analysis, 315-326.

Karpoff, J. M., 1987, The Relation between Price Changes and Trading Volume:A Survey, Journal of Financial and Quantitative Analysis, 22, 109-126.

LeBaron, B., 1998, Technical Trading Rules and Regime Shifts in Foreign Exchange, In: Acar, F., Satchell, S. (Eds.), Advanced Trading Rules. Butterworth-Heinemann, 5-40.

Lee, C., and B. Swaminathan, 2000, Price Momentum and Trading Volume, The Journal Of Finance,LV(5), 2017-2069.

Metghalchi, M., Y.H. Chang, and J. Marcucci, 2007, Is the Swedish Stock Market Efficient? Evidence from Some Simple Trading Rules, International Review of Financial Analysis, 475-490.

Morgan, I. G, 1976, Stock Price and Heteroskedasticity, Journal of Business, v.49, 496-508.

Pesaran, H ., and a. Timmermann, 1995, Predictabilities of Stock Returns: Robust and Economic Significance, The Journal of Finance, L (4), 1201-1228.

Pruitt , Stephen W. , Richard E. White , 1988, The CRISMA Trading System : Who Says Technical Analysis Can’t Beat the Market ? , Journal of Portfolio Management , 55-58.

Stickel, S., and Verrecchia, R., 1994, Evidence That Trading Volume Sustains Price Changes, Financial Analysts Journal, 50, 57-67 .

Van Horne, Jams C. and Parker, George G.. C, 1967, Technical Trading Rules: A Comment, Financial Analysts Journal, 35, 28-132.

White, H., 2000, A Reality Check for Data Snooping, Econometrica, 64, 1067-1126.

Wong W. K., M. Manzur, and B. K. Chew, 2003, How Rewarding Is Technical Analysis? Evidence from Singapore Stock Market, Applied Financial Economics, 13, 543-551.

Ying, C. C., 1966, Stock Market Prices and Volume of Sales, Econometrica 34, 676-686.

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top