臺灣博碩士論文加值系統

技術分析是一種藉由過去的資料和趨勢來預測未來價格的型態與變化。利用技術分析是否能獲得超額報酬不僅為投資者所關心,亦為財務經濟學家用以判斷市場是否具有效率性的方法之一。本篇論文採用Hsu and Kuan (2005,Journal of Financial Econometrics)所提出的方法分別去檢定技術分析在台灣指數期貨市場與股票市場的有效性。另一方面,釵h研究指出,期貨交易能促進市場訊息的傳遞而提升現貨市場的效率性。因此,我將台灣加權股票指數之日資料區分為台灣指數期貨發行前(1990~1997) 與台灣指數期貨發行後(2000~2005)
兩個樣本,利用上述方法來探討台灣指數期貨之發行是否能改善股票市場的效率性。

我們得到的實證結果指出,對於技術分析在台灣股票市場的有效性,我們發現在台股指數期貨發行前,技術分析是有效的,但是台股指數期貨發行後,技術分析就變的無效了,這意味著台灣期貨市場對於台灣股票市場確實能夠促進市場訊息的傳遞,進而使得台灣股票市場更有效率。至於技術分析在台灣期貨市場的有效性,我們發現技術分析是有效的,是可以獲得超額報酬。我們也發現交易成本對於技術分析的有效性有很大的影響, 在不包含交易成本時,
使用技術交易法則與策略的確會比買進長期持有好。然而包含交易成本後,使用技術交易法則與策略的表現在大部分的期間會比買進長期持有還要差。

目錄
1 緒論
1.1 研究動機與目的. . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 文獻回顧
2.1 效率市場理論. . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 技術分析在實證上的相關研究. . . . . . . . . . . . . . . . . 7
3 研究方法
3.1 技術分析法則的說明與設定. . . . . . . . . . . . . . . . . . . 14
3.1.1 簡單交易法則. . . . . . . . . . . . . . . . . . . . . . 15
3.1.2 反向交易法則. . . . . . . . . . . . . . . . . . . . . . 23
3.1.3 混合交易策略. . . . . . . . . . . . . . . . . . . . . . 24
3.2 White’s reality check 的理論與架構. . . . . . . . . . . . . . 25
3.3 執行White’s reality check . . . . . . . . . . . . . . . . . . . 28
4 實證分析與結果
4.1 資料來源與分析. . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 實證結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

參考文獻
Alexander, S. S. (1961). “Price Movements in Speculative Markets: Trends or Random Walks.” Industrial Management Review 2, 7–26.
Alexander, S. S. (1964). “Price Movements in Speculative Markets: Trends or Random Walks No.2.” Industrial Management Review 5, 25–46.
Blume, L., D. Easley, and M. O’Hara (1994). “Market Statistics and Technical Analysis: The Role of Volume.” Journal of Finance 49, 153–181.
Brock, W., J. Lakonishok, and B. LeBaron (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance 47, 1731–1764.
Chan, K. (1992). “A Further Analysis of the Lead-lag Relationship between the Cash Market and Stock Index Futures Market.” The Review of Financial Studies 5, 123–152.
Chan, L. K. C., N. Jegadeesh, and J. Lakonishok (1996). “Momentum Strategies.” Journal of Finance 51, 1681–1713.
Chan, L. K. C., J. Karceski, and J. Lakonishok (1998). “The Risk and Return from Factors.” Journal of Financial and Quantitative Analysis 33, 159–188.
Fama, E. F., and M. E. Blume (1966). “Filter Rules and Stock-Market Trading.” Journal of Business 39, 226–241.
Fama, E. F. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance 25, 383–417.
Gencay, R. (1998). “The Predictability of Security Returns with Simple Technical Trading Rules.” Journal of Empirical Finance 5, 347–359.
Ghosh, A. (1993). “Cointegration and Error Correction Model: Intertemporal Causality between Index and Futures Prices.” Journal of Futures Market 13, 193–198.
Hsu, P. H., and C. M. Kuan (2005). “Reexamining the Profitability of Technical Analysis with Data Snooping Checks.” Journal of Financial Econometrics, 3, 606–628.
James, F. E. (1968). “Monthly Moving Averages: An Effective Investment Tool.” Journal of Financial and Quantitative Analysis 3, 315–326.
Jensen, M. C., and G. A. Benington (1970). “Random Walks and Technical Theories: Some Additional Evidence.” Journal of Finance 25, 469–482.
Kawaller, I. G., Koch, P. D., and T. W. Koch (1987). “The Temporal Price Relationship between S&P 500 Futures and Index.” The Journal of Finance 42, 1309–1329.
Lakonishok, J., and S. Smidt (1988). “Are Seasonal Anomalies Real? A Ninety-Year Perspective.” Review of Financial Studies 1, 403–425.
Lakonishok, J., A. Shleifer, and R. W. Vishny (1994). “Contrarian Investment, Extrapolation, and Risk.” Journal of Finance 49, 1541–1578.
Levy, R. A. (1967a). “Relative Strength as a Criterion for Investment Selection.” Journal of Finance 22, 595–610.
Levy, R. A. (1967b). “Random Walks: Reality of Myth.” Financial Analysts Journal 23, 66–77.
Lo, A. W., and A. C. MacKinlay (1990). “Data-Snooping Biases in Tests of Financial Asset Pricing Models.” Review of Financial Studies 3, 431–467.
Lo, A. W., H. Mamaysky, and J. Wang (2000). “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation.” Journal of Finance 55, 1705–1765.
Lobato, I., J. C. Nankervis, and N. E. Savin (2001). “Testing for Autocorrelation Using a Modified Box-Pierce Q Test.” International Economic Review 42, 187–205.
Mandelbrot B. (1963). “The Variation of Certain Speculative Prices.” Journal of Business 36, 394–419.
Politis, D. N., and J. P. Romano (1994). “The Stationary Bootstrap.” Journal of the American Statistical Association 89, 1303–1313.
Rouwenhorst, K. G. (1999). “Local Return Factors and Turnover in Emerging Stock Markets.” Journal of Finance 54, 1439–1464.
Samuelson, P. (1965). “Proof that Properly Anticipated Prices Fluctuate Randomly.” Industrial Management Review 6, 41–49.
Stoll, H. R., and R. E. Whaley (1990). “The Dynamics of Stock Index and Stock Index Futures Returns.” Journal of Financial and Quantitative Analysis 25, 441–468.
Sullivan, R., A. Timmermann, and H. White (1999). “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” Journal of Finance 54, 1647–1691.
Sweeney, R. J. (1986). “Beating the Foreign Exchange Market.” Journal of Finance 41, 163–182.
Sweeney, R. J. (1988). “Some New Filter Rule Tests: Methods and Results.” Journal of Financial and Quantitative Analysis 23, 285–300.
Sweeney, R.J. (1990). “Evidence on Short-Term Trading Strategies.” Journal of Portfolio Management 17, 20–26.
van Horne, J. C. and G. G. C. Parker (1967). “The Random-Walk Theory: An Emperical Test.” Financial Analysts Journal 6, 87–92.
van Horne, J. C. and G. G. C. Parker (1968). “Technical Trading Rules: A Comment.” Financial Analysts Journal 4, 128–132.
White, H. (2000). “A Reality Check for Data Snooping.” Econometrica 68, 1097–1126.