簡易檢索 / 詳目顯示
| 研究生： |
艾祖鞍 Ai, Tsu-An |
|---|---|
| 論文名稱： |
道氏理論於台股指數之應用 An Application of the Dow Theory to the Taiwan Stock Index |
| 指導教授： |
顏盟峯
Yen, Meng-Feng |
| 學位類別： |
碩士 Master |
| 系所名稱： |
管理學院 - 財務金融研究所 Graduate Institute of Finance |
| 論文出版年： | 2018 |
| 畢業學年度： | 106 |
| 語文別： | 英文 |
| 論文頁數： | 42 |
| 中文關鍵詞： | 道氏理論 、頭肩頂形態 、三因子模型 、超額報酬 |
| 外文關鍵詞： | Dow Theory, Head-and-shoulders, Three-factor Model, Abnormal Returns |
| 相關次數： | 點閱：104 下載：20 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
在此篇研究中，我們將利用系統性的方法於台灣加權指數中辨認出道氏理論中的頭肩頂形態，並加以衡量技術指標的效率。首先我們先從資料中篩選出規律出現的天數，並以回歸分析找尋規律與其出現後的累積報酬之關係。其後再建立交易策略，且利用 Fama and French (1993)的三因子模型計算預期報酬，進而估算出異常報酬，經由檢測後，超額報酬皆於統計結果上顯示為顯著。最後，我們進行了模擬交易，希望藉由回測的方式來檢視這樣的交易策略之可行性，結果顯示我們所提出的策略之表現無論是在記入交易成本前後，皆優於簡單買進持有策略與無風險利率，除此之外，回測之結果也顯示我們的交易策略於熊市之獲利明顯高於其他交易時段。上述的結果皆說明道氏理論中的頭肩頂形態內具有某些資訊可供利用以獲取報酬。
In this paper, we propose a systematic approach to recognizing technical patterns, and we apply this method to TAIEX index from 1990 to 2017 to evaluate the effectiveness of technical analysis. Using OLS regression, we find the two technical indicators, head-and-shoulders top (HST) and head-and-shoulders bottom (HSB), are able to predict the accumulative returns after the patterns occur. Using the Fama-and-French (1993) three-factor model as a style benchmark for the accumulative returns, we find that the abnormal returns are statistically significant. At last, we back-test the trading strategies and find that the HST and HSB strategies outperform the sell-and-hold and buy-and-hold benchmarks respectively. The strategy combining both HST and HSB earns about 8% annual returns and around 6% annual returns for time period of five trading days before and after transaction costs respectively. Moreover, we find that the strategies we proposed tend to generate more returns in bear markets than in bull markets.
Anglin, P. M., Gen, xe, & ay, R. (1996). Semiparametric Estimation of a Hedonic Price Function. Journal of Applied Econometrics, 11(6), 633-648.
Caginalp, G., & Laurent, H. (1998). The predictive power of price patterns. Applied Mathematical Finance, 5(3-4), 181-205.
Cervelló-Royo, R., Guijarro, F., & Michniuk, K. (2015). Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data. Expert Systems with Applications, 42(14), 5963-5975.
Chang, P. H. K., & Osler, C. L. (1995). Head and shoulders: Not just a flaky pattern. Federal Reserve Bank of New York, Staff Report No. 4.
Chong Terence, T.-L., & Poon, K.-H. (2017). A new recognition algorithm for “head-and-shoulders” price patterns. Studies in Nonlinear Dynamics & Econometrics, 21(5), 1-18.
Dawson Edward, R., & Steeley James, M. (2003). On the Existence of Visual Technical Patterns in the UK Stock Market. Journal of Business Finance & Accounting, 30(1‐2), 263-293.
Edwards, R. D., & Magee, J. (1966). Technical analysis of stock trends. Boston, Mass: J. Magee.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Hu, Y., Feng, B., Zhang, X., Ngai, E. W. T., & Liu, M. (2015). Stock trading rule discovery with an evolutionary trend following model. Expert Systems with Applications, 42(1), 212–222.
Lee, K. H., & Jo, G. S. (1999). Expert system for predicting stock market timing using a candlestick chart. Expert Systems with Applications, 16(4), 357-364.
Lo A, W., Mamaysky, H., & Wang, J. (2002). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1765.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton, N.J.: Princeton University Press.
Malkiel, B. G. (1996). A Random Walk Down Wall Street: Including a Life-cycle Guide to Personal Investing. New York: Norton.
P. H. Kevin Chang, a., & Carol L. Osler, a. (1999). Methodical Madness: Technical Analysis and the Irrationality of Exchange- Rate Forecasts. The Economic Journal(458), 636-661.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
Pruitt, S. W. a. W., R.E. (1988). The CRISMA trading system: Who says technical analysis can't beat the market? Journal of Portfolio Management, 15, 55-58.
Ratner, M., & Leal, R. P. C. (1999). Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking & Finance, 23(12), 1887-1905.
Savin, G., Weller, P., & Zvingelis, J. (2007). The Predictive Power of “Head-and-Shoulders” Price Patterns in the U.S. Stock Market. Journal of Financial Econometrics, 5(2), 243-265.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21, 49-59.
Sortino, F. A., & Price, L. N. (1994). Performance Measurement in a Downside Risk Framework. The Journal of Investing, 3(3), 59.
Wong, W.-K., Manzur, M., & Chew, B.-K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
Yu, H., Chen, R., & Zhang, G. (2014). A SVM Stock Selection Model within PCA. Procedia Computer Science, 31, 406-412.
校內：立即公開校外：2023-06-01公開