臺灣博碩士論文加值系統

只要是投資就會有風險，而資本市場上的投資標的，根據CAPM理論，股票通常具有高風險、高投資報酬率的特性。高風險意味著股票價格波動率較大，因此，投資人常常利用技術面分析，找出投資標的最佳的交易時點。
本研究結合多個技術指標與加權模糊時間序列所預測的股票漲、跌訊號，來決定股票的買、賣時點；作法是採用組合方法的概念，計算各個指標過去所產生的決策的準確率，並標準化準確率產生出權重，再利用各技術指標的決策作加權，產生出買賣的決策。研究結果顯示，買賣時點預測的效果不錯，幾乎都是買在低點，賣在高點，賣點比買點價格高，應該是有利可圖。本研究採用"持續買進，賣出時的價格必須大於過去平均持有價格"的策略，經實驗顯示，本方法可以穩定獲利。

Investments in a financial market may incur risk. According to the Capital Asset Pricing Model (CAPM), a stock with higher return rate usually exhibits higher risk. The risk of a stock is expressed in terms of the volatility of the stock price. Traditionally, the investors often use the technical analysis to determine the time points for stock transactions.
This study is mainly based on the technical indicators and the weighted fuzzy time series. Borrowing the idea from the ensemble algorithms, we calculate the accuracy rate of the historical predictions of each technical indicator and the weighted fuzzy time series. With the accuracy rate as the weight, we then predict the decision as a buy, a sale or no action according to the weighted sum of the individual technical indicators. According to the experiment results, most of the predicted buy signals occur at the times when the stock prices are relatively low, while the predicted sale signals occur at the times when the stock prices are relatively high. To utilize the trading signals, we adopt the policy to continually buy a stock and to sale the stock only when the opening price of the stock is higher than the average holding price of the stock. The experiment results show that our method can acquire significant return rates from the stocks in the Taiwan stock market.

中文摘要 I
英文摘要 II
目錄 III
圖表目錄 V
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3研究架構 3
第二章 相關研究 5
2.1效率市場理論 5
2.1.1 效率市場假說之實證研究 7
2.2技術分析概論 8
2.2.1道氏理論 ( THE DOW THEORY ) 8
2.2.2艾略特波段理論 (THE ELLIOTT WAVE THEORY) 9
2.2.3葛蘭碧移動平均線原則 10
2.2.4技術分析之相關文獻 12
2.3組合方法(ENSEMBLE METHODS) 13
2.4模糊集合理論（FUZZY SET THEORY) 15
2.4.1模糊集合理論（FUZZY SET THEORY） 15
2.4.2歸屬函數 16
2.5模糊時間序列（FUZZY TIME SERIES）18
2.5.1加權模糊時間序列 19
2.5.2模糊時間序列相關文獻 20
2.6其他相關研究 22
第三章 研究方法 23
3.1實驗方法架構 23
3.2技術指標決策 25
3.2.1 RSI 相對強弱指標(RELATIVE STRENGTH INDICATOR)交易策略 25
3.2.2 BIAS乖離率交易策略 26
3.2.3 KD隨機指標交易策略 27
3.2.4 MACD (MOVING AVERAGE CONVERGENCE-DIVERGENCE)交易策略 29
3.2.5預測未來漲跌趨勢 32
3.3執行實驗流程步驟 37
第四章 實驗數據與分析 41
4.1資料來源及說明 41
4.2衡量指標 42
4.2.1 加權股價指數 42
4.3實驗環境與參數設定 43
4.4結果與分析 43
4.4.1 實驗結果 44
4.4.2 報酬率計算 47
第五章 結論與未來展望 49
5.1結論 49
5.2未來展望 50
參考文獻 51

1. C.H. Chen, T.L. Chen, H.J. Teoh, C.H. Chiang, “Fuzzy time-series based on adaptive expectation model for TAIEX forecasting,” Expert Systems with Applications, 34 (2008) 1162-1132.
2. Yu, H. K. (2005). “Weighted fuzzy time series models for TAIEX forecasting,” Physical A: Statistical Mechanics and its Applications, 349 (3-4) 609-624.
3. Fama, E. F. & French, K. R., “Dividend yield and expected stock return, ” Journal of Financial Economics, 22 (1988)3-25
4. William, B., Josef L., & Blake L. (1992). “Simple technical trading rules and the stochastic properties of stock returns,” Journal of Finance, 47, 1731-1764
5. Fang, Y., & Xu, D. (2003). “The predictability of asset returns: an approach combining technical analysis and time series forecasts, ” International Journal of Forecasting, 19, 369–385.
6. James, J. (2003). “Simple trend-following strategies in currency trading,” Quantitative Finance, 3, 75-77
7. Chen, S. M. (1996). “Forecasting Enrollments Based on Fuzzy Time Series, ” Fuzzy Sets and Systems, 81(3), 311-319.
8. Chen, S. M. (2002). “Forecasting Enrollments Based on High-Order Fuzzy Time Series,” Cybernetics and Systems: An International Journal, 33(1), 1-16.
9. Huarng, K. (1996). “Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series,” Fuzzy Sets and Systems, 81(3), 311-319.
10. Lee, L. W., Chen, S. M., & Leu, Y. H. (2004). “A New Method for Handling Forecasting Problems Based on Two-Factors High-Order Fuzzy Time series,” Proceedings of the 2004 Ninth Conference on Artificial Intelligence and Applications, Taipei, Taiwan, Republic of China, November 2004.
11. Leu, Y. H., Lee, C. P., & Jou, Y. Z. (2009). “A distance-based fuzzy time series model for exchange rates forecasting,” Expert Systems with Applications, 36(4), 8107-8114.
12. Chen, S. M., Senior Member, IEEE, & Wang, N. Y. (2010). “Fuzzy Forecasting Based on Fuzzy-Trend Logical Relationship Groups,” IEEE Transactions on Systems, Man, and Cybernetics—PART B: CYBERNETICS, VOL.40,NO.5, OCTOBER 2010,1343-1358
13. Tsai, C. F., &Hsiao, Y. C. (2010). “Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches,” Decision Support Systems ,50, 258-269
14. Chang, P. C., Fan, C.Y., & Liu, C. H. (2009). “Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction, ” IEEE Transactions on Systems, Man, and Cybernetics—PART C: APPLICATIONS AND REVIEWS, VOL. 39, NO. 1, JANUARY 2009,80-92
15. Chang, T. J., Yang, S. C., & Chang, K. J. (2009). “Portfolio optimization problems in different risk measures using genetic algorithm.” Expert Systems with Applications , VOL.36, 7 (2009) 10529-10537
16. Ni, L. P., Ni, Z. W., & Gao, Y. Z. (2011).“Stock trend prediction based on fractal feature selection and support vector machine,” Expert Systems with Applications, 38 (2011) 5569–5576
17. Andrew, W. L., Harry, M., & Jiang, W. (2000).“Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” The Journal of Finance, VOL. LV, NO. 4, AUGUST 2000
18. Yamamoto, R. (2008). Trading profitability of technical strategies in individual stocks.，碩士論文，國立政治大學國際企業學系。
19. 林呈勳(2009)，結合技術指標與財經新聞之股票趨勢預測,碩士論文，國立台北科技大學。
20. 何宗浩(2010)，技術指標、統計模型與資產配置在台灣股市的應用，碩士論文，國立臺灣師範大學。
21. 陳鳳玉(2009)，整合股價圖形辨識與技術指標分析股票之獲利力，碩士論文，國立雲林科技大學。

22. 洪振家(2009)，以模糊時間序列為主的類神經網路方法預測台灣證券交易所股價指數選擇權價格,碩士論文，國立台灣科技大學。
23. 周俊志(2007)，自動交易系統與策略評價之研究,碩士論文，國立臺灣大學。
24. 鄭雅仁(1994)，台灣股市弱式效率市場之再驗證，碩士論文，國立台灣大學。
25. 吳訂宜(2007)，台股之弱式效率市場檢定，高苑學報第十三卷p191~220，國立高雄第一科技大學。
26. 邱紫怡(2011)，使用基因演算法結合加權模糊時間序列在股票投資組合上的買賣時機策略，碩士論文，國立台灣科技大學。
27. Jiawei H., Micheline K. 著、王派洲 譯 資料探勘－概念與方法 (Data Mining: Concepts and Techniques, 2/e) ,CS,2008。
28. 徐俊明，投資學理論與實務 ，新陸，2003。
29. 杜金龍，最新技術指標 : 在臺灣股市應用的訣竅，財訊出版社股份有限公司，2006。