臺灣博碩士論文加值系統

本研究根據Vidyamurthy (2004)以及後續相關文獻所提出的統計套利配對交易方法對台灣股票市場進行實證研究。本文使用的模型為Engle and Granger (1987)提出的二階段共整合檢定。我們利用上述模型檢定台灣股票，找出具共整合性質之股票配對，利用技術指標－布林通道找出價格異常的時間點進行交易，建構配對交易投資組合；本研究進一步將類神經網路模型加入，用於預測共整合殘差走勢，建構類神經網路結合布林通道之配對交易策略並建構投資組合。實證結果顯示和Avellaneda and Lee (2010)結果相同，市場上確實存在市場中立性的報酬，且兩個策略的投資組合皆有優於大盤的績效和穩健性；此外類神經網路確實有幫助我們減少進場次數提高勝率，並且使投資組合的最大虧損下降，但也因此降低了投資組合的總報酬。

This paper used the statistic arbitrage pairs trading method according to Vidyamurthy (2004) and other papers based on this book. This paper followed papers to conduct empirical research on Taiwan stock market. The models used in this paper is two-steps cointegration test that proposed by Engle and Granger (1987). We tested Taiwan stocks through the above models to test cointegration, and find the investable pairs. After finding out investable pairs, we used Bollinger Band to find out abnormal stock price to trade. Then we constructed the portfolio to study its performance. This study further adds the neural network model to predict cointegral residual and constructs a strategy with Bollinger Band and neural network model. The result shows that the strategy helping us find market neutral return, which is the same as the result of Avellaneda and Lee (2010). Furthermore, our portfolio is also better than investing in benchmark. Neural network model truly helps us reduce trading frequency and decrease drawdown, but it also decreases return at the same time.

第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 研究架構 4
第二章 文獻探討 5
第一節 共整合配對交易 5
第二節 機器學習建構交易策略 8
第三章 研究方法 10
第一節 投資組合理論 10
第二節 單根檢定 12
第三節 共整合檢定 15
第四節 布林通道技術指標 17
第五節 倒傳遞類神經網路 18
第六節 配對交易策略 23
第七節 配對交易投資組合的建構方法 26
第八節 交易策略績效評估指標 27
第四章 實證分析 28
第一節 實證資料與研究期間 28
第二節 台灣股票配對組合篩選結果 30
第三節 布林通道配對交易策略實證結果 34
第四節 機器學習配對交易策略實證結果 41
第五章 結論 50
參考文獻 52
附錄 55

沈宣佑(2015)。三檔股票交易設計並與傳統配對交易之績效表現比較。交通大學財務金融研究所學位論文，1-92。
陳旭昇，2013。時間序列分析: 總體經濟與財務金融之應用。臺灣東華。
陳岱佑, & 王克陸. (2012)。台灣指數期貨與 ETF 價差交易之研究-以台股期貨, 電子期貨, 金融期貨與台灣 50ETF 為例。未出版之碩士論文，國立交通大學，財務金融研究所。
羅君昱(2005)。台灣股票市場執行統計套利之可行性分析。未出版之碩士論文，國立政治大學，經營管理研究所。
Chen, W. H., Shih, J. Y., & Wu, S. (2006). Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. International Journal of Electronic Finance, 1(1), 49-67.
Guenster, N., Kole, E., and Jacobsen, B. (2009). Riding bubbles, Working paper.
Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431.
Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 251-276.
Gatev, E., GOETZMANN, W., & ROUWENHORST, K. (1999). Pairs trading: performance of a relative value Arbitrage rule; Working Paper 7032, National Bureau of Economic Research, Cambridge.
Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111-120.
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210.
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Madhavaram G. R. (2013) Statistical arbitrage using pairs trading with support vector machine learning. Working paper. Saint Mary's University.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
Phillips, P. C., & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica: Journal of the Econometric Society, 165-193.
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. (1986). Sequential thought processes in PDP models. Parallel distributed processing: explorations in the microstructures of cognition, 2, 3-57.
Said, S. E., and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
Sharpe, W. F. (1994). The sharpe ratio. Journal of portfolio management, 21(1),49-58.
Vidyamurthy, G. (2004). Pairs Trading: quantitative methods and analysis (Vol. 217). John Wiley & Sons.