博碩士論文 103426029 詳細資訊




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姓名 蔡元翊(Yuan-Yi Tsai)  查詢紙本館藏   畢業系所 工業管理研究所
論文名稱
(Development and Analysis of Cluster Trading)
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摘要(中) 配對交易是投資理財中常見的操作行為,其交易的方式為選取兩兩相近的股票,並計算股票間歷史股價的價差平均與標準差,若兩股之新一期股價的價差大於由歷史股價的價差平均與標準差所形成的基準值,則對一支股票進行做多,並對另外一支進行放空。主要目的為消除市場變動造成的系統風險,但仍是會受到個別風險影響,如果能減少錯誤決策造成的損失,將可以提升投資報酬率。
本研究提出了以群集分析結合配對交易的新型態交易方式,命名為「群集交易」。這種交易方式突破傳統配對交易一次交易兩支股票(一對)的限制,可同時進行多支股票的操作,由於該方法同時考慮多支股票的資訊,使其在發生錯誤決策時,能以其他股票的獲利進行攤銷,降低損失,相較於舊有方法,群集交易將在獲利上有著更高的表現與穩健性。另外,由Gatev(2006)等人提出的相似度衡量方法中會遇到迭代的問題,使的相似度計算結果具有瑕疵,進而增加錯誤決策的機率。而本文將以指數平滑法改良其計算方式,同時加入趨勢走勢的資訊,使我們可以得到更精確的相似度計算結果,以提升投資報酬率。
我們利用近四年來台灣50成分股的資料依據產業分類並分別進行實驗,然後將大盤、配對交易、群集交易(原始相似度)和群集交易(新的相似度)進行比較,最後再依據各個產業找出投資報酬率最佳的參數設定。
摘要(英) Pairs trading is one of famous strategy in investment, the way of trading is being chosen by the two similar stocks, and being calculated on their historical price data’s gap of mean and standard deviation. If the gap of new price data is exceed the boundary, we will buy the stock and short selling the other stock. The purpose of it is removing the systemic risk by the market of changing, but it also be effected by idiosyncratic risk. If we can reduce the loss of making fault decision, we will get higher ROI.
We propose a new trading way which combine clustering analysis and pairs trading is named “Cluster trading”. The limit of pairs trading which only can trade two stocks (one pair) at each time is broken. It can be traded with more than two stocks at each time. More stock’s information can be considered and the loss can be shared by other stocks when making the fault decision to let the loss be decreased. Thus, we will have higher and more robust in the profit of making decision by comparing with pairs trading. However, we also find out the famous similarity which is proposed by Gatev, Goetzmann, and Rouwenhorst (2006) in pairs trading has a drawback about iterating, and it makes the similarity inaccurate. We use EWMA and stock’s trend to improve the similarity to get the better results and get higher ROI.
We use the stocks FTSE TWSE Taiwan 50 Index during recent four years and classify some of stocks into different industries. And then, compare TAIEX, pairs trading, cluster trading with original similarity and cluster trading with new similarity which is better. Finally, find the setting which have high ROI in cluster trading with new similarity in different industries.
關鍵字(中) ★ 配對交易
★ 分群
★ 群集交易
關鍵字(英) ★ Pairs trading
★ Clustering
★ Clustering trading
論文目次 摘要 I
Abstract II
List of Tables IV
List of Figures V
Chapter 1 Introduction 1
1-1 Background and motivation 1
1-2 Research Objectives and frameworks 2
Chapter 2 Literature Review 4
2-1 Pairs trading 4
2-2 Cluster analysis 6
2-2-1 Similarity measure 8
2-2-2 Clustering algorithms 10
2-3 Cluster trading 13
Chapter 3 Cluster Trading 17
Chapter 4 Empirical Results 26
4-1 Trading assumptions 27
4-2-1 Results of the experiment in semiconductor 27
4-2-2 Analysis the results of the experiment in semiconductor 31
4-3-1 Results of the experiment in bank 32
4-3-2 Analysis the results of the experiment in bank 35
4-4-1 Results of the experiment in computer accessories 36
4-4-2 Analysis the results of the experiment in computer accessories 39
Chapter 5 Conclusion and Future Research 40
References 42
參考文獻 1. Aghabozorgi, Saeed, Ali Seyed Shirkhorshidi, and Teh Ying Wah. "Time-series clustering–A decade review." Information Systems 53 (2015): 16-38.
2. Aghabozorgi, Saeed, and Ying Wah Teh. "Stock market co-movement assessment using a three-phase clustering method." Expert Systems with Applications 41.4 (2014): 1301-1314.
3. Broussard, John Paul, and Mika Vaihekoski. "Profitability of pairs trading strategy in an illiquid market with multiple share classes." Journal of International Financial Markets, Institutions and Money 22.5 (2012): 1188-1201.
4. Berndt, Donald J., and James Clifford. "Using Dynamic Time Warping to Find Patterns in Time Series." KDD workshop. Vol. 10. No. 16. 1994.
5. Ferreira, Leonardo N., and Liang Zhao. "Time series clustering via community detection in networks." Information Sciences 326 (2016): 227-242.
6. Francesco Gullo, Giovanni Ponti, Andrea Tagarelli, Giuseppe Tradigo, and Pierangelo Veltri. "A time series approach for clustering mass spectrometry data." Journal of Computational Science 3.5 (2012): 344-355.
7. Gegick, M. "Time series classification under more realistic assumptions." SIAM International Conference on Data Mining (2013): 578-586.
8. Guam, He-Shan, and Qing-Shan Jiang. "Cluster financial time series for portfolio." 2007 International Conference on Wavelet Analysis and Pattern Recognition. Vol. 2. Institute of Electrical and Electronics Engineers, 2007.
9. Gatev, Evan, William N. Goetzmann, and K. Geert Rouwenhorst. "Pairs trading: Performance of a relative-value arbitrage rule." Review of Financial Studies 19.3 (2006): 797-827.
10. Jacobs, Heiko, and Martin Weber. "On the determinants of pairs trading profitability." Journal of Financial Markets 23 (2015): 75-97.
11. Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. "Iterative incremental clustering of time series."International Conference on Extending Database Technology. Springer Berlin Heidelberg, 2004.
12. Kumar, Vasimalla, C. Narasimham, and B. Sujith. "Classification of Time Series Data by One Class Classifier using DTW-D." Procedia Computer Science 54 (2015): 343-352.
13. Kantardzic, Mehmed. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, 2011.
14. Lei, Yaoting, and Jing Xu. "Costly arbitrage through pairs trading." Journal of Economic Dynamics and Control 56 (2015): 1-19.
15. Liao, C. M., "Agglomerative Hierarchical clustering with the string data", National Central University Industry Management master thesis, 2014
16. Michael Johnstone, Vu Thanh Le, James Zhang, Bruce Gunn, Saeid Nahavandi, and Doug Creighton. "A dynamic time warped clustering technique for discrete event simulation-based system analysis." Expert Systems with Applications 42.21 (2015): 8078-8085.
17. Marica, Vasile-George. "Hyper-ellipsoid Clustering of Time Series. A Case Study for Daily Stock Returns." Procedia Economics and Finance 15 (2014): 777-783.
18. Martin Gavrilov, Dragomir Anguelov, Piotr Indyk, and Rajeev Motwani. "Mining the stock market (extended abstract): which measure is best?." Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. Association for Computing Machinery, 2000.
19. Qian, Jiang, et al. "Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions." Journal of molecular biology 314.5 (2001): 1053-1066.
20. Raza, Haider, Girijesh Prasad, and Yuhua Li. "Adaptive learning with covariate shift-detection for non-stationary environments." 2014 14th UK Workshop on Computational Intelligence (UKCI). Institute of Electrical and Electronics Engineers, 2014.
21. Ratanamahatana, Chotirat Ann, and Eamonn Keogh. "Making time-series classification more accurate using learned constraints." SIAM International Conference on Data Mining, 2004.
22. Syu, J. W., “Center-based algorithm with the string data”, National Central University Industry Management master thesis, 2014.
23. Yang, Jen-Wei, et al. "Pairs trading: The performance of a stochastic spread model with regime switching-evidence from the S&P 500." International Review of Economics & Finance 43 (2016): 139-150.
24. Zhang, Qiaoping, and Isabelle Couloigner. "A new and efficient k-medoid algorithm for spatial clustering." International Conference on Computational Science and Its Applications. Springer Berlin Heidelberg, 2005.
25. 沈宣佑. "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 (2015): 1-92.
26. 林奇生. "配對交易策略應用於台灣股票市場之實證研究." 淡江大學財務金融學系碩士在職專班學位論文 (2006): 1-138.
27. 張瀚文. "馬可夫轉換模型於配對交易策略之應用, 以台灣股票市場為例." 成功大學財務金融研究所學位論文 (2014): 1-33.
指導教授 曾富祥(Fu-Shiang Tseng) 審核日期 2016-7-13
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