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
(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。
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
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指導教授 曾富祥(Fu-Shiang Tseng) 審核日期 2016-7-13
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