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系統識別號 U0002-0806201112314700
中文論文名稱 適應性類神經模糊推論系統在動態交易決策的應用-台灣股票指數期貨的實證研究
英文論文名稱 The Application of Adaptive Neuro-Fuzzy Inference System(ANFIS) for Dynamic Trading Decision Support System-Evidence from TAIEX Stock Index Futures
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士班
系所名稱(英) Department of Banking and Finance
學年度 99
學期 2
出版年 100
研究生中文姓名 何東翰
研究生英文姓名 Tung-Han Ho
學號 698530564
學位類別 碩士
語文別 英文
口試日期 2011-05-06
論文頁數 53頁
口試委員 指導教授-李沃牆
共同指導教授-吳典明
委員-李沃牆
委員-吳典明
委員-沈大白
委員-池秉聰
委員-顧廣平
中文關鍵字 類神經網路  適應性模糊推論系統(ANFIS)  基因演算法 
英文關鍵字 Adaptive Neuro-Fuzzy Inference System(ANFIS)  Neural Networks  Genetic Algorithms 
學科別分類 學科別社會科學商學
中文摘要 股票市場的預測非常重要,因為成功的預測能帶來相當大的獲利。然而,能夠成功的預測是非常複雜而且困難的。
本研究擴展了適應性類神經模糊推論系統(ANFIS),創建一個交易決策支援系統,能夠利用模糊推論結合類神經網路的模型識別能力用於預測與交易台灣加權股價指數期貨。
本研究結果,提出了人工智能的方法結合模糊理論與類神經網絡來實現最佳化的交易規則。結果表明,結合模糊理論和類神經網絡產生的交易決策支援系統,能夠使交易員或是投資專家能夠克服眾多交易決策上資訊分析上的限制,提高投資效益,ANFIS能夠在預測未來的股市期貨指數上成為一個有用的工具,增加交易的獲利。

英文摘要 Stock market prediction is important because successful prediction of stock prices may promise attractive benefits. Yet, these tasks are highly complicated and very difficult.
This thesis extends the Adaptive Neuro-Fuzzy Inference System (ANFIS), to create a trading decision support system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in forecasting and trading the futures of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).
This study, as a result, proposes an approach of artificial intelligence by integrating fuzzy theory with neural networks to achieve the optimization of trading rules. The result indicates that integrating fuzzy theory with neural networks has produced a trading decision support system which overcomes the physical limitations of human experts and traders in taking decisions of trading and improve the investment performance. The experimental results indicate that ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock index future price and increase the returns of a trader's portfolio.
論文目次 Contents
Chapter Content
Abstract…………………………………………………………………………………………I
Chapter Content III
Table Content IV
Figure Content V
Chapter 1: Introduction 1
1.1 Motivation 1
1.2 Research Objectives 5
1.3 The Flow Chart 5
Chapter 2: Theories and Literature 7
2.1 Brief review of fuzzy 7
2.2 Review of Neuro-Fuzzy Systems 8
2.3 Review of ANFIS 13
Chapter 3: Methodologies 17
3.1 Fuzzy Clustering 17
3.2 ANFIS 18
3.3 Learning Algorithm of ANFIS 23
Chapter 4: Empirical Design and Results Analysis 25
4.1 Data description 27
4.2 Experiment Steps 33
4.3 Analysis’ result 43
Chapter 5: Conclusion and Suggestions 46
5.1. Conclusion 46
5.2. Suggestions 47
References ……………………………………………………………………………………48





Table Content
Table 1. Summary of training algorithm 24
Table 2. The TAIEX Futures 26
Table 3. Summary of input variables for training 32
Table 4. Transaction costs for example 40
Table 5. Calculate the profit for example 41
Table 6. The accuracy of initial training for one-month expiration contract (model 1). 43
Table 7. The accuracy of initial training for one-year expiration contract (model 2) 44
Table 8. The accuracy between model 1 and model 2 44
Table 9. The trading profits 45

















Figure Content
Fig. 1.1: the Flow Chart 6
Fig. 2.1: Connectionist fuzzy logic control/decision system (Lin and Lee, 1991) 10
Fig. 2.2: Network representation of fuzzy systems without linguistic rules 11
Fig. 2.3 The architecture of ANFIS. (Jang, 1993) 12
Fig. 3.1: An ANFIS architecture for a two rule Sugeno system 21
Fig. 4.1: Flowchart of proposed procedure 34
Fig. 4.2: The FIS structure 37
Fig. 4.4: Moving windows training 42
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