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研究生:陳詩郁
研究生(外文):Shi-Yu Chen
論文名稱:結合約略集合論與支援向量機於外匯漲跌幅預測之應用
論文名稱(外文):Applications of rough set theory and support vector machines in foreign exchange forecasting
指導教授:白炳豐白炳豐引用關係
指導教授(外文):Ping-Feng Pai
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:48
中文關鍵詞:外匯技術指標約略集合論支援向量機粒子群最佳化演算法
外文關鍵詞:Foreign exchangetechnical indicatorsrough set theorysupport vector machineparticle swarm optimization
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在金融市場逐漸趨向自由化及國際化的同時,各國紛紛解除對匯率及資本流動的限制,使得國際資本市場快速整合,此時當資本出現大量移動的現象時,首要衝擊的就是匯率的波動及社會的經濟活動。由此緣故外匯預測隨之重要,就企業而言,走向國際市場是現今的趨勢,不可必免的外匯管理已儼然成為目前避險首要議題,就投資人而言,若能掌握住匯率的走勢更是生財之道。
本研究提出以資料探勘的方式於大量不確定因素的外匯市場中,依歷史價格預測未來匯率的波動情形。以約略集合論於資訊表中選取重要屬性且刪除不必要的屬性,再由約簡後的決策表中擷取規則。而條件屬性與決策屬性分別為技術指標與隔日外匯收盤價的漲跌幅。接著計算規則的強度及分類能力後刪除強度較弱及分類能力較差的規則,並檢視測試資料是否能與規則匹配,若否,則以支援向量機加以訓練。而在使用支援向量機時需先以粒子群最佳化演算法搜尋參數,藉此提高支援向量機之最佳參數挑選時間及避開區域最佳解之問題。經實驗證明此混合模型確實可以提升約略集合論之預測能力。
Recently, financial markets have been characterized by rapid liberalization and globalization, many nations have removed restrictions on exchange rates and capital flow. As a result international capital markets have been characterized by rapid integration. As massive transfer of capital take place, the first impact in volatility of exchange rates and economic activities. This is why forecasting exchange rates have become an important problem, and an understanding of exchange rates trends is indispensable for both corporations and individual investors.
For this reason, this study proposes using data mining to deal with the uncertain factors in foreign exchange market, forecasting the volatility of next day’s exchange rate according to the history price. Using rough set theory (RST) to select the attributes of importance and reduce the unnecessary attributes in the information table, and then derives the decision rules from decision table. And condition attributes and decision attribute are the technical indicators and the volatility of next day’s exchange rate, respective. The weak strength and the inferior discrimination are discarded after computes strength and discrimination of the decision rules. Also ascertains which rule can match test data, otherwise uses support vector machine (SVM) to train test data. While doing SVM this study also uses the particle swarm optimization (PSO) to determine parameters for SVM. This reduces the time required for choosing optimal parameters for SVM and avoids the problem of local. This hybrid model is proved to improve the forecast ability of RST by the experiment.
Acknowledgement.............................................I
摘要........................................................II
Abstract...................................................III
Contents...................................................V
Figures....................................................VII
Tables.....................................................VIII
Chapter 1 Introduction........................................1
1.1 Research background and motivation........................1
1.2 Research objectives.......................................2
1.3 Organization..............................................3
Chapter 2 Literature review...................................5
2.1 A foreign exchange market.................................5
2.2 Feature selection.........................................7
2.3 Self-organizing map.......................................8
2.4 Rough set theory..........................................9
2.5 Support vector machine....................................11
2.6 Particle swarm optimization...............................12
Chapter 3 Methodology.........................................15
3.1 Technical indicators......................................15
3.2 Self-organizing map.......................................17
3.3 Rough set theory..........................................19
3.4 Support vector machine....................................24
3.5 Particle swarm optimization...............................27
3.6 Research model development................................29
Chapter 4 Empirical results...................................34
4.1 Data sources and preprocess...............................34
4.2 Rough set theory..........................................35
4.3 Support vector machine....................................37
4.4 Comparison................................................38
Chapter 5 Conclusion and future works.........................39
5.1 Conclusion................................................39
5.2 Future works 41
Reference 42
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