臺灣博碩士論文加值系統

本研究提出一個新的動態模糊模型，並結合支持向量機來預測股票市場動態。在新的整合模型中，模糊模型將整體經濟變數、股市技術指標及期貨技術指標，同時整合當做模型的輸入因素；基因演算法則動態調整各輸入因素的影響力模糊模型參數；支持向量機則被用來預測下一階段的股票市場動態；在研究中並設計multiperiod的實驗方式，來模擬股市的變動性。為了衡量新的整合模型的效能，我們與傳統的預測方法相比並且設計不同的實驗來驗證。實驗結果指出，本研究所提出之模型確實能取得較其他預測模型較佳的預測準確率。

In the study, a new dynamic fuzzy model is proposed in combination with support vector machine (SVM) to forecast stock market dynamism. In this new integrated model, the fuzzy model integrates various influence factors as the input variables, and the genetic algorithm (GA) adjusts the influential degree of each input variable dynamically. SVM then serves to predict stock market dynamism in the next phase. In the meanwhile, the multiperiod experiment method is designed to simulate the volatility of stock market. To evaluate the performance of the new integrated model, we compare it with the traditional forecast methods and design different experiments to testify. From the experiment results, the model from the study does generate better accuracy in forecast than other forecast models.

Abstract…………………………………………………………………ii
Acknowledgement…………………………….……………….…….. iii
Content…………………………..………………………...…………. iv
List of Figures…………………………………….……...…….……. v
List of Tables……………………………………..…..………….….. vi
Chapter One Introduction.............................................................1
Chapter Two Literature Review……………...............................3
2.1 Stock Market Modeling..... ..... ..... ..... ............................................ 3
2.2Fuzzy Theory.................................................................................... 6
2.3 Genetic Algorithm.... .... .... .... .... .... .... .... .... .... ..........................6
2.4 Support Vector Machine .... .... .... .... .... .... .... .... .........................7
Chapter Three Methedology……………................................... 8
3.1 Architecture..................................................................................... 8
3.2 Dynamic Fuzzy Model for Influence Factors...................................9
3.3 Model Optimization with Genetic Algorithm..................................10
3.4 Prediction of Stock Market Dynamism with SVM.........................11
Chapter Four Experiment…………..........................................13
4.1 Data Preparation.......................................................................... 13
4.2 Experimental Design.................................................................... 15
4.3 Experimental Data....................................................................... 17
4.4 Technical Indicators.....................................................................22
4.5 Experimental Result......................................................................31
Chapter Five Conclusion…………...........................................36
References.…………………………………………………....37
Appendix A Introduction on Fuzzy Theory……………………. 40
Appendix B Introduction on Genetic Algorithm………………. 45

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