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研究生:張凱婷
研究生(外文):Kai-Ting Chang
論文名稱:應用支撐向量迴歸及模糊規則於股價買賣點之預測
論文名稱(外文):A Collaborative Trading Model by Support Vector Regression and Fuzzy Rule for Stock Turning Points’ Prediction
指導教授:張百棧張百棧引用關係
學位類別:碩士
校院名稱:元智大學
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:2011
畢業學年度:99
語文別:中文
論文頁數:67
中文關鍵詞:支撐向量迴歸股價預測轉折點偵測TS模糊規則
外文關鍵詞:Support vector regressionStock forecastingturning point detectionTS fuzzy rule
相關次數:
  • 被引用被引用:5
  • 點閱點閱:231
  • 評分評分:
  • 下載下載:2
  • 收藏至我的研究室書目清單書目收藏:0
股票市場是一個現代最普遍額外獲利的投資管道,但投資人往往無法即時且準確的判斷買賣時機,而導致資金虧損,因此如何幫助投資者掌握買賣點且獲得穩定甚至更多的利潤顯得格外重要,然而股市環境變化迅速,故必須建立一套即時股市交易決策系統來提供投資人額外的參考資訊。過去許多研究中,透過資料探勘技術來辨識買賣點都有不錯的獲利空間,但仍然無法準確的掌握適當的買賣時機,而導致獲利狀況不穩定。而本研究嘗試利用多重預測模型的結合改善無法精確辨識買賣點時機的問題,期望能達成穩定且較高的投資利潤。
本研究主要是利用線段切割法(Piecewise Linear Representation)來從歷史片段中獲得股價轉折之時機點,並結合支撐向量迴歸(Support Vector Regression)技術來學習每日的交易知識,且透過TS模糊規則(Takagi-Sugeno fuzzy rule-based )來學習股票買點與賣點之交易知識以加強控制交易訊號,期望透過模型間交互作用,嘗試改善買賣時機偏差的問題。實驗的結果顯示本研究提出之交易決策模型確實優於其他模型,成功預測台灣及美國股票的最適買賣時機,其獲利狀況相當穩定。

The daily stock turning point detection problems are investigated in this study. The support vector regression (SVR) model has been applied in various forecasting applications and proved to be with stable performances. In this research, SVR has been used to predict the trading signal since it could handle overall information effectively even under the complex environment of stock price variations. The trading signals from the historic database is derived from the application of piecewise linear representation (PLR) of stock price. Therefore, the temporary bottoms and peaks of stock price within the studied period are identified by PLR. TS fuzzy rules were applied to calculate the dynamic threshold which intersects the trading signal and provides the trading points. The fuzzy rules were trained and obtained from the trading signals generated by PLR during the training period. A collaborative trading model of SVR and TS fuzzy rule is used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our system is more profitable and can be implemented in real time trading system.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究架構與流程 2
第二章 文獻探討 5
2.1 股價預測分析方法 5
2.1.1 技術分析 (Technical Analysis) 6
2.1.2 技術分析於股市預測之相關文獻 7
2.2 篩選變數的方法 9
2.2.1 迴歸分析法 9
2.3 線段切割法(Piecewise Linear Representation, PLR) 11
2.4 支撐向量迴歸(Support Vector Regression, SVR) 13
2.5 模糊推論系統 15
2.6 小結 18
第三章 問題定義與描述 19
3.1 研究資料範圍 19
3.2 研究變數說明 19
3.3 投資獲利之評估 23
第四章 研究方法 25
4.1 產生歷史片段之交易訊號 26
4.2 關鍵交易決策變數篩選 35
4.3 支撐向量迴歸法建立預測模型 36
4.4 建立Takagi-Sugeno-Kang模糊規則辨識買賣交易點 39
4.5 交易點判斷準則 44
第五章 實驗結果與分析 45
5.1 研究對象 45
5.2 參數設定 49
5.3 實驗設計 49
5.4 實驗結果評估 50
第六章 結論及後續研究建議 62
6.1 研究結論 62
6.2 後續研究方向 62
文獻參考 64

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