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研究生:湯韶元
研究生(外文):Tang,Shao-Yuan
論文名稱:柔性演算法於台灣股票期貨市場投資之應用
論文名稱(外文):The Application of Soft Computing for Investing Taiwan Stock Futures
指導教授:徐志明徐志明引用關係
口試委員:江宏志徐志明陳永琦
口試日期:2013-06-25
學位類別:碩士
校院名稱:明新科技大學
系所名稱:管理研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:68
中文關鍵詞:股價預測股票期貨基因規劃法支援向量迴歸
外文關鍵詞:stock price predictionstock futuresgenetic programmingsupport vector regression
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隨著台灣經濟快速發展,市場上提供投資者的金融商品越來越多,其中,『股票期貨』將股票與期貨兩者合而為一,囊括股票的穩健與期貨的高槓桿,擁有驚人的成長潛力也具備令人驚豔的多面向功能。故本研究使用基因規劃法與支援向量迴歸,並搭配技術指標、三因子模型與流動性指標以建構股價預測模型,同時設立投資策略以協助投資者進行股票期貨之投資。本研究之研究期間為2009年12月1日至2012年7月31日,以股票衍生之股票期貨為研究標的,並將研究期間區分為八個投資案例。實證結果顯示,透過本研究之預測模型並配合投資操作策略,可獲得優於大盤指數報酬率及台銀三個月定存利率之平均投資報酬率。
With rapid development of Taiwan's economy, the market provides more and more financial products for investors. Among them, stock futures combine stocks and futures, involving the robustness of stocks and high leverage of futures. The stock futures have surprising growth potential and multi-dimension functions. This thesis uses genetic programming and support vector regression with technical indicators. Moreover, three factor model and liquidity indicator are used to construct stock price prediction model, and investment strategies are established to help investors to invest stock futures. The research period is between December 1, 2009 and July 31, 2012. The stock futures derived from stocks is the research object. There are eight investment cases during the research period. The empirical results show that the proposed prediction model combined with investment operation strategies can obtain the average rate of return, which is greater than the market index return rate and interest rate of three-month fixed deposit in the banks of Taiwan.
目錄
摘 要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3研究流程 2
第二章文獻探討 4
2.1 股票與期貨價格之相關研究 4
2.1.1國內相關研究 4
2.1.2國外相關研究 8
2.2基因規劃演算法 12
2.2.1基因演算法概念 12
2.2.2基因規劃法 16
2.2.3使用基因規劃法前置處理 17
2.3支援向量迴歸 21
2.4 Fama-French三因子模型 24
2.4.1 Fama-French三因子模型概述 24
2.5流動性指標 24
2.5.1 市場深度 24
2.5.2證券買賣價差 25
2.5.3流動比率 25
第三章 研究架構與方法 27
3.1研究流程 27
3.2資料蒐集 28
3.3計算技術指標 28
3.3.1價之技術指標 28
3.3.2量之技術指標 32
3.3.3市場寬幅之技術指標 32
3.4三因子模型 33
3.5數據正規化 33
3.6建構預測模型 34
3.6.1以基因規劃法建構預測模型 34
3.6.2以支援向量迴歸建構預測模型 35
3.7操作策略制定 35
3.8分析與結論 36
第四章 實例驗證與分析 37
4.1資料蒐集與整理 37
4.2技術指標計算 37
4.2.1數據正規化 38
4.3模型建構 38
4.3.1基因規劃法 38
4.3.2支援向量迴歸 43
4.3.3選取最佳模型 46
4.4實例驗證 47
第五章 結論與建議 50
5.1結論 50
5.2研究限制 50
5.3未來研究建議 51
附錄A 技術指標、三因子模型、流動性指標及預測目標值之部分資料 52
附錄B 數據正規化之部分資料 53
參 考 文 獻 54
中文文獻 54
英文文獻 56


中文文獻
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