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學校名稱輔仁大學
系所名稱金融研究所
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學號493756086
研究生(中)劉翔瑜
研究生(英)Hsiang-Yu Liu
論文名稱(中)倒傳遞類神經網路、支援向量迴歸於日經225現貨指數之預測及交易策略之研究
論文名稱(英)Using Back-Propagation Neural Network and Support Vector Regression in Forecasting of Nikkei225 Stock Index and trading strategy
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指導教授(中)李天行
指導教授(英)Tian-ShyugLee
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學位類別碩士
畢業學年度94
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語文別中文
關鍵字(中)日經225現貨指數 支援向量迴歸 類神經網路
關鍵字(英)Nikkei 225 Stock Index Support Vector Regression Back-Propagation Neural Network
摘要(中)財務預測一直為各界研究的主題,本研究針對日經225指數為研究標的,逶過倒傳遞類神經網路(Back-Propagation Neural Network),及支援向量迴歸(Support Vector Regression),建立日經225現貨指數開盤價之預測模型,並檢驗在非現貨交易時段之期貨價格、國際傳遞效果及其主要因素是否具有其內涵價值,並增加模型的預測效果;最後,透過不同的交易策略,計算其投資報酬率,以檢視其是否能獲得超額報酬。 為驗證前述想法之正確性及有效性,本研究以日經225前一日現貨的收盤價、市值、芝加哥商業交易所(Chicago Mercantile Exchange)、新加坡衍生性商品交易所(Singapore Exchange Derivatives Trading)及大阪證?交易所(Osaka Securities Exchange)之日經225期貨指數為預測變數。實證結果發現,市值無法明顯提升模型的預測準確率,而非現貨交易時段及國際傳遞效果的確具有其內涵價值,能顯著提升模型的預測能力;而透過支援向量迴歸將非現貨交易時段及國際傳遞效果所得之預測值,並搭配當日沖銷的策略,其報酬率高達60%以上,具有超額報酬,可提供給投資人做為參考依據。
摘要(英)This study investigates the information content and spill over effect of Nikkei 225 futures prices during the non-cash-trading (NCT) period. The same day’s leading futures and previous day’s cash and futures market closing indices are firstly used to predict the opening cash price in the cash market by the back propagation neural network (BPN) and support vector regression (SVR) models. Sensitivity analysis is employed to address and solve the issue of finding the appropriate setup of the networks topology for both BPN and SVR. To demonstrate the effectiveness of our proposed method, the five-minute and one-minute intraday data of spot and futures index from September, 1998 to October, 2004 was evaluated using BPN and SVR. Analytic results demonstrate that the NCT futures prices do provide useful information in predicting the opening cash price index, the one-minute intraday data provide more information than the five-minute intraday data, and SVR has better prediction capability than BPN. Finally a proposed trading strategy using the observed results can provide significantly better investment return than the commonly discussed buy and hold strategy.
論文目次目錄 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 4 第三節 研究對象與範圍 6 第四節 研究架構 8 第二章 文獻探討 10 第一節 期貨與現貨之關係 10 第二節 國際間股市之訊息傳遞 13 第三節 類神經網路應用之相關文獻 15 第四節 支援向量機之財務時間序列相關文獻 21 第三章 研究方法 23 第一節 倒傳遞類神經網路 23 第二節 支援向量機 28 第四章 實證研究 34 第一節 實證資料 34 第二節 實證模型架構 36 第三節 實證結果。 38 第五章 結論與建議 59 第一節 結論 59 第二節 研究貢獻 60 第三節 後續研究建議 60 參考文獻 61 附錄 61
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