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題名:ARIMA模式分析與預測--以鴻海股票市場日收盤價與報酬率為例
書刊名:臺中教育大學學報. 數理科技類
作者:葉淑媚李佳樺許天維 引用關係
作者(外文):Ye, Shu-meiLi, Ghia-uaSheu, Tian-wei
出版日期:2007
卷期:21:2
頁次:頁51-69
主題關鍵詞:鴻海股票樣本自我相關函數延伸配適度殘差檢定ARIMA模式Foxconn stock priceExtended sample autocorrelation functionGoodness of fitResidual error eestARIMA model
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:2
  • 共同引用共同引用:2
  • 點閱點閱:82
本研究目的在於了解鴻海股票日收盤價與的報酬率最佳模式預測的準確性。研究方法爲利用延伸樣本自我相關函數(extended sample autocorrelation function,簡稱ESACF)提出原始模式,研究資料取自鴻海股票1999年1月1日至2006年4月30日,經由配適度及殘差檢定選取出最佳模式,最後進行模式的預測分析。研究結果發現報酬率的最佳模式ARIMA(0,0,4),而日收盤價最佳模式爲ARIMA(0,1,4),兩模式所預測出來的值雖與實際值有些許差距,但皆落於95%的信賴區間之內。
The aim of this research is to probe the best ARIMA model for the daily close prices and return rates of the Foxconn stock, and to forecast the accuracy of prediction. We applied Extended Sample Autocorrelation Function to determine the parameters of ARIMA model. The data of this research came from the Foxconn stock price during January 1, 1999-April 30, 2006. We employed the goodness of fit and residual error test to selects the best model. Finally, we performed the forecast and the analysis of ARIMA model. Our results showed that the best model for daily close prices is ARIMA (0, 1, 4). Moreover, we found that the best model for return rates is ARIMA (0, 0, 4). Although the forecast has a little difference between predicted values and the actual values for both models, all fall into 95% confidence interval.
期刊論文
1.林雅惠(200706)。靠股利賺50倍。理財週刊,365。  延伸查詢new window
2.曹銳勤(20040300)。股票投資規劃與分析--以上市銀行股票為例。玄奘管理學報,1(2),1-16。new window  延伸查詢new window
3.張殿文(200108)。鴻海為何是外資的「最愛」?。e天下雜誌。  延伸查詢new window
4.Wood, D.、Dasgupta, B.(1996)。Classifying trend movements in the MSCI U.S.A. capital market index--A comparison of regression, arima and neural network methods。Computers and Operations Research,23,611-622。  new window
5.陳仁惠、周麗芳、徐偉初(20031200)。我國全民健康保險藥品費用預測模式之探討。保險專刊,19(2),157-176。new window  延伸查詢new window
6.Shibata, R.(1976)。Selection of the order of an autoregressive model by Akaike's information criterion。Biometrika,63(1),117-126。  new window
7.Tsay, R. S.、Tiao, G. C.(1984)。Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Non-stationary ARMA Models。Journal of the American Statistical Association,79,84-96。  new window
8.Schwarz, Gideon(1978)。Estimating the Dimension of a model。The Annals of Statistics,6(2),461-464。  new window
9.Dickey, David A.、Fuller, Wayne A.(1981)。Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root。Econometrica: journal of the Econometric Society,49(4),1057-1072。  new window
10.Akaike, Hirotsugu(1974)。A new look at the statistical model identification。IEEE Transactions on Automatic Control,19(6),716-723。  new window
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12.Said, S. E.、Dickey, David A.(1984)。Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order。Biometrika,71(3),599-607。  new window
會議論文
1.李宏志、邱至中(2005)。自我迴歸整合移動平均--倒傳遞類神經網路與基因演算法在短期匯率預測績效之比較。財務金融學會年會暨學術論文研討會。  延伸查詢new window
研究報告
1.邱瑞科(2003)。NIIS全國性預防接種資訊管理中央資料庫系統之建置規劃與效益評估 (計畫編號:DOH92-DC-1111)。  延伸查詢new window
學位論文
1.商振綱(2006)。時間數列應用於利率預測模型之研究(碩士論文)。長庚大學。  延伸查詢new window
2.陳執中(2006)。台股加權指數隔月收盤價預測之研究(碩士論文)。國立成功大學。  延伸查詢new window
3.蔡屹彥(2004)。角落表法在轉換函數模型鑑定的研究(碩士論文)。國立臺北大學。  延伸查詢new window
4.蔡宗憲(2005)。運用長期記憶與ANFIS模型估計不同交易期間台灣股價指數之風險值(碩士論文)。國立臺灣科技大學。  延伸查詢new window
圖書
1.許純君(1999)。預測的原則與應用。台北:台灣西書出版社。  延伸查詢new window
2.吳柏林(1995)。時間序列分析導論。臺北:華泰。  延伸查詢new window
3.Fuller, Wayne A.(1976)。Introduction to Statistical Time Series。New York, NY:John Wiley and Sons。  new window
4.Box, G. E. P.、Jenkins, G. M.、Reinsel, G. C.(1976)。Time Series Analysis: Forecasting and Control。San Francisco:Holden-Day。  new window
5.林茂文(1992)。時間數列分析與預測。臺北:華泰書局。  延伸查詢new window
 
 
 
 
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