篇名 | 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 |
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卷期 | 21:2 |
並列篇名 | The Analysis and Forecast of ARIMA Model—Daily Close Price and Returns Rate of Foxconn Stock |
作者 | 葉淑媚 、 李佳樺 、 許天維 |
頁次 | 051-069 |
關鍵字 | 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 Price 、 Extended Sample Autocorrelation Function 、 Goodness of Fit 、 Residual Error Eest 、 ARIMA Model |
出刊日期 | 200712 |
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値雖與實際 値有些許差距,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.