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題名:台灣股票市場股價預測模式之研究
作者:劉美琦
作者(外文):Mei Chi Liu
校院名稱:淡江大學
系所名稱:管理科學學系
指導教授:張紘炬
許志義
學位類別:博士
出版日期:2000
主題關鍵詞:股價預測時間序列向量自我迴歸模式誤差修正模式轉換函數模式ARMA誤差迴歸模式stock priceforecastingARIMAvector autoregressive modelerror correction modeltransfer functionthe regression with ARMA error model
原始連結:連回原系統網址new window
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一個國家證券市場的榮與衰,皆會對一國之經濟有著重大的影響。在許多學者的研究中顯示,經濟因素確實是會影響著股市之股票價格。若想做好預測股價趨勢,則可加入經濟因素做預測,俾使預測股價績效良好,國家經濟危機發生的損害降到最低。
本研究對台灣股票市場之股價指數未來趨勢進行分析,首先建構其預測模式,續之為評量預測模式之預測績效,最後透過預測模式找出總體變數對股價之關聯性。建構台灣股票市場股價預測模式中,包含單一方程式的單變量時間序列Box-Jenkins ARIMA模式、多變量時間序列的轉換函數模式與ARMA誤差迴歸模式,以及聯立系統的向量自我迴歸模式及誤差修正模式。總體而言,以誤差修正模式(ECM)與向量自我迴歸(VAR)模式表現較佳,可見聯立系統模式之預測績效較單一方程式模式佳。另外在本研究中發現時間(期數超過12期)較長的預測模式以ARMA誤差迴歸模式之績效較佳,而在時間(期數不超過12期)較短的預測,則是以ECM模式之績效較佳。
由短時期預測佳的ECM模式顯示,利率對股價影響是同時來自於負向的短期動態調整以及長期均衡,且利率變動領先股價變動六期(即半年);由於利率並非模型中的弱外生變數,兩者間存在相互影響關係。工業生產指數對股價無顯著性的影響。第一次石油危機的發生對股價產生顯著的負向影響,導致股價下跌,對國家總體經濟之影響頗大。然第二次石油危機卻對股價無顯著影響。
另外,對於台灣電子業股票上市公司之股價進行預測配適模式探討,證實了古典時間序列分解模式利用趨勢變動因子、季節變動因子(第一型或第二型)以及循環變動因子等三因子相乘之預測結果,較僅利用趨勢變動因子、季節變動因子(第一型或第二型)等兩因子相乘性之預測結果佳。因此對電子業股價做預測時,應考慮循環變動因子加入預測模式,循環變動因子乃是一很重要的因子。另外比較第一型季節變動因子與第二型季節變動因子結果顯示,對於臺灣電子業股票上市公司之股價預測上,是以趨勢變動、第二型季節變動因子以及循環變動因子三者間相乘性之預測結果較佳。
The performance of a country’s economy is strongly related to the performance of its stock market. According studies indicated that the economic factors influenced stock prices. If we can use the economic factors in the forecasting stock price model, we will reduce the economic crisis to our country.
The purpose of the study is to explore the trend of the Taiwan stock price. We will conduct five forecasting models for Taiwan stock price. Models considered include the one equation, which is the Box-Jenkins ARIMA, the transfer function model and the regression with ARMA errors model, and the other simultaneous equation,which is the VAR and ECM models. The accuracy of these forecasting models is using the forecasting errors in a post-sample period. The results show that the simultaneous equation forecasting models outperform the one equation. In general, the ECM and VAR models are the best in the short term, and the regression with ARMA errors model is the best in the long term.
The other purpose of the study is that the classical multiplicative decomposition time series model decomposed into the trend factor, seasonal factor (included two types) and cyclical factor is a better forecasting tool than the one decomposed into the trend factor and seasonal factor (included two types). All of them are to fit the stock price of high technical electronic industry in Taiwan. It shows that the cyclical factor is an important feature of these series. While we make two type methods to handle the seasonal factor, it indicates the second type of seasonal factor is better than the first one.
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