摘要(英) |
In the global economical environment the semiconductor industry competition is increasingly fierce, production and delivery cycle is extremely important competitiveness and management indicators. As the consumer electronics products are light, thin, small, high performance and short life cycle characteristics, the process is increasingly complex, with the Moore′s Law may fail, semiconductor equipment, materials and processes continue to be refined to ensure that the company can meet customer′s Solutions, product customization, high yield, and on time delivery. The main purpose of investors to buy stocks is to obtain capital gains, it must be concerned about the factors that affect the stock price changes. In recent years, foreign investment to improve proportion of foreign capital in the semiconductor industry in Taiwan, the information has been able to foreign investors and investors to classify the interpretation of the research data is easy to be interpreted and analyzed. This article research into the effect of WIP and SHIP on the forecast of stock price for semiconductor foundry packaging and testing industry. This Article use the Autoregressive Integrated Moving Average with Explanatory Variable model to introduce the explanatory variables with high degree of correlation with the variables, which can improve the prediction effect of the model. The data range is based on the monthly average price data of January 2016 to March 2017, and the forecast of stock price is introduced WIP and SHIP amount through the ARIMAX model. The best model for forecasting stock price is the ARIMAX model established for the first six periods. The MAPE of the stock price forecast is 5.37% and the forecast of stock price is effective. Providing investors with more accurate forecasting of stock prices as a reference for investment decisions. |
參考文獻 |
1. 王琡閔. (2001). 股價預測之統計模型. 中央大學統計學系學位論文, 1-71.
2. 王群. (2013). 市場情緒指數之建構及其對市場報酬之影響—時間數列轉換函數模型之應用. 臺北大學企業管理學系學位論文, 1-74.
3. 李耕年. (2011). 景氣循環下資本支出對股價的影響-以台灣半導體產業為例. 長榮大學經營管理研究所 (博) 學位論文, 1-53.
4. 李宗翰. (2014). 運用 ARIMA 與向量自我廻歸模式探討新竹科學園區半導體產值預測. 交通大學管理學院管理科學學程學位論文, 1-59.
5. 沈小欣, 趙亞玲, & 朱海江. (2014). 集成模型 ARIMAX—GARCH 及其在股票预测中的應用. 绍興文理學院學報, 34(9), 60-63.
6. 吳宗原. (2014). 原物料鉬, 鋁, 銅及匯率之價格波動與半導體及面版產業股價關係探討. 成功大學財務金融研究所碩士在職專班學位論文, 1-35.
7. 郭翊翔, & 姜齊. (2007). 晶圓代工廠的需求預測模型-以 ARIMA 模式分析. 交通大學管理學院管理科學學程學位論文, 1-90.
8. 陳國玄. (2004). 人工神經網路與統計方法應用於台灣上市電子類股價指數預測與分類之研究. 成功大學統計學系學位論文, 1-102.
9. 陳韋豪. (2009). 台灣半導體產業上,中,下游股價關連性與波動性外溢效果研究-雙變量 EGARCH 模型的應用. 南華大學財務管理學系學位論文, 1-66.
10. 陳聰聰. (2016). 基於 ARIMA 模型和 ARIMAX 模型的山東省 GDP 的预测與分析. 山東大學應用統計學系學位論文, 1-75.
11. 許天維, 葉淑媚, & 李佳樺. (2007). 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. 臺中教育大學學報: 數理科技類, 2007, 21.2: 51-69.
12. 黃國銘. (2016). 貨幣型 ETF 與經濟因素之關聯性: 應用 ARIMAX-GARCH 模型分析. 中原大學企業管理研究所學位論文, 1-102.
13. 程燕. (2015). ARIMAX 模型方法及其應用-重慶城市居民可支配收入與消费支出. 重慶工商大學學報: 自然科學版, 32(11), 80-85.
14. 劉照群. (2008). 美國費城半導體股市, 台灣股市, 台積電股價的關聯性之實證研究. 臺北大學統計學系學位論文, 1-73.
1. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. In Computer Modelling and Simulation (UKSim), 2014 UKSim-AMSS 16th International Conference on (pp. 106-112). IEEE.
2. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
3. Chan, K. S., & Ripley, B. (2012). TSA: Time series analysis. R package version 1.01. URL: http://CRAN. R-project. org/package= TSA.
4. Kongcharoen, C., & Kruangpradit, T. (2013). Autoregressive integrated moving average with explanatory variable (ARIMAX) model for Thailand export. In 33rd International Symposium on Forecasting, South Korea (pp. 1-8).
5. Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 297-303.
6. Metghalchi, M., Chang, Y. H. & Garza-Gomez, X. (2012). Technical Analysis of the Taiwanese Stock Market. International Journal of Economics and Finance. 4(1), 90-102.
7. Nochai, R., & Nochai, T. (2006). ARIMA model for forecasting oil palm price. In Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications (pp. 13-15). |