WIP與SHIP量對股價預測之研究-以半導體代工封測產業為例

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题名:	WIP與SHIP量對股價預測之研究-以半導體代工封測產業為例
作者:	曹富凱;Tsao, Fu-Kai
贡献者:	會計研究所
关键词:	股價;預測;WIP;SHIP;ARIMAX;半導體;WIP;SHIP;ARIMAX;Forecast
日期:	2017-06-13
上传时间:	2017-10-27 14:19:03 (UTC+8)
出版者:	國立中央大學
摘要:	全球化半導體產業競爭日益劇烈，生產與交貨週期是極為重要的競爭力與管理指標。由於消費電子產品輕、薄、小、高效能及生命週期短的特性，使半導體製程日益複雜、隨著摩爾定律可能失效、半導體的設備、材料與製程不斷再精益求精，確保公司能滿足客戶的相關解決方案、產品客製化、高良率，並且準時交貨。投資者買賣股票主在的目的在於獲得資本利得，因此必須關心影響股價變動的因素。近年來外資在台灣半導體產業投資的比重提高，半導體產業的訊息已能被外資與投資人等進行分類解讀，所做的研究的數據容易被解讀與分析。研究對象以台灣第一線半導體代工封測大廠為主，研究目的在於探討半導體代工封測產業 WIP 與 SHIP 量對客戶股價的影響，並進行股價預測。研究方法利用自我迴歸移動整合解釋性變數(Autoregressive Integrated Moving Average with Explanatory Variable)的模型，引入與應變量相關程度較高的自變量，有助於提高模型的預測效果。資料範圍以民國105年1月至民國106年3月的月平均股價資料，透過 ARIMAX 模型引入 WIP 與 SHIP 量進行股價預測。實證結果發現預測股價的最佳模式為前六期的資料建立的 ARIMAX 模型，股價預測結果的 MAPE 平均為5.37%，股價預測效果良好。提供投資人更準確的預測股價，作為投資決策的參考。;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.
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