- 學位論文
線型組合近似分析預測台灣加權股價指數
The Similarity Analysis of Linear Combination for the Prediction of Taiwan Weighted Stock Indices
若您是本文的作者，可授權文章由華藝線上圖書館中協助推廣。
摘要
許多研究皆以股價指數過往的數據去預測未來的走勢，然而「過往的數據」代表多近、多久的期間？未來的收盤價又一定是現在價格趨勢所構成嗎？ 本文藉由基因演算法，運用長、短期移動平均線概念，做為灰關聯線型移動區間之參數，透過一般漲跌均數模型、灰關聯線型之漲跌均數模型、一般線型修正模型及灰關聯線型之線型修正模型四種方法預測台灣加權股價指數，欲以找出最適預測模型，並比較灰關聯線型是否有助提升預測準確度，期以提供一個更適合預測市場變化的參考工具。 本研究實證結果如下： 一、運用移動平均線交叉概念，以基因演算法求得結果，最適合灰關聯移動區間之參數，短期為15日、長期為32日。 二、從短期整體性灰關聯結果，發現比較序列中關聯度較高之序列，落在預測目標時間點前後各半年的比例並不高，表示預測參考序列與鄰近序列關聯度不高。 三、依長、短期灰關聯序高低排序結果之序列時間點進行預測，相較一般運用落後期數收盤指數的預測效果會較佳，即以歷史線型做未來走勢的預測準確率高於近期趨勢之預測。
並列摘要
Many researchers have been endeavoring to analyze and forecast the trend of Taiwan Weighted Stock Indices (TAIEX) from historical data for years. However, the effective duration of the past data applied for the prediction work is still arguable since the future closing prices are not always constituted by the related present closing prices. In this study, the Moving Average approach, both long-term and short-term, is integrated with Genetic Algorithm (GA) and their outcomes are employed as parameters for the grey relational linear moving analysis then others. Four different models including General Fluctuation Average Model, Grey Relational Fluctuation Average Model, General Linear Revision Model, and Grey Relational Linear Revision Model, are implemented to forecasts the trend of TAIEX. The results show that the Grey Relational Linear Model performs higher prediction accuracy. The empirical results are summarized as follows: 1.The best duration parameters acquired from GA for Grey Relational Linear Models are 15-days for short-term and 32-days for long-term respectively. 2.The results of the Globalized Grey Relational Analysis for short-term discovery that comparison sequences prior to or posterior six months are less related to the target sequence. 3.The Grey Relational Fluctuation Average Model and General Linear Revision Model perform better prediction accuracy than that of the General Fluctuation Average Model and General Linear Revision Model in terms of short-term and long-term periods. This result is conformable with our suggestion that historical linear patterns provide effective information for the trend prediction of TAIEX.
參考文獻
7.江耕南(2005)。進階Excel-投資理財寶典‧財務資訊系統。台北：知城數位科技股份有限公司。
17.黃彥聖(1995)。移動平均法的投資績效。管理評論，14(1)，47-68。
31.鄭美幸、詹志明(2002)。灰色理論與時間序列模型在匯率預測績效上之比較。台灣金融財務季刊，3(2)，95-104。
2.Chang K. H. (2004). Applying Grey Forecasting Model on the Systematic Risk Estimation: A Study of the Dow Jones Industry Index Component Securities. Journal of Grey System and Uncertainty, 7(2), 113-120.
4.李怡青(2005) 。利用已公開市場資訊預測大盤漲跌之研究－Logistic Regression之應用。未出版碩士論文，國立成功大學財務金融研究所，台南。