股票價格預測模式優化-實驗設計與類神經網路之應用

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Title:	股票價格預測模式優化-實驗設計與類神經網路之應用
Authors:	謝素真 Chen, Hsieh Su
Contributors:	財務管理學系 Finance
Keywords:	股價指數;倒傳遞神經網路;實驗設計方法 Stock price forecasting;Back-propagation neural network;Design of experime
Date:	2010
Issue Date:	2014-06-27 00:01:04 (UTC+8)
Abstract:	股票是金融市場中廣為個別投資人及法人機構所偏好的投資工具,因此如何提高對於股價變動的走勢,是許多投資人所關切的焦點,也是眾多學術論文的核心。有鑑於股票波動的非線性特性,類神經網路是一廣被採用且證明預測度高於一般之.歸模型的預測工具；然而關於類神經網路的參數設定,一般的論文研究是以試誤法做為設定的準則。為了能進一步提高預測的準確度,本文結合傳統實驗設計方法與倒傳遞神經網路,以台灣證券交易所(TWSE) 集中市場之加權股價指數與上市公司股價為研究資料來源,台灣股價市場投資人常用的技術指標為因子, Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always
Appears in Collections:	[Department of Finance] Seminar Papers

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