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    题名: 使用文字探勘結合多元有順序類別支持向量機預測股價漲跌趨勢之應用
    其它题名: Text mining techniques incorporating ordinal multi-class support vector machine on stock price movement prediction
    作者: 黃筑均;Huang, Jhu-Jyun
    贡献者: 淡江大學統計學系碩士班
    林志娟;Lin, Jyh-jiuan
    关键词: 文字探勘;多元支持向量機;文件分類;股價預測;text mining;Multi-class Support Vector Machines;Document categorization;Stock Price Prediction
    日期: 2017
    上传时间: 2018-08-03 14:52:30 (UTC+8)
    摘要: 股市為反映國家經濟的一項指標,投資者最終希望的就是能從中獲取最大的報酬,傳統股價趨勢預測考量的五大層面為市場面、基本面、技術面、籌碼面以及財務面,近年來由於網路科技及社群網路的大眾化,投資人對於接收股市投資的行為訊息相較以往更為便利以及快速,而這些新聞以及社群網路文章中與未來股市新聞漲跌的相關關鍵詞也將會成為影響投資者未來的買賣策略的一個面向。本研究主要以新聞文章萃取出之關鍵詞結合傳統的技術指標作為分析資料,建立模型時以兩種不同的多元支持向量機建立股價漲跌趨勢的預測模型,研究發現多元有順序類別支持向量機模型(OMSVM)在所有模型中預測表現最好。
    Stock market is an indicator to reflect the national economy. The ultimate goals of investors mostly are maximizing the returns. The forecast of stock price consists of five methods which are market analysis, fundamental analysis, technical analysis, chip analysis and financial analysis. Compared to the past, the investors receive information of stock market not only faster but also more convenient due to the popularization of interconnection networks and social networks. These terms of financial news articles on the network which are relevant to the trend of stock price have become a new implication. It would directly or indirectly affect investor’s trading decisions.This study used text mining techniques to extract the terms and combine the technical indicators. Two Multi-class Support Vector Machines (SVM) techniques are incorporated to analyze the stock news, build a forecasting model of stock price trend. It is found that OMSVM, which could handle multiple ordinal classes, outperformed among all the models.
    显示于类别:[統計學系暨研究所] 學位論文

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