ASIA unversity:Item 310904400/111753
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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/111753


    Title: Using Internet search trends and historical trading data for predicting stock markets by the least squares support vector regression model
    Authors: Pa, Ping-Feng;Pai, Ping-Feng;*;Ling-Chuang;Hong, Ling-Chuang;林國平;Lin, Kuo-Ping
    Contributors: 經營管理學系
    Date: 2018-07
    Issue Date: 2018-12-25 08:11:42 (UTC+0)
    Abstract: Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Instead of only theoretical causality in forecasting, the importance of data relations has raised. Thus, the aim of this study was to investigate performances of forecasting stock markets by data from Google Trends, historical trading data (HTD), and hybrid data. The keywords employed for Google Trends are collected from three different ways including users’ definitions (GTU), trending searches of Google Trends (GTTS), and tweets (GTT) correspondingly. The hybrid data include Internet search trends from Google Trends and historical trading data. In addition, the correlation-based feature selection (CFS) technique is used to select independent variables, and one-step ahead policy is adopted by the least squares support vector regression (LSSVR) for predicting stock markets. Numerical experiments indicate that using hybrid data can provide more accurate forecasting results than using single historical trading data or data from Google Trends. Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting stock markets.
    Relation: Computational Intelligence and Neuroscience
    Appears in Collections:[Department of Business Administration] Journal Article

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