Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model.

Ping-Feng Pai,Ling-Chuang Hong,Kuo-Ping Lin

doi:10.1155/2018/6305246

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.

Highlights

With the advances of the Internet and communication in recent years, the increasing amount of data from social networks leads to changes in ways of collecting and analyzing data
Proposed by Hall [30], the correlation-based feature selection (CFS) is a feature identification technology used for determining features with critical influence on prediction classes. e influence of features is related to the correlation between the feature and the prediction class labels. e correlation function is represented as follows: DOFp
Due to the rise of social networking and Internet search tools, types of data employed for predicting stock markets became diversified

Summary

Introduction

With the advances of the Internet and communication in recent years, the increasing amount of data from social networks leads to changes in ways of collecting and analyzing data. The forecasting of stock markets has relied heavily on historical trading data. Due to the popular use of the Internet search, people tend to seek data or information from the Internet and express opinions on social networks. Stephens-Davidowitz [1] indicated that when social censoring issues are studied, Internet search behaviors can better reflect the real thinking of people than survey data, and the timing to obtain data is more close to real time [2,3,4,5,6]. The importance of historical trading data in forecasting stock market values should not be disregarded. Is study attempts to incorporate the data from Google Trends and historical trading data together to predict stock markets. The importance of historical trading data in forecasting stock market values should not be disregarded. is study attempts to incorporate the data from Google Trends and historical trading data together to predict stock markets. e performance of hybrid data and the unique data type in forecasting stock market closing values

Objectives

Methods

Conclusion