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Google trends stock prediction
Google trends stock prediction





google trends stock prediction google trends stock prediction

This study attempts to incorporate the data from Google Trends and historical trading data together to predict stock markets. However, the importance of historical trading data in forecasting stock market values should not be disregarded. Stephens-Davidowitz 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.

google trends stock prediction

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. Most forecasting models using historical trading data are based on the causality theoretically. In the past, the forecasting of stock markets has relied heavily on historical trading data. However, the stock markets have profound effects on a country. Ever since the beginning of the stock market, it is hard to predict. Compared to structured data, collection data from social networks are another way to depict the issues concerned, and thus, some other interesting and essential insights that are not included in the traditional data collection may be discovered. Hence, the data from Google Trends data started to be applied to many fields such as economy, election, and medication. Google Trends ( ) can be used to search trends of keywords. 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.

google trends stock prediction

Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting 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. 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. The hybrid data include Internet search trends from Google Trends and historical trading 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. 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. Instead of only theoretical causality in forecasting, the importance of data relations has raised. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values.







Google trends stock prediction