Stock Price Forecasting with Support Vector Machines Based on Web Financial Information Sentiment Analysis
نویسندگان
چکیده
The stock price forecasting has always been considered as a difficult problem in time series prediction. Mass of financial Internet information play an important role in the financial markets, information sentiment is an important indicator reflecting the ideas and emotions of investors and traders. Most of the existing research use the stock's historical price and technical indicators to predict future price trends of the stock without taking the impact of financial information into account. In this paper, we further explore the relationship between the Internet financial information and financial markets, including the relations between the Internet financial information content, Internet financial information sentimental value and stock price. We collect the news of three stocks in the Chinese stock market in the GEM in a few large portals, use the text sentiment analysis algorithm to calculate the sentimental value of the corresponding Internet financial information, combined with the stock price data, implant Support Vector Machines to analyzes and forecasts on the stock price, the accuracy of the prediction of stock prices has been improved.
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