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1- Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran , golshid.ranjbaran@srbiau.ac.ir
2- ICT Research Institute, Tehran, Iran
3- Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- ICT Research Institute, Tehran, Iran
3- Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract: (232 Views)
In the stock market, which is a dynamic, complex, nonlinear and non-parametric environment, accurate prediction is crucial for trading strategy. It is assumed that news articles affect the stock market. We investigated the relationship between headline’s sentiment of news and their impact on stock prices changes. To show this relationship, we applied the sentiment data and the price difference between the day before the news was published and the day of the news, to machine learning regression and classification models. Regression is used to predict changes and classification is used to decide whether to buy or sell stocks. We used three stock datasets named Apple, Amazon and AXP and the results are shown in the mentioned dataset that using news with negative sentiments can make predictions just as correctly as using news with both positive and negative sentiments. In regression and classification models, Random Forest outperformed other machine learning algorithms in predicting stock price changes using news sentiment analysis. Additionally, we depicted that the results of computer and human tagging were almost similar, showing that using computer tools for text tagging will allow to tag text much more quickly and easily.
Type of Study: Research |
Subject:
Information Technology
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