ASYMMETRIC SENTIMENT ANALYSIS OF BUSINESS NEWS IN HISTORICAL DATA FORECASTING SYSTEMS
DOI:
https://doi.org/10.31649/1999-9941-2023-58-3-65-75Keywords:
historical data, business news, sentiment analysis, asymmetry forecasting, fundamental analysisAbstract
Abstract.Forecasting data in financial markets is a pertinent task in the modern world. The ability to predict market movements helps investors avoid obvious risks and spare themselves additional expenses. Numerous trading platforms have been developed to quickly access extensive historical data, enabling real-time analysis of the financial market from any corner of the planet using only a laptop or personal computer. Such platforms allow the development of unique strategies and approaches based on fundamental or technical analysis, taking into account news about a particular company, its earnings, capitalization, and the amount of dividends it is expected to pay on time.
Business news is a crucial source of information about the state of the economy and markets. They can be used for forecasting future events. One method of forecasting based on business news is sentiment analysis. Sentiment analysis allows assessing the positivity or negativity of business news.Traditional sentiment analysis methods employ a symmetric approach. This means that positive and negative news are equally considered in forecasting. However, in the real world, positive news may have a greater impact on markets than negative news. This is because positive news can stimulate economic activity, while negative news may hinder it.
The article explores the application of asymmetric sentiment analysis of business news in financial data forecasting systems. Various methods of sentiment analysis of business news, their advantages, and disadvantages are analyzed. A new approach to sentiment analysis of business news is proposed, which comprehensively utilizes artificial neural networks and principal component analysis.
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Tsai, C. F., & Wang, C. C. (2018). "Using deep neural network models for risk prediction and classification in real-time financial market." Expert Systems with Applications, 95, 12-27.
D. P. Kroese, S. J. Rubinstein, P. I. Frazier, et al. (2019). "Statistical Machine Learning for Asset Management." Journal of Financial Data Science, 1(1), 2-19.
Tsantekidis, A., Passalis, N., Tefas, A., et al. (2017). "Using deep learning and Google Trends to predict the stock price of a large IT company." Expert Systems with Applications, 73, 125-139.
Stock Market Sentiment Analysis in 2023, 2022. URL: https://research.aimultiple.com/sentiment-analysis-stock-market/
Types of Sentiment Analysis and How Brands Perform Them, 2020. URL: https://www.analyticsinsight.net/types-of-sentiment-analysis-and-how-brands-perform-them/
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Zhang, Y., Watson, J., & Johnson, J. (2019). "Deep learning in finance." Journal of Economic Dynamics and Control, 98, 1-16.
Yu, L., Wang, S., & Lai, K. K. (2017). "A deep learning and cross-domain approach for stock price movement prediction." Expert Systems with Applications, 83, 56-66.
Hagenau, M., Liebmann, M., & Neumann, D. (2013). "Automated news reading: Stock price prediction based on financial news using context-capturing features." Decision Support Systems, 55(3), 685-697.
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