Predicting the Effects of News Sentiments on the Stock Market

December 11, 2018 · Declared Dead · 🏛 2018 IEEE International Conference on Big Data (Big Data)

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Authors Dev Shah, Haruna Isah, Farhana Zulkernine arXiv ID 1812.04199 Category cs.CL: Computation & Language Cross-listed cs.LG Citations 106 Venue 2018 IEEE International Conference on Big Data (Big Data) Last Checked 4 months ago

Abstract

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.