Proceedings of the 16th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2021F79

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 147–150 (2021)

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Abstract. Stock market price prediction models have remained a prominent challenge for the investors owing to their volatile nature. The impact of macroeconomic events such as news headlines is studied here using a standard dataset with closing stock price rates for a chosen period by performing sentiment analysis using a Random Forest classifier. A Bi-LSTM time-series forecasting model is constructed to predict the stock prices by using the polarity of the news headlines. It is observed that Random Forest Classifiers predict the polarity of news articles with an accuracy of 84.92\%.

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