Financial News Effect Analysis on Stock Price Prediction Using a Stacked LSTM Model
Alexandre Heidein, Rafael Stubs Parpinelli
Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 233–240 (2022)
Abstract. In the age of information, it is understood that social media provides valuable reference for many contexts, including the financial market. Although having high volume, publications on social media are not necessarily reliable. In this context, this research aims to examine the influence of financial news coming from a more transparent source, the newspaper The New York Times. This source provides fact-checked news, but the volume of information is lower compared to social media. The strategy proposes a difficult challenge, the application of a Machine Learning model on a limited dataset. The LSTM-based stock price prediction model proposed has two features, news sentiment and historical data of the assets. Experiments indicate that the model performs better when the news' sentiments are considered and demonstrates potential to accurately predict stock prices up to around 35 days into the future, comparing the results obtained with the real prices on the period.
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