Abstract
To simulate the trading behavior of investors in the stock market, this study adopts parameters including technical, fundamental, and chip to build a LSTM model, and also observes the ability of news sentiment to predict stock prices. Influential stocks such as TSMC, Fulgent Sun, and HTC are chosen as the target of our experiment. Four common natural language processing packages are used to label news sentiment. Then the combined sentiment labels along with the LSTM model are used for backtesting. The results of the study found that FinBERT's ability to predict the price trend outperforms other methods, with an accuracy of 41.6%. In addition, combining news sentiment labels with the LSTM model generally leads to better outcome than using either the news label or the LSTM model alone. However, in certain extreme cases, traditional technical indicators or even buy-and-hold strategy have better performances.
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Li, JB., Lin, SY., Leu, FY., Chu, YC. (2023). Stock Price Trend Prediction Using LSTM and Sentiment Analysis on News Headlines. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2022. Lecture Notes in Networks and Systems, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-031-20029-8_27
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DOI: https://doi.org/10.1007/978-3-031-20029-8_27
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