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Improving stock price prediction using the long short-term memory model combined with online social networks

Author

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  • Liu, Keyan
  • Zhou, Jianan
  • Dong, Dayong

Abstract

To predict stock prices with effective information has always been a problem of great significance in the fields of behavioral finance. In this paper, we predict the stock prices with novel online data sources. For some emerging countries (such as China), individual investors often obtain trading information from online social media platforms. Therefore, stock features extracted from social media platforms are likely to include valuable information. We obtained the data of users and stocks they followed from EastyMoney, China’s largest social media platform, and generate daily social networks. Then we calculated the network variable of each stock as a supplement to traditional variables, and predicted the close prices of the SSE 50 constituent stocks using the LSTM model. The empirical results show that the social network variable can effectively improve the prediction accuracy. Our results can help investors improve forecasting accuracy. Our findings can help investors enhancing the understanding of the link between social networks and stock prices.

Suggested Citation

  • Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    • Handle: RePEc:eee:beexfi:v:30:y:2021:i:c:s2214635021000514
    DOI: 10.1016/j.jbef.2021.100507
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    2. Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    3. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Caparrini, Antonio & Arroyo, Javier & Escayola Mansilla, Jordi, 2024. "S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors," Research in International Business and Finance, Elsevier, vol. 70(PA).
    5. Tibor Bareith & Tibor Tatay & László Vancsura, 2024. "Navigating Inflation Challenges: AI-Based Portfolio Management Insights," Risks, MDPI, vol. 12(3), pages 1-16, March.

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