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Do Weibo platform experts perform better at predicting stock market?

Author

Listed:
  • Ziyuan Ma
  • Conor Ryan
  • Jim Buckley
  • Muslim Chochlov
Abstract
Sentiment analysis can be used for stock market prediction. However, existing research has not studied the impact of a user's financial background on sentiment-based forecasting of the stock market using artificial neural networks. In this work, a novel combination of neural networks is used for the assessment of sentiment-based stock market prediction, based on the financial background of the population that generated the sentiment. The state-of-the-art language processing model Bidirectional Encoder Representations from Transformers (BERT) is used to classify the sentiment and a Long-Short Term Memory (LSTM) model is used for time-series based stock market prediction. For evaluation, the Weibo social networking platform is used as a sentiment data collection source. Weibo users (and their comments respectively) are divided into Authorized Financial Advisor (AFA) and Unauthorized Financial Advisor (UFA) groups according to their background information, as collected by Weibo. The Hong Kong Hang Seng index is used to extract historical stock market change data. The results indicate that stock market prediction learned from the AFA group users is 39.67% more precise than that learned from the UFA group users and shows the highest accuracy (87%) when compared to existing approaches.

Suggested Citation

  • Ziyuan Ma & Conor Ryan & Jim Buckley & Muslim Chochlov, 2024. "Do Weibo platform experts perform better at predicting stock market?," Papers 2403.00772, arXiv.org.
    • Handle: RePEc:arx:papers:2403.00772
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    References listed on IDEAS

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    1. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    2. Xiaojun Chu & Chongfeng Wu & Jianying Qiu, 2016. "A nonlinear Granger causality test between stock returns and investor sentiment for Chinese stock market: a wavelet-based approach," Applied Economics, Taylor & Francis Journals, vol. 48(21), pages 1915-1924, May.
    3. Yingying Xu & Zhixin Liu & Jichang Zhao & Chiwei Su, 2017. "Weibo sentiments and stock return: A time-frequency view," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.
    4. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    5. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
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