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Do Weibo Platform Experts Perform Better at Predicting Stock Market?

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Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN 2021)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

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.

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Notes

  1. 1.

    https://github.com/majuanjuan/Doexpertsperformbetter.

  2. 2.

    https://www.investing.com/.

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Acknowledgement

This work was supported with the financial support of the Science Foundation Ireland grant 13/RC/2094_2.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Ziyuan Ma

  2. University of Limerick, LERO, Limerick, Ireland

    Conor Ryan, Jim Buckley & Muslim Chochlov

Authors
  1. Ziyuan Ma
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  2. Conor Ryan
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  3. Jim Buckley
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  4. Muslim Chochlov
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Corresponding authors

Correspondence to Conor Ryan , Jim Buckley or Muslim Chochlov .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Ma, Z., Ryan, C., Buckley, J., Chochlov, M. (2021). Do Weibo Platform Experts Perform Better at Predicting Stock Market?. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_40

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