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Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study

Author

Listed:
  • Qianwen Xu

    (Teesside University)

  • Victor Chang

    (Teesside University)

  • Ching-Hsien Hsu

    (Asia University
    China Medical University Hospital)

Abstract

This paper provides an improved method by introducing Sentiment Analysis into the Event Study and Principal Component Analysis. The model is constructed by using the heuristic mean-end analysis. This method enables us to take into investors’ feelings towards related stocks when we study the stock market’s reaction to a given event. This paper investigates the Chinese A-shared market over 2013–2019 to study the influence of rumors and the offsetting impact of rumor clarifications on the stock price. The results indicate that no matter investor sentiment is bullish or bearish, stock price reacts significantly to rumors before as well as when the rumor goes public. Furthermore, clarification offsets the positive abnormal returns caused by rumors with bullish sentiment substantially at a limited level. Still, after five days, it creates a positive effect like the positive rumor does on the stock price. Under the bearish sentiment, clarification brings an insignificant impact on the stock price. The results indicate that the source of rumor may not come from the media and investment decisions established on rumors would be beneficial to investors before as well as after they are published. Moreover, official clarification causes an offset effect, but it is very limited.

Suggested Citation

  • Qianwen Xu & Victor Chang & Ching-Hsien Hsu, 2020. "Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study," Information Systems Frontiers, Springer, vol. 22(5), pages 1021-1037, October.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10024-5
    DOI: 10.1007/s10796-020-10024-5
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    References listed on IDEAS

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    Cited by:

    1. Lydia Bouzar-Benlabiod & Stuart H. Rubin, 2020. "Heuristic Acquisition for Data Science," Information Systems Frontiers, Springer, vol. 22(5), pages 1001-1007, October.
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    5. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.

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