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Impact of Negative Tweets on Diverse Assets during Stressful Events: An Investigation through Time-Varying Connectedness

Author

Listed:
  • N. L. Balasudarsun

    (Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Electronic City, Phase-I, Bengaluru 560100, Karnataka, India)

  • Bikramaditya Ghosh

    (Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Electronic City, Phase-I, Bengaluru 560100, Karnataka, India)

  • Sathish Mahendran

    (Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Electronic City, Phase-I, Bengaluru 560100, Karnataka, India)

Abstract

Tweets seem to impact diverse assets, especially during stressful periods. However, their interrelations during stressful events may change. Cryptos are apparently more sensitive to the sentiment spread by tweets. Therefore, a construct could be formed to study such complex interrelation during stressful events. This study found an interesting outcome while investigating three major asset classes (namely, Equity, Gold and Bond) alongside negative sentiment (derived from tweets of Elon Musk) and Dogecoin (an emerging asset class) from 1 June 2015 to 20 February 2022. Negative sentiment emerged as the significant risk transmitter, while Gold emerged as the significant net recipient of shocks (risk). Interestingly, Dogecoin was found to be less impacted and not impactful (not transmitting shock and receiving tiny shocks) at the same time. In fact, the interconnectedness between negative sentiment (percolated through Twitter) and Dogecoin prices was found to be rather feeble. Further, the study showed that the COVID-19 breakout and Brexit referendum in 2016 were less stressful events compared to the Greek debt crisis back in 2015.

Suggested Citation

  • N. L. Balasudarsun & Bikramaditya Ghosh & Sathish Mahendran, 2022. "Impact of Negative Tweets on Diverse Assets during Stressful Events: An Investigation through Time-Varying Connectedness," JRFM, MDPI, vol. 15(6), pages 1-12, June.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:6:p:260-:d:835087
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    Cited by:

    1. Muhammad Nabil Rateb & Sameh Alansary & Marwa Khamis Elzouka & Mohamad Galal, 2024. "Predicting Cryptocurrency Prices During Periods of Conflict: A Comparative Sentiment Analysis Using SVM, CNN-LSTM, and Pysentimento," SN Operations Research Forum, Springer, vol. 5(3), pages 1-40, September.
    2. Ghosh, Bikramaditya & Pham, Linh & Gubareva, Mariya & Teplova, Tamara, 2023. "Energy transition metals and global sentiment: Evidence from extreme quantiles," Resources Policy, Elsevier, vol. 86(PA).

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