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Herd behavior in stock markets during COVID’ 19 Pandemic: A machine learning approach

Author

Listed:
  • Fatima Iqbal

    (PhD Scholar, Hailey College of Commerce, University of the Punjab, Pakistan)

  • Dr. Sadia Farooq

    (Assistant Professor, Hailey College of Commerce, University of the Punjab, Pakistan)

  • Dr. Sajid Nazir

    (Associate Professor, Institute of Administrative sciences, University of the Punjab, Pakistan)

Abstract

The COVID-19 pandemic brought unprecedented volatility and uncertainty to global financial markets. During this period, the concept of herd behavior emerged as a prominent factor influencing stock market dynamics. Understanding and quantifying herd behavior patterns during the pandemic is crucial for predicting market trends, detecting potential bubbles, and improving risk management strategies. Herd behavior is characterized by a sudden mimicry among investors which causes temporary deviation of stock market prices. This deviation exacerbates in the presence of extreme conditions or events such as the recent pandemic. Based on social media contextual data this study aims to investigate the presence of herd behavior during the global COVID’19 pandemic. For this purpose state of the art machine learning algorithms are employed as opposed to traditional methodologies that are being used in the past literature for quantifying herd behavior. The surprising results reveal how role of media sentiment during the pandemic shaped the stock markets and investor behavior.

Suggested Citation

  • Fatima Iqbal & Dr. Sadia Farooq & Dr. Sajid Nazir, 2023. "Herd behavior in stock markets during COVID’ 19 Pandemic: A machine learning approach," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(2), pages 268-273.
    • Handle: RePEc:rfh:jprjor:v:9:y:2023:i:2:p:268-273
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    References listed on IDEAS

    as
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