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Behavioral finance and market efficiency in the time of the COVID-19 pandemic: does fear drive the market?

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  • Evangelos Vasileiou

Abstract

In this study, we examine the efficiency of the US stock markets during the COVID-19 outbreak using a fundamental financial analysis approach, the constant growth model and a behavioral model including a Google-based Index. We juxtapose the released news and the performance of the US stock market during the COVID-19 outbreak and we show that during some periods the health risk was significantly underestimated and/or ignored. The Efficient Market Hypothesis (EMH) suggests that prices incorporate all the available information at any point in time, yet as we show a systemic factor, the health risk, was not always rationally incorporated in stock prices. The Runs-tests confirm our assumption that the market was not efficient during the examined period. The reason for this inefficiency could be that something is missing from traditional finance models, such as the impact of fear of COVID-19. For this reason we employ a Coronavirus Fear Index (CFI) based on Google searches and using Granger causality we provide empirical evidence that the fear drives the S&P500 performance, and using a GARCH model we show that the fear negatively influences the performance of the US stock market.

Suggested Citation

  • Evangelos Vasileiou, 2021. "Behavioral finance and market efficiency in the time of the COVID-19 pandemic: does fear drive the market?," International Review of Applied Economics, Taylor & Francis Journals, vol. 35(2), pages 224-241, March.
    • Handle: RePEc:taf:irapec:v:35:y:2021:i:2:p:224-241
    DOI: 10.1080/02692171.2020.1864301
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    Cited by:

    1. Andrew Phiri, 2022. "Changing efficiency of BRICS currency markets during the COVID-19 pandemic," Economic Change and Restructuring, Springer, vol. 55(3), pages 1673-1699, August.
    2. Jimei Shen & Zhehu Yuan & Yifan Jin, 2022. "AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment," Papers 2206.11072, arXiv.org, revised Dec 2022.
    3. Qingjie Zhou & Panpan Zhu & You Wu & Yinpeng Zhang, 2022. "Research on the Volatility of the Cotton Market under Different Term Structures: Perspective from Investor Attention," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    4. Kerry Liu, 2022. "China's Reform Spree in 2021: Common Prosperity and Others," Economic Papers, The Economic Society of Australia, vol. 41(3), pages 232-246, September.
    5. Khurram Ajaz Khan & Zdenko Metzker & Justas Streimikis & John Amoah, 2023. "Impact of negative emotions on financial behavior: An assessment through general strain theory," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(1), pages 219-254, March.
    6. Clement Moyo & Izunna Anyikwa & Andrew Phiri, 2023. "The Impact of Covid-19 on Oil Market Returns: Has Market Efficiency Being Violated?," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 118-127, January.
    7. Li, Sufang & Xu, Qiufan & Lv, Yixue & Yuan, Di, 2022. "Public attention, oil and gold markets during the COVID-19: Evidence from time-frequency analysis," Resources Policy, Elsevier, vol. 78(C).
    8. Zaheda Daruwala, 2022. "Reactive or Immune: Stock Market Behaviour During Subsequent Waves of the COVID-19 Pandemic," International Journal of Economics and Financial Issues, Econjournals, vol. 12(6), pages 92-106, November.
    9. Dash, Saumya Ranjan & Maitra, Debasish, 2022. "The COVID-19 pandemic uncertainty, investor sentiment, and global equity markets: Evidence from the time-frequency co-movements," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

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