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Overreaction, Investor Sentiment and Market Sentiment of COVID-19

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  • Ooi Kok Loang

Abstract

This study examines the impact of investor sentiment and market sentiment on overreaction in Europe and USA markets before and during COVID-19. The investor sentiment is calculated by the standard deviation, realized volatility, Parkinson’s estimator and Garman and Klass’s estimator. The market sentiment is measured by Business Confidence Index, Consumer Confidence Index, Labour Force Survey, Leading Index and Monetary Aggregates. The results of this study show that investor and market sentiments are correlated to stock return before COVID-19. Nonetheless, realized volatility is the only investor sentiment that is significant with the emergence of COVID-19. It shows that investors rely on the previous day’s stock prices to trade under market uncertainty. Market sentiment is observed to be insignificant in the pandemic. Furthermore, the existence of overreaction is detected in European portfolios but no evidence of overreaction is shown in the USA during pre-COVID-19. Surprisingly, overreaction is observed in Europe and USA markets in the pandemic. The USA market has a higher overreaction tendency than Europe. The results of this study assist academicians, practitioners and investors in understanding and creating awareness of the existence of market overreaction and its determinants before and during COVID-19.

Suggested Citation

  • Ooi Kok Loang, 2025. "Overreaction, Investor Sentiment and Market Sentiment of COVID-19," Vision, , vol. 29(5), pages 636-650, November.
  • Handle: RePEc:sae:vision:v:29:y:2025:i:5:p:636-650
    DOI: 10.1177/09722629221087386
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    References listed on IDEAS

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    1. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    2. Aparicio Fenoll, Ainoa & Grossbard, Shoshana, 2020. "Intergenerational residence patterns and Covid-19 fatalities in the EU and the US," Economics & Human Biology, Elsevier, vol. 39(C).
    3. Xiaolin Huo & Zhigang Qiu, 2020. "How does China’s stock market react to the announcement of the COVID-19 pandemic lockdown?," Economic and Political Studies, Taylor & Francis Journals, vol. 8(4), pages 436-461, October.
    4. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    5. Ruhani Ali & Zamri Ahmad & Shangkari V. Anusakumar, 2011. "Stock Market Overreaction and Trading Volume: Evidence from Malaysia," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 7(2), pages 103-119.
    6. Fenghua Wen & Yupei Zhao & Minzhi Zhang & Chunyan Hu, 2019. "Forecasting realized volatility of crude oil futures with equity market uncertainty," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6411-6427, December.
    7. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    8. Swati Dhingra & Gianmarco Ottaviano & John Van Reenen & Jonathan Wadsworth, 2016. "Brexit and the Impact of Immigration on the UK," CEP Brexit Analysis Papers 05, Centre for Economic Performance, LSE.
    9. Elena Ferrer & Julie Salaber & Anna Zalewska, 2016. "Consumer confidence indices and stock markets' meltdowns," The European Journal of Finance, Taylor & Francis Journals, vol. 22(3), pages 195-220, February.
    10. Pedro Piccoli & Mo Chaudhury, 2018. "Overreaction to extreme market events and investor sentiment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(2), pages 115-118, January.
    11. Ooi Kok Loang & Zamri Ahmad, 2021. "Market overreaction, firm-specific information and macroeconomic variables in US and Chinese markets during COVID-19," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 49(8), pages 1548-1565, December.
    12. Jason Zhe Ma & Kung-Cheng Ho & Lu Yang & Chien-Chi Chu, 2018. "Market Sentiment and Investor Overreaction: Evidence from New York Listed Asian Country Exchange Traded Funds," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(11), pages 2455-2471, September.
    13. Tsung-Pao Wu & Shu-Bing Liu & Shun-Jen Hsueh, 2016. "The Causal Relationship between Economic Policy Uncertainty and Stock Market: A Panel Data Analysis," International Economic Journal, Taylor & Francis Journals, vol. 30(1), pages 109-122, March.
    14. Baig, Ahmed S. & Butt, Hassan Anjum & Haroon, Omair & Rizvi, Syed Aun R., 2021. "Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 38(C).
    15. Blackburn, Douglas W. & Cakici, Nusret, 2017. "Overreaction and the cross-section of returns: International evidence," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 1-14.
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