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Stock return predictability in the time of COVID-19

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  • Ciner, Cetin

Abstract

We examine predictive ability of a relatively large number of variables from currency, bond and commodity markets for US stock returns during the COVID-19 crisis. As a novel contribution, we estimate robust Lasso predictive regressions with Cauchy errors, consistent with extreme movements and nonlinearities in the market. Both investment grade and high yield corporate bonds emerge as significant predictors of US stock returns in the period, lending support to recent policy decisions by the Federal Reserve.

Suggested Citation

  • Ciner, Cetin, 2021. "Stock return predictability in the time of COVID-19," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612320308345
    DOI: 10.1016/j.frl.2020.101705
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    References listed on IDEAS

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    Cited by:

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    3. ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.
    4. Hugo S. Gonçalves & Sérgio Moro, 2023. "On the economic impacts of COVID‐19: A text mining literature analysis," Review of Development Economics, Wiley Blackwell, vol. 27(1), pages 375-394, February.
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    6. Greta Keliuotyte-Staniuleniene & Julius Kviklis, 2021. "Assessing the reaction of the Baltic stock market to the spread of the COVID-19 pandemic," Technium Social Sciences Journal, Technium Science, vol. 25(1), pages 260-272, November.
    7. Afees A. Salisu & Ahamuefula E. Ogbonna & Tirimisiyu F. Oloko & Idris A. Adediran, 2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    8. Ciner, Cetin & Kosedag, Arman & Lucey, Brian, 2023. "Predictors of clean energy stock returns: An analysis with best subset regressions," Finance Research Letters, Elsevier, vol. 55(PA).
    9. Gregory, Richard Paul, 2022. "ESG scores and the response of the S&P 1500 to monetary and fiscal policy during the Covid-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 446-456.
    10. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    11. Raheem, Ibrahim, 2021. "Commentaries on the Global Fiscal Consequences of the COVID-19 Pandemic: The Good, the Bad, the Unknown, and the Way Forward," MPRA Paper 107629, University Library of Munich, Germany.
    12. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    13. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2022. "Determinants of cryptocurrency returns: A LASSO quantile regression approach," Finance Research Letters, Elsevier, vol. 49(C).
    14. A. Gómez-Águila & J. E. Trinidad-Segovia & M. A. Sánchez-Granero, 2022. "Improvement in Hurst exponent estimation and its application to financial markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
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