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Predicting stock returns in the presence of COVID-19 pandemic: The role of health news

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  • Salisu, Afees A.
  • Vo, Xuan Vinh
Abstract
This study derives its motivation from the current global pandemic, COVID-19, to evaluate the relevance of health-news trends in the predictability of stock returns. We demonstrate this by using data covering top-20 worst-hit countries, distinctly in terms of reported cases and deaths. The results reveal that the model that incorporates health-news index outperforms the benchmark historical average model, indicating the significance of health news searches as a good predictor of stock returns since the emergence of the pandemic. We also find that accounting for “asymmetry” effect, adjusting for macroeconomic factors and incorporating financial news improve the forecast performance of the health news-based model. These results are consistently robust to data sample (both for the in-sample and out-of-sample forecast periods), outliers and heterogeneity.

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  • Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:finana:v:71:y:2020:i:c:s1057521920301903
    DOI: 10.1016/j.irfa.2020.101546
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    1. Joakim Westerlund & Syed A. Basher, 2007. "Can panel data really improve the predictability of the monetary exchange rate model?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 365-383.
    2. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    3. Verardi Vincenzo & Wagner Joachim, 2011. "Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(4), pages 546-557, August.
    4. Liebmann, Michael & Orlov, Alexei G. & Neumann, Dirk, 2016. "The tone of financial news and the perceptions of stock and CDS traders," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 159-175.
    5. Simon Reese & Joakim Westerlund, 2016. "Panicca: Panic on Cross‐Section Averages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 961-981, September.
    6. Ciaran Driver & Katsushi Imai & Paul Temple & Giovanni Urga, 2004. "The effect of uncertainty on UK investment authorisation: Homogenous vs. heterogeneous estimators," Empirical Economics, Springer, vol. 29(1), pages 115-128, January.
    7. Akıncı, Özge & Chahrour, Ryan, 2018. "Good news is bad news: Leverage cycles and sudden stops," Journal of International Economics, Elsevier, vol. 114(C), pages 362-375.
    8. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    9. Büttner, David & Hayo, Bernd, 2010. "News and correlations of CEEC-3 financial markets," Economic Modelling, Elsevier, vol. 27(5), pages 915-922, September.
    10. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    11. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    12. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    13. Birz, Gene & Lott Jr., John R., 2011. "The effect of macroeconomic news on stock returns: New evidence from newspaper coverage," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2791-2800, November.
    14. William T. Gavin & Athena T. Theodorou, 2005. "A common model approach to macroeconomics: using panel data to reduce sampling error," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(3), pages 203-219.
    15. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
    16. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    17. Heiko Jacobs & Alexander Hillert, 2016. "Alphabetic Bias, Investor Recognition, and Trading Behavior," Review of Finance, European Finance Association, vol. 20(2), pages 693-723.
    18. Merton, Robert C, 1987. "A Simple Model of Capital Market Equilibrium with Incomplete Information," Journal of Finance, American Finance Association, vol. 42(3), pages 483-510, July.
    19. Narayan, Paresh Kumar, 2019. "Can stale oil price news predict stock returns?," Energy Economics, Elsevier, vol. 83(C), pages 430-444.
    20. Wang, Hui, 2019. "VIX and volatility forecasting: A new insight," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    21. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    22. Salisu, Afees A. & Isah, Kazeem & Akanni, Lateef O., 2019. "Improving the predictability of stock returns with Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 857-867.
    23. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    24. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    25. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
    26. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    27. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    28. Salisu, Afees A. & Raheem, Ibrahim D. & Ndako, Umar B., 2019. "A sectoral analysis of asymmetric nexus between oil price and stock returns," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 241-259.
    29. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    30. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    31. Chen, Xuanjuan & Kim, Kenneth A. & Yao, Tong & Yu, Tong, 2010. "On the predictability of Chinese stock returns," Pacific-Basin Finance Journal, Elsevier, vol. 18(4), pages 403-425, September.
    32. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    33. Maria Caterina Bramati & Christophe Croux, 2007. "Robust estimators for the fixed effects panel data model," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 521-540, November.
    34. Breitmayer, Bastian & Massari, Filippo & Pelster, Matthias, 2019. "Swarm intelligence? Stock opinions of the crowd and stock returns," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 443-464.
    35. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    36. Bange, Mary M., 2000. "Do the Portfolios of Small Investors Reflect Positive Feedback Trading?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(2), pages 239-255, June.
    37. Salisu, Afees A. & Adekunle, Wasiu & Alimi, Wasiu A. & Emmanuel, Zachariah, 2019. "Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries," Resources Policy, Elsevier, vol. 62(C), pages 33-56.
    38. Charles W. Calomiris & Harry Mamaysky, 2018. "How News and Its Context Drive Risk and Returns Around the World," NBER Working Papers 24430, National Bureau of Economic Research, Inc.
    39. Zhang, Jilin & Lai, Yongzeng & Lin, Jianghong, 2017. "The day-of-the-Week effects of stock markets in different countries," Finance Research Letters, Elsevier, vol. 20(C), pages 47-62.
    40. Even-Tov, Omri, 2017. "When does the bond price reaction to earnings announcements predict future stock returns?," Journal of Accounting and Economics, Elsevier, vol. 64(1), pages 167-182.
    41. Aouadi, Amal & Arouri, Mohamed & Teulon, Frédéric, 2013. "Investor attention and stock market activity: Evidence from France," Economic Modelling, Elsevier, vol. 35(C), pages 674-681.
    42. Tang, Wenbin & Zhu, Lili, 2017. "How security prices respond to a surge in investor attention: Evidence from Google Search of ADRs," Global Finance Journal, Elsevier, vol. 33(C), pages 38-50.
    43. Jan Ditzen, 2018. "Estimating dynamic common-correlated effects in Stata," Stata Journal, StataCorp LP, vol. 18(3), pages 585-617, September.
    44. Kim, Neri & Lučivjanská, Katarína & Molnár, Peter & Villa, Roviel, 2019. "Google searches and stock market activity: Evidence from Norway," Finance Research Letters, Elsevier, vol. 28(C), pages 208-220.
    45. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    46. Vincenzo Verardi & Christophe Croux, 2009. "Robust regression in Stata," Stata Journal, StataCorp LP, vol. 9(3), pages 439-453, September.
    47. Robertson, D & Symons, J, 1992. "Some Strange Properties of Panel Data Estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(2), pages 175-189, April-Jun.
    48. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    49. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    50. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    51. Adachi, Yuta & Masuda, Motoki & Takeda, Fumiko, 2017. "Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks," Pacific-Basin Finance Journal, Elsevier, vol. 46(PB), pages 243-257.
    52. Salisu, Afees A. & Swaray, Raymond & Oloko, Tirimisiyu F., 2019. "Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables," Economic Modelling, Elsevier, vol. 76(C), pages 153-171.
    53. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
    54. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, June.
    55. Salisu, Afees A. & Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "Testing the predictability of commodity prices in stock returns of G7 countries: Evidence from a new approach," Resources Policy, Elsevier, vol. 64(C).
    56. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
    57. Salisu, Afees A. & Isah, Kazeem O., 2017. "Revisiting the oil price and stock market nexus: A nonlinear Panel ARDL approach," Economic Modelling, Elsevier, vol. 66(C), pages 258-271.
    58. Yun, Jaeho, 2020. "A re-examination of the predictability of stock returns and cash flows via the decomposition of VIX," Economics Letters, Elsevier, vol. 186(C).
    59. Zhu, Sha & Liu, Qiuhong & Wang, Yan & Wei, Yu & Wei, Guiwu, 2019. "Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    60. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Westerlund, Joakim, 2016. "Are Islamic stock returns predictable? A global perspective," Pacific-Basin Finance Journal, Elsevier, vol. 40(PA), pages 210-223.
    61. John R. Nofsinger & Richard W. Sias, 1999. "Herding and Feedback Trading by Institutional and Individual Investors," Journal of Finance, American Finance Association, vol. 54(6), pages 2263-2295, December.
    62. Hongquan Zhu & Lingling Jiang, 2017. "Investor recognition and stock returns: evidence from China," China Finance Review International, Emerald Group Publishing Limited, vol. 8(2), pages 199-215, December.
    63. Bodnaruk, Andriy & Ostberg, Per, 2009. "Does investor recognition predict returns?," Journal of Financial Economics, Elsevier, vol. 91(2), pages 208-226, February.
    64. Taylor, Nick, 2019. "Forecasting returns in the VIX futures market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1193-1210.
    65. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2017. "Does Financial News Predict Stock Returns? New Evidence from Islamic and Non-Islamic Stocks," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 24-45.
    66. Rapach, David E. & Wohar, Mark E., 2004. "Testing the monetary model of exchange rate determination: a closer look at panels," Journal of International Money and Finance, Elsevier, vol. 23(6), pages 867-895, October.
    67. Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
    68. Mehmet Balcilar & Riza Demirer, 2015. "Effect of Global Shocks and Volatility on Herd Behavior in an Emerging Market: Evidence from Borsa Istanbul," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(1), pages 140-159, January.
    69. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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