key: cord-0722329-7r1jk4vw authors: Salisu, Afees A.; Vo, Xuan Vinh title: Predicting stock returns in the presence of COVID-19 pandemic: The role of health news date: 2020-06-29 journal: nan DOI: 10.1016/j.irfa.2020.101546 sha: 033d2f031adba17f8f8fdc75aa2d96708fabe7ce doc_id: 722329 cord_uid: 7r1jk4vw 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 using data for of top-20 affected countries, separately in terms of most cases and most deaths reporting countries. 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 improves the forecast performance. These results are consistently robust to data sample, both in the in-sample and out-of-sample forecast periods, outliers and heterogeneity. Theoretical evidence backed by the growing literature shows that news in general cannot be ignored when predicting the movements in economic/financial variables (see Narayan, 2019 for a review of the literature). The "price pressure hypothesis" or "attention theory" (see Barber and Odean, 2008) and "network analysis" (see Nofsinger and Sias, 1999) lend credence to this fact. The "pressure price hypothesis" on the one hand, states that individual investors tend to buy stocks that attract their attention because individual investors do not have enough time or resources to examine thousands of stocks. This often implies that stocks capturing investors' attention (often through news) and searched intensively tend to generate abnormally high returns and trading volume (Takeda and Wakao, 2014) . On the other hand, the rationale behind "network analysis" is underscored by the fact that individual or retail investors tend to adopt feedback strategies and rely primarily on stock information to infer value of a stock (Bange, 2000) . Therefore, co-attention networks promote information that captures investors' attention (Chen et al., 2010) . Understanding how this relationship works is crucial for certain reasons. First, buying and selling decisions made by individual investors are now more dependent on available news content. Second, the gradual emergence of social media investment platforms which now use crowd wisdom (or wisdom of crowds) and shared information to help users make better decisions (Breitmayer et al., 2019) . Meanwhile, how certain news -good or bad (positive or negative)affect macroeconomic variables especially stock returns remains a subject of debate among researchers. Cohen et al. (2017) , Akinchi and Chahrour (2018) and Svensson (1999) citing "bad news principle" argue that only bad news matters in investment decision but and Narayan (2019) find that both positive and negative news affect investment decisions. A number of studies have considered variable-specific news such as oil price news and economic news in predicting stock returns (see Narayan and Bannigidadmath, 2015; Liebmann et al., 2016; Shynkevich et al., 2016; Even-tov, 2017; Calomiris and Journal Pre-proof J o u r n a l P r e -p r o o f 4 Mamaysky, 2018; Nam and Seong, 2018; Narayan, 2019) while we utilized health news, the choice of which is influenced by the current pandemic. The outbreak of COVID-19 which triggered crisis in global financial economy is of special interest. Efforts to contain the spread of this disease such as quarantine and restrictions on mobility of labor are slowing down the world economy. Reduction in supply caused by a disrupted global supply chain and a fall in demand have continued to discourage investment and increased risk aversion which is now eroding business and consumer confidence. Commodity prices have nosedived, stock prices are at 10-year record low and still falling (OECD, 2020) . The global stock markets continue to sink in the absence of timely policy intervention. Emerging reports from around the world have shown a highly steeped downward sloping trend. Markets in Australia, South Korea and Hong Kong drop by more than 5% daily while in China, it is about 3%. Similarly, in the United States, the stock market has faced the same fate. Further aggravated by falling oil prices, investors are hurriedly selling their stocks and share prices are crashing. With the growing uncertainty in the business arena and no end in sight, the choice of making investment decisions under an extremely dicey condition becomes increasingly inevitable. To avoid the economy going into depression, investment must be sustained. Private investors will need sufficient information to restore their confidence and national government will require advice on the best policy intervention to create an enabling business environment. Knowledge of how stock prices might behave at later dates presents a unique opportunity to stakeholders. This does not only restore market efficiency but also allows investors enough room for strategic planning. Thus, the findings of this study will offer useful insights to investors seeking to maximize returns in the presence of global health crisis. Studies analyzing the impact of news on return predictability are gradually gaining prominence. The notable ones among them are Buttner and Hayo (2010) , Bank et al. (2011), Birz and Lott J o u r n a l P r e -p r o o f 5 other news sources such as prints and electronic media (see Narayan, 2019 ; for a review). Thus, the use of news to predict stock returns is not new and they involve macroeconomic and financial news. What has remained understudied in the literature is the use of health news in return predictability and this constitutes the main contribution of the study. Research in this area becomes crucial given investors' sentiment about the severe consequences of the COVID-19 pandemic on their returns coupled with the need to seek safe investments to minimize the impending high risks and uncertainties associated with pandemic. In this paper, we utilize health news obtained through Google searches to analyze the predictability of stock returns. The intention is to examine how the news associated with the outbreak of COVID-19 has influenced the trading activities in global stock exchanges particularly those that seem to be worse hit by the pandemic. Since the pandemic is healthrelated, we hypothesize that related news will be sought by investors when making investment decisions particularly in terms of the severity of the pandemic on the global economy. To the best of our knowledge, this is the first study to incorporate health news in the predictive model for stock returns. 3 To achieve this objective, we consider the following. First, we evaluate the predictability of health news as a potential predictor of stock returns during the pandemic period and beyond. Consequently, we evaluate both the in-sample and out-of-sample forecast performance of the health news-based predictive model for stock returns. This essentially requires comparing the forecast performance of the proposed model with the benchmark model (conventionally described as historical or constant returns model). Third, we further test whether controlling for macro-based predictors will enhance the forecast performance of the proposed model. Fourth, we use dataset that seems global in nature as we cover twenty (20) countries that appear to be worse hit by the COVID-19. Essentially, we use two parameters to identify these countries: the reported cases and deaths associated with the pandemic. Given the countries covered in our analyses with greater impacts on the global economy than other countries of the world put together, it becomes easier to draw meaning generalizations from our research findings. The main findings from our results reveal that incorporating health-related information in the valuation of stocks improves forecast accuracy. Besides, accounting for "asymmetry" J o u r n a l P r e -p r o o f 6 effect in terms of good and bad health news and adjusting for macroeconomic factors such as oil price and exchange rate further improves the forecast performance. Several robustness checks are considered to validate the results. The remainder of this study is organized as follows: Section 2 describes the data with some preliminary statistics and discussions on the behavior of relevant variables; the empirical methodology is detailed in Section 3; Section 4 discusses the findings of the study; and Section 5 concludes the paper. Our datasets consist of stock prices (in USD) of the 20 worst hit countries by the pandemic and corresponding volumes of searches relating to health news. Table 1 shows the list of these countries with the most reported cases and reported deaths of COVID-19 as of 30th of March 2020, as pulled from the website (www.cdc.gov) of the Centre for Disease Control and Prevention (CDC). The list shows the rank of individual countries according to the number of cases and deaths reported respectively. The stock index data for each of the country was obtained from www.investing.com historical data archive 4 . The keyword "health news" was to capture health-related news in order to accommodate all forms of health news searches during the considered period either directly related to COVID-19 or otherwise. The corresponding search volumes using keyword "health news" were obtained from Google trends (https://trends.google.com/trends/explore). Although that data is available in different time frequencies, the daily frequency is preferred and extracted on the 30th of March, 2020 starting from 1st of January, 2020. The general restriction of the start date to 1st of January, 2020 is as a result of data availability as Google trends allows for daily frequencies for data spanning a 90day period or less. Table 2 illustrates the descriptive analysis of countries' stock returns and evaluates its relationship with health-related news. The table summarizes the mean and standard deviation of stock returns across all the countries as well as the behavior of stock returns when health-news J o u r n a l P r e -p r o o f 7 searches increase or decline. The reported average values in Column I of Table 2 represent the average stock returns across all the countries at the average health-related news searches over the period under consideration, January 01 to March 30. However, Columns II & III report average country stock returns and its standard deviation when the health-news index is below and above its average value, respectively. It is evident from the table that all of the 20 COVID-19 worst hit countries both in terms of reported cases and deaths experienced a decline in their stock returns with all of them recording negative stock returns during this period with the exception of Australia. The positive returns seen in Australia was expectedly so because the country had suffered economic crises since before the announcement of COVID-19 with the incidences of wild fire disrupting economic activities around the country. The announcement period coincided with the halt of the crisis, when economy had just begun to recover. The United States and Italy despite being most hit with the highest recorded cases and deaths respectively experienced a modest negative stock return. Table 2 further show that as the health-related news search increases, stock returns decline across all the countries considered. On the other hand, when health news search declines, stock returns across these countries are above their averages. These findings have vast implications for the global economy. One, it implies that investment returns during this period will largely depend on the extent of reportage and global discourse surrounding COVID-19, investors would be very cautious to observe the trend of the pandemic before committing their wealth. For this reason, there is likely going to be absence of any serious investment while news of COVID-19 gathers momentum. If cases of infection continue, the global economy will plunge into an inevitable recession. Two, it also connotes that countries that heal fast from the pandemic will likewise achieve quicker economic recovery than those that heal later. Figure 1 is an illustration of the relationship between stock returns and changes in health news since the announcement of COVID-19. The graphical illustration reveals some co-movements between the two series for all the countries with health news being more volatile. The earlier part of the period of COVID-19 announcement witnessed a minimal fluctuation while an increased fluctuation was recorded in the later part of the period. The graph shows that stock returns did J o u r n a l P r e -p r o o f 8 not respond immediately to increased fluctuation in health news and only started responding until about the 3 rd month of its announcement. This maybe because of the gradual spread of the disease with only few cases reported in Europe and America at those times. Sweden 3700 Canada 60 Note: Although, Iran has high incidence of reported COVID-19 cases and deaths, it was omitted from this list because of the unavailability of the country's stock data. The figure represents what it was as of 30th of March, 2020 when it was retrieved from the website of CDC. The average stock returns are presented in percentages. Column I depicts the average stock returns and its corresponding standard deviation at the overall mean of health-related news search; Column II indicates the average stock returns and its standard deviation when the health news index is above its overall mean, while Column III considers the same requirements when the news index is below its average value. We construct a predictive model to evaluate the evident relationship between health-related news and stock returns of the worst-hit countries by the COVID-19 pandemic. In line with the study objectives, the predictive power is compared with other plausible forecast models for stock returns. The short-time span since the emergence of the pandemic informs our choice of panel data forecasting approach. By pooling the stock returns series for the 20 most affected countries in terms of cases and deaths, the likely problem of insufficient observations for a country-bycountry analysis is circumvented 5 (see Rapach and Wohar, 2004; Gavin and Theodorou, 2005) . A generic specification for a typical panel data regression model can be expressed as: 6 where for every i with T time series dimension, The panel data model in matrix form is specified this way to be able to isolate the slope coefficient for each country i without loss of generality (see Baltagi, 2013 for some computational details). In the empirical literature, some studies have favoured the choice of homogenous panels (see Baltagi and Griffin, 1997; Baltagi et al., 2000; Driver et al., 2004) . Baltagi et al. (2000) in particular find that homogeneous panel data estimators beat the heterogeneous and shrinkage type estimators in RMSE performance for outof-sample forecasts and a further complement from Driver et al. (2004) shows that pooled homogeneous estimators outperform their heterogeneous counterparts in out-of-sample forecasts as well. Another strand of empirical literature favours the heterogeneous panel models (see for example, Robertson and Symons, 1992; and Pesaran and Smith, 1995) . The analyses using 5 There are two approaches for forecasting stock returns using pooled data; (i) forecasting for the individual countries of the same variable and combining their forecasts to produce a single forecast, and (ii) pooling countries' stock returns data into panel and forecasting. For the former, it has been shown to increase forecast accuracy (see Bates and Granger, 1969; Diebold and Lopez, 1996; Newbold and Harvey, 2002; Watson, 2004, 2006; Timmermann, 2006; Westerlund and Basher, 2007) , there are also inherent instabilities and estimation errors particularly in the combination weights when multiple forecasts of the same variable are combined (see Timmermann, 2006; Westerlund and Basher, 2007) . On the other hand, the panel data approach involves the use of panel data procedures and the estimates may eliminate certain biases that may plague country by country estimates. 6 Previous studies that have examined stock return predictability using historical average as the baseline model include Bannigidadmath and Narayan (2015) , Narayan and Gupta (2015) J o u r n a l P r e -p r o o f 12 heterogenous panel can be done based each country's time series regression, or employing various estimation methods described in the earlier papers (see Robertson and Symons, 1992; Pesaran and Smith, 1995; Maddala et al., 1997; Baltagi, 2008; Reese and Westerlund, 2016; Isah, 2017 and Ndako, 2018) . However, the homogeneous panel model is parsimonious (particularly with short T which is the case here) compared to the more parameterconsuming heterogeneous estimators. Besides, it conforms with "keep it simple" principle advocated by Baltagi et al. (2002) and Clements and Hendry (2002) , among others. Consequently, we employ the homogenous panels given the short T dimension of our data. We begin our analyses with the baseline model involving the constant return (historical average) model which ignores any potential predictor of stock and is specified as: 7 where it r denotes stock returns;  is a constant parameter; and it e is the error term. We augment the historical average model with the health-news predictor by theoretically relying on the Investor Recognition hypothesis (Merton, 1987) . The Investor Recognition hypothesis assumes incomplete market information and investors are not aware of all information about the securities in a market. Therefore, emotions and sentiments based on available information and news influence their decision by selecting only familiar stocks in constructing portfolios (see also Bank et al., 2011; Da et al., 2011; Joseph et al., 2011; Aouadi et al. 2013; Preis et al., 2013; Adachi et al. 2017; . The health-news predictability model of stock returns is given as: where it hn denotes the health news index expressed in natural logs. The health news index is a measure of investors' awareness and emotions. We also explore an important feature of daily stock returns, the day-of-the-week effect (see Zhang et al., 2017 for a review of the literature). To account for this important feature while also avoiding parameter proliferation in the estimable [5] Where   1 and   1 are respectively the coefficients of the positive and negative asymmetry parameters. Lastly, the Arbitrage Pricing Theory provides the theoretical premise for incorporating systemic or macroeconomic risks in the predictability of stock returns. Therefore, J o u r n a l P r e -p r o o f 14 we also account for some other important factors that can influence stock returns. Some of the prominent macro-related fundamentals considered in the empirical literature include earnings expectations and interest rates, in addition to global factors such as exchange rates and crude oil prices (see also Chen, Roll and Ross, 1986; Rossi, 2013; Bannigidadmath and Narayan, 2015; Narayan et al., 2016; Devpura et al., 2018; Salisu et al., 2019a,b,c,d,&e) . Due to data limitation however, given the fact that our focus is on the COVID-19 period, our macro-related variables are limited to those that are available at a high frequency namely exchange rate and crude oil prices. On this basis, the single predictor model is extended to become: vector of additional (macroeconomic) variables, and  is   8 This idea is also technically motivated by the work of which provides some technical details and computational procedure on how to incorporate common factors in the predictability of stock returns. The approach followed in the estimation of this model is similar in spirit to that of . One major attraction to this approach is that it does not require integration property of the common factors used in the predictive model. 9 The macroeconomic variables considered include global crude oil prices and country's domestic currency exchange rates against the US Dollar. 10 Given the nature of our time series dimension, the forecast evaluation is limited to the in-sample and a single outof-sample forecast evaluation with one-week ahead period for brevity. (2007) test on the other hand is used to establish the statistical significance of the forecast evaluation procedure in the CT (2008). For a forecast horizon h , the CW (2007) (for additional literature, see Balcilar & Demirer, 2015; Psaradellis & Sermpinis, 2016; Taylor, 2019; Wang, 2019; Zhu, Liu, Wang, Wei, & Wei, 2019; Yun, 2020) . The VIX series is considered as a leading barometer of market volatility relating to listed options and it has been found to have larger in-sample predictability performance on stock markets ( Wang, 2019; Zhu et al., 2019; Yun, 2020) . Thus, for robustness, we evaluate the forecast performance of the combined news indices, i.e. VIX, an indicator of financial market news, and the health-news index, in the stock returns predictability of top-20 COVID-19 affected countries. The objective here is to see if including the two news indices will produce better forecast accuracy for stock returns relative to the benchmark model as well as the single-predictor health-based model. The second aspect of the additional results involves accounting for any inherent heterogeneity across the stock returns of the selected countries 11 . We apply the heterogeneous panel model approach suggested by Chudik and Pesaran (2015) and Chudik et al. (2016) which have been demonstrated to account for unobserved common factors among cross sections (see also Ditzen, 2018 We evaluate the health news predictability of stock returns since the emergence of COVID-19 by evaluating the stock returns behavior of top 20 most affected countries. We rely on official daily information on the number of reported cases and deaths in the selection of these countries. By pooling countries based on the number of reported cases and deaths, we evaluate the veracity of health-news predictability of stock returns. The four variant models estimated and compared with the historical average (constant returns) model as discussed in the methodology section 11 We thank the Anonymous reviewer for suggesting this additional robustness analysis. The model also helps to resolve any inherent nonstationarity in the series, can also accommodate mixed order of integration and is useful in the the estimation of long run and short run dynamics including the speed of adjustment. 13 We are grateful to Ditzen (2018 Ditzen ( , 2019 for providing the relevant codes for the estimation of dynamic panel data models with dynamic common correlated effects. Table 3 and we find that the estimated coefficients for almost all the models are correctly signed and statistically significant following the a priori expectation both across top-cases and top deaths reporting countries. However, while the coefficient of the positive asymmetry is negative, which conforms with the expected sign and statistically significant, the coefficient of the negative asymmetry is also negative over the period under consideration. By implication, regardless of the movements in health news, its impact on stock returns is negative during the pandemic, although, increased searches for health news have greater adverse effects on stock returns. Furthermore, the stock returns predictability estimates after controlling for macroeconomic variables are summarized in the MD4 column of Table 3 . The estimated coefficient of health-news is also negative and statistically significant conforming with the a priori expectation. indicates the single-predictor model with health news as the predictor; MD2 is the single predictor model with day-of-the-week adjusted stock returns series; MD3 is the heathnews predictor model with "asymmetry" effect; and MD4 is the predictability model with macro-adjusted stock returns series. Forecast performance of the variant models (MD2 to MD4) is evaluated and compared with the performance of the historical average as well as the single predictor model (MD1). Standard errors are reported in parentheses and *** indicates statistical significance at 1% level. The C-T stat indicates the Campbell-Thompson (2008) test statistics. As discussed in the methodology section, our first additional results involve evaluating the forecast performance of the stock returns predictability by introducing financial news captured with the VIX data into the health news model. The predictability results are presented in Tables 5 and 6 for top-20 countries with reported COVID-19 cases and reported deaths respectively. In line with the previous analyses, we also evaluate the forecast performance of the VIX-augmented health news model relative to the historical average as well as the single predictor health news model (MD1). The estimated predictability regression when VIX is combined with the health news index shows that both coefficients of one-period lagged health news and VIX are negative and statistically significant. For the forecast performance, the results show that the VIX-augmented model outperforms the historical average model both for the in-sample and out-of-sample data partition. 20 Similarly, the model that accommodates both news indices (health and financial news) perform better than the one with health news only, although the forecast accuracy is relatively equal for the in-sample period. This appears to mirror reality as rational investors seek for all the available information that will strengthen their understanding of the market risks. Table 7 . The coefficients conform with our a priori expectation that stock returns responds negatively to increasing health-news searches and it is in tune with earlier results using the homogenous panel estimator. Further, the in-sample and out-of-sample forecast performance evaluation further confirms that using health news as a predictor in stock returns predictability will outperform the historical average model, using both the Campbell-Thompson (2008) and Clark and West (2007) tests. ,1 it hn  indicate the coefficient of health-news predictor. The C-T stat indicates the Campbell-Thompson (2008) test statistics. For the two categories of countries, i.e top reporting COVID-19 cases and deaths, the tests evaluate the forecast performance of the historical average model against the HN predictor model. The asterisk *** indicates statistical significance at 1%, level. Lastly, we account for the possible influence of extreme observations or outliers in the predictability models. The empirical literature have established that having some units that are far away from the behavior of other observations in the sample could impact estimated results (see Bramati and Croux 2007; Verardi and Croux, 2009; Verardi and Wagner 2011) . Therefore, as an additional robustness test to check for the presence and influence of possible outliers in the dataset, we re-estimated the health-news predictability model of stock returns using robust-tooutliers panel estimator. We employed the robust least squares procedure which addresses both the potential outliers in the predictor and predicted variables and the results are summarized in J o u r n a l P r e -p r o o f 22 Table A1 in the Appendix. We find that the estimated predictability results and the forecast performance are consistent after accounting for outliers. Although the magnitude of impact of health news on stock returns declined after adjusting for outliers, the sign is still negative and statistically significant (see Table A1 ). In addition, both the forecast measures confirm that the single predictor health news model outperforms the historical average. By implication, predicting stock returns using health news index consistently outperforms the benchmark model regardless of the underlying assumptions for the parameter estimates. This study derives its motivation from the current global pandemic, COVID-19, to explore the significance of health news Google searches in predicting stock returns. Our analyses cover top-20 most affected countries during the pandemic in terms of reported cases and deaths. The empirical literature is replete with studies on how news and information trends can predict economic and financial variables (see Narayan and Baninigidadmath, 2015; Shynkevich et al., 2016; Liebmann et al., 2016; Even-tov, 2017; Nam and Seong, 2018; Calomiris and Mamaysky, 2018; Narayan, 2019; . However, the role of health news in the return predictability is less understudied, this is the main contribution of the study. Given the limited time dimension of available data since the emergence of the novel coronavirus, we employ panel data forecasting approach to evaluate the performance of health-news for stock return predictability. Alternative variants of the health news-based models are considered for robustness. We account for an important feature of the stock returns series, the day-of-the-week effects as well as the "asymmetry" effect and macro-common factors in the health-news predictive model for stock returns. We find that health-news has a negative and statistically significant effect on stock returns, indicating that that returns decline as more information is sought on health issues since the pandemic outbreak. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, accounting for daily effects and controlling for other macroeconomic variables and "asymmetry" effect improves the forecast accuracy of health news. On the implication of findings, rational investors seeking to maximize returns may need to evaluate the extent of uncertainty associated with infectious diseases before taking any investment decision in the stock market and perhaps other financial markets. By way of J o u r n a l P r e -p r o o f 23 suggestion for future research, extending the analyses to other financial market such as the commodity, foreign exchange, bond and money markets would offer more insightful outcomes. 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We the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from biomaterials@elsevier.com. hn  indicate the coefficient of health-news predictor. C-T stat indicates the Campbell-Thompson (2008) test statistics while C-W test is the Clark and West (2007) test. For the two categories of countries, i.e top reporting COVID-19 cases and deaths, the tests evaluate the forecast performance of the historical average model against the HN predictor models. both models are estimated using panel robust least squares estimators which accounts for inherent outliers in the predictor and predicted series. The asterisks *** indicate statistical significance at 1%, level.