key: cord-0999981-6je5r268 authors: Cepoi, Cosmin-Octavian title: Asymmetric dependence between stock market returns and news during COVID19 financial turmoil date: 2020-06-18 journal: Financ Res Lett DOI: 10.1016/j.frl.2020.101658 sha: 0e7b8e1f6a4e0cd5e4549288cd5e94a270dac1f5 doc_id: 999981 cord_uid: 6je5r268 • I investigate the stock market's reaction to coronavirus news in the top six most affected countries by the pandemic. • The fake news exerts a negative nonlinear influence on the inferior and the middle quantiles throughout the distribution of returns. • The media coverage leads to a decrease in returns across middle and superior quantiles and has no effects on the inferior ones. • During COVID19 turmoil superior quantiles of returns distribution exhibit negative dependence on past performances, while inferior and middle quantiles are not affected by this phenomenon. • The gold return has a positive correlation with the stock markets, which amplifies during extreme bearish and bullish periods indicating that it does not behave as a “Safe Havens” asset. The impact of public news sentiment on stock returns has received increasing attention in recent years. A growing body of empirical and theoretical studies has focused on understanding whether price movements in financial markets are driven by economic or political news (Smales, 2014; Broadstock and Zhang, 2019; Shi and Ho, 2020). The consensus is that the information arriving from social media channels exerts a significant influence on the stock market dynamic, especially in times of economic or political uncertainty. Given the COVID-19 pandemic and the considerable amount of related news, stock markets around the world have suffered enormous losses in the first three months of 2020. According to Bloomberg, "through 1 p.m. on March 18, the S&P 500 index was off 27% for the year to date, Germany's DAX was down 38% and Japan's Nikkei was off 29%." Consequently, the governments around the world have undertaken a series of stimulus packages to offset the damages produced by the pandemic and to regain investor's confidence. Although the major stock market indexes have partially recovered in the middle of April 2020, a great deal of financial uncertainty remains. (2020) suggest that cryptocurrencies do not act like safe havens during COVID 19 turmoil. In this paper, I contribute to the literature by investigating the stock market's reaction to coronavirus news in the top six most affected countries by the pandemic 1 . By employing a panel quantile regression model, I show that the stock markets present asymmetric dependencies with COVID-19 related information. Specifically, the fake news exerts a negative influence on the lower and the middle quantiles throughout the distribution of returns; however, their impact is not statistically significant for the extreme values. Moreover, the media coverage leads to a decrease in 1 I select the USA, the UK, Germany, France, Spain and Italy, considering the high number of persons infected with COVID-19. On 21 April 2020, the countries mentioned above were the only ones with more than 100.000 total cases according to Worldometer https://www.worldometers.info/coronavirus/ . returns across middle and upper quantiles and has no effects on the lower ones. Similarly, the financial contagion across companies is detrimental to returns from 50 th to 75 th quantiles. Furthermore, the estimates show that the gold price dynamic has a nonlinear impact on equity markets, especially during extreme bearish and bullish markets. The rest of the paper has the following structure: Section 2 presents the data, Section 3 discusses the econometric approach, and the results are in Section 4. Section 5 concludes the paper. To investigate the impact exerted by COVID19-related news on stock market return, I use a balanced panel covering 50 working days, from 3 February 2020 to 17 April 2020. The dependent variable includes daily returns of DJIA, FTSE 100, DAX, CAC 40, IGBM, and MIB. The choice of this sample displays some disadvantages since the dynamic of stock indexes was influenced by the same global event, i.e., the COVID19 pandemic, which thereby causes dependence between individual countries in the panel 2 . Zhang et al. (2020) confirm this empirical fact when investigating the correlation across the top 12 major stock markets before and after the World Health Organization declared COVID-19 to be a worldwide pandemic. According to them, "the correlations in February are relatively low, but they increase substantially upon entering March." Additional details of the correlation matrix during the analyzed period are in Table 1 . For example, Smales (2014) or Shi and Ho (2020) have previously used this news monitor database to investigate the link between news sentiment and implied volatility. Furthermore, to control for the sovereign default risk, I include country CDS spreads among covariates as recommended by Grammatikos and Vermeulen (2012). Additionally, I consider gold price as a benchmark for the common global factor 3 . A detailed data description and its source are presented in Table 2 . Considering the excessive market volatility during the COVID19 financial turmoil, I employ a panel quantile regression framework. Unlike other econometric approaches that only focus on the mean effects, the quantile regression model is a more powerful tool for handling fat tails or extreme values throughout the asset return distributions (du Plooy, 2019). Generally, at any level ( ) across the distribution of given a set of variables , the conditional quantile shows ( ) * ( ) + where ( ) is the conditional distribution function. Thereby, the panel quantile regression is illustrated by the following specification: In Eq. (1) ̅̅̅̅̅ and ̅̅̅̅̅ , denote the number of countries and days, respectively, is the stock market return, denotes the set of covariates, ( ) is the common slope coefficient while is individual-specific fixed effect coefficient. To account for the unobserved country heterogeneity, I follow Koenker (2004), which treats the fixed effects as nuisance parameters. The ingenuity of this approach comes from the introduction of a penalty term in the minimization problem leading to the following algorithm: In Eq. (2) is the quantiles' index, is the quantile loss function while is the relative weight given to the th quantile. The penalty term is diminishing the impact of individual effects on achieving higher efficiency for the global slope coefficients. The quantile regression represents an important class of nonlinear data models (Galvao et al., 2020) and has become a successful tool in economics and finance due to its ability to draw inferences about observations that rank below or above the population conditional mean. In some cases, the quantile-varying estimates reveal that OLS methods provide an incomplete picture regarding the link between variables, especially for extreme events. However, by estimating the entire quantile processes one can capture the presence of some potential nonlinear relationships Tables 3 provide the estimated coefficients for a representative selection of quantiles. To assess robustness for the results, I additionally report the estimates of a Seemingly Unrelated Regression (SUR), which is recommended for handling cross-sectional dependence across a panel with small N and large T (Sarafidis and Wansbeek, 2012). According to the estimation results, several interesting facts come to light. First of all, fake news appears to exhibit a negative nonlinear Ushaped impact during normal market conditions, i.e., from 25 th to 75 th , throughout the distribution of returns. This empirical fact is illustrating the growing importance of online fake news in the globalized financial markets and its implications for stock trading (Allcott and Gentzkow, 2016; Zhang and Ghorbani, 2020). However, it is worth mentioning that fake news is not affecting stock market returns at the times of extreme bearish (5 th quantile and lower) and bullish markets (95 th quantile and upper) and appears to influence the stock dynamic in a positive manner during periods of harshly decline (around the 10 th quantile). Second, the media coverage has a negative and monotonically decreasing impact from the middle to superior quantiles. This result is in line with the previous findings reported in the literature by Fang and Peress (2009), arguing that "the breadth of information dissemination affects stock returns." A slightly similar effect is noticeable for the contagion index, indicating that the higher the numbers of entities related to COVID-19 news, the lower the expected stock market returns, especially during recovering periods. Third, the superior quantiles of returns distribution exhibit negative dependence on past performances, while smaller and middle quantiles are not affected by this phenomenon. This This study offers novel empirical evidence on the relationship between COVID19-related news and stock market returns across the top six most affected countries by the pandemic. By employing a panel quantile regression model, I show that the stock markets present asymmetric dependencies with COVID-19 related information such as fake news, media coverage, or contagion. The result suggests the need for more intensive use of proper communication channels to mitigate COVID19 related financial turmoil. 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