Second, we analyze whether GT-COVID-19 indexes explain the stock market returns. Therefore, the delayed reaction of those indexes is likely due to the shock of the aforementioned events. The ensuing spread of the coronavirus disease in other countries provided a similar dynamic on the new cases and for the implemented lockdown measures. This lead-lag relationship is primarily due to the fact that Italy was the first European country to experience an outbreak of COVID-19, as well as being the first European country to implement lockdown measures since World War II. First, the GT-COVID-19 index for Italy is found to anticipate the GT indexes for all the other countries considered. In our analysis, we consider the six most impacted countries worldwide in terms of confirmed cases, as of May 1 2020: the United States, Spain, Italy, Great Britain, Germany, and France. Even if a priori a new pandemic is not an unlikely event ( Goodell, 2020), its origin, severity and impact cannot be reasonably foreseen and hence priced by markets. The COVID-19 pandemic is an interesting case to investigate since, due to its virulence and infectiousness, it represents a major exogenous shock to the economic and financial systems. We use them as proxies for country-level public attention and investigate their impact on the financial markets during the outbreak of the coronavirus disease. In this respect, we retrieve GT country indexes for the topic of coronavirus (GT-COVID-19) from January 2020 to April 2020. We also note that the massive amount of aggregate volumes of web data was different at that time, partially due to the facts that the number of internet users was significantly lower and the mobile internet was in its infancy. For instance, the previous H1N1 pandemic that occurred in the middle of the Global financial crisis in 2009 has left almost no traces in the stock market (see, for a comparative study, Schell et al., 2020). No previous disease outbreak has affected financial markets as the COVID-19 pandemic ( Baker et al., 2020). We believe that the outbreak of the pandemic on the financial markets represents an interesting and unprecedented case to investigate. To our knowledge, this paper is the first to analyze this type of relationship. We consider the relationship between stock markets and web-searches volumes related to the COVID-19 pandemic, a topic that, unlike the previously mentioned studies on Google search volumes, is not a priori related to any financial and economic activity. As noted in Gong et al. (2020), the latter has received limited attention so far. This paper takes a different perspective and relates to the literature that studies the impact of exogenous shocks on the financial market, such as natural disasters, terrorist events, and infection diseases ( Worthington, Valadkhani, 2004, Chesney, Reshetar, Karaman, 2011, Bourdeau-Brien, Kryzanowski, 2017). The second part of literature focuses on the use of GT indexes for the construction of economic uncertainty indicators that can explain several macroeconomic variables ( Donadelli, 2015, Castelnuovo, Tran, 2017, Donadelli, Gerotto, 2019). Overall, these papers are all concerned with a specific research question, that is, whether web-searches volumes on company names or stock market indexes can predict returns for a given individual firm. Dimpfl and Jank (2016) and Tantaopas et al. (2016) make use of keywords related to the stock market indexes. Da et al. (2011) consider the stock names listed in the Russell 3000 index while Takeda and Wakao (2014) study those ones included in the Nikkei 225. For instance, Joseph et al. (2011), Ding and Hou (2015), and Bijl et al. (2016) search for tickers and names of companies included in the S&P 500. Those papers make use of keywords related to the listed companies and to the stock market indexes. A first strand of literature studies how web-search volumes are related to financial markets in terms of returns, trading volume, and liquidity. In relation to economics and finance, there are two main strands of research concerning Google search data. GT data has been successfully used for disease surveillance purposes for MERS ( Shin et al., 2016), chickenpox ( Bakker et al., 2016) and flu ( Yang et al., 2015). In recent years, it has been shown that the informational content of GT data has explanatory and forecasting power in several fields of economics and finance. The interpretation of GT indexes is straightforward: the higher the value of a given GT index, the more public attention there is on that topic. These indexes can be retrieved for selected geographic areas or on a worldwide scale. Google Trends (GT) provides indexes based on the relative web-search volumes of a specific topic over time. Search engine data has been successfully used for tracking collective attention and public concerns, which are often correlated with social, environmental and economic events.
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