The impact of the COVID-19 pandemic on consumer and business confidence indicators

Teresiene, Deimante et al. Article The impact of the COVID-19 pandemic on consumer and business confidence indicators Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Teresiene, Deimante et al. (2021) : The impact of the COVID-19 pandemic on consumer and business confidence indicators, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 4, pp. 1-23, https://doi.org/10.3390/jrfm14040159 This Version is available at: https://hdl.handle.net/10419/239575 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators Deimante Teresiene 1,* , Greta Keliuotyte-Staniuleniene 1, Yiyi Liao 2, Rasa Kanapickiene 1, Ruihui Pu 3, Siyan Hu 4and Xiao-Guang Yue 5,6,7,8   Citation: Teresiene, Deimante, Greta Keliuotyte-Staniuleniene, Yiyi Liao, Rasa Kanapickiene, Ruihui Pu, Siyan Hu, and Xiao-Guang Yue. 2021. The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators. Journal of Risk and Financial Management 14: 159. https:// doi.org/10.3390/jrfm14040159 Academic Editor: Tullio Jappelli Received: 1 March 2021 Accepted: 28 March 2021 Published: 2 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Finance Department, Faculty of Economics and Business Administration, Vilnius University, LT-10223 Vilnius, Lithuania; [email protected] (G.K.-S.); [email protected] (R.K.) 2International Research Institute for Economics and Management, Wan Chai, Hong Kong, China; Y[email protected] 3Faculty of Economics, Srinakharinwirot University, Bangkok 10110, Thailand; [email protected] 4International Engineering and Technology Institute, Wan Chai, Hong Kong, China; [email protected] 5Department of Computer Science and Engineering, School of Sciences, European University Cyprus, Nicosia 1516, Cyprus; [email protected] 6Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand 7School of Domestic and International Business, Banking and Finance, Romanian-American University, 012101 Bucharest, Romania 8CIICESI, ESTG, Politécnico do Porto, 4610-156 Felgueiras, Portugal *Correspondence: deimante.ter[email protected] Abstract: The COVID-19 pandemic and induced economic and social constraints have significantly impacted the confidence of both consumers and businesses. Despite that, comprehensive studies of the impact of the COVID-19 pandemic on the consumer and business sentiment are still lacking. Thus, in our research we aim to identify consumer and business confidence indicators’ reaction to the spread of the COVID-19 pandemic in the Eurozone, the United States, and China. For this purpose, we used the method of correlation–regression analysis. We chose the consumer-confidence index, manufacturing purchasing manager’s index, and services purchasing manager’s index as dependent variables; and the number of confirmed cases of COVID-19, the number of deaths caused by COVID-19, and the mortality rate of COVID-19 infections as independent variables. The results showed a relatively rapid and robust effect of COVID-19 in the short period, but longer-term results depended on the region and were not so unambiguous: in the case of the Eurozone, the spread of COVID-19 pandemic did not affect the consumer-confidence index (CCI) or, in the cases of the United States and China, affected this index negatively; the purchasing managers’ index (PMI) in the services sector was significantly negatively affected by the mortality risk of COVID-19 infection; and the impact on the purchasing managers’ index (PMI) in the manufacturing industry appeared to be mixed. Keywords: COVID-19 pandemic; economic sentiment; consumer confidence; leading indicators 1. Introduction The COVID-19 pandemic has been a big shock for the global economy, but this crisis is different from other types of situations—especially a financial or banking crisis. The main difference is the level of impact and the causes of the problem. Consumer confidence and economic sentiment are the critical drivers for future economic growth. The health crisis of the COVID-19 pandemic and lockdowns had a significant impact on society. Those changes can be seen in consumer and business confidence indicators. Thus, it is essential to analyze how this demand and business sentiment changed in the COVID-19 environment to better analyze the possible consequences in the future. J. Risk Financial Manag. 2021,14, 159. https://doi.org/10.3390/jrfm14040159 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 159 2 of 23 The economic consequences of the COVID-19 pandemic, as well as its impact on the general economy and consumer and business sentiment, were analyzed by van der Wielen and Barrios (2020), Coibion et al. (2020), Andersen et al. (2020), Barro et al. (2020) , and Chronopoulos et al. (2020). As stated by van der Wielen and Barrios (2020), the COVID-19 crisis and crisis-induced constraints drastically affected households’ economic sentiment in Europe. Negative trends in consumption and the labor market emerged. Andersen et al. (2020) analyzed customer spending changes and indicated a spending drop that was larger in the sectors of goods and services directly affected by COVID-19 pandemic-induced restrictions. Baker et al. (2020) analyzed the indicators of newspaper-based economic uncertainty and subjective uncertainty in business-expectation surveys, and indicated an unprecedented decrease of these measures in the face of the COVID-19 pandemic. Although research on the impact of the COVID-19 pandemic on economic sentiment has become more widespread recently, most of the studies have focused on one area (consumer sentiment or business sentiment), or one region or country (the United States, the United Kingdom, Europe, etc.). Our research is not only focused on the analysis of the impact on different sentiment indicators (both consumer and business), but also includes the analysis and comparison of several regions. Some authors analyzed the sentiment of economics or consumers on a country level, while others focused on a regional level. Savin and Winker (2011) analyzed Germany and Russia’s cases, and pointed to the specifics of tendencies in different countries. Bhattacharyay et al. (2009) studied early-warning indicators impacting economic and financial risks in Kazakhstan. Kanapickiene et al. (2020) found that at the beginning of the COVID-19 crisis, there were different reactions in separate European countries. However, for more extended periods (i.e., covering both the onset of the pandemic in China and its global spread (the first and the second waves)), there is a lack of studies. With our research, we add value to the literature covering the COVID-19 pandemic impact. We use a longer time horizon and attempt to identify the differences in consumer and business sentiments in different regions. Armantier et al. (2020) analyzed the impact of the COVID-19 pandemic on consumer confidence in the United States. They tried to identify how the pandemic’s impact had changed over time and among different demographic groups. There is a large group of authors that have analyzed leading indicators as tools for predicting economic tendencies in the future, especially during recession periods Cesaroni and Iezzi (2015), Döpke (1999), Dovern and Ziegler (2008), Döpke (1998), Oh and Waldman (2005) ,Lysenko and Kolesnichenko (2016) ,Lehmann (2020) ,Drechsel and Scheufele (2010) , Kibritcioglu et al. (1999) ,Alleyne et al. (2013) ,Frale et al. (2009) ,Ferrara and Marsilli (2012) ,Etter and Graff (2003), Drechsel and Scheufele (2011) ,Dovern (2006) ,Garnitz et al. (2019), Buckman et al. (2020), Baker et al. (2020), Aguilar et al. (2020), Fritsche and Kouzine (2002), Ampudia et al. (2020), Juriova (2015), and Kitrar and Lipkind (2020); or used leading indicators for financial stability issues, especially for financial monitoring purposes (Bhattacharyay 2003). At the same time, other authors have focused on the problematic and main drivers that influence and have the most significant impact on leading indicators (Hüfner and Lahl 2003); or analyzed the main idea and construction of leading indicators Everhart and Duval-Hernández (2000), Elosegui et al. (2008), Bierbaumer-Polly (2010), Kellstedt et al. (2015), Martha Starr (2008), and Martinakova and Kapounek (2013). Considering that, this paper’s primary purpose is to assess the impact of the spread of the COVID-19 pandemic on consumer and business sentiment in different regions. We aim to identify consumer and business confidence indicators’ reaction to the COVID-19 pandemic in the Eurozone, the United States, and China. For this purpose, we chose the consumer confidence index as well as the purchase manager’s indices for manufacturing and services. J. Risk Financial Manag. 2021,14, 159 3 of 23 After analyzing the relevant scientific literature, we continue with the description of the research methodology (provided in Section 2) and the presentation of our results (provided in Section 3). To assess the impact of the COVID-19 pandemic, we chose to analyze economic sentiment in three different regions: the Eurozone, the United States, and China. For each area, we selected three different economic sentiment indicators: (i) consumer confidence index, (ii) purchase manager’s index in the manufacturing sector, and (iii) purchasing manager’s index in the services sector. 2. Literature Review As stated by Nowzohour and Stracca (2017), “Sentiment may be used to describe economic agents’ views of future economic developments that may influence the economy because they influence agents’ decisions today”. The economic sentiment includes two opposing dimensions—confidence and uncertainty (van der Wielen and Barrios 2020). The COVID-19 pandemic and social and economic constraints induced by this pandemic have undoubtedly brought more tension to the economy (Baker et al. 2020), while at the same time affecting the confidence of households and businesses. As economic-sentiment indicators are one of the most critical indicators showing the economy’s overall health, it is crucial to assess how the spread of the COVID-19 pandemic affects those indicators. Our analysis contributes to the theoretical issues analyzing how consumer and business sentiments can change in periods of external shocks. Consumer confidence is often described as a fundamental driving force of the economy, because when consumers are optimistic, consumption increases, and we have economic growth; while in pessimistic periods, consumers pull down the economy. Sentiment issues are significant in analyzing economic growth and tendencies in financial markets, and especially when trying to identify future trends. The sentiment of financial markets, consumers, and business entities can be measured by various surveys, after which different sentiment indices are created based on the responses to specific questions about current and expected economic conditions. These indices are used for forecasting household expenditures, consumer spending habits, tendencies in the labor market, or even industrial production growth. However, some authors have an opinion that sentiment surveys have little power for predictions (Roberts and Simon 2001). Not only are quantitative indices used in the forecasting process, but qualitative assessment of textual sentiment in news is becoming more and more popular. Ardia et al. (2019) found that news-based sentiment values help increase the accuracy of forecasting methods trying to identify the growth rates of industrial production in the United States. So, sentiment can be measured by using various survey quantitative indicators and including qualitative information such as news. Understanding the sentiment itself is also very important. When trying to describe the concept of sentiment, scientists usually use such proxies such as fear and uncertainty. Barone-Adesi et al. (2018) revealed that sentiment and fear are complementary risk-aversion measures that are linked with uncertainty. Sentiment indicators are a part of leading indicators that are used to predict future financial and economic trends, as those indicators change before factual changes in economy or business. Leading indicators are not new phenomena and include not only economic, business, or consumer sentiment indicators. Different indicators are essential and have been analyzed for various purposes. Leading inflation indicators are necessary for the monetary policy decision-making process. Ripatti (1995) investigated the mentioned inflation indicators by using Finland’s case and applying a pairwise analysis of Granger causality and cointegration. We even found news-sentiment indicators created using digital technology such as machine learning (Nguyen and La Cava 2020;Lee et al. 2012). Some authors (Gurcihan et al. 2013) studied leading indicators for unemployment rate forecasting. Gründler and Potrafke (2020) pointed out an exciting moment when experts J. Risk Financial Manag. 2021,14, 159 4 of 23 changed their policy decisions because of sentiment analysis. Burri and Kaufmann (2020) created a leading indicator using financial market and news data. Benhabib and Spiegel (2017) investigated how sentiment or consumer-confidence shocks can influence state outputs and consumptions, and they revealed a significant effect during a one-year horizon. The impact of consumer sentiment on consumption was analyzed by Gillitzer and Prasad (2016). Golinelli and Parigi (2004) investigated consumer sentiment and economic activity and, using a large set of observations, confirmed the consumer-confidence indices’ forecasting ability in the sample and out-of-sample periods. National sentiment and economic behavior can be crucial in the sports field of trying to bet on final match results (Braun and Kvasnicka 2013). Sentiments have strong power in any area. Charoenrook (2005) analyzed the University of Michigan Consumer Sentiment Index and found that consumer-sentiment changes were positively related to contemporaneous excess market returns, and that they were negatively related to future excess market returns at different horizons. The author pointed out that a shift in consumer sentiment can improve asset-return predictions. Consumer-confidence issues were also analyzed in the research of Daas and Puts (2014), who investigated country-level social-media messages and compared them with consumer confidence. Using the Granger causality test, the mentioned authors revealed that “changes in consumer confidence precede those in social media sentiment than vice versa” (Daas and Puts 2014). Fuhrer (1993) paid a lot of attention to consumer-sentiment analysis and tried to identify the role of consumer sentiment in the Unites States’ macroeconomy. The author stressed that “consumer sentiment, or consumer confidence, is both an economic concept and a set of statistical measures” (Fuhrer 1993). Consumer confidence as a household-sentiment indicator can help to explain tendencies in the consumer-loans segment (Rakovskáet al. 2020). Asset-return predictions are modeled not only using consumer-confidence indicators, but also including investor-sentiment indices. Chen et al. (2013) used a principalcomponent approach and constructed an investor-sentiment index for the Chinese stock market and found that the created index had good out-of-sample predictability. Investorsentiment issues were analyzed in the studies of Chu et al. (2015). The authors revealed that economic variables could increase forecasting accuracy when investor sentiment was low, and lose their prediction power when investor sentiment was high. Another author (Dieckelmann 2021) used corporate-bond and stock-market data as proxies for investorsentiment measurement, and used those proxies not only for forecasting tendencies in the financial market, but also for predicting banking crises and economic cycles. The role of market sentiment is very important for forecasting tendencies in the financial market, and a study (Frydman et al. 2019) showed that market sentiment was not related to the state of the economy. Investor sentiment was also analyzed by Jiang et al. (2020), García et al. (2019) ,Nartea et al. (2019), Jiang et al. (2020), Tuyon et al. (2016), and Uygur and Ta¸s (2014). While consumer-confidence analysis is much broader, there is still a lack of literature analyzing business sentiment. There are business-sentiment indicators for different sectors explaining the mood of separate parts of the economy. These business-sentiment indices mostly are based on a survey about present and future tendencies. As those indicators include information about the future, they can be used for forecasting as well. Vanhaelen et al. (2000) investigated the Belgian industrial-confidence indicator and tried to answer whether this country-level sentiment indicator precedes euro-area business cycles. The authors concluded that the turning points in the Belgian industrial confidence indicator significantly impact turning points in the euro area. Using graphical examination, correlation analysis, and Granger causality tests, Santero and Westerlund (1996) revealed that business-sentiment measures gave valuable information when trying to assess the present economic situation and to forecast future economic trends. Kukuvec and Oberhofer (2018) analyzed EU business-sentiment indicators and found substantial spillover effects. We agree that all sentiment indicators are more focused on the expectations about the future. A present economic situation has a very short impact, so such kind of indicators help in future trend predictions. It is essential to analyze how sentiment indicators interact J. Risk Financial Manag. 2021,14, 159 5 of 23 with each other, especially in such critical moments as the COVID-19 pandemic. In our paper, we attempt to add value to the literature analyzing sentiment issues during critical moments and shocks, and at the same time, we want to stress regional aspects, as the COVID-19 pandemic is global. 3. Methodology Seeking to evaluate the impact of the spread of the COVID-19 pandemic on economic sentiment, the economic-sentiment indicators of three different regions—the Eurozone, the United States, and China—were selected for further investigation. As we aim to analyze the reaction of both consumers and businesses to the COVID-19 pandemic, three different economic sentiment indicators—the consumer-confidence index (CCI), and the purchase manager’s indices for manufacturing and services (manufacturing PMI and services PMI, respectively) were selected. Our research consisted of two stages. In Stage 1, the trends of the selected economic sentiment indicators (indices) were analyzed (Section 4.1). Afterward, in Stage 2, the impact of the COVID-19 pandemic on the sentiment of both consumers and businesses was assessed (Section 4.2). In Stage 1, we used the method of graphical and statistical analysis and analysis of the trends of consumer and business confidence indicators in the face of the COVID-19 pandemic. We also used the method of correlation analysis (Pearson correlation coefficient) in order to identify possible similarities or differences between the dynamics of the selected economic-sentiment indicators in different regions. In Stage 2, the impact of the spread of the COVID-19 pandemic on selected consumer and business confidence indicators was assessed. When assessing the economic impact of the COVID-19 pandemic, many researchers (Verma et al. 2021; Chen et al. 2020;Lee 2020;Pavlyshenko 2020;Vasiljeva et al. 2020;Fetzer et al. 2020;Kanapickiene et al. 2020;Albulescu 2020;Ashraf 2020, and others) used the regression approach. For example, Verma et al. (2021) used the method of correlation– regression analysis in order to assess how COVID-19 was correlated with economic growth, and evaluated the impact on stock markets. Ashraf (2020) used panel-data regression models to assess the impact of COVID-19 on stock-market returns. Pavlyshenko (2020) discussed different regression approaches for modeling COVID-19’s impact on the stock market. Albulescu (2020) used simple OLS regression to evaluate the impact of COVID19-induced uncertainty on the volatility of financial markets. Fetzer et al. (2020) also used the regression technique in order to assess the relationship between the spread of COVID-19 and economic anxiety. Chen et al. (2020) provided the regression models of high-frequency indicators allowing the assessment of the economic impact of COVID-19. Lee (2020) conducted a correlation–regression analysis to explore the initial impact of COVID-19 sentiment. Given the wide application of the regression techniques in COVID-19-induced economicimpact studies, in our research, we used the methods of correlation and regression to assess the impact of the spread of the COVID-19 pandemic on selected economic-sentiment indicators. First of all, at the starting point of the assessment, seeking to identify a possible linear association between selected consumer and business sentiment indicators and COVID-19 related variables, the correlation was assessed (the Pearson correlation coefficient was calculated and interpreted). Second, the simple linear (bivariate) regression models for each pair of dependent and independent variables were constructed. Considering the statistical characteristics of these models (t-value, p-statistics, R squared), the conclusions regarding the impact of the spread of the COVID-19 on consumer and business economic sentiment were made. It is important to note that in our research, we were not able to construct multiple regression models due to: (i) a relatively small number of observations, and (ii) multicollinearity of regressors. J. Risk Financial Manag. 2021,14, 159 6 of 23 For this research, we selected the following nine dependent variables: (i) Eurozone CCI, (ii) Eurozone manufacturing PMI, (iii) Eurozone services PMI, (iv) United States CCI, (v) United States PMI, (vi) United States services PMI, (vii) China CCI, (viii) China PMI, (ix) and China services PMI. Based on previous studies (for example, Albulescu 2020,Ashraf 2020,Verma et al. 2021, and others), we selected five groups of COVID-19-related variables: (i) the cumulative number of cases of COVID-19 confirmed in each region selected and globally, (ii) new cases of COVID-19 confirmed in each region selected and globally per month, (iii) cumulative number of deaths from COVID-19 reported in each region selected and globally, (iv) new deaths from COVID-19 reported each region and globally per month, and (v) COVID-19 fatality rate in each region and globally. By choosing these groups of variables, we intended to check: (i) whether confidence was affected by the total prevalence of COVID-19 or its monthly growth, (ii) whether consumers and businesses tended to react to the increase of COVID-19 cases or its caused deaths, (iii) whether the changes of the fatality rate of COVID-19 affected confidence indicators, and (iv) whether the reaction to country-level and global-level changes differed. Based on this logic, 20 independent COVID-19-related variables were chosen for our research. Research variables (dependent and independent), their abbreviations, and data sources are provided in Table 1. Table 1. Research variables and abbreviations. Variable Name Full Name Source Dependent variables: CCI Eurozone Eurozone Consumer Confidence Indicator Thomson Reuters PMI Manuf. Eurozone Eurozone Purchasing Manager Index of the manufacturing sector PMI Serv. Eurozone Eurozone Purchasing Manager Index of the services sector CCI United States United States Consumer Confidence Indicator PMI Manuf. United States United States Purchasing Manager Index of the manufacturing sector PMI Serv. United States United States Purchasing Manager Index of the services sector CCI China China Consumer Confidence Indicator PMI Manuf. China China Purchasing Manager Index of the manufacturing sector PMI Serv. China China Purchasing Manager Index of the services sector Independent variables: Total cases Eurozone Cumulative number of cases of COVID-19 confirmed in the Eurozone World Health Organization Coronavirus disease 2019 (COVID-19) situation reports. New cases Eurozone New cases of COVID-19 confirmed in the Eurozone per month Total deaths Eurozone Cumulative number of deaths from COVID-19 reported in the Eurozone New deaths Eurozone New deaths from COVID-19 reported in the Eurozone per month Total cases United States Cumulative number of cases of COVID-19 confirmed in the United States New cases United States New cases of COVID-19 confirmed in the United States per month Total deaths United States Cumulative number of deaths from COVID-19 reported in the United States New deaths United States New deaths from COVID-19 reported in the United States per month Total cases China Cumulative number of cases of COVID-19 confirmed in China New cases China New cases of COVID-19 confirmed in China Total deaths China Cumulative number of deaths from COVID-19 reported in China New deaths China New deaths from COVID-19 reported in China per month Total cases World Cumulative number of cases of COVID-19 confirmed globally New cases World New cases of COVID-19 confirmed globally per month Total deaths World Cumulative number of deaths from COVID-19 reported globally New deaths World New deaths from COVID-19 reported globally Fatality rate Eurozone COVID-19 fatality rate in the Eurozone Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database Fatality rate United States COVID-19 fatality rate in the United States Fatality rate China COVID-19 fatality rate in China Fatality rate Worlds COVID-19 fatality rate globally Source: compiled by the authors. In our research, we analyzed the period of January 2020 to January 2021 and used monthly data (as the data of selected economic-sentiment indicators is provided on a monthly basis). The data of the selected economic-sentiment indicators were retrieved from J. Risk Financial Manag. 2021,14, 159 7 of 23 Thompson Reuters database, and the data for COVID-19-related variables were collected from World Health Organization coronavirus disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database (see Table 1). Data were analyzed using Eviews 11 software. It is important to emphasize that we did not seek to analyze a comprehensive set of economic-sentiment factors in our research. We only aimed to identify the presence or absence of the reaction to the COVID-19 pandemic (and its direction). The descriptive statistics of selected dependent (economic sentiment (CCI, manufacturing PMI, services PMI)) and independent (COVID-19-related) variables are provided in Table 2. The dynamics of COVID-19 related variables are shown in Appendices A–D. Table 2. Summary of descriptive statistics of models variables. Variable Mean Median Maximum Minimum Standard Deviation Skewness Kurtosis Obs. CCI Eurozone −14.508 −14.7 −6.6 −22.7 4.185 0.144 3.121 13 PMI Manuf. Eurozone 49.046 51.7 55.2 33.4 6.618 −1.187 3.471 13 PMI Serv. Eurozone 42.762 46.9 54.7 12 12.433 −1.392 3.907 13 CCI United States 100.6154 96.1 131.6 84.8 16.433 1.000 2.551 13 PMI Manuf. United States 53.008 54.2 60.7 41.5 5.961 −0.642 2.441 13 PMI Serv. United States 54.677 56.9 58.7 41.8 5.198 −1.692 4.401 13 CCI China 119.517 119.7 126.4 112.6 3.976 0.013 2.169 12 PMI Manuf. China 51.131 51.5 54.9 40.3 3.601 −2.196 7.543 13 PMI Serv. China 51.146 54.1 58.4 26.5 8.760 −1.892 5.862 13 Total cases Eurozone 4,871,279 1,451,245 19,633,934 13 6,539,193 1.301 3.204 13 New cases Eurozone 1,510,303 475,381 5,226,912 13 1,906,455 0.905 2.102 13 Total deaths Eurozone 161,534.8 136,136 471,122 0 137,704.6 0.961 3.243 13 New deaths Eurozone 36,240.15 21,913 103,184 0 41,068.39 0.743 1.816 13 Fatality rate Eurozone 6.075 6.1 10.9 2.2 3.618 0.157 1.357 12 Total cases United States 7,123,187 4,566,931 26,187,035 8 8,265,678 1.205 3.324 13 New cases United States 2,014,387 1,206,239 6,406,683 8 2,218,935 1.121 2.779 13 Total deaths United States 166,079.7 154,545 448,100 0 137,801.6 0.528 2.501 13 New deaths United States 34,469.23 26,593 95,371 0 29,443.6 0.794 2.713 13 Fatality rate United States 3.958333 3 10.3 1.7 2.506 1.439 4.342 12 Total cases China 82,517.85 87,655 100,063 9802 22,597.5 −2.809 9.733 13 New cases China 7697.154 2259 69,554 190 18,749.2 3.083 10.707 13 Total deaths China 4113.769 4661 4817 213 1326.33 −2.221 6.884 13 New deaths China 370.5385 35 2624 0 771.336 2.254 6.827 13 Fatality rate China 4.807692 5.2 5.5 2.2 0.941 −1.833 5.547 13 Total cases World 30,310,546 17,604,278 1.03 ×1089927 34,227,637 0.973 2.672 13 New cases World 7,857,240 7,146,442 19,445,486 9927 7,141,688 0.554 1.907 13 Total deaths World 803,218.4 675,905 2,234,722 213 719,483.6 0.609 2.297 13 New deaths World 171,901.7 167,373 409,144 213 121,859.7 0.394 2.597 13 Fatality rate World 3.707692 3.3 7.2 2.1 1.643 0.882 2.603 13 Source: authors’ calculations based on Thomson Reuters, WHO World Health Organization (2020), and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: Obs. = observations. 4. Results and Discussion This section analyzes the dynamics of the selected economic-sentiment indicators in the Eurozone, United States, and China. The effect of the COVID-19 pandemic on different sectors’ economic-sentiment indicators in three regions was assessed. 4.1. Analysis of the Trends of Economic Sentiment Indicators in the Face of the COVID-19 Pandemic The Eurozone dynamics and the United States’ and China’s CCI, manufacturing PMI, and services PMI are provided in Figure 1. J. Risk Financial Manag. 2021,14, 159 8 of 23 J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 8 of 24 This section analyzes the dynamics of the selected economic-sentiment indicators in the Eurozone, United States, and China. The effect of the COVID-19 pandemic on different sectors’ economic-sentiment indicators in three regions was assessed. 4.1. Analysis of the Trends of Economic Sentiment Indicators in the Face of the COVID-19 Pandemic The Eurozone dynamics and the United States’ and China’s CCI, manufacturing PMI, and services PMI are provided in Figure 1. (a) (b) (c) Figure 1. Dynamics of CCI (a), and manufacturing (b) and services (c) PMI, in the Eurozone, the United States, and China (January 2020–January 2021). Source: compiled by the authors based on Thomson Reuters data. Note: M1 = January, M2 = February, M3 = March, M4 = April, M5 = May, M6 = June, M7 = July, M8 = August, M9 = September, M10 = October, M11 = November, M12 = December. For CCIEZt, PMIMEZt, PMISEZt, CCIUSt, PMIMUSt, PMISUSt, CCICHt, PMIMCHt, and PMISCZt, see Table 1. -40 0 40 80 120 160 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M1 2020 2021 CCIEZ CCIUS CCICH 30 35 40 45 50 55 60 65 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M1 2020 2021 PMIMEZ PMIMUS PMIMCH 10 20 30 40 50 60 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M1 2020 2021 PMISEZ PMISUS PMISCH Figure 1. Dynamics of CCI ( a ), and manufacturing ( b ) and services ( c ) PMI, in the Eurozone, the United States, and China (January 2020–January 2021). Source: compiled by the authors based on Thomson Reuters data. Note: M1 = January, M2 = February, M3 = March, M4 = April, M5 = May, M6 = June, M7 = July, M8 = August, M9 = September, M10 = October, M11 = November, M12 = December. For CCIEZt, PMIMEZt, PMISEZt, CCIUSt, PMIMUSt, PMISUSt, CCICHt, PMIMCHt, and PMISCZt, see Table 1. From Figure 1, we can observe several essential trends of consumer confidence in the face of the COVID-19 pandemic: (i) The Eurozone CCI, being on average the lowest of all the regions analyzed (mean— 14.508), demonstrated much lower volatility (st. deviation—4.185) than the United States index (st. deviation—16.433), and only slightly higher volatility than the China index (st. deviation—3.796); the lowest value of CCI was observed in April 2020 J. Risk Financial Manag. 2021,14, 159 15 of 23 Eurozone and the United States, and a substantial correlation between consumer confidence in the Eurozone and the United States. The correlation analysis between sentiment indicators and COVID-19 variables showed that the Eurozone consumer-confidence index was not correlated with COVID-19 variables. In contrast, the same index in the United States was strongly negatively correlated with total cases of the COVID-19 pandemic confirmed globally. Consumer confidence in China was strongly negatively correlated with the COVID-19 fatality rate both in China and globally. These conclusions could be used for practitioners to model future economic tendencies or make investment decisions in stressful scenarios. We also observed that business sentiment in the manufacturing sector in all analyzed regions strongly positively correlated with the global spread of COVID-19 (cases and deaths). Simultaneously, the United States manufacturing PMI positively correlated with, while China manufacturing PMI negatively correlated with, country-level COVID-19 indicators. The Eurozone and United States manufacturing PMI negatively correlated with COVID-19 fatality rate both at the country and global levels. The Eurozone and United States service PMIs were negatively correlated with the COVID-19 fatality rate at both the in-country and international levels. In China, the service PMI was positively correlated with the global spread of COVID-19 and negatively correlated with country-level COVID-19 indicators. Our study showed no statistically significant impact of COVID-19-related indicators on consumer-confidence indicators. The Eurozone PMI in the manufacturing sector demonstrated a positive reaction to the increase in COVID-19 cases and deaths both in-country and globally. The fatality rate of COVID-19 infections (at the global level) appeared to significantly negatively impact the business-sentiment indicators in the manufacturing and service sectors. The regression analysis showed that the consumer-confidence indicator in the United States demonstrated an adverse reaction to the growth of deaths caused by COVID-19 infections. The Eurozone PMI in the manufacturing sector showed a positive response to the increase of COVID-19 cases and deaths of both in-country and globally. As in the Eurozone case, the fatality rate of COVID-19 infections (at the global level) appeared to have a statistically significant negative impact on the business-sentiment indicators in the manufacturing and services sectors in the United States. As in the short period, the adverse business-sentiment reaction was observed in the extended period. The regression models for COVID-19’s impact on consumerand business-sentiment indicators in China showed that the country’s consumer-confidence indicator demonstrated an adverse reaction to the fatality rate of COVID-19 infections (both at the country level and globally). In comparison, China PMI in the manufacturing and service sectors demonstrated an adverse reaction to new COVID-19 cases and deaths per month. The number of cases and recent deaths per month proved to have a statistically significant negative impact on China’s business sentiment. Our results showed that we could have entirely different reactions and tendencies, even in such a critical global situation. So, it is essential to pay attention to those factors while making strategic decisions or creating country-level risk limits, or even when considering regional diversification aspects. However, it is crucial to notice that this research encountered some limitations. As the economic sentiment indicators analyzed in this research are provided every month, these results are from a relatively small number of observations. Furthermore, given the dynamics of COVID-19-related variables and uneven growth of cases and deaths during the analyzed period, it would be appropriate to assess the impact of the COVID-19 pandemic on selected economic-sentiment indicators during different phases of the pandemic (onset of the pandemic, global spread, the second wave of the pandemic, beginning of vaccination, etc.). Nevertheless, the short data series did not allow this to be done. Dealing with these J. Risk Financial Manag. 2021,14, 159 16 of 23 limitations and the inclusion of more economic sentiment indicators (both general and sectorial) could be the direction for future research. Author Contributions: Conceptualization, R.K., D.T., G.K.-S.; methodology, D.T., G.K.-S.; software, D.T.; G.K.-S.; formal analysis, D.T., G.K.-S.; investigation, D.T., G.K.-S.; data curation, D.T., G.K.-S.; writing—original draft preparation, D.T., G.K.-S., Y.L.; writing—review and editing, D.T., G.K.-S., Y.L., R.P., S.H., X.-G.Y.; visualization, D.T., G.K.-S.; supervision, R.K. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. Appendix A. Dynamics of COVID-19-Related Variables in the Eurozone J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 17 of 24 Appendix A. Dynamics of COVID-19-Related Variables in the Eurozone Figure A1. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCEZt, NCEZt, TDEZt, NDEZt, and FREZt, see Table 1. 0 5,000,000 10,000,000 15,000,000 20,000,000 IIIIIIIVI 2020 2021 T CEZ T CEZ 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 IIIIIIIVI 2020 2021 NCEZNCEZ 0 100,000 200,000 300,000 400,000 500,000 IIIIIIIVI 2020 2021 TDEZTDEZ 0 20,000 40,000 60,000 80,000 100,000 120,000 IIIIIIIVI 2020 2021 NDEZNDEZ 2 4 6 8 10 12 IIIIIIIVI 2020 2021 FREZFREZ Figure A1. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCEZt, NCEZt, TDEZt, NDEZt, and FREZt, see Table 1. J. Risk Financial Manag. 2021,14, 159 17 of 23 Appendix B. Dynamics of COVID-19-Related Variables in the United States J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 18 of 24 Appendix B. Dynamics of COVID-19-Related Variables in the United States Figure A2. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCUSt, NCUSt, TDUSt, NDUSt, and FRUSt, see Table 1. 0 4,000,000 8,000,000 12,000,000 16,000,000 20,000,000 24,000,000 28,000,000 IIIIIIIVI 2020 2021 T CUS T CUS 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 IIIIIIIVI 2020 2021 NCUSNCUS 0 100,000 200,000 300,000 400,000 500,000 IIIIIIIVI 2020 2021 TDUSTDUS 0 20,000 40,000 60,000 80,000 100,000 IIIIIIIVI 2020 2021 NDUSNDUS 0 2 4 6 8 10 12 IIIIIIIVI 2020 2021 FRUSFRUS Figure A2. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCUSt, NCUSt, TDUSt, NDUSt, and FRUSt, see Table 1. J. Risk Financial Manag. 2021,14, 159 18 of 23 Appendix C. Dynamics of COVID-19-Related Variables in China J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 19 of 24 Appendix C. Dynamics of COVID-19-Related Variables in China Figure A3. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCCHt, NCCHt, TDCHt, NDCHt, and FRCHt, see Table 1. 0 20,000 40,000 60,000 80,000 100,000 120,000 IIIIIIIVI 2020 2021 TCCHTCCH 0 20,000 40,000 60,000 80,000 IIIIIIIVI 2020 202 1 NCCHNCCH 0 1,000 2,000 3,000 4,000 5,000 IIIIIIIVI 2020 2021 TDCHTDCH 0 400 800 1,200 1,600 2,000 2,400 2,800 IIIIIIIVI 2020 202 1 NDCHNDCH 2 3 4 5 6 IIIIIIIVI 2020 2021 FRCHFRCH Figure A3. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCCHt, NCCHt, TDCHt, NDCHt, and FRCHt, see Table 1. J. Risk Financial Manag. 2021,14, 159 19 of 23 Appendix D. Global Dynamics of COVID-19-Related Variables J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 20 of 24 Appendix D. Global Dynamics of COVID-19-Related Variables Figure A4. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCWt, NCWt, TDWt, NDWt, and FRWt, see Table 1. 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000 IIIIIIIVI 2020 2021 TCWTCW 0 5,000,000 10,000,000 15,000,000 20,000,000 IIIIIIIVI 2020 202 1 NCWNCW 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 IIIIIIIVI 2020 2021 TDWTDW 0 100,000 200,000 300,000 400,000 500,000 IIIIIIIVI 2020 202 1 NDWNDW 2 3 4 5 6 7 8 IIIIIIIVI 2020 2021 FRWFRW Figure A4. Source: compiled by the authors based on World Health Organization Coronavirus Disease 2019 (COVID-19) situation reports and the Our World in Data Coronavirus Pandemic (COVID-19) (Roser et al. 2020) database. Note: I = 1st quarter, II—2nd quarter, III—3rd quarter, IV—4th quarter. For TCWt, NCWt, TDWt, NDWt, and FRWt, see Table 1. J. Risk Financial Manag. 2021,14, 159 20 of 23 Appendix E. Correlation between the Eurozone, the United States, and China Economic-Sentiment Indicators of Different Sectors Table A1. Economic sentiment indicators correlation matrix. Correlation t-Statistic Probability CCI China CCI Eurozone CCI United States PMI Manuf. China PMI Manuf. Eurozone PMI Manuf. United States PMI Serv. China PMI Serv. Eurozone PMI Serv. United States CCI China 1 —– —– CCI Eurozone 0.408 1 1.411 —– 0.189 —– CCI United States 0.513 0.843 1 1.894 4.957 —– 0.088 0.001 ** —– PMI Manuf. China 0.177 −0.476 −0.573 1 0.569 −1.712 −2.211 —– 0.581 0.118 0.052 —– PMI Manuf. Eurozone 0.396 0.423 0.071 0.348 1 1.363 1.476 0.226 1.176 —– 0.203 0.171 0.825 0.267 —– PMI Manuf. United States 0.369 0.259 −0.082 0.482 0.961 1 1.256 0.851 −0.260 1.737 10.959 —– 0.238 0.415 0.799 0.113 0.000 * —– PMI Serv. China −0.006 −0.456 −0.599 0.930 0.323 0.440 1 −0.018 −1.618 −2.369 8.013 1.081 1.550 —– 0.986 0.137 0.039 * 0.000 ** 0.305 0.152 —– PMI Serv. Eurozone 0.154 0.633 0.256 0.050 0.813 0.689 0.160 1 0.493 2.588 0.838 0.158 4.409 3.008 0.514 —– 0.633 0.027 * 0.422 0.877 0.001 ** 0.013 * 0.619 —– PMI Serv. United States 0.262 0.634 0.279 0.138 0.920 0.842 0.154 0.906 1 0.858 2.591 0.919 0.441 7.444 4.929 0.494 6.778 —– 0.411 0.027 * 0.379 0.669 0.000 0.001 ** 0.632 0.000 ** —– ** 99% c.l., * 95% c.l. Source: compiled by the authors based on Thomson Reuters data. References Aguilar, Pablo, Corinna Ghirelli, Matias Pacce, and Alberto Urtasun. 2020. Can news help measure economic sentiment? An application in COVID-19 times. Economics Letters 2027: 29. [CrossRef] Albulescu, Claudiu Tiberiu. 2020. COVID-19 and the United States financial markets’ volatility. Finance Research Letters 38: 1–5. 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