Financial development during COVID-19 pandemic: The role of coronavirus testing and functional labs

Anser, Muhammad Khalid et al. Article Financial development during COVID-19 pandemic: The role of coronavirus testing and functional labs Financial Innovation Provided in Cooperation with: Springer Nature Suggested Citation: Anser, Muhammad Khalid et al. (2021) : Financial development during COVID-19 pandemic: The role of coronavirus testing and functional labs, Financial Innovation, ISSN 2199-4730, Springer, Heidelberg, Vol. 7, Iss. 1, pp. 1-13, https://doi.org/10.1186/s40854-021-00226-4 This Version is available at: https://hdl.handle.net/10419/237241 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. 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/ Financial development duringCOVID‑19 pandemic: therole ofcoronavirus testing andfunctional labs Muhammad Khalid Anser1, Muhammad Azhar Khan2, Khalid Zaman2*, Abdelmohsen A. Nassani3, Sameh E. Askar4, Muhammad Moinuddin Qazi Abro3 and Ahmad Kabbani5 Introduction The control of excess money supply and credit creations through bank rates is considered one of the oldest instruments for monetary policy adoption by the central bank to re-settle economic issues (Auclert 2019). The adverse shocks to monetary policy spur various differential outcomes in lowering interest rates and increasing asset pricing (Schmidt 2020). Empowering governance and capability structure for sustainable innovative programs, support corporate vision towards eco-friendly production (Awan 2020). The COVID-19 pandemic has hampered economic activities and financial transactions, leading to high volatility in the stock prices (Fallahgoul 2020; Procacci etal. 2020). Increased COVID-19 infected cases and deaths globally has slowed down stock Abstract The outbreak of the SARS-CoV-2 virus in early 2020, known as COVID-19, spread to more than 200 countries and negatively affected the global economic output. Financial activities were primarily depressed, and investors were reluctant to start new financial investments while ongoing projects further declined due to the global lockdown to curb the disease. This study analyzes the money supply reaction to the COVID-19 pandemic using a cross-sectional panel of 115 countries. The study used robust least square regression and innovation accounting techniques to get sound parameter estimates. The results show that COVID-19 infected cases are the main contributing factor that obstructs financial activities and decrease money supply. In contrast, an increasing number of recovered cases and COVID-19 testing capabilities gave investors confidence to increase stock trade across countries. The overall forecast trend shows that COVID-19 infected cases and recovered cases followed the U-shaped trend, while COVID-19 critical cases and reported deaths showed a decreasing trend. Finally, the money supply and testing capacity show a positive trend over a period. The study concludes that financial development can be expanded by increasing the testing capacity and functional labs to identify suspected coronavirus cases globally. Keywords: Financial development, COVID-19 pandemic, Infected cases, Testing capacity, Robust least square estimator, Innovation accounting matrix Open Access © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons .org/licen ses/by/4.0/. RESEARCH Anseretal. Financ Innov (2021) 7:9 https://doi.org/10.1186/s40854‑021‑00226‑4 Financial Innovation *Correspondence: [email protected] 2 Department of Economics, University of Haripur, Haripur Khyber Pakhtunkhwa, Pakistan Full list of author information is available at the end of the article Page 2 of 13 Anseretal. Financ Innov (2021) 7:9 market indices and increased negative financial values (Albulescu 2020; Sapkota and Madai 2020). Brown and Rocha (2020) concluded that entrepreneurial activities have been affected during the current pandemic, which affects the debt market, causing a global financial crisis to emerge. The entrepreneurial equity investment in a given scenario is disturbed, and strategic policy intervention is required to overcome the rising debt, to minimize uncertainty in the entrepreneurial business. Ali etal. (2020) showed that the COVID-19 outbreak started in China, moved to Europe and then to the US, and created disorder to global financial activities, leading to effects in the latter phase, when COVID-19 reached the US. The effects of the pandemic even affected relatively safer commodities in the latter phases. Shehzad etal. (2020) discussed the opportunity to invest in the Asian market, instead of Europe and the USA, for better portfolio optimization during the COVID-19 pandemic. They critically analyzes the effects of a global financial crisis in 2008 (GFC) and the COVID-19 crisis on the world economies. Figure1 shows the factual assessment of infected cases, money supply, and testing facility in the ten most affected countries. Figure1 illustrates that operational and testing lab capacity are higher in the USA, followed by Russia, India, UK, and Brazil, and cases increased at a tremendous rate in the USA, Brazil, India, and Russia. The money supply is higher in the UK and lowest in Mexico at the given point in time. This study used four main factors for analyzing the COVID-19 pandemic (infected cases, reported deaths, recovered cases, and critical cases) and the money supply, while earlier studies were limited to a few variables (see, Goodell etal. 2020). Second, coronavirus testing capacity was used as a mediator between COVID-19 cases and money supply, which was not done in the earlier literature. Third, the money supply is used as a “response variable” and a proxy for financial development, affected by COVID-19 associated factors. The relationship between money supply and COVID-19 factors is verified by innovation accounting matrix and forecast evaluation indices to reach certain policy conclusions. This study proposes to satisfy the following research objectives: (i) To examine the impact of coronavirus infected cases, reported deaths, critical cases, and recovered cases on money supply in a cross-sectional panel of 115 countries. 0 5000000 10000000 15000000 20000000 25000000 30000000 35000000 40000000 45000000 USA BrazilIndia Russia Peru ChileMexicoSouth Africa UK Pakistan 3616747 1970909 970169 746369 337724 321205 317635 311049 291911 255769 89 95 74 59 47 77 38 73 149 58 44844982 4911063 12739490 23754645 1983932 1334712 768457 2278127 12461869 1627939 CASES M2 TEST Fig. 1 COVID-19 infected cases, financial development, and testing scores. Source: Worldometer (2020, 16th July) Page 3 of 13 Anseretal. Financ Innov (2021) 7:9 (ii) To evaluate the role of coronavirus testing capacity in improving financial development across countries, and (iii) To analyze the forecast evaluation of the candidate variables over a time horizon. These objectives have been verified through a cross-sectional regression estimator, innovation matrix, and forecast indices. The study has the following sections. The introduction is presented in Sect.1. Literature review is in Sect.2. Data & methodology are presented in Sect.3. Section4 discussed the results, while the final section concludes the study. Literature review The number of scholarly works available on the COVID-19 pandemic shows its negative effect on every sector of the global world (Codagnone etal. 2020; Dzigbede and Pathak 2020). These adverse effects are not limited to healthcare casualties, but has also severely affected the financial sector, destabilizing investors’ confidence in stock trading (Topcu and Gulal 2020; Ibn-Mohammed etal 2020; Phan and Narayan 2020). The sustainable development goals are lost their momentum during the pandemic (Heggen etal. 2020). Hence, there is a greater need to devise innovative policies to combat the increased social and environmental sufferings (Amankwah-Amoah 2020; Awan 2020; Heinonen and Strandvik 2020). The increased need for coronavirus testing capacities may shorten the learning curves by sharing knowledge and technological spillovers (Kumar etal. 2020). The technical absorptive capacity is not only helpful to mitigate negative environmental concerns, but can be further used in many healthcare interventions to reduce the risk of infectious diseases (Sarkar etal. 2020, Cheng etal. 2020, Kanwal and Awan 2020, Tromberg etal. 2020). The financial risk reporting based on generating algorithms can use to avoid bankruptcy, which required prudential economic policies to prevent it from systemic risk (Kou etal. 2014, 2019, 2021; Chao etal. 2019, 2020). Specific literature is presented to observe financial volatility due to the COVID-19 pandemic across various economic settings. For instance, Topcu and Gulal (2020) found that stock markets negatively reacted because of shocks to real oil prices and currency exchange rates, due to increased COVID-19 infected cases. The stock market volatility is highest in emerging markets followed by the Middle East and South America; while Central & Eastern Europe is least affected. It is critical that governments minimize the financial crisis through various stimulus packages in order to reduce stock market volatility across countries. Al-Alwadhi etal. (2020) considered the Chinese stock market to analyze the vulnerability of COVID-19 infected cases and reported deaths on stock market returns across all companies, and found a negative correlation between them. The need for quick response time to contain coronavirus cases and reported deaths may help stabilize stock market trends. Li etal. (2020) found that the outbreak of the COVID19 virus, considered the primary driver of stock market volatility, resulted in increased insurance premiums. The optimal guarantee may have been enhanced through capital regulations during the current pandemic. The sound financial activities are vital for resolving the conflicts between board members and insurer to settle their accounts efficiently. Mazur etal. (2020) used the USA to analyze the stock market performance in the wave of coronavirus outbreak and found high volatility in stock trading of real Page 4 of 13 Anseretal. Financ Innov (2021) 7:9 estate business stocks, petroleum products, and services stocks while observed positive stock trading in information technology-based products, healthcare products, and food stocks. The impacts found in different trading stocks needs more critical review to understand the investors’ centric approach. Nicola etal. (2020) discussed the socio-economic consequences faced globally because of the COVID-19 pandemic such as, reduced workforce due to the lockdown; exacerbated unemployment because of closures of industries, educational institutes, and other corporate businesses; increased need for healthcare medical supplies; and increased demand for food items due to panic buying. Global strategic decision-making needs to develop resilient strategies to combat COVID-19. Waris etal. (2020) described the global efforts to reduce coronavirus infection. The study appreciated the efforts of Pakistan’s government that contained coronavirus cases by adopting rigorous measures to minimize the spread of coronavirus, that is, strict compliance to the Standard Operating Procedures (SOPs), designed special hospitals and healthcare facilities, increased laboratory testing capacity, and introduced multiple forms of lockdown initiatives. The efforts were highly valued that helps to formulate resilient healthcare policies for global prosperity. Fang and Meng (2020) emphasized the importance and need for clinical laboratories to detect infectious diseases (including COVID-19), at early stages to contain its spread. Vandenberg etal. (2020) suggested that continuously improving clinical diagnostic testing services, would not only support early detection of an infected person but can be used as a policy tool to optimize healthcare resources, to prevent further healthcare casualties globally. Oldekop etal. (2020) discussed four main economic factors affected by the COVID-19 pandemic. First, the global value chain has been disrupted due to restrictions to transportation systems, which led to severe consequences for essential healthcare medical supplies, food shortages, and manufacturing exports; second, digitalization, since the use of online working tools have increased because of working at home; third, public finance is now confined within the country and cannot be transformed into production workflows; and finally, climate change efforts to reduce global carbon emissions remains stagnant. These factors remain critical and need to be resolved with joint international collaboration. Barbier and Burgess (2020) pointed out that sustainable development goals were barely achieved in many parts of the global world due to exacerbated COVID-19 infected cases. The low adaptability of innovative technologies and the absence of international support make developing countries severely vulnerable, affecting the nation’s economic growth. The unexpected increase in the need for global healthcare infrastructure increased the strain on the already severe financial crises in developing countries. There is therefore, a need to pursue healthcare compatibility and innovative strategies to escape from the pandemic and the financial crisis. Yamey etal. (2020) stressed the need for the coronavirus vaccine, as it was the only way to control the pandemic. Study shows three imperatives for a possible coronavirus vaccine; first, the process for making the vaccine need to be quick; second, deployment at a massive scale; and third, ensuring global access to all. Priority must be given to healthcare workers and infected patients and then distribution to the non-infected cases on an equally. Based on the stated literature, the study selected the main predictors of the study defined in the subsequent section. Page 5 of 13 Anseretal. Financ Innov (2021) 7:9 Data sources andmethodological framework The study used a broad money supply relative to GDP (denoted by M2) as a proxy variable for financial development served as a “regressand variable.” In comparison, the counts of coronavirus registered cases (denoted by CASES), death cases (denoted by DEATHS), recovered cases (denoted by RECOVERED), critical cases (denoted by CRITICAL), and coronavirus testing (denoted by TEST) served as “regressors.” The available data for the M2 variable is taken from World Bank (2020), whereas the data for COVID19 variables are taken from Worldometer (2020, 16th July). The cross-sectional data is used as one point in time with a panel of 115 countries. Table1 shows the list of sample countries for easy reference. The study used the following equation to assess the effects of COVID-19 pandemic on money supply during increased testing capacity and reported critical cases: Equation(1) shows that the money supply is expected to decline when there is an increase in the COVID-19 registered cases, deaths, and critical cases. An increase in testing capacity and patients recovered from coronavirus diseases is expected to expand more money supply across countries. The following hypotheses need to be tested for this to be conclusive: H1 COVID-19 registered cases, critical cases, and reported deaths will negatively impact money supply across countries. H2 Money supply is expected to have a positive response to increased COVID-19 recovered cases, and. H3 An increase in testing capacity for COVID-19 will positively impact the money supply. (1) ln(M2) i,t= α 0+ α 1 ln(CASES) i,t+ α 2 ln(DEATHS) i,t +α3ln(RECOVERED)i,t+α4ln(CRITICAL)i, t +α5ln(TEST )i,t+εi,t∴ ln(M2) ln(CASES)<0, ln(M2) ln(DEATHS)<0, ln(M2) ln(RECOVERED)>0, ln(M2) ln ( CRITICAL ) <0, ln(M2) ln ( TEST ) >0 Table 1 List ofsample countries Total countries: 115 “USA, Brazil, India, Russia, Peru, Chile, Mexico, South Africa, UK, Pakistan, Turkey, Bangladesh, Colombia, Qatar, Egypt, Iraq, China, Indonesia, Sweden, Ecuador, Belarus, Kazakhstan, Philippines, UAE, Ukraine, Bolivia, Dominican Republic, Singapore, Poland, Afghanistan, Nigeria, Romania, Armenia, Guatemala, Honduras, Azerbaijan, Ghana, Japan, Algeria, Moldova, Serbia, Nepal, Morocco, Cameroon, Uzbekistan, Kyrgyzstan, Kenya, Australia, El Salvador, Costa Rica, Malaysia, North Macedonia, Senegal, Bulgaria, Bosnia and Herzegovina, Finland, Haiti, Tajikistan, Gabon, Madagascar, Luxembourg, Djibouti, Hungary, Greece, Albania, Thailand, Paraguay, Somalia, Equatorial Guinea, Maldives, Cuba, Slovakia, Iceland, Slovenia, Guinea-Bissau, Cabo Verde, Sierra Leone, Libya, Hong Kong, Yemen, Rwanda, Benin, Mozambique, Tunisia, Latvia, Niger, Zimbabwe, Liberia, Uganda, Cyprus, Uruguay, Georgia, Namibia, Andorra, Suriname, Jamaica, Malta, Angola, Syria, Botswana, Viet Nam, Mauritius, Comoros, Guyana, Burundi, Mongolia, Gibraltar, Bermuda, Brunei, Aruba, Barbados, Bhutan, Gambia, Macao, Belize” Page 6 of 13 Anseretal. Financ Innov (2021) 7:9 These hypotheses are essential to analyze and devise strong global financial policies. The study used sequential steps for statistical analysis. Step–I: robust least square (RLS) estimator The RLS estimator is the extended version of the simple least square regression, which addresses possible outliers of the dependent variable (by M-estimation technique), independent variables (by Sestimation technique) and simultaneously handle outliers from the regressand and regressors (by MM-estimation technique) from the model. The study used the M-estimation technique, as the money supply widely fluctuated during the COVID-19 pandemic. The M-estimation is abbreviated to "maximum likelihood estimator-like," magnifying the possible outliers in the regressand. This estimator is also known as the Huber-M estimator, as Huber (1973) proposed the way to minimize regressand outliers to transform robust least-square estimates. The S-estimation is abbreviated to “scale statistic” that addresses regressand outliers from the model. Rousseeuw and Yohai (1984) suggested that this estimation technique uses robust least-square estimates. Finally, the MM-estimator is proposed by Yohai (1987), considered the mixture of “M” estimator and “S” estimator, which handles the regressand and regressors outliers simultaneously, to provide more robust estimates (different from the simple least-square estimates). Step‑II: innovation accounting matrix (IAM) The innovation accounting matrix (IAM) procedure is based on the Vector autoregression (VAR) system’s two innovation techniques, that is, impulse response function (IRF) and variance decomposition analysis (VDA). The IRF estimates show that systematic shocks about the i-th variable has translated into other system endogenous variables via the dynamic lag structure. The function traces the i-th shock of innovations on present and future endogenous variables during the VAR system. The VDA innovation technique is somehow different from the IRF estimates, as IRF estimates mark out the system shock one to other endogenous variables. At the same time, VDA provides the importance of the components shocks that separate it from the endogenous variable in random innovation in the VAR system. Step‑III: forecast evaluation The study further moves towards the forecast evaluation by four methods: 1 "Root Mean Square Error (RMSE)," 2 "Mean Absolute Error (MAE)," 3 "Mean Absolute Percentage Error (MAPE)," and 4 "Theil inequality coefficient." These methods predict future deviation in individual variables where one can assess the candidate variables’ direction over a time horizon. Page 7 of 13 Anseretal. Financ Innov (2021) 7:9 Results anddiscussion Table2 shows the descriptive statistics of the variables. The mean value of money supply shows that the selected cross-sectional countries have more than 64% of its money supply relative to its GDP. The maximum total number of coronavirus registered cases, reported deaths, and recovered patients are about to reach 3,616,747, 140,140 and 1,645,962, respectively with a mean value of 102,928, 3537 and 61,299 respectively. The critical cases are about to reach 16,459 with a mean value of 453. The full testing service is at 90,410,000 with an average value of 2,080,156. The description of the variables is further used in the regression estimator. Table3 shows the robust least square regression estimates and found that COVID-19 infected cases substantially decrease money supply, due to investors’ low confidence in stock market trading, which ceased economic activities across countries. The elasticity estimates confirmed that there was a less elastic relationship between the two variables; if there is a 1% increase in the infected cases, money supply decreases by 0.390 percentage points. Increase in COVID-19 testing capacity and COVID-19 recovered cases, both increases the investors’ confidence in continuing economic activities, which positively affects the money supply across countries. The elasticity estimates show that if the increase in recovered cases is 1%, the money supply increases by 0.359%. On the other Table 2 Descriptive statistics Source: Worldometer (2020, 16th July) and World Bank (2020). Note: M2 shows broad money supply, CASES show COVID‑ 19 registered cases, DEATHS show COVID‑19 death cases, RECOVERED shows COVID‑19 recovered cases, CRITICAL shows COVID‑19 critical cases, and TEST show COVID‑19 testing capacity VDA estimates VDA estimates VDA estimates VDA estimates VDA estimates VDA estimates VDA estimates Mean 64.419 102,928 3537 61,299 453 2,080,156 Maximum 386.139 3,616,747 140,140 1,645,962 16,459 90,410,000 SD 49.769 412,127 15,830.980 220,446.700 1983.428 9,978,908 Skewness 3.347 6.878 7.088 5.733 6.324 7.422 Kurtosis 19.524 54.590 57.087 37.720 45.955 61.899 Table 3 Robust least square estimates ‘ln’ shows natural logarithm, M2 shows broad money supply, CASES show COVID‑19 registered cases, DEATHS show COVID‑ 19 death cases, RECOVERED shows COVID‑19 recovered cases, CRITICAL shows COVID‑19 critical cases, and TEST show COVID‑19 testing capacity Dependent variable: ln(M2) Variable Coefficient SE z‑Statistic Prob ln(CASES) − 0.390340 0.145110 − 2.689966 0.0071 ln(CRITICAL) 0.007461 0.045988 0.162243 0.8711 ln(DEATHS) − 0.082494 0.077710 − 1.061568 0.2884 ln(RECOVERED) 0.358547 0.127427 2.813745 0.0049 ln(TEST) 0.232845 0.055028 4.231390 0.0000 C 1.958477 0.579399 3.380188 0.0007 Robust statistics R20.166807 Adjusted R20.103687 Scale 0.484890 Deviance 0.235119 Rn-squared statistic 47.97638 Prob(Rn-squared stat.) 0.000000 Page 8 of 13 Anseretal. Financ Innov (2021) 7:9 hand, if the increase in testing capacity is 1%, the money supply increases by 0.232%. These results confirm the need for controlling COVID-19 through increased testing capacity and unified global policies. The results are in line with the earlier studies; Baker etal. (2020) found the adverse effects of the COVID-19 pandemic on a stock market performance that possibly be subsidized by expansionary economic policies. Ashraf (2020) confirmed the negative association between COVID-19 infected cases (and reported deaths) and stock market returns that may lead to more severe over time. Haroon and Rizvi (2020) confirmed that the immense disruption in the equity market arose because of coronavirus related panic-laden news across the globe. The results of the study further connected with the findings of the subsequent studies that concluded that financial trading is affected due to increase COVID-19 infected cases (see, Erdem 2020; Baek etal. 2020; Chia etal. 2020). These studies highlighted the need to subsidize the financial and healthcare sector to marginalize coronavirus’s adverse effects in economic and business processes. Table 4 IRF estimates ‘ln’ shows natural logarithm, M2 shows broad money supply, CASES show COVID‑19 registered cases, DEATHS show COVID‑ 19 death cases, RECOVERED shows COVID‑19 recovered cases, CRITICAL shows COVID‑19 critical cases, and TEST show COVID‑19 testing capacity Response ofln(M2) Months ln(M2) ln(CASES) ln(CRITICAL) ln(DEATHS) ln(RECOVERED) ln(TEST) September 2020 − 0.081765 0.089245 − 0.046740 − 0.199513 − 0.141159 0.100580 October 2020 0.082794 0.059305 − 0.119642 − 0.169229 − 0.056978 0.135813 November 2020 0.067628 0.081026 − 0.069807 − 0.131688 − 0.048519 0.123965 December 2020 0.098690 0.073576 − 0.060632 − 0.135930 − 0.097395 0.101724 January 2021 0.074980 0.072148 − 0.064648 − 0.155917 − 0.105412 0.103957 February 2021 0.089666 0.072536 − 0.084623 − 0.149722 − 0.067994 0.120638 March 2021 0.084261 0.078691 − 0.075475 − 0.141624 − 0.062926 0.117800 April 2021 0.082537 0.076289 − 0.064024 − 0.144353 − 0.088953 0.107160 May 2021 0.083375 0.073653 − 0.070595 − 0.151179 − 0.093733 0.108972 Table 5 VDA estimates ‘ln’ shows natural logarithm, M2 shows broad money supply, CASES show COVID‑19 registered cases, DEATHS show COVID‑ 19 death cases, RECOVERED shows COVID‑19 recovered cases, CRITICAL shows COVID‑19 critical cases, and TEST show COVID‑19 testing capacity Variance decomposition ofLNM2: Period SE ln(M2) ln(CASES) ln(CRITICAL) ln(DEATHS) ln(RECOVERED) ln(TEST) September 2020 0.730966 85.02804 1.490629 0.408863 7.449837 3.729274 1.893360 October 2020 0.780594 75.68494 1.884322 2.707693 11.23269 3.802952 4.687405 November 2020 0.812652 70.52407 2.732716 3.236178 12.98988 3.865297 6.651859 December 2020 0.847079 66.26541 3.269542 3.490804 14.53051 4.879474 7.564254 January 2021 0.882484 61.77686 3.680853 3.752984 16.50954 5.922606 8.357161 February 2021 0.916970 58.17375 4.034943 4.327654 17.95710 6.035329 9.471224 March 2021 0.947479 55.27853 4.469051 4.687984 19.05354 6.093993 10.41690 April 2021 0.977078 52.69369 4.812020 4.837623 20.09932 6.559189 10.99816 May 2021 1.007749 50.21954 5.057744 5.038375 21.14500 7.031139 11.50821