Newey-West result of the effect of Daily New confirmed deaths Beside determining the effect of total confirmed cases, we also conduct an estimation on how Daily COVID-19 deaths can lead
Introduction
The E-commerce sector has experienced significant growth due to innovations like the Internet and smartphones, rendering traditional commercial methods outdated and inefficient Unlike physical interactions that limit reach to specific geographical areas, E-commerce operates digitally, allowing access to a global audience This integration of E-commerce into daily life has become essential for global development However, the COVID-19 pandemic has profoundly impacted various aspects of life and business, leading to decreased revenue, workforce adjustments, and disrupted supply chains, which have adversely affected numerous companies worldwide.
Many individuals are questioning the connection between E-commerce, a crucial sector in our society, and the COVID-19 pandemic This article will analyze daily COVID-19 cases and deaths throughout 2020 to assess their impact on E-commerce.
5 biggest E-commerce businesses right now - Amazon, Alibaba, Jingdong, Ebay and Rakuten For the companies, we will be looking into their stock prices and interest over time of each company
Analyzing the impact of COVID-19 on E-commerce companies is crucial for two main reasons First, it helps us understand whether these businesses have been adversely or positively affected by the pandemic Second, it enables us to forecast necessary strategies to mitigate potential threats and leverage opportunities For example, we need to assess how fluctuations in COVID-19 cases and fatalities influence the stock prices of these firms If we discover that the pandemic has a positive effect on their financial performance, it suggests that these companies are thriving despite the crisis This insight prompts us to consider further actions to enhance success and adapt to the ongoing challenges posed by the pandemic.
This analysis draws on the methodology from Nguyễn Hoàng Nam's 2021 research, which quantified the impact of COVID-19 on Vietnam's economic activities through six hypotheses Additionally, it is inspired by the work of Hung-Hao Chang and Chad Meyerhoefer (2020), who used Ubox, Taiwan's largest business-to-consumer agri-food E-commerce platform, as a dependent variable to assess the pandemic's effects on the demand and growth of online food platforms.
Literature review
Previous studies
The United Nations Publications study highlights that the pandemic significantly slowed financial activities, leaving many businesses unprepared A key outcome has been the shift of numerous firms to E-commerce, driven by the urgent need to operate online This report evaluates the impact of the COVID-19 crisis on E-commerce, featuring surveys of E-commerce businesses, customers, and partners involved in the eTrade for all initiative, along with analyses from various multilateral organizations and inquiries conducted by the United Nations.
Luis Varona and Jorge R Gonzales (2021) conducted a study analyzing the short-term behavioral dynamics of economic activity during the COVID-19 pandemic, focusing on the daily transmission rates of the virus They collected data on various economic variables, such as the economic activity index, public expenditure index, real interest rate index, exchange rate index, international dong price index, and stock prices from the Lima Stock Exchange, employing the ARDL model to assess COVID-19's impact on Peru's economy The findings revealed a statistically significant negative effect Additionally, Akbulaev et al (2020) examined the broader economic implications of COVID-19, evaluating its effects on production, employment, and trade, as well as the government's support for producers during mandatory quarantine.
Last but not least, the study by Prince Asare Vitenu-Sackey and Richard Bar
(2021) aimed to assess the impact of the pandemic on poverty reduction and global [6]
This study analyzes the heterogeneous effects of COVID-19 on GDP across 170 countries using econometric panel techniques, including OLS and squared regression Key variables examined include total COVID-19 cases, confirmed deaths, rigor index, human development index, and GDP per capita The findings reveal that the severity of the pandemic has adversely affected poverty alleviation and economic growth, while the recorded deaths have shown a positive correlation with both poverty reduction and economic growth This suggests that controlling population growth is crucial, as it can hinder economic progress and efforts to alleviate poverty.
Overall effect of COVID-19
The COVID-19 pandemic has severely impacted the global economy, leaving many individuals in challenging situations It has introduced uncertainty in economic and social policies, particularly affecting the finance and various other industries The potential consequences of COVID-19 on globalization and global health, especially regarding mobility, trade, and travel, remain unpredictable and alarming As countries implement stay-at-home orders, social distancing, and nationwide shutdowns, the world economic order is undergoing significant changes.
In 2020, the COVID-19 pandemic significantly disrupted global economic progress, with development restrictions hampering financial activities across many regions The impact of the virus varied by location, influenced by the timing of infections and each country's economic status Developing and least developed nations faced heightened vulnerability to global economic downturns Conversely, some countries in the Asia-Pacific region reported lower infection rates, enabling a quicker recovery to pre-pandemic economic levels compared to Europe and the Americas, which anticipated further waves of COVID-19 cases However, without definitive evidence or clear trends regarding the virus's future, experts remain uncertain about the economic outlook.
Methodology and Data
Descriptive statistics
The variables utilized in this research paper are as follow in this table, with the source and time frame included:
Variable Description Source Expecte d sign Time amzn The stock price of Amazon
NASDAQ.com (The Nasdaq Stock Market)
01/11/2019 - 23/04/2021 baba The stock price of Alibaba jd The stock price of Jingdong ebay The stock price of Ebay rkuny The stock price of Rakuten amznint Amazon’s Interest over time
Google Trend This is left blank as babaint Alibaba’s Interest over time jdint Jingdong’s Interest over time
Variable Description Source Expecte d sign Time ebayint Ebay’s Interest over time we do not intend to study their effects
01/11/2019 - 23/04/2021 rkunyint Rakuten’s interest over time nc Daily new confirmed cases of COVID-19 worldometer.co m
+/- ttc Total confirmed cases of
+/- nd Daily new confirmed deaths of COVID-19
+/- ttd Total confirmed deaths of
Data description
Our analysis utilizes data from multiple sources, encompassing global coronavirus case and death statistics, stock prices of e-commerce companies, and trends in public interest over time.
According to Worldometer.com, a reliable source for COVID-19 data, the confirmed cases and death toll for the year 2020 were documented based on validated COVID-19 tests, distinguishing them from suspected cases that only exhibited early symptoms Death cases were confirmed immediately upon the patient's passing, and some confirmed deaths involved patients who were already deceased when the presence of the COVID-19 virus was detected through testing.
Stock price provides one measure of the efficiency of that company operating during the COVID-19 It also has daily data, which, along with cases and deaths of COVID-
We analyzed the impact of COVID-19 on the stock prices of E-commerce companies listed on the Nasdaq Stock Market by constructing a daily panel data set.
In their 2020 study, Hung-Hao Chang and Chad Meyerhoefer analyzed the impact of COVID-19 on the demand and growth of online food platforms using financial statistics from Ubox, Taiwan's largest business-to-consumer agri-food e-commerce platform By leveraging a comprehensive database, they created an extensive panel data set to effectively assess the pandemic's effects on Ubox's performance and draw conclusions about its broader impact on the industry.
This study identifies five leading companies in the global E-commerce industry—Amazon, Alibaba, Jingdong, Ebay, and Rakuten—to analyze the impact of COVID-19 By examining these top-ranked firms, we aim to statistically demonstrate the pandemic's effects on the E-commerce sector as a whole Data will be collected from November 1, 2019, to December 31, 2020, allowing us to account for stock price growth during the pre-COVID period for more accurate results.
During disease outbreaks, many countries enforce mandatory social distancing, forcing people to stay home and complicating in-store shopping due to high coronavirus infection rates Consequently, there has been a significant shift towards online shopping across various e-commerce platforms Our research analyzed the search volume data for selected companies on Google, highlighting this trend.
The Nasdaq stock market is closed on weekends, leading to gaps in the timeline of data from November 2019 to April 2021 To assess user interest in specific companies during this period, we utilized the Google Trends extension, which measures the popularity of search terms over time.
Companies with a high interest over time percentage indicate a growing popularity, leading to increased consumer favorability in purchasing decisions This trend is likely to influence the stock prices of these firms.
Hypotheses
This research is grounded in the methodology outlined in the 2021 paper by MBA Nguyễn Hoàng Nam, which established six hypotheses to assess the impact of COVID-19.
19 on Vietnam’s economic activities These hypotheses are as follow
- H1: COVID-19 has an effect on Vietnam’s Exchange rate
- H2: COVID-19 has an effect on gold price
- H3: COVID-19 has an effect on oil price,
- H4: COVID-19 has an effect on silver price
- H5: COVID-19 has an effect on cooper price,
- H6: COVID-19 has an effect on VN-index
Utilizing his model, he successfully managed to numerically quantify the effect COVID-19 has on the growth of these financial indicators, thus proving that COVID-
19 has an influence on Vietnam’s economic activities’ development
Inspired by Mr Nam's methodologies and models, we developed our own hypotheses to assess the impact of COVID-19 on the global E-commerce industry These hypotheses will form the basis for our final conclusions and findings in this paper.
Interest over time is measured by numbers that indicate search interest relative to the highest point on the chart for a specific region and timeframe A score of 100 signifies the peak popularity of a term, while a score of 50 indicates that the term is half as popular Conversely, a score of 0 suggests insufficient data for that term (Source: Google Trends)
- H1: COVID-19 has influenced Amazon’s financial development
- H2: COVID-19 has influenced Alibaba’s financial development
- H3: COVID-19 has influenced Jingdong’s financial development
- H4: COVID-19 has influenced Ebay’s financial development
- H5: COVID-19 has influenced Rakuten’s financial development.
Method of research
This research utilized Ordinary Least Square (OLS) Estimation to analyze the correlation between the stock prices of selected E-commerce companies, indicative of their business status, and two factors: COVID-19 cases or deaths and the company's interest over time The significance of the explanatory factors was assessed through p-values, with a null hypothesis (H0: B = 0) indicating statistical insignificance A p-value below 0.05 is preferred, while below 0.1 is still acceptable, suggesting the variable's importance to the model Additionally, various tests were conducted to identify potential issues in the models and database, including VIF for Multicollinearity, Breusch-Pagan, White, and Park tests for Heteroskedasticity, and Durbin-Watson and Breusch-Godfrey tests for Autocorrelation If any issues were detected, alternative methods would be employed to rectify the models, mitigate adverse effects, and optimize the research outcomes.
Model
We utilized a panel database, detailed in Appendix A, to assess the impact of COVID-19 on the selected companies, particularly regarding their stock price development To minimize model disturbances, we incorporated each company's online interest over time into their respective models.
The Ordinary Least Squares (OLS) estimation model is:
In this model, \(Y_i\) denotes the daily closing stock price of the \(i\)th company during 2020 The explanatory variables include \(X\), which represents daily new confirmed cases (nc), total confirmed cases (ttc), daily new confirmed deaths (nd), and total deaths (ttd) Additionally, \(Int_i\) serves as an independent variable reflecting the \(i\)th company's interest over time.
Result
Pearson correlation coefficients
The Pearson correlation coefficients method reveals that all dependent and explanatory variables exhibit statistically significant correlations, as indicated by the results in tables A.4 and A.5 (Appendix 2), with significance levels below 0.05.
OLS model results and problems with the model
The results of the OLS model are presented in tables A.6, A.7, A.8, and A.9 (Appendix 2), with all variables showing p-values less than 0.05, indicating their statistical significance We have excluded any results with insignificant variable coefficients, as they are deemed unreliable.
We conduct the Variance Inflation Factor (VIF) test to assess potential multicollinearity issues in our models Since all VIF values are below 5, we confidently determine that our models are free from multicollinearity problems.
Our models did not pass the White test for heteroskedasticity and the Durbin-Watson test for autocorrelation The p-values from the White test were below 0.05, indicating the presence of heteroskedasticity Additionally, the d-statistic fell outside the acceptable range, confirming that our models experienced autocorrelation issues.
Correction and final result
As we can observe from the testing result, our models and results are influenced by heteroskedasticity and autocorrelation Thus we employed the Newey-West (1987)
[8] test to attempt to mitigate and correct these two problems simultaneously
This article explores the correlation between daily new confirmed COVID-19 cases and the stock performance of five companies, as illustrated in the accompanying table.
Variable Beta Newey West – standard error t value P value
Table 2 Newey-West result of the effect of Daily new confirmed cases
The analysis reveals that COVID-19 significantly influences Amazon's stock price, with a Beta of 0.001913, attributed to its dominance in the e-commerce sector For every increase of 1,000 COVID-19 cases, Amazon's stock is projected to rise by $1.13 In contrast, while eBay, Alibaba, and JD also experience stock price increases with rising COVID-19 cases, their growth is less pronounced than Amazon's Rakuten shows only a slight impact from COVID-19 The results indicate a strong correlation, with a P-value of 0.000, confirming the effect of COVID-19 on stock prices at a 5% significance level Additionally, all regression models demonstrate no multicollinearity issues, as indicated by VIF values below 2.
Variable Beta Newey West – standard error t value P value
Table 3 Newey-West result of the effect of Total confirmed cases
The table above illustrates the relationship between total COVID-19 cases and stock prices While the P-values of the estimators indicate statistical significance, the influence of total COVID-19 cases on the stock prices of the five companies is relatively minor when compared to the effect of new COVID-19 cases.
Variable Beta Newy-west standard error T value P value
Table 4 Newey-West result of the effect of Daily New confirmed deaths
This study examines the impact of daily COVID-19 deaths on the stock prices of selected companies, revealing significant effects with P-values less than 0.05 The results indicate that daily death cases influence stock prices alongside daily new COVID-19 cases Additionally, an estimation of total COVID-19 death cases suggests a similar relationship with stock prices, although the effect is not as pronounced as that of total confirmed cases and daily new deaths Nonetheless, the influence of total death cases remains substantial, supported by a highly reliable P-value of less than 0.000.
Variable Beta Newy-west standard error T value P value
Table 5 Newey-West result of the effect of Total confirmed deaths
Research indicates that COVID-19 positively impacted the stock prices of Amazon, Alibaba, JD, Rakuten, and eBay, with high statistical significance Notably, Amazon's stock appears to be the most affected by COVID-19 case numbers All hypotheses were confirmed, demonstrating that COVID-19 influenced the stock prices of all selected companies.
As COVID-19 cases rise globally, countries are increasingly closing their borders to protect public health, which restricts movement and impacts access to essential daily needs In this challenging environment, e-commerce companies are thriving by delivering food and products to customers while adhering to social distancing guidelines.
Limitations
Our model currently lacks sufficient independent variables, which may lead to increased disturbances in the analysis This indicates that we may not have accounted for all factors influencing the stock price growth of companies, such as additional financial metrics and trader demand Despite the challenge of identifying relevant daily variables, we acknowledge this limitation in our model However, the R-squared values from our OLS models are generally acceptable, with the lowest being 0.2384 Consequently, we believe we have identified two key variables that significantly impact the stock prices of the selected companies.
A significant limitation of our model is the overgeneralization resulting from our focus on the impact of COVID-19 on the global e-commerce industry, specifically through the stock prices of five leading companies This approach may introduce bias, as the relationship between these companies and COVID-19 may not accurately reflect the pandemic's effects on the entire industry Smaller companies could experience vastly different outcomes, potentially contradicting our findings Factors such as size, market share, and business capabilities play crucial roles in how firms are impacted by COVID-19, and neglecting smaller companies can lead to biased results.
Relying solely on stock prices as the primary indicator of a company's financial performance presents limitations in our model While other financial metrics like net profit, cash flow, and expenses could provide a more accurate assessment, we faced challenges in compiling a comprehensive database due to the limited observations and the infrequent occurrence of COVID-19 in our dataset Consequently, we must depend on stock prices to evaluate the financial performance of the selected companies, as they offer a sufficient volume of daily data to construct a robust database that effectively illustrates the impact of COVID-19.
This research paper, despite its limitations, serves as a foundational resource for future studies exploring the impact of COVID-19 on e-commerce and various other industries.
Conclusion and Recommendation
Conclusion
The COVID-19 pandemic has significantly accelerated the shift towards online shopping as social distancing measures compel consumers to stay at home Many suppliers are experiencing a decline in leisure purchases while seeing a surge in demand for essential items like toiletries and basic supplies This disruption has led to instability in supply chains and has profoundly affected the E-commerce market, highlighting the changing landscape of consumer behavior during the pandemic.
The analysis conducted in this paper proves that COVID-19 has a positive effect on the stock price of all 5 E-commerce companies: Amazon, Alibaba, Jingdong, Ebay and Rakuten
Despite the challenges faced by many businesses during the pandemic, E-commerce has not only remained consistent but has also thrived Its contactless nature and global reach align perfectly with the quarantine measures in place, making it a safer option for consumers who can shop virtually without physical contact The increasing awareness of COVID-19's health risks has led to more people staying at home, shifting their work, hobbies, and shopping online This surge in homebound consumers has made E-commerce more profitable than ever However, the pandemic has caused supply chain disruptions, slowing down shipping and delivery processes Nevertheless, consumers continue to shop online due to the lack of alternatives.
Recommendation
A strategy for COVID and post-COVID situation is a must, and the crisis plan will have to be conducted fast and reactively according to many scenarios
Improving shipping speed is crucial for enhancing customer experience and fostering E-commerce growth, especially as logistics and postal services face delays due to COVID-19 safety guidelines Businesses should prioritize implementing recommended practices to navigate these challenges effectively.
Streamlining internal processes is essential to avoid complicating simple tasks, which can lead to inefficiencies and frustration If your current process is lengthy and cumbersome, it may be beneficial to reevaluate and design a more straightforward, efficient approach from the ground up.
- Using Electronic data interchange, which is a technique that replaces the paperwork with electronic data in order to speed up the process as a whole
Enhancing the online experience for customers involves providing reliable and helpful package tracking information Offering multiple tracking options, including on-site tracking, direct links to the carrier's website, and mobile device tracking capabilities, significantly improves customer satisfaction and engagement.
To maintain engagement with diverse communities, businesses should explore creative channels beyond their current strategies and be prepared to offer support during challenging times, as this fosters mutual support Key channels to consider include search engines like Google and Bing, as well as comparison shopping platforms such as Idealo and PriceGrabber Additionally, email marketing is an effective tool for reaching potential customers, allowing for targeted campaigns based on user behavior, such as reminders for abandoned carts or special discounts for birthdays By diversifying their outreach efforts, e-commerce companies can effectively connect with more target audiences and achieve their objectives.
TABLES AND FIGURES
Variable Number of observations Maximum Minimum Mean Standard deviation ttc 371 1.46e+08 0 3.81e+07 4.49e+07 nc 371 903747 0 279755.1 256828.8 ttd 371 3086756 0 938929 949495.1 nd 371 17906 0 6214.814 4839.212
Table A.1 Summary of COVID-19’s statistics
Variable Number of observations Maximum Minimum Mean Standard deviation amzn 371 3531.45 1676.61 2689.488 593.5119 baba 371 317.14 176.34 235.722 33.57524 jd 371 106.88 31.49 63.857 21.57024 ebay 371 64.93 26.34 47.457 9.974053 rkuny 371 14.12 6.22 9.538 1.394147
Table A.2 Summary of selected companies’ stock prices
Variable Number of observations Maximum Minimum Mean Standard deviation amznint 371 100 63 77.10512 8.841074 babaint 371 100 46 57.89757 8.749688 jdint 371 100 27 50.97305 13.10722 ebayint 371 99 65 81.72507 7.973937 rkunyint 371 100 64 79.79784 9.015578
Table A.3 Summary of selected companies’ interest over time
Table A.4 Summary of selected companies’ interest over time amzn baba ebay rkuny jd ttc nc ttd nd amzn 1 baba 0.8137* 1
0 0 0 0 amzn baba ebay rkuny jd amznint babaint ebayint jdint rkunyint amzn 1 baba 0.8137* 1
0 0 0 0 0 0 0.0167 0.0563 rkunyint 0.1851* -0.0205 0.1164* 0.1090* 0.2301* 0.4539* -0.059 0.1738* 0.1856* 1 0.0003 0.6944 0.0249 0.0358 0 0 0.2573 0.0008 0.0003 amzn baba ebay rkuny jd ttc nc ttd nd ttc 0.7047* 0.3982* 0.7579* 0.7784* 0.8234* 1
Table A.5 Correlation between stock price and COVID-19’s statistics
Variable Beta R squared Adj R-squared P value VIF White test d-statistic
Table A.6 OLS result of the effect of Daily new confirmed cases
Variable Beta R squared Adj R-squared P value VIF White test d-statistic
Table A.7 OLS result of the effect of Total confirmed cases
Variable Beta R squared Adj R- squared P value VIF White test d-statistic AMZN 0986512 0.6543 0.6524 0.000 1.00 0.0000 2689033 EBAY 0011214 0.6261 0.6241 0.000 1.42 0.0000 1510583
Table A.8 OLS result of the effect of Daily new confirmed death
Variable Beta R squared Adj R-squared P value VIF White test d-statistic
Table A.9 OLS result of the effect of Daily new confirmed death
Database of companies’ stock price and COVID 19’s statistics: -
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Date Total cases Daily cases new
Daily death new AMZN BABA EBAY RKUNY JD
Database of company’s interest over time: