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 Expected sign Time
Literature review
Previous studies
The United Nations Publications study highlights that the pandemic significantly slowed down financial activities, leaving many businesses unprepared As a result, numerous companies transitioned to E-commerce to adapt to the necessity of operating online This report provides an initial assessment of COVID-19's impact on E-commerce, featuring surveys of E-commerce businesses, consumers, and partners involved in the eTrade for All initiative, along with analyses from various multilateral organizations and inquiries conducted by the United Nations.
A study by Luis Varona and Jorge R Gonzales (2021) examined the short-term behavioral dynamics of economic activity during the COVID-19 pandemic, utilizing a baseline of daily COVID-19 transmission rates They collected data on various economic indicators, 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 the pandemic's impact on Peru's economy The findings revealed a statistically significant negative effect Additionally, research by Akbulaev et al (2020) explored the broader economic repercussions of COVID-19, analyzing its effects on production, employment, imports, exports, and the government's support for producers during mandatory quarantine.
Last but not least, the study by Prince Asare Vitenu-Sackey and Richard Bar
A 2021 study assessed the pandemic's impact on poverty reduction and global GDP by analyzing data from 170 countries using econometric panel techniques like OLS and squared regression Key variables included total COVID-19 cases, confirmed deaths, rigor index, human development index, and GDP per capita The findings revealed that the pandemic's severity has negatively affected poverty alleviation and economic growth, while recorded deaths have paradoxically shown a positive correlation with both poverty reduction and economic growth This suggests that controlling population growth may hinder economic progress and poverty alleviation efforts.
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, adversely affecting various industries, particularly finance The potential consequences of COVID-19 on globalization and global health—regarding mobility, trade, and travel—remain highly unpredictable and concerning As nations implement stay-at-home orders, social distancing measures, and even complete lockdowns, the world economic landscape is undergoing significant transformation.
In 2020, the COVID-19 pandemic significantly disrupted global economic progress, with restrictions hindering financial activities across many regions The impact varied by country, influenced by infection rates and existing economic conditions, with developing nations facing heightened vulnerability to economic downturns Conversely, some Asia-Pacific countries experienced lower infection rates, enabling a quicker recovery to pre-pandemic economic levels compared to Europe and the Americas, which anticipated further COVID-19 waves However, without definitive evidence or clear trends regarding the virus's future development, 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 diverse data 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 information, the confirmed and death cases reported in 2020 were based on validated COVID-19 tests, distinguishing them from suspected cases that only exhibited early symptoms Death cases were confirmed promptly following the patients' passing, and some confirmed deaths also included individuals 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, utilizing a daily-level panel data set.
In their 2020 study, Hung-Hao Chang and Chad Meyerhoefer analyzed the impact of COVID-19 on online food platforms using data from Ubox, Taiwan's largest business-to-consumer agri-food e-commerce platform By leveraging a comprehensive database, they developed an extensive panel data set that enabled them to effectively assess the pandemic's effects on Ubox's financial statistics and draw significant conclusions regarding its influence on the industry.
This article analyzes the impact of COVID-19 on the E-commerce industry by examining five leading companies: Amazon, Alibaba, Jingdong, eBay, and Rakuten These companies were selected based on their global rankings and will serve as representatives for the industry The research focuses on stock price growth from November 1, 2019, to December 31, 2020, to provide a comprehensive view of the pandemic's effects on E-commerce The findings aim to reflect the overall influence of COVID-19 on the industry as a whole.
During disease outbreaks, mandatory social distancing leads to challenges in shopping at physical stores due to high coronavirus infection rates Consequently, many individuals have shifted to online shopping across various e-commerce platforms Our research analyzed the search volume data for selected companies on Google.
The Nasdaq stock market is closed on weekends, resulting in gaps in the data timeline from November 2019 to April 2021 To assess the public interest in specific companies during this period, we utilized the Google Trends extension, which measures internet users' interest in various topics and search terms over time.
The increasing percentage of companies' high interest over time indicates a growing popularity, leading to a more favorable purchasing environment This trend is likely to influence the stock prices of these firms positively.
Hypotheses
In our research, we utilized 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
Drawing inspiration from Mr Nam's methodologies and models, we formulated our own hypotheses to assess the impact of COVID-19 on the global e-commerce industry These hypotheses will underpin the conclusions and findings presented in this paper.
Interest over time reflects the search popularity of a term relative to its highest recorded point within a specific region and timeframe A score of 100 indicates peak popularity, while a score of 50 signifies that the term is half as popular Conversely, a score of 0 indicates insufficient data for the 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 relationship between the stock prices of selected E-commerce companies 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 focus on rejecting the null hypothesis (H0: B = 0) for variables with p-values below 0.05, or 0.1 at a minimum To ensure model integrity, various tests were conducted, 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 identified, appropriate methods would be employed to rectify them, ensuring optimal models and results for the research.
Model
We utilized a panel database, detailed in Appendix A, to assess the impact of COVID-19 on the stock prices of selected companies To minimize model disturbances, we incorporated each company's interest over time from the Internet into our analysis.
The Ordinary Least Squares (OLS) estimation model is:
In this analysis, Y_i denotes the daily closing stock price of the ith company during 2020 The explanatory variables include 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 representing the interest of the ith company 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 p-values less than 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), demonstrating that all variables have p-values less than 0.05, indicating statistical significance Additionally, we excluded any results with insignificant variable coefficients to ensure reliability.
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 analysis revealed that the models did not pass the White test for heteroskedasticity, with p-values falling below 0.05, indicating significant heteroskedasticity Additionally, the Durbin-Watson test results showed a d-statistic outside the acceptable range, confirming the presence of autocorrelation issues in our models.
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 selected companies The impact of these daily case numbers on the stocks is summarized in the table provided below.
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 impacts Amazon's stock price, with a Beta of 0.001913, attributed to its dominance in the e-commerce sector An increase of 1,000 COVID-19 cases is associated with a projected rise of $1.13 in Amazon's stock price In contrast, while eBay, Alibaba, and JD also experience stock price increases amid rising COVID-19 cases, their growth rates are notably lower than Amazon's Rakuten shows only a slight response to COVID-19 The findings demonstrate a very low P-value (P-value = 0.000 < 0.05), confirming a significant effect of COVID-19 on stock prices at the 5% significance level Additionally, all regression models indicate no multicollinearity issues, as evidenced 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 illustrates the relationship between total COVID-19 cases and stock prices, revealing that while the P-values of the estimators are statistically significant, the overall effect of total COVID-19 cases on the stock prices of the five companies is relatively minor when compared to the impact 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
Our analysis explores the impact of daily COVID-19 deaths on the stock prices of selected companies, revealing significant correlations with very small P-values (P-value < 0.05) The data indicates that daily death cases significantly influence stock prices, alongside daily new COVID-19 cases Furthermore, our estimations suggest that total COVID-19 death cases also affect stock prices, albeit with a weaker influence compared to total confirmed cases and daily new deaths Notably, the results show a highly reliable P-value (P-value < 0.000), confirming that total COVID-19 death cases still exert a considerable impact on stock prices.
Variable Beta Newy-west standard error T value P value
Table Newey-West result of the effect of Total confirmed deaths 5
Research indicates that COVID-19 positively impacted the stock prices of major e-commerce companies, including Amazon, Alibaba, JD, Rakuten, and eBay, with high statistical significance Notably, Amazon's stock was most affected by COVID-19 case numbers Consequently, all hypotheses were confirmed, demonstrating that the pandemic influenced the stock prices of all selected businesses.
As COVID-19 cases rise globally, countries are closing their borders to protect public health, which restricts movement and affects daily necessities In this challenging environment, E-commerce companies are flourishing by delivering food and essential products to customers while adhering to social distancing guidelines.
Limitations
Our model currently suffers from a lack of independent variables, which may lead to increased disturbances and overlook key factors influencing stock price growth, such as additional financial metrics and trader demand Despite this limitation, our Ordinary Least Squares (OLS) models demonstrate relatively acceptable R-squared values, with the lowest at 0.2384, suggesting that we have identified two significant variables impacting the stock prices of the selected companies.
A significant limitation of our model is the overgeneralization caused by focusing solely on the stock prices of five major companies in the global e-commerce industry to assess the impact of COVID-19 This approach may lead to biased conclusions, as the pandemic's effects could vary greatly for smaller companies, potentially contradicting our findings Factors such as size, market share, and business capabilities significantly influence how firms respond to COVID-19 Neglecting smaller companies in our analysis risks producing skewed results.
Relying solely on stock prices as the primary indicator of a company's financial performance presents limitations, as other metrics like net profit, cash flow, and expenses may offer more accurate insights Unfortunately, due to the scarcity of data points and the infrequent occurrence of COVID-19 in our model, we were unable to compile a comprehensive database using these alternative statistics Consequently, stock prices must be utilized to assess the financial performance of the selected companies, as they provide a sufficient daily volume of data to create a robust database that effectively illustrates the impact of COVID-19.
This research paper serves as a foundational resource for future studies exploring the impact of COVID-19 on e-commerce and various other industries, despite its inherent limitations.
Conclusion and Recommendation
Conclusion
The COVID-19 pandemic has significantly shifted consumer behavior, leading to an increase in online shopping as social distancing measures compel people to stay home 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 resulted in supply chain instability, profoundly impacting the E-commerce market as it adapts to changing global customer needs.
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, thanks to its contactless nature and global reach, making it ideal for quarantine conditions Unlike traditional retail, E-commerce allows users to shop without physical interaction, enhancing safety during this health crisis The increasing awareness of COVID-19's health risks has led more people to stay home, shifting their work, hobbies, and shopping online This surge in homebound consumers has significantly boosted E-commerce profitability However, the pandemic has negatively impacted supply chains, causing delays in shipping and delivery 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 and enhancing customer experience during order fulfillment is crucial for overcoming supply chain challenges that hinder E-commerce growth The COVID-19 pandemic has led to delays in logistics and postal services across many countries due to new safety guidelines and government recommendations Businesses should adopt specific strategies to address these issues effectively.
Streamlining internal processes is essential to avoid unnecessary complications that can lead to frustration and inefficiency If your current workflow is lengthy and cumbersome, it may be beneficial to reassess and develop a simpler, more efficient system from the ground up Prioritizing clarity and speed in your processes can enhance productivity and reduce employee stress.
- 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, can significantly improve customer satisfaction and engagement.
To maintain engagement with diverse communities, businesses should explore creative channels beyond their current strategies, especially in challenging times when support and connection are crucial Recommended channels include search engines like Google and Bing, as well as comparison shopping platforms such as Idealo and PriceGrabber Email marketing is particularly effective for reaching potential customers on the verge of purchasing; for instance, targeting users who abandon shopping carts with reminders or offering birthday discounts can drive conversions By diversifying their outreach efforts, e-commerce businesses can effectively connect with more target audiences and achieve their objectives.
TABLES AND FIGURES
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
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
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
0.0003 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: -
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
AMZN BABA EBAY RKUNY JD
Database of company’s interest over time: