Introduction
The E-commerce sector has experienced significant growth due to transformative innovations such as the Internet and smartphones, rendering traditional commercial methods inefficient and limited by geographical constraints In contrast, E-commerce operates digitally, allowing it to reach a global audience This integration into the world's development has become increasingly vital However, the COVID-19 pandemic has profoundly impacted personal lives and various industries, leading to decreased revenues, workforce adjustments, and disrupted supply chains, which have taken a considerable toll on businesses worldwide.
The COVID-19 pandemic has led to widespread speculation about its impact on the e-commerce industry, one of the most significant sectors in our society This article examines the daily COVID-19 cases and deaths throughout 2020 to analyze their effects on e-commerce trends and consumer behavior.
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 Firstly, it helps us determine whether these businesses have experienced positive or negative effects from the pandemic Secondly, this understanding allows us to anticipate necessary strategies to mitigate potential threats and leverage opportunities for growth For example, we need to examine how fluctuations in COVID-19 cases and fatalities have influenced stock prices If we discover that the pandemic has positively impacted these companies' financial performance, it suggests that they are thriving despite the crisis In such a scenario, we must identify actionable steps to enhance success and adapt to the ongoing challenges presented by the pandemic.
This analysis is grounded in the methodology of Nguyễn Hoàng Nam's 2021 research, which quantified the impact of COVID-19 on Vietnam's economic activities through six hypotheses Additionally, we draw inspiration from the work of Hung-Hao Chang and Chad Meyerhoefer (2020), who employed Ubox, Taiwan's largest business-to-consumer agri-food e-commerce platform, as a dependent variable to assess the pandemic's influence on the demand and growth of online food platforms.
Literature review
Previous studies
The COVID-19 pandemic significantly slowed financial activities, leaving many businesses unprepared However, it accelerated the shift to E-commerce as companies adapted to the necessity of moving online This report evaluates the impact of the pandemic on E-commerce, featuring surveys of businesses and consumers, insights from the eTrade for All initiative, analyses by various multilateral organizations, and inquiries conducted by the United Nations.
Luis Varona and Jorge R Gonzales (2021) conducted a study that analyzed 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, including 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 Utilizing the ARDL model, their findings revealed a significant negative impact of COVID-19 on Peru's economy Additionally, Akbulaev et al (2020) explored the broader economic implications of the pandemic, examining its effects on production, employment, imports, and exports, as well as the government's support for producers during mandatory quarantine measures.
Last but not least, the study by Prince Asare Vitenu-Sackey and Richard Bar
A 2021 study assessed the impact of the COVID-19 pandemic 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 adversely affected poverty alleviation and economic growth, while recorded deaths were associated with positive effects on 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 in precarious situations and creating uncertainty around economic and social policies It has negatively affected various industries, particularly finance, and has led to volatile changes in globalization and global health, influencing mobility, trade, and travel As countries implement stay-at-home orders, social distancing measures, and nationwide shutdowns, the world economic landscape is undergoing significant transformations.
In 2020, the COVID-19 pandemic severely disrupted global financial progress, with development restrictions impacting economic activities across various regions The effects of COVID-19 varied significantly depending on the timing of outbreaks, infection rates, and each country's economic conditions Developing and least developed countries faced heightened vulnerability to global economic downturns Conversely, some nations in the Asia-Pacific region experienced 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 development, virologists and economists can only speculate about what lies ahead.
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
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 reliable data from Worldometer.com, the confirmed COVID-19 cases and death toll for the year 2020 were recorded based on validated testing, distinguishing them from suspected cases that only exhibit early symptoms Deaths were confirmed immediately following the passing of COVID-19 patients, and some confirmed death cases involved individuals who were already deceased when testing detected the presence of the virus.
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 online food platforms by utilizing financial statistics from Ubox, Taiwan's largest business-to-consumer agri-food e-commerce platform They created a comprehensive panel data set from their extensive database, allowing them to effectively estimate the pandemic's effects on Ubox's performance and derive conclusions regarding its influence on the industry.
This study focuses on the impact of COVID-19 on the E-commerce industry by analyzing five leading companies: Amazon, Alibaba, Jingdong, eBay, and Rakuten, ranked from highest to lowest These companies serve as representatives of the industry for our research, allowing us to draw conclusions about the overall effects of the pandemic We will collect data from November 1, 2019, to December 31, 2020, to include stock price growth during the pre-COVID period for more accurate analysis.
During disease outbreaks, many countries enforce mandatory social distancing, forcing people to stay home and limiting their ability to shop in stores and markets heavily affected by the coronavirus Consequently, there has been a significant shift towards online shopping through various e-commerce platforms Our research analyzed the search volume data for selected companies on Google, highlighting this trend.
Due to the Nasdaq stock market's closure on weekends, there will be gaps in the data timeline, particularly from November 2019 to April 2021, when it was the most popular market By using the Google Trends extension, which measures internet users' interest in specific topics or search terms over time, we analyzed the interest levels for the selected companies during this period.
The rising percentage of time that companies attract high interest indicates their growing popularity, which enhances their appeal to potential buyers 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-19has 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 developed our own hypotheses to assess the impact of COVID-19 on the global e-commerce industry These hypotheses will form the basis for the conclusions and findings presented in this paper.
Interest over time is measured by comparing search interest to the highest point on a chart for a specific region and timeframe A score of 100 indicates peak popularity for a term, while a score of 50 signifies that the term is half as popular A score of 0 indicates insufficient data for the term (Source: Google Trends)
-H1: COVID-19has influenced Amazon’s financial development
-H2: COVID-19has influenced Alibaba’s financial development
-H3: COVID-19has influenced Jingdong’s financial development
-H4: COVID-19has 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, indicative of their business performance, and two factors: COVID-19 cases or deaths and the company's interest over time The significance of the explanatory factors was assessed using 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 a higher likelihood of rejecting the null hypothesis and affirming the variable's importance Additionally, various tests were conducted to identify potential issues within the models and data, 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 results of the research.
Model
We utilized a panel database, detailed in Appendix A, to examine the impact of COVID-19 on the selected companies, focusing on its effect on their stock prices To enhance the accuracy of our model, we incorporated the companies' online interest over time into our analysis.
The Ordinary Least Squares (OLS) estimation model is:
In this model, Y_i denotes the daily closing stock price of the ith company during 2020 The explanatory variables include the number of daily new confirmed cases (nc), total confirmed cases (ttc), daily new confirmed deaths (nd), and total deaths (ttd) Additionally, the variable Int_i represents the interest of the ith company over time.
Result
Pearson correlation coefficients
The Pearson correlation coefficients method reveals statistically significant correlations between all dependent and explanatory variables, as shown 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 statistical significance We excluded any results that included insignificant variable coefficients due to their unreliability.
We conducted the Variance Inflation Factor (VIF) test to assess potential multicollinearity issues in our models With all VIF values being less than 5, we can confidently conclude that multicollinearity does not affect our models.
Our analysis revealed that the models did not pass the White test for heteroskedasticity and the Durbin-Watson test for autocorrelation Specifically, the p-values were below 0.05, indicating significant heteroskedasticity, while the d-statistic fell 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 analysis aims to explore the correlation between daily new confirmed COVID-19 cases and the stock performance of five specific 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, highlighting 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 increases with rising COVID-19 cases, their growth is less pronounced than that of Amazon Rakuten's stock appears to be only slightly affected by the pandemic The results indicate a strong correlation, with a P-value of 0.000, confirming COVID-19's impact on stock prices at a 5% significance level Additionally, all regression models show 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 these estimators are statistically significant, 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 evaluates the impact of daily COVID-19 deaths on the stock prices of selected companies, revealing significant findings The results, detailed in the accompanying table, indicate that daily death counts have a substantial effect on stock prices, with P-values below 0.05 Additionally, when analyzing the relationship between total COVID-19 death cases and stock prices, we anticipate similar outcomes to those observed with total confirmed cases As shown in Table 14, while the influence of total COVID-19 death cases on stock prices is not as pronounced as that of total confirmed cases or daily new deaths, it remains significant, evidenced by a highly reliable P-value of less than 0.000.
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 platforms, including Amazon, Alibaba, JD, Rakuten, and eBay, with strong statistical significance Notably, Amazon's stock exhibited the highest sensitivity to COVID-19 case fluctuations All hypotheses were confirmed, demonstrating that the pandemic influenced the stock prices of all selected businesses.
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's limited number of independent variables may lead to increased disturbance, indicating that we might have overlooked important factors influencing stock price growth, such as additional financial metrics and trader demand Unfortunately, we could not identify other relevant daily variables, which is a limitation of our model However, the R-squared values of our OLS models are generally acceptable, with the lowest being 0.2384, suggesting that we have successfully identified two key variables that significantly impact the stock prices of the selected companies.
A significant limitation of our study is the overgeneralization resulting from our focus on the stock prices of five major companies in the global E-commerce sector to assess the impact of COVID-19 This approach may introduce bias, as the relationship between these companies and the pandemic may not accurately reflect the broader industry's experience, particularly for smaller firms Factors such as size, market share, and business capabilities significantly influence how companies are affected by COVID-19 Consequently, neglecting smaller businesses can lead to skewed results in our analysis.
Relying solely on stock prices as the primary indicator of a company's financial performance poses limitations to our model While other financial metrics, such as net profit, cash flow, and expenses, may provide a more accurate assessment of a company's health, we faced challenges in compiling a comprehensive database due to the limited number of observations and the infrequent occurrence of COVID-19 in our dataset Consequently, we must use stock prices to evaluate the financial performance of the selected companies, as they offer a sufficient volume of daily data, enabling us 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 on 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 home Many suppliers are experiencing a decline in leisure purchases while facing increased demand for essential items like toiletries and basic supplies This disruption in supply chains, coupled with evolving global consumer needs, has profoundly affected the E-commerce market.
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 thrived, demonstrating resilience and even growth compared to pre-pandemic levels Its contactless nature and global reach make it ideally suited for the quarantine lifestyle, allowing users to shop safely without physical interaction As awareness of COVID-19's health risks has increased, more individuals are staying home and shifting their activities online, including work, hobbies, and shopping This surge in homebound consumers has significantly boosted E-commerce profitability, although the sector has faced supply chain disruptions due to the pandemic Nonetheless, the lack of alternatives has kept consumers actively purchasing online.
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 addressing the challenges posed by the supply chain on E-commerce growth Many countries have experienced delays in logistics and postal services due to COVID-19 safety guidelines and government recommendations To navigate these challenges effectively, businesses should adopt specific strategies aimed at optimizing their shipping processes.
Streamlining internal processes is essential to avoid complicating simple tasks, which can lead to inefficiencies and frustration If your current workflow is lengthy and cumbersome, it may be beneficial to reevaluate and redesign it from the ground up, focusing on creating a more straightforward, faster, and effective system.
- 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
To enhance the online shopping experience, it is essential to offer reliable and helpful package tracking information Providing customers with multiple tracking options—such as on-site tracking, direct links to the carrier's website, and mobile tracking capabilities—greatly improves their overall satisfaction and convenience.
To maintain engagement with diverse communities, businesses should explore creative strategies across various channels beyond their current usage, especially during uncertain times when connection and support are crucial Effective channels include search engines like Google and Bing, as well as comparison shopping engines such as Idealo and PriceGrabber Additionally, email marketing proves beneficial for targeting specific behaviors, such as sending reminders to customers who abandon their shopping carts or offering discounts to those with upcoming birthdays By diversifying their outreach efforts, e-commerce companies can effectively connect with more target audiences and achieve their business 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
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
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
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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: