Complex Risk Analysis of E-Commerce Companies Related to COVID 19

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Complex Risk Analysis of E-Commerce Companies Related to COVID 19
From the Edited Volume
Edited By:
Ebrahim Mazaheri
Content

ABSTRACT

The COVID-19 pandemic has a strong influence on the prices of all financial instruments. Financial markets had shivered at the end of January 2020 and crashed in the middle of March 2020. The shock was extremely forceful. COVID-induced shock hit almost all assets of all industries. Correspondingly, the shock affected investment portfolio management which led to decreasing portfolio value. Meanwhile, different assets have different dynamics of passing such a turbulent period. Does it necessary to change the asset allocation design of investment portfolios? This question became an actual one for individual and institutional investors. This paper aims to measure the risk of e-commerce company’s stocks from the COVID-induced shock. Two hypotheses were put forward in our research. First hypothesis conjectures differences of shock parameters for e-commerce companies’ stocks and S&P500. Especially, it was supposed differences in the renewal level. Second, our hypothesis focuses on the verification of the assumption that risk is higher after-shock than before the shock. In general, this is a typical effect and we have tried to estimate the level of such risk increasing. The shock of the stock market caused by COVID-19, in general, is characterized by a sharp decline and then a relatively slow rise. The post-shock period is characterized by high volatility.

To calculate the characteristics of the shock, three periods were identified: – 01.07.2019 – 15.01.2020 – pre-shock period; 16.01.2020 – 31.03.2020 – shock period; and 01.04.2020 – 14.10.2020 – post-shock period.

Keywords: E-commerce, Risk Measurement, COVID, Shock, Portfolio Management, Investment

1. Introduction

The COVID-19 pandemic has significantly affected all markets around the world. The e-commerce market has also undergone tremendous changes. Particularly noticeable were the changes in financial markets, which began a marked decline in late January and collapsed in mid-March 2020. The shock was extremely strong. The crisis caused by COVID has affected almost all assets of all industries. This caused problems in investment portfolio management, which led to a decrease in the value of portfolios. However, different assets have different dynamics of the crisis period. We investigate how e-commerce companies behaved during the shock period, and how the recovery took place after the shock. Two hypotheses were put forward in our research. First hypothesis conjectures differences of shock parameters for e-commerce companies’ stocks and S&P500. Especially, it was supposed differences in the renewal level. Second, our hypothesis focuses on the verification of the assumption that risk is higher aftershock than before the shock. In general, this is a typical effect and we have tried to estimate the level of such risk increasing.

The shock of the stock market caused by COVID-19, in general, is characterized by a sharp decline and then a relatively slow rise. The post-shock period is characterized by high volatility.

We used a comprehensive approach to risk analysis and assessment. Today, in the modern theory of financial risks, there are different approaches to measuring risk. Each approach focuses on one or another property of the multifaceted concept of “risk”. We used three approaches to risk assessment. The first approach is the classic one, which is to measure risk within the framework of variability. The second approach considers the risk in terms of losses in a negative situation. The importance of this approach is explained by the use of the regulatory risk indicator Value-at-Risk (VaR) and the agreed risk indicator Conditional Value-at-Risk (CVaR). The third approach is based on sensitivity to conception. It is logical to use the sensitivity analysis for the S & P500. The result of applying such an integrated approach is a generalized assessment of changes in risk characteristics. This study provided an opportunity to better understand the mechanisms of investment risk.

2. Risk Measurement Conception

Modern risk theory uses different approaches to risk assessment and analysis: quantitative and qualitative analysis, assessment of investment assets in particular and the portfolio as a whole, volatility analysis, assessment of the probability of adverse situations, and many others.In this study, we use several approaches that together provide a comprehensive risk assessment (Tiwari, 2017; Kaminskyi & Nehrey, 2019).

Risk assessment supposes to introduce mapping µ which each return of investment asset R (interpreting as a random variable) corresponds to some non-negative number µ(R) โธฆ [0; +∞]. The return of investment asset (in this paper – e-commerce companies’ stocks) over a period of time [t; t+1] will be expressed through the formula:

Rt,t+1 = (Pt+1 – Pt) / Pt                                                  (1)

where Pt and Pt+1 prices of shares in USD at times t and t+1 correspondingly. Rt,t+1 will be a random variable, because the future price Pt+1 is unknown. Thereafter R which reflects return through time is also a random variable. Mapping μ which corresponds to some rules interpret as risk measuring.

There are many measures of investment risk present which formalize in mapping µ different logic of risk interpreting (Szegö, 2004). In our research, we have divided risk measuring into three conceptual approaches:

  • Variability approach. Such an approach is based on the measurement of returns variability (volatility). This approach goes back to the papers of (Markowitz, 1971) and underlies the models of modern portfolio theory. Critiques of it using in the non-transparency connection between variability indicators and real losses.
  • Losses in a negative situation. This more practical and regulative approach. It focuses on measuring possible losses and fulfills capital requirements.
  • Sensitivity approach. According to such an approach, the risk is measured as the rate of response for occurring some factors.

Each of the abovementioned approaches had its pros and cons. Our point of that investment risk should be estimated by all these conceptual approaches. It provides a multifaceted understanding of investment risk.

Examples of coherent risk measures are Conditional Value-at-Risk (considered introduced below) (Babenko & Syniavska, 2017; Babenko at al., 2018) and T. Fischer measure (Fischer, 2003). It is necessary to note, that the presented approach for coherency is not unique. Other approaches of coherency are considered in (Kaminskyi, 2007).

The second block of risk measurement in the portfolio aspect corresponds to estimate interrelations of returns of different asset classes. It can be estimated as average correlation, reducing the value of chosen risk measure for a naïve diversified portfolio or risk value for the portfolio with minimum risk. Below we try to realize these ideas for e-commerce companies.

2.1. Risk Assessment During the Shock Period

Financial shocks are unique events that occur quite rarely and unexpectedly and have a significant impact on all markets. In such situations, classical approaches to risk theory often give inadequate risk assessments. Therefore, in times of shock, it is advisable to use slightly different risk assessments. To analyze the risk, we identified three periods: pre-shock, shock period, and recovery period aftershock (post-shock). We took the first and third periods of the same duration, to be able to adequately compare them. Thus, 01.07.2019 – 15.01.2020 – pre-shock period; 16.01.2020 – 31.03.2020 – shock period; and 01.04.2020 – 14.10.2020 – post-shock period.

The shock caused by COVID-19 has classic characteristics: it took the form of a “tick” sign. The onset of the shock was characterized by a gradual fall in asset prices, followed by a sharp and deep fall. A sharp drop occurred on March 17, 2020. After that, a gradual rise in prices began. Moreover, first, after the maximum fall, there is a “rollback”, and then the dynamics stabilize.

Given such market characteristics, the application of classical risk assessments is not possible, as a sharp decline in a short period of time will give an inadequate risk assessment. Therefore, we use an approach that estimates two quantities: the depth of the fall and the recovery over a period of time. The value that characterizes the depth of the fall is calculated as the ratio of the lowest price to the average price for the pre-shock period. Recovery for the post-shock period is calculated based on the average stabilization price after the maximum reduction.

The logic of calculating the parameters is shown in Figure 1 for the S & P500 index.

Figure 1. Shock risk assessment for S&P500.

Standard approaches were used to assess the risk before and after the shock period. The economic meaning of the study is to assess changes in risk as a result of shock.

3. Materials and Data

Our sample of e-commerce companies was created based on the volume of their shares, revenue, and a number of employees. (Table 1). We used the investing.com database (investing.com).

Table 1.
General characteristics of e-commerce companies

Company

Ticker Volume Revenue

Employees

Alibaba

BABA 9,422,891 12.86B

50092

Alphabet

GOOGL 2,740,000 171.7B

75606

Amazon

AMZN 3,260,676 347.95B

541900

Apple Inc.

AAPL 81,606,344 274.52B

116000

Baidu

BIDU 3,873,887 2.33B

43500

Best Buy Co Inc

BBY 2,973,232 45.52B

125000

Booking Holdings Inc

BKNG 342,349 8.9B

24500

Costco Wholesale Corp

COST 2,684,087 166.76B

126000

CVS Health Corp

CVS 6,038,275 266.04B

158000

Ebay

EBAY 7,086,818 12.6B

12600

Facebook

FB 10,727,007 78.98B

20658

H & M Hennes & Maurtiz AB

HMB 3,031,870 196.18B

148000

Home depot

HD 5,335,643 125.63B

406000

Kering SA

PRTP 151,178 13.62B

38000

LVMH Moet Hennessy Louis Vuitton SE

LVMH 323,337 46.98B

120000

Microsoft

MSFT 23,249,586 147.11B

114000

Nike Inc.

NIKE 6,951,563 37.34B

62600

Target Corporation

TGT 2,680,796 88.62B

341000

TJX Companies

TJX 4,290,398 33.4B

235000

Walgreens Boots Alliance Inc

WBA 6,204,188 139.54B

450000

Walmart Inc WMT 6,901,022 548.74B

220000

Alibaba Group Holding Limited (BABA) is a holding company that provides the technology infrastructure and marketing reach to help merchants, brands, and other businesses to leverage the power of new technology to engage with users and customers to operate. The Company operates four business segments: the Core Commerce segment provides China and International retail, China and International wholesale, logistics services, and local consumer services; the Cloud Computing segment provides a complete suite of cloud services, including database, storage, network virtualization services, big data analytics, and others; the Digital Media and Entertainment segment provides consumer services beyond the core business operations; the Innovation Initiatives and others segment are to innovate and deliver new services and products.

Alphabet Inc. (GOOGL) is a holding company. The Company’s businesses include Google Inc. (Google) and its Internet products, such as Access, Calico, CapitalG, GV, Nest, Verily, Waymo, and X. The Company’s segments include Google and other bets. The Google segment includes its Internet products, such as Search, Ads, Commerce, Maps, YouTube, Google Cloud, Android, Chrome, and Google Play, as well as its hardware initiatives. The Google segment is engaged in advertising, sales of digital content, applications and cloud offerings, and sales of hardware products. The Other Bets segment is engaged in the sales of Internet and television services through Google Fiber, sales of Nest products and services, and licensing and research and development (R&D) services through Verily. It offers Google Assistant, which allows users to type or talk with Google; Google Maps, which helps users navigate to a store, and Google Photos, which helps users store and organize all of their photos.

Amazon.com, Inc. (AMZN) offers a range of products and services through its Websites. The Company’s products include merchandise and content that it purchases for resale from vendors and those offered by third-party sellers. It also manufactures and sells electronic devices. It operates through three segments: North America, International, and Amazon Web Services (AWS). Its AWS products include analytics, Amazon Athena, Amazon CloudSearch, Amazon EMR, Amazon Elasticsearch Service, Amazon Kinesis, Amazon Managed Streaming for Apache Kafka, Amazon Redshift, Amazon QuickSight, AWS Data Pipeline, AWS Glue, and AWS Lake Formation. AWS solutions include machine learning, analytics, and data lakes, Internet of Things, serverless computing, containers, enterprise applications, and storage. Also, the Company provides services, such as advertising. It also offers Amazon Prime, a membership program that includes free shipping, access to streaming of various movies and television (TV) episodes.

Apple Inc. (AAPL) designs, manufactures, and markets mobile communication and media devices, personal computers, and portable digital music players. The Company sells a range of related software, services, accessories, networking solutions, and third-party digital content and applications. The Company’s segments include the Americas, Europe, Greater China, Japan, and the Rest of Asia Pacific. Its products and services include iPhone, iPad, Mac, iPod, Apple Watch, Apple TV, a portfolio of consumer and professional software applications, iPhone OS (iOS), OS X and watchOS operating systems, iCloud, Apple Pay, and a range of accessory, service and support offerings.

Baidu, Inc. (BIDU) is a Chinese language Internet search provider. The Company offers a Chinese language search platform on its Baidu.com Website that enables users to find information online, including Webpages, news, images, documents, and multimedia files, through links provided on its Website. In addition to serving individual Internet search users, the Company provides a platform for businesses to reach customers. Its business consists of three segments: search services, transaction services, and iQiyi. Search services are keyword-based marketing services targeted at and triggered by Internet users’ search queries, which mainly include its pay-for-performance (P4P) services and other online marketing services. Its transaction services include Baidu Nuomi, Baidu Takeout Delivery, Baidu Maps, Baidu Connect, Baidu Wallet, and others. iQiyi is an online video platform with a content library that includes licensed movies, television series, cartoons, variety shows, and other programs.

Best Buy Co., Inc. (BBY) is a provider of technology products, services and solutions. The Company offers products and services to the customers visiting its stores, engaging with Geek Squad agents, or using its Websites or mobile applications. It has operations in the United States, Canada and Mexico. The Company operates through two segments: Domestic and International. The Domestic segment consists of the operations in all states, districts and territories of the United States, under various brand names, including Best Buy, bestbuy.com, Best Buy Mobile, Best Buy Direct, Best Buy Express, Geek Squad, Magnolia Home Theater, and Pacific Kitchen and Home. The International segment consists of all operations in Canada and Mexico under the brand names, Best Buy, bestbuy.com.ca, bestbuy.com.mx, Best Buy Express, Best Buy Mobile and Geek Squad. As of December 31, 2016, the Company operated 1,200 large-format and 400 small-format stores throughout its Domestic and International segments.

Booking Holdings Inc. (BKNG), formerly The Priceline Group Inc., is a provider of travel and restaurant online reservation and related services. The Company, through its online travel companies (OTCs), connects consumers wishing to make travel reservations with providers of travel services across the world. It offers consumers an array of accommodation reservations (including hotels, bed and breakfasts, hostels, apartments, vacation rentals and other properties) through its Booking.com, priceline.com and agoda.com brands. Its other brands include KAYAK, Rentalcars.com and OpenTable, Inc. (OpenTable). As of December 31, 2016, Booking.com offered accommodation reservation services for over 1,115,000 properties in over 220 countries and territories on its various Websites and in over 40 languages, which included over 568,000 vacation rental properties.

Costco Wholesale Corporation (COST) is engaged in the operation of membership warehouses in the United States and Puerto Rico, Canada, the United Kingdom, Mexico, Japan, Australia, Spain, and through its subsidiaries in Taiwan and Korea. As of August 28, 2016, the Company operated 715 warehouses across the world. The Company’s average warehouse space is approximately 144,000 square feet. The Company’s warehouses on average operate on a seven-day, 70-hour week. The Company offers merchandise in various categories, which include foods (including dry foods, packaged foods, and groceries); sundries (including snack foods, candy, alcoholic and nonalcoholic beverages, and cleaning supplies); hardlines (including appliances, electronics, health, and beauty aids, hardware, and garden and patio); fresh foods (including meat, produce, deli and bakery); soft lines (including apparel and small appliances), and other (including gas stations and pharmacy).

CVS Health Corporation (CVS), together with its subsidiaries, is an integrated pharmacy healthcare company. The Company provides pharmacy care for the senior community through Omnicare, Inc. (Omnicare) and Omnicare’s long-term care (LTC) operations, which include distribution of pharmaceuticals, related pharmacy consulting, and other ancillary services to chronic care facilities and other care settings. It operates through three segments: Pharmacy Services, Retail/LTC, and Corporate. The Pharmacy Services Segment provides a range of pharmacy benefit management (PBM) solutions to its clients. As of December 31, 2016, the Retail/LTC Segment included 9,709 retail locations (of which 7,980 were its stores that operated a pharmacy and 1,674 were its pharmacies located within Target Corporation (Target) stores), its online retail pharmacy Websites, CVS.com, Navarro.com and Onofre.com.br, 38 onsite pharmacy stores, its long-term care pharmacy operations and its retail healthcare clinics.

eBay Inc. (EBAY), is a global commerce company. The Company’s technologies and services are designed to give buyers choice and relevant inventory and enable sellers worldwide to offer their inventory for sale, virtually anytime and anywhere. Its multi-screen approach offers downloadable, applications for iPhone operating systems (iOS) and Android mobile devices that allow access to some of its websites and vertical shopping experiences. The Company’s platforms are accessible through a traditional online experience, mobile devices, and its application programming interfaces (APIs). Its segments include Marketplace and Classifieds. Marketplace includes its online marketplace, its localized counterparts, and the eBay suite of mobile applications. Classifieds include a collection of brands such as Mobile.de, Kijiji, Gumtree, Marktplaats, and eBay Kleinanzeigen.

Facebook, Inc. (FB) is focused on building products that enable people to connect and share through mobile devices, personal computers, and other surfaces. The Company’s products include Facebook, Instagram, Messenger, WhatsApp, and Oculus. Facebook enables people to connect, share, discover and communicate with each other on mobile devices and personal computers. Instagram enables people to take photos or videos, customize them with filter effects, and share them with friends and followers in a photo feed or send them directly to friends. Messenger allows communicating with people and businesses alike across a range of platforms and devices. WhatsApp Messenger is a messaging application that is used by people around the world and is available on a range of mobile platforms. It’s Oculus virtual reality technology and the content platform offers products that allow people to enter an interactive environment to play games, consume content and connect with others.

H & M Hennes & Mauritz AB (HMB) is a Sweden-based company active in the clothing industry. It operates under such brand names, as H&M, H&M Home, COS, Monki, Weekday, Cheap Monday, and & Other Stories. It is engaged in the design, manufacture, and marketing of clothing items and related accessories. The Company’s product range comprises clothing, including underwear and sportswear, for men, women, children, and teenagers, as well as cosmetic products, accessories, footwear, and home textiles. The Company offers its products in many branded stores spread across over 40 markets. Additionally, the Company offers online and catalog sales in Sweden, Norway, Denmark, Finland, the Netherlands, Germany, Austria, and the United Kingdom, among others.

Home Depot, Inc. (HD) is a home improvement retailer. The Company offers its customers an assortment of building materials, home improvement products, lawn and garden products, and decor products and provides many services, including home improvement installation services and tool and equipment rental. It operates approximately 2,291 Home Depot stores located throughout the United States (U.S.), including the Commonwealth of Puerto Rico and the territories of the U.S. Virgin Islands and Guam; Canada, and Mexico. The Company serves two primary customer groups: do-it-yourself Customers and Professional Customers.

Kering SA (PRTP) is a France-based luxury group. It owns a portfolio of fashion brands, such as Saint Laurent, Gucci, Bottega Veneta, Alexander McQueen, Balenciaga, Boucheron, Brioni, Pomellato, Qeelin, and Ulysse Nardin, among others. The Group manufactures and sells, mostly through managed retail stores, a wide range of products, including leather goods, apparel, accessories, footwear, watches, and jewelry, among others, for man, woman, and child. The Group is active globally.

LVMH Moet Hennessy Louis Vuitton SE (LVMH) is a France-based luxury group active in six sectors: Wines and Spirits, Fashion and Leather Goods, Perfumes and Cosmetics, Watches and Jewelry, Selective Retailing and Other Activities. Wines and Spirits own brands, such as Moet & Chandon, Krug, Veuve Clicquot, Hennessy, and Chteau d’Yquem, among others. Fashion and Leather Goods owns brands, such as Luis Vuitton, Christian Dior, and Givenchy, among others. Perfumes and Cosmetics own brands, such as Parfums Christian Dior, Parfums Givenchy Guerlain, Benefit Cosmetics, Fresh and Make Up For Ever, among others. Watches and Jewelry own brands, including TAG Heuer, Hublo, Zenith, Bulgari, Chaumet, and Fred, among others. Selective Retailing owns the brands DFS, Miami Cruiseline, Sephora, and Le Bon Marche Rive Gauche, among others. Other Activities include lifestyle, culture, and the art brands, such as Les Echos, Royal Van Lent, and Cheval Blanc. The Company is active worldwide.

Microsoft Corporation (MSFT) is a technology company. The Company develops, licenses, and supports a range of software products, services, and devices. The Company’s segments include Productivity and Business Processes, Intelligent Cloud, and More Personal Computing. The Company’s products include operating systems; cross-device productivity applications; server applications; business solution applications; desktop and server management tools; software development tools; video games, and training and certification of computer system integrators and developers. It also designs, manufactures, and sells devices, including personal computers (PCs), tablets, gaming and entertainment consoles, phones, other intelligent devices, and related accessories, that integrate with its cloud-based offerings. It offers an array of services, including cloud-based solutions that provide customers with software, services, platforms, and content, and it provides solution support and consulting services.

NIKE, Inc. (NIKE) is engaged in the design, development, marketing, and selling of athletic footwear, apparel, equipment, accessories, and services. The Company’s operating segments include North America, Western Europe, Central & Eastern Europe, Greater China, Japan, and Emerging Markets. Its portfolio brands include the NIKE Brand, Jordan Brand, Hurley, and Converse. As of May 31, 2016, the Company focused its NIKE brand product offerings in nine categories: Running, NIKE Basketball, the Jordan Brand, Football (Soccer), Men’s Training, Women’s Training, Action Sports, Sportswear (its sports-inspired lifestyle products) and Golf. Men’s Training includes its baseball and American football product offerings. It also markets products designed for kids, as well as for other athletic and recreational uses, such as cricket, lacrosse, tennis, volleyball, wrestling, walking, and outdoor activities. The Company sells a range of performance equipment and accessories under the NIKE Brand name.

Target Corporation (TGT) is a general merchandise retailer selling products through its stores and digital channels. Its general merchandise stores offer an edited food assortment, including perishables, dry grocery, dairy, and frozen items. Its digital channels include a range of general merchandise, including a range of items found in its stores, along with an assortment, such as additional sizes and colors sold only online. Its owned brands include Archer Farms, Market Pantry, Sutton & Dodge, Art Class, Merona, Threshold, Ava & Viv, Pillowfort, Room Essentials, Wine Cube, Cat & Jack, Simply Balanced, and Wondershop. Its exclusive brands include C9 by Champion, Hand Made Modern, Mossimo, DENIZEN from Levi’s, Nate Berkus for Target, Fieldcrest, Kid Made Modern, Genuine Kids from OshKosh, and Liz Lange for Target. As of January 28, 2017, the Company had 1,802 stores across the United States, including 1,535 owned stores, 107 leased stores, and 160 owned buildings on leased land.

The TJX Companies, Inc. (TJX) is an off-price apparel and home fashions retailer in the United States and across the world. The company operates through four segments: Marmaxx, HomeGoods, TJX Canada, and TJX International. T.J. Maxx and Marshalls chains in the United States were collectively the off-price retailers in the United States with a total of 2,221 stores, as of January 28, 2017. The HomeGoods chain was an off-price retailer of home fashions in the United States with 579 stores. The TJX Canada segment operates the Winners, HomeSense, and Marshalls chains in Canada. Winners are the off-price apparel and home fashions retailer in Canada. HomeSense offers home fashions off-price concepts in Canada. The TJX International segment operates the T.K. Maxx and HomeSense chains in Europe. With 503 stores, T.K. Maxx operated in the United Kingdom, Ireland, Germany, Poland, Austria, and the Netherlands.

Walgreens Boots Alliance, Inc. (WBA), is a holding company. The Company is a pharmacy-led health and wellbeing company. The Company operates through three segments: Retail Pharmacy USA, Retail Pharmacy International, and Pharmaceutical Wholesale. The Retail Pharmacy USA segment consists of the Walgreen Co. (Walgreens) business, which includes the operation of retail drugstores, care clinics, and providing specialty pharmacy services. The Retail Pharmacy International segment consists primarily of the Alliance Boots pharmacy-led health and beauty stores, optical practices, and related contract manufacturing operations. The Pharmaceutical Wholesale segment consists of the Alliance Boots pharmaceutical wholesaling and distribution businesses. The Company’s portfolio of retail and business brands includes Walgreens, Duane Reade, Boots, and Alliance Healthcare, as well as global health and beauty product brands, including No7, Botanics, Liz Earle, and Soap & Glory.

Walmart Inc., formerly Wal-Mart Stores, Inc.(WMT), is engaged in the operation of retail, wholesale and other units in various formats around the world. The Company offers an assortment of merchandise and services at everyday low prices (EDLP). The Company operates through three segments: Walmart U.S., Walmart International, and Sam’s Club. The Walmart U.S. segment includes the Company’s mass merchant concept in the United States operating under the Walmart brands, as well as digital retail. The Walmart International segment consists of the Company’s operations outside of the United States, including various retail Websites. The Sam’s Club segment includes the warehouse membership clubs in the United States, as well as samsclub.com. The Company operates approximately 11,600 stores under 59 banners in 28 countries and e-commerce Websites in 11 countries.

4. Results and Discussion

4.1.Measurement of Shock Characteristics

According to the characteristics of the shock, the following parameters are defined:

1) Depth of shock relative to the pre-shock period.

2) The percentage of recovery in the post-shock period.

The depth of the shock was calculated as the ratio of the difference between the minimum share price in the shock period and the average value in the pre-shock period to the average value of the share price in the pre-shock period.

The characteristics of the stock market shock caused by COVID-19 are shown in Figure 2.

Figure 2Shock characteristics for e-commerce companies’ stocks and S&P500

Two observations are interesting. First, the depth of decline for most e-commerce companies was less than 30% (for S&P500 this value is 26%). However, the recovery rate is higher. The second observation is that e-commerce companies did not have a significant recovery rate for all companies more than 75%, and for 16 companies more than 100%, with Apple and Amazon characterized by a recovery of more than 150%.

4.2. The Variability Approach to Risk Measurement

Table 2 present the comparative analysis which characterizes differences in risk measures pre- and post-shock. After the shock e-commerce companies indicate higher values than the S & P500 and increasing risk.

Table 2.
Statistical analysis for risk measures

 

 

Stocks

min max Mean Std skewness kurtosis
Before shock Post-shock Before shock Post-shock Before shock Post-shock Before shock Post-shock Before shock Post-shock Before shock Post-shock
S&P500 -0,030 -0,059 0,019 0,070 0,001 0,002 0,008 0,016 -1,124 -0,202 3,279 3,426
AMZN -0,034 -0,076 0,045 0,079 0,000 0,004 0,012 0,024 0,122 0,100 1,309 0,850
BABA -0,052 -0,059 0,046 0,090 0,002 0,003 0,018 0,023 -0,429 0,420 0,500 1,669
HD -0,054 -0,059 0,044 0,071 0,001 0,003 0,012 0,018 -0,710 0,059 3,862 2,122
WMT -0,033 -0,034 0,061 0,068 0,000 0,002 0,009 0,016 1,578 1,214 12,318 3,324
COST -0,029 -0,034 0,050 0,057 0,001 0,002 0,010 0,013 0,459 0,685 3,578 2,538
PRTP -0,070 -0,067 0,087 0,061 0,001 0,002 0,017 0,021 0,420 0,046 6,757 0,779
BKNG -0,081 -0,084 0,066 0,125 0,001 0,002 0,014 0,027 -1,036 0,851 11,155 3,587
GOOGL -0,035 -0,055 0,096 0,089 0,002 0,002 0,014 0,021 2,119 0,379 15,105 3,221
FB -0,046 -0,083 0,031 0,082 0,001 0,004 0,014 0,026 -0,480 0,374 0,452 1,618
BIDU -0,071 -0,063 0,135 0,078 0,002 0,002 0,025 0,025 1,624 0,460 6,938 1,209
MSFT -0,034 -0,062 0,027 0,074 0,001 0,003 0,011 0,021 -0,527 -0,097 0,761 1,151
AAPL -0,052 -0,080 0,042 0,105 0,003 0,005 0,014 0,025 -0,652 0,110 1,784 2,435
WBA -0,058 -0,080 0,058 0,065 0,000 -0,001 0,016 0,025 0,013 -0,387 2,631 1,058
TGT -0,066 -0,039 0,204 0,127 0,002 0,004 0,025 0,020 4,959 1,954 36,428 9,735
EBAY -0,091 -0,043 0,039 0,065 -0,001 0,005 0,015 0,020 -1,677 0,114 10,054 0,436
LVMH -0,055 -0,058 0,056 0,060 0,001 0,002 0,015 0,018 -0,313 -0,119 2,461 0,886
HMB -0,034 -0,069 0,044 0,107 0,001 0,002 0,014 0,031 0,277 0,675 1,163 1,357
CVS -0,029 -0,064 0,075 0,045 0,002 0,000 0,014 0,017 1,206 -0,318 4,782 0,964
BBY -0,108 -0,073 0,099 0,125 0,002 0,006 0,022 0,028 -0,740 0,662 7,414 3,298
NIKE -0,034 -0,076 0,042 0,088 0,001 0,003 0,013 0,022 -0,191 0,059 0,949 2,832
TJX -0,039 -0,067 0,039 0,126 0,001 0,002 0,014 0,028 -0,189 0,646 0,747 2,007

It is interesting results we can identify by analysis of skewness, which indicates divergence from symmetry. Negative skewness indicates a long-left tail of the distribution or the possibility of larger losses than profits. Positive skewness is a desirable characteristic for risk-averse investors. The motivation of that is based on the expected utility theory (Scott and Horvath, 1980).

From this point of view, e-commerce companies have demonstrated higher positive skewness aftershock than before. Kurtosis indicators were multidirectional for e-commerce companies.

4.3. Risk measurement as losses in a negative situation

This conceptual approach is based on considering measures relating to the interpretation of the “negative situation” for the investor. The most popular in this group is Value-at-Risk (VaR), which presents a quantile of the probability distribution function. This quantile corresponding to some level of safety (it maybe 95%, 99%, or 99.9%). The logic of VaR is based on risk covering. If, for example, VaR orients for 95%, then 5% biggest losses will throw off. VaR will cover maximum losses at the framework of 95% possibilities. Risk measure Conditional Value-at-Risk (CVaR) is based on a generalization of VaR. This is the conditional mathematical expectation of losses higher than VaR (Table 3).

Table 3.
Risk measurement by VaR and CVaR

Stocks VaR CVaR
Before shock Post-shock Before shock Post-shock
S&P500 -0,0135 -0,0232 -0,0204 -0,0358
AMZN -0,0186 -0,0335 -0,0246 -0,0441
BABA -0,0296 -0,0303 -0,0422 -0,0443
HD -0,0202 -0,0249 -0,0288 -0,0384
WMT -0,0081 -0,0175 -0,0186 -0,0265
COST -0,0135 -0,0162 -0,0212 -0,0241
PRTP -0,0219 -0,0327 -0,0366 -0,0457
BKNG -0,0224 -0,0337 -0,0334 -0,0514
GOOGL -0,0069 -0,0280 -0,0245 -0,0443
FB -0,0240 -0,0345 -0,0326 -0,0477
BIDU -0,0230 -0,0340 -0,0408 -0,0474
MSFT -0,0184 -0,0321 -0,0253 -0,0461
AAPL -0,0220 -0,0345 -0,0313 -0,0526
WBA -0,0259 -0,0443 -0,0399 -0,0632
TGT -0,0118 -0,0318 -0,0271
EBAY -0,0281 -0,0276 -0,0389 -0,0373
LVMH -0,0239 -0,0289 -0,0364 -0,0404
HMB -0,0200 -0,0411 -0,0289 -0,0560
CVS -0,0145 -0,0295 -0,0242 -0,0389
BBY -0,0356 -0,0328 -0,0586 -0,0532
NIKE -0,0196 -0,0306 -0,0288 -0,0442
TJX -0,0217 -0,0384 -0,0309 -0,0535

4.4. Risk measurement based on sensitivity approach

Risk measurement in the framework of sensitivity analysis makes it possible to understand the role of systematic and non-systematic risks. We chose to analyze the sensitivity of the S & P500 as a systematic factor in the stock market. The main indicator in the sensitivity analysis is beta coefficients. This approach has been widely used in theoretical models (such as CAPM) and practical estimates of regression returns on assets to certain returns. Thus, the beta ratio indicates an assessment of the risk of including shares of e-commerce companies in the investment portfolio. A value that closes to zero points out to low risk and reverses.  An analysis should provide an answer to the question: How the stock market as a whole affects the return of e-commerce companies?

Table 4.
Regression Analysis

Stocks S&P500 beta coefficient Intercept R2 p-value
Before shock Post-shock Before shock Post-shock Before shock Post-shock Before shock Post-shock
AMZN 0,4784 0,3599 0,0009 0,0008 0,5314 0,2953 0,00000 0,00000
BABA 0,2781 0,3485 0,0002 0,0011 0,4296 0,2545 0,00000 0,00000
HD 0,3750 0,7236 0,0006 -0,0001 0,3352 0,6780 0,00000 0,00000
WMT 0,3916 0,3031 0,0007 0,0018 0,2301 0,0965 0,00000 0,00022
COST 0,3820 0,7025 0,0004 0,0008 0,2551 0,3513 0,00000 0,00000
PRTP 0,1971 0,3292 0,0006 0,0018 0,1814 0,2006 0,00000 0,00000
BKNG 0,3119 0,4090 0,0006 0,0014 0,3093 0,5075 0,00000 0,00000
GOOGL 0,4003 0,6102 0,0000 0,0009 0,5131 0,6504 0,00000 0,00000
FB 0,3820 0,3903 0,0004 0,0008 0,5021 0,4051 0,00000 0,00000
BIDU 0,1483 0,3210 0,0006 0,0015 0,2290 0,2537 0,00000 0,00000
MSFT 0,5801 0,5757 0,0000 0,0008 0,7216 0,6033 0,00000 0,00000
AAPL 0,4217 0,4375 -0,0006 0,0001 0,6123 0,5004 0,00000 0,00000
WBA 0,2287 0,3785 0,0008 0,0029 0,2407 0,3586 0,00000 0,00000
TGT 0,0845 0,3434 0,0006 0,0008 0,0756 0,1871 0,00120 0,00000
EBAY 0,3159 0,3450 0,0010 0,0007 0,3648 0,1987 0,00000 0,00000
LVMH 0,3095 0,4318 0,0004 0,0016 0,3631 0,2578 0,00000 0,00000
HMB 0,1831 0,1890 0,0006 0,0019 0,1042 0,1351 0,00013 0,00001
CVS 0,2656 0,4907 0,0002 0,0023 0,2479 0,2918 0,00000 0,00000
BBY 0,1699 0,4004 0,0005 0,0000 0,2398 0,5078 0,00000 0,00000
NIKE 0,3928 0,4732 0,0002 0,0007 0,4159 0,4283 0,00000 0,00000
TJX 0,3792 0,3676 0,0003 0,0017 0,4567 0,4447 0,00000 0,00000

The main result of the sensitivity analysis is minor changes for e-commerce companies of systematic risks. That is, we can conclude that for e-commerce companies, the systematic risk before and after the shock remained almost at the same level.

5. Conclusion

Thus, the trends emerging in the e-commerce market are intended to expand the activities of enterprises and attract even more customers. This is especially true in the context of the Covid-19 pandemic. Today, almost a third of enterprises in all spheres are implementing e-commerce technologies in their activities. The described trends allow e-commerce to become an integral part of modern society. This confirms the indisputable efficiency of the enterprise’s transition to work on e-commerce technologies and their implementation in their activities, which has become a key vector in the development of the modern economy. E-commerce has become one of its already successfully implemented parts. This area, although implemented in all areas of business, has unlimited development opportunities. Enterprises are moving from the real sector to the virtual one, the efficiency of which is supported by modern trends and built mathematical models.

Based on the study, we came to the following conclusion about the risks of e-commerce companies:

  • The depth of decline for most e-commerce companies was less than 30% (for S&P500 this value is 26%). However, the recovery rate is higher. E-commerce companies did not have a significant recovery rate for all companies more than 75%, and for 16 companies more than 100%, with Apple and Amazon characterized by a recovery of more than 150%.
  • E-commerce companies have demonstrated higher positive skewness aftershock than before. Kurtosis indicators were multidirectional for e-commerce companies.
  • For e-commerce companies, the systematic risk before and after the shock remained almost at the same level.

Thus, the above analysis of the main trends in the context of the Covid-19 pandemic proves the need for the introduction of IT-technologies and the benefits of switching to an e-commerce system. All this will eventually become an integral part of all areas of activity of absolutely any counterparty in the future.

References

Aikman, D., Lehnert, A., Liang, N., & Modugno, M. (2016). Financial vulnerabilities, macroeconomic dynamics, and monetary policy.

Macroeconomic Dynamics, and Monetary Policy (2016-07-07). FEDS Working Paper, (2016-055).

Andreasson, P., Bekiros, S., Nguyen, D. K., & Uddin, G. S. (2016). Impact of speculation and economic uncertainty on commodity markets.

International Review of Financial Analysis,

43, 115-127.

Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk.

Mathematical finance,

9(3), 203-228.

Babenko V., Kulczyk Z., Perevosova I., Syniavska O. and Davydova O. (2019). Factors of the development of international e-commerce under the conditions of globalization. SHS Web of Conferences, 65 (2019) Pp. 10-16. doi:

https://doi.org/10.1051/shsconf/20196504016

Babenko, V., Syniavska, O. (2018). Analysis of the current state of development of electronic commerce market in Ukraine. Technology audit and production reserves, Vol. 5, Nะพ 4(43). doi:

https://doi.org/10.15587/2312-8372.2018.146341

Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020).

Covid-induced economic uncertainty

(No. w26983). National Bureau of Economic Research.

Clapp, J. (2017). Responsibility to the rescue? Governing private financial investment in global agriculture.

Agriculture and human values,

34(1), 223-235.

Clark, B. M., Detre, J. D., D’Antoni, J., & Zapata, H. (2012). The role of an agribusiness index in a modern portfolio.

Agricultural Finance Review.

Fischer, T. (2003). Risk capital allocation by coherent risk measures based on one-sided moments.

insurance: Mathematics and Economics,

32(1), 135-146.

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions.

European Journal of Operational Research,

270(2), 654-669.

Hamilton, J. D., & Wu, J. C. (2015). Effects of indexโ€fund investing on commodity futures prices.

International economic review,

56(1), 187-205.

Hau, H., & Lai, S. (2017). The role of equity funds in the financial crisis propagation.

Review of Finance,

21(1), 77-108.

Holländer, D., Bauer, E., Mrusek, F., & Rotermann, B. (2020). European Banking Study 2020: How COVID might affect Europe’s banks.

Jensen, G. R., & Mercer, J. M. (2011). Commodities as an Investment.

The Research Foundation of CFA Institute Literature Review,

6(2), 1-33.

Johnson, M., Malcolm, B., & O’Connor, I. (2006). The role of agribusiness assets in investment portfolios.

Australasian Agribusiness Review,

14(1673-2016-136791).

Kaminskyi, A. (2006), Modelling of Financial Risks, Kyiv National University Pbl, Kyiv.

Kaminskyi, A., & Nehrey, M. (2019, September). Investment Risk Measurement for Agricultural ETF. In

Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019). Atlantis Press.

Kaminskyi, A., & Versal, N. (2018). Risk Management of Dollarization in Banking: Case of Post-Soviet Countries.

Liu, C., & Arunkumar, N. (2019). Risk prediction and evaluation of transnational transmission of financial crisis based on complex network.

Cluster Computing,

22(2), 4307-4313.

Markowitz, H. (1971).

Portfolio selection. Efficient diversification of investments. By Harry M. Markowitz. Yale University Press.

Martin, S. J., & Clapp, J. (2015). Finance for agriculture or agriculture for finance?

Journal of Agrarian Change,

15(4), 549-559.

Matviychuk, A. (2006). Fuzzy logic approach to identification and forecasting of financial time series using Elliott wave theory.

Fuzzy economic review,

11(2), 51.

Paul, P. (2020). A macroeconomic model with occasional financial crises.

Journal of Economic Dynamics and Control,

112, 103830.

Petajisto, A. (2017). Inefficiencies in the pricing of exchange-traded funds.

Financial Analysts Journal,

73(1), 24-54.

Racicot, F. É., & Théoret, R. (2016). Macroeconomic shocks, forward-looking dynamics, and the behavior of hedge funds.

Journal of Banking & Finance,

62, 41-61.

Rasool, A. (2018). Investing in Agribusiness Stocks and Farmland: A Boom or Bust Analysis.

Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk.

Journal of risk,

2, 21-42.

Skrypnyk, A., & Nehrey, M. (2015, May). The Formation of the Deposit Portfolio in Macroeconomic Instability. In

ICTERI

(pp. 225-235).

Spelta, A., Flori, A., Pecora, N., & Pammolli, F. (2019). Financial crises: uncovering self-organized patterns and predicting stock markets instability.

Journal of Business Research.

Stoll, H. R., & Whaley, R. E. (2010). Commodity index investing and commodity futures prices.

Journal of Applied Finance (Formerly Financial Practice and Education),

20(1).

Szegö, G. P. (Ed.). (2004).

Risk measures for the 21st century

(Vol. 1). New York: wiley.

Tiwari, A. K., Albulescu, C. T., & Yoon, S. M. (2017). A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices.

Physica A: Statistical Mechanics and its Applications,

483, 182-192.

Uthayakumar, J., Metawa, N., Shankar, K., & Lakshmanaprabu, S. K. (2020). Financial crisis prediction model using ant colony optimization.

International Journal of Information Management,

50, 538-556.

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