Analysis of Investment Style Changing: The U.S. Stock Market in Apparel Industry During Covid-19

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Analysis of Investment Style Changing: The U.S. Stock
Market in Apparel Industry During Covid-19
Xinyu Jiang†1, Zhuoer Li†2, Chenrui Xu†3
1School of Science, Xi’an Jiaotong-liverpool University (XJTLU), Suzhou, China, Xinyu.Jiang19@student.xjtlu.edu.cn
2School of Mathematics and Statistics University of Sheffield Sheffield, UK, zli192@sheffield.ac.uk
3School of Mathematics University of Edinburgh Edinburgh, UK, C.Xu-43@sms.ed.ac.uk
†These authors contributed equally.
ABSTRACT
The impact that COVID-19 brought to the financial market all over the world was huge, especially on the stock
market. Not only the stock prices became unpredictable, but also the stock traders’ decisions started to change in an
unexpected way. This paper focuses on analyzing the U.S. stock market of clothing industry by using Fama-French
five factor model, and comparing Fama-French five factor model with other competing asset pricing models, for
example Capital Asset Pricing Model. Also, the differences and changes that stock traders makes are discussed in the
U.S. stock market after the pandemic of COVID-19. The result shows that the t stats value of SMB (Small Minus
Big), HML(High Minus Low) and CMA (Conservative Minus Aggressive) becomes significant after the pandemic.
Therefore, it means that the companies with small-scall, value characteristic, and aggressive investment style are more
able to be more favored by the investors during this pandemic.
Keywords-component, Fama-French Model, Apparel Industry, Covid-19, U.S. Stock Market.
1. INTRODUCTION
The uncertainty and unpredictability form the risk in
the financial market. The asset pricing model
demonstrates that how investors optimally allocate their
wealth among numerous risky assets and how they
determine the equilibrium price of various risky assets in
the financial market. Considering the difference between
people’s expectation and the fluctuation of asset price,
the asset pricing model equates the future returns with
the asset prices. The most significant issue in the asset
pricing theory is to maximize the exposure of random
variations that represent the overall movement of risk in
the entire financial system. The asset pricing model
explores the expected rate of return of various securities
and the stock portfolios with different beta value levels,
which reflects the equilibrium relationship between the
risk and expected returns on financial or real assets. The
asset pricing model uses coefficient beta to clearly
indicate the expected return and the systematic risk when
investing in securities. This model has experienced
enormous changes throughout time. Taking COVID-19
as an example, the spread of the virus affected more than
210 countries and territories around the world. The
outbreak of COVID-19 pandemic has taken a heavy toll
on the global economy development. COVID-19 has
caused the disruption of supply chain as well as a sharp
decline in the international trade and commerce, thus
ultimately giving rise to the inevitable economic crisis.
Additionally, COVID-19 has impaired the finance sector
by causing a mild economic depression, such as the rise
in unemployment and the decrease in GDP growth.
Overall, the economic recovery after COVID-19 seems
shaky and sluggish. On the other hand, the COVID-19
epidemic, to some extent, facilitates the amelioration and
reformation of the asset pricing model in the financial
market. The asset pricing model reveals how mode of
production in enterprises or companies changes after the
COVID-19 epidemic. Simultaneously, these changes
offer guidance and reference for investors when electing
securities in the financial market.
Afterwards, scholars conducted studies in many
countries. Connor and Sehgal studied the Fama-French
three-factor stock return model in India. They found the
evidence of the market, size, and mark-to-market factors
that were prevalent in Indian stock returns. The research
figured out that the average cross-sectional return was
explained by the impact on these three factors, not just by
the market factors. It found mixed evidence regarding
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Proceedings of the 2021 International Conference on Public Relations and
Social Sciences (ICPRSS 2021)
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parallel markets, size, and book-market value factors. It
could not find any reliable connection between the
common risk factors of returns and the stock returns.
Overall, the empirical results were quite consistent with
the Fama French three-factor model [1]. Faff studied a
simple test of the Fama and French model by using daily
data. It showed the useful proxies for the Fama and
French factors could be readily constructed from ‘off the
shelf’ style index data. This research chose the
generalized method of moments (GMM) to test the Fama
and French three factor model and used the data of 24
Australian industry portfolios from Datastream
International. Overall, it came up to a question that, did
this wave of doubt over the size premium and more
generally over the Fama and French model herald that a
new ‘twist’ in the asset pricing saga is nigh? But for now,
only the time will tell [2]. Trimech et al studied the
application of Multiscale Fama-French model in French
market. It discussed a multiscale pricing model for the
French stock market by combining wavelet analysis and
Fama-French three-factor model. By doing the research,
the obtained results showed that as the wavelet scale
increased, the explanatory power of the Fama-French
three-factor model was enhanced. In addition, the
relationship between the portfolio returns and the risk
factors (ie, market, size, and value factors) depended, to a
large extent, on the time span under consideration [3].
Czapkiewicz and Skalna studied the CAPM and Fama-
French Models in Poland. They studied the ability of the
Fama-French three-factor model to explain the cross-
sectional difference in the asset returns from December
2002 to January 2010. The research was conducted on
the Warsaw Stock Exchange (WSE). Financial data came
from CEDULA1. CEDULA1 is a daily official
announcement that provides information from WSE. The
system risk and the risk premium were estimated using
the Generalized Method of Moments (GMM), which
took into account the autocorrelation and
heteroscedasticity in the time series. The test proposed by
MacKinlay and Richardson executed the process of
testing whether the intercepts were collectively zero. The
results indicated that three factor models could be used to
explain stock returns: the excess return relative to the
market (market factor), the difference between the
returns of portfolios with large and small capital (size
factor, SMB), and the difference between the two, as
well as the high and low book value versus market value
(HML) portfolio returns [4].
Moreover, Chiah et al studied an empirical
investigation of the Fama-French five-factor model in
Australia. It compared and evaluated the performance of
the Fama-French five-factor and other competing asset
pricing models, such as CAPM and three factor models,
in pricing Australian equities. Also, it investigated
whether the five-factor could explain more asset pricing
anomalies than other competing asset pricing models by
using the primary data source, Share Price & Price
Relative (SPPR) database of the Securities Industry
Research Centre of Asia-Pacific (SIRCA). Overall, the
results showed that the five-factor model was able to
explain more asset pricing anomalies than other
competing asset pricing models, but still had room for
improvement [5]. Gregory et al established and measured
the alternatives of the Fama-French model and Carhart
model for the financial market in the UK. It formed risk
factors, including value-weighted factor components and
various decompositions comprehensively, and
progressively with reference to the recent academic
literature. Moreover, it found out whether such Fama-
French model applied to large-scale enterprises. Overall,
the results demonstrate that such models were
inappropriate and inaccurate to reflect the cross-section
of returns in the UK [6]. Cakici, N. studied the five-
factor model in 23 developed stock markets. It referred to
three factor, four factor and five factor models to explain
the returns on different portfolios. The results showed
that the five-factor model in North America, Europe, and
Global markets resembled to that in the U.S. stock
market. Furthermore, it was discovered that regional
models were superior to global models, which indicated
that markets were not sufficiently integrated. Two new
factors, gross profitability (GP) and investment (Inv.),
failed to add additional power but still remained
important in the financial market [7]. Gregory et al
analyzed the Fama-French and momentum portfolios for
the financial market in UK. Different from other
research, this paper investigates the market by forming
portfolios methodically and systematically on an
appropriate date in the UK. The data is cross-matched
from two official database, then a UK proxy for the
NYSE break points is found in order to form the
portfolios and momentum factor based on website.
Additionally, it cautiously takes portfolios on different
bases into consideration. This research figures out that
any tests of long run abnormal returns in UK ought to be
based on characteristic-matched portfolios [8].
As for the effect of Covid-19, Zhang et al studied
about the financial markets under the global pandemic of
COVID-19. It mainly discussed about the significant
impacts that COVID-19 brought to the global financial
markets, and the substantial increases of volatility were
found in global markets due to the outbreak. The global
stock markets linkages displayed clear difference
patterns before and after the pandemic announcement
and future uncertainties that might be created by policy
responses in the financial markets. The research
compared different countries’ stock market data since the
coronavirus started. It also used graph theory and
minimum spanning tree for the analysis. Overall, the
results showed that global financial market risks had
increased substantially because of the pandemic, the
individual stock market reactions were clearly linked to
the severity of the outbreak in each country, and the
policy reactions to the virus were needed [9].
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In this article, Fama-French five-factor model was
adopted to study the changes in the U.S. stock market
under the new crown epidemic. The Fama-French model
is a relatively reliable asset pricing model and can be
used in many fields. By testing several fields, it is found
that Apparel industry is highly affected by the COVID-
19 epidemic. Therefore, it aims to investigate the effect
of COVID-19 on the clothing industry based on the
numeric analysis of the Fama-French five-factor model.
The investigation will also provide relative investment
examples of the clothing industry in the US stock market
and explain the motivation for the investment change.
2. METHOD
The Capital Asset Pricing Model (CAPM) was
developed by American scholars William Sharpe, John
Lintner, Jack Treynor, and Jan Mossin in 1964.
Developed on the basis of asset portfolio theory and
capital market theory in 2015, it mainly studies the
relationship between the expected rate of return of assets
in the securities market and risky assets, and how the
equilibrium price is formed. It is the backbone of
modern financial market price theory and is widely used
in investment decision-making and corporate financial
management. But CAPM's interpretation of the financial
market, in reality, is not good. Some long-term excess
returns cannot be explained by CAMP. So there is the
Fama-French three-factor model.
In the framework of classic financial theory, the risk
of a single stock is broken down into individual risks
and system risks. The so-called individual risk is the
return and fluctuation of the asset itself. For example,
the income and fluctuation of Apple's own business are
good or bad. System risk is the return and volatility of
the entire system, such as the return and volatility of the
market.The classical financial theory attempts to
describe system fluctuations with mathematical models
and establish the relationship between the fluctuations of
a single stock and the system fluctuations. For example,
in the CAMP model, the description of system risk is the
market portfolio.
Ri= ai+ biRM (1)
At the same time, it can also conclude that classic
financial theory only uses random variables to describe
the risks and returns of a single stock. In fact, the idea
behind it is that the short-term fluctuations of a single
stock are unpredictable (classic efficient market theory),
but in the long run The volatility of the stock will
eventually return to the volatility of the system. In other
words, when a stock has excess returns, market
arbitrageurs will appear to smooth out such excess
returns. From another perspective, the existence of
arbitrageurs corrects the mispricing of the market and
makes profits at the same time, which is an essential part
of the market. Classical financial theory has established
a relational paradigm. What the arbitrageur has to do is
to regress the relevant coefficients according to the data,
and then analyze the arbitrage strategy according to the
coefficients to obtain benefits. According to the analysis
of system risk and individual risk, income can also be
divided into income from the system and income from
individuals. This is the so-called alpha income and beta
income.
In a follow-up study of the CAPM model, Fama
(1993) found that the market factor β cannot explain
100% of the cross-sectional excess return of the
portfolio. Therefore, Fama and French (1993) analyzed
the influencing factors to better explain the excess return
of the investment portfolio and proposed the Fama-
French three-factor model. The model believes that the
factors in the capital asset pricing model cannot fully
explain the excess returns of the stock portfolio. The
size of the portfolio (represented by the market), the
value and variable characteristics of the owner’s equity
ME (book-to-market value ratio BE/ME). The ai here is
the unexplained excess return.
Ri= ai+ biRM+ siE(SMB)+ hiE(HMI)+ ei (2)
However, the model mentioned also meets the
anomaly problem. In the past 20 years, many scholars
have conducted empirical analysis on the three-factor
model and found that the interception of some stocks is
significantly non-zero, which indicates that the three
risks (factors) in the three-factor model cannot explain
all excess returns. Therefore, Fama and French
introduce more factors, including profitability and
investment factors to upgrade this issue, and its
expression is as follows:
Ri=ai+biRM+siE(SMB)+hiE(HMI)+riE(RMW)+ciE(CMA)+ ei (3)
The five-factor model has two more than the three-
factor model: E(RMW)is the return difference between
high and low- profit stock portfolios, and E(CMA) is the
low or high reinvestment ratio company The difference
in the return of the stock portfolio. These two items
describe the profit level risk and the investment level
risk respectively (note that the investment level here is
not the investment level of the secondary market, but
can be easily explained as the enterprise's ability to
expand reproduction). Similar to the three-factor model,
the method of parameter estimation is still the method of
multiple linear regression.
3. RESULTS
The data are selected from 38 Industry Portfolios in
Kenneth R. French – Data Library, and we especially
focused on two periods of time. Between the time when
COVID-19 just started to spread hugely all over the
world and after 9 months, we consider it as May 2019 to
February 2020. Start from March 2020 and until the time
when the first successful COVID-19 vaccine was
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reported on the news, and that is December 2020. Mkt-
RF, SMB, HML, RMW and, CMA are adopted as
independent variables to conduct the regression and
obtain the coefficients for Apparel.
TABLE 1. Results of Multi Regression From May
2019 to February 2020
Coefficients
Standard
Error
T Stat
P-value
Intercept
-0.020
0.048
-0.410
0.682
Mkt-RF
1.155
0.058
18.239
1.200
SMB
0.155
0.108
1.441
0.151
HML
0.067
0.111
0.604
0.547
RMW
0.642
0.182
3.524
0.001
CMA
0.020
0.214
0.092
0.927
TABLE 2. Results of Multi Regression From March
2020 to December 2020
Coefficients
Standard
Error
T Stat
P-
value
Intercept
0.005
0.088
0.061
0.951
Mkt-RF
1.030
0.041
25.205
3.805
SMB
0.405
0.105
3.847
0.001
HML
0.342
0.087
3.945
0.001
RMW
0.411
0.175
2.345
0.020
CMA
-0.495
0.224
-2.204
0.029
Table 1 is the result from May 2019 to February 2020
and Table 2 is the result from March 2020 to December
2020. It can see that the t-Stat value for SMB, HML, and
CMA from May 2019 to February 2020 were 1.44, 0.60,
and 0.09. However, after COVID-19 spread all over the
world, the t-Stat value of these three factors become
3.84, 3.94, -2.20. Therefore, they become significant,
which represents the change of investment trend.
4. DISCUSSION
4.1. Mkt and RMW
Mkt factor of the Apparel industry increases
from18.23 to 25.20, and the coefficients are above 1,
which indicates this industry is more sensitive than the
market in both periods. Throughout the pandemic, the
Apparel industry has been hit as hard as other traditional
industries. There is severe damage to the US economy
caused by COVID-19. [10] Mentioned in The Business
Research Company’s research report on the apparel
market, a survey was conducted in 2020 by McKinsey
and company on 290 fashion executives to understand
the state of the fashion industry in 2020. According to the
survey, 57%, 58%, and 58% of the fashion executives
were of the view that the luxury fashion segment, mid-
market, and value segments respectively will not see any
growth in 2020 when compared to 2019, while 31%,
39% , and 38% of the fashion executives were of the
view that the premium segment, mid-value segment, and
value segment respectively will grow at a similar rate in
2020 as they did in 2019. This is reflected in the decrease
in demand but a spur in online purchasing in the clothing
market. The slowed-down economic activity due to
COVID-19 across the globe has resulted in a decline in
the apparel market demand. The apparel manufacturing
industry is experiencing cuts in spending and poor
consumer confidence due to fear of coronavirus spread.
This low consumer demand keeps investors away from
new investments in the sector. In addition, factors of
RMW are both significant and positive, revealing that the
companies with good profitability have more
concentration from investors. According to the
International Monetary Fund (IMF), global GDP will
decline by 3% due to the COVID-19 impact. Many shop
owners curbed their stock purchases due to low demand
and increased inventory. According to the second survey
report by the International Textile Manufacturers
Federation (ITMF) between 28th March and 6th April,
the global textile orders experienced around a 31%
decline in orders in 2020. Fashion brands sales decreased
by around 70% in India, owing to enforced lockdown
and fear over coronavirus spread, which has piled up
apparel inventory.
4.2. SMB
As illustrated in Table 1 and Table 2, SMB became
significant and the coefficient is positive (0.405). This
phenomenon proves that the Apparel industry is more
inclined to small-scale enterprises during the COVID-19
epidemic. Due to the economic depression during
COVID-19, investing in small-scale enterprises has a
higher expected rate of return, which also requires
investors to bear correspondingly higher risks and larger
compensation for returns. Influenced by the new
coronavirus pandemic in 2020, the global economy has
been hit severely. The COVID-19 pandemic has also
taken a heavy toll on the Apparel industry. Nevertheless,
compared with the large-scale enterprises, small-scale
enterprises are less affected by the outbreak of COVID-
19, thus giving rise to a lower level of economic loss and
financial deficit. Moreover, companies with a small-scale
tend to possess superior opportunities to recover and
revive their development mode. Therefore, investors are
more willing to select small-scale enterprises when
taking the economic influence of COVID-19 into
consideration. As illustrated by Gregory, because of
liquidity constraints and limits to stock availability in
small-scale firms, people are reluctant to invest in small
enterprises in the general case. Nevertheless, under the
grim hit during COVID-19, small-scale companies
suffered less economic loss, which allured numerous
investors. For instance, large-scale enterprises like
Amazon and Facebook lowered the expected return of
advertisement to a great extent. Similarly, large catering
enterprises like McDonald’s close chain stores’ in remote
areas. The media industry also experienced a sharp
decrease in profit gains. Although no sectors in the
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Apparel industry are left unaffected by COVID-19,
investors are more inclined to opt for small-scale
enterprises in a bid to reap more profits. The prosperity
of these companies with small-scale has changed the way
how investors consider the Apparel industry. Besides,
under the promotion of favorable government policies in
various countries, investors are more willing to invest in
small-scale enterprises to gain a higher rate of return
despite the potential instability and high risk.
4.3. HML
Comparing Table 1 and Table 2, it is shown that the t
Stat of HML increased from 0.604 to 3.945. The
coefficient of this factor is positive(0.342), which
indicates that investors preferred to buy value stocks than
growth stocks after the pandemic. Value stock refers to
companies that are trading below what they really worth,
their ratio of book value to market value is higher, which
means they are relatively mature. Growth stocks,
however, consider as companies that have future
potential. Therefore, value stocks companies are
normally more experienced and mature, which means
that they are less risky compare to the growth stock. This
is one of the important reasons why stock traders switch
to invest in value stocks after the spreading of COVID-
19. It can conclude that after the pandemic, Apparel
companies like Topshop and New Look got a huge
financial crush that they have to close almost 40% of
their offline shops to a minimum their costs. Whereas
companies’ stocks like Louis Vuitton and Hermès go up
to the highest in past 5 years. Stock traders believed that
mature companies could survive from this COVID-19
situation because they are more experienced, more
reliable, and have more stable operating. Also, because
of the pandemic, the U.S. government provided a huge
subsidy to the society, which caused the inflation rate to
go up. What’s more, after the successful vaccine came
out, many countries start to cancel the lockdown
gradually. As people’s life get back to normal, a huge
amount of people will go shopping, therefore there is
more cash coming into the market in a short period of
time. This is another reason why the inflation rate went
up. Therefore, more people would invest in value stock
to keep their money value.
4.4. CMA
During the COVID 19 epidemic, aggressive
investments pay off better. The world is still in the period
of the new crown pneumonia epidemic, and there is no
sign of easing. The economic prospects are also helpless,
making many companies difficult and even leading to a
wave of bankruptcy. But this is a favorable opportunity
for corporate mergers and acquisitions, which is a
competitive and capital reserve. For leading companies,
take advantage of the current economic depression to win
rivals or upstream and downstream related companies at
a much cheaper price than before. The coefficient of
CMA changed after the pandemic, which is also a direct
reflection of the fervor trend of investors put money on
an aggressive investment. During the COVID 19
epidemic, aggressive investments pay off better. The
world is still in the period of the new crown pneumonia
epidemic, and there is no sign of easing. The economic
prospects are also helpless, making many companies
difficult and even leading to a wave of bankruptcy. But
this is a favorable opportunity for corporate mergers and
acquisitions, which is a competitive and capital reserve.
For leading companies, take advantage of the current
economic depression to win rivals or upstream and
downstream related companies at a much cheaper price
than before.
On 2021 January 7, US Eastern time, Tiffany issued
an announcement stating that LVMH has officially
completed its acquisition. However, the transaction price
was adjusted from the $135 per share and the total cash
price of $16.2 billion determined in November last year
to $131.5 per share, a decrease of $3.5 per share from the
original purchase price, and the total transaction price fell
to $15.8 billion. Under the influence of the new crown
epidemic, the sales of luxury brands have been hit hard
this year. According to Tiffany’s previously announced
performance report, Tiffany’s net sales fell 45% year-on-
year to US$556 million in the first quarter of the 2020
fiscal year as of April 30, with a net loss of US$64.6
million; In the second quarter within three months,
Tiffany's net sales fell 29% from the same period last
year to 747 million US dollars, and the net profit was 32
million US dollars. In November 2019, LVMH
announced that it would acquire Tiffany at a price of
US$135 per share, for a total price of approximately
US$16.2 billion. LVMH said that this acquisition aims to
improve the group's position in the jewelry industry,
while further enhancing its market share in the United
States. But in September 2020, LVMH suddenly
announced that it would not be able to complete the
acquisition of Tiffany. The reason was that the United
States announced in July that it would impose a 25%
tariff on cosmetics and other goods imported from
France. LVMH needs to change the acquisition date. put
off. Considering the impact of the epidemic and other
factors, the originally agreed Tiffany purchase price has
been too high. This is the core reason for LVMH's
abandonment. When the purchase price of Tiffany
deviated from LVMH's initial estimate of $135 per share,
LVMH was eligible to lower the price. Finally, LVMH
bought Tiffany at a low price.
5. CONCLUSION
Fama-French model investigates the influencing
factors to explain the expected rate of return of the
investment portfolios. In 2020, the global economy
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experienced tremendous depression because of the
influence of COVID-19. This article evaluates the
performance of Apparel industry during COVID-19
pandemic roundly and comprehensively. As shown in
Table 1, the factors, SMB, HML and CMA, are
insignificant before the COVID-19, but these three
factors turn out to be significant after the outbreak. The
results of this research demonstrate that investors are
more optimistic and confident about the value stock in
small enterprises. Companies with high book-to-market
ratio can arouse a greater number of investors.
Furthermore, during the epidemic, investors are more
inclined to trust in companies with aggressive investing
style, which are more probably to gain superior earnings.
The results provide references for investors when
choosing appropriate investing portfolios, such as the
Black Swan Theory.
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