– a comparison of the fashion and the food industry Short term effects of Covid-19 on stock market performance Bachelor’s degree Thesis

Thesis

Bachelor’s degree

Short term effects of Covid-19 on stock market

performance – a comparison of the fashion and the

food industry

A study on how volatility and the expected return affect the

share price

Authors: Alexandra Sömskar & Zlata Zapolskaia Supervisor: Catia Cialani

Examiner: Lena Nerhagen

Subject/main field of study: Economics Course code: NA2008

Credits: 15 hp

Date of examination: 2020-06-17

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Abstract

The aim of the study is to investigate how the share prices of food and fashion companies listed on the Stockholm Stock Exchange OMX have changed from when Covid-19 started until end of April 2020, by studying how stock price, volatility and expected return have affected the development of the stock. Using the financial theories of CAPM model and volatility, we investigate how the stock market has developed during the pre-19 period in comparison to the period when Covid-19 is ongoing. Our results show that the volatility increased a lot after the virus burst out and that the expected return changed to higher and more frequent fluctuations. We also compare the two industries showing that the food industry changed less during the post-Covid-19 compared to the fashion industry.

Table of contents

1. Introduction ... 1

2. Definitions ... 4

3. Literature review ... 5

4. Theoretical framework ... 7

4.1 Capital asset pricing model ... 7

4.2 Volatility... 10

5. Empirical framework ... 12

5.1 Data and descriptive statistics ... 12

5.2 Method ... 19

6. Results ... 21

7. Discussion of results ... 30

8. Conclusion... 33

References ... 34

Appendix ... 36

Appendix A: Deepening of CAPM model – CML & SML ... 36

Appendix B: Closing price and government bonds ... 39

Appendix C: Daily return ... 42

Appendix D: Excess return ... 45

Appendix E: Expected return ... 49

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1. Introduction

The introduction to this essay addresses the problem background about virus outbreaks and its effect on the stock market. It gives the reader an overview of studies and articles in the field and the identified research gap that is intended to be filled with this study. The problem background opens out to a presentation of the aim of the work and follows by the research question. The chapter ends with the limitation made for this study.

1.1 Background

The stock price and its development are influenced by various factors, with supply and demand being the biggest factors in the short term. In the long term, the price of the share reflects the company's income statement as it is affected by profit/loss for a period. When demand declines, the stock price is likely to fall, and the company loses value. However, events that are almost impossible to predict and prepare for, such as natural disasters, financial crises, or virus outbreaks, sometimes occur and creates shocks on the financial markets. Events like these can have devastating economic consequences in the affected country and, at worst, risk becoming a concern at global level. Shocks can have different impact on the stock market. Previous researches explain that the volatility dynamic on stock markets in Pakistan changed after the terrorist attack of 9/11, after comparing volatility behaviour during the post-9/11 period with volatility behaviour during the pre-reform and the post-reform periods (Ahmed & Farooq, 2008). According to Suleman (2012) news of terrorist attacks has also had a negative impact on the returns for the Karachi Stock Exchange with increased volatility of KSE100 index and financial sector index.

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as part of Dow Jones's ten largest stock market drops ever with a race of 12.9% on March 16. The Stockholm Stock Exchange ended the day at minus 4.7%, and the European market then continued to plunge. March 19 was reported Frankfurt's DAX 30 has rallied by 5.56%, Paris CAC 40 by 5.94% and London's benchmark FTSE 100 index fell 4.05% (DW, 2020).

Since the Second World War, the world has had five known flu pandemics. Asian Flu, the Hong Kong Flu, the Russian Flu, the Swine Flu and lastly, the current Covid-19. Looking back at how the stock market has been affected during previous pandemics cannot say anything about the consequences Covid-19 will have on the share development in the end, perhaps following the same pattern as before or perhaps not. Unique to Covid-19 is that the shares have fallen more than 10% in record time, which has resulted in the fear index VIX, an indicator of expected volatility in the S&P 500 index over the next 30 days, remaining at the same level as

in the 2008 financial crisis. It is clear that there is fear of investing in these times (Forbes,

2020). The reason why the stock market is interesting to study during a global event like a pandemic is because it affects society and the economy on a global level, and as far as we know, there is no research on this topic. Equity markets around the world are strongly influenced by each other in general and even more so under such a special and historical situation as this. Important to highlight is that the study is being carried out during the pandemic period, that is, it is not over yet, so the investigation is being done to study the impact of the virus outbreak so far. Many companies in Sweden are struggling now or have had to liquidate their operations and mainly a connection is seen within the clothing industry. One industry that, on the other hand, does not seem to be affected as negatively is the food industry. From this information came the idea to study the stock market based on companies within these two industries.

1.2 Aim

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and periods, show a change in stock exchange behavior during crises such as the financial crisis.

This paper presents previous studies, then the economic models that estimate the return requirement and volatility. The calculations are shown in the method section and finally the results of the study are presented.

1.3 Research question

In what way is the share price affected by industry affiliations? Is there any relationship between volatility and expected returns?

1.4 Limitations

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2. Definitions

In this section, concepts mentioned in the work will be defined.

Stock price

The current price used in this study is the closing price, thus the price of shares when the stock market closes for the day.

Expected return

The return requirement, also known as the calculation or discount rate, is the interest rate used to calculate the present value of future returns and is defined as the future profit an investor expects to make when investing in a company given the risk it takes to enter as a shareholder.

Volatility

Volatility is referred to as a risk measure that measures the percentage movements in a stock to get the difference between the highest and lowest price of the share during the period in relation to the average value. The measurement is calculated on an annual basis of 252 trading days a year and is based on the share trend over the past 30 days. High volatility for a stock means that the stock price tends to fluctuate a lot while low volatility indicates fewer fluctuations.

Covid-19

Covid-19 is the name of the disease that occurred in December 2019 and broke out as a global virus in early 2020, caused by the coronavirus SARS-CoV-2. World Health Organization (WHO, 2020) describes the official name of the disease as Covid-19 or the coronavirus disease and will continue to be referred to in this work as the coronavirus or just corona.

Fashion- and food industry

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3. Literature review

In this section, the literature review of empirical works on capital asset pricing model will be presented. These studies have tried to establish factors that contribute to the expected return of capital asset and volatility behaviour. We focus mainly on recent works which deal with exogenous shocks in the economy and how these shocks have affected the stock exchange. The CAPM model has been used in several studies to calculate stock performance. Choudhary & Choudhary (2010) test CAPM's validity in the Indian stock market while Coffie (2012) applies the model in the stock market in Africa. Both surveys show that there is a connection between studying stock performance and the CAPM model. Lundberg & Lundberg (2010) have studied the expected return, ownership structure and share distribution for specific industries from the Stockholm Stock Exchange index during the financial crisis. Through the CAPM model they confirm with their analysis that there is a correlation between the stock price's development and high dividend levels, even during times of crisis. It is more difficult to demonstrate how return requirements and ownership structure affect the development of the stock price. According to Lundberg & Salih (2010), the financial crisis affected the entire stock exchange in Sweden but notes that some industries are more stable than others during times of crisis. For example, the pharmaceutical industry was less volatile and recovered faster than other industries, a result also based on the CAPM model. Other shocks on the stock market is the terrorist attacks of September 11, 2001 which changed volatility behaviour in Pakistan with a significantly negative risk premium during the post-9/11 period (Ahmed & Farooq, 2008). These comparisons between a global crisis and equity development formed the basis of the idea for this study.

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4. Theoretical framework

In this section, we present a thorough description of the current paradigm withing parts of modern financial theory. The purpose is to provide the reader with a clear picture of which theories are relevant to this work.

4.1 Capital asset pricing model

CAPM was developed by Sharpe (1964) with purpose to use the model as a tool for calculating expected returns given a level of risk. By comparing the risk of a particular asset with its return for a certain period, a return requirement can be estimated. Like most models, CAPM is based on certain assumptions about reality. Since it is a further study on the modern portfolio theory founded on Markowitz (1952) CAPM has the same basic assumptions as Markowitz's theory, but with some additional assumptions added by Sharpe (1964). The model is based on the following (Sharpe, Alexander & Bailey, 1995):

1. Investors evaluate portfolios by looking at the portfolio's expected return and standard deviation over a specific time horizon.

2. Investors are never satisfied. If they can choose between two otherwise similar portfolios, they will choose the one with the highest expected return.

3. Investors are risk averse. If they can choose between two otherwise similar portfolios, they will choose the one that has the lowest standard deviation.

4. Individual assets are infinitely divisible, which means that investors can choose to buy only part of the asset if he or she so wishes.

5. There is a risk-free interest rate at which an investor can either lend (invest) money or borrow money.

6. Taxes and transaction costs are irrelevant.

The following assumptions can be added to the above ones: 7. All investors have the same time horizon.

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9. The information is free and immediately available to all investors.

10. Investors have homogeneous expectations, which means that they have the same perceptions of expected returns, standard deviations, and securities covariance.

CAPM is calculated using the risk-free interest rate, the β-value of the share and a risk premium consisting of the market interest rate. Using the various parts of the model, the return requirement for the share is calculated. The formula on which the model is based is presented below:

𝐸(𝑅𝑖) = 𝑅𝑓 + β𝑖 (𝐸(𝑅𝑚) - 𝑅𝑓) (1)

Where,

E(Ri) = the expected return on the asset (i), βi = the market risk of the asset (i), E(Rm) = market return and Rf = risk-free interest rate

An in-depth study of the model is done through the capital market line and the security market line. As the purpose can be fulfilled without an in-depth knowledge of the model, these parts are not presented in this chapter. For deeper understanding and visualization, this is described instead in Appendix A.

Beta

Investing in different individual assets can be more or less risky than the stock market average. By looking at a certain investment risk, the future return can be estimated. The theory behind CAPM is that there is a relationship between risk and return. The relationship means that if investment risk increases, compensation in the form of returns should also increase (Sharpe, et al., 1995). 𝛽𝑖 = 𝐶𝑜𝑣 (𝑅_𝑖, 𝑅_𝑚) 𝜎2_(𝑅 𝑚) (2)

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composition of all the market's securities) is equal to 1 and corresponds to the systematic risk of investing in the market portfolio. The basic idea of CAPM is that there is only one source of risk that affects the average return in the long term, namely the market risk measured as β. Andrén, Eriksson, & Hansson (2015) argue that:

β = 1: variations in the return on the current asset are equal to the market.

β > 1: the return on an individual asset exceeds that of the market average return on the rise and below the market average return on decline.

β < 1: the return on an individual asset is lower the market's average return on the rise and exceeds that of the market average return on decline.

Risk-free Interest

A risk-free interest rate (Rf) is obtained from a risk-free asset and is the return that can be obtained without taking any risk during a given period. Damodaran (2012) expresses two criteria that must be met for an asset to be risk-free:

• there is no risk of non-payment

• there is no risk of reinvestment

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Excess return

To calculate risk premium, also called excess return, for a financial asset, it is necessary to estimate the market risk premium (Rm) and the risk-free interest rate (Rf). The asset's excess return shows the return you expect to receive in addition to the risk-free alternative, thus the difference between the market's risk premium and the risk-free interest rate (Rm - Rf).

𝐸𝑥𝑐𝑒𝑠𝑠 𝑟𝑒𝑡𝑢𝑟𝑛 = 𝐸(𝑅_𝑚) − 𝑅_𝑓 (3)

The excess return on a financial asset measures what investors generally require and expect as a reward for investing in something other than a risk-free instrument. However, the excess return commonly used in calculations is a compromise because it stems from the historical difference between return on shares and risk-free return (Damodaran, 2012). The idea is that it should be a reliable estimate of the expected return.

4.2 Volatility

Volatility can be described as something that changes or varies and can be explained as the more a variable fluctuates over a period of time, the more volatile it is. In everyday language, volatility is considered to be synonymous with risk, with the concept also associated with unpredictability and uncertainty. In the financial market, volatility is of great importance and is discussed primarily in terms of unpredictability. There are significant negative effects on risk averse investors and significant changes in the return on the financial market (Daly, 2011). In this context, volatility is used as a measure of spread from expected value, price, or model. Consequences of high volatility affect, for example, consumption patterns, investment decisions in companies, leverage decisions and other cyclical and macroeconomic variables. Much of modern finance theory is based on the balance between risk and expected return. Schwert (1990, referred in Daly, 2011) shows that an increase in stock market volatility gives an increased chance of major stock price changes in both directions.

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will exhibit a volatility cluster. At higher frequencies, volatility is most likely to be based on noise, turbulence that prevails through trading. At lower frequencies, the most likely impacts are macroeconomic and institutional changes (Daly, 2011).

Formula for volatility

𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 = 𝑆𝑡𝑑𝑎𝑣 {𝑙𝑛 ( 𝑘𝑡

𝑘_𝑡−1)} × √𝑁

(4)

Where

Stdav = Standard deviation, 𝑘𝑡=Closing price for day t, 𝑘𝑡−1=Closing price the day before

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5. Empirical framework

In this section, we will first describe the data and then the method that has been used for this analysis.

5.1 Data and descriptive statistics

All three companies from each industry are selected based on placement in the so-called stock exchange list which categorises the companies into three separate sub-lists depending on the size of their stock market value. In Large Cap there are companies with a market capitalization of at least EUR 1 billion, which is the largest list. Mid Cap contains companies whose market capitalization is between EUR 150 million up to EUR 1 billion. The companies with a market capitalization of less than EUR 150 million are in Small Cap (Raihe, 2018). Based on the stock prices, figures for daily returns and average daily returns have been obtain for each company and are presented below from large to small cap. The stock price is defined in Swedish crowns (SEK). Daily return is the percentage change in the stock price of the previous day’s closing price. Average change is the mean of daily return with basis of 30 days before. Below there will be a presentation of the companies and what business they do followed by a trend of closing prices.

Axfood

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Stock price development

Figure 1.Monetary and percentage change of stock price for Axfood between 1st _{October 2019 and 30}th _April

2020. Closing price is read from the left axis. Daily return and average change are read from the right axis.

Table 1. Descriptive statistics of the variables in Figure 1.

Variable Obs Mean Std. Dev. Min Max

Closing price 145 202.4524 9.4201 168.4 217.6

Daily return 145 0.0001 0.0200 -0.0742 0.1460

Average change 145 0.0001 0.0022 -0.0054 0.0080

Figure 1 shows the development of the stock price for a period including the period for the coronavirus existence. The black line indicates a stable stock price before the period for when the virus started and then follows of smaller price changes with a few shocks leading the stock price to even increase before returning to the same level as before. Daily return follows the same trend as the stock price.

-10% -5% 0% 5% 10% 15% 20% 0 50 100 150 200 250 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Cha n ge in % Stock p ric e in SEK

Axfood

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Hennes & Mauritz

H&M is Sweden's largest retail chain with a selection of clothing, shoes and accessories. There are also a number of other brands under the same group and the company conducts business operations in all global regions. H&M has a market capitalization of SEK 225,917 million and is thus listed in Large Cap. It is also the seventh largest company in Sweden today at market value, according to Dagens Industri (retrieved 15 April 2020). In Figure 2 the stock price development is presented.

Stock price development

Figure 2. Monetary and percentage change of stock price for H&M between 1st _{October 2019 and 30}th _April

2020. Closing price is read from the left axis. Daily return and average change are read from the right axis.

Table 2. Descriptive statistics of the variables in Figure 2.

Variable Obs Mean Std. Dev. Min Max

Closing price 145 176.9741 29.9227 105.88 211.65

Daily return 145 -0.0018 0.0320 -0.1206 0.1321

Average change 145 -0.0022 0.0062 -0.0215 0.0069

Figure 2 shows a big fall of the stock price during the period for the virus with the price stock losing half of its value. It is shown that the changes in stock price makes big movements in the daily return. -15% -10% -5% 0% 5% 10% 15% 0 50 100 150 200 250 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Cha n e in % Stock p ric e in SEK

Hennes & Mauritz

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Boozt

Boozt is a relatively new company that started its retail business in 2011. The company conducts internet sales of clothing for a wide selection of brands for all customer groups, with the Nordic countries as the main customer market. On the stock exchange, the company is valued at SEK 2310 million, which places Boozt in the list for Mid Cap (Dagens industri, retrieved 15 April 2020). In Figure 3 the stock price development is presented.

Stock price development

Figure 3. Monetary and percentage change of stock price for Boozt between 1st _{October 2019 and 30}th _April

2020. Closing price is read from the left axis. Daily return and average change are read from the right axis.

Table 3. Descriptive statistics of the variables in Figure 3.

Variable Obs Mean Std. Dev. Min Max

Closing price 145 52.8486 8.1424 36.2 68.3

Daily return 145 -0.0002 0.0346 -0.104 0.2216

Average change 145 -0.0018 0.0042 -0.0123 0.0121

From Figure 3 it can be read that the stock price development is unstable with the black line moving a little bit all the time. From the beginning of April Boozt has had a steady rise. The trend for daily return has not change that much during the period for the graph, except for one shock each in November, March, and April.

-15% -10% -5% 0% 5% 10% 15% 20% 25% 0 10 20 30 40 50 60 70 80 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Cha n ge in % Sto ck p rice in SE K

Boozt

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Cloetta

Cloetta is active in the confectionery industry and mainly produces products such as chocolate and snacks through its own brands such as Läkerol, Cloetta, Jenki, Kexchoklad and Sperlari. Operations are conducted in both the Nordic and European markets. In the stock exchange list, the company is valued under Mid Cap with a market capitalization of SEK 6742 million (Dagens industri, retrieved 15 April 2020). In Figure 4 the stock price development is presented.

Stock price development

Figure 4. Monetary and percentage change of stock price for Cloetta between 1st _{October 2019 and 30}th _April

2020. Closing price is read from the left axis. Daily return and average change are read from the right axis.

Table 4. Descriptive statistics of the variables in Figure 4.

Variable Obs Mean Std. Dev. Min Max

Closing price 145 28.8889 3.5131 21.24 34

Daily return 145 -0.0011 0.0263 -0.1105 0.0854

Average change 145 -0.0011 0.00466 -0.0132 0.0050

Figure 4 illustrates how the stock price increased in the beginning of February and then followed by a negative trend to the middle of March. The biggest shock hit the stock price in the beginning of February with the daily return being very volatile. From the middle of March, the stock price has not changed that drastically but still being unstable with the daily return fluctuating a lot. -15% -10% -5% 0% 5% 10% 0 5 10 15 20 25 30 35 40 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Cha n ge in % Sto ck p rice in SE K

Cloetta

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Agromino

This is a company operating in the agricultural sector and produces various cereal and dairy products. The business is located throughout the value chain, from land acquisition to distribution and resale, and concentrates primarily on the European market. With a market capitalization of SEK 311 million, the company is in Small Cap (Dagens industri, retrieved 15 April 2020). In Figure 5 the stock price development is presented.

Stock price development

Figure 5. Monetary and percentage change of stock price for Agromino between 1st _{October 2019 and 30}th _April

2020. Closing price is read from the left axis. Daily return and average change are read from the right axis.

Table 5. Descriptive statistics of the variables in Figure 5.

Variable Obs Mean Std. Dev. Min Max

Closing price 145 14.8413 1.7780 11 18.2

Daily return 145 0.0029 0.0297 -0.1492 0.0964

Average change 145 0.0025 0.0026 -0.0099 0.0022

From Figure 5 the biggest change looking at the closing price is seen from the beginning of March, but still indicates that the stock price is moving somewhat even, steady slow downwards, from the 1st October. The daily return varies the most in connection to the fall in the stock price in early March with a negative change at almost 15%.

-20% -15% -10% -5% 0% 5% 10% 15% 0 2 4 6 8 10 12 14 16 18 20 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Cha n ge in % Sto ck p rice in SE K

Agromino

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Odd Molly International

The company operates in the fashion industry, with mainly sales through suppliers, but also through its own physical stores. The clothing brand, which goes by the name of Odd Molly, can be found around the world but the largest business can be found in the Nordic countries, Europe, and North America. The company is valued at SEK 134 million at OMX Stockholm Small Cap (Dagens industri, retrieved 15 April 2020). In Figure 6 the stock price development is presented.

Stock price development

Figure 6. Monetary and percentage change of stock price for Odd Molly International between 1st _{October 2019}

and 30th _{April 2020. Closing price is read from the left axis. Daily return and average change are read from the}

right axis.

Table 6. Descriptive statistics of the variables in Figure 6.

Variable Obs Mean Std. Dev. Min Max

Closing price 145 4.4106 1.3133 2.22 6.66

Daily return 145 .0028 0.0560 -0.1666 0.256

Average change 145 .0008 0.0135 -0.0286 0.0310

Figure 6 illustrates how the stock price increased a lot during November followed by a period at that higher price, until March when the stock fell. Since then the stock price development has started to recover.

-20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 0 1 2 3 4 5 6 7 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Cha n ge in % Sto ck p rice in SE K

Odd Molly International

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5.2 Method

The study is a multifaceted study where data is obtained through a quantitative approach. The target group is listed companies in OMX Stockholm, shortening OMXS, operating within the food and fashion industry. The study follows six companies from the two mentioned industries listed on OMX Stockholm, Large Cap, Mid Cap and Small Cap. The selection method is important when it comes to the extent to which the sample should generalize and represent the population. The companies that represent the selection frame were hand-picked and divided into two different clusters, considering industry affiliations. This resulted in a selection of three companies from each industry with one company from each stock exchange list.

As the study aims investigating stock performance for period October 1, 2019 to April 30, 2020, data was collected for the companies' share prices at daily intervals. All stock market data is taken from the Nasdaq database (Nasdaq, retrieved 8 May 2020) from August 20, 2019 to April 30, 2020, as some calculations require data 30 days before the first day of the period

studied. This resulted in 175number of trading days (weekends not included due to the closing

of the stock exchange). The period is chosen in light of the fact that companies in Sweden import and manufacturing large quantities from China and are thus influenced by China's production and social regulations (Pham, 2015). Thus, it is considered interesting to also investigate the period before the first noticed corona case in Sweden. To possibly indicate any connection between the share development and corona, the period for the calculation begins a couple of months before the virus occurred.

Based on data for share prices, volatility is calculated by converting the share price to return series, to get all the data in percentage changes. Thereafter, the standard deviation of the return is calculated with a period set to 30 days. Volatility on an annual basis is obtained by multiplying the standard deviation by an annualization factor, which is the root of 252, using the formula 4 for volatility. The calculation for volatility can be found in Appendix F.

To estimate the expected return for the different assets based on the CAPM model, the closing

price for each company is used, between dates 2019-09-30 - 2020-04-30.The data for the

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After data is collected, daily returns are calculated for the different variables, OMXSPI, AXFO, CLA B, AGRO, HM B, BOOZT and ODD. The daily return is calculated according to:

𝐷𝑎𝑖𝑙𝑦 𝑅𝑒𝑡𝑢𝑟𝑛 =𝑃𝑡−𝑃𝑡−1

𝑃_𝑡−1 × 100

(5)

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6. Results

In this section, the results that the study has produced will be presented, first for each company regarding volatility and expected return by using CAPM model, and then followed by a result for each industry (portfolio).

In the Stata program, a regression test is performed in which excess return for OMXSPI is placed on the x-axis and excess return for the companies is stated as dependent variable on the y-axis.

Table 7. Results of regression test for each company and industry, arrange in alphabetical order.

Share Variables Coef. t P>|t| [95% Conf. Interval]

AGRO (Rm-Rf)_OMXSPI 0.4620 3.76 0.000 0.2193 0.7046 _cons -0.0010 -0.44 0.662 -0.0058 0.0036 AXFOOD (Rm-Rf)_OMXSPI 0.2458 2.92 0.004 0.0795 0.4120 _cons 0.0027 1.64 0.103 -0.0005 0.0059 BOOZT (Rm-Rf)_OMXSPI 0.3644 2.49 0.014 0.0755 0.6533 _cons 0.0019 0.68 0.498 -0.0037 0.0075 CLAB (Rm-Rf)_OMXSPI 0.8410 9.36 0.000 0.6634 1.0186 _cons -0.0006 -0.35 0.730 -0.0040 0.0028 H&M (Rm-Rf)_OMXSPI 1.2874 14.79 0.000 1.1154 1.4594 _cons -0.0027 -1.59 0.113 -0.0060 0.0006 ODD (Rm-Rf)_OMXSPI 1.1818 5.35 0.000 0.7452 1.6185 _cons 0.0022 0.53 0.596 -0.0062 0.0108 PORTFOLIO (Rm-Rf)_OMXSPI 0.9446 10.17 0.000 0.7610 1.1282 FASHION _cons 0.0005 0.28 0.780 -0.0030 0.0041 PORTFOLIO (Rm-Rf)_OMXSPI 0.5162 8.59 0.000 0.3974 0.6351 FOOD _cons 0.0003 0.30 0.767 -0.0019 0.0026

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investment in the share comprises. The β-value for Agromino is 0.4620, 0.2458 for Axfood, 0.3644 for Boozt and 0.8410 for Cloetta. The value of β thus falls below 1 for the latter shares, which included that its risk is lower than the Stockholm Stock Exchange index. These shares are less affected compared to the market average for both positive and negative fluctuations.

Axfood

Figure 7 represents the volatility and expected return for Axfood shares for period pre-corona and during corona. Until February, both volatility and the expected return have had a stable trend. Then there is a clear upward trend for volatility with a change of about 40% between March and April. As volatility increases, a change in expected return can be explained by even greater fluctuations. When volatility increased the most, expected return only increased to over 3%. The stable expected return indicates that the company has not affected much during the time for corona. For the period shown in Figure 7, volatility peaked between mid-March and April, then declined again.

Figure 7. Percentage development of volatility and expected return for Axfood from 1st _{October 2019 to 30}th _April

2020. -3% -2% -1% 0% 1% 2% 3% 4% 0% 10% 20% 30% 40% 50% 60% 70% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Exp ected re tu rn ch an ge Vola tility ch an ge

Expected return & Volatility

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H&M

Figure 8 shows the volatility and expected return for H&M shares for period pre-corona and during corona. As shown the volatility was stable around 20% until February when it started increasing. As volatility increased sharply, the expected return also changed through larger and more frequent fluctuations. The expected return reached at most up to 15% and lowest down to -15%. The volatility of H&M went from about 20% up to over 90% during the current period, a change that occurred during the period for corona.

Figure 8. Percentage development of volatility and expected return for H&M from 1st _{October 2019 to 30}th _April

2020. -20% -15% -10% -5% 0% 5% 10% 15% 20% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Exp ected re tu rn ch an ge Vola tility ch an ge

Expected return & Volatility

24

Boozt

Figure 9 represents the volatility and expected return for Boozt shares for period pre-corona and during corona. The company has had a moving volatility since the beginning of the period which indicate a constantly variable stock price. When volatility increased in mid-November, the expected return contrasted with a decrease. The biggest change in volatility happened in late February, with one movement from around 25% to 65%. Despite varying volatility, the expected return has had a similar trend throughout the period, except for some deviating fluctuations.

Figure 9. Percentage development of volatility and expected return for Boozt from 1st _{October 2019 to 30}th _April

2020. -6% -4% -2% 0% 2% 4% 6% 8% 10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Exp ected re tu rn ch an ge Vola tility ch an ge

Expected return & Volatility

25

Cloetta

Figure 10 shows the volatility and expected return for Cloetta shares for period pre-corona and during corona. From the graph it is shown that volatility fluctuates between about 15-30% until February. At the beginning of February, there is a sudden rise which gradually increases and amounts to just over 70%, a level which it remains at throughout April. The expected return, like volatility, has risen at the end of October, and is followed by a more even period between November and February, where it is just under 0%. Another change in expected return occurs in early February when volatility suddenly rises again. The expected return then becomes more volatile, with ups and downs fluctuate more sharply and more frequently. The largest fluctuations are detected in mid-March. Figure 10 shows that the company is affected during the period for corona because of unstable expected return as a result of fluctuating share prices from February onwards.

Figure 10. Percentage development of volatility and expected return for Cloetta from 1st _{October 2019 to 30}th

April 2020. -12% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 0% 10% 20% 30% 40% 50% 60% 70% 80% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Exp ected re tu rn ch an ge Vola tility ch an ge

Expected return & Volatility

26

Agromino

Figure 11 sketches the volatility and expected return for Agromino shares for period pre-corona and during corona. Looking at the volatility it had a smaller decrease in November followed by a stable trend around 30-40% until late February. For that same period the expected return also had a stable trend with even fluctuations laying mostly between -2 and 2%. A big change is seen from March with volatility increasing a lot and the expected return having bigger fluctuations. By comparing the volatility with the expected return, Figure 11 shows that large changes in volatility do not make such a big difference to investors as the percentage change in expected return remains quite low, and therefore the company can be seen as less affected by corona.

Figure 11. Percentage development of volatility and expected return for Agromino from 1st _{October 2019 to 30}th

April 2020. -8% -6% -4% -2% 0% 2% 4% 6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Exp ected re tu rn ch an ge Vola tility ch an ge

Expected return & Volatility

27

Odd Molly International

Figure 12 displays the volatility and expected return Odd Molly International shares for period pre-corona and during corona. By observing volatility, it can be found that it varies regularly, rising in October from about 20% to about 110% at the beginning of December. Thereafter, a shorter period will follow at the same level before it will then decrease to about 45%. Volatility remains somewhat stable from January to March, to rise again to 140%, a change that is taking place during corona's time. In mid-November, it is possible to read out a link between increased volatility and greater percentage difference in expected return. When volatility then returns to a more even level, volatility stabilizes. At the beginning of March, volatility rises again, and the expected return receives larger fluctuations that move more frequently. Figure 12 shows that the maximum point for the expected return is about 30% and the lowest around -20%. In summary, Odd Molly's expected return is generally volatile even before the corona but becomes more frequent and greater after the outbreak.

Figure 12. Percentage development of volatility and expected return for Odd Molly International from 1st _October

2019 to 30th _{April 2020.} -30% -20% -10% 0% 10% 20% 30% 40% 0% 20% 40% 60% 80% 100% 120% 140% 160% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020 Exp ected re tu rn ch an ge Vola tility ch an ge

Expected return & Volatility

28

Portfolios

Figure 13 illustrates two equity portfolios, one for the fashion industry and one for the food industry, as well as one for the Stockholm Stock Exchange index. Clearly, the volatility for fashion is greater than for food, that is, stock prices for the fashion industry fluctuate more. The fashion industry had fluctuating stock prices even before the pandemic period started but shows a strong change from mid-February. Instead, the volatility of the food industry rose sharply in March, from a previously stable situation. During the time of the pandemic, the graph shows that the volatility of the clothing industry has increased more than for food, which has more of a parallel development to the Stockholm Stock Exchange.

Figure 13. A percentage development of volatility for the portfolios Food and Fashion and the Stockholm Stock Exchange index (OMX) between 1st _{October 2019 and 30}th _{April 2020.}

Figure 14 presents the return requirement for the two different portfolios, the fashion industry, and the food industry. According to the graph, the portfolio for the food industry during the current period has less fluctuations in comparison with the portfolio for fashion industry. It can be read that the food industry generally has a more even trend until the beginning of March, which mostly stays just below 0%, thus a negative expected return requirement from investors. The period from the beginning of March shows a more uneven trend with stronger ups and downs for the food portfolio. For the fashion industry, uneven fluctuations can be noticed for the entire period. Until the beginning of March, the upturn in the expected return is greater than

0% 20% 40% 60% 80% 100% 120% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020

Volatility

29

the negative fluctuations. On the other hand, a more troubled market can be observed after mid-March, where the declines fall to between -7% and about -11%. The portfolio becomes more volatile, which is also evident from the short-term high rises that occur up to between about 5% and 7%. In general, it can be implied that both portfolios reach a more volatile period from March onwards, with higher ups and downs moving more frequently.

Figure 14. A percentage development of expected return for the portfolios Food and Fashion between 1st _October

2019 and 30th _{April 2020.} -15% -10% -5% 0% 5% 10% 10/1/2019 11/1/2019 12/1/2019 1/1/2020 2/1/2020 3/1/2020 4/1/2020

Expected return

30

7. Discussion of results

The results of our study indicate that a previous shock affected the companies that represent the fashion industry even before the corona period. Cloetta can also be considered to have suffered a minor shock at the end of October when volatility rose about 10%. What underlying cause that affected these cannot be determined by this study but common to the companies is that the shock then did not hit the companies as severely as corona. At the beginning of February, volatility for Axfood, H&M and Cloetta all share a common trend of drastically increasing, while the same increase for the remaining companies occurs a month later. The virus outbreak was already reported on January 9 (ECDC, 2020) and despite news spreading rapidly, the effect is lagging, much like a domino effect. According to Suleman (2012) news of terrorist attacks influenced to increase volatility, which can be compared even with virus outbreaks. Lundberg & Salih (2010) and Ahmed & Farooq (2018) also confirm increased volatility in global crisis situations, such as corona.

Volatility appears to have some relation to the expected return. With a drastic increase in volatility, the expected return increases/decreases more than the trend before. The same trend is seen when volatility decreases as the fluctuations in the expected return then decrease. The expected return for each company thus follows the increase in volatility which is confirmed by the result which shows that the fluctuations are moving larger and more. Although Boozt's stock prices become more volatile from the end of February, a later reaction to the shock than Axfood, H&M and Cloetta, it is the only company that has its biggest fluctuations already during the same month. Common to the other companies is that the expected return has its extreme values in the middle of March. This is when the ups and downs are greatest and with higher frequency. The difference for all companies is the interval between its lowest point and peak.

31

33% for H&M and 50% for Odd Molly International. These are the only companies in the analysis that have a β-value exceeding 1, which means a greater fluctuation of the expected return for the company than the average for the stock exchange. The difference in the extreme values for the remaining companies is 17% for Cloetta, 11% for Agromino and 10% for Boozt. This contrast for the companies can be partly explained by its β-value and its leverage effect. Also, for Cloetta, the β value is slightly higher than for the remaining companies, it does not exceed 1 but is just below, β = 0.84 (Table 7). For Boozt and Agromino, β = 0.36 and β = 0.46 respectively, resulting in a difference of 10% and 11%. In summary, it can be noted that the companies from the same industry generally mimic each other. The companies that stand out from the development for each industry are Boozt from the fashion industry and Cloetta from the food industry. With the low β-value, Boozt tends to have a more stable development than H&M and Odd Molly International.

32

between the industries may be due to the different β-value, which is β = 0.52 for the food industry and β = 0.94 for the fashion industry (Table 7).

33

8. Conclusion

The conclusion that can be drawn from the volatility is that the food industry's share prices have so far been less affected by corona. There is a connection between an unstable share price and ever greater fluctuations in investor return requirements. However, no conclusion can be drawn if the relationship between volatility and expected return is positive or negative as the study only examined the percentage difference in volatility, regardless of direction. The food industry has been affected later and less and with a faster hint of recovery than the fashion industry has done through more stable stock prices, more uniform return requirements that reflect a lower investment risk.

34

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Appendix

Appendix A: Deepening of CAPM model – CML & SML CML

Like CAPM, the Capital Market Line (CML) is based on Markowitz's modern portfolio theory. According to CAPM, there is a linear equilibrium relationship between risk and expected return, known as the CML (Sharpe, 1964). The objective of the CAPM model is to find an optimal portfolio in relation to the investor's risk propensity. In theory, portfolios placed on CML optimize the risk and return relationship, thereby maximizing performance. In Figure 15,

M represents the market portfolio, 𝜎𝑀 the market's systematic risk is measured in standard

deviation and Rᴍ is the market portfolio's expected return.

Figure 15. The relationship between expected return and standard deviation, illustrated by CML. Source: Sharpe, et al., (1995). Own processing.

The x-axis represents the degree of risk in the form of standard deviation and the y-axis shows the degree of expected return given a risk level. For the CML model to be correct, there are assumptions. Assumptions that a risk-free interest rate exists without regard to inflation, that all investors possess the same information and thus invest in the same portfolio, that is the market portfolio (Haugen, 2001). The equation for the straight-line relationship is described according to Gavelin & Sjöberg (2012) as follows:

𝐸(𝑅𝑝) = 𝑅𝑓+

[𝐸(𝑅𝑚) − 𝑅𝑓]

𝜎_𝑀 × 𝜎𝑝

37

The function shows that the relationship between expected return for an efficient portfolio p and its standard deviation is linear. It can also be concluded from the formula that the risk

premium for the effective portfolio, 𝐸(𝑅𝑝) − 𝑅𝑓, is proportional to the market risk premium,

𝐸(𝑅_𝑚) − 𝑅𝑓. The more notable that can be read is that the risk premium set in relation to the

portfolio's risk (risk premium per unit risk) is constant for all effective portfolios that are on CML (Gavelin & Sjöberg, 2012).

SML

In addition to CML, which only considers effective portfolios, another model exists. This model shows the expected return for an individual asset or a combination of several securities, through the relationship between market risk and return. The model is called the Security Market Line (SML).

According to SML, a share does not have an equilibrium relationship if the share has a different expected return or risk premium or does not have the same combined risk as an effective CML portfolio. SML is often used to compare two similar securities that offer approximately the same return to determine which of them represents the least amount of market risk relative to the expected return. SML can also be used to compare securities i and securities j with the same risk to see which one offers the highest expected return against the risk level can be described as (Vinell & De Ridder, 1999):

𝑅𝑖 − 𝑅𝑓

𝜎_𝑖𝑀 =

𝑅𝑗− 𝑅𝑓

𝜎_𝑗𝑀

(7)

The formula can also be written as:

𝑅𝑖 = 𝑅𝑓− 𝛽𝑖𝑀(𝑅𝑀− 𝑅𝑓) (8)

According to the model, the risk of the share is defined as the systematic risk, or the market risk (β-value) and can be applied to all shares that are part of the market portfolio. If the market is effective, there is a linear relationship between expected return and β-value (Haugen, 2001).

38

Figure 16. The relationship between expected return and β-value, illustrated by SML.

Source: Sharpe, et al., (1995). Own processing.

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Appendix B: Closing price and government bonds

Date OMXSPI HM B BOOZT ODD AXFO CLA B AGRO SE GVB 2Y

42 2020-04-14 586,92 139,3 41,9 3,5 204,4 23,54 12,5 -0,23 2020-04-15 565,54 133,9 46,85 3,405 203,8 23,14 11,9 -0,28 2020-04-16 563,59 128,45 47,45 3,22 202,8 23,3 11,9 -0,26 2020-04-17 582,15 135,45 49,5 3,365 204,6 24,5 11,7 -0,25 2020-04-20 585,25 135,5 51,2 3,09 205,4 24,6 11,8 -0,25 2020-04-21 570,08 129,9 49,8 3,225 206,6 23,52 11,7 -0,25 2020-04-22 580,73 128,05 53,8 3,255 217,2 23,6 12,6 -0,25 2020-04-23 587,87 128,45 56,6 3,39 213,6 23,66 12,5 -0,25 2020-04-24 580,44 124,75 56,2 3,5 212,4 22,4 12,8 -0,20 2020-04-27 587,95 129,6 57,9 3,42 211 21,8 12,2 -0,26 2020-04-28 596,33 133,1 60 3,42 210,2 22,46 12 -0,22 2020-04-29 608,46 141 60,7 3,52 207,8 22,7 11,6 -0,22 2020-04-30 601,86 136,2 60,4 3,7 207,2 22,58 11 -0,21

Appendix C: Daily return

Date OMXSPI HM B BOOZT ODD AXFO CLA B AGRO FASHION_port FOOD_port

45 2020-03-24 7,2659% 13,2193% -2,4707% 4,9550% -1,8883% 3,2957% 7,8947% 5,2353% 3,1002% 2020-03-25 4,2998% 8,8340% -3,4667% 0,4292% -1,6807% 2,2789% -7,3171% 1,9329% -2,2395% 2020-03-26 1,9726% -3,0140% 2,2099% 2,9915% 2,7295% 2,3173% 9,6491% 0,7287% 4,8984% 2020-03-27 -3,0106% -5,3846% 1,7568% -1,6598% 4,4015% -2,8746% -4,0000% -1,7629% -0,8238% 2020-03-30 2,0417% -0,6016% 1,8592% -1,6878% 2,6735% 0,4484% -1,6667% -0,1434% 0,4853% 2020-03-31 2,7405% 4,6949% 4,3025% 8,5837% 1,2519% 5,0000% -0,8475% 5,8602% 1,8014% 2020-04-01 -3,4071% -3,8672% -1,3750% -1,5810% 1,5826% -0,6803% 0,0000% -2,2746% 0,3009% 2020-04-02 -0,0354% -6,4608% 0,3802% 0,2008% 1,8500% -3,5103% -0,8547% -1,9604% -0,8380% 2020-04-03 -0,8817% -0,9557% -3,4091% -3,8076% 3,0593% -1,4197% 0,8621% -2,7240% 0,8341% 2020-04-06 4,0696% 10,5263% 1,9608% 0,0000% 0,9276% 5,4005% 7,6923% 4,1630% 4,6731% 2020-04-07 3,5671% 7,1032% 1,5385% 4,1667% -2,5735% 3,0743% -2,3810% 4,2697% -0,6269% 2020-04-08 -0,0611% 1,7043% -2,9040% 25,6000% -0,7547% -3,3969% 1,6260% 8,1328% -0,8418% 2020-04-09 0,2898% -0,5464% 4,0312% 10,0318% -3,9924% 0,1715% 0,0000% 4,5050% -1,2739% 2020-04-14 2,1726% 2,0513% 4,7500% 1,3025% 1,1881% 0,7705% 0,0000% 2,7012% 0,6529% 2020-04-15 -3,6427% -3,8765% 11,8138% -2,7143% -0,2935% -1,6992% -4,8000% 1,7404% -2,2641% 2020-04-16 -0,3448% -4,0702% 1,2807% -5,4332% -0,4907% 0,6914% 0,0000% -2,7410% 0,0669% 2020-04-17 3,2932% 5,4496% 4,3203% 4,5031% 0,8876% 5,1502% -1,6807% 4,7577% 1,4523% 2020-04-20 0,5325% 0,0369% 3,4343% -8,1724% 0,3910% 0,4082% 0,8547% -1,5669% 0,5513% 2020-04-21 -2,5921% -4,1328% -2,7344% 4,3689% 0,5842% -4,3902% -0,8475% -0,8331% -1,5509% 2020-04-22 1,8682% -1,4242% 8,0321% 0,9302% 5,1307% 0,3401% 7,6923% 2,5123% 4,3878% 2020-04-23 1,2295% 0,3124% 5,2045% 4,1475% -1,6575% 0,2542% -0,7937% 3,2211% -0,7324% 2020-04-24 -1,2639% -2,8805% -0,7067% 3,2448% -0,5618% -5,3254% 2,4000% -0,1144% -1,1624% 2020-04-27 1,2938% 3,8878% 3,0249% -2,2857% -0,6591% -2,6786% -4,6875% 1,5426% -2,6749% 2020-04-28 1,4253% 2,7006% 3,6269% 0,0000% -0,3791% 3,0275% -1,6393% 2,1092% 0,3363% 2020-04-29 2,0341% 5,9354% 1,1667% 2,9240% -1,1418% 1,0686% -3,3333% 3,3423% -1,1355% 2020-04-30 -1,0847% -3,4043% -0,4942% 5,1136% -0,2887% -0,5286% -5,1724% 0,4047% -1,9964%

Appendix D: Excess return

Date OMXSPI HM B BOOZT ODD AXFO CLA B AGRO FASHION_port FOOD_port

49

Appendix E: Expected return

Date HM B BOOZT ODD AXFO CLA B AGRO FASHION_port FOOD_port

52 2020-04-17 7,087% 1,419% 5,367% 0,033% 4,293% -0,908% 4,481% 0,631% 2020-04-20 0,118% 1,096% -9,614% -0,089% 0,304% 0,263% -1,494% 0,166% 2020-04-21 -5,250% -1,152% 5,208% -0,041% -3,731% -0,523% -0,801% -0,919% 2020-04-22 -1,763% 2,772% 1,144% 1,076% 0,247% 3,422% 2,360% 2,147% 2020-04-23 0,473% 1,741% 4,946% -0,592% 0,175% -0,498% 3,029% -0,497% 2020-04-24 -3,651% -0,385% 3,871% -0,289% -4,511% 1,001% -0,119% -0,697% 2020-04-27 5,080% 0,938% -2,654% -0,357% -2,294% -2,305% 1,443% -1,506% 2020-04-28 3,539% 1,185% 0,039% -0,256% 2,512% -0,874% 1,981% 0,069% 2020-04-29 7,704% 0,288% 3,495% -0,444% 0,864% -1,656% 3,145% -0,691% 2020-04-30 -4,321% -0,316% 6,083% -0,232% -0,479% -2,505% 0,370% -1,134% Appendix F: Volatility

Date HMB BOOZT ODD AXFO CLAB AGRO FASHION_port FOOD_port