1
The effects of Quantitative Easing on
stock returns in the Eurozone
Abstract
Starting in March 2015, the European Central Bank (ECB) applied Quantitative Easing as a form of monetary policy to bring inflation at a stable level of 2% due to fears of deflation. Quantitative Easing should have resulted in an increase in consumption and an impact upon firm’s stock prices. This paper uses an event study to infer that Quantitative Easing did have a positive impact on Eurozone firms. Large capitalisation firms, non-cyclical firms and
countries exhibiting sustainable public debt benefitted the most from the non-conventional monetary policy.
Bachelor's Thesis Academic year 2017-2018 Faculty of Economics and Business
Universiteit van Amsterdam Milos Dragojevic (10853456) Supervisor: Dorinth W. van Dijk
2 Statement of Originality
This document is written by Milos Dragojevic, who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3
Table of Content
1
Introduction
4
2
Literature review
5
2.1
Quantitative Easing theory
5
2.2
QE in the Eurozone
7
2.3
Hypotheses
9
3
Methodology
9
3.1
Event study
9
3.2
Data description
10
3.3
The model
12
3.4
Independent variables
13
4
Results and Analysis
14
4.1
Small vs Medium vs Large
15
4.2
Cyclical vs Non-cyclical
16
4.3
High public debt vs Low public debt
18
5
Conclusion
20
6
Bibliography
22
4
1 Introduction
On the 15th of September 2008, the US investment bank Lehman Brothers collapsed.
This event triggered a banking panic which resulted in the 2008 global financial crisis, a recession which had an unprecedented impact on worldwide economies (Walisson, 2008).
The crisis was the product of a combination of asset complexity and a failure in predictive risk models (Jurado, Ludvigson & Ng, 2015). It resulted in years of economic stagnation and significant debt solvency problems for many countries and still represents a challenge for some nations to this day.
One of the causes of the crisis was the banking deregulation which allowed
investment banks to enjoy considerable growth by selling supposedly risk-free investments such as collateralised debt obligation (CDO). These complex financial assets were
introduced to enjoy an enhancement in profits when the market went well, with the risk of high cost if the market underperformed. This deregulation meant the power of Central Banks weakened, and their monetary policies had less effect. These conventional monetary
policies such as open market operations result in a stable inflation rate which is seen as vital for most Central Banks (Guthrie and Wright, 2000). The usual monetary process starts when the Central Banks buys back government bonds from various institutions to lower interest rates. This lower interest rate should promote an increased inward flow of capital which should result in higher economic expansion.
However, these usual methods did not manage to prevent the asset market
speculation and its ultimate demise. The first cause of the crisis resulted from Central Banks focusing on stable inflation at the expense of financial stability (Joyce, Miles, Scott &
Vayanos, 2012). This individual attention allowed the creation of an asset price bubble which was ready to burst in late 2008. The second issue was that conventional monetary policies were unable to stimulate economic growth (Priftis & Vogel, 2016). Interest rates could not be lowered anymore through the use of conventional monetary policy which led to economies experiencing minimal inflation and low consumption. Hence a new way to lower interest rates was needed. One method countries tried to do this was through the introduction of an unconventional monetary policy: Quantitative Easing (QE).
QE is a non-conventional policy which pursues the same goal as conventional policies: keeping inflation at a stable level of around 2%. This strategy was used as a way to ensure low-interest rates and therefore favour economic development. Contrary to other banks such as the US Federal Reserve System, the European Central Bank started using QE as a way to stimulate consumption much later than similar institutions. Even though QE was used to some extent by the ECB after the crisis, it only saw an extensive
5 Bank Mario Draghi announced that starting in March 2015 the ECB would perform a €60 billion per month asset purchase programme. Additionally, he further stated that this
programme would last until inflation came to its optimal level of 2% and would be extended if deemed necessary (Draghi, 2015). The ECB’s policy aims to provide price stability and stable inflation. The programme is performed for dysfunctional credit markets by buying back ECB bonds with the ultimate goal being a stimulation of aggregate demand (Baumeister & Benati, 2010). The policy was perceived favourably by companies which suffered extensively from the crisis, particularly investment banks and technological firms. Many of these had to restructure their debt after the crisis, and the QE policy pursued by the ECB was supposed to promote stable inflation. This entailed price stability for these firms and avoiding an exacerbation of their debt. However this “positive view” is questioned by some who say keeping the interest rate indefinitely low would only result in an indefinite cycle of booms and recessions (Palley, 2011).
This results in the central research question of this paper: to what extent did the Quantitative Easing policy pursued by the ECB impact the stock prices for Eurozone firms? This paper takes the form of an event study focused on the QE implementation by the ECB on the first working day of March 2015. Regressions for the daily historical returns of selected firms shall be performed to examine the impact of QE on stock prices.
This study begins with a presentation of the relevant literature concerning QE. Following, a presentation of the models used to analyse the effects of the unconventional monetary policy on firm’s returns is given. Subsequently, the data shall be tested and analysed. Finally, the conclusion resulting from this study shall be presented.
2 Literature review
The literature review starts with a presentation of the relevant research and debates on QE. A description of its operation and application shall be provided to understand what its impact on the economy should be. Following a more focused look at its usage in the Eurozone shall be studied. This would allow for an analysis of the variations in stock returns resulting from QE in the Eurozone.
2.1 Quantitative Easing theory
A Central Bank sets the conventional monetary policy as a way to adjust the overnight interest rate on the interbank money market. This process is also referred to as an open market operations and involves the purchase of risk-free government bonds. These open market operations have proven themselves to be useful over the years. Nonetheless, they
6 seem to be optimally used only when the interest rate is high. When interest rate approach a level of zero, the open market operations struggle to make a substantial positive change and can be completely ineffective (Guthrie & Wright, 2000).
Additionally, the increased money supply resulting from the operations can lead to a higher inflation rate which can be damaging for economies already experiencing high money growth (Berk & DeMarzo, 2017). These difficulties meant that a new way of lowering interest rate was required. One of the alternative strategies is Quantitative Easing, an
unconventional monetary policy which is the focus of this paper.
QE aims at bringing inflation to a stable level of two per cent. Doing so involves lowering interest rate through the purchase of financial assets from financial institutions by the Central Bank. QE is comparable to conventional monetary policy in that it also involves the purchase of government bonds and risky assets. However, these assets purchases are performed on a much larger scale under QE than under conventional policies (Kapetanios, Mumtaz, Stevens & Theodoridis, 2012). The acquisition of financial assets causes a soaring in these assets prices and an increase in the money supply and inflation. From economic studies, it is known that the price is inversely related to the interest rate (Berk & DeMarzo, 2017). Resulting from the theory, it is inferred that the interest rate should decrease.
A drop in interest rate would lead to a larger money supply, which subsequently results in higher investment and stimulation of economic growth (Smaghi, 2009). This leads to companies being able to borrow capital at a lower price than with higher interest rates (Fawley & Neely, 2013). Thus investment performed by these companies is boosted which results in an increase of consumption and leads inflation towards its optimal level of 2%. This increase in investment also benefits profits in the proximate future since consumption has risen (Claeys & Leandro, 2016). In consequence, it is expected that this increase in profits would reflect upon the price of these companies’ stocks.
Hence, QE manages to lower interest rates when conventional monetary are no longer effective in doing so (Jurado, Ludvigson & Ng, 2015). Further, QE succeeds to bring up the inflation rate to an ideal level of around 2%, thus avoiding potential adverse outcomes such as deflation (Christensen & Rudebusch, 2012).
Still, it is argued by some scholars that QE might not be an effective long-run
strategy. For Martin and Milas (2012), it would seem the beneficial effects of QE on interest rates would only be a temporary feature. In the long-run, interest rates would reach a level which is impossible to decrease further with QE, which is the same problem faced by conventional monetary strategies. Furthermore, Martin and Milas (2012) argue that
implementing QE is a one-time only strategy. The repeated purchases of financial assets will lead QE to lose its effectiveness as a way of increasing investment and subsequent
7 economic growth. Some others argue that QE might lose its value over time since its first implementation proves to risk-averse investors that the Central Bank is committed to stop the recession and reverse its negative outcomes (Hamilton & Wu, 2011). This assurance of stability and backup from the Central Bank would boost investment which would indicate that following programmes might have a lower impact on interest rates.
Moreover, Szczerbowicz (2011) declares that many rounds of QE can have a negative impact in the long run. Using the example of the Fed’s policy, she claims that only the first round of QE has an impact on interest rates and is effective at keeping inflation stable, while the second round does not influence interest rates and inflation may overshoot its desired level. Also, Szczerbowicz (2011) states that QE could lead to an overshooting of the desired inflation rate which would be in opposition with the inflation stability goal.
Another issue is the fact that financial transactions could become unprofitable under QE. Shirakawa’s research (2002) was a precursor in stating that a purchase of financial assets by a Central Bank might lead to a rise in costs of transactions, which results from perceived higher risk and uncertainty resulting from the policy. This might result from the fact that financial arrangements do not react accordingly to the policy in the short run.
Looking at the theory of QE, it is uncertain whether its effects would be beneficial in the Eurozone context.
2.2 QE in the Eurozone
QE started being used as a growth mechanism by the ECB much later compared to other Central Banks such as the United States Federal Bank (Fed) or the Bank of Japan. As an example, the Fed introduced the policy in November 2008, purchasing 600 billion dollars in mortgage financial assets only weeks after the collapse of Lehman Brothers (Duafala, 2015). These policies were seen as extremely urgent to combat the effects of the banking crisis which eventually turned into a global economic recession. Boosting an investment sector which was stagnating due to risk-averse investors was of paramount importance (Ivashina & Scharfstein, 2010).
Since its creation, the ECB’s historical focus has always solely been on keeping inflation at a stable rate of just under 2%. This meant that encouraging economic growth would require an increase in the money supply which could only lead to higher inflation (Claeys, Leandro & Mandra, 2015). Still, in January 2015 the ECB’s inflation rate was at its lowest level since 2009 and was actually at a deflation level of -0.6% (ECB, 2015). This signified that QE was needed to avoid an increase in the real value of debt and hence a worsening of the recession (Duafala, 2015). Immediately following the QE announcement, inflation started rising and reached a level above 0% in April 2015.
8 The use of QE in the Eurozone has raised concerns for some. QE could exacerbate inequalities between different social classes of the Eurozone. As stated by Claeys and Leandro (2016), only the wealthy would benefit. This also applies to differences in wealth between Eurozone states and the level of public debt that these countries hold.
De Santis (2016) affirms that the QE policy was successful in lowering interest rates in the Eurozone and that countries which suffered the most from the recession benefited the most from the policy. These countries usually exhibited high public debt which meant
experiencing an aggravation of their companies’ debt in periods of deflation or low inflation. Thus QE would help stabilise the inflation and aid firms, and countries recover from the recession.
Furthermore, the influence of QE on stock returns would differ depending on the industry in which Eurozone companies operate (Bernanke & Kuttner, 2005). Changes in the market would affect considerably firms operating in industries such as the technological and financial sectors (Fama & French, 2004). Companies which operate in less market sensible industries, such as the utilities or food sector are less affected by market fluctuations. This is in accordance with the Capital Asset Pricing Model theory which states that highly cyclical firms are more responsive to change in the market than non-cyclical firms (Berk & DeMarzo, 2017). The cyclicality of a firm is examined through the beta (β) coefficient. The market is defined by a beta equal to one. Firms that react sensibly to changes in the market would be considered cyclical (β>1). This implies that these firms provide services which are not essential in times of recession Firms that are unresponsive to market fluctuations are considered non-cyclical (β<1). Their products are seen as basic needs by consumers and consumed regardless of the economic trends (Berk & DeMarzo, 2017). In consequence, it is expected that a change in the Eurozone monetary policy should have a higher impact on market-sensitive industries than on less sensitive ones.
Seeing that public debt can have an impact on firm’s returns, it would be relevant to analyse if the firm’s size has an impact on the effects of the monetary policy change. It is argued that smaller firms would suffer the most from a potential deflation, hence if QE’s goal is to bring up inflation, small-sized firms should exhibit higher changes than bigger sized firms (Gertler & Gilchrist, 1994). This would be confirmed by the research of Thorbecke (1997) which stated that small-sized firms are highly reactive to monetary policy changes as well as to recessions. For example, a change in monetary policy would affect a firm’s access to credit. QE would boost investment hence smaller firms should receive proportionally more capital than larger firms which would imply higher stock prices.
It is further argued that QE seems effective in reducing the duration of public debt for Eurozone members (Bhattarai, Eggertsson & Gafarov, 2015). This occurs because the
9 policy raises inflation to a stable rate of 2%, which means avoiding the adverse output gap problem faced during deflation. Therefore, it avoids an exacerbation of the debt which would only push countries towards debt default and further recession. Knowing that the debt problem would be mitigated when QE is used, investors will be confident that interest rates will be kept to a low level and would promote investment which should be seen by an increase in stock prices (Eggertsson, 2006).
This research focuses on the effects of QE on Eurozone’s stock prices. In their research, Fawley and Neely (2013) state that stock prices would rise after the QE
implementation. According to them, this is explained by the fact that the purchase of assets at a significant rate would expand the monetary supply to a large extent which would result in a sharp drop in interest rates. This would increase consumer’s wealth leading to a boost in investment and therefore higher stock prices.
From past research, it can be concluded that QE succeeded in lowering interest rates to a level below which could be achieved with conventional monetary policies. Nonetheless, it is not sure whether the policy helped companies experience substantial change in returns.
2.3 Hypotheses
In view of the presented literature, three hypothesis are investigated.
Hypothesis 1: Small market capitalisation firms display higher returns increase than large market capitalisation firms.
Hypothesis 2: Cyclical companies generate a higher returns increase than non-cyclical companies.
Hypothesis 3: High-public debt Eurozone countries experienced higher increases in local companies return than sustainable-public Eurozone countries.
3 Methodology
This section describes the methodology and data used throughout this study. The upcoming section presents the event study performed in this paper. Ensuing, information covering the data used in this research and the data sources are provided. A description of the model used to estimate the effects of QE on firm’s returns as well as its component variables is further provided.
3.1 Event study
To perform an event study regarding the effects of QE on Eurozone companies, regressions are executed for each company on the three independent variables originating from the
10 Fama and French Three Factor model (1992). If companies did experience a change in their returns, they should exhibit a significant coefficient alpha. This alpha would describe how the firm performed over the period. A positive alpha would mean an increase in returns and thus QE having an impact on the firms’ stock prices. Following, a test of the cumulated abnormal returns shall be performed to compare firms and answer the hypotheses.
According to Campbell, Lo and Mackinlay (1997), abnormal returns are defined as the actual realised returns minus the expected return of the firm over the event study period. The equation is
𝜀̂ = 𝑅 − 𝐸(𝑅 |𝑋 )
where 𝜀̂ represent the abnormal returns, 𝑅 the actual returns and 𝐸(𝑅 |𝑋 ) the expected returns for the security according to the calculations performed with the regression model. Firstly each firm’s abnormal returns are aggregated across time:
𝐶𝐴𝑅 =∑𝜀̂ .
Succeeding, the cumulated abnormal returns are aggregated across firms using the 𝐶𝐴𝑅 formula. This formula is:
𝐶𝐴𝑅 = ∑𝐶𝐴𝑅 .
The aggregate variance is calculated for each firm. This ensues with the equation: 𝑉𝑎𝑟 𝐶𝐴𝑅 = 𝜎 = ∑𝜎 .
Finally, the aggregate standard deviations can be calculated by: 𝜎 = √𝜎 .
This all leads to the test of significance of aggregate abnormal returns which is done by using the student test statistic:
𝑡 = .
The p-value of this test allows to conjecture if the cumulated abnormal returns are
significant. Using the significant alphas and the cumulated abnormal returns, the hypotheses shall be confirmed or rejected.
3.2 Data description
On the 22nd of January 2015, Mario Draghi, the President of the European Central
Bank, announced a new asset purchase programme. This new plan would start in March 2015 and would see the ECB purchasing 60 billion euros of bonds per month from Eurozone financial agencies and institutions. This policy has been used extensively since, as seen by the announcement given by the ECB on 10 March 2016, in which it increased its monthly bond acquisition from 60 billion euros to 80 billion euros.
11 Given these recent development regarding the policy, it is presently relevant to study the effects of QE in the Eurozone. The study centres itself on the first working day of March 2015 (02/03/2015), when the policy started being implemented. The research covers a period of 1641 days. There are 820 daily observations before the QE date and 820 after. The data ranges from 09/01/2012 to 23/04/2018. It should be noted that countries stock exchanges differ in their holidays, for example, some engage in trade on the 24th of
December while others stay closed on that day. This means that some countries might have less or more than 820 working days per period. Nonetheless, since the study includes well over 1620 observations per company, a minor fluctuation in the number of daily values does not influence the research and therefore does not challenge its validity.
For the analysis of the influence of QE on differently sized firms in the Eurozone, 57 firms were selected. These firms operate in 19 sectors which represent the Eurozone economy. From each of these 19 sectors, three firms were selected: a large, mid and small capitalisation company. These companies are listed on the EURO STOXX, an index which includes 305 companies operating in the Eurozone and which represents most of the Eurozone’s capital trade. Hence the large firm represents the most significant company operating in the sector according to the EURO STOXX, the mid represents the median firm in the industry and the small represents the smallest business operating in the market area. These companies were chosen based on their market capitalisation and the activity sector in which they operate. The table for the selected companies ranked by country is available in the Appendix 1. The EURO STOXX component list is located in Appendix 3.
The sample companies stocks are all traded in Eurozone countries. Since the purpose of this research is to examine the effects of QE in the Eurozone, the national stock exchange equity index of every company’s country is suitable as the country-specific market proxy. This validity is further supported by the fact that every stock exchange index
represents the majority of the capital trade which occurs in the country, which implies a precise description of the nation’s market change. Listed below are each country’s index.
Table 1: National stock exchanges Country Stock Exchange Name Belgium BEL 20
France CAC 40
Germany DAX PERFORMANCE INDEX Ireland ISEQ 20
Italy FTSE MIB Index Netherlands AEX
12 The risk-free rate for the study is the official 3-month AAA-rated bond yield spot rate
provided by the ECB. It is compounded daily by dividing the provided value by 90 days. The majority of investors consider ECB bonds as a risk-free investment, and therefore they are seen as adequate to represent the risk-free rate (Berk & DeMarzo, 2014). Furthermore, the research is valid, and representative for the Eurozone since all the parameters and data used are listed in Eurozone stock exchanges.
The data for the 57 firms operating in the Eurozone is collected through Yahoo Finance. Yahoo Finance is a free online financial time series database which includes a majority of traded equity around the globe. Yahoo Finance is used extensively to extract historical daily stock prices needed to calculate the returns of firms and markets. The daily returns for each firm and market are calculated using the following formula.
𝑁𝑒𝑤 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑐𝑙𝑜𝑠𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒 − 𝑂𝑙𝑑 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑐𝑙𝑜𝑠𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒
𝑂𝑙𝑑 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑐𝑙𝑜𝑠𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒
∗ 100
3.3 The model
The Capital Asset Pricing Model (CAPM) is a model which states that differences in returns can be explained by a single factor: the risk premium (Blume& Friend, 1973). It manages to explain that firms exhibit different returns depending on their market change sensibility. However, this model is quite limited as stated by Fama and French (1992). Expanding the CAPM by adding a size and a value factor results in a much more accurate calculation of stock’s returns. They argue that their model, the Fama- French Three-factor model can explain 90% of the variation in stock’s returns. Its formula is defined as:
𝑅 – 𝑅𝐹 = 𝛼 + 𝛽(𝑅𝑀 –𝑅𝐹 ) + 𝑠𝑆𝑀𝐵 + ℎ𝐻𝑀𝐿 + 𝜀 . The variables are the following:
𝑅 = The return of the stock 𝑅𝐹 = The risk-free return
𝑅𝑀 = The return of the market index
𝑆𝑀𝐵 = The size factor (market capitalization) 𝐻𝑀𝐿 = The value factor (book-to-market ratio)
𝑎 = The deviations between the historical performance and the predicted performance of the security market line
𝛽, 𝑠 & ℎ = The slope coefficients which display factor sensitivity
𝜀 = The error term for returns which occurred in past events but which cannot be explained by the regression equation
13
3.4 Independent variables
The Return of the Market is calculated by using the historical adjusted closing values for the different market proxies. The risk-free rate is subtracted from the market return to result in the market premium (𝑅𝑀 –𝑅𝐹 ).
The 𝑆𝑀𝐵 is defined as the variable which represents risk factor dependent on the company’s size. This implies that its calculation requires knowledge of the market
capitalization for each company. After ordering the selected companies by size, a median is determined to separate small (S) from big firms (B).
The 𝐻𝑀𝐿 represents the risk factor depending on the book to market equity ratio of a firm. Its calculation follows a similar procedure to the one used in 𝑆𝑀𝐵 , except that
companies are ranked by their book to market equity ratio. This ranking is then divided using the 70% and 30% quantiles. The ones above 70% are labelled High, the ones between 70% and 30% are called Medium and the ones below 30% are ranked Low.
Using the size and book to market equity ratio parameters, 6 size/book to market portfolios are created. These portfolios are SH, SM, SL is small-sized and BH, BM, BL is big sized. These portfolios do include ranges and do not incur transaction costs (Kenneth R. French Website). According to Fama-French (1992), the two new variables 𝑆𝑀𝐵 and 𝐻𝑀𝐿 are representative of the sensitivity to common risk factors in returns.
Thanks to these portfolios, the daily returns of the 𝑆𝑀𝐵 and the 𝐻𝑀𝐿 can be calculated.
SMBt =1/3 (Small High + Small Medium + Small Low) - 1/3 (Big High + Big Medium + Big
Low).
HML t=1/2 (Small High + Big High) - 1/2 (Small Low+ Big Low).
For this research the daily data for the 𝑆𝑀𝐵 and the 𝐻𝑀𝐿 originates from the French database (French, 2018).
Ensuing the calculation of the independent variables, the regression are performed. The aim is to determine the alphas and betas of firms which would provide with an indication of a change in returns and cyclicality. The analysis of cumulated abnormal returns leads to a decision upon the stated hypotheses.
14
4 Results and Analysis
This section presents the results of the analysis. It is divided into three part, one for each hypothesis. Firstly, the change in returns experienced by differently sized companies shall be examined. Following, a comparison of the change in returns for companies exhibiting different cyclicality shall be examined. Further, the comparison of returns for companies located in countries with different levels of public debt shall be completed.
Each hypothesis testing follows the same procedure. First, an examination of the alphas shall be performed. Followed by an examination of the abnormal returns to confirm that a change in stock returns did occur. Using the results, a discussion of the stated hypothesis shall be done to see if the hypothesis holds.
The subsequent table lists the companies which exhibited significant changes in the coefficient alpha over the regression period. The full table for all 57 firms is in Appendix 2.
Table 2: Firms exhibiting significant alphas (* if p<0.1, ** if p<0.05 and *** if p<0.01)
Country Company Name Size α β SMB HML R² 𝐂𝐀𝐑 France KERING Large 0.0642 **
(2.1322) 1.0108 *** (23.3827) (0.2738) 0.0305 -0.5243 *** (-7.0308) 0.45 (1.2037) 65.6373 France PLASTIC OMNIUM Small 0.0756 * (1.78) 1.5606 *** (25.5655) 1.4637 *** (9.2952) -0.2301 ** (-2.1854) 0.376 -136.2096 (1.7139) Germany ALLIANZ Large 0.0388 **
(2.2793) 0.9329 *** (42.0824) -0.1007 * (-1.7365) 0.407 *** (9.8612) 0.743 (0.6773) 9.9929 Germany BEIERSDO
RF Medium 0.036 * (1.701) 0.5867 *** (21.2866) -0.5354 *** (-7.4223) -0.9869 *** (-19.2328) 0.497 -13.5506 (0.844) Germany DEUTSCHE
WHONEN Large (2.4604) 0.076 ** 0.6589 *** (16.3909) (-1.0806) -0.1137 -0.6064 *** (-8.1016) 0.275 -10.0815 (1.241) Germany FREENET Small 0.0538 *
(1.8805) 0.7676 *** (20.622) (0.8294) 0.0808 -0.288 *** (-4.1566) 0.343 -73.9668 (1.1366) Ireland SMURFIT KAPPA GROUP Medium 0.0909 ** (2.1078) 1.0319 *** (22.0268) -0.0318 (-0.2738) 0.366 *** (3.5083) 0.298 -67.5386 (1.7128) Italy SAIPEM Small -0.17 ***
(-2.6071) 0.9795 *** (14.5941) 0.9252 *** (4.4865) 0.3187 * (1.7658) 0.205 (2.6301) 23.8443 Netherlands HEINEKEN Medium 0.0506 **
(2.2323) 0.6497 *** (19.2618) -0.5463 *** (-7.0102) -0.9198 *** (-16.6413) 0.448 -24.7127 (0.905) Netherlands UNILEVER NV Large 0.0339 * (1.8715) 0.7631 *** (28.2992) -0.7106 *** (-11.4051) -1.0506 *** (-23.7764) 0.646 (0.7276) -0.546 Spain GAS NATURAL SDG Medium 0.0497 * (1.9353) 0.7755 *** (26.1343) (-1.4222) -0.1168 (-0.1506) -0.0106 0.518 (1.0391) -21.878 Spain VISCOFAN Small 0.0562 **
(2.0106) 0.4519 *** (13.9887) -0.28 *** (-3.1324) -0.9831 *** (-12.8553) 0.227 -53.1492 (1.1217)
Looking at the regression results, it can be seen that only 12 firms out of the 57 display a significant change in their alphas. This would mean that QE only affects certain types of firms. To investigate how these firms are characterised, the previously states hypotheses are tested.
15
4.1 Small vs Medium vs Large
In this section of the research, the difference in returns experienced by differently sized companies is examined. The following table splits companies by size and lists the alphas experienced by these three sizes.
Table 3: Regression results ranked by size
Size α=0 α>0 α<0 Total
Large 15 4 0 19
Medium 14 4 0 19
Small 15 3 1 19
Total 44 12 1 57
According to the literature available on QE, the null hypothesis to be tested would state that small-sized firms would exhibit a higher change in their returns compared to firms that are medium-sized or large-sized.
From the table above, it is seen that four small-sized companies exhibit a change in returns, four medium-sized and four large-sized. This would tend to reject the null hypothesis since the small firms exhibit the same number of significant change in returns as the medium and the large size companies. However, this is compromised by the fact that the company SAIPEM has a negative alpha which means it experienced a decrease in its returns over the study period. Thus the number of firms which display a higher return seems to be lower for smaller firms. This goes against the literature which states that smaller firms are the most influenced by changes in monetary policy. Also to be noted is that large firms exhibit a higher R-squared on average than medium or small firms. This would mean that the Fama-French Three Factor model explains a majority of the fluctuation in stock returns for large firms. This further confirms that the hypothesis does not hold. An examination of the cumulative
abnormal returns is now performed to confirm that the null hypothesis does not hold.
To do so, the Cumulated Aggregate Abnormal Returns (𝐶𝐴𝑅 ) were calculated for the large firms operating in 19 industries, for the sum of the 19 medium sized firms and for the sum of 19 small sized firms. Using the t-test and its p-values a conclusion can be reached regarding the hypothesis. Listed below are the results.
Table 4: Cumulated abnormal returns ranked by size
Size n 𝐂𝐀𝐑 𝐂𝐀𝐑 σ t p
Large 19 117.585 6.1887 4.4487 6.0638 <0.00001
Medium 19 -344.067 -18.1088 6.0014 -13.1527 <0.00001 Small 19 -860.799 -45.3052 6.7664 -29.1855 <0.00001
16 It is observed that the p values for each sized firm are very significant which means all sizes experienced a change in their 𝐶𝐴𝑅 . Further, the 𝐶𝐴𝑅 for small 45.3052) and medium (-18.1088) are both negative while large exhibits positive 𝐶𝐴𝑅 (6.1887). Large firms seem to have benefited the most from QE followed by medium firms. It confirms that the earlier comparison of alphas is correct and therefore that the null hypothesis does not hold.
4.2 Cyclical vs Non-cyclical
This part analyses the change in returns experienced by firms divided into two categories, cyclical and non-cyclical. The following table ranks these two types of firm and lists the different alphas.
Table 5: Regression results ranked by cyclicality
Type firm α=0 α>0 α<0 Total
Cyclical 22 3 0 25
Non-Cyclical 23 8 1 32
Total 45 11 1 57
From the table, it is seen that three cyclical firms exhibit significant alphas, while nine non-cyclical ones do. This demonstrates that cyclical firms are less reactive to the
introduction of QE than non-cyclical ones. This contradicts the literature and would lead to believe that companies that benefitted the most from the QE policy are unresponsive to market change. Hence, the null hypothesis would seem to be contradicted. Note that SAIPEM displays a negative alpha which means it experienced a decrease in returns over the period.
The following table lists the cumulated abnormal returns for the firms that exhibit a market return coefficient beta larger than one and firms that display a beta smaller than one.
17 Table 6: Cumulated abnormal returns ranked by industry
Industry n 𝐂𝐀𝐑 𝐂𝐀𝐑 σ t p
Automobiles & Parts 3 -197.351 -65.7835 2.8329 -40.2204 0.000618
Banks 3 1.8624 0.6208 2.7243 0.3947 0.733117
Basic Resources 3 178.6499 59.55 2.7408 37.6327 0.000706
Chemicals 3 13.0636 4.3545 2.2669 3.3271 0.079689
Construction & Materials 3 -51.1896 -17.0632 2.0735 -14.2534 0.004889 Financial Services 3 -121.248 -40.4159 2.0703 -33.8127 0.000874 Food & Beverage 3 -150.02 -50.0067 1.6839 -51.4366 0.000378 Health Care 3 -46.8847 -15.6282 1.5284 -17.7106 0.003173 Industrial Goods &
Services
3 -177.898 -59.2993 2.42 -42.4419 0.000555 Insurance 3 -36.291 -12.097 1.9736 -10.6164 0.00875
Media 3 -190.432 -63.4774 2.288 -48.0534 0.000433
Oil & Gas 3 110.9465 36.9822 3.0003 21.3495 0.002182 Personal & Household
Goods 3 -121.815 -40.605 1.6715 -42.0759 0.000565 Real Estate 3 -46.5602 -15.5201 1.787 -15.0429 0.004386 Retail 3 -77.9472 -25.9824 2.1944 -20.508 0.002369 Technology 3 -133.689 -44.5631 2.2811 -33.837 0.000873 Telecommunications 3 -74.8645 -24.9548 2.3312 -18.5411 0.002897 Travel & Leisure 3 -12.6857 -4.2286 3.086 -2.3733 0.14098
Utilities 3 47.0726 15.6909 2.1873 12.4251 0.0064
Looking at the results, it seems that non-cyclical industries do experience higher 𝐶𝐴𝑅 than cyclical ones. The oil and gas industry exhibits a positive 𝐶𝐴𝑅 (36.9822) as does the utilities sector (15.6909) and both sectors are composed of non-cyclical firms.
The banks sector is a cyclical industry which has positive 𝐶𝐴𝑅. It is the most cyclical sector, with BPER BANCA having the highest beta (1.6126) out of the 57 firm sample. The 2 other firms in the industry, BPER BANCA and COMMERZBANK also rank amongst the most cyclical firms, with betas of respectively 1.274 and 1.2409. However, the p-value for the t-test of the bank sector’s 𝐶𝐴𝑅 is also the most insignificant one of all sectors (p>0.1). Two other cyclical sectors, chemicals and travel & leisure, have insignificant 𝐶𝐴𝑅 . The only market sensitive firm which exhibits positive 𝐶𝐴𝑅 (59.55) is the basic resources sector.
When comparing all industries, a clear trend emerges. Even though most industries have experience negative 𝐶𝐴𝑅 over the period of testing, non-cyclical industries have higher 𝐶𝐴𝑅 than cyclical ones. These observations combined with the previous examination of alphas provide a strong affirmation towards the statement that non-cyclical firms react better to QE than cyclical ones. Thus the null hypothesis is rejected.
18
4.3 High public debt vs Low public debt
This part analyses the change in returns experienced by firms in their respective countries. The aim is to see whether countries which were unable to refinance their public debt after the 2008 crisis managed to enjoy positive changes in their companies’ returns after the implementation of QE compared to average public debt countries.
The following graph lists the public debt to GDP ratio of the country samples. The data originates from the Organisation for Economic Co-operation and Development(OECD).
Clearly, from the graph, it can be seen that countries such as Netherlands and Germany always have has the lowest public debt levels since the awakening of the crisis in 2008. Countries such as Italy and Belgium have had a public debt above 100% of GDP ever since the recession. However, the difference between these two countries is that Belgium was able to bail out over-indebted banks while Italy failed to do so (Büchel, 2013). The high public debt countries are defined as Italy, Spain and Ireland. The sustainable public debt countries are Belgium, France, Germany and the Netherlands.
Following, the 57 selected companies are allocated to their home countries.
Belgium France Germany Ireland Italy Netherlands Spain 20 40 60 80 100 120 140 160 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 % o f G D P Year
19 Table 7: Regression results ranked by country
Country α=0 α>0 α<0 Total Belgium 4 0 0 4 France 15 2 0 17 Germany 12 4 0 16 Ireland 2 1 0 3 Italy 5 0 1 6 Netherlands 4 2 0 6 Spain 3 2 0 5 Total 45 11 1 57
From the table, it can be seen that Germany leads with four companies that enjoy an increase in returns. France, Netherlands and Spain are equal with two increasing returns company each. Ireland exhibits only one firm with a positive increase in returns. Italy
performs the worst with one firm which experiences a significant adverse change in returns. Thus it would seem from the regression data that the null hypothesis does not hold and that countries which did not face debt default reaped most of the benefits from QE.
The following table lists the 𝐶𝐴𝑅 for the firms ranked by alphabetical order for their home countries.
Table 8: Cumulated abnormal returns ranked by country
Country n 𝐂𝐀𝐑 𝐂𝐀𝐑 σ t p Belgium 4 -97.5263 -24.3816 1.8497 -26.3628 0.000125 France 17 -741.686 -43.6286 5.8318 -30.8456 <0.00001 Germany 16 -198.672 -12.417 4.4336 -11.2026 <0.00001 Ireland 3 -47.7933 -15.9311 2.7602 -9.9969 0.00986 Italy 6 -19.5345 -3.2558 4.3763 -1.8223 0.128079 Netherlands 6 26.3193 4.3866 3.454 3.1109 0.026524 Spain 5 -8.3884 -1.6777 2.4089 -1.5573 0.194462
The table confirms the fact that countries which did not face problems refinancing their debt benefited the most from QE. Belgium, France, Netherlands and Germany exhibit significantly higher 𝐶𝐴𝑅 than countries such as Italy and Spain which do not display significance. Only the Netherlands experience positive 𝐶𝐴𝑅 (4.3866) over the period of the study, while Ireland is the only country with high-public debt that had a significant change in its 𝐶𝐴𝑅 .
Comparing between countries with significant 𝐶𝐴𝑅 and low public debt, it is seen that Germany has the highest 𝐶𝐴𝑅 while it has the lowest public debt when compared to Belgium and France. Thus the null hypothesis is rejected and it is confirmed that countries with low public debt benefitted the most from QE over the time period of the research.
20
5 Conclusion
This research was performed with the purpose of examining the effects of Quantitative Easing on the stock prices of Eurozone companies. To that aim, an event study was
performed centred around the QE implementation day (02/03/2015). Following a description of the previously existing literature on QE, the data and methodology used throughout the research were presented. Based on the literature, three hypotheses were generated. These hypotheses were examined through the use of the Fama-French Three Factor model which explains 90 per cent of the variations of the market over the study period. Using the model, regressions were performed for the returns of the 57 firms evolving in 19 key Eurozone industry sectors. Using the regression results, an event study was performed. The comparison of the alphas was done to see how individual firms performed when QE was implemented. Following, the cumulated abnormal returns for firms were compared according to size, cyclicality and the level of public debt.
The results show that firms reacted differently depending on the various tested
hypothesis. The comparison of firms according to size led to the conclusion that large market capitalisation firms display a higher returns increase than smaller firms. This suggests that large firms reap most of the benefits of QE and are sensitive to changes in monetary policy. This could result from large firms having a huge weight in certain country economies which would mean a change in monetary policy would impact the huge firm significantly. Analysing firms according to their cyclicality results in non-cyclical firms having a higher increase in stock prices than cyclical companies. A reason for that could be that in times of recession consumers privilege essential goods such as food and avoid investing in less vital industries such as technologies. Examining companies according to the level of public debt of their home country directs to the conclusion that average public debt enables countries to enjoy the effects of QE.
One of the possible explanation for these results could be that the inflation did not reach and maintain its 2% target over the study period. This can be seen by the official inflation reports of the ECB which state that only in February 2017 did the inflation reach 2% (ECB Statistical Data Warehouse). Thus QE would seem to not have an immediate effect on interest rates and stock prices in the Eurozone.
One of the limitations of this study is the number of sample selected firms. As seen by the comparison between countries, some nations included fewer firms in their sample than others. This means that some countries could have a very well performing firm in their sample which could pull up or down the cumulated abnormal returns by a large margin. Also, using as a market proxy a common Eurozone index such as the Eurostoxx 50 could have resulted in different results and conclusions.
21 Future research could focus itself on analysing what will occur regarding the ECB’s monetary policy in the next few years. It was announced on the 14th of June 2018 that the
ECB has the intention to completely stop its asset purchase programme by the end of the year 2018 (Praet, 2018).Would the effects on stocks of the QE policy pursued since 2015 fade? Would companies’ returns change for the better or worse? Will the new policies be able to perform as QE did? One way to replace QE would be to purchase assets from countries depending on their outstanding debt and not based on their Central banks weight in the Eurozone system. Assets coming from countries with high public debt such as Italy would be ignored in favour of assets from countries exhibiting a lower level of public debt such as Germany.
22
6 Bibliography
Bhattarai, S., Eggertsson, G.B., & Gafarov, B. (2015). Time Consistency and the Duration of Government Debt: A Signalling Theory of Quantitative Easing. (NBER Working Paper No. 21336).
Baumeister, C., & Benati, L. (2010). Unconventional monetary policy and the great recession. (ECB Working Paper Series, No 1258).
Berk, J., DeMarzo, P. (2017). Corporate Finance Fourth Edition, United States of America: Pearson Education, 120-436.
Bernanke, B., & Kuttner, K. (2005). What Explains the Stock Market's Reaction to Federal Reserve Policy? Journal of Finance, 60(3), 1221-1257.
Blume, M. E, & Friend, I. (1973). A new look at the capital asset pricing model. Journal of Finance, 28(1), 19-33.
Brown, R. I., & Warner, J. B. (1980). Measuring Security Price Performance. Journal of Financial Economics, 8(3), 205-258.
Büchel, K. (2013). Do words matter? The impact of communication on the PIIGS' CDS and bond yield spreads during Europe's sovereign debt crisis. European Journal of Political Economy, 32, 412-431.
Campbell, J. Y., Lo, A. W., & Mackinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.
Ceylan, N. B., Dogan, B., & Berument, M. H. (2015). Three-factor asset pricing model and portfolio holdings of foreign investors: evidence from an emerging market– Borsa Istanbul. Economic Research-Ekonomska Istraživanja. 28(1), 467-486.
Christensen, J. H., & Rudebusch, G. D. (2012). The Response of Interest Rates to US and UK QE. Economic Journal, 122(564), F385-F414.
Claeys, G., Leandro, A., & Mandra, A. (2015). European Central Bank QE: the detailed manual. (Bruegel Policy Contribution, Issue 2015/02, Bruegel, Brussels). Claeys, G., & Leandro, A. (2016). The European Central Bank's quantitative easing
programme: Limits and risks. (Bruegel Policy Contribution, Issue 2016/04, Brussels). De Santis, R. (2016). Impact of the asset purchase programme on euro area government
bond yields using market news. (ECB Working Paper Series, No 1939). Duafala, N. R. (2015). A Closer Look at the Impact of QE on the Capital Markets:
GARCH Analysis of the Exchange Traded Funds Market. Undergraduate Economic Review, 11(1).
Draghi, M. (2015). Speech at the European Central Bank, Frankfurt am Main, 22/01/2015. Eggertsson, G. B. (2006). The Deflation Bias and Committing to Being Irresponsible. Journal
23 Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The
Journal of Finance, 47(2), 427-427.
Fama, E. F., French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 18(3), 25-46.
French, K. R. (2018). Data Library. Retrieved from
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html
Fawley, B. W., & Neely, C. J. (2013). Four Stories of Quantitative Easing. Federal Reserve Bank of St. Louis Review, 95(1), 51-88.
Gertler, M., & Gilchrist, S. (1994). Monetary policy, business cycles, and the behaviour of small manufacturing firms. Quarterly Journal of Economics, 109, 310-338.
Guthrie, G., & Wright, J. (2000). Open Mouth Operations. Journal of Monetary Economics, 46(2), 489-516.
Guttmann, R. (2014). Asset Bubbles, Debt Deflation, and Global Imbalances, International Journal of Political Economy, 38(2), 46-69.
Hamilton, J. D., & Wu, J. C. (2011). The Effectiveness of Alternative Monetary Policy Tools in a Zero Lower Bound Environment, (NBER Working Paper No. 16956). Ivashina, V., & Scharfstein, D. (2010). Bank lending during the financial crisis of 2008.
Journal of Financial Economics, 97, 319–338.
Jagannathan, R., & Wang, Z. (1996). The Conditional CAPM and the Cross-section of Expected Returns. The Journal of Finance, 51, 3-53.
Joyce, M., Miles, D., Scott, A., & Vayanos, D. (2012). QE and unconventional monetary policy, an introduction. The Economic Journal, 122(564), F271-F288.
Jurado, K., Ludvigson, S., & Ng, S. (2015). Measuring Uncertainty. American Economic Review, 105 (3), 1177-1216.
Kapetanios, G., Mumtaz, H., Stevens, I., & Theodoridis, K. (2012). Assessing the Economy wide effects of QE. The Economic Journal, 122, F316-F347.
Kerr, W., & Nanda, R. (2009). Democratizing entry: Banking deregulations, financing constraints, and entrepreneurship. Journal of Financial Economics, 94(1), 124-149. Martin, C., & Milas, C. (2012). QE: A sceptical view. Oxford Review of Economic Policy,
28(4), 750–764.
Palley, T. (2011). QE: a Keynesian Critique. Investigación Económica, 70(277), 69-86. Praet, P. (2018). Assessment of quantitative easing and challenges of policy normalisation.
Speech at the ECB and Its Watchers XIX Conference, Frankfurt am Main, 14/03/2018.
Priftis, R., & Vogel, L. (2016). A framework for analysing QE in the open economy. (Connecting European Facility 2016).
24 Thorbecke, W. (1997). On Stock Market Returns and Monetary Policy. The Journal of
Finance, 52, 635-654.
Shirakawa, M. (2002). One Year Under QE. (IMES Discussion Paper No. 2002-E-3) Smaghi, L. (2009). Conventional and Unconventional Monetary Policy. International Center
for Monetary and Banking Studies (ICMB), Keynote Lecture in Geneva, 1-14. Szczerbowicz, U. (2011). Are unconventional monetary policies effective? (Center for Labor
and Economic Growth Working Paper Series No. 07).
Walisson, P. (2008). Cause and Effect: Government Policies and the Financial Crisis. American Enterprise Institute
25
7 Appendix
Appendix 1: List of selected firms ranked by country
Country Name Industry Size
Belgium ACKERMANS & VAN HAAREN Financial Services Medium Belgium ANHEUSER-BUSCH INBEV Food & Beverage Large
Belgium COFINIMMO Real Estate Small
Belgium SOLVAY Chemicals Medium
France AIR FRANCE-KLM Travel & Leisure Small
France ALTRAN TECHNOLOGIES Technology Small
France BIC Personal & Household Goods Small
France BOUYGUES Construction & Materials Medium
France CARREFOUR Retail Medium
France CASINO GUICHARD Retail Small
France GECINA Real Estate Medium
France ILIAD Technology Medium
France IMERYS Basic Resources Small
France JCDECAUX Media Small
France KERING Retail Large
France PEUGEOT Automobiles & Parts Medium
France PLASTIC OMNIUM Automobiles & Parts Small
France SES Media Medium
France TOTAL Oil & Gas Large
France VINCI Construction & Materials Large
France VIVENDI Media Large
Germany ALLIANZ Insurance Large
Germany BASF Chemicals Large
Germany BAYER Health Care Large
Germany BEIERSDORF Personal & Household Goods Medium
Germany COMMERZBANK Banks Medium
Germany DAIMLER Automobiles & Parts Large
Germany DEUTSCHE BOERSE Financial Services Large
Germany DEUTSCHE TELEKOM Telecommunications Large
Germany DEUTSCHE WHONEN Real Estate Large
Germany DUERR Industrial Goods & Services Small
Germany FREENET Telecommunications Small
Germany GERRESHEIMER Health Care Small
Germany QIAGEN Health Care Medium
Germany SAP Technology Large
Germany SIEMENS Industrial Goods & Services Large
Germany WACKER CHEMIE Chemicals Small
Ireland PADDY POWER BETFAIR Travel & Leisure Medium
Ireland RYANAIR Travel & Leisure Large
26
Country Name Industry Size
Italy A2A Utilities Small
Italy AZIMUT HOLDING Financial Services Small
Italy BPER BANCA Banks Small
Italy ENEL Utilities Large
Italy SAIPEM Oil & Gas Small
Italy TENARIS Basic Resources Medium
Netherlands AEGON Insurance Medium
Netherlands ARCELORMITTAL Basic Resources Large
Netherlands BOSKALIS WETSMINSTER Construction & Materials Small
Netherlands HEINEKEN Food & Beverage Medium
Netherlands KPN Telecommunications Medium
Netherlands UNILEVER NV Personal & Household Goods Large
Spain BANCO SANTANDER Banks Large
Spain GAS NATURAL SDG Utilities Medium
Spain MAPFRE Insurance Small
Spain REPSOL Oil & Gas Medium
27 Appendix 2: Regression results for the 57 selected companies
(* if p<0.1, ** if p<0.05 and *** if p<0.01) Company Name α β SMB HML R² 𝐂𝐀𝐑 BPER BANCA -0.0258 (-0.5332) 1.6216 *** (32.5244) 0.6095 *** (3.9787) 1.2284 *** (9.1612) 0.6432 -43.0142 (1.9287) ARCELORMITTAL -0.0553 (-1.1498) 1.6134 *** (22.5679) 0.8493 *** (5.1412) 2.2119 *** (18.8805) 0.4785 133.3065 (1.952) PLASTIC OMNIUM 0.0756 * (1.78) 1.5606 *** (25.5655) 1.4637 *** (9.2952) -0.2301 ** (-2.1854) 0.3758 -136.2096 (1.7139) PEUGEOT -0.0055 (-0.1056) 1.5526 *** (20.6443) 1.4154 *** (7.296) 0.9068 *** (6.9902) 0.3458 (2.0969) -7.4773 DUERR 0.0309 (0.7974) 1.4375 *** (28.4762) 0.8266 *** (6.2571) (-1.4991) -0.1409 0.4431 -128.9882 (1.5467) AIR FRANCE-KLM -0.0032 (-0.0585) 1.3258 *** (16.8493) 1.361 *** (6.7058) 0.5168 *** (3.8078) 0.2326 -32.431 (2.1996) AEGON 0.0129 (0.3736) 1.3164 *** (25.7268) 0.4346 *** (3.6758) 1.4269 *** (17.0176) 0.5308 -50.0855 (1.3742) ALTRAN TECHNOLOGIES (1.053) 0.0389 1.3147 *** (24.7677) 1.4548 *** (10.6249) (-0.9842) -0.0901 0.3449 -51.6971 (1.4954) RYANAIR 0.0175 (0.5082) 1.3056 *** (34.7939) 0.5252 *** (5.6455) -0.3806 *** (-4.5546) 0.4576 (1.3723) -0.4707 COMMERZBANK -0.0643 (-1.4308) 1.274 *** (21.7721) 0.8086 *** (5.2805) 2.3171 *** (21.2692) 0.488 46.2487 (1.7858) BANCO SANTANDER 0.0229 (1.3328) 1.2409 *** (62.4839) (-0.7677) -0.0422 (16.6754) 0.784 *** 0.8793 (0.7161) -1.3721 DAIMLER -0.0034 (-0.1636) 1.2321 *** (45.4496) 0.2513 *** (3.5424) 0.2271 *** (4.5001) 0.7261 -53.6636 (0.8313) AZIMUT HOLDING 0.0448 (1.1824) 1.2175 *** (31.1851) 0.6526 *** (5.4403) -0.3274 *** (-3.118) 0.5234 -139.7733 (1.5145) WACKER CHEMIE 0.0017 (0.036) 1.1365 *** (18.8908) 0.9679 *** (6.1478) 0.5331 *** (4.7597) 0.2628 (1.8401) 30.6051 SOLVAY 0.0167 (0.5873) 1.0872 *** (26.3557) -0.1237 (-1.3535) 0.1354 * (1.941) 0.4899 -30.1306 (1.1366) BASF -0.0085 (-0.5064) 1.0756 *** (49.1231) (-0.2918) -0.0167 0.115 *** (2.8205) 0.7744 12.5891 (0.679) BAYER 0.011 (0.534) 1.0694 *** (39.9626) -0.3885 *** (-5.5469) -0.5263 *** (-10.5636) 0.7157 -63.7953 (0.8231) BOUYGUES 0.0202 (0.5918) 1.0625 *** (21.6502) 0.5871 *** (4.638) 0.3523 *** (4.1619) 0.3879 -9.4865 (1.3665) VINCI 0.0337 (1.5349) 1.0349 *** (32.7624) 0.1847 ** (2.2673) (-1.3185) -0.0718 0.6147 (0.9028) 25.6343 SMURFIT KAPPA GROUP 0.0909 ** (2.1078) 1.0319 *** (22.0268) (-0.2738) -0.0318 0.366 *** (3.5083) 0.2982 -67.5386 (1.7128) IMERYS 0.0169 (0.6183) 1.0251 *** (26.1201) 0.6471 *** (6.3922) 0.1631 ** (2.4088) 0.4527 -25.1147 (1.0888) JCDECAUX 0.0001 (0.0021) 1.0182 *** (20.7263) 0.7621 *** (6.0138) -0.3401 *** (-4.0133) 0.298 -66.5525 (1.3618) CARREFOUR -0.0207 (-0.6674) (22.7339) 1.015 *** (0.058) 0.0067 0.2003 *** (2.6002) 0.4778 -85.0274 (1.2432) KERING 0.0642 ** (2.1322) 1.0108 *** (23.3827) (0.2738) 0.0305 -0.5243 *** (-7.0308) 0.4499 (1.2037) 65.6373 MAPFRE 0.023 (0.7395) 1.007 *** (28.07) (0.2774) 0.0275 0.4816 *** (5.6705) 0.5758 (1.2441) 3.8016 SAIPEM -0.17 *** (-2.6071) 0.9795 *** (14.5941) 0.9252 *** (4.4865) 0.3187 * (1.7658) 0.2049 (2.6301) 23.8443 SIEMENS -0.0077 (-0.4207) 0.9731 *** (40.777) 0.1035 * (1.6573) 0.1256 *** (2.8277) 0.6897 18.629 (0.7284)
28
Company Name α β SMB HML R² 𝐂𝐀𝐑
A2A 0.0386
(0.9491) 0.9599 *** (22.9403) 0.4838 *** (3.7633) (-1.4049) -0.1581 0.3834 (1.6502) 45.5322 ACKERMANS & VAN
HAAREN 0.0241 (1.0659) 0.956 *** (29.0886) 0.3536 *** (4.8551) -0.1011 * (-1.8198) 0.4496 2.2219 (0.9023) DEUTSCHE BOERSE 0.0327 (1.194) 0.9397 *** (26.3866) (3.5409) 0.33 *** (-2.1872) -0.145 ** 0.4336 (1.0855) 16.3037 BOSKALIS WETSMINSTER (-0.9568) -0.0305 (19.7328) 0.936 *** 0.3124 *** (2.85) 0.5092 *** (6.5507) 0.3422 -67.3374 (1.2716) ALLIANZ 0.0388 ** (2.2793) 0.9329 *** (42.0824) -0.1007 * (-1.7365) 0.407 *** (9.8612) 0.7426 (0.6773) 9.9929 GERRESHEIMER 0.0069 (0.2138) 0.9318 *** (22.0607) 0.437 *** (3.9535) -0.528 *** (-6.7143) 0.3261 17.3868 (1.2863) CASINO GUICHARD -0.0412 (-1.2283) (19.277) 0.93 *** 0.3774 *** (3.0326) 0.316 *** (3.7974) 0.354 -58.5571 (1.3495) REPSOL -0.0044 (-0.1533) 0.9119 *** (27.2413) -0.303 *** (-3.2712) 0.7776 *** (9.8134) 0.6258 (1.1851) 64.2093 ENEL 0.0136 (0.5558) 0.8823 *** (35.1468) -0.4014 *** (-5.2052) -0.1971 *** (-2.9188) 0.6775 (0.9907) 23.4184 SAP 0.0134 (0.6788) 0.8719 *** (33.9049) -0.0269 (-0.3995) -0.5146 *** (-10.7476) 0.5999 30.6874 (0.7828) PADDY POWER BETFAIR (0.204) 0.0085 0.8502 *** (18.6761) 0.3179 *** (2.8167) -0.4695 *** (-4.6315) 0.1991 (1.6739) 20.216 VIVENDI 0.016 (0.553) 0.8479 *** (20.4444) (0.9589) 0.1026 (0.0929) 0.0066 0.3961 (1.1597) -2.8269 ANHEUSER-BUSCH INBEV (1.4443) 0.0315 0.8343 *** (26.3028) -0.7426 *** (-10.5625) -0.8941 *** (-16.6691) 0.5698 -72.1583 (0.8708) KPN -0.0449 (-0.9955) 0.8257 *** (12.3198) -0.0748 (-0.4828) 0.0499 (0.4544) 0.1832 35.6944 (1.8072) DEUTSCHE TELEKOM (1.0666) 0.0249 0.8184 *** (26.9815) -0.3809 *** (-4.7991) -0.1689 *** (-2.9911) 0.5563 -36.5921 (0.9364) GECINA 0.0426 (1.6308) 0.7839 *** (20.8953) 0.4418 *** (4.5659) -0.1645 ** (-2.5423) 0.3312 -39.0194 (1.047) GAS NATURAL SDG 0.0497 * (1.9353) 0.7755 *** (26.1343) (-1.4222) -0.1168 (-0.1506) -0.0106 0.5177 (1.0391) -21.878 TOTAL 0.0169 (0.8256) 0.7752 *** (26.3629) -0.4401 *** (-5.8024) 0.526 *** (10.3714) 0.6587 22.8929 (0.8244) ILIAD 0.0154 (0.3996) 0.7747 *** (14.0147) 0.3087 ** (2.165) -0.5168 *** (-5.4202) 0.1879 -112.6797 (1.5344) FREENET 0.0538 * (1.8805) 0.7676 *** (20.622) (0.8294) 0.0808 -0.288 *** (-4.1566) 0.3428 -73.9668 (1.1366) UNILEVER NV 0.0339 * (1.8715) 0.7631 *** (28.2992) -0.7106 *** (-11.4051) -1.0506 *** (-23.7764) 0.6461 (0.7276) -0.546 DEUTSCHE WHONEN 0.076 ** (2.4604) 0.6589 *** (16.3909) -0.1137 (-1.0806) -0.6064 *** (-8.1016) 0.2749 -10.0815 (1.241) HEINEKEN 0.0506 ** (2.2323) 0.6497 *** (19.2618) -0.5463 *** (-7.0102) -0.9198 *** (-16.6413) 0.4479 -24.7127 (0.905) BIC 0.0095 (0.3047) 0.6092 *** (13.5598) (-0.0894) -0.0104 -0.701 *** (-9.0449) 0.2181 -107.7183 (1.2458) BEIERSDORF 0.036 * (1.701) 0.5867 *** (21.2866) -0.5354 *** (-7.4223) -0.9869 *** (-19.2328) 0.4965 -13.5506 (0.844) COFINIMMO 0.0109 (0.5841) 0.574 *** (21.1004) 0.0312 (0.5184) -0.258 *** (-5.6199) 0.3296 2.5407 (0.7464) TENARIS 0.0109 (0.2801) 0.5527 *** (13.7607) -0.5214 *** (-4.2252) 0.5209 *** (4.8223) 0.344 (1.5862) 70.4581 SES -0.0165 (-0.4604) 0.5184 *** (10.09) (0.1044) 0.0138 -0.3758 *** (-4.2406) 0.1303 -121.0529 (1.4268)
29 Company Name α β SMB HML R² 𝐂𝐀𝐑 VISCOFAN 0.0562 ** (2.0106) 0.4519 *** (13.9887) (-3.1324) -0.28 *** -0.9831 *** (-12.8553) 0.2273 -53.1492 (1.1217) QIAGEN -0.0006 (-0.4177) 0.0373 *** (19.4582) 0.0079 (1.5829) -0.0375 *** (-10.5015) 0.3061 -0.4762 (0.0619)
30 Appendix 3: Composition of the EURO STOXX and company weight
Company Sector Country Weight (%)
DAIMLER Automobiles & Parts DE 1.63
VOLKSWAGEN PREF Automobiles & Parts DE 0.70
BMW Automobiles & Parts DE 0.65
CONTINENTAL Automobiles & Parts DE 0.57
MICHELIN Automobiles & Parts FR 0.54
RENAULT Automobiles & Parts FR 0.47
FIAT CHRYSLER AUTOMOBILES Automobiles & Parts IT 0.44
VALEO Automobiles & Parts FR 0.32
FERRARI Automobiles & Parts IT 0.30
PEUGEOT Automobiles & Parts FR 0.25
PORSCHE PREF Automobiles & Parts DE 0.24
NOKIAN RENKAAT Automobiles & Parts FI 0.12
FAURECIA Automobiles & Parts FR 0.12
RHEINMETALL Automobiles & Parts DE 0.11
PIRELLI & C. S.P.A. Automobiles & Parts IT 0.06
PLASTIC OMNIUM Automobiles & Parts FR 0.06
SCHAEFFLER AG Automobiles & Parts DE 0.05
BCO SANTANDER Banks ES 2.08
BNP PARIBAS Banks FR 1.71
ING GRP Banks NL 1.32
INTESA SANPAOLO Banks IT 1.06
BCO BILBAO VIZCAYA ARGENTARIA
Banks ES 1.05
GRP SOCIETE GENERALE Banks FR 0.87
UNICREDIT Banks IT 0.86
DEUTSCHE BANK Banks DE 0.57
KBC GRP Banks BE 0.46
CREDIT AGRICOLE Banks FR 0.40
CAIXABANK Banks ES 0.33
ERSTE GROUP BANK Banks AT 0.30
COMMERZBANK Banks DE 0.28
ABN AMRO GROUP Banks NL 0.24
BCO SABADELL Banks ES 0.22
BANK OF IRELAND GROUP Banks IE 0.16
NATIXIS Banks FR 0.15 MEDIOBANCA Banks IT 0.14 BANKINTER Banks ES 0.13 BANCO BPM Banks IT 0.11 RAIFFEISEN BANK INTERNATIONAL Banks AT 0.10 FINECOBANK Banks IT 0.10
31
Company Sector Country Weight (%)
UBI BCA Banks IT 0.09
BANKIA Banks ES 0.09
BCO COMERCIAL PORTUGUES Banks PT 0.06
BPER Banca Banks IT 0.05
ARCELORMITTAL Basic Resources LU 0.40
UPM KYMMENE Basic Resources FI 0.37
STORA ENSO R Basic Resources FI 0.21
TENARIS Basic Resources IT 0.16
VOESTALPINE Basic Resources AT 0.12
IMERYS Basic Resources FR 0.06
AURUBIS Basic Resources DE 0.06
OUTOKUMPU Basic Resources FI 0.05
BASF Chemicals DE 1.85
AIR LIQUIDE Chemicals FR 1.03
LINDE TENDERED Chemicals DE 0.75
AKZO NOBEL Chemicals NL 0.48
KONINKLIJKE DSM Chemicals NL 0.34 COVESTRO Chemicals DE 0.27 UMICORE Chemicals BE 0.20 SOLVAY Chemicals BE 0.20 SYMRISE Chemicals DE 0.18 ARKEMA Chemicals FR 0.18 BRENNTAG Chemicals DE 0.17 LANXESS Chemicals DE 0.14 K + S Chemicals DE 0.11
EVONIK INDUSTRIES Chemicals DE 0.10
FUCHS PETROLUB PREF Chemicals DE 0.08
IMCD Chemicals NL 0.06
WACKER CHEMIE Chemicals DE 0.06
VINCI Construction & Materials FR 1.09
SAINT GOBAIN Construction & Materials FR 0.61
CRH Construction & Materials IE 0.56
HEIDELBERGCEMENT Construction & Materials DE 0.29
BOUYGUES Construction & Materials FR 0.28
EIFFAGE Construction & Materials FR 0.22
ACS Construction & Materials ES 0.22
FERROVIAL Construction & Materials ES 0.20
KINGSPAN GRP Construction & Materials IE 0.12
HOCHTIEF Construction & Materials DE 0.06
WIENERBERGER Construction & Materials AT 0.06
BOSKALIS WESTMINSTER Construction & Materials NL 0.05
DEUTSCHE BOERSE Financial Services DE 0.50
32
Company Sector Country Weight (%)
EXOR NV Financial Services IT 0.16
WENDEL Financial Services FR 0.10
ACKERMANS & VAN HAAREN Financial Services BE 0.08
AMUNDI Financial Services FR 0.08
EURONEXT Financial Services FR 0.08
AAREAL BANK Financial Services DE 0.06
BOLSAS Y MERCADOS ESPANOLES
Financial Services ES 0.05
AZIMUT HLDG Financial Services IT 0.05
ANHEUSER-BUSCH INBEV Food & Beverage BE 1.61
DANONE Food & Beverage FR 1.02
PERNOD RICARD Food & Beverage FR 0.66
HEINEKEN Food & Beverage NL 0.44
KERRY GRP Food & Beverage IE 0.28
HEINEKEN HLDG Food & Beverage NL 0.20
GLANBIA Food & Beverage IE 0.07
DAVIDE CAMPARI Food & Beverage IT 0.07
REMY COINTREAU Food & Beverage FR 0.06
VISCOFAN Food & Beverage ES 0.05
BAYER Health Care DE 1.87
SANOFI Health Care FR 1.80
PHILIPS Health Care NL 0.73
FRESENIUS Health Care DE 0.62
ESSILOR INTERNATIONAL Health Care FR 0.58
FRESENIUS MEDICAL CARE Health Care DE 0.41
MERCK Health Care DE 0.24
UCB Health Care BE 0.20
GRIFOLS Health Care ES 0.16
QIAGEN Health Care DE 0.15
EUROFINS SCIENTIFIC Health Care FR 0.13
ORPEA Health Care FR 0.12
IPSEN Health Care FR 0.11
GALAPAGOS Health Care BE 0.09
ABLYNX Health Care BE 0.08
RECORDATI Health Care IT 0.07
BIOMERIEUX Health Care FR 0.07
ORION B Health Care FI 0.07
GERRESHEIMER Health Care DE 0.05
STADA ARZNEIMITTEL Health Care DE 0.04
SIEMENS Industrial Goods & Services DE 2.16
AIRBUS Industrial Goods & Services FR 1.30
SCHNEIDER ELECTRIC Industrial Goods & Services FR 0.96
33
Company Sector Country Weight (%)
SAFRAN Industrial Goods & Services FR 0.75
AMADEUS IT GROUP Industrial Goods & Services ES 0.65
LEGRAND Industrial Goods & Services FR 0.41
KONE B Industrial Goods & Services FI 0.39
ABERTIS INFRAESTRUCTURAS Industrial Goods & Services ES 0.30
AENA SME Industrial Goods & Services ES 0.30
ATLANTIA Industrial Goods & Services IT 0.28
WIRECARD Industrial Goods & Services DE 0.27
CNH Industrial NV Industrial Goods & Services IT 0.26
THYSSENKRUPP Industrial Goods & Services DE 0.25
THALES Industrial Goods & Services FR 0.24
WARTSILA Industrial Goods & Services FI 0.20
SMURFIT KAPPA GRP Industrial Goods & Services IE 0.19
RANDSTAD Industrial Goods & Services NL 0.17
MTU AERO ENGINES Industrial Goods & Services DE 0.17 TELEPERFORMANCE Industrial Goods & Services FR 0.16
EDENRED Industrial Goods & Services FR 0.16
GEA GRP Industrial Goods & Services DE 0.14
PRYSMIAN Industrial Goods & Services IT 0.14
BUREAU VERITAS Industrial Goods & Services FR 0.14
ALSTOM Industrial Goods & Services FR 0.13
Getlink Industrial Goods & Services FR 0.13
ZODIAC AEROSPACE Industrial Goods & Services FR 0.12
ADP Industrial Goods & Services FR 0.12
KION GROUP Industrial Goods & Services DE 0.12
REXEL Industrial Goods & Services FR 0.10
AALBERTS INDUSTRIES Industrial Goods & Services NL 0.10
BOLLORE Industrial Goods & Services FR 0.10
SARTORIUS PREF. Industrial Goods & Services DE 0.09
ELIS Industrial Goods & Services FR 0.09
LEONARDO Industrial Goods & Services IT 0.09
DASSAULT AVIATION Industrial Goods & Services FR 0.08
HUHTAMAKI Industrial Goods & Services FI 0.08
ANDRITZ Industrial Goods & Services AT 0.08
EURAZEO Industrial Goods & Services FR 0.08
MAN Industrial Goods & Services DE 0.08
FRAPORT Industrial Goods & Services DE 0.07
METSO Industrial Goods & Services FI 0.07
PHILIPS LIGHTING NV Industrial Goods & Services NL 0.06
KONECRANES Industrial Goods & Services FI 0.06
VOPAK Industrial Goods & Services NL 0.06
DUERR Industrial Goods & Services DE 0.06
34
Company Sector Country Weight (%)
BPOST SA Industrial Goods & Services BE 0.05
ALLIANZ Insurance DE 2.00
AXA Insurance FR 1.13
MUENCHENER RUECK Insurance DE 0.69
SAMPO Insurance FI 0.53
ASSICURAZIONI GENERALI Insurance IT 0.51
NN GROUP Insurance NL 0.29
AEGON Insurance NL 0.24
AGEAS Insurance BE 0.21
HANNOVER RUECK Insurance DE 0.16
SCOR Insurance FR 0.14
ASR NEDERLAND NV Insurance NL 0.09
POSTE ITALIANE Insurance IT 0.08
CNP ASSURANCES Insurance FR 0.08
MAPFRE Insurance ES 0.07
VIVENDI Media FR 0.53
RELX NV Media NL 0.40
PUBLICIS GRP Media FR 0.29
WOLTERS KLUWER Media NL 0.29
PROSIEBENSAT.1 MEDIA Media DE 0.15
SES Media LU 0.12
SPRINGER (AXEL) Media DE 0.08
TELENET GRP HLDG Media BE 0.07
RTL GRP Media LU 0.06
LAGARDERE Media FR 0.06
EUTELSAT COMMUNICATION Media FR 0.06
JCDECAUX Media FR 0.05
TOTAL Oil & Gas FR 2.86
ENI Oil & Gas IT 0.85
REPSOL Oil & Gas ES 0.42
TECHNIPFMC Oil & Gas FR 0.27
GALP ENERGIA Oil & Gas PT 0.18
NESTE Oil & Gas FI 0.17
OMV Oil & Gas AT 0.16
SIEMENS GAMESA Oil & Gas ES 0.07
SBM OFFSHORE Oil & Gas NL 0.06
SAIPEM Oil & Gas IT 0.05
UNILEVER NV Personal & Household Goods NL 1.61
LVMH MOET HENNESSY Personal & Household Goods FR 1.58
L'OREAL Personal & Household Goods FR 1.04
ADIDAS Personal & Household Goods DE 0.84
HENKEL PREF Personal & Household Goods DE 0.47
