The Impact of European Central Bank’s (ECB) Tightening
Monetary Policy Announcements on Non-Bank Stocks in the
Eurozone
Abstract
The Covid-19 global pandemic led to large liquidity injections in the financial markets, which in
turn resulted in a high inflationary environment. The European Central Bank (ECB) has been
forced to end its expansionary economic cycle and to start combating the inflation pressures by
implementing tightening monetary measures. This paper analyzes the impacts of ECB’s
tightening announcements on 709 non-banking institutions publicly traded in the Eurozone,
using an event study methodology. The results indicate both negative and positive significant
abnormal returns in stock prices. Furthermore, it investigates the differences across firm size,
measured by their market capitalization. In addition, cross-sectional regressions are performed to
reveal the potential determinants of the abnormal returns. Further analysis revealed that the
effects were homogeneous across the firms with different sizes. Moreover, the relationship
between a firm’s debt-to-equity ratio and its stock returns was significantly negative for two of
the analyzed events. Lastly, the inability of isolating the studied events from other
macroeconomic factors and lacking data are major limitations of this paper and represent
avenues for future research.
Keywords: Tightening Monetary Policy, Event Study, European Central Bank, Non-Bank Stocks
Faculty of Economics and Business, BSc. Business Administration, Track Finance
Name: Vlad Tampa
Student Number: 13044877
Supervisor: Dr. Tanju Yorulmazer
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Statement of Originality
This document is written by student Vlad Tampa 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. I have not
used generative AI (such as ChatGPT) to generate or rewrite text.
UvA Economics and Business is responsible solely for the supervision of completion of the work
and submission, not for the contents.
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Table of Contents
Chapter 1: Introduction ................................................................................................................................. 4
Chapter 2: Theoretical Background and Hypothesis Development .............................................................. 8
2.1 Theoretical Framework for Stock Pricing ........................................................................................... 8
2.2 Background Information on Monetary Policy in the Eurozone .......................................................... 9
2.3 Monetary Policy Impact on Financial Markets: Equity Markets ...................................................... 12
2.4 Hypothesis Development .................................................................................................................. 15
Chapter 3: Methodology ............................................................................................................................. 16
3.1 Data ................................................................................................................................................... 16
3.2 Event Identification ........................................................................................................................... 17
3.3 Event Study Methodology ................................................................................................................ 18
3.3.1 Estimating the Expected Normal Returns: The Ordinary Least Squares (OLS) Model ....... 19
3.3.2 Calculation and Aggregation of Abnormal Returns.............................................................. 20
3.3.3 Testing the Abnormal Returns .............................................................................................. 21
3.3.4 Event Determinant Analysis for the Top 60 Components .................................................... 22
Chapter 4: Results ....................................................................................................................................... 25
4.1 Event Study Analysis ........................................................................................................................ 25
4.2 T-Test ................................................................................................................................................ 27
4.3 Cross Sectional Analysis ................................................................................................................... 28
Chapter 5: Discussion ................................................................................................................................. 32
5.1 Event Study ....................................................................................................................................... 32
5.2 Cross Sectional Analysis ................................................................................................................... 36
Chapter 6: Conclusion................................................................................................................................. 37
References ................................................................................................................................................... 39
Appendices.................................................................................................................................................. 42
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Chapter 1: Introduction
“We are committed to bringing inflation back down to our medium-term target, and we
will take the necessary measures to do so”, said Christine Lagarde, the president of the European
Central Bank (ECB), at the European Banking Congress on 18th of November 2022 (ECB, 2022).
Headline inflation increased sharply in the euro area since the start of 2021, in October 2022 the
Harmonized Index of Consumer Prices (HICP) peaking at 10.6% (Figure 1, Appendix 1). Supply
constraints in the global economy have emerged as a result of multiple factors, including
disruptions in supply chains, zero-COVID policies, reductions in energy production, and Russia's
incursion into Ukraine. The impact of these constraints has been compounded by a stagnation in
the responsiveness of demand to shifts in economic activity. Rather than responding to changing
demand by increasing output, prices have risen steeply. This phenomenon, along with escalating
energy costs, has been a significant driver of inflation in recent times (ECB, 2022). These
economic factors determined the ECB to revise their monetary policy and to take action in
bringing down inflation to the long-term target.
Traditionally, central banks manipulated key interest rates to achieve the objective of
preserving price stability, which entails keeping medium-term inflation levels at or below 2%.
However, after the Global Financial Crisis (GFC) expansionary monetary policies were taken by
major central banks to assure financial and economic stability. The ECB followed the path of
other central banks with a certain time lag and lowered the interest rates significantly and
expanded their balance sheet dramatically, effect known under the name of quantitative easing
(QE) (Cukierman, 2013). The decade prior to the Covid-19 pandemic was characterized by a
disinflationary environment with a period of low levels of inflation. This specific context
determined the majority of central banks to hold their interest rates close to zero or even at
negative levels (Aguilar et al., 2020).
To combat the negative economic impacts of the crisis and of the global Covid-19
pandemic, the ECB, similar to other central banks, relied heavily on necessary unconventional
monetary policies, namely, quantitative easing. Central banks adopted these measures with the
intention to provide a stimulus to the economy so that a recession could be avoided. These
included scale purchases of different securities and the provision of additional liquidity to banks
and financial institutes (Aguilar et al., 2020). However, theory proposes that the huge liquidity
4
injections together with low or even negative interest rates could raise the risk that when global
economies would return to normality, inflation will quickly pick up (Cukierman, 2013). The
described situation, was exactly what happened after the Covid-19 pandemic. To combat the
raising inflation, the ECB made public on the 16th of December 2021 its intention to start
steering the economy towards normalization and to implement tightening monetary conditions
(ECB, 2021). The main challenge in transitioning from an expansionary to a tight economic
environment is represented by the limited past occurrences of the prompt recovery of major
economies, such as the Eurozone, after facing financial crises that require significant liquidity
injections and abnormally low interest rates. This lack of precedent smooth transitions creates
substantial ambiguity around the optimal timing for initiating a tightening monetary cycle. The
underlying cause of disagreements between policymakers and economists is the uncertainty
regarding the appropriate time for removing liquidity injections (Cukierman, 2013).
Both tightening and expansionary monetary policies had significant effects on asset
prices in the past. Bonds and stock prices experienced changes in their prices after important
monetary policy decisions (Rigobon & Sack, 2002). Studies from Rigobon & Sack (2002)
identified that an increase in the short-term interest rate led to a decline in the stock prices and in
an upward shift in the yield curve that becomes smaller for longer maturities. The relationship
between asset prices and monetary policies is a crucial area of investigation for several reasons.
From policymakers’ point of view, having reliable estimates of the effect of the policy instrument
on asset prices is crucial designing and implementing effective policy decisions. The
transmission of monetary policy largely depends on the influence of short-term interest rates on
other asset prices, which include longer-term interest rates and stock prices. Real economic
activity is affected by private borrowing costs and changes in wealth levels, which in turn are
impacted by changes in asset prices. From the financial market participants’ point of view, they
are equally interested in this subject since monetary policy significantly influences financial
markets. Thus, having a reliable and accurate estimate of the responsiveness of asset prices to
monetary policy is a critical component in making effective investment and risk management
decisions (Rigobon & Sack, 2002).
Even if the impact of tightening monetary measures on various stock markets was already
addressed in a broad variety of previous papers, the vast majority of articles focused on the US
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stock market. Furthermore, there is not a great number of papers that study the effect of
conventional and unconventional effects of monetary policies after a global pandemic.
Conventional and unconventional monetary policies differ in terms of their primary instruments
and goals. The former, namely conventional policies, rely on setting a target for the short-term
interest rate, with the provision of additional short-term liquidity as necessary by the ECB. The
latter one, unconventional monetary policies, involves the purchase of significant amounts of
assets. This approach is intended to influence not only the short-term interest rate but also other
factors, and is therefore considered a departure from the conventional approach (Joyce et al., 2012).
In addition, the economic context is relatively unique, in the sense that during the Covid-19
pandemic, household savings increased significantly because of the imposed restrictions on
economic activity, despite the widespread of job losses (Cahill & Lydon, 2021). The higher savings
rate is associated with a higher investment activity, which affects the financial markets (Tesar,
1991). Further, as mentioned above, the past economic environment was characterized by low
interest rates, a low inflation rate and easy credit facilities. Most of the recent literature studied the
effect of expansionary monetary policy on stock market due to the macroeconomic context.
However, policymakers changed the perspective, starting to implement contractionary monetary
measures to combat the high inflation, transitioning to a tight economic environment. Thus, it is
particularly interesting, relevant and actual to measure the effect of the ECB’s tightening monetary
policy announcements on non-bank stock returns after a global pandemic that resulted in an
unprecedented economic context. Therefore, this paper will attempt to fill this gap and to
contribute to the previous literature by exploring the answer to the following research question:
“How will the transition from expansionary to tight monetary policy affect non-bank equity prices
in the Eurozone?”
The study from Thorbecke (1997) revealed that the impact of monetary policy shocks on
the returns of small firms is both significant and noteworthy. Additionally, monetary policy
decisions have a smaller effect on the returns of large firms. This can be explained by the fact
that large firms are usually well-collateralized and thus are not subject to binding credit
constraints. This observed difference in the effect of monetary shocks between large and small
companies is consistent with the notion that the mechanism of monetary policy mainly operates
by influencing firms’ access to credit facilities (Thorbecke, 1997). This paper will attempt to
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analyze the differences between the effects of tightening monetary measures announcements on
small, medium and large capitalization firms. In addition, it will analyze potential determinants
that might explain the abnormal returns. Consequently, this study employs the event-study
methodology, in which daily stock returns from various companies from countries in the
Eurozone are utilized to isolate the impact of changes in monetary policies. This specific
methodology allows for investigating the influence of monetary shocks on equity prices by
analyzing the information effect on stock markets resulting from the announcement of monetary
policy. However, one major challenge in event studies is to capture changes in stock prices to
specific events exogenously, without the inference of other macroeconomic factors (Kothari &
Warner, 2007).
The results of the event study indicate that the equity markets responded with both negative
and positive significant abnormal returns on the majority of the investigated announcements made
by the ECB regarding tightening conditions. However, the firm size, measured by the market
capitalization, did not influence the abnormal returns. The findings from the t-test and from the
cross-sectional analysis revealed that the differences between small and medium caps, small and
large caps were not statically significant.
The subsequent sections of this thesis have been structured as follows. Chapter 2 provides
an outline of the review of both empirical and theoretical literature that form the basis for
comprehending the impact of monetary policy on equity markets. At the conclusion of the chapter,
pertinent hypotheses are formulated to furnish a more profound understanding of how stock prices
react to the tightening of monetary policy in the Eurozone. Chapter 3 details the methodology used
and provides a comprehensive account of the data collection process. Chapter 4 presents the
findings from the empirical analysis, while Chapter 5 explains the results. Lastly, Chapter 6
summarizes the conclusions drawn from the study, while taking into account its limitations and
suggesting avenues for future research.
Chapter 2: Theoretical Background and Hypothesis Development
To proceed further, I will establish the theoretical framework underpinning stock pricing.
This will be followed by a succinct overview of the monetary policies adopted by the ECB. I will
7
then provide a literature review on the interplay between these instruments and the equity
markets.
2.1 Theoretical Framework for Stock Pricing
In order to analyze the connection between monetary policy actions and financial
markets, it is crucial to have an understanding of the fundamental principles underlying the
determination of financial asset prices. Considering stocks, theory posits that stock prices equal
the expected present values of future net cash flows.
𝑉0 =
𝐹𝐶𝐹1
1+𝑟𝑤𝑎𝑐𝑐
𝐹𝐶𝐹2
+ (1+𝑟
2
𝑤𝑎𝑐𝑐 )
+⋯+
𝐹𝐶𝐹𝑛 + 𝑉𝑛
(1+𝑟𝑤𝑎𝑐𝑐 )𝑛
(1)
Where V0 stands for the enterprise value at time zero, Vn for the terminal value, FCF for the free
cash flow at the specific time period and rwacc for the weighted average cost of capital. To derive
the stock price at time zero from the enterprise value calculated using the above equation, the
following formula is used (Berk & DeMarzo, 2019).
𝑃0 =
𝑉0 + 𝐶𝑎𝑠ℎ0 −𝐷𝑒𝑏𝑡0
𝑆ℎ𝑎𝑟𝑒𝑠 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔0
(2)
Where P0 stands for the stock price at time zero (Berk & DeMarzo, 2019).
The discounted cash flow model stipulates that stock prices correspond to the current
value of anticipated net cash flows. Consequently, monetary policy is expected to significantly
impact equity returns either by modifying the discount rate utilized by market agents or by
shaping market agents' projections of future economic operations. These mechanisms of
influence are interconnected since tighter monetary policy typically connotes elevated discount
rates and reduced future cash flows. Therefore, a contractionary monetary policy ought to
correspond with lower equity prices due to the higher discount rate applied to the predicted
stream of cash flows, and/or lower expected economic performance (Ioannidis & Kontonikas,
2008).
2.2 Background Information on Monetary Policy in the Eurozone
Since the onset of the GFC, the ECB faced exceptional circumstances. Their main purpose
of safeguarding the proper functioning of the monetary policy transmission mechanism and the
credit to the private sector, together with the necessity of combating the deflationary threats, has
8
put tremendous pressure on monetary policy. Despite the mentioned challenges, the operational
framework adopted by the ECB demonstrated flexibility, enabling the central bank to navigate
uncharted waters (Neri & Siviero, 2018). Therefore, it is important to illustrate how the ECB
managed this uncertain macroenvironment by altering its monetary policies. In this section, I will
provide a short overview of the previous economic outlook and on the measures that the ECB took
in different periods. To this end, it is convenient to divide the narrative into four different parts:
(1) The global financial crisis (2007 – 2009), (2) The sovereign debt crisis (2010 – 2012), (3) The
disinflation period (2013 – 2016) and (4) The COVID-19 pandemic (2020 – 2021).
1. The Global Financial Crisis (2007 – 2008)
In the early stages of September 2008, the financial turmoil escalated into a full crisis following
the collapse of Lehman Brothers. This resulted in a paralysis of the interbank trading, so a number
of major central banks, including the Federal Reserve and the ECB had to intervene to rescue the
financial system. They rapidly reduced their policy rates in early October. In the Eurozone, the
easing of monetary policy conditions continued to progress swiftly in late 2008, with the Main
Refinancing Operations rate (MRO) being lowered to 2.5% by December (Neri & Siviero, 2018).
Throughout 2009, the ECB sustained its easing cycle, culminating in the reduction of the MRO to
a historically low rate of 1% in May. Furthermore, the ECB strengthen its facilities available to
provide liquidity to the market, in the sense that they prolonged the duration of the refinancing
operations. These measures contributed to a substantial expansion of the balance sheet of the
Eurosystem, which surged to 2 trillion euros in early 2009 (Neri & Siviero, 2018).
2. The Sovereign Debt Crisis (2010 – 2012)
After the GFC turmoil experienced by the majority of economies, a recovery of the several of
euro area economies began to materialize in 2009, and gathered momentum in 2010. Nevertheless,
toward the end of 2009, apprehensions surfaced in the government bond market, prompted by the
revelation made by the newly installed government in Greece that the public deficit was
considerably higher than previously disclosed. The Eurozone’s economy, as a whole, appeared to
be on its way of recovering. Growth forecasts became optimistic and inflation started to rise,
reaching 2.8% in April. However, the Governing Council, which is the decision-making organ in
the ECB, was concerned about a “wage-price spiral” (Neri & Siviero, 2018). This situation refers
to an increase in overall demand that would result in higher production levels and employment,
9
prompting firms to seek higher prices and workers to demand higher wages. This could initiate a
cycle of inflationary pressure known as a "wage-price spiral," which could only be resolved if the
economy returned to a stable state by reducing the real money supply. The spiral could be triggered
by a variety of factors, including a desire by workers for higher wages, a desire by firms to increase
their profit margins, or attempts by both parties to maintain current wages and prices in the face of
supply shocks. Such a spiral would result in "cost-push" inflation and ultimately a recession, due
to the impact of inflation on real money balances (Blanchard, 1986). As a result, the ECB raised
the interest rates to stabilize the financial conditions. Initially, it was believed that the sovereign
debt crisis impacted a small number of countries. However, the spreading of the crisis in countries
like Italy and Spain, triggered a new expansionary cycle of policy rates that started at the end of
2011 (Neri & Siviero, 2018).
3. The Disinflation Period (2013 – 2016)
In late 2011, inflation peaked at 3% and it started to decline fast, so that by the second half of
2013 it became the ECB’s main concern. The ECB reduced the interest rates, bringing the MRO
to 0.25% in November 2013, to combat the falling inflation. In view of the deteriorating inflation
outlook over the summer of 2014, the Governing Council of the ECB concluded that it was
necessary to shift towards a proactive approach to managing the balance sheet of the Eurosystem.
Therefore, the Governing Council decided to procure covered bonds and asset-backed securities.
In addition, in the beginning of 2015, the ECB introduced the Expanded Asset Purchase
Programme (APP). It was a larger purchase scheme which encompassed the acquisition of public
securities known as the Public Sector Purchase Programme (PSPP). APP was seen as a suitable
measure of monetary policy by the Governing Council and it was implemented without any delays
(Neri & Siviero, 2018).
4. The COVID-19 Pandemic (2020 - 2021)
In March 2020, the emergence of the Covid-19 pandemic caused short-term negative demand
shocks, which further contributed to a decrease in inflation. Following the implementation of
lockdown measures in several European countries, the inflation outlook was severely impacted.
The ECB’s response to this situation was represented by the implementation of a range of both
traditional and non-traditional measures that intended to assure stability in the financial markets.
Their attention has been primarily directed towards the Asset Purchase Programme (APP),
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Pandemic Emergency Purchase Programme (PEPP), and longer-term refinancing operations
(LTRO), which encompass the LTRO, Targeted Longer-Term Refinancing Operations (TLTRO
III), and Pandemic Emergency Longer-Term Refinancing Operations (PELTRO) (Aguilar et al.,
2020). This approach permits fluctuations in the distribution of purchases across asset classes and
jurisdictions over time. The primary objective behind this strategy is to prevent any fragmentation
in the financial system, which could potentially hinder the transmission of the ECB's monetary
policy to the financial conditions of certain countries within the euro area. PEPP was initially
introduced with an allocation of €750 billion, which was set to expire by the end of 2020. However,
on 4 June 2020, the program was extended and the allocation increased to €1.35 trillion, to continue
at least until the end of June 2021. In addition, the ECB communicated the fact that any principal
payments received from securities purchased under the PEPP would be reinvested until at least the
end of 2022. The effect of both asset purchase programmes was an increase the portfolio of the
Eurosystem’s securities amounting to approximatively €4.4 trillion by June 2021 (Aguilar et al.,
2020). In conclusion, the efforts undertaken by the ECB have played a critical role in easing
financial conditions across all countries within the euro area. By doing so, the ECB has
successfully prevented the formation of negative loops between the real economy and financial
markets. This helped to increase confidence among economic actors, which has had positive
spillover effects on employment, economic activity, and inflation prospects (Aguilar et al., 2020).
On the 16th of December 2021, the ECB made public its intentions to discontinue net asset
purchases under the PEPP at the end of March 2022 and to invest the principal payments from
maturing securities purchased under PEPP until at least 2024 (ECB, 2022). This announcement
was in line with the rising inflation levels and it signaled a change in the macroeconomic context.
Figure 1 (Appendix 1), illustrates the inflation levels measured by the HICP, revealing the fact that
in December 2021 inflation rose to 5%, well above the medium-term target of 2%. However, the
ECB increased the interest rates by 50 basis points only on the 21st of July 2022. The Governing
Council decided to take “a larger first step” on its policy rate normalization path than it was
signaled in the previous meetings. The decision to take action has been grounded on the evaluation
conducted by the Governing Council with respect to the potential risks associated with inflation
(ECB, 2022).
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2.3 Monetary Policy Impact on Financial Markets: Equity Markets
Previous scholarly literature has analyzed the consequences of announcements related to the
tightening of monetary policies on diverse asset prices, such as stocks. However, the current study
does not aim to appraise all previous articles that have explored the impact of monetary policy
decisions on equity prices. Nonetheless, a clear and succinct overview of past research will
facilitate an improved comprehension of the subject matter.
As mentioned before, the role of financial institutions in interpreting changes in discount rates
is a crucial factor in the transmission channel linking these rates to the broader economy. The
existing literature has identified two channels through which adjustments in discount rates can
affect economic conditions and, in turn, influence stock prices. The first channel is based on the
fact that firm value is the sum of the present value of the cash flows, and thus an increase (decrease)
in the discounting rate will result in a decrease (increase) in the stock prices of the underlying firm.
Any adjustments made to the discount rate will affect the expectations regarding its future
trajectory, resulting in a shift in interest rates that aligns with the direction of the initial adjustment
of the discount rate (Cook & Hahn, 1988). Thus, an increase in the interest rate results in lower
stock market returns due to the increased cost of capital, which is perceived as a bad signal. On
the contrary, a decrease in the interest rate is perceived as a good signal because of the decreased
cost of capital and therefore, it raises stock market returns (Ahmad, Rehman & Raoof, 2010).
The second channel focuses on market expectations and illustrates the forward-looking role of
monetary policy decisions as an informative agent to the market. The central bank’s
communication channel regarding changes in the discount rate is an act of signaling that can shape
the expectations of financial institutions about the future of the economy. When the central bank
lowers the interest rate, it signals the possibility of future economic expansion. On contrary, when
the interest rate is increased, the signal is interpreted as a period of tightening in the future. These
different signals given by the central banks led to distinct interpretations and responses by stock
markets (Jensen & Johnson, 1993).
Previous papers from Rigobon and Sack (2002) addressed the relationship between
monetary policy and asset prices. They focused on how interest rates and other monetary tools can
affect the prices of stocks, bonds and other assets. The authors employed a method of estimation
based on heteroskedasticity to gauge the impact of shifts in monetary policy on specific dates, such
12
as Federal Open Market Committee (FOMC) meetings and the Chairman's bi-annual testimony on
monetary policy to Congress, on the fluctuation of equity prices and market interest rates. The
researchers discovered that changes in the short-term interest rate had an opposite effect on stock
prices, particularly on the Nasdaq index. The analysis revealed that a 25-basis point hike in the
three-month interest rate leads to a reduction of 1.7% in the S&P 500 index and 2.4% in the Nasdaq
index. This conclusion aligns with the conventional perspective that a monetary environment that
is expansive corresponds to favorable news for investors, while a restrictive environment is related
to negative news.
Furthermore, Conover and Johnson (1999) revealed in their study the fact that there is a
significant relationship between local monetary policies and local stock returns. They argued that
stock returns are higher during expansive monetary environments and lower during restrictive
conditions. Additionally, many foreign stock returns are linked to the US monetary environment,
with significantly higher returns observed when the Federal Reserve follows an expansive policy
compared to a restrictive one. Moreover, interestingly several stock markets exhibit a stronger
association with the US monetary environment than with local monetary conditions (Conover &
Johnson, 1999).
The work of Bohl et al. (2008) analyzed the immediate effects of unexpected interest rate
decisions made by the ECB on returns in major European stock markets. The findings indicated
that there is a substantial and detrimental reaction of European stock returns to monetary policy
surprises caused by the ECB, with stock markets declining between 1.42% and 2.30% on the day
when an unanticipated 25-basis point interest rate increase occurs. This is in line with previous
literature from Ioannidis and Kontonikas (2008), who revealed that in the 80% of the 13
Organization for Economic Cooperation and Development (OECD) countries studied, tight money
periods correspond with reductions in the value of the stock market. The present value model can
explain the reason for this consequence. An interest rate hike leads to a rise in discount rates and a
reduction in future cash flows, ultimately resulting in lower stock prices. The authors argued that
a shift in monetary policy can impact stock returns in a dual fashion. On the one hand, there is a
direct effect on stock returns due to the modification of the discount rate employed by market
participants. Tighter monetary policies lead to an increase in the rate used to discount future firms’
cash flows, leading to a decrease in stock values. This premise is based on two assumptions: firstly,
13
market participants' discount factors are generally connected to market interest rates, and secondly,
the central bank can influence market interest rates. On the other hand, monetary policy changes
have an indirect effect on firms' stock value by altering anticipated future cash flows. The
relaxation of monetary policy is expected to enhance the overall level of economic activity, and
the stock price reacts positively, anticipating higher cash flows in the future. Hence, this channel
generally presumes the existence of a correlation between monetary policy and the total real
economy (Ioannidis & Kontonikas, 2008).
A significant amount of literature aims to distinguish between the effects of tightening
monetary policy announcements on small firms and large firms, as measured by their market
capitalization. According to Gertler and Gilchrist (1994), the increase in interest rates can have a
negative effect on a firm’s cash flow net of interest, which in turn can weaken its balance sheet.
The authors used the size of a firm to measure the credit constraint and the results suggest that this
effect is more pronounced among small firms, due to their lower levels of collateralization
compared to larger firms and due to their higher dependency on bank loans. In a similar manner,
Thorbecke (1997) argued that monetary policy has substantial real effects on the economy in the
short term. In his analysis, he grouped firms by their market capitalization and revealed that smaller
firms are more sensitive to monetary shocks compared to larger companies. These findings lend
credence to the view that the effects of monetary policy on the availability of credit play a role in
the transmission of its effects on the economy (Thorbecke, 1997). In addition, Ehrmann and
Fratzscher (2004) studied the relationship between financially constrained firms and the changes
in the interest rates. They defined the “financially constrained firms” as the companies that find it
more difficult to raise funds internally by using existing cash flows, and externally via bank loans.
The degree of external financial constraints was measured using the debt to capital ratio and the
result revealed that firms hold low levels of debt because they are currently financially constrained
and they find it more difficult to borrow funds (Ehrmann and Fratzscher, 2004).
From a different perspective, Urbschat and Watzka (2020) revealed that the effects of the
APP conducted by the ECB resulted in a positive impact on the financial markets. The effect was
stronger in the short-run, decreasing gradually over time for every additional package. More recent
work of Aguilar et al. (2020) conducted in an event study approach, analyzed the immediate impact
of PEPP on financial markets. The main transmission channels of asset purchase programmes
14
include the effect of such announcements on capital markets. The effect of both the initial PEPP
announcement and a potential increase in the programme, had a positive impact on the main stock
indices in the Eurozone. This was in line with previous literature that presented that, on average,
an unanticipated 25 basis-point reduction in the Federal funds rate leads to a 1% increase in the
broad market indices (Bernanke, 2005).
2.4 Hypothesis Development
The compact analysis of literature and theory provided better understanding of the
effectiveness of both tightening and expansionary monetary policies implemented by the ECB and
other central banks in influencing the equity markets and asset prices by directing inflation
expectations towards the desired outcome. This formulates the primary justification for the
hypothesis development that aims to evaluate the effectiveness of the ECB’s announcements of
tightening conditions and whether it produces any significant outcomes.
Hypothesis 1 (𝑯𝟏)
“The tightening monetary policy announcements will result in negative abnormal returns for nonbank stock prices in the Eurozone.”
Based on the findings of multiple empirical studies (Rigobon and Sack, 2002; Bohl et al.,
2008; Ioannidis and Kontonikas, 2008), which demonstrate the unfavorable effect of tightening
monetary policies on stock and indices prices through diverse channels, it is hypothesized that
tightening monetary policies will result in a detrimental effect on non-bank stock returns.
Hypothesis 2 (𝑯𝟐)
“The tightening monetary policy announcements affect the small capitalization stocks more
compared to the medium and large capitalization stocks.”
Multiple studies, including those by Thorbecke (1997) and Gertler and Gilchrist (1994),
have highlighted the disparities in the response of stock prices among small, medium and large
capitalization companies which arise due to the higher dependency on bank loans and the credit
constraints of smaller firms. Hence, I examined if those differences hold under the current
tightening macroeconomic environment and after a global pandemic context that provided easy
credit facilities.
15
Hypothesis 3 (𝑯𝟑)
“There is a negative relationship between a firm’s debt-to-equity ratio and the abnormal returns.”
As mentioned above, Gertler and Gilchrist (1994) used the firm size as a proxy to measure
the credit constraint and showed that smaller firms are more dependent on bank loans. However,
a number of papers revealed that the firm size is not a perfect proxy for measuring the ability of
raising external funds (Ehrmann and Fratzscher, 2004). Therefore, inspiring from the study of
Ehrmann and Fratzscher (2004), this paper takes a slightly different approach in the sense that it
analyzes the effect of the debt-to-equity ratio of a firm on the abnormal returns. More specifically,
it is hypothesized that there is a negative relationship between a firm’s debt-to-equity ratio and its
abnormal returns which rises from the inability of accessing more external funds.
Chapter 3: Methodology
This chapter details the methodology and data used to analyze the effects of tightening
monetary policy announcements on stock prices in the Eurozone. The first part of the chapter
focuses on the data collection process, illustrating the sources used to gather it. The second section
provides a detailed description of the event study approach employed to detect any anomalous
stock returns. To achieve this, the methodology outlined by Brown & Warner (1985), MacKinlay
(1997) and by Kothari (2004) has been utilized as a reference. The subsequent section presents the
analytical framework utilized to assess the variations in the effects across different firm sizes and
potential determinants of the abnormal returns.
3.1 Data
The companies covered in this paper are part of one of the 20 countries that form the
Eurozone: Austria, Belgium, Croatia, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland,
Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Portugal, Slovakia, Slovenia, Spain,
and are disaggregated into small, medium and large capitalization institutions in the research area.
Given the impossibility of comprehensively covering all small, medium and large capitalization
stocks present in the Eurozone, I acknowledge the significance of utilizing companies that are
listed on various Euro STOXX Total Market Indices (TMI) based on their firm size. The Euro
16
STOXX TMI Large was selected to represent large capitalization firms. It is derived from the
STOXX Europe TMI index and includes the largest companies representing up to 70% of the
cumulative total market capitalization in the investible universe, with 117 constituents. The Euro
STOXX TMI Mid index was selected to represent medium capitalization firms, including
companies representing between 70% to 90% of the cumulative total market capitalization in the
investible universe, with 184 components. Similarly, the Euro STOXX TMI Small index was
chosen to represent small capitalization firms, including companies representing above 90% of the
cumulative total market capitalization in the investible universe, with 510 components. Companies
not already assigned to the large or mid segments are assigned to the small segment. Given the fact
that the above-mentioned indices include bank stocks and that not all companies were publicly
traded during our chosen estimation window, several adjustments were made. Firstly, all bank
stocks from the indices were excluded. Secondly, the companies that did not have data available
for the entire estimation window were removed. Thus, the final sample is composed of 709
components of which 97 are large cap, 162 medium cap and 450 small cap companies.
The fact that this study focuses on the impacts of monetary announcements in the
Eurozone, leads one to consider the Euro STOXX TMI as the proxy for the overall market
especially because it includes companies that operate only in countries that are part of the
Eurozone. It consists of 811 components and it covers approximatively 95% of the free float
market capitalization in Europe. All the daily price data was collected from FactSet in euros as the
currency for the years 2020 to 2023.
3.2 Event Identification
One of the most important steps in an event study is to identify the right events that should
be included in the analysis. There are several techniques of identifying the relevant events. For
example, according to Urbschat and Watzka (2020) a potential guideline in picking the most
appropriate events represents an analysis of 5-year inflation swaps, as they serve as a significant
indicator of inflation expectations. If the deviations from inflation target are significant, it may
signal an increase likelihood of the ECB implementing a QE program. However, establishing a
direct correlation between QE speculations and the fluctuations of inflation swaps is not at all an
easy analysis. The main challenge rises from the strong correlation between the inflation swaps
and oil prices (Urbschat & Watzka, 2020).
17
Another approach is to consider all the announcements and decisions made by central
banks as events. This implies that if the news is already anticipated by the market, the asset prices
will not change too much. That is because the news is already priced in and it doesn’t convey
anything unexpected. However, in reality the expectations of market agents about an event are
influenced by various channels, such as press releases (Urbschat & Watzka, 2020). The purpose of
press releases is to increase transparency and to prevent any potential misunderstandings that might
lead to false expectations. ECB implemented a standard two-step process to announce its monetary
decisions. First, it publishes the official decisions and after it holds a press release to communicate
the rationale behind the specific decision. Therefore, important monetary decisions related to
tightening conditions as well as press releases were identified as relevant and appropriate events.
Second, ECB announcements regarding weak future guidance were considered to be significant as
well. Weak future guidance suggests a slowdown in economic growth in which output systemically
falls and unemployment rises, after tightening monetary policy decisions (Friedman, 1995).
Following this approach, a total of thirteen events were included in this paper. See Table 1 in
Appendix 2 for a detailed overview of the events.
3.3 Event-study Methodology
To assess the impact of tightening monetary policy announcements on stock prices, the
event study methodology elaborated in papers such as MacKinlay (1997), Kothari (2004) and
Brown and Warner (1985) was utilized. This method is based on finding and analyzing any
statistically significant abnormal returns that may occur due to the mentioned events.
At the base of the investigation, the event window and the estimation period were
established. A common challenge encountered in event studies is represented by estimating the
impact of specific events on asset prices and to disentangle them from other macroeconomic
factors. However, Brown & Warner (1985) argue that the main advantage of event studies lies in
their ability to capture the genuine responses of equity prices to monetary decisions, owing to its
narrow event window. Unfortunately, this approach may be limited in its capacity to capture market
expectations both before and after the event. Therefore, to enhance the robustness of the
methodology, multiple event windows were incorporated [t1, t2], including [-1, 1] and [-5, 5]. The
estimation window represents the returns that are not affected by the event and it is used to
calculate the expected returns using the market model, which will be addressed in the following
18
section. The estimation window begins 252 trading days prior to the first event and it ends prior to
the first event window, namely 5 days before the first event. This larger estimation window [T1,
T2] will help to smooth the effect of potential outliers present in the data set. It is crucial that the
event window and the estimation window do not overlap, as this might result in an inaccurate
estimation of the expected returns (MacKinlay, 1997). Figure 2 represents the timeline of the event
study.
Figure 2:
T2
T1
t1
Estimation window
t2
Event date t = 0
Event window
3.3.1 Estimating the Expected Normal Returns: Ordinary Least Squares (OLS) Model
After identifying the estimation window and the event windows, one can then use the
returns realized in the estimation window to estimate the normal returns. The Ordinary Least
Squares (OLS) market model by Brown and Warner (1985) was used to calculate the expected
(normal) returns. The model assumes a linear relation between the market return and the individual
return of a stock.
𝐸(𝑅)𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖 𝑅𝑚,𝑡 + 𝜀𝑖,𝑡
(3)
𝐸(𝜀𝑖,𝑡 ) = 0
𝑣𝑎𝑟(𝜀𝑖,𝑡 ) = 𝜎𝜀2
Where 𝑅𝑚,𝑡 is the return of the Euro STOXX TMI at time t, and 𝐸(𝑅)𝑖,𝑡 is the expected return of
the firm i at time t, 𝜀𝑖,𝑡 is the zero mean disturbance term, and 𝛼𝑖 , 𝛽𝑖 , 𝜎𝜀2 are parameters estimated
during the estimation window by the OLS market model. This has an advantage compared to other
models because by removing part of the return that is related to the variance of the market’s return,
the variance of the abnormal return is reduced. This can increase the ability to determine and assess
the effects of events (MacKinlay, 1997).
19
3.3.2 Calculation and Aggregation of Abnormal Returns
Given the OLS market model parameter estimates, one could measure and analyze
abnormal returns 𝐴𝑅𝑖,𝑡 by subtracting the expected returns 𝐸(𝑅)𝑖,𝑡 from the actual returns 𝑅𝑖,𝑡 .
𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡 − 𝐸(𝑅)𝑖,𝑡
(4)
As previously indicated, the revision of expectations by the market may require a
significant amount of time. Consequently, in order to derive substantive inferences regarding a
given event, the estimation of an aggregate measure of abnormal returns across all relevant event
windows, as per Mackinlay's (1997) definition of Cumulative Abnormal Returns (CAR), is
necessary. It illustrates how individual firms react to the shock during the event windows.
𝐶𝐴𝑅𝑖 [𝑡1, 𝑡2]= ∑𝑡2
𝑡1 𝐴𝑅𝑖,𝑡
(5)
Additionally, in order to examine the overall impact of a specific event on all firms that are
part of the sample, it was imperative to consolidate the abnormal returns with respect to the
dimension of firm size. This is known as the Average Abnormal Return (AAR) and gives insight
in how all of the firms combined based on their market capitalization at a specific point in time t
react to tightening announcements.
𝐴𝐴𝑅𝑡 =
1 𝑡2
∑ 𝐴𝑅𝑖.𝑡
𝑁 𝑡1
(6)
Where N stands for the number of firms included in the sample.
Moreover, the aggregation of AAR across all event windows enables the determination of
the Cumulative Average Abnormal Returns (CAAR), encompassing the entirety of the sample.
𝐶𝐴𝐴𝑅(𝑡1;𝑡2) = ∑𝑡2
𝑡1 𝐴𝐴𝑅𝑡
(7)
Where t1 marks the start of the event window and t2 the end of it.
3.3.3 Testing the Abnormal Returns
After calculating the abnormal returns and aggregating them across the time and size
dimensions, it was crucial to test for their significance. This was done using the t-test. For the
cumulative abnormal returns, the formula for the t-statistic is:
20
𝐶𝐴𝑅
(8)
𝑡𝐶𝐴𝑅 = 𝑆
𝐶𝐴𝑅
Where 𝑆𝐶𝐴𝑅 is the standard deviation of CAR and it was calculated using the following formula:
2
𝑆𝐶𝐴𝑅 = √𝐿2 𝑆𝐴𝑅𝑖
(9)
L2 refers to the length of the event window in the number of days. For example, 3 days for the
event window [-1; 1] and 11 days for the event window [-5; 5].
In a similar manner, the t-statistic for AAR was calculated using the formula:
𝐴𝐴𝑅0
(10)
𝑡 = √𝑁 𝑆
𝐴𝐴𝑅0
Where N is the number of firms in the sample, and 𝑆𝐴𝐴𝑅0 is the standard deviation calculated as:
𝑆𝐴𝐴𝑅0 = √
1
∑𝑁 (𝐴𝑅𝑖,0
𝑁−1 𝑖=1
− 𝐴𝐴𝑅0 )2
(11)
The t-test for testing the significance of CAAR uses the t-statistic:
𝑡 = √𝑁
𝐶𝐴𝐴𝑅
𝑆𝐶𝐴𝐴𝑅
(12)
Where N is the number of instances and 𝑆𝐶𝐴𝐴𝑅 represents the standard deviation and was computed
using the formula:
1
2
𝑆𝐶𝐴𝐴𝑅 = √𝑁−1 ∑𝑁
𝑖=1(𝐶𝐴𝑅𝑖 − 𝐶𝐴𝐴𝑅)
(13)
Furthermore, the following formula for the two-sample t-test was used to test for
significance of the difference in the cumulative average abnormal returns (CAAR) between the
small and medium, and small and large capitalization groups:
𝑡=
𝑋1 −𝑋2
𝑠2 𝑠2
√ 1− 2
𝑁1 𝑁2
(14)
Where 𝑋1 is the mean CAAR for the small cap group for different event windows, and 𝑋2 is the
mean CAAR for either medium or large cap groups for different event windows. Similarly, 𝑠1 and
𝑠2 are the samples standard deviations, and 𝑁1 and 𝑁2 are the number of observations.
21
3.3.4 Event Determinant Analysis for the Top 60 Components
In addition to the event study that explored which events resulted in the most significant
impacts on equity prices in the Eurozone, this paper examines the differences in firm size.
Furthermore, it also analyses the effect of the debt-to-equity ratio on the abnormal returns for the
chosen events. To further work with reasonable sample sizes, a benchmark of top 20 firms that are
included in the 3 Euro STOXX TMI indices based on their size was used. This was done based on
the top 20 components weighted by their share in the index as of the 16th of April 2023 and on
their market capitalization, taken from the index’s prospectus, and on the availability of the data.
Furthermore, because of the inclusion of bank stocks in the 3 Euro STOXX TMI indices, it was
decided to replace those particular entities with the next according components using the abovementioned criteria. See Table 2 for a detailed view of the included companies.
To analyze the determinants of abnormal returns for the 60 firms included in the sample, I
used an OLS regression for each event window and for each event, meaning that 13 regressions
with 3 different dependent variables were run. The baseline OLS regression used to examine firm
specific aspects is as follows:
𝐶𝐴𝑅𝑖,𝑤 = 𝛽0 + 𝛽1 𝑀𝐼𝐷 + 𝛽2 𝐿𝐴𝐺 + 𝛽3 𝐿𝑒𝑣𝑟𝑖 + 𝛽4 𝑅𝑂𝐸𝑖 + 𝛽5 𝐶𝑅𝑖 + 𝛽6 𝐸𝑉𝐸𝐵𝑖 + 𝛽7 𝐸𝑃𝑆𝑖 +
𝛽8 𝐻𝐼𝐶𝑃𝑡 + 𝛽9 𝐶𝑂𝑅𝐸𝑖 + 𝜀𝑖 (15)
Where 𝐶𝐴𝑅𝑖,𝑤 acts as the dependent variable and is the cumulative abnormal return for stock i (of
the 60 companies in the sample) at the event window w. The variable 𝑀𝐼𝐷 is a binary variable that
takes the value of 1 if the company is part of the Euro STOXX TMI Mid index. The variable 𝐿𝐴𝐺
is also a binary variable, but it takes the value of 1 if the firm is included in the Euro STOXX TMI
Large. The variable 𝐿𝑒𝑣𝑟𝑖 indicates the firm’s level of leverage measured by its debt-to-equity
ratio. Also, the variable 𝐶𝑂𝑅𝐸𝑖 is a dummy that takes the value of 1 if the company is from
Germany, France, The Netherlands, Belgium and Austria (Core European countries) and the value
of 0, if the firms are from Spain, Finland and Italy (Periphery European countries). The following
variables, 𝑅𝑂𝐸𝑖 , 𝐶𝑅𝑖 , 𝐸𝑉𝐸𝐵𝑖 , 𝐸𝑃𝑆𝑖 are firm specific characteristics, namely, return on equity,
current ratio, enterprise value to EBITDA ratio and earnings per share for firm i, which act as
control variables. Data on the firm specific characteristics was downloaded from FactSet. In
addition, due to large outliers measured on the firm Prosus, regarding its enterprise value to
EBITDA, I decided to remove this particular observation that could affect the estimators of the
22
regression models. Furthermore, data regarding current ratios for Aedifica, flatexDEGIRO and
Argan, and regarding the enterprise value to EBITDA for Aegon and Allianz was not available. A
table with descriptive statistics could be found in Appendix 5 (Table 6). In addition, to control for
macroeconomic factors that could influence the abnormal returns, the variable 𝐻𝐼𝐶𝑃𝑡 was
introduced in the regression equation. It contains monthly values of the Harmonized Index of
Consumer Prices in the Eurozone from January 2016 to March 2023.
23
Table 2: Firm sample
Small Cap
Company
Market Cap
Valmet
5.81
Hugo Boss
4.69
Spie
4.46
Alten
5.19
Aixtron
2.82
Elis
4.06
Brunello Cucinelli
6.03
Verallia
4.50
Sopra Steria Group
3.88
Freenet
3.07
Aedifica
3.03
Gaztransport & Technigaz
3.53
Soitec
4.85
flatexDEGIRO
1.05
Fielmann
3.99
Argan
1.63
Varta
0.91
Melexis
3.49
Almirall
1.61
Nordex
2.28
Note. All numbers are in billions of euros.
Country
Finland
Germany
France
France
Germany
France
Italy
France
France
Germany
Belgium
France
France
Germany
Germany
France
Germany
Belgium
Spain
Germany
Medium Cap
Company
Market Cap
Publicis Grp
18.66
ASM International
15.89
Philips
16.95
Brenntag Societas
11.78
Moncler
18.35
Akzo Nobel
12.24
MTU Aero Engines
12.69
Rheinmetall
11.55
Carrefour
13.83
KPN
13.36
OMV AG
13.78
Getlink
8.95
Renault
9.50
Puma
7.76
Acciona
9.27
Aegon
8.10
Vivendi
10.04
Elia Group
9.26
Alstom
8.82
Prysmian
9.87
Country
France
Netherlands
Netherlands
Germany
Italy
Netherlands
Germany
Germany
France
Netherlands
Austria
France
France
Germany
Spain
Netherlands
France
Belgium
France
Italy
Large Cap
Company
Market Cap
LVMH Moet Hennessy
438.60
ASML HLDG
222.70
Total Energies
141.60
SAP
158.50
Sanofi
128.40
Siemens
114.90
L'Oreal
227.90
Allianz
91.19
Schneider Electric
84.50
Air Liquide
84.30
Prosus
88.06
Airbus
96.49
Christian Dior
150.60
Hermes International
208.60
Deutsche Post
50.20
Bayer
57.67
Heineken
60.09
Adyen
44.44
Ferrari
49.04
Mercedes-Benz Group
71.34
Country
France
Netherlands
France
Germany
France
Germany
France
Germany
France
France
Netherlands
France
France
France
Germany
Germany
Netherlands
Netherlands
Italy
Germany
24
Chapter 4: Results
4.1 Event Study Analysis
The initial step of the study was to analyze how the tightening announcements of the ECB
impacted the returns of the companies as a whole for different event windows. Table 4 provides
the cumulative average abnormal returns and the average abnormal returns of all 709 firms
included in the mentioned indices for all thirteen events.
Table 4: Cumulative average abnormal returns (%)
Date
16/12/2021
CAAR[-5,5]
-0.3372
(0.1567)
CAAR[-1,1]
0.2023
(0.0986)
AAR
0.0364
(0.6045)
03/02/2022
-0.1466
(0.5368)
-0.1019
(0.4077)
-0.0583
(0.4106)
10/03/2022
0.8169***
(0.0007)
1.2441***
(0.0000)
0.9311***
(0.0000)
24/03/2022
0.3219
(0.1753)
-0.3812***
(0.0020)
-0.5142***
(0.0000)
14/04/2022
0.3147
(0.1858)
0.1319
(0.2805)
0.2001***
(0.0047)
09/06/2022
-1.9120***
(0.0000)
-0.0512
(0.6826)
-0.0683
(0.3347)
21/07/2022
-0.3627
(0.1289)
0.3569***
(0.0037)
-0.1215*
(0.0846)
08/09/2022
-2.6540***
(0.0000)
-0.1649
(0.1790)
-0.1958***
(0.0057)
27/10/2022
0.7167***
(0.0028)
0.3031**
(0.0136)
0.3048***
(0.0000)
28/11/2022
0.5512**
(0.0208)
-0.4415***
(0.0004)
-0.2152***
(0.0024)
15/12/2022
-0.0542
(0.8197)
0.0254
(0.8381)
0.4769***
(0.0000)
02/02/2023
0.3467
(0.1451)
0.5022***
(0.0001)
0.4980***
(0.0000)
16/03/2023
-1.6237***
(0.0000)
-0.6996***
(0.0000)
-0.3625***
(0.0000)
p-values are in parenthesis
Note. This table illustrates the cumulative average abnormal returns for all thirteen events.
***p<0.01, **p<0.05, *p<0.1
25
The results show that the announcement made by the Governing Council on the 24th
of March 2022, regarding the decision to gradually remove the package of pandemic collateral
measures resulted in a significant negative abnormal return for both [-1,1] and [0] event
windows. The returns were equal to -0.3812% and -0.5142%, both significant at 1% significance
level. In a similar manner, the announcement made on the 9th of June 2022, related to the
discontinuity of the net asset purchases under the APP program ended in a -1.912% return, again
significant at the 1% significance level. On the 28th of November 2022, after the ECB published
data regarding decreasing monetary developments, the companies included in the sample
experienced a decrease of -0.2152% on the day of the event and with -0.4415% on average in
their stock prices. Both declines were significant at 1% significance level. Though, the same
event resulted in a positive abnormal return for the [-5,5] event window, which was significant at
the 5% level. The strongest negative price reaction happened on the 16th of March 2023, as a
result of a 50 basis-points interest rate hike decision. In fact, equity prices declined by more than
1.5% over the [-5,5] event window, this being significant at the 1% significance level. Figure 3
presents the cumulative average abnormal returns for the 16th of March 2023.
Figure 3: Cumulative average abnormal returns for the 16th of March 2023 for the [-5,5] event
window
26
However, the event study analysis revealed significant positive abnormal returns,
which were not in line with the previous literature. The announcement made on the 10th of March
2022, in which the Governing Council made public the possibility of ending the APP in the third
quarter of 2022, was greeted by the companies with positive abnormal returns. The measured
returns were equal to 0.8169% for event window [-5,5], 1.2441% for [-1,1] and 0.9311%, all
being significant at 1%. In addition, on the 14th of April 2022, the AAR was equal to 0.2001%,
which was significant at 1%. Moreover, the 75-basis points interest rate increase on the 27th of
October 2022 resulted in significant positive abnormal returns for all event windows too. The
CAAR for the [-5,5] event window was equal to 0.7167% and the AAR was equal to 0.3048%,
both significant at 1%. The CAAR for the event window [-1,1] was equal to 0.3031% and was
significant at the 5% significance level. The interest rate increases on the 15th of December 2022
and on the 2nd of February 2023, ended up in significant positive abnormal returns as well on the
day of the event. The increases were equal to 0.4729% and 0.498% (significant at p<0.01).
Looking at the interest increase on the 2nd of February 2023, we can notice that CAAR for the
event window [-1,1] was equal to 0.5022 and that it is significant at the 1% level. Considering
the first interest hike of the tightening cycle, namely the one on the 21st of July 2022, the CAAR
over the [-1,1] event window was 0.3569% (significant at p<0.01), but the AAR was equal to 0.1215% but merely significant at the 10% level. Additionally, the results revealed that the
announcements on the 16th of December 2021 and on the 3rd of February 2022, did not result in
any significant abnormal returns.
4.2 T-Test
The second step was to analyze how the firms with different sizes, measured by their
market capitalization, responded to tightening announcements made by the ECB. Table 5 in
Appendix 3 shows the cumulative average abnormal returns of the 709 sampled companies
grouped by their market cap for all thirteen events. Considering the individual groups, all
significance levels and all event windows, the small and medium capitalization firms had the
highest number of cumulative average abnormal returns, both negative and positive. In total, 22
abnormal returns at different significance levels were registered for those two groups, whereas
the large cap group had only 7 significant abnormal returns. We used the cumulative average
abnormal returns measured on the group level and test for the significance of the difference
27
between the small cap firms and the other two groups, medium and large caps. The results and
the t-values are presented in Table 6.
Table 6: Group difference t-test across all events
Test group
Variable
Mean Difference
t-value
Std. Error
Small - Medium
CAAR[-5,5]
-0.2089
-0.4157
0.5024
Small - Medium
CAAR[-1,1]
-0.0164
-0.0591
0.2779
Small - Medium
AAR
0.0182
0.0900
0.2027
Small - Large
CAAR[-5,5]
-0.495
-1.22
0.4042
Small - Large
CAAR[-1,1]
-0.0675
-0.4417
0.1527
Small - Large
AAR
0.0394
0.2796
0.1410
Combined Observations
26
The findings from the t-test showed that the most powerful difference was between
the small and large capitalization for the [-5,5] event window. It revealed that on average the
small capitalization firms experienced a decrease of -0.495% more, compared to the large
capitalization companies. However, the results are not significantly different from zero. To be
more precise, none of the differences are statistically significant at any significance level.
Therefore, there is not sufficient statistical evidence so that we can reject the null hypothesis for
𝐻2. This implies that there is no difference in abnormal stock returns between small and medium
cap companies and between small and large cap firms.
4.3 Cross-sectional Analysis for the Top 60 Components
Table 7 shows the results from the regression (15) for the most significant events
namely, 16th of March 2023 and the 10th of March 2022. The cumulative abnormal returns for
both [-5,5] and [-1,1] event windows, and the abnormal return on the day of the event of the top
20 components of the three different indices were used as dependent variables. They indicate that
there is not a significant relationship between a firm’s level of debt, measured by its debt-toequity ratio and the different abnormal returns for the mentioned event dates. Furthermore, as
depicted by the t-test, there is no difference between the abnormal returns and the firm’s size.
28
Both coefficients of 𝑀𝐼𝐷 and 𝐿𝐴𝐺 are not statistically significant at any level. However, it
appears that there is a significant negative relationship between the firm’s country position and
the abnormal returns measured on the 16th of March 2022 for the [-1,1] event window, for the 5%
significance level. This implies that the companies located in core European countries observed
lower bound returns compared to firms located in the periphery European countries on average.
Table 7: Cross-sectional regressions for 16th March 2023 & 10th March 2022
16/3/2023
Variables
10/03/2022
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
Levr
-0.000075
0.000094
0.000008
0.000006
0.000105*
0.000016
(-0.52)
(1.41)
(0.2)
(0.08)
(1.92)
(0.4)
MID
0.01517
-0.01131
-0.00155
-0.00838
0.01040
-0.00716
(0.56)
(-0.92)
(-0.2)
(-0.45)
(0.49)
(-0.49)
0.03336
0.0161
0.00488
0.0023
0.00234
-0.01315
(1.62)
(1.51)
(0.89)
(0.13)
(0.19)
(-1.45)
0.00127
0.00007
0.00054**
-0.00131
-0.00005
0.00074*
(1.44)
(0.13)
(2.68)
(-1.67)
(-0.75)
(1.82)
EVEB
-0.00005
0.00017*
0.00007**
0.00037
-0.00026
-0.00005
(-0.23)
(1.7)
(2.41)
(1.66)
(-0.5)
(-0.23)
ROE
-0.000005
0.00053
-0.00012
-0.00061
-0.00016
-0.00022
(-0.01)
(1.36)
(-1.02)
(-1.58)
(-0.45)
(-0.86)
0.0136*
0.01058*
0.00273
0.0067
0.00083
0.00045
(1.7)
(1.83)
(1.59)
(1.01)
(0.01)
(0.1)
-0.00786
0.00234
-0.000096
0.00579
-0.01015
0.00829
(-0.56)
(0.32)
(-0.27)
(0.52)
(-0.96)
(1.26)
CORE
-0.02302
-0.02682**
-0.01548
-0.01642
-0.00486
-0.00679
(-1.11)
(-2.16)
(-1.66)
(-0.95)
(-0.31)
(-0.64)
Constant
0.00166
-0.01173
0.00722
0.01615
0.01177
0.00749
(0.06)
(-0.75)
(0.73)
(0.76)
(0.47)
(0.51)
0.2518
0.3771
0.2409
0.1900
0.1239
0.1213
55
55
55
54
54
54
LAG
EPS
CR
HICP
R-squared
Observations
Note. This table illustrates the sampled firms specific regressions (15) with the CAR[-5,5], CAR[-1,1] and AR as dependent
variables. The regressions were performed for the following events, 26th of March 2023 and 10th of March 2022 with robust
standard errors. T-statistics are in parentheses. *** p<0.01, ** p<0.05, *p<0.1
Table 8 illustrates the results from the regressions performed with the cumulative
abnormal returns [-5,5] and [-1,1] and the abnormal returns for the second most significant
events. Those happened on the 27th of October 2022 and on the 28th of November 2022. They
indicate that the differences in the abnormal returns between small and large and small and
29
medium sized companies were not significant. Furthermore, there was a significant positive
relationship between a firm’s debt-to-equity ratio and the abnormal return only for the interest
rate increase on the 27th of October. The relationship was significant at 5% significance level.
Additionally, the control variable 𝐸𝑃𝑆 indicates that firms with lower earnings per share reacted
more for both [-5,5] and [-1,1] on the 27th of October 2022, compared to companies that had
lower earnings per share. The enterprise value to EBITDA registered a significant negative
relationship with the CAR [-1,1] on the 28th of November 2022.
Tables 9, 10 and 11 in Appendix 5 provide the results from the regressions performed
for all events and event windows. They reveal that the variables 𝑀𝐼𝐷 and 𝐿𝐴𝐺 were significant
at the 5% significance level in only three scenarios. Medium capitalization firms had
approximatively 3.46% more positive abnormal returns compared to the small cap stocks for the
[-5,5] event window on the 14th of April. In a similar manner, large cap stocks experienced a
1.27% more pronounced abnormal return in absolute terms compared to small cap companies on
the 24th of March 2022. Furthermore, on the 21st of July 2022, large cap companies reacted with
approximatively -2.24% less compared to the small cap firms. Besides the mentioned scenarios,
none of the 𝑀𝐼𝐷 and 𝐿𝐴𝐺 variables were significant.
The findings reveal that there was a significant negative relationship between the
abnormal returns and the firm’s debt-to-equity ratio for the [-1,1] and [0] event windows when
the ECB announced an interest rate increase on the 21st of July 2022. For the [-1,1] window the
results were significant at 1% and for the event day, at 5% significance level. Also, the firm’s
level of debt relative to its equity, negatively influenced the CAR [-1,1] on the 8th of September.
The relationship was significant at 5% significance level. However, on the 27th of October 2022,
the relationship between the variables 𝐿𝑒𝑣𝑟 and 𝐴𝑅 was positive at the 5% level.
30
Table 8: Cross-sectional regression for 27th October 2022 & 28th November 2022
27/10/2022
Variables
28/11/2022
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
Levr
0.00012
0.00011
0.00007**
0.00067
0.00004
0.000009
(1.00)
(1.62)
(2.21)
(0.76)
(1.16)
(0.37)
MID
-0.02089
-0.0023
0.00378
0.01205
0.015
-0.00367
(-0.69)
(-0.11)
(0.39)
(-0.6)
(1.22)
(-0.61)
0.02023
0.02215
0.00818
-0.00695
0.01255
0.00303
(0.82)
(1.35)
(1.07)
(-0.54)
(1.24)
(0.56)
-0.00238**
-0.00159**
-0.00002
0.00057
0.00016
0.00018
LAG
EPS
(-2.53)
(-2.04)
(-0.05)
(0.95)
(0.44)
(0.75)
-0.00026
-0.00008
-0.00021
-0.00042
-0.00059***
-0.00006
(-0.63)
(-0.26)
(-1.44)
(-1.26)
(-5.78)
(-1.07)
-0.00005
-0.00045
-0.00023
-0.00072
-0.00016
-0.0002
(-0.08)
(-1.05)
(-1.47)
(-1.58)
(-0.8)
(-1.33)
CR
-0.01006
-0.0172
-0.0065
0.00588
0.00592*
0.00308*
(-1.03)
(-1.84)
(-1.53)
(0.97)
(1.85)
(1.81)
HICP
0.02224
0.01207
0.00222
-0.0078
-0.00863
-0.00207
(1.45)
(1.04)
(0.4)
(-0.77)
(-1.49)
(-0.7)
CORE
0.02842
0.024
0.00965
-0.01056
-0.00983
-0.00438
(1.1)
(1.15)
(1.02)
(-0.70)
(-1.01)
(-1.06)
-0.03734
-0.01496
-0.00967
0.02089
0.00502
0.00284
(-1.3)
(-0.81)
(-1.12)
(1.00)
(0.45)
0.46
0.1957
0.3528
0.3405
0.1709
0.2485
0.1261
54
54
54
54
54
54
EVEB
ROE
Constant
R-squared
Observations
Note. This table illustrates the sampled firms specific regressions (15) with the CAR[-5,5], CAR[-1,1] and AR as
dependent variables. The regressions were performed for the following events, 27th of October 2022 and 28th of November
2022 with robust standard errors. T-statistics are in parentheses. *** p<0.01, ** p<0.05, *p<0.1
The variable 𝐶𝑂𝑅𝐸 influenced the abnormal returns for several event dates. On the
16th of December 2021, it revealed that firms located in core European countries experienced
more pronounced positive abnormal returns compared to firms located in periphery European
countries on the day of the event. The difference was significant at the 5% significance level.
Also, on the 3rd of February 2022 the mean difference was positive for both [0], at 1%
significance level, and [-5,5] at the 5% level. Similarly, on the 14th of April 2022 for the [-5,5]
event window, core European firms measured higher positive abnormal returns compared to
periphery European companies. In contrast to the above-mentioned results, on the 16th of March
2023, there was a negative relationship between 𝐶𝑂𝑅𝐸 and CAR [-1,1] significant at the 5%
level.
31
The chosen control variables had significant impacts on the different dependent
variables. The ratio between a firm’s enterprise value relative to its EBITDA negatively affected
the abnormal returns for the event dates: 16th of December 2021, 3rd of March 2022, 14th of April
2022, 8th of September 2022 and the 28th of November 2022 at different event windows. All of
the results were significant at 1% significance level. In contrast, the mentioned variable
positively influenced the abnormal returns as well. For the [-5,5] and [-1,1] event windows, on
the 21st of July the relationship between the firms’ enterprise value to their EBITDA was
positive, at 5% significance level. The earnings per share of a firm also showed significant
relationships with different abnormal returns. On the 12th of December 2021, 14th of April 2022
and on the 27th of October 2022 the relationship between 𝐸𝑃𝑆 and the abnormal returns was
negative at 5% significance levels, while on the 16th of March the relationship was positive and
significant at 5%. The return on equity positively influenced the abnormal returns only on the
21st of July 2022 at the 5% level, while for the all-other events the control variable did not have a
significant effect. The variable 𝐶𝑅 had a positive impact on the abnormal returns on the 24th of
March for different event windows, at both 1% and 5% significance levels, and for the 9th of
September. It also resulted in a negative effect on the 𝐴𝑅 on the 16th of December 2021. Lastly,
the variable 𝐻𝐼𝐶𝑃 had a negative effect on the abnormal returns for every event window only on
the 14th of April 2022, all being significant at 5% level.
Chapter 5: Discussion
5.1 Event Study
16th of December 2021 and 3rd of February 2022
The results revealed that the announcement of the ECB regarding the reduction of the
pace of conducting the APP and ending it on March 2022, made on the 16th of December did not
result in significant abnormal returns. Therefore, it is not possible to reject the null hypothesis for
𝐻1, for the mentioned event dates. One potential explanation is that the Governing Council
announced on the 28th of October 2021 the possibility of conducting PEPP at a slower pace. This
announcement might have also impacted the returns on the 3rd of February 2022, when the ECB
published their decision to conclude the net asset purchases under PEPP at the end of March
32
2022. Therefore, it is likely that the markets already incorporated the information in the asset
prices and thus we could not reject the null hypothesis of 𝐻1.
10th of March 2022
One of the strongest stock price reactions happened on the 10th of March when the
ECB made public its intention to conclude the asset purchases under the APP starting the third
quarter of 2022 and the possibility of increasing the interest rates. The announcement resulted in
positive abnormal returns, which are not in line with the previous literature and with the first
hypothesis (𝐻1). However, one potential explanation could be deduced from the increased
confidence regarding Central Bank’s target to restore price stability. With less than a week before
the Federal Open Market Committee, the markets already priced in a hike of 25 basis points in
the Federal Funds rates which marked the starting point of a tightening cycle in US. In principle,
a fully anticipated cycle should not trigger volatility in the financial markets (Arteta et al., 2015).
Furthermore, stock markets exhibit a strong association with the US monetary policy
environment (Conover & Johnson, 1999). Therefore, it is likely that the market perceived the
news as a good sign due to the central banks’ commitment to combat inflation.
24th of March 2022
The announcement made by the Governing Council in the press release on the 24th of
March 2022 regarding the gradual removal of the pandemic collateral package easing measures
was greeted by the equity markets with significant negative abnormal returns. This implies that
the null hypothesis of 𝐻1 can be rejected and evidence in favor of the alternative hypothesis was
found. The negative abnormal returns might have been also influenced by the UK Consumer
Price Index (CPI) release, which happened on day prior to the interest event. The CPI came
higher than expected, signaling that the inflation was increasing more aggressive. Markets tend
to experience higher correlations over time, especially during periods of uncertainty and crisis
(Professor et al., 2017).
14th of April 2022
The Governing Council’s announcement about the plan to conclude the asset
purchase program resulted in positive abnormal returns on the day of the announcement. In the
same meeting, ECB’s governing body made public its intention to not adjust interest rates at that
33
particular point in time. Therefore, it is likely that the equity markets reacted more to the last
announcement and thus, respond positively to the decision to keep the interest rates at record low
levels. This implies that the null hypothesis of 𝐻1 cannot be rejected.
9th of June 2022
The negative abnormal return on the 9th of June for the CAAR [-5,5] resulted from
the ECB’s announcement of increasing interest rates on their next meeting, which happened in
July 2022. The other event windows did not experience negative abnormal returns. Considering
this, one could adopt the alternative hypothesis 𝐻1 and reject the null for the [-5,5] event
window.
21st of July 2022
Governing Council’s decision to increase the interest rates with 50-basis points
compared to the expected 25-basis points resulted in positive abnormal returns, implying that the
null hypothesis of 𝐻1 cannot be rejected. This unexpected shock signaled to the markets that the
ECB is committed to bring back inflation at a normal level. However, the majority of important
companies included in the sample, such as ASM International, presented strong earnings and
beat the analysts estimates on the 20th of July 2022. The earnings analysis was done using data
from Factset. Thus, the positive abnormal returns might have been influenced by the strong
reported earnings.
8th of September 2022
The large 75-basis points interest rate hike resulted in significant negative abnormal
returns for two event windows, allowing us to reject the null hypothesis for 𝐻1. The findings
were in line with the previous literature that identified that a 25-baisis point increase in the
interest rates resulted in negative abnormal returns. Also, the magnitude of the interest rate
increase contributed to the negative abnormal returns (Fausch & Sigonius, 2018).
27th of October 2022
Even if at the meeting held on the 27th of October 2022 the ECB decided to raise the
interest rates with the same amount from its previous meeting, namely 75-basis points, the
markets greeted the decision with positive abnormal returns. The positive abnormal returns might
34
be explained by the fact that markets already anticipated another big interest rate hike which has
signaled at the previous meeting. Moreover, the US First Preliminary Gross Domestic Product
(GDP) report for the third quarter of 2022 was released on that day and the surprise factor was
positive 0.3%. Taking into consideration the results from Conover & Johnson (1999), that reveal
that stock markets respond to the US monetary environment, it is possible that the mentioned
factors might have influenced the abnormal returns. Thus, the null hypothesis for this specific
event could not be rejected.
28th of November 2022
The ECB’s publication regarding the monetary developments in the Eurozone was a
strong signal of a deteriorating outlook. The markets responded with both negative and positive
abnormal returns. The positive abnormal return might be attributed to the higher-than-expected
final GDP report in Germany which was published on the 24th of November 2022. On the
contrary, the negative price reaction was directly influenced by the ECB’s publication. Therefore,
we could reject the null hypothesis of 𝐻1 for the [-1,1] and [0] event windows, but not for the [5,5].
15th of December 2022 and 2nd of February 2023
On both dates, the Governing Council decided to increase the interest rates with 50
basis points and this resulted in positive abnormal returns for the equity markets in the Eurozone.
The interest rate hikes were highly anticipated by the markets due to the ECB’s previous
announcements made on the 27th of October regarding their strategy to combat the high inflation.
The ECB was explicit in their message, informing the market participants that more interest rate
increases will follow. Also, on the 14th of December the Federal Funds rate was increased with
0.5%. This might have provided more certainty to market participants about future interest rate
decisions. On the same day, data regarding the labor market was published in the US. The
Jobless Claims came lower with approximatively 9,000 than the estimates, signaling a strong
labor market. Considering evidence from Professor et al. (2017), the stock returns from the
countries of the Eurozone might exhibit similar patterns with the stock returns from the US due
to higher correlations especially during bear markets. The abnormal returns measured on the 2nd
of February 2023 might have been impacted by the Eurozone CPI report that was released one
35
day before the ECB’s meeting. The surprise component of the CPI was -0.4%, meaning that the
inflation was decelerating. Given the above, one could not adopt hypothesis 𝐻1.
16th of March 2023
The last investigated event, which consists of the ECB’s 50-basis points interest rate
increase negatively affected the returns of companies traded in the Eurozone, meaning that one
could reject the null of 𝐻1. This was highly in line with the previous literature from Bohl et al.
(2008) which identified that an increase in the interest rates leads to a decrease in the stock
returns.
5.2 Cross sectional analysis
Although the cross-sectional analysis did not cover all the 709 companies in the
event study, it is still relevant to address the effect on the abnormal returns. The findings of the
analysis with the chosen 60 firms are highly in line with the t-test performed on the 709
companies included in the event study. We failed to reject the null hypothesis of 𝐻2, implying
that there is no difference in the cumulative abnormal returns between firms with different
market capitalizations. Although the results from the majority of the analyzed events are not in
line with previous studies from Thorbecke (1997), a significant negative difference between the
small firms and large firms for the 21st of July 2022, [-1,1] event window was noticed. This
implies that the large firms reacted less compared to the small firms and thus, one would not
reject the null only for that event and event window.
The variable 𝐿𝑒𝑣𝑟, which measures the ratio of a firm’s debt relative to its equity,
indicated a negative relationship with the abnormal returns only for the events on the 21st of July
2022 and the 8th of September 2022, which was in line with pervious literature from Ehrmann
and Fratzscher (2004). Therefore, one could reject the null hypothesis of 𝐻3 for those two
events. In the remaining events, there was insufficient statistical evidence to reject the null. It is
challenging to explain the lack of significance for the coefficients of the debt-to-equity ratio for
the remaining events besides the small sample size. Further research should definitely explore
this limitation.
36
Next, looking at the effects of the variable 𝐶𝑂𝑅𝐸, the results indicated that the
companies located in core European countries experienced more pronounced abnormal returns
compared to firms located in the periphery European countries for three events. The effect was
similar to the results of Petrakis et al. (2022), who revealed that core countries tend to respond
more to monetary expansionary shocks due to their higher efficiency of transmission. However,
the relationship was negative for the last investigated event and insignificant for the remaining
events. Thus, a company’s location explained part of the abnormal returns only for four events
out of the thirteen studied events.
Lastly, the findings revealed several control variables with a significant coefficient.
Particularly noteworthy is the variable 𝐸𝑉𝐸𝐵 which presents both positive and negative
relationships with the abnormal returns for seven events out of the thirteen studied. It is
important to acknowledge that in five out of the seven significant events, the coefficients of the
control variable were significantly negative at 1% level.
Chapter 6: Conclusion
This paper analyzed the effects of tightening monetary announcements made by the
ECB on the equity markets, excluding the banking institutions, in the Eurozone and the potential
determinants of those reactions. The study implemented an event study methodology to assess
which announcements resulted in significant abnormal returns. A broad sample consisting of 709
publicly traded non-bank companies was used for the event-study and thirteen events were
investigated. The findings indicate that the announcements made on the 24th of March 2022, 9th
of June 2022, 8th of September 2022 and the 16th of March 2023 resulted in significant negative
abnormal returns. However, there were instances that contradicted the previous literature, such as
the 10th of March 2022, 14th of April 2022, 21st of July 2022 and the 27th of October 2022 that
resulted in significant positive abnormal returns and the event on the 28th of November 2022 that
resulted in both positive and negative significant abnormal returns. Moreover, using a t-test, this
paper revealed that the difference in cumulative average abnormal returns between firm size was
insignificant.
37
This study also evaluated the potential determinants of the abnormal returns for a sub
sample that consisted of the top 60 components of the broader sample. The cross-sectional
analysis revealed the same results as the t-test regarding the impact of a firm’s size on the
abnormal returns, implying that the difference was not significant. However, a negative
relationship between a firm’s debt to equity ratio only for two of the investigated events was
found, which was in line with the previous literature. Furthermore, a positive relationship
between the location of a company and the abnormal returns was identified for three events.
One major limitation of the study is the sample size. Several companies that represent
the top 60 components of the indicines did not have data regarding several ratios used in the
cross-sectional analysis and therefore they were removed. This resulted in samples with 54 and
55 companies, which barely crosses the threshold of Central Limit Theorem’s of 50 observations.
Furthermore, it is likely that the cross-sectional analysis suffers from omitted variable bias since
there might be other factors that explain the abnormal returns. Hence, further research should fill
this gap by increasing the sample size and by including more control variables or performing an
instrumental regression analysis.
Another challenge of this study was represented by the identification of isolated
events. As mentioned in the discussion section, several events coincided with other major
actions, such as the release of GDP report in US. These external factors might have influenced
the returns of the sampled companies. In addition, the latest interest rate increases (e.g., 4th of
May 2023 meeting) were not included in the event study.
Lastly, future research should take into consideration implementing more advanced
and robust econometric models to analyze the abnormal returns. Considering the study of
Bernanke (2005), the implementation of the vector autoregression (VAR) models will distinguish
between expected and unexpected policy actions and will capture the surprise element of ECB’s
decisions.
38
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https://doi.org/10.1016/j.qref.2019.10.008
41
Appendices
Appendix 1: HICP - Inflation Rate (ECB, 2023)
42
Appendix 2. Table 1: Event Overview
Date
Type of Announcement
16/12/2021
Monetary policy decision
Description
The European Central Bank (ECB) has announced a
reduction in the pace of its pandemic emergency
purchase programme (PEPP) and plans to discontinue it
by March 2022.
Day of the week
Thursday
3/2/2022
Monetary policy decision
The Governing Council announced that it will continue
the PEPP at a slower pace in the first quarter of 2022
and that it will discontinue the net asset purchases under
the PEPP at the end of March 2022.
Thursday
10/3/2022
Monetary policy decision
The Governing Council announced that it may be
possible to conclude APP starting Q3 2022 and that it
might be followed by interest rate increases.
Thursday
24/03/2022
Press release
The Governing Council of the ECB has decided to
gradually phase out the package of pandemic collateral
easing measures in place since April 2020.
Thursday
14/04/2022
Monetary policy decision
The Governing Council plans to conclude its asset
purchase program in Q3 2023, but will maintain
flexibility in conducting monetary policy due to high
uncertainty. The ECB will keep interest rates
unchanged and adjust them gradually after the
conclusion of the asset purchase program.
Thursday
9/6/2022
Monetary policy decision
ECB has announced that it will end net asset purchases
under its asset purchase programme (APP) as of 1st
July 2022. Also, the Governing Council plans to raise
its key interest rates by 25 basis points at its July
monetary policy meeting, and expects to gradually
increase rates beyond September.
Thursday
21/07/2022
Monetary policy decision
The Governing Council judged that it is appropriate to
take a larger first step on its policy rate normalisation
path than signaled at its previous meeting and it raised
three key interest rates by 50 basis points.
Thursday
8/9/2022
Monetary policy decision
The Governing Council today decided to raise the three
key ECB interest rates by 75 basis points.
Thursday
43
Press release
The Governing Council took today's decision, and
expects to raise interest rates further to preserve the
effectiveness of monetary policy transmission and
safeguard orderly market functioning.
Monetary policy decision
The Governing Council today decided to raise the three
key ECB interest rates by 75 basis points.
Press release
The Governing Council decided to recalibrate the
conditions of the third series of targeted longer-term
refinancing operations (TLTRO III) as part of the
monetary policy measures adopted to restore price
stability over the medium term.
28/11/2022
Monetary Developments in
the Euro area
ECB announced that in October 2022 annual growth
rate of broad monetary aggregate M3 decreased to
5.1%, annual growth rate of narrower monetary
aggregate M1 decreased to 3.8% and the annual growth
rate of adjusted loans to households decreased to 4.2%.
Monday
15/12/2022
Monetary policy decision
The Governing Council decided to raise the three key
ECB interest rates by 50 basis points and, based on the
substantial upward revision to the inflation outlook.
Thursday
2/2/2023
Monetary policy decision
The Governing Council decided to raise the three key
ECB interest rates by 50 basis points.
Thursday
16/3/2023
Monetary policy decision
The Governing Council decided to raise the three key
ECB interest rates by 50 basis points.
Thursday
27/10/2022
Thursday
44
Appendix 3. Table 5: Cumulative average abnormal returns per size (%)
Small Capitalization Stocks
Date
CAAR[-5,5]
CAAR[-1,1]
AAR
16/12/2021
-0.5611*
0.1892
0.0677
(0.0845)
(0.2570)
(0.4812)
Medium Capitalization Stocks
CAAR[-5,5]
CAAR[-1,1]
AAR
-0.1276
0.2777
-0.0469
(0.7649)
(0.2061)
(0.7107)
Large Capitalization Stocks
CAAR[-5,5]
CAAR[-1,1]
AAR
-0.3456
0.1362
0.0306
(0.4851)
(0.5925)
(0.8344)
03/02/2022
0.2691
(0.4070)
-0.1170
(0.4867)
-0.1077
(0.2662)
0.1798
(0.6713)
-0.0683
(0.7558)
0.1124
(0.3744)
-0.4840
(0.3259)
-0.0888
(0.7278)
-0.1143
(0.4366)
10/03/2022
1.0644***
(0.0012)
1.2274***
(0.0000)
1.1922***
(0.0000)
0.3978
(0.3485)
1.8194***
(0.0000)
0.8434***
(0.0000)
0.3120
(0.5266)
0.3361
(0.1962)
-0.1412
(0.3406)
24/03/2022
0.1844
(0.5696)
-0.5571***
(0.0010)
-0.7212***
(0.0000)
0.3376
(0.4262)
-0.2414
(0.2704)
-0.2742**
(0.0297)
0.9225*
(0.0613)
0.1973
(0.4367)
0.0423
(0.7712)
14/04/2022
0.1852
(0.5685)
0.1076
(0.5192)
0.1924**
(0.0460)
0.8537**
(0.0449)
0.2995
(0.1704)
0.2910*
(0.0211)
0.0100
(0.9828)
-0.0356
(0.8882)
0.0843
(0.5634)
09/06/2022
-2.3662***
(0.0000)
0.0331
(0.8468)
-0.0067
(0.9445)
-2.1331***
(0.0000)
-0.6079***
(0.0069)
-0.4501***
(0.0004)
0.5474
(0.2777)
0.4841*
(0.0633)
0.2819*
(0.0556)
21/07/2022
-0.4000
(0.2202)
0.5107***
(0.0024)
-0.0747
(0.4367)
-0.4457
(0.2955)
0.1914
(0.3804)
-0.3466***
(0.0061)
-0.0647
(0.8959)
-0.0822
(0.7455)
0.0370
(0.7995)
08/09/2022
-3.5119***
(0.0000)
-0.3457**
(0.0398)
-0.1204
(0.2106)
-1.6377***
(0.0001)
0.3410
(0.1198)
-0.3997***
(0.0016)
-0.3938
(0.4239)
-0.1757
(0.4896)
-0.2055
(0.1592)
27/10/2022
0.8273**
(0.0113)
0.3371**
(0.0442)
0.3856***
(0.0001)
1.0753**
(0.0118)
0.2824
(0.1960)
0.2918**
(0.0206)
-0.4121
(0.4033)
0.1772
(0.4845)
-0.0491
(0.7363)
28/11/2022
0.5591*
(0.0856)
-0.4242**
(0.0116)
-0.1729*
(0.0731)
0.6797
(0.1095)
-0.7035***
(0.0014)
-0.4908***
(0.0001)
0.2917
(0.5536)
-0.0859
(0.7348)
0.0483
(0.7409)
15/12/2022
-0.0690
(0.8318)
0.2184
(0.1989)
0.6242***
(0.0000)
0.2690
(0.5264)
-0.4746**
(0.0330)
0.3095**
(0.0167)
-0.5395
(0.2745)
-0.0398
(0.8773)
0.0712
(0.6339)
02/02/2023
0.2963
(0.3617)
0.2169
(0.1963)
0.1970**
(0.0426)
0.6373
(0.1337)
1.3751***
(0.0000)
1.3417***
(0.0000)
0.0797
(0.8715)
0.3593
(0.1586)
0.4770***
(0.0013)
-0.3861***
(0.0024)
1.1583**
(0.0196)
0.4428*
(0.0843)
-0.0427
(0.7708)
16/03/2023
-1.8308***
-0.6485***
-0.4231***
-2.7239***
-1.5295***
(0.0000)
(0.0001)
(0.0000)
(0.0000)
(0.0000)
p-values are in parentheses.
Note. This table illustrates the cumulative average abnormal returns per group size. ***p<0.01, **p<0.05, *p<0.1
45
Appendix 4.
Table 6: Descriptive Statistics
Variable
Observations
CAR1
60
Mean
0.0007
Std. dev.
0.0403
Min
-0.1122
Max
0.1216
CAR5
60
0.0021
0.0251
-0.062
0.0937
AB
60
-0.0007
0.0172
-0.058
0.0433
Levr
60
95.4203
82.7605
0.5661
373.3176
MID
60
0.3333
0.4754
0
1
LAG
60
0.3333
0.4754
0
1
CORE
60
0.3333
0.4754
0
1
EPS
60
4.6585
6.4268
-0.4885
27.4152
EVEB
57
19.2384
17.2
2.3076
107.2148
ROE
60
15.7948
16.2426
-26.9921
65.1854
CR
57
1.4639
0.8915
0.6337
4.6238
HICP
87
2.4724
2.9104
-0.3
10.6
46
Appendix 5.
Table 9: Cross-sectional regressions
12/16/2021
Variables
03/02/2022
10/03/2022
24/03/2022
14/04/2022
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
0.000076
0.0000087
-0.000029
0.00012
-0.000028
0.000034
0.000006
0.000105*
0.000016
0.00009
0.000059
0.000034
0.00009
0.00004
0.000035
(1.37)
(0.23)
(-1.56)
(1.36)
(-0.8)
(1.27)
(0.08)
(1.92)
(0.4)
(1.06)
(1.02)
(1.11)
(1.1)
(1.1)
(0.99)
MID
-0.00664
-0.00916
-0.01057
0.01059
0.00089
0.00102
-0.00838
0.01040
-0.00716
0.04315
0.01989
0.01071
0.03461**
0.01585*
0.00789
(-0.40)
(-0.93)
(-1.64)
(0.41)
(0.09)
(0.18)
(-0.45)
(0.49)
(-0.49)
(1.52)
(0.98)
(0.87)
(2.00)
(1.74)
(1.18)
LAG
0.00864
0.00368
-0.00399
-0.02096
-0.005369
-0.00234
0.0023
0.00234
-0.01315
0.01307
0.01449
0.012733**
-0.01
-0.00274
-0.00417
(0.52)
(0.34)
(-0.57)
(-1.44)
(-0.88)
(-0.59)
(0.13)
(0.19)
(-1.45)
(0.55)
(1.02)
(2.46)
(-0.56)
(-0.52)
(-0.88)
EPS
-0.00173**
-0.00158**
-0.00026
-0.00042
-0.000009
0.00007
-0.00131
-0.00005
0.00074*
-0.00048
-0.0004
-0.00031
-0.00285***
-0.00035
-0.00016
(-2.35)
(-2.31)
(-0.61)
(-0.46)
(-0.03)
(0.22)
(-1.67)
(-0.75)
(1.82)
(-0.47)
(-0.8)
(-1.16)
(-3.85)
(-1.11)
(-0.52)
EVEB
-0.00117***
-0.00029
-0.00026***
-0.00007
-0.00032***
-0.00054***
0.00037
-0.00026
-0.00005
-0.00009
-0.00034
-0.00007
-0.00068***
-0.00027***
-0.00009
(-8.61)
(-1.15)
(-2.99)
(-0.18)
(-2.9)
(-4.49)
(1.66)
(-0.5)
(-0.23)
(-0.39)
(-1.34)
(-0.65)
(-3.71)
(-3.11)
(-1.05)
ROE
0.00053
0.00058*
0.00018
0.00039
0.000009
-0.00008
-0.00061
-0.00016
-0.00022
0.00418
-0.00007
-0.00016
0.00063
0.00011
0.00012
(1.12)
(1.79)
(0.94)
(1.22)
(0.62)
(-0.77)
(-1.58)
(-0.45)
(-0.86)
(0.79)
(-0.23)
(-1.45)
(1.36)
(0.73)
(0.88)
CR
-0.00118
0.0021
-0.00621**
-0.01225
-0.00564*
-0.00331*
0.0067
0.00083
0.00045
0.01955**
0.01807***
0.00851***
0.00207
0.00241
-0.00271
(-0.23)
(0.52)
(-2.66)
(-1.48)
(-1.73)
(-1.72)
(1.01)
(0.01)
(0.1)
(2.5)
(3.47)
(3.39)
(0.31)
(0.55)
(-0.91)
HICP
0.00858
0.00601
0.00277
-0.01032
0.00656*
0.00495*
0.00579
-0.01015
0.00829
-0.00411
-0.00165
-0.00334
-0.02563**
-0.00966**
-0.00761**
(0.93)
(0.99)
(0.73)
(-0.9)
(1.76)
(1.69)
(0.52)
(-0.96)
(1.26)
(-0.34)
(-0.19)
(-0.63)
(-2.26)
(-2.09)
(-2.35)
CORE
0.02132
0.00371
0.01569**
0.04187**
0.00905
0.01281***
-0.01642
-0.00486
-0.00679
0.02368
0.026
0.00252
0.04245***
0.01315
0.0079
(1.43)
(0.44)
(2.08)
(2.44)
(1.03)
(2.72)
(-0.95)
(-0.31)
(-0.64)
(-0.34)
(1.35)
(0.28)
(3.32)
(1.5)
(1.07)
-0.01262
-0.0066
0.00301
-0.02833
-0.00034
-0.00739
0.01615
0.01177
0.00749
-0.06954*
-0.048
-0.0153
-0.00526
-0.00707
0.00168
(-0.71)
(-0.53)
(0.3)
(-1.46)
(-0.03)
(-1.09)
(0.76)
(0.47)
(0.51)
(-1.81)
(-1.6)
(-1.28)
(-0.23)
(-0.56)
(0.16)
0.3988
0.2628
0.3411
0.2404
0.2483
0.5407
0.1900
0.1239
0.1213
0.1767
0.1971
0.1401
0.4658
0.3147
0.2494
Levr
Constant
R-squared
Observations
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
Note. This table illustrates the sampled firms specific regressions (15) with the CAR[-5,5], CAR[-1,1] and AR as dependent variables. The regressions were performed for the following events, 16 th of December 2021, 3rd of February 2022, 10th of March 2022, 24th of March
th
2022, and 14 of April 2022 with robust standard errors. T-statistics are in parentheses. *** p<0.01, ** p<0.05, *p<0.1
47
Table 10: Cross-sectional regressions
9/6/2022
Variables
21/7/2022
8/9/2022
27/10/2022
28/11/2022
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
[-5,5]
[-1,1]
0
-0.000009
0.000066
-0.000022
-0.00008
-0.000167***
-0.000087**
0.000054
-0.00004
-0.000056**
0.00012
0.00011
0.00007**
0.00067
0.00004
0.000009
(-0.09)
(1.34)
(0.75)
(-0.77)
(-3.41)
(-2.17)
(0.58)
(-0.78)
(-2.33)
(1.00)
(1.62)
(2.21)
(0.76)
(1.16)
(0.37)
-0.00389
-0.01419
-0.00831
0.01251
-0.01007
0.00366
0.00424
-0.00939
-0.012*
-0.02089
-0.0023
0.00378
0.01205
0.015
-0.00367
(-0.12)
(-1.17)
(-1.28)
(0.4)
(-0.55)
(0.27)
(0.18)
(-0.78)
(-1.92)
(-0.69)
(-0.11)
(0.39)
(-0.6)
(1.22)
(-0.61)
0.01834
-0.00453
-0.00492
-0.02354
-0.02237**
-0.01303*
0.02105
-0.01169
-0.01024*
0.02023
0.02215
0.00818
-0.00695
0.01255
0.00303
(1.05)
(-0.5)
(-1.19)
(-1.1)
(-2.57)
(-1.76)
(1.24)
(-1.04)
(-1.93)
(0.82)
(1.35)
(1.07)
(-0.54)
(1.24)
(0.56)
EPS
-0.0006
0.00066
0.00029
0.00161
0.00059
0.00082*
-0.00114
-0.00041
-0.00033
-0.00238**
-0.00159**
-0.00002
0.00057
0.00016
0.00018
(-0.63)
(1.3)
(1.38)
(1.41)
(0.91)
(1.77)
(-1.11)
(-0.69)
(-0.84)
(-2.53)
(-2.04)
(-0.05)
(0.95)
(0.44)
(0.75)
EVEB
-0.00046
-0.00026*
-0.00026*
0.00153***
0.00085***
0.00039**
-0.00105***
-0.00016
-0.00009
-0.00026
-0.00008
-0.00021
-0.00042
-0.00059***
-0.00006
(-1.67)
(-1.85)
(-1.87)
(3.33)
(2.78)
(2.24)
(-5.49)
(-1.24)
(-0.7)
(-0.63)
(-0.26)
(-1.44)
(-1.26)
(-5.78)
(-1.07)
-0.000051
-0.00026
-0.00001
0.00081
0.00352**
0.00051**
-0.00054*
-0.00015
0.00007
-0.00005
-0.00045
-0.00023
-0.00072
-0.00016
-0.0002
(-0.14)
(-1.5)
(-0.14)
(1.54)
(1.92)
(2.62)
(-1.73)
(-0.59)
(0.65)
(-0.08)
(-1.05)
(-1.47)
(-1.58)
(-0.8)
(-1.33)
-0.00961
0.00808**
-0.00257
0.00639
0.00508
0.00311
0.0067
0.00771*
0.0015
-0.01006
-0.0172
-0.0065
0.00588
0.00592*
0.00308*
Levr
MID
LAG
ROE
CR
(-1.52)
(2.05)
(-1.06)
(0.59)
(0.77)
(0.83)
(0.97)
(1.88)
(0.51)
(-1.03)
(-1.84)
(-1.53)
(0.97)
(1.85)
(1.81)
HICP
0.0144
0.00589
0.00078
-0.01007
0.00364
-0.00433
-0.00367
-0.00196
0.00127
0.02224
0.01207
0.00222
-0.0078
-0.00863
-0.00207
(1.09)
(0.8)
(0.24)
(-0.63)
(0.43)
(-0.81)
(-0.34)
(-0.29)
(0.35)
(1.45)
(1.04)
(0.4)
(-0.77)
(-1.49)
(-0.7)
CORE
0.00835
0.00117
-0.00042
-0.02188
-0.01698
-0.02018
-0.01062
-0.00264
-0.0025
0.02842
0.024
0.00965
-0.01056
-0.00983
-0.00438
(0.44)
(0.11)
(-0.07)
(-0.64)
(-0.94)
(-1.66)
(-0.56)
(-0.31)
(-0.58)
(1.1)
(1.15)
(1.02)
(-0.70)
(-1.01)
(-1.06)
Constant
-0.00262
-0.01031
0.00911
-0.0025
0.01556
0.01036
0.00899
0.01025
0.01316*
-0.03734
-0.01496
-0.00967
0.02089
0.00502
0.00284
(-0.12)
(-0.67)
(0.99)
(-0.06)
(0.9)
(1.01)
(0.3)
(0.93)
(1.74)
(-1.3)
(-0.81)
(-1.12)
(1.00)
(0.45)
0.46
0.1144
0.1139
0.2026
0.2885
0.4023
0.3955
0.1674
0.2046
0.3780
0.1957
0.3528
0.3405
0.1709
0.2485
0.1261
R-squared
Observations
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
Note. This table illustrates the sampled firms specific regressions (15) with the CAR[-5,5], CAR[-1,1] and AR as dependent variables. The regressions were performed for the following events, 9 th of June 2022, 21st of July 2022, 8th of September 2022, 27th of October 2022
and 28th of November 2022 with robust standard errors. T-statistics are in parentheses. *** p<0.01, ** p<0.05, *p<0.1
48
Table 11: Cross-sectional regressions
Variables
Levr
MID
LAG
EPS
EVEB
ROE
CR
HICP
CORE
Constant
R-squared
Observations
[-5,5]
-0.00006
(-0.9)
0.01978
(0.72)
-0.01514
(-0.76)
-0.00091
(-1.15)
-0.00029
(-1.00)
0.00027
(0.06)
0.00302
(0.44)
-0.00885
(-0.65)
0.02389
(1.31)
-0.00568
(-0.26)
0.1242
55
15/12/2022
[-1,1]
0.000006
(0.15)
0.0219
(1.13)
-0.00464
(-0.38)
0.00002
(0.04)
-0.00019
(-0.7)
-0.00001
(-0.03)
0.00289
(0.78)
-0.01054
(-1.28)
0.00862
(0.8)
-0.0053
(-0.42)
0.1069
55
0
[-5,5]
0.000006
(0.24)
0.00629
(0.52)
-0.00585
(-0.69)
-0.0003
(-0.81)
-0.00028
(-1.12)
-0.00019
(-0.73)
0.00291
(0.99)
-0.00312
(-0.6)
0.00577
(1.1)
0.00405
(0.53)
0.1710
55
0.000054
(0.54)
0.00362
(0.2)
-0.01087
(-0.61)
-0.00068
(-0.54)
-0.00011
(-0.54)
-0.000009
(-0.03)
-0.00663
(-0.41)
-0.01313
(-0.95)
-0.00848
(-0.43)
0.03294
(1.15)
0.0882
55
2/2/2023
[-1,1]
0.000056
(0.55)
0.00925
(0.59)
-0.01128
(-0.76)
0.00071
(0.89)
0.00017
(1.02)
0.0001
(0.30)
0.00509
(0.59)
-0.01242
(-1.34)
-0.01113
(-0.79)
0.01139
(0.45)
0.1010
55
0
[-5,5]
0.000033
(0.43)
0.00805
(0.68)
0.00065
(0.05)
0.00019
(0.33)
0.00017
(1.22)
-0.00002
(-0.06)
0.00434
(0.72)
-0.00861
(-1.13)
-0.01574
(-1.23)
0.01243
(0.58)
0.1049
55
-0.000075
(-0.52)
0.01517
(0.56)
0.03336
(1.62)
0.00127
(1.44)
-0.00005
(-0.23)
-0.000005
(-0.01)
0.0136*
(1.7)
-0.00786
(-0.56)
-0.02302
(-1.11)
0.00166
(0.06)
0.2518
55
16/3/2023
[-1,1]
0.000094
(1.41)
-0.01131
(-0.92)
0.0161
(1.51)
0.00007
(0.13)
0.00017*
(1.7)
0.00053
(1.36)
0.01058*
(1.83)
0.00234
(0.32)
-0.02682**
(-2.16)
-0.01173
(-0.75)
0.3771
55
0
0.000008
(0.2)
-0.00155
(-0.2)
0.00488
(0.89)
0.00054**
(2.68)
0.00007**
(2.41)
-0.00012
(-1.02)
0.00273
(1.59)
-0.000096
(-0.27)
-0.01548
(-1.66)
0.00722
(0.73)
0.2409
55
Note. This table illustrates the sampled firms specific regressions (15) with the CAR[-5,5], CAR[-1,1] and AR as dependent variables. The regressions were performed for the following events,
15th of December 2022, 2nd of February 2023 and 16th of March 2023 with robust standard errors. T-statistics are in parentheses. *** p<0.01, ** p<0.05, *p<0.1
49