Macroeconomic Conditions and Corporate
Mergers: A study on the Swedish M&A market
Bachelor Thesis
Department of Finance
University of Gothenburg School of Business, Economics & Law
June 2024
Kevin Matnok Hansen & Malin Silverhem
Supervisor: Henrik Petri
Abstract
This thesis examines the relationship between the aggregate merger and acquisition (M&A) trans-
actions and macroeconomic variables of Sweden spanning from January 2001 to April 2024. The
study aims to explore how macroeconomic factors, such as interest and unemployment rates might
influence the M&A activity of the Swedish market and corporations willingness to engage in mergers
and acquisitions. This is done with the utilization of a multiple linear regression analysis, where
three different time lags also are incorporated. The inclusion of the time lags will account for delays
between planning and the announcement of M&A deals. The research indicates that there is a
statistically significant and negative relationship between the Swedish policy rate and the monthly
M&A transaction volume. This finding indicates that lower interest rates may encourage an increase
in the number of M&A transactions due to valuation and leveraging reasons. Interestingly, the find-
ings, furthermore, show a positive relationship between unemployment rates and M&A activity.
This may suggest that firms will engage in more mergers and acquisitions opportunistically during
economic downturns. These results contribute to the understanding of a more complex interplay of
the macroeconomic conditions and the corporate merger strategies in Sweden.
Acknowledgments
We would like to give special thanks to our supervisor Henrik Petri as well as Aineas Mallios, Mussa
Hussaini, and Joakim Lennartsson who provided us with their feedback, support, guidance, and
technical expertise which helped us shape the thesis into what it now is.
1
Contents
1 Introduction and Background 3
2 Literature Review 6
3 Purpose and Research Question 10
4 Theoretical Framework 11
4.1 Mergers and Acquisitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.2 Merger Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.3 Distressed Acquisitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.4 Interest Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.5 Unemployment Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5 Methodology 16
6 Data 19
6.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.2 Data Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.3 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.4 Empirical expectations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
7 Empirical Results 25
8 Discussion 33
8.1 The Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
8.2 Policy Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
8.3 Unemployment Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
8.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
9 Conclusion 39
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1 Introduction and Background
Mergers and acquisitions often plays a crucial role in a company’s business strategy, allowing com-
panies to achieve synergies, expand their market presence, and enhance their competitiveness (Mc-
Carthy and Dolfsma 2013). Since the late 1980s, there have been approximately 30,435 M&As in
Sweden, with a total value that exceeds 1.49 trillion dollars. During these three decades, the trend
shows several significant spikes in the number of M&A transactions (IMAA 2024).
In the wake of the dot-com crash of the early 2000s, Sweden experienced a remarkable economic
recovery, with GDP growth rates surpassing the average pace of the two preceding decades (NIER
2019). During this time, Sweden witnessed a surge in M&A activity. Approximately 1100 Swedish
M&A deals were announced in 2000, a 17% increase compared to the previous year (IMAA 2024).
This growth persisted until the financial crisis of 2008, when the M&A market spiked once again and
increased by 25% from 2006 to 2007 (IMAA 2024). The financial crisis of 2008 led to an economic
recession, resulting in a prolonged period of unemployment as well as low interest rates. The low
interest rate would subsequently be increased again as a consequence of the COVID-19 pandemic
(The Riksbank 2024).
In 2019, Sweden was struck by the coronavirus, which significantly affected the whole economy,
especially the production and labor markets (Ekholm et al. 2020). Despite this, the M&A market set
a record high in both value and trading volume in 2021, with 1 933 transactions valued at 229 824
billion dollars. This record number of transactions was followed by a decline in the next year (IMAA
2024). Since 2021, the policy rate has risen sharply from 0% to 4%, and Sweden is now experiencing
an economic downturn with increasing unemployment and high inflation (The Riksbank 2024).
What is interesting about these spikes is that they coincide with significant economic events.
These findings contrast some of the previous research regarding M&A as something that is often
looked at on the individual firm level (Choi and Jeon 2011).
At the same time, these three spikes suggest that M&A activity, indeed, is affected by macroe-
conomic influences. This raises the question of how macroeconomic factors influence companies’
decisions to engage in M&A.
3
For example, it could be reasoned that low interest rates encourage M&A activity since it lowers
the cost of borrowing and enables companies to leverage higher amounts of debt to use in the context
of M&A, this tactic is more commonly known as a leveraged buyout (LBO) which saw a surge in the
1970s and 1980s (Gaughan 2007). On the contrary, high interest rates can result in increased costs
for companies, which can cause companies with certain capital structures to struggle financially. This
scenario can provide opportunities for financially healthy companies with relatively high amounts of
liquidity to acquire competitors through M&A (Faulkner et al. 2012).
The same economic reasoning can also be found for other macroeconomic variables. Unem-
ployment, for example, is often used in economics to determine the health of an economy. A high
unemployment rate may indicate a slowing economy, which in turn may reduce firms’ willingness
to engage in M&A investments due to uncertainty about future performance and demand. Con-
versely, low unemployment typically indicates a strong economy which could potentially increase
M&A activity as companies seek to grow and expand their operations.
It is these contrasts that this paper seeks to investigate further to find conclusive evidence
regarding how different macroeconomic variables affect the M&A environment in Sweden.
4
2,000
Number of Transactions
1,500
1,000
500
0
1,990 1,995 2,000 2,005 2,010 2,015 2,020
Year
The graph shows the number of M&A transactions in Sweden per year from 1989 to 2024.
5
Number of Transactions
2 Literature Review
The academic literature on M&A is extensive, with numerous studies analyzing the factors influenc-
ing aggregated M&A activity. In this sub-field of M&A research, four perspectives on the drivers of
M&A have emerged. These are the stock market perspective, industry-level perspective, firm-specific
factors, and macroeconomic factors. This literature review aims to explore and analyze some of the
research on how various macroeconomic factors affect M&A activity. Interest rates, Gross Domestic
Product, or Gross National Income are often the focus of these studies, but variables such as money
supply and Consumer Price Index have also been examined.
In the paper ”Corporate Mergers and the Business Cycle” Becketti (1986) examines the relation-
ship between M&A activity and the business cycle. By studying M&A transactions in the US over 25
years, Becketti finds that M&A activity has a pro-cyclical character, meaning that merger activity
increases with economic expansions and decreases during economic downturns. To investigate this
observed merger pro-cyclicality, Becketti analyzes the relationship between M&A activity and a set
of macroeconomic variables.
The study uses two statistical models, where the first includes current values of M&A (frequency
and deal value) and historical values of macroeconomic variables. These variables are, for exam-
ple, S&P500, the 3-month T-Bill, Domestic Debt, M1, and GNP. In the second model, the same
variables are used, but in this case, the model is reversed. The second model sets current values of
the macroeconomic variables as endogenic and the historical values of M&A activity as exogenic.
Noteworthy here is that, in both models, lagged values are used for the macroeconomic variables to
better capture the effects these variables have on merger activity. This is done as Becketti believes
that there is a real lag in how companies respond to changes in the macroeconomic environment.
Becketti finds that the macroeconomic variables explain approximately one-third of M&A activity
during the examined period. Among all the included variables, interest rates had a significant impact
on merger activity, while GNP had a relatively significant impact. However, changes in M&A activity
appeared to have no impact on the macroeconomic variables included in the model (Becketti 1986).
Benzing (1991) also studied the determinants of M&A activity in her article ”The determinants
of aggregate-merger activity — Before and after Celler-Kefauver”.
6
In this article, Benzing studies the manufacturing and mining sector in the United States pre
and post-1950s antitrust regulations.To examine the determinants of M&A activity Benzing utilizes
a regression model that includes the annual number of completed mergers in the U.S from 1919-1979
as a dependent variable and past merger activity, stock prices, interest rates, and unemployment as
independent variables Benzing (1991).
When studying the entire period Benzing found a negative relationship between interest rate and
M&A activity, a positive relationship between M&A activity and stock prices, and unemployment
was found to be insignificant. When testing the period from 1919 to 1950, the positive relationship
between stock prices and M&A was confirmed, in contrast to the first regression, the relationship
between interest rates and M&A was also found to be positive. The regression analysis furthermore
showed that the coefficient for unemployment was significant and negative. The results from the
period 1951 to 1979 again showed a positive relationship between stock prices and M&A, as well as a
negative relationship between interest rate and M&A. Unemployment was found to be insignificant
(Benzing 1991).
Benzing finds that the results regarding stock prices and unemployment after 1950 might be
explained by the tightening of antitrust regulations in the period following 1950. However, the most
interesting conclusion Benzing finds concerns interest rates, where the relationship changes direction
when examining different periods. One explanation, as suggested by Benzing, is that interest rates
became more volatile following the Treasury Accord of 1951, which ended the practice of pegging
interest rates. Based on this result, Benzing suggests that conflicting results in previous studies may
be due to the different periods that were studied (Benzing 1991).
Another study that examines macroeconomic variables and their impact on M&A activity is
“The Impact of the Macroeconomic Environment on Merger Activity: Evidence from US Time-
Series Data” published in 2011 by Seung Hee Choi and Bang Nam Jeon. This study uses several
statistical models to investigate the relationship between M&A activity and common macroeconomic
variables from 1980 to 2004, both in the short and long term. Some of the macroeconomic variables
included are the 10-year Treasury bond rate, Real GDP, S&P 500 close, effective Fed funds rate,
and corporate net cash flow, as well as two dummy variables indicating the up and down phases of
the business cycle (Choi and Jeon 2011).
7
What sets this study apart from other similar studies is that it not only includes the frequency and
deal value of M&A transactions as dependent variables, but it also includes four different indicators
for aggregated merger activity. In addition to the frequency and transaction value of the M&As,
two additional measures are included, transaction value/S&P 500 trading volume and transaction
value/total assets. The purpose of this inclusion is to perform robustness checks and investigate
whether differing measures of M&A activity carries different information (Choi and Jeon 2011).
The authors find a long-term equilibrium relationship between macroeconomic factors and M&A
activity, indicating that the macroeconomic environment significantly influences M&A. Key macroe-
conomic determinants include real income, stock market conditions, and monetary policy (Choi and
Jeon 2011). The study also highlights that the upward phase of the business cycle is the most favor-
able for M&A activity. Additionally, there is evidence that shocks from the previously mentioned
macroeconomic variables affect mergers in the short term due to reactive adjustments in the market.
When using traditional measures for M&A activity the authors find that GDP and stock market
are the most important determinants, which is consistent with previous literature. When adding
additional measures of M&A activity the authors find that the bond market, monetary policy, and
corporate liquidity are relevant determinants in M&A activity (Choi and Jeon 2011).
While many studies suggest that M&A activity is procyclical, some propose a countercyclical
relationship. In the article “Are Bank Mergers Procyclical or Countercyclical?” Wen-Chung Guo and
Chih-Ching Yang examine the determinants of M&A activity in Taiwan’s banking sector and provide
an alternative perspective on the economic conditions surrounding merger activity. Guo and Yang
(2013) state that while previous research suggests that merger activity and merger waves coincide
with economic expansion, the banking sector in Taiwan demonstrates the opposite. Historically, most
bank mergers in Taiwan have occurred during economic downturns or under poor financial conditions
(Guo and Yang 2013). Guo and Yang argue that during a recession, banks operate in highly
competitive markets, where they compete for reduced revenue. In this challenging environment,
many banks turn to mergers as a way to reduce competition by acquiring less stable banks. In the
case of Taiwan, the government also played a role by using its stimulus to facilitate mergers, along
with a more favorable stock market.
8
The authors, furthermore, mention two additional examples of this phenomenon, namely the
bank mergers in Japan during the 1990s and Bank of America’s acquisition of Merrill Lynch in the
USA during the credit crisis in 2008 (Guo and Yang 2013).
The study covers the period from 1996 to 2007 and includes 41 commercial banks. A so-called
probit model is used to analyze the factors contributing to bank mergers in Taiwan. The model
includes various financial variables such as a stock price index, ROE, interest spread, overdue loans,
capital adequacy ratio, and a legal dummy variable.
The results showed, among other things, that economic factors like the financial price index and
interest spread had a significantly negative correlation with mergers, which supports the authors’
hypothesis and indicates that mergers tend to occur during economic downturns (Guo and Yang
2013).
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3 Purpose and Research Question
The purpose of this paper is to test how different macroeconomic variables affect the mergers and
acquisitions activity in Sweden. Our research will gather data on the number of monthly M&A
transactions, interest rate, and unemployment rate in Sweden. The aim is to explore how changes
in these variables affect the activity of mergers and acquisitions.
• Is there a significant relationship between M&A activity and the interest rate as well as the
unemployment rate?
• How do interest rates and unemployment rates affect firms’ decision to get involved in mergers
and acquisitions?
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4 Theoretical Framework
4.1 Mergers and Acquisitions
Mergers and acquisitions, commonly known as M&A, refers to the process of buying or merging
with another company. M&A can take various forms depending on the motives, but horizontal,
vertical, concentric, and conglomerate are the most common types. Mergers and acquisitions has
its roots in the late 19th century and has since become a common strategy and a widely applicable
tool for many companies. It can be used to expand, enter new industries, and acquire technical
expertise. An M&A is a complex and lengthy process that involves many steps, such as letters of
intent, valuations, negotiations, and due diligence. Depending on the size and complexity of the
transaction, the process can take anywhere from a few months to several years in order to reach
completion. However, considering the massive sums of capital involved in these deals, every step is
important to ensure a successful closure of an M&A deal (Faulkner et al. 2012).
4.2 Merger Strategies
In the fourth edition of his book ”Mergers Acquisitions, and Corporate Restructurings”, Patrik A.
Gaughan (2007) provides a comprehensive guide for the various aspects and concepts which are
found in the field of mergers and acquisitions. To narrow down the vast amounts of concepts into
something more digestible that can contextualize, and perhaps explain the empirical results, this
thesis will have a specific focus on the strategies employed for mergers and acquisitions.
In chapter four: Merger Strategy, Gaughan showcases three main types of merger strategies
along with a list of residuals, these strategies are viewed as the motives behind a corporate decision
to engage in mergers and acquisitions. They are as follows: Growth, Synergy, Diversification, and
Other Economic Motives. These main strategies also possess subcategories.
Growth, which Gaughan describes as “one of the most fundamental M&A motives”, is self-
explanatory in the sense that corporations have an inherent wish to grow. They are able to do this
either organically, that is, by expanding the internal operations of the company, or perhaps more
relevant here, inorganically through mergers and acquisitions.
11
Both options have their pros and cons, but what is certain is that inorganic growth happens
at a faster pace than organic growth (Gaughan 2007). This edge may be key in certain situations
where Gaughan gives the example of limited opportunity. Sometimes a “window of opportunity” will
present itself to corporations to expand and gain market shares. Here, the slower organic growth may
be out-competed by other competitors who are better positioned in the market. Thus, mergers and
acquisitions become a viable option to capture the resources needed to capitalize on the opportunity
that has presented itself. A sub-strategy to this motive may occur in the context of a slower industry.
Gaughan explains that managers of corporations have a constant expectation on them to perform
and grow by the shareholders. This pressure can become hard to meet when the demand for the
corporation’s goods, for one reason or another, is reduced. Mergers and acquisitions thus become a
solution to the problem by offering stable and relatively fast growth both in the form of increased
revenue from the acquired company but, hopefully also through increased synergy (Gaughan 2007).
Synergy here is, at the same time, the next strategy that Gaughan showcases. The concept has
a similar connotation to the natural sciences in that the combination of the merged corporations are
expected to generate a higher value than the sum of its parts. This strategy is then broken into two
sub-strategies, namely operating synergies and financial synergies (Gaughan 2007).
Operation synergies come in the form of revenue enhancements and the reduction of costs. Rev-
enue enhancements usually come from new marketing opportunities or when two companies with
differing specializations are able to play off each others strengths (Gaughan 2007). Cost reduction
synergy instead focuses on the concept of economies of scale. Economies of scale can be explained
as operations becoming cheaper when the now combined output of products and services share the
same overhead costs; as opposed to the alternative of having separate fixed costs each to deal with
as it is before the merger. Gaughan refers to this as “spreading overhead” (Gaughan 2007).
Financial synergy comes in the form of a lowered cost of capital, while at the same time not
being the consensus, Gaughan cites the concept of debt coinsurance which is when two companies
with differing income streams merge so that it becomes one diversified stream which is deemed to be
less risky by creditors and thus lessens creditor demand for risk-premiums, this in turn is supposed
to lower the cost of capital (Gaughan 2007).
12
The sheer size of a merged corporation is also deemed to be enough in order to create financial
synergies as larger corporations also enjoy perks that smaller ones do not. They are for example
perceived as less risky since they have a larger stream of income and may be ”too big to fail”
(Gaughan 2007).
The last main strategy which drives M&A is, according to Gaughan, diversification. Diversifica-
tion is defined as an expansion much like the growth strategy, the key difference is that diversification
targets expansions into new, uncharted, industries and segments(Gaughan 2007). Gaughan explains
that this strategy was very prominent during the 1960s when companies sought to become “con-
glomerates” by acquiring multiple market leaders within diverse segments. Among them, General
Electric would prove to be one of the most successful companies that used this strategy (Gaughan
2007). The motive behind diversification is, for example, to increase growth corporate as well as
revenue by entering new and growing markets. Another reason could be to increase its stock attrac-
tiveness; by having a diverse set of operations, the company creates a portfolio of companies that
becomes more attractive investment-wise (Gaughan 2007).
Gaughan finishes the chapter with perhaps less prominent strategies. Here tax, research and
development, as well as a plethora of other motives are showcased (Gaughan 2007). Among these
strategies is the motive of market power. This motive can take various shapes, but in the end
centers around acquiring or merging with competitors to gain market shares and lessen the market
competition. An interesting variant of the market power motive is the Roll-up acquisition strategy
(Gaughan 2007). Gaughan explains a roll-up acquisition as typically having a large company buying
up a series of smaller competitors, which then increases the acquirer’s market power through con-
solidation. Gaughan, furthermore, mentions the 1990s as a prominent time for roll-up acquisitions.
During this period, industries like bus transportation and funeral homes saw extensive roll-ups.
These owners usually found themselves in illiquid situations for one reason or another and thus the
acquiring companies that appeared were seen as a way out to regain liquidity (Gaughan 2007).
4.3 Distressed Acquisitions
Another variant that can relate to roll-ups and market power, depending on the perspective, is
distressed acquisitions which is showcased by Faulkner et al. (2012).
13
Distressed acquisitions occur when a company acquires a struggling, or perhaps more relevant
here, distressed company as a means of either saving or salvaging it for valuable parts. This is seen
by the authors as a useful alternative to liquidation or corporate restructuring with Faulkner noting
its predominance starting from the 1990s to the present during times of economic downturn and
financial crisis (Faulkner et al. 2012).
4.4 Interest Rate
In its simplest form, the interest rate refers to the rate at which someone pays for a loan, it can
thus be seen as the cost of borrowing money. A higher interest rate means that a loan is relatively
expensive compared to one with lower interest rates (Hayes 2024). The interest rates that banks offer
depend in turn on the policy rate which is set by the designated central bank of the country. The
policy rate is often a macroeconomic tool that central banks use to stimulate demand and control
inflation. When interest rates are low, the economy is often stimulated as businesses and individuals
gain access to cheap loans, leading to economic growth. This economic growth also tends to bring
with it increased inflation. Thus, higher interest rates may need to be implemented to reduce access
to cheap loans and in turn slow down the economy, as individuals and businesses, at the same time
receive higher premiums for saving money in bank accounts instead of spending and investing (The
Riksbank 2023). This staggered growth leads to a decrease in inflation and may at the same time
lead to a higher unemployment rate as corporations initiate layoffs in the face of reduced demand
for goods and increasing borrowing costs (Fredert and Jonung 2018).
The policy rate, furthermore, has a linkage to mergers and acquisitions in the form of valuation.
As the policy rate is said to influence all interest rates it also influences the discount rate, which is
a central concept in M&A valuation (Gaughan 2007). A lower interest rate is thus said to lower the
utilized discount rate for valuing a target acquisition and vice versa (Gaughan 2007). The lowering
of interest rates will therefore increase the net present value and return of the acquisition (Gaughan
2007). Higher net valuations on M&A investments will in turn promote the aggregate M&A activity
as they appear more profitable.
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4.5 Unemployment Rate
The unemployment rate measures the proportion of a nation’s workforce that is unemployed and is
often used to determine the health of an economy (Fredert and Jonung 2018). During an economic
downturn, the unemployment rate increases as production slows down and job opportunities dimin-
ish. On the contrary, the economy will see production surges in times of prosperity, thus creating
more job opportunities, which decreases the unemployment rate. Noteworthy here is also that an
extremely low unemployment rate can indicate an overheated economy with high inflation and a
lack of production capacity (Chen 2023).
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5 Methodology
This study will use a quantitative research approach to analyze the relationship between M&A
activity and macroeconomic variables of interest and unemployment rate in Sweden. All variables
that are included in the study will be collected in the format of monthly observations spanning the
time period from January 2001 to between February and April 2024, the difference in the end period
depends on practical circumstances that will be explained further down.
From this dataset, and much like in both (Becketti 1986) as well as (Benzing 1991), regression
analysis will be applied to measure the relationship and statistical significance of the collected
variables on the number of merger and acquisition. Regression analysis is one of the most widely
used instruments for analysis in fields of more natural leaning sciences like engineering as well as
more social leaning sciences such as business and economics (Jaggia and Kelly 2021). It was thus a
rather straightforward decision to use regression analysis for the study as it is a widely used tool for
both causal and correlation analysis (Jaggia and Kelly 2021). More specifically, the multiple linear
regression is employed because it can account for multiple variables at once, which will be relevant
here with regards to the control variables. The model will be fitted using the Ordinary Least Squares
(OLS) method, which is commonly used and widely regarded as the standard approach for fitting
regression models (Jaggia and Kelly 2021).
The data will initially be sorted in Microsoft Excel where it will be cleaned to meet the proper
criteria of the study. The dataset will then be imported into the statistical program Stata where
the regression analyses will be conducted. The significance levels in the analysis will be measured at
three different different levels, namely 10, 5, and 1 percent. These levels will be noted by *, **, and
*** respectively. The study will furthermore require the independent variable coefficients to possess
a minimum of at least five percent significance in order to be interpreted.
Since this paper has a main focus on the relationship between interest and unemployment rate
against M&A activity, the following regression will be used as an initial regression model:
Number of transactions = β0 + β1Policy rate + β2Unemployment rate + ϵ (1)
16
From this, the study will, furthermore, add additional controls for other variables that may exert
an influence on M&A transaction volume. The model is thus developed equivalently:
Number of transactions = β0+β1Policy rate+β2Unemployment rate+ γControl Variables+ ϵ (2)
In the additional variables, the model will control for other macroeconomic measures such as
the Consumer price index excluding mortgage costs. This exclusion is motivated on the basis of
multicollinearity, as the model otherwise would capture the interest rate twice in both the policy
rate and the consumer price index. Furthermore, the Swedish GDP indicator, the fiscal expenditure
of the Swedish government, and the change in money supply will also be controlled for. Due to the
high numbers of both the Swedish GDP indicator and the Money supply; a percentage change which
measures the increase in the numbers from the same month in the year prior, will be used instead.
These measurements give a more accurate representation as they smooth out any cyclical noise that
may be present, especially in GDP but also in money supply. The fiscal expenditure will at the same
time be logged in the model as there is a heavy inter-month variance which will be explained more
in the data section.
The second set of variables is supposed to capture the circumstances of Sweden’s aggregate
industry and exports. Here the model will control for the financial stress, which can be regarded
as a proxy for financial crisis, the aggregate business confidence, and the number of corporation
or limited liability bankruptcies in Sweden. The financial stress index is a measurement brought
forth by the European Central Bank to measure times of high risk and uncertainty in the financial
markets. The business confidence index is an index which is issued by National Institute of Economic
Research, a government agency in Sweden. The business confidence index is based of qualitative
data such as interviews. Additionally, the KIX index, which is a weighted average of the Swedish
currency against a portfolio of foreign currency will also be controlled for as Sweden is a nation that
relies heavily on exports to foreign nations (SCB 2024). These two control sets will also be combined
into one large control set to apply on the regression model, additionally, all the control observations
are collected on a monthly basis and showcased as such.
17
Since it has been used in previous literature such as (Becketti 1986), as well as being logical,
the study has also decided to create 4 variations of the original regression model and its controls.
Previous authors have found that the inclusion of a time lag may be necessary to better capture
the effects of the variables on M&A transaction volume (Becketti 1986). This makes intuitive sense
as well when considering that the planning of a merger or acquisition takes a long time from its
start date to the announcement date. Smaller ones may take months and the extremely large ones
may even take more than a year (Gallant 2024). Thus an M&A that is announced today may have
been started on the foundational circumstances that were present 6 months ago. Another argument
for the lags comes in the form of economic inertia. It usually takes time for businesses to react to
certain events in the environment which gives the company a lag period between the macroeconomic
circumstances changing and the businesses acting on it. The model takes these factors into account
by lagging the effects of the chosen variables on transaction volume. These lags will be 3 months, 6
months and 9 months. This will furthermore also increase the significance of the study by changing
the time horizons of the relationships between the variables and M&A activity to see if they still
hold.
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6 Data
6.1 Data Collection
The data included in this study will be collected from various databases and sources online. The
number of monthly merger transactions announced in Sweden is retrieved from S&P Capital IQ. In
Captial IQ, one was able to retrieve the number of merger transactions in the screening tool. In this
study, the following criteria was utilized to retrieve the target data:
• Transaction screening
• Geographic location (Buyers/Investors): Sweden (Primary)
• M&A Announced Date: [All History]
Additionally, it can be noted that the geographic location filter is limited to the buyers/investor.
This filtering is chosen to only apply to buyers/investors since these actors and companies are directly
affected by Swedish macro variables. An American company, for example, will not be as heavily
affected (if at all) by a change in the unemployment rate as a Swedish company would. This is
because it does not operate within the Swedish market and is thus not subject to Swedish market
conditions. Noteworthy here is that companies, which are headquartered elsewhere in the world,
but still have significant operations and subsidiaries in Sweden (such as LIDL), are at the same time
included in the dataset as they are subject to the Swedish market conditions.
When considering M&A announced date, it is logical to have all the M&A observations available
in Sweden to get as accurate of a representation as possible, hence the reasoning behind the inclusion
of [All History] in the screening tool. Upon further inspection, however, it became apparent that
the frequency of mergers declined to the point where there were months without any reported
M&A announcements. Thus, January 1996 became a fitting demarcation as a point where M&A
announcements occurred on a consistent and monthly basis.
Captured from this data was thus the monthly amount of merger announcements in which the
buying companies or investors involved in the M&A were Swedish, or had a direct linkage of operation
in Sweden, such as a subsidiary, for example.
19
Moving on with the collection of independent variables and control variables. The collection
of policy rate as well as the Kix-index came from the Swedish Central Bank: The Riksbank. The
Riksbank has a statistics service for collecting data on different variables of interest which is related
to the central bank itself. In the screening Policy rate and Kix-index was collected on a monthly
basis. The service allowed for easy access to the data in a relevant format.
The remaining variables are mainly collected from Statistics Sweden, or Statistiska Centralbyr̊an
in Swedish. The collection from Statistics Sweden was rather uncomplicated with files of interest
being readily available to download or accessed directly in the statistical service database. GDP,
Unemployment rate, Business confidence indicator, and LTD Bankruptcies were retrieved using
Statistics Sweden. Some additional variables were retrieved from other credible sources online such
as Ekonomifakta which cites the European Central Bank, Trading Economics which cites Statistics
Sweden, as well as Thomson Reuters Refinitiv Eikon.
When the collection was set and done 11 variables were gathered. These variables span different
areas and are explained further in the table on the next page.
20
Table 1: List of Variables
Variable name Definition Source
NumberofTransactions Number of M&A transactions for each S&P Capital IQ
month from 2001-2024
PolicyR The average policy rate in Sweden per The Riksbank
month from 2001-2024
UnemploymentR Percentage of the population aged 15 Statistics Sweden
to 74 that is unemployed. The obser-
vations are monthly and collected from
2001-2024
M2Growth The monthly money supply growth as Refinitiv Eikon
compared to the same month in the
year prior
CPIFRate The Consumer Price Index Fixed Inter- Statistics Sweden
est Rate in Sweden per month collected
from 2001-2024 This CPI excludes in-
terest rate cost on mortgages
GDPIndicator The preliminary estimate of the growth Statistics Sweden
in GDP for a given month as compared
to the same month in the prior year
FiscalExpenditure The total government expenses per Trading Economics
month, this includes buying goods and
services, investment, and transfer pay-
ments
FinStressIndex A proxy for financial crisis Ekonomifakta
LTDbankruptices Monthly bankruptcies of limited liabil- Statistics Sweden
ity companies in Sweden
BusinessConfidence Business confidence measurement is a National Institute of Eco-
metric collected by the National In- nomic Research
stitute of Economic Research through
qualitative sampling
KixIndex An exchange rate denominated in SEK The Riksbank
against a weighted portfolio of foreign
currencies
Table 1 presents the variable names, their definitions, and their sources of collection.
21
6.2 Data Cleaning
Due to the data being created, sorted, and summarized already by the organizations which they
were retrieved from, there was not much that actually needed to be cleaned and sorted. There
were monthly observations for virtually every variable. An exception that did need to be modified,
however, was the fiscal expenditure of the Swedish government. This variable showcased high inter-
month fluctuations. This variable thus became log-transformed in order to smooth out the variance.
Another thing that needed to be done was the implementation of another time demarcation so that
the data began in January 2001 instead of 1996. This was done as certain variables did not stretch
that far back in time and thus would not have been able to be included in the study otherwise.
An additional thing that needed to be done was the implementation of the time lags, which was
mentioned in the method section. As stated before the reasoning is that mergers take time to both
initiate and execute and there is thus a time lag between the effects of say a change in interest rates
and the change M&A activity. This implementation was rather simple and included just shifting
the explanatory variables’ time series back a given number of months. In this way, June 2023 would
interact with January 2023 in the dataset, for example. This shift caused a drop in observations
from 278 to 271 as the number of previously relevant months became omitted.
22
6.3 Descriptive Statistics
Table 2: Descriptive Statistics
Variable name Mean St.Dev Min Max
NumberofTransactions 99.79 39.48 12 212
PolicyR 1.45 1.62 -0.5 4.67
UnemploymentR 7.41 0.89 5.89 9.3
M2Growth 0.58 3.37 -10.71 9.70
CPIFRate 2.05 1.78 -0.4 10.2
GDPIndicator 2.03 3.57 -12.73 11.87
FiscalExpenditure 74.45 26.41 20.1 186
FinStressIndex 0.12 0.11 0.02 0.63
LTDbankruptices 493.26 112.97 273 1052
BusinessConfidence 99.91 10.55 57.2 120.1
KixIndex 114.50 6.48 100.75 131.18
Table 2 summarizes the statistics for the variables. Fiscal expenditures are reported in BSEK, KIX,
and business confidence are reported in absolute values, while all other variables are expressed as
percentages.
23
6.4 Empirical expectations
The expected results of this study are to be in line with the previous studies that found a negative
relationship between the policy rate and merger activity. The expectation is built partly on the
results of the previous studies, but also on the connection between merger valuation interest rate
which is mentioned in the theoretical framework. There is at, the same time, a merger motive which
contrasts this negative relationship expectation, which is financial synergy. As the cost of capital is
increased by the interest rate, there may be an increased motive to merge as companies seek to lower
their now higher cost of capital. The overall expectation, however, remains in favor of the negative
relationship, that is, when the policy rate increases, the number of monthly M&As transactions
decreases.
When considering the relationship between unemployment and M&A the expectations are less
clear. On the one hand, there are previous studies who point to a procyclical character in M&A, in
the sense that M&A activity tends to increase during economic prosperity. On the other hand there
is there are merger strategies and motives which points to a more countercyclical nature such as
roll-up and slow-growth acquisitions where M&As are favored during an economic downturn. This
expectation is also backed up by the findings of Guo and Yang (2013) who saw a countercyclical
relationship in the Taiwanese banking sector. The chosen expectation for this study, however, lands
in a procyclical one as economy upturns should intensify all types of economic activity on the
aggregate level, including mergers and acquistions.
24
7 Empirical Results
Table 3: Regression results from models with no lag
1 2 3 4
Variable
PolicyR -10.0208*** -9.3118*** -5.3023*** -3.8241***
(1.472) (1.432) (1.411) (1.400)
UnemploymentR 4.7280* 3.6837 6.9168*** 4.6786**
(2.667) (2.446) (2.532) (2.258)
M2Growth 0.3677** -0.0470
(0.158) (0.147)
CPIFRate 3.9354*** 3.7447***
(1.180) (1.163)
GDPIndicator 2.0407*** -1.9391***
(0.546) (0.629)
FiscalExpenditure 37.8355*** 41.8385***
(6.083) (5.884)
FinStressIndex 26.9278 28.9784
(21.432) (19.547)
LTDbankruptices 0.0626*** 0.0302*
(0.019) (0.017)
BusinessConfidence 2.0428*** 2.2265***
(0.229) (0.264)
KixIndex -0.1911 -1.2234***
(0.352) (0.358)
Adj R-squared 0.2164 0.3779 0.4084 0.5466
Standard errors in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
25
In table 3, policy rate is shown to be statistically significant with and without controls. There
is a strong negative relationship between the number of transactions and the policy rate. For
every increase in the percentage unit of the Swedish policy rate, the number of monthly M&A
transactions will decrease by between approximately 10 and 3.8 transactions depending on the
number of controls. This relationship is also diminishing as the increase of control weakens the
strength of the relationship. The choice of controls also affects the strength differently as showcased
in the difference between models 2 and 3.
In contrast, the unemployment rate is found to not have enough statistical significance in models
1 and 2 to be interpreted. In regression 3 the unemployment rate becomes heavily significant by
adding financial stress, bankruptcies, business confidence, and KIX index. In this regression, the
relationship is found to be positive, that is, for every percentage increase in unemployment the
number of monthly M&A transactions will increase by approximately 7 transactions. In model 4,
where all the controls are accounted for, there is a weaker statistical significance at the 5% level
which, once again, shows that there is a positive relationship between the number of monthly M&A
transactions and the unemployment rate. For a percentage point increase in the unemployment rate,
the number of monthly M&A transactions will increase by approximately 4.7 transactions.
In the table, one can also observe the adjusted R-squared values for all the models. Here it can
be noted that the explanatory power increases from 0.2164 to 0.5466 as more variables are added.
This is to say that in the most reduced model namely number one, 38.24% of the dependent variable
is explained by the model. This percentage rises to 53.28% in model four.
26
Table 4: Regression results from models with 3-month lag
1 2 3 4
Variable
PolicyR -10.096*** -10.8869*** -5.4629*** -5.2819***
(1.398) (1.468) (1.351) (1.465)
UnemploymentR 9.1447*** 8.8719*** 14.8068*** 13.439***
(2.526) (2.485) (2.412) (2.343)
M2Growth 0.5270*** 0.0874
(0.160) (0.151)
CPIFRate 3.4078*** 3.4653***
(1.193) (1.204)
GDPIndicator 1.4203** -1.6011***
(0.552) (0.650)
FiscalExpenditure 8.8477 19.3671***
(0.552) (6.070)
FinStressIndex -4.7098 -9.1546
(20.361) (20.199)
LTDbankruptices -0.0663*** -0.0809***
(0.018) (0.018)
BusinessConfidence 1.3227*** 1.4578***
(0.229) (0.264)
KixIndex 0.3454 -0.3200
(0.335) (0.371)
Adj R-squared 0.2956 0.3552 0.4606 0.5100
Standard errors in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
27
Table 4 showcases a 3-month lag for the explanatory variables. Here, the policy rate has statistical
significance across all models. Its impact on the number of monthly mergers remains negative, with
it possessing a coefficient of about -10.9 and -5.3 depending on the model. This means that a decrease
in the policy rate of 1 percentage point will correspond to a decrease in merger transaction volume by
between 10.9 and 5.2 depending on the model being utilized. Noteworthy is also that the relationship
strength is at its lowest when in model 4 where the most number of variables are controlled for. The
strongest relationship is found in model 2 which controls for other macroeconomic variables.
The unemployment rate also shows high statistical significance across all models. The relationship
between unemployment and M&A transaction volume is positive and exhibits a strong coefficient
where if unemployment rises by one percent the number of monthly M&A transactions will increase
by between 8.9 and 14.8 depending on the model utilized. There is also no linear pattern in which
coefficient strength increases or decreases with the number of added controls. The highest coefficient
of 14.8 is found in model 3 which controls for the corporate circumstances in Sweden and the lowest
of 8.9 is found in model 2 which controls for other macroeconomic variables.
Table 4 shows an adjusted R-squared between 0.29 and 0.51 depending on the model. Model
one showed the least amount of explanatory power where 29.56% of the dependent variable was
explained by the model, whereas the fourth model showed the highest explanatory power with 51%
of the dependent variable being explained by it. Notable here is also that model three has a higher
adjusted R-squared of 0.1054 more than model two despite the models having the same number of
explanatory variables.
28
Table 5: Regression results from models with 6-month lag
1 2 3 4
Variable
PolicyR -10.0402*** -9.4309*** -6.0937*** -4.5973***
(1.344) (1.407) (1.370) (1.504)
UnemploymentR 13.1962*** 11.9821*** 16.3684*** 14.6313***
(2.403) (2.315) (2.395) (2.310)
M2Growth 0.4675*** 0.2157
(0.148) (0.147)
CPIFRate 0.8719 -0.2084
(1.098) (1.195)
GDPIndicator 1.2804** -1.6478**
(0.506) (0.636)
FiscalExpenditure 25.7205*** 27.9523***
(6.219) (6.017)
FinStressIndex -13.4158 0.6207
(20.048) (19.829)
LTDbankruptices 0.0249 0.0169
(0.019) (0.019)
BusinessConfidence 1.3199*** 1.7009***
(0.217) (0.274)
KixIndex 0.0813 -0.05389
(0.330) (0.365)
Adj R-squared 0.3824 0.4551 0.4791 0.5328
Standard errors in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
29
Table 5 showcases a 6-month lag for the explanatory variables. Similar to the previous tables, the
coefficient for the policy rate is significant and shows a negative relationship with M&A transactions.
When examining the first regression in this table, the coefficient for the policy rate can be interpreted
as follows: when the interest rate increases by one percentage point, the number of M&A transactions
decreases by approximately 10 transactions per month. When controlling for additional variables,
the relationship continues to be significant, but the coefficients decrease as more variables are added.
In contrast, the unemployment rate shows a strong positive relationship with M&A transactions
across all models, with coefficients ranging from 11.9 to 16.4. This can be interpreted as, when
unemployment rises by one percent, the number of monthly M&A transactions will increase by 11.9
to 16.4, depending on the model utilized.
In Table 5, the adjusted R-squared values are 0.3824, 0.4551, 0.4791 and 0.5328. Which is
to say that model one, two, three, and four explain 38.24%, 45.51%, 47.91%, and 53.28% of the
dependent variable respectively. These values also indicate that the explanatory power increases as
more variables are included in the model.
30
Table 6: Regression results from models with 9-month lag
1 2 3 4
Variable
PolicyR -9.9463*** -9.8930*** -7.4796*** -6.3915***
(1.292) (1.418) (1.342) (1.542)
UnemploymentR 15.5371*** 14.6058*** 19.2211*** 18.2939***
(2.283) (2.290) (2.329) (2.334)
M2Growth 0.3682* 0.1647
(0.146) (0.145)
CPIFRate -0.1624 -0.9086
(1.071) (1.179)
GDPIndicator 0.9392* -0.8456
(0.494) (0.627)
FiscalExpenditure 10.9924* 16.8279***
(6.124) (5.939)
FinStressIndex -19.7901 -8.6235
(19.089) (19.572)
LTDbankruptices -0.0661*** -0.0690***
(0.019) (0.019)
BusinessConfidence 0.7033*** 0.9477***
(0.206) (0.272)
KixIndex 0.3907 0.4755
(0.327) (0.372)
Adj R-squared 0.4469 0.4727 0.5256 0.5407
Standard errors in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
31
Table 6 showcases a 9-month lag for the independent variables. Similar to all other models
presented in the previous tables, the policy rate is significant and has a negative relationship with
the number of M&A transactions across all models. The coefficients range from -9.9463 to -6.3915
as more control variables are added.
The coefficients for the unemployment rate again show a strong positive relationship with M&A
transactions across all models, with coefficients ranging from 14.6058 to 19.2211. It is worth noting
that the coefficients become more positive when the explanatory variables are lagged over a greater
number of months, in this case, nine months, compared to when they are lagged six or three months.
In table 6, featuring regressions with a 9-month lag, all models demonstrate relatively high
adjusted R-squared values, ranging between 0.4469 and 0.5407. Which is to say that approximately
half of the dependent variable is explained by the models with a varying spread of about 6%. It is
at the same time, even in this table, noticed that the adjusted R-squared increases when all control
variables are included, suggesting that Model 4 provides a better explanation of the data.
32
8 Discussion
8.1 The Results
This thesis aimed to investigate the impact of macroeconomic variables, with a specific focus on the
policy and unemployment rate in Sweden, on the volume of mergers and acquisitions transactions
in Sweden between January 2001 and April 2024. The empirical analysis showed that our selected
macroeconomic factors had a highly significant relationship with the Swedish M&A activity. This
holds for all the time-lagged models with adjusted R-squared ranging from 0.2956 to 0.5407 for the
lagged models and 0.2164 to 0.5466 for the unlagged models.
8.2 Policy Rate
The empirical results of the study suggest a significant negative relationship between the Swedish
policy rate and monthly M&A transaction volume. With statistically significant coefficients that
range from between negative 3.82 and negative 10.88 depending on the table and model, the rela-
tionship can be deemed to be highly relevant. This finding is in line with the expected results of the
study. Lower policy rates reduce borrowing costs, making financing more accessible for companies
that are considering mergers and acquisitions. Noteworthy here, however, is that the strength of
the relationship is reduced by the number added control variables. This suggests an overestimation
of the initial effects that policy rate has on the number of monthly merger announcements, never-
theless, this is partly adjusted for when adding the controls, when added policy rate still shows a
significant and negative relationship.
The empirical results also support Becketti’s (1986) findings, indicating that a decrease in interest
rate increases M&A activity. A contextual explanation behind this occurrence may be found in the
valuation of merger deals. As mentioned before, there is an observed phenomenon of lowering of
aggregate interest rates directly affecting the discount rates by lowering them as well. This lessening
of the discount rates used in the valuation process increases the value and ultimate profitability of
the M&A deal. Conversely, a higher interest rate would rate the discount rate which would make
the valuations of M&A deals less profitable (Gaughan 2007).
33
Another possible explanation behind the observed relationship is that the cost of conducting an
M&A deal decreases due to lower the cost of borrowing brought on by the lower policy rate. Just
as higher policy rates, conversely, would increase the cost of borrowing, which would in turn deter
M&A transactions as firms may become more cautious about taking on debt in an environment with
a high cost of debt (Gaughan 2007).
This empirical result is, furthermore, partly in line with Benzing’s paper, which also found a
negative relationship between interest rates and M&A activity in the majority of the studied period
between 1919 to 1979 in the US manufacturing and mining sectors.Benzing (1991) also draws upon
idea of the cost of capital as well as tighter monetary policy discouraging mergers and acquisitions.
8.3 Unemployment Rate
Contrary to the empirical expectations, the unemployment rate exhibited a positive and significant
relationship against merger activity. This relationship holds statistical significance at the 1% level
throughout all the tables except one model in the no-lag table. The lack of significance in the no
lag table can possibly be attributed to economic inertia, which refers to the resistance to change
in economic systems or behaviors. When changes appear in the economy, companies seldom adapt
instantaneously instead, there is a time lag in the adaptation. Another interesting finding was that
the positive relationship increased in strength for all tables in model 3 and model 4 where certain
control variables that relate to the business circumstances were added. This may suggest that the
initial effect of the unemployment rate is underestimated and that the additional controls reduce the
negative omitted variable bias which suggests that the unemployment rate actually may be stronger
than initially shown. This result, in turn, suggests that higher unemployment rates increase in
tandem with the number of mergers and acquisitions in Sweden.
This counter-cyclical behavior of Swedish merger activity is in line the findings of Guo and Yang
(2013), who observed similar trends in the Taiwanese banking sector during economic downturns
where Taiwanese banks capitalized on downturns to acquire their competitors and increase their
market share. This finding challenges the conventional view that M&A activity is primarily pro-
cyclical and underscores the complex motives behind corporate mergers and acquisitions.
34
At the same time, it is worth noting that Guo and Yang (2013) only focus on the banking industry
in Taiwan, this sector faces in front of its own unique challenges and opportunities which may not
be shared across other industries. Meanwhile, this study seeks to examine common effects across all
industries and thus the explanation provided may not hold for all industries as the other industries
can be intrinsically different from the banking sector. Further explanations behind the relationship
between the number of mergers and the unemployment rate.
Since the unemployment rate, once again, is indicative of an economic downturn, one explanation
may be found in the concept of distressed acquisitions. As the economy, due to one reason or another,
slows down, so does the demand for goods and services. This in turn puts certain companies
in financially stressful situations. These companies now find themselves struggling and become
financially distressed which presents the opportunity for financially stronger and healthier companies
to step in with distressed acquisitions and offer an exit opportunity for the struggling company. Thus
increasing the number of mergers and acquisitions.
Another closely related explanation to the distressed acquisitions, which many times can be
applied interchangeably, is the roll-up strategy which is utilized by some companies. As mentioned
before, roll-ups, were historically presented to smaller businesses that suffered from illiquidity, the
targeting of smaller firms also allowed larger corporations to conduct a series of this type of M&A
in a rapid fashion. The economic downturn that unemployment represents would at the same time
bring lower demand and induce lower revenue streams to smaller businesses which in turn puts them
in the illiquid position where a roll-up acquisition program now seems to be a viable exit strategy.
This explanation also takes into account the strength of the relationship between the variables as
roll-ups are serial acquisitions with a high number of smaller transactions which will be registered
more strongly in the number of M&A transactions as compared to larger and fewer transactions
which may be found in the other explanations.
A third explanation for the relationship between the unemployment rate and the number of
mergers and acquisitions can be found in the growth motive or more accurately the sub-strategy of
slow-growth acquisitions. As mentioned previously, there sometimes exists a rather heavy pressure
on corporate managers to perform and show growth as well as revenue for the corporate shareholders.
35
In times of a slower-moving economy such as when there is a downturn, corporate managers may
start to engage in mergers and acquisitions in an effort to meet shareholder pressure and deliver the
growth and revenue increases that are expected.
8.4 Limitations
Despite the statistically significant findings, it is at the same time good and reasonable to acknowl-
edge the limitations that might affect the scope and interpretation of the results.
The data used in this study spans from January 2001 to April 2024. A period of approximately 23
years, which allows for an analysis of trends and patterns over time, which provides a more significant
result of the relationship between M&A activity and the macroeconomic factors of choice. However,
(Benzing 1991) notes in her study that the relationship findings become contradictory when the study
period is split into two parts. She speculates, for example, that the underlying reason could be a
long period of unchanged interest rates. Our dataset shows a similar trend, with a relatively longer
period of zero and even negative interest rates between November 2014 and May 2022. Further
studies of a second period, alternatively a segmentation of the current period in the data might
provide a different picture of the relationships and thus results.
Furthermore, the lags of 3, 6, and 9 months indeed showed statistical significance in the rela-
tionship between the variables and M&A activity. At the same time, the findings may have been
different if other lags were chosen instead. The varying nature of mergers and acquisitions makes it
hard to pinpoint a time lag which can be deemed to be representative as some smaller mergers may
only need a month from planning to announcement and some of the larger, more complex ones, may
need more than a year to go from start to announcement. Mergers that thus have longer or shorter
timelines than the lags of the study may end up showing differing results from the findings of this
study.
The control variables in our study, which include GDP growth, inflation rate, fiscal expenditure,
business confidence, and financial stress et cetera, are used in the study to more accurately capture
the relationships of macroeconomic factors influencing M&A activity. At the same time, the thesis
still acknowledge that there may be other unobserved factors that influence M&A activity that have
not been accounted for.
36
Variables such as political stability, regulatory changes, and international conditions were not
explicitly controlled for in this study. This exclusion may partly be because of the national focus as
well as the difficulty in quantifying certain aspects. As a consequence of this exclusion, there may
be a bias towards more quantitative as well as national measurements, which potentially may skew
the results.
The thesis is also geographically limited to Sweden, which gives insights into the Swedish merger
market. However, this geographical focus also limits the applicability of the findings to Sweden only.
Other regions of the world, let alone bordering nations, might possess different economic structures,
regulatory environments, and other underlying characteristics. This makes the generalizability of
this study limited to the national border of Sweden. To validate whether the observed relationships
are held in regions a similar study would have to be conducted there. Another limitation of the
study comes in the form of granularity. Unlike many other studies that focus on specific sectors,
our research examines the aggregate merger market as a whole. This aggregation removes any inter-
industry variations that may be present in the market, something which might itself carry valuable
insights, and thus may be of interest for further research. This is also applicable with regards to the
merger types, as this study has not differentiated between different types of mergers such as growth,
synergy, and diversification there are also insights to be gained here from conducting further research.
One example would be how many roll-up acquisitions have taken place in Sweden as compared to
distressed acquisitions.
There is, furthermore, a limitation in the form of the one-sidedness of the dependent variable.
This thesis exclusively measured the M&A activity in the form of the volume of M&A transactions
that were announced per month. Previous studies such as (Choi and Jeon 2011) as well as (Benzing
1991) not only used the number of M&A transaction, but they also used merger deal value as
a measurement of M&A activity as the inclusion of this measurement gives a more nuanced and
therefore more representative picture of M&A activity. This idea can be reinforced with regards to
the roll-up acquisitions as the tend to be overrepresented in the number of transaction. Which may
create a sort of skew in the study. This thesis did, at the same time, not exclude the deal value
dimension on the basis of any bias.
37
Rather, the data for Swedish M&A deal value was not as readily available as the deal volume
so the inclusion of it would not be representative of the actual deal values that have taken place in
Sweden. If this data becomes readily available in the future, or already is available to others, one
suggestion would be to re-create this study with that dimension included.
A final limitation of this thesis is the lack of inverse regression for the number of M&A trans-
actions on the macroeconomic variables similar to the paper from (Becketti 1986). In this way
Becketti gains additional robustness as the inverse relationship is tested to search for any potential
effect of M&A transactions might have on the macro variables. From the findings of this paper one
interesting study of interest may be that if there is a potential relationship between M&A activity
and unemployment Gaughan (2007) mentions that one of the main merger strategies would be the
achievement of cost reduction through economies of scale. It would thus not be too far of a fetch
to assume that these synergies of cost reduction would come at the expense of layoffs where person-
nel that once served a purpose in the separate organizations are made redundant by spreading the
overhead, which in turn would increase the unemployment rate as they lose their jobs.
38
9 Conclusion
This bachelor thesis investigates how macroeconomic variables, with a particular focus on interest
and unemployment rates, affect the M&A transaction volume in Sweden. The findings show a
statistically significant and negative relationship between interest rates and M&A activity. Which
suggests that companies may be discouraged from participating in M&A when interest rates are
high.
This finding is in line with both previous studies and reasonable given the intricacies behind
mergers and acquisitions. An example of this complexity being found in the decrease in perceived
investment profits and the higher cost of debt during high interest rates. At the same time, and con-
trary to what was the expected result, higher unemployment rates were associated with an increase
in the volume of monthly M&A transactions. This finding may once again point to the intricacies
of mergers and acquisitions with certain merger strategies being benefited by an economic down-
turn, something which could suggest a corporate seizure of opportunities during times of economic
downturn by engaging in M&A.
While this thesis provides evidence of a significant relationship between the macroeconomic
variables of policy and unemployment rate against the M&A activity over multiple time lags. It
also highlights a need for further research to explore the possible effects of economic events, more
obscure variables, and different dimensions, which were not covered in this analysis. Future research
could also benefit from the inclusion of a broader set of control variables and the examination of the
macroeconomic effects in different countries, or in the same country across differing industries.
39
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