Group Coursework Submission Form Specialist Masters Programme Please list all names of group members: (Surname, first name) 1. Wei Bo 2. Agrawal Minakshi 3. Lemercier Jean GROUP NUMBER: MSc in: Finance Module Code: SMM 248 Module Title: STATISTICS IN FINANCE Lecturer: Professor Ana-Maria Fuertes Submission Date: 09 DECEMBER 2013 Declaration: By submitting this work, we declare that this work is entirely our own except those parts duly identi- fied and referenced in my submission. It complies with any specified word limits and the requirements and regulations detailed in the coursework instructions and any other relevant programme and module documentation. In submitting this work we acknowledge that we have read and understood the regula- tions and code regarding academic misconduct, including that relating to plagiarism, as specified in the Programme Handbook. We also acknowledge that this work will be subject to a variety of checks for academic misconduct. We acknowledge that work submitted late without a granted extension will be subject to penalties, as outlined in the Programme Handbook. Penalties will be applied for a maximum of five days lateness, after which a mark of zero will be awarded. Marker’s Comments (if not being marked on-line): Deduction for Late Submission: FinalMark: % 18
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Group Coursework Submission Form
Specialist Masters Programme
Please list all names of group members:
(Surname, first name)
1. Wei Bo
2. Agrawal Minakshi
3. Lemercier Jean
GROUP NUMBER:
MSc in: Finance
Module Code: SMM 248
Module Title: STATISTICS IN FINANCE
Lecturer: Professor Ana-Maria Fuertes Submission Date: 09 DECEMBER 2013
Declaration:
By submitting this work, we declare that this work is entirely our own except those parts duly identi-
fied and referenced in my submission. It complies with any specified word limits and the requirements
and regulations detailed in the coursework instructions and any other relevant programme and module
documentation. In submitting this work we acknowledge that we have read and understood the regula-
tions and code regarding academic misconduct, including that relating to plagiarism, as specified in the
Programme Handbook. We also acknowledge that this work will be subject to a variety of checks for
academic misconduct.
We acknowledge that work submitted late without a granted extension will be subject to penalties, as
outlined in the Programme Handbook. Penalties will be applied for a maximum of five days lateness,
after which a mark of zero will be awarded.
Marker’s Comments (if not being marked on-line):
Deduction for Late Submission: FinalMark: %
18
11/27/2013
Statistics in Finance
Mergers & Acquisition effects on
Economic Growth
Bo Wei – Minakshi Agrawal – Jean Lemercier
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Bo Wei – Minakshi Agrawal – Jean Lemercier
Introduction
Mergers and Acquisition global volumes amount to USD2.1 trillion for the first nine months of 2013, representing a
17% increase from 2012 levels. If this USD2.1 trillion figure is far from the peak of 2007 M&A volumes which were in
excess of $4 trillion, many economists and experts forecast that these volumes will continue to increase over the
next few years. Variation in M&A volumes are observed to follow patterns or waves, where volumes increase
substantially (1990s, 2001, 2008 – see graph) and then suddenly drop. Consequently, various literary works have
been increasingly focusing on explaining the determinants of change in M&A volumes, may it be fundamental factors
such as industrial/economic/regulatory shocks explained in the article “What drives merger waves?” (Harford, 2004)
and/or papers such as “The Free Cash Flow Theory of Takeovers: A Financial Perspective on Mergers and
Acquisitions and the Economy” where other factors such as agency costs, excess free cash flows and attempts of
market timing (Jensen, 1987).
The research on consequences of M&A activity and post-merger results at the firm level shows that there is a
widespread argument whether M&A creates value after taking cost (bid-premium) into account – however the
consensus is often that targeted companies performance improves post-acquisition (M. Healy, 1990). The commonly
used reasoning for justifying acquired companies’ outperformance is that M&A activities create value by constricting
agency costs and creating synergies between companies.
If acquisitions lead to sustainable long-term productivity gains at the firm level, one could argue that acquisitions at
the aggregate level may have an impact on economic growth. Literature displays two different theories. The first one
explains why M&A activity can induce GDP growth, thanks to increased productivity created though synergies and
slashing agency costs. The second theory support the idea that M&A transaction are detrimental to the economy in
the sense that they mostly result in more market control for the acquiring company, which rules out smaller
companies who lack in scale and size to remain viable or give them an incentive to cut down on R&D to remain
competitive. If the relationship between these two variables is hard to define, it seems as though a correlation could
reasonably be expected.
The aim of this paper is to investigate whether changes in volumes of M&A activity are correlated with growth at the
aggregate level – i.e economic growth.
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Bo Wei – Minakshi Agrawal – Jean Lemercier
Motivation It was reported that nine thousand billion dollars was spent by North American and Western European firms on
mergers and acquisitions (M&A’s) between 1995 and 1999. This is about seven times the GDP of the United Kingdom
by an incomprehensive comparison. There are numerous literatures talking about determinants and consequences
of merger and acquisition. However, the impact of M&A on economic growth is seldom explored. Monitoring the
relationship between M&A activity and economic growth could prove valuable to forecast economic growth or
recessions. In our model, we use the data from 2001 to 2013, including the periods of financial crisis. We also want
to know the performance of M&A after the financial crisis, emphasizing the relationship between M&A and
economic growth.
Data Description
Our model will feature the following variables
Y : Real GDP growth (QoQ, %, United Kingdom)
X1 M&A Volumes in the United Kingdom (% Change in sterling volume, QoQ)
X2 Deal count (% Change QoQ)
X3 Average premium paid over the period, (%, Quarterly)
X4 Bank of England base rate (%)
All the data has been taken from Bloomberg.
X1 X2 X3 X4 Y
Mean 0.174249 -0.002463 0.194067 3.029412 0.368627
Median -0.043002 0.001887 0.185500 4.000000 0.500000
Maximum 2.929415 0.269231 0.466800 5.750000 1.300000
Is there a multicollinearity problem in your regression?
Multicollinearity overestimates the Standard Error (S.E) of the explanatory variables. As a result, the t-
statistic of the regressors is underestimated when there is multicollinearity (as T-statistic for the null
hypothesis equals  /S.E (Â)) and as a direct consequence the null hypothesis for the regressors tends to not
be rejected, although the joint hypothesis (F-statistic) rejects the null hypothesis. In our case, two out of the
four regressors reject the null hypothesis at the 10% significance level (two-sided test) namely X1 & X3. In
addition, X4 is very close to not being rejected at the 10% level. Therefore the T statistic “symptom” does
not prove to be relevant in our case to detect whether our model suffers from multicollinearity or not.
In a model with multicollinearity, parameters estimates change notably when observations are
excluded/added. We tested by deleting 6 observations (Observation N°5,10,15,20,25,30) and running a new
regression, the coefficient did not change significantly (see below).
In a model with multicollinearity, the parameter estimates change significantly when one parameter is
dropped. In our model it is not the case (see below).
Variable Coefficient
C 0.878025
X1 -0.235082
X2 1.260784
X3 -3.677868
X4 0.082003
Variable Coefficient
C 0.890713
X1MINUS6 -0.230501
X2MINUS6 1.292053
X3MINUS6 -3.757550
X4MINUS6 0.088370
Variable Coefficient
C 0.932787
X1 -0.206127
X3 -3.899481
X4 0.075433
Table 4 – Linear Regression Coefficients
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Bo Wei – Minakshi Agrawal – Jean Lemercier
After running an auxiliary regression with X4: C X1 X2 X3, the R^2 result it 0.08, which is lower than 0.8, meaning there is no apparent multicollinearity. In conclusion, our model does not seem to have any significant multicollinearity and therefore does not need to be corrected for this.
Significance of the R²
Let’s test the significance of our model’s R² (29%):
Ho: X1=X2=X3=X4=0 or R²=0 / H1: X1 or X2 or X3 or X4 > 0 (at least one of them) and R²>0
Mean 6.59e-17Median -0.004087Maximum 1.167325Minimum -2.024367Std. Dev. 0.578690Skewness -0.563006Kurtosis 4.529200
Jarque-Bera 7.663500Probability 0.021672
Non-normality of Ԑt poses no problem for large T because in that case t-statistic follows N (0, 1). However, we only have 52 observations which is a relatively small T. Let’s test the normality of Ԑt
H0: Ԑt follows a normal distribution
H1: Ԑt does not follow a normal distribution
From the histogram of Ԑt, we can see that it is not bell-shaped. Additionally, Jarque-Bera equals 7.66300 which is
much bigger than 0. Therefore, reject the null hypothesis. Ԑt does not follow a normal distribution because of a
relatively small T.
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Bo Wei – Minakshi Agrawal – Jean Lemercier
Dummy Variables
Binary variable in the explanatory variable
Before using any dummy variable, it is important to recall what the initial data used comprises:
Y : Real GDP growth (QoQ, %, UNITED KINGDOM)
X1 M&A Volumes in the United Kingdom (% Change in sterling volume, QoQ) Used in the final model
X2 Deal count (% Change QoQ)
X3 Average premium paid over the period, (%, Quarterly) Used in the final model
X4 Bank of England base rate (%)
Some interesting dummy variable we could have used could be a dummy variable reacting to a specific industry. For
example we could have used a dummy variable taking the value 1 if the average premium paid over the period in the
financial industry had increased or 0 if the average premium paid over the period in the financial industry had
increased (or any other industry). This way we could have measured if any industry has a higher correlation with GDP
growth than another. However we were unable to use any of this due to lack of data.
The dummy variable we have created (DummyCrisis) is a structural break dummy variable taking the value 1 if the
values recorded in the sample are after quarter 1 2007 (Observation 23, beginning of the Global financial crisis) and
0 if the values have been recorded before quarter 1 2007 (the remaining observations). This variable will be useful to
assess whether the relationship between M&A premiums/volumes and GDP growth in the United Kingdom has
significantly changed after the crisis (Q1 2007).
Dependent Variable: Y
Method: Least Squares
Date: 12/08/13 Time: 20:03
Sample (adjusted): 1 51
Included observations: 51 after adjustments Variable Coefficient Std. Error t-Statistic Prob. DUMMYCRISIS 0.187783 0.189022 0.993444 0.3257