TILBURG UNIVERSITY

The impact of macroeconomic factors on mergers

and acquisitions in China from 1992 to 2013.

Analysis on domestic and cross-border mergers and acquisitions in two

sub-samples and one combined sample.

Supervisor: prof. dr. F.A. de Roon

Author: Xin Wang

ANR: 666193

1

Table of Contents Chapter 1 Introduction .................................................................................................................................. 2

Chapter 2 Data Collection & Summary Statistics ......................................................................................... 5

2.1 Data collection .................................................................................................................................... 5

2.2 Summary Statistics .............................................................................................................................. 6

Characteristic 1: China takes the biggest share of M&As within BRIC region. ................................... 6

Characteristic 2: SIC code pattern. ....................................................................................................... 8

Characteristic 3: Geographical Variety. ................................................................................................ 9

Characteristic 4: M&A value distribution ............................................................................................. 9

Chapter 3 Literature Review & Hypothesis Development .......................................................................... 10

3.1 Existing literatures on investigating the impact of macroeconomic factors on M&A activities. ..... 10

3.2 Hypothesis Development – macroeconomic factors that potentially have an impact on the volumes

of M&A in China between 1992 and 2013. ............................................................................................ 12

A. GDP and GDP growth. ................................................................................................................... 12

B. Inflation – expected inflation in six months and realized inflation. ............................................... 15

C. Real lending rate. ............................................................................................................................ 17

D. Climate for foreign investment – legal system. .............................................................................. 18

E. Political stability. ............................................................................................................................ 20

F. Exchange rate RMB/USD ............................................................................................................... 21

Chapter 4 Methodology .............................................................................................................................. 23

4.1 Dickey-Fuller test .............................................................................................................................. 23

4.2 Perfect collinearity test. .................................................................................................................... 24

4.3 Univariate and Multivariate time - series linear regressions. ............................................................ 26

Chapter 5 Results & Discussion ................................................................................................................. 28

5.1 Univariate time-series regression analysis. ....................................................................................... 28

5.2 Multivariate time - series regression analysis (with only base independent variables). ................... 31

5.3 Multivariate time - series regression analysis (with base independent variables plus two governance

factors: climate for FI - legal system and political stability). ................................................................. 34

5.4 Multivariate time - series regression analysis (with all independent variables). .............................. 37

Chapter 6 Conclusions & Recommendations ............................................................................................. 28

Reference .................................................................................................................................................... 41

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Chapter 1 Introduction

Mergers and acquisitions (M&A) are a big and very important part of the corporate finance

world. Every day there are M&A transactions arranged which bring different companies together

to form larger ones. M&A consists of two groups – cross-border merger and acquisitions

(CM&A) and domestic merger and acquisitions (DM&A). According to Erel, Liao and Weisbach

(2012), there are three main reasons for firms to undergo an M&A process: synergies,

diversification and the benefits from imperfect capital markets across countries. Historically,

DM&A has always dominated the whole M&A activities due to language and culture barriers,

government interventions, legal restrictions and etc. However, as the global economy is

becoming more connected and integrated, cross-border mergers and acquisitions (CM&A) are

increasing in importance.

Erel, Liao and Weisbach (2012) investigated the macroeconomic determinants of CM&A using a

worldwide sample of 56,978 CM&A deals between 1990 and 2007. They claimed that the

percentage of CM&A volume in the total M&A volume rose from 23% in 1998 to 45% in 2007.

Furthermore, Uddin and Boateng (2011) claimed that “most of the growth in foreign direct

investment (FDI) over the past two decades has been made via cross-border mergers and

acquisitions”. Current research on investigating M&A activities mainly focuses on developed

countries at microeconomic level. Visic and Peric (2011) mentioned in their research that

scientific studies on macroeconomic determinants of M&A is relatively scarce, and with a focus

on developing countries is even scarcer.

Since the 1990s, CM&A is increasing in significance and more scholars have investigated the

impact of macroeconomic determinants on M&A activities (Dunning 2009). Erel, Liao and

Weisbach (2012) explained well the reasons why macroeconomic factors play an important role

in influencing CM&A activities. Specifically, there are more M&A activities going on in

countries with high GDP and good governance. A country is more likely to be an acquirer if the

currency is appreciating while a country is more likely to be a target if the currency is

depreciating.

In addition, Uddin and Boateng (2011) investigated the impact of macroeconomic factors on the

number of CM&A deals in UK between 1987 and 2006. They found that real GDP has a

statistically significant and negative impact on cross-border M&A outflow1 (CM&AO) and a

statistically significant and positive impact on cross-border M&A inflow2 (CM&AI), both at 5%

level. Exchange rate has a statistically significant and positive impact on CM&AO at 5% level.

And the UK price index3 has a highly statistically significant (at 1% level) and positive impact

on both CM&AI and CM&AO.

1 In this study cross-border merger and acquisition outflow (CM&AO) is defined as a company in home country

acquires a company from a foreign country. 2 In this study cross-border merger and acquisition inflow (CM&AI) is defined as a company in home country being

acquired by a company from a foreign country. 3 In the paper Uddin, Boateng (2011) used UK all price index (1962 index=100), collected from Economic

Intelligence Unit (EIU) country database and UK national statistics database.

3

In a similar vein, Visic and Peric (2011) investigated the macroeconomic determinants that affect

the value CM&AI deals in European transition countries between 1994 and 2008, and found that

lagged value of GDP per capita, lagged GDP growth, interest rate, rule of law and control of

corruption have statistically significant and positive impacts on the value of CM&AI deals to

GDP ratio.

As discussed above, Visic and Peric (2011) noticed that scientific studies on macroeconomic

determinants of M&A are relatively scarce. When focus switches to developing countries the

studies conducted are even more sporadic. This paper will investigate the impact of

macroeconomic determinants on M&A activities (volumes) in China from the open-up policy

(after the second stage) in 1992 until 2013. The purpose of this study is not only to contribute to

academic literature, but also to generate advice for national macroeconomic policies. The aim is

to provide recommendations to emerging markets on a good preparation for CM&A in such a

fast growing, highly integrated but also volatile economy. Furthermore, the reasons of choosing

China as a case study are as follows:

Firstly, China is one of the biggest emerging markets in the world nowadays. China is becoming

more and more capable of absorbing foreign investments as well as expanding and making

investments into foreign markets. However, there are few studies in business literature solely

investigating the impact of macroeconomic determinants on M&A activities in China.

Changqi and Ningling (2010) investigated the impact of both macroeconomic and

microeconomic determinants on CM&A performance of Chinese companies between 2000 and

2006. They found that proportion of the state owned shares and pre-acquisition performances are

positively and statistically significant.

The sample period dates back to January 1st, 1992 to December 31st, 2013. We are going to

explain why January 1st 1992 is chosen as a starting point for this analysis. According to

Engardio and Peter (2005), China’s economic reform was introduced in 1978 by Deng Xiao

Ping4, and was executed in two stages. There were three main actions carried out in the first ten

years: reducing barriers for foreign investment into China, allowing entrepreneurs to start private

business and decollectivizing agricultural sector. Nevertheless, most of the industries and

business at this stage still remained state-owned.

In the second stage till the 1990s, the reform mainly focused on privatization through further

reducing the stated-owned shares in companies by mainly limiting government controls. As a

result, the private enterprises grew rapidly, which also led to huge losses for state revenues

inevitably. “After 1992, privatization began to accelerate, and the private sector surpassed the

state sector in share of GDP for the first time in the mid-1990s (Brandt, 2008)”. Therefore, 1992

is a reasonable starting point to investigate the impact of macroeconomic determinants on M&A

activities in China.

Due to economic and econometric reasons, the total sample set between 1992 and 2013 is

divided into two sub-samples: the period from 1992 to 2004 and the period from 2005 to 2013.

Firstly from an economic point of view, the number of state-owned enterprises has decreased

4 Deng Xiao Ping is the leader of the Communist Party if China between 1978 and 1992.

4

significantly along the process of privatization. By the end of 2004, “the number of state-owned

enterprises decreased by 48 percentage (Rawski, 2008)”. In the meantime, Jiang Ze Min and Zhu

Rong Ji who were the successors of Deng Xiao Ping further encouraged international trade by a

number of actions which included reducing trade barriers, loosening trade regulations and

joining the World Trade Organization (WTO). Secondly from an econometric point of view,

there is a structural break around the year 2004. Therefore statistically it is more precise to have

two models based on different situations. To explain more vividly, figure 1 below plots the

quarterly data of the number CM&A deals in China between 1992 and 2013.

Figure 1

The figure below plots the quarterly data of the volumes of cross-border M&A inflow (CM&AI) and cross-border

M&A outflow (CM&AO) between 1992 and 2013.

Besides dividing the whole sample into two sub-samples, CM&A and DM&A will also be

analyzed separately due to their different characteristics. The macroeconomic variables that will

be analyzed in this study are GDP, GDP growth, expected inflation in six months, realized

inflation, real lending rate, exchange rate, climate for foreign investment (FI) - legal system and

political stability.

The study is organized as follows: chapter 2 will discuss data collection and summary statistics.

Chapter 3 will discuss literature review and hypothesis development. Methodology will be

analyzed in chapter 4. Econometric results and discussions will be discussed in chapter 5. Last

chapter concludes by providing recommendations and the limitations of the paper.

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Chapter 2 Data Collection & Summary Statistics

2.1 Data collection

The whole data set in this study can be divided into dependent variables (M&A data) and

independent variables (macroeconomic factors data).

M&A data was collected from SDC in Tilburg University with the following detailed

information: acquirer and target’s company name, country of origin, industry SIC code and the

total value of transaction. Volumes of CM&AI, CM&AO, and DM&A are measured quarterly

between year 1992 and year 2013. There are 26400 deals in total including M&A from both

public and private companies. M&A are divided into two groups – cross - border mergers and

acquisitions (CM&A) and domestic mergers and acquisitions (DM&A). CM&A’s subgroups are

cross - border mergers and acquisitions inflow (CM&AI) and cross - border mergers and

acquisitions outflow (CM&AO). To illustrate this: we are dealing with a CM&AI when a Dutch

company acquires a domestic Chinese company. On the other hand, we have CM&AO when a

domestic Chinese company acquires a Dutch company. Noteworthy that, all M&A in this study

are measured in terms of volumes, so value will not be considered. There are a couple of reasons

that advocate the aforementioned statement. Firstly, M&A value is more complex to investigate

than M&A volume, M&A value is only comparable if all deals are measured in the same

currency, which means exchange rate will also be embedded, so this might have mixed effects on

the results of the analysis. Secondly, fewer studies have investigated the relationship between

macroeconomic variables with M&A values compared with M&A volumes. Therefore for the

sake of better comparison with the existing studies, only M&A volumes will be considered as

dependent variables in this study.

Data of macroeconomic factors including GDP, GDP growth rate, expected inflation in six

months, realized inflation, real lending rate, climate for FI - legal system, political stability and

exchange rate are collected from Datastream on a quarterly basis in Tilburg University. Table 1

below shows the summary statistics of M&A and macroeconomic variables.

Remarks: firstly, horizontal or vertical mergers will not be analyzed separately. Secondly, target

and acquirer financial advisor fees will not be included due to their insignificant value ($)

compared with the M&A’s deal value ($), Thirdly, Hong Kong and Macau are excluded from

China due to different economic and political environments. In other words, Hong Kong and

Macau are treated as “foreign countries”. Last but not least, there are 26400 deals in total which

include deals from both private and public companies.

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2.2 Summary Statistics Table 1

Summary statistics for M&A (including CM&AI, CM&AO and DM&A) variables and macroeconomic variables

(including GDP, GDP growth, expected inflation in 6 months, realized inflation, real lending rate, exchange rate,

political stability and climate for FI-legal system).

Variable Min Max Mean Median Std.Dev. Skewness Kurtosis

CM&AI 0,00 146,00 62,88 58,00 41,93 0,07 -1,47

CM&AO 1,00 60,00 16,95 10,50 15,38 0,89 -0,54

DM&A 0,00 813,00 220,16 163,50 233,49 0,73 -0,79

GDP 4794,33 181744,97 50060,50 32273,83 41903,91 1,18 0,56

GDP growth -29,91% 33,71% 6,24% 10,11% 0,20 -0,56 -0,88 Expected inflation (6m) 1,80 9,00 5,66 6,15 1,84 -0,43 -0,69

Realized inflation 1,00 9,00 4,47 4,05 2,77 0,50 -1,21

Real lending rate 5,31 12,06 7,15 6,03 2,09 1,06 -0,31

Exchange rate 5,44 8,70 7,55 8,28 0,97 -0,78 -0,92

Political stability 3,70 7,00 5,31 5,30 0,74 0,06 -0,17 Climate for FI-legal system 1,00 4,70 3,23 3,50 0,84 -0,58 -0,36

Besides the summary statistics showed in table 1 above, there are four main M&A characteristic

summarized from the Chinese M&A data.

Characteristic 1: China takes the biggest share of M&As within BRIC5 region.

China is considered as one of the biggest emerging markets in the world nowadays. As

summarized in table 2 and 3, China has the biggest share in terms of value and volume of M&A

within BRIC (Brazil, Russia, India, and China) region. According to a report6 named the Top 20

Emerging Markets published by Bloomberg in January, 2013, China scored the number one

emerging market in the world. The total score took factors including GDP growth rate, inflation

rate, Government debt to GDP ratio, and the environment of doing business into account.

5 BRIC is an abbreviation which stands for country Brazil, Russia, India and China.

6 Link of the report: http://www.bloomberg.com/slideshow/2013-01-30/the-top-20-emerging-markets.html#slide21

7

Table 27

Table 2 summarizes the Chinese yearly volumes of cross-border M&A inflow (CM&AI), cross-border M&A

outflow (CM&AO) and domestic M&A (DM&A) between year 1992 and year 2013. Also the corresponding

percentage of China’s M&A volumes to the total M&A volumes of BRIC is calculated.

Year Vol(CM&AI) %BRIC

Vol(CM&AI) Vol(CM&AO) %BRIC

Vol(CM&AO) Vol(DM&A) %BRIC

Vol(DM&A)

1992 10 35,7% 14 77,8% 4 18,2% 1993 36 53,7% 24 72,7% 9 27,3% 1994 59 56,2% 12 50,0% 15 31,3% 1995 44 39,3% 10 27,8% 25 22,5% 1996 61 40,1% 7 33,3% 39 36,8% 1997 94 40,7% 30 69,8% 61 43,9% 1998 118 47,0% 33 63,5% 77 33,3% 1999 121 44,2% 24 53,3% 75 24,8% 2000 168 36,8% 27 31,4% 109 20,5% 2001 168 43,4% 23 37,7% 145 27,5% 2002 215 62,3% 49 52,7% 430 51,9% 2003 294 65,5% 26 31,0% 844 69,5% 2004 386 68,1% 46 38,7% 1285 82,1% 2005 370 61,8% 48 33,1% 908 62,8% 2006 434 58,9% 65 32,8% 963 63,1% 2007 474 53,5% 134 39,6% 1421 65,3% 2008 389 50,9% 131 45,0% 1710 68,2% 2009 339 57,0% 143 58,8% 1709 71,1% 2010 461 58,7% 186 57,9% 2171 71,7% 2011 476 60,7% 159 57,0% 2555 71,7% 2012 432 58,6% 147 61,0% 2329 71,0% 2013 385 61,4% 154 70,6% 2490 76,1%

Average 52,5% 49,8% 50,5%

7 Author’s own calculation based on raw M&A data from SDC.

8

Table 38

Table 3 summarizes the Chinese yearly values of cross-border M&A inflow (CM&AI), cross-border M&A outflow

(CM&AO) and domestic M&A (DM&A) between year 1992 and year 2013. Also the corresponding percentage of

China’s M&A values to the total M&A values of BRIC is calculated.

Year Val(CM&AI) %BRIC

Val(CM&AI) Val(CM&AO) %BRIC

Val(CM&AO) Val(DM&A) %BRIC

Val(DM&A)

1992 630,253 67,1% 641,534 91,4% 171,084 5,6% 1993 1560,946 63,8% 487,221 48,3% 827,589 17,5% 1994 1175,767 49,1% 117,048 24,2% 565,975 4,9% 1995 664,909 19,5% 167,607 19,1% 589,327 8,2% 1996 2307,923 25,7% 224,986 37,5% 1003,524 13,6% 1997 7054,955 33,7% 1087,988 64,3% 2102,603 9,6% 1998 5346,098 16,1% 986,172 50,7% 2138,981 7,3% 1999 12979,724 50,3% 272,662 26,9% 2376,156 15,0% 2000 39370,063 51,2% 917,395 16,4% 7000,36 21,5% 2001 5505,122 43,5% 971,107 30,0% 6028,701 23,6% 2002 18886,909 68,6% 2593,335 33,3% 8212,502 33,4% 2003 6326,357 40,6% 1003,525 35,9% 23009,02 32,2% 2004 10475,65 34,6% 1501,933 11,4% 14691,881 36,4% 2005 32302,145 66,6% 6832,876 35,5% 12307,498 13,3% 2006 20767,797 34,7% 13869,503 27,6% 24799,016 32,9% 2007 23166,93 23,2% 30637,276 42,4% 57512,986 28,6% 2008 17869,147 24,3% 15910,893 35,2% 95450,875 42,1% 2009 22821,937 42,4% 23670,924 55,9% 79444,64 52,5% 2010 29340,08 24,9% 48643,123 54,3% 122456,166 41,5% 2011 33757,56 31,1% 38360,769 61,0% 104268,231 49,9% 2012 33641,279 39,9% 28578,398 61,3% 123145,995 45,9% 2013 43620,457 51,4% 42230,888 73,8% 176918,403 67,4%

Average 41,0% 42,6% 27,4%

Characteristic 2: SIC code pattern.

It’s noticed that 6960 (26.4%) out of 26400 deals have exactly the same four - digit SIC codes.

This means the acquirer and the target belong to exactly the same major industry group as well

as the specific industry.

Among the 26400 targets, the top three major industry groups are Manufacturing with 11316

(43%) deals, Finance & Insurance & Real Estate with 5647 (21%) deals and Services with 3074

(12%) deals, which together count for 76% of the total deals. The least two major industry

groups are Agriculture & Forestry & Fishing with 308 deals and Public Administration with 90

deals, which together account for 1.6% of the total deals.

Among the 26400 acquirers, the top three major industry groups are the Manufacturing industry

with 9675 (37%) deals, Finance & Insurance & Real Estate industry with 9488 (36%) deals, and

the Transportation & Public Utilities industry with 2048 (8%) deals, which together account for

81% of the total deals. The least two major industry groups are Agriculture & Forestry & Fishing

8 Author’s own calculation based on raw M&A data from SDC.

9

with 239 deals and Public Administration with 138 deals, which together account for 1.4% of the

total deals.

Scientific literature is found to support this finding. Liu (2011) investigated the main

determinants of foreign direct investment (FDI) into the manufacture industry in China and

claimed that, “over the period 1997 to 2008, the manufacturing sector has dominated China’s

FDI inflow, China became the largest destination of FDI inflow among developing nations since

1992”.

Characteristic 3: Geographical Variety.

Historically, DM&A has always dominated the overall M&A activities worldwide. This is due to

the fact that there are no languages and culture barriers, no gap between different governmental

regulations, and investors in general feel more familiar and safe with the home business

environment. Erel, Liao and Weisbach (2012) stated that for every country, the largest part of

M&A is domestic or regional (geographically neighboring countries). In a similar vein, Grote

and Umber (2006) also claimed that acquirers generally prefer a target company from a similar

geographical area sharing the same culture, language and social norms. Here is this study, not

surprisingly the number of Chinese DM&A deals also accounts for the majority of the total

M&A deals, that is 73.4% in terms of volume, and 57.9% in terms of value between 1992 and

2013.

The volume of CM&AI is 5534 deals which accounts for 21% of the total M&A deals. The

acquirers cover all continents, and Hong Kong is the biggest acquirer nation with 2544 deals,

which takes 46% of the total CM&AI share. The second largest acquirer nations are Singapore,

Japan and Taiwan, which together acquired 993 deals between 1992 and 2013, which translates

to an 18% of the total CM&AI deals.

The volume of CM&AO is 1492 deals, or 5.6% of the total M&A deals. The targets also cover

all continents, and Hong Kong is as well the biggest target nation with 512 deals, which takes

34% of the total CM&AO share. The second biggest target nations are Australia, US and

Canada, which together have 438 deals between 1992 and 2013, roughly 29% of the total

CM&AO deals.

Characteristic 4: M&A value distribution

The transaction values of M&As vary from 0,001 ($mil) to 341620 ($mil). However, among the

total M&A deals, 63.4% deals are in the value range of 0.001 ($mil) to 50 ($mil). There are some

outliers in the group CM&AO, 68 deals with transaction value exceeding 1000 ($mil). However,

it is still a small percentage compared with the rest of 1078 deals with transaction value less than

50 million US dollars. The figure below shows the distribution of the logarithm transaction

values for overall M&As.

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Figure 2

Figure 2 plots the values of log10 (transaction value) for overall M&A deals over the period 1992 to 2013.

Chapter 3 Literature Review & Hypothesis Development

Despite the vast amount of studies investigating M&A activities, the majority focuses on markets

from developed countries at microeconomic level. While this study is also relevant to investigate

the impact of macroeconomic factors on M&A volumes and with a data sample of China over

the period 1992 - 2013.

3.1 Existing literatures on investigating the impact of macroeconomic factors

on M&A activities.

Uddin and Boateng (2011) investigated the impact of macroeconomic variables on CM&A

volumes in UK from the period 1987 to 2006. They found that real GDP, exchange rate, stock

price index, interest rate and money supply are statistically significant. More specifically, stock

price index is highly statistically significant (at 1% level) with positive signs on both CM&AO

and CM&AI. The size of the coefficient on CM&AO is 0.69, which means that if the stock price

index increases by 1%, the volume of CM&AO will increase by 0.69%, while for CM&AO, the

volume will increase by 0.80%. Secondly, real GDP is also statistically significant (at 5% level)

with a negative sign on CM&AO and a positive sign on CM&AI. The size of the coefficient on

CM&AO shows that a 1% increase in real GDP will decrease the volume of CM&AO by -2.9%,

however increase the volume of CM&AI by 0.51%. Thirdly, exchange rate has a statistically

significant (at 5% level) and positive impact on CM&AO. In addition, the size of the coefficient

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000-3

.5 -3

-2.5 -2

-1.5 -1

-0.5 0

0.5 1 2 3

3.5 4

M&A Log10 (transactionvalue)

11

shows that if the British pound appreciates by 1%, the number of CM&AO will increase by 1%.

However, the coefficient on CM&AI is positive but not statistically significant. Last but not least,

interest rate9 and broad money supply

10 are both statistically significant but at a lower level of

10%.

Erel, Liao and Weisbach (2012) investigated the determinants of CM&A volumes with a sample

covering 56978 deals from 48 countries between 1990 and 2007. They found that market-to-

book ratio11

, bilateral trade12

, log GDP per capita and GDP growth are statistically significant in

a panel regression analysis. More specifically, on an entire sample of CM&A deals, market-to-

book ratio is highly statistically significant (at 1% level) with a coefficient of 0.004, which

means 1 unit increase in the difference of acquirer’s and target’s market-to-book ratio, the

volume of CM&A will increase by 0.013 deals. Secondly, bilateral trade is also statistically

significant (at 5% level). The coefficient shows that 1 unit increase in the maximum of bilateral

import and export between the target and acquirer will lead to 0.16 more deals in CM&A

between these country pairs. Last but not least, log GDP per capita with a coefficient size of

0.043 and GDP growth with a coefficient size of 0.003 are both positively and statistically

significant (at 10% level).

Furthermore, Changqi and Ningling (2010) investigated the determinants of CM&A performance

of publicly listed Chinese companies between 2000 and 2006. The sample covers 91 companies

(including private and public) with 165 CM&A deals. They found that the proportion of state

owned shares and the pre-acquisition performance have statistically significant and positive

impact on the performance of the Chinese company (measured by return on asset) who took the

CM&A. Particularly, the standardized coefficient of state owned shares shows that 1 unit

increase in the state owned shares will lead to a 0.364 unit increase in the Chinese company’s

performance13

. Moreover, the standardized coefficient of pre-acquisition performance shows that

1 unit increase in the company’s pre-acquisition performance will lead to a 0.329 unit increase in

the company’s performance after taking the CM&A.

However, no research has been found investigating the impact of macroeconomic factors on

M&A activities in China during the entire period from 1992 to 2013. Therefore, this study will

be the first attempt at investigating the impact in a time range between the 2nd

stage of Chinese

economic reform and 2013. Based on the discussion above, the hypothesis and the motivation of

macroeconomics are developed in this chapter.

9 Interest rate used in study Uddin, Boateng (2011) is the percentage interest rate on 3 month UK Treasury bill.

10 Money supply used in study Uddin, Boateng (2011) is M4, the total money supply in the UK economy.

11 Calculated by Erel, Liao and Weisbach (2012) as the difference between acquirer and target’s market-to-book

weighted value. 12

Calculated by Erel, Liao and Weisbach (2012) as the maximum of bilateral imports and exports between the

tareget and acquirer’s countries 13

Calculated by Changqi, Ningling (2010) as (ROA2-ROA-1)/ROA-1. ROA2 as two years after CM&A, and ROA-1 as

ROA in the year before.

12

3.2 Hypothesis Development – macroeconomic factors that potentially have an

impact on the volumes of M&A in China between 1992 and 2013.

The motivation of the macroeconomic factors (including GDP, GDP growth, expected inflation

in six month, realized inflation, real lending rate, climate for foreign investment – legal system,

political stability and exchange rate) that potentially have an impact on the volumes of M&A in

China between 1992 and 2013 is discussed in the following paragraph.

A. GDP and GDP growth.

GDP14

is generally considered a standard macroeconomic measure for national wealth as well as

an indicator of a country’s economic development level. Aside from the studies of Uddin and

Boateng (2011), and Erel, Liao and Weisbach (2012) mentioned above, other studies also

rendered the support of the important impact of GDP on M&A activities. Rossi and Volpin (2004)

investigated the cross-country determinants of M&A with a sample of 49 countries between

1990 and 2002. They found that GDP per capita has a statistically significant (at 1% level) and

positive impact on the number of M&A deals. In addition, Deng and Yang (2014) investigated

the major factors that determine CM&A activities in nine15

emerging countries between 2000

and 2012. They found that GDP growth rate is positively related with the number of CM&A

deals in a panel regression analysis, however, the coefficient is not statistically significant. In this

study, the motivation of considering GDP and GDP growth two of the independent variables are

as follows:

Firstly, GDP and GDP growth rate is expected to have a positive impact on the number of

CM&AI deals. The motivation goes as follows: countries with higher GDP or GDP growth rate

commonly have larger economic markets with more opportunities and are more capable of

absorbing foreign investment. European Economy (September 2004) investigated the

determinants of European CM&A covering 17 countries in Europe and US between 1991 and

2001, and found that GDP per capita is positively related with the volume of CM&A and

statistically significant at 5% level. The coefficient shows that 1% increase in GDP per capita

will lead to 0.352 more CM&A deals.

Secondly, GDP and GDP growth rate is expected to have a positive impact on the number of

DM&A deals, while a less clear impact on the number of CM&AO deals. DM&A has always

dominated the whole M&A activities historically in terms of both deals’ volumes and values.

This is due to the fact that investors feel more familiar with home countries economic, regulatory

14

Data extracted from Datastream at Tilburg University, currently price, not seasonally adjusted and in local

currency RMB. 15

In Deng and Yang (2014)’s study, the nine emerging markets are: Brazil, China, India, Indonesia, Mexico, Russia,

South Africa, Thailand and Turkey.

13

and cultural environment. As a result, investors might prefer targets in home country over the

ones in a foreign country. On the other hand, “higher GDP may result in higher level of cash

reserve in the hands of firms which may encourage them to acquire companies abroad (Boateng,

Hua, Uddin and Du, 2014)”. Last but not least, investors acquiring firms abroad also help to

reduce their overall portfolios’ risk via diversification. Thus, the impact of GDP and GDP

growth rate on the number of CM&AO deals is less clear.

It is worth mentioning that, due to the seasonality issue of the normal GDP growth rate, GDP

growth rate (moving average)16

instead will be considered as an independent variable in the

regression analysis in the next chapter. Based on the discussion above, the first hypotheses are

formed:

Hypothesis 1: the relationship between GDP and CM&AI will be positive.

Hypothesis 1.1: the relationship between GDP and DM&A will be positive.

Figure 3 & 4 & 5

The three figures below plot the values of Chinese nominal GDP (hundreds of millions Chinese Yuan), normal GDP growth rate,

and the moving average GDP growth rate for the whole sample period from the first quarter in 1992 to the last quarter in 2013. It

can be seen that nominal GDP is increasing sharply overtime; GDP growth rate is volatile but roughly within range of +30% to -

30%; and moving average GDP growth rate declined sharply in the period from moving average 5 to moving average 15, which

is between 1993 Q2 and 1996 Q3.

16

GDP growth rate moving average is calculated as MA1=Average (Q1+Q2+Q3+Q4), MA2=Average

(Q2+Q3+Q4+Q5), etc., there are 84 MA in total, the last one MA84=Average (Q85+Q86+Q87+Q88).

14

15

B. Inflation – expected inflation in six months and realized inflation.

Inflation rate17

is considered one of the most important macroeconomic factors and significantly

affects investors’ decision when making an investment. A few existing studies have done

research in investigating the relationship between inflation and M&A activities.

Boateng, Hua, Uddin and Du (2014) found a negative relationship between inflation18

and the

volume of CM&AO for UK firms during the period 1990 to 2008. The size of the coefficient

shows that CM&AO will decrease by 1.47% if CPI increases by one unit. However, the result is

not statistically significant. Furthermore, Black (2000) investigated the M&A activities with a

sample of at least one party being an American company in an M&A deal between 1985 and

1999. The result suggests that inflation rate is negatively related with the growth of CM&A deals.

In this study, the motivation for taking inflation as one of the independent variables is as follows:

Firstly, inflation rate is expected to have a negative impact on the number of CM&AI and

DM&A deals. A higher inflation rate in home economy makes the domestic targets more

expensive, thus decrease the willingness of foreign investors as well as domestic investors to

acquire domestic targets. On the other hand, a lower inflation rate in home economy makes the

domestic targets less expensive, thus attracts investors from foreign countries as well as domestic

investors to acquire domestic targets.

Secondly, inflation rate is expected to have a positive impact on the number of CM&AO deals. A

high inflation rate does not only make the domestic targets more expensive, but also decreases

the return on investments. Therefore, investors will seek targets from foreign countries instead,

which will increase the volume of CM&AO deals.

In this study, both the realized inflation rate and the expected inflation rate in six months will be

considered independent variables. The reason for also taking expected inflation in six months as

an independent variable is due to the fact that on average for M&A it takes six months between

the announcement date and the effective date. Therefore, it is essential to look at the expected

inflation rate as well. Based on the discussion above, the following hypotheses are formed:

Hypothesis 2: the relationship between inflation rate and CM&AI will be negative.

Hypothesis 2.1: the relationship between inflation rate and CM&AO will be positive.

Hypothesis 2.2: the relationship between inflation rate and DM&A will be negative.

17

Data extracted from Datastream at Tilburg University, average value and not seasonally adjusted. 18

Inflation rate used in study Uddin, Boateng (2011) is the absolutely value of CPI (with 1996=100).

16

Figure 6 & 7

The figures below plot the values of realized inflation rate as well the expected inflation rate in six months of China between the

first quarter of 1992 and the last quarter of 2013. It can be seen that both the realized inflation rate and expected inflation rate in

six months are very volatile and fluctuate a lot overtime.

17

C. Real lending rate.

Interest rate measures the cost of financing, thus it is considered one of the most important

macroeconomic factors and significantly affects investors’ decision when making an investment.

A few existing studies have investigated the relationship between interest rate and M&A

activities.

Tolentino (2010) investigated the relationship between macroeconomic variables in the home

country and outward foreign direct investment (FDI) using a sample of Chinese and Indian data

between 1980 and 2006. He found a statistically significant and positive relationship between

interest rate and the value of FDI in these two countries. However, Uddin and Boateng (2011)

found the opposite relationship. They claimed that interest rate19

has a statistically significant (at

10% level) and positive impact on the number of CM&AO deals in UK firms between 1987 and

2006. The size of the coefficient suggests that 1% increase in interest rate will lead to 0.28%

increase in the number of CM&AO deals. On the other hand, he found a negative relationship

between interest rates and the volume of CM&AI, but without a considerable statistically

significant level. In this study, the motivation of considering interest rate one of the independent

variables is as follows:

Firstly, interest rate is expected to have a positive impact on the number of CM&AI deals and a

negative impact on the number of CM&AO deals. A low interest rate at the home country means

that capital is abundant and return on investment is low, thus it motivates the domestic investors

to acquire targets overseas where capital is less abundant. The opposite, a high interest rate at

home country signifies that the capital is scarce and return on investments are high, thus it

attracts foreign companies to acquire domestic Chinese companies which will lead to a higher

CM&AI deals in China.

Secondly, real lending rate20

is used as a proxy for real interest rate in this study due to the

availability of data. Based on the discussion above, the following hypotheses are formed:

Hypothesis 3: the relationship between Real Lending Rate and CM&AI will be positive.

Hypothesis 3.1: the relationship between Real Lending Rate and CM&AO will be negative.

19

The interest rate used in Uddin and Boateng (2011) is the percentage interest rate on 3 month UK treasury bills. 20

Data extracted from Datastream at Tilburg University, end of period data, corrected from inflation rate.

18

Figure 8

The figure below shows that the real lending rate of China over time and it can be seen that real lending rate fluctuates a lot in

sample 1 (1992 Q1-2004 Q4). Real lending rate arrives at peak at the end of 1995 and beginning of 1996 with 12.06%, after the

first quarter in 1996, it decreases dramatically to 5.58% in the last quarter of 2004. In sample 2 (2005 Q1-2013 Q4), real lending

rate is more stable at an average level of 6.07%.

D. Climate for foreign investment – legal system.

The climate for foreign investment - legal system21

reflects a country’s law, norms, culture and

policy, so it is an important macroeconomic factor that can significantly affect investors’

decision on M&A activities.

Research done by Erel, Liao and Weisbach (2012) points to the motivation that “corporate

governance arguments predict that firms in countries that promote governance through better

legal or accounting standards will tend to acquire firms in countries with lower-quality

governance”. Furthermore, Changqi and Ningling (2010) suggest that governance is an important

factor determining the performance of the company in the economy. More specifically, it was

mentioned that state owned shares are much larger than the public shares in China, and indicates

that China has a unique political and economic setting, thus it is necessary to investigate the

impact of governance on the number of M&A deals in China. In this study, the motivation of

considering climate for foreign investment – legal system one of the independent variables is as

follows:

21

Data extracted from Datastream at Tilburg Unviersity.

19

Climate for foreign investment – legal system is expected to have a positive impact on the

number of CM&AI and DM&A deals. A better legal system in home country will bring synergy

from M&A and attract investors to acquire domestic firms. Therefore, it will lead to more

CM&AI and DM&A deals. On the other hand, a better legal system in the home country makes

the domestic targets more attractive to investors. Therefore it is expected to have a negative

impact on the number of CM&AO deals. Based on the discussion above, the following

hypotheses are formed:

Hypothesis 4: the relationship between Climate for Foreign Investment - legal system and

CM&AI will be positive.

Hypothesis 4.1: the relationship between Climate for Foreign Investment - legal system and

CM&AO will be negative.

Hypothesis 4.2: the relationship between Climate for Foreign Investment - legal system and

DM&A will be positive.

Figure 9

The figure below plots the value of climate for foreign investments (FI) – legal system of China between the first quarter in 1992

and the last quarter in 2013. It can be seen that on average the value of sample 2 (2005 Q1-2013 Q4) is higher than sample 1

(1992 Q1-2004 Q4).

20

E. Political stability.

In addition to the macroeconomic factors discussed above, another important one is political

stability22

. A stable political environment leads to less political risk in the home country thus

giving foreign and domestic investors more confidence in doing long - term business in China.

There are a few existing studies that investigated the relationship between political stability and

M&A activities.

Pajunen (2008) investigated the relationship between foreign direct investment and the different

degrees of membership in different institutions. The sample covers 47 countries between year

1999 and 2003. He found that a positive relationship between political stability, political rights,

civil liberties and foreign direct investment. Moreover, Visic and Peric (2011) used political

stability as one of the six indicators23

to measure governance. They found a positive and

significant relationship between governance and the total value of CM&AI. In this study, the

motivation of considering political stability one of the independent variables is as follows:

Political stability is expected to have a positive impact on the number of CM&A and DM&A

deals. China is one of the few communist countries in the world has its own unique political

setting. As discussed in chapter 1, after the economic reform in 1978, Chinese enterprises’ focus

changed dramatically from being heavily controlled by the state to more private-owned.

Especially after the second stage of reform around 1990s, further polices have been implemented

by reducing trade barriers, relaxing trade regulations and etc. As a result, foreign direct

investment and DM&A have increased significantly in this period. At the same time, by opening

up the border and encouraging free trade with foreign countries, also more and more Chinese

domestic companies are willing to acquire targets from foreign countries. Based on the

discussion above, the following hypotheses are formed:

Hypothesis 5: the relationship between political stability and CM&AI will be positive.

Hypothesis 5.1: the relationship between political stability and CM&AO will be positive.

Hypothesis 5.2: the relationship between political stability and DM&A will be positive.

22

Data extracted from Datastream at Tilburg University. Political stability is ranked from a scale 1 to 9, 1 means

high level of political instability and 9 means the opposite, the data was collected via sending out surveys . 23

World Bank analyzed the following governance indicators, (1) voice and accountability, (2) political stability and

absence of violence/terrorism, (3) government effectiveness, (4) regulatory quality, (5) rule of law, and (6) control

of corruption, Visic and Peric (2011).

21

Figure 10

The figure below plots the value of political stability of China between the first quarter of 1992 and the last quarter of 2013. It

can be seen that Chinese political stability fluctuates overtime, but roughly within a range of 4 to 7.

F. Exchange rate RMB/USD

Exchange rate24

has always been considered one of the most dominant factors influencing

investors taking CM&A decisions. Many scholars have discussed the relationship between

exchange rate and CM&A activities.

Uddin and Boateng (2011) investigated the impact of macroeconomic variables on CM&A

volumes in UK from the period 1987 to 2006. They found that the exchange rate (British Pounds

against a basket of world currencies) has a statistically significant (at 5% level) and positive

impact on the number of CM&AO deals in UK. More specifically, the size of the coefficient

suggests that if British Pounds against a basket of world currencies appreciates by 1%, this will

lead to 1% increase in the number of CM&AO deals. Furthermore, The Economist (August 5,

2010) pointed out an interesting phenomenon that the amount of CM&A deals that Japanese

firms took as an acquirer increased significantly during the period of the Japanese Yen’s

24

Data extracted from Datastream at Tilburg University. Average value of monthly data.

22

appreciation in the summer of 2010. In this study, the motivation of considering political stability

one of the independent variables is as follows:

Exchange rate (RMB/USD) is expected to have a positive impact on the number of CM&AI

deals and a negative impact on the number of CM&AO deals. When exchange rate (RMB/USD)

goes higher, that means more RMB can be exchanged with the same amount of US Dollars. Thus

it attracts foreign investors to acquire domestic Chinese companies as they become cheaper. On

the other hand, when exchange rate (RMB/USD) drops, it means that less RMB can be

exchanged with the same amount of USD. When RMB appreciates and become more expensive,

the foreign targets will become cheaper, which will motive the domestic Chinese companies to

acquire foreign targets. Based on the discussion above, the following hypotheses are formed.

Hypothesis 6: the relationship between exchange rate (RMB/USD) and CM&AI will be positive.

Hypothesis 6.1: the relationship between exchange rate (RMB/USD) and CM&AO will be

negative.

Figure 11

The figure below plots the exchange rate (RMB/USD) during period from the first quarter in 1992 to the last quarter in 2013. It

can be seen that in sample 1 there is a big jump in the first quarter in 1994, 50% increase compared with the last quarter in 1993,

afterwards stay at the level until 2004 Q4. In Sample 2, the exchange rate (RMB/USD) decreases gradually overtime.

23

Chapter 4 Methodology

The methodology of this study includes three steps: Dickey – Fuller test, perfect collinearity test

and univariate & multivariate time - series linear regressions test. In this chapter, these three

steps of econometric analysis and the reasoning are explained in detail.

4.1 Dickey-Fuller test

To start with, Dickey-Fuller test25

will be carried out to test the stationarity at 5% level of all the

independent and dependent variables. The reason for carrying out this test is: the data of

macroeconomic factors in this study are time series data. Due to the rapid economic growth in

China after the economic reform, high growth rate of the macroeconomic factors are expected.

Therefore, it is necessary to take the dickey-fuller test to detect unit root issue. Boateng, Hua,

Uddin and Du (2014) rendered support by taking the same approach. The below table 4 summarizes

the results of Dickey-Fuller test. Noteworthy to mention, the whole sample will be divided into

two sub-samples, sample 1 - from 1992 to 2004 with 52 quarters, and sample 2 - from 2005 to

2013 with 36 quarters.

Table 4 & 5: Dickey - Fuller Test of independent and dependent variables for stationarity (at 5% level).

Sample 1: 1992-2004 Reject non-stationarity p-value Reject non-stationarity p-value

(drift only) (trend+drift)

CM&AI (detrended) Yes 0,04

CM&AO (detrended) Yes 0,00

DM&AO (detrended) Yes 0,00

Expected inflation (6m) Yes 0,01

Climate for FI-legal system Yes 0,03

GDP growth Yes 0,01

Political stability Yes 0,03

Real lending rate No 0,90 No 0,62

Exchange rate Yes 0,01

GDP (detrended)/1000 Yes 0,00

Realized inflation (differenced) Yes 0,00

25

Knowledge extracted from course Empirical methods in Finance in Master in Finance program.

24

Sample 2: 2005-2013 Reject non-stationarity p-value Reject non-stationarity p-value

(drift only) (trend+drift)

CM&AI (detrended) Yes 0,00

CM&AO (detrended) Yes 0,01

DM&AO (detrended) Yes 0,00

Expected inflation (6m) Yes 0,02

Climate for FI-legal system Yes 0,02

GDP growth Yes 0,01

Political stability Yes 0,02

Real lending rate No 0,39 No 0,69

Exchange rate Yes 0,01

GDP (detrended)/1000 Yes 0,00

Realized inflation (differenced) Yes 0,00

For dependent variables, only CM&AO is stationary with the original data, CM&AI and DM&A

are not stationary with the original data in sample 1. In sample 2, only CM&AI is stationary with

the original data, while neither CM&AO nor DM&A is stationary with the original data. For the

purpose of consistency, all M&A data in sample 1 and sample 2 will be taken the detrended

value.

For independent variables in both samples, expected inflation in six months, climate for FI -

legal system, exchange rate, GDP growth rate and political stability are already stationary with

the original data, GDP data is stationary after taking the detrended value, realized inflation data

is stationary after taking the differenced value. The decision to choose between detrending and

differencing is based on comparing their P values. Last but not least, real lending rate which is

considered a proxy for real interest rate in this study is not stationary with the original data.

However, for the sake of economic intuition and explanation, the original data of real lending

rate will be considered one of the independent variables in the time – series regressions analysis

in the next chapter.

4.2 Perfect collinearity test.

The correlation between each independent variable and dependent variable as well as their P

values are tested to see whether perfect collinearity is an issue in this study or not.

Table 6 and table 7 summarize the test results of the correlation between each independent and

dependent variable for sample 1 and 2. In addition, their corresponding P value is also added in

bracket. Noteworthy to mention, all the independent variables are two quarterly lagged. The

reason is explained later in section 4.3. From the tables below it can be concluded that perfect

collinearity is neither an issue in sample 1 nor sample 2.

25

Table 6 & 7:

Correlation Matrixes of sample 1 and 2: explanatory variables are 2 quarterly lagged. The numbers in brackets are

the corresponding p-values.

Sample 1: 1992-2004 1 2 3 4 5 6 7 8 9 10 11

1. CM&AI (detrended) 1,00

(1,00)

2. CM&AO (detrended) 0,09 1,00

(0,53) (1,00)

3. DM&A (detrended) 0,82 0,05 1,00

(0,00) (0,73) (1,00)

4. GDP Growth 0,18 -0,24 0,25 1,00

(0,22) (0,10) (0,10) (1,00)

5. GDP (detrended)/1000 0,23 -0,15 0,18 0,19 1,00

(0,10) (0,30) (0,20) (0,21) (1,00)

6. Expected Inflation (6m) 0,28 0,10 0,31 -0,17 -0,21 1,00

(0,05) (0,48) (0,03) (0,27) (0,15) (1,00)

7. Realized Inflation (differenced) 0,11 -0,17 0,13 0,06 -0,03 0,38 1,00

(0,44) (0,25) (0,37) (0,71) (0,82) (0,01) (1,00)

8. Real Lending Rate -0,05 -0,12 -0,02 0,75 0,29 -0,46 -0,11 1,00

(0,71) (0,39) (0,91) (0,00) (0,04) (0,00) (0,44) (1,00)

9. Exchange Rate 0,05 0,01 0,05 0,33 -0,06 0,17 0,01 0,16 1,00

(0,73) (0,96) (0,71) (0,03) (0,69) (0,26) (0,96) (0,27) (1,00)

10. Political Stability 0,06 0,40 -0,01 -0,39 -0,06 0,31 0,34 -0,35 0,02 1,00

(0,66) (0,00) (0,94) (0,01) (0,68) (0,03) (0,02) (0,01) (0,92) (1,00)

11. Climate for FI-legal system 0,31 0,00 0,28 0,42 0,15 0,24 0,06 0,26 0,24 -0,13 1,00

(0,03) (0,99) (0,05) (0,00) (0,31) (0,09) (0,70) (0,07) (0,10) (0,38) (1,00)

26

4.3 Univariate and Multivariate time - series linear regressions.

After solving the unit root issue and tested that perfect collinearity does not exist in the data set

of this study, univariate and multivariate time - series linear regressions are carried out to test the

relationship between macroeconomic and M&A variables.

Firstly, all the independent variables are taken the lagged 2 (6 months) values. The reasons for

taking this approach is due to the fact that on average it takes 6 months between an M&A’s

announcement date and the effective date. Visic and Peric (2011) rendered support for this

approach. They investigated the relationship between macroeconomic variables and the value of

CM&AI deals in European transitional countries during period 1994 to 2008, and found that a

number of lagged value of macrocosmic variables (including GDP per capita, GDP growth rate,

interest rate and governance index) have statistically significant impact on the value of CM&AI

deals.

Secondly, the univariate and multivariate time – series linear regressions for sample 1 and 2 are

formed as follows. In other words, sample 1 and 2 are treated as two independent data sets at this

stage. For the multivariate time – series linear regressions, the mathematical models are formed

as follows:

Sample 2: 2005-2013 1 2 3 4 5 6 7 8 9 10 11

1. CM&AI (detrended) 1,00

(1,00)

2. CM&AO (detrended) 0,35 1,00

(0,05) (1,00)

3. DM&A (detrended) 0,51 0,60 1,00

(0,00) (0,00) (1,00)

4. GDP Growth 0,28 0,14 0,20 1,00

(0,12) (0,42) (0,26) (1,00)

5. GDP (detrended)/1000 -0,13 -0,11 -0,10 0,16 1,00

(0,48) (0,52) (0,59) (0,35) (1,00)

6. Expected Inflation (6m) 0,35 0,35 0,39 0,47 -0,10 1,00

(0,04) (0,04) (0,02) (0,01) (0,59) (1,00)

7. Realized Inflation (differenced) 0,48 0,28 0,33 0,37 -0,08 0,65 1,00

(0,00) (0,11) (0,06) (0,03) (0,65) (0,00) (1,00)

8. Real Lending Rate -0,19 -0,07 -0,03 0,62 0,06 0,03 0,07 1,00

(0,29) (0,71) (0,85) (0,00) (0,74) (0,87) (0,72) (1,00)

9. Exchange Rate 0,02 -0,02 -0,13 -0,63 -0,14 -0,22 -0,22 -0,75 1,00

(0,93) (0,91) (0,48) (0,00) (0,45) (0,22) (0,21) (0,00) (1,00)

10. Political Stability 0,34 0,53 0,51 0,08 -0,27 0,29 0,21 -0,26 0,00 1,00

(0,06) (0,00) (0,00) (0,68) (0,13) (0,11) (0,26) (0,15) (0,97) (1,00)

11. Climate for FI-legal system 0,10 -0,04 0,13 -0,17 -0,22 0,05 0,07 -0,30 0,30 -0,07 1,00

(0,57) (0,84) (0,48) (0,35) (0,23) (0,78) (0,70) (0,09) (0,10) (0,69) (1,00)

27

volumecmai_detrendedt = αt-2 + β1*detrended_gdpcnyt-2 + β2*inflation_6mt-2 +

β3*differenced_realizedinflationt-2 + β4*reallendingratet-2 + β5*climateforfit-2 +

β6*politicalstabilityt-2 + β7*GDPgrowth_ratet-2 + β8*exchange_rate t-2 + εt-2

volumecmao_detrendedt = αt-2 + β1.1*detrended_gdpcnyt-2 + β2.1*inflation_6mt-2 +

β3.1*differenced_realizedinflationt-2 + β4.1*reallendingratet-2 + β5.1*climateforfit-2 +

β6.1*politicalstabilityt-2 + β7.1*GDPgrowth_ratet-2 + β8.1*exchange_rate t-2 + εt-2

volumedma_detrendedt = αt-2 + β1.2*detrended_gdpcnyt-2 + β2.2*inflation_6mt-2 +

β3.2*differenced_realizedinflationt-2 + β4.2*reallendingratet-2 + β5.2*climateforfit-2 +

β6.2*politicalstabilityt-2 + β7.2*GDPgrowth_ratet-2 + β8.2*exchange_rate t-2 + εt-2

Where t denotes time. The definition of dependent variables and independent variables are as

follows:

volumecmai_detrended: detrended value of CM&AI volume.

volumecmao_detrended: detrended value of CM&AO volume.

volumedma_detrended: detrended value of DM&A volume.

detrended_gdpcny: detrended value of nominal GDP measured in local currency (RMB).

inflation_6m: value of expected inflation rate in 6 months.

differenced_realizedinflation: differenced value of realized inflation rate.

differenced_reallendingrate: differenced value of real lending rate (corrected from inflation).

climateforfi: present value of climate for foreign investment - legal system in China.

politicalstability: present value of political stability in China.

GDPgrowth_rate: the moving average rate of GDP growth rate.

exchange_rate: RMB to USD, quarterly data, average amount.

Thirdly, to test the whether sample 2 is different from sample 1, next step we will combine all

observations of sample 1 and 2 into one big sample and do the following regression:

y_m&a = α0, t-2 + α 1, t-2 *D + β0, t-2 *X + β1, t-2 *X*D + εt-2

Where dummy = 1 for sample 1 (1992 – 2004) and dummy = 0 for sample 2 (2005 – 2013).

Therefore, β0 measures the impact of macroeconomic variables (lagged two quarters) on M&A

volumes in sample 2 while using the whole data set. β1 measures the differences between the

impact of macroeconomic variables (lagged two quarters) on M&A volumes in sample 1 and 2.

28

Chapter 5 Results & Discussion

Univariate time-series regressions will firstly be carried out in two separate samples (1&2) and

the total combined sample (with Dummy variable). Secondly, three multivariate time-series

regressions will be carried out. The first multivariate time-series regression only contains the base

independent variables which are GDP, expected inflation in six months, realized inflation, real

lending rate and exchange rate. The second multivariate time-series regression adds two

governance factors (climate for FI - legal system and political stability) on top of the base

independent variables. The last multivariate time-series regression includes all independent

variables (base variables and the two governance factors and GDP growth rate).

5.1 Univariate time-series regression analysis.

Table 8 summarizes the results of univariate time-series regression for two separate samples (1&2)

and the big combined sample (with dummy variable).

(a) For the two separate samples, the following independent variables are statistically significant:

GDP growth rate, GDP, expected inflation in six months, realized inflation, political stability and

climate for FI - legal system. The interpretations of these statistically significant coefficients are

discussed in detail in the following paragraphs.

Firstly, GDP growth rate (moving average) has statistically significant and negative impact (at 10%

level) on CM&AO in sample 1, and has statistically significant and positive impact (at 10% level)

on DM&A in sample 1. Both signs are in line with the expectation. The coefficients show that a

one standard deviation increase in GDP growth rate (moving average) will lead to nearly 1.6 more

DM&A deals in sample 1, and 0.1 less CM&AO deals in sample 1. Although GDP growth rate

(moving average) is statistically significant, economically the significance level is little.

Secondly, GDP has statistically significant (at 10% level) and positive impact on CM&AI, which

is in line with hypothesis 1 discussed in chapter 3. The coefficient of 0.001 shows that a one

standard deviation increase in GDP, will lead to 42 more CM&AI deals in sample 1. Table 1 in

chapter 2 shows that the average number of CM&AI deals across the years is nearly 63, therefore,

GDP’s impact on CM&AI is not only statistically significant, but also economically significant.

Thirdly, the two inflation factors expected inflation in six months and realized inflation have quite

a number of statistically significant results. However, all the signs except for one are the opposite

compared with expectations. More specifically, expected inflation in six months has positive and

statistically significant impact (at 5% level) on CM&AO in sample 2. The coefficient shows that a

standard deviation increase in expected inflation in six months will lead to 3.13 more deals in

CM&AO. Compared with the average number of CM&AO deals (16.95) across years, the result

is both statistically and economically significant.

29

Fourthly, political stability has statistically significant and positive impact as hypothesized on all

three M&A groups in sample 2, and one statistically significant result in group CM&AO in

sample 1. All signs are in line with the hypothesis 5 – 5.2 in chapter 3. For example, the

coefficient of political stability on DM&A in sample 2 shows that one standard deviation increase

in political stability will lead to 41.04 more DM&A deals in sample 2. Compared with average

DM&A which is 220.16 deals across years, the coefficient is not only statistically significant but

also economically significant.

Furthermore, climate for FI-legal system has statistically significant and positive impact on group

CM&AI and DM&A in sample 1. The signs of climate for FI - legal system on CM&AI and

DM&A are in line with the hypothesis. The coefficient shows that one standard deviation increase

in this independent variable will lead to nearly 4 more CM&AI more deals in sample 1.

Compared with average CM&AI which is nearly 63 deals, the coefficient is statistically

significant but not much economically significant.

(b) For the combined sample, all the independent variables except exchange rate have statistically

significant impact on M&A volumes. In addition, the sizes of the coefficients are mostly in line

with the ones in the analysis of two separate samples discussed above. Particularly, GDP growth

rate and political stability are highly statistically (at 1% level) and economically significant.

Different from the analysis of two separate samples discussed above, GDP growth rate has a more

influential impact on the number of CM&AI deals in the combined sample. The coefficient shows

that 1% increase in GDP growth rate will lead to 9.71 more CM&AI deals in sample 2, compared

with the average number of CM&AI deals (62.88) across years, the result is both statistically (at 1%

level) and economically significant. Moreover, the coefficient that measures the different impact

of GDP growth rate on the number CM&AI deals in two samples is also highly statistically

significant. The negative sign shows that the impact of GDP growth rate by increasing 1% will

lead to 7.48 more deals in sample 2 than in sample 1.

In a similar vein, the impact of political stability on the number of DM&A deals is much bigger

than the one in separate samples’ analysis. More specifically, the coefficient shows that one

standard deviation increase in political stability will lead to 60 more deals in DM&A in sample 2,

which is nearly 1.5 times more than the impact in the two separate samples’ analysis. It is worth

mentioning that the coefficient that measures the different impact of political stability on the

number DM&A deals in two samples is also highly statistically significant. The negative sign

shows that the impact of political stability by increasing one standard deviation will lead to 69

more deals in sample 2 than in sample 1.

Last, exchange rate does not only appear to be statistically insignificant in this univariate time-

series regressions analysis, but also mostly with the opposite signs compared with the expectation.

30

31

5.2 Multivariate time - series regression analysis (with only base independent

variables).

Table 9 summarizes the results of multivariate time - series regression analysis (with only base

independent variables) for two separate samples (1&2) and the big combined sample (with

dummy variable).

(a) For the two separate samples, only two independent variables were found statistically

significant in group CM&AI and one was found in group DM&A. The interpretation of these

statistically significant coefficients will be discussed in detail in the following paragraphs.

Firstly, GDP has positive and statistically significant impact (at 5% level) on CM&AI in sample 1.

The sign of the coefficient is in line with hypothesis 1 discussed in chapter 3. The size of this

coefficient is exactly the same with the univariate regression summarized in table 8 and it shows

that a one standard deviation increase in GDP, will lead to 42 more deals increase in CM&AI in

sample 1, which is in line with the results in existing literature. Boateng, Hua, Udding and Du

(2014) investigated the impact of key macroeconomic variables on CM&AO in UK over the

period 1987 - 2008 using quarterly data, and found that GDP-1 is statistically significant (at 10%

level) with a coefficient of 0.0015 on a multivariate time-series regression. Similarly, Erel, Liao

and Weisbach (2012) investigated 56978 CM&A deals across 48 countries over the period 1990 -

2007 and found that GDP per capita has statistically significant and positive impact on the number

of CM&A deals in all target - all acquirer group with a coefficient of 0.004 in a panel analysis.

Secondly, realized inflation has statistically significant (at 10% level) and positive impact on

CM&AI in sample 2. The sign of the coefficient is not in line with hypothesis 2 discussed before.

The size of the coefficient shows that one standard deviation increase in realized inflation will

lead to 12.4 more CM&AI deals in sample 2. Compared with average volume of CM&AI across

years which is 62.88, the coefficient is both statistically and economically significant. Existing

literature usually found negative relationship between inflation and CM&A. Black (2000)

investigated M&As which involved at least one US party over the period 1985 - 1999, and found

that inflation rate is negatively related with the growth of CM&A. Boateng, Hua, Uddin and Du

(2014) also found a statistically significant and negative relationship between inflation26

and the

volume of CM&AO for UK firms over the period 1990 - 2008, the coefficient shows that

CM&AO will decrease by 1.47% if CPI increases by one unit.

Last but not least, expected inflation in six months also has positive and statistically significant

impact on DM&A and CM&AI in sample 1. The signs of the expected inflation are not in line

with hypothesis either. The size of the coefficient shows that one standard deviation increase in

expected inflation in six months will lead to 25.76 more DM&A and 4.3 more CM&AI deals in

sample 1. The possible reason that both expected inflation in six months and realized inflation 26

Inflation rate used in study Uddin, Boateng (2011) is the absolutely value of CPI (with 1996=100).

32

have opposite signs compared with hypothesis is: China’s economy is developing rapidly with

many unstable factors after the economic reform. A high domestic inflation rate can somehow be

reflected as an over-heated economy. Therefore it motivate the domestic investors to make

investment instead of keeping the money as money is losing value, which might lead to high

M&A activities.

(b) For the combined sample, among all the macroeconomic variables, only two inflation factors –

expected inflation in six months and realized inflation were found statistically significant in group

CM&AI and CM&AO. The results are mostly in line with the analysis of two separate samples.

To begin with, the impact of realized inflation on the number of CM&AI deals in sample 2 is

exactly the same compare with the one in separate samples’ analysis. Moreover, the coefficient

that measures the different impact of realized inflation on the number CM&AI deals in two

samples is statistically significant at 5% level. Dummy variable does play a role here by saying

that the impact of realized inflation by increasing one standard deviation will lead to 16.15 more

CM&AI deals in sample 2 than in sample 1.

Next to realized inflation, expected inflation in six months is statistically significant at 10% level

in group CM&AO. By combining two separate samples into one big sample, the coefficient

shows that one standard deviation increase in expected inflation in six months will lead to nearly

3 more CM&AO deals in sample 2. Table 1 in chapter 2 shows that the average number of

CM&AO deals across years is 16.95. Therefore, here the coefficient is both statistically and

economically significant.

Last, exchange rate continues to be statistically insignificant in all groups. The possible cause for

this is the structure of banking governance in China which is very different compared with most

countries in the world. More specifically, the central bank of China does not have full

independence but instead it is heavily regulated by the government. As a result, exchange rate is

not liquid in the capital markets and appears to be statistically insignificant correlated with the

number of CM&A deals.

33

34

5.3 Multivariate time - series regression analysis (with base independent

variables plus two governance factors: climate for FI - legal system and

political stability).

Table 10 summarizes the results of multivariate time - series regression analysis (including base

independent variables and two governance factors: climate for FI-legal system and political

stability) for two separate samples (1&2) and the big combined sample (with dummy variable).

(a) For the two separate samples, the independent variables which appear to be statistically

significant are GDP, expected inflation in six months, realized inflation, political stability and

climate for FI-legal system. The interpretation of these statistically significant coefficients will be

discussed in detail in the following paragraphs.

Firstly, GDP has exactly the same sign and size with the univariate time-series regression

summarized in table 8 and the multivariate time-series regression with base independent variables

summarized in table 9. The size of the coefficient shows that a one standard deviation increase in

GDP, will lead to 42 more deals increase in CM&AI in sample 1. More literatures have been

found which support this finding. European Economy (September 2004) investigated the

determinants of European CM&A covering 17 countries in Europe and US over the period 1991 -

2001, and found that GDP per capita is positively related with the volume of CM&A and

statistically significant at 5% level, the coefficient shows that 1% increase in GDP per capita will

lead to 0.352 more CM&A deals. Furthermore, Study done by Udding and Boateng (2011) found

statistically significant (at 5% level) and positive relationship between GDP and CM&AI.

Secondly, expected inflation in six months has statistically significant (at 10% level) and positive

impact on DM&A in both sample 1 and sample 2. The signs of the coefficients are the opposite

compared with hypothesis 2.2 discussed in chapter 3. The sizes of the coefficients are similar

compared with the regression results above in table 8 and 9. For example in sample 2, one

standard deviation increase in expected inflation in six months will lead to 28.2 more DM&A

deals. Compared with the average amount of 220.16 DM&A deals across the years, the regression

result is both statistically and economically significant.

Thirdly, realized inflation has statistically significant (at 1% level) and negative impact on

CM&AO. The sign of the coefficient is not in line with hypothesis in chapter 3. And the size of

coefficient shows that one standard deviation increase in realized inflation will lead to 3.5 less

CM&AO deals in sample 1. Compared with the average amount of 16.95 CM&AO deals across

the years, the regression result is both statistically and economically significant.

The regressions results show that realized inflation and expected inflation in six months have

opposite impact on M&A activities, similarly, existing studies on investigating the impact of

inflation on CM&A also show mixed findings. As discussed above, Boateng, Hua, Uddin and Du

35

(2014) found a statistically significant and negative relationship between inflation and the volume

of CM&AO for UK firms over the period 1990 - 2008. On the other hand, Zhang and Daly (2011)

investigated the impact of macroeconomic and environmental factors on CM&AO in China

between 2003 and 2006. They found a positive relationship between inflation and CM&AO

activities. However, the results are not statistically significant.

Last but not least, political stability appears to be highly statistically significant (at 1% level) with

both CM&AO and DM&A. Climate for FI-legal system also has statistically significant (at 5%

level) and positive impact on DM&A in sample 2. The coefficients signs are in line with

hypothesis stated in chapter 3. For example, a one standard deviation increase in political stability

will lead to 6.1 more CM&AO deals in sample 2, compared with average amount of CM&AO

across years which is 16.95 deals, the regression result is both statistically and economically

significant. Furthermore, existing study of Erel, Liao and Weisbach (2012) also found a positive

relationship between the quality of institution, accounting disclosure and the number of CM&A

deals. However, the coefficient size of 0.045 in their study is much smaller. The possible reason is,

China has a very unique political setting has been through rapid economic growth, thus the

enterprise structure is less mature compared with developing countries. Therefore, it is reasonable

to have bigger coefficients size compared with the study from Erel, Liao and Weisbach (2012)

which covered 56978 deals from 48 countries from 1990 to 2007.

(b) For the combined sample, among all the macroeconomic variables, GDP, expected inflation in

six months, political stability and climate for FI – legal system are statistically significant. The

signs and sizes of these coefficients are mostly in line with the analysis of two separate samples.

Noteworthy to mention, the different impact of GDP and the two governance factors on the

number of M&A deals in sample 1 and sample 2 are both economically and statistically

significant.

Firstly, the coefficient of GDP on the number of CM&AO deals in combined sample shows that if

GDP increases by one standard deviation, the number of CM&AO in sample 2 will increase 20

more deals than in sample 1. Table 1 in chapter 2 shows that the average number of CM&AO is

16.95 deals across years, therefore, the result here is not only statistically significant at 10% level

but also economically significant.

Secondly, among the two governance factors in combined sample, the coefficient of political

stability on DM&A in combined sample shows that if political stability increases by one standard

deviation, the number of DM&A in sample 2 will increase 66 more deals than in sample 1.

Moreover, if climate for FI – legal system increases by one standard deviation, the number of

DM&A in sample 2 will increase nearly 50 more deals than in sample 1. Based on the discussion

above, it is safely to draw the conclusion that the different impacts of these two governance

factors on sample 1 and 2 are both statistically and economically significant. Last but not least,

although some signs of the exchange rate coefficients match with hypothesis 6 – 6.1, statistically

it continues to be insignificant in this multivariate time-series regression analysis.

36

37

5.4 Multivariate time - series regression analysis (with all independent

variables).

Table 11 summarizes the results of multivariate time - series regression analysis with all

independent variables for separate samples (1 & 2) and combined sample.

(a) For the two separate samples, the independent variables appear to statistically significant are

GDP growth rate (moving average), GDP, real lending rate, political stability and climate for FI -

legal system. The interpretation of these statistically significant coefficients will be discussed in

detail in the following paragraphs.

Firstly, GDP growth rate (moving average) has statistically significant and positive impact on

CM&AI and DM&A. The signs of the coefficients are in line with hypothesis discussed in

chapter 3. The size of the coefficient shows that one standard deviation increase in GDP growth

rate (moving average) will lead to nearly 2 more CM&AI deals in sample 2 and 3.6 more

DM&A deals in sample 1. Similarly, an existing study Erel, Liao and Weisbach (2012) found

that the relationship between GDP growth and CM&A deals is positive, but with a much smaller

coefficient of 0.058. Deng and Yang (2014) investigated the major factor determining CM&A in

nine emerging markets from 2000 to 2012, and found positive relationship between GDP growth

and number of CM&A deals. However, the results are not statistically significant.

Secondly, real lending rate has a statistically significant (at 10% level) and negative impact on

CM&AI (detrended) in sample 2. The sign of the coefficient is the opposite as hypothesis stated in

chapter 3. The size of the coefficient shows that one standard deviation increase in real lending

rate will lead to 24.3 less CM&AI deals in sample 1. The average amount of CM&AI deals across

years is 62.88. Therefore, the regression result is both statistically and economically significant.

Research done by Uddin, Boateng (2011) found negative relationship between interest rates and

the volume of CM&A, but without serious significance level.

Thirdly, political stability continues to be highly statistically significant (at 1% level) in both

CM&AO and DM&A. And the sizes of coefficients are similar as the ones presented in table 11.

Besides the supportive finding by Erel, Liao and Weisbach (2012) that a positive relationship was

found between the quality of institution, accounting disclosure and the number of CM&A deals,

Deng and Yang (2014) also found a positive relationship between governance effectiveness and

the number of CM&A deals. Furthermore, Rossi and Volpin (2004) investigated the governance

determinants of M&A in 49 major countries during period 1990 to 2002 and found that the

relationship between accounting standard, shareholder protection and the number of M&A deals

is highly significant. However, the sizes of the coefficients are much smaller than this study

presented in table 11. The possible reason is: China has a very unique political setting. The

Chinese Party is very centralized and usually has much power in influencing the decisions of

38

enterprise. Therefore, it is reasonable to have bigger coefficients size compared with the other

studies.

(b) For the combined sample, among all the macroeconomic variables, GDP growth rate (moving

average), realized inflation, real lending rate, political stability and climate for FI – legal system

are statistically significant. The signs and sizes of these coefficients are mostly in line with the

analysis of two separate samples. The interpretations of these statistically significant coefficients

are discussed in detail in the following paragraphs.

Firstly, the coefficient of GDP growth rate on the number of CM&AI deals in combined sample

shows that if GDP growth rate increases by 1%, the number of CM&AI in sample 2 will increase

9.48 more deals. Furthermore, the coefficient of the different impact of GDP growth rate on the

number of CM&AI deals in two samples shows that the number of CM&AI deals in sample 2 will

increase 6,89 more than in sample 1 while GDP growth rate (moving average) increasing by 1%.

Secondly, for the first time real lending rate appears to be statistically significant (at 5% level) in

group CM&AI in the combined sample’s analysis. To be more specifically, the coefficient of real

lending rate in group CM&AI shows that if real lending rate increases by one standard deviation,

the number of CM&AI in sample 2 will decrease nearly 30 deals. This result is both statistically

and economically significant, however, the sign is the opposite compared with hypothesis 3. On

the other hand, if real lending rate increases by one standard deviation, the number of CM&AI in

sample 1 will increase 27 more deals than in sample 2.

Thirdly, the impacts of the two governance factors in combined sample are statistically and

economically significant. In addition, the sizes of the coefficients are consistent with the other

three analysis presented in the tables above.

It is worth mentioning that, different from the other three tables above, two inflation factors

(expected inflation in six months and realized inflation) appear to be less statistically significant

in this multivariate time - series regression. Last but certainly not least, exchange rate continues

to be statistically insignificant in all the four time-series regressions presented above.

39

40

Chapter 6 Conclusions & Recommendations

This study investigates the impact of macroeconomic factors on the number of M&A (including

CM&AI, CM&AO, and DM&A) deals in China using two separate sub - samples (1992-2004 and

2005-2013) and one combined big sample.

The results of time - series regressions in two separate samples appear to have similar findings

with each other. GDP has statistically significant and positive impact on the number CM&AI

deals. GDP growth rate (moving average) has statistically significant and positive impact on the

number of CM&AI and DM&A deals. GDP growth rate (moving average) is found to be

negatively correlated with the number CM&AO deals, which is in line with the hypothesis,

however, without any statistically significant level. Furthermore, the two governance factors

political stability and climate for FI - legal system are highly statistically significant and are

positively related with CM&AO and DM&A, which have more mixed findings than expected.

Climate for FI - legal system is expected to be positively correlated with the number of CM&AI

and DM&A deals, and negatively correlated with the number of CM&AO deals. However, the

regressions results are found to be positively correlated with all three M&A groups (CM&AI,

CM&AO and DM&A). In addition, the two inflation factors are mostly statistically significant

and positively related with the number of M&A deals, which are the opposite of what were

expected. A possible explanation could be that China is developing rapidly with many unstable

factors - a high domestic inflation rate can motivate the domestic investors to go overseas and

seek opportunities, thus this can explain the positive sign on the number of CM&AO deals that

Chinese firms undertook. Last but not least, exchange rate is not significant in any of the

regressions above, neither with the expected signs. The possible cause for this is the lack of

independence of the central bank of China.

The results of time - series regressions in one big combined sample mostly are in line with the

analysis of two separate samples in terms of sizes and signs of the coefficients. It is also worth

mentioning that the changes of independent variables mostly result in a bigger impact on sample

2 than in sample 1. Particularly these four following variables: GDP, GDP growth rate, political

stability and climate for FI – legal system. The empirical results reflect well the significant

impacts of the economic and regulatory policies which were implanted by Jiang Ze Min and Zhu

Rong Ji (the successors of Deng Xiao Ping) around 2005 to further encourage foreign direct

investments into China and promote international trade.

The existing research about the impact of macroeconomic factors on M&A activities in China

and developing countries is still underdeveloped and this research is the first study investigating

the relationship between only macroeconomic indicators and M&A activities in China from the

open-up policy (after the second stage) in 1992 till 2013. For developing countries like China,

the economic and regulatory environments are less mature than developed countries. At the same

41

time, the country’s economy is growing at a rapid speed. Therefore, it is important for the

government to play a key role in stabilizing the economy by implementing right policies,

meanwhile giving enough freedom for the market to grow and adapt to the new environment.

Lastly for further analysis, more advanced econometric models can be used. For example

integration model, can be used for detecting cointegration and common trends. It is also worth

mentioning that, in order to have a better representation of the developing markets other

countries could join the analysis such as Brazil, Russia and India. The limitations of this analysis

could be addressed to further research.

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