Horizontal Merger and Acquisition of Listed Companies and Its Empirical Research

ORIENT ACADEMIC FORUM
Horizontal Merger and Acquisition of Listed Companies and Its
Empirical Research
ZHOU Lin
School of Business Administration, Jiangxi University of Finance and Economics, China, 330013
Abstract: This paper study the horizontal mergers and acquisitions of listed companies in China
through factor analysis. The conclusions show that listed companies don’t improve their financial
conditions from the horizontal merger.
Keywords: Horizontal merger and acquisition, Factor Analysis, Financial Index
1 Introduction
Throughout the history of merger and acquisition of enterprises in the world, the first wave of merger in
western countries took horizontal merger and acquisition as the main form and till now it has happened
frequently with increasingly large scale. What drives enterprises to do horizontal merger and acquisition?
From the perspective of the history of the enterprises’ growth, horizontal merger and acquisition speeds
up the development of the enterprises, creates a large number of world-class enterprises and greatly
improves the enterprises’ level of economies of scale.
Before 1970s, most western scholars believed that the main reason for enterprise horizontal merger and
acquisition is to pursue scale economies effect. From the point of view of nature of the assets, horizontal
merger and acquisition is a superposition of homogenous assets. Under certain conditions, horizontal
merger and acquisition can enlarge the scale of the enterprise and increase the production factors so that
the scale profits increase. Specifically, the expansion of enterprise scale and the increase of production
facilities and labor force lead to that a large number specialized production has been achieved so
efficiency is enhanced while cost is reduces. It is a comparatively popular point of view in China at
present.
Foreign scholars’ study on horizontal merger and acquisition focuses on analysis on the horizontal
merger and acquisition happens in a specific industry. Jenny and Weber (1980) studied forty horizontal
mergers and acquisitions in France of the period between 1962 and 1972 and the result showed that the
profitability of the acquiring company decreased in eight years which are respectively four years before
and after merger and acquisition. Rhoades (1998) generalized the research reports of 1990s on the
banking performance after mergers and acquisitions and found that the cost, efficiency, profit
performance of banks had no prominent improvement after mergers and acquisitions. Becher (2000)
summed up the related statistics about mergers and acquisitions of banking industry. The results showed
that in the events of horizontal merger and acquisition, the performance of targeted companies was
obviously positive number while the performance of buyout companies was indistinct (sometimes
positive and sometimes negative), but there were little studies on the performance of the combined
companies. Delong (2001) found that the value of shares of those specializing in (business) activities
and geographic expansion increased by 3%, while the mergers and acquisitions of other types of banks
did not create value. Pesendorfer (2003) found that the mergers and acquisitions of papermaking
industry resulted in an increase of total welfare in mid 1980s.
There are also some domestic scholars doing empirical analysis on the performance of acquiring listed
companies, but only the following two literary works mentioning performance evaluation of horizontal
merger and acquisition. Fang Fang and Yan Xiaotong (2002) selected the related financial data of the
companies between 1999 and 2001 with the mergers and acquisitions happened in 2000 as the main line,
to analyze comprehensively the evaluations before, during and after mergers and acquisitions. They used
factorial analysis to respectively analyze the index of the year of mergers and acquisitions and one year
before and after mergers and acquisitions, as well as the performance of different types of mergers and
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ORIENT ACADEMIC FORUM
acquisitions (horizontal, vertical and mixed). The analysis on the 46 mergers and acquisitions showed
that in one year after the mergers and acquisitions, the overall performance was on the rise so the
mergers and acquisitions worked well. Lei Xinghui and Zhang Qi (2002) studied the 11 sample
companies undergoing mergers and acquisitions in 2000 in Shanghai Stock Exchange, and analyzed the
influence of mergers and acquisitions on performance using mode of constant rate of returns. They
found that the average abnormal return rate on the date of the public announcement of horizontal merger
and acquisition is 0.0051 having no significance. In addition, the other average abnormal return rates
and accumulate abnormal returns had no significance in the observation period, either. Therefore, they
believed that horizontal merger and acquisition had no impact on the value of companies.
Literature study shows that so far there have been no specific studies on transverse associated party
merger and acquisition performance. In addition, there are imperfection in the researches of horizontal
merger and acquisition, for example, the sample companies for study are too few; some researches on
abnormal return only calculate the share prices of the date of public announcement and study on merger
and acquisition incidents insufficiently; some researches do not exclude other factors which have impact
on the performance of acquiring companies in merger and acquisition incidents… Thus, in this paper,
we will use financial factor analysis method and more rigorous sample filtering method to try to exclude
other interfering factors so that it can reflect the functions and impacts of horizontal merger and
acquisition on Chinese listed companies more accurately.
2 Research Methods
Factorial analysis is a statistical method using a smaller number of factors to describe the connection
among a large number of indexes or factors and to reflect the most part of original information. The first
step of this method is to select a few factors to analyze on the basis of analysis of a large number of
indexes.
The mathematical model of factorial analysis is:
 x1 = a11 F1 + a12 F2 + L + a1m Fm + a1ε 1
x = a F + a F + L + a F + a ε
 2
21 1
22 2
2m m
2 2

L
 x p = a p1 F1 + a p 2 F2 + L + a pm Fm + a p ε p

x1 , x 2 , L x p are p original variables and standardized variables whose mean value is zero and
standard deviation is one. F1 , F2 L Fm are m factorial variables. M is smaller than p and the matrix is:
X = AF + aε . F is factorial variable or general factor and can be understood as m orthogonal
coordinate axes in higher dimensional space. A is factor loading matrix and aij is factor loading and
the load of i-th original variable on j-th factorial variable. A brief explanation of some concepts is as
follows.
1. Factor loading. In the case that all the factorial variables have no connections with each other, factor
loading aij is the correlation coefficient of the i-th original variable and the j-th factorial
variable—relative importance of xi on the j-th general factorial variable. Thus, the bigger the absolute
value of a ij is, the stronger the relation between the general variable F j and the original variable
xi is.
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2. The common degree of variables. It reflects the explanation proportion of the total variance of all
general factorial variables to the original variables. The common degree of the original variable xi is
the sum of squares of the i-th line factors in the factor loading matrix: hi =
2
m
∑a
2
ij
. The variance of
j =1
the original variable xi can be expressed in two parts: hi
2
and
ε i 2 . The first part hi 2
reflects the
explanation proportion of the variance of the general factors to the original variables and the second part
εi2
reflects the part which can not be expressed by the general factors in the variance of the original
variable. Therefore, if the first part hi
2
is closer to one, the general factors will explain more
information of the original variables. We can grasp how much information of this variable loses
according to this value. If the common degrees of most variables are higher than 0.8, it can be concluded
that the extracted general factors have reflected over 80% of the information of the original variables
and only a little information loses. In other words, the effect of the factorial analysis is good. So the
common degree of each variable is a index for measuring the effect of the factorial analysis.
3. The variance contribution of the general variable F j -- the sum of squares of the j-th line factors in
p
the factor loading matrix A: S j =
∑a
2
ij
, reflects the explanatory ability of this factor to the total
i =1
variance of all the original variables and the bigger the better. There are two core questions in factorial
analysis: one is how to construct factorial variable and the other one is how to name the factorial
variables for explanation. The solution is divided into five steps. Firstly, determine whether the original
variables suitable for factorial analysis. The main method is KMO inspection and when KMO<05, the
original variable is not suitable for factorial analysis. Secondly, construct factorial variables. Thirdly,
make the factorial variables more explanatory by rotation. Fourthly, calculate the results of factorial
variables: F j = β j1 x1 + β j 2 x 2 + L + β jp x p , (j 1 2 …m). Finally, further calculate the
＝，，
evaluation scores according to factor scores.
3 Selection of Samples
This paper selects 37 listed companies between 1998 and 2002 as samples for study on horizontal
merger and acquisition and the data source is Wind Financial Database. Shanghai Industrial
Pharmaceutical Investment Co., Ltd. did two times of horizontal merger and acquisition in 1999 and
2000 respectively. Shanghai Energy did two times of horizontal merger and acquisition in 2001 and
2002 respectively. These samples of horizontal merger and acquisition cover 17 industries and they are
textile, metallic and nonmetallic, petrochemicals, water, coal, power, social service, information
technology, pharmaceuticals, food, electronics, transportation and warehousing, real estate, mining,
mechanical equipment, construction, wholesale and retail trade, farming, forestry, animal husbandry and
fishery, papermaking and printing, all of which are numbered 1-17 in the following figure. The specific
distribution can be seen in figure one:
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ORIENT ACADEMIC FORUM
横向并购样本行业分布
4
4
4
4
3
3
3
2 2
2
22
2
1
1
1 11 11
1
0
1 3 5 7 9 11 13 15 17
Figure 1: sample distribution
We can see that most horizontal mergers and acquisitions happen in water, coal and power,
pharmaceuticals and mechanical equipment industries. It meets with the motive of horizontal merger
and acquisition because the biggest purpose of horizontal merger and acquisition is to achieve scale
effect. Water, coal and power industry is the industry with most prominent scale effect, followed by
textile industry and transportation and warehousing industry.
4 Empirical Analysis
In this paper we select 12 original financial indexes for study and they are earnings per share, current
ratio, quick ratio, assets liabilities ratio, net asset value per share, net assets earning ratio, ratio of profits
to assets, net profit margin of sales, cost ratio of main business, rate of stock turnover, account
receivable turnover ratio, turnover rate of circulating assets. But in fact, there are strong correlations
among many financial indexes. If there is no proper processing, the results are not scientific. The factors
reserved after correlation analysis are earnings per share, current ratio, assets liabilities ratio, net profit
margin of sales, cost ratio of main business, rate of stock turnover, account receivable turnover ratio,
turnover rate of circulating assets.
Firstly, we use Bartlett’s Test of Sphericity and KMO Test for the samples and see whether they can be
implemented factorial analysis. The result is:
Table 1 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of
Sampling Adequacy.
Bartlett's Test of Sphericity
0.583
Approx. Chi-Square
392.162
df
28
Sig.
0.000
KMO value is 0.583 and is smaller compared with that of vertical merger and acquisition, which means
it is not suitable for factorial analysis. But the testing value of Bartlett’s Test of Sphericity is bigger and
is 392.162, and its corresponding profitability value is zero smaller than the significance level. So null
hypothesis fails and the relevant data in this sample cannot be identity matrix. In other words, there are
correlations among the original variables and it is suitable for factorial analysis. So the results of
factorial analysis are:
Table 2 Total Variance Explained
Initial
Eigenvalues
Extraction Sums of
Rotation Sums of
Squared Loadings
Squared Loadings
Component Total % of VarianceCumulative %Total % of VarianceCumulative %Total % of VarianceCumulative %
1
2.601
32.517
32.517
2.601
32.517
32.517
1.786
22.321
22.321
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2
1.496
18.695
51.213
1.496
3
1.028
12.853
64.066
1.028
4
0.986
12.327
76.393
0.986
5
0.830
10.374
86.768
0.830
6
0.548
6.850
93.617
7
0.297
3.710
97.327
8
0.214
2.673
100.000
Extraction Method: Principal Component Analysis.
18.695
12.853
12.327
10.374
51.213
64.066
76.393
86.768
1.629
1.487
1.031
1.008
20.369
18.587
12.886
12.605
42.690
61.277
74.163
86.768
The results of the five factorial variables selected are good because the eigenvalues of them is more than
0.8 and the variance contributions which are the indexes measuring the importance of factors are over
10%. The total variance of the five factors takes up 86.768% of the variance of the original variables,
which shows that these factor are representative. To obtain the specific economic implication of these
factors, we use varimax rotation method for factor loading matrix and the results are as follows:
Table3 Rotated Component Matrix
Component
1
2
3
4
5
Earnings per share
0.317
-0.397
0.734
0.147
3.422E-02
Current ratio
0.905
Assets liabilities ratio
-0.740
0.299
-0.199
0.161
-1.821E-03
Net profit margin of sales
0.494
-0.679
0.303
0.113
0.100
Cost ratio of main business
-7.779E-02
0.932
0.131
Rate of stock turnover
-3.147E-02 -0.260 2.459E-02 -1.292E-02
8.127E-02 1.115E-02
-6.324E-04-2.528E-022.664E-02-8.574E-03
0.998
Account receivable turnover ratio -6.628E-02 2.374E-02 7.364E-03 0.978 -8.626E-03
0.223
0.855 -8.644E-02 1.578E-02
Turnover rate of circulating assets -0.252
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 8 iterations.
Rotated Component Matrix shows that the first factor reflects current ratio and assets liabilities ratio; the
second reflects net profit margin of sales and cost ratio of main business; the third reflects earnings per
share and turnover rate of circulating assets; the fourth reflects account receivable turnover ratio; the
fifth reflects rate of stock turnover and the last two factors only represent one original index. We can
also see from the rate of variance contribution that turnover rate of circulating assets, account receivable
turnover ratio and rate of stock turnover contribute a little because the first and second factors are most
important. That is to say these four indexes are more representative of the company’s financial status.
Finally, the computing formula of factor scores by rate of variance contribution is
Z = fac1 × 0.32517 + fac2 × 0.18695 + fac3 × 0.12853 + fac4 × 0.12327 + fac5 × 0.10374 .
Pair
Mean
Table 4 Paired Samples Test
Paired Differences
95% Confidence Interval of the
Std.
Std. Error
Difference
Deviation
Mean
Lower
Upper
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t
df
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ORIENT ACADEMIC FORUM
Pair 1
Pair 2
Pair 3
Pair 4
BG1 - BG2 0.10295
BG2 - BG3 5.0792E-02
BG3 - BG4 1.1330E-02
BG4 - BG5 -1.27708E-02
0.52743
0.33200
0.21739
0.27845
8.6709E-02
5.4581E-02
3.5738E-02
4.5777E-02
-7.29063E-02
-5.99036E-02
-6.11510E-02
-0.10561
0.27880
1.187 36
0.16149
0.931 36
8.3811E-02 0.317 36
8.0069E-02 -0.27936
0.243
0.358
0.753
0.782
We can see from the table that the testing values of the four paired samples T are very small but the
probabilities are big. That is to say the original assumption that they have no prominent differences
stands. It illustrates that these four paired samples have no prominent differences and in other words,
horizontal merger and acquisition does not improve company’s financial status.
5 Conclusion
This paper studies the horizontal mergers and acquisitions of listed companies in China through factor
analysis and 37 listed companies between 1998 and 2002 as samples, which shows that listed companies
don’t improve their financial conditions from the horizontal merger in short-term period. This
conclusion is as same as that of Lei Xinghui and Zhang Qi (2002). They studied the 11 sample
companies undergoing mergers and acquisitions in 2000 in Shanghai Stock Exchange, and analyzed the
influence of mergers and acquisitions on performance using mode of constant rate of returns. They
found that the average abnormal return rate on the date of the public announcement of horizontal merger
and acquisition is 0.0051 having no significance. In addition, the other average abnormal return rates
and accumulate abnormal returns had no significance in the observation period, either. Therefore, they
also believed that horizontal merger and acquisition had no impact on the value of companies.
References
～
[1]. Becher, D., 2000, Evaluation Effects of Bank Mergers, Journal of Corporate Finance 6.189 214.
[2]. DeLong G., 2001, Stockholder Gains from Focusing Versus Diversifying Bank Mergers. Journal of
Financial Economics, vol: 59, 22l 252.
[3]. Pesendorfer, M., 2003, Horizontal Mergers in the Paper Industry, RAND Journal of Economics, vol:
34.495 5l5.
[4]. Fang Fang, Yan Xiaotong, 2002, An Empirical Study of Acquisition Performance of Chinese Listed
Companies. Beijing: Economic Theory and Business Management(in Chinese)
[5]. Lei Xinghui, Zhang Qi, 2002, An Analysis on Model of Constant Rate of Return and Corporate
Acquisition Performance. Shanghai Management Science. (in Chinese)
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