Impact of Mergers and Acquisitions on Stock Price in Shenzhen A … · 2021. 1. 22. · Zijun (Katherine) Geng M.A. Economics, University of Victoria, 2020 An Extended Essay Submitted

1

Impact of Mergers and Acquisitions on

Stock Price

in Shenzhen A-Share Stock Market

by

Zijun (Katherine) Geng

M.A. Economics, University of Victoria, 2020

An Extended Essay Submitted in Partial Fulfillment

of the Requirements for the Degree of

MASTER OF ARTS

in the Department of Economics

We accept this extended essay as conforming

To the required standard

________________________________________________________________________

Dr. Pascal Courty, Supervisor (Department of Economics)

________________________________________________________________________

Dr. Ke Xu, Member (Department of Economics)

2

Abstract

Mergers and Acquisitions are commonly used for firm expansion and industrial

upgrade. Studies on the stock price effect of mergers and acquisitions have never stopped

since 1930s. This study focuses on the impact of mergers and acquisitions in Shenzhen A

stock market, aiming at measuring the abnormal returns and cumulative abnormal returns

for firms that made merger or acquisition announcement in 2019 using event study. The

study discovers value creation for the acquiring firms immediately after the merger and

acquisition announcement, but quickly disappear three days after the event. The abnormal

return is 1% in day 1 post-event and -1% in day 3 post-event under 10% significant level.

This contributes to the existing literature by investigating M&A effect on acquirers in

Shenzhen A-Stock market, which aims at the leading firms of traditional industries in

China. This will extend previous literature about the limited stock market respond on M&A

announcements effect to large size acquiring firms.

Keywords: Mergers and Acquisitions; Shenzhen A-stock market; China; Event study

________________________________________________________________________

3

Table of Contents

1. Introduction………………………………………………………………………..4

2. Literature review…………………………………………...……........................ ..6

3. Background data and methodology……………………………...………………..8

3.1. Methodology………………………………………………………………….....10

3.2. Event day and event window estimation…………………………….….……....10

3.3. Measurement of ARs and CARs…………………………………………….…..12

4. Empirical results………………………………………………………………....14

4.1. ARs for the overall A stock market……………………………………………..16

4.2. CARs for the overall A stock market……………………………………………17

4.3. Sign test………………………………………………………………………….17

4.4. Individual event analysis………………………………………………………...18

5. Conclusion ………………..……………………………………………………..19

Figure…………………………………………………………………………….20

Table……………………………………………………………………………..21

Appendix…………………………………………………………………………29

References………………………………………………………………………..37

4

1. Introduction

Mergers and acquisitions (M&As) 1 are two of the commonly used business

strategies for firm expansion, which enables industries and businesses to take advantage of

economies of scale and improve their market competitiveness. In 2019, the global M&A

volume has reached to $4.09 trillion (Dealogic, 2020). China started merging and acquiring

activities slow at the end of the twentieth century, but soon caught up and its M&A volume

ranked second only to America in 2010 (Economist, 2011).

The main reason for firms to merge or acquire lies under the potential growth of

market share firms may obtain and the restriction opportunity to extend industrial chain.

Cost efficiency is another reason for M&A application, interior production can reduce

transaction cost and production cost. Acquiring firms expect to occupy more market

capacity by actively absorbing new firms, thus identify whether there is value created or

damaged by the M&A is essential to both the acquiring firms and their shareholders. Since

efficient market hypothesis often applies in the financial market, we expect stocks trading

under the reflection of its fair value, thus stock return is a good indicator for M&A effect.

This paper evaluates the stock price effect of M&As in A-share firms in Shenzhen

stock market in 2019. An empirical model is built where abnormal returns (ARs) and

cumulative abnormal returns (CARs) are calculated to measure the change of stock price

1 Merger refer to two firms consolidate into one new firm and acquisition refer to less

positive and more aggressive takeovers. This paper studies the effect of both M&As to the

Chinese stock market, and do not make distinction between the two concepts.

5

after the announcement of merger is open to public. Student T-test and Sign test are also

applied to prove the significance of the value received by the acquirer. The results of the

study are expected to provide distinct evidence of value gained or lost by the transaction

of M&A for acquirers in Shenzhen A-share market.

Since most of the studies on M&A effect in China focused on the entire Chinese

stock market2, including growth enterprises market, small and medium stocks, the results

will not be representative of the reaction of Chinese leading companies on M&A effect.

Theoretically, small acquirers react slower to the M&A synergy than big acquiring firms,

since lager acquirers have bigger capacity and more experience to effectivity manage the

target firms (Park and Jang, 2011). This paper chooses the sample from Shenzhen

mainboard stock, which includes Chinese top listed firms with over RMB 300 million

accumulated revenue and aggregate net profit of RMB 30 million in the past 3 years

(Shenzhen Stock Exchange, 2018). We expect faster respond from the financial market for

these largely scaled firms than the reflection from previous studies on the entire Chinese

market.

Compared with Shanghai Exchange Market’s designated transaction where

investors were asked to find a delegate security company before transaction, free trading

in Shenzhen Stock Exchange plays a more active role in stimulating the market. According

to the results, our study of M&A effect for Shenzhen A-share market present more

significantly positive short-term reflection in the stock market than those using aggregated

Chinese stock market as their study sample. Value creation is captured in our event window

2 See for example Chi, Sun & Young (2011), Boateng, Qian & Tianle (2008), Gu and Reed (2013) among

others.

6

for two days after the M&A announcement. However, positive abnormal returns only last

for one day after the announcement in Gu and Reed (2013)’s study, and the result is not

significant.

We also find other evidence that Shenzhen A-share stock market is affected by the

M&A announcement, but the evidence is not as strong and convincing as the Student-T

Test for the abnormal return. For example, cumulative abnormal return remains positive

from day 1 after the announcement to day 9, reflecting the continuous value gain in

aggregate by the shareholders of acquirers, however the result is not significant enough to

make a strong statement. In fact, only 10 out of 23 acquiring firms received positive

abnormal return after the announcement within the (-10, 10) event window.

This paper poses the question of “will Shenzhen A-stock market observe the value

growth from M&A as more recent studies reported”, elaborates on the short period value

creation from M&A.

2. Literature review

Since the global rise of M&As activities in the twentieth century, much attention

has been directed to shareholder wealth and firm’s market power affected by M&A

activities. Jensen and Ruback (1983) examined 13 studies on the effects of takeovers on

the returns to both activity participants and asserted that corporate takeovers create profits

for target shareholders. However, they admitted that the results concerning the returns to

bidders are mixed, constituting an open issue for further research.

A few researches that explored the performance of acquiring firms in one specific

7

industry sector reported different results. According to Khanal, Mishra, and Mottaleb

(2014), both short run (with 4-day event window and 10-day event window) and long run

(with 60-day event window) stock price effect of M&As in ethanol-based biofuel industry

in the U.S. supported the positive response toward M&As for bidders. Whereas the event

study of the banking sector in Pakistan proved that bidders faced negative returns after

M&As, CARs for share price dropped after the event day (Rahman, Ali, & Jebran, 2018).

M&As do not lead to same direction of stock price fluctuation when the study

samples are of same industry sector in emerging markets. Goddard, Molyneux and Zhou

(2012), focusing on the impact of M&As in the banking sector in Asia and Latin America,

found that M&A transaction did not cause a loss to acquirer shareholders. This finding

does not agree with that of Rahman, Ali and Jebran (2018).

However, more studies have found observable value gained by the acquiring firms

in the 21th century. Alexandridis, Antypas and Travlos (2017) discovered the increase in

stock value on the acquiring side to a sizable scale. Significant value increase of 0.21% for

the acquiring firm was found in their study of a 3-day (-1, 1) announcement window during

2010 to 2015. Similar evidence is also available in Chinese oversea M&A studies through

recent two decades. By evaluating 27 overseas M&As events in China from 2000 to 2004,

Boateng, Qian, and Tianle (2008) found that M&As abroad create value to the acquirers.

Gu and Reed (2013) also found positive reaction towards M&As in the Chinese stock

market based on a similar study on cross-border acquisition between 1994 and 2008.

Unlike the well developed economies, China did not start corporate M&As until

1993. Despite the booming stock markets since late 1990s and the increasing M&A

transactions, studies on the M&As in China are very limited. In addition to the above

8

literature involving M&As in China, another study worth mentioning is done by Chi, Sun,

and Young (2011). By studying the acquiring firms of 1148 transactions in Shanghai Stock

Market and Shenzhen Stock Market from 1998 to 2003, they reported positive abnormal

returns before (6 months) and upon the announcements, and insignificant long-run

abnormal returns (6 months) after the announcements.

Shenzhen A-stock market contains over 400 top Chinese firms in the traditional

industry, However, to our knowledge, no studies have ever been reported to explore

specifically the stock price effect of M&As in Shenzhen A-share market. Even though our

sample size of 23 events is much lower than the 1148 transactions tested by Chi and his

peers, we narrow our geographic focus to present the M&A effect on firms holding on

average RMB 17.5 billion market value (Shenzhen Stock Exchange, 2018) and eliminates

the distraction of potential negative M&A effect by start-up firms and innovation firms.

We focus on the year 2019 since China Securities Regulatory Commission published

modification about regulation for Chinese listed company asset restructuring. The decision

was made to simplify restructure restriction. It canceled the requirement for net profit limit

to encourage corporate M&A. The present paper will analyze the influence of M&As on

the Shenzhen A stock market in 2019 to fill the gap and provide an addition to the limited

research on the market performance of M&As in China

3. Background data and methodology

The Chinese stock market is composed of firms from Shanghai Stock Exchange,

Shenzhen Stock Exchange and National Equities Exchange. It is an emerging market based

on retail investment and short-term business. Shenzhen stock exchange consists of three

9

investment types: mainboard, small and medium enterprise board, and growth enterprise

market, the enterprise scale of the listed firms in these boards decrease progressively.

In 2019, Chinese domestic M&A activities reached 1,705, which declined 27.7%

on year- to-year basis. However, the number of large volume transactions increased, 59

events with the transaction each over 7 billion RMB were reported in 2019, compared to

48 events with the same trading size in 2018. Within the 59 transactions, we collect 23

events with acquirers listed in Shenzhen A-share market as our sample. The detailed

information is shown in Table 1 where information of the M&As transaction including the

M&As announcement date, name of the bidding firms and their main business are listed.

Total asset of each target firm, shareholder equity of the target firm and target firm’s net

profit, are collected from RESSET Database. RESSET Database is a data platform which

provides professional financial transaction information that objectively reflects the Chinese

financial market for investment research. This database is designed by leading financial

experts from Tsinghua University and Peking University.

According to the Shenzhen stock exchange, there are 476 firms in Shenzhen A

share market. Compared with 1148 events in the overall Chinese stock market over 6 years

collected by Chi, Sun, and Young (2011), 23 M&As in one year for the Shenzhen stock

market is considered to be reasonable. To study the stock price effect of M&As in the

Chinese traditional mainboard stock market, we specify and focus on the Shenzhen A-share

market because it assembles the representative large-scale listed firms trading with RMB.

The sample of the study is reduced to 23 events on the following criteria.

1. The transaction was announced during the year 2019.

2. The target firm must be a Chinese domestic firm with total asset over billion.

10

3. The bidding firm is listed on the Shenzhen A-share Stock Exchange.

4. The data of financial transaction are available.

The stock price data were gathered from Netease Finance website, a Chinese

financial website tracking daily stock price of firms listed in Chinese stock market. Both

the opening price and the closing price for each transaction day in Shenzhen Stock

Exchange are provided in the website.

3.1. Methodology

We employ event study to analyze the impact of M&As on stock price and firm

value. Using ARs and CARs, we measure the difference between the actual returns and the

expected returns of a stock and calculate the summation of all ARs in the event window of

the study.

In addition, we conduct our short-term event study on three assumptions: 1)

Efficient market hypothesis is valid, so the financial market is effective and information

accessible to the public can be reflected in the stock market. 2) The events we study are

unexpected to the financial market, which is why we can use the abnormal return to

measure the response of stock market to the sudden event. 3) No other event occurs within

the event window dates. As a result, the interference terms can be removed and the mixed

effect eliminated.

3.2. Event day and event window estimation

11

To observe the change of stock price associated with M&As, this paper picks the

first trading day after the M&As announcement as the event date and sets it as t = 0. Event

window captures a certain period of time relevant to the research and frames the study

period of the event to measure and analyze the ARs and CARs for each sample stock. To

avoid the potential negative influence exerted by accidental asymmetric information

problem, the event window will include not only days after but days before the event.

Based on the time of duration for the event, the analytical method can be divided

into short-term event study and long-term event study (Brown & Warner, 1984). The

minimum window period selected by other studies is three days including one day before

and one day after the event. The maximum window period is over 810 days, which is 60

days before and 750 days after the event day (Tuch & O'Sullivan, 2007). Short-term event

study focuses on timely response, whereas long-term event study tends to pay more

attention to long term process and development. Even though yearly event study may

provide results for more comprehensive detail, it has multiple distractions. Compound daily

abnormal returns will have the risk of creating bias in the statistical result when event

window is set by months instead of by day (Brown & Warner, 1984). On the contrary,

daily event study offers a more straightforward and concise index for measuring the impact

of an event on firm’s wealth. Thus, we choose short-term event study to capture the stock

price effect of firm M&AS.

We choose the stock return of 10 days before and 10 days after the event date as

the event window for short-term event study, written as [t1, t2], with t1 < 0 and t2 > 0. In

this case, t1 = -10 and t2 = 10. This means to calculate the ARs and CARs for firms within

the 20 days around the M&As announcement. Given that most of the stock activities

12

happen closely around the announcement date, we further customize the event window into

pairs of matching groups starting from 5 days away from the event day, namely (-5, 0), (0,

5), (-4, 0), (0, 4), (-3, 0), (0, 3), (-2, 0), (0, 2), (-1, 0), (0, 1). We also test on individual

sample firm in event window of (-3, 0) and (0, 3) to analyze stock market behavior three

days before and three days after the event day.

3.3. Measurement of ARs and CARs

Because the stock price effect is hard to measure for the unlisted target firms, this

paper chooses public bidders as research objects. On the assumption that the events we

study are unexpected to the financial market, we can calculate the ARs for each listed firm

in each day within the event window. Given the opening price and closing price for the day,

the normal returns for each stock is calculated following the equation:

𝑅𝑖,𝑡 =𝑃𝑡−𝑃𝑡′

𝑃𝑡′ (1)

where 𝑅𝑖,𝑡 is the actual ex post return and 𝑃𝑡 , 𝑃𝑡′ represent the closing price at day t and the

opening price at day t respectively.

In this study, we use Shenzhen Stock Market Compositional Index as the indicator

for expected stock return of Shenzhen A stocks. Compositional Index of Shenzhen stock

market, marked as 399001, is an index of constituent stocks representing 40 typical firms’

weighted tradeable share price, reflecting the comprehensive stock price trend in the overall

Shenzhen stock market.

13

According to Brown and Warner (1980), different methodologies applied to

calculate AR will not lead to significant difference in result. The t-test results using Mean

Adjusted Returns, Market Adjusted Returns and Market and Risk Adjusted Returns

calculation shows almost not difference which is big enough to change the conclusion.

Using the market model, we use Single-Index Model and market-adjusted model to

calculate the stock return, in which the beta is referred as the degree of responsiveness to

the market return. It is written as:

𝑅𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝑚,𝑡 + 𝜀𝑖,𝑡 (2)

Single-Index Model is commonly used to calculate the expected market returns. The

expected return for a single stock is represented by the market index rate of return. The

calculation can be translated into the following equation:

𝐸(𝑅𝑖𝑡) = 𝑅𝑚𝑡 (3)

In market-adjusted model, we estimate normal return by assuming 𝛼𝑖 = 0 𝑎𝑛𝑑 𝛽𝑖 = 1,

therefore, the abnormal return will be the difference between the actual stock return and

the market return.

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝑅𝑚𝑡 (4)

𝐴𝑅𝑖𝑡 shows the abnormal return for stock i in day t. It calculates the difference between the

actual return of the stock and the expected return of the stock. Based on the Single-Index

Model, the equation can also be written as follows:

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝐸(𝑅𝑖𝑡) (5)

Because the major concern of this paper is the impact of M&As events on the entire stock

14

market over 21 days, it is essential to calculate the average abnormal return 𝐴𝐴𝑅𝑡 , the

cumulative abnormal return 𝐶𝐴𝑅𝑖(𝑡1,𝑡2) and the average cumulative abnormal return

𝐴𝐶𝐴𝑅𝑖(𝑡1,𝑡2). The equation is as follows:

𝐴𝐴𝑅𝑡 =1

𝑁∑𝑖=1

𝑁 𝐴𝑅𝑖𝑡 (6)

where N represents the number of firms and the equation takes the arithmetic mean of the

abnormal returns for N firms to reach approximation for abnormal return on average.

Unlike abnormal return, which reflects point-to-point difference in time for an

individual firm i, cumulative abnormal return covers the abnormal return over a period of

time from t1 to t2 for the same firm i.

𝐶𝐴𝑅𝑖(𝑡1,𝑡2) = ∑𝑡1𝑡2𝐴𝑅𝑖𝑡 (7)

On this basis, average cumulative abnormal return calculates the mean cumulative

abnormal return for N firms using time t1, t2 to define the length of event window.

𝐴𝐶𝐴𝑅(𝑡1,𝑡2) =1

𝑁∑𝑖=1

1 𝐶𝐴𝑅𝑖(𝑡1,𝑡2) (8)

4. Empirical results

The empirical study contains two parts. Part one examines the M&As impact on

the general mainboard Shenzhen A stock market, and part two details the M&As effect on

the individual acquirer.

Based on Table 2., we expect that events with bigger acquiring firm to acquired

firm ratio will have lager M&A effect reflected on stock return, since bigger acquiring

15

firms, theoretically, have more capacity to absorb firms with comparatively smaller size.

This logic seems to hold in this case.

The average mean returns for the individual sample stocks before and after M&As

are presented in Table 3. An increase in average return indicates a positive impact of M&As

events on the acquirer, whereas a decrease in average return shows a negative influence. In

Table 3, ten out of twenty-three of the firms (E2, E5, E6, E11, E12, E15, E16, E20, E21,

E22) experienced positive effect from M&As transactions, while the rest (E1, E3, E4, E7,

E8, E9, E10, E13, E14, E17, E18, E19, E23) show reduction in the mean returns. The result

indicates that over half of the bidding firms did not obtain satisfying financial returns after

M&As, which corroborates those of Chavaltanpipat, Kholdy, and Sohrabian (1999) and

Guest, Bild, and Runsten (2010), suggesting that M&As harm the benefit of stockholders

from the acquiring side. However, in general the ratio of positive and negative AARs are

almost even for the individual events. 11 sample stocks receive positive AARs and 12 of

the other stocks have negative AARs.

These results associate strongly with the value ratio of acquiring and acquired firms.

Event 4 with the highest value ratio of 209.34 has 0.86% AAR, which is second to the

strongest positive reflection of M&A announcement. And event 8 with the closest value of

acquiring and acquired firms, shows the strongest negative result of AAR equals to -1.33.

Noticeably, both the highest increase (39.0249%) and the greatest decrease (-

24.08%) in the stock returns are out of scale, which may be due to factors such as the size

of target companies, payment methods of the M&AS activities, etc.

16

4.1. ARs for the overall A stock market

The average abnormal return in each time period of the stock price is computed to

assess the effect of M&As on shareholders’ wealth before and after the M&As

announcement. Student T test is introduced to show the significance of the average

abnormal return. If the market is expected to receive 5% gain for the return, but increases

only 4% in reality, then the abnormal return would be -1% (4% - 5% = -1%). If the

significance level of the null hypothesis AAR=0 is 1%, 5% or 10%, we consider the result

of -1% of the abnormal return to be reliable.

Table 4 shows the result of AAR and its level of significance in both daily basis

and different event window periods. Even though AAR is generally positive after the

M&As announcement and negative before the announcement, the result for daily AAR is

not significant. The only reliable results are in day t = -3, t = 1 and t = 3 for the daily AAR.

At t = 1, AAR is positive with 10% significance; at t=-3 and t = 3, AARs are negative with

5% and 10% significance respectively.

Fig. 1 indicate the change of AAR in the given 21-day event window with the 95%

confidence interval below and above the estimated trend. Noise from the estimation sample

becomes smaller as the event day approach to (1, 3). Since confidence interval is affected

by variation and sample size, narrowed confidence interval reflects less dispersive data

distribution around the three days after M&A announcement, meaning that our result of

short-term value creation from M&A is reliable.

AAR result is more meaningful if we calculate the average mean abnormal return

over a period around the event date. We find that AAR is positive 2% at 10% significance

17

level, which is similar to the result we find in daily AAR calculation. AAR is 1% if we

consider the abnormal returns within the two days after M&As announcement, with 5%

significance. The AAR calculated by the sum of ARs over three days after the event

suggests that AAR is positive with only 1% possibility that null hypothesis will hold. The

fluctuation of AAR is also shown in Fig. 1. The result intuitively elaborates that M&As

increase the wealth of shareholders directly after the announcement and the effect of M&As

dribbles away as time stretches.

4.2. CARs for the overall A stock market

The average cumulative abnormal return captures the average adding-up change

over stock returns by days, if the market’s AR in t = 1 is 8% and AR in the next day is 3%,

then the CAR of the second day should be the summation of AR in both t = 1 and t = 2,

total of 11% (8% + 3% = 11%). The result showed in table 5 should also be marked at

significance level, however, none of the result is highly believable with noticeable

significance. For cursory reference, however, we also find positive ACAR after the event

announcement.

4.3. Sign test

We also use sign test to analyze whether the stock price change for bidders before

and after the M&As event is significant. The data sample is distributed into 10 pairs of

groups. Each pair of groups contains data from the dates with the same time distance from

the event date. For example, the group named (-10=10) covers the information of stock

returns from 10 days before to 10 days after the acquisition announcement. In Table 6, we

18

observe uneven numbers of positive and negative stock returns in each pair. Given such a

small sample, we can conclude that the volatility of stock price is recognizable, but this

conclusion is too weak to be our main supporting evidence.

4.4. Individual event analysis

Table 7 shows the results of ARs and CARs of the 23 events in a different

perspective. The mean average of ARs and CARs three days before and after the event for

each sample event is obtained. The difference in pre and post event stock returns is also

presented.

The difference in returns for both ARs and CARs before and after the event for

Event 3, 9, 10, 11, 13, 18, 19, 20, 22 shows negative results whereas that for Event 1, 2, 4,

7, 12, 14, 15, 16, 17 shows positive results. Besides, for Event 5, 8, 21, 23, the difference

in return is negative for ARs, but positive for CARs. On the contrary, the net return for

ARs is positive, while CARs negative for Event 6.

In addition, mixed results also exist for the sign of return on the announcement day.

Both ARs and CARs are reported as negative for Event 2, 12, 17. Furthermore, the results

indicate positive ARs and CARs for Event 3, 6, 13, 21. Event 1, 4, 8, 11, 14, 15, 20, 22

report only negative CARs on the event day, while Event 5, 7, 9, 10, 16, 18, 19, 23 report

only negative ARs on the announcement day. The numerical results of the trend for stock

return can also be plotted in to line graph, unfolded as Fig. 3 to Fig. 25 in Appendix. In

order to capture as much information as we can, these graphs include changes over the full

event window, (-10, 10).

19

5. Conclusion

The study analyzes the stock effect of M&As in Shenzhen A stock market in 2019

by catching the unexpected stock return in terms of ARs and CARs for the selected firm

stocks. The main result state on value created in short period immediately after M&A

announcements, the M&As stimulate the stock price, which is reflected by a quick but

transitory rise in ARs and CARs. Significant evidence is found in change over AR for one-

and two- and three-day event window right after the event date, and the 95% confidence

interval narrowed down in the same short period after the announcement.

However, the influence of M&A events upon the value of individual firm is hard to

predict and no trend to follow. In some cases, M&As may not bring profit to the bidder and

its shareholders. However, there still exist some opportunities to gain profit from the trade

because some of the acquirers receive positive stock outcome from the M&As. The

combination of the good and bad stock return after the M&As announcement suggests the

importance of prudence to those in stock market when M&As are involved. Bidders should

make careful and cautious investigation and survey before conducting the M&As process.

This study explores the impact of M&As on stock price using data from a less

studied Shenzhen A stock market in China. As some of the previous researches that reached

divergent, even opposite conclusions concerning the stock price effect of M&As, our study

finds some inconsistency in the conclusions. This inconsistency may be caused by multiple

factors including differences in time, industry sector, country, etc. Therefore, further

research with divergent focuses should be conducted.

20

Figures

Fig. 1. Average Abnormal Return With 95% Confidence Interval

Note: Fig. 1 shows the general fluctuation of average abnormal return over the 23 merger and acquisition

events in the event window of (-10, 10), with the 95% confidence interval.

Fig. 2. Average Cumulative Abnormal Return Fluctuation

Note: Fig. 2 shows the general fluctuation of average cumulative abnormal return over the 23 merger and

acquisition events in the event window of (-10, 10). Calculation refer to equation (8).

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

AR CI (lower) CI (upper)

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

-15 -10 -5 0 5 10 15

21

Tables

Table 1. 23 Merger and Acquisition Events in the Shenzhen A Stock Market of China in

2019

Event Bidders

Main Business

Date of

M&As

E-1 Henan shuanghui investment development co., LTD Processed meat 1/26/2019

E-2 Shenzhen humei group co., LTD Clothing 3/4/2019

E-3 Zhejiang longstone technology co., LTD Industrial stainless

steel 3/12/2019

E-4 Anhui jinhe industrial co., LTD Food additives 3/19/2019

E-5 Fujian SAN agricultural trade exhibition co., LTD Chicken processing 3/27/2019

E-6 Contact food co., LTD Snack food 4/12/2019

E-7 Shenzhen kodali industrial co., LTD Automotive

structure 4/13/2019

E-8 Multi-affection group co., LTD Textile 4/16/2019

E-9 Pedestrian and high business chain co., LTD Supermarket 4/20/2019

E-10 Zhejiang wanan science and technology co., LTD Vehicle control

system 4/25/2019

E-11 Zhejiang fangzheng motor co., LTD Automobile engine 4/29/2019

E-12 Guangzheng group co., LTD Eye care system 5/6/2019

E-13 Zhejiang dongjing electronic co., LTD Quartz crystal

component 5/25/2019

E-14 Dragon python, baililian group co., LTD Fine chemical

engineering 6/13/2019

E-15 Ningxia yinxing energy co., LTD New power

generation 6/19/2019

E-16 Tai chi computer co., LTD

Software and

information

technology services

7/24/2019

E-17 Guangzhou honga CNC machinery co., LTD Equipment

manufacturing 7/29/2019

E-18 Tianmao industrial group co., LTD Investment holding 8/27/2019

E-19 Jiangsu fishho medical equipment co., LTD Medical apparatus

and instruments 8/28/2019

E-20 Wuxi weifu high-tech group co., LTD Auto parts

manufacturing 9/26/2019

E-21 China federation co., LTD

Construction

machinery

manufacturing

10/31/2019

E-22 Guangyu group co., LTD Real estate

development 11/1/2019

E-23 Yantai and new materials co., LTD High-performance

fiber 11/2/2019

Note: There are 22 non-investment holding firms and 1 investment holding firm, Tianmao industrial group

co., LTD.

22

Table 2. Value ratio for acquiring firms to acquired firms

Value of acquiring firm on

the announcement day

(Million)

Value of acquired firm

(Million)

Ratio for acquiring to

acquired firm

5,501.4 22,864.5 0.24

57.5 4,944.2 0.01

4,055.9 25.4 159.64

6,927 33.1 209.34

17,140.1 596.8 28.72

2,484.1 271.6 9.15

442.7 95.7 4.63

159.2 68,629.4 0.00

425.8 16.9 25.26

762.3 97.2 7.84

415.1 68.4 6.07

840.7 135.1 6.23

10.6 1,129.6 0.01

2,171.4 5,644.8 0.38

386.5 32 12.09

1,395 32,711 0.04

155.1 153.2 1.01

2,630.3 170,779.2 0.02

2,183.5 808 2.70

1,073.9 230.8 4.65

1,819.2 14,982.8 0.12

127.6 104.8 1.22

805.6 4,317.4 0.19 Note: Both value of acquiring firms and acquired firms are measured under RMB.

Ratio for acquiring firms to acquired firms = value of acquiring firms / value of acquired firms.

23

Table 3. Summary Statistics of Stock Returns Before and After Merger and Acquisition

Events Pre-Event Post-Event Change AAR

E-1

1.00

-0.85

-1.85 0.075

E-2

-0.24

9.30

39.02 4.53

E-3

0.73

-0.22

-1.30 0.255

E-4

1.27

0.45

-0.65 0.86

E-5

-2.45

1.24

1.51 -0.605

E-6

-0.93

-0.03

0.97 -0.48

E-7

-0.03

-0.73

-24.08 -0.38

E-8

1.44

-4.10

-3.84 -1.33

E-9

-0.33

-1.54

-3.64 -0.935

E-10

-0.56

-0.96

-0.71 -0.76

E-11

0.17

0.65

2.72 0.41

E-12

-0.30

0.54

2.78 0.12

E-13

-0.33

-1.05

-2.17 -0.69

E-14

-0.16

-0.59

-2.80 -0.375

E-15

0.29

0.45

0.54 0.37

E-16

-0.36

-0.26

0.28 -0.31

E-17

-0.68

-1.00

-0.46 -0.84

E-18

1.13

0.02

-0.98 0.575

E-19

0.73

0.24

-0.67 0.485

E-20

-0.51

0.26

1.51 -0.125

E-21

-0.30

0.37

2.24 0.035

E-22

0.20

0.38

0.91 0.29

E-23 0.07 -0.13 -2.83 -0.03

Note: Pre-event return calculates the average of stock return over the period (-10, -1) under percentage,

equation represented as �̅�𝑃𝑟𝑒 =1

𝑁∑ 𝑅𝑖,(−10,−1)

𝑁𝑖=1 ; Post-event return calculates the average of stock return

over the period (1, 10) under percentage, equation represented as �̅�𝑃𝑜𝑠𝑡 =1

𝑁∑ 𝑅𝑖,(1,10)

𝑁𝑖=1 . AAR calculates

the arithmetic mean of abnormal returns for each event in the [-3, 3] event window.

24

Table 4. Student T Test for Abnormal Return

Date AAR T(AAR) Event Window AAR T(AAR)

-10 -0.02 -0.95 (-5, 0) 0.00 0.6

-9 -0.01 -0.9 (-4, 0) 0.00 1.15

-8 0.00 -0.1 (-3, 0) 0.00 0.7

-7 0.00 0.15 (-2, 0) 0.01 1.35

-6 0.00 0.35 (-1, 0) 0.01 2**

-5 0.00 -0.35 (0, 1) 0.02 1.65*

-4 -0.01 -1.3 (0, 2) 0.01 2**

-3 0.02 2.2** (0, 3) 0.02 2.85***

-2 0.01 1.15 (0, 4) 0.01 1.6*

-1 0.02 1.65 (0, 5) 0.01 1.4

0 0.01 0.65

1 0.01 2*

2 0.00 0.25

3 -0.01 -1.85*

4 0.01 1.4

5 -0.01 -0.85

6 0.00 0.35

7 -0.01 -1.4

8 -0.01 -1.1

9 0.00 -0.45

10 0.00 0.65

Note: Table 4 presents the significant level of merger and acquisition effect on stock price captured by

abnormal return. Null hypothesis is set to be AAR = 0; alternative hypothesis is set to be AAR ≠ 0.

* Estimate significant at the 10% level.

** Estimate significant at the 5% level.

*** Estimate significant at the 1% level.

25

Table 5. Student T Test for Cumulative Abnormal Return

Date ACAR T(ACAR)

-10 0.01 -0.85

-9 0 1

-8 -0.01 1.2

-7 -0.02 1.4

-6 -0.02 1.25

-5 -0.02 0.95

-4 -0.02 1

-3 -0.02 1.2

-2 -0.02 0.75

-1 -0.01 0.4

0 -0.01 -0.1

1 0.01 -0.4

2 0.02 -0.95

3 0.04 -1.05

4 0.03 -0.8

5 0.02 -1.15

6 0.03 -0.9

7 0.03 -0.95

8 0.02 -0.6

9 0.02 0

10 0 0.5

Note: Table 5 presents the significant level of merger and acquisition effect on stock price captured by

cumulative abnormal return. Null hypothesis is set to be ACAR = 0; alternative hypothesis is set to be ACAR

≠ 0.

26

Table 6. Result of Sign Test

Date

Observe

d Positive

Observed

Negative

Observe

d Zero

Expected

Positive

Expected

Negative

Expecte

d Zero

Differenc

e Level

-

10=1

0 9 14 0 11.5 11.5 0

Have

difference

-9=9 11 12 0 11.5 11.5 0

Doesn’t

have much

difference

-8=8 12 10 1 11 11 1

Doesn’t

have much

difference

-7=7 10 13 0 11.5 11.5 0

Have

difference

-6=6 11 12 0 11.5 11.5 0

Have

difference

-5=5 13 10 0 11.5 11.5 0

Have

difference

-4=4 9 14 0 11.5 11.5 0

Have

difference

-3=3 14 9 0 11.5 11.5 0

Have

difference

-2=2 13 10 0 11.5 11.5 0

Have

difference

-1=1 10 13 0 11.5 11.5 0

Have

difference Note: Table 6 reflects the sign difference of abnormal returns. Different than expected positive and negative

abnormal return indicates the selected stock sample exists value change over time.

27

Table 7. Difference in Average Abnormal and Cumulative Abnormal Returns

Event Day Pre-Event Post-Event Difference

Event 1 AR 0.17% -0.30% 0.37% 0.66%

CAR -1.57% -3.24% -1.65% 1.60%

Event 2 AR -0.48% -0.51% 0.26% 0.77%

CAR -1.91% -4.04% -2.05% 1.99%

Event 3 AR 9.94% 0.25% -0.66% -0.90%

CAR 0.51% 9.35% -0.87% -10.22%

Event 4 AR 9.98% 0.29% 0.45% 0.16%

CAR -3.09% 3.79% 12.54% 8.75%

Event 5 AR -1.37% 1.00% -0.85% -1.85%

CAR 3.24% 8.54% 9.42% 0.88%

Event 6 AR 0.64% 0.20% 0.38% 0.18%

CAR 0.25% -0.73% -1.35% -0.62%

Event 7 AR -3.39% 0.17% 0.65% 0.47%

CAR 3.63% -4.18% -2.40% 1.78%

Event 8 AR 10.04% -0.33% -1.05% -0.72%

CAR -5.30% 8.69% 18.80% 10.11%

Event 9 AR -2.20% 0.73% 0.24% -0.49%

CAR 1.40% 1.15% -0.15% -1.30%

Event 10 AR -1.22% -0.33% -1.54% -1.21%

CAR 3.13% 5.90% 0.67% -5.23%

Event 11 AR 2.12% 0.07% -0.13% -0.20%

CAR -1.27% 2.01% 1.42% -0.59%

Event 12 AR -9.37% -2.45% 1.24% 3.69%

CAR -16.10% -22.64% -9.26% 13.38%

Event 13 AR 3.88% 0.73% -0.22% -0.95%

CAR 1.69% 6.46% 3.32% -3.14%

Event 14 AR 9.97% -0.24% 9.30% 9.55%

CAR -30.41% -45.15% -17.04% 28.11%

Event 15 AR 2.81% -0.36% -0.26% 0.10%

CAR -1.37% 0.98% 2.26% 1.28%

Event 16 AR -9.91% -0.30% 0.54% 0.84%

CAR 5.38% -8.96% -3.74% 5.22%

Event 17 AR -1.62% -0.93% -0.03% 0.91%

CAR -0.53% -5.08% 0.50% 5.58%

Event 18 AR -6.11% -0.56% -0.96% -0.40%

CAR 3.87% -0.55% -2.88% -2.33%

Event 19 AR -6.89% 1.27% 0.45% -0.82%

CAR 3.05% 0.41% -1.59% -2.01%

Event 20 AR 1.01% -0.15% -0.59% -0.43%

CAR -2.08% 2.82% 1.77% -1.05%

Event 21 AR 9.98% 1.44% -4.10% -5.54%

CAR 4.83% 33.10% 33.72% 0.62%

28

Event 22 AR 1.10% -0.68% -1.00% -0.31%

CAR -0.05% 2.50% 0.78% -1.72%

Event 23 AR -0.17% -0.03% -0.73% -0.70%

CAR 2.89% 3.60% 8.45% 4.85%

Note: Pre-event abnormal return is calculated as ∑ 𝐴𝑅𝑖𝑡10𝑡=−10 , sums up the abnormal return for each of the

23 events before the event day. Post-event abnormal return is calculated as ∑ 𝐴𝑅𝑖𝑡10𝑡=1 , sums up the abnormal

return for each of the 23 events after the event day. Refer to equation (5) to see the calculation for abnormal

return.

Pre-event cumulative abnormal return is calculated as ∑ CARi(t1,t2)10𝑡=−10 , sums up the cumulative abnormal

return for each of the 23 events before the event day. Post-event abnormal return is calculated as

∑ 𝐶𝐴𝑅𝑖(𝑡1,𝑡2)10𝑡=1 , sums up the cumulative abnormal return for each of the 23 events after the event day. Refer

to equation (7) to see the calculation for cumulative abnormal return.

Difference is the change of abnormal return and cumulative abnormal return pre- and post-event.

29

Appendix

Fig. 3. Abnormal and Cumulative Abnormal Returns for Event 1



-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

000157AR 000157CAR

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

000581AR 000581CAR

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

000627AR 000627CAR

30




-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

000862AR 000862CAR

-10.00%

0.00%

10.00%

20.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

000895AR 000895CAR

-4.00%

-2.00%

0.00%

2.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002133AR 002133CAR

31




-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002196AR 002196CAR

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002199AR 002199CAR

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002223AR 002223CAR

32




-10.00%

-5.00%

0.00%

5.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002251AR 002251CAR

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002254AR 002254CAR

-30.00%

-20.00%

-10.00%

0.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002299AR 002299CAR

33




-10.00%

-5.00%

0.00%

5.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002318AR 002318CAR

-60.00%

-40.00%

-20.00%

0.00%

20.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002356AR 002356CAR

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002368AR 002368CAR

34




-20.00%

-10.00%

0.00%

10.00%

20.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002524AR 002524CAR

-10.00%

-5.00%

0.00%

5.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002557AR 002557CAR

-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002590AR 002590CAR

35




-10.00%

-5.00%

0.00%

5.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002597AR 002597CAR

-10.00%

-5.00%

0.00%

5.00%

10.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002601AR 002601CAR

-60.00%

-40.00%

-20.00%

0.00%

20.00%

40.00%

60.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002761AR 002761CAR

36



-4.00%

-2.00%

0.00%

2.00%

4.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002833AR 002833CAR

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

20.00%

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

002850AR 002850CAR

37

References

Ahern, K.R., Weston, J.F. (2007). M&As: the good, the bad, and the ugly. Journal of

Applied Finance, 17 (1), 5–19.

Alexandridis, G., Antypas, N., & Travlos, N. (2017). Value creation from M&As: New

evidence. Journal of Corporate Finance, 45, 632-650. doi:10.1016/j.jcorpfin.2017.05.010

Alexandridis, G., Petmezas, D., & Travlos, N. G. (2010). Gains from mergers and

acquisitions around the world: New evidence. Financial Management, 39(4), 1671-1695.

doi:10.1111/j.1755-053X.2010.01126.x

Andrade, G., Mitchell, M., & Stafford, E. (2001). New evidence and perspectives on

mergers. The Journal of Economic Perspectives, 15(2), 103-120. doi:10.1257/jep.15.2.103

Brown, S. J., & Warner, J. B. (1980). Measuring security price performance. Journal of

Financial Economics, 8(3), 205-258. doi:10.1016/0304-405X(80)90002-1

Brown, S. J., & Warner, J. B. (1984). Using daily stock returns: The case of event

studies. Journal of Financial Economics, 14(1), 3-31. doi:10.1016/0304-405X(85)90042-

X

Boateng, A., Qian, W., & Tianle, Y. (2008). Cross‐border M&As by Chinese firms:

An analysis of strategic motives and performance. Thunderbird International Business

Review, 50(4), 259-270. doi:10.1002/tie.20203

Chavaltanpipat, A., Kholdy, S., & Sohrabian, A. (1999). The wealth effects of bank

acquisitions. Applied Economics Letters, 6 (1), 5-11. doi:10.1080/135048599353780

Chi, J., Sun, Q., & Young, M. (2011). Performance and characteristics of bidders in

the Chinese stock markets. Emerging Markets Review, 12 (2), 152-170.

doi:10.1016/j.ememar.2010.12.003

38

China Securities Regulatory Commission. 2019.

http://www.csrc.gov.cn/pub/newsite/zjhxwfb/xwdd/201910/t20191018_364659.html

Dealogic. 2020. M&A highlights: full year 2019. https://www.dealogic.com/insight/ma-

highlights-full-year-2019/ (January, 2020).

Economist. 2011. Mergers and acquisitions.

http://www.economist.com/node/17851583 (February 2011).

Goddard, J., Molyneux, P., & Zhou, T. (2012). Bank mergers and acquisitions in

emerging markets: evidence from Asia and Latin America. The European Journal of

Finance, 18(5), 419–438.

Gu, L., & Reed, W. R. (2013). Chinese overseas M&As performance and the go global

policy 1. Economics of Transition, 21(1), 157-192. doi:10.1111/ecot.12007

Guest, P., Bild, M., & Runsten, M. (2010). The effect of takeovers on the fundamental

value of acquirers. Accounting and Business Research, 40(4), 333-352.

doi:10.1080/00014788.2010.9663409

Jensen, M. C., & Ruback, R. S. (1983). The market for corporate control: The scientific

evidence. Journal of Financial Economics, 11(1), 5-50. doi:10.1016/0304-

405X(83)90004-1

Khanal, A., Mishra, A., & Mottaleb, K. (2014). Impact of mergers and acquisitions on

stock prices: The U.S. ethanol-based biofuel industry. Biomass and Bioenergy, 61, 138 –

145.

Rahman, Z., Ali, A., & Jebran, K. (2018). The effects of mergers and acquisitions on

stock price behavior in banking sector of Pakistan. The Journal of Finance and Data

Science, 4(1), 44-54. doi:10.1016/j.jfds.2017.11.005

39

Shenzhen Stock Exchange. 2018. Listing Standards.

http://www.szse.cn/English/listings/standards/index.html

Tuch, C., & O'Sullivan, N. (2007). The impact of acquisitions on firm performance: A

review of the evidence. International Journal of Management Reviews, 9(2), 141-170.

doi:10.1111/j.1468-2370.2007.00206.x

Documents

Impact of Mergers and Acquisitions on Stock Price in Shenzhen A … · 2021. 1. 22. · Zijun (Katherine) Geng M.A. Economics, University of Victoria, 2020 An Extended Essay Submitted