Impact of on 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)

1 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

______

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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

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1. Introduction

Mergers and acquisitions (M&As) 1 are two of the commonly used strategies for firm expansion, which enables industries and 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 , we expect trading under the reflection of its , 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 . This paper studies the effect of both M&As to the

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

4 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 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 and aggregate net of RMB 30 million in the past 3 years

(Shenzhen , 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 were asked to find a delegate security before transaction, 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 -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. 5 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 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

6 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 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

7 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 firms.

We focus on the year 2019 since China Securities Regulatory Commission published modification about regulation for Chinese listed company 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 investment and short-term business. Shenzhen stock exchange consists of three

8 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 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.

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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

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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

11 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:

푃푡−푃푡′ 푅푖,푡 = (1) 푃푡′ where 푅푖,푡 is the actual ex post return and 푃푡, 푃푡′ 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.

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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:

푅푖푡 = 훼푖 + 훽푖푅푚,푡 + 휀푖,푡 (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:

퐸(푅푖푡) = 푅푚푡 (3)

In market-adjusted model, we estimate normal return by assuming 훼푖 = 0 푎푛푑 훽푖 = 1, therefore, the abnormal return will be the difference between the actual stock return and the market return.

퐴푅푖푡 = 푅푖푡 − 푅푚푡 (4)

퐴푅푖푡 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:

퐴푅푖푡 = 푅푖푡 − 퐸(푅푖푡) (5)

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

13 market over 21 days, it is essential to calculate the average abnormal return 퐴퐴푅푡, the cumulative abnormal return 퐶퐴푅푖(푡1,푡2) and the average cumulative abnormal return

퐴퐶퐴푅푖(푡1,푡2). The equation is as follows:

1 퐴퐴푅 = ∑푁 퐴푅 (6) 푡 푁 푖=1 푖푡 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.

푡2 퐶퐴푅푖(푡1,푡2) = ∑푡1퐴푅푖푡 (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.

1 퐴퐶퐴푅 = ∑1 퐶퐴푅 (8) (푡1,푡2) 푁 푖=1 푖(푡1,푡2)

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

14 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.

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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

16 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

17 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).

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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 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.

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Figures

Fig. 1. Average Abnormal Return With 95% Confidence Interval 0.05 0.04 0.03 0.02 0.01 0.00 -0.01 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -0.02 -0.03 -0.04 -0.05 -0.06

AR CI (lower) CI (upper)

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

5.00%

4.00%

3.00%

2.00%

1.00%

0.00% -15 -10 -5 0 5 10 15 -1.00%

-2.00%

-3.00%

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).

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Tables

Table 1. 23 Merger and Acquisition Events in the Shenzhen A Stock Market of China in 2019 Date of Event Bidders Main Business 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 Industrial stainless E-3 Zhejiang longstone technology co., LTD 3/12/2019 steel 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 Automotive E-7 Shenzhen kodali industrial co., LTD 4/13/2019 structure E-8 Multi-affection group co., LTD Textile 4/16/2019 E-9 Pedestrian and high business chain co., LTD Supermarket 4/20/2019 Vehicle E-10 Zhejiang wanan science and technology co., LTD 4/25/2019 system E-11 Zhejiang fangzheng motor co., LTD Automobile engine 4/29/2019 E-12 Guangzheng group co., LTD Eye care system 5/6/2019 Quartz crystal E-13 Zhejiang dongjing electronic co., LTD 5/25/2019 component Fine chemical E-14 Dragon python, baililian group co., LTD 6/13/2019 New power E-15 Ningxia yinxing energy co., LTD 6/19/2019 generation Software and E-16 Tai chi computer co., LTD information 7/24/2019 technology services Equipment E-17 Guangzhou honga CNC machinery co., LTD 7/29/2019 E-18 Tianmao industrial group co., LTD Investment holding 8/27/2019 Medical apparatus E-19 Jiangsu fishho medical equipment co., LTD 8/28/2019 and instruments Auto parts E-20 Wuxi weifu high-tech group co., LTD 9/26/2019 manufacturing Construction E-21 China federation co., LTD machinery 10/31/2019 manufacturing Real estate E-22 Guangyu group co., LTD 11/1/2019 development High-performance E-23 Yantai and new materials co., LTD 11/2/2019 fiber Note: There are 22 non-investment holding firms and 1 investment holding firm, Tianmao industrial group co., LTD.

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Table 2. Value ratio for acquiring firms to acquired firms

Value of acquiring firm on the announcement day Value of acquired firm Ratio for acquiring to (Million) (Million) 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.

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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, 1 equation represented as 푅̅ = ∑푁 푅 ; Post-event return calculates the average of stock return 푃푟푒 푁 푖=1 푖,(−10,−1) 1 over the period (1, 10) under percentage, equation represented as 푅̅ = ∑푁 푅 . AAR calculates 푃표푠푡 푁 푖=1 푖,(1,10) the arithmetic mean of abnormal returns for each event in the [-3, 3] event window.

23

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.

24

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.

25

Table 6. Result of Sign Test

Observe Observed Observe Expected Expected Expecte Differenc Date d Positive Negative d Zero Positive Negative d Zero e Level - 10=1 Have 0 9 14 0 11.5 11.5 0 difference Doesn’t have much -9=9 11 12 0 11.5 11.5 0 difference Doesn’t have much -8=8 12 10 1 11 11 1 difference Have -7=7 10 13 0 11.5 11.5 0 difference Have -6=6 11 12 0 11.5 11.5 0 difference Have -5=5 13 10 0 11.5 11.5 0 difference Have -4=4 9 14 0 11.5 11.5 0 difference Have -3=3 14 9 0 11.5 11.5 0 difference Have -2=2 13 10 0 11.5 11.5 0 difference Have -1=1 10 13 0 11.5 11.5 0 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.

26

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%

27

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% 10 Note: Pre-event abnormal return is calculated as ∑푡=−10 퐴푅푖푡, sums up the abnormal return for each of the 10 23 events before the event day. Post-event abnormal return is calculated as ∑푡=1 퐴푅푖푡, 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. 10 Pre-event cumulative abnormal return is calculated as ∑푡=−10 CARi(t1,t2), sums up the cumulative abnormal return for each of the 23 events before the event day. Post-event abnormal return is calculated as 10 ∑푡=1 퐶퐴푅푖(푡1,푡2), 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.

28

Appendix

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

4.00% 2.00% 0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00% -4.00% -6.00%

000157AR 000157CAR

Fig. 4. Abnormal and Cumulative Abnormal Returns for Event 2

4.00%

2.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00%

-4.00%

-6.00%

000581AR 000581CAR

Fig. 5. Abnormal and Cumulative Abnormal Returns for Event 3

15.00%

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

000627AR 000627CAR

29

Fig. 6. Abnormal and Cumulative Abnormal Returns for Event 4

40.00% 30.00% 20.00% 10.00% 0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -10.00% -20.00%

000862AR 000862CAR

Fig. 7. Abnormal and Cumulative Abnormal Returns for Event 5

20.00%

10.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -10.00%

000895AR 000895CAR

Fig. 8. Abnormal and Cumulative Abnormal Returns for Event 6

2.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00%

-4.00%

002133AR 002133CAR

30

Fig. 9. Abnormal and Cumulative Abnormal Returns for Event 7

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

-15.00%

002196AR 002196CAR

Fig. 10. Abnormal and Cumulative Abnormal Returns for Event 8

50.00% 40.00% 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 -20.00%

002199AR 002199CAR

Fig. 11. Abnormal and Cumulative Abnormal Returns for Event 9

6.00%

4.00%

2.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00%

-4.00%

002223AR 002223CAR

31

Fig. 12. Abnormal and Cumulative Abnormal Returns for Event 10

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

002251AR 002251CAR

Fig. 13. Abnormal and Cumulative Abnormal Returns for Event 11

4.00% 3.00% 2.00% 1.00% 0.00% -1.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00% -3.00%

002254AR 002254CAR

Fig. 14. Abnormal and Cumulative Abnormal Returns for Event 12

10.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -10.00%

-20.00%

-30.00%

002299AR 002299CAR

32

Fig. 15. Abnormal and Cumulative Abnormal Returns for Event 13

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

002318AR 002318CAR

Fig. 16. Abnormal and Cumulative Abnormal Returns for Event 14

20.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -20.00%

-40.00%

-60.00%

002356AR 002356CAR

Fig. 17. Abnormal and Cumulative Abnormal Returns for Event 15

6.00% 4.00% 2.00% 0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00% -4.00% -6.00%

002368AR 002368CAR

33

Fig. 18. Abnormal and Cumulative Abnormal Returns for Event 16

20.00%

10.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -10.00%

-20.00%

002524AR 002524CAR

Fig. 19. Abnormal and Cumulative Abnormal Returns for Event 17

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

002557AR 002557CAR

Fig. 20. Abnormal and Cumulative Abnormal Returns for Event 18

15.00% 10.00% 5.00% 0.00% -5.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -10.00% -15.00%

002590AR 002590CAR

34

Fig. 21. Abnormal and Cumulative Abnormal Returns for Event 19

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

002597AR 002597CAR

Fig. 22. Abnormal and Cumulative Abnormal Returns for Event 20

10.00%

5.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00%

-10.00%

002601AR 002601CAR

Fig. 23. Abnormal and Cumulative Abnormal Returns for Event 21

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 -40.00% -60.00%

002761AR 002761CAR

35

Fig. 24. Abnormal and Cumulative Abnormal Returns for Event 22

4.00%

2.00%

0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -2.00%

-4.00%

002833AR 002833CAR

Fig. 25. Abnormal and Cumulative Abnormal Returns for Event 23

20.00% 15.00% 10.00% 5.00% 0.00% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -5.00% -10.00%

002850AR 002850CAR

36

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