Searching for the Motives and Effectiveness of Chinese Mergers and

Acquisitions

Junxi Zhang and Xiaokun Wang*

School of Economics and Finance

The University of Hong Kong

Abstract

This study researches the M&A motives and their effectiveness by analyzing the

relationship between corporate governance, earnings management and the

performance of acquiring firms. We assume that if the long-term performance and

corporate governance of acquiring firms are significantly improved, M&As are driven

by the synergy motive; If the long-term performance of acquiring firms decreases

significantly and earnings management is obvious, M&As are driven by the agency or

hubris motive. The empirical evidence suggests that the long-term performance of

acquiring firms decreases significantly. Corporate governance of acquiring firms is

not improved and has no significant effect on the operating performance. However,

earnings management is very obvious and has significant effect on the short-term

performance of acquiring firms. Furthermore, the market reaction to acquisitions is

negative. Therefore, M&As in Chinese listed companies are mainly driven by agency

or hubris motive, and the synergy effect is not realized basically.

Keywords: M&A Motives, Operating Performance, Market Performance, Earning

Management, Corporate Governance.

* We acknowledge the data support from the Center for China Financial Research (CCFR) in School of Economics and Finance of the University of Hong Kong. Correspondence Address: School of Economics and Finance, the University of Hong Kong, Pokfulam Road, Hong Kong. Phone: (852)2857-8502. Fax: (852)2548-1152. E-mail: jjzhang@econ.hku.hk

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I. Introduction Since 1990s, the bull stock market and great technological innovation in the U.S. have

sparked a new wave of mergers and acquisitions. It involved large number of deals in

many industries with great dollar volumes. The famous cases include HP and Compaq,

Oracle and PeopleSoft, and SBC’s merger with AT&T etc. In January of 2005, P&G

bought Gillette for 57 billions, which made this wave of M&A to its peak. Through

over ten year’s development, Chinese stock market has become the eighth largest in

the world. As an important means of corporate restructuring, mergers and acquisitions

are more and more employed by Chinese listed companies to fulfill their strategic

objectives. According to the data of CSMAR (Chinese Stock Market & Accounting

Research Database), 149 listed companies launched 214 M&As1 with the volume of

115 billion yuan in 1998. In 2000, 244 listed companies launched 376 M&As with the

volume of 435 billion yuan. Even more, these numbers reached to 352, 540 and 520

respectively in 2003. Therefore, mergers and acquisitions are crucial to the

development of Chinese listed companies, and also have great implications to the

government and its regulatory authorities.

In China, the government and regulatory authorities always encourage listed

companies to realize the synergy effect and enhance the firm value through M&As.

However, due to the short history of securities market and incompleteness of relevant

laws and regulations, some listed companies launch M&As by special motives. For

example, controlling shareholders use M&As to tunnel listed companies, and local

governments force listed companies to acquire bankrupting firms. These M&As are

not market-oriented activities and harmful to the development of listed companies and

securities markets.

In order to effectively regulate the M&As in listed companies, the motives of M&As

should be identified and analyzed. Researchers argue that different M&A motives lead

to different firm performances, so they can identify the motives from the perspective

1 Acquisitions include acquisitions of share and acquisitions of asset.

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of firm performance. The empirical studies on the performance of M&A firms can be

divided into two categories: one is centered at the stock market return and the other

focuses on the operating performance. An important assumption of the first method is

the market efficiency, which remains a problem in Chinese stock markets. On the

other hand, some indicators of operating performance are prone to be manipulated.

Therefore, we need other evidences than the firm performance to identify the M&A

motives of Chinese listed companies.

This paper provides a new way to investigate the motives and their effectiveness on

the performance of acquiring firms, especially from the perspectives of corporate

governance and earnings management. We assume that if the long-term performance

and corporate governance of acquiring firms are significantly improved, M&As are

driven by the synergy motive; If the long-term performance of acquiring firms

decreases significantly and earnings management is obvious, M&As are driven by the

agency or hubris motive. The empirical results of this paper suggest that the long-term

performance of acquiring firms drops significantly and the corporate governance does

not change much, but earnings management is very obvious which has significant

effect on the firm’s performance. Therefore, M&As in Chinese listed companies are

mainly driven by the agency or hubris motive, and the synergy effect is not realized

basically.

This paper is organized as follows. Section II reviews the relevant literature and

proposes the hypotheses. Section III describes the data and sample, as well as the

research design. Section IV tests the hypotheses by studying the mechanisms of

corporate governance and earnings management, as well as their effects on the

performance of acquiring firms. Finally, we conclude in Section V. II. Literature Review and Hypotheses

The performance of M&A firms has been widely studied in the financial economics.

Although many studies investigate stock returns around acquisitions, few studies

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focus on changes in the operating performance. Perhaps the most notable study in this

field is by Healy et al. (1992). They find that cash flow performance improves

following acquisitions. However, following the research design prescribed by Barber

and Lyon (1996), Ghosh (2001) does not find evidence of improvements in the

operating performance following acquisitions. In contrast to the inconsistent empirical

results about the operating performance of M&A firms in other countries, a clear

conclusion about Chinese listed companies is reached by most researchers. For

example, Feng and Wu (2001) use sales to asset, return of asset, earning per share and

return of equity to measure the operating performance of acquiring firms between

1994 and 1998 and conclude that it increases in the announcement year and the next

year then decreases in the following years. Wan et. al. (2001) select sales’ growth,

earning before tax, earning after tax and return of equity to investigate the operating

performance of acquiring firms between 1997 and 1999, and find the similar empirical

results. In these studies, the changes of the operating performance after M&A are not

significant, which means that the event does not have material effect on the operating

performance. The study of Li and Chen (2003) further prove this point. The above

empirical studies rely heavily on the profitability indicators and use old samples of

M&As. Thus, this paper uses more comprehensive indicators to evaluate the effect of

recent M&As on the operating performance of Chinese listed companies. Furthermore,

we use Tobin’s q to measure the market valuation of acquiring firms, which also

reflects the market reaction to M&A event.

Why firms engage in mergers and acquisitions? In general, three major motives of

M&As have been advanced in the literature: the synergy motive, the agency motive

and the hubris motive (Berkovitch and Narayanan, 1993). The synergy motive

suggests that M&As occur because of economic gains that result from merging the

resources of two firms. The agency motive suggests that M&As occur because they

enhance the acquirer management’s welfare at the expense of acquirer shareholders.

The hubris hypothesis maintains that M&As are motivated by manager’s mistakes in

evaluating target firms even when there is no synergy. Theoretically, M&As are value

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increasing transactions for target firms in spite of which motives. However, for

acquiring firms, the agency and hubris motive have the negative effect on the firm

value. Although the synergy motive has the positive effect, it needs a long term to

work.

Some researchers try to identify M&A motives from the perspective of firm

performance. For example, Malatesta (1983) finds that acquisitions are value

increasing transactions for target firms but value decreasing transactions for acquiring

firms and concludes that acquisitions are motivated by agency. Berkovitch and

Marayanan (1993) distinguish among three M&A motives by investigating the

correlation between target and total gains. They argue that this correlation should be

positive if synergy is the motive, negative if agency is the motive, and zero if hubris is

the motive. They find that synergy is the primary motive in takeovers with positive

total gains and agency is the primary motive in takeovers with negative total gains.

The synergy motive, agency motive and hubris motive have some explaining power to

M&As in Chinese listed companies, especially in the situation that most listed

companies have non-tradable shares controlled by state owners. Some studies explore

the M&A motives of Chinese listed firms and provide valuable insights. Zhang (2003)

proposes the hypothesis of value transfer and redistribution dominated by institutional

factors, but he dose not test it empirically. Li et. al. (2005) investigates the effect of

tunneling or propping motives of controlling shareholders and local government to the

long-term performance of listed companies. Their result suggests that the M&As

driven by motives of avoiding deficit or rationing shares can enhance the companies’

operating performance in short term. When companies have not such motives, the

M&As are basically tunneling activities which will damage the companies’ value but

have no significant influences to their operating performance. However, this study

only captures the M&A motive of avoiding deficit or rationing shares, and tunneling

or propping can be viewed as one kind of agency problem.

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The existing empirical evidence does not clearly distinguish among different M&A

motives of Chinese listed firms. Although the method of Berkovitch and Marayanan

(1993) is useful, it can’t be applied to M&As of Chinese listed firms due to the fact

that most of target firms are not listed and the relevant data are unavailable. This

paper tries to identify the M&A motives of Chinese listed firms by investigating the

relationship between corporate governance, earnings management and the long-term

performance of acquiring firms.

Specifically, for acquirers driven by the synergy motive, M&As are value increasing

transactions. And the gains can be traced to efficiency increase, operating synergy,

diversification and financial synergy etc. (Weston et al., 2001). The synergy gains will

be reflected in the improvement of operating performance such as sales increase, cost

reduction, and profit enhancement, but need a long time to work. Furthermore,

corporate governance and firm performance work interactively. As suggested by

Dennis and McConnell (2003), corporate governance is the set of mechanisms-both

institutional and market based-that induce the self interested controllers of a company

to make decisions that maximize the value of the company to its owners. A firm’s

various corporate governance practices shape its behavior and eventually affect its

stock market performance and operating performance (Liu, 2005). We argue that the

synergy gain for acquiring firms cannot be realized without the good corporate

governance. On the other hand, M&As driven by the synergy motive may induce

improvements in corporate governance of acquiring firms. Under the rational market

expectations, the market reaction to the M&As driven by the synergy motive will be

positive. Therefore, we make the following hypothesis:

HI：Ceteris Paribus, if M&As are driven by the synergy motive, the long-term

performance and corporate governance of acquiring firms will be improved

significantly, and corporate governance has significant effect on the long-term

operating performance of acquiring firms, and the market reaction will be

positive.

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On the other hand, if acquirers driven by the agency or hubris motive, M&As are

value decreasing transactions. Due to manager’s self interest or overestimation of

target firms, M&As are detrimental to the operating performance of acquiring firms.

However, in order to keep their positions or reputation, the acquirers’ managers have

strong incentives to disguise the loss induced by M&As. Under this circumstance,

earnings management is a necessary choice for managers of acquiring firms. Earnings

management, the practice of manipulating earnings to show temporarily improved

financial performance, has been found around significant corporate events, including

mergers and acquisitions. Consistent with Erickson and Wang (1999), Louis (2004)

finds strong evidence suggesting that acquiring firms overstate their earnings reports

in the quarter preceding the announcement of a stock swap acquisition. Nevertheless,

earnings management is not costless and only works to the short term operating

performance. Under the rational market expectations, the market reaction to the

M&As driven by the agency or hubris motive will be negative. Thus, we provide the

following hypothesis:

HII：Ceteris Paribus, if M&As are driven by the agency or hubris motive, the

long-term performance of acquiring firms will decrease significantly, and

earnings management by managers will be obvious and has significant effect on

the short-term operating performance, and the market reaction will be negative.

III. Sample Selection and Research Design

A. Sample Selection

The main data source of this study is China Stock Market & Accounting Research

Database (CSMAR), which is compiled according to the format of CRSP and

Compustat. Since CSMAR is an authoritative and professional financial database

about Chinese listed companies, it is adopted by Wharton Research Data Service

(WRDS). We mainly use three subsets of CSMAR including Mergers and

Acquisitions Database, Financial Statements Database and Corporate Governance

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Database. Other sources such as Genius Database will also be employed as the

calibration and supplement to CSMAR.

Our sample is drawn from CSMAR Mergers and Acquisitions Database using the

following criteria:

The transaction is classified either as an acquisition of share or an acquisition of

asset;

The transaction is listed as successful and completed;

The announcement date2 of the acquisition lies between January 1, 2000 and

December 31, 20023;

If a listed company launched multiple acquisitions in this period, we only use the

first transaction4.

Our full sample is composed of 618 acquisitions. Table 1 shows the summary

statistics for sample firms about the yearly distribution, location of stock market and

type of acquisition. Since we exclude the possibility of multiple acquisitions by an

acquirer, one firm in our sample corresponds to one acquisition. That is the reason

why the amount of acquisitions decreases from 2000 to 2002. Moreover, all sample

firms only have A shares.

2 If it is not disclosed, we use the completion date as the substitute. 3 The sample period is restricted because the employee data is only available for the period from 1999 to 2003. 4 This criterion ensures that the performance of acquiring firms is only affected by a single acquisition.

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Table 1: Summary Statistics on Acquisitions, 2000-2002 The numbers in the table are the amounts of acquisitions or acquiring firms.

Year

1998 1999 2000 Total Location of Stock Market

Shanghai 134 137 102 373Shenzhen 109 92 44 245

Type of Acquisition Acquisition of Share 183 141 84 408Acquisition of Asset 60 88 62 210

Total 243 229 146 618

B. Research Design

B1. Measuring the Performance of acquiring firms

Drawing heavily on the work of Hu, Song and Zhang (2005), we use four groups of

indicators, i.e., production, profitability, cost and productivity, to measure the

operating performance of acquiring firms. Compared with former studies which rely

heavily on indicators of profitability, for example, Healy et al. (1992) and Ghosh

(2001), the performance indicators employed in this paper are more comprehensive.

Production is defined as sales revenue. Profitability is the most comprehensive

measure of firm efficiency and consists of profit, return of assets (ROA)5 and return of

sales (ROS) here. We abandon return of equity (ROE) because there are many cases

where both return and equity of an acquirer are negative and eliminating those firms

will bias the results. The cost indicators consist of total cost, total fee, total cost per

sales (COS) and total fee per sales (FOS). The total fee is defined as the sum of sales

fee, financial fee and administrative fee. The total cost per sales and total fee per sales

are the mirror indicators of profitability, reflecting a firm’s efforts in cost reduction.

The last category of indicators is productivity, including sales per worker (SOW), cost

per worker (COW) and profit per worker (POW). Productivity is another measure of

firm efficiency that is particularly sensitive to changes in the workforce and capital

stock. Totally, we have eleven indicators on the operating performance.

5 It is computed as the earnings before interest and tax over total value of assets.

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Furthermore, we use Tobin’s q to measure the market valuation or performance of

acquiring firms, which also reflects the market reaction to M&A event. Tobin’s q is

the widely used measure of valuation for listed companies and normalized with

respect to the firm size. Following Chung and Pruitt (1994), Tobin’s q is defined as:

BVTABVCABVCLBVINVBVLTDMVCSTq −+++

= (1)

Where MVCS is the market value of the firm’s common stock shares, BVLTD is the

book value of the firm’s long-term debt, BVINV is the book value of the firm’s

inventories, BVCL is the book value of the firm’s current liabilities, BVCA is the book

value of the firm’s current assets, and BVTA is the book value of the firm’s total assets.

Adopting the method of Bai et al. (2004), we adjust the measurement of Tobin’s q to

take account of illiquidity discount of 70% and 80% in the Chinese stock markets.

Specifically, we multiply the amount of tradable shares by the market price and the

amount of non-tradable shares by 30% and 20% of the market price when calculating

MVCS. Finally, we have two measurements of Tobin’s q denoted as Tq70 and Tq80,

respectively.

B2. Measuring corporate governance

Basing on the work of Bai et al. (2004), we employ eight corporate governance

variables to capture the control-based governance model adopted in Chinese listed

firms. As stated in Bai et al. (2004), there are two types of corporate governance

mechanisms in China. The first type consists of internal mechanisms, e.g., the

ownership structure, executive compensation, the board of directors, and financial

disclosure. The second are external mechanisms, e.g., the effective takeover market,

legal infrastructure, and product market competition. To capture the ownership aspect

of corporate governance, they use the stake of the largest shareholder to measure the

largest shareholder’s interest in a firm. Moreover, they uses a dummy variable, which

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equals to 1 if a firm has a parent company and 0 otherwise, to reflect the scope for

tunneling of the largest shareholder. With respect to the board of directors, they create

a dummy variable that equals to 1 if the CEO is the chairman or vice chairman of the

board of directors and 0 otherwise. To measure the degree of outside control of the

board, they take the ratio of outside directors, who are not members of the

management team, to inside directors. Regarding executive compensation, they

choose an alternative variable, i.e., the percentage of shares hold by the top executives

of the firm, to capture the alignment of interests between managers and shareholders.

Regarding financial transparency, they believe that companies that issue H shares or B

shares must adopt international accounting standards therefore have reliable

information. To capture this effect, they use a dummy variable that equals 1 if a

company has H shares or B shares and 0 otherwise. Since an active corporate control

market does not exist in China, they measure the market for corporate control by the

concentration of shares in the hands of the second to the tenth shareholders. In

addition to these variables, we use the percentage of state-owned shares to capture the

effect of government.

The corporate governance variables are defined as follows.

(1) CEO –– Whether the CEO is the chairman or vice chairman of the board of

directors.

(2) Out –– The proportion of outsider directors, i.e., the ratio of the number of

directors without pay with respect to the total number of directors.

(3) Top_executive –– Shareholding percentage by the top executives of the firm.

(4) Top1 –– Shareholding percentage of the largest shareholder.

(5) Parent –– Whether the firm has a parent company.

(6) Concentration –– Concentration of shareholding in the hands of the 2nd to the 10th

largest shareholders, i.e., sum of squares of the shareholding

percentage by the 2nd to the 10th largest shareholders, and then

take logarithm.

(7) State_control –– The percentage of state-owned shares.

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(8) Bh –– Whether the firm has H shares traded in Hong Kong Stock Exchange or B

shares traded in Shanghai or Shenzhen Stock Exchange.

Since the above eight variables represent the internal and external mechanisms of

corporate governance separately, it is important to form a composite measure of

corporate governance for the listed firms. Following Bai et al. (2003), we use the

principal component analysis (PCA) to establish the corporate governance index,

which is represented by the first principal component of the PCA. The central idea of

PCA is to reduce the dimensionality of a data set consisting of a large number of

interrelated variables, while retaining as much as possible of the variation present in

the data set. This is achieved by transforming to a new set of variables, the principal

components (PCs), which are uncorrelated, and which are ordered so that the first few

retain most of the variation present in all of the original variables (Jolliffe, 2002).

Since the eight corporate governance variables are highly and significantly correlated6,

the PCA method is very suitable and reliable in this condition.

B3. Estimating Earnings Management

Usually, the discretionary accruals are widely used in the literature to represent the

level of earnings management. Many models for measuring discretionary accruals

have been developed based on the relation between total accruals and hypothesized

explanatory factors, for example, Healy (1985) model, DeAngelo (1986) model, Jones

(1991) model, modified Jones model (Dechow, Sloan and Sweeney, 1995),

cross-sectional Jones model (DeFond and Jiambalvo, 1994), cross-sectional modified

Jones model (DeFond and Jiambalvo, 1994) and Kang & Sivaramakrishnan (1995)

model. The empirical results for American firms suggest that cross-sectional Jones

model and cross-sectional modified Jones model are more powerful than other models

to detect earnings management (Subramanyam, 1996; Bartov, Gul and Tsui, 2000).

Using the data of Chinese listed companies, Xia (2003) offers a comprehensive

analysis on the power of nine earnings management models and finds that 6 The details are presented in Table 3.

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cross-sectional Jones model is the most efficient one. Therefore, we use

cross-sectional Jones model and cross-sectional modified Jones model to estimate

discretionary accruals in this paper.

Specifically, these models apply cross-sectional regression on accruals of firms in the

same industry to obtain predicted values as non-discretionary accruals. We further

divide the discretionary accruals calculated by the modified Jones model into the

discretionary current accruals and the discretionary long-term accruals according to

Teoh et al. (1998). Current accruals are adjustments involving short-term assets and

liabilities that support the day-to-day operations of the firm. In contrast, long-term

accruals are adjustments involving long-term assets such as depreciation and

amortization. Guenther (1994) argues that managers have greater discretion over

current accruals than over long-term accruals.

Now, we have four kinds of discretionary accruals to measure earnings management

of the firm. They include discretionary total accruals by Jones model (DTAC),

discretionary total accruals by modified Jones model (MDTAC), discretionary current

accruals by modified Jones model (MDCA), and discretionary long-term accruals by

modified Jones model (MDLA). The details of computation are presented in the

appendix. It should be noted that the cross-sectional approach automatically adjusts

for changing industrywide economics conditions which influence accruals of earnings

management independently. Therefore, it is unnecessary to adjust above four

discretionary accruals by their industry medians7.

IV. Results

A. The Performance of Acquiring Firms

To examine the effect of M&A event on the performance of acquiring firms, we 7 Kothari et al. (2004) argue that the existing methods of estimating discretionary accruals are biased toward rejecting the null hypothesis of no earnings management. They recommend adjusting the discretionary accruals by the average discretionary accruals of a portfolio matched on prior-year return-of-asset (ROA) and industry. We abandon this method because on average each portfolio only has ten firms in our sample and this will result in the small sample bias of estimates.

13

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comparing eleven operating performance indicators and two market performance

indicators of acquiring firms over the year of the acquisition announcement, the

preceding year, and the year after. The median of each indicator, the time-series

differences of indicators and their statistical tests are reported in Table 2. It is obvious

that profitability of acquiring firms decreases significantly after the acquisition,

especially for indicators of ROA and ROS. For example, the median ROS decreases

from 11.01% in the preceding year to 5.52% in the year after. Moreover, sales rise

significantly after the acquisition. However, the large increase in cost, particularly in

the total cost and total fee, offsets sales increase and underpins the spectacular loss in

profitability. For the productivity, although there is a dramatic improvement in SOW

after the acquisition, ROW reduces 1450 Yuan in the announcement year and 6660

Yuan further in the year after due to the significant increase of COW after the

acquisition. All of above suggest that there are significant post-acquisition decreases

in the operating performance of acquiring firms. This finding is in sharp contrast with

the results of former studies, such as Feng and Wu (2001) and Wan et al. (2001). The

value of acquiring firm also suffers significant loss after the acquisition, which can be

reflected in the change of Tobin’q. For example, the median Tq70 decreases from 1.4

in the preceding year to 1.0 in the year after.

Therefore, the operating performance and market performance of acquiring firms

decreases significantly after the acquisition, especially in the long term. We suspect

that M&As of Chinese listed companies are mainly driven by agency or hubris motive.

To further prove this speculation, we need other evidence. In the next section, we will

analyze M&A motives form the perspective of corporate governance.

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Table 2: Time-Series Performance Comparison of Acquiring Firms ROA is return of assets and ROS is return of sales. The total fee is defined as the sum of sales fee, financial fee and administrative fee. COS is total cost per sales and FOS is total fee per sales. SOW, COW and ROW are sales per worker, cost per worker and profit per worker, respectively. Tq70 and Tq80 are Tobin’qs calculated by taking account of illiquidity discount of 70% and 80% in the Chinese listed companies, respectively. The numbers in the table are medians. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Items Indicators Unit The year before

(1)

The announcement year

(2)

The year after

(3) (2)-(1)

Wilcoxon

two-sample test

z-value

(3)-(1)

Wilcoxon

two-sample test

z-value

Production Sales 100 million ￥ 4.43 5.37 6.49 0.95 3.070*** 1.11 2.613***

Profitability Profit 100 million ￥ 0.47 0.47 0.38 0.00 0.251 -0.09 -3.002***

ROA Ratio 6.63% 5.97% 4.96% -0.66% -4.060*** -1.02% -4.725***

ROS Ratio 11.01% 8.64% 5.52% -2.38% -3.692*** -3.12% -6.427***

Cost Total Cost 100 million ￥ 3.12 3.79 4.93 0.67 2.849*** 1.14 2.670***

Total Fee 100 million ￥ 0.59 0.78 1.03 0.19 4.253*** 0.25 5.097***

COS Ratio 74.80% 75.48% 76.94% 0.68% 0.525 1.46% 1.819*

FOS Ratio 14.23% 14.52% 16.07% 0.29% 1.106 1.55% 3.007***

Productivity SOW 1000￥ 285.08 326.52 374.04 41.44 2.625*** 47.52 2.403**

COW 1000￥ 197.60 236.83 282.94 39.23 2.567*** 46.11 2.743***

ROW 1000￥ 29.87 28.42 21.76 -1.45 -0.426 -6.66 -2.799***

Market Performance Tq70 Ratio 1.4 1.3 1.0 -0.1 -1.779* -0.3 -9.617***

Tq80 Ratio 1.2 1.2 0.9 0.0 -1.319 -0.3 -9.193***

B. Corporate Governance and the Performance of Acquiring Firms

Given the definitions of eight corporate governance variables in the previous section, the

value of each variable for sample firms from 1999 to 2003 is collected or calculated

according to the CSMAR Corporate Governance Database. In order to get a consistent and

comprehensive measurement of the corporate governance mechanisms of acquiring firms,

we use the principal component analysis (PCA) to establish the corporate governance index,

which is represented by the first principal component of the PCA. Since different corporate

governance variables are measured in different units, we use the correlation matrix to derive

the first PC. The correlation coefficients between corporate governance variables are

presented in Table 3. It suggests that most of variables are significantly correlated, so the

PCA method is very suitable and reliable in this condition. Table 4 shows the factor loadings

from the first principal component of the PCA. It indicates that among the eight corporate

governance variables, Concentration, Top1, Out, Bh, Top_executive and state_control are

the more important components in the corporate governance index. The other two variables,

Ceo and Parent, are less important components. The sign and significance of each factor

loadings are consistent with the result of Bai et al. (2003).

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Table 3: Correlation Coefficients between Corporate Governance Variables CEO denotes whether the CEO is the chairman or vice chairman of the board of directors. Out is the proportion of outsider directors. Top_executive represents the percentage shareholding by top executives of the firm. Top1 is the shareholding percentage of the largest shareholder. Parent denotes whether the firm has a parent company. Concentration is the sum of squares of the shareholding percentage by the 2nd to the 10th largest shareholders. State_control is the percentage of state-owned shares. Bh is Whether the firm has H shares or B shares. p-values are in parentheses

Variables CEO Out Top_executive Top1 Parent Concentration State_control

Out -0.123

(0.000)

Top_executive 0.024 -0.047

(0.071) (0.001)

Top1 -0.053 -0.075 -0.082

(0.000) (0.000) (0.000)

Parent -0.086 0.088 -0.134 0.074

(0.000) (0.000) (0.000) (0.000)

Concentration 0.016 0.120 0.048 -0.602 0.002

(0.248) (0.000) (0.000) (0.000) (0.909)

State_control 0.024 -0.047 -0.072 0.056 -0.131 -0.040

(0.077) (0.001) (0.000) (0.000) (0.000) (0.003)

Bh -0.013 0.023 -0.017 -0.045 0.059 0.123 0.016

(0.321) (0.096) (0.199) (0.001) (0.000) (0.000) (0.236)

Table 4: Factor Loadings of the Corporate Governance Index

Variables Factor Loading

CEO 0.044

Out 0.190

Top_executive 0.117

Top1 -0.669

Parent -0.028

Concentration 0.679

State_control -0.108

Bh 0.166

We have argued that if M&As are driven by the synergy motive, the long-term performance

and corporate governance of acquiring firms will be improved significantly. To understand

the evolution of corporate governance in acquiring firms, we compare the corporate

governance index over the year of announcement, the year before and the year after. The

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result in Table 5 suggests that corporate governance of acquiring firms does not change

significantly after the acquisition. Since acquisitions do not induce improvements in

corporate governance of acquiring firms, we speculate that acquisitions of Chinese listed

companies are not driven by the synergy motive.

Table 5: Time-Series Corporate Governance Comparison of Acquiring Firms The numbers in the first row are mean values and numbers in the second row are medians. T test and Wilcoxon two-sample test are applied to mean values and medians respectively. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables

The year

before

(1)

The announcement

year

(2)

The year

after

(3)

(2)-(1)t/z

value (3)-(2)

t/z

value

CG_index 0.055 0.094 0.055 0.039 0.482 -0.039 -0.510

0.183 0.224 0.235 0.041 0.123 0.011 0.063

To further prove our speculation, we test the relationship between corporate governance

index and the operating performance of acquiring firms. If acquisitions do not induce

improvements in corporate governance of acquiring firms, the change in corporate

governance index is expected not to be related to the change in the operating performance of

acquiring firms. To perform this test, we first calculate the differences of performance

indicators and corporate governance index between the announcement year and the year

before, then regress the changes in eleven performance indicators on the change in corporate

governance index respectively.

Since corporate governance and operating performance always work interactively, there may

be the endogeneity problem in the regression model. We address this problem by using the

two stage least square (2SLS) regression model. For the endogenously determined variable,

we need an instrumental variable－a variable that is correlated with the variable of interest,

but is ideally uncorrelated with the error term. Here, we use the floating ratio, the percentage

of tradable shares, as the instrumental variable for the corporate governance index. Wang and

Xu (2005) suggest that firm-specific floating ratio is a good proxy for expected corporate

governance in China, which helps to predict a firm’s future cash flow. In our sample, the

18

floating ratio is highly and significantly correlated to the corporate governance index.

Specifically, we apply the following 2SLS regression model:

εβββββ +∆⋅+∆⋅+⋅+⋅+=∆ −−− )0,1(4)0,1(302010)0,1( __/_ ratioFloatingindexCGADassetLnPER

(2)

Where PER is the variable for the operating performance of acquiring firms measured by

eleven indicators described above9. Ln_asset and D/A are control variables for size and risk,

which are proxied by the natural logarithm of book value of total assets and debt to asset

ratio in the announcement year, respectively. CG_index represents the corporate governance

index and Floating_ratio is an instrumental variable for CG_index. △(-1,0) denotes the

changes of variables from the year before to the announcement year.

The regression results are reported in Table 6. As expected, the coefficient of △CG_index is

insignificant in every regression. It is very clear that the change of corporate governance

index have no significant effect on the change of operating performance of acquiring firms.

We also take account of the possibility that corporate governance affects operating

performance gradually. Thus, we replace the change of operating performance from the year

before to the announcement year with that from the announcement year to the year after in

the equation (2), keeping other variables unchanged. This new regression will capture the

effect of the change of corporate governance index on the lag change of operating

performance of acquiring firms. Table 7 exhibits the similar result with Table 6 that none of

coefficient of △CG_index is significant.

In addition, we test the relationship between corporate governance and market performance

of acquiring firms. In general, corporate governance should have positive effect on the firm’s

market performance, which has been proved by Bai et al. (2004). Here, we regress the

market performance on the corporate governance index cross-sectionally for a given year in

the estimation window, and compare the change of coefficient. From this, we can judge the

market reaction to the M&A event. Under the rational market expectations, the market 9 In order to account for the industry effect, we use the industry-adjusted performance indicator in the regression which is calculated as the difference between the gross value and the median value of firms in the same industry, hereafter.

19

20

iiiiiii ratioFloatingsqrindexCGindexCGADassetLnPER

Where PER is the variable for the market performance of acquiring firms measured by Tq70

and Tq80. Tq70 and Tq80 are Tobin’qs calculated by taking account of illiquidity discount of

70% and 80% in the Chinese listed companies, respectively. Ln_asset, D/A, CG_index and

Floating_ratio are defined in the same manner with equation (2). We also include the square

of CG_index, denoted by CG_index_sqr, to take account of the possibility that there may be

a nonlinear relationship between corporate governance and market performance. The

regression result is presented in Table 8. It suggests that in the year before acquisition,

corporate governance has significantly positive effect on the market performance of

acquiring firms, which is consistent with the finding of Bai et al. (2004). However, after the

acquisition, the magnitude and significance of this positive effect decrease gradually. In the

year after acquisition, corporate governance has no significant effect on the market

performance. This sends us a clear signal that the market does not believe the function of

corporate governance when a firm launches acquisitions. Therefore, the market reaction to

acquisitions is negative. We also note that the coefficients of CG_index_sqr are not

significant for most of regressions, which means that the relationship between corporate

governance and market performance is almost linear.

reaction to the M&As driven by the synergy motive will be positive. Specifically, we apply

the following 2SLS regression model:

We have found evidence that the change of corporate governance of acquiring firms is not

significant and have no significant effect on the change of operating performance. In

addition, market reaction to acquisitions is negative. Together with the fact that the long-term

performance of acquiring firms decreases significantly, hypothesis I is proved to be false.

Therefore, M&As by Chinese listed firms are not driven by the synergy motive basically. In

the next section, we will further prove this judgment from the perspective of earnings

management.

εββββββ +⋅+⋅+⋅+⋅+⋅+= ____/_ 543210

(3)

Table 6: The Effect of the Change of Corporate Governance Index on the Change of Operating Performance of Acquiring Firms-2SLS Regression

Ln_asset and D/A are proxied by the natural logarithm of book value of total assets and debt to asset ratio in the announcement year, respectively. CG_index represents the corporate governance index and is an endogenous variable. Changes are computed as the differences of variables between the announcement year and the year before. This table shows the result of the final stage of the 2SLS regression, so the coefficients of instrumental variable－Floating_ratio are not included. The absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables △Sales △Profit △ROA △ROS △Total Cost △Total Fee △COS △FOS △SOW △COW △ROW

Unit (Billion￥) (Billion￥)

(Billion￥) (Billion￥) (10000￥) (10000￥) (10000￥)

Ln_asset 2.118 0.053 0.019 0.131 1.808 0.242 -0.027 0.091 20.905 19.600 -2.378

[5.54]*** [0.99] [2.28]** [1.56] [5.73]*** [5.00]*** [1.97]** [1.09] [1.50] [1.64] [1.17]

D/A 2.801 0.420 0.104 -0.108 2.685 -0.163 0.287 -1.651 132.126 108.538 1.978

[1.66]* [1.79]* [2.80]*** [0.29] [1.93]* [0.76] [4.84]*** [4.50]*** [2.00]** [1.92]* [0.20]

△CG_index -4.578 -0.252 -0.041 0.062 -3.301 -0.576 0.138 -0.270 121.003 93.671 17.962

[1.29] [0.51] [0.53] [0.08] [1.12] [1.28] [1.11] [0.35] [1.34] [1.21] [1.36]

Constant -44.56 -1.212 -0.460 -2.800 -38.226 -4.881 0.431 -1.097 -481.504 -446.428 50.142

[5.61]*** [1.10] [2.62]*** [1.59] [5.82]*** [4.85]*** [1.53] [0.63] [1.65]* [1.79]* [1.18]

Obersevations 549 549 549 547 549 549 547 547 465 465 465

21

22

Table 7: The Effect of the Change of Corporate Governance Index on the Lag Change of Operating Performance of Acquiring Firms-2SLS Regression

Ln_asset and D/A are proxied by the natural logarithm of book value of total assets and debt to asset ratio in the announcement year, respectively. CG_index represents the corporate governance index and is an endogenous variable. Changes of independent variables are computed as the differences between the announcement year and the year before. Changes of dependent variables are computed as the differences between the year after and the announcement year. This table shows the result of the final stage of the 2SLS regression, so the coefficients of instrumental variable－Floating_ratio are not included. The absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

εβββββ +∆⋅+∆⋅+⋅+⋅+=∆ −− )0,1(4)0,1(302010)1,0( __/_ ratioFloatingindexCGADassetLnPER

Variables △Sales △Profit △ROA △ROS △Total Cost △Total Fee △COS △FOS △SOW △COW △ROW

Unit (Billion￥) (Billion￥)

(Billion￥) (Billion￥) (10000￥) (10000￥) (10000￥)

Ln_asset 6.055 0.310 -0.001 -1.651 4.941 0.675 -0.009 1.565 69.020 59.621 0.667

[5.82]*** [2.69]*** [0.09] [3.24]*** [5.22]*** [8.44]*** [0.80] [3.49]*** [2.61]*** [2.56]** [0.21]

D/A 4.099 -0.119 -0.022 -3.735 3.979 0.016 -0.080 2.879 268.266 235.511 -15.218

[0.90] [0.24] [0.32] [1.67]* [0.96] [0.04] [1.63] [1.46] [2.10]** [2.09]** [1.00]

△CG_index -12.426 -1.241 -0.062 0.035 -10.465 -0.745 0.160 1.141 85.712 33.642 28.065

[1.28] [1.15] [0.43] [0.01] [1.18] [1.00] [1.53] [0.27] [0.51] [0.23] [1.41]

Constant -125.528 -6.385 0.017 36.698 -102.603 -13.737 0.240 -34.612 -1,513.810 -1,309.740 -7.545

[5.82]*** [2.67]*** [0.05] [3.47]*** [5.23]*** [8.28]*** [1.03] [3.72]*** [2.74]*** [2.69]*** [0.12]

Obersevations 545 545 545 544 545 545 544 544 467 467 467

Table 8: The Effect of Corporate Governance Index on the Market Performance of Acquiring Firms-2SLS Regression

Tq70 and Tq80 are Tobin’qs calculated by taking account of illiquidity discount of 70% and 80% in the Chinese listed companies, respectively. Ln_asset and D/A are proxied by the natural logarithm of book value of total assets and debt to asset ratio, respectively. CG_index represents the corporate governance index and is an endogenous variable. CG_index_sqr is the square of CG_index. This table shows the result of the final stage of the 2SLS regression, so the coefficients of instrumental variable－Floating_ratio are not included. The absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Periods The year before The announcement year The year after

Variables Tq70 Tq80 Tq70 Tq80 Tq70 Tq80

Ln_asset -0.660 -0.554 -0.750 -0.614 -0.487 -0.403

[14.00]*** [11.75]*** [12.07]*** [11.29]*** [12.45]*** [11.57]***

D/A 1.173 1.083 -0.133 -0.100 0.973 0.891

[10.20]*** [9.19]*** [0.50] [0.39] [9.82]*** [10.18]***

CG_index 0.247 0.371 0.046 0.184 0.030 0.128

[2.56]** [3.85]*** [0.37] [1.69]* [0.34] [1.64]

CG_index_sqr -0.018 -0.028 -0.001 -0.019 0.011 0.021

[1.56] [2.49]** [0.06] [1.37] [0.55] [1.14]

Constant 14.927 12.559 17.488 14.427 10.986 9.105

[15.29]*** [12.87]*** [13.95]*** [13.15]*** [13.16]*** [12.24]***

Obersevations 552 552 597 597 599 599

C. Earnings Management and the Performance of Acquiring Firms

We have argued that if M&As are driven by the agency or hubris motive, earnings

management by managers will be obvious and has significant effect on the short-term

operating performance. To test this hypothesis, we first measure the level of earnings

management. Given the definitions of four discretionary accruals, the value of each variable

for acquiring firms from 1999 to 2003 is calculated according to the CSMAR Financial

Statement Database. Table 9 shows the summary statistics for four discretionary accruals of

acquiring firms over the year of announcement, the year before and the year after. It suggests

the level of earnings management increases gradually after the acquisition. In the

announcement year, although DTAC and MDTAC are not significantly different from zero

(but marginally), MDCA and MDLA are both significantly different from zero. In the year

after acquisition, all these four variables are significantly different from zero. This proves

the action of earnings management by the managers of acquiring firms.

23

Table 9: Time-Series Discretionary Accruals Comparison of Acquiring Firms DTAC is discretionary total accruals by Jones model, MDTAC is discretionary total accruals by modified Jones model, MDCA is discretionary current accruals by modified Jones model, and MDLA is discretionary long-term accruals by modified Jones model. The numbers in the table are mean values. The absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables The year before

(1)

The announcement year

(2)

The year after

(3)

DTAC 0.000 0.005 0.010

[0.107] [1.076] [2.455]**

MDTAC 0.001 0.007 0.010

[0.208] [1.330] [2.544]**

MDCA -0.006 -0.016 -0.010

[0.885] [1.877]* [1.715]*

MDLA 0.007 0.023 0.021

[1.123] [2.911]*** [3.701]***

To further test the effect of earnings management on the operating performance of acquiring

firms, we regress the changes in eleven performance indicators on the changes in earnings

management variables respectively. Specifically, we apply the following OLS regression

model:

εββββ +∆⋅+⋅+⋅+=∆ −− )0,1(302010)0,1( /_ DTACADassetLnPER (4)

Where △(-1,0) denotes the changes of variables from the year before to the announcement

year. The regression results are reported in Table 10. It suggests that the coefficients of

△DTAC are significantly positive when the dependent variables are △Profit, △ROA,

△ROS and △ROW, and significantly negative when the dependent variables are △Total

Fee and △COS. Moreover, when △MDTAC is used in regressions instead of △DTAC, the

results are almost identical. It is very clear that the change in earnings management level

significantly affects the change in the operating performance of acquiring firms, especially

on the aspects of improving profitability and lowering cost. To examine the method of

earnings management, we further divide discretionary accruals（MDTAC）into discretionary

24

current accruals（MDCA）and discretionary long-term accruals（MDLA）. The regression

results in Table 11 suggest that the sign and significance of the coefficient of △MDCA are

exactly same to the coefficient of △DTAC in Table 10, and △MDLA only has one

significant coefficient. Therefore, current accruals are easier to be manipulated than

long-term accruals, which is consistent with the result of Guenther (1994).

We also take account of the possibility that earnings management affects operating

performance gradually. Thus, we replace the change of operating performance from the year

before to the announcement year with that from the announcement year to the year after in

the equation (4), keeping other variables unchanged. This new regression will capture the

effect of the change of earnings management level on the lag change of operating

performance of acquiring firms. The results in Table 12 and 13 suggest that the coefficients

of earnings management variables turn to be insignificant for most of regressions. This

means that earnings management only has significant effect on the short-term operating

performance.

In addition, we test the relationship between earnings management and market performance

of acquiring firms. In general, there is a negative relationship between earnings management

and the firm’s market performance (Erickson and Wang, 1999). Similarly, we regress the

market performance on the earnings management variables cross-sectionally for a given year

in the estimation window, and compare the change of coefficient. From this, we can judge

the market reaction to the M&A event. Under the rational market expectations, the market

reaction to the M&As driven by the agency or hubris motive will be negative. Specifically,

we apply the following 2SLS regression model:

iiiiii sqrDTACDTACADassetLnPER εβββββ +⋅+⋅+⋅+⋅+= _/_ 43210 (5)

Where PER is the variable for the market performance of acquiring firms measured by Tq70

and Tq80. Ln_asset, D/A, and DTAC are defined in the same manner with equation (4). We

also include the square of DTAC, denoted by DTAC_sqr, to take account of the possibility

25

26

that there may be a nonlinear relationship between earnings management and market

performance. The regression result is presented in Table 14. It suggests that in the year

before acquisition, earnings management has insignificantly negative effect on the market

performance. However, after the acquisition, this negative effect turns to be significant. This

result is consistent with the finding of Louis (2004), which means that the market expects

acquiring firms manipulate its earnings and discounts its stock price sharply. Therefore, the

market reaction to acquisitions is negative. Moreover, when MDTAC is used in regressions

instead of DTAC, the results are almost identical. We also note that the coefficients of

DTAC_sqr are significant for only two regressions, which means that the nonlinear

relationship between corporate governance and market performance is not stable.

Up to now, we have found evidences that earnings management of acquiring firms is

obvious, the change of earning management has significant effect on the change of

short-term operating performance. In addition, earnings management has significantly

negative effect on the market performance after the acquisition, which means the market

reaction to acquisitions is negative. Together with the fact that the long-term performance

decreases significantly, the hypothesis II is proved to be true. Therefore, mergers and

acquisitions in Chinese listed companies are mainly driven by the agency or hubris motive.

The M&As are just tools of managers to pursue their own interests at the expense of

shareholders or the means of controlling shareholders to expropriate minority shareholders.

Actually, these two types of agency problems are very popular in Chinese listed firms due to

the weak legal enforcement and corporate governance, for example, Bai, Liu and Song

(2004) find evidences of tunneling in Chinese listed companies. Obviously, M&As driven

by the agency or hubris motive are value decreasing transactions for acquiring firms. In

order to disguise the loss induced by M&As, the acquirers’ managers have strong incentives

of earnings management. However, they can effectively manipulate earnings only in the

short term, and the negative effect of agency or hubris problem is inevitably reflected in the

long-term performance.

Table 10: The Effect of the Change of Earnings Management on the Change of Operating Performance of Acquiring Firms-OLS Regression

Ln_asset is a control variable proxied by the natural logarithm of book value of total assets. D/A is debt to asset ratio representing the firm’s risk. DTAC is discretionary total accruals by Jones model. The Changes are computed as the differences of variables between the announcement year and the year before. White-correct absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables △Sales △Profit △ROA △ROS △Total Cost △Total Fee △COS △FOS △SOW △COW △ROW

Unit (Billion￥)

(Billion￥) (Billion￥) (Billion￥) (10000￥) (10000￥) (10000￥)

Ln_asset 2.037 0.052 0.021 0.202 1.671 0.310 -0.022 0.056 28.288 25.300 -2.489

[3.50]*** [0.73] [0.78] [1.47] [3.12]*** [4.23]*** [0.82] [0.32] [1.97]* [1.28] [0.75]

D/A 3.784 0.573 0.111 -0.113 3.424 -0.016 0.309 -1.741 95.678 79.477 -4.477

[1.97]** [1.13] [1.03] [0.09] [1.93]* [0.04] [1.16] [1.03] [1.48] [1.60] [0.34]

△DTAC -3.524 0.834 0.261 2.450 -3.621 -0.743 -0.214 -1.127 123.259 72.892 29.885

[1.25] [2.03]** [1.91]* [2.03]** [1.37] [2.10]** [1.87]* [1.09] [1.47] [0.79] [1.98]**

Constant -43.678 -1.302 -0.495 -4.336 -36.008 -6.387 0.318 -0.277 -610.074 -545.915 56.975

[3.46]*** [0.91] [0.87] [1.67]* [3.08]*** [4.19]*** [0.71] [0.09] [2.01]** [1.30] [0.80]

Observations 489 489 489 487 489 489 487 487 407 407 407

R-squared 0.08 0.03 0.09 0.05 0.07 0.07 0.08 0.05 0.02 0.02 0.02

27

Table 11: The Effect of the Change of Earnings Management on the Change of Operating Performance of Acquiring Firms-OLS Regression (Continued)

εβββββ +∆⋅+∆⋅+⋅+⋅+=∆ −−− )0,1(4)0,1(302010)0,1( /_ MDLAMDCAADassetLnPER

Ln_asset is a control variable proxied by the natural logarithm of book value of total assets. D/A is debt to asset ratio representing the firm’s risk. MDCA is discretionary current accruals by modified Jones model, and MDLA is discretionary long-term accruals by modified Jones model. The Changes are computed as the differences of variables between the announcement year and the year before. White-correct absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables △Sales △Profit △ROA △ROS △Total Cost △Total Fee △COS △FOS △SOW △COW △ROW

Unit (Billion￥)

(Billion￥) (Billion￥) (Billion￥) (10000￥) (10000￥) (10000￥)

Ln_asset 2.031 0.049 0.020 0.197 1.666 0.311 -0.02 0.047 28.614 25.498 -2.408

[3.47]*** [0.70] [0.77] [1.48] [3.08]*** [4.25]*** [0.86] [0.29] [1.99]** [2.02]** [0.72]

D/A 3.752 0.552 0.107 -0.149 3.405 -0.01 0.318 -1.773 83.667 71.515 -6.808

[1.93]* [1.14] [1.04] [0.12] [1.89]* [0.02] [1.27] [1.08] [1.29] [1.25] [0.49]

△MDCA -2.777 0.918 0.269 2.442 -2.996 -0.703 -0.251 -0.817 172.613 107.688 35.55

[1.02] [2.35]** [1.99]** [2.13]** [1.17] [2.14]** [1.83]* [0.75] [2.14]** [1.52] [1.81]*

△MDLA -2.696 0.432 0.154 1.427 -2.834 -0.49 -0.003 -1.762 125.494 76.573 25.406

[1.30] [1.07] [1.22] [1.36] [1.48] [1.51] [0.03] [2.16]** [1.41] [0.98] [1.21]

Constant -43.549 -1.222 -0.479 -4.212 -35.905 -6.407 0.277 -0.069 -610.711 -545.941 56.467

[3.42]*** [0.86] [0.85] [1.67]* [3.04]*** [4.21]*** [0.71] [0.02] [2.02]** [2.05]** [0.79]

Observations 489 489 489 487 489 489 487 487 407 407 407

R-squared 0.07 0.05 0.11 0.06 0.07 0.07 0.14 0.07 0.03 0.02 0.03

28

Table 12: The Effect of the Change of Earnings Management on the Lag Change of Operating Performance of Acquiring Firms-OLS Regression

εββββ +∆⋅+⋅+⋅+=∆ − )0,1(302010)1,0( /_ DTACADassetLnPER

Ln_asset is a control variable proxied by the natural logarithm of book value of total assets. D/A is debt to asset ratio representing the firm’s risk. DTAC is discretionary total accruals by Jones model. Changes of independent variables are computed as the differences between the announcement year and the year before. Changes of dependent variables are computed as the differences between the year after and the announcement year. White-correct absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables △Sales △Profit △ROA △ROS △Total Cost △Total Fee △COS △FOS △SOW △COW △ROW

Unit (Billion￥)

(Billion￥) (Billion￥) (Billion￥) (10000￥) (10000￥) (10000￥)

Ln_asset 5.485 -0.004 -0.005 -1.830 4.742 0.670 -0.001 1.797 82.965 70.989 0.613

[2.93]*** [0.03] [0.30] [0.97] [2.63]*** [6.70]*** [0.10] [1.04] [1.34] [1.29] [0.19]

D/A 8.059 0.241 -0.015 -3.936 7.345 0.183 -0.103 2.868 239.475 223.527 -28.424

[1.37] [0.45] [0.21] [1.02] [1.28] [0.35] [1.62] [0.80] [1.34] [1.30] [1.96]*

△DTAC -12.635 0.014 0.126 -6.376 -11.960 -0.592 -0.065 6.201 -41.169 -87.130 39.141

[1.37] [0.03] [1.33] [0.80] [1.33] [1.44] [0.91] [0.85] [0.15] [0.33] [2.10]**

Constant -116.263 -0.091 0.083 40.639 -100.718 -13.730 0.100 -39.512 -1,784.40 -1,539.24 2.229

[2.83]*** [0.03] [0.24] [0.97] [2.53]** [6.59]*** [0.33] [1.03] [1.32] [1.27] [0.03]

Observations 485 485 485 484 485 485 484 484 411 411 411

R-squared 0.08 0.00 0.00 0.04 0.07 0.15 0.02 0.04 0.03 0.03 0.02

29

30

Table 13: The Effect of the Change of Earnings Management on the Lag Change of Operating Performance of Acquiring

Firms-OLS Regression (Continued)

εβββββ +∆⋅+∆⋅+⋅+⋅+=∆ −− )0,1(4)0,1(302010)1,0( /_ MDLAMDCAADassetLnPER

Ln_asset is a control variable proxied by the natural logarithm of book value of total assets. D/A is debt to asset ratio representing the firm’s risk. MDCA is discretionary current accruals by modified Jones model, and MDLA is discretionary long-term accruals by modified Jones model. Changes of independent variables are computed as the differences between the announcement year and the year before. Changes of dependent variables are computed as the differences between the year after and the announcement year. White-correct absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Variables △Sales △Profit △ROA △ROS △Total Cost △Total Fee △COS △FOS △SOW △COW △ROW

Unit (Billion￥)

(Billion￥) (Billion￥) (Billion￥) (10000￥) (10000￥) (10000￥)

Ln_asset 5.487 -0.008 -0.005 -1.836 4.747 0.672 -0.002 1.791 82.942 70.803 0.718

[2.92]*** [0.05] [0.33] [0.97] [2.62]*** [6.74]*** [0.11] [1.04] [1.34] [1.30] [0.22]

D/A 8.089 0.218 -0.017 -3.885 7.394 0.195 -0.103 2.771 228.752 222.286 -32.700

[1.36] [0.42] [0.24] [1.02] [1.27] [0.39] [1.60] [0.80] [1.13] [1.14] [1.14]

△MDCA -11.424 0.228 0.136 -6.354 -11.016 -0.623 -0.061 6.484 19.320 -48.172 48.248

[1.27] [0.46] [1.50] [0.80] [1.26] [1.59] [0.85] [0.89] [0.07] [0.18] [2.16]**

△MDLA -8.639 -0.251 0.045 -6.450 -8.094 -0.190 -0.064 4.925 -25.213 -48.317 29.076

[1.39] [0.50] [0.45] [0.88] [1.36] [0.48] [0.91] [0.74] [0.13] [0.29] [1.30]

Constant -116.361 0.004 0.096 40.759 -100.874 -13.79 0.102 -39.324 -1,778.34 -1,534.50 2.117

[2.81]*** [0.00] [0.27] [0.98] [2.52]** [6.62]*** [0.33] [1.03] [1.32] [1.27] [0.03]

Observations 485 485 485 484 485 485 484 484 411 411 411

R-squared 0.08 0.01 0.01 0.04 0.07 0.15 0.02 0.05 0.03 0.03 0.03

Table 14: The Effect of Earnings Management on the Market Performance

of Acquiring Firms-OLS Regression

Tq70 and Tq80 are Tobin’qs calculated by taking account of illiquidity discount of 70% and 80% in the Chinese listed companies, respectively. Ln_asset and D/A are proxied by the natural logarithm of book value of total assets and debt to asset ratio, respectively. DTAC is discretionary total accruals by Jones model. DTAC_sqr is the square of DTAC. White-correct absolute value of t statistics is in brackets. *, ** and *** represent significance level at 10%, 5% and 1% respectively.

Periods The year before The announcement year The year after

Variables Tq70 Tq80 Tq70 Tq80 Tq70 Tq80

Ln_asset -0.763 -0.680 -0.798 -0.702 -0.493 -0.434

[17.33]*** [17.10]*** [16.59]*** [16.78]*** [15.43]*** [15.46]***

D/A 1.281 1.238 -0.152 0.049 0.944 0.909

[12.15]*** [13.00]*** [0.70] [0.26] [9.87]*** [10.81]***

DTAC -0.147 -0.165 -2.765 -2.326 -0.835 -0.735

[0.38] [0.47] [6.66]*** [6.44]*** [2.47]** [2.48]**

DTAC_sqr 0.346 0.210 3.201 2.685 0.704 0.669

[0.35] [0.23] [5.37]*** [5.18]*** [0.97] [1.05]

Constant 17.032 15.108 18.468 16.165 11.158 9.796

[18.41]*** [18.08]*** [18.24]*** [18.36]*** [16.34]*** [16.32]***

Observations 491 491 578 578 612 612

R-squared 0.50 0.51 0.37 0.37 0.39 0.40

V. Conclusions

This study researches the M&A motives and their effectiveness by analyzing the relationship

between corporate governance, earnings management and the performance of acquiring

firms. We make two hypotheses in this paper. Hypothesis I postulates that if M&As are

driven by the synergy motive, the long-term performance and corporate governance of

acquiring firms will be improved significantly, and corporate governance has significant

effect on the long-term operating performance of acquiring firms, and the market reaction

will be positive. Hypothesis II assumes if M&As are driven by the agency or hubris motive,

the long-term performance of acquiring firms will decrease significantly, and earnings

management by managers will be obvious and has significant effect on the short-term

operating performance, and the market reaction will be negative. Using a sample of 618

acquisitions in Chinese listed companies, we test these hypotheses. The empirical evidence

suggests that the long-term performance of acquiring firms decreases significantly.

31

Corporate governance of acquiring firms is not improved and has no significant effect on the

operating performance. However, earnings management is very obvious and has significant

effect on the short-term performance of acquiring firms. Furthermore, the market reaction to

acquisitions is negative. Therefore, M&As in Chinese listed companies are mainly driven by

agency or hubris motive, and the synergy effect is not realized basically.

The contributions of this study lie in three aspects. First, this paper provides a new way to

identify and analyze the motives of acquiring firms, especially from the perspectives of

corporate governance and earnings management. Second, we measure the performance of

acquiring firms more comprehensively than the former studies. Thirteen indicators

employed in this study not only include profitability, but also comprise of production, cost

and productivity and market performance. This makes our results more robust. Finally, we

not only find the evidence of earnings management of acquiring firms, but also identify the

possible method of earnings management. In sum, our findings make an important

contribution towards a better understanding of the mechanisms behind the current massive

acquisitions in China. The main finding that acquisitions are mainly driven by the agency or

hubris motive suggests that corporate governance structure in Chinese listed companies is

far from effective and well developed. The corporate governance of Chinese listed

companies needs to be improved and M&As need to be instructed and regulated. Therefore,

this paper has important implications for listed firms and regulatory authorities in China.

Appendix: Calculation of Discretionary Accruals

In accounting, accruals are measured as follows:

TAC=Net Income-Cash Flow form Operations (6)

Where TAC denotes total accruals. As stated in Xia (2003), excluding extraordinary items

and discontinued operations form net income will enhance the model’s power in detecting

discretionary accruals in China. So, we use operating income instead of net income to

compute total accruals.

32

TAC=Operating Income-Cash Flow form Operations (7)

Total accruals can be classified into discretionary accruals and non-discretionary accruals

based on whether they are necessary adjusting or simply a result of management discretion.

We use the cross-sectional adaptation of the Jones (1991) model in derivation of

discretionary accruals and non-discretionary accruals. The model applies cross-sectional

regression on accruals of firms in the same industry to obtain predicted values as

non-discretionary accruals. Specifically, the regression model is as follows:

jttj

jt

tj

jt

tjtj

jt

TAPPE

bTASALES

bTA

bTATAC

ε+⎟⎟⎠

⎞⎜⎜⎝

⎛+⎟

⎟⎠

⎞⎜⎜⎝

⎛ ∆+⎟⎟⎠

⎞⎜⎜⎝

⎛=

−−−− 1,2

1,1

1,0

1,

1 (8)

Where TACjt is total accruals of firm j in year t; △SALESjt is calculated as SALESjt –

SALESj,t-1, or changes in sales revenue; PPEjt is gross property, plant and equipment for firm

j in year t; TAj,t-1, total assets in year t-1, is used as denominator to reduce heteroskedasticity.

To obtain non-discretionary accruals for firm j, the data of all firms in the same industry

except firm j are used in the regression. Here we use the industrial classification criterion

developed by China Securities Regulatory Commission (CSRC)10. The non-discretionary

accruals, NDTACjt, is computed as

⎟⎟⎠

⎞⎜⎜⎝

⎛+⎟⎟⎠

⎞⎜⎜⎝

⎛ ∆+⎟

⎟⎠

⎞⎜⎜⎝

⎛=

−−− 1,2

1,1

1,0

ˆˆ1ˆtj

jt

tj

jt

tjjt TA

PPEb

TASALES

bTA

bNDTAC (9)

Discretionary accruals, DTACjt, is calculated as the difference between total accruals and

non-discretionary accruals.

10 It divides all listed companies into thirteen main industries and subdivides every main industry into several subindustries. Due to the clustering of firms in the manufacturing industry (denoted by Code C), we treat ten subindustries of C as main industries.

33

jttj

jtjt NDTAC

TATAC

DTAC −=−1,

(10)

The modified Jones model employs the same regression model with Jones model. The only

difference is that it subtracts the increase in accounts receivable from sales growth when

estimating non-discretionary accruals. This treatment allows for the possibility of credit sales

manipulation by the firm (Dechow, Sloan and Sweeney, 1995). Here the non-discretionary

accruals by modified Jones model, MNDTACjt, is calculated as

⎟⎟⎠

⎞⎜⎜⎝

⎛+⎟

⎟⎠

⎞⎜⎜⎝

⎛ ∆−∆+⎟

⎟⎠

⎞⎜⎜⎝

⎛=

−−− 1,2

1,1

1,0

ˆˆ1ˆtj

jt

tj

jtjt

tjjt TA

PPEb

TAARSALES

bTA

bMNDTAC (11)

Where △ARjt is the change in accounts receivable for firm j in year t relative to year t-1, and

, and are estimated form equation (6). Therefore, the discretionary accruals by

modified Jones model, MDTAC

0b̂ 1̂b 2b̂

jt, is calculated as

jttj

jtjt MNDTAC

TATAC

MDTAC −=−1,

(12)

We further divide the discretionary accruals into the discretionary current accruals and the

discretionary long-term accruals according to Teoh et al. (1998). They point that accruals can

be classified into current accruals and long-term accruals based on the time period and

managerial control. Following Teoh et al. (1998), current accruals (CA) are the change in

noncash assets minus the change in operating current liabilities:

CA=△[current assets−cash]−△[current liabilities−current maturity of long-term debt] (13)

To obtain the discretionary current accruals, we first perform the intra-industry regression of

current accruals on the change in sales that is similar to equation (6).

34

jttj

jt

tjtj

jt

TASALES

bTA

bTACA

ε+⎟⎟⎠

⎞⎜⎜⎝

⎛ ∆+⎟⎟⎠

⎞⎜⎜⎝

⎛=

−−− 1,1

1,0

1,

1 (14)

As in Teoh et al. (1998), the following indicators of accruals are calculated by the modified

Jones model. The non-discretionary current accruals, MNDCAjt, is computed as

⎟⎟⎠

⎞⎜⎜⎝

⎛ ∆−∆+⎟⎟⎠

⎞⎜⎜⎝

⎛=

−− 1,1

1,0

ˆ1ˆtj

jtjt

tjjt TA

ARSALESb

TAbMNDCA (15)

Discretionary current accruals, MDCAjt, is calculated as the difference between current

accruals and non-discretionary current accruals. And discretionary long-term accruals,

MDLAjt, equals discretionary total accruals (MDTACjt) minus discretionary current accruals

(MDCAjt).

jttj

jtjt MNDCA

TACA

MDCA −=−1,

(16)

jtjtjt MDCAMDTACMDLA −= (17)

References Bai, Chong-En, Qiao Liu, Joe Lu, Frank Song, and Junxi Zhang, 2003. Corporate governance and market

valuation in China, Working Paper, the University of Hong Kong.

Bai, Chong-En, Qiao Liu, Joe Lu, Frank Song, and Junxi Zhang, 2004. Corporate governance and market

valuation in China, Journal of Comparative Economics 32, 519-616.

Bai, Chong-En, Qiao Liu, and Frank Song, 2004. Bad news or good news: propping and tunneling

evidence from China. Working Paper, the University of Hong Kong.

Barber, B. M., J. D. Lyon, 1996. Detecting abnormal operating performance: the empirical power and

specification of test statistics. Journal of Financial Economics 41, 359-99.

Bartov, E., Gul F.A., and S.L. Tsui., 2000. Discretionary-accruals models and audit qualifications. Journal

of Accounting and Economics 30, 421-52.

35

Berkovitch, E., Marayanan, M. P., 1993. Motives for takeovers: an empirical investigation. Journal of

Financial and Quantitative Analysis 28, 347-62.

Chung, Kee H., and Pruitt, Stephen W., 1994. A simple approximation of Tobin’s q. Financial

Management 23, 70-74.

DeAngelo, L., 1986. Accounting numbers as market valuation substitutes: a study of management buyouts

of public shareholders. The Accounting Review 61, 400-20.

Dechow, P.M., R.G. SLoan, and A.P. Sweeney., 1995. Detecting earnings management. The Accounting

Review 70, 193-225.

Defond, M. L., and J. Jiambalvo, 1994. Debt covenant violation and manipulation of accruals. Journal of

Accounting and Economics 17, 145-76.

Denis, Diane K., McConnell, John J., 2003. International corporate governance, Journal of Financial and

Quantitative Analysis 38, 1-36.

Erickson, M., Wang, S., 1999. Earnings management by acquiring firms in stock for stock mergers.

Journal of Accounting and Economics 27, 149-76.

Feng Genfu and Wu Linjiang, 2001. The empirical study of M&A performance in Chinese listed firms.

Economic Research Journal 36 (1), 54-68.

Ghosh, A., 2001. Does operating performance really improve following corporate acquisitions? Journal of

Corporate Finance 7, 151-178.

Guenther, D., 1994. Earnings management in response to corporate tax rate changes: evidence from the

1986 tax reform act. Accounting Review 69, 230-43.

Healy, P. M., 1985. The effect of bonus schemes on accounting decisions. Journal of Accounting and

Economics 7, 85-107.

Healy, P.; K. Palepu; and R. Ruback, 1992. Does corporate performance improve after mergers? Journal

of Financial Economics 31, 135-175.

Hu Yifan, Frank Song, and Junxi Zhang, 2005. Does privatization work in China? Working Paper, the

University of Hong Kong.

Jolliffe, 2002. Principal Component Analysis, 2nd Ed. Springer, New York.

Jones J., 1991. Earnings management during import relief investigations. Journal of Accounting Research

29, 193-228.

Kang, S. H., Sivaramakrishnan, K., 1995. Issues in testing earnings management and an instrumental

36

variable approach. Journal of Accounting Research 33, 353-67.

Kothari, s., Leone, A., Wasley, C., 2005. Performance matched discretionary accrual measures. Journal of

Accounting and Economics 39, 163-97.

Li and Chen, 2003. Empirical study on mergers and restructuring in Chinese listed companies. China

Financial and Economic Publishing House.

Li Zengquan, Yu Qian and Wang Xiaokun, 2005. Tunneling, propping M&A: evidence from Chinese

listed companies. Economic Research Journal 40 (1), 95-105.

Liu Qiao, 2005. Corporate governance in China: current practices, economic effects, and institutional

determinants. Working Paper, the University of Hong Kong.

Louis, H., 2004. Earnings management and the market performance of acquiring firms. Journal of

Financial Economics 74, 121-48.

Malatesta, P. H., 1983. The wealth effect of merger activity and the objective functions of merging firms.

Journal of Financial Economics 11,155-81.

Subramanyam K.R., 1996. The pricing of discretionary accruals. Journal of Accounting and Economics

22, 249-82.

Teoh, S., Welch, I., and Wong T., 1998. Earnings management and the underperformance of seasoned

equity offerings. Journal of Financial Economics 50, 63-99.

Wan Chaoling, Chu Chengzhong and Lixiang, 2001. The empirical study on the restructuring

performance in Shanghai and Shenzhen stock markets. China Securities Daily, January 9th.

Wang, F., and Y. Xu, 2005. What determines Chinese stock returns? Financial Analysts Journal,

forthcoming.

Weston, F., K. S. Cheung, J. A. Sin, 2001. Takeovers, Restructuring and Corporate Governance. Prentice

Hall.

Xia, Lijun, 2003. The application of earnings management models in Chinese stock market. China

Accounting and Finance Review 5.

Zhang Xin, 2003. Do mergers and restructuring create value? Theoretical and empirical studies on

Chinese securities market. Economic Research Journal 38 (6), 20-29.

37