performance commitment in acquisitions, regulatory change ... .pdfperformance commitment can be a...

Performance commitment in acquisitions, regulatory

change and market crash risk -- Evidence from China

Di Song

School of Business

Renmin University of China

Phone/Fax: + (86) 6898 4784

Email: [email protected]

Jun Su

School of Business

Beijing Technology and Business University

Phone/Fax: + (86) 6898 4784


Chao Yang

School of Accountancy

Central University of Finance and Economics

Phone/Fax: + (86) 6898 4784


Performance commitment in acquisitions, regulatory

change and market crash risk- Evidence from China

Abstract

We find that performance commitment provisions in Chinese acquisitions show positive economic

outcomes measured by improved abnormal returns and lower market crash risk using

hand-collected data. We further illustrate that the performance commitment contracts can reduce

stock price crash risk by reducing information asymmetry and improving information

transparency. We also investigate that, regulatory adjustments actually worsen the positive effect

of performance commitment in acquisitions. The fact shows the short-termism effect of Chinese

capital market imposed by the regulatory changing risk. The additional test shows that only the

ratio of the fair value of performance commitment to the acquisition payment other than specific

contract terms is relevant to the economic outcomes, which explains that the execution of the

performance commitment can be a tedious legal process with respect to disputing the final

payment between the counterparties. Our study complements the earnout literature and show

distinctive Chinese characteristics.

1. Introduction

There is a large academic literature on the principal–agent problem in financial contracting

(Bolton and Scharfstein, 1990; Aghion and Bolton, 1992; Kaplan and Strömberg, 2003; DeMarzo

and Fishman, 2007; Landier and Thesmar, 2008). The papers in this literature often begin with a

situation in which an investor negotiates with an entrepreneur over the financing of a project or

company (Admati and Pfleiderer, 1994; Kaplan and Strömberg, 2003). Kaplan and Strömberg

(2003) find that VC financings allow VCs to separately allocate cash flow rights, board rights,

voting rights, liquidation rights, and other control rights. These rights are often contingent on

observable measures of financial and non-financial performance. In general, board rights, voting

rights, and liquidation rights are allocated such that if the firm performs poorly, the VCs obtain

full control. As performance improves, the entrepreneur retains more control rights. If the firm

performs very well, the VCs retain their cash flow rights, but relinquish most of their control and

liquidation rights. Similar performance commitment contracts have been also studied in

acquisitions (Kohers and Ang, 2000; Datar et al., 2001; Cain et al., 2011; Barbopoulos and

Sudarsanam, 2012; Pan et al., 2017). Despite of the studies, relatively little empirical work exists

that analyzes the impact and characteristics of performance commitment contracts in acquisitions

especially in emerging markets. In this paper, we attempt to fill the gap by investigating in detail

the impact of performance commitment contracts between acquisition counterparties on economic

outcomes of acquirers in China.

The classical principal–agency approach, pioneered by Hölmstrom (1979), assumes that the

agent’s effort is unobservable to the principal. Signals, such as firm output or profits, however, are

correlated with effort and can be contracted on. The optimal incentive contract ensures that the

agent puts in enough effort by making the agent’s compensation dependent on the outcome of the

signals. In the basic model, and in the absence of risk aversion, the investor maximizes the

sensitivity of the agent’s compensation to the signal. Moreover, it is in the investor’s interest to

make the entrepreneur’s compensation contingent on as many verifiable signals correlated with

effort as possible (Hölmstrom, 1979; Harris and Raviv, 1979; Innes, 1990). The mechanism works

similarly in performance commitment contracts between acquisition counterparties where acquirer

the principal utilizing performance commitment contracts based mostly on financial profits to

make target the agents’ efforts observable.

Many financial contracting theories predict that the investor should hold a debt-like claim.

The security design theories based on classical principal–agent theory (Innes, 1990) show that

giving investors a senior claim is useful for incentive purposes as it makes the manager’s residual

claim more sensitive to performance. Similarly, signaling theories such as Myers and Majluf

(1984) and Demarzo and Duffie (1999), show that in an asymmetric information setting, the

manager can signal that success is more likely by offering the investor a senior claim that receives

all of the value in case of failure. According to these models, the acquirer should enjoy stronger

rights when there is greater uncertainty about target quality and future profitability. Bolton and

Scharfstein (1990), Fluck (1998) and Hart and Moore (1998) assume that profits are observable

but not verifiable to outsiders and courts. The optimal financial claim in both approaches is a

debt-like claim in which (1) the target company promises a fixed payment to the acquirer; and (2)

the acquirer takes control of the target and get payment if the commitment is not fulfilled. Hence,

these theories are consistent both with the seniority and the default aspects of debt contracts.

The debt-like claim between the acquirer and the target has been taken the form of earnout in

literature (Kohers and Ang, 2000; Datar et al., 2001; Cain et al., 2011; Barbopoulos and

Sudarsanam, 2012). The performance commitment contracts we study are similar to earnouts as

defined by Cadman et al. (2013), that “earnouts are provisions of acquisition agreements that

provide sellers with payments conditional on the occurrence of specified future events or meeting

certain conditions. These contracted outcomes, which generally extend up to five years after the

acquisition, are often based on financial performance measures, such as revenue and earnings

targets, and/or nonfinancial performance hurdles.” The difference is that, acquirers in performance

commitment contracts we study in China do not delay the acquisition payment as in earnouts.

Acquirers actually pay the targets in full amount1 in acquisitions, while targets pay back the

pre-specified amount as agreed in performance commitment contracts contingent upon conditions

similar to those in earnouts. We posit that the sequence of payment in such arrangement does not

alter the theoretical nature of such contracts. We use performance commitment instead of earnout

in our study to clarify the difference.

Prior work on earnouts primarily examines when acquiring firms are likely to include earnout

1 The payment methods have been specified in the Measures for the Administration of the Takeovers since 2006.

First, if the payment is made in cash, no less than 20% of the total price shall be deposited as security at a bank

designated by the securities registration and clearing institution; second, the payment in stocks shall be deposited

in full with the securities registration institution except for issuing new stocks. In addition, the banks are required

to guarantee the acquisition payment and the underwriters are also liable for default. The above policies show that

the absence of payment would legally forbid the transfer of ownership so that there can be no way of earnout

payment in China. To differentiate from earnout, we use performance commitment in this paper to depict the

reimbursement by the targets shareholders to the acquirers after the deal is closed once the per-specified

performance hurdle is not achieved.

provisions in acquisition agreements. Kohers and Ang (2000), Datar et al. (2001), and Chatterjee

et al. (2004) suggest that earnouts help acquiring firms hedge risk and reduce acquisition costs

when there is greater information asymmetry about target firms. Kohers and Ang (2000) and

Chatterjee et al. (2004) also provide evidence that acquisition premiums are greater when earnouts

are included in acquisition agreements. Cain et al. (2011) find that earnouts are larger when targets

operate in industries with high-growth or high-return volatility, consistent with earnouts being

structured to minimize the costs of valuation uncertainty. While Cadman et al. (2013) study the

impact of the new earnout fair value information required by SFAS 141(R) on the economic

determinants of earnout provisions in acquisition agreements. In our study, we take advantage of

the natural experiment of new acquisition regulation rollout to provide insights into the economic

outcome of performance commitment provisions in acquisition agreements in the largest emerging

market, China.

We present and test two types of economic outcome for the performance commitment

introduction in Chinese acquisitions, including abnormal market returns and market crash risk in

order to test that such provisions help to alleviate information asymmetry problems and bridge

valuation gaps. We find that performance commitment provisions in Chinese acquisitions show

positive economic outcomes measured by improved abnormal returns and lower market crash risk.

We further illustrate that the performance commitment contracts can reduce stock price crash risk

by reducing information asymmetry and improving information transparency.

We also investigate the economic outcome of regulation adjustments on acquisitions and

reorganizations in China. The empirical results show that regulation adjustments actually worsen

the positive effect of performance commitment provisions in acquisitions. The fact shows the

short-termism effect of Chinese capital market imposed by the frequent regulatory change. The

results also shows that only performance commitment dummy (PC) other than specific contract

terms significantly impact on the market performance after the regulatory change, that is, the

existence of such contracts is more effective than the exact contract terms. The results are parallel

to western studies (Caselli et al. 2006) that execution of the performance commitment can be a

tedious legal process with respect to disputing the final payment between the counterparties which

has been the fact from several cases disputed in Chinese courts.2

Our study makes four primary contributions. First, our findings contribute to the literature on

performance commitment and earnouts (Datar et al., 2001; Kohers and Ang, 2000; Cain et al,

2011; Barbopoulos and Wilson, 2013; Lukas and Heimann, 2014; Cadman et al., 2014) and more

generally, the literature on economic outcomes of the contingent payment mechanism. By

exploring the introduction of performance commitment rules of acquisitions in China, we provide

new insights into previously documented economic outcomes of performance commitment

provisions. Specifically, we provide evidence on the relation between contract characteristics and

economic outcomes to resolve information asymmetry problems and bridge valuation gaps.

Second, our results point out the possible way of mitigating stock crash risk through improved

information transparency. Third, by examining the impacts of regulatory adjustments on

performance commitments, we contribute to understanding why Chinese listed companies show

short-termis and speculative characteristics and side effects of the constantly changing regulatory

2 In 2013, Ourpalm Co. acquired 70% of Shangyou Co. and signed a performance commitment contract requiring

that stock should be the compensation method. However, Shangyou Co. changed to use cash to pay for the

unrealized profit in the end. In a similar way, after Steyr Motors Co. acquired Yingda Co., instead of using stock as

the pre- specified compensation method, Yingda Co. paid for the unrealized profit by cash.

environment. Finally, our evidence on the information content of performance commitment

contributes to the literature on the reliability of such mechanism in execution.

The rest of this paper is organized as follows. The next section introduces the background of

the performance commitment policy implementation process in China. Section 3 provides a

review of the relevant literature used in developing the main hypotheses. Section 4 describes the

sample and key variables. Section 5 reports the empirical results and section 6 presents the

robustness tests. The final section concludes.

2. Background

Performance commitment was first introduced to Chinese listed companies in the process of

listed company's shareholding structure reform which started in 2005 (Hou et al., 2015)3. In order

to protect the interests of investors, China Securities Regulatory Commission (CSRC) has called

for the disclosure of performance commitment by the listed companies who implemented

shareholding structure reform in assets reorganization. When the performance fails to meet the

standard, the listed companies shall compensate the outstanding shareholders with a certain

proportion of shares or cash. On April 16, 2008, CSRC issued “the Administration Measures for

Significant Asset Restructuring of Listed Companies” (CSRC Decree No. 53), which first legally

restricted the acquisition and reorganization of listed companies. It explicitly stipulated that the

target firms should sign a performance commitment agreement with the acquirers in the event that

the acquirers evaluate the price in a valuation method based on future expected earnings. In order

to further optimize the market environment of the acquisition and reorganization of listed

companies, in November 2014, CSRC revised “the Measures for the Administration of Major

Assets Reorganization of Listed Companies”. This revised “Measures for the Administration of

Major Assets Reorganization of Listed Companies” (CSRC Decree No.109) is the legal source of

the contingent payment in the current acquisition and reorganization of listed companies. Along

with the increasing acquisition and reorganization transactions, the CSRC has issued a series of

instructions to related problems and solutions to the performance commitment of acquisition and

reorganization. Especially, when replied to relative questions, CSRC emphasized that "no matter

whether the target assets belonged to or are controlled by the block-holders, actual controlling

shareholders, or related parties of actual controlling shareholders, or whether the target assets are

priced by asset approach, the block-holders, actual controlling shareholders, or related parties of

actual controlling shareholders, they all should make performance commitment with the stock and

cash they have obtained. It also emphasized that "the major assets reorganization of listed

companies should not subject to the provisions of “Article 5 of the No. 4 guidelines for regulating

listed Companies -- The Actual Controlling Shareholders, Shareholders, Related Parties,

Acquirers and Listed Companies’ promise and performance”, they cannot change the performance

commitment randomly 4 . As a contractual arrangement of price adjustment, performance

3 At this time performance commitment is what CSRC requires the listed companies to make, which is on

business performance after shareholding structure reform, aiming at protecting the interests of investors, promoting

the smooth progress of the listed companies’ split share structure reform, which formed the embryonic form of

earnout in acquisition. 4 On January 15, 2016, the CSRC issued Relevant Issues and Answers to Performance Commitment in acquisition.

On June 17, 2016, the Commission issued Relevant Issues and Answers to the Performance Commitment of Listed

commitment, which can reduce the information asymmetry and modify transaction price, has been

applied to the acquisition and reorganization of listed companies more, becoming an important

institutional guarantee for the smooth completion of acquisitions and reorganizations of listed

companies (Pan et al., 2017).

3. Literature review and hypothesis development

A considerable number of papers in the finance literature provide empirical evidence on the

determinants of the choice between stock and cash as the method of payment, including Carleton

et al. (1983), Amihud et al. (1990), Chaney et al. (1991), Martin (1996) and Officer (2004).

However, this literature typically ignores the increasing fraction of merger bids that contain

contingent payment provisions such as earnouts.

Corporate acquisitions often pose significant problems for both the acquiring and the selling

entities. For example, it is not uncommon to find that the parties to the transaction significantly

disagree as to the value of the target entity, with the purchaser believing that the asking price is

inflated, in contrast to the vendor’s perception that the price is fair or even too low. In addition,

purchasers are often concerned that key personnel (often owner-managers) will not remain with

the target entity or that if they do remain they will have little incentive or motivation to promote

and deliver the sort of synergies expected of the acquisition. These problems, referred to in the

literature as the problems of asymmetric information and moral hazard, are often overcome

through the use of earnouts (Cain et al., 2011). Arrangements whereby the consideration to be paid

for a business may involve additional and contingent payments based in some way on the future

performance of that business. The parties to the transaction agree that the buyer will pay an

additional amount in some agreed time frame based upon some agreed achievement of revenue,

earnings or other performance measure. The contingent payments made under the earnout

agreement therefore bridge the gap between what the vendor thinks the business is genuinely

worth and what the purchaser considers is a reasonable price based on the expectation of future

earnings. They also tie in and motivate key personnel in the transition phase.

The literature suggests a number of features that characterize earnouts (Cain et al., 2011).

They are more typically found where the target is a private company or a subsidiary of a public

firm rather than a public company; the performance measures vary. Some measure of profitability

(such as cash flow, pre-tax income, gross profit, net income, earnings per share) was used.

Earnouts are more typically found where there is a greater degree of uncertainty about the target’s

value. The key features of earnouts can vary significantly from case to case – there is not a “one

size fits all” approach to the financial contracts involved. As noted by Blough et al. (2007)

“Empirical analysis of earn-out clauses between third parties has revealed considerable

heterogeneity in the terms of earn-out contracts, the profit level indicator, the period over which

performance is measured, and the form of payment for the earn-out.” Elnahas et al. (2017) on the

contrary posit that conventional earnout agreement in M&A violates Islamic law from religious

perspective.

Companies, clearly stipulating that the performance commitment agreement should not be changed. On August 4,

2017, the Securities and Futures Commission spokesman Gao Li reiterated the requirements for regulation of

Performance Commitment in acquisition in the regular press conference.

3.1 Performance commitment in acquisitions and crash risk

Prior studies have argued that due to pressure from a variety of factors, including career

concerns, reputational concerns and compensation contracts, top managers have incentives to

withhold bad news and accelerate the release of good news, hoping that poor current performance

can be camouflaged (Jin and Myers, 2006; Kim et al., 2011a; Kim et al., 2011b; Kim and Zhang,

2016; Kim et al., 2016). Opacity combined with limited investor protection specifically enable

managers withhold and accumulate firm-specific bad news to protect his or her job, leading to

negative information stockpiled within a firm. Because of the cost and impossibility to infinitely

withhold bad news, there is an upper limit to the amount of bad news that managers can

accumulate. Once the accumulation of bad news reaches a tipping point, it will be released,

resulting largely in negative market-adjusted stock returns on the individual stocks concerned, that

is, stock price crashes (Hutton et al., 2009; Jin and Myers, 2006). Hoarding worse situations is

also possible for those acquirers with performance commitment signed with target firms. While

we are interested in finding out how signing performance commitment with targets affects the

situation, merger waves and waves of cash and stock purchases can be rationally driven by periods

of over- and undervaluation of the stock market. Thus, valuation fundamentally impacts mergers

(Rhodes-kropf and Viswanathan, 2004). As stock market valuation affects merger wave, so too

does mergers affect the stock valuation on the contrary in firm level.

Unlike prior studies using large listed companies’ targets, we focus on private or unlisted

companies as target firms, because the majority of acquisitions with performance commitment in

China are listed companies acquiring private companies. Under this scenario, Barbopoulos and

Sudarsanam (2012) present that information about a private firm’s performance and prospects is

much more limited than it is about listed companies, and the information asymmetry problem

between private firms and their potential acquirers is therefore likely to be more severe. Hence,

acquirers not only face valuation risk in negotiating a price and the payment in a takeover but also

have to suffer higher risk for future performance and prospects uncertainty of target firms, which

may induce potential losses for acquirers. Accordingly, earnout has been used as an effective way

to reduce potential risk for acquirers. Kohers and Ang (2000) show that during the critical

post-acquisition period, earnout could help in retaining ‘skilled managers’ from the target firm’s

side. Earnout motivates target firm’s managers to achieve pre-specified future profits. Retention of

the valuable and well-motivated management team in the target company is likely to reduce the

problems of post-acquisition integration and improve the chances of value creation. Thus earnout

may significantly contribute to value creation in the post-acquisition period and thus maximize

shareholder wealth (Cain et al., 2011).

The performance commitment contracts we study are similar to the function of earnout.

Performance commitment in acquisition is an adjustment mechanism adopted by acquirers in

valuing privately held targets. There is strong asymmetric information between acquirers and

targets about potential profitability of the private firms, and then to ensure the transaction is

successfully completed, a performance commitment can be signed to make adjustments to the

initial transaction pricing (Pan et al., 2017).

Performance commitment guarantees a fairer and transparent acquisition result and ensures

fair trade rights of both parties. Performance commitment contracts motivate target firm’s

managers to accomplish pre-specified performance hurdles, which mainly are based on net profits.

Once the pre-specified net profit is not reached, the shareholders of target firms should

compensate acquirers, using either stock or cash, to pay for the amount of difference between

pre-specified net profit and net profit actually achieved. Especially when the shareholders are also

top managers of target firms, the motivation and pressure of performance commitment is stronger.

Besides, performance commitment may also demand that the top management team of targets

should not change during the term of validity, which increases the likelihood of top management

team managing the target firm more efficiently during the post-acquisition period due to the

retention of key human capital and managers specialized in the target firm’s particular sector. With

all the constraints, performance commitment contracts mitigate the risk faced by the acquirers in

valuing privately held targets, and protect post-acquisition gains of bidders. As the acquirers can

predict the future profits of the target firms more accurately, the risk and uncertainty of the

post-acquisition listed companies will be reduced. With a certain future prediction, listed

companies will have a steady forecast on their profitability, managers have less bad news to hide

and would disclose information timely for the concerns of their career or reputation, therefore

stock price crash risk of the acquirers with existing performance commitment by the targets will

be lower. On the contrary, in the acquisition without performance commitment contracts, the

acquirers would have difficulties in discovering right information about the targets, especially the

post-acquisition profit target due to information asymmetry and moral hazard. Therefore, it is hard

to value target firms at fair prices, let alone predict the future profits of the targets. As the targets

will be part of the listed companies, their future performance will affect the performance of the

entire listed company. Therefore, in the acquisition without performance commitment contracts,

the acquirers will face higher risk and uncertainty in the future. Once the performances of the

targets deteriorate, the shareholders of listed companies face risky situation. The managers of the

acquirers may not want to disclose the negative news early with the aim of ensuring a stable stock

price of listed companies. With the accumulated bad news approaching a tipping point, a stock

price crash will occur. We, therefore, expect that the stock crash risk of acquirers using

performance commitment contracts will be lower than those without.

H1: Acquirers with signed performance commitment contracts, experience lower stock crash

risk, than those without.

3.2 The impact of regulatory change

The debate on stock market short-termism is long-running, dating back at least to the 1980s

when a massive number of corporate takeovers occurred in the United States, often for financial

reasons rather than any strategic rationale. Disruptive events such as the bursting of the stock

bubble in 2000 and the worldwide economic uncertainties of the last few years are raising new

concerns about the lack of long-term vision on the part of corporations and investors. Excessive

focus on quarterly results, scarce attention to value-creation strategies, and failure to probe deeply

enough into long-term performance are believed to be leading “short-termism” which damages

market credibility and depresses today’s economic development. Graham et al. (2005) surveyed

investment professionals, most of them recognize that discounted cash flow analysis (DCF), not

earnings-per-share (EPS), is the appropriate model for valuing financial assets, including equities;

but they believe that estimating distant cash flows is too time-consuming and costly to be

efficiently employed in their investment decision-making process. This and other aspects of the

economics of short-termism are eloquently explained by Rappaport (2005). Stock market

short-termism negatively affects the economic system, as it does not provide proper incentives for

businesses to pursue strategic opportunities that would translate into sustainable growth.

Short-termism inevitably exists in Chinese stock market. The specialty is that the regularity

commission of the market5 tends to issue lots of departmental regulations, some of which are not

coordinated, experimental and transient in nature such as the circuit breaker trading policy adopted

in January 2016, the shortest enacted policy on Chinese stock market. The trading policy caused

the market stopped automatically for 4 times, touched 5% market index down threshold and 7%

down threshold twice respectively. On the day of January 4th, the whole Chinese market was only

open for 140 minutes. Researches show that the circuit breaker trading policy actually worsens the

situation when market down-side risk is high. Such kind of policies exists across Chinese stock

market. We believe the policy by CSRC on acquisition did not have positive effect on acquisitions

and exacerbated the short-termism effect of listed companies.

In October 2014, CSRC promulgated “the Decision on Amending the Listed Companies

Acquisition Regulation”(CSRC Decree No. 108) and “the Listed Companies Major Asset

Reorganization Regulation” (CSRC Decree No. 109) at the same time, aiming at reducing and

simplifying administrative approval, strengthening information disclosure, strengthening

in-process and ex-post regulation, urging the intermediary agencies to fulfill their duties,

protecting the interests of investors and improving market efficiency.

On one hand, deregulation and simplifying the administrative approval of acquisition and

reorganization can improve the efficiency of the market operation and promote the competitive

mechanism. Therefore, under the idea of "deregulation and strengthening ex-post supervision",

acquisitions will be more efficient with less approval procedures. In addition, due to the function

of the performance commitment contracts, after the new policy is promulgated, acquisitions with

performance commitment could further reduce information asymmetry and moral hazard between

acquirers and targets, motivate target firms’ managers in line with acquirer’s, so that the stock

price crash risk will be reduced.

On the other hand, the deregulation simplifies the administrative approval of acquisitions and

reorganizations, on the contrary, the policy induces the short-termism effect of Chinese capital

market, damages the interests of minority shareholders in exchange for controlling shareholders’

benefits and restrain the long-term stabilization of the market. Under this scenario, regulation

adjustments actually worsen the positive effect of performance commitment provisions in

acquisitions. Performance commitment contracts possibly become means of manipulating stock

prices. In order to obtain more benefits during the acquisition process, controlling shareholders or

top managers of acquirers use the performance commitment contracts to anticipate sending

positive signals to the market and boosting companies’ stock price as the primary goal and neglect

the specific provisions of the commitment contracts. The counterparties may even work together

to exploit the benefits of increasing stock prices of the acquirers in the liberalized market. Due to

the short-termism behavior of acquirers, the performance commitment contracts become less

binding to the targets and can also become a means for controlling shareholders and top managers

5 The direct regulatory body is China Securities Regulatory Commission. Whereas, other financial regulatory

bureaus also have regulatory power on the listed companies for its own sector such as China Insurance Regulatory

Commission, China Banking Regulatory Commission, even State-Owned Assets Supervision and Administration

Commission on State-owned listed companies.

of listed acquirers squeezing minority interests. Therefore, after the regulation adjustments,

performance commitment contracts are possibly less effective. The controlling shareholders and

top managers of acquirers for the sake of their own interests, will be motivated to hide the

negative information resulting from hasty acquisitions. Therefore, in this scenario the stock price

crash risk will be higher in the future. This leads to next competing hypothesis:

H2a: Stocks of acquirers with performance commitment contracts show lower crash risk after the

regulation adjustments.

H2b: Stocks of acquirers with performance commitment contracts show higher crash risk after the

regulation adjustments.

4. Research design

4.1 Sample and data

To form our acquisition samples, we begin with all announced and completed Chinese

acquisitions with announcement dates between January 1, 2011 and December 31, 2015 from

Chinese Acquisition Database of Wind Info and CSMAR. 2011 is the year we start to see the

acquisitions with performance commitment contracts and the end of 2015 is the latest year we can

collect all stock price crash risk data for the next year (2016). We retain an acquisition only if the

acquirers were A-share6 listed company and the control right of the target firm changed after the

transaction completed, which means that the acquirer owns more than 50% of the target firm. We

require that: (1) eliminate the acquisition sample in which one of the counterparty belongs to

financial industry; (2) eliminate the acquisition sample whose purpose is backdoor listing; (3)

eliminate the data-missing samples. After the above preliminary screening, we obtain 9257

acquisition samples, 903 of them have performance commitment contracts signed.

It should be noted that, since the target firms are unlisted companies, their data cannot be

obtained directly from the public databases. We hand collected these data by checking each deal

draft, collected and compiled information on the performance commitment signed by both parties,

obtained the information of performance, compensation content and compensation mode of the

target parties’ performance commitments.

Table 1 and Figure 1 presents the distribution of acquisition events during 2011-2015. It can

be seen from the distribution table of the acquisition samples, there are 903 completed acquisitions

that have performance commitment, the acquirers of which are listed companies and the acquired

ownership of target companies are all more than 50%. Compared to 2012, the year-on-year growth

rate in 2013 was 617.65%, which was a pretty high growth rate. The average growth rate in the

five sample years was 227.74%. Since 2013, the number of acquisition with performance

commitment has increased significantly, which shows that since Haifu Investment Case 7 ,

6 Stocks listed either in Shanghai Stock Exchange or Shenzhen Stock Exchange, not including Chinese companies

listed overseas. 7 In October 2007, Haifu Investment and Gansu Shiheng signed a capital-increasing agreement with performance

commitment. In 2008, Gansu Shiheng’s net profit did not meet the committed standards. According to the terms of

the agreement, Gansu Shiheng needs to compensate Haifu Investment. There occurred a compensation dispute

between the two parts and Haifu investment took Gansu Shiheng to court. On December 31, 2010, Lanzhou

Intermediate People's Court made a verdict that performance commitment is invalid, Haifu investment refused to

performance commitment has started to gain attention by Chinese capital markets and was widely

applied to acquisitions since then.

Insert Table 1 and Figure 1 about here

4.2 Model

Generally, our hypothesis concerns the influence of performance commitment on stock price

crash risk. Therefore, we require measures of stock price crash risk, performance commitment,

and control variables that are known to stock price crash risk. Therefore, the general form of

equation we use to test hypothesis is as follows:

NCSKEW𝑡+1(DUVOL𝑡+1) = 𝛽0 + 𝛽1𝑃𝐶𝑡 + ∑ 𝛽𝑖 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑡 + 𝜀𝑡 (1)

Where NCSKEW𝑡+1(DUVOL𝑡+1) represents the stock price crash risk proxy; PC refers to the

performance commitment proxy. The follow discussion provides additional details for this

equation.

4.3 Measurement of key variables

4.3.1 Dependent variables

We use NCSKEW and DUVOL to present firm-specific crash risk. Chen et al. (2001) is the

first time to use Negative Coefficient of Skewness and Down-to-Up Volatility as the proxy variable

of stock price crash risk to study the relationship between heterogeneity of investors and stock

price crash risk. Their research showed that, the higher degree of investors’ heterogeneity, the

greater risk of stock price crash. Subsequent researches on stock price crash risk have shown an

improvement in the method of calculating individual stock returns (Hutton et al., 2009; Jin and

Myers, 2006; Kim et al., 2011a, 2011 b). Following prior researches, we employ two measures of

firm-specific crash risk. Both measures are based on firm-specific weekly returns (denoted by W)

estimated as the residuals from the market model.

First, we estimate the following expanded market model regression:

𝑅𝑖,𝑡 = 𝛽0 + 𝛽1𝑅𝑚,𝑡−2 + 𝛽2𝑅𝑚,𝑡−1 + 𝛽3𝑅𝑚,𝑡 + 𝛽4𝑅𝑚,𝑡+1 + 𝛽5𝑅𝑚,𝑡+2 + 𝜀𝑖,𝑡 (2)

𝑅𝑖,𝑡 is the return on stock i in week t on cash dividends reinvested, 𝑅𝑚,𝑡 is return on the

value-weighted market index in week t. The lead and lag terms for the market index return are

included to allow for nonsynchronous trading (Dimson, 1979). 𝜀𝑖,𝑡 is residual, which means the

part of individual stock returns that cannot be explained by the market. The larger the absolute

value of 𝜀𝑖,𝑡 is, the greater the degree of divergence between the stock i return and the market

return.

Then calculate the formula 𝑊𝑖,𝑡 = 𝐿𝑛(1 + 𝜀𝑖,𝑡), 𝑊𝑖,𝑡 is the firm-specific weekly return.

accept and appealed. On September 29, 2011, Gansu High Court made a second instance verdict, determining the

terms invalid, but Gansu Shiheng needed to return the increased money and the interests of Haifu Investment.

Gansu Shiheng refused to accept and appealed to the Supreme People's Court. On December 19, 2011, the

Supreme People's Court accepted the application of Shiheng Company and put it on trial. In November 2012, the

Supreme People's Court issued a verdict that Gansu High Court’s second instance verdict on the case be abrogated,

terms of gambling between the shareholders of the investor and the original shareholders of the investee effective

and Gansu Shiheng ought to pay agreed compensation to Haifu Investment.

Then construct the following two variables based on 𝑊𝑖,𝑡:

Negative Coefficient of Skewness, namely NCSKEW:

NCSKEW𝑖,𝑡 = −[𝑛(𝑛 − 1)3/2 ∑ 𝑊𝑖,𝑡3 ]/[(𝑛 − 1)(𝑛 − 2)(∑ 𝑊𝑖,𝑡

2 )3/2] (3)

Where n is the number of trading weeks of stock i each year. A higher value for NCSKEW

corresponds to a stock being more “crash prone” and vice versa.

Down-to-Up Volatility, namely DUVOL:

DUVOL𝑖,𝑡 = log {[(𝑛𝑢 − 1) ∑ 𝑊𝑖,𝑡2

𝐷𝑂𝑊𝑁 ]/[(𝑛𝑑 − 1) ∑ 𝑊𝑖,𝑡2

𝑢𝑝 ]} (4)

Where nu (nd) is the number of weeks when the firm-specific weekly return 𝑊𝑖,𝑡 is greater

than (less than) the yearly average return 𝑊𝑖. The higher value of DUVOL, the distribution of

returns tends to be more left-deviation, the greater risk of stock price crash.

Besides crash risk, we also test short-term economic outcomes: cumulative abnormal market

returns for the performance commitment introduction in Chinese acquisitions. The announcement

period cumulative abnormal return (CAR) is the sum of the abnormal returns of the 3-days (t-1 to

t+1) and 7-days (t-3 to t+3) surrounding the day of the announcement for the acquirers.

4.3.2 Independent Variable

Firstly, we use PC to represent whether the performance commitment contracts are signed.

This variable is a dummy variable. If the two parties signed performance commitment take 1,

otherwise, take 0. Then we use three variables to represent specific performance commitment

provisions. H_PC refers to performance hurdle for performance commitments. This variable refers

to the promised profit of each 10,000 yuan’s acquisition value, which is measured by the value of

the merger and acquisition transaction divided by the annual mean of the promised profit. C_PC

refers to compensation content for performance commitments. This variable is a dummy variable.

If the compensation content of performance commitment is to compensate for the price, take 1,

otherwise, take 0. P_PC refers to compensation method for performance commitments. This

variable is a dummy variable. If the performance commitment is compensated by the stock, take 1,

otherwise take 0.

4.3.3 Control variables

We control for several factors that have been shown to affect future stock price crash risk in

prior studies. We first control for the lag value of crash risk because Chen et al. (2001) find that

firms with high NCSKEW in year t are likely to have high NCSKEW in year t+1. And we add

other variables, the detrended average monthly stock turnover (OTurnover), past returns (Ret), the

standard deviation of firm-specific weekly returns over the fiscal year (Sigma), book-to-market

ratio (BM) and earnings management (AbsACC). We also control the firm total assets (Size), firm

financial leverage (Lev), the shareholding ratio of the largest shareholder (Top1), return on total

assets (ROA), firm’s growth (Growth), whether the firm's ultimate controlling shareholder is state

(SEO). At the acquisition level, method of payment (Pay), whether acquisition is related

transaction (Related) and whether the acquisition is belong to major asset restructuring (Major) are

controlled. All the variables are defined in Appendix A.

5. Empirical analyses

In order to control the sample’s self-selection and the potential endogeneity between

performance commitment contracts and stock price crash risk, we use propensity score matching

method by matching 903 acquisition samples with signed performance commitment contracts

against control acquisition samples without signed performance commitment contracts. The

primary benefit of using a control sample matched on propensity scores is that it allows us to

compare the treatment group to a set of firms that are the same on all observable dimensions, thus

allowing us to clearly attribute any observed effects to performance commitment itself, rather than

to the firm characteristics associated with performance commitment (Bowen et al., 2009).

Taking the first announcement day of acquisitions as the event day, using propensity score

matching method and selecting control group samples based on covariates -- the firm total assets

(Size), firm financial leverage (Lev), firm’s growth (Growth), return on total assets (ROA), the

shareholding ratio of the largest shareholder (Top1), whether the CEO is also chairman (Duality),

the shareholding ratio of institutional investors (Inst) , the net cash flow of fund-raising activities

(Finac) and whether the firm's ultimate controlling shareholder is state (SEO), all the variables are

defined in Appendix A. We matched samples of acquisition with performance commitment

(treatment group) to samples without performance commitment (control group) one by one.

Finally, the total number of successfully-matched samples was 1779, of which 890 have signed

performance commitment (treatment group).

5.1 Descriptive statistics

Table 2 presents descriptive statistics for the variables used in our analysis. The mean of

short-term performance CAR1t and CAR3t are 0.0759 and 0.103, respectively. The mean of crash

risk measures, NCSKEWt+1 and DUVOLt+1, are −0.350 and −0.118, respectively. The mean of

standard deviation of firm-specific weekly returns is 0.0998. The monthly excess turnover rate is

0.112. The average weekly rate of return is 0.02. The average book-to-market ratio is 0.353, an

average leverage is 0.403, and an average return on assets is 0.066. The average absolute value of

abnormal accruals is 0.071.

Insert Table 2 about here

Panel A of Table 3 reports the results of univariate tests of the dependent variables. The mean

(median) of CAR1t is 0.114 (0.124) for the acquisitions with performance commitment and 0.04

(0.013) for the acquisitions without performance commitment, the differences are statistically

significant at the 5% and 1% level, similarly for CAR3t. The mean of NCSKEWt+1 is -0.399

(-0.378) for the acquirers with performance commitment and -0.306 (-0.296) for the acquirers with

performance commitment, and the differences are both statistically significant at the 1% level,

similarly for DUVOLt+1. This means that the acquirers with performance commitment have lower

stock price crash risk than those without performance commitment.

To ensure that the propensity score matching is satisfactory, we assess covariate balance by

testing whether the means and medians of the covariates used in matching differ between the

treatment group and control group and report the results in panel B of Table 3. As panel B shows,

there are no significant differences in means and medians of any of the covariates, indicating that

the propensity-score matched control sample resembles the treatment group along virtually all

dimensions.


5.2 Multivariate analyses of performance commitment on crash risk

Before we test our hypothesis H1, Table 4 displays the results of eight regression models

which present the impact of performance commitment on short-term CAR. Table 4 focuses on

acquirer returns. The dependent variable is the 3-days (t-1 to t+1) and 7-days (t-3 to t+3)

cumulative abnormal returns (CAR1t, CAR3t) for the acquirer. Columns (1) and (2) report that the

coefficients associated with performance commitment are positive and significant at the 1% level,

indicating that using performance commitment has a robust positive value impact on shareholders

of the acquirer, all else equal.

Besides, as an important contractual arrangement in acquisition, performance commitment

contracts consist of some important terms. Although the specific terms of performance

commitment contracts vary in different acquisition events, regardless of the differences in contract

arrangement, they all include three aspects- performance commitment hurdle which is mainly

pre-specified net profit; compensation content which is based on the difference between

pre-specified net profit and actual net profit, shareholders in target firms should compensate the

acquirers for the difference between pre-specified net profit and actual net profit (compensating

profit) or the discount value of the difference between pre-specified net profit and actual net profit

to acquisition payment (compensating price); and compensation method which means

shareholders in target firms use cash or stock as a method of payment. The detail definition is in

Appendix A. Next, we give an analysis of three specific terms of performance commitment.

Columns (3) and (4) report that the performance commitment hurdle (H_PCt) is positively

related to CAR1t and CAR3t, significant at the 1% and 5% level respectively, indicating that as a

measure of the targets’ intrinsic value, performance commitment hurdle has a robust positive value

impact on shareholders of the acquirer, all else equal. As can be seen from columns (5) and (6), the

performance compensation content (C_PCt) is positively related to CAR1t and CAR3t, significant

at 10% and 5% level respectively, indicating that compared to compensating profit, compensating

price has a stronger positive value impact on acquirers’ shareholders. Columns (7) and (8) report

that performance compensation method (P_PCt) is positively related to CAR1t and CAR3t,

significant at 10% and 5% level respectively, which means that compared to using cash, using

stock has a stronger positive value impact on acquirers’ shareholders.


Table 5 displays the results of eight regression models used to test our hypothesis H1. These

models are derived from two measures of stock price crash risk. Columns (1) and (2) report that

the coefficients associated with performance commitment are negative and significant at the 1%

level, indicating that using performance commitment in acquisition can significantly reduce stock

price crash risk of acquirers in the future, which support hypothesis H1. Further analysis, columns

(3) and (4) report that the performance commitment hurdle (H_PCt) is negatively related to

NCSKEWt+1 and DUVOLt+1, significant at the 5% and 1% level respectively, indicating that as a

measure of the targets’ intrinsic value, performance commitment hurdle can significantly reduce

stock price crash risk of acquirers in the future. Columns (5) and (6) report that the performance

compensation content (C_PCt) is negatively related to NCSKEWt+1 and DUVOLt+1, significant at

1% and 5% level respectively, indicating that compared to compensating profit, compensating

price can significantly reduce stock price crash risk of acquirers in the future. Columns (7) and (8)

report that performance compensation method (P_PCt) is negatively related to NCSKEWt+1 and

DUVOLt+1, significant at 1% level, which means that compared to using cash, using stock can

significantly reduce stock price crash risk of acquirers in the future.


Next, in order to further illustrate that the performance commitment can reduce stock price

crash risk by reducing information asymmetry and improving information transparency, we add

information transparency index (difDA) and interaction variables to the regression model of

Hypothesis H1. difDA refers to information transparency index, when the difference of

information transparency between year t+1 and year t is negative take 1, otherwise, take 0.

Information transparency is defined by the accumulated three years before acquisition event year

of the absolute value of the estimated residuals from the adjusted-Jones model. The value of

information transparency is bigger, the information asymmetry is more serious. The regression

results are shown in Table 6. Columns (1) and (2) show that the interaction (difDA*PCt) is

negatively related to NCSKEWt+1 and DUVOLt+1, significant at the 1% level, indicating that

information transparency can further reduce stock price crash risk of acquirers which use

performance commitment contracts. Moreover, columns (3) and (4) report that the interaction

(difDA*H_PCt) is negatively related to NCSKEWt+1 and DUVOLt+1, significant at the 10% and 5%

level respectively, which indicates that the improvement of information transparency can further

reduce stock price crash risk of acquirers with higher performance hurdle. However, the

coefficients of interaction in columns (5) to (8) are not completely significant, indicating that the

improvement of information transparency has no obvious effect on the relation between C_PCt

(P_PCt) and stock price crash risk.


5.3 Multivariate analyses of the impact of regulatory change

Then, we test the impact of regulatory change. We use Event as the proxy variable of

regulatory change. Event is a dummy variable, the year after the new policy is issued equals 1,

otherwise equals 0. A regression analysis of short-term performance is presented in Table 7.

Columns (1) and (2) report that performance commitment is still positively related to CAR1t and

CAR3t, significant at the 1% level. However, the interaction variable (Event*PCt) is negatively

related to CAR1t and CAR3t, significant at the 1% and 5% level respectively, indicating that the

market value of acquirers is getting worse after the new policy implementation. Next, in order to

further study the effect of performance commitment contracts after the new policy implementation,

we regress models (3) to (8) respectively. The regression results show that the coefficients of

interactions are not significant, indicating that regulatory change has no significant impact on the

relation between the specific contract terms and abnormal accumulated returns of acquirers.


Next, Table 8 displays the results of eight regression models used to test our hypothesis H2.

Columns (1) and (2) report that performance commitment is still negatively related to

NCSKEWt+1 and DUVOLt+1, significant at the 1% level. However, the interaction variable

(Event*PCt) is positively related to NCSKEWt+1 and DUVOLt+1, significant at the 1% and 5%

level respectively, indicating that the stock price crash risk of acquirers using performance

commitment contracts is getting higher after implementing the new policy. Therefore, the new

policy makes the managers and controlling shareholders of listed companies pay more attention to

short-term stock price instead of long run growth, damage the minority interests and improve the

uncertainty and risk of the future stock price, which supports the hypothesis H2b. Next, in order to

further study the effect of performance commitment contracts arrangement after implementing the

new policy, we regress models (3) to (8) respectively. Columns (3) and (4) report that the

interaction (Event*H_PCt) is positively related to NCSKEWt+1 and DUVOLt+1, significant at the 1%

and 5% level respectively, which indicates that after implementing the new policy, the higher

performance hurdle, the higher stock price crash risk of acquirers, which also supports the

hypothesis H2b. Columns (5) and (8) show that the coefficients of interactions are not totally

significant, indicating that regulatory change has no significantly impact on the relation between

the specific contract items and stock price crash risk of acquirers. Results from Table 7 and Table

8 both suggest that the existence of such contracts outweighs the exact contract terms. The result

is parallel with the reality that execution of the performance commitment contracts can be a

tedious legal process with respect to disputing the final payment between the counterparties as

illustrated by Haifu Investment case mentioned previously.


6. Robustness test

In this section we perform several robustness checks to examine the validity of our results,

including adopting alternative dependent variables, alternative samples, Heckman two-step

selection test, and placebo test.

6.1 Alternative dependent variables

Firstly, we use Crash and Crashfreq as replacement variables of stock price crash risk. The

calculation method is as follows:

Crash: In one year, as long as the firm-specific weekly returns satisfies the following

equation at least one time, Crash takes 1, otherwise 0.

Wi,t ≤ Average(Wi,t) − 3.09σi3

𝐴𝑣𝑒𝑟𝑎𝑔𝑒(𝑊𝑖,𝑡) is the average of the firm-specific weekly return for stock i.; 𝜎𝑖 is the

standard deviation of the firm-specific weekly return for stock i.

Crashfreq: The stock price crash frequency - Crashfreq is equal to the number of weeks

which stock i crash in year t divide the number of trading weeks in year t.

The regression results indicate that significant negative correlation between performance

commitment variables and the stock price crash risk still holds, indicating that the result is robust.

6.2 Alternative sample

A major asset reorganization is when a listed company, its controlling parent or its affiliated

company purchase or sell the amount of asset accounting for more than 50% of the total asset, or

operating income for more than 50%, or net worth for more than 50% and more than 50 million

RMB based on audited consolidated financial accounting reports in the recent fiscal year. CSRC

regulates major asset reorganization of listed companies stricter than general acquisition events,

and listed companies must employ independent financial advisors or other security service

agencies to make further scrutiny. Therefore, the process of major asset reorganization is more

transparent and fairer and the stock price crash risk should be lower. To avoid the possible

influence of major reorganizations, we remove the major asset reorganization samples from the

total acquisition sample and test the remaining samples. The regression results present that all the

performance commitment variables are significantly and negatively related with the stock price

crash risk, indicating that our main results are robust and not affected by the sample of major asset

reorganization.

6.3 Heckman two-step sample selection model

A firm's decision to sign performance commitment may be non-random and this may cause a

self-selection bias. We adopt the Heckman two-step model to test the possible self-selection issue.

In the first step, we estimate a probit model with a binary performance commitment dummy (PC,

which equals 1 if a firm sign performance commitment in acquisition, 0 otherwise) as the

dependent variable using the matched sample with 1:1 matching.

We add the following determinants of signing performance commitment contracts: SEO (a

dummy variable that equals 1 when the ultimate controlling shareholder of a listed firm is the state,

0 otherwise), Related (A dummy variable that equals 1 if the acquisition is related transaction, 0

otherwise), Major (A dummy variable that equals 1 if the acquisition is belong to major asset

reorganization, 0 otherwise), Pay (If acquirers use cash as a way to pay in acquisitions, the

variable takes 1; using stock as a way to pay in acquisitions takes 2; otherwise takes 3), Consultant

(A dummy variable that equals 1 if the acquirers hired independent financial advisers, 0 otherwise),

Bvalue (The total payment of acquirers in acquisition), Duality (A dummy variable that equals 1 if

the chairman plays dual roles, 0 otherwise), Inst (The percentage of shares owned by institutional

investors in year t). Heckman's estimator requires exogenous variables that are correlated with an

acquirer's propensity to sign a performance commitment contract, but not with stock price crash

risk. The variables are defined in Appendix A.

The results of the second-step regressions in Table 9 show that the coefficients of the variable

performance commitment significantly negative when both NCSKEW and DUVOL are adopted.


6.4 Placebo test

A placebo test is designed to test whether the relation between performance commitment and

stock price crash risk is due to other uncontrollable and unobservable factors beyond the

acquisition events. Then we design the following experiment: we randomly select one year as the

new event occurrence year of acquisition during five years which is five years before the

acquisition event actually occurred, and rematch the data based on the new event year. If the

significant negative relation between performance commitment and stock price crash risk is due to

other unobservable variables, the coefficients of new regressions should still be negatively

significant. However, as we can see from Table 10, the coefficients of new regressions are not

significant, indicating that the stock price crash risk is indeed caused by the acquisition events,

and is unlikely to be spuriously caused by omitted variables.


7. Conclusion

We present and test two types of economic outcome for the performance commitment

introduction in Chinese acquisitions, including abnormal market returns and market crash risk, to

test the hypothesis that such provisions help to alleviate information asymmetry and bridge

valuation gaps. We find that performance commitment contracts in Chinese acquisitions induce

positive economic outcomes measured by improved abnormal returns and lower market crash risk.

We further illustrate that the performance commitment contracts can reduce stock price crash risk

by reducing information asymmetry and improving information transparency. We also investigate

the economic outcome of regulation adjustments on acquisitions and reorganizations in China.

The empirical results show that regulation adjustments actually worsen the positive effect of

performance commitment provisions in acquisitions. The fact shows the short-termism effect of

Chinese capital market imposed by the frequent regulatory change. The results also shows that

only performance commitment dummy (PC) other than specific contract terms significantly

impact on the market performance after the regulatory change, that is, the existence of such

contracts is more effective than the exact contract terms.

Our study makes four primary contributions. First, our findings contribute to the literature on

performance commitment and earnouts and more generally, the literature on economic outcomes

of the contingent payment mechanism. Second, our results point out the possible way of

mitigating stock crash risk through improved information transparency. Third, by examining the

impacts of regulatory adjustments on performance commitments, we contribute to understanding

why Chinese listed companies show short termism and speculative characteristics and side effects

of constant changing regulatory environment. Finally, our evidence on the information content of

performance commitment contributes to the literature on the reliability of such mechanism in

execution.

Due to the data availability, our study mainly focuses on how acquirers are impacted by the

performance commitment contracts because of their listing status while existing literature also

reveals how the acquisition targets and their top management team are involved in the earnout

setting. We expect as there are more acquisitions with performance commitment emerge,

especially with listed targets, we will be able to explore thoroughly the story of target side in

Chinese acquisitions.

Appendix A

Variables Definition

Variable name Description

CARt The announcement period cumulative abnormal return (CAR) is the sum of the abnormal returns of the 3-days

(t-1 to t+1) and 7-days (t-3 to t+3) surrounding the day of the announcement for the acquirers.

NCSKEWt + 1 The negative skewness of firm-specific weekly returns in year t + 1, calculating by taking the negative of the

third moment of firm-specific weekly returns for each sample year and dividing it by the standard deviation of

firm-specific weekly returns raised to the third power.

DUVOLt + 1 The down-to-up volatility. For any stock i in year t, we separate all the weeks with firm-specific weekly returns

below the annual mean (down weeks) from those with firm-specific weekly returns above the period mean (up

weeks) and compute the standard deviation for each of these subsamples separately. We then take the log of the

ratio of the standard deviation of the down weeks to the standard deviation of the up weeks.

PCt Whether the performance commitment contracts are signed. This variable is a dummy variable. If the two

parties signed performance commitment take 1, otherwise, take 0.

H_PCt Performance hurdle for performance commitments. This variable refers to the promised profit of each 10,000

yuan’s acquisition value, which is measured by the value of the merger and acquisition transaction divided by

the annual mean of the promised profit.

C_PCt Compensation content for performance commitments. This variable is a dummy variable. If the compensation

content of the promised performance is to compensate for the price, take 1, otherwise, take 0.

P_PCt Compensation method for performance commitments. This variable is a dummy variable. If the performance

commitment is compensated by the stock, take 1, otherwise take 0.

difDA Information transparency index is a dummy variable, when the difference of information transparency between

year t+1 and year t is negative takes 1, otherwise, takes 0. Information transparency is defined by the

accumulated three years before acquisition event year of the absolute value of the estimated residuals from the

adjusted-Jones model. The value of information transparency is bigger, the information asymmetry is more

serious.

Sizet The natural logarithm of the book value of total assets in year t

Levt Firm financial leverage, calculated by the book value of total debt divided by the book value of total assets in

year t

Growtht The increased percentage of sales growth in year t

ROAt Return on assets, calculated by net profit divided by the book value of total assets in year t

Top1t The percentage of shares owned by the largest shareholder in year t

Dualityt A dummy variable that equals 1 if the chairman plays dual roles, 0 otherwise

Instt The percentage of shares owned by institutional investors in year t

Finact Net cash flow from financing activities of listed companies in year t

SEOt A dummy variable that equals 1 if the ultimate controlling shareholder of a listed firm is the state in year t and 0

otherwise

Payt If acquirers use cash as a way to pay in acquisitions, the variable takes 1; using stock as a way to pay in

acquisitions takes 2; otherwise takes 3.

Relatet A dummy variable that equals 1 if the acquisition is related transaction, 0 otherwise

Majort A dummy variable that equals 1 if the acquisition is belong to major asset reorganization, 0 otherwise

Consultantt A dummy variable that equals 1 if the acquirers hired independent financial advisers, 0 otherwise

Bvaluet The total payment of acquirers in acquisition

OTurnovert The detrended average monthly stock turnover in year t, calculated as the average monthly share turnover in

year t minus the average monthly share turnover in year t − 1

Rett The mean of firm-specific weekly returns over the fiscal year t

Sigmat The standard deviation of firm-specific weekly returns over the fiscal year period t

BMt Book-to-market ratio, calculated by the book value of equity divided by the market value of equity in year t

AbsACCt The absolute value of the estimated residuals from the adjusted-Jones model (Dechow et al., 1995)

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Table 1

Sample Distribution. This table reports the distribution of acquisition events during 2011-2015.

Year Total

acquisitions

Acquisitions with

performance

commitment

Percentage of total

acquisitions Year to Year growth rate

Total 9,257 903 9.75% --

2015 15,21 498 32.74% 93.77%

2014 1,451 257 17.71% 110.66%

2013 2,705 122 4.40% 617.65%

2012 1,965 17 0.87% 88.89%

2011 1,615 9 0.56% --

Fig. 1. Sample Distribution.

0.56% 0.87% 4.40% 17.71% 32.74%0

88.89%

617.65%

110.66%93.77%

0.00%

100.00%

200.00%

300.00%

400.00%

500.00%

600.00%

700.00%

2011 2012 2013 2014 2015

Percentage of totalacquisitions

Year to Year growth rate

Table 2

Descriptive Statistics. This table reports descriptive statistics on crash risk, H_PC, C_PC, P_PC and control

variables for the sample in 2011–2015. All variables are defined in Appendix A.

Variable Mean Std. dev. Maximum Minimum Median Obs.

CAR1t 0.0759 0.653 19.820 -0.829 0.0402 1779

CAR3t 0.103 0.678 20.000 -1.510 0.0441 1779

NCSKEWt + 1 -0.350 0.694 1.435 -2.244 -0.302 1779

DUVOLt + 1 -0.118 0.488 1.172 -1.256 -0.123 1779

H_PCt 0.134 0.156 2.997 0.001 0.104 890

C_PCt 0.773 0.419 1 0 1 890

P_PCt 0.594 0.432 1 0 1 890

difDA 0.414 0.493 1 0 0 1779

Sizet 12.530 0.958 15.550 10.350 12.410 1779

Levt 0.403 19.480 0.923 0.051 0.394 1779

Top1t 0.321 14.160 0.715 0.073 0.301 1779

Instt 0.362 21.190 0.823 0.006 0.361 1779

Dualityt 0.326 0.469 1 0 0 1779

ROAt 0.066 5.539 0.281 -0.090 0.058 1779

Growtht 0.251 50.750 3.004 -0.645 0.153 1779

Finact 29307 81490 501364 -151847 7341 1779

SEOt 0.192 0.394 1 0 0 1779

Relatet 0.297 0.457 1 0 0 1779

Majort 0.273 0.446 1 0 0 1779

Payt 1.574 0.846 3 1 1 1779

OTurnovert 0.112 0.399 1.029 -1.314 0.141 1779

Rett 0.020 0.016 0.077 -0.009 0.018 1779

Sigmat 0.010 0.042 0.229 0.036 0.095 1779

BMt 0.353 0.195 0.962 0.044 0.315 1779

AbsACCt 0.071 0.0600 0.273 0.001 0.059 1779

NCSKEWt -0.441 0.843 1.690 -3.027 -0.359 1779

Table 3

Univariate Tests.

Panel A: This panel reports the results of univariate analysis on the mean and median differences of cumulative abnormal market

returns and the two crash risk measures NCSKEW and DUVOL between acquisitions with performance commitment and control

group. CAR1 and CAR3 are measured over year t; NCSKEW and DUVOL are measured over year t + 1. The t-values and z-values

for differences in means (medians) are based on t-tests (Wilcoxon tests).

Dependent

Variable

Acquisitions with performance

commitment Matched sample

T test P value Z test P value

Mean Median Mean Median

CAR1t 0.1144 0.1245 0.0412 0.0128 2.4503 0.0144 13.6710 0.0000

CAR3t 0.1627 0.1398 0.0498 0.0101 3.6455 0.0003 12.1580 0.0000

NCSKEWt + 1 -0.3993 -0.3768 -0.3056 -0.2963 -3.1910 -0.0014 -4.2750 -0.0000

DUVOLt + 1 -0.1751 -0.1693 -0.1055 -0.0947 -4.1906 -0.0000 -4.3890 -0.0000

Panel B: This panel reports the results of covariate balance checks on the mean and median difference in the covariates used in the

probit model between acquisitions with performance commitment and control group, when propensity score matching is adopted.

Control

Variable

Acquisitions with performance

commitment Matched sample

T test P value Z test P value

Mean Median Mean Median

Sizet 12.4798 12.4099 12.5547 12.4087 -1.5735 0.1158 -1.0480 0.2946

Levt 39.8156 38.1416 40.7537 40.3794 -1.0233 0.3063 -1.320 0.1870

Top1t 32.3467 30.0700 31.9717 30.0650 0.5728 0.5669 0.5320 0.5949

Instt 35.7462 35.4480 36.7438 36.9032 -1.0152 0.3101 -0.9260 0.3544

Dualityt 0.32486 0 0.3260 0 -0.0529 0.9578 -0.0530 0.9578

ROAt 7.2328 5.8648 10.0191 5.7666 -1.5242 0.1276 -0.4950 0.6203

Growtht 31.3153 15.8778 26.1852 14.5278 1.0183 0.3087 1.1390 0.2545

Finact 27051.2400 7690.7540 41697.9900 6951.2920 -1.9632 0.0498 -0.080 0.9361

SEOt 0.1867 0 0.1970 0 -0.5674 0.5705 -0.5670 0.5704

Table 4

Regression on CAR1 and CAR3. This table presents the results from the ordinary least squares regression of the

impact of performance commitment on cumulative abnormal market returns. Control variables include the firm

total assets (Sizet), firm financial leverage (Levt), the shareholding ratio of the largest shareholder (Top1t), the

shareholding ratio of institutional investors (Instt), whether the CEO is also chairman (Dualityt), return on total

assets (ROAt), firm’s growth (Growtht), the net cash flow of fund-raising activities (Finact), whether the firm's

ultimate controlling shareholder is state (SEOt) and method of payment (Payt). Reported in parentheses are t-values

based on robust standard errors clustered by firm, *, **, and *** indicate significance at the 10%, 5%, and 1%

levels (two-tailed test). All variables are defined in Appendix A.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES CAR1t CAR3t CAR1t CAR3t CAR1t CAR3t CAR1t CAR3t

PCt 0.0555*** 0.0780***

(6.1400) (6.3841)

H_PCt 0.1809*** 0.2417**

(3.3017) (2.5290)

C_PCt 0.0209* 0.0363**

(1.8917) (2.0671)

P_PCt 0.0170* 0.0375**

(1.6938) (2.2269)

Control Yes Yes Yes Yes Yes Yes Yes Yes

Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Constant -0.2067 -0.2426 0.2357*** 0.2225 0.2525*** 0.2408 0.2612*** 0.2552*

(-0.7237) (-0.8176) (2.6544) (1.5313) (2.7881) (1.6318) (2.9192) (1.7474)

Observations 1,749 1,749 879 879 879 879 879 879

Adj. R-Squared 0.024 0.030 0.099 0.103 0.095 0.102 0.094 0.103

Table 5

Regression on market crash risk. This table presents the results from the ordinary least squares regression of the

impact of performance commitment on future stock price crash risk. Other control variables are the firm total

assets (Sizet), firm financial leverage (Levt), the shareholding ratio of the largest shareholder (Top1t), return on

total assets (ROAt), firm’s growth (Growtht), whether the firm's ultimate controlling shareholder is state (SEOt). At

the acquisition level, method of payment (Payt), whether acquisition is related transaction (Relatedt) and whether

the acquisition is belong to major asset restructuring (Majort) are controlled. Reported in parentheses are t-values



(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES NCSKEWt + 1 DUVOLt + 1 NCSKEWt + 1 DUVOLt + 1 NCSKEWt + 1 DUVOLt + 1 NCSKEWt + 1 DUVOLt + 1

PCt -0.1468*** -0.0972***

(-3.5084) (-3.4549)

H_PCt -0.4863** -0.4186***

(-2.0153) (-2.6848)

C_PCt -0.2383*** -0.1066**

(-4.0136) (-2.3750)

P_PCt -0.1480*** -0.0979***

(-2.6834) (-2.7304)

OTurnovert -0.1075* -0.1007* -0.0572* -0.0104* -0.0670 0.0085 -0.0707 0.0066

(-1.7544) (-1.8152) (-1.7758) (-1.8919) (-0.9156) (0.1558) (-0.9667) (0.1219)

Rett 3.8239 -0.6712 -0.5271** 0.1443 0.8975 0.3566 1.0496 0.4086

(1.4430) (-0.3577) (-2.2914) (0.4218) (1.1768) (0.9237) (1.3013) (1.0425)

Sigmat 0.2209 0.4582 -0.0913 0.2924 -0.4162 0.3378 -0.5839 0.2597

(0.2070) (0.6278) (-0.0872) (0.4214) (-0.4053) (0.4848) (-0.5691) (0.3788)

BMt -0.4859** -0.3862*** -0.9503*** -0.4992*** -0.8568*** -0.4797*** -0.8590*** -0.4804***

(-2.3324) (-2.8113) (-4.3908) (-3.0894) (-3.9263) (-2.9191) (-3.9513) (-2.9606)

AbsACCt 0.3251 -0.0469 0.6520* -0.0125 0.3374 0.0456 0.3248 0.0219

(0.8477) (-0.1638) (1.9568) (-0.0493) (0.7575) (0.1744) (0.7029) (0.0834)

NCSKEWt 0.0239* 0.0295* 0.0636** 0.0013 0.0606** 0.0011 0.0602* 0.0001

(1.7929) (1.9422) (2.0204) (0.0531) (1.9820) (0.0449) (1.9443) (0.0030)

Other control Yes Yes Yes Yes Yes Yes Yes Yes



Constant 0.0794 -0.2531 -0.5126 -0.2196 -0.3640 -0.2692 -0.4612 -0.3164

(0.1817) (-0.9139) (-0.8638) (-0.5786) (-0.6203) (-0.7133) (-0.7942) (-0.8466)

Observations 1,779 1,779 888 888 888 888 888 888

Adj. R-Squared 0.091 0.100 0.117 0.091 0.132 0.086 0.123 0.087

Table 6

Regression with interactive term of difDA. This table presents the OLS regression results of the impact of

performance commitment on future stock price crash risk with interactive term of difDA. difDA refers to

information transparency index, when the difference of information transparency between year t+1 and year t is

negative, it equals 1, otherwise, equals 0. The value of information transparency index is bigger; the information

asymmetry is more serious with our measurement. Other control variables are the firm total assets (Sizet), firm

financial leverage (Levt), the shareholding ratio of the largest shareholder (Top1t), return on total assets (ROAt),

firm’s growth (Growtht), whether the firm's ultimate controlling shareholder is state (SEOt). At the acquisition

level, method of payment (Payt), whether acquisition is related transaction (Relatedt) and whether the acquisition is

belong to major asset restructuring (Majort) are controlled. Reported in parentheses are t-values based on robust

standard errors clustered by firm, *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed

test). All variables are defined in Appendix A.

(1) (2) (3) (4) (5) (6) (7) (8)


PCt -0.1238*** -0.0604**

(-3.0898) (-2.3860)

difDA* PCt -1.1519*** -1.3887***

(-2.6224) (-3.5347)

H_PCt -0.5369** -0.4049**

(-1.9873) (-2.4569)

difDA* H_PCt -0.5373* -0.6042**

(1.8690) (-2.2467)

C_PCt -0.3592*** -0.1446***

(-4.8511) (-2.6029)

difDA* C_PCt -0.2994** -0.0921

(-2.4967) (-1.0612)

P_PCt -0.2291*** -0.1666***

(-3.4011) (-3.7214)

difDA* P_PCt -0.1992* -0.1723

(-1.9539) (-1.5387)

difDA -0.3470 -0.0242 -0.0476 -0.0595 -0.2204** -0.0167 -0.0917 -0.0392

(-0.6861) (-0.0660) (-0.5183) (-0.8980) (-2.0359) (-0.1994) (-1.2210) (-0.6842)

OTurnovert -0.1281** -0.0095 -0.0623 0.0075 -0.0575 0.0089 -0.0715 0.0042

(-2.1148) (-0.2221) (-0.8522) (0.1393) (-0.7899) (0.1617) (-0.9858) (0.0792)

Rett 4.1095 -0.2273 -0.5333** -0.1400 0.8452 -0.1058 0.9919 -0.0327

(1.5556) (-0.1214) (-2.2941) (-0.8776) (1.1305) (-0.6616) (1.2608) (-0.2009)

Sigmat 0.0902 0.2245 -0.2795 0.3097 -0.7486 0.3777 -0.8226 0.3078

(0.0847) (0.3081) (-0.2671) (0.4441) (-0.7357) (0.5400) (-0.8020) (0.4494)

BMt -0.4677** -0.3713*** -0.9682*** -0.5097*** -0.8673*** -0.4969*** -0.8917*** -0.5111***

(-2.2475) (-2.7146) (-4.4346) (-3.1801) (-4.0452) (-3.0661) (-4.1132) (-3.1906)

AbsACCt 0.9451** 0.4224 0.6720* 0.1544 0.3213 0.2923 0.3271 0.2571

(2.0323) (1.2141) (1.8946) (0.5731) (0.6920) (1.1163) (0.6729) (0.9968)

NCSKEWt 0.0223 -0.0335 0.0409 -0.0027 0.0366 -0.0035 0.0348 -0.0062

(0.7440) (-1.4127) (1.2586) (-0.1088) (1.1728) (-0.1414) (1.0977) (-0.2516)




Constant 0.1703 -0.2115 -0.5132 -0.2475 -0.2486 -0.2801 -0.4639 -0.3576

(0.3935) (-0.7620) (-0.8572) (-0.6564) (-0.4153) (-0.7346) (-0.7960) (-0.9583)

Observations 1,779 1,779 888 888 888 888 888 888

Adj. R-Squared 0.099 0.119 0.115 0.093 0.137 0.089 0.125 0.095

Table 7

Regress with interactive term of regulatory change. This table presents the results from the ordinary least squares

regression of the impact of performance commitment on cumulative abnormal market returns with interactive term

of regulatory change. We use Event as the proxy variable of regulatory change. Event is a dummy variable equals 1

after the new policy is issued, otherwise equals 0. Control variables include the firm total assets (Sizet), firm

financial leverage (Levt), the shareholding ratio of the largest shareholder (Top1t), the shareholding ratio of

institutional investors (Instt), whether the CEO is also chairman (Dualityt), return on total assets (ROAt), firm’s

growth (Growtht), the net cash flow of fund-raising activities (Finact), whether the firm's ultimate controlling

shareholder is state (SEOt) and method of payment (Payt). Reported in parentheses are t-values based on robust

standard errors clustered by firm, *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed

test). All variables are defined in Appendix A.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES CAR1t CAR3t CAR1t CAR3t CAR1t CAR3t CAR1t CAR3t

PCt 0.0832*** 0.1025***

(6.9140) (6.5164)

Event * PCt -0.0484*** -0.0413**

(-3.3810) (-2.0430)

H_PCt 0.0995** 0.0946**

(2.3309) (2.2869)

Event * H_PCt -0.0486 -0.0286

(-1.4409) (-0.4351)

C_PCt 0.0557*** 0.0867***

(4.1423) (4.0342)

Event * C_PCt -0.0568 -0.0824

(-1.0694) (-0.6964)

P_PCt 0.0251** 0.0478**

(2.1705) (2.3707)

Event * P_PCt -0.0164 -0.0208

(-1.0039) (-0.7335)

Eventt -0.0201 -0.0634** -0.0960*** -0.1440*** -0.0437* -0.0734* -0.0796*** -0.1259***

(-0.9032) (-2.2100) (-4.3817) (-3.9223) (-1.7236) (-1.7436) (-3.5392) (-3.2510)

Control Yes Yes Yes Yes Yes Yes Yes Yes



Constant -0.2146 -0.2473 0.2935*** 0.2902* 0.2384*** 0.2236 0.2610*** 0.2592*

(-0.7342) (-0.8151) (3.1029) (1.8885) (2.6140) (1.5032) (2.8472) (1.7360)

Observations 1,779 1,779 888 888 888 888 888 888

Adj. R-Squared 0.024 0.030 0.114 0.115 0.120 0.122 0.112 0.118

Table 8

Regress with interactive term of regulatory change. This table presents the results from the ordinary least squares

regression of the impact of performance commitment on future stock price crash risk with interactive term of

regulatory change. We use Event as the proxy variable of regulatory change. Event is a dummy variable, the year

after the new policy is issued equals 1, otherwise equals 0. Other control variables are the firm total assets (Sizet),

firm financial leverage (Levt), the shareholding ratio of the largest shareholder (Top1t), return on total assets

(ROAt), firm’s growth (Growtht), whether the firm's ultimate controlling shareholder is state (SEOt). At the

acquisition level, method of payment (Payt), whether acquisition is related transaction (Relatedt) and whether the

acquisition is belong to major asset restructuring (Majort) are controlled. Reported in parentheses are t-values



(1) (2) (3) (4) (5) (6) (7) (8)


PCt -0.2683*** -0.2078***

(-4.4492) (-3.4675)

Event * PCt 0.2373*** 0.1793**

(3.4873) (2.1258)

H_PCt -1.4183*** -0.6377**

(-3.2238) (-2.2196)

Event * H_PCt 1.0775*** 0.2339**

(3.3362) (2.1328)

C_PCt -0.4074*** -0.1885***

(-4.4416) (-2.8462)

Event * C_PCt 0.2913 0.1415*

(1.4118) (1.6853)

P_PCt -0.2828*** -0.1972***

(-3.5964) (-3.6660)

Event * P_PCt 0.2337* 0.1767

(1.7407) (1.5643)

Event -0.1681* -0.0796 -0.1625 0.0043 -0.2697* -0.0879 -0.1850 -0.0855

(-1.9545) (-1.1399) (-1.4108) (0.0500) (-1.8977) (-0.8012) (-1.5326) (-0.9338)

OTurnovert -0.1028* 0.0028 -0.0593 0.0125 -0.0615 0.0119 -0.0656 0.0114

(-1.6849) (0.0627) (-0.8263) (0.2333) (-0.8587) (0.2212) (-0.9077) (0.2136)

Rett 3.7011 -0.9639 -0.4366* -0.1456 0.7150 0.2587 0.9477 0.3175

(1.4106) (-0.4978) (-1.9049) (-0.9455) (0.9881) (0.7037) (1.2014) (0.8634)

Sigmat 0.1216 0.4523 -0.4447 0.3395 -0.5025 0.3738 -0.7278 0.2949

(0.1144) (0.6074) (-0.4372) (0.4884) (-0.4929) (0.5334) (-0.7149) (0.4257)

BMt -0.4849** -0.3936*** -0.9096*** -0.5041*** -0.8894*** -0.4886*** -0.9042*** -0.5022***

(-2.3363) (-2.7528) (-4.2269) (-3.1245) (-4.0309) (-2.9811) (-4.1096) (-3.1117)

AbsACCt 0.3169 -0.0313 0.0836 -0.0840 0.3073 0.0265 0.3489 0.0363

(0.8248) (-0.1065) (0.2091) (-0.3086) (0.7120) (0.1026) (0.7641) (0.1409)

NCSKEWt 0.0204 -0.0316 0.0296 -0.0055 0.0343 -0.0055 0.0381 -0.0043

(0.6802) (-1.3066) (0.9221) (-0.2195) (1.0934) (-0.2230) (1.1835) (-0.1723)




Constant 0.1166 -0.1913 -0.0343 -0.1506 -0.2854 -0.2315 -0.3914 -0.2625

(0.2669) (-0.6640) (-0.0582) (-0.3886) (-0.4829) (-0.6040) (-0.6775) (-0.6968)

Observations 1,749 1,749 879 879 879 879 879 879

Adj. R-Squared 0.099 0.106 0.134 0.093 0.137 0.090 0.127 0.094

Table 9

Robustness test for selection bias. The regression results of Heckman model. This table reports the regression

results of Heckman model using the performance commitment sample and control sample matched by one-to-one.

The first step is a probit model with a binary PC dummy and the second step is the ordinary least square regression

of the impact of performance commitment on future stock price crash risk. The dependent variable NCSKEW and

DUVOL in the second step are measured over year t + 1. IMRt denotes the inverse Mills ratio generated from the

first step and included in the second step of this model. Reported in parentheses are t-values, *, **, and ***

indicate significance at the 10%, 5%, and 1% levels (two-tailed test). All variables are defined in Appendix A.

First-step regression Second-step regression

(1) (2) (3)

VARIABLES PCt VARIABLES NCSKEWt + 1 DUVOLt + 1

SEOt 0.0335 PCt -0.1860** -0.1205***

(0.3143) (-3.218) (-2.891)

Relatet -0.0495* Sizet -0.0008 0.0085

(-1.8237) (-0.0183) (0.2412)

Majort -0.3291** Levt 0.0029* 0.0025*

(-2.4031) (1.6511) (1.9279)

Payt 0.2195*** Top1t -0.0045** 0.0005

(3.3305) (-2.2553) (0.3111)

Consultantt 1.8705*** ROAt -0.0068 -0.0021

(13.2058) (-1.1552) (-0.4659)

Bvaluet 0.0024* Growtht 0.0004 0.0003

(1.7495) (0.6973) (0.7781)

Dualityt -0.0770 SEOt 0.0140 0.0916

(-0.9007) (0.1834) (1.5751)

Instt -0.0027 Relatet -0.0756 -0.0212

(-1.4345) (-1.2457) (-0.4570)

Majort -0.0301 0.0247

(-0.4480) (0.4812)

Payt 0.0115 -0.0137

(0.2564) (-0.3977)

OTurnovert -0.0073 0.0145

(-0.1028) (0.2698)

Rett 0.9232 -1.6072

(0.3035) (-0.6927)

Sigmat 0.4652 0.7669

(0.3876) (0.8377)

BMt -0.7802*** -0.5623***

(-2.9585) (-2.7955)

AbsACCt -0.1192 -0.1202

(-0.2623) (-0.3467)

NCSKEWt 0.0325 -0.0064

(0.9639) (-0.2489)

IMRt 0.0121 0.0188**

(1.1519) (2.3083)

Constant -1.0919*** Constant 0.1243 -0.4196

(-9.6684) (0.1875) (-0.8295)

Observations 1,779 Observations 1,779 1,779

Pseudo R-Squared 0.0127 Adj. R-Squared 0.0969 0.0986

Table 10

This table shows the placebo test on acquisitions with performance commitment in prior year. A significant

coefficient for PCt could be interpreted as the existence of unobservable variables omitted from our analysis but

correlated with the propensity to explain the future stock price crash risk. Other control variables are the firm total

assets (Sizet), firm financial leverage (Levt), the shareholding ratio of the largest shareholder (Top1t), return on

total assets (ROAt), firm’s growth (Growtht), whether the firm's ultimate controlling shareholder is state (SEOt). At

the acquisition level, method of payment (Payt), whether acquisition is related transaction (Relatedt) and whether

the acquisition is belong to major asset restructuring (Majort) are controlled. Reported in parentheses are t-values



(1) (2)

VARIABLES NCSKEWt + 1 DUVOLt + 1

PCt 0.0147 -0.0020

(0.3668) (-0.0771)

OTurnovert 0.0347 0.0003

(0.8147) (0.0090)

Rett 8.0624** 6.6077***

(2.1716) (2.6765)

Sigmat -1.1135 -1.1024

(-0.7075) (-1.0533)

BMt -0.7714*** -0.5637***

(-5.0569) (-5.5577)

AbsACCt -0.0035 0.1109

(-0.0097) (0.4604)

NCSKEWt 0.0465 0.0267

(1.5211) (1.3139)

Other control Yes Yes

Industry fixed effects Yes Yes

Year fixed effects Yes Yes

Constant -1.0636* -0.5582

(-1.7666) (-1.3942)

Observations 1,283 1,283

Adj. R-Squared 0.092 0.100

performance commitment in acquisitions, regulatory change ... .pdfperformance commitment can be a...

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