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What determines the market share of investment banks in Chinese domestic

IPOs?

Working paper

Nancy Huyghebaert

Weidong Xu*

Abstract:

In this paper, we examine what determines the market share of investment banks

in Chinese domestic IPOs in the period 1995–2010. Before the end of 2004, only

political connections significantly influenced the market share of investment

banks. As of 2005, both evaluation standards on IPO candidates and

underwriting fees significantly negatively affect market share, while the effect of

political connections has evaporated. We explain these results by changes in

government policy and by a lack of incentives for issuers to hire investment banks that apply high evaluation standards.

Keywords: IPO, Information asymmetry, Investment bank certification, Political

connection, market share

JEL classification: G24; G28; C22; D82; P21

* Corresponding author: Weidong Xu, Katholieke Universiteit Leuven/FWO, Department of Accountancy, Finance, and

Insurance, Naamsestraat 69, 3000 Leuven, Belgium. E-mail address: [email protected]

mailto:[email protected]

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1. Introduction

Investment banks have only a short history in the People‘s Republic of China. The first investment

bank – Shenzhen Special Economic Zone Securities Firm – was not formed until 1987. Yet, with the

re-establishment of stock markets at the beginning of the 1990s, the Chinese government has allowed

a whole new indigenous industry of investment baking to develop.1 These investment banks have led

a massive number of IPO firms to the stock exchanges of Shanghai and Shenzhen. According to the

data compiled by Dealogic, in 2010 Chinese domestic IPOs accounted for 45% of the number and 39%

of the gross proceeds of IPOs worldwide. Yet, academic research on the role and the development of

investment banks in Chinese IPOs is non-existent to date. Our paper tries to fill this void in the

literature by examining how Chinese investment banks have established their market share over time.

For this purpose, we combine existing theories developed in a Western context with the special

characteristics of the Chinese IPO market; we also account for the major institutional changes that

took place during our sample period. Specifically, we explore the role of political connections, IPO

underpricing, the evaluation standards applied by the investment bank, investment bank

compensation, and the presence of star analysts in determining the market share of investment banks

in Chinese domestic IPOs.

Several features about the Chinese IPO market make this research interesting. First, Chinese

investment banks have built up their market share from scratch over the past two decades, with the re-

establishment of Chinese domestic stock markets. During this process, the regulator has put

investment banks under heavy administration and has intervened directly in the IPO market to guide

investment banks to gradually take up a role in certifying the quality of IPO firms. This institutional

1 Chinese investment banks all started from scratch, with local owners. Foreign investment banks have had little, if any, influence on the development of Chinese investment banks. At present, investment banks still have to be majority-owned

by a domestic owner in order to underwrite IPOs in China. By the end of 2010, only Morgan Stanley, UBS, LCL,

Deutsche Bank, Goldman Sachs, and SMBC have established joint ventures with a domestic partner. Those joint ventures

have led only 60 A-share IPOs up till 2010, 2.8% of the total number of domestic IPOs.

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aspect makes the Chinese IPO market different from any Western IPO market, where the market

mechanism itself plays a major role in rewarding and punishing investment banks according to their

behavior in IPO markets. For example, in a US context, Dunbar (2000) finds that investment banks

that apply lower evaluation standards and thus introduce firms with inferior post-IPO performance

lose market share over time. Yet, along with the development of the Chinese IPO market, the

regulator has gradually reduced its direct market intervention and has allowed market forces to

become more influential. These institutional changes enable us to explore how the ‗visible hand‘ of

the government and the ‗invisible hand‘ of the market have affected the IPO underwriting business of

Chinese investment banks and how this has changed over time.

Second, the heavy government administration and direct market intervention offer us a unique

opportunity to investigate the effects of political connections on the development of IPO underwriting

business. Faccio (2006) notes that political connections could be valuable to firms, by securing

preferential treatment from the government. It is conceivable that heavy administration and direct

intervention have opened the door for the government to differentiate its treatment across investment

banks, thereby favoring the ones with stronger political connections. Meanwhile, the quality of

institutions remained weak in China, especially in the early years after stock market re-establishment

(see also Li et al., 2008). Such deficiencies tend to increase the dependency of investment banks on

government administration, enhancing the influence of political connections. While earlier research

has documented the effects of political connections on firm financial decisions and on firm value, this

paper examines its influence on the market shares of investment banks in the Chinese IPO market.

Third, empirical research to date has found it hard to quantify the effects of underwriter

compensation on market share. The reason is that investment banks in Western IPO markets are

compensated not only by means of underwriting fees. Loughran and Ritter (2004) report that in the

late 1990s, underwriters discretionally allocated hot IPOs to their institutional clients; those clients

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then paid back part of the realized first-day abnormal returns to the underwriter through brokerage

commissions on stock trading.2 So, by discretionally allocating IPO shares, investment banks could

secure and expand other businesses. However, that part of benefits is difficult to quantify, which

makes it complex to disentangle the empirical relation between underwriter compensation and market

shares. In contrast, Chinese investment banks never obtained the right to discretionally allocate IPO

shares to investors. Their compensation in IPO underwriting can therefore be measured more

precisely by their underwriting fees than in any Western market.

Our empirical results indicate that only political connections significantly influenced the

market share of investment banks before the end of 2004. On average, an investment bank directly

owned by the central government had a market share that is 7.7 percentage points larger than that of

an investment bank controlled by the local government or by a private owner. Given that the average

market share during that period was only 3.6%, political connections created a huge advantage. As of

2005, the role of political connections has become insignificant. Rather, investment banks applying

lower evaluation standards on IPO candidates and investment banks charging lower underwriting fees

could expand their market share. These findings are in line with the policy changes that took place in

the Chinese IPO market. Up till the end of 2004, the government restricted competition among

investment banks by imposing a yearly maximum number of IPO applications that could be

submitted by a single investment bank. As of 2005, this annual IPO limit was abolished. Also, up till

the end of 2003, the government granted underwriting permission to investment banks on an annual

basis, according to their performance in previous-year IPOs. Investment banks that had committed

serious errors or fraud in previous IPOs could lose their underwriting permission. From 2004

onwards, any comprehensive investment bank with at least two sponsors just needs to register with

2 A concrete sample was given by Loughran and Ritter (2004) by quoting the NASD Regulation news release: ―For

example, after a CSFB customer obtained an allocation of 13,500 shares in the VA Linux IPO, the customer sold two million shares of Compaq and paid CSFB $.50 a share—or $1million—as a purported brokerage commission. The

customer immediately repurchased the stock through other firms at normal commission rates of $.06 per share at a loss of

$1.2 million on the Compaq sale and repurchase because of the $1 million paid to CSFB. On that same day, however, the

customer sold the VA Linux IPO shares, making a one-day profit of $3.3 million.‖

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the regulator to carry out underwriting business. Also, before March 2004, underwriting fees were

restricted to be between 1.5% and 3% of total gross proceeds; this restriction was abolished thereafter.

Those policy changes show that in the early years after Chinese stock market re-establishment, the

government relied on direct intervention and heavy administration to manage the IPO underwriting

market. However, in later years, especially as of 2005, the state has dramatically reduced its direct

intervention and has left the monitoring of investment banks to the market. Moreover, these early

policies limited the ability of investment banks to build their own market strategy, such as by

differentiating their evaluation standards and underwriting fees from competitors. As of 2005, the

abolishment of those restrictions has allowed investment banks to compete freely against each other.

One of the interesting findings is that, unlike Western IPO markets, setting a higher evaluation

standard than competitors has never helped Chinese investment banks to expand their market share.

Before the end of 2004, IPO evaluation standards simply displayed no relation with the market share

of investment banks. Even more surprisingly, as of 2005, investment banks that set higher evaluation

standards actually lost market share. We argue that the unique Chinese IPO pricing mechanism – the

P/E ratio at which firms can issue new shares is fixed by the regulator – has incentivized issuers to

hire investment banks that apply low evaluation standards. With a fixed issuing P/E ratio, the only

way for issuers to ensure a higher offer price is to boost their historical and/or forecasted earnings.

So, by hiring an investment bank with a low IPO evaluation standard, the odds that this earnings

exaggeration goes unnoticed are considerably larger. This incentive on the part of issuers is

considerably distinct from that in any Western IPO market, where underwriters with a reputation of

applying strict evaluation standards help issuers to obtain a higher issue price and thus are preferred

by issuers (Chemmanur and Fulghieri, 1994). Our findings on the role of IPO evaluation standards

are somewhat related to those of DeFond et al. (1999), who examine the audit market for Chinese

IPO firms. They demonstrate that after the introduction of stricter audit standards, the top 10 audit

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firms that subsequently applied these stricter standards and issued more modified audit reports lost

market share. As both underwriters and auditors serve to certify the quality of IPO firms, both

findings are in line with each other. However, DeFond et al. (1999) did not examine in detail why

issuers lack incentives to demand certification from more independent auditors. In this article, we try

to dig deeper into the institutional reasons driving this phenomenon.

The remainder of the paper is organized as follows. In Section 2, we briefly review the

development of the Chinese IPO market; we focus on the policies that may have influenced the

market share of investment banks. In Section 3, we develop hypotheses by combining the current

literature with some unique institutional aspects of the Chinese IPO market. In Section 4, we

describe the data and the results of univariate comparisons. In Section 5, we discuss our multivariate

regression results and the results of some robustness checks. Section 6 offers conclusions and infers

public policy implications.

2. Historical review

2.1. Review of the Chinese domestic IPO mechanism

Since 1978, the Chinese government has reformed its centrally planned economy into a more market-

oriented one. Establishing a well-functioning financial market has been at the top of the

government‘s reform agenda. In Nov. 1984, Shanghai Feilo Co., Ltd made the first public offering of

common stock by self-issuing; the shares were sold and traded over the counter, as formal stock

exchanges were not yet in place. The stock exchanges of Shanghai and Shenzhen were re-established

in 1990 and 1991, respectively to facilitate securities transactions. Two kinds of shares can be traded

in Chinese domestic stock markets: A shares and B shares. A shares are traded only in RMB and

target domestic investors; foreign investors have been allowed to invest in these A shares since Dec.

2002, but only through qualified foreign investment funds who cannot hold more than 10% of a

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firm‘s stock. B shares are traded in USD/HKD and are offered to foreign investors.3 By the end of

2010, according to the figures compiled by the National Bureau of Statistics of China, 2,104 firms

have been listed into these two markets, with total market capitalization reaching RMB 26.54 trillion.

In 1993, the central government established the China Securities Regulatory Commission

(CSRC) to regulate Chinese securities markets. Since then, the IPO mechanism has been designed,

changed, and enforced by the CSRC. From 1993 till July 1999, stock offerings were subject to a

quota system. Under this system, the State Planning Commission, in conjunction with the People‘s

Bank of China and the CSRC, every year determined the number of new shares to be issued. The

quotas were subsequently allocated to provinces and to national ministries and committees who

recommended the companies under their jurisdiction to go public. The local securities authorities, i.e.

the CSRC‘s local branches, invited the enterprises in their region to apply for listing and made a first

selection among the IPO candidates. After their selection, firms had to engage an underwriter, who

would prepare and submit their IPO application to the central CSRC. Such application had to include

a detailed description of the operations, the financial performance, and the internal control procedures

of the issuing firm and a first draft of the IPO prospectus. Based on those application materials, the

central CSRC made a final decision on whether or not the IPO candidate could go public. In this way,

the CSRC itself was highly involved in examining the quality of IPO firms. Meanwhile, the CSRC

also required the investment banks to verify the validity and the accuracy of all information in the

IPO application, as a way to prepare investment banks to take up a role in certifying the quality of

IPO firms. After July 1999, with the enforcement of the China Securities Law, this quota system was

formally abolished. Also, firms eligible for IPO were no longer picked by the local CSRC branches.

Every company satisfying the listing criteria specified in the Company Law could henceforth apply

for listing. These listing criteria were as demanding as under the ‗quota system‘. Specifically, the

3 Due to the small number and the different nature of investors, we did not include B-share offerings in our study. Indeed,

by the end of 2010, only 108 firms have been listed on the B-share market, typically a few months before the firm‘s A-

share offer. The market value of these firms‘ B shares is typically less than 1% of the market capitalization of A shares.

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applicant had to record positive net earnings in each of the three years before its IPO. Also, it had to

establish sufficient internal control procedures and operate independently from other firms controlled

by the same controlling shareholder. As of July 1999, the CSRC relied on investment banks to

examine whether those conditions were fulfilled.

The formula to calculate the issue price was changed several times in history. Yet, the issue

price has always been determined as the product of a fixed P/E ratio (applying to all IPOs in that year)

and a weighted average of historical earnings preceding the IPO and forecasted earnings in the IPO

year.4 Every year, the fixed P/E ratio was set by the CSRC. To attract the interest of the public for

IPOs, the CSRC deliberately fixed the issuing P/E cap considerably below the prevailing market P/E

ratio. As shown by Francis et al. (2009), P/E caps were within the range of 13 to 16 during 1994–

1999, much lower than the secondary-market P/E ratio (15 to 58 in that period). During 2000–2004,

the P/E cap was about 20, while the market P/E ratio was between 24 and 58 (Tian, 2010). After Dec.

2004, with the publication of Circulation No. 162, the official P/E cap was given up. However, the

CSRC still managed issue prices to some extent (see Gao, 2010). However, in June 2009, with the

publication of ‗The guiding advice on further reform of the IPO pricing method‘, the CSRC

announced that it would no longer interfere in the pricing of IPO shares. Table 1 presents the annual

number of IPOs, the average issuing P/E ratio, and the market P/E ratio in the same year. Market P/E

ratios were obtained from the website of the stock exchanges. This table shows that the issuing P/E

ratio on average equals 64% of the prevailing market P/E ratio for the Shanghai stock exchange and

63% for the Shenzhen stock exchange. It also reveals a big increase in the issuing P/E ratio in 2010,

as the CSRC finally abolished its intervention in the pricing of IPO shares.

<Insert Table 1>

This pricing mechanism has given rise to extremely high first-day abnormal returns.

According to the numbers compiled on Jay Ritter‘s website, the first-day abnormal return in Chinese

4 For a more detailed discussion, see Gannon and Zhou (2008).

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domestic IPOs is the third largest in the world, averaging to 133% in 1990–2010. Studies report

different first-day abnormal returns in different time periods, however. Su and Fleisher (1999) find

949% in 1987–1995, while Chi and Padgett (2005) report 129% in 1996–2000; Guo and Brooks

(2008) find 93.49% between 2001 and 2005. We made a year-by-year analysis on the first-day

abnormal return for all IPOs with observable records in the GTA China listed firms‘ IPO research

database during 1992–2010. As the time lag between share offering and share listing can be quite

long, especially in the early years after stock market re-establishment (Huyghebaert and Quan, 2009),

we adjust the first-day abnormal return by the market return between the offering date and the listing

date. Our results are in line with those of previous studies. Specifically, we notice that only 49

(2.3%) IPOs realized a negative first-day abnormal return, while 65 (3.1%) realized a negative first-

day return.5

<Insert Table 2>

The high first-day abnormal returns created a ‗new issue fetish‘, as it was called by the

Chinese media. Investors blindly bought any new issues, paying only little attention to underlying

firm quality, as the CSRC almost guaranteed them to make money.6 Not surprisingly, Chinese IPOs

have been severely over-subscribed. According to the data available in the GTA China listed firms‘

IPO research database, the average (median) oversubscription multiple equalled 2,250 (219) between

1995 and 2010, much higher than that in any Western market. Cornelli and Goldreich (2001), for

example, find an average and median oversubscription rate of 5.2 and 3.0, respectively for a sample

of 39 firms becoming listed in the USA in 1995–1997. The allocation rules for oversubscribed IPOs

have changed several times in history as well. Before 1999, lottery cards were sold to investors and

5 As the ‗green shoe‘ mechanism was never used in Chinese A-share IPOs, underwriter price support cannot provide an

explanation for the limited number of IPOs with a negative first-day abnormal return. 6 These investors in Chinese domestic stock markets are typically small and unsophisticated. A study published on the website of the China Securities Depository and Clearing Corporation Ltd shows that by the end of 2007, small retail

investors still accounted for about 80% of the total transaction volume. About 56 million Chinese citizens were trading in

stocks; 70% of them have monthly income below RMB 5,000; over 50% of them hold stocks for less than three months.

Many investors thus invest in stocks for speculative purposes rather than for long-term investment purposes.

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IPO shares were allocated randomly, based upon the serial number of those cards. As of 1999,

buyers needed to make full prepayment and initial shares were rationed according to the amount of

prepayment. Most importantly, investment banks never have had any discretionary allocation rights,

which is again in sharp contrast to Western IPO markets, where allocating IPO shares is one of the

main tasks of investment banks.

2.2. Review of the development of investment banks in the Chinese A-share IPO market

In China, the first investment bank, Shenzhen Special Economic Zone Securities Firm, was not

formed until 1987. From 1987 till the end of 1992, three types of financial institutions could

underwrite IPOs: securities firms, trust and investment corporations, and commercial banks. They

were owned either by the central government or by a local government. So, they all started with state

ownership.7 During that period, public offerings were not mandated to involve an underwriter. Yet,

of the 180 firms that made a public offer between 1987 and 1992, only 36 (20%) of them offered

shares without engaging an underwriter.

In 1993, the CSRC issued ‗The circulation on enhancing the role of securities underwriters

and professional intermediaries in stock offerings‘, which henceforth mandated every issuer to select

an investment bank as lead underwriter for its IPO.8 Upon receiving a qualification from the CSRC,

investment banks had to organize the whole IPO process. Regulation defined the responsibility of

investment banks as to ensure the validity and accuracy of IPO application materials. It also

stipulated that the lead underwriter would be punished by the CSRC if the application material

contained serious misleading information or fraud. This punishment included fines, a suspension of

7 To protect a domestic industry, the Chinese government did not allow financial institutions majority-owned by

foreigners to carry out their business in Mainland China. Privately-owned investment banks are still scarce today. By the

end of 2010, only five investment banks are controlled by private owners; they have led 57 IPOs, 2.7% of the total

number of IPOs. Among the state-owned investment banks, some banks are owned directly by the central government, while others are owned by their local government. 8 Most IPOs were led by only one underwriter. In a few big IPOs, issuers hired several lead underwriters to organize the

whole IPO process. For the 2,104 Chinese A-share IPOs in our database, only 65 (3.1%) were led by more than one

underwriter. We explain how we dealt with those multi-lead underwriter IPOs in Section 4.1 of the paper.

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the underwriting qualification, and even disqualification. Besides, the lead underwriter had to buy

any unsold IPO shares at the issue price if the IPO could not attract enough investor interest; this

never happened in the history of Chinese A-share IPOs. Also in 1993, the Chinese government

clarified that commercial banking and investment banking should be separated. As a result, the

investment banking business previously owned by commercial banks was packed into independent

legal entities. Trust and investment corporations were asked to split their investment banking

business into independent legal entities a few years later. In the late 1990s, a wave of reorganizations

of the investment banking business originally owned by trust and investment corporations took place.

In 1996, to further regulate the behavior of investment banks in IPOs, the CSRC issued ‗The

circulation on issuing measures for the management of stock underwriting business by securities

firms‘. Under this regulation, the CSRC henceforth would review the performance of investment

banks at the end of every year and determine whether or not an investment bank could continue

carrying out underwriting business in the subsequent year. Investment banks that had committed

serious errors or fraud in the IPOs they led in the preceding year could lose their qualification. This

same regulation also mandated underwriting fees to be between 1.5% and 3% of total gross IPO

proceeds. The latter policy was maintained until the CSRC implemented ‗The interim measures for

stock issuance and listing recommendation‘ in February 2004. As of that date, investment banks

became free to set their underwriting fees.

By the end of 2003, the CSRC issued the trial implementation of the price inquiry system in

IPOs. Investment banks no longer needed to obtain a yearly underwriting qualification but could

carry out underwriting business upon registration with the CSRC and upon meeting certain criteria.

The investment banks eligible for registration should be comprehensive securities firms9 who hire at

9 A ‗comprehensive‘ investment bank should have registered capital of at least RMB 500 million and carry out the

different types of investment banking business, like brokerage business, consulting and asset management, etc.

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least two sponsors10

and establish efficient internal control procedures. The CSRC requires those

investment banks to be ―…bound by principles of good faith and due care and skill, as well as by the

CSRC‘s requirements for a sponsor‘s due diligence; they should make a full investigation into the

issuer to fully understand its financial and operating position, as well as its risks and problems‖ and

―based upon the information obtained in the due diligence, conduct prudent verification and make an

independent judgment on the information furnished and disclosed by the issuer‖.

As introduced in Section 2.1, with the abolishment of the quota system as of July 1999, the

CSRC became less involved in checking the quality of IPO firms. Instead, it relied on the investment

banks to certify firm quality. To ensure that the investment banks would carry out their certification

task, the CSRC resorted to direct market intervention. From 2001 till the end of 2004, the CSRC

granted ‗channels‘ to every investment bank, thereby directly influencing its market share. Those

channels limited the number of IPOs an investment bank could apply for at once. For example, if an

investment bank obtained four channels, it could apply for at most four IPOs at the same time. It

could not apply for any additional IPOs until one of the previous four applications had been approved

by the CSRC. So, the number of IPOs an investment bank could lead in one year depended on two

factors: the number of channels it was granted and the speed at which the CSRC handled its previous

applications. An application on average took half a year to be handled.11

If the CSRC had doubts on

the validity and accuracy of application materials, it could extend the approval period by asking for

more information, thereby further reducing the number of IPOs the investment bank could underwrite

in that year. The number of channels granted varied from one to eight, depending upon the CSRC‘s

assessment of the capacity of the investment bank. On January 1, 2005, the channel mechanism was

10 The sponsors are individuals certified by the CSRC. A firm should be recommended for listing by at least two sponsors

and by one qualified investment bank. The purpose of this policy is to incite individuals (the sponsors) to put their

reputation at stake to ensure the quality of IPO firms. The system started in the UK and has been widely used in Hong

Kong. In Chinese domestic stock markets, the sponsors are still acting mostly as employees of investment banks. Yet, time is not long enough to allow the more than 1,000 sponsors to have built up a clear track record. 11 The half-year application period is a norm in practice. According to the ‗IPO process guidance‘ published on the

Shanghai stock exchange website: http://www.sse.com.cn/sseportal/ps/zhs/sjs/nsszl/flow.shtml#1, the application period

is estimated to be between 3 and 9 months.

http://www.sse.com.cn/sseportal/ps/zhs/sjs/nsszl/flow.shtml#1

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abolished, thereby entirely leaving the determination of market shares to the market. Since then,

investment banks have become free to compete against each other in the IPO market.

3. Development of hypotheses

In the existing literature, direct empirical tests on the determinants of investment bank market share

are scarce. In a highly influential study, Dunbar (2000) examines the factors affecting the market

share of investment banks in US IPOs during 1985–1995. Somewhat related, Rau (2000) investigates

the relation between contingent fees, acquiring-firm performance and the market share of investment

banks in the M&A market. Based upon this research and paying attention to the Chinese context, we

develop five hypotheses, explaining the market share of investment banks in Chinese IPOs.

Specifically, we study the role of political connections, IPO underpricing, the evaluation standard

applied by the investment bank, investment bank compensation, and the presence of star analysts.

3.1. The political connections of investment banks

Faccio (2006) notes that political connections could be valuable to firms in various ways, including

preferential treatment in government contracts, preferential treatment by other state-owned firms,

relaxed regulatory oversight of the company itself, or stiffer regulatory oversight of its rivals. Li et al.

(2008) argue that political connections are especially valuable in a transition economy like China,

where both legal and market institutions are still weak. The reason is that firms depend more on the

political connections of their insiders to protect their property rights once conflicts arise in initiating,

modifying, and implementing contracts. Hillman (2005) concludes that the value of political

connections depends on the firm‘s reliance on government regulation. In heavily regulated industries,

political connections tend to have a larger effect on firm performance and value.

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Our research provides a good foundation for exploring the effects of political connections on

the market share of investment banks in Chinese domestic IPOs. First, unlike Western investment

banks that operate in a well-established legal and market context, Chinese investment banks had to

develop their market share from scratch, along with the corresponding institutional development. As

pointed out by Li et al. (2008), in such an environment firms have to rely more on government

administration when initiating and enforcing contracts. So, close ties with government officials may

help businesses to overcome institutional failures. In this article, we contend that political

connections were extremely important for investment banks especially in the early years after stock

market re-establishment, while their importance likely has diminished over time, along with

institutional development. Second, the underwriting business has been under heavy government

administration and direct intervention, especially before 2005. As pointed out by Hillman (2005) and

Faccio (2006), heavy government intervention opens the door for differentiated treatment of

connected firms. Investment banks with better political connections may have benefited from this,

for example by obtaining more ‗channels‘ under the channel system, or by evading severe

punishment after committing serious errors or fraud in previous IPOs. Having political connections

may also help investment banks to shorten the application period for IPO firms, to shorten the listing

lag, and/or to obtain a more favorable issuing price. Those advantages could have helped connected

investment banks to attract issuers when competing for underwriting business. As of 2005, the

regulator stopped its direct intervention in investment banking business. We therefore expect that the

likelihood of preferential treatment may have been reduced over time, thereby diminishing the value

of political connections. In summary, we postulate the following hypothesis:

Hypothesis 1: Political connections positively influence the market share of investment banks before

the end of 2004; as of 2005, such influence has disappeared.

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3.2. The pricing role of investment banks

The IPO market is characterized by asymmetric information; this feature is especially relevant in the

Chinese context, where investors are usually small retail ones. Under the assumption that certain

investors have an information advantage about firm value over other market participants, Beatty and

Ritter (1986) assign a pricing role to investment banks in IPOs. Specifically, they argue that

uninformed investors demand IPOs to be underpriced to compensate for their possible loss arising

from a ‗winner‘s curse‘, that is their larger (and smaller) allocation of shares in under-(over-)

subscribed issues. Investment banks, as repeated participants in the IPO market, set issue prices and

ensure that uninformed investors can earn a first-day abnormal return in order to break even on

average. They further predict that the investment banks that set a wrong price, by offering too much

or too little underpricing, will subsequently lose market share. Underpricing too much reduces the

attractiveness of an investment bank from the issuers‘ point of view while underpricing too little

annoys investors. The effect of first-day abnormal returns on market share will therefore depend on

the relative importance of these two forces. For the US market, Dunbar (2000) finds evidence that

too much underpricing hurts the market share of underwriters. For China, we expect to find no

evidence of such a relation. Indeed, in order for a relation between IPO underpricing and investment

bank market share to arise, it is necessary that investment banks have full discretion in setting the

offer price, so that investors and issuers can attribute wrong IPO pricing to the underwriter. In China,

the CSRC has been making final decisions on the issue price in IPOs. It is only very recently, as of

June 2009, that the government has fully given up this role. So, in the history of Chinese IPOs, the

pricing role of investment banks has been largely overtaken by the government. Any incorrect

pricing could therefore not be attributed to the incompetence of investment banks. We therefore put

forward the following hypothesis:

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Hypothesis 2: Abnormal underpricing does not significantly influence the market share of investment

banks in Chinese domestic IPOs.

3.3. The evaluation standard applied by investment banks

Booth and Smith (1986) show that investment banks, as repeated participants in the IPO market, can

offer certification for issuing firms. By putting their reputation at stake, investment banks are able to

assure the quality of IPO firms to investors. Chemmanur and Fulghieri (1994) develop a dynamic

model demonstrating how the evaluation standard adopted by the investment bank affects its

reputation among investors. This reputation subsequently influences its market share. The essence of

the model is that investors update their beliefs on investment banks by the quality of the firms

recently introduced, as the true quality of an IPO firm eventually becomes known in the aftermarket.

If an investment bank applies a high evaluation standard, it is better able to discover the true quality

of the issuer and thus introduces less bad-quality firms. An investment bank with a track record of

underwriting good-quality firms develops its reputation among investors. In this model, issuers

always choose the most reputable investment banks because engaging those investment banks helps

them to increase the issue price and to sell the IPO shares to investors. So everything else constant,

the investment banks that introduce better-quality firms should gain market share over time. For the

US market, Dunbar (2000) shows that the after-IPO performance of the firms introduced by an

investment bank is indeed positively correlated with its market share.

In China, an investment bank‘s reputation of strictly evaluating issuing firms could not help

issuers to increase the issue price of their offer and thus was not demanded by them. The reason is

again that the regulator influenced the issue price by the fixed issuing P/E cap for all IPOs in the

same year. In contrast, we claim that issuers in China actually had strong incentives to hire an

investment bank with a low evaluation standard, as firm earnings could be exaggerated more easily

when the investment bank does not scrutinize the IPO candidate. Exaggerating earnings could in turn

17

help to increase the issue price. So, we claim that issuers prefer investment banks with low

evaluation standards. As a result, we might find that investment banks with low evaluation standards

gain market share over time. This prediction seems to contradict the model of Chemmanur and

Fulghieri (1994), but is exactly the result after implementing its rationale to the Chinese context.

Issuers indeed are likely to select an investment bank with either a high or a low evaluation standard,

depending upon which standard may help them to maximize the issue price. In a Western context, a

high evaluation standard serves that purpose; in the Chinese context, a low evaluation standard did

the job during our period of analysis. A countervailing force to applying a low evaluation standard –

the legal costs to issuers and to underwriters – is unlikely to apply in the Chinese context, as investors

had only limited opportunities for suing misbehaving issuers and underwriters. As an example, the

class-action suit mechanism, which is a powerful instrument for US investors, does not exist in China

under the current legislative system. The only countervailing force left to impede investment banks

from competing with a low evaluation standard to secure investment banking business was

government administration and intervention. Indeed, as introduced in Section 2.2, the Chinese

government resorted to heavy administration before 2005 to enforce investment banks to hold full

responsibility for the quality of the firms they introduced. As of 2005, the CSRC no longer directly

intervenes in the underwriting market and investment banks may have adapted their evaluation

standards to attract underwriting business. The above arguments result in the following conjecture:

Hypothesis 3: Evaluation standards do not significantly affect the market share of investment banks

before 2005; as of 2005, investment banks with low evaluation standards may have gained market

share.

3.4. Investment bank compensation

Investment banks charge fees to the issuers for the services that they provide in IPOs, such as pricing

the offer, certificating the quality of the IPO firm, marketing the IPO to potential investors, etc. In

18

this sense, the fee rate is likely to be another important factor determining the market share of

investment banks. To build their market share, investment banks may initially set low fees. Once

market share is established, they could adjust their fees to a more normal rate. Based upon these

arguments, we argue that it is the deviation of actual fees from expected (equilibrium) fees that

matters. Dunbar (2000) uses two explanatory variables for the expected fee rate: gross IPO proceeds

and the natural log of gross IPO proceeds. These two variables depict an U-shaped relation between

gross IPO proceeds and fees (Altinkilic and Hansen, 2000).

In the Chinese IPO market, investment banks started from scratch; they might have set their

fees lower than the market equilibrium to attract issuers. By doing so, they might have sacrificed

short-term profits to grow their market share. However, before Feb. 2004, the CSRC limited the

underwriting fee to be between 1.5% and 3% of total gross IPO proceeds. After that date, investment

banks became free to set their underwriting fees. So, we postulate the following hypothesis:

Hypothesis 4: Fee rates bear no relation with market shares of investment banks before 2005; as of

2005, investment banks setting a fee rate below the expected level may have gained market share.

3.5. Star analysts employed by investment banks

Loughran and Ritter (2004) suggest that as of the 1990s, issuers in the US market have put a larger

importance on hiring lead underwriters with highly ranked analysts to guarantee analyst coverage

after the firm‘s first listing. Krigman et al. (2001) find that one of the main reasons why issuers

change their underwriters in seasoned equity offerings is better analyst coverage. According to Arbel

(1985) and Merton (1987), the more an asset is exposed to investors the higher is its price. In this

sense, issuers should care a great deal about star analyst coverage. For the US market, Dunbar (2000)

shows that having more star analysts helps investment banks to expand their market share.

19

The China Certified Security Analyst Committee was not established till 2001 and no

influential analyst ranking existed before the end of 2003. However, it is interesting to check the

influence of star analysts on the market share of investment banks in Chinese IPOs. The reason is

that Chinese stock markets are still dominated by small retail investors, who might be more

responsive to analyst recommendations. Thus, having star analyst coverage could be highly

beneficial to the listing firms. We expect this relation to arise only in the second subperiod, allowing

star analysts to have established their reputation among investors.

Hypothesis 5: The number of star analysts employed by an investment bank is positively related to its

market share as of 2005.

4. Sample selection and data description

We first collected data on all Chinese A-share IPOs on the Shanghai and Shenzhen stock exchanges

between 1990 and 2010. We obtained the data from GTA China listed firms‘ IPO research database.

We collected the offer date, the lead underwriter(s) of the offer, offer price, gross IPO proceeds,

underwriting fees, and total floating costs. From that database, we also downloaded pre-IPO

accounting data. We collected after-IPO accounting information from the CSMAR database. When

pre-IPO accounting data were available in the GTA IPO research database and in CSMAR, we used

the CSMAR records. Next, we cross-checked the offer information with the records published on

finance.sina.com.cn. In case of discordance, we used the original IPO prospectus to correct the data.

In the following subsections, we explain how we construct the variables used in our analyses. We

also provide descriptive statistics for these variables.

20

4.1. Investment bank market share

First, we need to calculate the market share of every investment bank in every year. The database

lists 174 different names of investment banks that have led at least one IPO during 1990–2010.

However, the GTA database did not develop a coding system to uniquely identify those investment

banks, which gives rise to a number of duplications and ambiguities. We indeed detected name

changes, ownership changes, mergers and acquisitions of investment banks from their websites and

annual financial reports. So, we applied the following coding system for the 174 different names:

1. If one investment bank owns another by more than 50%, we assign one code to both banks.

To investors, issuers, and the regulator, these two investment banks are actually one entity:

they have the same ultimate owner and even consolidate their financial statements under the

1994 Company Law. Consider the example of the Citic group in which Citic Securities Co.

Ltd. holds 60% of China Securities Co. Ltd. and holds 91.4% of Citic Wantong Securities Co.

Ltd. We identified 5 such cases.

2. Name changes. Most of the name changes came from ownership changes. Here, we exclude

those cases where the new owner is also an investment bank, acquiring the investment bank

under consideration (see further). In the 41 cases we discuss here, the new owner inherits the

business, the staff, and the reputation of the old bank. So, we assign one code to the bank

before and after its name change and treat it as one entity when calculating its market share.

3. Takeover by another investment bank. Before the acquisition, the two banks operate

independently from each other and have different names; afterwards, the business continues

under the acquirer‘s name. We identified 10 such cases. Before the takeover, we assign

different codes to the acquired bank and to the acquiring bank. Yet, as of the takeover, the

market likely perceives that the acquirer henceforth controls the behavior and the decisions of

the acquired investment bank. So, we assign the acquirer‘ code to the combined bank after

21

the acquisition. None of the 10 acquired banks led any IPO in the year before its acquisition.

When calculating the explanatory variables from previous IPOs underwritten by the

investment bank, we use only IPOs advised by the acquirer.

4. Merger. As two existing investment banks merge to form a new bank (under a new name),

we assign separate codes to the banks before and after their merger. We identified 3 such

cases. As we use lagged explanatory variables in the regressions, we calculate them from the

IPOs underwritten by both old banks before their merger. That is, we treat the old banks as

one bank when calculating the variables in the year of merger.

Applying the above coding rules results in a final sample of 126 investment banks. From 1990 till

the end of 2010, those 126 underwriters advised 2,065 IPOs. 65 (3.1%) of these IPOs involved more

than one lead underwriter. In those cases, we divide the gross IPO proceeds by the number of lead

underwriters in the IPO. We subsequently calculate the market share of an investment bank in a year

by first summing the gross proceeds that could be attributed to that investment bank in all IPOs it led

during that year and thereafter dividing this sum by the total gross IPO proceeds on an annual basis.

A year-by-year summary of market shares can be found in Table 3. The table shows that the

Herfindahl index was rather high during 1990–1992, which was the infant period of Chinese stock

markets, when underwriting was an absolutely new business. From 1993 to 2000, the Herfindahl

index was below 0.1 in most of the years. After 2001, the index was mostly above 0.1, indicating a

moderately concentrated market. This increase in market concentration is partly the consequence of

market competition, resulting in the failure of a few investment banks, and M&As during 1999–2001,

to restructure the investment banking business previously owned by investment and trust firms.

<Insert Table 3>

22

4.2. Political connections

A common measure of a firm‘s political connections is the presence (or number) of politically

connected directors (e.g., Agrawal and Knoeber, 2001; Hillman, 2005; Faccio, 2006). Unfortunately,

the resumes of board members of Chinese investment banks are only scarcely disclosed. In contrast,

their direct owners can be identified rather easily, which is usually either the central government or a

local government. The government that owns the investment bank appoints its board members, who

are in general current or former government officials or have close relationships with those officials.

Correspondingly, the board members in central-owned investment banks tend to have a higher

political hierarchy, which gives them better access to the market regulator (CSRC). So, we create a

Political hierarchy dummy equal to one if the investment bank is owned by the central government,

and zero otherwise. Considering the small number of private-owned investment banks, we included

them in the same group as the local-owned investment banks. Yet, we excluded those private-owned

investment banks from our sample in a robustness check.

We traced the main shareholder of each investment bank from its website and/or from its

annual reports. We collect ownership information in each year, as the ownership structure of

investment banks could change over time. For all bank-year observations, 8.1% have the central

government as majority owner. 2% have majority private ownership. So, investment banks are

mostly held by local governments. However, the importance of local governments is declining over

time: they held majority control in 93.6% of bank-year observations from 1990 to 2004, but only in

81% of observations between 2005 and 2010. This decline is due to the fact that investment banks

owned by local governments have typically been M&A targets, especially in the 1999–2001 wave.

23

4.3. Average unexpected first-day abnormal return

We use a standard model to calculate the expected first-day abnormal return in every A-share IPO,

relying on Biais and Perotti (2002), Huyghebaert and Quan (2009), and Tian (2010). This model

includes seven explanatory variables: the log of gross IPO proceeds, the log of firm total assets before

the IPO, the log of the number of days between share offering and listing (Listing lag), the market

return from one year before listing to the listing date (Market return), a dummy equal to one if the

firm operates in a regulated industry (Regulated), a dummy equal to one if the firm has issued B, H,

or N shares12

before it‘s A-share IPO (Foreign), and a dummy equal to one if the firm is privately

owned at IPO-time (Private). For every year, we run the regression using all IPOs listed in that year

and in the previous one. This modeling allows us to account for changes in the determinants of IPO

price setting over time, as stock markets developed. We report the results of those yearly regressions

in Table 4. In line with previous research, we find that the explanatory variables can explain about 16%

to 51% of the total variation in first-day abnormal returns of Chinese A-share IPOs. Market return

has a significant positive effect on the first-day abnormal returns in 15 out of 18 years (1993–2010),

revealing a strong influence of market sentiment on Chinese IPO underpricing. The log of gross IPO

proceeds has a significant negative coefficient in 14 years, indicating either a supply effect (Tian,

2010) or an asymmetric-information effect (Huyghebaert and Quan, 2009). Other variables are

significant in only two to six years.

<Insert Table 4>

For every IPO, we now can calculate its expected first-day abnormal return using the

coefficients obtained from those regressions. So, we compute the unexpected first-day abnormal

return of every IPO by subtracting the expected first-day abnormal return from its actual first-day

abnormal return. For each investment bank in every year, we thereafter average the unexpected first-

day abnormal return of all IPOs led by it in the previous two years. The corresponding average

12 H shares are listed on the Hong Kong stock exchange. N shares are listed on the New York stock exchange.

24

unexpected first-day abnormal return is supposed to measure how well the investment bank

performed its pricing role.

4.3. Evaluation standard applied by the investment banks

According to Chemmanur and Fulghieri (1994), investors and issuers can assess the evaluation

standards adopted by an investment bank by considering the after-listing performance of the firms

advised by it. Dunbar (2000) measures after-listing performance by the first-year aftermarket

abnormal return of the IPO firms. In the Chinese context, it has been argued that stock prices do not

reflect the fundamental value of listed firms (e.g., Allen et al., 2005; Pistor and Xu, 2005). One

reason is that the Chinese stock market is a highly speculative market, dominated by small retail

investors and marked by a high stock turnover. Besides, many firms have a large fraction of non-

tradable shares, which are typically owned by the state and by legal persons. We therefore rely on an

accounting indicator of post-IPO performance as our major metric. This choice is further justified by

the direct link between accounting profitability (earnings) and the IPO offer price. Due to the special

IPO-pricing mechanism, issuers have strong incentives to exaggerate their earnings to boost their

issuing price. Turning a blind eye on these exaggerations, i.e. applying low evaluation standard is

exactly what issuers would like underwriters to do. We calculate industry-adjusted return on sales

(ROS) as our main measure and implement a robustness check using industry-adjusted return on

assets (ROA). ROA could indeed be influenced by the amount of equity raised at IPO-time, which

could dramatically boost the issuing firm‘s balance sheet. An industry adjustment is needed to make

firm performance comparable across different industries. Using the 13 industry categories developed

by the CSRC, we construct the yearly industry ROS (ROA) by a simple average of ROS (ROA) of all

listed firms from that industry in that year. Next, we subtract the industry-adjusted ROS (ROA) of

every IPO firm in the year before listing from the industry-adjusted ROS (ROA) in the year after

25

flotation. Table 5 reports the average industry-adjusted ROS change of the firms listed in every year,

from 1993 till 2009.13

Operating performance deteriorates significantly after firm listing, in line with

previous studies (e.g., Wang, 2005). We also find that it drops considerably in 2005. Table 6

therefore summarizes the average of industry-adjusted ROS and industry-adjusted ROA for two sub-

periods: 1993 to 2004 and 2005 to 2009. The decline in performance turns out to be significantly

smaller for the firms listed before 2005 than for the firms listed as of 2005. For example, on average,

the industry-adjusted ROS of the firms listed between 1993 and 2004 declines by three percentage

points, while this is seven percentage points for firms listed between 2005 and 2009. A simple t-test

on the equality of means rejects the null hypothesis that the means are identical. Moreover, after-IPO

performance is not significantly different across both sub-periods. So, it is before-IPO performance

that differs: firms listed as of 2005 seem to have significantly higher pre-IPO operating performance

than the firms listed before 2005, suggesting that issuers have been inflating their before IPO

accounts as of 2005.

<Insert Tables 5–6>

We measure the evaluation standard adopted by an investment bank by the average change in

industry-adjusted ROS of all firms underwritten by it in the previous two years. A small average

ROS deterioration shows that the firms introduced by an investment bank perform relatively well

after IPO, implying that this investment bank sets a relatively strict standard when evaluating issuers.

The choice on how many lagged years to include is an empirical one. Including too many years

levels out the changes in evaluation standards, while including too few lags makes this measurement

too noisy. We implemented robustness checks using a one-year lag and a three-year lag and obtained

quantitatively similar results as using the two-year lag. We report the results of those robustness

13 For IPOs in the year 2010, we do not have their 2011 annual accounts yet. However, we do not loose these IPOs in our

analyses, as the explanatory variables are always one year lagged.

26

checks in Section 5.3. Finally, we implement robustness checks using post-IPO stock returns as

another measure of the evaluation standard adopted by the investment bank.

4.4. Investment bank compensation

The fee to be paid to the lead underwriter is composed of a sponsor fee and an underwriting fee. We

could obtain total floating costs for 1,823 IPOs in the sample (88.3%). Total floating costs contain

the sponsor fee, underwriting fee, lawyer fee, auditing fee, and other listing fees. However, total

floating costs are not always broken down into these different components. We could collect the

detailed information in only 180 IPOs, revealing that the underwriting fee and the sponsor fee

accounted for about 81% of total floating costs. Also, the correlation between total floating costs rate

and the fee rate charged by underwriters (the sum of underwriting fees and sponsor fees scaled by

total gross proceeds) equals 0.997 within these 180 observations. Based on those findings, we use

total floating costs divided by gross IPO proceeds as a proxy for the fee rate.

Dunbar (2000) argues that it is not the fee rate itself that should influence the market share of

investment banks; rather, it is the unexpected fee rate that matters. To determine expected fee rates,

he estimates a model based on gross IPO proceeds and the logarithm of gross IPO proceeds. We

follow this same procedure and run year-by-year regressions, using all observable floating-cost rates

in the previous two years. In line with Dunbar (2000), Table 7 reveals a significant positive effect of

gross IPO proceeds, while the log of gross IPO proceeds has a significant negative effect. These

results indicate a U-shaped relation between fee rates and gross IPO proceeds. In our regressions, the

adjusted R-squares are over 40%, except in the first two years.

<Insert Table 7>

For each IPO, we calculate the expected fee rate using the parameters reported in Table 7.

We deduct the expected fee rate from the actual fee rate to obtain the unexpected fee rate. A positive

27

unexpected fee rate implies that the investment bank charges more than expected, and vice versa. For

each investment bank in every year, we obtain its average unexpected fee rate by calculating a

weighted average of all unexpected fee rates in the IPOs underwritten by it in that year; the weights

are the gross IPO proceeds of each IPO that is has led. Using a weighted average calculation, we take

into account that pricing policies in large IPOs have a potentially higher influence on the market

share of investment banks than those in smaller IPOs. We use this average unexpected fee rate as

explanatory variable in the market share regression.

4.5. Star analysts

As of 2003, we obtained the list of star analysts from the magazine New Fortune. This magazine

publishes every year an evaluation report, ranking financial analysts in China. For each investment

bank in every year, we relate its number of star analysts to the total number of star analysts in that

year. By 2010, the number of investment banks that employ at least one star analyst has doubled

while the number of star analysts has increased by a factor four. However, the maximum number of

star analysts employed by a single investment bank has changed little. Those facts indicate that star

analysts have been allocated more equally among investment banks over time. We report summary

statistics on the number of star analysts in Table 8.

<Insert Table 8>

4.6. Summary

In summary, we have a panel data set including 126 investment banks and 16 years (from 1995 to

2010). The panel is unbalanced, as not all investment banks existed in all 16 years and as data is

missing on some of the independent variables in some of the years. We obtained 226 bank years with

28

full observations from 1995 to 2004 and 146 bank years from 2005 to 2010. We report summary

statistics for the sample in Table 9.

<Insert Table 9>

5. Multivariate analyses and results

We use Arellano and Bond dynamic panel model to perform multivariate analyses. We introduce this

methodology and discuss regression results in this chapter.

5.1. Methodology description

Our basic regression model looks as follows:

, 1 , 1 2 , 1 3 , 1 4 , 1 5 , 6 , 1 ,( )i t i t i t i t i t i t i t i j j i t

j

Ms C b Ms b Own b UFAR b ROSC b UFEE b SA C b Year

with: ,i tMs : Market share of investment bank i in year t.

, 1i tOwn : Dummy equal to one if the investment bank is owned directly by the central

government in year t1, zero otherwise.

, 1i tUFAR : Average unexpected first-day abnormal return of all IPOs led by investment bank i

in the years t1 and t2.

, 1i tROSC : Average of the industry-adjusted ROS change for all IPOs introduced by

investment bank i in the years t1 and t2.

,i tUFEE : Weighted average of unexpected fee rates in all IPOs led by investment bank i in

year t, using gross IPO proceeds as weighting factor.

, 1i tSA : Fraction of star analysts employed by investment bank i in year t1.

iC : Individual effect of investment bank i.

29

jYear : Year dummies. In the regression of first period, j starts from 1996 and ends with 2004.

In the regression of second period, j starts from 2006 and ends with 2010 (first years are assigned

to be zero).

Dunbar (2000) uses the change in market share from one year to the next as dependent variable. This

treatment is equivalent to fixing the coefficient on lagged market share to one. In a more mature

underwriting market, market shares tend to be rather stable, which can justify this assumption.

However, investment banks in China have developed their market shares from scratch and market

shares also fluctuate largely over time. So, the influence of lagged market share on current market

share may be far less than one. Following Rau (2000), we include the lagged market share as an

extra regressor rather than subtracting it from current-year market share. Specifying the regression

model in this way is actually performing a Granger causality test, gauging whether or not the

independent variables have any additional explanatory power on top of the lagged dependent variable.

As mentioned in Section 2.1, the CSRC normally needs half a year to process an IPO

application. Before submitting IPO applications, investment banks have to investigate the issuing

firm and prepare the IPO application material. So, for a stock market listing in this year, the lead

underwriter was typically already selected by the issuer in the previous year. This is why we lag the

explanatory variables. The only exception is fee rate. For an IPO issued in this year, its underwriting

fee rate is already quoted to issuers when investment banks bid to underwrite this IPO. As issuers

need to decided which investment bank to hire in the previous year, the fee rate for the IPOs issued in

this year is already known in the previous year. So, we can directly use the fee rates of the IPOs

issued in this year as independent variable..

We adopt the panel regression methodology with observations identified by investment banks

and by time. A normal way to account for the investment bank individual effect is either by a

random-effects regression or by a fixed-effects regression. However, including the lagged dependent

30

variable complicates the matter. The random-effects model requires the explanatory variables to be

uncorrelated with the individual effect iC . In a dynamic panel data model, by construction this

assumption cannot be satisfied, as the lagged dependent variable correlates with the individual effects.

So, the random effect is biased. The fixed-effects model adopts a within-transformation, so that the

error term includes the average error grouped per investment bank. By construction, the regression

results are biased because of the correlation between the error term and one of the explanatory

variables, that is the lagged dependent variable (Beggs and Nerlove, 1988).

Anderson and Hsiao (1981) offer a solution for the dynamic panel system. This solution is

based on the first-difference transformation: , , 1 , 1 , 2 , , 1 , , 1( ) ( ) ( )i t i t i t i t i t i t i t i ty y y y X X .

In this equation, the individual effect iC cancels out, but , 1 , 2( )i t i ty y is correlated with the error

term, , 1( )i t i t . But now

, 2i ty can be used as an instrument for

, 1 , 2( )i t i ty y , because by

construction, , 2i ty

correlates with , 1 , 2( )i t i ty y but does not correlate with

, , 1( )i t i t . Arellano

and Bond (1991) develop this idea into a GMM method. If , 2i ty

is a valid instrument for

, 1 , 2( )i t i ty y , then all lags before, 2i ty

are valid instruments as well. They suggest to include them

in the instrument matrix to construct stronger instruments.

Arellano and Bond (1991) show that this instrument construction method also applies to any

other predetermined explanatory variables where past shocks in the dependent variable Y influence

the current level of the explanatory variable. For example, in China issuers need to engage lead

underwriters about one year before their IPO. The market share of an investment bank in the

previous year could thus influence its fee rate policy when it negotiates with issuers on the IPOs to be

issued in the current year. So, the fee rates that we observe in this year‘s IPOs tend to be influenced

by last-year market shares. Conversely, the market share of investment banks in this year can hardly

influence their fee rates for IPOs issued in this year. In this sense, we define fee rates as

31

‗predetermined‘ rather than ‗endogenous‘. For predetermined X, the lagged X serve as good

instruments, as , 1i tX

correlates with, , 1( )i t i tX X but not with

, , 1( )i t i t . In our model, we consider

, 1 , 1 ,, and i t i t i tUFAR ROSC UFEE as predetermined explanatory variables, which allow us to account

for the feedback effects of previous market shares on these three explanatory variables. Arellano and

Bond (1991) suggest the Arellano-Bond test to check on the validity of instruments. Its basic

assumption is that if the instruments are valid, the correlation between , , 1( )i t i t and

, 2 , 3( )i t i t

should be zero. Arellano and Bond (1991) show that this test is superior to the Sargan test, as it

allows for heteroskedasticity of the error term.

Based on Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and Bond (1998)

build a more efficient method by including level equations into the GMM estimation. They construct

a system GMM with two parallel equations: the first-difference equation and the level equation.

They show that if the individual effects are not correlated with the first observation of first

differences, the lagged first difference can be used as instrument for the level explanatory variables.

In this way, the level equation and the first-difference equations can be estimated together to improve

efficiency.

We opted for two-step GMM estimation, as this method accounts for possible

heteroskedasticity and correlations among error terms. However, Arellano and Bond (1991) argue

that when using two-step GMM for dynamic panel models, the standard errors of the estimators are

often under-estimated. Windmeijer (2005) has developed a methodology that adjusts standard errors

for this potential bias. We report p-values using the Windmeijer (2005) adjusted standard errors.

<Insert Table 10>

32

5.2. Discussion of results

Before 2005, lagged market share had no significant influence on current-year market share,

indicating that in the early years after the re-establishment of Chinese stock markets, market shares

were far from stable. However, as a result of repeated market interactions, market shares started to

show some persistency in later years. Indeed, we find a significant coefficient of 0.40 in the sub-

period 2005–2010. The coefficient is far from one, indicating that the market share of investment

banks is still highly unstable; a high market share in this year thus hardly guarantees an equally high

market share in the next year.

Political connections positively affected investment-bank market share before 2005.

Everything else equal, an investment bank owned directly by the central government could expect its

market share to be 7.7% larger than that of an investment bank with other owners.14

This advantage

is economically meaningful, as the average market share during that period was only 3.6%. As of

2005, the investment banks owned by the central government can no longer expect any advantage at a

meaningful confidence level. Overall, these results are in line with Hypothesis 1.

The coefficients on ‗Average unexpected first-day abnormal return‘ are significant neither in

the first nor in the second sub-period. This result confirms Hypothesis 2; as the government severely

intervened in setting the issue price up to June 2009, which covers almost the entire sample period,

investment banks had little influence on how much money was ‗left on the table‘. Consequently, the

market share of investment banks did not depend on whether or not the IPOs were properly

(under)priced.

The empirical results on the influence of evaluation standards support Hypothesis 3. Before

2005, the influence is not significant. Yet, as of 2005, if the after-listing performance of IPO firms

(measured by industry-adjusted ROS) deteriorates by one percentage point, the investment bank that

14 In this paper, we adopt a linear model specification, because a non-linear dynamic panel model with predetermined

explanatory variables is too complicated to implement. So, we assume that the marginal effects are identical for all values

of the independent variables.

33

underwrote those firms could expect a 0.14% increase in its market share in the subsequent year.

This outcome is in contradiction with the predictions of Chemmanur and Fulghieri (1994) and the

empirical findings of Dunbar (2000). However, in the specific case of China, proper incentives to

demand investment banks with a high evaluation standard were still missing among issuers and

investors; on the contrary, investment banks applying low evaluation standard may allow issuers to

raise the IPO price.

Before 2005, the unexpected fee rate seems to have little impact on the market share of

investment banks; as of 2005, the unexpected fee rate becomes significant. In the sub-period 2005–

2010, if an investment bank sets its fee rate by 1 percentage point below the expected rate, it gains

0.72 percentage point gain in terms of market share. These results confirm Hypothesis 4.

Interestingly, when dividing these two numbers by the average fee rate and the average market share

in the sub-period 2005–2010, a one percent increase in the fee rate is associated with roughly a one

percent decrease in market share and vice versa. This finding indicates that, on average, the elasticity

of market share on fee rate in the Chinese underwriting market is close to one. This result suggests

that investment banks cannot immediately increase their total revenue by differentiating their fee

rates from the market equilibrium level. The fee-rate policy may serve as a long-term strategy rather

than a short-term one, which again complies with our conjectures in Hypothesis 4 that investment

banks apply a low-fee policy not to boost their revenue immediately, but to build up their market

status to reap economic rents in the long run.

We fail to find supporting evidence for the idea that star analysts significantly influence

investment bank market shares, although the coefficient is positive. We conjecture two possible

explanations: 1) Star analysts in China still need time to establish their reputation; issuers also need

time to realize that engaging star analysts is beneficial to their stock price. 2) Chinese domestic stock

market is still dominated by SOEs; managers in SOEs are not exposed to the threat of external

34

takeover as much as managers in listed firms in the Western market. Besides, stock options rarely

exist in executive compensation packages in China. Hence, managers of issuing firms may not care

about the aftermarket stock price as much as the managers in developed economies. So, they have

less incentives to engage star analysts to follow up on their firms. We recognize that we need a

longer sample period to draw more accurate conclusions on the relation between star analysts and

investment-bank market share.

5.3. Robustness checks

In this section, we report the results of a number of robustness checks. In Robustness check 1 to 6,

we use alternative variables to examine the robustness of our main result. Robustness check 7 to 9

examine several extensions of our basic model. Most of the results comply with our main result.

5.3.1 Robustness check 1

Du et al. (2010) use the political hierarchy of the cities where the head offices of Chinese state-owned

enterprises (SOEs) are located to measure the political connections of these SOEs. As Beijing is the

political center of China and Shanghai is the economic and financial center, Du et al. (2010) assign

the highest political hierarchy to these two cities. They find that, everything else equal, the SOEs that

have their head offices located in these two cites receive a higher performance ranking15

from the

State Asset Council, that is the bureaucratic agency that supervises all Chinese SOEs. So, as an

alternative way to measure political connections, we collected the locations of the head offices of

investment banks. We make a dummy equals to one if the head office of the investment bank is

located in Beijing or Shanghai, and zero otherwise. About 25% of investment banks have their head

offices located in these two cites. We use this dummy as the Political hierarchy dummy. The results,

15 The State Asset Council ranks the performance of SOEs into five categories every year. Please see Du et al. (2010) for

more details.

35

which are reported in Table 11, reveal that our conclusions on the role of political connections in

Table 10 are robust.

<Insert Table 11>


We notice that private institutions are the investment bank‘s majority owner in about 2% of sample

observations. We assigned a value of zero to the ‗Political connection‘ dummy for these investment

banks. To ensure that our conclusions on the role of political connections are not driven by those

privately-owned banks, we remove them from the sample in a robustness check. The results are

reported in Table 12; they are similar to those presented in Table 10.

<Insert Table 12>


We now use the average change in industry-adjusted ROS to measure the evaluation standard of

investment banks. ROS only relies on the firm‘s P&L records and thus does not fully consider how

efficiently a firm is utilizing its resources. As a robustness check, we replace it by the industry-

adjusted return on assets (ROA). Table 13 reports the results; they are in line with our earlier

findings in Table 10.

<Insert Table 13>


We calculated the average ROS change of the IPO firms listed in the previous two years to measure

the investment bank‘s evaluation standard. The choice on how many lags to include is an empirical

one. As argued by Dunbar (2000), including too many lags averages out the changes in evaluation

36

standard, while including too few lags makes the measurement noisy. We tried with a one-year lag

and with a three-year lag in a robustness check. The results are reported in Table 14. The results

using the three-year lag are consistent with what we found in Table 10. However, with the one-year

lag, the coefficient on average industry-adjusted ROS change is no longer significant in 2005–2010.

As shown in Table 2, in most years, half of the investment banks lead less than two IPOs. So, when

using only the IPOs in one year, half of the investment banks have less than two IPOs records. The

individual characteristics of IPO firms could largely influence the average ROS changes, and thus

introduce considerable noise into the measurement of evaluation standards.

<Insert Table 14>


Prior literature has relied on abnormal stock returns as a measure of the investment bank‘s evaluation

standard. Following Dunbar (2000), we construct a variable ‗Average aftermarket return‘ as an

alternative to the variable ‗Average industry-adjusted ROS change‘. For every stock, we calculate its

return from the closing price of the first listing date up to one year after listing, and adjust it by the

contemporary market return. We average those aftermarket returns of all stocks underwritten by the

same investment bank in years t–2 and t–1. The results are reported in Table 15, revealing that this

alternative measure of the evaluation standard adopted by investment banks is not significant. We

argued before that in the Chinese context, accounting returns may be preferable to stock returns.

<Insert Table 15>


To calculate the expected first-day abnormal return, we take the logarithm of gross IPO proceeds and

the logarithm of the firm‘s total assets before listing as explanatory variables. The literature has

37

shown that the fraction of shares floated relative to total shares (Floating fraction) is an important

determinant of underpricing in Chinese IPOs (Huyghebaert and Quan, 2009), as it represents the

relative size of the offer. To avoid multicollinearity with gross IPO proceeds and firm size, we did

not add Floating fraction to the regression model. As a robustness check, we replace the logarithm of

gross IPO proceeds with Floating fraction. The explanatory power of the new regressions is smaller,

varying between 9% and 51% (in most years over 20%). Using the expected first-day abnormal

returns that are constructed from the new regressions, we can calculate the average unexpected first-

day abnormal return in the same way as introduced in Section 4.3. Table 16 reports the results,

showing high similarity to those in Table 10.

<Insert Table 16>


Dunbar (2000) shows that in the US IPO underwriting market, the influence of the investment bank‘s

evaluation standard on its market share is more significant for the established investment banks than

for the non-established ones. He assumes that an investment bank is ‗established‘ when its last-year

market share exceeds 1.5%. He then constructs an interaction term as the product of the established-

bank dummy and the average one-year after-listing performance of the firms underwritten by this

investment bank. The market share of Chinese investment banks is still unstable during our sample

period, so it is hard to say which investment banks are really established. But we find it interesting to

examine whether or not this incremental effect also exists in the Chinese IPO market. We therefore

construct an interaction term between the investment bank‘s lagged market share and its average

industry-adjusted ROS change. Table 17 reports the results. The interaction term is indeed

significantly negative in the sub-period 2005–2010. Complying with Dunbar (2000), for the more

38

established investment banks, changing their evaluation standards could be more effective in

attracting underwriting business.

<Insert Table 17>


Dunbar (2000) also tests the effect of industry concentration on investment-bank market share. He

argues that the investment banks just entering the IPO market would focus on one industry to

establish their expertise, while the investment banks with an established market status may diversify

their underwriting businesses into different industries to secure a constant IPO flow. In China, all

investment banks started from scratch; in this sense, they are all ‗new entries‘. Nevertheless, we

checked the effect of industry concentration on investment-bank market share in a robustness test.

Following Dunbar (2000), we use the Herfindahl index to measure industry concentration. For every

investment bank in year t, we classify all IPOs it led in year t-1 into the 13 industry categories

published by the CSRC. We calculate the Herfindahl index by the fractions of IPO firms in every

category. We add this index as an additional variable in our model. Table 18 reports the results. In

both sub-periods, industry concentration has an insignificant effect. However, the coefficient on the

evaluation standard becomes insignificant in the subperiod from 2005 to 2010 after adding this

industry concentration variable.

<Insert Table 18>


We notice that as of 2005, an increasing number of privately-owned firms have been offering primary

shares in Chinese domestic stock markets. It is conceivable that private owners care more about the

total proceeds raised from the IPO and thus are more inclined to hire an investment bank with a low

39

evaluation standard. We construct an interaction term (Int_private) as the product of the fraction of

private-firm IPOs relative to the total number of IPOs in the current year and the average industry-

adjusted ROS change. Table 19 reports the results. However, the interaction term is not significant.

The correlation between this interaction term and the average industry-adjusted ROS change is 0.78,

which might explain our failure to find significant results.

<Insert Table 19>

We are also interested to explore whether, given the increasing number of private-firm IPOs,

investment banks can gain market share by building their expertise on private IPOs? We constructed

a new variable (Private fraction) to test this conjecture. For every investment bank in each year, we

calculate the fraction of private-firm IPOs relative to the total number of IPOs the investment bank

underwrote in the previous two years. A higher Private fraction indicates that the investment bank

focuses more on underwriting private-firm IPOs. We add the Private fraction as an additional

explanatory variable to the basic model; Table 20 reports the results. However, we do not find a

significant effect. One explanation could be that on average, private-firm IPOs are still smaller than

SIPs in term of gross IPO proceeds. So, the focus-on-private strategy may still take time to show its

effect when more and/or bigger private firms become listed in the coming years. The other

explanation might be that investment banks still need time to build up their expertise in the IPO

underwriting of private firms.

<Insert Table 20>

40

6. Conclusions and policy implications

This paper documents the forces that have influenced the market share of investment banks in

Chinese A-share IPOs from 1995 till 2010. In these 16 years, the Chinese A-share IPO market has

developed from RMB 2 billion to RMB 482 billion in terms of annual gross IPO proceeds. Before

2005, stronger political connections alone were sufficient to guarantee a relatively high market share

for investment banks. As of 2005, the effect of political connections has disappeared; investment

banks gain market share by competing on services and fees. Unsurprisingly, fee rates lower than the

expected levels have attracted underwriting business. A more interesting finding is that, against the

findings in developed economies, reducing the evaluation standard on issuing firms has helped

investment banks to increase their market share in IPOs. We view this phenomenon as the result of

issue-price distortion in the primary stock market.

By focusing on the evolution of the market share of investment banks in Chinese IPOs, our

paper analyses a small aspect of the reform of the Chinese economy in the past 20 years. The

Chinese government chose to interfere in the IPO market by directly assuming certain roles of market

participants, especially in the early years after stock market re-establishment. By selecting the firms

that are eligible for listing (before July 1999), the government replaced investment banks to examine

the quality of IPO candidates. By approving IPO applications, the government replaced investors in

demanding firm-quality certification from the underwriters. By setting a fixed issuing P/E ratio, the

government assumed the pricing role of underwriters. By limiting the maximum number of IPOs an

investment bank can handle under the channel mechanism, the government limited the issuers‘ choice

among underwriters. Maybe those interference were indispensable when the market was immature,

their side effects are also obvious. Our results show that although the CSRC took huge efforts to

enforce investment banks to take the certification role, these administrative efforts achieved little.

Investment banks never gained market share by underwriting relatively high-quality firms. Strong

41

administration only keeps those investment banks with high evaluation standard from losing market

share but never helped them to gain market share. Once the ‗visible hand‘ gets loose, those

investment banks applying a high evaluation standard start to lose market share. This finding offers

the evidence that administration mechanisms are hardly as efficient as the market mechanism in

monitoring market participants. Moreover, government administration could be influenced by

political connections, which further deteriorate its effectiveness.

Chinese regulators are aware of the deficiencies in direct government intervention. They try

to leave the market to regulate. However, if they still guarantee investors with an almost riskless

return from investing in the primary market, investors will not value the certification from investment

banks. If investment bank‘s reputation of carefully examining the true quality of issuing firms does

not help issuers to increase gross IPO proceeds, issuers will not care about such reputation. It was

therefore a correct step for the Chinese government to give up its interference in setting the issue

price from June 2009 onwards. Indeed in 2010, we see more investors lose money by blindly buying

IPO shares. However, the ‗new issue fetish‘ has been formed for almost 20 years; it will take time to

educate investors to carefully evaluate IPO firms before subscribing. It will also take time for

investment banks to build up their reputation in certification. But the step taken is definitely a right

one.

We would like to recommend the government to offer investors more legal protection against

fraud in IPOs. This is especially important in China, as Chinese investors are still relatively small

and inexperienced compared to their Western counterparts. Small investors typically lack the

resources to sue large institutions, especially when those institutions have close political connections.

The imbalance of power makes issuers and investment banks less likely to respect investors‘ interests

in IPOs. So, investment banks could be tempted to collude with issuers rather than safeguard

investors.

42

In summary, from the evolution of market share of investment banks in the Chinese A-share

IPO market, we might conclude that the government should not directly take the role of market

players. The roles like pricing underlying assets, selecting trading partners, negotiating fees should

be left to the market. Rather, the government should focus on establishing and enforcing rules to

induce proper incentives among market participants and to keep them play fairly.

43

References

Agrawal, A., Knoeber, C.K. (2001). Do some outside directors play a political role? Journal of Law

and Economics 44(1), pp. 179–198.

Allen, F., Qian, J., and Qian, M. (2005). Law, finance, and economic growth in China. Journal of

Financial Economics 77(1), pp. 57–116.

Anderson, T.W. and Hsiao, C. (1981). Estimation of dynamic models with error components. Journal

of the American Statistical Association 76(375), pp. 589–606.

Arbel, A. (1985). Generic stocks: An old product in a new package. Journal of Portfolio

Management 11(4), pp. 4–12.

Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence

and an application to employment equations. Review of Economic Studies 58(2), pp. 277–297.

Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of error-

components models. Journal of Econometrics 68(1), pp. 29–51.

Altinkilic, O. and Hansen, R.S. (2000). Are There Economies of Scale in Underwriting Fees?

Evidence of Rising External Financing Costs. Review of Financial Studies 13(1), pp. 191–218.

Beatty, R.P. and Ritter, J.R. (1986). Investment banking, reputation, and the underpricing of initial

public offerings. Journal of Financial Economics 15(1-2), pp. 213–232.

Beggs, J.J. and Nerlove, M. (1988). Biases in dynamic models with fixed effects. Economics Letters

26(1), pp. 29–31.

Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data

models. Journal of Econometrics 87(1), pp. 115–143.

Booth, J.R. and Smith, R.L. (1986). Capital raising, underpricing and the certification hypothesis.

Journal of Financial Economics 15(1-2), pp. 261–281.

Carter, R.B. and Manaster, S. (1990). Initial public offerings and underwriter reputation. The Journal

of Finance 45(4), pp. 1045–1067.

Chang, E., Chen, C., Chi, J. and Young, M. (2007). IPO underpricing in China: New evidence from

the primary and secondary markets. Emerging Markets Review 9(1), pp. 1–16.

Chemmanur, T.J. and Fulghieri, P. (1994). Investment bank reputation, information production, and

financial intermediation. The Journal of Finance 49(1), pp. 57–79.

Chen, G., Firth, M. and Kim, J.B. (2004). IPO underpricing in China‘s new stock markets. Journal of

Multinational Financial Management 14(3), pp. 283–302.

Chi, J. and Padgett, C. (2005). Short-run underpricing and its characteristics in Chinese initial public

offering (IPO) markets. Research in International Business and Finance 19(1), pp. 71−93.

DeFond, M.L., Wong, T.J., Li, S. (1999). The impact of improved auditor independence on audit

market concentration in China. Journal of Accounting and Economics 28(3), pp. 269–305.

Du, F., Tang, G., Young, S.M. (2009). The influence of political connections and geographic

proximity on subjective performance evaluation: evidence from state-owned Chinese enterprises.

Working paper.

Dunbar, C.G. (2000). Factors affecting investment bank initial public offering market share. Journal

of Financial Economics 55(1), pp. 3–41.

Faccio, M. (2006). Politically connected firms. American Economic Review 96(1), pp. 369–386.

Fernald, J. and Rogers, J.H. (2002). Puzzles in the Chinese stock market. The Review of Economics

and Statistics, 84(3), pp. 416–432.

Gannon, G. and Zhou, Y. (2008). Conflicts of interest and China‘s A-share underpricing.

International Review of Financial Analysis 17(3), pp. 491–506.

Granger, C.W.J. (1969). Investing causal relations by econometric models and cross-spectral

methods. Econometrica 37(1), pp. 424–438.

44

Gao Y. (2010). What comprises IPO initial returns: Evidence from the Chinese market. Pacific-Basin

Finance Journal, 18(1), pp. 77–89.

Guo, H. and Brooks, R. (2008). Underpricing of Chinese A-share IPOs and short-run

underperformance under the approval system from 2001 to 2005. International Review of

Financial Analysis 17(5), pp. 984–997.

Hillman, A.J. (2005). Politicians on the board of directors: Do connections affect the bottom line?

Journal of Management, 31(3), pp. 464–481.

Huyghebaert, N. and Quan, Q. (2009). Share issuing privatizations in China: Sequencing and its

effects on public share allocation and underpricing. Journal of Comparative Economics 37(2), pp.

306–320.

Krigman, L., Shaw, W.H. and Womack, K.L. (2001). Why do firms switch underwriters? Journal of

Financial Economics 60(2-3), pp. 245–284.

Li, H., Meng, L., Wang, Q. and Zhou, L.A. (2008). Political connections, financing and firm

performance: Evidence from Chinese private firms. Journal of Development Economics 87(2), pp.

283–299.

Loughran, T., Ritter, J.R. and Rydqvist, K. (1994). Initial public offerings: International insights.

Pacific-Basin Finance Journal 2(2-3), pp. 165–199.

Loughran, T. and Ritter, J.R. (2002). Why don‘t issuers get upset about leaving money on the table in

IPOs? Review of Financial Studies 15(2), pp. 413–443.

Loughran, T. and Ritter, J.R. (2004). Why has IPO underpricing changed over time? Financial

Management 33(3), pp. 5–37.

Merton, R.C. (1987). A simple model of capital market equilibrium with incomplete information. The

Journal of Finance 42(3), pp. 483–510.

Miller, E.M. (1977). Risk, uncertainty, and divergence of opinion. The Journal of Finance 32(4), pp.

1151–1168.

Pistor, K., and Xu, C, (2005). Governing stock markets in transition economics: Lessons from China.

American Law and Economics Review 7(1), pp. 184–210.

Rau, P.R. (2000). Investment bank market share, contingent fee payments, and the performance of

acquiring firms. Journal of Financial Economics 56(1), pp. 293–324.

Ritter, J.R. and Welch, I. (2002). A review of IPO activity, pricing, and allocations. The Journal of

Finance 57(4), pp. 1795–1828.

Rocker, K. (1986). Why new issues are underpriced. Journal of Financial Economics 15(1-2), pp.

187–212.

Su, D.W. and Fleisher, B.M. (1999). An empirical investigation of underpricing in Chinese IPOs.

Pacific-Basin Finance Journal 7(2), pp. 173−202.

Sun, Q. and Tong, W.H.S. (2003). China share issue privatization: The extent of its success. Journal

of Financial Economics 70(2), pp. 183–222.

Tian L. (2010). Regulatory underpricing: Determinants of Chinese extreme IPO returns. Journal

of Empirical Finance 18(1), pp. 78–90.

Wang, C. (2005). Ownership and operating performance of Chinese IPOs. Journal of Banking

& Finance 29(7), pp. 1835–1856.

Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM

estimators. Journal of Econometrics 126(1), pp. 25–51.

45

Table 1

Comparison of issuing P/E ratio with market P/E ratio.

This table presents the issuing P/E ratio and the prevailing market P/E ratio from 1996 to 2010. The market P/E ratios are obtained from the

websites of the Shanghai and the Shenzhen stock exchanges. They are calculated using end-of-year stock prices divided by net earnings in

that year. The average issuing P/E ratio is the simple average of the issuing P/E ratio of all IPOs in the corresponding year. ‗Issue vs.

market‘ is calculated by dividing the average issuing P/E ratio by the market P/E ratio in the same year.

*N/A means that no IPO was observed in that year, so that the issuing P/E ratio was unavailable.

Panel A: Shanghai stock exchange

Year 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

2010

1996

—

2004

2005

—

2010

1996

—

2010

Number of IPOs 84 82 51 46 95 67 69 67 59 3 14 24 6 11 28 620 86 706

Average issuing P/E ratio 17.7 14.3 15.9 17.1 24.4 28.1 19.0 19.2 17.9 10.9 20.1 27.8 21.5 30.4 40 19.2 25 21.6

Market P/E ratio 31.3 39.9 34.4 38.1 58.2 37.7 34.4 36.5 24.2 16.3 33.3 59.1 14 29 21.6 35 28.9 33.9

Issue vs. market 57% 36% 46% 45% 42% 75% 55% 53% 74% 67% 60% 47% 154% 105% 185% 55% 87% 64%

Panel B: Shenzhen stock exchange

Year 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

1996

—

2004

2005

—

2010

1996

—

2010

Number of IPOs 86 106 51 46 43 0 1 0 39 12 56 97 72 101 321 372 659 1031

Average issuing P/E

ratio 18.6 14.3 16.1 17.1 24.0 N/A 38.1 N/A 17.1 13.7 17.2 20.2 18.6 35.7 55 20.8 29.3 23.9

Market P/E ratio 35.4 41.1 32.3 37.6 56.0 39.8 36.9 36.2 25.6 16.4 35.7 69.7 16 46 44.6 37.9 38 38.1

Issue vs. market 52% 35% 50% 46% 43% N/A* 103% N/A* 67% 84% 53% 29% 117% 78% 123% 55% 69% 63%

46

Table 2

Summary of first-day market-adjusted returns from 1992 to 2010.

This table summarizes the first-day market-adjusted returns of all IPOs from 1992 to 2010. First-day market-adjusted returns are

calculated as: (first-day closing price - issuing price ) (listing day market index - issuing-day market index) First-day market adjusted return

issuing price issuing-day market index

Note: 1.When a stock is listed in Shanghai stock exchange, we use the Shanghai A-share index to calculated market return; if it is listed in Shenzhen stock market, we

use Shenzhen A-share index. The market indexes are downloaded from Datastream.

2. The number of firms listed could be different from the number of firms issued in certain years, due to the lag between issuing and listing.

Year Number of

firms listed

Average (median)

first-day market-

adjusted return

Average (median)

first-day return

before market

adjustment

Number of IPOs with a

negative first-day

market-adjusted return

Number of IPOs with a

negative first-day return

1992 40 142%

(142%)

485%

(271%)

0 0

1993 124 218%

(150%)

403%

(163%)

18 26

1994 111 145%

(96%)

159%

(100%)

2 4

1995 24 440%

(166%)

599%

(412%)

1 2

1996 203 166%

(100%)

335%

(113%)

6 3

1997 207 193%

(131%)

271%

(138%)

0 0

1998 106 178%

(117%)

287%

(126%)

1 1

1999 98 115%

(94%)

116%

(102%)

0 0

2000 135 150%

(141%)

152%

(141%)

0 0

2001 79 214%

(130%)

220%

(127%)

0 0

2002 71 148%

(118%)

148%

(117%)

0 0

2003 67 72%

(65%)

72%

(70%)

0 0

2004 100 72%

(60%)

70%

(59%)

3 3

2005 15 49%

(45%)

45%

(46%)

0 0

2006 66 80%

(69%)

83%

(75%)

1 0

2007 126 189%

(170%)

193%

(175%)

0 0

2008 77 119%

(82%)

114%

(83%)

0 0

2009 99 73%

(69%)

74%

(76%)

1 0

2010 349 41%

(32%)

41%

(31%)

16 26

1992--2010 2097 130%

(89%)

185%

(95%)

49 65

47

Table 3

Summary of market shares of investment banks in Chinese A-share IPOs.

This table summarizes the market share of investment banks in Chinese A-share IPOs. The market share of an

investment bank in a given year is the sum of the gross proceeds raised in the IPOs in which the investment

bank acts as leading manager, divided by the total gross proceeds raised in all IPOs in that year. The Herfindahl

index is the sum of the squared market shares of all investment banks in a given year.

* Investment banks enter and drop out from the market from 1990 to 2010, so the mean and median market share of investment banks in the 21 years is far less than the mean and median of their market shares in any particular year. Calculating overall Herfindahl index in the 21 years is not necessary as the 126 investment banks do not coexist in the entire period of 21 years.

Year Number of

firms

issued

Total

gross

proceeds

(Million

RMB)

Number of

underwriters

that led at least

one IPO

Mean IPO

number per

underwriter

Median

IPO

number per

underwriter

Mean market

share per

underwriter

Median market

share per

underwriter

Herfindahl

index of

market

concentration

1990 7 594 7 1 1 14.3% 3.4% 0.5728

1991 17 872 13 1.31 1 7.7% 3.6% 0.1441

1992 110 35900 36 3.06 2 2.8% 0.2% 0.2736

1993 142 23600 43 3.35 2 2.3% 1.0% 0.0855

1994 38 5230 22 1.73 1 4.5% 2.9% 0.0753

1995 13 2190 10 1.3 1 10% 5.7% 0.3064

1996 170 22300 36 4.72 2 2.8% 1.1% 0.0806

1997 188 65500 44 4.27 2 2.3% 0.9% 0.0636

1998 102 40900 34 3 2 2.9% 1.6% 0.1158

1999 92 49600 33 2.79 2 3.0% 1.7% 0.0716

2000 138 83900 33 4.18 2 3.1% 1.3% 0.0706

2001 67 56300 20 3.35 2 5.0% 3.3% 0.1042

2002 70 55200 32 2.19 2 3.1% 1.4% 0.1969

2003 67 45400 35 1.94 2 2.9% 1.3% 0.1021

2004 98 37100 46 2.13 2 2.2% 1.7% 0.0375

2005 15 57600 12 1.25 1 8.3% 6.9% 0.1584

2006 70 158000 30 2.53 2 3.3% 0.4% 0.1737

2007 121 459000 37 3.65 2 2.7% 0.2% 0.1681

2008 78 106000 29 2.79 2 3.4% 1.1% 0.1281

2009 112 202000 41 2.8 1 2.4% 0.6% 0.1637

2010 349 482000 56 6.34 3 1.8% 0.6% 0.0505

1990—

2010 2064 1989186 126 17 6 0.8% 0.06% *

48

Table 4

Summary of annual OLS regressions on first-day abnormal returns. To explain the investment-bank market share in year t, we calculate the average unexpected first-day abnormal return in year t-1 and year t-2. So first, we need to estimate the expected first-day abnormal return for the firms listed in year t-1 and year t-2. To estimate the expected first-day abnormal return for the firms listed in year t-1, we take all observable first-day returns (adjusted by the market return from issuing to listing) of the firms listed in year t-1 and year t-2. We regress those first-day abnormal returns on the logarithm of their corresponding gross proceeds, on the logarithm of total

assets before issuing, on the logarithm of the days between issuing and listing, on the market return from one year before listing till the listing day, on a dummy equals one if the firm belongs to regulated industries (Regulated), a dummy equals one if the firm has issued B/H/N shares before (Foreign) and a dummy equals to one if the firm is controlled by private owners when listing (Private). Same, to estimate the expected first-day abnormal returns of the IPOs listed in year t-2, we take all IPOs listed in t-3 to t-2. The column ‗Year‘ indicates the years in which the IPO firms are listed. 'R-square' is the adjusted R-square reported with the OLS regressions. p-values are reported between parentheses. *Indicates any coefficient that is significant under 10% level.

Year Intercept Logarithm of

gross proceeds

Logarithm of

total assets

Logarithm of

Listing lag

Market

return

Regulated Foreign Private R-

square

Number of

observation

1993 16.148

(0.012)*

-2.003

(<0.001)*

1.050

(0.001)*

0.694

(0.217)

0.434

(0.521)

-0.612

(0.464)

-0.654

(0.331)

1.040

(0.511)

57% 47

1994 24.118

(0.002)*

-0.990

(0.051)*

-0.319

(0.222)

0.780

(0.073)*

5.109

(0.001)*

-0.386

(0.323)

0.599

(0.274)

1.023

(0.371)

62% 49

1995 11.391

(0.243)

-0.224

(0.650)

-0.370

(0.346)

0.364

(0.220)

2.545

(0.031)*

0.088

(0.804)

0.660

(0.180)

0.117

(0.623)

51% 19

1996 5.006

(0.010)*

-0.249

(0.040)*

-0.006

(0.925)

0.115

(0.425)

0.454

(<0.001)*

0.264

(0.016)*

0.681

(0.017)*

0.339

(0.370)

46% 137

1997 5.267

(<0.001)*

-0.043

(0.607)

-0.153

(0.011)*

-0.131

(0.154)

0.311

(<0.001)*

0.181

(0.082)*

0.440

(0.020)*

0.224

(0.267)

18% 294

1998 13.396

(<0.001)*

-0.618

(<0.001)*

-0.057

(0.416)

0.282

(0.199)

0.843

(0.001)*

-0.044

(0.734)

0.219

(0.173)

0.151

(0.440)

41% 257

1999 16.196

(<0.001)*

-0.713

(<0.001)*

-0.089

(0.524)

0.162

(0.474)

1.658

(<0.001)*

0.319

(0.072)*

0.087

(0.745)

0.228

(0.177)

41% 181

2000 13.182

(<0.001)*

-0.458

(<0.001)*

-0.113

(0.209)

-0.172

(0.067)*

0.836

(0.028)*

0.248

(0.076)*

-0.364

(0.066)

0.098

(0.463)

26% 198

2001 17.326

(<0.001)*

-0.886

(<0.001)*

0.072

(0.300)

0.151

(0.229)

-0.090

(0.740)

-0.003

(0.975)

-0.364

(0.033)

0.118

(0.422)

44% 165

2002 15.254

(<0.001)*

-0.917

(<0.001)*

0.193

(0.039)*

0.169

(0.284)

1.013

(0.088)*

0.126

(0.269)

-0.273

(0.189)

-0.179

(0.195)

49% 115

2003 11.760

(<0.001)*

-0.644

(<0.001)*

0.141

(0.096)

-0.324

(0.186)

-0.708

(0.172)

0.210

(0.180)

-0.212

(0.356)

-0.338

(0.001)*

37% 122

2004 6.474

(0.001)*

-0.420

(0.004)*

0.100

(0.160)

0.199

(0.457)

0.876

(0.103)*

0.084

(0.333)

-0.101

(0.517)

-0.137

(0.122)

16% 137

2005 7.184

(0.033)*

-0.520

(0.041)*

0.156

(0.265)

0.213

(0.448)

2.221

(<0.001)*

0.106

(0.412)

0.818

(0.046)*

0.010

(0.923)

20% 88

2006 5.700

(0.002)*

-0.119

(0.175)

-0.107

(0.116)

-0.227

(0.428)

0.416

(0.026)*

0.398

(0.093)*

0.232

(0.375)

0.044

(0.643)

35% 75

2007 5.517

(0.006)

-0.056

(0.606)

-0.229

(0.004)*

0.223

(0.609)

1.280

(<0.001)*

0.401

(0.040)*

0.524

(0.046)*

-0.115

(0.426)

48% 175

2008 8.401

(<0.001)*

-0.277

(0.071)*

-0.106

(0.259)

0.206

(0.569)

0.663

(<0.001)*

0.172

(0.353)

0.055

(0.047)*

-0.121

(0.481)

40% 174

2009 7.05

(<0.001)*

-0.589

(<0.001)*

0.236

(0.001)*

0.397

(0.002)*

0.224

(0.072)*

0.025

(0.812)

0.306

(0.557)

-0.134

(0.226)

30% 172

2010 5.511

(<0.001)*

-0.302

(<0.001)*

0.055

(0.029)*

0.067

(0.277)

0.343

(<0.001)*

0.039

(0.378)

-0.015

(0.937)

-0.204

(<0.001)*

24% 434

49

Table 5

Summary of changes in industry-adjusted ROS from one year before IPO to one year after IPO.

This table summarizes all industry-adjusted ROS change from one year before IPO to one year after IPO, from

1993 till 2009. In every IPO, we calculate its industry-adjusted ROS one year after listing and subtract it by its

industry-adjusted ROS one year before listing. We report the mean and median industry-adjusted ROS changes

for all IPOs listed in every year.

Year Mean Median

1993 -0.040 -0.040

1994 0.064 0.072

1995 -0.017 0.008

1996 -0.030 -0.015

1997 -0.037 -0.022

1998 0.006 0.013

1999 -0.026 -0.019

2000 -0.035 -0.016 2001 0.007 0.020

2002 -0.067 -0.033

2003 -0.056 -0.042

2004 -0.039 -0.015

2005 -0.099 -0.067

2006 -0.091 -0.079

2007 -0.074 -0.046

2008 -0.032 -0.018

2009 -0.068 -0.057

50

Table 6

Summary of change in firm performance after IPO to before IPO.

In this table we summarize the firm performance changes before and after IPO. We use industry-adjusted ROS and industry-adjusted ROA

to measure the firm performance. We report those accounting ratios one year before listing and one year after listing. The change of firm

performance is measured by subtracting after IPO ROS/ROA by before IPO ROS/ROA. The whole data period is from 1993 till 2009. We

also divide the whole period into two sub-periods: 1993—2004 and 2005—2009. We perform equal mean tests to check whether or not the

firm performances and firm-performance changes differ significantly between these two sub-periods. The result of t-test is reported in the

column ‗Compare of the means‘.

1993–2004 2005–2009 Comparison of

the means

1993–2009

Variables Obs. Mean Median Obs. Mean Median p-value

H0=equal means

Obs. Mean Median

Industry-adjusted ROS one year before

IPO 1011 0.08 0.06 387 0.13 0.10 <0.01 1648 0.10 0.08

Industry-adjusted ROS one year after

IPO 1023 0.04 0.04 377 0.05 0.04 0.31 1400 0.05 0.04

Industry-adjusted ROS change one

year after IPO to one year before IPO 973 -0.03 -0.02 372 -0.07 -0.06 <0.01 1345 -0.04 -0.03

Industry-adjusted ROA one year

before IPO 1000 0.05 0.04 380 0.09 0.09 <0.01 1380 0.06 0.06

Industry-adjusted ROA one year after

IPO 1082 0.02 0.02 394 0.02 0.02 0.27 1476 0.02 0.02

Industry-adjusted ROA change one

year after IPO to one year before IPO 1000 -0.03 -0.02 380 -0.06 -0.05 <0.01 1380 -0.04 0.03

51

Table 7

Summary of annual OLS regressions on fee rates.

To explain the investment bank market share in year t, we need to calculate the unexpected fee

rates for every IPO issued in year t. So first, we need to obtain the expected fee rates for those

IPOs. For the IPOs issued in year t, we take all observable floating cost rates from the year t-2

to t-1. We regress those floating cost rates on their corresponding gross proceeds and on

logarithm of the gross proceeds. 'R-square' is the adjusted R-square reported with the OLS

regressions. p-values are reported between parentheses. The column ‗Year‘ indicates the years

in which the IPOs are issued.

Year Intercept Gross proceeds Logarithm of gross proceeds R-square

1995 0.139

(0.100)

4.16E-12

(0.700)

-0.006

(0.208)

2%

1996 0.266

(0.507)

2.21E-11

(0.895)

-0.012

(0.601)

11%

1997 0.409

(<0.001)

3.12E-11

(0.384)

-0.020

(<0.001)

33%

1998 0.360

(<0.001)

9.85E-12

(0.001)

-0.017

(<0.001)

62%

1999 0.398

(<0.001)

1.07E-11

(<0.001)

-0.019

(<0.001)

73%

2000 0.384

(<0.001)

6.32E-12

(0.001)

-0.018

(<0.001)

75%

2001 0.334

(<0.001)

3.90E-12

(<0.001)

-0.015

(<0.001)

62%

2002 0.311

(<0.001)

2.98E-12

(<0.001)

-0.014

(<0.001)

57%

2003 0.319

(<0.001)

2.50E-12

(<0.001)

-0.014

(<0.001)

62%

2004 0.274

(<0.001)

1.55E-12

(0.023)

-0.012

(<0.001)

51%

2005 0.334

(<0.001)

2.28E-12

(0.117)

-0.015

(<0.001)

35%

2006 0.471

(<0.001)

6.94E-12

(0.134)

-0.021

(<0.001)

37%

2007 0.508

(<0.001)

2.08E-12

(<0.001)

-0.023

(<0.001)

75%

2008 0.483

(<0.001)

1.64E-12

(<0.001)

-0.021

(<0.001)

72%

2009 0.514

(<0.001)

1.81E-12

(<0.001)

-0.023

(<0.001)

69%

2010 0.512

(<0.001)

1.68E-12

(<0.001)

-0.022

(<0.001)

59%

1995–2010 0.378

(<0.001)

1.02E-12

(<0.001)

-0.018

(<0.001)

67%

52

Table 8

Summary of star analysts.

This table summarizes the number of star analysts employed by Chinese investment banks from

2003 to 2010. Star- analyst lists are obtained from New Fortune‘s annual analyst evaluation

reports.

Year Total number

of star

analysts

Number of

investment banks

that employ at least

one star analyst in

that year

Average number

of star analysts

among investment

banks that employ

at least one star

analyst

Maximum

number of star

analyst

employed by

any investment

bank

Minimum

number of star

analyst

employed by

any investment

bank

2003 26 6 4.3 11 1

2004 84 10 8.4 25 1

2005 34 4 8.5 16 3

2006 104 14 7.4 28 1

2007 97 16 6.1 22 1 2008 134 13 10.3 21 2

2009 116 13 8.9 24 1

2010 141 18 7.8 26 1

53

Table 9

Summary statistics for the variables used in the multivariate regression.

We summarize the investment bank market share, the average industry-adjusted ROS changes, the

average unexpected fee rates and the fraction of star analysts the investment bank has in total star

analysts. All observations are in bank years. We report the statistics covering the whole sample

period in panel A. In panel B and C we report the statistics covering the two sub-periods: 1995–2004

and 2005–2010 respectively.

Panel A: 1995—2004

Variable Obs.

Bank year

Mean Median Std.

Dev.

Min. Max.

Investment bank market share 226 0.036 0.022 0.046 0.000 0.422

Average unexpected first-day abnormal return 226 0.004 -0.08 0.445 -1.714 1.925

Average industry-adjusted ROS change 226 -0.017 -0.017 0.063 -0.290 0.288

Average unexpected fee rate 226 0.005 0.005 0.007 -0.018 0.035

Proportion of star analysts * - - - - - -

Panel B: 2005–2010

Variable Obs.

Bank year

Mean Median Std.

Dev.

Min. Max.

Investment bank market share 146 0.036 0.009 0.063 0.000 0.344

Average unexpected first-day abnormal return 146 0.044 0.006 0.381 -0.999 1.851

Average industry-adjusted ROS change 146 -0.069 -0.056 0.078 -0.548 0.096

Average unexpected fee rate 146 0.006 0.006 0.015 -0.057 0.045

Proportion of star analysts 146 0.044 0.006 0.050 0.000 0.257

*We do not include the proportion of star analysts in our market share regression for the sub-period 1995—2004,

as there was no star-analyst list before 2003.

54

Table 10

Determinants of investment bank market share in Chinese A-share IPOs.

This table reports the two-step system GMM regression results. We regress current-year investment

bank market share on its previous-year market share; on a dummy that equals one if the investment

bank was owned directly by the central government in the previous year and zero otherwise; on the

average unexpected first-day abnormal return of all IPOs the investment bank led in the previous two

years; on the average industry-adjusted ROS changes of all IPOs the investment bank led in previous

two years; on the average unexpected fee rate in all IPOs the investment bank leads in this year and

on the proportion of star analysts employed by the investment bank in total star analysts in last year;

The explanatory variables also include year dummies. Coefficients significant at the 10%, 5%, and 1%

level are respectively marked with *, **, and ***. p-values are reported between parentheses. The

p-values are calculated against Windmeijer bias adjusted standard errors. We also report the p-value

from Abond test to verify the validity of the instruments; a high p-value means we cannot reject the

validity of the instruments.

1995–2004 2005–2010

Intercept 0.029

(0.561)

0.014

(0.478)

Last-year market share 0.151

(0.483)

0.401***

(<0.001)

Political hierarchy dummy 0.077**

(0.019)

0.029

(0.465)

Average unexpected first-day abnormal return 0.003

(0.850)

-0.017

(0.237)

Average industry-adjusted ROS change 0.051

(0.678)

-0.142**

(0.051)

Average unexpected fee rate -0.642 (0.712)

-0.721** (0.023)

Proportion of star analysts 0.298

(0.199)

Year dummies Yes Yes

p-value of Wald Chi-square <0.001 <0.001

p-value of Abond test 0.870 0.292

Number of observations 226 146

55

Table 11

Robustness check by using head-office location as proxy for political connection.

This table reports the robustness check result by using the location of head office as the proxy

for political connection. We assign a dummy equals to one if the head office of an investment

bank is located in Beijing or in Shanghai, and zero otherwise. Coefficients significant at the

10%, 5%, and 1% level are respectively marked with *, **, and ***. p-values are reported

between parentheses. The p-values are calculated against Windmeijer bias adjusted standard

errors. We also report the p-value from Abond test to verify the validity of the instruments; a

high p-value means we cannot reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.006

(0.944)

0.018

(0.415)


(0.213)

0.347***

(<0.001)

Political hierarchy dummy (by

head-office location)

0.047***

(0.001)

0.034

(0.277)

Average unexpected first-day

abnormal return

0.008

(0.340)

-0.012

(0.353)

Average industry-adjusted ROS

change

0.118

(0.446)

-0.154**

(0.034)

Average unexpected fee rate -1.184

(0.250)

-0.851***

(0.001)

Proportion of star analysts 0.361**

(0.033)





56

Table 12

Robustness check by excluding the bank-years with majority private owners.

This table reports the robustness check results by excluding the bank-years with majority private

owners. Coefficients significant at the 10%, 5%, and 1% level are respectively marked with *, **,

and ***. p-values are reported between parentheses. The p-values are calculated against Windmeijer

bias adjusted standard errors. We also report the p-value from Abond test to verify the validity of the

instruments, a high p-value means we cannot reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.031

(0.306)

0.014

(0.503)


(0.194)

0.374***

(0.001)

Political hierarchy dummy 0.073***

(<0.001)

0.269

(0.442)

Average unexpected first-day abnormal

return

0.003

(0.873)

-0.016

(0.443)

Average industry adjusted ROS change 0.059

(0.509)

- 0.161*

(0.084)


(0.243)

-0.808*

(0.077)

Proportion of star analysts 0.348*

(0.067)





57

Table 13

Robustness check by replacing industry-adjusted ROS with industry-adjusted ROA.

This table reports the robustness check result by replacing ‗Average industry-adjusted ROS

change‘ in Table 9 with ‗Average industry-adjusted ROA change‘. Coefficients significant at

the 10%, 5%, and 1% level are respectively marked with *, **, and ***. p-values are reported

between parentheses. The p-values are calculated against Windmeijer bias adjusted standard

errors. We also report the p-value from Abond test to verify the validity of the instruments; a

high p-value means we cannot reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.028

(0.628)

0.000

(0.997)

Last-year market share 0.210**

(0.011)

0.382***

(<0.001)

Political hierarchy dummy 0.060***

(<0.001)

0.024

(0.540)


return

0.001

(0.962)

-0.016

(0.258)

Average industry-adjusted ROS change -0.079

(0.617)

-0.075*

(0.101)


(0.150)

-0.867**

(0.012)


(0.153)





58

Table 14

Robustness check using one-year and three-year average ROS changes.

This table reports the robustness check results by using average industry-adjusted ROS of firms

listed in the previous three years and in the previous year. Coefficients significant at the 10%,

5%, and 1% level are respectively marked with *, **, and ***. p-values are reported between

parentheses. The p-values are calculated against Windmeijer bias adjusted standard errors. We

also report the p-value from Abond test to verify the validity of the instruments; a high p-value

means we cannot reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.039

(0.261)

0.039

(0.579)

0.001

(0.950)

0.026

(0.421)


(0.415)

0.072

(0.718)

0.272***

(<0.001)

0.375**

(0.035)


(0.039)

0.066*

(0.058)

0.038

(0.308)

0.014

(0.632)

Average unexpected first-

day abnormal return

0.001

(0.977)

0.006

(0.621)

-0.014

(0.546)

-0.021

(0.456)

Average industry-adjusted

ROS of firms listed in the

previous three years

-0.010

(0.927)

-0.231***

(0.006)

Average industry-adjusted

ROS of firms listed in the

previous year

0.090

(0.484)

-0.054

(0.234)

Average unexpected fee

rate

-0.480

(0.722)

-0.36

(0.794)

-0.747**

(0.021)

-0.865*

(0.103)


(0.126)

0.314

(0.325)

Year dummies Yes Yes Yes Yes

p-value of Wald Chi-

square

<0.001 <0.001 <0.001 <0.001

p-value of Abond test 0.971 0.756 0.330 0.301

Sample number 226 196 147 121

59

Table 15

Robustness check by using average abnormal aftermarket return instead of average

industry-adjusted ROS change.

This table reports the robustness check results replacing ‗Average industry-adjusted return‘ with

‗Abnormal aftermarket return‘. Coefficients significant at the 10%, 5%, and 1% level are

respectively marked with *, **, and ***. p-values are reported between parentheses. The p-

values are calculated against Windmeijer bias adjusted standard errors. We also report the p-

value from Abond test to verify the validity of the instruments; a high p-value means we cannot

reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.024

(0.465)

-0.001

(0.958)

Last-year market share 0.162*

(0.073)

0.433***

(<0.001)


(0.035)

0.025

(0.466)


return

-0.002

(0.882)

-0.012

(0.600)

Average abnormal aftermarket return -0.027

(0.133)

0.006

(0.535)


(0.235)

-0.745**

(0.022)


(0.195)





60

Table 16

Robustness check by replacing the logarithm of gross proceeds with Floating fraction in

the first-day abnormal return regression.

This table reports robustness check result with the average unexpected first-day abnormal return

constructed with the outcome of first-day abnormal return regression using Floating fraction

instead of Logarithm of gross proceeds. Coefficients significant at the 10%, 5%, and 1% level

are respectively marked with *, **, and ***. p-values are reported between parentheses. The p-

values are calculated against Windmeijer bias adjusted standard errors. We also report the p-

value from Abond test to verify the validity of the instruments, a high p-value means we cannot

reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.034

(0.698)

0.010

(0.642)


(0.796)

0.382***

(<0.001)

Political hierarchy dummy 0.072*

(0.071)

0.031

(0.539)

Average unexpected first-day abnormal return 0.002

(0.966)

-0.008

(0.672)


(0.690)

-0.145**

(0.036)


(0.440)

-0.705**

(0.030)


(0.361)





61

Table 17

Robustness check by adding an interaction term between last-year market share and

average industry-adjusted ROS change.

This table reports the robustness check results by adding interaction term between last-year

market share and average industry-adjusted ROS change. Coefficients significant at the 10%,

5%, and 1% level are respectively marked with *, **, and ***. p-values are reported between

parentheses. The p-values are calculated against Windmeijer bias adjusted standard errors. We

also report the p-value from Abond test to verify the validity of the instruments; a high p-value

means we cannot reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.034

(0.293)

0.031

(0.776)

Last-year market share 0.200***

(0.003)

-0.053

(0.845)

Political hierarchy dummy 0.051*** (0.001)

0.047 (0.440)


return

0.003

(0.779)

-0.011

(0.864)

Average industry-adjusted ROS change 0.089 (0.580)

-0.065 (0.792)

Interaction :

Last-year market share * Average

industry-adjusted ROS change

-0.946

(0.580)

-4.807**

(0.036)

Average unexpected fee rate -1.176 (0.144)

-0.453* (0.087)


(0.407)





62

Table 18

Robustness check by adding industry concentration.

This table reports the robustness check results by adding last-year industry concentration.

Coefficients significant at the 10%, 5%, and 1% level are respectively marked with *, **, and

***. p-values are reported between parentheses. The p-values are calculated against

Windmeijer bias adjusted standard errors. We also report the p-value from Abond test to verify

the validity of the instruments, a high p-value means we cannot reject the validity of the

instruments.

1995–2004 2005–2010

Intercept 0.048

(0.542)

0.051

(0.738)


(0.318)

0.315**

(0.033)


(0.016)

0.005

(0.919)


return

0.008

(0.313)

-0.018

(0.644)


(0.426)

-0.138

(0.154)


(0.339)

-0.976***

(0.004)


(0.254)

Last-year industry concentration -0.026

(0.503)

0.028

(0.724)





63

Table 19

Robustness check by adding an interaction term: Int_private.

The table reports the regression results by adding an interaction term: the fraction of private IPOs *

average industry-adjusted ROS change (Int_private). Coefficients significant at the 10%, 5%, and 1%

level are respectively marked with *, **, and ***. p-values are reported between parentheses. The

p-values are calculated against Windmeijer bias adjusted standard errors. We also report the p-value

from Abond test to verify the validity of the instruments; a high p-value means we cannot reject the

validity of the instruments.

1995–2004 2005–2010

Intercept 0.347

(0.289)

0.029

(0.728)


(0.267)

0.392**

(0.045)


(0.085)

0.028

(0.696)


return

0.004

(0.796)

-0.018

(0.327)

Int_private 0.346

(0.722)

-0.565

(0.647)

Average industry-adjusted ROS change -0.043

(0.820)

0.153

(0.797)


(0.506)

-0.808*

(0.102)


(0.653)





64

Table 20

Robustness check by adding Private fraction.

This table reports the regression result by adding an explanatory variable: the fraction of private

IPOs in total IPOs an investment bank underwrote in the previous two years (Private fraction).

Coefficients significant at the 10%, 5%, and 1% level are respectively marked with *, **, and ***.

p-values are reported between parentheses. The p-values are calculated against Windmeijer bias

adjusted standard errors. We also report the p-value from Abond test to verify the validity of the

instruments, a high p-value means we cannot reject the validity of the instruments.

1995–2004 2005–2010

Intercept 0.043

(0.430)

0.022

(0.375)


(0.336)

0.369***

(<0.001)


(0.105)

0.024

(0.566)


return

0.004

(0.937)

-0.019

(0.223)


(0.739)

-0.163*

(0.097)


(0.415)

-0.833**

(0.003)


(0.377)

Private fraction -0.028

(0.768)

-0.014

(-0.359)





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