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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]
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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.
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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>
5.3.2 Robustness check 2
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>
5.3.3 Robustness check 3
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>
5.3.4 Robustness check 4
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>
5.3.5 Robustness check 5
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>
5.3.6 Robustness check 6
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>
5.3.7 Robustness check 7
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>
5.3.8 Robustness check 8
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>
5.3.9 Robustness check 9
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
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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)
Last-year market share 0.268
(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)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.486 0.274
Number of observations 226 146
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)
Last-year market share 0.142
(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)
Average unexpected fee rate -0.971
(0.243)
-0.808*
(0.077)
Proportion of star analysts 0.348*
(0.067)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.868 0.283
Number of observations 219 141
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)
Average unexpected first-day abnormal
return
0.001
(0.962)
-0.016
(0.258)
Average industry-adjusted ROS change -0.079
(0.617)
-0.075*
(0.101)
Average unexpected fee rate -1.132
(0.150)
-0.867**
(0.012)
Proportion of star analysts 0.374
(0.153)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.623 0.306
Number of observations 227 149
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)
Last-year market share 0.113
(0.415)
0.072
(0.718)
0.272***
(<0.001)
0.375**
(0.035)
Political hierarchy dummy 0.068**
(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)
Proportion of star analysts 0.375
(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)
Political hierarchy dummy 0.058**
(0.035)
0.025
(0.466)
Average unexpected first-day abnormal
return
-0.002
(0.882)
-0.012
(0.600)
Average abnormal aftermarket return -0.027
(0.133)
0.006
(0.535)
Average unexpected fee rate -0.888
(0.235)
-0.745**
(0.022)
Proportion of star analysts 0.263
(0.195)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.721 0.300
Number of observations 224 149
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)
Last-year market share 0.137
(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)
Average industry-adjusted ROS change 0.068
(0.690)
-0.145**
(0.036)
Average unexpected fee rate -0.609
(0.440)
-0.705**
(0.030)
Proportion of star analysts 0.279
(0.361)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.958 0.263
Number of observations 226 146
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)
Average unexpected first-day abnormal
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)
Proportion of star analysts 0.264
(0.407)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.583 0.396
Number of observations 226 146
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)
Last-year market share 0.178
(0.318)
0.315**
(0.033)
Political hierarchy dummy 0.052**
(0.016)
0.005
(0.919)
Average unexpected first-day abnormal
return
0.008
(0.313)
-0.018
(0.644)
Average industry-adjusted ROS change 0.082
(0.426)
-0.138
(0.154)
Average unexpected fee rate -0.958
(0.339)
-0.976***
(0.004)
Proportion of star analysts 0.380
(0.254)
Last-year industry concentration -0.026
(0.503)
0.028
(0.724)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.935 0.309
Number of observations 223 146
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)
Last-year market share 0.158
(0.267)
0.392**
(0.045)
Political hierarchy dummy 0.062*
(0.085)
0.028
(0.696)
Average unexpected first-day abnormal
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)
Average unexpected fee rate -0.727
(0.506)
-0.808*
(0.102)
Proportion of star analysts 0.285
(0.653)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.788 0.325
Number of observations 226 146
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)
Last-year market share 0.153
(0.336)
0.369***
(<0.001)
Political hierarchy dummy 0.065*
(0.105)
0.024
(0.566)
Average unexpected first-day abnormal
return
0.004
(0.937)
-0.019
(0.223)
Average industry-adjusted ROS change 0.060
(0.739)
-0.163*
(0.097)
Average unexpected fee rate -1.330
(0.415)
-0.833**
(0.003)
Proportion of star analysts 0.262
(0.377)
Private fraction -0.028
(0.768)
-0.014
(-0.359)
Year dummies Yes Yes
p-value of Wald Chi-square <0.001 <0.001
p-value of Abond test 0.648 0.282
Number of observations 226 146
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