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Financial Market Development, Market Transparency and IPO Returns
Ying Sophie Huanga, Carl R. Chenb, *, Mengyu Lic
a School of Management, Zhejiang University, Hangzhou, Zhejiang 310058, China b Department of Economics and Finance, University of Dayton, Dayton, OH 45469-2251, United States c College of Economics, Zhejiang University, Hangzhou, Zhejiang 310027, China
Abstract
We examine the effects of financial market development on IPO underpricing and firm long-run performance. We bring forth new ideas to test IPOs for firms located in places with differential development levels while the financial market is “under one regulation umbrella” by exploiting the natural regional development disparity in the Chinese financial market. We find evidence that firms located in better-developed markets experience less underpricing and perform better in the long run. We also show that regulation reforms reduce underpricing and enhance the marginal impact of financial market development on underpricing by making the market more transparent. Our results hold especially for private firms. Furthermore, we find that benefits of financial market development on IPO initial pricing are stronger for financially constrained firms. JEL classification: G30 Keywords: IPO; Financial market development; Market transparency
* Corresponding author. Email address: [email protected]. Ying Sophie Huang gratefully
acknowledges the financial support provided by the National Natural Science Foundation of China
(Grant No. 71573228). All errors are our responsibility.
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1. Introduction
One of the stock market anomalies is IPO underpricing and its long-run
underperformance. IPO return anomalies have been documented and the literature has
attempted to explain the abnormal initial returns (Baron, 1982; Rock, 1986; Allen and
Faulhaber, 1989). In particular, Ritter (1991) brings IPO long-run underperformance into
the discussion. While most of the theoretical models and empirical tests target the US
market, Loughran et al. (1994) are among the first to discuss both IPO short- and long-run
returns in 25 countries worldwide. They contribute to this debate by interpreting the
varying initial IPO returns from the angle of market development and regulation
differences among different countries.
Cross-country investigations have been further carried out over the years. For example,
Chowdhry and Sherman (1996) conclude that differences in IPO underpricing across
markets may be explained by the method used for firm commitment offerings. In
comparative studies, the long-standing distortions in IPO pricing in various countries have
generally been explained from the perspective of law and regulation, such as shareholder
protection laws. A higher quality of a country’s legal framework is found to be associated
with a lower level of underpricing (e.g. Engelen and Essen, 2010). Similarly, an effective
contract enforcement mechanism or stronger law enforcement can greatly help reduce IPO
underpricing (Banerjee et al., 2011; Hopp and Dreher, 2013).
While the extant empirical evidence on international IPO underpricing differential is
concentrated on the country-specific legal factors and offering methods, changes in the
3
macroeconomic environment, such as financial market development, might also have
important implications for a country’s IPO market. As financial markets in emerging
markets and developing economies have been increasingly developed, such development
of financial markets has seen beneficial effects on firm operating performance (Mitton,
2006), equity price (Bekaert and Harvey, 2000) and the cost of equity capital (Henry, 2000).
Cross-country IPO underpricing, therefore, may vary across countries with different
degrees of financial market development. More importantly, different degrees of financial
market development have bearings on the transparency of the market, hence the
asymmetric information environment. However, literature examining the relation between
financial market development and IPO underpricing is scant as it may encounter a difficulty,
i.e., institutional and legal environments differ among countries and they are shown to have
an impact on cross-country level IPO underpricing (e.g., Banerjee et al., 2011; Hopp and
Dreher, 2013).
Building upon these findings, in the current study we attempt to examine the impact
of financial market development on IPO underpricing under a unified regulatory
environment including the legal condition and offering methods. To this end, we exploit a
unique setting and assess the impact of differing financial market development on IPO
stock performance when Chinese IPO firms are subject to the same set of institutional and
legal constraint and changes in regulations. A key feature of the Chinese economy is that
4
there are 31 province-level administrative units1 (excluding Hong Kong and Macau) that
experience financial market development disparity. Within the last few decades, we have
witnessed the emergence of some highly developed cities/provinces like Beijing, Shanghai
and Guangdong, along with other underdeveloped areas. At the same time, all firms in
different regions are “under one regulation umbrella” and subject to the same regulations
and laws in both the stock market and the IPO market. As a result, the extent to which the
financial market development diverges across the country resembles a pseudo international
market, yet it provides us an ideal natural experimental ground to examine the impact of
financial development on IPO returns without encountering other institutional differences.
Variations in the level of financial market development in geographical areas and new
regulations put forward and enforced during our sample period, which allows us to uncover
the dynamics in IPO return patterns over time is another advantage of our study.
Specifically, we try to evaluate how financial market development influences the
initial returns of IPOs and firms’ long-run performance. Several interesting results emerge
from our analyses. Overall, the financial market development in China has significant
effects on IPO returns - underpricing is reduced to a large extent with better development
conditions. For long-run performance, the 3-year BHAR tests indicate that financial market
1 The 31 provinces, including four municipalities with the same level of authority as the provinces, are
Anhui, Beijing, Chongqing, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hainan, Hebei, Heilongjiang,
Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Inner Mongolia, Ningxia, Qinghai, Shanxi, Shandong,
Shanghai, Shanxi, Sichuan, Tianjin, Xinjiang, Tibet, Yunnan and Zhejiang.
5
development may also benefit the long-run performance of IPOs. Second, we find that
state-owned enterprises (SOEs) are less sensitive to financial market development
compared to non-state-owned enterprises (non-SOEs). This could be due to the fact that
SOEs have easier access to capital from financial institutions that are also government-
owned. Consistent with our conjectures, we also find that if a firm faces financial
constraints, the influence of financial market development on IPO underpricing would be
strengthened.
Finally, we argue that financial market development nourishes IPO rationality because
better developed financial markets foster higher market transparency. Madhavan (1995)
concludes that when market develops from fragmentation to consolidation, increasing
market transparency reduces price volatility and enhances pricing efficiency. Levine and
Zervos (1998) find that the stock market tends to become larger, more liquid and more
integrated following market development. Market transparency, therefore, largely reduces
information asymmetry in the process of IPO price formation, which mitigates the need for
issuers to underprice the IPOs (e.g., Neupane and Poshakwale, 2012; Allen and Faulhaber,
1988; and Chemmanur, 1993). Consistent with our reasoning, we observe a strong and
negative relation between financial market development and price volatility and stock
illiquidity, and a strong positive relation between financial market development and stock
turnover ratio and analyst attention.
The rest of our paper is organized as follows. In Section 2, we provide the financial
market development background in China by introducing provincial market development
6
disparity and key reforms on financial markets. Section 3 introduces data and how IPO
performance is measured. In Section 4, we present empirical results showing the channel
through which financial market development impacts IPO underpricing. In this section we
also examine the effects of financial constraints and the ownership structure. In Section 5,
the relation of long-run underperformance and financial market development are tested.
Section 6 provides additional robustness checks. Section 7 concludes.
2. Institutional background
2.1 Provincial financial market development disparity
Since the late 1970s, China has achieved astonishing economic growth. However, it
has also been accompanied by unbalanced distribution of resources, widening the income
gap between coastal and inland regions and creating large disparities among different
provinces in financial market development. Fan et al. (2003, 2004, 2007, 2010 and 2011)
of the National Economic Research Institute (NERI) develop a series of indices to measure
regional disparities in institutional environment. Their index is scaled to range from 0 to
10, with 1997 as the base year. In particular, their index of marketization of the financial
industry is relevant for our research, as it provides a quantitative measurement to depict
the financial market development disparity. This index is a weighted average of two ratios,
the first being the percentage of non-state-owned financial institutions’ deposits to all
financial institutions’ deposits and the second being the percentage of short-term loans to
non-state-owned sectors over all short-term loans made by financial institutions. This
7
measurement is consistent with the conclusions drawn by Demirguc-Kunt and Levine
(1996, 2004) that stock market indicators are highly correlated with the development and
efficient functioning of banks. Since it is unique in China that SOEs have privileges in
getting access to capital and information (Sun and Tong, 2003), the index developed by
Fan et al. (2011) reliably captures the market development disparity among various
provinces in China. The alternative measurement of financial market development using
market capitalization over GDP in Demirguc-Kunt and Levine (1996) and Love (2003)
may not be suitable for China as it does not consider the special ownership structure in
Chinese firms. The index we employed has also been exploited by Chen et al. (2017),
Zeng et al (2012), Jiang et al (2008) and others in different studies. Moreover, China’s
banking system plays a more important role in the economy relative to capital markets
(Allen et al., 2005; Hasan et al., 2009). China’s banking industry is dominated by state-
owned commercial banks that have historically funneled financial capital into government-
run projects including SOEs. In the process of banking system reform, commercial banks
and foreign banks are successively established to replace the old mono-banking system
(Liang, 2006).2 The establishment of these banks, however, is not an overnight
accomplishment. Special Economic Zones (e.g. Shenzhen) and coastal areas (e.g. Shanghai,
2 Key players in the domestic banking system currently include the “Big Four” (state-owned commercial banks: Bank of China, the China Construction Bank, the Agricultural Bank of China and the Industrial and Commercial Bank of China), policy banks (Agricultural Development Bank of China, China Development Bank and the Export-Import Bank of China) and commercial banks (some big ones include Shenzhen Development Bank, Pudong Development Bank, China Minsheng Bank, Shenzhen City Commercial Bank, Bank of Beijing, Bank of Tianjin) and trust and investment corporations (China International Trust and Investment Corporation, Guangdong International Trust and Investment Corporation) and foreign banks ( some big ones include Bank of East Asia, Woori Bank, JP Morgan Chase, Standard Chartered).
8
Zhejiang Province, Jiangsu Province) are among the first beneficiaries of the banking
system development. As self-fundraising and bank loans are the two most important
financing channels in China (Allen et al., 2005), being located in a better-developed
financing environment would offer a company choices of financing method and thus their
going public. China’s capital market has grown rapidly with the promulgation of the 1998
Securities Law. The transaction, registration and settlement systems of the stock exchanges
have become more efficient. At the same time, the relevant legal and accounting
regulations have been strengthened and the secondary market has become more active.
Table 1 shows the summary statistics of the financial market development index (FMD
Index for short hereafter) among 31 provinces/regions from 1997 to 2009. We include the
number of IPOs and average underpricing in these provinces in the last two columns. As
can be seen, Shanghai, Guangdong, Beijing, Tianjin and Zhejiang rank the “top 5” in terms
of the mean FMD index. The underpricing of IPOs in these areas is relatively moderate
compared to Sichuan (471.06%), Hubei (244.25%) and Shaanxi (171.88%). It is noted that
more companies initiate their first public offerings in better-developed areas. The five
locations with the highest number of IPOs possess nearly 50% of all IPOs in China; they
are Guangdong (141), followed by Zhejiang (117), Beijing (110), Jiangsu (107) and
Shandong (76). Even though all areas in China have benefited from the evolution of the
finance sector (the mean FMD Index surges from 2.5 in 1997 to 10.2 in 2009, a 308%
increase), the geographic disparity continues.
[Insert Table 1 here]
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A better developed market always enjoys better transparency (Bloomfield and Ohara,
1999), which in turn may reduce information asymmetry and IPO underpricing. We provide
some preliminary evidence by separating all provinces into better-developed and less-
developed groups by their FMD ranks. The top 15 locations are classified as High Group
and the remaining provinces are classified as Low Group. We find that there are significant
differences on stock volatility, stock illiquidity, stock turnover ratio and analyst attention
between these two groups. In general, stocks of companies in better developed areas are
more liquid, less volatile and attract more attention from analysts. Our findings are
consistent with prior literature in that information asymmetry would be reduced in better
developed financial markets since fund managers and analysts may have an informational
advantage with respect to local firms and a preference for firms in big cities (Loughran and
Schultz, 2005; Bae et al., 2008; Malloy, 2005; Coval and Moskowitz, 1999, 2001; Ivkovic
and Weisbenner, 2005).
2.2 Key financial market reforms
China has undergone many financial market reforms. These major reforms include the
non-tradable shares (NTS for short hereafter) reform, improving the quality of listed
companies, restructuring securities firms, strengthening institutional investors, and
improving the legal and regulatory frameworks for the market. Among these reforms over
the years, the NTS reform is the most revolutionary one.
Ever since its establishment in 1991, the split-share structure has been widely criticized
for its detriments on the liquidity and transparency of the stock market (Beltratti et al.,
10
2012). As a segmented financial market with A-shares, B-shares and H-shares traded for
different investors, a significant amount of non-tradable shares makes the situation even
worse. The small public float causes shares to be illiquid and vulnerable to manipulation.
The structure also puts public investors in an inferior position relative to controlling
shareholders in making decisions regarding corporate policies and disposing of corporate
profits. The non-tradable shares entrench incompetent corporate managers (Beltratti and
Bortolotti, 2006; Deng et al., 2008).
To protect the legitimate interests of investors and enhance the transparency of the
stock market, reforms were carried out in 1999, 2001 and 2005. The earlier two reforms,
however, failed due to improper mechanism designs.3 On April 29, 2005, China Securities
Regulatory Commission (CSRC) issued the Circular on Relevant Issues Regarding Pilot
Programs of Non-Tradable Share Reform of Listed Companies and initiated the non-
tradable share reform. By the end of 2005, all 50 companies listed on the small and
medium-sized enterprises board of the Shenzhen Stock Exchange have finished converting
their non-tradable shares into tradable shares (Li et al., 2011; Liao et al., 2014).4 By the
end of Sep. 2017, the total shares of listed Chinese companies were 6001.6 billion, among
which 851.0 billion shares, or 14% of the total, were non-tradable, significantly reduced
from 64% in 2004.5
3 In the first attempt, two companies were selected to sell their state shares to the floating shareholders. The experiment did not meet the investors’ expectations and within 15 days from the announcement of the transfer program the share price of the two companies had fallen by about 40%. The second attempt failed in 2001 because the proposal envisaged an equal pricing for tradable and non-tradable shares (Beltratti et al., 2012). 4 For a detailed timeline of the reform, please refer to (Li et al., 2011; Liao et al., 2014). 5 The statistics are downloaded from monthly market review of CSRC.
11
This reform of non-tradable shares constitutes a landmark event in the Chinese stock
market. It enhances the transparency and efficiency of the market by aligning the
information and interests of the government and public investors (Liao et al., 2004),
reducing controlling shareholders’ tunneling activities and enhancing corporate
governance (Marcelin and Mathur, 2015). Disparity of financial markets among various
provinces/regions together with the key stock market reform provide a natural background
for us to test the impacts of financial market development on IPO pricing.
3. Data and performance measurements
In this section we describe our data source and show how initial returns and long-run
performance are measured. Our data are retrieved from China Stock Market and
Accounting Research (CSMAR) database. The sample consists of A-share IPOs only as B-
shares cannot be invested by mainland Chinese residents and thus cannot directly reflect
the stock investment activities in mainland China. The sample period for IPOs spans from
January 1997 to December 2009. We limit our data to 2009 since the original data on
provincial market development index ends in 2009. Although the first publicly traded firm
in China appears in 1990, to ensure that we have sufficient matching firms traded for more
than 5 years for each IPO firm, we choose 1997 as the starting year. We exclude financial
firms as their financial data are not comparable to others. Our final sample includes 1,246
IPOs after excluding firms with insufficient data requirement. For each IPO, we have initial
stock returns, 1-year and 3-year buy-and-hold abnormal returns, firm size, book-to-market
12
ratio, firm’s provincial location, industry code, issuing shares, listing time lag, firm age,
public-traded shares ratio, P/E ratio, stock volatility, liquidity/illiquidity in the first year,
trading volume, and financial constraint index (KZ index) before going public.6 We choose
our control variables following related research (e.g. Cai et al., 2008; Chang et al., 2010).
The definitions of all variables are summarized in the Appendix.
IPO initial return (underpricing) is defined as the percentage difference between the
first day closing price and the offer price.
��� =��,��
� (1)
where ��� is the percentage initial return (underpricing if positive) of firm�,��, is the
closing price of firm � on the first trading day and �� is the offer price of firm �.
For the long-run performance measurement, two approaches have been widely
employed, namely the buy-and-hold abnormal return (BHAR) and the calendar time
portfolio approach. It has been documented that the calendar time portfolio approach can
be misspecified in nonrandom samples, while the BHAR method is relatively robust (Lyon,
et al., 1999). Furthermore, the calendar time portfolio approach may be subject to
“rebalancing bias” (Barber and Lyon, 1997). In contrast, the BHAR method is largely free
of such bias and directly reflects investors’ actual experience. Thus, we measure the stock
long-run performance by adopting the BHAR method.
Following the literature in measuring IPO long-run performance, we use size and book-
6 Data for computing their financial constraint (KZ Index) before IPO are available in the CSMAR database. We use the end of the year before IPO to calculate their financial constraint before they actually go public.
13
to-market matched firms as benchmarks for each IPO firm. To be sure, each IPO firm is
matched with a firm such that the absolute percentage difference between size and book-
to-market ratio is minimal7 (Loughran and Ritter, 1995; Barber and Lyon, 1997; and Eckbo,
et al., 2000). The matching firm should be publicly traded for more than 5 years. Matching
firm-adjusted BHAR for IPO firm �, we do the following calculation:
�����, = �∏ �1 + ��, ����� � − [∏ �1 + �!, ��
��� ] (2)
where ��, is the monthly return of the IPO firm and �!, is the monthly return of the
matching firm.
4. Financial market development and IPO underpricing
In this section we develop our hypotheses on financial market development, market
transparency and IPO underpricing. Empirical results are presented after each hypothesis.
4.1 The relation between financial market development and IPO underpricing
As depicted in Table 1, firms located in different provinces show disparity in IPO
underpricing. The underpricing for firms in Shanghai, Guangdong, Beijing, Tianjin and
Zhejiang are relatively moderate compared to firms from less-developed areas.
Financial market development first caught the eyes of researchers by its impacts on
economic growth (King and Levine, 1993; Jayaratne and Strahan, 1996; Bekaert et al.,
2001) and economic growth leads to the formation of developed markets (Greenwood and
7 We have also tried the propensity score matching as an alternative matching method. However, the sample size is reduced to nearly half of the original sample. Due to this drawback, we have to drop this alternative method.
14
Smith, 1997). A more developed financial market enjoys more transparency (Bloomfield
and Ohara, 1999) and market transparency would reduce information asymmetry
(Loughran and Schultz, 2005; Bae et al., 2008; Malloy, 2005; Coval and Moskowitz, 2001;
Ivkovic and Weisbenner, 2005). Greenwood and Smith (1997) claim that bankers,
stockbrokers, insurance agents, realtors and other agents, who enforce trading transparency,
require resource expenditures and tend to stay in better developed areas while put their eyes
on areas that are easy to cover.
Information asymmetry is taken as one of the key factors driving IPO underpricing.
Rock (1986) posits that information asymmetry exists between informed and uninformed
investors and to entice the uninformed to participate, IPO shares must be underpriced.
Beatty and Ritter (1986) test Rock (1986) model by examining the relation between
underpricing and ex ante uncertainty in the firm value as they argue greater risk due to
uncertainty must be compensated by higher yield. We believe that the information
asymmetry caused by the regional disparity in financial market development influences the
offer price decisions of a company and consequently impacts the extent to which its IPO is
underpriced. Ceteris paribus, companies located in less-developed markets face more
uncertainty regarding their quality compared with those from other areas. Based on these
beliefs, we propose to test the following hypothesis.
H1: Firms in better developed financial market has lower IPO underpricing.
In Table 2, we show some preliminary univariate results on this hypothesis by
categorizing all IPOs into two groups based on the financial market development indices
15
for their firms’ locations. In the first group, firms in provinces with development scores
higher than the median are included. Correspondingly, IPOs from provinces with
development scores lower than the median are in the second group.8 Panel A presents the
differences of FMD Index, IPO Numbers, Underpricing, BHAR1 and BHAR3 between the
two groups. The differences are statistically significant based upon t-test and Wilcoxon z-
test. For less developed areas, IPO underpricing is significantly higher than that in more
developed areas. Also, firms in more developed areas perform better in the long run. The
findings indicate that though under the same regulatory environment, firms perform
differently when their geographical locations differ, hence FMD Indices differ. In Panel B,
a number of firm characteristics such as Firm Age, Public Ratio, List Lag, P/E Ratio and
Ln (Offering Shares) are compared and we see that in more developed areas, firms are
typically older, having more shares traded publicly, shorter listing time lag, higher P/E
ratios before going public9 and less shares issued. In short, Table 2 shows that there are
significant differences both in firm characteristics and their stock performance on the first
trading day and in the long run for firms located in different regions.
[Insert Table 2 here]
To further test our first hypothesis, we examine the relation between IPO underpricing
and the financial market development environment using multivariate analyses. We
8 Group one contains firms from Zhejiang, Shanghai, Guangdong, Jiangsu, Shandong, Chongqing, Liaoning, Hainan, Hebei, Henan, Fujian, Tianjin, Ningxia, Shaanxi, Anhui and Beijing. Group two includes firms located in Hunan, Yunnan, Shanxi, Hubei, Sichuan, Jiangxi, Guangxi, Guizhou, Jilin, Inner Mongolia, Gansu, Xinjiang, Heilongjiang, Qinghai and Tibet. 9 The P/E ratio of a firm before IPO is available in the CSMAR database. We use the P/E ratio at the end of the year before the firm goes public, where P is the issue price.
16
estimate the following equation by including the key explanatory factor FMD Index and
using UP (Underpricing) as the dependent variable while controlling for other firm
characteristics.
���, = #� + $�%&'()*+,�, + $-%��.�/+�, + $0�123�4�56�7�, + $89�:695/�, +$;�/=�56�7�, + $>9)(�@@+��)/Aℎ5�+:)�, + D+5� + ()*1:6�E + 1�, (3)
The FMD Index is a measurement of financial market development for firm i at time
t. All firm-level independent variables are measured at the end of the preceding year. Firm
Age is firm age, Public Ratio measures the percentage of public holding, List Lag measures
delays in floating the issue, P/E Ratio is measured by issuing price over earnings, and
Offering Shares is the number of shares offered. Detailed definitions can be found in the
Appendix. The year and industry effects are also controlled for in each model, and standard
errors are clustered at the firm level.
Table 3 presents coefficients from the estimation of Equation (3) for IPOs from 31
provinces drawn over the period of 1997 to 2009. The first two columns indicate that FMD
Index is statistically significant in influencing IPO underpricing at the 1% level with or
without considering other factors. The effect of FMD Index is also economically significant
as IPO underpricing is reduced by 0.1 percent for every 1% increase in the FMD index. In
Columns 3 and 4, we replace FMD Index by FMD Dummy, which is defined to take on the
value of 1 if the firm is located in a more developed area (the top half of the FMD Index)
and 0 otherwise. Again, a higher degree of financial market development is associated with
a lower level of IPO underpricing. Therefore, results in Table 3 support our first hypothesis
17
that IPO underpricing is lower for firms located in better developed markets. Also
consistent with Tian (2011), our regression results suggest a higher IPO underpricing with
a longer listing time lag. Offering shares are negatively related to IPO underpricing, which
is in line with the literature that the larger the issue size, the more relative bargaining power
the issuer has and the less initial underpricing by the underwriter (Cheung et al., 2009).
[Insert Table 3 here]
4.2 Influence mechanism
The above subsection shows that IPO underpricing is mitigated when the firm is located
in a better developed financial market. Here we attempt to examine the channel through
which financial market development influences IPO returns. To this end, we propose that
companies located in different provinces are exposed to differing information asymmetry,
and the resulting differences in financial market transparency may lead to the disparity on
IPO underpricing. Neupane and Poshakwale (2012) find that transparency in the offering
mechanism leads to higher retail investors’ participation and in turn it results in higher IPO
price. Akyol et al. (2014) study the effect of regulatory changes on European IPOs and find
that IPO underpricing declined on Member State-regulated markets after Member States
adopted corporate governance codes containing SOX-like provisions. These authors
conclude that elevating corporate governance standards increases transparency and reduces
information asymmetries that affect IPO valuations. If these arguments are true, then we
expect firms located in higher FMD index regions exhibit better market transparency. This
leads to our second hypothesis.
18
H2: Better developed financial markets have higher market transparency.
In Table 4, we present univariate analyses on market transparency for firms in the low
and high FMD Index groups using t-test and Wilcoxon z-test. Measurements of market
transparency include stock volatility, stock liquidity (illiquidity), stock turnover and analyst
attention. Definitions of these variables are in the Appendix. The results show significant
differences in all transparency measures between the high and low FMD Index groups.
Specifically, both measurements of stock liquidity confirm that stocks are more liquid in
the high FMD Index group, which suggests that firms located in better developed areas
may be more attractive to both underwriters and investors. Consistent with Greenwood and
Smith (1997), we also find that firms located in better developed areas enjoy more analyst
attention.
[Insert Table 4 here]
In Table 5, we report the findings on the relationship between financial market
development and market transparency by running the following multivariate regression.
&5�F+6G�5):H5�+)4E�, = #� + $�%&'Index�, + $-9)(G�5*�)/N731.+)�, +$09)(()*1:6�EA�O+)�, + $8�/&�56�7�, + D+5� + ()*1:6�E + 1�, (4)
The dependent variable is Market Transparency, measured by different proxies
including Stock Volatility, Stock Illiquidity (Amihud), Stock Liquidity (P-S), Turnover Ratio
and Analyst Attention. All firm-level independent variables, Trading Volume, Industry Size,
and Book-to-Market ratio (B/M Ratio), are measured at the end of the preceding year. Year
19
effect and Industry effect are controlled for in each model, and standard errors are clustered
at the firm level.
The results indicate that development in financial market improves market
transparency across all measures of market transparency, strongly support our second
hypothesis. From the first and second columns, we find that a more developed financial
market contributes to declining stock volatility. In Columns 3-6, our empirical results
suggest that if a market is better developed, stocks are traded in a more liquid manner no
matter which measurement is used. Our findings are consistent with Rock (1986) and Elllul
and Pagano (2006). In the last four columns, we find that stocks in better-developed areas
have higher turnover ratios and receive more analyst attention. Our results imply that with
a better developed financial environment, market transparency is enhanced, thus
information asymmetry is reduced.
[Insert Table 5 here]
4.3 The impact of financial market reforms
By this point, our findings suggest that firms located in better developed financial
markets tend to underprice less since information asymmetry is reduced with better market
transparency. Although disparity of financial market development exists among provinces,
overall, market transparency has improved over time. After the 2005 NTS reform, many of
the previously non-tradable shares have become tradable. As Akyol et al. (2014) find,
regulatory changes increase transparency, reduce information asymmetries and improve
IPO valuations, we believe that with the development of financial market in China,
20
similarly, we could see a reduction in IPO underpricing after the 2005 NTS reform.
Therefore, we propose to test the following hypothesis.
H3: Reforms on financial market reduce IPO underpricing.
To test our third hypothesis, we develop the following model:
���, = #� + $�%&''1..E�, + $-�+@7�.'1..E�, + $0�+@7�.'1..E�, ∗%&''1..E�, + $8%��.�/+�, + $;�123�4�56�7�, + $>9�:695/�, +$Q�/=�56�7�, + $R9)(�@@+��)/Aℎ5�+:)�, + D+5� + ()*1:6�E + 1�, . (5)
The dependent variable, UP is the firm’s IPO underpricing. FMD Dummy is defined
to take on the value of 1 if the firm is located in more developed areas (the top half of the
FMD Index), otherwise 0. Reform Dummy is a dummy variable used to measure the effect
of the 2005 NTS reform. For the years before 2005, this dummy is set to 0; otherwise 1.
All firm-level independent variables are measured at the end of the preceding year, and are
similarly defined as in Equation (3). Year and industry effects are controlled for in each
model, and standard errors are clustered at the firm level.
In Columns 1 and 2 of Table 6, we divide the sample into two subsamples (before and
after the 2005 NTS reform) to compare the variation in relation between financial market
development and IPO underpricing. We find that after the reform the degree of financial
market development plays a more important role in shaping IPO pricing, which is
consistent with our Hypothesis 3 that financial market reform reduces IPO pricing in
general. In Columns 3 and 4, we compare how the reform influences IPO underpricing in
two groups of provinces classified by the rank of FMD index. Since the magnitude of
21
Reform Dummy coefficient is more than 50% higher in the high financial market
development group, the results imply that for highly-developed markets the reform helps
to reduce IPO underpricing to a greater extent. In Column 5, we employ all observations
and conduct an additional test using method that is similar to the difference-in-difference
specification. The variable of interest is the interaction term between FMD Dummy and
Reform Dummy.10 Since the coefficient of (FMD Dummy) * (Reform Dummy) is negative
and significant, we conclude that the 2005 reform has enhanced the negative impact of
financial market development on IPO underpricing. Hence, our evidence supports our third
hypothesis that reforms on financial market reduce IPO underpricing, hence financial
market development plays a more significant role in IPO underpricing after the reform.
[Insert Table 6 here]
4.4 What if a firm is financially constrained?
From the discussions above, we have reached the conclusion that market development
lowers the underpricing in IPOs. We now proceed to investigate how the impact of financial
market development on IPO pricing differs for financially constrained firms. It is widely
emphasized in previous works that firms in China face severe financial constraints due to
limited access to capital (Chong and Ongena, 2013; Poncet et al., 2010). Firms rely more
on the external financing resource if they are financially constrained (Berger and Udell,
1994; Chaddad and Reuer, 2009). To obtain external capital, financially constrained firms
10
The specification can be viewed from the following three equations. First, we specify UP=a + b FMD, and b= c + d Reform. Substituting the second equation into the first, we obtain UP = a +c FMD + d FMD*Reform.
22
may lower their initial offering price to ensure the success of IPO regardless of their
geographical locations. However, since firms located in better developed areas will be able
to reduce IPO underpricing, the benefit of better pricing due to locations may be even more
evident for financially constrained firms. We propose that if a firm faces financial
constraints, the influence of financial market development on IPO underpricing would be
strengthened.
H4: Benefit of financial market development on IPO underpricing is greater for
financially-constrained firms.
To test this hypothesis, we follow Kaplan and Zingales (1997) to construct the KZ
index for each firm. The index is calculated as in Equation (6) and definition for each
variable in follows Kaplan and Zingales (1997). Detailed definitions are summarized in
Appendix.
ST()*+, = −1.001909 × XYZ[\]^_Z
`+ 0.2826389 × e + 3.139193 ×
fgh
�^ Y]XYi� Y]− 39.3678 × f�k�lgmlZ
`− 1.314759 × XYZ[
` (6)
Using KZ Index as proxy of financial constraint, we estimate the following equation
to test the fourth hypothesis.
���, = #� + $�%&'()*+,�, + $-%�)5)4�53p7):6�5�)6�, ∗ %&'()*+,�, +$0%��.�/+�, + $8�123�4�56�7�, + $;9�:695/�, + $>�/=�56�7�, + D+5� +()*1:6�E + 1�,
(7)
The dependent variable, UP is the firm’s IPO underpricing. FMD Index is financial
market development index following Fan et al., (2011). Financial Constraint is the KZ-
23
index, a proxy of financial constraint following (Kaplan and Zingales, 1997). In Table 7,
Columns 3 and 4, the FMD Index is replaced by FMD Dummy. The dummy is defined to
be 1 if the firm is located in more developed areas (the top half of the FMD Index);
otherwise 0. Other control variables follow the same definitions as in Equation (5). All
firm-level independent variables are measured at the end of the preceding year. Year and
industry effects are controlled for in each model, and standard errors are clustered at the
firm level.
Table 7 presents the results of Equation (7). The coefficients of the interaction term
between financial market development and financial constraint are all significantly
negative, indicating that the benefits of financial market development on IPO pricing are
stronger for financially constrained firms. This result supports our Hypothesis 4.
[Insert Table 7 here]
4.5 The effect of ownership structure: SOEs vs. non-SOEs
SOEs and non-SOEs are unique and crucial institutional features in the Chinese market.
SOEs exist ever since the establishment of planned economy in China. Though
privatization of inefficient SOEs starts during the past decade (Bai et al., 2006), state
ownership is the mainstay of China’s spectacular economic growth. China is still pushing
ahead with partial privatization (to be more exact, promoting mixed ownership) of SOEs
in key industries to overhaul the state sector. It is widely discussed and documented in the
literature about the difference of IPOs between SOEs and non-SOEs. Chen et al. (2004)
find that high governmental and legal entity shareholdings are associated with underpricing.
24
Wang et al. (2004) also state that ownership structure affects post-listing performance.
Chen et al. (2013) document that only SOEs are sensitive to underwriter reputations. In
China SOEs often enjoy organizational legitimacy and favorable access to valuable
information and they have preferential financial treatment and less policy discrimination
(Huang, 2003). Specifically, with the support of government, SOEs enjoy favorable access
to bank loans (Brandt et al., 2005) and lower cost of capital (Borisova and Megginson,
2011). Thus, in line with previous studies, we argue that all privileges enjoyed by SOEs
spare them from highly reliant on the development of financial market. That is, we believe
non-SOEs are more sensitive to financial market development compared to SOEs in IPO
pricing.
H5: Compared to SOEs, non-SOEs are more sensitive to financial market development in
IPO pricing.
To test our fifth hypothesis, we separate our sample into two parts by firms’ ownership
structure. Our sample has 547 SOEs and 699 non-SOEs. Our data on firm’s controlling
shareholders are extracted from the CSMAR database, which identifies major equity
blockholders and their control rights. We apply the same test as in Equation (3) on the
subsamples. In Table 8, our results show that compared to SOEs, non-SOEs are more
sensitive to financial market development. Specifically, in Columns 2 and 4, we see twice
as large a decrease in IPO underpricing for non-SOEs compared to SOEs if they are located
in better developed areas. In fact, the FMD Index in Columns 3 and 4 shows insignificant
coefficient suggesting that IPO pricing of SOEs is not sensitive to the financial market
25
development.
[Insert Table 8 here]
In Subsection 4.3, we find that reforms on financial market reduce IPO underpricing
overall and financial market development plays a more significant role in IPO underpricing
after the reform. The reform has two-sided impacts on SOEs. First, since SOEs are less
prone to rely on the market, one can argue that the underpricing in non-SOEs are more
sensitive after the 2005 NTS reform. On the other hand, the reform reduces the aggregate
power of the SOEs, hence its impact on IPO pricing. Therefore, we are interested in finding
(1) if the reform lowers IPO underpricing for both SOEs and non-SOEs; and (2) whether
the impact is greater or smaller for SOEs in comparison to non-SOEs. To this end, we run
the following OLS regression, which is similar to the design of Equation (5).
���, = #� + $��+@7�.'1..E�, + $-A�='1..E�, + $0�+@7�.'1..E� ∗A�='1..E�, + $8%��.�/+�, + $;�123�4�56�7�, + $>9�:695/�, + $Q�/=�56�7�, + $R9)(�@@+��)/Aℎ5�+:)�, + D+5� + ()*1:6�E + 1�, (8)
The dependent variable, UP is the firm’s IPO underpricing. Reform Dummy is a
dummy variable which indicates the 2005 reform. For years before 2005, it is set to 0;
otherwise 1. SOE Dummy takes on the value of 1 if the firm is owned by the state; otherwise
0. In Column 3 of Table 9, the cross term of SOE Dummy and Reform Dummy is included
which is similar to a difference-in-difference design. See the Appendix for the definitions
or calculations for each control variable. All firm-level independent variables are measured
at the end of the preceding year. Year and industry effects are controlled for in each model
26
and standard errors are clustered at the firm level.
Results in Table 9 show that the 2005 reform indeed has impact on both SOEs and
non-SOEs as the coefficient of Reform Dummy is negative and significant in both columns
although the magnitude of the non-SOE coefficient is a little larger. Column 3 reports the
results using the entire sample. The variable of interest in this equation is the interaction
term of Reform Dummy and SOE Dummy. The coefficient of the interaction term is not
statistically significant, indicating that the impacts of the reform on SOE and non-SOE
IPOs, though negative, are not different.
[Insert Table 9 here]
5. IPO long-run performance
Besides underpricing, IPO long-run underperformance is also well documented in the
literature (Carter et al., 1998; Chan et al., 2004). In this section, we test whether IPO firms
located in more developed areas would perform better in the long run.
As mentioned in Section 3, we measure the IPO long-run performance by using the
buy-and-hold abnormal return. Instead of using market return as the benchmark, we choose
a specific matching firm for each IPO firm by size and book-to-market ratio following
Chan et al. (2004). Since Ritter (1991) finds that significantly underpriced IPOs
underperform in the long run, we also test if this “over-optimism” hypothesis holds in
China under the backdrop of disparate financial market development in different areas
within the country. Thus, besides financial market development, we also include
27
underpricing as independent variable in our model of IPO long-run performance and the
relevant supposition is stated as follows.
H6: Development in financial market improves IPO firms’ performance in the long run.
To test this hypothesis, we specify our model as follows.
����3�, = #� + $����, + $-%&'()*+,�, + $0%��.�/+�, + $8�123�4�56�7�, +$;9�:695/�, + $>�/=�56�7� + D+5� + ()*1:6�E + 1�, (9)
The dependent variable BHAR3 is the 3-year buy-and-hold abnormal return calculated
from the second day after the firm is publicly listed. UP is the firm’s IPO underpricing, and
FMD Index is a measurement of financial market development for firm i at time t. See the
Appendix for the definitions or calculations for each control variable. All firm-level
independent variables are measured at the end of the preceding year. Year and industry
effects are controlled for in each model and standard errors are clustered at the firm level.
Table 10 presents the results on the impact of financial market development on IPO
long-run performance. Two results are relevant to our discussions. First, the findings in
Columns 1 and 2 are consistent with the idea of over-optimism hypothesis. The negative
coefficients suggest that if a firm is more underpriced in the IPO, it would perform worse
in the following three years. This is consistent with Ritter (1991) that significantly
underpriced IPOs underperform in the long run. Second, the positive and significant
coefficients of FMD Index imply that firms located in higher-developed regions achieve
better long-term performance, supporting Hypothesis 6. Finally, according to the estimates
28
in Column 5, the conclusions from Columns (1) through (4) remain when both UP and
FMD Index are included in the same model. Overall, we conclude that better financial
market development not only benefits firms in their initial offerings, but also their
operations, hence their long-term operating performance. It is interesting to note that most
of the control variables which influence IPO underpricing are no longer important factors
in driving stock long term performance.
[Insert Table 10 here]
6. Robustness checks
In the previous sections, we reach the conclusion that firms located in better developed
financial markets are subject to less IPO underpricing and experience better long-run
performance. In this section we conduct some additional analyses to check the robustness
of our findings.
6.1 Self-selection bias
A concern about the current research design is that firms choose their locations ahead
of going public, so there is a possibility that more transparent companies tend to locate
themselves in better developed financial markets. To mitigate the concern on this self-
selection bias, we adopt the Hackman two-stage regression following Hribar and Yang
(2010). In the first stage regression, the dependent variable is FMD Dummy, which equals
to 1 if a firm is located in better developed areas (rank within the top half in their FMD
29
Index) and 0 otherwise. Explanatory variables include Firm Age, Public Ratio, List Lag,
P/E Ratio and Ln (Offering Shares).
The resulting fitted values from stage 1 estimation are used to compute the Inverse
Mill’s Ratio (IM Ratio). In the second stage regression, the Inverse Mill’s Ratio is included
in the regression to test the relationship between UP and FMD Index. The equation is as
follows.
���, = #� + $�%&'()*+,�, + $-%��.�/+�, + $0�123�4�56�7�, +$89�:695/�, + $;�/=�56�7�, + $>9)(�@@+��)/Aℎ5�+:)�, + $Q(&�56�7� + D+5� +()*1:6�E + 1�, (10)
Table 11 shows the results from the two-step Heckman regression. In Column 2, FMD
Index continues to be negative and significant, suggesting that our conclusions are not
driven by self-selection bias. This is further supported by the fact that the IM Ratio is not
significant suggesting that self-selection bias is not an issue in our model settings
[Insert Table 11 here]
6.2 Extreme sample bias
From our summary statistics in Table 1, we see that firms located in Sichuan province
have an average underpricing of 471.06%. It is not clear whether our conclusions are biased
by such extreme sample observation. In other words, one might worry about the bias
resulted from outliers. Thus we perform empirical estimations on reduced samples to
exclude the possible influence of extreme observations. In Table 12, samples are reduced
30
by excluding Sichuan (Column 1), Tibet, Qinghai and Xinjiang (Column 2), Tibet, Qinghai,
Xinjiang and Sichuan (Column 3). The results, nevertheless, are consistent with those
reported in Table 3 and our conclusions that financial market development significantly
lowers IPO underpricing continue to hold.
[Insert Table 12 here]
6.3 Evidence from a more recent sample period
As mentioned in Section 3, our sample period lasts from January 1997 to December
2009. We limit our data to 2009 since the data on the provincial market development index
ends in 2009. In 2016, Fan et al., (2016) from the National Economic Research Institute
(NERI) updated their index from year 2008 to 2014. Though the calculation of financial
market development is done in the same manner as in the previous index, they choose 2008
as their base year to estimate the score of each province in each year. Thus, their updated
version of the FMD Index in 2016 is not congruent with the previous one. We re-run the
main tests using the more recent sample to see if our conclusions still hold. Table 12 reports
the relevant results. In Columns 4 and 5, we test our new sample with Equation (3) as in
Section 4.1. Again, our main findings are confirmed using the more recent sample.
7. Conclusions
In this paper we provide evidence that financial market development helps to reduce
IPO underpricing and enhance stock long-run performance. We consider not only
provincial disparities in financial market development, but also the impact of the NTS
31
reform. Our main findings are summarized below.
First, with respect to IPO underpricing, our tests make contribution by taking financial
market development into consideration. It is well documented that IPO underpricing is
influenced by different legal system, institutional environment and issuing methods (e.g.,
Banerjee et al., 2011; Hopp and Dreher, 2013). Our paper employs the setting of China’s
provincial financial market development disparity as an ideal experiment field which rules
out the influence of legal and regulation differences. Also, our research design includes the
impact of reform on financial market as a dynamic check of our main results. Prior research
proposes asymmetric information hypothesis to explain the abnormal underpricing of IPOs,
our arguments and evidence are rooted in this hypothesis. More generally, our paper adds
to the growing literature on the impact of market transparency on firms’ pricing and
performance.
Second, we provide evidence suggesting that SOEs have information and capital
access advantages compared to non-SOEs. Though similar conclusions are documented in
previous works (e.g. Chen et al., 2004; Wang et al., 2004), our research design is the first
to address the problem by suggesting that non-SOEs are more sensitive to the financial
market development. The original intention for Fan et al., (2011) to develop their
marketization index is to capture the development of non-SOEs. Thus, our results suggest
that even though every part of China has been developing in recent years, non-SOEs are
still inferior in terms of having access to capital and information.
Third, this paper sheds additional lights on financially-constrained firms’ financing
32
decision. Our results suggest that the impact of financial market development is larger for
financially-constrained firms since they are more eager to ensure the success of IPOs.
Fourth, although financial reforms reduce IPO underpricing for both SOE and non-SOE
firms, no difference in impact is observed.
Finally, we find the impact of financial market development lasts beyond IPO initial
underpricing. The 3-Year buy-and-hold results also indicate that firms located in better
developed financial markets perform better in the long run.
33
References
Akyol, Alic C., Tommy Cooper, Michele Meoli and Silvio Vismara. “Do regulatory changes affect the underpricing of European IPOs?” Journal of Banking and Finance 45 (2014): 43-58.
Allen, Franklin, and Gerald R. Faulhaber. "Signalling by underpricing in the IPO market." Journal of Financial Economics 23.2 (1989): 303-323.
Allen, Franklin, Jun Qian, and Meijun Qian. "Law, finance, and economic growth in China." Journal of Financial Economics 77.1 (2005): 57-116.
Amihud, Yakov. "Illiquidity and stock returns: cross-section and time-series effects." Journal of Financial Markets 5.1 (2002): 31-56.
Bae, Kee-Hong, Takeshi Yamada, and Keiichi Ito. "Interaction of investor trades and market volatility: Evidence from the Tokyo Stock Exchange." Pacific-Basin Finance Journal 16.4 (2008): 370-388.
Bai, Chong-En, Jiangyong Lu, and Zhigang Tao. "The multitask theory of state enterprise reform: empirical evidence from China." The American Economic Review 96.2 (2006): 353-357.
Banerjee, Suman, Lili Dai, and Keshab Shrestha. "Cross-country IPOs: What explains differences in underpricing?" Journal of Corporate Finance 17.5 (2011): 1289-1305.
Barber, Brad M., and John D. Lyon. "Detecting long-run abnormal stock returns: The empirical power and specification of test statistics." Journal of Financial Eeconomics 43.3 (1997): 341-372.
Baron, David P. "A model of the demand for investment banking advising and distribution services for new issues." The Journal of Finance 37.4 (1982): 955-976.
Beck, Thorsten, and Ross Levine. "Stock markets, banks, and growth: Panel evidence." Journal of Banking & Finance 28.3 (2004): 423-442.
Bekaert, Geert, and Campbell R. Harvey. "Foreign speculators and emerging equity markets." The Journal of Finance 55.2 (2000): 565-613.
Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad. "Emerging equity markets and economic development." Journal of Development Economics 66.2 (2001): 465-504.
Bortolotti, Bernardo, and A. Beltratti. "The Nontradable Share Reform in the Chinese Stock Market." Working Papers (2006).
34
Beltratti, Andrea, Bernardo Bortolotti, and Marianna Caccavaio. "The stock market reaction to the 2005 split share structure reform in China." Pacific-Basin Finance Journal 20.4 (2012): 543-560.
Berger, Allen N., and Gregory F. Udell. "Did risk-based capital allocate bank credit and cause a" credit crunch" in the United States?" Journal of Money, Credit and Banking 26.3 (1994): 585-628.
Bessembinder, Hendrik, and Feng Zhang. "Firm characteristics and long-run stock returns after corporate events." Journal of Financial Economics 109.1 (2013): 83-102.
Bloomfield, Robert, and Maureen O'Hara. "Market transparency: Who wins and who loses?" The Review of Financial Studies 12.1 (1999): 5-35.
Borisova, Ginka, and William L. Megginson. "Does government ownership affect the cost of debt? Evidence from privatization." The Review of Financial Studies 24.8 (2011): 2693-2737.
Brandt, Loren, Hongbin Li, and Joanne Roberts. "Banks and enterprise privatization in China." Journal of Law, Economics, and Organization 21.2 (2005): 524-546.
Cai, Xiaoqiong, Guy S. Liu, and Bryan Mase. "The long-run performance of initial public offerings and its determinants: the case of China." Review of Quantitative Finance and Accounting 30.4 (2008): 419-432.
Carter, Richard B., Frederick H. Dark, and Ajai K. Singh. "Underwriter reputation, initial returns, and the long-run performance of IPO stocks." The Journal of Finance 53.1 (1998): 285-311.
Chaddad, Fernando R., and Jeffrey J. Reuer. "Investment dynamics and financial constraints in IPO firms." Strategic Entrepreneurship Journal 3.1 (2009): 29-45.
Chan, Kalok, Junbo Wang, and KC John Wei. "Underpricing and long-term performance of IPOs in China." Journal of Corporate Finance 10.3 (2004): 409-430.Chang, Xin, Lin, Shi Hua, Tam, Lewis H. K "Cross‐ sectional determinants of post‐ IPO stock performance: evidence from China." Accounting & Finance 50.3 (2010): 581-603.
Chemmanur, Thomas J. "The pricing of initial public offerings: A dynamic model with information production." The Journal of Finance 48.1 (1993): 285-304.
Carl R. Chen, Yingqi Li, Danglun Luo, and Ting Zhang "Helping hands or grabbing hands? An analysis of political connections and firm value." Journal of Banking & Finance 80 (2017): 71-89.
35
Chen, Gongmeng, Michael Firth, and Jeong-Bon Kim. "IPO underpricing in China’s new stock markets." Journal of Multinational Financial Management 14.3 (2004): 283-302.
Yibiao Chen, Steven Shuye Wang, Wei Li. "Institutional environment, firm ownership, and IPO first-day returns: Evidence from China." Journal of Corporate Finance 32 (2015): 150-168.
Cheung, Yan-leung, Zhiwei OUYANG, Weiqiang TAN. "How regulatory changes affect IPO underpricing in China." China Economic Review 20.4(2009):692-702.
Chong, Terence Tai-Leung, Liping Lu, and Steven Ongena. "Does banking competition alleviate or worsen credit constraints faced by small-and medium-sized enterprises? Evidence from China." Journal of Banking & Finance 37.9 (2013): 3412-3424.
Chowdhry, Bhagwan, and Ann Sherman. "International differences in oversubscription and underpricing of IPOs." Journal of Corporate Finance 2.4 (1996): 359-381.
Coval, Joshua D., and Tobias J. Moskowitz. "Home bias at home: Local equity preference in domestic portfolios." The Journal of Finance 54.6 (1999): 2045-2073.
Coval, Joshua D., and Tobias J. Moskowitz. "The geography of investment: Informed trading and asset prices." Journal of political Economy 109.4 (2001): 811-841.
Demirgüç-Kunt, Asli, and Ross Levine, eds. Financial structure and economic growth: A cross-country comparison of banks, markets, and development. MIT press, 2004.
Demirgüç-Kunt, Asli, and Ross Levine. "Stock market development and financial intermediaries: stylized facts." The World Bank Economic Review 10.2 (1996): 291-321.
Ellul, Andrew, and Marco Pagano. "IPO underpricing and after-market liquidity." The Review of Financial Studies 19.2 (2006): 381-421.
Engelen, Peter-Jan, and Marc Van Essen. "Underpricing of IPOs: Firm-, issue-and country-specific characteristics." Journal of Banking & Finance 34.8 (2010): 1958-1969.
Fan, Gang, Wang Xiaolu., Zhu, Hengpeng, 2003, 2004, 2007, 2010, 2011. The report on the relative process of marketization of each region in China. Economic Science Press, Beijing (in Chinese).
Fan, Gang, Wang, Xiaolu, 2001. The report on the relative process of marketization of each region in China (2000). Ecomomic Science Press, Beijing (in Chinese).
Greenwood, Jeremy, and Bruce D. Smith. "Financial markets in development, and the development of financial markets." Journal of Economic Dynamics and Control 21.1
36
(1997): 145-181.
Hasan, Iftekhar, Paul Wachtel, and Mingming Zhou. "Institutional development, financial deepening and economic growth: Evidence from China." Journal of Banking & Finance 33.1 (2009): 157-170.
Henry, Peter Blair. "Do stock market liberalizations cause investment booms?" Journal of Financial Economics 58.1 (2000): 301-334.
Hong, Harrison, Terence Lim, and Jeremy C. Stein. "Bad news travels slowly: Size, analyst coverage, and the profitability of momentum strategies." The Journal of Finance 55.1 (2000): 265-295.
Hopp, Christian, and Axel Dreher. "Do differences in institutional and legal environments explain cross-country variations in IPO underpricing?" Applied Economics 45.4 (2013): 435-454.
Huang, Yasheng. "One country, two systems: Foreign-invested enterprises and domestic firms in China." China Economic Review 14.4 (2003): 404-416.
Ivković, Zoran, and Scott Weisbenner. "Local does as local is: Information content of the geography of individual investors' common stock investments." The Journal of Finance 60.1 (2005): 267-306.
Jayaratne, Jith, and Philip E. Strahan. "The finance-growth nexus: Evidence from bank branch deregulation." The Quarterly Journal of Economics 111.3 (1996): 639-670.
Jiang, Guohua, Charles MC Lee, and Heng Yue. "Tunneling through intercorporate loans: The China experience." Journal of Financial Economics 98.1 (2010): 1-20.
Kaplan, Steven N., and Luigi Zingales. "Do investment-cash flow sensitivities provide useful measures of financing constraints?" The Quarterly Journal of Economics 112.1 (1997): 169-215.
King, Robert G., and Ross Levine. "Finance and growth: Schumpeter might be right." The Quarterly Journal of Economics 108.3 (1993): 717-737.
Levine, Ross, and Sara Zervos. "Capital control liberalization and stock market development." World Development 26.7 (1998): 1169-1183.
Kai Li, Tan Wang, Yan-Leung Cheung. "Privatization and risk sharing: Evidence from the split share structure reform in China." The Review of Financial Studies 24.7 (2011): 2499-2525.
37
Liang, Qi, and Teng Jian-Zhou. "Financial development and economic growth: Evidence from China." China Economic Review 17.4 (2006): 395-411.
Liao, Li, Bibo Liu, and Hao Wang. "China׳ s secondary privatization: Perspectives from the split-share structure reform." Journal of Financial Economics 113.3 (2014): 500-518.
Loughran, Tim, and Paul Schultz. "Liquidity: Urban versus rural firms." Journal of Financial Economics 78.2 (2005): 341-374.
Loughran, Tim, Jay R. Ritter, and Kristian Rydqvist. "Initial public offerings: International insights." Pacific-Basin Finance Journal 2.2 (1994): 165-199.
Love, Inessa. "Financial development and financing constraints: International evidence from the structural investment model." The Review of Financial Studies 16.3 (2003): 765-791.
Lyon, John D., Brad M. Barber, and Chih‐Ling Tsai. "Improved methods for tests of long‐run abnormal stock returns." The Journal of Finance 54.1 (1999): 165-201.
Madhavan, Ananth. "Consolidation, fragmentation and the disclosure of trading information." The Review of Financial Studies 8.3 (1995): 579-603.
Malloy, Christopher J. "The geography of equity analysis." The Journal of Finance 60.2 (2005): 719-755.
Marcelin, Isaac, and Ike Mathur. "Privatization, financial development, property rights and growth." Journal of Banking & Finance 50 (2015): 528-546.
Mitton, Todd. "Stock market liberalization and operating performance at the firm level." Journal of Financial Economics 81.3 (2006): 625-647.
Neupane, Suman, and Sunil S. Poshakwale. "Transparency in IPO mechanism: Retail investors’ participation, IPO pricing and returns." Journal of Banking & Finance 36.7 (2012): 2064-2076.
Pástor, Ľuboš, and Robert F. Stambaugh. "Liquidity risk and expected stock returns." Journal of Political Economy 111.3 (2003): 642-685.
Poncet, Sandra, Walter Steingress, and Hylke Vandenbussche. "Financial constraints in China: firm-level evidence." China Economic Review 21.3 (2010): 411-422.
Ritter, Jay R. "The long‐run performance of initial public offerings." The Journal of Finance 46.1 (1991): 3-27.
38
Rock, Kevin. "Why new issues are underpriced." Journal of Financial Economics 15.1-2 (1986): 187-212.
Sun, Qian, and Wilson HS Tong. "China share issue privatization: the extent of its success." Journal of Financial Economics 70.2 (2003): 183-222.
Tian, Lihui. "Regulatory underpricing: Determinants of Chinese extreme IPO returns." Journal of Empirical Finance 18.1 (2011): 78-90.
Wang, Xiaozu, Lixin Colin Xu, and Tian Zhu. "State-owned enterprises going public The case of China." Economics of Transition 12.3 (2004): 467-487.
Zeng, Saixing, Xiaodong Xu, Haitao Yin, Chiming Tam. "Factors that drive Chinese listed companies in voluntary disclosure of environmental information." Journal of Business Ethics 109.3 (2012): 309-321.
39
Appendix Definition of Variables.
Variable Definition
Analyst Attention The number of times a company is covered by analysts within a year.
BHAR1 1-year Buy-and-Hold Abnormal Return is calculated from the second day
after IPO. The benchmark is selected as a firm with similar size and book-
to-market ratio following Chan et al. (2004).
BHAR3 3-year Buy-and-Hold Abnormal Return is calculated from the second day
after IPO. The benchmark is selected as a firm with similar size and book-
to-market ratio following Chan et al. (2004).
Financial Constraint Following Kaplan and Zingales (1997), we calculate the KZ-index as a proxy
for financial constraint.
Firm Age The number of years since the founding of the firm.
FMD Dummy The dummy is defined to be 1 if the firm is located in more developed areas
(the top half of the FMD Index); otherwise 0.
FMD Index Financial Market Development Index following Fan et al. (2011). This index
is a weighted average of two ratios, the first being the percentage of non-
state-owned financial institutions’ deposits to all financial institutions’
deposits and the second being the percentage of short-term loans to non-
state-owned sectors over all short-term loans made by financial institutions.
List Lag The number of days delayed to be floated onto the secondary market after
making public offers.
Ln (Industry Size)
Firm Size
B/M Ratio
Cash Flows
K
Q
Debt
Dividends
Cash
IPO Numbers
SOE Dummy
IM Ratio
The logarithm of industry size by year. The industry code is in the CSRC-
2001 form. Industry size is the summary of firm sizes within the industry in
a certain year.
The total assets of firm by the end of fiscal year.
The total assets of the firm at the end of fiscal year divided by market size
(calculated by multiplying stock price and trading volume at the end of year).
It is calculated as income before extraordinary items plus total depreciation
and amortization of the current time period.
It refers to property, plant and equipment of the last time period.
Tobin’s Q is calculated as the total shareholder’s equity divided by the sum
of market capitalization and total shareholder’s equity minus book value of
common equity and deferred tax asset in the current time period.
It is the total long term debt plus notes payable and current portion of long
term debt in the current time period.
Total cash dividends paid in the current time period.
Cash and short-term investment in the current time period.
It refers to the number of IPOs within the province.
The dummy equals to 1 if the firm is SOE; otherwise 0.
It is the Inverse Mill’s ratio in a Heckman two-step self-selection model.
40
Marketization Index The newly updated Marketization Index by Fan et al. (2016). The index
indicates the marketization of 31 provinces in China from 2008 to 2014.
Their method of computation and base time has changed from their earlier
method in measuring marketization.
Ln (Offering Shares) The logarithm of offering shares.
Ln (Trading Volume) The logarithm of yearly total trading volume of stock.
P/E Ratio The issue price over earnings per share before the firm going public.
Public Ratio Percentage of public shareholding at the time of IPO. It is calculated as the
number of publicly traded shares over the total number of common shares.
Reform Dummy A dummy variable which indicates the 2005 NTS reform in China. For the
years before 2004 (2004 included), it is set to 0; otherwise, it is set to 1.
Stock Illiquidity
(Amihud)
It is measured as the average daily ratio of absolute stock return to trading
volume following Amihud (2002).
Stock Liquidity (P-S) The liquidity measure for stock � at time 6 is the ordinary least squares
estimate of q�, in the following regression:
��,lr�, g = s�,l, + ∅�, ��,l, + q�, :�/)���,l,
g � ∙ v�,l, + w�,lr�, , * = 1,… , '
where ��,l, is the return on stock � on day * in month 6; ��,l, g =��,l, −
�!,l, , �!,l, is the return on the benchmark market return on day * in
month 6 ; and v�,l, is the volume for stock � on day * in month 6
(Pastor and Stambaugh, 2003).
Stock Volatility It is measured as the annualized standard deviation of the residuals in
monthly regressions of daily stock returns on the Fama and French (1993)
three factors following Ang, et al. (2006).
Turnover Ratio It is defined as the value of trades of the share divided by the total value of
listed shares (Beck and Levine, 2004).
UP Underpricing. The first-day initial return of IPO, which is the percentage of
the difference between the first-day closing price and the initial offering price
over the initial offering price.
41
Table 1 Descriptive statistics on sample of IPOs among 31 provinces. This table presents descriptive statistics on our sample of 1,246 observations drawn from the CSMAR
database on listing firms. Provinces contain 31 province-level administrative units (excluding Hong Kong
and Macau). FMD Index is financial market development index following Fan et al. (2011). The calculation
is defined in the Appendix. All provinces are ranked by their mean FMD Index from 1997 to 2009. Reform
is the NTS reform that took place in 2005 which changes the ownership structure and market transparency
of the stock market. The means of FMD Index before and after Reform are also presented. IPO Numbers are
the sum of all IPOs within a certain province from 1997 to 2009. UP is the average underpricing of all IPOs
within a certain province.
Provinces FMD Index IPO
Rank Mean Mean
before
Reform
Mean
after
Reform
Numbers UP
Shanghai 1 7.49 7.64 11.02 55 102.91%
Guangdong 2 7.06 5.99 10.48 141 126.56%
Beijing 3 7.02 4.80 8.30 110 126.63%
Tianjin 4 6.95 5.04 8.72 21 155.01%
Zhejiang 5 6.88 8.00 11.99 117 128.89%
Fujian 6 6.51 5.21 9.31 40 141.97%
Jiangsu 7 5.89 6.73 9.70 107 122.31%
Chongqing 8 5.45 5.65 10.17 19 142.76%
Liaoning 9 5.29 5.41 10.35 42 192.97%
Shandong 10 4.77 6.41 10.41 76 146.95%
Hunan 11 3.88 4.96 8.31 46 118.95%
Hainan 12 3.85 6.49 8.40 13 135.85%
Hubei 13 3.62 3.56 8.46 46 244.25%
Henan 14 3.49 5.40 9.38 37 133.31%
Sichuan 15 3.34 3.99 7.85 52 471.06%
Jiangxi 16 3.32 3.89 8.07 24 147.76%
Hebei 17 3.32 5.94 8.62 29 103.35%
Yunnan 18 3.30 3.80 9.18 22 147.86%
Anhui 19 3.07 4.82 8.60 47 149.02%
Shaanxi 20 2.98 5.04 8.39 22 171.88%
Shanxi 21 2.77 3.92 8.75 22 94.54%
Guangxi 22 2.63 3.48 7.90 21 228.34%
Ningxia 23 2.57 3.77 9.44 8 123.70%
Gansu 24 2.56 3.28 6.76 18 139.73%
Guizhou 25 2.31 3.07 7.43 15 90.52%
Inner Mongolia 26 2.29 2.78 7.67 17 95.16%
Xinjiang 27 2.24 2.42 6.23 28 151.70%
42
Jilin 28 2.04 3.02 6.82 20 118.71%
Heilongjiang 29 1.86 2.06 6.35 21 154.33%
Qinghai 30 1.70 1.75 5.82 5 186.99%
Tibet 31 1.58 1.12 4.40 5 213.96%
Average 3.94 4.50 8.49 40 152.6%
43
Table 2 Univariate comparisons between low and high FMD Index groups. Panel A presents descriptive statistics on our sample by dividing all firms into low and high FMD Index
groups. Top 16 province-level administrative units are contained in the high FMD group; the rest are
defined as the low FMD group. FMD Index is financial market development index following Fan et al.,
(2011). IPO Numbers are the sum of all IPOs within a certain province from 1997 to 2009. UP is the
average underpricing of all IPOs within a certain province. BHAR1 is 1-year Buy-and-Hold Abnormal
Return calculated from the second day after IPO. BHAR3 is 3-year Buy-and-Hold Abnormal Return
calculated from the second day after IPO. The ***, **, * indicates that the difference in means between
low and high FMD Index groups is significant at the 1%, 5%, 10% levels using a t-test and Wilcoxon z-
test, respectively. Panel B presents differences on IPO firm characteristics between low and high FMD
Index groups. Firm Age is number of years since the founding of the firm. Public Ratio is the percentage
of public shareholding at the time of IPO. List Lag is number of days delayed to be floated onto the
secondary market after making public offers. P/E Ratio is the issue price over earnings per share before
the firm going public. Ln (Offering Shares) is logarithm form of offering shares, which serves as a proxy
of offering shares. The ***, **, * indicate that the difference in the means between the low and high
FMD Index groups is significant at the 1%, 5%, 10% levels using a t-test and a Wilcoxon z-test,
respectively.
Panel A: IPO patterns and differences between Low and High FMD Index groups
IPO Patterns
FMD Index Group
Low High T-test Wilcoxon Z-test
FMD Index 4.037 10.042 -24.321*** -29.908***
IPO Numbers 33.978 68.386 -8.055*** -14.313***
UP 1.391 1.172 2.065** 5.940***
BHAR1 0.020 0.204 -3.829*** -1.859*
BHAR3 0.952 1.373 -6.277*** -6.649***
Panel B: IPO firm characteristics and differences between Low and High FMD Index groups
IPO Firm Characteristics
FMD Index Group
Low High T-test Wilcoxon Z-test
Firm Age 3.565 5.881 -9.550*** -12.438***
Public Ratio 0.830 0.923 9.540*** 11.543***
List Lag 0.263 0.085 2.541*** 14.309***
P/E Ratio 23.978 31.615 -3.173*** -4.092***
Ln (Offering Shares) 8.645 8.460 1.861* 5.461***
44
Table 3 Impact of financial market development on IPO underpricing. This table presents the regression results from Equation (3). In this model, the dependent variable is
firm’s IPO underpricing. The FMD Index is the financial market development index following Fan et al.,
(2011). In Columns 3 and 4, FMD Index is replaced with FMD Dummy. The dummy is defined to be 1 if
the firm is located in more developed areas (within the top half of the FMD Index); Otherwise 0. Firm
Age is the number of years since the founding of the firm. Public Ratio is the percentage of public
shareholding at the time of IPO. List Lag is number of days delayed to be floated onto the secondary
market after making public offers. P/E Ratio is the ratio of price to earnings before the firm goes public.
Ln (Offering Shares) is the logarithm of total shares offered, which serves as a proxy of offering shares.
All firm-level independent variables are measured at the end of the preceding year. Year and industry
effects are controlled for in each model and standard errors are clustered at the firm level. The ***, **,
* denote statistical significance at the 1%, 5%, 10% levels, respectively, using a two-tailed test. Robust
standard errors are in parentheses.
(1) (2) (3) (4)
Variables UP UP UP UP
FMD Index -0.104*** -0.031***
(0.034) (0.010)
FMD Dummy -0.381** -0.150**
( 0.193) (0.060)
Firm Age 0.001 0.001
(0.005) (0.005)
Public Ratio -0.140 -0.130
(0.132) (0.132)
List Lag 0.192*** 0.193***
(0.017) (0.017)
P/E Ratio 0.012*** 0.012***
(0.001) (0.001)
Ln (Offering
Shares)
-0.195*** -0.190***
(0.017) (0.017)
Constant 2.633*** 2.614*** 2.324*** 2.558***
(0.205) (0.229) (0.169) (0.227)
Year Effect
Yes
Yes
Yes
Yes
Industry Effect Yes Yes Yes Yes
Observations 1,246 1,246 1,246 1,246
R-squared 0.082 0.594 0.078 0.593
45
Table 4 Univariate test between low and high FMD Index groups on market transparency. This table presents differences on market transparency by dividing all firms into low and high FMD
Index groups. Top 16 province-level administrative units are contained in the High FMD group; the rest
are defined as the Low FMD group. The FMD Index is financial market development index following
Fan et al., (2011). Stock Volatility is a measurement of the trading volatility of the stock in its first publicly
traded year following Ang, et al., (2006). Stock Illiquidity (Amihud) and Stock Liquidity (P-S) are two
ways to measure the liquidity of a certain stock following Amihud (2002) and Pastor and Stambaugh
(2003), respectively. Turnover Ratio is the value of the trades of shares on domestic exchanges divided
by total value of listed shares (Beck and Levine, 2004), which captures stock’s liquidity as well. Analyst
Attention is the number of times a company is covered by analysts within a year, which is always used
as an indicator of firm transparency. The ***, **, * indicate that the difference in means between low
and high FMD Index groups is significant at the 1%, 5%, 10% levels using a t-test and a Wilcoxon z-test,
respectively.
Market Transparency
Characteristics
FMD Index Group
Low High T test Wilcoxon Z test
FMD Index 4.037 10.042 -24.321*** -29.908***
Stock Volatility 1.491 1.332 1.297 2.120**
Stock Illiquidity (Amihud) -17.829 -17.996 1.769* 1.822*
Stock Liquidity (P-S) 1.19e-06 2.58e-05 -4.492*** -5.717***
Turnover Ratio 446.625 456.530 -1.348 -1.469
Analyst Attention 2.214 8.251 -10.557*** -11.988***
46
Table 5 Impact of financial market development on market transparency. This table presents the regression results from Equation (4). In this model, the dependent variable is Market Transparency, which is measured by Stock Volatility, Stock Illiquidity
(Amihud), Stock Liquidity (P-S), Turnover Ratio, and Analyst Attention. The FMD Index is the financial market development index following Fan et al., (2011). Stock Volatility
is a measurement of the trading volatility of the stock in its first publicly traded year following Ang, et al., (2006). Stock Illiquidity (Amihud) and Stock Liquidity (P-S) are two
ways to measure the liquidity of a certain stock following Amihud, (2002) and Pastor and Stambaugh, (2003), respectively. Turnover Ratio is the value of the trades of shares
on domestic exchanges divided by total value of listed shares (Beck and Levine, 2004), which captures stock’s liquidity as well. Analyst Attention is the number of times a
company is covered by analysts within a year, which is always used as an indicator of firm transparency. Ln (Trading Volume) is the logarithm of yearly total trading volume
of stock. Ln (Industry Size) is the logarithm of industry size by year. The industry code is in the CSRC-2001 form. Industry size is the summary of firm sizes within the industry
in a certain year. B/M Ratio is the total assets of the firm at the end of fiscal year divided by market size. All firm-level independent variables are measured at the end of the
preceding year. Year and industry effects are controlled for in each model and standard errors are clustered at the firm level. The ***, **, * denote statistical significance at the
1%, 5%, 10% levels, respectively, using a two-tailed test. Robust standard errors are in parentheses.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Variables Stock
Volatility
Stock
Volatility
Stock
Illiquidity
(Amihud)
Stock
Illiquidity
(Amihud)
Stock
Liquidity
(P-S)
Stock
Liquidity
(P-S)
Turnover
Ratio
Turnover
Ratio
Analyst
Attention
Analyst
Attention
FMD Index -0.057** -0.066** -0.022** -0.025** 2.430** 2.290* 12.212*** 12.140*** 1.263*** 1.408***
(0.029) (0.027) (0.011) (0.011) (1.210) (1.231) (1.511) (1.503) (0.100) (0.098)
Ln (Trading
Volume)
-0.461***
(0.068)
-0.986***
(0.024)
-6.270
(5.302)
-18.971***
(6.607)
2.183***
(0.232)
Ln (Industry
Size)
-0.019
(0.068)
-0.019
(0.023)
8.620
(5.280)
-5.387
(6.611)
-0.017
(0.333)
47
B/M Ratio 0.011 -0.031 -8.899 -13.182 0.007
(0.111) (0.039) (7.974) (10.063) (0.006)
Constant 1.980*** 6.252*** -17.750*** -8.726*** -1.220 -1.100 185.730*** 465.121*** 4.514*** -24.480***
(0.351) (1.459) (0.086) (0.507)
(9.260) (11.425) (11.740) (142.201) (0.888) (6.998)
Year Effect
Industry Effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations 1246 1246 1246 1246 1246 1246 1246 1246 608 608
R-squared 0.089 0.163 0.090 0.785 0.005 0.015 0.075 0.116 0.206 0.305
48
Table 6 Impact of financial market development and reform on IPO underpricing. This table presents the regression results from Equation (5). In this model, the dependent variable is firm’s
IPO underpricing. In Columns 1 and 2, we divide the sample into two subsamples (before and after the 2005
NTS reform) to compare the relationship between financial market development and IPO underpricing. In
Columns 3 and 4, we compare how the reform influences IPO underpricing short-term patterns in two
different groups of provinces classified by the rank of FMD Index. The FMD Dummy is defined to be 1 if the
firm is located in more developed areas (within the top half of the FMD Index); Otherwise 0. Reform Dummy
is a dummy variable which is used to measure the effect of the 2005 NTS reform. For the years before 2004
(2004 included), this dummy is set to 0; otherwise 1. The cross term of FMD Dummy and Reform Dummy is
included in Column 5. The FMD Index is the financial market development index following Fan et al., (2011).
Firm Age is the number of years since the founding of the firm. Public Ratio is the percentage of public
shareholding at the time of IPO. List Lag is number of days delayed to be floated onto the secondary market
after making public offers. P/E Ratio is the ratio of price to earnings before the firm goes public. Ln (Offering
Shares) is the logarithm of total shares offered, which serves as a proxy of offering shares. All firm-level
independent variables are measured at the end of the preceding year. Year and industry effects are controlled
for in each model and standard errors are clustered at the firm level. The ***, **, * denote statistical
significance at the 1%, 5%, 10% levels, respectively, using a two-tailed test. Robust standard errors are in
parentheses.
(1) (2) (3) (4) (5)
Before
Reform
After
Reform
Low
Market
High
Market
Total
Variables UP UP UP UP UP
FMD Dummy
-0.025**
-0.039**
-0.150**
(0.012) (0.019) (0.060)
Reform Dummy -0.866***
(0.284)
-1.349***
(0.128)
FMD Dummy*
Reform Dummy
-1.017***
(0.096)
Firm Age 0.010 -0.011 0.011 -0.006 0.001
(0.007) (0.007) (0.010) (0.006) (0.005)
Public Ratio -0.146 -0.240 -0.167 -0.083 -0.130
(0.136) (0.408) (0.163) (0.229) (0.132)
List Lag 0.177*** -4.207*** 0.169*** 0.199*** 0.193***
(0.018) (1.061) (0.020) (0.044) (0.017)
P/E Ratio 0.014*** 0.012*** 0.013*** 0.011*** 0.012***
49
(0.001) (0.001) (0.001) (0.001) (0.000)
Ln (Offering
Shares)
-0.232*** -0.175*** -0.301*** -0.150*** -0.190***
(0.027) (0.027) (0.036) (0.020) (0.017)
Constant 2.833*** 2.466*** 3.453*** 2.512*** 2.558***
(0.299) (0.538) (0.383) (0.354) (0.227)
Year Effect
Yes
Yes
Yes
Yes
Yes
Industry Effect Yes Yes Yes Yes Yes
Observations 856 390 621 625 1,246
R-squared 0.611 0.602 0.607 0.582 0.593
50
Table 7 Impact of financial market development on financially constraint firms’ IPO underpricing. This table presents the regression results from Equation (7). In this model, the dependent variable is firm’s
IPO underpricing. The FMD Index is the financial market development index following Fan et al., (2011).
Financial Constraint is the KZ-index, a proxy of financial constraint following Kaplan and Zingales, (1997).
In Columns 3 and 4, FMD Index is replaced with FMD Dummy. The dummy is defined to be 1 if the firm is
located in more developed areas (within the top half of the FMD Index); Otherwise 0. Firm Age is the number
of years since the founding of the firm. Public Ratio is the percentage of public shareholding at the time of
IPO. List Lag is number of days delayed to be floated onto the secondary market after making public offers.
P/E Ratio is the ratio of price to earnings before the firm goes public. All firm-level independent variables
are measured at the end of the preceding year. Year and industry effects are controlled for in each model and
standard errors are clustered at the firm level. The ***, **, * denote statistical significance at the 1%, 5%,
10% levels, respectively, using a two-tailed test. Robust standard errors are in parentheses.
(1) (2) (3) (4)
Variables UP UP UP UP
FMD Index -0.035*** -0.048**
(0.013) (0.020)
FMD Index
*Financial Constraint
-0.001***
-0.007***
(0.000) (0.002)
FMD Dummy -0.131** -0.148*
(0.062) (0.086)
FMD Dummy
*Financial Constraint
-0.014***
-0.006**
(0.003) (0.003)
Firm Age -0.001 -0.004
(0.009) (0.009)
Public Ratio 0.987** 0.325
(0.418) (0.440)
List Lag 5.754** 4.612*
(2.519) (2.504)
P/E Ratio 0.001 0.002
(0.003) (0.003)
Constant 1.373*** 0.305 1.198*** 0.750
(0.302) (0.566) (0.294) (0.578)
51
Year Effect
Industry Effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations 605 448 605 448
R-squared 0.331 0.344 0.332 0.360
52
Table 8 Impact of financial market development on IPO underpricing in terms of SOEs and non-SOEs. This table presents the same test as in Equation (3) on the subsamples. The definitions of variables are in
accordance with those in Table 3. All firm-level independent variables are measured at the end of the
preceding year. Year and industry effects are controlled for in each model and standard errors are clustered
at the firm level. The ***, **, * denote statistical significance at the 1%, 5%, 10% levels, respectively, using
a two-tailed test. Robust standard errors are in parentheses.
(1) (2) (3) (4)
Non-SOE Non-SOE SOE SOE
Variables UP UP UP UP
FMD Index -0.055*** -0.033** -0.029 -0.016
(0.019) (0.014) (0.023) (0.017)
Firm Age 0.005 -0.002
(0.007) (0.008)
Public Ratio -0.004 -0.182
(0.220) (0.177)
List Lag 0.187*** 0.199***
(0.028) (0.025)
P/E Ratio 0.012*** 0.012***
(0.001) (0.001)
Ln (Offering Shares) -0.241*** -0.190***
(0.035) (0.024)
Constant 1.702*** 2.860*** 1.487*** 2.581***
(0.075) (0.409) (0.081) (0.313)
Year Effect Yes Yes Yes Yes
Industry Effect Yes Yes Yes Yes
Observations 699 699 547 547
R-squared 0.263 0.596 0.157 0.608
53
Table 9 Impact of reform on IPO underpricing in terms of SOEs and non-SOEs. This table presents regression results from Equation (8). In this model, the dependent variable is firm’s IPO
underpricing. Reform Dummy is a dummy variable which is used to measure the effect of the 2005 NTS
reform. For the years before 2004 (2004 included), this dummy is set to 0; otherwise 1. SOE Dummy equals
to 1 if the firm is SOE; otherwise 0. In Column 3, the cross term of SOE Dummy and Reform Dummy is
included in the test. Firm Age is the number of years since the founding of the firm. Public Ratio is the
percentage of public shareholding at the time of IPO. List Lag is number of days delayed to be floated onto
the secondary market after making public offers. P/E Ratio is the ratio of price to earnings before the firm
goes public. Ln (Offering Shares) is the logarithm of total shares offered, which serves as a proxy of offering
shares. All firm-level independent variables are measured at the end of the preceding year. Year and industry
effects are controlled for in each model and standard errors are clustered at the firm level. The ***, **, *
denote statistical significance at the 1%, 5%, 10% levels, respectively, using a two-tailed test. Robust standard
errors are in parentheses.
SOE Non-SOE Total
Variables UP UP UP
Reform Dummy -0.843** -0.997*** -1.157***
(0.336) (0.198) (0.245)
SOE Dummy 0.104**
(0.052)
Reform Dummy* SOE Dummy -0.030
(0.096)
Firm Age -0.009 0.004 0.001
(0.012) (0.008) (0.007)
Public Ratio -0.924 -0.152 -0.374
(0.713) (0.497) (0.563)
List Lag 1.121*** 0.850*** 1.085***
(0.161) (0.098) (0.155)
P/E Ratio 0.014*** 0.016*** 0.015***
(0.002) (0.004) (0.002)
Ln (Offering Shares) -0.209*** -0.251*** -0.203***
(0.046) (0.077) (0.042)
Constant 3.507*** 2.503** 2.672***
(1.074) (1.193) (0.869)
Year Effect
Industry Effect
Yes
Yes
Yes
Yes
Yes
Yes
Observations 547 699 1246
R-squared 0.839 0.657 0.761
54
Table 10 Impact of financial market development on IPO long-run performance. This table presents regression results from Equation (9). In this model, the dependent variable BHAR3 is 3-
year Buy-and-Hold Abnormal Return calculated from the second day after publicly listed. UP is firm’s IPO
underpricing. The FMD Index is the financial market development index following Fan et al., (2011). Firm
Age is the number of years since the founding of the firm. Public Ratio is the percentage of public
shareholding at the time of IPO. List Lag is number of days delayed to be floated onto the secondary market
after making public offers. P/E Ratio is the ratio of price to earnings before the firm goes public. All firm-
level independent variables are measured at the end of the preceding year. Year and industry effects are
controlled for in each model and standard errors are clustered at the firm level. The ***, **, * denote
statistical significance at the 1%, 5%, 10% levels, respectively, using a two-tailed test. Robust standard errors
are in parentheses.
(1) (2) (3) (4) (5)
Variables BHAR3 BHAR3 BHAR3 BHAR3 BHAR3
UP -0.078* -0.167** -0.071**
(0.044) (0.068) (0.028)
FMD Index 0.037*** 0.044*** 0.040***
(0.014) (0.015) (0.015)
Firm Age 0.008 -0.004 -0.004
(0.018) (0.007) (0.007)
Public Ratio 0.506 0.339 0.443*
(0.621) (0.254) (0.256)
List Lag 0.031 -0.009 0.021
(0.126) (0.051) (0.052)
P/E Ratio 0.001 -0.003 -0.002
(0.007) (0.003) (0.003)
Constant -0.455*** -1.037 1.262*** 0.871*** 0.842***
(0.085) (0.741) (0.143) (0.307) (0.306)
Year Effect Yes Yes Yes Yes Yes
Industry Effect Yes Yes Yes Yes Yes
Observations 1246 1246 1246 1246 1246
R-squared 0.243 0.397 0.539 0.640 0.643
55
Table 11 Heckman two steps tests on self-selection bias. This table presents the results from the two-step Heckman regression. In the first stage regression, the
dependent variable is FMD Dummy, which equals to 1 if a firm is located in better developed areas (rank
within the top half in their FMD Index) and 0 otherwise. Explanatory variables include Firm Age, Public
Ratio, List Lag, P/E Ratio and Ln (Offering Shares). The first stage regression is based on the entire sample
of 1246 observations. The resulting fitted values from stage 1 estimation are used to compute the Inverse
Mill’s ratio. In the second stage regression, the Inverse Mill’s ratio is included in the regression to test the
relationship between UP and FMD Index. All firm-level independent variables are measured at the end of
the preceding year. Year and industry effects are controlled for in each model and standard errors are clustered
at the firm level. The ***, **, * denote statistical significance at the 1%, 5%, 10% levels, respectively, using
a two-tailed test. Robust standard errors are in parentheses.
Step 1
Probit Regression
Step 2
OLS Regression
Variables FMD Dummy UP
IM Ratio 0.174
(0.458)
FMD Index -0.076***
(0.015)
Firm Age 0.131*** 0.0040
(0.017) (0.012)
Public Ratio -2.286*** -0.024
(0.368) (0.220)
List Lag -6.483*** 0.136
(1.002) (0.580)
P/E Ratio 0.019*** 0.001
(0.004) (0.002)
Ln (Offering Shares)
-0.031
(0.052)
-0.281***
(0.026)
Constant 1.771*** 3.582***
(0.675) (0.886)
Year Effect
Industry Effect
Yes
Yes
Yes
Yes
Observations 1246 1246
R-squared 0.256 0.318
56
Table 12 Results from excluding extreme data and newly updated FMD index This table presents the same test as in Equation (3) on the reduced sample (Columns 1 to 3) and more recent
data (Columns 4 to 5). The definitions of variables are in accordance with those in Table 3. Samples are
reduced by excluding Sichuan (Column 1), Tibet, Qinghai and Xinjiang (Column 2), Tibet, Qinghai, Xinjiang
and Sichuan (Column 3). In Columns 4 and 5, we test sample from 2008 to 2014 with Equation (3) as in
subsection 4.1. All firm-level independent variables are measured at the end of the preceding year. Year and
industry effects are controlled for in each model and standard errors are clustered at the firm level. The ***,
**, * denote statistical significance at the 1%, 5%, 10% levels, respectively, using a two-tailed test. Robust
standard errors are in parentheses.
(1) (2) (3) (4) (5)
Excluding
Sichuan
Excluding
Tibet,
Qinghai and
Xinjiang
Excluding
Tibet,
Qinghai,
Xinjiang and
Sichuan
More
Recent
Sample
More
Recent
Sample
Variables UP UP UP UP UP
FMD Index -0.076*** -0.050*** -0.065*** -0.018*** -0.014**
(0.017) (0.018) (0.019) (0.007) (0.006)
Firm Age 0.001 -0.001 0.001 0.006**
(0.007) (0.007) (0.007) (0.003)
Ln (Offering
Shares)
-0.284*** -0.272*** -0.278*** -0.063***
(0.024) (0.024) (0.024) (0.016)
Public Ratio -0.028 -0.041 -0.040 -0.471
(0.201) (0.201) (0.201) (0.637)
List Lag 0.293 0.208*** 0.394 0.007**
(0.619) (0.022) (0.636) (0.003)
P/E Ratio 3.46e-06 -0.002 -0.001 -0.003***
(0.002) (0.002) (0.002) (0.001)
Constant 3.888*** 3.840*** 3.856*** 1.244*** 1.719***
(0.346) (0.346) (0.349) (0.111) (0.202)
Year Effect Yes Yes Yes Yes Yes
Industry Effect Yes Yes Yes Yes Yes
Observations 1194 1208 1156 1,089 1,083
R-squared 0.318 0.302 0.314 0.239 0.267