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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: rchen1@udayton.edu. 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

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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

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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.

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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

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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

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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).

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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.,

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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.

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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

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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.

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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.

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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

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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.

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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

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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.

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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

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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,

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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

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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