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Does Having a Credit Rating Leave Less Money on the Table When Raising Capital? A Study

of Credit Ratings and Seasoned Equity Offerings in China

Abstract

We examine the impact of unsolicited credit ratings on seasoned equity offering (SEO) underpricing in China using issuer credit rating data of listed companies on the Shanghai and Shenzhen Stock Exchanges for the period 2002 to 2009. Our findings suggest that, after controlling for other factors, a SEO firm in China with a credit rating is able to reduce its SEO underpricing, on average, by 13.26% to 15.80%. In addition, the underpricing of an SEO firm that receives a speculative-grade credit rating is not significantly different from an SEO firm with an investment-grade rating. Thus, SEO firms appear to benefit from receiving an unsolicited rating. In general, credit ratings reduce information asymmetry and hence leave less money on the table when raising capital. This may lead firms to actively solicit credit ratings in the future, especially those who plan to access the capital markets.

JEL code: G24, G14, G32 Key words: Seasoned equity offerings; credit rating; information asymmetry

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Does Having a Credit Rating Leave Less Money on the Table When Raising Capital? A Study

of Credit Ratings and Seasoned Equity Offerings in China

1. Introduction

Studies on seasoned equity offerings (SEOs) suggest that SEOs, on average, are underpriced. That

is, the SEO offering price is significantly lower than the first day trading price. SEO underpricing

research in the U.S. is primarily focused on placement issues. Some studies investigate the

determinants of SEO underpricing (Corwin, 2003; Kim and Shin, 2004). Liu and Malatesta (2007)

incorporate credit ratings in their analysis of SEO underpricing and find that, on average, rated

SEO firms have lower underpricingwhen compared to unrated SEO firms in the U.S. They report

that underpricing is 1.3% to 4% less than the underpricing of similar non-rated firms.

Recently, there have been a few studies on SEOs in China. China SEOs are interesting for two

reasons. First, as suggested in Chen and Wang (2007), China has a unique regulated environment

for SEOs. Before May 22, 2000, Chinese firms were not allowed to have placement SEOs. After

that date, the regulators required SEO applicants to have positive earnings and a minimum of a 10%

return on equity. These regulations were believed to be effective in allowing only ‘good’ Chinese

firms to enter the SEO process. Under such a heavily regulated environment in China, we would

expect that the magnitude of SEO underpricing and the determinants of the underpricing to be

different from those in the U.S. Second, Chinese SEOs have significantly higher underpricing

relative to U.S. firms. The average SEO underpricing in the U.S. is 2.92% in Corwin (2003)

whereas it is a reported 21.6% in China in Chen and Wang (2007).

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The main objective of this study is to examine the impact of credit ratings on SEO underpricing in

China. Consistent with Liu and Malatesta (2007), we argue that the large SEO underpricing in

China is partially attributed to information asymmetry. The relatively new and speculative equity

market in China is prone to a high degree of information asymmetry between investors and SEO

firms. By incorporating credit ratings in the Chinese SEO analysis, we examine if the ratings

themselves help alleviate information asymmetry in China. Specifically, we study whether rated

SEOs are less underpriced vis-à-vis unrated SEOs. Our findings complement those of Poon and

Chan (2008), who found credit rating change annoucements have information content for Chinese

stock prices.

Our findings suggest that, after controlling for other factors, a SEO firm in China with a credit

rating is able to reduce its SEO underpricing, on average, by 13.26% to 15.8%, depending on the

model specification. The impact of credit ratings in lowering SEO underpricing in our Chinese

samples is substantially more than those reported in Liu and Malatesta, which is consistent with the

conjecture that the equity market in China is prone to greater information asymmetry than in the

U.S. Our results also echo those in An and Chan (2008), which show that IPO firms with credit

ratings in the U.S., on average, reduce IPO underpricing by about 25%. In general, firms are able to

use credit ratings to reduce information asymmetry and hence leave less money on the table when

raising capital.

2. Background of Research and Hypotheses Development 2.1. Background of Research

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There are three main clusters of research literature on seasoned equity offerings that are related to

our study. The first cluster examines the determinants of SEO underpricings with an emphasis on

the U.S. markets. Corwin (2003), Mola and Loughran (2004), and Kim and Shin (2004) offer

comprehensive analyses on the issue. Corwin (2003) finds that SEO relative offer size and stock

price uncertainty are positively correlated with the SEO underpricing. A larger SEO relative offer

size means a higher price pressure for the issuing firm stock and greater uncertainty regarding a

firm’s value, which leads to a larger underpricing. Corwin (2003), however, found no evidence

that the magnitude of SEO underpricing is related to information asymmetry variables, such as firm

size and the bid-ask spread.

Mola and Loughran (2004) offer evidence to suggest that variables reflecting the uncertainty of an

issuing firm’s value help explain SEO underpricing. In addition, SEO firms with no prior SEOs

and less reputable underwriters, on average, have larger underpricing. Consistent with Mola and

Loughran, Kim and Shin (2004) also find that SEO relative offer size, stock price uncertainty, and

underwriter quality are related to underpricing. The literature suggests that relative offer size and

stock price uncertainty are important determinants in SEO underpricing. Prior SEO experience and

underwriter quality are also related to SEO underpricings.

The second cluster of research focuses on the impact of a credit rating on SEO and initial public

offering (IPO) pricing. In a recent study of U.S. SEOs, Liu and Malatesta (2007) suggest that firms

with an existing credit rating before an SEO are, on average, underpriced significantly less than

firms without a credit rating. Liu and Malatesta argue that a credit rating reduces the information

asymmetry between SEO firms and investors. By having a credit rating before a SEO, a firm is

able to reduce investors’ uncertainties regarding its value. Thus, SEO underpricing is lower.

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Relating credit ratings to IPO pricing, An and Chan (2008) document that rated IPOs have

significantly less underpricing than unrated IPOs. These recent studies suggest that credit ratings

are effective in reducing information asymmetry in the equity issuance process (both SEOs and

IPOs).

The third cluster of related literature specifically studies SEOs and credit ratings in China.1

1 Prior to 2002, SEOs were in the form of rights issues. Wang et al. (2006) examine the stock returns of rights issue firms in China using data from 1994 to 1999. In general, they found positive returns.

Chen

and Wang (2007) offer a number of insights into the Chinese SEO market. For example, there is a

strict profit requirement in the Chinese SEO market (see Table 1). The latest regulation is that

Chinese firms need to have an average of a 10% return on equity for the three years before an

application is made to make a SEO. Chen and Wang find that the minimum return on equity

requirement helps mitigate the adverse selection problem in the SEO process. After a tightened

SEO regulation in 2002, stock returns for firms react less negatively to their SEO announcements.

Luo, Rao, and Yue (2010) study the relation between underwriting quality and information risk of

SEO firms in China. They use accounting accruals quality as a proxy for information risk. Luo et al.

suggest that SEO firms with a lower (higher) degree of information risk are more likely to change

to underwriters with a better (worse) reputation between their IPO and SEO processes. Here,

information risk is positively correlated with information asymmetry. Hence, the findings in Luo et

al. suggest that SEO firms with a lower degree of information asymmetry will switch to a reputable

underwriter because it can help lower the underpricing magnitude. Poon and Chan (2008) study

credit ratings in China. They find that, contrary to the critics (e.g., Harrison, 2003; Kennedy 2003;

and Baglole, 2004), Chinese credit ratings have information content for the equity market when a

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rating downgrade is announced. That is, Chinese investors pay attention to credit ratings for

Chinese firms.

[Insert Table 1 here]

In summary, some U.S. SEO studies, such as Corwin (2003), find that information asymmetry

variables do not help explain SEO underpricing. Instead, Corwin (2003) documents that the

uncertainty in firm value, price pressure, prior SEO experience, and underwriter quality play a

significant role in explaining SEO underpricing. The credit rating literature, such as Liu and

Malatesta (2007) and An and Chan (2008), documents that rated SEOs and IPOs can alleviate

information asymmetry in the pricing of SEOs and IPOs. So far, there is no study of how credit

ratings in China affect SEO underpricing. Given the unique SEO market in China (a high degree of

information asymmetry and a regulated environment), it is interesting and timely to relate credit

ratings and SEO underpricing in China.

2.2. Hypotheses Development

Corwin (2003) argues that uncertainty and asymmetric information are possible explanations for

the underpricing of SEOs. He believes that underpricing can be caused by differences in

information between the parties involved in the offer (asymmetric information), and SEOs made by

firms with a high level of asymmetric information tend to be more underpriced than other offers,

other things being equal. Liu and Malatesta (2007) propose that if the presence of credit ratings

reduces information asymmetry between the parties involved in SEOs, then the SEOs of firms with

credit ratings will tend to be underpriced less than the SEOs of firms without credit ratings.

Empirically, they find that the presence of credit ratings and rating levels tend to be negatively

related to SEO underpricing. That is, the underpricings in rated SEOs are less than in unrated

SEOs. In addition, Liu and Malatesta (2007) find that the higher the rating level, the lower the SEO

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underpricing is, all else equal. They argue that a lowly rated firm signals a higher probability of

default relative to a highly rated firm. Therefore, SEO investors expect a larger SEO underpricing

to compensate for the relatively higher probability of default. However, in a credit rating and IPO

study in the U.S., An and Chan (2008), document that having a credit rating can significantly

reduce IPO underpricing but the specific rating level does not matter. An and Chen argue that a

credit rating reduces uncertainty about firm value and it is the value certainty that matters, not the

value per se.

We contend that it is not clear what the specific credit rating grade implies for SEO underpricing. In

an efficient market, the stock price should have already reflected the credit rating (presumably a

lower stock price for a speculative grade firm). In support of this, Poon and Chan (2008) show that

Chinese investors do react to credit rating announcements. It is also possible that a speculative

grade indicates weak management and investors are very concerned when these managers want to

increase their operations (funded from the SEO). Then, investors require a deeper discount for

speculative-rated SEOs relative to investment-grade rated SEOs. Alternatively, firms with weak

balance sheets may receive low credit ratings and the injection of new equity (via the SEO) can

improve the liquidity and solvency of these firms. Thus, the SEO underpricing in these

speculative-grade SEOs could be less than those for investment-grade rated SEOs. Overall, there

are contrasting arguments on what a high credit rating compared to a low rating has on

underpricing.

An important implication of Liu and Malatesta (2007) and An and Chen (2008) is that having an

issuer rated by a recognized rating agency before its SEO can potentially reduce the cost of equity

capital. This leads to our first hypothesis, which is as follows:

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H1: Other things being equal, rated SEOs are underpriced significantly less than unrated SEOs

in China.

The arguments and empirical evidence on whether the level of the rating (e.g., investment or

speculative) has a differential impact on the level of underpricing are mixed. Our null hypothesis is

as follows:

H2: Other things being equal, the level of credit rating has no impact on underpricing.

3. Research Methods Credit rating agencies, among other financial institutions, use financial strength characteristics to

determine credit rating levels. Hence, some of the characteristics that help explain the probability

of a firm being rated by a credit rating agency might not be independent of the characteristics that

determine the SEO underpricing level of the same firm. In our case, the financial profile of a firm

that affects the rating agency’s decision to rate the firm may also be the financial characteristics that

determine the SEO’s underpricing. It is possible that a rating agency is more (less) likely to rate a

larger (smaller) and financially stronger (weaker) Chinese firm because these firms are more able

or probably more keen to seek additional funding from the domestic and international financial

markets. We expect that a financially stronger firm will have a higher probability of being rated

and will have a lower SEO underpricing. Therefore, we need to control for any endogeneity

inherent in underpricing and the decision to give a rating. Endogeneity occurs when the firm

characteristics that affect the probability of being rated also determine its SEO underpricing. That

is, there is a sample-selection bias. The credit rating literature discusses such bias in detail (see

Poon, 2003, and Poon and Firth, 2005). In our analysis, we model the rating decision using return

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on equity (ROE) as a criterion for making an unsolicited rating of a specific Chinese firm. Because

of the minimum 10% ROE application rule, we also use ROE as a possible explanatory variable in

SEO underpricing.

Many studies use Heckman’s (1979) two-step estimation method to mitigate sample-selection bias.

The first step is to estimate the selection equation (or rating decision equation) to determine the

probability of a firm being rated. A sample-selection bias variable (called the inverse Mill’s ratio)

is estimated in the process. The second step is to estimate the main equation to study the

determinants of the SEO underpricing by incorporating a set of explanatory variables and the

inverse Mill’s ratio (from the first-step) using a regression model. A disadvantage of using

Heckman’s procedure is that the credit rating literature does not offer a concrete theory to guide the

selection of specific determinants in models of the probability of a firm receiving a credit rating.

The decision as to what variables to include is primarily based on conceptual arguments or other

practical reasons such as data availability. Therefore, the estimated inverse Mill’s ratio may change

depending on the extent of variables used in the selection equation. With a different Mill’s ratio,

the second step in the Heckman procedure may yield different estimation results.

To mitigate the challenge of using Heckman’s procedure, we adopt Wooldridge’s (2002, p.621)

two-step instrumental variable method. Similar to Heckman’s method, Wooldridge’s

instrumental-variable approach also uses a probit model to estimate the rating decision equation

with a set of firm characteristics in the first step. Then, a fitted probability of being rated (Y_hat) is

obtained from the estimated probit equation for each firm. The fitted probability is then used as the

instrumental variable to replace the dummy variable that measures the effect of a credit rating on

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the SEO underpricing equation (the main equation). In the second step (the main equation), the

SEO underpricing determinants are estimated using a set of explanatory variables and the fitted

probability instrumental variable. Wooldridge (2002, p. 621) shows that such an approach does not

require a perfect specification of the selection equation in Step 1. This mitigates the concern about

specification errors in the first step in Heckman’s method. Recent studies such as Faulkender and

Petersen (2006) and Lin and Su (2008) also use Wooldridge’s approach to deal with the

endogenous selection issue. The two-step Wooldridge treatment effects model is illustrated below.

Step 1: Rating Decision Equation (Selection Equation based on a probit model)

iii ZY ξ+γ=∗ (1) The observed decision is 1=iY if 0>∗

iY

0=iY if 0≤∗iY

Step 2: SEO Underpricing Equation (Main Equation based on a regression model)

iiii YXU ε+δ+β= (2) where Ui = SEO underpricing for firm i;

Xi = a vector of explanatory variables for firm i in the SEO underpricing equation;

Yi = a binary variable representing whether an issuer has a credit rating (1 for a rated

SEO firm);

Yi* = an unobserved continuous latent variable for the selection decision;

Zi = a vector of explanatory variables in the rating decision equation;

β ,δ ,γ = a vector of coefficients or coefficient;

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iε , iξ = the random error terms that follow a bivariate normal distribution with zero mean

and correlationρ εξ; and

ρ εξ = correlation between iε and iξ .

In Step 1, we estimate the rating decision with a number of explanatory variables using a probit

model. The explanatory variables of the probit model are return on equity (ROE), natural

logarithm of total assets (LNTA), total debts to total assets (DTA), market-to-book ratio (MTB),

and year dummy variables. The inclusion of some of these financial variables follow the work in

Faulkender and Petersen (2006), Liu and Malatesta (2007), Poon and Firth (2005), and Poon, Lee

and Gup (2009). We expect that a firm will be more likely to be rated when it is larger and

financially stronger.

In Step 2, the dependent variable is the SEO underpricing (UNDERPRICING). The explanatory

variables include: (1) ROE = return on equity, (2) rel_offersize = relative offer size, (3) volatility =

stock return volatility of the issuer , (4) privateoffer where 1 = private offering, 0 otherwise, (5)

besteffort where 1 = underwriting method is by best efforts, 0 otherwise, (6) percent of state

ownership = percentage of state ownership shares, (7) priorSEO where 1 = issuer had another SEO

issue before the SEO in the prior year, 0 otherwise , (8) U_mktshare = market share of the lead

underwriter which is used to proxy for the lead underwriter’s reputation or quality, (9) industry

dummies for the issuers’ industries, and (10) Y_HAT = the fitted probability of the likelihood of a

firm being rated, which is estimated from the probit model in Step 1. Y_HAT is an instrumental

variable to Y in Equation (2). The control variables for the SEO underpricing equation follow the

SEO literature.

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To examine H2, we separate the credit ratings into investment and speculative grades based on the

credit rating levels. Wooldridge’s approach is unable to examine H2 because it can account for a

binary dependent variable only. Instead, we use an ordinary least sqaures (OLS) approach to

examine the impact of credit rating levels on underpricing. We use the following regression

equation:

iL

LiH

Hiii RRXU εδδβ +++= ,, (3)

Where Ri,H is a dummy variable with a value of 1 if the ith firm has an investment-grade rating and

Ri,L is a dummy variable with a value of 1 if the ith firm has a speculative-grade rating. We use the

ordinary least squares methodology in estimating Equation (3) to examine H2 after we confirm that

there is no severe selectivity bias in Equations (1) and (2) (i.e., the inverse Mill’s ratio, from the

Heckman model, is not statistically significant).

We expect that the relative offer size and stock return volatility are positively correlated with the

magnitude of the underpricing. Corwin (2003) suggests that a larger relative offer size and a higher

stock return volatility represent greater uncertainty regarding the SEO firm’s future. SEO

investors, therefore, require a larger SEO underpricing. We argue that, other things equal, a

private issue and best effort SEOs are expected to have a larger magnitude of underpricing. During

the SEO process, many Chinese firms use private issue SEOs to re-distribute the relative

percentage of ownership among various classes of shareholders. Some favored investors receive

shares cheaply via the SEO. Thus, raising outside capital is perhaps only one of several motivations

for private SEO issues. Therefore, we would expect these private issue SEOs will have a larger

underpricing. When uncertainty is very high, underwriters will refuse to undertake firm

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commitment offers and will, instead, use their best efforts to sell the shares. Thus, best effort

underwriting indicates a lot of uncertainty, which will induce greater underpricing.

Return on equity (ROE) is expected to have a negative sign in the second stage regression. A

higher ROE implies the SEO firm is financially strong and it does not need to give high

underpricing to attract SEO investors. In common with other China studies, we add a variable,

percent of state ownership, to control for the state ownership of listed firms. We also include

industry dummy variables to control for any industry impact on SEO underpricing.

Prior SEO experience and underwriter quality are negatively related to the magnitude of

underpricing. Mola and Loughran (2004) find that investors require smaller discounts from more

frequent issuers (i.e., with a recent history of SEO issues) than firms without recent offerings.

Their result provides some preliminary evidence consistent with the “leaving a good taste

hypothesis”. That is, firms are willing to leave some money on the table for investors (leaving a

good taste) at earlier offerings because issuers want to come back later for additional funding.

Without this past good taste, more marketing effort is required for an SEO, which is manifested in

a higher required SEO discount. Kim and Shin (2004) find that the underwriter ranking is

negatively related to the SEO discount, that is, SEOs that are underwritten by less prestigious

underwriters have greater SEO discounts than those that are underwritten by more prestigious

underwriters.

Because of the possible sample-selection bias in the decision to seek a credit rating, we use Y_HAT

(the fitted probability of the likelihood for a firm having a credit rating in Step 1) as an instrumental

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variable to replace Y in Equation (2). The test of the null hypothesis that δ = 0 is used to examine

H1.

4. Sample Data and Variable Construction 4.1. Sample Data

We examine the impact of credit agency ratings on the pricing and returns of companies listed on

the Shanghai and Shenzhen Stock Exchanges that make SEOs. In particular, we contrast the

underpricing of SEOs between the companies with and without long-term issuer ratings from

Xinhua Far East China Ratings . Xinhua Financial Network (XFN) and Shanghai Far East Credit

Rating Company Limited (Shanghai Far East) formed a strategic alliance, named Xinhua Far East

China Ratings (Xinhua-Far East), which aims to provide investors with independent, objective, and

forward looking credit opinions on Chinese corporations (People’s Daily, 2002). Xinhua-Far East

rates large Chinese firms that are listed on domestic and overseas stock exchanges (Xinhua-Far

East, 2007) and the agency states that it uses international rating standards to assign issuer ratings

based on its unique knowledge of the Chinese market (Asiainfo, 2002). Xinhua-Far East mainly

assigns non-paid issuer ratings based on public information (Xinhua-Far East, 2003) while it

generates its income from other financial services. According to Xinhua-Far East, a long-term

issuer rating “assesses the obligors’ ability and willingness to meet financial obligations and

commitments over a period of one year or above” and the ratings range from AAA to C

(Xinhua-Far East, 2003).

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We use Xinhua-Far East rating data for the following reasons. First, Shanghai Far East was the first

credit rating agency in China to join the Association of Credit Rating Agencies in Asia (ACRAA)2,

which attests to it meeting minimum quality standards (PRNewswire, 2004). Second, Poon and

Chan (2008) empirically show that that Xinhua-Far East credit ratings have a signalling effect and

informational content using a sample of 170 companies listed on the Shanghai and Shenzhen Stock

Exchanges. Third, a few other domestic credit ratings agencies in China were criticized for giving

upward biased or overly optimistic credit ratings (e.g. mainly giving investment-grade ratings or a

relatively high proportion of AAA ratings)3

. Xinhua-Far East appears to have a wider distribution

of credit ratings and is consistent with the ratings from other global credit rating agencies.

The SEO data on listed companies on the Shanghai and Shenzhen Stock Exchanges and their

related financial and accounting data are obtained from the CSMAR Financial Database

(CSMAR). The sample used in this study consists of SEO issuers in China that meet the following

conditions: (1) The SEO issuers must be listed companies on the Shanghai or Shenzhen Stock

Exchange with detailed financial reports including daily price and return data in CSMAR. (2) The

issuers had SEOs during the study period from 2002 to 2009. Since Xinhua-Far East began its

issuer rating services in 2002, our study period starts from 2002. The sample SEO issuers are

divided into two groups – rated issuers and unrated issuers. If a SEO issuer has a long-term issuer

rating from Xinhua-Far East before the announcement date of its SEO, it is considered as a “rated

2 The Association of Credit Rating Agencies in Asia (ACRAA) was organized by 15 Asian credit rating agencies from 13 countries at the Asian Development Bank headquarter on September 14, 2001 to promote “the adoption of best practices and common standards that enure high quality and comparability of credit ratings throughout the region”. The membership has increased to 29 members from 15 countries as of June 2011 (ACRAA, 2011a). Japan Credit Rating Agency Ltd. (JCR) which is one of the Nationally Recognized Statistical Rating Organizations (NRSROs) of the U.S. Securities and Exchange Commission (ACRAA, 2011b, and US SEC, 2011), is also a member of ACRAA. 3 See Poon and Chan (2008) for a detailed discussion of the criticisms against some domestic credit ratings agencies in China.

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issuer” and the other SEO issuers are considered as “unrated issuers”. The respective SEOs are

called “rated SEOs” (ratedSEO) and “unrated SEOs”, respectively.

4.2. Construction of Variables

We explain the construction of the variables in this Section. The financial information variables

(return on equity (ROE), natural logarithm of total assets (LNTA), total debts to total assets ratio

(DTA), percent of state ownership, and market-to-book value (MTB)) are obtained from the latest

financial statements prior to the SEO announcement date in the CSMAR database.

SEO underpricing (UNDERPRICING) is calculated as the pre-issue day closing price minus the

SEO offer price and divided by pre-issue day closing price4

. Following Corwin (2003) and Kim

and Shin (2004), the SEO relative offer size (rel_offersize) is the number of shares offered by the

SEO issue divided by the total number of shares outstanding of the company before the SEO.

Whether the SEO is a private issue (privateoffer) and the underwriting method is by best effort

(besteffort) are obtained from the CSMAR database.

Following Megginson and Weiss (1991) and Luo, Rao and Yue (2010), the market share of the lead

underwriter (U_mktshare) is used as the proxy for the lead underwriter’s reputation or quality. We

assume that the larger the market share of the lead underwriter, the better is the underwriter’s

quality. Following Luo, Rao, and Yue (2010), the market share of the lead underwriter is based on

the underwriter’s market share of the initial public offering (IPO) market during the sample period.

Specifically, we calculate the market share as the gross proceeds of the IPO issue(s) where the

4 Both Kim and Shin (2004) and Mola and Loughran (2004) compared the pre-issue day closing price to the SEO offer price.

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underwriter serves as the lead underwriter divided by the sum of the gross proceeds of all IPO

issues in the sample period. If there is more than one lead underwriter for an IPO issue, the IPO’s

gross proceeds are divided by the number of lead underwriters before assigning the gross proceeds

of the issue to each underwriter.

Following Kim and Shin (2004), stock return volatility (volatility) is measured by the standard

deviation of daily stock return on the company from 260 trading days before to 10 trading days

before the SEO issue date, t (i.e., from t-260 to t-10). Similar to Mola and Loughran (2004), we

create a “priorSEO” dummy variable. If the firm had another SEO issue before the SEO in the prior

250 trading days, the value of the variable is coded as one. The detailed definitions of all variables

are shown in Appendix 1.

[Insert Appendix 1 here]

5. Discussion of Results

5.1. Descriptive Statistics

Table 2 reports the detailed rating definitions and distribution of Xinhua-Far East’s long-term

issuer credit ratings by rating categories for SEOs made during January 2002 to May 2009. There

were a total of 62 rated SEOs. Among the 62 rated SEO firms, 42 of them are investment grade and

20 are speculative grade. Consistent with the results in Poon and Chan (2008), Xinhua-Far East

provides a wide spectrum of credit ratings. Table 3 presents the distribution of SEO firms by year.

There were a total of 379 SEO firms without credit ratings. For the eight years, the SEO firms

were clustered more in 2007 and 2008 with 159 and 136 SEOs, respectively. Because some firms

have missing SEO or financial information, our final sample consists of 359 unrated and 58 rated

SEOs.

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[Insert Tables 2 and 3 here]

Table 4 contains the summary statistics of all variables in Equations (1) and (2). We partition the

sample into rated and unrated SEOs. Overall, the mean underpricing is 0.247 (or 24.7%) for

unrated and 0.095 (or 9.5%) for rated SEOs. The difference in SEO underpricing is statistically

significant irrespective of using a parametric t-test or a non-parametric Z-test. Other variables

showing signficant differences between the rated and unrated SEO samples include total assets,

market-to-book ratio, stock return volatility, private offer, best effort, and underwriter quality. The

preliminary statistics suggest that rated and unrated SEOs have different profiles over many

dimensions.

[Insert Table 4 here]

5.2. Credit ratings and SEO underpricing

Table 5, Panels A and B, present the results from the credit rating decision and SEO underpricing

equations. We use a probit estimation to predict the likelihood of a SEO firm getting a Xinhua-Far

East credit rating. The total asset coefficient is positive while the debt to asset ratio coefficient is

negative and both are significant at the 1% level. If a firm is larger (in terms of total assets) and

less debt (in terms of the debt to assets ratio), then it has a higher probability of being rated by

Xinhua-Far East. Panel B shows the coefficients from using the Wooldridge instrumental variable

approach in explaining SEO underpricing. Because prior SEO and underwriter quality variables

are not used in some SEO literature (e.g., Corwin, 2003), we present four slightly different models

of SEO underpricing in China. Irrespective of the models, the control variables (relative offer size,

private offer, and best effort) carry the expected signs and are significant at the 1% or 5% levels.

Hence, our general findings are consistent with the U.S. SEO literature (Corwin, 2003, Mola and

Loughran, 2004, and Kim and Shin, 2004). The prior SEO experience, stock return volatility, and

underwriter quality variables, contrary to some U.S. studies, are not significant. For Hypothesis 1,

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the rated SEO instrumental variable is negatively signficant at either at the 5% or 10% levels. The

coefficients range from -0.1326 (Model 2) to -0.1580 (Model 1). Hence, other things being equal,

a SEO firm with a credit rating, is able to reduce its underpricing. The magnitudes of these SEO

underpricings are economically significant. On average, a firm’s underpricing is cut in half

resulting in considerably less money being left on the table. Consistent with the findings in Poon

and Chan (2008) and An and Chan (2008), credit ratings in general (and particularly in China) can

alleviate information asymmetry and convey useful information to investors.

[Insert Table 5 here]

5.3. Credit rating levels and SEO underpricing

We present the results of credit rating levels and SEO underpricing in Table 6 using Equation (3).

We first estimate Equations (1) and (2) using a Heckman approach to calculate the inverse Mill’s

ratio and assess the severity of the selection bias. To conserve space, we do not report the results

here. The inverse Mill’s ratio is not significant, suggesting that the selection bias is not severe. We

then estimate the same Equation (2) specification by ordinary least squares with the addition of

speculative-grade and investment-grade dummy variables. As expected, in all four models, both

investment-grade and speculative-grade dummy variables are negatively significant suggesting

that when a firm receives a credit rating (either investment-grade or speculative-grade), its SEO

underpricing is less than a firm without a credit rating. The magnitude of the speculative-grade

dummy variable is larger than the investment-grade dummy variable in all four models but the

F-statistics of the differences are not statistically significant, that suggesting speculative-grade

firms’ underpricings are not significantly different from the underpricings of investment-grade

firms. The results in Table 6 are consistent with those in An and Chan (2008), i.e., the different

rating levels do not explain SEO underpricing.

20

[Insert Table 6 here]

6. Conclusions

Credit rating agencies are well established in the U.S. and other developed markets, and they have

existed for more than 100 years in some cases. They are held in high esteem by investors5

and high

credit ratings can lead to reduced costs of capital and can make fund raising both easier and

cheaper. However, credit rating agencies are relatively new in emerging markets, including China,

which is the focus of our study. Unlike U.S. firms, Chinese firms are not obliged to have credit

ratings and nor do they typically solicit them. Instead, credit rating agencies make unsolicited

ratings of firms they wish to cover. In this study, we set out to explore if credit ratings of China’s

corporate sector have any information value to investors. The specific area we focus on is whether

a credit rating has an impact on the way a firm prices its seasoned equity offering (SEO).

Using data for the period 2002 to 2009, we examine whether an unsolicited rating from Xinhua Far

East China Ratings (Xinhua-Far East) has an impact on SEO underpricing. In doing so, we control

for other known factors that affect underpricing and control for possible endogeneity using the

two-step Wooldridge procedure. We find that a credit rating results in a reduction in underpricing

of 13.26% to 15.80%, depending on model specification. Thus, the typical underpricing of a SEO

falls substantially for a rated firm. In addition, the underpricing for an SEO firm that receives a

speculative-grade credit rating is not significantly different from an SEO firm with an

investment-grade rating. The results are consistent with An and Chan’s (2008) argument that it is

the act of receiving a credit rating that matters, not the rating per se. SEO firms appear to benefit

5In recent years the accuracy and independence of major rating agencies has been questioned (Poon et al., 2009). Despite these attacks, credit ratings still seem to have a lot of credibility in the market place.

21

from receiving an unsolicited rating. This may lead firms to actively solicit credit ratings in the

future, especially those that plan to access the capital markets.

22

References An, Heng, and Kam C. Chan, 2008. Credit ratings and IPO pricings, Journal of Corporate Finance,

14, 584-595. Asiainfo Daily China News (Asiainfo), 2002. XFN & Shanghai Far East form alliance on credit

rating, February 6, 2002. Retrieved from: ProQuest On-line Database, January 26, 2009. Association of Credit Rating Agencies in Asia (ACRAA), 2011a. About us – Objectives.

Retrieved from: http://www.acraa.com/final.asp, October 4, 2011. Association of Credit Rating Agencies in Asia (ACRAA), 2011b. ACRAA members. Retrieved

from: http://www.acraa.com/acraamembers.asp, October 4, 2011. Baglole, Joel, 2004. Chinese credit ratings: A huge leap of faith, Far Eastern Economic Review,

January 8, 2004, 39-42. Chen, Kevin C.W., and Jiwei Wang, 2007. Accounting-based regulation in emerging markets: The

case of China's seasoned-equity offerings, International Journal of Accounting, 42, 221-236. Corwin, Shane A., 2003. The determinants of underpricing for seasoned equity offers, The Journal

of Finance, Vol. 58, No. 5, 2249-2279. CSMAR Financial Database (CSMAR), 2011. GTA Finance & Education Group. Faulkender, Michael, and Mitchell A. Petersen, 2006. Does the source of capital affect capital

structure? Review of Financial Studies, 19, Issue 1, 45-79. Harrison, Matthew, 2003. Asia-Pacific Securities Markets, Fourth Edition, Sweet & Maxwell

Asia, 237-280. Heckman, James J., 1979. Sample selection bias as a specification error, Econometrica, 47(1),

153-161. Kennedy, Scott, 2003. China’s credit rating agencies struggle for relevance. The China Business

Review, November-December 2003, 36-40. Kim, Kenneth A., and Hyun-Han Shin, 2004.The puzzling increase in the underpricing of seasoned

equity offerings, The Financial Review, 39, 343-365. Lin, Chen, and Dongwei Su, 2008. Industrial diversification, partial privatization and firm

valuation: Evidence from publicly listed firms in China, Journal of Corporate Finance, 14(4), 405-417.

Liu, Yang, and Paul H. Malatesta, 2007.Credit ratings and the pricing of seasoned equity offerings,

Working Paper, University of Washington.

23

Luo, Wei, Pingui Rao, and HengYue, 2010. Information risk and underwriter switching in SEOs:

Evidence from China, Journal of Business Finance and Accounting, 37(7)&(8), 905-928. Megginson, William L., and Kathleen A. Weiss, 1991. Venture capitalist certification in initial

public offerings, The Journal of Finance, 46(3), 879-903. Mola, Simona, and Tim Loughran, 2004. Discounting and clustering in seasoned equity offering

prices, Journal of Financial and Quantitative Analysis, Vol. 39, No. 1, 1-23. People’s Daily, 2002. XFN & Shanghai Far East form alliance on credit rating, February 6, 2002.

Retrieved from: http://english.peopledaily.com.cn/200202/06/eng20020206_90009.shtml, October 4, 2011.

Poon, Winnie P.H., 2003.Are unsolicited credit ratings biased downward?Journal of Banking and

Finance, 27(4), 593-614. Poon, Winnie P.H., and Michael Firth, 2005.Are unsolicited credit ratings lower? International

Evidence From Bank Ratings, Journal of Business Finance and Accounting, 32(9-10), 1741-1771.

Poon, Winnie P.H., and Kam C. Chan, 2008.An empirical examination of the informational content

of credit ratings in China, Journal of Business Research, 61, 790-797. Poon, Winnie P.H., Junsoo Lee, and Benton E. Gup, 2009. Do solicitations matter in bank credit

ratings? Results from a study of 72 countries, Journal of Money, Credit and Banking, 41(2-3), 285-314.

PRNewswire, 2004. Xinhua Finance subsidiary Shanghai Far East Credit Rating becomes the first

China member of the Association of Credit Rating Agencies in Asia, January 13, 2004. Retrieved from: http://www.prnewswire.com/news-releases/xinhua-finance-subsidiary-shanghai-far-east-credit-rating-becomes-the-first-china-member-of-the-association-of-credit-rating-agencies-in-asia-58949497.html, October 4, 2011.

U.S. Securities and Exchange Commission (US SEC), 2011. Nationally Recognized Statistical

Rating Organizations (“NRSROs), September 16, 2011. Retrieved from: http://www.sec.gov/divisions/marketingreg/ratingagency.htm, October 4, 2011.

Wang, Junbo, K.C. John Wei, and Stephen W. Pruitt, 2006. An analysis of the share price and

accounting performance of rights offerings in China, Pacific-Basin Finance Journal 14, 49-72.

Wooldridge, Jeffrey M., 2002. Econometric Analysis of Cross Section and Panel Data, The MIT

Press, 603-644. Xinhua Far East China Credit Ratings (Xinhua-Far East), 2007. Xinhua Far East China Issuer

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Credit Ratings Results by August 31, 2007. Retrieved from: http://www.xinhuafinance.com/uploadedFiles/products-and-services/ratings/far-east-china-ratings/Ratings_Results.pdf, October 3, 2011.

Xinhua Far East China Credit Ratings (Xinhua-Far East), 2003. Xinhua Far East China Rating:

Rating scale and definitions 2003. Retrieved from: http://www.xinhuafinance.com/uploadedFiles/products-and-services/ratings/far-east-china-ratings/Rating_Methodology.pdf, October 3, 2011.

25

Table 1 Regulations of SEOs in China

Date

Regulation

Before May 22, 2000

No placement SEOs were allowed.

May 22, 2000

The 2000 Regulation: companies with three years' profits can apply to the CSRC for permission to make a SEO (The regulation was released by the CSRC on May 22 and published in newspapers the next day).

March 15, 2001

The 2001 Regulation: three-year average ROE≥6% but not definitive. Companies not meeting the threshold can be qualified provided that the management and the underwriter provide a detailed explanation that shows a healthy condition for the company (The regulation was released by the CSRC to all listed companies on March 15 and published in newspapers on March 28).

July 24, 2002

The 2002 Regulation: Three-year average ROE≥10% and ROE≥10% in the previous year (The exposure draft was released on June 22 and the final regulation was released by the CSRC on July 24. It was published in newspapers on July 26).

Source: Chen and Wang (p. 223, 2007)

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Table 2 Rating Definitions and Distribution of Xinhua-Far East’s Long-Term Issuer Credit Ratings

by Rating Categoriesin the Sub-sample of Rated SEOs During the Period January 2002 - May 2009

Rating Frequency (percentage) in the

sample

Rating definitions/sub-total

AAA 0 (0%) Excellent: “An obligor has the strongest ability to meet its financial commitments and the lowest likelihood of credit loss relative to other obligors in China.”

AA 4 (6.5%) Very Good: “An obligor has a very strong ability to meet its financial commitments and a low likelihood of credit loss relative to other obligors in China.”

A 11 (17.7%) Above Average: “An obligor has above average ability to meet its financial commitments and a below average likelihood of credit loss relative to other obligors in China.”

BBB 27 (43.5%) Average: “An obligor has an average ability to meet its financial commitments and an average likelihood of credit loss relative to other obligors in China.”

Investment grade 42 (67.7%) Sub-total of “BBB” or above ratings

BB 15 (24.2%) Below Average: “An obligor is the least vulnerable and speculative in the near term compared to other lower rated obligors.”

B 3 (4.8%)

Weak: “An obligor is more vulnerable than obligors rated BB. The obligor currently has the ability to meet its financial commitments but its credit strength is considered weak, relative to other obligors in China.”

CCC 0 (0%)

Very Weak: “An obligor has very weak credit strength and is currently vulnerable, is dependent upon favorable operating, financial and external environments to meet its financial commitments, relative to other obligors in China.”

CC 2 (3.2%) Extremely Weak:

27

“An obligor has extremely weak credit strength and is currently highly vulnerable to defaulting on its financial commitments, relative to other obligors in China.”

C 0 (0%) Default: “An obligor has already defaulted on its financial commitments and has the weakest credit strength relative to other obligors in China.”

Speculative grade 20 (32.3%) Sub-total of “BB” or below ratings

Total 62 (100%) Notes : 1. All ratings with “+” or “-“ designations are grouped into the rating sub-groups of their corresponding letter grades.

2. Source: Xinhua Far East Credit Ratings (Xinhua-Far East) (2003). “Xinhua Far East China Ratings – pioneering undertaking to rank credit risks of enterprises in China. Rating Methodology Paper”. The rating definitions are extracted from this rating methodology paper.

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Table 3 Distribution of SEO Announcements by year

Year Frequency in the sample

(Percentage of the sample)

Sub-total/ Total Unrated SEO Rated SEO

2002 27 (6.12%) 1 (0.23%) 28 (6.35%) 2003 13 (2.95%) 4 (0.91%) 17 (3.85%) 2004 6 (1.36%) 5 (1.13%) 11 (2.49%) 2005 2 (0.45%) 3 (0.68%) 5 (1.13%) 2006 44 (9.98%) 10 (2.27%) 54 (12.24%) 2007 144* (32.65%)* 15 (3.4%) 159* (36.05%)* 2008 116 (26.3%) 20* (4.54%)* 136 (30.84%)

Jan. 2009 – May 2009

27 (6.12%) 4 (0.91%) 31 (7.03%)

Total

379

(85.94%)

62

(14.06%)

441

(100%)

Notes : 1. * indicates the highest number/percentage in each column.

2. Percentage in the sample is in parenthesis.

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Table 4 Descriptive statistics, t-test, and Mann-Whitney U test results of financial variables of the sample

Descriptive statistics include mean, median, standard deviation (std dev), and number of observations (N) of each variable. T-values refer to the t-test statistics of the means between the unrated SEO group and the rated SEO group, and Z-values refer to the Z-test statistics of the Mann-Whitney U between the rated group and the unrated group. Variable definitions are in Appendix 1. ***, **, and * indicate significance at the 1%, 5%, and 10% levels.

Unrated SEO Rated SEO Mean median std dev N Mean median std dev N t-value Z-value UNDERPRICING 0.247 0.235 0.289 359 0.095 0.075 0.375 58 2.94 *** 4.166 *** ROE 0.172 0.117 0.581 371 0.134 0.143 0.109 62 1.143 -1.818 * LNTA 21.435 21.378 1.023 376 23.181 23.015 1.142 62 -11.316 *** -10.057 *** DTA 0.582 0.566 0.313 376 0.555 0.613 0.203 62 0.874 -0.189 MTB 1.681 1.345 1.125 376 1.419 1.217 0.741 62 2.369 ** 2.629 *** YEAR_2002 0.071 0 0.258 379 0.016 0 0.127 62 2.642 *** 1.647 * YEAR_2003 0.034 0 0.182 379 0.065 0 0.248 62 -0.921 -1.143 YEAR_2004 0.016 0 0.125 379 0.081 0 0.275 62 -1.828 * -3.028 *** YEAR_2005 0.005 0 0.073 379 0.048 0 0.216 62 -1.555 -2.966 *** YEAR_2006 0.116 0 0.321 379 0.161 0 0.371 62 -0.906 -1.004 YEAR_2007 0.38 0 0.486 379 0.242 0 0.432 62 2.291 ** 2.095 ** YEAR_2008 0.306 0 0.461 379 0.323 0 0.471 62 -0.256 -0.26 YEAR_2009 0.071 0 0.258 379 0.065 0 0.248 62 0.197 0.191 rel_offersize 0.36 0.201 0.572 366 0.344 0.19 0.560 59 0.204 0.718 volatility 0.043 0.037 0.087 368 0.033 0.035 0.010 61 2.188 ** 2.771 *** privateoffer 0.734 1 0.443 379 0.597 1 0.495 62 2.047 ** 2.206 ** besteffort 0.124 0 0.330 379 0.016 0 0.127 62 4.61 *** 2.525 ** percent of state ownership 26.813 25.040 25.671 379 35.220 38.242 24.710 62 -2.47 ** -2.635 *** priorSEO 0.058 0 0.234 379 0.048 0 0.216 62 0.322 0.303 U_mktshare 0.014 0.001 0.037 379 0.052 0.009 0.075 62 -3.91 *** -4.386 ***

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Table 5. Credit Ratings and SEO Underpricing: Results of the Two-Step Models using Wooldridge’s instrumental variable method

For Panel A (Rating Decision Equation), the explanatory variables of the probit model are (1) ROE = return on equity, (2) LNTA = natural logarithm of total assets, (3) DTA = total debts to total assets, (4) MTB = market-to-book value, and (5) Year dummies for the SEO announcement years. For Panel B (Underpricing Determinant Equation), the dependent variable of the primary regresson equation of interest is the amount of SEO underpricing. The explanatory variables include: (1) ROE = return on equity, (2) rel_offersize = relative offer size, (3) volatility = stock return volatility , (4) privateoffer where 1 = private offering, 0 otherwise, (5) besteffort where 1 = underwriting method is by best efforts, 0 otherwise, (6) percent of state ownership = percentage of state ownership shares, (7) priorSEO where 1 = issuer had another SEO issue before the SEO in the prior year, 0 otherwise , (8) U_mktshare = market share of the lead underwriter which is used to proxy for the lead underwriter’s reputation or quality, (9) Y_HAT = a fitted probability of the likelihood of a firm being rated, which is estimated from the probit model in Panel A. Y_HAT is an instrumental variable to Y in Equation (2). (10) industry dummies for the issuers’ industries, and (11) year dummies for SEOs. Variable definitions are in Appendix 1. ***, **, and * indicate significance at the 1%, 5%, and 10% levels.

Panel A: Rating Decision Equation (models the probability of being rated)

Explanatory

variable Coefficient χ2 statistic

Intercept -19.3804 84.07 *** ROE -0.8443 0.83 LNTA 0.8966 84.80 *** DTA -2.4982 15.92 *** MTB -0.0069 0.00 Year dummies Yes log_likelihood -105.815 No. of observations 433

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Table 5. Credit Ratings and SEO Underpricing: Results of the Two-Step Models using Wooldridge’s instrumental variable method (continued)

Panel B: Underpricing Determinant Equation (main equation: dependent variable is “UNDERPRICING”)

Explanatory variable expected sign Model 1 Model 2 Model 3 Model 4

Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat Intercept 0.0498 1.29 0.0500 1.30 0.0488 1.27 0.0491 1.28 ROE - 0.1267 0.92 0.1460 1.08 0.1214 0.88 0.1389 1.02 rel_offersize + 0.0774 2.38** 0.0846 2.67*** 0.0831 2.59*** 0.0891 2.84*** volatility + 0.2929 1.36 0.2839 1.32 0.2960 1.38 0.2879 1.34 privateoffer + 0.1737 5.10*** 0.1689 5.01*** 0.1675 4.97*** 0.1637 4.90*** besteffort + 0.1103 2.33** 0.1083 2.29** 0.1110 2.34** 0.1092 2.31**

percent of state ownership + 0.0002 0.34 0.0003 0.45 0.0002 0.33 0.0002 0.43 priorSEO - -0.0760 -1.24 -0.0700 -1.15 U_mktshare - 0.3776 0.98 0.3309 0.86 Y_HAT - -0.1580 -2.08** -0.1326 -1.86* -0.1564 -2.06** -0.1341 -1.88* Industry dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Adjusted R2 0.1311 0.1312 0.1299 0.1305 F-statistics 6.50*** 7.05*** 6.99*** 7.69*** Number of Observations 402 402 402 402

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Table 6. Level of Credit Ratings and SEO Underpricing: Results of the Ordinary Least Squares Models

The dependent variable of the regresson equation is the amount of SEO underpricing. The explanatory variables include: (1) ROE = return on equity, (2) rel_offersize = relative offer size, (3) volatility = stock return volatility , (4) privateoffer where 1 = private offering, 0 otherwise, (5) besteffort where 1 = underwriting method is by best efforts, 0 otherwise, (6) percent of state ownership = percentage of state ownership shares, (7) priorSEO where 1 = issuer had another SEO issue before the SEO in the prior year, 0 otherwise , (8) U_mktshare = market share of the lead underwriter which is used as a proxy for the lead underwriter’s reputation or quality, (9) speculative-grade rating = 1 when the credit rating of the rated SEO issuer is speculative grade, 0 otherwise, (10) investment-grade rating = 1 when the credit rating of the rated SEO issuer is investment grade, 0 otherwise,. (11) industry dummies for the issuers’ industries, and (12) year dummies for SEOs. Variable definitions are in Appendix 1. ***, **, and * indicate significance at the 1%, 5%, and 10% levels.

Explanatory variable expected sign Model 1 Model 2 Model 3 Model 4

Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat Intercept 0.0481 1.29 0.0504 1.35 0.0469 1.25 0.0490 1.31 ROE - 0.1142 0.84 0.1361 1.01 0.1091 0.80 0.1289 0.95 rel_offersize + 0.0763 2.37** 0.0843 2.68*** 0.0827 2.59*** 0.0893 2.86*** volatility + 0.2988 1.40 0.2869 1.34 0.3020 1.41 0.2912 1.36 privateoffer + 0.1804 5.36*** 0.1735 5.24*** 0.1735 5.21*** 0.1679 5.11*** besteffort + 0.0987 2.09** 0.0973 2.06** 0.0998 2.11** 0.0985 2.08**

percent of state ownership + 0.0001 0.16 0.0002 0.34 0.0001 0.15 0.0002 0.31 priorSEO - -0.0843 -1.38 -0.0770 -1.27 U_mktshare - 0.4251 1.14 0.3710 0.99 speculative-grade rating - -0.1930 -2.75*** -0.1794 -2.59*** -0.1869 -2.66*** -0.1754 -2.53** investment-grade rating - -0.1007 -1.99** -0.0865 -1.77* -0.1002 -1.98** -0.0878 -1.79* Industry dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Adjusted R2 0.1421 0.1415 0.1401 0.1401

33

F-statistics 6.54*** 7.01*** 6.94*** 7.54*** Number of Observations 402 402 402 402 F-test of equal coefficient in speculative-grade rating and investment-grade rating variables

1.27 1.29 1.12 1.15

34

Appendix 1 List of variables used for statistical analyses

Variable Code Variable Name / Brief Explanations

SEO Characteristics of SEO

rated SEO (or ratedSEO) unrated SEO UNDERPRICING rel_offersize

privateoffer

Underwriter/underwriting characteristics

besteffort U_mktshare

ROE

Financial ratios of the sample company

LNTA DTA MTB

volatility Others

priorSEO percent of state ownership investment-grade rating speculative-grade rating

Seasoned equity offering of the company during the sample period from January 1, 2002 to May 31, 2009. Xinhua Far East China Ratings (Xinhua-Far East) has assigned initial long-term issuer’s credit ratings to the company before the company’s announcement of the SEO during the sample period. They are called “rated SEOs”. The dummy variable “ratedSEO” = 1 for rated SEO firms, 0 otherwise. Xinhua-Far East has not assigned initial long-term issuer’s credit ratings to the company before the company’s announcement of the SEO during the sample period. They are called “unrated SEOs”. (pre-issue day closing price – SEO Offer price) / pre-issue day closing price (number of shares offered by the SEO issue) / (total number of shares outstanding of the company before the SEO) 1 = private issue or offering, 0 otherwise 1 = underwriting method is by best efforts, 0 otherwise The market share of the lead underwriter is used as the proxy for the lead underwriter’s reputation or quality. The market share of the lead underwriter is based on the underwriter’s market share of the initial public offering (IPO) market during the sample period. The market share of each underwriter in the IPO market = (gross proceeds of the IPO issue(s) for the issue(s) when the underwriter served as the lead underwriter*) / (sum of the gross proceeds of all IPO issues in the sample period). [Note: *If there are more than one lead underwriter for an IPO issue, the IPO’s gross proceeds are divided by the number of lead underwriters before assigning the gross proceeds of the issue to each underwriter.] Return on stockholders’ equity (i.e., net profit / stockholders’ equity) Natural logarithm of total assets Total debts to total assets Market-to-book value Volatility is measured by the standard deviation of daily stock return on the company from 260 trading days before to 10 trading days before the SEO issue date, t (i.e., from t-260 to t-10). 1= issuer had another SEO issue before the SEO in the prior year, 0 otherwise. (i.e., priorSEO =1 when the company had another SEO issue within the 250 trading days prior to the SEO) Percentage of state ownership shares of the SEO issuer. 1 = credit rating of the rated SEO issuer is investment grade, 0 otherwise. 1 = credit rating of the rated SEO issuer is speculative grade, 0 otherwise.

Note: All financial ratios are derived from the latest financial statements prior to the SEO announcement date.