2010-55
Does the Intended Use of Proceeds
and Bank's Characteristics Affect the
Bank Underwriters’ Certification Role?
Evidence from Seasoned Equity Offerings
Katsushi Suzuki Kazuo Yamada

Does the Intended Use of Proceeds and Bank's Characteristics Affect the Bank
Underwriters’ Certification Role? Evidence from Seasoned Equity Offerings

byKatsushi Suzuki1 and Kazuo Yamada2This version: 19 October 20101

Graduate School of Business Administration, Kobe University, 2-1,Rokkodai,Nada,Kobe 657-8501 , phone: +81 78
803 6914, mail: suzukatsu@b.kobe-u.ac.jp
2
Graduate School of Business Administration, Kobe University, 2-1,Rokkodai,Nada,Kobe 657-8501, and Japan
Society for the Promotion of Science, mail: mailto:yamada@stu.kobe-u.ac.jp

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Does the Intended Use of Proceeds and Bank's Characteristics Affect the Bank
Underwriters’ Certification Role? Evidence from Seasoned Equity Offerings

Abstract

This paper examines the effects of the intended use of proceeds on bank underwriters’certification roles in Japan following deregulation in 1999. We find that bankunderwriters have a positive effect on announcement returns. However, when theissuer discloses that the intended use of proceeds is repayment, the bank underwriter has
no effect on the announcement returns. Although banks’ holding loans have a positiveeffect on announcement returns, banks’ equity holdings are negatively associated withthe announcement return. Furthermore, issuers underwritten by banks whose use ofproceeds is loan repayment are more likely to be weak in their ex-post performance thanthose without repayment. We conclude that banks have different effects on issuers
according to the intended use of proceeds and the banks’ characteristics.
JEL classification: G21; G24
Keywords: Commercial bank; Intended use of proceeds; Announcement return; Certification;
Conflicts of interest.

2

1.

Introduction

On average, firms’ stock prices react negatively to their announcements of seasoned
equity offerings (SEOs).3 Several hypotheses have been advanced to explain theabnormal negative average common stock return following an SEO announcement.
Prominent among these is the adverse selection model of Myers and Majluf (1984).
Myers and Majluf (1984) attribute the average negative return to information
asymmetry between corporate insiders and outside investors. If managers are better
informed than outside investors, firms are more likely to issue equity when the equity isovervalued. Thus, the announcement of an equity offering conveys negative information
about firm value. Under this condition, Booth and Smith (1986) and Cooney, Kato, andSchallheim (2003) argue that the informative underwriter can guarantee that the market
price is not overvalued for investors, and their certification of the market price has amore favorable effect on the SEO market reaction.
In general, banks can obtain private information about a firm through loans orclearing functions. This private information may affect the announcement return in two
ways: through a certification effect and through a conflict of interest effect.4,5 A bankwith private information can assess the issuers’ market valuation and set the security
price more suitably than an investment house can (i.e., the certification effect).63

In Japan, Cooney, Kato, and Schallheim (2003) and Kang and Stulz (1996) found that the average abnormal returnfollowing SEO announcements was positive before 1991. However, after 1995, the abnormal return of SEO
announcement is negative, similar to United States SEO market (Suzuki 2009).
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There are also several papers that examine the effect of bank entry into the market. Gande, Puri, and Saunders(1999) and Takaoka and McKenzie (2006) show that competition intensifies within the underwriting market, because
issue costs, following the entrance of banks, decrease on the U.S. and Japanese bond markets.
5

Kanatas and Qi (1998) and Puri (1999) show the effect of certification in the presence of a conflict of interest rather

than treating certification and conflict as two mutually exclusive effects.
6

Suzuki (2010) argues that because the bank underwriter can set the security price suitably, the bank issuers lower

the issue discounts more than those employed by investment houses.

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Conversely, if a bank has the issuer’s bad information about a future prospectus that is
not spread to investors, the bank may have an incentive to misrepresent the value of afirm’s securities and use the proceeds to repay its own loans (i.e., the conflict of interest
effect). If the conflict of interest effect is supported, the SEO announcement return andlong-term performance of issuers underwritten and borrowed by the bank’s underwriters
will be negative. Extensive research using bond market data demonstrates that thecertification effect has a stronger influence on credit availability than the conflict ofinterest effect does.7,8This paper examines the market reaction to SEO announcements and post-issue
performance with a particular emphasis on commercial bank-affiliated underwriters.
This paper differs from previous empirical research in several respects. First, we
examine the relationship between the bank underwriter and the issue costs of the stock
market instead of the bond market. Compared to a bond market, the problem of inside
information is more serious in SEO markets (Brennan and Kraus 1987; Myer andMajluf 1984; Burch, Nanda, and Warther 2005).9 Thus, it is suitable to use the equity
market to examine the effect, because the announcement effect is more relevant to firmsthat face serious information problems. Suzuki (2010) examines the relationshipbetween the bank underwriter and the flotation costs (i.e. discount rate and the7

For example, Puri (1996), Gande, Puri, Saunders, and Walter (1997), and Takaoka and McKenzie (2006) examine

the question of whether investors paid higher prices for bonds underwritten by banks than those underwritten by
investment houses. See Kroszner and Rajan (1994), Puri (1994), and Konishi (2002) for studies of bond performanceusing pre-war data for the United States or Japan.
8

In contrast, Ito and Konishi (2000) and Kang and Liu (2007) show that Japanese banks create conflicts of interest inbond markets.
9

Brennan and Kraus (1987) and Myer and Majluf (1984) wrote theoretical papers on capital structure, based on the

problem of insider information at the time of new equity issuance. Burch, Nanda, and Warther (2005) argue that the
problem of inside information is more serious in SEO markets than bond markets, because bond markets usuallyobserve calibrated debt ratings provided by third parties, such as Standard and Poor’s and Moody’s.

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underwriting fee) of SEOs. In addition, we examine the effect of between bankunderwriters on the market impact of SEO announcements and the post-issue long-term
performance.
Second, this paper examines the effect of the relationship between the bankunderwriter and the intended use of the proceeds on the SEO announcement return andlong-term performance. In Japan, when issuers offer new shares, they must disclose theintended use of the proceeds to the investors in their prospectus. If the conflict ofinterest effect is supported, the SEO announcement’s return will be more negative whenissued by a bank that uses the proceeds to repay bank loans. Walker and Yost (2008)
find that the pre-issue disclosure of the intended use of SEO proceeds reduces
informational asymmetry and have an effect of the announcement price reactions.
Duarte-Silva (2010) examines the relationship between the underwriter lending and the
SEO announcement return. Regarding the relationship between long-term performanceand the use of proceeds, Autore, Bray, and Peterson (2009) find that management canrecapitalize debt by issuing stock when investors are overly optimistic about a firm’s
future prospects, potentially resulting in relative devaluation over the long term. In
addition, we examined the relationship between the intended use of proceeds to repay
the bank underwriter and the market impact on SEO announcement returns andlong-term performance.
Third, Japanese banks differ from U.S. banks in several respects. Japanese banks
have close and long-term relationships with their borrowers. Such ties enable a bank to
produce more information than an investment house.10 Moreover, unlike the United

10

In this paper, the term “investment house” refers to securities companies that do not have a bank as their parent

company.

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States where banks are banned from having equity holdings, Japanese banks are allowed
to own equity in their client firms. While previous theoretical research has strenuously
examined the difference between allowing and denying bank equity holdings (e.g.,
Kanatas and Qi 2003; Puri 1999), empirical examinations have not been fully performedin the United States. Puri (1999) argues that the impact of equity holdings isinternationally testable in countries in which banks are allowed to hold equity. Thus, we
examine the effect of the equity holdings of bank underwriters on SEO announcement
returns and post-issue performance.
This research contributes to the existing literature in several ways. We find that thebank underwriter, and especially the bank lending relationship, has a positive effect on
SEO announcement returns and post-issue long-term performance. This result is
consistent with the certification effect in which investors evaluate if a bank with private
information can assess the issuer’s intrinsic value and set the security price moresuitably than an investment house. This result is also consistent with the bond market(Puri 1996; Gande, Puri, Saunders, and Walter 1997; Konishi 2002; Takaoka and
McKenzie 2006; Yasuda 2007) and equity market (Duarte-Silva 2010; Kutsuna, Smith,and Smith 2007; Suzuki 2010) literature. However, when the issuers intend to use
proceeds to repay bank loans, the bank underwriter is not significantly associated withthe announcement return and the post-issue performance. This result is novel andimplies that the intended use of proceeds to repay bank loans offsets a bank’s
certification effect, which is consistent with the conflict of interest effect.
Bank equity holdings have significantly negative effects on SEO announcement
returns and post-issue performance. These results imply that the bank equity holding
offsets the bank’s certification effect, which is consistent with the conflict of interest
6effect. Yasuda (2007) and Suzuki (2010) find that the underwriting fee of issuers inwhich the underwriters’ parent banks concurrently have shareholdings is higher thanthat of the issuers underwritten by investment houses or banks with no lending in thebond and SEO market.11 We find additional effects of the concurrent bank equity
holdings on SEO announcement returns.
The verification method of an abnormal return also differs from that used inprevious studies. This paper examines abnormal SEO returns using acharacteristic-based benchmark model. Event studies typically use data that arecharacteristically non-representative of the overall market and are often grouped byunderlying traits, such as size, momentum, and valuation. For instance, firms that
initiate dividends, split their stock, or issue new shares are likely to be large with highprior returns.12 Ahern (2009) indicates that a characteristic-based benchmark model
produces the least biased returns with the fewest rejection errors. To control for any
possible selection bias in the estimation, we used a benchmark portfolio sample that
employs a propensity score-matching estimation approach.
We assumed that the bank is exogenously determined. Additionally, to investigate
the robustness of our results, we used sample selectivity methods. The results discussed
above do not differ when controlling for the endogeneity of the bank’s underwriting
decisions using selectivity methods.
The remainder of this paper consists of six sections. Section 2 discusses the related
literature and develops our hypotheses. Section 3 describes the sample selection and11

Drucker and Puri (2005) find that issuers with underwriters who concurrently lend to issuers pay lower

underwriting fees and loan yield spreads than those with underwriters who do not lend to issuers.
12

Under these conditions, Ahern (2009) evaluates eight methods, including a characteristic-based benchmark model,

a market model, the Fama French Three-Factor and Carhart Four-Factor models, and four test statistics, to determine
which method has the least mean bias, the best power, and the best specification of the tests.

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data. Sections 4 and 5 present the empirical methodology and results, respectively.
Finally, section 6 presents concluding remarks.

2. Related Literature and Hypotheses
Myers and Majluf (1984) argue that if managers typically have a more accurate
information set than the market and the market is aware of this information asymmetry,investors interpret the decision to sell equity by issues as an incidence of managerial
opportunism. Therefore, the asymmetric information between the issuer and outside
investors produces negative stock return following an SEO announcement.
The role of the underwriter is to alleviate the asymmetric information. The
underwriter has incentive to underwrite the issuers with a suitable stock price. Boothand Smith (1986), Chemmanur and Fulghieri (1994), and Cooney, Kato, and Schallheim(2003) argue that because the underwriter has an incentive to price appropriately tomaintain its reputation for future business, the informative underwriter certifies its
issuers to the market.13
Unlike investment houses, which do not offer loans to issuers in Japan, banks canobtain private information about issuers through loans or clearing functions. Diamond(1984) argues that through debt contracts, banks can access information that is not
publicly available and enjoy a comparative cost advantage in information production.
This advantage allows bank underwriters to gather more information about issuers anduse their information to signal to investors the quality of issued securities, as formallymodeled in Puri (1999). The certification hypothesis implies that if banks obtainsuperior information via lending activities, issuers underwritten by banks that are
13

Dunbar (2000) and Fernando, Gatchev, and Spindt (2005) indicate that mispricing in offer prices significantly

affects changes in the underwriter market share and leads to changes in the lead underwriter.

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concurrently lending should have higher announcement returns and post-issue
performance than those that are underwritten by banks without lending and investment
houses.
Japanese banks can hold both loans and stocks of firms.14 There is no definitive
evidence from previous research that a bank’s equity holdings will enhance or lower its
certification ability. Berlin, John, and Saunders (1996) argue that equity holdings byfinancial intermediaries enhance their certification capacity. Moreover, Li and Masulis(2004) suggest that equity investments in issuers improve the alignment of underwriterand issuer interests, thus causing underwriters to set relatively higher offer prices. If abank’s equity holdings heighten the bank’s certification ability and improve thealignment of underwriter and issuer interests, then issues underwritten by banks holding
the equity of issuers will have lower announcement returns and post-issue performance
than those underwritten by banks that do not hold the equity of issuers.
In contrast, Puri (1999) argues that when the equity held by the bank underwriteris retired by the proceeds of the issue, equity damages the credibility of the bank. The
reason is that equity increases the incentives of the bank to underwrite a bad quality
firm more than debt does. If the certification ability of a bank that is concurrently
underwriting and holding the shares of an issuer is weaker than that of a bankunderwriter engaged only in lending, then issues underwritten by banks holding theequity of issuers will have lower announcement returns and post-issue performance thanthose underwritten by banks that do not hold the equity of issuers.
A bank with superior information about issuing firms can misrepresent the value ofa firm’s securities and use the proceeds to repay its own debts (Benston 1990). This is
14

By law in Japan, bank shareholdings among firms are limited to a maximum of 5%.

9known as the conflict of interest effect. Because Japanese investment houses do notextend loans to firms, the issuer with an investment house underwriter is not altered by
the conflict of interest effect, though the issuer with a bank underwriter may face thiseffect. This is especially true when issuers have lending relationships with banks
disclose that the intended use of proceeds is repayment; this increases outsiders’concerns about the bank’s conflict of interest. Thus, the conflict of interest hypothesis
implies that when the intended use of proceeds disclosed by issuers is loan repayment,these issues underwritten by banks will have lower announcement returns and post-issue
performance than issuers with banks that do not use proceeds for loan repayment.

3. Sample Selection
We analyze an initial sample of 693 SEOs from January 2000 to December 2007. The
sample includes all registered stock offerings made by Japanese firms during that period.
We exclude utilities, financial companies, and REITs, as well as firms that release
important information (e.g., such as earnings, dividends, and switch the exchange) withthe announcement of the issue. The final sample consists of 346 offerings.
We obtain the data on equity issues (including the offer price, proceeds, and originalunderwriter names) from the IN Information Systems and the eol ESPer database. Bank
shareholding information is accessed via a subscription to the Japanese Group Company
database and the NIKKEI NEEDS Financial Quest database. Data on the age of the
SEO firms and the timing of the listed changes are obtained from Quarterly Firms
Statistics and the NIKKEI NEEDS Financial Quest database. The remainder of the data
is gathered from the NIKKEI NEEDS Financial Quest database.

4. Empirical Model
104.1. Announcement return
Following Ahern (2009), we use a characteristic-based benchmark model to examine
abnormal returns around the SEO pricing date. We defined abnormal returns for anindividual firm i , ARi ,t as:

ARi ,t  Return i ,t  Return p ,t,(1)

where Return i ,t is the daily stock return on day t for firm i , and Returni ,t is thedaily stock return on day t for the matching portfolio p . A simple comparison of thedaily return of firms that issued equity and those that did not is inappropriate due to apossible selection bias. If firms issuing securities are riskier than those that do not issue
securities, then a simple comparison of the returns between these groups confounds
ex-ante riskiness and ex-post riskiness (i.e., changes in the riskiness of the borrowers
after issuing the security). To circumvent this problem, we need to control for any
possible selection bias in the estimation. Thus, we employ a propensity score matching
estimation approach. The matching procedure is presented in Appendix A. The CAR is
the three-day cumulative abnormal return around the SEO announcement date (day -1, 0,+1).
We estimate the following equation using an ordinary least squares regression(OLS):CAR j    1Bank j   2 Payment j   3 Investment j   4 Working capital j  5 ln(Capitalization j )   6 RelSize j   7 TobinQ j 8STDR j   9 MajorUW j  10 Days j,(2)   k EXCHANGE jk    l YEAR jl  u j .
kl

The dependent variable CAR represents the cumulative abnormal returns around theannouncement date (day -1, 0, +1). The first independent variable is Bank, which is the11variable of primary interest. Bank is a dummy variable is equal to one if the leadunderwriter is a bank-affiliated securities company and zero otherwise. When thecertification effect is considered, the CAR should be positively correlated with the Bank.
Conversely, considering the conflict of interest effect, the CAR should be negatively
correlated with the Bank.
Payment, Investment, and Working capital are the variables concerning the firm’s
intended purpose for the proceeds. Payment is a dummy variable, which is equal to one
if the intended use of proceeds, which issuers disclose in the SEO prospectus, is a loan
payment. Otherwise, the variable equals zero. Autore, Bray, and Peterson (2009)indicate that management may recapitalize debt by issuing stock when investors areoverly optimistic about a firm’s future prospects, which potentially results in relative
devaluation in the long term. Thus, we expected CAR to be negatively associated with
Payment. Investment is a dummy variable, which is equal to one if the intended use ofproceeds is investment for facilities and zero otherwise. Walker and Yost (2008) findthat firms that have valuable growth opportunities are specific in their S-filings, and themarket views their anticipated investments favorably. Furthermore, Autore, Bray, andPeterson (2009) indicate that firms issuing equity specifically for investment purposes
are more likely to use the proceeds for value adding investments and less likely to beopportunistic market timers. Thus, we expected CAR to be positively associated with
Investment. Working capital is a dummy variable, and it is equal to one if the intended
use of proceeds is working capital and zero otherwise. The use of working capital does
not necessarily reflect future investment opportunities. Thus, we did not expect CAR tobe associated with Working capital.
The remaining independent variables are used to control for other factors that may
12affect the announcement return. The adverse selection problem results in a negative
relationship between announcement returns and ex-ante uncertainty concerning thevalue of the issue. We define two proxies, ln(Capitalization) and STDR, for asymmetric
information and uncertainty, respectively. The first proxy, ln(Capitalization) is thenatural logarithm of total market capitalization on the day prior to the offer. STDR is thestandard deviation of daily stock returns over 120 trading days and ending 20 days prior
to the offer. We expect CAR to be positively associated with ln(Capitalization) and
STDR. Cooney and Kalay (1993) argue that the investment opportunities to invest in apositive net present value (NPV) project are positively associated with SEO
announcement returns. TobinQ is the ratio of the market value of a firm to its total assets.
We expected CAR to be positively associated with TobinQ. Prestigious underwriters
certify the quality of the issue and alleviate informational asymmetry problems. As aproxy variable for prestigious underwriters, Major UW was used in the present study as
the dummy variable for a prestigious underwriter. This variable assumes a value of one
if the lead underwriter is a major underwriter (e.g., such as Nomura, Nikko, or Daiwa)
and zero otherwise. Based on the certification role of prestigious underwriters, Major
UW was expected to be positively associated with the CAR. Days is the number of days
between the pricing date (PD) and the issue day (ID). If the number of days between PD
and ID is high, price uncertainty will increase (Corwin 2003). We define the relative
offer size, RelSize, as the number of offered shares divided by the size of the existing
market for the firm’s shares. EXCHANGE and YEAR are sets of exchange and year
dummy variables, respectively.

4.2. Long-term performance

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Following Li and Zhao (2006), we use a propensity score matching method to examine
the post-issue abnormal buy and hold returns. We define the post-issue abnormal buy
and hold returns for an individual firm i , ABHR i as:

ABHR i  BHR i  BHR p(3)

where BHR i is the buy and hold return for the 24-month return after issue for firm
i , and BHR p is the buy and hold return for the 24-month return after issue for thematching portfolio p . The propensity score matching method finds matches by thepropensity score. The matching procedure is presented in Appendix A. In the long-term
performance examination, we exclude the issuers that issued equity 2 years before theissue examined. Therefore, the long-term performance sample includes 300 issuers.

5. Empirical Results
5.1. Descriptive statistics
In this paper, a bank-affiliated underwriter is defined as a securities company in which
the bank holds at least a 20% equity stake. Table 1 shows the number of bank-affiliatedunderwriting and investment houses that underwrote SEOs from 2000 to 2007. The
underwriting market share of bank-affiliated underwriters is nearly unchanged over thisperiod: from 40.90% in 2000 to 53.85% in 2006. This market share accounts for 47.69%
of the overall issues during 2000–2007. These results are consistent with Suzuki (2010).
The high market share of bank underwriter in 2000 is because the bank acquired thestocks of certain major investment houses just before 2000, including Daiwa Securities
SMBC and Kokusai Securities. Daiwa Securities, second in the underwriting market,
merged with Sumitomo Securities, which handled securities for the Sumitomo

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Bank-Group. Daiwa Securities SMBC then joined the Sumitomo Bank-Group. In 2000,
Daiwa Securities SMBC underwrote 19 SEO firms and held a 25% market share.
Kokusai Securities, the fifth largest in the underwriting market, became a groupcompany of the Bank of Tokyo Mitsubishi when Nomura Securities (an investment
house and the largest stockholder) sold off equity to the Bank of Tokyo Mitsubishi.15
There were five underwriting firms of Kokusai Securities in 2000, accounting for a
6.5% market share. Unlike the underwriting market for Japanese bonds, there were no
rapid changes in market share in the SEO underwriting market. This implies that driving
an investment bank out of the SEO market is relatively difficult. We indicate the detailof bank underwriters in Appendix B.
Table 2 presents descriptive statistics for the sample. The total number of offers isdivided into two mutually exclusive categories: bank and investment house SEO
samples. The mean (median) cumulative abnormal return, i.e., CAR, is -3.88% (-3.68%).
This level is low at ~1.5% compared with Eckobo, Masulis, and Norli (2007) in the
United States (-2.22%). The mean market capitalization, i.e., Capitalization, is 189,000
million yen, and the average offering raised 14,200 million yen in proceeds. The mean
relative offer size, i.e., RelSize, is 13%, and the mean (median) STDR is 3.20 (2.84). The
period from the pricing date to the offer date is, on average, 8.44 days. The share ofmajor underwriters in the Japanese SEO underwriter market is 71%. The percentage ofissuers who intend to repay loans in Japan is 35%. The share of issuers who intended toinvest their SEO proceeds for facilities is 64%. The share of firms who intended to use
proceeds for working capital is 32%. The mean level of offering characteristics in the
15

In September 2002, Kokusai Securities, Tokyo Mitsubishi Securities, and Tokyo Mitsubishi Personal Securities

formed Mitsubishi Securities.

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bank underwriter group is not statistically different from that in the investment housegroup, excluding Major UW and RelSize.

5.2. Announcement returns
In Table 3, we report the cumulative abnormal return, which divides the entire sample
by the issuer’s intended use of proceeds. In Panel A, we divide the sample into thepayment issuer and the non-payment issuer groups and compare the announcement
returns between these groups. The payment group is defined as the firms whose
intended use of the SEO proceeds is loan payment, and the non-payment group isdefined as the firms that do not intend to use the SEO proceeds in loan payment. The
level of announcement returns in the payment group (mean: -4.82%; median: -4.88%) islower than that in the non-payment group (mean: -3.37%; median: -3.41%), and thedifference is both economically and statistically significant. Furthermore, we divide thesample into issuers underwritten by banks and those underwritten by investment houses.
We then compare the announcement returns with the payment and non-payment groups,
respectively. Although the cumulative abnormal returns of the payment group are notstatistically different from that in the non-payment group in the investment housesubsample, the announcement returns of the payment group are 2.5% lower than that in
the non-payment group in the bank underwriter subsample. In Panel B, we divide thesample into the investment and non-investment groups and compare the cumulative
abnormal returns with these groups. The investment group is defined as the firms whose
intended use of SEO proceeds disclosed in the prospectus is investing in facilities, andthe non-investment issuer’s intended use of SEO proceeds is not investing in facilities.
The level of announcement returns in the investment group (mean: -3.59%; median:16

-3.62%) was not statistically and economically different from that in the non-investmentgroup (mean: -4.39%; median: -3.81%). We divided the issuers into bank andinvestment house subsamples and then compared the announcement returns with theinvestment and non-investment groups, respectively. Neither comparison resulted in astatistical or economic difference in CAR between the investment and non-investmentgroups. In Panel C, we divided the sample into the working capital and non-working
capital groups and compared the cumulative abnormal returns for each. The working
capital group is defined as the firms whose intended use of SEO proceeds in theprospectus is working capital, and the non-working capital issuer’s intended use of SEO
proceeds is not working capital. The level of announcement returns in the working
capital group (mean: -4.52%; median: -5.04%) is not statistically or economicallydifferent from that in the non-working capital group (mean: -3.58%; median: -3.40%).
Moreover, we divided the sample into issuers affiliated with banks and investment
houses and compared the cumulative abnormal returns with the working capital andnon-working capital groups, respectively. There is neither a statistical nor economic
difference in CAR between the working capital and non-working capital groups.
Table 4 shows the effect of bank underwriting and the intended loan payment on theannouncement cumulative abnormal returns. In Model 2, the estimated Bank coefficientis positively associated with the cumulative abnormal returns, consistent with thecertification effect. However, in Models 3 and 5, the coefficient of the interaction of
Bank and Payment, Bank*Payment, is negatively and significantly associated withcumulative abnormal returns. This result is consistent with the bank’s conflict of interest
effect, which implies that the announcement abnormal returns of banks with the intent
to repay are lower than that of banks without loan repayment intentions and no different
17

from that of issuers affiliated with investment firms. The average Japanese firm usually
has a bank that is its largest lender and sometimes supplies one or two board members.
In such a case, the bank is referred to as the “main bank” of the firm. To investigate theimpact of the main bank’s underwriter on announcement returns, we include the Main
Bank in the base model. The Main Bank is a dummy variable that assumes a value of
one if the bank underwriter’s parent bank is the greatest lender to the issuer and zerootherwise. In Models 4 and 5, the estimated coefficients of the Main Bank are notsignificantly associated with the discount rate, which is consistent with Suzuki (2010)
and Takaoka and McKenzie (2006). We assume that the bank is exogenously
determined. To investigate the robustness of the results, we use sample selectivity
methods, as in Suzuki (2010) and Takaoka and McKenzie (2006). However, the results
discusses above do not differ when we control for the endogeneity of the bank’s
underwriting decisions using selectivity methods. The sample selectivity methods arepresented in Appendix C.
The results in Table 4 do not allow us to examine whether bank lending andshareholding affect SEO announcement returns. To examine the impact of lending andshareholding relationships between bank underwriters and issuers, issuers are classified
into various groups, as shown in Table 5. They are divided into two groups based on theparent underwriter of the loans to the issuing firm: issuing firms with a loan relationshipand issuing firms with no loan relationship. The number of issuers with a loan
relationship is 112 (67.88%), and the number of firms with no loan relationship is 53(32.12%). The loan share is the total loan from the parent bank of the underwriter justbefore the issue, divided by the issuer’s total assets. The average loan share of issuerswith a loan relationship is 6.58%. Based on the parent’s equity share in the issuing firm,18issuers are divided into two groups: issuing firms with an equity relationship and thosewith no equity relationship. The number of issuing firms with an equity relationship is
52 (31.51%), and the number with no equity relationship is 113 (68.49%). The Equity
share is the equity held by the parent bank of the underwriter just before issuing
securities, divided by the number of shares outstanding. The average equity share ofissuers with an equity relationship is 2.17%.
A base model is explained in Section 4, which included two variables, Loan shareand Equity share, to investigate the impact of loan and equity relationships on
announcement returns. Models 1-4 in Table 6 show the effect of concurrent loan andequity holdings by a bank underwriter on the announcement returns. The Loan sharecoefficient is positively associated with the cumulative abnormal returns. This result is
consistent with Duarte-Silva (2010), who indicates (with respect to the certification
prediction) that the level of the SEO announcement return is positively associated withthe underwriter who lends concurrently. In contrast, the Equity share coefficient isnegatively associated with the announcement return. This result is consistent with thecertification effect, which predicts that the level of the SEO announcement return ispositively associated with the bank underwriter who holds equity concurrently. If theconflict of interest effect is supported, an announcement return may be lower from anissuers who discloses the intention for loan repayment and is underwritten by bankunderwriters who concurrently lend. LoanD is a dummy variable that is equal to one if
the bank underwriter is concurrently lending and a value of zero otherwise. In Model 6,
the coefficient of the interaction variable of LoanD and Payment, LoanD*Payment, isnegatively associated with the announcement abnormal returns. These results imply thatthe intended use of proceeds to repay bank loans offsets a bank’s certification effect,19

which is consistent with the conflict of interest effect.

5.3. Long-term performance
To examine issuers’ long-term performance, we exclude firms that offered new stocks
within 2 years before and after the SEO offering. Therefore, the long-term performancesample consists of 222 offerings. If the certification effect is supported, issuers
underwritten by banks that are concurrently lending should have higher post-issue
performance than those underwritten by banks without lending and investment houses.
Conversely, if banks misuse their superior information to repay their own claims more
actively than investment houses, the issuers with banks that use proceeds to repay bankloans may have lower post-issue performance than those underwritten by banks without
lending or by investment houses. In this section, post-issue performance is comparedwith the bank relationship and the issuers’ intended uses of proceeds.
To examine the impact of bank relationships and the issuers’ payment intentions,issuers are classified into various groups as shown in Table 7. In Panel A, they are
divided into two groups based on the underwriter: issuing firms with bank underwriters
and investment houses. The mean (median) ABHR for bank underwriters is -8.09%(-10.51%), which is greater than the mean (median) ABHR for investment houses,-16.71% (-20.80%). Panel B of Table 7 shows the ABHR of issuers who intended to
repay bank loans and those who did not. The average (median) ABHR for issuers who
intended to repay loans is -16.38% (-18.82%), which is lower than the average (median)
ABHR for issuers who did not intend to repay loans, -10.65% (-12.22%). This result is
consistent with Autore, Bray, and Peterson (2009), who state that management may
recapitalize debt by issuing stocks when investors are overly optimistic about a firm’s

20future prospects, potentially resulting in long-term relative devaluation.
Panels C and D of Table 7 indicate the ABHR of issuers with bank loans and theirequity relationships. The average (median) ABHR of the firms that have a loan
relationship with a bank is 5.72% (-1.20%). The average and median ABHR of issuerswith bank underwriters’ loan relationships is significantly higher than those without aloan relationship. This result is consistent with the certification effect by the bank loan
relationship. The average (median) ABHRs of the equity relationship and non-equity
relationship are -6.56% (-16.65%) and -8.92% (-8.20%), respectively. Although the
ABHR of banks without an equity relationship (Non equity) is marginally greater thanthat of investment houses (z-statistics = 1.62), the ABHR of banks with an equity
relationship (Equity) is not different from that of investment houses. These results imply
that the level of ABHR is marginally affected by the difference in bank equity
relationships, which is consistent with the conflict of interest effect by the bank equity
relationship.
Panel E of Table 7 indicates the ABHR of issuers with the interaction of the payment
intention and bank loan relationship. The average (mean) ABHR of banks with
LoanD*Payment is not significantly different from that of investment houses (t-statistics= -1.07, and z-statistics = -1.23). In contrast, the average (median) ABHR of banks with
LoanD*Nonpayment is significantly higher than that of investment houses (t-statistics =2.96, and z-statistics = 2.87). These results imply that firms’ intentions to use proceeds
to repay bank loans offset a bank’s certification effect, which is consistent with theconflict of interest effect.

6. Concluding Remarks
21

We examine the effects of bank underwriters on the market reaction to SEO
announcements and post-issue long-term performance in Japan following deregulationin 1999. We find that according to the issuers’ payment intentions and bankrelationships, bank underwriters have different effects on SEO announcement returns
and post-issue performance.
First, we demonstrate that the bank underwriter, and especially the bank lending
relationship, have a positive effect on SEO announcement returns and post-issue
long-term performance. This result is consistent with the certification effect, whichstates that investors who evaluate a bank with private information can assess the issuer’s
intrinsic value and set the security price more suitably than an investment house can.
Second, when the issuers intended to use proceeds to repay bank loans, the bankunderwriter is not significantly associated with the announcement returns and post-issue
performance. This result is novel, and it implies that the intended use of proceeds torepay bank loans offsets a bank’s certification effect, which is consistent with theconflict of interest effect. Finally, bank equity holdings have significantly negative
effects on SEO announcement returns and post-issue performance. These results imply
that the bank equity holding offsets a bank’s certification effect, which is consistent
with the conflict of interest effect. We conclude that banks have different effects on theissuers according to the banks’ characteristics and intended uses of the proceeds.
These results support both the certification and conflict of interest effects withregard to SEO announcement returns according to the bank’s relationships and theissuers’ repayment intentions. However, because the conflict of interest effect on the
SEO announcement returns does not exceed the certification effect, the announcement
returns of issuers affiliated with banks is not lower than that of issuers affiliated with22

investment houses. Thus, the evidence supports the deregulation of the internal flow of
information obtained through lending relationships.

Appendix A: Matching Procedure
The matching procedure was performed as follows. First, we implemented a probit
estimation that models the probability of an equity offering in year t, conditional on thecovariates observed in year t-1. Firms that issue equity ( SEOt  1 ) are labeled astreatment observations. Next, we attached a propensity score to each observation. The
propensity score e() is defined ase( X t 1 )  Pr( SEOt  1 X t 1 ),(A1)where X t 1 is a vector of covariates in the probit estimation.
Next, we implemented another set of probit estimations, including cross-terms, which
are multiplied by the variables that measure the extent of the external control of a firm.
For each treatment observation, we identified matched observations from the sample offirms not issuing securities. The matched observations are those that demonstrate the“closest” propensity scores to a particular treatment observation; they are labeled ascontrol observations. These matched observations were selected from the same calendar
year as the treatment observation. It should also be noted that we used a non-treatedobservation more than once as a control, i.e., a non-treatment observation may havebeen used as a control for more than one treatment observation at the same time. Several
matching algorithms can be used to find the “closest” control observations. As a23

baseline for this analysis, we employed the ten nearest matches in which the arbitrarily
determined ten observations with propensity scores closest to each treatment
observation were selected.16
One of the benefits of employing a propensity-score-matching estimation is that we
could match treatment and control observations using the scalar propensity score. The
propensity score, which is the conditional probability of a treatment given the value ofthe observed characteristics, is a useful variable when dealing with highly dimensional
vectors of covariates. Rosenbaum and Rubin (1983) show that treatment observations
and control observations with the same propensity score value have the same
distribution as the full vector of covariates. Thus, it is sufficient to match firms in termsof the propensity score to obtain the same probability distribution of covariates fortreatment and control observations. We began with the baseline probit estimation. In the
probit estimation, we obtained the conditional probabilities of a firm issuing equity inyear t given the values of the observed firm’s characteristics in year t-1. The dependent
binary variable represents a security offering in year t ( SEOt ). The following
explanatory variables were used. Regarding firm performance, the return on total assets( ROAt 1 ) and the capitalization ( CAPt 1 ), which is defined as the natural logarithm of
the total market capitalization, were employed. We also used the debt ratio ( DEBTt 1 ) asa variable for a higher risk of bankruptcy and the market to book ratio ( MTBt 1 ) as avariable for the firm’s value gap.

16

We found that the results obtained using different matching algorithms (i.e., nearest five matches) are similar to

those obtained using the ten nearest matches.

24Appendix B: Wave of securities company mergers between 2000 and 2007
Table A1 shows the list of bank underwriters, their parent commercial banks, and shares.
In our sample period, most bank underwriters merged due to the regulation change andthe merger of their parent commercial banks. Before 1993, the Securities and Exchange
Law, which was modeled after the Glass-Steagall Act in the U.S., separated the banking
and securities business. The Financial Institution Reform Act of 1993 allowed banks
and securities companies to enter each other’s business areas. For example, theIndustrial Bank of Japan formed the subsidiary securities company, Industrial Bank ofJapan Securities, in July 1993. This act affected the structure of securities companies in
two ways. First, in the deregulation process, securities companies were allowed to form
equity brokerage businesses. In contrast, subsidiaries of commercial banks were notallowed to engage in equity brokerage businesses, including equity underwriting.
However, the Financial System Law of October 1999 enabled securities companies toengage in equity brokerage businesses.
Second, after The Financial Institution Reform Act of 1993 took effect, the structureof the securities industry was concentrated. Thus, banks established subsidies forsecurities companies. For example, the Industrial Bank of Japan established theIndustrial Bank of Japan Securities in September 1997. Furthermore, the Financial
System Reform Law of 1998 allowed the creation of financial holding companies.
Large banks established or merged other banks or financial service companies. As aresult of the concentration of financial companies, four securities companies remained:
Shinko Securities, Mizuho Securities, Mitsubishi UFJ Securities, and SMBC Friends
Securities, at the end of December 2007.
In April 2000, two subsidiaries of the Industrial Bank of Japan, Shin-Nihon
25

Securities and Wako Securities, formed Shinko Securities (a). This was an equal merger,
and the surviving entity was the former Shin-Nihon Securities. Mizuho Investor
Securities (b), formerly Kankaku Securities (which is an affiliated company ofDaiichi-Kangyo Bank), became a member of the Mizuho Financial Group in April
2002; it acquired Daito Securities in April 2002. In September 2000, the Mizuho group
was created through the establishment of Mizuho Holdings as a holding company of
Dai-Ichi Kangyo Bank, Fuji Bank, and the Industrial Bank of Japan, and in October
2000, their respective securities companies (Fuji Securities, Dai-Ichi Kangyo Bank
Securities, and Industrial Bank of Japan Securities) merged with Mizuho Securities (c).
The surviving entity was the Industrial Bank of Japan Securities.
In July 2001, Sanwa Securities and Tokai International Securities merged into
UFJ Capital Markets Securities via the merger of their parent banks. Furthermore, UFJ
Capital Markets Securities merged with Tsubasa Securities, which was comprised of theformer Taiyo Securities, Towa Securities and Dai-Ichi Securities in June 2002 andnamed UFJ Tsubasa Securities (d). UFJ Tsubasa Securities is a subsidiary of UFJ Bankand a member of the former UFJ Group. In September 2002, Kokusai Securities, Tokyo
Mitsubishi Securities (e), and Tokyo Mitsubishi Personal Securities merged to form
Mitsubishi Securities (f), which simultaneously became a subsidiary of The Bank ofTokyo-Mitsubishi and a member of the former Tokyo-Mitsubishi Group. Kokusai
Securities (g) was an affiliate of Nomura Securities but became the subsidiary of Tokyo
Mitsubishi Bank. Tokyo Mitsubishi Securities was a former subsidiary of the Mitsubishi
Trust Bank and was named Mitsubishi-Shin Securities. It became the subsidiary of theTokyo Mitsubishi Bank in September 1999 and changed its name to Tokyo Mitsubishi
Securities. In October 2005, the two largest financial group banks, Mitsubishi Tokyo
26

Financial Group and UFJ Financial Group, merged to form the Mitsubishi UFJ
Financial Group. As a result of this merger, their securities subsidiaries (UFJ Tsubasa
Securities and Mitsubishi Securities) merged into Mitsubishi UFJ Securities (h). In April
2003, Meiko National Securities, which was a member of the Sumitomo Group, andSakura Friend Securities, which was a subsidiary of the Sakura Bank and a member ofthe former Mitsui Group, formed SMBC Friend Securities (i). In April 1999, thewholesale division of Daiwa Securities and the operations of Sumitomo Capital
Securities Operations formed under the name of Daiwa Securities SB Capital Market.
At first, Daiwa Securities held 60% of the shares and Sumitomo Bank held 40%. In
April 2001, Daiwa Securities SB Capital Market absorbed part of the wholesale
securities operations of Sakura Bank and changed its name to Daiwa Securities SMBC
Securities (j).

Appendix C. Selection Bias
To examine the difference in the cumulative abnormal returns between firms with abank or an investment house underwriting, we used OLS regression and estimated thecoefficient of a dummy variable for the type of underwriter (Table 4). Specifically, we
estimated the following model:
yi  X i    Ci   ,(A2)

where yi is the cumulative abnormal return of the i th issue, X i represents the set of
publicly-observable characteristics of the i th issuer that affect the cumulative abnormal
return, Ci is a dummy variable that is equal to one if the i th issue is underwritten by abank and zero otherwise, and  is the disturbance term that can contain information on
the i th issuer’s unobservable characteristics. It is well-known that the estimatedcoefficient of the dummy variable in the OLS regression is inconsistent if the dummy
27

variable is endogenously determined. Because a bank’s decision to underwrite an issue
depends on both the issuer’s observable and unobservable characteristics, the dummy
variable Ci is potentially correlated with the disturbance term  . To illustrate the natureof this problem, suppose that the bank’s decision to underwrite an issue depends on:d i  I i  u ,(A3)

where I i represents a set of the issuer’s publicly-observable characteristics, and u isthe disturbance term that includes a set of the issuer’s publicly-unobservablecharacteristics. A bank underwrites the issue only when di  0 . Otherwise, a house
underwrites the issue. The selection bias of the OLS arises because u can becorrelated with  , in which case the underwriter can partially reveal hidden
characteristics and thereby affect stock pricing. To assess the effect of the issuer’s
unobservable characteristics on cost, we followed Puri (1996) and replaced the dummy
variable in Equation (1) with:
BANKINFO  Ci * ( I i )
 ( I i )
 (1  Ci ) *, ( I i )1   ( I i )(A4)where  () and  () are the density and cumulative probability functions of thestandard normal distribution, respectively. Thus, Equation (A2) becomes:
yi  X i   wBANKINFOi  v.

(A5)

The test for the difference in the issue cost between bank-underwritten firms andinvestment house-underwritten firms consists of the sign and significance of w . A
positive (negative) and significant value of w suggests a higher (lower) issue cost whena bank underwrites the issue. We estimated Equation (A5) using Heckman’s two-step
approach (Heckman, 1979). In the first step, we calculated  in Equation (A3) via probit
regression and substituted this estimate of  into Equation (A4) in order to obtain an
approximation for BANKINFOi . We calculated w in Equation (A5) by performing an
28

OLS regression and testing the null hypothesis that w  0 .
The explanatory variables used in a probit model explaining Bank were variables
contained in the base models for the discount rate and underwriter spread,
Ln(Capitalization), STDR, Major UW, RelSize, Ln(Proceeds), and two additionalvariables: the main bank’s loan share and the number of issues the issuer has made.
Table A2 presents the results of the cumulative abnormal returns on the OLS regression
in the second step of the test. In this step, the signs and significance of all independent
variables are similar to those in Table 4. In particular, the BANKINFO coefficient is
positive and significant at the 5% level. This result also supports the certification
hypotheses.
[2010.10.19 1005]

29

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34

Table 1.
The number of seasoned equity offerings from 2000 to 2007

Bank

Investment house

Total sample
year

Num

%

Num

%

(a)

(b)

(b)/(a)(c)(c)/(a)

2000

44

18

40.91

26

59.09

2001

24

10

41.67

14

58.33

2002

27

12

44.44

15

55.56

2003

31

13

41.94

18

58.06

2004

87

49

56.32

38

43.68

2005

58

28

48.28

30

51.72

2006

49

21

42.86

28

57.14

2007

26

14

53.85

12

46.15

Total

346

165

47.69

181

52.31

This table reports the number of bank underwriter and investment houses underwriting for eachyear. The sample consists of 346 seasoned equity issues from January 2000 to December 2006. A
bank underwriter is defined as a securities company in which the bank holds at least 20% equity.

35

Table 2.
Summary statistics for seasoned equity offerings
t -statistics,
z -statistics

Total Sample

Bank

Investment house

Mean
MedianStd.dev

-3.88
-3.68
6.36

-3.30
-3.41
6.78

-4.40
-3.78
5.92

1.61
1.33

Capitalization(mm yen)

Mean
MedianStd.dev

18900024700
1390000

82700
18800
20200028600028300
1900000-1.37-2.84 ***

Proceeds(mm yen)

Mean
MedianStd.dev

14200
2710
73400

8520
2370
20500

19400
2880
99500

-1.37
-1.89 *

RelSize

MeanMedianStd.dev

0.13
0.12
0.05

0.14
0.13
0.05

0.12
0.11
0.05

3.60 ***
3.42 ***

STDR

Mean
MedianStd.dev

3.20
2.84
1.58

3.25
2.91
1.67

3.15
2.75
1.49

0.57
0.30

Days

Mean
MedianStd.dev

8.44
7.00
3.01

8.23
7.00
2.58

8.63
7.00
3.35

-1.23
1.42

TobinQ

MeanMedianStd.dev

3.01
1.60
4.25

2.83
1.57
4.35

3.17
1.68
4.16

-0.74
-1.33 *

Major UW

Mean

0.71

0.53

0.88

-7.78 ***

Intended use of proceeds
Payment (%)

Mean

35.26

35.15

35.36

-0.04

Investment (%)

Mean

63.58

60.61

66.30

-1.10

Working capital (%)

Mean

31.79

34.55

29.28

1.05

346

165

181

SEO characteristics
CAR[-1 ~ +1]

Observations

This table reports the summary statistics for each variables by subsamples. CAR is the cumulative abnormal return for which the characteristicbased benchmark model is used for the matching portfolio. Capitalization is the market value of the firm at the last day of the previous month.
Proceeds is the amount of money firms earn by SEO. RelSize is the numver of offerd shared divided by the size of the existing market for the
firm's shares. STDR is defined as the standard deviation of daily stock returns over 120 trading days and ending 20 days prior to the offer.
Days is defined as the numver if days between PD and ID. TobinQ is defined as the ratio of the market value to its total assets. Major UW isequal to one if the lead underwriter is a major underwriters, Nomura, Nikko, and Daiwa, and zero otherwise. Paymend takes a value one if theintended use of proceeds is a loan payment, and a value of zero otherwise. Investment takes a value equal to one if the intended use ofproceeds is the investment for facilities and a value of zero otherwise. Working capital is the dummy variable and takes a value equal to one if
the intended use of proceeds is the working capital and a value of zero otherwise. Test statistics are t -test and Man-Whitney z -test results fordifference in mean and median. *** and * indicate statistical significance at the 0.01 and 0.1 levels, respectively.

36

Table 3.
Announcement returns by the intended use of the proceeds and the type of underwriter
Panel A. Intended use of the proceeds: repayment
CAR[-1~+1]

Repayment(a)Non-repayment(b)t -statistics,
z -statistics

-4.82
-4.88
6.32
122

-3.37
-3.41
6.34
224

2.04 **
1.98 **

Total sample

MeanMedianStd.dev
Obs.

Bank

Mean
MedianStd.dev
Obs.

(A)
(B)

-4.96
-5.21
6.62
58

-2.41
-2.44
6.73
107

-2.34 **
-2.32 **

Investment house

Mean
MedianStd.dev
Obs.

(C)
(D)

-4.69
-3.74
6.09
64

-4.25
-3.78
5.85
117

-0.49
0.36

-0.23
-0.40

2.19**
2.05**

t -statistics (A) - (C)z -statistics (B) - (D)Panel B. Intended use of the proceeds: investment facilities
CAR[-1~+1]

t -statistics,
z -statistics

Investment(a)

Non-investment
(b)

-3.59
-3.62
5.83
220

-4.39
-3.81
7.20
126

1.13
1.28

Total sample

MeanMedianStd.dev
Obs.

Bank

Mean
MedianStd.dev
Obs.

(A)
(B)

-3.00
-3.39
6.27
100

-3.77
-3.84
7.53
65

0.71
0.91

Investment house

Mean
MedianStd.dev
Obs.

(C)
(D)

-4.08
-3.78
5.40

-5.05
-3.78
6.84

1.05
0.99

1.37
1.15

1.00
0.88

t-statistics (A) - (C)z-statistics (B) - (D)37

Panel C. Intended use of the proceeds: working capital
CAR[-1~+1]

t -statistics,
z -statistics

Working capital(a)Non-working capital
(b)

-4.52
-5.04
7.42
110

-3.58
-3.40
5.80
236

-1.28
-1.62

Total sample

MeanMedianStd.dev
Obs.

Bank

Mean
MedianStd.dev
Obs.

(A)
(B)

-3.86
-3.94
7.44
57

-3.01
-3.39
6.43
108

-0.77
-0.81

Investment house

Mean
MedianStd.dev
Obs.

(C)
(D)

-5.23
-5.78
7.41
53

-4.06
-3.40
5.18
128

-1.20
-1.69 *

0.96
1.07

1.40
1.03

t-statistics (A) - (C)z-statistics (B) - (D)This table reports the cumulative abnormal returns in the three trading days around the announcement date (i.e., days 1, 0, and 1). Panel A divides the entire sample into the payment group and the non-payment group and compares the
CAR with the payment group and the non-payment group. Panel B divides the entire sample into the investment groupand the non-investment group and compares the CAR with the investment group and the non-investment group. Panel
C divides the entire sample into the working capital group and the non-working capital group and compares the CAR
with the working capital group and the non-working capital group. Test statistics are t -test and Man-Whitney z -test
results for difference in mean and median. ** and * indicate statistical significance at the 0.05 and 0.1 levels,
respectively.

38

Table 4.
The effect of bank underwriter and the intended use of proceeds on the announcement return(Obs = 346)

Model 2Model 3Model 4Model 51.43 **(2.02)

2.30 ***(2.68)

1.74 **(2.19)-0.94(-0.91)-1.18(-1.63)0.15(0.19)-0.16(-0.18)-0.02(-0.05)-9.62(-1.25)-0.07(-0.37)-0.98 ***(-3.45)-0.80(-1.00)0.09(0.22)-1.17(-1.62)0.24(0.32)-0.12(-0.14)-0.01(-0.03)-11.63(-1.51)-0.06(-0.32)-0.98 ***(-3.46)-0.23(-0.27)0.13(0.33)-2.64 *(-1.80)0.08(0.08)
0.18(0.24)-0.28(-0.32)-0.01(-0.03)-12.02(-1.54)-0.07(-0.42)-0.97 ***(-3.44)-0.40(-0.47)0.15(0.40)-1.14(-1.58)0.16(0.20)-0.10(-0.11)0.04(0.15)-10.98(-1.42)-0.06(-0.30)-1.01 ***(-3.55)-0.31(-0.36)0.12(0.32)

2.60 ***(2.72)-0.90(-0.87)-2.61 *(-1.78)0.09(0.10)
0.10(0.13)-0.25(-0.29)0.04(0.15)-11.39(-1.46)-0.07(-0.39)-0.99 ***(-3.53)-0.48(-0.55)0.15(0.39)

yes
yes

yes
yes

yes
yes

yes
yes

yes
yes

-0.08
(-0.01)-1.38(-0.19)-2.20(-0.30)-2.41(-0.33)-3.31(-0.44)

0.08

0.09

0.10

0.09

0.10

Model 1Bank
Main Bank
Bank*Payment
Payment
Investment
Working capitalln(Capitalization)
RelSize
TobinQ
STDR
Major UW
Days

Exchange dummyYear dummyIntercept

Adj-R

2

This table reports the effect of bank underwriter and the intended use of proceeds on the announcement cumulative abnormal
return. The dependent variable, CAR , represents the cumulative abnormal returns around the announcement date (day -1, 0, +1).
Bank is the dummy variable which take the value of one if the underwriter is bank. Main Bank is the dummy variable thattakes the value of one if the bank underwriter's parent bank is the greatest lender to the issuer and zero otherwise. Paymend
takes the value of one if the intended use of proceeds which issuers disclose in the SEO prospectus is a loan payment, and zerootherwise. Bank *Payment is the interaction term of Bank and Payment . Ln(Capitalization ) is the natural logarithem ofmarket value at the last day of previous month. RelSize is the numver of offerd shared divided by the size of the existing market
for the firm's shares. TobinQ is defined as the ratio of the market value to its total assets. STDR is deefined as the standard
deviation of daily stock returns over 120 trading days and ending 20 days prior to the offer. Major UW takes a value one if thelead underwriter is a major underwriters, Nomura, Nikko, and Daiwa, and zero otherwise. Days is defined as the numver ifdays between PD and ID. We also include Stock Exchange dummy and year dummy. Heteroskedasticity-adjusted t-statistics
are presented in parentheses below the regression coefficients. ***, **, and * indicate statistical significance at the 0.01, 0.05,
and 0.1 levels, respectively.

39

Table 5.
Relationship between issuers and bank underwriters, loan repayment, and announcement returns

Bank loan ratio(%)

Bank equity ratio
(%)

Mean

MedianMeanMedianRepayment(%)

CAR[-1~+1] (%)

MeanMedianLoan relationship
yes
no

112
53

6.58

4.48

0.84
0.36

0.00
0.00

41.07
22.64

-3.48
-2.93

-3.51
-3.04

Equity relationship
yes
no

52
113

4.68
4.37

3.50
0.59

2.17

2.09

40.38
32.74

-3.66
-3.14

-3.22
-3.61

Note:
This table presents the relationship between issuers and bank underwriters, loan repayment, and announcement returns.
The sample is restricted to issuers with bank underwriters. Upper (lower) rows divide the sample into groups dependingon whether a loan (equity) relationship existed between the firm and parent bank of the underwriter.

40

Table 6.
The effect of concurrent loan and equity holdings by bank underwriters on announcement returns(Obs = 346)

Model 1Model 2Model 3Model 4Model 5Bank

1.03
(1.44)
0.10 *(1.82)

1.91 **(2.56)

1.50 **(2.01)
0.11 **(2.21)-0.88 **(-2.46)2.35 **(2.56)
0.11 **(2.24)-0.86 **(-2.43)-2.61 *(-1.82)2.40 *(1.87)

Loan Share
Equity Share-0.81 **(-2.29)Bank*Payment
LoanD-1.23 *(-1.72)0.41(0.54)-0.20(-0.23)-0.09(-0.31)-10.65(-1.39)-0.04(-0.24)-0.94 ***(-3.30)-0.10(-0.11)
0.12(0.31)-1.10(-1.55)0.18(0.24)-0.10(-0.12)0.08(0.26)-10.23(-1.34)-0.07(0.37)-1.04 ***(-3.67)-0.49(-0.57)0.13(0.35)-1.16 *(-1.65)0.37(0.48)-0.20(-0.23)0.00(-0.01)-9.02(-1.19)-0.05(-0.29)-0.99 ***(-3.52)-0.36(-0.42)0.13(0.32)

0.07(0.07)
0.32(0.41)-0.36(-0.41)-0.01(-0.02)-9.42(-1.23)-0.07(-0.37)-0.97 ***(-3.49)-0.51(-0.60)0.15(0.39)-0.17(-0.12)-2.96 *(-1.93)-1.11(-0.49)0.06(0.07)
0.26(0.33)-0.22(-0.24)0.03(0.10)-12.45(-1.60)-0.08(-0.42)-0.99 ***(-3.52)-0.43(-0.49)0.15(0.38)

yes
yes

yes
yes

yes
yes

yes
yes

yes
yes

-1.27
(-0.16)-2.96(-0.40)-1.27(-0.18)-2.00(-0.29)-2.92(-0.40)

0.09

0.10

0.11

0.11

0.09

LoanD*Payment
NonLoanD*Payment
Payment
Investment
Working capitalln(Capitalization)
RelSize
TobinQ
STDR
Major UW
Days
Exchange dummyYear dummyIntercept
Adj-R

2This table reports the effect of bank underwriter and the intended use of proceeds on the announcement cumulative abnormal return. The
dependent variable, CAR , represents the cumulative abnormal returns around the announcement date (day -1, 0, +1). Bank is the dummy variablewhich take the value of one if the underwriter is bank. Loan Share is the share of outstanding loans made by bank. Equity Share is the equity
share held by the bank underwriter. Paymend takes the value of one if the intended use of proceeds which issuers disclose in the SEO prospectus
is a loan payment, and zero otherwise. Bank *Payment is the interaction term of Bank and Payment . LoanD a dummy variable that take thevalue of one if the bank underwriter is concurrently lending and zero otherwise. LoanD *Payment is the interaction term of LoanD and Payment .
NonLoanD *Payment is the interaction term of NonLoanD , which take the value of one if the underwriter is not concurrently lending and zerootherwise, and Payment . Ln(Capitalization ) is the natural logarithem of market value at the last day of previous month. RelSize is the numver ofofferd shared divided by the size of the existing market for the firm's shares. TobinQ is defined as the ratio of the market value to its total assets.
STDR is deefined as the standard deviation of daily stock returns over 120 trading days and ending 20 days prior to the offer. Major UW takes avalue one if the lead underwriter is a major underwriters, Nomura, Nikko, and Daiwa, and zero otherwise. Days is defined as the numver if days
between PD and ID. We also include Stock Exchange dummy and year dummy. Heteroskedasticity-adjusted t-statistics are presented inparentheses below the regression coefficients. ***, **, and * indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.

41

Table 7.
Long-term returns by underwriter type and issuers’ loan repayment intents
Panel A. Comparison by type of underwriter
ABHR 24

Total sample

Bank
(a)

Investment house(b)-12.71-15.27222-8.09-10.51103-16.71-20.80119

Payment

Nonpayment

MeanMedianObs.

t-statistics,
z-statistics(a)-(b)1.03
1.63

Panel B. Comparison by issuer’s loan repayment intents
ABHR 24
Mean
Median
Obs.

(a)

(b)

-16.38-18.8280-10.65-12.22142

t-statistics,
z-statistics(a)-(b)
-0.66
-1.16

Panel C. Comparison by bank loan relationshipBank

Investment house
ABHR 24
Mean
Median
Obs.

(a)

Loan
(b)

Non loan(c)-16.71-20.80
119

5.72
-1.20
73

-41.71-31.4030

t -statistics,
z -statistics(a)-(b)t- statistics,
z -statistics(a)-(c)t -statistics,
z -statistics(b)-(c)-2.68 ***
-2.73 ***

-2.01 **
-1.16

3.23 ***
2.97 ***

t -statistics,
z -statistics(a)-(b)t- statistics,
z -statistics(a)-(c)t -statistics,
z -statistics(b)-(c)

-0.96
-0.87

-0.81
-1.62

0.16
-0.35

z -statistics(a)-(b)t- statistics,
z -statistics(a)-(c)t -statistics,
z -statistics(b)-(c)

-1.07
-1.23

-2.96 ***
-2.87 ***

-1.18
-1.42

Panel D. Comparison by bank equity relaitonshipBank

Investment house
ABHR 24
Mean
MedianObs.

(a)Equity(b)

Non equity(c)-16.71-20.80119-6.56-16.65
36

-8.92
-8.20
67

Panel E. Comparison by bank loan relationship and issuers’ loan repayment intents
Bank
Investment houset -statistics,
LoanD
LoanD
*Payment

ABHR 24
Mean
MedianObs.

*Nonpayment

(a)

(b)

(c)

-16.71-20.80119-4.54-10.43
29

12.48
9.80
44

ABHR is the 24 month abnormal buy and hold return, which propensity score matching method are used to identify the matching portfolio.
Panel A reports the ABHR by the type of underwriter; which with banks or with investment houses. Panel B reports the ABHR by issuer's
loan payment intend. Panel C reports the ABHR by the type of the underwriter, and the loan relationship if the parents of underwriter isbank. Panel D reports the ABHR by the type of the underwriter, and the equity relationship if the parents of underwriter is bank. Panel E
reports the ABHR by the type of the underwriter, and the interaction term of LoanD and Payment (Nonpayment ), which take the value of
one if the firm has (does not has) loan relationship with bank underwriter, and zero otherwise. LoanD a dummy variable that take the valueof one if the bank underwriter is concurrently lending and zero otherwise. Test statistics are t -test and Man-Whitney z -test results fordifference in mean and median. *** and ** indicate statistical significance at the 0.01 and 0.05 levels, respectively.

42

Table A1.
Bank Underwriter and Market Share
Bank underwriter

Parent commercial bank

Number of Firms

Share (%)

Daiwa SMBC Securitiesi

Mitsui Sumitomo Bank

87

25.14

Kokusai SecuritiesfTokyo Mitsubishi Bank

7

2.02

Mitsubishi SecuritieseTokyo Mitsubishi Bank

8

2.31

Mitsubishi UFJ Securitiesx

Mitsubishi Tokyo UFJ Bank

7

2.02

Mizuho SecuritiescMizuho Corporate Bank

13

3.76

Mizuho investors Securitiesb

Mizuho Bank

5

1.45

Shinko SecuritiesaMizuho Bank

22

6.36

SMBC Friend SecuritieshSumitomo Bank

1

0.29

Tokyo Mitsubishi SecuritiesgTokyo Mitsubishi Bank

1

0.29

UFJ Tsubasa Securitiesd

UFJ Bank

14

4.05

Note:
Security company mergers
April 2000, Shinnihon Securities and Wako Securities formed Shinko Securities
Octorber 2000, Daiichi Kangyo Securities, Fuji Securities, and IBJ Securities formed Mizuho Securities
July 2001, Sanwa Securities and Tokai International Securities formed UFJ Capital Markets Securities
June 2002, Tsubasa Securities and UFJ Capital Markets Securities formed UFJ Tsubasa Securities
September 2002, Kokusai Securities, Tokyo Mitsubishi Securities, and Tokyo Mitsubishi Personal Securities formed Mitsubishi Securities
April 2003, Meiko National Securities and Sakura Frend Securities formed SMBC Friend Securities
Octorber 2005, Mitsubishi Securities and UFJ Tsubasa Securities formed Mitsubishi UFJ Securities

43

Table A2.
The effect of the bank underwriter on the announcement returns: selectivity bias adjustments
Two-step(Obs = 346)
t-statistics

Coeff.
BANKINFO
Payment
Investment
Working capitalln(Capitalization)
RelSize
TobinQ
STDR
Major UW
Days

0.85
-1.15
0.24
-0.12
-0.03
-9.94
-0.06
-0.98
-0.78
0.10
yes
yes

Exchange dummyYear dummyIntercept
Adj-R

1.98 **
-1.60
0.32
-0.14
-0.10
-1.29
-0.34
-3.46 ***
-0.97
0.26

3.22

2

0.98
0.09

Note:
This table reports the effect of bank underwriter and the intended use of proceeds on the announcement
cumulative abnormal return. The dependent variable, CAR, represents the cumulative abnormal returns
around the announcement date (day -1, 0, +1). BANKINFO is the selectivity bias adjustments bankvariable. Paymend takes the value of one if the intended use of proceeds which issuers disclose in the
SEO prospectus is a loan payment, and zero otherwise. Bank*Payment is the interaction term of Bankand Payment. Ln(Capitalization) is the natural logarithm of market value at the last day of previous
month. RelSize is the numver of offerd shared divided by the size of the existing market for the firm's
shares. TobinQ is defined as the ratio of the market value to its total assets. STDR is deefined as thestandard deviation of daily stock returns over 120 trading days and ending 20 days prior to the offer.
Major UW takes a value one if the lead underwriter is a major underwriters, Nomura, Nikko, and Daiwa,
and zero otherwise. Days is defined as the numver if days between PD and ID. We also include Stock
Exchange dummy and year dummy. Heteroskedasticity-adjusted t-statistics are presented in parentheses
below the regression coefficients. *** and ** indicate statistical significance at the 0.01 and 0.05 levels,
respectively.

44