2010-35
The type of short selling and issue discounts
in seasoned equity offerings
Katsushi Suzuki

The type of short selling and issue discounts in seasoned equity offerings†

Katsushi Suzukia
Graduate School of Business Administration
Kobe University

This version: April 2010_____________________________________________________________________________
Abstract
This paper examines whether issue discounts and stock return around seasoned equity offerings(SEOs) differ according to the type of short selling, manipulative or informative, in Japan where a
special short selling regulation around SEOs, such as Rule 105 in the United States, does not exist.
Following Gerard and Nanda (1993), this paper measures the type of short selling based on the ratioof covering short sales with new shares (SCR). This paper finds that a higher level of short sellingwith a high SCR has a negative effect on the informativeness of secondary market prices, but ahigher level of short selling with a low SCR has a positive effect on the information content. We alsofind evidence that short selling with a high SCR is associated with a price recovery, but short sellingwith a low SCR is associated with a price drop. Although the level of short selling has a negative
effect on issue discounts, the level of short selling with a high SCR has a positive effect onunderpricing. These results indicate that manipulative short selling increases by covering shorts withnew shares.
JEL classification: G24; G38; G39
Keywords: Seasoned equity offerings; Short sales; Discounts; Price pressure; Manipulative shortsales; Informative short sales_____________________________________________________________________________†

I would like to thank seminar participants at Hitotsubashi University (in 2009), and the Institute of Statistical
Research (in 2008). This paper was presented at the 2010 Japan Finance Association Conference. I have benefited
from the comments of Hideki Hanaeda, Taiji Baba, Yukio Kimura, Masato Hirota, Tadao Miyakawa, Xu Peng,
Masayoshi Takahashi, and Toshio Serita. I also thanks Masahito Kato for substantial assistance with the data. I am
grateful for financial support from the KAKENHI projects (No. 19730228).

asuzukatsu@b.kobe-u.ac.jp (K.Suzuki).

11. Introduction
There are at least two motives for short selling during a seasoned equity offering (SEO) period:
manipulative short selling and informative short selling. The motive for manipulative short selling isthat the investors launch short selling to artificially depress offering prices, which are lower than
anticipated offering prices, and cover the short sales with the discounted new shares. To prevent theoccurrence of such transactions in the United States, the Securities and Exchange Commission(SEC) formulated Rule 105 as part of Regulation M (an anti-manipulative rule concerning securities
offerings). Rule 105, short selling in connection with a public offering, prohibits any person frompurchasing a new public securities offering share if that person has sold short within five days prior
to the pricing date.1 On the other hand, the motive of informative short selling is that investors
decide to short sell based on private information concerning the issuers. Many academics find thatshort sellers enhance the informational efficiency of the price (e.g., Boehmer, 2009; Boehmer, Jones,
and Zhang, 2008; Reed, 2007). If informative short selling is regulated within pre-pricing dates, theprivate negative information may not have been fully reflected in offering prices, and investors maypurchase the over-valued stocks or require more issue discounts from issuers.
The purpose of this paper is verifying whether issue discounts and stock return around SEOs
differ according to the type of short selling, manipulative or informative, in Japan where a special
short selling regulation around SEOs, such as SEC Rule 105 in the United States, is not introduced.
Gerarad and Nanda (1993) develop models of interaction for the type of secondary market
short selling prior to the pricing of an offering. According to their model, manipulative short selling
activity prior to the pricing occurs among issuers which the investors expect that they can cover theirpositions with discounted new shares, even if their information is positive. Manipulative shortselling activity prior to the pricing will reduce the informativeness of the secondary market net order
1

On June 2007, the SEC tightened Rule 105. This is an important change from the prior Rule 105, which prohibitedthe use of offering shares to cover short sales affected within the five days prior to the pricing.

2

flow and exacerbate the winner's curse problem. As a result, a larger discount is required to sell theoffered shares. On the other hand, informative strategy occurs among issuers which the investor's
expected allocation is so small. Informed short selling activity prior to pricing will generate more
informative prices and will decrease issue discounts.
Whereas much empirical work has focused on testing the model, there is no broad consensus.
Corwin (2003), Kim and Shin (2004), Safieddine and Wilhelm (1996) and Singal and Xu (2005) test
whether the issue discount changed after the introduction of Rule 10b-21, which was designed tocurtail manipulative short selling around SEOs.2 However, these empirical studies do not use thelevel of daily short selling data.3 Henry and Koski (2010) examine the relationship between returns
and short sales over the entire SEO period, using daily level short selling data. However, they cannot
distinguish between informed and manipulative short selling. The findings of Safieddine andWilhelm (1996) and Henry and Koski (2010) are consistent with manipulative short sellers. In
contrast, Corwin (2003), Kim and Shin (2004) and Singal and Xu (2005) find evidence consistent
with informed short sellers.4
This paper differs from previous empirical research in some respects. First, we use not only
daily stock-borrowing data but also daily short-covering data. Gerard and Nanda (1993) argue thatthe types of short selling differ by whether investors can cover their short positions with discounted
new shares. Previous empirical research does not use daily short-covering data and cannot directly
investigate the validity of Gerard and Nanda (1993) model. This paper investigates the number ofshort selling stocks that are covered on an issue date and whether the types of short selling prior to2

SEC Rule 10b-21 was adopted in 1988. Rule 10b-21 prohibits the use of shares purchased in an SEO to cover shortpositions established between the announcement date and the offer date. In April 1997, the SEC adopted Rule 105 asa replacement for Rule 10b-21.
3
Corwin (2003) and Kim and Shin (2004) do not use the level of short selling data. Safieddine and Wilhelm (1996)
and Singal and Xu (2005) use the monthly short interest data.
4
Chemmanur, He, and Hu(2009) find that institutional investors trade in the same direction as their private
information, and their post-SEO trading significantly outperforms a naive buy-and-hold trading strategy. They statewith institutions possessing private information about SEOs and with information production instead of amanipulative trading role for institutional investors in the United States with Rule 105.

3

the pricing differ based on the difference in a short covering ratio.
Second, short selling regulations around SEOs, such as SEC Rule 105 in the United States, do
not exist in Japan. If short selling before pricing is manipulative trading, a short selling regulation
around SEOs will prevent the inefficient formation of the price and reduce the cost of raising funds
for an issuing firm. However, if the informative short selling is regulated prior to the pricing, thenegative private information may not be fully reflected in the offering prices, and investors maypurchase over-valued stocks or demand more SEO discounts from issuers. In this case, it is moredesirable to abolish the regulation around SEO. This paper directly examines the relationshipbetween short selling and its SEO offer price without SEO short selling regulations in Japan, and it
suggests whether a regulation around SEOs, i.e., Rule 105 in the United States, is necessary.
Third, the issuers in Japanese SEO markets must set the pricing date five or more trading days
prior to an offering date. These data show that the mean number of days between the pricing date
and the issue date is about seven trading days. In an offering date, a large number of new shares flow
into the market. A great amount of research indicates that these large inflows (outflows) result in apermanent decrease (increase) in the stock price due to a "downward-sloping demand curve" effect(Lock-up provisions: Field and Hanka (2005)，Stock splits: Greenwood, 2009)5. The "price pressure"effect also may have an effect on stock price around the issue date. A temporary price drop may benecessary in equilibrium in order to attract liquidity providers. Mikkelson and Partch (1988) and
Barclay and Litzenberger (1988) examine price changes around the issue date of SEOs. The large
flow of new shares have an effect on not only a "downward-sloping demand curve" and a "pricepressure" effect, but also on a "manipulative short selling" effect. Using Japanese SEO data, this5

In the presence of downward-sloping demand curve, if the market is semi-strong form market efficiency, apermanent stock price decrease may occur on the announcement day rather than the issue day. Harris and Gurel(1986) and Lynch and Mendenhall (1997) find temporary price changes around the announcement of S&P 500 index
changes. However, Field and Hanka (2005) find that although the terms of the lockup, including the unlock date, aredisclosed in the IPO prospectus, share price drop at the unlock date and is not quickly reversed, even though theexpiration date is well-known.

4paper can directly investigate whether short selling activity affects the secondary market pricearound the pricing date of SEOs, by dividing a "downward-sloping demand curve" effects and a"price pressure" effects that occur around the issue date.6
Finally, this paper examines an abnormal return around SEO by using a characteristic-based
benchmark model. Event studies typically use data that are characteristically non-representative of
the overall market and are often grouped by underlying 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 high prior returns. Under these conditions, Ahern (2009) run a hose race between 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 and the best power and specification of the tests. He indicate that a characteristic-based
benchmark model produces the least biased returns with the least rejection errors. To control for any
possible selection bias in the estimation, this paper uses a benchmark portfolio sample, which
employs a propensity score-matching estimation approach.
This paper contributes to the body of previous research in several ways. First, the paper findsthat a high level of pre-pricing short selling with a high short covering ratio at the issue date has anegative effect on the informativeness of secondary market prices, but a high level of pre-pricing
short selling with a low short covering ratio at the issue date has a positive effect on the information
content. These results imply that manipulative short selling is more likely to occur in a firm with ahigher short covering ratio, and informative short selling is more likely to occur in a firm with alower short covering ratio. These findings are consistent with the predictions of the Gerard andNanda (1993) model but have not been empirically proven by previous research.
Second, this paper finds that the level of short selling prior to the pricing is associated with a
6

Kim and Musulis (2009) finds that in post-SEO issue date, the price support activity level affect post-issue return,"price support" effect. The paper can also directly investigate the short selling effect, dividing a "price support"
effects.

5

price drop prior to pricing, consistent with Corwin (2003) and Henry and Koski (2010). However,depending on the short covering ratio on the issue date, a post-pricing return is different, inconsistent
with previous empirical research. Short selling prior to the pricing with a high short covering ratioon the issue date is associated with a price recovery, but short selling with a low short covering ratioon the issue date is associated with a price drop. These results indicate that the price impact of shortselling among issuers with higher levels of a short covering ratio on the issue date are temporary, but
the price impact of short selling among issuers with lower levels of a short covering ratio arepermanent, consistent with Gerard and Nanda (1993).
Finally, irrespective of the level of a short covering ratio, the level of pre-pricing short selling
has a negative effect on issue discounts measured at the end of the prior day, inconsistent with
Gerard and Nanda’s (1993) manipulative model. These results are consistent with Corwin (2003),
Kim and Shin (2004) and Singal and Xu (2005) and are inconsistent with Safieddine and Wilhelm(1996) and Henry and Koski (2010). However, Gerard and Nanda (1993) discuss another effect, i.e.,
the underwriters' pricing effect, where underwriters who account for manipulative temporary pricedrops and price relative to the expected value set the discount of the issuers lower than the true
discounts. The results obtained for issuers with a higher level of a short covering ratio do not support
the manipulative prediction but rather the prediction of the underwriters' pricing effect. The evidencecomplements the results of other studies supporting a certification role for underwriters in Japan[Cooney, Kato, and Schallheim, 2003] and the United States. [Booth and Smith, 1986]. However,underpricing which is the ratio of the closing price of the day after the pricing date to the offer priceis positively associated with the level of short selling with a high short-covering ratio on the issue
date. These results indicate that although underwriters set a lower discount rate of issuers with a highshort covering ratio, they cannot set a complete offer price on the issuer's intrinsic value completely
through the discount rate.

6

Our findings indicate that manipulative short selling and informative short selling coexist in
short selling before SEOs, and they provide some implications for the regulation of short selling
activity around SEOs, e.g., Rule 105 in the United States. For issuers that do not cover the shortposition with the new offer, we find no evidence of manipulative short selling activity, which
worsens the informativeness of secondary market prices and exerts a temporary price pressurearound the pricing date. Therefore, these findings imply indirectly that Rule 105 in U.S. effectively
restrict manipulative short selling before SEOs. However, if underwriters can account for
manipulative temporary price drops and set an offer price at a suitable price, issuers may not suffer
any loss by manipulative trading. In this case, the need for the regulation of short selling activity
around an SEO may be low. However, our findings indicate that underwriters cannot set an offer
price at the issuer's fundamental value accurately in Japan without regulation. The results imply that
it is necessary to introduce a regulation on short selling, such as Rule 105, in many countries thatlack such a regulation. Although this analysis is based on the Japanese experience, these results may
have important implications for many countries.
The remainder of the paper is organized as follows. Section 2 discusses the relevant theories
and develops our hypotheses. Section 3 describes the data and the measures of short selling activity.
In Section 4, we report the empirical results of the tests used to evaluate the relationships between
short selling activity and stock returns around the pricing date, the discount rate and the underpricing.
Section 5 concludes the paper.

2. Relevant Theory and Hypotheses
2.1. Informativeness of secondary market prices
As discussed above, there are at least two motives for short selling activity during a seasoned equity
offerings (SEOs) period: manipulative short selling and informative short selling. In markets where7there are no regulations on short selling around SEOs, Gerard and Nanda (1993) argue that
according to the possibility that new shares can be used to cover shorts, the types of short selling aredifferent. When the possibility of covering shorts with new shares is high, manipulative short selling
activity occurs. This trading strategy is profitable as long as the secondary market trading loss issmaller than the additional gain from a lower issue price. Therefore, Gerard and Nanda argue that thetrader is willing to sell in the secondary market even when his or her signal is positive, to ensure alower issue price and a larger gain on his or her share of the SEO. Furthermore, they argue that a
trader might attempt to influence the offer price by selling heavily in the secondary market just prior
to the offering, expecting to recoup his losses and make larger profits by bidding within the SEO. If
manipulative short selling occurs among issuers when new shares are intended to cover shortpositions, the secondary market prices of these issuers may be less efficient on the pricing date. On
the other hand, informed traders sell short in the secondary market when their information isnegative, and the expected allocation of the new shares is so small that attempts to cover their shortposition with new shares are difficult. If informative short selling occurs among issuers when newshares are not used to cover short positions, the secondary market prices may be more efficient.

H1A. Higher levels of short selling before a SEO pricing date for issuers with high levelsof covering short positions with new shares should be associated with lessinfomativeness of market prices on the pricing date.

H1B. Higher levels of short selling before a SEO pricing date for issuers with low levelsof covering short position with new shares should be associated with the more
infomativeness of market prices on the pricing date.

8

2.2. Pre-pricing and post-pricing performance
Types of short selling may change the secondary market price before or after the pricing date. Gerard
and Nanda (1993) indicate that for the issuers where manipulative trading occurs before the pricing
date, secondary market prices will drop in the days preceding the pricing date and will recover in the
post-issue market. Corwin (2003) and Henry and Koski (2010) find that these price pressures are
consistent with manipulative short selling. On the other hand, Gerard and Nanda (1993) also indicatethat for the issuers in which informative short selling occurs, secondary market prices will drop in
the days preceding the pricing date and will not change or drop in the post issue market. However,there are no empirical studies supporting the effects of informative short selling on changes in thestock price before and after the pricing date.

H2A. Higher levels of short selling before a SEO pricing date for issuers with high levelsof covering short positions with new shares should be associated with sliding prices inthe days preceding the pricing date and recovering prices in the post-pricing days.

H2B. Higher levels of short selling before a SEO pricing date for issuers with low levelsof covering short positions with new shares should be associated with sliding prices inthe days preceding the pricing date and dropping or unchanging prices in the post-pricing
days.

2.3. Discount rate and underpricing
Many of the theories advanced to explain the price of new issue shares are based on uncertainty andinformation asymmetry.7 Rock (1986) presents a model for the IPO market with two types of7

Allen and Faulhaber (1989), Beatty and Ritter (1986), Benveniste and Spindt (1989), Chemmanur (1993), andWelch (1989) present theoretical models of IPO underpricing based on uncertainty and information asymmetry.

9investors: informed investors and uninformed investors. This information asymmetry may lead to awinner's curse problem. The uninformed investors primarily obtain the less successful IPOs.
Keeping these investors in the market, therefore, requires an additional premium.8 Gerard andNanda (1993) extend these insights to the SEO discounts. They developed a model of SEO pricing inwhich the nature of information asymmetry at the time of the offering is depends on the type of shortselling, manipulative or informative, in the secondary market preceding the offering. The
manipulative short selling activity prior to the pricing reduces the informativeness of the secondary
market net order flow and exacerbates the winner's curse problem. As a result, a larger discount isrequired to sell the offered shares. On the other hand, informed short selling activity prior to thepricing will increase the informative of the prices and decrease the issue discounts.
A large amount of empirical research has focused on testing the work of Gerard and Nanda(1993). Corwin (2003), Kim and Shin (2004), Safieddine and Wilhelm (1996), and Singal and Xu(2005) test whether the issue discount changed after the introduction of Rule 10b-21, which was
designed to curtail manipulative short selling around SEOs. Corwin (2003) and Kim and Shin (2004)
conclude that this rule resulted in larger issue discounts, but they do not use the level of short selling
data prior to the pricing and covering shorts on the issue date. Using the monthly short interest data,
Safieddine and Wilhelm (1996) conclude that Rule 10b-21 reduces short interest, and higher shortinterest is associated with larger issue discounts. Singal and Xu (2005) also use the monthly shortinterest data and find that the issue discount increases after the introduction of Rule 10b-21. Henry
and Koski (2010) use the daily short selling data following the introduction of Rule 105 and examine
the relationship between short selling and the issue discount. They find that higher levels of shortselling are significantly associated with larger issue discounts.9

8Rock (1986) assumes that informed traders cannot purchase all new shares of public offerings.
SEC Rule 105 in the United States prohibited traders from covering short sales made within five days of theoffering with shares obtained in the SEO. Henry and Koski (2010) find that higher levels of short selling at least six
days before an offering are significantly associated with larger issue discounts.

910

If, according to Gerard and Nanda (1993), the types of short selling prior to the pricing differ
by the difference in covering shorts with new shares, the relationship between the level of shortselling and discount rates may change with a short covering ratio.

H3A. Higher levels of short selling before a SEO pricing date for issuers with high levelsof covering short positions with new shares should be associated with larger issue
discounts.

H3B. Higher levels of short selling before a SEO pricing date for issuers with low levelsof covering short positions with new shares should be associated with smaller issue
discounts.

Easterbrook (1984) suggests that when a firm issues new securities, its affairs are reviewed byan investment banker or similar intermediary who acts as a monitor for the collective interests of theshareholders and purchasers of the new securities.10 If the underwriters possess the information
about the SEO's value, they may account for whether a temporary price drop depends on
manipulative short selling prior to the pricing. Furthermore, the underwriters have incentives to set
an appropriate offer price. Dunbar (2000) and Fernando, Gatchev, and Spindt (2005) indicate thatmispricing in offer prices significantly affects changes in the underwriter market share and lead tochanges in the lead underwriter. Cooney, Kato, and Schallheim (2003) argue that since theunderwriter has the incentive to price appropriately to maintain its reputation for future business, theunderwriter's agreement to issue the new stock certifies to the market in Japan. In U.S. issue markets,
Booth and Smith (1986) argue that underwriters provide a valuable service by certifying the validity

10

Corwin and Schutlz (2005) find that syndicate members product information about IPO firms.

11

of the issuing firm's current stock price. If underwriters account for these temporary price drops andthe price relative to the expected value, the discount rate will be lower than the true discounts.

H3C. Higher levels of short selling before a SEO pricing date for issuers with high levelsof covering short positions with new shares should be associated with lower issue
discounts.

To confirm whether the underwriters have set the offer price appropriately, it is necessary toverify the relationship between manipulative short selling and underpricing. This paper defines
underpricing as the percent change from the offer price to the closing price on the day of trading
after pricing. If the underwriters can set the offer price of issuers with high manipulative shortselling appropriately through lowering the discount rate, the level of manipulative short selling will
not affect the underpricing of SEOs. However, if the underwriter’s capacity to set an appropriate
offer price is insufficient, underpricing may be associated with the level of manipulative short selling
prior to the pricing.

H4A. (Perfect adjustment) Higher levels of short selling before a SEO pricing date forissuers with high levels of covering short positions with new shares should not be
associated with underpricing.

H4B. (Imperfect adjustment) Higher levels of short selling before a SEO pricing date inissuers with high levels of covering short positions with new shares should be associatedwith larger underpricing.

123. Sample Selection and Data
3.1. SEO data
This paper employs a sample of seasoned equity offerings of common stock of Japanese public firms
between 1998 and 2009. The paper exclude initial public offerings, rights offerings, unit issues,
closed-end funds, and REITs. Consistent with previous studies, this paper excludes financial firms,
investment corporations, and firms from regulated industries. In addition, the paper excludes SEOs
that lack short selling data and that include other events such as account settlements, bonds,
convertible bonds, private equity placements, stock splits, and stock repurchases, during theannouncement date to the issue date.
To construct a proxy for the level of short selling and covering shorts, this paper uses the dataon borrowing and covering stocks from the Japan Securities Finance Co., Ltd., which is the mostimportant participant Japanese stock lending markets. In Japan, when investors borrow stocks fromthe Japan Securities Finance Co., Ltd., there are restrictions on the stocks that can be sold short, thereimbursement terms, and the fees, all of which are determined by the stock exchange according tovarious standards, such as individual stock liquidity. Borrowing is processed after brokerage firms
accept short selling orders from investors. Lending stocks are borrowed from securities financecompanies, whereas investors receive borrowed stocks from brokerage firms. The data cover all
information concerning borrowings through brokerage firms and provide the following information:
date, identification code, number of shares in inventory, number of shares that are newly lent andlent back, transaction price, and backwardation rate. The number of shares that are newly lent to themarket maker refers to the number of shares short sellers borrow to sell short, whereas the number ofshares lent back to the market maker refers to the number of shares short sellers cover to liquidate
short positions.

13This study obtained the data on borrowing and covering stocks from the Nikkei NEEDS
Financial Quest database. Data on the age of the SEO firms and the timing of the listed changes wereobtained from Quarterly Firms Statistics and the Nikkei NEEDS Financial Quest database. The
present study also gathered data on equity issues, including the date of the announcement, pricing,issuing, offer price, proceeds, and original underwriter names from the IN Information Systems andthe eol ESPer database. Data on stock market prices and returns were obtained from the Nikkei
media marketing database. The remaining data were gathered from the Nikkei NEEDS Financial
Quest database.

3.2. The timing of the announcement, pricing, and issue date
In contrast to the United States, the issuers in Japan must set the pricing date five or more days
before an offering date in Japan. Figure 1 illustrates the timing of the announcement date (AD),pricing date (PD), and issue date (ID). The announcement date of the SEO is the next date of the first
board meeting date.11 The period between AD and PD is approximately one to three weeks. Figure 2
shows the frequency distribution of the number of trading days between AD and PD for the entire
sample of SEOs. From AD to PD, most issuers are concentrated between six and seven days. The
greatest frequency of issuers (approximately 80 issuers) falls at six business days. The offering pricedetermines 90 percent of issuers within 10 business days of AD. The period between PD and ID isapproximately one to two weeks. Figure 3 shows the frequency distribution of the number of trading
days between PD and ID for the entire sample of SEOs. From the day after the pricing date until theend of the subscription period, formal stock price stabilization by the underwriter is permitted.12 The

11

Cooney, Kato, and Schallheim (2003) use the board meeting date to investigate market reactions to announcements
regarding SEOs in Japan, whereas Kang and Stulz (1996) use the newspaper announcement date.
12
The stabilization regulation is set under conditions of the Cabinet Order for Enforcement of the Securities andExchange Law.

14

stabilized activities occur in the issuers whose stock price drop after the pricing date.13 During theperiod from AD to PD, most issuers are concentrated between six to seven days. Figure 4 shows thefrequency distribution of the number of trading days between AD and ID for the entire sample of
SEOs. Over 50 percent of the full sample falls on days 13 and 14.

3.3. Data construction
3.3.1 Shorts selling and covering
To examine the relationship between short sale activity prior to the pricing and market stock returns
and issue discounts, this paper constructs several statistics to measure abnormal short sale trading
and returns. The paper defines abnormal the level of short selling for an individual firm i on day t ,ABSSVOLi ,t , as
ABSSVOLi ,t = SSVOLi ,t − AVSSVOLiSSVOLi ,t =AVSSVOLi =,(1)

SSi ,tVOLi ,t,1 −21 SSi ,d∑
120 d =−140 VOLi ,d

(2)

,

(3)

where SSi ,t is the total short selling volume on day t for that firm i , and VOLi ,t is the total
daily trading volume on day t for that firm i . SSVOLi ,t is the total short selling volume SSi ,trelative to the total volume VOLi ,t . AVSSVOLi is the average daily SSVOLi for that firm
during the benchmark period. The benchmark period extends from day -140 to day -21 relative to theannouncement date.14
13

To prevent sample selection bias, this paper does not exclude these samples. There is no difference in thefollowing results, if the samples with the activity are excluded.
14
The results are sufficiently robust for the use of different benchmark periods extending from day +21 to +140

15

Gerard and Nanda (1993) argue that the types of short selling differ according to whether theinvestors can cover their short position with discounted new shares; they indicate that investors
launch manipulative short selling to increase the probability of covering their position with newshares. The sample in this paper indicates that there are few equity volumes of short sales and shortcovers between the pricing date and the issue date. The most intensive period of short selling isimmediately prior to the pricing date.15 On the other hand, the most intensive date of covering shorts
is the issue date. Figure 5 shows the total number of short selling stocks without covered shorts
divided by the total shares around the issue date. Although there is no change in the number of shortselling stocks between ID-5 to ID-1, the number of short selling stocks decreases greatly on the issue
date. This result implies that investors use new shares to cover their short position. Due to data
restrictions, this research is unable specify whether the covered stocks are new shares. Therefore, theissuers with a high possibility of covering shorts with new shares may be the issuers with a highlevel of covering shorts relative to the total number of shorts on the issue date. This paper defines thelevel of covering shorts relative to the total number of shorts on the issue date for an individual firmi , SCRi as

SCRi =

COVERi , ID
NSHORTSi , ID −1,(4)where COVERi , ID is the total number of covering shorts on the issue date for that firm i , and

NSHORTSi , ID −1 is the total number of shorts that are not covered one day prior to the issue date.

3.3.3 Abnormal return

relative to the issue date.
15
The results are not shown in the table. The percentage of short sales is defined as the number of daily short sales
divided by the total share. The percentage of short sales immediately before the pricing date is about four to six times
higher than the percentage of short sales before and after the pricing date.

16

Following Ahern (2009), this paper uses a characteristic-based benchmark model to examine
abnormal returns around the pricing date. This paper defines abnormal returns for an individual firmi , ARi ,t , and the cumulative abnormal return for that firm i , CARi ,t , asARi ,t = Return i ,t − Return p ,t

,

⎛ T
⎞
CARi [d , T ] = ⎜ ∏ ( ARi ,t /100 + 1) − 1⎟ *100
⎝ t =d
⎠
,

(5)

(6)

where Return i ,t is the daily stock return on day t for firm i , and Return i ,t is the daily stock
return on day t for the matching portfolio p . A simple comparison of the ex-post performance offirms that issued equity and those that did not is inappropriate due to a possible selection bias. If
firms with issuing securities are riskier than those without, then a simple comparison of the ex-post
performance between these two groups confounds ex-ante riskiness and ex-post riskiness (changes in
the riskiness of the borrowers after issuing the security). To circumvent this problem, we need tocontrol for any possible selection bias in the estimation. To achieve this goal, this paper employs apropensity score matching estimation approach. The matching procedure is presented in Appendix A.

3.4. Description of the data
Table 1 presents the descriptive statistics for the sample. The mean (median) discount rate, where
DISCOUNT is defined as the percent change from the closing price before an application to the offer
price,16 is 3.01% (3.02%). With respect to the discount rate, almost no difference was detected uponcomparison to the level of U.S. offerings in the 1990s (Corwin, 2003; Mola and Loughran, 2004).
Underpricing is defined as the percent change from the offer price to the closing price on the firstday of trading after pricing. The mean underpricing is 4.24% (offer price to closing price on PD+3)

16

This notion is consistent with previous studies. See Corwin (2003) and Mola and Loughran (2004).

17and 1.89% (offer price to closing price on ID+10). Although the mean (median) CAR before pricing
is negative [from PD-3 to PD: -2.82% (-2.79%)], the mean (median) CAR after the pricing date is
positive [from PD+1 to PD+3: 1.03% (0.72%)]. These results are consistent with temporary pricepressures around the pricing date. The mean (median) level of short selling relative to the trading
volume on the pricing date, SSVOL [PD], is 0.24 (0.17). The mean (median) ABSSVOL on thepricing date, ABSSVOL [PD], is 0.19 (0.13). These results show that twenty percent of dealings on
the pricing date occur via short selling. The mean (median) SCR is 53.94% (55.50%). Over half ofthe total numbers of short positions before the issue date are covered on the issue date.
The mean (median) market capitalization on the last day of the month immediately prior to theoffer, Capitalization, is 353 (57.70) billion yen. The average offering raised 29.90 billion yen inproceeds. The mean relative offer size, RelSize, which represents the percentage of outstanding
pre-issue shares that were issued in the offering, is 12%. Major UW is the dummy variable for aprestigious underwriter, which assumes a value equal to one if the lead underwriter is a major
underwriter, such as Nomura, Nikko, or Daiwa, and a value of zero otherwise. The share of major
underwriters in the Japanese SEO underwriter market is 79%. The stock price volatility of issuers,
STDR, is defined as the standard deviation of daily stock returns over 20 trading days and ending 10days prior to the offer. The mean (median) STDR is 2.92 (2.56). The periods from AD to PD andfrom PD to ID are, on average, 7.33 and 7.63 days, respectively.
Table 2 shows the level of short selling prior to PD. Panel A presents the average SSVOL prior
to PD, and Panel B shows the average ABSSVOL prior to PD. In the first row of Panel A, the total
sample, the average SSVOL on PD (0.24), is higher than the average SSVOL prior to PD. In thesecond to fifth rows, the total sample is divided into tertile based on SCR. The SSVOL in the highest
tertile group is no different from the SSVOL in the lowest tertile group. Furthermore, in Panel B, the
ABSSVOL in the highest tertile group is no different from the ABSSVOL in the lowest tertile group.

18

These results indicate that according to SCR, there is no difference with respect to the short selling
scale.

4. Empirical Results
4.1. Information content of stocks prior to pricing
Gerard and Nanda’s (1993) model indicates that the information content of issuers with manipulative
short selling, as measured by the price change per unit of trade, would be smaller immediately prior
to the pricing. Following Gerard and Nanda (1993), this paper defines information content for anindividual firm i , ILLQi ,t , as

ILLQi ,t =Return i ,tVOLi ,t，(7)

where Return i ,t is the daily stock return on day t for firm i , and VOLi ,t is the total daily
trading volume on day t for firm i . ILLQi ,t is defined as the average daily ratio between theabsolute value of a stock.17 AVILLQi ,n is the average daily ILLQi ,t for that firm during thebenchmark period. The benchmark period extends from day -200 to day -21 (6 months) relative tothe announcement date.18 The ILLQi ,t relative to AVILLQi ,t , ABILLQi ,t is defined as

ABILLQi ,t =ILLQi ,tAVILLQi.

(8)

Table 3 presents the average ABILLQ prior to the pricing. In the first row of Panel A, the total
sample, the average ABILLQ on PD (0.53), is lower than the average ABILLQ prior to PD. In the17

Amihud (2002) argues that this measure is positively related to the high-frequency measures used in the market
microstructure literature, such as price impacts and trading costs.
18
The results are sufficiently robust for the use of different benchmark periods extending from day +21 to +200
relative to the issue date.

19

second to fifth rows, the total sample is divided into quartiles based on the level of SCR. Although
the ABILLQ in the highest quartile group before PD-2 is no different from the ABILLQ in thelowest tertile group before [PD-2], the ABILLQ in the highest tertile group on [PD] and [PD-1] islower than the ABILLQ in the lowest quartile group on [PD] and [PD-1] (t-statistics = -2.13 and-2.06). Furthermore, the ABILLQ in the lowest tertile group from [AD] to [PD] and from [PD-3] to[PD] is higher than the ABILLQ in the lowest tertile group during the same periods.
To test hypotheses H1A and H1B, the paper estimates the following equation using an
ordinary least squares regression (OLS):ABILLQi , PD = b 0 + b1 ABSSVOLi , PD + b 2 HighestSCR*ABSSVOLi , PD
+ b 3MiddleSCR*ABSSVOLi , PD + b 4 HighestSCR i , PD
+ b 5 MiddleSCR i + b 6 RelTurnoveri + u j .

(9)

The most relevant information regarding the calculation of the offer price is the information content
of stocks on the pricing date. The dependent variable, ABILLQ, is a proxy variable for the
information content of stocks on the pricing date. The first independent variable is ABSSVOL on[PD], which is the variable that represents the primary interest. To investigate the impact of SCR and
ABSSVOL on the ABILLQ, the paper two variable are included in the model:
HighestSCR*ABSSVOL, MiddleSCR*ABSSVOL variable is cross-term of the dummy variable
HighestSCR that assumes a value equal to one if issuers with highest tertile SCR and zero otherwise
and ABSSVOL. The MiddleSCR*ABSSVOL variable is a cross-term of the dummy variable
MiddleSCR, which assumes a value equal to one for issuers with a SCR in the second tertile of thetotal sample and zero otherwise, and their ABSSVOL. If hypotheses (1A) and (1B) are supported,the coefficient of ABSSVOL will be positively associated with ABILLQ, and the coefficient ofHighestSCR*ABSSVOL will be negatively associated with ABILLQ.
Amihud and Mendelson (1986) indicate the turnover is related negatively to the illiquidity cost.

20

The present model includes the RelTurnover variable, which is the average daily turnover for a firm
during the benchmark period. The benchmark period extends from day -200 to day -21 (6 months)
relative to the announcement date.
The results in Panel B of Table 3 show that although in Model 2 the coefficient of the
ABSSVOL variable does not have a statistically or economically significant effect on the ABILLLQ
variable, in Model 3, the coefficient of the ABSSVOL variable is positively associated with the
ABILLQ variable (t-value = 1.85), and the coefficient of HighestSCR*ABSSVOL is negatively
associated with the ABSSVOL variable (t-value = -2.50). These results are consistent with (H1A),(H1B), and Gerard and Nanda’s (1993) model, which shows that higher levels of short selling
immediately prior to the pricing date for issuers with high levels of covering short positions withnew shares should be associated with less informativeness.

4.2. Pre-pricing and post-pricing performance
To investigate the relationship between short selling immediately prior to pricing and the market
price performance around the pricing date, this paper first examines abnormal returns (AR) andcumulative abnormal returns (CAR) around the pricing date. Table 4 illustrates AR and CAR aroundthe pricing date. The first and second rows of Panel A show the average AR and CAR based on the
total sample. In pre-pricing days (from PD-3 to PD), the market price return drops (average CAR =-2.84%). On the pricing date, the AR falls sharply (average AR = -1.38%). On the other hand, after
the pricing date, the CAR increases gradually and within two days hits 1%. In the third and sixth
rows of Panel A, this paper divides the data into a High ABSSVOL group and a Low ABSSVOL
group based on the median of ABSSVOL on the pricing date. In pre-pricing days (from PD-3 to PD),
the CAR in the High ABSSVOL group (average CAR = -3.68%) is lower than the CAR in the Low
ABSSVOL group (average CAR = -1.95%). Furthermore, in post-pricing days (from PD+1 to PD+3),

21although the average CAR with the Low ABSSVOL group increases only 0.75%, the average CAR
for the High ABSSVOL group increases about 1.20%.
Panel B of Table 4 shows the average AR and CAR around PD in the group divided into tertiles

based on the level of SCR. Regarding the pre-pricing date, the average AR[PD] and CAR[PD-3 to PD]
in the highest SCR group (AR[PD] = -2.11% and CAR[PD-3 to PD] = -4.60%) demonstrates the lowest

value compared with the AR and CAR of the other groups. In addition, for the post-pricing date, theaverage AR[PD+1] and CAR[PD+1 to PD+3] in the highest SCR group (AR[PD+1] = 1.17% andCAR[PD+1 to PD+3] =2.35%) demonstrates the highest value compared with the AR and CAR of theother groups.

To test hypotheses H2A and H2B, the following equation is estimated using an ordinary leastsquares regression (OLS):CAR i = b 0 + b1 ABSSVOLi + b 2 HighestSCR*ABSSVOLi+ b 3 MiddleSCR*ABSSVOLi + b 4 HighestSCR i + b 5 MiddleSCR i + u j .

,

(10)

The dependent variable, CAR, is the cumulative abnormal returns around the pricing date. The firstindependent variable, ABSSVOL, is the proxy of the short selling scale prior to PD. According to the
manipulative short selling hypothesis (H1A), ABSSVOL is expected to be negatively associated withthe pre-pricing CAR and to be positively associated with the post-pricing CAR. On the other hand,according to the informative short selling hypothesis (H1A), ABSSVOL is expected to be negatively
associated with the pre-pricing CAR and to not be (or to be negatively) associated with thepost-pricing CAR. To investigate these hypotheses, two variables are included in the model:
HighestSCR*ABSSVOL, MiddleSCR*ABSSVOL. If the issuers experience manipulative shortselling over informative short selling prior to the pricing, the coefficient of theHighestSCR*ABSSVOL and the MiddleSCR*ABSSVOL should be positively associated with
post-pricing CAR. In an informative scenario, this paper would expect ABSSVOL to be22

insignificantly different from zero or to be negatively associated with the post-pricing CAR.
Table 5 shows the effect of the level of short selling prior to the pricing date on the pre-pricing
CAR. In Models 1 and 2, the dependent variable is CAR during PD-3 to PD, and in Models 3 and 4
the dependent variable is CAR during AD to PD. The ABSSVOL coefficient is negatively associatedwith the pre-pricing CAR in all Models, consistent with the hypotheses (H2A) and (H2B). In Model
2 and 4, according to the SCR, there is no difference in the influence of ABSSVOL on the
pre-pricing CAR.
Table 6 presents the regression results for the post-pricing CAR. The post-pricing CAR period
is three days from PD+1 to PD+3 in Models 1 to 4 and the period between PD+1 and ID+10 inModels 5 to 8. Models 1, 3, 5, and 7 exclude the two variables, HighestSCR*ABSSVOL,
MiddleSCR*ABSSVOL, from the base model explained in equation (10). Although the coefficientsof ABSSVOL in Models 1, 3, 5, and 7 are insignificantly different from zero, the coefficients of
ABSSVOL in Models 2, 4, 6 and 8 are negatively associated with the post-pricing CAR.
Furthermore, in Models 2, 4, 6 and 8, HighestSCR*ABSSVOL and MiddleSCR*ABSSVOL arepositively correlated with the post-pricing CAR. The level of HighestSCR*ABSSVOL (8.09, t-value= 2.01) is larger than those of MiddleSCR*ABSSVOL (5.65, t-value = 1.83) in Model 4. The
coefficient level of ABSSVOL with HighestSCR in Model 4 is 3.70 (= HighestSCR*ABSSVOL +ABSSVOL). These results indicate that higher levels of short selling prior to PD for issuers with thehigh levels of SCR are positively associated with post-pricing CAR, consistent with hypothesis(H2A). On the other hand, higher levels of short selling prior to PD for issuers with high levels of
SCR and for issuers with low levels of SCR are negatively associated with the post-pricing CAR,
consistent with hypothesis (H2B) and the Gerard and Nanda model.

4.3. Discount rate and Underpricing

23

To investigate the relation between the discount rate and the level of short selling prior to pricing
date, this paper estimates the following equation using an ordinary least squares regression (OLS):19

Discounti = α + β1 ABSSVOLi + β 2 HighestSCR*ABSSVOL i+ β 3 MiddleSCR*ABSSVOLi + β 4 HighestSCR i+ β 5 MiddleSCR i + β 6 ln(Capitalizationi )+ β 7 STDRi + β8 Reloffsizei + β 9 MajorUWi(11)+ β10 Daysi + β k ∑ Exchange dummyi ,k +ε ikThe dependent variable, DISCOUNT, is defined as the percent change from the closing price before
an application to the offer price. The first independent variable is ABSSVOL, which is the variableof primary interest. The coefficient estimate of ABSVOL facilitates an understanding of whether
higher levels of short selling before the SEO pricing date affect the discount rate. When short sellingwith superior information decreases the costs that are reflected in the discount rate (H3B), thediscount rate should be negatively correlated with the ABSSVOL variable. According to the Gerard
and Nanda (1993) model’s manipulative effects (H3A), the discount rate should be positively
correlated with the HighestSCR*ABSSVOL and MiddleSCR*ABSSVOL variable. On the other
hand, according to the underwriters' ability of pricing effect (H3C), the discount rate should besignificantly correlated with the HighestSCR*ABSSVOL variable.
The remaining independent variables are used to control for other factors that may affect thelevel of the discount rate. Several prior studies document the discount rate as a function of priceuncertainty and information asymmetry. Rock (1986) assumes that some investors are better
informed than are others and can thus avoid participating in overvalued IPOs. The resulting winner’s
curse experienced by uninformed investors must be countered by deliberate underpricing.
Furthermore, the winner’s curse results in a positive relationship between underpricing and ex-ante
uncertainty concerning the value of the issue (Beatty and Ritter, 1986).

19

In this equation, LN refers to the natural logarithm.

24

Following Corwin (2003), this paper defines two proxies, ln(Market Cap) and STDR, forasymmetric information and uncertainty, respectively. The first proxy is ln(Market Cap), defined as
the natural logarithm of the total market capitalization on the day prior to the offer. The second
proxy is STDR, defined as the standard deviation of the daily stock returns over 120 trading days
ending 20 days prior to the offer. In the present paper, Discount is expected to be negatively
associated with ln(Market Cap) and positively associated with STDR. Corwin (2003) argues thatprice pressures affect the discount rate. This price pressure hypothesis suggests that the discount rateshould be most pronounced among the largest offers, reflecting the market’s ability to absorb the
new shares. Following Corwin (2003), this paper defines the relative offer size, Rel offer size, as thenumber of shares offered divided by the size of the existing market for the firm’s shares.
Booth and Smith (1986) and Carter and Manaster (1990) argue that, by agreeing to be
associated with an offering, prestigious underwriters certify the quality of an issue and alleviate
informational asymmetry problems. As a proxy variable for prestigious underwriters, the MajorUW
is used in the present study as the dummy variable for a prestigious underwriter, which assumes avalue equal to 1 if the lead underwriter is the major underwriter, such as Nomura, Nikko, or Daiwa,
and a value of zero otherwise. Based on the certification role of prestigious underwriters, MajorUW
is expected to be positively associated with the discount rate. Days is defined as the number of days
between PD and ID. If Days between PD to ID is high, the price uncertainty will increase (Corwin,2003). In the present paper, Days is expected to be positively associated with the discount rate.
EXCHANGE indicates sets of exchange dummy variables20.
Table 7 shows the effect of the level of short selling on the discount rate. The relationshipbetween pre-issue short selling and the issue discount is strongest for PD (Model 2). The coefficientof ABSSVOL[PD] is negative and significant at the 1% level (t-value = -2.91). In Models 7, the20

The paper uses the Nikkei medium classification (Nikkei chyu bunrui).

25

coefficient HighestSCR*ABSSVOL and MiddleSCR*ABSSVOL are not significantly associatedwith issue discounts. These results indicate that as the level of short selling increases, the issuers pay
lower discounts, consistent with hypotheses (H3B) and (H3C) and inconsistent with hypothesis(H3A) and Gerard and Nanda's (1993) manipulative effect. The coefficient of the ln(Market Cap)variable is negative and significant in the discount rate, suggesting that the ex-ante uncertainty of a
SEO firm has a positive impact on discount rates. The coefficients of the RelOfferSize are positiveand significant in the discount rate model. The coefficients of MajorUW and STDR are insignificant.
The coefficient of Days is positive and significant in the discount rate model.
The results shown in Table 7 preclude our assessment of whether underwriters set lower
discounts on issuers with manipulative short selling prior to PD. If the underwriters adjust the offer
price of issuers with manipulative short selling completely, the manipulative short selling should not
be associated with underpricing, defined as the percent change from the offer price to the closing
price on the day after pricing (H4A). If the adjustment is perfect, investors will not participate inmanipulative short selling. However, if the adjustment of the offer price by underwriters is
insufficient, the level of manipulative short selling may be correlated with the underpricing (H4B).
This paper examines the regression analysis by using underpricing instead of discounting.
Table 8 shows the effects of the level of short selling prior to the pricing date on theunderpricing. In Models 1 to 4, the dependent variable, Underpricing, is defined as the percent
change from the offer price to the closing price on PD+3. The underpricing in Model 5 to 8 isdefined as the percent change from the offer price to the closing price on ID+10. In Models 1, 3, 4, 5,
7, and 8, the ABSSVOL is not associated with the underpricing. However, in Models 2, and 6, which
include the cross-term, ABSSVOL is negatively and significantly correlated with the underpricing.
The coefficient of Highest*ABSSVOL is positive and significant at the 1% level in Models 2, 4, 6,
and 8. The level of HighestSCR*ABSSVOL (9.32, t-value = 2.96) is larger than that of26

MiddleSCR*ABSSVOL (3.44, t-value = 1.26) in Model 2. The coefficient of ABSSVOL with
HighestSCR in Model 2 is 8.22 (= HighestSCR*ABSSVOL + ABSSVOL). These results indicatethat higher levels of short selling prior to PD for issuers with high levels of SCR are positively
associated with the underpricing, consistent with hypothesis (H4B). These results in Table 7 and 8
imply that even if underwriters adjust offer prices for issuers with manipulative short selling prior toPD, issuers with manipulative short selling must pay a higher discount for a suitable price.

5. Concluding Remarks
The present paper examines whether issue discounts and stock returns around seasoned equity
offerings (SEOs) differ according to the type of short selling, manipulative or informative, in Japan,
where a special short selling regulation around SEOs does not exist. First, the paper finds that ahigher level of pre-pricing short selling with a high covering ratio at the issue date has a negative
effect on the informativeness of secondary market prices. In addition, a higher level of pre-pricing
short selling with a low cover ratio at the issue date has a positive effect on the information content.
Second, the paper finds that pre-pricing short selling is associated with a price drop prior to
the issue date, consistent with Corwin (2003) and Henry and Koski (2010). Short selling prior to
pricing with a high short covering ratio on the issue date is associated with a price recovery, but
short selling with a low covering ratio on the issue date is associated with a price drop.
Finally, irrespective of the level of the cover ratio, the level of pre-pricing short selling has anegative effect on issue discounts measured from the prior day's close, consistent with Corwin(2003), Kim and Shin (2004), and Singal and Xu (2005). However, the underpricing is positively
associated with the level of short selling with a high covering ratio on the issue date.
These results imply that manipulative practices are more likely in a firm with a higher shortcovering ratio, and informative practices are more likely in a firm with a lower short covering ratio,27

consistent with the predictions of Gerard and Nanda’s (1993) model but not empirically proven by
previous research. Furthermore, the discount and underpricing results indicate that although
underwriters set a lower discount rate for issuers with a high covering ratio, they cannot lower a
discount rate to the issuer's fundamental value completely, which is not consistent with the Gerard
and Nanda (1993) model's manipulative hypothesis. The present paper attempts to include theinfluence of the price adjustment function of the underwriters in Gerard and Nanda’s (1993) model.
Our findings provide some implications regarding the regulation of short selling activity
around SEOs, e.g., SEC Rule 105 in the United States. For issuers who do not cover their shortposition with the new offer, this paper finds no evidence of manipulative short selling activity, which
worsens the informativeness of secondary market prices and exerts temporary price pressure aroundthe pricing date. Therefore, these findings imply indirectly that Rule 105 in the United Stateseffectively restricts manipulative short selling before a SEO. In addition, our findings indicate that
underwriters cannot set an offer price at the issuer's fundamental value completely in Japan without
such a regulation. These results imply that it is necessary to introduce a short selling regulation, suchas Rule 105, in many countries that lack such a regulation. Although this analysis is based on theJapanese experience, these results may have important implications for countries worldwide.

28

Appendix A: Matching Procedure
The matching procedure is as follows. First, I implement a probit estimation that models theprobability of an equity offering in year t conditional on the covariates observed in year t-1. Firms
that issue equity ( SEOt = 1 ) are labeled as treatment observations. Next, I attach 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, I implement another set of probit estimations, including cross-terms, multiplied by thevariables that measure the extent of the external control of a firm. For each treatment observation,
the paper identifies matched observations from the sample of firms without issuing securities. The
matched observations are those that demonstrate the “closest” propensity scores to a particulartreatment observation and are labeled as control observations. These matched observations are
selected from the same calendar year as the treatment observation. It should also be noted that I use a
non-treated observation more than once as a control; that is, a non-treatment observation may be
used as a control for more than one treatment observation at the same time. Several matchingalgorithms can be used to find the “closest” control observations. As a baseline for this analysis, I
employ the ten nearest matches in which the arbitrarily determined ten observations with propensity
scores closest to each treatment observation are selected.21
One of the benefits of employing propensity score matching estimation is that I can match
treatment and control observations using the scalar propensity score. The propensity score, which is21This paper finds that the results obtained using different matching algorithms (nearest five matches) are similar to
those obtained using the ten nearest matches.

29the conditional probability of being treated given the value of the observed characteristics, is a very
useful variable in dealing with highly dimensional vectors of covariates. Rosenbaum and Rubin(1983) show that treatment observations and control observations with the same propensity scorevalue have the same distribution of the full vector of covariates. Thus, it is sufficient to match firms
in terms of the propensity score to obtain the same probability distribution of covariates fortreatment and control observations.
I start with the baseline probit estimation. In the probit estimation, this paper obtains the conditional
probabilities of a firm issuing equity in year 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 are 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, are employed. I employ the debt ratio ( DEBTt −1 ) as a variable for higher riskof bankruptcy and the market to book ratio ( MTBt −1 ) as a variable for the firm's value gap.

[2010.6.21 985]

30

【References】
Ahern, K.R., 2009. Sample selection and event study estimation. Journal of Empirical Finance 16, 466-482.
Amihud, Y., 2002. Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets 5,
31-56.
Altinkilic, O., and R.S. Hansen, 2003. Discounting and underpricing in seasoned equity offers. Journal of Financial
Economics 69, 285–323.
Altinkilic, O., and R.S. Hansen, 2005. The puzzling wealth loss around equity offerings: Is it merely price pressure oris there more information? Unpublished working paper.
Barclay, M.J., and R.H. Litzenberger, 1988. Announcement effects of new equity issues and the use of intraday pricedata. Journal of Financial Economics 21, 71-99.
Boehmer, E., and J. Wu, 2009. Short selling and the informational efficiency of prices. Unpublished working paper,
University of Georgia.
Boehmer, E., and C.M. Jones, and X. Zhang, 2008. Which short are informed? Journal of Finance 63, 491-527.
Booth, J.R., and R.L. Smith, 1986. Capital raising, underwriting, and the certification hypothesis. Journal of Financial
Economics 15, 261-281.
Brav, A., and P.A. Gompers, 2003. The role of lockups in Initial public offerings. Review of Financial Studies 16,
1-29.
Chemmanur, T.J., and Y. Jiao, 2007. Institutional trading, information production, and the SEO discount: A model ofseasoned equity offerings. Unpublished working paper, Boston college.
Chemmanur, T.J., S. He, and G. Hu, 2009. The role of institutional investors in seasoned equity offerings. Journal of
Financial Economics 94, 384-411.
Cooney, J.W., H.K Kato, and J.S. Schallheim, 2003. Underwriter certification and Japanese seasoned equity issues.
Review of Financial Studies 16, 949-982.
Corwin, S.A., 2003. The determinants of underpricing for seasoned equity offers. Journal of Finance 58, 2249-2280.

31

Dunbar, C.G., 2000. Factors affecting investment bank initial public offering market share. Journal of Financial
Economics 55, 3–41.
Easterbrook, F. H., 1984. Two agency-cost explanations of dividends. American Economic Review 74, 650-659.
Fernando, C.S., V.A. Gatchev, and P.A. Spindt, 2005. Wanna Dance? How Firms and Underwriters Choose Each
Other. Journal of Finance 60, 2437-2469.
Field, L., and G. Hanka, 2001. The expiration of IPO share lockups. Journal of Finance 56, 471-500.
Gerard, B., and V. Nanda, 1993. Trading and manipulation around seasoned equity offerings. Journal of Finance 48,
213-245.
Greenwood, R., 2009. Trading restrictions and stock prices. Review of Financial Studies 22, 509-539.
Harris, L., and E. Gurel, 1986, Price and volume effects associated with changes in the S&P 500 list: New evidence
for the existence of price pressures. Journal of Financial Economics 41, 815-829.
Henry, T.R., and J.L. Koski, 2010. Short selling around seasoned equity offerings. Review of Financial Studies,
forthcoming.
Kang, J., Stulz, R., 1996. How different is Japanese corporate finance? An investigation of the information content of
new security issues. Review of Financial Studies 9, 109-139.
Kim, S.T., and R.W. Masulis, 2009. Order imbalances around seasoned equity offerings. Unpublished working paper,
Vanderbilt University.
Kim, K., and H. Shin, 2004. The puzzling increase in the underpricing of seasoned equity offerings. Financial Review
39, 343–365.
Kim, Y., and M.S. Park, 2005. Pricing of seasoned equity offers and earnings management. Journal of Financial andQuantitative Analysis 40, 435–463.
Lynch, A.W., and R.R. Mendenhall, 1997. New evidence on stock price effects associated with changes in the S&P
500 Index. Journal of Business 70, 351-383.
Mikkelson, W., and M. Partch, 1988. Withdrawn security offerings. Journal of Financial and Quantitative Analysis 23,32119-133.
Mola, S., and T. Loughran, 2004. Discounting and clustering in seasoned equity offering prices. Journal of Financial
and Quantitative Analysis 39, 1–23.
Reed, A.V., 2007. Costly short-selling and stock price adjustment to earnings announcements. Unpublished working
paper, University of North Carolina.
Rock, K., 1986. Why new issues are underpriced. Journal of Financial Economics 15, 187–212.
Rosenbaum, P., and D. Rubin, 1983 "The central role of the propensity score in observational studies for causal
effects. Biometrika 70, 41-55.
Safieddine, A., and W.J. Wilhelm, 1996. An empirical investigation of short-selling activity prior to seasoned equity
offerings. Journal of Finance 51, 729-749.
Singal, V. and L. Xu, 2005. Do short sellers know more? Evidence from a natural experiment. Unpublished working
paper, Virginia Tech.

33

Table 1
Summary Statistics for Seasoned Equity Offers
Statistics for a sample of 218 seasoned equity offers during 1998 to 2009. DISCOUNT is the percent change from the closing price before an application
to the offer price. Underpricing is the percentage price change from the offer price to the closing price on the day after the pricing date. CAR is the
cumulative abnormal return. SSVOL is the total short selling volume (SS) relative to the total volume (VOL). ABSSVOL is the SSVOL minus the average
daily SSVOL from day -140 to day -21 relative to the announcement date. SCR is the total number of covering shorts on an issue date relative to the total
number of shorts that is not covered on a day prior to the issue date. Capitalization is the market capitalization on the day of the month just prior to theoffer. RelOffSize represents the percentage of pr-issue shares outstanding that were issued in the offering. M ajorUW is the dummy variable for theprestigious underwriter that assumes a value equal to 1 if the lead underwriter is the major underwriter, such as Nomura, Nikko, or Daiwa, and a value ofzero otherwise. STDR is the standard deviation of daily stock returns over 20 trading days, ending 10 days prior to offer. Days from the announcement
date (AD) to the pricing date (PD), Days from PD to the issue date (ID), and Days from AD to PD are the periods from AD to PD, PD to ID, and AD toID, respectively.

(Number of observation = 218)

Mean

MedianStd.dev.

Discount and Underpricing
Discount (Closing price on PD to offer price) (%)
Underpricing (Offer price to closing price on PD+3) (%)
Underpricing (Offer price to closing price on ID+10) (%)

3.01
4.24
1.89

3.02
3.23
2.12

0.81
5.30
6.78

Cumlative Abnormal return
CAR from PD-3 to PD (%)
CAR from PD to PD+3 (%)
CAR from PD to ID+10 (%)

-2.82
1.03
-1.11

-2.79
0.72
-0.79

6.43
4.86
6.84

Short selling
SSVOL[PD]ABSSVOL[PD]SCR (%)

0.24
0.19
53.94

0.17
0.13
55.50

0.30
0.27
25.16

Offering characteristics
Capitalization (billion yen)
Proceeds (billion yen)
RelOffSize (%)
Major UW
STDR
Days from AD to PD
Days from PD to ID
Days from AD to ID

353.00
29.90
12.39
0.79
2.92
7.33
7.63
14.92

57.70
5.86
11.40
1.00
2.56
7.00
7.00
14.00

1750.00
84.60
6.50
0.41
1.47
2.04
2.30
3.29

34

Table 2
The scale of short selling prior to the pricing dateThis table reports the level of short selling prior to the pricing date (PD). Panel A presents the average SSVOL, defined as thetotal short selling volume relative to the total volume prior to PD, and Panel B shows the average ABSSVOL, defined as the
SSVOL minus the average daily SSVOL from day -140 to day -21 relative to the announcement date prior to PD. In the second
to forth row, the total number of offers is divided into tertile based on SCR. Test statistics are t-test results for difference inmeans (highest SCR group - lowest SRC group).

Panel A：SSVOL
SCR[PD-5][PD-4][PD-3][PD-2][PD-1][PD][PD-3 to PD][AD to PD]
Obs.

Total Sample

High
(a)

Middle

Low
(b)

0.14
0.14
0.13
0.17
0.20
0.24
0.19
0.16
218

0.12
0.12
0.12
0.17
0.19
0.23
0.18
0.16
73

0.12
0.14
0.15
0.18
0.22
0.27
0.21
0.18
72

0.16
0.15
0.13
0.17
0.18
0.22
0.17
0.14
73

(a)-(b)t-statistics

-0.04
-0.03
-0.01
0.00
0.01
0.01
0.00
0.02

-0.83
-0.41
-0.26
-0.10
0.15
0.22
0.03
0.73

(a)-(b)t-statistics

-0.03
-0.01
0.00
0.01
0.02
0.02
0.01
0.03

-0.67
-0.23
0.11
0.17
0.55
0.46
0.45
1.35

Panel B：ABSSVOLSCRABSSVOL

Total Sample

High
(a)

MiddleLow(b)[PD-5][PD-4][PD-3][PD-2][PD-1][PD][PD-3 to PD][AD to PD]
Obs.

0.09
0.09
0.09
0.13
0.15
0.19
0.14
0.11
218

0.08
0.08
0.08
0.12
0.15
0.19
0.13
0.12
73

0.08
0.10
0.10
0.14
0.18
0.23
0.16
0.13
72

0.11
0.09
0.08
0.12
0.13
0.16
0.12
0.09
73

35

Table 3
Informativeness of secondary market prices prior to the pricing dateT his table reports the informativeness of secondary market prices prior to the pricing date. ABILLQ is defined as the ILLQ, definedas the average daily ratio between the absolute value of a stock, relative to AVILLQ, defined as the average daily ILLQ for that firmfrom day -200 to day -21 (6 months) relative to the announcement date. Panel A presents the average ABILLQ prior to PD. T he
first row of Panel A shows the average of ABILLQ, which are based on the total sample. In the second to forth row of Panel A, thetotal number of offers is divided into tertile based on SCR. T est statistics are t-test results for difference in means (highest SCR group lowest SRC group). T est statistics of Panel A are t-test results for difference in means. ** and * denote significance at the 5% and
10% levels, respectively. Panel B shows various ordinary least squares regressions that predict ABILLQ. T he dependent variable is
ABILLQ on the pricing date. T he first independent variable is ABSSVOL on [PD]. T he HighestSCR is the dummy variable thatassumes a value equal to 1 if issuers with highest tertile SCR and zero otherwise. T he MiddleSCR is the dummy variable that assumes avalue equal to 1 if issuers with middle tertile SCR and zero otherwise. T he HighestSCR*ABSSVOL variable is the cross-term ofHighestSCR and their ABSSVOL. T he MiddleSCR*ABSSVOL variable is the cross-term of MiddleSCR and their ABSSVOL. T he
RelT urnover variable is the average daily turnover for a firm during the benchmark period. T he benchmark period extends from day 200 to day -21 (6 months) relative to the announcement date.T -ratios of robust standard errors are presented in parentheses. *** and** denote coefficients significant at the 1% and 5% levels, respectively.

Panel A: the average ABILLQ prior to PD
SCR
Total Sample[t][PD-5][PD-4][PD-3][PD-2][PD-1][PD][PD-3 toPD][AD to PD]
Obs.

0.72
0.70
0.62
0.62
0.79
0.53
0.64
0.68
218

High
(a)

Middle

Low
(b)

0.72
0.74
0.62
0.56
0.45
0.44
0.52
0.61
73

0.76
0.67
0.61
0.71
0.74
0.44
0.62
0.67
72

0.68
0.68
0.64
0.58
1.18
0.71
0.78
0.76
73

(a)-(b) t-statistics

0.04
0.05
-0.01
-0.02
-0.73
-0.27
-0.26
-0.15

0.36
0.38
-0.11
-0.22
-2.13
-2.06
-2.34
-1.70

**
**
**
*

Panel B: Ordinary least squares regressions predicting ABILLQ
Model 1Model 2Model 3

ABILLQ [PD]Coeff.

t-statistics

ABSSVOL[PD]

Coeff.

t-statistics

0.03

0.19

Coeff.

t-statistics

0.16

1.85 *HighSCR*ABSSVOL[PD]-0.66-2.50 **

MiddleSCR*ABSSVOL[PD]

-0.24

-0.57

HighSCR dummy-0.16-1.78 *

-0.16

-1.82 *

0.03

0.27

MiddleSCR dummy

-0.12

-1.48

-0.13

-1.57

-0.10

-0.86

RelTurnover-2.95-3.01 ***

-2.87

-2.99 ***

-3.00

-3.19 ***

Intercept

0.58

8.57

0.58

7.43 ***

0.55

6.90 ***

2

R
Observations

0.05
218

0.05
218

36

0.07
218

Table 4
Pre-pricing and post-pricing performanceThis table shows the abnormal return (AR) and cumulative abnormal return (CAR) around the pricing date (PD). Panel A presents the average AR and CAR
prior to PD. The first row of Panel A shows the average of AR and CAR that are based on the total sample. In the third and sixth row of Panel A, the paper
divides into the High ABSSVOL group and Low ABSSVOL group based on the median of ABSSVOL on the pricing date. Panel B shows the average AR
and CAR around PD in the group divided into tertile based on the level of SCR. Test statistics are t-statistics for the significance of this difference fromzero. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.

Panel A: Total sample, High ABSVOL and Low ABSSVOL
Total sample[PD-3][PD-2][PD-1][PD][PD+1][PD+2][PD+3]
Obs.

High ABSSVOL[PD]

AR

CAR

AR

-0.35 *
-0.60 ***
-0.54 **
-1.38 ***
0.55 ***
0.46 **
0.00-0.35 *
-0.95 ***
-1.48 ***
-2.84 ***
-2.30 ***
-1.86 ***
-1.85 ***

-0.04
-0.51 **
-1.24 ***
-1.94 ***
0.48 *
0.49 *
0.23
109

218

Low ABSSVOL[PD]

CAR

AR

CAR

-0.04
-0.55
-1.78 ***
-3.68 ***
-3.22 ***
-2.74 ***
-2.52 ***

-0.58 *
-0.69 **
0.17
-0.86 **
0.60 **
0.37-0.21-0.57 *
-1.27 ***
-1.10 **
-1.95 ***
-1.36 *
-1.00
-1.20
109

Panel B: SCR
HighSCR[PD-3][PD-2][PD-1][PD][PD+1][PD+2][PD+3]
Obs.

MiddleSCR

AR

CAR

AR

CAR

-0.87 **
-1.23 ***
-0.47
-2.11 ***
1.17 ***
1.05 **
0.03-0.87 **
-2.08 ***
-2.57 ***
-4.60 ***
-3.46 ***
-2.52 ***
-2.50 ***

0.09
-0.22
-0.58 **
-1.49 ***
0.13
0.23
0.33

0.09
-0.12
-0.68
-2.18 ***
-2.06 ***
-1.85 ***
-1.55 **

73

72

37

LowSCR
AR

CAR

-0.28
-0.34
-0.55
-0.53
0.36
0.10
-0.36

-0.28
-0.59
-1.12 *
-1.63 *
-1.33
-1.33
-1.69 **
73

Table 5
The effect of the level of short selling prior to the pricing on the pre pricing performanceThis table shows various ordinary least squares regressions that predict cumulative abnormal return (CAR) prior to the pricing date (PD). The
dependent variable, CAR, is a cumulative abnormal return around the pricing date. The ABSSVOL, which is defined as the SSVOL minus the average
daily SSVOL from day -140 to day -21 relative to the announcement date, is the proxy of the level of short selling prior to PD. The SCR is the total
number of covering shorts on an issue date relative to the total number of shorts that is not covered on a day prior to the issue date. The HighestSCR
is the dummy variable that assumes a value equal to 1 if issuers with highest tertile SCR and zero otherwise. The M iddleSCR is the dummy variable
that assumes a value equal to 1 if issuers with middle tertile SCR and zero otherwise. The HighestSCR*ABSSVOL variable is the cross-term ofHighestSCR and their ABSSVOL. The M iddleSCR*ABSSVOL variable is the cross-term of M iddleSCR and their ABSSVOL. T-ratios of robust
standard errors are in parentheses. ***, ** and * denote coefficients significant at the 1%, 5%, 10% levels, respectively.

CAR [PD-3 to PD]

ABSSVOL[PD]Model 1Model 2

-3.22 **(-2.39)-3.31 **(-2.01)HighSCR*ABSSVOL[PD]-0.09(-0.02)

MiddleSCR*ABSSVOL[PD]0.71(0.21)ABSSVOL[PD-3 to PD]

Model 3Model 4-4.06 *(-1.97)-6.24 *(-1.78)HighSCR*ABSSVOL[PD-3 to PD]

3.63(0.58)

MiddleSCR*ABSSVOL[PD-3 to PD]

4.22(0.95)HighSCR dummy-3.13 ***(-2.81)-3.11 **(-2.08)-3.15 ***(-2.80)-3.61 **(-2.49)MiddleSCR dummy-0.49(-0.48)-0.65(-0.51)-0.53(-0.51)-1.13(-0.95)Intercept-0.96(-1.04)-0.94(-0.98)-0.99(-1.14)-0.72(-0.78)

0.064
218

0.064
218

0.058
218

0.061
218

2

R
Observations

38

Table 6
The effect of the level of short selling prior to the pricing on the post pricing performance
T his table shows various ordinary least squares regressions that predict cumulative abnormal return (CAR) after the pricing date (PD). T he dependent variable, CAR, is a cumulative abnormal returnaround the pricing date. T he ABSSVOL, which is defined as the SSVOL minus the average daily SSVOL from day -140 to day -21 relative to the announcement date, is the proxy of the level of shortselling prior to PD. T he SCR is the total number of covering shorts on an issue date relative to the total number of shorts that is not covered on a day prior to the issue date. T he HighestSCR is thedummy variable that assumes a value equal to 1 if issuers with highest tertile SCR and zero otherwise. T he MiddleSCR is the dummy variable that assumes a value equal to 1 if issuers with middle tertile
SCR and zero otherwise. T he HighestSCR*ABSSVOL variable is the cross-term of HighestSCR and their ABSSVOL. T he MiddleSCR*ABSSVOL variable is the cross-term of MiddleSCR and their
ABSSVOL. T -ratios of robust standard errors are in parentheses. ***, ** and * denote coefficients significant at the 1%, 5%, 10% levels, respectively.

CAR [PD+1 to PD+3]
Model 1ABSSVOL[PD]Model 2Model 3

-1.13

0.10

-1.90 *

(0.07)(-1.82)

6.26 **(2.44)
2.24

3.92(1.04)(1.32)

Model 8-0.83

-4.39 *

0.08

-3.34

(-0.58)(-1.80)(0.03)(-0.97 )MiddleSCR*ABSSVOL[PD-3 to PD]

2Model 78.90 **(2.49)HighSCR*ABSSVOL[PD-3 to PD]

R
Observations

Model 6(-1.37)ABSSVOL[PD-3 to PD]

Intercept

Model 50.19

MiddleSCR*ABSSVOL[PD]MiddleSCR dummy

Model 4(0.21)HighSCR*ABSSVOL[PD]HighSCR dummy

CAR [PD+1 to ID+10]

8.09 **

9.09(2.01)(1.63)

5.65 *

4.67(1.83)(1.09)

2.13 **

0.99

2.14 **

1.10

2.49 **

0.87

2.49 **

1.31(2.37)(0.90)(2.39)(0.95)(2.09)(0.59)(2.09)(0.85)

0.60

0.17

0.64

-0.13

1.66

0.91

1.67

1.05

(0.72)(0.17)(0.78)(-0.12)(1.45)(0.64)(1.46)(0.77)

0.05(0.06)

0.27(0.31)

0.18(0.22)

0.62 **(0.65)-2.53 ***(-2.61)-2.21 **(-2.21)-2.53 ***(-2.61)-2.11 *
( -1.91)

0.034
218

0.050
218

0.035
218

0.049
218

0.023
218

0.039
218

0.023
218

0.022
218

39

Table 7
The effect of the level of short selling prior to the pricing date on the discount rate
This table shows various ordinary least squares regressions that predict the discount rate. The dependent variable, DISCOUNT, is defined as the percent change from the closing pricebefore an application to the offer price. The ABSSVOL, which is defined as the SSVOL minus the average daily SSVOL from day -140 to day -21 relative to the announcement date, isthe proxy of the level of short selling prior to PD. The SCR is the total number of covering shorts on an issue date relative to the total number of shorts that is not covered on a dayprior to the issue date. The HighestSCR is the dummy variable that assumes a value equal to 1 if issuers with highest tertile SCR and zero otherwise. The M iddleSCR is the dummy
variable that assumes a value equal to 1 if issuers with middle tertile SCR and zero otherwise. The HighestSCR*ABSSVOL variable is the cross-term of HighestSCR and their
ABSSVOL. The M iddleSCR*ABSSVOL variable is the cross-term of M iddleSCR and their ABSSVOL. The ln (M arket Cap) is defined as the natural logarithm of total market
capitalization on the day prior to the offer. The STDR is defined as the standard deviation of daily stock returns over 120 trading days and ending 20 days prior to the offer.
RelOffSize is defines as the number of offered shares divided by the size of the existing market for the firm’s shares. The M ajorUW is the dummy variable for prestigious underwriterthat assumes a value equal to 1 if the lead underwriter is the major underwriter, such as Nomura, Nikko, or Daiwa, and a value of zero otherwise. Days is defined as the days between
the pricing date (PD) to the issue date (ID). EXCHANGE are sets of exchange dummy variables. T-ratios of robust standard errors are in parentheses. ***, ** and * denotecoefficients significant at the 1%, 5%, 10% levels, respectively.

M odel 1

ABSSVOL[PD]M odel 2

M odel 3

M odel 4

M odel 5

M odel 6

M odel 7-0.38 ***

-0.44 **

-0.44 ***(-2.91)(-2.12)(-2.73)ABSSVOL[PD-1]

-0.10

0.14

(-0.45)(0.45)ABSSVOL[PD-2]

-0.14

-0.06

(-0.66)(-0.21)ABSSVOL[PD-3]

0.39

0.42

(0.84)(0.79)HighSCR*ABSSVOL[PD]-0.12(-0.37)

M iddleSCR*ABSSVOL[PD]0.24(0.57)HighSCR

M iddleSCR

ln(Capitalization)

RelOffSize

M ajor UW

STDR

Days[PD to ID]

Exchange dummyIntercept

R2
Observations

-0.19

-0.19

-0.20

-0.20

-0.21

-0.19

-0.17

(-1.56)(-1.54)(-1.61)(-1.62)(-1.65)(-1.58)(-1.01)-0.24 *

-0.22 *

-0.24 *

-0.24 *

-0.25 *

-0.24 *

-0.28(-1.78)(-1.70)(-1.76)(-1.76)(-1.82)(-1.80)(-1.61)-0.21 ***

-0.22 ***

-0.21 ***

-0.21 ***

-0.19 ***

-0.20 ***

-0.21 ***(-6.14)(-6.42)(-6.14)(-6.23)(-5.15)(-5.63)(-5.88)1.83 **

1.76 **

1.77 **

1.78 **

1.66 **

1.63 **

1.75 **(2.38)(2.30)(2.25)(2.28)(2.03)(2.01)(2.28)

-0.10

-0.16

-0.11

-0.11

-0.08

-0.13

-0.15

(-0.61)(-0.91)(-0.66)(-0.71)(-0.43)(-0.74)(-0.84)

0.02

0.01

0.02

0.02

0.03

0.02

0.01

(0.48)(0.32)(0.50)(0.46)(0.59)(0.36)(0.30)

0.06 **

0.06 **

0.06 **

0.06 **

0.06 **

0.06 **

0.06 *(2.01)(2.03)(2.01)(2.01)(1.99)(2.00)(1.90)

yes

yes

yes

yes

yes

yes

yes

7.37 ***

7.71 ***

7.48 ***

7.53 ***

6.85 ***

7.12 ***

7.68 ***(9.05)(9.26)(8.80)(8.97)(7.00)(7.67)(8.63)

0.220

0.236

0.223

0.223

0.229

0.246

0.237

218

218

218

218

218

218

218

40

Table 8
The effect of the level of short selling prior to the pricing date on the underpricing
This table shows various ordinary least squares regressions that predict the underpricing. In M odels 1 to 4, the definition of dependent variable Underpricing is the percent change from offer price to closing
price on the pricing date (PD)+3. In M odels 5 to 8, the definition of dependent variable Underpricing is the percent change from offer price to closing price on the issue date (ID) +10. The ABSSVOL, which isdefined as the SSVOL minus the average daily SSVOL from day -140 to day -21 relative to the announcement date, is the proxy of the level of short selling prior to PD. The SCR is the total number of covering
shorts on an issue date relative to the total number of shorts that is not covered on a day prior to the issue date. The HighestSCR is the dummy variable that assumes a value equal to 1 if issuers with highest
tertile SCR and zero otherwise. The M iddleSCR is the dummy variable that assumes a value equal to 1 if issuers with middle tertile SCR and zero otherwise. The HighestSCR*ABSSVOL variable is the crossterm of HighestSCR and their ABSSVOL. The M iddleSCR*ABSSVOL variable is the cross-term of M iddleSCR and their ABSSVOL. The ln (M arket Cap) is defined as the natural logarithm of total market
capitalization on the day prior to the offer. The STDR is defined as the standard deviation of daily stock returns over 120 trading days and ending 20 days prior to the offer. RelOffSize is defines as the number
of offered shares divided by the size of the existing market for the firm’s shares. The M ajorUW is the dummy variable for prestigious underwriter that assumes a value equal to 1 if the lead underwriter is the
major underwriter, such as Nomura, Nikko, or Daiwa, and a value of zero otherwise. Days is defined as the days between PD to ID. EXCHANGE are sets of exchange dummy variables. T-ratios of robust
standard errors are in parentheses. ***, ** and * denote coefficients significant at the 1%, 5%, 10% levels, respectively.

ABSSVOL[PD]Underpricing [Offer price to Closing price on PD+3]

Underpricing [Offer price to Closing price on PD+10]

Model 1Model 2Model 5Model 60.56
(0.47)-1.10 *(-1.67)-0.57(-0.32)-2.67 **(-2.19)Model 3Model 4HighSCR*ABSSVOL[PD]9.32 ***(2.96)

11.56 ***
(2.73)

MiddleSCR*ABSSVOL[PD]3.44(1.26)

4.57(1.21)ABSSVOL[PD-3 to PD]

0.48(0.28)-1.80(-1.17)

Model 7

Model 8-0.10(-0.03)-3.30(-0.83)HighSCR*ABSSVOL[PD-3 to PD]

9.03 **(2.93)

11.74 *
(1.67)

MiddleSCR*ABSSVOL[PD-3 to PD]

3.64(1.29)

4.49(0.81)HighSCR dummy

2.52 ***(2.96)

0.72(0.60)

2.53 ***(2.99)

0.78(0.65)

2.90 **(2.37)

0.68(0.44)

2.88 **(2.35)

1.28(0.77)

MiddleSCR

0.85(1.13)

0.14(0.14)

0.86(1.15)

0.10(0.10)

1.40(1.16)

0.47(0.30)

1.38(1.14)

0.77(0.50)ln(Capitalization)

0.04(0.23)

0.21(1.00)

0.05(0.25)

0.17(0.83)

0.19(0.60)

0.39(1.13)

0.20(0.60)

0.30(0.90)

RelOffSize-10.48 **(-2.22)-10.14 **(-2.17)-10.54 **(-2.23)-10.07 **(-2.15)-23.14 ***(-2.68)-22.70 ***(-2.66)-23.06 ***(-2.67)-22.74 ***(-2.63)Major UW

-0.08(-0.10)0.26(0.31)-0.12(-0.14)0.25(0.30)-0.53(-0.56)-0.59(-0.48)-0.94(-0.78)-0.68(-0.55)STDR

0.74(1.65)

0.82 *(1.79)

0.73(1.63)

0.81 *(1.77)-0.16(-0.43)-0.07(-0.18)-0.15(-0.39)-0.08(-0.21)

Days[PD to ID]

0.08(0.64)

0.03(0.19)

0.08(0.46)

0.04(0.21)-0.07(-0.36)-0.13(-0.70)-0.07(-0.35)-0.10(-0.51)

Exchange dummyIntercept
2

R
Observations

yes

yes

yes

yes

yes

yes

yes

yes

0.62
(0.12)-3.16(-0.55)0.58(0.10)-2.08(-0.36)-0.59(-0.07)-5.22(-0.61)-1.00(-0.11)-3.75(-0.43)

0.11
218

0.15
218

0.11
218

0.15
218

0.09
218

0.10
218

0.08
218

0.09
218

41

Announcement Date [AD]Pricing Date [PD]

About one - three weeks

Issue Date [ID]

About one - two weeks

Fig. 1 The timing of announcement date (AD), pricing date(PD), and issue date(ID)

42

90

80

70

Number of SEOs

60

50

40

30

20

10

0
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Days between announcement date and pricing dateFig. 2. Frequency distribution of the number of trading days between from announcement date (AD) to the pricing date (PD)

43

140

120

Number of SEOs

100

80

60

40

20

0
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

Days between pricing date and issue dateFig. 3. Frequency distribution of the number of trading days between from the pricing date (PD) to the issue date (ID)

44

20

70

60

50

Number of SEOs

40

30

20

10

0
5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

Days between announcement date and issue dateFig. 4. Frequency distribution of the number of trading days between from the announcement date (AD) to the issue date (ID)

45

29

30

0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
ID‐5

ID‐4

ID‐3

ID‐2

ID‐1

ID

ID+1

ID+2

ID+3

ID+4

ID+5

Fig. 5. The total number of short selling stocks without covered which is divided by total shares around the issue
date (ID)46