An Empirical Analysis of the Efficiency of Online Auction IPO

8/6/2019 An Empirical Analysis of the Efficiency of Online Auction IPO

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An empirical analysisof the efciency of online auction

IPO processes and traditionalIPO processes

Nayantara HenselGraduate School of Business and Public Policy, US Naval Postgraduate School,

Monterey, California, USA

AbstractPurpose – The purpose of this paper is to examine whether the online auction mechanism in the USAis more effective at pricing initial public offerings (IPOs) than the traditional book building process.Design/methodology/approach – The analysis compares the performance of online auction IPOswith traditional IPOs issued in the same industry area and in the same year to assess the differences inrst day mispricing and its persistence. The paper compares the characteristics of rms choosing theauction process relative to the traditional process. It also uses regression models to examine whetheronline auction IPOs had a signicantly lower rst day price increase than traditional IPOs.Findings – The results indicate that for 60 percent of the auction IPOs, over 40 percent of thetraditional IPOs issued in that year and in that three-digit Standard Industry Classication (SIC) areahad greater mispricing. The mispricing of online auction IPOs relative to traditional IPOs persist overtime for 50-80 percent of online auction IPOs. Regression analyses controlling for industry effects, yeareffects, size of the issue, and type of traditional underwriter (low, medium, and high volumeunderwriters) suggest that the auction’s rst day price surges are not signicantly lower than those of traditional underwriters. Moreover, high volume traditional underwriters have statisticallysignicantly higher rst day price surges than low volume traditional underwriters, supporting thetheory that they intentionally misprice to benet their preferred clients. Firms choosing the auctionprocess tend to be smaller in terms of the number of shares of their IPO and their annual sales thanrms choosing the traditional IPO process. There is some overlap in industry sector and age, althoughthis varies by year.Originality/value – This paper suggests that the auction process may not be as efcient in pricingIPOs as was initially intended and that there are opportunities for further innovation andimprovement.Keywords Auctions, Flotation of companies, Pricing, Electronic commerce, United States of AmericaPaper type Research paper

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1743-9132.htm

JEL classication – G32, G24, G12, M21The author appreciated discussions with Professor Dale Jorgenson. The author also

appreciated the comments on earlier drafts of this paper from participants at the NBEICConference at the Federal Reserve Bank of Dallas in 2008, at the San Francisco Chapter meetingof the National Association of Business Economists at the Bureau of Labor Statistics in 2008, atthe 2007 European Financial Management Conference, at the 2007 National Association of Business Economists Annual Conference, at the 2006 Western Economics AssociationConference, at the 2006 Midwest Economics Association Conference, and at the DePaulUniversity Economics Seminar in 2006. The views and analysis in this paper represent onlythose of the author, not any institution with which the author is afliated.

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International Journal of ManagerialFinanceVol. 5 No. 3, 2009pp. 268-310q Emerald Group Publishing Limited1743-9132DOI 10.1108/17439130910969729



I. Introduction and literature reviewThe resurgence in the initial public offering (IPO) market in 2007 in the USA andoverseas raises the question of whether the traditional IPO issuance process is more orless efcient in pricing IPOs than the online auction process. Minimizing the rst dayprice surge of IPOs has been an important topic in the USA since the dot-com era, whenvarious dot-coms experienced substantial price growth from their initial price on therst day; examples include Enel (966.9 percent), VA Linux (896.7 percent), andSycamore Networks (612.8 percent). In recent years, the average rst day price surgefor an IPO has fallen. This is partially because the exuberance and uncertainty overnew technology in the dot-com boom has ended, and partially due to the initiation andconclusion of legal proceedings by the then – New York State Attorney General ElliotSpitzer and the Securities and Exchange Commission (SEC) against many of theunderwriting investment banks which alleged that the investment banks hadmanipulated IPO prices during the dot-com era to benet their preferred clients.Despite this, mispricing has continued[1], although on a smaller scale than was seenduring the dot-com boom[2]. This paper compares the performance of the traditionalIPO process in pricing new issues with the performance of the online auction process inthe USA, as well as provides some possible explanations for the results.

In the traditional IPO “book building” process, the underwriting investment bankstake the issue on a “road show” to various possible investors (often large mutual fundsor preferred clients of the investment bank) and build a demand curve of possibleprices for the IPO based upon the indications of interest that they receive from theinvestors in terms of the price that they are willing to pay for the IPO and the numberof shares that they are willing to buy. Based on demand curve developed through this“book building” process, the underwriting investment banks determine an “offer price”for the IPO. The offer price is usually about 7 percent below the price at which theunderwriters purchase the shares of the IPO from the issuing company. The

underwriters then resell the shares of the IPO at the “offer price” to the institutionalinvestors and preferred clients of the investment bank who assisted them in pricing theIPO through the “book building” process. The 7 percent spread between the price atwhich the underwriters buy the IPO from the issuing company and the offer price atwhich they resell the issue to their preferred clients represents the prots for theunderwriters[3]. When the IPOs price jumps up from the offer price on the rst day of trading, the investors receiving the initial allotments of the IPO benet because theywere allocated the shares at the offer price by the underwriting investment bank. Thisunderpricing –, i.e. setting the offer price too low such that it jumps up on the rst day – suggests that the IPO may not have been accurately priced and represents “moneyleft on the table” to the issuing rm, because they sold the issue to the underwriters at7 percent below the offer price and therefore could have obtained greater proceeds if the offer price had been higher and had more accurately reected the value of thecompany. Indeed, Aggarwal et al. (2002) nd a stronger positive relationship betweeninstitutional allocation and underpricing than would be predicted by the premarketdemand. Consequently, developing a more accurate offer price, which will minimizemispricing, is important (the Wall Street Journal , 2005a; Ian, 2005).

Loughran and Ritter (2004) discuss some of the causes for the underpricing in thetraditional IPO process during the dot-com era. They argue that executives of issuingrms were more willing to accept underpricing of shares of their rm because they were

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allocated shares of other new IPOs,so that they could benet from the underpricing (andthe resulting rst day price increase) in these IPOs – a practice known as “spinning.”Loughran and Ritter (2004) also discuss the greater importance of analyst coverageduring the 1990s for IPOs (relative toother time periods), which made issuing rmsmorelikely to go to investment banks with the top analysts[4].

The development of the Dutch auction process, OpenIPO.com (which was developedby Hambrecht,whohadpreviouslyco-foundedthe investment bank Hambrecht & Quist(the San Jose Business Journal , 1999)) was the mechanism for the issuance of Google’sIPO and represents oneof the most recent of the attempts to efciently price IPOs so thatthe underwriter receives a more accurate reection of the economicvalue of the rm. Theonline auction process debuted in February 1999 with the issuance of the IPO forRavenswood Wineries. As numerous press articles have noted, the Dutch auctionmethod would supposedly minimize or eliminate the rst day price surge in IPOs[5] bydeveloping an offer price which is a more accurate reection of the company’s valuethrough an auction rather than through the “book building” process. Under this method,bidders post the price that they are willing to pay and the number of shares that theywish to purchase. The auction is open for about two weeks, and bidders can change orcancel their bids until the close of the auction. After the auction ends, OpenIPO.comassembles the bids in order from highest to lowest, and then sets the offer price. Allbidders who bid above this price have their bids satised at this offer price, while thosewho bid below it do not have their bids satised. This offer price is usually the highestprice at which all of the shares are sold, based on the cumulation of the bids[6]. If thenumber of shares in the bids above the offer price exceeds the total number of sharesavailable in the offering, then shares are allocated on a “pro-rata” basis[7]. Unlike thetraditional method, in which the individuals participating in the setting of the offer priceare institutional investors and/or preferred clients of the underwriters, bidders inthe auction method can includesmaller investors. The role of the investment bank as the

middleman is minimized. Generally, Hambrecht’s usual fee ranges from 4 to 6 percentof the proceeds and is sometimes even less than that, which is lower than the 7 percentfeecharged by traditional underwriters (the Wall Street Journal , 2005a). But, is the onlineauction process likely to solve the problem of underpricing and of the resulting rst dayprice surges for IPOs, or will it generate additional problems?

This analysis compares the rst day mispricing of the online auction processthrough OpenIPO.com with that of the IPO process using traditional underwriters andexamines whether the rst day mispricing from the auction process persists over time.The paper is organized as follows: Section II describes the data and provides summarystatistics indicating differences in the rst day mispricing of online auction IPO’s andtraditional IPO’s over time, as well as compares the characteristics of IPOs debutingusing each process in terms of their size, age, and industry sector. Section III presentsempirical results comparing the performance of the online auction IPO process and thetraditional IPO process by matching IPOs issued in the same year and in the sameindustry sector by the two processes, as well as by using regression models controllingfor the industry sector and the size of the IPOs. Section III also examines whether therst day mispricing from the online auction process persists over time. Section IVpresents some possible explanations for the lack of success of the online auctionprocess in efciently pricing IPOs and provides some possible solutions. Finally,Section V presents the conclusions.

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Little work has been done on the empirical performance of IPO auctions; thisanalysis contributes to the literature in that it empirically examines the performance of the US online auction process and compares it to the results of the traditional IPOprocess. Kandel et al. (1999) examine the demand schedules and elasticities of 27 IsraeliIPOs between 1993 and 1996, which debuted using a uniform price auction and ndunderpricing and an average abnormal return on the rst day of trading of 4.5 percent.Biais and Faugeron-Crouzet (2002) examine the Mise en Vente auction-like mechanismused in France using data on 92 IPOs between 1983 and 1996 and nd averageunderpricing of about 13 percent. They also argue that the Dutch auction format canlead to tacit collusion on the part of bidders and can lead to underpricing. Severalpapers examine optimal mechanism design, including Biais and Faugeron-Crouzet(2002) and Jagannathan and Sherman (2005). Sherman (2000) models how in a repeatedsetting, underwriters can lower the excess returns of uninformed investors and thuslower the degree of underpricing, as well as discusses how hybrid book building canlead to greater underpricing than straight book building.

Carter et al. (2000) focus on the use of the internet to distribute an IPO to smallinvestors, but not to price it (as the online auction process does). They explore thecharacteristics of IPOs which are underwritten by traditional underwriters, but whichallocate some portion of the initial shares to an online investment bank, such as WitCapital, which can then distribute them to small investors. Their analysis examined thecharacteristics and behavior of 27 IPOs debuting between July and December 1998, soboth the dataset and the date of publication of the paper largely pre-dated the onlineIPO auction pricing process, although the paper notes that the online auction processhad recently been developed. Their ndings suggested that rms using the internet fordistribution were larger, used more reputable underwriters, and had younger CEOs.They had greater volume and volatility than IPOs that chose the traditionaldistribution mechanism. They also had higher returns, which the authors suggested

was due to the underwriter discounting the offer price to reect the riskiness inherentin this new method of distributing the IPO or due to the rms having younger CEOs.Differences in information sets for investors provide explanations for IPO

underpricing in the traditional process. Rock (1986) develops a winner’s curse model inwhich informed investors only invest in issues that they know are underpriced, thusleading to greater underpricing by underwriters. Beneviste and Spindt (1989) developan information-gathering model, in which underpricing rewards informed investors forproviding information on demand, price, and quantity to the underwriters. Welch(1989) provides a signaling model in which high quality rms tend to underprice.Lee et al. (1999), using data on the Singapore stock exchange, show that large investorswith better information request participation in IPOs with higher initial returns, thusbeing one of the rst papers to document that larger investors have an informationaladvantage[8]. Other papers, such as Field (1997), suggest that institutional investorsmay be better informed about IPO value than other investors. Carter and Manaster(1990) discuss how IPOs with higher returns have more informed investor capital. Theanalysis in this paper nds that the online process did not exhibit signicantly lowerunderpricing than the traditional process, as had been initially intended by itsfounders. In Section V, the paper discusses how one reason for the auctionunderpricing may be that there is greater participation of smaller investors, who areless informed, and so are less able to accurately price an IPO.


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Another strand of the literature examines how IPO rms perform following theirdebut, such as Jain and Kini (1994), Ritter (1991), and Teoh et al. (1998). Krignam et al.(1999) examine underwriters pricing errors and show that rst-day “winners” continueto be “winners” and that rst-day “dogs” continue to be “dogs.” Section IV of thisanalysis examines whether the mispricing of online auction IPOs continues to exceedthe mispricing of traditional IPOs over time (one week, two weeks, four weeks, 60 days,90 days, 180 days, one year after debuting) and nds that a greater degree of mispricing relative to traditional IPOs persists for 50-80 percent of online auction IPOs.

II. Data and summary statisticsThis section describes some of the differences between the online auction process andthe traditional IPO issuance process, both in terms of their comparative performanceover the years, and in terms of their characteristics, such as the size and the industrysector of the rms using each of the processes to issue an IPO. The data, which arefrom SDC Platinum, consist of all IPOs issued by both the traditional underwriters and

the online underwriter in the USA between February 1999 and June 2005.Table I subdivides the IPOs into those issued by the online auction process andthose issued by traditional underwriters. Traditional underwriters are then subdividedinto low volume underwriters, medium volume underwriters, and high volumeunderwriters. This paper denes low volume underwriters to be underwritinginvestment banks which served as primary lead bookrunner on under 14 IPOs between1999 and 2005, medium volume underwriters to be underwriting investment bankswhich served as primary lead bookrunner for between 14 IPOs and 40 IPOs, and highvolume underwriters to be underwriting investment banks which served as primarylead bookrunner for over 40 IPOs. Table I shows summary statistics, by underwritercategory, on the total number of underwriters in that category, the number of IPOs, thepercentage of total IPOs underwritten by each category of underwriter, and theaverage principal amount, size, and rst day price surge of an IPO underwritten by anunderwriter in each category.

Several interesting observations can be drawn from Table I. First, despite thepublicity surrounding the process, the number of IPOs issued by the online auctionprocess is small relative to the number issued by the traditional process. Within thetraditional process, there are comparatively few high and medium volumeunderwriters, but they issue collectively over 80 percent of the IPOs. Second, basedon these averages, the rst day price increase of an IPO issued through the auctionprocess is 29-30 percent, which is lower than the average for all of the traditionalunderwriters at 38 percent. This is consistent with the intention of the auction processto minimize mispricing. Nevertheless, when one segments the traditional underwritersinto low, medium, and high volume underwriters, the auction process is more efcientthan high volume underwriters (48 percent), as efcient as a medium volumeunderwriter (29 percent), and less efcient than low volume underwriters (10 percent).One would think that low volume underwriters, since they have less experience, shouldbe less efcient at pricing IPOs than high volume underwriters. Nevertheless, the highvolume underwriters were the investment banks who paid nes for allegedlyintentionally underpricing IPOs to benet their preferred clients, who received theinitial allocations. Third, despite the fact that the degree of rst day mispricing is muchgreater for the auction process than for low volume underwriters, auction IPOs tended

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N u m b e r o f

u n d e r w r i t e r s

N u m b e r

o f I P O s

P e r c e n t a g e o f

t o t a l I P O s

A v e r a g e

n u m b e r o f

I P O s

A v e r a g e p r i n c i p a l

a m o u n t ( $ m i l l i o n ) o f I P O

A v e r a g e s i z e ( n u m b e r

o f s h a r e s ) o f I P O

A v e r a g e r s t d a y

p r i c e i n c r e a s e

O n l i n e p r o c e s s ( w i t h

G o o g l e )

1

1 4

0 . 8 0

1 4

1 6 4 . 4

4 , 9 7 6 , 4 0 9

0 . 2 9 0

O n l i n e p r o c e s s

( w i t h o u t G o o g l e )

1

1 3

0 . 7 4

1 3

4 9 . 1 5

3 , 8 5 1 , 1 2 9

0 . 3 0

A l l t r a d i t i o n a l


1 4 8

1 , 7 3 8

9 9 . 2

1 1 . 7

1 3 6 . 3 1

1 1 , 7 0 0 , 0 0 0

0 . 3 8 3

H i g h v o l u m e

t r a d i t i o n a l


1 3

1 , 1 4 6

6 5 . 4 1

8 8 . 2

1 6 7 . 6

1 4 , 6 0 0 , 0 0 0

0 . 4 8 2

M e d i u m v o l u m e



1 1

2 8 1

1 6 . 0 4

2 5 . 5

1 0 1 . 6

7 , 8 0 6 , 6 4 9

0 . 2 8 9

L o w v o l u m e



1 2 5

3 1 1

1 7 . 7 5

2 . 5

5 7 . 5

5 , 8 4 1 , 2 6 6

0 . 1 0 1

N o t e s : T h i s t a b l e p r o v i d e s s u m m a r y s t a t i s t i c s o n n u m b e r o f

u n d e r w r i t e r s , n u m b e r o f I P O s , p e r c e n t a g e o f t o t a l I P O s , a v e r a g e n u m b e r

o f I P O s p e r

u n d e r w r i t e r , a v e r a g e p r i n c i p a l a m o u n t , a v e r a g e n u m b e r o f s h a r e s p e r I P O , a n d

a v e r a g e r s t d a y p e r c e n t a g e p r i c e i n c r e a s e ; t h e t a b l e c o m p a r e s t h e e n t i r e

s a m p l e o f t r a d i t i o n a l I P O

u n d e r w r i t e r s w i t h t h e o n l i n e a u c t i o n

p r o c e s s , a n d t h e n s e g m e n t s t h e t r a d i t i o n a l I P O u n d e r w r i t e r s i n t o l o w v o l u m e , m e d i u m

v o l u m e , a n d h i g h v o l u m e t r a d i t i o n a l u n d e r w r i t e r s

S o u r c e : S D C P l a t i n u m

Table ISummary statisticon various types o

underwriter


273



to be similar to IPOs underwritten by low volume underwriters in terms of the numberof shares in the average IPO and in terms of the average principal amount raised in theIPO (if one excludes Google in examining the auction process). Fourth, Google raisedan unusually large amount for an auction IPO at almost $2 billion.

Table II compares the online auction IPOs and the IPOs issued through thetraditional process based on industry sector as measured by the two-digit SIC code.Almost two third of traditional IPOs were in the same industry area as the IPOs issuedby the auction process. Both processes had over 20 percent of their IPOs in thesoftware/online services area, due to the growth of the internet at the time, and bothhad 7-8 percent of their IPOs in the area of manufacturing telecomm equipment andenergy components. The auction process, however, had a greater emphasis than thetraditional process in the soft drink/coffee industry area, the biotech andpharmaceutical area, the restaurant area, the online retail and catalog area, and theonline nancial services and loan services area. Table III shows the industrybreakdown for traditional IPOs across all industry sectors (not just the onesoverlapping with auction IPOs). Two of the top four industry areas for auction IPOswere also in the top four sectors for traditional IPOs (SIC 7300 and 2800). Six of the topten industry sectors for traditional IPO issuance were the same industry areas as forauction IPOs. Nevertheless, when one compares IPOs issued in the same industrysector between the traditional process and the auction process (Table II), the size of theissue in terms of number of shares is much smaller. The average number of shares of an auction IPO ranges from 5 percent of the average number of shares of a traditionalIPO (SIC 2800) to over 50 percent (SIC 6700). Similarly, the average principal amountraised in an auction IPO ranged from 12 percent of the amount raised by the traditionalIPO (SIC 5800) to 66 percent of the amount raised in a traditional IPO (SIC 6200). Inmany industry areas, the principal amount raised in auction IPOs was 30-50 percent of the amount raised in traditional IPOs (SIC 2800, 3600, 5900, 6100, and 6700). The only

sector in which the share size and the average principal amount raised was greater forauction IPOs than traditional IPOs was in SIC 7300 when Google is included. If Googleis excluded (see note in Table II), then the average number of shares for auction IPOs inSIC 7300 is about 58 percent of the average number of shares of a traditional IPO in SIC7300 and the average principal amount for auction IPOs is 66 percent of the amountraised by traditional IPOs. In short, while there was some overlap in industry areabetween online auction IPOs and traditional IPOs, auction IPOs tended to be smaller interms of the principal amount raised and the number of shares in the issue.

Table IV compares the percentage of technology IPOs issued in each year throughthe online auction process and through the traditional IPO process. The denition of atechnology IPO follows Loughran and Ritter (2004), and includes IPOs in SIC codes3571, 3572, 3575, 3577, 3578, 3661, 3663, 3669, 3671, 3672, 3674, 3675, 3677, 3678, 3679,3812, 3823, 3825, 3826, 3827, 3829, 3841, 3845, 4812, 4813, 4899, 7371, 7372, 7373, 7374,7375, 7378, and 7379. In four of the seven years, the online auction process had no techIPOs and, consequently, had a smaller percentage of IPOs debuting in this morebroadly dened technology sector than in the traditional process. In three of the sevenyears, the online auction process had a higher percentage of technology IPOs than thetraditional process. One explanation for the greater degree of mispricing in the onlineauction IPO process relative to the traditional process (seen in the averages in Table I)could have been that a greater percentage of technology IPOs were debuting using the

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T w o - d i g i t S I C

c o d e ( i n d u s t r y

a r e a )

B u s i n e s s d e s c r i p t i o n f o r

c o m p a n i e s g o i n g p u b l i c i n

t h i s S I C

c o d e b e t w e e n 1 9 9 9

a n d 2 0 0 5

P e r c e n t a g e o f t r a d i t i o n a l

I P O s i n t h i s i n d u s t r y a r e a

P e r c e n t a g e o f o n l i n e

I P O s i n t h i s i n d u s t r y

a r e a

A v e r a g e n u m b e r o f s h a r e s o f

t r a d i t i o n a l I P O s ( p r i n c i p a l

a m o u n t i n $ s m i l l i o n )

A v e r a g e n u m b e r o f s h a r e s o f

o n l i n e I P O s ( p r i n c i p a l

a m o u n t i n $ s m i l l i o n )

2 0 0 0

M e a t p a c k i n g p l a n t s ;

m a n u f a c t u r i n g o f c a n n e d

f o o d s , s o f t d r i n k s , c o f f e e ,

f r u i t j u i c e s , e t c .

0 . 6 3

1 4 . 2 9

3 5 , 7 0 0 , 0 0 0 ( 1 1 1 . 1 8 )

2 , 1 5 0 , 0 0 0 ( 1 8 . 4 5 )

2 8 0 0

M a n u f a c t u r i n g o f

p h a r m a c e u t i c a l s a n d b i o t e c h

d r u g s

6 . 7 9

1 4 . 2 9

7 , 6 5 6 , 7 5 4 ( 8 8 . 1 )

3 , 9 5 0 , 0 0 0 ( 3 3 . 4 5 )

3 6 0 0

M a n u f a c t u r i n g o f e l e c t r i c a l

a n d e n e r g y c o m p o n e n t s a n d

t e l e c o m m u n i c a t i o n s

e q u i p m e n t

7 . 8 8

7 . 1 4

1 2 , 2 0 0 , 0 0 0 ( 1 2 7 . 8 5 )

3 , 5 0 0 , 0 0 0 ( 4 2 )

5 8 0 0

O w n a n d o p e r a t e r e s t a u r a n t s

0 . 8 1

7 . 1 4

8 , 5 3 9 , 4 4 6 ( 1 3 6 . 3 1 )

2 , 0 0 0 , 0 0 0 ( 1 6 )

5 9 0 0

O w n a n d o p e r a t e v a r i o u s

s e r v i c e b u s i n e s s e s : s p o r t i n g

g o o d s , c a t a l o g c o m p a n i e s ,

o n l i n e r e t a i l

2 . 1 3

1 4 . 2 9

5 , 3 3 7 , 0 1 1 ( 8 0 . 8 8 )

2 , 6 0 0 , 0 0 0 ( 3 4 . 9 )

6 1 0 0

L o a n s e r v i c e s ( p a y d a y l o a n s ,

s t u d e n t l o a n s ) , m o r t g a g e

b a n k s , c r e d i t c a r d s e r v i c e s

1 . 1 5

7 . 1 4

1 6 , 5 0 0 , 0 0 0 ( 9 4 . 3 9 )

3 , 0 5 2 , 1 7 4 ( 3 5 . 1 )

6 2 0 0

P r o v i d e b r o k e r a g e a n d

i n v e s t m e n t b a n k i n g s e r v i c e s ;

o n l i n e n a n c i a l s e r v i c e s

1 . 8 4

7 . 1 4

2 1 , 4 0 0 , 0 0 0 ( 2 1 2 . 2 4 )

7 , 6 1 2 , 5 0 0 ( 1 4 0 . 8 )

6 7 0 0

B a n k h o l d i n g c o m p a n i e s a n d

c l o s e d - e n d i n v e s t m e n t f u n d s

1 8 . 4 7

7 . 1 4

1 8 , 9 0 0 , 0 0 0 ( 2 5 4 . 0 5 )

1 0 , 0 0 0 , 0 0 0 ( 1 3 0 )

7 3 0 0

D e v e l o p m e n t o f s o f t w a r e a n d

o n l i n e s e r v i c e s

2 6 . 7 0

2 1 . 4 3

5 , 5 5 1 , 7 6 2 ( 7 4 . 3 2 )

8 , 7 0 1 , 6 8 4 a

( 5 8 8 . 1 ) a

T o t a l p e r c e n t a g e

o f I P O s

6 6 . 4 0

1 0 0

N o t e s :

a N o t e t h a t t h e s e n u m b e r s i n c l u d e G o o g l e , i f o n e e x c l u d e s G o o g l e , t h e n t h e a v e r a g e n u m b e r o f s h a r e s i n S I C 7 3 0 0 i s s u e d b y t h e o n l i n e a u c t i o n

p r o c e s s w a s 3 , 2 5 0 , 0 0 0 s h a r e s a n d t h e a v e r a g e p r i n c i p a l a m o u n t r a i s e d w a s $ 4 9 . 1 5 m i l l i o n

S o u r c e : T h e s o u r c e o f t h e u n d e r l y i n g r a w d a t a i n t h e t a b l e i s t h e S D C P l a t i n u m d a t a b a s e

Table II.Comparison of traditionaIPOs and online IPOs b

industry, 1999-200


275



Two-digit SIC codeBusiness description for companies going public inthis SIC code between 1999 and 2005

Percentage of traditional IPOsin this category

7300 Development of software and online services 26.706700 Bank holding companies and closed-end investment

funds 18.473600 Manufacturing of electrical and energy components

and telecommunications equipment 7.882800 Manufacturing of pharmaceuticals and biotech

drugs 6.794800 Provide wireless internet and telecomm service,

broadband, VOIP; own and operate radio, cable, andTV stations 5.98

3800 Manufacturing medical and biotech equipment 4.098700 Drug R&D services, cancer treatment research 2.476300 Health services (including insurance) 2.42

5900 Own and operate various services: sporting goods,online retail, catalog companies 2.135900 Own and operate various service businesses:

sporting goods, catalog companies, and online retail 2.136200 Provide brokerage and investment banking services;

online nancial services 1.846200 Provide brokerage and investment banking services;

online nancial services 1.843500 Manufacturing surface mining machinery, data

storage, ber optics, printing equipment, and ightinfo computers 1.78

1300 Oil and gas exploration and production 1.616000 Commercial banks, savings and loans, and

associated holding companies 1.384900 Electric and gas utilities; electrical and geothermal

power 1.216100 Loan services (payday loans, student loans),

mortgage banks, credit card services 1.156100 Loan services (payday loans, student loans);

mortgage banks; and credit card services 1.158000 Own and operate hospitals, surgery centers,

laboratories 1.044400 Shipping companies; water transportation services 0.928200 Own and operate schools, educational services,

educational software, etc. 0.865000 Wholesaler of autos, computers, and roong

materials 0.86

5800 Own and operate restaurants 0.812000 Meat-packing plants; canned foods, soft drinks,coffee, fruit juices 0.63

3700 Manufacturing aircraft, railroad cars, tanks,snowmobiles, and auto parts 0.58

6500 Real services 0.467800 Motion picture services (incl. distribution); cable TV

services 0.46( continued )

Table III.Industry breakdownfor traditional IPOs,1999-2005

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process and that technology IPOs were more underpriced because they were indeveloping industries whose potential was harder to estimate at the time. Nevertheless,the data do not support this hypothesis because there was often a smaller percentage of technology IPOs debuting using the auction process than in the traditional process.

Two-digit SIC codeBusiness description for companies going public inthis SIC code between 1999 and 2005

Percentage of traditional IPOsin this category

3300 Steel and aluminum production 0.404600 Crude oil pipeline operator 0.404500 Airlines 0.401200 Coal mining services 0.405100 Manufacturing herbal drugs, dietary supplements;

pharmacies 0.405600 Clothing retail stores 0.404200 Provided trucking, relocation, and courier services 0.401500 Residential and commercial construction 0.295700 Furniture and appliance stores; video game stores;

online lm stores 0.297000 Own and operate casino hotels and hotel casinos 0.293900 Manufacturing collectibles – candles, golf equation 0.296400 Insurance agents and brokers 0.295400 Convenience stores; bread stores, doughnut and ice

cream stores 0.232700 Publishing 0.231000 Metal, gold, and palladium mining 0.177900 Operate gyms; golf instruction 0.173000 Manufacturing plastic containers and products 0.173200 Manufacturing glass containers, ready-mix concrete,

and chemicals 0.177200 Misc – coin-operated laundries; tax preparation

services, and weight reduction services 0.17250023005200

75003100160034001400220074017001802600550053002900750490 , 0.12 Table III.


277



The rms which went public using the online auction process had characteristicssimilar to rms that went public using the traditional process. The author obtaineddata on the age of the issuing rm, sales and net income of the rm in the 12 monthsprior to its IPO, and on the age and salary of its CEO by examining the S-3 that eachrm, which debuted using the online auction process, had led with the SEC. IPOsdebuting using the online auction process were about nine years old, on average. If oneexcludes Morningstar and Peet’s Coffee, which were over 20 years old when they wentpublic, the average age was 6.3 years. Table V compares the age of a rm using thetraditional process with the age of a rm using the online process on a year-by-yearbasis. In four of the seven years, rms using the online auction process were actuallyslightly older than rms using the traditional process. Examination of the S-3s alsoindicated that the base salary of the CEOs averaged $185,294 and the age of the CEOaveraged around 51 years. The Ernst & Young IPO Study found that the average agefor CEOs of rms who immediately qualied for listing in the Russell 2000 Indexfollowing their IPO using the traditional process was between 50 and 54 years and thatthe median age of the rm itself was eight to nine years old. This is similar to thepattern seen in this analysis for rms using the online auction process.

Median age (traditional) Median age (online)

1999 4 72000 6 72001 12 18

2002 15 52003 11 72004 8 62005 11 13.5

Notes: Data for the median age of online issuers comes from the aggregation of information on the ageof each online issuer obtained by the author through examining each company’s S-3 ling with theSEC; composite median age data on traditional issuers are from the Field-Ritter dataset on foundingdates for rms going public in the US during 1975-2006

Table V.Comparison of medianage of rms using theonline auction processand the traditionalprocess: 1999-2005

Percentage of technology IPOs throughtraditional underwriters

Percentage of technology IPOs throughonline underwriters

1999 53.97 66.62000 57.25 02001 20.64 02002 10.47 02003 13.18 502004 21.12 332005 15.32 0

Notes: The yearly percentages of technology IPOs issued through traditional underwriters and theonline auction process were derived by the author from underlying raw data on SIC codes from SDCPlatinum, the denition of technology IPOs includes IPOs in SIC codes 3571, 3572, 3575, 3577, 3578,3661, 3663, 3669, 3671, 3672, 3674, 3675, 3677, 3678, 3679, 3812, 3823, 3825, 3826, 3827, 3829, 3841,3845, 4812, 4813, 4899, 7371, 7372, 7373, 7374, 7375, 7378, and 7379Source: Loughran and Ritter (2004, Appendix D)

Table IV.Yearly comparison of thepercentage of technologyIPOs issued through thetraditional IPO processand through the onlineauction process:1999-2005

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Table VI compares the distribution of sales for a rm debuting using the traditionalIPO process in the year preceding its IPO during the 1980s, the 1990s, the height of thedot-com bubble, and the 2000s, with the distribution of sales for rms debuting usingthe online auction process. The table suggests that the online auction process had agreater percentage of smaller rms with annual sales of under $10 million than thetraditional IPO process at any period, with the exception of the 1999-2000 period (thepeak of the dot-com era). The online process had similar percentages to the traditionalIPO process in terms of the percentage of rms with sales between $10 and $20 million,between $20 and $50 million, and between $50 and $100 million. The online auctionprocess had a smaller percentage of rms with sales greater than $100 million andgreater than $200 million than the traditional process in every period, except during thepeak of the dot-com period, when the percentages were similar. The conclusion thatthe online auction process tended to have a greater fraction of smaller rms than thetraditional process, the same fraction of intermediate-size rms, and a smaller fractionof really large rms is consistent with the pattern in the data on the number of shares

issued for online auction IPOs relative to the number of shares of IPOs issued by thetraditional process (Tables I and II).Table VII compares the average rst day price surge for online auction IPOs with

those of low, medium, and high volume traditional underwriters across the entireve-year period, and on a yearly basis. The rst row reects the ndings previouslydiscussed for Table I: although the average rst day price increase for online issuers ishigher than the average for traditional issuers, when traditional issuers aredecomposed by volume of IPOs issued, the online process is less efcient than lowvolume underwriters, as efcient as medium volume underwriters, and more efcientthan high volume underwriters. Second, the table compares the rst day price surge bytype of underwriter on a yearly basis – the degree of mispricing (as measured byabsolute value) for online underwriters exceeded that of low volume, medium volume,

1980-1989(traditional,

%)


%)


%)


%)

1999-2005(online,

%)

Sales between $0 and $10million 19.5 19.4 38.9 15.3 36.36Sales between $10 and $20million 12.4 11.0 16.3 4.7 9.09Sales between $20 and $50million 23.3 22.2 17.6 14.5 18.8Sales between $50 and $100million 17.4 16.6 10.4 15.6 18.18Sales between $100 and $200million 11.8 12.8 6.7 13.5 9.09Sales greater than $200 million 14.3 18.0 10.1 36.5 9.09

Notes: The chart compares the sales in the 12 months preceding an IPO for rms using the traditionalIPO process and rms using the online auction IPO process; data for the sales of rms using the onlineauction process comes from the aggregation of information on the sales of each online issuer obtainedby the author through examining each company’s S-3 ling with the SECSource: Data on the sales for rms using the traditional IPO process comes from Ritter (2008)

Table VI.Comparison of averag

annual sales for IPOdebuting using th

traditional process anIPOs debuting using th

online proce


279




p r i c e s u r g e b y y e a r

a n d S I C c o d e


p r i c e s u r g e ( t o t a l

s a m p l e )


p r i c e s u r g e ( i s s u e d

t h r o u g h o n l i n e

a u c t i o n )



t h r o u g h t r a d i t i o n a l

p r o c e s s )



b y l o w v o l u m e


u n d e r w r i t e r s )



b y m e d i u m v o l u m e


u n d e r w r i t e r )



b y h i g h v o l u m e


u n d e r w r i t e r s )

T o t a l

0 . 3 8 3

0 . 2 9 0

0 . 3 8 3

0 . 1 0 1

0 . 2 8 9

0 . 4 8 2

D u r i n g 1 9 9 9

0 . 5 8 9

1 . 0 9 8

0 . 5 8 6

0 . 1 4 2

0 . 3 8 7

0 . 7 8 0

D u r i n g 2 0 0 0

0 . 5 4 2

2 0 . 2 1 6

0 . 5 4 4

0 . 1 0 2

0 . 5 4 4

0 . 6 3 5

D u r i n g 2 0 0 1

0 . 8 9 0

0 . 3 1 8

0 . 9 0 0

0 . 2 3 1

0 . 1 3 7

1 . 1 1 1

D u r i n g 2 0 0 2

0 . 0 5 7

0 . 0 0 2

0 . 0 5 7

0 . 1 6 0

0 . 0 3 8

0 . 0 4 2

D u r i n g 2 0 0 3

0 . 0 6 8

0 . 0 7 5 2

0 . 0 6 7

0 . 1 1 0

0 . 0 3 0

0 . 0 6 8

D u r i n g 2 0 0 4

0 . 0 7 9

0 . 0 3 7

0 . 0 8 0

0 . 0 4 2 3

0 . 0 7 9

0 . 0 9 2

D u r i n g 2 0 0 5

0 . 0 1 3

0 . 0 4 2

0 . 0 1 3

2 0 . 0 8 9

0 . 0 5 5

0 . 0 4 1 0

N o t e s : T h i s t a b l e c o m p a r e s t h e a v e r a g e r s t d a y p r i c e s u r g e b e t w e e n t h e t o t a l s a m p l e o f t r a d i t i o n a l u n d e r w r i t e r s a n d t h e o n l i n e a u c t i o n p r o c e s s , a s w e l l

a s a m o n g t h r e e t y p e s o f t r a d i t i o n a l u n d e r w r i t e r s –

l o w v o l u m e , m e d i u m v o l u m e , a n d h i g h v o l u m e u n d e r w r i t e r s ; t h e t a b l e e x a m i n e s t h i s f o r t h e e n t i r e

1 9 9 9 - 2 0 0 5 p e r i o d , a s w e l l a s o n a y e a r l y b a s i s

S o u r c e : S D C P l a t i n u m

Table VII.Summary statistics of rst day price surges byyear and type of underwriter

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and high volume underwriters 42.8 percent of the time (in three of the seven years).Consequently, this suggests that the online process may not be as efcient in pricing aswas originally intended. Moreover, an analysis of the average rst day price surge perunderwriter over the ve-year period across all underwriters indicates that the averagerst day price surge for IPOs issued by WR Hambrecht’s OpenIPO.com online auctionmechanism met or exceeded the rst day price surge for 82.4 percent of the primarylead bookrunners (108 out of 131) in the sample.

The results in Table VII also suggest that while the rst day surges in an IPOs priceare important, the problem that the online auction mechanism was partially developedto solve has lessened. The average rst day price increase has dropped substantiallyfrom the 55.4-92.5 percent range during 1999-2001 into the 6.3-8.6 percent range of 2002-2004. This gradual reduction in the magnitude of rst day price surges over timemay be a reection of more careful pricing by some underwriters in the wake of substantial litigation concerning their alleged manipulation of IPOs, as discussed in theintroduction[9]. Many of the investment banks with the top analysts and whichengaged in “spinning” and “laddering” included some of the high volume traditionalissuers in my analysis, which explains why their underpricing was so substantial – inmany cases, the executives of the issuing rms were willing to accept underpricing inreturn for shares in other “hot” IPOs or in return for favorable coverage from the topanalysts in the industry.

A second explanation for the high degree of underpricing during the dot-com eraand the subsequent reduction in underpricing following the collapse of the bubble maybe that many of the technology IPOs during the dot-com boom were in emergingindustries, and, since most investors had a supercial understanding of thefundamentals and technology in these industries, they had difculty in accuratelypricing the issues and were inuenced by “herd behavior.” Indeed, at the peak of thedot-com bubble in 1999, over half of the IPOs issued through the online process and the

traditional process were technology IPOs, using Loughran and Ritter’s (2004) criteria,as was evident in Table IV.In short, the summary statistics in this section yield several key ndings. First, the

degree of underpricing has fallen over time, possibly due to the role of litigation ineliminating anticompetitive practices, such as “spinning,” as detailed in Loughran andRitter (2004), and possibly due to the declining fraction of technology IPOs. Second,IPOs issued through the online auction process had a greater degree of averageunderpricing than low volume issuers and a lesser degree of average underpricing thanhigh volume issuers. This suggests that the online auction process may not have beensuccessful in its objective to minimize underpricing, although it did have lower averageunderpricing than traditional issuers when the three types are aggregated.

Are there differences in the types of rms choosing the online auction processrelative to the traditional process for their IPO? Almost two third of traditional IPOswere in the same industry area as the IPOs issued by the online auction process,although the auction process had a greater emphasis in the soft drink/coffee industryarea, the biotech and pharmaceutical area, the restaurant area, the online retail andcatalog area, and the online nancial services and loan services area. In four of theseven years, the online auction process had no tech IPOs and, consequently, had asmaller percentage of IPOs debuting in this more broadly dened technology sectorthan in the traditional process, while in three of the seven years, the online auction


281



process had a higher percentage of technology IPOs. Second, the IPOs debuting usingthe online auction process was smaller than IPOs debuting using the traditionalprocess. This was true even if one matched the industry areas and compared thenumber of shares in an auction IPO and in a traditional IPO, as in Table II. The averagenumber of shares of an auction IPO ranged from 5 percent of the average number of shares of a traditional IPO to over 50 percent. Similarly, the average principal amountraised in an auction IPO ranged from 12 percent of the amount raised by the traditionalIPO to 66 percent. Not surprisingly, when the author examined sales in the 12 monthspreceding an IPO, the online auction process tended to have a greater fraction of smaller rms (as measured by sales) than the traditional process, the same fraction of intermediate-size rms, and a smaller fraction of larger rms. Age of the debuting rmdid not differ substantially between rms choosing the auction process and rmschoosing the traditional process: in four of the seven years, the auction IPOs wereslightly older, and in three of the seven years, they were somewhat younger than IPOsusing the traditional process.

III. Empirical results and sensitivity analysisThis section analyses whether the rst day price surge exhibited by online auctionIPOs is signicantly higher than the rst day price surge exhibited by traditional IPOs,controlling for various factors, such as the year, and several factors capturing thecharacteristics and risk of these IPOs. These factors include the industry of the issuingrm (as measured by the two-digit SIC code), as well as the size of the rm (asmeasured by the number of shares in its IPO). Other measures describing thecharacteristics of the rms going public were highly correlated with the industry andthe size of the issue and so were not helpful in describing the differences inunderpricing. For example, smaller rms as measured by sales or net income werelikely to have smaller IPOs (as measured by the number of shares) than larger rms, sothe number of shares variable picked up most of these differences, as was suggested bythe summary data in Section II. Also, the number of shares was important to includebecause IPOs with a smaller number of shares could have greater underpricing if therewere alot of pent-up demand relative to the supply of shares offered, which then wouldmanifest itself when the IPO began to trade. The industry variables were important indescribing risk because many of the traditional and online IPOs during this periodwere technology IPOs, and were often smaller, younger companies. Age of thecompany was not helpful in the regression specications during this period bothbecause, as described in Section II, there were not signicant differences in agebetween the online IPOs and the traditional IPOs, and also because the industry codesoften picked up the same type of information as age variables. The lack of signicanceof age was also seen in Loughran and Ritter (2004) during the 1999-2000 period and the2001-2003 period[10]. Consequently, the regression results reported in the tables focuson the specications using industry xed effects by two-digit SIC code and IPO size interms of number of shares to capture the rm-specic characteristics, since otheriterations using similar metrics (age, etc.) yielded similar results.

As noted in the earlier tables, signicantly more traditional IPOs were issued thanauction IPOs. Consequently, Table VIII matches each auction IPO with a traditionalIPO issued in the same year and in the same industry area as measured by thethree-digit SIC code and compares the degree of mispricing[11]. Some auction IPOs had

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I s s u e d a t e

U n d e r w r i t e r

S I C c o d e

F i r s t d a y

p r i c e

i n c r e a s e

( % )

D i f f e r e n c e b e t w e e n

t h e a u c t i o n I P O

m i s p r i c i n g a n d t h e

t r a d i t i o n a l I P O

m i s p r i c i n g ( % )

P e r c e n t a g e o f I P O s

i s s u e d i n t h e s a m e

y e a r a n d

i n t h e s a m e

t h r e e - d i g i t S I C c o d e

w i t h l e s s m i s p r i c i n g

t h a n t h e a u c t i o n I P O

( % )

A p r i l 8 , 1 9 9 9

H a m b r e c h t I P O : R a v e n s w o o d W i n e r y

2 0 8 4

3 . 6 1

9 . 3

0

M a r c h 3 0 , 1 9 9 9

C o m p a r a b l e T r a d i t i o n a l I P O : P e p s i B o t t l i n g G r o u p

2 0 8 6

2 5 . 6 9

J a n u a r y 2 5 , 2 0 0 1

H a m b r e c h t I P O : P e e t ’ s C o f f e e & T e a

2 0 9 5

6 3 . 2 5

5 9 . 2 5

1 0 0

M a y 2 , 2 0 0 1

C o m p a r a b l e t r a d i t i o n a l I P O : C o f f e e H o l d i n g C o .

2 0 9 5

4

A u g u s t 5 , 2 0 0 4

H a m b r e c h t I P O : N e w R i v e r P h a r m a c e u t i c a l s

2 8 3 4

2 6 . 2 5

2 1 4 . 5 3

5 0

A v e r a g e o f t r a d i t i o n a l I P O s i n 2 0 0 4 f o r S I C 2 8 3 0 s

8 . 2 8

O c t o b e r 2 9 , 2 0 0 3

H a m b r e c h t I P O : G e n i t o p e C o r p

2 8 3 6

1 1 . 1

7 . 9 8

7 7 . 7


3 . 1 2 5

M a y 1 8 , 2 0 0 0

H a m b r e c h t I P O : N o g a t e c h

3 6 7 4

2 2 1 . 5 8

2 8 3 . 3 1

3 5 . 3


6 1 . 7 3

M a y 2 , 2 0 0 1

H a m b r e c h t I P O : B r i a z z

5 8 1 2

0 . 3 7 5

2 6 . 8 7

0


7 . 2 4

M a r c h 1 7 , 2 0 0 4

H a m b r e c h t I P O : S u n s e t F i n a n c i a l R e s o u r c e s , I n c .

6 7 9 8

2 0 . 7 6 9

2 4 . 3 0

4 3 . 3


4 . 2 2

J u n e 2 2 , 1 9 9 9

H a m b r e c h t I P O : S a l o n . c o m

7 3 7 2

2 4 . 7 6

2 8 9 . 8 6

8 . 7 8

D e c e m b e r 8 , 1 9 9 9

H a m b r e c h t I P O : A n d o v e r . n e t

7 3 7 9

3 3 0 . 5

2 4 5 . 4

9 7 . 5


8 5 . 1

A u g u s t 1 8 , 2 0 0 4

H a m b r e c h t I P O : G o o g l e

7 3 7 5

1 8

1

7 0


1 7

N o t e s : T h i s t a b l e c o m p a r e s I P O s w h i c h d e b u t e d u s i n g t h e O p e n I P O a u c t i o n m e t h o d w i t h I P O s w h i c h d e b u t e d u s i n g t h e t r a d i t i o n a l m e t h o d i n t h e s a m e

y e a r a n d i n t h e s a m e t h r e e - d i g i t S I C c o d e , n o t e t h a t t h i s t a b l e d o e s n o t i n c l u d e t h e f o l l o w i n g O p e n I P O I P O s b e c a u s e t h e r e w e r e n o o t h e r c o m p a r a b l e

t r a d i t i o n a l I P O s i n t h e i r t h r e e - d i g i t S I C c o d e i n t h e y e a r o f i s s u e : R e d E n v e l o p e O v e r s t o c k . c o m , M

o r n i n g s t a r , B o H o l d i n g

Table VIII.Comparison of auctio

IPOs and traditional IPOin the same three-digSIC code issued in t

same yea


283



more than one traditional IPO issued in the same three-digit SIC code and in the sameyear, so their performance was compared to the average of the traditional IPOs issuedin the same three-digit SIC code in the same year. The fth column shows thedifference in the percentage mispricing between the auction IPO and the traditionalIPO – in four tenth of the IPOs (40 percent), the auction IPOs degree of mispricing wasmore than 7 percent greater than the percentage price surge of the comparabletraditional IPO. The last column in Table VIII shows the percentage of traditional IPOsissued in the same three-digit SIC code and in the same year which had greatermispricing than the auction IPO. For 60 percent (six out of ten) of the auction IPOs,over 40 percent of the traditional IPOs issued in that year and in that three-digit SICarea had greater mispricing. This suggests that the online auction mechanism is not asefcient in minimizing the rst day price surge as some have thought.

Table IX shows the results of the regression estimating the rst day price surge foran IPO as a function of SIC code dummies (for industry effects), year dummies (for yeareffects), a dummy variable for whether the IPO was issued through the online auctionprocess, and variable for the number of shares issued in each IPO (to control for the sizeof the issue and of the rm) during the 1999-2005 period. The results indicate thatonline auction IPOs do not have statistically signicantly higher or lower rst dayprice surges than traditional IPOs, when controlling for year/industry effects, and thesize of the issue. The industry effects were jointly signicant ( F ¼ 3.78, p ¼ 0.0000),the year effects were jointly signicant ( F ¼ 4.72, p ¼ 0.0001), and the coefcient forthe number of shares outstanding was signicant.

The models in Tables X-XII include dummy variables for the various types of traditional underwriters – low volume traditional underwriters, medium volumetraditional underwriters, and high volume traditional underwriters – with one of themleft out to serve as the base case. The model in Table X includes dummy variables formedium volume underwriters and high volume underwriters, in addition to dummy

variables for year effects, industry effects, and whether the IPO debuted using theonline auction process. The results indicate that online auction IPOs do not have astatistically signicantly higher or lower rst day price surges than traditional IPOs,when controlling for year and industry effects, relative to the base case. The resultsalso suggested that high volume underwriters had statistically signicantly higherrst day price surges than low volume underwriters (the base case). Although the rstday price surges are not statistically signicantly different between online auctionIPOs and low volume underwriters (the base case) or between medium volumeunderwriters and the base case, the sign on the online auction IPO coefcient ispositive and the sign for the coefcient for medium volume underwriters is alsopositive. The industry effects were jointly signicant ( F ¼ 4.40, p ¼ 0.0000), the yeareffects were jointly signicant ( F ¼ 2.43, p ¼ 0.0296), and the medium and highvolume effects were jointly signicant ( F ¼ 2.88, p ¼ 0.0563).

The model in Table XI differs from the model in Table X in that the dummyvariables for low volume underwriters and high volume underwriters are includedsuch that the base case is medium volume traditional underwriters, rather than highvolume traditional underwriters (Table IV). Again, the results indicate that onlineauction IPOs do not have a statistically signicantly higher or lower rst day pricesurge than traditional IPOs, when controlling for year and industry effects, and thetype of traditional underwriter. The sign on the coefcient for online auction IPOs

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Variable Coefcient SE

Dummy 1999 0.482* 0.209Dummy 2000 0.291 0.212Dummy 2002 2 0.095 0.236Dummy 2003 2 0.353 0.251Dummy 2004 2 0.169 0.211Dummy 2005 2 0.258 0.257Online auction dummy 0.526 0.530 Number of shares 0.000 * 0.000SIC 6300 2 1.672 1.965SIC 1300 2 0.570 1.974SIC 3600 2 0.378 1.950SIC 3800 2 0.422 1.955SIC 5100 2 0.452 2.075SIC 6700 2 1.043 1.946SIC 7300 2 0.154 1.946

SIC 28002

0.519 1.951SIC 3500 0.006 1.974SIC 6000 2 0.605 1.983SIC 1500 2 0.521 2.128SIC 1000 2 1.149 2.244SIC 3700 2 0.934 2.035SIC 3100 2 0.812 2.378SIC 4800 2 1.008 1.954SIC 5900 2 0.421 1.968SIC 5000 2 0.478 2.006SIC 5800 2 0.610 2.003SIC 8000 2 0.469 1.991SIC 8200 2 0.725 2.006SIC 5600 2 0.364 2.077SIC 8700 2 0.453 1.965SIC 1600 2 0.614 2.377SIC 6500 2 0.553 2.061SIC 4200 2 1.144 2.079SIC 4400 2 0.523 2.002SIC 4700 2 0.069 2.077SIC 6200 2 1.270 1.970SIC 7800 2 0.810 2.061SIC 7900 2 0.363 2.242SIC 5700 2 0.416 2.128SIC 3400 2 1.432 2.379SIC 4900 2 1.053 1.986SIC 6100 2 0.993 1.990SIC 2200 2 0.638 2.748SIC 7000 2 0.799 2.128SIC 1200 2 0.969 2.073SIC 3000 2 1.182 2.242SIC 3900 2 0.847 2.129SIC 4500 2 0.847 2.077SIC 740 2 0.981 2.736SIC 2000 2 1.771 2.015

( continued )

Table IX.The impact of the onlin

auction process on aIPO’s rst day pric

surge, controlling for yeaeffects, industry effect

and size of the issu


285



suggests a higher price surge for online auction IPOs relative to the base case of medium volume underwriters, but, again the coefcient is not signicant. Similarly,neither high volume underwriters nor low volume underwriters have signicantlydifferent rst day price surges from medium volume underwriters, although the sign

for high volume underwriters was positive and the sign for low volume underwriterswas negative, as is consistent with the averages for low, medium, and high volumeunderwriters in Tables I and VII. The industry effects were jointly signicant( F ¼ 4.40, p ¼ 0.0000), the year effects were jointly signicant ( F ¼ 2.35, p ¼ 0.0293),and the medium and high volume effects were jointly signicant ( F ¼ 2.93, p ¼ 0.0539).

The model in Table XII differs from the model in Table XI in that the dummyvariables for low volume and medium volume underwriters are included such that thebase case is high volume traditional underwriters, rather than medium volumetraditional underwriters. Again, the results indicate that online auction IPOs do nothave a statistically different rst day price surge in magnitude than high volumeunderwriters (the base case), when controlling for year/industry effects, and the type of traditional underwriter, although the sign on the online auction IPO (relative to thebase case) is negative. The industry effects were jointly signicant ( F ¼ 4.40, p ¼ 0.0000), the year effects were jointly signicant ( F ¼ 2.35, p ¼ 0.0290), and themedium and high volume effects were jointly signicant ( F ¼ 2.97, p ¼ 0.0514). Theresults also suggested that low volume underwriters have a signicantly lower rstday price surge relative to the high volume underwriter base case. Medium volumeunderwriters did not have a signicantly different rst day price surge (although thesign on the coefcient was negative).


SIC 3200 2 0.510 2.242SIC 7200 2 1.067 2.239

SIC 1400 2 0.743 2.378SIC 5400 0.552 2.172SIC 1700 2 0.790 2.744SIC 180 2 1.260 2.744SIC 2300 2 0.029 2.379SIC 2500 2 0.582 2.379SIC 2600 2 2.376 2.745SIC 3300 8.606* 2.083SIC 4600 2 0.440 2.075SIC 5500 2 0.513 2.746SIC 6400 2 0.553 2.126SIC 5300 2 0.886 2.744SIC 5200 2 0.707 2.378SIC 7500 2 1.082 2.379SIC 2700 2 1.033 2.172SIC 2900 2 0.841 2.746SIC 750 1.384 2.744 _Cons 0.262

Notes: Signicant at *5 percent; (rst day price surge) i ¼ a þ m (SIC code dummies)i þ

b (year dummies) i þ C (online auction IPO dummy) I þ r (number of shares offered in each IPO)i Table IX.

IJMF5,3

286




Dummy 1999 2 0.232 0.252Dummy 2000 2 0.294 0.255Dummy 2002 2 0.694* 0.284Dummy 2003 2 0.680* 0.303Dummy 2004 2 0.666* 0.254Dummy 2005 2 0.740* 0.310 Auction dummy 0.300 0.650 Medium volume traditional underwriter 0.151 0.199 High volume traditional underwriter 0.354* 0.156 SIC 6300 0.075 2.368SIC 1300 0.043 2.381SIC 3600 0.530 2.352SIC 3800 0.184 2.358SIC 5100 0.153 2.503SIC 6700 0.078 2.346

SIC 7300 0.517 2.347SIC 2800 0.184 2.354SIC 3500 0.729 2.380SIC 6000 0.085 2.392SIC 1500 0.078 2.566SIC 1000 2 0.008 2.707SIC 3700 0.075 2.454SIC 3100 2 0.015 2.868SIC 4800 0.092 2.356SIC 5900 0.239 2.374SIC 5000 0.227 2.419SIC 5800 0.113 2.418SIC 8000 2 0.009 2.401SIC 8200 2 0.174 2.419SIC 5600 0.308 2.505SIC 8700 0.227 2.369SIC 1600 0.032 2.868SIC 6500 0.223 2.486SIC 4200 0.150 2.507SIC 4400 0.217 2.415SIC 4700 0.607 2.505SIC 6200 2 0.020 2.375SIC 7800 0.124 2.485SIC 7900 0.278 2.704SIC 5700 0.385 2.567SIC 3400 2 0.048 2.869SIC 4900 2 0.104 2.395

SIC 6100 0.185 2.399SIC 2200 0.167 3.314SIC 7000 0.207 2.567SIC 1200 2 0.115 2.500SIC 3000 2 0.380 2.704SIC 3900 0.012 2.567SIC 4500 0.072 2.505SIC 740 2 0.639 3.299

( continued )

Table X.The impact of the onlin

auction process on aIPOs rst day pric

surge, compared witmedium and high volum

traditional underwriter


287



In summary, Tables IX-XII suggest that online auction IPOs do not have a statisticallysignicantly higher or lower price surge than traditional IPOs or any subset of

traditional IPOs (low, medium, or high volume underwriters). Nevertheless, within thecategory of traditional underwriters, low volume underwriters have a statisticallysignicantly lower rst day price surge than high volume underwriters, which issurprising given that high volume underwriters have had more experience inunderwriting IPOs and therefore should exhibit a lesser degree of mispricing. Thismay be because high volume underwriters were intentionally underpricing IPOs,which is consistent with the allegations against many investment banks by theNew York State Attorney General’s Ofce during 2002 and 2003, as previouslydiscussed. Medium volume underwriters did not have statistically signicantlydifferent rst day price surges from high volume underwriters, although the sign onthe coefcient was negative.

Tables XIII-XX provide a sensitivity analysis of the regression models. Theyincludes several different formulations, some of which include or exclude year effects,industry effects, dummy variables for type of traditional underwriter, and measures of the size of the issue, in various combinations. While the models in Tables IX-XII are themost appropriate, as will be discussed, for measuring whether online auction IPOshave a signicantly lower rst day price surge than traditional underwriters, themodels listed in the table also show that, regardless of the specication, the onlineauction IPOs do not have a statistically signicantly lower price surge than traditionalIPOs, as had been originally intended. Tables XIII, XV, XVII, and XIX provide a


SIC 2000 0.019 2.430SIC 3200 0.072 2.705SIC 7200 2 0.047 2.699SIC 1400 2 0.096 2.868SIC 5400 1.183 2.620SIC 1700 2 0.279 3.309SIC 180 2 0.337 3.309SIC 2300 0.477 2.870SIC 2500 0.224 2.868SIC 2600 2 0.279 3.309SIC 3300 14.290* 2.500SIC 4600 0.014 2.503SIC 5500 0.140 3.312SIC 6400 0.157 2.564SIC 5300 0.013 3.312SIC 5200 0.068 2.869SIC 7500 0.427 2.870SIC 2700 0.223 2.619SIC 2900 0.299 3.312SIC 750 2.225 3.312 _Cons 0.218

Notes: Signicant at *5 percent; (rst day price surge) i ¼ a þ b (year dummies) i þ m (SIC codedummies) i þ C (online auction IPO dummy) I þ t (medium volume underwriter dummy) i þ w (highvolume underwriter dummy) i Table X.

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Dummy 1999 2 0.232 0.252Dummy 2000 2 0.295 0.255Dummy 2002 2 0.695* 0.284Dummy 2003 2 0.681* 0.303Dummy 2004 2 0.667* 0.254Dummy 2005 2 0.741* 0.310 Auction dummy 0.143 0.651 Low volume traditional underwriter 2 0.161 0.198 High volume traditional underwriter 0.197 0.162 SIC 6300 0.075 2.368SIC 1300 0.044 2.381SIC 3600 0.530 2.352SIC 3800 0.185 2.358SIC 5100 0.153 2.503SIC 6700 0.079 2.346

SIC 7300 0.517 2.346SIC 2800 0.184 2.353SIC 3500 0.730 2.380SIC 6000 0.087 2.392SIC 1500 0.080 2.566SIC 1000 2 0.004 2.707SIC 3700 0.077 2.454SIC 3100 2 0.012 2.868SIC 4800 0.093 2.356SIC 5900 0.240 2.374SIC 5000 0.227 2.419SIC 5800 0.112 2.418SIC 8000 2 0.009 2.401SIC 8200 2 0.174 2.419SIC 5600 0.309 2.505SIC 8700 0.228 2.369SIC 1600 0.036 2.868SIC 6500 0.224 2.486SIC 4200 0.151 2.507SIC 4400 0.219 2.415SIC 4700 0.609 2.505SIC 6200 2 0.013 2.375SIC 7800 0.127 2.485SIC 7900 0.281 2.704SIC 5700 0.386 2.566SIC 3400 2 0.046 2.869SIC 4900 2 0.102 2.395SIC 6100 0.186 2.399SIC 2200 0.168 3.314SIC 7000 0.208 2.566SIC 1200 2 0.116 2.500SIC 3000 2 0.377 2.704SIC 3900 0.015 2.567SIC 4500 0.074 2.505SIC 740 2 0.639 3.299

( continued )

Table XI.The impact of the onlin

auction process on aIPOs rst day price surg

with low and higvolume traditiona

underwriter


289



summary of the coefcients and their p-values in the various model formulations, whileTables XIV, XVI, XVIII, and XX provide a summary across the models of jointsignicance for year effects, industry effects, and inclusion of dummy variables bytype of traditional underwriter.

Tables XIII and XIV show the statistical results and the tests for the modelspecications for Models I-III. All three models indicate that the online auctiondummy is statistically insignicant. Model I only includes year effects, which are jointly signicant. Model II includes year and industry effects, both of which are jointlysignicant. Model III includes year effects, industry effects, and a variable controllingfor the size of the issue. The year effects and industry effects are jointly signicant, andthe variable controlling for the size of the issue is statistically signicant. Inclusion of the variable controlling for the size of the issue increased the adjusted R 2 of the modelto 0.392, which is consequently an improvement on Model I (0.009) and Model II (0.114).Model III is the basic model in Table IX and is the most effective of the basic models inestimating whether online auction IPOs have a lower rst day surge than traditionalIPOs, without including variables on whether the traditional underwriters are lowvolume, medium volume, or high volume underwriters.

Tables XV and XVI show the statistical results and the tests for modelspecications for Models IV-VI. Again, all three models indicate that the online auctiondummy is statistically insignicant. Models IV-VI have year effects, but not industryeffects or a variable for the size of the issue, and include dummy variables for whetherthe traditional underwriters are low volume, medium volume, or high volume.


SIC 2000 0.021 2.430SIC 3200 0.074 2.704SIC 7200 2 0.047 2.699SIC 1400 2 0.095 2.868SIC 5400 1.187 2.620SIC 1700 2 0.278 3.309SIC 180 2 0.337 3.309SIC 2300 0.481 2.870SIC 2500 0.225 2.868SIC 2600 2 0.278 3.309SIC 3300 14.291 2.500SIC 4600 0.014 2.502SIC 5500 0.141 3.312SIC 6400 0.159 2.564SIC 5300 0.018 3.311

SIC 5200 0.066 2.869SIC 7500 0.428 2.870SIC 2700 0.225 2.619SIC 2900 0.300 3.312SIC 750 2.231 3.312 _Cons 0.375

Notes: Signicant at *5 percent; (rst day price surge) i ¼ a þ b (year dummies) i þ m (SIC codedummies) i þ C (online auction IPO dummy) I þ y (low volume underwriter dummy) i þ w (highvolume underwriter dummy) i Table XI.

IJMF5,3

290




Dummy 1999 2 0.234 0.252Dummy 2000 2 0.296 0.255Dummy 2002 2 0.697* 0.284Dummy 2003 2 0.682* 0.303Dummy 2004 2 0.668* 0.254Dummy 2005 2 0.742 0.310 Auction dummy 2 0.056 0.641 Low volume traditional underwriter 2 0.360 * 0.155 Medium volume traditional underwriter 2 0.204 0.163SIC 6300 0.077 2.368SIC 1300 0.046 2.381SIC 3600 0.532 2.352SIC 3800 0.187 2.358SIC 5100 0.157 2.503SIC 6700 0.081 2.346

SIC 7300 0.520 2.346SIC 2800 0.187 2.353SIC 3500 0.732 2.380SIC 6000 0.090 2.392SIC 1500 0.081 2.566SIC 1000 2 0.002 2.707SIC 3700 0.079 2.454SIC 3100 2 0.010 2.868SIC 4800 0.095 2.355SIC 5900 0.243 2.373SIC 5000 0.230 2.419SIC 5800 0.117 2.418SIC 8000 2 0.007 2.401SIC 8200 2 0.171 2.419SIC 5600 0.311 2.505SIC 8700 0.230 2.369SIC 1600 0.037 2.868SIC 6500 0.227 2.486SIC 4200 0.152 2.507SIC 4400 0.221 2.415SIC 4700 0.610 2.505SIC 6200 2 0.006 2.375SIC 7800 0.128 2.485SIC 7900 0.282 2.704SIC 5700 0.389 2.566SIC 3400 2 0.044 2.869SIC 4900 2 0.101 2.395SIC 6100 0.189 2.399SIC 2200 0.169 3.314SIC 7000 0.209 2.566SIC 1200 2 0.114 2.499SIC 3000 2 0.376 2.704SIC 3900 0.016 2.567SIC 4500 0.075 2.505SIC 740 2 0.639 3.299

( continued )

Table XII.The impact of the onlin

auction process on aIPOs rst day price surg

compared with low anmedium volum

traditional underwriter


291



Models IV-VI differ based on which one is the base case. The year effects and thetraditional volume IPO underwriter dummies are jointly signicant in all of the models.

Tables XVII and XVIII show the statistical results and the tests for modelspecications for Models VII-IX. Again, all three models indicate that the onlineauction dummy is statistically insignicant. Models VII-IX differ from Models IV-VI inthat they have added industry effects, which are jointly signicant. Again, the yeareffects and the traditional underwriter indicator variables are jointly signicant.Owing to inclusion of year effects, these models are an improvement on Models IV-VIand their results are described in more detail in Tables X-XII, which were discussedearlier in this section.

Tables XIX and XX show the statistical results and the tests for modelspecications for Models X-XII. As in the other models, the online auction dummy isstatistically insignicant. These models differ from Models VII-IX in that they includea variable for the size of the IPO, in addition to year effects, industry effects, anddummy variables for the type of underwriter. With the inclusion of a variablecontrolling for the number of shares in each issue, the volume dummies for traditionalunderwriters become jointly insignicant, although their inclusion was jointlysignicant in the models which did not include a variable controlling for the number of shares in each issue. This is not surprising since high volume underwriters have, onaverage, a greater number of shares in the IPOs that they issue (the average number of shares in an IPO is 14,600,000) than medium volume underwriters (7,806,649 shares)and low volume underwriters (5,841,266 shares) so the dummy variables for type


SIC 2000 0.023 2.430SIC 3200 0.075 2.704SIC 7200 2 0.046 2.699SIC 1400 2 0.094 2.868SIC 5400 1.188 2.620SIC 1700 2 0.277 3.309SIC 180 2 0.335 3.309SIC 2300 0.482 2.870SIC 2500 0.226 2.868SIC 2600 2 0.277 3.309SIC 3300 14.293* 2.500SIC 4600 0.016 2.502SIC 5500 0.142 3.311SIC 6400 0.161 2.564SIC 5300 0.021 3.311

SIC 5200 0.071 2.869SIC 7500 0.433 2.870SIC 2700 0.226 2.619SIC 2900 0.301 3.311SIC 750 2.233 3.312 _Cons 0.573

Notes: Signicant at *5 percent; (rst day price surge) i ¼ a þ b (year dummies) i þ m (SIC codedummies) i þ C (online auction IPO dummy) I þ y (low volume underwriter dummy) i þ t (mediumvolume underwriter dummy) i Table XII.

IJMF5,3

292



M o d e l s

C o e f c i e n t f o r o n l i n e

a u c t i o n I P O

d u m m y

( p - v a

l u e )

C o e f c i e n t f o r l o w

v o l u m e u n d e r w r i t e r

d u m m y ( p - v a l u e )

C o e f c i e n t f o r m e d i u m


( p - v a l u e )

C o e f c i e n t f o r h i g h


( p - v a l u e )

C o e f c i e n t f o r n u m b e r

o f s h a r e s o f f e r e d

( p - v a l u e )

A

d j u s t e d R

2

( n u m b e r o f

o b s e r v a t i o n s )

M o d e l I

2 0 . 0 3 4 8 ( 0 . 9 5 8 )

N A

N A

N A

N A

0 . 0 0 9 0

( 1 , 7 5 2 )

M o d e l I I

0 . 0 6 3 9 ( 0 . 9 2 0 )

N A

N A

N A

N A

0 . 1 1 4 3

( 1 , 7 5 2 )

M o d e l I I I

0 . 5 2 8 8 ( 0 . 3 2 1 )

N A

N A

N A

4 . 7 3 e 2

0 8 ( 0 . 0 0 0 )

0 . 3 9 2 4

( 1 , 7 5 2 )

N o t e s : M o d e l I , ( r s t d a y p r i c e s u r g e ) i

¼ a

þ b ( y e a r d u m m i e s ) i

þ C ( o n l i n e a u c t i o n I P O d u m m y ) I ; M o d e l I I , ( r s t d a y p r i c e s u r g e ) i ¼

a þ b ( y e a r

d u m m i e s ) i

þ m

( S I C c o d e

d u m m i e s ) i

þ C ( o n l i n e a u c t i o n I P O

d u m m y ) I ; M o d e l I I I , ( r s t d a y p r i c e s u r g e ) i

¼ a

þ b ( y e a r d u m m i e s ) i þ

m ( S I C c o d e

d u m m i e s ) i

þ C ( o n l i n e a u c t i o n I P O d u m m y ) I

þ r

( n u m b e r o f s h a r e s o f f e r e d i n e a c h I P O ) i

Table XIII.Impact of the onlin


increa


293



M o d e l s

F - t e s t f o r j o

i n t

s i g n i c a n c e o f

y e a r e f f e c t s

F - t e s t f o r

j o i n t


i n d u s t r y e f f e c t s

F - t e s t f o r j o i n t

s i g n i c a n c e o f l o w

a n d m e d i u m v o l u m e


F - t e s t f o r

j o i n t


a n d h i g h v o l u m e


F - t e s t

f o r j o i n t


m e d i u m a n d h i g h

v o l u m e

u n d e r w

r i t e r s

t - T e s t f o r


n u m b e r o f s h a r e s f o r

e a c h I P O

A

d j u s t e d R

2

( n u m b e r o f


M o d e l I

F ¼

3 . 8 0 ( 0 . 0 0 0 9 )

N A

N A

N A

N A

N A

0 . 0 0 9 0

( 1 , 7 5 2 )

M o d e l I I F ¼

2 . 4 0 ( 0 . 0 2 5 8 ) F ¼

4 . 4 0 ( 0 . 0 0 0 0 ) N A

N A

N A

N A

0 . 1 1 4 3

( 1 , 7 5 2 )

M o d e l I I I F ¼

4 . 7 2 ( 0 . 0 0 0 0 ) F ¼

3 . 7 8 ( 0 . 0 0 0 0 ) N A

N A

N A

2 7 . 7 7 ( 0 . 0 0 0 0 )

0 . 3 9 2 4

( 1 , 7 5 2 )

N o t e s : M o d e l I , ( r s t d a y p r i c e s u r g e ) i

¼ a


þ C ( o n l i n e a u c t i o n I P O d u m m y ) I ; M o d e l I I , ( r s t d a y p r i c e s u r g e ) i ¼

a þ b ( y e a r

d u m m i e s ) i

þ m

( S I C c o d e

d u m m i e s ) i


d u m m y ) I ; M o d e l I I I , ( r s t d a y p r i c e s u r g e ) i

¼ a


m ( S I C c o d e

d u m m i e s ) i


þ r


Table XIV.Model specications

IJMF5,3

294





M o d e l s




F - t e s t f o r

j o i n t




i n t


a n d m e d i u m v o l u m e


F - t e s t f o r

j o i n t




F - t e s t

f o r j o i n t


m e d i u m

a n d h i g h

v o l u m e u n d e r w r i t e r s

t - T e s t f o r



e a c h I P O

A

d j u s t e d R

2

( n u m b e r o f


M o d e l I V F ¼

3 . 7 8 ( 0 . 0 0 1 0 ) N A

F ¼

3 . 0 8 ( 0 . 0 4 6 1 )

N A

N A

N A

0 . 0 1 1 3

( 1 , 7 5 2 )

M o d e l V

F ¼

3 . 7 8 ( 0 . 0 0 1 0 ) N A

N A

N A

F ¼

3 . 0 3 ( 0 . 0 4 8 5 )

N A

0 . 0 1 1 3

( 1 , 7 5 2 )

M o d e l V I F ¼

3 . 7 7 ( 0 . 0 0 1 0 ) N A

N A

N A

F ¼

2 . 9 7 ( 0 . 0 5 1 4 )

N A

0 . 0 1 1 2

( 1 , 7 5 2 )

N o t e s : M o d e l I V , ( r s t d a

y p r i c e s u r g e ) i

¼ a


þ C ( o n l i n e a u c t i o n I P O d u m m y ) I þ

y ( l o w v o l u m e u n d e r w r i t e r d u m m y ) i

þ t

( m e d i u m

v o l u m e u n d e r w r i t e r d u m m y ) I ; M o d e l V , ( r s t d a y

p r i c e s u r g e ) i

¼ a



þ y

( l o w v o l u m e u n d e r w r i t e r

d u m m y ) i

þ w

( h i g h v o l u m e u n d e r w r i t e r d u m m y ) I ; M o d e l

V I , ( r s t d a y p r i c e s u r g e ) i

¼ a



d u m m y ) I

þ t

( m e d i u m v o l u m e u n d e r w r i t e r d u m m y ) i

þ w

( h i g h v o l u m e u n d e r w r i t e r d u m m y ) i

Table XVI.Model specications

IJMF5,3

296



M o d e l s


a u c t i o n I P O d u m m y

( p - v a l u e )






( p - v a l u e )



( p - v a l u e )



( p - v a l u e )

A

d j u s t e d R

2

( n u m b e r o f


M o d e l V I I

2 0 . 0 5 6 3

( 0 . 9 3 0 )

2 0 . 3 5 9 7 ( 0 . 0 2 1 )

2 0 . 2 0 4 1 ( 0 . 2 0 9 )

N A

N A

0 . 1 1 6 4

( 1 , 7 5 2 )

M o d e l V I I I

0 . 1 4 3 2

( 0 . 8 2 6 )

2 0 . 1 6 1 5 ( 0 . 4 1 6 )

N A

0 . 1 9 7 5 ( 0 . 2 2 4 )

N A

0 . 1 1 6 3

( 1 , 7 5 2 )

M o d e l I X

0 . 3 0 0 2

( 0 . 6 4 4 )

N A

0 . 1 5 1 3 ( 0 . 4 4 7 )

0 . 3 5 4 1 ( 0 . 0 2 3 )

N A

0 . 1 1 6 3

( 1 , 7 5 2 )

N o t e s : M o d e l V I I , ( r s t d a y p r i c e s u r g e ) i

¼ a


þ m

( S I C c o d e d u m m i e s ) i


þ y

( l o w v o l u m e

u n d e r w r i t e r d u m m y ) i þ

t ( m e d i u m v o l u m e u n d e r w r i t e r d u m

m y ) I ; M o d e l V I I I , ( r s t d a y p r i c e

s u r g e ) i

¼ a


m ( S I C c o d e

d u m m i e s ) i


þ y

( l o w v o l u m e u n d e r w r i t e r d u m m y ) i

þ w

( h i g h v o l u m e u n d e r w r i t e r d u m m y ) I ; M o d e l I X , (

r s t d a y p r i c e

s u r g e ) i

¼ a


þ m

( S I C c o d e d u m m i e s ) i þ

C ( o n l i n e a u c t i o n I P O d u m m y ) I

þ t


þ w

( h i g h

v o l u m e u n d e r w r i t e r d u m m y ) i

Table XVII.Impact of the onlin


increa


297



M o d e l s




F - t e s t f o r

j o i n t





a n d m e d i u m

v o l u m e


F - t e s t f o r

j o i n t







v o l u m e


t - t e s t f o r



e a c h I P O

A

d j u s t e d R

2

( n u m b e r o f


M o d e l V I I F ¼

2 . 3 5 ( 0 . 0 2 9 0 ) F ¼

4 . 4 0 ( 0 . 0 0 0 0 )

F ¼

2 . 9 7 ( 0 . 0 5 1 4 )

N A

N A

N A

0 . 1 1 6 4

( 1 , 7 5 2 )

M o d e l V I I I F ¼

2 . 3 5 ( 0 . 0 2 9 6 ) F ¼

4 . 4 0 ( 0 . 0 0 0 0 )

N A

F ¼

2 . 9 3 ( 0 . 0 5 3 9 )

N A

N A

0 . 1 1 6 3

( 1 , 7 5 2 )

M o d e l I X

F ¼

2 . 3 4 ( 0 . 0 2 9 6 ) F ¼

4 . 4 0 ( 0 . 0 0 0 0 )

N A

N A

F ¼ 2 . 8 8

( 0 . 0 5 6 3 ) N A

0 . 1 1 6 3

( 1 , 7 5 2 )

N o t e s : M o d e l V I I , ( r s t d a y p r i c e s u r g e ) i

¼ a


þ m

( S I C c o d e d u m m i e s ) i þ C ( o n l i n e a u c t i o n I P O d u m m y ) I

þ y

( l o w v o l u m e

u n d e r w r i t e r d u m m y ) i þ

t ( m e d i u m v o l u m e u n d e r w r i t e r d u m

m y ) I ; M o d e l V I I I , ( r s t d a y p r i c e

s u r g e ) i

¼ a


m ( S I C c o d e

d u m m i e s ) i


þ y


þ w

( h i g h v o l u m e u n d e r w r i t e r d u m m y ) I ; M o d e l I X , (

r s t d a y p r i c e

s u r g e ) i

¼ a


þ m



þ t


þ w

( h i g h

v o l u m e u n d e r w r i t e r d u m m y ) i

Table XVIII.Model specications

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M o d e l s


a u c t i o n I P O

d u m m y

( p - v a

l u e )






( p - v a l u e )



( p - v a l u e )



( p - v a l u e )

A

d j u s t e d R

2

( n u m b e r o f


M o d e l X

0 . 5 2 8 6 ( 0 . 3 2 1 )

2 0 . 0 1 8 0 ( 0 . 8 9 0 )

0 . 0 3 3 7 ( 0 . 8 0 3 )

N A

4 . 7 3 e 2

0 8 ( 0 . 0 0 0 )

0 . 3 9 1 7

( 1 , 7 5 2 )

M o d e l X I

0 . 4 5 5 2 ( 0 . 4 0 0 )

2 0 . 0 9 3 3 ( 0 . 5 7 1 )

N A

2 0 . 0 8 7 1 ( 0 . 5 1 9 )

4 . 7 3 e 2

0 8 ( 0 . 0 0 0 )

0 . 3 9 1 9

( 1 , 7 5 2 )

M o d e l X I I

0 . 5 1 0 9 ( 0 . 3 4 4 )

N A

0 . 0 1 3 6 ( 0 . 9 3 4 )

2 0 . 0 3 1 6 ( 0 . 8 0 8 )

4 . 7 3 e 2

0 8 ( 0 . 0 0 0 )

0 . 3 9 1 8

( 1 , 7 5 2 )

N o t e s : M o d e l X , ( r s t d a


¼ a


þ m



þ y


d u m m y ) i

þ t


þ r

( n u m b e r o f s h a r e s o f f e r e d i n I P O ) I ; M

o d e l X I , ( r s t d a y p r i c e s u r g e ) i ¼

a þ b ( y e a r

d u m m i e s ) i

þ m



þ y


þ w

( h i g h v o l u m e u n d e r w r i t e r

d u m m y ) i

þ r

( n u m b e r o f s h a r e s o f f e r e d i n e a c h I P O ) I ; M o d e l X I I , ( r s t d a y p r i c e s u r g e ) i

¼ a


þ m


þ C ( o n l i n e

a u c t i o n I P O d u m m y ) I

þ t


þ w


þ r


Table XIX.Impact of the onlin


increa


299



M o d e l s


i n t

s i g n i c a n c e

o f


F - t e s t f o r

j o i n t



F - t e s t f o r

j o i n t

s i g n i c a n c e

o f l o w

a n d m e d i u m

v o l u m e


F - t e s t f o r

j o i n t




F - t e s

t f o r j o i n t



v o l u m e


t - t e s t f o r



e a c h I P O

A

d j u s t e d R

2

( n u m b e r o f


M o d e l X

F ¼

4 . 7 3 ( 0 . 0 0 0 1 ) F ¼

3 . 7 6 ( 0 . 0 0 0 0 )

F ¼

0 . 0 5 ( 0 . 9 5 0 4 )

N A

N A

2 7 . 6 0 ( 0 . 0 0 0 0 )

0 . 3 9 1 7

( 1 , 7 5 2 )

M o d e l X I F ¼

4 . 7 7 ( 0 . 0 0 0 1 ) F ¼

3 . 7 5 ( 0 . 0 0 0 0 )

N A

F ¼

0 . 2 3 ( 0 . 7 9 6 1 )

N A

2 7 . 6 2 ( 0 . 0 0 0 0 )

0 . 3 9 1 9

( 1 , 7 5 2 )

M o d e l X I I F ¼

4 . 7 0 ( 0 . 0 0 0 1 ) F ¼

3 . 7 6 ( 0 . 0 0 0 0 )

N A

N A

F ¼ 0 . 0 7

( 0 . 9 3 1 7 )

2 7 . 6 1 ( 0 . 0 0 0 0 )

0 . 3 9 1 8

( 1 , 7 5 2 )

N o t e s : M o d e l X , ( r s t d a


¼ a


þ m



þ y


d u m m y ) i

þ t


þ r

( n u m b e r o f s h a r e s o f f e r e d i n I P O ) I ; M

o d e l X I , ( r s t d a y p r i c e s u r g e ) i ¼

a þ b ( y e a r

d u m m i e s ) i

þ m



þ y


þ w

( h i g h v o l u m e u n d e r w r i t e r

d u m m y ) i

þ r

( n u m b e r o f s h a r e s o f f e r e d i n e a c h I P O ) I ; M o d e l X I I , ( r s t d a y p r i c e s u r g e ) i

¼ a


þ m


þ C ( o n l i n e

a u c t i o n I P O d u m m y ) I

þ t


þ w


þ r


Table XX.Model specications

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of underwriter are picking up some of the same effects that were incorporated into thevariable for the number of shares of the issue. Also, as seen in other studies, such asLoughran and Ritter (2004), smaller rms are less likely to go public using a “highprestige” underwriter[12]. These “high prestige” underwriters, as dened by Loughranand Ritter (2004), Carter and Manaster (1990), and Carter et al. (1998), are largely thesame as the high volume underwriters in this analysis – large, well-known investmentbanks which underwrite a number of IPOs.

Since separation of traditional underwriters into low, medium, and high volumeunderwriters is important to the analysis, the models in Tables XVII and XVIII aremore appropriate than the models in Tables XIX and XX for comparing the size of therst day price surge. These models were described in more detail in Tables X-XII. Formodels in which all of the traditional underwriters are combined together, the mostappropriate model is one which includes a variable controlling for the size of the issueand of the rm, but which excludes dummy variables for the type of traditionalunderwriter. This was the model in Table IX.

In summary, these models suggest that regardless of the specication of the model,IPOs which are issued through the online auction mechanism do not have rst pricesurges which are statistically signicantly different from the traditional IPOs. The signon the online auction IPO is positive in nine of the 12 models; the only models in whichthe sign is negative are in three of the models with among the poorer ts (Models I, IV,and VII). This suggests that the rst day price increase tends to be statisticallyinsignicantly higher for IPOs issued through the online auction process, controllingfor year xed effects, industry xed effects, size of the IPO, and whether the traditionalunderwriter is a high volume underwriter, a low volume underwriter, or a mediumvolume underwriter.

The models also provide information on the size of the rst day price surge betweenthe various types of traditional underwriters. Models IV-VI, which have only year

effects, but not industry effects, suggest that low volume underwriters have astatistically signicantly lower price surge than high volume underwriters, and thatmedium volume underwriters have a lower price surge than high volume underwritersand a higher price surge than low volume underwriters, although the results are notstatistically signicant. These ndings are consistent with the average rst day pricesurges for low volume, medium volume, and high volume underwriters in Tables I andVII. Models VII-IX differ from Models IV-VI in that they add industry xed effects,which are jointly signicant; the conclusions and results, however, are identical.Models X-XII differ from Models VII-IX in that they add a variable controlling for thesize of the issue. Since inclusion of this variable makes the volume dummies fortraditional IPO underwriters unnecessary (as can be seen in their statisticalinsignicance) because high volume underwriters, on average, issue larger IPOs, thesemodels are not as useful as the previous sets in evaluating the relative behavior of thethree types of traditional underwriters. Nevertheless, although the results arestatistically insignicant, the sign of the low volume coefcients tend to supportthe hypothesis that low volume underwriters have lower rst day price increases thanmedium or high volume underwriters.

Although the online auction IPOs exhibit some degree of mispricing, does it persistover time to a greater degree than the mispricing exhibited by IPOs issued through thetraditional process? Table XXI examines the degree of mispricing for IPOs over several


301



time periods: after the rst day of trading, after one week of trading, after two weeks of trading, after four weeks of trading, after 60 days of trading, after 90 days of trading,after 180 days of trading, and after one year of trading. The second column shows thepercentage of online auction IPOs whose absolute percentage price increase fromthe original offer price exceeds that of the comparable traditional IPO, issued in thesame year and in the same three-digit industry SIC code. The table indicates that, formost time periods, generally over 60 percent of online IPOs still have a greater degreeof mispricing relative to their original offer price, when compared to the degree of mispricing exhibited by their traditional counterpart. This suggests that the offer priceset in auctions is less reective of the true value of the company than the offer price setthrough the traditional process.

In summary, the analysis suggests that the auction process tends to be moreefcient, on average, than the traditional processes of many medium to high volumeunderwriters, and less efcient on average than the traditional processes of manylow volume underwriters. Although one would have expected medium and highvolume underwriters to be more efcient at pricing IPOs because they have had theopportunity to price so many of them, they are also more likely than low volumeunderwriters to have a large client base and to possibly reward their clients who assistthem in the initial book-building process by mispricing the IPO so that these clients canbenet from the price increase on the rst day of trading. When analysing the averagerst day price surges by underwriter across all underwriters, the average rst dayprice surge for OpenIPO.com exceeds the average rst day price increase for82.4 percent of the underwriters. When examining together all IPOs in all SIC codesissued by all underwriters, use of the online auction issuance process had aconsistently statistically insignicant impact on the rst day price increase. This was

Time period

Percentage of online auction IPOs whose absolutepercentage price change from the offer price exceedsthat of comparable traditional IPOs issued in their

three-digit SIC industry area in the same year

After one day of trading 77.78 (7/9)After one week of trading 66.67 (6/9)After two weeks of trading 62.5 (5/8)After four weeks of trading 75 (6/8)After 60 days of trading 50 (4/8)After 90 days of trading 37.5 (3/8)After 180 days of trading 62.5 (5/8)After one year of trading 83.3 (5/6)

Notes: This table compares the frequency for which the absolute value of the percentage change inthe stock price for IPOs issued through the online auction mechanism exceeds the absolute value of thepercentage change of the average of IPOs issued through the traditional process in the same three-digitSIC industry area in the same year, the analysis compares these numbers at various time intervalsfollowing issuance of the online auction IPO and the traditional IPOs; the absolute value of thepercentage change measures the degree to which mispricing (relative to the offer price) occurs; thendings in this table are consistent with the hypothesis that the online auction IPO process does notreduce mispricing relative to the traditional processSource: SDC Platinum

Table XXI.Percentage of onlineauction IPOs whosepost-issue mispricingexceeds that of comparable traditionalIPOs

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also conrmed by t -tests of the differences between the mean degree of underpricingfor online auction IPOs and low, medium, and high volume underwriters.

IV. Possible causes and solutions to potential problems in the onlineauction processThe online process is not as successful as had been hoped in minimizing the rst-dayprice surges of IPOs. Although, by its structure, it eliminates potentially intentionalmispricing by underwriters to benet the larger nancial institutions who assist themin building a “book” of orders, the online auction process still leads to mispricing.Possible causes of this mispricing include:

. a lack of information on the part of the small investors relative to larger nancialinstitutions;

. an adverse selection problem concerning the types of rms which choose to issueonline; and

.

inherent conservatism on the part of investors in using a new process.This section discusses these factors as possible causes of the mispricing probleminherent in auctions, examines applications of these factors in the IPO debut of Google(August 2004) and Morningstar (May 2005), and assesses possible solutions.

The mispricing in online auctions may be due to the involvement of small investors,who do not have access to detailed sources of information on the company. Theinvestment banks and institutional investors who price the IPO in the traditionalprocess analyse companies routinely, and also have a greater capability than smallinvestors to meet with and interview representatives of the issuing rm. Firms that dotheir IPO through the online auction process do not have to provide as muchinformation concerning their uses of funds as they would in the traditional process.

This is because the investment banks, under the online auction process, do not engagein the “book-building” process. Examples of types of information that issuing rmscould provide which would be useful to investors include:

. the uses for the capital that they are raising;

. their strategies for overcoming potential challenges;

. the corporate governance mechanisms within the rm (share of outsiders on theboard, etc.);

. their reasons for using the online auction process, rather than the traditionalprocess; and

. their involvement in any current or potential litigation.

As investors become more familiar with the auction process, they may be less likely tobid conservatively in the presence of greater information.

The degree of mispricing for Google was more substantial than the performance of comparable IPOs in its industry group which debuted in the same year using thetraditional method. Google’s offer price was $85 and it closed at $100.34, reecting an18 percent increase, while the average rst day price increase for all IPOs issued in2004 was 8.6 percent. Indeed, analysis of the data shows that 82 percent of the IPO’sissued in 2004 experienced less of a jump from the offer price to the closing price on the


303



rst day of trading than Google did. Google’s rst day price increase also exceeded thatof IPOs in its peer group: about 70 percent of the IPOs in its three-digit SIC code issuedin 2004 experienced less of a rst day price increase and about 60 percent of the IPOs inGoogle’s four-digit SIC code, focusing largely on the internet search arena, exhibitedless of a rst day price increase.

One of the criticisms of Google was that it was “secretive” in how it would use itsfunds and conveyed little detailed information (the Wall Street Journal , 2004a). At thetime, Google faced several strategic issues, which in the absence of more detailedinformation on the uses of the capital to be raised, may have been difcult for thesmaller investors to evaluate. These included Google’s lack of diversication inrevenue sources and reliance on online advertising, rather than on the othersubscription-based services (unlike Yahoo and Microsoft). Despite the lack of externaltransparency in Google’s strategic processes, it has continued to surpass analysts’earnings expectations. Nevertheless, the online process could be used more bycompanies, which may not have a clear sense of the uses for the funds that they areraising or their uses of funds may not be appropriately assessed by smaller investors if enough information is not provided to them.

In the case of Google’s auction, many informed investors – institutional investorsand hedge funds – entered in the market after the stock had debuted, contributing toits upward price momentum ( The Washington Post , 2004a, b); their earlier entry mighthave provided greater price support in developing the offer price. Nevertheless, thelarger investment banks felt “locked out of the process,” and lacked nancial incentivesto push Google’s IPO to their clients. This may also have been the case for other onlineauction IPOs.

The recent SEC proposals to liberalize the “quiet period” may provide a solution tothe possible informational problems inherent in the online auction process. During the“quiet period,” companies traditionally have been only allowed to give out information

orally (in presentations), but not in written form (except for the company’s prospectus).Consequently, the “quiet period” increased the informational disadvantage of smallinvestors relative to larger institutional investors, since smaller investors are less likelyto be able to attend company presentations and more likely to be reliant on writtendocuments available to the public. In late October 2004, the SEC voted to liberalizethese rules by allowing companies planning an IPO to communicate information toinvestors verbally or in writing, provided that this information would be led with theSEC (the Wall Street Journal , 2004b). Indeed, the SEC has also proposed to allow themarketing “roadshows” of IPOs to be broadcast online to all investors, although this isnot a requirement such that companies which only want to present materials before thetraditional audience can do so (the Wall Street Journal , 2005b). Smaller and lesswell-known companies, previously handicapped by their inability to use the media andthe web during the “quiet period” to generate interest, are more likely to stimulateinvestor enthusiasm in their IPO (the Investment Dealers Digest , 2004). This relaxationof the “quiet period” restrictions will make it easier for companies using the onlineauction process to provide small investors with information if the company wants to doso. With the relaxation of the “quiet period” restrictions, companies can minimize theinformational problems inherent in auctions.

A second potential aw of the online process lies in the possibility of an adverseselection problem inherent in the types of rms choosing to use the online process,

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which could lead to a lemons “discount” being placed on these issues by uninformedinvestors[13]. Since rms using the online process are not subjected to the rigorousinterviews of the investment banks in the traditional “book-building” process, rmswhich are well-known, but which would have had difculty in going public for somereason using the traditional IPO process may be more likely to use the online auctionmechanism. For example, when Morningstar, which rates mutual funds, decided to gopublic using the online auction process in January 2005, it did so at a time when it wasunder legal investigation and when its IPO ling under the traditional process hadconsequently been dormant since May 2004. In September 2004, Morningstar was thesubject of an SEC investigation concerning inaccurate data which had been on itswebsite. In December 2004, Eliot Spitzer, as well as the SEC, began looking intopossible conicts of interest from Morningstar Associates’ recommendations to 401(k)on mutual fund investment options, while Morningstar itself provides fund ratings(the Wall Street Journal , 2005a; Stein, 2005). Indeed, Morgan Stanley executives warnedMorningstar that “an auction carried a high risk of an ‘adverse outcome,’” such that

when Morningstar continued with the auction, instead of switching back to thetraditional process, Morgan Stanley resigned as the lead underwriter (Smith, 2005).Morningstar’s pricing behavior suggests that the online auction initially underpricedit: the stock opened on May 3, 2005 at $18.66 and closed at $20.05. By the second weekin June 2005, it was trading in the high $20s per share, by November 2005, it wastrading in the mid $30s per share, and, by December 30, 2005, it was trading around$34-$35 per share. As of April 2008, it was trading around $54 per share. Thisexplanation is similar to the “changing risk composition” hypothesis, discussed inRitter (1984) in which underpricing increases in an IPO market as the proportion of IPOs that are perceived as “risky” increases. Nevertheless, the sources of “risk” wouldlie more in the lack of information provided by the issuing rm than in othercharacteristics. As we showed previously in our comparison of characteristics of issuing rms using the online auction IPO process and the traditional IPO process, therms using the online auction IPO process have many similarities to rms using thetraditional IPO process in terms of company age, and industry. The main difference isthat, on average, they tend to issue a smaller number of shares, and that a higherpercentage of online auction IPOs are likely to have sales below $10 million thantraditional IPOs.

Inherent conservatism of investors may be a third explanation for the underpricing.One of the lessons that investors learned in the dot-com boom is that a knowledge of the underlying company is important in valuing it. Consequently, at least initially,investors may be setting low offer prices for IPOs because of this lesson and because of the relative youth of the auction process. Indeed, OpenIPO served as lead manager for

only four IPOs prior to 2001, by which time the market was declining and investorswere more wary in their bids. The inherent conservatism of investors in auctions mayhave been a factor in the Google auction: the fund manager of the Legg Mason ValueTrust noted in his letter to shareholders that:

[. . .] many investors may have steered clear of the Google auction because “the determinationof what to bid would require considerable work [ . . .]” we were delighted when the so-calledFUD [fear, uncertainty, doubt] dominated the process, resulting in the shares coming in at thebargain price of $85 (Ian, 2005).


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V. ConclusionThe online auction process was developed in response to the substantial increasesbetween IPOs offer prices and their open prices at the peak of the dot-com era. Table VIIsuggests that while the rst day surges in an IPOs price are important, the problemthat the online auction mechanism was partially developed to solve has lessened. Theaverage rst day price increase has dropped substantially from the 55.4-92.5 percentrange during 1999-2001 into the 6.3-8.6 percent range of 2002-2004. This gradualreduction in the magnitude of rst day price surges over time may be a reection of more careful pricing by some underwriters in the wake of substantial litigationconcerning their alleged manipulation of IPOs, as well as that many of the IPOs duringthe dot-com era were in emerging industries and therefore were difcult to price.

The ndings of this analysis suggest that the online auction process does notminimize the rst day price surge of an IPO to the degree that was initially intendedwhen the process was developed. The types of rms choosing the auction IPO processrelative to the traditional IPO process tended to be smaller rms, as measured by salesand number of shares in their IPO issue. Even controlling for the two-digit SIC industryarea, auction IPOs tended to be smaller than traditional IPOs. The percentage of technology rms and the age distribution of the rms were similar between the onlineand traditional process, although they varied from year to year – in four of the sevenyears, the auction process hosted slightly olderrms or had fewer technology IPOs thanthe traditional process, while in three of the seven years, the opposite effects were seen.Industry sector and number of shares in the IPO reected many of the characteristics of online and traditional rms, which also could be seen in age, sales, etc.

Regression analyses controlling for year and industry effects, as well as the size of the IPO, suggest that the rst day price surge of the online auction IPO process did notstatistically signicantly differ from the rst day price surge of the traditional process,even when controlling for type of traditional underwriter (low volume, medium

volume, and high volume). A comparison of auction IPOs with traditional IPOs issuedin the same year and in the same three-digit SIC code suggests that for 60 percent (sixout of ten) of the auction IPOs, over 40 percent of the traditional IPOs issued in thatyear and in that three-digit SIC area had greater mispricing. High volume traditionalunderwriters have a statistically signicantly higher rst day price surge than lowvolume traditional underwriters, which is surprising because high volumeunderwriters, due to the number of IPOs that they price, should be better able toprice an issue than low volume underwriters. This nding provides evidencesupporting the legal allegations against larger investment banks in 2002-2003 that theymay have been manipulating IPO prices during the dot-com era in an effort to rewardtheir preferred clients who assisted them in pricing the IPO. This analysis also ndsthat the mispricing of online IPOs relative to their traditional counterparts issued in thesame three-digit SIC code and in the same year persists over time (one week, twoweeks, four weeks, 60 days, 90 days, 180 days, and one year after debuting) for 50-80percent of online auction IPOs.

Although the online auction process increases the ability of small investors toparticipate in the IPO process, the process may suffer from several problems. First,small investors may lack the ability to efciently price an IPO due to informationalasymmetries either because small investors lack access to the sources of informationthat institutional investors have, or because companies are not required to provide

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detailed information in the online process since they do not undergo the rigorousscrutiny of investment banks in the traditional bookbuilding process. Second, since theinformational scrutiny is reduced, the online process could be used more by companieswhich may not have a clear sense of the uses for the funds that they are raising or bywell-known companies which may not have been successful in the traditional issuanceprocess. This could lead to an adverse selection problem, as investors could havedifculty in distinguishing successful companies from “lemons,” and consequentlycould end up discounting the price of all online IPOs.

Nevertheless, with interest in IPOs rebounding and the growing belief of smallinvestors that they can become involved early with new issues, it is likely that some of the weaknesses of the online process may be improved. Some of the solutions include:

. the SEC reforms on the “quiet period,” which will minimize the informationalasymmetry between small investors and larger institutional investors; and

. greater provision of information by issuing companies concerning uses of capitalto be raised, etc. so that small investors can better distinguish good companies

from “lemons.”Developing new methods of IPO issuance, increasing available information, andinvolving more parties in the process are likely to lead to a more egalitarian andtransparent process for providing new companies with capital.

Notes1. Indeed, in the rst quarter of 2008, several IPOs exhibited substantial underpricing on their

rst day, despite the declining nancial markets. Asia Time Corporation (debuted February11, 2008) exhibited a rst day price increase of 142.9 percent, the RiskMetrics Group(debuted January 24, 2008) exhibited a rst day price increase of 35.7 percent, IPC (debutingon January 24, 2008) and Visa (debuting on March 18, 2008) increased about 28-29 percent ontheir rst day, and Heritage Crystal Clean (debuted March 11, 2008) increased 23.4 percenton its rst day of trading (Renaissance Capital, 2008).

2. The decline in mispricing since the collapse of the dot-com boom is discussed in Loughranand Ritter (2004).

3. Chen and Ritter (2000) examine various explanations for the large spreads in the USArelative to other countries, and as well as why 7 percent is such a common spread.

4. Clarke et al. (2003) discussed how investment banks signicantly boosted their share of IPOswhen they gained an all-star analyst who was highly ranked in the Institutional Investor annual survey, and that this effect was greater in the mid to late 1990s than in the precedingperiods. Ljungqvist and Wilhelm (2003) noted that the presence of an all-star analyst raisedthe probability that the underwriting investment bank employing the analyst would bechosen as the lead underwriter. Both of these studies were also discussed in Loughran andRitter (2004).

5. For example, the Wall Street Journal (2005a) noted that the auction “method can sap the rstday price surges that IPOs typically enjoy,” while Ian (2005) notes “With an auction, the priceis usually set after aggregating bids and deciding the highest price at which the companycan sell its shares. This often saps any rst-day pops in price that IPO’s typically enjoy.”

6. Available at: www.wrhambrecht.com (accessed January 21, 2008).7. For example, if the offer price is set at $11 share, and there are 1 million shares available, but

the cumulation of bids above $11 indicates that 1.25 million shares were requested, then


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Wall Street Journal (2005b), “IPO outlook: SEC proposes increasing role of web in IPOs”,Wall Street Journal , January 3.

( The ) Washington Post (2004a), “Because of, or despite, news, bids placed in IPO”,The Washington Post , August 14.

( The ) Washington Post (2004b), “Google ends auction for IPO shares”, The Washington Post ,August 19.

Welch, I. (1989), “Seasoned offerings, imitation costs, and the underpricing of initial publicofferings”, Journal of Finance , Vol. 44 No. 2, pp. 421-49.

Further readingDelaney, K.J. (2005), “Google revisited: insiders got choice other sellers didn’t”,Wall Street

Journal , September 16.Ernst & Young LLP (2007), “Quantitative economics and statistics group on behalf of the

strategic growthmarkets practice”, The Ernst & Young IPOStudy , availableat: www.ey.com(accessed April 15, 2008).

Field, L.C. and Jonathan, K. (2002), “Takeover defenses of IPO rms”,Journal of Finance , Vol. 57No. 5, pp. 1857-89.Forno, R. (2005), “Google to offer instant messaging and voice communications on web”,

The New York Times , August 24.Karmin, C., Cowan, L. and Fowler, G.A. (2005), “Baidu.com sees IPO pop, but some fear a burst

bubble”, Wall Street Journal , August 14.Pagano, M., Panetta, F. and Zingales, L. (1998), “Why do companies go public: an empirical

analysis”, Journal of Finance , Vol. 53 No. 1, pp. 27-64.Ritter, J.R. and Welch, I. (2002), “A review of IPO activity, pricing, and allocations”,Journal of

Finance , Vol. 57 No. 4, pp. 1795-828.Weisman, R. (2004), “Google IPO fails to nd results it sought”, Knight Ridder Tribune Business

News, August 19.Zingales, L. (1995), “Insider ownership and the decision to go public”, The Review of Economic

Studies , Vol. 62 No. 3, pp. 425-48.

Corresponding authorNayantara Hensel can be contacted at: [email protected]

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An Empirical Analysis of the Efficiency of Online Auction IPO

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