MSc Finance Dissertation

Syndicate Underwriting and the Extent of Underpricing in
Initial Public Offerings (IPOs)
A dissertation submitted in fulfilment for the degree:
MSc Finance with Risk Management
By
Michael Tack
Student Number: 159292031
Supervisor: Mr. Michael Willes
Submission Date: 5 September 2016

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Abstract
This study examines whether the degree of syndicate underwriting significantly
influences the underpricing of IPOs. The study also considers syndicate composition and
industry sector influence on underpricing. Using data from the NASDAQ stock exchange
during 2005-2015, it is shown that 67% of IPOs are underpriced with underpricing
averaging 23.78%. A regression analysis, incorporating six independent variables and
one dummy variable for industry type, found that increasing the number of underwriters
in a syndicate corresponds to decreasing the underpricing. The explanatory power of the
regression is significantly enhanced with the inclusion of industry sectors. Analysis of
the sample shows that underpricing does not correlate to general market returns,
eliminating the possibility of a cyclical pattern.
Keywords: IPOs, underpricing, syndicate underwriting

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Acknowledgements
I would like to thank my supervisor for his guidance and feedback throughout the
dissertation period as well as the University of Bath for facilitating this degree. I would
also like to thank my parents for their continued support and providing me with the
opportunity to study at this great university.

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Table of Contents
ABSTRACT..................................................................................................................................I
ACKNOWLEDGEMENTS ...................................................................................................... II
LIST OF FIGURES ....................................................................................................................V
LIST OF TABLES.................................................................................................................... VI
1. INTRODUCTION...............................................................................................................1
1.1 INITIAL PUBLIC OFFERINGS..........................................................................................1
1.2 RESEARCH QUESTION AND HYPOTHESES.....................................................................2
1.3 MOTIVATION ................................................................................................................4
2. LITERATURE REVIEW...................................................................................................6
2.1 THE ROLE OF UNDERWRITING SYNDICATES ................................................................6
2.2 THE TYPES OF UNDERWRITING COMMITMENT ............................................................8
2.3 SYNDICATE FUNCTIONS..............................................................................................10
2.3.1 Information Production..........................................................................................10
2.3.2 Certification and Underwriter Reputation.............................................................11
2.3.3 Analyst Coverage ...................................................................................................12
2.3.4 Market Making.......................................................................................................13
2.4 FACTORS AFFECTING UNDERPRICING........................................................................14
2.4.1 Market conditions ..................................................................................................14
2.4.2 The Impact of Gross Spreads.................................................................................15
2.4.3 The Monopoly Power of Underwriters ..................................................................16
2.5 INFORMATION ASYMMETRY MODELS........................................................................17
2.5.1 The Winner’s Curse ...............................................................................................17
2.5.2 Information Revelation Theories ...........................................................................19
2.5.3 Principal-Agency Theory .......................................................................................21
2.5.4 Signaling Model .....................................................................................................22

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3. METHODOLOGY............................................................................................................24
3.1 RESEARCH SCOPE .......................................................................................................24
3.2 DATA DESCRIPTION....................................................................................................24
3.3 TESTING FOR THE PRESENCE OF UNDERPRICING .......................................................25
3.3.1 Model 1 ..................................................................................................................26
3.3.2 Model 2 ..................................................................................................................26
3.4 COMPARING TWO MEANS ..........................................................................................28
3.5 REGRESSION ANALYSIS..............................................................................................29
3.5.1 Significance Tests...................................................................................................30
3.5.2 Determinants of Underpricing...............................................................................32
4. RESULTS AND DISCUSSION .......................................................................................35
4.1 IPO DISTRIBUTION .....................................................................................................35
4.2 DESCRIPTIVE STATISTICS ...........................................................................................36
4.3 LEVEL OF UNDERPRICING...........................................................................................40
4.4 INFERENCE BASED ON THE COMPARISON OF TWO MEANS........................................45
4.5 REGRESSION ANALYSIS..............................................................................................45
4.5.1 Model 1 ..................................................................................................................46
4.5.2 Model 2 ..................................................................................................................50
5. CONCLUSIONS ...............................................................................................................55
6. LIMITATIONS .................................................................................................................58
7. RECOMMENDATIONS FOR FUTURE STUDIES.....................................................59
BIBLIOGRAPHY......................................................................................................................60
APPENDIX A: MODEL 1 DIAGNOSTIC TESTS ................................................................64
APPENDIX B: MODEL 2 DIAGNOSTIC TESTS ................................................................67

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List of Figures
Figure 1: Syndicate Structure (Ramirez, 2011) ................................................................6
Figure 2: Underpricing and Market Movement Correlation...........................................36
Figure 3: Model 1 and Model 2 Comparison..................................................................40
Figure 4: Relationship between Model 1 Underpricing and No. of Underwriters..........41
Figure 5: Relationship between Model 2 Underpricing and No. of Underwriters..........42
Figure 6: Syndicate Members and Model 1 Underpricing..............................................43
Figure 7: Syndicate Members and Model 2 Underpricing..............................................43
Figure 8: Number of IPOs per Year................................................................................44
Figure 9: Normality Test for Model 1 with Dummy Variables......................................66
Figure 10: Normality Test for Model 2 with Dummy Variables....................................69

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List of Tables
Table 1: Summary of Data for Each IPO........................................................................25
Table 2: No. of Underpriced and Overpriced IPOs ........................................................35
Table 3: Descriptive Statistics for Model 1 and Model 2...............................................37
Table 4: IPO Distribution per Industry Type..................................................................38
Table 5: T-test Results for Comparing Two Means........................................................45
Table 6: Evidence of Multicollinearity in Model 1 ........................................................46
Table 7: OLS Regression for Model 1............................................................................47
Table 8: OLS Regression with Industry Dummies for Model 1.....................................48
Table 9: Joint Significance Test for Model 1..................................................................50
Table 10: Correlation Table for Model 2........................................................................50
Table 11: OLS Regression for Model 2..........................................................................51
Table 12: OLS Regression with Industry Dummies for Model 2...................................52
Table 13: Joint Significance Test for Model 2................................................................53
Table 14: Heteroskedasticity Test for Model 1 with Dummies......................................64
Table 15: Autocorrelation Test for Model 1 with Dummies ..........................................65
Table 16: Heteroskedasticity Test for Model 2...............................................................67
Table 17: Autocorrelation Test for Model 2...................................................................68

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1. Introduction
The concept of an Initial Public Offerings (IPO) is introduced together with the factors
that result in such offerings being underpriced. The research question is stated along with
two hypotheses that are thought to influence the underpricing of an IPO. The testing of
the hypotheses will form the body of this dissertation. The relevance of this topic in
today’s current economic climate is briefly discussed.
1.1Initial Public Offerings
An IPO is the first sale of a company’s shares to the general public and in doing so the
company becomes a listed entity on a stock exchange. An IPO enables a company to raise
equity capital for project funding, growth acquisitions and enhancement of credit
standing depending on the strategic requirement at the time of the offering (Iannotta,
2010). IPOs are important in capital markets as they facilitate significant capital inflow
to growth focused small and medium enterprises (SMEs) that may have limited capacity
to raise funds by the more traditional means, such as, retained earnings or bank loans.
Pursuing the traditional means of raising finance would result in many SMEs raising
insufficient equity capital (Saunders, 1990). A secondary benefit of an IPO is the increase
in media coverage a firm receives when “going public”. Although unquantifiable, the
coverage is seen to have a positive impact on the issuing firm as it helps to more
effectively market the equity offering (Ritter, 1998).
The IPO process would normally be managed by one or more investment banks. The
banks underwrite the equity issue and when multiple underwriters are involved, a
syndicate would be formed (Ramirez, 2011). The degree of responsibility and risk a

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syndicate undertake in terms of placing the equity offering is broadly defined within four
categories (Ramirez, 2011). This will be discussed in more detail in section 2.1. The IPO
syndicate does not necessarily guarantee the complete sale of all the equity on offer. This
is dependent on the type of underwriting commitment as agreed between the syndicate
and the issuing firm i.e. the company going public. The commitment specifies the degree
of risk and compensation taken by the underwriter, as detailed in section 2.2.
An important consideration of an IPO is the associated costs which have to be absorbed,
mainly by the issuing firm. These would include direct costs such as underwriters’ fees
and the cost of introducing the offer to the market via, for example, investor roadshows
(Ritter, 2014). The underwriting spread, discussed in section 2.4.2, is another example of
a direct cost. In addition to the direct costs there is an indirect cost associated with IPOs
reflected by the phenomenon of underpricing. Underpricing occurs when the firm’s
shares are offered to the public for the first time at a price lower than what investors seem
willing to pay when they start trading in the market (Saunders, 1990). In most instances,
this indirect cost would be the most significant cost absorbed by the issuing firm. The
extent of the underpricing is measured as the percentage difference between the offer
price and the first day closing price of the equity offering.
1.2Research Question and Hypotheses
There is a vast amount of literature dedicated to the phenomenon of IPO underpricing.
The majority of studies either focus on the impact of the various asymmetrical
information models or the pricing mechanisms used to set the offer price, which are
discussed in the literature review. This dissertation takes an alternate approach to
determine what may influence the level of underpricing evident in the majority of IPOs.

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The main focus will be on whether the number of underwriters in the syndicate influences
the level of underpricing. Therefore, the main research question for this dissertation is as
follows:
Two models will be used to calculate underpricing and both of these models will be
compared to determine which is the most effective when used in a regression analysis.
Underpricing will be regressed against the number of underwriters and the “other external
factors” mentioned in the research question. These factors, or, explanatory variables
include: IPO industry type, market capitalization, gross underwriter spread, offer price
and the market return for the day of the IPO. This will allow for an inference to be made
on whether the size of an underwriting syndicate, influences the level of the underpricing.
One main hypothesis will be evaluated in order to answer the research question with an
additional sub-hypothesis about the relevance of industry type to underpricing. They are
as follows:
H1: The greater the number of underwriters in the syndicate, the lower the level of
underpricing
H2: The industry sectors contribute to the explanatory power of the regression in
explaining the variability in underpricing.
The two models used to calculate underpricing will be explained in more detail in section
3.3 whilst the explanatory variables and their expected relationship with the level of
underpricing is discussed in more detail in section 3.5.2. The IPOs in question are those
“Does the extent of syndicate underwriting influence the level of underpricing in
IPOs more than other factors?”

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that primarily listed their equity offering on the NASDAQ stock exchange between
January 2005 and December 2015.
1.3Motivation
Companies looking to grow have to raise capital either through debt or equity. Debt
financing is the borrowing of funds with the premise of paying back the principal amount
plus some agreed upon interest. Traditionally, debt financing either involves bank loans
or the selling of bonds to investors. Unlike debt financing, equity financing requires no
re-payment which may prevent solvency issues later in the company’s life-cycle. Equity
financing also eliminates a major downside of debt financing: the risk associated with
guaranteeing the loan repayment with some form of collateral (Coplan, 2009).
Equity financing can take many forms as companies can either issue shares to the public
in an IPO or issue a large portion of equity to venture capitalists (VC’s) or private equity
(PE) investors. Given the current global economic slowdown, VC funding has declined
as cautious investor are requiring better terms when offering their funds to companies
(Steinberg, 2016). Therefore, those companies not turning to debt financing options are
looking to tap into the capital markets through the offering of their shares to the public.
This being said, even the IPO market has seen a downturn of late as economic conditions
have caused some companies to postpone their IPO plans (Kelley, et al., 2016).
The first quarter of 2016 saw the slowest rate of IPO activity since 2009. Compared to
the same period in 2015 alone, there had been a 39% drop in volume and 70% decline in
total capital raised (Kelley, et al., 2016). For example, in the U.S. there were only ten
IPOs in the first quarter of 2016; all of whom came from the health care sector. Notable

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absentees were the technology sector who usually predominate U.S. IPO activity.
According to (Kelley, et al., 2016), tech companies were delaying their IPOs due to poor
economic conditions which caused the market to view their valuations as far too high. If
they had gone public, their shares would have been under-subscribed as investors would
not buy at the given offer prices.
“Going public” is an expensive process for any company. The issuing firm looks to raise
as much capital as possible and underwriters are looking to minimize risk and maximize
fees. The two aspirations are to a large extent completely opposed but both parties need
one another. Many factors can influence the success of an IPO and understanding these
is crucial to all parties involved. Therefore, this study aims to contribute to the body of
knowledge surrounding IPOs so that both issuing firms and underwriters can maximize
the potential benefits an IPO can offer. Furthermore, this study can serve as a basis for
future studies to build on and develop. For instance, the methodology can be extrapolated
to determine whether the same trends are applicable in other IPO markets.

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2. Literature Review
Section 1 introduced the topic of IPOs and presents the objective of this dissertation.
Reviewed in this section is past literature on IPO underpricing with particular emphasis
placed on theories behind why the phenomenon of underpricing exists. The underwriters’
role in the IPO process is also discussed.
2.1The Role of Underwriting Syndicates
There is a vast amount of literature dedicated to the topic of IPO underpricing, however,
very little of it is concerns the role of underwriters and the formation of syndicates
(Corwin & Schultz, 2005). In general, the roles given to the various underwriters
syndicating a deal can be represented as follows:
Figure 1: Syndicate Structure (Ramirez, 2011)
It is far less common for an IPO to have one underwriter fulfilling all of the roles
stipulated in Figure 1. Often, the size of the equity offering is too large for an individual

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investment bank to solely underwrite the issue. That being said, for smaller IPOs this
isn’t necessarily the case. In the case of syndicated underwriting, each tier in Figure 1
represents a different commitment by the underwriter in terms of placing the offering of
the issuer. As one moves down the pyramid the amount of underwriting risk reduces. The
underwriting risk arises when the underwriter, such as an investment bank, overestimates
the demand for the underwritten issue. As a result, the underwriter would have to hold
the unsold part of the issue on its balance sheet as it has made the commitment of placing
that issue. Alternatively, it would have to sell the issue at a discount and absorb the loss.
As one moves down the pyramid in Figure 1, the amount of underwriting commitment
decreases, hence, decreasing the exposure to underwriting risk.
According to Ramirez (2011) the number of underwriters syndicating an IPO is
dependent on the size of the offering as well as the type and size of tranches included in
the deal. The typical IPO has a retail and an institutional tranche, with the latter making
up 70-80% of the offerings (Iannotta, 2010). The institutional tranche is made up of the
desired institutional investors and is vital in the price-setting process. When underwriters
set the offer price using the book-building process, they suggest a price range to these
investors and allow them to bid for the issue which reveals their demand for the offering
(Iannotta, 2010). From these bids, the “book is built” and the pricing and allocations of
the offerings is determined. The retail tranche contains investors from the ‘general public’
who require much more detailed information (when compared to the institutional
investors) which is contained in the prospectus (Ramirez, 2011). The reason for this is
discussed in section 2.5.2. Ramirez (2011) concludes that as the size of the offering
increases, the number of syndicate members’ increases with a large IPO typically
containing two or three global coordinators, two to six joint bookrunnners, approximately

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three co-bookrunners/co-lead managers and a substantial number of co-lead
managers/co-managers.
Corwin and Schultz (2005) stressed that when forming a syndicate in an IPO deal,
relationships between every tier of underwriter is crucial. An underwriter has a greater
chance of being included in a syndicate if they had been part of a previous IPO led by the
same bookrunner. Also, if a bookrunner received an allocation of shares in another
underwriter’s previous deals, then there is a higher chance of that underwriter being
included in the bookrunners syndicate (Corwin & Schultz, 2005).
2.2The Types of Underwriting Commitment
Once an underwriter is chosen, or the syndicate is decided upon, the issuing firm and the
underwriters need to enter into a sales and marketing agreement. This agreement
determines the degree of responsibility underwriters have in terms of selling and
marketing the offering (Solomon & Schaller, 2013). It also establishes who is financially
accountable for any unsold shares (Ejara, 2007). There are two main forms of
underwriting commitment: firm and best efforts.
Firm commitment underwriting is the commitment that the underwriter will buy the
equity offering from the issuer at the bid price and then offer them to the public at a set
offer price (Solomon & Schaller, 2013). The underwriters assume all responsibility for
marketing and selling the equity offering as well as bearing the entire underwriting risk.
Thus, underwriters are liable for any unsold shares to the public. Undertaking a firm
commitment underwriting can be done in one of two ways. Either the issuing firm lets
underwriters bid for the underwriting contract in a competitive bidding process where the

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contract is given to the most competitive bid and contact terms, or, the issuing firm
decides to negotiate with predetermined underwriters for the contract (Ramirez, 2011).
Firm commitment underwriting occurs with larger equity issues and more well-known
issuing firms as underwriters wouldn’t put themselves under financial risk if there were
no incentives to do so (Ejara, 2007).
Best efforts commitment underwriting occurs with smaller, lesser-known issuing firms.
In this type of agreement, the underwriter(s) commit to using their best efforts to sell the
issuers offering (Solomon & Schaller, 2013). Unlike firm commitment underwriting,
there is no guarantee from the underwriters that the entire offering will be allocated and
sold (Ramirez, 2011). Similarly, the underwriter is not liable for any shares they do not
sell. From this point of view there is far less underwriting risk involved. Within a certain
time frame, underwriters attempt to sell the offering but if a portion remains unsold, then
the IPO is cancelled with the equity returned to the issuing firm and the money returned
to the already subscribed investors (Solomon & Schaller, 2013). A key characteristic of
this type of underwriting is that underwriters are permitted to cancel their agreement with
the issuers under certain conditions (Ejara, 2007). For example, a sudden market
downturn or a pending recession would make selling the equity offering extremely
difficult at the agreed upon price. In this instance, the underwriters can void their
agreement with the issuing firm with no penalty incurred. Major investment banks rarely
involve themselves in best efforts agreements as the potential market capitalization is of
limited profitability (Ramirez, 2011).

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2.3Syndicate Functions
Corwin and Schultz (2005) conducted a study on 1600 IPOs in the US, spanning from
1997 to 2002. Their hypotheses were that underwriting syndicates can provide four main
services: “information production, certification and underwriter reputation, analyst
coverage and market making”.
2.3.1 Information Production
According to Iannotta (2010): “pricing an IPO is part art and part science”. Whilst
valuation methods can be used to determine the price of an IPO’s offering, determining
the market’s actual interest in the offering is what is considered the “art” of going public.
Companies going public have no prior trading history but underwriters are tasked with
pricing their stocks in a manner that appeals to investors to such a degree that the entire
equity offering is allocated (Iannotta, 2010). Individual underwriters have access to
different client bases i.e. either retail or institutional and may also specialize by region in
terms of where the majority of their clients are based (Corwin & Schultz, 2005). Thus,
because of their clientele differences, most underwriters will convey different
information about the market demand for an IPO. In terms of syndicate structure, this is
vital as co-managers and syndicate members may provide additional information to the
bookrunners. Therefore, it would be suggested that with increased syndication,
information production on total market demand for an IPO is improved. In Corwin and
Schultz’ (2005) study, there is evidence that “larger syndicates produce useful
information between the filing of the preliminary prospectus and the offer date”, leading
to more precisely priced IPOs. The result is that in an IPO with a large number of
syndicate members, the offer price is adjusted away from the midpoint of the filing range
during the book building process as useful information is produced by the syndicate

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members (Corwin & Schultz, 2005). This conclusion is heavily dependent on the role
that syndicate members play under different bookrunners. It is noted by Corwin and
Schultz (2005) that some bookrunners cooperate fully with syndicate members in terms
of discussing pricing, allocation amounts and other issue characteristics whilst other
bookrunners disregard their syndicate completely.
2.3.2 Certification and Underwriter Reputation
The certification and underwriter reputation hypothesis is a suggestion that more
reputable underwriters are associated with less underpricing as there is less uncertainty
surrounding their valuation abilities (Megginson & Weiss, 1991). The underwriters use
their reputational capital to underwrite the IPO deal and thus certify the quality of the
equity offering (Lee, 2011). This builds on the theory proposed by (Rock, 1986), that the
level of IPO underpricing increases as asymmetrical information becomes more
prevalent. Corwin and Schultz (2005) argue that having a greater number of syndicate
members, especially co-managers, can provide additional certification on an offering than
what is provided solely by the bookrunners. The concept of certification is important
because it leads to the fact that the underwriter’s own reputation is at risk when
underwriting the equity offering. By being involved in a mispriced IPO, the reputation of
the underwriter is damaged which could impact their ability to take part in future
syndicates. Also, underwriters may be liable for major losses/damage that arise from
providing investors with misleading information, which is another reason why
certification is meaningful (Beatty & Welch, 1996). Thus, there is incentive for all
underwriting parties involved, to correctly price the offering which ultimately reduces
the level of underpricing. While seeming to be theoretically valid, empirical findings on
the certification hypothesis provide conflicting results. For instance, Carter and Manaster

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(1990) as well as Carter et al. (1998) conclude that more reputable underwriters, or in
their case investment banks, are associated with lower levels of underpricing whilst
Beatty and Welch (1996) concluded that the top performing investment banks are
inclined to underprice IPOs more so than less reputable ones.
2.3.3 Analyst Coverage
An important determinant of which underwriters should be included in a syndicated IPO,
is the amount of aftermarket analyst coverage each can provide. Analyst coverage refers
to the amount of analysts covering the issuer’s stock in the market once it goes public.
The role additionally involves providing research reports and issuing buy
recommendations for the newly trading stock (Chen & Ritter, 2000). This is beneficial to
firms as it increases their market exposure, resulting in increased trading activity and
enhanced liquidity (Iannotta, 2010). The individual ability of the analysts provided by the
underwriters is also crucial in securing underwriting business, as Dunbar (2000)
confirmed, when concluding that underwriters increase their overall involvement in IPOs
if they have an analyst highly rated in the Institutional Investor Survey (Cliff & Denis,
2003). Corwin and Schultz (2005) determine that increasing the number of co-managers
in an IPO syndicate increases the amount of analyst coverage in the aftermarket whilst
increasing the amount of any other syndicate members seems to have little impact. Also,
for larger IPOs, when an underwriter can provide a highly rated analyst for aftermarket
services, then they are more likely to be added to the syndicate. Cliff and Dennis (2003)
determine that there is a positive correlation between underpricing and post IPO analyst
coverage. The level of underpricing is, in part, an additional compensation that the
underwriters receive for the analyst coverage they provide. Ultimately, Cliff and Dennis
(2003) conclude that perhaps the reason why issuing firms are not overly upset with the

13
underpricing of their IPO, is that they see the underpricing as compensation for the
aftermarket analyst coverage they end up receiving.
2.3.4 Market Making
As with the analyst coverage service, the importance of underwriters continues beyond
the IPO issue date as they become market makers for the newly traded stock (Ellis, et al.,
2000). In general terms, a market maker is a firm which can facilitate the bringing
together buyers and sellers of a security in order to keep the financial markets liquid. For
an IPO, the underwriters can provide this service in aftermarket activities once the issuing
firm’s stock is trading in the open market. Simplistically, this is done by the underwriter’s
quoting bid and ask prices for the shares. Generally, it is found that underwriters of an
IPO quote the highest bids and so support the prices of underperforming IPO stocks
(Dunbar, 2000). Once an order is received from an investor then the market maker, i.e.
the underwriter in this case, sells shares from its own inventory or finds an offsetting
order from another seller. Corwin and Schultz (2005) find that the lead underwriters, i.e.
bookrunners, almost always serve as market makers whilst co-managers also contribute
to this process. Though Ellis et al. (2000) also find that the bookrunners contribute
significantly to market making, their findings suggest that other syndicate members have
negligible impact on actively supporting newly trading stocks. The reason for this is that
bookrunners often hold large volumes of the issuing firm’s stock or at least underwrite a
much larger proportion than other syndicate members. The amount of stock which they
hold also varies depending on the price at which it is trading. When performing well, they
tend to hold less with the opposite being true when the stock is performing badly (Ellis,
et al., 2000). This suggests that the lead underwriters not only act as market makers but

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also provide a type of price stabilization to give the IPO the best chance of surviving
(Ellis, et al., 2000).
2.4Factors Affecting Underpricing
Deciding whether to go public using a large syndicate is something which needs to be
taken into account by the issuing firm in all IPOs. The benefits of a large underwriting
syndicate are well documented but there is also evidence that increasing the size of a
syndicate limits profits realized by the issuing firm as underwriters’ fees increase with
increasing members. Other factors influencing the level of underpricing are mentioned
below.
2.4.1 Market conditions
Market conditions are often an integral determinant of how syndicates determine at which
price to set the offering (Lee & Cho , 2011). If market conditions are favorable, then there
is less incentive for underwriters to underprice the IPO offering as the demand for the
IPO will most likely cause it to be over-subscribed, causing the stock price to increase as
soon as it is offered on the market. Alternatively, when market conditions are more
uncertain then there is more incentive for underwriters to underprice IPOs due to the
volatility of market prices as a whole (Yon & Park, 2009). According to Loughran and
Ritter (1995) there is a tendency for the highest number of IPOs to correlate with market
peaks and that there is a greater chance for them to underperform following the high
volumes of IPOs. An example of this would be the dotcom bubble in 2000 where the
boom of the IPO market was witnessed and soon after, its demise (Rajan & Servaes,
1995).

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2.4.2 The Impact of Gross Spreads
In an IPO, the gross spread is the difference between the price the underwriters pay the
issuing firm (bid price) and the price at which the equity offering is sold in the market
(offer price) (Chen & Ritter, 2000). It can be seen as the compensation, on top of their
fee, that underwriters receive to cover expenses, management fees and risk (Lee, et al.,
2011). Previous research shows that the underpricing inherent in an IPO is greater than a
reasonable risk premium would require (Carter & Manaster, 1990). Thus, it appears that
issuing firms and underwriters are often deliberately underpricing IPOs. This
consequently reduces the compensation underwriters receive as it reduces the gross
spread. Chen and Ritter (2000), in their paper titled “The Seven Percent Solution”, find
that in U.S. IPOs, underwriting spreads are almost always set at 7%. Corwin and Schultz
(2005) note that because the gross spread will have to be shared amongst syndicate
members, bookrunners may charge higher fees to add additional co-managers to the
syndicate. They find that underwriter spreads are generally negatively related to offer
sizes and that underwriter spreads increase with the addition of more co-managers into
the syndicate. This suggests that it is not costless for the issuer to include more co-
managers into the underwriting syndicate.
Chen and Ritter’s (2000) study suggest that the reason spreads are so high, on average, is
because of game theory. They argue that if spreads were determined primarily by costs,
then deals worth $40 million and $80 million would have different spreads respectively.
Also, deals worth the same amount but with different risk profiles should display different
spreads. However, in both cases they do not. Their assessment is that the recurring 7%
spread is due to “implicit collusion” between investment bankers. In other words,
underwriters keep prices above competitive levels without explicitly colluding (Chen &

16
Ritter, 2000). This is a form of “strategic pricing” where underwriters realize that by
undercutting competitors to secure a deal, these same competitors will charge lower
spreads in the future, steadily decreasing the profitable nature of IPOs for underwriters
(Dutta & Madhavan, 1997). Due to each underwriter’s self-interest, gross spreads are
much higher than they would be if fees were universally competitive i.e. set at a level
where no profit is achievable (Dutta & Madhavan, 1997). In effect, a price war would
emerge with lower profits in the long-run if the 7% gross spread wasn’t used as a
reference point.
2.4.3 The Monopoly Power of Underwriters
A possible explanation for the persistent underpricing in IPOs, is the monopoly power
underwriters hold over the issuing firm. Underwriters, often in the form of investment
banks, have expertise in countries and markets that issuing firms require. Their ability to
better access a larger client base as well as provide all the services mentioned in section
2.3, allows them to impose more favorable terms when underwriting an IPO contract.
This hypothesis is also based on the fact that there is a lack of competition amongst
investment banks which gives them leverage when negotiating terms with issuing firms
(Cao, 2011). If underwriters possess monopoly power, they can set the offer price further
below the intrinsic value of the equity being offered which increases underpricing
(Benveniste & Spindt, 1989). Also, they can increase the gross spread between the offer
price and the bid price paid to the issuing firm (Saunders, 1990). The incentive to use this
monopoly power stems from the fact that by increasing underpricing, underwriters
decrease the underwriting risk whilst also earning more due to the higher gross spread
(Benveniste & Spindt, 1989). However, if the lack of competition between investment
banks was the main contributor to underpricing, then there would be a case for allowing

17
commercial banks into the underwriting business (Saunders, 1990). As a result, when the
Glass Steagall Act was partially repealed in 1999 and commercial banks were allowed to
enter the underwriting business, the expectation would have been a decrease in
underpricing as the increased competition should have decreased the monopoly power.
As this has not been the case, there is limited evidence to support the hypothesis that the
monopolist behavior of underwriters is a significant contributor to underpricing
(Ljungqvist, 2007).
2.5Information Asymmetry Models
Possibly the most prominent explanation, and the one with the most empirical support as
to why underpricing exists, is Informational Asymmetry. Information asymmetry is
created when informational disparities exist between the parties involved in a particular
deal. Many models and theories have been created which relate to information
asymmetry. A select few are discussed below.
2.5.1 The Winner’s Curse
A simple description of the winner’s curse is: “In auctions, if you end up overbidding and
win, you lose” (Prince, 2013). Bidders find difficulty in determining the true value of an
item being sold due to lack of information, emotional involvement or any other number
of pressures and impulses. The result is that the bidder who most over estimates the value
of the item ends up winning the auction. The highest bid wins the auction regardless of
how overpriced that bid may be; this is the winner’s curse.
When applied to IPOs, the problem of the winner’s curse is the extension of an
informational asymmetry model introduced by Rock (1986). The theory suggests that

18
underpricing in an IPO market exists because one group of investors is viewed as
informed and the other, much larger group, is viewed as uninformed. Informed investors
are those who have the means to acquire information on IPOs whilst uninformed
investors could be described as members of the public, for whom the acquisition of
information is far too costly. Therefore, underpricing is directly related to the degree of
informational imperfection between them as well as the cost of collecting that
information (Saunders, 1990). Rock (1986) proposes that there are two types of IPO
issues: good issues and bad issues. Informed investors, who possess information that the
vast majority don’t, will be able to assess the true value of an IPO. As a result, they will
only bid for IPOs which are good. On the other hand, uniformed investors cannot engage
in extensive research/information collection because of the costs, therefore, they will bid
randomly across the entire distribution of good and bad issues (Saunders, 1990). An
assumption placed on informed investors is that as a group, they are never large enough
to purchase the entire equity offering (Rock, 1986).
If one first considers a good issue i.e. one where the offer price is below the firm’s
intrinsic value. Then, both informed and uninformed investors will bid. Informed
investors will bid because they know it is a good issue and uninformed investors because
of the random nature of their bidding. As a result, the IPO will most likely be
oversubscribed which means that each investor (informed or uninformed) will receive
fewer shares than they actually bid for (Saunders, 1990). Subsequently, in a bad issue,
informed investors will not bid at all as their ability to conduct extensive research allows
them to determine the issue is bad. Therefore, the only bidders will be the uninformed
investors and due to the lack of competition from the informed investors, each
uninformed investor is more likely to receive a greater amount of shares than they bid

19
for. “That is, the uninformed bidder suffers from the problem of the winners curse: he
achieves large allotments for bad IPOs and small allotments for good IPOs” (Saunders,
1990).
Rock (1986) argues that IPOs have to be underpriced on average because of the winner’s
curse problem. The underpricing of the IPOs, on average, needs to be significant enough
to hold uninformed investors in the market because informed investors, as a group, cannot
absorb the entire IPO (Rock, 1986). By providing uninformed investors with a high
enough unexpected return due to the average underpricing of issues, there will be
incentive for these uninformed investors to continually bid for IPOs regardless of whether
they are good or bad (Rock, 1986).
2.5.2 Information Revelation Theories
If a situation exists where some investors are seen as more informed than others, or even
more informed than the issuing firm, then it is the role of the underwriter to extract this
information before setting the offer price (Ljungqvist, 2007). However, there is no
incentive for the informed investor to reveal any positive information they possess to the
underwriter. In the presence of positive information, the underwriter will most likely set
the offer price higher which reduces potential profits informed investors can earn.
Ljungqvist (2007) argues that informed investors are more likely to actively misrepresent
positive information by portraying a falsely bleak outlook on the issuing firm’s future.
This causes underwriters to set low offer prices which could partly explain how IPOs are
underpriced. The challenge is for underwriters to devise a mechanism for investors to
reveal their information truthfully but also keep their best interests in mind.

20
The two main mechanisms for determining the offer price are: fixed price and book-
building (Iannotta, 2010). The fixed price method allows investors to know the share
price before the company goes public and the allocation of the offering is done on a pro-
rata basis depending on the demand (Iannotta, 2010). This pro-rata allocation rule, which
resulted in Rock’s (1986) winner’s curse issue, has given way to the now more popular
bookbuilding method (Ljungqvist, 2007). The bookbuilding method gives the
underwriter a wide discretion over allocations as it allows them to determine the market
demand for the issue prior to any price being set (Ljungqvist, 2007). Investors bid on the
shares with the final price being set once the bidding has closed. By bidding for the shares
investors reveal their demand for the share as well as the price they are willing to pay.
Ljungqvist (2007), Spatt and Srivastava (1991) and Benveniste and Spindt (1989)
determine that the bookbuilding mechanism can act as a means of extracting investor’s
information on the issuing firm. During the bidding process investors display their
interest in the equity offering and if informed investors wanted to misrepresent positive
information, they would bid very conservatively. However, this would result in them
receiving either very little, or no share allocation when the bidding is closed. Hence, there
is little incentive to misrepresent information as doing so could result in exclusion from
the IPO (Ljungqvist, 2007). Conversely, those investors who bid aggressively, revealing
positive information about the issuer, receive large share allocations (Spatt & Srivastava,
1991). Aggressive bidding simultaneously raises the offer price, thus, in order to facilitate
continued bidding and information revelation, the issue has to involve underpriced
stocks. The underwriters have to be seen as leaving “money on the table” through the
underpriced stock to continually incentivise investor bids which reveal information about
the issuing firm (Benveniste & Spindt, 1989).

21
The role of syndicates also influences the nature of information revelation. The more
underwriters and institutional investors deal with one another, the lower the cost of
information (Ljungqvist, 2007). The inclusion of more underwriters who are active in the
IPO market, in a syndicate, increases the amount of total repeated interaction with
investors. This decreases the cost of information due to their wider range of investor
interaction. Ljungqvist (2007) states that underwriters “more active in the IPO market
have a natural advantage in pricing IPOs: their larger IPO deal flow allows them to
obtain investors’ cooperation more cheaply than less active underwriters could.”
2.5.3 Principal-Agency Theory
Extracting information from investors gives underwriters discretion on how to better
allocate shares, as mentioned in the information revelation theory. However, there is the
potential for agency problems between the underwriters and the issuing firm (Ljungqvist,
2007). The agents are generally the underwriters and the principals the issuing firm or
the investing public. By underpricing the offering, the underwriters decrease their
underwriting risk and the effort required to market the shares. But underwriter fees are
proportional to IPO proceeds and therefore inversely related to underpricing (Ljungqvist,
2007). Thus the agency problem is the conflict of interest underwriters face in decreasing
their risk and effort at the expense of the higher fees and IPO proceeds.
Early models focused on whether the informational advantage underwriters had over
issuers caused them to put in sub-optimal effort when it came to marketing and selling
the offering. As effort is not perfectly observable, underwriters find themselves in a case
of moral hazard when selling the equity offering (Loughran & Ritter, 2004). Similarly,

22
there is moral hazard in the case where underwriters ‘oversell’ an IPOs profitability to
the public. This may lead to increased fees as the proceeds from the IPO increase, but, it
could be at the detriment of the investors and the issuers as underwriters are not
responsible for the consequences of poor issuing firm performance once the IPO is
complete (Loughran & Ritter, 2004).
According to Baron (1982), the optimal solution to mitigating agency problems occurs
when issuers delegate the pricing decision to underwriters. This induces the optimal use
of the underwriters’ superior knowledge of investor demand (Baron, 1982). The
underwriters select the IPO price and underwriting spread together so that if market
demand is high, a low spread and a high price is selected and vice versa for low demand.
This optimizes the underwriters’ unobservable marketing and distribution effort by
making it dependent on market demand (Ljungqvist, 2007).
2.5.4 Signaling Model
This model treats underpricing as a signal of firm quality. Essentially, Rock’s (1982)
assumption of asymmetrical information between issuers and investors is reversed.
Issuing firms have better information about their investible projects and, hence, the
present value of their future cash flows than investors do (Ljungqvist, 2007). In this
scenario, underpricing is used to signal the “true” high value of the company. This
signaling mechanism is costly to the issuer due to the ‘money left on the table’ as a result
of the underpricing (Corwin & Schultz, 2005). However, if the underpricing does signal
to investors that the firm is of a high quality, then the signaling may allow the issuer to
return to the market at a later stage and sell equity at more favorable prices. This would
usually be in the form of a secondary equity offering (SEO).

23
Firms can be broadly categorised as either low-quality or high-quality, with investors not
being able to distinguish between the two (Ljungqvist, 2007). Firms are assumed to raise
equity in two stages, an IPO and most likely an SEO at a later date. For an IPO, the signal
would be the offer price of the equity offering. High-quality firms have the incentive to
correctly signal their higher quality due to the advantageous terms that can be gained
when selling again at a later date (Allen & Faulhaber, 1989). Low-quality firms have an
incentive to exaggerate their quality and imitate what high-quality firms do. According
to Allen and Faulhaber (1989), there is a high probability that a firm’s ‘true’ type will be
revealed before any benefits can be realized in a secondary selling of equity. This would
expose low-quality firms if they attempt to replicate a high-quality firms’ underpricing
signal.
The repercussions of false signaling need to be significant enough to deter low-quality
firms from imitating high-quality firms. For instance, if the reduction in IPO proceeds
were sufficiently great to prevent this deception, then high-quality firms can influence
investor beliefs on their value by deliberately leaving money on the table at the IPO and
recovering it at a later date when the firm goes to market again (Ljungqvist, 2007). Low-
quality firms would be unable to recoup the cost of the signal if they were to return to the
market at a later stage and would therefore not take this risk (Allen & Faulhaber, 1989).
It is via this mechanism that underpricing can act as a signal to firm quality.

24
3. Methodology
Section 2 introduced the literature behind IPO underpricing. This section outlines
procedures followed in terms of collecting, analyzing and interpreting the data. The scope
of the research topic as well as limitations in the data are also discussed.
3.1Research Scope
The IPOs used in this study were gathered from the Thomson ONE database available at
the University of Bath. All IPOs with their primary listing on the NASDAQ stock
exchange, between January 2005 and December 2015, were taken into account. The
NASDAQ was chosen over other exchanges such as the New York Stock Exchange
(NYSE) because of more lenient listing requirements. The considerably higher listing
fees on the NYSE are a deterrent to smaller issuers which is why the majority of new
listings are found on the NASDAQ (Draho, 2004). Although the NYSE attracts the larger
IPOs of the two, the NASDAQ boasts a larger volume of IPOs. In summary, the NYSE
is more expensive but offers more ‘prestige’ but in today’s tech-orientated world, many
companies see listing on the NASDAQ as the most logical as it is the most cost efficient
(Weinberg, 2003). For these reasons the NASDAQ was chosen. There were no
restrictions placed on industry type or issue size when collecting the data. Every IPO
available in the database was collected and used for analysis and empirical testing.
3.2Data Description
For the given eleven-year period, a total of 1052 IPOs were collected. For each IPO, the
relevant information will be categorized as set out in Table 1:

25
Table 1: Summary of Data for Each IPO
Issue
Date
Industry
Type
No. of
Shares
Issued
Offer
Price
($)
No. of
Primary
Bookrunners
No. of
Co-
managers
No. of
Syndicate
Members
Total No. of
Underwriters
Gross
Spread
(%)
- - - - - - - - -
IPOs with missing data relating to the offer price or the total number of underwriters were
eliminated as they would provide no information on underpricing or the influence of
syndicates. Of the 1052 IPOs collected, 970 contained complete data sets. The first day
closing price for each of the 970 companies’ stocks was then collected in order to
determine the level of first day underpricing. Underpricing did not apply to every IPO,
as a result, the overpriced and underpriced issues were separated so as to not skew the
analytics of the data. This can be seen in section 4.1.
In addition to the company specific data, general market data was also collected. The
closing price for the NASDAQ composite index was extracted from the Thomson ONE
database and used to determine whether the level of underpricing was influenced by the
general movements of the market. The NASDAQ composite index tracks all companies
listed on the NASDAQ stock exchange and gives a reliable indication of market
performance over the time horizon considered.
3.3Testing for the Presence of Underpricing
Two methods were used to test for the presence of underpricing in the data sample. The
first is a standard model employed in numerous other studies whilst the second model is
an adjusted method which accounts for market movement. Both of these are discussed
below.

26
3.3.1 Model 1
Direct costs for a firm going public can be classified as the underwriter spread (section
2.4.2) plus any fees and commission (Saunders, 1990). However, the indirect cost of
going public can be measured by the degree of underpricing. This is why determining the
level of underpricing is crucial when analysing IPOs. Using the data obtained for the
aforementioned IPOs, the level of underpricing for each was determined using the
formula:
𝑈𝑃 =
$%&$
&$
×100 (1)
where OP is the offer price and P is the price observed at the end of the first trading day.
If UP is positive, then the issue has been under-priced whereas if it is negative, then it
has been over-priced. If UP is zero, then the issue has been accurately priced but this is
not to be expected for the majority of IPOs due to the theories mentioned in section 2.4
about accurately determining public demand and their opinion on a fair price for an issue.
3.3.2 Model 2
According to Saunders (1990), UP can be viewed as the initial percentage rate of return
of buying an IPO issue. More formally it can be denoted as:
𝑈𝑃 = 𝑅 = 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 % 𝑟𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑡ℎ𝑒 𝑠𝑡𝑜𝑐𝑘 (2)
There is a lag between the setting of the offer price and the beginning of trading on a
stock exchange. Although this lag may be anywhere from one day to one week or more,
for the purpose of this study, it was assumed that the lag is one day. This is relevant as
the price the stock is trading at in the market on the first day may be high relative to the

27
offer price as a result of a rise in the stock market as a whole over the lag period
(Saunders, 1990). Similarly, the first day market price of the stock may be low due to a
general slump in the stock market. Therefore, stock market performance needs to be
controlled for when investigating underpricing (Saunders, 1990). Using the data collected
from the NASDAQ composite index, the excess market adjusted returns were calculated
as follows:
𝑅: =
;<=%;<>
;<>
×100 (3)
Where Rm is the return on the market portfolio, NC1 is the level of the NASDAQ
composite index the day of listing and Nc0 is the level of the NASDAQ composite
index when the offer price is set. As mentioned, it is assumed that the lag between NC1
and NC0 is one day. When Rm is positive then the market is going up and vice versa if
Rm is negative.
Model 2, the excess market return, can thus be defined as in Saunder’s (1990) paper:
𝐸𝑀𝑅 = 𝑅 − 𝑅: (4)
The significance of equation (4) is that underpricing only occurs when EMR is positive
i.e. when the initial return of the stock is greater than the market portfolio return. After
taking into account the movement of the stock market, the data sample was analyzed to
see whether or not the number of underpriced issues had changed and whether or not the
market movement had an effect on underpricing as a whole. Also, the data could be used
to see whether underpricing, in general, followed the same cyclical pattern as the market
over the past eleven years.

28
3.4Comparing Two Means
For the sample of IPOs, there were IPOs with no co-managers or syndicate members
underwriting the deal. This posed an issue for the regression analysis as the natural
logarithm of the number of underwriters would have to be taken to preserve the unit
integrity of the regression. Taking into consideration that underpricing, gross spread and
market return were all percentage values, logarithms of the remaining explanatory
variables would have to be taken so that they could be compared on a like for like basis.
As the log(0) does not exist, which would be the case for the number of co-managers and
syndicate members in some IPOs, the regression could not be defined.
The solution to this was testing whether underpricing was significantly higher on average
for IPOs underwritten solely by bookrunners when compared to IPOs underwritten by
bookrunners and co-managers. To test this, the following hypothesis was constructed:
𝐻C: 𝜇𝐼𝑃𝑂HIIJKLMMNKO & <I%:QMQRNKO = 𝜇𝐼𝑃𝑂HIIJKLMMNKO
𝐻S: 𝜇𝐼𝑃𝑂HIIJKLMMNKO & <I%:QMQRNKO > 𝜇𝐼𝑃𝑂HIIJKLMMNKO
If H1 is true, then IPOs underwritten by syndicates containing bookrunners and co-
managers are, on average, more underpriced than IPOs underwritten by bookrunners
only. However, if H0 is true, then there is no significant difference in the average
underpricing and the regression analysis can be done by only looking at the total number
of underwriters as opposed to looking at each type of underwriter separately. By making
this inference on two samples’ mean value, the solution would be found if H0 is true.

29
3.5Regression Analysis
Testing the influence a set of explanatory variables have on underpricing, requires a
multivariate regression model. As according the Brooks (2014), an Ordinary Least
Squares (OLS) analysis was used in Eviews. The general model used for OLS regression
analysis is as follows:
𝑦V = 𝛼 + 𝛽Z 𝜒VZ
M
ZS + 𝜇V (5)
In the above equation yi is the dependent variable, a is a constant, bj is the coefficient on
the jth
explanatory variable, cij is the set of explanatory variables regressed against yi and
µi is the random error term. This error term represents the deviation of the dependent
variable from its true value which is not explained by the explanatory variables (Brooks,
2014). Based on equation (5), the following multivariate regression models were
established:
Model 1:
eqn. (6a)
Underpricingf = α + βS ∗ log no. of underwriters + βr
∗ log market capitalisation + 𝛽v ∗ log offer price
+ 𝛽w ∗ log no. of shares issued + 𝛽y ∗ gross spread + 𝛽z
∗ 𝑚𝑎𝑟𝑘𝑒𝑡 𝑟𝑒𝑡𝑢𝑟𝑛 + 𝜇V
eqn. (6b)
+ 𝛽w ∗ log no. of shares issued + 𝛽y ∗ gross spread + 𝛽z
∗ 𝑚𝑎𝑟𝑘𝑒𝑡 𝑟𝑒𝑡𝑢𝑟𝑛 + 𝛽Z ∗ 𝐷𝑢𝑚𝑚𝑦(𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑇𝑦𝑝𝑒)VZ
MSr
Z‚
+ 𝜇V

30
Model 2:
eqn. (7a)
+ 𝛽w ∗ log no. of shares issued + 𝛽y ∗ gross spread + 𝜇V
eqn. (7b)
+ 𝛽w ∗ log no. of shares issued + 𝛽y ∗ gross spread
+ 𝛽Z ∗ 𝐷𝑢𝑚𝑚𝑦(𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑇𝑦𝑝𝑒)VZ
MSr
Zz
+ 𝜇V
The regressions for model 1 (6a) and model 2 (7a) differ in that model 2 loses market
return as an explanatory variable as it is already accounted for in the calculation for
underpricing. For both models, a secondary regression is created where a dummy variable
is added to account for the industry type. This dummy variable takes the value of 1 for
the industry of the ith
IPO for which the underpricing is being determined and 0 for all
other industry types. Using this dummy variable, the significance of each industry type
can be determined. Heteroskedasticity is a phenomenon where the variance of the series
is not constant throughout the sample. It is common in cross-sectional data which is why
all the standard errors in the regression have been corrected for the presence of
heteroskedasticity using White’s heteroskedasticity-consistent standard errors and
covariance matrix transformation in Eviews (Brooks, 2014).
3.5.1 Significance Tests
The OLS analysis in Eviews estimates the beta coefficients of each explanatory variable
which indicates whether the variable is positively or negatively related to underpricing.

31
It also produces various test statistics which can be used to evaluate the individual
significance of each variable in determining the level of underpricing. The T-test is used
to test for significance of each explanatory variable’s coefficient in the following
hypothesis test:
𝐻C: 𝛽 = 0
𝐻S: 𝛽 = 1
𝑇OƒQƒVOƒV„ =
…%…>
†‡ …
(8)
If the Tstatistic is greater than Tcritical, as specified in Brooks (2014), then the null hypothesis
is rejected and that variable is said to be statistically significant. Similarly, the F-statistic
will be used to determine whether the industry dummy variables are jointly significant in
explaining the variability of underpricing. Equations 6a and 7a will serve as the restricted
regressions and 6b and 7b the unrestricted regressions. The following hypothesis to test
for joint significance can then be established as follows:
𝐻C: 𝛽S = 𝛽r … . = 𝛽z = 0
𝐻S: 𝛽S ≠ 0, 𝛽r ≠ 0, … , 𝛽z ≠ 0
𝐹OƒQƒVOƒV„ =
(Œ••Ž••‘Ž’“‘•”•Œ••–—Ž••‘Ž’“‘•”)
˜
Œ••–—Ž••‘Ž’“‘•”
(—•™)
(9)
As before, if the Fstatistic is greater than the Fcritical specified by Brooks (2014), then the
industry type as a whole, will be a significant determinant of underpricing.

32
3.5.2 Determinants of Underpricing
In total, six explanatory variables were used across model 1 and model 2. According to
academic theory, each has an expected relationship with the level of underpricing as seen
below.
Number of Total Underwriters:
Academic literature doesn’t provide a conclusive argument on the relationship between
the number of underwriters and the extent of underpricing. For instance, Carter et al.
(1998) find that with added certification, underpricing decreases. However, Beatty and
Welch (1996) find that the opposite occurs. Assuming that an increased number of
underwriters in a syndicate corresponds to a higher amount of certification, the
expectation for this study is in line with Carter et al.’s (1998) results. Also, having more
underwriters increases the access to more client channels which increases the chances of
an IPO being successful. The higher the chance of success, the lower the perceived risk
and the lower the required underpricing. Hence, a negative relationship is expected.
Market Capitalization and Number of Shares Issued:
The market capitalization of an IPO can also be defined as the actual proceeds raised in
the market from the IPO. The proceeds are restricted to shares primarily issued on the
NASDAQ stock exchange. Any secondary listings on other stock exchanges are not taken
into account. According to Clarkson and Simunic (1994), market capitalization indicates
the uncertainty surrounding an IPO. The larger the proceeds from an IPO, the lower the
perceived risk from an investor’s point of view. According to Carter and Manaster
(1990), investors take into account the size of the offering when valuing the performance
of an IPO. Therefore, a negative relationship between market capitalization and

33
underpricing is expected. The same relationship is expected between the number of
shares issued and underpricing.
Offer Price:
The offer price is the price of the equity offered to both institutional and retail investors
and it is not set in an arbitrary way. Daily et al. (2003) suggest that when the aim of an
IPO is to attract retail investors then the offer price is set low, which leads to an excess
demand for the offering and larger underpricing. Conversely, when the aim of an IPO is
to attract institutional investors, the offer price is set high as these types of investors
generally avoid lower priced equity offerings (Gompers & Metrick, 2001). Institutional
investors need to be compensated for the information they provide in better marketing an
IPO which is why higher underpricing may be associated with higher offer prices
(Benveniste & Spindt, 1989). Therefore, empirical findings are varied in terms of the
relationship offer price has with underpricing.
Gross Spread:
There is a risk compensation component included into the gross spread earned by the
underwriters. This risk component is linked to the price uncertainty of the IPO. The
higher the uncertainty of the IPO, the greater the perceived risk and the higher the level
of underpricing (Cao, 2011). Therefore, a positive relationship is expected between gross
spread and underpricing.
Market Return:
According to Loughran and Ritter (2002), the greater the market return leading up to an
IPO, the greater the underpricing. Bakke et al. (2011) find this relationship puzzling as

34
this type of public information is available to the underwriter at no cost. The expectation
would therefore be a negative relationship, but, their model also predicts a positive
relationship between market return and underpricing.
Industry Type:
The dummy variable for industry type contains 12 separate industries with their
corresponding IPOs. Namely, these industries are: Consumer and Product Services,
Consumer Staples, Energy and Power, Financials, Healthcare, High Technology,
Industrials, Materials, Media and Entertainment, Real Estate, Retail and
Telecommunications. In the regression analysis, the real estate sector was left out to avoid
the issue of perfect multicollinearity amongst the dummy variables. It was excluded as it
contributed the lowest volume of IPOs for both model 1 and model 2. It is expected that
the level of underpricing is significantly different for each industry sector and that the
industry type as a whole contributes to determining the underpricing.

35
4. Results and Discussion
This section discusses the results obtained from the methodology mentioned in section 3.
The IPO distribution and descriptive statistics of the data are presented first. Then the
level of underpricing, as calculated by model one and two, is discussed and the results
from the regression analysis are interpreted.
4.1IPO Distribution
There were 1053 IPOs in the data set before any analysis was conducted. In total, 82 were
removed as they were missing valuable information. The remaining 971 IPOs were then
analyzed, using both model 1 and 2, to determine whether their stocks were underpriced
or overpriced when going to market. The summary of this can be seen in Table 2 below:
Table 2: No. of Underpriced and Overpriced IPOs
No. of
Underpriced IPOs
No. of Overpriced
IPOs
No. of “Correctly”
Priced IPOs
Model 1 650 238 57
Model 2 656 289 0
Without accounting for the market movement (model 1), slightly fewer IPOs in the data
set are underpriced. Moreover, once the market movement is taken into account for the
day of the IPO and the underpricing calculated (model 2), none of the issues can be
considered as accurately priced. It is evident from Table 2 that market movement has
some influence on how underpricing is distributed through the data set. Determining the
significance of this influence is important in terms of which model is more relevant.
Therefore, determining the correlation between the two is of particular interest.

36
Figure 2: Underpricing and Market Movement Correlation
The scatter plot in Figure 2 graphically represents the correlation between the movement
of the NASDAQ Composite index and the level of underpricing for IPOs using model 1.
If correlation were present, then the data points would congregate in quadrant 1 and 3 as
they increase or decrease together. Alternatively, they could congregate in quadrants 2
and 4 if they exhibited an inverse relationship. From Figure 2 there doesn’t appear to be
any clear evidence of correlation between the two. However, a more concrete assessment
will be made during the regression analysis when market movement is regressed against
underpricing.
4.2Descriptive Statistics
As the focus of this study is underpricing, the overpriced and correctly priced IPOs were
removed from the data set once they were identified. As a result, the statistical description
for underpricing as well as the explanatory variables from 2005 to 2015 is as follows:
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
45
50
-2.25 -2 -1.75 -1.5 -1.25 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25
First Day Underpricing (%)
Market Movement (%)
Correlation Between Underpricing and Market Movement from
2005-2015
Quadrant 1Quadrant 2
Quadrant 3 Quadrant 4

37
Table 3: Descriptive Statistics for Model 1 and Model 2
Variable Mean Minimum Maximum Median
Std.
Deviation
Model
1
Underpricing
(%)
23.88 0.04 410 14.58 32.89
Total No. of
Underwriters
4.9 1 21 4 2.58
Offer Price ($) 13.99 4 65 13.50 5.54
Market
Capitalization
($ millions)
149.86 2.8 2155.91 100 198.314
Market
Return (%)
0.06 -6.53 4.19 0.11 1
Gross Spread
(%)
6.76 0.35 10.75 7 0.83
Model
2
Underpricing
(%)
23.68 0.01 409.4 14.46 32.79
Total No. of
Underwriters
5 1 65 4 3.74
Offer Price ($) 13.94 4 65 13.50 5.60
Market
Capitalization
($ millions)
147.47 2.8 2155.91 98.07 194.45
Gross Spread
(%)
6.78 0.35 10.75 7 0.78
Statistically, there appears to be very little separating the two models. This is to be
expected as the market return fluctuations are considerably less than the deviations in
underpricing for both models. The average underpricing is 23.88% and 23.68% for model
1 and 2, respectively. The median underpricing percentage is lower than the average in
both model 1 and 2. This is common of a distribution which is skewed to the right i.e. a
clustering of data to the left with a “tail” extending out to the right. The implication of
this is that the standard deviation is not a good measure of the variability in the
underpricing distribution. This skew witnessed in the data could be as a result of outliers.
An example of this is in underpricing, for both model 1 and model 2, where the average
is considerably lower than the maximum value.

38
Offer price and gross spread represent what is closest to a normal distribution. Although
both are slightly skewed to the left, their mean and median values are relatively close to
one another. This is expected for gross spread as underwriters predominately settle on a
gross spread of 7%, as discussed in section 2.4.2. The most notable difference between
model 1 and 2 is the difference in the maximum value of the total number of underwriters
which are 21 and 65, respectively. The IPO with 65 underwriters was either overpriced
or correctly priced in model 1, but when the market movement was taken into account it
became underpriced. The average proceeds per IPO in model 1 and model 2 are $149.86
and $147.47 million.
Grouping the underpriced IPOs per industry type allowed for the following distribution
to be obtained for the eleven-year period. The industry types are listed in descending
order in terms of their average underpricing.
Table 4: IPO Distribution per Industry Type
Model 1 Model 2
Industry Type
No. of
IPOs
Average
Underpricing
(%)
No. of
IPOs
Average
Underpricing
(%)
Retail 48 35.35 49 34.55
Consumer Staples 10 34.86 11 31.78
Technology 154 31.18 155 30.89
Consumer and
Product Services
36 30.63 38 29.44
Media and
Entertainment
17 27.18 16 28.54
Healthcare 196 23.05 199 22.69
Industrials 38 20.27 37 20.92
Energy and Power 27 16.36 27 16.56
Telecommunications 12 14.23 12 14.45
Real Estate 5 12.48 5 12.15
Financials 94 11.24 94 11.26
Materials 13 7.49 13 7.57

39
From Table 4, the three largest industries according to the number of IPOs that occurred
are healthcare, technology and financials. Of these three, the financial sector was the least
underpriced and technology the most underpriced. The average underpricing varies from
7.5% to 35.35% which indicates there is a significant difference in underpricing
depending on the industry sector. For instance, in the retail sector, issuing firms and
underwriters are leaving a considerably greater percentage of their potential proceeds on
the table when compared to the materials sector. Although the technology sector
continually outperforms other sectors in terms of the volume of IPOs, it still remains
highly underpriced in comparison. The reason is that issuing firms based on new and
innovative technologies are hard to value as they have no real assets on their balance
sheet (Dembosky, 2011). Conversely, the consumer staples sector is highly underpriced
as investors often overlook it for more ‘exciting’ opportunities. As everyday items
constitute the consumer staple industry, the stocks belonging to this sector often display
low volatility which decreases the probability of large payoffs (Lee, 2015). Consequently,
the higher underpricing in this sector is used to attract investors as the average first day
return they can realize is 34.86% or 31.78%, depending on the model used.

40
4.3Level of Underpricing
Statistically there was very little difference between model 1 and 2 when determining the
level of underpricing. Graphically, this can be seen in Figure 3 below:
Figure 3: Model 1 and Model 2 Comparison
The average underpricing per quarter in both model 1 and 2 are relatively consistent with
one another. The only notable exception being in the third quarter of 2009, where model
2 saw an increase in underpricing compared to the decrease model 1. It comes as no
surprise that the largest deviations witnessed between the two models come in the period
during, and just after, the recent financial crash. In the eleven-year period considered,
stock market volatility was at its highest during and immediately after the crash. As a
result, when model 2 takes these larger price movements into account, there is a notable
difference between the models.
It is also interesting to note that in the few IPOs which did occur during the financial
crisis, underpricing appears to be at some of its lowest values for the given time horizon.
0
5
10
15
20
25
30
35
40
45
50
Average Underpricing (%)
Time Horizon (Quarters)
Comparing the Two Models of Underpricing
Model 1 Model 2

41
This could be due to the financial strain the entire economy was under and IPO
participants wanted to leave as little money on the table as possible for each deal. In
general, there appears to be no trend in the level of underpricing other than the fact that
sharp average increases in underpricing seem to be followed by sharp decreases in the
subsequent quarter(s).
Figure 4 and Figure 5 below, begin the analysis on the research question, in section 1.2,
by determining whether there is a correlation between the number of underwriters and
the level of underpricing.
Figure 4: Relationship between Model 1 Underpricing and No. of Underwriters
Graphically, there seems to be little correlation between the average number of
underwriters and the level of underpricing for model 1. At times the pair display a
relationship of mutual increase or decrease but it does not apply to every quarter in the
eleven-year period. Figure 4 does make it evident that no IPOs took place during the
second quarter of 2008 as well as the first quarter of 2009. More recently, it seems as
though underpricing has increased and the number of underwriters has decreased slightly.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
Average First Day Underpricing (%)
Average No. of Underwriters
Time Horizon (Quarters)
Total Underwriters (Bars) and Model 1 Underpricing
(Diamonds)

42
Perhaps as underwriting firms recover from the latest recession, they are more able to
underwrite IPO deals as they can commit more of their funds. Hence, there is a need for
less underwriters.
Figure 5: Relationship between Model 2 Underpricing and No. of Underwriters
The same general comment can be made for the relationship in model 2. Although Figure
4 varies slightly to Figure 5, no immediate conclusion can be made on whether the total
number of underwriters influences the level of underpricing. As a result, the total number
of underwriters were broken up into the individual types of underwriters to determine
whether their relationship with underpricing was representative of a more deterministic
trend. This can be seen in Figure 6 and Figure 7 below.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
Average Excess Market Return (%)
Average No. of Total Underwriters
Time Horizon(Quarters)
Total Underwriters (Bars) and Model 2 Underpricing
(Diamonds)

43
Figure 6: Syndicate Members and Model 1 Underpricing
It is clear from Figure 6 and Figure 7 that post financial crisis, syndicate members are
almost never included into a syndicated IPO deal. As a result of this, they will be excluded
from any further analysis. In both model 1 and model 2, the number of co-managers
exceeds the number of book runners, up until 2012, where the opposite becomes apparent.
Figure 7: Syndicate Members and Model 2 Underpricing
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005
Average Underpricing (%)
Average No. of Syndicate Members
Calendar Year
The Syndicate and Model 1 Underpricing
Total Underwriters Primary Bookrunner(s) Co-Managers
Syndicate Members Model 1 Underpricing
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005
Average Excess Market Return (%)
Number of Syndicate Members
Calendar Year
The Syndicate and Model 2 Underpricing
Total Underwriters Primary Bookrunners Co-Managers
Syndicate Members Model 2 Underpricing

44
Figure 6 shows that there has been a steady increase in the number of bookrunners
involved in a syndicate, with the exception of 2010. Similarly, the number of co-
managers increased until 2009, after which they have been steadily decreasing. The same
general pattern is seen in Figure 7, although it is not as consistent. By changing the time
horizon to years, instead of quarters, the total number of underwriters appears to better
correlate with underpricing. A general increase or decrease in the number of underwriters
tends to correspond to the same movement in underpricing. However, when interpreting
Figure 6 and Figure 7 the number of IPOs in each year need to be considered.
Figure 8: Number of IPOs per Year
There were 4 IPOs in 2008 compared to the 89 in 2007, but this wouldn’t be evident from
Figure 6 or Figure 7. The average underpricing is therefore based on significantly
different sample sizes each year. In Figure 8, the number of IPOs per year has steadily
increased post financial crisis and in 2014 IPO activity exceeded that preceding the
financial crisis. However, with the worldwide economic slowdown in 2015 the number
of IPOs decreased yet again. A more definitive answer to the research question will be
presented in the remaining sections.
75
99
79
48
35
40
15
4
89 89
7677
101
77
44
38 41
18
4
89 90
77
0
20
40
60
80
100
120
2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005
Number of IPOs
Calendar Year
Total Number of IPOs per Calendar Year
Model 1 Model 2

45
4.4Inference Based on the Comparison of Two Means
According to the methodology, two sub samples were compared to make an inference on
which variables to use in the regression. The first sub-sample contained IPOs with
bookrunners and co-managers present in the syndicate whilst the second sub-sample
contained IPOs underwritten solely by bookrunners. Table 5 summarizes the results from
the test.
Table 5: T-test Results for Comparing Two Means
Bookrunners and
Co-Managers
Bookrunners Only
Mean 24.34% 27.09%
No. of Observations in Sample 540 54
T statistic -0.57
Probability (|Tstatictic|<Tcritical) 0.5645
T critical (5% significance level) 1.96
The absolute value of the test statistic is less than the critical value found in Brooks
(2014). This, paired with the fact that the probability of 56.45% is greater than the 5%
needed to reject the null hypothesis, means that the result is insignificant. Therefore, there
is no statistically significant difference in underpricing when looking at the type of
underwriters comprising the syndicate. Based on the hypothesis established in section
3.4, the null is not rejected and as a result only the total number of underwriters will be
considered for the regression analysis. This allows for the logarithm transformation to be
used in the regression analysis for the explanatory variables.
4.5Regression Analysis
This section discusses the results from the regression analysis. It is composed of two
parts, the first on model 1 and the second on model 2. Conclusions and limitations drawn
from the models are discussed in section 5 and 6 respectively.

46
4.5.1 Model 1
Before any regressions corresponding to model 1 were estimated, it was necessary to test
whether the explanatory variables were correlated to one another. The problem with
multicollinearity is that the regression may “look good” with a high R2
statistic but the
individual variables may not be significant. As a result, any conclusions made using the
significance tests mentioned in section 3.5.1 would be inappropriate as the estimate of
the coefficients would be distorted. The easiest was to check for multicollinearity is to
establish a matrix of correlations between the explanatory variables as in Table 6.
Table 6: Evidence of Multicollinearity in Model 1
Correlation Table
Number
of Shares
Issued
Offer
Price
Total
Underwriters
Gross
Spread
Market
Return
Market
Capitalization
Number of
Shares Issued
1 0.0549 0.4441 -0.4506 0.0721 0.9168
Offer Price 0.0549 1 0.1959 -0.0855 -0.0017 0.2445
Total
Underwriters
0.4441 0.1959 1 -0.1545 -0.0008 0.5001
Gross Spread -0.4506 -0.0855 -0.1545 1 -0.0354 -0.4203
Market Return 0.0721 -0.0017 -0.0008 -0.0354 1 0.0519
Market
Capitalization
0.9168 0.2445 0.5001 -0.4203 0.0519 1
The number of shares issued appear to be highly correlated to market capitalization
(0.9168) which could lead to distorted results if both are left in the regression. This was
to be expected as companies who issue a large amount of shares generally generate higher
proceeds. As a result, the number of shares issued was removed from the regression

47
equations. The first regression, according to equation 6a in section 3.5, was estimated
with the Eviews output seen in Table 7:
Table 7: OLS Regression for Model 1
Dependent Variable: MODEL_1_UNDERPRICING
Method: Least Squares
Date: 08/12/16 Time: 12:22
Sample: 1 650
Included observations: 650
White heteroskedasticity-consistent standard errors & covariance
Variable Coefficient Std. Error t-Statistic Prob.
C -92.27101 60.93656 -1.514214 0.1305
LOG(TOTAL_UNDERWRITERS) -5.410996 3.160887 -1.711860 0.0874
LOG(MARKET_CAP) 3.864551 2.804997 1.377738 0.1688
LOG(OFFER_PRICE) 7.125486 2.933837 2.428726 0.0154
GROSS_SPREAD 5.115537 1.599729 3.197752 0.0015
MARKET_RETURN -0.214976 0.994508 -0.216163 0.8289
R-squared 0.025059 Mean dependent var 23.88441
Adjusted R-squared 0.017490 S.D. dependent var 32.88754
S.E. of regression 32.59867 Akaike info criterion 9.815608
Sum squared resid 684361.5 Schwarz criterion 9.856934
Log likelihood -3184.073 Hannan-Quinn criter. 9.831637
F-statistic 3.310614 Durbin-Watson stat 2.012344
Prob(F-statistic) 0.005834 Wald F-statistic 5.431039
Prob(Wald F-statistic) 0.000066
Not many conclusions will be drawn from the results in Table 7 as the main value of
interest is the residual sum of squares (highlighted in yellow) which will be used in the F
test for joint significance in accordance to the methodology. It is interesting to note that
the only significant variables in regression 6a are offer price and gross spread. The final
step was to estimate the unrestricted regression for model 1: equation 6b. Various
diagnostic tests were also conducted on equation 6b to determine the adequacy of model
1. These can be seen in Appendix A. The Eviews output for model 1: equation 6b can be
seen in Table 8 below.

48
Table 8: OLS Regression with Industry Dummies for Model 1
Date: 08/12/16 Time: 12:15
Sample: 1 650
C -58.27704 65.94306 -0.883748 0.3772
LOG(MARKET_CAP) 2.284726 2.894520 0.789328 0.4302
LOG(OFFER_PRICE) 9.790129 2.785221 3.515028 0.0005
GROSS_SPREAD 2.568743 1.758482 1.460773 0.1446
MARKET_RETURN -0.420422 1.017156 -0.413331 0.6795
CONPRODSERVICES 17.08563 6.413946 2.663825 0.0079
CONSTAPLES 19.54386 7.589904 2.574982 0.0103
ENERGY 3.249545 5.170794 0.628442 0.5299
FINANCIALS -3.308778 5.728751 -0.577574 0.5638
HEALTH 9.056797 5.374723 1.685072 0.0925
TECH 18.15077 5.402433 3.359739 0.0008
INDUSTRIALS 5.547446 5.504952 1.007719 0.3140
MATERIALS -8.069997 5.096244 -1.583519 0.1138
MEDIA 13.34713 9.046844 1.475336 0.1406
RETAIL 22.51305 6.788730 3.316239 0.0010
TELECOMS 1.409250 5.590007 0.252102 0.8010
Estimating the unrestricted regression for model 1 yields six statistically significant
determinants for underpricing. The other variables possess very little explanatory power
in terms of explaining the variability in the dependent variable. The regression equation
can therefore be re-written as follows:

49
Regression Estimates:
eqn. (10)
Underpricingf = −58.277 − 8.550 ∗ log no. of underwriters + 9.279
∗ log offer price + 17.085
∗ 𝐷𝑢𝑚𝑚𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑎𝑛𝑑 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 + 19.544
∗ 𝐷𝑢𝑚𝑚𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑆𝑡𝑎𝑝𝑙𝑒𝑠 + 18.151 ∗ 𝐷𝑢𝑚𝑚𝑦 𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦
+ 22.513 ∗ 𝐷𝑢𝑚𝑚𝑦 𝑅𝑒𝑡𝑎𝑖𝑙
The relationship between the total number of underwriters and the level of underpricing
is negative which is consistent with the expectations in section 3.5.2. Therefore, as more
underwriters are added to the syndicate, the level of underpricing is reduced and vice
versa. There was no explicit relationship expected between the offer price and the level
of underpricing. However, the positive relationship is consistent with Benveniste and
Spindt (1989), suggesting that IPOs are trying to attract institutional investors. Market
return is not significant in determining underpricing which definitively agrees with the
suggested relationship between the two in section 4.1. As expected, the different industry
types had varying degrees of significance. Of the twelve industries, only consumer and
product services, consumer staples, technology and retail contributed to the explanatory
power of the regression, each of whom display a positive relationship with the level of
underpricing. Of the three largest industries contributing to IPO activity, only the
technology sector was found to be significant.
Testing whether the industries are jointly significant, as opposed to individually
significant in determining underpricing was conducted according to the F-test in section
3.5.1. The results are as follows:

50
Table 9: Joint Significance Test for Model 1
Joint Significance Test
Restricted Regression
(eqn. 6a)
Unrestricted Regression
(eqn. 6b)
Residual Sum of Squares 684361.5 644858.3
Number of Restrictions
(q)
11
Degrees of Freedom (k) 16
Number of Observations 650
F-statistic (eqn. 9) 3.531
F-critical (Brooks, 2014) 1.81
As the F-statistic is greater than the critical value, the null hypothesis of no joint
significance can be rejected. Therefore, industry type as a group has an effect on the
model’s efficiency in being able to explain the variability in the level of underpricing.
Following these conclusions on model 1 from the regression analysis, the same procedure
will be carried out on model 2 to determine whether the coefficient estimates are
significantly different.
4.5.2 Model 2
To avoid excessive repetition, only brief comments will be made on the results obtained
for model 2’s regression analysis. As before, a matrix of correlations was created to check
for multicollinearity as in Table 10.
Table 10: Correlation Table for Model 2
Correlation Table
Number of
Shares
Issued
Offer
Price
Total
Underwriters
Gross
Spread
Market
Return
Market
Capitalization
Number of
Shares Issued
1 0.1262 0.3309 -0.4785 0.0804 0.9146
Offer Price 0.1262 1 0.2613 -0.1648 0.0204 0.3623

51
Total
Underwriters
0.3309 0.2613 1 -0.2282 -0.0129 0.3895
Gross Spread -0.478 -0.1642 -0.2282 1 -0.0149 -0.4510
Market
Return
0.0804 0.0204 -0.0129 -0.0149 1 0.0724
Market
Capitalization
0.9146 0.3623 0.3895 -0.4510 0.0724 1
Again, as in the previous model, the number of shares issued appears to be highly
correlated to market capitalization and was subsequently excluded from the regression in
model 2. When the initial regression, equation 7a, for model 2 was estimated the
following results were obtained from Eviews:
Table 11: OLS Regression for Model 2
Date: 08/12/16 Time: 17:02
Sample: 1 656
C -65.93998 63.80639 -1.033438 0.3018
LOG(MARKET_CAP) 0.933664 2.974395 0.313901 0.7537
LOG(OFFER__PRICE) 18.02162 4.227140 4.263312 0.0000
GROSS_SPREAD 5.132892 1.884874 2.723201 0.0066

52
It is interesting to note that the major difference between model 1 and 2 is that for model
2, the total number of underwriters is statistically significant. As with model 1, the
diagnostic tests carried out on model 2 can be seen in Appendix B. When the industry
dummy variables are added to the regression, as in equation 7b, the following output is
obtained:
Table 12: OLS Regression with Industry Dummies for Model 2
Date: 08/12/16 Time: 17:22
Sample: 1 656
C -12.96951 71.90518 -0.180370 0.8569
LOG(MARKET_CAP) -1.748487 3.205327 -0.545494 0.5856
LOG(OFFER__PRICE) 24.02973 4.617584 5.203962 0.0000
GROSS_SPREAD 1.600630 2.204913 0.725938 0.4681
CONPRODSERVICES 13.81578 7.582453 1.822073 0.0689
CONSTAPLE 13.85531 9.202973 1.505526 0.1327
ENERGY -2.022983 7.003850 -0.288839 0.7728
FINANCIALS -6.134698 7.544897 -0.813092 0.4165
HEALTH 9.984034 6.920791 1.442615 0.1496
TECH 18.91208 6.904034 2.739280 0.0063
INDUSTRIALS 5.817360 7.104060 0.818878 0.4132
MATERIALS -11.69934 7.689546 -1.521460 0.1286
MEDIA 14.51045 9.529711 1.522654 0.1283
RETAIL 20.39200 7.979729 2.555475 0.0108
TELECOMS 3.217724 7.087548 0.453997 0.6500

53
Adding the industry dummy variables to the regression results in four individually
significant variables. The same variables from model 1 are significant but there are fewer
significant dummy variables. However, the R2
statistic is higher for model 2 than in
model 1 (0.116 vs. 0.081). This R2
statistic represents how well the explanatory variables
explain the variability in the dependent variable. Therefore, even though there are fewer
significant variables in model 2, the ones that are significant, better explain the variation
in underpricing. The following regression can be established from the results in Table 12.
Regression Estimate:
eqn. (11)
Underpricingf = −12.97 − 8.920 ∗ log no. of underwriters + 24.029
∗ log offer price + 18.912 ∗ 𝐷𝑢𝑚𝑚𝑦 𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 + 20.392
∗ 𝐷𝑢𝑚𝑚𝑦 𝑅𝑒𝑡𝑎𝑖𝑙
The relationship between each explanatory variable is consistent with equation 10 for
model 1 and therefore consistent with the academic literature and expectations in section
3.5.2. The two significant industry sectors are also amongst the ones with the highest
levels of underpricing over the eleven-year period. In terms of the test for joint
significance of the industries, the following result was obtained:
Table 13: Joint Significance Test for Model 2
Joint Significance Test
Restricted Regression
(6a)
Unrestricted Regression
(6b)
Residual Sum of Squares 668456.5 622208
Number of Restrictions (q) 11
Degrees of Freedom (k) 15
Number of Observations 656
F-statistic (equation 9) 4.331
F-critical (Brooks, 2014) 1.81

54
The F-statistic is greater than the critical value which means that the dummy variables
for industry type are jointly significant as the null hypothesis in section 3.5.1 is rejected.
As a group, the industry types add explanatory power to the regression in terms of better
explaining the variation in underpricing.

55
5. Conclusions
This dissertation answers the research question: Does the extent of syndicate
underwriting influence the level of underpricing in IPOs more than other factors?
To conduct this study a sample of 1053 IPOs with their primary listings on the NASDAQ
stock exchange was compiled. The time horizon considered is January 2005 to December
2015. Further investigation was conducted into whether the industry an IPO belongs to,
influences the amount of underpricing. The literature reviewed in section 2 is used to
interpret the relationship between a set of explanatory variables and underpricing in a
regression analysis.
Two models were used to calculate underpricing. The first model takes the difference
between the first day closing price of the equity offering and its offer price whilst the
second model adjusts the model 1 underpricing to account for overall market return. In
the sample of 1053 IPOs, the two models gave close correlation for the average
underpricing during the eleven-year period under review. The calculated number of
underpriced IPOs were 650 and 656 whilst the average underpricing was 23.88% and
23.68% in model 1 and model 2, respectively. Both models determined that IPOs offered
in the retail sector were underpriced the most at an average of 35% while the materials
sector was the least underpriced at an average of 7.5%. The largest volume of IPOs during
the eleven-year period was offered by the healthcare sector which had an average
underpricing of 22.8%. Thus, it was concluded that underpricing varied considerably
depending on the industry sector of the IPO.

56
The composition of the underwriting syndicates (bookrunners and co-managers) varied
during the eleven-year period, mainly influenced by the financial crisis of 2007/08.
Syndicate members were prevalent pre-financial crisis but their influence completely
diminished in the subsequent years. As a result, their influence was no longer considered
in any statistical tests or regressions. Using a T-test of mean equality, average
underpricing was not significantly different for different compositions of underwriting
syndicates. Thus, it was acceptable to only use the total number of underwriters as an
explanatory variable in the regression analysis.
The addition of industry sectors to the regression analysis increased both models’
explanatory power, with model 2 having the highest correlation coefficient (R2
statistic)
of 0.1166. The F-test for joint significance also confirmed that for both models, the
addition of the industry sectors aided the regression in better explaining the variability in
underpricing. Thus, the second hypothesis in section 1.2 is confirmed. Model 2 possess
the greatest explanatory power, however, with the variables used, it still only explains
11.66% of the variation in underpricing over the eleven-year period.
From the research question, it can be concluded that the number of underwriters in a
syndicate does influence underpricing more than other factors. Due to the negative
relationship between underpricing and the number of underwriters, companies going
public with a large syndicate should expect their offering to be underpriced less than
companies going public with a smaller syndicate, in the same sector.
The conclusion supports academic theory that the added certification and greater
information production gained from larger syndicates, results in decreased underpricing.

57
Companies conducting IPOs should understand the ramifications of choosing a small
syndicate over a large one as it will decrease the amount of equity capital raised. In doing
so, companies would defeat the main goal of conducting an IPO: to raise the maximum
amount of equity capital to facilitate the strategic requirement at the time of the offering.

MSc Finance Dissertation

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