Why are IPO Investors Net Buyers Through Lead Underwriters?
JOHN M. GRIFFIN, JEFFREY H. HARRIS, AND SELIM TOPALOGLU*
August 1, 2004.
Griffin is at the University of Texas at Austin, Harris is at University of Delaware, and Topaloglu is at Queen’s
University. Griffin can be reached at john.griffin@mccombs.utexas.edu, Harris at harrisj@lerner.udel.edu, and
Topaloglu at stopaloglu@business.queensu.ca. We thank the Nasdaq Stock Market for providing essential data and
Jie (Olivia) Lian, Johan Sulaeman, and Yongjun (Dragon) Tang for research assistance. We are also grateful for
helpful discussions with Aydogan Alti, Kirsten Anderson, David Brown, Sonia Falconieri, Eric Falkenstein, Mark
Flannery, Bruce Foerster, James Griffin, Jay Hartzell, Darius Palia, Bob Parrino, Jay Ritter, Jeff Smith, Matt
Spiegel, Laura Starks, Ivo Welch, Donghang Zhang, and seminar participants at the University of Florida,
University of Missouri—Columbia, Rutgers University, Tilburg University, and the University of Texas at Austin.
*
Why are IPO Investors Net Buyers Through Lead Underwriters?
Abstract
On the first day of trading in Nasdaq IPOs from 1997 to 2002, clients of the lead
underwriter buy an amount equal to 20.64 percent of shares issued but sell only 11.85
percent. We investigate many alternative explanations for this buy imbalance through the
lead underwriter. First, contrary to clientele demand-based explanations, brokerage
houses with large client buying when the broker is the lead underwriter experience net
client selling when the broker is just another member of the syndicate. Second,
inconsistent with the hypothesis that underwriter clients obtain superior information on
certain IPOs, we find that the strong buying activity from lead underwriter clients is
present in 85.16 percent of all IPOs. Third, contrary to client buying being driven by
superior price execution, we find that clients executing large block trades through the
bookrunner often pay slightly higher prices. Fourth, inconsistent with initial client buying
being driven by long-term shareholders, IPOs with large first-day net buying by lead
underwriter clients experience more institutional investor selling over the subsequent
quarters. Fifth, the strong net buying activity through the lead underwriter is driven by
large block trades, widespread in cold, warm, and hot IPOs as well as at most price
levels, with slightly more prevalence at prices near the offer price. These patterns are
most consistent with theories where underwriters allocate shares to extract rents from
clients--such as help with aftermarket price support and stimulating IPO demand.
In 1999 and 2000, shares of initial public offerings (IPOs) soared an average of 71.7 and 56.1
percent on the first day of trading, respectively, transferring more than $65 billion of wealth from
issuers to fortunate shareholders through underpricing.1 While many have focused on the puzzle
of why issuing firms leave large sums of money on the table, it is perhaps even more perplexing
that sophisticated underwriters also presumably forego the substantial fees associated with
underpricing (more than $4.5 billion during 1999-2000).2 This paper provides evidence
consistent with a partial explanation for this puzzle, namely that underwriters may receive
benefits from underpricing in the form of strong aftermarket client demand.
Using a unique sample of Nasdaq IPO trading by brokerage houses from 1997-2002, we
find that the clients of the bookrunner (lead underwriter) buy an amount equal to $35.36 billion
on the first day of public trading but sell shares worth only $21.45 billion, for a net buying
imbalance of $13.91 billion. These patterns of net buying for bookrunner clients and small net
selling through other syndicate members are widely prevalent across most brokerage houses,
sample periods, and IPOs with varying degrees of underpricing. We examine predictions of
competing explanations for this phenomenon including those based on clientele effects,
underwriter reputation, information from underwriters, familiarity, differential execution quality,
strategic allocation, and ‘laddering.’
Lead underwriter or bookrunner clients generally receive large allocations and hence may
be most likely to sell for diversification purposes. However, there are many possible reasons why
clients of the bookrunner might be net buyers. A popular explanation promoted recently in the
press called ‘laddering’ finds motivation from models by Fulghieri and Spiegel (1993) and
Loughran and Ritter (2002) where underwriters are able to extract indirect rents from clients in
1
2
These numbers are taken from the Ritter and Welch (2002) survey of the extensive IPO literature.
Based on a flat seven percent of capital raised (Chen and Ritter (2000)).
1
exchange for underpriced shares. In Fulghieri and Spiegel’s model an investment bank allocates
underpriced shares to clients who provide business for other parts of the bank. In this context,
investment banks signal their quality to clients by the total dollar value of underpricing they can
provide.
Loughran and Ritter (2002) provide an explanation for the firm’s ex ante choice of an
underwriter who is likely to ex post underprice the issue.3 Because underpricing is lucrative for
clients of the underwriter who receive IPO allocations, these clients engage in rent-seeking
behavior to increase their probability of receiving shares. Underpricing generates higher revenue
to underwriters in other business areas. Indirect underwriter fees are more acceptable than higher
direct commissions because issuers underweight underpricing costs and these costs are limited in
poor performing IPOs.
Underwriters are judged by both the amount of demand that they can generate for the IPO
and their ability to stabilize prices in the aftermarket. The working assumption in the IPO
industry and in many recent index listing and delisting papers is that there is a negatively-sloped
demand curve. Thus, if a brokerage house fails to generate sufficient client demand for an IPO
then subsequent issuers may view the underwriter as a poor promoter. In the case of particularly
weak demand, the bookrunner may be forced to provide costly aftermarket price support. Thus,
both in terms of generating sufficient IPO demand and protecting itself from costly aftermarket
support, an underwriter could benefit from encouraging their clients to buy aftermarket shares in
exchange for current or future share allocations. The practice of the bookrunner implicitly or
explicitly encouraging clients to engage in unconditional buying support is often called
‘laddering’ (Pulliam and Smith (2000a, b)) since it is said to generate artificially inflated prices.
3
Ljungqvist and Wilhelm (2004) provide some support for the prospect theory predictions of Loughran and Ritter
(2002) by focusing on subsequent security issues.
2
There are many other reasons to think that the bookrunner’s clients will be stronger net
buyers than customers from other brokerage houses. For instance, customers with excess demand
for IPOs may migrate to brokerage houses that specialize in issuing IPOs and where they can
receive larger share allocations. The strong aftermarket buying could simply reflect strong excess
client demand for IPO shares. However, in contrast to the clientele explanation, we find that
clients are consistently large net buyers when their brokerage house is the bookrunner but are
typically small net sellers when this same brokerage house is a co-manager or another member of
the syndicate.4
Strong bookrunner client buying could reflect better, or more important information
passed on from the bookrunner to clients. If so, these clients would likely take large positive
positions only in IPOs that trade at prices lower than their fundamental value. However, we find
that net bookrunner client buying activity prevails in over 85 percent of all IPOs in our sample.
We also explore whether the bookrunner offers relatively attractive prices to encourage buying.
We find that block trades executed through the bookrunner actually receive slightly worse
executions than those through non-syndicate brokerage houses.
The bookrunner may strategically allocate shares to encourage aftermarket buying. Zhang
(2003) argues that institutional investors only wish to hold positions above a certain size and that
additional aftermarket buying may be to establish positions of sufficient size for institutions. One
could couple Zhang’s argument with the argument that since the bookrunner has more of an
interest in generating long-term shareholders than other members of the syndicate, they may
strategically allocate “toeholds” to those institutions to encourage them to establish more
sizeable positions. Thus, the strategic allocation and laddering hypotheses generate similar
4
These patterns also hold within IPOs with the most reputable lead underwriters and within an internet IPO
subsample.
3
predictions of substantial buying from bookrunner clients. Additionally, consistent with
predictions of both hypotheses being driven by large institutional investors, we find that 99.6
percent of bookrunner net client buying is due to trade sizes of 2,000 shares or more.
However, a distinguishing prediction of strategic allocation to long-term shareholders is
that those IPOs with large initial buying by long-term institutional clients through the
bookrunner should have a more long-lasting shareholder base than other IPOs without this base
of institutional investor support. To examine this hypothesis, we track the percentage of shares
held by these initially reported shareholders from the first 13f quarterly reporting date following
the IPO to subsequent quarters. In contrast to having stable long-term shareholders, IPOs with
higher levels of client buying through the bookrunner experience more institutional selling than
other IPOs over the subsequent four quarters by these first-reporting shareholders.
The features of the data that are inconsistent with clientele effects, underwriter reputation,
information from underwriters, familiarity, differential execution quality, and strategic allocation
are all consistent with the laddering explanation. Additionally, with laddering arrangements,
clients offer unconditional aftermarket buying support although buying is clearly more beneficial
to underwriters near the offer price. Consistent with these aspects of laddering, we find slightly
higher proportions of net client buying at lower prices, but strong client buying at all but the
most excessive price ranges. Nevertheless, there may be other unexamined explanations for our
findings.
Our paper is related to a growing body of literature that examines the dual nature of
underwriting and market making activities by investment banks. Ellis, Michaely, and O’Hara
(2000), for instance, find that the lead underwriter becomes the dominant market maker and
generates positive trading profits that are increasing in the level of underpricing. Schultz and
4
Zaman (1994) show that underwriters stabilize cold IPOs, while Aggarwal (2000) and Ellis,
Michaely, and O’Hara find that market makers accumulate inventory in cold IPOs to cover initial
short positions generated through the use of the overallotment option.
We focus less on the underwriter’s proprietary trading activity and more on the trades
executed as agents for their clients. In a similar vein, Aggarwal (2003) finds that IPO flippers
represent a relatively small amount of trading volume and flipping (as a percentage of the
allocation) is much more likely in IPOs with large abnormal first-day returns.5 We focus on
buying and selling activity through the lead underwriter, other syndicate, and non-syndicate
members and our findings support Ritter and Welch’s (2002) conjecture that “agency
explanations will play a bigger role in the future research agenda.”
The remainder of the paper is organized as follows. Section I discusses the alternative
hypotheses which might generate differential buying and selling activities across members of the
syndicate as well as their distinguishing testable predictions. We discuss our data and summarize
the characteristics of our Nasdaq IPO sample in Section II. Section III provides empirical
evidence on aftermarket client trading imbalances and Section IV tests predictions of the
previously outlined hypotheses. We explore the tradeoff between bookrunner client and
bookrunner trading as well as their relation to short- and long-run returns in Section V. We
discuss accounting issues of how initial shares sold might arise from short-selling or flipping in
Section VI and conclude in Section VII.
I. Explanations for Differential Buying Patterns through the Lead Underwriter
5
Krigman, Shaw and Womack (1999) find a negative relation between a proxy for institutional flipping and longrun price performance. Aggarwal (2003) and Corwin, Harris and Lipson (2004) suggest that this negative relation
may result from the variation in total volume and not necessarily institutional flipping, per se.
5
There are a number of explanations why clients might buy more shares than they sell
through the lead underwriter. Here we detail each hypothesis and distinguishing testable
implications associated with it.
A. Laddering
There has been substantial controversy surrounding the turbulent IPO activity during the
‘internet bubble’ period. The controversy focuses on three main issues. First, some claim that
underwriters allocated issues to favored institutional clients with the understanding that the
clients would buy more shares in the aftermarket, so this ‘laddering’ helps to support prices
through artificial demand. Second, underwriters are alleged to have allocated shares to those
clients who would rebate some of the underpricing profits back to the underwriter through active
trading (perhaps at inflated commission rates) in other liquid stocks.6 Third, there are charges
that underwriters gave shares to favored clients, such as CEOs hoping that they would use the
underwriter in a future offering (known as spinning). Our results are related to the first issue but
we do not directly examine the other two.
Laddering involves a quid pro quo relationship between underwriters and their clients
where clients receive IPO share allocations with the implicit or explicit understanding they
would purchase additional shares in the aftermarket. Underwriters enforce these alleged
arrangements by withholding future IPO allocations.7 If underwriters engage in laddering, IPO
prospectuses may be misleading for not disclosing agreements for the purchases of additional
6
On a related note, Reuter (2004) finds a positive relation between brokerage commissions paid by mutual funds to
lead underwriters and the aftermarket shares these mutual funds end up holding in the underwriter’s IPO.
7
The buying and selling activities of the clients are easy to track if they make the transactions through the same
underwriter. Furthermore, the Depository Trust Corporation (DTC) IPO tracking service assists in tracking client
trading (see Section V below). According to the SEC (Litigation Release No. 18385, 2003), an email from a J.P.
Morgan sales representative says that the institutional customer “followed up in the aftermarket exactly as promised
(every share through us).”
6
aftermarket shares (and the sources of commissions that accompany this activity). 8 Concerns
about laddering are not new. In both 1961 and 1984 the SEC issued warning statements that
laddering violates antifraud and anti-manipulative security laws.9 Jay Ritter notes that such
laddering practices have occurred in the past with penny stock underwriters but “generally has
not been a focus of allegations against prestigious underwriters until recently” (Harris (2001)). In
August 2000, the SEC issued another warning that these ‘tie-in’ agreements violate antifraud and
stock manipulation provisions.10
Laddering is difficult to prove. The Economist states: “Seasoned regulators admit that it
will be hard to prove that Wall Street firms were systematically up to no good” (Getty (2001)).
Barry Barbash, former director of the SEC's Division of Investment Management, speculates that
aftermarket purchases simply reflect strong demand and that “proving this kind of conspiracy as
a practical matter is very difficult” (Loomis (2001)).11 Nevertheless, legal claims of IPO
laddering are migrating from the criminal to the civil arena as shareholder groups claim that they
unknowingly bought shares at inflated prices.
Our analysis focuses on the different brokers that handle secondary market trades in
IPOs. The lead underwriter plays a key role the IPO process. The lead underwriter risks more
reputational capital than other syndicate members and is judged by its ability to generate postIPO demand and provide aftermarket price support. Laddering arrangements likely involve lead
underwriter clients assisting with aftermarket support. Other syndicate members have limited
Deneen and Hooghuis (2001) note that lawsuits charge that underwriters and certain issuers violated “Sections 11,
12(a) (2) and 15 of the Securities Act of 1933 and Section 10(b) of the Securities Exchange Act of 1934 and Rule
10b-5.” (See SEC Release No. 4358, 1933, and SEC Release No. 6536, 1934.)
9
SEC Lexis 25, 1961, and SEC Bulletin, 1984, Hot issues report: report of the security and exchange commission
concerning the hot issues market.
10
SEC Staff Legal Bulletin No. 10, 2000.
11
Perhaps this is why the SEC fined J.P. Morgan only $25 million on October 1, 2001 under Rule 101 of Regulation
M instead of imposing larger fines that might have been levied under a 10b-5 violation (Plitch and Hennessey
(2003)). Rule 101 of Regulation M prohibits underwriters from inducing people to purchase securities in the
aftermarket. (See an interpretation of Regulation M in SEC Litigation Release No. 18385, 2003).
8
7
incentives to support aftermarket prices. Therefore, a necessary, but not sufficient, distinguishing
feature of the laddering hypothesis is that net buying activity by lead underwriter clients exceeds
that of other syndicate and non-syndicate members and their clients. Laddering allegations
suggest prearranged unconditional buying support by large institutional clients of the bookrunner
which should lead to persistent net buying through the bookrunner.
H1: Client Net Buy large trades, Bookrunner > Client Net Buy small trades, Bookrunner
Of course, net bookrunner client buying could be caused by other factors as well. We discuss
other factors and their empirical predictions compared to the laddering hypothesis below.
B. Clienteles
Certain investors prefer to hold young stocks with high volatility and growth potential
like IPO firms. These clienteles may migrate their trading to brokerage houses with a propensity
to issue IPOs and it may be natural for them to seek additional shares in the aftermarket. Indeed,
Cornelli and Goldreich (2001) and Jenkinson and Jones (2004) find that bidders will generally
receive a larger pro-rata portion of the IPO when their broker is the bookrunner. To investigate
whether aftermarket client buying is due to pent-up demand by IPO investors, we examine client
trading by brokerage house. The clientele hypothesis predicts that net client aftermarket demand
may be greatest when clients of brokerage houses who typically handle IPOs are not the
bookrunner (relative to demand when the broker is the bookrunner).
H2: Client Net Buyu, Bookrunner ≤ Client Net Buyu, Non-lead
where u denotes a particular underwriter.
C. Informational advantages
There are also several informational explanations for net buying differences across
brokerage houses. For example, clients of the bookrunner may be better informed (than other
8
investors) about true IPO values but either only partially reveal their demand in the primary
market or are rationed by the IPO pricing process, so that they seek additional shares in the
secondary market. If so, we would expect bookrunner client buying activity to be associated with
positive future abnormal returns. Although it is difficult to know the average investment horizon
of the aftermarket investors and long-run return tests lack power, if IPOs are priced at fair value
on average, then net bookrunner client buying would occur only in IPOs that initially trade below
fair value. Since the median IPO underperforms in the long-run, not more than half of IPOs can
be underpriced in the aftermarket and for each IPO, ‘i’,
H3: Proportion (Client Net Buyi, Bookrunner >0) < 0.50
D. Marketing to create familiarity
In a similar vein, a familiarity bias could explain differences in net buying propensity
across brokerage houses. Investors are more likely to hold securities they are familiar with (see
Kang and Stulz (1997), Coval and Moskowitz (2001), and Huberman (2001), among others). If
familiarity drives net aftermarket buying patterns, we should see little difference between
bookrunner and co-manager client trading since all are exposed to similar information during the
road show.
H4: Client Net Buyu, Bookrunner ≈ Client Net Buyu, Co-manager
Underwriters that take multiple IPOs to market may also have larger, more effective
marketing forces. One marketing hypothesis predicts that bookrunner initial client buying should
be greatest for underwriters that are involved with relatively more IPOs (compared to less active
underwriters). This marketing hypothesis would apply to all members of the syndicate, since
marketing expertise can be applied no matter what role the underwriter takes in the syndicate.
Laddering, however, specifically applies to the bookrunners but not co-managers. In this regard,
9
a distinguishing prediction of marketing to create familiarity is that co-managers who issue
multiple IPOs should generate more aftermarket demand than those that issue fewer IPOs.
H5: Client Net BuyLarge, Co-manager > Client Net BuySmall, Co-manager
E. Reputation
The bookrunner’s reputation could attract more net buying activity. However, controlling
for the reputation of the underwriter (which is known to all traders), we would expect to see
consistent demand from both clients and non-clients alike. To examine reputational effects, we
focus on trading only through underwriters with high reputation and then examine whether client
behavior for a given underwriter varies depending on the role of the underwriter.
H6: If underwriter reputation = high, Client Net Buyu, Bookrunner ≈ Client Net Buyu, Non-lead
F. Superior bookrunner buy prices
The bookrunner may also encourage clients to buy IPO shares in the aftermarket by
offering superior buy trade execution to their clients (i.e. allow their customers to buy within
posted spreads, for instance).12 Indeed, bookrunners may dominate secondary market trading by
offering superior prices. We investigate whether the bookrunner offers superior prices relative to
all other non-syndicate brokerage houses. Specifically, we document the percent of time that buy
trades are executed at or below the bid-ask midpoint (or below the ask price) for the bookrunner,
other non-syndicate market makers, and non-lead brokerage houses.13 Because execution varies
by trade size and buy imbalances may cluster by trade size, we investigate across multiple tradesize groups. The superior price hypothesis predicts that bookrunner clients receive better prices
than if they traded through other market makers.
12
Merely posting the lowest ask prices would allow all investors, even those who trade through other brokerage
houses, to buy at these lower prices.
13
We also investigate the percentage of executed buy trades relative to bid prices.
10
H7: Bookrunner (% of buy trades ≤ mid-point) > Non-syndicate MM (% of buy trades ≤
mid-point)
G. Strategic allocations to long-term shareholders
Bookrunners may strategically allocate shares to investors with long-term interests in the
stock to encourage them to buy more shares in the aftermarket.14 Because long-term share
holders are often thought to be stable institutional investors, the strategic allocation hypothesis
predicts, like laddering, that buy imbalances are due to large trades (H1). Since the lead
underwriter bears primary responsibility for fostering long-term shareholders, the strategic
allocation hypothesis also predicts, like laddering, that stronger client buying persists when the
brokerage house is the bookrunner versus just another syndicate member. Thus, in contrast to
H2, H4, and H6, both strategic allocation and laddering predict unconditionally:
H2A: Client Net Buyu, Bookrunner > Client Net Buyu, Non-lead
A distinguishing feature of the strategic allocation hypothesis is that strong aftermarket
buy imbalances should reflect long-term institutional shareholder purchases. IPO firms with
strong aftermarket bookrunner client buying from these stable shareholders should have fewer
shareholders selling their shares than IPOs with weak initial bookrunner client buying.
H8: Decreases in Institutional Ownership (large client buy imbalances) < Decreases in
Institutional Ownership (small client buy imbalances)
In contrast, laddering predicts that the clients that buy aftermarket shares immediately after the
IPO may unwind their positions over time.
We have discussed many possible explanations for bookrunner client net buy activity and
many distinguishing testable implications. For brevity, we do not thoroughly compare and
14
Zhang (2003) argues that institutions prefer substantial blocks and buy IPO aftermarket shares to satisfy this
preference.
11
contrast all hypotheses.15 Nevertheless, we discuss contrasting implications further when we take
the testable hypotheses to the data below.
II. Data and Summary Statistics
Using the Thomson Financial Securities Data Company (SDC) new issues database we
identify IPOs from January 1997 to December 2002 and exclude certificates, ADRs, shares of
beneficial interest, units, closed-end funds, REITs, companies incorporated outside the U.S., and
IPOs not covered by CRSP. We also collect characteristics of the offerings from SDC but note
that for some of the variables, coverage is incomplete across the sample. We calculate total
shares issued by aggregating all the underwriter allocations for an IPO. We gather CarterManaster underwriter reputation rankings from Jay Ritter’s website.16 We restrict our attention to
Nasdaq IPOs since we use proprietary Nasdaq data. Our final sample consists of 1298 IPOs, with
most listing in the first half of our sample period.
We collect Nasdaq clearing data that identifies all trades by brokerage house from
January 1997 to December 2002. We restrict our attention to the first 21 days of trading in each
IPO. In addition to the brokerage house identification, the Nasdaq data provides other essential
items for our purposes. Both sides of each trade are reported, including the buyer and the seller,
allowing us to avoid misclassification errors that result from tick test rules. In addition, each side
of the trade is classified as to whether the market maker is trading for her own account (as a
principal) or handling a trade for a brokerage client (as an agent). When a customer submits an
15
For example, as previously mentioned, laddering has opposing predictions to H2, H4, H6, and H7. While the
laddering hypothesis makes no concrete prediction regarding the proportion of IPOs with client net buying (H3), one
would expect a bookrunner who engages in laddering to have a high proportion of IPOs with positive client net
buying. In contrast to H5, laddering predicts that co-manager buying activity will be universally small in comparison
to that through the bookrunner.
16
Jay Ritter’s website has IPO data available at: http://bear.cba.ufl.edu/ritter/ipodata.htm
12
order to sell shares, the market making firm can either buy the client’s shares for their own
inventory or they can act as an agent and trade against a bid (buy) price from another market
maker.17 Market makers who fill orders for their own (typically large) clients report trades
marked with a blank counterparty, allowing us to identify a subset of trades that can be traced
directly to clients of each market maker.18 We classify both sides of each trade as a principal
trade (for the market maker’s own inventory), client trade (for brokerage clients of the market
maker), or non-client trades (all other trades not in the above two categories).
Although each trade is reported only once in our final data, some trades are routed
multiple times. The Nasdaq data includes both reported and non-reported legs of some trades.
We check for consistency in assigning whether a market maker acted as a principal or an agent
for each leg of routed trades and do not classify trades that are inconsistently reported in each leg
of the routing report.19 We are able to consistently classify 97.19 percent of trades and 96.08
percent of volume over the first three months of trading and a similar proportion (97.19 percent
of trades and 95.29 percent of volume) on the first day of trading.
We manually match the underwriter firms from SDC and the market maker identities in
the Nasdaq data, accounting and adjusting for market making firms that have merged, changed
names, or used multiple market maker codes (for different trading desks, for instance). We are
More recent Nasdaq data includes “riskless principal” trades where the market maker buys from one client for
their own account, but immediately sells to another party.
18
In discussions with Nasdaq officials, we confirm the reporting standard is uniform throughout the data.
19
A trade via some electronic communications networks (ECNs) typically includes three reports in our data, one
identifying the selling market maker with the ECN as counterparty, a second with the buying market maker with the
ECN as counterparty and a third with the individual market makers on each side of the trade. In this instance, only
one transaction is reported to the tape (and included in the NYSE’s Trade and Quote, or TAQ data and Nasdaq’s
NASTRAQ data, for instance). Since ECNs take no positions, we pair up the market makers on each side of the
trade.
17
13
able to match 96.2 percent of allocated IPO shares to a syndicate member.20 Since our focus is on
the trading behavior of clients, we separately analyze trades executed through bookrunners, comanagers, other syndicate members, and non-syndicate members.
Table 1 presents some summary statistics for our data, first for the whole period and then
by year. Not surprisingly, the number of IPOs is highest in 1999 (393) and 1997 (333) and
lowest in 2001 (45) and 2002 (27). For the whole sample period (1997-2002), the mean one-day
return is 44.8 percent and the median return is 18.2 percent.21 The year-by-year IPO returns show
that the large one-day returns are primarily driven by the 75.5 and 60.2 percent average returns in
1999 and 2000. The average one-week abnormal return is only slightly higher than the one-day
return. However, the one-month return is substantially higher at 57.6 percent mainly due to large
returns in 1999 (108.9 percent).
For a typical IPO in our sample, there is one lead underwriter, two co-managers, and
twelve syndicate members. The median lead underwriter underwrites 40 percent of the total
shares issued and the mean amount underwritten is 43.88 percent. This fraction has increased
slightly over the period, particularly in 2001 and 2002 where the average lead underwriter
underwrites 55.6 and 54.8 percent of the shares, respectively. On average, co-managers
underwrite 35.7 percent of the shares and other syndicate members underwrite the remaining
20.4 percent. The amount of shares allocated by the bookmaker is typically greater than the
number of shares the bookmaker underwrites (Chen and Ritter (2000)). However, unfortunately,
Hence, what we label as ‘non-syndicate trading’ likely contains a small amount of trading from ‘unmatched’
syndicate members. These unmatched syndicate members are typically small market makers so we likely miss less
than ½ of one percent of the total shares issued and less than 0.6 percent of total trading volume.
21
This is substantially higher than the mean return of 18.8 percent reported by Ritter and Welch (2002) but is in line
with the estimates they present for the late 1990s through 2001. In addition, our sample excludes larger IPOs (that
tend to have smaller underpricing) that list on the New York Stock Exchange (NYSE).
20
14
we do not have data on the actual allocations. The over-allotment allocation is used in 75 percent
of our firms with mean and median over-allotment allocations of 10.6 and 15 percent.
III. Trading through Lead Underwriter, Co-Manager, and Other Syndicate Members
We first document patterns of buying and selling behavior of clients of lead underwriters,
co-managers, syndicate members, and non-syndicate members on the first day of the IPO. Table
2, Panel A shows buy and sell trading volume by group scaled by total shares issued. Most of the
volume comes from buying and selling of lead underwriter clients and clients of market makers
that are not part of the syndicate. On average bookrunner clients buy 20.9 percent of total shares
offered and sell 12.1 percent, resulting in a first-day net buy imbalance of 8.9 percent of the total
shares issued.
Co-manager clients are responsible for a smaller proportion of trading and are small net
sellers of about 0.6 percent of shares issued. Clients of other syndicate members account for only
a small proportion of trading, buying and selling approximately equal amounts. Non-syndicate
member clients do not receive primary shares through non-syndicate brokerage houses, so we
might expect them to be net buyers in the aftermarket. However, these clients are net sellers,
likely reflecting an attempt to mask their sales by flipping through non-syndicate brokerage
houses.
Consistent with Ellis, Michaely, and O’Hara (2000), the lead underwriter is a very active
market maker, buying back 23.5 percent of shares issued and selling 22 percent, on average. This
net buying (just 1.5 percent) seems small compared to the 8.9 percent net buying activity by their
clients. Conversely, co-managers and other syndicate members experience little or no change in
inventory positions on the first day of trading. Non-syndicate members and their clients sell to
15
offset bookrunner client buying. Overall, Panel A shows that not only do bookrunners increase
their inventory positions (or buy to cover short positions), so do their clients and on a much
greater scale.
We also examine the trading dynamics by various investor types over the first 21 trading
days. Figure 1 examines trading by bookrunner clients, bookrunner inventory, and changes in comanager client and other syndicate member client holdings scaled by the initial number of shares
issued. Figure 1 shows that most of the buying by bookrunner clients and the bookrunner takes
place on the first day of trading.22 Bookrunner client net buying is 8.9 percent of the initial
allocation on the first trading day, but only an additional 1.43 percent on the second day. In
unreported results, we find that clients of the bookrunner continue to be net buyers throughout
the first 18 days although, as can be seen in Figure 1, the magnitude of their buying activity
quickly diminishes within the first week.
Consistent with Ellis, Michaely and O’Hara (2000) we find that the lead underwriter is a
net buyer for the first 15 trading days, buying the most during the first day and week of trading. 23
Co-manager client selling activity prevails throughout the first 21 trading days as these clients
likely unload some of their initial allocation. Co-manager and other syndicate member
inventories remain largely unchanged throughout the first month of trading.
Panel B, C, and D of Table 2 present the trading as a fraction of total trading volume,
total dollar trading, and in terms of the average share-weighted prices, respectively. Imbalances
scaled by volume are similar in magnitude to those scaled by shares outstanding. Bookrunner
clients represent 19.45 percent of first-day buy volume and 10.11 percent of sell volume. Panel C
22
We also examine first-day intraday imbalances to see if imbalances cluster at the opening and find that the net
positive client buying imbalance is fairly persistent throughout the trading day.
23
Ellis, Michaely and O’Hara (2000) find a median inventory increase of 3.7 percent of shares offered vs. 1.5
percent in our sample, likely reflecting stronger IPO performance in our sample requiring less aftermarket support.
16
shows that over the full sample bookrunner clients buy $35.36 billion worth of shares on the first
day and sell $21.44 billion. In contrast, for their own inventory the bookrunner buys $36.77
billion and sells only slightly more $37.06 billion. The net bookrunner dollar position is nearly
flat, despite net buying of 1.2 percent because the bookrunner generally sells stock at higher
prices than purchase prices (Panel D). Panel D also shows that bookrunner clients generally buy
at slightly lower prices as well, whereas non-syndicate client buying is more prevalent at higher
prices.
Table 3 presents the buying and selling for each group as a fraction of shares outstanding
for each year separately. The net buying pattern by bookrunner clients is prevalent throughout
the period. In 2000 and 2001, the pattern of net buying by bookrunner clients is extremely strong
with over twelve percent net buy imbalances from bookrunner clients. Bookrunner clients bought
only 3.5 percent of the shares for the 38 IPOs in 2002 but the underwriter picked up a net buy
imbalance of 8.6 percent on the first day of trading (likely buying back shares sold short through
the overallotment option). In contrast, in 2000 the bookrunner buy imbalance of less than onehalf a percent paled in comparison to the 12.5 percent buy imbalance from bookrunner clients.
In sum, we find extremely strong and persistent patterns of bookrunner client buying that
are persistent across the sample period. We more closely examine features of this finding to
disentangle competing explanations below.
IV. Comparison by Brokerage Houses, Trade Size, Execution Quality, Long-term
Ownership, and Price Patterns
We use various features in the cross-section of IPOs to shed light on the testable
hypotheses outlined in Section I. We first examine patterns of the same underwriter when they
17
are the bookrunner versus when acting as another syndicate member to examine predictions most
closely related to the clientele, information advantage, familiarity, and reputation hypotheses.
We then examine imbalances by trade size to test predictions of laddering and strategic
allocation that buy-side imbalances are generated primarily by institutional clients. We then
examine whether differences in imbalances for certain trade-size categories are explained by
differences in execution quality within groups. We analyze institutional shareholdings and
buying activity by price levels to separate predictions of strategic allocation and laddering.
A. Activity by Issuer
The clientele hypothesis (H2) predicts that clients who have an affinity for IPOs may
migrate to brokerage houses that specialize in issuing IPOs and will be more likely to buy IPO
shares through the bookmaker in aftermarket trading than other clients. To explore this
hypothesis, we examine the client trading of each market maker when they are the lead
underwriter compared to when they join the syndicate in another capacity.
Figure 2 plots net bookrunner client buying on the y-axis and net client buying when the
firm is a syndicate member (Panel A) or a co-manager (Panel B) on the x-axis as a fraction of
total shares issued. Very few lead underwriters face net selling pressure from their clients in the
secondary market (or at least are successful in deterring net selling). Out of 121 underwriters,
only 10 have net client selling when acting as bookrunner whereas 71 brokers face net client
selling when participating as another syndicate member. Those that do face net client selling
pressure tend to be less active underwriters, handling fewer than five IPOs in our sample. Most
of the points are in the northwest quadrant (63 of 121), indicating that most brokerage houses
have positive net client buying when they are the bookrunner but net client selling otherwise.
18
The information hypothesis posits that lead underwriters might provide superior
information to their clients relative to other syndicate members. To explore this possibility, Panel
B of Figure 2 displays net buying levels for bookrunner clients and co-manager clients—parties
that are likely to be equally informed and familiar with the IPO. These results show that clients
are much more likely to be net buyers when their broker is the lead underwriter (93 of 99 have
positive bookrunner client net buying, but only 37 have positive co-manager client net buying).
These results do not support the clientele hypothesis, where some brokers simply attract clients
with an insatiable demand for IPOs, nor do they support the hypothesis that bookrunner clients
trade differently based on superior information or familiarity.
Net client buying might also be related to reputation effects where bookrunner clients bet
on a successful offer due to past underwriter reputation. However, if reputation is visible to all
investors, this hypothesis predicts no differences between lead clients and clients of other
brokerage houses, holding reputation constant. We examine IPOs underwritten by the most
reputable investment bankers.24 Panel C of Figure 2 presents the trading behavior of their clients
both when their broker is the lead and when their broker is a co-manager in another IPO with a
high-reputation lead underwriter. Although this combination limits our analysis to 24
underwriters, it is clear that clients of highly reputable firms buy at least five percent of initial
shares underwritten when the broker is the lead but are generally net sellers when the broker is a
co-manager. Overall, the underwriter’s role as the bookrunner, rather than reputation, seems to
drive the observed patterns in client trading.
To examine underwriting activity more thoroughly, Table 4 reports the mean and median
client buying for four underwriter groups based on activity. Highly active lead underwriters
High reputation is those scoring 9 for the 1992-2000 period according to data on Jay Ritter’s website; those
rankings are loosely based on Carter and Manaster (1990) and Carter, Dark, and Singh (1998).
24
19
(underwriting more than 45 IPOs) have clients that buy an additional 10.9 percent of the total
shares allocated their IPO allocation on the first day of trading. Clients of firms that lead few
syndicates (underwriting fewer than five IPOs) buy only approximately four percent of their
shares on the first day. Tests reported in Panel B indicate significant differences across lead
underwriter activity categories. Active bookrunners are associated with greater net client buying.
Other syndicate member clients have small net mean and median sales in all categories.
Although we do not have allocation data, we can extrapolate the size of this allocation per
bookrunner from Aggrawal (2003). Aggrawal reports that the lead underwriter allocates 59
percent of shares in cold IPOs and 63 percent in hot deals. Using the more conservative hot deal
number, the average bookrunner would have first day client buying of 7.78 percent of the shares
they allocate and the large bookrunner clients would buy 16.83 percent as a fraction of the
bookrunner allocation. If this net buying activity as a fraction of the initial allocation was taken
by other members of the syndicate then for the average underwriter, clients should purchase 2.87
percent of outstanding shares when another syndicate member and large underwriter clients
should purchase 6.22 percent. These numbers are much larger than the one-tenth of one percent
co-manager net selling for the average issuer and one-half a percent client co-manager selling for
large underwriters. Other syndicate member trading is nearly flat for all issuer sizes. These
results reinforce conclusions from Figure 2 that clientele (H2) and familiarity (H4) hypotheses
are not supported by the data.
The fact that brokerage houses with more IPOs have larger bookrunner client buying is
consistent with both the marketing to create familiarity and laddering hypotheses. However, in
contrast to the marketing prediction (H5) that co-managers of large brokerage houses will have
20
more co-manager net buying, Table 4 shows that co-managers that issue more than 45 IPOs
actually have more client selling than co-managers that issue fewer IPOs.
Table 4 also provides evidence against the informational advantage hypothesis (H3). If
bookrunner clients have superior information, one might expect large purchases only in those
IPOs where they have material information. However, Table 4 shows that median levels of IPO
client buying look similar to those of the mean, suggesting no skewness in the distributions.
More importantly, clients of the most active underwriters are net buyers in 87.58 percent of
IPOs, providing evidence that clients almost always unconditionally buy. Given the low
subsequent negative performance of IPOs, it is clearly not the case that this many IPOs were
bought because they were underpriced.
B. Trade size
Laddering and strategic allocation predict that buy imbalances primarily involve
institutional clients whereas familiarity through large marketing efforts would likely impact
small investors. While not a perfect measure, only institutional or extremely wealthy traders can
execute block trades of 10,000 shares or more.25 We break down buy, sell, and buy-sell
imbalances for client trades of each syndicate type and report the fraction of shares in each
category in Table 5. Table 5 shows that bookrunner client trading is dominated by large trades
when compared to co-manager, other syndicate member, or non-syndicate member clients. For
example, only three (1.2 + 1.8) percent of bookrunner client buying is driven by trades less than
1,000 shares compared to 57.5 percent of non-syndicate buys. More importantly, bookrunner
imbalances are entirely driven by trades of more than 1,000 shares with the greatest proportion of
the imbalance driven by block trades. A full 99.6 percent of this buy imbalance is due to trades
25
With the average buy trade price of 25.57 from Panel B of Table 1, a typical block trade requires more than
$250,000 of capital. Griffin, Harris and Topaloglu (2003) document that for Nasdaq trades, brokerage houses that
primarily deal with individual clients rarely execute block trades.
21
of 2,000 shares or more. Conversely, little of the co-manager net buy-sell imbalance is due to
large trades. Clients of non-syndicate brokerages purchase more shares than they buy in small
trade sizes but sell more than they buy in large trades. Overall, these results demonstrate that
large trader buying is driving the bookrunner client buy imbalance and these patterns are not
prevalent in other syndicate or non-syndicate brokerages.
C. Execution quality
The bookrunner may offer better prices than other market makers to induce clients to
purchase shares through the lead underwriter. To examine this hypothesis (H7) further, we
benchmark all first-day transactions to the posted (inside) bid-ask quotes and present the
percentage of buy activity by clients of the bookrunner trading activity that occurs a) below the
best posted bid, b) below or at the bid-ask midpoint, and c) below or at the best posted ask price.
Since it is advantageous for clients to buy at lower prices, higher fractions of these numbers
across brokerage houses mean that clients of the broker are receiving superior execution. It is
important to note that quotes are only valid up to a posted depth, so execution costs may vary
substantially across trade size. For this reason, and because buy imbalances derive from large
trades, we examine executions by trade-size categories.
Panel A of Figure 3 compares the execution of client buy trades from bookrunners to
those of non-syndicate market makers at various prices relative to the offer price.26 While the
fraction of clients who buy either below the bid or above the ask is small near the offer price, the
fraction increases at prices above the offer price (likely because of rapidly moving prices) and
26
For each return grouping, we exclude upper and lower tail end price ranges because few observations in each price
interval (and some intervals with no observations for certain market participants) make comparisons unreliable.
22
increases with trade size (likely reflecting trades exceeding the quoted size).27 In the less than
1,000-share trade-size category results are mixed.
However, in trades of 5,000 shares or more, clients of non-syndicate members are
generally able to buy either at or below the bid-ask midpoint or at or below the ask price a
greater fraction of the time than can bookrunner clients. This result contrasts with Ellis (2003)
who finds that bookrunners generally execute client block buy trades at better prices than other
market makers over an earlier October 1996 to June 1997 Nasdaq period.28 We also examine the
average buy price relative to bid-ask mid-point and find that large trades are generally executed
at prices closer to the mid-point for clients of non-syndicate market makers. These results
provide no support for the superior execution hypothesis--that the bookrunner offers superior
aftermarket prices to buyers.
The net buy imbalance could also be driven by a lack of selling through the bookrunner if
they offer clients poor selling executions to discourage flipping. Panel B of Figure 3 reports the
fraction of trades where sellers receive prices a) above the best ask price, b) at or above the midpoint, and c) prices at or above the bid. For the smallest three trade-size groups, Panel B shows
that investors generally receive better selling prices a greater fraction of the time when executing
trades through the non-syndicate market maker than for the bookrunner. However, for trades at
or above 10,000 shares (those that generate the large buy imbalances) the bookrunner and nonsyndicate market makers offer similar execution.
27
Timing mismatches between trades and quotes may also result in trades outside the quotes. We lag each quote two
seconds before mapping to trades based on conversations with Nasdaq officials. For timing mismatches to affect our
findings would require systematic reporting differences between the lead underwriter and other brokers or
systematic reporting differences across time within the same market maker. Neither seems likely.
28
One difference in our analyses is that we examine executed prices relative to the offer price rather than
unconditionally.
23
In sum, large bookrunner clients generally pay slightly higher prices than clients of nonsyndicate market makers. The largest buy imbalances are generated by trades executed for
10,000 shares or more and here non-syndicate dealers offer their clients slightly better buying
prices and similar sell prices. Overall, there is little evidence that underwriter client buy
imbalances can be explained by superior execution quality.
D. Long-term share holding patterns
We also examine whether strong net client buying stems from the bookrunner
reallocating secondary shares to long-term shareholders. Since our trading data does not contain
the identity of the investors, we utilize quarterly 13f Spectrum data where all institutional
investors are required to report.29 In addition to our previous evidence that share imbalances are
mainly due to large block trades, Aggarwal, Prabhala, and Puri (2002) and Ljungqvist and
Wilhelm (2002) show that 72.77 and 80.4 percent of IPO shares are allocated to institutional
investors in their respective U.S. and international samples. We calculate the change in
ownership from the first date of reported Spectrum ownership to the next quarter’s reported
ownership by these same shareholders. Of course, we can not capture ownership changes
between the first day of aftermarket trading and the first required reporting date. Consequently, if
transitory investors purchase shares on the first day but sell before the first reporting date, our
estimates will understate the amount sold.
Since changes in institutional holdings in IPO firms may naturally differ from other
firms, we compare institutional ownership changes within our IPO sample. If bookrunner client
buying is due to long-term shareholders aggregating secondary market shares then we expect to
see those brokerage houses with large initial buy imbalances to have more original shareholders
who continue to hold shares across quarters (H8). We estimate regressions of the level of
29
The data has many known reporting issues that we thoroughly clean and correct before applying to our sample.
24
institutional holding by initial shareholders on the magnitude of first-day client buying while
controlling for the first-day IPO returns, the log of the number of days from the IPO date to the
first Spectrum reporting date, and fixed effects for each quarter and report the results in Table 6.
Panel A contains dependent variables with raw changes in initial institutional holdings
and Panel B uses abnormal changes in initial holdings for each firm by computing the difference
between IPO institutional holdings and matched firms from the same size and book-to-market
quintile.
30
Panel A shows that at horizons up to the following four reporting quarters, larger
bookrunner client net buying is consistently significantly associated with a decrease in
institutional holdings by initial shareholders. Similarly, Panel B with abnormal holdings finds
that initial buy-sell imbalances are significantly negatively related to ownership levels by initial
holders for the first two subsequent quarters. These results are inconsistent with initial buying
due to information advantages or allocations to long-term shareholders, but consistent with the
laddering hypothesis where aftermarket buyers are transient investors.
E. Buying across price levels
The laddering hypothesis predicts that clients unconditionally buy. Client buying may be
particularly beneficial to the underwriter (and costly to the client) at prices at or near the offer
price. Alternatively, if aftermarket buying is driven by familiarity or marketing through road
show activities then one might expect this over-optimistic behavior to be greatest in the hottest
IPOs--those that receive the most attention and rise in price on the first trading day.
In Figure 4 we examine net buying for IPOs classified by the first-day return. When first
day returns are zero or negative, both the bookrunner and the bookrunner clients are net buyers
in the aftermarket. As first-day returns increase, however, the bookrunner buys much less,
30
We follow Brav and Gompers (1997) and require BE/ME to be available within one-year of the offering date.
This reduces the size of the sample in the benchmark analysis from 1,118 with 1 st quarter Spectrum holdings to 958
IPOs with size and BE/ME.
25
becoming a net seller in IPOs with first-day returns higher than ten percent. On the other hand,
net client buying is pervasive across first-day return categories. Bookrunner clients consistently
buy shares whereas co-manager clients sell shares during the first few days and weeks in the
aftermarket. Although bookrunner client trading mimics aftermarket support in cold IPOs, these
results suggest a more permanent relation that is not consistent with familiarity or marketing.
If clients supplement the bookrunner’s price stabilization efforts for cold IPOs, we would
expect more client buying at or near the offer price and less at higher prices. Panel A of Figure 5
displays the buying activity of bookrunner clients compared to non-syndicate market maker
clients across first-day return categories. We scale share volume for each IPO by the total
number of shares issued and present the volume at prices relative to the offer price. For first-day
returns below 10 percent, buy volume peaks just above the offer price. In these IPOs, bookrunner
clients buy slightly more shares at or near the offer price. With first-day returns above ten
percent, bookrunner and non-syndicate clients buy significant shares at higher prices (more than
twenty percent above the offer price). For these IPOs, non-syndicate client buying exceeds
bookrunner client buying at prices more than thirty percent above the offer price.
Panel B of Figure 5 shows net buy-sell volume for these two client groups. Lead
underwriter clients are consistent net buyers in the IPO aftermarket, particularly in IPOs with
first-day returns at or below ten percent. This result stands in stark contrast to the net selling by
non-syndicate clients (that peaks at the offer price) in these same IPOs..
Figure 6 compares buy and buy-sell volumes for the lead underwriter and non-syndicate
market makers. Panel A shows that for IPOs with first-day returns less than 10 percent, both the
lead underwriter and non-syndicate member market making buy volume peaks at exactly the
26
offer price. For IPOs with greater than 10 percent first-day returns, both the lead underwriter and
non-syndicate members are active buyers at prices well above the offer price.
Panel B of Figure 6 shows net buy-sell volume for lead underwriters and non-syndicate
market makers across first-day return categories. Non-syndicate market makers take very few net
positions in any IPO. Lead underwriters consistently display net buying at the offer price for
IPOs with first-day returns at or below ten percent. However, lead underwriters are net sellers at
prices just above the offer price, consistent with profitable market making activity (Ellis,
Michaely, and O’Hara (2000)). Notably, the consistent net bookrunner client buying documented
in Figures 5 and 6 is not mirrored by the bookrunner.
In Figure 7 we pool all IPOs to examine co-manager and other syndicate trading activity
and their clients in relation to the offering price. For comparison, we also display the activity of
the bookrunner and non-syndicate brokers and their clients. Panel A of Figure 7 shows that both
buy and sell volumes for clients of co-managers and other syndicate members are only
approximately 1/10 of the client volume through the bookrunner and non-syndicate members.
Co-manager and other syndicate clients are small net sellers at the offer price with few
imbalances at other prices. In Panel B we observe net buying by the co-manager (but not other
syndicate members) right at the offer price but only approximately 1/30 of the net buying by the
bookrunner. The difference in magnitude between bookrunner and co-manager support at the
offer price demonstrates that the co-managers, plays only a token role in price support.
V. Determinants of client and underwriter activity and returns
The results presented above are largely consistent with the laddering hypothesis. A
motivation for laddering is that buying by bookrunner clients mitigates the need for the
27
bookrunner to accumulate share inventory. We control for other IPO features that may affect net
client buying and investigate the relation between bookrunner and client trading. In addition, we
also examine if there is a systematic relation between client buying and short- and long-run
returns as might be expected if bookrunner clients have superior information or if prices are
distorted by artificial demand.
A. Determinants of client and market maker purchases
Hot IPO markets, partial pre-market price adjustments, first-day returns, high tech firms,
and prestigious underwriters may all be factors affecting aftermarket demand for IPO shares.
Likewise, aftermarket support by the bookrunner may also affect the trading decisions of
bookrunner clients.31 Table 7 examines the relations between net bookrunner client buying, net
bookrunner buying and other IPO characteristics in a multivariate setting. Net bookrunner client
buying is inversely related to net bookrunner buying activity, even after controlling for other
factors, suggesting a connection between the behavior of the lead underwriter and their clients.
Net buying by bookrunner clients is larger in the post-crash period (consistent with Table 3),
when IPO prices are set high relative to the initial filing range, and when IPOs have low first-day
returns.
Table 7 also shows the determinants of net bookrunner proprietary buying on the first day
of trading. Bookrunner buying is not only negatively related to their clients’ buying, but also
strongly negatively related to net buying by non-syndicate clients. Consistent with aftermarket
support, the bookrunner is a net buyer when non-syndicate clients sell shares. Bookrunner
buying is also lower when the overallotment option is utilized, when the IPO is priced higher
31
Corwin, Harris, and Lipson (2004) show that traders commonly submit limit buy orders at the offer price,
presumably knowing the lead underwriter will support the price at this level, effectively limiting downside risk.
28
relative to the initial filing range and with larger syndicates, perhaps reflecting the lesser need for
aftermarket support in these situations.32
B. Aftermarket returns and buying behavior
Laddering or other arrangements may lead to artificially high prices in the short run and
create long-run price reversals. Table 8 shows the determinants of post-IPO performance at 1month, 3-month, 6-month, 1-year, 2-year and 3-year horizons. We test whether the first-day net
buying activity of various market participants is related to returns at these horizons. We measure
performance as the simple excess buy-and-hold return relative to the Nasdaq Composite Index
return. Many of our control variables are motivated by Ritter (1991) and Carter, Dark, and Singh
(1998).
Table 8 shows that IPOs with higher first-day bookrunner client net buying have higher
first month returns. However, this relationship diminishes when including net bookrunner buying
in the regression. Net bookrunner client buying is not significantly related to longer-term IPO
returns at most frequencies after controlling for other IPO characteristics. At the two-year
horizon there is weak evidence that net bookrunner client buying is negatively related to long-run
returns. The negative relation between bookrunner client buying and future returns is generally
inconsistent with the hypothesis that bookrunner clients enjoy superior information.
Laddering, by producing artificial demand, should create short-term price pressure and
long-run reversals. We find evidence for the former but only weak evidence for the latter. These
results suggest that net buying by bookrunner clients may not be linked to harmful long-run
effects on shareholders. By extension, if laddering generates large net bookrunner client buying,
these results suggest that the effects are primarily at short horizons. However, price reversals can
32
Bookrunner net buying is relatively invariant to high-tech IPOs, secondary shares offered, an ex post measure of
risk, hot IPO markets, the age of the firm, whether the firm was backed by venture capital and underwriter
reputation.
29
occur over a multitude of horizons and given the short sample, volatile market conditions, and a
variety of potentially non-linear factors influencing long-run returns, these tests may simply lack
the power to link net client buying with price distortions.
VI. Short-selling and Flipping
One unresolved issue is that non-syndicate members’ and their clients’ sales exceed
purchases by 3.2 and 6.5 percent, respectively. Since these market makers do not receive
allocations in the IPO, these shares must come from short selling or from another party who
received shares in the initial allocation. Institutional brokerage house clients may have multiple
accounts with different market making firms. In fact, a large mutual fund or hedge fund might
use three or four large brokerage houses for trading purposes with one or two of those brokerage
houses acting as ‘clearing accounts’ for their trades. An institutional investor might receive
primary market shares from one brokerage house and sell the same shares through a second
broker who appears to be unrelated to the IPO. Thus in the absence of any method to track
physical shares, clients of the syndicate may flip shares without directly revealing this practice to
the underwriting syndicate.
However, during our sample period, the Depository Trust Corporation (DTC) IPO
tracking service allows underwriters to track shares. The lead underwriter receives a report that
has the clearing agent’s participation number but not the exact details of the client (Aggarwal
(2003)). However, each syndicate member receives a report which details the activity of their
customers. Because of the complexity of the DTC system, a company called Commscan
30
produced a Penalty Bid Tracker system for the DTC system but the DTC tracking system is not
seamless (Wisz (1997a, b)) and all underwriters do not subscribe to the system.33,34
The large net selling patterns by non-syndicate clients appear to be either covered by
shares obtained through a syndicate member and then sold through a non-syndicate account or
covered through short selling.35 Investors are required to report short interest to Nasdaq once a
month. Using this data, we are able to compute the average short interest on the first reporting
date after the IPO.
In unreported results we find that the median short interest across firms is 0.72 percent of
shares issued and the mean short interest is 2.67 percent. One problem with our short interest
measure is that it can be reported as late as one month following the IPO. To investigate whether
patterns change drastically from the IPO date we examine the short interest distribution for the
subset of firms that has short interest reported within one day, one week, and two weeks after the
IPO. We find slightly higher short interest close to the IPO date, but nothing approaching the 9.7
percent average net selling through non-syndicate market makers (3.2 percent from their own
inventory and 6.5 percent from their clients). Thus, it appears that short selling can only explain
a relatively small fraction of the net selling by non-syndicate market makers and their clients.
The large net selling by non-syndicate clients suggests that either some investors are
unaware of the tracking system (unlikely, in our view), that there are imperfections in the
tracking system which clients exploit, or that these reports do not influence client trading
According to Mario Patella, Merrill Lynch’s vice president of operations, “there will always be guys who will try
to beat the system” [Wisz (1997b)].
34
Corwin and Schultz (2004) suggest that co-managers afford firms greater leverage in pricing of IPOs, suggesting
that the presence of a co-manager weakens the relative strength of the lead underwriter since the lead underwriter
may not be able to present a unified front in negotiating offer prices. It is possible that the presence of co-managers
and other syndicate members present a lack of unified front when dealing with IPO flippers as well.
35
Although syndicate members typically do not accommodate short sales within the first thirty trading days (Houge,
et al. (2001)), Geczy, Musto, and Reed (2002) show that shares of medium and large IPOs are freely available for
shorting, presumably through non-syndicate market makers. The monthly settlement date is on or before the 15 th.
Because of a 3-day settlement period, this maps to a trading day of the 12 th.
33
31
practices. Given that (as previously discussed) the lead underwriter receives specific information
related to investor identity of shares they allocated but only clearing agent participation number
of shares from other syndicate members (Aggarwal (2003)), investors who receive shares from
someone besides the bookrunner have an extra layer of transparency. Coupled with less
incentives for non-lead syndicate members to find flippers, it would seem logical that the
flipping through non-syndicate brokers may be due to shares allocated through syndicate
members besides the bookrunner.
VII. Conclusion
Theoretical and anecdotal evidence motivate an examination of whether bookrunner
clients help provide price stability by purchasing additional shares in the aftermarket. We find
that trades by clients of the bookrunner have a strong buy bias of 8.9 percent of the total issue
size or $13.91 billion over the entire 1997-2002 period. This net client buying activity is present
in each year of the sample, for both small and large underwriters, but more prevalent with
underwriters that issue more IPOs. We examine competing explanations for this persistent net
buying activity including laddering, clientele effects, underwriter reputation, superior
information from underwriters, marketing that creates familiarity, differential execution quality,
and strategic allocations to long-term shareholders.
We find little evidence of clientele effects. When the brokerage house is the lead
underwriter, clients consistently buy additional shares in the secondary market, but when the
broker is just a syndicate member or a co-manager, clients are net sellers. Similarly, we find little
evidence that superior information or familiarity affect net client buying. Lead underwriter and
32
co-manager clients, who are more likely to have similar information, trade much differently in
the aftermarket, even within the subset of high quality underwriters.
We find that the strong bookrunner client demand is more prevalent for active
underwriters, who might have the greatest ability to extract rents from their clients or provide
superior marketing efforts in the aftermarket. However, inconsistent with the marketing
hypothesis, we find that large clients of active co-managers actually sell more than large clients
of less active co-managers. Likewise, we find that bookrunners offer slightly inferior execution
quality to large trades, suggesting that execution quality does not generate the observed client
buy imbalances.
We find that IPOs with large initial bookrunner client buying have more institutional
selling activity over the subsequent four reporting quarters, suggesting that these initial client
imbalances are not consistent with strategic allocations to long-term shareholders but rather with
transitory shareholders who purchase due to laddering agreements. Furthermore, bookrunner
client net buying is slightly stronger at prices close to the offer price and in IPOs with lower firstday returns, with bookrunner client net buying complementing that of the bookrunner. These
patterns suggest that client buying plays an important role in price support and may substitute for
price support from the lead underwriter. Bookrunner client buying is positively related to onemonth abnormal returns and (insignificantly) negatively related to longer-run returns.
Although there may be other explanations for our findings, the patterns of large
aftermarket buying by clients of the bookrunner under a variety of conditions (even more intense
for cold IPOs, for instance) are consistent with laddering, where underwriters extract rents from
clients by implicitly or explicitly requiring aftermarket purchases. Although some facets of our
findings are inconsistent with other hypotheses, these explanations likely offer partial influence
33
on aftermarket client buying as well. We hope to see additional research explore quid pro quo
explanations for the relation between underwriters and their clients. Although our research does
not examine the issuer/underwriter relation directly, our findings motivate an additional rationale
for why issuers tolerate underpricing--if issuers believe that IPO underpricing is partially due to
artificial demand generated by the lead underwriter they may be more content to accept a price
less than what they view to be the perhaps excessive market price.
34
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