IPO Waves, Information Spillovers, and Analyst Biases*
Susan Christoffersena,b,c
Amrita Nainb
Ya Tangd
September 2010
_________________________________
*Previously circulated under the title “IPO Cycles, Firm Characteristics, and the Role of Underwriters”
a
Rotman School of Management, University of Toronto, 105 St. George St., Toronto, ON, M5S 3E6
b
Desautels Faculty of Management, McGill University, 1001 Sherbrooke St.West, Montreal, QC H3A 1G5
c
Copenhagen Business School, Solbjerg Plads, DK-2000 Frederiksberg
d
Guanghua School of Management, Peking University, 5 Summer Place Road, Beijing, 100871, P.R.
China
Contact author: Amrita Nain, amrita.nain@mcgill.ca, 514-398-8440.
Susan Christoffersen is grateful for support from SSHRC, IFM2, and the Leibovitch Award. Amrita Nain is
grateful for support from SSHRC and FQRSC. The authors are grateful for helpful comments from
Michelle Lowry, J. Ari Pandes, and participants of the Northern Finance Association Meetings, 2010. All
remaining errors are our own.
IPO Waves, Information spillovers, and Analyst Biases
Abstract
Existing research suggests the presence of learning and information spillovers in IPO waves.
Consistent with information spillover models, we find that the level of information asymmetry
surrounding IPO firms is significantly higher in the early stages of an IPO wave than in the late
stages. Thus, the early stages of an IPO wave are characterized by high valuation levels as well as
high valuation uncertainty. According to current thinking, private firms learn about IPO
valuations by observing the price outcomes of recent IPOs. We propose a more direct method for
underwriters to convey this information to potential issuers: affiliated analysts. We show that
affiliated analysts issue excessively optimistic recommendations early in the wave, but not later in
the wave. Institutional holdings are unaffected by the early-stage bias of affiliated analysts and
the stock market appears to discount affiliated analysts in the early stages of a wave. We suggest
that the affiliated-analyst bias in the early stages of an IPO wave is an attempt to convey positive
demand information to private firms.
1. Introduction
It is a well-established fact that there are pronounced cycles in IPO volume and
initial returns.1 Recent theoretical and empirical work indicates the existence of learning
and information spillovers during IPO waves. Lowry and Schwert (2002) find that more
companies file IPOs following periods of high underpricing because high initial returns
reflect positive information learned during the registration period. Evidence of
information spillovers can be found in Benveniste, Ljungqvist, Wilhelm, and Yu (2003)
(henceforth, Benveniste et. al. (2003)) who show that the decision to complete an IPO
and the terms of the IPO depend on the experience of contemporaneous IPOs in the same
industry. More recently, Alti (2005) presents a model of endogenous information
spillovers in which firms with higher growth opportunities go public earlier in the wave
when information asymmetry is high.
A central tenet of the information spillover argument is that information
asymmetry declines as an IPO wave progresses. The price outcome of each additional
IPO reveals information about market demand and common valuation factors. Although
several existing papers discuss the link between underpricing and information asymmetry,
there is a dearth of evidence on the dynamics of information asymmetry within an IPO
wave. 2 Thus, the first objective of this paper is to examine a key feature of the
information spillover argument – does uncertainty surrounding IPO firms decline during
the course of an IPO wave? Using different methods for identifying spurts in IPO volume
1
See, for example, Ibbotson and Jaffe (1975), Ibbotson, Sindelar, and Ritter (1988, 1994), Lowry
(2003).
2 The positive correlation between underpricing and information asymmetry is discussed in Beatty
and Ritter (1986), Rock (1986), Grinblatt and Hwang (1989), Benveniste and Spindt (1989),
Michaely and Shaw (1994), and Lowry, Officer, and Schwert (2010).
1
and several different proxies of valuation uncertainty, we show that valuation uncertainty
is higher in the early stages of an IPO wave than in the late stages.
Having provided evidence that the informational environment is opaque at the
beginning of an IPO wave, we examine the mechanism of information spillovers. Like
other papers, we find extensive empirical evidence that price realizations early on in the
wave provide information to the market about valuations and demand. However, we
explore an alternative and more direct method for investment banks to communicate their
private information to firms: affiliated analysts. During the road show, issuing firms and
underwriters obtain information about investor demand and valuations. Underwriters
would like to convey positive information gleaned during the registration period to
private firms in order to build up the supply of IPOs. Investment banks can signal high
market valuations by encouraging their analysts to provide favorable views of IPO
performance. Accordingly, we expect the views of affiliated analysts to be biased early in
the IPO wave when investment banks are responding to new demand information and
building up the supply of IPO offerings.
Consistent with this idea, we find strong evidence of an affiliated analyst bias
early in the wave, but not later on. Specifically, we show that in the early stages of an
IPO wave, when valuations are high but information quality is poor, analysts affiliated
with underwriters provide disproportionately more positive recommendations to issuing
firms as compared with unaffiliated analysts. Later in the wave, when valuations and
information asymmetry are much lower, the recommendations of affiliated and
unaffiliated analysts are similar. In a related test, we divided IPOs into two mutually
exclusive groups - IPOs that receive a stronger recommendation from affiliated analysts
2
than from unaffiliated analysts (termed Affiliated Favored IPOs), and IPOs that receive a
stronger recommendation from unaffiliated analysts (termed Unaffiliated Favored IPOs).
We find that there are significantly more Affiliated Favored IPOs in the early stages of an
IPO wave than in the late stages.
It is well accepted that analysts affiliated with underwriters issue more favorable
recommendations. 3 This bias is generally viewed in a negative light as an attempt by
underwriters to appease issuing clients and institutional investors who are long the stock.
In contrast, we suggest that the early-stage analyst bias is an attempt by underwriters to
convey positive demand information to potential issuers during periods of high
information asymmetry. However, we consider two potential alternative explanations for
the affiliated-analyst bias in the early stages of an IPO wave. First, we explore whether
affiliated analysts provide more favorable recommendations to early issuers than
unaffiliated analysts because they have private information about the higher quality of
early issuers. However, we find no significant differences in profitability, sales growth,
cash holdings, liquidity, and abnormal stock returns of early and late issuers two years
after issue date. Thus, there are no notable quality differences that would justify the more
favorable outlook of affiliated analysts early in an IPO wave.
The second alternative explanation we explore is that the analyst bias early in the
wave is a mechanism to attract institutional demand rather than an attempt to signal
positive valuations to private firms. To test this alternative explanation, we investigate
how institutional holdings respond to analyst recommendations. Institutional investors
need to trade-off two features of affiliated analysts. On the one hand, affiliated analysts
3
See, for example, Michaely and Womack (1999) and Bradley, Jordan, and Ritter (2008).
3
are likely to have an informational advantage over unaffiliated analysts. On the other
hand, their recommendations may be biased due to pressure from the investment banking
divisions. Our tests reveal that institutional investors optimally balance these competing
factors and do not respond to the affiliated analysts bias early in the wave.
Specifically, we find that in the early stages of an IPO wave, when affiliated
analysts provide biased recommendations, institutional holdings of Affiliated and
Unaffiliated Favored IPOs are similar. Later in the wave, when the analyst bias is absent,
institutional holdings are significantly higher in Affiliated Favored IPOs (38.9%) than in
Unaffiliated Favored IPOs (30.69%). Thus, institutions pay more attention to affiliated
analysts later in the wave, when they are not biased and the recommendations reflect
private information about the firms. We also find that within the subset of Unaffiliated
Favored IPOs, institutions hold 39.3% of early issuers and 30.69% of late issuers.
Institutional investors are, therefore, more likely to follow the recommendations of
unaffiliated analysts early in the wave because affiliated analysts are overly optimistic at
that time.
Since institutional investors appear to be unmoved by the affiliated-analyst bias
early in the wave, we also explore the possibility that the analyst bias is targeted to
influence uninformed investors. We follow existing literature and calculate cumulative
abnormal returns (CARs) at the announcement of strong-buy recommendations. We find
that the market reacts positively to strong-buy recommendations from both affiliated and
unaffiliated analysts. However, the CARs of early issuers are higher when unaffiliated
analysts issue strong-buy recommendations than when affiliated analysts issue strong buy
recommendations. In contrast, CARs of late issuers are higher when affiliated analysts
4
issue strong-buy recommendations than when unaffiliated analysts issue strong buy
recommendations. These stock market reactions indicate that the market pays more
attention to affiliated analysts’ opinions of late issuers and to unaffiliated analysts’
opinions of early issuers.
Overall, the affiliated analyst bias early on in the wave is not successful in
convincing institutions to increase their holdings of early issuers and appears to be
discounted by the market as a whole. Since investors appear to be unmoved by the earlystage bias of affiliated analysts, the bias is possibly targeted at a different audience. We
propose that the affiliated analyst bias serves as a mechanism of conveying information
learned by underwriters during the book building process to the pool of private firms that
are considering an IPO but are uninformed about IPO demand and valuations.
Our paper makes several contributions to existing literature. Recent theory
suggests that firms issuing early in a wave are likely to be different from firms that issue
late in a wave. Ours is one of the first papers to show empirically that issuer
characteristics proxying for valuation certainty change during the course of an IPO wave.
We are also the first to demonstrate that the bias of affiliated analysts dissipates as an IPO
wave progresses and that sophisticated investors like institutions know when to ignore
affiliated analysts and when to follow their advice. Our results suggest that the analyst
bias plays a positive role of disseminating information to the supply side of the IPO
market. Finally, given the high correlation between IPO volume and IPO initial returns,
existing literature tends to use high volume and high initial returns as substitute measures
of IPO heat. Our results show that IPO wave periods are not uniformly hot and that
5
treating all issuers in an IPO wave as a homogenous group can result in a significant loss
of information.
Our paper adds to a small set of empirical studies that take into account the order
of moves within corporate event waves. Benveniste, et. al. (2003) provide evidence that
information spillovers influence issuers’ decision on whether to complete an IPO and
how to price it. They examine industry IPO waves and show that information spillovers
are stronger among pioneers (first movers) and early followers in an industry than among
late followers. Çolak and Günay (2010) find that the average quality of issuing firms is
lower in the early stages of rising IPO cycle. Bouwman, Fuller, and Nain (2009) examine
mergers and acquisitions and show that acquirers that buy early in a merger wave
perform worse than those that purchase later in a merger wave.
The rest of the paper is organized as follows. Section 2 describes the data, Section
3 discusses IPO characteristics and valuation uncertainty, Section 4 examines the analyst
bias, and Section 5 studies issuer quality. In Section 6, we present an analysis of
institutional holdings and market reactions. Section 7 discusses robustness and Section 8
concludes.
2. Data Description
2.1. Sample
To identify IPO waves, we use the 12,648 initial public offerings between January
1st, 1970 and December 31st, 2005 provided by Jay Ritter.4 To obtain offer-specific and
4
The data are available on Jay Ritter’s website at http://bear.warrington.ufl.edu/ritter/ipodata.htm
6
firm-specific information, we rely on Securities Data Company (SDC). This dataset
include 11,490 firm-commitment IPOs between 1970 and 2005. We exclude unit
offerings, real estate investment trusts (REITs), closed-end funds, and ADRs, and further
restrict our analysis to stocks with information available on CRSP. If a firm’s stock price
appears on CRSP more than 14 days after the IPO date, we drop it from the sample. This
leaves us with a final sample of 7,043 IPOs. All the analyses in this paper are conducted
on this sample of 7,043 IPOs.
2.2. Classification of IPO waves
Our primary method of identifying IPO waves follows the simulation
methodology of Harford (2005).5 Since each of the four decades in our sample period is
characterized as a distinct era, we do the simulation separately for the 1970s, 1980s,
1990s and 2000s. From the sample of 12,648 IPOs, we take the total number of IPOs in
each decade and create a simulated distribution by randomly assigning each actual IPO to
a quarter within the decade. The probability of assignment is 1/40 for each quarter. We
repeat this process 1000 times to generate 1,000 randomly drawn series of quarterly IPO
volume. Any quarter where the actual number of IPOs exceeds the 95th percentile from
the simulated distribution is designated as a high-volume quarter. Quarters where the
actual number of IPOs lies below the 5th percentile are designated low-volume quarters.
As robustness check, we also identify IPO waves using a methodology based on Helwege and Liang
(2004). Details of this method and the robustness of our results are provided in Section 7.
5
7
This method gives us 38 high-volume quarters and 28 low-volume quarters. Since
it takes time for a wave to form, peak, and disappear, we define an IPO wave as a period
of three or more consecutive high-volume quarters. This method results in 6 IPO waves
between January 1st, 1970 and December 31st, 2005. Out of the 38 high-volume quarters
identified above, 5 are not included in our waves because the high volume period did not
last long enough. Table 1 provides summary statistics of the 6 IPO waves. Panel A shows
the start and end date of all six waves along with the total number of IPOs and the
number of IPOs per quarter in each wave. A total of 3,052 IPOs occurred during IPO
wave periods with an average of 99 IPOs per quarter. Panel B presents the number of
IPOs, dollar volume of IPOs, initial returns and oversubscription in the first and last
month within in each IPO wave. Initial returns, IR, are calculated as the first-day closing
price less the offer price divided by the first-day closing price. First-day closing prices
are collected from CRSP daily file and offer prices are obtained from SDC. The
percentage of oversubscription, OVERSUBPT, is calculated as shares offered and
oversold minus shares filed divided by shares filed. Panel B shows that IPO volume as
measured by number of IPOs and dollar volume of IPOs is high in both the first and the
last months of the six waves. This is to be expected since our classification of wave
periods is based on the number of IPOs. However, underpricing and oversubscription is
noticeably lower in the last month of the IPO waves than in the first month. Thus, by the
end of a wave period, volume may still be high but IPO demand and underpricing have
dropped significantly. This is consistent with Lowry and Schwert’s finding that initial
returns lead volume by a few months. The low initial returns at the end of a wave period
are indicative of the imminent drop in IPO activity. The difference in initial returns and
8
demand in the first and last month of the IPO wave provide a preview of how market
conditions change within a high volume period. Since there is no set definition of what is
‘early’, we use three different classifications throughout the paper. In all the tests that
follow, we define early (late) movers as the first (last) 10%, 15% or 20% of the IPOs in
each wave.
3. IPO Characteristics
3.1. IPO Market Characteristics
We begin by demonstrating that the state of the IPO market changes significantly
during the course of an IPO wave. We compare initial returns, oversubscription, and the
difference between the offer price and the original filing price of early and late issuers in
a wave. Initial returns and oversubscription are calculated as described in Section 2.2.
The variable HIGH_TO_OFFER is computed as highest original price filed less the offer
price divided by highest original price filed. LOW_TO_OFFER is computed as the offer
price less the lowest original price filed divided by lowest original price filed. Since the
highest original price filed is fixed, smaller values of HIGH_TO_OFFER indicate better
price outcomes for the issuing firms. Likewise, since the lowest original price filed is
fixed, higher values of LOW_TO_OFFER indicate better price outcomes.
Table 2 reports that for all three classifications of early/late movers, initial returns
and oversubscription are significantly higher for early movers than for late movers.
Likewise, for all three cut-offs, HIGH_TO_OFFER is lower and LOW_TO_OFFER
9
higher in the early stages of an IPO wave. Thus, all variables indicate that market
conditions are significantly poorer in the late stages of an IPO wave than in the early
stages. This is to be expected since IPO waves tend to die out after observing the
unfavorable outcomes for late issuers. Thus, the pattern of oversubscription, initial
returns, and offer prices within an IPO wave is consistent with existing evidence that
volume responds to the experience of recent IPOs. The more interesting fact is that the
differences between the early and late stages of an IPO wave are greater than the
differences between IPO waves and low-volume periods. Table 2 also presents mean
values of initial returns, oversubscription, HIGH_TO_OFFER and LOW_TO_OFFER for
wave periods and low-volume periods. Recall that low-volume periods are quarters when
IPO volume is lower than the 5th percentile of the simulated distribution. During lowvolume periods, each of the four variables takes a value that lies in between the extremes
achieved during the early and late stages of an IPO wave. At first, it may seem
counterintuitive that low-volume periods have stronger demand and valuations than the
later stages of an IPO wave. However, low-volume periods are often periods of rising
initial returns, which are then followed by an IPO wave (see Lowry and Schwert, 2002).
In contrast, the coldest IPO market conditions are achieved shortly before the end of an
IPO wave. The latter finding is consistent with existing theory suggesting that corporate
event waves end after firms observe the poor outcomes of recent adopters (Persons and
Warther, 1997; Rhodes-Kropf and Viswanathan, 2004).
3.2. Valuation Uncertainty
10
Information spillover arguments of Benveniste et. al. (2003) and Alti (2005)
predict that valuation uncertainty is high at the beginning of an IPO wave but declines as
the market learns about a common valuation factor from the outcomes of successive
IPOs. Several existing papers have studied uncertainty surrounding IPO firms. For
example, Lowry, Officer, and Schwert (2010) show that IPO valuation uncertainty varies
over time and is highly correlated with the level of underpricing. The Yung, Çolak, and
Wang (2008) model predicts greater dispersion in firm quality and greater information
asymmetry during high IPO valuation periods because poorer quality firms pool with
better quality firms when valuations are high. Since valuations are higher earlier in the
wave than later in the wave, the Yung, Çolak, and Wang (2008) model would imply that
information asymmetry is higher in the early stages of an IPO wave than in the late
stages. Despite the recent discussion of valuation certainty in the literature, none of these
papers directly examine whether information asymmetry declines during the course of an
IPO wave as predicted by the information spillover models.6 Therefore, we use various
measures of valuation uncertainty from each of aforementioned papers, and directly test
for differences in valuation certainty in the early and late stages of an IPO wave
Following Lowry, Officer, and Schwert (2010), we capture valuation uncertainty
of issuing firms with the cross-sectional volatility of initial returns (IR_VOL). Panel A of
Table 3 shows that the volatility of initial returns is higher for early movers than for late
movers, regardless of the cut-off used to classify issuers as early or late. For comparison,
the same table also presents volatility of initial returns for low-volume (non-wave)
periods. We see that the extreme values of initial return volatility are achieved at the
6
One exception to this statement is the finding in Benveniste et. al. (2003) that IPO withdrawals are higher
earlier in an industry wave than later.
11
beginning and the end of an IPO wave and not during low-volume periods consistent
with an information spillover model.
We also compare other proxies of information asymmetry like the tightness of the
filing price range, the percentage of withdrawn IPOs, the percentage of NASDAQ stocks
issued, and issue size. Since less information is available about smaller issues, these tend
to be harder to value correctly. Similarly, technology stocks tend to be small, highgrowth stocks with greater valuation uncertainty. More uncertainty about the value of the
issuer will lead to a wider filing price range and a higher incidence of withdrawn IPOs.7
We compare these issue characteristics for the early and late stages of an IPO wave and
present the results in Table 3, Panel B. We find that the filing price range is wider, and
the percentage of withdrawn IPOs higher in the early stages of an IPO wave. We also
find that issue size is significantly smaller in the early stages of an IPO wave and that a
larger fraction of early movers are NASDAQ firms as compared with late movers. Thus,
stocks of more uncertain value go public earlier in the wave when market valuations are
high. Finally, in the spirit of Yung, Çolak, and Wang (2008), we calculate the 12-month
and 24-month buy-and-hold abnormal returns and measure dispersion of firm quality as
the cross-sectional standard deviation of buy-and-hold abnormal returns (BHAR_VOL).
Table 3, Panel A presents a comparison of BHAR_VOL for early issuers and late issuers.
We see that volatility of buy-and-hold abnormal returns is higher for early IPOs than for
late IPOs.
7
Benveniste et. al. (2002) suggest that IPO withdrawals capture firms which are exante difficult to value
and are likely to decline as a wave progresses. Benveniste et. al. (2003) show that pioneers and early
followers in an industry IPO wave are more likely to withdraw the offering than late followers.
12
In summary, the results in Table 3 provide robust evidence that early issuers are
characterized by greater valuation uncertainty than late issuers. Interestingly, we find that
the harder-to-value early movers (which tend to experience higher underpricing) use
underwriters with a higher Carter-Manaster (1990) ranking score (Table 3, Panel B).
Therefore, it seems that firms with high levels of uncertainty surrounding their valuation
use more reputable investment banks to issue their stock. This result is consistent with
existing literature suggesting that firms are willing to accept higher underpricing (notably
early in a wave) in return for higher quality analyst coverage.8
4. Analyst Recommendations
The results in the previous section show that firms that go public early in an IPO
wave are of more uncertain value and display greater dispersion in quality than firms that
go public later in the wave. These early movers use underwriters that are ranked higher
than underwriters used by the late issuers. This result is intuitive insofar as riskier firms
benefit more from the information produced by top-tier investment banks. One of the
primary considerations in the choice of underwriters is analyst coverage. In light of this,
we examine what kind of analyst coverage the hard-to-value early movers receive from
the analysts of their reputable underwriters.
We are particularly interested in examining whether investment banks use analyst
coverage as a means to provide information to private firms considering an IPO. In the
8
See Rajan and Servaes (1997), Loughran and Ritter (2004), Cliff and Denis (2004), and Lowry,
Officer, and Schwert (2010)
13
early price-discovery phase of an IPO wave, investment banks obtain positive demand
information from institutional investors. While underpricing levels convey some of this
information to potential issuers, we explore another, more direct method for investment
banks to signal high demand and valuations to the pool of private firms. We predict that
the early stages of an IPO wave, which are characterized by high valuations and high
uncertainty, will also coincide with overly optimistic coverage by affiliated analysts. The
affiliation of the analyst is important because it is through the affiliation with the
underwriter that analysts obtain information about institutional demand. Hence, we do
not expect the same optimism among unaffiliated analysts during the early stages of a
wave. Although it is a well-established fact that affiliated analysts provide more positive
recommendations to IPO stock than unaffiliated analysts, we propose that affiliated
analyst bias is a feature of the early stages of an IPO market, when IPO demand and
valuation uncertainty are high. 9
For each IPO firm, we calculate recommendation value, RECD, as mean value of
I/B/E/S analyst recommendations in the first year after IPO. We calculate mean RECD
received from affiliated analysts and unaffiliated analysts separately. I/B/E/S
recommendations take a value between 1 and 5 for recommendations ranging for Strong
Buy to Strong Sell. Thus, lower mean values of RECD variable indicate more positive
analyst recommendations. We also calculate the percentage of positive recommendations,
PER_RECD, as the number recommendation of buy or strong buy divided by the total
9
See, for example, Michaely and Womack (1999), Cliff and Denis (2003), James and Karceski (2006),
Agrawal and Chen (2008), Bradley, Clarke, and Cooney (2009), and Ljungqvist, Marston, and Wilhelm
(2009).
14
number of recommendations. Higher values of PER_RECD indicate more favorable
coverage.
Table 4 presents mean RECD and PER_RECD of affiliated and unaffiliated
analysts for early and late IPOs within a wave. Panel A contains results at the 10% cutoff
for early and late IPOs. Panel B presents the same results for the 15% and 20% cutoffs. In
the early-mover subsample, we see that RECD is significantly lower and PER_RECD
higher for affiliated analysts than for unaffiliated analysts. Thus, early movers receive
more favorable recommendations from affiliated analysts than from unaffiliated analysts.
In the late mover sub-group, RECD and PER_RECD are statistically indistinguishable.
That is, late IPOs receive similar recommendations from affiliated and unaffiliated
analysts. These results are robust for all three cutoffs for early and late.
In Table 4, Panel C, we present more evidence of affiliated analyst bias in the
early stages of an IPO wave. We define an IPO that receives more favorable
recommendations on average from affiliated (unaffiliated) analysts as an Affiliated
Favored (Unaffiliated Favored) IPO. We find that, at the 10% cutoff level, 53.4% of early
movers are Affiliated Favored while 41.5% of late movers are Affiliated Favored. The
difference in these percentages is statistically significant at the 95% confidence level.
Similar evidence emerges if we use the 15% cutoff level. At the 20% cutoff there are still
more Affiliated Favored IPOs in the early stages than in the late stages, but the difference
is not statistically significant. The evidence for Unaffiliated Favored IPOs is the reverse,
albeit weaker. The fraction of Unaffiliated Favored IPOs is higher in the later stages of
the wave than in the earlier stages, and the difference is statistically significant at the 15%
15
cutoff. Together these results show that affiliated analysts issue disproportionately more
favorable recommendations in the early stages of an IPO wave when firms of more
uncertain value go public. Since early movers use better-ranked underwriters on average
and experience higher underpricing, we control for these factors by examining analyst
bias in a multivariate setting.
Table 5 presents several regressions in which the dependent variable is the analyst
recommendation value RECD. Each regression includes a dummy variable, AFF, which
is equal to one if the recommendation is provided by an affiliated analyst and zero
otherwise and a dummy variable, EARLY, which is equal to one if the IPO is an early
issue and zero if it is a late issue. The cutoffs used to define early and late movers are
10%, 15% and 20% in columns 1, 2 and 3 respectively. In these three columns, the
sample is restricted to early and late movers only. The key variable of interest in these
three regressions is the interaction of the affiliated analyst dummy AFF and the early
mover dummy EARLY. The interaction term is negative and statistically significant,
confirming the univariate finding that affiliated analysts are biased in the early stages of
an IPO wave. All of the aforementioned regressions include underpricing, the size of the
issue, and reputation of underwriters as control variables.
Previous research suggests a link between analyst coverage and underpricing. 10
Since underpricing is high during the early stages of a wave and low during the late
stages, it could be argued that the difference in analyst bias between early and late
movers is attributable to the difference in underpricing and not to the timing of the IPO
10
See, for example, Cliff and Denis (2004).
16
within the wave. We address this concern in two ways. First, as indicated above, we
include the level of underpricing (IR) as a control variable in each regression. Despite
controlling for underpricing, the early-mover dummy is still statistically significant. The
coefficient on the underpricing variable is negative and statistically significant in all
regressions. Thus, consistent with prior literature, we find that firms going public during
periods of high underpricing receive more favorable recommendations on average (across
both types of analysts). Second, in the last regression (column 4) we include the full
sample of IPOs in order to distinguish between analyst recommendations issued during
the early stages of an IPO wave and those issued during any period of high underpricing.
In this regression, the dummy EARLY2 equals one if the IPO is an early mover in an IPO
wave and zero for all other IPOs. HIGHIR is a dummy variable, which is equal to one for
IPOs whose initial returns lie in the top quartile and zero otherwise. Both EARLY2 and
HIGHIR are interacted with the affiliation dummy variable, AFF. As expected, the
coefficient on HIGHIR is negative indicating that firms that go public during periods of
very high underpricing receive more favorable recommendations. Similarly, the negative
coefficient on AFF shows that, on average, affiliated analysts provide more favorable
recommendations. More interestingly, the interaction off AFF and EARLY2 is negative
and significant while the interaction of AFF and HIGHIR is statistically insignificant.
Thus, we find evidence that affiliated analysts provide more favorable recommendations
than unaffiliated analysts during the early stages of an IPO wave but not during all
periods of high underpricing.
On the basis of these results, we conclude that affiliated analysts provide biased
recommendations specifically for IPOs that come out at the beginning of a wave when
17
volume, valuation uncertainty, underpricing, and demand are all simultaneously high.
The findings are consistent with the view that analysts serve as a mechanism to signal
high institutional demand to firms considering an IPO. However, there could be other
explanations for the favorable outlook of affiliated analyst early on in the wave. First, the
biased recommendations in the early stages of the IPO wave may be targeted towards
institutional investors and not private firms. That is, the bias may be an attempt to ramp
up demand for IPOs rather than encourage a greater supply of IPOs. Second, it could be
argued that affiliated analysts are not biased, but have private information about better
growth prospects for early issuers. In the next section, we explore these alternative
explanations in more detail.
5. Issuer Quality
Our finding that affiliated analysts give more favorable recommendations than
unaffiliated analysts to early issuers is in itself not sufficient evidence that affiliated
analysts are biased. Analysts affiliated with underwriters have an informational
advantage relative to unaffiliated analysts. It could be argued that affiliated analysts give
more favorable recommendations to early issuers because these firms have better growth
prospects that unaffiliated analysts are not aware of. Therefore, we compare profitability,
sales growth, change in market share, capital expenditures, and cash-to-net asset ratio of
early and late issuers two years after IPO. Our measures of profitability are cash flow
18
margin, return on assets and asset turnover. Data for these variables are obtained from
Compustat and, to reduce the impact of outliers, all variables are winsorized at the one
percent and 99 percent level. Cash flow margin is calculated as operating income before
depreciation divided by total sales. Return on assets is net income over total assets. Asset
turnover is total sales over total assets. All three variables are adjusted for industry
performance by subtracting the industry median, where an industry is defined as all firms
in the same 3-digit SIC code. Table 6 shows that the differences in industry adjusted cash
flow margin, return on assets and asset turnover of early and late issuers are mostly
statistically insignificant.
To compare growth potential of early and late issuers, we calculate realized
growth in the two years following IPO. We calculate sales growth of an issuing firm as
sales two years after issue date less sales in the IPO year divided by sales in the IPO year.
The sales growth variable is adjusted for industry growth by subtracting the growth rate
of the median firm in the three-digit SIC code. We also calculate the change in market
share of an issuing firm over this two-year period as market share two years after IPO
less market share in the IPO year divided by market share in the IPO year. Table 6 shows
some differences in the sales growth and changes in market shares of early and late
issuers, but these differences are not robust across different cutoffs used to define early
and late issuers. At the 10% cutoff, early issuers have significantly slower growth than
late issuers. At the 10% and 15% cutoff, early issuers have a significantly smaller
increase in market share than late movers.
Next, we test whether late issuers are more likely to sit on the cash raised during
the IPO due to lack of investment opportunities. We compare capital expenditures and
19
cash-to-net asset ratio of early and late issuers. Capital expenditure ratio is calculated as
the issuing firm’s capital expenditure divided by total assets. Cash-to-net asset ratio is
calculated as cash and cash equivalents divided by total assets less cash and cash
equivalents. Both variables are industry-adjusted. Table 6 shows that capital expenditure
ratio and cash to net asset ratio of early and late issuers two years after IPO date are
similar. Finally, we compare abnormal returns over the 2-years following IPO and find
that the 24-month BHARs of early and late issuers are statistically indistinguishable.
Thus, just as Helwege and Liang (2004) find no differences in firm quality between wave
and non-wave period, we find no quality differences within the wave. In light of these
results, we conclude that the disproportionately more favorable recommendations issued
by affiliated analysts in the early stages of an IPO wave are not justified by the
subsequent performance of early IPOs.
6. Market Reaction and Institutional Trading
Results in Section 4 and 5 indicate that affiliated analysts do indeed provide
excessively optimistic recommendations during the early stages of a wave. We have
conjectured that the biased recommendations are the underwriter’s attempt to signal high
valuations to private firms considering an IPO. However, we recognize that the
affiliated-analyst bias may be an attempt by underwriters to build up institutional
demand rather than a means to convey information to private firms. Therefore, our
final set of tests explores this alternative explanation for the analyst bias. We investigate
whether institutions respond to analyst bias early in the wave.
20
6.1 Institutional Response to Analyst Bias
To examine if informed investors react to the biased recommendations of
affiliated analyst, we examine secondary institutional trading activity in the post-IPO
market. We collect information on institutional holdings from CDA/Spectrum from 1980
to 2005 (data prior to 1980 is sparse). For each IPO firm, institutional holding
(HOLDING) is calculated as the shares held by institutional investors divided by total
shares outstanding at the end the first year after the IPO. We define an institutional
investor as a “buyer” (“seller”) if it’s holding at the 4th quarter after IPO is more (less)
than its holding one quarter after IPO. Percentage of institutional buys (BUY) is defined
as the number of shares bought by institutions divided by the total shares outstanding at
the end of 4th quarter after the initial offering. Percentage of institutional sells (SELL) is
defined as the number of shares sold by institutions divided by the total shares
outstanding at the end of 4th quarter after the initial offering.
Affiliated analysts may have better information about the issuing firms but
they provide overly optimistic recommendations early in the wave. Do institutional
investors know when to pay attention to affiliated analysts and when to listen to
unaffiliated analysts? To answer this question, we identify IPOs for which there is a
divergence between the recommendations of affiliated and unaffiliated analysts.
Specifically, we divide early and late issuers into Affiliated-Favored and UnaffiliatedFavored IPOs. As described in Section 4 above, Affiliated Favored IPOs receive, on
average, more favorable recommendations from affiliated analysts than from unaffiliated
21
analysts. On the other hand, Unaffiliated Favored IPOs receive more favorable
recommendations from unaffiliated analysts.
Table 7, Panel A presents institutional holdings of Affiliated Favored and
Unaffiliated Favored IPOs one year after IPO. Results for early and late issuers are
provided separately for all three cut-offs—10%, 15% and 20%. We see that, for the earlymover subgroup, institutions hold similar amounts of Affiliated Favored and Unaffiliated
Favored IPOs. In the late-mover subgroup, institutions hold significantly more Affiliated
Favored IPOs than Unaffiliated Favored IPOs. These findings are intuitive in light of
two opposing factors – the informational advantage of affiliated analysts and their biased
recommendations for early issuers. The informational advantage of affiliated analysts is
likely to have a positive impact on how much Affiliated Favored stock institutions hold.
In contrast, affiliated-analyst bias is likely to have a negative impact. Our finding that
institutions hold similar amounts of Affiliated-Favored and Unaffiliated-Favored early
issuers suggests that these two factors offset each other in the early stages of a wave. In
the late stages of a wave, when the analyst bias is absent, the informational-advantage
effect dominates and therefore, institutions hold more Affiliated-Favored late issuers than
Unaffiliated-Favored late issuers. This pattern is consistent across all three early/late
cutoffs and suggests that institutions are likely to listen to affiliated-analyst
recommendations of late issuers and discount their recommendations of early issuers.
Another interesting result emerges when we focus on the subset of Unaffiliated
Favored IPOs in Table 7, Panel A. One year after issue date, institutions hold more
Unaffiliated-Favored early issuers than Unaffiliated-Favored late issuers. This finding is
also robust across all three cutoffs used to define early and late movers. It appears that
22
institutions give more weight to the optimistic opinion of unaffiliated analysts in the early
stages of a wave than in the late stages. A potential explanation for this finding can be
found in Table 4, which shows that unaffiliated analysts tend to be less optimistic about
early issuers than late issuers. The results in Table 7 suggest that when unaffiliated
analysts do issue more favorable recommendations than affiliated analysts to early
movers, institutions appear to take it as a strong signal that the IPO stock is worth holding.
A similar picture emerges when we look at the institutional buying variable (BUY).
Table 7, Panel B shows that, during the year following the IPO, institutions increase their
holdings of Affiliated-Favored and Unaffiliated-Favored early movers by similar
amounts—26% and 28.8.3% respectively. However, institutional holdings in AffiliatedFavored late movers increase much more than institutional holdings of UnaffiliatedFavored late movers—38.7% and 23.3% respectively. In Panel B, we also see that within
the subset of Affiliated Favored IPOs, institutional holdings increase more for late issuers
than for early issuers. In stark contrast, we find that, within the subset of Unaffiliated
Favored IPOs, institutional holdings increase much more for early issuers than for late
issuers. Thus, institutions pay more attention to favorable recommendations by affiliated
analysts in the later stages of a wave but listen more closely to favorable
recommendations of unaffiliated analysts in the early stages of a wave – precisely when
affiliated analysts are less reliable. These results provide compelling evidence that
institutions know about the affiliated analyst bias in the early stages of a wave. Panel C
presents the institutional selling variable (SELL). There are no discernable patterns in
institutional selling which may reflect the fact that analysts rarely issue Sell or Strong
Sell recommendations for IPOs.
23
In Table 8, we subject these findings to multivariate regressions that control for
size of the issue, initial returns and underwriter rank. Panel A examines institutional
holdings and Panel B examines institutional buys. Focusing on institutional holdings first
(Panel A), we identify the sample of early issuers using the 10 % cutoff. We regress
institutional holdings of early issuers one year after IPO date on a dummy variable
AFF_FAVORED. AFF_FAVORED is equal to one if the IPO is Affiliated Favored and
zero if it is Unaffiliated Favored. In column 1 we see that for the early-mover subsample,
AFF_FAVORED is insignificant. Similarly, we identify the set of late issuers using the
10% cutoff and regress institutional ownership of late issuers one year after issue date on
AFF_FAVORED. For the late-mover subsample in column 2, AFF_FAVORED is positive
and significant at the 10 percent level. This confirms the univariate result that institutions
holdings of late movers are positively related to favorable recommendations of affiliated
analysts while institutional holdings of early movers are not. These results are robust to
the 15% and 20% cutoff for identifying early/late movers (not shown).
Next, we split the sample into Affiliated Favored and Unaffiliated Favored IPOs
and regress institutional holdings in each group on a dummy variable EARLY, which is
equal to one for early issuers and zero for late issuers. In the Affiliated Favored
subsample (column 3, Panel A), the EARLY dummy is insignificant. However, in the
Unaffiliated Favored Subsample, EARLY is positive and statistically significant (column
4, Panel A). Thus, institutions hold more Unaffiliated-Favored early issuers than
Unaffiliated-Favored late issuers. This confirms the univariate test that institutions listen
closely to positive recommendations given by unaffiliated analysts to early movers.
Regressions of institutional buys shown in Table 8, Panel B present a similar picture.
24
Institutions pay more attention to favorable recommendations from affiliated analysts
during the late stages of a wave, when the analyst bias is absent. They give more weight
to favorable recommendations by unaffiliated analysts in the early stages of a wave since
affiliated analysts provide biased forecasts at this stage.
Consistently the results suggest that institutions do not react to analyst biases
early on in the wave. Hence, we know that in equilibrium overly optimistic analyst biases
cannot be targeted towards institutions as a mechanism to build institutional demand.
However, affiliated analysts may try to influence uninformed investors since the
uninformed are more likely to fall prey to the favorable recommendations issued by
affiliated analysts. The next sub-section gauges the impact of the affiliated-analyst bias
on uninformed investors by examining the overall market reaction to positive
recommendations of affiliated analysts
6.2 Market Response to Analyst Bias
To determine whether the market as a whole discounts affiliated analysts in the
early stages of a wave, we follow past research and calculate the market’s reaction to
strong-buy recommendations from affiliated and unaffiliated analysts separately. As
before, we divide the IPO sample into firms that issue early in a wave and those that issue
late in the wave. The market’s reaction is the cumulative abnormal return earned from
one day before the recommendation date till one day after the recommendation date.
Abnormal return is calculated as the firm’s return less the return on the CRSP valueweighted index. A univariate comparison of the mean CAR across all strong-buy
25
recommendations is presented in Table 9, Panel A. We see that the market reacts
positively to strong-buy recommendations from affiliated and unaffiliated analysts both
in the early and late stages of an IPO wave. However, the differences in mean CARs
presented in the last row of Panel A show that in the early stages of a wave, CARs are
significantly higher when unaffiliated analysts issue strong-buy recommendations than
when affiliated analysts issue strong-buy recommendations. In contrast, later in the wave,
CARs are significantly higher when affiliated analysts issue strong-buy recommendations.
These results show that early in the wave, when affiliated analysts issue biased
recommendations, the market gives more weight to unaffiliated analysts. Later in the
wave, when the affiliated-analyst bias is absent, the market reacts more strongly to
recommendations of affiliated analysts than to those of unaffiliated analysts. This
comparison of mean CARs suggests that, early on in a wave, the market is relatively
skeptical of affiliated analysts. The response of stock market prices is in line with the
trading behavior of institutional analysts.
We examine this issue in more detail using multivariate regressions presented in
Table 9, Panel B.
The dependent variable in Panel B is the issuer’s CAR at the
announcement of a recommendation by an analyst. In both the regressions shown, we
include initial returns (IR), underwriter rank (UWRANK), and issue size (LNSIZ) as
control variables. In column 1, the variable of interest is the dummy variable AFF, which
is equal to one if the recommendation is from an affiliated analyst and zero otherwise.
The AFF dummy is negative and significant indicating that the market’s reaction to
recommendations from affiliated analysts is, on average, more muted. In column 2, we
include the dummy variable EARLY, which is equal to one if the issuer is an early mover
26
and zero if it is a late mover. Since the affiliated analyst bias exists only in the early
stages of an IPO wave, we want to see if the market reacts differently to
recommendations issued by affiliated analysts to early issuers. Therefore, we include an
interaction of AFF and EARLY in the second column. The interaction term is negative
and significant indicating that the market’s reaction is less positive when affiliated
analysts give strong-buy recommendations to early issuers.
Bradley, Jordan, and Ritter (2008) show that recommendations initiated
immediately after the quiet period are different from those initiated later. In unreported
tests, we divide the sample of strong-buy recommendations into quiet period and postquiet period recommendations. Quiet-period recommendations are those issued within 30
days of going public. Post-quiet period recommendations are those issued more than 30
days after going public.11 In the subsample of quiet-period recommendations, we again
find that the market reacts more positively to unaffiliated (affiliated) analysts earlier
(later) in the wave. This result also holds, although weakly, in the subsample of postquiet period recommendations. Overwhelmingly, the results of Section 6.1 and 6.2 show
no reaction of either institutions or the overall market to affiliated analyst
recommendations early in wave. This non-result provides strong evidence that the bias is
not meant to build a demand for shares. We, therefore, argue that the bias exists as
mechanism for underwriters to provide information to private firms deciding whether to
go public.
7. Robustness
During the quiet period, which lasts for 25 calendar days, the issuing firm and members of the
underwriting syndicate are not allowed to issue opinions or recommendations about the issuing firm.
11
27
In this section, we discuss the robustness of our results. Our results are likely to
be sensitive to how an IPO wave is classified. Our tests require us to identify sustained
periods of high IPO volume. The main classification of IPO waves used in this paper
relies on a simulation method similar to that used by Harford (2005) to identify merger
waves. We also identify IPO waves following Helwege and Liang’s (2004) movingaverage procedure. For each decade, we calculate a three- quarter moving average of the
number of IPOs per quarter. Periods of at least three consecutive quarters with a moving
average in the top quartile are classified as an IPO wave. Quarters with a moving average
lower than the bottom 30% of the quarterly moving average are considered low-volume
periods. This method results in six IPO waves between January 1970 and December 2005.
As before, we define early (late) movers as the first (last) 10%, 15% or 20% of issuers in
a wave. Table 10 repeats the key tests of the paper using this alternative definition of IPO
waves. Panels A and B present a comparison of the various measures of information
uncertainty described in Section 3.2 above. We find that BHAR volatility and volatility
of initial returns is higher for early issuers than for late issuers. Moreover, filing price
ranges are wider for early movers than for late movers. Withdrawals are also higher for
early movers than for late movers, although the difference is statistically insignificant.
Panel C compares the recommendations of affiliated and unaffiliated analysts in
the early and late stages of an IPO wave. As before, we find that affiliated analysts issue
more favorable recommendations for early issuers than unaffiliated analysts. This
difference does not exist in the late stages of an IPO wave. Panel D presents institutional
buying of IPO stock and confirms that institutional analysts are cognizant of the trade-off
28
between the information advantage of affiliated analyst and their bias in the early stages
of an IPO wave. Focusing on the subset of late issuers, we see that institutions buy more
affiliated-favored late movers than unaffiliated-favored late movers. Since affiliated
analysts do not provide biased forecasts for late issuers, this result suggests that the
informational advantage of affiliated analysts dominates. However, institutions do not
show a similar preference for affiliated-favored early movers. Thus, institutions appear to
discount affiliated analyst views of early issuers. This result is also evident when we look
within the subset of affiliated-favored IPOs. Institutions buy more affiliated-favored late
movers as compared with affiliated-favored early issuers.
Finally, to allow for the possibility that each IPO wave is different and that
relationships between the main variables used in this paper may have changed over time,
we run our tests for the 1970s, 1980s, 1990s and 2000s separately if data availability
permits it. We find that early movers have higher IR volatility and higher BHAR
volatility in all the decades, although the results are weaker for the 1990s. Thus, the
decade-by-decade analysis confirms our finding that uncertainty is higher early in the
wave than later. We examine the analyst bias and institutional trading for the 1990s and
2000s only because data availability prior to the 2000s is insufficient. We find that in
both the 1990s and 2000s the analyst bias existed only in the early stages of an IPO wave
and
that
institutional
investors
discounted
the
overly optimistic
early-stage
recommendations of affiliated analysts. Thus, our results are robust to alternative
methods of classifying IPO waves and appear to be stable over time.
8. Conclusion
29
Fluctuations in IPO volume have received significant attention in the financial
literature. Several recent papers suggest that learning and information spillovers
occur during IPO waves. The outcome of recent and contemporaneous IPOs
provides potential issuers with information on the price they can expect to receive
for the IPO.
A key implication of the information spillover arguments is that
information asymmetry declines as an IPO wave progresses.
We provide strong
evidence in favor of information spillovers. Using several different proxies of
valuation uncertainty, we show that the early stages of an IPO wave are
characterized by much higher levels of information asymmetry than the late stages
of an IPO wave. These hard-to-value early issuers receive unjustifiably positive
recommendations from affiliated analysts. Interestingly, the affiliated-analyst bias is
absent later in the wave when the value of issuing firms is more certain. Thus,
affiliated analysts provide biased recommendations precisely when information
asymmetry is the highest and investors’ need for information the greatest. We try to
discern whom the affiliated analysts hope to influence with these biased forecasts:
firms on the supply side or institutions on the demand side? We find strong
evidence that institutional investors discount affiliated analysts when they are
biased and listen to them closely when they are not. Hence, the bias does not seem
successful in building a demand for securities but rather is targeted at building a
supply of firms to take to market. We conclude that the affiliated-analyst bias serves
as another source of information spillover – it provides a positive signal of
institutional demand to private firms considering an IPO.
30
The difference in valuation uncertainty, analyst bias, and institutional
holdings of early and late issuers indicates that firms that go public in an IPO wave
are not a homogeneous group of firms. Moreover, the drastic change in IPO market
conditions during a wave indicates that high-volume periods are not uniformly hot.
Thus, financial researchers may learn more from deconstructing an IPO wave than
from comparing IPO waves with periods of low IPO volume.
31
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34
Table 1
Summary of IPO Waves
12,648 IPOs from January 1970 to December 2005 in U.S. market are used to identify IPO waves for each
decade. Panel A reports start and end time, total number of IPOs and the number of IPOs per quarter of each
IPO wave. IPO waves are identified based on simulation of aggregate initial offering activities in 1970s,
1980s, 1990s, and 2000s separately. There are six IPO waves from January 1970 to December 2005 and 28
quarters are defined as a non-wave period. Within each IPO wave, early-movers and late-movers are defined
as the first and last 10%, 15%, and 20%. Panel A reports the start and end time, the total number of IPOs and
the average number of IPOs per quarter of each IPO wave. Panel B report the number of IPOs, the dollar
volume, the underpricing level (IR) and the percentage of oversubscription (OVERSUBPT) in the first and
last month of each IPO wave. IR is defined as the difference between the first-day close price and offer price
over the first-day close price where first-day close prices are obtained from CRSP daily file and offer prices
are obtained from the SDC. Percentage of over-subscription OVERSUBPT is calculated as shares offered and
oversold minus shares filed divided by shares filed. Shares offered and oversold and shares filed are obtained
from SDC.
Panel A: IPO Waves from 1970 to 2005
70s
Wave1
71Q1
72Q4
Total number of
IPOs
522
80s
Wave2
Wave3
Wave4
Wave5
Wave6
83Q2
85Q4
93Q4
95Q4
00Q1
84Q1
87Q3
94Q2
96Q4
00Q3
483
779
363
702
202
121
98
121
140
67
3052
99
90s
00s
Start
End
All
Number of IPOs
per quarter
65
Panel B: Volume, Underpricing, and Oversubscription during IPO Waves
Wave1
Wave2
Wave3
Wave4
Wave5
Wave6
Number of
IPOs
First month
Last month
14
27
17
19
24
33
42
35
49
30
15
17
Dollar
Volume
First month
Last month
60.554
221.175
438.378
161.313
645.794
1343.787
1582.050
1360.544
1534.218
1027.955
2429.280
1002.488
IR
First month
Last month
12.42
4.66
16.82
3.46
13.03
2.02
13.88
8.04
12.43
10.33
19.85
18.77
OVERSUBPT
First month
Last month
N/A
N/A
13.27
-2.78
7.28
3.29
12.22
-0.49
33.38
10.96
12.81
-3.47
35
Table 2
IPO Demand and Underpricing of Early and Late Issuers
This table reports the underpricing level and market demand for early movers in the IPO waves, late movers in the IPO waves, IPOs in the wave periods, and IPOs in the
cold issue periods, respectively. IPO waves are identified based on the simulation of aggregate initial offering activities in 1970s, 1980s, 1990s, and 2000s separately.
There are six IPO waves from January 1970 to December 2005. Within each IPO wave, early-movers and late-movers are defined as the first and last 10%, 15%, and 20%
of the firms go to public, respectively. Correspondingly, quarters which have actual number of IPOs below the 5 th percentile from the simulated distribution are identified
as non-wave periods. Initial return IR is defined as the difference between the first-day close price and offer price over the first-day close price where first-day close
prices are obtained from CRSP daily file and offer prices are obtained from the SDC. Percentage of over-subscription, OVERSUBPT is calculated as shares offered and
oversold minus shares filed divided by shares filed where the shares offered and oversold and shares filed are obtained from SDC platinum. HIGH_TO_OFFER is
calculated as difference between the highest original filed and offer scaled by the highest original price filed and LOW_TO_OFFER is calculated as difference between
the lowest original filed and offer scaled by the lowest original price filed, where highest, lowest priced filed and offer price are obtained from SDC platinum.
LOGSHARE is calculated as the logarithm of the number of shares (in millions) offered in the IPO where the number of shares offered is obtained from SDC platinum;
For example, the IR value 20.29% reported in the 3 rd column and the 2nd row indicates that the first 10 percent of the firms go to public in the IPO waves has underpricing
level as high as 20.29%. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively..
INITIAL RETURN
(IR)
OVERSUBPT
HIGH_TO_OFFER
LOW_TO_OFFER
Cutoffs
Early-Mover in
the waves
Late- Mover in
the waves
10%
20.29
8.44
15%
22.68
9.02
20%
23.57
9.37
10%
11.67
2.81
15%
11.84
3.77
20%
11.68
4.86
10%
4.77
12.88
15%
4.06
12.33
20%
3.39
11.55
10%
10.35
-0.94
15%
11.23
0.06
20%
12.19
0.75
Diff
(early late)
11.84***
(4.62)
13.66***
(5.80)
14.20***
(6.68)
8.86***
(6.66)
8.07***
(7.03)
6.83***
(7.27)
-8.11***
(-6.14)
-8.26***
(-7.57)
-8.16***
(-8.50)
11.30***
(7.50)
11.17***
(8.88)
11.40***
(10.39)
Wave
Period
IPOs
Low Volume
Period
Diff
(Wave –
Low Vol)
INITIAL RETURN
(IR)
14.62
20.28
-5.66***
(-3.19)
OVERSUBPT
7.96
6.02
1.94
(1.59)
HIGH_TO_OFFER
8.29
6.93
1.36*
(1.88)
LOW_TO_OFFER
5.23
7.57
-2.05**
(-2.25)
Diff
(Early –
Low Vol)
0.01
(0.05)
2.40
(0.71)
3.29
(1.50)
5.65***
(3.64)
5.64***
(3.18)
5.66***
(2.95)
-2.16**
(-2.10)
-2.87*
(-1.92)
-3.54**
(-2.39)
2.78***
(-2.96)
3.66***
(3.16)
4.62**
(2.46)
Diff
(Late –
Low Vol
-11.84***
(-3.72)
-11.26***
(-3.66)
-10.91***
(-3.69)
-3.21**
(-1.98)
-2.25*
(-1.81)
-1.16
(-1.17)
5.95***
(4.91)
5.40***
(5.66)
4.62***
(4.68)
-8.51***
(-5.46)
-7.71***
(-6.24)
-6.82***
(-6.41)
36
Table 3
Valuation Uncertainty of Early and Late Issuers
This table reports measures of valuation uncertainty and dispersion of firm quality for firms that go public early in
an IPO wave and firms that go public late in an IPO wave. IPO waves are identified by simulating aggregate IPO
activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual
IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. We identify
six IPO waves from January 1970 to December 2005. Within each IPO wave, early (late) movers are defined as the
first (last) 10%, 15%, and 20% of the firms go to public. The table also summarizes valuation uncertainty variables
for wave periods on average, and low-volume periods. Low-volume periods are quarters in which the actual number
of IPOs lies below the 5th percentile of the simulated distribution. Variables shown in this table are as follows. In
Panel A, IR_VOL is the volatility of initial returns and is calculated as standard deviation of IPO initial returns; 12m
BHAR Volatility is the cross-sectional volatility of 12-month buy-and-hold abnormal returns (BHAR) where an
issuers 12-month BHAR is calculated as the issuer’s buy-and-hold return less the buy-and-hold return of a portfolio
of size-matched firms. Likewise, 24m BHAR volatility is the cross-sectional volatility of 24-month BHARs. In
Panel B, PER_NASDAQ is the percentage of IPOs listed on NASDAQ. UWRANK is the average Carter-Manaster
(1990) underwriter ranking score, as updated by Carter, Dark, and Singh (1998) and Loughran and Ritter (2004).
LNSIZE is calculated as the logarithm of the number of shares (in millions) offered in the IPO where the number of
shares offered is obtained from SDC platinum; RANGE is the difference between the maximum original price filed
and the minimum original price filed. The values reported are the mean of each group. The symbols, *, **, and ***,
indicate significance at the 10%, 5%, and 1% levels, respectively.
37
PANEL A: IR and BHAR Volatility
Early
Late
F-test
(Prob >F)
IRVOL:
Volatility of
Initial Returns
10%
15%
20%
0.658
0.637
0.610
0.231
0.273
0.272
<0.0001***
<0.0001***
<0.0001***
12m BHAR
Volatility
10%
15%
20%
0.796
0.776
0.740
0.683
0.605
0.718
0.008***
<0.0001***
0.06*
10%
15%
20%
0.906
0.856
0.815
0.778
0.792
0.797
0.004***
0.08*
0.15
24m BHAR
Volatility
Wave
Periods
LowVolume
Periods
F-test
(Prob >F)
0.437
0.373
<0.0001***
0.749
0.521
<0.0001***
0.759
1.089
<0.0001***
PANEL B: Issue Size, Percentage NASDAQ, Price Range, and Percentage Withdrawals
Cutoff
LNSIZE:
Issue Size
PER_NASDAQ:
Percentage of
NASDQQ firms
UWRANK:
Underwriter
Rank
WITHDRAW:
Percentage of
Withdrawals
RANGE:
Filing Price
Range
Observations
Early
Late
10%
14.336
14.471
15%
14.381
14.489
20%
14.396
14.480
10%
49.39%
48.15%
15%
53.32%
48.94%
20%
53.22%
48.87%
10%
6.565
6.047
15%
6.453
6.209
20%
6.513
6.269
10%
23.89
19.81
15%
23.73
20%
22.82
20.71
10%
1.70
1.45
15%
1.69
1.52
20%
1.73
1.53
10%
15%
20%
316
467
622
324
472
620
20.86
Early Late
-0.134**
(-2.01)
-0.108*
(-1.77)
-0.084*
(-1.65)
1.24
(1.39)
4.38**
(2.59)
4.35**
(2.23)
0.527**
(2.34)
0.243*
(1.66)
0.244*
(1.65)
4.07**
(2.24)
2.87*
(1.69)
2.11
(1.47)
0.25
(2.72)**
0.17
(2.27)*
0.20
(3.09)**
Wave
Period
IPOs
NonWave
Period
Diff
(Wave –
Non-Wave)
LNSIZE:
Issue Size
14.462
14.732
-0.267***
(-5.99)
PER_NASDAQ:
Percentage of
NASDQQ firms
51.57%
55.92%
-4.35**
(-2.24)
6.341
6.432
-0.091
(-1.00)
20.48%
18.53%
1.95*
(1.75)
1.62
1.40
0.22
(0.68)
3052
633
UWRANK:
Underwriter
Rank
WITHDRAW:
Percentage of
Withdrawals
RANGE:
Filing Price
Range
Observations
38
Table 4
Analyst Recommendation for Early and Late Issuers
This table reports the recommendations given by affiliated and unaffiliated analysts to firms that issue in the early
and late phases of IPO waves. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s,
and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th
percentile of the simulated distribution is classified as an IPO wave. Correspondingly, quarters which have actual
number of IPOs below the 5th percentile from the simulated distribution are identified as non-wave periods. We
identify six IPO waves from January 1970 to December 2005. Within each IPO wave, early (late) movers are
defined as the first (last) 10%, 15%, and 20% of the firms go to public. For each IPO firm, we calculate the
recommendation value, RECD, as the mean value of I/B/E/S recommendations from affiliated analysts or
unaffiliated analysts in the first year after IPOs. For each issuing firm, the percentage of positive recommendations,
PER_RECD, from affiliated (unaffiliated) analysts is calculated as the number buy or strong buy recommendation
issued by affiliated (unaffiliated) analysts divided by the total number of recommendations issued by affiliated
(unaffiliated) analysts. Panel A reports RECD and PER_RECD from affiliated and unaffiliated analysts for early and
late moves using the 10 percent cutoff to define early and late movers. Panel B reports RECD and PER_RECD from
affiliated and unaffiliated analysts for the 15 and 20 percent cutoffs. Panel C reports the fraction of Affiliated
Favored and Unaffiliated Favored IPOs in the early and late stages of a wave. Affiliated Favored IPOs are IPOs that
get a higher average recommendation from affiliated analysts than from unaffiliated analysts. Unaffiliated Favored
IPOs are IPOs that get a lower average recommendation from affiliated analysts than from unaffiliated analysts. No
difference IPOs are defined as those that get a same average recommendation from affiliated analysts as from
unaffiliated analysts. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively.
39
Panel A : Early (Late) movers defined as the first (last) 10% of IPOs in a wave
Unaffiliated
RECD
Affiliated
Diff
(Unaff - Aff)
Unaffiliated
PER_RECD
Affiliated
Diff
(Unaff - Aff)
Observations
Early
Late
1.814
1.724
1.646
1.693
0.168**
(2.28)
0.032
(0.39)
0.821
0.842
0.891
0.820
-0.070**
(-2.38)
159
0.022
(0.62)
167
Wave Periods
0.090
(1.14)
-0.046
(-0.60)
1.795
1.808
1.683
1.773
0.112***
(4.58)
0.035
(0.78)
0.824
0.809
0.870
0.828
-0.046***
(-4.33)
1752
-0.019
(-0.86)
439
-0.021
(-0.65)
0.071**
(2.25)
Diff
(Wave – NonWave)
-0.013
(-0.33)
-0.089**
(-2.27)
Non-Wave
Periods
Early - Late
0.015
(0.84)
0.042**
(2.46)
Panel B: Early (Late) movers defined as the first (last) 15% or 20% of IPOs in a wave
15% cut-off
20% cut-off
RECD
PER_RECD
Early
Late
Unaffiliated
1.859
1.751
Affiliated
1.690
1.674
Diff
(Unaff - Aff)
0.169**
(2.60)
0.077
(1.19)
Unaffiliated
0.796
0.838
Affiliated
0.859
0.829
-0.063**
(-2.32)
214
0.009
(0.30)
212
Diff
(Unaff - Aff)
Observations
Diff
(Early - Late)
0.108*
(1.64)
0.016
(0.25)
-0.042
(-1.43)
0.030
(1.06)
Early
Late
1.822
1.759
1.692
1.667
1.30**
(2.31)
0.092
(1.62)
0.807
0.837
0.863
0.847
-0.057**
(-2.37)
264
-0.011
(-0.42)
267
Diff
(Early - Late)
0.063
(1.10)
0.023
(0.46)
-0.030
(-1.16)
0.016
(0.66)
Panel C: IPOs Favored by Affiliated and Unaffiliated Analysts
Early
10%
15%
20%
Late
Affiliated Favored
53.38%
41.45%
Unaffiliated Favored
34.75%
41.45%
Affiliated Favored
52.11%
41.86%
Unaffiliated Favored
34.98%
44.34%
Affiliated Favored
52.96%
45.97%
Unaffiliated Favored
36.86%
42.93%
Diff
(Early – Late)
12.07%**
(1.97)
-6.71%
(1.47)
10.25%*
(1.86)
-9.36%
(1.81)
6.99%
(1.58)
-6.07%
(1.46)
40
Table 5
Multivariate Analysis of Analyst Recommendations
This table reports a multivariate analysis of recommendations given by affiliated and unaffiliated analyst to issuing
firms. In each regression, the dependent variable is the recommendation value, RECD, calculated as the mean value
of I/B/E/S recommendations from affiliated analysts or unaffiliated analysts in the first year after IPO. We regress
RECD on LNSIZE, UWRANK, IR, AFF, EARLY, AFF_EARLY. The variable LNSIZE is the coefficient of the
logarithm of number of shares (in millions) offered in the IPO. UWRANK is the underwriter ranking scores
(Loughran and Ritter,1994). Initial return, IR, is defined as the difference between the first-day close price and offer
price over the first-day close price. AFF is a dummy variable that is equal to one if the recommendation is from the
affiliated analysts and zero otherwise. EARLY is a dummy variable that is equal to one if the IPO is an early mover
and zero if it is a late mover within an IPO wave. AFF_EARLY is the interaction of AFF and EARLY. In columns 1,
2, and 3 respectively, early movers are defined as the first 10%, 15%, or 20% of the firms to go public in an IPO
wave. EARLY2 is a dummy variable equal to one if the issuer is an early mover in an IPO wave and zero otherwise.
AFF_EARLY2 is the interaction of AFF and EARLY2. IPO waves are identified by simulating aggregate IPO activity
in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO
activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Within each IPO
wave, early (late) movers are defined as the first (last) 10%, 15%, and 20% of the firms go to public. t-statisics are
provided in parenthesis. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels,
respectively..
RECD
(10%)
RECD
(15%)
RECD
(20%)
RECD
(10%)
1
2
3
4
Intercept
0.235
(0.42)
0.656
(1.22)
0.726
(1.35)
0.791***
(4.49)
LNSIZE
0.075*
(1.88)
0.059
(1.55)
0.048
(1.24)
0.056***
(4.18)
UWRANK
0.031*
(1.82)
0.036*
(1.93)
0.034*
(1.86)
0.022***
(3.90)
IR
-0.334***
(-3.92)
-0.214***
(-2.84)
-0.206***
(-2.91)
-0.082***
(-4.59)
AFF
-0.091
(-1.27)
-0.048
(-0.74)
-0.079
(-1.05)
-0.063**
(-3.42)
EARLY
0.133
(1.58)
0.085
(1.28)
0.097
(1.41)
AFF_ EARLY
-0.212**
(-2.08)
-0.185**
(-2.02)
-0.203**
(-2.03)
EARLY2
-0.009
(-0.17)
AFF_EARLY2
-0.071*
(-1.91)
HIGHIR
-0.057*
(-1.75)
AFF_HighIR
-0.031
(-0.69)
Ad_R (%)
3.90
3.84
3.87
2.82
Obs
542
706
902
4995
41
Table 6
Operating Performance and BHARs of Early and Late Issuers
This table compares operating performance and abnormal stock returns of firms that go public early and firms that
go public late in an IPO wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s,
and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95 th
percentile of the simulated distribution is classified as an IPO wave. We identify six IPO waves from January 1970
to December 2005. Within each IPO wave, early (late) movers are defined as the first (last) 10%, 15%, and 20% of
the firms go to public. The following variables are calculated as of 2 years after IPO date. Cash Flow Margin is
calculated as operating income before depreciation over net sales. Return on Assets is calculated as net income over
Book Assets. Asset Turnover is calculated as net sales over total assets. Cash to Net Assets is cash and cash
equivalents divided by total assets less cash and cash equivalents. Capital Expenditure ratio is calculated as the
issuing firm’s capital expenditure divided by total assets. Sales Growth is calculated as firm as sales two years after
issue date less sales in the IPO year divided by sales in the IPO year. Change in Market Share of an issuing firm
over this two-year period is market share two years after IPO less market share in the IPO year divided by market
share in the IPO year. Market share is firm sales over total sales of all firms in the same three-digit SIC code. For all
aforementioned variables except Change in Market Share, an abnormal value is calculated by subtracting the
median value of the 3-digit SIC industry. 24-month BHAR is the buy-and-hold-abnormal return 24 months after IPO
date. It is calculated the buy-and-hold return of the issuing firm over the 24 months following IPO less the buy-andhold return of an size-matched portfolio over the same period. Since data availability for each variable differs, the
table presents the minimum number of observations used in each subgroup. t-statistics presented in absolute values
are in provided in parentheses. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels,
respectively.
42
Abnormal Cash-Flow
Margin
Abnormal Return on
Assets
Abnormal Asset
Turnover
Abnormal Sales
Growth
Change in Market
Share
Abnormal Cash to
Net-Asset Ratio
Abnormal Capital
Expenditure Ratio
Cutoff
10%
Early
-1.181
Late
-0.488
15%
-0.973
-0.615
20%
-0.831
-0.543
10%
-0.118
-0.068
15%
-0.096
-0.082
20%
-0.088
-0.094
10%
0.037
0.123
15%
0.069
0.101
20%
0.061
0.085
10%
2.685
6.500
15%
3.199
5.341
20%
3.844
4.763
10%
1.819
5.379
15%
2.133
4.405
20%
2.679
3.923
10%
-0.367
-0.161
15%
0.515
-0.028
20%
0.081
-0.194
10%
0.0284
0.031
15%
0.031
0.029
20%
0.033
0.032
-0.129
-0.081
-0.113
-0.098
-0.111
-0.097
114
188
264
127
173
236
10%
24-Month BHAR
15%
20%
Observations (min)
10%
15%
20%
Early-Late
-0.692*
(1.83)
-0.357
(1.17)
-0.288
(1.19)
-0.049
(1.33)
-0.014
(0.47)
0.005
(0.21)
-0.085
(1.28)
-0.031
(0.57)
-0.025
(0.52)
-3.813**
(2.05)
-2.141
(1.61)
-0.919
(0.78)
-3.560**
(2.43)
-2.271**
(2.17)
-1.243
(1.35)
-0.206
(0.47)
0.079
(0.22)
0.276
(0.92)
-0.002
(0.35)
0.001
(0.22)
-0.001
(0.11)
-0.048
(1.21)
-0.015
(0.41)
-0.014
(0.40)
43
Table 7
Institutional Trading and Analyst Bias
This table reports institutional holdings and institutional buying of early and late issuers depending on whether they are Affiliated Favored or Unaffiliated
Favored IPOs. Early (late) issuers in an IPO wave are defined as the first (last) 10%, 15%, and 20% of the firms go to public in a wave. IPO waves are identified
by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity
greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Affiliated Favored IPOs are IPOs that get a higher average
recommendation from affiliated analysts than from unaffiliated analysts; Unaffiliated Favored IPOs are IPOs that get a lower average recommendation from
affiliated analysts than from unaffiliated analysts. For each IPO firm, institutional holding, HOLDING, is calculated as the shares held by institutional investors
divided by total share outstanding at the end the 4 th quarter after IPO. We define an institutional investor as a “buyer” (“seller”) if its holding at the 4th quarterend after issue date is more (less) than its holding at 1st-quarter-end after issue date. For institutional “buyers”, the percentage of institutional buys, BUY, is
defined as 4th quarter-end institutional holding less the 1st quarter-end institutional holding divided by the total shares outstanding at end of 4th quarter after IPO.
For institutional “sellers”, the percentage of institutional sells, SELL, is defined as the 1st quarter-end institutional holding less the 4th quarter-end institutional
holding divided by the total shares outstanding at end of 4th after IPO. Panel A reports HOLDING, Panel B reports BUY and Panel C reports SELL. Each panel
presents results for all three early/late cutoff points: 10%, 15% and 20%. t-statistics are provided in parenthesis and the symbols, *, **, and ***, indicate
significance at the 10%, 5%, and 1% levels, respectively.
44
Panel A Institutional Holding
10% Early/Late Cutoff
Affiliated
Favored
Unaffiliated
Favored
Difference
15% Early/Late Cutoff
20% Early/Late Cutoff
Early
Late
Diff
(Early-Late)
Early
Late
Diff
(Early-Late)
Early
Late
Diff
(Early-Late)
35.52
38.88
34.10
35.91
37.73
30.69
36.64
31.00
-1.82
(-0.68)
6.73*
(1.72)
35.63
39.30
-3.36
(-1.08)
8.61*
(1.75)
37.63
32.13
-2.10
(-0.90)
5.50*
(1.64)
-2.67
(-0.90)
8.19*
(1.78)
-2.75
(-0.94)
5.80*
(1.64)
-1.99
(-0.74)
5.61*
(1.78)
Panel B Institutional Buys
10% Early/Late Cutoff
Affiliated
Favored
Unaffiliated
Favored
Difference
15% Early/Late Cutoff
20% Early/Late Cutoff
Early
Late
Diff
(Early-Late)
Early
Late
Diff
(Early-Late)
Early
Late
Diff
(Early-Late)
26.05
38.72
28.00
36.44
34.94
23.29
28.16
18.79
-8.44*
(-1.94)
9.85**
(2.28)
30.94
28.83
-12.67**
(-2.31)
5.54
(1.23)
29.38
21.41
-3.39
(-1.00)
7.97*
(1.95)
-2.78
(-0.86)
15.34**
(2.22)
-0.65
(-0.32)
17.64***
(3.26)
1.56
(0.32)
12.93**
(2.68)
Panel C Institutional Sells
10% Early/Late Cutoff
Affiliated
Favored
Unaffiliated
Favored
Difference
15% Early/Late Cutoff
20% Early/Late Cutoff
Early
Late
Diff
(Early-Late)
Early
Late
Diff
(Early-Late)
Early
Late
Diff
(Early-Late)
24.66
16.89
24.68
20.26
20.81
22.02
21.05
24.33
4.43
(1.27)
-3.28
(-0.83)
24.62
17.06
7.77
(1.53)
-4.96
(-1.11)
21.58
25.31
3.81
(1.16)
-3.73
(-0.94)
7.60
(1.38)
-5.13
(-1.11)
3.63
(0.83)
-4.07
(-1.23)
3.04
(0.93)
-4.50
(-1.36)
45
Table 8
Multivariate Analysis of Institutional Activities and Analyst Bias
This table examines the relation between institutional holdings and analyst recommendations in a
multivariate setting. In Panel A, the dependent variable is institutional holding (HOLDING) one year after
issue date. In Panel B, the dependent variable is the percentage increase in institutional holdings (BUY)
from the first quarter after issue date till the fourth quarter after issue date. Columns 1 and 2 of both panels
restrict the sample to early issuers and late issuers respectively. Early (Late) issuers are defined as the first
(last) 10% of issuers in an IPO wave. IPO waves are identified by simulating aggregate IPO activity in
1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual
IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave.
Columns 3 and 4 of both panels restrict the sample to Affiliated Favored and Unaffiliated Favored issuers
respectively. Affiliated Favored IPOs are IPOs that get a higher average recommendation from affiliated
analysts than from unaffiliated analysts, Unaffiliated Favored IPOs are IPOs that get a lower average
recommendation from affiliated analysts than from unaffiliated analysts. The variable LNSIZE is the
logarithm of the number of shares(in million) offered in the IPO. IR is the initial return which defined as
the difference between the first-day close price and offer price over the first-day close price. UWRANK is
the underwriter ranking score (Loughran and Ritter,1994). AFF_FAVORED is a dummy variable equal to 1
if the issuer is Affiliated Favored and 0 if it is Unaffiliated Favored. EARLY is a dummy variable that is
equal to one if the IPO is an early mover and zero if it is a late mover. t-statistics are in parenthesis and the
symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively.
PANEL A: INSTITUTIONAL HOLDINGS
Intercept
LNSIZ
IR
UWRANK
AFF_FAVORED
PANEL B: INSTITUTIONAL BUYS
1
2
3
4
1
2
3
4
Early
Late
Late
-1.287
(-1.26)
0.150*
(1.67)
-0.080
(-0.97)
0.081
(0.97)
0.129**
(2.42)
Unaffiliated
Favored
-0.545
(-0.84)
0.036
(0.72)
0.001
(0.01)
0.041
(1.54)
Early
0.343
(0.64)
0.027
(0.77)
0.013
(0.07)
0.045*
(1.69)
0.004
(0.07)
Affiliated
Favored
-0.485
(-0.70)
0.048
(1.00)
-0.069
(-0.21)
0.027
(0.82)
0.018
(0.04)
0.001
(0.01)
-0.132
(-0.76)
0.032
(1.30)
0.011
(0.18)
-1.536*
(-1.78)
0.146*
(1.87)
0.359
(0.64)
0.061
(1.96)
0.146*
(1.84)
Affiliated
Favored
-0.069
(-0.11)
0.022
(0.48)
0.351
(1.09)
0.011
(0.35)
Unaffiliated
Favored
-0.581
(-1.09)
0.030
(0.73)
-0.232
(-1.25)
0.041
(1.22)
-0.023
(-0.26)
3.25
0.114*
(1.85)
4.98
0.083*
(1.82)
5.67
EARLY
Ad_R (%)
3.19
8.11
Obs
249
237
226
207
5.52
10.38
-0.132
(-1.51)
3.48
139
137
104
107
46
Table 9
The Market’s Reaction to Analyst Recommendations
This table presents the market’s reaction to strong-buy recommendation given to early and late issuers by
affiliated and unaffiliated analysts. The market’s reaction is captured by the cumulative market adjusted
return (CAR) over three days (-1,+1) centered on the recommendation date. Information on strong-buy
recommendations and recommendation dates are obtained from I/B/E/S. An issuer’s CAR is calculated as
its return cumulated over the three-day window less cumulated return of the CRSP value-weighted market
index. Early (late) issuers in an IPO wave are defined as the first (last) 10%, 15%, or 20% of the firms go to
public in a wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s,
and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater
than the 95th percentile of the simulated distribution is classified as an IPO wave. Panel A presents mean
CARs for early and late issuers split by analyst affiliation. A recommendation is defined as Affiliated if a
lead manager or co-manager of the IPO employs the analyst; otherwise the recommendation is defined as
Unaffiliated. Panel B presents a multivariate analysis of the abnormal returns. We regress the CAR on the
following variables LNSIZE, UWRANK, IR, AFF, EARLY, AFF_EARLY. The variable LNSIZE is the
logarithm of the number of shares(in million) offered in the IPO. UWRANK is the underwriter ranking
scores (Loughran and Ritter,1994). IR is the initial return defined as the difference between the first-day
close price and offer price divided by the first-day close price. AFF is equal to one if the recommendation
is from the affiliated analysts and zero otherwise. EARLY is equal to one if the IPO is an early mover and
zero if it is a late mover. AFF_EARLY is the interaction term of AFF and EARLY. Patell (1976) Z test
statistics are reported in parenthesis and the symbols, *, **, and ***, indicate significance at the 10%, 5%,
and 1% levels, respectively.
Panel A: Univariate analysis of CARs (All Recommendations)
10%
15%
Early
Late
Early
Late
Affiliated
1.81*
2.19***
2.92**
1.99***
RECD
(1.60)
(3.23)
(1.63)
(3.51)
Unaffiliated
4.29***
0.85*
3.96***
0.72*
RECD
(4.99)
(1.34)
(6.56)
(1.55)
Diff
-2.48***
1.34*
-1.04*
1.27
(Aff-Unaff)
(-2.79)
(1.68)
(-1.68)
(1.54)
Early
2.26**
(2.01)
3.68***
(7.22)
-1.42**
(-1.99)
20%
Late
2.16***
(4.36)
1.01**
(1.94)
1.15*
(1.72)
Panel A: Univariate analysis of CARs (All Recommendations)
1
2
2.539***
2.531***
Intercept
(4.59)
(4.58)
-0.181***
-0.175***
LNSIZE
(-4.53)
(-4.35)
0.038
0.037
UWRANK
(1.55)
(1.52)
0.021
0.020
IR
(0.24)
(0.23)
-0.097*
-0.128
AFF
(-1.82)
(-1.37)
0.073
EARLY
(0.58)
-0.164**
AFF_EARLY
(-2.00)
3.99
4.87
Adjusted R-square
Obs
527
527
47
Table 10
Robustness to Moving Averages Classification of IPO Wave
This table presents compares of valuation uncertainty, analyst recommendations, and institutional buys for
early and late issuers in IPO waves. IPO waves are identified using 12,648 IPOs from January 1970 to
December 2005 in U.S. market following the moving-average method of Helwege and Liang (2004). For
each decade, we calculate three-quarter moving averages of the number of IPOs for each quarter. Periods
with at least three consecutive quarters that have a moving average exceeding the top quartile of the
quarterly moving average are labeled as wave periods, and those with a moving average lower than the
bottom 30% of the quarterly moving average are considered non-wave periods. This method identifies six
six IPO waves from January 1970 to December 2005. Within each IPO wave, early-movers and latemovers are defined as the first and last 10% firms go to public. Panels A and B compare measures of
valuation uncertainty in the early and late stages of IPO waves. All variables in Panel A and B are
calculated as defined in Table 3. Panel C compares recommendations issued by affiliated and unaffiliated
analysts for early and late issuers. The variables RECD and PER_RECD are as defined in Table 4. Panel D
compares institutional buying of early and late IPOs. Institutional buys, affiliated favored, and unaffiliated
favored IPOs are calculated as defined in Table 7. The values reported are the mean of each group. The
symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively.
48
Panel A: Uncertainty (IR and BHAR Volatility)
Early
Late
F-test
(Prob>F)
IRVOL:
Volatility of
Initial Returns
0.440
0.283
<0.0001***
IRVOL:
Volatility of
Initial Returns
24m BHAR
Volatility
0.990
0.762
<0.0001***
12m BHAR
Volatility
Wave
Periods
0.517
NonWave
periods
0.358
0.757
1.021
F-test
(Prob>F)
<0.0001***
<0.0001***
PANEL B: Uncertainty (Issue Size, Percentage NASDAQ, Price Range, and Percentage
Withdrawals)
LNSIZ
Issue Size
PER_NASDAQ:
Percentage of
NASDAQ firms
EarlyMover
in the
waves
14.425
Late
Mover
in the
waves
14.480
Diff
(early late)
-0.055
(-1.09)
54.92%
8.65***
(2.69)
46.27%
NonWave
periods
Wave
Periods
Diff
(early late)
LNSIZ
Issue Size
14.461
14.634
-0.173***
(-4.43)
PER_NASDAQ:
Percentage of
NASDAQ firms
55.88%
57.72%
-1.84%
(-1.62)
UWRANK:
Underwriter
Rank
6.529
6.073
0.456**
(2.43)
UWRANK:
Underwriter
Rank
6.331
6.533
-0.202*
(-1.72)
WITHDRAW:
Percentage of
Withdrawal
21.13
20.00
1.13
(1.05)
WITHDRAW:
Percentage of
Withdrawal
21.57
18.86
2.71*
(1.90)
RANGE:
Filing Price
Range
1.74
1.44
0.29***
(4.01)
RANGE:
Filing Price
Range
1.59
1.51
0.08*
(1.72)
Observations
452
450
Observations
2261
519
Panel C: Analyst Recommendations
RECD
Unaffiliated
Earlymovers in
the waves
1.808
Latemovers in
the waves
1.711
Affiliated
1.688
1.728
Diff
(Unaff – Aff)
0.120*
(1.91)
-0.016
(-0.28)
PER_RECD
Diff
(early – late)
0.097
(1.52)
-0.040
(-0.70)
Earlymovers in
the waves
0.813
Latemovers in
the waves
0.847
0.866
0.855
-0.053**
(-2.09)
-0.007
(-0.29)
Diff
(early – late)
-0.035
(-1.29)
0.011
(0.46)
Panel D: Institutional Buys (BUY) of IPOS
Affiliated
Favored
Unaffiliated
Favored
Difference
Early Mover
Late Mover
29.53
36.81
33.67
28.21
-4.14
(-0.75)
10.14**
(2.22)
Diff
(early – late)
-7.28*
(1.65)
5.48
(0.88)
49