yung by yaosaigeng


									IPO Information Aggregation and Underwriter Quality

                        Wei Wang
                 Leeds School of Business
                  University of Colorado

                       Chris Yung
                 Leeds School of Business
                  University of Colorado

                  First Draft: July, 2006
                 Current Draft: Sept, 2007

A key distinction between some models of IPO pricing (e.g., auctions and bookbuilding)
and others (e.g., fixed-priced models) is whether price discovery occurs in the primary
market or the secondary market. We show that higher investment bank reputation is
associated with 1) more active filing price revisions and 2) reduced secondary market
return variability, both of which are consistent with primary market price discovery.
Reputable underwriters exhibit partial adjustment to private information but virtually
complete adjustment to information implicit in the returns of public comparables. Non-
reputable underwriters partially adjust to all types of information. Taken together, this
evidence suggests that theoretical models of primary (secondary) market information
aggregation are better suited for reputable (non-reputable) underwriters.
1 Introduction
        IPO auction and bookbuilding models emphasize the economic importance of
information flows from investors to the issuer and/or underwriter during the pre-IPO
phase.1 The importance of these flows is motivated as follows. Information asymmetry
among investors creates a winner’s curse, which prevents the seller from obtaining a high
price (Rock, 1986). A rational response, therefore, is to design a mechanism to elicit this
information before the price is set, thereby mitigating the winner’s curse.
        Any such mechanism entails costs. The question then becomes which is cheaper:
designing a pre-IPO mechanism to extract investors’ information (Benveniste and Spindt,
1989) or simply bearing the adverse selection costs (Rock, 1986)? Or, equivalently: is
price discovery is more efficient in the primary market or in the secondary market?
        We study primary market information aggregation by looking at price revisions,
defined as the percentage change from the midpoint of the original filing range to the
final IPO offer price. Because soliciting investor opinions before the initial filing range
is set is prohibited by the Securities Act of 1933, the period between the filing and
offering dates fully captures the impact of the underwriter’s bookbuilding efforts. The
private information revealed during this period may be either good or bad news.
Information aggregation thus leads to diverse movements in prices, i.e., cross-sectional
volatility. Our primary methodology is to compare cross-sectional variation in price
movement during this phase2 to that of the secondary market. By doing so, we hope to
establish a measure of how much information is aggregated in primary markets rather
than secondary market.
        Our first observation is that reputable underwriters revise their prices much more
actively in the pre-IPO phase. This is a very robust and dramatic feature of our data. For
underwriters ranked 8.5 or above on the Carter-Manaster scale, the standard deviation of
price revisions is 28% of the midpoint of the initial filing price range, while for
underwriters ranked below 6.75 it is only 17% of the filing price midpoint. This evidence

  Much of the rest of the literature emphasizes information flows in the opposite direction. This category
includes signaling models (e.g., Allen and Faulhaber (1989), Grinblatt and Hwang (1989) and Welch
(1989)), certification models (e.g., Carter and Manaster (1990)) and models based upon moral hazard in
selling effort (e.g., Baron (1982) and Baron and Holmstrom (1982)).
  Public information is revealed during the bookbuilding period as well. In Section 4, we discuss how this
public component is factored out in order to focus on private information.

is consistent with prestigious underwriters acquiring information much more actively
during the bookbuilding phase and incorporating that information into the price.
        Price discovery in the primary market should substitute for price discovery in the
secondary market. Hence, given the strong difference in primary market pattern
mentioned earlier, one might expect reduced aftermarket volatility for IPOs taken public
by reputable underwriters. In the full sample, the univariate evidence for this hypothesis
is surprisingly weak. We do find lower secondary market cross-sectional return
variability for reputable underwriters for every holding period horizon and in every sub-
period we consider except 1996-2000. Yet this one sub-period rejection is sufficiently
dramatic as to cause the hypothesis to fail in the full sample.
        This univariate evidence is of limited power, however, since reputable and non-
reputable underwriters issue very different types of IPOs. Offerings by reputable
underwriters are much more likely to be high-tech and venture capital backed, among
other differences.
        We control for these differences in two ways. We employ the standard firm
characteristic controls (size, age, VC backing, etc.) in our regressions. These controls are
insufficient, however, if the asymmetric information profile of firms approaching high
and low quality underwriters is different. Recall also that we are primarily interested in
the relative return variability in the primary and secondary markets; that is, not how
much learning is done in total, but rather when this learning tends to be done. We
therefore form the following ratio: cross-sectional variability of price movements in the
primary market (i.e., percentage change from the filing range midpoint to the offer price)
divided by cross-sectional variability of secondary market returns. This ratio functions as
an additional control for unobservable differences, because heightened ex-ante
asymmetric information inflates both the numerator and the denominator.
        We find that reputable underwriters have lower variance ratios in every subperiod
except 1996-2000. This lone reversal owes to the crash of tech stocks (disproportionately
underwritten by reputable underwriters) near the end of this subperiod. Despite this
outlier, reputable underwriters have much lower variance ratios (p-value < .001 for all
holding periods studied) in the full sample, both unconditionally and controlling for firm

1.1 Partial Adjustment to Private Information
        Benveniste and Spindt (1989) show that primary market information acquisition
necessitates partial adjustment to private information. Briefly, the intuition is as follows.
Investors know that divulging good information leads to higher prices. Naturally, this
makes it difficult to extract favorable information. To counteract the incentive to hide
good information, the underwriter commits to adjusting prices by less than warranted
when good information is revealed. Equilibrium underadjustment leads to a positive
correlation between file price revisions and initial returns. In the context of our
aforementioned results – high-quality underwriters are more active in the information
aggregation role – this paradigm predicts more pronounced partial adjustment for
reputable underwriters.3
        To isolate the component of the price revision due to private information, we
admit two control variables used in previous literature. For each IPO we measure the
average return of public firms in the same Fama-French 48 industry classification
between the IPO filing date and the offer date. In addition, we define the “heat” of the
IPO market as the average initial return of all IPOs in the 30 day window preceding the
offer date. Private information is then orthogonalized against these two pieces of public
        Partial adjustment to private information is observed throughout our sample, but
the effect is much stronger (both economically and statistically) for high-quality
underwriters. A one-standard-deviation increase in price revision leads to a .43 standard
deviation increase in initial returns for high-quality underwriters, but only a .20 standard
deviation increase for low-quality underwriters. Again this evidence suggests the
information acquisition paradigm is a better fit for reputable underwriters.

1.2 Partial Adjustment to Public Information
        Recent studies find that partial adjustment of IPO prices to public information is
incomplete as well (Bradley and Jordan, 2002). There is disagreement over how to
interpret these results, which are unanticipated by classical models. It may point to the

  The aforementioned results concerning primary vs. secondary market information aggregation are not
mechanism-specific, i.e. they do not assume bookbuilding. In contrast, the partial adjustment hypotheses
follow directly from Benveniste and Spindt (1989).

importance of factors other than information asymmetry in determining average
underpricing. Loughran and Ritter (2002), Ljungqvist and Wilhelm (2005) and Ince
(2007) take it as evidence of an agency problem between issuers and underwriters in
which there is bargaining over prices. An exogenous increase in wealth leads issuers to
bargain less aggressively and accept more underpricing. Edelen and Kadlec (2005)
develop a tradeoff argument that does not rely on agency costs. In their model, issuers
attempt to go public but face the possibility of inefficient failure; in particular, they may
be forced to withdrawal from the market. Underpricing reduces the likelihood of this
failure. Higher valuations indicate a higher relative “cost” of failure, in which case
issuers price their offerings more conservatively, leading to partial adjustment.
       We depart from previous literature in arguing that partial adjustment to the two
proxies for public information (return on public comparables and IPO market heat) merit
different interpretation. In classical models, an exogenous increase in the value of public
comparables ought to be fully incorporated into the offer price. By contrast, high
underpricing may simply indicate an environment of heightened adverse selection. If
market conditions are sticky, then adverse selection and high underpricing will persist.
Statisticians then observe autocorrelated underpricing, which could be erroneously
interpreted as the incomplete adjustment of prices to past valuation surprises.4
       With respect to public comparables, our full sample demonstrates a moderate
amount of partial adjustment, replicating Edelen and Kadlec’s result. We find, however,
that this effect is almost exclusively limited to low-quality underwriters. A one-standard
deviation shock to public comparables’ returns leads to a .25 (.04) standard deviation
increase in underpricing for low-quality (high-quality) underwriters. This finding is
consistent with Loughran and Ritter’s agency theory if reputable underwriters are better
able to commit to acting in the interests of issuers. It is also consistent with Edelen and
Kadlec’s tradeoff model if inefficient withdrawal – the key piece of their analysis – is
more likely for low-quality underwriters, which seems like a reasonable assumption.
       The results with respect to IPO market heat are reversed. While the full sample
exhibits strong partial adjustment, the effect is stronger for reputable underwriters. A

  In the model of Yung, Colak and Wang (2007) it is shown that positive shocks to investment
opportunities lead to heightened adverse selection. Hence, high underpricing follows good news.

one-standard deviation shock to IPOheat leads to a .42 (.24) standard deviation increase
in underpricing for low-quality (high-quality) underwriters. From the point of view of
the agency or tradeoff theories, this reversal is puzzling: it is not clear why increases in
wealth coming from public markets and from the IPO market should have differential
effects for high and low-quality underwriters. Alternatively, this result may simply
indicate more autocorrelation in the asymmetric information profile of firms brought
public (i.e., more clustering) by high-quality underwriters for whatever reason. Still other
explanations may emerge as the literature on partial adjustment to public information
matures. Resolving this puzzle is outside the scope of the current analysis, which instead
focuses on the extent to which private information is incorporated in the primary market
rather than the secondary market. In other words, partial adjustment to public
information acts primarily as a control variable in our analysis rather than a variable of
direct interest.
        The plan of the paper is as follows. In Section 2, we describe the data and the
methodology. Section 3 presents the main tests, comparing the cross-sectional volatility
of price revisions with that of the secondary market. Section 4 examines how partial
adjustment to private information varies with underwriter quality. Section 6 concludes.

2 Data and Methodology
        The data for this study was drawn from the Thompson SDC database and consists
of initial public offerings of equity for the period 1980 through 2006. We exclude unit
offers, ADRs, REITs, limited partnerships, closed-end funds and IPOs with an offer price
lower than five dollars. Information for each IPO was collected regarding the initial filing
range, the offer price, the number of shares sold, the identity of the underwriter, whether
the firm was backed by a venture capitalist or not, and whether the firm operates in a
high-tech industry or not. The above sample is supplemented with a hand-collected
dataset of IPOs from 1980 to 1984, available from Jay Ritter’s website. In the case of
overlapping observations (e.g., an IPO in both datasets), the Ritter’s data enable us to
backfill missing variables in SDC. Whenever there is disagreement on a variable, we use
Ritter’s value.

       We supplement the dataset with the Carter-Manaster reputation rank of each
underwriter and age of the firm, both of which are obtained from Jay Ritter’s website.
Carter-Manaster ranks range from 1 (lowest quality) to 9 (highest quality), and Ritter’s
website evaluates these ranks over separate time periods. When an IPO is underwritten by
multiple lead underwriters, we average the reputation of all involved banks, though rank
differences are typically small. In what follows, we use “high-reputation” and “high-
quality” interexchangeably.
       We order our IPO sample by (average) underwriter rank. In what follows we
group IPOs into one of three categories: average rank 8.5 or higher, rank between 6.75
and 8.5 and rank below 6.75. These breakpoints divide our sample into three nearly
equal subsamples. Our results are qualitatively similar with other divisions.
       We merge this database with CRSP in order to compute monthly returns,
excluding those IPOs which lack data in six months or more during the first 12 months
following the offering. When time-series volatility is studied, we use daily prices instead,
also extracted from CRSP. Our final sample consists of 7,124 completed offerings.
       In addition, we extract the deal characteristics (including underwriter
identification) for 1,700 withdrawn IPOs for the same period. This inclusion is important
in our study because we focus on the information revealed during bookbuilding. Clearly
this information is correlated with the decision to continue or withdraw, and so our
completed offering sample suffers from a censoring problem. Section 4 discusses
Heckman’s (1976, 1979) two-step procedure employed to address this censoring.

2.1 Pre-IPO Price Revisions
For each IPO, we calculate the offer price’s deviation from the midpoint of the filing
range as follows
                                 Offer Pr ice − MidFile
                        PREV =                                                        (1)
where MidFile represents the midpoint of the initial filing range, respectively. This is the
standard definition of pre-IPO price revision (e.g., Hanley, 1993). An alternative
definition uses the dollar-value filing price range width as the denominator in (1) to
account for the possibility that the filing range spread is associated with ex-ante level of

uncertainty (Cornelli & Goldreich 2003). Our results under this measure are significantly
stronger than our reported results.5
        Obviously, part of this price revision is due to public information. Following
Edelen and Kadlec (2005) we use the average return of firms in the same Fama-French
48 industry (COMPS) between the filing date and the offer date as a proxy for publicly
observable changes in valuation. We also employ the average underpricing of all IPOs in
the 30 days preceding the offer date as a measure of overall IPO activity (IPOHeat)
following Bradley and Jordan (2002). IPOHeat is orthogonalized against COMPS in
order to single out the information coming from the IPO market specifically rather than
the overall health of the equity markets. Finally, PREV is orthogonalized against both
COMPS and IPOHeat to obtain a measure (REV) that isolates the private information
revealed by bookbuilding.
        Earlier drafts of this paper used raw price revisions PREV rather than
orthogonalized revisions REV, with similar results.

2.2 Holding Period Horizons
        Our methodology requires us to specify a horizon over which prices incorporate
private information; see Figure 1.
        There are several institutional factors that may contribute to the inefficiency of
immediate aftermarket prices. First, lead underwriters engage in price support during the
first month or so of trading (Aggarwal, 2000), which truncates the distribution of returns
and therefore reduces the informativeness of prices. Second, IPOs are difficult to short-
sell in the immediate aftermarket (Loughran and Ritter, 1995) which further impedes the
price discovery role of the market. Third, underwriters employ a system known as the
Depository Trust Company’s IPO tracking system. This system identifies shares that are
“flipped”, that is, trading immediately for quick profit – a strategy which is said to be
opposed to underwriters’ interests. If a syndicate member’s customers are
disproportionately identified as “flippers”, the lead underwriter reserves the right to recall

 We view this difference as an artifact of low-quality underwriters’ lower offer prices, rather than a
genuine economic effect. Note that Offer − Mid = Offer − Mid Mid , and the last fraction is smaller for
                                      Hi − Lo        Mid    Hi − Lo
low-quality underwriters. Low-quality underwriters therefore have even smaller revisions under this
alternative measure.

underwriting fees earned by that syndicate member (a “penalty bid”). This recall does
occasionally occur in practice, and the tracking system is often monitored for thirty days
or more (Aggarwal, 2000). IPO investors, sensitive to their standing with underwriter,
may refrain from trading as aggressively as they would in the absence of such a system.
Taken together, all of these institutional factors suggest that in seeking an appropriate
proxy for true value of an IPO, one should require at least a month (if not more) of
       Long horizons, however, introduce their own problems. First, estimate of long-
run abnormal returns are extremely noisy and biased. Kothari and Warner (1997)
conclude that inferences from long-run studies “require extreme caution.” Second, much
of the variation occurring at very long horizons is unrelated to ex-ante private
information of IPO investors, which is the information of interest in the current study. It
is our view that most of this private information will be resolved through the trading of
investors within the first several months after the IPO.
       To balance these tradeoffs, we focus on buy-and-hold returns at 3-month
horizons. We also follow the stocks for up to twelve months after the IPOs, in part as a
robustness check. Differential patterns at the six to twelve month horizons may be of
independent interest, however, since they may shed light on the private information of
insiders (since most lockups expire at six months) rather than that of secondary market

2.2 Cross-Sectional Variance in Aftermarket Returns
For our cross-sectional results, we use monthly CRSP returns. The abnormal return for
stock i at month T is defined as
                            T            T
                BHARiT = ∏ (1 + rit ) − ∏ (1 + mit )                         (2)
                           t =1         t =1

where rit denotes the return on stock i in month t, and mit denotes the return on the market
(proxied by the CRSP stock file value-weighted market index) at time t. Here the
implicit assumption is that the market serves as a reasonable measure of expected return
for stocks in our sample. Our cross-sectional results are insensitive to the choice of
benchmark, however. We note that the benchmark choice is more critical for event

studies that focus on the mean rather than (as in our case) the variance, because nearly all
of our cross-sectional variance is firm-specific rather than driven by the benchmark.

2.4 Time Series Variance in Aftermarket Returns
        To examine patterns in secondary market returns in more depth, we use CRSP to
obtain daily prices in the first year after the IPO for all the 7,124 completed IPO firms in
our data set. For each firm, we run a regressison of the daily returns on the Fama-French
factors and the momentum factor to obtain daily abnormal returns. The mean and
standard deviation in time-series variances are compared across the subsets of IPOs
underwritten by high-quality and by low-quality banks.

2.5 Testable Implications
        We test the following hypotheses, motivated in the introduction:

H1: Cross-sectional dispersion in price revisions is higher for more reputable

        Hypothesis H1 is consistent with a scenario in which low quality underwriters do
not extract information from investors; rather, they serve in an uninformative
distributional role.
        This hypothesis has another implication. If no uncertainty is resolved during the
bookbuilding phase for low-quality underwriters, this means that all of the uncertainty
must be resolved in the secondary market, leading to the following claim.

H2: Information aggregation during the bookbuilding period leads to reduced
subsequent uncertainty. Therefore high-quality underwriters should be associated with
lower (cross-sectional) aftermarket return variability.

        To test Hypothesis H2 we calculate the cross-sectional dispersion of buy-and-hold
returns (BHARs), identifying reduced uncertainty with reduced dispersion in BHARs. We
also consider the analogous time-series version of this hypothesis.

H3: Firms taken public by more reputable underwriters exhibit lower time-series return

        Again, the intuition is that more active price discovery in the pre-market leads to
less need for price discovery in the secondary market.
        The aforementioned tests are univariate. It may be the case, however, that high-
quality underwriters are associated with systematically different types of issuers. It is
important therefore to control for firm characteristics. We do this in two ways. First, as
is standard in the literature, we introduce firm characteristics as additional independent
variables in our regressions. However, controlling for firm characteristics is a highly
imperfect control if the asymmetric information profile of firms is different across
underwriters, which seems likely here. As a second control, we therefore consider the
following variant of H1 and H2:

H4: Of the total (post-filing) uncertainty to be resolved, for high-quality underwriters a
smaller proportion is resolved in the secondary market than for the low-quality

        To test Hypothesis H4 we form the ratio of cross-section dispersion in abnormal
aftermarket returns to the cross-sectional dispersion in filing price revisions. This
fraction is termed a variance ratio. To the extent that either category of underwriter is
associated with higher ex-ante risk, both the numerator and denominator of that ratio will
be inflated. In effect, the denominator “factors out” the level of unobservable risk so that
investigating Hypothesis H4 is equivalent to investigating whether the proportion of
uncertainty resolved in the bookbuilding period varies with underwriter quality.
        Hypotheses H1 through H4 are not mechanism-specific. That is, they assume that
information is aggregated in the primary markets but do not specify how. In contrast, our
final hypothesis depends on bookbuilding models in particular. As mentioned earlier,
these models predict a positive correlation between underpricing and private information

revealed.6 Under the maintained hypothesis that the information gathering paradigm
better models the behavior of high-quality underwriters, we predict:

H5: Partial adjustment to private information is more pronounced for reputable

           Finally, we turn to partial adjustment to public information. Three possible
explanations for this phenomenon were summarized in the introduction: agency costs, the
trade-off theory and autocorrelated information asymmetry. We ask now whether the
strength effects should vary with underwriter quality. The relevant hypotheses are
summarized in the following table and discussed below.

                                       Agency/                                  Autocorrelated
                                                           Trade-off Theory
                                       Bargaining Theory                        Information Asymmetry
    Partial Adjustment to Return of
                                       Yes                 Yes                  No
    Public Comparables?
    Partial Adjustment to IPOHeat?     Yes                 Yes                  Yes

    Effects Stronger for Low Quality
                                       Yes                 Yes                  No prediction

           Both the agency and tradeoff theory rely upon valuation shocks to insiders’
personal wealth. Theoretically, it should not matter which channel the news comes from:
publicly traded comparables or the returns of contemporaneous IPOs. In contrast, the
autocorrelated information asymmetry argument is based upon the amount of adverse
selection in the IPO market itself. Controlling for IPO market heat, the movements of
public comparables should contain no information regarding the amount of adverse
selection for current offerings.
           The arguments behind the agency and tradeoffs theories are particularly strong for
low-quality underwriters. Reputable underwriters tend to mitigate agency problems.
Issuers able to attract high-quality underwriters tend to be larger and more well-known,

  This prediction would not hold in an auction, for example, in which case prices would be weak-form

leading to better outside opportunities. Inefficient withdrawal may also be less likely for
IPOs with a high quality underwriter. All of these factors suggest stronger partial
adjustment to public information for low-quality underwriters in both the agency theory
and the trade-off theory. In contrast, the autocorrelated information asymmetry theory
makes no prediction regarding underwriter quality: a priori, either type of underwriter
could exhibit stronger intertemporal clustering in the asymmetric information profile of

3. Volatility in the Primary and Secondary Markets
        Table 1 presents descriptive statistics for the full sample, and for the three terciles
of IPO firms sorted by underwriter’s reputation ranking in two panels, respectively.
High-quality underwriters are more likely to take public firms that are venture capital
backed and in high-tech industries. Their offerings are older on average and tend to raise
larger proceeds than firms taken public by median or low prestige underwriters. Taken
together, these variables have an ambiguous impact on the severity of asymmetric
information across terciles: firm seasoning should mitigate market imperfections whereas
high-tech industry affiliation may increase them.
        Price revisions relative to the original filing price range, both in raw form (PREV)
and industry-return-adjusted form (REV), are approximately symmetrically distributed
around zero in the full sample. On average, they are negative for the low-reputation
tercile, near zero for the medium-reputation tercile, and positive for the high-reputation
tercile. However, central tendency differences are not large; the median PREV is exactly
zero for all three quality terciles.
        The absolute value of price revision increases monotonically with bank
reputation. The mean absolute value of price revision is 0.168 for firms taken public by
high-quality underwriters, but only 0.104 for those by low-quality underwriters. This
preliminary evidence is consistent with the notion that high-quality underwriters are more
active in aggregating information in the marketing phase.
        Consistent with Beatty and Welch (1996), Kumar McGee and Womack (1998),
Habib and Ljungqvist (2002), and Cooney, Singh, Carter and Dark (1999), we find that
prestigious underwriters are associated with higher average underpricing than low-quality

underwriters (29.8% vs. 14.3%). For all three terciles, the underpricing variable is highly
skewed: in all subsamples, the mean underpricing is at least double the median
        Long-run IPO returns in our sample are disappointing. The average 12-month
buy-and-hold return in our full sample is –6.0%. Also consistent with existing literature,
much of this poor return is concentrated in the offerings of low prestige underwriters
(Brav and Gompers 1997). The average 12-month BHAR is –14.2% in the bottom
quality tercile, while it is –0.6% in the top quality tercile. However, over shorter
horizons there is little or no underperformance: the average 3-month BHAR is 5.6% in
the top tercile and -1.0% in the bottom tercile.

3.1 REV and BHAR Variability
        Table 2 presents the cross-sectional standard deviations of our key variables. Over
the whole sample, dispersion in pre-IPO price revision (REV) shows a strongly positive
monotonic trend ( σ =28.1% vs. σ =17.0%; top vs. bottom tercile). BHAR dispersion
displays a milder, if still identifiable, trend. Buy-and-hold dispersion is greater for high
prestige underwriters, but these differences are small. For instance, in the case of 12-
month BHARs, the cross-sectional standard deviation for the top tercile is σ =79%,
whereas for the bottom tercile it is σ =72%.
        In contrast to this full sample result, the subperiod analysis indicates that
throughout most of our sample high-quality underwriters have been associated with lower
secondary market return variability. For example, in the 1980s, the standard deviation of
3-month BHARs was 33% for the bottom tercile and only 22% for the top tercile, and the
difference is statistically significant at the 1% level. This pattern persists for all holding
time horizons and subperiods 1980s, 1990-1995 and 2001-2006. The only rejection of
Hypothesis H2 was during the internet bubble period (1996-2000) in which high-quality
underwriters were associated with higher volatility.
        This difference between subperiod results and full sample results is partly
explained by the Simpson’s paradox effect. To see why, note that cross-sectional
variation was dramatically higher during hot market of 1996-2000, consistent with Yung,
Colak and Wang’s (2007) argument that hot markets are associated with heightened

adverse selection. For example, for high-quality underwriters, the standard deviation of
3-month BHARs is 70% during the 1996-2000 period but only 22% during the 1980s.
On the other hand, there was a strong substitution towards high-quality underwriters
during this market. Hence, even though high-quality underwriters are associated with
lower variance in most subperiods, it appears in the full sample as if they are associated
with higher variance.
           To summarize, hypothesis H1 is supported throughout the sample and hypothesis
H2 is supported in all subperiods except 1996-2000.

3.1 Variance Ratios
           The aforementioned descriptive statistics include dispersion in each of our key
variables. Recall, however that our analysis is confounded by the fact that different
profiles of asymmetric information associated with each quality tercile.
           To account for this unobservable factor, our main hypothesis is that information
aggregation should increase the dispersion of REV relative to that of BHAR, i.e.,
relatively more information is incorporated in the primary market. Perhaps the simplest
way to measure the relative size of these two variables is to form their ratio.
           Our methodology is as follows. We treat each bank (rather than each IPO) as an
observation. For a given bank, we measure the buy-and-hold return on each of its
offerings and the cross-sectional variance of these returns. We then compute the cross-
sectional variance of the pre-IPO price revisions of these same offerings. Finally, we
define the “variance ratio” of this bank (VR) to be the ratio of these two cross-sectional
variances (with the variance of BHAR in the numerator since the variance of REV could
be very close to zero for small investment banks that have underwritten only a few IPOs).
To ensure reasonably stable estimates of second moments, we keep in our sample only
underwriters that has made more than 6 offerings (using different cutoffs does not change
our results.) Using the same Carter-Manaster tercile cutoffs of 6.75 and 8.5 described in
Section 2, we are left with 25 high-quality banks, 41 medium-quality and 145 low-quality
banks. Finally, we compute the descriptive statistics of variance ratios within each

        Untabulated examination shows the variance ratios thus obtained are highly
skewed. In all three terciles, the skewness of variance ratios is an order of magnitude
larger than mean, median or standard deviation. Therefore, subsequent statistical analysis
employs the natural logarithm of the variance ratio for each bank.
        Table 3, Panel A summarizes the statistical significance of the differences in log
variance ratios for the full sample. Low-quality underwriters have higher ratios than
either other terciles, and this difference is significant at all horizons (p-value < 0.01 in
three cases and equals to 0.011 in the other), which is consistent with hypothesis H4.
        The abnormal pattern in aftermarket returns during 1996-2000 shown in Table 2
suggests that closer examination in warranted here as well. In Table 3, Panel B, we
isolate the bubble period and find log variance ratios are of roughly identical levels across
underwriter groups. Thus the lone failure for H2 applies to H4 as well. In the rest of time,
low-quality underwriters exhibit significantly higher ratios than medium-quality and
high-quality underwriters, as shown in Table 3, Panel C.
        The multivariate version of this result tells the same story; see Table 4. We
regress log variance ratios on underwriter reputation, controlling for firm characteristics,
including the amount filed, firm age, whether the firm is venture capital-backed or in a
high-tech industry. We also include in the regression the IPO market spillover
information measure, IPOHeat, to control market condition around the IPO time. For an
underwriter firm characteristics and IPOHeat are the average of all firms it takes public.
Since different underwriters might attract different types of issuers, we orthogonalize
mean firm characteristics against underwriter reputation. Columns 2-9 display the
estimated coefficients on the reputation variable, all of which are negative, and 7 out of 8
coefficients are statistically significant.
        In untabulated results, we also consider the possibility that underwriter reputation
is the endogenous result of issuer firm characteristics and use a two-stage-least-square
style procedure to estimate the model. Again we obtain significant, negative coefficients
for bank reputation.
        Overall, we conclude that prestigious underwriters are associated with increased
dispersion of pre-IPO price revisions relative to that of aftermarket buy-and-hold returns,
and this result holds after controlling for different firm characteristics. Hypothesis H4 is

thus supported. This evidence is consistent with these underwriters taking a more active
information aggregation role during the bookbuilding period than do low-quality

3.2 Factoring Out the Predictable Component of Underpricing
       In Section 3.1 we investigate the dispersion of REV relative to that of BHARs, the
aftermarket returns, and find that high-reputation banks exhibit higher variance ratios
than their low-reputation peers, indicating that a larger proportion of information
asymmetry is resolved in the bookbuilding stage by a high-reputation bank than by a low-
reputation bank. By design, those tests avoid the underpricing stage (UP) in Figure 1.
The reason for this omission is that UP is a contaminated measure. It incorporates both
(1) a predictable component resulting from REV, because of the partial adjustment effect
and (2) some early aftermarket price revelation. It therefore contains information from
the two stages that we want to measure separately.
       In this section we define an “implied value” from the empirical relationship
between pre-IPO price revision and underpricing. This implied value is, effectively, the
expected true value conditional on information revealed by REV. The specific method is
to run the regressions in Section 4, defining the “implicit offer price” as the real offer
price times the sum of one and the predicted underpricing. Doing so identifies the
predicted underpricing as the investment bank’s intended underpricing. The residuals are
price changes resulted from new information from investors and/or noise during the first
day trading.
       Secondary market price discovery is then defined by deviations from this implied
value, rather than from the (biased) offer price. With this new baseline price, we redefine
aftermarket price changes, denoted as PChange3, Pchange6, Pchange9 and Pchange12,
respectively for 3-month, 6-month, 9-month and 12-month time horizons. The difference
between Pchange and BHAR lies in the baseline price: BHAR uses the closing price on
the first trading day as the baseline, while Pchange uses the implied value.
       Table 5 summarizes the comparison of variance ratios derived across firm groups.
The average variance ratio is higher for low-reputation underwriters and the differences
between the high-reputation group and the low-reputation group are significant at about

the 1 percent level. The regression results are displayed in Table 6. Again, the estimated
coefficients of reputation are consistently negative and mostly significant, indicating
firms taken public by a higher quality investment bank exhibit lower aftermarket return
dispersion relative to that of pre-offering price revisions, compared to those taken public
by a lower quality bank. The results are qualitatively identical to those in Table 3 and
Table 4 when BHARs is used as the measure of aftermarket returns.

3.3 Time-series volatilities
       In this subsection, we repeat the analysis of Section 3.1, using time-series
volatility rather than a cross-sectional measure. Again the argument is that primary
market price discovery should reduce secondary market risk.
       We employ daily return observations and find abnormal daily returns as the
residuals of the non-intercept Fama-French-Carhart four-factor model. The market, size,
value and momentum factor are all from Kenneth R. French’s website. We then compute
the standard deviations of abnormal daily returns for each month and each quarter in the
first 12 months following the IPO. The first trading day is excluded.
       Table 7 displays the average of aftermarket return volatility across the high-
reputation-bank-underwritten and low-reputation-bank-underwritten groups. In the
whole period of 1980-2006 (Panel A), the results are mixed: for the 3-month and 6-month
horizons, the high-reputation group has significantly return volatility, while the pattern
switches for the 9-month and 12-month horizons. However, this full sample result masks
the relationship seen in the subperiods. Mirroring our cross-sectional results, the variance
is lower firms underwritten by reputable banks, for all the four time horizons, in every
subperiod except 1996-2000 (Panels B-E).
       Table 8 summarizes the multivariate version of this test. We regress aftermarket
volatility on bank reputation and a variety of firm characteristics. The results are as
expected. VC-backed and high-tech firms are riskier. Larger and older firms are less
risky. Consistent with Yung, Colak and Wang (2007), initial public offers that occur
during hot markets are riskier, ceteris parabus. As Hypothesis 3 predicts, the coefficients
of bank reputation for 6-, 9-, and 12-month horizons are all negative.

4. Partial Adjustment as a Function of Underwriter Quality
       Our fifth hypothesis is that the partial adjustment to private information is more
pronounced for prestigious underwriters. This prediction will hold provided that they act
in the costly information extraction role suggested by Benveniste and Spindt (1989),
while low-quality underwriters act in the uninformed, distributional role suggested by
Rock (1986).
       Figure 2 offers preliminary evidence on this question. Raw price revisions are
plotted against underpricing. Along the top panel (the lowest quality) there appears to be
no relation, and in the middle panel (medium quality) there is a mildly identifiable
association. For the highest quality, there is an obvious positive correlation. Note also
that this figure re-iterates our core finding of Section 3: higher quality underwriters
exhibit much more dispersion in REV.
       To test Hypothesis H5 formally, we look into whether REV’s predictive power for
IPO initial returns differ across underwriter reputation terciles by regressing underpricing
on REV. As emphasized by Edelen and Kadlec (2005), this type of regression (though
standard in the literature) suffers from a censoring problem since only firms that were
received favorably (and therefore didn’t withdraw) are typically included in the sample.
To address this issue, we employ a Heckman (1976, 1979) two-stage model. In the first
stage, we use the full sample (including both completed and withdrawn IPOs) to run the
following Probit regression,

  I w = α 0 + α 1COMPS + + α 2 COMPS − + α 3WRate + α 4 Re pu + α 5 AmtFile + ε           (3)

where Iw equals 1 for withdrawn offerings and 0 for completed offerings.
COMPS + equals COMPS, the equally-weighted return of listed firms in the same Fama-

French 48 industry during the bookbuilding period, if COMPS >0, and 0 otherwise;
COMPS − equals COMPS if COMPS < 0, and 0 otherwise. COMPS + and COMPS −

together capture the market movement and account for potential asymmetric impact of
positive and negative market movements on the IPO withdrawal/completion decision.
The latest IPO withdrawal rate, wrate, is computed as the number of IPO withdrawals

within 106 days (the median length of registration period in our sample) of filing, divided
by the number of active IPOs under registration during the 30 days preceding the offer or
withdrawal date. We orthogonalize wrate with respect to COMPS and use the residual
WRate in order to isolate the spillover information from activity in the withdrawn IPO
market. Repu and AmtFile are lead underwriters’ (average) reputation ranking and the
issuing firm’s amount filed. The above specification that uses public information to
explain withdrawal/completion decision is in essence similar to that in Edelen & Kadlec
          The estimation results of (3) are reported in the Appendix. Using estimated
coefficients, we calculate the inverse Mills ratio (sometimes called “selection hazard”), λ.
In the second step, we run an OLS regression (using the sample of completed offerings)
in which the dependent variable is IPO underpricing. The set of independent variables
includes the inverse Mills ratio from the first step, private information REV, market
movement variable COMPS, IPO market spillover information, IPOHeat, and risk
proxies identified in the underpricing literature. In a separate specification, asymmetric
adjustment to both public and private information is allowed. Likewise, we define REV+
as equal to REV for positive REV and 0 otherwise, and REV- as equal to REV for negative
REV and 0 otherwise. Partial adjustment will be indicated by positive and significant
coefficients on REV, or REV+ and REV- , in the two specifications respectively.
          We report the results of this regression in Table 9. The estimated coefficient for
REV is monotonic in bank reputation, rising from 0.338 for the bottom reputation tercile
in to 1.166 for the top reputation tercile in specification (1). Thus, partial adjustment to
private information is a much more pronounced phenomenon for reputable underwriters.
In addition, this partial adjustment shows significant asymmetry (as indicated by higher
coefficients on REV+ than on REV-) for high-quality underwriters, again consistent with
payment for information aggregation in the Benveniste and Spindt (1989) framework. For
low-quality underwriters, the partial adjustment is nonexistent given positive private
information, but big given negative private information, contrary to Benveniste and
Spindt’s prediction.
          The coefficient on COMPS decreases monotonically with reputation. Thus
reputable underwriters more fully adjust prices to the information implied by the return of

public comparables. In contrast, the coefficient on IPOHeat increases monotonically
with reputation. This differential response to two different types of public information is
somewhat puzzling from the point of view of the agency/bargaining theory and the
tradeoff theory. Both theories emphasize the role of the issuer’s personal wealth, and it is
unclear why shocks to wealth coming from the public equity markets and the IPO market
should have differential effects.
       An alternative view discussed in Section 2.5 is that partial adjustment to IPOHeat
simply reflects autocorrelation in the severity of frictions in the IPO market. The larger
coefficient on IPOHeat for reputable underwriters would then indicate more
intertemporal clustering of firms types (i.e., states in which particularly risky IPOs are
underwritten are more persistent).
       Another interesting observation from Table 9 is the differences in R² across
underwriter groups. With specification (1), this goodness-of-fit statistic increases from
0.108 for the low-reputation underwriter tercile to 0.466 for the high-reputation
underwriter tercile. The differences in R² are statistically significantly, as shown in the
bottom panel. With specification (2) and an untabulated univariate specification with
REV being the only explanatory variable, the same pattern in R² is observed. This
pattern, indicating pre-offering price revisions made by high-reputation underwriters are
more informative about underpricing, buttresses our argument above that the Beveniste-
Spindt dynamic bookbuilding framework describes the behavior of high-reputation
underwriters better.
       The economic impact of information flows during the bookbuilding period on
underpricing is displayed in Table 10. A one-standard-deviation shock to REV causes the
underpricing to change by 0.43 standard deviation if the underwriter is a high-reputation
one, but only by 0.20 standard deviation if the underwriter is a low-reputation one. Shock
to comparable firm return barely alters the underpricing for a high-reputation underwriter,
but does cause change to the underpricing for a low-reputation underwriter.
       In summary, high-reputation underwriters exhibit more pronounced partial
adjustment and asymmetric adjustment to private information, both consistent with the
bookbuilding model’s prediction; The bookbuilding model, however, fails to describe the
behavior of low-reputation underwriters.

5. Conclusions
       Our evidence suggests that reputable banks are more active in primary market
information aggregation than non-reputable banks. High-quality banks have more active
price revisions: the standard deviation of pre-offering price revisions of high-quality
banks is nearly twice that of low-quality banks. This information aggregation apparently
substitutes for secondary market price discovery. Both cross-sectional aftermarket return
variance and time-series volatility are lower for high-quality banks in all time periods
except the bubble period, 1996-2000. Thus, reputable banks have a higher relative
proportion of information asymmetry resolved during the bookbuilding period rather than
in the secondary market.
       We also confirm that a central prediction of bookbuilding models – partial
adjustment to private information – has much stronger support in the high reputation
subsample. Again this is consistent with a stronger price discovery role for reputable
       Finally, we turn to partial adjustment to public information. We show that partial
adjustment to two separate pieces of public information (IPO market spillover
information and returns of comparable public firms) show reversed patterns for low and
high-quality underwriters. These differential effects do not seem to have an obvious
basis in either the tradeoff theory or the agency/bargaining theory. It is hypothesized that
this result may indicate differences in the intertemporal clustering in types of firms
underwriters by different underwriters, but a full investigation along these lines is beyond
the scope of this paper.

Appendix: Heckman first-step regression

The table below presents the probit estimation in the first step of the Heckman (1976, 1979) two-
step procedure that is used to correct selection bias. The dependent variable is WITHDRAW,
which equals 1 for withdrawn offering and 0 for completed offerings. Independent variables
include a series of public information. COMPS + equals COMPS if COMPS > 0, and 0

otherwise; COMPS − equals COMPS if COMPS < 0, and 0 otherwise. the IPO withdrawal rate,

wrate, is computed as the number of offerings withdrawn within 106 days of their initial filing
date divided by the number of active registrants during the 30 days preceding the offer or
withdrawal date. REPU is the underwriters’ average Carter-Manaster reputation ranking. AmtFile
is the filing size in million dollars.

                            Estimate         Std error            Chi-Sq            Pr > Chisq
COMPS +                      -0.644           0.0619              108.29             <.0001
COMPS-                       5.276            0.2972              315.09             <.0001
wrate                        -1.004           0.0598              281.44             <.0001
repu                         0.162            0.0081              398.23             <.0001
AmtFile                      -0.166           0.0169               97.27             <.0001


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    Table 1: Descriptive Statistics

                                        Panel A: Full Sample (N=7,124)
                                         Mean            Median          10th percentile   90th percentile
    Filing Amount ($m, 1980)             14.26             6.63               1.63             25.65
    Age (years)                          15.86               8                  2               43
    High Tech dummy                       0.45               0                  0                1
    Venture Capital dummy                 0.37               0                  0                1
    Bank Reputation                       7.06               8                  3                9
    PREV                                  0.001              0               -0.241            0.217
    REV                                   0.000           -0.001             -0.235            0.209
    Underpricing                         0.206            0.091              -0.028            0.500
    3-month BHAR                          0.025           -0.029             -0.348            0.404
    6-month BHAR                          0.004           -0.080             -0.527            0.563
    9-month BHAR                         -0.024           -0.125             -0.666            0.647
    12-month BHAR                        -0.060           -0.189             -0.775            0.734

                                              Panel B: SubSamples
                                         Low Reputation      Medium Reputation          High Reputation
                                      Mean        Median     Mean      Median         Mean       Median
    Filing Amount ($m, 1980)           4.09        2.41       9.66      6.78          27.51        12.77
    Age (years)                         7.42         7         9.01       9            10.34         9
    High Tech dummy                     0.36          0        0.49       0             0.48         0
    Venture Capital dummy              0.23          0        0.42        0             0.43         0
    Bank Reputation                    4.08         4.81      7.69        8             8.93         9
    PREV                              -0.033          0      -0.012       0            0.043         0
    REV                               -0.035      -0.010     -0.013    -0.006          0.043       0.018
    abs(REV)                           0.104       0.066      0.151     0.113          0.168       0.115
    Underpricing                      0.143        0.071     0.180      0.093          0.298       0.111
    3-month BHAR                      -0.010      -0.060      0.028    -0.021          0.056      -0.009
    6-month BHAR                      -0.043      -0.132      0.009    -0.066          0.039      -0.051
    9-month BHAR                      -0.088      -0.204     -0.010    -0.115          0.018      -0.072
    12-month BHAR                     -0.142      -0.279     -0.043    -0.194         -0.006      -0.115

Table 2: Cross-Sectional Variation in Pre-IPO Price Revision and Aftermarket Returns
This table presents the mean and standard deviation of the pre-IPO price revision and aftermarket buy-and-
hold returns for 3-, 6-, 9-, and 12-month periods. REV is the price revision from the midpoint of the
original filing range to the offer price, scaled by the midpoint of the offer range and then orthogonalized
against industry return during the bookbuilding period. Our sample of IPOs is partitioned according to
Carter-Manaster rank of the lead underwriters involved, or average quality if the issue is co-underwritten.
IPOs are in the top tercile of our sample if their average underwriter rank is 8.5 or higher, and in the
bottom tercile if it is below 6.75.

Panel A: 1980-2006
                                    Bottom Tercile    Medium Tercile      Top Tercile     Bottom vs. Top
                                      (n=2116)          (n=2511)           (n=2497)           Pr > F

                      Mean              -0.019            -0.013              0.029
REV                                                                                            0.000
                      Std Dev            0.170             0.194              0.281
                      Mean              -0.010             0.029              0.056
3-month BHAR                                                                                   0.000
                      Std Dev            0.365             0.381              0.468
                      Mean              -0.043             0.009              0.039
6-month BHAR                                                                                   0.000
                      Std Dev            0.534             0.548              0.620
                      Mean              -0.088            -0.010              0.018
9-month BHAR                                                                                   0.000
                      Std Dev            0.631             0.694              0.695
                      Mean              -0.142            -0.043             -0.006
12-month BHAR                                                                                  0.000
                      Std Dev            0.723             0.811              0.793
Panel B: 1980s
                                    Bottom Tercile Medium Tercile Top Tercile             Bottom vs. Top
                                       (n=879)        (n=712)         (n=628)                 Pr > F

                      Mean               0.016            -0.005              0.009
REV                                                                                            0.000
                      Std Dev            0.111             0.139              0.137
                      Mean               0.007             0.001              0.012
3-month BHAR                                                                                   0.000
                      Std Dev            0.332             0.246              0.221
                      Mean              -0.009            -0.016              0.003
6-month BHAR                                                                                   0.000
                      Std Dev            0.522             0.377              0.332
                      Mean              -0.057            -0.027              0.000
9-month BHAR                                                                                   0.000
                      Std Dev            0.638             0.522              0.436
                      Mean              -0.111            -0.044             -0.021
12-month BHAR                                                                                  0.000
                      Std Dev            0.766             0.589              0.526

Panel C: 1990-1995
                               Bottom Tercile Medium Tercile Top Tercile   Bottom vs. Top
                                  (n=610)        (n=766)        (n=644)        Pr > F

                     Mean          -0.017         0.013          0.039
REV                                                                            0.000
                     Std Dev        0.152         0.198          0.198
                     Mean           0.007         0.056          0.062
3-month BHAR                                                                   0.061
                     Std Dev        0.327         0.299          0.303
                     Mean          -0.008         0.078          0.083
6-month BHAR                                                                   0.022
                     Std Dev        0.495         0.472          0.452
                     Mean          -0.046         0.066          0.069
9-month BHAR                                                                   0.000
                     Std Dev        0.595         0.577          0.507
                     Mean          -0.084         0.050          0.045
12-month BHAR                                                                  0.006
                     Std Dev        0.673         0.743          0.603
Panel D: 1996-2000
                               Bottom Tercile Medium Tercile Top Tercile   Bottom vs. Top
                                  (n=511)        (n=842)        (n=864)        Pr > F

                     Mean          -0.073         -0.027          0.045
REV                                                                            0.000
                     Std Dev        0.239         0.225           0.410
                     Mean          -0.077         0.028           0.093
3-month BHAR                                                                   0.000
                     Std Dev        0.435         0.526           0.700
                     Mean          -0.158         -0.033          0.037
6-month BHAR                                                                   0.000
                     Std Dev        0.593         0.731           0.903
                     Mean          -0.222         -0.076         -0.017
9-month BHAR                                                                   0.000
                     Std Dev        0.647         0.906           0.982
                     Mean          -0.306         -0.141         -0.058
12-month BHAR                                                                  0.000
                     Std Dev        0.676         1.028           1.091
Panel E: 2001-2006
                               Bottom Tercile Medium Tercile Top Tercile   Bottom vs. Top
                                  (n=116)        (n=191)        (n=361)        Pr > F

                     Mean          -0.066         -0.040         0.010
REV                                                                            0.920
                     Std Dev        0.195         0.189          0.197
                     Mean           0.077         0.017          0.030
3-month BHAR                                                                   0.000
                     Std Dev        0.423         0.308          0.267
                     Mean           0.035         0.003          0.022
6-month BHAR                                                                   0.000
                     Std Dev        0.462         0.354          0.343
                     Mean           0.064         0.052          0.050
9-month BHAR                                                                   0.000
                     Std Dev        0.598         0.544          0.423
                     Mean           0.082         0.048          0.066
12-month BHAR                                                                  0.000
                     Std Dev        0.718         0.583          0.517

    Table 3: Variance Ratios Across Underwriter Quality Terciles
    This table presents the folowing statistic. For each underwriter in our sample with at least seven IPOs, we compute the cross-sectional
    variance of secondary market returns of IPOs underwritten by that bank, employing 3-,6-,9- and 12-month holding periods. We also
    compute cross-sectional variance of pre-IPO price revisions, again for IPOs underwritten by that bank. The variance ratio is defined as the
    ratio of these two statistics. The high-rep subsample consists of underwriters with Carter-Manaster rank 8.5 or higher, and the low-rep
    subsample consists of underwriters with Carter-Manaster rank below 6.75.
    Panel A: 1980-2005
                                           Number of Banks                    Average Log Variance Ratio
                                       Low-Rep        High-Rep             Low-Rep Banks High-Rep Banks                              Pr > t
    3-month VR                           145             25                    1.676             1.199             Low vs. High      0.005
    6-month VR                           145             25                    2.475             1.815             Low vs. High      0.000
    9-month VR                           145             25                    2.844             2.300             Low vs. High      0.003
    12-month VR                          145             25                    3.051             2.587             Low vs. High      0.011

    Panel B: 1996-2000
                                          Number of Banks                     Average Log Variance Ratio
                                       Low-Rep       High-Rep              Low-Rep Banks High-Rep Banks                              Pr > t
    3-month VR                           41             13                     1.413             1.451             Low vs. High      0.912
    6-month VR                           41             13                     1.921             1.958             Low vs. High      0.922
    9-month VR                           41             13                     2.361             2.253             Low vs. High      0.723
    12-month VR                          41             13                     2.455             2.576             Low vs. High      0.772

    Panel C: Other time
                                           Number of Banks                    Average Log Variance Ratio
                                       Low-Rep        High-Rep             Low-Rep Banks High-Rep Banks                              Pr > t
    3-month VR                           123             23                    1.688             0.997             Low vs. High      0.000
    6-month VR                           120             23                    2.557             1.666             Low vs. High      0.000
    9-month VR                           120             23                    2.934             2.210             Low vs. High      0.000
    12-month VR                          120             23                    3.220             2.538             Low vs. High      0.000

Table 4: The Determinants of Underwriters' Variance Ratios
This table presents the the coefficient estimates, and heteroscedasticity-consistent standard errors in parentheses, of OLS regressions. The
dependent variable is the observation for each underwriter (with at least 5 IPOs in our sample) of the following statistic: the log of the cross-
sectional dispersion in secondary market returns divided by the cross-sectional standard deviation of price revisions. Statistical significance at the
1%, 5% and 10% level are denoted with superscripts ***,** and *, respectively.

                                                                      Dependent Variables (N=211)
Variables                  VR3             VR6             VR9            VR12                   VR3            VR6           VR9           VR12
                        -0.158***       -0.210***       -0.207***       -0.166***             -0.119**       -0.146**      -0.169**         -0.101
Bank Reputation
                         (0.042)         (0.043)         (0.048)         (0.048)               (0.059)        (0.065)       (0.074)        (0.072)
                                                                                                -2.870         -2.158        -3.213         -1.628
                                                                                               (2.660)        (2.894)       (2.938)        (3.158)
                                                                                                -0.109       -0.339**        -0.224         -0.309
Filing Amount
                                                                                               (0.152)        (0.148)       (0.145)        (0.141)
                                                                                                -0.108          0.005        -0.099          0.078
                                                                                               (0.199)        (0.202)       (0.214)        (0.206)
                                                                                                -0.698         -0.568        -0.801         -0.614
                                                                                               (0.667)        (0.653)       (0.670)        (0.626)
                                                                                                1.273*         1.130*         0.871          0.588
                                                                                               (0.661)        (0.604)       (0.640)        (0.631)

R²                        0.081           0.127           0.110           0.072                 0.154         0.205          0.178          0.126

Table 5: Variance Ratios Across Underwriter Quality Terciles Using Pchange

This table presents the folowing statistic. For each underwriter in our sample with at least seven IPOs, we compute the cross-sectional
variance of secondary market returns of IPOs underwritten by that bank, employing 3-,6-,9- and 12-month holding periods, and using the
estimated "implicit offer price" as the benchmark price. We also compute cross-sectional variance of pre-IPO price revisions, again for IPOs
underwritten by that bank. The variance ratio is defined as the ratio of these two statistics. The high-rep subsample consists of underwriters
with Carter-Manaster rank 8.5 or higher, and the low-rank subsample consists of underwriters with Carter-Manaster rank below 6.75.
Panel A: 1980-2005
                                    Number of Banks                    Average Log Variance Ratio
                                 Low-Rep      High-Rep              Low-Rep Banks    High-Rep Banks                                 Pr > t
3-month VR                         145           25                     2.178             1.725                Low vs. High         0.011
6-month VR                         145           25                     2.734             2.075                Low vs. High         0.000
9-month VR                         145           25                     2.881             2.412                Low vs. High         0.008
12-month VR                        145           25                     2.953             2.534                Low vs. High         0.012

Panel B: 1996-2000
                                   Number of Banks                     Average Log Variance Ratio
                                 Low-Rep     High-Rep               Low-Rep Banks    High-Rep Banks                                 Pr > t
3-month VR                         41           13                      1.782             1.983                Low vs. High         0.503
6-month VR                         41           13                      2.122             2.190                Low vs. High         0.800
9-month VR                         41           13                      2.446             2.255                Low vs. High         0.477
12-month VR                        41           13                      2.527             2.410                Low vs. High         0.713

Panel C: Other time
                                    Number of Banks                    Average Log Variance Ratio
                                 Low-Rep      High-Rep              Low-Rep Banks    High-Rep Banks                                 Pr > t
3-month VR                         120           23                     2.233             1.500                Low vs. High         0.000
6-month VR                         120           23                     2.855             1.940                Low vs. High         0.000
9-month VR                         120           23                     2.994             2.391                Low vs. High         0.002
12-month VR                        120           23                     3.123             2.549                Low vs. High         0.001

Table 6: The Determinants of Underwriters' Variance Ratios using Pchange
This table presents the the coefficient estimates, and heteroscedasticity-consistent standard errors in the parentheses, of OLS regressions.
The dependent variable is the observation for each underwriter (with at least 5 IPOs in our sample) of the following statistic: the log of the
cross-sectional dispersion in secondary market returns relative to the estimated "implicit offer price", divided by the cross-sectional standard
deviation of price revisions. White's correction is employed to estimate standard errors, which are enclosed in parentheses. Statistical
significance at the 1%, 5% and 10% level are denoted with superscripts ***,** and *, respectively.
                                                                    Independent Variables (N=205)
Dependent Variables           VR3          VR6           VR9         VR12               VR3              VR6            VR9           VR12
                           -0.155***    -0.183***     -0.187***    -0.141***         -0.107**          -0.093*       -0.144**         -0.041
Bank Reputation
                            (0.042)      (0.038)       (0.044)      (0.043)           (0.052)          (0.052)        (0.063)        (0.058)
                                                                                     -5.556**          -4.032*         -3.896         -1.892
                                                                                      (2.283)          (2.266)        (2.393)        (2.782)
                                                                                       -0.226         -0.317**         -0.168       -0.292**
Filing Amount
                                                                                      (0.145)          (0.132)        (0.140)        (0.128)
                                                                                       -0.212            0.064         -0.188          0.155
                                                                                      (0.174)          (0.163)        (0.200)        (0.196)
                                                                                       -0.543           -0.573       -1.022**         -0.755
                                                                                      (0.561)          (0.494)        (0.492)        (0.531)
                                                                                        0.732           0.912*         0.847*         0.885*
                                                                                      (0.560)          (0.499)        (0.511)        (0.515)

R²                           0.084        0.127         0.095        0.059               0.178          0.223          0.172          0.127

Table 7: Time-Series Volatility Comparison
This table presents the mean aftermarket daily return volatility of IPO firms. Firms are classified into
terciles according to the Manaster-Carter reputation ranking of their underwriters and comparisons are
made between those underwritten by low-reputation banks and those by high-reputation banks. The last
column shows the T-test results indicating whether the differences in the mean volatility are statistically
Panel A: 1980-2006
                                 Low-Reputation          High-Reputation            Comparison
                                   (n=2116)                 (n=2497)                  Pr > t
3 months                            0.0017                   0.0020                   0.000
6 months                            0.0019                   0.0020                   0.021
9 months                            0.0021                   0.0020                   0.209
12 months                           0.0023                   0.0020                   0.000
Panel B: 1980s
                                 Low-Reputation          High-Reputation            Comparison
                                    (n=879)                 (n=628)                   Pr > t
3 months                             0.0010                  0.0008                   0.001
6 months                             0.0011                  0.0009                   0.002
9 months                             0.0012                  0.0009                   0.000
12 months                            0.0013                  0.0010                   0.000
Panel C: 1990-95
                                 Low-Reputation          High-Reputation            Comparison
                                    (n=610)                 (n=644)                   Pr > t
3 months                             0.0018                  0.0011                   0.000
6 months                             0.0020                  0.0012                   0.000
9 months                             0.0022                  0.0012                   0.000
12 months                            0.0025                  0.0012                   0.000
Panel D: 1996-2000
                                 Low-Reputation          High-Reputation            Comparison
                                    (n=511)                 (n=864)                   Pr > t
3 months                             0.0029                  0.0038                   0.000
6 months                             0.0032                  0.0037                   0.002
9 months                             0.0037                  0.0036                   0.885
12 months                            0.0039                  0.0036                   0.142
Panel E: 2001-06
                                 Low-Reputation          High-Reputation            Comparison
                                    (n=116)                 (n=361)                   Pr > t
3 months                             0.0015                  0.0011                   0.079
6 months                             0.0015                  0.0012                   0.082
9 months                             0.0015                  0.0011                   0.058
12 months                            0.0016                  0.0011                   0.036

Table 8: Explaining Time-Series Return Volatilities

The table shows the results of regressing a firm's aftermarket daily return volatility on firm
characteristics, underwriter reputation and time period indicators. The bubble dummy is equal
to 0.001 if an IPO is conducted in 1999 or 2000, and 0 otherwise. All other explanatory
variables are scaled down by a factor of 1000 to secure proper magnitudes for estimated
coefficients. Enclosed in parenthesis are standard errors of estimated coefficents above.
Statistical significance at the 1%, 5% and 10% level are denoted with superscripts ***,** and *,

                           3-month            6-month           9-month          12-month
Bank Reputation             -0.005           -0.040***         -0.080***         -0.109***
                            (0.014)            (0.013)           (0.014)           (0.015)
IPOHeat                    0.079***           0.074***          0.076***          0.080***
                            (0.021)            (0.019)           (0.020)           (0.022)
Filing Amount             -0.222***          -0.213***         -0.215***         -0.237***
                            (0.030)            (0.027)           (0.029)           (0.031)
Age                       -0.156***          -0.131***         -0.146***         -0.166***
                            (0.023)            (0.021)           (0.022)           (0.024)
VC-Backed                  0.333***             0.413           0.385***          0.383***
                            (0.055)            (0.051)           (0.053)           (0.057)
Hi-Tech                    0.697***           0.662***          0.625***          0.620***
                            (0.054)            (0.049)           (0.052)           (0.056)
Bubble dummy               4.113***           3.844***          3.723***          3.684***
                            (0.076)            (0.070)           (0.074)           (0.079)

Adj. R²                      0.411             0.426             0.385              0.355

    Table 9: Partial Adjustment and Underwriter Quality

    This table presents the coefficient estimates, and heteroscedasticity-consistent standard errors in parentheses, of
    OLS regressions in which the dependent variable is IPO underpricing. Statistical significance at the 1%, 5% and
    10% level are denoted with superscripts ***,** and *, respectively. REV + equals REV if REV > 0, and 0
    otherwise; REV - equals REV is REV <0, and 0 otherwise. COMPS + equals COMPS if COMPS >0, and 0
    otherwise; COMPS - equals COMPS if COMPS <0, and 0 otherwise. In the lower panel of the table, Cramer's test is
    used to test the equality of R2 for the long specification.
                                Full Sample           Bottom Tercile       Medium Tercile             Top Tercile
                               (1)       (2)          (1)       (2)         (1)      (2)            (1)        (2)
    REV                     0.845***               0.338***              0.606***                1.166***
                             (0.068)                (0.102)               (0.045)                 (0.097)
    REV+                               1.035***                0.185                0.909***                 1.318***
                                        (0.155)               (0.134)                (0.084)                  (0.163)
    REV-                               0.559***               0.516***              0.281***                 0.840***
                                        (0.076)                (0.093)               (0.080)                  (0.113)
    COMPS                   0.165***               0.212***              0.180***                  0.175
                             (0.047)                (0.057)               (0.054)                 (0.109)
    COMPS+                             0.162***               0.160**               0.213***                   0.196
                                        (0.060)               (0.074)                (0.066)                  (0.139)
    COMPS-                                0.131               0.686***              -0.101                      -0.051
                                         (0.154)               (0.209)             (0.216)                     (0.299)
    IPOHeat              0.844***       0.810***    0.376*** 0.408***    0.758*** 0.725***        0.979***    0.941***
                          (0.048)        (0.046)     (0.088)   (0.086)    (0.066)  (0.063)         (0.075)     (0.074)
    Filing Amount       -0.023***      -0.025***   -0.021*** -0.017**    -0.014** -0.014**        -0.024**    -0.023**
                          (0.006)        (0.006)     (0.008)   (0.008)    (0.006)  (0.006)         (0.011)     (0.011)
    Age                 -0.023***      -0.022***   -0.020*** -0.020***     -0.005   -0.003       -0.031***   -0.030***
                          (0.004)        (0.004)     (0.005)   (0.005)    (0.005)  (0.004)         (0.008)     (0.008)
    VC-Backed            0.039***       0.033***      0.019     0.025      -0.002   -0.012        0.073***    0.068***
                          (0.011)        (0.012)     (0.016)   90.016)    (0.015)  (0.015)         (0.025)     (0.025)
    Hi-tech              0.034***       0.028***      0.015     0.021      0.023*    0.015         0.051**     0.044**
                          (0.009)        (0.009)     (0.014)   (0.014)    (0.013)  (0.013)         (0.020)     (0.021)
    λ                     0.066**         0.047       0.021     0.072     0.091**    0.028          0.060       -0.004
                          (0.030)        (0.035)     (0.048)   (0.057)    (0.038)  (0.042)         (0.065)     (0.066)
    Bank Reputation      0.010***       0.009***
                          (0.003)        (0.003)
    Adjusted R2              0.388       0.393       0.108     0.116       0.417     0.432         0.466      0.469
                                                     Low vs. Medium        Medium vs. High           Low vs. High
    Equality of R2                         Z             -94.85                 -15.91                  -107.24
    for specification (1)               Pr > |Z|         0.000                  0.000                    0.000

    Table 10: Impact of Pre-Offering Information on Underpricing
    This table shows how many standard deviations' change is caused by a one-standard-deviation shock in
    pre-offering price revision (REV ), comparable firms return (COMPS ) and mean underpricing during the
    30 days prior to an IPO (IPOHeat ). Panel A doesn't differentiate positive and negative information; Panel
    B look at the impact of positive (REV + ) and negative (REV - ) price revisions, positive (COMPS + ) and
    negative (COMPS - ) industry return, and mean past underpricing (IPOHeat ) on underpricing.

    Panel A: Not Differentiating positive and negative information
                                               causes change in underpricing   ( # of standard deviations)
                                            Full Sample      Low Tercile       Med Tercile       High Tercile
          1 st. dev. shock to REV               0.38             0.20              0.36              0.43
          1 st. dev. shock to RInd              0.08             0.25              0.08              0.04
        1 st. dev. shock to Spillover           0.42             0.24              0.47              0.42
    Panel B: Differentiating positive and negative information
                                               causes change in underpricing   ( # of standard deviations)
                                            Full Sample      Low Tercile       Med Tercile       High Tercile
         1 st. dev. shock to REV_P              0.32             0.07               0.33             0.36
        1 st. dev. shock to REV_N               0.14             0.20               0.10             0.15
         1 st. dev. shock to RInd_P             0.08             0.19               0.09             0.04
        1 st. dev. shock to RInd_N              0.01             0.08              -0.01             0.00
        1 st. dev. shock to Spillover           0.40             0.26               0.45             0.40


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