Dotcom bubble and underpricing: conjectures and evidence

Working
Paper 441
Dotcom Bubble and Underpricing:
Conjectures and Evidence
Antonio Gledson de Carvalho
Roberto B. Pinheiro
Joelson Oliveira Sampaio
CEQEF - Nº28
Working Paper Series
06 de março de 2017

WORKING PAPER 441 – CEQEF Nº 28 • MARÇO DE 2017 • 1
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1
Dotcom Bubble and Underpricing: Conjectures and
Evidence
Antonio Gledson de Carvalho
Fundacao Getulio Vargas
School of Business at Sao Paulo
Roberto B. Pinheiro**
Federal Reserve Bank of Cleveland
Research Department
Joelson Oliveira Sampaio***
Fundacao Getulio Vargas
School of Economics at Sao Paulo
© 2016 Antonio Gledson de Carvalho. All rights reserved.
Abstract. We provide conjectures for what caused the price spiral and the high underpricing of the
dotcom bubble of 1999–2000. We raise two conjectures for the price spiral. First, given the uncertainty
about the growth opportunities generated by the new technologies and their spillover effects across
technology industries, investors saw the inflow of a large number of high-growth firms as a sign of high
growth rates for the market as a whole. Second, investors interpreted the wave of highly underpriced IPOs
as an opportunity to obtain gains by investing in newly public companies. The underpricing resulted from
the emergence a large cohort of firms racing for market leadership. Fundamentals pricing at the IPO was
part of their strategy. We provide evidence for our conjectures. We show that returns on NASDAQ
composite index are explained by the flow of high-growth (or highly underpriced) IPOs; the high
underpricing can be fully explained by firms’ characteristics and strategic goals. We also show that,
contrary to alternatives explanations, underpricing was not associated with top underwriting, there was no
deterioration of issuers’ quality, and top underwriters and analysts became more selective.
Keywords: Internet bubble, underpricing, spinning, analyst lust, risk composition hypothesis
JEL codes: G14, G24, L1, O33
FGV-EAESP, Av. Nove de Julho, 2029 – Sala 912, São Paulo, SP, Brasil 01313-902. Tel: (5511) 3281-7767.
Email: gledson.carvalho@fgv.br.. **
P.O. Box 6387,Cleveland, OH, 44101-1387’ E-
mail: roberto.pinheiro@clev.frb.org. ***
FGV-EAESP, Av. Nove de Julho, 2029 – Sala 912, São Paulo, SP, Brazil
01313-902. Email: joelson.sampaio@fgv.br. Tel: (5511) 3799-3362. We thank Charles Calomiris, Marcelo
Fernandes, Tim Loughran, and seminar participants at the University of São Paulo and WHU Otto Beischeim
School of Management for invaluable comments. We acknowledge Humberto Gallucci Netto and Pedro Luiz
Aprigio for their excellent research assistantship. Carvalho acknowledges support from FAPESP [2016-06826-6].
The views in this article do not necessarily reflect those of the Federal Reserve System or the Board of Governors.

2
1 – Introduction
The Dotcom bubble and the concomitant high underpricing are two puzzles. From 2,000
points in the beginning of 1999, the NASDAQ composite index escalated to 5,048 in March
2000, returning to near the 2,000 points after 2000. The average underpricing escalated from
14.3 percent in the 1991-1998 period to 64.9 percent in 1999-2000 (Ritter, 2014). This article
presents a rational explanation connecting these two phenomena. We conjecture that the large
inflow of IPOs of fast growing firms affected the expectation about the long-term growth rate of
New Economy firms and consequently, their evaluation, feeding the price spiral. The high
underpricing originated from the strategic behavior of some issuers for which the continued use
of capital market was a need: the possible overvaluation of their shares forced them to highly
underprice their IPOs.
The birth of the Internet spurred a variety of new products and processes (the so-called
New Economy). The Internet also changed the structure of many traditional businesses. For
example, it allowed for improved inventory management and new manners of marketing
products, stimulating market consolidation in some traditional industries. It even allowed some
local businesses to become global (e.g., Amazon.com). The use of new processes even raised
doubt on whether these new businesses would subsume traditional ones. Such business
revolution fostered a large cohort of new firms in a race for leadership (or survivorship). These
firms needed to go public either to raise cash to fund organic growth, or to turn their shares into
currency to pay for acquisitions.1
Thus, for them the continued use of the capital markets was
important. New Economy firms also presented abnormal realized growth rate, but their track
record was short because they were young. Short track record along with uncertainty about the
effectiveness the new processes made difficult the assessment of long-term growth rates
(Schwert 2002; Schultz and Zaman, 2001).
We conjecture that information on long-term growth rate coming from the IPO market
fed the price spiral. The stock of public New Economy firms was small and the flow of IPOs was
1
Brau and Fawcett (2006) examining IPOs from 2000-2001 find that the desire to create an acquisition currency
ranks as the most important reason for an IPO. Over a longer sample period, Celikyurt, Sevilir, and Shivdasani
(2010) report that newly public firms make acquisitions at a torrid pace.

3
large. Thus, the IPO market became relevant to assess the long-term growth rate of the whole
industry. The continued flow of highly underpriced high-growth firms lead investors to update
upwards their estimation of the long-term growth rate, causing upward price revision for the
whole industry. Therefore, the increase in price was a rational.
Our explanation for the abnormal underpricing builds on the behavior of firms with the
need for continued use of capital markets. Such firms, facing the frenzy for their shares, had
reasons to be conservative in the pricing of their IPO. First, the continued use of the stock market
requires good shares performance (e.g., good price returns, regular analyst coverage and low bid-
ask spreads). Overvalued shares could lose their attractiveness if a market reversion occurred.
Second, race for leadership required large pre-IPO capital infusion, lowering CEO’s ownership.2
Maintenance of control with small ownership requires good shares dilution,3
preferentially
among buy-and-hold investors (e.g., small number of shares floating makes difficult for
threatening investors to acquire significant ownership). However, underwriters would hardly
place potentially overpriced shares among their premier buy-and-hold investors. Third, for an
issuer intending to do a series of acquisitions, ownership dilution could come mostly from
expensive acquisitions, rather than from IPO underpricing. By selling overvalued shares at their
IPOs, strategic acquirers could create unrealistic expectations for acquisition multiples, making
acquisitions expensive. Last, venture capital (VC) sponsored firms could want to protect VCs
ability to tackle underwriters, auditors, analyst and investors (Barry et al., 1990; Megginson and
Weiss, 1991).
We present evidence supporting our conjectures. First, we show that during the bubble
the flow of IPOs of fast growing firms explains returns on the NASDAQ composite index. Next,
we show that the abnormally high underpricing can be fully accounted by issuers’ strategic
purposes.
2
Ljungqvist and Wilhelm (2003) report that average CEO ownership dropped from 23 percent during 1996-1998
dropped to 17.3 percent in 1999 and to 11.6 percent in 2000.
3
Brennan and Franks (1997) find that when shares are placed more widely rather than placed with just a few
powerful large shareholders, the entrepreneur is less easy ousted from the company.

4
For robustness purposes, we also investigated other conjectures on the origin of the
bubble’s underpricing. Following Loughran and Ritter (2004), there are three alternative
conjectures: 1) Change in Risk Composition (Helwege and Liang, 2004; Howe and Zhang, 2005;
Ibbotson, Sindelar, and Ritter, 1994; Loughran and Ritter, 2004; Lowry and Schwert, 2002;
Ritter 1984; and Yung, Colak, and Wang, 2008): changing in average underpricing over time
reflects change in the riskiness of the IPOs. The main evidence supporting this hypothesis for the
Dotcom period is the increased failure rate in three and five years from the IPO (Yung, Colak,
and Wang, 2008). However, we show that this increased failure rate prevailed only for a period
inferior to 11 years. Furthermore, high-quality firms where the ones that bore the highest
underpricing; 2) Change in Issuer Objective Function: based on what Loughran and Ritter
(2004) call Analyst Lust. As issuers placed more importance on hiring lead underwriters that
would bring highly ranked analyst coverage, they became less concerned with avoiding
underwriters with the reputation for excessive underpricing. Analyst Lust is inconsistent with our
findings that analysts’ coverage during the bubble is unrelated to underpricing or top
underwriting, and that underpricing during the bubble is fully accounted by issuers’ strategic
goals. Moreover, once one accounts for firms’ characteristics, analysts’ coverage actually
decreased; and 3) Realignment of Incentives: increased underpricing resulted from lower
incentives for firms’ insiders to monitor underwriters. Ljungqvist and Wilhelm (2003) interpret
the observed sharp drop in average CEO ownership as a reduction in incentives for CEOs to
monitor underwriters. Loughran and Ritter (2004) based their explanation on spinning:
underwriters that usually force high underpricing co-opted firms’ insiders by allocating to them
stocks in highly underpriced IPOs. These two conjectures are not consistent with the fact that
abnormal underpricing is fully explained by firms’ strategic behavior rather than top
underwriting.
This article is structured as follows: Section 2 presents our hypotheses and methodology;
Section 3 describes our data, sample and variables; Section 4 presents our results; and Section 5
concludes.
2 – Hypotheses and methodology

5
2.1 – Hypotheses
We conjecture that the large inflow of high-growth IPOs influenced the expectation about
the growth rate of whole New Economy feeding the price spiral. We motivate this conjecture
with the following simple model:
Suppose that a technology could induce high growth, gH, or low growth rates, gL (gH > gL).
Investors a priori do not know the technology growth rate. The ex-ante probability of high-
growth is given by   (0,1). As in Benveniste and Spindt (1989) we assume that during the
book-building, institutional investors provide information about the firm that becomes public and
common knowledge after the IPO. In particular, we assume that the information gathering
process generates a signal about the quality of the technology. Signals can be good (G) or bad
(B). If the growth rate is gH, the probability of a good signal is 𝜃 𝐻. If the technology is low
growth, the probability of a good signal is 𝜃 𝐿 (𝜃 𝐻 > 𝜃 𝐿). After every IPO, the growth rate for all
the firms using the same technology is updated using Bayes’ rule:
𝑃𝑟𝑜𝑏( 𝑔 𝐻|𝐺) =
𝛾𝜃 𝐻
𝛾𝜃 𝐻 + (1 − 𝛾)𝜃 𝐿
To keep argument simple, let us assume that stocks are valued using the Gordon’s Constant
Growth Model. Let 𝑘 be the cost of capital and assume that agents are risk-neutral. Then, after
each successful IPO, the expected value of the share is given by:
𝛾𝜃 𝐻
𝛾𝜃 𝐻 + (1 − 𝛾)𝜃 𝐿
×
𝐷0(1 + 𝑔 𝐻)
𝑘 − 𝑔 𝐻
+
(1 − 𝛾)𝜃 𝐿
𝛾𝜃 𝐻 + (1 − 𝛾)𝜃 𝐿
×
𝐷0(1 + 𝑔 𝐿)
𝑘 − 𝑔 𝐿
.
Before the signal was issued, the expected share value was given by:
𝛾 ×
𝐷0(1 + 𝑔 𝐻)
𝑘 − 𝑔 𝐻
+ (1 − 𝛾) ×
𝐷0(1 + 𝑔 𝐿)
𝑘 − 𝑔 𝐿
.
Consequently, the expected change in price after a good signal is revealed is given by:
𝛾(1 − 𝛾)(𝜃 𝐻 − 𝜃 𝐿)
𝛾𝜃 𝐻 + (1 − 𝛾)𝜃 𝐿
×
(1 + 𝑘)(𝑔 𝐻 − 𝑔 𝐿)𝐷0
(𝑘 − 𝑔 𝐻)(𝑘 − 𝑔 𝐿)
> 0,

6
where the first term indicates the shift in probability from low to high growth and the second
term indicates the net gain in valuation by moving towards a high-growth stock. If the signals are
not perfectly correlated, the market valuation of all firms using that technology increases with
the number of positive signals. Moreover, the higher the difference between 𝑔 𝐻 and 𝑔 𝐿 the
bigger the appreciation following a good signal.
It is natural that IPO of firms with historical high sales growth are the candidates to generate
good signals about the new technology. The good signal coming from the IPO market is that
investors estimate that the historical sales growth is projected into the future. The high
underpricing indicates that the growth rate that investors estimate is higher than the one used by
underwriters in the in the IPO valuation. Thus, if high sales growth IPO are associated to high
underpricing (as we will see later on) our first hypothesis can be stated as:
Hypothesis 1: During the Dotcom bubble, returns on NASDAQ composite index responded to the
flow of high growth IPOs.
With respect to underpricing, we conjecture that the possibility of overvaluation led firms
that needed the continued use of capital markets (and thus, could not risk distributing overvalued
shares) to highly underprice their IPOs. Thus, underpricing was a consequence of their strategic
goals:
Hypothesis 2: The high underpricing prevailing during the bubble can be fully accounted by
issuers’ strategy.
2.2 – Methodology
2.2.1 – Variables
Table 1 described our variables. Most of them are standard in the IPO literature. We
discuss here only three variables that proxy for the issuers’ strategic goals. These variables are:
Acquisition pre-IPO, which is a dummy variable indicating that the issuer made acquisitions in
the 3-year period before the IPO. The creation of shares for acquisition plays an important role in
our conjecture. Ideally, one would want to use acquisitions post-IPO (a dummy variable
indicating acquisition within 5 years from the IPO), but such variable would be endogenous to

7
the analysis of underpricing. Alternatively, we use Acquisition pre-IPO. The correlation between
these two variables is high: 0.79. Furthermore, only pre-IPO acquirers made acquisitions after
their IPOs and only 28 percent of the pre-IPO acquirers did not make a post-IPO acquisition;
Big-Four auditing, which is a dummy that indicates if the external auditor is one of the top
auditing firms. Notice that although the choice of auditing is a strategic decision, it is exogenous
to the underpricing analysis since auditors do take part in the going public process; and Venture
capital, a dummy variable indicating venture capital sponsorship: the strategic role of venture
capital in IPOs and its concern with reputation has already been extensively discussed (Barry et
al. 1990; and Megginson and Weiss, 1991).
2.2.2 – Econometric models
Hypothesis 1 states that the returns on the NASDAQ composite index responded to the
inflow of high-growth IPOs. To measure this correlation one must control for high-frequency
macroeconomic shocks. Fortunately, the bubble was mostly restricted to NASDAQ, both in
terms of price spiral and IPO flow, barely affecting the New York Stock Exchange index
(NYSE). 4
Thus, we use the NYSE index returns to control for high-frequency macroeconomic
shocks. Hence, our econometric models to test Hypothesis 1 are:
∆𝑁𝐴𝑆𝐷𝐴𝑄𝑡 = 𝛽1∆𝑁𝑌𝑆𝐸𝑡 + 𝛽2#𝐼𝑃𝑂𝑠_𝐿𝑜𝑤𝑡 + 𝛽3 #𝐼𝑃𝑂𝑠_𝐻𝑖𝑔ℎ 𝑡 + 𝜀𝑡, (1)
and
∆𝑁𝐴𝑆𝐷𝐴𝑄𝑡 = 𝛽1∆𝑁𝑌𝑆𝐸𝑡 + 𝛽2#𝐼𝑃𝑂𝑠𝑡 + 𝛽2 𝑝𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛(𝐼𝑃𝑂𝑠_𝐻𝑖𝑔ℎ) 𝑡 + 𝜀𝑡, (2)
where
∆ is the percent change in the market index in period 𝑡;
#𝐼𝑃𝑂𝑠_𝐿𝑜𝑤𝑡 is the number of IPOs with low underpricing in period 𝑡;
#𝐼𝑃𝑂𝑠_𝐻𝑖𝑔ℎ 𝑡 is the number of IPOs with high underpricing in period 𝑡; and
#𝐼𝑃𝑂𝑠𝑡 is the number of IPOs in period 𝑡.
4
During 1999 and 2000 the New York Stock Exchange Composite index (NYSE) varied between 6,092 and 7,164
(only 17.5% variation) and few IPOs in the period occurred at NYSE: 94.6% of our IPO sample during the bubble
was at NASDAQ (in Loughran and Ritter, 2004, sample it was 88.6%).

8
We estimate Equations 1 and 2 using both weekly and three-week rolling returns. In the
latter case, we use index returns over three weeks and the number (or proportion) of highly
underpriced IPOs over the same three weeks as a moving sum of returns and IPOs. Estimations
come from least squares regressions with Newey-West (12 lags) standard errors (Newey and
West, 1987).
Robustness: One may be concerned with reverse causality in Models 1 and 2. Accordingly, high
market returns could force high underpricing, increasing the number of highly underpriced IPOs.
We address this concern in two ways. First, we replace the number and proportion of highly
(lowly) underpriced IPOs by the number and proportion of IPOs of high (low) sales growth
issuers. Sales growth is highly correlated to underpricing (Table 5), completely predetermined,
and hardly correlated to market returns at the time of the IPO. Second, we estimate predicted
underpricing and use it to classify IPOs into either highly or lowly underpriced. To predict
underpricing we run a least square regression with robust errors on an estimation window and
use the estimated parameters to project underpricing over the entire sample period. The
econometric model is:
𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔𝑖 = 𝛾0 + 𝛾1 𝐼𝑠𝑠𝑢𝑒𝑖 + 𝛾2 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑑𝑢𝑚𝑚𝑖𝑒𝑠𝑖 + 𝜇𝑖, (3)
where
𝐼𝑠𝑠𝑢𝑒𝑖 is a vector of issue i’s characteristics, including: dummy variables indicating VC-
sponsorship and Big-four auditing, sales growth, technology, age, firm size, offer size, offer-
to-firm size, and the size of the price interval scaled by its middle point.
One should note that, in order to avoid any by-construction correlation with market
returns, our model does not include variables that could be related to the timing of the IPO (ex-
ante demand, top underwriting dummy and quarter dummies). We use two estimation windows:
the whole sample period (1991-2000) 5
and the pre-bubble period (1991-1996). Based on
predicted underpricing, we classify firms as highly underpriced using several cut-offs (when one
5
The exclusion of all variables that could be related to the timing of the IPO minimizes the risk of endogeneity
when the estimation window includes the bubble period.

9
uses the pre-bubble period as estimation window, predicted underpricing during the bubble is
unsurprisingly low).
Hypothesis 2 states that the high underpricing prevailing during the bubble can be fully
accounted by the characteristics and strategic goals of issuers. To test this hypothesis, we run
underpricing regressions controlling for the issues’ characteristics, issuers’ strategic
characteristics, and underwriting. Our econometric model is:
𝑈𝑛𝑑𝑒𝑟𝑝𝑟𝑖𝑐𝑖𝑛𝑔𝑖 = 𝛾0 + 𝛾1 𝐵𝑢𝑏𝑏𝑙𝑒 + 𝛾2 𝐼𝑠𝑠𝑢𝑒𝑖 + 𝛾3 𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐𝑖 + 𝛾4 𝑈𝑛𝑑𝑒𝑟𝑤𝑟𝑖𝑡𝑖𝑛𝑔𝑖
+𝛾5 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖 + 𝛾6 𝐵𝑢𝑏𝑏𝑙𝑒 × 𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐𝑖 + 𝛾7 𝐵𝑢𝑏𝑏𝑙𝑒 × 𝑈𝑛𝑑𝑒𝑟𝑤𝑟𝑖𝑡𝑖𝑛𝑔𝑖 + 𝜇𝑖,
(4)
where
𝐼𝑠𝑠𝑢𝑒𝑖 is a vector of characteristics of issue 𝑖: technology, age, offer size, firm size, offer-to-
firm size, sales growth and the size of the price interval scaled by its middle point;
𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐𝑖 is a set of three dummy variables indicating pre-IPO acquisitions, Big-four auditing
(𝐵𝑖𝑔_𝑓𝑜𝑢𝑟) and VC-sponsorship; and
𝑈𝑛𝑑𝑒𝑟𝑤𝑟𝑖𝑡𝑖𝑛𝑔𝑖 is a dummy variable indicating that the Carter-Manaster reputation index is
above 8.
In our basic setting, we do not control for the ex-ante demand for the IPO (price revision up and
price revision down) because it can be related to the underwriter’s selling effort and to the IPO
timing. Nevertheless, we also present estimations controlling for ex-ante demand for robustness
purposes.
If Hypothesis 2 is correct, interactions of the bubble dummy with our proxies for strategic
goals (Acquisitions pre-IPO, Big-four and Venture Capital) fully explain the abnormal
underpricing observed during the bubble; i.e., the coefficients on the bubble dummy and its
interaction with top underwriting will not be statistically significant.
We then move to discuss our robustness checks by considering the alternative scenarios
proposed by the literature. In order to investigate the change in risk composition we use means
comparison of the average quality of the bubble and pre-bubble IPO cohorts. Our quality

10
measure is the frequency of failures. Our indirect measures are: institutional ownership6
and its
concentration, certification by reputable underwriters and auditors, and enhanced tradability
conditions (analysts’ coverage and bid-ask spreads). We measure tradability conditions and
institutional ownership at the end of the second year from the IPO. This allows for the effect of
underwriter efforts at the IPO to wear out.
There are two objections to the use of top underwriting and analysts’ coverage as quality
measures during the bubble, which are related to our other robustness tests. First, Loughran and
Ritter (2004) suggest that top underwriters increased market share by lowering their standards
(we call this Underwriters’ Opportunistic Behavior). If so, top underwriting would not signal
quality. Second, the same authors conjecture that during the bubble, firms coped with
underwriters with a reputation for severe underprice in exchange for analyst coverage (Analyst
Lust). Therefore analysts’ coverage was related to underpricing and underwriting rather than
issuer quality. We test these two conjectures using probit analysis on the choices of underwriters
and analyst coverage. Our specification for these tests is:
𝐷𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡𝑖 = 𝛽0 + 𝛽1 𝐵𝑢𝑏𝑏𝑙𝑒 + 𝛽2 𝐼𝑠𝑠𝑢𝑒𝑖 + 𝛽3 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑖 + 𝜇𝑖, (5)
where
𝐷𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡𝑖 is either a dummy variable indicating Carter-Manaster index for underwriters’
reputation above 8, or a dummy variable indicating analysts’ coverage during the
second year from the IPO;
𝐵𝑢𝑏𝑏𝑙𝑒 is a dummy variable indicating the bubble years (1999-2000);
𝐼𝑠𝑠𝑢𝑒𝑖 is a vector of predetermined characteristics of issue 𝑖 : VC-sponsorship, Big-four
auditing, high-growth, pre-IPO acquisition, age, technology, firm size, offer size, offer-
to-firm size and sales growth (in the analysis for analysts’ coverage we also include
underpricing, the top-underwriting dummy and their interactions with the bubble
dummy); and
𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑖 is a set of 9 industry dummies.
If the Underwriters’ Opportunistic Behavior is right, one would observe a positive
coefficient on the bubble dummy in the probit analysis for top underwriting. If the Analyst Lust
6
One would expect institutional investors to run away from bad quality firms, decreasing institutional ownership
and increasing the Herfindahl index.

11
is right, one would observe positive coefficients on the interactions of bubble dummy with
underpricing and top-underwriting
3 – Data and sample
Our dataset combines data from several sources. From Securities Data Corporation
(SDC-Platinum) we obtained an exhaustive list of IPOs and information on offer price, offer
date, proceeds, leading underwriter, price interval, issuer age and seasoned equity offerings
(SEO). We complemented and corrected SDC-Platinum database following suggestions in Jay
Ritter’s website (Ritter, 2014). From Compustat we obtained data on quarterly and annual
fundamentals: sales, book value of assets, and Big-Four auditing. Information on VC-sponsoring
comes from Venture Economics database. Analysts’ coverage comes from the I/B/E/S database.
Data on institutional ownership and its Herfindahl index comes from Thomson Reuters
Institutional Holdings (13F). As measure of underwriter quality we use the Carter and Manaster
index (1990) updated by Loughran and Ritter (2004). Information on bid-ask spreads, delisting
due to bankruptcy, mergers and drops, and daily quotation for NYSE and NASDAQ composite
indices come from the CRSP-US. We use Loughran and Ritter’s (2004) classification to identify
High-tech firms. We define three periods: Pre-bubble (1991-1996), Transition (1997 and 1998)
and Bubble (1999 and 2000).
Our sample consists of firms completing an IPO between January 1991 and December
2000. As usual, we exclude offerings from closed-end funds, limited partnerships, financial
institutions (SIC codes 6000–6999), utilities (SIC codes 4900–4999), real-estate investment
trusts, unit offerings, IPOs with offer price below five dollars, and American depositary receipts
(ADRs). Our final sample consists of 2,754 IPOs with complete information on all variables used
in underpricing regressions. Table 2 describes the drop in sample size due to missing values in
relevant variables.
Table 3 compares our sample to that of Ritter (2014). During the pre-bubble and
transition periods our coverage is 58 and 61 percent of his sample. During the bubble period, the
coverage is higher: 70 percent. Overall, our sample comprises 62 percent of his sample. Samples
are similar in terms of underpricing, proportion of IPOs at NASDAQ, VC-sponsorship, median

12
age, and proportion of IPOs with price revision up (down) in an annual basis. Differences are
large for gross proceeds in 2000 and technology during the pre-bubble and bubble periods.
During the pre-bubble period our sample has a higher proportion of technology firms, but during
the transition and bubble periods, that proportion is significantly lower.
3.1 – Change in IPO characteristics
Table 4 (Panel A) reports issuers’ characteristics in three distinct periods pre-bubble
(1991-1996) transition (1997 and 1998) and bubble (1999-2000). It also compares pre-bubble to
bubble periods. Panel A describes the entire sample while Panel B only the subsample of high-
growth firms. Pre-IPO sales growth – measured by the average quarterly growth (over the
previous three quarters) - increased significantly during the bubble, from 56 to 91 percent. To
classify firms into high-growth we used the cutoff for the highest quartile of the pre-bubble
subsample (81 percent). Thus the proportion of high-growth firms increased from 25 percent in
the pre-bubble period to 36 percent in the transition period and 57 percent during the bubble.
The general message of Table 4 is that IPO characteristics changed from the pre-bubble
to the bubble period due to not only an increase in the proportion of high-growth issuers but also
a change in the characteristics high-growth IPOs. From the pre-bubble to the bubble period, firm
age declined from 14.6 to 9.4 years. In the high growth subsample such decline was from 9.85 to
6.55 years. Thus, high growth firms during the bubble were very young. The fraction of IPO
from technology firms increased from 29 to 50 percent, similar change was occurred among high
growth firms: from 34 to 55 percent; firm size (measured by book value of assets) increased from
$164 to $255 mi. Among high growth firms size almost tree folded: from $90 to $230 mi; offer
size increased from $58 million to $116 million. Among high growth firms offer size increased
from $53 million to $107 million; underpricing increased from 16 to 66 percent. The increase
was more pronounced for high-growth firms: from 23 to 78 percent; top underwriting increased
from 68 to 84 percent but among high-growth firms the increase was lower: from 76 to 85
percent; VC backing increased from 41 to 67 percent. In the high-growth subsample, the increase
was from 55 to 75 percent; there was no sizable change in Big-four auditing: an increase of 3 to

13
4 percent in both samples; and a decrease on pre-IPO acquisitions from 33 to 25 percent in the
whole sample and from 31 to 27 percent among high-growth firms.
One condition embedded in our Hypothesis 1 is that high growth IPOs were highly
underpriced during the bubble. In Table 4 we have seen that underpricing was significantly
higher in the high-growth sample. One could be concerned that such high average was driven by
some few outliers. However, this is not the case: underpricing was above 50 percent for near half
of the high-growth sample and above 40 percent for 54 percent of the sample. It was below 20
percent for only 32 percent of the high-growth sample.
4 – Results
4.1 – Price spiral
Tables 5 presents estimations for Models 1 and 2 that test for a positive relation between
the number (or proportion) of IPOs of high-growth firms and the variation of the return on
NASDAQ composite index (Hypothesis 1). Regressions 1-6 analyses the bubble period.
Regressions 1-3 report analysis on rolling three-week returns while regressions 4-6 report results
on weekly returns. Regression 1 includes only the variation on the NYSE composite index
(ΔNYSE) and the total number of IPOs (#IPOs) in the period. As expected, the coefficient on
ΔNYSE composite index is positive, near to one (0.948), although its statistical significance is
only marginal (t-statistics is 1.77). The coefficient on #IPOs is virtually zero in magnitude and
statistical significance. R-square coefficient is only 0.09. In Regression 2 we include also the
mean sales growth of the IPOs during the equivalent period. Now both the coefficients on the
numbers of IPOs and mean sales growth are marginally statistically significant. The coefficient
for the number of IPOs is negative (-0.007, t-statistics is -1.95), while the coefficient for the
mean sales growth is positive (0.001, t-statistics is 1.86). The coefficient on ΔNYSE loses
statistical significance even though does not change much in value (t=1.62). R-square coefficient
increases sharply to 0.23, showing a significant increase in the model’s predictive power.
Regression 3 includes instead of the mean sales growth, the number of IPOS with high sales
growth in the period pre-IPO, i.e., the number of IPOs at the top quartile in the sales growth
distribution. Results become significantly stronger in terms of statistical significance: the

14
coefficient for the number of IPOs with high sales growth in the pre-IPO period is 0.027 and t-
statistics increases to 2.85. The coefficient on the number of IPOs becomes -0.010 with t-
statistics of -2.71. Once more, the coefficient on ΔNYSE loses statistical significance (t=1.63).
R-square statistics showed a minor change to 0.21.
Regressions 4-6 focus on NASDAQ weekly variations. Results are qualitatively the
same. As expected, the coefficient on NYSE becomes larger in size and statistical significance
than in regressions 1-3, indicating that weekly variation on NASDAQ is more responsive to
macroeconomic high frequency shocks. Regression 4 shows that the number of IPOs by itself is
not statistically significant. In Regression 5 the coefficient on the average sales growth is 0.001
with five percent statistical significance and the coefficient on the number of IPOs is -0.002 and
statistically significant at ten percent with t-statistic -1.93. Finally, in Regression 6 the coefficient
on the number of high growth IPOs is 0.01 with statistical significance at the one percent level.
The coefficient on the number of IPOs is -0.004, significant at the five percent level. From
Regression 4 to Regressions 5 and 6 the R-square statistics increases only slightly from 0.19 to
0.22 and 0.23. Regressions 6 allows one estimate the effect of IPOs on NASDAQ returns: the
coefficient on the number of IPOs is -0.0039 and in our bubble sample there were 633 IPOs. The
coefficient on the number of high-growth IPOs is 0.0103 and there were 261 of such IPOs. Thus
over the bubble period the predicted variation on NASDAQ index is 19 percent. Over the same
period the actual variation was 20 percent (from 2,192 to 2,626 points)
Regressions 7-9 replicate the results for regressions 1-3 while focusing only on the 1999
sub-sample. As we know, the technology IPO market already started showing some weakness in
2000, so focusing on 1999 is likely to strength our results. As expected, results become stronger
but qualitatively the same. In regression 7, the coefficient on NYSE is positive and statistically
significant at the one percent level. The coefficient for the number of IPOs is 0.004 and
statistically significant at the one percent level. In regression 8, we introduce mean sales growth
of the IPOs during the equivalent period. As expected due to the results in regression 2, the
coefficient for number of IPOs becomes negative (-0.005) and statistically significant at the five
percent level. Finally, the coefficient for mean sales growth is 0.001 with t-statistic 4.00.

15
Regression 9 substitutes the mean sales growth by the number of IPOs with high sales growth,
i.e. IPOs with sales growth in the top quartile. As in regression 3 results, in regression 8 the
coefficient for the number of IPOs stays negative and statistically significant at the one percent
level, while the number of high-growth IPOs is 0.025 with t-statistic 4.16. Regressions 10-12
replicate the specifications presented for regressions 4-6 while focusing on the 1999 subsample.
As expected, results are qualitatively similar and quantitatively stronger to the ones obtained in
regressions 4-6, corroborating the assumption that the 1999 subsample is a cleaner sample to
study the impact of the signal of long-term growth on the market. Regressions 12 also allows one
estimate the effect of IPOs on NASDAQ returns: the coefficient on the number of IPOs is -
0.0007 and in our 1999 sample there were 345 IPOs. The coefficient on the number of high-
growth IPOs is 0.0076 and there were 51 of such IPOs. Thus, over the bubble period the
predicted variation on NASDAQ index is 15 percent. Over the same period the actual variation
was much higher: 77 percent (from 2,192 to 3,887 points)
Regressions 13-24 replicate specifications used in regressions 1-12 but using two
different samples: The pre-bubble period (1990-1996) and the transition period (1997-1998). As
we can observe, throughout all these regressions, the only coefficient that is statistically
significant it is the one for the variation on the NYSE composite index, indicating that only
macroeconomic variations play a role in the movements of the NASDAQ index. Consequently,
our results corroborate Hypothesis 1’s statement that during the bubble, NASDAQ index
responded to the flow of high-growth IPOs, a fact that it is not present in other adjacent periods.
4.2 – Underpricing in the bubble
Our Hypothesis 2 states that the high underpricing prevailing during the bubble is fully
accounted by issuers’ characteristics and strategic goals rather than underwriter’s behavior. Table
6 investigates underpricing during the bubble (Model 2). Regression 1 includes only the usual
controls plus the Bubble dummy.7
The coefficient on the bubble dummy is 0.358 (35.8 percent)
with statistical significance at the one percent level. Thus the increase in underpricing during the
7
We do not include controls for the ex-ante demand because they could be correlated to the bubble itself.

16
bubble is not fully explained by change in firms’ characteristics. Notice that the coefficients on
the dummies for VC, Big-four, High-growth, Acquisition pre-IPO and Young are positive and
statistically significant at the one percent level.
Regression 2 includes the interaction between top-underwriting and the bubble dummies.
Now the coefficient on the Bubble dummy drops to 0.249 that is significant at the one percent
level. The coefficient on the interaction is 0.137 that is significant at the 10% level, suggesting
that part of the underpricing incurred during the bubble was due to the action of top underwriters.
This result is similar to that of Loughran and Ritter (2004).8
Regression 3 examines the effect of VC sponsorship and pre-IPO acquisitions during the
bubble on underpricing. The interaction of VC and bubble dummies has coefficient of 0.273 that
is statistically significant at the one percent level indicating that VC sponsored firms bore
additional 27.3 percent underpricing. Similarly, the interaction with the acquisition dummy has
coefficient of 0.213 that is statistically significant at the one percent level. The coefficients on
the bubble dummy drops to 0.074 and that on its interaction with top underwriting to 0.087. Both
coefficients lose statistical significance. Thus, the underpricing during the bubble can be fully
accounted by the behavior of VC and pre-IPO acquiring firms. Notice that once we include the
interaction of VC and bubble dummies, the coefficient on VC loses statistical significance.
Therefore, out of the bubble period VC sponsorship does not affect underpricing. Distinctively,
the coefficient on Acquisition pre-IPO remains statistically significant at the 10 percent level,
even though its magnitude drops from 0.072 to 0.023.
Regression 4 includes additionally the interaction between the bubble and Big-four
dummy. The coefficient on such interaction is 0.217 that is statistically significant at the one
percent level. Notice that now the coefficient on the bubble dummy becomes negative but still
non-significant. The inclusion of this interaction does not change the magnitudes of the
coefficients on the interactions of VC and Acquisition pre-IPO.
8
We note that these authors did not include in their analysis any other interaction with the bubble dummy.

17
It is possible that our three strategic variables also have a selection component. For
instance, VC-sponsored firms are frequently young, focused on technology and belong to high-
growth industries. In order to disentangle these two components, regression 5 includes
interactions between the bubble dummy and the dummies three for firms’ characteristics (high-
growth, technology and young). All of these interactions bear coefficients near 15 percent that
are statistically significant at the five or 10 percent levels. Now the coefficient on the interaction
of the bubble and VC dummies drops from 0.27 (Regressions 3 and 4) to 0.18, but remains
statistically significant at the one percent level. This means that from the 27 percent extra
underpricing bore by VC-sponsored firms, only 9 percent was due to their characteristics. The
magnitude of the coefficient on the interactions between the Acquisition pre-IPO, Big-four and
bubble dummies are only marginally affected by firms’ characteristics, suggesting that the
Acquisition pre-IPO and Big-four variables capture only strategic behavior.
Regression 6 drops the bubble dummy to include its interaction with the Non-top
underwriting dummy. The coefficient on the interaction with the top-underwriting dummy
becomes negative (-0.077) but still fails to present statistical significance (t-statistics is -1.13).
Regression 7, for robustness purposes, includes controls for the ex-ante demand (price revision
up and price revision down). The coefficient on the bubble dummy becomes -0.173 that is
statistically significant at the 10 percent level. The coefficient on the interaction of the bubble
and top-underwriting dummies remain positive but without statistical significance (t-statistics is
1.10).
Overall, our underpricing analysis indicates that the abnormal underpricing observed
during the bubble was due mostly to firms’ characteristics and their strategic behavior,
corroborating Hypothesis 2. We find no evidence that such abnormal underpricing was driven by
top-underwriters’ actions.
4.2.1 – Robustness checks
4.2.1.1 – Risk Composition Hypothesis

18
We will now discuss some robustness tests that consider alternative theories for the
market’s bubble behavior. We start with the Risk Composition hypothesis (RCH) that assumes
that issuers’ quality decreased during the bubble period. We now investigate changes in issuers’
quality.
One of the main evidences supporting RCH is the increased rate of failure (delisting due
to bankruptcy or drop reasons) within the first five years from the IPO (Yung, Colak and Wang;
2008). We look at the evolution of failure rates along the years from the IPO (Table 7).
Compared to the pre-bubble period, failure rate within three years from the IPO during the
bubble is almost three times bigger: 14.7 vs. 5.3 percent (difference statistically significant at the
one percent level). Within five years, it is almost twice: 18.6 vs. 10.3 percent (significant at the
one percent level). These results are in accordance to those of Yung, Colak and Wang (2008).
However, difference in failure rates decreases monotonically over time. Within 14 years from the
IPO, failure rates are similar: 26.5 vs. 24.0 percent (with no statistical difference). In fact, the
difference loses statistical significance after the 11th
year. Business cycles can explain difference
in failure rates across cohorts: firms that went public during the bubble faced its burst just few
months after their IPOs and a great credit crunch (2007-2009) within the first 10 years from their
IPOs. Therefore, it is likely that the effect of macroeconomic shocks on failure rates is stronger
for the bubble cohort. Summing up, there is no evidence of higher failure rates during the bubble
in the long run.
Table 7 also reports the difference in failure rates across highly and lowly underpriced
IPOs during the bubble. If underpricing is related to drop in quality, highly underpriced IPOs
should be associated increases failure rate. We find just the opposite. Failure rate remains five to
seven percent lower for highly underpriced IPO regardless of the time horizon (difference always
statistically significant at the one percent level). Thus, failure rate seems negatively related to
underpricing.
We also look at other quality measures: bid-ask spreads at the end of the second year
from the IPO and analysts’ coverage at the end of the first year, and institutional ownership and
its Herfindahl index at the end of the second year from the IPO (Table 4, Panel C). Bid-ask

19
spreads decreased from 4.2 to 3.2 percent and were considerably lower for highly underpriced
firms: 2.4 vs. 3.9 percent. Analysts’ coverage increased from 70 to 76 percent and was higher
among highly underpriced IPOs: 80 vs. 73 percent. Institutional ownership was constant at 33
percent over both pre-bubble and bubble periods and it was slightly lower for highly underpriced
IPOs: 31 vs. 34 percent (difference statistically significant at the 10 percent level). Similarly, the
Herfindahl index was constant at 0.21 across periods and slightly lower for highly underpriced
IPOs: 0.20 vs. 0.22 (no statistical difference). Thus, once more we find no evidence for drop in
quality.
Finally, we run regression analysis to search for drop in quality associated to issuers
characteristics (Table 8). Initially we focus on VC-sponsorship. VC bubble firms were less
frequently M&A targets, and they also experienced lower bid-ask spreads and institutional
ownership. There was no relative change in failure rate, analysts’ coverage and the Herfindhal
index for institutional ownership. Therefore, VC-sponsorship at the bubble implies a higher
liquidity and lower likelihood of becoming an M&A target. Thus, there is no evidence of a
decrease in quality for VC-sponsored issuers. For pre-IPO acquirers there was increased
likelihood of M&A, decreased bid-ask spreads and institutional concentration. Moreover, the
failure among pre-IPO acquirers was so rare that the variable drops in failure regression.
Consequently, apart from some evidence of industry concentration, all results point to an
increase in quality. For issuers with Big-four auditing there was decreased failure rate, improved
analyst coverage and institutional ownership, and lower institutional concentration. These results
point towards an improvement in quality.
The only groups of firms for which we see any evidence of decrease in quality are
technology and high-growth firms. Evidence is stronger for technology firms: there was an
increase in the rate of failure, even though they had improved their liquidity (lower bid-ask
spreads and higher analysts’ coverage). High-growth firms experienced a reduction in
institutional ownership and an increase in its concentration. For both technology and high-
growth firms there was an indication of sector consolidation, with higher likelihood of becoming
an M&A target.

20
Contrary to what one could expect, there is no evidence of any change in quality for
young issuers during the bubble. Issuers that went public earlier in their life cycle during the
bubble were equally good as their predecessors. This result goes against the idea that during the
bubble firms were speeding up their IPOs to time the market and exploit investors’ gullibility.
Overall, the evidence of a decrease in the quality of issuers seems weak. In particular,
there is no evidence of deterioration in quality among VC-sponsored, acquiring, audited by Big-
four or young companies. This leaves little room for a demand-driven explanation for the high
underpricing.
4.2.1.2 -- Change in underwriters’’ certification
Certification increased during the bubble (Table 4). The proportion of top underwriting
increased from 68 to 84 percent (difference significant at the one percent level), and was much
higher among highly underpriced IPOs: 87 vs. 81 percent (significant at the five percent level).
This led Loughran and Ritter (2004) to raise concern as top underwriting as measure of quality.
Accordingly, the increase in top-underwriting during the bubble was due to top underwriters’
opportunistic behavior to increase market share. We address such concern by running probit
analysis on the probability of top underwriting (Model 5). Regressions 1-3 in Table 9 report
marginal effects. Regression 1 includes only predetermined characteristics of issues. Top
underwriting is more likely for VC-sponsored, technology and large firms, and for large
offerings (in terms of absolute and relative size). Regression 2 includes a dummy variable for the
bubble period. The marginal effect of the bubble dummy is -0.160 (statistically significant at the
one percent level). Thus, the likelihood of any firm hiring a top underwriter fell by 16.0 percent
during the bubble. Finally, Regression 3 also controls for issuers’ strategic goals. The marginal
effect of the bubble dummy remains the same both in terms of magnitude and statistical
significance. In short, our results reject the idea of underwriters’ opportunism. In fact, top
underwriters became more selective. The increase in top underwriting during the bubble was due
to the raise in the proportion of firms with the fit for it.
4.2.1.3 – Change in analysts’ coverage

21
Loughran and Ritter (2004) suggest that during the bubble some firms coped with high
underpricing to obtain analysts’ coverage (Analyst Lust). We investigate such conjecture by
running probit analysis on analysts’ coverage (Model 5). Regressions 4-6 in Table 9 report
marginal effects. Regression 4 controls for firms’ characteristics, underpricing and the bubble
period. The coefficient on underpricing is 0.020 and bears no statistical significance. The
coefficient on the bubble dummy, -0.052, is statistically significant at the 10 percent level. The
marginal effect on Top-underwriting (0.055), even though positive and statistically significant at
the 5% level is relatively small when compared to that of acquisition dummy (0.211 with t-
statistics of 13.23) or VC sponsorship (0.133 with t-statistics of 7.18). Regression 5 also includes
the interactions of underpricing and Top-underwriting with the bubble dummy. Both interactions
are not statistically significant. Finally, regression 6 excludes Big-four auditing and Pre-IPO
acquisition dummies, but results remain similar to those in regressions 4 and 5. Summing up,
there is no evidence that during the bubble analyst coverage increased uniformly or that coverage
was related to underpricing or top underwriting. Consequently, our results contradict the Analyst
Lust conjecture.
5 – Conclusion
We conjecture that the Internet bubble and the concomitant high underpricing was
consequence of the emergence of the Dotcom industry and its large cohort of firms racing for
market leadership.
Our empirical findings support our conjecture. We begin by showing that during the
bubble there was no decrease in the quality of the average issuer. In fact, there is indication that
quality increased. For example, we find that the tradability conditions and certification improve
during the bubble. Finally, in order to reconcile our result with the previous literature, we show
that the increase in failure rate previously reported in the literature prevailed only in the short-
run. However, the mid- and long-run failure rates are not different for the bubble and pre-bubble
IPO cohorts. Furthermore, we observe that highly underpriced firms presented better quality than
their low-underpriced counterparts. Therefore, our evidence goes against the Risk Composition
Hypothesis.

22
By examining the determinants of top underwriting an analysts’ coverage we found that
underwriters and analysts became more selective during the bubble. Controlling for firms’
characteristics, the likelihood of obtaining top underwriting fell by near 15 percent, while that of
analysts’ coverage fell by near three percent. Top underwriting and analysts’ coverage increased
during the bubble because of the increase in the proportion of firms with the fit for them. This
evidence is contrary to the Analyst Lust Conjecture and to the idea that top underwriters took
advantage of the moment to increase their market share.
Next, we show that the abnormally high underpricing observed during the bubble can be
fully accounted by issuers’ characteristics and strategic purposes. The strategic dimensions we
consider were firms that were doing acquisitions, sought Big-four auditing, and had VC
sponsorship. The issuers associated with high underpricing were high-growth, technology and
young. When one controls for these characteristics, it emerges that the abnormally high
underpricing is not related to underwriters’ behavior. Thus our evidence is contrary to the
Realignment of Incentives Hypothesis.
We also checked for the possibility that investors forced high underpricing as a reaction
to a decrease in quality for specific issuers. We did not find supporting evidence that firms with
the characteristics associated to high underpricing presented decreased quality. The exception
was technology firms that presented increased failure rates. Consequently, we do not find
empirical support for the conjecture of a demand-driven high underpricing.
Finally, based on a model that controls for macroeconomic low-frequency shocks through
the variation in NYSE composite index, we show that the number or proportion of highly
underpriced IPOs explains large part of the variation on NASDAQ composite index. This result
remains even if we proxy for the proportion of highly underpriced IPOs with the number of high-
growth IPOs, or the number of IPOs with predicted high underprice (predictions coming from a
regression that includes only issue’s predetermined characteristics). This result is robust to the
estimation period used to predict underpricing.

23
8 – References
Barry, Christopher, Chris Muscarella, John Peavy and Michael Vetsuypens, 1990, The role of
venture capital in the creation of public companies: evidence from the going public process.
Journal of Financial Economics 27, 447-471.
Brau, James and Stanley Fawcett, Initial public offerings: an analysis of theory and practice.
Journal of Finance 61, 399–436.
Brennan, Michael and Julian Franks, 1997, Underpricing, ownership and control in initial public
offerings of equity securities in the UK. Journal of Financial Economics 45, 391–414.
Celikyurt, Ugur, Merih Sevilir and Anil Shivdasani, 2010, Going public to acquire? The
acquisition motive in IPOs. Journal of Financial Economics 96, 345–363.
Helwege, Jean and Nellie Liang, 2004. Initial public offerings in hot and cold markets. Journal
of Financial and Quantitative Analysis 39, 541–569.
Howe, John and Shaorong Zhang, 2005. Underwriting in hot and cold markets. Unpublished
working paper. University of Missouri at Columbia.
Ibbotson, Roger, Jody Sindelar and Jay Ritter, 1994. Initial public offerings. Journal of Applied
Corporate Finance 1, 37–45.
Ljungqvist, Alexander and William Wilhelm Jr., 2003, IPO pricing in the Dot-com bubble.
Journal of Finance 58, 723-752.
Loughran, Tim and Jay Ritter, 2004, Why has IPO underpricing changed over time? Financial
Management 33, 5-37.
Lowry, Michelle and G. William Schwert, 2002, IPO market cycles: bubbles or sequential
learning? Journal of Finance 57, 1171–1200.
Megginson, William and Kathleen Weiss, 1991, Venture Capitalist Certification in Initial Public
Offers. Journal of Finance 46, 879-903.
Newey, Whitney and Kenneth West, 1987, A simple, positive semi-definite, heteroskedasticity
and autocorrelation consistent covariance matrix. Econometrica 55, 703–708.
Ritter, Jay, 1984, The ‘hot issue’ market of 1980. Journal of Business 57, 215–240.
Ritter, Jay, 2014, Initial Public Offereings: Updated Statistics. Available at
http://bear.warrington.ufl.edu/ritter/IPOs2014Statistics.pdf. access on Oct/30/2014.
Scheinkman, Jose A. and Wei Xiong, 2003, Overconfidence and speculative bubbles. Journal of
Political Economy 111, 1183-1220.

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Schultz, Paul, Zaman, Mir, 2001. Do the individuals closest to internet firms believe they are
overvalued? Journal of Financial Economics 59, 347–381.
Schwert, G. William, 2002. Stock volatility in the new millennium: how wacky is Nasdaq?
Journal of Monetary Economics 49, 3–26.
Yung, Chris, Conul Colak and Wei Wang, 2008, Cycles in the IPO market. Journal of Financial
Economics 89, 192-208.

25
Table 1
Variables Definition
Underpricing
The percent change from the IPO offer price to the closing price
of the first trading day.
Acquisition post-IPO
Dummy variable indicating that the firm made acquisitions in the 5-
year period after the IPO.
Acquisition pre-IPO
Dummy variable indicating that the firm made acquisitions in the
3-year period before the IPO.
Big-four auditing
Dummy variable indicating whether financial statements were
audited by Big-Four auditor
Venture Capital (VC) Dummy variable indicating VC sponsorship.
M&A
Dummy variable indicating that the firm was target for merger or
acquisition between 3th
and 10th
years from the IPO.
Top underwriting
Dummy variable indicating whether the Carter-Manaster index
(updated for the period 1992-2003 by Loughran and Ritter
(2004) for the member of the underwriting syndicate with the
highest score is bigger than 8.
Bid-ask spread
Difference between bid and ask prices, divided by the arithmetic
average between the two.
Failure Dummy variable indicating delisting for bankruptcy or drop.
Institutional
ownership
Percentage of outstanding shares held by institutional investors.
Herfindhal index Herfindhal index for institutional ownership.
Analysts’ coverage
Dummy variable indicating that the firms is followed by at least
one analyst during the year
Sales growth
Geometric average of quarterly sales growth during the last three
quarters before the IPO (or available period if less).
High-growth
Dummy variable indicating that Sales growth was above the cut
off for the top quartile using the sample from 1991-1996
Price revision up
Dummy variable indicating that the offer price was higher than
original filing high price.
Price revision down
Dummy variable indicating that the offer price is lower than
original filing low price.
Price interval
Original filing high price minus original filing low price divided
by their average.
Offer size Filled amount in the IPO prospectus (MM).
Firm size
Book value of assets in the last financial statement before the
IPO (MM).
Technology
Dummy variable indicating technology industries as defined in
Loughran and Ritter (2004).
Age IPO year minus founding year.
Young
Dummy variable indicating if the firm is younger than 8.1 years
old (the median age during the bubble).
Industry dummies Mapped into US 2-digit SIC codes

26
Table 2
Reasons for drop in sample
Description Number of IPO
Original Sample from Ritter (2014) sample including founding date 9003
IPOs missing prospectus and information from SDC Platinum -2888
IPOs with multiple entries -1
Firms without information on Institutional Holdings -770
Firms without CRSP information on bid-ask spread -60
Firms without Compustat Annual or Quarterly Fundamental's data -1034
Firms with offer size less than US$ 5 -124
Firms that opened capital in unknown or foreign exchanges -433
Firms with IPO after 2001 -880
Firms with IPO before 1990 -59
Total 2754

27
Table 3
Comparing samples
Pre-bubble Transition period Bubble period
1991-1996 1997 1998 1999 2000
Sample
Ritter (2014) 2801 474 282 476 381
Our Sample 1661 291 172 333 300
coverage 58% 61% 61% 70% 79%
IPO at NASDAQ
Ritter (2014) 86% 79% 79% 92% 85%
Our sample 88% 80% 73% 95% 94%
VC-backed IPOs
Ritter (2014) 37% 28% 27% 58% 64%
Our sample 41% 33% 35% 63% 71%
Tech IPOs
Ritter (2014) 24% 58% 61% 78% 69%
Our sample 29% 30% 28% 44% 57%
Gross Proceeds
Ritter (2014) 57.6 68.7 122.2 136.4 170.3
Our sample 58.8 72.7 118 115.9 116.3
Median age
Ritter (2014) 8 7 6 4 5
Our sample 8 9 7 5 6
Price revision up
Ritter (2014) 24.2% 24.2% 22.5% 47.7% 38.8%
Our sample 27.0% 26.0% 25.0% 54.0% 38.0%
Price revision
down
Ritter (2014) 27.0% 29.9% 28.0% 14.8% 22.1%
Our sample 29.0% 32.0% 31.0% 17.0% 22.0%
Underpricing
Ritter (2014) 14.3% 14.0% 22.2% 71.7% 56.1%
Our Sample 15.5% 15.1% 22.7% 75.1% 56.3%

28
Table 4
Characteristics of Sample across Periods
Underpricing: first trading day closing price relative to the offer price; Age: IPO year minus founding year; Firm size: book value of assets in the last
financial report before the IPO; Technology: dummy variable indicating technology industries as defined in Loughran and Ritter (2004); Sales growth:
geometric average of quarterly sales growth during the last three quarters before the IPO (or available period, if less); High-growth: dummy variable
indicating quarterly sales growth above 100%; Offer size: filled amount in the IPO prospectus; and Top underwriting: dummy variable indicating that the
Carter-Manaster index for the member of the underwriting syndicate with the highest score is bigger than 8. Number of firms with the attribute and t-
statistics are in parentheses. *, ** and *** to denote statistical significance at the 10, 5 and 1percent levels (in boldface). The number or observations is
2,754.
Panel A: entire sample Panel B: only growth firms (third quartile)
Pre Bubble
Transition
period
Bubble
Period
Difference Pre Bubble
Transition
period
Bubble
Period
Difference
1991-1996 1997-98 1999-2000
Bubble - Pre-
bubble
1991-1996 1997-98 1999-2000
Bubble - Pre-
bubble
Sample 1660 464 633 -
416
(25%)
165
(36%)
362
(57%)
Sales growth
(Average of quarterly growth)
56% 68% 91%
35%***
(0.000)
Age 14.6 15.2 9.4
-5.2*
(0.077)
9.85 10.61 6.55
-2.96*
(0.077)
Technology 29% 30% 50%
21%***
(0.000)
34% 35% 55%
22%***
(0.000)
Firms size
(Book value of assets in millions)
164.1 233.6 254.6
90.5***
(0.000)
90 231 230
140***
(0.000)
Offer size
(in million)
58.8 95.35 116.1
57.3***
(0.000)
53 90 107
54***
(0.000)
Offer-to-firm size 1 1.09 1.12
0.12***
(0.000)
1.13 1.21 1.10
-0.03*
(0.082)
Underpricing 16% 20% 66%
50%***
(0.000)
23% 25% 78%
55%***
(0.000)
Top underwriting 68% 67% 84%
16%***
(0.000)
76% 72% 85%
10%***
(0.000)
Venture capital 41% 34% 67%
26%***
(0.000)
55% 39% 75%
20%***
(0.000)
Big-four auditor 33% 31% 36%
3%*
(0.082)
31% 29% 35%
4%*
(0.082)
Acquisition pre-IPO
(3 years)
33% 33% 25%
-8%*
(0.094)
31% 25% 27%
-5%*
(0.094)

29
Table 5
The Price Spiral
Least squares estimations using Newey-West (12 lags) standard errors for the models ∆𝑁𝐴𝑆𝐷𝐴𝑄𝑡 = 𝛽0 + 𝛽1∆𝑁𝑌𝑆𝐸𝑡 + 𝛽2#𝐼𝑃𝑂𝑠_𝐿𝑜𝑤𝑡 + 𝛽3#𝐼𝑃𝑂𝑠_𝐻𝑖𝑔ℎ 𝑡 + 𝜀𝑡 and ∆𝑁𝐴𝑆𝐷𝐴𝑄𝑡 = 𝛽0 + 𝛽1∆𝑁𝑌𝑆𝐸𝑡 +
𝛽2#𝐼𝑃𝑂𝑠𝑡 + 𝛽2 𝑝𝑟𝑜𝑝𝑟𝑡𝑖𝑜𝑛(𝐼𝑃𝑂𝑠_𝐻𝑖𝑔ℎ) 𝑡 + 𝜀𝑡, where ∆ is the percent change in the index over period 𝑡; NASDAQ is the NASDAQ composite index and NYSE is the NYSE composite index. #𝐼𝑃𝑂𝑠𝑡
is the number of IPOs in period 𝑡. #𝐼𝑃𝑂𝑠_𝐿𝑜𝑤𝑡 is the number of IPOs with low underpricing (or sales growth) in period 𝑡. #𝐼𝑃𝑂𝑠_𝐻𝑖𝑔ℎ 𝑡 is the number of IPOs with high sales growth in period 𝑡.
Sample consists of 107 weeks from Jan/1999 to Dec/2000 (633 IPOs in this sample period). T-statistics are shown in parentheses. We use *, ** and *** to denote statistical significance at the 10, 5 and 1
percent levels (two sided).
Bubble Period Bubble 1999
Δ NASDAQ in 3 Weeks Δ NASDAQ in 1 Week Δ NASDAQ in 3 Weeks Δ NASDAQ in 1Week
Regression
number
1 2 3 4 5 6 7 8 9 10 11 12
Δ NYSE (in 1 or 3
weeks)
0.948* 0.935 0.954 1.222*** 1.206*** 1.217*** 1.556*** 1.548*** 1.641*** 1.361*** 1.347*** 1.369***
(1.77) (1.62) (1.63) (3.68) (3.55) (3.55) (11.21) (12.98) (15.13) (9.28) (10.64) (10.61)
# IPOs
0.001 -0.007* -0.010*** 0.000 -0.002* -0.004** 0.004*** -0.005** -0.007*** 0.003** -0.000 -0.001
(0.50) (-1.95) (-2.71) (0.13) (-1.93) (-2.51) (2.77) (-2.09) (-2.68) (2.46) (-0.20) (-0.50)
Mean Sales
Growth
0.001* 0.001** 0.001*** 0.001***
(1.86) (2.07) (4.00) (3.17)
# high growth
IPOs (75%)
0.027*** 0.010*** 0.025*** 0.008***
(2.85) (3.85) (4.16) (3.21)
R-Squared 0.09 023 0.21 0.19 0.22 0.23 0.40 0.64 0.62 0.45 0.52 0.51
Pre-bubble period (1990-1996) Transition period (1997-1998)
13 14 15 16 17 18 19 20 21 22 23 24
Δ NYSE (in 1 or 3
weeks)
1.460*** 1.461*** 1.458*** 1.385*** 1.381*** 1.380*** 1.260*** 1.297*** 1.275*** 1.262*** 1.273*** 1.267***
(10.21) (10.41) (10.31) (11.73) (11.69) (11.58) (19.54) (17.18) (14.33) (15.12) (14.64) (13.97)
# IPOs
-0.000 0.001 0.001 -0.000 0.000 0.000 -0.001 -0.003 -0.002 -0.001 -0.001 -0.001
(-0.42) (1.05) (1.18) (-0.52) (0.28) (0.32) (-0.71) (-1.03) (-0.57) (-0.89) (-1.22) (-0.78)
Mean Sales
Growth
-0.000 -0.000 0.000 0.000
(-1.6) (-1.22) (0.87) (0.91)
# high growth
IPOs (75%)
-0.005 -0.002 0.004 0.001
(-1.57) (-1.06) (0.36) (0.37)
R-Squared 0.63 0.64 0.63 0.63 0.63 0.62 0.74 0.76 .075 0.73 0.74 0.73
=

30
Table 6
Underpricing Analysis
Least square analysis of the underpricing. Bubble dummy indicates the years of 1999 and 2000; Venture Capital dummy: indicates VC
sponsorship; Top underwriting dummy: indicates that the Carter-Manaster score for the highest syndicate member ≥ 8; Big-four auditing dummy:
indicates Big-Four auditing; High-growth dummy: indicates quarterly sales growth ≥ 100%; Acquisition pre-IPO dummy: indicates acquisitions
in the 3-year period before the IPO; Young dummy: indicates firm age ≤ 8.1 years; Age: IPO year minus founding year; Technology dummy:
indicates technology industries as defined in Loughran and Ritter (2004); Firm size: book value of assets; Offer size: filled amount in the IPO
prospectus; Sales growth: geometric average of quarterly sales growth over the last three quarters before the IPO (or available period if less); Price
interval: original filing upper bound minus lower bound divided by their average; Price Revision Up dummy: indicates offer price higher than
filing high price; and Price Revision Down dummy: indicates offer price lower than original filing low price. T-statistics in parentheses. Estimates
use White standard errors. We use *, ** and *** to denote statistical significance at the 10, 5 and 1 percent levels (two sided). The number or
observations is 2,754.
1 2 3 4 5 6 7
Bubble dummy
0.358*** 0.249*** 0.074 -0.003 -0.143 -0.173*
(10.67) (3.67) (1.01) (-0.04) (-1.53) (-1.88)
Bubble x Top underwriting
0.137* 0.087 0.084 0.065 -0.077 0.083
(1.77) (1.14) (1.08) (0.85) (-1.13) (1.10)
Bubble x Non-top underwriting
-0.143
(-1.53)
Bubble x Acquisition pre-IPO
0.213*** 0.229*** 0.232*** 0.232*** 0.239***
(2.64) (2.85) (2.89) (2.89) (2.99)
Bubble x Venture capital
0.273*** 0.270*** 0.181*** 0.181*** 0.185***
(4.36) (4.31) (2.78) (2.78) (2.90)
Bubble x Big-four
0.217*** 0.215*** 0.215*** 0.221***
(2.97) (2.96) (2.96) (3.06)
Bubble x High-growth
0.134* 0.134* 0.146**
(1.80) (1.80) (1.97)
Bubble Dummy x Technology
0.165** 0.165** 0.186***
(2.47) (2.47) (2.80)
Bubble Dummy x Young
0.130** 0.130** 0.129**
(2.05) (2.05) (2.06)
Acquisition pre-IPO
0.072*** 0.072*** 0.023** 0.074*** 0.023** 0.023** 0.014
(3.74) (3.73) (2.10) (3.88) (2.14) (2.14) (1.33)
Venture capital
0.053*** 0.054*** -0.005 0.051*** 0.016 0.016 0.010
(2.89) (2.97) (-0.38) (2.83) (1.35) (1.35) (0.95)
Big-four
0.070*** 0.071*** 0.018 0.066*** 0.015 0.015 0.011
(3.57) (3.60) (1.55) (3.43) (1.29) (1.29) (1.06)
High-growth
0.112*** 0.110*** 0.095*** 0.039 0.037 0.037 0.047*
(3.00) (2.95) (2.60) (1.35) (1.29) (1.29) (1.72)
Technology
0.088*** 0.089*** 0.085*** 0.034** 0.041*** 0.041*** 0.019
(4.04) (4.05) (3.91) (2.55) (3.12) (3.12) (1.60)
Young
0.045*** 0.045*** 0.039** 0.011 0.013 0.013 0.011
(2.72) (2.69) (2.38) (0.88) (1.07) (1.07) (0.98)
Top underwriting
0.034* 0.010 0.042** 0.036* 0.030** 0.030** 0.028
(1.75) (0.66) (2.22) (1.91) (2.18) (2.18) (1.56)
Age
-0.001** -0.001** -0.001*** -0.001*** -0.001*** -0.001*** -0.001***
(-2.29) (-2.21) (-2.84) (-3.64) (-3.86) (-3.86) (-2.81)
Firm size
0.056*** 0.054*** 0.052*** 0.055*** 0.051*** 0.051*** 0.047***
(4.13) (4.05) (4.11) (4.21) (4.14) (4.14) (3.80)
Offer size
-0.058*** -0.054*** -0.055*** -0.054*** -0.050*** -0.050*** -0.047***
(-3.62) (-3.48) (-3.59) (-3.56) (-3.48) (-3.48) (-3.34)
Offer-to-firm size
10.754 10.734 10.602 10.395 10.256 10.256 9.852
(1.39) (1.39) (1.42) (1.35) (1.37) (1.37) (1.34)
Sales growth
0.042 0.043* 0.048* 0.044* 0.049* 0.049* 0.028
(1.61) (1.66) (1.86) (1.69) (1.92) (1.92) (1.10)
Price interval
-0.005*** -0.005*** -0.005*** -0.006*** -0.005*** -0.005*** -0.008***
(-3.68) (-3.47) (-3.40) (-3.78) (-3.45) (-3.45) (-6.03)
Price revision up
0.203***
(11.63)
Price revision down
-0.057***
(-6.65)
R-squared 0.238 0.247 0.257 0.251 0.275 0. 275 0.301
Industry dummies yes yes yes yes yes Yes yes
Constant yes yes yes yes yes Yes yes

31
Table 7
Failure along firms life
Failure: dummy variable indicating delisting for bankruptcy or drop. Number of firms with the attribute and t-statistics are shown in
parentheses. We use *, ** and *** to denote statistical significance at the 10, 5 and 1 percent levels (two sided). The number or observations
is 2,754.
Cumulative
Failure
Pre Bubble
Transition
period
Bubble Period Difference Bubble Period Difference
Years from the
IPO
1991-1996 1997-98 1999-2000
Bubble - pre-
bubble
High
Under>50%
Low
Under≤50%
High-Low
3
5.3%
(88)
12.5%
(58)
14.7%
(93)
9.4%***
(0.000)
10%
(27)
9%
(35)
1%
(0.723)
4
7.6%
(126)
18.3%
(85)
16.7%
(106)
9.2%***
(0.000)
13%
(34)
20%
(72)
-7%***
(0.000)
5
10.3%
(171)
22.0%
(102)
18.6%
(118)
8.3%***
(0.000)
15%
(39)
21%
(79)
-7%***
(0.000)
6
12.7%
(210)
24.1%
(112)
19.7%
(125)
7.1%***
(0.000)
16%
(42)
22%
(83)
-7%***
(0.000)
7
15.7%
(260)
25.4%
(118)
21.3%
(135)
5.7%***
(0.000)
18%
(48)
24%
(87)
-5%***
(0.000)
8
17.5%
(291)
26.1%
(121)
22.6%
(143)
5.1%**
(0.034)
20%
(52)
25%
(91)
-5%***
(0.000)
9
19.4%
(322)
26.9%
(125)
24.2%
(153)
4.8%**
(0.027)
22%
(57)
26%
(96)
-5%***
(0.000)
10
20.5%
(341)
27.8%
(129)
25.1%
(159)
4.6%*
(0.082)
22%
(57)
28%
(102)
-6%***
(0.000)
11
20.5%
(341)
27.8%
(129)
25.1%
(159)
4.6%*
(0.091)
22%
(58)
28%
(103)
-6%***
(0.000)
12
22.6%
(375)
29.7%
(138)
25.8%
(163)
3.2%
(0.124)
23%
(60)
28%
(105)
-6%***
(0.000)
13
23.5%
(390)
30.2%
(140)
26.2%
(166)
2.7%
(0.132)
23%
(61)
28%
(105)
-5%***
(0.000)
14
24.0%
(399)
30.8%
(143)
26.5%
(168)
2.5%
(0.133)
24%
(63)
28%
(105)
-5%***
(0.000)
15
24.3%
(404)
31.0%
(144)
26.5%
(168)
2.2%
(0.137)
24%
(63)
28%
(105)
-5%***
(0.000)

32
Table 8
Quality Analysis
(Marginal effects)
The dependent variables are Failure: dummy variable indicating delisting for bankruptcy of drop in the first 10 years from the IPO;
M&A: dummy variable indicating that the firm was target for acquisition in the first 10 years from the IPO; Bid-ask: bid-ask
spreads at the end of the second year from the IPO; Analysts: dummy variable indicating analysts coverage at the end of the second
year from the IPO; Institutional ownership: Percentage of outstanding shares held by institutional investors at the end of the second
year from the IPO; and Herfindhal: Herfindhal index for institutional ownership at the end of the second year from the IPO.
Explanatory variables are: Bubble dummy indicates the years of 1999 and 2000; Venture Capital: dummy variable indicating VC
sponsorship; Acquisition pre-IPO: dummy variable indicating that the firm made acquisitions in the 3-year period before the IPO;
Big-four auditing: dummy variable indicating auditing by Big-Four auditors; High-growth: dummy variable indicating quarterly
sales growth above 100%; Technology: dummy variable indicating technology industries as defined in Loughran and Ritter (2004);
Young: dummy variable indicating if the firm is younger than 8.1 years old; Age: IPO year minus founding year; Firm size: book
value of assets in the last financial report before the IPO; Offer size: filled amount in the IPO prospectus; and Sales growth:
geometric average of quarterly sales growth during the last three quarters before the IPO (or available period if less). T-statistics
are shown in parentheses. Estimates use White standard errors. We use *, ** and *** to denote statistical significance at the 10, 5
and 1 percent levels (two sided). The number or observations is 2,754.
Failure M&A Bid-Ask Analysts
Institutional
ownership
Herfindhal
Bubble dummy
0.065 -0.083 -0.010*** -0.207*** -0.026 0.078***
(1.36) (-1.55) (-7.00) (-3.96) (-0.82) (3.17)
Bubble x Venture capital
-0.010 -0.095** -0.003* -0.033 -0.079*** 0.010
(-0.24) (-1.99) (-1.81) (-0.65) (-2.73) (0.39)
Bubble x Acquisition pre-IPO dropped
0.200*** -0.002* 0.050 0.012 -0.053**
(3.20) (-1.88) (0.97) (0.47) (-2.49)
Bubble x Big-four
-0.067* 0.098 -0.001 0.135*** 0.046* -0.047**
(-1.93) (1.56) (-0.80) (3.62) (1.79) (-2.07)
Bubble x High-growth
-0.023 0.116* 0.002 -0.087 -0.067** 0.073***
(-0.54) (1.75) (1.32) (-1.58) (-2.47) (2.84)
Bubble x Technology
0.095** 0.120** -0.004*** 0.104*** -0.002 0.025
(2.02) (2.05) (-2.99) (2.59) (-0.07) (1.07)
Bubble x Young
0.013 -0.080 -0.001 0.036 -0.003 0.009
(0.27) (-1.54) (-0.95) (0.73) (-0.09) (0.37)
Venture capital
-0.034 0.060** -0.000 0.155*** 0.088*** -0.066***
(-1.64) (2.54) (-0.31) (6.79) (7.91) (-6.61)
Acquisition pre-IPO dropped
0.601*** -0.000 0.294*** 0.063*** -0.056***
(30.01) (-0.52) (15.71) (5.77) (-6.24)
Big-four
-0.020 -0.183*** 0.000 0.005 0.029*** -0.032***
(-1.01) (-8.45) (0.07) (0.22) (2.60) (-3.51)
High-growth
0.114*** -0.046 -0.002 0.014 -0.067*** 0.041**
(2.91) (-1.18) (-1.27) (0.35) (-3.34) (2.39)
Technology
-0.083*** -0.021 -0.000 -0.046* -0.026** 0.014
(-3.63) (-0.85) (-0.45) (-1.80) (-2.25) (1.24)
Young
0.047** 0.002 0.000 -0.089*** -0.017 0.020*
(2.05) (0.09) (0.32) (-3.53) (-1.36) (1.91)
Age
-0.000 -0.000 0.000 -0.000 0.001** -0.001***
(-0.52) (-0.18) (1.10) (-0.39) (1.97) (-2.84)
Firm size
-0.007 0.025* -0.002*** -0.015 0.011 -0.022***
(-0.62) (1.82) (-3.38) (-1.10) (1.62) (-4.29)
Offer size
-0.057*** -0.025 -0.004*** 0.060*** 0.074*** -0.059***
(-4.15) (-1.51) (-5.57) (3.54) (9.34) (-9.12)
Offer-to-firm size
4.335 1.100 0.361 1.659 -4.512*** -0.965
(1.12) (0.19) (0.79) (0.41) (-2.60) (-0.52)
Sales growth
-0.066** 0.036 -0.001 0.061** 0.041*** -0.049***
(-2.47) (1.17) (-1.27) (2.06) (2.85) (-3.58)
R-squared 0.333 0.165 0.188
Industry dummies yes yes yes yes yes Yes
Constant yes yes yes yes yes Yes

33
Table 9
Determinants of Top Underwriting and Analysts’ Coverage
(Marginal effects)
The dependent variables are Top underwriting: dummy variable indicating that the Carter-Manaster index for the member of the
underwriting syndicate with the highest score is bigger than 8; and Analysts’ coverage: dummy variable indicating that the firms
was followed by at least one analyst in the second year from the IPO; Bubble dummy indicates the years of 1999 and 2000;
Acquisition pre-IPO: dummy variable indicating that the firm made acquisitions in the 3-year period before the IPO; Venture
Capital: dummy variable indicating VC sponsorship; Big-four auditing: dummy variable indicating auditing by Big-Four
auditors; Age: IPO year minus founding year; Technology: dummy variable indicating technology industries as defined in
Loughran and Ritter (2004); Firm size: book value of assets in the last financial report before the IPO; Offer size: filled amount
in the IPO prospectus; and Sales growth: geometric average of quarterly sales growth during the last three quarters before the
IPO (or available period, if less). T-statistics are shown in parentheses. Estimates use White standard errors. We use *, ** and
*** to denote statistical significance at the 10, 5 and 1 percent levels (two sided). The number or observations is 2,754.
Top underwriting Analyst’s Coverage in year 1
1 2 3 4 5 6
Bubble Dummy
-0.160*** -0.157*** -0.052* -0.046 -0.063*
(-5.23) (-5.12) (-1.72) (-1.38) (-1.84)
Underpricing x Bubble
-0.021 -0.030
(-0.40) (-0.55)
Top underwriting x Bubble
0.033 0.026
(1.33) (1.04)
Underpricing
0.020 0.036 0.052
(0.96) (0.75) (1.03)
Acquisition pre-IPO
0.036* 0.211*** 0.211***
(1.95) (13.23) (13.21)
Big-four auditing
0.012 -0.035* -0.035*
(0.66) (-1.88) (-1.89)
Venture capital
0.160*** 0.173*** 0.171*** 0.133*** 0.133*** 0.135***
(8.93) (9.58) (9.47) (7.18) (7.18) (7.19)
Top underwriting
0.055** 0.037 0.058**
(2.15) (1.56) (2.30)
Age
-0.001 -0.001* -0.001* 0.001 0.001 0.001
(-1.31) (-1.72) (-1.71) (1.50) (1.52) (1.45)
Technology
0.049** 0.061*** 0.059*** 0.015 0.015 0.027
(2.42) (3.06) (2.95) (0.75) (0.74) (1.35)
Firm size
0.118*** 0.124*** 0.124*** -0.018 -0.018 -0.014
(8.36) (8.63) (8.57) (-1.49) (-1.48) (-1.18)
Offer size
0.195*** 0.219*** 0.219*** 0.065*** 0.065*** 0.064***
(10.87) (11.50) (11.45) (4.20) (4.21) (4.21)
Offer-to-firm size
9.480* 10.187* 10.274* -1.740 -1.721 -1.916
(1.77) (1.82) (1.83) (-0.47) (-0.47) (-0.53)
Sales Growth
0.010 0.027 0.028 0.043** 0.043** 0.040**
(0.48) (1.34) (1.40) (2.23) (2.18) (1.98)
Industry dummies yes yes yes yes yes yes
Constant yes yes yes yes yes yes

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