Investor Sentiment and IPO Pricing during Pre-Market and Aftermarket Periods
Li Jianga, *, Gao Lia
a School of Accounting and Finance, Hong Kong Polytechnic University, Hong Kong, China
This version: January 14, 2012
Abstract
In this study, we separately measure pre-market and aftermarket investor sentiment and investigate their impact on IPO pricing in a two-stage framework. We find that compensation for institutional investors is associated with their fractional allocation and the risk they bear. This helps explain the partial adjustment of offer prices to pre-market sentiment. We also show that pre-market sentiment tends to spill over to the aftermarket period, and aftermarket sentiment causes a further price run-up in the secondary market. Overall, our findings suggest that institutional investors play an important role in re-distributing shares in the secondary market and the momentum in investor sentiment makes it possible for underwriters to implement the staged distribution strategy.
JEL Classification Code: G02; G14
Keywords: Pre-market sentiment; Aftermarket sentiment; IPO pricing; Institutional investors
* Corresponding author. Tel.: +852 2766 4672; Fax: +852 2330 9845
E-mail addresses: [email protected] (L. Jiang); [email protected] (G. Li)
1
1. Introduction
IPO underpricing phenomenon has been studied extensively in finance literature. So far,
evidence has shown that investor sentiment is an important determinant for IPO pricing and
investor sentiment causes issuers to set offer price above intrinsic value (Derrien, 2005; Cornelli
et al., 2006; Ljungqvist et al., 2006; and Dorn, 2009). In this study, we show that it is important
to examine investor sentiment separately for pre-market and aftermarket periods.
A recent study by Chen and Wilhelm (2008) focuses on the transition from pre-market
bookbuilding to the secondary market trading, to account for the sequential arrival of informed
traders. Underwriters adopt a staged distribution strategy in order to circumvent the restriction of
a uniform offer price and to extract greater surplus from the informed investors over time. We
adopt the two-stage framework to address for the possibility that sentiment investors may arrive
sequentially and pre-market sentiment may spill over to the aftermarket period. The transition
from primary to secondary markets gives rise to an important role played by institutional
investors to re-distribute shares in the secondary market (Ljungqvist et al., 2006). To extract
greater surplus from sentiment investors over time, underwriters adopt a similar staged
distribution strategy. However, the fact that most of prior IPO studies on investor sentiment use
initial return (offer to close return) implies that they have not paid sufficient attention to the
transition from pre-market stage to aftermarket trading.
In this study, we fill this gap by separating pre-market sentiment from aftermarket
sentiment and examining their impacts on IPO pricing in the two-stage framework. By
considering pre-market and aftermarket investor sentiment, we can address two important
questions: First, how institutional investors are compensated? In their theoretical model,
Ljungqvist et al. (2006) show that underwriter partially adjusts for investor sentiment and leaves
2
room for institutional investors, in exchange for their role of re-distributing shares in the
secondary market. Only by partitioning the transition into pre-market and aftermarket periods,
we can empirically test their prediction. Second, why would investors (sentiment investors in
particular) pay an even higher price than the inflated offer price when trading starts in the
secondary market? If investor sentiment has a momentum over time, sentiment investors at an
earlier stage would be willing to chase the hot issues as long as more optimistic sentiment
investors are expected to arrive later.
Hong Kong provides a unique institutional setting to study investor sentiment and IPO
pricing. First, Hong Kong IPOs have two separate tranches for retail and institutional investors.
The oversubscription rate for retail tranche indicates retail demand, which is subject to investor
sentiment. Second, retail investors have a prominent presence in Hong Kong market, making it
ideal to study the impact of investor sentiment on IPO pricing. Prior research suggests that Asian
markets are more prone to investor overconfidence (Huang et. al, 2011). Third, the claw-back
clause in Hong Kong IPOs implies that the fractional allocation to institutional investors is
inversely related to retail demand. This generates a greater variation in the fractional allocation
to institutional investors (as compared with a more stable allocation of 70% in the US), which
allows us to examine the determinants of compensation for institutional investors.
Our study proceeds as follows. First, we posit that underwriter revises offer price to
incorporate pre-market sentiment. We find supporting evidence that offer price revision is
positively related to oversubscription rate and investor attention. Offer-to-open return remains
positively related to pre-market sentiment, suggesting a partial adjustment to pre-market
sentiment.
3
Second, we posit that institutional investors are compensated for their role in the staged
distribution in the secondary market. We show that compensation for institutional investors is
associated with the risk facing institutional investors. Our findings provide an explanation for the
observed partial adjustment phenomenon and confirm the prediction by Ljungqvist et al. (2006).
Third, we posit that investor sentiment has a momentum over the transition from primary
market to the secondary market periods. We use small trade order imbalance and turnover as
proxies for aftermarket sentiment and find that pre-market sentiment proxies positively affect
aftermarket investor sentiment.
Finally, we posit that aftermarket sentiment pushes stock price higher. While small trade
order imbalance captures investor overconfidence, the divergence of opinion captured by trading
volume in conjunction with short-sales constraints causes overpricing (Miller, 1977). Our
findings support our prediction for a positive impact of aftermarket sentiment on secondary
market returns.
Our study makes the following important contributions: First, we shed lights on the
impact of investor sentiment on IPO pricing during the transition from primary market to
secondary market. Second, we identify the sources of investor sentiment at different stages.
Investor attention appears to drive retail demand at the pre-market stage, whereas the divergence
of opinion in conjunction with short-sales constraints leads to overpricing in the secondary
market. Third, by linking pre-market and aftermarket sentiments, we provide reasons for why
sentiment investors are willing to participate in the hot IPOs. Finally, by separately considering
pre-market and aftermarket periods, we establish the link between compensation for institutional
investors and their risk. We offer an explanation for partial adjustment to pre-market investor
4
sentiment and shed lights on the role played by institutional investors in re-distributing new
shares in the secondary market.
The rest of paper proceeds as follows. Section 2 reviews related studies and develops the
hypotheses. Section 3 describes the data and discusses the measurement of investor sentiment.
Section 4 presents the empirical results. Section 5 conducts further analysis. Section 6 concludes.
2. Related research and hypothesis development
In this study, we adopt the two-stage framework of Chen and Wilhelm (2008) in order to
address for the dynamics of investor sentiment over the transition from pre-market to aftermarket
period. Chen and Wilhelm (2008) focus on the transition to account for the sequential arrival of
informed traders. The same framework allows us to investigate the impact of sequentially
arriving sentiment investors on IPO pricing during pre-market and aftermarket stages. By
reviewing related research on IPO pricing, we can generate empirical implications under the new
framework.
We first examine the impact of pre-market investor sentiment on IPO offer price. Derrien
(2005) and Ljungqvist et al. (2006) show that underwriter sets the offer price above a firm’s
intrinsic value to take advantage of investor sentiment. Using abnormal Google search volume
index (ASVI) to proxy investor attention, Da et al. (2011) find that ASVI is positively related to
IPO price. Using the number of news headlines as proxy for investor sentiment during the
roadshow period, Cook et al. (2006) find a positive relationship between numbers of news
headlines and offer price revision. We make a similar prediction for the offer price revision in
relation to pre-market sentiment.
H1: IPO offer price revision is positively related to pre-market sentiment.
5
The partial adjustment of offer price is a well-known phenomenon. Benveniste and
Spindt (1989) propose that underwriter reward the informed investors for revealing their private
information by partially incorporating the private information into the offer price. Hanley (1993)
confirms their partial updating hypothesis. Loughran and Ritter (2002) explain the partial
adjustment phenomenon based on prospect theory that investors’ preference for wealth depends
on whether it is long-held or recently acquired. Edelen and Kadlec (2005) argue that issuers
partially adjust the offer price in order to increase the probability of completion. When issuer’s
surplus is large and therefore the marginal benefit of increasing the probability of success is high,
he would set a more conservative offer price to ensure the success of the offer. Bradley and
Jordan (2002) and Lowry and Schwert (2004) find that even public information is not fully
incorporated into the offer price. In this study, we predict that the offer price partially adjusts to
(publicly observable) investor sentiment during the pre-market period.
H2: Offer price partially adjusts to pre-market investor sentiment: Offer-to-open return is
positively related to pre-market sentiment.
We further examine the causes for the partial adjustment in the offer price. Prior research
suggests that the role played by regular investors in re-distributing shares in the secondary
market is a reasonable cause for the partial adjustment. Specifically, Ljungqvist et al. (2006)
suggest that regular investors are compensated for the risk of re-distributing shares in the
secondary market. Derrien (2005) argues that sentiment is only partially incorporated into the
offer price because underwriter intends to strike a balance between taking advantage of
sentiment investors and costly price support during the aftermarket period. To the extent that
6
partial adjustment in the offer price is intended to be the compensation for institutional investors,
there should be a connection between the compensation and the risk facing institutional investors.
H3: Partial adjustment is due to compensation for regular investors, which is positively
related to the fractional allocation and risk.
Next, we establish the link between pre-market and aftermarket sentiments. In their
theoretical model, Ljungqvist et al. (2006) show that pre-market demand by sentiment investors
exceeds their allocation and regular investors are allocated shares for them to re-distribute in the
secondary market. As a result, it is natural to expect that the unfulfilled demand may spill over to
aftermarket period. Kaustia and Knupfer (2008) show that investors overweight their personal
experiences in IPO and the reinforcement learning drives investor sentiment. Their findings
across IPOs can be extended to different stages of the same IPO, such that good experiences (in
terms of positive offer-to-open returns on the allocated shares) bolster investors’ confidence. In
addition, hot IPO issues and positive market reactions attract investors’ attention. Barber and
Odean (2008) argue that momentum investors would chase recent attention-grabbing stocks. We
therefore predict that pre-market sentiment tend to spill over to the aftermarket period.
H4: Pre-market sentiment is positively related to aftermarket sentiment.
Finally, we investigate whether aftermarket sentiment pushes up stock prices in the
secondary market trading. As informed and sentiment investors trade the newly listed shares, an
increase in divergence of opinion in conjunction with short sales constraints may lead to a
speculative premium (Miller, 1977). Using prices from pre-IPO grey market to proxy for retail
investors’ valuation, Cornelli et al. (2006) and Dorn (2009) show that overoptimistic retail
7
investors push the aftermarket IPO prices higher than its intrinsic value. Bradley et al. (2009)
find that information asymmetry and retail sentiment lead to an open-to-close return of 2.3% on
the first day of trading. Chan (2010) find that retail investors’ trading such as small trade order
imbalance is positively related to aftermarket returns. We make the following prediction:
H5: Aftermarket sentiment is positively related to open-to-close return.
3. Data and measurements
3.1. Data
We collect a sample of 567 IPOs in Hong Kong main board market from 1999 to 2009.
We limit our sample to IPOs using bookbuilding method and exclude IPOs of closed-end funds,
REITs, unit offering, companies switching from growth market. Our final sample contains 316
IPOs.
Firms in Hong Kong use dual-tranche mechanism in an IPO and reserve a fraction of IPO
shares for individual investors (“Public Tranche”). When applying for shares in an IPO, retail
investors are required to pay for the full cost of shares under their application. We download IPO
prospectus and the Factbook from the HKEx website to obtain offer price, price range,
oversubscription rate for the Public Tranche, proceeds for the Public Tranche and Placing
Tranche.
We obtain daily prices and trading volume from the Datastream. We obtain intraday
transaction prices and quotations from HKEx to calculate the return volatility. Since Loughran
and Ritter (2004) do not cover most of the Hong Kong underwriters in their ratings, we acquire
the annual market share of underwriters from Bloomberg.
3.2. Investor sentiment measures
8
In this study, we intend to capture investor sentiment for pre-market and aftermarket
stages. Previous studies use several indirect proxies to gauge investor sentiment:
oversubscription rate for public tranche (Derrien, 2005); the number of news headlines (Cook et
al., 2006); and price in grey market (Cornelli et al., 2006). We use two proxies for pre-market
sentiment.
First, the availability of oversubscription rate for retail tranche allows us to measure pre-
market retail demand for an IPO. Thus, we use oversubscription rate (SUBRATE) to proxy for
pre-market sentiment. Second, we use abnormal Google search volume index (ASVI) as our
second proxy for pre-market sentiment. Da et al. (2011) argue that Google search volume is a
direct and unambiguous measure of investor attention. We obtain the Google Insights1 Search
Volume Index (SVI) of company names in Chinese for our sample and compute the abnormal
SVI (ASVI) by calculating the percentage difference between the SVI in one week before the
listing day and the median SVI during the eight weeks earlier.
We use two proxies for aftermarket sentiment. First, we use turnover on the listing day to
proxy for aftermarket sentiment. Baker and Stein (2004) argue that liquidity is a proxy for
sentiment because sentiment investors systematically underestimate the information contained in
adverse trading decision, thus make the adverse price impact lower and result in more liquidity in
market. We obtain the turnover by dividing trading volume by the total number of shares
outstanding. Second, we use small trade order imbalance to proxy for aftermarket sentiment.
Following Lee and Ready (1991) procedure, we identify trades into buyer-initiated and seller-
initiated by comparing the transaction price with the midpoint of concurrent bid-ask quote. We
use dollar-value cut-off points at HK$ 50,000 and HK$ 500,000 to classify each of the trades
1 http://www.google.com/insights/search/#
9
into small, medium, and larger trades. Under the small trade category, we use the order
imbalance between buyer-initiated and seller-initiated trades to generate the net-buying measure
for small trade group and then scale it by the total dollar trading volume.
3.3. Descriptive statistics
Panel A of Table 1 provides the descriptive statistics of our sample. The average offer
price is HK$4.14 and the mean offer proceeds are approximately HK$4,371 million. More than
half of the firms (56%) on the main board use prestigious investment bank as their underwriter
(i.e., UWREP=1). The mean of initial return (offer-to-close return) on the first day is 13.47%.
However, once the initial return is decomposed into pre-market (offer-to-open) return and
aftermarket (open-to-close) return, offer to open return dominates the initial return (12.71%) and
open-to-close return has a mean close to zero (0.60%).
The IPO market in Hong Kong appears to be affected by investor sentiment. The average
oversubscription rate is 161 times higher than the number of shares assigned for retail tranche.
Although over-subscription is common in Hong Kong market, some firms are under-subscribed;
the lowest subscription rate is merely 0.02. The offer price revision has a mean of 3.48%,
indicating that underwriter typically increases offer price after soliciting private information and
gauging investor sentiment from bookbuilding process.
There is a significant variation in the price range for Hong Kong IPOs, ranging from 7%
to 67% of the mid-point offer price, and the average price range is 25%. This is in contrast with
the price range in the US, which is usually set at two dollars (Kutsuna et al., 2009). We use a
dummy variable, TOP, which equals one if the offer price reaches the upper bound of the price
range. About 40% of the IPOs are priced at the upper bound (i.e., TOP = 1).
10
Panel B provides year-by-year statistics. Our sample incorporates hot market periods like
2006-2007 and also the subprime crisis period in 2008. Although the first bookbuilding case was
introduced to Hong Kong in 1994, they were rare before 2003. Since 2003 the bookbuilding
method has prevailed for the IPOs in Hong Kong. The investor sentiment varies a great deal over
time. The IPO market in 2006 and 2007 was extremely hot in terms of oversubscription rates for
retail tranche; the median oversubscription rate was above 100 times. In contrast, IPOs in 2008
had a median of oversubscription rate of 2 times. In line with the oversubscription rate, the
average initial returns are about 22%-25% in 2006 and 2007, but merely 7% in 2008. Relatively,
the open-to-close return is less volatile than initial return. The mean and median 18-month
adjusted returns are negative for IPOs in 2006 and 2007, but positive for 2008. This suggests that
strong retail demand in hot IPO periods results in overpriced IPO shares, as compared with their
long-run prices.
Table 2 reports the correlation matrix that generates some preliminary results. The offer
price revision (REVISION) is positively correlated with each of the two pre-market sentiment
measures: abnormal Google search volume and oversubscription rate (ASVI and SUBRATE).
This suggests that underwriter incorporates pre-market sentiment in the offer price. However,
either ASVI or SUBRATE is positively correlated with offer-to-open returns (OTO), suggesting
that the offer price revision is only partial. The compensation for institutional investors is
positively correlated with either RANGE or VOLATILITY. The former indicates pre-market value
uncertainty, the latter indicates aftermarket value uncertainty. It is worth noting that
oversubscription rate (SUBRATE) is positively correlated with aftermarket volatility
(VOLATILITY). This reveals that those heavily over-subscribed IPOs tend to have greater
aftermarket value uncertainty. To the extent that the investor sentiment towards oversubscribed
11
stocks is more likely to change in aftermarket period, IPO shares held by regular investors
beyond their optimal inventory level would be risky.
Aftermarket trading volume (TURNOVER) is positively correlated with either ASVI or
SUBRATE, confirming our conjecture that pre-market sentiment tends to spill over to aftermarket.
However, small trade order imbalance (SMALLNET) is weakly correlated with pre-market
sentiment measures. The market-adjusted open-to-close return on the first day is significantly
and positively correlated with either SMALLNET or TURNOVER, suggesting that aftermarket
sentiment leads to significant secondary market return. The long-run adjusted return is negatively
correlated with pre-market and especially aftermarket sentiment measures,
[Insert Tables 1 and 2 about here]
4. Empirical results
In this section, we conduct several tests that investigate the impact of pre-market and
aftermarket sentiment on IPO pricing. We first examine whether pre-market sentiment positively
affects offer price revision. After documenting offer price is only partially adjusted to pre-market
sentiment, we then examine whether compensation for institutional investors is related to the risk
for re-distributing new shares in the secondary market. We also examine whether pre-market
investor sentiment spills over to aftermarket period and test if aftermarket sentiment causes
further overpricing.
4.1. Pre-market sentiment and offer price revision
Hypothesis 1 predicts a positive relationship between pre-market sentiment and offer
price revision. In this section, we use the oversubscription rate (SUBRATE) and abnormal search
volume index (ASVI) to proxy for pre-market investor sentiment. We test their impact on the
12
offer price revision. In addition, we also include several control variables into our regressions to
control other IPO characteristics that would influence IPO price.
Table 3 presents the regression results. The coefficient of subscription rate (SUBRATE) is
positive and significant at the 1% level. Similarly, the coefficient of abnormal Google search
index (ASVI) is positive and significant at the 1% level. This suggests that pre-market sentiment
positively influences the offer price revision and Barber and Odean’s (2008) attention-induced
price pressure hypothesis holds for IPOs. This impact is also economically significant. On
average, a one standard deviation increase in subscription rate leads to a 61% higher price
relative to initial midpoint price. In addition, Derrien (2005) suggests that the bullishness of
investors towards an IPO is determined by current market condition and finds positive
association between market returns and offer price. Thus, we include the average initial return of
prior five IPOs (PRE_IPO_RTN) in the regression. Its coefficient is positive and significant at
the 1% level, consistent with the finding of Derrien (2005). Our findings suggest that both firm-
specific and market-wide pre-market sentiment cause the underwriter to revise the offer price
upward. The coefficient of price range (RANGE) is negative and significant at the 5% level,
suggesting that price range proxies for ex-ante value uncertainty. Economically, increasing price
range by one standard deviation will cause offer price to drop by nearly 17%-22% on average.
Our result is consistent with Kutsuna et al. (2009) that value uncertainty negatively influences
offer price.
Overall, the results in Table 3 confirm that underwriter takes into consideration of pre-
market investor sentiment, as reflected by oversubscription and abnormal Google search volume,
and revises the offer price upward to capitalize on investor sentiment.
[Insert Table 3 about here]
13
4.2. Partial adjustment to pre-market sentiment
In this section, we investigate whether underwriter adjusts the offer price fully to
incorporate information (including investor sentiment) gathered during the pre-market period.
Hanley (1993) finds that offer price revision is positively correlated with initial return,
suggesting a partial adjustment of offer price. In this regard, we regress offer-to-open returns on
pre-market sentiment measures to test our Hypothesis 2.
Table 4 presents the regression results. The coefficients of oversubscription rate
(SUBRATE) are positive and significant at the 1% level, suggesting that underwriter only
partially responds to pre-market sentiment when setting the offer price. On average, a one
standard deviation increase in subscription rate generates a return of 119% to 126% assuming
flipping new shares once the market trading starts. The result based on ASVI as the proxy for pre-
market sentiment is similar though the coefficients of ASVI are positive and significant at the 5%
level. The coefficients of PRE_IPO_RTN are positive and significant at the 5% or 1% level,
suggesting that the offer price does not fully incorporate market-wide investor sentiment. We
conclude that underwriter does not fully take advantage of investor sentiment when setting the
offer price, and leaves some profit to investors who acquire new shares at the offer price. These
results are consistent with previous findings that investor sentiment causes IPO partial
adjustment (Derrien, 2005; Cornelli et al., 2006).
[Insert Table 4 about here]
4.3. Compensation for institutional investors
So far, our findings indicate that underwriter partially adjusts the offer price in response
to pre-market investor sentiment. In this section, we attempt to provide an explanation for the
14
partial adjustment phenomenon by examining the compensation for institutional investors.
Specifically, we investigate whether compensation for institutional investors is positively related
to their fractional allocation and value uncertainty of an IPO.
In this study, we construct a measure for compensation for institutional investors, which
is defined as the offer-to-open return times the number of shares allocated to institutional
investors divided by total asset. This measure is more relevant than offer-to-open return itself
since it focuses directly on the reward for institutional investors. To remove the fractional
allocation to institutional investors that can be explained by pre-market sentiment due to the
Claw-back Clause in Hong Kong, we regress PLACING on SUBRATE and use the residual
(R_PLACING) in the regression. We use price range (RANGE) as a proxy for pre-market value
uncertainty and use the first-day return volatility (VOLATILITY) as proxy for aftermarket
uncertainty. To investigate whether compensation for institutional investors depends on the
“hotness” of IPO, we divide the full sample into two subsamples based on the sign of the offer
price revision. 237 IPOs are classified into “hot” IPO group and 79 into “cold” IPO group in our
subsample tests.
Table 5 presents the regression results. The coefficients of fractional allocation
(R_PLACING) are positive and significant at the 10% to 1% levels, for the full sample and hot-
IPO subsample, respectively. Specifically, in column 1, one standard deviation increase in
institutional placing results in an average 23% higher compensation for institutional investors. In
contrast, the coefficient of R_PLACING is not significant for the cold-IPO subsample. This
indicates that the positive relationship between fractional allocation to institutional investors and
their compensation holds only for hot IPOs.
15
Table 5 also reports the results on the impact of risk on the compensation for institutional
investors. The coefficients of price range (RANGE) are positive and significant at the 1% level,
for hot-IPO subsamples. For hot IPO subsample, a one standard deviation wider in price range
causes underwriter on average to give up 26% of the compensation to institutional investors for
bearing the risk. Similarly, the coefficients of VOLATILITY are positive and significant at the 1%
level, for both the full sample and hot-IPO subsamples (columns 2 and 4). However, the
coefficient of VOLATILITY for cold IPOs is insignificant. This result implies that the
compensation for aftermarket uncertainty depends largely on market conditions; underwriter
offers rewards to institutional investors for firm-specific uncertainty only when market is hot.
It is interesting to note that the coefficient of oversubscription rate (SUBRATE) is no
longer significant, while the sign of coefficients is even negative for hot IPO subsample
(columns 3 and 4). For hot IPOs, underwriter chooses (and the claw-back clause requires the
underwriter) to sell more shares directly to retail investors, rather than relies on institutional
investors. Therefore, underwriter may compensate institutional investors less when investor
sentiment is high.
We further investigate whether underwriter reputation affects the compensation for
institutional investors. We collect the yearly market share of underwriters in Hong Kong from
Bloomberg. We then construct a dummy variable UWREP which has a value of 1 for the top ten
underwriters in ranking and otherwise 0. As shown in Table 5, underwriter reputation is
negatively related to compensation for institutional investors at the 5% significance level.
Moreover, we find that the reputation effect is more pronounced in the hot market. As shown in
columns 3 to 6, high-reputation investment banks provide less compensation for hot IPOs than
16
for cold IPOs, consistent with Daniel (2002) that underwriter’s bargaining power is stronger in
good business period.
Overall, Table 5 provides empirical evidence supporting our hypothesis (H3) regarding
compensation for institutional investors. The results show institutional investor’s fractional
allocation and valuation uncertainty are important determinants of their compensation.
Furthermore, the observed partial adjustment to pre-market sentiment can be explained by the
compensation for institutional investors.
[Insert Table 5 about here]
4.4. Spill-over from pre-market sentiment to aftermarket sentiment
In this section, we investigate whether pre-market sentiment spills over to the aftermarket
period. Table 6 presents the regression results. In Panel A, the dependent variable is the first-day
turnover (TURNOVER), our first proxy for aftermarket sentiment. The coefficient of
oversubscription rate (SUBRATE) in columns 1 is positive and significant at the 5% level. In
Panel B, the dependant variable is small trade order imbalance (SMALLNET), our second proxy
for aftermarket sentiment. Chan (2010) proposes that small trade order imbalance increases
open-to-close return in the secondary market. The coefficient of SUBRATE is positive and
significant at the 10% level, providing similar evidence that pre-market and aftermarket
sentiments are positively linked.
In addition, offer price revision may serve as a signal that stimulates aftermarket
sentiment2. To test the signaling effect, we include in the regression price revision (REVISION)
and a dummy variable (TOP) to indicate offerings at the upper bound of the price range. As
2 Cornelli et al. (2006) constructs a dummy variable indicating whether grey market price exceeds midpoint price of price range.
Using it as their pre-market sentiment proxy, they find that this dummy variable effectively explains aftermarket turnover.
17
shown in columns 1 and 4, price revision shows positive but insignificant relation to our two
aftermarket sentiment proxies. However, the coefficients of TOP in columns 1 and 2 are positive
and significant at the 5% or 1% level. The fact that the revised offer price is set at the upper
bound of the price range attracts investors’ attention and generates aftermarket trading.
Several other characteristics also exert significant impact on aftermarket sentiment. SIZE
is negatively related to turnover at the 5% significance level, implying sentiment investors are
more likely to speculate with small-cap firms, which are more subject to information asymmetry.
This is consistent with prior studies (Ritter and Welch, 2002; Ofek and Richardson, 2003; and
Kumar and Lee, 2006). The positive coefficients of UWREP are also consistent with the claim of
Cook et al. (2006) that underwriter’s marketing effort enhances investor sentiment.
In sum, pre-market investor sentiment tends to spill over to the aftermarket stage. It
appears that the unfulfilled demand by sentiment investors in pre-market period continue to
affect aftermarket trading, and the initial market response to the IPO attract additional sentiment
investors to participate in the aftermarket trading. The fact that investor sentiment exhibits a
momentum over time can help explain why sentiment investors would be willing to chase the hot
issues even though pre-market sentiment is likely to produce an overpriced offer price.
[Insert Table 6 about here]
4.5. Aftermarket sentiment and secondary market returns
In this section, we investigate whether aftermarket sentiment further drives IPO
overpricing in the secondary market. Table 7 reports the regression that examines the
relationship between aftermarket sentiment and open-to-close return. Through columns 3 to 6,
the coefficients of TURNOVER and SMALLNET are positive and significant at the 1% level.
18
Based on the parameter estimates in column 3, a one standard deviation change in turnover ratio
leads to a 33% change in open-to-close return in the same direction. In column 4, a one standard
deviation increase in the small trade order imbalance leads to a 61% increase in the open-to-close
return. Thus, aftermarket sentiment affects open-to-close return with both statistical and
economical significance. In columns 5 and 6, we include both TURNOVER and SMALLNET in
the regressions. Their coefficients remain positive and significant at the 1% level. These results
provide strong empirical evidence that aftermarket sentiment further increases the IPO
overpricing in the secondary market.
Interestingly, we find that our pre-market sentiment measures, oversubscription rate
(SUBRATE) and abnormal search volume index (ASVI), as well as prior IPO returns
(PRE_IPO_RTN), lose their power in explaining the open-to-close returns. This is different from
previous studies with the offer-to-close return as the dependant variable, and implies that
investors have fully incorporated pre-market sentiment into the opening price when trading starts
in the secondary market. Another worth-noting variable is VOLATILITY. Falconieri et al. (2009)
use intraday volatility as the proxy for aftermarket uncertainty and find it positively related to
open-to-close return. The coefficients of VOLATILITY through columns 3 to 6 are positive and
significant at the 1% level, suggesting that aftermarket uncertainty contributes to secondary
market return.
In columns 6, we incorporate additional control variables, including large trade order
imbalance (LARGENET) and institutional fractional allocation (R_PLACING). Only coefficient
of LARGENET is positive and significant at the 1% level. Chan (2010) finds that medium and
large trades explain aftermarket price movement in cold or neutral market, while small trades can
only predict share price in hot market. Our finding indicates that net buying by large investors
19
also contributes to IPO price run-up in the aftermarket. Overall, Table 7 shows that aftermarket
sentiment results in a further price run up once the secondary market trading starts. In contrast,
pre-market sentiment no longer has any impact on open-to-close return in the secondary market.
[Insert Table 7 about here]
5. Further Analysis
5.1. Investor attention and retail demand
In this section, we further investigate the potential source of pre-market retail demand for
IPOs. Investor attention is a major factor in determining the purchase decision by individual
investors (Barber and Odean, 2008 and Barber et al., 2009). Da et al. (2011) show that Google
search volume (ASVI) is a direct and timely measure of investor attention. Using it to proxy for
individual investor attention, they find increased attention leads to high initial return and
subsequent long-run underperformance of IPOs.
Table 8 presents the regression results to test whether investor attention drives
oversubscription rate (SUBRATE). As expected, the coefficient of ASVI is positive and
significant at the 1% level. This suggests that retail investors’ attention indeed increases the
oversubscription rate of the IPO. Similarly, the coefficient of prior IPO returns (PRE_IPO_RTN)
is also positive and significant at the 1% level. In contrast, the coefficient of price range (RANGE)
is negative and significant at the 5% level. Overall, our findings confirm that those attention-
grabbing IPOs are more subject to the demand by sentiment investors.
[Insert Table 8 about here]
5.2. Long-run underperformance
20
In this section, we examine whether pre-market and aftermarket sentiment measures can
explain the long-run underperformance of IPOs. A consensus view is that as sentiment fades
away and short-sale constraints are lifted, the overpriced share price is expected to revert to its
normal level in the long run. Previous research provides both theoretical argument (Ritter and
Welch, 2002; Ljungqvist et al. 2006) and empirical evidences (Derrien, 2005; Cornelli et al.,
2006; Agarwal et al., 2008; Dorn, 2009; Da et al., 2011) of IPO long-run underperformance. In
this study, we adopt similar methods to test whether such phenomenon exists.
First, we calculate the buy-and-hold returns of IPOs in the 6-month, 1-year, and 18-
month horizons from the first-day closing price. Table 9 presents the reports on mean and
median values in each holding period. The long-run median market-adjusted returns are -
5.58%�-11.52%, and -16.90%, respectively, indicating an increasing underperformance relative
to the benchmark market index (Hang Seng Index). To further investigate the cause of long-run
underperformance, we run regressions with the long-run market adjusted returns as the
dependent variables.
Table 10 reports the estimation results of 1-year market-adjusted returns. Various
sentiment measures are our focus. The coefficients of aftermarket sentiment measures,
TURNOVER and SMALLNET, are negative and significant at the 5% or 10% level. This is in line
with Barber et al. (2009) that purchase by small investors pushes up price and results in a
subsequent price reversal. In contrast, the coefficients of pre-market sentiment measures,
SUBRATE and ASVI, are not significant. This is different from Agarwal et al. (2008) that pre-
market sentiment is negatively related to long-run performance. The difference might be due to
our incorporation of aftermarket sentiment. The coefficients of SIZE are both positive and
21
significant. Since big firms are often less opaque, they might be less influenced by investor
sentiment. Overall, Table 10 confirms that aftermarket sentiment causes IPO overpricing.
[Insert Tables 9 and 10 about here]
6. Conclusion
In this study, we examine the impact of investor sentiment over pre-market and
aftermarket stages on IPO pricing. By allowing sentiment to evolve over the transition from
primary market to secondary market trading, we are able to show empirically how institutional
investors are compensated for re-distributing new shares and who are leaving the money on the
table. Our findings can be summarized as follows: Underwriter capitalizes on pre-market
investor sentiment by revising the offer price upward. Investor attention appears to drive pre-
market retail demand for IPO. The offer price revision is only partial in the sense that pre-market
sentiment still affects offer-to-open return. We empirically confirm the prediction by Ljungqvist
et al. (2006) that compensation for regular investors is one of the main reasons for partial
adjustment.
We also shed lights on why sentiment investors are willing to participate in the IPOs by
paying an even higher price than the offer price in the secondary market. We show that pre-
market sentiment tends to spill over to aftermarket period, suggesting that additional sentiment
investors may arrive in the aftermarket stage. The aftermarket sentiment pushes up the stock
price further, making those sentiment investors who participate in the early stage benefit from
the sequential arrival of sentiment investors. The long-term underperformance confirms that
sentiment eventually fades away and overpricing is corrected over time. However, the presence
22
of investor sentiment and its momentum make it possible for underwriter to successfully
implement the staged distribution strategy.
23
References
Agarwal, S., Liu C., Rhee S. G., 2008, Investor demand for IPOs and aftermarket performance: Evidence from the Hong Kong stock market, International Financial Market, Institutions & Money 18, 176-190.
Baker, M., Stein, J. C., 2004. Market liquidity as a sentiment indicator. Journal of Financial Markets 7, 271-299.
Barber, B. M., Odean T., 2008, All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors, Review of Financial Studies 21, 785 – 818.
Benveniste, L. M., Spindt P. A., 1989, How investment bankers determine the offer price and allocation of new issues? Journal of Financial Economics 24, 343-361.
Bradley, D. J., Gonas, J. S., Highfield, M. J., Roskelley, K. D., 2009. An examination of IPO secondary market returns. Journal of Corporate Finance 15, 316-330.
Bradley, D. J., Jordan, B. D., 2002. Partial adjustment to public information and IPO underpricing. Journal of Financial and Quantitative Analysis 37, 595 – 616.
Chan, Y. C., 2010. Retail trading and IPO returns in the aftermarket, Financial Management 1475-1495.
Chen, Z., Wilhelm, W., 2008. A theory of the transition to secondary market trading of IPOs. Journal of Financial Economics 90, 219-236.
Cook, D., Kieschnick, R., Van Ness, R., 2006. On the marketing of IPOs. Journal of Financial Economics 82, 35-61.
Cornelli, F., Goldreich, D., Ljungqvist, A., 2006. Investor sentiment and pre-IPO markets. Journal of Finance 61, 1187-1216.
Da, Z., Engelberg, J., Gao, P., 2011. In search of attention. Journal of Finance 66, 1461-1499.
Daniel, K., 2002. Discussion of “Why don’t issuers get upset about leaving money on the table in IPOs?”. Review of Financial Studies 15, 445-454.
Derrien, F., 2005. IPO pricing in “hot” market conditions: Who leaves money on the table? Journal of Finance 60, 487-520.
Dorn, D., 2009, Does sentiment drive the retail demand for IPOs? Journal of Financial and Quantitative Analysis 44, 85-108.
24
Edelen, R. M., Kadlec, G. B., 2005, Issuer surplus and the partial adjustment of IPO prices to public information. Journal of Financial Economics 77, 347-373.
Espinasee, P., 2011, IPO: A Global Guide. The Hong Kong University Press.
Falconieri, S., Murphy, A., Weaver, D., 2009. Underpricing and ex post value uncertainty. Financial Management 285-300.
Hanley, K. W., 1993. The underpricing of initial public offerings and the partial adjustment phenomenon, Journal of Financial Economics 34, 231-250.
Huang, Z., Heian, J.B., Zhang, T., 2011. Differences of opinions, overconfidence, and the high-volume premium. Journal of Financial Research 34, 1-25.
Kaustia, M., Knupfer, S., 2008. Do investors overweight personal experience? Evidence from IPO subscriptions. Journal of Finance 63, 2679 – 2702.
Kumar, A., Lee, M.C., 2006. Retail investor sentiment and return comovements. Journal of Finance 61, 2451-2486.
Kutsuna, K., Smith, J. K., Smith, R. L., 2009. Public information, IPO price formation, and long-run returns: Japanese evidence. Journal of Finance 64, 505-546.
Lee, M. C., Ready, M., 1991. Inferring trade direction from intraday data. Journal of Finance 2, 733-746.
Ljungqvist, A., Nanda, V., Singh, R., 2006. Hot markets, investor sentiment and IPO pricing. Journal of Business 79, 1667-2702.
Loughran, T., Ritter, J. R., 2002. Why don’t issuers get upset about leaving money on the table in IPOs? Review of Financial Studies 15, 75-109.
Lowry, M., Schwert, G. W., 2004. Is the IPO pricing process efficient? Journal of Financial Economics 71, 3-26.
Miller, E. M., 1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32, 1151-1168.
Ofek, E., Richardson, M., 2003. Dot-com mania: The rise and fall of Internet stock prices. Journal of Finance 58, 1113-1138.
Ritter, J. R., Welch, I., 2002. A review of IPO activity, pricing and allocations. Journal of Finance 57, 1795-1828.
25
Table 1 Descriptive Statistics
The full sample consists of 316 IPOs in Hong Kong from 1999 to 2009. The sample is restricted to IPOs using bookbuilding method. We exclude IPOs of closed-end funds, REITs, unit offerings, and companies switching from growth market. OFFER_PRICE is the offer price after price revision. PLACING is the portion of shares allocated to the Placing Tranche in an IPO. SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. PROCEEDS is the amount raised in millions of HK dollars. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. LARGENET is the buyer-initiated large trades minus seller-initiated large trades divided by the total dollar trading volume on the first trading day. ADJOTC is the market-adjusted open-to-close return. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. COMPEN is the compensation for institutional investors and calculated as the offer to open return times the number of shares allocated to institutional investors scaled by total asset. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. 6MADJRTN, 1YADJRTN, and 18NADJRTN are 6-month, 1-year and 18-month buy-and-hold market-adjusted returns from close price on the first trading day. Panel A: Full Sample Variable N Mean Median Maximum Minimum Std Dev
OFFER_PRICE 316 4.14 2.70 84.00 0.34 5.99
PLACING 316 69.32 69.78 99.34 0.00 18.94
SUBRATE 316 161.81 54.85 1703 0.07 242.44
PRE_IPO_RTN 316 12.97 8.84 61.92 -13.59 14.12
RANGE 316 24.82 24.61 66.67 7.00 8.32
SIZE 316 22.03 21.73 29.65 18.01 2.04
PROCEEDS 316 4371.77 1260.83 124947.90 2.75 10919.99
UWREP 316 0.56 1 1 0 0.50
REVISION 316 3.48 6.51 20.42 -33.33 10.02
TOP 316 0.40 0 1 0 0.49
OTO 316 12.71 5.95 122.22 -27.62 20.60
SMALLNET 316 -0.15 -0.15 36.35 -51.58 5.26
TURNOVER 316 0.48 0.33 5.84 0.01 0.59
LARGENET 316 -2.09 -0.13 51.75 -61.85 12.39
ADJOTC 316 0.60 -0.03 76.02 -27.99 10.61
VOLATILITY 316 3.27 2.40 23.45 0.24 2.84
ASVI 158 75.02 41.67 900.00 -52.84 116.55
COMPEN 316 3.68 0.94 53.78 -27.68 9.19
6MADJRTN 316 -2.09 -5.09 220.19 -100.15 46.01
1YADJRTN 316 -1.10 -10.54 381.49 -108.91 66.75
18MADJRTN 316 1.91 -16.60 977.46 -132.33 101.58
26
Table 1 Descriptive Statistics (Continued)
Thi
s ta
ble
prov
ides
yea
r-by
-yea
r de
script
ive
stat
istic
s fo
r ou
r sa
mpl
e. N
is
the
num
ber
of I
POs. O
FFER_P
RIC
E i
s th
e of
fer
pric
e af
ter
pric
e re
visi
on. SU
BRATE
is
the
ratio
be
twee
n th
e to
tal d
eman
d su
bmitt
ed b
y in
divi
dual
inve
stor
s du
ring
the
appl
icat
ion
period
, and
the
amou
nt o
f sha
res as
sign
ed to
pub
lic tr
anch
e. P
RO
CEED
S is th
e am
ount
rai
sed
in
mill
ions
of
HK
dol
lars
. U
WREP is
a du
mm
y va
riab
le w
hich
equ
als
one
if o
ne o
f sp
onso
rs is
amon
g th
e to
p te
n ba
sed
on the
und
erw
ritin
g m
arke
t sh
are
rank
ing.
AD
JIR a
nd
AD
JOTC
are
mar
ket-ad
just
ed o
ffer
-to-
clos
e an
d op
en-to-
clos
e re
turn
s, res
pect
ivel
y. 1
8MAD
JRTN
is 1
8-m
onth
buy
-and
-hol
d m
arke
t-ad
just
ed ret
urns
fro
m c
lose
price
on
the
firs
t trad
ing
day.
Panel B: B
y Year
Yea
r N
OFFERPRIC
E
PRO
CEED
S (H
K$
Mil)
S
UBRATE
AD
JIR
AD
JTO
C
18M
AD
JOTC
Mea
n M
edia
n M
ean
Med
ian
Mea
n M
edia
n M
ean
Med
ian
Mea
n M
edia
n M
ean
Med
ian
1999
1 1.
70
1.70
71
4.68
71
4.68
32
.50
32.5
0 -1
7.22
-1
7.22
-2
.03
-2.0
3 10
7.30
10
7.36
2000
7 4.
61
1.59
15
249.
40
1078
7.00
14
.48
4.06
0.
97
-2.2
7 -5
.76
-4.4
5 45
.57
28.0
2
2001
5 4.
20
4.10
35
84.6
9 12
74.5
0 22
.64
7.14
6.
90
8.95
3.
40
7.78
26
.49
36.7
5
2002
9 12
.45
1.68
45
30.3
4 16
37.0
3 9.
43
3.70
2.
87
4.74
0.
88
1.50
-0
.54
7.26
2003
20
3.44
2.
04
2745
.98
1045
.41
130.
61
64.8
0 16
.47
10.9
1 2.
45
4.08
2.
20
-4.0
8
2004
31
4.34
2.
50
2351
.82
1040
.37
157.
80
55.7
0 4.
40
1.73
-1
.40
-0.9
6 5.
60
-17.
16
2005
45
2.36
1.
70
3593
.18
517.
50
95.6
0 14
.00
4.40
1.
95
-0.4
4 -0
.38
37.7
5 16
.18
2006
47
3.16
2.
28
7009
.63
1367
.21
257.
54
142.
70
25.3
2 19
.91
2.26
1.
39
-17.
17
-43.
73
2007
72
4.64
3.
84
3951
.32
1866
.45
234.
64
169.
00
22.4
7 9.
51
1.38
-1
.06
-28.
77
-29.
03
2008
22
3.15
2.
77
2924
.92
2115
.20
32.1
1 2.
07
7.17
-0
.11
2.61
0.
51
18.7
1 2.
66
2009
57
4.91
3.
45
4342
.23
1620
.00
163.
02
57.7
0 10
.14
6.91
-0
.47
-0.5
9 11
.92
-4.9
8
All
316
4.14
2.
70
4371
.77
1260
.83
161.
82
54.8
5 13
.47
6.08
0.
60
-0.0
3 1.
91
-16.
86
27
Table 2 Correlation M
atrix
PLA
CIN
G is
the
portio
n of
sha
res
allo
cate
d to
the
Pla
cing
Tra
nche
in
an I
PO. SU
BRATE
is
the
num
ber
of s
hare
s su
bscr
ibed
by
indi
vidu
al inv
esto
rs d
ivid
ed b
y th
e nu
mbe
r of
sh
ares
ass
igne
d to
the
Publ
ic tr
anch
e. P
RE_I
PO
_RTN
is th
e av
erag
e in
itial
ret
urn
of fiv
e la
test
IPO
s be
fore
the
IPO
.RAN
GE is
the
pric
e ra
nge
anno
unce
d in
its pr
ospe
ctus
div
ided
by
its
mid
poin
t price
. SIZ
E is
the
loga
rith
m o
f to
tal a
sset
. PRO
CEED
S is
the
loga
rith
m o
f th
e am
ount
rai
sed
in m
illio
ns o
f H
K d
olla
rs. R
EVIS
ION
is th
e of
fer pr
ice
divi
ded
by th
e m
idpo
int o
f in
itial
price
ran
ge m
inus
one
. OTO
is th
e of
fer to
ope
n re
turn
, tha
t is, th
e op
en p
rice
on
the
firs
t tra
ding
day
div
ided
by
the
offe
r pr
ice
min
us o
ne. S
MALL
NET
is th
e bu
yer-
initi
ated
sm
all t
rade
s m
inus
sel
ler-
initi
ated
sm
all t
rade
s di
vide
d by
the
tota
l dol
lar t
radi
ng v
olum
e on
the
firs
t tra
ding
day
. TU
RN
OVER is
the
tota
l tra
ding
vol
ume
divi
ded
by
the
num
ber
of s
hare
s ou
tsta
ndin
g on
the
first
-tra
ding
day
. LA
RG
EN
ET
is the
buy
er-ini
tiate
d la
rge
trad
es m
inus
sel
ler-
initi
ated
lar
ge t
rade
s di
vide
d by
the
tot
al d
olla
r trad
ing
volu
me
on th
e firs
t tra
ding
day
. AD
JOTC
is th
e m
arke
t-ad
just
ed o
pen-
to-c
lose
ret
urn.
VO
LATI
LITY
is th
e st
anda
rd d
evia
tion
of in
trad
ay p
rice
s on
first
trad
ing
day
norm
aliz
ed b
y th
e of
fer pr
ice.
ASV
I is
the
abno
rmal
Goo
gle
sear
ch v
olum
e, d
efin
ed a
s th
e se
arch
vol
ume
inde
x du
ring
the
book
build
ing
wee
k m
inus
the
med
ian
of s
earc
h vo
lum
e in
dex
in the
pr
evio
us e
ight
wee
ks.C
OM
PEN
is
the
com
pens
atio
n fo
r in
stitu
tiona
l in
vest
ors
and
calc
ulat
ed a
s th
e of
fer
to o
pen
retu
rn tim
es t
he n
umbe
r of
sha
res
allo
cate
d to
ins
titut
iona
l in
vest
ors sc
aled
by
tota
l ass
et. 1
YAD
JRTN
is 1
-yea
r buy
-and
-hol
d m
arke
t-ad
just
ed re
turn
s from
clo
se p
rice
on
the
firs
t tra
ding
day
.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
� 8
�
� 9
�
� 10
� � 11�
(12)
(13)
(1
4)
(15)
(1
6)
PLA
CIN
G
(1)
1
SUBRATE
(2)
-0.4
873
1
PRE_I
PO
_RTN
(3)
-0.3
859
0.32
18
1
RAN
GE
(4)
0.17
87
-0.1
344
-0.1
762
1
SI
ZE
(5)
0.17
49
-0.0
049
0.04
32
-0.1
449
1
PRO
CEED
S (6
) -0
.037
4 0.
0588
0.
1622
-0
.192
4 0.
8232
1
REVIS
ION
(7)
-0.5
831
0.40
83
0.34
49
-0.1
492
0.01
1 0.
0506
1
O
TO
(8)
-0.4
450
0.57
34
0.36
26
-0.1
439
0.02
08
0.14
35
0.39
69
1
SM
ALL
NET
(9)
-0.1
476
-0.0
515
-0.1
449
-0.0
263
-0.0
947
-0.1
496
-0.0
583
0.00
54
1
TURN
OVER
(10)
-0
.483
1 0.
3628
0.
2488
-0
.166
9 -0
.083
2 0.
1292
0.
3876
0.
6172
0.
2307
1
LARG
EN
ET
(11)
-0
.207
1 0.
2094
0.
0641
-0
.153
6 -0
.114
1 -0
.104
6 0.
2843
0.
2655
0.
4081
0.
3132
1
AD
JOTC
(12)
-0
.073
2 -0
.057
1 -0
.060
5 -0
.026
5 -0
.020
3 -0
.119
3 0.
0389
0.
0695
0.
6500
0.
2673
0.
4920
1
VO
LATI
LITY
(13)
-0
.244
9 0.
0612
0.
1241
-0
.192
2 -0
.020
0 0.
1206
0.
0557
0.
4427
0.
1844
0.
3189
0.
2164
0.
3272
1
ASV
I (1
4)
-0.2
098
0.36
72
0.04
29
0.14
62
0.01
45
0.01
24
0.29
49
0.25
17
0.04
19
0.16
44
0.08
07
-0.0
061
0.00
29
1
C
OM
PEN
(1
5)
-0.2
913
0.13
53
0.27
77
0.00
20
-0.1
100
0.10
20
0.34
34
0.53
02
0.07
41
0.20
62
0.13
29
0.04
38
0.30
66
0.06
33
1
1YRTN
(16)
0.
2435
-0
.062
8 -0
.075
3 -0
.033
0 0.
1591
0.
0523
-0
.144
7 -0
.116
8 -0
.198
7 -0
.270
1 -0
.178
9 -0
.066
5 -0
.099
1 -0
.103
7 -0
.016
4 1
28
Table 3 Pre-Market Sentiment and Offer Price Revision
This table tests of Hypothesis 1. The dependant variable, REVISION, is the offer price divided by the midpoint of initial price range minus one. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Dependent Variable: Offer price revision (REVISION) Variables Model 1 Model 2
SUBRATE 0.015***
(6.10) ASVI 0.028*** (2.88)
PRE_IPO_RTN 0.154*** 0.201*** (4.31) (3.96) RANGE -0.193** -0.215*
(-2.23) (-1.66) SIZE -0.170 -0.368
(-0.62) (-0.98)
UWREP 1.894 0.743 (1.52) (0.39) Constant 6.652 12.330
(1.09) (1.45) Observations 316 158
R-squared 0.268 0.206
29
Table 4 Partial Adjustment of Offer Price to Pre-Market Sentiment
This table tests Hypothesis 2. The dependant variable, OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Dependent Variable : Offer-to-Open Return (OTO) Variables Model 1 Model 2 Model 3 Model 4 SUBRATE 0.041*** 0.039*** (6.14) (5.78) ASVI 0.035** 0.032** (2.59) (2.40) PRE_IPO_RTN 0.240*** 0.211** 0.516*** 0.406*** (2.79) (2.45) (4.08) (3.30) RANGE 0.138 0.147 -0.176 -0.148 (1.11) (1.20) (-0.81) (-0.74) SIZE -0.348 -0.308 -1.023 -0.788 (-0.74) (-0.66) (-1.40) (-1.10) UWREP 1.850 1.391 -0.138 -2.227 (0.77) (0.57) (-0.03) (-0.52) REVISION 0.262** 0.174 0.529*** 0.192 (2.27) (1.41) (3.41) (1.06) TOP 3.725 11.680*** (1.41) (2.86) Constant 5.311 3.905 29.020* 21.890 (0.49) (0.36) (1.69) (1.33) Observations 316 316 158 158 R-squared 0.376 0.380 0.289 0.316
30
Table 5 Compensation for Institutional Investors
This table tests Hypothesis 3. In models 3 to 6, the full sample is divided into “Cold” and “Hot” IPO subsamples based on the sign of REVISION. The dependent variable, COMPEN, is the compensation for institutional investors, defined as the offer to open return times the number of shares allocated to institutional investors scaled by total asset. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. R_PLACING is the residuals from regressing PLACING on SUBRATE. RANGE is the price range announced in its prospectus divided by its midpoint price. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. SIZE is the logarithm of total asset. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level. Dependent Variable: Compensation for institutional investors (COMPEN) Full Sample Hot IPOs s Cold IPOs s
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 SUBRATE 0.005 0.005 -0.003 -0.001 0.007 0.009 (1.28) (1.04) (-1.07) (-0.36) (1.22) (1.03) R_PLACING 0.090** 0.074** 0.075* 0.119*** 0.018 0.025 (2.53) (2.14) (1.84) (2.75) (0.60) (0.83) RANGE 0.106* 0.219*** -0.052 (1.69) (2.85) (-0.99) VOLATILITY 0.764*** 0.837*** -0.163 (4.21) (4.06) (-0.61) UWREP -2.276** -3.455** -3.538** -4.295** -2.680** -2.594** (-2.22) (-2.55) (-2.32) (-2.59) (-2.17) (-2.09) SIZE 0.843*** 0.833** 0.933* 1.095** 0.432 0.389 (2.62) (2.15) (1.81) (2.02) (1.15) (1.00) PRE_IPO_RTN 0.049 0.005 0.001 -0.026 0.063* 0.068** (1.23) (0.12) (0.012) (-0.50) (1.96) (2.05) REVISION 0.175** 0.145** 0.149 0.160* -0.021 -0.014 (2.52) (2.10) (1.60) (1.73) (-0.37) (-0.25) TOP 2.202 1.877 3.127* 2.671 -1.211 -1.183 (1.52) (1.26) (1.81) (1.56) (-0.77) (-0.75) Constant 16.460** -4.957* -7.205* -4.484 -2.874 -3.670 (2.29) (-1.84) (-1.79) (-1.34) (-1.03) (-1.39) Observations 316 316 237 237 79 79 R-squared 0.161 0.181 0.174 0.196 0.234 0.229
31
Table 6 Pre-Market and Aftermarket Sentiment
This table tests Hypothesis 4. The dependant variables, TURNOVER and SMALLNET, are defined as follows: TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. .REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Panel A Panel B Dependant Variables: TURNOVER TURNOVER SMALLNET SMALLNET Model 1 Model 2 Model 3 Model 4
SUBRATE 0.002** 0.002* (2.27) (1.79) ASVI 0.003* 0.002 (1.72) (1.60) RANGE 0.007* 0.006 -0.018 0.033 (1.87) (0.73) (-0.44) (0.61) SIZE -0.041** -0.056** -0.050 0.231 (-2.29) (-2.20) (-0.28) (0.90) UWREP 0.239*** 0.194** 0.304 0.085 (4.15) (2.31) (0.55) (0.10) PRE_IPO_RTN -0.003 -0.005* -0.029 -0.055** (-1.24) (-1.87) (-1.52) (-2.52) REVISION 0.004 0.005 0.071* 0.060 (1.32) (1.02) (1.82) (0.97) TOP 0.197*** 0.247** 0.782 0.785* (2.88) (2.35) (0.88) (1.77) OTO 0.010*** 0.012*** 0.008 0.011 (3.96) (4.31) (0.52) (0.65) Constant 0.834** 1.322** 1.316 -6.278 (2.50) (2.57) (0.26) (-0.83) Observations 316 158 316 158 R-squared 0.335 0.298 0.031 0.041
32
Table 7 Aftermarket Sentiment and Open-to-Close Return
This table tests Hypothesis 5. The dependant variable, ADJOTC, is the market-adjusted open-to-close return. Explanatory variables: TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. R_PLACING is the residual from regressing PLACING on SUBRATE. LARGENET is the buyer-initiated large trades minus seller-initiated large trades divided by the total dollar trading volume on the first trading day. SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. SIZE is the logarithm of total asset. RANGE is the price range announced in its prospectus divided by its midpoint price. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Dependent Variable: Market-adjusted open-to-close returns (ADJOTC) Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 TURNOVER 3.788*** 2.989*** 2.940*** (3.15) (2.86) (2.93) SMALLNET 0.618*** 0.817*** 0.759*** (5.75) (7.40) (7.11) VOLATILITY 1.482*** 1.257*** 1.350*** 1.225*** (6.06) (5.21) (6.11) (5.73) R_PLACING 0.032 (0.90) LARGENET 0.206*** (4.90) SUBRATE -0.002 -0.000 0.003 -0.000 -0.001 (-0.56) (-0.08) (0.901) (-0.00) (-0.38) ASVI -0.005 (-0.88) PRE_IPO_RTN -0.065 -0.086 -0.074 -0.071 -0.057 -0.043 (-1.44) (-1.55) (-1.58) (-1.57) (-1.38) (-1.07) SIZE -0.458 -0.348 0.195 0.185 0.130 0.288 (-1.22) (-0.74) (0.65) (0.50) (0.37) (0.85) RANGE -0.040 -0.009 -0.780 -0.036 -0.101 -0.093 (-0.49) (-0.10) (-1.07) (-0.52) (-1.56) (-1.50) UWREP 2.668 0.935 0.340 1.046 0.049 -0.207 (1.25) (0.47) (0.23) (0.75) (0.03) (-0.17) REVISION -0.038 0.090 -0.039 -0.094 -0.090 -0.090 (-0.39) (1.07) (-0.47) (-1.19) (-1.25) (-1.30) TOP 0.990 0.650 0.060 0.213 0.798 0.518 (0.53) (0.28) (-0.03) (0.14) (0.53) (0.36) OTO 0.040 0.039 -0.097* -0.071* -0.112*** -0.112*** (0.73) (0.69) (-1.89) (-1.84) (-3.13) (-3.26) Constant 11.186 8.716 -17.309** -7.450 -13.969* -15.649** (1.31) (0.78) (-2.39) (-1.11) (-1.95) (-2.09) Observations 316 158 316 316 316 316 R-squared 0.021 0.038 0.165 0.238 0.361 0.410
33
Table 8 Investor Attention and Pre-Market Retail Demand
This table examines the relation between oversubscription rate and investor attention during the pre-market period. The dependant variable, SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. Explanatory variables: ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Dependant Variables: Oversubscription Rate (SUBRATE) Variables ASVI 0.825*** (4.08) PRE_IPO_RTN 6.147*** (3.59) RANGE -3.971** (-2.57) SIZE -9.950 (-1.23) UWREP 1.179 (0.023) Constant 347.800* (1.90) Observations 158 R-squared 0.283
34
Table 9 Long-run Returns
This table shows the IPO long-run returns. Raw Return is calculated as the percentage difference between the close prices of 6 months, 12 months, and 18 months after the listing, and the close price of the listing day. Benchmark Return is the contemporaneous market return. The Adjusted Return is the difference between the corresponding Raw Return and the Benchmark Return. Variables 6 Months 12 Months 18 Months Raw Return (%) Mean 2.14 6.87 9.83
Median -2.71 -5.11 -11.29
Benchmark Return (%) Mean 4.40 8.25 10.08
Median 5.07 11.34 13.61
Adjusted Return (%) Mean -2.13 -1.10 0.35
Median -5.58 -11.52 -16.90
Observations 316 316 316
35
Table 10 Investor Sentiment and Long-run Underperformance
This table examines the determinants of long-run IPO underperformance. The dependant variable, 1YADJRTN, is the 1-year buy-and-hold market-adjusted return from the closing price on the first trading day. Explanatory variables: TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. SIZE is the logarithm of total asset. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level. Dependant Variable: One-year market-adjusted buy-and-hold return (1YADJRTN) Variables Model 1 Model 2 Model 3 Model 4 TURNOVER -11.850** -11.950** (-2.20) (-2.23) SMALLNET -0.196** -0.174* (-2.01) (-1.79) SUBRATE -0.016 -0.026 (-0.75) (-1.30) ASVI -0.050 -0.061 (-1.33) (-1.55) PRE_IPO_RTN -0.543* -0.504 -0.548* -0.503 (-1.92) (-1.11) (-1.94) (-1.11) RANGE -0.415 -0.599 -0.499 -0.647 (-1.04) (-1.01) (-1.24) (-1.07) UWREP -12.34 -17.10 -13.33 -16.46 (-1.19) (-0.93) (-1.28) (-0.90) SIZE 7.182*** 8.554** 7.423*** 8.852** (2.80) (2.24) (2.90) (2.30) VOLATILITY -0.108 -1.275 -0.719 -2.501 (-0.06) (-0.73) (-0.43) (-1.40) Constant -8.733 -13.17 -3.485 -7.751 (-0.34) (-0.32) (-0.14) (-0.18) Observations 316 157 316 157 R-squared 0.057 0.080 0.054 0.057