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Underpricing, Partial Adjustment and the Effects of Entry on Taiwan’s IPO Auctions*
Yao-Min Chiang Department of Finance, National Chengchi University
NO.64, Sec.2, ZhiNan Rd.,Wenshan District,Taipei City 11605; Taiwan +886 -2 -29393091 ext. 81140
ymchiang@nccu.edu.tw
Yiming Qian
Department of Finance, University of Iowa S382 Pappajohn Business Building, 21 E. Market Street
Iowa City, IA 52242; USA (+1 319) 335-0934
yiming-qian@uiowa.edu
Ann E. Sherman
Department of Finance, University of Notre Dame Notre Dame, IN 46556; USA
(+1 574) 631-3373 asherman@nd.edu
October 22, 2006
VERY PRELIMINARY – COMMENTS WELCOME
* We would like to thank .
Abstract
Auction theory predicts that, for a large multi-unit common value auction with endogenous
entry, fluctuations in ex post entry are a major determinant of returns even when bidders are
shaving their bids optimally, given their information sets. If, in addition, some bidders are not
shaving their bids sufficiently, there may be substantial variations in returns. We examine
these predictions for discriminatory (pay what you bid) IPO auctions in Taiwan from 1995 -
2000. The bids of institutional investors are consistent with the predictions of auction theory,
displaying partial adjustment to both private and public information. Both the entry and the
bidding decisions of individual investors, however, appear to violate the predictions of current
auction theory regarding the optimal bids, both because their entry decisions are significantly
influenced by the returns on recent past IPO auctions, and because the unexpected entry of
more individual investors or high bids placed by those investors leads to lower expected
returns, a sign of systematic overbidding perhaps due to inadequate bid-shaving. Our results
also shed light on the causes of underpricing under other methods besides auctions, since our
dataset allows us to isolate one of the three explanations for the partial adjustment to public
information that has been observed for US book building IPOs.
JEL Categories: G24, G28, G32
Countries have been experimenting with initial public offering (IPO) methods since
Margaret Thatcher, the British Prime Minister, began the global trend towards privatizations in
the 1980s. In the United States (US), serious debate about IPO methods began with the internet
boom and later scandals1, and received further impetus when Google, a popular search engine
company, chose to use an auction for its IPO in 2004. The IPO auction method was hailed in
the US as a new one, first ‘pioneered’ by the investment bank WR Hambrecht in 19992. But in
fact, countries around the world had been experimenting with IPO auctions for decades. One
country that preceded the US in its use of IPO auctions is Taiwan, which began using them in
December, 1995.
The Taiwan IPO auction sample is unique in several respects. First, it is one of only a
few reasonably large samples of IPO auctions, since many of the countries that used this
method dropped it fairly quickly. Second, Taiwan used discriminatory (pay what you bid)
auctions3, which will allow us to focus on the winner’s curse and the effects of entry without
also having to adjust for the free rider problem. We discuss these aspects of sealed bid auctions
in more detail in Section I.B, but discriminatory auctions allow us to isolate only certain
predictions of theory, in a way that uniform price auctions would not.
Last, we have access to all bids in these auctions. Kandel, Sarig and Wohl (1999) were
the first to analyze all bids for a set of IPO auctions, and in fact the first to examine the whole
1 See Loughran and Ritter (2002, 2004) for discussions of the problems and changes that have occurred. 2 See, for example, “ Instinet's IPO to test traditional vs. online channels”, by Laura Santini, The Investment Dealers' Digest : IDD; New York; Apr 30, 2001. 3 The two main type of multi-unit sealed bid auctions are discriminatory and uniform price. With a discriminatory auction, all winning bidders pay the price that they bid. For a uniform price auction (sometimes mistakenly known as a Dutch or Vickrey auction), all bidders pay the same price. This price is often the market-clearing price – the highest price that allows all units to be sold – but a surprising number of IPO auctions have been ‘dirty Dutch’, where the price is set strictly below market-clearing, in order to ‘leave something on the table’. For general information on the methods that have been used for IPOs in various countries, as well as for more extensive analysis of problems that have occurred with IPO auctions, see Jagannathan and Sherman (2006).
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demand schedule for any asset4, analyzing the bids for 27 uniform price IPO auctions in Israel.
It is even more important to be able to observe the full range of bids for discriminatory
auctions, where there may be wide variation in returns even among winning bidders. The only
other country for which there is a large sample of discriminatory IPO auctions, Japan, has
released only summary statistics such as the weighted average winning bid, rather than
information regarding all bids. Thus this dataset is unique in allowing us to examine the full
range of returns for a fairly large sample of discriminatory auctions – we have data on more
than 80,000 bids, including more than 17,000 winning bids, from 84 auctions5.
We are not the only ones to examine this important dataset – Taiwan’s IPO auctions
were first analyzed by Liu, Wei and Liaw (2001). Other studies include Lin, Lee and Liu
(2003), Hsu and Shiu (2004), and Hsu and Hung (2005). We are, however, the first to explore
the role of entry in these auctions, testing how well the data fit the predictions of theory in this
respect. This dataset allows us to test several predictions of auction theory in an IPO setting.
Much of the theoretical and empirical work on auctions has focused on settings with
small, stable groups of sophisticated, often exogenously informed potential bidders. With
IPOs, on the other hand, tens of millions of shares may be auctioned off to tens of millions of
potential bidders, although the number of actual bidders may be only a tiny fraction of the
potential. IPO auctions differ from the very successful auctions for US Treasury securities
because IPOs come along sporadically, rather than frequently and at regular intervals. Each
4 Note that this is the auction demand schedule, not the underlying ‘true demand curve’ (if there is such a thing). For a common value auction with endogenous entry, the bids actually placed may not capture all demand if some do not bid, and may not accurately reflect demand if people bid strategically for various reasons (such as free riding or bid-shaving for the winner’s curse). Moreover, in a common value or even affiliated values environment, demand will change as people observe the results of the auction and later trading, since they will be constantly updating their estimate of the value of the shares. Nevertheless, this data is far more than is normally available and gives a clear, detailed picture of the bidding strategies of entrants. 5 We have data on all IPO auctions in Taiwan from December, 1995 (when auctions were first allowed) through 2000. We are missing the six auctions that came after 2000 – there were three IPO auctions in Taiwan in 2001, two in 2002 and one in 2003. To the best of our knowledge there have been none since 2003.
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offering is relatively unique and will naturally attract a somewhat different set of bidders, The
uniqueness and speculative nature of IPOs (relative to 13 week US T-bills) also make valuation
and bid preparation more challenging and costly6. Not enough work has been done exploring
the track records of sealed bid auctions in such a setting.
In particular, little work has been done on the effects of endogenous entry. Or, when
entry decisions have been considered, the focus has been on expected entry, without explicit
recognition of the added complications of entry fluctuations. We find, however, that these
fluctuations are a significant determinant of realized returns for Taiwan’s IPO auctions. We
examine the entry decisions as well as the returns to both institutional and individual investors
in Taiwan’s auctions. The bids of institutional investors are largely consistent with the
predictions of auction theory. The bids of individual investors, however, appear to violate the
predictions of current auction theory regarding the optimal bids. Overall, we find that
endogenous entry is important at explaining bidder returns in a large multi-unit auction.
Besides offering evidence on the cause of underpricing in IPO auctions, our results may
also shed light on the causes of underpricing for other methods, particularly the US book
building method. Of the various explanations that have been offered for IPO underpricing,
many involve the actions and preferences of the issuer and/or the underwriter. Auctions, on the
other hand, are priced based on investor bids, thus allowing theories that apply to investors to
be tested separately. In addition, there are three theoretical explanations for the partial
adjustment of IPO prices to public information (first pointed out by Loughran and Ritter
(2002); see section I.D for more information). Our auction data allow us to isolate one of the
three explanations, in order to test for it separately.
Last, by giving us a better understanding of both auctions and other IPO methods such
6 And yet Goldreich (2005) shows that underpricing occurs even in US Treasury auctions.
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as book building, our results may help to guide regulators. Regulators in many countries
around the world have faced and are still facing many questions regarding IPOs, including:
Should the use of auctions be required for IPOs? If not required, should IPO auctions be
allowed? If allowed, should issuers be forced to use a specific type (for example,
discriminatory, standard uniform price or ‘dirty Dutch’), or should they be allowed to choose?
Is it more efficient for individual and institutional investors to all face the same choices and
restrictions in an auction, or for the two groups to have separate tranches with perhaps different
rules regarding prices and allocations? While we do not pretend to answer all of these
questions, our findings may contribute to the debate.
The paper is organized as follows: Section I discusses the theories that we will be
testing, while Section II describes the data and the form of auction used in Taiwan. Section III
presents our results on entry into auctions and Section IV gives our results on underpricing.
Section V is the conclusion.
I. Predictions of Auction Theory
Auctions go back centuries, perhaps millennia, while formal auction theory began with
Vickrey’s 1961 Journal of Finance article. There has since been extensive research on
auctions, including theory, empirical research and experimental work. In this long tradition,
one branch still relatively unexplored is that regarding large, multi-unit common value auctions
open to many potential bidders. In this section we will try to summarize the theory that is
relevant to our dataset.
I. A. Endowed Information/Full Entry Models
We will use the endowed information/full entry model as a base case from which to
compare the results for a more appropriate environment for IPOs. The environment and model
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that seem to drive many people’s intuitions regarding IPO auctions can be described as a ‘no
cost’ model. If we assume that information is freely endowed to at least some investors, and
that there are no entry or bidding costs at all, then all informed investors will bid, and the
auction will yield a highly accurate price with little or no initial return or aftermarket volatility.
Even in this costless environment, investors will still need to shave their bids to adjust
for the winner’s curse. The winner’s curse problem in common value auctions stems from the
fact that, even if each investor has some information on the value of the shares, each individual
signal is less accurate than the aggregation of all of the signals. Since each signal has a “noise”
component to it, if a bidder were to bid the value indicated by her signal and win in the auction,
in part it would be because the bid was “too high” – the bidder probably bid more than the
value indicated by the aggregation of all signals. Thus, observing the consensus estimate of all
bidders will cause each bidder to revise her original estimate. Since the winning bidders are, by
definition, the highest bidders, they are most likely to revise their estimates downward. If
unwary bidders bid their full valuation without adjusting for this, they will tend to overbid.
The solution to the winner’s curse is for all entrants to shave their bids accordingly, to
adjust for the upward bias in unadjusted winning bids. This adjustment must take into account
both the expected number of other bidders and the nature of the information sets of those other
bidders. Even when information gathering is costless (endowed information), as in our base
case model, a high level of sophistication and computational capability is required to figure out
how to bid in an auction while taking the winner’s curse into account.
However the standard assumption is that all investors are highly sophisticated and can
easily calculate complicated optimal bidding strategies. In this ‘no cost’ environment, there
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will be full entry, meaning that everyone7 bids in every auction. Thus with a stable, predictable
number of informed and sophisticated bidders, it is relatively straightforward for everyone to
shave their bids by the optimal amount, so that auction prices are highly accurate, on average.
In this base case with endowed information and full entry, IPO auctions will produce
initial returns with means, medians and standard deviations of approximately zero. There will
be little or no underpricing, and entry will be stable and predictable. The number of bidders
might perhaps fluctuate based on firm characteristics, if endowed information follows certain
industry or firm-specific patterns, but any underpricing or entry fluctuations that randomly
occur will not be tied to market returns or to the returns on past auctions.
I. B. Information Production/Endogenous Entry Models
The information production/endogenous entry auction model of Sherman (2005) offers
predictions regarding the determinants of entry into sealed bid auctions, and regarding how
entry affects underpricing8. In the model, investors choose whether to enter each auction and
whether to devote resources to producing a more accurate valuation of the shares. Bidders will
not enter or spend resources on evaluation unless they expect to recover their costs, so there
will be underpricing in equilibrium. However, the evaluation costs are sunk costs by the time
that bids are placed, and bidders are competing with each other, so it may seem that
underpricing would be driven out by competition. This does not happen on average, if
investors are jointly following the optimal entry and bidding strategies, because the optimal
entry probability (in a symmetric or mixed strategy equilibrium) will be low enough to allow
the bidders that enter to recover their costs9.
However, even if the expected number of entrants is optimal ex ante, there will still be
7 everyone whose valuation is above the reservation price or minimum bid in the auction. 8 Other IPO auction models have included Bias and Faugeron-Crouzet (2002), Biais, Bossaerts and Rochet
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ex post fluctuations. Each entrant decides for him- or herself whether to enter. Without some
sort of coordination, the number of actual entrants will not be the same every time.
Jagannathan and Sherman (2006) show that optimal bid-shaving for the winner’s curse is
greatly complicated by endogenous entry, because the optimal amount of bid-shaving depends
on the number of actual entrants, but that number is not known when the bids are placed.
Thus this model predicts that entry and underpricing are determined jointly and are both
influenced by both firm and market conditions and characteristics, and it predicts that there will
be substantial uncertainty that may be related to unexpected entry. Underpricing, according to
this model, is related to the costs of evaluation and thus will be higher for firms with more
uncertainty or higher costs of evaluation. In addition, since the main cost of evaluation is the
opportunity cost of the investor’s time, underpricing (and entry) will be positively related to
recent market returns but negatively related to recent market volatility.
One problem that occurs with some auctions in this environment is the free rider
problem. However, this problem only occurs in uniform price, not in discriminatory auctions
such as those used in Taiwan. In a uniform price auction (where each winning bidder pays the
same price), bidding a higher price increases a bidder’s chance of winning the auction (i.e. of
getting shares) but does not increase the price paid, conditional on winning. Thus, rather than
devote time and resources to improving her estimate of the value of the shares, a bidder can
instead simply bid high, if she expects the marginal (price-setting) bid to be a reasonable one
that incorporates a good estimate of the share value.
In other words, such free riding on the efforts of others may be profitable, as long as
there are not so many free riders that they end up setting the price. Sherman (2005) shows that
(2002) and Srivastava and Spatt (1991). 9 See, for example, French and McCormick (1984).
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each investor optimally collects less information in a uniform price than in a discriminatory
auction, because of the moral hazard or free rider problem. Sherman and Jagannathan (2006)
argue that the winner’s curse and the free rider problem are the two main problems that theory
would predict for IPO auctions. One strength of our dataset is that we are able to isolate only
one of these two problems. In a discriminatory auction, each bidder pays what he bids and
hence cannot free ride off of the information production of others. Thus, our dataset allows us
to focus on the winner’s curse problem with endogenous entry.
I. C. Return-chasers
Adjusting for the winner’s curse with endogenous entry is complicated, as is
determining the optimal joint entry and bidding strategy in a setting with many potential
bidders and an underlying security whose value is difficult to estimate. If even a fraction of the
potential bidders do not correctly calculate and implement the optimal entry strategy, then the
expected number of entrants will not be optimal and auctions will be mispriced on average
(beyond the expected underpricing). For a stable equilibrium, it is important that all, rather
than only some or most, potential bidders follow the optimal mixed strategy. Thus, IPO
auctions place a substantial computational burden on all potential investors, whereas
experimental and other evidence indicates that bidders find it difficult to adequately adjust their
bids for the winner’s curse even in relatively simple settings10.
Given that it takes time to learn auction theory and calculate the optimal strategy, there
is the potential here for another type of free rider, which Jagannathan and Sherman (2006) call
a return-chaser. Suppose a person skips all the hard work of learning auction theory and simply
enters an auction if similar auctions appear to have offered high returns recently. This person is
10 See, for example, Bazerman and Samuelson (1983), Kagel and Levin (1986), and Hendricks, Porter, and Boudreau (1987). Engelbrecht-Wiggins and Katok (2005) showed that bidders have an even harder time
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attempting to free ride off of the efforts of other investors, the issuer and ‘the system’, hoping
that the auction has been set up to offer good returns even to those that have not solved for the
optimal joint entry and bidding strategies.
Return-chasing is not necessarily irrational on the part of individuals, given the costs of
learning auction theory. However, such behavior imposes a cost on other bidders. If there are
more return-chasers than expected, the excess entry alone will reduce the expected returns for
all winning bidders. In addition, since these investors by definition have not calculated the
optimal joint entry and bidding strategies, they may also fail to shave their bids sufficiently and
thus may overbid, reducing the expected returns for winning bidders even further.
Since the expected return would be positive without return chasers (since underpricing
was already expected, to compensate investors for their information costs), return-chasers
won’t necessarily lead to negative expected initial returns to auctions, but they will lead to
insufficiently high initial returns to sophisticated investors, unless those investors shave their
bids even more to adjust for the costs imposed by the return-chasers. But, all else equal, more
bid-shaving by sophisticated investors will lead to higher initial returns, which will attract more
return-chasers. Once the return-chasers have completely overwhelmed the process, leading to
low or negative initial returns, they will begin to drop out again, having gotten the message that
auctions are not currently offering easy profits. But, if enough drop out so that initial returns
are high once again, that very fact will attract more of them again, restarting the cycle.
Thus it is hard to imagine IPO auctions reaching a stable equilibrium if there are return-
chasers. Moreover, given the large number of potential bidders in most IPO auctions, only a
small fraction of potential bidders choosing such a strategy may overwhelm the system. For
Taiwan, if only one eligible individual bidder in 10,000 chose to enter an auction for the first
calculating their bids in experimental auctions with endogenous entry.
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time, perhaps due to high returns on past auctions, the number of bidders would be more than
triple the average (assuming that entry was average except for this unexpected surge of new
bidders). In other words, it is not sufficient for only 9,999 out of 10,000 potential bidders to
follow a predictable bidding strategy, since that last 1 in 10,000 can swamp the usual bidders.
Whether it is reasonable to think of individuals as rational agents, able to solve
complicated mathematical problems effortlessly without formal training, is an old debate, and
one that we would prefer not to engage in. But one argument often given for stock market
efficiency is that the market may be self-correcting – even if some people may be irrational
noise traders, they may not be able to systematically disrupt prices because other, better
informed agents will have an incentive to step in and either buy or short sell until prices are
pushed back in line. We bring up this argument only to point out that auctions are not self-
correcting in this sense. Rational agents cannot short-sell auctions even if they learn the return-
chasing pattern and expect a surge of overbidders in a particular auction. All that informed
investors can do is to stay away from such auctions11.
This brings up a last question, which is whether return-chasers are good for issuers.
After all, if underpricing is bad for issuers, shouldn’t potential overpricing (or at least the
elimination of underpricing in this framework) be good? The problem with this is that return-
chasers add risk to the process. If return-chasers pile into auctions only when the last few have
done well, and then stop bidding when returns lately have been low, then issuers cannot count
on them to participate and bid reasonably in each auction. And, if return-chasers drive away
the other bidders, auctions may end up either oversubscribed and/or heavily underpriced.
In the stable equilibrium discussed in Section I.B., informed investors earned positive
11 In a different, non-auction setting, Cornelli, Goldreich and Ljungqvist (2006) find evidence that irrational 'sentiment' investors may have a significant effect even on aftermarket trading prices for IPOs.
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expected returns but did not get economic rents or excess returns, beyond their evaluation and
bid-preparation costs. Although there is room for debate regarding whether issuers would
willingly choose to compensate investors for more careful valuations, nevertheless the
equilibrium in an information production/endogenous entry model may lead to relatively
accurately priced offerings (except for the expected underpricing) and thus to liquid aftermarket
trading that may allow the issuer to achieve the other objectives that may have led the company
to go public originally12. Issuers may prefer paying regular investors for steady participation
and more accurate valuation, as in Sherman’s (2005) book building model, to taking on a lot of
risk in an auction and perhaps still being forced to compensate investors for the uncertainty
caused by return-chasers. Investors have many alternative investments, and thus issuers
ultimately have to pay for any added risk in the IPO process.
This explanation, return-chasing, implies that investors may tend to over-enter IPOs, to
the point that investors receive an inadequate return. There is evidence of such over-entry for
the fixed price tranches of these auctions (see Section II for a description of Taiwan’s hybrid
auction/fixed price method). For the fixed price portions of the very same IPOs that we are
analyzing, the equally-weighted mean initial return on shares received is 31%, implying that
these IPOs were very profitable for fixed price investors. However, as Rock (1986) points out,
the expected return for fixed price IPOs must be weighted by the probability of getting shares,
since investors may tend to receive fewer shares in hot IPOs and more in cold ones.
The mean initial return to ordering shares in a fixed price tranche, weighted for the
probability of receiving shares, is only 0.3%, substantially below the equally-weighted average
of 31%. Given the NT$30 subscription fee (see Liu, Wei and Liaw, 2001) and given that less
than 1 in 100 of those that order fixed price shares and pay the fee actually receive a round lot
12 Brau and Fawcett (2006) offer survey data on companies’ motivations for going public.
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(1,000 shares), on average, the return to ordering fixed price shares is negative, even though the
shares are heavily underpriced from the issuer’s standpoint. The negative return occurs
because of over-entry on the part of investors – investors hear of high returns and thus flood
into IPOs in such large numbers that the final expected return to participating fails to
compensate them for their trouble and risk, or even for their subscription fee. Such over-entry
on the part of investors in the fixed price tranches makes over-entry and return-chasing on the
part of investors in the auction tranches of the same IPOs more plausible.
Jagannathan and Sherman (2006) discuss the possibility of return-chasers and find
evidence of return-chasing behavior in Singapore’s uniform price IPO auctions, but such
investors have not yet been formally modeled for IPO auctions. If return-chasers were present
in Taiwan’s IPO auctions, we would expect to see a positive correlation between entry for the
current auction and returns on past auctions, as well as evidence of overbidding.
I. D. Implications for Book Building
The oldest debate in the IPO literature is over the causes of underpricing. Many
explanations have been proposed for it in the US under the book building method13. However,
as was first pointed out by Kandel, Sarig and Wohl (1999), most of these explanations do not
apply to standard sealed bid auctions, since most rely on the choices and preferences of the
issuer and/or underwriter, whereas allocation and pricing decisions in standard auctions are
based on the bids of investors. Thus, auction data can give us further insights into the
determinants of IPO underpricing by allowing us to test separately for the costly information
production theory, which predicts underpricing for both book building and auctions14.
In addition, our study sheds light on the question of what drives partial adjustment to
13 See Ritter and Welch (2002) and Ljungqvist (2004) for surveys of the various explanations. 14 Auction data cannot definitively establish a sole determinant of IPO underpricing, of course, since underpricing
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public information in IPO pricing. There are three theories that offer an explanation of partial
adjustment under bookbuilding, but only one of the three can also explain it for auction data.
Thus, if we observe whether or not there is partial adjustment to public information in Taiwan’s
IPO auctions, we have a better idea of what drives partial adjustment under either IPO method.
Partial adjustment to private information was first documented by Hanley (1993), who
pointed out that it could be explained by Benveniste and Spindt’s (1989) model of book
building in which issuers underprice their shares to induce investors to accurately reveal their
endowed information. However, Loughran and Ritter (2002) showed that U.S. IPOs also only
partially adjust to public information such as recent stock market performance, a fact that
cannot be explained by an endowed information model such as Benveniste and Spindt’s.
Three models offer explanations for partial adjustment of IPO prices to public
information. The first is Loughran and Ritter’s prospect theory model of issuers’ preferences:
if issuers anchor on an early estimate of the offering price, they may be more willing to accept
underpricing when the offering price is being set above the original expected amount, whether
the increase is due to the revealed private information of investors or to a run-up in the stock
market. If underwriters are anxious to give high returns to certain investors through some sort
of quid pro quo arrangement, they will be most able to deliver these excess returns when the
offering price is above the original expected value, for whatever reason. Thus, prospect theory
can explain partial adjustment to both private and public information for book building IPOs.
The second theoretical explanation of partial adjustment to public information is offered
by Edelen and Kadlec (2005), who argue that general market returns may affect a company’s
decision on whether or not to withdraw an IPO. Low or negative market returns may mean that
explanations are not, in general, mutually exclusive. Nevertheless, we will get a better idea of whether the explanation that applies to auctions is likely to be one of the causes of underpricing under various methods.
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an issuer can expect relatively little surplus even if the offering succeeds. Thus the issuer is
more anxious to push for the highest possible offering price, even though this makes it more
likely that the offering will fail, since the issuer has relatively little to lose from such a failure15.
When comparable firm valuations are high, however, the issuer has more to lose if the offering
fails and so does not insist on aggressive pricing.
The third explanation is offered by Sherman and Titman (2002), which builds on the
classic models of Benveniste and Spindt (1989) and Benveniste and Wilhelm (1990), by
incorporating costly evaluation16. In this model of book building, underpricing is a way for
issuers to induce investors to devote time and effort to evaluating an offering, as well as a way
to compensate investors for then reporting that information. Issuers are paying investors for the
opportunity cost of their time, and this opportunity cost is greater when returns to the market
are high. Thus, underpricing must be greater when publicly observable variables such as the
current return to the market are high (assuming that recent past returns are a good estimate of
short term future returns), leading to partial adjustment to public information.
The first two theories – Loughran and Ritter’s prospect theory and Edelen and Kadlec’s
greater willingness to withdraw when market returns are low – cannot explain partial
adjustment (or underpricing in general) for auction IPOs, because both rely on the preferences
and choices of issuers and/or underwriters, whereas auction prices are set by bidders.
The third theory, information acquisition, explains underpricing and partial adjustment
to both private and public information, for both auctions and book building. This was shown
15 Edelen and Kadlec’s hypothesis should also imply a higher proportion of firm commitment or book building IPOs being turned into best efforts offerings when market returns have fallen. By converting the offering to a best efforts basis, the issuer can attempt the offering at a higher price than the underwriter is willing to guarantee, but there is of course a risk of failure, for an offering that is not underwritten. See Sherman (1992). 16 Other models in which IPO underpricing is driven by costly evaluation include Sherman (1992), Chemmanur (1993), Sherman (2000), and Busaba and Chang (2003). Yung (2005) models costly evaluation by both investors and the underwriter. Cornelli and Goldreich (2001 and 2003) and Jenkinson and Jones (2004) offer evidence on
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by Sherman (2005), which models book building, discriminatory auctions and uniform price
auctions for the same environment, one driven by costly evaluation and endogenous entry17. If
Taiwan’s discriminatory auction prices display partial adjustment to public and private
information, it would indicate that underpricing in auctions, and perhaps under book building,
is driven at least in part by information acquisition.
II. Data and Taiwan’s IPO Method
Our sample includes a complete list of the 84 initial public offerings that used the
hybrid auction method in Taiwan between 1995 and 2000.18 We obtain detailed bidding
information on each auction from the Taiwan Securities Association, including bidder IDs and
the bidding price and quantity of every bid by each bidder. The format of the bidder ID tells us
whether that bidder is an institutional or individual investor. It is worth noting that such
detailed bidding information was not available to the public. Instead, after each auction was
done, the Association publicly announced the auction size, the reserve price, the total bidding
quantity, the lowest and highest winning prices, the quantity-weighted winning price, the total
proceeds received from the auction and the offer price for the subsequent fixed price tranche.
Background information about the IPO firms such as assets, venture capital ownership
and P/E ratio are collected from the firms’ prospectuses, which are available from the Taiwan
Securities & Futures Information Center database. Stock returns for individual stocks and the
market are from the Taiwan Economic Journal (TEJ).
The method used for auctions in Taiwan is a sequential hybrid, where 50% of the shares
whether book building allocations have been consistent with this explanation in practice. 17 Chemmanur and Liu (2004) later extended the analysis to compare uniform price auctions to fixed price public offers, in a setting with costly evaluation. They do not explicitly consider endogenous entry. However, this may be less relevant to a comparison of auctions and fixed price public offers, since both are subject to problems with random entry, unlike book building where the underwriter co-ordinates entry. 18 When we refer to IPOs from here on, we are referring only to that used the hybrid auction method, unless
16
are offered through a discriminatory auction, and later the remaining shares (including any
shares unsold in the auction) are offered at a fixed price19. In this later fixed price tranche, the
offering price is the maximum of 1.5 times the reserve price in the auction (or 1.3 times the
reserve price beginning in 2000) or the weighted average winning bid price for all winning bids
within the range from the reserve price to 1.5 (1.3 since 2000) times the reserve price.
Thus, the offering price for the fixed price tranche is almost always below the weighted
average winning bid price, except in cases where even the highest bid is no more than 50%
(30%) above the reservation price or minimum bid in the auction20. The fixed offering price
may on the other hand be below the clearing price (the lowest winning bid price) in the auction.
On average, the offering price for the fixed price tranche in our sample was 20% below the
weighted average winning bid price and 17% below the clearing (lowest winning bid) price.
Because these are sequential hybrids, with a fixed price tranche occurring after the
auction is completed, there is an average delay of 57.36 days from the closing date of the
auction to the first official trading day. A similar delay was found to be significant for
bookbuilding IPOs in France. Derrien and Womack (2003) compared auctions to sequential
hybrid bookbuilding (plus an unspecified number of pure bookbuilds) and found that the
restriction for sequential hybrid bookbuilding, forcing the price to be set well in advance to
allow time for the fixed price tranche, put those sequential hybrids at a disadvantage to
otherwise noted. 19 For an excellent, more detailed description of the entire IPO process in Taiwan, see Liu, Wei and Liaw (2001). 20 Note then that issuers and underwriters have no discretion in setting the offering price for the fixed price tranche in Taiwan. Japan’s sequential hybrid auctions were surprisingly similar except that issuers were given some discretion in setting the fixed offering price below the weighted average winning bid price. Kerins, Kutsuna and Smith (2006) take advantage of this regulatory feature to analyse issuers’ choices in setting the price for these fixed price offerings that occur after feedback has been obtained from the auction but before the shares have begun to trade. Kaneko and Pettway (2001) and Kutsuna and Smith (2004) analyze pricing in Japan’s discriminatory auctions themselves, based on the weighted average winning bid prices. Unlike Taiwan, Japan gave issuers no choice and mandated the use of these hybrid auctions from 1989 until 1997, when Japan first allowed issuers to choose book building. Once an alternative was allowed in Japan, auctions quickly vanished.
17
France’s uniform price auctions, which did not face the same restriction21. Chowdhry and
Sherman (1996a) model IPOs in which the price is set in advance, showing that it increases
underpricing due to the added risk to investors.
Issuers in Taiwan actually had a choice between three methods – 1) they could use a
hybrid auction for selling secondary shares (from current shareholders rather then new shares
issued by the company); 2) they could use bookbuilding for selling primary (newly issued)
shares; or 3) they could use a fixed price public offer regardless of whether they were selling
primary or secondary shares. In practice, book building has been allowed since 1995 but has
not been used, since IPOs in Taiwan usually involve secondary rather than primary shares22.
Issuers have chosen between pure fixed price offerings and hybrid auctions with fixed price
tranches. Hsu and Hung (2005) compare issuers under the two methods.
Of the 84 auctions in our sample, three were undersubscribed, including Chunghwa
Telecom in 2000, the largest IPO ever on the Taiwan Stock Exchange (TSE). Two auctions
drew no institutional bidders at all, and another 9 had no winning institutional bidders.
Table 1 presents the descriptive statistics for our IPO sample. Panel A displays the
statistics for the entire sample. During 1995-2000, about half of the IPO firms are listed on the
TSE rather than on the over the counter (OTC) market, and about half of the firms are from the
high-tech industry. Prior to the IPO, the average firm has an asset value of 7.3 billion New
Taiwan Dollars (about US$221 million). The average VC ownership is 13.5%. The average
earnings-to-(auction reservation) price ratio is 0.07 (which corresponds to a P/E of 14.3). In an
21 Derrien and Womack’s sample ends in 1998. In 1999, France began allowing simultaneous as well as sequential hybrids. With a simultaneous hybrid, also known as ‘open pricing’, the fixed price tranche occurs at the same time as the other method and thus does not delay the offering. Once France began allowing the more modern form of hybrid book building, auctions quickly vanished. Note also that France’s auction method was somewhat unique in that the top bids were thrown out to discourage free riding. 22 We have been told that issuers in Taiwan believe that they will receive more regulatory scrutiny if they sell new shares in their IPO, and so it is common practice, when new funds are needed, for the company to issue more
18
average IPO, the firm aims to sell 11.0 million shares and receives NT$854.0 million (US$25.9
million) in auction proceeds.
Panel B of Table 1 displays IPO background information by year. The number of IPO
auctions increases from 1995 through 1998, and then decreases. More and more IPO firms are
from the high-tech industry over our sample period, which coincides with the internet boom.
Related to that, a smaller percentage of IPO firms are listed on the TSE instead of the OTC
market over the years. Otherwise there are no obvious time trends for these IPO firms.
III. Results on Entry into IPO Auctions
Table 2 presents summary statistics of bidding activities by all bidders (Panel A),
institutions (Panel B) and individuals (Panel C), respectively. Each panel shows by year the
mean, median and standard deviation of number of bids, number of bidders, subscription ratio
and average bidding premium over the reservation or reserve price (the minimum bid).
Panel A shows that in an average auction, there are 708.8 bidders who submit 986.7
bids. There is substantial variation for both of these numbers, however. The standard deviation
is 843.3 for the number of bidders and 1,205.0 for the number of bids, which is larger than
either the mean or the median in both cases. The variation is similarly large for each year in the
sample period. It is evident that entry into IPO auctions is not stable. Overlooking this fact and
assuming otherwise may lead to inaccurate predictions regarding auction results.
All but three of the auctions in our sample are oversubscribed, with an average
subscription ratio of 3.77. However even this variable has a wide range of variation, with a
minimum of 0.4 (i.e. 60% undersubscribed), a maximum of 17.2, and a standard deviation of
2.9. The average bidding premium, measured as the quantity-weighted average of bidding price
shares to existing stockholders who then sell those shares in the IPO itself.
19
over the reserve price, has an overall mean of 1.58. There seems to be a trend over time, with
the bidding premium increasing from a mean of 1.2 in 1995 to 1.9 in 2000, although the
increase is not monotonic and does not coincide with an increase in the number of bidders or
the subscription ratio. Instead, the number of bidders and subscription ratio are highest in 1996
and 1997, and then decrease. This coincides with the fact that the number of auctions picks up
quickly from 1996 to the peak in 1998 and then decreases after that, as mentioned earlier.
When comparing Panels B and C, we see that most bidders in these auctions are
individuals. In an average auction, there are 676.8 individual bidders whose total bidding
quantity is about 3 times the auction size, whereas there are 32.0 institutional bidders whose
total bidding quantity is about 76% of the auction size. Therefore individual bidders are
undoubtedly the dominant group of players. In terms of potential bidders, the pool of
individuals obviously is much larger than that of institutions, making it more difficult to predict
the entry decisions of this group. Individuals also have larger variations than institutions in
terms of their wealth, sophistication and incentives to gather information. Thus their
dominance may introduce added uncertainty into IPO auctions. In comparison, institutions
play the dominant role in traditional auction markets such as treasury auctions.
Next, we examine what factors influence bidders’ entry decisions. Sherman’s (2005)
information acquisition/endogenous entry model predicts that more bidders will participate in
an auction and/or each bidder will bid more when (1) the information uncertainty (about the
firm or about the market) is lower; (2) the cost of acquiring information is lower; (3) the
auction size is larger so that each bidder can expect to get more shares given the information
cost. On the other hand, some bidders may try to get a free ride through return-chasing, i.e.,
participating more if returns from recent IPO auctions have been higher.
We use regression analysis to examine whether these predictions hold. The dependent
20
variable, entry into an auction, is measured by the natural logarithm of the number of bidders.
Results are similar when we use the natural logarithm of the number of bids or the subscription
ratio. To measure information uncertainty about the firm, we use three variables: the natural
logarithm of the firm’s assets, VC ownership in the firm and the earnings-to-price ratio. We
conjecture that uncertainty about the firm decreases with each of these variables: the more
assets in place, the more stake venture capitalists put in the firm, and the more earnings the firm
is already producing, the less risky the firm’s prospects. To measure uncertainty about the
market in general, we use the market volatility (standard deviation of daily market return) over
the three months prior to the auction.
To measure the opportunity cost of information acquisition, we use the market (TSE
index) return over the three months prior to the auction. The assumption is that the higher the
market return prior to the auction, the higher the opportunity cost to devoting time and effort to
the auction offering, and therefore the lower the auction entry. Auction size is measured in
thousands of shares to be sold. To measure recent IPO returns, we calculate the weighted-
average initial returns (i.e. the closing price on the first non-hit day over the quantity-weighted
average winning price, minus one) of the last three IPO-auctions. We assign a weight of 3/6 to
the most recent IPO, 2/6 to the next and 1/6 to the earliest one.23 Finally, we also include two
dummies as control variables: a high-tech dummy equal to one if the firm is in a high-tech
industry, and a TSE dummy equal to one if the firm is listed on the TSE.
Table 3 presents the results of our entry regressions. Column (1) reports results when
the dependent variable is log(number of all bidders). Consistent with the theory, we find that
entry into auctions decreases with firm uncertainty (increases with log(assets)) and market
23 For the second IPO in our sample period, the recent IPO return is based on the first IPO’s return only. For the third IPO in our sample, it is based on the first two IPOs’ returns with a weight of 2/3 on the second IPO and 1/3
21
uncertainty (decreases with market volatility), and increases with auction size. On the other
hand, we do not find evidence that entry decreases with opportunity cost as measured by the
recent market return, which shows an insignificant and positive coefficient. Moreover, we find
that recent IPO-auction returns has a significantly positive influence on entry decisions.
We then separate institutions and individuals to see whether they make entry decisions
differently. Column (2) shows the entry regression results when the dependent variable is
log(number of institutional bidders) and Column (3) shows the results when the dependent
variable is log(number of individual bidders). Interestingly, these two groups of investors seem
to be influenced by the same factors in similar fashions except for one thing: recent auction
return. Institutions’ entry decision is insignificantly related to recent auction return while
individuals’ entry is significantly and positively related to this variable. This suggests that
individuals are return-chasers and institutions are not. As discussed before, return-chasing
behavior may cause instability in auctions.
IV. Results on Auction Returns (IPO Underpricing)
In this section, we examine bidder returns from our IPO auctions (or, underpricing of
these IPOs). Table 4A presents the summary statistics of bidding results at the auction level,
i.e., with each observation being one auction. Panel A shows results for all bidders, Panel B for
institutional bidders and Panel C for individual bidders.
We calculate auction returns over the winning bidding price based on two after-IPO
market prices for the stock: the closing price on the first non-hit day, and the closing price on
the 10th trading day after the first non-hit day. In Taiwan, a daily return limit of 7% in each
direction is imposed on all publicly traded stocks including IPO shares during our sample
on the first. In addition, we include a recent IPO only if its first non-hit day is prior to the current IPO’s auction
22
period. IPO shares frequently continue to hit this limit for a few days after the day that they are
first officially ‘traded’, particularly since the base price for that first day’s price limit is the
offering price from the fixed price tranche24. We call the first day when the stock price falls
within the limit “the first non-hit day” and the return based on that day’s closing price the
“initial return” of the IPO. This initial return is comparable to an IPO’s first-day return in the
US, where there is no such return limit. In our sample, the holding period for the initial return
has a mean (median) of 5.37 (3) trading days and ranges between 1 and 28 trading days.
Considering that the market price might be noisy and that it may take time for a stock
price to adjust to information, we also calculate the return over a longer holding period, that is,
from the auction until the 10th trading day after the first non-hit day. For each holding period,
we calculate a raw return and a market-adjusted return (i.e. minus the TSE index return). Note
that both of these holding periods may vary for different IPO stocks, since the first non-hit day
varies. We also calculate returns until the 20th trading day of the IPO stock. In other words,
this last measure was for a constant number of days after the first trading day, regardless of the
first non-hit day25. Results are very similar to those based on returns until the 10th trading day
after the first non-hit day and therefore are not tabulated for the sake of space.
We first examine the weighted average returns for each auction, i.e. the returns over the
quantity-weighted average winning bidding price. As can be seen from Panel A of Table 4.A.,
the mean (median) weighted-average initial return across auctions is 7.5% (3.5%). The market-
adjusted initial return has an even higher mean (median) of 8.6% (8.9%). This implies that, on
average, winning bidders receive underpriced IPO shares and earn positive returns. However,
even for the average winning bidder, there is large variation in her returns across different
date. After imposing these restrictions, we lose two observations in the entry regressions. 24 which was, on average, around 20% below the weighted average winning bid price, as reported earlier.
23
auctions: the standard deviation for the raw (adjusted) initial return is 24.7% (22.0%).
The corresponding weighted-average returns until the 10th trading day after the first
non-hit day tend to be lower than the initial returns: the raw return has a mean (median) of
5.2% (-1.25%), and the adjusted return has a mean (median) of 6.3% (0.3%). These returns
over the longer period show similarly large standard deviations across auctions. The negative
median raw return over the longer period indicates that the average winning bidder loses money
in at least half of the auctions if she holds shares until the 10th trading day after the first non-hit
day. Examining the mean weighted average returns by year also discloses substantial variation.
Based on all four return measures, the average winning bidder performs the best in year 1996,
which, however, is immediately followed by the worst or near worst performance in 1997. A
similar mini-cycle seems to be repeated in 1999 and 2000.
We then compare the summary statistics of these weighted average returns between
institutional bidders (Panel B of Table 4.A.) and individual bidders (Panel C of Table 4.A.).
Both groups exhibit similar patterns to those described above – the mean weighted average
returns are positive but show large variations across auctions and from year to year. However,
there is one striking contrast between the two groups: the average institutional winning bidder
performs better than the average individual winning bidder. For the overall sample,
institutional bidders have higher mean and median weighted average returns than those earned
by individual bidders based on each of the four return measures, while the standard deviations
of these returns are very similar between the two groups of bidders. The same thing can be said
for the returns for each year separately. In Table 2, we see that the average bidding premium
(for all bids) offered by institutions is very close to that by individuals. However, the current
evidence shows that they earn higher returns from the bidding process.
25 Out of the 84 stocks in our sample, one stock’s non-hit day was more than 20 days after the first trading day.
24
Together, this indicates that institutions bid more smartly than individuals in the
following sense: institutions’ bids are more on target, which gives them a higher chance to win
the bids (Table 4 shows that the percentage of winning bidders (bids) for institutions is 21.5%
(19.2%) whereas the percentage of winning bidders (bids) for individuals is 17.6% (17.0%));
on the other hand, institutions’ weighted-average winning price is lower than that of the
individuals, as evidenced by their higher weighted-average return. This is consistent with the
notion that institutions are informed bidders.
So far, we have learned that the average winning bidder earns positive returns if she
participates in all auctions. Next, we examine the percentage of successful bids (bidders), i.e.
winning bids (bidders) that earn positive returns in each auction26. For the overall sample, only
56.0% (57.0%) of all winning bids (bidders) earn positive initial returns, i.e. the (bidders’
quantity-weighted average) bidding price is lower than the price on the first non-hit day. The
percentage of successful bids (bidders) based on 10th trading day after the first non-hit day is
46.9% (47.4%). In other words, even if one wins in all the auctions in our sample, she can
expect to earn positive returns only about half of the time.
Similar to the weighted-average returns, these variables also exhibit a lot of variation
across auctions and from year to year. For an auction winner in 1995 or 1996, the chance of
earning a positive return is much higher compared to winning in 1997-2000. The median
percentage of successful bidders based on the first non-hit day price is 90.5% in 1995, reaches
100% in 1996, decreases to around 65% in the next two years, and then increases again to
91.8% in 1999 only to drop to 0.0% in 2000. This means that in at least half of the auctions in
2000, every winning bidder loses money. The above patterns hold for both institutional and
26 Note that we cannot be sure what actual returns specific bidders received, since we do not know when they sold their shares. One way to think of this statistic, then, is the percentage of bidder positions that were ‘in the
25
individual bidders. However, the mean and median percentages of successful bids (bidders) are
higher for institutions than for individuals for the overall sample and for most of the years,
again indicating that institutions are smarter bidders.
Table 4.B. presents summary statistics of returns at the bidder level, i.e. each
observation being a winning bidder’s (quantity-weighted average) return in a specific auction.
With hundreds of bidders in each auction, there are many more observations for this analysis.
The basic patterns are similar to those in Table 4.A. where each observation is one auction,
although most returns are slightly lower. Bidders on average still earn positive returns (with
the exception that the mean raw return until the 10th trading day after the first non-hit day is
negative), but there is substantial variation from bidder to bidder and (for the average bidder)
from year to year. In addition, institutional bidders on average perform better than individual
bidders for the overall sample period and in most of the years.
Next, we examine the determining factors of auction returns, to see if they are
consistent with the predictions of existing theories. Auction models based on endogenous entry
and information production predict that IPO returns (or the extent of underpricing) will be (1)
positively related to information uncertainty about the firm and the market; (2) positively
related to the cost of information acquisition (when we use the market return as a proxy for the
opportunity cost of information acquisition, this predicts partial adjustment to public
information); (3) negatively related to auction size; and (4) positively related to entry and the
aggressiveness of bids (partial adjustment to private information).
As before, we use the natural logarithm of firm assets, VC ownership and earnings-to-
price ratio as inverse measures for the extent of information uncertainty about the firm. We use
money’ rather than ‘out of the money’ on the first non-hit day, i.e. the proportion that would have led to a profit if the positions had been closed on that day.
26
the market volatility over the three months prior to the auction to measure the uncertainty about
the market in general. We use the market return over the three months prior to the auction to
measure the opportunity cost of information acquisition.
The information acquisition theory predicts that the above factors influence both entry
and returns of IPO auctions. In addition, IPO returns should also depend on unexpected entry
and on the aggressiveness of bids. If bidders produce information and then bid based on that
information, they will participate in an auction and will bid aggressively when they receive a
positive information signal. However, if they are following an optimal bid strategy, they will
still shave their bids sufficiently to get a return on their information, shaving more heavily
when they receive a better information signal (see Sherman, 2005). This implies that returns
will be positively related to unexpected entry and the aggressiveness of the bids27.
Alternatively, if bidders bid without understanding auction theory and following the
optimal entry and bidding strategies (for example, if they are simply chasing past IPO returns),
they will not shave their bids sufficiently, tending to both over-enter and overbid. Thus returns
will be negatively related to these two variables if investors are return-chasers. We measure
unexpected entry as the residuals from the entry regressions in Table 3, and measure the
aggressiveness of bids as the bidding premia over the reserve price. We already see some
evidence in Table 4 that institutions bid more smartly than individuals. We therefore measure
these two variables separately for institutions and individuals.
We also look at the direct impact of recent IPO returns. If there is return-chasing
behavior among bidders, and these bidders tend to overbid (i.e. to not shave their bids
adequately), we would expect that the return on the current IPO will be negatively related to
27 But the signal may be somewhat weaker for unexpected entry than for aggressive bids, depending on the reservation price This is because bidders that evaluate a stock and get a neutral or mildly positive signal will still
27
recent IPO returns. To measure recent IPO returns, we calculate the weighted-average returns
of the last three IPOs, as described in the previous section.
Another factor that might affect the IPO return is the demand in the fixed-price offering
subsequent to the auction. Since investors put in orders in the fixed-price offering after the
basic results from the auction (the auction subscription ratio, weighted-average winning bid
price, and lowest and highest winning bids) are made public, they are able to put in an informed
order if they invest based on this information. If that is the case, a high subscription ratio in the
fixed-price offering would indicate a positive information signal received by investors, which
in turn predicts higher IPO returns28.
As before, we include a high-tech dummy and TSE dummy as control variables. In
addition, we control for the market return between the auction date and the first non-hit day (or
10th trading day after the first non-hit day), if the dependent variable is the raw return. We also
control for market volatility during the same period. If the market turns out to be riskier than
expected, then stocks may be discounted for the added risk and bidders may end up with lower
returns because it is too late for them to shave their bids to adjust for the higher risk level.
We run regressions of IPO returns on the variables described above. Regression results
are reported in Table 5. Panel A of Table 5 reports the results of regressions at the auction
level, using four measures of IPO returns as dependent variables: initial raw returns in Column
(1); initial market-adjusted returns in Column (2); raw returns until the 10th trading day after
the first non-hit day in Column (3); and market-adjusted returns until the 10th trading day after
the first non-hit day in Column (4) .
Results are similar under each measure of returns. Possibly due to the small number of
bid (although they won’t be as high a price), unless the optimal bid is below the reservation price in the auction. 28 even though it did not, on average, lead to positive returns for those ordering shares in the fixed price tranche
28
observations at the auction level, many of the coefficients are statistically insignificant.
However, two results come out strong: the returns are positively related to unexpected entry by
institutions and negatively related to recent IPO returns. As discussed before, if bidders bid
based on information, higher than expected entry indicates positive information and predicts
higher returns. The significant positive coefficient on unexpected entry by institutions suggests
that institutional bidders are informed and sophisticated bidders. In other words, we find
evidence of partial adjustment to private information for institutions.
Also consistent with this conjecture, the coefficient on bidding premium by institutions
is positive as well, albeit insignificant. In contrast, the coefficients on unexpected entry and bid
premia by individuals are both negative (albeit insignificant), suggesting that individuals are
not informed bidders. Of course, we cannot put too much stock in these two coefficients since
they are insignificant here, but we will re-examine them for bidder level data.
The coefficient on recent IPO return is negative in all four regressions and is significant
when the dependent variable is either of the two market-adjusted returns. This is consistent
with our prediction of return-chasing behavior. That is, more investors bid in the current
auction if the returns to the recent IPO-auctions are good. These return-chasers, however, do
not shave their bids enough to adjust the winner’s curse problem, and their bids therefore raise
the clearing price and lower the average return of all bidders.
Panel B of Table 5 reports the results of return regressions at the bidder level. With the
largely increased number of observations, these regressions exhibit many more significant
regression coefficients, based on which we can say more about the validity of our predictions.
In the rest of the paper, we only show regressions at the bidder level29.
because of over-entry, as discussed earlier. 29 Recall that, because this is a discriminatory auction, various winning bidders genuinely receive varying
29
For Panel B of Table 5, we first observe that the two results we noted above in Panel A
become even stronger. In all four regressions (with different measures of the dependent
variable), the auction return is significantly and positively related to the unexpected entry and
bidding premia of institutions, and it is significantly and negatively related to the unexpected
entry and bidding premia of individuals, indicating institutions are informed and sophisticated
bidders, while individuals are not. Also in all four regressions, the auction return is negatively
related to recent auction returns, suggesting return-chasing behavior.
Consistent with the theory, we find that the return is negatively related to auction size.
We also find that the return is positively related to market return prior to the auction, i.e., the
return only partially adjusts to public information, as predicted by Sherman’s (2005)
information production/endogenous entry auction model.
The impact of market volatility prior to the auction is mixed. It is negatively related to
unadjusted returns but positively related to market-adjusted returns. Perhaps bidders were able
to shave their bids sufficiently to adjust for risk on a market-adjusted basis but not on a raw
basis. Returns are positively related to assets, VC ownership and E/P ratio. This seems to
suggest that the lower the information uncertainty about the firm, the higher the return, which is
inconsistent with what rational theories would predict.
Table 6 reports bidder-level return regressions for institutions (Panel A) and individuals
(Panel B) separately. The key results are similar to those above. Perhaps the main difference is
that the previous auction returns are not significantly related to institutions’ raw initial returns,
although they are negatively and significantly related to the other three return measures. They
are more strongly significant (and negative) for individuals using any measure of returns.
(sometimes widely varying) returns, as we saw in Table 4. The auction level data treated bidders as if they each received the weighted average return in each auction, which was not accurate.
30
Bidders are allowed to place multiple bids in these auctions, so in Table 7 we give
summary statistics on bidder wealth, measured as the total $ amount bid (in thousands of NT$),
and intra-bidder dispersion, which is the quantity-weighted standard deviation of bidder i’s bids
in auction j. For single unit bidders, this variable will be 0. There are an average of 1.33 bids
per bidder. Institutional investors average 1.70 bids per bidder, while individuals average only
1.32. Multiple bids are placed by 38.7% of institutional investors and only 18.5% of
individuals. Not surprisingly, institutions on average place larger total bids: the total amount
bid averages NT$13.52 million (US$410,000) for institutions and NT$2.26 million
(US$68,500) for individuals.
If we interpret bidding quantity as a proxy for wealth, then we could get some
indication of whether wealthy bidders are better informed. Another interpretation, however, is
that better informed bidders should optimally make larger bids. This was modeled by
Chowdhry and Sherman (1996b), who showed that among ex ante identical bidders that varied
only in the quality of their information, risk averse investors optimally order more in an IPO if
their information is stronger. The intuition is that one is willing to place a larger bet on a ‘sure
thing’ than on a mere guess. This would predict that those placing larger orders thought that
they had better information. If they actually were relatively well informed, then large orders
will tend to make better returns than smaller orders.
The bidder wealth and intra-bidder dispersion variables are taken from Nyborg,
Rydqvist and Sundaresan (2002), who look at Swedish Treasury auction data, another setting
with multi-unit discriminatory auctions. They argue that initial returns and intra-bidder
dispersion should increase and bid size should decrease with increased market uncertainty.
They use volatility (uncertainty) as their dependent variable, but their hypothesis would imply
that intra-bidder dispersion should be positively related to returns. Intra-bidder dispersion may
31
also proxy for a lack of information – investors may be more likely to spread their bids across
several prices when they have no idea how much the shares are worth and thus no idea what to
bid. In this case, we would expect intra-bidder dispersion to be negatively related to returns.
Table 8 gives the same regressions as Table 6 but with these two additional explanatory
variables, bidder wealth and intra-bidder dispersion. For institutional investors, neither variable
is significant. Even though institutional bids vary more than individuals bids by either
measure, this variation does not seem to be related to returns. Individual and overall investor
returns are positively and significantly related to bidder wealth, indicating that individual
investors tend to bid more when they are better informed, or perhaps that wealthier bidders are
better informed. Intra-bidder dispersion is negatively related to returns. This result is only
marginally significant for all bidders but is strong, by three of the four return measures, for
individuals, implying that individuals are more likely to spread their bids across several prices
when they are poorly informed and thus uncertain about what price to bid.
V. Conclusion
We have explored the determinants of entry, and the effects of entry on returns, for
Taiwan’s sequential hybrid discriminatory IPO auctions. Using data on more than 17,000
winning bids from 84 auctions between 1995 and 2000, we found that unexpected fluctuations
in entry are important in explaining IPO auction returns. We also found that, consistent with
auction theory (in an endowed information, endogenous entry environment such as Sherman,
2005), auctions are underpriced, and institutional investor returns display partial adjustment to
both private and public information. When unexpected institutional entry or the premium bid
for the shares by institutional investors is high, returns to winning bidders tend to be
significantly higher, indicating that institutions are relatively well informed and are shaving
32
their bids as predicted by auction theory, to adjust for the winner’s curse.
For individual investors, we find evidence of return-chasing. A significant determinant
of individual investor entry is the return on recent past auctions. Moreover, when unexpected
individual entry or the premium bid for the shares by individuals is high, returns to winning
bidders tend to be significantly lower, indicating that individuals are overbidding. Individual
investor returns display partial adjustment to public information, which indicates that they are
able to shave their bids to adjust for their opportunity costs, but they display over-adjustment to
private information (returns are lower when individuals bid higher prices), indicating that they
are not shaving their bids sufficiently to adjust for the winner’s curse.
In terms of regulatory implications, our results question whether all individual
investors, as a group, have the sophistication to participate in pricing highly risky securities
such as IPOs, given the complexity of optimal bidding strategies under endogenous entry. We
found evidence of return-chasing and overbidding by individuals, which would tend to make
entry and bidding less attractive for more sophisticated investors, discouraging them from
devoting time and effort to evaluating the stock and preparing a bid. If too many sophisticated
investors are discouraged from devoting time to IPO auctions, the IPO process may be very
risky for issuers. Thus, countries may want to allow (not force, but allow) issuers to restrict
auction participation to only institutional investors, while individuals could still be allowed to
participate through a fixed price tranche30.
Limiting individuals to a fixed price tranche would not necessarily prevent them from
over-entering to the point at which they may actually be getting negative expected returns, as
we saw for the fixed price tranches of Taiwan’s auctions, but it would at least prevent any
30 It might appear that closing individuals out of the auction tranches of Taiwan’s IPOs would have limited their choices. However, their choices were limited anyway in the end, because issuers in Taiwan have given up using
33
excess entry by individuals from disrupting the pricing of the auction. Sherman (2005) showed
in a discriminatory (and uniform price) auction model that limiting the number of potential
entrants may in some cases actually increase the number of expected entrants. This might be
particularly true if the entrants that are shut out had a tendency to miscalculate the optimal
entry and bidding strategies, adding risk for all bidders.
These results shed light on the causes of underpricing and of partial adjustment to both
private and public information, both under auctions and under the US book building method.
Many of the theories that predict underpricing and partial adjustment under book building can
not explain such patterns for auctions, since the theories rely on the preferences and choices of
issuers and/or underwriters, but underpricing as compensation for investor time and attention
can explain these patterns for both auctions and book building. The fact that Taiwan’s IPO
auction data is consistent with this explanation (except perhaps for the evidence of individual
investor return-chasing behavior) makes it more likely that the need to compensate investors
for their efforts is one of the factors driving underpricing under both methods.
Our results also contribute to the overall understanding of large, multi-unit sealed bid
auctions in practice. The internet has made it more feasible than ever to open up all sorts of
auctions to millions of potential bidders. Thus theory and empirical work on auctions should
begin to focus on this relatively unexplored area.
IPO auctions. The auction method is still allowed, but for the last several years, only pure fixed price public offers have been chosen.
34
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37
Table 1. Summary statistics of IPOs by year The sample consists of all 84 IPOs in Taiwan that used the hybrid auction method from 1995-2000. This table shows the frequency and summary statistics of these auction IPO firms’ basic information. Panel B gives means, and medians in ( ), by year. We identify firms as high-tech or non high-tech, and as listed on either the Taiwan Stock Exchange (TSE) or the over the counter (OTC) market. Assets are the total assets of the issuing firm. The currency of Taiwan is New Taiwan Dollars (NT$). US$1 is about NT$33. VC ownership is the percentage of shares held by venture capitalists before the firm went public. E/P is the ratio of earnings to share price (based on the reservation price in the auction). Auction size is the total shares sold in the auction, in round lots (not including the later fixed price tranche). Total proceeds are measured in millions of NT$. N is the sample size (number of auctions). Panel A: Overall sample N Mean Median Std. Dev. Minimum Maximum(1) % of IPOs in high tech industry 84 53.57 (2) % of IPOs on TSE 84 55.95 (3) Assets (in NT$MM) 84 7,340.3 1,961.3 30,789.8 117.6 277,576.7(4) VC ownership (%) 84 13.5 6.8 16.2 0.0 69.4(5) E/P 84 0.070 0.063 0.038 0.001 0.210 (6) Auction size (in 1,000 shares) 84 11,007.1 5,646.5 31,490.5 1,040.0 89,431.0 (7) Auction proceeds (in NT$MM) 84 854.0 406.7 2516.4 20.1 22,745.1 Panel B: By year 1995 1996 1997 1998 1999 2000(1) % of IPOs in high tech industry 0 27.27 31.58 58.62 73.33 88.89(2) % of IPOs on TSE 100 81.82 68.42 55.17 33.33 33.33(3) Assets (in NT$MM) 277,576.71 4,682.15 2,548.69 3,303.74 5,935.70 6,025.72
(277,576.71) (2,602.28) (2,155.15) (1,927.72) (1,748.12) (1,417.91)(4) VC ownership (%) 1.61 9.96 10.51 15.68 17.97 10.62
(1.61) (4.12) (2.23) (10.92) (9.67) (5.67)(5) E/P 0.114 0.069 0.049 0.076 0.089 0.063
(0.114) (0.057) (0.050) (0.070) (0.074) (0.058)(6) Auction size (in 1,000 shares) 10,227.00 14,276.27 8,678.68 6,384.24 6,304.67 34,746.44
(10,227.00) (9,120.00) (7,260.00) (5,550.00) (4,980.00) (3,150.00)(7) Auction proceeds (in NT$MM) 204.89 766.38 671.42 591.49 521.27 2818.88
(204.89) (530.01) (412.24) (284.65) (455.53) (234.19)(8) N 1 11 19 29 15 9
38
Table 2. Summary statistics of bids by year This table reports mean, median in ( ) and standard deviation in [ ] of bids across auctions by year (1995-2000). Auction size is the total number of shares sold in the auction, in round lots. Number of bids is the total number of bids at each auction. Number of bidders is the total number of bidders at each auction. Subscription ratio is the ratio of total bidding quantity over auction shares. Premium is defined as the ratio of the weighted average of bidding price to the reservation price of each auction. Panel A: All bids
All years 1995 1996 1997 1998 1999 2000(1 Auction size (in 1000 shares) 11,007.05 10,227.00 14,276.27 8,678.68 6,384.24 6,304.67 34,746.44 (5,646.50) (10,227.00
)(9,120.00) (7,260.00
)(5,550.00
)(4,980.00
)(3,150.00)
[31,490.50] [11,061.16]
[6,899.76]
[4,961.91]
[4,635.43]
[95,519.52](2
)Number of bids 986.65 298.00 2,398.00 1,091.16 561.66 650.07 1,048.00
(600.00) (298.00) (1,402.00) (727.00) (373.00) (790.00) (365.00) [1,205.04] [1,955.44] [872.55] [604.45] [507.89] [1,774.12](3)
Number of bidders 708.76 237.00 1,645.09 787.42 400.97 486.60 812.78 (442.00) (237.00) (952.00) (523.00) (268.00) (611.00) (278.00) [843.34] [1,306.53] [628.92] [414.19] [365.28] [1,350.90](4)
Subscription ratio 3.77 2.54 6.18 4.19 3.17 3.33 2.72 (2.97) (2.54) (5.35) (2.68) (2.85) (3.60) (2.72) [2.92] [4.71] [3.27] [2.12] [2.00] [1.58](5)
Weighted avg. of bidding price/ 1.58 1.20 1.32 1.62 1.48 1.75 1.94 reservation price
( ll)(1.51) (1.20) (1.36) (1.58) (1.50) (1.63) (1.44)
[0.42] [0.23] [0.20] [0.15] [0.47] [0.91](6)
N 84 1 11 19 29 15 9
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Table 2 (continued). Summary statistics of bids by year Panel B: Bids by institutions
All years 1995 1996 1997 1998 1999 2000(7) Number of bids 59.88 2.00 105.82 43.05 47.31 58.07 89.22 (36.50) (2.00) (62.00) (43.00) (24.00) (52.00) (22.00) [83.71] [91.93] [37.94] [67.93] [54.48] [178.94](8) Number of bidders 31.95 2.00 49.73 24.37 27.38 33.73 41.33 (20.50) (2.00) (41.00) (27.00) (17.00) (29.00) (16.00) [37.05] [38.59] [20.62] [38.29] [30.65] [61.51](9) Subscription ratio 0.76 0.12 1.29 0.70 0.68 0.84 0.48 (0.53) (0.12) (1.54) (0.48) (0.54) (0.57) (0.43) [0.67] [0.82] [0.63] [0.53] [0.82] [0.39](10) Weighted avg. of bidding price/ 1.58 1.02 1.31 1.60 1.47 1.77 1.95 reservation price (premins) (1.51) (1.02) (1.37) (1.55) (1.53) (1.64) (1.44) [0.44] [0.23] [0.19] [0.17] [0.52] [0.95] Panel C: Bids by individuals All years 1995 1996 1997 1998 1999 2000(11) Number of bids 926.77 296.00 2,292.18 1,048.11 514.34 592.00 958.77 (544.00) (296.00) (1,369.00) (683.00) (338.00) (738.00) (343.00) [1,140.07] [1,883.28] [846.55] [548.18] [456.03] [1,596.07](12) Number of bidders 676.81 235.00 1,595.36 763.05 373.58 452.86 771.44 (407.50) (235.00) (911.00) (496.00) (251.00) (575.00) (260.00) [816.10] [1,278.20] [614.25] [383.61] [337.01] [1,290.48](13) Subscription ratio 3.00 2.41 4.88 3.48 2.49 2.50 2.24 (2.35) (2.41) (3.72) (2.26) (1.94) (2.26) (2.33) [2.58] [4.42] [3.01] [1.88] [1.25] [1.32](14) Weighted avg. of bidding price/ 1.58 1.20 1.31 1.62 1.47 1.75 1.94 reservation price (premind) (1.52) (1.20) (1.34) (1.59) (1.50) (1.63) (1.45) [0.41] [0.22] [0.20] [0.15] [0.46] [0.90]
40
Table 3. Entry regressions This table reports coefficient estimates with t-statistics for the regression of auction entry (number of bidders) on the issuing firm’s characteristics, auction properties, and market conditions. LN(asset) is the natural log of issuing firm’s total asset. VC ownership is the percentage of shares held by venture capitalist before the firm went listed. E/P is the ratio of earning and share price. Auction size is the total shares sold at the auction IPO. If the issuing firm is a high-tech firm, the high-tech dummy is 1, otherwise, 0. If the firm is listed at the Taiwan Stock Exchange (TSE), the TSE dummy is 1, otherwise, 0. Market return and volatility is measured 3 months before the auction day. Previous auction returns are calculated based on the rule: (1)weighted average of the last 3 IPOs. (2)weights: 3/6 for the most recent, 2/6 for the next, 1/6 for the earliest one. (3)weights for the 2nd firm: 6/6 for the first firm. (4)Weights for the 3rd firm: 5/6 for the 2nd firm, 1/6 for the 1st firm. We lost two samples when calculated previous auction IPO return. ***, **, * significant at the 1%, 5%, and 10% level, respectively.
log(# of bidders) (1)
log(# of institutional bidders)(2)
log(# of individual bidders)(3)
(1) Intercept 5.2172 ( 6.34 ) *** -0.2669 ( -0.24 ) 5.2885 ( 6.44 ) *** (2) LN(asset) 0.1981 ( 2.25 ) ** 0.3683 ( 3.09 ) *** 0.1903 ( 2.17 ) ** (3) VC ownership 0.509 ( 0.98 ) 1.0282 ( 1.46 ) 0.4895 ( 0.94 ) (4) E/P -0.9295 ( -0.41 ) 1.89 ( 0.61 ) -1.1685 ( -0.52 ) (5) High-tech 0.5598 ( 3.22 ) *** 0.7335 ( 3.12 ) *** 0.5565 ( 3.21 ) *** (6) TSE dummy 0.5687 ( 3.33 ) *** 0.8927 ( 3.86 ) *** 0.549 ( 3.22 ) *** (7) auction size 0.0073 ( 2.7 ) *** 0.0048 ( 1.31 ) 0.0074 ( 2.75 ) *** (8) Market return (-3m) 1.2648 ( 1.5 ) 1.7328 ( 1.52 ) 1.1648 ( 1.39 ) (9) Market volatility (-3m) -112.3304 ( -3.7 ) *** -68.0232 ( -1.65 ) -115.0357 ( -3.79 ) ***
(10) Previous auction return 1.8367 ( 2.75 ) *** 0.5101 ( 0.56 ) 1.9075 ( 2.86 ) *** R2 65.98% 52.04% 66.13% N 82 82 82
41
Table 4. A. Summary statistics of returns (or bidding results) by year: mean, median and standard deviation – Auction level This table shows summary statistics of returns: mean, median in ( ), and standard deviation in [ ]. We measure IPO return based on the quantity weighted price of winning bids and price on the first non-hit day and the price on the 10th trading day after the first non-hit day. The previous is represented by rh and the later by rh10. Adjusted return are adjusted with the market returns between the auction day and the first non-hit day and between the auction day and the 10th trading day after the first non-hit day, (arh and arh10). % of successful bids are those winning bids whose bidding price is lower than the price on the first non-hit day or than the price on the 10th trading day after the first non-hit day. % of successful bidders are those winning bidders whose quantity weighted winning price is lower than the price on the first non-hit day or than the price on the 10th trading day after the first non-hit day. Panel A: All bidders
All years 1995 1996 1997 1998 1999 2000(1) % of successful bids (out of 55.97 90.70 99.30 50.02 50.29 66.15 13.08 winning bids) based on first (74.70) (90.70) (100.00) (62.11) (62.96) (87.50) (0.00) non-hit day price [43.59] [1.66] [42.00] [43.52] [40.20] [33.00](2) % of successful bidders (out of 57.01 90.48 99.35 52.63 51.34 66.36 13.45 winning bids) based on first (80.00) (90.48) (100.00) (67.18) (65.65) (91.79) (0.00) non-hit day price [44.06] [1.83] [43.27] [44.00] [41.32] [33.03](3) % of successful bids (out of 46.93 100.00 96.11 37.12 39.41 58.43 6.73 winning bids) based on 10th trading (31.17) (100.00) (100.00) (0.00) (0.00) (98.57) (0.00) days after first non-hit day [47.46] [9.59] [43.84] [48.06] [49.33] [20.18](4) % of successful bidders (out of 47.39 100.00 96.58 38.72 39.40 58.53 6.94 winning bids) based on 10th trading (33.62) (100.00) (100.00) (0.00) (0.00) (97.96) (0.00) days after first non-hit day [47.69] [8.70] [45.22] [47.97] [49.34] [20.83](5) Weighted average raw return 7.49 6.82 37.39 0.70 4.21 11.97 -11.51 until the first non-hit day, (3.52) (6.82) (23.75) (2.48) (0.99) (4.57) (-9.73) in % (rh) [24.73] [27.17] [15.48] [20.76] [26.47] [18.08] (6) Weighted average adjusted 8.58 9.08 25.96 0.53 10.17 5.30 4.68 return until the first non-hit (8.88) (9.08) (14.86) (-2.12) (10.78) (-3.18) (2.90) day, in % (arh) [21.98] [26.58] [14.78] [19.88] [26.09] [21.17] (7) Weighted average raw return 5.20 31.28 34.56 -9.16 4.46 20.55 -26.44 until the 10th trading day after the (-1.25) (31.28) (27.56) (-8.28) (-3.81) (8.71) (-33.96) first non-hit day, in % (rh10) [30.38] [26.46] [17.28] [27.56] [32.12] [15.44] (8) Weighted average adjusted return 6.34 31.89 22.86 -7.14 11.57 8.27 -8.28 until the 10th trading day after the (0.29) (31.89 ) (12.47) (-3.66) (4.02) (-3.69) (-3.31) first non-hit day, in % (arh10) [26.14] [25.76] [16.68] [26.37] [31.45] [15.25] N 84 1 11 19 29 15 9
42
Table 4. A. (continued), Summary statistics of returns (or bidding results) by year: mean, median and standard deviation – Auction level Panel B: Institutional bidders
All years 1995 1996 1997 1998 1999 2000(1) % of successful bids (out of 60.14 100.00 56.61 56.95 75.36 11.76 winning bids) based on first (90.91) (100.00) (80.34) (80.00) (95.83) (0.00) non-hit day price [46.45] [0.00] [46.74] [47.47] [39.34] [33.15](2) % of successful bidders (out of 61.42 100.00 59.90 57.94 76.53 11.97 winning bids) based on first (100.00) (100.00) (90.89) (90.00) (100.00) (0.00) non-hit day price [47.10] [0.00] [48.16] [48.17] [40.40] [33.11](3) % of successful bids (out of 50.49 99.71 43.35 40.50 71.88 11.11 winning bids) based on 10th trading (62.50) (100.00) (0.00) (0.00) (100.00) (0.00) days after first non-hit day [49.59] [0.93] [50.79] [49.15] [44.63] [33.33](4) % of successful bidders (out of 50.64 100.00 43.42 40.58 72.22 11.11 winning bids) based on 10th trading (66.67) (100.00) (0.00) (0.00) (100.00) (0.00) days after first non-hit day [49.65] [0.00] [50.87] [49.14] [44.57] [33.33](5) Weighted average raw return 8.77 37.45 3.33 5.31 0.1515 -11.18 until the first non-hit day, (3.88) (26.25) (3.95) (1.66) (4.51) (-9.13) in % (rh) [25.09] [27.19] [14.61] [21.33] [27.71] [19.02](6) Weighted average adjusted 10.46 27.21 4.96 10.84 7.05 5.01 return until the first non-hit (10.31) (16.56) (9.67) (10.11) (-3.52) (3.50) day, in % (arh) [22.00] [24.93] [13.17] [20.53] [27.98] [22.18](7) Weighted average raw return 6.30 33.45 -8.06 6.31 27.19 -26.22 until the 10th trading day after the (1.31) (22.10) (-10.77) (-2.90) (18.55) (-32.85) first non-hit day, in % (rh10) [30.68] [25.97] [17.29] [27.97] [30.98] [15.72](8) Weighted average adjusted return 8.14 22.31 -3.83 12.79 13.62 -8.07 until the 10th trading day after the (2.66) (16.76) (-0.42) (6.41) (-1.60) (-1.79) first non-hit day, in % (arh10) [2607] [24.54] [16.49] [27.19] [31.88] [15.88]
Note: There was only one auction in 1995. This auction had only two institutional bids and no winning institutional bids.
43
Table 4 A (continued), Summary statistics of returns (or bidding results) by year: mean, median and standard deviation – Auction level Panel C: Individual bidders
All years 1995 1996 1997 1998 1999 2000(1) % of successful bids (out of 55.65 90.70 99.27 49.51 49.66 66.21 13.09 winning bids) based on first (72.69) (90.70) (100.00) (60.42) (61.71) (86.67) (0.00) non-hit day price [43.46] [1.75] [41.90] [43.17] [39.93] [33.02](2) % of successful bidders (out of 56.64 90.48 99.32 51.92 50.73 66.38 13.47 winning bids) based on first (78.03) (90.48) (100.00) (66.03) (64.81) (91.27) (0.00) non-hit day price [43.92] [1.92] [43.16] [43.64] [41.09] [33.06](3) % of successful bids (out of 46.90 100.00 95.93 36.95 39.50 58.49 6.61 winning bids) based on 10th trading (32.36) (100.00) (100.00) (0.00) (0.00) (98.36) (0.00) days after first non-hit day [47.41] [10.10] [43.71] [48.02] [49.34] [19.82](4) % of successful bidders (out of 47.36 100.00 96.44 38.58 39.46 58.57 6.81 winning bids) based on 10th trading (34.22) (100.00) (100.00) (0.00) (0.00) (97.62) (0.00) days after first non-hit day [47.65] [9.14] [45.11] [47.93] [49.34] [20.43](5) Weighted average raw return 7.25 6.82 35.96 0.36 4.13 12.17 -11.40 until the first non-hit day, (3.22) (6.82) (23.69) (1.89) (0.83) (4.52) (-9.82) in % (rh) [24.38] [27.04] [15.53] [20.27] [26.52] [18.04](6) Weighted average adjusted 8.34 9.08 24.53 0.19 10.09 5.50 4.79 return until the first non-hit (8.87) (9.08) (14.94) (-2.32) (10.80) (-3.17) (2.81) day, in % (arh) [21.65 ] [26.03] [14.67] [19.48] [26.11] [21.23](7) Weighted average raw return 4.96 31.28 33.10 -9.43 4.35 20.78 -26.33 until the 10th trading day after the (-1.18) (31.28) (27.46) (-8.81) (-4.59) (10.01) (-34.25) first non-hit day, in % (rh10) [30.09] [25.82] [17.45] [27.14] [32.25] [15.46](8) Weighted average adjusted return 6.10 31.88 21.40 -7.41 11.47 8.50 -8.18 until the 10th trading day after the (0.08) (31.88) (12.47) (-3.69) (4.26) (-3.68 ) (-3.43) first non-hit day, in % (arh10) [25.82] [24.67] [16.66] [26.04] [31.54] [15.30]
44
Table 4. B. Summary statistics of returns (or bidding results) by year: mean, median and standard deviation – Bidder level This table shows summary statistics of returns: mean, median in ( ), and standard deviation in [ ]. We measure IPO return based on the quantity weighted price of winning bids and price on the first non-hit day and the price on the 10th trading day after the first non-hit day. The previous is represented by rh and the later by rh10. Adjusted return are adjusted with the market returns between the auction day and the first non-hit day and between the auction day and the 10th trading day after the first non-hit day, (arh and arh10). % of successful bids are those winning bids whose bidding price is lower than the price on the first non-hit day or than the price on the 10th trading day after the first non-hit day. % of successful bidders are those winning bidders whose quantity weighted winning price is lower than the price on the first non-hit day or than the price on the 10th trading day after the first non-hit day. Panel A: All bidders
All years 1995 1996 1997 1998 1999 2000(1) % of successful bids (out of 50.47 90.70 99.07 57.17 59.29 67.55 1.66 winning bids) based on first non-hit day price (2) % of successful bidders (out of 50.28 90.48 98.96 58.76 59.09 67.99 2.01 winning bids) based on first non-hit day price (3) % of successful bids (out of 42.16 100.00 94.85 35.89 49.16 65.67 0.36 winning bids) based on 10th trading days after first non-hit day (4) % of successful bidders (out of 41.73 100.00 94.89 37.97 47.88 64.31 0.40 winning bids) based on 10th trading days after first non-hit day (5) Weighted average raw return 5.85 11.78 33.00 3.22 6.19 6.87 -8.95 until the first non-hit day, (0.36) (14.68) (23.53) (2.80) (2.32) (4.40) (-8.64) in % (rh) [23.07] [6.82] [27.30] [15.49] [20.75] [21.76] [7.95](6) Weighted average adjusted 13.02 14.04 22.18 2.78 12.16 0.11 20.74 return until the first non-hit (13.63) (16.94) (12.78) (3.35) (9.82) (-3.91) (23.78) day, in % (arh) [19.74] [6.82] [25.94] [13.37] [20.35] [19.85] [11.00](7) Weighted average raw return -0.48 37.37 26.82 -8.75 7.59 21.40 -23.63 until the 10th trading day after the (-7.30) (40.94) (16.98) (-8.97) (-0.45) (16.32) (-23.18) first non-hit day, in % (rh10) [27.79] [8.38] [25.10] [15.73] [27.19] [27.64] [6.78](8) Weighted average adjusted return 5.90 37.98 14.73 -5.62 15.04 8.56 02.41 until the 10th trading day after the (3.64) (41.55) (6.91) (-4.09) (7.80) (-3.03) (4.45) first non-hit day, in % (arh10) [21.33] [8.38] [25.15] [14.92] [26.97] [24.65] [8.40] N 17,008 42 2,975 3,943 3,329 1,743 4,976
45
Table 4 B (continued), Summary statistics of returns (or bidding results) by year: mean, median and standard deviation – Bidder level Panel B: Institutional bidders
All years 1995 1996 1997 1998 1999 2000(1) % of successful bids (out of 52.93 100.00 71.53 80.58 78.01 0.98 winning bids) based on first non-hit day price (2) % of successful bidders (out of 64.09 100.00 78.02 83.16 81.95 1.68 winning bids) based on first non-hit day price (3) % of successful bids (out of 39.02 99.50 28.13 58.93 70.68 0.16 winning bids) based on 10th trading days after first non-hit day (4) % of successful bidders (out of 46.13 100.00 29.12 60.94 69.92 0.42 winning bids) based on 10th trading days after first non-hit day (5) Weighted average raw return 11.55 41.21 5.50 18.33 13.06 -7.94 until the first non-hit day, (4.61) (27.02) (4.56) (10.45) (4.22) (-6.36) in % (rh) [26.10] [31.98] [11.60] [26.28] [24.87] [7.27](6) Weighted average adjusted 18.68 31.20 9.16 22.19 8.70 20.91 return until the first non-hit (18.72) (18.10) (13.90) (17.75) (-2.95) (25.60) day, in % (arh) [22.74] [29.68] [12.86] [25.10] [26.34] [12.20](7) Weighted average raw return 6.09 35.71 -10.02 17.86 27.06 -22.82 until the 10th trading day after the (-2.70) (21.82) (-14.97) (22.76) (17.00) (-20.78) first non-hit day, in % (rh10) [31.37] [27.36] [13.74] [28.82] [30.67] [6.51](8) Weighted average adjusted return 13.80 24.86 0.64 25.06 16.32 2.88 until the 10th trading day after the (7.50) (16.12) (4.49) (24.36) (0.95) (5.92) first non-hit day, in % (arh10) [25.29] [25.87] [14.74] [28.55] [30.97] [9.21] N 969 119 182 297 133 238
46
Table 4 B (continued), Summary statistics of returns (or bidding results) by year: mean, median and standard deviation – Bidder level Panel C: Individual bidders
All years 1995 1996 1997 1998 1999 2000(1) % of successful bids (out of 50.26 90.70 99.02 56.34 56.80 66.49 1.73 winning bids) based on first non-hit day price (2) % of successful bidders (out of 49.44 90.48 98.91 57.83 56.73 66.83 2.03 winning bids) based on first non-hit day price (3) % of successful bids (out of 42.43 100.00 94.62 36.34 48.02 65.16 0.38 winning bids) based on 10th trading days after first non-hit day (4) % of successful bidders (out of 41.46 100.00 94.68 38.39 46.60 63.85 0.40 winning bids) based on 10th trading days after first non-hit day (5) Weighted average raw return 5.50 11.78 32.66 3.11 5.00 6.36 -9.00 until the first non-hit day, (0.00) (14.68) (23.53) (2.59) (1.83) (4.40) (-8.64) in % (rh) [22.83] [6.82] [27.04] [15.65] [19.74] [21.42] [7.97](6) Weighted average adjusted 12.68 14.04 21.81 2.47 11.18 -0.60 20.73 return until the first non-hit (13.28) (16.94) (12.78) (2.67) (9.50) (-4.12) (23.78) day, in % (arh) [19.49] [6.82] [25.71] [13.31] [19.55] [19.05] [10.93](7) Weighted average raw return -0.88 37.37 26.45 -8.69 6.58 20.94 -23.67 until the 10th trading day after the (-7.51) (40.94) (16.98) (-8.85) (-0.82) (16.00) (-23.18) first non-hit day, in % (rh10) [27.51] [8.38] [24.94] [15.82] [26.82] [27.33] [6.79](8) Weighted average adjusted return 5.42 37.98 14.31 -5.92 14.06 7.92 2.39 until the 10th trading day after the (3.37) (41.55) (5.97) (-4.53) (7.57) (-3.28) (4.45) first non-hit day, in % (arh10) [20.98] [8.38] [25.03] [14.86] [26.61] [23.95] [8.36] N 16,039 42 2,856 3,761 3,032 1,610 4,738
47
Table 5. Return regressions for all bids This table reports coefficient estimates with t-statistics for the regression of return on the issuing firm’s characteristics, auction properties, auction results and market conditions. LN(asset) is the natural log of issuing firm’s total asset. VC ownership is the percentage of shares held by venture capitalist before the firm went listed. E/P is the ratio of earning and share price. Auction size is the total shares sold at the auction IPO. If the issuing firm is a high-tech firm, the high-tech dummy is 1, otherwise, 0. If the firm is listed at the Taiwan Stock Exchange (TSE), the TSE dummy is 1, otherwise, 0. Market return and volatility (-3m) are measured 3 months before the auction day. Probability on fixed price offering is the probability to win shares at the fixed-price offerings. Previous auction returns are calculated for the previous auction firms with complete data to calculate post-IPO returns, auction day to the first non-hit day (AH) and auction day to the 10th trading day after the first non-hit day (AH10). Unexpected entries are error of the entry regression for institutional investors and for the individual investors. Premium is defined as the ratio of weighted average of bidding price over the reserved price of each auction. Market return and market volatility is measured based on market return between auction day and the first non-hit day (AH) and between auction day and the 10th trading day after the first non-hit day (AH10). ***, **, * significant at the 1%, 5%, and 10% level, respectively. We lost two samples when calculated previous auction IPO return based on the first non-hit day and lost two more samples based on the 10th trading day after the first non-hit day. Panel A: Auction level
rh rh10 arh arh10
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat
(1) Intercept 0.3206 ( 1.19 ) 0.3714 ( 1.39 ) 0.3287 ( 1.11 ) 0.3092 ( 1.02 ) (2) LN(asset) 0.0149 ( 0.56 ) 0.0087 ( 0.33 ) -0.0016 ( -0.06 ) -0.0107 ( -0.36 ) (3) VC ownership 0.0446 ( 0.26 ) 0.0787 ( 0.46 ) 0.1011 ( 0.53 ) 0.1838 ( 0.96 ) (4) E/P 0.8084 ( 1.15 ) 0.9585 ( 1.32 ) 0.9078 ( 1.17 ) 0.8781 ( 1.1 ) (5) High-tech dummy 0.1958 ( 3.45 ) *** 0.1885 ( 3.34 ) *** 0.2457 ( 3.96 ) *** 0.2482 ( 3.89 ) ***(6) TSE dummy 0.0208 ( 0.39 ) 0.0038 ( 0.07 ) 0.0808 ( 1.38 ) 0.0756 ( 1.25 ) (7) Auction size -0.0006 ( -0.48 ) -0.0004 ( -0.32 ) -0.0009 ( -0.71 ) -0.0006 ( -0.48 ) (8) Market return (-3m) 0.2632 ( 0.93 ) 0.2196 ( 0.78 ) 0.0595 ( 0.19 ) 0.1578 ( 0.52 ) (9) Market volatility (-3m) -3.6592 ( -0.39 ) -5.6285 ( -0.65 ) 5.3792 ( 0.57 ) 8.951 ( 0.96 )
(10) Unexpected entry by institutions 0.0798 ( 2.04 ) ** 0.0862 ( 2.17 ) ** 0.0741 ( 1.72 ) * 0.0848 ( 1.91 ) * (11) Bidding premium by institutions 0.3095 ( 1.07 ) 0.3839 ( 1.33 ) 0.2062 ( 0.65 ) 0.2566 ( 0.79 ) (12) Unexpected entry by individuals -0.057 ( -1.16 ) -0.0776 ( -1.55 ) -0.0121 ( -0.22 ) -0.0267 ( -0.48 ) (13) Bidding premium by individuals -0.3813 ( -1.25 ) -0.4744 ( -1.58 ) -0.203 ( -0.61 ) -0.3134 ( -0.93 ) (14) Market return (AH) 0.4501 ( 1.86 ) * (15) Market volatility (AH) -24.7378 ( -3.63 ) *** -18.4225 ( -3.08 ) *** (16) Market return (AH10) 0.5151 ( 2.2 ) ** (17) Market volatility (AH10) -38.5926 ( -4.52 ) *** -29.7325 ( -3.94 ) ***(18) Probability on fixed price offering 0.1128 ( 1.28 ) 0.1138 ( 1.29 ) 0.1481 ( 1.49 ) 0.1233 ( 1.22 ) (19) Previous auction return (AH) -0.0946 ( -0.43 ) (20) Previous auction adjusted return (AH) -0.5281 ( -2.25 ) ** (21) Previous auction return (AH10) -0.2294 ( -1.33 ) (22) Previous auction adjusted return (AH10) -0.3527 ( -1.83 ) *
R2 50.95% 35.64% 59.74% 40.68% N 80 80 80 80
48
Table 5 (continued), Return regressions for all bids Panel B: Bidder level
rh rh10 arh arh10
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat
(1) Intercept 0.3565 ( 23.88 ) *** 0.3252 ( 22.62 ) *** 0.2913 ( 18.44 ) *** 0.1808 ( 11.15 ) ***
(2) LN(asset) 0.0209 ( 15.49 ) *** 0.0235 ( 17.76 ) *** -0.0039 ( -2.83 ) *** -0.0076 ( -5.29 ) ***
(3) VC ownership 0.0359 ( 3.23 ) *** 0.0705 ( 6.59 ) *** 0.1084 ( 9.33 ) *** 0.2131 ( 18.12 ) ***
(4) E/P 1.0493 ( 22.34 ) *** 1.6556 ( 34.02 ) *** 1.0357 ( 21.76 ) *** 1.1881 ( 23.03 ) ***
(5) High-tech dummy 0.2177 ( 59.59 ) *** 0.2062 ( 57.35 ) *** 0.3035 ( 80.26 ) *** 0.3017 ( 76.45 ) ***
(6) TSE dummy 0.0074 ( 1.98 ) ** -0.0115 ( -3.08 ) *** 0.0814 ( 20.66 ) *** 0.0837 ( 20.02 ) ***
(7) Auction size -0.0008 ( -11.97 ) *** -0.0008 ( -11.09 ) *** -0.0014 ( -19.35 ) *** -0.0013 ( -17.59 ) ***
(8) Market return (-3m) 0.2663 ( 13.69 ) *** 0.1909 ( 10.15 ) *** 0.251 ( 12.24 ) *** 0.3877 ( 19.13 ) ***
(9) Market volatility (-3m) -11.0631 ( -16.82 ) *** -15.1796 ( -26.76 ) *** 1.6038 ( 2.67 ) *** 6.7392 ( 11.46 ) ***
(10) Unexpected entry by institutions 0.0752 ( 28.41 ) *** 0.0938 ( 35.88 ) *** 0.0726 ( 25.6 ) *** 0.1045 ( 35.96 ) ***
(11) Bidding premium by institutions 0.4242 ( 19.04 ) *** 0.4043 ( 18.35 ) *** 0.2326 ( 10.08 ) *** 0.222 ( 9.18 ) ***
(12) Unexpected entry by individuals -0.0832 ( -28.48 ) *** -0.1099 ( -37.93 ) *** -0.0493 ( -16.01 ) *** -0.0728 ( -22.92 ) ***
(13) Bidding premium by individuals -0.5327 ( -22.79 ) *** -0.5262 ( -22.84 ) *** -0.2237 ( -9.25 ) *** -0.2892 ( -11.46 ) ***
(14) Market return (AH) 0.498 ( 29.9 ) ***
(15) Market volatility (AH) -20.3075 ( -45.31 ) *** -14.0493 ( -38.96 ) ***
(16) Market return (AH10) 0.4644 ( 31.02 ) ***
(17) Market volatility (AH10) -35.9058 ( -67.33 ) *** -25.2639 ( -53.58 ) ***
(18) Probability on fixed price offering 0.1082 ( 15.3 ) *** 0.122 ( 17.56 ) *** 0.1989 ( 26.65 ) *** 0.1979 ( 25.38 ) ***
(19) Previous auction return (AH) -0.1668 ( -12.33 ) ***
(20) Previous auction adjusted return (AH) -0.578 ( -44.15 ) ***
(21) Previous auction return (AH10) -0.2332 ( -22.11 ) ***
(22) Previous auction adjusted return (AH10) -0.335 ( -27.09 ) ***
R2 58.68% 44.33% 68.51% 41.06%
N 16,791 16,791 16,791 16,791
49
Table 6. Return regressions for institutional and individual bidders (Bidder level) This table reports coefficient estimates with t-statistics for the regression of each bidder’s return on the issuing firm’s characteristics, auction properties, auction results and market conditions. LN(asset) is the natural log of issuing firm’s total asset. VC ownership is the percentage of shares held by venture capitalist before the firm went listed. E/P is the ratio of earning and share price. Auction size is the total shares sold at the auction IPO. If the issuing firm is a high-tech firm, the high-tech dummy is 1, otherwise, 0. If the firm is listed at the Taiwan Stock Exchange (TSE), the TSE dummy is 1, otherwise, 0. Market return and volatility (-3m) are measured 3 months before the auction day. Probability on fixed price offering is the probability to win shares at the fixed-price offerings. Previous auction returns are calculated for the previous auction firms with complete data to calculate post-IPO returns, auction day to the first non-hit day (AH) and auction day to the 10th trading day after the first non-hit day (AH10). Unexpected entries are error of the entry regression for institutional investors and for the individual investors. Premium is defined as the ratio of weighted average of bidding price over the reserved price of each auction. Market return and market volatility is measured based on market return between auction day and the first non-hit day (AH) and between auction day and the 10th trading day after the first non-hit day (AH10). ***, **, * significant at the 1%, 5%, and 10% level, respectively. We lost two samples when calculated previous auction IPO return based on the first non-hit day and lost two more samples based on the 10th trading day after the first non-hit day. Panel A: Institutional Bidder
rh rh10 arh arh10
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat
(1) Intercept 0.2417 ( 3.39 ) *** 0.1992 ( 2.94 ) *** 0.2119 ( 3.05 ) *** 0.0347 ( 0.51 )
(2) LN(asset) 0.0332 ( 5.39 ) *** 0.0339 ( 5.6 ) *** 0.0092 ( 1.52 ) 0.0149 ( 2.4 ) **
(3) VC ownership -0.0137 ( -0.23 ) 0.0071 ( 0.13 ) 0.1086 ( 1.91 ) * 0.2616 ( 4.76 ) ***
(4) E/P 0.4944 ( 2.28 ) ** 0.878 ( 4.01 ) *** 0.1784 ( 0.86 ) 0.0073 ( 0.03 )
(5) High-tech dummy 0.2817 ( 15.07 ) *** 0.2921 ( 15.98 ) *** 0.4042 ( 22.66 ) *** 0.421 ( 22.97 ) ***
(6) TSE dummy -0.0057 ( -0.3 ) -0.0049 ( -0.26 ) 0.087 ( 4.64 ) *** 0.1255 ( 6.71 ) ***
(7) Auction size -0.0019 ( -3.09 ) *** -0.0024 ( -3.95 ) *** -0.0032 ( -5.41 ) *** -0.0038 ( -6.29 ) ***
(8) Market return (-3m) 0.3616 ( 3.79 ) *** 0.289 ( 3.1 ) *** 0.3031 ( 3.35 ) *** 0.3833 ( 4.19 ) ***
(9) Market volatility (-3m) 2.7607 ( 0.93 ) -1.0117 ( -0.41 ) 3.3492 ( 1.29 ) 5.5315 ( 2.24 ) **
(10) Unexpected entry by institutions 0.101 ( 6.7 ) *** 0.1443 ( 9.93 ) *** 0.1292 ( 8.65 ) *** 0.1757 ( 11.94 ) ***
(11) Bidding premium by institutions 0.4879 ( 3.17 ) *** 0.4887 ( 3.24 ) *** 0.5823 ( 4.03 ) *** 0.5139 ( 3.45 ) ***
(12) Unexpected entry by individuals -0.0661 ( -4.61 ) *** -0.0972 ( -6.82 ) *** -0.0437 ( -3.1 ) *** -0.0692 ( -4.86 ) ***
(13) Bidding premium by individuals -0.6141 ( -3.85 ) *** -0.6438 ( -4.15 ) *** -0.6087 ( -4.07 ) *** -0.5844 ( -3.81 ) ***
(14) Market return (AH) 0.2976 ( 3.53 ) ***
(15) Market volatility (AH) -28.2983 ( -12.37 ) *** -19.4098 ( -10.35 ) ***
(16) Market return (AH10) 0.4979 ( 8.18 ) ***
(17) Market volatility (AH10) -35.0846 ( -15.32 ) *** -27.0626 ( -12.73 ) ***
(18) Probability on fixed price offering 0.20947 ( 3.22 ) *** 0.2806 ( 4.44 ) *** 0.3628 ( 5.8 ) *** 0.431596 ( 6.75 ) ***
(19) Previous auction return (AH) -0.1123 ( -1.51 )
(20) Previous auction adjusted return (AH) -0.7633 ( -10.52 ) ***
(21) Previous auction return (AH10) -0.1567 ( -3.42 ) ***
(22) Previous auction adjusted return (AH10) -0.2744 ( -5.29 ) ***
R2 55.72% 43.09% 71.42% 53.09%
N 969 969 969 969
50
Table 6 (continued), Return regressions for institutional and individual bidders (Bidder level) Panel B: Individual Bidder
rh rh10 arh arh10
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat
(1) Intercept 0.3846 ( 25.26 ) *** 0.3549 ( 24.26 ) *** 0.308 ( 18.95 ) *** 0.2047 ( 12.26 ) ***
(2) LN(asset) 0.019 ( 13.8 ) *** 0.0219 ( 16.22 ) *** -0.0054 ( -3.84 ) *** -0.0097 ( -6.56 ) ***
(3) VC ownership 0.0389 ( 3.46 ) *** 0.0711 ( 6.58 ) *** 0.1101 ( 9.28 ) *** 0.2108 ( 17.54 ) ***
(4) E/P 1.0567 ( 22.07 ) *** 1.6795 ( 33.94 ) *** 1.0586 ( 21.66 ) *** 1.2353 ( 23.34 ) ***
(5) High-tech dummy 0.2118 ( 57.33 ) *** 0.1998 ( 55.17 ) *** 0.2961 ( 76.78 ) *** 0.2932 ( 72.92 ) ***
(6) TSE dummy 0.0062 ( 1.64 ) -0.0137 ( -3.63 ) *** 0.0814 ( 20.22 ) *** 0.0816 ( 19.08 ) ***
(7) Auction size -0.0008 ( -11.12 ) *** -0.0007 ( -10.1 ) *** -0.0013 ( -18.27 ) *** -0.0012 ( -16.19 ) ***
(8) Market return (-3m) 0.2516 ( 12.68 ) *** 0.1659 ( 8.69 ) *** 0.2434 ( 11.52 ) *** 0.3773 ( 18.13 ) ***
(9) Market volatility (-3m) -12.798 ( -19.01 ) *** -16.9703 ( -29.09 ) *** 0.9803 ( 1.58 ) 6.161 ( 10.12 ) ***
(10) Unexpected entry by institutions 0.07 ( 25.99 ) *** 0.0881 ( 33.18 ) *** 0.0681 ( 23.42 ) *** 0.0993 ( 33.29 ) ***
(11) Bidding premium by institutions 0.4261 ( 19.09 ) *** 0.4074 ( 18.52 ) *** 0.2278 ( 9.77 ) *** 0.2227 ( 9.12 ) ***
(12) Unexpected entry by individuals -0.0837 ( -28.06 ) *** -0.1107 ( -37.58 ) *** -0.0501 ( -15.81 ) *** -0.0737 ( -22.55 ) ***
(13) Bidding premium by individuals -0.5327 ( -22.73 ) *** -0.5263 ( -22.85 ) *** -0.2181 ( -8.91 ) *** -0.2903 ( -11.38 ) ***
(14) Market return (AH) 0.5197 ( 30.75 ) ***
(15) Market volatility (AH) -19.6432 ( -43.26 ) *** -13.6751 ( -37.62 ) ***
(16) Market return (AH10) 0.4717 ( 30.45 ) ***
(17) Market volatility (AH10) -35.6472 ( -64.99 ) *** -25.058 ( -51.94 ) ***
(18) Probability on fixed price offering 0.1038 ( 14.8 ) *** 0.1155 ( 16.8 ) *** 0.1929 ( 25.8 ) *** 0.189 ( 24.22 ) ***
(19) Previous auction return (AH) -0.1772 ( -12.96 ) ***
(20) Previous auction adjusted return (AH) -0.5826 ( -44.04 ) ***
(21) Previous auction return (AH10) -0.239 ( -22.11 ) ***
(22) Previous auction adjusted return (AH10) -0.3402 ( -26.82 ) ***
R2 59.38% 45.33% 68.51% 40.28%
N 15,822 15,822 15,822 15,822
51
Table 7. Summary statistics of bidder wealth ($ bidding quantity) and uncertainty (intra-bidder dispersion) This table shows summary statistics of bidder’s wealth and intra-bidding dispersion: mean, median in ( ), and standard deviation in [ ]. Bidders are allowed to have multi bids. Bidder’s wealth is the total product of each bid’s quantity and price, measured in thousands of NT$. Intra-bidder dispersion is the quantity-weighted standard deviation of bidder i’s bids in auction j. For single unit bidders, this variable will be 0. Panel A: All bidders All years 1995 1996 1997 1998 1999 2000(1) Number of bids 986.65 298.00 2,398.00 1,091.16 561.66 650.07 1,048.00 (600.00) (298.00) (1,402.00) (727.00) (373.00) (790.00) (365.00) [1,205.04] [1,955.44] [872.55] [604.45] [507.89] [1,774.12](2) Number of bidders 708.76 237.00 1,645.09 787.42 400.97 486.60 812.78 (442.00) (237.00) (952.00) (523.00) (268.00) (611.00) (278.00) [843.34] [1,306.53] [628.92] [414.19] [365.28] [1,350.90](3) Bidder wealth (total $ 2,900.81 1,931.19 2,158.44 2,375.69 3,416.62 3,268.14 2,750.16 amount bid per bidder, (2,659.66) (1,931.19) (2,218.19) (1,897.24) (3,371.02) (3,290.01) (2,260.63) in 1,000s) [1,762.44] [827.71] [1,553.36] [2,283.72] [1,322.16] [1,362.11](4) Number of bids per bidder 1.3348 1.2574 1.3942 1.3826 1.3453 1.2741 1.2376 (1.3329) (1.2574) (1.4614) (1.3620) (1.3577) (1.2982) (1.2744) [0.1377] [0.1413] [0.1205] [0.1378] [0.1470] [0.0776](5) % of bidders with multiple bids 0.1847 0.1181 0.2150 0.2075 0.2034 0.1696 0.1541 (0.1936) (0.1181) (0.2384) (0.2128) (0.2127) (0.1898) (0.1626) [0.0610] [0.0626] [0.0399] [0.0636] [0.0792] [0.0351](6) Intra-bidder dispersion 0.4014 0.0625 0.2759 0.3774 0.4367 0.4067 0.5203 (0.2997) (0.0625) (0.2940) (0.2871) (0.3077) (0.2316) (0.3983) [0.3609] [0.1444] [0.1788] [0.4749] [0.3715] [0.4061]
52
Table 7 (continued), Summary stats of bidder wealth ($ bidding quantity) and uncertainty (intra-bidding dispersion) Panel B: Institutional bidders All years 1995 1996 1997 1998 1999 2000(1) Number of bids 59.88 2.00 105.82 43.05 47.31 58.07 89.22 (36.50) (2.00) (62.00) (43.00) (24.00) (52.00) (22.00) [83.71] [91.93] [37.94] [67.93] [54.48] [178.94](2) Number of bidders 31.95 2.00 49.73 24.37 27.38 33.73 41.33 (20.50) (2.00) (41.00) (27.00) (17.00) (29.00) (16.00) [37.05] [38.59] [20.62] [38.29] [30.65] [61.51](3) Bidder wealth (total $ 13,520.61 9,195.00 17,468.46 14,288.27 12,309.08 11,982.55 13,937.15 amount bid per bidder, (11,719.31) (9,195.00) (17,720.58) (12,197.33) (10,942.91) (12,926.93) (5,598.89) in 1,000s) [11,370.60] [10,599.57] [11,365.85] [9014.52] [5,939.27] [22,751.45](4) # of bids per bidder 1.6985 1.0000 2.0709 1.7158 1.6520 1.7034 1.4285 (1.6726) (1.0000) (2.0000) (1.7618) (1.6111) (1.7792) (1.3571) [0.4789] [0.3811] [0.3365] [0.5187] [0.4187] [0.5752](5) % of bidders with multiple bids 0.3872 0.0000 0.4943 0.4083 0.3648 0.4505 0.2307 (0.3810) (0.0000) (0.5063) (0.4327) (0.3667) (0.4349) (0.2195) [0.2394] [0.1091] [0.2003] [0.2631] [0.2858] [0.1818](6) Intra-bidder dispersion 0.6057 0.0000 0.5408 0.6811 0.5846 0.6968 0.5278 (0.4741) (0.0000) (0.5510) (0.6301) (0.3581) (0.5382) (0.5215) [0.5470] [0.2319] [0.4250] [0.7175] [0.5626] [0.4181]
53
Table 7 (continued), Summary stats of bidder wealth ($ bidding quantity) and uncertainty (intra-bidding dispersion) Panel C: Individual bidders All years 1995 1996 1997 1998 1999 2000(1) Number of bids 926.77 296.00 2,292.18 1,048.11 514.34 592.00 958.77 (544.00) (296.00) (1,369.00) (683.00) (338.00) (738.00) (343.00) [1,140.07] [1,883.28] [846.55] [548.18] [456.03] [1,596.07](2) Number of bidders 676.81 235.00 1,595.36 763.05 373.58 452.86 771.44 (407.50) (235.00) (911.00) (496.00) (251.00) (575.00) (260.00) [816.10] [1,278.20] [614.25] [383.61] [337.01] [1,290.48](3) Bidder wealth (total $ 2,261.08 1,869.37 1,529.66 1,786.06 2,664.64 2,699.84 2,169.70 amount bid per bidder, (2,040.03) (1,869.37) (1,528.66) (1,533.93) (2435.10) (2,533.53) (2,066.70) in 1,000s) [1,251.10] [362.15] [1,020.72] [1,612.91] [992.51] [806.78](4) # of bids per bidder 1.3172 1.2596 1.3724 1.3684 1.3245 1.2548 1.2283 (1.3113) (1.2596) (1.4262) (1.3351) (1.3414) (1.2814) (1.2364) [0.1347] [0.1439] [0.1244] [0.1289] [0.1451] [0.0729](5) % of bidders with multiple bids 0.1847 0.1191 0.2056 0.1991 0.1942 0.1578 0.1500 (0.1936) (0.1191) (0.2275) (0.2029) (0.2067) (0.1770) (0.1585) [0.0610] [0.0655] [0.0425] [0.0586] [0.0797] [0.0367](6) Intra-bidder dispersion 0.3918 0.0631 0.2681 0.3648 0.4249 0.3966 0.5221 (0.2944) (0.0631) (0.2935) (0.2784) (0.3073) (0.2196) (0.3644) [0.3531] [0.1454] [0.1737] [0.4576] [0.3663] [0.4149]
54
Table 8. Effects of bidder wealth ($ bidding quantity) and uncertainty (intra-bidding dispersion) This table reports coefficient estimates with t-statistics for the regression of return on the issuing firm’s characteristics, auction properties, auction results, market conditions, and bidders’ wealth and intra-bidding dispersion. LN(asset) is the natural log of issuing firm’s total asset. VC ownership is the percentage of shares held by venture capitalist before the firm went listed. E/P is the ratio of earning and share price. Auction size is the total shares sold at the auction IPO. If the issuing firm is a high-tech firm, the high-tech dummy is 1, otherwise, 0. If the firm is listed at the Taiwan Stock Exchange (TSE), the TSE dummy is 1, otherwise, 0. Market return and volatility (-3m) are measured 3 months before the auction day. Probability on fixed price offering is the probability to win shares at the fixed-price offerings. Previous auction returns are calculated for the previous auction firms with complete data to calculate post-IPO returns, auction day to the first non-hit day (AH) and auction day to the 10th trading day after the first non-hit day (AH10). Unexpected entries are error of the entry regression for institutional investors and for the individual investors. Premium is defined as the ratio of weighted average of bidding price over the reserved price of each auction. Market return and market volatility is measured based on market return between auction day and the first non-hit day (AH) and between auction day and the 10th trading day after the first non-hit day (AH10). We lost two samples when calculated previous auction IPO return based on the first non-hit day and lost two more samples based on the 10th trading day after the first non-hit day. Bidders are allowed to have multi bids. Bidder’s wealth is the total product of each bid’s quantity and price, in NT$1,000s. Intra-bidder dispersion is the quantity-weighted standard deviation of bidder i’s bids in auction j. For single unit bidders, this variable will be 0. ***, **, * significant at the 1%, 5%, and 10% level, respectively. Panel A: All Bidders
Rh (1)
Arh (2)
rh10 (3)
arh10 (4)
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat (1) Intercept 0.3568 ( 23.86 ) *** 0.3262 ( 22.64 ) *** 0.2912 ( 18.4 ) *** 0.18308 ( 11.27 ) ***(2) LN (asset) 0.0210 ( 15.54 ) *** 0.0236 ( 17.75 ) *** -0.0038 ( -2.74 ) *** -0.00769 ( -5.35 ) ***(3) VC ownership 0.0360 ( 3.24 ) *** 0.0705 ( 6.59 ) *** 0.1084 ( 9.33 ) *** 0.21266 ( 18.09 ) ***(4) E/P 1.0489 ( 22.32 ) *** 1.6532 ( 33.96 ) *** 1.0360 ( 21.75 ) *** 1.18426 ( 22.95 ) ***(5) High-tech dummy 0.2175 ( 59.54 ) *** 0.2059 ( 57.28 ) *** 0.3034 ( 80.21 ) *** 0.30133 ( 76.33 ) ***(6) TSE dummy 0.0072 ( 1.92 ) * -0.0119 ( -3.18 ) *** 0.0814 ( 20.6 ) *** 0.08303 ( 19.81 ) ***(7) Auction size -0.0008 ( -12.04 ) *** -0.0008 ( -11.13 ) *** -0.0014 ( -19.4 ) *** -0.00134 ( -17.58 ) ***(8) Market return (-3m) 0.2653 ( 13.64 ) *** 0.1897 ( 10.09 ) *** 0.2500 ( 12.2 ) *** 0.38635 ( 19.06 ) ***(9) Market volatility (-3m) -11.1723 ( -16.99 ) *** -15.2717 ( -26.92 ) *** 1.5224 ( 2.54 ) ** 6.66385 ( 11.33 ) ***(10) Unexpected entry by institutions 0.0749 ( 28.31 ) *** 0.0935 ( 35.75 ) *** 0.0724 ( 25.53 ) *** 0.10415 ( 35.84 ) ***(11) Bidding premium by institutions 0.4245 ( 19.07 ) *** 0.4048 ( 18.39 ) *** 0.2327 ( 10.09 ) *** 0.22244 ( 9.2 ) ***(12) Unexpected entry by individuals -0.0829 ( -28.29 ) *** -0.1097 ( -37.78 ) *** -0.0489 ( -15.83 ) *** -0.07304 ( -22.92 ) ***(13) Bidding premium by individuals -0.5329 ( -22.81 ) *** -0.5267 ( -22.88 ) *** -0.2236 ( -9.24 ) *** -0.28998 ( -11.49 ) ***(14) Market return (AH) 0.4980 ( 29.91 ) *** (15) Market volatility (AH) -20.2819 ( -45.28 ) *** -14.0234 ( -38.91 ) *** (16) Market return (AH10) 0.4638 ( 30.94 ) *** (17) Market volatility (AH10) -35.8955 ( -67.31 ) *** -25.26029 ( -53.57 ) ***(18) Probability on fixed price offering 0.1083 ( 15.33 ) *** 0.1220 ( 17.57 ) *** 0.1990 ( 26.67 ) *** 0.19773 ( 25.37 ) ***(19) Previous auction return (AH) -0.1674 ( -12.38 ) *** (20) Previous auction adjusted return (AH) -0.5779 ( -44.17 ) *** (21) Previous auction return (AH10) -0.2335 ( -22.14 ) *** (22) Previous auction adjusted return (AH10) -0.33519 ( -27.11 ) ***(23) Bidder’s wealth 2.10E-07 ( 4.46 ) *** 2.10E-07 ( 4.66 ) *** 1.50E-07 ( 3.12 ) *** 1.70E-07 ( 3.41 ) ***(24) Intra-bidding dispersion -0.0014 ( -1.87 ) * -0.0008 ( -1.11 ) -0.0013 ( -1.69 ) * 0.00046 ( 0.56 ) R2 58.73% 44.40% 68.53% 41.10% N 16,791 16,791 16,791 16,791
55
Table 8 (continued), Effects of bidder wealth ($ bidding quantity) and uncertainty (intra-bidding dispersion) Panel B: Institutional Bidders
rh (1)
arh (2)
rh10 (3)
arh10 (4)
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat (1) Intercept 0.2467 ( 3.44 ) *** 0.2040 ( 2.99 ) *** 0.2166 ( 3.1 ) *** 0.04279 ( 0.62 ) (2) LN (asset) 0.0326 ( 5.23 ) *** 0.0334 ( 5.45 ) *** 0.0086 ( 1.41 ) 0.01399 ( 2.24 ) ** (3) VC ownership -0.0141 ( -0.24 ) 0.0065 ( 0.12 ) 0.1078 ( 1.89 ) * 0.25949 ( 4.71 ) ***(4) E/P 0.4871 ( 2.24 ) ** 0.8724 ( 3.98 ) *** 0.1742 ( 0.84 ) 0.00205 ( 0.01 ) (5) High-tech dummy 0.2815 ( 15.05 ) *** 0.2920 ( 15.96 ) *** 0.4040 ( 22.62 ) *** 0.42057 ( 22.92 ) ***(6) TSE dummy -0.0075 ( -0.39 ) -0.0063 ( -0.33 ) 0.0856 ( 4.53 ) *** 0.12331 ( 6.54 ) ***(7) Auction size -0.0019 ( -3.09 ) *** -0.0024 ( -3.94 ) *** -0.0032 ( -5.39 ) *** -0.00381 ( -6.26 ) ***(8) Market return (-3m) 0.3617 ( 3.79 ) *** 0.2904 ( 3.12 ) *** 0.3042 ( 3.36 ) *** 0.38498 ( 4.2 ) ***(9) Market volatility (-3m) 2.8615 ( 0.96 ) -0.9803 ( -0.4 ) 3.4058 ( 1.31 ) 5.57928 ( 2.26 ) ** (10) Unexpected entry by institutions 0.1008 ( 6.69 ) *** 0.1441 ( 9.91 ) *** 0.1292 ( 8.65 ) *** 0.17538 ( 11.91 ) ***(11) Bidding premium by institutions 0.4896 ( 3.18 ) *** 0.4894 ( 3.25 ) *** 0.5825 ( 4.02 ) *** 0.51427 ( 3.45 ) ***(12) Unexpected entry by individuals -0.0671 ( -4.67 ) *** -0.0980 ( -6.85 ) *** -0.0447 ( -3.15 ) *** -0.07048 ( -4.93 ) ***(13) Bidding premium by individuals -0.6177 ( -3.87 ) *** -0.6457 ( -4.16 ) *** -0.6105 ( -4.07 ) *** -0.58664 ( -3.82 ) ***(14) Market return (AH) 0.2983 ( 3.54 ) *** (15) Market volatility (AH) -28.2040 ( -12.31 ) *** -19.35492 ( -10.3 ) *** (16) Market return (AH10) 0.499693 ( 8.2 ) *** (17) Market volatility (AH10) -34.98823 ( -15.24 ) *** -26.968542 ( -12.67 ) ***(18) Probability on fixed price offering 0.2090 ( 3.21 ) *** 0.2801 ( 4.43 ) *** 0.3622 ( 5.78 ) *** 0.4301882 ( 6.72 ) ***(19) Previous auction return (AH) -0.1083 ( -1.45 ) (20) Previous auction adjusted return (AH) -0.76041 ( -10.46 ) *** (21) Previous auction return (AH10) -0.1551 ( -3.38 ) *** (22) Previous auction adjusted return (AH10) -0.2718154 ( -5.23 ) ***(23) Bidder’s wealth 4.00E-08 ( 0.59 ) 2E-08 ( 0.37 ) 2.00E-08 ( 0.28 ) 2.00E-08 ( 0.29 ) (24) Intra-bidding dispersion 0.0018 ( 0.72 ) 0.0015 ( 0.61 ) 0.001661 ( 0.68 ) 0.00246 ( 0.98 ) R2 55.77% 43.12% 71.44% 53.15% N 969 969 969 969
56
Table 8 (continued), Effects of bidder wealth ($ bidding quantity) and uncertainty (intra-bidding dispersion)
Panel C: Individual Bidders
rh (1)
arh (2)
rh10 (3)
arh10 (4)
Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat (1) Intercept 0.3891 ( 25.5 ) *** 0.3610 ( 24.64 ) *** 0.3104 ( 19.06 ) *** 0.2107 ( 12.58 ) ***(2) LN (asset) 0.0190 ( 13.84 ) *** 0.0219 ( 16.23 ) *** -0.0054 ( -3.78 ) *** -0.0098 ( -6.63 ) ***(3) VC ownership 0.0383 ( 3.41 ) *** 0.0694 ( 6.44 ) *** 0.1097 ( 9.25 ) *** 0.2094 ( 17.43 ) ***(4) E/P 1.0560 ( 22.07 ) *** 1.6760 ( 33.92 ) *** 1.0571 ( 21.62 ) *** 1.2299 ( 23.24 ) ***(5) High-tech dummy 0.2105 ( 56.92 ) *** 0.1982 ( 54.71 ) *** 0.2953 ( 76.46 ) *** 0.2919 ( 72.5 ) ***(6) TSE dummy 0.0050 ( 1.31 ) -0.0156 ( -4.12 ) *** 0.0807 ( 19.97 ) *** 0.0798 ( 18.6 ) ***(7) Auction size -0.0008 ( -11.16 ) *** -0.0007 ( -10.11 ) *** -0.0013 ( -18.3 ) *** -0.0012 ( -16.15 ) ***(8) Market return (-3m) 0.2518 ( 12.71 ) *** 0.1624 ( 8.53 ) *** 0.2428 ( 11.5 ) *** 0.3748 ( 18.02 ) ***(9) Market volatility (-3m) -13.1458 ( -19.5 ) *** -17.3179 ( -29.68 ) *** 0.7748 ( 1.25 ) 5.9128 ( 9.7 ) ***(10) Unexpected entry by institutions 0.0694 ( 25.8 ) *** 0.0874 ( 32.95 ) *** 0.0677 ( 23.29 ) *** 0.0987 ( 33.09 ) ***(11) Bidding premium by institutions 0.4302 ( 19.3 ) *** 0.4121 ( 18.77 ) *** 0.2304 ( 9.89 ) *** 0.2262 ( 9.27 ) ***(12) Unexpected entry by individuals -0.0836 ( -27.97 ) *** -0.1110 ( -37.61 ) *** -0.0498 ( -15.67 ) *** -0.0741 ( -22.62 ) ***(13) Bidding premium by individuals -0.5378 ( -22.96 ) *** -0.5326 ( -23.16 ) *** -0.2214 ( -9.04 ) *** -0.2954 ( -11.58 ) ***(14) Market return (AH) 0.5233 ( 30.97 ) *** (15) Market volatility (AH) -19.5196 ( -43.02 ) *** -13.5908 ( -37.46 ) *** (16) Market return (AH10) 0.4725 ( 30.45 ) *** (17) Market volatility (AH10) -35.5646 ( -64.84 ) *** -25.0129 ( -51.88 ) ***(18) Probability on fixed price offering 0.1039 ( 14.84 ) *** 0.1153 ( 16.8 ) *** 0.1929 ( 25.81 ) *** 0.1888 ( 24.21 ) ***(19) Previous auction return (AH) -0.1811 ( -13.25 ) *** (20) Previous auction adjusted return (AH) -0.5854 ( -44.34 ) *** (21) Previous auction return (AH10) -0.2402 ( -22.23 ) *** (22) Previous auction adjusted return (AH10) -0.3420 ( -26.98 ) ***(23) Bidder’s wealth 9.00E-07 ( 6.64 ) *** 1.12E-06 ( 8.39 ) *** 6.60E-07 ( 4.57 ) *** 8.80E-07 ( 5.85 ) ***(24) Intra-bidding dispersion -0.0024 ( -3.12 ) *** -0.0019 ( -2.43 ) ** -0.0022 ( -2.63 ) *** -0.00048 ( -0.56 ) R2 59.51% 45.59% 68.57% 40.41% N 15,822 15,822 15,822 15,822
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