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

39

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