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IPO Underpricing Explanations: Implications from Investor Application and AllocationSchedulesAuthor(s): Philip J. Lee, Stephen L. Taylor and Terry S. WalterReviewed work(s):Source: The Journal of Financial and Quantitative Analysis, Vol. 34, No. 4 (Dec., 1999), pp.425-444Published by: University of Washington School of Business AdministrationStable URL: http://www.jstor.org/stable/2676228 .

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JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS VOL 34, NO. 4, DECEMBER 1999

IPO Underpricing Explanations: Implications

from Investor Application and Allocation

Schedules

Philip J. Lee, Stephen L. Taylor, and Terry S. Walter*

Abstract

Initial Public Offers (IPOs) made on the Stock Exchange of Singapore routinely provide sufflciently detailed data to allow reconstruction of both the application and allocation schedules. We show that large investors tend to preferentially request participation in IPOs with higher initial returns, consistent with these investors being better informed. We also show that inferences based exclusively on application strategies are quite different from those drawn on investor allocations. Our results suggest that caution is necessary in assess-

ing the relative merit of competing explanations for IPO underpricing where the underlying demand is not identified.

I. Introduction

Institutional practices adopted in initial public offerings (IPOs) are an impor? tant determinant of initial underpricing (Loughran, Ritter, and Rydqvist (1994)). The "book-building" approach of U.S. underwriters means that there should be a

close correspondence between final applications for shares in an IPO and the allo?

cation of these shares.1 Final applications, however, may differ substantially from

the underlying demand for an issue, reflecting potential investors' perceptions of

bias in underwriters' allocation decisions and the futility of an application for

shares when not on the "favored client" list. When underlying demand is dampened in this way, it is not possible to observe the probability of an allocation, and

theories that rely on differentially informed investors cannot be directly tested.2

* All authors, Department of Accounting (H04), University of Sydney, NSW 2006, Australia. This paper has benefited from comments by participants at the 1996 PACAP/APFA annual meeting, as well as workshops at the following universities: Sydney, Adelaide, Western Australia, Macquarie, and Queensland. The authors gratefully acknowledge these comments. They also acknowledge detailed comments and suggestions received from Philip Brown, Paul Malatesta (the editor), and Ivo Welch (associate editor and referee).

following pre-selling, U.S. underwriters must set a common price for all investors (Benveniste and Wilhelm (1990)). The subsequent offers of shares reflect underwriters' allocation discretion, as well as the information gained from pre-selling. Hence, "applications," as such, will already reflect both the information provided in the pre-selling period and the underwriters' ability to discriminate between applicants.

2It is possible that investors might strategically apply for a larger number of shares than they actually want in an issue. To do so, however, involves an additional application cost, and an increased

425



426 Journal of Financial and Quantitative Analysis

Indeed, the extent of differentially informed investors becomes a maintained as?

sumption, rather than a subject of investigation. We examine stock issues in Singapore and show that large investors prefer-

entially request participation in IPOs with higher initial returns, consistent with

them having better information. While larger investors are commonly assumed to

have an information advantage, our paper is the first to document it empirically. We also show that the winner's curse (Rock (1986)) is far more apparent in applications than it is in allocations. Data for Singapore IPOs are frequently sufficient

to reconstruct the demand schedule within various application sizes, as well as a

schedule of how that demand is rationed (i.e., allocation data). Thus, we can make

a direct comparison of the inferences drawn from allocation data vis a vis those

that derive from application data (i.e., unconstrained demand). To the extent that

differences between unconstrained demand and eventual allocation patterns exist

in the U.S. and elsewhere, our results also suggest that conclusions based only on

allocation data need to be cautiously interpreted. Our results need to be considered in light of the prevailing institutional set-

ting for Singapore IPOs. Underwriting in Singapore is evenhanded, in contrast

to the book-building approach adopted in the U.S. All investors who apply for

the same number of shares in an IPO have an equal chance of success (though issuers can choose to ration requests for different numbers of shares more or less

severely), because over-subscribed issues are allocated using a process of random

drawing. Koh and Walter (1989) and Lee, Taylor, and Walter (1996b) confirm the

winner's curse prediction for short-run underpricing, and also show that long-run returns for Singapore IPOs are consistent with efficient market expectations. The

long-run secular decline in price documented in several international studies (see

Loughran, Ritter, and Rydqvist (1994) and Lee, Taylor, and Walter (1996a)) is not

present in Singapore. The next section highlights the role of informed investors in two widely

tested explanations for IPO underpricing, as well as the key institutional arrangements for the Singapore IPO market. The data used in this study are described in

Section III, together with the results. Application and allocation details are ana?

lyzed to discover the nature of demand expansion and its relationship with IPO

rationing. The conclusions are presented in the final section.

II. Background

A. Prior Research

Differentially informed investors play a crucial role in many explanations of

IPO underpricing. For example, in Rock's (1986) winner's curse model, informed

investors only request stock in issues they know to be underpriced, thereby cre?

ating an adverse selection problem for issuers that leads to underpricing, on av?

erage, of IPOs. In Benveniste and Spindt's (1989) information-gathering model, informed investors provide valuable demand and pricing feedback to underwrit?

ers, and underpricing, on average, compensates for the value of this information,

probability that, if the issue is overpriced, they will face the consequent winner's curse. More detail on the cost structure and institutional arrangements in Singapore IPOs is provided in Section II.B.



Lee, Taylor, and Walter 427

as well as the cost of participating in overpriced issues. While distinguishing be? tween the role of these (and other) models requires consideration of the prevailing institutional setting, Hanley and Wilhelm (1995) also argue that the behavior of informed investors will differ in important aspects between the winner's curse and

information-gathering models. Hence, Hanley and Wilhelm endeavor to "test"

these competing explanations for IPO underpricing by examining outcomes for

informed investors. They use confidential allocation data for a limited number of

U.S. IPOs and show that institutional (i.e., informed) investors receive approxi?

mately the same proportions of shares in overpriced and underpriced issues. They

interpret this result as support for Benveniste and Spindt's (1989) explanation of IPO underpricing, which is premised on discriminatory behavior by U.S. un?

derwriters, and reject the winner's curse explanation proposed by Rock (1986).

Specifically, they show that variation in the proportion of the issue allocated to

institutional investors explains a statistically insignificant (though positive) pro?

portion of cross-sectional variation in initial returns.

Hanley and Wilhelm interpret their evidence to imply that informed investors

are forced by underwriters to take shares in overpriced issues. It is also plausible that informed investors take more reliably overpriced issues because of unob?

servable "scaling back" via the pre-selling process or a bias in rationing relative

to uninformed investors. More generally, it is even possible that results such as

those reported by Hanley and Wilhelm may simply reflect an absence of supe? rior information among institutional investors, so that they cannot be expected to

discriminate between overpriced and underpriced issues. Another possible expla? nation for the result reported by Hanley and Wilhelm is that the regression masks

a statistically significant institutional investor coefficient due to institutions be?

ing more prominent in larger underpriced issues, because each IPO is equally

weighted. The variety of possible interpretations of the results in Hanley and Wilhelm

(1995) highlights two important related issues. First, do allocation data provide a representative picture of differential information among investors? We know

of no existing evidence on this issue, but provide such information for the Sin?

gapore IPO market. Biases created by pre-selling methods or explicit rationing

guidelines may disguise unconstrained demand, the measure most likely to re?

flect differential information among investors. Second, is it appropriate to even

assume (as so many explanations of IPO underpricing do) that some investors are

differentially informed? The extent to which investors are differentially informed

has received little attention. Rather, it is typically assumed that institutional and/or

larger investors are better informed about the "true" value of an IPO. This assump? tion underlies tests of competing equilibrium models of IPO underpricing (e.g., Koh and Walter (1989), Hanley and Wilhelm (1995)). Recent evidence (Field

(1997)) indicates that institutional investors may be better informed about IPO

value. Field, however, examines only post-issue institutional holdings, showing that IPOs with the smallest institutional investment at the end of the first post-

listing quarter tend to have the poorest long-run returns. Hence, Field does not

actually demonstrate that institutional investors are better informed in the absence

of an observable market price (i.e., at the time of deciding whether, and to what

extent, to subscribe to the IPO).




To investigate the extent to which allocations reflect differential information

between investors, it is necessary to examine the relationship between uncon?

strained demand and actual allocations. These data are not typically available, so

studies such as Hanley and Wilhelm (1995) are confined to assuming that alloca?

tion data reflect the underlying demand (i.e., applications). The relationship between application and allocation data, and direct evidence

of the extent to which large investors are better informed, can be tested using our data for Singapore IPOs. We use the dollar value of the shares applied for

as a proxy for information advantage, rather than the applicants' identity, which

we cannot observe. We expect that large applications will be based on better

information. In other environments, institutional investor identity and applica? tion size should be highly correlated. Using private data for 28 Finnish IPOs,

Keloharju (1997) is able to distinguish retail investors' orders from those placed

by institutions. He shows that institutional investors place much larger orders

than those placed by retail investors, consistent with order size capturing the re-

tail/institutional distinction. More fundamentally, the role of order size is sup?

ported by Chowdhry and Sherman (1996a), who argue that informed investors

place larger orders even when they have the same wealth as the uninformed. Fi?

nally, the dilemma of revealing information to other market participants purely

by means of order size does not apply to Singapore IPOs. Other IPO participants cannot observe the number of shares applied for by a particular investor, and the

total application pool is not publicly known until after the issue closes. Large in?

vestors are hidden in the crowd (if they so desire) and need not fear that the size of

their order will reveal their information advantage. Hence, the adverse selection

problem identified in Easley and O'Hara (1987) is avoided, as too are cascades of

the type described by Welch (1992).

B. Institutional Arrangements

The main features of the Stock Exchange of Singapore's (SES) IPO mar?

ket are described in Koh and Walter (1989), Saunders and Lim (1990), and Lee,

Taylor, and Walter (1996b). These papers highlight that:

i) Rationing of shares in over-subscribed issues is evenhanded; all investors

who apply for the same number of shares in an IPO have an equal probability of receiving an allocation.

ii) Disclosure of the rationing process is provided routinely, allowing the re-

searcher to determine, conditional on various application strategies, the ex

post probability of success.

iii) Sufficient post-issue disclosure is frequently made to allow estimation of the

demand schedule, allowing the researcher to observe the application pool for

an issue, the rationing process used in the issue, and the actual allocation

distribution for the issue.3

3 Some corporate disclosures made in Singapore IPOs allow the researcher to estimate the prob? ability of receiving an allocation conditional on the number of shares applied for, but do not give sufficient details to allow the demand schedule to be reconstructed. For example, a disclosure might indicate that one in 10 investors who applied for 1,000 shares were randomly selected as success-




Previous Singapore research has utilized the disclosures in i) and ii). Koh and

Walter (1989) and Lee, Taylor, and Walter (1996b) show that there is a systematic

preference for the applications of smaller investors in the allocation process, pos-

sibly induced by the listing requirements of the SES. Both papers also show that

large investor demand is significantly more responsive to expected levels of un?

derpricing (proxied by realized underpricing) than demand in smaller investor size

categories. However, neither study exploits data contained in the full application and allocation schedules for the Singapore IPO market to show small and large investor profits and losses. This paper employs the 91 (out of 132) IPOs made in

the period July 1973 to December 1992 with sufficient disclosure to reconstruct

the application and allocation proportion and split these into groups based on the

application size.

These features of the Singapore IPO market mean that the observable de?

mand for an issue is not dampened by any ex ante expectation that a particular issue will be discretionally allocated to favored clients of the underwriter, issuer, or broker for the issue.4 That demand is not dampened by discriminatory allo?

cations has, however, resulted in quite pervasive over-subscription levels. Lee,

Taylor, and Walter (1996b) document a median over-subscription level of 14.1

times for the 128 Singapore IPOs included in their study. Chowdhry and Sher-

man (1996b) suggest that, given high levels of over-subscription, the cost to the

issuing firm of underpricing may be mitigated by the interest earned on the sub?

scription pool. To investigate this, we calculated a standardized measure of the

interest on the subscription pool expressed as a percentage of the issue size. Our

standardized interest measure shows a maximum interest accruing to the issuer of

39% (this issue was priced at 65<?, came on to the market three days later at $1.93

and was over-subscribed 783 times), though nearly 70% of issues have a value

at less than 1%. This can be compared to the average underpricing we report in

Table 1 of 31.73%. We conclude that the interest on the subscription pool is not a

major incentive to underprice.5 It is also worth noting that the IPO process in Singapore follows the general

principles of British law and, thus, the offer price must be set and stated in a

prospectus prior to a formal invitation to the public to apply for shares. The

elapsed time between fixing an offer price for inclusion in the prospectus and the

listing of the shares averages four to five weeks. Comparing evidence for different

countries, Loughran, Ritter, and Rydqvist (1994) suggest that the longer the time

period between setting the offer price and listing, the greater will be the level of

underpricing, conditional on the offer not being withdrawn.

Another feature of relevance in this paper is the SES listing rule, which re?

quires a minimum percentage of the issued capital (after excluding the holdings of directors, the parent company, and companies associated with the parent com?

pany) to be held by shareholders who hold between 500 and 10,000 shares. The

ful, but the proportion of the total issue allocated to such investors is not disclosed. Koh and Walter ((1989), pp. 270-271) provide an example of both a "complete" disclosure and a "partial" disclosure.

4Typically 10% of the issue is reserved for staff of the issuing company. Seventy-five of the 132 IPOs had a 10% staff priority entitlement.

5The statistics for the interest on the subscription pool expressed as a percentage of issue size are: mean 1.53, median 0.49, standard deviation 3.81, maximum 39.04, minimum 0.01, deciles 4.28, 1.89, 1.13, 0.79, 0.49, 0.27, 0.16, 0.08, 0.04, 0.01.




TABLE 1

Summary Statistics for Singapore IPOs

Summary statistics for various firm attributes for 132 Singapore IPOs made between July 1973 and Dec. 1992, and for the 91 firms for which sufficient data are disclosed to allow the application and allocation proportions for the issue to be reconstructed (the included firms), and for the remaining 41 firms for which application and allocation proportions cannot be reconstructed (the excluded firms).

a Underpricing = The last sale on the first day of listing minus the subscription price, divided by the subscription price, multiplied by 100 (%). Over-subscription = The multiple by which total applications exceed available shares (times). Age of the firm = Length of prior operating history of the firms (years). Retained ownership = Proportion of the equity retained by previous owners (%). Listing lag = Time between prospectus registration and exchange listing (days). Log of issue size = Natural log of equity issue size * (Singapore $ millions). Log of total assets = Natural log of total assets after initial equity issue * (Singapore $ millions). 250-, 500-, 750-Day wealth relatives = The investment performance of the observation relative to a market return over the relevant number of trading days. The beginning point for the wealth relative is the first day listing price (ratio). Number of firms = The number of firms used in the calculation of the relevant wealth relative (firms). The number of observations for the 500- and 750-day wealth relative decreases due to truncation of the share price series at Dec. 31, 1993. No sample company failed or delisted during the period of the study.

* These figures are CPI-adjusted to a Jan. 1993 base, to reflect a constant dollar value. For comparison purposes, the exchange rate between the Singapore dollar and the U.S. dollar increased during the study from approximately 2.1S$: 1 US$ to 1.4S$: 1 US$.




minimum percentage varies from 10% (for issues with paid-up capital of $150 million or more) to 20% (for issues with a paid-up capital of $50 million or less). This rule creates a bias in favor of smaller investors, clearly evident in Koh and

Walter's (1989) figure 2, which shows that the probability of receiving an alloca?

tion of shares for a 1,000 share application (0.35) is more than twice the proba? bility of success (0.16) when 1 million shares are sought. However, whether this

bias actually reduces the extent of any winner's curse (as suggested in Chowdhry and Sherman (1996a) has not been addressed empirically.

III. Data and Empirical Results

A. Data and Descriptive Statistics

We use a combined sample of IPOs previously studied by, and described in, Koh and Walter (1989) and Lee, Taylor, and Walter (1996b). During the period from July 1973 (which marks the inception of the SES) to December 1992, there

were 132 IPOs. We manually searched the company files at the SES library to

extract details of the application and allocation patterns for each issue. Ninety- one issues provided sufficient information to allow us to estimate the total demand

schedule for the issue (see the Appendix for details of the estimation methods

used), classified by size of application. The reasons why data are not available for

41 IPOs are as follows:

i) Prior to June 1978, detailed disclosure of demand for the issue was not re?

quired and was rarely provided on a voluntary basis (22 out of the 25 IPOs

made between January 1973 and June 1978 did not voluntarily disclose the

demand schedule).

ii) Six IPOs were under-subscribed and they did not provide sufficient details to

allow demand to be determined.6

iii) A further 13 IPOs were over-subscribed but the details of the application

patterns cannot be estimated from the (less complete) information disclosed.7

Nine of these come from the earlier IPOs studied by Koh and Walter (1989).

In summary, the exclusions are primarily caused by insufficient voluntary disclosure in the earlier IPOs. Thirty-two of the first 66 IPOs in our sample period do not provide sufficient disclosure for our tests, while only nine of the second 66

are omitted due to incomplete application and allocation data.

Various attributes of the IPO population (132) and the included (91) and ex?

cluded (41) IPOs are reported in Table 1. Underpricing for the excluded IPOs is

approximately 24%, which is insignificantly different (r-statistic 1.43) from the

35% for the included firms. However, the over-subscription level of 55 times for

the included firms is significantly higher (r-statistic 4.31) than for the excluded

firms (10 times). This is expected because under-subscribed firms are in the

excluded set. Included firms have issue sizes that are significantly larger than

6Of these six omitted (under-subscribed) firms, three were overpriced (by an average of 8.2%). Another two firms were underpriced (by an average of 7.0%) and one was neither over- nor underpriced. These omissions may introduce a bias to the results.

7The mean over-subscription for these was 21.0 times, and the median was 8.0.




excluded IPOs Ostatistic 2.00), but firm size as measured by total assets, is in-

significantly different (/-statistic 1.27) between the two groups. The lag between the prospectus date and the listing date is significantly smaller (7-statistic 2.09) for

the included firms. This is also expected as a result of efficiencies associated with

technology improvements introduced into the IPO process over the sample period, and also because under-subscribed offerings are probably shopped longer. Across

all other dimensions (retained ownership, age, and three measures of long-run

performance), the included and excluded firms have similar characteristics.

As noted in Lee, Taylor, and Walter (1996b), there is no evidence that Singa?

pore IPOs display poor long-run performance of the type encountered in several

international studies (see Loughran, Ritter, and Rydqvist (1994)). Further, while

initial underpricing is significantly positive, Koh and Walter (1989) and Lee, Tay? lor, and Walter (1996b) show that once the probability of receiving shares in an

issue is considered, initial returns are insignificantly different from the risk-free

rate of interest. This result is consistent with the prediction (for uninformed in?

vestors) of Rock's (1986) equilibrium model of why new issues are underpriced. A direct test of Rock's model requires data on the rationing process used in each

IPO, and this information is routinely supplied to the market in Singapore. The

probability of an allocation, conditional on the quantity applied for, can be estab-

lished for 128 of the 132 IPOs; these probabilities were used in previous Singa?

pore tests of Rock's model; however, the more stringent disclosure requirements that we impose restrict the analysis to 91 firms.

Figure 1 plots the difference in the allocation proportion and the applica? tion proportion for investors in four categories (small, medium/small, medium, and large investors) for each ofthe 91 issues. Small investors are defined as those

who apply for up to $5,000 dollars worth of shares in each issue, medium/small in?

vestors apply for $5,001 to $50,000, medium investors apply for $50,001 to $250,000, and large investors are defined as those who apply for more than

$250,000 worth of stock. It is clear in Figure 1 that large investors are allocated

relatively less than they apply for, when compared to small investors. The data on

which Figure 1 is based show that large (informed) investors, on average, account

for approximately half the applications, though they are allocated only 28%. In

contrast, small (uninformed) investor demand constitutes, on average, 12% ofthe

applications, but they receive an average of 32%.

An analysis of the IPOs plotted in Figure 1 shows that the range of appli? cation proportions differs considerably from the allocation proportions. Large investors had a minimum demand of zero (as too did medium investors for one

issue that was underpriced by significantly less than the average underpricing for

the sample) and a maximum demand of 97.55% (the issue was the most heav-

ily over-subscribed IPO and it was the most underpriced; small investors were

crowded out of this particular issue and accounted for only 1.22% of the total ap?

plication pool for the issue, though they were allocated 20.2% ofthe shares). We

examine these matters in greater detail in subsequent regression analysis, but point out that substantial differences in application and allocation proportions mean that

inferences drawn solely from an analysis of allocations may give a false picture of application proportions for the Singapore IPO market. Likewise, the allocation

data examined by Hanley and Wilhelm (1995) may also disguise the underlying,




unconstrained demand for U.S. IPO shares, making a reliable test of this particular

prediction of Rock's (1986) winner's curse model difficult.

Panels A, B, and C of Table 2 combine the information in Figure 1 with data

on issue size and underpricing to develop total profit (loss) statistics for all 91

issues, and separately for the underpriced issues (84) and the overpriced or fully

priced issues (seven). The profit (loss) statistics in panels A, B, and C of Table

2 are developed using the actual allocations for each issue and show that large investors made an average gross profit before application and information search

costs of $3,897 million per issue, while small investors made $3,766 million per issue. The total realized value of the information advantage of large investors

is $11.9 million for the 91 issues. However, as shown in Figure 1 and further

quantified below, large investor gains are scaled back by the allocation system used in Singapore. The maximum profit earned by large investors is $54,122

million, while their maximum loss is $31,189 million.8 Across all issues, $920.7

million dollars are left "on the table" (i.e., 91 times the sum of the mean profits for the four application size categories). In underpriced issues, the total gain to

investors is $1,078.6 million, while the total loss in overpriced issues is $157.9

million. Large investors are shown to make substantial gains ($406.6 million) in

the 84 underpriced issues (which are 12 times as likely for our data), but they also

suffer some large losses ($52.0 million) in the seven overpriced issues.

Panels D and E of Table 2 show the wealth transfer associated with the fa-

vorable treatment small investors receive in rationing IPOs, which was evident

in Figure 1. Panel D shows the wealth transfers and panel E shows profits that

would have been achieved by each investor category if allocations were strictly

proportional to applications. The systematic favoring of small investors results

in an average increase in profit per issue of $2,674 million for small investors, whereas large investors have their profits reduced on average by $2,630 million.

Large investors apply for issues that would produce average gains of $6,527 mil?

lion, but the allocation process scales these profits back to an average gain of

$3,897 million. Median results show similar patterns of profit transfer from large to small investor categories. The application data can also be used to estimate

the information advantage that large investors have over small investors. Large investors apply for issues that would have realized gains of $6,527 million per

issue, whereas small investor applications would have produced gains of $1,092

million per issue. The total value of this information advantage is $494.6 million

for the 91 issues. However, as we have shown here, allocations in Singapore IPOs

systematically favor small investors, reinforcing our point that inferences drawn

from allocations are quite different to those based on applications. Hence, tests

of competing IPO pricing models (e.g., Hanley and Wilhelm (1995)) that rely on allocation decisions may not produce the same conclusions as tests based on

application decisions.

8The maximum loss occurred for DBS Land, which issued 300 million shares at $1.35. These shares ended trading on the first day at $1.03 (above the low of 80<?), giving investors a loss of $96 million ((1.03 ? 1.35) * 300 million). This loss was then adjusted for the change in the CPI between October 1987 and the CPI base of January 1993 by applying a factor of 86.8.




FIGURE 1

Differences in the Application and Allocation Proportions for Various Investor Classes

r Small

Medium/small Medium

Differences in the application and allocation proportions for various investor classes (based on dollar value of applications), from 91 Singapore IPOs made between July 1973 and December 1992 that have sufficient data disclosed to allow the application and allocation proportions for the issue to be reconstructed. The vertical axis is the change (bias) between the application and allocation proportion.

B. Evidence on Application and Allocation Schedules as Explanations of Initial Underpricing

This section investigates the ability of application and allocation schedules to

explain variations in initial underpricing. We are particularly interested in whether

different inferences flow from looking at the allocation decisions of issuers, com?

pared to the application patterns of investors, across various application size cat?

egories. As we detail below, the proportions applied for by investors are substan-

tially different from the proportions allocated to those investors.

Table 3 reports results for eight regressions in which either the application

proportion (within four investor size categories) or the allocation proportion (for the same four investor size categories) is regressed on initial profit. Initial profit is calculated as initial underpricing multiplied by the size ofthe issue (in millions

of Singapore dollars). The regression equation for Table 3 is

P, = a + /HP,- + e,

where i = 1,2,..., number of firms;

P = Proportion for small, medium/small, medium, and large applica? tions and allocations;

IP = Initial Profits.




TABLE 2

Singapore IPO Profits

Profits to various investor classes (based on dollar value of applications) for Singapore IPOs where sufficient disclosures were made to allow reconstruction of the total demand for the issue and the basis of allocation in the issue (91 firms), drawn from 132 IPOs made between July 1973 and Dec. 1992. The sample is then split into those IPOs that were overpriced or had zero initial return (seven firms) in Panel B, and the underpriced IPOs (84 firms) in Panel C. Panel D shows the change in profits caused by the allocation proportions differing from the application proportions, while Panel E describes the profits that would have been earned if the allocation proportions had been determined by the application proportions. Profits are denominated in Singapore dollars, CPI-adjusted to a Jan. 1993 base, to reflect a constant dollar value.

a Small investors are defined as those who apply for up to $5,000 dollars worth of shares in each issue, medium/small investors apply for $5,001 to $50,000, medium investors apply for $50,001 to $250,000, and large investors are defined as those who apply for more than $250,000 worth of stock.

The results in Table 3 show that relative demand by large investors is significantly

positively associated with underpricing, consistent with large investors being bet?

ter informed.9 Indeed, demand by the largest investors is sufficient to reduce the

proportional applications and allocations in the three smaller investor categories,

9We repeated the regressions in Table 3 using weighted least squares, where the weights are the size of the issue. The unreported results are very similar to those reported.




TABLE 3

OLS Regression Results for Application and Allocation Proportions

Results for the OLS regression of application and allocation proportions for various investor classes (based on dollar value of applications) on initial underpricing profits, for 91 IPOs where sufficient disclosures were made to allow reconstruction of the total demand for the issue and the basis of allocation in the issue, drawn from 132 Singapore IPOs made be? tween July 1973 and Dec. 1992. Initial underpricing profits are determined by multiplying the initial underpricing by the size of the issue (issue price multiplied by the number of shares issued) and are denominated in millions on Singapore dollars, then CPI-adjusted to a Jan. 1993 base, to reflect a constant dollar value. All f-statistics have been adjusted for heteroskedasticity using White's (1980) procedure.

Statistic Application Proportions Allocation Proportions

Coefficient r-Statistic

Panel A. Small Investor3 Relative Demand

Intercept 0.15373 10.51** Initial profits -0.00076 -1.24 Adjusted R2 0.0051

Panel B. Medium/Small Investor3 Relative Demand

Intercept 0.11159 11.35** Initial profits -0.00036 -0.88 Adjusted R2 -0.0031

Panel C. Medium Investor3 Relative Demand

Intercept 0.22915 15.98** Initial profits -0.00121 -1.90* Adjusted R2 0.0435

Panel D. Large Investor3 Relative Demand

Intercept 0.50553 17.45** Initial profits 0.00234 1.90* Adjusted R2 0.0319 0.0264

3Small investors are defined as those who apply for up to $5,000 dollars worth of shares in each issue, medium/small investors apply for $5,001 to $50,000, medium investors apply for $50,001 to $250,000, and large investors are defined as those who apply for more than $250,000 worth of stock.

The regression equation is

P/ = ot+p\Pi+et

where / = 1,2,..., number of observation firms; P = proportion for small, medium/small, medium, and large applications or al?

locations; IP = initial profits in millions of Singapore dollars.

*Significant at 5% **Significant at 1%

such that the coefficient on underpricing profit is negative, either insignificantly different from zero (five cases) or significantly negative (one case). In all eight

cases, White's (1980) adjusted f-statistics show the intercept to be highly signif?

icant, suggesting that there are omitted variables in this test of cross-sectional

differences in underpricing. A test similar to Table 3 is reported by Hanley and Wilhelm (1995), who

find an insignificant positive relationship between the proportion of an issue al?

located to institutional (i.e., large) investors and initial underpricing. When we




repeat the regressions in Table 3 using underpricing, rather than initial profits as the dependant variable, our results are similar to, though somewhat weaker than, those reported in Table 3.

Koh and Walter (1989) report that the Spearman rank correlation coefficient

between the over-subscription level and underpricing in their sample (n = 66) is

0.951, which is significant at the 1% level. Lee, Taylor, and Walter (1996b) report results for a univariate regression of over-subscription on underpricing (n = 128), which is also significant at the 1% level. Table 4 explores these issues further.

For each of three samples (all 91 IPOs, the 46 least underpriced IPOs, and the

45 most underpriced IPOs), we regress the level of over-subscription achieved

within four investor categories (small, medium/small, medium, and large) on ini?

tial underpricing. Rock's (1986) theory predicts that, among other things, the

estimated coefficients, which are measures of how responsive investors are to

expected underpricing (proxied by actual underpricing), should reflect their infor?

mation advantage. For example, Rock's (1986) model also predicts that issuers

intentionally underprice IPOs to overcome the adverse selection problem that un?

informed investors face in dealing in a market in which informed traders exist.

Looking first at panel A for all 91 IPOs, the results are strongly in accord with

this theory, providing that the value of shares applied for is a valid proxy for the

information advantages of groups. While small investor demand expansion is sta?

tistically significant (each 1 % increase in underpricing results in small investor

demand expanding by 7.9%), it expands far less markedly than large investor de?

mand (each 1 % of underpricing causes demand to expand by 176.7%, or 22 times

as much). Rock ((1986), pp. 194-196) noted that it is essential to establish that

uninformed demand expands as the issue price is reduced. This creates an in?

centive for the issuer to underprice, because underpricing increases the chance

of achieving full subscription in the event of informed demand being withdrawn

(i.e., the issue is overpriced). It is also worth noting that the panel A regressions in

Table 4, with the adjusted R2s in the range of 0.2467 (small investors) to 0.3088

(medium/small investors), indicate that underpricing explains over-subscription levels within investor size categories quite well.

Further evidence consistent with demand expansion by informed investors is

presented in the sub-category results drawn by dividing the sample at the median

level of underpricing. For the most underpriced subsample in Table 4, panel C,

large investor demand expands sufficiently (the coefficient is 278.3)10 to crowd

out uninformed investors, and small investor demand expansion is no longer sta?

tistically significant. The reverse is true for small investor demand in the least

underpriced subsample in panel B; where small investor demand expansion is

statistically significant at the 1% level.11

10White's f-statistic is significant at 5% in Table 4 whereas the raw f-statistic is 3.931. This result is influenced by one extreme observation caused by the most heavily over-subscribed (and most under? priced) issue. White's f-statistic increases to 2.035 (significant at 1%) when this outlier is removed.

12The standard error of the estimate for the underpricing coefficient on this regression is 32.4. Thus, it is clear that some of the least underpriced IPOs still attract significant informed demand. White's (1980) adjusted f-statistics were also calculated. These are consistent with the reported results, except that the f-statistic for large investor demand expansion for the most underpriced issues is significant at 5%, rather than at 1%. The weaker result is caused by one extreme observation.




TABLE 4

OLS Regression Results for Over-Subscription Levels

Results for the OLS regression of over-subscription level for various investor classes (based on dollar value of applications) on initial underpricing where the over-subscription level is determined by actual application levels for 91 IPOs where sufficient disclosures were made to allow reconstruction of the total demand for the issue and the basis of allocation in the issue, drawn from 132 Singapore IPOs made between July 1973 and Dec. 1992. Underpricing is calculated by taking the natural logarithm of the last sale price on the first day of listing divided by the subscription price. All f-statistics have been adjusted for heteroskedasticity using White's (1980) procedure.

Small Medium/Small Medium Large Investors3 Investors3 Investors3 Investors3

Coefficient f-Statistic Coefficient f-Statistic Coefficient f-Statistic Coefficient f-Statistic

Panel A. All 91 Issues

Intercept 1.43 3.95** 0.82 1.98* 2.77 3.13** -8.77 -0.56 Underpricing 7.93 5.38** 6.55 2.88** 21.66 5.45** 176.75 2.14* Adjusted R2 0.2467 0.2824 0.3088 0.2601 Panel B. Least Underpriced Issues (46) Intercept 1.22 6.45** 0.92 7.01** 2.47 4.95** 8.68 3.51** Underpricing 4.41 2.44** 3.72 3.12** 18.36 2.88** 57.15 2.01* Adjusted R2 0.0931 0.1147 0.1482 0.0458 Panel C. Most Underpriced Issues (45) Intercept 3.34 1.96* 1.05 0.59 5.08 1.36 -64.30 -1.00 Underpricing 4.81 1.47 6.29 1.35 17.85 2.27* 278.26 1.68* Adjusted R2 0.0252 0.1053 0.0939 0.2473

3Small investors are defined as those who apply for up to $5,000 dollars worth of shares in each issue, medium/small investors apply for $5,001 to $50,000, medium investors apply for $50,001 to $250,000, and large investors are defined as those who apply for more than $250,000 worth of stock. The regression equation is

OS/ = a + pUPj + e,

where / = 1,2,..., number of observation firms; OS = Over-subscription for small, medium/small, medium, and large investors; UP = Underpricing.


We also estimated these regressions using the allocation proportions (rather than the over-subscription levels or application proportions used in Table 4) within

investor categories. In summary, the demand expansion coefficients for the four

investor categories for the full sample are -0.0563 for small investors, -0.0787

for medium/small, 0.0046 for medium, and 0.1308 for the large group. None

of these coefficients is statistically significant. This evidence provides a further

cautionary note for inferences drawn from observation of the allocation outcomes.

In Singapore, the results for application proportions and allocation proportions are

quite different. Specifically, large investor demand expands significantly based on

applications, but the allocation data mask this relationship. Taken together, the results in Table 4 are consistent with Rock's model,

which predicts that informed demand expansion in underpriced issues crowds

out other investors. More generally, other models (e.g., Welch's (1992) cascade

theory) of underpricing also require that demand expands in response to price decreases.




Table 5 further explores the issue of demand expansion. Here, our concern

is to determine the extent to which applications by (and allocations to) investors in each of the four size categories are explained by the level of over-subscription. It is clearly evident in the Table 5 result that, overall, over-subscription levels

are determined by the large investor group. The estimated coefficients of 0.0012

for applications and 0.0010 for allocations have f-statistics of 3.05 and 4.17, re?

spectively. In sharp contrast, all six coefficients for the three smaller investor

categories are significantly negative.

TABLE 5

OLS Regression Results for Total Demand Proportions

Results for the OLS regression of proportion of the total demand for an issue applied for by various investor classes (based on dollar value of applications) as a function of total over- subscription, and the proportion of the total demand for an issue allocated to various in? vestor classes (based on the size of the application) as a function of total over-subscription, for 91 IPOs where sufficient disclosures were made to allow reconstruction of the total de? mand for the issue and the basis of allocation in the issue, drawn from 132 Singapore IPOs made between July 1973 and Dec. 1992. All f-statistics have been adjusted for het? eroskedasticity using White's (1980) procedure.

Statistic Application Proportions Allocation Proportions

Coefficient f-Statistic Coefficient f-Statistic

Panel A. Small Investor3 Relative Demand

Intercept 0.1709 9.79** 0.4228 22.63** Over-subscription -0.0005 -2.40** -0.0005 -3.66** Adjusted R2 0.0982 0.0633

Panel B. Medium/Small Investor3 Relative Demand

Intercept 0.1267 10.82** 0.1474 12.41** Over-subscription -0.0004 -2.35* -0.0003 -2.31* Adjusted/?2 0.1281 0.1160

Panel C. Medium Investor3 Relative Demand

Intercept 0.2343 17.29** 0.1791 17.16** Over-subscription -0.0003 -5.17** -0.0002 -3.29** Adjusted R2 0.0601 0.0208

Panel D. Large Investor3 Relative Demand

Intercept 0.4681 15.23** 0.2506 11.11** Over-subscription 0.0012 3.05** 0.0010 4.17** Adjusted/?2 0.1744 0.2048

3Small investors are defined as those who apply for up to $5,000 dollars worth of shares in each issue, medium/small investors apply for $5,001 to $50,000, medium investors apply for $50,001 to $250,000, and large investors are defined as those who apply for more than $250,000 worth of stock.

The regression equation is

P/ = a + pOSj + e,

where / = 1,2,..., number of observation firms; P = proportion for small, medium/small, medium, and large applications or al?

locations; OS = over-subscription.





C. Long-Run Returns

In unreported results, we also tested for an association between long-run returns and initial investor demand for an issue. Long-run returns are measured

as individual firm wealth relatives. Our methodology is consistent with that of

Lee et al. (1996b) in their equations 5, 6, and 7 and their Table 5. The wealth

relative compares the investment in each firm (from the first day of listing) with

the performance of the market index over an equivalent period. The index used

is the Straits Times index, which is an equally-weighted price index of 30 stocks

(adjusted for capital changes but not adjusted for dividends). Ritter (1991) suggests that long-run under-performance of IPOs reflects the

overly optimistic expectations of investors, followed by a marked reversal in de?

mand post-listing. Such an association is inconsistent with an efficient market

(though perhaps consistent with a speculative bubble or fad argument). While

Lee, Taylor, and Walter (1996b) examine the relationship between long-run re?

turns and initial demand, they do not break demand down into categories that are

proxies for information advantage. However, none of the estimated coefficients

on over-subscription by these different investor groups was significant, and all

models had insignificant explanatory power. Fad or speculative bubble arguments are not supported by this analysis. Market efficiency explanations better fit the

results.

We further explore the issue of whether long-run IPO returns are associated

with initial demand for the issue. Specifically, we constructed an index of the

level of informed demand for an issue by dividing the over-subscription level for

large investors by the sum of the over-subscription levels for the other three cat?

egories. IPOs with larger values for this statistic have a larger level of informed

demand. This proxy for the level of informed investor participation in an IPO was

regressed on the 250-, 500-, and 750-day wealth relatives for the sample. Field

(1997) shows that IPOs that have larger institutional shareholdings at the end of

the quarter after listing have better equally-weighted long-run (three-year) abnor?

mal returns. However, we are unable to find any significant association between

long-run performance and our proxy for the level of informed investor participa? tion in the IPO. This is in contrast to our evidence that informed investors reveal

their information advantage in relation to initial IPO underpricing in Singapore.

IV. Conclusions

Using confidential allocation data for a limited number of U.S. IPOs, Hanley and Wilhelm (1995) show that institutional (i.e., informed) investors receive ap-

proximately the same proportions of shares in overpriced and underpriced issues.

They interpret this result as support for Benveniste and Spindt's (1989) explanation of IPO underpricing, which is premised on discriminatory behavior by U.S.

underwriters, and reject the winner's curse explanation proposed by Rock (1986). This conclusion, however, is subject to the criticism that the final allocation of

IPO shares may look very different from the underlying demand for the shares.

Unfortunately, this criticism cannot be evaluated using U.S.-sourced data, for at least two reasons. First, application and allocation data will likely be very




similar if, in fact, underwriters' discriminatory allocation powers dampen under?

lying (i.e., unconstrained) demand. Second, even if this were not the case, such

application data are simply not available to researchers in the U.S.; however, IPOs

made on the Stock Exchange of Singapore (SES) routinely provide details of how

over-subscribed issues are rationed. Frequently, the disclosure is sufficiently de?

tailed to allow the application schedule for an IPO to be reconstructed. For a

sample of 91 IPOs made on the SES between July 1973 and December 1992, we

investigate the profits and losses made by investors in various size-of-application based categories. We argue that investors who apply for larger quantities of IPO

shares are likely to be better informed than small investors are. Based on the al?

location decisions of underwriters and issuers, we show that large investors make

substantial profits in underpriced issues, though they also lose in overpriced is?

sues. Similarly, based on allocation decisions, small investors gain in underpriced issues and lose in overpriced issues. We also show, however, that allocation deci?

sions favor smaller investors and cause a transfer of some profits that would have

accrued to large investors if allocations were strictly proportional to applications. Most important, we show that inferences drawn from an analysis of the allo?

cation decisions of underwriters and issuers in Singapore are in contrast to those

arising from an investigation of investors' application strategies. We show that

larger investors are more informed in that they apply for relatively more of the un?

derpriced issues. Our results are consistent with the predictions of Rock's (1986)

theory of why, in equilibrium, IPOs are underpriced. We note that a direct test

of the uninformed investor equilibrium conditions in Rock's model requires that

the probability of receiving an allocation in IPOs can be estimated. These data

are routinely reported for IPOs made on the SES. We show that, in underpriced IPOs, large investor demand is far more responsive than small investor demand.

We split our sample at the median level of underpricing and show that large in?

vestors crowd small investors out of the most underpriced issues and leave small

investors to take larger proportions of the less attractive issues. Thus, we provide, for the first time, evidence that large investors are better informed and that this

information advantage is revealed in their IPO application strategies. While sev?

eral authors assume such a relationship, we provide strong empirical support for

its existence.

Appendix

The following is the full text of an announcement made by United Merchant

Bank Limited (the underwriter) on April 19, 1988 for and on behalf of Fuji Offset

Plates Manufacturing Ltd (the issuer).




ANNOUNCEMENT

Fuji Offset Plates Manufacturing Ltd (Incorporated in the Republic of Singapore)

New Issue of 5,000,000 shares of $0.20 each at $0.75 per share

1. The Board of Directors of Fuji Offset Plates Manufacturing Ltd ("Fuji Offset" or the "The Company") is pleased to announce that, at the close of the Application List at 12 noon on 19 April 1988, Fuji Offset's issue was 11.0 times subscribed. Of the 4,250,000 shares available for subscription by the public, a total of 5,237 applica? tions were received for 46,866,000 shares, amounting to $35,149,500.

2. In order to ensure a reasonable spread of shareholders in the Company, the Board has decided on the following basis of allotment:

No. of Shares Allotted Range of Shares per Successful Percentage of

Applied for Balloting Ratio Application Shares Allotted

3. The 500,000 shares reserved for the management and staff of Fuji Offset and those who have contributed to the success of the Company were fully subscribed. The 250,000 shares placed privately with the Sponsoring Market Makers were also fully subscribed.

4. The Board of Directors wishes to thank all applicants for their interest and confidence in the Company. Unsuccessful applications and refund cheques for successful applications will be sent to applicants as soon as possible.

We estimate demand for the issue for investors in the following categories:

i) small investors, defined to be those applying for $5,000 worth of shares or less,

ii) medium/small investors, defined to be those applying for between $5,001 and

$50,000 worth of shares,

iii) medium investors, defined as those applying for between $50,001 and $250,000 worth of shares, and

iv) large investors, those applying for more than $250,001 worth of shares.

(1)

We estimate total demand (D) at each range of application (i) as follows:

AP/* AS * IB/* LR, Dt

NA;

where AP/ =

AS

IB

LR,

NA,

the proportion of shares allocated to investors with an applica? tion in the range /, the number of shares available for subscription, the inverse of the balloting ratio for applications in the range i, the low point of the range for an application in range i, and

the number of shares allotted to each successful application in

range i.




Thus, demand (D) for application i = 1 is

0.367*4,250,000*25/10* 1,000 ?>i

1,000 = 3,899,375 shares.

Thus, we estimate that there were 3,899.375 applications for 1,000 shares giving an over-subscription rate of 0.9175. That a minimum parcel was 1,000 shares for

this issue means that some rounding error exists in this calculation. It is probable that 3,900 or 3,899 applications were received. The subscription price per share for this issue was $0.75, thus 1,000 shares would cost $750. This is less than the

upper limit ($5,000) of our definition for the cost of shares for small investors, while a subscription for 2,000 shares (the smallest application size in the next dis-

closed category) would cost $1,500. This is again less than the $5,000 upper limit

(and, hence, will be included in the small category), though the next category has

a cost of $7,500 and is, thus, in the medium/small category. The estimated number

of shares applied for in category D2 is 1,782,639. Thus, the over-subscription rate

for small investors is estimated at (3,899,375 + 1,782,639)/4,250,000 or 1.3369

times.

Another source of rounding error exists in our calculations because we do

not know the distribution of applications within a range. Here, we assume all

applicants apply for the smallest number of shares in a range and, thus, our esti?

mates of demand are downward biased. To illustrate this rounding error, we show

below, in summary, all analogous calculations for this issue.

Medium/small demand over-subscription is estimated at 1.4525 times, medi?

um demand results in an over-subscription rate of 2.2833 times, and large in?

vestor demand is over-subscribed 5.6583 times. Our total demand is estimated as

10.7310 times, whereas actual over-subscription was 11.0273 times.

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