Long – run performance of Initial Public Offerings in the...
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MSc in Finance & International Business Author: Teresa Darmetko Academic Advisor: Jan Bartholdy Long – run performance of Initial Public Offerings in the Polish capital market A review of the IPO stock performance and its determinants Aarhus School of Business January 2009
Transcript of Long – run performance of Initial Public Offerings in the...
MSc in Finance & International Business
Author: Teresa Darmetko
Academic Advisor: Jan Bartholdy
Long – run performance of Initial Public Offerings in the
Polish capital market
A review of the IPO stock performance and its determinants
Aarhus School of Business
January 2009
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Abstract
The first purpose of this thesis is to analyze the long-run market performance of Initial
Public Offerings in Poland by applying different methodologies of measuring and
testing abnormal returns. Moreover, the second aim of the study is to examine the
influence of both IPO firm and the offer characteristic on the likelihood of the
successful long-run stock performance.
The study is based on the sample of IPOs that took place in Poland during the period of
July 1998 till June 2005. The long-run abnormal performance of the IPOs is evaluated
using event-time abnormal returns and calendar-time portfolio returns methods with
application of various benchmarks. The determinants of the IPOs’ performance have
been checked with the logistic regression models where the independent variables are
either the abnormal return on the IPO three year after going public, or the “raw” three-
year IPO return. The variables related to a firm and offer characteristics have been
applied as explanatory factors.
The results of measuring and testing long-run abnormal returns support the claim that
the existence of IPOs’ long-run abnormal performance is highly dependent on the
methodology and benchmarks used. Generally, I have found some evidence of
underperformance when event-time abnormal returns are used, however, no existence of
abnormal returns is discovered if the calendar-time abnormal returns are employed.
As far as the influence of an IPO firm and an offer characteristic is concerned, I have
found the evidence of a positive and significant relationship between the success of IPO,
measured as a positive three-year “raw” buy-and-hold return after going public and the
size of IPO firm, the size of the offer, the reputation of the lead manager of the offer and
PE/VC backing. I have also noticed a negative relationship between the return and the
company maturity and underpricing. When the three-year long-run abnormal return is
employed as independent variable there is reported only a significant positive relation
with the offer size.
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Table of contents
1. Introduction ............................................................................................................ 4
2. Literature Overview............................................................................................... 7
2. 1. Evidence of long-run performance of IPOs among various equity markets .... 7
2.2. Issues in measuring and testing long-run performance of IPOs....................... 10 2.2.1. Event–time abnormal returns ....................................................................... 10
2.2.2. Bias in test statistics of event–time abnormal returns................................. 12
2.2.3. Benchmarks in measuring long-run abnormal returns .............................. 14
2.2.4. Calendar-time portfolio returns.................................................................... 17
2.2.5. Cross–sectional dependence and bad–model problems .............................. 18
2.3. Determinants of long-run performance............................................................... 19 2.3.1. Long-run market underperformance theories ............................................ 20
2.3.2. Firm and IPO characteristics influencing long-run performance of IPOs21
3. Methodology and data description...................................................................... 29
3.1. Methodology of measuring and testing long–run performance of IPOs .......... 29
3.2. Analysis of the determinants of the long-run performance of IPOs................. 39
3.3. Market, sample and data sources characteristics............................................... 47 3.3.1. Polish IPO market .......................................................................................... 47
3.3.2. The IPO sample .............................................................................................. 51
3.3.3. Data sources .................................................................................................... 54
4. Results and discussion.......................................................................................... 57
4.1. Results of event-time abnormal returns analysis ............................................... 57 4.1.1. Results of the analysis of buy-and-hold abnormal returns......................... 57
4.1.2. Results of the analysis of cumulative abnormal returns ............................. 64
4.2. Results of calendar-time abnormal returns analysis.......................................... 66 4.2.1. Results of the analysis of mean calendar-time returns ............................... 66
4.2.2. Results of the analysis using the Fama–French three-factor model .......... 67
4.3. Results of the analysis of determinants of the long-run performance of IPOs 69
5. Conclusions, limitations and suggestions for further research ........................ 75
List of tables .................................................................................................................. 81
List of graphs ................................................................................................................ 81
List of supplementary materials.................................................................................. 81
References ..................................................................................................................... 82
1. Introduction
Among the problems related to the pricing anomalies of Initial Public Offering(s)
there are: the short-run underpricing phenomenon, the “hot issues” market phenomenon,
and long-run underperformance of IPOs. Starting with Ritter (1991), the third issue has
found significant attention among researchers. The underperformance is discussed both
with respect to the measurement methodologies and testing of the IPO abnormal returns,
as well as the reasons of the underperformance.
While reviewing the financial literature on the long-run performance of IPOs, I have
found substantial number of papers analyzing IPO performance for a particular country
or a region, however, the studies mostly examine the new issues conducted in the US or
in the Western European countries. I have also noticed that the topic has been hardly
investigated in Poland, probably due to the young capital market. The research devoted
to the IPO performance in Poland, conducted by Ausseneg (2000), Jelic and Briston
(2003), Lyn and Zychowicz (2003), consider the sample of IPOs from the period
between 1991 and 1999 and they are primarily focused on privatization IPOs.
Thus, it seems challenging to examine the IPOs that represent a different sample
period and background characteristics to the previous studies (at present, majority of
debuting companies are private rather than privatized ones). The fact that Warsaw Stock
Exchange is currently one of the leading European capital markets with respect to the
volume of IPOs provide another argument to perform the IPO performance research.
In this study, I examine the long-run performance of IPOs in Poland based on the
sample of 103 new issues companies that went public on the Warsaw Stock Exchange
between July 1998 and June 2005. While the long-run performance is analyzed, both
event-time methods and calendar-time portfolio methods are applied by calculating
returns of the IPOs for 12, 24 and 36 months of listing in the market.
I have observed that the conclusion regarding the existence of the long-run
underperformance of IPOs is highly sensitive to the measurement methodology and
benchmark used. The event-time abnormal returns analysis are more likely to suggest
the worst performance of IPO relative to the benchmark than the analysis of calendar-
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time abnormal returns. Significant negative abnormal returns have been found for 36 -
months buy-and-hold abnormal returns and cumulative abnormal returns adjusted to
market index, as well as the reference portfolio of similar size and book-to-market
companies returns. For shorter-horizon returns the evidence of abnormal performance
(with respect to market index return benchmark and the reference of similar size and
book-to-market portfolio) is mixed. On the contrary, the mean event-time returns
adjusted to the control firm mean returns are usually positive and insignificant for each
horizon analyzed. Moreover, when the significance of buy-and-hold returns adjusted to
the reference portfolio returns is evaluated via the bootstrap skewness adjusted t-
statistics (the procedure recommended by Lyon et al. 1999, p.173 ), no evidence of
abnormal returns is found.
In the case of the calendar-time portfolio methods, neither the average monthly
calendar-time abnormal returns analysis, nor the intercept from the Fama-French three-
factor model provide the evidence to reject the null hypothesis of the zero mean
abnormal return. The average calendar-time portfolio abnormal returns for the horizon
analyzed are usually slightly negative but insignificant; the intercept from the Fama-
French model is actually positive but also insignificantly different from zero. Moreover,
the explanatory variables of the model - such as the market risk premium, size factor
and book-to-market factor - explain a great share of the variance of the mean monthly
calendar-time returns of the IPOs.
With respect to the second aim of the thesis - the analysis of the influence of a firm and
IPO offer characteristics on the success of the IPO, I have found the only significant
relation between 36-months buy-and-hold abnormal return adjusted to the size and
book-to-market reference portfolio with the size of the IPO offer. When the “raw” 36-
months buy-and-hold abnormal returns are applied, I have found significant positive
relation between the long-run IPO return and its offer size, the size of company assets,
VC/PE backing and the reputation of the lead manager of the offer. There is also a
negative relation between the long-run return and the age of the company and the level
of underpricing.
The inspiration for the study has been provided by the research conducted by
Alvarez and Gonzalez (2001) performed on the sample of Spanish IPOs. Moreover, the
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methodology of the long-run returns analysis have been enhanced by the findings of
Lyon et all. (1999) with respect to the skewness problem. Additionally, a logistic
regression analysis performed on the sample of Polish IPOs has been conducted with
respect to a broader choice of explanatory variables in comparison to the Spanish study.
As far as the determinants of IPO long-run performance are concerned, it was not
possible to test the influence of all variables reported in the financial literature on the
long-run abnormal performance. However, some of them, which are not included in the
analysis, are also discussed in the study.
Although during the recent years there has been a substantial progress in the
methodology of measuring and testing of long-run stock performance, all existing
methodologies have their various weak points (for instance, the limited application for
non-random samples of returns both in the case of buy-and hold abnormal returns and
calendar-time portfolio abnormal returns as reported by Lyon et al (1999, p. 167). It can
be expected that the future research will develop more reliable methods to interference
from IPO returns. At the moment, the analysis is “treacherous” and requires
cautiousness.
More information regarding current research on IPO performance, as well as more
detailed results of the empirical analysis, are reported in the remainder of the study. The
literature overview is covered in Section 2 which provides evidence on IPO stock
performance across the various country markets, includes methodological issues of
measuring and testing long-run IPO returns, and discusses possible determinants of
long-run performance. Data and methodology of the analyses are presented in Section 3.
Section 4 provides empirical findings, while Section 5 summarizes the main results and
concludes the dissertation.
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2. Literature Overview
2. 1. Evidence of long-run performance of IPOs among various equity markets
In number of countries researchers have studied aftermarket performance of IPOs. One
of the first examining this issue have been Ritter (1991) and Loughran and Ritter (1995)
who, based on the large US data sets, found that IPOs underperform in the long-run.
Ritter (1991 p.9, 23) have evaluated the long-run performance of IPO for the 36 months
after the offering date for the sample issued from 1974 till1984 using buy-and-hold
returns and cumulative average returns adjusted for a set of matching firm in terms of
industry and market capitalization. He has discovered that for both methods the
underperformance is economically and statistically significant and reported that the
wealth relative ratio for IPOs compared to similar firms in terms of size and industry
amounts to 0,83.
Loughran and Ritter (1995 p. 28) have reported that the average wealth relative ratio for
three years returns of IPOs issued in 1970-1990 and adjusted to matching firms is equal
to 0,80, close to the wealth relative reported by Ritter (1991). Moreover, they have
found that the underperformance of IPOs continues till the fifth year after going public,
with the mean wealth relative ratio falling to 0,70. In order to check weather the
underperformance is sensitive to the benchmark used, Loughran and Ritter (op. cit., p.
35-36) have measured the returns on IPOs adjusting them also to the market indexes
benchmarks (equally weighted and value–weighted Amex-NYSE and Nasdaq indices,
and the S&P 500). It has been noticed that the five-years wealth relative ratio for IPOs
adjusted to each of the benchmark is less than 1, thus showing long-run
underperformance.
The results of the studies on long-run underperformance in other countries are usually
consistent with that on the U.S. market but there are also significant exceptions. In UK,
Levis (1993, p. 35-37) has found that a sample of 712 IPOs companies during the period
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of 1980 till 1988 underperformed three alternative benchmarks. 1 Three-year wealth
relative ratios adjusted to all market indexes have been found to be less than one. While,
for the US market, Ritter (1991) has reported under-performance of up to 29 %
(measured by cumulative abnormal return) over the first three years after the IPO, for
the UK market, Levis has found underperformance between - 8 % to - 23 % depending
on the benchmark used.
Espenlaub et al. (1998, p. 12) also give the evidence on the long-run returns in the UK
over the period 1985-1995 and they find significant negative returns. While Levis (op.
cit., p. 35-37) has reported that the British IPOs underperform the HGSC Index (Hoare
Govett Small Companies Index) over a three-years period by -8,31%, Espenlaub et al.
(op. cit p. 12) have found significant negative returns of -8,12% at the same index.
Leleux and Muzyka (1998, p. 113) have examined the post-issue performance of
European IPOs issued between 1988 and 1992. They have revealed that the 36-months
average cumulative market-adjusted returns for France and the UK are equal to -29,2%
and -21.8% respectively. Buy-and-hold returns, indicate similar underperformance, with
French stocks underperforming by 30,3% and the UK stocks by 19,2% over the 36
months post-IPO interval. The mean abnormal returns are statistically different from
zero at the usual levels of confidence.
Stehle et al.(2000, p. 173), in their study on 187 German IPOs and SEOs listed during
1960 - 1992, find out that the buy-and-hold abnormal returns on average underperform
a portfolio consisting of the stocks of similar market capitalization companies by 6% in
the 3-year post-offering period. They conclude that the underperformance level is less
then reported by Ritter (1991).
In Spain, Alvarez and Gonzalez (2001, p. 14-20) find that the Spanish IPOs
underperform after three and five years of listing independently of the benchmark used.
1 Levis (1993) used Financial Times Actuaries All Share Index (FTA) value weighted index to compute
abnormal returns as well as Extended Hoare Govet Smaller Companies (HGSC) Index – value weighted
index comprising the lowest the percent by capitalization of the Main and USM (Unlisted Securities
Markets) equity markets and Al Share Equally weighted (ASEW) Index.
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There are observed negative buy-and-hold abnormal returns ranging from -4.14% to
- 37.05%, occasionally statistically significant. The returns measured by calendar-time
portfolio methods provide no evidence of long-run performance. In general the authors
conclude that they reveal non-existence of long-run underperformance.
Market adjusted returns have usually been found negative with the notable exceptions
of Sweden where it has been noticed IPO overperformance, rather than negative long-
term returns. Loughran et al. (1994, p.165) have found that in Sweden IPO companies
outperform the market by 1,2 %. Also, Kim et al. (1995 p. 449) using a sample of 169
firms listed on the Korea Stock Exchange during period 1985-1989, report that Korean
IPOs outperform seasoned firms with similar characteristics. In Korea, IPO companies
outperformed the market by 91,6 %
Aussenegg (2000, p. 94) have examined Polish IPOs between 1991-1999 and has found
out that the three-year average buy-and-hold abnormal return relative to Warsaw Stock
Exchange Index return is 11,5% and the wealth relative amounts to 1,04. However, he
reports that the median buy-and-hold abnormal returns is -61.1% significantly different
from zero with 66% of the issue experiencing negative long-run performance. Jelic and
Briston (2003, p. 473) have also researched the Polish IPO with respect to the same
period as Aussenegg and provide similar results. Lyn and Zychowicz (2003, p.190)
have analyzed long-run performance of IPOs in Hungary and Poland in the period 1991-
1998. For both markets they have computed three-years returns measured by means of
cumulative abnormal returns and found that the abnormal returns are negative, however
statistically insignificant in both markets.
The findings related to the long run performance of IPOs usually report the negative
returns of IPOs with respect to the benchmark. However, as example of Sweden or
Korea show, notable exceptions exist. Despite the literature focused on reporting the
magnitude of IPO abnormal returns found in the particular country market, the are
articles which try to assets the methodology of measuring and testing long-run abnormal
returns. The following part of the thesis present the recent research on this issue.
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2.2. Issues in measuring and testing long-run performance of IPOs
The literature devoted to the influence of the various methodologies and benchmarks,
on the results of reported abnormal performance of IPOs as well as the size and power
of statistical tests include the studies of Barber and Lyon (1997), Kothari and Warner
(1997) Lyon et al. (1999), Barber et al (1996), Fama (1998), Mitchell and Stafford
(2000) among others. Lyon et al (1999, p.165) suggest that there is no leading optimal
methodology in measuring and testing the long-run abnormal returns and the
methodological problems evoking from the use of different methods result in difficulties
in assessing the performance of long-run returns of IPOs.
The section discusses the methodological issues related to the use of the event-time and
calendar-time approaches of measuring long-run abnormal returns and present the two
methodologies within each approach.
2.2.1. Event–time abnormal returns
An event return performance of sample firms is measured for a period of time that
follows major corporate events or decisions (i.e. dividend initiation, stock splits,
acquisitions, or security offerings). Two measures of event-time abnormal IPO returns
are buy-and-hold abnormal returns (BHARs) and cumulative abnormal returns (CARs).
It is common among researchers to analyze abnormal returns using cumulative
abnormal returns. A mean CAR is developed by summing across periods, usually
monthly, abnormal returns calculated as the difference between the month t simple
return on a sample firm ( tiR , ) and the month t expected return for the sample firm
)( ,tiRE .
∑=
−=T
ttitiTi RERCAR
1,,, ))(( (1)
Barber and Lyon (1997, p. 346) argue that when calculating event-time returns
researchers should apply buy-and-hold abnormal returns. BHAR is “the return on a buy-
and-hold investment in the sample firm less the return on a buy-and-hold investment in
an asset/portfolio with an appropriate expected return” (op. cit., p. 344).
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∏ ∏= =
+−+=T
t
T
ttitiTi RERBHAR
1 1,,, ))(1()1( (2)
It is recommended by Barber and Lyon (op. cit., p. 370) to use buy-and-hold abnormal
returns because of two reasons. First, cumulative abnormal returns are biased predictors
of long term buy-and-hold abnormal returns which is referred as measurement bias.
They document that, in random samples, researchers would draw different inferences
using CARs instead of BHARs in roughly 4% of all sampling situations.
The second argument in favor of BHAR is based on the fact that the magnitude of CAR
does not accurately measure the return to an investor who holds a security for a long
post-event period. According to Lyon et al. (1999, p. 192), the analysis of buy–and-hold
abnormal returns can well answer the question of whether sample firms earned
abnormal stock return or not over a particular horizon of analysis. Alternatively, the
cumulative abnormal return or mean monthly abnormal return is warranted if a
researcher is interested in answering the question: do sample firms persistently earn
abnormal monthly returns?
Although the buy-and-hold abnormal return has the advantage of being a “precise
measure of investor experience”, it is likely to magnify underperformance – even if it
occurs in only a single period due to the nature of compounding (Brav et al 2000, p.
210). Fama (1998, p. 294) admits that capturing the investor’s experience is important
but he advocates to use cumulative abnormal returns or mean monthly abnormal returns
for formal tests for abnormal returns. The returns calculated based on these methods are
more likely to be normally distributed, while normality is assumed for asset pricing
models.
Fama (1998, p.295) argues that abnormal performance measures, such as cumulative
abnormal returns and time-series regressions, are less likely to yield spurious rejections
of market efficiency relative to methodologies that calculate buy-and-hold returns by
compounding single period returns at the monthly frequency. Cumulative or mean
monthly abnormal returns might be used because they are less skewed and therefore less
problematic statistically.
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Despite the skewness issue of buy-and-hold abnormal returns, the test of BHARs also
suffer from the cross-sectional dependence problem. Cross-sectional dependence
inflates test statistic of BHARs because the number of sample firms overstates the
number of independent observations. Fama (1998, p. 195) quotes the study of Brav
(1997) which claims that” all existing methods for drawing inferences from BHARs,
fail to correct fully for cross-sectional dependence in sample observations.”
Barber and Lyon (1997, p. 342-343) report that event-time abnormal returns calculated
using reference portfolio (market index or size and book-to-market ratios portfolios) can
yield misspecified test statistics. The reasons of misspecificantion are: new listing bias,
rebalancing bias and skewness bias which have different impact on buy-and-hold
abnormal returns and cumulative abnormal returns. As a result, cumulative abnormal
returns yield positively biased test statistics, while buy-and-hold abnormal returns and
the associated test statistics are generally negatively biased.
2.2.2. Bias in test statistics of event–time abnormal returns
Long-horizon tests generally focus on a test statistic, such as the ratio of the sample
mean cumulative abnormal return or buy-and-hold abnormal return to its estimated
standard deviation. With long horizons, it is more difficult to obtain an unbiased
estimate of each component of this ratio. The potential significant bias in the test
statistics of long-run abnormal returns is discussed in this section.
New listing bias
According to Barber and Lyon (1997, p. 342), in event studies of long-run abnormal
returns, sampled firms are tracked for a long post-event period, but firms that constitute
the index (or a reference portfolio) usually include firms that begin trading subsequent
their initial event month. The inclusion of these newly listed firms in the market index
and their exclusion from the potential sample in the initial event month can cause the
population mean CAR or BHAR to depart from zero. The bias is referred as the new
listing bias and leads to a positive bias in the population mean of long-run buy-and-hold
abnormal return and cumulative abnormal returns. New listing bias may be lightened by
carefully constructing reference portfolios. A reference portfolio that control well for
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new listing bias allows a population mean abnormal return to be identically zero and,
therefore, reduce the misspecification of test statistics.
Rebalancing bias
Lyon et al. (1999, p. 169) also document that there are significant biases in test statistics
when long run abnormal returns are calculated using a reference portfolio (such as a
market index or book-to-market reference portfolio). The long-run return on the index is
compounded assuming monthly rebalancing of all securities constituting the index
whereas the returns of sample firms are compounded without rebalancing The effect of
rebalancing can cause an inflated return on the market index and a negative bias in the
population mean for long-run buy-and-hold abnormal returns. As far as the size and
book-to-market reference portfolio is concerned, the rebalancing bias can be alleviated
by carefully constructed reference portfolio. As reported by Lyon et al (op. cit.), there
are two methods for calculating size and book-to-market ratio portfolios. The “buy-and-
hold” reference portfolio computed by first compounding the returns on securities
constituting the portfolio and then summing across securities2 alleviates the rebalancing
bias and also new listing bias. The method allows to obtain the portfolio return that
represents a passive equally weighted investment in all securities constituting the
reference portfolio traded for the period of investment.
Rebalancing bias does not affect the calculation of cumulative abnormal returns, since
the monthly returns of sample firms and the index are both summed rather that
compounded.
Skewnes bias
The skewness bias occurs due to the distribution of long-run abnormal stock returns
which is positively skewed. The positive skewness of buy-and-hold abnormal returns is
more pronounced than cumulative abnormal returns, because of compounding of
monthly returns. The positive skewness of buy-and-hold abnormal returns results in
a negative bias in test statistics calculated as the mean buy-and-hold abnormal return of
2 The more precise description of the calculation procedure for size and book-to-market reference see page 33.
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sample firms divided by the cross-sectional standard deviation of sample firms. The
negative skewness (as reported by Barber et al (1996, p. 9) “leads to an inflated
significance level for lower-tailed tests (i.e. reported p values will be smaller than they
should be) and a loss of power for upper-tailed tests (i.e. reported p values will be too
large)”.
Nevertheless, in the literature, for instance Lyon et al. (1999, p. 173-176), there can be
found statistical methods that allow to control for the skewness bias in tests of long-run
abnormal returns. The methods that eliminate the skewness bias in random samples are:
a bootstrapped version of a skewness-adjusted t-statistics, and empirical p values
calculated from the simulated distribution of mean long-run abnormal returns estimated
from pseudoportfolios.
To sum up, the two methods allow to control well for usually negative skewness bias
while a carefully constructed reference portfolios make it possible to control well for the
usually positive new listing bias and usually negative rebalancing bias. However, the
methods are unable to control well for two additional sources of mispricing: cross-
sectional dependence in sample observations, and a poorly specified asset pricing model
which are also discussed in this section.
2.2.3. Benchmarks in measuring long-run abnormal returns
Current empirical research on IPOs, in measuring long-run event-time abnormal returns,
adopts several, popular benchmarks: (i) the market return, as measured by official
indexes, (ii) the return on the reference portfolio of similar companies with respect to
size or/and book-to market, and (iii) the return on control listed firms.
In the early studies of long-run abnormal returns of IPOs also industry and size have
been taken into consideration when benchmark returns were computed. Such approach
is applied in the studies of Ritter (1991) and Rajan and Servaes (1993). However,
Loughran and Ritter (1995, p. 27) do not advise to match IPOs by industry and point out
several reasons. First, companies may time their offers to take advantage of industry-
wide misevaluation and thus controlling for industry effects will reduce the ability to
identify abnormal performance. Moreover, they claim, that there are frequently only a
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few publicly traded companies in an industry with approximately the same market
capitalization as the issuing firms. It can result in the same non issuing firm being
matched with numerous issuers.
While using the market return as a benchmark, a test of abnormal return can be biased
towards no abnormal returns because the benchmark include also the IPOs firms. This is
confirmed by Loughran and Ritter (2000, p. 364), who find substantially greater
underperformance using decontaminated factors than when using simple market
benchmark. When a benchmark is contaminated with many of the firms that are the
subject of the test, than the test can be biased towards high explanatory power and no
abnormal returns. The maximum power test uses a benchmark that is constructed to
have none of the stocks in the sample as part of the benchmark.
When event-time abnormal returns are calculated, it is common practice to calculate the
benchmark by matching on characteristics such as size and book-to-market. According
to Barber et al. (1996, p. 1-5) the key issue in analyzing long-run abnormal performance
using such benchmark is a carefully constructed reference portfolio that is free of the
new listing and rebalancing biases. They distinguish between two methods of
calculating the reference portfolio: “rebalanced” reference portfolio and “buy-and-hold”
reference portfolio. Only the second method assures the carefully constructed reference
portfolio and accurately reflect the buy-and-hold strategy of investing equally in
securities that constitute the reference portfolio. 3 When testing long-run abnormal
returns calculated using the “buy –and-hold” reference portfolios it is possible to
eliminate the skewness bias using bootstrap skewness-adjusted t-statistic or empirical p
values calculated from the simulated distribution of mean long-run abnormal returns
estimated from pseudoportfolios.
Barber and Lyon (1997), discuss the way of calculating long-run abnormal returns in
event time by matching sample firms to control firms of similar sizes and book-to-
market ratios. Such approach is free from test statistics misspecification present when
size and book-to-market ratio reference portfolio is applied. The control firm approach
eliminates the new listing bias (since both the sample and control firm must be listed in
3 The detailed calculation procedure is described on the pages 33 and 34
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the identified event month), the rebalancing bias (since both the sample ad control firms
are calculated in an analogous fashion - without rebalancing) and the skewness problem
(since the sample and control firms are equally likely to experience large positive
returns). When cumulative abnormal returns are used to detect long-run abnormal
returns, however, the measurement bias remains when the control firm approach is used.
The results of the simulation analysis performed by Lyon et al. (1999, p. 178) indicate
that the event-time return methods that yield tests that are well-specified in random
samples are: a conventional t-statistics using size and book-to-market matched control
firms, a bootstrapped skewness-adjusted test statistics using “buy-and-hold” size and
book-to-market reference portfolios as well as empirical p values derived from the
distribution of mean long-run abnormal stock returns in pseudoportfolios.
However, when the power of the well specified test statistics methods is evaluated by
Lyon et all (op. cit.), the bootstrapped skewness –adjusted t-statistics and empirical p
values both yield improved power in random samples relative to the control firm
approach.
The financial economists debates the use of equally-versus value-weighted portfolios.
Loughan and Ritter (2000, p. 363) claim that if misevaluations are higher among small
firms than among big firms, tests that weight firms equally should find greater abnormal
returns than tests that weight firms by market capitalization. If in a value-weighted
portfolio a single firm constitutes a large proportion of the portfolio, it can result in a
high variance of returns. Consequently, large standard errors and low t-statistics will
make evident low power of statistical test.
Brav et al. (2000, p. 212) also find that small stock are undervalued by more than large
ones, consequently the tests based o equally-weighted returns are more powerful. They
suggest that if researcher is concerned in “the managerial implications of the potential
stock market mispricing” equal-weighted returns should be applied. On the other hand,
the value-weighting method is more appropriate when the researcher is interested in
quantifying investors’ average wealth change subsequent to an event.
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2.2.4. Calendar-time portfolio returns
The measurement approach that eliminates the bias due to cross-sectional dependence
and yields well specified test is a calendar-time portfolio returns method. According to
Lyon et al. (1999, p.193), this approach avoids cross-sectional dependence problem
because the returns on sample firms are aggregated into a single portfolio. Nevertheless,
unlike buy-and-hold abnormal returns, the calendar-time abnormal returns do not
precisely measure investor experience.
The most recent variants of the calendar-time portfolio approach account for calculating
calendar-time portfolio returns for event firms and calibrate whether they are abnormal
in a multifactor (e.g. Fama-French three factor) regression. The estimated intercept from
the regression of portfolio returns is used to test the abnormal performance. The Fama-
French three-factor model using returns on calendar-time portfolios of issuing firms has
been applied in the studies of Loughran and Ritter (1995), Brav et al. (1996), Brav and
Gompers (1997) and Lyon et al. (1999).
Lyon et al. (1999, p. 197) have also used the mean monthly calendar-time abnormal
returns adjusted to reference portfolios and the Fama-French three-factor model to
performed the empirical simulation of the two methods4 The results of the simulation
analysis of Lyon et al. (op. cit.) show that calendar-time portfolio methods based on the
reference portfolios “generally dominate those based on the Fama-French three-factor
model”. Empirical rejection levels for test statistics based on calendar time abnormal
returns calculated using reference portfolios are generally lower than based on Fama-
French three-factor model. The second method implicitly assumes linearity in the
constructed market, size, and book–to market factors, as well as no interaction between
the three factors.
The calendar-time portfolio returns approach is advocated by many researchers
including Fama (1998), Brav and Gompers (1997) and Mitchell and Stafford (2000)
among others. Fama (1998, p. 294) gives both theoretical and statistical considerations
of supporting the use of mean monthly returns (also cumulative abnormal returns) rather
4 For the detailed calculation procedure see pages 38-39
18
then buy-and-hold abnormal returns. The advantages of the methods based on a short
horizons like a month include better approximation of normality distribution and lack of
the skewness problem. Fama points out that the empirical tests of asset pricing models
typically use monthly returns. He claims that the average abnormal returns methods are
simpler than BHARs and also comparable in terms of interference reliability. Moreover,
the methods based on average monthly returns alleviate the problem of cross-correlation
of returns across events, while the buy-and-hold abnormal returns does not provide the
full solution for the cross-correlation problem.
On the other hand, Loughran and Ritter (2000, p. 362) criticize the use of calendar-time
approach suggesting that it can be biased towards accepting the null hypothesis of no
abnormal performance. In their opinion, it has lower power to identify time-varying
misvaluations resulting in the managers’ timing decisions because it weights each
period equally. They claim that the test based on Fama-French model using calendar-
time approach is biased towards high explanatory power and no abnormal returns
because it applies a benchmark including many of the firms that are the subject of the
test. The version of Fama-French three-factor regressions after purging the factors of
new issues results in substantially greater underperformance.
The simulation study of Loughran an Ritter (op. cit., p. 365) reveals that equally
weighted buy-and-hold returns using size and book-to-market benchmark portfolios
capture about 80% of the true abnormal returns, while the Fama-French three factor
model used with value-weighted portfolios is able to detect only about half of the true
abnormal returns.
2.2.5. Cross–sectional dependence and bad–model problems
Statistical inference is difficult when the returns on individual IPOs overlap. When
cross-sectional dependence exists, the number of sample firms overstate the number of
independent observations. This makes abnormal returns positively cross-correlated and
leads to misspecified, overstated test statistics. Lyon et al (1999, p. 188-190) mention
the two types of cross-sectional dependence which are calendar clustering and
overlapping return calculations. Well specified methods for testing event-time abnormal
returns (t-statistic for control firm benchmark, bootstrap skewness-adjusted t statistic
19
and empirical p values) deal with the calendar clustering of the event dates. However,
when the impact of overlapping periods of returns calculations on the test of abnormal
performance is assessed, the methods provide misspecified test statistics. When
searching for the better solution for the cross-sectional dependence problem, the
calendar-time portfolio methods can be applied which fully solve the problem of cross-
sectional dependence.
In his study, Fama (1998, pp. 291, 299) expresses the opinion with regard to the
specification of the models testing abnormal performance. He claims (as stated in Fama
(1970)) that the market efficiency must be test jointly with a model for expected returns.
The trouble is, that none of the models provide complete description of the systematic
patterns in average returns. The model misspecification may be of two types. Either the
asset pricing model does not completely explain expected returns or there are systematic
deviations (sample specific patterns) in a sample period that can arise even if the true
asset pricing model exist. Although Fama claims that all methods for estimating
abnormal returns are subject to the bad-model problems, they are most serious in case of
buy-and-hold abnormal returns because of “compounding an expected –return model’s
problems in explaining short-term returns”
Lyon et al. (1999, p. 167), when analyzing event-time abnormal returns and calendar-
time portfolio methods which control for size and book-to market, have noticed that in
nonrandom samples the methods yield misspecified test statistics and attribute it to the
bad-model problems. They argue that “the most serious problem with interference in
studies of long-run abnormal stock returns is the reliance on a model of asset pricing”
and “the rejection of the null hypothesis in tests of long–run abnormal returns is not
sufficient condition to reject the theoretical framework of market efficiency”.
2.3. Determinants of long-run performance
The literature on stock performance points out several hypotheses about the reasons of
long-run underperformance of IPOs. There are also studies that relate particular
variables with the long-run performance of IPOs. Below, I present the theoretical claims
explaining long-run performance of IPOs. Next, based on the IPO theory and empirical
20
studies as well as more general firm theory, I describe the variables possibly correlated
with the long-run performance of IPOs and applied in the empirical analysis.
2.3.1. Long-run market underperformance theories
Divergence of opinions, optimistic investors theory
One of the first theories explaining why the initial public offering underperform in the
long run has been proposed by Miller (1977, p 1155-1156). He suggests that divergence
of opinion among investors about the value of the initial public offering leads to both
short-run overvaluation and long-run underperformance of IPOs. Miller contended that
share prices in markets with restricted short selling, such as IPO market, are determined
by marginal, optimistic investors. As information flows increase with time and
additional information becomes available about the firm, the variance of opinions
decreases among IPO investors. Eventually, the marginal investor’s valuation converges
towards the mean valuation, and the prices of IPO are adjusted downwards. This
hypothesis suggests that long run performance is the worst when divergence of opinions
is high.
Investors sentiment, fads phenomenon, market timing theory
According to Ritter (1991, p. 4) and Rajan and Servaes (1994, p. 3), long-run
underperformance of IPOs is related to investors sentiment explained as “propensity to
overpay for the stocks of certain industries at times” (Rajan and Servaes (op. cit., p. 3)).
They claim that companies go public when investors are over-optimistic about the
growth prospects of the companies. The temporary over-optimism periods about the
prospects of IPOs are named as “fads” (first called by Shiller (1990, p. 62) Aggarwal
and Rivoli (1990, p.47)). The IPO market is a good candidate for fads because the
intrinsic value of an IPO firm is hard to estimate, IPO investors may be more
speculative and IPOs are difficult to short. Firms realize of the market fads and go
public when investors sentiment is high and equities are substantially overvalued i.e. at
a high market-to-book ratio. Ritter (1991, p. 13) has advanced the fads theory and
21
showed that IPO firms with a high risk profile (i.e. younger, smaller and active in
certain sectors) are sooner subject to shareholder sentiment and the fads of the stock
market.
The fact that companies go public when their shares are overvalued is consistent with
the market timing hypothesis of dynamic version of Myers’ pecking order theory as
well as the underperformance’s findings reported by Ritter (1991, p. 19) and Loughran
and Ritter (1995, p. 46-47). Loughran and Ritter report the evidence that issuers and
lead managers have the ability to take advantage of the “window of opportunity”.
The volume of IPOs display large variation over time. Large cycles in IPO volume may
be firms timing IPOs to swing in investor sentiment, as a result their long-run returns
appear to be low. Lee, Shleifer, and Thaler (1991, p. 106) found that the annual number
of operating companies going public in period 1966-85 was strongly negatively related
to the discount on closed-end mutual funds which was interpreted as a measure of
individual investor sentiment. Decreases in the average discount imply that investors are
more optimistic and should be correlated with higher returns. Loughran and Ritter (2000,
p. 342) posit that underperformance is more severe in high-volume trading periods than
in low-volume periods.
2.3.2. Firm and IPO characteristics influencing long-run performance of IPOs
Initial return and the number of secondary equity offerings as indicators of good
after-market performance
There are several studies that attempt to correlate long-run performance of stock to IPO
characteristic. Allen and Fauhaber (1989), Grinblatt and Hwang (1989) and Welch
(1989) suggest that good firms underprice their shares to signal quality.
According to the signaling hypothesis of Welch (1989, p. 445), better quality issuers
intentionally sell their shares at a lower price than the market thinks due to the
possibility of coming back to the market to sell securities on more favorable terms.
Moreover, the marginal cost of underpricing is lower for high-quality firms than for
22
low-quality firm owners. “To imitate high-quality firms, low-quality firms would not
only have to incur the signaling costs but also expend the resources to imitate the
observable real activities and attributes of high-quality firms.”
Allen and Fauhaber (1989, p. 304) suggest that “underpricing the firm’s initial offering
(which is an immediate loss to the initial owners) is a credible signal that firms are good
to investors, because only good firms can be expected to recoup this loss after their
performance is realized).
Grinblatt and Hwang (1989, p. 415) present a two-parameter signaling model, in which
there is information asymmetry about both the expected values and variance of a project
that is about to be capitalized and report that “given the variance of the firm, firm value
and the degree of underpricing are positively related”.
The evidence that underpricing firms show a good aftermarket performance is revealed
in Germany (Ljungvist 1997, p.1378) and in Spain (Alvarez and Gonzalez 2001, p. 19).
Contrary to the signaling models, that suggest that companies underprice their stock in
order to gain more money during the SEOs, Michaely and Shaw (1994, p. 179) find that
firms that underprice more come back to the reissue market less frequently, and for
lesser amounts, than firms that underprice less.
High initial return as an indicator of bad after-market performance
On the contrary Shiller (1990, p. 61) argues that the IPO market is subject to fads
opportunistically exploited by intermediaries through underpriced issues. Such
temporary fads must eventually fade away, resulting in long-run bad performance.
Rock (1986, p. 188) claims that the purpose of underpricing is to attract less informed
investors into the IPO market (“winner’s course theory”). The idea is supported by
Michaely and Shaw (1994, p. 279) who find that larger IPOs and those issued by more
reputable investment bankers experience less underpricing. Firms that underprice less
experience higher earnings ad pay higher dividends.
23
There are findings of a negative relation between initial returns and long-run returns for
IPO firms. Ritter (1991, p. 15) give evidence that the higher the return on the first
trading day the worst is the performance of IPOs in the long-run. He presents the
sample of initial public offering divided according to the extend of the initial
underpricing and reveals that the lowest total return over three years is for the
companies with the highest initial return. Poor long-run returns are consistent with
divergence of opinion theory. The higher the variance of opinions among the IPO
investors (and uncertainty regarding the appropriate price per share) the more
underpriced are the shares and lower long-term returns. The evidence that underpricing
firms show a low performance return to the market is also revealed in United Kingdom
(Levis 1993, p. 41).
Size of the offer
The literature shows a positive relationship between the size of the IPO offer and the
long run performance of shares. Brav and Gompers (1997, p. 1819) have found that the
underperformance is the greatest for the smallest (by market value) initial public
offerings. They claim that small IPOs may be more affected by investor sentiment and
subject to fads. The equity of small IPOs is held primary by individuals who are usually
less informed investors. Many institutions like pension funds and insurance companies
retain from holding shares of very small companies because taking a meaningful
position in a small firm may make an institution a large block-holder in the company.
Consequently, a small size of IPO can be perceived as a surrogate for difficulty of
predicating the future of a company, and for divergence of opinion. The long run
performance of small IPOs is lower because such IPOs are the most speculative ones.
The offerings with lower gross proceeds are often of start up firms who do not yet have
an established history of operations.
The study of Fields (1995, p. 24) shows that three-years buy-and-hold returns are the
highest for the largest IPO’s (measured by capitalization). She also shows that the size
variable is correlated with institutional shareholdings. The cross sectional study of Levis
(1993, p. 38) has again revealed that the larger firms, in terms of gross proceeds from
the offering, the better are their long-run returns.
24
In this study, as a proxies of size, both gross proceeds from offering and value of assets
are used.
Maturity / age
Ritter (1984a, 1991), Fields (1995) and Carter et al. (1998) found significant
relationship between the age of the firm and its long-term performance.
Ritter (1984a, p 223) suggests that there is a relationship between difficulty in valuation
and long-run underperformance and claims that ”the age of the firm is probably the best
proxy for the initial uncertainty about its future”. In his later study, Ritter (1991, p. 20),
has found that the three-years wealth relatives (value compared to a portfolio of
matching firms) increase monotonically with age. Wealth relatives for firms aged 0-1
years (at the time of the IPO) increase from 0,6 to 1,14 for firms aged over 20 years. He
has interpreted the evidence of the higher underperformance for younger IPOs as the
evidence of investors overoptimism.
Fields (1995, p. 24) has investigated the impact of age on long-run performance and has
found that wealth relatives after three years from IPO are between 0,72 – 0,76 for firms
aged 0-5 years of going public, while firms aged over 16 years outperformed
comparison firms, with the wealth relative of 1,07. She has suggested that the more
established companies are associated with less divergence of opinion and asymmetry of
information, thus the age of the company may have impact on the long-run performance
of IPOs.
Reputation of IPO coordinator
Michaely and Shaw (1995), Brav and Gompers (1997), and Carter et al. (1998) have
related indicators of issue quality to the after-market performance of IPOs.
According to Michaely and Shaw (1994, p. 279) IPOs managed by high prestige
underwriters have smaller initial returns and less negative long-run returns than IPOs
handled by lower reputation underwriters. The result of their study indicates that IPOs
25
lead by more prestigious investment banks have, on average, a less negative
performance over the three-years period after IPO.
Carter et al. (1998, p. 296) also has found that the underperformance of IPO shares
relative to the market is less severe for IPOs handled by more prestigious underwriters.
They claim that “the long-run market adjusted returns become less negative
monotonically with increasing underwriter reputation”. It is possible that the
underwriter’ reputation reflects the quality of the information available, and that the
IPOs underwritten by lower reputation underwriters have greater divergence of opinion.
The underwriters with better reputations have more to lose from a failed underwriting,
and as a result they refrain from underwriting IPOs whose future is very uncertain or
whose returns are hard to predict.
Chemmanur and Fulghieri (1994, p. 57) claim that investors evaluate the investment
banks’ past performance to asses their credibility. Consequently, investment banks'
“equity-marketing history” play an important role. By marketing IPOs that have
relatively better long-term performance, investment banks protect their reputation.
Ownership structure, retention ratio
The IPO literature documents a relationship between the change of share ownership at
the time of IPO and long-run performance. It is found that the higher the proportion of
equity sold at the time of offering (i.e. the higher the dilution of original share holdings)
the worse is the long-run performance.
Ritter’s (1984b, p. 1232 ) “wealth effects” hypothesis assumes that to raise a given
amount of money, the initial owners sell a smaller proportion of the stock if the value of
a firm is greater. Consequently, the block of stock sold by initial owners decreases with
higher firm value. He also points out the agency theory, suggesting “that managerial
compensation schedules do not induce managers to produce as much as would be the
case with 100% owner-management” as a result the lower the fraction of insider
holdings, the lower will be the firm value.
26
The evidence provided in the study of Jain and Kini (1994, p. 1725), suggest that there
is a significant positive relation between post-IPO operating performance and equity
retention by the original shareholders. However, they can not explain is it because there
is lower agency conflicts due to higher ownership retention or entrepreneurs signaling
quality with ownership retention, or for other reasons.
On the contrary Mikkelson et al. (1997, p. 306) have found that the proportion of
secondary shares sold in the IPO is not related to the post-IPO operating performance
of IPO. They claim that managers’ compensation linked to stock price, potentially
substitute for the incentive benefits of large ownership stakes.
Goergen (1998, p.24), based on both German and UK Stock Market, investigated the
relation between long-run performance and ownership and found that the bad long-term
performance of IPOs can not be explained by the dilution of ownership by the original
shareholder after IPO ad possible agency conflicts evoked.
VC / PE backing
The presence of venture capital in the ownership structure of US firms going public has
been associated with both improved long-term performance and superior ‘certification’
at the time of the initial public offerings (IPOs).
Brav and Gompers (1997, p. 1791) has found that venture-backed firms outperform
nonventure-backed IPOs over a five-years period when returns are weighted equally.
They have adopted the Fama - French three – factor model when estimating long-run
returns and have found that although the VC – backed sample outperforms the non-VC
backed sample, the underperformance is not an IPO effect. Underperformance is a
characteristic of small, low book-to-market firms regardless of whether they are, or are
not IPO firms.
According to, Brav and Gompers (op. cit.) venture-backed companies’ prices may be
less susceptible to investor sentiment because of the lower potential asymmetric
information between the firms and investors and the higher institutional shareholding.
Venture capitalists have contacts with top-tier, national investment banks and may be
27
able to entice more and higher quality analysts to follow their firms. Similarly, because
institutional investors are the primary source of capital for venture funds, institutional
may be more willing to hold equity in firms that have been taken public by venture
capitalists.
Align with that, research by Megginson and Werner (1999, p. 901) suggest the
certification role of venture capitalists in bringing new issues to market. They claim that
VC capitalists have impact on the pricing and subsequent ownership structure of IPOs.
By retaining their share ownership after the offering, the venture capitalists can provide
assurance of continued monitoring and can credibly signal their belief in the firm’s
prospects.
More over, Gompers (1996, p. 134) demonstrates that reputational concerns affects the
decisions venture capitalists make when they take firms public. He claims that because
venture capitalists repeatedly bring firms public, it is necessary for them not to tarnish
their reputation and ability to bring firms public in the future, which may happen if they
become associated with failures in the public market. Venture capitalists may
consequently be less willing to hype a stock or overprice it.
Profitability
The literature finds a negative relationship between the profitability of a firm prior to
going public and its long-run performance. The more profitable a firm is prior to going
public, the worse is the long-run performance. Mikkelson and Shah (1994, p.2) show
that long-run share price performance and the change in operating performance from
before to after flotation are negatively related. When operating performance fails to
sustain pre-listing levels of profitability, share prices fall, indicating that investors were
surprised by the change in operating performance. It suggests that firms go public at the
height of their performance thus seizing their window of opportunity.
Other firm and IPO characteristic that have found to be linked with long run
performance of IPO but not applied in the study include: initial trading volume and
flipping, analyst recommendation and earning management.
28
Initial trading volume, flipping
Krigman et al. (1999, p. 1015-1043) and Houge et al. (2001, p. 7) find link between
flipping by institutions and long-term returns on IPOs. They give evidence suggesting
that institutions succeed in identifying IPOs that are being overvalued when trading
commences. On the first day of listing, they observe large informed investors (flippers)
selling issues that have the worst future performance, they conclude that flipping
predicts bad long-run performance.
Analyst recommendation
The long-run aftermarket performance of IPOs is claimed to be affected by underwriters
and analyst activism. Michaely and Womack (1999, p. 653) show that IPO stocks that
underwriter analysts recommend perform more poorly than recommendations by
unaffiliated brokers, this suggesting that underwriting relationship biases analysts’
coverage.
Earning management hypothesis
Teoh, Welch, and Wong (1997, p. 63) show that IPO underperformance is positively
related to the size of discretionary accruals in the fiscal year of the IPO. They believe
that the level of discretionary accruals is a proxy for earning management and find
evidence that investors may be systematically fooled by earnings management
operations of “window dressing”. These operations aim at reporting earnings in excess
of cash flows by taking opportunistic positive accruals. Buyers rely on earnings reported
in the prospectus, but are unaware that they are inflated by accruals, and pay too high a
price. In fact, when inflating accruals, firms borrow income from future periods so that
managers cannot overstate earnings over long periods of time without being detected.
The same results are obtained by Roosenboom et al. (1999, p. 243) who analyze a
sample of Dutch IPOs. They find that IPO firms do manage their earnings during the
fiscal year of the issue. Companies which lavish on discretionary accruals experience
worse long-run stock price performance.
29
3. Methodology and data description
The first aim of the thesis is to provide additional evidence on the long-run performance
of IPOs based on the sample of companies which went public during the period 1998-
2005 on the Warsaw Stock Exchange. Moreover, the second goal of the study is to
analyze the influence of a firm and an offer characteristics of IPO companies on their
long – run stock performance. The inspiration for the thesis was a research conducted
by Susana Alvarez and Victor M. Gonzalez (2001) on the Spanish capital market. They
have also checked the robustness of IPO performance taking into consideration various
methods as well as studied whether investors can relay on a firm and offer information
available before IPO to distinguish firms with good and bad long-run performance.
In my study I have followed the approach of the Spanish authors, however, I have also
used the methodologies of measuring and testing statistical significance for long – run
abnormal returns provided in Barber and Lyon (1997), Lyon et al. (1999). Especially, I
have applied skewness adjusted t-statistics and bootstrap skewness adjusted t-statisics
for testing statistical significance of abnormal returns described in Lyon et al. (1999)
which has not been discussed in the Spanish study. What is more, in the logit model
analyzing determinants of IPO success or failure, I have extended the choice of
explanatory variables.
3.1. Methodology of measuring and testing long–run performance of IPOs
I measure the magnitude and statistical significance of long-run performance of IPOs by
means of the methodologies most often and applied in the current literature5
a) event time abnormal returns method including:
- buy-and-hold abnormal returns
- cumulative abnormal returns
b) calendar-time portfolios returns method including:
- mean monthly calendar-time portfolio abnormal returns
- the Fama-French three-factor model applying mean monthly calendar-time
portfolio returns. 5 Barber and Lyon (1997), Kothari and Werner (1997), Fama (1998), Lyon et al. (1999)
30
Calculation procedures of buy-and-hold returns and cumulative abnormal returns
The long-run event returns are computed for the period of 12, 24 and 36 months after
IPO using monthly returns according to the methodology described in the study of
Ritter (1991, s.7). Excluding initial return period defined as month 0, monthly returns
are calculated for the following 12, 24 or 36 months which are defined as successive 21-
trading-day periods relative to the IPO date. As a result, month 1 consists of event days
2-22 and month 2 consists of event days 23-42. If the initial return period is greater than
1 day, the month 1 period is shortened accordingly. For instance, if the initial return
period is 6 days, month 1 consists of event days 7-22.
Long-run buy-and-hold returns are obtained by compounding monthly returns. For n
IPOs constituting the sample, monthly returns tir , (for each i firm and in each t month)
are compounded for T months after IPO, as shows the equation (1).
)]1([11
,1∏∑==
+=T
tti
n
iT r
nBHR (1)
Next, the returns are adjusted to the compounded returns of its benchmarks tmr , (for each
m firm benchmark and each t month), summed and divided by the number of the
companies, as shows the equation (2).
)1()1([11
,1 1
, ∏∑ ∏== =
+−+=T
ttm
n
i
T
ttiT rr
nBHAR ] (2)
The components of buy-and-hold abnormal returns are used to compute wealth relative
ratio TWR which measures performance of the mean buy–and–hold return on IPOs
relative to its mean benchmark return.
∏∑
∑ ∏
==
= =
+
+= T
ttm
n
i
n
i
T
tti
T
rn
rnWR
1,
1
1 1,
))1((1
))1((1
(3)
31
If wealth relative ratio is greater than 1, it means that IPOs outperforms its benchmark,
wealth relative ratio less than 1 indicates that IPOs underperform.
In order to obtain an average CAR for n IPOs, for each initial public offering monthly
abnormal returns tiar , are calculated according to the equation (4). The monthly returns
are averaged and cumulated as show equation (5) and (6).
tmtiti rrar ,,, −= (4)
∑=
=n
itit ar
nAR
1,
1 (5)
∑=
=T
ttT ARCAR
1 (6)
Calculation procedure of benchmarks for event-time abnormal returns
Abnormal returns are computed by adjusting the returns of the IPO sample by selected
benchmarks: (a) the returns on the Warsaw Stock Exchange Index (WIG); (b) the
returns on reference portfolio of similar size and book–to–market ratio companies and
(c) the returns on control firms of similar size and book-to-market ratio. The portfolio of
IPOs are compared to the benchmarks weighting the new issues equally.
Reference portfolios of similar size and book-to-market companies are formed on the
basis of methodology provided in the study of Alvarez and Gonzalez (2001, p. 10). At
the end of June of each year from 1998 to 2005 the firms listed on the Warsaw Stock
Exchange are ranked on the basis of a firm market value of equity calculated as a price
per share multiplied by shares outstanding and classified into size tertiles. Within each
size tertile the firms are again classified into tertiles formed on the basis of book-to-
market ratio in December of the previous year. In order to eliminate the contamination
of the benchmark portfolio, IPO firms are eliminated from the portfolios.
32
Table 1: IPO companies classification in portfolios according to size and book-to-
market ratio The table present the classification of the sample IPOs companies according to their size and book-to-
market ratio with respect to their benchmark reference portfolios in the month following the IPO.
Reference portfolios have been constructed by classifying firms listed at the Warsaw Stock Exchange
according to their market value of common equity at the end of June each year and forming size tertiles.
In each size tertile, the firms are classified according to book-to-market ratio tertiles. IPOs are assigned to
each of the corresponding portfolios and their returns are compared with the potfolio’s returns in order to
obtain the abnormal return. The classification takes place in the month following the IPO and later in June
each year. The classification of IPO companies in the month after the IPO in the portfolios of similar size
and book-to-market ratio companies is based of the IPO company size - market value of equity at the end
of the first month after the IPO and book–to-market ratio for which book value of equity correspond to
December of the previous year to the IPO. Next, in July each year the sample IPO companies are
reclassified to the portfolios of companies of similar size, measured as the market value of equity at the
end of June and book-to-market ratio in December previous year.
Book – to- market ratio
High Medium Low TOTAL
Big 2 6 23 31
Medium 0 4 37 41
Small 3 5 23 31
Market value
of equity
TOTAL 5 15 83 103
In July of each year the IPO sample companies are assigned to their benchmark
portfolios based on firm size and book-to-market ratio. In the first year, the market
value of equity is calculated using the stock price at the end of the first month following
going public. The book value of equity of IPO firms correspond to December of the
year prior to going public.
In each month, the IPO sample company return is compared to the portfolio return in
order to obtain the abnormal return. The distribution of the firms into size and book-to-
market portfolios in the month following the IPO is illustrated in the Table 1.
The assignment of an IPO company to a control firm follows in the same manner. First,
companies are placed in the appropriate size tertile based on their June market value of
equity. Second, the firm with the book-to-market value ratio closest to that of the
33
sample firm is assigned as a control firm benchmark. In July of each year this process is
carried out.
As far as the portfolio of similar size and book-to-market ratio companies is concerned,
for buy-and-hold abnormal returns two possible ways of calculating the benchmark is
possible. As it is described in the study of Lyon et al (1999, p. 165) , researchers usually
apply “rebalanced” size and book-to-market ratio portfolio or while it is better to
compute “buy-and-hold” size and book-to-market ratio portfolio.
“Rebalanced” reference portfolio assumes that in each month the mean return for each
portfolio is calculated and then compounded over investment period. However, such
approach of calculation of reference portfolio does not accurately reflect the returns
earned on a passive buy-and-hold strategy of investing equally in the securities that
constitute the reference portfolio because it assumes monthly rebalancing to maintain
equal weights. Moreover, the portfolio is subject to the rebalancing and also new listing
bias. However, on the purpose of this study the new listing companies are eliminated
from the “rebalanced” portfolio.
The procedure of computing the mean “rebalanced” portfolio return depicts equation (1),
where s is the beginning period, T is the period of investment (T months), tiR , is the
return on security i in month t and tn is the number of securities in month t.
1)1( 1,
−+=∏∑+
=
=Ts
st t
n
iti
rebpsT n
RR
t
(1)
The second manner of calculating the long-horizon returns on the reference portfolio
first require compounding the returns on the securities constituting the portfolio and
then summing across securities. The procedure of computing the mean “buy-and-hold”
portfolio return presents equation (2), where sn is the number of securities traded in
month s which is the beginning period for the return calculation.
34
∑∏
=
+
=
−+=
tn
t s
Ts
stit
bhpsT n
RR
1
1))1(( (2)
The return on this portfolio represent a passive equally –weighted investment in all
securities constituting the reference portfolio in period s. There is no investment in
firms newly listed subsequent to periods, nor is there monthly rebalancing of the
portfolio.
Test statistics for event time abnormal returns
To test the null hypothesis that the mean buy-and hold or cumulative abnormal returns
are equal to zero (hypothesis of no long-run abnormal performance) for a sample of n
firms, following parametric tests statistics are employed (equations 1 and 2):
nBHARBHAR
tti
tiBHAR )( ,
,
δ= (1)
or
nCAR
CARt
ti
tiCAR /)( ,
,
δ= (2)
Where the tiCAR , and tiBHAR , are the sample averages and )( ,tiCARδ and
)( ,tiBHARδ are the cross-sectional sample standard deviations of abnormal returns for
the sample of n firms. If the sample is drawn randomly from a normal distribution, these
test statistics follow a Student’s t-distribution under the null hypothesis. However, the
distribution of returns of CARs and BHARs are usually nonnormal.
Lyon and Barber (1997, p. 370) document that cumulative abnormal returns are most
affected by new listing bias which result that associated test statistics are generally
negatively biased. The use of reference size and book-to-market reference portfolios not
35
contaminated by the new listings and control firms benchmark should eliminate this
problem.
In contrast, long-run buy-and-hold abnormal returns are more affected by the
rebalancing and skewness biases. The use of control firm approach benchmark eliminate
rebalancing and skewness bias but the use of size and book –to-market “buy-and-hold”
reference portfolio does not eliminate the skewness problem resulting in the negatively
biased test statistics. Skewness leads to an inflated significance level lower-tailed tests
(i.e. reported p values will be smaller than they should be) and a loss of power for
upper-tailed tests (i.e. reported p values will be too large).
In order to control for the skewness bias in tests of long-run abnormal returns when
buy-and-hold abnormal returns are computed with size and book-to-market reference
portfolio, Lyon et al. (1999, p. 165) recommend two solutions a) a bootstrapped version
of skewness –adjusted t-statistics or b) empirical p values calculated from the simulated
distribution of mean long-run abnormal returns estimated from pseudoportfolios.
In order to control for skewness bias in the test of buy-and-hold abnormal returns, I
apply bootstrapped skewness-adjusted t-statistics. The first step in the estimation of
bootstrap skewness adjusted t- statistics is the estimation of “basic” skewness t-statistics
(1).
)61
31( 2
∧∧
++= γγ nSSntsa (1)
Where γ∧
is an estimate of the coefficient of skewness and Sn is the conventional t-
statistic equation6 3
31
)(
)(
T
n
iTiT
ARn
ARAR
δγ∑=
∧ −= (2)
6 nAR
ARt
T
T
/)(δ= , where TAR is the sample mean and )( TARδ is the cross-sectional sample
standard deviation of abnormal returns for the sample of n firms.
36
)( T
T
ARARS
δ= , and (3)
Lyon et al. (1999, p. 174) quote Sutton (1993) who argues that a bootstrapped
application of Jonhson’s statistic “should be preferred to the t test when the parent
distribution is asymmetrical, because it reduces the probability of type I error in cases
where the t test has an inflated type I error rate and it is more powerful in other
situations.” As a result bootstrapping the test statistics yields well-specified test
statistics.
The procedure of bootstrapping the test statistics described by Lyon et. al (1999, p. 174)
requires drawing b resamples of size bn from the original sample of returns. In each of
the b bootstrapped resamples of size 4/nnb = , the skewness-adjusted test statistics is
calculated in a way shown by equation (4),
)61
31( 2
b
b
bb
bb
bsa n
SSnt γγ∧∧
++= (4)
where b
γ∧
is an estimate of the bootstrap coefficient of skewness and bSn is the
bootstrap sample t-statistic equation7.
31
3
)(
)(
Tb
b
nb
i
BT
biTb
ARn
RAAR
δγ
∑=
∧−
= (5)
)( Tb
TbTb
ARARARS
δ−
= (6)
7 nAR
ARt
T
T
/)(δ= , where TAR is the sample mean and )( TARδ is the cross-sectional sample
standard deviation of abnormal returns for the sample of n firms.
37
bsat , bS , and
b∧
γ from the bootstrapped resamples are analogues of sat , S, and ∧
γ from
the original sample for the b=1,….1000 resamples. The null hypothesis is rejected when
the long-run abnormal return is zero, which means if: sat < *lx or sat > *
ux . Lower critical
value *lx and upper critical value *
ux calculated from the 1000 resamples points out the
rejection regions of null hypothesis for the test statistics sat . The null hypothesis of the
mean long-run abnormal return is zero at the α significance level by solving equation
(7):
Pr [ bsat <= *
lx ] =Pr[ sat >= *ux ]=α /2 (7)
This means that for 1000 bsat ordered from the smallest to the greatest value, at
α significance level of 5%, the lower critical value is bsat for b=25 and upper critical
value is bsat for b=975 respectively.
Calculation procedure of calendar-time portfolios returns
The buy-and-hold abnormal returns method suffers from already discussed cross-
sectional correlation bias. The method that alleviates this issue is based on calculation
of calendar-time portfolios. In this study I follow Fama (1998) and Lyon et al. (1999)
approaches and apply mean monthly calendar-time returns and Fama-French model
based on calendar –time returns portfolios.
The procedure for calculating mean monthly portfolio abnormal returns starts with the
decision of the horizon of abnormal return period (T). In case of this study, it is either
12, 24 or 36 months. For the period of T months, for each calendar month, it is
calculated the abnormal return tiAR , for each security that had carried out an IPO with
in the period of T months from the t calendar month using the returns on reference
portfolios ptR . The abnormal return is calculated using: market index, size and book-to-
market portfolios benchmarks.
38
tptiti RRAR ,,, −= (1)
In each calendar month t , it is calculated a mean abnormal return tMAR across tn firms
in the portfolio. tMAR is computed assuming equal weighting.
ti
n
i tt AR
nMAR
t
,1
1∑=
= (2)
Next, the grand mean abnormal returns MMAR is calculated as the sum of the mean
abnormal returns tMAR divided by the number of calendar months T.
∑=
=T
ttMAR
TMMAR
1
1 (3)
To test the null hypothesis of zero mean monthly abnormal returns, a t-statistic
)(MMARt is calculated using the time-series standard deviation of the mean monthly
abnormal returns.
TMMRMMARMMARt
T /)()(
δ= (4)
Because of the changes in the composition of the portfolio’s abnormal return, the
heteroskedasticity problem may occur. In line with the suggestion of Alvarez and
Gonzalez (2001, p. 13), the portfolio return for each month is divided by the estimate of
its standard deviation. The grand mean abnormal return is then estimated by averaging
the standardized monthly abnormal returns and standardized t-statistic is obtained.
Estimation of the Fama-French three-factor model based on calendar-time
portfolios
The mean monthly calendar-time portfolio returns, where the portfolio is composed of
firms that had issued equity within the last three years of the calendar month are applied
to estimate the Fama-French regression model (1).
39
tititiftmtiiftpt HMLhSMBsRRRR ,)( εβα +++−+=− (1)
In this equation ptR stands for the equally weighted mean monthly return on the
calendar-time portfolio, ftR is a monthly risk free rate of return which in this study is
computed as the average of the 1 month Warsaw Interbank Offer Rate (WIBOR) and 1
month Warsaw Interbank Bid Rate (WIBID), mtR is the return on WIG value - weighted
market index, tSMB is the difference in returns of value-weighted portfolios of small
stocks (portfolio of firms whose equity market value is less than the median value of
firms quoted at the Warsaw Stock Exchange) and big stocks portfolio (portfolio of firms
whose equity market value is higher than the median value of the firms quoted at the
Warsaw Stock Exchange); tHML is the difference in the returns of value-weighted
portfolios of high book-to-market stocks (represents the top 30% of all firms on the
Warsaw Stock Exchange) and low book-to-market stocks (portfolio contains firms in
the lowest 30% of the firms quoted at the Warsaw Stock Exchange).
The regression yields parameters estimates of iiii hs ,,,βα and the error term in the
regression is denoted by ti,ε . By means of the estimate of the intercept term iα it is
possible to test the null hypothesis that the mean monthly excess return on the calendar-
time portfolio is zero. The error term in this regression may be heteroskedastic, since the
number of securities in the calendar-time portfolio varies from one month to the next.
Although, Lyon et al (1999, p.193) find that this heteroskedasticity does not
significantly affect the specification of the intercept test in random samples, I correct for
heteroskedasticity using weighted least squares estimation, where the weighting factor
is based on the number of securities in the portfolio in each calendar month.
3.2. Analysis of the determinants of the long-run performance of IPOs
The analysis of the determinants of the long-run performance of IPOs’ success or failure
is conducted by estimating logit model which explanatory variables are related to a firm
and an offer characteristics. In this section, I present theoretical assumptions of logit
40
model based on Greene (2000) text book for Applied Econometrics. Next, I describe the
variables used in this model.
Theoretical assumptions of logit model
Logit model belongs to the group of binary choice models and is applied to model
a relationship between a dependent variable tiy and independent variables itx , where
tiy is a discrete variable that represent choice, or category, from a set of mutually
exclusive choices or categories. In case of dichotomous dependent variable ity , the
expected value of dependent variable )( tiyE takes value 1 if the event occurs with
probability itp . The expected value of ity is depicted by the equation (1).
titititi pppyE =−⋅+⋅= )1(01)( (1)
The expected value is modeled as a function of independent variables tix , where β is a
parameter to be estimated, and F is the logistic cumulative distribution function.
tititititi xFxyEyprobp '()(]1[ β==== ) (2)
In the logit model, logistic cumulative distribution (CDF) function is applied, the
properties of CDF function allow )'( tixF β to range [0,1] probability.
Binary variable can be applied, when dependent variable is continuous but unobservable.
For example, a company decide to accept the project if its net present value ( *tiy )
exceeds a certain level (3):
1=tiy if 0* >tiy and 0=tiy if 0* ≤tity , where tititi xy εβ += '* , (3)
consequently,
prob ( 1=ity ) = prob ( 0* >tiy ) = prob ( )'()' ititit xFx ββε =−> (4)
41
Logit model: it
it
x
x
itit eexxF '
'
1)'()'( β
β
ββ+
=Λ= (5)
The logistic regression model is estimated by maximum likelihood. As a result,
goodness of fit and statistical interference is based on the log likelihood and chi-square
test statistics. The maximum likelihood parameter estimation (MLE) allow to determine
the parameters that maximize the probability of the sample data. A regression of the
probability model is depicted by the equation (6):
)]'([1)]'(1[0][ xFxFxyE ββ ⋅+−⋅= (6)
As far as the interpretation of the estimated logistic regression function is concerned,
the first step is to test and describe the overall goodness of fit of the model. In case of
maximum likelihood approaches, the common method is to examine the difference
between the residuals of the model under the constraint that all regression coefficients
are zero and the residuals where the coefficients are estimated from the sample data.
The reduction in “the badness of fit” as a result of freeing parameters for each X can be
tested as a chi-square with as many degrees of freedom as freed parameters.
Likelihood ratio test
A common test, similar to the F test that all slopes in a regression are zero, is the
likelihood ratio statistic (LR) test that all the slopes coefficients in the binary model (for
instance logit model) are zero. The likelihood-ratio test statistic is given by equation (7),
where r is the number of restrictions imposed on a full model, RL∧
and UL∧
are the log
likelihood functions evaluated at the restricted and unrestricted estimates, respectively.
)(~][ln2 2 rLLLR UR χ∧∧
−−= (7)
The likelihood-ratio examines whether a reduced model provides the same fit as a full
model. For this test, the constant term remains unrestricted.
42
Pseudo R-squared
The often applied measure of goodness-of-fit for binary choice models, an analog to the 2R in a conventional regression, is McFadden’s likelihood ratio index also known as
pseudo R-squared or McFadden R-squared.
The pseudo R-squared measures how well the variations in the data can be explained by
the model and is calculated depending upon the likelihood ratio. In order to evaluate the
model goodness of fit, R-squared compares the likelihood for the intercept only model
to the likelihood for the model with the explanatory variables. In the equation, Lln
reports the maximized value of the log-likelihood function and 0ln L the log-likelihood
that all the slopes in the model are zero.
Pseudo R-squared =0ln
ln1LL
− (8)
The pseudo R-squared specify the proportions of variations in the outcome variable
accounted for by the explanatory variables. The greater the value of the pseudo R-
squared the better the model fitting. McFadden’s R-squared is bounded by 0 and 1.
Explanatory variables of the logit model
The analysis of the determinants of the likelihood of success of IPO is conducted by
means of the logit model, following Alvarez and Gonzalez (2001), the Spanish IPO
study. The dependent variable (3-years abnormal return or 3-years “raw” return) is a
qualitative attribute and equals 1 if the return is positive or 0 if the return is negative.
F(.) is the cumulative distribution function of a standard normal variable.
The long-run stock performance of IPOs may be influenced by the range of factors
related to an IPO offer of a firm characteristics described in the international literature
and discussed in previous section. The logit model tests several determinants for which
data has been collected based on the sample of Polish IPOs going public within the
43
period of July 1998 and December 2005. The logit model equation is given by equation
(9):
(9)
)Re_/Re
_()1(
987,6
,5,4,31,21,13,
putationBackingPEVCSEOtention
SizeOfferngUnderpriciAGEROAAssetsFBHRP
ti
titititititi
ββββ
βββββ
+++
+++++== −−+
Where:
Assets- natural logarithm of firm’s assets at the year end before IPO
ROA – return on firm’s assets at the year end before IPO, measured net profit over total
assets
Age – firm’s maturity by number of years from incorporation till IPO
Underpricing – logarithm of 1 plus market adjusted initial stock return
Offer Size– logarithm of offer size
Retention - % of shares retained at IPO by initial owners
SEO – number of secondary equity offerings in the five year period after IPO
VC/PE – Venture Capital of Private Equity backing of a firm before IPO
Reputation – Reputation of lead manger of a firm’s offer.
Based on the international literature’s findings, several hypotheses have been created
testing the effects of an IPO offer and a firm characteristics on the long run IPO success
or failure. Below, I briefly present the theories and evidence supporting the created
hypotheses. The broader discussion about the influence of these factors is provided in
the literature overview section.
Hypothesis 1 – OFFER SIZE, ASSETS
The offer size and the value of a company’s assets is positively correlated with the
long-run performance of IPO
Ritter (1991, p.15) has found that IPO firms with a high risk profile (for instance small
companies) show more underperformance and he relates his findings with the “fads”
theory claiming that such companies are more subject to investor sentiment. The cross
sectional study of Levis (1993, p.38) reveals that the larger firms, in terms of gross
proceeds from the offering, the better are their long-run returns. Brav and Gompers
44
(1997, p. 1792) have found that the underperformance is the greatest for the smallest (by
market value) initial public offerings.
Hypothesis 2 - ROA
Company profitability is negatively correlated with the long-run performance of
IPO
The assumption of the negative correlation of pre-IPO profitability with the post-IPO
long-run market performance hypothesis is based on the window of opportunity
hypothesis that the companies go public when their operating results are very high.
After IPO, the operating performance fail to meet previous results and consequently
share price falls. The study of Mikkelson and Shaw (1994, p.2) supports the hypothesis
of the negative influence of the high pre IPO profitability on the long-run stock
performance of IPO.
Hypothesis 3 – AGE
Company’s maturity is positively correlated with the long-run performance of IPO
The hypothesis that the maturity is positively correlated with the long-run performance
of IPOs in supported by Ritter (1991, p. 20), Fields (1995, p. 24) and Carter et al. (1998,
p. 294) that have found significant relationship between the age of the firm and its long-
term performance. More established companies are associated with less divergence of
opinion and asymmetry of information, thus the age of the company may have positive
impact on the long-run performance of IPOs.
Hypothesis 4 – UNDERPRICING, SEO
Underpricing and the number of subsequent equity offerings is positively
correlated with the success of IPO
In line with the signaling hypothesis of Allenand and Faulhaber (1989, p. 304), the best
firms are characterized by the greater amount of underpricing at the IPO because they
want to signal they quality and gain more funds in later SEOs, in which the firms will
sell stock at a higher price closer to their intrinsic value. Consequently, according to the
signaling hypothesis, this types of firms should present better long-run performance.
45
The studies of Ljungqvist (1996, p. 1378) in Germany and Alvarez and Gonzalez (2001,
p. 19) in Spain provide evidence that the firms underprice their offerings in order to
conduct SEOs.
Hypothesis 5 - RETENTION
The percentage of shares retained by the initial investors is positively correlated
with the likelihood of success of IPO
Based on Ritter’s (1984b, p. 1232 ) “wealth effects” hypothesis I assume that to raise
a given amount of money, the initial owners sell a smaller proportion of the stock if the
value of a firm is greater. Consequently, the block of stock sold by initial owners
decreases with higher firm value. Moreover, the evidence provided in the study of Jain
and Kini (1994, p. 1725), suggest that there is a significant positive relation between
post-IPO operating performance and equity retention by the original shareholders.
Hypothesis 6 – VC/PE backing
VC/PE backing is positively correlated with the success of IPO
The involvement and facilitating role of VC/PE funds is expected to have a positive
effects on the aftermarket performance of IPOs. The hypothesis is in line with the
researches by Brav and Gompers (1997, p. 1791) which find that VC/PE backed public
firms perform better than the non-VC/PE backed public companies. The study of
Frederikslust and van det Geest (2001) conducted on the sample of IPOs on the
Amsterdam Stock Exchange also reveals better performance of VC/PE backed IPOs.
Hypothesis 7 - REPUTATION
The higher the reputation of the lead manager of the offer the grater chances for
the success of IPO.
In the financial literature in can be find that those issues for which highly prestigious
underwriters have been chosen should present better long-run returns. The study of
Carter et al. (1998, p. 296) examines the lead managers’ short and long-run influence
and supports the idea that IPOs by more prestigious and reputable lead managers show
46
less underperformance. The results are similar also in case of the study of Michaely and
Shaw (1994, p. 279).
Table 2: Summary of the hypotheses about firm and IPO offer factors influencing
long-run performance of IPOs Determinant Relationship
Hypothesis 1 OFFER_SIZE, ASSETS The offer size an the value of company assets are
positively correlated with the long-run performance of
IPO
Hypothesis 2 ROA Company profitability is negatively correlated with
the likelihood of IPO success
Hypothesis 3 AGE Company maturity is positively correlated with the
long-run stock performance of IPO
Hypothesis 4 UNDERPRICING, SEO Amount of underpricing and number of SEO are
positively correlated with the success of IPO
Hypothesis 5 RETENTION The percentage of shares retained by the initial
investors is positively correlated with the likelihood of
success of IPO
Hypothesis 6 VC/PE_BACKING VC/PE backing is positively correlated with the
success of IPO
Hypothesis 7 REPUTATION High reputation of lead manager of IPO offer
increases chances for the success of IPO
47
3.3. Market, sample and data sources characteristics
3.3.1. Polish IPO market
Since the analysis of the long-run IPO stock performance and the likelihood of an IPO
success or failure is based on the sample of the IPO companies which went public on
the Warsaw Stock Exchange, it is a good idea to present the characteristics of the Polish
IPO market taking into consideration its historical development and the current position
in comparison to leading European markets.
Polish IPO market vs. other European markets
In the first half of 2008, there have been conducted 55 initial public offerings on the
Warsaw Stock Exchange. This represented 27% of European IPOs and gave the WSE
the second position among the European stock exchanges taking into consideration the
IPO volume. The first place was held by the London market (33%) and the third
position by NYSE Euronext (16%). In 2007, the Warsaw Stock Exchange hosted 104
IPOs which accounted for the third position in terms of the IPO volume, after the
London market and NYSE Euronext. The year earlier, there were only 38 IPOs on the
WSE, which still accounted for the leading stock exchange in Europe with respect to the
IPO volume
Table 3: IPO volume in European stock exchanges Stock Exchange IH 2008 2007 2006London 68 324 26WSE** 55 (21) 104*(81) 38 NYSE Euronext 32 127 134OMX 14 85 59Luxemburg 11 13 25Deutsche Borse 9 62 89Oslo Bors & Axess 9 37 15Borsa Italiana 4 29 21SWX 4 10 9BME (Spanish Exchanges) 0 12 10ISE 0 10 8Wiener Borse 0 6 7Athens Stock Exchange 0 3 2Europe Total 204 813 838
Source: IPO Watch Europe 2007, Q1 2008, Q2 2008 , PWC * the figure do not include the offer of Austrian company Immoeast
** the numbers in parentheses indicate the number of IPOs on the WSE main market
48
The substantial increase in the volume of IPOs on the WSE in 2007 was partly driven
by the opening, in the second half of the year, the New Connect market, an exchange-
regulated market which generally attracts smaller offering value IPOs. The number of
companies entered on the WSE main market in the first half of 2008 amounted to 21, in
2007 it reached 81 IPOs.
At the end of the first half of 2008, the Warsaw Stock Exchange held a third position
taking into consideration the value of IPO offering amounting to Euro 1 933 million
which represented 17% of the total European offerings. The greater share of the total
offering value had the London market - 64% and NYSE Euronext - 20%. The amount of
the offering on the WSE in the first half of 2007 was similar to the whole offering value
in 2007 when it amounted to Euro 2 021 million. In 2007, the value of money raised on
the Warsaw Stock Exchange almost doubled in comparison to 2006.
Table 4: Offering value in European stock exchanges Offering value (Euro million) IH 2008 2007 2006London 7 273 39 087 42 182NYSE Euronext 2 235 8 032 21 287WSE 1 933 2 021* 1 045SWX 412 1 975 1 022Deutsche Borse 330 6 984 6 997Luxemburg 245 1 295 1 355OMX 193 3 138 2 848Borsa Italiana 120 3 943 433Oslo Bors & Axess 33 1 993 1 457BME (Spanish Exchanges) 10 084 2 969ISE 1 678 597Wiener Borse 1 427 1 715Athens Stock Exchnage 479 612Europe Total 11 366 80 367 87 849
Source: IPO Watch Europe 2007, Q1 2008, Q2 2008 , PWC * the figure do not include the offer of Austrian company Immoeast.
Development of Polish IPO market
The basic institution of the Polish capital market is the Warsaw Stock Exchange. Within
the last 18 years, the number of companies listed on the Warsaw Stock Exchange main
market have increased from 9 in 1991 to 367 in 2008, in the same time the capitalization
of shares has soared from PLN 161 million to PLN 452 115 million, reaching its peak in
2007 of PLN 1 080 257 million.
49
Graph 1: Total number of listed companies and the market capitalization on the
Warsaw Stock Exchange in years 1991 – 2008*
9 16 2244
6583
143
198221 225 230 217
203230
255284
351367
161 351 5 8457 45011 27124 00043 76672 442123 411130 085103 370110 565
167 716
291 698
424 900
635 909
462 115
1 080 257
0
50
100
150
200
250
300
350
400
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
No
of c
ompa
nies
0
200 000
400 000
600 000
800 000
1 000 000
1 200 000
Mar
ket c
apita
lizat
ion
PLN
mill
ion
Total number of companies
Total market capitalization
Source: WSE statistics
*Total number of companies and market capitalization in the period between January 1991 and October
2008
During the 18 years of the Warsaw Stock Exchange there have been noticed various
periods of IPO activity. In 1991-1996 the number of debuts on the WSE ranged from 6
to 22 annually and none of the companies decided to withdraw from the market. A very
dynamic growth in terms of the number of offerings was recorded in 1997-1998 with
almost 60 companies making their flotation each year. To some extent, this growth can
be credited to the National Investment Funds, part of the country’s mass privatization
programme. However, increasing investors’ interest in the stock market was also
encouraging private companies to go public.
In 1999, much less companies decided to go public, however contrary to the previous
years the great majority of them were private. In between of 1999 and 2000, in line with
the world’s trends, it could have been observed an increase in popularity of IT
companies. Consequently, the “internet bubble” was inflating the WIG index and
resulted in increase in the stock turnover. Soon the “internet bubble” blew up and the
50
recession in economy appeared. As a result, the first years of the twenty one century on
the Polish capital market were marked with a visible slowdown.
Graph 2: Value of WIG index and total turnover in years 1991-2008*
0
10000
20000
30000
40000
50000
60000
199119921993199419951996199719981999200020012002200320042005200620072008
WIG
0
100000
200000
300000
400000
500000
600000
Turn
over
(PLN
mill
ion
WIG Turnover value
Source: WSE statistics * The value of WIG index and total turnover in years in the period between January 1991 and October
2008
In 2002 and 2003 more companies withdrew than came to the WSE as many strategic
investors decided to delist their companies. Consequently, in that period the number of
companies quoted on the Warsaw stock Exchange decreased. Only 5 ad 6 companies
went public in 2002 and 2003 respectively while 19 were delisted each year.
Yet the following three years, 2003-2007, saw a renewed interest in public listings. In
line with the booming economy the value of WIG index and the turnover value were
systematically growing. The Warsaw Stock Exchange was attracting more investors and
started to host more and more international companies. As a result, annually over 30
companies was making their debuts in years 2004-2006. In the record year 2007, there
were 81 companies which decided to go public on the main market of the Warsaw Stock
Exchange while market capitalization amounted to PLN 1 080 257 million. As a
consequence of the increasing popularity of rising equity through the stock exchange, in
51
the second half of 2007, an exchange-regulated market was opened under the name New
Connect, which is generally predestined for smaller offering value IPOs.
Graph 3: Number of debuts and delistings in years 1991 - 2008*
9 7 622 21 18
62 57
2813 9 6 5
36 35 38
81
26
-2 -2 -5 -9 -4 -9 -10 -9 -10-14-19 -19
-40
-20
0
20
40
60
80
100
19911992
19931994
19951996
19971998
19992000
20012002
20032004
20052006
20072008
Number of Debuts Number of Delist ings
Source: WSE statistics
*Number of debuts and delisting in the period between January 1991 and October 2008
In 2008, the Warsaw Stock Exchange has again been affected by the global economy
downturn evoked by the financial crisis. As a result, the capitalization of the WSE has
shrank by half of the level from 2007 and at the end of October 2008 it amounted to
PLN 462 115 million. In the period between January and October 2008 there were 24
new entries on the main market of the Warsaw Stock Exchange.
3.3.2. The IPO sample
Sample characteristics for event-time returns analysis
The sample of IPOs that have been used for the event-time returns analysis contains
companies which went public between July 1998 and June 2005. The beginning of the
period is July 1998 because the most recent book values of the companies contained in
Notoria database come from December 1997. Thus, it is only possible to obtain book –
to - market reference portfolios for the companies which went public beginning from
the second half of 1998. The end of the period is June 2005 and as a result the three-
years long run returns are computed till June 2008.
52
Several criteria have been used to select the final sample of the companies which went
public between July 1998 and June 2005. First, IPOs of investment funds are excluded
from the sample because their unique characteristics make them incomparable with
other IPOs. Second, foreign companies are also eliminated from the sample as well as
those IPOs for which information found in the data source was incomplete.
Consequently, the number of companies for which event time-returns are analyzed
encompasses 103 out of 125 IPO companies that went public between July 1998 and
June 2005 (see supplementary materials I and II).
Table 5: Number of IPO companies included in the sample of event-time returns
analysis Year Number of IPO companies Number of analyzed IPO companies
From July 1998 10 9 1999 28 20 2000 13 10 2001 9 6 2002 6 5 2003 5 5 2004 36 31
Till June 2005 18 17 Total 125 103
Source: Author’s own calculations
Sample for calendar-time portfolio returns methods
Mean-monthly calendar-time portfolio returns analysis and the estimation of Fama-
French three-factor model are computed based on the monthly stock returns beginning
from January 2002 till June 2005 (the monthly WSE statistics is available from January
2002). As a result the sample of IPOs used in the analysis of monthly calendar-time
portfolio returns are companies which went public between January 1999 and June 2005
and their returns are included in the WSE monthly statistics. As in case of event-time
returns, foreign companies and investment funds are excluded from the sample. The
input data for the analysis of calendar-time portfolio returns are covered in
supplementary materials III and IV.
53
Sample for logit model
Inclusion to the logit analysis is reserved to those IPOs for which the complete company
and offer characteristics as well as three-years event-time returns are
available. Consequently, the sample includes 94 IPO companies (see supplementary
material V). The table below presents the summary of the firm and the offer
characteristics for the sample IPO companies.
Table 6: Characteristics of IPO sample companies for logit model The table presents the summary statistics for the explanatory variables for the IPO sample used in the
logit model. ASSETS – is the natural logarithm of total firm assets one year before IPO, ROA – return on
assets one year before IPO, AGE – company age from incorporation till IPO, UNDERPRICING – natural
logarithm of 1 plus initial market adjusted return, OFFER SIZE – natural logarithm of the offer size,
RETENTION - % of stock retained by the initial owners, SEO – number of secondary equity offering in
five year period following IPO, VC/PE BACKING – dummy variable representing value 1 if the IPO was
VC/PE equity backed or 0 if not; REPUTATION – variable taking value from 1 (for the most prestigious
and reputable offer lead managers) and 3 (for the least prestigious and reputable lead managers).
Mean Median Std. Dev Min Max ObsASSETS 11,55 11,10 1,83 8,15 18,25 94ROA 7% 7% 9% -48% 27% 94AGE 20,00 12,00 17,39 1,00 68,00 94UNDERPRICING 0,08 0,06 0,23 -0,79 0,74 94OFFER SIZE 17,35 17,24 1,74 11,24 22,79 94RETENTION 66% 69% 18% 14% 95% 94SEO 0,85 1,00 1,05 0,00 5,00 94VC/PE BACKING 0,14 0,00 0,35 0,00 1,00 94REPUTATION 1,59 1,00 0,75 1,00 3,00 94
Source: author’s own analysis
The mean value of assets for the sample of Polish IPO companies that went public in
the period from July 1998 till June 2005 measured as the natural logarithm value is
11,55 (median 11,10). The highest value of the assets of 18,25 has PKO BP – the
greatest Polish bank. The average return on assets is 7% (median also 7%) but the scale
of observed returns on assets one year before IPO is ranging from -48% to 27%.
The average age of the sample of the IPOs is 20 years (median 12). The age of observed
companies varies largely due to the fact that the WSE was opened in 1991 and the
companies incorporated long before that period did not have a chance to went public
54
through the IPO. The minimum age (1 year) represent Optimus Technologie the spin off
company of Optimus S.A.
In case of the offer size, the average value measured as a natural logarithm is equal to
17,35 (median 17,24). Again the offer size varies substantially form 11,24 to 22,79. the
highest value offers are those conducted by the large state companies such as PKO BP,
WSIP, PKN Orlen, Lotos. Initial owners retains on average two-third of the stock and in
the period of 5 years after IPO they perform the secondary equity offering directed
either to the public or limited number of investors.
As VC/PE backing is concerned, only 13 companies out of 94 are backed by VC/PE
funds before their IPO. For the 54 of 94 companies, the offers were conducted by the
most prestigious lead managers ranked with 1 on the scale from 1 to 3; 25 IPOs were
held by semi-prestigious lead managers (rank as 2)and 15 by least reputable lead
managers (ranked as 3).
To sum up, it can be noticed large variations in the values of the particular categories of
explanatory variables. For some of the categories (AGE, OFFER SIZE) the extreme
values appear for large privatized companies or financial institutions (ASSETS, ROA).
As a result it is a good idea to perform a version of the logit analysis with exclusion of
financial institutions and privatized companies.
3.3.3. Data sources
Data source for event time returns analysis
The data for computing the event–time abnormal returns analysis of the IPO companies
comes from Notoria database and includes:
- daily stock quotations for WSE listed companies, which have been used to compute
“raw” event-time monthly returns according to the Ritter’s (see page 30)
methodology,
- daily stock quotations for the benchmark returns (WIG index quotations, size and
book-to-market portfolio companies quotations, control firms quotations).
55
In order to construct size and book-to-market portfolios the information about the
capitalization and book-to-market value for each of the portfolio company has been
taken from WSE monthly statistics (available from January 2002) and Notoria database
(accounting and quotations data before January 2002).
Data source for calendar-time portfolio returns and Fama-French three-factor
model
The mean-monthly calendar-time returns analysis of the IPO companies have been
obtained from WSE monthly statistics and which includes:
- monthly returns for the IPO sample companies and size and book-to-market ratio
portfolios’ companies,
- information about capitalization and book-to-market ratio of listed companies
The data for estimating Fama-French regression model include:
- the series of mean – monthly calendar-time returns computed based on WSE
monthly statistics,
- the series of monthly risk free rates of returns based on the data from NBP (National
Bank of Poland) statistics,
- the series of SMB (difference between small and big companies portfolios’ returns)
and HML (difference between high book-to-market ratio and low book-to-market
ratio companies portfolios’ returns) obtained from WSE monthly statistics
- the series of monthly rates of returns on WSE value-weighted index computed based
on the data from Notoria database.
Data source for logit analysis
In order to analyze the likelihood of IPO success or failure, there has been gathered
information regarding explanatory variables related to an IPO firm and an offer
characteristics.
The sources of information include:
- the companies’ prospectuses –offer size, VC/PE backing, lead manager information,
retention ratio, number of SEO,
56
- companies web page information, issue information form Polish financial internet
portals such as Bankier.pl, Money.pl. –number of SEO,
- brokerage houses’ rankings of the WSE and Nasz Rynek Kapitałowy (monthly
financial magazine) – reputation of lead manager,
- WSE IPO statistics - IPO companies initial returns,
- Notoria data base - value of assets and ROA in a year following IPO.
57
4. Results and discussion
The chapter provides the results of the analysis of long-run performance of IPOs
conducted applying event-time returns methods (buy-and-hold abnormal returns and
cumulative abnormal returns) and calendar-time returns methods (mean monthly
calendar-time portfolio returns, Fama-French three-factor model using calendar-time
portfolio returns). Moreover, the results of the logistic regression models have been
discussed with respect to the influence of firm and IPO offer characteristics on the
probability of success of IPO.
The data for the analyses has been prepared in Excel and the analyses have been
performed both with Eviews 5 and Excel (with respect to skewness adjusted t-statistics
and bootstrap skewness adjusted t-statistics).
4.1. Results of event-time abnormal returns analysis
4.1.1. Results of the analysis of buy-and-hold abnormal returns
Buy-and-hold abnormal returns are calculated for 12, 24 and 36-months investment
horizon after the first day’s trading using the returns of the IPOs adjusted to the Warsaw
Stock Exchange Index, the appropriate size and book-to-market matched portfolio
returns computed both as “rebalanced reference portfolio” and “buy-and-hold”
reference portfolios as well and control firms returns. The sample of IPO include the
companies which began to be quoted between the second half of 1998 and the first half
of 2005. Such horizon was influenced by data availability for composing portfolios.
The returns are equally weighted because it provides more power of t-statistic test to
detect underperformance as noticed by Brav et al. (2000, p. 212) and Loughan and
Ritter (2000, p. 363). Moreover, the method of value-weighting the returns was not
applied due to data mining problems rather than the lack of the interest in quantifying
investors’ average wealth change subsequent to an event. Nevertheless, the sample of
Polish IPOs under study is proportionally distributed according to the size measured by
capitalization (as reported in Table 1, the sample consists of approximately 30% of
58
small firms, 40% of medium-sized firms and 30% of big firms) which should diminish
the differences between the two weighting schemes reported in the literature.
The distribution of the long-run buy-and-hold abnormal returns is consistent with those
reported in the literature, for instance Kothari and Warner (1997, p. 317). The
distribution of long horizon returns is highly positively skewed (see Graphs 4-6 in the
appendicies) with the majority of negative values observed. The skewness coefficient
increases with the horizon of measuring long-run abnormal returns and it is the highest
for the returns adjusted to WIG in each of the analyzed horizon. High skewness is also
observed in case of returns adjusted to control-firms returns measured for horizon of 24
and 36 months, although the Lyon and Barber suggest there should be less problems
with skewness when the control firm benchmark is applied. Skewness coefficient is
lower for returns compared to “rebalanced” and “buy-and-hold” of size and book-to-
market reference portfolios.
When considering the “peakedness” of the IPO sample returns distribution, the kurtosis
coefficient varies from 3,3 to 4,0 for the 12-months abnormal returns. It highly
increases in line with the horizon of measuring up to 15 for the 36-months abnormal
returns. The kurtosis is again the highest for the 24 and the 36-months WIG adjusted
abnormal returns and substantial for the control firm adjusted returns. Kothari and
Werner (1997, p. 308) also report that the distribution of buy-and-hold returns is
severely fat-tailed, with kurtosis coefficients in excess of 23 for all buy-and-hold returns
models considered.
The statistical test of abnormal returns use the mean and standard deviation of the
sample of abnormal returns. The observed means of abnormal returns are negative for
each benchmarks applied despite control firms returns which are also the most close to
the zero mean. Abnormal returns adjusted to control firms benchmarks have also
standard deviation which is lower when compared to the returns adjusted to size and
book-to-market portfolios.
The results of the long-run buy-and-hold abnormal returns analysis reveal that the
average abnormal returns benchmarked to the WIG index return as well as capitalization
59
and book-to-market reference portfolio benchmarks are negative, whereas they are
positive when adjusted to the control firms return in each of the period analyzed.
Table 7: Long-run buy-and-hold abnormal returns on IPOs The table shows the results of buy-and-hold strategy on IPOs, after 12, 24 and 36 months from the first
day of trading. Long-run returns are computed monthly up to the investment horizon considered (12, 24
and 36 months). Returns are adjusted to the return considered normal, that is alternatively the Warsaw
Stock Exchange Index, a capitalization and book-to-market “rebalanced” and “buy-and-hold” reference
portfolio return and control firms return.
BHARs Equally Weighted Buy and Hold returns 12 months
N=103 Mean abnormal
return T student Probability % AR <0 Wealth
RatioWIG index -0,1996 -3,9501*** 0,0001 71% 0,85Cap and B/M portfolio rebalanced -0,0943 -1,7612* 0,0812 68% 0,91Cap and B/M portfolio buy and hold -0,0876 -1,6470 0,1026 66% 0,92Control firm 0,0100 0,1437 0,8860 48% 1,01 24 months
N=103 Mean abnormal
return T student Probability % AR <0 Wealth
RatioWIG index -0,1553 -1,3989 0,1649 69% 0,90Cap and B/M portfolio rebalanced -0,2360 -1,7091* 0,0905 63% 0,86Cap and B/M portfolio bh -0,2251 -1,6145 0,1095 65% 0,86Control firm 0,1153 0,8867 0,3774 50% 1,09 36 months
N=101 Mean abnormal
return T student Probability % AR <0 Wealth
RatioWIG index -0,3107 -1,9418* 0,0550 74% 0,82Cap and B/M portfolio rebalanced -0,4723 -2,2388** 0,0274 67% 0,77Cap and B/M portfolio buy and hold -0,4259 -2,1619** 0,0330 67% 0,77Control firm 0,1586 0,8798 0,3811 50% 1,13
***,**,* Statistically significant at the 1%, 5% and 10% level, respectively
In the first 12 months of the stock trading, the greatest and significant
underperformance of the IPOs has been noticed with respect to the market index return
(close to 20%). When “rebalanced” reference portfolio is applied, the underperformance
decreases by half but it is also statistically significant. Slightly lower and insignificant
underperformance is reported for returns adjusted to “buy-and-hold” portfolio
60
benchmark, whereas mean return adjusted to control firms return is positive and amount
to 1%.
24-months mean returns adjusted to WIG return or the reference portfolios return range
between -15,5% and -23,6% but they are only significantly different from zero in case
of mean return adjusted to “rebalanced” reference portfolio benchmark. In contrast, the
24-months average return compared to control firm return is positive and amounts to
11,5%.
When 36 months horizon is considered, there can be observed significant, negative
mean abnormal returns, with values between -31,1% for WIG benchmark and -47,2%
for “rebalanced reference portfolio benchmark”. Only the mean control firm abnormal
return is positive (15,9%) and insignificantly different from zero.
In each horizon analysed, the negative abnormal returns are noticed for approximately
two third of the observations for buy-and-hold IPO returns adjusted to WIG index as
well as to capitalization and book-to-market reference portfolios returns. IPOs returns
compared to control firms returns are negative in about 50% of the cases.
Wealth relative ratios for IPO returns adjusted to WIG and capitalization and book-to-
market ratio portfolio also show that IPO companies underperform the benchmarks. The
wealth ratio indicates the greatest underperformance in case of 3-year abnormal returns
compared to size and book-to-market reference portfolios and amounts to 0,77.
The wealth relative ratios for returns adjusted to control firms return for the 12 months
horizon are close to 1 which suggest equal performance with respect to the benchmark.
In case of 24 and 36 months of measuring the returns of IPOs, the results of the
portfolio strategy of investing an equal amount in every issuing firm versus an equal
amount in control nonissuing firm indicate better performance of IPOs than control
firms (reported wealth relatives of 1,09 and 1,13 for 24 and 36 months respectively).
The results of the analysis with respect to the returns adjusted to the market benchmark
are similar with those achieved for instance by Loughan and Ritter (1995, p. 36) for US
stocks (although, they have measured five-years wealth relative ratios). Brav and
61
Gompers (1997, p.1799) have also found out underperformance of IPOs relative to the
market.
Brav and Gompers (op. cit., p.1780) also show that underperformance is eliminated
when firms are matched to portfolios based on size and book-to-market, they claim that
matching by size and book-to-market eliminates the underperformance of IPO stocks,
because the majority of IPO firms are in the portfolio of the smallest and lowest book-
to-market portfolio. Contrary, the Polish sample of IPOs does show underperformance
relative to size-and book-to-market portfolio, what is more the IPO companies are quite
equally distributed among size groups (see Table 1).
When examining Spanish IPOs, Gonzalez and Alvarez (2001, p. 34) have found out
underperformance after 36 months from IPO relative to the size and book-to-market
portfolio (wealth relative 0,80) and also control firm (0,81). In contrast, the Polish
sample does not underperform if compared to control firm (1,13 for 36 months wealth
relative ratio).
Kothari and Werner (1997, p. 308) claim that the test statistics of event-returns does not
conform the standard parametric assumptions (returns are independent and identically
distributed), as a result buy-and-hold abnormal returns over reject the null hypothesis of
no abnormal performance after the end of three years. According to Mitchell and
Stafford, (2000, p. 303), the over rejection is caused by the downward biased estimate
of the standard deviation of the cross-sectional distribution of buy-and-hold abnormal
returns for the event sample of firms because the cross-dependence (lack of
independence) of returns is likely to be ignored. Consequently, the nonnormal
distribution of long-run abnormal returns of IPOs requires cautiousness when testing
significance of the zero mean abnormal return. Barber and Lyon (1997 p, 343)
recommend the use of control firm approach when testing long-run abnormal returns
because “it yields well-specified test statistics in virtually all sampling situations”. Lyon
et al. (1999, p. 165) also suggest the interference based on either a bootstrap skewness-
adjusted t-statistic or the empirically generated distribution of long-run abnormal
returns using “buy-and-hold” reference portfolio. These methods represent improved
power relative to control firm approach.
62
Following the study of Lyon et al. (op, cit) it is checked the statistical significance of
the IPO buy-and-hold abnormal returns abnormal returns (constructed with carefully
constructed “buy-and-hold” reference portfolio) using first skewness adjusted t-statistics
and finally bootstrap skewness adjusted t-statistics, To analyze the influence of the
rebalancing bias the skewness adjusted t-statistic test is also performed for abnormal
returns adjusted to the “rebalanced portfolio”.
Table 8: Skewness adjusted T-statistic for buy-and-hold abnormal returns of IPOs The hypothesis that the mean buy-and hold-abnormal returns, computed for 12, 24 and 36 months and
adjusted to the capitalization and book-to-market ratio portfolios‘ returns as well as market adjusted
returns, are zero have been check applying skewness adjusted statistics.
BHARs Equally Weighted Buy and Hold returns 12 months N=103 T student (skewness adjusted) ProbabilityWIG - value weighted index -3,4696*** 0,0008Cap and B/M portfolio rebalanced -1,6707* 0,0979Cap and B/M portfolio buy and hold -1,5578 0,1224 24 months N=103 T student (skewness adjusted) ProbabilityWIG - value weighted index -1,2065 0,2305Cap and B/M portfolio rebalanced -1,6223 0,1079Cap and B/M portfolio buy and hold -1,5583 0,1223 36 months N=101 T student (skewness adjusted) ProbabilityWIG - value weighted index -1,5353 0,1279Cap and B/M portfolio rebalanced -2,0848** 0,0396Cap and B/M portfolio buy and hold -1,9839* 0,0500
***,**,* Statistically significant at the 1%, 5% and 10% level, respectively The results of the skewness adjusted t-statistics reduce the probability of rejection of the
zero-mean abnormal return hypothesis, thus show less evidence for underperformance
than “simple” t-statistics. The values of skewness adjusted t-statistic are significant and
negative for 12-months market adjusted return and “rebalanced portfolio” adjusted
return. The evidence of underperformance id also found for the 36-months abnormal
returns adjusted to similar size and book-to-market value portfolio returns.
63
When evaluating the skewness-adjusted t-statistic, Barber et al. (1996, p.10) indicate
that only the bootstrap application of this skewness-adjusted test statistic for IPO returns
adjusted to carefully constructed reference portfolio return yields well-specified test
statistics. The results the bootstrap analysis presented in the table 9 show that in each of
the horizon analysed there is no evidence for the abnormal performance of IPOs returns
adjusted to capitalization and book-to-market ratio returns. For 10% significance levels
skewness adjusted t-statistic values are within the region limited by the bootstrap
critical values. The returns adjusted to WIG still show underperformance relative to the
market.
Table 9: Bootstrap skewness adjusted t-statistics for buy and hold abnormal
returns adjusted to capitalization and book-to-market ratio The procedure of bootstrapping the test statistics described by Lyon and Barber (1999) requires drawing
b resamples of size bn from the original sample of returns, In each of the b bootstrapped resamples of
size 4/nnb = , the skewness-adjusted test statistics is calculated Lower critical value *lx and upper
critical value *ux calculated from the 1000 resamples points out the rejection regions of null hypothesis
for the test statistics sat ,
Benchmark
skewness adjusted t-statistics empirical value
lower critical value *
lx upper critical value
*ux
12 months N=103 WIG index -3,4696 -4,1400 4,3152Cap and B/M portfolio rebalanced -1,6707 -4,6691 4,2327Cap and B/M portfolio buy and hold -1,5578 -4,2024 4,405924 months N=103 WIG index -1,2065 -5,4375 4,1530Cap and B/M portfolio rebalanced -1,6223 -4,9845 4,5800Cap and B/M portfolio buy and hold -1,5583 -5,2884 4,551436 months N=101 WIG index -1,5353 -5,6084 3,8369Cap and B/M portfolio rebalanced -2,0848 -4,8496 4,4447Cap and B/M portfolio buy and hold -1,9839 -5,0560 4,6175
***,**,* Statistically significant at the 1%, 5% and 10% level, respectively
64
To sum up, the methods of testing significance of buy-and-hold abnormal returns that
are reported to have generally misspecified test statistics (Lyon et al, p. 197) (t-statistics
for control firm, bootstrap skewness adjusted statistic for size and book-to-market
portfolio) give the consistent results and find no significant underperformance of the
sample of Polish IPOs.
4.1.2. Results of the analysis of cumulative abnormal returns
Measuring the long-run abnormal returns via cumulative abnormal returns is not
recommended because they do not measure the investor’s experience well and because
they are biased predictors of buy-and-hold abnormal returns (Barber and Lyon 1997, p.
344-345). However, to provide more robust analysis of abnormal returns and to
compare distributional properties of cumulative abnormal returns with buy-and-hold
returns, this method is also applied.
In comparison to buy-and-hold abnormal returns, distribution of cumulative abnormal
returns is substantially less skewed (see graphs 7-9). The skewness coefficient revolves
around zero and is usually slightly positive. The absence of skewness is also suggested
by median values which are close to the means for all horizons and benchmarks. When
the kurtosis value is compared, the distribution of cumulative abnormal returns has
much lower kurtosis coefficient than buy-and-hold abnormal returns and in majority of
cases it is less than 3. As a result, the distribution of cumulative abnormal returns is
more similar to the normal distribution and provide less troubles with testing the
statistical significance of the mean abnormal returns. This is consistent with the
argument given by Fama (1998, p. 295) who favors the use of CARs instead of BHARs,
On the other hand, Kothari (1997, p. 308) claims that CARs distributions are fat-tailed
relative to a normal distribution. He suggests that buy-and-hold returns over reject the
null hypothesis of no abnormal performance after the end of three years but the rejection
frequencies are comparable to those using CARs.
The results of cumulative abnormal returns analysis (Table 10) show that the magnitude
of underperformance is slightly greater that in case of buy-and-hold abnormal returns
for the 12 months horizon but lower for longer periods. The returns adjusted to WIG
and reference portfolio of similar size and book-to-market companies are significantly
65
negative for each horizon considered. The greatest underperformance is reported for
WIG index adjusted returns, with means ranging from -24,1% (for the 12 months
horizon) to -35,4% (for the 36 months period). The abnormal reference portfolio returns
vary from -12,8% (for the 12 months period) to -26,4% (for the 36 months period).
Unlike, for buy-and-hold abnormal returns method, the control firm abnormal returns
are slightly negative for the first 12 and 24 months after IPO. After the 36 months of
trading, the mean CAR is positive and amounts to 5,6%, Control firm abnormal returns
again show no significant underperformance.
Table 10: Long –run cumulative abnormal returns The table shows the mean 12, 24 and 36 months cumulative abnormal returns on IPOs. The Long-run
returns are computed monthly up to the investment horizon considered. Returns are adjusted by the return
considered normal, that is alternatively the return on the Warsaw Stock Exchange Index, the
capitalization and book-to-market portfolio return and the control firms return.
CARs Equally cumulative abnormal returns 12 months N=103 Mean abnormal Return T student Probability % AR <0 WIG index -0,2407 -4,6622*** 0,0000 68,0% Cap and B/M portfolio -0,1284 -2,3910** 0,0186 60,2% Control firm -0,0236 -0,3469 0,7294 47,6% 24 months N=103 Mean abnormal Return T student Probability % AR <0 WIG index -0,2441 -3,3233* 0,0012 66,0% Cap and B/M portfolio -0,1832 -2,2915** 0,0240 59,2% Control firm -0,0180 -0,1957 0,8452 50,5% 36 months N=101 Mean abnormal Return T student Probability % AR <0 WIG index -0,3539 -4,3067*** 0,0000 50,5% Cap and B/M portfolio -0,2640 -2,6602*** 0,0091 69,3% Control firm 0,0562 0,4614 0,6455 56,4%
***,**,* Statistically significant at the 1%, 5% and 10% level, respectively
The negative cumulative abnormal returns after 36 months from IPO have been reported
in the study of Ritter (1991, p.10). I find that the long-run underperformance is greater
when the IPO returns are compared to the market returns rather than to the capitalization
and book-to-market reference portfolio. Ritter, however, give evidence that the
abnormal returns adjusted to the portfolio of similar size companies are greater than the
market adjusted returns.
66
4.2. Results of calendar-time abnormal returns analysis
4.2.1. Results of the analysis of mean calendar-time returns
Both buy and hold abnormal returns and cumulative abnormal returns methods suffer
from cross-sectional dependence among sample firms. To address this potential problem
with event-time returns also calendar-time returns for the sample of IPO firms are
examined. As in case of cumulative abnormal returns, the mean monthly calendar-time
returns are characterized by the distribution which is close to normal - skewness close to
zero, kurtosis close to 3, (see graphs 10-12 in the appendix).
Due to changes through time in the composition of the portfolio and possible
heteroskedasticity, the statistical significance of the underperformance of IPO is tested
using not only simple t-statistic but also standardized t-statistic (series of abnormal
portfolio returns for each month are divided by an estimate of its standard deviation) are
computed. The results of mean calendar-time analysis are depicted in Table 11.
The analysis reveals predominantly negative long-run performance of IPOs when it is
calculated as the mean monthly calendar-time abnormal returns. However, the results
are not statistically significant (except the value of the 24-months abnormal return
adjusted to WIG). Consequently, the hypothesis of mean zero abnormal return can not
be rejected and there are no evidence supporting the underperformance of IPOs. It can
be observed that for each horizon analyzed, the mean abnormal returns are lower when
WIG is applied as a benchmark as compared to size and book-to-market benchmark.
The specification of mean monthly calendar-time returns have been provided by Lyon
and Barber (1999, p. 196) who find that the methods yields well-specified test statistic.
Fama (1998, p. 295) argue that “improved methods for BHARs produce inferences no
more reliable than simpler methods based on average monthly returns“. The results of
the analysis of the Polish IPO sample based on these improved methods (control firm
approach, bootstrap skewness adjusted t-statistic) are consistent with mean monthly
abnormal returns and provide no evidence of underperformance.
67
The outcome of the calendar-time portfolio returns analysis for the Spanish sample of
IPOs (Alvarez and Gonzalez, 2001, p. 37) also reveals non-existence of significant
abnormal returns after 36 months from IPO (expect from single cases when portfolios
are formed based on the market index).
Table 11: Mean calendar-time abnormal returns on IPOs The table shows the 12, 24 and 36-months calendar-time portfolio returns on IPOs. The performance is
calculated as the return of a portfolio composed in each month by the stocks of those firms that have
carried out an IPO in the previous 12, 24 or 36 months. Abnormal returns are computed in relation to the
WIG value weighted market index and reference portfolio of book-to-market companies. To test the null
hypothesis of zero mean monthly abnormal returns, a t-statistic is calculated using the time-series of
abnormal portfolio returns for each month as well as standardized t-statistic using the time-series of
abnormal portfolio return for each month divided by an estimate of its standard deviation. Standardized t-
statistic considers the heteroskedasticity of the portfolio abnormal return due to changes in portfolio
composition over time.
Equally weighted mean monthly calendar time returns
12 months Mean abnormal Return T Student
Probability (%)
Standardized T Student
Probability (%)
N=42 WIG index -0,0010 -0,9275 0,3591 -0,3026 0,7637 Cap and B/M portfolio 0,0008 0,0658 0,9478 0,5835 0,5627
24 months Mean abnormal Return T Student
Probability (%)
Standardized T Student
Probability (%)
N=42 WIG index -0,0010 -1,3804 0.1749 -1.9573* 0,0571 Cap and B/M portfolio -0,0018 -0,2023 0.8407 -0.4347 0,6661
36 - months Mean abnormal Return T Student
Probability (%)
Standardized T Student
Probability (%)
N=42 WIG index -0,0046 -0,5413 0.5912 -1,4251 0,1617 Cap and B/M portfolio -0,0014 -0,1629 0.8714 -1,1601 0,2527
***,**,* Statistically significant at the 1%, 5% and 10% level. respectively
4.2.2. Results of the analysis using the Fama–French three-factor model
The works by Fama and French (1992, 1993) indicate that the three-factor model may
explain the cross section of stock returns. I use the model (following Brav and Gompers
(1997) and Lyon et al. (1999), Gompers and Lerner (2003) among others) to analyze
returns on calendar-time portfolios of IPOs.
68
Table 12: Fama-French Three-Factor Model The table shows the results of the Fama-French three-factor model. Assuming that the investment horizon
to be analyzed is three years, I have calculated the monthly returns on a portfolio composed of all IPO
firms during a period of 3 years. The dependent variable is simply monthly return on the calendar-time
portfolio-equally weighted (the portfolio is composed of all IPO firms during the last three years) less the
monthly risk free rate of return. The independent variables are RMRF - the difference between the return
on the value-weighted marked index and the return on the monthly risk free rate of return; SMB is the
difference in the returns of the value-weighted portfolios of small and big stocks. and HML is the
difference in the returns of the value-weighted portfolios of high book-to-market stocks and low book-to-
market stocks. Since the number of securities in the calendar-time portfolio varies from one month to the
next, the error term in this regression may be heteroskedastic. Due to the fact, weighted least squared
estimation has been used, where the weighting factor is based on the number of securities in the portfolio
in each calendar month.
Dependent Variable: Y_R_PT__R_FT_ Method: Least Squares N=42 White Heteroskedasticity-Consistent Standard Errors & Covariance Variable Coefficient Std. Error t-Statistic Prob. C 0,5350 1,0682 0,5009 0,6194 X1_RMRF 1,0967 0,1031 10,633*** 0,0000 X2_SMB 0,2771 0,0891 3,1088*** 0,0036 X3_HML 0,3865 0,1288 3,0003*** 0,0047 Weighted Statistics R-squared 0,8537 Mean dependent var -6,9149 Adjusted R-squared 0,8422 S.D. dependent var 9,7113 S.E. of regression 3,8579 Akaike info criterion 5,6285 Sum squared resid 565,57 Schwarz criterion 5,7940 Log likelihood -114,20 F-statistic 53,780 Durbin-Watson stat 2,3607 Prob(F-statistic) 0,0000 Unweighted Statistics R-squared 0,7667 Mean dependent var -5,6619 Adjusted R-squared 0,7483 S.D. dependent var 8,9982 S.E. of regression 4,5141 Sum squared resid 774,33 Durbin-Watson stat 2,2455
***.**.* Statistically significant at the 1%. 5% and 10% level. respectively
The results obtained for the Polish IPO sample using the Fama-French three-factor
model provide no evidence for existence of long-run abnormal returns. The observed
value of intercept takes positive but not statistically significant value. The intercept is
used as an indicator of risk-adjusted performance with the interpretation analogous to
69
Jensen’s alpha in the CAMP model. Moreover, the coefficients for the market premium
(RM-RF), the return on a zero investment portfolio formed by subtracting the return on
a large firm portfolio from the return on a small firm portfolio (SMB), the return on a
zero investment portfolio calculated as the return on a portfolio of high book-to-market
stocks minus the return on a portfolio of low book-to-market stocks (HML) have
statistically significant values. The independent variables explain the large variation of
the model, R–squared of 85.4% shows that the model is well specified.
The literature suggests that abnormal returns are much higher when measured in event-
time (CAR and BHAR) than in calendar-time (mean calendar-time returns. Fama-
French three-factor model). The study of Alvarez and Gonzalez (2002, p.18) shows that
when using buy-and-hold method, abnormal returns calculated for the periods of 36 and
60 months are present and rarely significant. However, the results of the methodologies
based on the calendar-time portfolios state non-existence of long-run performance.
Gompers and Lerner (2003 p.1256) have analyzed the US IPOs issued between 1935
and 1972, their results suggest that the underperformance depend on the method of
measurement, it exist in event-time but not in calendar-time.
4.3. Results of the analysis of determinants of the long-run performance of IPOs
Tables 13 and 14 report the results of the logit models estimations. Table 13 provides
the results of the logistic regression where the dependent variable is 3-years buy-and-
hold abnormal return adjusted to the reference portfolio of similar capitalization and
book-to-market companies, table 14 depicts the results of the logistic regression model
where the depended variable is 3-years “raw” buy-and-hold return. As independent
variables the firm and the offer characteristics have been used.
The results of the logit model with 3-years buy-and-hold abnormal return as dependent
variable (Table 13) reveal no significant impact of the explanatory variables when all of
the variables are applied. When the variables with the highest probability of zero
hypotesis are gradually removed from the model, it is found only one significant
relation between the long-run abnormal performance of IPOs and the offer size.
70
Table 13: Long-run abnormal performance of IPOs and the Firm and Offering
Characteristics The table shows the effects of the variables on the probability of the success of IPO estimated by a logit
model. The independent variable is the 3-years buy-and-hold abnormal return adjusted to the return on the
portfolio of similar capitalization and book-to-market ratio companies. It takes on a value 1 if the firm has
the positive abnormal return and 0 if the return is negative. The only explanatory variables that positively
influence on the long-run abnormal performance of IPO is the offer size described as OFFER – natural
logarithm of a company offer size.
Dependent Variable: 3-year buy-and-hold abnormal return Method: ML - Binary Logit (Quadratic hill climbing) Included observations: 94 Variable Coefficient Std. Error z-Statistic Prob. C OFFER 0.309827 0.131083 2.363602 0.0181 Mean dependent var 0.333333 S.D. dependent var 0.473804S.E. of regression 0.461644 Akaike info criterion 1.251063Sum squared resid 20.67220 Schwarz criterion 1.303490Log likelihood -59.92763 Hannan-Quinn criter. 1.272275Restr. log likelihood -63.01490 Avg. log likelihood -0.605330LR statistic (9 df) 6.174555 McFadden R-squared 0.048993Probability(LR stat) 0.012960 Obs with Dep=0 63 Total obs 94Obs with Dep=1 31
***,**,* Statistically significant at the 1% 5% and 10% level. respectively
On the other hand, the results of the logit model with 3-years buy-and-hold “raw” return
as dependent variable (Table 14) show significant impact of IPO company assets size,
IPO offer size, vc/pe backing and the reputation of offer lead manager on the probability
of the success of IPO. When the variables with the highest p-value were gradually
eliminated from the model, there have been also found significant negative relation
between the long-run performance of IPOs and the company age as well as the level of
underpricing.
The likelihood ratio chi-square of 29,13 with a very small p-value indicates that the
model (Table 14) as a whole is statistically significant (significantly different from zero
at the 1 percent level). Moreover, the logit analysis taking as an dependent variable 3-
years “raw” buy-and-hold return performed for the sample of IPOs excluding financial
71
institutions and privatized companies also finds the size of the assets as the significant
explanatory variable (results not reported).
Table 14: Long-run performance of IPOs and Firm and Offering Characteristics The table shows the effects of the variables on the probability of the success of IPO estimated by a logit
model. The independent variable is the 3-years “raw” buy-and-hold return that takes on a value 1 if the
firm has a positive 3 return and 0 if the return is negative. The explanatory variables are ASSETS which
is the natural logarithm of a company assets year end before IPO, ROA - the return on a company assets
year end before IPO, RETENTION – percentage of shares retained by the initial owners after IPO,
OFFER – natural logarithm of a company offer size, SEO – number of secondary equity offering in the
five year period following IPO, UNDERPRICING – natural logarithm of one plus market adjusted initial
return, AGE – number of a company year from incorporation till IPO, VC/PE – VC/PE backed IPO,
REPUTATION – reputation of lead manager of the offer
Dependent Variable: 3-years “raw” buy-and-hold return
Method: ML - Binary Logit (Quadratic hill climbing)
Included observations: 94
Variable Coefficient Std. Error z-Statistic Prob.
C -13,676 3,4659 -3,9459 0,0001
ASSETS 0,3654 0,1886 1,9373 0,0527
OFFER 0,4847 0,2007 2,4146 0,0158
UNDERPRICING -2,5740 1,3008 -1,9788 0,0478
AGE -0,0264 0,0160 -1,6473 0,0995
VC_PE 1,5769 0,7195 2,1915 0,0284
REPUTATIOON 0,7394 0,3503 2,1105 0,0348
Mean dependent var 0,4255 S.D. dependent var 0.497074
S.E. of regression 0,4355 Akaike info criterion 1.203114
Sum squared resid 16,497 Schwarz criterion 1.392508
Log likelihood -49,546 Hannan-Quinn criter. 1.279615
Restr. log likelihood -64,1094 Avg. log likelihood -0.527089
LR statistic (6 df) 29,1261 McFadden R-squared 0.227159
Probability(LR stat) 0,0000576
Obs with Dep=0 54 Total obs 94
Obs with Dep=1 40 ***.**.* Statistically significant at the 1%. 5% and 10% level. respectively Based on the results received from the logit model with raw return as independent
variable, the hypotheses about the influence of the firm and the offer characteristics on
the long run performance of IPOs are discussed.
72
Hypothesis 1
The offer size and the value of company assets is positively correlated with the long
run performance of IPOs
The results of the analysis indicate significant, positive relation between the proxies of
the IPO company size (value of assets, value of IPO offer) and the long run performance
of IPO represented by the positive “raw” 3-years buy-and-hold return. Greater
companies are usually more established ones and are characterized with lower
divergence of opinion and are less subject to fads. The analysis supports the findings of
Levis (1993, p. 38), Fields (1995, p. 24), Brav and Gompers (1997, p. 1819) that the
greater the IPO companies the more likely they are to experience positive long-run
performance.
Hypothesis 2
Company profitability is negatively correlated with the likelihood of IPO success
Although the study of Mikkelson and Shah (1994) has showed that the long-run share
price performance and the change in operating performance from before to after
flotation are negatively related. No evidence has been found to support the idea that the
more profitable an IPO firm is prior to going public the worse is the long run
performance. Consequently, also the suggestion that companies go public at the height
of their performance in order to take advantage of “the window of opportunity” has not
been proved.
Hypothesis 3
Company maturity is positively correlated with the long-run stock performance of
IPO
The analysis reveals negative and significant relation between the age of the company
understand as the number of years from establishment till IPO and the after-market
stock performance. However, the studies of Ritter (1991), Fields (1995) and Carter et al.
(1998) provide opposite results. The reverse finding may suggest that more established
companies rising at a lower rate than the young, dynamically growing companies.
73
Such a surprising finding may also be connected with fact that the age variable is likely
to produce misleading results in case of the Polish IPO sample. The Warsaw Stock
Exchange has been opened in 1991, as a result many companies had to wait for a chance
to be quoted. Moreover, there were periods that the IPOs companies were
predominantly old ones (mean age in 1997 equals to 27) while in others young
companies dominated (mean age of 10 in 2004). Consequently, the influence of the age
variable on the after-market performance can not be fully comparable with international
research.
Hypothesis 4
Amount of underpricing and number of SEO is positively correlated with the
success of IPO
The study finds no evidence for the positive impact of underpricing and the number of
SEO on the long-run returns of IPOs and the support for the signaling hypothesis of
Allenand Faulhaber (1989, p. 304). Instead, the negative, significant impact of
underpricing on the long run performance of IPOs supports the finding of Ritter (1991,
p. 15) and Levis (1993, p. 41) who give evidence that the higher the return on the first
trading day the worst is the performance of IPOs in the long-run.
Hypothesis 5
The percentage of shares retained by the initial investors is positively correlated
with the likelihood of success of IPO
As far as the retention ratio is concerned, I have found no relation between the
proportion of equity retained by the initial owners and the long-run performance of
IPOs. My results are similar to those obtained by Mikkelson et al. (1997, p. 306) and
Goergen (1998, p. 24) who investigated the relation between long-run performance and
ownership. They have found that the long-run performance both within one year of
offering and during the first ten years of public trading is unrelated to the ownership
structure. Consequently, I can not support either “wealth effects” hypothesis of Ritter’s
(1984b. p. 1232 ) either “agency problem” suggested by Jain and Kini (1994. p. 1725).
74
Hypothesis 6
VC/PE backing is positively correlated with the success of IPO
The analysis reveals the positive significant impact of the presence of the venture
capital/ private equity in the ownership structure of the companies before IPO on their
long-run performance. Based on the findings, I can confirm, in line with Brav and
Gompers (1997, p. 1791) that vc/pe capital provide assurance of continued monitoring
and can credibly signal their belief in the firm’s prospects. Venture-backed / private
equity backed companies’ prices may be less susceptible to investor sentiment because
of the lower potential asymmetric information between the firms and investors and the
higher institutional shareholding.
Hypothesis 7
High reputation of lead manager of IPO increases chances for the success of the
company
The results of the analysis show statistically significant, positive relation between the
reputation of lead managers handling IPOs and successful after-market performance of
stocks. It is explained by the fact that the lead managers / underwriters with better
reputations have more to lose from a failed IPO, and as a result they refrain from
underwriting IPOs whose future is very uncertain or whose returns are hard to predict.
The results of the analysis are in line with those obtained by Michaely and Shaw (1994.
p. 274) and Carter et al. (1998. p. 296).
75
5. Conclusions, limitations and suggestions for further research
The aim of this paper was to investigate the long-run performance of IPOs based on the
sample of Polish companies which went public between July 1998 and June 2005. The
second objective of the thesis was to find out whether the firm or the IPO offer
characteristics variables can predict a success of an IPO.
With respect to the first aim, some evidence of the long-run underperformance have
been noticed when the abnormal returns were computed with the application of event-
time abnormal returns, both buy-and-hold abnormal returns and cumulative abnormal
returns, with the use of such benchmarks as the WIG index return and also the reference
portfolio of similar size and book-to-market ratio companies return. No significant
underperformance was found when the control firm approach was used to adjust the
IPO returns.
However, when buy and hold abnormal returns were tested with skewness adjusted t-
statistics, the evidence of the long-run underperformance appeared to be less significant.
When the bootstrap skewness adjusted statistics was applied no evidence of long-run
underperformance of IPOs was found in case of buy-and-hold abnormal returns adjusted
to similar size and book-to-market reference portfolio returns.
The conclusion of no significance of abnormal returns was also raised from the analysis
of calendar-time abnormal returns. The returns computed by the mean calendar-time
returns method, as well as the estimation of the Fama-French model, found no evidence
for long-run underperformance of IPOs.
As far as the limitations of the study are concerned, the analysis of IPO stock
performance was conducted weighting the returns equally, while it would be interesting
to see the results of the analysis comparing the outcome of value-weighting and equal-
weighting schemes. Furthermore, no much attention has been given to the relative
change of IPO performance with respect to the different subperiods of the analysis’
horizon. As it is claimed by Ritter and Welch (2002, p. 1796), the long-run performance
of IPOs is sensitive not only to the choice of measuring methodology, but also it
76
depends on the sample period used; thus, the changing market conditions definitely
influence the stock performance.
While finding the underperformance of IPOs abnormal returns, one needs to be cautious
reporting the evidence of IPO mispricing. The results of the analysis of the returns
adjusted to matching firms or tested via the multifactor model should rather be
interpreted as the existence or lack of similarity to certain public firms, rather than
provide evidence of market (in)efficiency.
With respect to the second goal of the thesis, the analysis of the association between the
long-run performance of IPOs, assumed as the three-years “raw” buy-and-hold
abnormal return post IPO, and the IPO offer and firm characteristics variables, I have
noticed significant relation between the size of company assets, size of the IPO offer,
the reputation of the lead manager and the vc/pe backing, whereas the age of the
company and the level of underpricing are found to be negatively related to the long-run
performance. Moreover, when the three-years long-run abnormal return is applied as an
independent variable, the only significant positive relation is found for the offer size
variable.
The logit analysis does not cover the full list of possible determinants that can influence
the post-IPO stock performance. The several hypotheses that seem to be interesting to
examine are, for instance, “earning management” theory (Teoh, Welch, Wong, 1998)
and the influence of analyst recommendation on the long-run performance of IPOs
(Michaely and Womack (1999). One can also test if “flipping” by institutional investors
(Krigman et al. (1999)) succeeds in identifying long-run performance o IPOs.
77
Appendices
Graph 4: Distribution and descriptive statistics of 12-months buy-and-hold returns The graphs present distribution and descriptive statistics of 12-months buy–and-hold abnormal returns (12-months buy-and-hold abnormal returns adjusted to the Warsaw Stock Exchange Index returns, 12- months buy-and hold abnormal returns adjusted to the capitalization and book-to-market “rebalanced” portfolio returns, 12-months buy-and-hold abnormal returns adjusted to the capitalization and book-to-market “buy-and-hold” portfolio returns and 12-months buy-and-hold abnormal returns adjusted to control firm returns)
Graph 5: Distribution and descriptive statistics of 24-months buy-and-hold returns The graphs present distribution and descriptive statistics of 24-months buy–and-hold abnormal returns (24-months buy-and- hold abnormal return adjusted to the Warsaw Stock Exchange Index returns, 24-months buy-and hold abnormal returns adjusted to the capitalization and book-to-market “ rebalanced” portfolio returns, 24-months buy-and-hold abnormal returns adjusted to the capitalization and book-to-market “buy-and-hold” portfolio returns and 24-months buy-and-hold abnormal returns adjusted to control firm returns).
0
2
4
6
8
10
12
14
16
-1.25 0.00 1.25 2.50 3.75 5.00 6.25
Series: BHAR_CONTR_FIRM_2_YEARSample 1 103Observations 103
Mean 0.115337Median 0.001953Maximum 6.341929Minimum -2.110206Std. Dev. 1.320185Skewness 1.382869Kurtosis 7.321055
Jarque-Bera 112.9602Probability 0.000000
0
5
10
15
20
25
30
-4 -2 0 2 4 6
Series: BH_BHAR_CAP_AND_B_M__2Sample 1 103Observations 103
Mean -0.225072Median -0.288013Maximum 5.674734Minimum -4.846625Std. Dev. 1.414855Skewness 0.558693Kurtosis 6.825891
Jarque-Bera 68.17738Probability 0.000000
0
2
4
6
8
10
12
14
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Series: BHAR_CONTR_FIRM_1_YEARSample 1 103Observations 103
Mean 0.010031Median 0.025758Maximum 2.071819Minimum -1.529801Std. Dev. 0.708260Skewness 0.324432Kurtosis 3.950855
Jarque-Bera 5.687101Probability 0.058219
0
2
4
6
8
10
12
14
-1.0 -0.5 -0.0 0.5 1.0 1.5
Series: BH_BHAR_CAP_AND_B_M__1Sample 1 103Observations 103
Mean -0.087578Median -0.178198Maximum 1.685770Minimum -1.082766Std. Dev. 0.539667Skewness 0.857524Kurtosis 3.707544
Jarque-Bera 14.77195Probability 0.000620
0
2
4
6
8
10
12
14
-1.0 -0.5 -0.0 0.5 1.0 1.5
Series: BHAR_WIG__1_YEARSample 1 103Observations 103
Mean -0.199633Median -0.316552Maximum 1.445893Minimum -1.116182Std. Dev. 0.512916Skewness 0.921832Kurtosis 3.347917
Jarque-Bera 15.10729Probability 0.000524
0
2
4
6
8
10
12
14
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Series: REBALANCED_BHAR_CAP_ANSample 1 103Observations 103
Mean -0.094302Median -0.163245Maximum 1.688197Minimum -1.534050Std. Dev. 0.543410Skewness 0.776378Kurtosis 4.008205
Jarque-Bera 14.70981Probability 0.000639
0
4
8
12
16
20
-1.25 0.00 1.25 2.50 3.75 5.00
Series: BHAR_WIG__2_YEARSample 1 103Observations 103
Mean -0.155325Median -0.433911Maximum 5.664249Minimum -1.478818Std. Dev. 1.126871Skewness 2.418851Kurtosis 11.09876
Jarque-Bera 381.9295Probability 0.000000
0
4
8
12
16
20
-2.5 0.0 2.5 5.0
Series: REBALANCED_BHAR_CAP_A0Sample 1 103Observations 103
Mean -0.236012Median -0.275853Maximum 5.726969Minimum -3.303920Std. Dev. 1.401512Skewness 0.783627Kurtosis 6.006201
Jarque-Bera 49.32640Probability 0.000000
78
Graph 6: Distribution and descriptive statistics of 36-months buy-and-hold returns The graphs present descriptive statistics of 36-months buy–and-hold abnormal returns (36-months buy-and- hold abnormal return adjusted to the Warsaw Stock Exchange Index returns, 36-months buy-and hold abnormal returns adjusted to the capitalization and book-to-market “ rebalanced” portfolio returns, 36-months buy-and-hold abnormal returns adjusted to the capitalization and book-to-market “buy-and-hold” portfolio returns and 36-months buy-and-hold abnormal returns adjusted to control firm return).
Graph 7: Distribution and descriptive statistics of 12-months cumulative returns The graphs present descriptive statistics of 12-months cumulative abnormal returns (12-months cumulative abnormal return adjusted to the Warsaw Stock Exchange Index returns, 12-months cumulative abnormal returns adjusted to the capitalization and book-to-market portfolio returns and 12-months cumulative abnormal returns adjusted to control firm return).
0
2
4
6
8
10
12
14
-1.5 -1.0 -0.5 0.0 0.5
Series: CAR_WIG_1_YEARSample 1 103Observations 103
Mean -0.240710Median -0.245472Maximum 0.831206Minimum -1.445035Std. Dev. 0.523991Skewness 0.079498Kurtosis 2.415063
Jarque-Bera 1.576893Probability 0.454550
0
2
4
6
8
10
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Series: CAR_CONTR_FIRM_1_YEARSample 1 103Observations 103
Mean -0.023574Median 0.069031Maximum 2.162516Minimum -1.604828Std. Dev. 0.689723Skewness 0.222294Kurtosis 3.568859
Jarque-Bera 2.237072Probability 0.326758
0
4
8
12
16
20
24
-2.5 0.0 2.5 5.0 7.5 10.0
Series: BHAR_CONTR_FIRM__3_YEASample 1 103Observations 101
Mean 0.158611Median 0.000473Maximum 10.59471Minimum -4.105158Std. Dev. 1.811808Skewness 2.067084Kurtosis 13.02348
Jarque-Bera 494.7381Probability 0.000000
0
5
10
15
20
25
-4 -2 0 2 4 6 8
Series: BH_BHAR_CAP_AND_B_M__3Sample 1 103Observations 101
Mean -0.425853Median -0.369586Maximum 9.107571Minimum -5.410185Std. Dev. 1.979643Skewness 1.052912Kurtosis 8.535062
Jarque-Bera 147.5922Probability 0.0000000
5
10
15
20
25
30
-2 0 2 4 6 8
Series: BHAR_WIG__3_YEARSample 1 103Observations 101
Mean -0.310727Median -0.637846Maximum 9.405405Minimum -2.447847Std. Dev. 1.608216Skewness 2.912965Kurtosis 15.99450
Jarque-Bera 853.4434Probability 0.000000
0
4
8
12
16
20
24
28
-6 -4 -2 0 2 4 6 8
Series: REBALANCED_BHAR_CAP_A0Sample 1 103Observations 101
Mean -0.472310Median -0.305567Maximum 9.426699Minimum -5.723251Std. Dev. 2.120143Skewness 0.855112Kurtosis 7.652222
Jarque-Bera 103.3905Probability 0.000000
0
2
4
6
8
10
12
-1.5 -1.0 -0.5 0.0 0.5 1.0
Series: CAR_CAP_B_M_1_YEARSample 1 103Observations 103
Mean -0.128354Median -0.125474Maximum 1.106238Minimum -1.502729Std. Dev. 0.544806Skewness 0.021407Kurtosis 2.742093
Jarque-Bera 0.293331Probability 0.863583
79
Graph 8: Distribution and descriptive statistics of 24 months cumulative returns The graphs present descriptive statistics of 24-months cumulative abnormal returns (24-months cumulative abnormal returns adjusted to the Warsaw Stock Exchange Index returns, 24-months cumulative abnormal returns adjusted to the capitalization and book-to-market portfolio returns and 24- months cumulative abnormal return adjusted to control firm returns).
Graph 9: Distribution and descriptive statistics of 36-months cumulative returns The graphs present descriptive statistics of 36-months cumulative abnormal returns (36-months cumulative abnormal returns adjusted to the Warsaw Stock Exchange Index returns, 36-months cumulative abnormal return adjusted to the capitalization and book-to-market portfolio returns and 36- months cumulative abnormal returns adjusted to control firm return).
0
2
4
6
8
10
12
-3 -2 -1 0 1 2 3
Series: CAR_CONTR_FIRM_3_YEARSample 1 103Observations 101
Mean 0.056166Median -0.011208Maximum 3.444772Minimum -3.108508Std. Dev. 1.223449Skewness 0.198855Kurtosis 2.808007
Jarque-Bera 0.820771Probability 0.663395
0
4
8
12
16
20
-2 -1 0 1 2
Series: CAR_WIG_2_YEARSample 1 103Observations 103
Mean -0.244063Median -0.301441Maximum 2.030915Minimum -2.232071Std. Dev. 0.745344Skewness -0.070498Kurtosis 3.465031
Jarque-Bera 1.013407Probability 0.602478
0
2
4
6
8
10
12
14
-2 -1 0 1 2 3
Series: CAR_CONTR_FIRM_2_YEARSample 1 103Observations 103
Mean -0.018032Median -0.036685Maximum 3.061033Minimum -2.351017Std. Dev. 0.935020Skewness 0.332488Kurtosis 3.292585
Jarque-Bera 2.265133Probability 0.322205
0
2
4
6
8
10
12
14
16
-2 -1 0 1 2
Series: CAR_CAP_B_M_2_YEARSample 1 103Observations 103
Mean -0.183200Median -0.204601Maximum 2.111926Minimum -2.370202Std. Dev. 0.811372Skewness -0.068839Kurtosis 2.879683
Jarque-Bera 0.143478Probability 0.930774
0
2
4
6
8
10
12
14
16
-2 -1 0 1 2
Series: CAR_CAP_B_M_3_YEARSample 1 103Observations 101
Mean -0.264018Median -0.148530Maximum 2.127419Minimum -2.123231Std. Dev. 0.997406Skewness 0.018420Kurtosis 2.515037
Jarque-Bera 0.995467Probability 0.607907
0
4
8
12
16
20
-2 -1 0 1 2
Series: CAR_WIG_3_YEARSample 1 103Observations 101
Mean -0.353912Median -0.377417Maximum 2.098622Minimum -2.286743Std. Dev. 0.825859Skewness 0.155968Kurtosis 2.904237
Jarque-Bera 0.448082Probability 0.799282
80
Graph 10: Distribution and descriptive statistics of 12-months monthly calendar-
time returns The graphs present distribution and descriptive statistics of 12-months calendar-time portfolio abnormal returns (12- months calendar-time portfolio abnormal returns adjusted to the Warsaw Stock Exchange Index returns and 12-months calendar-time portfolio abnormal returns adjusted to the capitalization and book-to-market portfolio returns).
Graph 11: Distribution and descriptive statistics of 24-months monthly calendar-
time returns The graphs present distribution and descriptive statistics of 24-months calendar-time portfolio abnormal returns (24-months calendar-time portfolio abnormal returns adjusted to the Warsaw Stock Exchange Index returns and 24 month calendar-time portfolio abnormal returns adjusted to the capitalization and book-to-market portfolio returns). Graph 12: Distribution and descriptive statistics of 36-months monthly calendar-
time returns The graphs present descriptive statistics of 36-months calendar-time portfolio abnormal returns (36- months calendar-time portfolio abnormal returns adjusted to the Warsaw Stock Exchange Index returns and 36-months calendar-time portfolio abnormal returns adjusted to the capitalization and book-to-market portfolio return).
0
2
4
6
8
10
12
-15 -10 -5 0 5 10 15
Series: __YEAR_AR_WIGSample 1 42Observations 42
Mean -0.461869Median -0.328907Maximum 15.07313Minimum -14.22944Std. Dev. 5.529758Skewness 0.237911Kurtosis 3.857404
Jarque-Bera 1.682711Probability 0.431126
0
2
4
6
8
10
12
-20 -10 0 10
Series: __YEAR_AR_WIGSample 1 42Observations 42
Mean -1.027475Median -1.059387Maximum 17.18089Minimum -22.60069Std. Dev. 7.179075Skewness -0.160544Kurtosis 4.353411
Jarque-Bera 3.385935Probability 0.183973
0
2
4
6
8
10
12
-10 -5 0 5 10
Series: __YEAR_AR_WIGSample 1 42Observations 42
Mean -1.006357Median -1.012171Maximum 9.005890Minimum -12.37068Std. Dev. 4.724705Skewness -0.306717Kurtosis 3.003310
Jarque-Bera 0.658547Probability 0.719446
0
4
8
12
16
-10 -5 0 5 10 15
Series: __YEAR_AR_B_MSample 1 42Observations 42
Mean -0.139485Median -1.085819Maximum 13.52436Minimum -11.93543Std. Dev. 5.549311Skewness 0.253107Kurtosis 3.317280
Jarque-Bera 0.624609Probability 0.731759
0
1
2
3
4
5
6
7
-10 0 10 20
Series: __YEAR_AR_B_MSample 1 42Observations 42
Mean 0.084593Median -0.641775Maximum 20.88718Minimum -16.50837Std. Dev. 8.323952Skewness 0.353673Kurtosis 3.191438
Jarque-Bera 0.939726Probability 0.625088
0
2
4
6
8
10
12
-15 -10 -5 0 5 10 15
Series: __YEAR_AR_B_MSample 1 42Observations 42
Mean -0.178850Median -0.055691Maximum 12.61237Minimum -13.26965Std. Dev. 5.730545Skewness -0.085608Kurtosis 3.360173
Jarque-Bera 0.278318Probability 0.870089
81
List of tables
Table 1: IPO companies classification in portfolios according to size and book-to-market ratio..................................................................................................................... 32 Table 2: Summary of the hypotheses about firm and IPO offer factors influencing long-run performance of IPOs ................................................................................................ 46 Table 3: IPO volume in European stock exchanges ....................................................... 47 Table 4: Offering value in European stock exchanges ................................................... 48 Table 5: Number of IPO companies included in the sample of event-time returns analysis ........................................................................................................................... 52 Table 6: Characteristics of IPO sample companies for logit model ............................... 53 Table 7: Long-run buy-and-hold abnormal returns on IPOs .......................................... 59 Table 8: Skewness adjusted T-statistic for buy-and-hold abnormal returns of IPOs ..... 62 Table 9: Bootstrap skewness adjusted t-statistics for buy and hold abnormal returns adjusted to capitalization and book-to-market ratio ....................................................... 63 Table 10: Long –run cumulative abnormal returns ........................................................ 65 Table 11: Mean calendar-time abnormal returns on IPOs.............................................. 67 Table 12: Fama-French Three-Factor Model ................................................................. 68 Table 13: Long-run abnormal performance of IPOs and the Firm and Offering Characteristics ................................................................................................................ 70 Table 14: Long-run performance of IPOs and Firm and Offering Characteristics ........ 71 List of graphs
Graph 1: Total number of listed companies and the market capitalization on the Warsaw Stock Exchange in years 1991 – 2008*.......................................................................... 49 Graph 2: Value of WIG index and total turnover in years 1991-2008*......................... 50 Graph 3: Number of debuts and delistings in years 1991 - 2008* ................................. 51 Graph 4: Distribution and descriptive statistics of 12-months buy-and-hold returns..... 77 Graph 5: Distribution and descriptive statistics of 24-months buy-and-hold returns..... 77 Graph 6: Distribution and descriptive statistics of 36-months buy-and-hold returns..... 78 Graph 7: Distribution and descriptive statistics of 12-months cumulative returns ........ 78 Graph 8: Distribution and descriptive statistics of 24 months cumulative returns......... 79 Graph 9: Distribution and descriptive statistics of 36-months cumulative returns ........ 79 Graph 10: Distribution and descriptive statistics of 12-months monthly calendar-time returns ............................................................................................................................. 80 Graph 11: Distribution and descriptive statistics of 24-months monthly calendar-time returns ............................................................................................................................. 80 Graph 12: Distribution and descriptive statistics of 36-months monthly calendar-time returns ............................................................................................................................. 80 List of supplementary materials
Supplementary material I: Input data for buy-and-abnormal returns analysis Supplementary material II: Input data for cumulative abnormal returns analysis Supplementary material III: Input data for mean monthly calendar-time portfolio returns analysis Supplementary material IV: Input data for Fama-French three-factor model Supplementary material V: Input data for the logit model
82
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Supplementary material I: Input data for buy-and-abnormal returns analysis
No Comany name IPO year Raw BHR 1 year BHAR WIG 1 yearBHAR rebalanced cap and B-M 1 year
BHAR "buy-and-hold" cap and B-M 1 year
BHAR control firm 1 year
1 PEKAO 1998 -22,7% -31,7% -13,8% -14% 33,0%2 LZPS 1998 -31,6% -31,6% 0,0% -35% -31,8%3 MOSTOSTAL PLC 1998 -12,4% -36,7% -29,9% -31% 11,1%4 SUWARY 1998 -25,4% -59,9% -63,6% -63% -146,5%5 ENERGOPOL 1998 -1,0% -11,5% -13,4% -14% -19,2%6 TPSA 1998 47,6% 23,7% 39,5% 44% 51,9%7 GROCLIN 1998 -85,7% -111,6% -108,7% -108% -75,5%8 GANT 1998 -14,8% -52,7% -40,3% -37% -1,8%9 BEDZIN 1998 -24,4% -62,5% -109,3% -106% -152,9%
10 PEMUG 1999 -4,0% -40,7% -3,6% -5,3% 14,6%11 SKOTAN 1999 14,8% -28,3% -20,3% -24,6% 39,5%12 TUP 1999 -16,7% -53,3% -7,2% -8,5% 23,9%13 POLNORD 1999 -20,6% -73,9% -13,0% -13,9% 26,4%14 CSS 1999 10,3% -54,7% 1,1% 0,2% 34,3%15 NAFTOBUDOWA 1999 -28,3% -82,7% -34,2% -35,0% 11,4%16 SZEPTEL (MNI) 1999 86,3% 43,9% 83,8% 97,9% 73,6%17 AGORA 1999 112,8% 87,9% 80,1% 88,1% 66,9%18 INSTAL KRAKOW 1999 123,5% 95,1% 128,5% 124,6% 131,3%19 COMARCH 1999 20,8% -7,1% -3,6% 2,4% 27,4%20 PROSPER 1999 -28,4% -43,3% -27,8% -28,4% -42,1%21 CASPOL(FON) 1999 -9,4% -38,0% -9,9% -20,0% 34,0%22 TU EUROPA 1999 96,7% 72,5% 103,6% 93,9% 5,1%23 FARMACOL 1999 -2,4% -20,7% -0,1% -0,9% 12,8%24 PROJPRZEM 1999 -40,5% -53,2% -33,0% -37,5% 8,0%25 PEKABEX 1999 -43,5% -54,2% -56,5% -56,7% -6,0%26 POLLENA EWA 1999 -10,5% -20,3% -22,2% -23,1% -102,7%27 PKN 1999 -8,2% -12,6% -20,8% -21,1% -18,3%28 KZWM 1999 -60,5% -61,7% -48,1% -48,1% -17,8%29 LTL 1999 -28,6% -26,3% -15,4% -16,3% -15,9%
30 BEEFSAN 2000 -74,0% -38,5% -42,5% -43,9% -6,3%31 STALPROFI 2000 -2,1% 18,6% 19,3% 18,6% -10,0%32 KOGENERA 2000 -8,4% 6,7% -0,2% 2,2% -28,6%33 MACROSOFT 2000 -79,0% -58,7% -55,9% -52,8% -61,1%34 NETIA 2000 -80,2% -49,5% -30,5% -32,8% -2,8%35 PUE 2000 -12,6% 17,9% 24,3% 23,7% 32,9%36 FASING 2000 -71,3% -39,2% -33,6% -32,0% -57,1%37 TALEX 2000 -58,0% -43,4% -41,8% -41,0% 2,6%38 WANDALEX 2000 -33,8% -19,9% -3,8% -4,6% 13,6%39 SIMPLE 2000 -65,7% -42,1% -47,3% -47,4% 2,3%
40 MCI 2001 -86,8% -74,1% -56,3% -59,6% -28,8%41 ELKOP 2001 -84,1% -84,1% -52,7% -54,1% -28,6%42 LPP 2001 95,4% 91,2% 105,6% 101,1% 167,4%43 BZWBK 2001 51,8% 49,2% 47,1% 45,5% 51,1%44 TRASTYCHY 2001 61,3% 62,3% 66,7% 64,8% 45,1%45 HOGA 2001 -50,0% -53,7% -13,2% -12,4% 35,5%
46 ELDORADO 2002 -1,7% 0,2% -2,5% -1,6% 25,9%47 OPTIMUS 2002 -60,6% -47,4% -44,0% -46,4% -54,8%48 SPIN (TELMAX) 2002 -70,6% -67,0% -54,6% -50,5% -153,0%49 KRUK 2002 -36,4% -50,8% -25,8% -27,0% 16,7%50 EMAX 2002 102,3% 66,2% 57,4% 90,6% 183,7%
51 DUDA 2003 167,3% 109,6% 125,0% 128,5% 207,2%52 HOOP 2003 -49,8% -73,3% -85,3% -83,7% -115,0%53 IMPEL 2003 -62,6% -89,3% -76,6% -75,3% -143,3%54 REDAN 2003 -35,8% -61,7% -37,5% -42,1% 45,2%55 SNIEZKA 2003 -5,6% -28,0% -18,9% -17,8% -22,3%
56 ATMGRUPA 2004 16,2% -0,6% -2,1% 3,1% 58,5%57 PLASTBOX 2004 -22,1% -38,6% -153,4% -102,3% -29,0%58 BETACOM 2004 -44,3% -63,1% -78,2% -108,3% -143,4%59 DGA 2004 -15,5% -20,2% -41,6% -42,0% 13,9%60 GTC 2004 8,8% -0,5% 3,9% 5,6% 4,1%61 TECHMEX 2004 -43,5% -53,9% -48,5% -44,8% 12,6%62 INTERCARS 2004 36,1% 24,2% 50,3% 54,3% 51,8%
Supplementary material I1 / 6
Supplementary material I: Input data for buy-and-abnormal returns analysis
No Comany name IPO year Raw BHR 1 year BHAR WIG 1 yearBHAR rebalanced cap and B-M 1 year
BHAR "buy-and-hold" cap and B-M 1 year
BHAR control firm 1 year
63 JCAUTO 2004 -6,5% -24,1% -10,8% -7,4% 17,1%64 ARTMAN 2004 -60,2% -78,4% -65,7% -62,8% -45,0%65 MEDIATEL 2004 -61,3% -82,4% -44,5% -41,3% -101,4%66 HYGENIKA 2004 -54,4% -75,5% -71,6% -68,7% -36,1%67 RMFFM 2004 32,5% 5,3% 20,0% 23,4% 52,4%68 NOWAGALA 2004 -17,6% -44,7% -31,7% -28,3% -2,9%69 ELSTAROIL 2004 -7,1% -31,8% -16,9% -13,8% 19,5%70 PBG 2004 62,7% 33,7% 64,7% 62,9% 58,6%71 ASSECO 2004 3,5% -24,5% 4,2% 7,7% -3,7%72 ATM 2004 84,1% 51,6% 95,3% 71,8% 9,3%73 SWISSMED 2004 -48,5% -77,4% -60,0% -66,7% -58,4%74 FAM 2004 -24,9% -55,0% -19,7% -20,9% 40,4%75 WSIP 2004 -28,5% -58,6% -51,6% -49,3% -26,7%76 PKOBP 2004 15,8% -13,0% 7,6% 7,9% -51,6%77 TORFARM 2004 4,3% -28,8% 3,2% 12,2% -0,9%78 PEKAES 2004 -21,8% -53,8% -42,9% -42,6% -25,5%79 KOELNER 2004 9,0% -23,4% 7,9% 1,9% 3,7%80 CCC 2004 177,0% 144,6% 168,8% 168,6% 171,2%81 PRATERM 2004 6,8% -24,2% 6,4% 6,0% -41,2%82 TVN 2004 108,2% 75,1% 99,3% 99,1% 95,7%83 POLCOLOR 2004 -35,9% -69,7% -38,5% -38,3% -7,8%84 DWORY 2004 -8,0% -42,1% -31,0% -30,3% 18,9%85 DROZAPOL 2004 -38,0% -75,9% -38,7% -36,5% 7,2%86 EUROFAKTOR 2004 -26,6% -64,8% -37,4% -38,6% 13,3%
87 ATLANTAPL 2005 32,6% -15,4% 3,4% 3,7% -6,9%88 COMP 2005 58,6% 10,8% 33,8% 34,0% -1,5%89 ZELMER 2005 53,3% 1,7% 24,1% 22,3% -34,7%90 EUROCASH 2005 81,7% 40,4% 59,7% 58,8% 168,5%91 CIECH 2005 30,0% -11,2% 17,4% 15,1% 21,2%92 SRUBEX 2005 -36,2% -75,4% -74,1% -73,8% -39,7%93 POLMOSBN 2005 -20,5% -60,9% -57,1% -55,4% -124,3%94 GRAAL 2005 128,1% 91,9% 92,1% 86,0% 155,4%95 BIOTON 2005 93,5% 47,8% 56,8% 58,0% 101,7%96 ZTSRG 2005 96,6% 37,1% -28,0% -16,8% 54,6%97 ZETKAMA 2005 109,7% 39,5% -16,3% 24,5% 0,2%98 POLMOSBIA 2005 11,4% -65,5% -43,3% -38,6% -92,3%99 PEP 2005 86,8% 15,1% -16,3% -18,4% -88,3%
100 LENA 2005 14,4% -34,7% -56,5% -58,2% -103,6%101 LOTOS 2005 58,0% 17,4% -11,6% 4,3% 64,0%102 DECORA 2005 58,1% 27,0% -4,9% -7,5% -55,7%103 OPOCZNO 2005 -44,7% -86,5% -62,1% -74,8% -82,1%
Supplementary material I2 / 6
Supplementary material I: Input data for buy-and-abnormal returns analysis
No Comany name IPO year Raw BHR 2 year BHAR WIG 2 yearBHAR rebalanced cap and B-M 2 year
BHAR "buy-and-hold" cap and B-M 2 year
BHAR control firm 2 year
1 PEKAO 1998 -7,7% -30,1% -12,6% -7,9% 29,0%2 LZPS 1998 -52,6% -66,1% 13,5% -86,2% -124,1%3 MOSTOSTAL PLC 1998 -25,8% -63,6% -39,8% -49,4% -3,4%4 SUWARY 1998 -16,4% -65,8% -60,4% -63,5% -54,2%5 ENERGOPOL 1998 -19,0% -44,7% -44,4% -58,8% -31,2%6 TPSA 1998 52,4% 20,7% 52,9% 56,6% 85,4%7 GROCLIN 1998 -73,5% -103,0% -67,3% -66,4% -70,3%8 GANT 1998 -4,5% -52,6% -31,0% -41,0% -6,5%9 BEDZIN 1998 -56,4% -102,0% -102,4% -100,5% -193,1%
10 PEMUG 1999 -23,5% -39,8% -3,9% -17,5% 23,1%11 SKOTAN 1999 -73,0% -96,9% -95,4% -114,9% -40,0%12 TUP 1999 -77,8% -101,3% -55,8% -62,0% 6,3%13 POLNORD 1999 -4,7% -11,9% 29,1% 15,3% 52,8%14 CSS 1999 -30,8% -46,3% -8,4% -7,1% -15,8%15 NAFTOBUDOWA 1999 -72,9% -73,9% -54,1% -57,1% -11,4%16 SZEPTEL (MNI) 1999 47,2% 39,5% 89,0% 96,3% 39,1%17 AGORA 1999 42,8% 41,3% 61,1% 67,0% 106,2%18 INSTAL KRAKOW 1999 111,1% 113,6% 135,5% 132,3% 138,7%19 COMARCH 1999 -67,3% -64,6% -38,0% -32,8% 12,0%20 PROSPER 1999 -33,7% -30,5% -19,3% -17,1% -34,5%21 CASPOL(FON) 1999 -45,6% -43,4% -29,5% -37,5% 12,6%22 TU EUROPA 1999 -4,0% 4,4% 22,1% 14,9% -58,6%23 FARMACOL 1999 -11,7% 2,4% 11,5% 11,1% 26,7%24 PROJPRZEM 1999 -47,6% -21,8% -5,9% -9,9% 17,9%25 PEKABEX 1999 -52,3% -25,3% -26,7% -29,0% 1,9%26 POLLENA EWA 1999 -44,1% -45,2% -27,6% -27,8% -69,0%27 PKN 1999 -13,2% -4,3% -29,4% -30,2% 3,4%28 KZWM 1999 -56,6% -35,4% -16,3% -17,0% -7,7%29 LTL 1999 -20,7% -3,7% 19,9% 18,8% 33,3%
30 BEEFSAN 2000 -73,8% -42,4% -22,0% -23,8% 7,8%31 STALPROFI 2000 -35,4% -13,3% 22,5% 22,1% -51,3%32 KOGENERA 2000 -56,2% -42,2% -47,7% -46,8% -86,7%33 MACROSOFT 2000 -90,9% -71,0% -33,3% -32,3% -41,0%34 NETIA 2000 -97,4% -67,7% -43,4% -46,1% -0,3%35 PUE 2000 -30,7% -4,5% 36,8% 31,2% 4,0%36 FASING 2000 -73,6% -46,3% -28,9% -28,8% -101,9%37 TALEX 2000 -45,6% -34,1% -18,9% -17,6% 31,1%38 WANDALEX 2000 -33,8% -25,3% 13,9% 15,1% 32,5%39 SIMPLE 2000 -82,6% -62,0% -27,6% -25,2% -8,6%
40 MCI 2001 -95,8% -74,2% -46,8% -56,5% -6,3%41 ELKOP 2001 -92,3% -82,9% -39,7% -49,6% -24,0%42 LPP 2001 566,0% 566,4% 572,7% 567,5% 634,2%43 BZWBK 2001 58,8% 45,7% 50,2% 44,3% 100,8%44 TRASTYCHY 2001 -37,0% -66,8% -17,2% -31,0% -126,0%45 HOGA 2001 72,3% 15,8% 80,0% 85,6% 149,6%
46 ELDORADO 2002 102,6% 56,4% 72,6% 59,4% 136,3%47 OPTIMUS 2002 -42,2% -78,5% -64,1% -65,5% -63,2%48 SPIN (TELMAX) 2002 -50,4% -98,9% -100,7% -90,0% -191,8%49 KRUK 2002 -34,4% -106,0% -273,5% -262,7% -173,5%50 EMAX 2002 107,9% 32,7% 38,8% 77,1% 184,0%
51 DUDA 2003 503,7% 421,5% 353,5% 393,5% 412,1%52 HOOP 2003 -56,5% -115,6% -98,5% -92,9% -67,3%53 IMPEL 2003 -49,4% -115,3% -67,4% -67,4% -82,3%54 REDAN 2003 -75,7% -147,9% -87,2% -93,5% 2,7%55 SNIEZKA 2003 -6,0% -74,9% -21,2% -19,9% 0,8%
56 ATMGRUPA 2004 16,5% -48,9% -46,2% -43,7% 27,2%57 PLASTBOX 2004 -59,7% -126,6% -291,1% -226,2% -141,4%58 BETACOM 2004 0,7% -68,8% -262,4% -484,7% -94,7%59 DGA 2004 -24,2% -94,0% -126,0% -129,2% -40,2%60 GTC 2004 177,5% 86,9% 73,6% 94,6% 140,1%61 TECHMEX 2004 -34,7% -118,8% -136,2% -129,6% -59,0%62 INTERCARS 2004 8,7% -58,5% -8,3% -4,2% 50,4%
Supplementary material I3 / 6
Supplementary material I: Input data for buy-and-abnormal returns analysis
No Comany name IPO year Raw BHR 2 year BHAR WIG 2 yearBHAR rebalanced cap and B-M 2 year
BHAR "buy-and-hold" cap and B-M 2 year
BHAR control firm 2 year
63 JCAUTO 2004 0,0% -67,3% -65,1% -64,2% 65,2%64 ARTMAN 2004 16,9% -54,2% -151,0% -85,2% 35,9%65 MEDIATEL 2004 -29,2% -101,9% -188,9% -113,5% -169,1%66 HYGENIKA 2004 -72,8% -146,8% -146,4% -130,8% -17,8%67 RMFFM 2004 80,3% 1,3% 32,0% 32,2% 83,0%68 NOWAGALA 2004 -35,3% -116,7% -139,9% -132,6% -66,0%69 ELSTAROIL 2004 248,3% 172,5% 177,5% 181,3% 310,6%70 PBG 2004 272,9% 185,5% 241,9% 238,0% 112,8%71 ASSECO 2004 114,9% 19,5% 88,6% 93,0% 39,3%72 ATM 2004 419,3% 343,2% 348,8% 337,2% 151,8%73 SWISSMED 2004 -9,0% -98,5% -236,7% -146,4% -189,5%74 FAM 2004 -11,9% -99,5% -120,8% -136,3% -0,6%75 WSIP 2004 27,7% -59,9% -76,0% -52,5% 0,2%76 PKOBP 2004 66,9% -25,0% 19,3% 14,3% -100,9%77 TORFARM 2004 63,2% -36,4% -151,9% -23,8% -36,9%78 PEKAES 2004 75,6% -19,9% 3,8% -2,7% -31,5%79 KOELNER 2004 138,8% 42,0% 96,6% 85,5% 162,9%80 CCC 2004 332,0% 237,7% 277,5% 274,8% 327,9%81 PRATERM 2004 150,2% 49,3% -20,1% -17,2% -20,1%82 TVN 2004 247,0% 146,3% 195,5% 190,7% 207,8%83 POLCOLOR 2004 -39,5% -142,9% -157,9% -156,5% -74,2%84 DWORY 2004 104,9% 12,3% 39,0% 36,4% 97,7%85 DROZAPOL 2004 152,9% 58,9% 55,2% 57,0% 163,8%86 EUROFAKTOR 2004 -40,2% -129,3% -275,4% -246,2% -81,4%
87 ATLANTAPL 2005 38,0% -61,1% -153,2% -171,2% -149,2%88 COMP 2005 -15,3% -124,1% -194,1% -232,2% 10,0%89 ZELMER 2005 241,2% 129,7% 147,4% 154,7% 67,0%90 EUROCASH 2005 177,2% 72,6% 120,8% 118,3% 246,8%91 CIECH 2005 180,9% 79,8% 120,0% 105,6% 72,0%92 SRUBEX 2005 -18,4% -114,0% -296,7% -252,6% -131,5%93 POLMOSBN 2005 -6,1% -87,9% -153,1% -154,2% -200,6%94 GRAAL 2005 197,8% 108,3% 99,8% 87,8% 202,4%95 BIOTON 2005 136,6% 25,1% 47,9% 47,4% 197,7%96 ZTSRG 2005 144,4% 17,9% -327,9% -237,0% -210,2%97 ZETKAMA 2005 113,6% -18,7% -232,8% -176,8% -47,3%98 POLMOSBIA 2005 36,1% -97,3% -60,7% -62,1% -54,5%99 PEP 2005 295,8% 164,2% 34,7% 38,1% 21,2%
100 LENA 2005 30,4% -110,4% -330,4% -421,9% -192,1%101 LOTOS 2005 63,4% -68,1% -100,8% -83,6% 39,5%102 DECORA 2005 196,2% 64,4% 49,0% 28,1% 134,0%103 OPOCZNO 2005 -2,0% -140,1% -119,5% -141,3% -211,0%
Supplementary material I4 / 6
Supplementary material I: Input data for buy-and-abnormal returns analysis
No Comany name IPO year Raw BHR 3 year BHAR WIG 3 yearBHAR rebalanced cap and B-M 3 year
BHAR "buy-and-hold" cap and B-M 3 year
BHAR control firm 3 year
1 PEKAO 1998 14,5% 31,6% 67,1% 69,0% 73,4%2 LZPS 1998 -56,1% -30,5% -25,6% -66,1% -166,0%3 MOSTOSTAL PLC 1998 -54,5% -49,2% -24,8% -43,8% -131,8%4 SUWARY 1998 -38,8% -62,8% -21,9% -39,6% -3,7%5 ENERGOPOL 1998 -76,2% -87,3% -61,5% -71,8% -8,0%6 TPSA 1998 -4,6% -16,1% 25,8% 23,0% 63,1%7 GROCLIN 1998 -85,1% -100,3% -68,7% -67,2% -29,8%8 GANT 1998 -31,8% -49,6% -18,0% -26,9% -42,6%9 BEDZIN 1998 -63,8% -77,2% -64,7% -63,6% -45,2%
10 PEMUG 1999 -90,0% -99,7% -35,9% -48,7% -1,4%11 SKOTAN 1999 -90,0% -102,8% -66,5% -84,7% -26,3%12 TUP 1999 -87,8% -99,6% -39,4% -45,8% 2,9%13 POLNORD 1999 -11,8% -20,7% 25,2% 16,8% 47,3%14 CSS 1999 -47,9% -63,8% -25,0% -25,2% -19,7%15 NAFTOBUDOWA 1999 -84,2% -91,3% -36,1% -42,8% -3,0%16 SZEPTEL (MNI) 1999 -27,6% -30,3% 31,8% 34,3% 9,2%17 AGORA 1999 30,9% 32,7% 74,9% 74,1% 126,5%18 INSTAL KRAKOW 1999 3,7% 4,3% 57,6% 56,1% 71,3%19 COMARCH 1999 -77,8% -79,1% -29,1% -30,3% 19,0%20 PROSPER 1999 -40,3% -38,2% -8,1% -5,3% -28,2%21 CASPOL(FON) 1999 -62,5% -62,2% -12,0% -27,6% 28,2%22 TU EUROPA 1999 110,7% 115,1% 165,8% 163,1% 164,2%23 FARMACOL 1999 42,4% 55,7% 59,7% 52,6% 50,0%24 PROJPRZEM 1999 -62,0% -43,4% 0,5% -1,1% 16,9%25 PEKABEX 1999 -55,3% -34,3% 3,1% 3,5% 33,9%26 POLLENA EWA 1999 -63,6% -60,2% -13,2% -8,5% -65,8%27 PKN 1999 -16,4% -10,5% -28,6% -33,2% -129,0%28 KZWM 1999 -71,2% -53,7% -7,5% -6,4% 5,7%29 LTL 1999 -96,7% -78,2% -32,8% -28,5% -29,0%
30 BEEFSAN 2000 -86,0% -48,2% -18,0% -22,5% 8,8%31 STALPROFI 2000 53,9% 78,6% 121,3% 119,9% 102,9%32 KOGENERA 2000 -72,6% -55,8% -69,7% -66,1% -69,0%33 MACROSOFT 2000 -92,6% -72,3% -30,6% -28,6% -83,5%34 NETIA 2000 -97,0% -86,9% -48,6% -54,6% 0,0%35 PUE 2000 -20,6% -35,6% 26,6% 9,1% -99,6%36 FASING 2000 -47,3% -56,5% -117,5% -74,6% 3,3%37 TALEX 2000 -39,5% -55,4% -29,3% -37,0% 43,1%38 WANDALEX 2000 -12,3% -34,1% 28,0% 29,5% 51,9%39 SIMPLE 2000 -82,4% -98,4% -64,3% -58,7% -15,0%
40 MCI 2001 -83,5% -110,7% -95,1% -119,7% 8,1%41 ELKOP 2001 -80,6% -134,3% -176,1% -178,7% -204,2%42 LPP 2001 996,0% 940,5% 942,7% 910,8% 1059,5%43 BZWBK 2001 104,0% 34,1% 56,8% 50,5% 143,0%44 TRASTYCHY 2001 -60,3% -137,7% -166,0% -126,3% -271,5%45 HOGA 2001 96,7% 6,4% 32,3% 45,8% 183,6%
46 ELDORADO 2002 168,0% 90,6% 96,8% 56,7% 191,9%47 OPTIMUS 2002 -48,9% -108,3% -274,2% -171,3% -60,2%48 SPIN (TELMAX) 2002 -68,9% -137,1% -90,7% -87,5% -152,0%49 KRUK 2002 -20,6% -122,4% -336,6% -314,7% -61,2%50 EMAX 2002 70,4% -58,7% -7,8% 44,1% 149,0%
51 DUDA 2003 459,9% 275,7% 230,1% 282,0% 374,8%52 HOOP 2003 -34,7% -159,2% -204,5% -200,0% -72,5%53 IMPEL 2003 -15,9% -159,8% -37,0% -48,7% -35,8%54 REDAN 2003 -73,5% -215,9% -140,6% -153,6% 0,4%55 SNIEZKA 2003 40,4% -93,3% -40,7% -54,2% 1,5%
56 ATMGRUPA 2004 301,2% 155,8% 38,8% 8,2% 363,0%57 PLASTBOX 2004 -12,4% -134,2% -512,8% -420,7% -35,9%58 BETACOM 2004 -32,7% -169,8% -572,3% -541,0% -410,5%59 DGA 2004 -23,3% -158,8% -429,4% -512,3% 22,0%60 GTC 2004 -53,7% -208,8% -269,7% -237,0% -122,6%61 TECHMEX 2004 9,6% -152,6% -322,6% -329,9% -67,0%62 INTERCARS 2004 395,7% 229,6% 126,8% 83,2% 312,7%
Supplementary material I5 / 6
Supplementary material I: Input data for buy-and-abnormal returns analysis
No Comany name IPO year Raw BHR 3 year BHAR WIG 3 yearBHAR rebalanced cap and B-M 3 year
BHAR "buy-and-hold" cap and B-M 3 year
BHAR control firm 3 year
63 JCAUTO 2004 38,1% -140,7% -348,5% -442,0% 121,4%64 ARTMAN 2004 226,8% 45,0% -316,4% -137,5% 99,8%65 MEDIATEL 2004 -33,7% -219,6% -426,3% -437,6% -276,0%66 HYGENIKA 2004 -52,3% -235,1% -255,1% -219,8% -86,4%67 RMFFM 2004 95,8% -88,4% -4,3% -3,2% -170,9%68 NOWAGALA 2004 0,0% -180,6% -354,0% -328,6% -22,6%69 ELSTAROIL 2004 -69,6% -244,8% -204,1% -208,1% -19,5%70 PBG 2004 692,8% 533,5% 632,5% 625,1% 487,2%71 ASSECO 2004 245,8% 40,4% 202,4% 200,7% 91,9%72 ATM 2004 478,5% 339,2% 241,7% 152,6% 1,5%73 SWISSMED 2004 -38,3% -191,5% -551,9% -264,3% -303,2%74 FAM 2004 -31,3% -172,5% -288,8% -252,9% -90,4%75 WSIP 2004 60,9% -80,3% -53,8% -32,0% -50,8%76 PKOBP 2004 117,8% -8,5% 77,4% 66,6% -274,0%77 TORFARM 2004 79,0% -46,4% -166,7% 3,8% -198,5%78 PEKAES 2004 21,8% -98,6% -10,8% -20,0% -205,7%79 KOELNER 2004 30,7% -90,6% 5,0% -20,4% 79,4%80 CCC 2004 238,3% 121,2% 188,8% 180,0% 11,4%81 PRATERM 2004 126,6% 3,6% -66,7% -61,9% -9,5%82 TVN 2004 260,2% 133,9% 216,7% 203,5% 333,2%83 POLCOLOR 2004 -71,9% -199,6% -226,8% -226,6% -136,2%84 DWORY 2004 282,5% 172,7% 216,4% 210,2% 239,8%85 DROZAPOL 2004 30,5% -81,8% -105,6% -87,9% 64,0%86 EUROFAKTOR 2004 -63,9% -160,7% -312,2% -147,8% -162,0%
87 ATLANTAPL 2005 -49,6% -131,2% -205,4% -236,9% -15,8%88 COMP 2005 -14,3% -104,6% -167,5% -222,5% 39,5%89 ZELMER 2005 378,8% 293,2% 336,9% 330,4% -65,8%90 EUROCASH 2005 245,3% 162,1% 225,4% 216,3% 316,6%91 CIECH 2005 246,3% 159,1% 179,0% 149,5% 343,0%92 SRUBEX 2005 -3,7% -81,7% -300,2% -183,9% -45,8%93 POLMOSBN 200594 GRAAL 2005 96,3% 31,1% 61,4% 45,7% 142,1%95 BIOTON 2005 -13,7% -93,5% -30,5% -36,7% 63,0%96 ZTSRG 2005 -31,6% -106,8% -218,6% -205,8% -122,0%97 ZETKAMA 2005 35,9% -38,0% -67,4% -83,5% 111,1%98 POLMOSBIA 200599 PEP 2005 278,5% 194,9% 175,8% 168,3% 132,4%
100 LENA 2005 -64,9% -136,0% -193,1% -257,0% -121,3%101 LOTOS 2005 2,0% -66,9% -80,6% -92,3% 16,9%102 DECORA 2005 -18,8% -64,5% -33,1% -54,4% 76,7%103 OPOCZNO 2005 -38,4% -110,3% -79,5% -117,7% -33,3%
Supplementary material I6 / 6
Supplementary material II: Input data for cumulative abnormal returns analysis
No Company nameCAR WIG 1 year
CAR Cap B-M 1 year
CAR contr firm 1 year
CAR WIG 2 years
CAR Cap B-M 2 year
CAR contr firm 2 years
CAR WIG 3 years
CAR Cap B-M 3 years
CAR contr firm 3 years
1 PEKAO 1998 -32% -15,6% 23% -24% -10,4% -17% 36% 85,7% 41%2 LZPS 1998 26% 25,6% 17% -20% -33,6% -74% 33% 16,3% -89%3 MOSTOSTAL PLC 1998 -20% -13,1% -8% -47% -26,8% 1% -57% -26,8% 96%4 SUWARY 1998 -51% -52,3% -106% -40% -35,2% -41% -52% -10,8% 2%5 ENERGOPOL 1998 -6% -9,9% -20% -38% -38,8% -30% -136% -109,9% -29%6 TPSA 1998 27% 38,2% 52% 29% 49,6% 96% 7% 44,9% 123%7 GROCLIN 1998 -136% -133,6% -104% -72% -41,1% -49% -110% -76,5% -24%8 GANT 1998 -49% -40,7% 8% -43% -26,9% 34% -56% -22,9% 47%9 BEDZIN 1998 -61% -93,4% -125% -119% -123,1% -186% -101% -92,7% -98%
10 PEMUG 1999 -38% -8,3% 15% -45% -10,9% 26% -165% -80,1% 36%11 SKOTAN 1999 -16% -12,5% 41% -82% -81,9% -30% -169% -129,5% -67%12 TUP 1999 -50% -10,4% 25% -153% -105,8% 33% -172% -92,4% 57%13 POLNORD 1999 -63% -13,6% 33% -6% 39,4% 71% -18% 36,4% 60%14 CSS 1999 -22% 18,2% 50% -24% 14,8% 0% -44% -1,8% -4%15 NAFTOBUDOWA 1999 -65% -28,9% 20% -117% -95,0% -33% -163% -90,3% -9%16 SZEPTEL (MNI) 1999 80% 110,6% 100% 84% 139,2% 75% 33% 121,5% 61%17 AGORA 1999 76% 69,7% 57% 66% 83,1% 148% 65% 118,1% 319%18 INSTAL KRAKOW 1999 58% 87,0% 93% 78% 102,0% 97% 4% 78,9% 101%19 COMARCH 1999 54% 57,3% 88% -12% 14,5% 121% -49% 13,7% 226%20 PROSPER 1999 -34% -18,8% -37% -23% -10,0% -33% -35% 4,8% -37%21 CASPOL(FON) 1999 -32% -9,2% 39% -52% -36,5% 20% -44% 26,8% 167%22 TU EUROPA 1999 76% 106,3% 14% 50% 72,2% -24% 130% 206,8% 164%23 FARMACOL 1999 -14% 5,2% 20% 8% 18,9% 40% 54% 58,7% 47%24 PROJPRZEM 1999 -58% -39,0% 14% -31% -6,3% 40% -71% 7,5% 43%25 PEKABEX 1999 -63% -64,7% -8% -31% -35,4% -24% -47% 15,9% 84%26 POLLENA EWA 1999 -18% -17,9% -80% -45% -23,6% -77% -84% -14,9% -100%27 PKN 1999 -12% -20,4% -18% -1% -24,0% 1% -6% -21,1% -101%28 KZWM 1999 -89% -72,5% -35% -39% -8,1% -4% -76% 8,8% 30%29 LTL 1999 -30% -17,2% -21% 5% 39,2% 57% -229% -165,5% -145%30 BEEFSAN 2000 -82% -87,9% -28% -87% -52,7% 21% -89% -24,5% 108%31 STALPROFI 2000 27% 28,3% -7% -11% 66,7% -58% 91% 190,5% 75%32 KOGENERA 2000 6% -1,1% -30% -68% -71,3% -108% -96% -107,8% -113%33 MACROSOFT 2000 -114% -112,1% -123% -175% -113,6% -139% -137% -67,3% -169%34 NETIA 2000 -93% -66,2% -119% -202% -164,7% -235% -185% -134,4% -205%35 PUE 2000 27% 37,0% 47% -5% 76,9% 4% -38% 36,3% -103%36 FASING 2000 -73% -64,1% -95% -86% -55,5% -142% -29% -85,5% -52%37 TALEX 2000 -58% -54,6% 7% -20% 3,3% 99% -25% 1,8% 144%38 WANDALEX 2000 -12% 9,1% 33% -11% 45,2% 84% 4% 76,6% 122%39 SIMPLE 2000 -111% -105,0% 24% -160% -92,6% -36% -177% -135,0% -51%40 MCI 2001 -136% -110,9% -37% -223% -178,4% -148% -121% -109,3% -140%41 ELKOP 2001 -145% -106,0% -71% -168% -103,2% -73% -94% -137,2% -311%42 LPP 2001 66% 82,3% 187% 203% 211,2% 306% 210% 212,7% 344%43 BZWBK 2001 47% 46,5% 52% 44% 51,1% 110% 27% 45,8% 119%44 TRASTYCHY 2001 51% 59,4% 35% -54% 5,8% -110% -123% -131,8% -201%45 HOGA 2001 -67% -17,1% 59% 32% 80,2% 148% 29% 41,2% 204%46 ELDORADO 2002 0% -1,4% 31% 35% 49,4% 109% 46% 50,2% 122%47 OPTIMUS 2002 -66% -60,6% -75% -70% -58,1% -61% -92% -210,2% -69%48 SPIN (TALEX) 2001 -70% 48,4% -160% -49% -82,0% -133% -103% -22,5% -148%49 KRUK 2002 -45% -21,5% 26% -65% -179,3% -149% -56% -176,7% -84%50 EMAX 2002 58% 51,8% 216% 36% 40,2% 193% -10% 16,1% 179%51 DUDA 2003 59% 70,6% 144% 146% 99,4% 95% 99% 68,8% 93%52 HOOP 2003 -84% -95,5% -118% -113% -104,7% -94% -103% -127,4% -77%53 IMPEL 2003 -94% -83,1% -132% -85% -50,1% -68% -65% -2,8% -1%54 REDAN 2003 -63% -41,7% 2% -184% -140,1% -105% -196% -160,6% -100%55 SNIEZKA 2003 -25% -17,2% -21% -57% -18,1% 1% -50% -23,8% 1%56 ATMGRUPA 2004 2% 0,2% 58% -32% -32,2% 9% 69% 23,7% 177%57 PLASTBOX 2004 -36% -150,3% -35% -121% -237,0% -163% -65% -212,3% -105%58 BETACOM 2004 -64% -80,6% -125% -32% -121,8% -64% -97% -212,1% -199%59 DGA 2004 -19% -40,1% 9% -74% -95,1% -51% -104% -187,6% -17%
Supplementary material II1 / 2
Supplementary material II: Input data for cumulative abnormal returns analysis
No Company nameCAR WIG 1 year
CAR Cap B-M 1 year
CAR contr firm 1 year
CAR WIG 2 years
CAR Cap B-M 2 year
CAR contr firm 2 years
CAR WIG 3 years
CAR Cap B-M 3 years
CAR contr firm 3 years
60 GTC 2004 1% 5,2% 5% 49% 42,1% 82% -17% -38,8% 25%61 TECHMEX 2004 -60% -56,6% 10% -87% -100,1% -76% -52% -110,1% -47%62 INTERCARS 2004 26% 51,9% 52% -37% -0,1% 62% 87% 50,3% 108%63 JCAUTO 2004 -20% -8,4% 12% -47% -47,6% 90% -48% -109,8% 151%64 ARTMAN 2004 -93% -82,1% -68% -1% -57,6% 59% 67% -29,4% 66%65 MEDIATEL 2004 -99% -62,3% -121% -53% -103,3% -102% -102% -204,8% -176%66 HYGENIKA 2004 -85% -82,2% -50% -140% -188,2% -20% -69% -211,6% -123%67 RMFFM 2004 5% 16,0% 41% 1% 19,6% 39% -36% -3,4% -106%68 NOWAGALA 2004 -42% -32,1% -12% -96% -112,3% -75% -90% -146,4% -20%69 ELSTAROIL 2004 -28% -16,1% 16% 83% 82,5% 226% -73% -59,9% 87%70 PBG 2004 25% 52,8% 46% 75% 112,2% 39% 125% 173,7% 104%71 ASSECO 2004 -26% 4,7% -6% 25% 58,1% 11% 47% 65,7% 16%72 ATM 2004 38% 78,7% 7% 124% 125,6% 45% 110% 69,6% 13%73 SWISSMED 2004 -80% -66,3% -89% 46% -20,5% -69% -15% -121,1% -142%74 FAM 2004 -53% -20,8% 69% -15% -31,3% -11% -21% -80,7% -79%75 WSIP 2004 -55% -49,4% -28% -31% -39,4% 7% -32% -22,3% -24%76 PKOBP 2004 -10% 7,5% -39% -15% 12,9% -59% -4% 45,6% -95%77 TORFARM 2004 -24% 14,1% -3% -19% -13,9% -28% -13% -0,5% -82%78 PEKAES 2004 -47% -37,8% -27% 4% 17,7% -8% -34% 13,7% -92%79 KOELNER 2004 -10% 17,6% 8% 37% 70,4% 124% -20% 35,5% 107%80 CCC 2004 83% 104,1% 103% 91% 115,5% 143% 59% 97,2% -14%81 PRATERM 2004 -20% 6,6% -36% 29% -4,5% -8% 14% -22,1% -11%82 TVN 2004 49% 69,5% 58% 60% 89,4% 81% 53% 99,9% 178%83 POLCOLOR 2004 -71% -45,4% -13% -119% -128,4% -77% -184% -202,8% -138%84 DWORY 2004 -38% -28,3% 21% 13% 29,4% 70% 74% 97,5% 108%85 DROZAPOL 2004 -77% -44,2% 10% 64% 60,9% 133% 21% 5,3% 127%86 EUROFAKTOR 2004 -60% 0,0% 19% -108% 0,0% -99% -143% 0,0% -193%87 ATLANTAPL 2005 -7% 5,8% -7% -30% -73,5% -78% -117% -160,0% -102%88 COMP 2005 9% 24,8% -107% -36% -68,4% -72% -24% -64,6% -70%89 ZELMER 2005 1% 17,6% -28% 61% 70,4% 18% 42% 68,4% -40%90 EUROCASH 2005 31% 46,0% 140% 42% 70,4% 96% 78% 121,7% 124%91 CIECH 2005 -6% 16,8% 14% 41% 65,2% 22% 81% 89,2% 227%92 SRUBEX 2005 -69% -68,6% -41% -76% -150,7% -93% -50% -144,7% -57%93 POLMOSBN 2005 -54% -52,1% -95% -63% -95,6% -119%94 GRAAL 2005 59% 57,7% 117% 56% 50,4% 120% 29% 44,3% 132%95 BIOTON 2005 48% 53,8% 95% 47% 59,2% 135% -29% 13,8% 92%96 ZTSRG 2005 39% 1,1% 35% 31% -77,6% -99% -64% -143,4% -125%97 ZETKAMA 2005 25% -8,6% -3% -2% -75,4% -27% -19% -47,1% 21%98 POLMOSBIA 2005 -47% -33,2% -63% -57% -37,0% -34%99 PEP 2005 14% -6,5% -40% 71% 20,6% 11% 93% 72,6% 50%
100 LENA 2005 -21% -37,9% -71% -56% -128,1% -102% -138% -175,9% -153%101 LOTOS 2005 13% -6,7% 54% -33% -46,9% 30% -43% -56,3% 24%102 DECORA 2005 23% 0,0% -37% 38% 30,7% 61% -32% -11,7% 196%103 OPOCZNO 2005 -91% -71,1% -89% -78% -68,2% -110% -87% -67,6% -45%
Supplementary material II2 / 2
Supplementary material III: Input data for mean monthly calendar-time portfolio returns analysis
1 year AR1 year AR WIG
1 year AR Cap B/M 2 year AR
2 year AR WIG
2 year AR Cap B/M 3 year AR
3 year AR WIG
3 year AR Cap B/M
1 sty-02 10,76 -1,77 10,02 10,88 -1,65 9,58 4,06 -8,55 2,412 lut-02 -0,35 2,64 -0,80 -3,89 -0,90 -0,70 -5,53 -2,42 -1,203 mar-02 -8,25 -5,75 -6,47 -9,08 -6,58 -8,28 -7,66 -5,16 -8,354 kwi-02 -9,76 -11,69 -6,52 -10,44 -12,37 -10,17 -12,30 -14,23 -10,735 maj-02 8,06 3,68 8,30 6,56 2,17 4,04 5,13 0,75 2,186 cze-02 -10,83 -0,51 -5,36 -7,77 2,56 -2,49 -5,48 3,69 -0,737 lip-02 -8,96 -0,05 20,89 -17,81 -8,90 12,61 -19,03 -10,12 7,568 sie-02 8,02 5,41 8,65 5,95 3,34 5,93 5,04 2,30 7,259 wrz-02 -2,83 1,74 2,07 -4,84 -3,08 -1,70 -9,16 -4,60 -2,46
10 paź-02 -3,93 -14,54 -3,45 8,90 -1,72 8,87 12,86 2,24 11,3011 lis-02 -4,08 -7,36 -11,90 3,79 0,50 -1,24 3,73 0,44 -1,7612 gru-02 7,82 12,21 10,27 2,65 7,04 4,30 -0,86 3,53 -1,2413 sty-03 -1,52 2,19 0,11 -3,64 0,07 -0,72 2,05 5,76 3,9514 lut-03 6,40 8,88 4,00 -0,34 2,14 1,79 -1,76 0,71 -0,9315 mar-03 -3,45 -3,98 -10,36 1,88 1,36 -4,44 -0,67 -1,20 -4,7516 kwi-03 20,78 17,18 17,16 1,88 9,01 11,93 15,20 11,61 13,5217 maj-03 -1,70 -7,82 -4,87 2,15 -3,98 -2,76 6,06 -0,06 -0,0718 cze-03 -0,37 -5,20 -0,63 1,79 -3,05 1,38 -1,66 -6,49 -3,1219 lip-03 -9,00 -22,60 -16,51 2,31 -11,29 -6,97 15,98 2,37 2,6120 sie-03 15,90 -2,77 4,03 14,53 -4,15 3,78 20,81 1,09 5,3821 wrz-03 -6,97 5,30 1,84 -10,13 2,14 -3,59 -7,87 4,40 -2,3822 paź-03 15,47 8,61 17,77 3,31 -3,54 2,36 0,20 -6,65 -3,6523 lis-03 3,90 11,23 8,45 -3,01 4,32 -0,21 -5,43 1,90 -3,1824 gru-03 10,40 5,35 5,45 9,49 4,44 4,70 11,95 6,89 6,3425 sty-04 -2,62 -5,66 -10,57 2,95 -0,09 -6,98 3,43 0,39 -6,5826 lut-04 2,00 -2,90 -1,66 8,41 3,51 0,27 19,98 15,07 10,6227 mar-04 -0,70 -0,81 -3,27 4,71 4,60 1,46 4,38 4,27 -1,4328 kwi-04 0,86 -0,90 -13,71 7,85 6,08 -13,27 8,15 6,39 -10,4329 maj-04 -3,85 -0,39 -4,69 -5,12 -1,65 -3,95 -4,45 -0,99 -2,7730 cze-04 2,15 0,26 -0,66 2,11 0,23 0,10 0,35 -1,53 -0,9731 lip-04 -3,88 -2,78 -5,42 -4,37 -3,27 -4,81 -4,32 -3,21 -4,0632 sie-04 -7,46 -10,00 -14,20 -6,88 -9,42 -13,16 -4,90 -7,45 -11,9433 wrz-04 -0,77 -4,72 -3,13 -0,04 -3,99 -2,43 -0,04 -3,99 -2,4334 paź-04 -4,12 -4,77 -1,23 -4,18 -4,83 -1,31 -4,18 -4,83 -1,3135 lis-04 -5,40 -4,01 -2,40 -4,57 -3,19 -1,19 -4,22 -2,84 -1,4336 gru-04 2,09 -1,88 3,01 2,84 -1,12 3,76 2,92 -1,04 3,8337 sty-05 0,32 3,00 2,59 0,94 3,62 2,67 0,67 3,35 2,1838 lut-05 2,36 -5,23 3,36 2,16 -5,43 2,88 2,12 -5,48 2,8839 mar-05 -4,07 -0,67 -2,59 -3,38 -0,45 -2,26 -3,57 -0,59 -2,1940 kwi-05 -3,91 1,18 1,07 -4,69 0,40 0,11 -5,06 0,03 -0,4241 maj-05 2,10 -1,21 1,36 1,71 -1,60 0,82 1,55 -1,76 0,5942 cze-05 4,91 -2,01 3,56 3,38 -3,54 1,77 3,52 -3,40 2,04
Supplementary material III1 / 1
Supplementary material IV: Input data for Fama-French three-factor model
Y X1 X2 X3 WeightsR(pt)-R(ft) R(mt)-R(ft) SMB HML
1 sty-02 -7,13 1,34 -18,98 -11,01 462 lut-02 -15,74 -14,37 2,71 4,08 453 mar-02 -18,05 -12,89 -4,28 -12,94 434 kwi-02 -22,29 -8,06 -7,30 -16,53 415 maj-02 -4,41 -5,15 -1,69 -1,70 386 cze-02 -14,55 -19,39 3,91 5,64 377 lip-02 -27,81 -17,69 -6,03 -12,76 368 sie-02 -3,28 -5,72 12,04 -6,76 369 wrz-02 -16,86 -12,26 4,80 -7,97 34
10 paź-02 5,77 3,52 -6,92 -4,73 3111 lis-02 -3,12 -3,56 -0,73 2,80 3112 gru-02 -7,69 -11,22 2,36 -5,08 2813 sty-03 -4,53 -10,28 14,40 22,60 2514 lut-03 -8,05 -8,77 -0,29 -4,40 2715 mar-03 -6,75 -5,55 -0,68 -1,39 2616 kwi-03 9,45 -2,16 -4,06 7,33 2417 maj-03 0,53 0,59 1,89 6,45 2318 cze-03 -6,89 -0,40 -5,15 -5,49 2019 lip-03 10,72 8,35 -4,91 -5,68 2020 sie-03 15,59 13,45 -16,70 -1,90 1821 wrz-03 -13,04 -17,44 4,19 -0,43 1922 paź-03 -5,25 1,41 0,54 -4,97 1823 lis-03 -10,85 -12,74 3,91 -0,34 1824 gru-03 6,64 -0,26 0,17 -0,05 1725 sty-04 -1,86 -2,25 11,57 6,24 1826 lut-04 14,68 -0,39 8,62 0,21 1827 mar-04 -0,93 -5,20 6,29 -6,90 1928 kwi-04 2,56 -3,83 12,00 0,54 1929 maj-04 -10,05 -9,06 -1,16 3,94 1930 cze-04 -5,30 -3,77 -2,96 -0,16 2031 lip-04 -10,38 -7,16 1,76 -2,98 2032 sie-04 -11,46 -4,02 2,91 -2,58 2533 wrz-04 -6,64 -2,65 3,03 -3,98 2634 paź-04 -10,75 -5,92 -5,30 -2,79 3035 lis-04 -10,84 -8,00 -5,12 -0,73 2936 gru-04 -3,64 -2,60 -4,02 -4,43 3537 sty-05 -5,84 -9,19 2,68 -3,23 4238 lut-05 -4,23 1,24 -6,15 -6,61 4339 mar-05 -9,62 -9,44 0,41 -1,15 4740 kwi-05 -10,56 -10,59 -1,04 0,49 5041 maj-05 -3,89 -2,13 -1,80 -6,80 5142 cze-05 -1,47 1,93 -0,63 0,21 52
Supplementary material IV1 / 1
Supplementary material V: Input data for the logit model
Year Company name Assets ROA Retention Offer SEO Underpricing AGE VC/PE Reputation Lead manager
1 1998 PEKAO 1 67% 15% 1 17 0,01 85% 21 1 0,22 48 0 1 CDM Pekao2 1998 LZPS 0 -26% -56% 0 10 0,07 60% 16 0 -0,22 39 0 2 DM WBK3 1998 MOSTOSTAL PLC 0 -25% -54% 0 10 0,18 16% 17 0 0,00 36 0 3 East brokers4 1998 SUWARY 0 -22% -39% 0 17 0,11 50% 15 0 0,40 40 0 2 BM PBK5 1998 ENERGOPOL 0 -61% -76% 0 10 0,17 91% 11 0 -0,47 30 0 3 Elimar6 1998 GROCLIN 0 -69% -85% 0 11 0,13 80% 17 0 -0,03 8 0 2 WBK7 1998 BĘDZIN 0 -65% -64% 0 11 0,04 86% 16 0 0,23 48 0 1 DM Banku Handlowego8 1999 POLNORD 1 25% -12% 0 11 0,07 56% 16 0 0,06 8 0 1 DI BRE9 1999 CSS 0 -25% -48% 0 10 0,18 90% 16 0 0,70 6 0 1 DI BRE
10 1999 NAFTOBUDOWA 0 -36% -84% 0 10 0,12 55% 17 1 0,13 47 0 3 Biuro Maklerskie Elimar11 1999 SZEPTEL 1 32% -28% 0 11 0,01 80% 18 2 0,74 7 0 1 DI BRE12 1999 AGORA 1 75% 31% 1 13 0,12 78% 20 0 0,31 10 0 1 CSFB13 1999 INSTAL KRAKÓW 1 58% 4% 1 11 0,07 66% 16 0 -0,10 49 0 3 Elimar14 1999 COMARCH 0 -29% -78% 0 11 0,19 80% 16 2 0,48 8 0 1 DI BRE15 1999 PROSPER 0 -8% -40% 0 9 0,12 51% 18 0 -0,05 9 0 2 Raiffeisen16 1999 CASPOL 0 -12% -63% 0 10 0,07 38% 15 2 0,62 8 0 2 DM Banku Śląskiego17 1999 TU EUROPA 1 166% 111% 1 10 0,13 81% 15 0 0,01 5 1 2 Millennium DM18 1999 FARMACOL 1 60% 42% 1 12 0,06 51% 18 0 -0,14 6 0 1 Societe Genrele19 1999 PROJPRZEM 1 0% -62% 0 11 0,06 91% 15 0 -0,21 51 0 1 CA IB 20 1999 PEKABEX 1 3% -55% 0 11 0,02 76% 18 0 0,34 28 0 3 Elimar21 1999 POLLENA EWA 0 -13% -64% 0 10 0,15 66% 17 0 -0,20 47 0 1 CDM Peako22 1999 PKN 0 -29% -16% 0 16 0,08 65% 22 0 0,12 39 0 1 DM Banku Handlowego23 1999 KZWM 0 -8% -71% 0 9 0,05 80% 15 0 -0,33 9 0 2 Beskidzki Dom Maklerski24 1999 LTL 0 -33% -97% 0 13 0,01 95% 15 1 0,19 8 0 1 DM Banku Handlowego25 2000 BEEFSAN 0 -18% -86% 0 10 -0,08 48% 15 2 0,24 9 0 2 BM BOŚ26 2000 STALPROFI 1 121% 54% 1 11 0,08 64% 16 0 0,14 2 0 3 Beskidzki Dom Maklerski27 2000 KOGENERACJA 0 -70% -73% 0 13 0,02 64% 19 1 0,07 49 0 3 Dom Maklerski BZ28 2000 NETIA 0 -49% -97% 0 15 -0,12 84% 20 2 0,08 10 0 1 CDM Peako29 2000 PUE 1 27% -21% 0 11 0,08 85% 15 0 -0,09 4 0 1 BDM PKO BP 30 2000 FASING 0 -117% -47% 0 11 -0,13 67% 17 0 0,25 50 0 3 Elimar31 2000 TALEX 0 -29% -40% 0 9 0,27 86% 17 0 0,03 11 0 1 DM WBK32 2000 WANDALEX 1 28% -12% 0 10 0,24 64% 16 0 -0,08 5 0 2 Millennium DM33 2000 SIMPLE 0 -64% -82% 0 9 0,21 84% 15 1 0,05 2 0 2 Raiieisen34 2001 MCI 0 -95% -84% 0 11 0,01 86% 17 0 0,40 2 0 1 CDM Pekao35 2001 LPP 1 943% 996% 1 11 0,07 76% 16 1 0,01 10 0 1 DM Banku Handlowego
3 years BHAR 3 years raw BHR
Supplementary material V1 / 3
Supplementary material V: Input data for the logit model
Year Company name Assets ROA Retention Offer SEO Underpricing AGE VC/PE Reputation Lead manager
36 2001 BZWBK 1 57% 104% 1 17 0,01 45% 18 0 0,06 30 0 1 CA IB37 2001 TRASTYCHY 0 -166% -60% 0 11 0,10 77% 16 5 0,04 4 0 3 Krakowski Dom Maklerski38 2001 HOGA.PL 1 32% 97% 1 8 -0,48 50% 16 0 -0,79 2 0 3 Beskidzki Dom Maklerski39 2002 ELDORADO 1 97% 168% 1 12 0,05 54% 17 1 -0,05 10 1 2 Millennium DM40 2002 OPTIMUS 0 -274% -49% 0 12 -0,05 27% 16 0 -0,24 1 0 1 CDM Peako41 2002 TELMAX (SPIN) 0 -91% -69% 0 10 0,06 14% 17 2 0,24 11 1 1 DB securities42 2002 KRUK 0 -337% -21% 0 11 0,01 54% 17 1 -0,08 28 1 1 CDM Peako43 2002 EMAX 0 -8% 70% 1 12 0,03 75% 13 2 0,00 14 1 3 Dom maklerski BMP 44 2003 DUDA 1 230% 460% 1 12 0,04 69% 17 3 0,10 13 0 3 IDMSA45 2003 HOOP 0 -204% -35% 0 12 0,10 77% 18 2 0,28 11 0 1 CA IB46 2003 IMPEL 0 -37% -16% 0 12 0,15 68% 19 1 0,04 13 0 1 CA IB47 2003 REDEN 0 -141% -73% 0 11 0,15 40% 18 1 0,46 8 0 2 Millennium DM48 2003 SNIEŻKA 0 -41% 40% 1 12 0,14 70% 18 1 0,26 19 0 1 Beskidzki Dom Maklerski49 2004 ATMGRUPA 1 39% 301% 1 10 0,14 62% 18 2 0,17 9 0 2 Millennium DM50 2004 PLASTBOX 0 -513% -12% 0 10 -0,08 56% 17 2 0,20 10 0 1 BDM PKO BP 51 2004 BETACOM 0 -572% -33% 0 10 0,13 74% 16 0 0,04 9 0 1 BM Banku Handlowego52 2004 DGA 0 -429% -23% 0 10 0,16 75% 17 2 0,12 9 0 1 BDM PKO BP 53 2004 GTC 0 -270% -54% 0 14 0,11 74% 20 2 0,14 10 0 1 CDM Pekao54 2004 TECHMEX 0 -323% 10% 1 12 0,04 54% 19 0 -0,04 17 1 2 Millennium DM55 2004 INTERCARS 1 127% 396% 1 12 0,04 68% 18 1 0,05 14 0 1 CDM Pekao56 2004 JCAUTO 0 -348% 38% 1 11 0,17 67% 18 0 0,14 10 0 3 IDMSA57 2004 ARTMAN 0 -316% 227% 1 11 0,04 62% 17 2 0,11 13 0 1 DM Banku Handlowego58 2004 MEDIATEL 0 -426% -34% 0 9 0,00 84% 16 0 -0,15 13 0 2 Millennium DM59 2004 HYGIENIKA 0 -255% -52% 0 10 -0,05 60% 17 1 -0,07 13 0 1 DI BRE60 2004 RMFFM 0 -4% 96% 1 12 0,00 73% 18 0 -0,09 14 0 2 IDMSA61 2004 NOWAGALA 0 -354% 0% 1 12 0,03 81% 18 2 0,33 9 0 1 DM BZ WBK62 2004 ELSTAROIL 0 -204% -70% 0 12 0,02 66% 18 1 0,30 21 0 1 BDM PKO BP 63 2004 PBG 1 632% 693% 1 12 0,08 71% 18 2 0,20 10 0 2 Millennium DM64 2004 ASSECO 1 202% 246% 1 12 -0,02 91% 19 3 0,26 13 1 2 Millennium DM65 2004 ATM 1 242% 479% 1 11 0,03 64% 17 1 0,04 10 1 1 BDM PKO BP 66 2004 SWISSMED 0 -552% -38% 0 10 -0,06 85% 15 2 0,43 8 0 1 CA IB67 2004 FAM 0 -289% -31% 0 10 0,11 74% 17 3 -0,11 10 0 2 Millennium DM68 2004 WSIP 0 -54% 61% 1 12 0,12 15% 19 1 0,15 30 0 1 BDM PKO BP
69 2004 PKOBP 1 77% 118% 1 18 0,01 62% 23 0 0,18 17 0 2 DM PENETRATOR
3 years BHAR 3 years raw BHR
Supplementary material V2 / 3
Supplementary material V: Input data for the logit model
Year Company name Assets ROA Retention Offer SEO Underpricing AGE VC/PE Reputation Lead manager
70 2004 TORFARM 0 -167% 79% 1 12 0,03 74% 17 1 -0,06 14 0 2 DM BGŻ
71 2004 PEKAES 0 -11% 22% 1 13 0,07 52% 19 0 -0,02 46 0 3 DM Banku Handlowego72 2004 KOELNER 1 5% 31% 1 11 0,13 73% 18 1 0,03 22 0 1 DI BRE73 2004 CCC 1 189% 238% 1 12 0,03 79% 18 0 0,03 8 0 1 CA IB Securities S.A. 74 2004 PRATERM 0 -67% 127% 1 11 0,07 47% 19 0 0,04 9 1 1 CDM Pekao SA75 2004 TVN 1 217% 260% 1 14 0,05 76% 20 1 0,10 7 0 1 CDM Pekao, CA IB 76 2004 POLCOLOR 0 -227% -72% 0 11 0,14 56% 19 1 -0,05 20 0 2 Millennium DM77 2004 DWORY 1 216% 283% 1 13 0,02 24% 20 1 0,01 59 0 2 DM BZ WBK 78 2004 DROZAPOL 0 -106% 31% 1 10 0,20 80% 16 2 0,15 11 0 1 BDM PKO BP 79 2004 EUROFAKTR 0 -312% -64% 0 12 0,01 52% 18 0 0,06 8 0 1 BDM PKO BP 80 2005 ATLANTAPL 0 -205% -50% 0 11 0,07 77% 16 1 0,16 15 0 1 BDM PKO BP 81 2005 COMP 0 -167% -14% 0 11 0,10 76% 17 3 0,14 15 1 1 CA IB Securities 82 2005 ZELMER 1 337% 379% 1 13 0,09 15% 19 0 0,28 68 0 1 DM BZ WBK 83 2005 EUROCASH 1 225% 245% 1 13 0,06 55% 19 0 0,04 10 0 1 CA IB Securities 84 2005 CIECH 1 179% 246% 1 14 0,02 59% 19 0 0,15 60 0 2 Millennium DM85 2005 ŚRUBEX 0 -300% -4% 0 11 0,16 43% 18 0 0,05 56 0 2 Millennium DM86 2005 GRAAL 1 61% 96% 1 11 0,04 74% 17 1 -0,12 15 0 3 IDMSA87 2005 BIOTON 0 -30% -14% 0 12 0,03 91% 18 4 0,44 16 0 1 CA IB88 2005 ZTSERG 0 -219% -32% 0 10 0,01 54% 16 1 -0,12 56 0 1 BDM PKO BP 89 2005 ZETKAMA 0 -67% 36% 1 11 0,04 78% 16 0 -0,50 59 1 1 CDM Pekao90 2005 PEP 1 176% 278% 1 12 0,13 74% 17 1 -0,07 8 1 1 DM BZ WBK 91 2005 LENA 0 -193% -65% 0 11 0,05 70% 18 0 0,06 16 0 1 CDM Pekao92 2005 LOTOS 0 -81% 2% 1 15 0,13 69% 20,7 0 0,03 30 0 1 DI BRE93 2005 DECORA 0 -33% -19% 0 11 0,22 75% 18 0 0,05 11 0 1 CDM Pekao94 2005 OPOCZNO 0 -80% -38% 0 13 0,16 50% 20 0 0,02 60 1 1 DM Banku Handlowego
3 years BHAR 3 years raw BHR
Supplementary material V3 / 3
