Survivability and Intra-Industry Effects of Initial Public Offerings – An

Transcription

Survivability and Intra-Industry Effects of Initial Public Offerings –
An Empirical Analysis
DISSERTATION
of the University of St.Gallen,
School of Management,
Economics, Law, Social Sciences
and International Affairs
to obtain the title of
Doctor of Philosophy in Management
submitted by
Adrian Peller
from
Germany
Approved on the application of
Prof. Dr. Dr. h.c. Klaus Spremann
and
Prof. Dr. Andreas Grüner
Dissertation no. 4102
Difo Druck GmbH, Bamberg 2013
The University of St.Gallen, School of Management, Economics, Law, Social
Sciences and International Affairs hereby consents to the printing of the present
dissertation, without hereby expressing any opinion on the views herein expressed.
St.Gallen, October 29, 2012
The President:
Prof. Dr. Thomas Bieger
To my parents
Acknowledgements
The past two years have been an amazing experience for me. Facing the academic as
well as personal challenges during the process of writing a dissertation taught me a
lot. Mastering these challenges would not have been possible without several persons
whom I hereby would like to thank for their enduring support.
First of all my deepest gratitude goes to my supervisors Prof. Dr. Klaus Spremann
and Prof. Dr. Andreas Grüner. Providing me with guidance and support throughout
the dissertation, they have given me the freedom to research the topics of my interest.
Furthermore I want to thank them for the time I was able to spend at the St.Gallen
Institute of Management in Singapore which has been one of the highlights of writing
this dissertation. My gratitude further goes to Dr. Sebastian Lang who has helped me
to initially start the dissertation process.
Besides, my friends and colleagues from the University of St.Gallen as well as from
my home deserve a special notation. I want to mention Lucia Ehn, Tatiana
Dvinyaninova, Julius Agnesens, Albert Gebhardt and Florian Neuhaus. I very much
enjoyed the constructive feedback and comments in many discussions which clearly
have enriched my dissertation and the time while writing it. Special thanks also go to
my brother Maximilian, my sister Theresa and my dear friends Nina Etterer and
Martin Liebmann who have supported me throughout the whole dissertation and have
been the necessary balance for my personal life.
Finally I would like to deeply thank my parents to whom I want to dedicate this
dissertation. Writing this dissertation would not have been possible without their
constant trust and unconditional support through all my years. They have always
encouraged me in times of doubt and provided me with so much generosity, devotion,
patience and love, making my family the most important pillar in my life.
Höhenkirchen, December 2012
Adrian Peller
Contents
I
Contents
Contents ......................................................................................................................... I List of Tables .............................................................................................................. III List of Figures ............................................................................................................. IV Abstract ........................................................................................................................ V Abstract in German ..................................................................................................... VI 1 Introduction ...................................................................................................... - 1 - 1.1 Motivation ................................................................................................... - 1 - 1.2 Research Ideas ............................................................................................. - 4 - 1.3 Course of the Analysis ................................................................................ - 7 - References ........................................................................................................... - 10 - 2 The Survivability of Initial Public Offerings – Insights from the Product
Market Competition ...................................................................................... - 14 - 2.1 Introduction ............................................................................................... - 15 - 2.2 Related Literature ...................................................................................... - 16 - 2.2.1 IPO Survival ....................................................................................... - 16 - 2.2.2 Product Market Competition .............................................................. - 17 - 2.3 Hypothesis Development .......................................................................... - 18 - 2.4 Data............................................................................................................ - 21 - 2.5 Methodology.............................................................................................. - 26 - 2.6 Empirical Results....................................................................................... - 29 - 2.6.1 Kaplan-Meier Survival Functions ...................................................... - 29 - 2.6.2 Duration Analysis ............................................................................... - 29 - 2.7 Conclusion ................................................................................................. - 38 - References ........................................................................................................... - 40 - II
3 Contents
Intra-Industry Effects of Initial Public Offerings – Insights from the
Product Market Competition on the IPO’s Competitive Effect .................... - 45 - 3.1 Introduction ............................................................................................... - 46 - 3.2 Related Literature ...................................................................................... - 48 - 3.3 Hypothesis Development .......................................................................... - 51 - 3.4 Data and Descriptive Statistics .................................................................. - 55 - 3.5 Methodology.............................................................................................. - 59 - 3.6 Empirical Results....................................................................................... - 62 - 3.6.1 Event Study Analysis.......................................................................... - 62 - 3.6.2 Analysis of the IPO’s intra-industry Effect ........................................ - 66 - 3.6.3 Comparison of different Levels of Industry Competitiveness ........... - 70 - 3.7 Conclusion ................................................................................................. - 73 - References ........................................................................................................... - 75 - 4 Intra-Industry Effects of withdrawn Initial Public Offerings ........................ - 79 - 4.1 Introduction ............................................................................................... - 80 - 4.2 Hypothesis Development .......................................................................... - 86 - 4.3 Data and Descriptive Statistics .................................................................. - 95 - 4.4 Methodology.............................................................................................. - 99 - 4.5 Empirical Results..................................................................................... - 100 - 4.5.1 Intra-industry Effect of a withdrawn IPO ......................................... - 100 - 4.5.2 Cross-sectional Results ..................................................................... - 105 - 4.5.3 Results from the subsample Analysis ............................................... - 108 - 4.6 Conclusion ............................................................................................... - 111 - References ......................................................................................................... - 113 - 5 Conclusion ................................................................................................... - 117 - List of Tables
III
List of Tables
Table 2.1 Variables Definition ............................................................................... - 22 - Table 2.2 Summary of Delisting Reasons.............................................................. - 24 - Table 2.3 Descriptive Statistics.............................................................................. - 25 - Table 2.4 Regression Results of Cox proportional Hazard Model and loglogistic Survival Function ............................................................................. - 32 - Table 2.5 Robustness Test using the exponential, Weibull and log-normal
Distribution as baseline Hazard Rate ............................................................ - 35 - Table 2.6 Robustness Test of the log-logistic Survival Model excluding
Internet IPOs and using an alternative Measure for Industry
Concentration ................................................................................................ - 36 - Table 2.7 Robustness Test using a logit discretionary Response Model ............... - 37 - Table 3.1 Variables Definition ............................................................................... - 57 - Table 3.2 Descriptive Statistics of completed IPO Sample ................................... - 60 - Table 3.3 Distribution of completed IPOs (1992-2010) ........................................ - 60 - Table 3.4 Abnormal Returns of the Competitor Portfolio on the Completion
and Filing Date of IPOs ................................................................................. - 65 - Table 3.5 Cross-sectional Variation in Competitors’ Valuation Effects upon
a completed IPO ............................................................................................ - 67 - Table 3.6 Abnormal Returns of the Competitor Portfolio on the Completion
Date of an IPO for different Levels of Industry Competitiveness ................ - 71 - Table 4.1 Variables Definition ............................................................................... - 96 - Table 4.2 Descriptive Statistics of the Sample of withdrawn IPOs ....................... - 98 - Table 4.3 Abnormal Returns of the Competitor Portfolio on the Withdrawal
Date of IPOs ................................................................................................ - 102 - Table 4.4 Cross-sectional Variation in Competitors’ Valuation Effects upon
a withdrawn IPO.......................................................................................... - 106 - Table 4.5 Abnormal Returns of the Competitor Portfolio sorted by Industry
Concentration and Leverage on the Withdrawal Date of IPOs ................... - 109 - IV
List of Figures
List of Figures
Figure 2.1 Histogram of Time-to-Failure of IPO Companies ............................... - 26 - Figure 2.2 Kaplan-Meier Estimate of the Survival Function................................. - 30 - Figure 2.3 Kaplan-Meier Estimate of the Hazard Rate.......................................... - 30 - Figure 3.1 Competitors’ abnormal Return around the Completion and Filing
of IPOs........................................................................................................... - 63 - Figure 4.1 Competitors’ abnormal Return around withdrawn IPOs .................... - 101 - Figure 4.2 Competitors’ abnormal Return around withdrawn IPOs
(controlling for confounding positive IPO Events) ..................................... - 104 - Abstract
V
Abstract
This dissertation investigates two topics concerning a company’s Initial Public
Offering (IPO): survivability in the aftermarket and intra-industry effects. The first
study (Part 2) explores the role of the firm’s product market competition by assessing
the survival times of IPOs in the aftermarket. To test the possible relationship, a
prediction model using factors that characterize the firm’s product market
competition together with firm-, deal- and market-related variables is employed. My
results suggest that industry concentration, the degree of competitive interaction
among companies and the industry’s entry rate all significantly affect survival time.
The second and third study (Parts 3 and 4) focus on the intra-industry effects of IPOs.
Part 3 analyzes the valuation effects of IPOs on listed industry competitors and
provides evidence that competitors exhibit a negative price reaction to the filing and
completion of an IPO in their industry. This reaction is attributed to a competitive
effect associated with an IPO. I further find that the competitive effect upon a
completed IPO is negatively related to the toughness of the product market
competition. IPOs in industries that have higher industry concentration, a lower
degree of strategic interaction and lower industry demand uncertainty exhibit a
stronger competitive effect on their rivals. Part 4 analyzes the valuation effect of
withdrawn IPOs on listed industry competitors. When controlling for confounding
events, the competitor portfolio does not, on average, show a significant reaction.
However, a cross-sectional analysis, using variables that account for the market
environment as well as industry, deal and company characteristics, shows that market
variables have a strong impact on competitors’ responses. Furthermore, industry
concentration is found to have a significantly negative impact and the filing firm’s
financial leverage to have a significantly positive impact on competitors’ abnormal
returns. This finding is consistent with the theory that the ex-ante risk of withdrawal
of the IPO filing affects competitors’ reactions. These results provide evidence for the
existence of an intra-industry effect of withdrawn IPOs.
VI
Abstract in German
Abstract in German
Diese Dissertation befasst sich mit zwei Aspekten von Börsengängen: der
Überlebensdauer an der Börse und intra-Industrie Effekten. In der ersten Studie (Teil
2), wird der Einfluss der Wettbewerbssituation des Unternehmens auf seine
Überlebensdauer an der Börse untersucht. Dabei wird ein Vorhersagemodell, welches
Faktoren beinhaltet, die den Wettbewerb sowie Firmen-, Transaktions- und
Marktcharakteristiken abbilden verwendet. Die Resultate zeigen, dass die
Industriekonzentration, das Maß der Wettbewerbsinteraktion und das Wachstum der
Unternehmensanzahl einen signifikanten Einfluss auf die Überlebensdauer haben.
Die zweite und dritte Studie (Teile 3 und 4) befassen sich mit intra-Industrie Effekten
von Börsengängen. Teil 3 analysiert den Bewertungseffekt eines Börsengangs auf
gelistete Wettbewerber und zeigt, dass diese nach der Ankündigung und
Durchführung eines Börsengangs in ihrer Industrie eine negative Reaktion aufweisen.
Die Reaktion ist auf einen Wettbewerbseffekt des Börsengangs zurückzuführen.
Dieser Wettbewerbseffekt ist negativ mit der Wettbewerbsintensität in der Industrie
korreliert. Börsengänge in Industrien mit einer hohen Industriekonzentration, einem
geringen Maß an Wettbewerbsinteraktion und geringer Nachfrageunsicherheit weisen
einen stärkeren Wettbewerbseffekt auf. Teil 4 analysiert den Bewertungseffekt eines
zurückgezogenen Börsengangs auf gelistete Wettbewerber. Wenn man irritierende
Vorfälle beachtet, zeigt sich, dass das Wettbewerberportfolio keine signifikante
Reaktion aufweist. Eine Regressionsanalyse, welche Variablen für das Marktumfeld,
die Industrie, die Transaktion und das Unternehmen beinhaltet, zeigt, dass die
Marktstimmung einen großen Einfluss auf die Wettbewerberreaktion hat. Außerdem
hat die Industriekonzentration einen signifikant negativen Einfluss und der
Verschuldungsgrad des Unternehmens einen signifikant positiven Einfluss auf die
Wettbewerberreaktion. Dieses Ergebnis unterstützt die Theorie, dass das VorabRisiko eines Börsengangrückzugs einen Einfluss auf die Wettbewerberreaktion hat.
Die Resultate weisen daher auf einen intra-Industrie Effekt von zurückgezogenen
Börsengängen hin.
Introduction
-1-
1 Introduction
1.1 Motivation
The Initial Public Offering (IPO) is one of the most significant steps in the company’s
life cycle and one that probably triggers the most public attention. The immense
media coverage of prominent IPOs, such as Groupon’s $700 million offering in 2011
and Facebook’s $16 billion IPO in 2012, demonstrates the extensive importance of
such events. For the issuing company and the accompanying investment banks, a
successful IPO is crucial because a very large amount of money is involved. Yet, an
IPO is not simply a firm-related event; it also affects and it is affected by other factors
including investors, competitors and further related financial market members. For
these groups, it is important to understand the complex dynamics and effects that
accompany an IPO. In this dissertation, I thus provide further insights into these
dynamics by focusing on two topics concerning a company’s IPO: the survivability of
the IPO in the aftermarket and the intra-industry effects of the IPO.
For a company, going public means significant changes in terms of legal
requirements, disclosure, public attention and, most importantly, access to the capital
market. These changes and the IPO preparations that are necessary are costly. It is
thus of great interest for companies to receive a satisfactory valuation at the time of
the IPO. For investors, the performance of the IPO is of greatest interest. As a
consequence, the vast majority of research in the academic finance literature focuses
on the valuation and performance of IPOs. This stream of research is focused on
solving the well-known IPO puzzles, namely underpricing and the long-run
underperformance of new issues. Underpricing refers to the phenomenon that IPOs
experience huge returns in the initial trading days and explores why issuing
companies leave that money on the table. This puzzle has first been observed by
Ibbotson (1975), with many subsequent studies attempting to explain this
observation. Most prominent are theories that assume a relationship between
underpricing and information asymmetry, such as Rock’s (1986) “winner’s curse
theory”, Beatty and Ritter (1986), Baron (1982) and Benveniste and Spindt (1989).
-2-
Introduction
However, the studies have thus far been unable to fully explain the puzzle but
confirm the existence and persistence of underpricing in different countries (e.g.,
Loughran, Ritter and Rydqvist, 1994). The second puzzle refers to the
underperformance of IPO companies in the initial years compared with size and
book-to-market matched peers and has also been well documented in the literature
(see Ritter, 1991; Ritter and Welch, 2002). As in the case of underpricing, previous
studies have failed to provide a satisfactory explanation for the performance puzzle.
However, an IPO is not just an isolated firm-specific event. A central factor thereby is
the competitive environment, which includes competitors and competition in the
product market. Several studies have described the important interaction of an IPO
with its competitive environment. For instance, Chemmanur, He and Nandy (2010)
show that product market competition is a significant factor in a firm’s decision to go
public. Furthermore, Chod and Lyandres (2011) as well as Chemmanur and He
(2011) find that competition in the product market also significantly influences the
company’s post-IPO performance. These studies therefore suggest that product
market competition significantly affects the dynamics of an IPO. By contrast, the
studies by Akhigbe, Borde and Whyte (2003) and Hsu, Reed and Rocholl (2010)
suggest that an IPO also affects the competitive environment. These two studies
examine competitors’ responses to an IPO in their industries and find evidence for a
competitive and an information effect of an IPO on industry competitors. The
competitive effect arises because the IPO affects the competitive balance in the
industry. The information effect refers to the release of private information about the
industry from the IPO.
These
studies
show
that
together
with
firm-specific
and
market-related
considerations, the competitive environment influences the IPO. In return, the IPO
also affects the competitive environment. Therefore, an IPO should not be seen as
firm-specific but in interaction with the company’s environment. To study this
interaction, this dissertation explores how the competitive environment influences the
IPO and vice versa. In particular, it focuses on the influence of product market
Introduction
-3-
competition on the survival time of an IPO in the aftermarket (Part 2) and the effect
of an IPO on competitors (Parts 3 and 4).
Survival time represents the time a newly listed company survives in the capital
market. This is a basic but crucial way of performance measurement. In this context,
the company’s survival ends when it is delisted from the stock exchange for negative
reasons. Studying the survival times of new firms in the aftermarket has gained more
interest as the number of defaulting companies has been increasing over the last
decades as documented by Fama and French (2004). According to the authors, this
has occurred because of the changing characteristics of newly listed firms. The
supply of capital has increased over recent years, leading to a decline in the required
returns and consequently to lower costs of capital. Lower costs of capital turn the net
present values of more firms that have low profitability levels but high growth
expectations positive. As a result, the number of new risky firms increases, as do
delisting rates. Thus far, few studies investigate the survival times of IPO companies.
Hensler, Rutherford and Springer (1997) are the first to connect the company’s
survival time with factors at the time of the IPO. In a more recent article, Demers and
Joos (2007) study IPO survival times using firm-specific as well as market- and dealrelated variables.1 The research on IPO survival is strongly related to that on firm
survival in general. Traditional models of bankruptcy prediction such as Altman’s
(1968) Z-score focus on accounting variables. It has been shown by Shumway (2001)
that these static models are outperformed by dynamic hazard models when predicting
firm survival and that including market-driven variables strongly increases the default
predictability.2 Including product market competition thus expands the existing
literature on IPO survival.
The second topic of this dissertation are the intra-industry effects of IPOs. Although
the major impact of an IPO relates to the issuing company, when a company goes
public, the impact of the IPO is not limited to the company alone. For competitors
1
Other studies explore the survival of internet IPOs (Kauffman and Wang, 2003; van der Goot, van Giersbergen, and
Botman, 2009), the survival of penny-stock IPOs (Bradley, Cooney, Dolvin and Jordan, 2006; Carpentier and Suret, 2011)
and the influence of underwriters (Jain and Kini, 1999) and auditors (Weber and Willenborg, 2003; Willenborg and
McKeown, 2001) on survival.
2
Chava and Jarrow (2004) find that including accounting variables adds little predictive power when market-driven
variables are already included in the prediction model.
-4-
Introduction
and investors, it is thus important to understand the impact of the IPO’s intra-industry
effect. For example, Ritter and Welch (2002) notice that an IPO affects the
company’s relations with its customers, creditors and suppliers. Going public is a
strong commitment and the result of a successful company strategy. Therefore, being
public increases the company’s reputation and credibility in the relation with these
stakeholders. Most importantly, the IPO brings fresh capital into the company. These
changes have an impact on the company’s competitive situation, and therefore, are
relevant for competitors as well. Empirical support for this assumption comes from
Akhigbe et al. (2003) and Hsu et al. (2010), who study the intra-industry effects of
IPOs. Additionally, an IPO affects competitors not only by influencing product
market competition, but also through an information effect. Previous studies have
shown that corporate events provide an information signal to competitors. For
instance, Lang and Stulz (1992), Cheng and McDonald (1996) as well as Ferris,
Jayaraman and Makhija (1997) all show that bankruptcy announcements have a
significant information effect on competitors. Akhigbe and Madura (1999) report a
significant information effect of acquisition announcements in an industry. Further,
the studies by Akhigbe et al. (2003) and Cotei and Farhat (2011) find empirical
evidence for the existence of an IPO’s information effect.
1.2 Research Ideas
Although the interaction of the IPO with product market competition and competitors
is an under-researched topic, it is important for investors and managers, both of the
issuing firm and of competitors, to understand the driving forces behind the dynamics
of an IPO. These insights should help investors and senior management assess the
risks and opportunities associated with an IPO.
The first topic in this dissertation thus explores the question of whether the
survivability of a newly listed company in the aftermarket is influenced by product
market competition. In the context of interaction, this question examines the
influence of product market competition on the IPO. This research is motivated by
the findings from Chemmanur and He (2011), Chemmanur et al. (2010) and Chod
and Lyandres (2011), who show the significant impact of product market competition
Introduction
-5-
on the firm’s decision to go public and its post-IPO performance. Furthermore, Hou
and Robinson (2006) find that product market competition is a significant risk factor
in explaining average stock returns.
Taking the firm’s product market competition into account when explaining the
survivability of IPO companies adds a further dimension to previous research, which
has only explored firm-, deal- and market-related factors. To my best knowledge, no
study so far has taken into consideration the company’s product market competition
when studying IPO survival.3 Including product market competition makes sense
though because every firm faces competition from its competitors in the product
market. As product market competition differs between industries, it is easier, given
the same financial endowment, to survive in one industry while it might be more
difficult in another industry. It is thus the company’s success in its product market
that ultimately determines its financial success and therefore its stock performance
and survival in the financial market. Therefore, Part 2 of this dissertation is dedicated
to answering whether product market competition does have an effect on the survival
time of an IPO firm with the following two research questions:
Do companies operating in a tough competitive environment have a shorter
survival time in the aftermarket?
Do companies operating in a weak competitive environment have a longer survival
time in the aftermarket?
The second topic focuses on how an IPO affects industry competitors. In the context
of interaction, this topic examines the influence of the IPO on the competitive
environment. The starting point is that an IPO is not only a firm-specific event but
that it influences competitors as well. This influence can be caused either by an
information effect or by a competitive effect as previous studies have shown
3
Demers and Joos (2007) employ the gross profit margin to account for production efficiencies, brand names, pricing
power and competitive conditions in a firm’s product market. Li and Zhou (2006) and Seguin and Smoller (1997) control
for six major industries in their studies. The recent studies by Carpentier and Suret (2011) and Wagner and Cockburn
(2010) also control for industry affiliation.
-6-
Introduction
(Akhigbe et al., 2003). Hsu et al. (2010), Chod and Lyandres (2011) and Chemmanur
and He (2011) all find evidence that the competitive effect is caused by a competitive
advantage of the issuing firm, which affects the competitive balance in the industry
and causes an adverse effect on rival companies. This leads to the question of
whether the degree to which IPO firms are able to gain a competitive advantage
depends on the existing competition in the product market. To give insights into this
question, Part 3 focuses on the interaction between the IPO’s competitive effect and
the competitive environment with the following research questions:
Is the IPO’s competitive effect stronger in industries with a weak competitive
environment?
Is the IPO’s competitive effect weaker in industries with a tough competitive
environment?
Given that the previous research questions explore the intra-industry effect of a
successful IPO event (either an IPO filing or a completion), an ensuing question is
how a negative IPO event, that is the withdrawal of an IPO filing, affects industry
competitors. In the context of interaction, this topic also examines how an IPO
influences the competitive environment. The withdrawal can have only firm-specific
implications or it can trigger an intra-industry effect, caused by an information effect
and/or a competitive effect. Part 4 therefore examines the possible intra-industry
effects of withdrawn IPOs and focuses on the following two research questions:
Do withdrawn IPOs have an intra-industry effect?
If withdrawn IPOs have an industry wide impact, which factors have an influence
on the intra-industry effect?
To summarize, this dissertation investigates whether product market competition
influences the survival time of an IPO, what role product market competition plays
Introduction
-7-
for the intra-industry effects of IPOs and whether withdrawn IPOs have an intraindustry effect (and if so, which factors influence this effect). The dissertation thus
provides insights into the interaction of the IPO with the competitive environment to
better understand the implications for the dynamics upon an IPO. To answer these
research questions, I carry out an empirical analysis using IPO data from the US,
which is by far the largest capital market.
1.3 Course of the Analysis
The remainder of this dissertation is structured as follows. Part 2 explores the
question whether product market competition influences the survival time of newly
listed firms in the aftermarket. Part 3 focuses on how the IPO affects the competitive
environment by looking at the intra-industry effects of IPOs and the role of product
market competition. The analysis in Part 3 leads to the research question in Part 4:
What is the effect of withdrawn IPOs on industry competitors? Finally, Part 5
summarizes and concludes the overall findings of this dissertation.
The empirical analysis starts in Part 2, which focuses on the role of the firm’s product
market competition by assessing the survival times of IPOs in the aftermarket. To test
the possible relationship, a prediction model using factors that characterize the firm’s
product market competition together with firm-, deal- and market-related variables is
employed. For this analysis, a parametric log-logistic model and the Cox (1972)
proportional hazard model are applied. The results suggest that product market
competition significantly affects survival time. Further, using different baseline
hazard functions does not change the results. The analysis using a logit discrete
response model also confirms these findings. The results show that product market
competition is a further dimension that influences the survival of newly listed firms in
the aftermarket. These insights should help investors assess the risk structure of a
firm that is going public.
The analysis continues by looking at the intra-industry effects of IPOs in Part 3. The
study focuses on the competitive effect of an IPO on industry competitors. I
hypothesize that the degree to which a newly listed company benefits from going
-8-
Introduction
public depends on the competition in the product market. This implies that the
benefits of going public are higher when competition in the industry is rather weak
whereas the benefits of going public are lower when competition in the industry is
rather tough. To test these hypotheses, I apply the event-study methodology in order
to analyze competitors’ abnormal returns around a successful IPO in their industries.
I find that industry competitors yield negative abnormal returns in response to a filing
or completed IPO in their industries. Analyzing the competitors’ abnormal returns,
the results show that product market competition significantly influences
competitors’ abnormal returns. In other words, the weaker the competitive
environment, the stronger is the IPO’s competitive effect. I further find that IPOs in
industries with a tough competitive environment do not trigger a significant
competitors’ response. These findings have important implications for private firms
in their decision to go public. When operating in an industry that has a weak
competitive environment, going public is associated with more benefits. The
presented results should help investors of competing firms assess the risks associated
with an IPO in the industry.
Part 4 continues with the analysis of the intra-industry effects of IPOs by exploring
the possible impact of a withdrawn IPO on industry competitors. I focus on the
information effect of a withdrawn IPO. The underlying assumption is that the
withdrawal of an IPO is a signal of a worsening industry outlook. This signal would
be stronger when the withdrawal is less anticipated. I therefore identify such IPO
withdrawals with a strong information effect using the ex-ante risk of withdrawal. In
a first step, I use the event-study methodology in order to get an insight whether a
withdrawn IPO is merely a firm-specific event or has industry-wide implications. In a
second step, I analyze competitors’ abnormal returns around a withdrawn IPO in the
industry to assess which factors have an impact on the competitors’ reaction. The
analysis shows that on average, there is no observable competitors’ reaction around
the withdrawal of an IPO in the industry. However, further analysis finds that market
sentiment, industry concentration and the issuing firm’s financial leverage all have a
significant impact on the competitors’ reaction. While the impact of industry
concentration is negative, the impact from leverage is positive. These findings are
Introduction
-9-
consistent with the theory that IPO filings with a low ex-ante risk of withdrawal have
a strong information effect when withdrawn. Overall, the results provide evidence for
the existence of an intra-industry effect of withdrawn IPOs.
- 10 -
Introduction
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Introduction
- 11 -
Cheng, L. & McDonald, J. (1996). Industry Structure and Ripple Effects of
Bankruptcy Announcements. Financial Review, 31 (4), 783-807.
Chod, J. & Lyandres, E. (2011). Strategic IPOs and Product Market Competition.
Journal of Financial Economics, 100 (1), 45-67.
Cotei, C. & Farhat, J. (2011). Information Externalities of Initial Public Offerings:
Does Venture Capital Backing Matter? Journal of Economics and Finance,
forthcoming.
Cox, D. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical
Society, 34 (2), 187-220.
Demers, E. & Joos, P. (2007). IPO Failure Risk. Journal of Accounting Research, 45
(2), 333-371.
Fama, E. & French, K. (2004). New Lists: Fundamentals and Survival Rates. Journal
of Financial Economics, 73 (2), 229-269.
Ferris, S., Jayaraman, N. & Makhija, A. (1997). The Response of Competitors to
Announcements of Bankruptcy: An Empirical Examination of Contagion and
Competitive Effects. Journal of Corporate Finance, 3 (4) 367-395.
Hensler, D., Rutherford, R. & Springer, T. (1997). The Survival of Initial Public
Offerings in the Aftermarket. Journal of Financial Research, 20 (1), 93-110.
Hou, K. & Robinson, D. (2006). Industry Concentration and Average Stock Returns.
Journal of Finance, 61 (4), 1927-1956.
Hsu, H.-C., Reed, A. & Rocholl, J. (2010). The New Game in Town: Competitive
Effects of IPOs. Journal of Finance, 65 (2), 495-528.
Ibbotson, R. (1975). Price Performance of Common Stock New Issues. Journal of
Economics, 2 (3), 235-372.
Jain, B. & Kini, O. (1999). On Investment Banker Monitoring in the New Issues
Market. Journal of Banking and Finance, 23 (1), 49-84.
- 12 -
Introduction
Kauffman, R. & Wang, B. (2003). A Survival Theory of Internet Firm Duration.
Working Paper, Carlson School of Management, University of Minnesota.
Lang, L. & Stulz, R. (1992). Contagion and Competitive Intra-Industry Effects of
Bankruptcy Announcements. Journal of Financial Economics, 32 (1), 45-60.
Li, J. & Zhou, J. (2006). Earnings Management and Delisting Risk of Initial Public
Offerings. Working Paper, Tsinghua University.
Loughran, T., Ritter, J. & Rydqvist, K. (1994). Initial Public Offerings: International
Insights. Pacific-Basin Finance Journal, 2 (2), 165-199.
Ritter, J. (1991). The Long-Run Performance of Initial Public Offerings. Journal of
Finance, 46 (1), 3-27.
Ritter, J. & Welch, I. (2002). A Review of IPO Activity, Pricing and Allocations.
Rock, K. (1986). Why New Issues are Underpriced. Journal of Financial Economics,
15 (1-2), 187-212.
Seguin, P. & Smoller, M. (1997). Share Price and Mortality: An empirical Evaluation
of Newly Listed NASDAQ Stocks. Journal of Financial Economics, 45 (3), 333363.
Shumway, T. (2001). Forecasting Bankruptcy more Accurately: A Simple Hazard
Model. Journal of Business, 74 (1), 101-124.
van der Goot, T., van Giersbergen, N. & Botman, M. (2009). What Determines the
Survival of Internet IPOs? Applied Economics, 41 (5), 547-561.
Wagner, S. & Cockburn, I. (2010). Patents and the Survival of Internet-related IPOs.
Research Policy, 39 (2), 214-228.
Weber, J. & Willenborg, M. (2003). Do Expert Informational Intermediaries add
Value? Evidence from Auditors in Microcap IPOs. Journal of Accounting
Research, 41 (4), 681-720.
Introduction
- 13 -
Willenborg, M. & McKeown, J. (2001). Going-Concern Initial Public Offerings.
Journal of Accounting and Economics, 30 (3), 279-313.
- 14 -
The Survivability of Initial Public Offerings
2 The Survivability of Initial Public Offerings –
Insights from the Product Market Competition4
In this study, I explore the role of the firm’s product market competition when
explaining the survival time of IPOs in the aftermarket. To test the possible
relationship, a prediction model using factors characterizing the firm’s product
market competition together with firm-, deal- and market-related variables is
employed. For this analysis a parametric log-logistic model and the Cox proportional
hazard model are applied. My results suggest that industry concentration, the degree
of competitive interaction among companies and the industry’s entry rate
significantly affect the survival time. For industry demand uncertainty and the market
share the results are not statistically significant. Various robustness tests confirm
these findings. The results contribute to the existing literature by showing the
important role of taking the firm’s product market competition into account when
predicting the IPO’s survival time in the aftermarket.
4
This study has been published in the Corporate Finance biz Journal. Acknowledgements: I wish to thank my supervising
professors Klaus Spremann and Andreas Grüner, an anonymous referee, Lucia Ehn, Julius Agnesens, Florian Neuhaus and
the participants of several seminars at my university for their helpful comments and suggestions.
- 15 -
2.1 Introduction
When a new firm enters the financial market, the Initial Public Offering (IPO) is
accompanied by expectations but also by uncertainty. For investors it is essential to
evaluate the IPO’s potential and risk before investing. One dimension of the IPO’s
aftermarket performance is the expected survival time in the financial market which
is the topic of this study. A delisting due to negative reasons from the market is
mostly accompanied with serious financial distress or even bankruptcy. Therefore,
the survival time is a measure of performance, not only for investors but also for
further stakeholders like debtees, banks, employees or suppliers. Empirical evidence
shows the critical phase of a new firm in the market is the time shortly after going
public (Hensler, Rutherford and Springer, 1997 or Demers and Joos, 2007). Factors at
the time of the IPO are expected to provide valuable insights on the expected survival
time.
Starting with Hensler et al. (1997), prior literature has already connected IPO survival
with factors upon the IPO (also see Demers and Joos, 2007; Jain and Kini, 2008; van
der Goot, van Giersbergen and Botman, 2009). These studies identify variables which
can be categorized into three groups: firm specific, market- and deal-related
variables. Prior studies only focus on the IPO company itself and on relations of the
company with the financial market and intermediaries. However, these studies omit
the company’s product market environment when studying IPO survival.5
Firm-specific factors like leverage or profitability should be interpreted together with
the competitive environment. Recent studies show that product market competition
plays a significant role for the firm’s decision to go public and also for the post-IPO
performance (see Chemmanur, He and Nandy, 2010; Chod and Lyandres, 2011;
Chemmanur and He, 2011). Every firm faces competition from its rivals in the
product market. The degree in which a firm succeeds in the product market
competition determines the chances of survival and financial success. As product
5
Demers and Joos (2007) employ the gross profit margin to account for production efficiencies, brand names, pricing
power and competitive conditions in a firm’s product market. Li and Zhou (2006) and Seguin and Smoller (1997) control
for six major industries in their studies. The recent studies by Carpentier and Suret (2011) and Wagner and Cockburn
(2010) also only control for industry affiliation.
- 16 -
market competition differs between industries, it is easier for some companies to be
successful in the product market whereas others might fail given the same financial
endowment. This study is an attempt to connect the expected survival time of an IPO
with product market competition.
2.2 Related Literature
2.2.1 IPO Survival
The literature on the survival of IPOs origins in the literature on bankruptcy
prediction. Traditional models like Altman’s Z-score (Altman, 1968) or Ohlson’s Oscore (Ohlson, 1980) focus on accounting variables. Shumway (2001) shows that
these static models are outperformed by a dynamic hazard model to predict firm
survival. Shumway further adds market-related variables to his model and finds a
strong increase in the default predictability (see also Chava and Jarrow, 2004 and
Beaver, McNichols and Rhie, 2005).
Only a limited number of studies deal with the survival time of IPOs so far. Fama and
French (2004) document that over the last decades, more firms characterized by low
profit/high growth went public which has decreased the average survival time of an
IPO significantly. The literature on IPO survival is an attempt to discover the reasons
why a new firm actually fails in the market. A comprehensive study on the survival
predictability of IPOs is conducted by Demers and Joos (2007). The authors use
accounting information, deal-related variables as well as variables accounting for the
role of information intermediaries to study the factors which influence IPO survival
in a non-tech and a high-tech sample. Hensler et al. (1997) use firm-specific as well
as market-related factors to study the effect on the survival time of firms going
public. Their study is the first to link the IPO’s time-to-failure to factors at the time of
the IPO. Using the same approach as Hensler et al., van der Goot et al. (2009) focus
on the determinants of the survival of internet IPOs. Internet IPOs which have been
dominating the IPO market during the dotcom bubble in the late 1990s exhibit a far
higher hazard rate than traditional IPOs.6
6
For the survival of internet IPOs see also Kauffman and Wang (2003).
- 17 -
Carpentier and Suret (2011) as well as Bradley, Cooney, Dolvin and Jordan (2006)
study the survival of penny stock IPOs.7 Both studies report a significantly higher
hazard rate for penny stock IPOs than for ordinary IPOs. Wagner and Cockburn
(2010) study the role of intellectual property rights for “new economy” firm survival.
The authors find that the number of patents positively influences firm survival in a
sample of internet IPOs going public at NASDAQ in the bubble period of 1998 to
2001. Further studies focus on the role of financial intermediaries. Weber and
Willenborg (2003) as well as Willenborg and McKeown (2001) report the positive
influence of auditors in assuring a new firm’s quality and therefore the survival
probability. Jain and Kini (1999) study the role of underwriter reputation, finding a
positive relation between reputation and aftermarket performance of IPO companies.
Moreover, a positive influence on IPO survival is associated with venture backing
(Jain and Kini, 2000). These studies demonstrate that financial intermediaries provide
valuable information about the firm’s quality.
2.2.2 Product Market Competition
Prior literature has already found that product market competition affects the firm
performance. Earlier studies focus on the connection between industry concentration
and accounting profitability. Cowling and Waterson (1976) find that industry
concentration is positively related to the price-cost margin and Qualls (1972) shows
that the positive relation is also true for further profit margins (also see Bain, 1951 or
Mann, 1966). More recent studies show that the competitive environment also affects
stock returns. Hou and Robinson (2006) find that firms in more concentrated
industries earn lower returns after controlling for common risk factors. Supporting
evidence further comes from Hashsem (2010) who studies an UK sample. In an
ensuing study, Sharma (2011) confirms the influence of industry concentration and
also shows that firms with relatively low product substitutability and a low market
size earn lower stock returns as well. A possible explanation for this empirical
observation comes from Gaspar and Massa (2006) who find that industry
7
There exists no clear cut definition for the term „penny stock IPO“. Generally it refers to an initial public offering with an
offering price below some threshold value. In the US, e.g. stocks below an offering price of 5 USD are considered as penny
stocks.
- 18 -
concentration is also connected to idiosyncratic volatility. Firms in industries with a
higher degree of concentration exhibit lower idiosyncratic volatility.
In the case of an IPO, product market competition is also found to have a significant
influence. Studying the decision to go public of a large sample of private US
manufacturing firms, Chemmanur et al. (2010) find that product market competition
is a significant determinant in the going public decision. Product market competition
also affects the post-IPO performance. Chod and Lyandres (2011) show that the
strategic benefit of being public is associated with product market competition. The
authors find that going public is accompanied with an increase in the IPO firm’s
market share and this increase is influenced by product market competition.
Chemmanur and He (2011) report further evidence that going public is associated
with an increase in the product market share after the IPO and the role of product
market competition. Additionally, the authors report a significant relationship
between factors from the product market competition and operating performance after
the IPO.
The purpose of this study is to combine the literature on IPO survival with the
literature on product market competition. The literature review suggests that the
competitive environment is a significant risk factor which influences firm
performance. So far, this risk factor has not been employed in explaining the survival
time of IPOs. My study is an attempt to close this research gap.
2.3 Hypothesis Development
One possible indicator for the industry’s competitive environment is industry
concentration. This is an often used measure to account for the industry’s
competitiveness, both in academic literature and practice.8 It is assumed that the more
concentrated an industry, the weaker is the competition among the firms. Economic
theory suggests that competition ranges from perfect competition over an oligopoly to
8
See for example Bikker and Haaf (2002) who find evidence of the adverse effect of concentration on competition for the
banking industry or Lang and Stulz (1992), Chevalier (1995), Sundaram, John and John (1996) or Akhigbe, Borde and
Whyte (2003) who use industry concentration to control for competition in their studies on the intra-industry effect of firm
announcements. Also see Tirole (1988) for a theoretical discussion. The industry concentration is also a crucial factor in
decisions by the US Department of Justice or the European Commission on merger and acquisition cases.
- 19 -
a monopoly with decreasing “toughness” of competition. Industries with low
concentration exhibit characteristics of perfect competition whereas industries with
high concentration exhibit characteristics of an oligopoly. For the IPO survival time, I
expect that chances of survival are higher for IPOs facing competition in more
concentrated industries, leading to the first hypothesis.
H1: The industry concentration has a positive influence on the IPO’s survival time.
As suggested by Raith (2003) as well as Sharma (2011), industry concentration does
not capture all dimensions of product market competition. I therefore use several
further factors to create a more comprehensive picture of product market competition.
An alternative measure for the industry’s competitive environment is the way a
company responds to strategic choices of its competitors. This is represented by the
degree of competitive interaction and can either be in “strategic substitutes” or in
“strategic complements” according to the oligopoly literature (Bulow, Geanakoplos
and Klemperer, 1985). The companies’ interaction in an industry, i.e. whether they
take their decision rather dependent or independent of competitors’ decisions, is a
further characteristic of product market competition. More interaction among the
firms increases competition, making the competitive environment tougher. I therefore
expect that survival is more difficult in industries in which the competitive interaction
among firms is higher.
H2: The degree of competitive interaction in the product market has a negative
influence on the IPO’s survival time.
To account for the risk a company faces in its product market regarding earnings and
cash flows, I use the industry’s demand uncertainty in my study. In the literature on
industrial organization, Ghosal (1991) as well as Guiso and Parigi (1999) find that
demand uncertainty leads to a suboptimal level of investment in capital goods,
decreasing production efficiency. For the survival time I expect the influence of
- 20 -
demand uncertainty to be negative as operating is more risky due to the uncertainty
about cash flows.
H3: The demand uncertainty in the product market has a negative influence on the
IPO’s survival time.
The market share in the product market is an indicator for the firm’s market power.
The higher the market power, the easier it is for a company to handle competition in
the product market. Additionally, Chemmanur and He (2011) report a significant
negative relation between the IPO company’s market share and private as well as
public competitor’s market share growth after the IPO. The company’s market share
at the time of the IPO is hence expected to increase the IPO’s survival time.
H4: The market share has a positive influence on the IPO’s survival time.
The number of competitors in an industry is not constant over time. New entrants in
the market can change the competitive structure by increasing competition and
decreasing profits. This poses a potential threat to incumbent companies. The threat
increases with the entry rate. According to Porter’s famous five forces framework, an
industry is more attractive, the higher the barriers to entry for new companies are
(Porter, 1979). A low entry rate may reflect the existence of entry barriers. In a study
about business failure in the Japanese manufacturing sector, Honjo (2000) finds that
new businesses in an industry with a high entry rate exhibit higher mortality rates.
His study differs in the basic approach: Honjo calculates the entry rate from an insample period as he tries to explain business failure with the entry rate. The purpose
of my study is rather the prediction of the IPO survival time. Therefore I construct the
entry rate at the time of the IPO to omit possible endogeneity issues. I expect the
entry rate to negatively influence the IPO’s survival time.
H5: The entry rate in the product market has a negative influence on the IPO’s
survival time.
- 21 -
To test these hypotheses, I use a set of control variables which account for the results
of previous studies as mentioned in the literature review. The whole set of variables
can be seen in Table 2.1. The control variables comprise firm-related variables, dealrelated variables and market-related variables.
2.4 Data
The dataset contains 1,840 IPOs of firms going public on the three major stock
exchanges in the US (NYSE, AMEX and NASDAQ) between 1990 and 2010 and is
obtained from the Thomson SDC New Issues database. Following prior literature,
ADRs, REITs, Unit IPOs, penny stock IPOs (offer price < 5 USD) as well as
financial institutions are excluded from the sample. Furthermore, the IPO company
has to be available in Compustat and the CRSP database with available delisting
information as well as available company founding date. Finally two more
modifications have to be made. IPOs with delistings due to mergers and acquisitions,
exchanges or going privates are excluded from the sample as these delistings are not
for negative reasons. IPOs from the NAICS sector Mining (21) and Construction (23)
have to be excluded as there is no data available about industry concentration from
the US Census Bureau. These restrictions yield the final sample size of 1,840 IPOs of
which 703 have not survived during the observation period which ends Dec. 31 2010.
Table 2.1 lists and describes the variables used in the study. Deal-related information
about the IPO is obtained from the SDC database. The founding date is further
merged with Jay Ritter’s data on IPO companies’ founding date, available on his
website.9 The underwriter reputation ranking is the modified Carter-Manaster (1990)
reputation ranking by Jay Ritter and also available on his website. Internet IPOs have
been identified using Ofek and Richardson (2003), Loughran and Ritter (2004) and
have also been identified by the business description from the SDC database.10
Accounting-related variables and the six-digit NAICS code are obtained from
Compustat. Accounting variables are used from the fiscal year prior to the IPO. First
trading day closing prices and delisting information are obtained from the CRSP
9
I want to thank Jay Ritter for making his data publicly available. See http://bear.warrington.ufl.edu/ritter/ipodata.htm.
The Ofek and Richardson list is available at http://people.stern.nyu.edu/eofek/InternetDatabase.prn and Loughran and
Ritter’s list is available at http://bear.warrington.ufl.edu/ritter/ipodata.htm
10
- 22 -
Table 2.1
Variables Definition
Variable Name
survival_time
Description
Time the company is listed on the stock exchange since its IPO date until
delisting or end of observation period in years
Binary variable set to one if the observation is right censored
censor
Product Market
Variables
concentration
Market share of the 20 largest companies in six-digit NAICS industry
hhi
Herfindahl-Hirschman Index of six-digit NAICS industry
market_share
Market share of the company: Sales over Total Sales in the same sixdigit NAICS industry
idu
Industry demand uncertainty in six-digit NAICS industry
dci
Degree of competitive interaction in six-digit NAICS industry
entry_rate
Average net entry (number of firms over number of firms last year – 1)
over 3 years in six-digit NAICS industry
Firm Variables
age
Age of the company at the time of IPO in years (IPO date – founding
date)
internet
Binary variable set to one if the IPO is an internet IPO
logcapex
log of 1 + capital intensity (Capital Expenditures over Total Assets)
logleverage
log of 1 + leverage (Total Liabilities over (Total Assets + IPO proceeds))
roa
Return on Assets: Net Income over Total Assets
ocf
Operating Cash Flow over Total Liabilities
logsales
log of 1 + Sales over Assets
Deal Variables
offer_price
IPO offer price
vc
Binary variable set to one if the IPO got backed by Venture Capital
underwriter
Carter-Manaster (1990) underwriter reputation ranking
auditor
Binary variable set to one if the IPO is audited by a big 8 auditor*
exchange
Binary variable set to one if the IPO went public on NASDAQ
size
IPO size: Number of shares offered x IPO offer price
Market Variables
initial_return
First day return: Closing price of first trading day over IPO offer price -1
hotness
IPO market hotness: Average IPO underpricing in the 3 months before
the IPO
earlyipo
Binary variable set to one if the IPO has less than four preceding IPOs in
the same six-digit NAICS industry 3 month before the IPO
otb
Offer-to-book ratio: Firm value at offer price over book value of equity
uncertainty
Valuation uncertainty: Spread of price range over average price range
*
Big 8 stands for Arthur Andersen, Arthur Young, Coopers & Lybrand, Ernst & Young, Deloitte & Touche, KPMG,
Pricewaterhouse Coopers and Touche Ross
- 23 -
database. CRSP provides the delisting date as well as the reason for the delisting.11
Only delisting for negative reasons are accounted as failures and included in the
sample. Table 2.2 shows a summary of the delisting reasons in the sample. Data for
constructing the IPO market hotness variable is available on Jay Ritter’s website
where the number of IPOs and the average initial return per month are listed.12 From
the US Census Bureau I acquire data about industry size and industry concentration
which are available in the Economic Census.13 The Census data is available for the
years 2007, 2002 and 1997. I match each IPO with the closest available Economic
Census. An alternative would have been to calculate industry concentration and
market shares from all firms available in Compustat which would yield yearly
observations.14 The drawback of this alternative is that Compustat only contains data
about public firms. As a result, concentration ratios and market shares do not consider
the effect of private firms. However, this effect is significant as Ali, Klasa and Yeung
(2009) report. In their study, they find a correlation of only 13% between the
concentration ratio from Compustat and from the US Census. Additionally, the
benefit of having yearly observations is rather limited as industry concentration does
not vary significantly from year to year. Therefore I decide to use data about industry
concentration and industry size from the Economic Census.
For calculating the industry demand uncertainty (idu) and the degree of competitive
interaction, data from private firms is not available. These two variables are
calculated using all companies available in Compustat. The demand uncertainty is
constructed as the standard deviation of quarterly sales growth over a 20 quarter
period using a rolling window. This allows for variation in time of the variable. I
follow Chod and Lyandres (2011) and calculate seasonally adjusted sales growth. In a
first step I regress the industry sales growth, calculated as the sum of sales of all
companies in the same six-digit NAICS industry which have reported data available
over a 20 quarter period, on four dummy variables set to one if the observation is
11
See http://www.crsp.com/documentation/product/stkind/data_coding_schemes/delisting_codes.html
information about CRSP delisting codes.
12
See http://bear.warrington.ufl.edu/ritter/ipoisr.htm
13
See http://www.census.gov/econ/
14
As can be seen in Chod and Lyandres (2011) or Chemmanur and He (2011).
for
further
- 24 -
Table 2.2
Summary of Delisting Reasons
Number of
CRSP Delisting Code
IPOs
100 Issue still trading
1137
552 Price below acceptable level
161
574 Bankruptcy
91
561 Insufficient float or assets
86
584 Not meeting exchange's financial guidelines
83
580 Delinquent in filing, non-payment of fees
74
560 Insufficient capital, surplus and/or equity
60
Other
148
as %
61.79
8.75
4.95
4.67
4.51
4.02
3.26
8.04
from the first, second, third or fourth quarter respectively and zero otherwise. The
residuals from these regressions are referred to as seasonally adjusted quarterly sales
growth. The industry demand uncertainty is then calculated as the standard deviation
of seasonally adjusted sales growth. IPOs are matched with the demand uncertainty in
their six-digit NAICS industry from the quarter in which they go public.
The degree of competitive interaction (dci) is based on Sundaram et al.’s (1996)
competitive strategy measure. The dci for firm i is defined as the correlation of its
quarterly sales growth with the quarterly sales growth of the sum of sales of all other
companies in the same six-digit NAICS industry over a 20 quarter period using a
rolling window.
∆
∆
(I)
I calculate the dci for each firm available in Compustat. The sign of this variable
provides insight whether the company is competing in strategic substitutes (negative
sign) or in strategic complements (positive sign). This of course assumes that a
company’s sales are a valid proxy for its strategic actions. In my study, I am
interested in the degree of interaction and not in whether the industry competes in
strategic substitutes or strategic complements, hence I use the absolute value of the
correlation. To calculate the industry-dci for a given quarter, I take the average value
of the absolute company-dci over all companies in the same six-digit NAICS industry
which have at least ten non-missing sales growth observations.
Variable
survival_time (years)
censor (binary)
concentration (%)
hhi
market_share
idu
dci
entry_rate
age (years)
internet (binary)
logcapex
logleverage
roa
ocf
logsales
offer_price (USD)
vc (binary)
underwriter
auditor (binary)
exchange (binary)
size (million USD)
initial_return
hotness (%)
earlyipo (binary)
otb
uncertainty
Table 2.3
Descriptive Statistics
Total sample
Surviving IPOs
Mean Median Mean Median
8.48
6.97
10.29
10.89
0.62
59.93
62.70
61.47
65.40
665.04 529.90 696.97 529.90
0.02
0.02
0.03
0.02
0.12
0.05
0.11
0.05
0.39
0.35
0.39
0.35
0.04
0.02
0.03
0.02
15.37
7.97
17.48
8.96
0.10
0.08
0.08
0.05
0.07
0.05
0.27
0.25
0.27
0.25
-0.25
0.01
-0.19
0.02
-0.39
0.04
-0.30
0.07
0.73
0.75
0.72
0.75
13.15
13.00
13.99
14.00
0.44
0.46
7.58
8.00
7.93
8.50
0.91
0.91
0.80
0.75
108.33 48.00 139.86
60.00
0.23
0.09
0.22
0.10
23.35
15.44
20.97
14.65
0.74
0.75
2.55
3.11
3.33
3.05
0.14
0.14
0.14
0.13
- 25 -
Failing IPOs
Mean Median
5.55
4.55
57.46
60.00
611.17 462.40
0.01
0.00
0.12
0.05
0.38
0.35
0.05
0.03
11.96
6.47
0.15
0.09
0.05
0.27
0.25
-0.35
-0.01
-0.53
-0.03
0.75
0.77
11.79
11.00
0.41
7.02
8.00
0.89
0.88
57.35
33.64
0.23
0.08
27.20
16.73
0.72
1.29
3.24
0.14
0.15
Variables are defined in Table 2.1
To calculate the entry rate I use the yearly number of companies in a six-digit NAICS
industry. The data is made available by the US Small Business Administration.15 The
entry rate is calculated as the three year average net entry rate, defined as new
number of firms over previous number of firms minus one. To allow time variation of
the variable I again apply the rolling window method. Data about entry rates is only
available from 1990 to 2009. IPOs after 2009 have been matched with the entry rate
from 2009. Since there has been a change in the industry classification system from
SIC to NAICS codes in 1997, data from 1990 until 1998 is available using the SIC
15
See http://www.sba.gov/advocacy/849/12162.
- 26 -
Figure 2.1
Histogram of Time-to-Failure of IPO Companies
0
.05
Density
.1
.15
.2
The figure shows the density histogram of failing IPOs as time from going public to delisting in years.
0
5
10
Survival Time
15
20
classification system and data from 1997 to 2009 is available using the NAICS
classification system. To ensure comparability, I use the official concordance tables
provided by the US Census Bureau to match the NAICS industries with the
corresponding SIC industries.16 For the transition years from SIC to NAICS I use the
overlapping data sets.17
Table 2.3 provides the summary statistics of the variables used in this study. As can
be seen, the average survival time (survival_time) is about 8.5 years for the whole
sample and 5.6 years for failed IPOs. The majority of IPOs fail within the first 5 years
after going public as can be seen from Figure 2.1. This allows for the conclusion that
factors at the time of the going public already provide insights about the expected
survival time.
2.5 Methodology
To research the factors influencing the survival time of IPOs in the aftermarket, I
follow prior literature and use a duration analysis model to study the influence on the
16
See http://www.census.gov/eos/www/naics/concordances/concordances.html.
For the 1999 entry rate, I calculate the growth rate from 1996 to 1997 using the matched SIC industry classification and
the growth rates from 1997 to 1998 and 1998 to 1999 using the NAICS industry classification.
17
- 27 -
expected survival time (for example: Hensler et al.,1997 or van der Goot et al., 2009).
Duration analysis deals with the time-to-failure and answers the question how long an
IPO firm is expected to survive. Let T be a continuous random variable measuring the
time until an IPO is getting delisted. The cumulative distribution function of T is
defined as
(II)
The survivor function, measuring the probability of surviving past time t is given by
(III)
The conditional probability of leaving the initial state in the time interval t until t+h,
given survival up to time t, can be expressed by
(IV)
The resulting hazard function
is the instantaneous rate of leaving per unit of time
and is defined as
→
|
(V)
To get a first insight on the survivor function of my sample, I use the Kaplan-Meier
(1958) methodology (KMM) to estimate the survival function. The KMM is a nonparametric approach as it does not take explanatory variables into account and is
given by
(VI)
where
is the number of firms that have survived until time
and
is the number
of firms that have been delisted at time .
To be able to make inferences about the survival of IPO firms, I use the widely
applied proportional hazard model as proposed by Cox (1972) which takes the form
(VII)
- 28 -
is the baseline hazard function and x a vector containing the explanatory
variables. Cox has suggested a partial likelihood method to estimate the betas. The
advantage of this model is that it does not require any assumption about the
distribution of the baseline hazard function and still produces accurate and efficient
estimates (Hosmer and Lemeshow, 1999). It can be shown that the log-likelihood
function for the Cox proportional hazard model is given by
(VIII)
∈
where
is the censoring indicator, set to 1 if the observation is not censored and set
to 0 if the observation is censored (Hosmer and Lemeshow, 1999).
is called the
risk set of firms, consisting of all firms which have not been delisted and are
uncensored just before the time of delisting of observation i, .
The disadvantage of the Cox model is that it only provides insights about a firm’s
hazard rate but not about its expected survival time. Therefore, I additionally use a
parametric log-logistic hazard model to study the expected survival time of IPOs. The
difference to the Cox approach is that the distribution of the baseline hazard function
is now employed in the analysis. In this specification I assume that the baseline
hazard function follows a log-logistic distribution. The log-logistic hazard function is
defined as
(IX)
where
is a positive density parameter. If
decreasing and when
< 1, the hazard is monotonically
> 1, the hazard is increasing until
and then
decreases to zero. The resulting log-likelihood function is given by
(X)
where
is the censoring indicator which takes the value 1 if the data is not censored
and 0 when the observation is censored.
- 29 -
2.6 Empirical Results
This section presents the results of the analysis on IPO survival. In the first part, the
dataset is analyzed using the Kaplan-Meier method to estimate the survival function.
The second part presents the results of the duration analysis using the Cox
proportional hazard model and a parametric log-logistic hazard model. To check the
robustness of my study, I test various specifications of the hazard model. Additionally
I perform a logit regression, to further test the results.
2.6.1 Kaplan-Meier Survival Functions
As a preliminary analysis, I estimate the survival function using the non-parametric
Kaplan-Meier estimator. Figures 2.2 and 2.3 show the estimated survival function and
hazard rate of the whole sample using the Kaplan-Meier method. The hazard rate can
be interpreted as the first derivative in absolute terms of the survival function. As can
be seen in Figure 2.3, the hazard rate increases steadily and reaches its peak at about
4 years after the IPO and decreases afterwards. Figure 2.2 shows that the probability
of surviving the first 5 years after the IPO is about 75%. Surviving 10 years after the
IPO still has a probability of about 60%. The two figures again demonstrate that the
company’s critical phase of surviving in the aftermarket is in the first 5 years
following the IPO, allowing for the conclusion that factors at the time of the IPO have
a significant influence on the survival chances.
2.6.2 Duration Analysis
Table 2.4 presents the results of the duration analysis using the Cox proportional
hazard model and the log-logistic survival model. As the log-logistic model is in the
accelerated time-to-failure metric, the two models differ in their coefficients’ sign as
the hazard rate in the Cox model is scaled by a positive factor whereas in the loglogistic model, the hazard rate is scaled by a negative factor. The coefficients in the
Cox model can therefore be interpreted as the proportional effect on the hazard rate of
a unit change in the independent variable and in the log-logistic model as the
proportional effect on the survival time of a unit change in the independent variable.
The likelihood ratio statistic is significant at the 1% level in all four regressions. For
each model I perform two regressions. In the first and third regression I use the whole
- 30 -
0.00
0.25
0.50
0.75
1.00
Figure 2.2
Kaplan-Meier Estimate of the Survival Function
0
5
10
years
15
20
.02
.03
.04
.05
.06
.07
Figure 2.3
Kaplan-Meier Estimate of the Hazard Rate
0
5
10
years
15
20
- 31 -
sample. In regressions (II) and (IV) I add the variable entry_rate to the sample. As
this variable is not available for my whole sample, IPOs before 1993 are excluded in
this specification.
Hypotheses 1-5 suggest that the firm’s product market competition, described by
industry concentration, the degree of competitive interaction, industry demand
uncertainty, the IPO firm’s market share and the industry’s entry rate, affect the
survival time of new firms in the financial market. The results from Table 2.4 confirm
the assumption that product market competition affects the survival time. For industry
concentration (concentration), the results from the Cox model suggest a negative
influence on the hazard rate and the log-logistic model suggests a positive influence
on the survival time. This means companies operating in industries with higher
concentration, have a higher expected survival time. An increase in industry
concentration by one standard deviation increases the survival time by about 10%.
The results confirm H1 that industry concentration has a positive influence on the
IPO’s survival time. A possible explanation for this finding is that industry
concentration indicates the toughness of competition in the industry. Firms going
public in industries with a tougher competition, have a shorter expected survival time.
The market share of the company (market_share) going public exhibits the expected
effect on the hazard rate and survival time as H4 predicts, however without statistical
significance. This result indicates that a company’s market power at the time of the
IPO is not able to predict the chances of survival. Industry demand uncertainty (idu)
exhibits counter-intuitive signs as to what H3 proposes, however the results again
lack statistical significance. H3 suggests that a higher industry demand uncertainty
leads to a suboptimal level of investments which in turn leads to inefficiency in the
production process. This effect may already be covered by logcapex and logsales
which is why the effect of idu is not statistically significant. In unreported results, I
also test H3 using the intra-industry correlation of unadjusted sales growth instead of
seasonally adjusted growth which does not affect the results. The degree of
competitive interaction (dci) exhibits statistical significance at the 5% level in both
model specifications. The effect on the survival time is negative as suggested by H2.
Companies operating in industries with higher competitive interaction have a shorter
- 32 -
Table 2.4
Regression Results of Cox proportional Hazard Model and log-logistic Survival Function
The sample consists of 1,840 IPOs going public between 1990 and 2010 on NASDAQ, NYSE or AMEX. The independent variable is survival_time, measured as the difference between the IPO date and the date of
delisting or end of observation period which is Dec 31 2010. If the IPO continues to be listed through the end of the observation period, the observation is right-censored. offer_price is the IPO price, uncertainty is
the price range spread over average price range, age is the age of the company at the time of the IPO, vc is a binary variable indicating venture backing, underwriter is the Carter-Manaster (1990) underwriter
reputation ranking, auditor is a binary variable indicating whether the IPO is audited by a Big 8 auditor company, exchange is a dummy variable indicating whether the IPO got listed on NASDAQ, internet is a
binary variable indicating whether it is an internet-related IPO, initial_return is the first day return of the IPO as first-day closing price over IPO price minus one, size is the IPO proceeds as number of shares offered
times IPO price, logcapex is the log of one plus capital intensity, logleverage is the log of one plus leverage, roa is the return on assets, otb is the offer-to-book ratio, ocf is operating cash-flow over liabilities,
logsales is the log of one plus sales over assets, concentration is the market share of the 20 largest companies in a six-digit NAICS industry, market_share is a company’s market share in its six-digit NAICS
industry, idu is the industry demand uncertainty calculated as the std of seasonally adjusted sales growth, dci is the degree of competitive interaction in the industry measured as intra-industry correlation of sales
growth, earlyipo is a binary variable indicating whether the IPO has less than four preceding IPOs during three months in the same industry, hotness is the average IPO underpricing in the three months before the
IPO and entry_rate is the average net entry rate in the three years before the IPO. The Breslow method is applied for ties in the Cox model. The log-logistic model is presented in the accelerated failure-time form.
***/**/* indicates statistical significance at the 1%/5%/10% level.
Variables
concentration
market_share
idu
dci
entry_rate
age
internet
logcapex
logleverage
roa
ocf
logsales
offer_price
vc
underwriter
auditor
exchange
size
initial_return
hotness
earlyipo
otb
uncertainty
constant
Observations
Likelihood ratio (chi)
(I)
-0.004***
-0.812
-0.019
0.719**
-0.013***
0.753***
1.165***
1.432***
-0.041**
-0.008
-0.185*
-0.036***
-0.181**
-0.091***
-0.069
0.409***
-0.000
-0.234**
0.008***
-0.154*
-0.002**
0.368
1840
256.00***
Cox proportional hazard model
t-stat
(II)
-2.58
-0.004**
-1.06
-0.606
-0.39
-0.019
2.38
0.694**
0.557
-4.50
-0.013***
5.81
0.726***
4.45
1.174***
5.75
1.387***
-2.12
-0.039**
-0.62
-0.015
-1.86
-0.172
-3.10
-0.033***
-2.04
-0.161*
-4.05
-0.100***
-0.52
-0.111
3.14
0.381***
-0.59
-0.000
-2.21
-0.239**
4.08
0.008***
-1.66
-0.151
-2.03
-0.001*
0.68
0.631
1747
247.90***
t-stat
-2.25
-0.75
-0.39
2.23
1.06
-4.21
5.44
4.30
5.29
-1.96
-0.72
-1.61
-2.76
-1.75
-4.32
-0.81
2.80
-0.60
-2.24
4.04
-1.56
-1.89
1.11
(III)
0.005***
0.736
0.025
-0.617**
0.011***
-0.773***
-1.016***
-1.197***
0.033*
0.008
0.178**
0.034***
0.154**
0.092***
0.006
-0.409***
-0.000
0.203**
-0.007***
0.094
0.001*
0.138
1.985***
1840
291.17***
Log-logistic survival model
t-stat
(IV)
2.98
0.004**
1.27
0.517
0.58
0.028
-2.40
-0.580**
-0.862*
4.89
0.011***
-6.32
-0.745***
-4.25
-1.101***
-5.44
-1.221***
1.77
0.028
0.56
0.033
2.09
0.141
3.40
0.031***
1.99
0.124
4.33
0.103***
0.05
0.066
-3.80
-0.391***
-0.01
-0.000
2.32
0.207**
-4.10
-0.007***
1.13
0.072
1.82
0.001*
0.27
-0.061
7.80
2.020***
1747
284.40***
t-stat
2.51
0.86
0.64
-2.17
-1.92
4.67
-5.93
-4.34
-5.21
1.46
1.40
1.53
2.95
1.53
4.69
0.52
-3.47
-0.06
2.34
-3.96
0.84
1.68
-0.11
7.43
- 33 -
survival time. An increase of dci by one standard deviation decreases the survival
time by about 9%. This result suggests that not only industry concentration, but also
the strategic interaction among the companies affects the survival time. To test H5, in
regressions (II) and (IV) I add the entry rate (entry_rate) to the regression model.
This reduces the number of observations to 1,747. As can be seen in regression (IV),
the influence of the entry rate on an IPO’s survival time is negative and statistically
significant at the 10% level. An increase in the entry rate by one standard deviation
reduces the survival time by about 7%, in line with H5. In the Cox model however,
the effect of the entry rate on the IPO’s hazard rate is positive but without statistical
significance. This result provides weak evidence that the entry rate affects a firm’s
survival chances making industries with lower entry rate more attractive, as suggested
by Porter’s (1979) five forces framework.
Looking at the control variables, the results are in line with prior research. A
company’s age (age) is highly significant in determining the IPO’s survival time in
the aftermarket, consistent with prior studies (Hensler et al., 1997, Demers and Joos,
2007). Being an internet IPO (internet) significantly reduces the survival time by
53.84% compared to non-internet IPOs. As this special type of IPO seems to exhibit a
particularly high hazard rate, I will perform an analysis excluding internet IPOs in the
robustness section to check whether these IPOs drive my sample. The accounting
variables have the expected sign and, with the exception of ocf, are significant at least
at the 10% level. The results suggest that firms with a higher capital intensity
(logcapex), higher leverage (logleverage) and lower profitability (roa) have a shorter
expected survival time. There is also weak evidence that less efficient firms,
measured by sales over assets (logsales) have a shorter expected survival time. For
the deal-related variables I find that the survival time is positively influenced by the
offer price (offer_price), the underwriter reputation (underwriter), VC backing and
the whether the IPO gets listed on the NYSE or AMEX (exchange) which is again
consistent with prior studies (Seguin and Smoller, 1997, Demers and Joos, 2007, Jain
and Kini, 2000). For the offer size (size) and the auditor reputation (auditor) I cannot
find a significant effect. Controlling for the market environment, my results further
show that the IPO’s first day return (initial_return), the offer-to-book ratio (otb) and
- 34 -
when a firm goes public in a cold market (hotness) all have a positive influence on
the survival time. Weak evidence is found for the IPO’s earliness to affect the
survival time. For the valuation uncertainty (uncertainty), no significant effect can be
found.
To further test the robustness of these results, I perform various analyses which I am
briefly going to present here. One common problem with duration analysis is the
specification of the underlying baseline hazard function. In the study I use the loglogistic distribution. To see whether my results depend on the specified baseline
distribution, I perform the analysis again using the exponential, the Weibull and the
log-normal distribution. The results are presented in Table 2.5. Using different
distributions has no major effect on my previous results.
Furthermore, my results suggest an abnormally high hazard rate for internet IPOs.
Bartov, Mohanram and Seethamraju (2002) report an anomalous behavior in the
pricing and valuation of internet IPOs. To see whether these IPOs drive my results, I
perform the model again excluding internet IPOs. As can be seen from regression (II)
in Table 2.6, excluding the IPOs has no significant effect on my results.
To test the robustness of the industry concentration measure, I use the HerfindahlHirschman Index (hhi) as an alternative measure. This index is used by the US
Department of Justice and the Federal Trade Commission to evaluate the impact of
horizontal mergers on the competitive environment. As the US Census Bureau only
provides this index for the manufacturing sector (NAICS codes beginning with 3) the
analysis is performed using only manufacturing IPOs which reduces the sample to
892. The results can be seen in regression (III) in Table 2.6. The hhi is statistically
significant and increases the IPO’s survival time. An increase in the HerfindahlHirschman Index by one standard deviation increases the survival time by about 10%
as does an increase in concentration.
Finally, Table 2.7 provides the results of the robustness test using a logit discrete
response model. A discrete response model is an alternative way to analyze the
factors influencing the IPO’s delisting from the stock market. The drawback of this
- 35 -
Table 2.5
Robustness Test using the exponential, Weibull and log-normal Distribution as baseline Hazard Rate
the price range spread over average price range, age is the age of the company at the time of the IPO, vc is a binary variable indicating venture capital backing, underwriter is the Carter-Manaster (1990)
underwriter reputation ranking, auditor is a binary variable indicating whether the IPO is audited by a Big 8 auditor company, exchange is a dummy variable indicating whether the IPO got listed on NASDAQ,
internet is a binary variable indicating whether it is an internet-related IPO, initial_return is the first day return of the IPO as first-day closing price over IPO price minus one, size is the IPO proceeds as number of
shares offered times IPO price, logcapex is the log of one plus capital intensity, logleverage is the log of one plus leverage, roa is the return on assets, otb is the offer-to-book ratio, ocf is operating cash-flow over
liabilities, logsales is the log of one plus sales over assets, concentration is the market share of the 20 largest companies in a six-digit NAICS industry, market_share is a company’s market share in its six-digit
NAICS industry, idu is the industry demand uncertainty calculated as the std of seasonally adjusted sales growth, dci is the degree of competitive interaction in the industry measured as intra-industry correlation of
sales growth, earlyipo is a binary variable indicating whether the IPO has less than four preceding IPOs during three months in the same industry, hotness is the average IPO underpricing in the three months
before the IPO and entry_rate is the average net entry rate in the three years before the IPO. The models are presented in the accelerated failure-time form. ***/**/* indicates statistical significance at the
1%/5%/10% level.
Variables
age
internet
logcapex
logleverage
roa
ocf
logsales
offer_price
vc
underwriter
auditor
exchange
size
initial_return
hotness
earlyipo
otb
uncertainty
concentration
market_share
idu
dci
entry_rate
constant
Observations
Log-logistic
Coeff.
t-stat
0.011***
4.67
-0.745***
-5.93
-1.101***
-4.34
-1.221***
-5.21
0.028
1.46
0.033
1.40
0.141
1.53
0.031***
2.95
0.124
1.53
0.103***
4.69
0.066
0.52
-0.391***
-3.47
-0.000
-0.06
0.207**
2.34
-0.007***
-3.96
0.072
0.84
0.001*
1.68
-0.061
-0.11
0.004**
2.51
0.517
0.86
0.028
0.64
-0.580**
-2.17
-0.862*
-1.92
2.020***
7.43
1747
284.40***
Exponential
Coeff.
t-stat
0.013***
4.19
-0.699***
-5.25
-1.186***
-4.42
-1.384***
-5.26
0.037*
1.91
0.017
0.81
0.172
1.62
0.034***
2.85
0.158*
1.73
0.095***
4.11
0.103
0.75
-0.366***
-2.70
0.000
0.52
0.215**
2.05
-0.008***
-4.25
0.145
1.50
0.001
1.50
-0.647
-1.14
0.004**
2.25
0.650
0.80
0.019
0.38
-0.675**
-2.18
-0.330
-0.64
2.514***
8.30
1747
242.63***
Weibull
Coeff.
0.011***
-0.647***
-1.084***
-1.229***
0.035**
0.013
0.167*
0.031***
0.138*
0.083***
0.116
-0.326***
0.000
0.199**
-0.007***
0.141*
0.001
-0.608
0.004**
0.636
0.016
-0.619**
-0.144
2.456***
1747
254.60***
t-stat
4.31
-5.65
-4.66
-5.41
2.05
0.75
1.82
2.96
1.76
4.16
0.99
-2.80
0.17
2.20
-4.37
1.70
1.58
-1.26
2.37
0.92
0.40
-2.33
-0.32
9.46
Log-normal
Coeff.
0.011***
-0.692***
-1.071***
-1.172***
0.032
0.029
0.135
0.028***
0.106
0.098***
0.102
-0.368***
0.000
0.184**
-0.007***
0.050
0.001
-0.070
0.004***
0.523
0.011
-0.497*
-0.854*
2.058***
1747
269.58***
t-stat
4.74
-5.55
-4.08
-5.04
1.57
1.33
1.52
2.79
1.35
4.50
0.82
-3.31
0.01
2.28
-3.82
0.60
1.38
-0.13
2.60
0.93
0.27
-1.91
-1.94
7.79
- 36 -
Table 2.6
Robustness Test of the log-logistic Survival Model excluding Internet IPOs and using an alternative Measure for Industry
Concentration
the price range spread over average price range, age is the age of the company at the time of the IPO, vc is a binary variable indicating venture capital backing, underwriter is the Carter-Manaster (1990) underwriter
reputation ranking, auditor is a binary variable indicating whether the IPO is audited by a Big 8 auditor company, exchange is a dummy variable indicating whether the IPO got listed on NASDAQ, internet is a
binary variable indicating whether it is an internet-related IPO, initial_return is the first day return of the IPO as first-day closing price over IPO price minus one, size is the IPO proceeds as number of shares offered
times IPO price, logcapex is the log of one plus capital intensity, logleverage is the log of one plus leverage, roa is the return on assets, otb is the offer-to-book ratio, ocf is operating cash-flow over liabilities,
logsales is the log of one plus sales over assets, concentration is the market share of the 20 largest companies in a six-digit NAICS industry, hhi is the Herfindahl-Hirschman Index, market_share is a company’s
market share in its six-digit NAICS industry, idu is the industry demand uncertainty calculated as the std of seasonally adjusted sales growth, dci is the degree of competitive interaction in the industry measured as
intra-industry correlation of sales growth, earlyipo is a binary variable indicating whether the IPO has less than four preceding IPOs during three months in the same industry, hotness is the average IPO underpricing
in the three months before the IPO and entry_rate is the average net entry rate in the three years before the IPO. The log-logistic model is presented in the accelerated failure-time form. ***/**/* indicates statistical
significance at the 1%/5%/10% level.
Variables
age
internet
logcapex
logleverage
roa
ocf
logsales
offer_price
vc
underwriter
auditor
exchange
size
initial_return
hotness
earlyipo
otb
uncertainty
concentration
hhi
market_share
idu
dci
entry_rate
constant
Observations
(I)
0.011***
-0.745***
-1.101***
-1.221***
0.028
0.033
0.141
0.031***
0.124
0.103***
0.066
-0.391***
-0.000
0.207**
-0.007***
0.072
0.001*
-0.061
0.004**
t-stat
4.67
-5.93
-4.34
-5.21
1.46
1.40
1.53
2.95
1.53
4.69
0.52
-3.47
-0.06
2.34
-3.96
0.84
1.68
-0.11
2.51
0.517
0.028
-0.580**
-0.862*
2.020***
1747
284.40***
0.86
0.64
-2.17
-1.92
7.43
(II)
0.011***
t-stat
4.61
-1.140***
-1.144***
0.044*
0.025
0.093
0.031**
0.126
0.104***
0.014
-0.344***
0.000
0.332**
-0.007***
0.021
0.001*
0.261
0.004**
-3.76
-4.67
1.78
1.03
0.92
2.53
1.44
4.38
0.10
-2.97
0.59
2.15
-2.96
0.21
1.76
0.45
2.40
0.269
0.029
-0.500*
-1.236**
1.975***
1555
194.08***
0.44
0.67
-1.74
-2.24
6.76
(III)
0.006**
-1.058***
-1.022
-0.944***
0.092**
0.040
-0.011
0.016
0.044
0.085***
0.122
-0.433***
-0.000
0.233
-0.004
-0.060
-0.001
-0.627
t-stat
1.97
-4.59
-1.56
-2.93
2.44
1.49
-0.07
1.09
0.40
2.92
0.75
-2.83
-0.08
1.58
-1.40
-0.51
-0.54
-0.84
0.0002*
0.509
-1.103
-1.279***
-0.310
3.028***
892
116.00***
1.86
0.60
-1.32
-3.25
-0.35
8.54
- 37 -
Table 2.7
Robustness Test using a logit discretionary Response Model
The sample consists of 1,840 IPOs going public between 1990 and 2010 on NASDAQ, NYSE or AMEX. In this specification only IPOs
are included from 1993 to 2005. The independent variable is a binary variable set to 1 if the IPO got delisted within 5 years after the IPO
and 0 otherwise. offer_price is the IPO price, uncertainty is the price range spread over average price range, age is the age of the
company at the time of the IPO, vc is a binary variable indicating venture capital backing, underwriter is the Carter-Manaster (1990)
underwriter reputation ranking, auditor is a binary variable indicating whether the IPO is audited by a Big 8 auditor company, exchange
is a dummy variable indicating whether the IPO got listed on NASDAQ, internet is a binary variable indicating whether it is an internetrelated IPO, initial_return is the first day return of the IPO as first-day closing price over IPO price minus one, size is the IPO proceeds
as number of shares offered times IPO price, logcapex is the log of one plus capital intensity, logleverage is the log of one plus leverage,
roa is the return on assets, otb is the offer-to-book ratio, ocf is operating cash-flow over liabilities, logsales is the log of one plus sales
over assets, concentration is the market share of the 20 largest companies in a six-digit NAICS industry, market_share is a company’s
market share in its six-digit NAICS industry, idu is the industry demand uncertainty calculated as the std of seasonally adjusted sales
growth, dci is the degree of competitive interaction in the industry measured as intra-industry correlation of sales growth, earlyipo is a
binary variable indicating whether the IPO has less than four preceding IPOs during three months in the same industry, hotness is the
average IPO underpricing in the three months before the IPO and entry_rate is the average net entry rate in the three years before the
IPO. ***/**/* indicates statistical significance at the 1%/5%/10% level.
Variables
age
internet
logcapex
logleverage
roa
ocf
logsales
offer_price
vc
underwriter
auditor
exchange
size
initial_return
hotness
earlyipo
otb
uncertainty
concentration
market_share
idu
dci
entry_rate
constant
Observations
R²
Coeff
-0.027***
1.171***
1.754***
1.468***
-0.058
-0.026
-0.103
-0.048**
-0.093
-0.121***
-0.245
0.729***
0.001
-0.315**
0.008**
-0.023
-0.001
-0.247
-0.005*
1.588
-0.200
1.285**
2.484***
-0.759
1461
13.30%
216.90***
t-stat
-5.38
5.14
3.13
3.19
-1.17
-0.68
-0.62
-2.25
-0.62
-2.95
-0.96
2.97
0.55
-2.22
2.28
-0.14
-0.74
-0.24
-1.69
-1.09
-0.28
2.49
3.02
-1.57
- 38 -
model is that it is static, i.e. it can only analyze the factors influencing the delisting
after a fixed time period whereas information about IPOs surviving the observation
period and delist later is omitted. Duration analysis is able to employ this information
through censoring. In the robustness test I use a five year window given that the
hazard rate is highest in the first years after the IPO (see Figure 2.1). The logit
regression again confirms my previous results and provides further evidence for the
influence of the entry rate. A higher entry rate increases the probability of not
surviving the first five years after going public.
The results from the duration analysis suggest that firms going public in industries
which exhibit a higher industry concentration, a lower degree of competitive
interaction and a lower industry entry rate have a higher expected survival time in the
aftermarket. The results are robust to controlling for firm-, deal- and market-related
factors as suggested in prior studies as well as various model specifications. My
results provide certain implications for investors as well as for firms when deciding to
go public. Investors should take the expected survival time of a new company into
account when assessing the firm’s risk-return profile. As shown, the expected
survival time does not just depend on firm, deal or market factors but also on product
market competition. For private firms, my results provide valuable insights for the
decision to go public. Going public is associated with high direct as well as indirect
issuing costs and also with the costs of disclosing private information about the firm.
Overall, my results suggest that the competitive environment affects the expected
survival time for new firms in the market and is therefore associated with failure risk.
Firms should include the competitive environment in their going public decision as
the risk of failure may be a significant factor when comparing the costs and benefits
of going public. Actually, Chemmanur et al. (2010) find that industry concentration is
positively related to the decision to go public versus stay private which supports this
conclusion.
2.7 Conclusion
The purpose of this study is to show the relationship between the firm’s product
market competition and the survival time in the aftermarket. While previous research
- 39 -
on IPO survival has already discovered that factors related to firm, deal and market
characteristics are influencing the survival time of a new company in the aftermarket,
this is the first study to look at the influence of the company’s product market
competition. Using a sample of 1,840 IPOs in the US between 1990 and 2010, I use a
log-logistic duration analysis model along with a Cox proportional hazard model to
test the influence of factors from the firm’s product market competition on the
survival time in the aftermarket. I find that the IPO’s survival time increases with
industry concentration and decreases with the degree of competitive interaction in the
industry and also decreases with the entry rate in the industry. I cannot find evidence
that the firm’s market share or industry demand uncertainty have a statistically
significant influence on the IPO’s survival time. The results are robust to controlling
for firm-, deal- and market-related variables, various model specifications and are
further confirmed by a logit discrete response model.
Overall, the results show the important role of taking the firm’s product market
competition into account when predicting the IPO’s survival time in the aftermarket.
For investors the results provide valuable insights when assessing the risk structure of
firms going public and also for the firms themselves in their decision to go public.
Variables relating to firm characteristics, deal characteristics or the financial market
form just one dimension in affecting the firm’s survival. It is the interaction of these
variables with conditions in the product market competition which ultimately decides
over the firm’s success or failure. The firm’s product market competition should
therefore be taken into account when studying IPO survival. An interesting topic for
further research would be the ensuing question whether investors take the risks
associated with the competitive environment into account when pricing firms or IPOs
in particular. I leave that for further research.
- 40 -
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Intra-Industry Effects of Initial Public Offerings
- 45 -
3 Intra-Industry Effects of Initial Public Offerings
– Insights from the Product Market Competition
on the IPO’s Competitive Effect
In this study I want to analyze the valuation effect of Initial Public Offerings (IPOs)
on listed industry competitors and provide evidence that competitors exhibit a
negative price reaction to the filing and completion of an IPO in their industry. This
reaction is attributed to a competitive effect associated with an IPO. I further find that
the competitive effect upon a completed IPO is negatively related to the toughness of
the product market competition. IPOs in competitively weak industries, indicated by
a higher industry concentration, a lower degree of strategic interaction and a lower
industry demand uncertainty exhibit a stronger competitive effect on their rivals.
Overall, my results provide evidence that the competitive environment is an
important factor in determining the competitors’ reaction upon an IPO in the same
industry.
- 46 -
3.1 Introduction
The Initial Public Offering (IPO) is an important step in the firm’s life cycle,
allowing further growth by having access to financing from the capital market. The
majority of the research on IPOs focuses on valuation effects of the issuing firm with
IPO underpricing and the long-run underperformance being the most popular topics
in IPO research (see e.g. Ritter, 1991; Ritter and Welch, 2002; Loughran, Ritter and
Rydqvist, 1994). Yet, IPOs are not simply a firm-specific event but can have an
industry wide valuation impact as the studies by Akhigbe, Borde and Whyte (2003),
Hsu, Reed and Rocholl (2010) or Cotei and Farhat (2011) show. An IPO can have
several, even contradicting effects on its competitors and, a priori, it is not clear
which effect is dominating. This explains why different studies come to different
conclusions about an IPO’s intra-industry effect.18 The purpose of this study is to
further analyze the IPO’s intra-industry effect and thereby to focus on the competitive
effect by looking at the firm’s product market competition using a comprehensive
sample of IPOs.
Previous studies show that the IPO’s external effect on listed industry competitors
can be caused by information externalities or by a competitive effect. The
information effect assumes that an IPO reveals private information not only about the
issuing firm but about the whole industry. An IPO can have positive information
externalities on competitors as the IPO is a sign for a positive or improved industry
outlook. Companies do not perform an IPO when the industry outlook is not
favorable as it would be more difficult to persuade investors of the equity story and
the risk of paying the costs of an unsuccessful IPO would also be too high. Thus an
IPO reveals private information about an improved outlook for the industry. As it is a
signal for the whole industry, public competitors can also benefit from this
information spillover (Akhigbe et al., 2003).
Along with the information externalities, an IPO also has a competitive effect as it
affects the competitive balance in the industry. Chemmanur and He (2011) argue that
18
The study of Hsu et al. (2010) finds that competitors negatively react to an IPO in the same industry. The study of
Akhigbe et al. (2003) does not find a significant competitors’ response and the study by Cotei and Farhat (2011) finds a
positive competitors’ reaction.
- 47 -
issuing firms gain an advantage over their competitors as they are provided with fresh
capital, which is cheaper than debt financing and allows them to increase production
as well as to employ more qualified employees or acquire related firms. Additionally,
a firm enjoys greater public attention upon an IPO.
In this study I will present valuable new insights for the research on the IPO’s
competitive intra-industry effect by looking at the industry’s competitive
environment. Previous studies have found out that industry concentration is a
significant factor in explaining the intra-industry effect of IPOs (see Akhigbe et al.,
2003; Cotei and Farhat, 2011; Lee, Bach and Baik, 2011). I expand the existing
research by looking at further factors characterizing product market competition.
Raith (2003) as well as Sharma (2011) suggest that industry concentration does not
capture all dimensions of the product market competition. By using these additional
variables, I am therefore able to account for an IPO’s competitive environment more
accurately. The research is motivated by recent studies which have demonstrated that
the firm’s product market competition is an important factor in explaining the firm’s
decision to go public (Chemmanur, He and Nandy, 2010), the firm’s post-IPO
operating performance (Chemmanur and He, 2011) and the rival’s post IPO market
share development (Chemmanur and He, 2011 and Chod and Lyandres, 2011). Chod
and Lyandres (2011) report a significant influence of the firm’s product market
competition on the increase in a firm’s product market share after going public. The
study by Chemmanur and He (2011) reports further evidence that going public is
associated with an increase in the product market share in the years after the IPO. The
authors study the decline in the product market share of both private and public
competitors and find a significant influence from product market characteristics.
Additionally, the authors report a significant relationship between the factors from
product market competition and the operating performance in the year after the IPO.
The main assumption in this study is that the IPO’s competitive effect depends on the
industry’s competitive environment. In order to test this, I will analyze the influence
of several factors which describe the firm’s product market competition on the rivals’
valuation response to the IPO. In a first step, I look at the valuation response of rival
- 48 -
firms to the filing and the completion of a firm’s IPO using the event study
methodology. In a second step, I look at the factors influencing the valuation
response of completed IPOs using a cross-sectional analysis.
I find that the competitors’ response to an IPO filing or completion is negative,
concluding that the competitive effect is dominating the information effect on
average. The results further suggest that the competitors’ response is positively
related to the toughness of the competitive environment. The weaker the competitive
environment, the stronger is the competitive effect of an IPO in that industry. I also
find that the subsample of IPOs in industries with the toughest competitive
environment does not exhibit a significant response to an IPO, whereas the subsample
of IPOs in industries with the weakest competitive environment exhibits a significant
negative response to the IPO. The study contributes to the literature on IPO’s intraindustry effects by demonstrating the important role of the competitive environment
when analyzing the IPO’s competitive effect on competitors.
The remainder of this study is structured as follows: Section 3.2 provides a brief
overview of the existing and related literature. Section 3.3 introduces the hypotheses
about the influence of product market competition on the IPO’s external effect.
Section 3.4 describes the data set and Section 3.5 presents the methodology I use for
this study. In Section 3.6 the results of the event study and the cross-sectional
analysis are presented and discussed. Finally, the study is concluded and summarized
in Section 3.7.
3.2 Related Literature
The motivation for this research reaches back to the literature on the valuation effect
of firm-level announcements on competitors and deals with the question whether
firm-specific events only affect the firm or have an effect on rival firms as well.
Slovin, Sushka and Ferraro (1995) study the effects of equity carve-outs, IPOs, spinoffs and asset sell-offs on industry rivals. They observe a negative price reaction of
rival firms in response to equity carve-outs and IPOs but find no negative effect for
the other transaction types. The authors argue that these transactions convey adverse
- 49 -
information about the industry, as an equity sale is perceived as a sign of
overvaluation, which holds true for the whole industry. In an earlier study, Slovin,
Sushka and Poloncheck (1992) find that seasoned equity offerings in the banking
industry trigger a negative stock price reaction in rival commercial and investment
banking firms, which is attributed to adverse information of the corporate transaction.
Looking at the effect of going private transactions in the US, Slovin, Sushka and
Bendeck (1991) find positive abnormal intra-industry stock price reactions upon
announcement. The authors offer three factors as explanation for the observed
positive reaction: a buyout bid may convey private information about future industry
expectations, it may increase the probability of becoming a buyout target in the
industry and it may reduce the agency costs in an industry by inducing managers to
improve corporate governance to prevent becoming a buyout target. Chevalier (1995)
who researches the effect of leveraged buyouts on industry rivals comes to similar
results. She focuses on the US supermarket industry and finds out that a leveraged
buyout announcement leads to a positive stock price reaction of rivals. Chevalier
hypothesizes that increasing leverage in an industry softens product market
competition, which increases expected future profits. The studies of Lang and Stulz
(1992) and Ferris, Jayaraman and Makhija (1997) cover bankruptcy announcements
and find a negative intra-industry price reaction. The authors discover two different
effects, however. A negative contagion effect, which arises due to a decreased
industry outlook implied by the bankruptcy, and a positive competitive effect due to
an improved competitive situation in the industry with the contagion effect
dominating on average.
Akhigbe and Madura (1999a) focus on the effect of an acquisition announcement in
an industry and find a significant positive stock price reaction in the target’s industry.
The authors explain their observation with an increased probability for rival firms to
become acquisition targets as well. In a related study, Akhigbe and Madura (1999b)
confirm their findings, studying acquisition announcements in the banking industry.
The price reaction is positively related to factors that indicate the probability of a
future acquisition of corresponding rivals. Bley and Madura (2003) study the effects
- 50 -
of European M&A transactions and confirm the positive intra-industry price reaction
upon the announcement in their sample of European acquisitions.
Studying the intra-industry effect of IPO announcements, Akhigbe et al. (2003) study
a sample of IPOs in the US and confirm the hypothesis that IPOs not only convey
firm-specific information but industry-wide information. However, the authors find
out that on average the effect of an IPO on traded rival firms is insignificant. The
authors assume that the impact of an IPO is twofold as they find evidence for a
positive information effect and a negative competitive effect, which offset each other.
Akhigbe et al. argue that an IPO “could signal a change in the outlook for the
industry as a whole, resulting in significant valuation effects for rival firms” (2003,
p.532). A negative effect is attributed to competitive effects of an IPO deriving from
changes in the industry’s competitive balance due to an IPO. It is assumed that a firm
is able to gain a competitive advantage by pursuing an IPO. Hsu et al. (2010) find in a
sample of large firms going public that rivals exhibit a negative price reaction to the
IPO announcement and the actual IPO completion and that there is a positive price
reaction to withdrawn IPOs. The authors find evidence that new firms have an
advantage over incumbent firms as they have lower leverage, recent financial
intermediary certification and a higher degree of knowledge capital. Cotei and Farhat
(2011) examine the role of venture capital in their study on the IPO’s intra-industry
effect. The authors find that rivals positively respond to VC backed IPOs in their
industry while there is no observable reaction to non-VC backed IPOs. The authors
argue that VC backed IPOs convey superior information concerning industry
outlooks compared to non-VC backed IPOs, which explains the positive valuation
reaction by competitors. Lee et al. (2011) focus on the IPO’s intra-industry effect in
the growing computer-related service industry. They conclude that the positive
information spillover is stronger for rivals competing in the same product market than
rivals competing in related product markets and that the valuation effect is positively
influenced by R&D expenses and negatively by industry concentration.
My research is distinctive from the above mentioned studies as I use various variables
accounting for the firm’s product market competition and not only industry
- 51 -
concentration. Furthermore, I analyze the factors upon the completion of the IPO
instead of the IPO announcement as the above mentioned studies do.19 Additionally I
use a substantially larger data sample than Hsu et al. (2010) and Lee et al. (2011) and
use data about industry concentration from the US Census Bureau as opposed to
calculating industry concentration from firms available in Compustat, which does not
account for the influence of private firms.20
A different reason for the observed intra-industry effect of IPOs is brought up by
Braun and Larrain (2009). Instead of focusing on the information or competitive
effect, the authors study an IPO as a possible equity supply shock in the market. In a
sample of IPOs in emerging countries, the authors find significant evidence that
aggregate asset prices decline after an IPO. This effect is more pronounced for larger
IPOs and for assets with higher return covariance with the IPO, which can be
assumed for competitors from the same industry. It is noteworthy that the results need
to be interpreted with awareness of the dataset. An IPO in an emerging country with a
less sophisticated capital market has a different impact than an IPO in a sophisticated
capital market such as the US market.
As already stated, an IPO’s effect on rivals is not clear a priori. The competitors’
response depends on which effect of the IPO is dominating. Akhigbe et al. (2003)
show that the information effect of an IPO is positive as it accounts for a positive
industry outlook. This is further supported by the results from Rau and Stouraitis
(2011), who show that corporate events occur in waves and follow a distinctive
pattern with IPOs initiating a new wave of corporate events. This suggests that IPOs
do contain information regarding the industry outlook. Thus, an IPO’s influence on
competitors is positive when the information effect is dominating and negative when
the competitive effect is dominating. The main assumption in this study is that
product market competition influences the degree of the competitive effect. This
19
I expect the competitors’ reaction to be more meaningful upon completion of the IPO, since the success of going public is
still accompanied by uncertainty at the time of filing (Busaba, 2006). Therefore, I decide to analyze the completion instead
of the announcement. See the methodology section for further discussion.
20
. Ali, Klasa and Yeung (2009) find that the correlation between industry concentration calculated from Compustat and the
industry concentration from the US Census Bureau is only 13%.
- 52 -
assumption further implies that the IPO’s information effect is independent of the
competitive environment.
The recent studies by Hsu et al. (2010), Chod and Lyandres (2011) and Chemmanur
and He (2011) find evidence that IPOs exhibit a competitive advantage over both
their listed and their private competitors. This competitive advantage changes the
competitive balance in an industry and causes the adverse effect on rival companies.
The degree to which IPO firms are able to gain a competitive advantage depends on
the existing competitive environment in the product market. In industries, in which
product market competition is already tough, the marginal benefit of going public is
smaller compared to industries with a weak competitive environment. Firms can
benefit more from the competitive advantage of going public, when the competitive
environment is weak. Economic theory suggests that firms in a perfectly competitive
environment are not able to earn economic rents. The weaker the competitive
environment, i.e. the more imperfect, the more are firms able to earn economic rents.
Profiting from a competitive advantage would mean earning economic rent.
Therefore, the ability to benefit from a competitive advantage depends on whether the
competitive environment is rather weak (imperfect) or tough (perfect) and this
ultimately determines the degree of the IPO’s competitive effect on rivals. As a
consequence the competitive effect is expected to be stronger for IPOs in industries
with a weak competitive environment and weaker for IPOs in industries with a strong
competitive environment.
To test this assumption, I analyze the effect of industry concentration, the degree of
strategic interaction and demand uncertainty in the industry as well as the industry’s
entry rate on the rivals’ response to a competitor’s IPO. All these factors influence
and shape the competitive environment in an industry.
A widely accepted measure for the competitive environment in an industry, both in
academic literature and in practice, is industry concentration.21 Industries with a low
21
See for example Cowling and Waterson (1976) who find that industry concentration affects firms’ price-cost ratio, Bikker
and Haaf (2002) who empirically confirm the adverse effect of concentration on competition or Lang and Stulz (1992),
Chevalier (1995), Sundaram, John and John (1996) or Akhigbe et al. (2003) who use industry concentration to control for
competition in their studies on the intra-industry effect of firm announcements. Also see Tirole (1988) for a theoretical
- 53 -
concentration exhibit characteristics of perfect competition whereas industries with a
high concentration exhibit characteristics of an oligopoly. The more concentrated an
industry, the weaker is the competitive environment in that industry. Therefore, IPOs
in industries with higher concentration are expected to have a stronger competitive
effect, leading to the first hypothesis.
H1: IPOs in industries with higher concentration have a stronger competitive
effect.
A different measure of the competition in an industry is the degree of strategic
interaction (dsi) among the firms. The dsi refers to the way a firm responds to
strategic choices of its competitors. According to the oligopoly literature, the
interaction can either be in “strategic substitutes”, for which the dsi is negative, or in
“strategic complements”, for which the dsi is positive. Competition in strategic
substitutes refers to a situation in which competitors will react in the opposite way to
a strategic move by a firm, which relaxes the competition and is accommodating.
Competition in strategic complements refers to a situation in which competitors will
react in the same way to a strategic move by a firm, which leads to an escalation and
increases the competition (Bulow, Geanakoplos and Klemperer, 1985). Therefore,
competition in strategic complements is more aggressive and the competitive
environment is weaker in industries competing in strategic substitutes.
H2: IPOs in industries competing in strategic complements have a weaker
competitive effect while IPOs in industries competing in strategic substitutes have a
stronger competitive effect.
The uncertainty a company is facing in its demand, regarding the product market,
also affects the competitive environment in the industry. Demand uncertainty
increases the risks of future earnings and cash flows, which decreases the
discussion. The industry concentration is also a crucial factor in decisions by the US Department of Justice or the European
Commission on merger and acquisition cases.
- 54 -
predictability of strategic choices and makes them more difficult to make. In the
literature on industrial organization, Ghosal (1991) as well as Guiso and Parigi (1999)
find out that demand uncertainty in the product market is affecting the firms’
investment decisions. It is easier for firms to compete in an environment with lower
demand uncertainty as the results of strategic decisions are easier to evaluate. I
therefore expect that demand uncertainty makes the competitive environment
tougher, leading to a weaker competitive effect of an IPO.
H3: IPOs in industries with a lower industry demand uncertainty have a stronger
competitive effect.
New entrants in the product market can change the competitive structure in an
industry by increasing competition and decreasing profits. They therefore pose a
potential threat to incumbent companies. In a study about business failure in the
Japanese manufacturing sector, Honjo (2000) finds that new firms in an industry with
a higher entry rate also exhibit higher bankruptcy rates. One factor, which influences
an industry’s entry rate, are the barriers to entry (Geroski and Schwalbach, 1991).
According to Michael Porter’s famous five forces framework, an industry is more
attractive, the higher the barriers to entry are for new companies (Porter, 1979). It is
easier for incumbent companies to compete in an industry with a low threat of new
entrants, which softens the competitive environment.
H4: IPOs in industries with a lower entry rate have a stronger competitive effect.
In order to test these hypotheses, I additionally use a set of control variables. To
account for the firm-related factors I use the size, leverage and the profitability of the
IPO firm. I expect the effect to be negative for the size of the IPO firm. Larger firms
constitute a higher threat to incumbent firms. I expect that the leverage has a positive
influence on the intra-industry effect as higher leveraged firms have a smaller range
in competitive choices, which decreases the competitive effect of the IPO. The
- 55 -
profitability is expected to affect the rivals’ reactions negatively, as a more profitable
firm has a larger range in competitive choices, which increases the competitive effect.
To control for the market environment, I employ the overall market sentiment using
the S&P 500 performance, the hotness of the IPO market and the level of the
volatility index VIX. For the first two variables, I expect the effect to be positive. In
hot market periods, investors are more optimistic about a firms’ growth outlook,
which makes it easier for new firms to go public. This allows low quality firms to
enter the market as well (Demers and Joos, 2007). In cold market periods, rather high
quality firms manage to go public.22 The competitive effect is assumed to be stronger
for high quality firms and therefore should be stronger in cold market periods. The
VIX, also known as the “investor fear gauge”, accounts for the overall market risk. I
expect the relation to be negative. The higher the systematic risk in the market, the
more investors are concerned about new competitors and the stronger is the negative
reaction to an IPO.
Furthermore, I control for the number of listed competitors in the industry, whether
the IPO got backed by a venture capital firm and whether the IPO is the first in the
industry after a long silence period. The effect of the number of listed competitors is
not clear a priori, yet I include this variable to test whether the rival’s reaction is
depending on the number of listed companies. Cotei and Farhat (2011) assume that
venture backed IPOs convey a strong positive market signal as they find evidence for
a positive market reaction upon venture backed IPOs. I therefore expect the influence
to be positive. The expected sign for the first IPOs in the industry after a long silence
period is also expected to be positive. These IPOs should contain the strongest market
signal about an improved industry outlook. Akhigbe et al. (2003) find evidence
confirming this assumption.
3.4 Data and Descriptive Statistics
The initial sample contains all IPO filings on NASDAQ, NYSE or AMEX between
1990 and 2010 available in the Thomson SDC New Issues Database. Financial
22
Chemmanur and He (2011) provide empirical evidence that IPOs going public in a cold market phase have higher
productivity than IPOs going public in a hot market phase.
- 56 -
institutions, REITs, unit IPOs, spin-offs, depository receipts and penny stock IPOs
(offer price below $5) are excluded from the sample leaving an initial sample of
5,651 IPO filings. By further imposing the restriction that the completed IPOs have to
be available in Compustat and CRSP, the sample is reduced to 5,382. The sample is
then divided in two groups: IPO filings and completed IPOs. In a next step, all events
that have a confounding filing or completion in the same industry within 30 days
before the event are excluded. Finally, the last restriction requires that every firm in
the sample has at least five listed competitors to get an adequate picture of the
competitors’ reaction. Competitors are identified using their six–digit NAICS
industry code. As there is no data available about the industry entry rate for the years
1990 and 1991, which is required for the cross-sectional regression, I further have to
exclude the completed IPOs in these years. This leads to a final sample consisting of
1,599 completed IPO events with 58,798 competitors from 206 different industries
and 2,107 filing events with 70,619 competitors from 215 different industries.
Table 3.1 provides an overview and description of the used variables in the analysis.
Industry concentration data is acquired from the US Census Bureau, available in the
Economic Census.23 The Census data is available for the years 2007, 2002 and 1997.
I match each IPO with the closest available Economic Census. An alternative would
have been to calculate industry concentration and market shares from all firms
available in Compustat, which would yield yearly observations (see e.g. Chod and
Lyandres, 2011 or Chemmanur and He, 2011). The drawback of this alternative is
that Compustat only contains data about public firms. As a result, concentration ratios
and market shares, which are constructed by using only Compustat data, do not
consider the effect of private firms. However, this effect is significant as Ali, Klasa
and Yeung (2009) report. In their study, they find a correlation of only 13% between
the concentration ratio from Compustat and from the US Census concluding that
concentration variables constructed from Compustat data are poor proxies.
Additionally, the benefit of having yearly observations is rather limited as industry
23
- 57 -
Table 3.1
Variable Name
concentration
dsi
idu
entry
rel_size
logleverage
roa
s&p
vix
hotness
competitors
vc
first
Description
Average cumulative abnormal return of the portfolio of competitors
in response to a filed/completed IPO
Cumulative market share of the 20 largest companies in a six-digit
NAICS industry
Degree of strategic interaction in a six-digit NAICS industry
Industry demand uncertainty in a six-digit NAICS industry
Average net entry (number of firms over number of firms last year
– 1) over three years in six-digit NAICS industry
Relative size of the IPO firm in terms of sales compared to the
median sales of listed competitors in the year of the IPO
log of 1 + leverage. Leverage is defined as total liabilities over the
sum of total assets and IPO proceeds
Return on assets, defined as net income over total assets
One-month average return of the S&P 500 index at the beginning
of the event window
One-month average level of the Chicago Board Options Exchange
Market Volatility Index before the IPO
IPO market hotness: Average IPO underpricing in the three months
before the IPO
Number of listed competitors in the same six-digit NAICS industry
Binary variable set to one if the IPO got backed by Venture Capital
and zero otherwise
Binary variable set to one if the IPO is the first in its industry after
a silence period of at least one year and zero otherwise
concentration does not vary significantly from year to year. Therefore I decide to use
data about industry concentration from the Economic Census.
For calculating the industry demand uncertainty (idu) and the degree of strategic
interaction (dsi), quarterly sales data are necessary, which are not available for private
firms. Therefore, these two variables are calculated using all companies available in
Compustat. The degree of strategic interaction (dsi) is based on Sundaram, John and
John’s (1996) competitive strategy measure. The dsi for a firm is defined as the
correlation of its quarterly marginal profit with the change in the combined quarterly
sales of industry competitors over a 20 quarter period, using a rolling window and
- 58 -
allowing for time variation of the variable. The marginal profit is defined as the ratio
of change in the firm’s net income to changes in its sales.
∆
∆
(I)
I calculate the dsi for each firm available on Compustat. The sign of this variable
provides insight whether the company is competing in strategic substitutes (negative
sign) or in strategic complements (positive sign). This, of course, assumes that the
company’s sales are a valid proxy for its strategic actions. To calculate the industrydsi for a given quarter, I take the average value of the company-dsi over all
companies in the same six-digit NAICS industry that have at least ten non-missing
sales observations.
The demand uncertainty is constructed as the standard deviation of quarterly industry
sales growth over a 20 quarter period using a rolling window. Industry sales are
calculated as the sum of sales of all companies in the same six-digit NAICS industry
available in Compustat, which have reported data available over the 20 quarter
period. IPOs are matched with demand uncertainty and the degree of strategic
interaction in their six-digit NAICS industry from the quarter in which they go public.
To calculate the entry rate I use the yearly number of companies in a six-digit NAICS
industry. The data is made available by the US Small Business Administration.24 The
entry rate is calculated as the three year average net entry rate, defined as new
number of firms over previous number of firms minus one. To allow time variation of
the variable I apply again the rolling window method. Data about entry rates are only
available from 1990 to 2009. IPOs after 2009 have been matched with the entry rate
from 2009 and the entry rates for IPOs in 1992 are calculated using the two year
average net entry rate. Since there has been a change in the industry classification
system from SIC to NAICS codes in 1997, data from 1990 until 1998 are available
using the SIC classification system und data from 1997 to 2009 are available using
the NAICS classification system. To ensure comparability, I use the official
concordance tables provided by the US Census Bureau to match the NAICS
24
See http://www.sba.gov/advocacy/849/12162
- 59 -
industries with the corresponding SIC industries.25 For the transition years from SIC
to NAICS I use the overlapping data sets.26
Accounting-related variables are used from the fiscal year prior to the IPO and,
together with the six-digit NAICS code, are obtained from Compustat. Information
on the IPO proceeds and VC backing are received from Thomson Financial. Data
about the S&P 500 index and the VIX are received from CRSP. Finally, data for
constructing the IPO market hotness variable is available on Jay Ritter’s website
where the number of IPOs and the average initial return per month are listed.27
Table 3.2 provides the summary statistics of the variables used in the cross-sectional
analysis and Table 3.3 shows the distribution of all completed IPOs between 1992
and 2010 that comply with the mentioned requirements. The number of completed
IPOs ranges from a minimum of 18 in 2008 to a maximum of 178 in 1996. Table 3.3
does not represent the distribution of all completed IPOs in these years. Especially the
relatively low number of sample IPOs during the boom years 1997 to 2001 can be
explained by the exclusion of confounding events and the requirement to have at least
five listed competitors.
3.5 Methodology
To analyze the effect of an IPO event, such as a filing or completion on listed
competitors, I employ the standard event study methodology. This is an appropriate
approach to analyze corporate events and their implications for valuation
(MacKinlay, 1997). The abnormal returns are calculated for the filing and the
completion date according to Thomson Financial. I follow Akhigbe et al. (2003) and
form equally-weighted portfolios of listed companies that share the same six-digit
NAICS code as the event firm. This approach controls for potential cross-sectional
correlation of returns in the industry. Abnormal returns for a day are calculated as the
25
See http://www.census.gov/eos/www/naics/concordances/concordances.html
For the 1999 entry rate, I calculate the growth rate from 1996 to 1997 using the matched SIC industry classification and
the growth rates from 1997 to 1998 and 1998 to 1999 using the NAICS industry classification.
27
See http://bear.warrington.ufl.edu/ritter/ipoisr.htm. I would like to thank Jay Ritter for making this data publicly
available.
26
- 60 -
Variable
[-5; 5]
concentration
dsi
idu
entry
rel_size
logleverage
roa
s&p
vix
hotness
competitors
vc (binary)
first (binary)
n=1599
Table 3.2
Descriptive Statistics of completed IPO Sample
Mean
Median
Std
Max
-0.0066
-0.0066
0.0457
0.2042
59.90%
63.20%
21.18%
100.00%
-0.0031
-0.0053
0.0599
0.3099
0.5157
0.1488
2.7364
73.5452
0.0375
0.0232
0.0828
0.6632
2.3673
0.6497
8.2085
142.7552
0.2697
0.2485
0.1841
1.0919
-0.3509
0.0097
5.1401
9.9784
0.0108
0.0121
0.0335
0.1518
17.6746
16.5095
5.2311
41.1832
21.53%
15.38%
20.06%
112.09%
36.7717
17.0000
42.1403
276.0000
0.4822
0.2896
-
Min
-0.2534
6.10%
-0.3315
0.0162
-0.8221
0.0000
0.0000
-202.1000
-0.1334
10.6682
-3.38%
5.0000
-
Variable
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Table 3.3
Distribution of completed IPOs (1992-2010)
Completed IPOs
Competitors
Different industries
113
3,112
69
147
3,723
80
126
3,286
75
133
3,971
74
178
4,708
94
149
4,402
89
83
2,974
52
121
4,392
57
96
3,940
55
30
1,540
24
32
1,446
25
26
1,631
20
71
3,382
46
71
4,079
40
68
3,699
41
70
3,895
34
18
713
15
20
1,349
14
47
2,511
29
- 61 -
difference between the realized return of the competitor portfolio and its expected
return, and are expressed by:
(II)
Expected returns for the competitor portfolio are estimated using a one factor market
model.28 The parameters for the market model are estimated in the period [-250; -30]
relative to the event date. As benchmark market index, I use the S&P 500 index. The
market model can be expressed as follows:
(III)
The abnormal returns are calculated for each day in the event period [-30; 20] and are
cumulated for each competitor portfolio:
(IV)
Finally, the cumulative abnormal returns are aggregated across events by computing
the equally-weighted average of CARs of the competitor portfolios:
(V)
Average cumulative abnormal returns of competitor portfolios are reported for
several event windows ranging from [-10; 10] to [-1, 1]. To test the statistical
significance of the CCARs, I follow the methodolgy of Mikkelson and Partch (1988)
and compute a z-statistic involving prediction errors of the market model.
For analyzing the influence of the competitive environment in explaining the
competitors’ reaction upon an IPO, I use the following cross-sectional model:
(VI)
28
MacKinlay (1997) states that “the gains from employing multifactor models for event studies are limited” (p.18).
Therefore I follow prior literature (Akhigbe et al., 2003; Lee et al., 2011; Hsu et al., 2010) and use the one factor market
model. The study by Cotei and Farhat (2011) uses a constant mean return model to estimate the expected returns for their
event study.
- 62 -
I estimate four versions of the above model. To test my results’ robustness, I control
for year-fixed effects in the last two versions. The first and third version include only
the variables describing the competitive environment. The second and fourth version
further include the control variables to account for firm factors, the market
environment and further factors.
An alternative to test the effect of an IPO would be to look at the competitors’
response upon the IPO filing date. However, I expect that the competitors’ response
is more meaningful upon the completion of the IPO. The management’s
announcement to go public is still accompanied by great uncertainty of success and a
withdrawal can occur at any point in time during the filing period (Busaba,
Benveniste and Guo, 2001). In the initial sample of IPO filings, 1,234 filed IPOs at
NYSE, AMEX and NASDAQ are withdrawn in the period 1990 to 2010, which is
about 22%. The uncertainty about actually going public decreases steadily in time
and reaches its low only at the date of issuance. I therefore decided to analyze the
factors influencing the competitors’ response upon the completion instead of the IPO
announcement date.
This section provides the empirical results to test the assumption that the competitive
environment in an industry determines the degree of the IPO’s competitive intraindustry effect. In a first step, the abnormal returns of the competitor portfolio around
the completion and filing of an IPO are computed. In a second step, the factors
influencing the abnormal return around a completed IPO are analyzed. Finally, in a
third step, I use the results from the cross-sectional analysis to create subsamples of
IPOs with respect to the prevalent industry competitiveness.
3.6.1 Event Study Analysis
Figure 3.1 shows the development of the cumulative abnormal returns of the
competitors’ portfolio for a completed (straight line) and filed (dotted line) IPO over
the event window [-30; 20]. The competitors’ reaction to a filing and completion is
negative and in line with the results from Hsu et al. (2010). As the information effect
- 63 -
Figure 3.1
Competitors’ abnormal Return around the Completion and Filing of IPOs
The figure shows the abnormal returns of the competitor portfolio upon completion and filing of an IPO for the event window. The sample
consists of all completion and filing events at the NYSE, AMEX and NASDAQ between 1990 and 2010 (1992 to 2010 for completed
IPOs) that have no confounding event in the same six-digit NAICS industry in the surrounding 30-day period and for which the prices of at
least five listed competitors in the same industry are available. There are 2,107 filing events and 1,599 IPO events. Cumulated abnormal
returns are on the y-axis and are calculated from the difference between actual return of the equally-weighted portfolio of competitors and
the expected return, estimated from a one-factor market model. The parameters for the market model are estimated in the period [-250; -30]
relative to the event. The x-axis comprises the timeline in days around the event (day 0).
is assumed to be positive, a negative reaction indicates that the competitive effect
dominates over the information effect. Investors of public rivals rather see the IPO as
a threat than a sign for an improved industry outlook. The graph also shows that
competitors substantially respond to a filing or a completion of an IPO already a
couple of days in advance. This trend starts at 10 trading days before the event and
lasts for a significant period of time after the event. Akhigbe et al.’s (2003) event
window only starts at the event date which may explain why they do not find a
significant competitors’ reaction in their study. A possible explanation for the strong
pre-event reaction is that the uncertainty about the success of the IPO already starts to
decrease in the days before the event. Hsu et al. (2010) argue that in the days before
the issue date, more and more information becomes available from the road show and
about potential investor demand which reduces the uncertainty about a successful
completion.
- 64 -
The results from Figure 3.1 provide a first piece of evidence that the competitive
effect dominates when a firm goes public or announces to do so. To formally test this
result, I look at the abnormal returns of the competitor portfolio for different event
windows and test for statistical significance. The results are presented in Table 3.4.
As the graphical analysis already indicates, the competitors’ abnormal returns in
response to an IPO completion or filing are negative. The results are all highly
significant at the 1% level, with the exception of the [-1; 1] window for completed
IPOs, which is significant at the 5% level. The negative reaction holds true for the
immediate days surrounding the event and also for a number of days before and after
the event. Interestingly, the competitors’ reaction is relatively similar upon an IPO
filing and an IPO completion. For the longest event window [-10; 10], the abnormal
return is -1.12% for a completed IPO and -1.02% for an IPO filing. For the 3-day
window around the event, the abnormal return is -0.15% for completed IPOs
and -0.18% for IPO filings. To increase robustness, I also perform the analysis using
the Fama-French-Carhart four factor model (Fama and French, 1993; Carhart, 1997)
which does not change the overall results.
The results are also economically significant. The average market capitalization of
incumbent firms in my sample of completed IPOs is $3.556 billion. For the longest
event window, this yields an average loss of $39.823 million per competitor upon
completion of an IPO in the same industry.29 On an event basis, the average total loss
of all competitors amounts to $2.708 billion. Overall the results indicate that listed
industry competitors exhibit a negative reaction in their share price when an IPO
filing or completion occurs in their industry.
To further study the IPO’s competitive effect I look at the relation between the
competitors’ reaction upon the filing and the completed IPO to see whether the
reaction is persistent. A reasonable assumption is to expect a positive correlation
between the competitors’ reaction upon the filing and the completion of an IPO. If the
competitive effect is strong upon the filing (i.e. strong negative competitors’
reaction), the competitive effect should also be strong upon the first trading day of the
29
The average market capitalization is calculated at the beginning of the event window. The average loss is calculated by
multiplying the CCAR [-10; 10] with the average market capitalization.
- 65 -
Table 3.4
Abnormal Returns of the Competitor Portfolio on the Completion and Filing
Date of IPOs
The table shows the average competitor cumulative abnormal returns (
) in response to the completion and filing of an IPO for
different event windows. The sample consists of all completion and filing events at the NYSE, AMEX and NASDAQ between 1990 and
2010 (1992 to 2010 for completed IPOs) that have no confounding event in the same six-digit NAICS industry in the surrounding 30-day
period and for which the prices of at least five listed competitors in the same industry are available. Abnormal returns are calculated from
the difference between actual return of the equally-weighted portfolio of competitors and the expected return, estimated from a one-factor
market model. The parameters for the market model are estimated in the period [-250; -30] relative to the event. To test for statistical
significance, the z-statistic from Mikkelson and Partch (1988) is used. ***/**/* indicates statistical significance at the 1%/5%/10% level.
Completed IPOs
IPO Filings
z-statistic
Days
z-statistic
[-10; 10]
-1.12%***
-6.43
-1.02%***
-7.04
[-5; 5]
-0.66%***
-5.70
-0.62%***
-6.24
[-3; 3]
-0.50%***
-5.79
-0.43%***
-5.74
[-1; 1]
-0.15%**
-2.01
-0.18%***
-3.66
[-10; 5]
-0.88%***
-5.90
-0.72%***
-5.80
[-10; 3]
-0.83%***
-5.94
-0.58%***
-5.22
[-10; 1]
-0.61%***
-4.45
-0.46%***
-4.47
[-5; 3]
-0.60%***
-5.86
-0.48%***
-5.65
[-5; 1]
-0.39%***
-4.08
-0.36%***
-4.85
[-3; 1]
-0.29%***
-3.74
-0.32%***
-4.84
[-3; 5]
-0.56%***
-5.55
-0.57%***
-6.37
[-3; 10]
-0.80%***
-6.13
-0.87%***
-7.60
[-5; 10]
-0.89%***
-6.24
-0.91%***
-7.49
[-1; 3]
-0.37%***
-4.68
-0.29%***
-4.78
[-1; 5]
-0.42%***
-4.48
-0.43%***
-5.47
[-1: 10]
-0.66%***
-5.29
-0.73%***
-6.86
n=1,599
n=2,107
IPO. However, there is no significant correlation observable for the [-10; 10], [-5; 5],
[-3; 3] and [-1; 1] window, which is surprising. This may have several reasons. For
one, the market environment or firm specific factors could have changed since the
filing date. Given an average time of only 93 days between filing and issue date, this
does not seem to be the reason. Alternatively, the result may stem from the possibility
that competitors show a reaction either at the time of the filing or at the issue time.
- 66 -
This may depend on the credibility of a successful IPO at the time of the filing. As
already stated, there is a significant risk of withdrawal at the time of the filing. I find
evidence that the response upon the issue date is significantly more negative than
upon the filing date. On average, for the [-10; 10] window, the reaction upon issue is
more negative by 0.58% and more negative by 0.38% for the [-5; 5] window, both
statistically significant at the 5% level.
3.6.2 Analysis of the IPO’s intra-industry Effect
Table 3.5 presents the results from the cross-sectional analysis of the competitors’
response upon a completed IPO in their industry for the [-5; 5] window. Models 1 and
3 only include the factors describing the industry competitiveness. Models 2 and 4
also include the control variables accounting for firm-specific factors, the market
environment and further factors. In the first two columns, results are presented
without year-fixed effects and, in order to test for robustness, the last two columns
also show the results, which control for year-fixed effects.
The primary result from the cross-sectional analysis is that the toughness of industry
competition is negatively related with the competitive effect. The weaker the
competitive environment, the more adverse is the competitors’ reaction. The
influence of industry concentration (concentration) is negatively related to abnormal
returns. This holds true for all model specifications and is statistically significant at
the 5% level. The result is consistent with H1 that competitors’ in higher concentrated
(less competitive) industries experience a stronger competitive effect. Akhigbe et al.
(2003) also find an inverse relation between industry concentration and competitors’
abnormal returns. The study by Hsu et al. (2010) finds no relation between
competitor returns and industry concentration.30 The reason for this could be that Hsu
et al. only use a rather small sample of only the largest IPOs. My results indicate that
an increase in industry concentration by one standard deviation decreases abnormal
returns by 0.25% on average. The degree of strategic interaction (dsi) has a positive
influence on the abnormal returns. Competitors in industries that are rather competing
30
Hsu et al. (2010) do not perform a cross-sectional analysis, but divide their sample in three groups according to the
industry concentration and find that returns are more negative in the most concentrated group. The abnormal returns of the
three groups are not significantly different though.
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Table 3.5
Cross-sectional Variation in Competitors’ Valuation Effects upon a completed
IPO
The table shows the results of estimating a cross-sectional regression on the competitors’ cumulative abnormal return (CCAR) upon a
completed IPO in their industry for the event window [-5; 5]. The sample consists of all completed IPOs at the NYSE, AMEX and
NASDAQ between 1992 and 2010 that have no confounding event in the same six-digit NAICS industry in the surrounding 30-day period
and for which the prices of at least five listed competitors in the same industry are available. Abnormal returns are calculated from the
difference between the actual return of the equally-weighted portfolio of competitors and the expected return, estimated from a one-factor
market model. The parameters for the market model are estimated in the period [-250; -30] relative to the event. concentration is the
cumulated market share of the 20 largest companies in a six-digit NAICS industry, dsi is the degree of strategic interaction in the industry
measured as intra-industry correlation of the change in firms’ marginal profit and change in total industry sales, idu is the industry demand
uncertainty calculated as the std of sales growth, entry is the industry’s average net entry rate in the three years before the IPO, rel_size is
the relative size in terms of sales of the issuing company relative to the median sales of listed competitors, logleverage is the log of one
plus leverage, roa is the return on assets, s&p is the one-month average return of the S&P500 ten days before the event date, vix is the onemonth average level of the Chicago Board Options Exchange Market Volatility Index before the IPO, hotness is the average IPO
underpricing in the three months before the IPO, competitors is the number of listed competitors with available prices, vc is a binary
variable indicating venture backing and first is a binary variable indicating whether the IPO is the first in its industry after a silence period
of at least one year. Year-fixed effects are included in the third and fourth column. ***/**/* indicates statistical significance at the
1%/5%/10% level.
Without year-fixed effects
Year-fixed effects included
Variables
Model 1
Model 2
Model 3
Model 4
concentration
-0.0001**
(-2.16)
0.054***
(2.85)
0.001***
(2.83)
0.030**
(2.14)
-0.0001**
(-2.23)
0.050***
(2.67)
0.001***
(3.10)
0.031**
(2.20)
-0.0003**
(-2.49)
0.013*
(1.91)
-0.0001
(-0.45)
0.162***
(4.78)
-0.0004
(-1.52)
0.0002***
(3.63)
0.0001
(1.10)
0.002
(0.98)
0.001
(0.48)
-0.007
(-1.11)
1599
-0.0001**
(-2.17)
0.048**
(2.56)
0.001***
(2.70)
0.018
(1.21)
-0.0001**
(-1.98)
0.043**
(2.35)
0.001***
(2.95)
0.019
(1.31)
-0.0004***
(-2.91)
0.007
(1.12)
-0.0001
(-0.62)
0.109***
(3.07)
-0.0009**
(-2.04)
0.0008***
(7.43)
0.0001
(0.81)
0.002
(0.77)
0.001
(0.40)
1599
1599
dsi
idu
entry
rel_size
logleverage
roa
s&p
vix
hotness
competitors
vc
first
constant
Observations
R²/Adj. R²
-0.001
(-0.31)
1599
1.76%/1.51%
4.70%/3.92%
5.92%/4.66%
10.79%/9.08%
- 68 -
in strategic complements (positive dsi) suffer less from the competitive effect than in
industries that are rather competing in strategic substitutes (negative dsi), which is
consistent with H2. The results are statistically significant in all models and robust to
year-fixed effects. A decrease in the dsi by one standard deviation decreases
abnormal returns by 0.32%. When looking at industry demand uncertainty (idu), there
is also an inverse relation with the competitive effect observable. As suggested by
H3, IPOs in industries with lower demand uncertainty (weaker competitive
environment) exhibit a stronger competitive effect. This effect is again significant in
all model specifications. Reducing the demand uncertainty by one standard deviation
leads to a decrease in the abnormal returns by 0.32%. Finally, H4 suggests that the
entry rate is also inversely related to the competitive effect. In all four model
specifications, the entry rate (entry) exhibits the expected sign. However, this result is
only statistically significant in the random effects specification and loses significance
when controlling for year-fixed effects. Therefore, H4 can only be accepted with
limitations.
Overall, the results provide evidence for the assumption that the toughness of the
competitive environment in an industry is inversely related to the degree of the
competitive effect. The results are also consistent with Lang and Stulz (1992), who
find in a study on the intra-industry effect of bankruptcy announcements that the
competitive effect is stronger for highly concentrated industries with low leverage,
which indicates a weak competitive environment.
Looking at the control variables in Models 3 and 4, the relative size of the issuing
firm (rel_size) is positively related to the competitive effect, which is expected. The
result is statistically significant and indicates that relatively larger IPOs pose a bigger
threat to competitors. A firm’s leverage (logleverage) is inversely related to the
competitive effect. However, this only holds true when not controlling for year-fixed
effects. The effect of the new firm’s profitability (roa) on the competitors’ abnormal
returns is negative, as expected, but lacks statistical significance at conventional
levels. With the exception of the volatility index (vix), the market control variables
are all highly statistically significant and exhibit the expected effect on abnormal
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returns. The results for s&p and hotness suggest that the competitors’ investors draw
conclusions about the new firm’s quality from the market environment around the
IPO. The market volatility is only statistically significant when year-fixed effects are
included and exhibits the expected sign. The number of listed competitors
(competitors) does not yield statistical significance. Furthermore, I test for the
influence of venture backing (vc) and whether the IPO is the first in its industry after
a long silence period (first). The two variables should indicate a strong information
effect as suggested by Akhigbe et al. (2003) and Cotei and Farhat (2011). As
expected, the variables exhibit a positive influence on abnormal returns. However, the
results fail to provide statistical significance at conventional levels.
My results have implications for the firm’s decision to go public. Competitors show a
more adverse reaction when a firm goes public in an industry with a weak product
market competition. Firms going public in a weak competitive environment are
considered to be a greater threat than IPOs in industries with a tough competitive
environment. This suggests that these firms can benefit more from the competitive
advantage of going public. Put differently, the marginal benefit of the competitive
advantage of going public is higher in industries with a weak product market
competition compared to industries with a tough product market competition.
According to the theories by Bhattacharya and Ritter (1983) and Maksimovic and
Pichler (2001), a firm not only faces monetary costs when going public, but also the
costs of making private company information public. Only when the benefits of going
public outweigh the costs, the firm decides to go public. My results suggest that the
firm’s benefits of going public are higher in competitively weak industries. Therefore
a weak competitive environment should induce more firms to go public. This is
indeed consistent with the study by Chemmanur et al. (2010), who research the
factors that influence a firm’s decision to stay private or go public. Chemmanur et al.
find that industry concentration is positively related to the decision to go public.
Firms in industries with a higher concentration (weaker competitive environment) are
more likely to go public.
- 70 -
3.6.3 Comparison of different Levels of Industry Competitiveness
The results from the cross-sectional regression indicate that the competitive
environment affects the degree of the IPO’s competitive effect on competitors. In this
subsection I want to further analyze how the competitive effect depends on the
competitive environment. I therefore construct several subsamples according to the
factors describing the firm’s product market competition. The results are presented in
Table 3.6.
By sorting the sample according to industry concentration it is shown that the top
quartile (weak competition) exhibits an average cumulative competitors’ return
of -1.03% for the [-5; 5] window. The bottom quartile (tough competition) does not
exhibit a significant competitors’ response. The test for difference is significant at the
5% level. By sorting according to the degree of strategic interaction or demand
uncertainty the same picture is shown. The top quartile (tough competition) does not
exhibit a statistically significant competitors’ reaction whereas the bottom quartile
(weak competition) shows a strong negative response. Again, the test for difference is
significant in both cases. These results further confirm the results from the crosssectional analysis that the competitive effect is stronger in a weaker competitive
environment. Dividing the sample into IPOs in industries competing in strategic
substitutes and in strategic complements does not yield a statistically significant
difference. Both subsamples show a significant negative response upon an IPO. As
expected, the reaction in industries competing in strategic substitutes is more negative
than for strategic complements. The difference is not statistically significant though. I
also sort the sample according to the industry’s entry rate, for which I find mixed
results in the cross-sectional analysis. The bottom quartile subsample exhibits a
significant negative price reaction while the top quartile shows no significant
reaction. The difference is statistically significant at the 5% level supporting the
hypothesis that the entry rate influences the competitive effect.
Furthermore, I construct subsamples according to the overall competitive
environment as suggested by the results from the cross-sectional analysis. I identify
three factors, which describe an industry’s competitive environment and are found to
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Table 3.6
Abnormal Returns of the Competitor Portfolio on the Completion Date of an
IPO for different Levels of Industry Competitiveness
The table shows the average competitor cumulative abnormal returns (
) in response to the completion of an IPO. Subsamples are
constructed according to the industry’s concentration, degree of strategic interaction, demand uncertainty and entry rate. The “tough”
subsample is constructed as IPO events with below median industry concentration and above median degree of strategic interaction and
industry demand uncertainty. The “weak” subsample is constructed as IPOs with above median industry concentration and below median
degree of strategic interaction and industry demand uncertainty. ***/**/* indicates statistical significance at the 1%/5%/10% level.
[-5; 5]
Concentration
Top quartile
Bottom quartile
Difference
n=400
n=400
t-statistic
-1.03%***
-0.34%
-0.69%**
-4.07
-1.58
-2.09
Degree of Strategic Interaction
Top quartile
n=400
Bottom quartile
n=400
Difference
Strategic Complements
n=720
Strategic Substitutes
n=879
Difference
-0.32%
-1.09%***
0.77%**
-0.49%***
-0.79%***
0.30%
-1.35
-4.41
2.27
-2.93
-5.07
1.34
Industry Demand Uncertainty
Top quartile
n=400
Bottom quartile
n=400
Difference
0.10%
-1.32%***
1.42%***
0.47
-6.54
-4.71
Entry Rate
Top quartile
Bottom quartile
Difference
-0.31%
-0.99%***
0.68%**
-1.38
-4.27
2.10
-0.12%
-1.83%***
1.71%***
-0.36
-4.42
-3.22
n=400
n=400
Competitive Environment
Tough
n=185
Weak
n=183
Difference
- 72 -
have a statistically significant influence on the competitors’ abnormal returns:
industry concentration, the degree of strategic interaction and industry demand
uncertainty. Using these variables I form a subsample of IPOs in a tough competitive
environment and a subsample of IPOs in a weak competitive environment. The
“tough” subsample consists of all IPOs that exhibit a below median industry
concentration, an above median degree of strategic interaction and an above median
industry demand uncertainty (see Table 3.2). The “weak” subsample consists of all
IPOs that exhibit an above median industry concentration, a below median degree of
strategic interaction and a below median industry demand uncertainty. As expected,
the “weak” subsample exhibits a statistically significant negative return of -1.83% for
the [-5; 5] window. The “tough” subsample does not exhibit a significant abnormal
return. The test for the statistical significance of the difference indicates a
significance level of 1%.
The results provide further evidence that the IPO’s competitive effect is stronger for
competitors in industries that can be characterized by a weak competitive
environment. Furthermore, the results in Table 3.6 not only indicate that the
competitive effect is stronger if the competitive environment is weak but also that
there is no observable competitor reaction for IPOs in industries with a tough
competitive environment. The competitive effect is only observable in industries with
a weak competitive environment. This can have two reasons. First, IPOs in “tough”
industries do not exhibit a competitive effect. This seems rather questionable. In this
case I would expect a positive competitors’ reaction due to the information effect.
However the returns are negative but not statistically significant. Second, the
information and the competitive effect offset each other in “tough” industries. Given
my previous results, this seems more reasonable. The competitive effect is weaker in
“tough” industries and cancels out with the information effect. This is how Akhigbe
et al. (2003) also interpret their results as they find evidence for the existence of both
the competitive and the information effect in their sample but do not find a significant
competitors’ reaction overall.
- 73 -
3.7 Conclusion
In this study, I analyzed the reaction of listed competitors in response to a filing or
completion of an IPO in the same industry and test whether the IPO’s competitive
effect on industry competitors is linked to the competitive environment in the
industry. I find that industry competitors yield negative abnormal returns in response
to a filing or completed IPO in their industry. This result indicates that the
competitive effect dominates positive information externalities associated with the
event. Analyzing the competitors’ abnormal returns, I find that the industry’s product
market competition is affecting the IPO’s competitive effect. Industry concentration,
the degree of strategic interaction and industry demand uncertainty are all found to
have a significant influence on the competitors’ abnormal returns. The weaker the
competitive environment, described by higher industry concentration, lower degree of
strategic interaction and lower industry demand uncertainty, the stronger is the IPO’s
competitive effect. For the industry’s entry rate I only find minor evidence for
influencing the competitive effect. I further find that the subsample of IPOs in
industries with the toughest competitive environment does not exhibit a significant
response to the IPO whereas the subsample of IPOs in industries with the weakest
competitive environment exhibits a significant negative response to the IPO. A
possible conclusion is that in “tough” industries the positive information effect and
the competitive effect offset each other.
Overall, my results present evidence that the degree of the IPO’s competitive effect
depends on the surrounding competitive environment. This has implications for both
private and public firms as well as for investors. When a private firm considers going
public, it should also include in its decision the benefits from a competitive advantage
associated with going public. Going public in industries with a weak competitive
environment is accompanied with larger benefits compared to going public in
industries with a tough competitive environment. Therefore, firms in competitively
weak industries should, ceteris paribus, have a higher incentive to go public than
firms in competitively tough industries. For investors, my results provide valuable
insights on assessing the risk structure of companies, especially in young industries
- 74 -
with a high likelihood of new IPOs. Lastly my results are also beneficial for already
listed companies as the results help to better understand the dynamics associated with
an IPO in their industry. An interesting topic for further research would be to see
whether the observed valuation reaction reverses over time. To answer this, a
considerably longer event window would be necessary. Moreover it would be
interesting to analyze if withdrawn IPOs also affect the competitors’ prices. Part 4
will cover this question.
- 75 -
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Intra-Industry Effects of withdrawn Initial Public Offerings
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4 Intra-Industry Effects of withdrawn Initial Public
Offerings
In this study, I analyze the valuation response of listed competitors to a withdrawn
IPO in the same industry. After controlling for confounding events, the competitor
portfolio does not, on average, show a significant reaction. However, a crosssectional analysis, using variables accounting for the market environment and
industry, deal and company characteristics, shows that market sentiment, industry
concentration and the filing firm’s financial leverage have a significant impact on the
competitors’ response. Industry concentration has a negative impact, while the impact
of leverage is positive. Subsamples of withdrawn IPOs with above median industry
concentration and below median leverage exhibit a statistically significant negative
competitor response. The finding is consistent with the theory that the ex-ante risk of
withdrawal of the IPO filing affects the competitors’ reaction. These results provide
evidence for an intra-industry effect of withdrawn IPOs.
- 80 -
4.1 Introduction
The filing process of an Initial Public Offering (IPO) is characterized by uncertainty
of a successful outcome. Throughout the IPO process, the company has the option to
withdraw its IPO at any point. Going public is an important step in a firm’s life cycle
and this step is accompanied by many changes and challenges for the company.
During the process of going public, the company is subject to thorough due diligence
by an auditor, the underwriters, the exchange supervisory authority and, most
importantly, by future investors. The result of the filing process is naturally of great
interest not only to the filing company, but also to industry competitors. In the case of
a successful IPO within the industry, competitors are confronted with a company that
is provided with fresh capital and increased public attention. Hsu, Reed and Rocholl
(2010) argue that companies that successfully accomplish an IPO exhibit a
competitive advantage over their competitors. Akhigbe, Borde and Whyte (2003)
argue that a successful IPO in an industry is also a sign of a favorable outlook in the
product market, which can also affect competitor firms. In the case of a withdrawn
IPO, the reverse occurs. Competitors no longer have to face the threat of the issuing
firm’s competitive advantage, while the withdrawal can also indicate a poor or
declining industry outlook.
The purpose of this research is to study the competitors’ response to a withdrawn IPO
within the same industry. Previous studies have only focused on the intra-industry
effect of an IPO filing or the successful completion of an IPO (see Hsu et al., 2010;
Akhigbe et al., 2003; Cotei and Farhat, 2011; Lee, Bach and Baik, 2011; or Slovin,
Sushka and Ferraro, 1995). Studying the intra-industry effect of withdrawn IPOs is an
expansion of existing literature on the intra-industry effects of IPOs in general. This
study has two objectives. First, I want to detect whether competitors show a valuation
reaction in response to a withdrawn IPO in the same industry. This will provide a first
insight on the question of whether a withdrawn IPO is merely a firm-specific event or
has industry-wide implications. Second, I want to shed light on the factors that
influence the competitors’ reactions to a withdrawn IPO.
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I find that there is a significant drop in the share price of competitors after a
withdrawn IPO. This result seems to be driven by confounding positive IPO events
(filing or completion). After controlling for these confounding events, the
competitors’ reactions to a withdrawn IPO in the same industry becomes
insignificant. However, I find that industry concentration, the issuing company’s
financial leverage and market variables significantly influence competitors’
responses. A withdrawn IPO in a highly concentrated industry or from a filing
company with low leverage yields a significant negative response in an equally
weighted competitor’s portfolio around the announcement of the withdrawal. The
subsamples of withdrawn IPOs in industries with above-median concentration and
below-median leverage exhibit a significant negative reaction in the competitors’
valuation. The opposite subsamples of withdrawn IPOs, with below-median industry
concentration and above-median leverage, do not yield a significant reaction from
competitors. The results provide evidence that the withdrawal of an IPO can trigger
an information effect with an industry-wide impact; these results are consistent with
the assumption that more surprise regarding the withdrawal leads to a stronger
information effect.
After officially filing the IPO with the SEC, the underwriter and possible investors
are most interested in the company’s IPO pricing. In the US, the common price
discovery process is through bookbuilding. In the pre-marketing phase, the
underwriter collects valuation information from key investors and sets a price range
for the offered shares (the bookbuilding range) on the basis of this preliminary
information. During the subsequent bookbuilding period, investors can indicate the
number of shares they want to order as well as the price. After taking orders, the
underwriter builds a demand curve and sets the final IPO price. If demand exceeds
supply, the underwriter allocates the shares according to the bids and the preferred
investor mix. However, when a company engages an underwriter to initiate the
bookbuilding process, there is no commitment by the company to actually sell the
shares at the resulting IPO price. The company keeps the option to withdraw the IPO
at any time. In other words, if the company feels that the resulting IPO price is too
low, the IPO can be withdrawn. According to Busaba (2006), this option is an
- 82 -
important factor in finding the fair company valuation. Investors will reveal a fair
valuation in order to not deter the IPO candidate from proceeding.
The reasons why a company perceives its market valuation as being too low, and
therefore decides to withdraw its IPO, can be manifold. These reasons can
theoretically be classified into two categories: firm-specific factors (e.g. unfavorable
corporate news) or bad market conditions. However, it is most often a combination of
reasons from both categories that leads to the company’s decision to withdraw the
IPO. Both Busaba, Benveniste and Guo (2001) and Dunbar and Foerster (2008) find
that approximately 20% of IPOs filed with the SEC are withdrawn during the filing
period and that the most often-stated reason for the withdrawal request are
unfavorable market conditions. According to the sample of this study, approximately
55% of the companies state this reason in their official withdrawal letter with the
SEC. The number of studies dealing with the question of why companies withdraw
their IPO is rather small. Busaba et al. (2001) find that there is no significant
difference in the company size, profitability or underwriter reputation between
companies withdrawing and succeeding with IPOs. However, the authors find that
leverage, issue size, venture backing, general market conditions and IPO market
conditions do significantly affect the probability of withdrawal. In a similar study,
Dunbar and Foerster (2008) find that the issue size, venture backing, the average
industry book-to-market ratio and the number of IPO filings influence the probability
of withdrawal. In contrast to Busaba et al., the authors find that the underwriter does
influence the withdrawal probability.
The studies by Busaba et al. and Dunbar and Foerster have an important implication
for the current research: investors are able to assign an ex-ante risk of withdrawal to a
filing company. The higher the withdrawal probability of a filed IPO, the weaker is
the surprise effect. When competitors place a high probability of withdrawal on a
filed IPO, the reaction to the withdrawal announcement is rather weak, as it has been
anticipated, whereas the reaction to a withdrawal announcement of a filed IPO with a
rather low withdrawal probability is expected to be stronger. Therefore, the degree of
surprise upon a withdrawn IPO influences competitors’ reactions.
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Theoretically, there are two ways in which an IPO can have an impact on industry
competitors: as a result of information externalities or by a competitive effect. The
two effects oppose each other and, a priori, it is not clear which effect will dominate.
This explains why different studies come to different conclusions regarding an IPO’s
intra-industry effect.31 The information effect assumes that an IPO reveals private
information, not only about the issuing firm, but about the entire industry (Akhigbe et
al, 2003). The IPO is a sign of a positive or improved industry outlook. Companies do
not perform an IPO when the industry outlook is not favorable, as this would make it
more difficult to attract investors, sell the equity story and the risk of paying the costs
of an unsuccessful IPO is too high. Thus, an IPO reveals private information
regarding a favorable outlook for the industry. As this is a signal for the entire
industry, public competitors also benefit from this information spillover. In the case
of a withdrawn IPO, the reverse is true. Withdrawing an IPO is a signal of a declining
industry outlook. This release of private information affects investors’ valuations of
competitor companies, leading to a downward adjustment. Therefore, the impact of
the information effect of a withdrawn IPO on competitors is negative.
An IPO can also have a competitive effect on competitors. Hsu et al. (2010) argue
that an IPO is associated with a competitive advantage of the issuing firm. This
would affect the competitive balance in the industry and investors may reevaluate the
relative competitiveness of the firms in the industry. This assumption is supported by
Chemmanur and He (2011) and Chod and Lyandres (2011), who find that going
public is associated with a significant increase in the product market share.
Chemmanur and He argue that issuing firms gain an advantage over their
competitors, as they are provided with fresh capital that is cheaper than debt
financing. This capital allows them to increase production, employ more qualified
employees or acquire related firms. Additionally, a firm enjoys greater public
attention upon the filing of an IPO. In the situation when an IPO is withdrawn, there
are no changes in the competitive balance in an industry. Depending on whether the
31
Hsu et al.’s (2010) study finds that competitors react negatively to an IPO in the same industry. Akhigbe et al.’s (2003)
study does not find a significant response of competitors and Cotei and Farhat’s (2011) study finds a positive reaction of
competitors. However, these studies are only partially comparable, as they vary substantially in the methodologies they
apply.
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market has already priced the competitive effect of a successful IPO, the competitors’
reaction to the withdrawal announcement should be positive, as the withdrawal
eliminates the threat of the issuing company’s competitive advantage.
As the two effects oppose each other, an insignificant competitors’ response does not
necessarily mean that an IPO withdrawal is merely a firm-specific event. As Akhigbe
et al. (2003) indicate it is possible that an IPO or a withdrawn IPO, will trigger both
effects at the same time, which leads to the two effects cancelling each other out.
For positive IPO events, that is the filing or completion of an IPO, several studies
have dealt with the question of whether an IPO is a firm-specific or an industry-wide
event. Akhigbe et al. (2003) study a sample of IPOs in the US and confirm the
hypothesis that IPOs not only convey firm-specific information but also industrywide information. The authors find that, on average, the effect of an IPO on traded
competitors is insignificant. However, in their study, they find evidence for the
existence of a positive information effect and a negative competitive effect and
conclude that the impact of an IPO is twofold with the effects offsetting each other.
Hsu et al. (2010) use a sample of large firms going public and find that rivals exhibit
a negative price reaction to the announcement of an IPO and the actual IPO date and
a positive price reaction to a withdrawn IPO. The authors find evidence that new
firms have an advantage over incumbent firms, as they have lower leverage, recent
financial intermediary certification and a higher degree of knowledge capital. Hsu et
al. also consider the intra-industry effects of an IPO withdrawal and, to the best of my
knowledge, theirs is the only other study to examine this particular event. The authors
report a positive abnormal return of the competitor portfolio upon a withdrawn IPO in
the industry. However, there is a significant distinction between Hsu et al.’s sample
and the one used in this study, which can explain the opposing observations. Hsu et
al.’s sample comprises only 37 withdrawn IPO events. The authors only include
events that have no other event in the surrounding six years with a larger issuing
volume. Their research design is intended to study the intra-industry effect of only the
largest completed or withdrawn IPOs. This sample selection clearly creates a bias
towards the competitive effect, since the degree of the competitive effect is positively
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correlated to the size of the IPO (Peller, 2011), whereas the signal sent to the market
by a withdrawal is not necessarily stronger for larger IPOs. Hence, it is not surprising
that the competitive effect dominates in their sample of withdrawn IPOs.
Cotei and Farhat (2011) examine the role of venture capital in their study on an IPO’s
intra-industry effect. The authors find that rivals positively respond to venture capital
(VC)-backed IPOs in their industry, while there is no observable reaction to non-VC
backed IPOs. The authors argue that VC-backed IPOs convey superior information
regarding the industry outlook when compared to non-VC-backed IPOs, which
explains the positive valuation reaction by competitors.
Lee et al. (2011) focus on an IPO’s intra-industry effect in the growing computerrelated service industry. They conclude that the positive information spillover is
stronger for rivals competing in the same product market than rivals competing in
related product markets. They also conclude that the valuation effect is positively
influenced by R&D expenses and negatively influenced by industry concentration.
From the results of these studies, it is evident that an IPO possesses an industry-wide
impact. Therefore, it is just a natural step to examine the possible impact of a
negative IPO event, that is, a withdrawn IPO. In this regard, the closest related topic
is probably the literature on the intra-industry effect of bankruptcy announcements. In
a pioneering work, Lang and Stulz (1992) find a negative intra-industry price reaction
to a bankruptcy announcement in the industry. However, the authors discover two
different effects: a negative contagion effect, which arises from the decreased
industry outlook implied by the bankruptcy; and a positive competitive effect as a
result of an improved competitive situation in the industry. They find that, on
average, the contagion effect is the more dominating of the two.
Ferris, Jayaraman and Makhija (1997) also find a significant negative reaction around
a bankruptcy announcement, concluding that the contagion effect dominates the
competitive effect. In further analyses, the authors find that, after splitting their
sample into a group of competitors that are prone to the competitive effect and a
group of competitors that are prone to the contagion effect, the competitive effect is
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still dominated by the contagion effect. Ferris et al. conclude that the market already
anticipated the firm’s bankruptcy and thus has already priced the competitive effect in
the period leading to the bankruptcy announcement. Consistent with this hypothesis,
the authors find a significant positive stock price reaction by the competitor portfolio
for the hundred days prior to the bankruptcy announcement.
Finally, Hertzel, Li, Officer and Rodgers (2008) expand the scope and also look at the
reaction along the supply chain, together with the reaction of competing firms. They
too find that, on average, the contagion effect dominates the competitive effect.
For the current study, the insights from literature on the intra-industry effect of
bankruptcy announcements provide an important input. As the competitive effect
upon a bankruptcy announcement is found to be rather weak, I expect that, for an
announcement of a withdrawal, the competitive effect will be dominated by the
information effect as well. Therefore, in this study, I focus on the possible
information effect of a withdrawn IPO.
The remainder of this study is organized in the following manner. Section 4.2
develops the hypotheses for the empirical tests. Section 4.3 describes the applied
dataset. Section 4.4 introduces the methodology. Section 4.5 presents the results of
the empirical analysis together with robustness tests. Finally, Section 4.6 summarizes
and concludes the study.
After officially filing for an IPO, the company enters a stage of great uncertainty
regarding the success of the transaction. This is also true for the competitors. As long
as the new shares have not yet been sold, there is general uncertainty over whether
the filing company will be able to successfully place its shares in the market.
However, as shown in the studies by Busaba et al. (2001) and Dunbar and Foerster
(2008), the probability of withdrawal is not the same for each company filing an IPO.
There exist certain characteristics that affect the probability of withdrawal. The
market can evaluate a firm’s risk of withdrawal. Busaba et al. (2001) find that
underpricing is lower for firms that have a higher ex-ante probability of withdrawing
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the IPO. A possible explanation for this observation is that investors are able to
evaluate the probability of withdrawal of the company and therefore reduce
underpricing in order not to deter the company from proceeding. This assumption
builds on Benveniste and Spindt’s (1989) information revelation theory of
bookbuilding. In their model, investors are rewarded, through underpricing, for
revealing private information regarding the pricing of the issue. In this context, the
option to withdraw the issue is an important factor, so that investors actually reveal
their private information.
This has certain implications for the intra-industry effect of withdrawn IPOs. When
investors assign a high withdrawal risk to a company’s IPO filing, the actual
withdrawal is not considered a surprise. In contrast, when an IPO that has been
assigned a rather low withdrawal risk is withdrawn, the withdrawal is more of a
surprise. Furthermore, the withdrawal indicates that the initial assessment of the
firm’s withdrawal risk might have been flawed. This can be caused by a misjudgment
of the firm’s quality or by private information regarding the industry outlook. In the
second case, informed investors signal that the expected offer price is too high, given
the industry outlook, leading to the withdrawal. Therefore, the withdrawal of an IPO
that has been assigned a low withdrawal risk can indicate a poor or worsening
industry outlook. The surprising withdrawal reveals private information from
informed investors and so provides an information effect for the entire industry. For
the intra-industry effect of withdrawn IPOs, this implies that more surprising
withdrawals are expected to have a stronger effect. Since the withdrawal indicates
negative information, the competitors’ response is expected to be negative.
In order to assess the issuing company’s ex-ante risk of withdrawal, I use variables on
the basis of the insights from Busaba et al. (2001) and Dunbar and Foerster (2008).
Both studies use company and deal specifications, as well as variables that account
for the market environment. I extend the set of variables by also examining industry
specifications. In addition to the company’s ex-ante risk of withdrawal,
characteristics of competitors might also have an effect on their response. This is
supported by Lang and Stulz’s (1992) and Ferris et al.’s (1997) studies, who show
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that competitor characteristics do have an effect on how they react on a bankruptcy
announcement in their industry. Therefore, I also include competitor characteristics in
my analysis of the intra-industry effect of a withdrawn IPO.
The first industry-specific variable is industry concentration. This variable accounts
for the industry’s competitive environment and is a widely used measure, both in
academic literature and in practice.32 According to economic theory, industries with
low concentration exhibit characteristics of perfect competition, whereas industries
with high concentration exhibit characteristics of an oligopoly. In other words, the
more concentrated an industry, the weaker is the competitive environment in that
industry. The studies by Hsu et al. (2010), Chod and Lyandres (2011) and
Chemmanur and He (2011) assume that the decision to go public is a strategic move
and is associated with a competitive advantage for the issuing firm. The degree to
which IPO firms are able to gain a competitive advantage depends on the existing
competitive environment in the product market. In industries in which product market
competition is already tough, the marginal benefit of going public is smaller when
compared to industries with a weak competitive environment. Firms can benefit more
from the competitive advantage of going public when the competitive environment is
weak. This is consistent with the results of Chemmanur, He and Nandy (2010) who
research factors that influence a firm’s decision to stay private or go public.
Chemmanur et al. find that industry concentration is positively related to the decision
to go public. Firms in industries with a higher concentration (weaker competitive
environment) are more likely to go public. With regard to the probability of
withdrawal, this means that, ceteris paribus, firms operating in more concentrated
industries have a lower probability of withdrawing their IPO, as the benefits of going
public are higher. Therefore, a withdrawal would come as a greater surprise. This
leads to the first hypothesis:
H1: Industry concentration has a negative influence on the competitors’ reaction
to a withdrawn IPO.
32
See, for example, Bikker and Haaf (2002), Lang and Stulz (1992), Chevalier (1995), Sundaram, John and John (1996) or
Akhigbe et al. (2003). The industry concentration is also a crucial factor in decisions by the US Department of Justice or the
European Commission on merger and acquisition cases.
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The second industry-specific variable is the uncertainty a company faces regarding
future sales. The uncertainty about industry demand increases the volatility of future
earnings and cash flows, thereby making company valuation more difficult. Further,
predicting future sales becomes more challenging. This increases the probability that
the company will fail to meet expectations during the intense observation period after
the IPO filing and therefore increases the risk of withdrawal. As a consequence, it is
less surprising when an IPO is withdrawn in an industry with a high demand
uncertainty.
H2: Industry demand uncertainty has a positive influence on the competitors’
reaction to a withdrawn IPO.
Industry specifications do not just affect the probability of withdrawal of the issuing
firm but also the manner in which industry competitors react to the news. For
example, Lang and Stulz (1992) find that the industry leverage affects competitors’
responses to a bankruptcy announcement. The higher the leverage in the industry, the
stronger is the fear of contagion after a bankruptcy filing, resulting in a more negative
valuation response. For withdrawn IPOs, this implies that competitors in highly
leveraged industries are, ceteris paribus, more concerned with regard to the signal of
a withdrawn IPO.
H3: Industry leverage has a negative influence on the competitors’ reaction to a
withdrawn IPO.
Another factor that must be included when considering industry specifications is the
size of the competitors. Ferris et al. (1997) find that the size of a competitor does
affect how they react to a bankruptcy announcement. Large competitors tend to only
show a reaction to a bankruptcy announcement of another large firm, but small
competitors show a strong reaction to a bankruptcy announcement of large and small
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firms. Therefore, I expect that the response to a withdrawn IPO is stronger when the
size of competitors is smaller.
H4: Competitors’ size has a positive influence on their reaction to a withdrawn
IPO.
With respect to company-related variables, the first factor is the company size.
Dunbar and Foerster (2008) find that the filing size has a positive effect on
withdrawal risk. They conclude that large issues have more options when it comes to
sources of capital, which makes them more sensitive to the expected offer price.
Busaba et al. (2001) also find that the expected offer size is positively related to the
risk of withdrawal. Additionally, the authors find that the size of the filing company,
in terms of revenue, has a negative impact on the withdrawal risk. Companies with
large revenues are easier to value, which reduces the withdrawal risk. With regard to
the response to a withdrawn IPO, I expect that the impact of the offer size is positive
on the competitors’ reaction while the impact of the company size is negative.
H5: The expected offer size has a positive influence on the competitors’ reaction to
a withdrawn IPO.
H6: The filing company’s size has a negative influence on the competitors’
reaction to a withdrawn IPO.
Another crucial company characteristic, apart from its size, is the financial leverage
of the company. The firm’s leverage is a basic measure of its bankruptcy risk. Here,
higher leverage is associated with a higher bankruptcy risk (see Altman, 1986;
Shumway, 2001; Hillegeist, Keating, Cram and Lundstedt, 2004). Higher leverage is
also strongly associated with the survival chances of an IPO company. Demers and
Joos (2007) and Peller (2012) find a strong negative relationship between leverage
and the chance of survival in the aftermarket for IPO companies. Busaba et al. (2001)
also use financial leverage to explain withdrawal risk. However, the authors do not
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interpret leverage as a company risk factor but that a higher debt ratio indicates better
access to alternative sources of financing. Therefore, the authors expect that leverage
increases the risk of withdrawal, as these companies are more sensitive to the IPO
price because they have better access to alternative financing. Busaba et al. find that
leverage has a significant positive influence on withdrawal risk. In this study, I
interpret the company’s financial leverage as a risk factor. Studies on bankruptcy and
survival prediction show that firms with low leverage are more stable. Therefore,
these companies should be less sensitive to the industry outlook and current market
conditions. However, both interpretations suggest a positive relation between
financial leverage and the risk of withdrawal. As a consequence, the signal associated
with the withdrawal of a company with low leverage should be stronger.
H7: The filing company’s financial leverage has a positive influence on the
competitors’ reaction to a withdrawn IPO.
The time span between filing and completing the IPO depends on a variety of factors
and is not foreseeable at the time of the filing. According to the Thomson Financial
New Issues Database, between 1990 and 2010, the average time between filing and
issue date was 100 days. It can be assumed that investors associate a higher
withdrawal risk with filing companies that have passed a certain amount of time.
Therefore, IPO filings that are withdrawn after a certain time span come as less of a
surprise. A possible benchmark is the average time between filing and completion,
plus one standard deviation. For competitors, a withdrawn IPO after this period
would have a positive influence.
H8: The amount of time between filing and withdrawal has a positive influence on
the competitors’ reaction to a withdrawn IPO.
A further deal-related factor is the issuing firm’s underwriter. It is assumed that
underwriters with high reputation are able to select high-quality issues. Jain and Kini
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(1999) find evidence for the successful selection process of prestigious underwriters;
they find that underwriter reputation is positively related to the post-issue
performance of IPO companies. In addition, Demers and Joos (2007) find that the
likelihood of survival of a new company in the market is also positively influenced by
underwriter reputation. This would suggest that underwriter reputation decreases the
withdrawal risk. However, findings on the influence of the underwriter on withdrawal
risk are inconclusive. While Busaba et al. (2001) do not find a significant relation,
Dunbar and Foerster (2008) find that the underwriter does have a significant impact
on the withdrawal risk. I will test if there is a relation between the issuing firm’s
underwriter and the competitors’ response and assume that more prestigious
underwriters are associated with a stronger reaction.
H9: The prestige of the issuing firm’s underwriter has a negative influence on the
According to Jain and Kini (2000), another sign of a quality company is the
engagement of a private equity or venture capital company. In their study, they find
that venture-backed IPO companies have better chances of survival in the
aftermarket. Brav and Gompers (1997) find that venture-backed issues also
outperform non-venture-backed IPOs in the long-run.33 The presence of venture
capital also has an impact on the company’s withdrawal risk. Both Busaba et al.
(2001) and Dunbar and Foerster (2008) find that the presence of venture capital
decreases the company’s risk of withdrawal. With respect to the intra-industry effect,
I expect that both the level of surprise and, therefore, the effect are stronger when the
issuing firm has been backed by venture capital or by a private equity investor.
H10: The presence of a private equity or venture capital investor has a negative
influence on the competitors’ reaction to a withdrawn IPO.
33
However, the outperformance found in Brav and Gompers (1997) is only significant when returns are equally weighted.
For value weighted returns, the outperformance is not significant.
- 93 -
The third group of variables controls for the market environment. This is a very
important group, as the market situation provides the overall sentiment with which
the signal is interpreted by the market. The first market variable is the overall market
situation, represented by the performance of the S&P 500 index. The performance
after the official filing has a strong influence on whether the company can achieve an
acceptable IPO price during the bookbuilding phase. Dunbar and Foerster (2008) find
that the post-filing return of the NASDAQ Composite Index has a significant
negative impact on withdrawal risk. Busaba et al. (2001) show similar findings.
When the market sentiment is negative, it is not surprising that an IPO is withdrawn,
that is, the information spillover is very low. However, when the overall market
environment is positive, a withdrawn IPO sends a strong signal that indicates private
information regarding the industry outlook. Thus, the intra-industry effect should be
stronger when the post-filing performance has been more positive.
H11: The post-filing market performance has a negative influence on the
A further variable used to measure the overall market sentiment is market volatility.
Market volatility is represented by the VIX, which accounts for the systematic risk in
the market. Whaley (2000) finds that the VIX is a good measure of investors’ overall
level of fear. However, the study discovers an asymmetric behavior of the volatility
index. The stock market reacts more negatively to an increase in the VIX than it
reacts positively to a decrease in the VIX. Therefore, the VIX is more a measure of
investors’ fear than it is of their excitement. Market volatility also plays an important
role in the price finding of a new issue. Volatile markets make it more difficult for the
underwriter to set an acceptable IPO price. Therefore, it is less surprising when a
company withdraws its IPO in times of high volatility. In addition, high volatility
already indicates a negative outlook, which reduces the marginal information gain
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from a withdrawn IPO. Therefore, I expect that the information signal of a withdrawn
IPO is stronger in times of low volatility.
H12: The market volatility before the withdrawal has a positive influence on the
Along with variables for the overall market, the new issues market can also provide
valuable insight into how competitors react to a withdrawn IPO. As is true for most
corporate events, IPOs occur in waves (Rau and Stouraitis, 2011). Ritter (1991)
argues that, when going public, companies are able to take advantage of opportunity
windows of great optimism among investors, which is a possible explanation for the
wave phenomenon (Lowry, 2003). The impact of hot IPO markets on the intraindustry effect of a withdrawn IPO is attenuating. When an IPO is withdrawn while
other companies are able to successfully place their new shares in the market, the
withdrawal is unlikely to provide information regarding the industry outlook. Instead,
the withdrawal is rather considered to be due to firm-specific factors. Therefore, the
information signal of a withdrawn IPO is expected to be stronger in cold IPO
markets.
H13: IPO market hotness has a positive influence on the competitors’ reaction to a
withdrawn IPO.
Finally, the number of previously withdrawn IPOs is also expected to influence the
intra-industry effect. In a period of numerous withdrawn IPOs, the information gain
of a further withdrawn IPO is rather limited. It is also less surprising that an IPO is
withdrawn when the market has recently witnessed many withdrawals. Hence, it is
reasonable to expect that the information signal is weaker in times of numerous IPO
withdrawals.
H14: The number of previously withdrawn IPOs has a positive influence on the
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4.3 Data and Descriptive Statistics
The dataset comprises all withdrawn IPOs between 1996 and 2011 that have been
filed for the NYSE, AMEX or NASDAQ and has been obtained from the Thomson
Financial New Issues database. This yields an initial sample of 1,164 withdrawn
IPOs. The year 1996 is chosen because it is the first year of good coverage of filing
documentation in the SEC’s EDGAR database. The initial sample excludes
Depository Shares/Receipts, Unit Offerings, Spin-Offs, Financial Institutions, REITs
and closed-end funds. It also excludes filings from mining (NAICS 21) and
construction (NAICS 23) companies, as there is no data on industry concentration
available for these industries from the US Census Bureau. Further restrictions are
imposed on the sample. All events that have a confounding withdrawal event either
30 days before or 20 days after the event are exluded in order to increase the
robustness of the results.34 Furthermore, the initial IPO filing and the registration
withdrawal letter for the event have to be available in the SEC EDGAR database.
Finally, every withdrawing company in the sample is required to have at least five
listed competitors to get an adequate picture of the competitors’ reactions.
Competitors are identified using their six-digit NAICS industry code. This yields a
final sample of 428 withdrawn IPO filings from 107 different industries, with an
accumulated total of 21,045 competitors. Table 4.1 provides an overview of the
variables used to explain the intra-industry effect of a withdrawn IPO.
Accounting data on industry competitors are acquired from Compustat. Stock prices
of the competitors and market data are retrieved from the Center for Research in
Security Prices (CRSP). Information regarding the filing company is acquired from
the Thomson Financial New Issue Database and cross-checked against the filing in
the SEC EDGAR database. The underwriter reputation ranking is the modified
Carter-Manaster (1990) reputation ranking by Jay Ritter and is available on his
website.35 The binary variable for a private equity (PE) or venture capital (VC)
investor is set to one when, according to the S-1 filing, at least one PE or VC investor
with a stake of 5% or more exists before the offering. The binary variable time is set
34
35
A confounding event is defined as a withdrawn IPO in the same six-digit NAICS industry.
I wish to thank Jay Ritter for making his data publicly available. See http://bear.warrington.ufl.edu/ritter/ipodata.htm.
- 96 -
Variable
Industry Variables
concentration
idu
lev_ind
size_ind
Company/Deal
Variables
offer_size
size
lev
time
uw_rank
pe/vc
Market Variables
s&p
vix
hotness
num_withdrawn
Table 4.1
Description
Average cumulative abnormal return of the portfolio of
competitors in response to a withdrawn IPO
Cumulative market share of the 20 largest companies in a
six-digit NAICS industry
Industry demand uncertainty in a six-digit NAICS industry
Median leverage (total liabilities over total assets) of the
competitor portfolio
Median sales of the competitor portfolio
Expected offer size (shares filed × midpoint of price range)
Sales of the filing company before the offering
Financial leverage of the filing company before the offering.
Leverage is defined as total liabilities over the sum of total
assets and expected IPO proceeds
Binary variable set to one if the time between filing and
withdrawal is longer than 200 days, and 0 otherwise
Carter-Manaster (1990) underwriter reputation ranking
Binary variable set to one if the IPO is backed by venture
capital or private equity investor and zero otherwise
Average daily return of the S&P 500 index between the
filing date and the withdrawal date (beginning of the event
window)
One-month average level of the Chicago Board Options
Exchange Market Volatility Index before withdrawal
IPO market hotness: Average IPO underpricing in the three
months before withdrawal
Number of withdrawn IPOs in the two months before
withdrawal
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to one when the time between filing and withdrawal is more than 200 days, which is
the average time span between filing and issue date plus one standard deviation.36
Industry concentration data is acquired from the US Census Bureau, available from
the Economic Census.37 The Census data is available for the years 2007, 2002 and
1997. I match each IPO with the closest available Economic Census. An alternative
would have been to calculate the industry concentration for all firms available in
Compustat, which would yield yearly observations. The drawback of this alternative
is that Compustat only contains data on public firms. As a result, concentration ratios
constructed using only Compustat data do not consider the effect of private firms.
This effect is significant, as reported by Ali, Klasa and Yeung (2009). In their study,
they find a correlation of only 13% between the concentration ratio from Compustat
and from the US Census, concluding that concentration variables constructed from
Compustat data are poor proxies. In addition, the benefit of having yearly
observations is rather limited, as industry concentration does not vary significantly
from year to year. Therefore, I decided to use data from the Economic Census in
order to represent industry concentration more accurately.
Demand uncertainty is constructed as the standard deviation of quarterly industry
sales growth over a 20-quarter period using a rolling window. Here, I follow Chod
and Lyandres (2011) and calculate seasonally adjusted sales growth. As a first step, I
regress the industry sales growth on four dummy variables, set to one if the
observation is from the first, second, third, or fourth quarter respectively, and zero
otherwise. The industry sales growth is calculated as the sum of the sales of all
companies in the same six-digit NAICS industry, available from Compustat, that
have reported data available over the 20-quarter period. The residuals from these
regressions are referred to as seasonally adjusted quarterly sales growth. The industry
demand uncertainty is then calculated as the standard deviation of seasonally adjusted
sales growth. IPO filings are matched with demand uncertainty in their six-digit
NAICS industry from the quarter in which they request the withdrawal.
36
According to the Thomson Financial New Issues Database, the average time between filing and issue date is 100 days
with a standard deviation of 100 days.
37
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Table 4.2
Descriptive Statistics of the Sample of withdrawn IPOs
Variable
Mean
Median
Std. Dev.
Industry Variables
concentration
60.51%
61.00%
22.66%
idu
0.25
0.06
0.89
lev_ind
0.42
0.39
0.14
size_ind
1,059.44 M$
142.91 M$
5,648.46 M$
Company/Deal
Variables
offer_size
size
lev
time (binary)
uw_rank
pe/vc (binary)
Market Variables
s&p
vix
hotness
num_withdrawn
122.99 M$
314.92 M$
0.46
0.55
7.78
0.73
75.00 M$
25.51 M$
0.43
–
8.50
–
296.69 M$
1,762.63 M$
0.36
–
1.79
–
–0.0001
23.20
20.45%
17.09
0.0001
22.22
14.30%
14.00
0.001
8.60
21.61%
12.17
n = 428
Data for constructing the IPO market hotness variable is available on Jay Ritter’s
website, where the number of IPOs and the average initial return per month are
listed.38 The number of withdrawn IPOs is taken from the Thomson Financial New
Issues Database.
Table 4.2 presents the summary statistics for the sample variables. One comparable
variable between the sample of withdrawing companies and industry competitors is
the financial leverage. The descriptive statistics reveal that the withdrawing
companies have a higher leverage than their industry competitors. The difference is
statistically significant at the 5% level (t-statistic: 2.32). Busaba et al. (2001) show
similar results. In their study, they compare the debt ratio of successful IPO
companies and withdrawing companies and find that withdrawing companies exhibit
38
See http://bear.warrington.ufl.edu/ritter/ipoisr.htm
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a significantly higher debt ratio. Not reported in Table 4.2 is the average number of
competitors, which is 49.2 with a median of 18.5. The minimum number of
competitors, as required by the sample selection, is 5 and the maximum number is
255.
4.4 Methodology
In order to analyze whether a withdrawn IPO is just a firm-specific event or whether
it has an intra-industry effect, I use the standard event study methodology. This is an
appropriate approach for analyzing corporate events and their implications for
valuation (MacKinlay, 1997). Abnormal returns are analyzed for the surrounding
time of the official date of withdrawal according to the SEC filing. Here, I follow
Akhigbe et al. (2003) and Hsu et al. (2010) and form equally-weighted portfolios of
listed companies that share the same six-digit NAICS code as the event firm. This
approach controls for potential cross-sectional correlation of returns in the industry.39
Abnormal returns for a day are calculated as the difference between the realized
return of the competitor portfolio and its expected return and are expressed in the
following manner:
(I)
Expected returns for the competitor portfolio are estimated using a one-factor market
model.40 The parameters for the market model are estimated in the period [–250; –30]
relative to the event date. As a benchmark market index, I use the S&P 500 index.
The market model can be expressed in the following manner:
(II)
39
In order to check for the robustness of my result, I also test a value-weighted competitor portfolio.
MacKinlay (1997) states that “the gains from employing multifactor models for event studies are limited” (p.18).
Therefore, I follow prior literature (Akhigbe et al., 2003; Lee et al., 2011; Hsu et al., 2010) and use the one-factor market
model. The study by Cotei and Farhat (2011) uses a constant mean return model to estimate the expected returns for their
event study. In order to check the robustness of my result, I also perform the event study using the Fama-French-Carhart
four-factor model (Fama and French, 1993; Carhart, 1997).
40
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The abnormal returns are calculated for each day in the event period [–30; 20] and are
cumulated for each competitor portfolio:
(III)
Finally, the cumulative abnormal returns are aggregated across events by computing
the equally-weighted average of CARs of the competitor portfolios:
(IV)
The average cumulative abnormal returns of competitor portfolios are reported for
several event windows. In order to test the statistical significance of the CCARs, I
follow the methodolgy of Mikkelson and Partch (1988) and compute a z-statistic
involving prediction errors of the market model.
The purpose of this section is to provide empirical results to answer the question of
whether a withdrawn IPO is merely a firm-specific event or has industry-wide
implications. Therefore, as a first step, results regarding competitors’ valuation
responses to a withdrawn IPO in the same industry are presented in order to ascertain
whether there is a significant overall reaction to a withdrawal. As a second step, the
competitors’ reactions are analyzed using a cross-sectional model in order to
ascertain the influence of variables that account for the ex-ante risk of withdrawal. To
explain competitors’ reactions, I use industry, firm, deal and market variables. In the
third step, using the results from the cross-sectional model, I form subsamples and
test the competitors’ reactions.
4.5.1 Intra-industry Effect of a withdrawn IPO
Figure 4.1 depicts the development of the cumulative abnormal returns of the
competitor portfolio around a withdrawn IPO. As can be seen, the competitors’
reaction is negative, which indicates that competitors are concerned about the implied
information effect of a withdrawn IPO in the industry. Interestingly, the reaction
already begins a couple of days before the actual event date. Hsu et al. (2010) also
find that competitors’ reaction to a completed or a withdrawn IPO begins a couple of
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Figure 4.1
Competitors’ abnormal Return around withdrawn IPOs
This figure shows the abnormal returns of the competitor portfolio upon a withdrawn IPO within the event window. The sample comprises
all withdrawn IPOs at the NYSE, AMEX and NASDAQ between 1996 and 2011. These IPOs have no confounding withdrawal events in
the same six-digit NAICS industry in the surrounding 30-day period and the prices of at least 5 listed competitors in the same industry are
available. There are 428 withdrawn IPO events. The cumulated abnormal returns are shown on the y-axis and are calculated as the
difference between the actual return of the equally-weighted portfolio of competitors and the expected return, estimated from a one-factor
market model. The parameters for the market model are estimated in the period [–250; –30], relative to the event. The x-axis represents the
timeline, in days, around the event (day 0).
days before the official announcement date. The reason might be that IPO
withdrawals are often preceded by bad news, either firm-specific or market-related.
Furthermore, Figure 4.1 shows that, at approximately 14 days after the event date, the
cumulative abnormal returns begin to rise again, which is a sign of a price reversal.
This could indicate that the withdrawal is more of a sentiment shock than a release of
private information.
The results from Figure 4.1 provide the first evidence that competitors suffer a drop
in their share price after a withdrawn IPO in the industry. In order to formally test
these results, panel A in Table 4.3 provides the abnormal returns of the competitor
portfolio for different event windows and tests for statistical significance. With the
exception of the [–5; 5] window, all event windows exhibit significantly negative
abnormal returns, which supports the graphical analysis. For the [–20; 10] window,
the drop in share price is –0.64% and is highly significant. For the event windows
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Table 4.3
Abnormal Returns of the Competitor Portfolio on the Withdrawal Date of IPOs
This table presents the average competitor cumulative abnormal returns (
) in response to the withdrawal of an IPO for different event
windows. The sample comprises all withdrawal events at the NYSE, AMEX and NASDAQ between 1996 and 2011. The IPOs have no
confounding withdrawal event in the same six-digit NAICS industry in the surrounding 30-day period and the prices of at least 5 listed
competitors in the same industry are available. Abnormal returns are calculated as the difference between the actual return of the equallyweighted portfolio of competitors and the expected return, estimated from a one-factor market model. The parameters for the market model
are estimated in the period [–250; –30], relative to the event. In order to test for statistical significance, the z-statistic from Mikkelson and
Partch (1988) is used. Panel A contains the results for the full sample of 428 withdrawn IPOs. Panel B includes only those withdrawn IPO
events that have a confounding positive IPO event (i.e. filing or completion) in the industry 20 days before or after the withdrawal event.
Panel C contains only those withdrawn IPO events that have no confounding positive IPO event in the industry 20 days before or after the
withdrawal event. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels.
Panel A: Full Sample
Panel B: Confounding
positive IPO Events
z-statistic
Days
Panel C: No
confounding positive
IPO Events
z-statistic
z-statistic
[-30; 20]
–0.39%***
–2.74
–2.52%***
–3.38
0.51%
–1.01
[-20; 20]
–0.14%**
–2.27
–2.49%***
–3.67
0.48%
–0.23
[-20; 10]
–0.64%***
–3.19
–2.65%***
–4.65
0.31%
–0.67
[-20; 5]
–0.56%***
–3.11
–2.04%***
–3.77
0.13%
–1.19
[-10; 10]
–0.42%**
–2.19
–1.73%***
–3.70
0.19%
–0.12
[-10; 5]
–0.35%**
–2.00
–1.12%**
–2.49
0.02%
–0.72
[-10; 3]
–0.37%**
–2.31
–1.19%***
–3.16
0.07%
–0.64
[-5; 5]
–0.10%
–1.40
–0.39%
–1.41
0.03%
–0.73
[-5; 3]
–0.13%*
–1.79
–0.46%**
–2.21
0.02%
–0.65
[-5; 1]
–0.08%*
–1.74
–0.40%**
–2.45
0.07%
–0.43
[-3; 3]
–0.22%**
–2.51
–0.48%***
–2.84
–0.09%
–1.09
[-3; 5]
–0.19%*
–1.95
–0.41%*
–1.83
–0.09%
–1.11
[-1; 1]
–0.08%**
–1.99
–0.16%**
–2.44
–0.04%
–0.73
[-1; 3]
–0.13%**
–1.99
–0.22%**
–2.01
–0.08%
–1.03
n = 428
n = 137
n = 291
close to the actual event date, the returns are also significantly negative but the
strongest reaction clearly occurs in the days before the event date.
In unreported results, the competitors’ reaction is also tested using the Fama-FrenchCarhart four-factor model (Fama and French, 1993; Carhart, 1997) to estimate the
expected returns and also using a value-weighted competitor portfolio. Both
- 103 -
specifications provide the same result of a significantly negative competitors’
response to a withdrawn IPO in the industry.
The sample selection controls for IPO withdrawals that are confounding events.
However, other withdrawals are not the only events that can interfere with
competitors’ reactions. The opposite event, that is, an IPO filing or a successful
launch of an IPO, can also interfere. Previous studies (e.g. Hsu et al., 2010; Peller,
2011) find a strong, negative response from a competitor portfolio upon the filing or
completion of an IPO. It is possible that these events also interfere with competitors’
responses to a withdrawn IPO. Therefore, I examine each withdrawal event and check
whether it has a confounding positive IPO event (filing or completion) in the same
industry 20 days before or after the withdrawal event. This creates a subsample of
withdrawn IPO events without confounding positive IPO events (a clean subsample
with 291 withdrawn IPOs), and a subsample of withdrawn IPO events that include
confounding positive IPO events (a contaminated subsample with 137 withdrawn
IPOs). Figure 4.2 depicts the development of the cumulative abnormal returns for the
two subsamples graphically. The dotted line shows competitors’ reactions in the clean
sample, while the straight line indicates competitors’ responses in the contaminated
subsample. The graphic clearly shows that there is a strong, negative response in the
contaminated subsample, while there is barely any observable reaction in the clean
subsample. This indicates that the observed negative competitor reaction in the full
sample is driven by contaminated positive IPO events. Examining the cumulative
abnormal returns for different event windows provides the same conclusion. As panel
B in Table 4.3 shows, competitors’ reactions are significantly negative when there is
a confounding positive IPO event, while the competitors’ reaction is not statistically
significantly different from zero in the clean subsample (see panel C in Table 4.3).
Hsu et al. (2010) also examine intra-industry effects of IPO withdrawals. The authors
report a positive abnormal return of 1.35% in the [–10; 10] window. However, as
already stated, the different results can be due to the study design. Their sample
comprises only 37 withdrawal events, as their research design is intended to study the
intra-industry effect of only the largest completed or withdrawn IPOs.
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Figure 4.2
Competitors’ abnormal Return around withdrawn IPOs (controlling for
confounding positive IPO Events)
This figure depicts the abnormal returns of the competitor portfolio upon a withdrawn IPO for the event window. The sample is divided
into a subsample of withdrawn IPOs that have a confounding positive IPO event (filing or completion) in the same industry 20 days before
or after the withdrawal event (contaminated subsample) and a subsample of withdrawn IPOs that have no confounding positive IPO event
in the same industry 20 days before or after the withdrawal event (clean subsample). There are 137 contaminated withdrawn IPO events
and 291 clean withdrawn IPO events. Cumulated abnormal returns are shown on the y-axis and are calculated as the difference between the
actual return of the equally-weighted portfolio of competitors and the expected return, estimated from a one-factor market model. The
parameters for the market model are estimated in the period [–250; –30], relative to the event. The x-axis comprises the timeline in days
around the event (day 0).
My results imply that withdrawn IPOs, on average, have no observable effect on
competitors. As already outlined, this does not have to mean that a withdrawn IPO is
merely a firm-specific event. It is possible that two opposing effects occur upon a
withdrawal and that these effects offset each other (Akhigbe et al., 2003). A further
possible explanation is that not all withdrawn IPOs trigger an information effect. As
already outlined, I assume that a withdrawn IPO’s information effect depends on its
degree of surprise. Non-surprising withdrawals provide no new information.
Therefore, it is possible that the sample, which comprises both surprising and nonsurprising withdrawals, yields no significant reaction on average. In order to finally
answer whether a withdrawn IPO has an industry-wide impact, I perform a crosssectional analysis in the following section.
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4.5.2 Cross-sectional Results
In order to analyze competitors’ responses to a withdrawn IPO in their industry, I use
variables that account for the industry, firm and deal characteristics, as well as for the
market environment. Since I am interested in the intra-industry effect of a
withdrawal, I use the clean subsample of withdrawn IPOs, that is, the subsample
adjusted to only include IPO withdrawals with no confounding positive IPO event.
As a dependent variable, I choose the cumulative abnormal return over the [–10; 5]
window. As already mentioned, both my results and previous studies have shown that
a large portion of competitors’ responses occurs before the actual event date. To
cover this reaction, I choose a rather long event window. However, the results are
also robust to shorter event windows.
Table 4.4 presents the results from the cross-sectional analysis using the clean
subsample. Panel A shows the influence of industry-related variables. With the
exception of industry concentration (concentration), all variables lack statistical
significance. Industry concentration is significant and shows a negative influence on
competitors’ reactions, as suggested by H1. Higher industry concentration means
stronger adverse intra-industry effects of a withdrawn IPO. The result is also robust
when the entire model is applied, as can be seen in panel D of Table 4.4. These
results suggest that industry demand uncertainty (idu), competitors’ leverage
(lev_ind) and size (size_ind) have no influence on competitors’ reactions. Therefore,
hypotheses 2 to 4 are not accepted.
Panel B in Table 4.4 shows the impact of the firm and deal characteristics. As with
the industry variables, the explanatory power is rather low. Only the firm’s leverage
(lev) exhibits a statistically significant correlation with competitors’ responses. As
proposed by H7, the impact is positive. Here, higher financial leverage of the filing
company (lev) implies more positive competitors’ reactions to a withdrawal. This is
consistent with the assumption that more surprising withdrawals mean more adverse
intra-industry effects. In other words, when a firm with high leverage withdraws its
IPO, competitors rather interpret the withdrawal to be due to firm-specific factors
than to private information regarding the industry outlook. The variable lev is also
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Table 4.4
Cross-sectional Variation in Competitors’ Valuation Effects upon a withdrawn
IPO
This table shows the results of estimating a cross-sectional regression on the competitors’ cumulative abnormal return (
) upon a
withdrawn IPO in their industry for the event window [–10; 5]. The sample comprises all withdrawn IPOs at the NYSE, AMEX and
NASDAQ between 1996 and 2011. The IPOs have no confounding withdrawal event in the same six-digit NAICS industry in the
surrounding 30-day period and the prices of at least 5 listed competitors in the same industry are available. The sample has further been
adjusted to include only withdrawn IPOs that have no confounding positive IPO event (filing or completion) in the surrounding 20-day
window. Abnormal returns are calculated as the difference between the actual return of the equally-weighted portfolio of competitors and
the expected return, estimated from a one-factor market model. The parameters for the market model are estimated in the period
[-250; -30], relative to the event. The variable constant is the cumulated market share of the 20 largest companies in a six-digit NAICS
industry, idu is the industry demand uncertainty calculated as the std of sales growth, lev_ind is the median leverage of the competitor
portfolio, size_ind is the median sales of the competitor portfolio, offer_size is the expected proceeds from the IPO, size is the company’s
sales, lev is the company’s leverage, time is a binary variable set to one if the time between filing and withdrawal is longer than 200 days
and zero otherwise, uw_rank is the Carter-Manaster (1990) underwriter reputation ranking, pe/vc is a binary variable set to one if the IPO
received backing from a VC or PE investor and zero otherwise, s&p is the return of the S&P 500 index between the filing date and the
withdrawal date (beginning of the event window), vix is the one-month average level of the CBOE Market Volatility Index before the
withdrawal, hotness is the average IPO underpricing in the three months before the withdrawal and num_withdrawn is the number of
withdrawn IPOs in the two months before the withdrawal. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.
Variables
constant
concentration
idu
lev_ind
size_ind
Panel A
0.021
(1.09)
–0.0004**
(–2.15)
–0.007
(–1.57)
0.011
(0.36)
0.000
(0.50)
offer_size
Panel B
–0.008
(–0.35)
0.000
(0.09)
0.000
(0.27)
0.032**
(2.26)
0.010
(1.07)
–0.001
(–0.28)
–0.011
(–0.97)
size
lev
time
uw_rank
pe/vc
s&p
vix
hotness
num_withdrawn
Observations
R²/Adj. R²
Panel C
-0.056***
(-3.64)
291
2.64%/1.28%
291
2.71%/0.65%
–2.535
(–0.36)
0.002***
(3.87)
0.0006**
(2.42)
–0.0007**
(–1.99)
291
10.48%/9.23%
Panel D
-0.043
(-1.32)
–0.0004**
(–2.16)
–0.004
(–1.10)
–0.003
(–0.10)
0.000
(0.01)
–0.000
(–0.06)
0.000
(0.49)
0.024*
(1.75)
0.007
(0.78)
0.000
(0.03)
–0.007
(–0.64)
–3.416
(–0.47)
0.002***
(3.57)
0.0006**
(2.55)
–0.0005
(–1.40)
291
14.63%/10.29%
- 107 -
significant in the full model, as shown in panel D. The offer size (offer_size), the
filing company’s size (sales), the time of the withdrawal (time), underwriter
reputation (uw_rank), and private equity or venture backing (pe/vc) have no
significant impact. Therefore, hypotheses 5 and 6, and 8 to 10 are not accepted.
The influence of the market environment is presented in panel C of Table 4.4. As
expected, the market environment has a strong impact on competitors’ reactions.
With the exception of the overall market (s&p) all variables are statistically
significant. However, the number of withdrawn IPOs (num_withdrawn) becomes
insignificant when all variables are applied (see panel D). The market volatility (vix)
has a positive impact and is highly significant. As suggested by H12, this result
indicates that, during times of high uncertainty, the marginal gain of information from
a withdrawn IPO is rather small. H13 suggests that during a hot IPO market phase, a
withdrawn IPO is less considered a bad market signal, which is supported by the
results in Table 4.4. The impact of hotness is positive and statistically significant. The
results are unchanged when the full set of variables is applied in panel D. The
influence of the number of withdrawn IPOs (num_withdrawn) is negative, which
contradicts H14. It seems as if the information effect (and with that also the adverse
effect) from a withdrawn IPO increases with the number of previous IPO
withdrawals. A possible explanation is that the information signal becomes more
credible when more IPOs have already been withdrawn. However, the effect of
num_withdrawn becomes insignificant when applying the whole set of variables (see
Panel D). The return of the overall market between filing and withdrawal (s&p) has
the expected negative impact but lacks statistical significance. It seems as if the other
market sentiment variables already cover all the information from the overall market
performance.
The results from the cross-sectional analysis show that a large part of competitors’
reactions is due to the market environment. However, the cross-sectional analysis
does show that industry concentration and the filing firm’s financial leverage have a
significant impact even when controlling for the market environment. The results are
also unchanged when controlling for year-fixed effect. Thus, the industry
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environment and firm characteristics have a significant impact on how competitors’
investors interpret a withdrawn IPO in the industry. This is evidence that a withdrawn
IPO is not just a firm-specific event, but also has an intra-industry effect.
4.5.3 Results from the subsample Analysis
In order to gain further insight into how competitors respond to a withdrawn IPO in
the industry, I examine two particular factors, namely industry concentration and the
filing firm’s leverage. These two factors exhibit a statistically significant impact on
competitors’ responses. Therefore, I construct several subsamples, sorted by industry
concentration and financial leverage. The sample is divided into an above-median and
below-median subsample and tested for a statistically significant difference. Table 4.5
presents the results of the subsample analysis.
Examining the results of the sample sorted by industry concentration shows that, for
withdrawn IPOs in industries with a high concentration, competitors exhibit a
significantly negative response for different event windows. For the [–15; 1] window,
the abnormal return is even –1.21%. The group of withdrawn IPOs in industries with
a low concentration exhibits positive abnormal returns, although without statistical
significance. The test for difference is statistically significant, at least at the 10%
level, with the exception of the [–1; 3] window. These results provide further
confirmation for H1. As going public in high-concentration industries is associated
with more benefits, a withdrawal is considered to be less likely. The opposite case,
that is, a withdrawal in a low-concentration industry, does not exhibit a significant
impact on competitors.
The filing company’s leverage also has a significant impact on how competitors react
to a withdrawn IPO. As is evident from Table 4.5, the withdrawal of a highly
leveraged company does not trigger a significant competitor reaction. In contrast, the
withdrawal of a company with low leverage has a significantly adverse effect on the
competitor portfolio. The abnormal returns for below-median leverage withdrawals
all exhibit statistical significance, except in the [–10; 5] window. As is the case with
industry concentration, the drop begins a couple of days before the actual event, with
a value of –0.87% for the [–15; 1] window. The differences of the below- and above-
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Table 4.5
Abnormal Returns of the Competitor Portfolio sorted by Industry
Concentration and Leverage on the Withdrawal Date of IPOs
This table presents the average competitor cumulative abnormal returns (
) in response to the withdrawal of an IPO for different event
windows. The total sample comprises all withdrawal events at the NYSE, AMEX and NASDAQ between 1996 and 2011. The IPOs have
no confounding event in the same six-digit NAICS industry in the surrounding 30-day period and the prices of at least 5 listed competitors
in the same industry are available. The sample has further been adjusted to include only withdrawn IPOs that have no confounding positive
IPO event (filing or completion) in the surrounding 20-day window. Abnormal returns are calculated as the difference between the actual
return of the equally-weighted portfolio of competitors and the expected return, estimated from a one-factor market model. The parameters
for the market model are estimated in the period [–250; –30], relative to the event. In order to test for statistical significance of the
abnormal returns, the z-statistic from Mikkelson and Partch (1988) is used and presented in parentheses. The t-statistic for the test of
difference is presented in brackets. The sample is sorted according to industry concentration and the filing company’s financial leverage.
***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.
Industry concentration
Days
Above
median
–1.21%***
(–2.89)
Below
median
0.78%
(0.54)
Difference
[–10; 5]
–1.08%**
(–2.21)
[–5; 5]
Leverage
–1.99%**
[–2.14]
Above
median
0.39%
(–0.25)
Below
median
–0.87%**
(–2.13)
1.05%
(1.13)
–2.13%**
[–2.36]
0.57%
(0.19)
–0.65%
(–1.28)
1.21%
[1.33]
–0.63%*
(–1.92)
0.65%
(0.84)
–1.28%*
[–1.77]
0.67%
(0.61)
–0.73%*
(–1.74)
1.40%*
[1.90]
[–5; 3]
–0.53%*
(–1.79)
0.54%
(0.83)
–1.07%*
[–1.67]
0.70%
(0.98)
–0.78%**
(–2.03)
1.48%**
[2.31]
[–3; 3]
–0.64%**
(–2.39)
0.41%
(0.80)
–1.05%*
[–1.79]
0.51%
(0.95)
–0.82%***
(–2.65)
1.33%**
[2.28]
[–3; 5]
–0.74%**
(–2.42)
0.52%
(0.80)
–1.26%*
[–1.89]
0.48%
(0.53)
–0.76%**
(–2.21)
1.24%*
[1.84]
[–1; 3]
–0.35%
(–1.47)
n = 141
0.16%
(–0.01)
n = 150
–0.51%
[–1.05]
0.44%
(0.99)
n = 158
–0.71%***
(–2.60)
n = 133
1.15%**
[2.38]
[–15; 1]
Difference
1.26%
[1.35]
median sample are significant for the shorter windows around the event date. The
longer windows show no significant difference. As proposed in H7, competitors’
reactions to a withdrawn IPO are stronger for companies with low leverage. Investors
do not seem to derive industry-related information from a withdrawal by a highly
leveraged company, but do so in the case of companies with low leverage.
As shown above, the competitor portfolio experiences a statistically significant drop
in the share price in highly concentrated industries and for filing companies with low
leverage. For investors, it is also important to analyze the economic significance of
the competitor reaction. The average market capitalization of a competitor company
in the sample used in this study is $4.3 billion. Multiplying the average competitors’
- 110 -
cumulative abnormal return in the [–3; 3] window by the market capitalization yields
an average value loss per competitor of $27.8 million for withdrawn IPOs in highly
concentrated industries and an average value loss of $35.7 million for a withdrawn
IPO for a company with low leverage. On an event basis, the cumulated loss around a
withdrawn IPO for all competitors amounts to $1.36 billion in highly concentrated
industries and $1.75 billion for a company with low leverage. These figures are
economically significant and reveal the substantial impact of the intra-industry effect
of a withdrawn IPO.
These results reveal that not all withdrawn IPOs trigger an information effect but only
IPOs in highly concentrated industries or with a low financial leverage. Further, it is
important to notice that competitors do not show a positive reaction to a withdrawal
in the below-median sample (for industry concentration) and in the above-median
sample (for financial leverage), which would indicate a possible competitive effect,
but rather show no significant reaction. These results suggest that withdrawn IPOs
only exhibit a very weak competitive effect, if at all. The results are similar to those
of Ferris et al. (1997), who find that the competitive effect of a bankruptcy
announcement is weak at best. This is an interesting observation and shows that
investors have different interpretations of a positive IPO event (i.e. a filing or
completion) and a negative IPO event (i.e. a withdrawal). Previous studies report a
dominating competitive effect to a positive IPO event (Hsu et al., 2010; Peller, 2011).
The results of this study suggest that the information effect dominates for negative
IPO events. Competitors’ investors are more concerned about the potential threat of
the benefit to the firm going public and not about the information signal for the
industry in the case of a positive IPO event. In contrast, in the case of a negative IPO
event investors are more concerned about the implied information regarding the
industry outlook and not the removal of the threat caused by the issuing firm’s benefit
from going public.
To sum up, the results from Section 4.5 provide evidence that a withdrawn IPO can
exhibit an industry-wide effect. In this case, competitors experience a significant
negative stock price reaction around the time of the withdrawal. While there is no
- 111 -
observable competitor reaction upon a withdrawal, on average, the results from
Sections 4.5.2 and 4.5.3 show that industry concentration and the filing company’s
financial leverage, together with market sentiment, have a significant impact on how
the competitors’ stock value reacts to a withdrawn IPO.
4.6 Conclusion
In this study, I analyze competitors’ reactions to a withdrawn IPO within the same
industry in order to test if a withdrawn IPO is merely a firm-specific event or has an
industry-wide impact. I find that an equally-weighted competitor portfolio exhibits a
significant negative valuation response to a withdrawn IPO. However, further
analysis shows that this result is driven by confounding positive IPO events (i.e. a
filing or a completion). After controlling for these confounding positive IPO events,
the competitor portfolio does not show a significant reaction, on average. A possible
explanation for this observation is that not all withdrawn IPOs trigger an information
effect, which leads to an insignificant reaction on average. In order to further analyze
the competitors’ reaction I apply a cross-sectional analysis using variables that
account for industry, deal and company characteristics, as well as the market
environment. In the cross-sectional model, I find that competitors’ responses are
substantially driven by market sentiment. Furthermore, the analysis reveals that
industry concentration has a significantly negative impact on competitors’ responses,
which is in line with the hypothesis that IPO filings in industries with a high
concentration have a lower withdrawal risk and therefore a withdrawal comes as a
greater surprise. Further, the filing firm’s financial leverage is found to have a
significant impact on the competitor portfolio. The lower the leverage, the stronger is
the drop in valuation for competitors. This provides evidence for the hypothesis that
companies with low leverage have a lower withdrawal risk, which makes a
withdrawal more surprising. Finally, when I control for industry concentration and
financial leverage, I find that a subsample of withdrawals in industries with abovemedian concentration exhibits a significantly negative competitor reaction, while the
below-median subsample does not exhibit a significant reaction. For the subsample of
withdrawing companies with below-median leverage, the competitors’ response is
- 112 -
also significantly negative, while for the above-median subsample there is no
significant reaction. This suggests that only these withdrawn IPOs with a low ex-ante
withdrawal risk trigger an adverse information effect as they come as a greater
surprise. Other withdrawn IPOs show no significant impact on competitors.
These results suggest that the withdrawal of an IPO can have an industry-wide
impact. When an IPO is surprisingly withdrawn, competitors are concerned about the
implied private information on the industry outlook. My results further suggest that
investors have different interpretations of a positive and a negative IPO event. After a
positive IPO event, investors are more concerned about the filing company’s benefits
of going public and a possible competitive advantage (Hsu et al., 2010; Peller, 2011),
while after a negative IPO event, they are more concerned about what the withdrawal
implies regarding the industry outlook. Overall, the results of this study contribute to
and expand the literature on the intra-industry effect of an IPO. These results help
competitor companies and their investors in understanding the dynamics of a
withdrawn IPO in their industry and in the assessment of the opportunities and threats
around the withdrawal in the industry. An interesting question for further research is
whether the withdrawal of related capital market transactions, such as secondary
offerings or dual listings, has a similar intra-industry effect.
- 113 -
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Product Market Competition on the IPO’s Competitive Effect. Working Paper,
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Conclusion
- 117 -
5 Conclusion
This dissertation explores the interaction of Initial Public Offerings (IPOs) with the
competitive environment by focusing on two topics concerning a company’s IPO: the
survivability of the IPO in the aftermarket and the intra-industry effects of the IPO.
The first topic examines the influence of product market competition on an important
aspect of IPOs, namely survivability in the aftermarket. The second topic takes a
different perspective by assessing how an IPO influences the competitive
environment, to be more specific, the firm’s competitors.
The first study (Part 2) shows the relationship between the firm’s product market
competition and survival time in the aftermarket. While previous research on IPO
survival has already found that factors related to firm, deal and market characteristics
influence the survival time of a new company in the aftermarket, this is the first study
to look at the influence of the company’s product market competition. Based on a
sample of US IPOs, I use a log-logistic duration analysis model along with a Cox
proportional hazard model to test the influence of the firm’s product market
competition on the survival time in the aftermarket. I find that an IPO’s survival time
increases with industry concentration, decreases with the degree of competitive
interaction in the industry and decreases with the entry rate in the industry. I find no
evidence that the firm’s market share or industry demand uncertainty have a
statistically significant influence on the IPO’s survival time. The findings are robust
after controlling for firm-, deal- and market-related variables as well as different
underlying baseline hazard functions. The results are further confirmed using a logit
discrete response model. These results show that product market competition is a
further dimension to evaluate when explaining the survival chances of a newly listed
company. Variables relating to firm characteristics, deal characteristics or the
financial market form just one dimension of factors that affect firm survival. It is
rather the interaction of these variables with product market competition that decides
the firm’s success or failure. Investors should therefore take into account the
competitive environment of a company when assessing the risk structure of a firm
- 118 -
that is going public. Moreover, the results are also valuable for companies in their
decision to go public.
The second study (Part 3) looks at the intra-industry effects of completed IPOs by
focusing on the IPO’s competitive effect on rival firms and the interaction with
product market competition. I analyze the reactions of listed competitors in response
to a filing or completion of an IPO in the same industry in order to test whether the
IPO’s competitive effect on industry competitors is linked to the competitive
environment. I find that industry competitors yield negative abnormal returns in
response to a filing or completed IPO in their industry. This result indicates that the
competitive effect dominates the positive information externalities associated with
the event. By analyzing competitors’ abnormal returns, I find that the industry’s
product market competition influences the IPO’s competitive effect. Industry
concentration, the degree of strategic interaction and industry demand uncertainty are
all found to significantly influence competitors’ abnormal returns. The weaker the
competitive environment, denoted by a higher industry concentration, a lower degree
of strategic interaction and a lower industry demand uncertainty, the stronger is the
IPO’s competitive effect. For the industry’s entry rate, I only find minor evidence that
it influences the competitive effect. I further find that the subsample of IPOs in
industries that have the toughest competitive environment does not show a significant
response to the IPO, whereas the subsample of IPOs in industries that have the
weakest competitive environment shows a significant negative response to the IPO. A
possible conclusion is that the positive information effect and the competitive effect
offset each other in “tough” industries. The results present evidence that the degree of
the IPO’s competitive effect depends on the competitive environment. These results
are of particular interest for investors in industries that have a high likelihood of new
IPOs. The results also help firms that are going public as well as firms that are facing
an IPO in their industry to understand the implications of an IPO as a strategic move.
The third study (Part 4) continues from Part 3 by exploring how a withdrawn IPO
event affects competitors given that a successful IPO event such as the filing or
completion has an industry-wide impact. Theoretically, competitors should display
Conclusion
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the opposite reaction as to a positive IPO event. To provide insights, I analyze
competitors’ reactions to a withdrawn IPO within the same industry in order to test
whether a withdrawn IPO is merely a firm-specific event or has an industry-wide
impact. I find that the competitor portfolio exhibits a significant negative valuation
response to a withdrawn IPO. However, further analysis shows that this result is
driven by confounding positive IPO events (i.e., a filing or a completion). After
controlling for these confounding positive IPO events, the competitor portfolio no
longer shows a significant reaction on average. A possible explanation for this
observation is that not all withdrawn IPOs trigger an information effect, which leads
to a non-significant reaction on average. In order to analyze in depth the competitors’
reaction I apply a cross-sectional analysis using variables that account for industry,
deal and company characteristics as well as for the market environment. In this crosssectional model, I find that competitors’ responses are substantially driven by market
sentiment. Furthermore, the analysis reveals that industry concentration has a
significantly negative impact on competitors’ responses, which is in line with the
hypothesis that IPO filings in industries that have a high concentration have a lower
withdrawal risk and therefore that a withdrawal comes as a greater surprise. Further,
the withdrawing firm’s financial leverage is found to have a significant impact on the
competitor portfolio. The lower the leverage, the stronger is the drop in valuation for
competitors. This supports the hypothesis that companies that have low leverage have
a lower ex-ante withdrawal risk, which makes a withdrawal more surprising. Finally,
when I control for industry concentration and financial leverage, I find that a
subsample of withdrawals in industries that have above-median concentration
exhibits a significantly negative competitor reaction, while the below-median
subsample does not exhibit a significant reaction. For the subsample of withdrawing
companies that have below-median leverage, the competitors’ response is also
significantly negative, while for the above-median subsample there is no significant
reaction. These results suggest that both the filing and completion of an IPO as well
as the withdrawal of an IPO have an industry-wide impact. When an IPO is
surprisingly withdrawn, competitors are concerned about the implied private
information on the industry outlook. My results further suggest that investors
- 120 -
interpret positive and negative IPO events differently. After a positive IPO event,
investors are more concerned about the filing company’s benefits of going public and
the possible competitive advantage, while after a negative IPO event, they are more
concerned about what the withdrawal implies regarding the industry outlook.
To conclude, this dissertation supports the view that it is insufficient to interpret an
IPO as merely a firm-specific event and emphasizes the importance of considering
the interaction of the IPO with the competitive environment. Parts 3 and 4 provide
empirical evidence that an IPO, either the completion, filing or withdrawal, has a
significant impact on competitors. Going public can be a strategic move and the
company considering going public as well as companies facing an IPO in their
industry need to understand the implications of an IPO. This study demonstrates that
IPOs have an impact on the competitive environment either by affecting the
competitive balance in the industry (the competitive effect) or by signaling insights
about the industry outlook (the information effect). At the same time, the competitive
environment affects the IPO. Part 2 of this dissertation provides evidence that the
competitive environment affects the survival chances in the aftermarket of a firm
going public. For further research, it might be fruitful to consider the interaction with
the competitive environment when analyzing further topics concerning a company’s
IPO.
Curriculum Vitae
Date of Birth:
February 24, 1984 (Munich, Germany)
Citizenship:
German
Education
2010 - 2012
University of St.Gallen HSG
PhD Program in Management
2008 – 2010
Master in Quantitative Economics and Finance (M.A. HSG)
2009
University of Michigan Ross School of Business (MI, USA)
Exchange Semester
2005 – 2008
Bachelor in Economics (B.A. HSG)
2007
Babson College (MA, USA)
Exchange Semester
2004 – 2005
University of Zurich
1994 – 2003
Gymnasium Ottobrunn
Abitur
Professional Experience
since 2012
zeb/rolfes.schierenbeck.associates
Munich
2009
Deutsche Bank AG
Frankfurt a.M.
2008
Deutsche Bank AG
Frankfurt a.M.
2005
EADS Astrium
Ottobrunn
2003-2004
Nursing Home “KWA Hanns-Seidel Haus”
Ottobrunn

Survivability and Intra-Industry Effects of Initial Public Offerings – An

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