UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN
BEDRIJFSKUNDE
ACADEMIEJAAR 2015 – 2016
INITIAL PUBLIC OFFERING:
PERFORMANCE BEFORE AND AFTER THE
EUROPEAN FINANCIAL CRISIS
Masterproef voorgedragen tot het bekomen van de graad van
Master of Science in de
Toegepaste Economische Wetenschappen: Handelsingenieur
Thibault Degrande
onder leiding van
Prof. dr. D. Heyman
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN
BEDRIJFSKUNDE
ACADEMIEJAAR 2015 – 2016
INITIAL PUBLIC OFFERING:
PERFORMANCE BEFORE AND AFTER THE
EUROPEAN FINANCIAL CRISIS
Masterproef voorgedragen tot het bekomen van de graad van
Master of Science in de
Toegepaste Economische Wetenschappen: Handelsingenieur
Thibault Degrande
onder leiding van
Prof. dr. D. Heyman
i
PERMISSION
I declare that the content of this Master’s Dissertation can be consulted and/or reproduced
if the sources are mentioned.
Degrande Thibault
Abstract
Since some market observers predict that the world is on the brink of a new global financial crisis, this dissertation engaged in reviewing the impact of the recent financial crisis in
today’s light. In particular, the stock price performance of newly listed firms were examined in both the short- and the long-term, to determine whether or not the performances
of European Initial Public Offering (IPO) stocks were affected by the financial crisis. In
order to do so, 849 stocks going public between 2002 and 2012 on major European stock
exchanges have been evaluated. Evidence has been found for the existence of hot and cold
issue markets in the research timeframe. This study finds an average market-adjusted level
of underpricing to be at 5.03%, but there is little to no evidence of the level of underpricing
being directly affected by the crisis. Research of long-term performance has been conducted
by analysing 36-month buy-and-hold abnormal returns (BHARs), using the Fama-French
model as benchmark. This study finds substantially different BHARs when applying different weighting schemes; these findings are in line with the literature on the subject.
In this research, weighting each IPO stock equally is preferred, resulting in an average abnormal return of a passive buy-and-hold strategy that amounts to -18.53% for the 3-year
period, or -6.60%, on a yearly basis. This research finds that firms going public in the hot
market prior to the crisis perform significantly less well than IPOs during the colder market before and after that hot issue market. Going public during the crisis did not result in
outperforming the Fama-French benchmark (-9.36%) after three years, probably because
of the on-going financial turmoil in Western European financial markets. This thesis thus
finds that IPO investors with a short time horizon were not directly affected by the financial crisis, whereas IPO investors with a longer time horizon and passive strategy saw their
IPO stock portfolio collapse during the financial meltdown. The fact that results differ depending on the weighting scheme, as well as the fact that the analysis was performed using
only the Fama-French benchmark, deserve further attention.
Keywords: Initial Public Offering, Underpricing, Underperformance, Buy-and-Hold Abnormal Return, Hot issue markets, Fama-French, financial crisis
Samenvatting
Deze masterproef probeert de impact van de financiële crisis op de stockprijs performantie
van beursgaande bedrijven te evalueren en kwantificeren. Daarbij wordt in het bijzonder
gelet op het al dan niet overleven van twee welbekende en uitvoerig gedocumenteerde abnormaliteiten in de prijszetting van beursintroducties: het feit dat het initiële aanbod van
aandelen aan een te lage prijs gebeurt, getuige substantiële rendementen op de eerste dag
-onderprijzing van de stocks- en het slechter presteren van dergelijke stocks op de lange termijn in vergelijking met een ’verwachte’ prestatie -onderprestatie. Ten einde dit onderzoek
te kunnen voeren werden de aandelen van 849 bedrijven die een beursintroductie deden op
grote Europese aandelenmarkten tussen 2002 en 2012 geanalyseerd.
Het onderzoek toont het bestaan van ’hot’ en ’cold’ uitgifte periodes aan, een verschijnsel
dat eveneens gedocumenteerd staat als een bekende abnormaliteit in de Initial Public Offering (IPO) literatuur. The beginjaren 2000 staan bekend als een zeer ’koude’ uitgifte markt,
opgevolgde door een ’warmere’ uitgifte market tussen 2005-2007. Vanaf de financiële crisis
in 2008 start een nieuwe koudere periode, die aanhoudt tot het einde van het onderzochte
tijdsbestek.
Analyse van de initiële rendementen werd gevoerd door zowel de rauwe als marktgecorrigeerde rendementen te onderzoeken. Het verschil tussen deze rendementen bleek niet
significant. Een gemiddeld initiëel rendement van 5.08% en 5.03%, respectievelijk, werd
gevonden over de elf onderzocht jaren. Onderprijzing werd, op een uitzondering na, in alle
aandelenmarkten waargenomen, consistent met de literatuur. Het fenomeen is echter niet
significant in de ’koude’ uitgifte markt van 2002-2004. Daarna ondergaat het gemiddelde
initiële rendement een significante stijging in de daaropvolgende ’warme’ uitgifte markt,
voorafgaand aan de crisis. De daaropvolgende daling van het gemiddelde initiële rendement
gedurende de crisis en het herstel ervan na de crisis blijken geen significante wijzigingen
voor te stellen. Er kan dus besloten worden dat er weinig tot geen bewijs wordt gevonden
iii
iv
voor een impact van de crisis op de korte-term prestatie van IPO-aandelen.
Zoals gedocumenteerd in de literatuur, is het bestaan van onderprestatie een betwistbaar
gegeven -die door sommige auteurs wordt afgedaan als een methodologische kwestie- omdat
de resultaten erg afhankelijk zijn van de gebruikte methodologie en gekozen benchmark.
Analyse van de prestatie op lange-termijn werd in deze masterproef gevoerd door het onderzoeken van de abnormale rendementen op drie jaar, wanneer een passieve ’buy-and-hold’
strategie wordt toegepast, met het drie-factor model van Fama-French als normale return.
Consistent met literatuur zijn de resultaten erg afhankelijk van het gekozen weegschema.
Dit onderzoek opteert, op basis van vorige onderzoeken, voor het gelijk wegen van elke
beursintroductie. Er wordt een gemiddelde abnormaal rendement van -18.53% op drie jaar,
of -6.60% op jaarlijkse basis gevonden. De gevonden resultaten zijn significant en dus consistent met het fenomeen van onderprestatie.
Voor bedrijven die een beursintroductie doen tijdens de ’hot’ uitgifte markt voorafgaand
aan de crisis presteren significant slechter dan ’koudere’ markten die deze ’hot’ market omsluiten, wat bewijs is voor de ’windows of opportunity’ hypothese. Bedrijven die naar de
beurs gaan tijdens de crisis presteren in vergelijking met de benchmark dus beter dan bedrijven die voor de crisis een eerste uitgifte doen, maar niet beter dan na de crisis. Dit kan te
wijten zijn aan het feit dat financiële onrust na de crisis niet meteen helemaal verdween. De
impact van de crisis op de lange termijn performantie is dus vooral te merken voor bedrijven die in jaren voorafgaand aan de crisis naar de beurs stappen, waardoor de crisis in hun
holding periode zit.
Acknowledgements
This dissertation is the capstone in graduating as a Master of Science in Applied Economics
– Business Engineering. In this preface, I would like to take the opportunity to attribute
some words of gratitude to a number of people in particular.
First of all, I would like to offer my special thanks to Prof. dr. Dries Heyman, my research
supervisor, for his willingness to guide me on this topic. His advice was useful and essential
for the successful completion of this research.
I wish to thank Professor Jay Ritter, University of Florida, United States, for providing
data of initial returns.
I would also like to express my gratitude to David Poissonnier for providing me with valuable insights and constructive advice regarding Visual Basic.
Furthermore, I owe special thanks to my parents, friends and girlfriend. Their confidence
and supportive guidance were essential in the accomplishment of this masters’ dissertation
and my education as a whole.
v
Contents
Samenvatting
v
Acknowledgements
v
List of Acronyms
xii
List of Figures
xiii
List of Tables
xviii
Introduction
I
1
Research Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Outline of the Dissertation
4
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Literature Review
5
1 Key Concepts
1.1
7
IPOs: theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
1.1.1
Why companies go public . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.1.2
IPO activity
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.1.3
European IPO framework . . . . . . . . . . . . . . . . . . . . . . . . .
12
vi
Contents
vii
1.2
Financial Crisis: Impact for Europe
. . . . . . . . . . . . . . . . . . . . . . .
14
1.3
Investor Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
2 Previous literature on IPO performance
2.1
Short-term performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.1.1
Empirical evidence for short-term performance . . . . . . . . . . . . .
22
2.1.2
Theories explaining Short-Term Performance . . . . . . . . . . . . . .
23
2.2
Hot and cold issue markets . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
2.3
Long-term performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
32
2.3.1
Empirical evidence on Long-Term Performance . . . . . . . . . . . . .
33
2.3.2
Theories explaining long-term Performance . . . . . . . . . . . . . . .
33
Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
2.4
II
21
Methodology and Data
3 Methodology to evaluate IPO performance
39
41
3.1
Short-term performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
3.2
Long-term performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
4 Data Sample
51
4.1
Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
4.2
Descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
viii
III
Contents
Analysis and Discussion
5 Short-term performance
59
5.1
Raw and Market-Adjusted Initial Returns . . . . . . . . . . . . . . . . . . . .
61
5.2
Comparison with previous evidence on short-term performance . . . . . . . .
64
5.3
Discussion on underpricing before and after the Financial Crisis . . . . . . . .
69
6 Long-term performance
6.1
75
Buy-and-Hold Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
6.1.1
BHRs year on year: Discussion . . . . . . . . . . . . . . . . . . . . . .
79
Buy-and-Hold Abnormal Returns . . . . . . . . . . . . . . . . . . . . . . . . .
87
6.2.1
Equally weighted Scheme . . . . . . . . . . . . . . . . . . . . . . . . .
87
6.2.2
Value-weighted scheme . . . . . . . . . . . . . . . . . . . . . . . . . . .
95
6.3
Comparison with previous evidence on long-term performance . . . . . . . . .
103
6.4
Discussion on underperformance before and after the Financial Crisis . . . . .
105
6.2
IV
57
Summary and Conclusions
7 Summary of findings
108
110
7.1
Overview of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
110
7.2
Short-Term Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
110
7.3
Long-term performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
112
7.3.1
112
Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contents
7.3.2
ix
Discussion of findings . . . . . . . . . . . . . . . . . . . . . . . . . . .
8 Conclusions
114
117
8.1
Conclusions and remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
117
8.2
Directions for further research . . . . . . . . . . . . . . . . . . . . . . . . . . .
119
Bibliography
120
Appendices
133
A Financial Crisis
134
A.1 Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
134
A.2 Impact and consequences for America . . . . . . . . . . . . . . . . . . . . . .
140
B Sample: Zephyr search
145
C Stock Exchanges
146
D Short-run Performance
148
E Hot Markets: Scatter
152
F Yearly Buy-and-Hold Return
153
G Average Buy-and-hold Abnormal Returns by IPO year
157
H BHR: Means per SE and per Year
160
x
I
Contents
Outlier labelling
165
J Bootstrapped BHAR Statistics per Year
166
K Bootstrapped BHAR Statistics per Stock Exchange
170
L BHAR: Value-weighted scheme
174
M T-tests for BHAR Periods
176
xi
xii
Contents
List of Acronyms
ABS
Asset-Backed Securties
AIM
Alternative Investment Market
BHR
Buy-and-Hold Return
BHAR
Buy-and-Hold Abnormal Return
CAR
Cumulative Abnormal Return
CDO
Collateralised Debt Obligation
df
Degrees of Freedom
ECB
European Central Bank
EMU
European Monetary Union
EU
European Union
EW
Equally weighted
FF
Fama-French
FTSE
Financial Times Stock Exchange
IMF
International Monetary Fund
IPO
Initial Public Offering
LSE
London Stock Exchange
MAR
Market-adjusted Return
RQ
Research Question
RR
Raw Returns
SE
Stock Exchange
SME
Small- and Medium-sized Enterprise
Sig.
Significance level
Std. Dev
Standard Deviation
t
t-test statistic
VW
Value-weighted
WR
Wealth-Relative
List of Figures
1
Outline of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.1
Financing cycle from startup . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
3.1
Overview Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
3.2
Event-time Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
4.1
Sample Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
4.2
Overview Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
5.1
First-day returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
5.2
Volumes for France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
5.3
Initial Returns in France . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
5.4
Volumes for Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
5.5
Initial Returns in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
5.6
Volumes for UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
5.7
Initial Returns in UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
5.8
Level of Underpricing from 2002 to 2012 . . . . . . . . . . . . . . . . . . . . .
69
5.9
Level of Underpricing from 2002 to 2012: Periods . . . . . . . . . . . . . . . .
69
xiii
xiv
List of Figures
6.1
Average 36-month BHRs for 2002-2012 . . . . . . . . . . . . . . . . . . . . . .
78
6.2
Average 36-month BHRs for 2002-2012 . . . . . . . . . . . . . . . . . . . . . .
80
6.3
BHRs from 2002 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
6.4
BHRs from 2003 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
6.5
BHRs from 2004 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
6.6
BHRs from 2005 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
6.7
BHRs from 2006 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
6.8
BHRs from 2007 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
6.9
BHRs from 2008 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
6.10 BHRs from 2009 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
6.11 BHRs from 2010 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
6.12 BHRs from 2011 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
6.13 BHRs from 2012 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
6.14 BHR distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
6.15 BHAR distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
6.16 Average 36-month BHAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
6.17 BH(A)Rs with equally weighted years . . . . . . . . . . . . . . . . . . . . . .
89
6.18 BH(A)Rs from 2002 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
6.19 BH(A)Rs from 2003 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
6.20 BH(A)Rs from 2004 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
List of Figures
xv
6.21 BH(A)Rs from 2005 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
6.22 BH(A)Rs from 2006 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
6.23 BH(A)Rs from 2007 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
6.24 BH(A)Rs from 2008 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
6.25 BH(A)Rs from 2009 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
6.26 BH(A)Rs from 2010 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
6.27 BH(A)Rs from 2011 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
6.28 BH(A)Rs from 2012 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
6.29 Equally weighted scheme: BHR & BHAR Summary . . . . . . . . . . . . . .
95
6.30 Average 36-month BHARs for 2002-2012: Value-weighted . . . . . . . . . . .
97
6.31 VW BH(A)Rs with equally weighted years . . . . . . . . . . . . . . . . . . . .
97
6.32 BH(A)Rs from 2002 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
6.33 BH(A)Rs from 2003 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
6.34 BH(A)Rs from 2004 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
6.35 BH(A)Rs from 2005 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
6.36 BH(A)Rs from 2006 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
6.37 BH(A)Rs from 2007 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
6.38 BH(A)Rs from 2008 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
6.39 BH(A)Rs from 2009 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
6.40 BH(A)Rs from 2010 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
xvi
List of Figures
6.41 BH(A)Rs from 2011 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
6.42 BH(A)Rs from 2012 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
6.43 Value-weighted scheme: BHR & BHAR Summary
. . . . . . . . . . . . . . .
102
6.44 Volumes: a comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
103
6.45 BHR: a comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
103
6.46 LT Performance 2002-2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
106
6.47 LT Performance: Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
106
A.1 Expansion of the subprime lending . . . . . . . . . . . . . . . . . . . . . . . .
136
B.1 Sample refinement Zephyr . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
145
E.1 Hot Markets: scatter plot (R-squared: 0.185) . . . . . . . . . . . . . . . . . .
152
G.1 BHARs from 2002 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
G.2 BHARs from 2003 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
G.3 BHARs from 2004 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
G.4 BHARs from 2005 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
G.5 BHARs from 2006 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
G.6 BHARs from 2007 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158
G.7 BHARs from 2008 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
G.8 BHARs from 2009 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
G.9 BHARs from 2010 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
List of Figures
xvii
G.10 BHARs from 2011 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
G.11 BHARs from 2012 IPOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
List of Tables
2.1
Evidence from Europe: Short-run Performance . . . . . . . . . . . . . . . . .
23
2.2
Evidence from Europe: Long-term Performance . . . . . . . . . . . . . . . . .
34
4.1
Procentual distribution of the IPOs . . . . . . . . . . . . . . . . . . . . . . . .
53
4.2
Decomposition of Eurnonext
. . . . . . . . . . . . . . . . . . . . . . . . . . .
54
4.3
Average Deal Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
4.4
Deal Value per Stock Exchange . . . . . . . . . . . . . . . . . . . . . . . . . .
55
5.1
Short-term performance: statistic descriptives . . . . . . . . . . . . . . . . . .
61
5.2
Statistical Significance Short-term performance . . . . . . . . . . . . . . . . .
61
5.3
Short-term performance: statistic descriptives per year
. . . . . . . . . . . .
62
5.4
Statistical Significance Short-term performance per year . . . . . . . . . . . .
63
5.5
Statistical Significance Short-term performance per Stock Exchange . . . . .
64
5.6
Average level of underpricing (MAR) per Stock Exchange per Year . . . . . .
66
5.7
Defining the period variable . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
5.8
Initial Returns in (non) crisis years . . . . . . . . . . . . . . . . . . . . . . . .
71
5.9
Independent t-test for Pre-Crisis and Post-Crisis . . . . . . . . . . . . . . . .
73
6.1
Statistic descriptives BHR . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
76
xviii
List of Tables
xix
6.2
Market Values: descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
6.3
BHR per Year: Descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
6.4
BHR per Stock Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
84
6.5
BHR per Country
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
6.6
Percentage decomposition of positive Buy-and-Hold Returns . . . . . . . . . .
86
6.7
Descriptives BHAR of trimmed sample . . . . . . . . . . . . . . . . . . . . . .
88
6.8
Bootstrapped t-test for Monthly Average BHARs . . . . . . . . . . . . . . . .
90
6.9
BHAR per Year: Bootstrapped Statistics . . . . . . . . . . . . . . . . . . . .
94
6.10 BHAR per Stock Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94
6.11 BHARs in equally-weighted and value weighted scheme . . . . . . . . . . . .
100
6.12 Comparison with FTSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
102
6.13 Evidence from Gajewski and Gresse (2006): 1995-2004 . . . . . . . . . . . . .
104
6.14 Defining the period variable . . . . . . . . . . . . . . . . . . . . . . . . . . . .
105
6.15 Buy-and-Hold (Abnormal) Returns: Summary . . . . . . . . . . . . . . . . .
106
D.1 Independent t-test for Pre-Crisis and Crisis . . . . . . . . . . . . . . . . . . .
148
D.2 Independent t-test for Crisis and Post-Crisis . . . . . . . . . . . . . . . . . . .
149
D.3 Independent t-test for Pre(0) and Crisis . . . . . . . . . . . . . . . . . . . . .
150
D.4 Independent t-test for Pre(0) and Pre(1) . . . . . . . . . . . . . . . . . . . . .
151
F.1 BHR Year by Year: Descriptive Statistics . . . . . . . . . . . . . . . . . . . .
153
xx
List of Tables
H.1 BHR: Comparison of the means . . . . . . . . . . . . . . . . . . . . . . . . . .
160
H.2 BHR Means per SE per Year: London . . . . . . . . . . . . . . . . . . . . . .
163
I.1
Outlier Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
165
J.1
BHAR per year: Bootstrapped Statistics
. . . . . . . . . . . . . . . . . . . .
166
K.1 BHAR per Stock Exchange: Bootstrapped Statistics . . . . . . . . . . . . . .
170
L.1 Value-weighted scheme: Overview Market Values per Year . . . . . . . . . . .
175
M.1 Independent t-test for Pre-Crisis(0) and Pre-Crisis(1) LT
. . . . . . . . . . .
177
M.2 Independent t-test for Pre-Crisis(1) and Crisis (2): LT . . . . . . . . . . . . .
178
M.3 Independent t-test for Crisis(2) and Post-Crisis(3) . . . . . . . . . . . . . . .
179
Introduction
”In 2015 – the most successful year since 2007 – the European Initial Public Offering (IPO)
market rounded out the year with total annual proceeds increasing by 16% year on year.
The year was dominated by 14 mega IPOs raising more than e1bn, the highest number
since 2007 when 18 issuers raised over e1bn” (Hughes, 2016, p. 4).
PwC’s IPO Watch Europe 2015’s opening paragraph lets the reader to believe that the European IPO market is heading to the pre-financial crisis levels again. Have European IPO
markets definitively recovered from the recent financial crisis?
Ironically, 2015 might further have in common with 2007 that it possibly stands on the
brink of a new global financial crisis, as some market observers predict a global financial
crisis in 2016–’17 — one just as serious, if not more so, than that of 2008–’09 (Patrick Atrus,
2016). Moreover, the International Monetary Fund’s Global Financial Stability report in
2015 stated that ’sustainable recovery has failed to materialised and cheap money has led
to bubbles and debt’ and concluded that ’the next financial crisis is coming, it’s a just a
matter of time’ (The Guardian, 2015).
In the face of a possible new global financial crisis, this dissertation tries to estimate the
impact on the volumes and performance of the IPO market when such event occurs. By
mapping the consequences of the recent financial crisis on the IPO market in Europe, this
master’s thesis aims to provide guidance for investors who wish to inquire about the likely
performance of investment opportunities in the face of another global financial crisis. Therefore, this study evaluates the performance of 849 stocks going public between 2002 and
2012 on the biggest European stock exchanges. Since short-run underpricing and long-run
underperformance are two well-known and widely documented anomalies in the literature
on Initial Public Offerings, this study attempts to assess the impact of the financial crisis
by examining both of these issues in the light of the financial crisis, as the identified time-
1
2
Introduction
frame (2002–2012) captures data before and after the crisis. Restricting the timeframe to
2012 enables the researcher to evaluate the long-term performance of all IPOs in our sample. The issues account for a total amount of e76,503,745,380.00 raised.
This research contributes to previous literature by year-on-year analysis of the performance
of firms that went public before, during, and after the crisis. Furthermore, an analysis based
on ‘hot’ and ‘cold’ issue markets is conducted, making it possible to compare and study the
effects of the crisis on both the short- and long-run performance of stock issues in Europe.
This master’s thesis adds to the global body of literature by using the most recent available
data, thus updating former research findings.
Research Statement
The main aim of this research is to investigate the short-run and long-run market performance of IPOs in Europe throughout the financial crisis: are the short-run and long-run
market performances of European IPO stocks affected by the financial crisis?
The next chapter serves to sketch some key concepts and, in particular, underline the impact of the financial crisis on investor sentiment and, in turn, the impact of investor sentiment on the stock markets. This dissertation therefore hopes to examine and quantify a
potential evolution in IPO stock performance throughout the crisis.
Secondly, the extensive body of IPO literature largely covers the U.S. markets. This thesis
therefore hopes to contribute to the literature by discussing the main stock exchanges of
Europe in light of the financial crisis.
To achieve the aforementioned aim of this study, this thesis seeks to answer the following
questions:
Outline of the Dissertation
Research Question 1
3
Are IPOs in the examined European markets underpriced
in the short term ( 2002-2012)?
Research Question 2
Do levels of underpricing change over time (where the
timeframe comprises the crisis)?
Research Question 3
Do IPOs in the examined European markets underperform
in the long term ( 2002-2012)?
Research Question 4
Does the long-run performance changes over time (where
the timeframe comprises the crisis)?
Outline of the Dissertation
The remainder of this thesis is organized as given in Figure 1: Part I describes some key
concepts and previous literature on both short- and long-term IPO performance. Part II
defines the data sample and methodology that will be used in this research and Part III
presents the analysis and discussion of respectively the short- and long term performance of
the stocks, based on Part II . Part IV concludes.
Literature
Methodology
Analysis and
Summary and
Review
and Data
Discussion
Conclusions
Introduction
Figure 1: Outline of this Dissertation
Part I
Literature Review
5
Chapter 1
Key Concepts
This chapter provides the reader with some valuable insights on the matter, prior to the
research. The chapter begins with a short theoretical background of IPOs and continues
with a discussion of the financial crisis and its connections with investor trust.
1.1
IPOs: theoretical background
Two broad categories of companies can be distinguished: privately-held and public companies. Although large firms can also be private (e.g. IKEA), both types of firm are frequently said to be at different stages of the life cycle of firms, as shown in Figure 1.1. 1 .
The IPO, then, represents the first significant stage in the evolution of a public company.
When high growth mid-sized companies transition into established global players, public
markets are vital (European Commission, 2015). In the post-issue phase, the IPO firm can
evolve into one of three basic states: (1) survive as an independent firm, (2) get acquired
and lose its current identity, or (3) fail outright (Jain and Kini, 1999).
An entrepreneurial concept or idea is initially funded with private equity capital or debt. In
later stages in the development of a company, it will try to raise money for expanding its
business into the broadest set of funding providers’ sources, such as retained earnings and,
eventually, by issuing equity to the public (Jain and Kini, 1999). For mid-size and large
companies willing to raise equity in excess of e50 million, public offers of debt or equity
1
By Kmuehmel [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0) or GFDL
(http://www.gnu.org/copyleft/fdl.html)], via Wikimedia Commons
7
8
Chapter 1. Key Concepts
Figure 1.1: Financing cycle from startup
instruments are the principal modus of funding (European Commission, 2015). The process
of the first sale of the equity shares of a private company to the public is its ‘Initial Public
Offering, or IPO. The company either issues new shares or sells existing ones, where the
proceeds of selling new shares help to raise capital for the company, and the sale of existing
shares are accrued to the original investors (Jenkinson and Ljungqvist, 2001a).
Initial public offering became one of the most popular ways for companies across all industries and company sizes to acquire additional equity. It is therefore also one of the most
popular topics among researchers. From the beginning of the last century onwards, the interest in the topic resulted in many publication on this field of finance, with contributions
from researchers all over the world and on many different areas of IPO. The most interesting and valuable findings were documented concerning IPO performance, see Chapter 2.
The existence of two anomalies, being underpricing and long-term underperformance, have
driven the scale of the academic research in large part.
IPO has become one of the most popular ways for companies across all industries and company sizes to acquire additional equity. It is, therefore, also one of the most popular topics
for researchers. From the beginning of the last century, interest in the topic has resulted in
many publications in this particular field of finance, with contributions from all over the
Chapter 1. Key Concepts
9
world and on many different areas of IPO. For the most interesting and valuable documented findings concerning IPO performance, see chapter 2. The existence of two anomalies – underpricing and long-term underperformance – has fueled academic research on
IPOs.
1.1.1
Why companies go public
Bancel and Mittoo (2009)identified that acquiring funds for business growth is indeed the
most important benefit to a company transforming into an IPO. In addition to the desire
to raise equity capital for the firm, Ritter and Welch (2002) stated that IPOs also generate a public market where the business’ founders and its other shareholders can convert
some of their wealth into cash at a future date by making it possible to sell their share of
the company to the public. It thus also provides, in addition to financing, an ‘exit route’
for the company’s original entrepreneurs and investors (Jenkinson and Ljungqvist, 2001a).
Bancel and Mittoo (2009) only found moderate support for theories focusing on exit strategies. This is in line consistent with Black and Gilson (1998), who argue that an exit via the
stock market through an IPO is critical to the existence of a vibrant venture capital market, but that IPOs are not so much an exit for entrepreneurs as they are for venture capitalists, since entrepreneurs often regain control from venture capitalists in venture-capitalbacked companies at the time of an IPO.
Firms may, nevertheless, seek multiple benefits in going public. In addition to funding for
reasons of growth,Bancel and Mittoo (2009) identified enhanced visibility and prestige, as
well as financial flexibility, as being the most important benefits that were common across
all the firms and countries they investigated. However, those perceived benefits might differ
across firms. Furthermore, their main finding was that motivations for an IPO differ significantly across firms and regulatory environments, depending on ownership structure, size,
age, country.
In their survey, Bancel and Mittoo (2009) found strong support for theories focusing on
financial and strategic considerations, such as increased credibility and reputation. Ritter
and Welch (2002) on the other hand, found that non-financial reasons play a minor role
10
Chapter 1. Key Concepts
for most firms, arguing that entrepreneurs would not concern themselves with the complex
public market process if not for funding considerations.
The decision to go public has important consequences for the firm, as it subjects the firm
to the enhanced disclosure and regulatory requirements of stock exchanges, as well as to the
intense scrutiny of analysts and the media (Bancel and Mittoo, 2009). The authors summarise the benefits and costs of going public, ascertaining that CFO respondents perceived
benefits to significantly outweigh the costs of going public. That in some way explains why
many privately owned companies all over the world have become public.
Ritter and Welch (2002) note that the questions as to why IPOs are the best way for entrepreneurs to raise capital – considering the strict legislation and requirements concerning
the transformation in a publically owned company – and why the motivation to do an IPO
is stronger in some situations or at some times than it is in others, remain.
1.1.2
IPO activity
The latter question in the previous section refers to significant differences in IPO issuing
activity over different periods of time. These are difficult to test since researchers usually
only observe a set of firms that go public instead of all the potential firms that are suited
for an IPO.
In the context of this thesis, (Ritter et al., 2012, p. 2) state that ”due to the Panic in 2008
and the Eurozone crisis in 2011, the 280 companies going public on the London, Euronext,
Frankfurt, and Milan stock exchanges from 2008 to 2011 is lower than the 353 companies
going public in 2007 alone”, analogous to Gao et al. (2013), especially notice that diminution to manifest within small firms. The theories or explanations proposed by Ritter et al.
(2012) for that recent drop are threefold:
1. The economies of scope hypothesis, introduced by Gao et al. (2013), states that small
firms can create greater operating profits by being acquired by a firm in the same or a related industry, rather than operating as an independent firm and relying on organic (i.e.
internal) growth. The reasons for this are that the profitability of small companies are re-
Chapter 1. Key Concepts
11
duced since the importance of growing quickly has increased over time due to an increase
in the development of technological innovations. Earnings would thus be higher as part of a
larger organization that can benefit from economies of scope.
2. The regulatory overreach hypothesis states that small firms remain private due to an increase in the regulatory costs carried by publicly traded firms. These additional compliance
requirements directly or indirectly discourage other IPOs through lowering the market valuation.
3. The market conditions hypothesis. (Ritter and Welch, 2002, p. 1822) define ”market
conditions as the most important factor in the decision to go public”. Low IPO volume
is here largely ascribed to depressed stock market levels. The opposite reasoning, known
as hot issue markets, is the subject of market-timing theories. (cf. Alexander Ljungqvist
(2006); Shiller (1990)). This phenomenon of hot and cold IPO market cycles has been widely
documented and will be further addressed in Section 2.2. The reader may wish to know, in
the context of the following section on the financial crisis, that a market-timing theory is
about a favourable investor sentiment, resulting in increased valuations. Firms respond to
overoptimistic investors by issuing equity in a ‘window of opportunity’ (Baker and Wurgler,
2000). The next section will point out that these overoptimistic investors are the very foundation of the financial crisis.
(Ritter et al., 2012, p. 16) conclude that the evidence found in 1995–2011 in Europe suggest that ”small firm IPO activity is indeed experiencing a long-term secular decline”. Consistent with the market conditions hypothesis, market valuations were lowered following
the dot-com bubble and its collapse, the financial panic of 2008 and the Eurozone crisis, all
contributing to a drop in European IPO volume. Similarly, the economies of scope hypothesis is not refuted since it is largely consistent with the negative trends that Ritter et al.
(2012) have found in IPO volume after adjusting for the influence of market conditions.
No evidence, however, was found to support the regulatory overreach hypothesis: even in
secondary markets that were loosely regulated and had no change in the minimal costs of
being a publicly trade firm, there was a significant negative trend in the yearly number of
IPOs to be found.
12
Chapter 1. Key Concepts
To conclude, Ritter and Welch (2002) remark that when stock markets decline, many firms
will withdraw their public offering rather than proceed with their IPO at lower prices. Thus
it is necessary to ask why there is quantity adjustment rather than price adjustment.
1.1.3
European IPO framework
As mentioned in Section 1.1.2, one of the three possible explanations for the drop in IPO
activity as suggested by Ritter et al. (2012) is the regulatory overreach hypothesis, which
argues that small firms are discouraged to go public due to the increase in regulatory costs.
Europe has a culture of family businesses, which made up more than 60% of all European
companies in 2009 (Flören et al., 2010). In The Action Plan on Building a Capital Markets
Union (2015), the European Commission reported that for IPOs with a deal size below e6
million, the cost of listing fees represents 10-15% of the deal’s value (European Commission, 2015), implying that the EU regulatory environment is not favourable for Small and
Medium-sized Enterprises (SMEs) to take advantage of the IPO funding channel. In this
report, the authors compare costs for SMEs with 5-8% of the deal’s value for larger deals
(e50-100 million). In addition, setting up the prospectus – a legally required document for
investors – is both costly and circuitous for SMEs. Many SMEs therefore see the benefits of
going public.
The European public equity markets are characterized by their large variety across EU
member states. Although they have developed significantly, they are still not on the same
level as other developed economies (European Commission, 2015). Here, the regulatory
framework of Europe is briefly discussed.
Stricter regulations have their origins in 2001, when the U.S. Enron scandal revealed failing external and internal auditing in companies (Bloomberg, 2001). To restore confidence
in auditing , U.S. authorities introduced the Sarbanes-Oxley Act in 2002 to prevent new
scandals through corporate governance.
The Sarbanes-Oxley Act incited European governments to make analogous changes to regulations, as cited by the European Commission in its Report of High Level Group of Company Law Experts on Modern Regulatory Framework for Company Law in Europe, which
Chapter 1. Key Concepts
13
recommended some of its corporate governance practices (Akyol et al., 2014). No more
than two years after that report, fifteen EU-member states had already responded either
by revising existing regulations or by issuing new corporate governance codes (Akyol et al.,
2014). The authors provide a complete table on the governments and stock exchanges that
initiated corporate governance codes. Although those codes vary across EU member states
due to tailoring to legal systems, cultures and other reasons, they are highly aligned with
the guidelines that the European Commission had set (Ritter et al., 2012). Akyol et al.
(2014) find that after EU Member states adopted corporate governance codes, IPO underpricing2 on the regulated market of the member state, on average, declined.
There are exceptions to national codes in both stricter and less regulated ways. Firstly,
stock exchanges may issue their own codes in addition to national codes, occasionally adding
additional requirements (Akyol et al., 2014). The opposite effect – less regulation – is true
for secondary markets, where requirements are less strict than they are in the main markets
(Vismara et al., 2012). All major continental European stock exchanges now have such secondary markets (also known as the exchange-regulated markets) – typically there for small
companies – following the success of London’s exchange-regulated Alternative Investment
Market (AIM) (Ritter, 2012). As defined by the European Financial Service Directive, the
looser regulation of the exchange-regulated markets are not affected by national regulatory
changes (Ritter et al., 2012). For a more detailed overview of the developing structure of
the main European stock markets, see Table 1 in (Vismara et al., 2012, p. 355).
Inconsistent with the regulatory overreach hypothesis, Ritter (2012) notes that few of the
companies listed on those secondary markets reported positive earnings per share, even
though they were exempt from the stricter regulations applicable to the main markets.
Although conclusions can be made that regulations similar to the Sarbanes-Oxley Act are
not the primary reason for drops in IPO volume, the effect cannot be completely excluded.
Regulators have to find the balance between efficient capital raising and the protection of
their investors Ritter (2012). This complex web of regulations can influence potential IPO
firms in their choice of exchange, as they may prefer to list in a less stringently regulated
jurisdiction. That tendency is strongly reduced by a strong alignment of market regulations
2
Underpricing is discussed in Section 2.1.
14
Chapter 1. Key Concepts
around the world. However, in the EU, the requirements for the IPO prospectus have been
harmonised to make such comparisons possible (European Commission, 2015). The European Commission has attempted to deal with the problematic of family-owner preferences
in Europe with its REFIT programme for reducing and simplifying regulatory burdens, so
modernising the Prospective Directive through exploring other sources of support and advice for SMEs in the listing process. However, higher regulation might also offer a number of benefits, since it offers transparency through more stringent reporting and auditing,
which positively impacts investor confidence. Gajewski and Gresse (2006) give a detailed
overview of the stock exchange listing process and the various listing requirements across
European exchanges.
1.2
Financial Crisis: Impact for Europe
”The crisis of September 2008 is said being the biggest crisis since 1929. It brought the
largest bankruptcies in world history, pushed over 30 million people into unemployment and
brought many countries on the edge of insolvency.”
Meltdown: The Men Who Crashed The World (2013), documentary
In order to fully understand the impact of the global financial and economic crisis, its causes
and the impact must be discussed. An elaborate description of the causes of the financial
crisis, as well as the consequences and impact for the United States can be found in Appendix A. This section focuses on the financial crisis’ impact on Europe, as this research
examines the major European stock exchanges.
Appendix A makes clear the magnitude and severity of the financial crisis in the U.S. One
can easily understand that, due to interconnected financial markets, the crisis quickly spread
to financial overseas markets. Thus, the global economy was affected and slipped into a severe recession. The financial crisis in Europe became an economic one, and posed profound
challenges for individual countries as well as the Eurozone, as it threatened the existence of
the European monetary union (EMU) (Hall, 2014).
Chapter 1. Key Concepts
15
U.S. investment bankers sold their mortgage-backed securities to Europe, intertwining the
European financial markets with their own markets. It is not a coincidence that U.S. investment banks came to Europe. At first, it seemed that these two transatlantic parties
were highly complementary. On the one hand, Europe was characterized by its overcapacity. It is an aging continent, and its (near-)retired generation had made a great deal of
money during the economic upturn in the second half of the twentieth century, resulting in
comfortable pension funds. In addition, this aging Europe was risk-averse, and lacked capabilities for financial innovation at the time, resulting in poor yields (The Globalist, 2009).
On the other hand, Americans tend to be rather reluctant to engage with simple strategies,
preferring instead to chase high margins, taking risks and financially innovating. The U.S.,
and to a lesser extent London, have been global pioneers when it comes to financial innovation. Collateralized mortgage, bond or debt obligations, asset-backed commercial papers
and other financial products were created in the U.S. in order to earn the high premiums
by those innovations (The Globalist, 2009).
The U.S. had thus found an ideal partner in Europe to target with their frequently-exotic
assets, since the continent and its financial managers craved decent returns, as their financial markets and products had not yielded anything of value. As the European managers
of financial institutions desired high returns but were unable to create innovative products themselves and were risk-averse, they were easily convinced, perhaps even destined, to
buy financial products from the U.S. Moreover, the risks they added to their balance sheets
looked safe, and were in no way comparable to the risks they would have to take with European products to obtain a similar return. For the U.S. institutions, this was also a welcome deal as they were able to discharge their balance sheets with the credit risks they produced.
European managers were living their dream: to chase high yields with other people’s money.
However, they suffered from a herd mentality: carrying out poor risk assessment as everybody in the sector was doing it. If anyone was to blame for the failure, it was the U.S. with
its exotic products. Americans made good use of that reasoning and, as a result, European
institutions ended up with a great deal of subprime loans and mortgage-backed securities,
together with financial risks made in the U.S., on their balance sheets. The turmoil in the
16
Chapter 1. Key Concepts
United States’ housing sector thus quickly spread to European banks. Additionally, U.S.
and European financial markets had some things in common, from excessive lending and
borrowing at artificially low interest rates (partly due the ECB) to Spain, Ireland and, to a
lesser extent, Italy, like transatlantic counterpart, to the development of property bubbles,
putting their banking systems at risk (Karanikolos et al., 2013). Not only the private but
also the public sector was at risk by taking national debt to increasingly dangerous levels
(Hall, 2014).
The outbreak of the subprime mortgage crisis in America shook financial markets around
the world. By the end of 2008, all of the major world economies were struggling. By the
end of 2009, there was – according to the year-end assessment of the World Bank – virtually no country that had escaped the impact of the crisis.
Reinhart and Rogoff (2009) describe the worldwide crisis as a classic debt crisis. Most of
the excessive lending and borrowing had to be refunded in a period of deep recession caused
by unrealistic expectations about asset prices and deficient regulations. Those levels of
debt that were present in Europe, in particular the unsustainable levels of debt and deficits
in Greece, alarmed investors against purchasing European bonds. Although credit rating
downgrades were restricted to countries like Greece, Iceland, Ireland, Portugal and Spain,
the financial markets all over the EMU came under pressure. This is, next to other channels for spillovers between European economies, because European banks and governments
throughout the Eurozone have substantial amounts of sovereign debt (Arezki et al., 2011).
The economic crisis, then, developed into a Eurocrisis, since the single currency of Europe –
its proudest achievement – was in grave danger (Krugman, 2011).
Credit risks also significantly increased because of the importance and amount of common
factors in bank credit default swaps with the U.S. that had been rising since the outbreak
of the subprime crisis. In addition to the strong interdependencies caused by the common
factors, there were direct spillovers from the credit default swaps of U.S. banks to those of
European banks, further worsening the loan portfolio of banks (Eichengreen et al., 2009).
Soon, the financial crisis spilled over into the overall economy, resulting in an economic crisis. In the second and third quarter of 2008, the output from the EMU countries dropped
on average by 0.2%, which heralded the first recession since the inauguration of the Euro
Chapter 1. Key Concepts
17
in 1999 (Encyclopædia Britannica, 2016). Karanikolos et al. (2013) provide us with some
numbers on the economic crisis in 2009 in Europe from the Eurostat database: the gross
domestic product (GDP) dropped for all members of the EU in real terms except for Poland.
The mean decrease across the board was 4.3%, but losses were as far ranging as 1.9% in
Cyprus to 17.7% in Latvia.
Furthermore, unemployment rate skyrocketed. The following percentages show how unemployment increased in select European countries: Portugal, Slovakia and Bulgaria ( 3%),
Denmark, Hungary and Greece (4%), Iceland (5%), Ireland (9%), Spain and Estonia (12%),
Latvia (13%) and Lithuania (14%) (Karanikolos et al., 2013). As before stated, the crisis
therefore had significant psychological and physical consequences, resulting in a serious humanitarian problem. De Vogli et al. (2013) describe the increase in suicide rates during the
crisis in Europe, most problematic in the worst-affected economies. However, because of the
regulation on layoffs and the strong social-welfare programs, the human cost of the crisis in
Europe was far less serious than it was in America (Krugman, 2011).
The farthest corners of Europe were also involved in the crisis: Iceland’s three largest banks
which had outgrown the country’s output by a factor of ten somewhat inevitably collapsed,
crashing their economy. Ireland was comparable, and Hungary and Latvia also soon moved
in the same way (Encyclopædia Britannica, 2016).
In August 2008, the U.K. government announced a plan to stabilize banks by partly nationalizing the major banks for £50 billion ($88 billion), and promised to guarantee bank loans
of up to £250 billion ($438 billion) to shore up the financial sector (NBC News, 2008). The
governments of the Benelux countries were involved in a e11.2 billion ($16.6 billion) deal to
buy a 49 per cent share in order rescue the Fortis Group and guarantee its liquidity. This
intervention came after rumours of a massive liquidity crisis and a drop of 21 per cent in
the stock price of Fortis. The intervention was incentivised by the concern for a destabilisation of the entire Eurozone if Fortis collapsed, since it is among Europe’s top 20 banks (The
Telegraph, 2008).
The European Commission approved a plan from the Germany government to recapitalize
Commerzbank in May 2009, the second largest private bank of Germany, with an injection
18
Chapter 1. Key Concepts
of e8 billion ($10.7 billion) (Budapest Business Journal, 2009). In October 2008, the Swiss
Government had to bail out the Union Bank of Switzerland to save the bank from insolvency (Odekon, 2015). Credit Suisse in Switzerland and Barclays in the U.K. both rejected
government help and raised funds from their private investors: the United Arab Emirates,
Qatar and Abu Dhabi (Encyclopædia Britannica, 2016).
Governments in Europe adopted policies to prevent the recession from becoming too severe,
and to keep the crisis as short as feasible. European institutions and banks imposed measures in terms of monetary and fiscal policy. First of all, central banks performed interestrate reductions. Furthermore, the European Commission unveiled a public-spending program worth e200 billion ($258 billion) as a co-ordinated fiscal stimulus. It imposed policies that would boost the economy in the short term, such as temporary cuts in sales taxes
and more generous state support (The Economist, 2008). Most European governments approved, beginning with the French government. Germany was more reluctant, as Angela
Merkel argued for fiscal restraints (Encyclopædia Britannica, 2016).
The Troika (International Monetary Fund (IMF), European Commission (EC), and European Central Bank (ECB)) later imposed austerity policies as a condition for financial help
to Greece, Ireland and Portugal (Karanikolos et al., 2013). Such austerity policies aimed
to cut public expenditure and reform the public sector, since falling tax revenues and increased spending on bank bailouts had increased deficits to enormous proportions. However, the austerity technique was very controversial from the outset, and opposition grew
as the policy began to adversely affect the economic growth of Europe. An alternative to
austerity measures might be a fiscal stimulus policy, since countries that opted for fiscal
stimulus, Germany for instance, tended to recover more quickly (Karanikolos et al., 2013).
However, since the outcome of the crisis, the European Union has been trying to find a way
out of the severe recession on the continent and to restore confidence in its financial markets (Hall, 2014).
Summarized, Europe allocated the large pools of assets that craved high yields to innovative American financial products, thus adding financial risk to their balance sheets. To
avoid that, European business and finance would have to reconfigure itself in a more riskoriented manner towards promising risks from within Europe itself (The Globalist, 2009).
Chapter 1. Key Concepts
1.3
19
Investor Psychology
This section considers the importance of sentiment in stock markets. In the following chapter, the role of investor psychology and its relation to pricing in IPOs will be addressed
again. This section focuses on the influence of the financial crisis on trust and confidence
in stock markets. The financial crisis has been extensively discussed in the previous section,
with an aim to give readers a clear picture of the true impact of the crisis and its causes.
Section 1.2 has made clear that the crisis removed all trust and confidence not only in the
financial markets, but also the economic system as a whole. Many researchers have studied the relationship between social trust and economical prosperity, finding a positive relationship between levels of trust and levels of national wealth. Tonkiss (2009) explains the
relationship by arguing that resources of trust reduce the transaction costs of economic exchange and thus enhance economic efficiency. The same relationship is consistently found
on the European continent, as evidenced by Delhey and Newton (2005), who conclude with
data from the European Social Survey that higher levels of trust are to be found in societies with high scores on wealth indicators. They refer to this as ‘Nordic exceptionalism’, since countries such as Denmark and Finland report the highest scores in trust, while
economies with low GDP per capita, such as Hungary or Poland, perform poorly on trust
measures.
Tonkiss (2009) identifies information, contracts and federal regulation as the formal mechanisms and enablers of confidence in economic systems and institutions, as also cited in
(Sapienza and Zingales, 2012). (Guiso et al., 2008, p. 1) add that ”unreliable information
or corporate misconduct may change not only the distribution of expected payoffs, but also
the fundamental trust in the system that delivers those payoffs”. (Guiso et al., 2008, p. 1)
examine trust in the stock market as the explanatory variable in everyday investment activity: ”The decision to invest in stocks requires not only an assessment of the risk-return
trade-off given the existing data, but also an act of faith (trust) that the data in our possession are reliable and that the overall system is fair”. The authors base their research on
survey data from the Netherlands and Italy, which report significant differences between
the levels of investment for investors with higher and lower levels of trust. This leads to the
20
Chapter 1. Key Concepts
conclusion that ”trusting individuals are significantly more likely to buy stocks and risky
assets and, conditional on investing in stock, they invest a larger share of their wealth in
it”.(Guiso et al., 2008, p. 2) However, the moral agency of individuals has also been factor of great importance during the crisis, as managers’ greed was perceived by 36% of respondents in the United States as being the most significant cause of the financial crisis
(Sapienza and Zingales, 2012).
Given the above evidence, it may be assumed that in both Europe and the U.S., investors
decided not to invest in stock markets due to the events of the financial crisis and the subsequent economic crisis. This thesis hopes to examine and quantify these decisions.
Chapter 2
Previous literature on IPO performance
As Chapter 1 attempted to make clear, the importance of IPO must not be underestimated.
Initial public offerings, therefore, have been one of the most researched areas in finance literature. The enormous body of literature is largely focused on three anomalies: (1) the
short-term underpricing of IPOs, (2) the hot issue market phenomenon, and (3) the longrun performance of IPOs Bansal et al. (2012). In this chapter, these anomalies that exist
in the various developed and developing markets, as well as the connectedness of the three
phenomena, will be discussed.
2.1
Short-term performance
The phenomenon that stocks of IPO firms earn substantially positive returns on the first
day of trading is well known and has been well observed and documented. In studying the
results from 38 different IPO researchers, Jenkinson and Ljungqvist (2001b) report the underpricing in the markets of 35 countries, with the exception of tender offers in the U.K.
The phenomenon of underpricing has been extensively researched, since Ibbotson (1975)
documented the anomaly for the first time. He found an initial return of 11.4% on average
for American stocks going public in the 1960s. This section provides an overview of previous empirical findings, followed by possible explanations for the anomaly.
21
22
2.1.1
Chapter 2. Previous literature on IPO performance
Empirical evidence for short-term performance
Although underpricing has been a persistent empirical observation for many decades, the
level of underpricing differs over time (Loughran and Ritter, 2004) and across markets (Jenkinson and Ljungqvist, 2001b). The latter study reports that the phenomenon is far less pronounced in industrialised countries (15%) than in emerging economies (60%). This study
is particularly interested in the former group, since the stock exchanges discussed in this
paper are industrialised and developed Western European markets.
Gajewski and Gresse (2006) examined 2,104 European IPOs from 1995 to 2004. They found
a mean raw return of IPO stocks of 22.06% on the first day of trading. When adjusting for
market return, the value did not significantly differ, being at 22.01%. However, the level of
underpricing varied substantially from one country to another. This is also indicated by the
large standard deviations they found, indicating heterogeneous levels of underpricing in the
sample. In particular, the short-term performance of the Belgian, French, Italian, German,
Dutch, Portuguese and British markets is interesting for this research. Their initial returns,
as found by Gajewski and Gresse (2006), will be briefly discussed here.
Portugal (21.30%), the Netherlands (22.92%) and the United Kingdom (21.27%) were found
to have initial returns that were close to the sample average. Germany’s level of underpricing (38.93%) exceeded the sample mean by more than 15%, but Gajewski and Gresse
(2006) argue that this value can be explained by the high concentration of German IPOs
in the dot-com bubble. There was a sub-mean level of underpricing in Belgium (12.21%),
France (5.36%) and Italy (10.26%).
Table 2.1 shows Loughran et al. (2010) evidence from Europe, with updated data from 2015
given by J. Ritter. Only the countries relevant for this thesis have been selected for the table. Table 2.1 displays comparable results to the findings made by citegajewski2006survey,
with initial returns ranging from 10.2% in the Netherlands to 23.0% in Germany. However,
neither study can be compared since they cover different time periods.
Chapter 2. Previous literature on IPO performance
23
Table 2.1: Evidence from Europe: Short-run Performance
Source: From Loughran et al. (2010), 2015 updated table by J. Ritter
Country
Researchers
Sample size
Time frame
Level of
Underpricing
Rogiers, Manigart &,Ooghe; Manigart, Du Mortier;
Belgium
114
1984-2006
13.5%
697
1983-2010
10.5%
779
1978-2014
23.0%
312
1985-2013
15.2%
181
1982-2006
10.2%
32
1992-2013
11.9%
4,932
1959-2012
16.0%
Ritter
Husson & Jacquillat;
Leleux & Muzyka;
France
Paliard & Belletante;
Derrien & Womack;
Ljungqvist;
Rochel;
Germany
Vismara;
Dealogic
Arosio, Giudici &,Palerai;
Italy
Cassia, Paleari & Redondo;
Vismara
Wessels;
Netherlands
Eijgenhuijsen & Buijs;
Jenkinson, Ljungqvist & Wilhelm;
Ritter
Almeida & Duque;
Portugal
Dealogic
Dimson;
United Kingdom
Vismara;
Levis
2.1.2
Theories explaining Short-Term Performance
The first anomaly in the pricing of IPOs is the short-term underpricing of new issues. The
pricing of IPOs has been extensively researched due to the remarkable empirical regularity
that, when companies go public, the equity they sell tends to be underpriced, resulting in a
substantial price jump on the first day of trading, as first documented by (Ibbotson, 1975).
This phenomenon inspired a large body of literature that attempted to formulate theories
on rationalising underpricing.
24
Chapter 2. Previous literature on IPO performance
The underpricing of IPOs is considered an anomaly in its contradictory nature to the efficient markets hypothesis. The existence of initial returns or shares sold at discounted prices
should incentivise the majority of investors to want benefits from those profit opportunities,
thus making the underpricing of IPOs disappear over time. However, the observation seems
to persist in both developed and developing markets (Bansal et al., 2012).
The first section of this chapter reviews the most important theories in the literature that
have been formulated to explain the existence of initial returns. Various authors have tried
to categorise the variety of theories on the subject. Ritter and Welch (2002) classified the
theories based on whether asymmetric or symmetric information was assumed. It was found
that most of the theories relied on asymmetric information. In line with (Woloszyn and
Zarzecki, 2013), among others, this research follows Ljungqvist (2007)’s theory classification into four broad categories: (1) asymmetric information, (2) institutional, (3) control,
and (4) behavioural. This section only briefly addresses the most important theories, which
are not mutually exclusive. Some of the models have been criticized for being over simplistic, however they provide meaningful insights on the matter. For more detailed information,
empirical evidence and critic on theory, consult Ljungqvist (2007).
Asymmetric information
The models based on one type of asymmetric information are the most widely examined
and established (Ljungqvist and Wilhelm, 2005). They assume that one of the key agents1
of an IPO transaction is better informed than the other(s). The party with access to more
information is called the ‘agent’; the other party – who has to rely on the agent while making a decision – is known as the ‘principal’ (Woloszyn and Zarzecki, 2013). The information
frictions between agent and principal explain the lasting nature of underpricing. Ljungqvist
and Wilhelm (2005) give a substantial body of evidence in favour of such frictions being an
explanation for at least a part of the well-known anomaly.
Winner’s curse Theory
1
An IPO transaction is characterised by three key parties, being, next to the issuing firm, the under-
writer and the investor
Chapter 2. Previous literature on IPO performance
25
The inequality in the availability of information between different groups of investors leads
to the most important theory: the winner’s curse theory, as formulated by Rock (1986).
Rock assumes the existence of two groups of investors: well-informed and underinformed.
The investors that have superior information on the issuing firm will (over)subscribe if issues are underpriced, and avoid participating in IPOs where the price is above the company’s fair value, while the investors with inferior information will have random investing
habits, resulting in the latter being exclusively allocated overpriced issues that earn negative returns. Thus, uninformed investors will only ‘win’ when paying an unfavourable
price (Ibbotson et al. (1994); Bansal et al. (2012)), resulting in the so-called winner’s curse.
Moreover, since only uninformed investors are participating in over-valued issues, the demand is lower than if both groups are interested, making it even more likely for the shares
to yield a negative initial return (Woloszyn and Zarzecki, 2013).
However, investors that are trading with inferior information are aware that they are likely
to suffer the winner’s curse and will therefore only subscribe to issues where the expected
return of their investment is positive. Thus, to attract and encourage participation with
this group of investors, IPOs must be underpriced or discounted to counter the winner’s
curse (Ljungqvist, 2007). Additionally, Kulendran and Perera (s.d.) states that first-day
returns should equal the risk-free rate in equilibrium after adjusting the allocation rate.
Principle-agent theory
Baron (1982) formulated a second theory based on asymmetric information. His hypothesis involves an agency-based explanation for underpricing. Baron (1982)’s theory of information asymmetry regards the disparity between issuers that are less informed about the
demand conditions relative to the underwriter of the IPO, as the latter has expertise in
marketing shares (Agathee et al., 2012). If the underwriter’s effort in marketing and distributing the stock is not perfectly observable and verifiable, the banks will find themselves
in a morally hazardous situation to perform a sub-optimal level of effort and they will not
act in the best interests of the issuer (Ljungqvist (2007); Loughran and Ritter (2004)).
Ritter and Welch (2002) state that it is best for the issuer to permit some form of underpricing because the underwriter cannot be monitored without cost. Leaving money on the
26
Chapter 2. Previous literature on IPO performance
table for the investment bank should, therefore, induce the underwriter (agent) to optimal
selling effort (Ljungqvist, 2007) and insure the full disclosure of all information about the
issuer (Karlis, 2000).
Loughran and Ritter (2004) in particular highlight the potential for agency problems regarding the institutional arrangements between underwriters and issuers. The motivations
for underwriters not to act in the best interests of the issuer are two-fold: they underprice
shares to favour established clients – as formulated in Baron (1982)– or they reduce the
marketing efforts (Agathee et al., 2012). Additionally, Ljungqvist (2007) states that investors offer underwriters side-payments while competing for the allocations of (underpriced) stock. Another case where the underwriter gains from underpricing the shares is
the practice of ‘spinning’2 .
Of course, issuers will try to incorporate certain mechanisms in those institutional arrangements to control the agency problem, such as underwriting fees proportional to IPO proceeds. However, Agathee et al. (2012) and Ljungqvist (2007) both indicate that the underwriter’s benefits of underpricing (supra: side-payments) can significantly exceed the increase in underwriting fees. Another mechanism is optimising the selling effort of the underwriter by making him/her dependent on market demand. This is effected by allowing
the underwriter to select a contract from a variety of combinations of IPO prices and underwriting spreads (Ljungqvist, 2007).
The greater the asymmetry of information, thus, the more uncertain the issuer is about the
value of the firm, the greater the level of underpricing. This is because the issuer is more
dependent on the auditing of the investment bankers to report accurate information (Karlis
(2000); Ljungqvist (2007)).
Signalling theory
The third theory that assumes asymmetric information is the signalling theory. Among others, Welch (1989) proposed a signalling model with the idea that high-quality firms use the
underpricing of their IPOs to signal their high valuations to the market (Agathee et al.,
2
Spinning involves allocation of stocks by underwriters to executives at other companies, with the aim of
bringing in potential future investment banking business of those companies (Ljungqvist, 2007)
Chapter 2. Previous literature on IPO performance
27
2012).
The information asymmetry exists between the better-informed issuing firm and the investors. Since, in this hypothesis, the issuer has superior information about the value of
the company, rational investors fear a “lemons problem”3 .
Allen and Faulhaber (1989) argue that underpricing can indeed signal a firm’s favourable
prospects. Firms of high quality try to identify themselves as such by underpricing their
initial issue of shares (Kulendran and Perera, s.d.), knowing that investors are aware of the
fact that only the best firms can recover the cost of the underpricing signal by obtaining a
higher price in subsequent offerings (Welch, 1989). The author states that the greater the
level of underpricing, the stronger the likelihood of higher firm quality.
Ritter and Welch (2002) argues that leaving money on the table in the IPO is indeed a way
to demonstrate high quality to investors, but question why it is a more efficient signal than
other ways of throwing money away as high-quality firm. On this topic, Allen and Faulhaber (1989) declare that underpricing is just one of various possible signals to convey a
firm’s quality. Other ways might be, for example, the choice of underwriters or auditors, or
the board of directors (Agathee et al., 2012).
Bookbuilding theory
The final explanation of underpricing derived from the hypothesis of asymmetric information as discussed in this dissertation is the bookbuilding theory, also known als the market
feedback hypothesis. According to Ritter and Welch (2002), the theory was originally proposed by (Benveniste and Spindt, 1989), followed by (Benveniste and Wilhelm, 1990) and
(Spatt and Srivastava, 1991). The bookbuilding theory comprises a mechanism that can
induce investors to thruthfully reveal information about the issuing firm, by making it in
their best interest to do so (Ljungqvist, 2007).
As has seen in the section on the winner’s curse theory, Rock (1986) assumes that some
investors have superior information about the issuing company. For the underwriter, then,
3
The situation in which the only shares available at the average price will be from low-quality firms,
since they are the only ones willing to sell for that price (Emons, 1988)
28
Chapter 2. Previous literature on IPO performance
one of the key tasks is to obtain that information, in order to set the right offer price when
taking a company public (Ljungqvist, 2007). Bookbuilding can allow underwriters to obtain
information from informed investors, say Ritter and Welch (2002).
With bookbuilding, underwriters and issuers undertake marketing campaigns (or road shows)
to market their company to prospective investors (Kulendran and Perera, s.d.), prior to
pricing their shares4 . The road show serves to gauge demand as they canvass the opinions
and indications of interest of potential investors (Ritter and Welch, 2002). It is clear that
revealing such information, in the absence of compensation through underpricing, is not in
the interest of the investor, since that can lead to higher offer prices. Even worse, investors
with superior information will have the incentive to give false information (Ljungqvist,
2007).
Moreover, the strict pro-rata allocation rules on which the theory of Rock (1986) is based,
has been replaced in many countries by bookbuilding, the latter of which allows the underwriter to largely decide on allocations (Ljungqvist, 2007). Bookbuilding is used by investment bankers to reward investors that reveal favourable information, in addition to underpricing, with a disproportionately large allocation of the (underpriced) stock (Ljungqvist,
2007).
Institutional theories
Ljungqvist (2007)’s classification is of institutional theories. These theories offer institutional explanations for underpricing by focusing on the legal aspects and changes concerning regulations of trade in capital markets (Woloszyn and Zarzecki, 2013), including litigation, price stabilising activities of the bank, and taxes (Ljungqvist, 2007). Woloszyn and
Zarzecki (2013) remarks that these concepts may only be considered as a second driver of
underpricing and thus complementary to other theories and concepts, since each country
has its own specific legal framework and market structure. The literature study in this dissertation only discusses the concept of lawsuit avoidance.
4
However, a preliminary offer price range is set (Ritter and Welch, 2002)
Chapter 2. Previous literature on IPO performance
29
As discussed in Section 1.1.3, the regulations concerning due diligence and corporate governance worldwide have but increased since the year 2001. Before that time, Tinic (1988)
had already put forward that due to stringent disclosure rules, issuers5 underprice their
shares to protect themselves from being sued by investors (Woloszyn and Zarzecki, 2013)
on the grounds that material facts have been misstated or omitted from the IPO’s prospectus (Ljungqvist, 2007). Tinic (1988) argued that the consequences of a lawsuit for the defendants are costly and their origin are two-fold: (1) direct costs consist of damages and legal fees, and (2) indirect costs imply the potential damage to the reputation capital. Intentional underpricing thus reduces the exposure to the risk of litigation. Hughes and Thakor
(1992) state that the trade-off for an underwriter is between maximising the gross proceeds
from the issue on the one hand, and minimising the probability of costs implied by a lawsuit on the other.
Ownership and control theories
Ljungqvist (2007)’s next classification is of ownership and control theories. These theories argue that underpricing can help to shape the shareholder structure. Booth and Chua
(1996) found that a dispersion of ownership creates increases liquidity in secondary markets, which in turn reduces the return required by investors. This creates an incentive for
issuing firms to underprice their issues in order to obtain oversubscription and thus attract
a large number of small shareholders (Kulendran and Perera, s.d.). The latter research also
states that underpricing is needed to offset the information costs known to the investors
that are implied by a broad initial ownership.
Secondly, in many cases, the public offering of the stock results in the eventual separation
of ownership and control (Ljungqvist, 2007). He quotes Jensen and Meckling (1976) in saying that when the separation of ownership is incomplete, an agency problem between nonmanaging and managing shareholders can arise. On the concept of ownership dispersion,
Brennan and Franks (1997) argue that by attracting a large number of investors, underpricing can allow managers to strategically allocate shares when taking their company public.
5
According to the securities acts in different countries, all participants who have signed an IPO prospec-
tus can be held liable for any misleading information or material omissions (Kulendran and Perera, s.d.).
30
Chapter 2. Previous literature on IPO performance
By strategically allocating shares , they obtain more investors with smaller stakes and are
able to avoid single investors with a large stake of the shares (Ljungqvist, 2007). Underpricing the issue can thus be used by insiders to retain control, since it avoids monitoring by a
single, large external shareholder (Brennan and Franks, 1997).
Behavourial theories
The final classification is an alternative to the majority of theoretical work in the area of
IPO pricing. While the aforementioned concepts have been built on the assumption of rational market participants, behavioural theories assume the presence of ‘irrational’ investors
and their effect on stock prices. Ljungqvist and Wilhelm (2005) emphasise the particularly
high potential for such an effect in IPO cases, as sentiment can come into play when confronted with young and immature IPO firms that are in general hard to evaluate. Due,
among other observations, excessively high levels of underpricing during the dot-com bubble, more attention has been given to this perspective. However, behavioural theories ’provoke considerable scepticism among economists on both philosophical and methodological
grounds’ (Ljungqvist and Wilhelm, 2005). The reader may consult Ljungqvist and Wilhelm
(2005) for further inquiries about scepticism on behavioural theories.
Investor sentiment theory
The first author to show the existence of sentiment investors in a way to explain underpricing is Alexander Alexander Ljungqvist (2006). Ljungqvist combines his theory in particular with hot markets, as the impact of the sentiment investors is seen as especially acute in
such markets.
Some of the sentiment investors will hold (overly) optimistic beliefs about the future prospects
for IPO companies, , and this is especially the case in hot markets (Ljungqvist, 2007). Ljungqvist
(2007) further states that in order to maximise the excess valuation of those sentiment investors, the optimal strategy is to hold back a part of the shares, since bringing an excessive amount of stocks into the market would depress their price. However, due to regulatory
constraints, the issuer cannot pursue such a strategy directly and therefore must allocate
Chapter 2. Previous literature on IPO performance
31
stock to ’regular’ investors, which will subsequently sell this stock to sentiment investors
Alexander Ljungqvist (2006).
However, regular investors must restrict supply in order to maintain prices. This means
carrying IPO stocks in company inventories. As the demand of the sentiment investors may
fall sooner than expected, the keeping inventory is risky, therefore IPO underpricing is intended as compensation for the losses expected from holding stocks in inventories Alexander Ljungqvist (2006).
Prospect Theory
The second reason for IPO underpricing, as based on behavioural theories, is the prospect
theory. This descriptive theory was developed by Loughran and Ritter (2002) and combines
the prospect theory of Kahneman and Tversky (1979) with the notion of mental accounting
developed by Thaler (1985) (Ljungqvist, 2007). Loughran and Ritter (2002) attempted to
explain the fact that issuers do not ‘get upset’ about leaving huge amounts of money on
the table at the IPO. They found that issuers initially value their company as a benchmark
against which the outcome of the IPO can be assessed (Ljungqvist, 2007).
Under the assumption that the issuer will retain shares after the IPO, an initial return is
perceived as both a gain and a loss in wealth. According to Loughran and Ritter (2002),
the issuer tends to consider gain by the price jump in the after-market activity of his/her
retained shares against the loss of selling the shares at a lower price prior to the price jump.
The issuer will thus not be dissuaded with the underwriter’s performance if the underpricing loss does not exceed the perceived gain (Ljungqvist, 2007), as people tend to focus on
fluctuations in wealth rather than its absolute level (Kulendran and Perera, s.d.).
2.2
Hot and cold issue markets
The second anomaly of IPOs is the existence of hot and cold issue markets. The phenomenon
of hot markets is closely related to underpricing. (Ibbotson and Jaffe, 1975, p. 1) were one
of the first to document the anomaly, and defined the phenomenon as ‘markets defined as
32
Chapter 2. Previous literature on IPO performance
periods in which the average first month performance (or aftermarket performance) of new
issues is abnormally high’. With regard to underpricing, their study concluded that companies that go public in hot issue markets are able to obtain higher offer prices relative to the
efficient price as compared to their issue in cold issue markets. Ritter (1984)identified the
properties of hot markets as markets that have an unusually high volume of new offerings.
Ritter later argues that underpricing is a cyclical observation, with periods characterised as
having much higher average initial returns (Ritter, 1991) .
Ritter (1984) attempted to explain the hot market of 1980 by examining the risk composition of firms going public. He reasoned that an unusually large fraction of high-risk initial
public offerings in some periods could explain hot markets, as high-risk offerings are underpriced more than low-risk offerings. However, he found that the average high initial return
in 1980 was almost entirely attributable to firm offerings in the natural resources industry,
while other IPOs experienced no observable influence from the hot market. This conclusion
is comparable to the findings of Ljungqvist and Wilhelm (2003), who reported first-day returns of internet firms were on average 89 per cent during the dot-com bubble, dwarfing the
other IPOs’ initial returns. However, this study does not suggest that hot markets can be
attributed to specific industries. Multiple factors come into play, and the impact of investor
sentiment is regarded as particularly acute in hot markets (Alexander Ljungqvist, 2006).
2.3
Long-term performance
The second anomaly in the pricing of IPOs is the price performance of IPO stocks in the
long run. Researchers generally agree on the finding that IPO stocks tend to perform poorly
in the first one to five years after going public. In contrast to the observation of underpricing, the phenomenon of underperformance has been frequently challenged since findings
tend to depend significantly on the methodology and benchmarks used while analysing
long-term performance, leading to conflicting results. The evidence, hence, is not that extensive as has been found for short-term performance.
Chapter 2. Previous literature on IPO performance
2.3.1
33
Empirical evidence on Long-Term Performance
Ibbotson (1975) was one of the first to report a below average performance of U.S. stocks
in the first four years after going public. However, due to high standard deviations and
a small sample size, his findings were not statistically significant. Since then, the finding
has been widely reported and documented across markets and time periods. Ritter (1991),
among other authors, also confirmed that IPOs significantly underperform in the long-term,
after-market. He examined the price performance of 1,526 US IPO stocks from 1975–1984
and found an average three-year return of 34.47%, while the control sample yielded a 61.86%
return. Other authors also confirmed the anomaly in the U.S. Saleh and Mashal (2008)remark that the existence of underperformance can be seen as reliable, since multiple research
have yielded similar results despite scholars using other methodologies and statistical methods. The latter issue has been widely discussed in the literature on long-term performance,
as Brav et al. (2000), among others, have shown that the methodology strongly determines
the performance measurement.
Gajewski and Gresse (2006) though, in light of this thesis, found that no level of underperformance has been measured in the same way in Europe as that observed in the U.S. stock
markets. They combine previous research to reach this conclusion, even finding overperformance in Sweden and Greece, and underpricing being challenged in France and Switzerland. The phenomenon, however, has been established in a large number of European countries. Table 2.2 summarises the findings of Gajewski and Gresse (2006) for the markets being discussed in this paper. All studies found negative abnormal returns in the long run,
ranging from -2.80% in Portugal to -32.80% in Germany.
2.3.2
Theories explaining long-term Performance
As stated in previous section, the methodology of measuring long-term performance has
been widely debated (see Brav et al. (2000)). Due to conflicting and controversial findings
based on various methodologies and benchmarks, the anomaly of underperformance has
been classified as a methodological issue by some researchers (among others, Loughran and
34
Chapter 2. Previous literature on IPO performance
Table 2.2: Evidence from Europe: Long-term Performance
Source: Based on Table 14 of Gajewski and Gresse (2006)
Country
Authors
Period
Sample size
Methodology
Time Horizon
Mean abnormal
performance
France
Brounen and Eichholtz (2002)
1984-1999
17
CAR
1
–12.62%
Chahine (2004a)
1996-1998
168
BHAR
2
–9.94%
Leleux and Muzyka (1997)
Nov. ’87 - Mar. ’91
56
CAR
3
–29.2%
Jaskiewicz et al. (2005)
1990-2000
-
BHAR
3
-32.8%
Ljungqvist (1997)
1970-1993
180
BHAR
3
–12.11%
–20%
Germany
Portugal
Sapusek (1998)
1983-1993
142
CAR
3
Stehle et al. (2000)
1960-1992
187
BHAR
3
–6%
Duque and Almeida (2000)
1992-1998
21
CAR
1
–2.80%
CAR
1
–4.53%
Brounen and Eichholtz (2002)
1984-1999
24
-17.81%
United-Kingdom
BHAR
–5.83%
Khurshed et al. (1999)
1991-1995
240
BHAR
3
Leleux and Muzyka (1997)
Nov. ’87-Mar. ’91
220
CAR
3
Levis (1993)
1980-1988
483
CAR
3
–21.8%
From –8.31% to -22.96%
according to the benchmark
Ritter (2000)).
Perera and Kotalawala (2014) indicate the intertwined relationship between underpricing
and long-term post-IPO performance. The authors propose the same theoretical explanations for both empirical observations. As has previously been stated in the section on shortterm performance, some issuing firms use a high level of underpricing as a signal of quality.
Grinblatt and Hwang (1989) find that those high-quality firms tend to have better longrun performance. The reasoning is that those firms issue only a low share of their stock and
are able, when their true quality has been observed and confirmed by investors, to sell the
remaining equity more expensively in the secondary market. Among others,Belghitar and
Dixon (2012) also reported a positive relationship between the initial returns and the performance on the long run.
Another underpricing theory that is applicable to the long-term performance is the prospect
theory, as proposed by Ma and Shen (2003). This theory argues that investors continue to
invest in IPOs because they perceive the probability of high returns as more likely than
they actually are, and they in turn underestimate the probability of low returns.
The remainder of this section is mainly based on frequently cited behavioural theories that
Chapter 2. Previous literature on IPO performance
35
claim to be an explanation of long-term underperformance of IPO stocks, theories proposed
by Ritter (1998). The earning management hypothesis, the windows of opportunity hypothesis, and the divergence of opinion hypothesis are all discussed in brief. These hypotheses,
however, are not mutually exclusive, as they can all contribute to the explanation of the
observation of underperformance at the same time.
Earnings management hypothesis
To attract investors, managers of firms doing a first issue are incentivised to window-dress
financial statements prior to the IPO in order to reduce both the cost of capital and the
risk of failure. Teoh et al. (1998) examined the relation between the long-term price performance of IPO firms and their earnings management, and found that discretionary current
accruals –proxies for earnings management – are high around the time of IPO when compared to non-issuing companies, meaning that managers will deliberately manipulate their
earnings ahead of the IPO.
On average, investors tend to identify false projections of future profitability that the earnings management implies after three years. Of course, when discovering the true value of
the firm, they will revise their portfolio, causing the stock price to fall (Teoh et al., 1998).
(Teoh et al., 1998, p. 32) also state that issuers with more earnings management ‘have
poorer stock returns in the subsequent three years’. The latter authors subdivided the firms
they examined into quartiles, based on the aggressiveness of their earnings management.
After three years, the firms that were in the most aggressive quartile performed on average
15 to 30 worse than the firms in the least aggressive quartile.
Windows of opportunity hypothesis
As been discussed in the section on hot markets, Aggarwal and Rivoli (1990) formulated
a ‘fads’ hypothesis, suggesting that share prices can rise more significantly than their fair
value once investors become overoptimistic. One well-known case example came a decade
after their theory’s publication, with excessive investment in internet firms during the dot-
36
Chapter 2. Previous literature on IPO performance
com bubble. Based on the fads hypothesis, Ritter (1991) formulated the window of opportunity hypothesis, or ‘timing hypothesis’. According to this hypothesis, managers choose a
window of opportunity in which to take their company public. This window can be identified as the result of favourable market conditions (hot markets) or as being ideal for the
firm’s performance cycle. The latter means that managers will prefer to launch an IPO
when the firm is performing well, or is at its peak in its cycle of activities Gajewski and
Gresse (2006), while former is an extension of the fads hypothesis of Aggarwal and Rivoli
(1990). In this sense, the window of opportunity is determined by a bullish market and
strong optimism among investors.
Loughran and Ritter (1995) discuss the phenomenon of a substantially higher IPO activity in hot periods, also known as IPO clusters. They show that firms that launch their
IPO during such hot markets perform poorly relative to other firms. Gajewski and Gresse
(2006) report that, unlike the U.S., no study has formally tested the market timing hypothesis in Europe. They instead refer to Derrien and Kecskes (2006) for empirical evidence of
market timing IPOs on Alternative Investment Market (AIM) London.
The window of opportunity hypothesis is similar to the investors’ optimism hypothesis,
where long-term underperformance is the result of an excess of optimism and confidence of
investors at the time of IPO. Cornelli et al. (2006) examined 486 IPOs in Europe and found
that only for stocks where prices were high in the pre-IPO market (indicating optimism)
was a price reversal was detected in the long-run, thus supporting the investors’ optimism
hypothesis.
Divergence of Opinion hypothesis
Another interesting hypothesis is that presented by Miller (1977). He argues that ‘in a market with little or no short selling, the demand for a particular security will come from the
minority who hold the most optimistic expectations about it’ (Miller, 1977, p. 16). The divergence in opinion of investors arises from the uncertainty about projected earnings. The
valuation of the group of optimistic buyers will determine the initial return. In the long
term, however, these previously divergent opinions will tend to converge as more and more
Chapter 2. Previous literature on IPO performance
37
information comes available. This convergence leads to a decline in stock prices, explaining
the underperformance of IPO stocks.
As with all hypotheses, researchers have tried to find evidence for or against the motion.
Gao et al. (2006) examined 4,057 IPOs in the U.S. and found a negative relation between
post-IPO long-term abnormal returns and early-market return volatility, a proxy for a divergence in opinion. This observation is more pronounced in IPO markets, where shortselling is more restricted, than in non-IPO markets. Jenkinson and Ljungqvist (2001a) also
detect a negative correlation between long-term performance and opinion heterogeneity.
They also found the former to be positively related to the speed of reduction of the divergence of opinions when new information becomes public. These findings are therefore consistent with the hypothesis of Miller (1977).
2.4
Hypotheses
Based on the literature review and empirical evidence highlighted in this chapter, the researcher was able to develop hypotheses for Research Questions 1 and 3 concerning the
short- and long-term performance of IPO stocks.
To investigate the short-term market performance and thus provide an answer to Research
Question 1, the following hypothesis was developed:
H10 : European IPOs are fairly priced in the short run.
H11 : European IPOs are underpriced in the short run.
A similar hypothesis was developed to answer Research Question 3 and so investigate the
long-term performance:
H30 : European IPOs do not perform in the long run.
H31 : European IPOs underperform in the long run.
Furthermore, this study seeks to answer research questions 2 and 3 by considering an evolution of performance throughout the financial crisis of 2008. Based on the discussion about
the financial crisis in Section 1.2 and Appendix A, hypotheses 2 and 4 were developed:
38
Chapter 2. Previous literature on IPO performance
H20 : There is no evolution of underpricing throughout the financial crisis.
H21 : Different levels of underpricing can be found throughout the financial crisis .
And analogous:
H40 : There is no evolution of long-term performance throughout the financial crisis.
H41 : Different levels of long-term performance can be found throughout the financial crisis.
This dissertation seeks to examine if these null hypotheses hold by applying a methodology
that will be described in the following chapter.
Part II
Methodology and Data
39
Chapter 3
Methodology to evaluate IPO performance
In line with past authors, this research about the market performance of IPOs has been
carried out by computing both the short- and long-term performances of stock returns.
The closing prices of the stocks in our sample were obtained from the Thomson Reuters
Datastream. The issue of methodology is a widely discussed topic in the literature, since it
is highly determinative to the results of this research. The choice of performance methodology for both short- and long-term performance in this master’s thesis is given below. An
overview of the methodologies in literature is given in Figure 3.1.
3.1
Short-term performance
In this thesis, short-term performance refers to the initial, first day return. Two main methods were used to evaluate the short-term market performance in previous literature: Raw
Return method (RR) and the Market-Adjusted Return (MAR).
Raw Returns are computed by Formula 3.1. Market-Adjusted Return represents the return
relative to the market benchmark, as shown in Formula 3.2, with Pi,1 the closing price of
the first day, Pi,0 as the offer price, RRi,t as equal to the raw return of a share i on its first
day, and Mt as the market index on the first day of listing, M Rt the market return on the
first day of listing, where Equation 3.2 is M ARi , the Market-Adjusted Return of a share i
on its first day.
41
42
Chapter 3. Methodology to evaluate IPO performance
Performance
of IPOs
Short-term
Long-term
performance
performance
First day returns:
Event-time ap-
-RR
proach:
-MAR
-CAR
-BHR
-BHAR
-WR
Figure 3.1: Overview Methodology
RRi =
M ARi =
Pi,1 − Pi,O
Pi,O
Pi,1 − Pi,O
Mt − Mt−1
−
Pi,O
Mt−1
(3.1)
(3.2)
= RRi,t –M Rt
Hence, the initial IPO return is defined as the offer-to-close return, in line with Ritter and
Welch (2002). Although this definition is generally accepted by most researchers, some
studies have broken down the initial return into the initial return of the primary market
(offer-to-open return) and the initial return of the secondary market (open-to-close return)
(cf Bradley et al. (2009)). However, such data can not be obtained from Thomson Reuters
Datastream, as it only provides the closing prices. Therefore, the definition from Ritter and
Welch (2002) is used, being offer-to-close return.
In line to Vismara et al. (2012), the FTSE Eurotop 100 index is used to compute market
returns. Vismara et al. (2012) used the index as a normal return for computing long term
Chapter 3. Methodology to evaluate IPO performance
43
abnormal performance. In this thesis, it is used to evaluate the short-term performance.
Averages per year, per stock exchange or per combination can be computed in order to
come to meaningful conclusions. Results from both methods can then be interpreted by
analysing the sign of the initial return. A negative average return would indicate overpricing of the IPO, while a positive sign suggest underpricing of the IPO. The corresponding
t-statistics are subsequently analysed to determine whether or not the underpricing or overpricing is statistically significant.
3.2
Long-term performance
To measure long-term performance, (equally-weighted) calculations were performed on the
first three post-listing years, in accordance with Ritter (1991), and widely accepted and applied to later IPO literature on long-term performance. This research uses an event-time
approach, as showed by figure 3.2.1 Typically, three event windows are determined: (1) the
estimation window, (2) the event window and (3) the post-event window.
Figure 3.2: Event-time Approach
In the event-time approach, the following methods have been used to evaluate long-term
performance: Cumulative Abnormal Returns (CARs), Buy-and-Hold Returns (BHRs), Buyand-Hold Abnormal Returns (BHARs) and Wealth Relative (WR). The different methodologies and measures of long-term performance has been a hotly debated issue, and will be
briefly discussed here.
The Wealth Relative (WR) method, as used by Ritter (1991) reflects the ratio of the three1
Obtained from Event Study Metrics (2012).
44
Chapter 3. Methodology to evaluate IPO performance
year BHR of a stock over the BHR of the market benchmark.
WR =
1 + BHRi,3Y
1 + BHRM,3Y
(3.3)
An outcome of Formula (3.3) higher than 1 indicates that the IPO outperforms the market
and vice versa. In line with Barber and Lyon (1997) and the methodology of the short-term
performance, emphasis is placed on the abnormal returns measures (CAR, BHAR), since
abnormal returns adjust the returns for the movement of the market and thus capture the
impact of the event – in this case, the financial crisis.
As shown in Formula 3.2the abnormal return of stock is defined as the difference between
the realized and the expected, or normal, return. The latter of which will be discussed later
on in this section. Cumulating these abnormal returns over the first three post-listing years
gives the CAR, as shown in Formula 3.4.
CARi =
36
X
ARi,t
(3.4)
i=1
Instead of the arithmetic sum being applied to the CAR measure, the BHAR of Formula
3.5 calculates the geometric sum.
min(36,delist)
min(36,delist)
Y
Y
BHAR = [
i=1
(1 + Ri,t )–1]–[
(1 + E[Ri,t )–1]
(3.5)
i=1
Unlike short-term research, where CAR and BHAR give similar results, BHAR seems to
be preferred in the literature for measurements of long-term performance. However, as
stated before, the choice of methodology has been widely debated, since abnormal returns
in the long term are sensitive to the procedures and benchmarks that are used (Kothari
and Warner (1997); Chopra et al. (1992)). Barber and Lyon (1997) and Ritter (1991) discuss the differences between the measures of CAR and BHAR and their conclusions are
recommended to the reader. Clayton and Qian (2003) summarises that both Barber and
Lyon (1997) and Kothari and Warner (1997) argue BHAR as being the most appropriate
Chapter 3. Methodology to evaluate IPO performance
45
for measuring long-term performance since the CARs can result in incorrect inferences with
respect to long-term stock return performance. Moreover, (Clayton and Qian, 2003, p. 6)
cite findings from Lyon et al. (1999) by stating that ’buy-and-hold excess returns (BHARs)
compared against carefully constructed matching portfolios yield well-specified test statistics under the null hypothesis, together with empirical p-values calculated from the simulated distribution of mean BHARs via bootstrapping’. In line with Vismara et al. (2012),
Ritter (1991), Loughran and Ritter (1995), and many other researchers, this study therefore
opted for the BHAR methodology to measure and test the long-term performance of IPO
stocks.
The three-year buy-and-hold returns are computed through their monthly returns, as can
be seen in Equation 3.5, that uses data from the Thomson Reuters Datastream. As proposed by Lyon et al. (1999), initial day returns are excluded as the first monthly return
refers to the first return of the next month, which is on average fourteen days after the first
day of listing. The approach of Lyon et al. (1999) therefore controls for the non-independence
of returns over time. Stocks that delist before the end of the three-year period are retained
in order to avoid a survivor bias (Loughran and Ritter, 1995).
The choice for a normal return is thus substantial for our study, as conflicting results can
be obtained when different benchmarks are used. Although the FTSE 100 market index
was used to compute abnormal returns in the short-term, Kothari and Warner (1997) argue
that the use of a market index in long-horizon tests results in misspecified test statistics.
Vismara et al. (2012) further state that a single benchmark index for all IPOs means comparing asset classes with different levels of risks or betas, and that they should therefore be
interpreted with caution in case of obtaining a negative or positive market risk premium.
Additionally, Loughran and Ritter (2000) argue that the use of a market index may result
in underestimating the underperformance of stocks since the index includes issuing firms.
The latter is why the market return model has been excluded from our set of normal return models for benchmarking. The following benchmarks, or normal return models, are
normally used for calculating abnormal returns:
– Capital Asset Pricing Model (CAPM, 1964)
46
Chapter 3. Methodology to evaluate IPO performance
– Multi-factor model
– Reference portfolio with matched firms
– Control firm
Each of these normal return benchmark are widely discussed on their advantages and drawbacks. The last two options are based on matching procedures, as, to name a few, discussed
in the appendix of (Ritter, 1991, p. 22) and by (Lie, 2001, p. 8). In this thesis, the multifactor model was used for calculating normal returns. The most popular and best known
multi-factor model among researchers is developed by Fama and French (Fama and French,
1993). Due to two additional factors, as displayed in Equation 3.6, that should increase
the explanatory power, the three factor model is preferred over the CAPM method. SMB
stands for small-minus-big size portfolio return and attempts to capture the excess returns
of small stocks over big stocks. HML stands for high-minus-low book-to-market portfolio
returns, capturing the excess returns of high market-to-book ratios over stocks with a low
market-to-book ratios.
The Fama-French model, however, has been criticised for a number of reasons. First of all,
Fama and French (1993) themselves have documented the systematic problem that the
model has in explaining the returns of small stocks. Loughran and Ritter (2000) also argue that if the sample consists of small stocks (such as typical IPOs), then the three-factor
regressions will fail to detect abnormal returns. Therefore, both an equally weighted and
a value-weighted scheme are needed to calculate the abnormal returns. In this thesis, the
value-weighted scheme will be based on market capitalisation, with data obtained from the
Thomson Reuters Datastream. Second, Loughran and Ritter (2000) show that the use of
the multi-factor model may lead to abnormal returns that are biased towards zero, since
the firms that are being regressed may be incorporated into the factors.
The third point of criticism that is specific to this thesis, is that Duong (s.d.) has found a
reduced explanatory ability of the Fama-French multi-factor for stock returns during the
crisis. However, the latter author documents that the model is ideal as it works for many
markets. Duong (s.d.) tested both US market data, which was strongly affected by the crisis, and Turkish data, where no such linkage with the crisis was detected. Although he finds
Chapter 3. Methodology to evaluate IPO performance
47
that the Fama-French model performs outside of the financial crisis2 , the results from his
research ”supported a main argument that the Fama and French model survives against
criticism when shocks are taken into account”(Duong, s.d., p. 5). This is why, given these
findings and in light of this thesis, the use of the Fama-French three factor model is justified. Another argument in favour of the multi-factor model is that several researchers have
stated that more reliable results are obtained with benchmarks built upon size and book-tomarket (Gajewski and Gresse, 2006).
Ri,t = rf,t + βi,M (RM,t − rf,t ) + βi,SM B SM Bt + βi,HM L HM Lt
(3.6)
The time-series data of the Fama and French three factor model for Europe were obtained
from the Kenneth French website (Kenneth R. French, 2015), which supplied an updated
version of the factors isolated by Fama and French (2012). The SMB and HML factors are
constructed with data from sixteen European countries. This considerably reduces the statistical issue of benchmark contamination, meaning that ”a test is biased towards high explanatory power and no abnormal returns if it uses a benchmark that is contaminated with
many of the firms that are the subject of the test” (Loughran and Ritter, 2000, p.3).
Since our parameters are thus predefined, there is no need for a separate estimation window, as displayed in Figure 3.2. The next step is the interpretation of the BHARs to identify whether the IPOs underperform or outperform the normal return over the three-year
period. The sign of the BHAR measure can be used for this identification, where ’+’ and
’−’ indicate out- or underperformance respectively.
The final step in analysing the long-term performance is determining whether or not the
results found in the previous steps are statistically significant. In line with Kothari and
Warner (1997), the test statistic for the buy-and-hold abnormal return test is defined as
shown in Equation 3.7, where its components are unpicked in Equation 3.8 and 3.9. BHARp,T
is the equally weighted 36-month average BHAR of the portfolio of n stocks, and σBHARp,T
is the standard deviation of that portfolio.
2
Duong (s.d.) finds that the Fama-French three-factor model even performs well and better than other
alternatives in markets that were relatively little affected by the crisis
48
Chapter 3. Methodology to evaluate IPO performance
tBHAR =
BHARp,T
σBHARp,T
(3.7)
n
1X
=
BHARi,T
n
BHARp,T
(3.8)
i=1
n
"
σBHARp,T
1 X
(BHARi,T − BHARp,T )2
=
n−1
#1/2
(3.9)
i=1
This test statistic, however, assumes independent and identically distributed BHARs. Since
some firms do not survive the 36-month test period, the latter condition will be upheld.
Moreover, any overlap in the 36-month test period indicates that the independency condition is also not met. Therefore, Kothari and Warner (1997) and Barber and Lyon (1997),
amongst others, conclude that buy-and-hold abnormal returns are positively skewed and
that, consequently, the test statistics must be transformed to eliminate the bias to Equation
3.10, with Equation 3.11, 3.12 and 3.13 describing the components of Equation 3.10.
tBHARadj =
√
γ̂ =
"
σ̂BHARp,T
1
1
n S + γ̂S 2 +
γ̂
3
6n
(3.10)
BHARp,T
σBHAR
ˆ p,T
S=
n
P
(3.11)
(BHARi,T − BHARp,T )3
i=1
(3.12)
3
nσ̂BHAR
p,T
n
1 X
=
(BHARi,T − BHARp,T )2
n−1
#1/2
(3.13)
i=1
In addition, the construction of a bootstrapped distribution3 of test statistics can be used
to test whether or not a statistically significant abnormal performance can be detected
3
From the original sample of abnormal returns, a large number of resamples must be drawn. As sug-
gested by both Kothari and Warner (2004) and SPSS, a 1000 resamples is used.
Chapter 3. Methodology to evaluate IPO performance
49
(Kothari and Warner, 2004). Furthermore, (Lyon et al., 1999, p. 10) states that ’only the
bootstrapped application of this skewness-adjusted test statistic yields well- specified test
statistics’. This is why, in this research, the abnormal returns are tested with bootstrapped
skewness-adjusted t-statistics. The bootstrap process is performed by the Statistical Package for the Social Sciences (SPSS).
Chapter 4
Data Sample
4.1
Sample
This thesis’ sample of 849 IPOs between 2002 and 2012 accounts for a total deal value of
e76,503,745,380.00. Based on Ritter et al. (2012) and Vismara et al. (2012), this research
focuses on the stock exchanges of Brussels, France, Amsterdam and Lisbon (Euronext),
Germany (Frankfurt Stock Exchange), Italy (Borsa Italiana) and the U.K. (London Stock
Exchange), since these are the largest economies in Western Europe. A concise overview of
these stock exchanges is given in Appendix C.
Both primary and secondary markets are considered, since the second markets, which are
inherent to the European stock exchanges, play a relevant role. As mentioned in Section
1.1.3, these secondary markets are characterised by their looser regulation and are typically designed for small companies (Ritter et al., 2012). The London Alternative Investment Market (AIM) is by far the most popular secondary market in Europe, accounting for
70.12% of the London IPOs in our sample data, which is in line with the 79% of the IPOs
taking place in London from 1995 to 2011, as found by Ritter et al. (2012). Note that the
difference of 8.88 % can be explained by the restriction concerning the offer price (cf. next
paragraph), which eliminated a substantial number of the London AIM IPOs.
By choosing this particular timeframe, data before and after the crisis can be captured, as
it can be concluded from Section 1.2 that 2008-2009 are the years during which the financial crisis occurred in Europe. Based on the methodology used by Ritter in Chapter 2, a
long-term performance analysis of three year can be conducted if we limit the sample to
51
52
Chapter 4. Data Sample
IPO of the 2012.
The data on the Initial Public Offerings were obtained from the Zephyr Database, as displayed in Appendix B.1. From our sample of IPOs, financial sector firms, real estate firms,
equity, trust or close-end funds and unit offers or spin-offs were excluded, in lin with Ritter
(1991), and other researchers following the same reasoning (among others, Helwege et al.
(2004); Ahmad-Zaluki et al. (2007); Dimovski and Brooks (2004)). Based on the data available from the Zephyr Database, the sample was further refined to all IPOs with a known
offer price, which, in addition, is higher than e1.00, as proposed by Ritter (1991). A funnel
chart overview of the sample refinement is given in Figure 4.1.
Initial Offerings in Europe
Restriction to 2002-2012
Main Stock Exchanges
Datastream restriction
Non-Financial sectors
∀ Offer Price >e1.00
The research sample
Figure 4.1: Sample Refinement
4.2
Descriptives
The bar chart in Figure 4.2 clearly displays the volumes of the sample per stock exchange
and per year. The years following the dot-com bubble are characterised by low volumes,
increasing to peak volumes of around 200 IPOs per year in the years before the financial
crisis.
In 2008, the volume of IPOS collapsed from 181 to 50 and continued to decline in 2009 to
23. What is striking is that, due to the sample’s constraints, no IPOs in that year come
from London, although Price waterhouse Cooper (2010) report that there were 25 IPOs in
London in 2009.
Chapter 4. Data Sample
53
Number of IPOs
200
150
100
50
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
0
Frankfurt Euronext Paris Alternext LSE London AIM Italian Stock Exchange AIM Italia
Figure 4.2: Overview Sample
From that point on, volumes barely exceeded 50 IPOs a year. The bar chart also indicates
that the biggest contributors to the sample are Euronext and London’s AIM, at 34.86% and
27.09% of IPOs in the sample respectively. The procentual distribution of the IPOs is listed
in Table 4.1.
Table 4.1: Procentual distribution of the IPOs
Stock exchange \ year
2002
AIM Italia
Euronext
2003
2004
2005
2006
0.00
0.00
0.00
0.00
0.00
27.27
28.57
27.41
26.82
30.14
Frankfurt Stock Exchange
9.09
0.00
6.45
7.32
Italian Stock Exchange
12.12
14.29
4.84
8.13
London AIM Stock Exchange
21.21
38.10
46.77
London Stock Exchange
30.30
19.05
14.52
2007
2008
2009
2010
2011
2012
Total
0.00
0.00
8.70
5.00
2.00
2.70
0.82
35.91
56.00
69.56
33.33
54.00
32.43
34.86
25.84
18.23
10.00
13.04
15.00
12.00
16.22
15.55
6.22
11.60
8.00
4.35
1.67
4.00
2.70
7.42
46.34
27.75
19.34
16.00
0.00
18.33
14.00
27.03
27.09
9.76
7.66
11.60
6.00
0.00
21.67
12.00
10.81
11.54
Paris Alternext
0.00
0.00
0.00
1.63
2.39
3.31
4.00
4.35
5.00
2.00
8.11
2.71
Grand Total
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
AIM Italia is the smallest contributor to the sample, with only 0.82% over a ten-year timeframe. Likewise, the secondary market of France contributes rather little with 2.71% of the
total number of IPOs. The smallest number of IPOs was found in Portugal on the Lisbon
Stock Exchange, which falls within the Euronext. Euronext numbers largely reflect Euronext Paris volumes as they represent, on average, 81.40% of the Euronext total. A break-
54
Chapter 4. Data Sample
down of the Euronext can be found in Table 4.2.
Table 4.2: Decomposition of Eurnonext
Stock Exchange
Volume
Procentual share
Euronext Amsterdam
14
4.70%
Euronext Brussels
35
11.80%
Euronext Lisbon
6
2.00%
Euronext Paris
241
81.40%
Total
296
100%
Table 4.3 provides an overview of the total and average deal values in the sample years. It
is important to notice that, regardless of the drop in volume, the average deal value peaks
in 2008 with an average deal value ofe181 million, and drops to an average of e0.89 million
in 2009, when the crisis in Europe was fully underway (see Section 1.2).
Table 4.3: Average Deal Value
Year
Deal Values [103 e]
Number of IPOs
Average Value [103 e]
2002
4.696.503,26
33
142.318,28
2003
1.507.033,36
21
71.763,49
2004
6.536.861,36
62
105.433,25
2005
15.066.362,29
123
122.490,75
2006
14.156.355,56
209
67.733,76
2007
13.821.086,41
181
76.359,59
2008
9.098.950,96
50
181.979,02
2009
20.464,90
23
889,78
2010
6.833.685,90
60
113.894,77
2011
2.825.375,45
50
56.507,51
2012
1.941.065,93
37
52.461,24
76.503.745,38
849
90.110,42
Grand Total
Table 4.4 shows the deal value decomposition per stock exchange. The Lisbon Euronext
IPOs were the most valuable, with on average e403.95 million per deal. The markets that
Chapter 4. Data Sample
55
target SMEs typically have the lowest average deal values, being AIM Italia (e3.85 million), Paris Alternext (e8.90 million) and London AIM (e37.76 million, in comparison with
the e215.35 million of the LSE). Amongst the Euronext Stock Exchanges, Brussels has a
deal value of only e12.38 million, far below the average Euronext deal value of e167.01 million.
Table 4.4: Deal Value per Stock Exchange
Stock Exchange
AIM Italia
Euronext Amsterdam
Euronext Brussels
Euronext Lisbon
Euronext Paris
Frankfurt Stock Exchange
Italian Stock Exchange
London AIM Stock Exchange
London Stock Exchange
Paris Alternext
Deal Value
Number of IPOs
Average Deal value
26.966,00
7
3.852,29
2.149.279,53
14
153.519,97
433.583,00
35
12.388,09
2.423.702,00
6
403.950,33
23.661.998,00
241
98.182,56
6.396.310,23
132
48.456,90
11.418.546,00
63
181.246,76
8.684.184,54
230
37.757,32
21.104.422,08
98
215.351,25
204.754,00
23
8.902,35
Part III
Analysis and Discussion
57
Chapter 5
Short-term performance
In the first instance of the analysis, the first value of the price indices was used to compute
the short-term performance. However, these offer-to-close returns produced over 40% zero
returns. To resolve this issue, the trading volumes were obtained from Thomson Reuters
Datastream. Hence, the first day with a non-zero trading volume was considered as being
the first trading day, resulting in the first closing price. The distribution of those first-day
returns is displayed in a stacked bar chart in Figure 5.1. On average, 62.78% of the stocks
ended the first trading day with a closing prices that were greater than the offer price, or,
in other words, were underpriced. We also speak of ’money left on the table’, since for the
company that issues stock, those initial returns represent lost capital that could have been
raised had the offer price been equal to the first day closing price. The number of stocks
with a initial return of exactly zero was reduced to 17.31%. These numbers are highly comparable with the findings of Ritter and Welch (2002), who found respectively approximately
70% and 16% (respectively) for those shares of the first-day returns in the United States for
firms going public between 1980 and 2001.
59
60
Chapter 5. Short-term performance
Zero
Positive
Negative
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
TOTAL
0%
20%
40%
60%
80%
100% stacked percentages
100%
Figure 5.1: First-day returns
Since the yearly samples are often not sufficiently large, it is important to take into account
the influence of the outliers when performing statistical analyses. In line with Moore and
McCabe (2006), outliers should be eliminated if the sample size is smaller than 40 observations, which is the case for 2002, 2003, 2009 and 2012. However, sample sizes for those
years all exceed 15, the minimum number of observations, when ridded from severe outliers,
to safely perform a t-test, as proposed by Moore and McCabe (2006). Their reasoning is
based on the central limit theorem, which says that a sample mean is approximately normally distributed when sample size is moderately large. For the aforementioned years, severe outliers for which no reasonable explanation or information was found were eliminated.
Chapter 5. Short-term performance
5.1
61
Raw and Market-Adjusted Initial Returns
Table 5.1 indicates that the average initial return for the 849 sample stocks is 5.08%, or
5,03% when they are market-adjusted, with a standard deviation of 15,6%. For the remainder of this chapter, RR will stand for Raw Returns and MAR for Market-Adjusted Returns.
Table 5.1: Short-term performance: statistic descriptives
One-Sample Statistics
N
Mean
Std. Deviation
Std. Error Mean
RR
849
0,0508
0,15613
0,00536
MAR
849
0,0503
0,15604
0,00536
A ’One-Sample t-test’ was performed on the RR and MAR, with the conclusion that the
values given above are statistically significantly higher than zero on a 1 per cent level, as
shown in Table 5.2. This means the first null hypothesis given in Section 2.4 can be rejected and the alternative hypothesis can be adopted. This means that the answer to research question 1 is that, on average, IPOs are underpriced, at least for in the examined
stock markets.
Table 5.2: Statistical Significance Short-term performance
Test Value = 0
95% Confidence Interval
of the Difference
t
df
Sig. (2-tailed)
Mean Difference
Lower
Upper
RR
9,479
848
0,000***
0,05079
0,0403
0,0613
MAR
7,432
848
0,000***
0,05051
0,0400
0,0610
*: Statistically significant on 10% confidence interval
**: Statistically significant on 5% confidence interaval
***: Statistically significant on 1% confidence interval
Table 5.4 shows the annual breakdown of the initial returns. In the early 2000s and the
year of the crisis itself (2009), are the only years that show a non-significant (positive) re-
62
Chapter 5. Short-term performance
turn. It is clear that the years before the financial crisis, given in Section 1.2 as 2005-2007,
also known as the ’housing bubble years’ in the U.S., statistically have the most different
returns (interpretation: ’higher’, from confidence interval) from the zero value.
Table 5.3: Short-term performance: statistic descriptives per year
N
Mean
Std. Deviation
Std. Error Mean
RR02
33
0,0076
0,1038
0,01807
MAR02
33
0,0058
0,10651
0,01854
RR03
21
0,0224
0,07085
0,01546
MAR03
21
0,0233
0,07095
0,01548
RR04
62
0,0284
0,14789
0,01878
MAR04
62
0,0255
0,14796
0,01879
RR05
123
0,0748
0,15797
0,01424
MAR05
123
0,0735
0,15749
0,0142
RR06
209
0,0459
0,15634
0,01081
MAR06
209
0,0457
0,15535
0,01075
RR07
181
0,0571
0,14081
0,01047
MAR07
181
0,0576
0,14028
0,01043
RR08
50
0,0424
0,11374
0,01609
MAR08
50
0,0404
0,11537
0,01632
RR09
23
0,0213
0,09818
0,02047
MAR09
23
0,0204
0,09975
0,0208
RR10
60
0,0858
0,24792
0,03201
MAR10
60
0,0843
0,24891
0,03213
RR11
50
0,045
0,18425
0,02606
MAR11
50
0,0458
0,183
0,02588
RR12
37
0,0476
0,14703
0,02417
MAR12
37
0,0462
0,14473
0,02379
Table 5.5 provides an overview of the statistical significance of the first-day returns per
stock exchange for both RR and MAR. Except for the Paris Alternext (and RR Italian
Stock Exchange), all stock exchanges yield a return that are on average significantly higher
Chapter 5. Short-term performance
63
Table 5.4: Statistical Significance Short-term performance per year
Test Value = 0
95% Confidence Interval
of the Difference
t
df
Sig. (2-tailed)
Mean Difference
Lower
Upper
RR02
0,419
32
0,678
0,00758
-0,0292
0,04438
MAR02
0,311
32
0,758
0,00576
-0,032
0,04352
RR03
1,448
20
0,163
0,02238
-0,0099
0,05463
MAR03
1,507
20
0,147
0,02333
-0,009
0,05563
RR04
1,320
61
0,192
0,02479
-0,0128
0,0623
MAR04
1,320
61
0,179
0,02555
-0,0120
0,0631
RR05
5,251
122
0,000***
0,0748
0,0466
0,10299
MAR05
5,176
122
0,000***
0,0735
0,0454
0,10161
RR06
4,248
208
0,000***
0,04593
0,0246
0,06725
MAR06
4,257
208
0,000***
0,04574
0,0246
0,06693
RR07
5,458
180
0,000***
0,05713
0,0365
0,07778
MAR07
5,521
180
0,000***
0,05757
0,037
0,07814
RR08
2,636
49
0,011**
0,0424
0,0101
0,07472
MAR08
2,476
49
0,017**
0,0404
0,0076
0,07319
RR09
1,041
22
0,309
0,0213
-0,0212
0,06376
MAR09
0,982
22
0,337
0,02043
-0,0227
0,06357
RR10
2,682
59
0,009***
0,08583
0,0218
0,14988
MAR10
2,624
59
0,011**
0,08433
0,02
0,14863
RR11
1,727
49
0,090*
0,045
-0,0074
0,09736
1,77
49
0,083*
0,0458
-0,0062
0,09781
RR12
1,968
36
0,057*
0,04757
-0,0015
0,09659
MAR12
1,942
36
0,060*
0,04622
-0,002
0,09447
MAR11
*: Statistically significant on 10% confidence interval
**: Statistically significant on 5% confidence interaval
***: Statistically significant on 1% confidence interval
64
Chapter 5. Short-term performance
than zero on the 5 per cent confidence level.
Table 5.5: Statistical Significance Short-term performance per Stock Exchange
Test Value = 0
95% Confidence Interval
of the Difference
t
df
Sig. (2-tailed)
Mean Difference
Lower
Upper
Frankfurt SE
2,538
131
0,012**
0,05420
0,0120
0,0964
Frankfurt SE MA
2,580
131
0,011**
0,05500
0,0128
0,0972
Euronext
4,305
295
0,000***
0,03374
0,0183
0,0492
Euronext MA
4,092
295
0,000***
0,03216
0,0167
0,0476
Paris Alternext
0,897
22
0,380
0,01593
-0,0209
0,0528
Paris Alternext MA
0,859
22
0,399
0,01532
-0,0217
0,0523
Italian SE
1,940
62
0,057*
0,03077
-0,0009
0,0625
Italian SE MA
2,128
62
0,037**
0,03354
0,0020
0,0650
AIM Italia
3,304
6
0,016**
0,1844
0,0478
0,321
AIM Italia MA
3,235
6
0,018**
0,18045
0,044
0,3169
London SE
3,945
97
0,000***
0,05555
0,0276
0,0835
London SE MA
3,897
97
0,000***
0,05498
0,027
0,083
AIM London
8,316
229
0,000***
0,07202
0,055
0,0891
AIM London MA
8,388
229
0,000***
0,07234
0,0553
0,0893
*: Statistically significant on 10% confidence interval
**: Statistically significant on 5% confidence interaval
***: Statistically significant on 1% confidence interval
5.2
Comparison with previous evidence on short-term performance
Across all markets and years, this study finds initial returns of 5.08% on average. This is
consistent with the widely recognised pattern of underpricing, and thus with the empirical evidence discussed in Chapter 2. Similar to the findings of Gajewski and Gresse (2006),
Chapter 5. Short-term performance
65
the initial returns prove themselves as heterogeneous, as their stardard deviations are large
(15.61%). What is also consistent with Gajewski and Gresse (2006) is that there is no statistical difference found between adjusted and raw returns. In addition, the findings are
in line with Section 2.2 on the hot and cold issue markets hypothesis, as a cautious positive relation is found between level of underpricing and IPO activity, as given in Figure
E.1 in the Appendix. Moreover, the cold market of the early 2000s in Europe, as reported
by Gajewski and Gresse (2006), is also found in this thesis’ sample. This cold issue market is followed by a more hot issue market in the run up to the financial crisis, since these
years represent both the highest volumes and highest levels of underpricing. The crisis subsequently heralds the beginning of a new cold market, that seems, more or less, to endure
until the end of the timeframe given.
However, the difference in level of underpricing between the previous empirical evidence
and this research can not pass unnoticed. This chapter finds initial returns of 5.08% on average, while the other empirical evidence that is covered in this thesis report initial returns
that range much higher. For example, as stated, Gajewski and Gresse (2006)finds 22.06%
for European IPOs from 1995 to 2004, more than four times the figure this research observes.
It is important to notice that in constrast with this research, all of the timeframes in Table 2.1, as well as the sample used by Gajewski and Gresse (2006), include the years 1999
and 2000 the time of the dot-com bubble1 . Averages of initial returns will undoubtedly be
biased by those dot-com years, which can serve to explain at least part of the difference.
Also, in contrast to Gajewski and Gresse (2006), the timeframe of this study comprises the
years of the financial crisis, which yield lower initial returns (see Table 5.4).
Furthermore, this research attempts to make a statistical comparison with previous research from Jay R. Ritter, who provided data for France, Germany and the U.K.
Table 5.6 breaks down the average market-adjusted returns per stock exchange per year of
the research sample. Although most of these samples are too small for important conclu1
The dot-com bubble is a so-called ’hot issue’ market, as discussed in Section 2.2, a market characterised
by high IPO activity and excessive levels of underpricing. The dot-com bubble met those requirements for
the large part by Internet IPOs (Ljungqvist and Wilhelm, 2003).
66
Chapter 5. Short-term performance
Table 5.6: Average level of underpricing (MAR) per Stock Exchange per Year
Stock Exchange
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
AIM Italia
.
.
.
.
.
.
.
0,22
0,24
0,00
0,11
Euronext Amsterdam
.
.
.
-0,01
0,02
0,02
-0,01
.
.
.
0,14
Euronext Brussels
.
.
0,16
0,07
0,05
0,05
0,03
0,01
.
.
.
Euronext Lisbon
.
0,05
0,01
.
0,04
0,29
-0,03
.
.
.
.
0,01
0,02
0,00
0,05
0,03
0,04
-0,01
0,01
0,02
0,07
-0,01
Frankfurt Stock Exchange
-0,07
.
-0,05
0,21
0,03
0,05
0,12
-0,08
0,21
0,02
0,00
Italian Stock Exchange
-0,05
-0,02
0,03
-0,02
0,05
0,03
0,14
0,09
-0,02
0,03
0,49
London AIM Stock Exchange
0,04
0,02
0,00
0,10
0,07
0,08
0,15
.
0,09
0,08
0,12
London Stock Exchange
0,02
0,07
0,10
0,03
0,04
0,11
-0,04
.
0,08
-0,03
-0,04
.
.
.
-0,03
0,05
0,02
0,01
0,00
0,01
-0,07
0,01
Euronext Paris
Paris Alternext
sions to be drawn, it is important to notice that no severe outliers are found, with average
returns ranging from -8% to 24%. In addition, all stock exchanges yield on average positive
first-day returns for IPOs in the growth (’bubble’) years 2006 and 2007.
With the data received from Jay R. Ritter (Jay R. Ritter, 2015), a comparison can be made
between his findings and the findings in this research concerning volumes and initial returns. Jay R. Ritter provided data for France, Germany and the UK.
Figures 5.2 and 5.3 show the volumes and initial returns of both Jay R. Ritter and this
thesis’ sample for France. The comparison only goes to 2011, as the data from Ritter did
not provide information for 2012. The volumes of the sample in this thesis are, on average, 55.86% smaller than the sample of Ritter until the year 2007, but they clearly follow
the same trend. What is remarkable is that number of IPOs in the sample of this thesis
is on average 3.89 times higher from the year 2008 onwards, which is not in line with the
other years and markets and the logic assumption that the sample in this thesis should be
smaller due to the number of constraint set (see Section 4). As Jay R. Ritter obtained his
data from different sources, confirmed by his Excel file, an explanation might be found in
the exclusion of some of the many IPO sources (Eurolist, Premier Marché, Second Marché,
Alternext, Marché Libre, Hors-Coté IPOs,..) for those years in comparison with the preceding years.
Chapter 5. Short-term performance
67
The initial returns in Figure 5.3 run a strikingly high similar course from 2002 up to mid2007. From that point onwards, the number of IPOs collapse and the data from Ritter is
characterised by rather extreme returns, with an underpricing of 18.30% in 2008 and an
overpricing of 35.80% the year after that in 2009. The respective returns found in this research are -1.18% and 0.92%. The assumption here is that severe outliers in Ritter’s data
might have caused the returns in the aforementioned years to rise and drop so dramatically,
since the impact of outliers is significant in small sample sizes (respectively 4 and 2 IPOs).
150
50
20
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
0
20 8
0
20 9
1
20 0
11
0
Figure 5.2: Volumes for France
20
0
−20
20
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
0
20 8
0
20 9
1
20 0
11
100
Ritter
This research
40
Initial Returns in %
Number of IPOs
Ritter
This research
Time
Figure 5.3: Initial Returns in France
Figures 5.4 and 5.5 provide a comparison for the German market. Again, except for 2009,
the sample sizes are smaller for this research (on average 31.33%), but follow a pattern that
is comparable. For 2003, neither samples have IPOs. Figure 5.5 indicates that our sample
exhibits more extreme returns in 2003, 2005, 2009 and 2010, years that have, unsurprisingly, low sample sizes and that are thus sensitive to outliers. The opposite is true for the
similar pattern from mid-2004 to 2008, which are the years with the highest sample-sizes.
Figures 5.6 and figure 5.7 show the comparison for the United Kingdom. What is striking
is that the volume bar chart shows a far smaller sample size of this thesis (70.61% smaller).
Main reason for this is likely the restriction to IPOs with offer prices that are higher than
e1, which cancels out a great number of the London AIM IPOs. As expected, almost no
correlation can be found in the levels of underpricing, as shown in Figure 5.7.
68
Chapter 5. Short-term performance
40
60
40
20
30
20
10
0
−10
20
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
0
20 8
09
20
1
20 0
11
20
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
0
20 8
0
20 9
1
20 0
11
0
Time
Figure 5.4: Volumes for Germany
Figure 5.5: Initial Returns in Germany
25
Number of IPOs
Ritter
This research
150
100
50
Initial Returns in %
250
200
Ritter
This research
Ritter
This research
20
15
10
5
0
20
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
0
20 8
0
20 9
1
20 0
11
0
Figure 5.6: Volumes for UK
20
0
20 2
0
20 3
0
20 4
05
20
0
20 6
0
20 7
0
20 8
0
20 9
1
20 0
11
Number of IPOs
80
Ritter
This research
Initial Returns in %
100
Time
Figure 5.7: Initial Returns in UK
Ritter’s findings for the French and German markets are thus highly comparable with the
findings of this research. Due to a huge difference in sample sizes, there is less correlation
found with the initial returns of UK IPOs.
Chapter 5. Short-term performance
5.3
69
Discussion on underpricing before and after the Financial
Crisis
In order to provide a meaningful answer to research question 2 -whether or not the firstday returns change throughout the crisis - a new variable (Period) is defined based on hot
and cold issue markets, as shown in Table 6.14. From Section 1.2, 2008 and 2009 are commonly considered to be the years of the financial crisis in Europe; this thesis defines them
as the Crisis(2) years (cold issue market). The three years before and after the crisis are
respectively defined as Pre(1) and Post (3), and are respectively the hot issue market (the
housing bubble years) and the post-crisis recovery years2 . Pre(0) comprises the early 2000s
years, which was a cold issue period, and can be used to compare with the cold Crisis(2)
years. Figure 5.8 shows the yearly average level of underpricing. Graphically clustering by
hot and cold issue markets has the advantage that volatility can be levelled out and a clear
pattern can emerge: the level of underpricing peaks in the ’hot’ Pre(1)-crisis periods, falls
back during the crisis and recovers in the years thereafter.
12
12
Initial Returns
Initial Returns in %
Initial Returns
8
6
4
2
8
6
4
2
2012
)
(3
Po
st
2)
is(
C
ris
Pr
e(
0)
Figure 5.8: Level of Underpricing from 2002 to
e(
20
0
20 2
0
20 3
0
20 4
0
20 5
0
20 6
0
20 7
0
20 8
0
20 9
1
20 0
1
20 1
12
Time
1)
0
0
2
10
Pr
Initial Returns in %
10
Time
Figure 5.9: Level of Underpricing from 2002 to
2012: Periods
The number between brackets after the name represents the variable in the SPSS tests that is assigned
to that period
70
Chapter 5. Short-term performance
These results are also given in Table 6.14. In addition to the average level of underpricing
(MAR), the sample sizes are also included in this table. As discussed in Section 5.2, the
table shows a low initial returns of 1.64% for the first three years of the sample.3
A small drop in the level of underpricing is seen in the crisis years, which recovers and slightly
exceeds the Pre(1)-crisis level in the Post(3)-crisis years. An independent Samples Test is
performed to see if the Pre(1) and Post(3) levels differ statistically, as well as if the drop
and recovery in level of underpricing is significant throughout the crisis.
Table 5.7: Defining the period variable
Avg Underpricing
Number of IPOs
Year
Period
2002
1,64%
116
2003
Pre (0)
2004
2005
5,67%
513
2006
Pre (1)
2007
2008
3,58%
73
Crisis (2)
2009
2010
6,25%
147
2011
Post (3)
2012
Table 5.9 provides the outcome of the test. The results Levene’s test show that the first
row with equal variance should be read, since these variance between groups are not statistically different, as shown in a significance level of 0.338, which is greater than the 5 per
cent confidence interval. From the t-test for Equality of Means, no statistical significant difference is found between the pre- and post-crisis periods, meaning that the initial returns
do not differ statistically between those periods.
The tests for periods 1 and 2 and period 2 and 3 yield comparable results, meaning that
3
These years are, as mentioned in Section 5.2, following the collapse of the dotcom bubble, and literature
speaks about the ’early 2000s recession’ (among others, Breneman (2002)).
Chapter 5. Short-term performance
71
the decline in first-day returns during the crisis and the recovery the period thereafter are
not statistically significant. The outcomes of those tests can be found in Appendix D , Table D.1 and Table D.2. It is interesting to note the outcome of the comparisons between the
Pre(0)-period and the crisis in Table D.3 and the Pre(0) period and Pre(1) period in Table
D.4. The years in the Pre(1) show in statistically higher levels of underpricing in comparison with the years after the dot-com bubble (p-value RR = 0.008), but the recession in the
early 2000s shows that initial returns of on average 1.64% are not statistically lower than
the financial crisis’ 3.58% average level of underpricing (p-value RR = 0.278 ), even though
the averages would suggest as much.
Thus, in contrast with the Crisis(2) years, the initial returns in Periods 1 and 3 are statistically different from Period 0, but Crisis(2) and Pre(0) do not differ statistically from
one another. Hence, it seems that the collapse of the dot-com bubble had a more profound
impact on the level of underpricing than the financial crisis had. Possible explanations for
this is that the astronomical levels of IPO underpricing reached during the dot-com bubble
were due to specific characteristics of the issuing firms which incentivised pricing behaviour
(Ljungqvist and Wilhelm, 2003). In this study’s sample, financial companies (which are
at the centre of the financial crisis) are excluded4 , and would more than presumably have
given the outcome of our research a more extreme character. As stated in the previous section, subsamples per stock exchange per year are, for the large part, too small to analyse on
the stock exchange-level.
Table 5.8: Initial Returns in (non) crisis years
non-Crisis
Crisis
Overall
776
73
849
RR
0,0517
0,0358
0,0504
MAR
0,0516
0,0340
0,0501
N
Table 5.8 provides another comparison of the crisis and non-crisis years. The financial crisis
does not seem to have a statistically significant influence on initial returns, despite showing
a 1.76% lower initial return during those years.
4
As discussed in Section 4
72
Chapter 5. Short-term performance
The findings in this section are consistent with null hypothesis 2. This hypothesis can hence
not be rejected on the 5% confidence level. It is important to note that a statistically different level of underpricing is has been found, but these do not include the levels of underpricing in the periods before, throughout or after the crisis. The first period of our timeframe shows initial returns being statistically different from period 1 and 3. The answer to
Research Question 2, then, is that the level of underpricing rises significantly in the years
prior to the crisis, but subsequently did not change after that time. There is thus no evidence of a significantly different level of underpricing throughout the crisis. These findings
are consistent with U.S. findings by King and Banderet (2014).
MAR
RR
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
0,911
0,921
F
0,340
0,338
Sig.
Equality of Variances
Levene’s Test for
-0,288
-0,340
-0,319
-0,376
t
193,735
658
193,985
658
df
0,774
0,734
0,750
0,707
Sig. (2-tailed)
-0,00523
-0,00523
-0,00579
-0,00579
Mean Difference
0,01815
0,01538
0,01817
0,01542
Std. Error Difference
t-test for Equality of Means
Independent Samples Test
Table 5.9: Independent t-test for Pre-Crisis and Post-Crisis
-0,04103
-0,03543
-0,04163
-0,03607
Lower
0,03057
0,02498
0,03004
0,02448
Upper
Interval of the Difference
95% Confidence
Chapter 5. Short-term performance
73
Chapter 6
Long-term performance
In this section, the performance of the sample stocks in the long-term is examined and discussed in accordance with the methodology given in Chapter 3. The Chapter is organised
as follows: firstly, the statistical description and analysis of the Buy-and-Hold Returns are
given. Subsequently, the analysis of the Buy-and-Hold Abnormal Returns is conducted.
Next, the findings of this research are tested alongside results from previous literature on
the subject, and the hypotheses formulated in Chapter ?? are tested.
6.1
Buy-and-Hold Returns
First, an overview is given for an investor with a passive strategy for holding a long position in the IPO stocks in this sample from the first month after the offering until at least 36
months afterwards, or at the moment of delisting. The mathematical formula for the Buyand-Hold Return for a stock is displayed in Equation 6.1.
min(36,delist)
BHRi =
Y
(1 + Ri,t )
(6.1)
i=1
On average, across all sample years, the investor is left with only 83.32% of the value of his
initial investment after a 36-month period. Therefore, he earns a negative return of 16.68%
on average, which translates to -5.90% on yearly basis, or -0.51% on monthly basis Table
6.1 provides an overview of the statistical descriptives of the BHRs. The negative average
return of 16.68% is statistically significant from zero, as the confidence interval is entirely
negative.
75
76
Chapter 6. Long-term performance
Table 6.1: Statistic descriptives BHR
Mean
95% Confidence Interval for Mean
Statistic
Std. Error
-0,1667564
0,03003801
Lower Bound
-0,2257140
Upper Bound
-0,1077988
5% Trimmed Mean
-0,2760570
Median
-0,4130000
Variance
Std. Deviation
0,766
0,87523574
Minimum
-1,00000
Maximum
4,9501
Range
5,98940
Interquartile Range
6,98940
Skewness
2,328
0,084
Kurtosis
7,710
0,168
Table 6.2 provides the statistic descriptives of the 5% trimmed dataset of market values. As
market value-based weights ranged from 0.0008 % to 14.59%, stocks with an outlier market value were excluded from the value-weighted analysis. The value-weighted analysis now
comprises 797 stocks. For the current sample, weights range from 0.00173% to 1.79%1 . The
average market value of firms in the sample amounts to e237.96 million.
When applying a value-weighted scheme, thus giving less weight to smaller firms, the average BHR is -2.74%. At first, this empirical result offers up an interesting observation, as it
provides evidence against the widely documented anomaly that stock price performance
of small firms tend to outperform these of large firms, giving rise to the so-called ‘small
firm effect’ (see citestoll1983transaction; Fama and French (1993)). Additionally, according to (Loughran and Ritter, 2000, p. 3), greater (abnormal) returns should be found for
equally weighted firms in comparison with market capitalisation-weighted firms, since there
are good reasons to believe that percentage misvaluations are more common among small
firms.
1
1/(837*95%) equals 0.125%. Thus, weights now range approximately from one tenth of the average
weight to ten times the average weight
Chapter 6. Long-term performance
77
Table 6.2: Market Values: descriptives
Mean
95% Confidence Interval for Mean
Statistic
Std. Error
237,96
16,921
Lower Bound
204,74
Upper Bound
271,17
5% Trimmed Mean
Median
Variance
Std. Deviation
152,89
61,2800
228.200,375
477,70
Minimum
3,28
Maximum
3.400,00
Range
3.396,72
Interquartile Range
184,63
Skewness
3,630
0,087
Kurtosis
14,887
0,173
However, these findings are consistent with (Ritter et al., 2012, p. 9), who argue that large
firm IPOs outperform small firm IPOs and that ’the underperformance of IPOs is concentrated among small firms’. These findings are in line with evidence from the U.S. studies
(Gao et al., 2013)2 . (Brav et al., 2000, p. 1) also document that ’underperformance is concentrated primarily in small issuing firms’.
The discussion of the year-on-year BHRs in this study focuses on equally weighted firms,
as the yearly samples are often limited in size and thus highly bias-sensitive by returns of
a single firm that has a high proportion of market capitalisation in its portfolio. Moreover,
this results in a high variance of returns3 and thus low power when testing for statistical
significance (Loughran and Ritter, 2000).
Figure 6.1 displays the progress of the sample average buy-and-hold returns on a monthly
basis. At six months, the BHR graph reaches its peak with a return of 4.18% (8.36% annu2
Ritter et al. (2012) finds, on average, a 3-year BHR of 14.6% for large and -2.9% for small companies.
Gao et al. (2013) finds similar results in te U.S. In contrast to this thesis, both authors rely on pre-issue annual sales to distinguish small from large companies, being less than e30 million and less than $ 50 million
respectively.
3
The specific firm’s unique risk is not being diversified away
78
Chapter 6. Long-term performance
ally). From that point onwards, the graph gradually declines until the end of the period of
36 months, with negative returns from month 16 onwards. As discussed in this section, the
negative Buy-and-Hold Returns are mainly due to the large weights (largest samples) in the
years before the crisis (2005-2007). As these were times of economic growth, they are characterised by large IPO activity. At the same time, these are the sample years that include
the 2008 and 2009 crisis years in their 36-month holding period, resulting in -13.13%, 38.71% and -40.81% buy-and-hold returns for 2005, 2006 and 2007 respectively. Since IPOs
in 2007, unlike the two preceding years, were not able to benefit from growth, its returns
after 36 months were the worst affected. If the mean BHRs of all sample years were to be
equally weighted, thus irrespective of their sample size, the average BHR after 36 months
for all sample years would amount to a 0.8% positive return. The equally-weigted firms
BHR graph, as well as the equally-weighted years graph are included in Figure 6.1.
A graphical overview of the 36-month Buy-and-Hold strategy per year in which the IPOs
took place, thus, when the investment started, is given in Figure 6.2.
Average BHR
Average BHR’
Buy-and-hold Return
0.2
0.1
0
−0.1
−0.2
6
12
18
24
Month
30
Figure 6.1: Average 36-month BHRs for 2002-2012
36
Chapter 6. Long-term performance
6.1.1
79
BHRs year on year: Discussion
Figures 6.3 to 6.13 provide an graphical annual breakdowns of Figure 6.1. Figure 6.3, for
example, displays the average BHR from 1 to 36 months after going public in the year 2002.
The patterns in the graphs clearly display the period of economic growth4 and the subsequent period of the financial crisis. The years 2002, 2003 and 2004 reap the benefits of the
years with economic growth, as they enclose the years 2005, 2006 and 2007 in their holding period. These years’ average Buy-and-Hold Returns 36 months after the stocks started
trading are 34.35%, 46.73% and 24.89% respectively.
Figure 6.6 firstly shows the influence of the financial crisis: 24 months after going public
in 2005, being mid-2007, (following the reasoning that the average IPO takes place in the
middle of the year), the returns start to drop. In the final 12 months in the three-year holding period, the average BHR for that year plummeted by approximately 50%. The same
observation is made for the average BHR for firms going public in 2006, as evident in Figure 6.7, where the decline in BHR starts after 12 months; the same mid-2007 period. The
subsequent downturn amounts to over 60%. The end of the graph in Figure 6.7 indicates
the first signs of recovery of the financial markets: at 33-months, which corresponds to the
end of 2009, the declining BHR trend comes to an end and starts to slightly recover. That
can also be observed after 21 months in Figure 6.8, and 9 month in Figure 6.9, All of these
moments in time refer to the same period, late 2009. Firms that go public during that recovery period, as shown in Figure 6.10, result in the first positive 36-month BHR since
2004. Thus, although one may expect firms to go public during the crisis as having positive Buy-and-Hold Returns due to their being able to capture the recovery period within
their holding timeframe, 2008 IPOs show negative returns after 36 months. The reason for
this is that the recovery beginning in late 2009 apparently did not offset the downturn in
the first year for those stocks.
No conclusive evidence can be found for the Eurozone crisis in 2011, as mentioned by Ritter et al. (2012), although months 12 to 24 of figure 6.11 and first monts (2-6) of Figure
6.12 might suggest as much. While the 2010 graph shows a quick recovery, the average 2011
4
The years of the so-called housing bubble in the U.S., see Chapter A of Appendix
80
Chapter 6. Long-term performance
BHR remains negative. Table 6.3 confirms the poor long-run performance of the 2011 IPO
stocks. Furthermore, except for a 35% growth from months 3 to 16 in 2012, no particular
phenomena are evident. As that raise cannot be observed in the graphs for 2010 or 2011,
the rise is considered as a 2012’s sample-specific characteristic rather than as an overall economic trend.
0.6
0.4
0.2
0
−0.2
08
20
09
20
10
20
11
20
12
20
07
06
20
20
04
05
20
20
03
20
02
−0.4
20
Mean Buy-and-hold Return
Average BHR per year
Year
Figure 6.2: Average 36-month BHRs for 2002-2012
Chapter 6. Long-term performance
81
1
Average BHR 2002
0.4
0.2
0
−0.2
6
Buy-and-hold Return
Buy-and-hold Return
0.6
0.4
0.2
0
Buy-and-hold Return
Buy-and-hold Return
Average BHR 2004
0.4
0.2
0
−0.2
6
0
−0.2
−0.4
6
12 18 24 30 36
Month
Figure 6.7: BHRs from 2006 IPOs
12 18 24 30 36
Month
Figure 6.6: BHRs from 2005 IPOs
Buy-and-hold Return
0.2
Average BHR 2005
0.6
12 18 24 30 36
Month
Average BHR 2006
12 18 24 30 36
Month
Figure 6.4: BHRs from 2003 IPOs
Figure 6.5: BHRs from 2004 IPOs
Buy-and-hold Return
0
6
0.6
−0.6
0.5
12 18 24 30 36
Month
Figure 6.3: BHRs from 2002 IPOs
6
Average BHR 2003
0.2
Average BHR 2007
0
−0.2
−0.4
−0.6
6
12 18 24 30 36
Month
Figure 6.8: BHRs from 2007 IPOs
Chapter 6. Long-term performance
Average BHR 2008
0.2
0
−0.2
−0.4
6
0.3
Buy-and-hold Return
Buy-and-hold Return
82
−0.2
6
0.1
0
Average BHR 2011
0
−0.2
6
0.2
0
6
12 18 24 30 36
Month
Figure 6.12: BHRs from 2011 IPOs
Average BHR 2012
0.4
12 18 24 30 36
Month
0.2
12 18 24 30 36
Month
Figure 6.11: BHRs from 2010 IPOs
6
Figure 6.10: BHRs from 2009 IPOs
Buy-and-hold Return
0
Buy-and-hold Return
Buy-and-hold Return
Figure 6.9: BHRs from 2008 IPOs
0.2
0.2
−0.1
12 18 24 30 36
Month
Average BHR 2010
Average BHR 2009
12 18 24 30 36
Month
Figure 6.13: BHRs from 2012 IPOs
Chapter 6. Long-term performance
83
Statistic descriptives of the yearly breakdowns are given in Table 6.3. More descriptives
statistics of the BHRs per year can be found in Appendix F, Table F.1. It is important
to note that the negative returns confidence intervals for the previously mentioned years
(2005), 2006 and 2007 correspond with the years in which some firms go bust. Their return
over 36 months is -100%, as can be derived from their minimum values.
Table 6.3: BHR per Year: Descriptives
95% Confidence Interval
N
Mean
Std. Deviation
Std. Error
Lower Bound
Upper Bound
Minimum
Maximum
2002
33
1,3435
0,90858
0,15816
1,0214
1,6657
0,11
3,44
2003
21
1,4673
1,61387
0,35218
0,7327
2,2020
0,04
6,99
2004
62
1,2489
0,99552
0,12643
0,9961
1,5018
0,06
4,79
2005
123
0,8687
0,88566
0,07986
0,7106
1,0268
0,00
4,50
2006
209
0,6129
0,80916
0,05597
0,5026
0,7232
0,00
5,83
2007
181
0,5919
0,63084
0,04689
0,4994
0,6844
0,00
5,02
2008
50
0,8583
0,74400
0,10522
0,6469
1,0698
0,02
3,10
2009
23
1,0719
0,86885
0,18117
0,6962
1,4476
0,07
3,40
2010
60
1,0065
0,81939
0,10578
0,7948
1,2181
0,02
4,11
2011
50
0,8448
0,51587
0,07295
0,6982
0,9914
0,07
2,05
2012
37
1,1502
1,23918
0,20372
0,7370
1,5634
0,00
4,60
Total
849
0,8332
0,87524
0,03004
0,7743
0,8922
0,00
6,99
In Table 6.4, an overview of the statistic descriptives per stock exchange is given. It is important to notice that only little attention should be given to the most extreme mean BHRs,
as found in Euronext Lisbon (34.90%) and AIM Italia (-46.93%), as the corresponding samples are very small (6 and 7 observations respectively). Their 95% confidence intervals are
broad: the 34.90% positive return from the Euronext Lisbon, for example, is not statistically significant from zero.
In addition to the Euronext Lisbon, the best performing stock, that is, the only one that
has a positive average return, is the London Stock Exchange. This is a remarkable finding,
as one may expect the London Stock Exchange to be one of the most affected by the crisis, since London is the financial centre of Europe and houses a number of investment banks
directly involved in the financial crisis. An explanation for this could be that the growth
84
Chapter 6. Long-term performance
years prior to the crisis were also more affected, and that those years offset any negative returns. Surprisingly, a 30.33% gap in return between the premier and secondary market in
London is found. In order to find an explanation for the aforementioned anomalous values,
Table H.1 in Appendix H was designed to include all means of BHR per Stock Exchange
per annum. Furthermore, a year-on-year comparison of the London Stock Exchanges is
given in Table H.2 in Appendix H, together with a brief discussion on both Table H.1 and
Table H.2.
Table 6.4 further reveals that the Euronext Paris, which represent 241 IPOs or 28.39% of
the observations, as well as London AIM (representing 27.09%) and the Frankfurt Stock
Exchange (representing 15.55%) largely determine the negative average return of 16.68%.
This is because the IPOs on those stock exchange resulted, on average, in negative returns
after 36 months (-14.58%, -26.79%, -19.49% respectively). Although those returns differ
greatly, there are no statistically significant differences if when comparing the averages on
a country level (p-value 91.20% between groups in a one-way ANOVA test), as shown in
Table 6.5.
Table 6.4: BHR per Stock Exchange
95% Confidence Interval
N
Mean
0,8403
Std. Deviation
0,62349
Std. Error
0,16663
Lower Bound
Upper Bound
0,4803
1,2003
Minimum
Maximum
0,22
2,33
Euronext Amsterdam
14
Euronext Brussel
35
0,7559
0,56743
0,09591
0,5610
0,9509
0,02
2,73
Euronext Lisbon
6
1,3490
0,95800
0,39110
0,3436
2,3543
0,17
2,57
Euronext Paris
241
0,8542
0,77722
0,05007
0,7556
0,9528
0,00
4,90
Paris Alternext
23
0,9695
0,95843
0,19985
0,5550
1,3840
0,07
4,11
Italian Stock Exchange
63
0,8431
0,66296
0,08353
0,6762
1,0101
0,06
2,70
7
0,5307
0,44286
0,16739
0,1211
0,9402
0,07
1,40
132
0,8051
1,00670
0,08762
0,6318
0,9785
0,00
5,83
AIM Italia
Frankfurt Stock Exchange
London Stock Exchange
98
1,0354
1,05498
0,10657
0,8239
1,2469
0,00
6,99
London AIM
230
0,7321
0,89936
0,05930
0,6152
0,8489
0,00
4,79
Total
849
0,8332
0,87524
0,03004
0,7743
0,8922
0,00
6,99
Figure 6.14 displays a histogram of the BHR distribution, with 100% as the initial value of
the investment. What is striking here are the 218 (25.68%, or over a quarter of the stocks)
observations that drop to a quarter (or less) of their original value after the 36-month period. 70% of the IPOs stocks have a value that is equal to or less than the stock price at
Chapter 6. Long-term performance
85
Table 6.5: BHR per Country
95% Confidence Interval
N
Euronext (France)
Mean
Std. Deviation
Std. Error
Lower Bound
Upper Bound
Minimum
Maximum
319
0,8604
0,76865
0,04304
0,7757
0,9451
0,00
4,90
70
0,8119
0,64877
0,07754
0,6572
0,9666
0,06
2,70
132
0,8051
1,00670
0,08762
0,6318
0,9785
0,00
5,83
United Kingdom
328
0,8227
0,95704
0,05284
0,7187
0,9266
0,00
6,99
Total
849
0,8332
0,87524
0,03004
0,7743
0,8922
0,00
6,99
Italy
Germany
the first day of the month following the month of the IPO. Table 6.6 provides a yearly breakdown of the BHR in terms of positive or negative returns. As discussed in this section, the
years 2006-2007, and to a lesser extent 2005, are characterised by far lower percentages of
positive returns.
250
BHR Distribution
Observations
200
150
100
50
0
1
2
3
4
Return (in 100%)
Figure 6.14: BHR distribution
5
86
Chapter 6. Long-term performance
Table 6.6: Percentage decomposition of positive Buy-and-Hold Returns
Year
Positive returns
IPOs
Percentage
2002
20
33
60,61%
2003
12
21
57,14%
2004
32
62
51,61%
2005
41
123
33,33%
2006
31
209
14,83%
2007
30
181
16,57%
2008
17
50
34,00%
2009
10
23
43,48%
2010
23
60
38,33%
2011
18
50
36,00%
2012
17
37
45,95%
TOTAL
251
849
29,56%
Chapter 6. Long-term performance
6.2
87
Buy-and-Hold Abnormal Returns
As discussed in Section 3.2, the Buy-and-Hold Abnormal Returns are calculated by substracting the normal Buy-and-Hold Return, obtained by a Fama-French three factor regression, from the Buy-and-Hold Return of a particular stock. Some of these regressions
resulted in abnormally high normal returns, leading to deeply negative BHARs that skewed
the results. Therefore, the sample was trimmed from the most severe outliers based on the
outlier labelling procedure of Hoaglin and Iglewicz (1987). The outliers labelling procedures
results in 12 of the 849, or 1.41% of the observations being eliminated. The outlier labelling
procedure is discussed in Appendix I.
Figure 6.15 gives a graphical overview of the BHARs. The statistical descriptives are given
in Table 6.7. For all 837 BHARs, an average underperformance of 18.53% is found. Section 6.1 states that the high IPO activity in the growth years results in a high weight for
the most affected BH(A)Rs. If the mean BHARs for all research years were to be equally
weighted, the average BHARs after 36 months for all sample years would be -8.62%. Table
6.7 further points out that median BHARs over a period of 36 months are inferior to the
mean BHAR. It is also important to note that a substantial positive skewness (1.456), as
assumed in Section 3.2, was found. The statistical tests will, therefore, have to be corrected
from their skewness, as discussed in Section 3.2 of Chapter 3.
6.2.1
Equally weighted Scheme
In line with the discussion of the Buy-and-Hold Returns, Figure 6.16 provides a graphical
overview of the average BHAR from 1 month after the IPO until 36 months of trading.
In the eleventh month, after almost one year of trading on a stock exchange, the average
BHAR of sample stocks is around zero. In the subsequent 25 months, the average BHAR
steadily falls towards the 18.53% in negative returns after 36 months. This equates to a
yearly negative return of 6.60%, or -0.57 % per month. The average BHR is also included
in Figure 6.16. The abnormal returns graph reveals that the stocks, in comparison with the
normal return, perform 1.85 pe rcent worse than the Buy-and-Hold strategy suggests. Sim-
88
Chapter 6. Long-term performance
200
BHAR Distribution
180
160
Observations
140
120
100
80
60
40
20
0
-4
-3
-2
-1
0
1
2
Return (in 100%)
3
4
Figure 6.15: BHAR distribution
Table 6.7: Descriptives BHAR of trimmed sample
Mean
95% Confidence Interval for Mean
Statistic
Std. Error
-0,185339
0,031531
Lower Bound
-0,247228
Upper Bound
-0,123449
5% Trimmed Mean
-0,247163
Median
-0,354180
Variance
Std. Deviation
0,832
0,912225
Minimum
-2,837750
Maximum
2,260100
Range
8,787850
Interquartile Range
0,771355
Skewness
1,456
0,085
Kurtosis
5,532
0,169
ilar to the BHR discussion, average BHAR are also computed for all years being equally
weighted, in order to eliminate the differences in IPO activity. In this were the case, the
Chapter 6. Long-term performance
89
Buy-and-Hold strategy would yield a -8.62% abnormal return on average. Substraction of
normal returns thus leads to a 9.42% less return than the equally weighted BHR per year.
Figure 6.17 shows a graphical comparison for the BHAR and BHR if the averages per annum were equally weighted. Loughran and Ritter (2000) discussed equally weighting each
firm versus equally weighting each time period and stated that, in general, tests that give
firms equal weighting should have more power than tests that give each time period equal
weighting. This is why this research tests the average BHARs with equally weighted firms,
as displayed in Figure 6.16.
Return
Average BHAR
Average BHR
0
−0.2
Buy-and-hold Return
0.2
0.2
0.1
0
−0.1
−0.2
6
12 18 24 30 36
Month
Average BHAR
Average BHR
6
12 18 24 30 36
Month
Figure 6.17: BH(A)Rs with equally weighted
Figure 6.16: Average 36-month BHAR
years
Table 6.8 displays the bootstrapped t-test for the average BHARs across all sample years
for each month. It reveals that from month 16 onwards, the (negative) BHARs are statistically significant different (smaller)5 from zero. This finding rejects H30 that European
stocks do not have significant performance in the long-run, meaning that the alternative
hypothesis that European stocks underperform can be adopted.
5
Based on 95% confidence intervals
90
Chapter 6. Long-term performance
Table 6.8: Bootstrapped t-test for Monthly Average BHARs
Bootstrapa
Month
Mean Difference
95% Confidence Interval
Bias
Std. Error
Sig. (2-tailed)
Lower
Upper
1
0,01293
-0,00019
0,00720
0,079
0,00006
0,02728
2
0,01175
-0,00041
0,00838
0,160
-0,00503
0,02836
3
0,01157
-0,00055
0,01088
0,314
-0,00890
0,03325
4
0,01054
-0,00081
0,01395
0,465
-0,01640
0,03590
5
-0,00252
-0,00090
0,01687
0,877
-0,03897
0,02910
6
-0,00053
-0,00091
0,01815
0,980
-0,03940
0,03304
7
-0,00541
-0,00088
0,01719
0,743
-0,04177
0,02868
8
-0,00745
-0,00073
0,01906
0,697
-0,04633
0,02929
9
-0,00584
-0,00020
0,01794
0,733
-0,04093
0,03172
10
-0,00214
-0,00007
0,01931
0,907
-0,03968
0,03740
11
-0,00369
0,00027
0,02185
0,872
-0,04436
0,04165
12
-0,02081
0,00068
0,02248
0,367
-0,06165
0,02693
13
-0,02656
0,00102
0,02420
0,282
-0,06888
0,02668
14
-0,02545
0,00104
0,02472
0,312
-0,06727
0,02752
15
-0,03968
0,00105
0,02532
0,113
-0,08245
0,01563
16
-0,05387
0,00080
0,02590
0,030*
-0,09890
0,00240
17
-0,05565
0,00082
0,02636
0,033*
-0,10284
0,00183
18
-0,06430
0,00065
0,02769
0,017*
-0,11350
-0,00610
19
-0,07644
0,00036
0,02883
0,010*
-0,13061
-0,01643
20
-0,08682
0,00028
0,02898
0,004*
-0,13804
-0,02658
21
-0,09909
0,00034
0,02911
0,001*
-0,15093
-0,03916
22
-0,10089
0,00040
0,02896
0,001*
-0,15263
-0,04052
23
-0,11182
0,00028
0,02831
0,001*
-0,16323
-0,05045
24
-0,12305
0,00011
0,02891
0,001*
-0,17854
-0,06281
25
-0,14251
-0,00015
0,02745
0,001*
-0,19355
-0,08809
26
-0,15211
-0,00015
0,02774
0,001*
-0,20476
-0,09748
27
-0,16111
-0,00005
0,02828
0,001*
-0,21570
-0,10472
28
-0,16509
-0,00011
0,02834
0,001*
-0,22090
-0,11080
29
-0,16888
-0,00021
0,02870
0,001*
-0,22444
-0,11486
30
-0,15954
-0,00009
0,02935
0,001*
-0,21682
-0,10212
31
-0,17519
0,00008
0,02993
0,001*
-0,23121
-0,11554
32
-0,18308
0,00000
0,03021
0,001*
-0,24120
-0,12168
33
-0,18435
-0,00007
0,02992
0,001*
-0,24219
-0,12446
34
-0,17545
-0,00008
0,03067
0,001*
-0,23346
-0,11097
35
-0,17950
0,00001
0,03134
0,001*
-0,23824
-0,11577
36
-0,18534
-0,00003
0,03211
0,001*
-0,24756
-0,11913
a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples
*: Statistically significant on the 5% significance level
Chapter 6. Long-term performance
91
Average BHR 2002
Average BHAR 2002
0.5
0
6
Buy-and-hold Return
Buy-and-hold Return
EW BHARs year on year: Discussion
Average BHR 2003
Average BHAR 2003
1
0.5
0
6
12 18 24 30 36
Month
12 18 24 30 36
Month
Figure 6.19: BH(A)Rs from 2003 IPOs
Figure 6.18: BH(A)Rs from 2002 IPOs
Average BHR 2004
Average BHAR 2004
0.4
0.2
0
−0.2
6
Buy-and-hold Return
Buy-and-hold Return
0.6
0
6
0
−0.5
12 18 24 30 36
Month
Figure 6.22: BH(A)Rs from 2006 IPOs
12 18 24 30 36
Month
Figure 6.21: BH(A)Rs from 2005 IPOs
Buy-and-hold Return
Buy-and-hold Return
Average BHR 2006
Average BHAR 2006
6
0.5
12 18 24 30 36
Month
Figure 6.20: BH(A)Rs from 2004 IPOs
0.5
Average BHR 2005
Average BHAR 2005
Average BHR 2007
Average BHAR 2007
0
−0.5
6
12 18 24 30 36
Month
Figure 6.23: BH(A)Rs from 2007 IPOs
92
Chapter 6. Long-term performance
0.6
Average BHR 2008
Average BHAR 2008
0.2
Buy-and-hold Return
Buy-and-hold Return
0.4
0
−0.2
−0.4
6
0
−0.2
Figure 6.25: BH(A)Rs from 2009 IPOs
Buy-and-hold Return
−0.2
Average BHR 2011
Average BHAR 2011
0
−0.2
12 18 24 30 36
Month
6
Figure 6.26: BH(A)Rs from 2010 IPOs
Average BHR 2012
Average BHAR 2012
0.2
0
6
12 18 24 30 36
Month
Figure 6.27: BH(A)Rs from 2011 IPOs
0.6
0.4
12 18 24 30 36
Month
0.2
0
Buy-and-hold Return
Buy-and-hold Return
0.2
6
Average BHR 2010
Average BHAR 2010
6
0.4
12 18 24 30 36
Month
Figure 6.24: BH(A)Rs from 2008 IPOs
0.2
Average BHR 2009
Average BHAR 2009
12 18 24 30 36
Month
Figure 6.28: BH(A)Rs from 2012 IPOs
Chapter 6. Long-term performance
93
Figures G.1 to G.11 in Appendix G represent the annual breakdown of figure 6.16. In order to make a meaningful comparison with the Buy-and-Hold returns, BHARs are plotted
together with the BHRs in Figures 6.18 to 6.28. In general, the course that BHAR graphs
run is similar to the the ones of the respective BHR graphs. The BHAR graphs however, as
expected, tend to smooth out the BHR returns: both the influence of the growth years and
the crisis is diminished.
The findings in this section are consistent with the window of opportunity hypothesis, formulated by Ritter (1991)6 . As the period 2005-2007 can be considered as a hot(ter) issue
market due to its larger IPO activity, the findings in this study are consistent with those
made by Loughran and Ritter (1995), who show that firms launching their IPO during such
fad or hot market periods perform poorly relative to other firms.
Appendix J provides a full table of the bootstrapped statistics of the BHAR per year. Table 6.9 gives an overview of the bootstrapped confidence intervals for the means of the BHAR
per year7 . The years 2006, 2007 and 2011 have BHARs that are significantly lower than
zero. The highest BHARs are, in line with the BHRs, the years that include the years of
growth before the crisis. Yet, due to high standard errors, those returns are not significantly positive.
A breakdown of the BHAR per stock exchange can be found in Table 6.10. This Table
summerises the bootstrap-performed test, which can be found in Appendix K. In line with
the BHR, the most extreme results are found in Euronext Lisbon and AIM Italia. However,
as stated in the discussion about the BHR, the sample sizes of respectively 6 and 7 are
not enough to result in reliable findings. Section 6.1 offers a more profound analysis. Furthermore, the Euronext Paris, the Frankfurt Stock Exchange and the London AIM Stock
Exchange, the three largest contributors to the sample, are the only stock exchanges that
display a statistically significant negative performance over a 3-year period. Similar to the
BHR discussion, the gap between the primary and secondary markets in London is striking: the AIM performs almost 30% worse than the LSE over 36-months, equal to a monthly
overperformance of 3.37% by the LSE in comparison with its secondary market. According
6
7
Discussed in Section 2.3.2
The columns with an asterix contain bootstrapped values
94
Chapter 6. Long-term performance
Table 6.9: BHAR per Year: Bootstrapped Statistics
95% Confidence
N
Mean
Std. Deviation
Std. Error*
Interval for Mean*
Lower Bound
Upper Bound
Minimum
Maximum
2002
29
0,135463
1,354224
0,240599
-0,324500
0,605591
-2,458570
2,949080
2003
17
0,188913
1,423726
0,335915
-0,519695
0,848129
-2,389580
2,976870
2004
59
-0,009399
1,143283
0,150943
-0,317214
0,260049
-3,837750
3,518540
2005
123
-0,154141
0,954616
0,086233
-0,318593
0,015174
-2,893830
3,533810
2006
208
-0,331949
0,748467
0,050573
-0,427457
-0,228407
-2,124370
3,372200
2007
182
-0,281175
0,760987
0,055063
-0,383471
-0,164278
-1,262460
3,950100
2008
49
-0,074308
0,792150
0,116519
-0,297128
0,167569
-1,878840
2,048760
2009
23
-0,134843
0,969420
0,203579
-0,547799
0,274144
-2,838710
1,999540
2010
60
-0,105417
0,876322
0,113320
-0,305429
0,134215
-2,384200
2,499400
2011
50
-0,238779
0,567479
0,078360
-0,399647
-0,084285
-1,375990
1,272630
2012
37
0,066788
1,445799
0,233578
-0,405966
0,535207
-3,549860
3,538140
Total
837
-0,185339
0,912225
0,031872
-0,248102
-0,121834
-3,837750
3,950100
to a one-way ANOVA test, no statistically significant differences are found between stock
exchanges groups (p-value: 0.416).
Table 6.10: BHAR per Stock Exchange
95% Confidence Interval*
N
Mean
Std. Deviation
Std. Error*
Minimum
Lower Bound
Maximum
Upper Bound
Euronext Amsterdam
14
-0,103997
0,601985
0,160995
-0,396250
0,228380
-0,703600
1,328890
Euronext Brussels
35
-0,133717
0,612697
0,103579
-0,326582
0,079954
-1,235510
1,973650
6
0,189153
0,914773
0,381699
-0,508137b
,985170b
-0,766870
1,623190
Euronext Lisbon
Euronext Paris
236
-0,179630
0,882083
0,054780
-0,286491
-0,075968
-3,837750
4,372200
Paris Alternext
23
-0,153337
0,894545
0,177858
-0,473517
0,199196
-1,421560
2,499400
132
-0,189670
1,049948
0,088474
-0,354143
-0,009483
-2,458570
4,950100
63
-0,150366
0,621728
0,077874
-0,300768
0,009254
-1,111720
1,806550
7
-0,503926
0,430878
0,159546
-0,717283
-0,120405
-0,784440
0,461170
Frankfurt Stock Exchange
Italian Stock Exchange
AIM Italia
London Stock Exchange
97
-0,003524
0,939678
0,092247
-0,171392
0,177536
-2,384200
2,754520
London AIM Stock Exchange
224
-0,293881
0,974141
0,064695
-0,419662
-0,166121
-3,549860
3,520780
Total
837
-0,185339
0,912225
0,030207
-0,246514
-0,126068
-3,837750
4,950100
Figure 6.29 summarises the BHR and BHARs per annum. Note that for the years 2009,
2010 and 2011, the comparison with the normal return does not result in levelling off the
Buy-and-Hold returns, unlike other years. Also, while the BHR only has negative values
Chapter 6. Long-term performance
95
in years that capture the years of the crisis, the BHAR is negative in eight of the eleven
years.8 This is in line with the generally accepted finding that IPOs underperform in the
long run, as discussed in Section 2.3.
−40
−13.48
+6.67
−14.93
−23.98
−38.77
−30.69
−20
−41.48
−30.88
−12.99
−15.17
−14.70
−7.74
−0.91
0
−10.54
+0.98
+7.60
+17.18
+25.29
+34.35
+18.89
20
+13.04
Return (in 100%)
40
EW BHR
EW BHAR
+46.73
60
−60
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Figure 6.29: Equally weighted scheme: BHR & BHAR Summary
6.2.2
Value-weighted scheme
As proposed in Section 3.2, a value-weighted scheme should be used to lower the weight
of the small stocks. As previously stated, it is important to be aware that value-weighted
returns in the sample can result in subsamples where single stock makes up a large proportion of the portfolio due to its market capitalization. This in turn results in a high variance of returns because the firm’s unique risk is not diversified away (Loughran and Ritter,
2000).
In line with the findings in Section 4, where the deal values during the crisis years, espe8
2002, 2003 and 2012 have positive BHARs
96
Chapter 6. Long-term performance
cially 2009, collapsed, Table L.1 in Appendix L also reveals the lowest average market values for 2009 (and to a lesser extent 2008) IPO firms. Except for the higher average market
values of 20029 and 201010 , the market values of all years is near to the common mean of
e237.96 million.
Figure 6.30 displays the graphical outcome of BHARs with the value-weighted scheme,
together with the equally weighted scheme (of the trimmed sample), from months 1 to
36. The result is striking: after 36 months, there is a 20.40% gap between the 797 BHARs
of the two weighting schemes: a return of -19.77% for the equally weighted BHARs, and
0.63% for the value-weighted BHAR. Using the value-weighted scheme thus seems to make
the strong level of underperformance in the equally weighted scheme disappear. Only between months 15 and 28 does the VW BHAR seems to underperform slightly11 . Moreover, if the IPO activity-effect is eliminated, the IPO stocks outperform the benchmark by
20.03% in the VW-scheme, as shown in Figure 6.31. The difference with the EW scheme,
then, is 28.65%. Annual breakdown is shown to identify the origin of the difference.
e480.53 million, mostly due to one outlier of 3.2 billion
e422.83 million
11
Maximum underperformance found is -2.43%
9
10
Chapter 6. Long-term performance
97
Buy-and-hold Abnormal Return
0.2
Average EW-BHAR
Average VW-BHAR
0.1
0
−0.1
−0.2
6
12
18
24
Month
30
36
Figure 6.30: Average 36-month BHARs for 2002-2012:
Value-weighted
Buy-and-hold Abnormal Return
0.5
Average EW- BHAR
Average VW-BHAR
0.4
0.3
0.2
0.1
0
−0.1
−0.2
6
12
18
24
Month
30
36
Figure 6.31: VW BH(A)Rs with equally weighted years
98
Chapter 6. Long-term performance
VW BHARs year on year: Discussion
EW BHAR 2002
VW BHAR 2002
BHR 2002
1
0
6
Buy-and-hold Return
Buy-and-hold Return
2
1
0
6
12 18 24 30 36
Month
Figure 6.33: BH(A)Rs from 2003 IPOs
1
EW BHAR 2004
VW BHAR 2004
BHR 2004
0.5
0
6
Buy-and-hold Return
1
Buy-and-hold Return
2
12 18 24 30 36
Month
Figure 6.32: BH(A)Rs from 2002 IPOs
EW BHAR 2005
VW BHAR 2005
BHR 2005
0.5
0
12 18 24 30 36
Month
6
EW BHAR 2006
VW BHAR 2006
BHR 2006
0.5
0
−0.5
6
12 18 24 30 36
Month
Figure 6.36: BH(A)Rs from 2006 IPOs
12 18 24 30 36
Month
Figure 6.35: BH(A)Rs from 2005 IPOs
Buy-and-hold Return
Figure 6.34: BH(A)Rs from 2004 IPOs
Buy-and-hold Return
EW BHAR 2003
VW BHAR 2003
BHR 2003
3
0.5
EW BHAR 2007
VW BHAR 2007
BHR 2007
0
−0.5
6
12 18 24 30 36
Month
Figure 6.37: BH(A)Rs from 2007 IPOs
99
EW BHAR 2008
VW BHAR 2008
BHR 2008
0.5
Buy-and-hold Return
Buy-and-hold Return
Chapter 6. Long-term performance
0
−0.5
6
0
−0.2
0
Buy-and-hold Return
EW BHAR 2011
VW BHAR 2011
BHR 2011
0.5
0
12 18 24 30 36
Month
6
Figure 6.40: BH(A)Rs from 2010 IPOs
12 18 24 30 36
Month
Figure 6.39: BH(A)Rs from 2009 IPOs
Buy-and-hold Return
Buy-and-hold Return
0.2
6
EW BHAR 2010
VW BHAR 2010
BHR 2010
6
0.4
12 18 24 30 36
Month
Figure 6.38: BH(A)Rs from 2008 IPOs
0.5
EW BHAR 2009
VW BHAR 2009
BHR 2009
0.6
12 18 24 30 36
Month
Figure 6.41: BH(A)Rs from 2011 IPOs
EW BHAR 2012
VW BHAR 2012
BHR 2012
0.5
0
6
12 18 24 30 36
Month
Figure 6.42: BH(A)Rs from 2012 IPOs
Figures 6.32 to 6.42 display both the VW and EW BHAR, as well as the BHR for all sample years. Where the equally weighted scheme resulted in long-term underperformance in
100
Chapter 6. Long-term performance
all years except for 2002, 2003 and 2012, the outcome of the value-weighted scheme results
in outperforming the benchmark in all years except for the period 2005-2007.
Table 6.11: BHARs in equally-weighted and value weighted scheme
Year
EW (%)
VW (%)
∆ (in%)
2002
13,04
71,95
58,91
2003
18,89
110,16
91,27
2004
-0,91
33,92
34,84
2005
-15,17
-6,54
8,63
2006
-30,69
-27,99
2,70
2007
-30,88
-26,24
4,65
2008
-7,75
6,25
13,99
2009
-13,48
4,45
17,94
2010
-10,54
30,08
40,62
2011
-23,98
2,75
26,74
2012
6,68
21,60
14,92
-19,77
0,63
20,40
TOTAL
Table 6.11 indicates that, for the stocks capturing the economic downturn in their holding period,12 the value-weighted and equally weighted schemes result in highly comparable
results after 36 months. Figures 6.35, 6.36 and 6.37 show that throughout the three-year
holding period, the VW and EW BHAR are more or less equal in their course. However,
the other years display significant differences between the returns.
First of all, for all the years in our sample, giving less weight to small stock will result in
higher abnormal returns. For the years that have not been affected by the crisis, the difference in abnormal returns is substantial (Table 6.11). Section 6.1 showed that large firms
tend to outperform small firms; those finding is in line with those made by other authors13 .
Hence, when adopting a value-weighted scheme, findings, except for those in the years 2005,
2006 and 2007, contradict the well-known anomaly of underperformance. Moreover, the
12
13
Firms that issued stocks in 2005, 2006 and 2007
Gao et al. (2013) finds a market-adjusted BHR of on average 3.2% decomposed as -19.7% for small
firms and 13.0% for large firms going public between 2001-2009 in the U.S.
Chapter 6. Long-term performance
101
contradictory results are substantial and thus provide evidence against the anomaly.
Regarding the many similarities between the BHAR and BHR graph in Figure 6.16, it tends
to provide evidence for the finding of Loughran and Ritter (2000) that multi-factor regressions fail in detecting abnormal returns that are present especially when the target population comprises small stocks like typical IPOs, as discussed in Section 3.2. The observation that the VW and EW scheme are highly comparable only in the -by the crisis- most
affected holding periods can be explained by the reduced explanatory ability of the FamaFrench model when a crisis appears, as documented by Duong (s.d.).
In line with Figure 6.29, Figure 6.43 also shows the BHR and BHAR per year in the valueweighted scheme. It shows that the VW BHARs only have a negative sign in the 20052007 period, (in contrast to Figure 6.29, where eight of eleven sample years where characterised by negative BHARs). Also, all years have an equal sign for the EW BHR and EW
BHAR. When comparing Figures 6.29 and 6.43, one can see that, as previously discussed,
the equally weighted and value-weighted schemes result in substantially different outcomes
when calculating the return of the passive strategy used by this study. For the Buy-andHold Returns, the difference between the two weighting schemes has a p-value of 2.29%,
making it significant. The p-value for the Buy-and-Hold Abnormal Returns amounts to
0.53%, resulting in the same conclusion.
The value-weighted scheme results in substantially different abnormal returns, especially
in those years where the samples are the smallest, whereas high resemblance is observed in
the years with big sample-sizes. Given this result, this thesis prefers the equally weighted
scheme, as Loughran and Ritter (2000) also follow the reasoning that a single firm with a
high market-capitalisation can heavily skew the results.
For the sake of completeness, Table 6.12, displays the Buy-and-Hold Returns for both the
IPO stocks and the FTSE 100 index. It reveals that, except for the year 2002, the IPO
stocks underperform in comparison with the index, except for 2003 and 2012. These findings are equal to the findings in the EW BHAR scheme. However, as mentioned in Chapter
3, the Fama-French model is preferred as the normal return since, among other reasons, it
incorporates a market-factor as well, but adjusted for risk.
102
Chapter 6. Long-term performance
+149.36
180
160
−39.30
−27.99
−20
−40
−60
+20.62
+21.60
−40.46
−26.24
−8.22
−6.54
0
+1.10
+2.75
20
+33.42
+30.08
40
+20.78
+4.45
60
+8.72
+6.25
80
+33.92
Return (in 100%)
100
+54.57
+71.95
120
+80.85
+110.16
140
VW BHR
VW BHAR
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Figure 6.43: Value-weighted scheme: BHR & BHAR Summary
Table 6.12: Comparison with FTSE
Year
BHR IPOs
BHR FTSE
2002
34,85
10,14
2003
46,73
48,45
2004
24,89
48,95
2005
-13,13
6,18
2006
-38,71
-26,23
2007
-40,81
-27,08
2008
-14,17
9,30
2009
7,19
29,95
2010
0,65
31,25
2011
-15,52
13,58
2012
15,02
17,16
Chapter 6. Long-term performance
6.3
103
Comparison with previous evidence on long-term performance
In line with Chapter 5, a comparison is made with the findings from other researchers. Section 2.3 makes clear that results of the Buy-and-Hold Abnormal Returns may significantly
differ based on various methodologies and benchmarks. This is why, in the first instance,
the BHR instead of the BHAR is compared with previous evidence, since there is only one
way to calculate the BHR. The findings of this study can be compared to the results of Ritter et al. (2012), who examined European IPOs. Due to the earlier timeframe used by Ritter et al. (2012), the comparison is limited to 2008.
Figure 6.44 shows the IPO activity volumes of both this research and the research conducted by Ritter et al. (2012). The findings are in line with Section 5.2. The volumes in
this research are a great deal smaller14 but nevertheless display the same evolution and pattern. Figure 6.45 shows the average BHR for the sample years. What is particularly striking is the 25% gap in Buy-and-Hold Returns of both studies, a gap that seems to persist for
Ritter
This research
Number of IPOs
600
400
200
Buy-Hold Returns in %
the first five years.
Ritter
This research
40
20
0
−20
Figure 6.44: Volumes: a comparison
20
05
20
06
20
07
20
08
4
20
0
2
20
0
2
20
03
20
04
20
05
20
06
20
07
20
08
20
0
20
0
03
−40
Time
Figure 6.45: BHR: a comparison
The main take-away is that the findings are equal in the sense that both BHR-graphs decline until 2006 (for this thesis) or 2007 (Ritter’s research), only to recover afterwards. In
14
The sample size in this research is on average 37.5% of the sample size of Ritter
104
Chapter 6. Long-term performance
2008, where the sample sizes are merely equal, an almost equal return is displayed (-4% and
-7.43%). The difference in BHRs between the two studies could be attributed to the different sample sizes.
The BHARs discussed in Chapter 2 are in line with the common finding of underperformance. A comparison with this research, however, is impossible as Table 2.2 only provides
averages and the timeframes are not equal. Therefore, this section additionally provides
the results of Gajewski and Gresse (2006). In line with this thesis, the empirical evidence
on long-term performance of Gajewski and Gresse (2006) is less clear. He finds that results
are not benchmark-dependent but that the results differ when applying various measurement methods. The relevant markets are summarised, as given in Table 6.13. Gajewski and
Gresse (2006) examined European IPOs from 1995 until 2004 and found substantial underperformance in every country except for Belgium. No conclusions can be drawn from a
comparison with our data15 , as Gajewski and Gresse (2006) do not provides annual subdivisions.
Table 6.13: Evidence from Gajewski and Gresse (2006): 1995-2004
Country
15
Table 6.10
Index
mean 3y-BHAR (%)
N
St. Dev
Belgium
BEL20
14,98
51
180,3
France
CAC40
-36,33
355
128,33
Germany
DAX30
-30,28
408
210,89
Italy
MIB30
-30,47
127
61,73
Netherlands
AEX
-18,81
44
117,64
Portugal
PSI20
-19,24
15
111,78
UK
FTSE100
-24,88
306
64,32
Chapter 6. Long-term performance
6.4
105
Discussion on underperformance before and after the Financial Crisis
In line with Section 5.3, this section attempts to provide an answer to Research Question
4 by dividing the timeframe of this study’s sample into four periods based on hot or cold
issue markets.16 . Table 6.14 summarises the most important statistical descriptives of the
four subsamples.
Table 6.14: Defining the period variable
Avg Underperformance
St. Dev
Number of IPOs
Year
Period
2002
6,27%
1,2416
105
2003
Pre (0)
2004
2005
-27,13%
0,8082
513
2006
Pre (1)
2007
2008
-9,36%
0,8463
72
Crisis (2)
2009
2010
-10,74%
0,9734
147
2011
Post (3)
2012
In order to reject the null hypothesis 4, a t-test will be needed to determine if the evolution
of the BHAR contains significantly different levels. Figure 6.47 graphically shows the average BHAR evolution over these periods. The outcomes of the t-tests are given by Tables
M.1, M.2 and M.3 in Appendix M.
The t-tests reveal that the drop in underperformance from the pre(0) to pre(1) time periods is significant on the 1 per cent level17 . Furthermore, companies going public during
16
17
The reader may consult Section 5.3 of Chapter 5 for the reasoning behind the subdivisions.
p-value: 0,009253613
Chapter 6. Long-term performance
−20
−30
Po
st
C
Time
(3
)
2)
(1
(0
Pr
e
20
0
20 2
0
20 3
0
20 4
0
20 5
06
20
0
20 7
0
20 8
0
20 9
1
20 0
1
20 1
12
)
−40
is(
−20
−10
ris
0
0
)
20
Average BHAR
10
Pr
e
Long-run performance in %
Average BHAR
Long-run performance in %
106
Time
Figure 6.46: LT Performance 2002-2012
Figure 6.47: LT Performance: Periods
the crisis perform better on the 10 per cent level18 than companies that issue stocks for the
first time prior to the crisis. Additionally, going public after the crisis results in, on average,
equal performance than does going public during the crisis19 .
The results from the t-test imply that the null hypothesis H40 can be rejected and the alternative hypothesis can be adopted. The answer on Research Question 4 is thus that going
public in the years prior to the crisis results in substantial underperformance in the longrun relative to going public in the previous period or issuing stock during the crisis itself.
Table 6.15: Buy-and-Hold (Abnormal) Returns: Summary
Non-crisis
Crisis in holding period
Overall
301
496
797
BHR EW
0,0777
-0,3433
-0,1843
BHR VW
0,4274
-0,3146
-0,0274
BHAR EW
-0,0598
-0,2814
-0,1977
BHAR VW
0,3578
-0,2155
0,0063
N
To conclude, Table 6.15 shows an overview of average Buy-and-Hold (Abnormal) Returns in
18
19
p-value: 0.083000905
p-value: 0.914368198
Chapter 6. Long-term performance
107
both equally and value-weighted schemes for IPOs going public during (the hot issue market of) 2005–2007 and the remainder of the sample years, which respectively equates to (1)
having the crisis in the holding period and (2) not having the crisis in the holding period.
For the equally weighted schemes, the t-tests point out that the crisis-affected years perform significantly less well than in the years outside the crisis.20 .
20
P-values of the t-test are 1.17936E-11 for the EW BHRs and 0.000934 for the EW BHARs
Part IV
Summary and Conclusions
109
Chapter 7
Summary of findings
7.1
Overview of the study
This dissertation aims to provide an answer to whether or not two well-known and widely
discussed anomalies of short-run underpricing and long-run market underperformances persist during the financial crisis. Therefore, 849 stocks going public from 2002 and 2012 on
European stock exchanges were evaluated by their short- and long-term performance. The
analysis for short-term performance was conducted by examining both the raw and the
market-adjusted first-day returns. Long-run performance was evaluated by analysing 36month Buy-and-Hold Abnormal Returns, with the Fama-French three factor model as a
‘normal return’.
7.2
Short-Term Performance
Chapter 5 examines the first-day returns and finds that for 62.78% of the stocks, the closing price after the first day was greater than the offer price, strongly indicating underpricing. The average change in stock price on the first day amounts to +5.08%, or +5.03%
when adjusted for market returns. Those initial returns are significant at the 1 per cent
level and thus reject H10 . The answer to Research Question 1 is hence that the examined
IPOs are underpriced in the short-term. These findings are consistent with previous empirical evidence in Europe.
111
112
Chapter 7. Summary of findings
Except for the period 2002-2004 and 2009, all years show significant underpricing1 . This
finding is in line with Gajewski and Gresse (2006), who report an extremely cold market
in the early 2000s. The time period from 2005-2007 show the most significant level of underpricing. Since these years also represent the highest volumes, this period can be characterised as a hot(ter) issue market. Both findings are thus consistent with Section 2.2.
Except for the Paris Alternext, with a rather limited number of 22 IPOs, all stock exchanges
exhibit significant levels of underpricing. The phenomenon of underpricing hence is present
in all of the examined European stock markets, which is in line with previous literature,
as discussed on the subject. The AIM Italia represents the most significant level of underpricing, with 18.05% initial returns, significant on the 5% level. However, it is important to
notice that this stock exchange represents only 6 IPOs.
In order to examine if there are significant differences between the average levels of underpricing per year, the examined timeframe was subdivided into four subsamples. In testing
the average levels of underpricing of the four subsamples could not reject H20 , could not be
rejected, as it states that the level of underpricing does not change throughout the crisis.
Firms going public in 2008–2009 have a below average level of underpricing (3.58%), but
the difference is not significant. The difference between the early 2000s (1.64%) to the years
prior to the crisis (5.67%), however, is significant. The highest level of underpricing can be
observed in the 2010-2012 subsample, being 6.25%. Yet, statistically, that level does not
differ from the previous two periods. The answer to Research Question 2 hence is that the
level of underpricing raised significantly in the years prior to the crisis, but subsequently
did not change after that. is that the level of underpricing significantly increased in the
years prior to the crisis but did not change after that. These findings are consistent with
those from the U.S. compiled by King and Banderet (2014).
1
2010-2012 only on 10% level
Chapter 7. Summary of findings
7.3
113
Long-term performance
Chapter 6 analyses the long-term performance of the IPO stocks in the research sample. As
recommended in the literature, this has been carried out by analysing the 36-month Buyand-Hold (Abnormal) Returns. This section summarises these findings.
7.3.1
Main findings
Main findings of the BHR analysis are:
– The average return of a Buy-and-Hold strategy amounts to -16.68%, or -5.90% on a
yearly basis.
– The BHRs are heterogeneous, as can been concluded from the standard deviation of
87.5%.
– When eliminating the IPO activity effect by weighting each year equally, the BHR is
0.8%, or 0.27% on a yearly basis.
– Weighting stocks by their market capitalisation results in value-weighted average
BHR of -2.47%, or -0.83% on yearly basis.
– The annual BHRs clearly show a period of economic growth prior to the crisis, followed by the economic crisis itself. Firms that went public in years during times of
growth in their holding period are characterised by positive average Buy-and-Hold
Returns. 55.17% of the IPOs going public in 2002–2004 have a positive BHR, with an
average 36-month Buy-and-Hold return of 31.54% for all stocks.
– Firms going public during those exact years of growth (2005–2007), capture the crisis
and their holding period, and are therefore confronted with deeply negative Buy-andHold returns. 80.12% of IPOs going public in those years result in negative returns
over the 36-month period, at an average of -33.32%.
114
Chapter 7. Summary of findings
– Going public during the crisis or after the crisis on average yield comparable returns
(-7.44% and -1.23% respectively). In 36.99% and 39.46% (respectively) of the stocks,
the investor yielded a positive return.
– – Except for a few more extreme values in the smallest stock exchanges, the largest
stock exchanges perform rather close to average. BHRs amount to -14.58%, -26.79%
and -19.49% for Euronext Paris, London AIM and Frankfurt Stock Exchange respectively (with a p-value of 91.20% between groups in a one-way ANOVA test).
Main findings of the BHAR analysis are:
– The equally weighted average abnormal return of 837 IPO stocks in a Buy-and-Hold
strategy amounts to -18.53%, or -6.60% on a yearly basis.
– BHARs are heterogeneous, as can been concluded from the standard deviation of
91.22%.
– When eliminating the IPO activity effect by equally weighting each year (EW scheme),
the BHAR is -8.62%, or -2.96% on a yearly basis.
– Weighting 797 stocks by their market capitalization results in an value-weighted average BHAR of 0.63%, or 0.21% on yearly basis.
– Eliminating the IPO activity effect in the value-weighted scheme by weighting each
year equally results in an average BHAR is 20.03%, or 6.27% on a yearly basis.
– The course of the annual BHAR graphs shows many similarities with the respective
graphs from the BHR, although BHARs tends to smooth out the evolution in returns.
– Applying a value-weighted scheme results in, on average, a 20.40% higher BHAR
– Differences between stock exchanges are not significant, as pointed out by a one-way
ANOVA test with a p-value of 41.6%.
– The four subsamples of the timeframe based on hot or cold issue market characteristics yield, on average, 6.27%, -27.13%, -9.36% and -10.74% respectively
Chapter 7. Summary of findings
7.3.2
115
Discussion of findings
When enumerating the findings in this chapter, the significant differences in BHAR outcomes when applying different weighting schemes stand out. In what follows, a summary
will be given of the choices made when answering the postulated research questions and the
academic literature from which they have been formulated.
This research prefers the scheme where all firms are equally weighted. The equally weighted
scheme is preferred over the value-weighted scheme given that the 116 value-weighted scheme
results in substantially different abnormal returns, especially in years where the samples are
smallest, whereas high similarity is observed in the years with large sample sizes. Loughran
and Ritter (2000) concur with the reasoning that a single firm with a high market cap can
heavily skew results. Secondly, the scheme for equally weighting each firm is preferred over
equally weighting each year. This choice is based on findings from the same authors, (Loughran
and Ritter, 2000, p. 3), who noted that, in general, tests that weight firms equally should
have more power than tests that weight each time period equally.
The findings of this thesis are also consistent with the window of opportunity hypothesis as
formulated by Ritter (1991). Since firms have gone public from 2005–2007, a hot(ter) issue
market will result in the worst levels of underperformance. The findings in this study are
consistent with Loughran and Ritter (1995) who show that firms which launch their IPO
during such fad or hot markets perform poorly, relative to other firms.
In addition, the findings in the value-weighted scheme are consistent with Ritter et al. (2012),
who argue that large firm IPOs outperform small firm IPOs, and that the underperformance of IPOs is hence concentrated in small firms. These findings are in line with evidence about the U.S. from Gao et al. (2013).
Furthermore, based on equally weighted scheme, Chapter 6 was able to provide an answer
to Research Questions 3 and 4. When applying an equally weighted scheme, the IPO stocks
of the research sample significantly underperform after 36-months2 . H30 hence can be rejected and the alternative hypothesis that European stocks underperform in the long-term
2
From month 16 onwards, the BHARs are significantly different from zero
116
Chapter 7. Summary of findings
can be adopted, which provides an answer to Research Question 3. Section 6.4, subsequently,
attempted to answer Research Question 4 by analysing the different level of underpricing in
four sections of the timeframe. The null hypothesis H40 can be rejected since going public
prior to the crisis resulted in substantial underperformance in the long-term, relative to going public in the previous period or issuing stock during the crisis. The alternative hypothesis, that different levels of long-term performance can be found throughout the financial
crisis, answers Research Question 4.
Chapter 8
Conclusions
8.1
Conclusions and remarks
This dissertation studies the short- and long-term market stock price performance of newly
listed firms over an 11-year timeframe surrounding the financial crisis. Since the financial
crisis is both a radical and a unique event, this study hopes to specifically evaluate the impact of such market conditions on the stock price performance of IPO firms. In particular,
the widely documented phenomena of underpricing and underperformance are examined
in the light of the crisis. 849 IPOs going public from 2002–2012 are subject to this study,
which seeks to test hypotheses on whether or not the financial crisis had a significant impact on the performance of the stock market of IPOs.
Short-term analyses found an average level of underpricing of 5.08% across this thesis’ timeframe. Little to no evidence was found of a significant impact on the level of underpricing
during the crisis. Investors purchasing stocks of firms going public in 2008–2009 experienced below average initial returns of 3.58% but these results are not statistically different.
However, the raise of initial returns from IPO stocks from 2002–2004 to firms going public
in 2005–2007 is significant. Those periods are labelled ‘cold’ and ‘hot’ markets, in line with
the hot issue market anomaly.
Results from the analysis of long-term performance are more difficult to interpret unilaterally, since the study of abnormal returns is inherent to numerical (statistical) issues. The
long-term performance study has been conducted by analysing 36-month Buy-and-Hold Abnormal Returns. The abnormal returns are obtained by comparing the IPO stock returns
117
118
Chapter 8. Conclusions
with the Fama-French factor model. This study based its conclusions on results from all
IPO firms being equally weighted, and found an average abnormal return of -18.53%, or 6.60% on a yearly basis. Going public in the hot issue market prior to the crisis led to both
the highest level of underpricing (5.67%) and the significantly highest level of underpricing (-27.13%) consistent with the windows of opportunity hypothesis. Issuing stock during
the crisis resulted in significantly better performance (-9.36%), but still underperformance
relative to the benchmark. A comparable abnormal return is observed for going public in
the aftermath of the crisis. Hence, it seems that the financial crisis mostly affected the IPO
firms that went public prior to the crisis and thus had the crisis within their holding period.
The findings of this study thus seem to support the suggestions made in Chapter 1 about
the likely link between investor sentiment and investor behaviour, resulting in overreaction
both prior to (investor optimism, windows of opportunity for issuers) and during (investor
pessimism, high level of underperformance for IPO stocks trading) the crisis.
However, some further remarks should be made. First, it is important to be aware that any
conclusions about long-term performance are dependent on the choice of weighting scheme.
Despite the fact that the findings from the value weighting scheme are in line with other
authors’ observations that large stocks outperform small stocks, they do not provide evidence of underperformance in general. Moreover, substantial levels of overperformance has
been found for IPOs that do not have the financial crisis in their 36-month holding period.
This study also shows the limitations of the Fama-French returns as normal return. The
strong resemblance of the BHAR and BHR graphs 16 suggest that there is a limited ability
for the Fama-French model to detect abnormal return. Therefore, one should in take into
account the limited power of the BHAR analysis. This thesis thus tends to provide further
support for the findings of Loughran and Ritter (2000) who argue that multi-factor regressions fail in detecting abnormal returns that are present especially when the target population comprises small stocks like typical IPOs. Furthermore, only for the IPOs going public
during the hot issue market prior to the crisis is the course of the VW and EW graphs similar. This could partly be due to the fact that those years involve the largest samples, but
it in particular lends itself as evidence for the lack of ability for the Fama-French model to
explain when a crisis appears, as documented by Duong (s.d.).
Chapter 8. Conclusions
8.2
119
Directions for further research
The evidence presented in this study offers up a number of profitable directions for further
research. To begin, this research can be expanded by conducting the long-term analysis
with other methodologies and benchmarks, such as a reference portfolios or matched firms,
to see if these results conform to or challenge the findings of this research.
Furthermore, this dissertation mainly provides descriptive statistics and the analysis of
those descriptives. Further research could propose variables to explain short- and long-term
performance, such as underwriters that may have been involved in the financial crisis, variables that describe firm-specific characteristics, operational performance measures, and so
on. The latter variable could help to separate the investor sentiment reflected in stock price
performance with actual operational performance of the firms, which would undoubtedly
lead to interesting insights.
Additionally, performing the study for other markets in Europe and other less developed
economies worldwide might lead to new results that can be alternative or consistent, so inducing more profound discussion. Finally, broadening the timeframe might result in valuable further insights, as a comparison could be made with the crash of the dot-com bubble
on the one hand, with more data to analyse post-financial crisis performance on the other.
This would shed light on the impact and effects of the financial crisis on stock price performance in the IPO market.
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Appendices
133
Appendix A
Financial Crisis
A.1
Causes
The root cause of this crisis is the real estate market (Udell, 2009) and the burst of its
housing bubble, the so-called US subprime mortgage crisis.
The seed of the financial crisis is said to be planted in 2004, when some specific factors
came into play (Blundell-Wignall et al., 2008).
To start with, Alan Greenspan, chairman of the Federal Reserve had been cutting interest
in the early 2000s, bringing down the Federal Funds rate to 1% in 2004, the lowest level
since years.
Easy borrowing facilities would keep the economy strong, but added with general surpluses
from Japan (interest rates at zero percent), China (fixed exchange rate) and the MiddleEast (accumulation of reserves in Sovereign Wealth Funds), it provided the banks on Wall
St. with a abundance of cheap credit (Blundell-Wignall et al., 2008). The overflow of liquidity gave opportunity to Wall St. to operate with excess leverage, a gate-way to making
millions of dollars.
Wall Street was able to wield such disproportionate leverage ratio since the Securities and
Exchange Commission (SEC) installed the ‘consolidated supervised entities program’, in
which investment banks could increase their debt to net equity ratio towards 40:1 if they
agreed voluntarily to SEC consolidated oversight, with much less stringent rules than prior
to 2004 (15:1 ratio). The US (investment) banks strongly supported and lobbied for new
SEC rules, because the previous Glass-Steagall Act was said to be too restrictive compared
134
Appendix A. Financial Crisis
135
to Europe: banks such as UBS and Deutsche bank had already been benefiting from their
new business model based on securitization(Blundell-Wignall et al., 2008). The main advantage of such a financial system of securitizaton is making the illiquid asset class of mortgages liquid (Udell, 2009).
The lowered US Federal Funds rate had another important consequence: the Treasury bills
did not yield a satisfactory return on investment for the investors, who represent, through
their money, large institutions like pensions funds, insurance companies, mutual funds, etc.
Definitely not while Wall Street was making that much money.
As being said, some factors came into play in 2004, and they gave Wall Street an idea that
could solve the current situation. As an attempt to revive the economy with his New Deal
program, Franklin Delano Roosevelt founded Fannie Mae, which is an acronym for Federal
National Mortgage Association. Its purpose was to create and expand the secondary mortgage market by securitizing mortgages (mortgage-backed securities). Buying the mortgages
from the lenders of the mortgages, the traditional banks, allowed the traditional banks to
fund additional borrowing with that federal money, as they were not more solely dependent
on the deposits they were receiving from their customers to finance lending. Those contributions were in order to ensure a raise in the levels of home ownership and the availability
of affordable houses.
Although it was privatized in 1968, Fannie Mae was still under strict government oversight
and government sponsored. In 1970, Freddie Mac, Federal Home Loan Mortgage Corporation, was established to create competition with Fannie Mae.
In 2004, the regulator of those two mortgage securitization institutions, the Office of Federal Housing Enterprise Oversight (OFHEO), imposed greater capital requirements and balance sheet controls. Banks who had been selling them mortgages were now faced with an
interruption in their earnings.
As the Fannie Mae and Freddie Mac became more regulated, they opened the way for (investment) banks. The reader might want to know that the business model and corporate
culture of banks had been evolving from a traditional credit culture to a more equity culture (Blundell-Wignall et al., 2008). The drive to more revenues and return on capital put
the focus on earnings expansion and shares price growth instead of balance sheets and loan-
136
Appendix A. Financial Crisis
spreads. This strategy towards growing earnings with trading income and fees via securitization, shortly, risk taking, was partly incentivized by the announcement of transition from
Basel I to Basel II accord in 2004, as it provokes banks to originate mortgages and issue
residential mortgage-backed securities (RMBS) (Blundell-Wignall et al., 2008), as displayed
in Figure A.1.
Figure A.1: Expansion of the subprime lending
Basel II would namely reduce the capital weight given to mortgages from 50 percent under
Basel I to 35 per cent or, depending on the use of the internal ratings-based version (IRB),
to as little as 15-20 per cent. Because that raises the return on capital, the low-capitalweighted mortgages raised the overall bank return, proportional with their concentration.
The transition thus provided an arbitrage opportunity for banks to accelerate mortgage
securitization and push a proportion of the on-balance sheet mortgages into off-balancesheet activity, since banks could in this way raise the return on capital to the return likely
to emerge under Basel II, without having to wait for the new regulation to come into effect.
(Blundell-Wignall et al., 2008)
Thus, in the context of all these concurrent conditions, Wall St. and the banks came to the
idea to connect the investors with the homeowners through the mortgages, which represent the houses. Housing prices had been rising since the early 1990s, so when home owners
would default on their mortgage, the lender gets the house, making mortgages a solid and
safe investment. In large part, the real bubble in house estate was made from 2004 onwards
to early 2006, when appreciation in housing prices per year exceeded 10 per cent.
Appendix A. Financial Crisis
137
The five largest investment bankers, Lehman Brothers, Merrill Lynch, Morgan Stanley,
Bear Stearns and Goldman Sachs were the first beyond the Fannie Mae and Freddie Mac
to start issuing mortgage-backed securities (MBS). They borrowed millions of dollars to
buy mortgages from the traditional banks, lenders of the mortgages. They bundled all the
mortgages into a financial instruments for which the homeowners were providing the inflow
of the money: they created new Fannie- and Freddie-like financial instruments, being the
structured investment vehicles (SIVs) and collateralized debt obligations (CDOs). While
the mortgage bond market had been dominated previously by the government sponsored
agencies, the private sector was now significantly expanding its role in a market that was
expanding significantly itself. In 2007, the mortgage bond market was worth $6.8 trillion,
being the largest piece of the $27 trillion US bond market as a whole.
The CDO was an instrument divided in three slices based on credit risk. Simply said: from
safe to okay to risky. The most junior tranche pays off only if all above tranches are paid
first. To make the most senior tranche even safer, the investment bankers insured it with
credit default swaps, insurance policies that, in return for a fee, take on any losses caused
by mortgage-holder defaults. However it started as an insurance, soon a market arose in
those CDSs, with financial institutions speculating by buying or selling CDSs on assets
they did have on their balance sheets. From $ 900 billion in credit in 2001 being insured by
CDSs, the total amounted to $62 trillion by 2008 (Encyclopædia Britannica, 2016). Since
the top slice was insured, they obtained an AAA-rating from rating agencies, the highest
degree of credit quality. It is clear that the ratings of those sliced MBS trances were euphoric, and the errors large (Udell, 2009). Udell (2009) states on this topic that the problem is the inherent conflict of interest for those rating agencies. The company that gets a
rating is the one who pays for the rating, and thus ensuring highly biased ratings. Because
of the high creditworthiness of the top MBS slice, it also yielded the lowest return, although
still being significantly higher than the one per cent Treasury Bills. The middle slice was
rated BBB and ‘risky’ left unrated. Mortgage backed security issuers could thus raise capital from investors of all kind of risk-aversion.
In the beginning, the new financial construction worked like a charm. Everyone profited as
long as the real estate market prices kept rising. Mortgage holders who would have difficul-
138
Appendix A. Financial Crisis
ties with their down payment could borrow additionally against their rising home equity.
Banks could sell of their mortgages (and their risk) to the investment bankers. In addition,
the deals were highly profitable as they earned a fee for every mortgage they sold. The investment bankers in turn sold the CDO slices (and their risk) to the investment funds, also
making millions of dollars. The investment funds got a very decent return on investment on
their new financial instrument, making all players in the financial market happy and rich.
The result is that everyone wanted more of that new financial instrument. That is the moment we can see that the abundance of liquidity had, due to changes in the regulatory system, been directed to the specific mortgages securities area. (Blundell-Wignall et al., 2008)
The demand for homeowners with a mortgage quickly exceeded the amount of qualified
people to obtain a mortgage. Since the prices of the collateral, the houses, were constantly
on the rise and both the banks and the investment banks were basically selling of their risk,
they were not longer incentivized to carefully check the mortgages and the qualifications of
the borrower. In contrast, they encouraged mortgage brokers to sell as much as possible of
these mortgages. This is when even with poor credit histories and a weak to no documentation of income, one could get a mortgage, making almost everyone ‘qualified’ for a home
loan.
Subprime lending further increased the demand for housing and thus the house prices. While
“prime” lenders like Fannie Mae and Freddie Mac were more reluctant to those sub-prime
borrowers, sub-prime mortgages became a specialization of the new private sector-players in
the mortgage bond market. Estimates suggest that $2 trillion out of the $11 trillion of US
household mortgages were subprime.
However, also the government-sponsored institutions were heavily easing their requirements
and began to purchase subprime loans for securitization as a result of the American Dream
Downpayment Initiative. This is a zero equity mortgage proposal signed into law in December 2003 by President George W. Bush, becoming operative in, what did the reader expect,
2004:
”We can put light where there’s darkness, and hope where there’s despondency in this country. And part of it is working together as a nation to encourage folks to own their own home.”
(President George W. Bush, Oct. 15, 2002).
Appendix A. Financial Crisis
139
Where traditional mortgages required a substantial downpayment, typically 20 per cent,
mortgages were now offered with highly reduced or zero money down. The Bush Administration aimed at helping 22.000 low-income families a year to obtain mortgages. Those
efforts resulted in a record home-ownership rate in America of 68.60 per cent of households
in 2007.
The big difference of those subprime mortgages with conventional mortgages is that they
were adjustable rate mortgages (ARMs). After two years of fixed payments with a low initial interest (“teaser”) rate, many of those mortgages reset to higher, even double-digit
rates. That is why the subprime segment was targeted in the first place: higher interest
rates because of the lower creditworthiness promised high return.
Since the value of the mortgages outstanding in 2007 was worth around $11trillion, only a
subtle raise in the default rate would have a significant negative impact on the condition
of the balance sheets of the banking sector and their net income (Heilpern et al., 2009). As
more and more homeowners had to default on their mortgage, more and more houses were
put up for sale. Basic economic laws learn us that the rising supply and the falling demand
will make the housing prices drop sharply: the asset bubble had burst. The amount of foreclosures rose with the delinquency rates on subprime mortgages.
The effect spread to the prime mortgage segment as well: credit capable home owners walked
away from their house, since they were not willing to pay expensive mortgages for a house
that is not worth that much, accelerating the downwards spiral.
The new financial instrument had become a ‘box’ full of worthless houses. Everyone simultaneously tried to sell off MBSs, so prices were plummeting. Worse, nobody knew what
these instruments were really worth, because of the lack of transparency in what these instruments really represented. A shortage of transparency was also to be found in the bank
MBS portfolios and the CDS market. Combined with a substantial risk of insolvency regarding those financial institutions, a highly increased difficulty in finding sources for external financing for banks was the outcome(Udell, 2009). In short, liquidity in the money
market and interbank market dried up. The financial system had completely frozen.
140
A.2
Appendix A. Financial Crisis
Impact and consequences for America
The crisis of 2007-2009 is universally recognized as the biggest recession since the Great
Depression. Its consequences penetrate into all aspects of the US society, as well as beyond
the borders of the United States of America.
Next to the enormous loss of economic output and financial wealth and the cost of all government interventions, there are other substantial costs that sometimes are hard to quantify: national trauma, psychological and physical effects and squander of talents and skills
through prolonged unemployment, to mention a few. It is important to assess the cost of
the implications of the recession to weigh it against the cost of policies to prevent such
events in the future (Atkinson et al., 2013).
Atkinson et al. (2013) estimates that cost to amount to 40 to 90 percent of one year’s output. This is equal to in between $50,000 and $120,000 for every household in the United
States, or $6 trillion up to $14 trillion.
In what follows, we will first discuss the direct consequences for the financial institutions
involved. However, in the light of this thesis, we are also in particular interested in the effects of the kind of psychological and physical health. The crisis left the spirit of the country broken, and the faith in the American dream reduced. Lost health care, delayed retirements, unemployment, abandoned college education and even worse: poverty, homelessness
and increased crime rates: the crisis had brought unquantifiable and enormous fears for the
population (The Globalist, 2012).
Regardless the calculability of the different components of the total effect of the collapse
and its subsequent crisis, one cannot deny that the costs of the greatest recession since the
1930s add up to a double digits amount in trillions of dollars.
As said in previous section, the root cause of the crisis lies in the real estate market. The
consequences are thus very pronounced in that market: over 3,6 million families were expelled from their homes due to foreclosure. The bursting of the bubble led to declines of
33% in home values up to 2011, equal to a total lost value of $7 trillion or 46% of that
Appendix A. Financial Crisis
141
year’s GDP. Because of that steep decline in home values, 22,8% of residential property
with a mortgage own a home that is worth less.
The Sub Prime Mortgage crisis indeed struck those who had those mortgages and the mortgage backed securities or other derivatives on their balance sheets. Major financial institutions suffered from huge losses in write-offs or filed for bankruptcies. We provide a concise
overview, going from the investment banking industry (Merrill Lynch, Bearn Stern, Goldman Sachs, Morgan Stanley, Lehman Brothers), the biggest insurance company (American
International Group (AIG)), the Fannie Mae and Freddie Mac, the largest mortgage lender
(Countrywide Financial Corp.), the largest savings and loan bank (Seattle-based Washington Mutual), to the biggest commercial banks (Citigroup & Wachovia Corp). We will discuss briefly how the crisis affected those institutions by a limited overview of the year 2008.
In January 2008, The Bank of America agreed to terms for the purchase of the largest American mortgage lender, Countrywide Financial Corp. (San Jose Mercury News, 2008). The
deal was valued at roughly $ 4 billion, a fraction of the recent market value of Countrywide.
Two months later, in March 2008, Bear Stearns was rescued from bankruptcy by JPMorgan Chase as shareholders approved the sale of the bank for $10 a share, or $2.2 billion.
One year before, Bear traded above $150 a share (Financial Times, 2008). Bear was the investment bank with the second thickest portfolio of mortgage-backed securities, of which
the value was plummeting. (Udell, 2009). The merger/bailout was assisted by the Federal
Reserve that itself assumed $ 30 billion of the assets of Bear Stearns (Encyclopædia Britannica, 2016).
September was an eventful month in 2008. Fannie Mae and Freddie Mac, who held or guaranteed about half of the mortgages in the U.S. (Encyclopædia Britannica, 2016), suffered
enormous losses due the wave of defaults of subprime mortgages. The Bush administration decided on September 7 to place the two private mortgage companies under “conservationship” of the U.S. Department of the Treasury, a legal status related to Chapter 11
bankruptcy (Washington Post, 2008a).
Next to Bearn Stearns in March, two other investment banks with great exposure to mortgage-
142
Appendix A. Financial Crisis
backed securities, Lehman Brothers and Merrill Lynch, had difficulties not to go under.
Merrill Lynch & Co. agreed on September 14 to run into the arms of Bank of America
Corp. and to sell itself for only $ 50 billion. (Wall Street Journal, 2008). The fate of Lehman
Brothers was less favorable: the day after the Merrill-deal, September 15, they filed for
bankruptcy, becoming the biggest bankruptcy in American history and triggering worldwide financial panic. After the withdrawal of Barclays to buy (most of) the company, the
Bank of America, who committed already enormous amounts of taxpayers’ money for Bear
Stearns & Freddie Mae, was the only potential rescuer left. However, the US Treasury refused to commit more of its capital for the bailout of Lehman (BBC News, 2008a).
The financial market did not get much time to recover from Lehman brothers’ bankruptcy.
The biggest insurer, American International Group (AIG), was facing large losses on CDSs.
The Federal Reserve decided to engage itself in one of its most radical interventions in private business in its history: the bailout of the troubled insurance giant for $85 billion on
September 16. The decision was mainly out of fear for a financial crisis worldwide due the
possible chain reaction and spillover effects. (New York Times, 2008a). The Treasury authorized an additional loan of $38 billion when the previous amount proved insufficient, obtaining a 79.9% equity stake (Washington Post, 2008b).
September was far from at the end of his rope. In an attempt to prevent being the next
target, both Morgan Stanley and Goldman Sachs announced on September 21 that they
will become Bank Holding Companies and thus will be regulated by the U.S. Federal Reserve (The Goldman Sachs Group, 2008), (Morgan Stanley, 2008). By being granted that
status, they get access to the credits and other funding opportunities the FED provides.
September still had something in store: on September 25, regulators brokered the sale of
the largest savings and loan bank of the U.S., Washington Mutual (WaMu) to JPMorgan
Chase for $1.9 billion. Since the bankruptcy of Lehman brothers, $16.7 billion of deposits
had been withdrawn (BBC News, 2008b).
On October 3, Well Fargo announced its purchase of the by subprime-mortgages troubled
Wachovia Corp in a $15.1 billion all-stock merger (New York Times, 2008b). Wachovia was
not the only bank that foundered: Federal Regulators agreed to shore up the losses of Cit-
Appendix A. Financial Crisis
143
igroup by guaranteeing on about $306 billion of its risky assets and injecting new capital.
The Treasury hopes to make clear to investors that the government will provide support to
large banks in the light of the current recession (Reuters, 2008).
“Derivatives are financial weapons of mass destruction.” The events in the year 2008 suggest that Warren Buffet was right in his 2002 Berkshire Hathaway Inc. Annual Report.
The collapse of these financial giants however is not the end of the impact of the crisis.
The Dow Jones Industrial Average fell by 33.8% in 2008 (Encyclopædia Britannica, 2016)
and in total more than 50% in between October 2007 and March 2009, which equals $11
trillion (The Globalist, 2012). However, it is plausible to assume that the downturn in 2008
on the stock market is not solely due to the financial crisis. Even without a financial crisis,
the oil-price shock might have triggered the downturn, since the price of crude oil was at
historic highs in 2008 (Atkinson et al., 2013).
In June 2013, The Bureau of Labor Statistics reported that the unemployment rate had
recovered to 7.6% from its 10.1% peak in October 2009, in full recession. However, that recover is far less pronounced in the share of the unemployed who find employment in the
following month, which recovered to only 19% after a drop from 26% to 17% over the recession (Atkinson et al., 2013). There is thus an augmentation of prolonged or permanently
unemployed workforce. In 2008-2012, unemployed people related to the economical crisis
missed out on roughly $900 billion in earnings (The Globalist, 2012).
Besides the financial implications, various surveys have confirmed the negative consequences
of unemployment on life satisfaction (amongst others: Di Tella et al. (2001)). The loss of
earnings does namely not include the psychological strains that population must endure
such as shame, loss of control, anxiety, stress and a worse physical health. A (higher) unemployment rate also affects the employed as it affects the society as a whole since it decreases job security and the chances of a new job if a dismissal would take place. The impact of unemployment, both psychological and physical grows with the length of unemployment as stress accumulates and savings deplete (Mckee-Ryan et al., 2005). The aggregate
societal effects are at the least not trivial, but are amongst the unquantifiable cost of the
national trauma (Atkinson et al., 2013).
Low employment rates imply low consumer spending. From 2007 to 2010, the net worth
144
Appendix A. Financial Crisis
of the median household fell by 38.8% or over $7 trillion in total. The median family income was skimmed of from $49,600 in 2007 to $45,800, or a 7.7% decline (Forbes, 2012).
The Globalist (2012) also states an increase in the number of families falling below poverty
line from 12.5% to 15.1%.
Next to fewer taxpayers, the low employment rates also imply increased unemployment
benefit costs for the government. These come on top of its expenditures on bailouts, extraordinary assistance and stimulus programs. In total, the governments commitments of
support above and beyond the previously existing public safety are estimated by the International Monetary Fund at $12.6 trillion or 82 per cent of U.S. GDP of 2007 (Atkinson
et al., 2013). That GDP had known a severe crash and full recovery is not expected until 2018 (The Globalist, 2012). Both reduced revenues and government expenditures as a
result of the financial crisis contribute the significant increased U.S. national debt. The
national debt increased by $7.4 trillion to $18 trillion between January 2009 and January
2015 (The Washington Post, 2015), which is more than 100% of 2014 GDP ($17.419 trillion, databank.worldbank.org). It is important that society must deal with the fact that
the nation is vulnerable and less able to respond to future downturns due to that swollen
federal debt. Also, it has to live with the expanded balance sheet of the FED and the government intervention and regulations in the near future. (Atkinson et al., 2013)
Appendix B
Sample: Zephyr search
Figure B.1: Sample refinement Zephyr
145
Appendix C
Stock Exchanges
This appendix provides the reader with a short description of the Stock Exchanges used in
this research. The descriptions are largely based on the markets descriptions of the EurIPO
site of Universoft, a spin-off company by the University of Bergamo (Universoft, 2010).
London Stock Exchange (LSE) is a stock exchange seated in the City of London, United
Kingdom. It was founded in 1801 and has become the largest stock exchange of Europe
by measures of market capitalisation (6.06 trillion USD in December 2014). It is also the
third-largest stock exchange in the world. Additionally, it is the first one in terms of number of new issues in Europe. Beside the Official List, the LSE launched in 1995 the Alternative Investment Market (AIM) for small and medium enterprises. In 2005, there were more
than 3.000 listed companies.
Euronext is a stock exchange seated in Amsterdam, Brussels, London, Lisbon and Paris.
It is born from the merger of the Paris, Amsterdam and Bruxelles stock exchanges in September 2000 and nowadays it is a single exchange, that consists of three markets with different
rules and requirements: Eurolist, Marché Libre and Alternext. In 2014, the Euronext had a
market capitalisation of e2.6 trillion, consisting of 1,300 issuers.
Deutsche Börse or the Frankfurt Stock Exchange is a stock exchange seated in Frankfurt,
Hesse, Germany. Its roots are said to go back to the The Frankfurt autumn fair in, mentioned for the first time in 1150. The reader may consult Deutsche Börse Group (2016) for
an elaborate history of the Frankfurt Stock Exchange. Nowadays, it is divided into three
markets. Amtlicher and Geregelter are the official markets, having European rules and similar admission and ongoing requirements. The third market, born in 2005, is called Freiverkehr
146
Appendix C. Stock Exchanges
147
and is similar to Alternext and AIM, facilitating SME to go public. That year, approximately 750 firms listed were listed on the Frankfurt Stock Exchange, with a total market
value of e1 trillion.
Borsa Italiana is a stock exchange seated in Milan, Italy. Since 2007, it is a subsidairy of
the London Stock Exchange Group plc. It also consists of three markets: the main market (MTA), the technology companies market (MTax) and the Expandi. The latter was
founded in 2003 from the restructuring of the Mercato Ristretto and is, like the AIM, Alternext and Freiverkehr, dedicated to SMEs. In 2005, 282 companies were listed on the
Borsa Italiana, equally to a market capitalisation of e0,67 trillion.
MAR
RR
148
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
1,986
2,718
F
1,557
1,233
1,450
1,336
t
114,465
584
115,903
584
df
0,122
0,218
0,150
0,257
Sig. (2-tailed)
0,02260
0,02260
0,02086
0,02086
Mean Difference
0,01452
0,01834
0,01439
0,01837
Std. Error Difference
t-test for Equality of Means
Independent Samples Test
Table D.1: Independent t-test for Pre-Crisis and Crisis
0,159
0,100
Sig.
Equality of Variances
Levene’s Test for
95% Confidence
-0,00616
-0,01341
-0,00763
-0,01522
Lower
0,05136
0,05862
0,04936
0,05695
Upper
Interval of the Difference
Appendix D
Short-run Performance
MAR
RR
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
2,173
2,686
F
-1,310
-1,085
-1,260
-1,041
t
216,978
218
216,978
218
df
0,192
0,279
0,209
0,299
Sig. (2-tailed)
-0,02783
-0,02783
-0,02666
-0,02666
Mean Difference
0,02125
0,02564
0,02116
0,02562
Std. Error Difference
t-test for Equality of Means
Table D.2: Independent t-test for Crisis and Post-Crisis
0,142
0,103
Sig.
Equality of Variances
Levene’s Test for
Independent Samples Test
95% Confidence
-0,06971
-0,07837
-0,06836
-0,07714
Lower
0,01405
0,02271
0,01504
0,02383
Upper
Interval of the Difference
Appendix D. Short-run Performance
149
MAR
RR
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
0,003
0,050
F
-1,004
-0,973
-1,123
-1,087
t
168,508
187
169,037
187
df
0,317
0,332
0,263
0,278
Sig. (2-tailed)
-0,01752
-0,01752
-0,01941
-0,01941
Mean Difference
0,01745
0,01801
0,01728
0,01785
Std. Error Difference
t-test for Equality of Means
Table D.3: Independent t-test for Pre(0) and Crisis
0,959
0,823
Sig.
Equality of Variances
Levene’s Test for
Independent Samples Test
95% Confidence
-0,05197
-0,05305
-0,05352
-0,05463
Lower
0,01693
0,01801
0,01470
0,01581
Upper
Interval of the Difference
150
Appendix D. Short-run Performance
MAR
RR
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
2,675
2,975
F
-2,969
-2,658
-2,994
-2,663
t
196,306
627
198,087
627
df
0,003
0,008
0,003
0,008
Sig. (2-tailed)
-0,04012
-0,04012
-0,04027
-0,04027
Mean Difference
0,01351
0,01510
0,01345
0,01512
Std. Error Difference
t-test for Equality of Means
Table D.4: Independent t-test for Pre(0) and Pre(1)
0,102
0,085
Sig.
Equality of Variances
Levene’s Test for
Independent Samples Test
95% Confidence
-0,06677
-0,06977
-0,06680
-0,06997
Lower
-0,01347
-0,01048
-0,01374
-0,01058
Upper
Interval of the Difference
Appendix D. Short-run Performance
151
Appendix E
Hot Markets: Scatter
10
Initial Returns
9
Level of underpricing (in %)
8
7
6
5
4
3.14 + 1.55 · 10−2 · Samplesize
3
2
1
0
0
20
40
60
80
100 120 140 160 180 200 220 240
Number of IPOs
Figure E.1: Hot Markets: scatter plot (R-squared: 0.185)
152
Appendix F
Yearly Buy-and-Hold Return
Table F.1: BHR Year by Year: Descriptive Statistics
Year
2002
Mean
95% Confidence
Statistic
Std. Error
1,343524
0,1581637
Lower Bound
1,021355
Upper Bound
1,665693
Interval for Mean
2003
5% Trimmed Mean
1,297712
Median
1,332000
Variance
0,826
Std. Deviation
0,9085813
Minimum
0,1071
Maximum
3,4392
Range
3,3321
Interquartile Range
1,3606
Skewness
0,582
0,409
Kurtosis
-0,242
0,798
1,467348
0,3521762
Mean
95% Confidence
Lower Bound
0,732721
Upper Bound
2,201974
Interval for Mean
2004
5% Trimmed Mean
1,248708
Median
1,098400
Variance
2,605
Std. Deviation
1,6138740
Minimum
0,0420
Maximum
6,9894
Range
6,9474
Interquartile Range
1,8094
Skewness
2,230
0,501
Kurtosis
6,217
0,972
Mean
1,248945
0,1264308
153
154
Appendix F. Yearly Buy-and-Hold Return
95% Confidence
Lower Bound
0,996131
Upper Bound
1,501759
Interval for Mean
5% Trimmed Mean
2005
1,159954
Median
1,058650
Variance
0,991
Std. Deviation
0,9955174
Minimum
0,0560
Maximum
4,7895
Range
4,7335
Interquartile Range
1,2445
Skewness
1,304
0,304
Kurtosis
1,990
0,599
0,868683
0,0798573
Mean
95% Confidence
Lower Bound
0,710597
Upper Bound
1,026768
Interval for Mean
2006
5% Trimmed Mean
0,768667
Median
0,555600
Variance
0,784
Std. Deviation
0,8856601
Minimum
0,0022
Maximum
4,5000
Range
4,4978
Interquartile Range
0,9440
Skewness
1,811
0,218
Kurtosis
3,692
0,433
Mean
0,612900
0,0559708
95% Confidence
Lower Bound
0,502557
Interval for Mean
Upper Bound
2007
0,723242
5% Trimmed Mean
0,492329
Median
0,390000
Variance
0,655
Std. Deviation
0,8091598
Minimum
0,0000
Maximum
5,8270
Range
5,8270
Interquartile Range
0,5990
Skewness
3,680
0,168
Kurtosis
17,176
0,335
Mean
0,591924
0,0468902
95% Confidence
Lower Bound
0,499399
Interval for Mean
Upper Bound
5% Trimmed Mean
0,684449
0,509937
Appendix F. Yearly Buy-and-Hold Return
2008
155
Median
0,402400
Variance
0,398
Std. Deviation
0,6308432
Minimum
0,0027
Maximum
5,0164
Range
5,0137
Interquartile Range
0,5509
Skewness
3,293
0,181
Kurtosis
16,448
0,359
Mean
0,858324
0,1052168
95% Confidence
Lower Bound
0,646883
Upper Bound
1,069765
Interval for Mean
2009
5% Trimmed Mean
0,798664
Median
0,694050
Variance
0,554
Std. Deviation
0,7439952
Minimum
0,0205
Maximum
3,1004
Range
3,0799
Interquartile Range
1,0335
Skewness
1,098
0,337
Kurtosis
0,874
0,662
Mean
1,071883
0,1811681
95% Confidence
Lower Bound
0,696163
Interval for Mean
Upper Bound
5% Trimmed Mean
2010
1,447602
1,002620
Median
0,932700
Variance
0,755
Std. Deviation
0,8688515
Minimum
0,0687
Maximum
3,4016
Range
3,3329
Interquartile Range
1,2327
Skewness
1,165
0,481
Kurtosis
1,253
0,935
1,006455
0,1057827
Mean
95% Confidence
Lower Bound
0,794784
Interval for Mean
Upper Bound
5% Trimmed Mean
1,218126
0,929367
Median
0,829750
Variance
0,671
Std. Deviation
0,8193893
156
2011
Appendix F. Yearly Buy-and-Hold Return
Minimum
0,0173
Maximum
4,1091
Range
4,0918
Interquartile Range
0,9718
Skewness
1,562
0,309
Kurtosis
3,165
0,608
0,844838
0,0729547
Mean
95% Confidence
Lower Bound
0,698230
Upper Bound
0,991446
Interval for Mean
5% Trimmed Mean
2012
0,822827
Median
0,888350
Variance
0,266
Std. Deviation
0,5158675
Minimum
0,0723
Maximum
2,0526
Range
1,9803
Interquartile Range
0,7414
Skewness
0,518
0,337
Kurtosis
-0,217
0,662
1,150208
0,2037197
Mean
95% Confidence
Lower Bound
0,737045
Upper Bound
1,563371
Interval for Mean
5% Trimmed Mean
1,035058
Median
0,795300
Variance
1,536
Std. Deviation
1,2391787
Minimum
0,0020
Maximum
4,6031
Range
4,6011
Interquartile Range
1,2341
Skewness
1,401
0,388
Kurtosis
1,294
0,759
Appendix G
Average Buy-and-hold Abnormal Returns by IPO year
157
Appendix G. Average Buy-and-hold Abnormal Returns by IPO year
Average BHAR 2002
0.2
0
−0.2
6
Buy-and-hold Return
Buy-and-hold Return
158
0
−0.1
6
Buy-and-hold Return
Buy-and-hold Return
Average BHAR 2004
0
6
12 18 24 30 36
Month
Average BHAR 2005
0.2
0
−0.2
12 18 24 30 36
Month
6
12 18 24 30 36
Month
Figure G.4: BHARs from 2005 IPOs
Figure G.3: BHARs from 2004 IPOs
0.2
Average BHAR 2006
0
−0.2
−0.4
6
12 18 24 30 36
Month
Figure G.5: BHARs from 2006 IPOs
Buy-and-hold Return
0.2
Buy-and-hold Return
0.2
Figure G.2: BHARs from 2003 IPOs
Figure G.1: BHARs from 2002 IPOs
0.1
0.4
−0.2
12 18 24 30 36
Month
Average BHAR 2003
0.6
Average BHAR 2007
0
−0.2
−0.4
6
12 18 24 30 36
Month
Figure G.6: BHARs from 2007 IPOs
Average BHAR 2008
0.1
Buy-and-hold Return
Buy-and-hold Return
Appendix G. Average Buy-and-hold Abnormal Returns by IPO year
0
−0.1
−0.2
6
Figure G.8: BHARs from 2009 IPOs
Buy-and-hold Return
−0.1
−0.2
Average BHAR 2011
0
−0.1
−0.2
−0.3
12 18 24 30 36
Month
Figure G.9: BHARs from 2010 IPOs
6
12 18 24 30 36
Month
Figure G.10: BHARs from 2011 IPOs
0.4
Average BHAR 2012
0.2
0
6
12 18 24 30 36
Month
0.1
0
Buy-and-hold Return
Buy-and-hold Return
−0.2
6
Average BHAR 2010
6
0
12 18 24 30 36
Month
Figure G.7: BHARs from 2008 IPOs
0.1
Average BHAR 2009
0.2
12 18 24 30 36
Month
Figure G.11: BHARs from 2012 IPOs
159
Appendix H
BHR: Means per SE and per Year
Table H.1: BHR: Comparison of the means
Euronext Amster-
Year
Mean
N
Std. Deviation
2005
0,6204
3
0,61768
2006
-0,3580
4
0,25052
2007
-0,5486
5
0,37457
2008
-0,5623
1
2012
0,6402
1
Total
-0,1597
14
0,62349
2004
0,0587
2
0,36579
2005
-0,0828
6
0,92812
2006
-0,4287
11
0,49713
2007
-0,4268
9
0,36154
2008
-0,0317
5
0,52199
2009
0,2764
2
0,34210
Total
-0,2441
35
0,56743
dam
Euronext Brussels
Euronext Lisbon
Euronext Paris
2003
1,5680
1
2004
1,0024
1
2006
0,9840
1
2007
-0,5596
2
2008
-0,3414
1
Total
0,3490
6
0,95800
2002
-0,1852
9
0,56313
2003
-0,0891
5
0,43814
2004
0,1837
14
1,19907
2005
0,0523
24
0,82335
2006
-0,2557
47
0,79219
2007
-0,4737
49
0,45017
2008
-0,3155
21
0,52247
2009
0,0738
14
0,97027
2010
0,1388
20
0,59557
2011
-0,2012
27
0,41999
160
0,38092
Appendix H. BHR: Means per SE and per Year
Paris Alternext
Frankfurt Stock
161
2012
0,6020
11
1,49364
Total
-0,1458
241
0,77722
2005
0,7250
2
1,43295
2006
-0,5442
5
0,31725
2007
-0,4688
6
0,64506
2008
0,2376
2
0,75554
2009
0,6358
1
2010
1,0698
3
2011
0,1603
1
2012
-0,3662
3
0,51837
Total
-0,0305
23
0,95843
2002
0,0138
3
0,62893
2004
0,3619
4
1,45308
2005
-0,3737
9
0,57252
2006
-0,2074
54
1,19750
2007
-0,2045
33
0,87793
2008
-0,1435
5
1,04809
2009
-0,0701
3
0,59324
2010
0,0161
9
1,13186
2011
-0,3364
6
0,73746
2012
-0,5161
6
0,53133
Total
-0,1949
132
1,00670
2002
0,7975
4
0,66948
2003
0,7805
3
0,51482
2004
0,2722
3
0,34754
2005
-0,3622
10
0,33522
2006
-0,3990
13
0,52543
2007
-0,4734
21
0,51369
2008
-0,1700
4
0,64451
2009
1,2835
1
2010
0,1364
1
2011
0,7016
2
2012
0,3767
1
1,78284
Exchange
Italian Stock Exchange
AIM Italia
London Stock Ex-
0,49639
Total
-0,1569
63
0,66296
2009
-0,8208
2
0,15627
2010
-0,6169
3
0,07785
2011
-0,1892
1
2012
0,3963
1
Total
-0,4693
7
0,44286
2002
0,5915
10
0,76516
2003
2,1301
4
2,82413
2004
0,5180
9
0,85035
change
162
London AIM Stock
Appendix H. BHR: Means per SE and per Year
2005
0,0494
12
0,97227
2006
-0,3945
16
0,81816
2007
-0,3620
21
0,53411
2008
0,3003
3
1,13206
2010
-0,3411
13
0,50297
2011
-0,2312
6
0,29037
2012
0,6538
4
1,61116
Total
0,0354
98
1,05498
2002
0,5510
7
1,40782
2003
-0,2713
8
1,09476
2004
0,1661
29
0,99402
2005
-0,2426
57
0,97104
2006
-0,6604
58
0,32401
2007
-0,4526
35
0,77600
2008
0,0781
8
1,17255
2010
0,0369
11
0,81934
2011
-0,0419
7
0,72098
2012
-0,0898
10
1,31581
Total
-0,2679
230
0,89936
2002
0,3435
33
0,90858
2003
0,4673
21
1,61387
2004
0,2489
62
0,99552
2005
-0,1313
123
0,88566
2006
-0,3871
209
0,80916
2007
-0,4081
181
0,63084
2008
-0,1417
50
0,74400
2009
0,0719
23
0,86885
2010
0,0065
60
0,81939
2011
-0,1552
50
0,51587
2012
0,1502
37
1,23918
Total
-0,1668
849
0,87524
Exchange
Total
The high performance of the Euronext Lisbon is explained by the BHR of 156.80% in 2003
and 100.24% in 2004, years with only one IPO. The very low long-term performance of the
AIM Italia is due to a -82.08% 36-month BHR (2 observations) for stocks that went public in 2009 and a -61.69% for IPOs in 2010 (3 observations), while no observations for the
growth years are included in the AIM Italia sample.
A summary of the London Stock Exchanges is given in Table H.2. Concerning the primary
market, the years that do not capture the 2008 and 2009 years in their Buy-and-Hold pe-
Appendix H. BHR: Means per SE and per Year
163
Table H.2: BHR Means per SE per Year: London
London Stock Exchange
London AIM Stock Exchange
Year
Mean
N
Std. Deviation
Mean
N
Std. Deviation
2002
0,5915
10
0,76516
0,5510
7
1,40782
2003
2,1301
4
2,82413
-0,2713
8
1,09476
2004
0,5180
9
0,85035
0,1661
29
0,99402
2005
0,0494
12
0,97227
-0,2426
57
0,97104
2006
-0,3945
16
0,81816
-0,6604
58
0,32401
2007
-0,3620
21
0,53411
-0,4526
35
0,77600
2008
0,3003
3
1,13206
0,0781
8
1,17255
2010
-0,3411
13
0,50297
0,0369
11
0,81934
2011
-0,2312
6
0,29037
-0,0419
7
0,72098
2012
0,6538
4
1,61116
-0,0898
10
1,31581
Total
0,0354
98
1,05498
-0,2679
230
0,89936
riod of 36 months are characterised by very high returns. The 23 firms that went public
on the LSE from 2002-2004 have an average BHR of 83.03%, with a striking 213% for 2003
IPOs (4 observations). Surprisingly, the average BHR that is starting in 2005 is still positive. With 2005 included, the average BHR is 56.26% for 35 observations. The next 37
(negative) observations for 2006 and 2007 bring the average to 8.02%, explaining the positive mean BHR for the LSE. An explanation for this is that the crisis affected the long-run
performance less than the years prior to the crisis (-27.19%1 versus 83.03%) and evidence
for this may be found by excluding the financial institutions in our sample.
However, the findings for the secondary market are different. Surprisingly, the AIM market
has a negative average BHR in 2003, resulting in a mean BHR of only 14.78% for the years
2002-2004 (in comparison with 83.03% for LSE). In addition, the years including the crisis
are worse affected: the mean BHR amounts to-45.31% (in comparison with the -27.19% for
the LSE), bringing the average of 2002-2007 to -31.69%, explaining the deep negative mean
1
The average BHR for the years 2005, 2006 and 2007, capturing 2008 and 2009 in their 36 month Buy-
and-Hold period
164
Appendix H. BHR: Means per SE and per Year
BHR for the London AIM sample. What is also striking is that the LSE underperforms the
AIM by more than 10% in the years 2010-2012. Explanations for these findings, however,
go beyond the scope of this research.
Appendix I
Outlier labelling
Equations I.1 and I.2 provide the equations for the procedure of of Hoaglin and Iglewicz
(1987), with k=2.2, as proposed by the original authors.
Fl − k(Fu − Fl )
(I.1)
Fu + k(Fu − Fl )
(I.2)
Table I.1 gives the percentiles for the calculation of the upper and lower bound. The outliers labeling procedure results in 12 of the 849, or 1.41% of the observations being eliminated.
Table I.1: Outlier Labeling
Percentiles
Weighted Average(Definition 1)
TotalSample
Tukey’s Hinges
TotalSample
5
10
25
50
75
90
95
-1,2596
-0,9716
-0,6941
-0,3622
0,0838
0,8753
1,5173
-0,6936
-0,3622
0,0837
165
Appendix J
Bootstrapped BHAR Statistics per Year
Table J.1: BHAR per year: Bootstrapped Statistics
Bootstrapa
Statistic
95% Confidence Interval
Bias
2002
Std. Error
Lower
Upper
N
29
0,085000
5,155226
19
39
Mean
0,135463
0,008014
0,240599
-0,324500
0,605591
Std. Devia-
1,354224
-0,027940
0,152740
1,020684
1,610183
tion
Std. Error
0,251473
95% Con-
Lower
fidence
Bound
-0,379657
Interval for
Mean
Upper
0,650582
Bound
2003
Minimum
-2,458570
Maximum
2,949080
N
17
0,092000
4,117826
9
26
Mean
0,188913
-0,007008
0,335915
-0,519695
0,848129
Std. Devia-
1,423726
-0,047244
0,216389
0,931482
1,799815
tion
Std. Error
0,345304
95% Con-
Lower
fidence
Bound
-0,543099
Interval for
Mean
Upper
0,920925
Bound
2004
Minimum
-2,389580
Maximum
2,976870
N
59
0,017000
7,164219
45
74
Mean
-0,009399
-0,001029
0,150943
-0,317214
0,260049
166
Appendix J. Bootstrapped BHAR Statistics per Year
Std. Devia-
1,143283
tion
Std. Error
0,148843
95% Con-
Lower
fidence
Bound
167
-0,014478
0,166457
0,806757
1,463609
10,167604
105
144
-0,307340
Interval for
Mean
Upper
0,288542
Bound
Minimum
2005
-3,837750
Maximum
3,518540
N
123
-0,198000
Mean
-0,154141
0,000275
0,086233
-0,318593
0,015174
Std. Devia-
0,954616
-0,007199
0,094472
0,758133
1,129853
tion
Std. Error
0,086075
95% Con-
Lower
fidence
Bound
-0,324535
Interval for
Mean
Upper
0,016252
Bound
Minimum
2006
-2,893830
Maximum
3,533810
N
208
0,310000
12,616222
184
234
Mean
-0,331949
-0,000898
0,050573
-0,427457
-0,228407
Std. Devia-
0,748467
-0,009423
0,106819
0,518578
0,939111
tion
Std. Error
0,051897
95% Con-
Lower
fidence
Bound
-0,434264
Interval for
Mean
Upper
-0,229635
Bound
Minimum
2007
-2,124370
Maximum
4,372200
N
182
0,334000
12,217762
159
206
Mean
-0,281175
-0,002665
0,055063
-0,383471
-0,164278
Std. Devia-
0,760987
-0,016535
0,132646
0,478742
1,011201
tion
Std. Error
0,056408
95% Con-
Lower
fidence
Bound
-0,392477
Interval for
Mean
Upper
Bound
-0,169873
168
2008
Appendix J. Bootstrapped BHAR Statistics per Year
Minimum
-1,262460
Maximum
4,950100
N
49
-0,495000
6,759709
36
62,974595
Mean
-0,074308
0,002939
0,116519
-0,297128
0,167569
Std. Devia-
0,792150
-0,011038
0,087830
0,610064
0,952094
tion
Std. Error
0,113164
95% Con-
Lower
fidence
Bound
-0,301839
Interval for
Mean
Upper
0,153224
Bound
2009
Minimum
-1,878840
Maximum
2,048760
N
23
-0,002000
4,722531
15
33
Mean
-0,134843
0,000065
0,203579
-0,547799
0,274144
Std. Devia-
0,969420
-0,039093
0,200361
0,538456
1,298262
tion
Std. Error
0,202138
95% Con-
Lower
fidence
Bound
-0,554051
Interval for
Mean
Upper
0,284366
Bound
2010
Minimum
-2,838710
Maximum
1,999540
N
60
-0,238000
7,374262
45,025405
74
Mean
-0,105417
0,003007
0,113320
-0,305429
0,134215
Std. Devia-
0,876322
-0,009083
0,105930
0,652432
1,068510
tion
Std. Error
0,113133
95% Con-
Lower
fidence
Bound
-0,331795
Interval for
Mean
Upper
0,120961
Bound
2011
Minimum
-2,384200
Maximum
2,499400
N
50,000000
0,422000
6,726006
38
64
Mean
-0,238779
0,000974
0,078360
-0,399647
-0,084285
Std. Devia-
0,567479
-0,009005
0,057450
0,445006
0,665896
tion
Std. Error
0,080254
Appendix J. Bootstrapped BHAR Statistics per Year
95% Con-
Lower
fidence
Bound
169
-0,400055
Interval for
Mean
Upper
-0,077504
Bound
2012
Minimum
-1,375990
Maximum
1,272630
N
37
-0,327000
5,963022
26
48,974595
Mean
0,066788
-0,004388
0,233578
-0,405966
0,535207
Std. Devia-
1,445799
-0,044935
0,204844
0,986667
1,787653
tion
Std. Error
0,237688
95% Con-
Lower
fidence
Bound
-0,415266
Interval for
Mean
Upper
0,548841
Bound
Total
Minimum
-3,549860
Maximum
3,538140
N
837
0,000000
0,000000
837
837
Mean
-0,185339
-0,000741
0,031872
-0,248102
-0,121834
Std. Devia-
0,912225
-0,001280
0,043317
0,827292
0,997025
tion
Std. Error
0,031531
95% Con-
Lower
fidence
Bound
-0,247228
Interval for
Mean
Upper
-0,123449
Bound
Minimum
-3,837750
Maximum
4,950100
a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples
Appendix K
Bootstrapped BHAR Statistics per Stock
Exchange
Table K.1: BHAR per Stock Exchange: Bootstrapped Statistics
Bootstrapa
Statistic
95% Confidence Interval
Bias
Std. Error
Lower
Euronext
Upper
N
14
0,097000
3,562061
7
21
Mean
-0,103997
0,007433
0,160995
-0,396250
0,228380
Std. Devia-
0,601985
-0,033810
0,128223
0,305679
0,801434
5,761391
24
47
Amsterdam
tion
Std. Error
0,160887
95% Con-
Lower
fidence
Bound
-0,451573
Interval for
Mean
Upper
0,243579
Bound
Euronext
Minimum
-0,703600
Maximum
1,328890
N
35
-0,163000
Mean
-0,133717
0,002428
0,103579
-0,326582
0,079954
Std. Devia-
0,612697
-0,018447
0,107313
0,411617
0,811133
tion
Std. Error
0,103565
Brussels
95% Con-
Lower
fidence
Bound
-0,344186
Interval for
Mean
Upper
0,076752
Bound
Minimum
-1,235510
170
Appendix K. Bootstrapped BHAR Statistics per Stock Exchange
Euronext
171
Maximum
1,973650
N
6
0b
2b
2b
11b
Mean
0,189153
-
,38169914b
-
,98517019b
Std. Devia-
0,914773
,00303661b
-
,276389923c
,50813667b
,146623051c
1,211678719c
260
Lisbon
tion
Std. Error
,129347339c
0,373455
95% Con-
Lower
fidence
Bound
-0,770842
Interval for
Mean
Upper
1,149149
Bound
Euronext
Minimum
-0,766870
Maximum
1,623190
N
236
-0,405000
12,679738
211
Mean
-0,179630
-0,001161
0,054780
-0,286491
-0,075968
Std. Devia-
0,882083
-0,007848
0,085604
0,702666
1,040210
Paris
tion
Std. Error
0,057419
95% Con-
Lower
fidence
Bound
-0,292751
Interval for
Mean
Upper
-0,066509
Bound
Minimum
Paris Al-
-3,837750
Maximum
4,372200
N
23
-0,107000
4,675833
14
33
Mean
-0,153337
0,011161
0,177858
-0,473517
0,199196
Std. Devia-
0,894545
-0,034226
0,198575
0,437488
1,195217
tion
Std. Error
0,186526
ternext
95% Con-
Lower
fidence
Bound
-0,540167
Interval for
Mean
Upper
0,233494
Bound
Frankfurt
Minimum
-1,421560
Maximum
2,499400
N
132
0,408000
10,617922
112
153
Mean
-0,189670
-0,000632
0,088474
-0,354143
-0,009483
Stock Exchange
172
Appendix K. Bootstrapped BHAR Statistics per Stock Exchange
Std. Devia-
1,049948
tion
Std. Error
0,091386
95% Con-
Lower
fidence
Bound
-0,011314
0,139623
0,775803
1,313444
-0,370453
Interval for
Mean
Upper
-0,008886
Bound
Minimum
Italian
-2,458570
Maximum
4,950100
N
63
0,303000
7,731686
48
79
Mean
-0,150366
-0,000833
0,077874
-0,300768
0,009254
Std. Devia-
0,621728
-0,007819
0,070422
0,467965
0,750569
tion
Std. Error
0,078330
Stock Exchange
95% Con-
Lower
fidence
Bound
-0,306946
Interval for
Mean
Upper
0,006214
Bound
AIM Italia
Minimum
-1,111720
Maximum
1,806550
N
7
0,150000
2,608122
3
12
Mean
-0,503926
-0,006839
0,159546
-0,717283
-0,120405
Std. Devia-
0,430878
-
,218595131b
,032456719b
,641741052b
tion
Std. Error
0,162857
,101137062b
95% Con-
Lower
fidence
Bound
-0,902422
Interval for
Mean
Upper
-0,105430
Bound
Minimum
London
-0,784440
Maximum
0,461170
N
97
-0,234000
9,158883
79
115
Mean
-0,003524
-0,001557
0,092247
-0,171392
0,177536
Std. Devia-
0,939678
-0,012236
0,085775
0,751417
1,102475
tion
Std. Error
0,095410
Stock Exchange
Appendix K. Bootstrapped BHAR Statistics per Stock Exchange
95% Con-
Lower
fidence
Bound
173
-0,192911
Interval for
Mean
Upper
0,185863
Bound
London
Minimum
-2,384200
Maximum
2,754520
N
224
-0,097000
13,175021
198
Mean
-0,293881
-0,000044
0,064695
-0,419662
-0,166121
Std. Devia-
0,974141
-0,009060
0,079665
0,807057
1,122944
tion
Std. Error
0,065088
837
250
AIM Stock
Exchange
95% Con-
Lower
fidence
Bound
-0,422147
Interval for
Mean
Upper
-0,165616
Bound
Minimum
Total
-3,549860
Maximum
3,520780
N
837
0,000000
0,000000
837
Mean
-0,185339
-0,000287
0,030207
-0,246514
-0,126068
Std. Devia-
0,912225
-0,003135
0,042833
0,824429
0,993943
tion
Std. Error
0,031531
95% Con-
Lower
fidence
Bound
-0,247228
Interval for
Mean
Upper
-0,123449
Bound
Minimum
-3,837750
Maximum
4,950100
a. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples
b. Based on 995 samples
c. Based on 972 samples
Appendix L
BHAR: Value-weighted scheme
174
Appendix L. BHAR: Value-weighted scheme
175
Table L.1: Value-weighted scheme: Overview Market Values per Year
Bootstrapa
Year
Statistic
95% Confidence Interval
Bias
Std. Error
Lower
2002
Mean
480,5271
-0,5978
146,2937
224,3588
794,8706
28
0
5
19
38
766,45864
-36,04455
179,48907
450,20944
1.008,71949
220,3838
-1,9796
103,8418
57,7252
415,8197
16
0
4
10
23
437,11700
-49,13786
164,00914
51,58416
623,43156
202,0945
-0,7952
62,9434
106,0330
329,2714
55
0
7
44
67
495,44825
-48,83793
193,42946
166,00560
762,94330
263,1993
-1,4550
47,8782
186,9764
350,0935
118
-1
10
100
135
504,50073
-14,30830
88,62924
352,90734
630,12229
190,5134
-0,5113
25,7541
146,9395
240,3140
201
0
12
180
222
362,20512
-6,82223
59,78029
256,51400
459,79407
264,9981
-1,2754
36,1788
202,4781
329,7252
177
0
12
156
199
508,38435
-10,12223
75,36847
362,14705
625,55078
123,8302
-1,0913
43,5290
57,4176
207,2955
41
0
6
31
51
279,87967
-28,71183
111,95496
82,62650
432,62295
46,3822
-0,2990
14,7950
22,7331
75,6186
23
0
5
15
32
Std. Deviation
69,39152
-4,65159
18,25869
27,43115
91,53261
Mean
422,8335
-6,0577
94,7161
255,6243
589,5522
55
0
7
42
69
709,06452
-25,88580
119,76780
467,32276
866,42843
206,6103
-3,2101
64,5570
101,0860
320,3361
48
0
7
36
61
444,19317
-19,26698
94,43808
259,73841
567,66568
170,9937
0,3691
47,1046
95,6181
266,8292
35
0
6
25
46
279,41449
-19,23840
86,92068
130,35561
398,10792
237,9589
-1,2285
16,7469
207,8761
268,1238
797
0
0
477,70323
-4,38912
34,20972
411,30519
532,94059
N
Std. Deviation
2003
Mean
N
Std. Deviation
2004
Mean
N
Std. Deviation
2005
Mean
N
Std. Deviation
2006
Mean
N
Std. Deviation
2007
Mean
N
Std. Deviation
2008
Mean
N
Std. Deviation
2009
Mean
N
2010
N
Std. Deviation
2011
Mean
N
Std. Deviation
2012
Mean
N
Std. Deviation
Total
Upper
Mean
N
Std. Deviation
Appendix M
T-tests for BHAR Periods
176
BHAR
not assumed
Equal variances
assumed
Equal variances
30,338
F
0,000
Sig.
Equality of Variances
Levene’s Test for
2,644
3,48
t
122,633
616
df
0,009
0,001
Sig. (2-tailed)
0,33402
0,33402
Mean Difference
0,12631
0,09599
Std. Error Difference
t-test for Equality of Means
Independent Samples Test
Table M.1: Independent t-test for Pre-Crisis(0) and Pre-Crisis(1) LT
0,083985
0,145506
Lower
0,58406
0,522537
Upper
Interval of the Difference
95% Confidence
Appendix M. T-tests for BHAR Periods
177
BHAR
not assumed
Equal variances
assumed
Equal variances
3,006
F
0,083
Sig.
Equality of Variances
Levene’s Test for
-1,677
-1,737
t
90,134
583
df
0,09698
0,083
Sig. (2-tailed)
-0,17766
-0,17766
Mean Difference
0,10593
0,10231
Std. Error Difference
t-test for Equality of Means
Independent Samples Test
Table M.2: Independent t-test for Pre-Crisis(1) and Crisis (2): LT
-0,3881
-0,37859
Lower
0,03278
0,02328
Upper
Interval of the Difference
95% Confidence
178
Appendix M. T-tests for BHAR Periods
RR
not assumed
Equal variances
assumed
Equal variances
0,074
F
0,785
Sig.
Equality of Variances
Levene’s Test for
0,108
0,103
t
160,12
217
df
0,91437
0,91832
Sig. (2-tailed)
0,01379
0,01379
Mean Difference
0,12803
0,13431
Std. Error Difference
t-test for Equality of Means
Independent Samples Test
Table M.3: Independent t-test for Crisis(2) and Post-Crisis(3)
-0,23906
-0,25093
Lower
0,26664
0,27851
Upper
Interval of the Difference
95% Confidence
Appendix M. T-tests for BHAR Periods
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