Saloua El Bouzaidi - Bibliothèque Universitaire d`Evry

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Saloua El Bouzaidi - Bibliothèque Universitaire d`Evry
UNIVERSITE EVRY VAL D'ESSONNE
ECOLE DOCTORALE SDS
CENTRE DE RECHERCHE EPEE
ESSAYS ON VENTURE CAPITAL MARKET AND EXIT STAGE
THESE
pour l'obtention du titre de
DOCTEUR EN SCIENCES ÉCONOMIQUE
Présentée et soutenue publiquement par
Saloua El Bouzaidi
le 26 Septembre 2014
JURY
Directeur de thèse : Monsieur Jérôme GLACHANT
Professeur à l'Université Evry Val d'Essonne
Rapporteurs :
Monsieur Philippe DESBRIÉRES
Professeur à l'Université de Bourgogne
Monsieur Ulrich HEGE
Professeur à HEC Paris
Suragants :
Monsieur Fabio BERTONI
Professeur à l'EM de Lyon
Madame Emmanuelle DUBOCAGE
Maître de Conférences à l'Université Paris Sud
.
It always seems impossible until it's done, Nelson Mandela
Acknowledgement
Mes remerciements particuliers vont à mon superviseur, le professeur Jérôme Glachant
pour ses conseils professionnels et son soutien tout au long de cette thèse. Je voudrais
également remercier le personnel de l'Université d'Evry Val d'Essonne, en particulier le
service des aaires doctorales.
Je remercie également les membres de mon comité de thèse: Monsieur Fabio Bertoni, le
professeur Philippe Desbrières, Madame Emmanuelle Dubocage et, professeur Ulrich Hege
d'avoir accepté de participer à commenter et à améliorer ce travail.
Enn, je suis très redevable à ma famille, spécialement mes parents, mes surs Houda
et Najoua, et mon frère Rachid, pour leur soutien et leur encouragement tout au long de
mes années d'études et pendant cette laborieuse aventure de doctorat.
3
Table of Contents - Overview
Table of Contents - Overview
5
1 Introduction
6
1.1
Industry Statistics
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2
Outline of The Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Survey About Venture Capital Financing Exit Stage
9
34
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2
Venture Capital Financing Process . . . . . . . . . . . . . . . . . . . 40
2.3
Exit Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.4
Exit Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.5
Some Open Research Questions . . . . . . . . . . . . . . . . . . . . . 55
3 Competitive Eect of Venture Capital Backing In IPOs
65
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3
Data and Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . 73
3.4
Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.5
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4 The Role of Venture Capital Backing in Mergers and Acquisitions 99
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.3
Data and Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . 107
4.4
Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4
TABLE OF CONTENTS - OVERVIEW
4.5
5
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5 Duration Analysis Of VC Staging In Cross Border Investment
131
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.2
Literature Review and Hypotheses development . . . . . . . . . . . . 135
5.3
Data and variables measures . . . . . . . . . . . . . . . . . . . . . . . 139
5.4
Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
5.5
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6 Conclusion
161
6.1
Summary of Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . 161
6.2
Outlook and Future Research . . . . . . . . . . . . . . . . . . . . . . 166
List of Figures
171
List of Tables
174
Table of Contents
178
Chapter 1
Introduction
L'une des spécicités stratégiques des petites et moyennes entreprises (PME)1 découle de leur choix technologiques. En eet, la technologie des PME tend à être
intermédiaire entre des technologies à très forte intensité de travail caractérisant
les micro-entreprises, et les technologies à forte intensité de capital caractérisant
les grandes entreprises. Les micro-entreprises consacrent une place importante à la
technologie dans leur processus de production ce qui se traduit par une faible productivité moyenne du travail. Diéremment, les grandes entreprises se caractérisent
par une forte intensité de capital ce qui permet d'atteindre une productivité élevée
du travail, mais qui en contre partie requiert un large capital. Par conséquent, le
niveau technologique intermédiaire des PME peut permettre à ce segment de jouer
un rôle particulier dans la création d'emplois.
Selon l'oce statistique de l'Union européenne (Eurostat), les chires sur les
PME rapportés dans le tableau 1.1 montrent que 20 millions de PME européennes
jouent un rôle important dans l'économie européenne 2 . En 2012, les entreprises
PME employaient environ 86.8 millions de personnes, soit les deux tiers des emplois
du secteur privé en Europe. Le secteur des PME dans son ensemble livre 57.6 % de
1 La
dénition récente des petites et moyennes entreprises (PME) adoptée en mai 2003, correspond à des entreprises qui emploient moins de 250 personnes et dont le chire d'aaires annuel
est inférieur à 50 millions d'euros, et / ou qui présentent un total annuel de bilan n'excédant pas
43 millions d'euros.
2 Les catégories de taille employées dans ce tableau correspondent à des dénitions utilisées par
la base de données Eurostat sur les statistiques structurelles des entreprises: les micro-entreprises
(0-9 salariés), les petites entreprises (10-49 salariés), les entreprises moyennes (50-250 salariés), et
les grandes entreprises (plus de 250 salariés)
6
7
la valeur ajoutée brute
3
générée par l'économie privée, non-nancière en Europe en
2012, ce qui suggère que le secteur privé est le principal moteur de la croissance en
Europe.
Table 1.1 Enterprises, Employment and Gross Value Added of SMEs in the EU-27,
2012
Size
Micro
Small
Medium
SMEs
Number of Enterprises
222 628 20 355 839
1.10%
99.80%
Large
Total
Number
%
18 783 480 1 349 730
92.10%
6.60%
43 454 20 399 291
0.20%
100%
Number
%
Employment
37 494 458 26 704 352 22 615 906 86 814 717 43 787 013 130 601 730
28.70%
20.50%
17.30%
66.50%
33.50%
100%
Value Added at Factor Costs
Million Euros 1 242 724 1 076 388 1 076 270 3 395 383 2 495 926
%
21.10%
18.30%
18.30%
57.60%
42.40%
5 891 309
100%
Source: Eurostat
Selon l'association Européenne de l'investissement en capital (EVCA) plus de 80
% des sociétés nancées par capital investissement en Europe entre 2007 et 2012,
sont des petites et moyennes entreprises (PME). Par conséquent, les capitaux privés
contribuent à la croissance économique en facilitant l'augmentation des investissements du secteur privé dans les PME. Par exemple, en 2007, l'industrie du capital
investissement représente 0.53 % du PIB
4
en Europe, avec moyenne globale de 0.34
% de PIB pour la période 2007-2013 (voir gure 1.1).
Le capital Investissement (CI) est une forme de prise de participation dans des
entreprises privées qui ne sont pas cotées sur une bourse de valeurs. Les investissements sont réalisés par l'intermédiaire d'un fonds d'une durée de vie de dix ans.
3 La
valeur ajoutée brute est la diérence entre la production et la consommation intermédiaire
s'agit d'une mesure globale de la production, le PIB est égal à la somme de la valeur ajoutée
brute de l'ensemble des unités institutionnelles résidentes (industries) engagées dans la production,
augmentée des impôts moins les subventions sur les produits qui ne sont pas aectés aux sect eurs
et aux branches d'activités
4 il
8
Figure 1.1 Private Equity Investment as % of GDP
Les gestionnaires des fonds de capital investissement (Général partners : GP)
5
s'engagent activement dans le management des entreprises détenues en portefeuille.
Une fois les entreprises réussissent à atteindre leur échéance, la phase désinvestissement (ou sortie) est réalisée et le GP rembourse ses investisseurs ou réinvestis à
nouveau les fonds.
Le forum économique mondial (WEF) publie annuellement un rapport sur la
compétitivité mondiale qui classe les pays en fonction de leur niveau de compétitivité.
L'une des mesures que le WEF utilise pour évaluer la compétitivité est la disponibilité des fonds de capital-risque (CR). Ceci est basé sur l'idée que l'augmentation
de la disponibilité des fonds du CR encourage la création d'entreprises.
Popov et Roosenboom (2009) [3] examinent cette idée académiquement en utilisant une base de données des entreprises européennes; l'objectif est d'étudié comment le CI impacte le taux d'entrée des entreprises dans 21 pays européens entre 1998
5 Le
type et le fonctionnement du partenariat liant les investisseurs de CI et leurs pourvoyeurs de
fonds sont discutés dans le chapitre suivant Survey About Venture Capital Financing Exit Stage.
Le chapitre Survey About Venture Capital Financing Exit Stage également dénit et distingue les
diérents segments de capital investissement
1.1.
Industry Statistics
9
et 2008. Les auteurs constatent qu'une augmentation de deux écart-type du capitalinvestissement explique près de 5.5 % de la diérence d'entrée entre les secteurs à
hausse et faible entrées.
De même, Samila et Sorenson (2011) [7] dans un échantillon d'entreprises américaines trouvent que entre 1993 et 2002, le doublement de l'ore de capital risque
dans une région se traduit par la création en moyenne de 0.49 % à 2.6 % entreprises
de plus. Les auteurs expliquent ce résultat par deux mécanismes: premièrement,
l'entrepreneur qui anticipe un besoin futur de nancement est susceptible de créer
son entreprise quand l'ore de capital augmente. Deuxièmement, les entreprises de
capital-risque peuvent transférer des connaissances tacites à leurs propres employés,
les encourageant ainsi de devenir des entrepreneurs par un eet de démonstration.
Ce chapitre d'introduction commence par présenter des statistiques récentes sur
l'industrie européenne du capital investissement, puis par résumer la structure de
cette thèse.
1.1
Industry Statistics
1.1.1 Fundraising Activity
L'Europe est historiquement un marché important dans le secteur du capital investissement. La crise économique subie par la région, notamment la crise nancière
mondiale de 2008 et la crise de la dette pendant la seconde moitié de 2011, a menacé
de l'eondrement plusieurs économies en particulier dans le sud du continent, notamment au Portugal, en Italie et en Espagne. L'Irlande et la Grèce ont également
souert de façon signicative, et la conance générale a chuté dans toute l'Europe.
Cette situation se reète également dans le secteur du CI, où les uctuations du
marché sont apparentes à travers la baisse de l'activité de collecte de fonds depuis
les dernières années.
Comme le montre la gure ??, la crise nancière internationale a aecté négativement l'industrie du capital investissement dans son ensemble, où la collecte de fonds
sur le marché de CI à baissé de manière signicative. La gure montre également
1.1.
Industry Statistics
10
Figure 1.2 Aggregate Capital Commitments by Fund Geographic Focus, 2000 - 2011
Source: Preqin
comment jusqu'à récemment, les fonds investissant en Europe ont été la deuxième
destination des fonds, juste après l'Amérique du Nord et avant les véhicules investissant en Asie et reste du monde. Cependant, depuis la crise nancière le marché
de levé des fonds a changé, avec notamment le niveau de capital alloué aux fonds
investissant en Asie et reste du monde dépassant pour la première fois le niveau de
fonds attribué à des véhicules investissant en Europe. Cette tendance est maintenue
depuis 2011, avec des véhicules investissant en Europe recueillant $62.4 milliards et
des fonds investissant en Asie et reste du monde enregistrant un total de fonds levé
égal à $ 63.5 milliards.
La gure 1.3 indique le montant total levé entre 2000 et 2013 par l'industrie
européenne du capital investissement. En 2009, l'activité de levé de fonds enregistre
une chute spectaculaire avec un total de capitaux levés qui chute de 80 e milliards en
2008 à 18 e milliards en 2009, l'activité de collecte de fonds commence à reprendre en
2011, mais se repli à nouveau en 2012. La levé de fond par les investisseurs en capital
Européen s'est nettement améliorée en 2013 en enregistrant une augmentation de
118% (par rapport à 2012) pour atteindre 54 e milliards.
Généralement, les gestionnaires de fonds de capital investissement appellent le
capital auprès de diérents types d'investisseurs.
Les pourvoyeurs de fonds en
1.1.
Industry Statistics
11
Figure 1.3 Total Capital Raised by European Private Equity Funds, 2000-2013
Source: EVCA
CI peuvent être: des investisseurs institutionnels (fonds de pension, compagnies
d'assurance, des établissements universitaires et des fondations), des familles fortunées, des organismes gouvernementaux et des fonds souverains. Le capital investissement (y compris le capital-risque) a attiré plus de 318 milliards en Europe
entre 2007 et 2013.
La gure 1.4 montre par type d'investisseur la proportion des capitaux levés en
Europe entre 2007 et 2013, avec 47 % des fonds levés par l'industrie du capital
investissement en 2013 provenant des investisseurs institutionnels, pour une proportion moyenne globale à 25 % pour la période 2007-2013. La gure montre également
quelques changements dans la nature des investisseurs dans les fonds européens de
CI, à savoir les fonds de pension et les banques qui perdent leur position de chef de
le en terme d'engagement en capital après la crise nancière de 2009. Inversement,
la participation des agences gouvernementales dans l'industrie Européenne de CI à
augmenté an soutenir le marché pendant les périodes de turbulences.
La gure 1.5 indique le type d'investisseurs dans les 5 premiers pays de l'industrie
européens du CI en 2013. Le type et la proportion d'investisseurs dièrent entre les
pays. Les fonds de pension sont les principaux fournisseurs de capital pour l'industrie
de CI en Royaume-Uni (41 % de total des fonds levés) et en Suède (25 % de total
1.1.
Industry Statistics
Figure 1.4 Funds raised by Type of Investors in Europe, 2007-2013
Figure 1.5 Funds Raised by Type of Investor in 2013-in top 5 countries
12
1.1.
Industry Statistics
13
des fonds levés). D'une manière diérente, les particuliers nancent une partie importante de marché de CI allemand avec 30 % du total des fonds levés. Le secteur
de CI français a la particularité de lever plus de 18 % du capital auprès d'organismes
gouvernementaux; ceci représente la plus forte participation gouvernementale dans
le secteur CI en Europe. Les compagnies d'assurance ont également une place importante dans le secteur de CI français, avec 31 % du capital total des fonds levés. Enn,
les fonds familiaux occupent une première position aux Pays-Bas et au Suède, où ils
nancent plus de 32 % de l'indsutrie de CI néerlandaise et 21 % du marché de CI
suédois. Ces statistiques donnent une idée sur l'hétérogénéité du type d'investisseur
dans l'industrie de CI européenne.
Les fonds de capital investissement sont classés en 4 grandes catégories en fonction de leur stade de nancement. Un fond peut être spécialisé dans le nancement
de capital-risque, capital développement, capital transmission (Leverage Buy Out :
LBO), ou capital retournement. Les fonds de capital-risque nancent des entreprises
au stade d'amorçage ou en début d'activité. Les fonds de développement investissent dans des sociétés qui cherchent à se développer ou à eectuer des opérations
de restructuration. Les fonds de transmission sont spécialisés dans l'acquisition de
participations majoritaires dans des entreprises établies. Enn les fonds de retournement facilitent le nancement de rachat.
La gure 1.6 montre l'évolution du capital total levé par type de fonds en Europe
entre 2007 et 2013. Les plus grand écarts dans les activités de collecte de fonds ont
été enregistrés dans le secteur de capital transmission, avec une profonde récession en
2009. En eet, le montant total des capitaux levés par les fonds de transmission chute
de 64 e milliards en 2008 à 10 e milliards en 2009. Cette tendance s'est inversée
ensuite pour commencer à augmenter doucement, mais à diminuer a nouveau en
2012. Les récentes levées de fonds se sont améliorées en 2013, principalement tirées
par le stade de transmission (169 % à l'44,9 e milliards), qui constitue , en plus du
capital de développement, la plus importante partie du marché en terme de montant
élevé (+124 % à 1,2 e milliards).
Les fonds levés par le capital risque (CR) ont connu moins de variation, mais à
partir d'un montant de départ plus réduit, par exemple le segment CR a levé 3 e
1.1.
Industry Statistics
14
Figure 1.6 Capital Raised by Stage Focus of Funds, 2007-2013
Source: EVCA
milliards en 2010 et en 2009, soit environ la moitié du montant levé en 2008. Le
niveau total de levée des fonds a augmenté en 2013, cependant, ça n'a pas dépassé les
niveaux d'avant les années de crises. Cela suggère que "les investisseurs en capital
risque en Europe font face à une pénurie de fonds" (EVCA, 2013).
Un autre aspect révélé par la gure 1.6 est le stade de spécialisation des fonds
de capital investissement en Europe. En fait, les fonds généralistes ne représentent
que 2 % des fonds levés en 2013, pour un total moyen égal à 3 % entre 2007 et 2013.
Enn, en fonction de leur structure organisationnelle, les fonds sont classés en
fonds indépendants ou captifs. Les fonds indépendants n'ont pas de liaison directe
avec un parent. Les fonds captifs sont détenus par une organisation mère (banques
ou entreprises).
La gure 1.7 montre la proportion des fonds levés par fonds indépendants et
captifs en Europe. La majorité des fonds de capital investissement en Europe sont
indépendants, la proportion des fonds captifs dans l'industrie a légèrement augmenté
de 2007 à 2009, puis a diminué après pour ne représenter que 1 % de l'industrie en
2013. Cette répartition de l'industrie est diérente de celle observée sur le marché
américain, où selon l'association nationale de capital risque (NVCA), les fonds levés
1.1.
Industry Statistics
15
Figure 1.7 Capital Raised by Funds Organization Type, 2007-2013
Source: EVCA
par les investisseurs en capital aliés à des grandes entreprises représentent 10,5 %
du capital de risque investi. L'étude comparative de Hege et al. (2009) [131] entre
les marchés américain et européen du capital risque souligne également la diérence
dans la participation des sociétés de gestion aliés aux grandes compagnies dans le
marché du capital de risque. Les auteurs constatent que pour la période 1997-2003,
les fonds de CR aliés à des entreprises sont deux fois plus fréquemment impliqués
dans le marché de CR aux États-Unis.
1.1.2 Investment Activity
Une fois l'investissement est réalisé, les investisseurs en capital s'engagent activement dans la gestion de l'entreprise, où souvent ils occupent un siège au conseil
d'administration et s'impliquent dans la gestion courante de l'entreprise.
La gure 1.8 indique le montant total des capitaux investis et de nombre d'entreprises
nancées par capital investissement en Europe entre 2000 et 2013. Les années 2006
et 2007 représentent les années de prospérité pour le capital-investissement européen,
avec un total de 72.9 e milliards investis en 2007. Le marché européen du capitalinvestissement a ensuite souert d'une importante baisse d'investissement en raison
1.1.
Industry Statistics
16
de la crise nancière, avec un niveau d'investissement total égal à 25 e milliards en
2009. Bien que le CI avait partiellement rebondi durant les années 2010 et 2011,
toutefois, la reprise à subi un revers en 2012, mais est restée bien supérieure au
niveau d'investissement pendant la crise de 2009. En 2013, les investissements de CI
se sont stabilisés à 37.72 e milliards d'euros (0.1 % par rapport à 37.7 e milliards
d'euros en 2012). Le nombre d'entreprises qui ont bénécié d'un nancement de CI
en 2013 est également resté pratiquement inchangé (+0,3 % par rapport à 2012) à
un niveau de près de 5300.
Figure 1.8 Investment Activity of Private Equity Firms Located in Europe, 2000-2013
Source: EVCA
1.1.
Industry Statistics
17
Le nancement par capital investissement répond à plusieurs besoin de l'entreprise.
Ainsi l'investissement peut être adressé aux entreprises qui ont besoin de nancer
la recherche et le développement d'un concept initial (seed investment), ou bien
à des entreprises qui démarrent le développement de produit et sa commercialisation préalable (starts up), ou bien à des entreprises d'exploitation qui ont besoin de
plus de fonds pour leur expansion (later stage). Ces trois objets d'investissement
sont communément appelés investissement en capital risque. Les investissements
de développement concernent les entreprises relativement matures qui s'engagent
dans la diversication des marchés ou dans des opérations de réorganisation. Les
investissements de transmission (LBO : leverage buy out) consistent à acquérir des
entreprises en utilisant une quantité importante de la dette. Les investissements de
retournement visent à rétablir les entreprises en diculté. Enn le capital de remplacement consiste en l'achat des parts d'une autre société ou d'un autre actionnaire.
Figure 1.9 Amount Invested by stage, 2007-2013
Source: EVCA
La gure 1.9 montre que la part importante des montants investis se concentre
dans le stade LBO. Le LBO a également souert d'une baisse sérieuse des montants investis courant la crise nancière, où ils chutent de 39 e milliards en 2008 à
1.1.
Industry Statistics
18
13 e milliards en 2009. Le niveau d'investissement des fonds LBO a rebondi avec
un doublement du montant total investi en 2010-2011 (30 e milliards en 2010 par
rapport à 13 e milliards en 2009), cependant en 2012, le montant investi baisse à
28 e milliards et se stabilise ensuite à 29 e. En 2013, les investissements en capital
risque étaient relativement stables, même si le niveau d'activité en 2013 était encore
loin de celui de 2008 (EUR3.4 milliards en 2013 contre EUR6.6 milliards en 2008).
Dans l'ensemble, ces chires suggèrent que le niveau d'activité dans le LBO mène
la tendance globale de l'activité du capital investissement, ceci est principalement dû
à l'importance relative des valeurs des transactions en LBO, et que ces variations ont
été entraînées par la turbulence économique générale dont le capital investissement
et en particulier le secteur de LBO ont souert.
1.1.3 Exit and Performance
1.1.3.1 Exit Patterns
Quand les entreprises du portefeuille atteignent leur échéance, les investisseurs en
capital vendent leurs parts et distribuent le capital à leurs bailleurs de fonds. En
eet, la rentabilité des fonds de CI découle des gains de capital réalisés lors de la
cession, et comme généralement les entreprises nancées par capital investissement
ne paient pas de dividendes, c'est la phase de sortie qui détermine le succès des fonds
de CI qui sera ensuite décisif pour la survie des sociétés de CI eux mêmes, plaçant
ainsi les sorties rentables au cur de l'industrie de CI.
La gure 1.10 indique le montant de désinvestissement dans l'industrie de capital
investissement en Europe entre 2000 et 2013. La gure montre que la crise nancière
a également impacté négativement l'activité de sortie, qui a diminué en 2009 en
enregistrant 12 e milliards de valeur à la sortie. L'activité de désinvestissement s'est
sensiblement améliorée en 2010 et 2011, avant de baisser à nouveau en 2012, puis
augmenter en 2013, pour enregistrer un plus haut niveau avec un total de cessions
égal à 33 e.6 milliards (+ 53 % de plus que le niveau atteint en 2012).
En pratique, il y a 8 modes de sorties; les plus connues sont ceux en relations
avec le marché public, mais il est utile de distinguer entre une ore publique initiale
1.1.
Industry Statistics
19
Figure 1.10 Divestment (by amount at cost divested) by European Private Equity Firms
2000-2013
Source: EVCA
d'achat (IPO) qui est la première vente des actions de l'entreprise sur une bourse de
valeurs et la vente des restants des actions cotées après l'écoulement de la période de
verrouillage (lock-up). En eet, les actionnaires sont souvent tenus de ne pas vendre
leurs actions pendant une certaine période de temps après l'introduction en bourse,
habituellement de 6 à 12 mois; ce qui permet à l'entreprise de susciter l'intérêt parmi
les acheteurs potentiels de ses actions.
La vente des entreprises du portefeuille en dehors du marché public est classée
selon le type de l'acquéreur. La sortie est appelée vente industrielle 6 , lorsque
l'acquéreur est un industriel. Il s'agit d'une vente secondaire lorsque l'acquéreur
est une autre société de capital-investissement; il s'agit d'un rachat si l'entrepreneur
rachète les actions de sa propre entreprise. Enn, les entreprises du portefeuille peuvent également être vendues à un acteur nancier autre que les sociétés de capital
investissement.
Une sortie non réussie en capital investissement consiste à une vente partielle ou
complète. Une perte est partielle lorsque la valeur de l'investissement est réduite
6 les
termes acquisition ou vente à un industriel sont utilisées d'une manière interchangeable
dans cette thèse. Les deux termes représentent un événement de sortie où la société de portefeuille
est vendue à un acheteur industriel
1.1.
Industry Statistics
20
avec une réduction des valeurs des actions de l'entreprise. Une perte est complète
lorsque la valeur du portefeuille de l'entreprise est mise à zéro ou à un montant
symbolique.
Figure 1.11 Divestment evolution by exit route in European PE market, 2007-2013
Source: EVCA
La gure 1.11 rapporte le nombre d'entreprises par type de sortie en Europe entre
2007 et 2013. Pour les sorties réussies, les acquisitions et les ventes secondaires sont
les modes de sorties les plus fréquents. Les sorties par introduction en bourse ne
constitue pas une sortie fréquente, en particulier pendant la période de crise, avec 4
introductions en bourse en 2009 et en 2012. La liquidation est la principale forme
de sortie pour le capital investissement en période de turbulences avec 20% des
sorties par liquidation en 2009 et en 2012, ceci reète les dicultés que rencontre le
marché de CI pour réaliser des sorties rentables. Dans l'ensemble, ces statistiques
documentent que les investisseurs en CI ont du mal à réaliser des sorties prospères et
que les sorties par acquisition sont plus fréquentes que les introductions en bourse.
En général, il est communément admis que les introductions en bourses et les
sorties par acquisitions sont les deux sorties les plus réussies; elles sont aussi les plus
étudiées. Cependant, l'importance des sorties par ventes secondaires et des rachats
par le management est également en train d'augmenter.
1.1.
Industry Statistics
21
1.1.3.2 PE Industry Performance
Du point de vue de l'investisseur en capital et de ses investisseurs institutionnels,
c'est avant tout la rentabilité nancière qui constitue l'attractivité de cette classe
d'actifs. Le cycle de nancement par CI exige que des investisseurs privés réalisent
des rendements nanciers susants pour réussir à lever de nouveaux fonds.
Selon la gure 1.12, le taux de rendement interne (TRI) à l'horizon de 3, 5 et 10
ans s'est stabilisé à un niveau faible en enregistrant ainsi un petit recul, ceci après
une période de rendements négatifs au cours des années 2008 à 2010. En eet pour la
première fois depuis 2008, le TRI de 5 ans (1,3 %) et de 10 ans (0,8 %) sont positive
en même temps. Il est intéressant de noter que le rendement des fonds exposés à la
période 2008-2010 continue d'être freiné ce qui est particulièrement visible dans le
TRI à l'horizon de 5 ans.
Figure 1.12 3, 5 and 10-year Rolling-Horizon Internal Rate of Return
La gure 1.13 compare le TRI net à l'horizon de 5 ans pour des fonds de capital
risque et des fonds de capital transmission, la performance du secteur du capital
risque en Europe est en dessous du niveau des rendements du segment du capital
transmission; ce fut également le cas lorsque l'on regarde le passé, en particulier
depuis 2001, toutefois, les chires de TRI pour le capital transmission et le capital risque ont convergé jusqu'en 2012, avant que la performance augmente pour le
1.1.
Industry Statistics
22
segment des LBO et puis après pour le segment de capital risque en 2013.
Figure 1.13 5 Year Rolling Horizon Net IRR of Venture and Buyout Funds
Source: EVCA
Malgré la modeste performance de l'industrie du CI, il continue d'attirer l'enthousiasme
des investisseurs. Kaplan et Lerner (2010) [140] argue que l'une des raisons pour
cela est "la grande fraction des IPO soutenus par capital-investissement ". En effet, le capital-risque à participer au nancement de nombreuses sociétés publiques
prospères aux États-Unis. Par exemple, Microsoft, Apple, Google, eBay, Amazon,
Yahoo, Adobe, Starbucks et Cisco ont été nancés en partie par du capital-risqueur
au cours des 30 dernières années.
La gure 1.14 indique le nombre d'introductions en bourse aux États-Unis entre
2000 et 2013. Par exemple dans l'année 2000, les entreprises introduites en bourse et
soutenues par un capital-risque représentent 67 % du total des introductions sur le
marché public, pour une moyenne globale de 45 % pendant la période 2000-2013. Ces
statistiques montrent qu'une grande partie des entreprises qui deviennent publiques
sont nancées par du capital risque, ce qui suggère que le nancement par CR aux
États-Unis augmente de façon signicative la probabilité qu'une entreprise devienne
public.
La grande fraction des introductions en bourses soutenues par capital-risque
1.1.
Industry Statistics
23
Figure 1.14 VC IPOs in US, 2000-2013
Source: NVCA
anime également les recherches sur le rôle des investisseurs de capital risque dans
le processus d'introduction en bourse. En eet, le rôle des capital-risqueurs dans
les introductions en bourse est une question de recherche spécique dans cette thèse
(Competitive Eect of Venture Capital Backing In IPOs), je fais cela en analysant
le rôle concurrentiel des introductions en bourse soutenues par un investisseur en
capital risque. De façon complémentaire, The Role of Venture Capital Backing in
Mergers and Acquisitions vise à comprendre le rôle des investisseurs en capital risque
dans les acquisitions, ceci en examinant le rendement de l'acheteur d'une entreprise
nancée par capital risque. L'objectif est d'analyser le rôle de l'investisseur en
CR dans les acquisitions. Le troisième travail empirique de cette thèse (Duration
Analysis Of VC Staging In Cross Border Investment) examine un nouvel aspect du
marché du capital de risque, c'est l'investissement à l'international et l'inuence des
cultures sociales sur le nancement par capital risque. La prochaine section décrit
les objectifs et les résultats de chaque chapitre de cette thèse en donnant un aperçu
de ce travail.
1.2.
Outline of The Thesis
1.2
24
Outline of The Thesis
1.2.1 Venture Capital Investor Role in IPOs Exits
En général, la littérature reconnaît 4 rôles aux investisseurs de CR dans les sorties
par IPO, à savoir, "la certication", "la sélection et le suivi", "le pouvoir de marché"
et "le timing du marché".
La littérature utilise le ratio de sous-évaluation pour analyser le rôle de certication que joue l'investisseur en capital dans les introductions en bourse. Comme
le montre la gure 1.15, la sous-évaluation mesure l'augmentation du prix d'ore
au moment de l'introduction par rapport au prix de clôture de la 1er journée sur le
marché secondaire.
Figure 1.15 Underpricing ratio
Source: Chemmanur and Loutskina, (2007)
Par exemple Megginson et Weiss (1991) [149] constatent que les nouvelles introductions en bourse soutenues par CR sont moins sous-évaluées que les introductions
qui ne sont pas soutenues par un investisseur en capital. Les auteurs trouvent que
la variation entre le prix d'ore et le prix de clôture pour les introductions avec un
investisseur en capital risque est plus faible (7,1 % pour les introductions soutenus
par un CR et 11,9 % pour les introductions non nancées par CR). Ceci montre que
les investisseurs de capital-risque xe pour leurs entreprises un prix d'ore qui se
rapproche de leur valeur intrinsèque, ce qui confère aux investisseurs en CR un rôle
de certication.
1.2.
Outline of The Thesis
25
Le pouvoir de marché des investisseurs en capital risque se reète par la qualité
des participants lors de l'introduction en bourse d'entreprises nancées par CR (assureurs, investisseurs institutionnels et analystes) et par l'évaluation après l'introduction.
L'idée derrière est qu'un grand nombre et une meilleure qualité de participants
pendant l'introduction rend les investisseurs plus optimistes quant à la perspective d'évolution de l'entreprise introduite, ce qui par la suite impacte positivement
l'évaluation de l'entreprise introduite.
Par exemple, Chemmanur et Loutskina,
(2007) [111] trouvent que l'introduction en bourse d'entreprises nancées par un
capital-risqueur est réalisée par des assureurs réputés et de que ces introductions
reçoivent une grande couverture des analystes, comparé à des introductions qui ne
sont pas nancées par CR. Ce qui suggère que les investisseurs en capital risque ont
un pouvoir de marché pendant introductions en bourse.
Le rôle de sélection des investisseurs en capital risque réfère à la nature sélective
de ce type de nancement, où les capital-risqueurs nancent seulement une minorité
d'entreprises sélectionnées. Le rôle de suivi désigne le temps et l'eort que les investisseurs de capital-risque fournissent en étant impliqués dans la gestion de leurs
entreprises de portefeuilles. Les deux rôles entraînent que la qualité des entreprises
soutenues par CR est supérieure au moment de l'introduite en bourse. Par exemple,
Chemmanur et Loutskina (2006) [111] documentent que les résultats d'exploitation
après l'introduction en bourse (marges bénéciaires, retour sur capital, et la croissance des ventes) des entreprise nancées par CR, pendant l'année d'introduction en
bourse et dans les deux années qui suivent, sont plus élevés que ceux des entreprises
non nancées par CR.
Enn, le timing du marché réfère à la capacité des investisseurs en capital risque
de choisir le moment adéquat pour côter leur entreprise en bourse. Par exemple, Lerner (1994) [146] établit que les investisseurs de capital-risque introduisent
leurs entreprises en bourse quand les valorisations du marché public sont au pic et
s'appuient sur des nancements privés lorsqu'elles sont moins élevés.
Dans le chapitre 3 j'étudie le lien entre le nancement par CR et la décision
d'introduction en bourse.
L'objectif est d'examiner le rôle des investisseurs en
capital-risque dans les introductions en bourse, ceci en étudiant comment les en-
1.2.
Outline of The Thesis
26
treprises nouvellement introduites et ayant un nancement par capital risque impactent la rentabilité de leur concurrents. C'est une façon indirecte pour étudier
le rôle des investisseurs en CR dans les introductions en bourse. En eet, au lieu
d'analyser le prix d'ore et la performance des entreprises nouvellement introduites,
ce qui nécessite l'accès à des données historiques des entreprises privées, j'utilise
la méthode d'étude d'événement pour évaluer l'impact d'introduction en bourse
d'entreprise nancée par CR sur la valeur de les entreprises concurrentes. Je choisis
des paires comparables de 120 entreprises avec et sans nancement de CR qui sont
introduites sur le marché public Français entre 1994 et 2011.
Le résultat montre que les introductions en bourse d'entreprises nancées par
CR sont associées à des rendements boursiers positifs des concurrents, En d'autres
termes, la présence de capital-risqueur au moment de l'introduction en bourse a
un eet de valorisation positive sur les concurrents du même secteur d'activité,
ceci indique que les investisseurs réévaluent à la hausse la valeur des entreprises
similaires déjà présentes sur le marché boursier. Ce qui suggère que les investisseurs
de capital-risque ont un rôle du marché timing qui transmet un signal positif sur les
perspectives du secteur.
Il est important de garder à l'esprit que l'introduction en bourse n'est pas le seul
moyen pour le capital-risqueur pour désinvestir de son portefeuille d'entreprises, la
vente industielle est aussi une voie de sortie fréquente. En eet, les grandes compagnies sont devenues actives dans l'acquisition d'entreprises en début de croissance, en
raison de leur besoin de développer leurs stratégies d'innovation.
1.2.2 Venture Capital Investor Role in M&As Exits
Les statistiques récentes du marché européen du capital-risque signalent le rôle croissant des grandes compagnies en tant qu'acheteurs de jeunes entreprises. Figure 1.16
montre le nombre d'entreprises cédées par type de sortie sur le marché de capital
risque européen entre 2007 et 2013. Par exemple en 2013, les grandes compagnies
acquièrent 211 entreprises (+ 28 % comparé au nombre d'entreprises acquises en
2012), représentant 54 % de la valeur de cession totale du marché (1.2 e milliards).
Entre 2007 et 2013 le nombre total des entreprises soutenues par CR et acquises
1.2.
Outline of The Thesis
27
par une compagnie est de 1545, alors que pour la même période, le nombre total
d'introduction en bourse d'entreprises nancées par CR est de seulement 62.
Figure 1.16 Divestment Evolution by Exit Route in European VC market
Source: EVCA
Le chapitre 4 s'intéresse aux opérations de sorties par acquisitions. L'objectif
est d'analyser comment l'acquisition d'une entreprise soutenue par CR impacte la
rentabilité de l'acheteur; il s'agit d'un moyen d'étudier le rôle de l'investisseur en CR
dans les opérations de fusions acquisitions. J'utilise un échantillon de 172 acquisitions européennes similaires réalisées entre 1997 et 2012 pour comparer la rentabilité
des acheteurs pendant l'annonce d'acquisition des cibles avec ou sans de capitalrisqueur.
Le résultat concernant le rôle de l'investisseur en capital risque dans les opérations d'acquisitions documente un impact négatif de la présence d'un investisseur
en capital risque sur les rendements de l'acheteur. Ce qui suggère que le marché
considère que les investisseurs de capital-risque obtiennent des prix élevés pour leurs
entreprises. Ce résultat insinue que les investisseurs de CR ont un rôle de certication
dans les opérations de sortie par acquisition. En fait, le rôle de certication consiste
à réduire l'asymétrie d'information entre l'acheteur et la cible, ainsi la présence un
investisseur en capital risque va inciter l'acheteur à payer un prix plus élevé pour
1.2.
Outline of The Thesis
28
conclure le deal, et en conséquence avoir une rentabilité boursière moindre.
Globalement chapitre 3 et le chapitre 4 partagent deux points en commun.
Le premier est que les deux visent à explorer le rôle de l'investisseur en capital
risque pendant le stade de sortie, et le second est que les deux chapitres utilisent
la méthodologie d'étude d'événement pour évaluer l'eet d'annonce d'une décision
spécique.
L'objectif d'étude d'événements est de mesurer les rendements anormaux des prix
d'actions suite à l'annonce d'événements spéciques. Les études d'événements sont
utilisées dans cette thèse pour mesurer les changements de rendement d'actions résultant d'annonces spéciques (introductions en bourse et acquisitions). L'hypothèse
d'ecience des marchés suggère que le prix des valeurs mobilières absorbe instantanément toutes les informations et les reète dans le prix du marché, ceci implique
que les prix des actions s'ajustent rapidement en fonction de nouvelles informations disponibles, de sorte qu'aucun rendement excédentaire ne peut être gagné par
arbitrage sur des informations nouvelles.
1.2.3 Cross Border Investment and Staging Decisions
Avant les années 1990, le CI était principalement un phénomène américain. La
mondialisation de l'activité informatique encouragée par l'industrie du capital-risque
a incité les gestionnaires des fonds de capital-risque à exporter leurs compétences.
Ainsi les États-Unis dominent le marché internationale de CI, où la plupart des
transactions internationales sont depuis ou vers les Etats-Unis (Aizenman et Kendall,
2008 [159]).
La gure 1.17 montre l'évolution de l'origine géographique des fonds levés en Europe entre 2007 et 2013. En dehors de la période de crise, les engagements de fonds
non européens sont plus prépondérants, avec 28 e milliards en 2008 (représentant
35% du total des fonds soulevés) et 26 e milliards en 2013 (soit 48 % du total des
fonds). Pendant la période de crise, l'industrie européenne de CI repose principalement sur des fonds nationaux et des fonds européens.
La gure 1.18 montre l'évolution de la répartition géographique des investissements de CI entre 2007 et 2013, l'industrie européenne de CI intervient princi-
1.2.
Outline of The Thesis
Figure 1.17 Geographic Sources of PE Funds, 2007-2013
Source: EVCA, Euronext
Figure 1.18 European PE Investment Destination, 2007-2013
Source: EVCA, Euronext
29
1.2.
Outline of The Thesis
30
palement dans les transactions domestiques et intra-européennes. Les fonds de CI
européens investissent en dehors de l'Europe à hauteur de 4% (en 2012) à 7,6 % (en
2013) du montant total de l'investissement.
Ces statistiques soulèvent des questions sur les aspects de l'investissement international de l'industrie du CI. La littérature de CI commence aussi à développer
l'intérêt en ce qui concerne la façon dont la distance géographique et culturelle entre
l'entrepreneur et l'investisseur peut inuer le processus de nancement par CR. Dans
cette thèse, je cherche à examiner comment la distance et le niveau de conance entre
les investisseurs de capital-risque et l'entrepreneur impactent les décisions de réinvestissement. L'objectif est d'analyser comment l'asymétrie de l'information associée
aux investissements internationaux impacte le modèle de nancement par CR. L'idée
est que l'asymétrie de l'information est associée aux distances géographiques, culturelles et institutionnelles, et que la variation du niveau de conance entre nations
va inuencer les décisions de réinvestissements. Pour étudier ceci nous analysons
un échantillon de 317 jeunes entreprises européennes nancées par CR entre 1994 et
2008.
Nous trouvons que les investissements distants ont des courts tours de nancement, et que les investissements associés à un degré élevé de conance entre le
capital-risqueur et l'entrepreneur encouragent les investisseurs de capital-risque à
augmenter la durée de leur tour de nancement. Nos résultats conrment ceux de
la littérature que les investissements lointains sont associés à un niveau plus élevé
d'asymétrie d'information et que dans ce cas les décisions de réinvestissement sont
utilisées comme substitut au management directe (Gompers (1995) [195], Tian 2011
[196]). Nos résultats suggèrent également que l'asymétrie d'information et les coûts
d'agence augmentent avec le niveau de méance, en conséquence le capital-risqueur
va raccourcir la durée des tours de nancement associés à un faible niveau de conance.
Cette thèse est sous forme papiers; elle commence par un survey (2). Les principales contributions académiques appartiennent aux trois documents annexés au
présent document (3 jusqu'au 5). L'accent du lecteur devrait être mis sur les quatre
documents annexés. Chapitre 2 donne un examen approfondi des recherches an-
1.2.
Outline of The Thesis
31
térieures sur les sorties de capital-risque. Chapitre 3 par chapitre 5 sont les travaux
empiriques basées sur diérentes bases de données et diérents marchés.
Bibliography
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an initial public oering, Journal of Financial Intermediation, 14 (2), 253277,
2005.
[3] , Popov, Alexander and Roosenboom, Peter, On the real eects of private equity investment: Evidence from new business creation, European Central Bank
Working Paper, 2009.
[4] , amila, Sampsa and Sorenson, Olav, Venture capital, entrepreneurship, and
economic growth, The Review of Economics and Statistics, 93 (1), 338349,
2011.
[5] , Gompers, Paul A, Optimal investment, monitoring, and the staging of venture
capital, The journal of nance, 50 (5), 14611489, 1995.
[6] , Tian, Xuan, The causes and consequences of venture capital stage nancing,
Journal of Financial Economics, 101 ( 1),132159, 2011.
[7] , Samila, Sampsa and Sorenson, Olav, Venture capital, entrepreneurship, and
economic growth, The Review of Economics and Statistics, 93(1), 338-349, 2011.
[8] , Hege, Ulrich and Palomino, Frédéric and Schwienbacher, Armin,Venture capital performance: the disparity between Europe and the United States, Finance,
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33
[9] , Kaplan, Steven N and Lerner, Josh, It ain't broke: The past, present, and
future of venture capital,Journal of Applied Corporate Finance, 22(2), 36-47,
2010.
[10] , Megginson, William L and Weiss, Kathleen A,Venture capitalist certication
in initial public oerings,The Journal of Finance, 46 ( 3), 879-903,1991.
[11] , Chemmanur, Thomas J and Loutskina, Elena, The role of venture capital
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Chapter 2
Survey About Venture Capital
Financing Exit Stage
Résumé1
Dans ce chapitre, je présente un résumé des recherches qui traitent la phase de sortie
du capital risque. Ce travail commence par exposer le processus de nancement des
investissements en capital, en soulignant comment l'asymétrie d'information entre
l'entrepreneur et l'investisseur impacte les diérentes étapes de nancement. Je
discute ensuite les déterminants de l'étape " sortie ", et comment le choix du véhicule
de sortie dépend des objectifs de l'entrepreneur et de l'investisseur en capital risque.
Enn j'expose le rôle de l'investisseur en capital risque dans la sortie et la façon dont
ses caractéristiques impactent la performance de la sortie. Je conclus ce travail en
suggérant quelques idées pour de futures recherches.
1 Ce
chapitre est un extrait de survey publié dans Edward Elgar Handbook on Entrepreneurial
Exit, édité par prof. Karl Wennberg and prof. Dawn DeTienne.
34
abstract2
In this survey I present a summary of relevant research related to venture capital
exit stage. The venture capital exit is the end of the nancing relationship between
the entrepreneur and the venture capital investor. This work starts by introducing
the VC nancing process and highlighting how information asymmetry between the
entrepreneur and the venture capital investor impacts the dierent nancing stages.
I then discuss the determinant of the exit, and how the choice of the exit route
depends on the entrepreneur and the venture capitalist objectives. Finally I expose
the role of venture capital investor at exit and how its characteristics impact the
exit outcomes. I conclude this work by suggesting some ideas for future research.
2 This chapitre is an extract from a survey published in the Edward Elgar Handbook on Entrepreneurial Exit, edited by prof Karl Wennberg and prof. Dawn DeTienne.
2.1
Introduction
New business creation is shown to be a potent force for economic development and
productivity improvement. In their empirical work using a British data in manufacturing industries during the period 1980 to 1993, Aghion et al (2004)[96] compare
the overall change in the industry productivity growth, distinguishing between incumbents, entrants, and exitors shares. The ndings document a substantial growth
in incumbent share of productivity, especially during entry threat. The authors
argue that productivity growth by incumbent rms pass through its incentives to
escape entry by innovating. Similarly, Aghion and Howitt (2006)[97] argue theoretically that among others, a higher rm entry and exit constitute countries' growth
enhancing mechanisms.
Though, creation of rms with new entrepreneurial ideas and product development requires substantial capital during their seminal stages. Banks are an important source of nancing for a subset of new businesses rms, but a major part of
young rms remains unlikely to receive signicant bank loans because they have substantial intangible assets, they are associated with signicant ex ante uncertainty
and they are still producing operating losses that enable them to assure interest
payment on debt obligations (Ueda, 2004)[158]. Furthermore, young entrepreneurial
rms need, in addition to capital, substantial competences to transform their ideas to
mature business. The specicity of a Venture capitalist (VC) is its ability to bridge
these competences gaps and to nance the high-risk and potentially high-reward
ventures (Sahlman, 1990[155]; Hellmann and Puri, 2000[132], 2002[136]; Brander et
al, 2002[?]).
Venture capitalists serve as intermediaries between investors (fund providers) and
entrepreneurial rm in need of capital and competences; they are at the same time,
seekers of capital from investors and professionals managing a fund that invest equity
securities in private ventures. The contract that underlies this double relationship
is traditionally a partnership, where the VC rm exerts active management of the
entrepreneurial company and therefore assumes unlimited liability, whereas the institutional investors don't interfere with the fund' operations and accordingly have
limited liability. This structure originates the common terms of Limited Partners
(LPs), for the investors, and of General Partners (GPs) for the venture capitalists.
The VC funds have a dened lifetime, with typical life spans of seven to ten years
before they are terminated and the capital distributed to the investors (Cumming,
2008[114]; Sahlman, 1990[155]).
Venture Capitalists major challenge is to handle the information asymmetry associated with the investment in young rms operating in new industries and having
short historical data (Amit et al., 1998[98]). The entrepreneur often has an information advantage over the VC investor, as he/she is usually the founder of the rm
and thus better informed about the projects (Cochrane, 2005[113]; Gompers and
Lerner, 2001[125]).
Information asymmetry creates two major problems: the risk for adverse selection and the risk for moral hazard (Holmström, 1982[137]; Amit et al., 1998[98];
Cumming, 2006[118]). The risk for adverse selection is the risk that "hidden information" leads to bad investments. The risk for moral hazard is the risk that
the entrepreneur acts opportunistically to venture capitalist' disadvantage. Agency
theory suggests that the greater the information advantage possessed by insider (entrepreneur), the greater the danger that they pursue self-interested decisions that
the principal (VC investors) will fail to detect.
Though, the information asymmetry problem may exist also between the limited
partners and venture capitalists. Indeed, the legal structure of limited partnerships
gives VCs investors the possibility to be involved in portfolio rm management and
thus to have an information advantage over the LPs, who don't monitor the evolution
of the investment as closely and thus are less informed than VC investors. In fact,
GPs are agents and they can choose to invest in securities that serve their own
private interests at the expense of the LPs ones.
Prospective conicts between the agent (general partner) and the principal (limited partner) are mainly addressed by aligning VCs and LPs interests using a typical compensation form, where the fund managers receive management fee and
performance-based payments. In addition to that, the life of a VC fund is limited,
the venture capitalist needs to return the money to his investors and the investors
enjoy the right to invest or not in later fund managed by the same venture capitalists.
The limited fund life constitutes a constraint that put exit stage at the heart
of VC investment process. It is one of the most important events in the life of the
VC backed rm, where the entrepreneur and venture capitalists liquidate (at least
partially) some of their equity holdings in the rm. Exit stage is also a way for the
rm to raise external nancing for new investment projects; accordingly the term
and the conditions of exit will determine the entrepreneur and the VC investor'
payos.
Before exposing VC nancing process, it is useful to briey explain the dierence
between formal and informal venture capital (business angel) and the dierence
between formal venture capital and private equity nancing.
Young rms may also be candidate to receive funding from "business angels".
Business angels are high net worth individuals investing their own monies and their
managerial experience into private start-up with high potential of growth. Kerr,
lerner and schoar (2014)[142] notice that angels began forming groups to collectively
evaluate and invest in entrepreneurial rms. Similar to venture capitalists, angel
groups are often involved in the management of the deals in which they invest,
providing entrepreneurs with advice and contacts. In practice, the dierence between
informal and formal venture capital nancing is diuse (Ratio report Business angel
network and investments, 2014[157]). However, the structure of nancing remains
the main dierence between the two types of investors. Informal venture capital
is investments that are made by wealthy individuals using their own capital, while
formal venture capital is a professionally managed fund raised from institutional
investors with a specic investment strategy and limited life time. The "venture
capital cycle" starts with raising a fund; selecting and investing in rms; monitoring
and adding value to rms; and nally divesting from successful deals and returning
capital to the fund investors. Accordingly, it's natural that the type of contract and
the investment strategy (sector, rm' size and stage, etc.) will be dierent between
formal and informal venture capital. Finally, Kerr, Lerner and schoar (2014)[142]
argue that angels may be a bridge to other nancing sources, like prestigious venture
capitalists.
Venture Capital nancing process main common point with private equity nancing is their structure, both have the common structure of funds with pre-determined
life spans; the main dierence is that private equity rms invest in mid-sized and
large rms acquired through leveraged buyouts, while venture capitalists typically
invest in rms in the start-up and expansion stage associated with high potential
and risk. The 'European Private Equity and Venture Capital Association' (EVCA),
however, denes venture capital as a subset of private equity activity "Private Equity
provides equity capital to enterprises not quoted on a stock market. Private Equity
can be used to develop new products and technologies, to expand working capital,
to make acquisitions, or to strengthen a company' balance sheet. Venture capital is,
strictly speaking, a subset of private equity and refers to equity investments made
for the launch, early development, or expansion of a business."
Along this survey, we focus on the entrepreneur-venture capitalist' relationship
and describe how the information asymmetry problems may impact VC investment
stages. The organization of this section mirrors the VC nancing process; though
we will focus more on the exit stage. The purpose of this article is to provide an
overview of the important issues and questions regarding VC exit, and to emphasize
the impact of entrepreneur preferences and objectives at the exit stage.
The literature shows that VC market functioning and performance diers among
the two largest VC markets: US and Europe (Hege et al, 2009[131]), nevertheless
comparative study between the two markets is out of the focus of this work. The
choice of theoretical and empirical papers to include in this survey is mainly motivated by the objective to document how the characteristics and the asymmetry of
information associated with VC nancing will impact the exit stage decisions.
The rest of the survey is organized as follows. Section 2 describes the VC nancing process from entrepreneurial rm selection to exit stage. Section 3 examines
the determinants of the timing and the choice of exit route. Section 4 presents the
exit outcomes and the role of VC investors at and after the exit stage. Section 5
concludes.
2.2
Venture Capital Financing Process
In this section I start by briey explaining the typical functioning of VC investment
process and then highlighting the development of the exit stage.
2.2.1 Investment Selection
Amit, Muller, and Cockburn (1995)[99] consider the matching between entrepreneurs
and venture capitalists. The authors explain that VC investors either receive unsolicited proposals from entrepreneurs or search for new deals through their network
and contacts. At this stage, VC might not be able to distinguish, a priori, between
bad or good entrepreneur projects. As Sahlman (1990)[155] presents, entrepreneurs
may deliberately overstate the value of their venture to attract more funds which may
lead to an adverse selection. Previous studies on the VC decision making process
show that adverse selection is limited through an extensive due diligence process.
Kaplan and Lerner (2010)[140] argue that VC investors consider factors that
include market size, competition, strategy, technology, and the quality of the management team in their evaluation of the attractiveness and risks of the projects3 .
The screening process is an intensive and disciplined one that often takes several
months, according to the National Venture Capital Association, (NVCA), usually
only 10% of business plans that come to a venture capital rm for funding get a
serious look.
Contracts between venture capitalists and entrepreneurs also aim to overcome
the problems of information asymmetry and moral hazard. For instance, Kaplan and
Strömberg (2003)[141] examine the term sheets from 14 US VC rms, making 213
investments into 119 companies. The authors nd that contracts usually separate
cash ow and control rights, make extensive use of state contingent clauses depending
on observable measures of nancial and non-nancial performance, and that the
control right shift between the VC investor and the entrepreneur depending on the
rm performance, where the VC keep control in bad states and inversely give up
control to the entrepreneur in good states.
3 For
a detailed description and analysis of VC screening, see Kaplan and Strömberg(2003)[141]
Overall, It is worth noting that VC investment is a two-sided matching process
where VC investors select the entrepreneurs and likewise the entrepreneurs accept
the venture capitalists oer (Hsu, 2004)[138]. In the end, only a small fraction of the
investment proposals end up being funded by VCs. For instance, Fried and Hisrich,
(1994)[123] nd in their study that only 20% of entrepreneurial project candidate
get nanced.
2.2.2 Post-Investment Monitoring
Once investment is made, there is much more than just capital that ows from the
VC investors to the entrepreneurial rm. For instance, VC investors provide nancial and strategic management, services, and experience in recruiting (Hellmann,
1998[133]; Hellmann and Puri, 2002[136]; Ueda, 2004[158]). Monitoring is typically
exercised by venture capital through sitting on the board of the backed rm and
having voting power. Lerner (1995)[145] nds that the VC investors' representation
on the boards is stronger when the agency risk is severe, and Xu (2004)[121] nds
that venture capital investors' involvement in entrepreneurial rm monitoring and
value adding activities is a control mechanism to elevate the information asymmetry
between entrepreneurs and investors. This is in line with the corporate governance
literature considering the board representation as a control mechanism employed by
outside owners to detect and correct agency problems (Fama and Jensen, 1983[122]).
A second common control mechanism used by venture capitalist is staging investment. Staging consists on providing the necessary nancing in structured successive
rounds. Staged nancing provides VC with a real option which can be exercised or
abandoned over time depending on the uncertainty and the performance of the entrepreneurial rm. Sahlman (1990)[155] argues that through staging, VC investors
encourage entrepreneurs both to perform and to reveal accurate information. Gompers (1995)[195] studies empirically the staging of VC investment; he nds that
staging is related to expected agency costs, which are increasing with the ratio of
intangible assets, the market-to-book ratio and the R&D intensity.
Chemmanur, Krishnan and Nandy (2011)[109] investigate a much discussed question about the screening and monitoring role of VC investors. Indeed, there is a
potential endogeneity issue related to this question. In other words, VC investors
may merely be providing funding to better-quality rms, which then perform better subsequently. Accordingly, VC nancing eect on entrepreneurial rms may be
due to VC investors better selection ability than to the extra-nancial value-added
services that they provide to the rm.
Using the U.S. Census Bureau Database, Chemmanur et al (2011)[109] disentangle the screening and monitoring eects of VC backing using three dierent econometric methodologies: switching regression with endogenous switching, regression
discontinuity analysis, and propensity score matching. The rst methodology controls for unobservable characteristics that aect both the probability of getting VC
nancing as well as its productivity. The second methodology uses a discontinuous
jump in the probability of obtaining VC nancing to identify the causal eect of
VC involvement. The third methodology matches the sample of VC-backed rms
to comparable non-VC-backed private rms along many dimensions. The authors
use the total factor productivity (TFP) as an indicator of the residual growth in a
rm' output after accounting for the growth in output attributable to the factors
of production. The results show that VC-backed rms TFP prior to receiving venture nancing is higher than that of non-VC-backed rms, and that the growth in
TFP subsequent to receiving venture nancing is greater for VC-backed rms relative to non-VC-backed rms. Thus the authors document both a screening and a
monitoring role for US VC investors in improving rm eciency.
The same research question is investigated by Bertoni, Colombo and Grilli (2011)[105]
in an Italian sample of 538 rms, observed over a 10-year period (1994-2003). The
authors control for the endogeneity of VC investments using generalized method
of moments (GMM) estimation techniques. The results show that VC investments
positively inuence rm employees and sales growth, and that the monitoring eect
of VC investments is of large economic magnitude, but inversely to Chemmanur et
al (2011)[109] their data show that the selection eect of VC appears to be negligible
in the Italian context.
Overall the literature has managed by using better econometric tool to separate
selection versus treatment eects. However it remains unclear how VC investors
selection and value-adding roles dier across countries, and how the characteristics
of VC investors (experience, sector specialization, etc.) may impact its selective and
monitoring skills.
2.2.3 Exit Stage
The critical nature of exit stage is enforced by the type of the partnership between
GPs and LPs. The closed-end nature of most private equity funds is a control
mechanism applied by limited partners to the managers of the venture capital rm
in order to ensure the eventual redemption of their capital and investment returns
(Neus and Walz, 2005[151]). Typically, venture capitalists invest in entrepreneurial
rms for 5 to 10 years prior to an exit event. In the exit phase, the VC investors
divest their holdings in the portfolio rms and return exit proceeds to the LPs.
As in Cumming and MacIntosh (2003)[119] I present the ve principle exit vehicles distinguishing between full and partial exits. The ve principle exits routes
are: IPO, acquisition, secondary sale, buyback, and write-o.
2.2.3.1 Initial Public Oerings
Initial public oering (IPO) is the sale of rm' shares in the public market. It's
useful to distinguish between partial and full IPO exit. A partial IPO exit is when
the VC investor retains a fraction of the issued rm equity post-IPO. A full IPO
exit is when the VC divests all of his holdings; it's usually realized within a year
after the oering date. In fact, public market regulation requires VC investors to
respect the lock-up agreements by retaining some of their shares after the oering
date and divesting in the following period (Barry et al. 1990)[101]. Therefore, IPOs
oer only a partial immediate VC exit, the full VC exit occurs typically one year
after the entrepreneurial rm issuance.
For the entrepreneur, IPO exit is the ultimate route to remain in the control
of the rm. Typically in an IPO exit, VC investors sell their holdings to new and
diverse shareholders and the entrepreneur stays in the management of the standalone
rm, thus preserving her/his personal benets of control.
Another distinctive aspect of IPO exit is the level of information asymmetry
between issuers and prospective investors. Indeed, IPOs involve a large number
of diverse shareholders, many of which do not have time or expertise to carry out
due diligence on the quality of the issued rms, therefore IPOs are characterized
by high level of information asymmetry between rms' insiders and potential new
shareholders. In addition to that, the new issued rms have to consider the cost
of the preparation of obligatory legal and nancial report. Accordingly, only best
rms that are able to overcome the problem of information asymmetry faced by new
shareholders and to hurdle the cost of listing requirements will end up listed on a
stock exchange.
From a more general perspective, an important dierence need to be highlighted
regarding the IPO of VC backed and non VC backed rms. In fact, the VC backed
rm decision to go public is not only a way to adjust the rm' capital structure (i.e.,
debt to equity ratio), but it is mainly driven by the need to raise more funds and
to allow the VC investor to divest. Therefore, stock market conditions are crucial
for VC backed' IPOs timing because it will determine VC investors payos. This
idea meets the nding that VC are successful in timing the decision to take the
entrepreneurial rm public, where VC investor choose to exit their portfolio rm
by IPO when its valuation is at peak and when the industry valuations are highest
(Ball et al, 2011[100]; Lerner, 1994[146]).
2.2.3.2 Trade Sale
An acquisition is dened as the sale of the entrepreneurial rm to another company.
It is also commonly referred to as a trade sale. Cumming and MacIntosh (2003)[119]
observe that a trade sale often oers to the VC investors and to the entrepreneur a
full exit from the rm. However, the nature of the exit (full or partial) will depends
on the acquisition method of payment. A stock method of payment will allow the
insiders to receive shares in the acquiring company, and a cash acquisition method
of payment translate into insiders full exit.
Gompers and Xuan (2009)[129] analyze the structures of trade sale exit. The
authors nd that successful acquisition paid with stock are more likely when there is
a common VC investor between the acquirer (previously VC backed) and the target
(the actual VC backed rm).
It's well noting that potential acquirers in a trade sale exit have the industrial
expertise to evaluate the rm value and its business model in the product market.
Therefore, rms' insiders know that acquiring company suers less from informational asymmetry than atomistic investors in the IPO market. Accordingly one may
expect that the level of information asymmetry in the IPO and M&A market may
impact the exit route choice. Furthermore, the number of buyers and thus competition between bidders and the acquisition price may also dier depending on the
industry and the market conditions.
2.2.3.3 Other Exits Routes: Secondary Sale, Buyback and Liquidation
A secondary sale is the sale of the VC investors' shares to a strategic acquirer or to
another private equity rm. The dierence between a trade sale and a secondary
sale is that in a secondary sale it is only the VC' shares that are sold, while in a trade
sale the entrepreneur needs also to give up control to the acquirer. A full secondary
sale involves the sale of all of the VC investor' shares, while a partial secondary sale
involves the sale of only parts of the initial VC' holdings. Therefore, the two types
of the secondary sales (full and partial) don't involve an entrepreneurial exit.
A buyback is when the entrepreneurial rm buys the VC' shares. A partial buyback exit is dened as a sale of a part of the VC' holdings, while a full buyback exit
occurs when the VC sells all of his shares in the portfolio rm to the entrepreneur.
Cumming and MacIntosh (2003)[119] argue that in a buyback the vendor and the
acquirer are insiders and thus this exit strategy is characterized by the least degree
of information asymmetry.
A write-o is conducted when the entrepreneurial rm has failed or when it is
unprotable. A full write-o means the bankruptcy of the rm. A partial write-o is
a write-down of the entrepreneurial rm assets value. Such ventures are commonly
denoted as a "living dead" investment (Cumming and MacIntosh, 2003[119]).
Overall, it is widely accepted in private equity literature that an IPO is the
ultimate and most successful exit (i.e.; Gompers, 1996[127]; Gompers and Lerner,
2004[128]; Neus and Walz, 2005[151]). Nonetheless, Wright and Robbie (1998)[154]
argue that the European public market is less liquid than the US public market for
high growth rms, therefore, IPOs may not be the preferred divestment route in
Europe, and European venture capitalists may favor an exit by selling the rm to
another company.
Furthermore, recent statistics of the venture capital professional associations in
US (National Venture Capital Association, NVCA) show that the ratio of acquisitions to IPOs among private rm exits has increased in recent years, in addition
to that the literature documents that venture capital industry has underperformed
since the nancial crisis, mainly because of its vulnerable dependency to the public
markets (Harris, Jenkinson, and Kaplan, 2013[130]).
Figure 2.1 Evolution of Exit Type in US
Source: NVCA
Figure 2.1 summarizes the IPOs and M&A exits activities in the most important
VC market, the US, between 1985 and 2013. Three patterns appear from this gure.
First, both exits are highly dependent on market conditions, but the consequences of
an economic downturn are relatively more evident for IPOs. Second, the progression
of IPO exits provides evidence of the volatility of the public market; for instance,
years of exceptional high public listing activities are followed by years of calm listing
activities, this is specically apparent in the important decreases in the number of
IPOs exits after the dotcom bubble in 2001 and after the nancial crisis of 20072008. Finally, the proportion of IPOs exits diers across years, IPOs exits were more
frequent in the eighties and nineties, in the last decade M&A exits became by far
the most common exit route.
In general, most exit studies start to interpret IPOs and acquisitions as success
events, and consider it failure if the rm closed down or remains alive after many
years (i.e. Chemmanur, Krishnan, and Nandy, 2011[110]; Cumming, 2008[114];
Cumming and Dai, 2010[115]).
2.3
Exit Determinants
In this section, I discuss more specic features of exit stage. I start by analyzing the
timing of exit and then discussing the determinant of the exit route choice.
2.3.1 Exit Timing
One would naturally expect that the achievement of company milestones correlates
with exits. However, Giot and Schwienbacher (2007)[124] show that the timing
exit pattern vary with the exit route. Indeed, the likelihood of an IPO exit rst
increases with time (up to four years) and then decreases sharply; for trade sales
exits, the likelihood of M&A exits reach it maximum later and tend to decrease slowly
thereafter. Giot and Schwienbacher 2007)[124] ndings suggest that in contrast to
an IPO, a trade sale is a more universal exit channel that is adopted as a second
best exit choice after an IPO.
In their general theory of venture capital exits, Cumming and Johan (2010)[117]
state that:" a VC will exit from an investment when the projected marginal value
added (PMVA) is less than the projected marginal cost (PMC)". PMC refers to
the costs related to creating value activities and to the opportunity cost associated
with alternative investment. PMVA is resulting from VC eorts, it increases at the
beginning and then decrease over time as the rm matures. The PMC function
also declines over time as the VC' intensity of eort is greater at the earlier stages.
The authors show that PMC and PMVA are higher for high-tech and early-stage
investments, that PMC is negatively related to the rm age and positively related
to strong market conditions, as the opportunity cost increases during such periods.
The authors also show that PMVA is positively related to syndication. Therefore,
duration is shorter for syndicated investment, for older rms and for investment in
times of strong market conditions.
Schwienbacher (2008)[156] compares VC investment duration in US and Europe.
The results show that the duration of the exit stage is longer in Europe. The
author argues that this dierence is driven by the dierence in the exit market
liquidity, where the European exit market is less liquid than the US one. Hege et
al. (2009)[131] extend this nding and document that disparity between US and
European exit market liquidity lead to signicant performance dierences, with US
investments generating much higher returns. The authors also document dierent
contracting patterns in the two markets. For instance, US venture capitalists make
a larger portion of funding contingent on the completion of the rst round, organize
themselves in larger syndicates, involve corporate VC more frequently in the deals
and tend to be more specialized.
2.3.2 Exit Route Choice
In this subsection, I start by analyzing how the preferred exit decision may dier
depending on the entrepreneur and venture capitalist objectives. Then I expose
important ndings regarding the determinants of the exit route.
2.3.2.1 VC Investors and Entrepreneurs Objectives
Bascha and Walz (2001)[102] emphasize that an exit stage can potentially lead to a
disagreement between venture capital investors and the entrepreneurial rm' management. Indeed, VC and entrepreneur objectives may be dierent regarding the
exit strategy. Typically, when exiting from the venture VC needs to end up its
relationship with the rm and to maximize the immediate value of their invest-
ment. Entrepreneur also cares about the exit return but he seeks to preserve his
private benet as well. Accordingly, arguments such as prestige, fear of jobs cutting
and independence from a large corporate parent are motivating entrepreneurial rm
management' exit preference. In fact, entrepreneur knows that his private benet of
control is likely to be lost in case of an M&A exit (Bascha and Walz 2001[151]; Black
and Gilson 1998[106]; Neus and Walz 2005[151]; Hellmann, 2006[135]). Therefore,
for their private benet entrepreneurs may prefer IPOs to M&A exits.
Poulsen and Stegemoller (2008)[152] measure insiders' stock retention after IPOs
and M&A exits, the authors nd that on average, insider retain 49.4% of equity in
the rm after an IPO, while liquidating almost all their equity holdings after an
acquisition. In fact, due to lock up period requirement, IPOs allow selling only a
part of the equity holdings, while in a sale exit insiders are more likely to divest their
entire equity holdings in the rm, with the entrepreneur giving up control to the
acquirer who satises the target rm' funding requirements. Bayar and Chemmanur
(2012)[103] argue that independent VC investors are more concerned about the
immediate nancial returns that they can get from their portfolio rms. Thus,
among successful exits, independent VC prefers immediate exit routes. Accordingly
for their immediate pay o, VC investors may favor M&A to IPOs exits.
2.3.2.2 Contracts and Exit
Several studies connect exit decision with VC investors and entrepreneurs levels of
control. Cumming (2008)[114] nds that promising ventures are associated with
higher degree of entrepreneur control. Indeed, in case of promising projects, when
the successful exit route is more guaranteed, the entrepreneur enjoys a sucient
negotiation power to extract control right from VC investors. The author also studies
the relationship between VC level of control, the nancing security type and the
exit route. He nds that VC' use of convertible preferred equity is associated with
strong VC' control rights, while common equity securities are associated with lower
VC' rights of control. In addition to that, the author shows in a sample of 223
investments in 11 European countries that acquisitions are more likely (and IPOs
less likely) when convertible securities are used. Which suggests that VC strong
control right are associated with M&A exits routes, and VC weak control right are
associated with IPOs exits.
In a unique database constructed from interviews and contracts analysis, Cumming and Johan (2008)[116] nd that it is more likely to use convertible securities
when an acquisition exit route is preplanned at the time of initial contract with the
entrepreneur. Furthermore, the data shows that acquisitions are associated with
stronger investor' veto and control rights. Accordingly, ex ante, stronger VC control
rights increase the likelihood that an entrepreneurial rm will exit by a trade sale.
2.3.2.3 Entrepreneurial Firms Characteristics and Exit
Regarding entrepreneurial rms characteristics and its impact on exit choice, Bayar
and Chemmanur (2011)[103] call to consider the rm' ability to face competition as a
crucial factor when analyzing the private rm' choice of exit route between IPO and
M&A. The author argue that the product market is important as "after going public,
the VC backed rm has to stand-alone and to fend for itself, while an acquired rm
benet from considerable support from the acquirer". The model predict that higher
quality rms, which are more viable in the face of product market competition, are
more likely to go public, while lower product quality rms are more likely to be
acquired. Accordingly, the authors nd that on average, more established rms
with business models viable against product market competition are more likely to
go public through an IPO rather than to be acquired.
Schwienbacher (2008)[156] analyzes how startups nanced by venture capitalist
choose their innovation strategy based on exits preferences. The author argues that
the entrepreneur' personal benet from remaining in control after the exit stage
(IPO exit), and the fact that innovative project makes the rm more attractive for
an IPO, may create strong motivation for the entrepreneur to enhance rm' innovation strategy. Thus, IPOs exits are more likely for innovative ventures (Gompers,
1995[195]; Cochrane, 2005[113]; Cumming and MacIntosh, 2003[119]).
2.3.2.4 Cost of Exits Routes
Finally, another variable to consider in the exit route decision is the cost to private
rm of going public rather being acquired. PricewaterhouseCoopers (PwC) analyzes
the IPOs' costs in US between 2009 and 20114 . The report distinguishes between the
cost of going public and the cost of being public. The costs of going public are the
direct costs, such as underwriter, external auditor, and legal and nancial reporting
advisor fees. The results nd that on average companies incur $3.7 million of costs
directly attributable to their IPO. The costs of being public are the longer-term
costs such as the need to develop external reporting, investor relations and human
resource functions. The survey estimates that companies incur more than $1 million
of one-time costs to convert their organization to a public company.
M&A deals major costs concern merging corporate expenditure for preparing,
negotiation and contracting, and more important for the acquired rm, a trade sale
exit implies the transfer of the property right and the loss of control.
The big knowledge gaps that remain in the literature are the reasons and dynamics around rms' failures. In fact, there is relatively little information about
failure; this is mainly due to the lack of accurate information, specically that the
failure exit route is weakly distinguished in the commercial VC databases. The main
work about failure is Puri and Zarutskie (2012)[153], using the US census level data
the authors compare the failure rate of VC backed and non VC backed rms. The
authors nd that VC backed rms are less likely to fail, and that the dierence in
the failure rate between the two sub-samples is mainly due to the lower initial failure
rates of VC-backed rms.
2.4
Exit Outcomes
In this section I start by reviewing the ndings about VC characteristics and its
impact on exit outcomes, then I discuss the documented VC role at the exit stage,
nally I present the main results about VC backed rms development after VC
nancing.
4 http://www.pwc.com/publications/assets/pwc-cost-of-ipo.pdf
2.4.1 VC Investors Characteristics and Exit Performance
Regarding VC investors' characteristics and exit outcome, the literature considers
VC investor reputation as a key factor for better exit performance. VC reputation
is primarily built on past success, such that VC will be able to raise greater followon funds only if the performance of their prior funds has been successful. A large
number of VC reputation measures have been used e.g., age, cumulative aggregate
investment, the number of investment rounds and the market share of the amount
of funds raised by the VC.
Hsu (2006)[139] nds that relative to a control group, VC-backed companies
are more likely to go public especially if nanced by more reputable VC investors.
The author measures VC investor reputation using the number of companies a VC
rm had taken public in the year prior to funding the entrepreneurial target rm.
Nahata (2008)[150] nds similar results, where VC reputation measure is based on
cumulative dollar capitalization share of IPOs backed by the VC.
Another major nding about VC reputation and exit decisions is Gompers (1996)[127]
results.
The author identies the phenomenon of "grandstanding", it is when
younger VC takes companies public earlier than older VC in order to establish a
reputation and raise capital for new funds. The author argues that the reputation
of older VC rms is already established while less-established VC investors need to
signal quality by taking portfolio rms public.
Lee and Wahal (2004)[144] conrm this result using the underpricing ratio. The
author documents that VC IPOs are more underpriced than non VC IPOs with a
return dierential ranging from 5.0% to 10.3% over the entire sample period. In fact,
greater underpricing represents a real cost to the venture capital fund because there
is a transfer of wealth to new shareholders. The underpricing cost is measured as
the number of shares issued multiplied by the dierence between the closing price on
the rst day of trading and the oer price. In the sample of Lee and Wahal (2004)
composed of 6,413 IPOs between 1980 and 2000, with 37% VC IPOs, the underpricing cost represent about $10 million left on the table by VC backed IPOs. However,
the authors' ndings about VC investors reputation (measured by the number of
previous IPOs that the venture capital rm has conducted) and underpricing give
an interesting explanation to this result. In fact, the authors nd that less reputed
VC (less backed IPOs) accepts to bear a higher cost of underpricing and that higher
VC IPOs underpricing leads to larger future ows of capital into venture capital
funds. Therefore, the results suggest that VC investors' grandstanding provides a
compensating benet to underpricing cost.
Overall we would say that the literature agrees that there is a correlation between
certain investor and the exit outcomes.
2.4.2 VC Role at The Exit Stage
Studies about the role of venture capital backing in IPOs exits are more abundant.
To document VC role at IPO, the literature uses underpricing ratio, it measures the
price rise of a rm' equity from the IPO oer price to the rst day closing price in
the secondary market; it reects the initial returns in an IPO.
pioneering eorts in this literature document that VC backed IPOs are less
underpriced than non-VC backed IPOs, (Barry et al 1990[101]; Megginson and
Weiss 1991[149]), attributing this result to the VC "certication" eect (Megginson and Weiss, 1991[149]), or to the VC screening and monitoring eect (Barry et
al, 1990[101]).
The "certication" eect signies that venture capitalists set the oer price of
rms backed by them closer to intrinsic value due to their concern to preserve their
reputation in the IPO market. Inversely, Liu and Ritter (2011)[147] nd that VC
backed rms are more underpriced if they are covered from star analysts, the authors argue that VC investors allow higher levels of underpricing because they are
especially concerned about analyst coverage when shares are distributed to limited
partners.
Beside this divergent results, Chemmanur and Loutskina (2006)[111] argue that
underpricing is not an appropriate proxy for VC' role in IPO process, because underpricing is aected both by the pricing of the equity at the IPO and at the closing
of the rst trading day in the secondary market. This implies that the closing price
at the secondary market includes the eect of VC backing as well, thus underpricing
is no longer useful in determining the economic role of venture backing in IPOs.
The authors propose three direct measures of VC role at IPO, the rst measure
is the ratio of the oer price to the rm intrinsic value, the second measure compares
the quality and the level of participation of reputed underwriters and analysts, and
the third is the fraction of institutional investors' equity sold in the IPO. The authors
test two hypothesis about VC' role in IPOs: If the role of venture backing in IPOs
is that of certication, it's expected that the rst ratio would be closer to 1 for
venture backed rms, and If VC investors have a market power role, where the eect
of VC investors is to attract higher quality market participants to VC IPOs, the
authors expect that the second and the third measures will be greater for IPOs
with VC backing. The results show that VC backed IPO rms are characterized
by more reputable underwriters, a larger fraction of equity holding by institutional
investors, and more extensive analyst coverage than non-VC backed IPO rms. Thus
the ndings reject the VC certication hypothesis in favor of the market power
hypothesis.
Studies' considering the eect of VC-backing on acquisition are recent and focus
merely on the US market. Masulis and Nahata (2011)[187] use a sample of 490
acquisition in US between 19912006, after controlling for the endogeneity of VC
nancing the authors nd that VC backing leads to signicantly higher acquirer'
announcement returns than non VC backed deals which runs counter to the VC
certication eect. In fact, the certication role of VC at M&A exit is expected to
reduce the information asymmetry faced by acquirers, who thus would pay a higher
purchase price and accordingly earn a lower acquirer announcement returns. The
data show that the presence of VC investors with funds closer to liquidation, VC
investors with nancial ties to acquirers, or corporate venture capitalists enforce the
results, and thus enhancing acquirer protability and reducing target shareholder
gains. These ndings suggest that ,in some cases,entrepreneur and the VC exit
interests' may diverge.
2.4.3 VC Role After The Exit Stage
A number of papers investigate VC backed rms development after the IPO exit.
The objective is to evaluate venture capitalists screening and monitoring role. In
fact, the natural eect of VC screening and monitoring role is that the rms going
public with venture backing will be on average of a higher quality than rms going
public without VC backing, generating superior post-IPO operating performance.
Brav and Gompers (1997)[108] investigate the long-run abnormal stock returns
of VC-backed and non-VC-backed IPOs and nd that in general VC-backed IPOs
outperform non-VC-backed IPOs. Krishnan, Masulis, and Ivanov (2011)[143] nd
that VC reputation is positively associated with long run company performance
measured by return on asset (ROA), market-to-book ratio, survival, and long-run
abnormal stock returns. However, Bottazzi and Da Rin (2002)[107] nd that postIPO growth of sales and employees of VC backed rms are not signicantly dierent
from those of other public rms. Chemmanur and Loutskina (2006)[111] compares
the post-IPO operating performance of VC backed and non-VC backed IPOs, they
nd that VC backed IPOs exhibit higher prot margins, ROA, and sales growth in
the IPO year and in the two years subsequent to the IPO.
Overall, it's obvious that studies of IPOs exits attract much more attention than
the other exit' routes. This is mainly due to the information availability that public
market oers. As a consequence we would argue that more research on alternative
exit mechanisms, most notably acquisitions and secondary sales, is warranted to
have the whole picture of the venture capital exit stage.
2.5
Some Open Research Questions
To conclude this survey, I expose some open research questions for future work.
Many of these issues remain to be investigated and to add to the literature on
venture capital exits.
For example, most studies that are empirically exploring exit aspects do not
discuss the impact of VC investors' heterogeneity. For instance, one may expect different exit behavior of large versus smaller or specialist versus generalized venture
capital rms. Furthermore, the exit process need to be benchmarked to an industry
perspective, indeed one may, for example, expect that the bidding and bargaining
power of prospective acquirers in a trade sale exit to depend on the industry characteristics which may accordingly impact the exit sale terms. Finally, an important
distinguishing aspect of the VC market that needs to be explored regarding its impact on exit is the VC rms' organizational structure. Indeed there are two types of
VC investors, Independent VC (IVC) that invest on behalf of institutional investors
and wealthy individuals, and captive VC that belong to corporations making VC
investments (CVC), or to banks that use similar structures to invest in the VC market. The literature shows that VC investors objectives may dier depending on their
organizational structure, which in its turn impacts their investment proles. The
main dierence is that unlike IVCs who mainly seek nancial gains, CVCs care also
about their parent strategic benets that arise from synergies with entrepreneurial
rm activities (Hellmann, 2002[134]). Furthermore, compared to rms nanced by
IVCs, CVCs invest in younger rms and in less mature and more R&D intensive
industries (Chemmanur et al, 2011[112]). Accordingly, one may expect that the exits
preferences may dier depending on VC investors' type.
There is also room for more future research to be conducted about the impact of
the entrepreneur characteristics on exit patterns. DeTienne and Cardon (2012)[120],
for example, nd that entrepreneurial characteristics such as entrepreneurial experience, industry experience, age, and education aect exit intentions. Accordingly,
one may expect, for example, that entrepreneur industry experience will be associated with more successful exits. In a somehow related vein, Bengtsson and Hsu
(2010)[104] document that personal and professional characteristics' similitude between the VC and the entrepreneur increases the likelihood of an investment matching. Furthermore the authors argue that such similitude reduce the VC nancing
transaction cost. Accordingly, one may expect that higher similitude between the
entrepreneur and the VC investors will be helpful to develop an ecient climate for
collaboration and thus increases the probability of a successful exit.
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64
Chapter 3
Competitive Eect of Venture
Capital Backing In IPOs
Résumé
Dans ce chapitre, j'analyse l'eet concurrentiel des introductions en bourse, et
j'examine l'impact de la présence d'un investisseur en capital risque dans l'actionnariat
de l'entreprise introduite sur le rendement des entreprises concurrentes cotées. L'étude
porte sur un échantillon de 120 introductions en bourse réalisées en France entre
1994 et 2011. Les résultats montrent que le rendement boursier des concurrents
augmente si l'entreprise introduite est nancé par un investisseur en capital risque,
alors qu'il baisse si l'entreprise introduite ne l'est pas. Ces résultats suggèrent que
les concurrents publics considèrent que les investisseurs de capital risque sont capables de choisir la meilleur période pour introduire les entreprises de leur portefeuille
en bourse. Ainsi les introductions en bourse accompagnées d'un investisseur en
capital risque vont signaler des conditions favorables du marché et entrainer une
augmentation des rendements boursiers des entreprises concurrentes déjà présentes
en bourse.
65
abstract
In this paper I analyze the competitive eect of initial public oerings (IPOs)
of private rms and explore the impact that venture capital-backing has on competitors' market returns. I nd that industry competitors experience positive stock
return around the IPO if the new issued rm is backed by venture capital investors
(VC) and negative reaction if not. The results suggest that public competitors consider that VC investors select to take their portfolio rms public when the market
is favorable, therefore, supporting that VC IPOs constitute a signal about sector'
favorable conditions.
3.1
Introduction
Going public or remaining private is a strategic decision in rms' lifetime. A direct
consequence of going public is improving rm liquidity by raising capital from much
larger number of investors. Public trading also allows to infer rm quality from its
stock price and to inspire more reliance in the rm from other investors, suppliers,
and customers. However, to achieve this, public rms need to incur additional
cost of information release to convince investors about the quality of its projects,
which presents a possibility of divulging valuable information that can benet to
competitors in the same sector.
This paper has two objectives, the rst is about how product market competitors' react towards new listings. Chemmanur and He (2011)[189] in a development model for IPOs rational based on product market consideration argue that
higher-productivity rms need more capital to achieve their ecient operating scales.
Therefore when they go public they will be associated with a signicant increase in
their market shares. Chod and Lyandres (2011)[190] argue that the strategic benet of going public stems from public investors' lower risk aversion and resulting in
greater aggressiveness in the product market, which then will impact negatively the
equilibrium aggressiveness of IPOs rms' sector competitors.
In other words, the literature suggests that IPOs rms can capture additional
market shares from their competitors. Accordingly, one may expect that competitors
will react negatively to the announcement of IPOs in the industry, as they will
consider the new issued rm able to grab additional market shares.
The second objective of this study is to examine the dierential impact of venture
and non-venture backed IPOs on rival rms. It is an indirect way to investigate VC
investors' roles in IPOs.
The literature recognizes dierent roles for VC investors in going public process.
For instance Ball et al. (2011)[100] nd that VC IPOs occur in periods when demand
for growth capital is high. This suggests VC investors market timing ability to react
to market favorable conditions.
In fact VC investors market timing role need to be linked to the organization
structure of private equity funds. Private equity funds are "closed-end" vehicles with
a limited contractual lifetime, requiring from VC investors to liquidate the fund and
to return the capital to their institutional investors. The fund payos will determine
whatever or not institutional investors will participate in future funds managed by
the same Venture capitalist. For instance in IPOs exits, VC investors need to consider the public market conditions to achieve better exit' performance. Accordingly,
VC investor market timing rational may persuade VC bakced rms' competitors to
consider VC IPOs as a positive signal about the whole market. Therefore,one may
expect that competitors consider VC IPOs announcement as good news about the
industry prospect from which they can benet as well.
In this paper, I aim rst to document that IPOs events have information externalities on competitors, and second that the nature of the externality diers depending
on the status of the issued rm. A key piece of evidence on the competitive eect
of IPOs can be obtained by analyzing the stock return of industry competitors at
and around the IPO announcement date. Using a sample of 120 IPOs in France
between 1994 and 2011, I rst select comparable pairs of VC and non VC backed
IPOs. Second, for each new issued rm I build a portfolio of rival public companies
operating in the same sector. Then I compute the abnormal stock returns for different windows around the IPO announcement date. The event study results proxy
for IPOs announcements eects on rivals returns. The ndings show that the overall competitors reaction to IPOs in their sector is negative, and that competitors
reaction diers on the status of the issued rm: rival stock returns fall if the issued
rm is not VC backed and increase if not.
The structure of the paper is as follows: Section 2 reviews the relevant literature related to the paper. Section 3 introduces the data sources, variables and the
methodology. Section 4 shows and discuss the results. Section 5 concludes.
3.2
Literature Review
This paper is related to two strands of literature. The rst is the literature on the
going public decision and the interactions between nancial and product markets,
and the second is on IPOs' VCs exit stage.
3.2.1 Going Public Decision and The Product Market Competition
Chod and Lyandres (2011)[190] model relates IPOs rms' product market aggressiveness to shareholder diversication. The idea is that the owners of public rms
tend to hold more diversied portfolios than owners of private rms; therefore they
tend to be less concerned with idiosyncratic prot variability. The authors conclude
that rms pursue aggressive product market strategy when they go public which in
equilibrium will reduce the aggressiveness of issued rms rivals. The authors use a
US sample of 3,871 IPOs between 1990 and 2008 to test the prediction of their model.
The results document that going public increases the issued rm' market share and
that the relative increase in the IPO rm' market share is positively related to the
degree of competitive interaction in the industry.
The empirical ndings of Chod and Lyandres (2011))[190] are consistent with
Chemmanur and He (2011)[189] and with Hsu, Reed, and Rocholl (2010)[188] results.
The three papers agree on the positive relationship between going public and the
increases in the issued rm' market share.
Chemmanur and He (2011)[189] study the impact of product market competition
in the appearance of IPOs waves. The authors argue that in equilibrium, even
rms with sucient internal capital to fund their investments needs may go public,
driven by the anticipation of increased market share of their competitors that became
public. The authors use a US sample of 6647 IPOs between 1970 and 2006 to
document that new issued rms' market shares increase while competitors' market
shares decrease, specically if they are private. Similarly, Hsu, Reed, and Rocholl
(2010) use a US sample of 4188 IPOs between 1980 and 2001 to show that rms
going public exhibit sizable post-IPO sales growth.
Overall, the literature gives interesting intuitions about informational externalities of IPO announcements. For instance, due to documented increase of issuer
market share after an IPO, I expect that product market competitors will have neg-
ative valuation reaction to IPO announcements, by experiencing a decrease in their
stock price in response to completed IPOs in their industry.
3.2.2 VC Investor Role in IPOs Exits
The second strand of literature is related to the VC nancing and exit decision. The
literature interprets IPOs and acquisitions as successful exits, and considers it as
a failure if the company closed down or remains alive after many years. In IPOs,
generally investors sell some of their equity holdings and the entrepreneur continues
managing the stand alone rm. In exits by sale, the backed rm is acquired; the
VC investors divest their entire equity holdings in the rm, with the entrepreneur
giving up control of the rm to the acquirer who satises the target rm' funding
requirements.
The VC literature' ndings on exit stage related to this paper give two competing
intuitions about the VC IPOs impact on competitors. The rst nding relates the
VC backed rm' product quality to the exit route choice, and the second studies
the role of VC investors at the IPO exit. In the next two sub-section I discuss the
hypothesis development derived from each nding.
3.2.2.1 VC Backed Firm' Product Quality and The Exit Choice
Bayar and Chemmanur (2012)[103] argue in their model that VC backed rm' ability
to face competition is an additional variable to consider in the exit decision. The
model predict that higher quality rms, that are more viable in the face of product
market competition, are more likely to go public, while lower product quality rms
are more likely to be acquired. The intuition here is that the stand-alone rm (rm
that go public) will be able to face competition and to establish itself in the product
market with a viable business model, while acquired rm needs additional support
from the acquirer. In an related empirical work, Bayar and chemmanur (2012)[69]
compare the private rms' choice between IPOs and acquisitions, the results show
that the likelihood of an IPO over an acquisition is greater for venture backed rms
and those characterized by higher pre-exit sales growth.
Schwienbacher (2008)[156] studies how VC backed rms choose their innovation strategy based on exit preferences. The author argues that the entrepreneur'
personal benet of remaining in the rm control after VC investors exit, and the
attractiveness of innovative projects to the public market, create strong motivations
for the rm to enhance its innovation strategy. Thus, IPO exit are more likely
for innovative ventures (Gompers, 1995[195]; Cochrane, 2005[113]; Cumming and
MacIntosh, 2003[70]).
Overall, the literature documents that the product quality of rms brought public
with VC backing is likely to be higher. Accordingly, one may expect that competitors
will consider VC backed rms as strong rival able to gain competitors' market share.
Therefore competitors would view VC IPOs announcements as negative news and
their stock price will decrease.
3.2.2.2 VC Investor Role in IPOs Exits
Several papers study VC investors' role in the public market using the underpricing
ratio. However the results of underpricing level of VC versus non-VC IPOs dier
depending on data period. Data prior to the 1990s show that venture backed IPOs
are associated with a lower degree of underpricing than non-venture backed IPOs
(Megginson and Weiss, 1991[149]), thus suggesting that VC investors objective in
IPOs is to price the equity of rms backed by them closer to intrinsic value, it's
a certication role. The motivation behind VC investors certication role is that
their repeated participation in the public market makes them attentive about their
reputation among public market participants to not sell over-valued rms.
Data after the1990s document that IPOs of VC backed rms are more underpriced than those of non-VC backed rms (Lee and Wahal, 2004[144]; Loughran
and Ritter, 2003[89]). Liu and Ritter (2011)[147] argue that VCs allow higher levels
of underpricing because they are especially concerned about analyst coverage when
shares are distributed to limited partners. Accordingly, VCs investors care mainly
about their reputation with their own venture fund investors and entrepreneurs.
Beside this divergent results, Chemmanur and Loutskina (2007)[111] discuss the
appropriateness of IPO underpricing as a measure of VC investors role in the IPO
market. The authors argue that underpricing ratio is not a good proxy for venture
capitalist role in IPO process. In fact, underpricing ratio express the price rise of a
rm' equity from the IPO oer price to the rst day closing price in the secondary
market, thus it is aected by the two prices (oer and closing price), this implies
that the closing price at the secondary market includes the VC backing' eect as
well.
Chemmanur and Loutskina (2007)[111] propose three direct measures of VC
role at IPO, the rst measure is the ratio of the oer price to the rm intrinsic
value, the second measure compares the quality and the level of participation of
reputed underwriters and analysts, and the third is the fraction of institutional
investors' equity sold in the IPO. The results show that VC backed IPO rms are
characterized by more reputable underwriters, more extensive analyst coverage, and
a larger fraction of equity held by institutional investors. This ndings support a VC
market power role, where VC investors attract a greater number and higher quality
of market participants for their portfolio rms' IPOs.
Another set of questions regarding VC investors and the public market concerns
the timing of IPOs. Lerner (1994)[146] uses a sample of 350 venture-backed rms
in the biotech sector in the period 19781992 to establish that VC investors take
the rms public at market peaks and rely on private nancings when valuations
are lower. Ball et al. (2011)[100] use a sample of 8163 venture backed companies
over three decades to test if IPOs and M&A exits are aected by market timing.
The authors nd that venture-backed issuers react to market or sector run ups.
Accordingly, one may expect that VC investors' market power and timing ability
constitute positive signal about the whole industry, and that VC IPOs may signal
favorable sector conditions from which competitors of VC backed rms can benet
as well.
In sum, one may expect that competitors' stock price return will fall after VC
IPO announcement if the news conveys more positive prospect for the issuing rm
than for the growth of the sector. While competitors' stock price return will increase
if competitors consider VC IPO as a signal about sector' positive outcome. In other
words, the competitive eect of VC IPOs will be negative if competitors consider
that VC backed rms are able to deal optimally with the public market and that
this eect is more dominant than the positive signaling eect that VC investors may
have due to their market timing ability.
Most similar to our paper are Hsu, Reed, and Rocholl (2010)[188] and Cotei and
Farhat (2013)[191], although, their empirical evidences about IPOs eect on rival
evaluation are mixed. Hsu, Reed, and Rocholl (2010)[188], for instance, report that
IPOs in US public market between 1980 and 2001 result in abnormally negative
returns to the rm' competitors. However, the authors don't distinguish the impact
of VC and non VC IPOs, and they do not control for competitors confounding
events that could potentially contaminate their stock price reaction around IPO
announcements. Cotei and Farhat (2013)[191] use also a sample of US IPOs between
1983 and 2001 to compare VCs to non VCs IPOs competitive eect, they nd that
rivals have positive valuation eects in response to venture backed IPOs and no
signicant reaction in response to non-venture backed IPOs. But the authors don't
control for the endogeneity in the receipt of venture nancing. Indeed, VCs may
merely be providing funding to better-quality rms, which then at the IPOs exit
create a dierent competitor reaction.
In this paper I analyze the going public' competitive eect in the French market.
I control for the endogeneity of VC nancing, by matching comparable VC and non
VC IPOs. I use the market model to estimate competitor' abnormal returns, and
take into account competitors' conicting events.
3.3
Data and Descriptive Statistics
In this section I start by describing the data collection stages . Then I describe the
matching approach to be used as a solution to the endogeneity problem associated
to the research question.
3.3.1 Data Description and Sample Selection
3.3.1.1 Data Collection
The IPO data used in this study come from Thomson One Banker. I construct a
sample of IPOs in the French market between 1994 and 2011. In common with others
IPOs studies, I eliminate equity oerings of nancial institutions (SIC codes between
6000 and 6999), and keep rms that issue ordinary common shares, that are not a
spin o, and that are not issued in other foreign public markets. I then distinguish
between VC and non VC IPOs using Thomson One private equity module. The
initial sample is composed of a total of 257 IPOs. I dene competitors of issued
rms as public companies operating in the same four-digit SIC code as the issued
rm. I restrict competitors to those that are public at least one year before the
IPO date. I use Thomson One Banker to get competitors list; and Datastream
for competitors' nancial information and daily stock prices. To avoid conicting
events, I check in Factiva database if any competitor made important announcement
20 days around the IPO day, I drop competitors who announced earnings, dividends,
stock splits, mergers and acquisitions and strategic alliances. I also drop competitors
with non sucient stock price information to estimate their returns. I end up with
a sample of 171 issued rms and 343 rival rms.
3.3.1.2 Sample Selection and Matching
VC investment selection process requires that entrepreneurial rm candidate for VC
nancing pass through a due diligence process before getting nanced (or not), and
naturally VC investors select to nance the more promising rms. Accordingly, even
before the IPO event, VC backed rms are likely to have dierent characteristics
than non VC backed ones, and thus dierent quality. This raises a selection bias
problem that impedes to know the dierence between the participants' outcome
(issued rms impact on competitors) with and without treatment (venture capital
nancing).
The matching approach is one possible solution to the selection problem. Propensity scores matching are used to select "control" units that are most like the "treat-
ment" units across a variety of characteristics considered important to the analysis
(Dehejia and Wahba, 1999[73]). The "treatment" and "control" units for the purpose of this analysis are VC-backed and non VC-backed rms, respectively.
I start by estimating the propensity score using a logistic model predicting
whether an IPO involves a VC-backed or a non-VC-backed rm. The estimated
likelihood is based on the sample of all IPOs. The dependent variable is equal to 1
if the issued rm is VC backed and 0 otherwise. The explanatory variables are: IPO
proceeds, high tech sector dummy, IPO announcement year, the size of the issued
rm measured by its total asset in the year before IPO announcement' year and the
rm' book value of sale in the year preceding the IPO announcement year.
The high tech dummy control for industry patterns in VC investing, since VC
investors focus largely on innovative and technological rms in selective industries.
IPO year controls for time trends and year variation in nancing activity. Firms'
asset control for issued rm size, rms' sale and IPO proceeds control for rms'
performance.
Table 3.1 The likelihood of VC IPOs
This table reports the logistic model results predicting whether an IPO involves a VC-backing or
not. The dependent variable is equal to 1 if the issued rm is VC backed and 0 otherwise, Log (IPO
Proceeds) is the logarithm of the amount raised by the issued rm, high tech sector is a dummy
equal to 1 if the issued rms is in high-technology industries and 0 otherwise, log (total asset) and
log (total sale) are the book value of asset and the book value of total sale in the year preceding
the IPO' announcement year. ***, **, and * indicate signicance at the 1%, 5%, and 10% levels,
respectively.
Pr(VC IPO)
log(proceeds) 0.4665***
(0.1334)
High Tech dummy 0.7590
(0.5349)
log(total asset) -0.0002
-0.0002
log(sale) 0.0006*
-0.0004
Constant -344.1649***
(102.0103)
Year Fixed eect
Yes
Observations
125
The estimated results reported in table 3.1 show that VC-backed IPOs are likely
to raise larger proceeds, and to have larger total sale.
The results of the logistic model serve to estimate the propensity scores for
IPOs involving VC and non-VC-backed rms. The propensity score of all IPOs is
stratied into blocks dened by quantiles. Then a balancing test is performed based
on dierences in means t-tests between VC-backed and non-VC-backed rms within
each quantile to keep only well balanced blocks.
The Final step is to seek for each "treatment" observation a matched rm from
the "control" sample using the kernel method, where the treated rms are matched
to a weighted sum of rms who have similar propensity scores.
Over the period 1994 to 2011, I report 50 VC-backed IPOs versus 70 non VCbacked IPOs. The corresponding 120 competitors' portfolios contain a total of 403
competitors operating in 77 dierent four-digit sic codes. Portfolio' competitors
contain at least one competitor and a maximum of 28 competitors.
3.3.2 Descriptive Statistics
In this section I rst present the descriptive statistics of the full sample before
matching, then I describe the matched sample.
3.3.2.1 Descriptive Statistics Before Matching
Table 3.2 reports descriptive statistics of the sample before matching. Panel A shows
the total IPOs proceeds, the frequency of High tech industry,and the issued rms
total asset and total sale in the year before the IPO announcement year. Panel B and
C report the same variables across the sub-samples of VC and non VC backed IPOs.
table 3.2 also shows the result of a standard t-test for dierence in means between
the two sub-samples. In general, VC IPOs' proceeds are larger,with an average of
64 e million of proceeds raised by VC issued rms and 19 e million raised by non
VC issued rms. VC backed issued rms are also bigger and present higher level
of sale. For instance VC backed issued rms have an average total sale in the year
preceding the IPO year equal to 137 e million, which is signicantly larger than the
average total sale of non VC backed rms (42 e million). Finally, the statistic shows
that on average 24% of backed issued rms operate in a high tech industry, which
is signicantly higher than the proportion of non VC backed issued rms operating
in high tech industry (14%).
Accordingly, I conclude that the dierence between the two sub-samples across
the listed characteristics is statistically signicant. This indicates that issued rms
characteristics are substantially dierent across the two sub-samples.
Table 3.2 Descriptive Statistics for VC Backed and non VC Backed IPOs, Before Match-
ing
This table shows the descriptive statistics of the dierent variables used in the matching.
Proceeds are the amount raised by the issued rm in million euros. Sale are the issued
rms total sale (in million euros) in the year before the IPO date. Total Assets are the
issued rms total assets (in million euros) in the year before the IPO date. High-tech
sector dummy equals one if the issued rm belongs to a high-technology sector, zero
otherwise. A standard t-test for a dierence in means is used to compare VC-backed and
non-VC-backed issued rms. ***, **, and * denote signicance at the 1%, 5%, and 10%
levels, respectively.
Mean Standard Minimum Maximum
Deviation
Proceeds
Sale
Total Asset
High Tech Industry
Panel A: Total Sample
37.96
116.44
84.50
343.94
137.56
734.25
0.19
0.39
0.01
0.43
0.77
0.00
1078.00
3247.90
5831.60
1.00
0.01
0.43
1.65
0.00
1078.00
3247.90
5831.60
1.00
Panel C: Non VC Backed Firms
Proceeds
19.16
42.65
0.01
Sale
42.72
127.29
0.86
Total Asset 106.35
681.02
0.77
High Tech Industry
0.14
0.35
0.00
347.72
1018.40
5707.16
1.00
Panel B: VC Backed Firms
Proceeds
64.35
170.73
Sale 137.69
495.77
Total Asset 177.28
801.57
High Tech Industry
0.26
0.44
T-Test For Dierence Panel B vs Panel C
Proceeds (2.27)**
Sale (1.54)*
Total Asset (0.53)*
High Tech Industry (1.85)**
3.3.2.2 Descriptive Statistics After Matching
Table 3.3 reports descriptive statistics of the sample after matching VC IPOs to
comparable non VC IPOs. The comparison between the two sub-samples shows
that the issued rms are similar in their average size and level of sale. However the
total proceeds of VC backed IPOs remain larger, with 32 e million proceeds raised
by VC IPOs and 18 e million proceeds raised by non VC IPOs. Also the proportion
of VC backed issued rms operating in high tech sector is larger, with 22% of VC
backed issued rms and 13% of non VC backed issued rms operating in a high tech
sector. The dierence between the two sub-samples in the total proceeds and the
frequency of high tech industry is signicant at 10% level. Therefore, I also control
for these characteristics in the multivariate analysis.
Overall, the 2 groups of issued rms after matching are similar in their total
asset and sale characteristics, I consider that the similarity of the two sub-samples
after the matching helps to lower the concerns about selection bias that came from
the endogeneity of VC investment. Therefore, the rest of the empirical analysis will
be conducted in the matched sample.
Table 3.4 reports the IPO sample distribution across years. 37% of the IPOs
in the sample occur during 1998-2000. This is consistent with the internet bubble
and the "hot period". The market then underwent a cold period where the number
of IPOs dropped between 2001 and 2004. The public market reports a renewal of
activity during 2005-2008, but again a strong decrease after this.
Table 3.5 describes the industry distribution of the VC IPOs and the matched
non VC IPOs. The industry distribution between the two sub-samples remains
dierent in some sectors.
Table 3.3 Descriptive Statistics for VC Backed and non VC Backed IPO, After Matching
This table shows the descriptive statistics of the dierent variables after matching VC
backed IPOs to comparable Non VC backed IPOs. Proceeds are the amount raised by the
issued rm in million euros. Sale are the issued rms total sale (in million euros) in the
year before the IPO date. Total Assets are the issued rms total assets (in million euros)
in the year before the IPO date. High-tech sector dummy equals one if the IPO occurs in
a high-technology sector, zero otherwise. A standard t-test for a dierence in means is
used to compare VC-backed and non-VC-backed issued rms. ***, **, and * denote
signicance at the 1%, 5%, and 10% levels, respectively.
Mean Standard Minimum Maximum
Deviation
Panel A: Total Sample
Proceeds 23.92
45.01
0.01
Sale 43.30 106.45
0.43
Total Asset 81.53 522.28 767.00
High Tech Industry 0.16
0.37
0.00
347.71
1018.42
5707.16
1.00
Panel B: VC Backed Firms
Proceeds 32.14
44.62
0.01
Sale 44.12
68.59
0.43
Total Asset 46.79
85.16
1.65
High Tech Industry 0.22
0.41
0.00
261.12
285.25
435.65
1.00
Panel C: Non VC Backed Firms
Proceeds 18.06
44.68
0.01
Sale 42.72 127.29
0.86
Total Asset 106.35 681.02
0.76
High Tech Industry 0.13
0.34
0.00
347.72
1018.42
5707.16
1.00
T-Test For Dierence Panel B vc Panel C
Proceeds (1.7)*
Sale -0.07 no star
Total Asset 0.61 no star
High Tech Industry (1.32)*
Table 3.4 IPOs Distribution by Year
The sample consists of 120 completed IPOs of private rms in France between 1994 and
2011. IPOs are listed by IPO year announcement and status of the private issued rm.
Total VC Backed Non VC Backed
Filling_year Sample
Firms
Firms
N % N
% N
%
1994
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2010
2011
Total
2
5
12
16
17
7
7
1
2
6
17
12
4
5
7
120
2
4
10
13
14
6
6
1
2
5
14
10
3
4
6
100
2
1
2
6
4
1
4
0
0
5
10
7
2
3
3
50
4
2
4
12
8
2
8
0
0
10
20
14
4
6
6
100
0
4
10
10
13
6
3
1
2
1
7
5
2
2
4
70
0
6
14
14
19
9
4
1
3
1
10
7
3
3
6
100
Table 3.5 IPOs Distribution by Industry
This table presents the industry distribution of a sample of 120 completed IPOs in France
between 1994 and 2011. IPOs are listed by the industries and the status of the issued rms.
Total VC Backed Non VC Backed
Issued Industry Sample
Firms
Firms
N % N
% N
%
Electronics, Computers and Communication
Business Service
Chemical, Rubber and Glass products
Construction
Food
Health Service
Manufacturing
Metal Industry
Textiles and clothing
Transportation, Communication and utilities
Wholesale and retail trade
Wood and paper products
Total
18
47
12
1
3
2
6
4
1
7
12
7
120
15
39
10
1
3
2
5
3
1
6
10
6
100
13
19
5
0
1
1
1
1
0
5
3
1
50
26
38
10
0
2
2
2
2
0
10
6
2
100
5
28
7
1
2
1
5
3
1
2
9
6
70
7
40
10
1
3
1
7
4
1
3
13
9
100
3.4
Empirical Results
rst I present univariate analysis results by investigating competitors' cumulative
abnormal returns. Then I examine the regressions results.
3.4.1 Univariate Analysis: Cumulative Abnormal Return
3.4.1.1 Event Study Methodology
Event study methodology was introduced by Ball and Brown (1968)[74] and Fama,
Fisher, Jensen, and Roll (1969)[75]. It has been widely used to measure the impact
of an economic event on the value of rms.
In this paper the event is the IPO announcement and the rms studied are
public competitors of new issued rms. The event study measures the impact of the
IPO announcement on competitors' stock returns by detecting the changes triggered
exactly by the IPO event.
The four steps of the event study are the following:
− I start by estimating the market model for each competitor rm' stock returns
during an estimation period prior to the IPO date (i.e. t=0). The estimation
period starts 40 days prior the IPO date (i.e. t=-40) over a period of 260 days.
The model parameters are estimated using OLS regressions following a market
model for each stock:
rit = αi + βi rmt + it
(3.1)
Where rit denotes the daily return for rm i on day t, rmt represents the
corresponding daily return for the value-weighted SBF 250 price index. αi
and βi are rm-specic parameters and it are independent and identically
distributed (i.i.d) errors.
− The estimated coecients, (αi and βi ), are then used to predict daily returns
for each competitor i over the "event window" - i.e. in the days immediately
surrounding the IPO date:
Rit = αi + βi Rmt
(3.2)
Where Rit denotes the predicted daily returns for each incumbent rm i on
day t. For this study and as in Hsu, Reed, and Rocholl (2010)[188] I use
dierent event windows ranging from 10 days before the IPO date to 10 days
after the IPO day: [−1, 1], [−3, 3], [−5, 1], [−5, 5], [−10, 1], , [−10, 5], [−10, 7]
and [−10, 10].
− I calculate the abnormal returns (AR) for each incumbent rm i on each day
of the event window by subtracting the predicted return Rit from the actual
return rit .
− I nally compute for each event window the cumulative abnormal returns
(CAR) for each competitor i which is the sum of the daily abnormal return
over the event window (i.e. from m days before the event to n days after it):
CARimn =
t=n
X
Rit
(3.3)
t=−m
3.4.1.2 Event Study Results
The key questions in this paper are whether IPOs have an impact on the stock
returns of companies competing in the same industry as the issued rms, and if yes
do competitors reactions dier on the status of the issued rm. I state that the
competitive eect of IPOs can be obtained by analyzing rivals' stock market returns
at and around the issuing date. In this part, I will analyze competitors' cumulative
abnormal returns. This is an evidence of the short-term competitive eect of IPOs.
Table 3.6 presents competitors' mean CARs for the full sample and for VC and
non VC backed IPOs. The table 3.6 also shows the t-test results for dierence in
means reactions to VC and non VC IPOs, and Wilcoxon test for signicance of
abnormal stock price changes. The events windows start up to 10 days before the
IPO date. The subsequent analysis shows that there is a substantial price reaction
20 days around the IPO announcement day.
The return evidence reported in table 3.6 suggests that in general the market
perceives IPOs as bad news for industry competitors, where the mean CARs for
the full sample over all event windows is negative and signicantly dierent from
zero. This suggests that going public decisions have negative externalities eects on
existing publicly held rms that share a common valuation factor (market factor).
This result is consistent with Hsu et al (2010)[188].
However competitors' stock market returns after IPOs announcements are dierent depending on the status of the issued rms. For instance, non VC backed rms'
competitors have signicant negative cumulative abnormal returns around IPOs'
announcement for the dierent event windows. Conversely, VC backed rms IPOs
have positive and signicant eects on competitors. This result is consistent with
Cotei and Farhat (2013)[191].
For instance, competitors' CARs in the period between 10 days before and 5
days after the IPO are equal to 1.95% for the VC IPOs, compared to a CARs'
decrease of -2.31% for the non VC IPOs, and the dierence between the two subsamples is signicant at 1% level. This suggests that when a non VC-backed IPO is
announced, rival' rms in the same sector consider it as bad news, and view the new
issued rm as able to compete more aggressively. Dierently, when a VC backed
rm' issuance is announced, the public market consider it as a positive signal about
the whole market, and that competitors can as well benet from the market positive
outcomes..
The results in the table 3.6 are statistically signicant, yet it is also important to
analyze its economic signicance. The value loss (gain) for each rival is calculated by
multiplying the competitor' CAR in the event window by its market capitalization
at the beginning of the event window. For example, as shown in the table 3.6, for
the (-5, 1) event window, VC backed rms competitors experience an average stock
price increase of 1.52% in their stock return, while non VC backed rms competitors
experience a loss of 2.27%, this corresponds to an average incumbent loss of 2.037
million around the non VC backed IPOs, and an average gain of 1.039 million around
the VC backed IPO.
In general, the univariate results document overall negative information externalities for the total IPOs in the sample. Distinguishing VC and non VC IPOs shows
that rivals react dierently to the two sub-samples IPOs' announcement, where competitors react positively to VC backed rm going public announcement and react
negatively to non VC IPOs. This nding suggests that VC IPOs decisions signal positive prospects for the industry and that this information conveyed at the ling date
is transferred to competitors publicly traded rms, conversely non VC backed IPOs
announcement are informative about the issued rm productivity increase which
accordingly creates an average negative competitors stock market return.
Table 3.6 Competitors' Cumulative Abnormal Returns (CAR) for VC IPOs and
Matched non VC-IPOs Announcements
This table reports competitors' cumulative abnormal returns (CAR) for comparables VC
IPOs and non VC IPOs. The sample consists of 403 competitors operating in 77 different four-digit sic codes, over the period 1994 to 2011 that satisfy the following criteria: they have daily stock returns available in the Datastream, there are at least 1 rival
rms in each four-digit SIC code, competitors rms have no major confounding event
in 20 days surrounding the IPO announcement day. The table also report a standard
t-test for a dierence in means and Wilcoxon test for signicance of abnormal stock returns. ***, **, and * denote signicance at the 1%, 5%, and 10% levels, respectively.
Event Windows Full sample CAR VC-backed' CAR Non VC-backed' CAR T-statistic
[−1, 1]
[−3, 3]
[−5, 1]
[−5, 5]
[−10, 1]
[−10, 5]
[−10, 7]
[−10, 10]
-0.09%***
(-2.77)
-0.76%**
(-2.17)
-0.69%**
(-2.23)
-0.60%**
(-2.48)
-0.69%*
(-0.90)
-0.58%*
(-0.89)
-1.17%*
(-1.31)
-1.81%*
(-1.10)
1.40%
(-1.43)
1.26%
(-0.45)
1.52%
(-0.83)
1.46%
(-0.76)
1.94%
(0.23)
1.95%
(0.40)
1.53%
(-0.02)
0.78%
(-0.03)
-1.16%**
(-2.49)
-1.74%**
(-2.49)
-2.27%**
(-2.21)
-2.02%***
(-2.55)
-2.56%*
(-1.43)
-2.3%*
(-1.75)
-3.03%**
(-1.75)
-3.58%*
(-1.51)
-1.12
-1.52**
-1.36*
-1.24*
-1.27*
-1.24*
-1.10*
-0.95*
3.4.2 Regression Results
The univariate analysis show that IPOs announcements convey positive (negative)
information about industry if the issued rm is VC backed (non VC backed) and
that this information is transferred to rival rms. In this subsection I carry out
a multivariate analysis to investigate how the intra-industry information transfer
varies with rivals' characteristics.
To examine the CAR cross-sectional variation, I model competitors' CARs as a
function of issued rms, competitors, and IPO characteristics. Issued rms characteristics included in the regression model are: VC backing status, rm relative size,
and rm relative sale. Competitors characteristics are: competitor' age, competitor
relative size,competitor relative market to book ratio (MB), and whether the competitor belongs to a concentrated or competitive industry. The IPO characteristics
are: IPO proceeds, and the public market conditions.
The dependent variable is the cumulative abnormal return for each individual
competitors in the [-10, 1] window. VC backing is a dummy equal 1 if the issued
rms is VC backed, , zero otherwise. Issued rms relative size is dened as the
natural logarithm of the ratio of the rm' size at the IPO year announcement, to
median book assets of all public rms operating in the rm' industry, dened at the
4 SIC digit. Issued rms relative sale is the natural logarithm of the ratio of the
rm' sale at the IPO year announcement, to median book sale of all public rms
operating in the rm' industry, dened at the 4 SIC digit.
Competitors' characteristics are age, relative size and relative MB ratio. As in
Hsu, et al (2010)[188], competitor age is the number of years a rm has been publicly
traded. The advantage of measuring competitors' age since trading date instead of
measuring it from the founding date is that rm operating performance may follow
its life cycles. In fact, rms operating performance tends to increase shortly at
the beginning of the rm' life span and then increase less, or even decrease, at
later stages. Therefore, if we proxy age from rm creation date, competitors rms'
performance may be declining in the years measured in our study.
Competitor relative size is dened as the natural logarithm of the ratio of the
rm' size, one year before the IPO announcement, to median book assets of all public
rms operating in the rm' industry, dened at the 4 SIC digit code. Competitor
relative MB is dened as the natural logarithm of the ratio of the rm' MB, one
year before the IPO announcement, to median MB of all public rms operating in
the rm' industry, dened at the 4 SIC digit code.
I use two proxies for sector competition aspect; the rst is Leader Market Share
which is equal to the value of the largest market share of the public competitors at a
three-digit SIC level. The second proxy is the total number of competitors operating
in the same 3 SIC code as the issued rms. The intuition is that more concentrated
industry is characterized by a smaller number of players with higher market power.
I also Control for the hotness of the IPO environment and the market conditions.
Following Chemmanur and He (2008)[189] for their denition of IPOs hotness activities, for each IPO in the sample I count the total number of IPOs in the 3 SIC digit
code industry within a 90 days window symmetrically surrounding the issuance date
for the given IPO. I use this number as a raw measure of the hotness of the IPO
market in that industry for the particular issuance event under consideration. In
order to control for overall equity market conditions, I use S&P Euro 350 Returns,
which is dened as the monthly return on the Standard & Poor' Euro 350 Index.
Table 3.7 presents the results. Model 1 includes only variables about competitors'
characteristics. Model 2 examines the eects of issued characteristics on competitors'
CAR, independently of competitors' characteristics. Model 3 and 4 are the full model
specication after controlling for competitors and issued rms characteristics. Model
5 include results for only the sub-sample of VC back issued rms, where I control
also for the VC syndication size, which is the total number of VC investors investing
in the issued rm at the last nancing round.
Based on competitors' characteristics in model 1 in table 3.7, the result documents that competitors' reaction in response to IPO announcement is higher for
rivals with higher relative growth options. Market-to-book ratio is a common proxy
for growth opportunities. Rivals' growth opportunities may inuence their ability to
Table 3.7 The Eect of IPO Announcement Events on Competitor's Cumulative Ab-
normal Returns
This table reports the cumulative abnormal returns (CAR) of incumbent rms around the IPO
date of VC and non VC-backed rms. The [−10, 1] event window is considered in these
regressions. Overall we have 403 competitors of 120 IPO in 77 dierent four-digit sic codes
industry from 1994 to 2011. Within these IPOs, 50 are PE-backed rms and 70 non PE-backed
rms. We compute the cumulative abnormal returns (CAR) for each rm rival i by adding the
AR over the event window. VC-backing dummy equals one if the issued company is backed by a
venture capital investor, zero otherwise. Log(IPO Proceeds) is the logarithm of the amount raised
by the issued rm. Relative size (sale) is dened as the natural logarithm of the ratio of the
rm's size (sale) at the IPO year announcement, to median book assets (sale) of all public rms
operating in the rm' industry, dened at the 4 SIC digit. Competitor relative size (MB ratio) is
dened as the natural logarithm of the ratio of the rm's size (MB ratio), one year before the
IPO announcement, to median (book assets, MB) of all public rms operating in the rm's
industry, dened at the 4 SIC digit code. competitor age is the number of years a rm has been
publicly traded. Leader Market Share is equal to the value of the largest market share of the
public competitors at a three-digit SIC level. Number of competitors is the total number of
competitors operating in the same 3 SIC code as the issued rms. Number of IPOs is total
number of IPOs in the 3 SIC digit code industry within a 90-day window symmetrically
surrounding the issuance date. S&P Euro 350 Returns is the monthly return on the Standard &
Poors Euro 350 Index. We estimate our regressions using OLS with robust standards errors. ***,
**, and * indicate signicance at the 1%, 5%, and 10% levels, respectively.
model1
model2
model3
model4
model5
VC dummy
0.0629*
(0.0377)
0.0226
(0.0219)
-0.0275
(0.0190)
0.0314*
(0.0204)
-0.0018
(0.0061)
-0.0005
(0.0070)
-0.0012
(0.0052)
0.0041*
(0.0088)
-0.0093
(0.0140)
0.0400
(0.0414)
0.0018
(0.0017)
0.0020**
(0.0008)
0.5399**
(0.2170)
0.0420
(0.0716)
0.0019
(0.0039)
-0.0001
(0.0013)
0.1137
(0.3278)
-0.0023
(0.0049)
0.0131
(0.0096)
0.0007
(0.0127)
0.0438
(0.0396)
0.0007
(0.0014)
0.0002
(0.0006)
0.5676***
(0.1997)
0.0010
(0.0055)
-0.0032
(0.0051)
0.0056
(0.0100)
0.0016
(0.0134)
0.0484
(0.0407)
0.0004
(0.0014)
0.0003
(0.0005)
0.4780**
(0.2018)
-0.0232
(0.0414)
Yes
Yes
0.1661
262
-0.3502
(0.2585)
Yes
Yes
0.2740
321
-0.0340
(0.0509)
Yes
No
0.1185
241
-0.1157**
(0.0536)
Yes
No
0.1094
252
Log(Proceeds)
Log(Issued Relative Size)
Log(Issued Relative Sale)
Log(Competitor Relative Size)
Log(Competitor Relative M/B)
Log(Competitor Age)
Leader Market Share
Number of Competitors
Number of IPOs
S&P Euro 350 Returns
Syndication size
Constant
Year Fixed Eect
Industry Fixed Eect
R-squared
Observations
0.0335*
(0.0186)
0.0011
(0.0052)
-0.0212
(0.0177)
-0.0111
(0.0127)
-0.0109
(0.0085)
0.0048
(0.0138)
-0.0132
(0.0225)
-0.0221
(0.1679)
0.0097
(0.0074)
0.0031*
(0.0018)
0.6072
(1.2478)
0.0164
(0.0114)
0.0318
(0.3431)
Yes
Yes
0.3288
115
respond to the competitive threat of a new publicly traded rm within an industry or
to incorporate new growth opportunities available in that industry. The coecient
estimate for competitors' relative market to book ratio is positive and statistically
signicant at 10% level. This suggests a positive relationship between the rivals'
ability to take advantage of growth opportunities (or to respond to a competitive
threat) and their reaction at the IPO announcement.
Consistent with nding in the univariate analysis, the regression models show
that competitors have positive and signicant valuation eects when the IPO is
venture backed (0.06). This suggests that venture backed IPOs signal positive information about the related industry and this has a signicant positive impact on
competitors' stock price returns. In other words, with the presence of venture capitalists at the time of IPO, a new entrant does not represent a competitive threat
for competitors; it represents instead a signicant positive valuation eect because
investors reassess the value of similar existing publicly traded rms at the time a venture capitalist brings a rm public. When considering rivals' characteristics, venture
backed IPOs keep generating positive externalities eects for industry competitors.
The models specication also control for IPO market hotness and market conditions. The results about VC eect on competitors' valuation remain the same.
Therefore the positive externality of VCs IPOs is not only derived by favorable IPO
environment and market conditions. In fact, the results also document a positive
and signicant eect of IPO market hotness and market conditions on competitors'
reaction to IPOs announcements. This naturally shows the positive relationship
between competitors stock market return and public market environment.
Overall, the results suggest that VC investors market timing is an important
determinant of VC backed rm issuance decision. And that the signaling ability of
venture backed IPOs have a positive impact on industry competitors.
The other control variables, namely indicators for sector competition level, issued rms relative size and sale, competitors' relative size and age are statistically
insignicant.
3.5
Conclusion
While some private rms compete well with public companies in the same industry
and thus stay private, other companies may decide to go public and thus change their
competitive position and eventually impact rivals one. It is therefore important for
investors to know how an IPO aects the operating and stock market performance
of existing rms when making portfolio allocation decisions. Similarly, rms that
compete with IPO candidates need to understand how the new issuance aects their
competitive environment.
In this paper I'm interested in competitor'stock market reaction to IPOs announcement of VC and non VC backed issued rms. The results show that in
France public market, VC-backed IPOs impact positively competitor's CAR, while
non VC-backed IPOs create a negative competitors reaction. However, it is worth
noting that the average competitors' CAR for the full sample is negative, and that
the signicant positive externality driven by the venture backed IPOs is overcame
by the negative eect of non VC backed IPOs. The results also document that competitor' reaction to IPOs announcement is inuenced by public market conditions
and competitors relative level of growth options.
We are aware that additional control variables may improve our results. For instance, Chemmanur and Krishnan (2012)[76] nd that the reputation and the quality of IPOs' participant (high-reputation underwriters and co-managers, important
number of institutional investors and analysts) make retail investors more optimistic
about the IPO rm's prospects and consequently increase equity valuation in IPOs.
One may expect that IPOs participants' quality will impact competitors' reaction
as well, specically to VC backed ones. In fact repetitive participation of VC investors in the public market allow them to develop a long term relationship with
the market participants, and therefore to attract higher public market participants,
giving an optimistic valuation about the future of the issued rm. Further development of this work need also to include more robust proxies about sector competition
characteristics.
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Chapter 4
The Role of Venture Capital Backing
in Mergers and Acquisitions
Résumé
Dans ce chapitre, j'explore les caractéristiques et la réaction du marché à l'acquisition
d'une entreprise soutenue par un investissement en capital risque. L'objectif est de
déterminer le rôle de l'investisseur en capital dans les opérations de sortie par vente
à un industriel. L'étude porte sur un échantillon de 172 acquisitions en Europe entre
1997 et 2012. Les résultats montrent que la valeur des deals et la taille de l'acquéreur
sont plus grandes quand la cible achetée est soutenue par un investisseur en capital.
En plus, après contrôle du risque de sélection associé à l'investissement en capital, les
résultats montrent que la rentabilité boursière de l'acheteur est nettement plus faible
quand la cible est soutenue par un investisseur en capital. Ces résultats suggèrent
que les investisseurs de capital-risque dans les opérations de sortie par acquisition
certient la qualité de leur entreprise, ce qui leur permet d'obtenir un prix plus élevé
et ainsi réduire la rentabilité de l'acheteur.
99
abstract
In this paper, I explore acquirer characteristics and market reaction to acquisition
of venture capital backed rms. I nd that deal value and acquirer size are larger for
venture backed targets.Further, after controlling for VC nancing endogeneity and
deals characteristics, I nd that acquirer announcement return is signicantly lower
for venture backed targets. Overall,the results suggest that VC investors in M&A
certify the quality of their portfolio rms.Thus, obtaining higher price and reducing
acquirer return.
4.1
Introduction
Venture capital investors (therefore VC) are active investors who invest in start-up
rms for which they obtain an equity position. The VC investor stays involved in the
development of his portfolio rm until the exit, when he sells his shares. Generally,
successful exits can occur through an initial public oering (IPO), with a subsequent
sale of VC stake in the public market, through a sale of the rm to another investor
(secondary sale), or through the sale of the rm to a larger company (trade sale).
The European Private Equity and Venture Capital Association (EVCA) statistics
show that in 2010, trade sales comprised approximately 41,2% in Venture capital
market while divestments by public oerings represent 13,7%. The objective of this
paper is to study the trade sale exit patterns in Europe, more specically the role
of VC in M&A exits.
Several papers study VC investors' role in the public market. For instance data
before the 1990s shows that VC backed IPOs are associated with lower degree of underpricing than non-venture backed IPOs (Megginson and Weiss ,1991[149]), therefore documenting a VC certication role, where VC objective in IPOs is to price
the equity of backed rms closer to intrinsic value. Data after the 1990s shows
that IPOs of VC backed rms are more underpriced than those of non-VC backed
rms (Lee and Wahal, 2004[144]; Loughran and Ritter, 2004[89]). Liu and Ritter
(2011)[147] argue that VC investors allow higher levels of underpricing because they
are especially concerned about analyst coverage when shares are distributed to limited partners. Similarly Chemmanur and Loutskina (2007)[111] nd that VC IPOs
attract higher quality market participants, therefore suggesting a VC market power
role in IPOs exits.
Overall "Going public" is an important and well-known exit mechanism that has
been extensively studied in the literature. However, acquisition that is an equally
important exit route remains under studied. In this paper I aim to study the VC
role in M&A exit. I use a sample of 172 similar Europeans acquisitions to analyze acquirer announcement return for VC backed and comparable non VC backed
targets. I use the short-window event study to investigate acquisition creation of
value for acquirer shareholders, where the average abnormal stock market reaction
at merger announcement proxies for value creation or destruction. In other words,
acquirer positive (negative) abnormal return indicate a positive (negative) rate of
return on the acquisition. Furthermore a dierence in stock market reaction between
comparable VC and non VC acquisition signies that the VC investor has an impact
on the deal valuation.
I nd that acquirer announcement return for acquisition of VC backed target
is signicantly lower than when the target is non VC backed. The result suggests
that during acquisition, VC investors certify the quality of their portfolio rms by
reducing the information asymmetry faced by the acquirers. Target presenting less
information asymmetry to the acquirer is necessary with higher purchase price and
thus lower acquirer announcement return.
The remainder of the paper is organized as follows. Section 2 reviews the related
literature. Section 3 presents the sample and methodology. Section 4 analyses
the empirical results. In Section 5, I will conclude and give suggestions for future
research
4.2
Literature Review
This paper is related to two strands of literature. The rst is on mergers and acquisitions and the determinants of acquisitions returns.The second is on VC investors
role in acquisitions exits.
4.2.1 Acquirer Reaction to Acquisition Announcement
Empirical works on M&A document dierent results of acquirer reaction to acquisition announcement depending on target status, acquirer and deal characteristics.
4.2.1.1 Target Status
The target status - i.e., whether the target is public or private is shown to impact
acquisition return. For instance, Fuller et al. (2002)[192] examine acquisition in
a US sample of 3,135 acquisitions between 1990 and 2000; the authors nd that
acquirers receive a better price when they buy unlisted rms. Ocer (2007)[193]
nd the same result for a sample of 12,716 acquisitions in US between 1979 and
2003. The authors document a discount for acquisitions of unlisted targets that
average 15% to 30% relative to multiples paid to acquire comparable publicly traded
rms. Finally, Faccio et al. (2006)[194] nd in a sample of 4429 acquisitions in
Western European countries between 1996 and 2001 that acquirers of listed targets
earn an insignicant average abnormal return of -0.38%, while acquirers of unlisted
targets earn a signicant average abnormal return of 1.48%. The literature infers
this result to the information asymmetry and illiquidity concerns characterizing the
unlisted rm, which reduce the unlisted rms' price acquisition and therefore increase
acquirer return.
4.2.1.2 Acquirer Characteristics
The literature shows that larger acquirers earn lower announcement returns than
do smaller ones. Moeller (2004)[80], for instance nd that the announcement return
for acquiring-rm shareholders is roughly 2% points higher for small acquirers irrespective of the form of nancing and whether the acquired rm is public or private.
The authors conclude that this nding is consistent with managerial hubris role in
acquisition decisions of large rms. In fact, managers of large rms may develop an
unrealistic belief that they can manage the assets of a target rm more eciently
than the target rm' current management, and therefore engage in disproportionate
acquisition deals.
Fuller et al. (2002)[192] document a signicant positive relation between relative
deal size and acquirers announcement returns. The relative deal size is the target size
relative to the acquirer size. The authors nd that taken together, the deal size and
relative deal size also account for the size of the acquiring rm, which signicantly
aects acquirer returns (Moeller et al., 2004[80]).
4.2.1.3 Deal characteristics
Regarding deal characteristics, empirical evidences nd that the form of payment,
the acquirer and the target geographic location and industrial relatedness are deter-
minant in acquisition wealth eects.
Fuller et al., (2002)[192] and Ocer et al (2009)[81], for example, document
that the impact of deal methods of payment on acquirer return diers on acquirer
status. In fact for private rms, acquirers returns associated with stock payment are
more positive than those associated with cash payment. Conversely, acquisitions of
public targets show that stocks deals trigger more negative abnormal returns. The
authors argue that when using stock to acquire private rms, the acquirer mitigates
information asymmetry concerns associated with private targets and shares the risk
of a target' over-valuation with the target' owners. While in public rms' acquisition,
the manager will prefer to use stocks for deal payment when he knows that they are
overvalued, therefore inducing a negative acquirers' reaction.
Acquirer and target geographic locations also impact cross-border deals returns.
However, the literature reveals mixed results about the acquirers returns in cross
border deals. In fact, cross-border transactions may bring to acquirers additional
benets of international diversication and access to new markets, but at the same
time they are likely to be more costly and complex to execute than domestic ones.
In US, Fuller et al. (2002)[192] nd that acquirers of private targets show lower
returns when buying a foreign rm, suggesting that cross-border acquisitions come
with diculties in managing the post merger process due to regulatory and national
cultural dierences. Moeller and al (2005)[82] nd similar results in a sample of
4430 acquisitions between 1985 and 1995. The results document that US rms
who acquire cross-border targets relative to those that acquire domestic targets
experience signicantly lower announcement stock returns of approximately 1% and
signicantly lower changes in operating performance. Conversely, Francis et al.
(2008)[83] document a positive cross-border eect for US acquirers during late 1990s
and early 2000s.
In a sample of acquisitions involving rms in the European Union over the period 1998-2000, Campa and Hernando (2004)[84] nd bidders to perform better in
domestic than in cross-border acquisitions, although the dierence is only signicant
for a long pre-announcement window. Conn et al. (2005)[85] examine the announcement and post-acquisition share returns of UK acquirers in over 4,000 acquisitions of
domestic, cross-border, public and private targets. The authors nd that both domestic and cross-border acquisitions result in signicantly positive returns of 0.68%
and 0.33%, respectively.
In a recent work, Danbolt and Maciver (2012)[86] compare between domestic and
cross-border mergers in UK, basing their analysis on 397 cross-border deals and a
similar sample of comparable domestic acquisitions. The authors nd that bidding
companies perform better in cross-border than in domestic acquisitions, where the
bidding companies in domestic acquisitions present negative abnormal returns of
-1.8%, and an insignicantly dierent from zero return to cross border acquisitions.
It is worth noting that the dierence in the sample composition and the sample period may explain the divergence of Conn et al (2005)[85] and Danbolt et al
(2012)[86] ndings. In fact, Conn et al (2005) sample is dominated by acquisitions
of private rms that account for 83.7% of the sample, compared to 14.5% in Danbolt
et al (2012)[86] sample. Furthermore, the sample periods are also dierent, Conn et
al (2005)[85] study cover the 1984-1998 period, while Danbolt et al (2012)[86] study
extends the sample period to 1980-2008. Finally, Conn et al.(2005) [85] do not match
similar domestic and cross-border acquisitions, as Danbolt et al (2012)[86] do.
Finally, it is also unclear whether industrial diversication leads or not to acquirer
value creation. Moeller et al. (2004)[80], for example nd that acquirers abnormal
returns are higher in within-industry acquisitions than in inter-industry acquisitions.
Although, Fuller and al (2002)[192] results for unlisted target acquisitions are not
supportive of an industry focus-increasing eect on acquirer returns. This suggests
that industrial diversied acquisitions are expected to create operational synergies,
but involve more monitoring costs as the acquisitions are made outside of acquirer'
main activities.
4.2.2 VC Investor Role in Acquisition Exits
The second strand of literature is related to VC role in sale exit. The literature
considering the eects of VC-backing on acquisition returns is recent and focus only
in the US market.
Gompers and Xuan (2008)[129] explore a sample of 1,261 acquisitions of ven-
ture capital-backed private companies from 1992 and 2006. The authors nd that
successful acquisition paid with stock is more likely when there is a common VC
investor between the acquirer and the target, and that announcement acquisition is
more positive for acquisition in which both the target and the acquirer are nanced
by the same VC rm. The authors argue that a common venture capital investor has
credibility with both the buyer and the seller and thus has the ability to "bridge"
the information gap between the two rms.
A contemporaneous paper by Masulis and Nahata (2011)[187] use a sample of
490 acquisitions with important portion of high-tech private targets (about 70%).
The authors nd that Venture Capital backing leads to signicantly higher acquirer
announcement return, averaging 3%. The authors explain this nding by the presence around the acquisition bid of conicts of interest with the VC fund providers
. For instance, VC funds approaching maturity need to be liquidated as soon as
possible; therefore pressuring the portfolio rms to negotiate rapidly a sale of the
company, which harms target negotiation power and increases acquirer returns. A
second example of conict of VC interest documented by the authors occurs when a
VC investor in a target also has a direct nancial tie to the acquirer, which impact
negatively the target negotiation position and thus increase the average acquirer
announcement returns. Finally the authors argue that CVCs can also prioritize
CVC parents' strategic objectives with the acquirer and willingly sacrice nancial
returns on their venture investments, which results in higher acquirer wealth gains.
Overall the Masulis and nahata (2011)[187] ndings meet IPO evidence in Lee and
Wahal (2004)[144] that VC investors can have incentives to accept lower values for
their portfolio companies when they are divesting their private equity investments.
In this paper I investigate the eect of VC nancing for European acquisition
using a new database from Bureau van Dijk Zephyr and studying recent time period.
My results are dierent form Masulis and Nahata (2011)[187] nding about VC role
in M&A. The main dierence is that I nd a less positive acquirer reaction to
VC backed announcement than acquirer reaction to comparable non VC backed
acquisition. This result document a VC certication role. In fact, a direct eect of
VC certication role is to reduce the VC backed rms asymmetric information, which
may promote acquirers to pay a higher price for the VC backed targets. Everything
else being equal, this might imply a smaller surplus for the acquiring rm and a
lower abnormal return.
4.3
Data and Descriptive Statistics
In this section I start by describing the data collection stages . Then I describe the
matching approach to be used as a solution to the endogeneity problem associated
to the research question.
4.3.1 Data Description and Sample Selection
4.3.1.1 Data Collection
I obtain a sample of completed acquisitions involving European private targets from
Zephyr database. The target must be a privately held European incorporated company, and acquirers a public European incorporated company. Acquirers stock must
be publicly listed on a European public market and available in DataStream. I
exclude deals where the acquirer or the target are regulated utilities or nancial
institutions. I keep only deals where acquirer has no toehold position prior to the
deal announcement, and where the buyer acquires 100% of target rm shares. I
also exclude clustered acquisitions by a single acquirer within 5 days and deals with
relative size (deal value divided by acquirer' market value) less than 10%.
Applying the above criteria to the initial sample, I constitute a sample of completed oers containing 1690 acquisitions of non venture backed targets and 272
acquisitions of venture backed rms. From this sample I exclude deals where acquirers don't have sucient stock price information to estimate their returns, and
nally, I use Lexis-Nexis and Factiva databases to remove deals confounded by other
acquirers major news announcements (earnings, dividends, strategic alliances, stock
splits, etc.) in the 5-day trading period (event days -2 though 2). The nal sample
contains a total of 496 completed acquisitions announced between 1997 and 2012,
with 98 private VC backed targets and 398 private non VC backed rms.
4.3.1.2 Sample Selection and Matching
VC-backed targets are likely to have dierent characteristics than non VC backed
ones. For example, VC investors concentrate their investments in rms with high
growth potential, and they seek to exit from their investments within limited number of years. This constitute a selection bias problem for this study. In fact the
comparison between acquirers' return to VC and to non VC targets' acquisitions is
biased as VC investors select his backed rms, and thus they are naturally dierent
from non VC backed ones.
To better evaluate the eect of VC investors on acquirers returns, I create a
comparable sample of non-VC-backed targets using propensity score matching. In
this approach, propensity scores are used to select "control" units that are most like
the "treatment" units across a variety of characteristics that are important to this
study.
The rst step in propensity score matching is to estimate a logistic regression
predicting whether an acquisition involves a VC-backed or a non-VC-backed rm.
The dependent variable is equal to 1 if the issued rm is VC backed, and is 0
otherwise. The explanatory variables used in the matching criteria are: high tech
sector dummy, a method-of-payment dummy (deals involving stock for payment),
deal size (target purchase price), and relative deal size (target purchase price divided
by acquirer' market value).
The result in table 4.2 indicates that deals involving VC-backed targets are likely
to be larger in size.
After estimating the propensity scores and performing balancing test in each
quantiles of the propensity scores distribution, I match VC backed target to comparable non VC backed target using the nearest-neighbor matching method. The nal
data set consists of a total of 86 VC backed targets matched with a comparable 86
non VC backed targets,
Based on this sample, a standard event procedure is then used to assess whether
the stock prices of the listed acquirer react to the acquisition announcement.
Table 4.1 The likelihood of VC Target Firms Acquisition
Table 4.2 This table reports the logistic model results predicting whether an acquisition
involves a VC-target or not. The dependent variable is equal to 1 if the issued rm
is VC backed, and is 0 otherwise. Deal size (target purchase price), relative deal size
(target purchase price divided by acquirer' market value), method-of-payment dummy
(deals involving stock for payment), and high tech sector dummy.
Variables Pr (VC target)
deal size
relative deal size
stock in payment
High Tech
Constant
Observations
4.3.2 Descriptive Statistics
0.0017*
(0.0009)
0.0048
(0.1242)
0.3364
(0.3615)
0.1074
(0.2636)
-1.4864***
(0.1545)
496
In this section I rst present the descriptive statistics of the full sample before
matching, then I describe the matched samples.
4.3.2.1 Descriptive Statistics Before Matching
Table 4.3 reports descriptive statistics for the acquisition samples before matching
the VC and non-VC-backed targets. Panel A reports the dierences in rms characteristics using a standard t-test for dierence in means and a Wilcoxon test for
dierence in medians. In general, VC backed targets are twice as large as non-VCbacked targets, and the dierence is statistically signicant at the 1% level. A similar
pattern is observed for acquirers size; the mean (median) size of VC-backed targets'
acquirers is e222 (e111) million, which is signicantly larger than the mean (median) size of non-VC-backed targets' acquirers, e94 (e35) million. This indicates
that targets and their acquirers are substantially dierent across the 2 samples.
Panel B reports the nancing methods frequency of the 2 samples. Acquisitions
of VC-backed targets are less often nanced by stock, but the dierence between the
two samples is not signicant. In fact, 58% of VC backed targets acquisitions are
nanced with stock or a mixture of cash and stock, while 64 % of non- VC-backed
acquisitions involve stock as an acquisition currency.
Panel C reports the frequency of high tech targets in the sample, nearly 27% of
the total sample belongs to technology-intensive industries, with 24% of VC-backed
targets and 27% of non-VC-backed targets are in technology-intensive sectors. The
dierence between the two sub-samples regarding operating intensity in high tech
sector is not statistically signicant.
Panel D reports the frequency of industry unrelated deals, it is when the target
and the acquirer belong to dierent 3-digit SIC code. Inter-industry deals represent
40% of the total sample, with 37% of VC backed targets and 40% of non VC backed
targets belonging to dierent 3-digit SIC code than the acquirer. The industry
diversication aspect of the sample is not signicantly dierent between VC and
non VC backed targets.
Panel E reports the frequency of cross border deals, it is when acquirer and target
are located in dierent countries. The cross border deals are more common when
the target is VC backed, with 26% of VC backed targets and 18% of non VC backed
targets are located in a dierent country than the acquirer. The dierence in cross
border acquisition aspect is statistically dierent between the two sub-samples at a
10% level.
Overall, deals involving VC-backed targets have substantially dierent properties
from other private rms acquisitions in terms of deal value and acquirer size, and not
signicant dierences regarding the type of nancing, and industries. This however,
raises some important concerns about sample selection bias that I addressed using
propensity score matching method.
Table 4.3 Descriptive Statistics for VC backed and non VC backed Targets, Before
Matching
The sample consist of private rms acquisitions for the period 1997 - 2012, where the acquirers
are European public rms and the targets are European private rms dierentiated by whether or
not they are VC-backed. Acquisitions must have a relative deal size (deal size divided by acquirers
market value of equity 1 month prior to the acquisition announcement) of at least 10%. The
table compares VC-backed to non-VC-backed targets acquisition characteristics. Acquirer size
is measured by the market value of acquirer equity 1 month prior to acquisition announcement.
Target size is the price paid for acquisition of the target. High-technology industries are classied
as belonging to SIC codes 283 (biological products, genetics, and pharmaceuticals), 481 (hightechnology communications), 365-369 (electronic equipment), 482-489 (communication services),
357 (computers), and 737 (software services). An acquisition is classied as related if the target
and the acquirer have the same 3-digit SIC code. An acquisition is qualied "cross-border"' if the
acquirer rm and the target are located in dierent countries. A standard t-test for a dierence
in means and Wilcoxon test for a dierence in medians are used to compare VC-backed and nonVC-backed targets. ***, **, and * denote signicance at the 1%, 5%, and 10% levels, respectively.
Total Sample
VC-backed
Non VC-backed
Test of Equality
Mean
MedianMean MedianMean
Median Mean
Median
Panel A: Deal Characteristics
Target size
39.51
12.12 71.51
31.59 31.45
Acquirer size
120
41 222
111 94
Relative size
0.49
0.26 0.52
0.30 0.48
No of obs
477
96
Total Sample
%
No
0.63
Panel C: Frenquency of 129
High Tech targets
(7.47)***
(6.22)***
(2.30)***
No
Non VC-backed
%
56
0.58 245
0.64
1.08
0.27
23
0.24 106
0.27
0.76
Panel D: frequency of in- 191
dustry unrelated deals
0.40
36
0.37 155
0.40
0.56
Panel E: Frenquency of
cross border deals
0.20
25
0.26
0.18
(1.74)*
No
Panel B: Deal nancing
methods
Involving stock
301
Panel F: Acquirer CARs
[−2, 2]
94
3.91%
VC-backed
%
381
8.92(2.61)***
35(4.35)***
0.25(0.34)***
3.88%
69
3.92%
Test of Equality
0.03%
4.3.2.2 Descriptive Statistics After Matching
Panel A of Table 4.4 reports the dierences in rms characteristics across VC-backed
and non VC backed matched targets. The dierences in target size, acquirer size and
relative deal size are statistically insignicant. I conclude that the two sub-samples
are similar regarding their rms characteristics.
Panel B reports the acquisition nancing methods for the 2 matched samples. A
comparison of the 2 samples on the basis of nancing methods also indicates close
matching on this dimension, with 60% of the acquisitions of VC backed targets and
70% of acquisitions of non VC backed targets involve stock payment, the dierence
between the two sub-samples is not statistically signicant.
Panel C reports the frequency of high tech sector in the sample. The dierence between VC backed and non VC targets sub-samples regarding their operating
intensity in high tech sector is also not statistically signicant.
Panel D shows that industry diversied deals are more common for acquisition of
non VC backed targets, with 48% of non VC backed targets and 34% of VC backed
targets belonging to dierent 3 digit SIC codes than the acquirers. The dierence is
signicant at 10% level.
For cross border deals, the results in Panel E show that cross border acquisitions
represent 27% of the total sample, with 25% of VC backed targets and 29% of non
VC backed targets are located in dierent countries than the acquirer. The dierence
between the two sub-samples is not statistically signicant.
Overall, the 2 groups of targets are well matched in all characteristics listed in
the table 4, (except in the industry relatedness aspect). Therefore, I can consider
that the similarity of the two sub-samples after the matching helps to substantially
alleviate concerns about selection bias that came from the endogeneity of VC investment.
Table 4.5, reports the distribution of the sample across years after matching.
Over the period 1997 to 2012, I report 86 VC-backed targets acquisitions and 86
non VC-backed targets acquisitions. The table shows that the period between 2005
Table 4.4 Descriptive Statistics for VC backed and non VC Backed Targets, After
Matching
The sample consist of European acquisitions of comparable private target rms for the period
1997 - 2012. Non-VC-backed targets are selected based on propensity score matching using hightechnology industry indicator, method-of payment indicator, deal size, and relative deal size. Acquisitions must have a relative deal size (deal size divided by acquirer's market value of equity 1
month prior to the acquisition announcement) of at least 10%. The table compares VC-backed to
non-VC-backed targets acquisition characteristics. Acquirer size is measured by the market value
of acquirer equity 1 month prior to acquisition announcement. Target size is the price paid for
acquisition of the target. High-technology industries are classied as belonging to SIC codes 283
(biological products, genetics, and pharmaceuticals), 481 (high-technology communications), 365369 (electronic equipment), 482-489 (communication services), 357 (computers), and 737 (software
services). An acquisition is classied as related if the target and the acquirer have the same 3digit SIC code. An acquisition is qualied "cross-border"' if the acquirer rm and the target are
located in dierent country. The variables used in the matching of VC and non VC sub samples
are: high tech sector dummy, a method-of-payment dummy (deals involving stock for payment),
deal size (target purchase price), and relative deal size. A standard t-test for a dierence in
means and Wilcoxon test for a dierence in medians are used to compare VC-backed and non-VCbacked targets. ***, **, and * denote signicance at the 1%, 5%, and 10% levels, respectively.
Total Sample VC Backed Non VC Backed Test of Equality
Mean
MedianMeanMedianMean Median Mean
Median
Deal Characteristics
Target size
Acquirer size
Relative size
No of obs
43.76
169
0.52
No
Panel B: Deal nancing
methods
Involving stock
113
Frenquency of High Tech 41
targets
frequency of industry re- 72
lated deals
Frenquency of cross bor- 47
der deals
Acquirer CARs
[−2, 2]
27.81 45.62 27.94 41.89
27.41(-0.50)
85 166
100 172
76 (0.15)
0.28 0.48 0.29 0.56
0.27 (0.48)
172
86
86
Total Sample
%
No
0.65
0.24
4.6%
VC
(-0.51)
(0.43)
(0.45)
%
No
Non VC
%
52
23
0.60
0.26
61
18
0.70
0.20
1.44
(-0.89)
0.41
30
0.34
42
0.48
1.86*
0.27
22
0.25
25
0.29
0.51
3.2%
6.%
Test of Equality
1.81**
and 2007 represent the highest number of acquisition with about 30% of the total
acquisitions.
Table 4.5 Acquisitions Distribution by Year for VC-Backed Targets and a Matched
Sample
The sample consists of 172 completed acquisition of private rm target by 15 western european
countries beteween 1997 and 2012. Acquisition are listed by announcement year and the status of
the private target rm.
Total
VC-backed
Non VC-backed
Announcement
Acquisition
targets
targets
Year
No
%
No
%
No
%
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
7
11
17
15
9
4
8
14
17
14
21
6
7
12
5
5
4
6
10
9
5
2
5
8
10
8
12
3
4
7
3
3
1
3
11
5
2
2
6
9
6
8
11
4
5
6
4
3
1
3
13
6
2
2
7
10
7
9
13
5
6
7
5
3
6
8
6
10
7
2
2
5
11
6
10
2
2
6
1
2
7
9
7
12
8
2
2
6
13
7
12
2
2
7
1
2
Total
172
100
86
50
86
50
Table 4.6 presents the number of acquisitions by acquirer home country. The
sample is dominated by U.K. acquirers (more than 58% of the acquisitions). This
observation is in line with the proportion of U.K. acquirers of unlisted rms in
Europe found by Faccio et al. (2006)[194].
Table 4.7 lists by industry groups the number of VC-backed targets and the
matched non-VC-backed ones. 26% of VC backed target and 20% of non -VC-backed
targets are from technology-intensive sectors.
Table 4.6 Acquisitions by Country for VC-Backed Targets and a Matched Sample
The sample consists of 172 completed acquisition of private rm by 15 western european
countries between 1997 and 2012. Acquisition are listed by home countries of the acquirers and
the targets. Targets are distinguished by their status of VC backing
Acquirers
Targets
Country
Total
VC-backed
Non VC-backed
N
%
N
%
N
%
Austria
Belgium
Switzerland
Germany
Denmark
Spain
Finland
France
United kingdom
Ireland
Italy
Netherlands
Norway
Portugal
Sweden
0
4
1
6
1
1
2
9
100
6
8
4
10
1
19
0
2
1
3
1
1
1
5
58
3
5
2
6
1
11
1
1
0
3
1
0
2
7
54
0
4
2
3
1
7
1
1
0
3
1
0
2
8
63
0
5
2
3
1
8
1
0
1
7
2
1
0
7
46
1
6
3
3
0
8
1
0
1
8
2
1
0
8
53
1
7
3
3
0
9
Total
172
100
86
100
86
100
Table 4.7 Acquisitions by Industry for VC-Backed Targets and a Matched Sample
This table presents the industry distribution of the sample of 172 completed acquisition of private rms by 15 western european countries between 1997 and 2012. Acquisition are listed
by the industries of the acquirer and the target, and the status of the private target rm.
Acquirers
Targets
Industry Total Acquirers VC-backed Non VC-backed
N
% N
% N
%
Electronics, computers, and communication
Food
Oil, gas, and energy
Business services
Construction
Health services
Manufacturing
Software services
Textiles and clothing
Transportation, communications, and utilities
Wholesale and retail trade
Wood and paper products
Total
7
6
7
29
4
2
11
50
3
20
22
11
172
4
3
4
17
2
1
6
29
2
12
13
6
100
3
5
2
13
1
3
6
25
1
9
11
7
86
3
6
2
15
1
3
7
29
1
10
13
8
100
3
1
5
13
3
0
10
29
3
6
9
4
86
3
1
6
15
3
0
12
34
3
7
10
5
100
4.4
Empirical results
In this section I will start by analyzing the results of the cumulative abnormal returns
of the nal sample, and then I examine the results of the regression models.
4.4.1 Univariate Analysis: Cumulative abnormal return
I use event study methodology to capture acquirers' share price reactions to acquisitions announcements, where the announcement date of each completed acquisition
represents the event date.
I estimate the market model parameters for each acquirer using the daily return
of the value-weighted local price index . The estimation period starts 40 days before
the announcement date (i.e. t=0) and ends 300 days after it.
The estimated coecients serve then to predict the daily returns for each rm
over the "event window" [−2, 2] i.e. the ve days immediately surrounding the
announcement date. The abnormal return (AR) for each acquirer rm on each day
of the event window is the dierence between the predicted return Rit and the actual
observed return rit . The cumulative abnormal return (CAR) for each acquirer rm
is the sum of the daily abnormal return over the event window.
Table 4.8 tabulates the CAR from day -2 to day +2 for both VC and non VC
targets. Table 6 also presents mean acquirer CARs depending on whether the payment involve stock, whether the acquirer and the target rms belong to the same
industry based on their 3-digit SIC codes, and whether the acquirer and the target
are located in the same country.
The results in Table 4.8 show that the acquirers react positively to announcement
of private rm acquisitions. This result is consistent with the literature (Moeller,
Schlingemann, and Stulz ,2004[80]; Ocer, Poulsen, and Stegemoller,2009[81]; Faccio, McConnell, and Stolin, 2006[194]; Fuller, Netter, and Stegemoller ,2002[192]).
The average CAR is 4.41% for all acquisitions in the total sample, acquirers announcement returns are equal to 6.60% for non VC backed targets which is signicantly higher than the mean CAR of 4.21% for the matched VC backed acquisitions.
The dierence in mean CAR for acquisitions announcements of VC backed and non
VC backed targets is statistically signicant at 5%. This result is dierent from
Masulis and Nahata (2011)[187] ndings, where the authors nd that VC backing
lead to higher acquirer announcement return.
The deal payment structure has dierent impacts on acquirer announcement
return depending on the private rm status. For non VC backed rms, the mean
acquirer CAR for deals involving stock is more important (6.48 %) than the mean
acquirer CAR for acquisition without stock in payment (4.83 %). However, the
dierence in acquirer reaction to non VC backed rms acquisition method of payment
is not statistically signicant.
For VC backed targets, the mean CAR for acquisition nanced with stock is
lower (2.54 %) than the mean CAR for acquisition without stock in the payment
(4.27%). The impact of method of payment on acquirer of VC backed targets is also
not statistically signicant. Overall the acquirer reaction is equal to 4.67% if the
acquisition involves stock in payment and equal to 4.50% if the acquisition payment
is without stock. Therefore, the dierence in methods of payment doesn't have
a signicant impact in the total sample. However, distinguishing the acquisition
announcement return for stock nanced oers by VC backed status presents more
signicant results. The comparison of acquirer CARs for acquisitions with stock
nancing shows that the mean abnormal return for acquirer of VC backed targets is
signicantly lower than the mean abnormal return for the matched non VC backed
acquisitions. The dierence in acquirer reaction is signicant at 5% level. This result
suggests that acquirers' investors consider that the acquisition of VC backed target
paid for with stock as less good news than the acquisition of non VC backed paid
with stock.
Panel B shows that industrial relatedness between the acquirer and the target has
a positive eect on acquirer abnormal return, where the acquirer of private rm earns
more when the target is in the same industry. Furthermore the results document that
the industrial relatedness has dierent impacts on acquirer announcement return
depending on the target status. Industrial related deals have a more positive impact
on acquirers stock return when the target is not VC backed, with acquirer CAR
equal to 8.50% if the target is in the same 3 digit SIC codes as the acquirer, and
equal to 3.38% if the target is in a dierent sector than the acquirer. For VC backed
rms, to be in the same industry as the acquirer or not doesn't have a signicant
impact on acquirer stock return.
Panel C of the table shows that the geographical diversication has a negative
impact on acquirer return, especially when the target is VC backed. The mean CAR
in the total sample is equal to 5.09% if the target and the acquirer are located in the
same countries, and equal to 3.34% if the target and the acquirer are located in different countries. The geographical diversication has a more statistically important
impact for the VC backed sub-sample. Where the acquirer investor earns 0.62% if
the target and the acquirer are located in dierent countries and earns 4.12% if the
deal is local. Furthermore, the acquirers shareholders consider cross border deals
that are VC backed to be signicantly less positive than cross border deals that are
not VC backed.
The table 4.9 shows the mean CAR for acquirers of VC backed and non VC
backed rms across years. The results show that the mean CAR of the total sample
in most years is positive and statistically dierent from zero. Furthermore, when
distinguishing between VC and non VC backed rms, the acquirer mean CAR of
non VC backed target is larger than the mean CAR of VC backed target, except in
3 isolated years (1998, 1999, and 2001) where the mean CAR of acquisition of non
VC backed rms is smaller than the mean CARs of comparable VC backed rms,
however the dierence during these years is not statistically signicant. Accordingly
in the multivariate analysis I use a year xed eect to control for year variation
impacts.
To determine whether the acquirer reaction to deal announcement is aected by
its the home country, I calculate the mean CAR by acquirer' country, and separate
the acquirer abnormal return by the status of the private target rm. Table 4.10
reports the mean acquirer CAR by country, the standard t-test for dierence in
Table 4.8 Acquirer Cumulative Abnormal Returns for Acquisition of Private Target:
VC backing, Method of payment, Industrial Diversication,and Cross border deals
Cumulative abnormal returns (CARs) for acquirer stocks are calculated over the 5 trading days
(-2, 2) around the acquisition announcement (day 0). Abnormal returns are estimated using a
market return model. All acquirers are publicly traded rms. Acquisitions must have a relative
deal size (deal size divided by acquirers' market value of equity 1 month prior to the acquisition
announcement) of at least 10%. The VC and non VC samples are matched on the basis of hightechnology industry indicator, method-of payment indicator, deal size, and relative deal size. The
table presents acquirer CARs for the full sample,and for the sub-samples of VC and non VC bakced
taregt, distinguishing the CARs results by the deals method of payment, the deals industrial
relatedness and the deal geographical diversication. The table also report a standard t-test for a
dierence in means and Wilcoxon test for a dierence in medians that compare VC-backed and nonVC-backed targets. ***, **, and * denote signicance at the 1%, 5%, and 10% levels, respectively.
Full Sample VC-Backed Non VC-Backed T-Test
Acquirers' CAR Acquirers' CAR Equality
(1)
(2)
(1)-(2)
Full sample
4.61%***
(6.08)
6.00%***
(4.48)
1.80**
Panel A: Acquirer CARs distinguished by
Deal Method of payment
Stock deals
4.67%***
(4.57)
Cash deals
4.50%***
(4.08)
T-Test Stock vs Cash Deals
-0.09
3.22%***
(4.14)
2.54%**
(2.54)
4.27%***
(3.47)
1.12
6.48%***
(3.88)
4.83%**
(2.24)
-0.55
1.84**
Panel B: Acquirer CARs distinguished by Industrial diversication
Related deals
5.42%***
(4.66)
Unrelated deals
3.48%***
(3.88)
T-Test Related vs Unrelated Deals
1.24*
3.01%***
(3.09)
3.63%***
(2.86)
-0.39
8.50%***
(3.50)
3.38%***
(2.73)
1.94**
2.33**
Panel C: Acquirer CARs distinguished by geographical diversication
Local deals
5.09%***
(5.50)
Cross-border deals
3.34%***
(2.63)
T-Test local vs cross-border Deals
1.00
4.12%***
(4.35)
0.62%
(0.21)
2.06**
6.11%***
(3.35)
5.73%***
(3.18)
0.12
1.02
0.29
-0.15
2.29**
Table 4.9 Acquirer Cumulative Abnormal Returns for Acquisition of Private Targets:
Announcement Year and VC backing
Cumulative abnormal returns (CARs) for acquirer stocks are calculated over the 5 trading days
(-2, 2) around the acquisition announcement (day 0). Abnormal returns are estimated using a
market return model. Acquisitions must have a relative deal size (deal size divided by acquirer'
market value of equity 1 month prior to the acquisition announcement) of at least 10%. The two
VC and non VC sample are matched on the basis of high-technology industry indicator,
method-of payment indicator, deal size, and relative deal size. The table presents acquirer' CARs
for the full sample, Medians and Wilcoxon test statistics for a signicant dierence are shown in
parentheses. ***, **, and * denote signicance at the 1%, 5%, and 10% levels,respectively.
Full Sample VC- Backed Non VC-Backed T-Test
target
target
Equality
(1)
(2)
(1)-(2)
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Total Sample
7.07%*
(1.18)
3.05%*
(1.68)
1.73%
(1.20)
6.67%*
(1.70)
4.99%*
(1.12)
3.59%*
(1.09)
4.42%**
(1.4)
2.17%*
(1.03)
2.71%*
(1.25)
5.88%**
(2.04)
4.70%***
(2.72)
2.86%*
(1.57)
4.99%**
(2.36)
13.39%**
(2.04)
2.57%*
(0.94)
0.53%
(0.13)
4.61
1.37%
(1.00)
4.28%*
(1.60)
1.78%
(0.71)
2.75%*
(0.94)
7.83%**
(1.34)
-0.42%
(-0.44)
3.67%**
(0.94)
-0.08%
(-0.29)
2.13%*
(0.94)
2.63%*
(1.12)
4.60%*
(1.68)
2.02%*
(1.46)
4.02%**
(2.02)
9.26%*
(1.78)
3.34%*
(1.09)
4.05%*
(1.06)
3.22
8.02%**
(1.15)
2.59%*
(1.26)
1.64%
(0.94)
8.63%**
(1.37)
4.17%
(0.50)
7.62%*
(1.34)
6.65%**
(1.34)
6.23%**
(2.02)
3.03%*
(0.88)
10.23%**
(1.57)
4.81%**
(2.19)
4.56%*
(0.44)
7.42%**
(1.34)
17.52%*
(0.94)
0.51%
(1.00)
4.74%**
(1.34)
6.00
-0.47
-0.03
0.87
** -0.36
1.82*
0.33
1.77**
0.21
1.34*
0.06
0.72
0.82
0.63
1.71
1.8**
means and the Wilcoxon z-statistic to test the signicance of the acquirer reaction.
the results show that the overall acquirer reaction in the sample is positive in most
countries expect in Denmark and Finland. It is worth noting that the sample' deals
in Denmark and Finland contain only VC backed targets, and that acquirers mean
CARs to VC backed targets in these countries are negative but not signicantly different from zero. Furthermore, Except in Germany, the result shows that mean CAR
of VC backed target is higher than the mean CAR of non VC backed ones, however
the dierence between the two sub-samples mean CARs is not statistically significant. Accordingly, and given these inter-country results' variation, I use country
xed eects in the multivariate analysis.
These results provide rst evidences about acquirers' reaction to private rms'
acquisitions and about VC investors' role in trade sale exits. In the next section,
I will investigate if these eects persist after controlling for deals and acquirers
characteristics.
Table 4.10 Acquirer Cumulative Abnormal Returns for acquisition of private targets:
Acquires and Target countries and VC backing
Cumulative abnormal returns (CARs) for acquirer stocks are calculated over the 5 trading days
(-2, 2) around the acquisition announcement (day 0). Abnormal returns are estimated using a
market return model. Acquisitions must have a relative deal size (deal size divided by acquirer's
market value of equity 1 month prior to the acquisition announcement) of at least 10%. The
table presents acquirers' CARs for the full sample, Medians and Wilcoxon test statistics for a
signicant dierence are shown in parentheses. ***, **, and * denote signicance at the 1%, 5%,
and 10% levels,respectively.
Full Sample VC Backed Non VC-Backed T-Test
Target
Target
Equality
(1)
(2)
(1)-(2)
Belgium
4.41%*
(1.82)
Switzerland
10.17%*
(1.00)
Germany
8.42%**
(1.78)
Denmark
-2.05%
(-1.00)
Spain
12.78%*
(1.00)
Finland
-5.62%
(-1.34)
France
6.46%*
(1.48)
United Kingdom 4.70%***
(4.66)
Ireland
5.86%*
(0.73)
Italy
2.63%*
(1.82)
Netherlands
6.81%*
(1.82)
Norway
0.28%
(0.25)
Portugal
4.33%
(1.00)
Sweden
5.68%*
(1.77)
4.21%
(1.34)
12.04%*
(1.60)
12.78%*
(1.00)
-5.62%
(-1.34)
-0.64%
(0.00)
4.17%***
(3.83)
-0.98%
(0.00)
1.21%
(1.09)
7.78%
(1.34)
0.19%
(0.13)
4.33%
(1.00)
0.08%
(0.33)
4.60%*
(1.34)
10.17%*
(1.00)
4.81%*
(0.53)
-2.05%
(-1.00)
10.01%*
(1.78)
5.29%***
(2.81)
12.71%*
(1.06)
4.06%*
(1.46)
5.83%*
(1.34)
0.37%
(0.13)
8.95%*
(1.88)
0.09
(0.90)
1.39*
0.51
1.44*
1.10
0.03
1.56*
4.4.2 Regression Results
In the multivariate analysis I model the acquirer CAR as a function of the deal and
acquirer' characteristics. Deal characteristics used as controls are those nd in the
literature to impact acquirer announcement return: a dummy for VC backed targets,
the log of acquirer size, relative deal size, a dummy for deals involving stocks in
payment, a dummy for within industry acquisition based on the 3-digit SIC codes, a
dummy for high-technology-intensive targets, a dummy for cross border acquisitions,
the market-to-book ratio in the target rm' industry in the year of the acquisition
announcement, and volatility of the acquirer' excess stock returns (measured from
270 to 6 trading days prior to the acquisition announcement).
Table 4.11 presents regression estimates of acquirer announcement CARs for the
combined sample of VC-backed and the matched non-VC-backed targets. Consistent
with the earlier univariate analysis, acquisitions of VC-backed rms are signicantly
less protable for acquirer shareholders than acquisitions of non- VC-backed rms;
the regression models show that acquirer CAR decline signicantly by more than 3%
for VC backed target acquisition. This suggests that VC investors are considered
to be able to negotiate better terms for their backed rms and, hence obtain higher
price for the targets, inducing a less positive acquirer' CAR.
To investigate more this result, from model 3 I include a control variable for the
number of IPOs in the acquirer' public market in the month before the acquisition
announcement. The idea behind including the number of IPOs is that one may
expect that VC investors will enjoy more negotiation power if they consider that
the public market conditions allow achieving alternative successful exits. The results show that an active IPO market impact negatively the acquirer announcement
return. Suggesting that VC investors obtain better price for their backed rms in
sale exit, especially during positive public market conditions. Therefore supporting
the idea that VC investors certify the quality of their portfolio rm in M&A exit by
reducing the information asymmetry faced by the acquirer who would accept to pay
higher purchase price and thus earns lower returns.
Turning to the control variables, consistent with the ndings in Moeller et al.
(2004)[80], the coecient on acquirer size is negative and statistically signicant,
the results show that bigger acquirers loss around 2% in their CARs, the eect is
not driven by relative transaction size as I control for how large the acquisition
is relative to the acquirer market value. Consistent with Fuller et al (2002)[192]
and Masulis (2011)[187], the coecient estimate on relative deal size is positive and
statistically signicant, documenting the positive impact of the economic signicance
of the acquisition on the acquirer share value.
The coecient on target industry market-to-book is also signicantly positive,
where a higher market-to-book ratio (MB) in the target industry induces an increase
in acquirer stock market return by 1.8%. Consistent with Masulis et al (2011)[187]
that nd a greater stock price reaction for bidders acquiring high growth rms.
In model 5, I include an interaction term between VC dummy and target industry
market to book. The results show a signicant negative eect of the interaction term.
In other words, an increase in VC backed target' MB industry will have a smaller
increase in acquirer CAR than it would be the acquirer CAR increase for a non VC
backed target' industry growth. The coecient on VC dummy variable in model 5, is
positive but not statistically signicant, this however may indicate that VC backed
targets belonging to sector with null growth opportunities will impact negatively
acquirer negotiation power and therefore allow acquirer to have a better acquisition
term.
I control also for the idiosyncratic volatility of acquiring rms, which has been
found to partially account for dierences in acquirer announcement returns. The
coecient on acquirer stock return volatility is positive and statistically signicant,
consistent with the ndings in Moeller et al (2004)[80] and Masulis et al (2011)[187].
The other control variables, namely indicators for inter-industry acquisition, cross
border deal, stock in deal payment and shareholders protection, are statistically
insignicant.
In the model 6, I include a control variable for the dierence between the acquirer
and the target countries shareholders level of protection. Shareholders protection
score is issued from the La Porta et al. (1998)[92]. The coecient on the variable
dierence between acquirer and target level of shareholders protection is positive
but not statistically signicant.
In summary, I document that acquisition of VC-backed target lead to signicantly
lower acquirer announcement return than acquisitions of non-VC-backed target. Our
results conrm the literature ndings about the negative impact of the acquirer size,
the positive impact of the relative deal size, the target' MB industry, and acquirer'
stock return volatility.
The result regarding our main research question about the VC role in acquisition returns, documents a negative impact of the VC backing status on acquirers'
abnormal returns, suggests that VC investors negotiate better trade sale price for
their portfolio rms.
Table 4.11 The Impact of Acquisition of Private Firms on Acquirers' CAR
The table reports OLS estimates. The dependent variable is the CAR return for the acquirer that
is calculated over the 5 trading days (-2, 2) around the acquisition announcement (day 0).
Abnormal returns are estimated using a market return model. The sample represents matched
pairs of privately held acquisitions, half of which are VC-backed and the other half are
non-VC-backed. The VC-backed target variable indicates when a target has VC backing.
Acquirer size is the equity market value measured 1 month prior to the acquisition
announcement, and relative deal size is the deal size divided by acquirer size. Stock acquisition is
an indicator for the use of common stock in acquisition payment. The high-technology target
denote targets in high-technology industries. Inter-industry deal is an indicator variable denoting
whether the target and acquirer rms belong to the same industry, based on their 3-digit SIC
codes. An acquisition is "cross-border" if the acquirer rm and the target are located in dierent
country.Number of IPOs is the total number of new issuance in the public market in the month
before the acquisition announcement month. Acquirer stock return volatility is Acquirer stock
return volatility denotes the standard deviation of the acquirer's excess stock returns measured
from trading days -6 to -270 prior to the announcement date (day 0). Target industry
market-to-book denotes the median value of the market-to-book ratio in the target rms industry
in the year of the takeover announcement. VC the interaction term: dummy*Target industry
market to book is the interaction between the VC dummy and the target median industry market
to book ratio. Dierence in Shareholder Protection is La Porta et al. (1998) shareholders
protection score. P-values based on heteroskedastic-consistent robust standard errors adjusted for
industry clustering are reported in brackets below the
parameter estimates. ***, **, and * denote signicance at the 1%, 5%, and 10% levels, respectively.
CARmodel1model2 model3 model4 model5 model6
VC backed target
-0.038* -0.031** -0.009
0.030 -0.032*
(0.020) (0.014) (0.019) (0.022) (0.017)
Acquirer size -0.008 -0.008 -0.002 -0.023** -0.019** -0.016*
(0.010) (0.010) (0.007) (0.009) (0.008) (0.010)
Relative deal size 0.037 0.036 0.035 0.022*** 0.023*** 0.018*
(0.026) (0.025) (0.021) (0.008) (0.008) (0.011)
Acquisition involving stock 0.007 -0.007 -0.016 -0.019 -0.014 -0.016
(0.025) (0.025) (0.013) (0.021) (0.020) (0.017)
High tech target 0.034 0.033 0.034* 0.057** 0.050** 0.012
(0.036) (0.036) (0.019) (0.022) (0.022) (0.030)
Inter-industry deal -0.004 -0.006 -0.005 -0.002 -0.010 -0.003
(0.017) (0.017) (0.014) (0.022) (0.022) (0.015)
Cross border deal -0.008 -0.011 -0.020 -0.051 -0.042 -0.032
(0.027) (0.026) (0.016) (0.032) (0.034) (0.021)
Number of IPOs
-0.001* -0.004** -0.005*** -0.001
(0.001) (0.002) (0.002) (0.001)
Acquirer stock return volatility
1.636*** 1.586*** 1.539***
(0.326) (0.344) (0.448)
Target industry market to book
0.002* 0.018***
(0.001) (0.005)
VC dummy*Target industry market to book
-0.017***
(0.005)
Dierence in Shareholder Protection
0.003
(0.006)
Intercept -0.053 -0.072 0.073* -0.542***-0.243*** -0.017
(0.099) (0.091) (0.040) (0.087) (0.084) (0.081)
Country xed eects Yes Yes
No
Yes
Yes
No
Year xed eects Yes Yes
No
Yes
Yes
Yes
Industry xed eects Yes Yes
No
No
No
Yes
R-squared 0.441 0.463 0.209 0.599 0.639 0.525
Observations 172 172 172
103
103
172
4.5
Conclusion
This paper examines the eect of acquirer and deal characteristics in acquisition
returns; the objective is to study the role of venture capital investors in private
rms' acquisitions. Several papers investigate the role played by VC investors in
IPOs, but analyses of VC role in trade sales are rare, although trade sales of VC
portfolio rms are by far the largest exit routes in Europe.
The results show that acquirers react positively to private rms' acquisitions, and
that the acquirer reaction to VC backed rm' deal announcement is less positive than
the acquirer reaction for a similar non VC backed acquisition announcement. The
results suggest that the market believes that VC investors obtain higher prices for
their portfolio rms. This nding indicate a VC certication role in trade sale exit.
In fact, certication role consists in reducing the information asymmetric between
the acquirer and the VC backed target and accordingly promote the acquirer to pay
higher price for VC backed targets.
One may believe that VC investors don't have a long term interest in selling
overvalued rms, because they are repeated players in the market, and selling overvalued private rms to public acquirers would impact negatively their reputation
and impede their ability to sell rms to other public acquirers in the future. Accordingly, acquirers bidding for VC backed rm will believe that the target is not
overvalued and will be more willing to pay higher price for it.
Another explanation for acquirer less positive reaction to VC backed rm acquisition could be the VC investors' extensive contacts with potential buyers that may
result in more competitive bidding and hence lower acquirer announcement eects.
One way to evaluate the VC investor' ability to attract more bidders for their portfolio rms acquisitions is to control for the acquisition negotiation term, which our
database don't allow to do.
A further part of this research will be the analysis of VC investors' characteristics eect in acquirer announcement return. One may expect that experienced
or industrial specialized VC investors to be good negotiators, and therefore impact
negatively acquirers return on VC backed acquisitions.
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Chapter 5
Duration Analysis Of VC Staging In
Cross Border Investment
Résumé1
Ce chapitre s'intéresse aux décisions de renancement d'entreprises soutenues par
capital risque dans un contexte d'investissement à l'international. En eet les investissements en capital risque se déroulent sur plusieurs tours de nancement. Ici
nous nous intéressons aux facteurs qui déterminent la durée entre deux tours successives, notamment la distance géographique entre l'investisseur en capital risque
et l'entreprise nancée. Nous explorons aussi comment le niveau de conance entre
pays peut inuencer ces décisions de renancements. Nous exploitons une base de
sur données européennes, l'échantillon porte sur 317 entreprises dont les tours de
nancement s'échelonnent entre 1997 et 2008. En eet la distance géographique
et le niveau de conance entre pays reètent le niveau d'asymétrie d'information
et de coûts d'agence que peut engendrer les investissements distants. Les résultats montrent que les investissements lointains sont associés à des courts tours de
nancement, et que un niveau de conance élevé entre l'investisseur en capital et
l'entrepreneur se traduit par des tous de nancement plus long.
1 Ce
Chapitre est un extrait d'un article co-écris avec Massimo Colombo de l'école Politecnique
de Milan et, et avec Massimiliano Guerini de l'université de Pisa.
131
abstract2
This study examines venture capital staging decisions in cross-border investment
context. Considering that information asymmetry and behavioral uncertainty increase with geographical, cultural and institutional distances, and that trust level
diers among nations. We use data from a unique European sample of 310 venture
capital backed rms to support our predictions. We nd that distant investments are
associated with shorter nancing rounds, and that high trust investments encourage
venture capital investors to increase rounds duration.
2 This Chapter is an extract of an article co-written with Massimo Colombo of the school Politecnico di Milano and, and with Massimiliano Guerini of the university of Pisa.
5.1
Introduction
One new interesting aspect of VC rms' strategies is to invest across national border.
Aizenman and Kendall (2008)[159] use a large rm-level data covering three decades
and about 100 countries to document that prior to the 1990s VC was a US-only
phenomenon, that the globalizations of IT activities change the situation and the
cross-border deals became more common.
In fact, investing abroad allows VC investors to explore growth opportunities
in foreign markets and to diversify the risk of their portfolio (Dai et al., 2012[160];
Guler and Guillen, 2010[161]). However, cross border investment came also with
the cost of managing distance and dealing with foreign institutional and new business environment. Dai et al (2012)[160], for instance argue that among the various
risks and challenges in VC cross-border investments are the information asymmetry
and agency cost that arise from geographical, cultural, and institutional distances
between VCs and the entrepreneurial rm countries.
Information asymmetry between VC investors and the entrepreneur persist even
after investment decisions are made. In fact, it remains challenging to monitor the
rm progress because VCs investors and the entrepreneur are likely to have dierent
level of information about the rm' projects evolution, as generally, the VC investor
is less informed about the portfolio rm daily operations and entrepreneur eorts.
The literature considers VC staging as a way to mitigate such agency problem
by giving to VCs investors the option to abandon the project if it doesn't meet the
expected results (Gompers and Lerner, 1999[163]; and Neher, 1999[162]). Indeed,
staging VC investment consists on providing the necessary additional nancing in
structured successive rounds (Sahlman, 1990[155]; Gompers and Lerner, 1999[163]),
thus by staging the investment, VC investor threats to abandon the rm by denying
capital, such decision is very costly, specially for young entrepreneurial rms with
few tangible asset and little chance to get alternative source of funding.
Several theoretical papers consider the role of staging. Neher (1999)[162] for
example argues that staging helps VC investors to build collateral that limits the
entrepreneur's hold-up power, particularly at the beginning of the project when the
entrepreneur is the only one able to implement it. Bergemann and Hege (2009)[164]
model the optimal staging decisions, they nd that staging is used as a screening
device specically when the risk of failure is high, and when VCs need to adjusts its
investment according to interim information. Gompers (1995)[163] studies empirically the staging of VC investments, he shows that staging is related to expected
agency costs and that industry-level of information asymmetry aects staging patterns.
In this paper we aim to analyze how VCs investors deal with information asymmetry and agency cost related to distant investments. For instance, how frictions
related to geographic distance between the entrepreneur and the VC investors impact VCs' staging decisions. Furthermore, considering the cultural and institutional
dierences between VCs and rm countries we investigate how the level of trust
between them impact staging decisions.
Distance is likely to intensify disadvantages related to the lack of local knowledge
and makes monitoring more dicult. Infusing capital over several rounds instead
of providing all the necessary nancing upfront, give to the venture capitalist the
opportunity to learn progressively about the entrepreneur rm, therefore to escape
the information asymmetry risk associated with distant investment.
Studies show that countries with higher cultural distance display higher mutual
distrust (Chakrabarti et al., 2008[171]; Guiso et al., 2008[172]). Given that trust
is the foundation for a healthy climate of information exchange and that the luck
of trust may be very costly specically in nancial relationship characterized by a
high level of asymmetry information and agency cost. We suggest that lower level
of trust between VCs and entrepreneur citizens is associated with higher level of
information asymmetry, and expect that the level of investment trust will impact
positively the nancing rounds duration.
Overall, VCs assume an active role advising and monitoring the entrepreneurs
to add value to the portfolio rms. Therefore, the information asymmetry and
moral hazard associated with distance and cultural disparity is particularly acute.
Our study provides distinct empirical evidence to this literature by investigating
VCs' staging decisions when investing across national borders. The results from a
sample of European venture capital-backed rms show that the nancing rounds
duration are shorter for distant investment, conrming the literature ndings that
distant investment is associated with higher level of information asymmetry and
therefore staging may be used as substitute to direct monitoring. Second in this
work we explore a central issue during all the VC nancing process that is trust. We
document a positive relationship between the trust level and the staging decisions.
The results suggest that the potential of asymmetric information and agency cost
increase with the level of distrust, and accordingly drive VCs investors to shorten
the duration of the nancing rounds.
The structure of the paper is as follows: Section 2 reviews the relevant literature
and formulate hypothesis. Section 3 introduces the data sources, variables and the
methodology. Section 4 shows and discuss the results. Section 5 concludes.
5.2
Literature Review and Hypotheses development
5.2.1 Geographical Distance and Staging
The literature considers many aspects of the international VC market. Several
papers look at the determinants of VC cross border funding inow and investment
decisions (Schertler and Tykvová, 2012[166]; Jeng and Wells, 2000[167]; Bottazzi et
al., 2011[168]). Other studies compare the quality of legal enforcement and its impact
on VC contracting (Balcarcel et al., 2010[169]; Bottazzi et al., 2008[?]; Kaplan
et al., 2007[173]; Lerner and Schoar, 2005[174]). Finally, recent papers study the
syndication composition and staging in cross border deals and its impact on rm
performance (Chemmanur et al. 2010[175]; Dai et al.2012[160])
Cumming and Dai (2010)[115], for instance, study the local bias in VC investments by investigating the rationales for VC investors' preference for geographic
proximity. Their ndings suggest that geographical distance is an important factor
that impacts VCs' investment decision. The authors argue that VCs prefer to invest
in geographically proximate rm to avoid information asymmetry and the higher
cost of monitoring associated with distance. Chemmanur et al. (2010)[175] study
foreign VCs investors distance from the entrepreneurial rm and its impact on performance; the authors nd that the distance of the international VCs is negatively
correlated with the probability of successful exits, and that syndicating with local
VCs enhances the performance, the authors argue that local VCs have the advantage to access easily to local information, networks, and resources and thus they are
more able to mitigate the negative eect of distance, the authors conclude that syndicating with local VCs and infusing capital over more rounds are used to overcame
asymmetric information associated with international investment.
In his seminal work, Gompers (1995)[195] uses the agency theory to shed light on
factors aecting staging decisions. The author argues that expected agency cost is
positively related to the industry ratio of intangible asset, market-to-book and R&D
intensity. The results in a US sample of 794 VC backed rms show that staging
is positively related to agency cost. For instance, the frequency of monitoring is
positively related the ratio of intangible asset, market to book ratio, R&D to sale
ratio and R&D to total asset. Overall, the ndings document that rms subject to
greater agency costs should be monitored more often.
Tian (2011)[196] examines how staging pattern depends on the geographical distance between VCs investors and the entrepreneurial rm in US; the author control
for the endogeneity problem where the entrepreneur may endogenously chooses the
optimal distance from the VC investor. In fact it is common in US that VC investors
refuse funding to start up if they are not within a 20-minute drive of the VC rm's
oces, which may forces the entrepreneur seeking VC funding to move closer to a
VC investor. The author control for this endogeneity in distance by constructing an
instrumental variable for distance and by conducting a 2SLS regression. The results
show that staging is more likely when there is greater geographic distance between
the entrepreneurial rm and the VC investor. The author concludes that staging
and monitoring are substitutes, and that VCs investors will use more staging when
it's less costly than direct monitoring.
In this paper we aim to analyze the geographical distance impact in European
VCs cross-border investment staging pattern. Compared to national VC investment,
cross-border investment suers less from endogenous co-location by entrepreneur
near to VCs investor. In fact, it is not obvious for an entrepreneur to move from a
country to another just in order to be closer to a specic VC investor. Chemmanur
et al (2010)[175] study is similar to our work; however chemmanur et al (2010)[175]
paper focuses only on the physical distance and measure staging by the number of
nancing rounds, whereas we are interested in the inter-round duration aspect of
staging, more specically how distances between VC investors and the rm impact
the inter-round duration. As the literature ndings suggest that monitoring cost
is positively related to the distance between the entrepreneurial rm and the VCs
investors (Cumming and dai 2010[115], Chemmanur et al 2010[175]), we expect that
VCs will use staging as a control device for distant portfolio rms, and that VCs will
stage the investment on distant entrepreneurial rm over shorter round; accordingly,
the inter-round duration is expected to be negatively related to the distance between
the VC and the entrepreneurial rm.
5.2.2 Trust and Staging
Recent studies investigate the issue of cultural and institutional distance and its
impact on VC cross-border investment. Yong, Ilan and Jing Li (2014)[177] examine how institutional and cultural distances between the environments of VCs and
entrepreneurial rm aect the performance of cross-border VC investments. The
results show that the likelihood of a successful exit is negatively related to the institutional and cultural distance. Dai et al. (2012) [160]study the same research
question in the Asian market, they nd that when investing alone, foreign VCs are
more likely to invest in informationally transparent rms and that foreign VCs develop partnerships with local VCs to overcome the frictions associated with distance,
which has a positive exit performance implication.
In this work we aim to explore additional impacts of cultural dierences; we
do this using the trust measure between VC investor and the entrepreneur. Guiso,
Sapienza and Zingales (2006) [160] argue that trust is originated at least in part
from the cultural dierences between countries, and that trust aects economic outcomes. In their empirical study, Guiso, Sapienza and Zingales (2009)[179] establish
at the level of country pairs, the importance of trust for aggregate trade and foreign
direct investment ows, they document that trade portfolio investment, and direct
investment are positively correlated to the level of trust between two countries. In
an another study, Guiso, Sapienza and Zingales (2008)[172] nd that trust aects
the willingness to invest money in shares, and thus contribute to explaining limited
participation in the stock market.
Guiso et al (2009)[179] conclude that "trust is particularly relevant when transactions involve some unknown counterpart, when the transaction takes place over a
period of time , and when the legal protection is imperfect". It's well known that the
role of VC investors is to nance for many years young ventures characterized by uncertain outcome and information asymmetry, and hence limited hard information;
VC investors may therefore be more prone to rely on soft information, including
social beliefs such as trust.
Bottazzi, Da Rin, and Hellmann (2011)[168] is, to our knowledge, the unique
work in VC literature that study the trust' role, the authors investigate how the level
of trust between VCs investors and entrepreneur impact VC cross-border investment
decision and performance outcomes. Indeed, the results show that IPO and M&A
exits are more likely for deal associated with lower level of trust, and that inversely
failure are more likely for deals associated with a higher level of trust. The authors
conclude that low trust investments are more likely in more attractive markets, and
that performance is negatively related to the level of trust.
In this work we highlight the eect of trust on VC nancing staging. The objective is to explore how to the level of trust among VCs investors and the entrepreneur
nations inuence the investment staging decisions. Given that trust is the foundation
for a healthy climate of information exchange, and that less the entrepreneur' citizen
country trust the VC investor citizen country, less disposed will be the entrepreneur
to share condential information with the VC investor, and therefore higher will be
the information asymmetry and the agency cost between the entrepreneur and the
VC investor. Accordingly we expect that the level trust will be positively related to
staging decisions, and that more trustful is the entrepreneur citizen country by the
VC investor citizen, longer will be the inter-round duration.
As in Guiso et al (2008[172], 2009[179]), we use a commonly accepted denition of
trust, as "the subjective probability with which an agent assesses that another agent
or group of agents will perform a particular action". Our trust variable measures
how people from VCs investors countries trust people from rm country. We take the
investors' perspective measure because the staging decision is on investor's hands.
5.3
Data and variables measures
Our sample includes 632 VC investments rounds for 317 European VC-backed rms.
All VC-backed rms received their rst round of venture capital nancing between
1994 and 2004 and were less than 10 years old at that time. The sample is extracted
from the VICO database, a large-scale dataset on European young high-tech and
independent entrepreneurial rms. The database was created with the support of
the Seventh European Framework Program. It includes rms operating in seven European countries (Belgium, Finland, France, Germany, Italy, Spain, and the United
Kingdom). First the data were collected by local teams from each country, using
several databases (VentureXpert, Zephyr, Amadeus and its local equivalent, investor
annual reports and websites, press releases, local Venture Capital Association yearbooks, etc.), and then the data was double checked by a centralized data collection
unit. The dataset consists of detailed information for each rm (rm address, status
and several accounting variables from income statement and balance sheet, etc.), furthermore, the data contains round-by-round information about the investors (VCs
address, identity and type, VCs company size and experience), deal characteristics
(date, stage and amount of investment, syndication composition and size), and exit
details (date and the eventual exit route, etc.). The description of sampling process
and the overall structure of the VICO database are detailed by Bertoni and Martí
(2011)[180].
We use Hofstede (2001)[181] culture index to control for the cultural distance. We
generated the Kogut and Singh (1988)[182] distance calculation to compute distance
between VCs investors and the entrepreneur countries in the space of ve Hofstede
(2001)[181] cultural dimensions.
In an original work Hofstede (2001)[181] divides culture into ve dimensions:
power distance, individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance and long vs. short-term Orientation. Power distance express how
power may be unequally distributed in the society. Individualism vs. Collectivism,
show how the citizen are integrated into groups. Masculinity vs femininity considers
how society denes social roles for men and women. Uncertainty avoidance indicates
how the members of a culture feel threatened by uncertain or unknown situations.
The long vs. short term orientation describes term orientation characterizing and
ordering relationships.
We also control for the institutional quality dierences between countries using
the World Governance Index (WGI) constructed by Kaufmann et al. (2007)[183].
Kaufmann et al. (2007)[183] identied six dimensions of governance infrastructure
quality along which countries dier: Voice and Accountability, Political stability,
Government eectiveness, Regulatory quality, Rule of law, and Control of corruption. We start by computing a composite index by averaging the six dimensions,
and then we calculate the Manhattan distance between VCs' and the entrepreneur
countries.
Finally, to measure trust we adopt the approach of Guiso, Sapienza and Zingales (2009)[179] of using the Eurobarometer survey data of bilateral trust among
nations. The surveys are conducted by Eurobarometer and sponsored by the European Commission; they were designed to monitor attitudes on European integration,
life satisfaction, and social goals. The surveys were conducted on a representative
sample of about 1,000 individuals per country. In a sub sample of these surveys,
respondents were asked to report how much they trust their fellow citizens and how
much they trust the citizens of each of the countries in the European Union. More
specically, they were asked the following: "I would like to ask you a question about
how much trust you have in people from various countries. For each, please tell me
whether you have a lot of trust, some trust, not very much trust or no trust at all."
Bottazi et al (2011)[168] notice that trust among nations is remarkably persistent
over time and that the correlation coecients across Eurobarometer waves is often
over 90% and always above 84%.
We adopt Guiso et al (2009)[179] method, where they re-coded the answers to the
trust question setting them to 1 (no trust at all), 2 (not very much trust), 3 (some
trust), and 4 (a lot of trust). Then the responses are aggregated by country and year
computing the mean value of the responses to each survey. The measure reects the
average level of trust that citizens from each country have toward citizens of other
countries. In another words, it informs about the average subjective probability
with which an agent assesses that another agent or group of agents will perform a
particular action. (Guiso, Sapienza and Zingales (2006)[178]).
The Three distance proxies are measured at the round level. For syndicated
deals, the geographical, cultural and institutional distances are the average distance
between the nanced rm and VC investors participating in the round. The trust
measure is based on how much the lead VC investor in the round trust the target rm.
The VICO database indicates the identity of lead investor. When this information
is not available, we assumed that among the investors involved in a particular round
of nancing, the lead investor is the one investing the highest amount. When the
invested amount is not available we use equity interest. When neither amount nor
equity interest are available we assume the lead investor to be the one located at
the minimum distance to the company.
5.4
Empirical Results
5.4.1 Univariate Analysis
Table 5.1 reports the number of VC investments and the number of VC backed rms
per country. The sample is dominated by Belgian, Spanish and UK investors and
VC backed rms, with more than 74% of the VC investors and of VC backed rms
located in Belgium, Spain and UK. This is mainly due to availability of information
in these countries during the data collection process; the data collection activity for
instance was more restricted in France and Germany, despite the relative importance
of the VC market in these countries.
Table 5.2 reports the summary statistics for distances measures, trust measure,
VC staging characteristics, as well as VC investment and entrepreneurial rm char-
Table 5.1 Distribution of the Number of VC Investments, by VC Investors and Backed
rms Countries
The sample consists of 317 venture capital backed rms that receive nancing from a total number
of 1084 venture capitalists from 14 countries.
Country
Investor Backed Firms
N Percent N Percent
Australia 2
Belgium 315
Finland 69
France 95
Germany 6
Ireland 2
Israel 3
Italy 38
Japan 2
Luxembourg 4
Netherlanfs 20
Spain 235
UK 255
USA 38
Total 1084
0.18 0
29.06 69
6.37 26
8.76 23
0.55 0
0.18 0
0.28 0
3.51 32
0.18 0
0.37 0
1.85 0
21.68 78
23.52 89
3.51 0
100 317
0
21.77
8.2
7.26
0
0
0
10.09
0
0
0
24.61
28.08
0
100
acteristics. The average distance between VC investors in each rond of nancing
and the company is 282 km, ranging between a minimum approximating 0 km and a
maximum equal to 6079 km.The average cultural distance is 0.25, the minimum is 0
when investors and the entrepreneur are from the same country, and the maximum
is 10.74. The average institutional distance is 0.05, the minimum institutional distance is 0 when the lead investor and the entrepreneur are from the same countries,
and the maximum is 1.52.
The average level of trust ranges from a minimum trust of 2.51 (the trust of
British toward French) to a maximum of 3.69 (the trust of Finns toward Finns),
with an average trust level equal to 3.28. Among the trust measure rakings there
seem to be some common views: people usually have the highest trust for their
own country (except Italians); the most trusted are Finns and German, and the less
trusted are Italian and Spanish. There is reciprocity between trusting and being
trusted, for instance, Finns are at the more trusted and tend to trust others the
most; the British trust the French less than other nations; and the French trust the
British and then Italians.
An entrepreneurial rm receives an average investment amount of 2.20 e million
along 1.99 rounds with an average round duration of 1.96 years. The average age
of the rms when it receives the rst round of VC nancing is 2.19 years and the
maximum age is 11. The average size of the rm (when it receives the rst round)
measured by its total asset is 4.4 e million .
Finally, on average entrepreneurial rms receive investments from a syndicate
composed of 1.71 VCs investor, a captive VC participate in 61% of the nancing
rounds in the sample.
The entrepreneurial rms operate in industries in which
the average industry market to book ratio is 2.96, and the average industry asset
tangibility asset is 47%.
Table 5.2 Descriptive Statistics of the VC Financing Rounds
The sample consists of 317 venture capital backed rms that receive 632 nancing rounds
between 1994 and 2008. Distance is the average of the distance between the target rm and VC
investors participating in the round. Cultural distance is the average cultural distance between
the target rm and VC investors in the round. Institutional distance is the average institutional
distance between the target rm and VC investors in the round. Trust is the lead VC trust
measure. Round amount is the log of the amount invested in the round. Time to next rounds is
the duration in years between two successive rounds. Age at rst round is the log age of the
target rm at the rst round. Firm size is the rm' total asset. Syndication size the number of
VC investors in the round. Captive VC is a dummy equal to 1 if a captive VC investor (bank,
corporate or public VC) is involved in the round. Industry Market Book is the Median value of
Industry Market/Book ratio (based on NAICS 3 digit). And Industry Asset Tangibility is the
Median Industry Tangible Assets /Total Assets (based on NAICS 3 digit).
Variable Obs Mean Std. Dev. Min Max
Distance
Cultural distance
Institutional distance
Trust lead VC
Round amout
Number of rounds3
Time to next rounds4
Age at rst round
Firm Size
Syndication size
Captive VC
Industry Market Book
Industry Asset Tangibility
3 The
632
625
625
622
632
317
315
632
632
632
632
625
625
282
0.25
0.05
3.28
2205
1.99
1.96
2.19
4435
1.71
0.61
2.96
0.47
820 0 6079
1.07 0 10.75
0.17 0 1.52
0.19 2.51 3.69
4392 0 47012
1.27 1
7
1.33 1
9
2.81 0
11
10936 3 184960
1.32 1
11
0.49 0
1
1.90 1.00 7.62
0.08 0.29 0.66
unit of analysis is the number of rms
only for rms that receive at least a follow-on round
4 dened
5.4.2 Multivariate Analysis
In this work we aim to analyze VC nancing staging patterns in cross border investments context, we focus on round funding duration, which is the time elapsed
between two successive rounds of nancing for a portfolio rm.
The dependent variable is the natural logarithm of the duration in years of a
particular venture nancing round. If the entrepreneurial rm eventually exits from
VC portfolio (went public, was acquired, or went bankrupt), we calculate the last
investment duration as the time interval in years between the last nancing round
date and the exit date. For rms on the middle of ongoing nancing rounds for which
we do not observe the subsequent round of nancing, we consider the censoring date
to be ve years after receipt of the last round.
We describe the behavior of duration through its survival function. We consider an Accelerated failure-time (AFT) model to estimate the probability that the
duration of a round lasts at least to time t, the dependent variable is the natural
logarithm of the duration (years), and we assume that the distribution of the hazard
function ows a Weibull density distribution. Finally, we integrate control variables
that are found to impact VC staging decisions. We control for the entrepreneurial
rm specic characteristics such as the ratio of tangible asset which is the ratio of
tangible asset to total asset. Gompers (1995)[195] argue that agency cost increase as
assets are less tangible, and that rm associated with higher agency cost should be
monitored more often, thus shorter will be the round duration of funding for rms
associated with high level of asset tangibility.
We also control for the age of the entrepreneurial rm which is the number of
years between the rm founding date and the rst nancing round date. Gompers (1995)[195] ndings document that younger rms are associated with shorter
duration rounds. The idea behind is that young rms are characterized by more
asymmetric information because they have less information available to assess their
value, accordingly their round duration need to be shorter. Finally we control for
the rm total asset value, as a proxy for rm size.
At the industry level, we use the median tangibility asset ratio and market to
book ratio at the 3 dit NAICs code to control for industry eects.
We control for the size of syndicated deals using the total number of VCs investors
investing in the round. There have been very important analyses of syndicates and
its roles. Casamatta, Haritchabalet (2007)[184], for example, explain that syndication helps to gather information specically when uncertainty is high and that VCs
need the assessment of a peer. The literature also nd that the joint eort in the
syndication improve the project selection and monitoring of the ventures (Lerner
1995[145]; Sorenson and Stuart 2001[185]). Finally, accordingly we may expect that
syndication avantages will increase the syndicate partners' incentive to invest and
increase the funding duration.
Specic to the international VC investments setting, foreign VCs syndication
with local VCs helps to reduce their lack of local knowledge and thus improving
monitoring eectiveness (Chemmaur et al 2010[175]), accordingly we control for the
presence of a local VC in the round. We also control for the presence of a captive
VC in the syndicate by including a dummy for captive VCs. In fact the literature
shows that depending on their type, VCs dier in their objectives, structures and
investment strategies (Bottazi et all, 2008[170]; Hellmann et al, 2008[186]; Cumming
and Johan, 2010[117]), accordingly, one may expect that this dierences impact
rounds nancing duration. Finally as in Gompers (1995)[195] we include a dummy
variable to represent the outcomes of venture nancing (the dummy equal one 1 if
the rm has been listed or acquired, and zero otherwise).
This section presents multivariate analysis of VC nancing staging patterns in
cross border investments context. The objective is to study how VCs investors
deal with asymmetric information associated with cross border investment, more
specically how VCs investors organize the duration of nancing rounds when the
entrepreneurial rm is physically, culturally or institutionally distant from the VC
investors. Furthermore, we are interested on how the trust level between the entrepreneur and the VC nations may impact staging decisions.
Table 5.3 reports the estimation results. Model 1 is the baseline model that
includes all the control variables. Model 2 to model 5 examine the eects of the
variables of our interest. Model 6 is the full model specication with all the ex-
Table 5.3 Weibull Model of Funding Duration
The table reports the maximum likelihood accelerated failure-time (AFT) model estimation. The
dependent variable is the natural logarithm of the duration (years). The error term is assumed
to follow an extreme value Weibull density distribution.Distance is the log average of the distance
between the target rm and VC investors in the round. Local is a dummy variable equal to 1 if
all the VC investors in the round are from the same country of the target rm zero otherwise.
Cultural distance is the average cultural distance between the target rm and VC investors in the
round. Institutional distance is the average institutional distance between the target rm and VC
investors in the round. Trust is the lead VC trust measure. Follow on round is a dummy equal to
1 if the round is a follow on round (from the second round), zero otherwise. Age at rst round is
the log age of the target rm at the rst round. Round amount is the log of the amount invested
in the round. Syndication size is the number of VC investors in the round. Industry Market Book
is the Median value of Industry Market/Book ratio (based on NAICS 3 digit) at the round year.
Industry Asset Tangibility is the Median Industry Tangible Assets /Total Assets (based on NAICS
3 digit) at the round year. Firm Size is the log of the total Assets of the target rm at the round
year. Firm Asset Tangibility is the ratio of Tangible Assets to Total Assets of the target rm at
the round year. Successful Exit is a dummy equal to 1 if the rm has been listed or acquired, zero
otherwise. Captive VC is a dummy equal to 1 if a captive VC investor (bank, corporate or public
VC) is involved in the round.
m1
Distance (log)
Local
Cultural distance
0.059
(0.040)
m2
m3
m5
m6
0.102
(0.184)
0.557***
(0.037)
0.566***
(0.025)
-0.016
(0.011)
0.025**
(0.011)
0.021
(0.016)
-0.285
(0.276)
0.030***
(0.010)
0.114
(0.083)
-0.007
(0.024)
-0.031
(0.023)
0.420***
(0.619)
615.000
- 291.962
3437.98
-0.015**
(0.006)
0.037
(0.060)
-0.016
(0.020)
0.223
(0.179)
0.234*
(0.131)
0.558***
(0.037)
0.571***
(0.025)
-0.010
(0.012)
0.017
(0.012)
0.020
(0.016)
-0.307
(0.281)
0.030***
(0.010)
0.109
(0.081)
-0.000
(0.025)
-0.021
(0.023)
-0.019
(0.429)
608.000
289.671
4814.637
-0.016***
(0.006)
-0.006
(0.011)
Institutional distance
Trust lead VC
Follow on round 0.556***
(0.036)
Age at rst round (log) 0.565***
(0.025)
Round amout (log) -0.017
(0.011)
Syndication size 0.031***
(0.011)
Industry Market Book
0.016
(0.015)
Industry Asset Tangibility -0.237
(0.274)
Firm Size (log) 0.032***
(0.011)
Firm Asset Tangibility
0.106
(0.081)
Successful Exit -0.010
(0.025)
Captive VC -0.032
(0.023)
Constant 0.640***
(0.178)
Observations 625.000
Log Likelihood - 294.589
Wald Chi2 2018.64
m4
0.555***
(0.036)
0.572***
(0.025)
-0.013
(0.011)
0.029***
(0.011)
0.018
(0.015)
-0.288
(0.272)
0.032***
(0.011)
0.106
(0.079)
-0.013
(0.025)
-0.028
(0.023)
0.713***
(0.163)
625.000
- 295.423
5081.06
0.554***
(0.037)
0.565***
(0.026)
-0.015
(0.012)
0.019
(0.013)
0.014
(0.015)
-0.232
(0.272)
0.029***
(0.011)
0.110
(0.081)
-0.002
(0.025)
-0.027
(0.024)
0.754***
(0.166)
618.000
- 290.708
1915.59
-0.029
(0.070)
0.554***
(0.036)
0.565***
(0.026)
-0.015
(0.012)
0.018
(0.012)
0.015
(0.015)
-0.231
(0.272)
0.029***
(0.010)
0.110
(0.081)
-0.003
(0.025)
-0.027
(0.024)
0.753***
(0.166)
618.000
- 290.679
1911.61
planatory variables.
We expect that the geographical distance between the venture capitalist and
the entrepreneurial rm will impact staging decisions. In fact, venture capitalists
that are located farther away from the entrepreneurial rm may nd it harder to
directly monitor their investments; this is mainly due to the asymmetry of information associated with distant investment. Accordingly our rst hypothesis is that
venture capitalists that are farther away from the entrepreneurial rm organize their
nancing in shorter rounds duration. The regression results reported in Table 5.3
support the implications of distance on staging. As reported in column 2, the coefcient estimate of the distance variable is negative and signicant at the 1% level.
In other words, a greater distance between the entrepreneurial rm and the VC investor implies a shorter nancing duration between successive rounds. This nding
show that VC investors tend to shorten the duration of consecutive rounds when
entrepreneurial rms are located far away from them, thus suggesting that in distant investment, staging is used as a substitute for direct monitoring. This result is
consistent with the results of Tian (2010)[196] in the US market.
We also explore an important and not suciently explored topic in international
business studies that is the dierence in trust level across countries, and the resultant
impact on dierent business activity. For instance, we highlight the impact of trust
level between the entrepreneur and the VCs investors on staging decisions. We
expect that a high level of trust between the entrepreneur and VCs investor nations
reect a better work climate with less asymmetric information and agency cost
problems, and thus longer nancing rounds. The coecient estimate in model 6 is
positive and statistically signicant. This result document that more trustful are
citizens from the entrepreneur country by citizens form VC investor country, longer
is the round nancing duration.
In this paper, we control for additional aspects specic to cross-border investment
that are related to cultural and institutional dissimilarities. In fact, VCs investors
nancing entrepreneurial rms in foreign countries face a dierent cultural and institutional environment; we expect that cultural and institutional distance between
VCs investors and the entrepreneur countries will increase the potential asymmetric
information, and given that rms subject to higher level of information asymmetry
and agency problems should be monitored more often (Gompers (1995)[195], we
expect that nancing round duration will decrease with cultural and institutional
distance. The results in column 3 and 4 show a negative coecient estimate of cultural and institutional distance, however, the results are not statistically signicant.
The age and the size of the entrepreneurial rm and the syndication size present
worth highlighting ndings. The results show that the rm age is positively related to
the funding duration, this suggest that older rm requires lower monitoring intensity
and thus longer nancing round duration. This is in accordance with Gompers
(1995)[195] results. The results document also a positive relationship between the
size of the entrepreneurial rm and the round duration. This suggest that bigger
rms present less asymmetric information issues and thus they can be monitored
more eciently and the associated nancing round duration will be longer. Finally,
the syndication size has a consistently positive and statistically signicant eect on
the funding duration. This suggests that the collaborative advantages of syndication
give an incentive to VCs investors to participate in the round, furthermore the
documented positive eect of syndication on entrepreneurial rm monitoring explain
the longer duration of syndicated round.
Overall, the empirical results oer supportive evidence for the negative impact
of physical distance between the entrepreneurial rm and the VCs investor on round
duration. Furthermore, we document an original result about the positive relationship between the level of trust between the entrepreneur and VC nations and the
nancing round duration. Finally, our data conrm the staging literature ndings
of the positive impact of the rm age and syndication size on staging decision.
5.5
Conclusion
The current study examines the staging decision and analyzes when portfolio rms
will receive each round of nancing from venture capital investors in the context
of distant investments. We document that the staging of venture capital investments is related to the distance between VCs investor and the entrepreneurial rm.
The results from a sample of European venture capital-backed rms show that the
nancing rounds duration are shorter for distant investment, conrming the literature ndings that distant investment is associated with higher level of information
asymmetry and that in this case staging is used as substitute to a direct monitoring.
Second in this work we explore a central issue during all the VC nancing process
that is trust. We document a positive relationship between the trust level and the
staging decisions. The results suggest that the potential of asymmetric information
and agency cost increase with the level of distrust, and accordingly VCs investors
will shorten the duration of the nancing rounds associated with low level of trust.
This study can be extended to investigate the relationship between trust and investment performance. In fact Bottazi et al (2012)[168] studying the investment
decisions, show that performance is negatively related to trust. The idea behind is
that investment associated with a low level of trust will requires a supplement of
time and eorts in due diligence process which translate into better performance.
Our results document a positive relation between trust and inter-round duration
suggesting that entrepreneurial rm from less trustful country is associated with
more frequent staging activities, which may translate into better performance.
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Chapter 6
Conclusion
6.1
Summary of Results
Cette thèse poursuit quatre objectifs de recherche, leur combinaison à pour
but de contribuer à la compréhension du fonctionnement du marché du CR.
Le premier objectif est de fournir une synthèse de la littérature sur l'étape
de sortie du capital risque. Le survey commence par décrire le processus de
nancement par CR en soulignant comment le fonctionnement de l'industrie
permet de gérer l'asymétrie d'information entre l'entrepreneur et l'investisseur
de CR. en eet, les capital-risqueurs investissent dans des jeunes entreprises
en croissance ayant peu de données historiques et de garanties. L'implication
directe des investisseurs en capital risque dans le management des entreprises,
les types et caractéristiques des contrats et les décisions de réinvestissement
visent à réduire le risque d'asymétrie d'information entre l'investisseur en CR
et l'entrepreneur.
Le survey met également en évidence la position centrale de l'étage de sortie
dans le cycle de nancement par CR. En fait, les entreprises de capital-risque
sont gérées par des professionnels qui fournissent des capitaux et des compétences aux entreprises entrepreneuriales. Les entreprises de portefeuille ne
paient pas des dividendes, et les rendements générés par l'investissement sont
161
6.1.
Summary of Results
162
réalisées par l'intermédiaire d'un événement de sortie, indiquant qu'une sortie
rentable est au cur de l'industrie du capital de risque.
L'objectif principal du survey est de mettre l'accent sur les déterminants de
la phase de sortie, et d'examiner comment le choix de sortie peut varier en
fonction des objectifs du capital-risqueur et de l'entrepreneur. Par exemple,
le bénéce privé de l'entrepreneur émanant du pouvoir de contrôle que lui
confère un poste de direction dans une entreprise autonome l'incite à favoriser
une sortie par introduction en bourse. Toutefois, la période de lock-up ou
verrouillage associée aux introductions en bourse ne permet pas à la société
de capital-risque de céder la totalité de ses parts immédiatement après une
introduction en bourse. Par conséquent, et étant donné la durée de vie limitée
des fonds de capital-risque, le capital-risqueur préférait les sorties qui sont
associés à un désinvestissement immédiat. Ce qui suggère que, en plus des
caractéristiques de l'entreprise et les conditions du marché, les objectifs de
l'entrepreneur et de la société de CR vont également impacter le choix du
mode de sortie.
Par exemple, les motivations du capital-risqueur vont faire à ce qu'il encourage
les acquisitions rapides qui peuvent être moins rentables pour l'entrepreneur
et donc plus avantageuses pour l'acheteur. En eet, les fonds de capital-risque
s'approchant de la maturité doivent être liquidés rapidement, ce qui peut faire
une pression sur les entreprises du portefeuille pour négocier rapidement une
sortie par acquisition, ainsi diminuant le pouvoir de négociation de l'entreprise
cible et augmentant les rendements des acheteurs (Masulis et Nahata 2011
[187])
Le deuxième, troisième et quatrième objectifs de cette thèse sont déclinés dans
des résultats empiriques basés sur diérentes bases de données. Chacun des
trois documents présentés (chapitre 3, jusqu'au chapitre 5) sont explicitement
consacrés à une question de recherche spécique.
Le premier chapitre 3 a commencé en arguant que l'eet concurrentiel des
investissements en CR est une question sous-explorée. Cette idée a été couplée
6.1.
Summary of Results
163
avec le fait que l'introduction en bourse est l'événement le plus importante
dans la vie d'une entreprise. Par conséquent, la question de recherche porte
sur l'eet concurrentiel de nancement par risque dans le cas d'introduction
en bourse.
Deux hypothèses sous-tendent cette étude. Tout d'abord, si les annonces de
cotation sur la bourse de valeur révèlent de précieux renseignements sur les
entreprises introduites, il est probable que les investisseurs des entreprises concurrentes utilisent cette information pour réévaluer la valeur et les perspectives d'évolution de leurs propres entreprises. Par conséquent, les annonces
d'introduction en bourse sont susceptibles d'avoir des eets d'externalité sur
les entreprises rivales. Deuxièmement, si les investisseurs de capital-risque sont
considérés comme ayant un rôle en aidant leurs entreprises à traiter avec le
marché public, il est probable que les concurrents réagissent diéremment aux
introductions en bourse des entreprises soutenues par CR. En conséquence, les
deux questions de recherche suivantes ont été formulées: (1) quelle est la réaction des concurrents à l'annonce d'introductions en bourse? Et (2) la réaction
des concurrents dière-t-elle en fonction du statut des entreprises émises?
En accord avec la littérature (Hsu et al 2010 [188]), les résultats montrent
qu'en France, les introductions en bourse d'entreprises sans nancement de
CR créent une réaction négative à l'égard des concurrents opérant dans le
même secteur d'activité. Ce qui suggère que les entreprises introduites seront
en mesure d'améliorer leur position concurrentielle (Chemanur et al 2011 [189];
Chod et Lyandres 2011 [190]).
D'une manière diérente, l'introduction en bourse d'entreprise nancée par
CR a un eet d'annonce positive sur le rendement boursier de ses concurrents.
Ce résultat suggère que le marché public considère l'introduction en bourse
d'entreprises soutenues par CR comme un signal positif sur les perspectives
de l'ensemble de marché, dont les concurrents peuvent également tirer prot.
Ce résultat est en cohérence avec Cotei et Farhat (2011 [191]).
L'eet positif sur des concurrents d'introductions en bourse d'entreprises -
6.1.
Summary of Results
164
nancése par CR met en évidence le rôle du capital-risqueur dans des IPO. En
fait, cette relation positive suggère que les IPOs d'entreprises nancées par CR
signalent des conditions positives du marché. Conférant ainsi aux investisseurs
de capital-risque un pouvoir de prévoyance, où le CR décidera de coter son
entreprise quand la valorisation du marché boursier est élevée.
Le deuxième article de cette thèse est une suite du premier, il vise à documenter le rôle du capital-risqueur dans les fusions et acquisitions. En eet, les
statistiques du marché de capital-risque montrent que la vente industrielle est
le véhicule de sortie le plus commun. Cependant, cette domination ne se reète
pas dans la littérature académique. En conséquence, de nombreuses questions
de recherche restent sans réponse comme le rôle de l'investisseur de CR dans
les sorties par acquisitions.
La principale hypothèse de cette étude porte sur le rôle du capital-risque dans
les opérations de fusions acquisitions. Si l'investisseur de CR certie la valeur
de ses entreprises, il sera en mesure de réduire l'asymétrie d'information rencontrée par les acheteurs. En conséquence, il est probable d'observer des prix
d'achat élevés pour l'acquisition d'entreprises soutenues par CR, ce qui correspondrait à des rendements plus faibles à l'annonce d'acquisitions d'entreprises
nancées par CR.
Les résultats empiriques de l'étude conrment ceux de la littérature, où
l'annonce d'achat d'entreprises privées engendre une réaction positive de
l'acheteur (Fuller et al, 2002 [192]; 2007 [193]; Faccio et al, 2006 [194]). En plus
on documente que l'acheteur réagit moins positivement à l'acquisition d'une
cible soutenue par CR.
Dans l'ensemble, les résultats montrent que les investisseurs en CR ont un
rôle de certication pendant les sorties par acquisitions. La certication consiste à réduire l'asymétrie d'information entre l'acheteur et la cible, ce qui en
conséquence encourage l'acheteur à payer un prix plus élevé pour acquérir la
cible.
6.1.
Summary of Results
165
Les résultats du chapitre 3 et 4 de cette dissertation ont aussi un aspect pratique pour les actionnaires de sociétés publiques. C'est aussi important pour
les investisseurs d'entreprises publiques de connaître comment une introduction en bourse aecte le fonctionnement et la performance boursière des entreprises existantes et en conséquence ajuster leur allocation de portefeuille. De
La même façon Les actionnaires des acheteurs doivent évaluer l'impact d'une
nouvelle acquisition sur la performance de leurs entreprises. Ainsi les résultats
de nos études d'événements devraient être aussi d'intérêt pour investisseurs
d'entreprises pour leurs décisions allocation à court terme.
Le troisième papier empirique de cette thèse considère les décisions de réinvestissement. En fait, le nancement par CR est généralement organisé graduellement le long de dates successives autour desquelles des tours de tables supplémentaires sont requis. D'une perspective d'agence (par exemple, Gompers,
1995[195]), cette étude prolonge la ligne de recherche sur les décisions de réinvestissement en examinant la durée entre deux tours de table successives dans
un contexte d'investissement à l'international. Le premier objectif de ce papier
est d'analyser les décisions de réinvestissement selon la distance géographique
entre l'investisseur du CR et l'entreprise entrepreneuriale. Le deuxième objectif est d'examiner une question largement sous-explorée dans la littérature qui
concerne l'inuence des cultures sociales sur les investissements internationaux
en CR. Nous faisons ceci en utilisant la notion de conance entre les notions.
Le premier résultat montre que la distance entre l'investisseur de capital-risque
et les entreprises entrepreneuriales raccourcit la durée entre les tours de nancement. Ceci suggère que les décisions de réinvestissement se substituent
au management direct qui est onéreux pour les investissements distants. Ce
résultat est conforme a celui de Tian (2011[196]) pour le marché américain.
Le deuxième résultat original de cet article documente la relation positive entre
le niveau de conance et la durée de nancement. En eet, plus élevé est le
niveau de conance entre l'investisseur en capital risque et l'entrepreneur plus
longue sera la durée entre deux tours de nancement. Ce résultat suggère que la
6.2.
Outlook and Future Research
166
conance et les cultures sociales peuvent diminuer l'asymétrie de l'information
et les coûts d'agence liés aux investissements en fonds propres.
6.2
Outlook and Future Research
En regardant la conclusion de littérature sur le rôle de capital-risqueur à la
sortie, on peut remarquer que les résultats d'une sortie spécique ont tendance
à être généralisés, sans considérer les diérences du marché, (les États-Unis,
l'Europe), la période d'étude (avant ou après la crise), le mode de sorties
(Introduction en bourse, acquisition). Par exemple dans cette dissertation je
documente que le rôle du capital-risqueur dans une acquisition dière de son
rôle dans une introduction en bourse, (le rôle de certication dans le premier
cas et de marché timing dans le deuxième).
On peut aussi s'attendre à ce que les modèles de sortie soient diérents selon le
type et les caractéristiques des investisseurs en capital risque. En eet, comme
discuté dans le survey (2) les préférences de sortie des investisseurs en capital
risque peuvent diérer en fonction de leur objectifs, et naturellement les objectifs des capital-risqueurs dépendent de leurs types et de leurs caractéristiques
(2.5).
Je suis convaincue que l'impact de nancement par CR sur le secteur
d'intervention des entreprises nancées par CR ouvre des nouvelles perspectives de recherche. Par exemple, les résultats documentés dans le chapitre 3
sur les eets compétitifs des sorties par introductions en bourse ore une intuition intéressante sur l'eet compétitif de nancement par CR. Ainsi, on
peut s'attendre à ce que l'investissement par CR impacte également les concurrents des entreprises nancées. Autrement dis, au lieu d'étudier l'impact
direct du nancement par CR sur leurs entreprises de portefeuille, on peut
explorer son eet indirect en examinant le développement des concurrents des
sociétés soutenues par CR.
6.2.
Outlook and Future Research
167
En n, les aspects internationaux de marché de CR restent aussi sous - explorés, spéciquement comment les diérences culturelles parmi les nations
peuvent avoir un impact sur le fonctionnement de marché de CR. En eet,
l'utilisation d'informations " soft " pour étudier le marché de CR est approprié en raison de l'incertitude et de l'asymétrie de l'information qui caractérise
les données " hard " des sociétés entrepreneuriales, ainsi on peut s'attendre à
ce que des informations " soft " puissent être aussi informatives sur le fonctionnement de marché de CR.
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List of Figures
1.1
Private Equity Investment as % of GDP . . . . . . . . . . . .
1.2
Aggregate Capital Commitments by Fund Geographic Focus,
2000 - 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3
8
10
Total Capital Raised by European Private Equity Funds, 20002013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
1.4
Funds raised by Type of Investors in Europe, 2007-2013 . . . .
12
1.5
Funds Raised by Type of Investor in 2013-in top 5 countries .
12
1.6
Capital Raised by Stage Focus of Funds, 2007-2013 . . . . . .
14
1.7
Capital Raised by Funds Organization Type, 2007-2013 . . . .
15
1.8
Investment Activity of Private Equity Firms Located in Europe,
1.9
2000-2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
Amount Invested by stage, 2007-2013 . . . . . . . . . . . . . .
17
1.10 Divestment (by amount at cost divested) by European Private
Equity Firms 2000-2013 . . . . . . . . . . . . . . . . . . . . .
19
1.11 Divestment evolution by exit route in European PE market,
2007-2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
1.12 3, 5 and 10-year Rolling-Horizon Internal Rate of Return . . .
21
1.13 5 Year Rolling Horizon Net IRR of Venture and Buyout Funds
22
1.14 VC IPOs in US, 2000-2013 . . . . . . . . . . . . . . . . . . . .
23
1.15 Underpricing ratio . . . . . . . . . . . . . . . . . . . . . . . .
24
170
List of Figures
171
1.16 Divestment Evolution by Exit Route in European VC market
27
1.17 Geographic Sources of PE Funds, 2007-2013 . . . . . . . . . .
29
1.18 European PE Investment Destination, 2007-2013 . . . . . . . .
29
2.1
46
Evolution of Exit Type in US . . . . . . . . . . . . . . . . . .
List of Tables
1.1
Enterprises, Employment and Gross Value Added of SMEs in
the EU-27, 2012 . . . . . . . . . . . . . . . . . . . . . . . . . .
7
3.1
The likelihood of VC IPOs .
. . . . . . . . . . . . . . . . .
75
3.2
Descriptive Statistics for VC Backed and non VC
Backed IPOs, Before Matching . . . . . . . . . . . . . . .
77
Descriptive Statistics for VC Backed and non VC
Backed IPO, After Matching . . . . . . . . . . . . . . . .
79
3.4
IPOs Distribution by Year
80
3.5
IPOs Distribution by Industry
3.6
Competitors' Cumulative Abnormal Returns (CAR)
for VC IPOs and Matched non VC-IPOs Announcements
84
The Eect of IPO Announcement Events on Competitor's Cumulative Abnormal Returns . . . . . . . . . . . .
87
3.3
3.7
4.1
. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . .
The likelihood of VC Target Firms Acquisition
172
. . . . .
80
109
List of Figures
4.2
173
This table reports the logistic model results predicting whether
an acquisition involves a VC-target or not. The dependent variable is equal to 1 if the issued rm is VC backed, and is 0 otherwise. Deal size (target purchase price), relative deal size (target
purchase price divided by acquirer' market value), method-ofpayment dummy (deals involving stock for payment), and high
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
tech sector dummy. . . . . . . . . . . . . . . . . . . . . . . . .
109
Descriptive Statistics for VC backed and non VC
backed Targets, Before Matching . . . . . . . . . . . . . .
111
Descriptive Statistics for VC backed and non VC
Backed Targets, After Matching . . . . . . . . . . . . . .
113
Acquisitions Distribution by Year for VC-Backed Targets and a Matched Sample . . . . . . . . . . . . . . . . .
114
Acquisitions by Country for VC-Backed Targets and a
Matched Sample . . . . . . . . . . . . . . . . . . . . . . . .
115
Acquisitions by Industry for VC-Backed Targets and a
Matched Sample . . . . . . . . . . . . . . . . . . . . . . . .
115
Acquirer Cumulative Abnormal Returns for Acquisition of Private Target: VC backing, Method of payment, Industrial Diversication,and Cross border deals
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
119
Acquirer Cumulative Abnormal Returns for Acquisition of Private Targets: Announcement Year and VC
backing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
120
Acquirer Cumulative Abnormal Returns for acquisition
of private targets: Acquires and Target countries and
VC backing . . . . . . . . . . . . . . . . . . . . . . . . . . .
122
The Impact of Acquisition of Private Firms on Acquirers' CAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
126
List of Tables
174
Distribution of the Number of VC Investments, by VC
Investors and Backed rms Countries . . . . . . . . . . .
142
5.2
Descriptive Statistics of the VC Financing Rounds
. .
143
5.3
Weibull Model of Funding Duration
. . . . . . . . . . . .
146
5.1
Contents
Table of Contents - Overview
5
1 Introduction
6
1.1
1.2
Industry Statistics
. . . . . . . . . . . . . . . . . . . . . . . .
9
1.1.1
Fundraising Activity . . . . . . . . . . . . . . . . . . .
9
1.1.2
Investment Activity . . . . . . . . . . . . . . . . . . . .
15
1.1.3
Exit and Performance . . . . . . . . . . . . . . . . . .
18
1.1.3.1
Exit Patterns . . . . . . . . . . . . . . . . . .
18
1.1.3.2
PE Industry Performance . . . . . . . . . . .
21
Outline of The Thesis . . . . . . . . . . . . . . . . . . . . . . .
24
1.2.1
Venture Capital Investor Role in IPOs Exits . . . . . .
24
1.2.2
Venture Capital Investor Role in M&As Exits . . . . .
26
1.2.3
Cross Border Investment and Staging Decisions . . . .
28
2 Survey About Venture Capital Financing Exit Stage
34
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
2.2
Venture Capital Financing Process . . . . . . . . . . . . . . .
40
2.2.1
Investment Selection . . . . . . . . . . . . . . . . . . .
40
2.2.2
Post-Investment Monitoring . . . . . . . . . . . . . . .
41
2.2.3
Exit Stage . . . . . . . . . . . . . . . . . . . . . . . . .
43
175
Table of Contents
2.3
2.4
2.5
176
2.2.3.1
Initial Public Oerings . . . . . . . . . . . . .
43
2.2.3.2
Trade Sale . . . . . . . . . . . . . . . . . . . .
44
2.2.3.3
Other Exits Routes: Secondary Sale, Buyback
and Liquidation . . . . . . . . . . . . . . . . .
45
Exit Determinants . . . . . . . . . . . . . . . . . . . . . . . .
47
2.3.1
Exit Timing . . . . . . . . . . . . . . . . . . . . . . . .
47
2.3.2
Exit Route Choice . . . . . . . . . . . . . . . . . . . .
48
2.3.2.1
VC Investors and Entrepreneurs Objectives .
48
2.3.2.2
Contracts and Exit . . . . . . . . . . . . . . .
49
2.3.2.3
Entrepreneurial Firms Characteristics and Exit
50
2.3.2.4
Cost of Exits Routes . . . . . . . . . . . . . .
51
Exit Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . .
51
2.4.1
VC Investors Characteristics and Exit Performance . .
52
2.4.2
VC Role at The Exit Stage
. . . . . . . . . . . . . . .
53
2.4.3
VC Role After The Exit Stage . . . . . . . . . . . . . .
55
Some Open Research Questions . . . . . . . . . . . . . . . . .
55
3 Competitive Eect of Venture Capital Backing In IPOs
65
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
3.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . .
68
3.2.1
3.2.2
Going Public Decision and The Product Market Competition . . . . . . . . . . . . . . . . . . . . . . . . . .
69
VC Investor Role in IPOs Exits . . . . . . . . . . . . .
70
3.2.2.1
Exit Choice . . . . . . . . . . . . . . . . . . .
70
VC Investor Role in IPOs Exits . . . . . . . .
71
Data and Descriptive Statistics . . . . . . . . . . . . . . . . .
73
3.2.2.2
3.3
VC Backed Firm' Product Quality and The
Table of Contents
3.3.1
Data Description and Sample Selection . . . . . . . . .
74
3.3.1.1
Data Collection . . . . . . . . . . . . . . . . .
74
3.3.1.2
Sample Selection and Matching . . . . . . . .
74
Descriptive Statistics . . . . . . . . . . . . . . . . . . .
76
3.3.2.1
Descriptive Statistics Before Matching . . . .
76
3.3.2.2
Descriptive Statistics After Matching . . . . .
78
Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . .
81
3.4.1
Univariate Analysis: Cumulative Abnormal Return . .
81
3.4.1.1
Event Study Methodology . . . . . . . . . . .
81
3.4.1.2
Event Study Results . . . . . . . . . . . . . .
82
Regression Results . . . . . . . . . . . . . . . . . . . .
85
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
3.3.2
3.4
3.4.2
3.5
177
4 The Role of Venture Capital Backing in Mergers and Acquisitions
99
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
101
4.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . .
102
4.2.1
Acquirer Reaction to Acquisition Announcement . . . .
102
4.2.1.1
Target Status . . . . . . . . . . . . . . . . . .
102
4.2.1.2
Acquirer Characteristics . . . . . . . . . . . .
103
4.2.1.3
Deal characteristics . . . . . . . . . . . . . . .
103
VC Investor Role in Acquisition Exits . . . . . . . . . .
105
Data and Descriptive Statistics . . . . . . . . . . . . . . . . .
107
4.3.1
Data Description and Sample Selection . . . . . . . . .
107
4.3.1.1
Data Collection . . . . . . . . . . . . . . . . .
107
4.3.1.2
Sample Selection and Matching . . . . . . . .
108
4.2.2
4.3
Table of Contents
4.3.2
4.4
4.5
178
Descriptive Statistics . . . . . . . . . . . . . . . . . . .
109
4.3.2.1
Descriptive Statistics Before Matching . . . .
109
4.3.2.2
Descriptive Statistics After Matching . . . . .
112
Empirical results . . . . . . . . . . . . . . . . . . . . . . . . .
116
4.4.1
Univariate Analysis: Cumulative abnormal return . . .
116
4.4.2
Regression Results . . . . . . . . . . . . . . . . . . . .
123
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
127
5 Duration Analysis Of VC Staging In Cross Border Investment131
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
133
5.2
Literature Review and Hypotheses development . . . . . . . .
135
5.2.1
Geographical Distance and Staging . . . . . . . . . . .
135
5.2.2
Trust and Staging . . . . . . . . . . . . . . . . . . . . .
137
5.3
Data and variables measures . . . . . . . . . . . . . . . . . . .
139
5.4
Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . .
141
5.4.1
Univariate Analysis . . . . . . . . . . . . . . . . . . . .
141
5.4.2
Multivariate Analysis . . . . . . . . . . . . . . . . . . .
144
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
149
5.5
6 Conclusion
161
6.1
Summary of Results
. . . . . . . . . . . . . . . . . . . . . . .
161
6.2
Outlook and Future Research . . . . . . . . . . . . . . . . . .
166
List of Figures
171
List of Tables
174
Table of Contents
178