joint venture stock market reactions: scandinavian...

Joint Venture Stock Market Reactions:

Scandinavian Evidence

Niklas Karl Kamp

Master Thesis in M.Sc. Finance and International Business

Aarhus University, Denmark

Supervised by Asst. Prof. Ph.D. Cristina M. Scherrer

1 June 2015

Abstract

This Master Thesis examines the stock market reactions and announcement effects in Joint

Ventures with Scandinavian participation. Using the event study methodology and a cross-

sectional regression analysis, the abnormal returns as well as five explanatory variables, which

potentially impact the announcement effect, are investigated.

With respect to the underlying sample of 127 Joint Ventures, the findings yield significant

positive abnormal returns and suggest that previous Joint Venture experience as well as cultural

relatedness positively influence the announcement effect and the simultaneous shareholder value

creation. Further, the findings are applied to a real-life case study in form of the recent Joint

Venture between Vestas Wind Systems A/S and Mitsubishi Heavy Industries, Inc.

Characters (no spaces): 129,840 (approx. 59.02 pages)

Master Thesis in M.Sc. Finance and International Business 1 June 2015

Aarhus BSS, School of Business and Social Sciences ii

Contents

Contents .......................................................................................................................................... ii

Abbreviations ................................................................................................................................. iv

List of databases and software ....................................................................................................... iv

List of figures .................................................................................................................................. v

List of tables .................................................................................................................................... v

1. Introduction .............................................................................................................................. 1

1.1. Problem statement ............................................................................................................ 4

1.2. Structure ........................................................................................................................... 4

1.3. Delimitations and assumptions ......................................................................................... 5

2. Literature review & hypotheses ............................................................................................... 6

2.1. Theoretical motivations for JVs over other cooperative modes ....................................... 6

2.1.1. Cost minimization and economies of scale ............................................................... 7

2.1.2. Synergies and knowledge sharing ............................................................................. 8

2.1.3. Market access and diversification of risk .................................................................. 8

2.2. Joint ventures and stock valuation.................................................................................. 10

2.3. Variables affecting the stock market return ................................................................... 13

2.3.1. Partner-venture relatedness ..................................................................................... 13

2.3.2. Partner-partner relatedness ...................................................................................... 14

2.3.3. JV Experience ......................................................................................................... 16

2.3.4. Domestic vs. international JVs ................................................................................ 17

2.3.5. Cultural relatedness ................................................................................................. 18

3. Methodology .......................................................................................................................... 20

3.1. Event study methodology ............................................................................................... 20

3.2. Estimation period and event window ............................................................................. 21

3.3. Measuring normal and abnormal returns........................................................................ 22

3.4. Test statistics .................................................................................................................. 24

3.4.1. Parametric tests ....................................................................................................... 25

3.4.2. Nonparametric tests................................................................................................. 27

3.4.3. Cross-sectional regression....................................................................................... 28

4. Data ........................................................................................................................................ 31

4.1. Data selection ................................................................................................................. 31

4.2. Descriptive statistics ....................................................................................................... 33


Aarhus BSS, School of Business and Social Sciences iii

5. Empirical evidence and discussion ........................................................................................ 38

5.1. Parametric tests............................................................................................................... 38

5.2. Nonparametric tests ........................................................................................................ 40

5.3. Cross-sectional regression .............................................................................................. 42

6. Case study .............................................................................................................................. 47

6.1. Industry and market overview ........................................................................................ 48

6.2. Company description – Vestas Wind Systems A/S – 2010-2013 .................................. 50

6.3. Establishment of the JV MHI Vestas Group – 2013/2014 ............................................. 53

6.4. Theoretical incentives for JV participation .................................................................... 54

6.4.1. Cost minimization and economies of scale ............................................................. 54

6.4.2. Synergies and knowledge sharing ........................................................................... 55

6.4.3. Market access and diversification of risk ................................................................ 55

6.5. Stock price reaction ........................................................................................................ 56

6.6. Development after the JV MHI Vestas Group announcement – 2014-Q1/2015 ........... 58

7. Implications and conclusion .................................................................................................. 60


Aarhus BSS, School of Business and Social Sciences iv

Abbreviations

Abbreviation Name

AR Abnormal Return

BLUE Best Linear Unbiased Estimator

CAGR Cumulative Annual Growth Rate

CAR Cumulative Abnormal Return

CLM Central Limit Theorem

DK Denmark

EMH Efficient Market Hypothesis

EU European Union

FDI Foreign Direct Investment

FI Finland

FTE Full-Time Equivalent (employee)

GWEC Global Wind Energy Council

IJV International Joint Venture

JV Joint Venture

M&A Mergers & Acquisitions

MHI Mitsubishi Heavy Industries Ltd.

MNE

Multinational Enterprise

NBC Net Borrowing Cost

NO Norway

NPV Net Present Value

OLS Ordinary Least Square

RBV Resource-Based View

RNFA Return on Net Financial Assets

RNOA Return on Net Operating Assets

ROCE Return On Common Equity

R&D Resource & Development

SAS Statistical Analysis Software

SIC Standard Industrial Clarification

SWE Sweden

TCT Transaction Cost Theory

UK United Kingdom

US(A) United States (of America)

VWS Vestas Wind Systems A/S

WTG Wind Turbine Generator

List of databases and software

Name Source

Bureau van Dijk (BvD) Zephyr database

All databases and software have been accessed through

the Aarhus University library.

EViews 8

Statistical Analysis Software (SAS)

Thomson Reuter database


Aarhus BSS, School of Business and Social Sciences v

List of figures

Figure 1: Joint Venture deals/year .................................................................................................. 2

Figure 2: Timeline of event study ................................................................................................. 22

Figure 3: Sample distribution of JV deals/year ............................................................................. 34

Figure 4: Sample distribution ........................................................................................................ 35

Figure 5: Sample variable coding – binary= 1 .............................................................................. 36

Figure 6: Sample variable coding – binary= 0 .............................................................................. 36

Figure 7: Timeline of case study ................................................................................................... 47

Figure 8: Global turbine industry value: mEUR, 2010-2013 ........................................................ 49

Figure 9: Market share of leading players in the wind turbine industry, 2010-2013 .................... 50

Figure 10: VWS stock price reaction ............................................................................................ 57

List of tables

Table 1: Overview of empirical JV effect studies ......................................................................... 11

Table 2: Explanatory variables ..................................................................................................... 13

Table 3: Coding classification....................................................................................................... 29

Table 4: Search strategy ................................................................................................................ 31

Table 5: Sample descriptive statistics ........................................................................................... 34

Table 6: Parametric test results ..................................................................................................... 39

Table 7: Nonparametric test results .............................................................................................. 41

Table 8: Cross-sectional regression results, entire data sample (ALL) ........................................ 42

Table 9: Cross-sectional regression results, sub-samples ............................................................. 44

Table 10: Overall results ............................................................................................................... 45

Table 11: Overview of VWS's financial figures, 2010-2013 ........................................................ 51

Table 12: VWS DuPont profitability analysis, 2010-2013 ........................................................... 52

Table 13: Overview of VWS's financial figures, 2014-Q1/2015 .................................................. 58

Master Thesis in M.Sc. Finance and International Business 1 June 2015 1 June 2015

Aarhus BSS, School of Business and Social Sciences 1

1. Introduction

Given the economic pressures of the on-going globalization including the ever-increasing

competition in the terrestrial marketplace, multinational enterprises (MNE) are bound to expand

their businesses across the globe and engage in cross-border transactions with other players. As

such, it is no longer sufficient for MNEs to solely focus on their respective domestic market,

rather to broaden their economic horizon through an international approach of conducting

business. Due to a vast variety of entry modes into foreign markets, e.g. Exporting, Licensing,

Joint Ventures (JVs), Mergers & Acquisitions (M&As) and Greenfields Investments, MNEs

need to assess their internal and external environment, in order to determine the most appropriate

and compatible entry mode. According to Phatak (1997), it is essential for MNEs to match their

corporate strategies and objectives with the specific features of each mode and carefully analyze

the apparent trade-off between control, given by their resource commitment, as well as risk,

including systemic (political, economic and financial risk) and dissemination risk (risk of

expropriation of know-how by the partner company). Intuitively, risk and control are positively

correlated, as a successive increase in control entails gradually ascending risk levels and higher

exposure (Anderson & Gatignon, 1986). With respect to the entry modes, Export and Licensing

exhibit lower degrees of control and risk, since both are based on contractual agreements,

whereas JVs, M&As and Greenfield Investments present medium or high control and risk levels,

due to necessary equity investments (Hill, Hwang & Chan Kim, 1990; Phatak, 1997).

Shifting the attention to JVs, as a subset of strategic alliances, they involve two or more partners,

who create a new entity in which both partners hold equity, in order to facilitate the sharing,

exchange or co-developing of resources (Glaister, 2004). The exclusive features of shared

ownership and control make it especially interesting to examine this particular entry mode, in

comparison with other forms of cooperative alliances, as it entails specific opportunities and

challenges for both JV partners. Following this notion, the establishment of JVs encompasses

several advantages and motives for the partner companies, such as economies of scale,

diversification or risk sharing. In reference to Figure 1, since the establishment of the BvD

Zephyr database, the popularity of JVs increased over the period between 1997-2004, after

which the number of completed/confirmed JV deals decreased from 2005-2013 and in turn

slightly increased in 2014.



Figure 1: Joint Venture deals/year

Source: Contribution by author. Data retrieved from BvD Zephyr database.

Concerning this trend and the fact that JVs have not been the focal point of corporate alliance

studies, it is of special interest to investigate the concrete incentives and motives of MNEs to

participate in a JV. This also includes the means by which MNEs generate additional shareholder

value through the establishment of a JV partnership.

Whereas especially in M&As, the announcement effect, defined as “the difference between the

actual and expected return of a security“ (Bartholdy, Olesen & Paere, 2007) and the

simultaneous creation of additional shareholder value is well documented, this has been done to

a much lesser degree for JVs. Regarding the profit distribution in M&As, there is extensive

empirical evidence that target shareholders realize excess returns in contrast to acquiring

shareholders (Jensen & Ruback, 1983; Fuller, Netter & Stegemoller, 2002). Despite the fact that

there are several similarities between M&As and JVs, this academic consensus does not hold for

JVs, for which empirical findings in this regard are inconclusive. Thus, not only positive

(Kumar, 2010; Hanvanich, Miller, Richards & Cavusgil, 2003; McConnell & Nantell, 1985), but

also negative (Chang & Chen, 2002; Lee & Wyatt, 1990; Barkema, Bell & Pennings, 1996) and

insignificant (Borde, Whyte, Wiant & Hoffman, 1998; Chen, Hu & Shieh, 1991) effect studies of

shareholder value creation following JV announcements exist.

Additionally, it is noteworthy that most of the aforementioned studies focus on US companies

and consequently do not account for potential effects with respect to the international/European

geographic area. With the aim of contributing to the existing evidence of international/European

effect studies, this study concentrates on JVs with Scandinavian participation and sets out to fill

the prevailing empirical gap. As such, this Master Thesis not merely examines whether an

announcement effect exists, but recapitulates the collective impact of cardinal influences on

shareholder value creation affiliated with the JV participation of the Scandinavian partners.

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1000

1500

2000

2500

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

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Joint Venture announcements by year



By means of an event study comprising a data sample of 127 JV deals, partitioned into four sub-

ordinated samples (Denmark, Finland, Norway and Sweden), the potential value creation for

shareholders of the JV partners is examined. In independently applying a battery of parametric

and nonparametric tests, the validity and reliability of the event study are reinforced. Further, a

cross-sectional regression analysis casts light on the influence of five empirically-tested variables

(partner-venture relatedness, partner-partner relatedness, domestic vs. international JVs (IJVs),

JV experience and cultural relatedness) which potentially amplify the announcement effect

(Merchant, 2002, 2004, 2012). As there is no pre-specified time range (JV data retrieved up until

31 December 2014) with respect to the search strategy of the data sample, the Master Thesis

aims for an integral approach to assess and explore the underlying objective.

To facilitate the understanding of the financial and economic consequences of the announcement

effect, an exemplary case study of the JV MHI Vestas Group between the Danish-based Vestas

Wind System Systems A/S (VWS) and the Japanese MNE Mitsubishi Heavy Industries Ltd.

(MHI) is conducted to show how far the theoretical incentives and motivations for a JV

participation transfer into practice. Further, the stock market reaction with respect to potential

shareholder value creation is investigated in comparison to the local MSCI index.

This particular JV, which is also contained in the underlying sample of the event study, is of

special interest since VWS decided to source its entire offshore wind business segment into the

JV, in response to the prevailing economic pressures and VWS’ financial situation in times of an

internal turn-around process. This includes all R&D activities and know-how in relation to the

V164-8.0MW, the world’s largest and record-setting wind turbine generator (WTG), which

ensures VWS’ front-runner position in the highly competitive offshore wind market. Thus, on

the basis of the empirical findings of the event study and the cross-sectional regression analysis,

the real-life case study is put forth not merely taking into account VWS’ financial, but also its

economic situation. Hereby, it is especially intriguing to examine the repercussions of the JV

MHI Vestas Group announcement in terms of potential shareholder value creation as well as the

strategic notion behind the decision to contribute VWS’ offshore wind business segment into the

JV with MHI.



1.1. Problem statement

In line with the compelling motivation and the shortcomings of the existing

international/European evidence concerning shareholder value creation upon JV announcements,

the following problem statement has been outlined as the central objective of this Master Thesis:

To what extent do Joint Venture announcements with Scandinavian participation impact

shareholder value creation?

In pursuit of answering the above-mentioned problem statement, two sets of research questions

have been formulated:

RQ1: Does an announcement effect in JVs with Scandinavian participation exist?

RQ2: What are the primary influences on shareholder value creation related to the

establishment of JVs?

Whereas the first two research questions are investigated by means of the event study and the

cross-sectional regression analysis, the second set of research questions build on the previous

discussion of the theory as well as the empirical findings and focuses on the case study of the JV

MHI Vestas Group:

RQ3: What are VWS’ strategic and economic incentives to engage in a JV with MHI?

RQ4: How is the partnership reflected in VWS’ financial statements and its stock price?

As such, the aim of the Master Thesis is to provide a holistic approach in combining theory and

practice.

1.2. Structure

Following the introduction in Chapter 1, Chapter 2 provides an overview of the existing

empirical literature concerning the main motives and incentives for the establishment of a JV

based on the transaction cost theory (TCT) and the resource-based view (RBV). Further,

empirical findings of existing event studies exploring announcement effects are discussed.

Afterwards, the event study methodology including the tests statistics of the underlying

(non)parametric tests and the cross-sectional regression analysis is outlined in Chapter 3, before

reviewing the data selection process as well as the descriptive statistics in Chapter 4. Following

the presentation and discussion of the empirical findings in Chapter 5, the case study of the JV

MHI Vestas Group is put forth in Chapter 6 in light of the previously discussed elaborations and



the empirical findings of the underlying study. Finally, Chapter 7 concludes the Master Thesis

and reveals possible implications of the overall findings.

1.3. Delimitations and assumptions

The study at hand bears the following delimitations and is based on several underlying

assumptions:

Whereas the total size of the underlying data sample (n= 127) exceeds the minimum sample size

of n= 30, determined as a rule of thumb on the basis of the central limit theorem (CLM) to

approximate a normal distribution, the Danish (n= 12) as well as the Norwegian (n= 27) sub-

sample do not fulfill this assumption. However, this delimitation is partially offset, as all Danish

and Norwegian JV deals contained in the BvD Zephyr database which meet the criteria of the

search strategy (see Table 4) have been included. Regarding the test statistics utilized in the

study at hand, the underlying parametric test and cross-sectional regression assumptions are

outlined in Appendix E and F, respectively. Concerning the case study of the JV MHI Vestas

Group, publically available information has been used exclusively.



2. Literature review & hypotheses

After introducing the main objective of the study in Section 1.1, this chapter aims to provide a

theoretical basis for the Master Thesis, in order to provide an integral assessment of the

empirical findings of this study. Rooted in the TCT and RBV, the principal theoretical incentives

for MNEs to engage in JVs in comparison to other forms of cooperative mechanisms and foreign

entry modes are described in Section 2.1. In close connection to the theoretical motivations of JV

participation, Section 2.2 shifts the focus towards potential shareholder value creation in

distinguishing the empirical concepts of the Shareholder Value Maximization-, Rational

Expectations- and Institutional Investor hypotheses. Those concepts are discussed by virtue of

previous event studies, intending to investigate potential announcements effects upon JV

participation. Further, Section 2.3 reviews five task-, partner- and country-specific variables

affecting the stock market returns, in reference to Merchant (2002, 2004, 2010).

In light of the existing theories and concepts, hypotheses corresponding to the research questions

are put forth in advance of analyzing them as part of the event study as well as the cross-

sectional regression analysis.

2.1. Theoretical motivations for JVs over other cooperative modes

This section is set out to provide a general overview of motives and incentives for MNEs to

engage in a JV. As such, advantages and disadvantages of JVs in comparison to other entry

modes with a special focus on M&As are outlined. Naturally, depending on the economic,

political and financial situation, JVs prove to be a valuable option for MNEs to create

shareholder value.

Following Glaister’s (2004) argumentation, “the increasing number of JVs is due to

internationalization of technology and of product markets, turbulence in world markets and

higher economic uncertainty, more pronounced cost advantages and shorter life cycles”. When

selecting a foreign entry mode, MNEs face the omnipresent trade-off between control and the

associated risks. Given the fact that control and ownership in a JV are shared between/amongst

the JV participants, the partners have certain responsibilities towards one another, so that

decisions need to be undertaken and implemented collectively (Balakrisham & Koza, 1993).

Compared to other cooperative mechanisms, JVs constitute an exclusive foreign entry mode

based on shared partnership, entailing a vast amount of theoretical motives which have been

suggested and outlined in the recent academic literature. Oftentimes those revolve around the

TCT and the RBV, which shall be viewed as complementary rather than in isolation (Glaister,



2004). Whereas the TCT focuses on the cost minimizations with respect to transaction costs

(Williamson, 1975, 1985), the RBV proposes that firms gain competitive advantages through a

bundle of valuable tangible and intangible assets (Wernerfelt, 1984). Thus, in the following the

most pronounced incentives for JVs rooted in both, the TCT and RBV are discussed.

2.1.1. Cost minimization and economies of scale

As MNEs seek to grow their business through foreign market entries in response to external

pressures and strategic incentives, they automatically engage in cross-border transactions with

other firms and partners. Naturally, those transactions entail certain transaction costs e.g. search-,

information-, communication- and coordination costs comprising the major elements of the TCT.

According to Coase (1937), “transactions will be handled in such a way as to minimize the costs

in carrying them out”.

As the JV partners share all potential costs arising through transactions, JVs present an

opportunity for cost sharing, since only portions of assets are required (Balakrisham & Koza,

1993). M&As are considerably costly in comparison to the minimized administrative and

coordination costs in JVs. Beneficial synergistic gains are off-set and the oversharing of relevant

assets by multiple business lines lead to subsequent transaction costs following the acquisition of

assets (Balakrisham & Koza, 1993). Costs related to M&As further arise through information

asymmetry. Ravenscraft & Scherer (1987) point out that the mispricing or withholding of

information about the asset quality or organizational problems leads to an understatement of the

true value in pre-merger situations by the target. This results in adverse selection by the acquirer

who realizes the information asymmetry and accordingly discounts the acquisition price.

Therefore, the pooling of assets in JVs, on the basis of shared ownership, mitigates adverse

selection and leads to greater synergistic benefits. As one of the factors increasing transaction

costs is the opportunistic behavior by partner firms (Williamson, 1975, 1985), mutual obligations

as well as strategic objectives with respect to the JV partners, reduce opportunism and the

associated costs (Balakrisham & Koza, 1993).

Another aspect of cost minimization is increasing economies of scale (Hennart, 1988). As the

cost per unit of output decreases in relation to the increasing scale, costs are distributed over a

wider range of units of output. This is in line with Borde, Whyte, Wiant & Hoffman (1997)

stating that economies of scale is one of the primary incentives for MNEs to engage in a JV, as

they likely contribute to enhanced operative efficiency, lowering variable costs.



2.1.2. Synergies and knowledge sharing

Apart from cost minimization and economies of scale, an additional motive for MNEs to

establish JVs is the potential creation of synergies by pooling complementary resources of both

partner firms. Typically, synergies are distinguished between horizontal and vertical synergies

(McConnell & Nantell, 1985). Whereas “horizontal synergies include increased market power

through collusion and scale economies of production and distribution”, vertical synergies

comprise cost savings in relation to “inventory, transportation and other cost savings when

production steps are adjacent” (Johnson & Houston, 2000). In reference to the RBV, in

leveraging on synergies, firms have the opportunity to “achieve and sustain a competitive

advantage by configuring strategic assets in a way that is not possible to imitate perfectly, or by

resources, or capabilities that are durable and not perfectly transferable or replicable” (Glaister,

2004). As firms may not be able to create those capabilities by themselves, engaging in a JV

provides a valuable option to gain access to capabilities by partnering with other firms.

Moreover, the notion of the RBV can also be transferred to knowledge sharing, which is a

further motive for MNEs to participate in JVs. According to Polanyi (1966), JVs are utilized to

transfer organizationally embedded tacit knowledge, which cannot be effectively transferred in

codified and secured form e.g. via a patent. Hence, as it relies on intimate human contact upon its

exchange, a considerable amount of uncertainty rests with the buyer, since the true value of tacit

knowledge remains unknown until after the transfer (Hennart, 1988). Due to the nature of tacit

knowledge and the interdependence of both JV partners, JVs “are likely to be chosen to transfer

organizational knowledge” (Berg & Friedman, 1981). Kogut (1988) outlines two conditions

under which JVs are encouraged, “either (1) one or both firms desire to acquire the other’s

organizational knowhow; or (2) one firm wishes to maintain an organizational capability while

benefitting from another firm’s current knowledge or cost advantage”. Thus, JVs are viewed as

an instrument of knowledge sharing as well as continuous organizational learning (Lorange,

Roos & Brønn 1992).

2.1.3. Market access and diversification of risk

Building upon the aforementioned incentive for knowledge sharing, many MNEs use JVs to

internationally expand and gain access to foreign markets. “As the venture partner is already

familiar and accustomed with the local environment” (Borde, Whyte, Wiant & Hoffman, 1998),

JVs are a well-suited vehicle to facilitate the market entry. Hereby, the sharing of tacit

knowledge is used to overcome entry barriers (Hennart, 1988). Following the market entry, the



partner also needs to take cultural differences of the host market into account. Naturally, it

proves beneficial to leverage on the market as well as customer knowledge of the venture

partner, when conducting business in the foreign market (Barkema, Bell & Pennings, 1996).

Intuitively, entering a foreign market is associated with certain risks, such as country risks

accompanied with risks of economic and political changes as well as exposure to exchange rates

(Borde et al., 1998). Even though those risks may be reduced due to the experience and

knowledge of the venture partner, MNEs need to take them into account and assess them

carefully. The participation of a MNE in a JV can facilitate efficient risk sharing as a response to

demand uncertainty. Since the size of an investment is reduced through the partnership, resource

exposure can be limited and diversified through JV participation (Johnson & Houston, 2000).

Following this notion, Koh & Venkatraman (1991) find that JVs are superior to licensing

arrangements, technology exchange and supply & marketing arrangements, due to the shared

control and ownership features of JVs.

After elaborating on the most frequently highlighted motives and incentives for MNEs to engage

in JVs discussed by recent academic studies, it is in turn necessary to outline potential negative

contingencies of JVs.

With respect to the principal-agent dilemma, managers tend to overinvest in unprofitable projects

which in turn have a negative effect on the overall firm value, hence have a negative net present

value (NPV). As such, managers face a moral hazard and act in their self-interest rather than in

the interest of the MNE’s shareholders (Borde et al., 1998). In line with the behavioral

misconception of decision-makers, Knickerbocker (1973) argues that the establishment of JVs is

“often motivated by the desire to deny benefits to competitors rather than gain benefits on its

own”.

Concerning shareholder value creation through the establishment of a JV, the succeeding section

is aimed to function as a transition between the previously discussed general theoretical motives

for JVs and the practical analysis of shareholder value creation in JVs. As such, the focus is

shifted towards financial objectives driving the ultimate maximization of the MNE’s market

capitalization and cash flow generation.



2.2. Joint ventures and stock valuation

According to traditional valuation theory, “the market value of the firm is the sum of (a) the

discounted value of future cash flows expected to be generated from assets in place and (b) the

NPV of expected cash flows from investment opportunities that are expected to and undertaken

by the firm in the future” (Brealey, Myers & Allen, 2008). In line with the efficient market

hypothesis (EMH), all publicly available information is reflected in the stock price and

correspondingly changes the market value of a firm. In other words, “the value of the firm

changes as the stock market receives general or firm-specific information that changes the

market expectations about the cash returns from current and future assets” (Woolridge & Snow,

1990). The reaction of the stock price in an efficient market, as a response to a particular event

e.g. earnings-, dividends, M&As as well as JV announcements, is analyzed in examining the

abnormal or excess return (the actual return minus the expected return) following the event study

methodology outlined in Chapter 3. In relation to the orientation of the potential announcement

effect of an event Woolridge & Snow (1990) have introduced three hypotheses: Shareholder

Value Maximization-, Institutional Investor- and Rational Investor hypothesis.

In line with the general objective of managers to increase the firm value along with the key

financial figures and ratios, “the Shareholder Value Maximization hypothesis predicts that the

stock market will react positively to corporate announcements of strategic investments decisions”

(Woolridge & Snow, 1990). Pioneering in the research of JVs, McConnell & Nantell (1985)

examine 136 JVs involving 210 US-based companies in the time period of 1972-1979 and

recorded significantly positive 2-day average announcement period ARs of 0.73% and a

cumulative abnormal returns (CARs) over the event window of 2.15%. In line with that, Koh &

Venkatraman (1991) tested 39 US companies and a total of 175 JVs between 1972-1986, finding

a significant 2-day announcement-period ARs of 0.87%. More recently, Kumar (2010)

constructed a data sample including 688 firms participating in 344 JVs between 1985-2003, and

discovered positive ARs for shareholders of both JV partners.

In contrast to the Shareholder Value Maximization hypothesis, the Institutional Investor

hypothesis suggests the direct opposite; hence that the stock market will react negatively to

cooperative announcements of strategic investment decisions (Woolridge & Snow, 1990). The

rationale driving this view is based on the believe that US investors are more concerned with

short-term earnings rather than the long-term success of companies (Ellsworth, 1985). Thus,

“managers who do not maintain quarterly earnings to satisfy institutional investors will see the

companies’ stock prices decline” (Woolridge & Snow, 1990). In their sample of 109 US-based



JVs recorded in the time period of 1974-1986, Lee & Wyatt (1990) find that the overall investor

reactions to JVs with foreign firms are negative and experience a CAR of -3.84% over the event

window. Further, Chung, Koford & Lee (1993) also find support for the Institutional Investor

hypothesis. In their study comprising 230 JVs over the time span between 1969-1989,

shareholders suffer significant wealth losses amounting to a CAR of -2.79%.

In striking the balance between the two aforementioned hypotheses, the Rational Expectations

hypothesis “predicts that the stock market will not react quickly or strongly to corporate

announcements of strategic investment decisions” (Woolridge & Snow, 1990). Thus, the

hypothesis is associated with neutral ARs. The argumentation behind this notion is contradictory

to the EMH, since public information and announcements are not immediately reflected in the

stock price resulting in an insignificant effect following the announcement of the event. One of

the first studies supporting the Rational Expectations hypothesis was conducted by Finnerty,

Owers & Rogers (1986), analyzing 110 JVs involving US companies in the period between

1976-1979. The study revealed insignificant ARs from the viewpoint of the shareholders in the

short run. Further, Borde, Whyte, Wiant & Hoffman (1998) examine 100 JVs of US firms

establishing JVs in Asian countries in the period of 1979-1994 showing an insignificant market

reaction in relation to JV announcements.

Building upon the previously outlined examples, Table 1 exhibits a selection of academic effect

studies, which are categorized by their support for the aforementioned hypotheses. Along those

lines, the overview confirms that no consensus with respect to the impact of JV announcements

exists, as the empirical literature supports different hypotheses with either positive, negative or

insignificant announcement effects.

Table 1: Overview of empirical JV effect studies

Shareholder Value Maximization

Author(s) Publication

year

# (I)JVs # Participants Location Time

period McConnell & Nantell 1985 136 210 USA 1972-1979

Woolridge & Snow 1990 767 248 USA 1972-1987

Lummer & McConnell 1990 416 n/a USA 1971-1980

Chen, Hu & Shieh 1991 88 56 USA/China 1979-1990

Koh & Venkatraman 1991 175 39 USA 1972-1986

Crutchley, Guo & Hansen 1991 146 n/a Japan/USA 1979-1987

Balakrishnan & Koza 1993 64 85 USA 1974-1977

Etebari 1993 31 37 USA/Europe 1988-1991

Park & Kim 1997 158 174 USA 1979-1988

Johnson & Houston 2000 215 n/a USA 1991-1995

Hanvanich et al. 2003 20 23 USA 1985-1998

Gleason, Mathur & Wiggins III 2003 638 728 n/a 1985-1998



Jones & Daubolt 2004 158 n/a UK 1991-1996

Kumar 2008 597 1,194 USA 1985-2003

Kumar 2010 344 688 USA 1985-2003

Liu, Aston & Acquaye 2014 394 n/a n/a 2005-2010

Institutional Investors

Author(s) Publication # (I)JVs # Participants Location Time

period Knickerbocker 1973 187 n/a USA n/a

Lee & Wyatt 1990 109 n/a USA 1974-1986

Chung et al. 1993 230 173 USA 1969-1989

Chang & Chen 2002 69 137 Taiwan 1988-1999

Rational Expectations

Author(s) Publication # (I)JVs # Participants Location Time

period Finnerty et al. 1986 208 n/a USA 1976-1979

Borde et al. 1998 100 n/a USA 1979-1994

Notes

Table 1 lists the name of the author(s), publication year, the number of (international) JV announcements and

participants/firms included in the study, the home country/location of the respective firms as well as the time period

over which the study was conducted.

Source: Contribution by author.

Additionally, it is noteworthy that the majority of studies involve companies based in the USA,

whereas only a few studies focus entirely on non-US data sets. Non-US studies were published

by Meschi (2004), analyzing shareholder value creation of 67 French companies entering into

JVs with Chinese companies, Barkema & Vermeulen (1997) examining 828 foreign market

entries of 25 Dutch MNEs as well as Cleeve (1997) focusing on 170 Japanese foreign direct

investments (FDI) in Europe and the UK. As such, the underlying study aims to fill the empirical

gap of international/European evidence and overcome the US-based company bias by examining

127 JVs with Scandinavian participation.

In light of RQ1, presented in Section 1.1, and in line with the Shareholder Vale Maximization

hypothesis, the first hypothesis, H1, is formulated as follows:

H1: The stock market will react positively to Joint Venture announcements with Scandinavian

participation.

Hence, it is expected that the event study yields positive ARs which is reflected in the underlying

rationale of H1. Further details about the announcement effect are provided in the event study

methodology outlined in Section 3.1.

With respect to RQ2, the remainder of the literature review briefly touches upon five explanatory

variables which potentially reinforce the impact of the announcement effect tested in H1. Further

elaborations regarding those variables, which are targeted within H2-H6, are presented in the

succeeding sections.



2.3. Variables affecting the stock market return

This section elaborates on five empirically-tested binary variables with respect to their

explanatory power of the JV announcement effect. In reference to Merchant (2002, 2004, 2010),

Table 2 presents a framework which distinguishes between the explanatory task-, partner- and

country-specific variables included in this study:

Table 2: Explanatory variables

Task-specific Partner-specific Country-specific

Partner-venture relatedness

(PVRELATED)

Partner-partner relatedness

(PPRELATED)

Domestic vs. international JVs

(DOMESTIC) JV experience

(JVEXPERIENCE)

Cultural relatedness

(CULTURE) Source: Contribution by author.

Even though a wide range of variables have been discussed in academic journals and articles, the

variables at hand have been chosen for two specific reasons: Firstly, the impact of these variables

has not been as frequently investigated as others e.g. firm size, relative firm size, political risk,

level of competition etc. Secondly, the academic literature presents largely inconsistent findings;

hence it is of special interest to contribute to a better understanding of the explanatory power of

these specific variables. Intuitively, the different variables are tightly related to the theoretical

motivations of MNEs to engage in JV participation, which are outlined in Section 2.1.

Task-specific variable

2.3.1. Partner-venture relatedness

The first variable of interest, which potentially influences the value creation in JVs, is partner-

venture relatedness (PVRELATED). This variable is rooted in the task-related context of a JV, as

it aims at the impact of industry similarity in relation to overlapping business activities of the JV

partners and those of the upcoming JV (Merchant, 2010). According to Merchant & Schendel

(2000), “partner-venture relatedness refers to the nature of business activity undertaken by a

stand-alone firm, the JV partner, vis-à-vis that undertaken by the venture in which it

participates”. Building on the notion of economies of scale, greater similarity between the

business of the partner and the business of the resulting JV consequently leads to cost reductions

in e.g. production and/or manufacturing. Further, partner-venture relatedness also presents the

opportunity to leverage on existing economies of scope, since “opportunities for learning and/or

transferring skills and knowledge across value chains increase with similarity of business”

(Merchant & Schendel, 2000). The partner-venture variable goes hand in hand with the

theoretical objectives of forming JVs discussed in Section 2.1, as economies of scale and also



knowledge sharing comprise two of the main motives for MNEs to engage in JVs. Simply put, it

is expected that greater overlap and similarity between business activities of the partner and the

JV in which the MNE is engaging in, results in positive shareholder value creation. As the MNE

has the opportunity to contribute and put its existing resources/experience into practice, the

rationale seems straight-forward. As such, the following hypothesis is pronounced:

H2: Partner-venture relatedness is associated with positive abnormal returns upon JV

announcement.

Previous findings support H2 (Merchant & Schendel 2000, Merchant, 2012, Koh &

Venkatranam, 1991), as those confirm that partner-venture relatedness positively impacts the

announcement effect of JVs and thus facilitate the creation of shareholder value. Specifically,

JVs undertaken including R&D- as well as Marketing-related business activities show a distinct

increase in stock value of the partner (Merchant, 2002), whereas JVs intended to engage in

Manufacturing business activities record the opposite (Koh & Venkatranam, 1991).

Partner-specific variables

2.3.2. Partner-partner relatedness

The second variable at hand is partner-partner relatedness (PPRELATED), which as well as

partner-venture relatedness refers to the similarity in industry and business activities. However,

in this instance the variable is partner-specific, as it focuses on industry relatedness of all

partners prior to the formation of the JV.

The variable has been discussed by the academic literature to some extent, yielding controversial

and inconsistent results. Oftentimes, the underlying foundation of the variable is rooted in two

opposing hypotheses – the complementary hypothesis as well as the previously discussed TCT

hypothesis (Merchant & Schendel, 2000; Chang & Chen, 2002).

“The TCT literature suggests that greater similarity between partners’ businesses confers

production and transaction orientated gains upon these firms” (Merchant & Schendel, 2000). In

other words, JVs comprised of partners from related business segments are associated with

superior performance and thus positive stock market reactions. Further, it is argued that a higher

degree of relatedness reduces the chance of opportunistic behavior as well as information

asymmetry, hence facilitating the communication between the JV partners (Alchian & Demsetz,

1972). Thus, it is suggested that stock markets anticipate a higher level of productivity associated

with JV formations including business-related partners. The TCT hypothesis is supported by Koh



& Venkatraman (1991) who analyzed 175 US JVs in the time period between 1972-1986, stating

that “JVs involving related partners are more productive for the partners than those involving

unrelated parents”. A similar approach is taken by Merchant & Schendel (2000) who tested 393

US-based JVs between 1986-1990. The authors constructed the partner-partner relatedness

variable in favor of the TCT hypothesis, however were not able to find significant results.

In contrast to the TCT hypothesis, the complementary hypothesis suggests that the stock market

reacts positively to JV formations of JV partners from unrelated businesses (Chang & Chen,

2002). The main rationale is that the JV partners possess dissimilar resources, which complement

each other and thus result in higher productivity and stimulate cooperative behavior (Park &

Russo, 1996). The hypothesis is strongly related to the theoretical incentive of MNEs to benefit

from pooling complementary resources in JVs, as specified in Section 2.1.2. Therefore, rather

than producing similar, overlapping or even duplicative assets, JVs including partners from

unrelated businesses offer higher learning potential and knowledge sharing opportunities (Chang

& Chen, 2002). Park & Russo (1996) state that JVs “between relatively direct competitors tend

to be fragile and unstable [resulting in] a learning race and a tit-for-tat behavior”. In previous

studies, the complementary hypothesis is supported by Balakrishan & Koza (1993) who tested

64 JVs in the time period of 1974-1977. One of the main findings of the authors is that JVs

involving unrelated partners result in positive ARs, thus lending support to the complementary

hypothesis and underscoring the importance of synergies as one of the main drivers for JV

formations. This evidence has been more recently confirmed by Mohanram & Nanda (1996)

focusing on 253 US-based JVs as well as by Chang & Chen (2002) who tested 69 Taiwanese JVs

over a time span between 1988-1999. As such, it has been shown that the complementary

hypothesis holds for US-based as well as for international studies.

Based on the discussion of the partner-partner relatedness variable and in line with the

theoretical motives for JV formation, the following hypothesis, H3, is suggested:

H3: Partner-venture relatedness is associated with negative abnormal returns upon JV

announcement.

As such, it is believed that shareholder value creation is in line with the complementary

hypothesis and positively related to the pooling of synergistic assets of both JV partners.



2.3.3. JV Experience

Similar to the previously discussed variables, prior JV experience (JVEXPERIENCE) has been

investigated in the empirical literature to a limited degree, resulting in varying outcomes with

respect to its impact on the firm value upon JV announcement. According to Meschi (2004), “the

key concept in explaining the relationship between experience and performance is organizational

learning”. Naturally, previous JV experience can be categorized as partner-specific variable and

is connected to the theoretical incentive of mutual knowledge sharing and collective learning by

the JV partners, as discussed in Sections 2.1.2-2.1.3.

Accordingly, the level of JV experience, which is gathered through accumulated learning

experiences in previous JVs, forms expectations and realized best practice approaches in

managing a JV (Merchant & Schendel, 2000). Thus, a JV presents not only the opportunity for

MNEs to share knowledge regarding proprietary, intangible assets or to learn new insights from

the other JV partner(s), it also serves as an option to accumulate experience through constantly

repeated actions with respect to the general management of the JV (Meschi, 2004). Merchant &

Schendel (2000) state that “this enables more experienced firms to better anticipate and respond

to exogenous challenges related to the JV implementation [and] permits firms to better attend to

endogenous challenges originating form a partner’s opportunistic propensity”. With respect to

the negatively connoted opportunistic behavior of the other partner(s), Kumar (2010) suggests

that “firms with experience in operating JVs are more likely to recognize the importance of

devoting efforts toward common benefits and cooperative behavior in sustaining a mutually

beneficial relationship from the outset”. Thus, rather than developing a competitive drive

towards the other partner, experienced MNEs are likely “to understand the mutual value creation

logic” (Kumar, 2010).

As stated before, the empirical evidence offers mixed findings with respect to the influence of

previous JV experience on shareholder value creation. Several studies conducted by Merchant

(2002, 2012) do not reveal any support for a positive impact of previous JV experience. In

contrast, Meschi (2004) examines 67 Sino-French JVs during a period ranging from 1994-2002

and suggests that “French companies entering into JVs in China create more value as they

accumulate alliance experience, [defined as] experience in setting up and managing Sino-French

JVs”. As the authors focus exclusively on JVs between French (home country) and Chinese (host

country) MNEs, the study presents a unique location specification of the JV partners. In a more

broadly specified study, Kumar (2010) tests 344 US-based JVs established in the period between

1985-2003 which supports the positive influence of previous JV experience of both partners.



In line with the findings of Meschi (2004) and Kumar (2010), the following hypothesis, H4, is

suggested for the study at hand:

H4: Previous Joint Venture experience is associated with positive abnormal returns upon JV

announcement.

Country-specific variables

2.3.4. Domestic vs. international JVs

The final set of variables relates to the country-specific nature of the JV. Regarding domestic vs.

international JVs (DOMESTIC), globalization has been one of the main drivers for international

investments and continuous expansion of MNE businesses, resulting in an increasing number of

JVs over the previous decades (Phatak, 1997). Gleason, Mathur & Wiggins III (2003) outline

that in face of increasing market competition, “IJV transactions provide alternative strategies for

accomplishing growth objectives”. In other words, MNEs have been moving their strategic focus

from domestic investments in their home market towards international investments in foreign,

unknown markets. Naturally, those international investments are accompanied by e.g. systemic

and/or dissemination risk with respect to the host country as well as the JV partner(s) (Phatak,

1997). Based on the elevated level of perceived risk in IJVs, MNE investments in domestic JVs

have been consistently associated with significantly positive ARs, whereas the results for IJVs

are varying (Liu, Aston & Acquaye, 2014).

Support for value creation in domestic JVs is provided by Jones & Daubolt (2004) who

investigate 158 JV announcements of UK listed companies which engaged in the partnership

between 1991-1996. The authors observe higher ARs where the JV is either located in the UK

(domestic) or within the EU. Those results are consistently confirmed by other studies (Min &

Prather, 2002; Merchant, 2012). Opposing, as presented in Table 1 in Section 2.2, the research of

the effect of (I)JVs on shareholder value creation is inconclusive.

Finally, with respect to the comparison of domestic and IJVs, the empirical literature does not

always favor one over the other. In their study, Liu, Aston & Acquaye (2014) observe significant

shareholder value creation for both, domestic and IJV. As such, European-based domestic and

IJVs recorded significantly positive ARs, suggesting largely efficient stock markets.



Based on the previous discussion and the consistent evidence for domestic JVs, the following

hypothesis, H5, is suggested:

H5: Domestic Joint Ventures are associated with positive abnormal returns upon JV

announcement.

2.3.5. Cultural relatedness

The last variable of interest is cultural relatedness (CULTURE), which is closely related to

knowledge sharing and organizational learning, discussed in Sections 2.1.2-2.1.3. According to

Hofstede (1980), culture can be defined as “a collective programming of the mind that

distinguishes the members of on group or category of people from another”. With respect to IJV,

culture therefore determines the strategic mindset of both partners and bears the risk of

opportunism (Merchant, 2002), miscommunication (Barkema, Bell & Pennings, 1996),

increased costs in terms of JV coordination and management (Sirmon & Lane, 2004) as well as

diverging business practices (Park & Ungson, 1997). In fact, “cultural similarity facilitates

better JV execution […], eliminates a firm’s need to incur incremental bonding costs to sustain

its partner’s […] incentive to continue participation in the JV” (Merchant & Schendel, 2000). As

such, culturally related JV partners are able to minimize the exposure of potential failure and

facilitate better coordination and control between firms, given mutually shared expectations

(Hofstede, 1991).

In a more generic study, Merchant & Schendel (2000) utilized the Kogut & Singh (1988) index,

which equally weights the first four (Power Distance, Individualism, Masculinity, Uncertainty

Avoidance) of Hofstede’s (1980) cultural dimensions, to investigate the effect of cultural

difference on shareholder value creation. However, the authors were not able to find sufficient

significance in their results. Other studies have deviated from this approach in focusing on only

specific dimensions such as Individualism (Merchant, 2002) as indicator of culturally-embedded

opportunism, however again without significant outcomes. These outcomes are opposed by those

of Barkema & Vermeulen (1997), who examined 828 IJVs over the time period of 1966-1994,

supporting the positive impact of cultural relatedness.

In line with the notion that cultural relatedness increases potential shareholder value creation, the

following hypothesis, H6, has been put forth in this study:



H6: Cultural relatedness between/amongst the Joint Venture partner(s) is associated with

positive abnormal returns upon JV announcement.

After elaborating on the empirical evidence regarding the variables, the following chapter

provides a description and explanation of the event study methodology including the

determination of the estimation period. Moreover, the event window as well as the test statistics

applied in the event study and the cross-sectional regression analysis are outlined.



3. Methodology

Subsequent to the empirical evidence put forth in literature review presented in Chapter 2, this

chapter contains the overall event study methodology as well as the test statistics utilized in the

analysis to uncover the JV announcement effect. Consequently, an introduction to the general

concept of an event study is provided in Section 3.1, ensuing the determination of the estimation

period and the event window in Section 3.2. Moreover, Section 3.3 specifies the model selected

to capture the announcement effect of the JV announcement, prior to providing the test statistics

of the (non)parametric tests and the cross-sectional regression analysis in Section 3.4.

3.1. Event study methodology

In pursuit of answering RQ1 and gaining insights with respect to the central objective of the

study at hand, H1 sets out to examine whether shareholders are able to gain ARs following the

announcement of a JV with Scandinavian participation. In order to obtain the ARs, the event

study methodology has been applied. This methodology has been utilized as the primary concept

in several empirical event studies, as it is applicable to a range of announcements effects, e.g.

dividend payouts (Asquish & Mullins, 1983; Auerbach & Hasset, 2005; Brown, Liang &

Weisbenner, 2007), M&A announcements (Bruner, 2002; Cybo-Ottone & Murgia, 2000; Beitel,

Schiereck & Wahrenburg, 2004), seasonal anomalies (Kunkel, Andsager, Liang, Arritt, Takle,

Gutowski & Pan, 2002; Dimson & Marsh, 1986; Pezzi & Cavalcanti, 2001) etc. The essential

objective in conducting event studies is “to detect abnormal price changes in financial assets in

the time period around various events” (Bartholdy, Olson & Peare, 2007).

Additionally, event studies are used to test the efficiency of stock markets. Regarding the

different classification of the EMH, the event study methodology assumes semi-strong efficient

markets, absorbing publically available information and reflecting those through an adjustment

of the stock prices (Yolsal, 2011). Hence, private information is not taken into consideration as

this would eliminate the any profit-making opportunities for shareholders and investors

(MacKinley, 1997). As such, event studies are typically set up to compare a company’s stock

before and after the occurrence of an exogenous well-defined event relative to the expected

return (Brown and Warner, 1980; MacKinlay, 1997). Given that the underlying study focuses on

JVs with Scandinavian participation, the event study automatically provides insights and draws

conclusions about the efficiency of the respective Scandinavian stock markets.



For the quantification of the impact of the JV announcement as well as the determination of the

ARs, a specific model has to be selected. Typically, three primary models are used in practice to

calculate the ARs:

(1) mean adjustment model;

(2) market adjustment model and

(3) market model adjustment.

“Since the market model adjustment encompasses the other two” (Bartholdy et al., 2007), the

market model has been applied in the event study at hand. According to Corrado (2011), the

market model is “a more sophisticated procedure […] (which) adjusts the event date return to

remove the influence of the market”. A detailed overview and description of the market model is

provided in Section 3.3. Prior to measuring the normal and ARs, the estimation period as well as

the event window of the event study has to be specified.

3.2. Estimation period and event window

The initial task in conducting an event study is to determine the estimation period as well as the

event window (MacKinlay, 1997). In doing so, first the day of the occurrence of an exogenous

event, in this case the JV announcement needs to be obtained. When selecting the event date, one

is presented with various choices, including the rumor-, announcement- or completion-date of

the JV establishment retrieved from the BvD Zephyr database. With respect to the EMH, the

possibility of information leakages to individual stakeholders (private information as part of the

strong-market efficiency classification) or potential positive and negative rumors needs to be

eliminated. Assuming that the market immediately reflects all publically available information, it

is necessary to adjust for asymmetric information. This issue is addressed by exclusively

selecting deals with identical rumor- and announcement-date. Evidently, this contributes to the

reliability and robustness of the findings.

Following the determination of the event date, the event window needs to be chosen. In order to

increase the likelihood of detecting ARs, the event window usually comprises the event date, so

that not only early trades in anticipation of the announcement, but also late trades can be

accounted for. Consequently, the entire price effect of the occurrence of the exogenous event can

be captured. Empirical event studies have selected several different ranges for the event window.

Whereas early studies rely on 2-day event windows [0; +1] (McConnell & Nantell, 1895;

Woolridge & Snow, 1990; Koh & Venktraman, 1991), more recent studies select much larger



event windows of 5 [-2; +2] to 21 [-10; +10] days (Havanich, Richards & Cavusgil, 2001; Liu,

Aston & Acquaye, 2014). In striking a compromise, a primary 3-day [-1; +1] event window has

been chosen for this event study. Alternatively, a 7-day [-3; +3] event window has been set up to

decrease the likelihood of missing the potential stock market reaction prior or in retrospect of the

JV announcement. In surrounding the event date, the probability of capturing the announcement

effect is increased and the robustness of the study is enhanced.

After the selection of the event windows, it is necessary to determine the estimation period.

Peterson (1989) points out that it is crucial to “weigh the benefits of a longer period (an

improved prediction model) and the cost of a longer period (model parameter instability)”. As

the chosen length of the estimation period varies for daily return studies, e.g. 239 days (Corrado

& Zivney, 1992), 200 days (Liu et al., 2014), 100 days (Cowan, 1992), Peterson (1989) suggests

that “the typical lengths of the estimation period range from 100 to 300 days”. Following

academic consensus, this study will use a sampling interval of daily stock returns over an

estimation period of 200 days in advance of the event window. The timeline for this study is

visualized in Figure 2.

Figure 2: Timeline of event study

Notes Figure 2 visualizes the estimation period of 200 trading days prior to the event windows [-1; +1] and [-3; +3],

surrounding the event date, t0.


3.3. Measuring normal and abnormal returns

As pointed out in Section 3.1, this study applies the market model, which has been initially

introduced by Fama, Fisher, Jensen & Roll (1969), to determine the AR for each individual

security over the estimation period. The model is “the commonest approach to estimate the

relationship between a stock’s return and returns on the market by ordinary least squares (OLS)

regression and use this relationship to estimate expected returns, given returns on the market”

(Armitage, 1995). Based on the assumption of a linear relationship between the stock return and

the market proxy return, variations in the stock return caused by market movements are

eliminated. As such, the probability of finding an announcement effect in response to the

exogenous event, i.e. the JV announcement, is increased.

t-3 t-1 t+1 t+3

Estimation period= 200 trading days Event windows

Event date

t0



For any security i at day t, the market model is determined by:

Ri,t = αi + βiRmkt,t + εi,t (1)

E(εi,t = 0) var(εi,t) = Ϭ2

ε,t

By assumption of the market model, the actual return, Ri,t, is equal to the market return, Rmkt,t.

Thus, the firm-specific return, specified by the error term, εi,t, is essentially the shareholders’

excess return over the market return. According to Cowan (1992), the error term “has an

expected value of zero, is not autocorrelated and has constant variance”. Consequently, this

implies that it is unrelated to the overall market return.

In this study, the market return, Rmkt,t, has been obtained using the local MSCI indices of the four

Scandinavian countries (Denmark, Finland, Norway and Sweden), which measures the

performance of the large- and mid-cap segment of the respective market. Despite being a proxy

for the market return, choosing local MSCI indices increases the reliability and consistency of

the study compared to selecting a more general index, e.g. MSCI Europe. An overview of the

MSCI indices retrieved from Thomson Reuter Datastream is provided in Appendix A. Ri,t and

Rmkt,t are provided by:

Ri,t = ln (Stock pricet

Stock pricet-1

) (2)

Rmkt,t = ln (MSCI pricet

MSCI pricet-1

) (3)

Utilizing the natural logarithm slightly enhances the test statistic specification compared to

arithmetic returns (Corrado & Truong, 2008). Additionally, the authors find that the natural

logarithm eliminates any negative values and converges the return distribution towards

normality.

The expected return, E[Ri,t], can be determined by using the regression coefficients, α and β, as

the best linear unbiased estimator (BLUE) of the market model OLS estimation:

E[Ri,t

] = α ̂+ β̂Rmkt,t (4)

By rearranging formula (1), the market model for calculating the AR is specified as:

ARi,t = Ri,t - E[Ri,t] = Ri,t - α̂i - β̂iRmkt,t (5)



As such, the AR, ARi,t, is the day-zero firm-specific return εi,t identified by the market model

(Corrado, 2011). At the same time. formula (5) indicates that by incorporating the market model,

ARi,t equals the difference between the actual and expected return of a security, as specified in

formulas (1) and (4), respectively.

To capture the announcement effect in its entirety, the CAR method which is calculated as the

sum of all ARs contained in the event window, has been selected. Since the primary event

window consists of three days, the ARs over this time span are summed up.

The CARi is determined by:

CARi = Ai, -1 + Ai, 0+ Ai, +1 = ∑ ARi,t (6)

The CARs are later used in the cross-sectional regression analysis, to examine the impact of five

empirically-tested variables on potential announcement effect. As mentioned in Section 3.2,

CAR [-1; +1] is used as the prevailing interval for the event window, however CAR [-3; +3] is

also applied in the event study, in order to ensure that the risk of missing the effect of the JV

announcement on the stock performance is minimized (Kothari & Warner, 2004).

3.4. Test statistics

After elaborating on the measurement of normal and ARs, this section outlines the test statistics

for the (non)parametric tests, in order to secure the reliability of the event study findings. In line

with the academic consensus, a battery of parametric and nonparametric test is applied in

combination, to enhance the validity of the study, as the two sets of tests are based on different

assumptions (Bartholdy, Olesen & Paere, 2007). The following (non)parametric tests are applied

to the data sample as part of the event study:

Parametric tests

T1 t-test with cross-sectional independence;

T2 t-test with standardized abnormal returns;

T3 Parametric test with variance adjustment;

Nonparametric tests

T4 Rank-test;

T5 Sign-test and

T6 Generalized Sign-test.



Appendix D provides additional details about the specification of the above-mentioned tests and

presents the corresponding formulas and t-statistics. Overall, following the general principles of

inferential statistics, both sets of tests are aimed to examine the following hypothesis:

H0: CAR = 0 (7)

H1: CAR ≠ 0

As such, H0 suggests the absence of ARs, whereas in case H0 is rejected as result of the

significant (non)parametric tests, H1 lends support for the presence of ARs, hence shareholder

value creation upon the JV announcements.

3.4.1. Parametric tests

In general, according to Bartholdy, Olesen & Paere (2007), “parametric test statistics for

abnormal performance on event days are based on a standard t-test of the difference between two

means”. As mentioned before, in contrast to the nonparametric tests, parametric tests are based

on a variety of assumptions (Campbell & Wesley, 1993):

(1) The sample must be independently distributed;

(2) the sample must be normally distributed and

(3) the sample variance must be constant.

Given the case that all of the assumptions are fulfilled, parametric tests are believed to be

superior to nonparametric tests (MacKinlay, 1997). However, if one or more of the above-

mentioned assumptions are not met, it is possible to use the nonparametric tests as a robustness

check, as the parametric tests are likely to be misspecified and nonparametric tests are less likely

to commit Type 1 errors (MacKinlay, 1997; Yolsal, 2011). Appendix E reveals that assumptions

(2) and (3) are violated, as the data sample exhibits skewedness (non-normal distribution) as well

as a distinct increase in sample variance on the event date , t0 (inconstant variance). This can be

justified, provided that the event study contains time series data including daily returns, ARs are

likely to follow a non-normal (skewed) distribution due to event induced variance around the

event date (Yolsal, 2011). Consequently, as two out of three parametric test assumptions are not

fulfilled, it can be argued that “the nonparametric tests are more reliable than the parametric

measures of abnormal performance” (Bartholdy et al., 2007).



All (non)parametric tests have been conducted via the Statistical Analysis Software (SAS). The

corresponding codes and log-files are included in Appendix I.

T1 – t-test with cross-sectional independence

The T1 t-test investigates the ARs with an adjustment of cross-sectional dependence (Brown &

Warner, 1985). Initially introduced by Brown & Warner (1980), T1 divides each AR in the event

window by its estimated standard deviation, in order to yield standardized ARs. With respect to

the parametric test assumptions, Bartholdy, Olesen & Paere (1997) state that “in its unadjusted

form, the variance of the T1 test statistics is the sum of the variances of ARs of the individual

stocks”. Based on the parametric test assumptions the ARs under H0 are independently and

identically distributed following a student t-distribution, it is improbable to rely on time series

data which is likely to be skewed (Brown & Warner, 1985). Even though the T1 t-test is

comparatively simple, it overcomes the problem of cross-sectional correlation and volatility by

standardizing the ARs (Patell, 1976).

T2 – t-test with standardized abnormal returns

As the ARs in event studies using financial data are generally not independently and identically

distributed, Bowman (1983) states that “the accepted procedure for this circumstance is to

standardize the individual returns”. As such, the T2 t-test adjusts the ARs by an estimate of the

standard distribution of the ARs (Bowman, 1983). Regarding the T2 test statistics, “each excess

return is divided by its estimated standard deviation to yield a standardized excess return”

(Corrado & Zivney, 1992).

T3 – Parametric test with variance adjustment

As is the case for the data sample at hand, the data exhibits an increase in variance around the

event date. According to Boehmer, Musumeci & Poulsen (1991), this is due to a temporary

increase in systematic risk and uncertainty. Following this notion, Bartholdy, Olesen & Paere

(2007) argue that the standard deviation during the estimation period relative to the standard

deviation during the event window might be understated. Consequently, the T3 Parametric test is

adjusted for deviations in variances during the pre-specified event window. In response to the

changes in variance, “the residuals are first standardized and then the variance is estimated

during the event window” (Bartholdy et al., 2007).



3.4.2. Nonparametric tests

As mentioned in Section 3.4, nonparametric tests do not rely on the assumptions of the

parametric tests. Since the underlying sample is non-normally distributed (skewed) and exhibits

increased variance around the event date, t0, the nonparametric tests “perform better […] than

parametric tests that assume stable variances“ (Cowan, 1992). Several other empirical studies

support this statement (Peterson, 1989; Bowman, 1989; Corrado, 2011). Whereas early studies

(Dyckman, Philbrick & Stephan, 1984; Bernard, 1987) generally conclude that the standard

parametric event study tests are well specified with good test power, more recent studies

including nonparametric tests, such as the T4 Rank-, (Corrado, 2011; Maynes & Rumsey, 1993)

the T5 Sign- (Cowan, 1992; Corrado & Zivney, 1992; Bartholdy, Olesen & Paere, 2007) and the

T6 Generalized Sign-test (Cowan, 1992; Cowan & Sergeant, 1996; Bartholdy et al., 2007),

outline that those tests outperform the parametric tests, given the violation of the normality

assumptions as a result of daily stock return data.

T4 – Rank-test

The initial task of performing the T4 Rank-test is to transform “each security’s time series of

market-model excess return into their respective ranks” (Corrado, 1989), in order to arrive at a

rank statistic for t0 (event date). With respect to the sample at hand [-200; +1], the average rank

is 0.5 plus half the number of observed returns, i.e. 101.5 (Corrado & Zivney, 1988). As such,

“the ranking procedure transforms the distribution of security excess returns into a uniform

distribution across the possible rank values regardless of any asymmetry in the original

distribution” (Corrado, 1989). According to Yolsal (2011), this procedure “is more resistant

against event-induced variance on day zero (event date) and has a better performance than

traditional [parametric] tests”. This is in line with studies by several studies arguing that the T4

Rank-test outperforms parametric tests (Campbell & Wesley (1993); Maynes & Rumsey, (1993);

Bartholdy, Olesen & Paere, (2007).

T5 – Sign-test

The T5 Sign-test, initially introduced by Corrado & Zivney (1992), assumes that the probability

of observing either a negative or positive AR is 0.5 (Bartholdy et al., 2007). As such, the

essential rationale of the T5 Sign-test is that it is equally probable, that the security excess returns

are either denoted with a positive or negative sign under H0 (MacKinlay, 1997). With respect to

the test statistic, the observed proportion of positive returns minus 0.5 is divided by the standard

deviation of a binominal distribution MacKinley, 1997). Whereas McConnell & Muscarella

(1985) and Lummer & McConnell (1990) state that the T5 Sign-test is the most successful



nonparametric tests in terms of validity and power, MacKinlay (1997) points out that, given the

assumed equally probable negative and positive distribution of ARs, the T5 Sign-test is likely to

be misspecified, as the distribution oftentimes is skewed due to daily data returns (MacKinlay,

1997). Subsequently, Corrado (1989) outlines that the previously discussed T4 Rank-test for

abnormal performance in event studies “offers improved specification under H0 and more power

under H1”, since the T5 Sign-test requires the ARs to follow a symmetrical distribution for

correct test specification.

T6 – Generalized Sign-test

Unlike the traditional T5 Sign-test proposed by Corrado & Zivney (1992), the T6 Generalized

Sign-test, does not assume equally probable excess security returns under H0, however specifies

them “as a fraction of positive returns computed across stocks and across days in the parameter

estimated period” (Cowan & Sergeant,1996). As such, “the T6 Generalized Sign-test compares

the proportion of positive ARs around an event to the proportion from a period unaffected from

the event” (Cowan & Sergeant, 1996). In this way, the T6 Generalized Sign-test addresses the

potential asymmetric return distribution under H0 (Cowan, 1992), which has been previously

criticized by Corrado (1989).

Provided that the underlying sample of this study does not meet the parametric test assumptions,

as outlined on Section 3.4, the nonparametric tests are superior to the parametric tests. Given the

likely misspecification of the T5 Sign-test, (assumption of equally probable negative and positive

ARs), the T4 Rank-test as well as the T6 Generalized Sign-test are believed to be the most

appropriate and powerful tests regarding the study at hand.

Following the detailed elaborations with respect to the (non)parametric test statistics as part of

the underlying event study to provide insights with respect to RQ1 and H1, the next section puts

forth further details in connection to the cross-sectional regression analysis utilized to answer

RQ2.

3.4.3. Cross-sectional regression

With the aim of investigating the empirically-discussed influences on shareholder value creation

related to the announcement of the Scandinavian JV establishment, the cross-sectional

relationship between the different JV deals is tested using regression analysis, in order to gain

additional insights with respect to RQ2. In doing so, explanatory task-, partner- and country-

specific variables which potentially impact the JV announcement effect are introduced. SAS as



well as EViews were used to specify and conduct the cross-sectional regression analysis.

Appendix F gives an overview of the cross-sectional regression assumptions (Gauß-Markow) on

which the following model is based:

CAR = β0+β

1PVRELATED+β

2PPRELATED+β

3JVEXPERIENCE+β

4DOMESTIC+β

5CULTURE (8)

With the exception of the aforementioned normality assumption, all Gauß-Markow assumptions

are fulfilled. Despite the fact that not all assumption are met, the validity of the empirical

findings is granted, as an asymmetric distribution due to skewedness is caused by the given

nature of time series data including daily returns.

The coding classification of the explanatory variables, included in formula (8), is listed in Table

3. This is in line with the discussion of the literature review in Section 2 and corresponds to the

formulation and thus the expected outcome of H2-H6.

Table 3: Coding classification

H# Variable Coding Source


H2 Partner-Venture relatedness

(PVRELATED)

1=Related

0=Unrelated

BvD Zephyr database

3-digit US SIC code


H3 Partner-Partner relatedness

(PPRELATED)

1=Related

0=Unrelated

BvD Zephyr database

3-digit US SIC code

H4 JV experience

(JVEXPERIENCE)

1=Experienced

0=Unexperienced

BvD Zephyr database

Previous JV deals


H5 Domestic JV

(DOMESTIC)

1=Domestic

0=IJV

BvD Zephyr database

Partner country locations

H6 Cultural relatedness

(CULTURE)

1=Related

0=Unrelated Kogut – Singh Index


In reference to the PVRELATED and PPRELATED, the binary coding is based on the respective

3-digit US Standard Industrial Classification (SIC) codes obtained via the BvD Zephyr database.

In using the US SIC codes as indicator for both relatedness variables (H2 and H3), this study

follows the majority of the empirical literature which merely varies in the selection of digits.

Amongst others, previous studies of used single SIC codes (Harrigan, 1988: 2-digit SIC codes;

Balakrishnan & Koza, 1993: 3-digit SIC codes), combination of SIC codes (Merchant &

Schendel, 2000; Keown, Laux & Martin, 2005: 2- and 4-digit SIC codes) or absolute distant

values (Kumar, 2010; Mohanram & Nanda, 1996), in order to quantify the PVRELATED as well

as the PPRELATED variable. In the coding process of the sample, all of the above-mentioned



approaches have been applied to the sample. The 3-digit US SIC codes have been ultimately

selected, since those yield the highest explanatory power, given by adjusted squared multiple

correlation coefficient (adj. R2) with respect to the cross-sectional regression analysis.

Concerning H4, the coding classification of the DOMESTIC variable is straightforward, as

domestic JV is simply located in the home country of all JV partners, distinguishing domestic

JVs from IJVs (Liu, Aston & Acquaye, 2014; Jones & Daubolt, 2004). Further with respect to

H5, JVEXPERIENCE is grounded upon previous JV engagements of the Scandinavian partner

(Meschi, 2004; Kumar, 2010). The binary distinction has been made utilizing the BvD Zephyr

database. The final variable, CULTURE included in H6, has been coded on the basis of the Kogut

& Singh (1988) index using the first four of Hofstede’s (1980) cultural dimensions (Power

Distance, Individualism, Masculinity and Uncertainty Avoidance). The index calculates the

cultural distance between country P1 and PX (e.g. Denmark and China), CDP1,PX, using the

following formula:

CDP1, PX=∑

(IiP1-IiPX)2

Vi

4i=1

4 (9)

As such, the index divides the sum of cultural differences of two countries, P1 and PX, along the

cultural dimension, i, by the variance of the index of that dimension, Vi. The score is then equally

weighted by the number of dimensions (in this case by four). With respect to the sample at hand,

the average CD score is 1.69. Accordingly, the CULTURE variable distinguishes cultural

relatedness between countries/deals above and below the average CD score. The calculation of

the Kogut & Singh (1988) index for all deals is presented in Appendix I, whereas the indices of

Hofstede’s (1980) four cultural dimensions divided by countries can be obtained in Appendix C.

The succeeding chapter provides insights into the data selection procedure and further outlines

the descriptive statistics of the underlying data sample of this study.



4. Data

After elaborating on the event study methodology (Section 3.1), including the selection of the

event window and the estimation period (Section 3.2) as well as the determination of normal and

ARs (Section 3.3) and the corresponding test statistics (Section 3.4), this chapter provides further

details about the data sample at hand. Moving forward, Section 4.1 discloses the data selection

procedure after which Section 4.2 provides on overview of the descriptive statistics of the ARs

contained in the data sample.

4.1. Data selection

The sample data for this study have been obtained by the BvD Zephyr database. Following the

initial filtering process via the database (Zephyr Search), a manual selection process (Manual

Search) was undertaken. Hence, the data selection process comprises two parts and a total of 14

search criteria, in order to enhance the validity and representativeness of the data. The search

strategy applied is presented in Table 4.

Table 4: Search strategy

Zephyr Search Search result

1. Deal type: Joint-venture 32,583

2. Listed/Unlisted/Delisted companies: listed acquirer 16,601

3. Current deal status: Completed 12,631

4. Time period: up to and incl. 31 December 2014 12,622

5. Country: DK, FI, NO, SE (Acquirer) 566

Sub-Total 566

Manual Search Search result

6. Primary Scandinavian JV partner 433

7. Acquirer without ISIN – limits the generation of return data via Datastream 377

8. Announced date = Rumor date 362

9. Targets without country code – limits coding of cross-sectional regression 346

10. Duplicates 187

11. Thomson Reuter Datastream errors/Missing return data 171

12. Dead/thinly traded companies 137

13. Individual/Private investors 132

14. 9999/Non-classifiable SIC codes – limits coding for cross-sectional regression 127

Total

127

Source: BvD Zephyr database (Zephyr Search). Contribution by author (Manual Search).

In line with RQ1, the objective is to examine whether an announcement effect in JVs with

Scandinavian participation exists and the respective shareholders are able to gain ARs.

Correspondingly, the first set of Zephyr search criteria filters all completed (criterion 3)

Scandinavian (criterion 5) JVs (criterion 1) in the time period up to 31 December 2014

(criterion 4), given at least one listed acquirer (criterion 2). It is noteworthy that the BvD Zephyr



database maintains the original M&A setup which typically includes one “Acquirer” and one

“Target” company. Following the description of the BvD Zephyr database guide regarding JVs,

“two or more companies create a new joint-owned company. The “Acquirer” companies

continue to exist and a new entity is created. The new company is coded as the “Target”

company and the investors are coded as joint “Acquirer” companies”. Thus, when investigating

JVs, it is essential to account for all companies listed as “Acquirer” companies, since those

comprise the JV partners. Another remark has to be made regarding criterion 5, as the

Scandinavian region is typically classed as five countries, i.e. Denmark, Finland, Iceland,

Norway and Sweden. However, due to an insufficient representation of a potential Icelandic sub-

sample (n= 2), the study focuses on the remaining four countries. As pointed out in Table 4, the

initial Zephyr Search yields an interim total of 566 deals.

After the Zephyr Search, a Manual Search was conducted, not only to identify missing data

entries (criterion 7, 9, 11, 14) as this limits either the generation of return data via Thomson

Reuter Datastream and the coding of the cross-sectional regression analysis, but also to further

tailor the sample data to the central objective of the study. As the study focuses on JVs with

Scandinavian participation, only JVs including primary Scandinavian partner have been selected

(criterion 6). More specifically, deals have been selected in which a Scandinavian investor was

listed as first (primary/lead) “Acquirer” as defined by the database. One of the cardinal

objectives of the data selection process is the validity of the sample data. As mentioned in

Section 3.2, only deals with identical announcement and rumor date were selected (criterion 8).

This avoids offsetting a premature reflection of asymmetric information in the stock performance

of the “Acquirer”, which would have a deteriorative influence on the consistency of the study.

Prior to generating the historical daily return data in Thomson Reuters Datastream on the basis of

the “Acquirers” ISIN code, the data sample was adjusted in order to avoid misspecification of

the return data following the generation process. As such, in case one “Acquirer” was involved

in multiple deals, the most recent deal has been selected (criterion 10). Hence, duplicative data

entries in the data sample have been avoided. After importing the historical daily return data and

matching it to the corresponding local MSCI return data, the sample size was adjusted for thinly

traded companies. In reference to Bartholdy, Olson & Peare (2007), companies with >50%

trading inactivity (no change in stock price from one to the next day) over the estimation period

have been removed (criterion 12). As this study focuses on announcements effects regarding

publically listed companies, all deals involving individual and private investors were eliminated

from the data sample (criterion 13). In applying a total of 14 search criteria (Zephyr: 5; Manual:



9) a data sample of 127 deals (DK: n= 12; FI: n= 39; NO: n= 24; SWE: n= 49) tailored to

support the aim of this study, was created. An overview of all deals is provided in Appendix B.

In accordance to the event study methodology, discussed in Section 3.1, Thomson Reuters

Datastream was used to generate the historical, daily prices of all relevant stocks and local MSCI

indices within the time intervals [-201; +1] and [-203; +3] corresponding to the 3-day and 7-day

event window and the estimation period of 200 days. After the data generation, the return strings

were adjusted, so that potential deals announced during weekends are aligned. In applying the

log-normal function outlined in formulas (2) and (3) introduced in Section 3.3, it is possible to

calculate the returns of both, the stocks and the MSCI indices. Utilizing the selected market

model put forth in formula (5) the ARs are obtained, which together with the CARs ([-1; +1] and

[-3; +3] provided the necessary components for the (non)parametric tests (Sections 3.4.1-3.4.2)

as well as the cross-sectional regression analysis (Section 3.4.3). In the succeeding section, the

descriptive statistics of the data sample is presented, in order to provide further insights about the

main statistical features and the sample distribution.

4.2. Descriptive statistics

In advance of elaborating on the traditional descriptive statistics of ARs, Figure 3 provides an

overview of the distribution of deals by year, divided into the four sub-sample countries. The

figure indicates that with the exception of 1999, the data sample includes JV deals ranging over

the entire time span (1998-2014). After an initial JV spike in 2001 and a consequent drop in

2002-2003, the JV participation increased steadily over the years 2004-2007 after which a rapid

decline followed in 2008. Intuitively, this development can be attributed to the negative

repercussions of the financial crisis occurring during that time span, discouraging MNEs and

investors to engage in JVs. In the following years 2009-2011, JV participation stagnated on a

relatively constant level before slightly decreasing during 2012-2014.



Figure 3: Sample distribution of JV deals/year

Notes

Figure 3 displays all JV deals contained in the Zephyr BvD database which meet the search criteria listed in Table 4

(Section 4.1).


After briefly elaborating on the sample distribution over the given time-span with respect to the

four sub-samples, the descriptive statistics of the ARs are provided in Table 5. Additionally to

the elaboration on the parametric test (Appendix E) and cross-sectional regression assumptions

(Appendix F), the output gives further indications about the distribution of the ARs.

Table 5: Sample descriptive statistics

Sample ALL DK FI NO SWE

n 127 12 39 27 49

Mean 0.00231 -0.00204 0.00360 0.00176 0.00265

Median 0.00035 0.00048 0.00393 -0.00023 -

0.00169 Mode -0.00136 n/a n/a -0.00136 n/a

Std. dev. 0.03122 0.03685 0.02686 0.03814 0.02889

Variance 0.00097 0.00136 0.00072 0.00145 0.00083

Skewedness 0.56571 -1.73160 1.84671 1.20549 0.42990

Kurtosis 10.9748 4.35585 13.3533 11.8596 9.19873

Std. error mean 0.00159 0.00614 0.00248 0.00424 0.00238

Source: Data generation via SAS (see Appendix I – Descriptive_Statistics_Output)

According to the CLM, under common conditions, the sum of many random variables has an

approximately normal distribution. Thus, in increasing the sample size, the sample converges

towards a normal, “bell-shaped” distribution with a sample mean equal to zero, and a variance

equal to one (Rosenblatt, 1956). In reference to the first three descriptive measures in Table 5,

this is the case for the (sub)-sample means; however, the variances rather tend towards zero than

one. Those values give a first indication that the sample is non-normally, hence asymmetrically

distributed.

1 3 2 1 1 2 1 1 1

1 1 2 3 5 4

5 3

2 4 3 2 1

1

2 2 1

3 3

6

1 2

2 1 2 1 2

1

6

2

1

4

5 6

5

4 5 1 5 1 2 1 1

0

5

12

6

2

9

13 13

18

9 9 7

9

5 5 4

0

5

10

15

20

25

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

# o

f J

V d

eals

DK FI NO SWE



Further specification about the distribution of the ARs can be made by investigating the

skewedness and kurtosis measures in Table 5. Whereas skewedness gives an indication about the

symmetry, kurtosis provides insights about the peakedness or flatness of the distribution.

According to Corrado (2011), the skewedness and kurtosis values of a normally distributed, large

sample fluctuate around zero and three, respectively. Regarding the descriptive statistics in Table

5, especially the kurtosis values clearly deviates from those suggested.

The sample distribution presented in Figure 4, confirms the previously discussed observations.

Figure 4: Sample distribution

Source: Data generation via SAS (see Appendix I – Descriptive_Statistics_Output)

With a mean of 0.00231, a median of 0.00035 and a mode of -0.00136 (mean>median>mode)

recorded in Table 5, the distribution of the ARs included in the entire sample (ALL) is

asymmetrically skewed to the right, indicating the potential for outliers reflected in the

skewedness value of 0.56571. In reference to Figure 4, the kurtosis value explains the high peak

around the center of the distribution output. As the ARs are measured by the difference between

the actual and expected returns, which are assumed to be equal under the market model (Section

3.3), deviations from the center of the distribution are only impacted by the error term which

comprises the firm-specific excess shareholder return. Several more formal tests regarding the

symmetry of the distribution are provided in Appendix E and F as part of the parametric test and

cross-sectional regression assumptions. In combination with the Q-Q plot, those tests including

the Shaprio-Wilk, Cramer von Mises and Anderson Darling test are consistent with this



observation and confirm the existence of a non-normal distribution of the ARs contained in the

sample.

Shifting the focus towards the five explanatory variables included in the cross-sectional

regression analysis introduced in Section 3.4.3, Figure 5 and 6 display the distribution of the

binary coding classification and provide further insights with respect to the underlying sub-

samples in relation to JV participation preferences of the Scandinavian MNEs.

Figure 5: Sample variable coding – binary= 1


Whereas Figure 5 presents the sample variable coding among the countries for binary variables =

1, Figure 6 depicts the one for binary variables= 0, on the basis of the coding classification

visualized in Table 3 introduced in Section 3.4.3. On a general level, it is apparent that all sub-

samples are considerably equally represented in both binary coding distributions.

Figure 6: Sample variable coding – binary= 0


5 6 6 5 7

19 18 13 26 23

5 8 12

16 19 12

17 17

22 27

41

49 48

69

76

0

10

20

30

40

50

60

70

80

PVRELATED PPRELATED DOMESTIC JVEXPERIENCE CULTURE

# o

f b

ina

ry=

1

DK FI NO SWE

7 6 6 7 5

20 21 26 13 16

22 19 15

11 8

37 32 32

27 22

86

78 79

58 51

0

10

20

30

40

50

60

70

80

90

PVRELATED PPRELATED DOMESTIC JVEXPERIENCE CULTURE

# o

f b

ina

ry=

0

DK FI NO SWE



Even though this study, amongst other objectives specified in the research questions, aims to test

the impact of the five binary variables on the stock market reaction by means of potential

announcement effects as set out by RQ2, additional conclusions can be drawn in terms of the

investment behavior and engagement preferences of the MNEs in the four sub-samples.

The Danish sub-sample does not indicate any specific preferences across the variables

concerning JV participation, whereas Finish MNEs prefer to invest in IJVs (DOMESTIC: 13<26)

based on their previous JV experience (JVEXPERIENCE: 26>13). Further, Norwegian MNEs

tend to engage in unrelated JVs (PVRELATED: 5<22) with partners primarily operating in

differing business industries (PPRELATED: 8<19), however with related cultural backgrounds

(CULTURE: 19>8). Finally, initial indications reveal that Swedish MNEs also predominately

participate in unrelated JVs (PVRELATED: 12<37) with unrelated partners (PPRELATED:

17<37). Here, similarities to the Norwegian sub-sample become apparent. Further, Swedish

MNEs mainly invest in IJVs (DOMESTIC: 17<32), similar to Finish MNEs.



5. Empirical evidence and discussion

After presenting the data selection process and the descriptive statistics, this chapter applies the

event study methodology described in Sections 3.1. As such, Section 5.1 and 5.2 provide the test

results of the parametric and nonparametric tests, respectively. Moreover, Section 5.3 further

puts forth the test results of the cross-sectional analysis. Throughout this chapter, the test results

are discussed in light of the findings by the academic literature with respect to JVs and stock

valuation, which has been discussed in Section 2.2.

5.1. Parametric tests

Both sets of tests (parametric and nonparametric) are conducted in order to investigate whether

an announcement effect in JVs with Scandinavian participation exists, as outlined in RQ1 as well

as H1. As mentioned in the discussion of the descriptive statistics in Section 4.2 and further

outlined in Appendix E, the data sample does neither meet the normality assumption nor the

constant variance assumption of parametric tests. As such, the nonparametric tests presented in

the following section hold more explanatory power. Nevertheless, in applying a battery of tests

in combination, the validity of the findings is secured.

The results of the parametric tests are provided in Table 6. All tests have been applied to the

entire data sample as well as to the four country-specific sub-samples. As the primary event

window contains three days [-1; +1], the t-statistic and the corresponding p-value of the event

date, t0, as well as the surrounding days, t-1 and t+1 are presented.

In addition to those, the t-statistics and p-values of the CARs over the intervals [-1; +1] and [-3;

+3] are provided. The event window interval has been extended in order to ensure capturing the

announcement. Additionally, in testing over both intervals, further conclusions can be drawn

with respect to the efficiency of the respective markets. In general, significant values are

highlighted in grey and specified as shown in the footnotes of the tables displaying the results.

By looking at Table 6, a multitude of significant results have been generated. Starting with the

findings of the entire data sample and focusing on the CARs, T2 and T3 show highly significantly

positive p-values of both, CAR [-1; +1] (p= 0.04356; 0.00325) as well as of CAR [-3; +3] (p=

0.00262; 0.00003), respectively. This gives an initial indication that there is a positive

announcement effect associated with Scandinavian JV participation and simultaneous

shareholder value creation.



Shifting the attention towards the sub-samples, all countries show evidence of a significantly

positive announcement effect. With the exception of T2 t-test of the Danish and Swedish sample,

all country-specific samples exhibit CARs with significant p-values, whether over the 3- or 7-

day event window.

Table 6: Parametric test results

Parametric tests

Sample EW T1 p-value T2 p-value T3 p-value

ALL

-1 1.20389 0.23008 1.99581 0.04733**

1.89407 0.05968*

0 1.76116 0.07976* 1.65756 0.09900

* 1.20737 0.22874

1 -0.20631 0.83676 -0.13487 0.89285 -0.12166 0.90329

CAR [-1; +1] 1.59276 0.11282 2.03140 0.04356**

2.97978 0.00325***

CAR [-3; +3] 1.19903 0.23197 3.04892 0.00262***

4.27291 0.00003***

DK

-1 -0.01331 0.98939 0.89077 0.37414 0.98126 0.32767

0 -0.50168 0.61645 -0.19438 0.84608 -0.34031 0.73399

1 0.12198 0.90304 0.86350 0.38891 1.45137 0.14827

CAR [-1; +1] -0.22691 0.82073 0.90061 0.36890 2.09232 0.03769**

CAR [-3; +3] -1.92069 0.05623* 0.06261 0.95014 0.59847 0.55022

FI

-1 0.59106 0.55516 0.75021 0.45403 0.66695 0.50558

0 1.16846 0.24403 1.58938 0.11358 1.12745 0.26093

1 1.67187 0.09614* 1.45931 0.14607 1.39254 0.16533

CAR [-1; +1] 1.98111 0.04897**

2.19329 0.02946**

3.18694 0.00167***

CAR [-3; +3] 0.69033 0.49081 0.99102 0.32290 0.58689 0.55796

NO

-1 -0.31145 0.75579 0.41354 0.67966 0.39234 0.69523

0 1.96524 0.05079* 0.03564 0.97161 0.01843 0.98532

1 -0.44084 0.65981 -0.45274 0.65124 -0.38747 0.69883

CAR [-1; +1] 0.70030 0.48457 -0.00205 0.99836 0.02330 0.98143

CAR [-3; +3] 2.74637 0.00659***

2.59410 0.01020**

3.85098 0.00016***

SWE

-1 1.85836 0.06461* 1.79371 0.07439

* 1.70719 0.08936

*

0 1.48691 0.13864 1.31517 0.18998 1.20181 0.23088

1 -1.28131 0.20159 -1.60965 0.10907 -1.32408 0.18701

CAR [-1; +1] 1.19163 0.23484 0.86558 0.38778 1.58492 0.11459

CAR [-3; +3] 1.77865 0.07686* 0.53765 0.59142 3.82293 0.00018

***

Notes

Significance level: * ≙ p <0.10;

** ≙ p<0.05;

*** ≙ p<0.01

EW= Event window

Values are rounded to 5 decimal places

Source: SAS data output.

With respect to previous studies discussed in Section 2.2, the significantly positive findings

support the Shareholder Maximization hypothesis advocated by, amongst others, McConnel &

Nantell (1985), Balakrishan & Koza (1993) and Kumar (2008, 2010). Moreover, in offering an

interim conclusion of H1 and RQ1, the empirical evidence of the underlying sample on the basis

of the parametric tests suggests that an announcement effect in JVs with Scandinavian

participation exists. Additional cross-references to the empirical evidence and an overall

conclusion of the test results is presented after elaborating on the findings of the nonparametric



tests in the upcoming section, which play a pivotal role in providing more detailed insights to

answer the RQ1.

5.2. Nonparametric tests

After the presentation of the parametric test results in the previous section, the nonparametric test

results are put forth in the following. As discussed in Section 3.4.2 and in reference to the

academic literature, the nonparametric tests are not bound to the parametric test assumptions. As

the underlying sample is asymmetrically distributed and shows significant fluctuations in

variance around the event date, the nonparametric tests are better suited to test H1. As such the

second set of tests complements the first one and relativizes the findings of the parametric tests.

More specifically, based on the elaboration of the nonparametric tests, the T4 Rank-test and the

T6 Generalized Sign-test are believed to be the most appropriate and powerful tests regarding the

study at hand.

Table 7 provides the nonparametric test results. In reference to the entire data sample, only the T4

Rank-test shows significant positive CARs [-1; +1] and [-3; +3], even though over both intervals.

Further, the fact that the T4 Rank-test as well as the Generalized Sign-test include significant

value over the event window, more specifically on the day before the event date, t-1, confirms the

efficiency of the Scandinavian markets, as investors tend to trade/invest in anticipation of the

event date.

Whereas the Danish and Swedish sub-samples show no significant p-values for the CARs, the

Finish sample exhibits CARs with significant p-values (p= 0.01947; p= 0.04952; p= 0.09761,

respectively) over the 3-day event window of the T4 Rank-, T5 Sign- as well as the T6 Generalized

Sign-test. Since the CAR [-3; +3] is insignificant, the announcement effect revolves around the

event date and disperses after its temporary existence. Hence, shareholders of Finish MNEs

significantly benefit upon the announcement of a JV with Finish participation, as there is the

potential for temporary value creation. The remaining Norwegian sample shows a CAR with

positive p-value (p= 0.03167) over the wider 7-day event window of the T4 Rank-test, capturing

the delayed announcement effect.

Comparable to the results of the parametric tests discussed in the previous section, the tests

exclusively show significantly positive p-values of the ARs. This further substantiates the

support for the Shareholder Maximization hypothesis. As the underlying data sample only

contains European JV deals, the findings contribute to the existing evidence and are in line with



those of Etebari (1993) and Jones & Daubolt (2004) who examine European as well as UK JV

deals, respectively.

Table 7: Nonparametric test results

Nonparametric tests

Sample EW Rank p-value Sign p-value Gen Sign p-value

ALL

-1 2.53389 0.01206**

1.51606 0.13365 2.18475 0.03009**

0 0.63347 0.52716 -0.17718 0.85955 -0.12462 0.90095

1 0.36959 0.71209 0.08859 0.92950 0.40832 0.68349

CAR [-1; +1] 2.04206 0.04248**

0.81838 0.41413 1.47418 0.14203

CAR [-3; +3] 2.23391 0.02663**

0.15455 0.87734 0.95478 0.24061

DK

-1 1.18926 0.23577 1.42546 0.15561 1.92155 0.05611*

0 -0.38114 0.70351 -0.85528 0.39344 -0.39121 0.69606

1 1.39430 0.16480 0.57019 0.59200 0.76517 0.44509

CAR [-1; +1] 1.27157 0.20503 0.65839 0.51105 1.34336 0.18070

CAR [-3; +3] 0.18014 0.85723 -0.49525 0.62098 -0.38455 0.70099

FI

-1 1.59665 0.11195 0.93354 0.35169 1.34402 0.18049

0 1.00068 0.31821 0.77795 0.43753 0.70310 0.48283

1 1.48300 0.13967 1.71149 0.08857* 1.68139 0.09429

*

CAR [-1; +1] 2.35578 0.01947**

1.97625 0.04952**

1.66448 0.09761*

CAR [-3; +3] 0.43015 0.66756 0.17942 0.85779 1.04041 0.29944

NO

-1 0.61340 0.54032 0.58467 0.55944 0.90814 0.36493

0 -0.21134 0.83284 -0.58467 0.55944 -0.61226 0.54107

1 0.37113 0.71094 0.19489 0.84568 0.54509 0.58631

CAR [-1; +1] 0.44640 0.65580 0.11252 0.91053 0.93087 0.35306

CAR [-3; +3] 2.16408 0.03167**

1.02479 0.30673 0.90814 0.36493

SWE

-1 1.51654 0.13099 0.40200 0.68812 0.67847 0.49827

0 0.44470 0.65703 -0.13400 0.89354 -0.17922 0.85795

1 -1.57127 0.11772 -1.74200 0.08307* -1.60870 0.10928

CAR [-1; +1] 0.22515 0.82210 -0.85102 0.39579 -0.46511 0.64236

CAR [-3; +3] 1.47768 0.14111 -0.39207 0.69544 0.12536 0.90037

Notes


** ≙ p<0.05;

*** ≙ p<0.01

EW= Event window


Source: SAS data output.

In specifically focusing on the results of the T4 Rank-test as well as the T6 Generalized Sign-test

in combination with the parametric test set, it can be confirmed that a positive announcement

effect in JV with Scandinavian participation exits. This lends support to H1 as well as answers

RQ1. As such, shareholders are able to earn excess return over their respective home market. The

announcement effect is preeminently pronounced with respect to the Finish sub-sample inferred

by the highly significant p-values of CAR [-1; +1] throughout all nonparametric tests.

After establishing that an announcement effect in the underlying sample exists, the next section

elaborates on the findings of the cross-sectional regression analysis, in which the explanatory



power of five binary variables with respect to the announcement effect is investigated, as

specified by RQ2. Hence, further conclusions can be drawn regarding H2-H6.

5.3. Cross-sectional regression

Following the presentation of the (non)parametric test results which reveal the existence of an

announcement effect in JVs with Scandinavian participation, this section investigates the

explanatory power of five empirically-tested task-, partner- and country-specific binary

variables. Thus, building on the insights from the previous chapter, the focus is shifted towards

RQ2. The cross-sectional regression analysis has been conducted for the entire sample as well as

for all country sub-samples. Table 8 provides the cross-sectional regression results for the entire

sample (n= 127). Initially, it is of cardinal importance that the F-value of 2.20432, reported at the

bottom of the table is significant to less than 10% (Prob. F-statistic= 0.05817), indicating the

affirmed validity of the results. As stated in the notes of the table, the expected sign reported in

the second column of the table refers to the formulation of the respective hypothesis.

Table 8: Cross-sectional regression results, entire data sample (ALL)

Variable Expected sign Coeff. t-statistic

Intercept

-0.00457 -0.54450

(0.5871)


Partner-venture relatedness (+) 0.00611 0.60811

(PVRELATED) (0.5443)


Partner-partner relatedness (-) -0.00317 -0.33390

(PPRELATED) (0.7390)

Joint Venture experience (+) 0.02121 2.48270**

(JVEXPERIENCE) (0.0144)


Domestic vs. International JV (+) 0.00003 0.00039

(DOMESTIC) (0.9969)

Cultural relatedness (+) 0.00064 2.25826**

(CULTURE) (0.0257)

R2 0.08348

Adjusted R2 0.04561

F-value 2.20432*

Notes


** ≙ p<0.05;

*** ≙ p<0.01

The p-values are displayed in parentheses

Expected sign is based on H2-H6


Source: EViews Regression_output.



Building upon the initial findings, two of the five binary variables are of special interest. With a

t-statistic of 2.48270 (p-value= 0.0144), JVExperience is highly significant, thus lending support

to H4. As such, the results indicate that previous JV experience is positively associated with

shareholder value creation in JVs. Hence, the binary variable exhibits high explanatory power

impacting the announcement effect in JVs with Scandinavian participation. The findings support

those of Merchant & Schendel (2000), as previous JV experience is associated with accumulated

learning which forms expectations and crystalizes best practice approaches in coordinating and

executing JVs. As such, MNEs with previous experience are likely to benefit from a repetition

effect and shape common cooperative behavior in sustaining a mutual beneficial relationship

with their respective partners from the initial incept of the JV (Kumar, 2010).

In addition, CULTURE is highly significant with a t-statistic of 2.25826 (p-value= 0.0257).

Similar to JVEXPERIENCE this is in line with the expectations and the corresponding

formulation H6. Hence, the empirical evidence supports H6, as cultural relatedness between the

JV partners positively impacts the announcement effect and is associated with positive ARs.

These findings are underscored by several academic studies confirming the positive effect of

cultural relatedness (Merchant & Schendel, 2000; Berkema & Vermeulen, 1997). As such, it is

suggested that cultural relatedness minimizes “the exposure of potential failure and facilitate

better coordination and control between firms, given mutually shared expectations” (Hofstede,

1991). Following this notion, shareholders and investors react positively to JV announcements of

culturally related partners.

Further, it is noteworthy that all other binary variables PVRELATED, PPRELATED and

DOMESTIC are insignificant. Hence, H2, H3 and H5 are not supported. However, the tendencies

of the respective t-statistics are in line with the expected signs affirming the orientation

formulation of the hypotheses. Similarly, several academic studies recorded insignificant results

related to those variables (PVRELATED: Balakrishan & Koza, 1993; PPRLEATED: Merchant &

Schendel, 2000; DOMESTIC: Borde, Whyte, Wiant & Hoffman, 1998). Thus, it can be

concluded that partner-venture and partner-partner relatedness as well as domestic partnerships

in JVs do not significantly impact the announcement effect in JVs. Turning to the sub-samples,

the cross-sectional regression results are displayed in Table 9.



Table 9: Cross-sectional regression results, sub-samples

Variable Expected sign DK Coeff. t-statistic FI Coeff. t-statistic

Intercept

-0.19327 -2.55416

(0.0432) -0.00402

-0.30754

(0.7604)


Partner-venture relatedness (+) 0.12684 1.92846 -0.00737 -0.58633

(PVRELATED) (0.1021) (0.5616)


Partner-partner relatedness (-) 0.01801 0.28222 0.00385 0.30417

(PPRELATED) (0.7873) (0.7629)

Joint Venture experience (+) 0.09715 1.38797 0.03070 2.50250**

(JVEXPERIENCE) (0.2145) (0.0175)


Domestic vs. International JV (+) 0.08474 0.64775 -0.00677 -0.53794

(DOMESTIC) (0.5411) (0.5942)

Cultural relatedness (+) 0.05885 0.45758 0.00080 2.10593**

(CULTURE) (0.6634) (0.0429)

R2 0.56794 0.26147

Adjusted R2 0.20788 0.14957

F-value 1.57736 2.33670*

Variable Expected sign NO Coeff. t-statistic SWE Coeff. t-statistic

Intercept 0.01905

1.02913

(0.3151) 0.00127

0.11748

(0.9070)


Partner-venture relatedness (+) -0.01450 -0.56804 0.00977 0.68525

(PVRELATED) (0.5760) (0.4969)


Partner-partner relatedness (-) -0.00760 -0.35630 -0.01494 -1.15883

(PPRELATED) (0.7252) (0.2529)

Joint Venture experience (+) 0.01753 0.90211 0.00741 0.69341

(JVEXPERIENCE) (0.3772) (0.4918)


Domestic vs. International JV (+) -0.03380 -1.67316 0.00989 0.65950

(DOMESTIC) (0.1091) (0.5131)

Cultural relatedness (+) 0.00063 1.71849 0.00467 0.32384

(CULTURE) (0.1004) (0.7476)

R2 0.21817 0.06963

Adjusted R2 0.03201 -0.03855

F-value 1.17198 0.64364

Notes


** ≙ p<0.05;

*** ≙ p<0.01

The p-values are displayed in parentheses

Expected sign is based on H2-H6


Source: EViews Regression_output



The findings from the entire sample can be partially transferred to the country-specific sub-

samples. Especially the Finish sub-sample exhibits strong resemblance as it is not only overall

significant with a F-value of 2.33670 (p-value= 0.06374), but also displays similar effects with

respect to the variables included. As such, JVEXPERIENCE and CULTURE are highly

significant with t-statistics of 2.50250 (p-value= 0.0175) and 2.10593 (p-value= 0.0429)

supporting the rationale put forth in relation to the results of the entire sample. Similarly to the

results of the entire sample, the remaining binary dummy variables have no effect on the stock

price reaction, as they are all insignificant.

Taken all findings of the results into consideration, it is possible to arrive at an overall

conclusion of the empirical evidence induced through the (non)parametric test and the cross-

sectional regression analysis conducted in this chapter. Table 10 provides a visual representation

of the overall results.

Table 10: Overall results

Significance

Hypothesis Variable ALL DK FI NO SWE Support

H1 Announcement effect

(AR)

H2 Partner-venture relatedness

(PVRELATED)

H3 Partner-partner relatedness

(PPRELATED)

H4 Joint Venture experience

(JVEXPERIENCE)

H5 Domestic vs. International JV

(DOMESTIC)

H6 Cultural relatedness

(CULTURE)

Notes

green = significance/support; yellow = partial significance/support; red = no significance/support

The highlights are based on (non)parametric test and cross-sectional regression results Source: Contribution by author.

In Sections 5.1 and 5.2 the analysis of the (non)parametric tests supported the existence of an

announcement effect in JVs with Scandinavian participation. Hence, the Scandinavian

shareholders and investors are able to earn excess returns surrounding the announcement date.

The effect is especially pronounced with respect to the Finish sub-sample, whereas the remaining

sub-samples merely offer partial significance. Regarding the more suitable nonparametric T4

Rank-test and T6 Generalized Sign-test, the Danish, Norwegian and Swedish sub-sample record



insignificant results for either one or both tests. Thus, they are only partially supported. In

response to RQ1, these overall findings grant support for H1 and the existence of an

announcement effect in JVs with Scandinavian participation. Moreover the findings are in line

with the Shareholder Maximization hypothesis, as the announcement effect positive ARs,

leading to additional shareholder value creation.

Concerning the potential impact of the binary variables tested within the cross-sectional

regression analysis formulated in RQ2, the findings in Section 5.3 suggest that previous JV

experience (JVEXPERIENCE) of the Scandinavian MNEs as well as cultural relatedness

(CULTURE) between the JV partners positively impact the announcement and thus favorably

influence the announcement effect established in H1. Again, the results are especially distinct for

the Finish sub-sample. The empirical evidence therefore supports H4 and H6. As the findings for

the remaining variables (PVRELATED, PPELATED, DOMESTIC) are insignificant, H2, H3 and

H5 are not supported, as the variables do not impact the announcement effect and the

simultaneously shareholder value creation.



6. Case study

Based on the presentation and discussion of the findings of the event study and the cross-

sectional analysis, this chapter aims to depict a transition towards gaining more practical insights

into the topic of JV formations and the associated implications. As such, an exemplary case

study of the JV between VWS and MHI is conducted. This particular JV has been selected since

it is not only part of the underlying data sample of the event study, but also well-known due to its

reputation, size and VWS’s headquarter location in Aarhus, Denmark. Further, as the JV was

announced in September 2013 and finally established in April 2014, it is possible to examine the

JV over a large time period, given an extensive amount of publically available information. In

closing the loop, the case study sets out to offer a holistic approach in applying practical issues in

reference to theoretical concepts, such as the primary incentives for MNEs to engage in a JV or

the empirical findings of the event study, which have been presented in Section 2.1. Figure 7

depicts the timeline for the case study.

Figure 7: Timeline of case study

Notes

The timeline of the case study is split into three time periods, covering (1) the time prior and including the

JV announcement, (2) the transition period between the JV announcement and commencement including

2014 and (3) Q1/2015 going forward.


A brief industry and market overview is provided in Section 6.1, covering the time period

between 2010-2013 containing the years prior to the JV announcement as well as the JV

announcement date itself (27 September 2013). A company description is put forth in Section

6.2, outlining the economic and financial situation during the same time period, including

VWS’s internal two-year turn-around process. As such, the first two sections serve as a basis for

the case study, as it is of special interest to examine the impact of the JV formation in

comparison to the prior time period. After a short description of the JV MHI Vestas Group in

Section 6.3, the main incentives driving VWS to engage in the JV are outlined in Section 6.4, in

reference to RQ3. This is done, since the case study serves as an application of the previously

discussed theoretical concepts. As the prior sections involve not only financial, but also

economic, political as well as environmental aspects, close attention is paid to the impact of the

announcement effect on the stock market in Section 6.5. In doing so, it is investigated in how far

Q1/2015

2012-2013

2-year turn-around

27 September 2013

JV announcement

1 April 2014

JV commencement

2010-2013 2014



the repercussions of JV participation, over and beyond the transition period between the JV

announcement and actual commencement on 1 April 2014, is reflected in the financial

statements and the firm value of VWS, as outlined in RQ4. A future outlook which concludes the

case study is provided in Section 6.6.

6.1. Industry and market overview

In response to the omnipresent danger of climate change and global warming, the wind industry

has been growing rapidly over the previous decades, as wind energy constitutes a valuable

renewable resource. According to the Global Wind Energy Council (GWEC Report, 2013),

“wind power is one of the main energy sources of the future which not only generates clean and

climate-friendly electricity, but also creates jobs and reduces risks, such as the exposure to

particular matter and susceptibly to the price volatility of imported fuel”. As such, the number of

wind farms and electric power transmission network (grid) installations has been ever-increasing

over the recent past (GWEC Report, 2013). Hereby, it is important to distinguish between on-

and offshore wind power. As the wind stream is steadier in offshore locations than on land, the

offshore wind segment has been one of the major focal areas for players in the global wind

industry despite the considerably higher installation and maintenance costs associated with

offshore grid locations (Gipe, 1993).

Market value 2010-2013

Even though the global wind industry has been generally associated with continuous value

generation, the industry has experienced a downward trend in growth percentage during 2010-

2013. Figure 8 shows the global turbine industry value and the annual growth rate for the

respective time period.



Figure 8: Global turbine industry value: mEUR, 2010-2013

Notes

The primary y-axis depicts the industry value in mEUR, whereas the secondary y-axis shows the annual growth rate

over the time period 2009-2013. Source: Marketline Industry Profile (2014)

With regard to Figure 8, the global turbine industry value, including the on- as well as the

offshore wind segment, has been increasing from mEUR 47,183 to mEUR 57,658 between 2009-

2012, exhibiting dynamic fluctuations in positive growth percentages. In 2013, the value

decreased to mEUR 57,230, resulting in the first negative growth percentage of -0.70% for more

than 20 years (GWEC Report, 2013). This development is mainly rooted in a 92% decrease in

installations in the US, due to uncertain federal policies, after recording all-time highs in 2012

(GWEC Report, 2013). Further, the rise of other renewable energy sources, i.e. solar-, bio- or

hydro-energy increases the competition in the market and limits the demand for wind energy.

Key players

The global wind turbine market consists of four main players – VWS, Xinjiang Goldwind

Science & Technology Co., Ltd., Enercon GmbH and Siemens AG – dividing over 40% of the

market share amongst them (Marketline Industry Profile, 2014). Figure 9 displays the market

share by volume of the largest companies in the global wind turbine market between 2010-2013.

47183

52166 53113 57658 57230

10,70%

1,80%

8,60%

-0,70%

-2,0%

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

0

10000

20000

30000

40000

50000

60000

70000

2009 2010 2011 2012 2013

Gro

wth

%

Va

lue

in m

EU

R

Value in mEUR Growth %



Figure 9: Market share of leading players in the wind turbine industry, 2010-2013

Source: Marketline Industry Profile (2014); Cleantechinvestor.com (2012)

Despite VWS’s leading position, its market share has experienced a sharp drop since 2007,

where VWS had a 28% share (Romanowicz, 2007). The succeeding section provides a company

description of VWS and outlines the financial and economic situation over the time period 2010-

2013.

6.2. Company description – Vestas Wind Systems A/S – 2010-2013

VWS has been founded in 1945 by Peder Hansen and is headquartered in Aarhus, Denmark.

Following VWS’s slogan “Wind. It means the world to us”, VWS “is the only global energy

company dedicated to wind energy” (VWS.com). Whereas several competitors differentiate their

business activities over multiple renewable energy sources, VWS focuses solely on offshore and

onshore wind energy. Pursuing the strategy of vertical integration, VWS’s core business covers

the entire supply chain, including R&D, manufacturing, sales, installation and maintenance of

their products and power plants (Windpowermonthly.com, 2012). Referring to the production of

VWS’s WTGs, ranging from V80-2.0MW to V164-8.0MW (rotor diameter in meters followed

by the energy generation capacity), VWS produces blades, generators, power converters and

castings (Windpowermonthly.com, 2012). With a total wind turbine installation of 60,232MW

generated by 51,147 WTGs located in 73 countries around the world, VWS is the world’s

leading wind turbine manufacturer, accounting for 13.1% of market share in 2013 (VWS.com).

Table 11 provides an overview of the development of VWS’s key financial figures reported in

the respective annual reports.

14,8%

12,7%

14,0% 13,1%

8,7% 9,5%

6,0%

11,0%

7,2% 7,8% 8,2%

9,8%

5,9% 6,3%

9,5%

7,4%

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

16,0%

2010 2011 2012 2013

Ma

rket

sha

re %

VWS Xinjiang Golwind S&T Co., Ltd. Enercon GmbH Siemens AG



Table 11: Overview of VWS's financial figures, 2010-2013

in mEUR 2010 2011 2012 2013

Income Statement

Revenue 6,920 5,836 7,216 6,084

EBIT (before

special items) 310 -60 -697 102

Net income 156 -166 -963 -82

Financial Ratios

EBIT margin % 4.5 -1.0 -9.7 1.7

ROIC % 10.8 -1.3 0.2 7.7

Share Ratios

EPS 0.8 -0.8 -4,8 -0,4

Share price at year-

end (EUR) 23.6 8.3 4.3 21.5

Shares outstanding

at year-end 203,704,103 203,704,103 203,704,103 203,704,103

Market

capitalization 4,807,416,831 1,690,744,055 875,927,643 4,379,638,215

Source: VWS Annual Report (2013)

VWS has been able to generate relatively stable revenues between 2010-2013, however recorded

negative net income over this time period. With an EBIT margin of -9.7% and a return on

common equity (ROIC) of 0.2%, 2012 marks the least profitable year over the respective time

period. The overall declining pattern in financial figures and ratios throughout 2010-2012 also

extends to the share ratios. In connection with the ROE values, VWS shareholders and investors

suffered losses throughout 2011-2013, with EPS fluctuating between EUR -0.40 and EUR -4.80.

With a positive EBIT and a share price increase from EUR 4.30 to EUR 21.50, VWS was able to

recover in 2013. This provides the first indication of the positive impact of VWS’ JV

participation with MHI.

Even though that the financial figures contained in Table 11 provide a useful snapshot of VWS

financial position, it is necessary to reformulate the statements, in order to align them with the

business activities of VWS. The “reformulation readies the statements for the (profitability)

analysis […] which uncovers the factors that determine residual earnings” (Penman, 2013). By

focusing on shareholder value, the main drivers of VWS’ profitability are exposed. “The key

element is the separation of operating and investment activities from financing activities in the

financial statements, for it is the operating and investment activities that typically generate

value” (Penman, 2013).

Table 12 displays a DuPont profitability analysis based on the reformulated financial statements

of VWS which are contained in Appendix G. By looking at the key items, which are highlighted

in the table, VWS has been recording a negative return on common equity (ROCE) value

between 2010-2013. This observation is in line with the financial figures included in Table 11.



“Residual earnings are determined by the profitability of shareholders’ investment, ROCE“

(Penman, 2013). In turn, the ROCE is driven by the return on net operating assets (RNOA), the

profitability measure for a firm’s operations, and the net borrowing cost (NBC) or the return on

net financial assets (RNFA). After a significant drop in 2012 with a ROCE of -20.64%, VWS

improved the ROCE to -6.49% in 2013, however was still not able to create value for its

shareholders. For further explanations about the generation of values, please refer to Appendix

H.

Table 12: VWS DuPont profitability analysis, 2010-2013

Level 1 ROCE drivers 2010 2011 2012 2013

Financial liability leverage

ROCE_NFA -6.49%

ROCE_NBC 9.28% -0.55% -20.64%

RNOA 8.39% -3.55% -40.51% 6.03%

FLEV_NFA 16.20%

FLEV_NBC -12.77% -8.12% -47.12%

RNFA or NBC 15.38% 33.41% 1.66% -71.28%

FLEV*SPREAD 0.89% 3.00% 19.87% 12.52%

SPREAD -6.99% -36.97% -42.17% 77.31%

Operating liability level

RNOA 8.39% -3.55 -40.51% 6.03%

Implicit interest 85.1 105.15 90.58 88.45

ROOA 5.31% 0.10% -14.43% 3.42%

OLLEV 1.10 1.52 1.54 2.83

OLSPREAD 2.81% -2.40% -16.93% 0.92%

Level 2 Operating profit drivers 2010 2011 2012 2013

RNOA 8.39% -3.55% -40.51% 6.03%

Profit Margin, PM 3.76% -1.69% -13.20% 1.24%

Average Turnover, ATO 2.23 2.11 3.07 4.86

Notes

The values contained in Table 12 are calculated using the reformulated financial statements which are based on

those from VWS Annual Report (2013). The reformulation of the financial statements is based on Penman (2013).


The declining trend in financial figures and ratios throughout 2010-2012 is caused by a

combination of economic and political challenges, not meeting the previous expectations of

VWS. In 2011, VWS realized its first loss since 2005 and had to abandon its initial plan to

generate bEUR 15 in revenues including a 15% profit margin in 2015 (Triple 15)

(Rechargenews.com, 2011). VWS’ profit warnings were issued at the end of 2011, due to

deferred shipments cause by slower-than-expected commissioning of the generator factory in

Travemünde, Germany (VWS Annual Report, 2011). Additionally, general postponements of

project deliveries corresponding to approximately bEUR 1.2 and higher industrialization costs



for the V112 WTG and the GridStreamerTM

technology led to a -4.6% decline in gross profit

compared to 2010 (VWS Annual Report, 2011). Overall, write-downs of individual projects

accumulated to total costs of EUR 149m. Further, the drop in return on invested capital (ROIC)

before special items of -12.1% compared to 2010 was due to large-scale investments in new

facilities and technology, not utilized in 2011 and resulting in an overcapacity of wind turbine

plans (VWS Annual Report, 2011). Moreover, the US market, which is VWS’ key market,

experienced a slowdown caused by a combination of low gas prices and the lack of a national

energy plan with ambitious climate targets exacerbating the market conditions (VWS Annual

Report, 2011). Due to the above-mentioned factors and conditions, VWS’ share price dropped by

65% and the MNE was forced to lay-off 2,000 FTEs in 2011.

In response to the negative trend, VWS launched a two-year turn-around initiative in the end of

2011 striving to improve profitability and cash flows, hence stabilizing its financial position. One

of the focal areas of the process was cost reductions. As such, the VWS’ new chairman, Bert

Nortberg, together with the new five-person management team decided to slackened its product

range to four turbine platforms (VWS Annual Report, 2012). Additionally, improved production

efficiency and another lay-off of 4,923 full-time equivalent (FTE) employees lowered the need

for investments, resulting in a revenue increase of 24% in 2012 compared to 2011 (VWS Annual

Report, 2012). As part of the new operating business model, VWS set out to improve cash flows

and earnings in the short term, while not sacrificing long-term opportunities such as capitalizing

on the growing service business or developing the V164 WTG for the offshore business segment

(VWS Annual Report, 2012).

Due to the prevailing competitive market pressures as well as VWS’ economic and financial

situation, VWS decided to engage in a JV together with the Japanese-based MHI, in which VWS

sourced its offshore business segment including the R&D projects related to the V164 WTGs

(VWS Annual Report, 2013). Based on the main objectives of the new operating model, with

MHI, VWS has found a lucrative investor providing financial stability, allowing the MNE keep

the core business intact and offloading a potentially significant burden during a period of

economic and political uncertainty (Bloomberg.com, 2013).

6.3. Establishment of the JV MHI Vestas Group – 2013/2014

On 27 September 2013, the 50/50 JV, MHI Vestas Group, was announced and finally

commenced on 1 April 2014 (Bloomberg.com, 2013; Reuters.com, 2014). The Group includes

one principal which is also based in Denmark (MHI Vestas A/S) as well as seven wholly-owned



subsidiaries, located in Belgium, Germany, the Netherlands, Sweden and the UK (VWS Annual

Report, 2013). In engaging into the JV, VWS effectively combined its brand and technical

knowledge in relation to the on- and offshore wind business segment including its existing track

record with MHI’s strong reputation and financial strength, in order to re-establish its position in

the offshore wind industry (VWS Annual Report, 2014). As part of the JV agreement, VWS

additionally transferred all R&D business activities with respect to the V164, the world’s largest

WTG, the V112 WTG offshore order backlog (494MW as of 31 December 2013), existing

offshore service contracts and 380 FTEs to the new entity (VWS Annual Report, 2014). In turn,

MHI injected mEUR 100 into the JV and another mEUR 200 depending on certain milestone

achievements tied to the R&D project of the V164 WTG (VWS Annual Report, 2014).

6.4. Theoretical incentives for JV participation

Based on the description of VWS and its economic and financial situation prior to the

engagement in the JV with MHI, the succeeding sections elaborate on the main economic

objectives for the JV participation. This is done by referring to the theoretical motivations and

incentives for MNEs put forth in the literature review in Section 2 and aims at answering RQ3.

6.4.1. Cost minimization and economies of scale

As one of the main objectives of the new operating model, cost minimization is of fundamental

importance for VWS. After several cost-saving changes, e.g. the reduction of product range,

FTEs lay-offs and a slackened management team, VWS concentrates its efforts on implementing

the turn-around process since the end of 2011 (VWS Annual Report, 2011; VWS Annual Report,

2013). In engaging into the JV MHI Vestas Group, VWS’ entire offshore business segment is

contributed to the JV, which reduces the risk of potential liquidity and solvency problems,

especially since MHI provides financial stability with an initial investment of mEUR 100 and an

eventual second investment of mEUR 200 (VWS Annual Report, 2013). As such, R&D costs in

relation to the development of the V164 WTG are covered by the JV MHI Vestas Group; hence

reducing VWS’ R&D costs significantly.

In terms of economies of scale, VWS is able to achieve cost advantages granted by an increase

of output and size of its WTGs, due to a centralization of production facilities and a globally

standardized manufacturing process of the V112 and V164 WTGs (VWS.com). As of the JV

agreement, VWS sources all WTG parts related to the manufacturing process, e.g. blades,

nacelles and rotors to the JV MHI Vestas Group (VWS Annual Report, 2013). Thus, VWS



continues to be responsible for all centrally based manufacturing and R&D business activities

and is able to exercise a high degree of control in monitoring the production and manufacturing

process. As pointed out in Section 2.2.1, given the omnipresent trade-off between risk and

control and the associated make-or-buy decisions, the JV partnership with MHI poses a

beneficial opportunity for VWS in relation to cost minimization and favorable options to

leverage on the existing economies of scale.

6.4.2. Synergies and knowledge sharing

As mentioned before, one of the major incentives for VWS and MHI to engage into the JV MHI

Vestas Group is to mutually benefit from the prevailing synergies. The resources contributed by

both JV partners complement each other. While VWS provides its tacit knowledge concerning

the R&D including the manufacturing/production process of the V164, MHI provides financial

stability needed to execute this process. As such, a promising partnership has been created which

is set out to gain a competitive advantage in the offshore wind industry.

This setup also offers the opportunity for knowledge sharing. As discussed in Section 2.1.2, the

provision of highly-specialized technology, as is the case in the JV MHI Vestas Group, increases

the likelihood of dissemination risk and opportunism by the partner. However, since VWS

assumes full control of the internally-kept tacit know-how in relation to the production,

manufacturing and maintenance of the V164 WTG, VWS is able to protect its firm-specific

know-how. According to Mody (2013), JVs “will ensure a close knit relationship, where there is

a joint management and greater control over affairs and also greater protection of risks”, opposed

to completely outsourcing R&D business activities.

6.4.3. Market access and diversification of risk

Regarding the theoretical JV incentive to access foreign markets when engaging in a JV, in order

to expand business activities as discussed in Section 2.1.3, VWS has accumulated significant

experience as it currently operates globally in over 70 countries (VWS.com). Following its JV in

India which had been established in 1987 and setting up a wholly-owned subsidiary in the highly

competitive Chinese market, VWS was able to benefit from its acquired networking position in

Asia to engage in the JV with MHI (VWS.com). As the JV MHI Vestas Group operates on a

global level, the partnership with MHI grants instant access to the Japanese as well as the

Asian/Pacific market.



Naturally, joining another MNE and forming a 50/50 JV partnership is accompanied with the

diversification of risk. After having discussed dissemination risk in the previous section, country

and political risk also impose challenges. As the JV MHI Vestas Group is based in Denmark and

includes five wholly-owned subsidiaries in EU countries, the JV partners benefit from VWS’

location familiarity (VWS Annual Report, 2014). Hence, country-specific and political risks are

limited due to the possibility to exercise a high level of control in countries which follow the

similar European regulatory standards. Therefore, due to shared ownership of control in the JV

MHI Vestas Group, political and country-specific risk can be mitigated and diversified through

the mutual contribution of resources by both parties.

Based on the previous discussion and in reference to RQ3, it is apparent that all theoretical

incentives to engage in a JV covered by Section 2.1, are well-applicable to the case study of the

JV MHI Vestas Group. Further, it is noteworthy that those should not be regarded as exhaustive.

As pointed out before, financial stability provides an additional incentive for VWS to engage in

the JV. As a result of the partnership, VWS also stays competitive in the global offshore wind

turbine segment as the JV creates the opportunity to gain a higher market share. Further, in

sourcing the R&D business activities in relation to the V164 WTG into the JV, VWS is able to

significantly reduce risks. Finally, the JV MHI Vestas Group is likely to be able to respond

quicker to market changes and pressures as it constitute an autonomous entity with the sole focus

on the offshore wind turbine business. Hence, VWS’ decision to participate in the JV MHI

Vestas Group allows the MNE to effectively compete with its main competitors, i.e. Xinjiang

Goldwind Science & Technology Co., Ltd., Enercon GmbH and Siemens AG and grants a

number of additional opportunities which provide a positive future outlook. Those are further

discussed in Section 6.6.

After applying the theoretical incentives and motivations to engage into JVs to the JV MHI

Vestas Group, the following section provides more details about the stock price reaction upon

the announcement of the JV. This is done in relation to the established existence of an

announcement effect in JVs with Scandinavian participation.

6.5. Stock price reaction

In line with the Shareholder Maximization hypothesis discussed in Section 2.2 and established in

the analysis of the empirical evidence of the underlying sample, Figure 10 displays the positive

excess return of the VWS stock in comparison to the MSCI return, in pursuit of answering RQ4.



Figure 10: VWS stock price reaction

Notes

Figure 10 displays the return values of the VWS stock return in comparison to the local MSCI index.

Source: Contribution by author. Data retrieved fromThomson Reuter Datastream.

By looking at Figure 10, it easily observable that the VWS stock return clearly outperforms the

local MSCI index, around the JV announcement date, t0 (27 September 2013). The VWS stock

return and the local MSCI index initially move together (t-10 – t-5), after which distinct positive

spikes in VWS’ stock return are recorded one day prior to the announcement, t-1, as well as two

days after the announcement t+2. Following the visual representation, the positive announcement

effect induced by the JV announcement is striking and grants further support for H1.

Further, with respect to the binary dummy-variables examined in the cross-sectional regression,

especially previous JV experience (JVEXPERIENCE) as well as cultural relatedness

(CULTURE) positively impacted the announcement effect. The empirical findings are partially

in line with the setup of the JV MHI Vestas Group. As both JV partners can leverage on previous

experience in relation to multiple forms of partnerships and alliance, both MNEs benefit from a

repetition effect suggesting a similar mindset with respect to the coordination and management

of the JV. As such, both partners are likely to engage in cooperative behavior resulting in mutual

beneficial outcomes. As both JV partners are not culturally related, it can be argued that this

fosters knowledge sharing of complementary resources, leading to a favorable shareholder

reaction which is reflected by the stock price increase around the announcement date.

-0,060

-0,040

-0,020

0,000

0,020

0,040

0,060

0,080

0,100

t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t0 t+1 t+2 t+3 t+4 t+5

Ret

urn

VWS stock return MSCI return



6.6. Development after the JV MHI Vestas Group announcement – 2014-Q1/2015

After elaboration on the positive stock market reaction following the JV MHI Vestas Group

announcement, this section sets out to touch upon the development and consequences with

respect to VWS financial and economic performance over the time period between 2014-

Q1/2015. The succeeding elaborations provide further insights with respect to RQ4.

After the commencement of the JV MHI Vestas Group on 1 April 2014 (primo Q2), VWS’

performance significantly increased over the following quarters. Table 13 provides an overview

of VWS’ key financial figures and ratios during the time period 2014-Q1/2015.

Table 13: Overview of VWS's financial figures, 2014-Q1/2015

in mEUR Q1/2014 Q2/2014 Q3/2014 2014 Q1/2015

Income Statement

Revenue 1,283 1,341 1,813 6,910 1,519

EBIT (before

special items) 27 104 163 559 79

Net income 2 94 102 392 56

Financial ratios

EBIT margin % 0.2 7.8 9.0 8.1 5.2

ROIC % 14.5 19.0 25.7 35.3 43.8

Share ratios

EPS

1.8

Share price at year-

end (EUR) 30.4

Shares outstanding

at year-end 224,074,513

Market

capitalization 6,811,865,195

Source: VWS Interim Financial Report Q1-Q3/2013; VWS Annual Report (2014); VWS Interim Financial Report

Q1/2015

In comparison to Table 11 presented in Section 6.2, the year-end financial figures and

profitability measures have improved drastically. As a consequence of the 2-year turn-around

processes (2012-2013) including the establishment of the JV MHI Vestas Group, VWS was able

to increase its total revenue from EUR 6,084m in 2013 to EUR 6,910m in 2014 (+13.6%) and

even more importantly raise its profitability measures, as cost minimizations led to an EBIT of

559 in 2014 compared to 102 in 2013 (+448%) (VWS Annual Report, 2014). As such, total

R&D costs decreased by 34% to mEUR 159 in 2014, “driven by the V164 WTG development

costs now transferred to the JV” (VWS Annual Report, 2014). Further, “Vestas has recognized

special items of mEUR 48 in 2014, mainly driven by a gain from the establishment of the

offshore JV, leading to EBIT after special items of mEUR 607” (VWS Annual Report, 2014)

and resulting in a net income of mEUR 392 in 2014 after three loss-making years. This positive

trend is also reflected in the corresponding financial ratios, as these have continuously increased

over the course of 2014. Thus, VWS recorded an EBIT margin of 8.1% (2013: 1.7%), and a



ROIC of 35.3% (2013: 7.7%). In comparison to 2012, VWS’s share price increased by over 85%

resulting in a year-end share price of EUR 30.4.

The positive forecast for the wind turbine market, including on- and offshore, additionally

underscores VWS’ opportunities in the future. The market is expected to grow by 7.8% and

9.2% in 2015 and 2016, respectively (Marketline Industry Profile, 2014). In combination with a

forecasted CAGR of 10.5% over the time period between 2015-2018, the market trend is

believed to be positive. With the V164 WTG successfully commission and in light of the future

industry outlook, the JV MHI Vestas Group “is expected to be a strong platform for winning and

expanding the share of global offshore market” (VWS Annual Report, 2014). Concerning the

product demand, the JV MHI Vestas Group received its first order of four V164 WTGs by

Skovgaard Invest Aps, Energicenter Nord on 2 July 2014 (Windpoweroffshore.com, 2014).

Later, on 22 December 2014, the JV MHI Vestas Group received a “breakthrough” order of 32

V164 WTGs by Dong Energi (DK) (Compositesworld.com, 2014).

In conclusion of the case study, the JV MHI Vestas proves to be a suitable example. With respect

to RQ3, the theoretical incentives of MNEs for a JV participation presented in the literature

review in Section 2.1 have been applied to the case study. Concerning RQ4, the positive impact

of the announcement effect reflected in the stock price reaction and the simultaneous shareholder

value creation has been illustrated. Moving beyond, it is noteworthy that the transition between

the theoretical concepts and the real-life example entails additional aspects which need to be

taken into account. As is the case for the underlying example, competitive forces and the

overarching political/economic conditions impacted VWS’ decision to engage in the partnership

with MHI. Another driver for VWS’ decision was cost minimization as part of the internal turn-

around process. This is reflected in the transfer of the R&D business activities of the V164 WTG

into the JV MHI Vestas Group, which effectively lowers VWS’ R&D costs. Further, the

additional gain received from the business activities of the JV MHI Vestas Group contributes to

VWS’ enhanced profitability. Given the financial stability provided by MHI, the JV MHI Vestas

Group can leverage on their synergistic assets, effective risk sharing as well as mutual learning

opportunities going forward.



7. Implications and conclusion

In pursuit of answering the problem statement presented at the outset, this Master Thesis set out

to examine to what extent JV announcements with Scandinavian participation impact

shareholder value creation. Supplementary to the central objective of the study, four research

questions were formulated focusing on both, empirical evidence as well as a real-life case study

in form of the JV MHI Vestas Group.

With respect to RQ1, the study applied the event study methodology including a battery of

(non)parametric tests to the underlying sample of 127 JVs with Scandinavian participation to

investigate a potential announcement effect. In line with the Shareholder Value Maximization

hypothesis, the empirical findings yield significantly positive p-values of the CARs, lending

support to H1. The announcement effect and the simultaneous shareholder value creation are

especially pronounced for the Finish sub-sample. Given the confirmed efficiency of the

Scandinavian stock markets, shareholders are able to earn excess returns upon JV

announcements, as the underlying stock outperforms the respective market around the

announcement date.

Concerning RQ2, five empirically-test explanatory variables have been tested by means of a

cross-sectional regression analysis in order to uncover the primary influences on the

announcement effect established along the lines of H1. As a result, previous JV experience of the

JV partners (JVEXPERIENCE) as well as cultural relatedness among the JV partners

(CULTURE) positively impact the announcement effect and contribute to the significant

shareholder value creation. As such, H4 and H6 are supported, whereas no significant impact has

been found for partner-venture (PVRELATED) and partner-partner relatedness (PPRELATED) as

well as domestic (DOMESTIC) JVs. Thus, H2, H3 and H5 are no supported.

Further, the empirical findings recorded in RQ1 and RQ2 were applied to the JV MHI Vestas

Group, in order to gain practical insights using a real-life case study.

Regarding RQ3, it has been shown that the theoretical incentives for MNEs to engage into JVs

are comparable to VWS’ motivations to participate in a partnership with MHI. VWS can

leverage on existing, complementary synergies and explicitly minimize costs and share risks in

sourcing its R&D business activities in relation to the V164 WTG into the JV with MHI, which

in turn provides financial stability. It is essential to point out, that the theoretical incentives

discussed are not to be regarded as exhaustive. Given the economic industry pressures, a high

level of competition and VWS’ negative financial performance prior to the JV establishment,



VWS’ decision to participate in the JV marks the end of a successful turn-around process

granting future opportunities.

Following this notion with respect to RQ4, the JV announcement was positively reflected in

VWS’ stock price. In relation to H1, the stock price increased by up to 12%, enabling

shareholders to earn excess returns on their investments. The JV participation was also positively

reflected in VWS’s financial performance. As the development costs of the V164 WTG are

transferred to the JV, R&D costs decreased significantly. Further, Vestas recognized gains

associated with the JV participation, which overall contributes to its enhanced financial

performance and profitability.

joint venture stock market reactions: scandinavian...

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