The Government of the Russian Federation
The Federal State Autonomous Educational Institution
of Higher Professional Education
"National Research University
"Higher School of Economics"
Faculty of Economics
Department of Economic Theory
Admit to defense
Head of the Department
Senior Lecturer of the Economic Theory Department
Anastasiya Y. Redkina
"______" __________________20____
GRADUATION THESIS
On the topic: MARKET REACTION TO RUSSIAN MERGER
REGULATION
Student: Ilia D. Samarin
Group: E-10-4
________________________
Supervisor: Anastasiya Y. Redkina
Senior Lecturer of the Economic Theory Department
________________________
Perm 2014
Contents
Introduction ............................................................................................................... 4
1.
Institutional Framework ................................................................................... 7
1.1 Merger Control: the Necessity and Complexity of Enforcement ................... 7
1.2 Procedure of Regulation .................................................................................. 8
1.3
The Scope of Intervention ........................................................................ 10
2.
Literature Review ........................................................................................... 13
3.
Research Design ............................................................................................. 17
4.
Methodology ................................................................................................... 22
5.
Data ................................................................................................................. 28
6.
Empirical Results ............................................................................................ 30
6.1 Initial Merger Announcement ....................................................................... 30
6.2 Decision Announcement ............................................................................... 32
6.3 The Relationship between Generated CAR .................................................. 34
6.4 Endogeneity and Self-Selectivity .................................................................. 37
6.5 Robustness Check ......................................................................................... 39
7.
Discussion of Results...................................................................................... 41
Conclusion ............................................................................................................... 43
References ............................................................................................................... 45
Appendices .............................................................................................................. 50
2
Abstract
The present research employs the event study methodology in order to quantitatively assess
the merger regulation in Russia. We examined a sample of 230 transaction that took place between
2005 and 2014. Empirical results reveal no significant reaction to initial merger announcements.
However, both merging companies and their rivals react negatively to the remedies implications by
the Federal Antimonopoly Service. Further analysis of relationship between these two reactions
demonstrated surprising findings. Remedies do not provide a systematic rent reversion and make only
a one-time impact on merging companies. This result assumes the ineffective use of remedies in
Russian merger control. We also found that merging firms and competitors positively respond to the
decision of unconditional approval, which may indicate the type II errors made by Russian
competition authority.
Аннотация
Представленное исследование посвящено анализу государственного контроля сделок
слияний и поглощений в России при помощи метода анализа событий. Изучаемая выборка
включает в себя 230 сделок, которые произошли в период с 2005 по 2014 года. Результаты
эмпирического анализа показали, что фондовый рынок не демонстрирует значимой реакции
на
первоначальные
объявления
о
намерении
вступить
в
сделку.
Однако
и
компании-участники, и их конкуренты отрицательно реагируют на решения Федеральной
Антимонопольной Службы в виде выдачи предписаний. Анализ зависимости между
реакциями на эти события показал, что выдаваемые предписания не обеспечивают сохранение
конкурентной среды и имеют только одномоментный эффект. Кроме того, компанииучастники и конкуренты положительно реагируют на безусловное одобрение сделок, что
может говорить о совершении антимонопольным органом ошибок второго рода.
3
Introduction
The merger control is an essential component of competition policy. Mergers can reduce
competition and lead to higher prices or inefficiency in production. On the other hand, merger can
cause positive effects, for example, in the form of economies of scale. Therefore, a competition
authority should act as a judge prohibiting anticompetitive combinations and clearing procompetitive
ones, so encouraging economic growth. The problem related to competition enforcement is that
potential benefits and risks of a merger are difficult to assess before a merger takes place. This may
lead to so-called type I and type II errors made by a competition authority. Type I error occur when
an authority prohibits procompetitive mergers, whereas type II errors imply outright clearance of
anticompetitive mergers that should have been prohibited or challenged by remedies. Consequently,
the decisions of a competition authority need to be revised and analyzed in order to reveal their
weaknesses and inconsistencies and to eliminate them.
In the last three decades, there has been growing interest in merger control assessment.
Although the topic is quite well researched, there is no unified approach applied to this research topic.
The majority of studies focus on investigation of factors influencing the competition authorities’
decision or on the stock market reaction to initial merger proposals and further authorities’ decisions
announcements. Although all of these studies are devoted to evaluation of merger regulation, they
serve for different goals and looking for answers to different research questions. The first group of
studies aim to understand the actual logic of a competition authority by identification of factors
influencing its decision and to compare their findings with economic reasons for merger control (e.g.
Khemani, R. and Shapiro, D., 1992; Bergman, M., Jakobsson, M. and Razo, C., 2005). Another
bundle of papers investigates the impact of merger events on the competition level by observing the
investors’ response (Eckbo, E., 1983; Aktas, N., Bodt, E. and Roll, R., 2004).
Despite a variety of existing studies dedicated to the appraisal of merger regulation, little
attention has been paid to possible policy implementation of gained results. Duso, T., Neven, D. and
Roller, L. (2007) and Duso, T., Gugler, K. and Yurtoglu, B. (2011) presented a clear framework for
analysis of effectiveness of regulation by identifying the type I and type II errors made by the
competition authorities.
As for the Russian studies, there is a strong lack of qualitative examinations devoted to
Russian merger regulation. Several Russian researchers addressed this research question using a
qualitative analysis. For example, Avdasheva, S. and Kalinina, M. (2012) performed a comparative
analysis, where they examined decision made by the FAS and the European Commission on similar
4
cases. Other studies are of methodological nature and discuss the existing problems in Russian
procedure of merger control and possible ways to deal with them (Shastitko, A., 2011; Shastitko, A.,
2012; Sushkevich,A., 2012). To our knowledge, there is the single study containing econometric
analysis of Russian merger processes. Using the so-called event study methodology, Tsytsulina, D.
(2012) examined the stock market response to merger announcements among Russian steel
companies. However, this paper did not consider the effect of the competition authority’s actions.
Our study possess several distinct features. First of all, it applies econometric techniques to
Russian data on merger control. Secondly, we do not concentrate on the single industry and analyze
a broader range of transactions. Furthermore, we focus on economic effects of Russian antimonopoly
body’s decision.
The aim of the present paper is to assess Russian merger regulation using the event study
approach. Particularly, the effect of using remedies in merger control will be investigated. To reach
the goal, a series of question needs to be addressed: How does the stock market react to the initial
merger announcements? How does the Federal Antimonopoly Service’s (FAS) decision influence the
investors’ anticipations about transactions? Does the FAS take into consideration the stock market
reaction to the initial merger proposals? Do remedies in Russia truly reduce the anticompetitive
effects of mergers?
Although the event study methodology is quite complicated, it has several advantages
compared to others. First, the stock market provides objective reaction to the events. Additionally,
while the use of stock market data allows separating the merger and the decision announcement effect,
data on accounting profits present only the net effect (Duso, T., Gugler, K. and Yurtoglu, B.,2011).
On the first step of the research, we analyzed the effect of initial merger announcements on
both participating companies and their rivals. Our data revealed no reaction to the announcements in
general case.
The next step included the investigation of effect of the antimonopoly body’s decisions
announcements. The main findings are the positive reaction of merging companies to outright
clearance and negative response of both merging firms and their competitors.
This reaction was revised during the third stage of our analysis, which showed that negative
reaction relates to only nonsystematic costs of remedies imposed to merging companies. Finally, we
investigated the problem of possible endogeneity.
5
The dataset for the empirical analysis was constructed based on the Zephyr database that
contains the initial merger announcements dates. The sample comprises 230 transactions that took
place in Russia between 2005 and 2014.
The outcomes of the present research may be valuable for Russian competition authority
because they highlight the flaws in the current merger control enforcement, so stimulating to find the
ways for improving and refining of the existing procedure of regulation. Moreover, our study is an
essential step in the process of Russian merger regulation assessment.
The paper is organized as follows: firstly, institutional framework of merger control in Russia
is presented. After that, existing relevant studies are reviews. Next section present design of our
research. Then, the methodology is discussed. This one is followed by the description of data and
data collection process. After that, results of empirical analysis are presented. The final chapter
discusses the obtained results and concludes the paper.
6
1. Institutional Framework
This paper aims to assess the merger control in Russia. However, we firstly need to discuss
why mergers have to be regulated and why this regulation is worth being assessed. Furthermore, we
should have a clear vision of how the merger regulation is put into practice in the Russian Federation.
Thus, this section considers the general complications that any antimonopoly body faces during the
merger control implementation and provides a legal context of Russian merger regulation procedure.
1.1 Merger Control: the Necessity and Complexity of Enforcement
Mergers, acquisitions, and other types of business combinations are subject to a high extent
of antitrust scrutiny. The reason is that a merger can cause both negative and positive effects.
Companies merge for variety of reasons, and not all of them have anticompetitive nature. The main
benefit from a merger is the synergy effect, which may take several forms. A deal may result in
economies of scale and further cost efficiency. For other companies, a merger can reduce transaction
costs. Moreover, many companies see in a merger a way to diversify, reduce operational risks and
cost of capital (Trautwein, F. 1990; Amihud, Y. 1986). Avdasheva, S., Shastitko, A. and
Kalmychkova, E. (2007) indicated a Russia-specific incentive for business combinations. This motive
is connected with relative underdevelopment of Russian economic law. According to researchers,
many companies prefer to arrange business agreements due to poorly protected property rights and
very high costs related to this protection. As a result, this fact negatively affects the investment
attractiveness of many companies.
Although benefits from mergers gained by the participating companies may transform into
benefits for consumers in the form of lower prices, a wider range of products or higher quality of
goods, mergers may also lead to deadweight losses and reduced consumer surplus. Some estimates
showed that the deadweight losses might reach a very high level of 6% of GDP (Avdasheva, S.,
Shastitko, A. and Kalmychkova, E. 2007). Williamson, O. (1968). There is a bunch of theoretical
studies, which shows the possible negative effects of mergers and acquisitions. Most researchers
concluded that horizontal mergers lead to higher prices and lower output, so decreasing the social
welfare (Gaudet, D. and Salant, S., 1988; Perry, M. and Porter, R., 1985; Salant, S., Switzer, S. and
Reynolds, R, 1983). The losses might be even higher if the industry deviates from the Cournot
oligopoly (Farrell, J. and Shapiro, C., 1990). Salinger, M. (1988), on the other hand, analyzed vertical
7
agreements and concluded, that although the monopolization due to vertical combination might be
not very obvious, it would lead to the price increase.
The simultaneous existence of both positive and negative consequences of a merger reveal the
necessity of merger regulation: negative effects should be prevented, whereas positive ones need to
be fulfilled. Williamson, O. (1968) developed an analytical model, which stated that there should be
a trade-off between cost savings and a price increase caused by a merger. Therefore, a competition
authority should act as a judge prohibiting anticompetitive combinations and clearing procompetitive
ones, so encouraging economic growth. It needs to weigh possible benefits and risks of a transaction.
From this point of view, Motta, M. (2004) provided an excellent definition of a competition policy
by emphasizing its economic goals: “the set of policies and laws which ensure that competition in the
marketplace is not restricted in such a way as to reduce economic welfare”. However, the competition
authority has to carry out its analysis before a merger or acquisition takes place. As a results, the
errors may occur and negatively affect the competition level. For this reason, the merger control is
worth being revised and investigated in details.
1.2 Procedure of Regulation
The merger control in Russia is conducted by the Federal Antimonopoly Service (FAS), which
activity is governed by the regulations that were first implemented in 1991. In 2006, the new Federal
Law № 145-FZ “On Protection of Competition” was passed. This law is still valid today and defines
the transactions subject to state control over economic concentration and the procedure of this control.
Further, we will use “merger control” instead of “control over economic concentration”.
According to the Federal Law, the transactions subject to the state investigation may be
divided into three groups:

mergers, i.e. transactions resulting in combination of several companies into new one;

transactions with voting shares, including acquisitions, when a bidder company gains
control over a target company;

transactions with assets, which book value exceeds 25% of the total assets of a selling
company.
Despite the different nature of the above-mentioned transactions, we will call them mergers
and acquisitions, since they have similar economic consequences.
The law also defines conditions for transactions that have to receive a prior FAS’s consent.
Currently, the main thresholds are as follows: a merger or acquisition of several commercial
8
organizations needs to be approved beforehand if the aggregate value of assets according to the
balance sheet at the latest reporting date exceeds seven billion rubles or if the aggregate revenues
from sale of commodities of such organizations for the calendar year preceding the merger
exceed ten billion rubles, or where one of the organizations is included into the Register of economic
entities.
Figure 1 describes the general procedure of Russian merger control. Article 33 of the Federal
Law № 145 specifies that the FAS has 30 days to make its decision on a transaction. Based on the
performed analysis, it can either clear a transaction unconditionally or clear and give remedies, or
block a deal or prolong the period of examination. If the FAS decides to prolong, it has two month
more to make the final solution.
2 months
30 days
(optional)
Merger
Announcement
Merger
Notification
Decision:

Cleared

Cleared with
remedies

Blocked

Prolongation
Decision:



Cleared
Cleared with
remedies
Blocked
Figure 1. The procedure or merger control in Russia
Remedies are usually defined as special conditions developed by the competition authority to
remove the concerns identified during the investigation. There are two basic groups of remedies:
structural and behavioral (Balto, D. and Parket, R., 2000; Merger Remedies Study, 2005). Structural
remedies refer primarily to the divestiture, i.e. alienation of assets in order to restore the competition
level in an industry. Behavioral remedies constrain the behavior of merging companies after the
completion of transaction. Article 23 of the Federal Law “On Protection of Competition” enumerates
examples of possible remedies; so these may be: to grant a right to facilities of industrial property
protection, to transfer the property rights, to preliminary inform the antimonopoly body about
intention to fulfill actions provided for in the determinations, to sell particular volume of products
through commodity exchange.
9
At a first glance, Russian merger control procedure is similar to the European one: in both
cases, the value of assets and revenues act as a threshold determining the subjectivity of a deal to
prior state approval. Moreover, the definitions of negative consequences of a merger are also alike.
There are, however, differences as well. The European regulation implies a two-stage procedure
(Duso, T., Gugler, K. and Yurtoglu, B., 2011). At the end of the Phase I, the EC may proceed the
examination if a transaction seems dangerous to competition. However, the EC cannot block a deal
after the first stage. This is the crucial difference between Russian and European procedures.
Although Russian law states that prolongation may be a result of dangerous transaction, the FAS
usually decides to prolong in case the information provided by the merging companies is not
sufficient.
A distinctive feature of Russian merger control is that, unlike the European legislation, there
is no unified instruction about conducting a merger analysis. The only existing document that allows
supposing of how this analysis is performed is Order № 108 «On Approval of the Proceedings of
Analysis and Assessment of Competition Environment on Goods Markets». According to this
document, the study of competition environment can be divided into following steps:

to establish the geographical and product borders of a goods market;

to estimate market capacity, market shares of entities, and concentration level;

to define the entry barriers;

to assess the competition environment and possible risks due to transaction.
However, these stages are generic and do not relate directly to the merger control. Therefore,
they only enable to hypothesize the logic of Russian antimonopoly body.
1.3 The Scope of Intervention
The dynamics of cases processed by the FAS is quite variable. Figure 2 demonstrates the total
number of applications and decisions on them during the period between 2007 and 2012. The
information about other years is not publicly available. As the bar chart presents, the total number of
applications considered by the FAS decreased more than twice during the particular period. This is
caused mainly by several amendments of the assets and revenues thresholds. As a result, the FAS’s
overload decreased due to higher values of the thresholds and a lower number of applications
processed. However, the quantity of applications remains very high comparing with the number of
applications notified to the European Commission (EC). For example, during the same period, the
EC considered 1875 cases and only 283 notifications in 2012.
10
7000
90
6000
353
141
404
5000
106
4000
232
60
3000
5654
5276
2000
57
232
308
2675
2914
2010
2011
50
224
3822
2220
1000
0
2007
2008
Clearances
2009
Remedies
2012
Prohibitions
Figure 2. Number of applications considered by FAS. Source: the FAS’s annual reports
Figure 3 shows the structure of decisions made by the FAS. First of all, there is a growing
trend in frequency of remedies. Talking about the type of remedies given, behavioral remedies prevail
over structural ones. In 2007, behavioral remedies accounted for 96% of total number of remedies
given by the FAS. This is opposite to the Europe, where more than 80% of remedies were structural
(Avdasheva, S., Shastitko, A. and Kalmychkova, E., 2007).
Another remarkable fact is a lower and quite stable rate of prohibitions, which fluctuates about
2% of total number of decisions. It is interesting that the EC’s decisions are characterized with the
same low rate of rejections (Aktas, N., Bodt, E. and Roll, R., 2004; Duso, T., Gugler, K. and Yurtoglu,
B., 2011). However, unlike the EC, Russian FAS usually blocks transactions due to missing
documents or insufficient amount of information provided along with the application, rather than due
to negative economic consequences.
11
To conclude, Russian merger control has several features similar to the European one. It
allows for use of remedies and is also characterized by a low number of prohibitions. However, unlike
the EC regulation, it lacks for clear and unified instructions on conducting merger analysis, so creating
difficulties in the process of assessment the FAS’s activity.
9.38%
10.00%
8.98%
9.00%
7.83%
8.00%
6.94%
7.00%
6.00%
5.79%
5.58%
5.00%
Remedies to application ratio
4.00%
Prohibitions to applications ratio
2.42%
3.00%
2.00%
2.55%
1.48%
1.92%
1.83%
2.00%
2010
2011
2012
1.00%
0.00%
2007
2008
2009
Figure 3. The structure of decisions
12
2. Literature Review
There is a great variety of studies covering the assessment of merger regulation in the European
Union, the United States and Canada. Existing studies may be divided into three categories according
to the classification presented by Bougette, P. and Turolla, S. (2006) and based on the applied
approach. They are the cost-benefit analysis, the discrete choice models approach and the event study
approach.
The cost-benefit analysis. This group of studies are based on weighing the benefits and costs of
mergers. The first studies in this field were carried out by Long, W., Schramm, R. and Tollison, R.
(1973), Asch, P. (1975) and Siegfried, J. (1975). Their aim was to quantitatively assess the welfare
effects related to the antitrust regulation. These effects were primarily linked to the deadweight losses
(“Harberger’s triangle”, Harberger, A., 1954). For example, Long et al. estimated a linear regression
model to examine the factors affecting antitrust activity. Costs in this model consisted of litigation
costs initiated by the government. The results showed the quadratic relationship between
concentration level and the number of filed cases. Another study was conducted by Postema, B.,
Goppelsroeder, M. and Bergeijk, P. (2006). Using the applied game theoretic approach and MonteCarlo simulations (Crooke, P., Luke, F., Tschantz, S. and Werden, G., 1999), the authors estimated
the net annual welfare gains resulted from merger regulation in the Netherlands, which amounted to
100 million euros. They also concluded that the gross gain over the analyzed period between 1998
and 2002 was about 770 million euros. However, cost-benefit analysis is a very complicated and often
inaccurate technique, since it is difficult to provide strong evidence of selecting appropriate proxy for
benefits and costs of a merger.
The discrete choice models approach. This approach involves the use of econometric models
with discrete dependent variable, such as probit- or logit-models and their ordered and multinomial
modifications. The approach allows identification of important factors explaining a competition
authority’s decisions. One of the most cited research was carried out by Khemani, R. and Shapiro, D.
(1993). Using Canadian data for running the ordered probit model, the authors found that the most
important factors were market shares, concentration index and barriers to entry. Their results were
close to the conclusions of Coate, M., Higgins, R. and McChesney, F. (1992), who applied the same
approach to the American data. They also emphasized a low predictive power of ordered models due
to low rate of prohibitions in the sample. Weir, C. (1992) used the UK’s data and obtained different
results: the post-merger market shares did not influence the authority’s decisions. Williams, F. (2003)
analyzed the European merger cases by estimating a probit-model. They concluded that the post
13
merger market share and the increase in market share significantly increased the probability on a
merger to be prohibited. Bergman, M., Jakobsson, M. and Razo, C. (2005) found that market share
was an important determinant of an authority’s decisions. Moreover, they included several political
variables, such as nationality of merging companies and the identity of the commissioner. However,
these variables appeared to be insignificant. In contrast, Bougette, P. and Turolla, S. (2006), using the
multinomial logit-regression, concluded that the probability of giving remedies increased when the
acquirers were from the U.S. or France. Moreover, the authors identified that the pattern changed
when Mario Monti took up a post of the commissioner. They also found the inter-industries
differences, e.g. energy and communication sectors were subject to “stricter” decisions, i.e. remedies,
while the retail trade increased the probability of an outright acceptance. They did not try, however,
to implement the ordered probi- model, which could be more suitable for their analysis, since the
EC’s decisions can be naturally ranked depending on their “severity”. Nevertheless, important
contribution of this paper was the use of artificial neural networks, which allows for in-depth study
of factors influencing the EC’s decision. Andreasson, J. and Sundqvist, C. (2008) studied the effect
of EC merger control before and after 2004. Their main conclusion was that the post-merger market
share became less significant after the alterations in the EU regulations.
The event study approach. This approach is based on the efficient markets hypothesis, which
enables to examine market reaction to the merger and authority’s decisions announcements. Ellert, J.
(1976) presented one of the pioneering studies using event study in the analysis of antitrust regulation.
However, this study did not investigate the reaction of rivals to the merger events. Later, Eckbo, E.
(1983) addressed this question on his research. The analysis of stock market data of Canadian
companies showed that both merging parties demonstrated positive and statistically significant
abnormal return after merger announcement. After the blocking decision these returns turned
negative, which meant efficiency of regulation. However, in some cases the abnormal returns of rival
firms remained positive even after the blocking decision. After that, Eckbo, E. and Wier, P. (1985)
found again that rivals generated positive abnormal returns around a merger announcement date.
Authors rejected the collusion hypothesis in favor of their information hypothesis. Simpson (2001)
received similar results. Solvin M., Sushka, M. and Hudson, C. (1991), however, obtained opposite
results. They analyzed 42 horizontal mergers in the airline industry and concluded that the Civil
Aeronautics Board promoted the collusion among existing market players. Brady and Feinberg (2000)
examined the effects of the change in the EU merger enforcement regime on the market indices and
individual companies. They found that the indices did not demonstrate a significant reaction, while
many companies did. On the contrary, Aktas, E. and Derbaix, A. (2003) analyzed the automobile
sector and found that neither participating companies nor their competitors demonstrated any
14
significant reaction to the initial merger announcement, while several customers showed significant
and negative reaction.
Aktas, N., Bodt, E. and Roll, R. (2004) analyzed the stock market reaction to the European
Commission’s (the EC) announcements. The authors concluded that actions of the EC were consistent
with its antimonopoly goals, and there was a clear evidence that shareholders took into consideration
the decisions of the EC. Moreover, the authors showed that the probability of the EC’s intervention
did not depend on acquirers’ nationality. However, the market expected a more costly operation when
an acquirer was outside the EU. This study was the first attempt to address the problem of endogeneity
in merger analysis.
A great contribution to the merger analysis based on the event study methodology has been
made by Duso and his colleagues. Duso, T., Gugler, K. and Yurtoglu, B. (2010) addressed the
problem of usefulness of event study for merger analysis. Specifically, they considered the
relationship between generated abnormal returns and the ex-post profitability (Gugler, K. and Siebert,
R., 2004) of merging companies. The results showed a strong and significant dependence between
these variables, especially for a long pre-announcement windows (-25;+5) and (-50;+5). On the
contrary, McAfee, R.P., Williams, M.A. (1988) asserted that event studies are not able to detect
anticompetitive mergers. However, their analysis lacked for theoretical reasoning and was based on
the simple case. Moreover, Fridolfsson, S. and Stennek, J. (2009) showed that anticompetitive merger
might reduce the rivals’ share prices just like McAfee and Williams observed.
Duso, T., Neven, D. and Roller, L. (2007) combined the event study approach and the discrete
choice analysis. Estimating the abnormal returns, they identified type I and type II errors made by the
competition authority. Type I errors occur when an authority prohibits a procompetitive merger, while
type II errors take place when an authority clears an anticompetitive merger. They defined type II
error as the clearance of a transaction, which generated positive abnormal returns for rivals at the
announcement date. However, this definition is questionable. In many studies competitors
demonstrated positive reaction to merger announcement (Eckbo, E.,1983; Simpson, J., 2001), but no
one related this directly to the anticompetitive nature of a deal. After that, authors estimated binary
choice models to identify determinants of the frequency of errors. Eventually, they concluded that
institutional and political factors did matter and influenced the error occurrence.
Duso, T., Gugler, K. and Yurtoglu, B. (2011) presented a new view on this problem and tried
to econometrically assess the effectiveness of European merger control using stock market data on
companies involved in business combinations during 1990-2002. They looked at the relationship
between companies’ abnormal returns around the merger and antitrust decision announcements. They
15
concluded that outright prohibition eliminated completely the rents generated around the merger
announcement. However, remedies seemed to be only half-efficient. Furthermore, outright clearance
led to the increase in rivals’ profitability, which indicated the possible type II-errors.
As for Russia, there is a lack of quantitative examination of merger regulation. Avdasheva, S.
and Kalinina, M. (2012) presented a comparative study analyzing decisions of Russian and European
competition authorities on similar transactions. They found that many Russian behavioral remedies
just replicated the law. Nevertheless, they concluded that the FAS conducted an in-depth analysis of
markets borders. Shatitko, A. (2011, 2012) and Suchkevich, A. (2012) analyzed existing issues and
flaws of Russian merger control and suggested adopt several procedures from the European law. The
only published Russian study was performed by Tsytsulina, D. (2012). She analyzed the market
reaction to merger announcements of Russian metal companies. She found that Russian participants
did not demonstrate significant reaction, while both foreign and local competitors reacted negatively
to these announcements. She also considered separately effects of vertical and horizontal mergers and
concluded that vertical mergers did not cause significant reaction. She did not, however, investigate
market reaction to decisions of the FAS.
To sum up, although the topic is quite well researched by American, Canadian and European
authors, “there is almost no systematic econometric evidence on whether merger policy achieves what
it is supposed to achieve” (Duso, T., Gugler, K. and Yurtoglu, B., 2011). The choice of a particular
approach depends on the hypotheses that are to be tested and will be discussed in the next chapter.
16
3. Research Design
This section describes the framework of this research and the hypotheses that need to be tested.
Our study is focused on the assessment the merger regulation in Russia. As it was mentioned in the
literature overview, different approaches could be used to investigate this research topic. We use the
event study analysis to evaluate the FAS’s decisions through the stocks reaction. This approach has
several strong advantages. First of all, stock market provides an independent assessment of the events
(Duso, T., Neven, D. and Roller, L., 2007), which means that the evaluation is exogenous to the
antimonopoly body’s decisions. In Addition, the stock market data are prospective, so they allow
capturing dynamic effects of the observed events on companies’ performance (Aktas, E. and Derbaix,
A., 2003).
The present research will be carried out in four major steps. The first step is to investigate the
stock market reaction to initial merger announcements, the second one is analyze the response to the
FAS’s actions. Then, based on results of the first two stages, the examination of the relationship
between these reaction will be done to gain conclusions about the effectiveness of the FAS’s activity.
And finally, the test for possible endogeneity should be performed in order to discuss the reliability
of the results.
Implementation of first two steps is complicated due to simultaneous existence of two basic
hypotheses: the Market Power Hypothesis (MPH) and the Economic Efficiency Hypothesis (EEH)
(Eckbo, E., 1983; Eckbo, E. and Wier, P., 1985; Aktas, E. and Derbaix, A., 2003). The MPH is derived
from the oligopoly theory (Stigler, G.,1950; Salant, S., 1983) and states that a merger provides
companies with opportunity to restrict the competition and gain benefits. At the same time, it is
possible to consider two sub-hypotheses within the MPH. The first one is the Collusion Hypothesis,
which implies higher probability of collusion among fewer number of companies after the merger. In
this case, both merging and rival companies benefit from the higher post-merger prices, while the
consumer surplus would decrease. This fact should be reflected in higher stock prices around the
merger announcement. The second sub-hypothesis is associated with the Predatory Pricing Model,
according to which a new bigger firm is able to decrease its costs and start the price war (Eckbo, E.
and Wier, P., 1985). Under this sub-hypothesis, rivals’ share prices should fall due to their inability
to win this war.
The EEH consists of two effects. The productivity effects implies the ability to reach economy
of scale after the merger. As a result, a merged company would decrease its average costs and product
17
price, and rivals’ stock prices would fall. Because of the information effect rivals get able to use the
merged company’s technology, so the rivals’ market value increases.
Results of the first two steps enables one to test a set of following hypotheses.
H_1: Merging companies demonstrate positive and significant reaction to merger
announcement and outright clearance of the FAS.
As both the EEH and the MPH regardless of their sub-hypotheses imply higher profitability for
participating companies, market anticipation should be reflected in higher current stock prices when
a merger is announced and the antimonopoly body gave unconditional permission to complete the
transaction. The second event is important because it allows the fulfilling of previously expected
profitability increase. The similar hypotheses have been tested by many researchers (e.g., Aktas, N.,
Bodt, E. and Roll, R., 2004, 2004; Eckbo, E., 1983). However, the results vary considerably from
highly significant and positive reaction (Eckbo, E. and Wier, P., 1985; Duso, T., Neven, D. and Roller,
L., 2007) to insignificant (Aktas, E. and Derbaix, A., 2003). Significance also varied across different
windows of estimation and a country of origin of participating companies (Tsytsulina, D., 2012). We
will also anticipate diverse results depending on the estimated window.
H_2: Reaction of competitors to both merger announcement and the FAS’s clearance has the
same sign.
It is difficult to assume the exact sign of market response due to a complicated nature of the
MPH and the EEH discussed before. Nevertheless, it is still reasonable to suppose that the sign should
not alter after the FAS’s outright decision just for the same reason as in case of previous hypothesis:
this decision does not change the market conditions, it only fulfils either the benefits or the threatens
for rivals. Duso, T., Gugler, K. and Yurtoglu, B. (2011) obtained the same signs, which, however,
were insignificant.
H_3: Reaction of merging companies to the decision of giving remedies is negative.
This hypothesis refers to the fact that remedies are supposed to restore the pre-merger level of
competition in the market. On the one hand, decision with remedies does not prevent from gaining
benefits of a merger, but it causes extra costs and restrictions, so decreasing the gains of merging
companies. Therefore, more costly merger should be reflected in negative anticipations of investors.
The similar hypothesis was also suggested by Duso, T., Gugler, K. and Yurtoglu, B. (2011).
Unfortunately, it is impossible to estimate the effect of prohibitions due to non-availability of
data. For further clarifications, see the data description chapter.
18
H_4: There is a difference in reaction among vertical/conglomerate and horizontal transactions.
Vertical mergers demonstrate lower significance.
This hypothesis has been addressed by many authors (e.g. Tsytsulina, D., 2012; Eckbo, E.,
1983). It is linked to the theoretical studies (Salinger, M., 1988), which assert that negative influence
of vertical mergers on competition is less than of the horizontal ones. Empirical studies confirm that
reaction to initial merger announcement depends on the nature of a transaction (Eckbo, E. and Wier,
P., 1985; Duso, T., Gugler, K. and Yurtoglu, B., 2011).
Although abnormal return estimations might give some insights about market’s perception of
merger events in Russia, they are not able to draw conclusions about the quality of regulation. Hence,
to make inferences about the effectiveness of control, we implement the methodology developed by
Duso, T., Gugler, K. and Yurtoglu, B. (2011). This is the third step of the present research.
The idea is based on the underlying assumption that the market power effects generated by a
merger can be partially separated from its efficiency effects. Therefore, the analysis of relationship
between the rents generated around the decision and merger announcement may make sense. For
example, if the merger announcement generated positive rents for both rivals and merging companies,
which is consistent with the MPH, then the effective merger control should eliminate this rent. It
should be reflected in negative reaction when a decision is announced. Furthermore, there should be
a systematic negative relationship between these rents around two events. Thus, the core idea of this
approach is to investigate the phenomena of rent reversion. This approach allows one to assess the
antimonopoly body’s decisions. To tackle this question, authors proposed to estimate the “degree of
effectiveness” by running the following regression separately for competitors and participating
companies:
𝐷
𝐴
Π𝑖𝑗
= 𝑎𝑖𝑑𝑗 + 𝑏𝑖𝑑𝑗 Π𝑖𝑗
+ 𝑔𝑖 𝑋𝑗 + 𝜀𝑖𝑗 ,
(1)
where: X is a vector of exogenous control variables (e.g. industry-dummies);
Π denotes the rents generated around particular event of interest.
The subscript i denotes merging (M) or rival (R) firms, the subscript j stand for the transaction.
The subscript d denotes the FAS’s decision (C=clearance; R=remedies). The upper index A stands for
the merger announcement event, while D - for the authority’s decision announcement. This technique
19
is able to provide more robust results since it employs the regression analysis, rather than analysis of
significance of the rents.
Regressions results can be used to test a set of hypotheses concerning the sings of slope
coefficients and the intercepts. These hypotheses are generalized in Table 1.
Table 1
Expected Coefficients1
Decision
Clearance (d=C)
Remedies (d=R)
Predictions
Rivals (i=R)
Merging companies (i=M)
𝑎𝑅𝐶 = 0; 𝑏𝑅𝐶 = 0
𝑎𝑀𝐶 = 0; 𝑏𝑀𝐶 = 0
𝑎𝑅𝑅 < 0; 𝑏𝑅𝑅 < 0
𝑎𝑀𝑅 = 0; 𝑏𝑀𝑅 < 0
1
Source: Duso, T., Gugler, K. and Yurtoglu, B., 2011
Let us provide several comments on the Table 1. In case of clearance decision, it is quiet
straightforward that no systematic relationship should exist. As was mentioned above, the outright
clearance does not change the industry circumstances, therefore coefficients are assumed to equal
zero. In case of remedies implementation, the slope coefficients for both merging firms and their
rivals is supposed to be negative, as it indicates the rent reversion. The intercept for competitors
became negative, because it means the shift occurring because of the eradication of the market power
rents.
To conduct first three steps of the research, we will use the event study technique. An in-depth
review of this methodology will be presented in the next section. In brief, this approach enables one
to separate the effect of a merger or decision announcements from the “normal” performance of a
company’s shares. The main object of event studies is the abnormal returns, which is the difference
between actual and normal returns. Hence, if the abnormal return is significant, it means that event
does really influence the market’s anticipation about company’s future performance.
In order to be able to carry out the event study analysis, we need primarily three sorts of data.
First of all, dates of initial merger announcements are required. Since there is no specific Russian
database containing them, we should turn to the foreign sources, such as the Zephyr database collected
by the Bureau van Dijk. Secondly, we need the FAS’s decision announcements dates. These can
easily be extracted from the official Website of the Federal Antimonopoly Services, which contains
a publicly available decisions database. However, despite a legal obligation to place all the decisions
texts on the Internet, it is occasionally impossible to find several cases. Finally, the event study
technique requires the quotations of companies’ shares. There are several specialized stock data
20
sources, but the Finam is one of the most complete and conveniently accessed databank. A more
detailed overview of the data collection process will be presented later in the data section.
After obtaining the results on market reaction to various merger events and inferences about
the quality of merger regulations, it is highly important to assess the reliability of gained results. It is
the final step of our study. One of the most common and dangerous phenomena, which might
negatively affect the validity of results, is the problem of endogeneity. To our knowledge, only few
studies devoted to event study applications addressed this question directly (Eckbo, E., 1990; Aktas,
N., Bodt, E. and Roll, R., 2004). To accomplish a test for endogeneity, we use the discrete choice
models analysis. The idea of this test is to examine the behavior of two parties of a merger control
process: investors and the FAS. We will firstly study whether investors take into account the
probability of the FAS’s decision to impose remedies. Next, we will examine whether the FAS
considers the initial investors’ reaction to a merger proposal. If both of these questions appear can be
answered positively, then the endogeneity does really exist and the results of previous steps cannot
be treated as reliable.
21
4. Methodology
As was stated previously, the present study concentrates on the stock market reaction to
Russian merger events. This reaction can be analyzed using the event study approach. The event study
came originally from the finance (Myers, J. and Bakay, A., 1948; Fama, E.F., Fisher, L., M.C. Jensen
and Roll, R. , 1969; Brown, S. and Warner, J., 1985) and is based on the efficient markets hypothesis
(the EMH), specifically on its semi-strong form (Fama, E., 1970). According to this form of the EMH,
current stock market prices reflect all the publicly available information. This allows using current
prices and their changes as reliable indicators of future profitability. In terms of the present paper, the
events are the merger and antitrust decision announcements. Therefore, assuming that semi-strong
EMH holds, it is possible to assess the merger control by examining the reaction of the efficient
market.
A question of applicability of the event study analysis to the Russian stock market data needs
to be discussed at a basic level. Frankly speaking, today there is no direct evidence of the semi-strong
efficiency of Russian stock market despite several attempts to conduct the event study using data on
Russian public companies (Pogozheva, A., 2013; Tsytsulina,D., 2012). However, emerging markets,
to which Russia refers according to the International Monetary Fund’s list, draw interest of many
researchers. For example, Griffin, J., Hirschey, N. and Kelly, P. (2008) concluded that the difference
in reaction of emerging and developed markets did really exist. Main reasons for that were
enumerated, such as investors inattentiveness, poor informational environment, and possible insider
trading leading to the leakages. Nevertheless, those findings did not imply he prohibition to conduct
event study for emerging market data. Later, Griffin, J., Kelly, P. and Nardari, F. (2010) found that
traditional efficiency tests might give misleading results. Taking into account the transaction costs,
which investors of emerging markets face, and the speed of information incorporation, these markets
cannot be regarded as considerably less efficient than the developed markets. The study of Hall, S.,
Urga, G. and Zalewska-Mitura, A. (1998) used the data on Russian stock market indices and found
the Russian market was inefficient, however, it would become efficient in about two years. Today,
more than 10 years have passed since then. Thus, the present paper is based on the assumption that
the event study approach is an appropriate and feasible technique for Russian data.
The main idea of the event study is to assess the impact of an event using the abnormal return.
The abnormal return is the difference between the actual ex post return of a stock and its normal
return. The normal return is the expected return without conditioning on the event taking place. So,
for company i and the event date τ the abnormal return is:
22
𝐴𝑅𝑖,𝜏 = 𝑅𝑖,𝜏 − 𝐸[𝑅𝑖,𝜏 |𝑋𝜏 ],
(2)
where 𝐴𝑅𝑖,𝜏 , 𝑅𝑖,𝜏 , 𝐸[𝑅𝑖,𝜏 |𝑋𝜏 ] are the abnormal, actual and normal returns respectively for the
period τ. 𝑋𝜏 stands for the information set for the normal return model. For further clarification, Figure
4 presents the timeline for an event study.
T0
Estimation
Event
Post-Event
Window
Window
Window
T1
0
T2
T3
τ
Figure 4. The timeline for an event study
The choice of the normal return model has been discussed in several studies (e.g. MacKinlay,
A., 1997) and varies from the constant return model to the multi-factor models. However, none of the
researchers used the Fama-French model. The majority estimated the market model (Eckbo, E., 1983;
Duso, T., Gugler, K. and Yurtoglu, B., 2011) and used the constant return model to perform the
robustness check (Aktas, N., Bodt, E. and Roll, R. 2004). The market model implies the dependence
of a stock daily return 𝑅𝑖,𝑡 from the market portfolio daily return 𝑅𝑚,𝑡 :
𝑅𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖 𝑅𝑚,𝑡 + 𝜀𝑖,𝑡 , 𝜀𝑖,𝑡 ~𝑖𝑖𝑑 (0; 𝜎𝜀2 ),
(3)
where: 𝛽𝑖 is the sensitivity of a company’s return to the market return.
The slope 𝛽𝑖 should not be estimated using the Ordinary Least Squares (OLS) technique due
to the nonsynchronous data. Otherwise, the inconsistent and biased estimated will be obtained. The
problem of nonsynchronous trade has been addressed by many researchers (Brady, U. and Feinberg,
R., 2000). This problem is associated with the fact that closing prices of stocks cannot be observed
every day at the exactly same time. Fortunately, Scholes, M. and Williams, J. (1977) solved this
problem and introduced another way to estimate beta. Let us consider this technique closer.
23
The simple OLS estimate of beta, which is inconsistent with the nonsynchronous data, can be
written as:
𝛽𝑖 =
𝑐𝑜𝑣(𝑅𝑖,𝑡 ;𝑅𝑚,𝑡 )
𝑣𝑎𝑟(𝑅𝑚,𝑡 )
,
(4)
where: 𝑐𝑜𝑣 stands for covariance;
𝑣𝑎𝑟 is the variance.
For the consistent estimate, however, we need to consider two auxiliary coefficients:
𝛽𝑖− =
𝑐𝑜𝑣(𝑅𝑖,𝑡 ;𝑅𝑚,𝑡−1 )
𝑣𝑎𝑟(𝑅𝑚,𝑡−1 )
,
(5)
and
𝛽𝑖𝑠+ =
𝑐𝑜𝑣(𝑅𝑖,𝑡 ;𝑅𝑚,𝑡+1 )
𝑣𝑎𝑟(𝑅𝑚,𝑡+1 )
.
(6)
After that, it is possible to estimate the consistent beta and the intercept:
−
+
𝛽 +𝛽 +𝛽
𝛽̂𝑖 = 𝑖1+2𝜌𝑖̂ 𝑖 ,
(7)
𝑚
and
1
1
𝑇−1
̂
𝛼̂𝑖 = 𝑇−2 ∑𝑇−1
𝑡=2 𝑅𝑖,𝑡 − 𝛽𝑖 𝑇−2 ∑𝑡=2 𝑅𝑚,𝑡 ,
(8)
where: the 𝜌̂𝑚 the estimated first-order autocorrelation coefficient of the market return.
24
To catch the effect of event, the cumulative abnormal return (CAR) for every company and
its average (CAAR) for the whole sample should be estimated:
𝐶𝐴𝑅𝑖 = ∑𝑇𝑡=1 𝐴𝑅𝑖,𝑡 ,
(9)
and
𝐶𝐴𝐴𝑅 =
1
𝑁
∑𝑁
𝑖=1 𝐶𝐴𝑅𝑖 ,
(10)
where: 𝑁 is the sample size.
Under the null hypothesis that the event has no impact on the stock returns and the zero mean
of the market model’s disturbances, the abnormal return is supposed to be normally distributed
2
𝐴𝑅𝑖,𝜏 ~𝑁 (0; 𝜎𝐴𝑅
). If this conditions holds, the cumulative abnormal return should also follow the
2
normal distribution (MacKinlay, A., 1997): 𝐶𝐴𝑅𝑖 ~𝑁 (0; 𝜎𝐶𝐴𝑅
). Therefore, it is possible to implement
the t-test in order to estimate the significance of the cumulative abnormal returns.
The null hypothesis is as follows:
𝐸(𝐶𝐴𝑅𝑖 ) = 0.
(11)
The formula for statistic then takes the form:
𝐶𝐴𝐴𝑅
𝜃 = 𝑠𝑑(𝐶𝐴𝑅 )√𝑁 ,
𝑖
(12)
where: 𝑠𝑑(𝐶𝐴𝑅𝑖 )is the unbiased estimate of the standard deviation of the cumulative
abnormal returns.
25
We also investigate the problems of self-selectivity bias, which were first addressed by Eckbo,
E.B., Maksimovic, V. and Williams, J. (1990) and then developed by Aktas, N., Bodt, E. and Roll,
R. (2004). Eckbo et al. (1990) stated that if an events, such as merger or acquisition, results from
voluntary decision, then rational managers will proceed only those transactions that are expected to
be profitable and value creating. As a result, such a self-selection leads to a truncation of the
distribution of the cumulative abnormal returns. Therefore, it may make sense to consider the
truncated regression model (Green, W., 2003):
𝜑[(𝑎−𝛽𝑥 )/𝜎]
𝐸[𝑦𝑖 |𝑦𝑖 > 𝑎] = 𝛽𝑥𝑖 + 𝜎 1−Φ[(𝑎−𝛽𝑥𝑖 )/𝜎],
𝑖
(13)
where: 𝑦𝑖 is the dependent variable;
𝑥𝑖 is the vector of explanatory variables;
𝜑 is the normal density function;
Φ is its cumulative function;
a is the truncation threshold;
𝜎 is the standard deviation of the regression errors;
𝛽 is the set of coefficients.
The model is estimated by the Maximum Likelihood Estimation (MLE). If the significance of
the coefficients estimated by the MLE differs from those obtained after the OLS procedure, then the
selectivity bias does exist and influences the results.
The problem of endogeneity implies the simultaneous dependence of the cumulative abnormal
returns and the probability of the antimonopoly body to intervene, i.e. to give remedies or to prohibit
a deal. It means that investors initially, when a merger is announced, take into account the possible
probability of intervention, but at the same time the competition authority may take into consideration
the initially generated rents and then decide whether to intervene or not. To deal this problem, we
will firstly estimate the relationship between the FAS’s decision, several independent factors and the
estimated cumulative abnormal returns. The common technique is to run a probit- or logit-regression.
Please find a detailed overview of these models in the Appendix 1.
26
The estimated cumulative abnormal returns as the instrument for the cumulative returns will
also be included as a possible predictor. This procedure is called the two-stage instrumental variables
estimation and described by Aktas, N., Bodt, E. and Roll, R. (2004). To obtain the instrument, it is
necessary to estimate the dependence of CAR from the same set of variables as for the decision
probability. After running this regression, the predicted CAR can be obtained and included into the
probit- and logit- models described above.
The next step is to predict the probability of intervention and run a regression of the actual
CAR generated around the merger announcement on this probability of intervention. The idea is
similar to the Heckman’s lambda (Heckman, J., 1979). If the coefficient of the probability is positive,
then the endogeneity problem does exist. Moreover, the last model will be estimated by both OLS
and truncated techniques in order to investigate the problem of self-selectivity discussed above.
27
5. Data
To analyze the reaction of Russian stock market on merger events, a sample based on the
Zephyr (Bureau van Dijk) search strategy was constructed. The main advantage of Zephyr database
is the availability of merger announcements dates. First of all, all the mergers, acquisitions, and
minority stakes that had taken place, been announced or been withdrawn between 2005 and 2014
years were chosen. The next step was to select those transactions, in which at least one of the
participated companies was Russian company. After that, at least one of the merging firms needed to
be a public stock company in order to do the event study technique applicable. Finally, the number
of transactions was limited by only those ones, which were scrutinized by the Federal Antimonopoly
Service, because vast number of deals were not subject to control of the FAS. As a result, the sample
includes 230 observations. The timing distribution of the transactions based on the year of completion
of a deal is presented in the Table 2.
Table 2
The Timing Distribution of the Sample
Year
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
Total
Number
of deals
3
32
48
22
41
39
18
16
8
3
230
In spite of the variety of advantages of the Zephyr database, many of the transaction there
missed the dates of the FAS’s decisions and/or the type of decision. Therefore, all the transactions
were subjected to the auxiliary check using the FAS’s official website. However, it was not possible
to find the decision dates for several transactions. As a result, a set of three sub-samples will be
analyzed in the present paper. The first sub-sample includes 126 transactions with specified dates of
the initial merger announcement. The second one consists of 200 observations that have a stated date
of the FAS’s decision. Finally, the third sub-sample includes 97 transactions that have both dates
identified. The Table 3 demonstrates the frequency of outright approvals, remedies and prohibitions
that fell into the sample. There is no division of remedies into structural and behavioral due to their
little number in both sample and population, so such separation is likely to make results insignificant.
28
Table 3
Decision Distribution
Decision
Number of Cases
Percent of the Total Number
Remedies
34
14,78%
Prohibitions
4
1,74%
Outright Approvals
192
83,48%
After constructing the sample, the quotations needed to be collected. The source was “Finam”,
Russian investment holding providing financial services and data. Different sets of stock prices were
obtained. To estimate betas, we used 200 daily observations starting from 250 days before the merger
announcement. 50 days before the announcement were excluded because the merger information
might influence stock prices (Aktas, N., Bodt, E. and Roll, R., 2004). To estimate the abnormal returns
around the events, two windows were investigated: a wider and a shorter one, which is a common
approach in the event study (Maquieria, C., Megginson, W. and Nail, L., 1998; Tsytsurina, D., 2012;
Duso, T., Gugler, K. and Yurtoglu, B., 2011). The wider window includes 20 days before an event
and 20 days after, while the shorter one – 5 days before and 5 days after the event. The use of wider
windows allows catching the reaction to possible rumors and information leakages. As a market
portfolio for Russian firms, the MICEX Index (Moscow Interbank Currency Exchange) was
employed (see the exception in the robustness check section). Since several companies involved in
the mergers do not operate today, it appeared to be impossible to collect stock prices for them. For
this reason, prohibitions will not be analyzed in this paper.
To identify the rivals, we took into account both product and geographic borders of markets.
The product borders were determined based on the Russian Industry Classification Standards. After
that, the information provided by the RosBusinessConsulting (RBC) agency, which is the Russian
analogue for YahooFinance and MarketWatch, were used to reveal the publicly traded competitors
of the merging companies.
As it was mentioned in the methodology section, the problem of endogeneity will be
investigated through this research. To enable doing it, a set of additional variables needed to be
collected. The choice of the variables was based on the studies of Aktas, N., Bodt, E. and Roll, R.
(2004) and Duso, T., Gugler, K. and Yurtoglu, B. (2011). These can be divided into two groups. The
first group includes the financial information about the target company: the value of total assets and
29
the revenues obtained during the fiscal year prior to the transaction. These indicators may primarily
influence the decision made by the FAS, because higher valued targets may result in higher market
power of the combined firm (Andreasson, J. and Sundqvist, C., 2008; Bougette, P. and Turolla, S.,
2006). The second group of variables characterizes each particular transaction. First of all, it
comprises the estimated value of a deal, which may make impact on both the decision and the
investor’s anticipations about the deal, i.e. the abnormal returns (Aktas, N., Bodt, E. and Roll, R.,
2004). Moreover, there is a variable indicating the vertical transactions, as they are supposed to make
less significant impact on the competition level (Eckbo, E., 1983; Tsytsulina,D., 2012). All the
financial data were extracted from the Zephyr. The main advantage of this source comparing to the
ordinary annual financial reports is linked to the fact that all the companies are subject to the financial
audit (due diligence), so this information can be considered as correct. Finally, there are industry
dummies and indicator of whether a bidder company is Russian or not. These variables may act as
institutional characteristics (Bergman, M., Jakobsson, M. and Razo, C., 2005) and affect both decision
of the antimonopoly body and the investors reaction. For example, industry dummies may account
for entry barriers, which should be considered by the FAS when making a decision. Appendix 2
contains the descriptive statistics, industries description, and indistry distribution.
All the collected data were subjected to the econometric analysis using the R and STATA12
software.
6. Empirical Results
6.1 Initial Merger Announcement
Our first findings relate to the reaction to the initial merger announcements and are presented
in the Table 4. Since the CAR’s are not normally distributed (see Appendix 3 for normality check),
we cannot rely on results of the ordinary Student’s t-test. Therefore, the bootstrap technique was
implemented to tackle this problem. We used 1000 bootstrap sample of the same size as the original
one to estimate the bootstrapped t-statistic and corresponding p-values, which are presented in the
Table 4.
30
Table 4
Reaction to the Merger Announcements
Merging Firms
Short Window
Long Window
(-5;+5)
(-20;+20)
-0,0039
-0,0017
Competitors
Short Window
Long Window
(-5;+5)
(-20;+20)
0,0010
0,0100
CAAR
bootstrapped
0,458
0,489
0,464
0,459
p-value
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level
The study has shown that neither merging companies neither their rivals demonstrate any
significance reaction to the merger announcements. Therefore, we should partly reject the H_1
hypothesis about singinicant response to initial merger proposals. These results are contrary to many
European studies (for example, Aktas, N., Bodt, E. and Roll, R. 2004; Eckbo, E. 1983), where
merging companies had strong positive and significant abnormal returns around the announcement
date. In the Russian research (Tsytsulina, D. 2012) merging companies reacted to the announcements
significantly only on the day of announcement, while rivals showed strong negative reaction to these
events using both short and long windows. However, that research focused only on the metal sector,
but the present paper investigates a broad variety of industries. The obtained discrepancy may be
attributed to specificity of Russian stock market. First of all, Russian investors may see no great
opportunities for increasing future profitability or any threatens (in case of rivals). Another reason
may be associated with wrong announcement dates. Finally, the most likely reason for such results is
the poor information environment, including insider trading and information leakage, and investor’s
inattentiveness, which are common for all emerging markets (Griffin, J., Hirschey, N. and Kelly, P.
2008). Even several European researchers European stock market in existing insider trading (Aktas,
N., Bodt, E. and Roll, R. 2004).
We also tested the hypothesis about different responses to horizontal and vertical merger
announcements. Estimates are presented in the Table 5.
31
Table 5
Reaction to the Horizontal and Vertical Mergers
Merging Firms
Horizontal
Vertical
(-5;+5)
(-20;+20)
(-5;+5)
(-20;+20)
-0,0005
-0,0029
0,0340
-0,0319*
Competitors
Horizontal
Vertical
(-5;+5) (-20;+20)
(-5;+5) (-20;+20)
-0,0086
-0,0134
0,0730
0,2001
CAAR
bootstrapped
0,504
0,483
0,090
0,425
0,283
0,398
0,331
p-value
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level
0,302
Surprisingly, when we tried to control for vertical mergers, results contradicted many
empirical (Tsytsulina, D. 2012) and theoretical studies. They also reject the H_4 hypothesis, which
assumed lower significance of vertical agreements. On the one hand, merging companies do not react
to horizontal mergers announcements, but they show negative and significant at 10%-level response
to vertical agreements, despite obvious benefits of vertical mergers in the form of lower costs
(Salinger, M. 1983). At the same time, rivals have no reaction at all, although Riordan, M. (1998)
showed that vertical combination may lead to the increase in inputs prices, which implies threatens
for competitors. It is difficult to explain this result, but it might be related to negative expectations of
investors about the consequences of a deal for a company. The sub-sample consists of 126 transaction,
and 15 of them were regarded as vertical ones. Furthermore, almost a third of those vertical mergers
(5 cases) was not approved unconditionally by the FAS. Therefore, it is possible to assume that such
market response may appear because investors anticipate a higher scrutiny of the government and
higher probability of its intervention, so they expect more costly process of combination. This
assumption becomes even more sensible when looking at the list of the vertical mergers closer. Many
of these deals involve such huge corporations as “Noriskii Nikel”, “Gazprombank” and “OJSC
Sistema”. Apparently, big-scope companies are likely to draw high attention of the antimonopoly
body. Additionally, a negative reaction can be a reflection of investors’ disapprobation concerning
managers’ decision to extend company’s scope. Appendix 4 contains the list of these vertical mergers.
6.2 Decision Announcement
The next step was to analyze the stock market reaction to the FAS’s decisions announcements.
Results are presented on the Table 6.
32
Table 6
Reaction to the Decision Announcements
Merging Firms
Outright Approval
Remedies
CAAR
bootstrapped
p-value
Competitors
Outright Approval
Remedies
(-5;+5)
(-20;+20)
(-5;+5)
(-20;+20)
(-5;+5)
(-20;+20)
(-5;+5)
(-20;+20)
0,0090
0,0339**
-0,0331*
-0,0368
0,0032
-0,0444
-0,0280*
-0,0242
0,300
0,0407
0,060
0,179
0,467
0,368
0,081
0,249
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level
Results demonstrate that cumulative abnormal returns of merging companies are significant
at the 5%-level in case of unchallenged transactions. Hence, we can partly accept the hypothesis H_1.
As for the remedy-decisions, both rivals and participating firms demonstrate negative and significant
at 10%-level abnormal returns. These results are consistent with the H_3 hypothesis about negative
reaction of merging companies to remedies. We cannot accept the hypothesis H_2 because rivals
demonstrated significant reaction to neither initial merger announcement nor further decision
declaration.
Let us first discuss the merging companies. We can see the positive and significant response
to outright decision. It can mean that Russian investors perceive this decision as a feasible opportunity
to fulfill the benefits from the deal and increase the profitability. These results differ from European
ones. For example, Duso, T., Gugler, K. and Yurtoglu, B. (2011) showed that market did not react to
the unconditional clearance. Moreover, significant and positive reaction may indicate the type II
errors made by the FAS, because it approved the deals, which are likely to increase the market power
of the merged companies. Talking about the decision of giving remedies, the negative reaction can
mean that this is a bad news for merging companies because such decision implies extra costs, so
decreasing the abnormal returns, which demonstrate the elimination of rents generated initially due
to increased opportunities to monopolize the market.
As for competitors’ reaction, they only show significant response to giving remedies in the
short window. It can indicate the anticompetitive mergers, which created benefits for rivals as well,
however, the decision of giving remedies successfully destroyed the abnormal rent to the certain
extent. Similar results were also obtained by Duso, T., Gugler, K. and Yurtoglu, B. (2011) for
European companies.
33
6.3 The Relationship between Generated CAR
Using the available results, it is still quite difficult to make any inferences about the effects of
merger regulation. Therefore, in this study, we applied a novel approach developed by Duso, T.,
Gugler, K. and Yurtoglu, B. (2011), which none of the researches (except for its inventors) have
implemented before. This concept was described in details in the hypotheses section. Its main idea is
to estimate a linear regression of the CAR generated around the decision announcement on the CAR
caused by the initial merger proposal and to analyze the phenomena of rent reversion. Results are
presented in Table 7 and Table 8. Table 7 serves for the long window, and Table 8 – for the short one.
We employed the Newey-West sandwich estimator to obtain reliable standard errors, which are robust
to heteroscedasticity and autocorrelation.
Table 7
Relationship between Generated Rents for the Long Window
Merging Firms
Outright
Rivals
Remedies
Outright Clearance
Remedies
0,007
-0,006
-0,115
0,002
(0,013)
(0,005)
(0,108)
(0,003)
0,462***
0,453***
0,049
0,455***
(0,144)
(0,116)
(0,048)
(0,140)
Adj. R-squared
0,243
0,473
0,001
0,578
𝑝𝑟𝑜𝑏 > 𝐹
0,001
0,000
0,998
0,000
Clearance
𝑎𝑀𝐶 /𝑎𝑀𝑅 /𝑎𝑅𝐶 /𝑎𝑅𝑅
𝑏𝑀𝐶 /𝑏𝑀𝑅 /𝑏𝑅𝐶 /𝑏𝑅𝑅
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level. The Newey-West
corrested standard errors are presented in parentheses.
As we can see from the Table 7 and 8, the use of different windows enables to catch various
relationships. In both cases, the slope is positive and significant at the 1%-level for merging
companies in case of remedies-decision. Among the long window estimates, the slope coefficient for
merging firms is also positive and significant at 1%-level. It became insignificant, however, through
the short window estimation, but the intercept term in case of remedies appeared to be negative and
at 5%-level significant. As for the rivals, there are no significant relationships in case of outright
approval. Nevertheless, in both panels the slope coefficients are positive and significant when
remedies were given. Only significance dropped from the 1%-level in the long window estimation to
the 10%-level in the short window.
34
Table 8
Relationship between Generated Rents for the Short Window
Merging Firms
Outright
Rivals
Remedies
Outright Clearance
Remedies
0,014
-0,006**
0,012
-0,001
(0,009)
(0,003)
(0,010)
(0,004)
0,104
0,502***
-0,049
0,226*
(0,157)
(0,078)
(0,018)
(0,129)
Adj. R-squared
0,001
0,437
0,014
0,175
𝑝𝑟𝑜𝑏 > 𝐹
0,997
0,000
0,998
0,023
Clearance
𝑎𝑀𝐶 /𝑎𝑀𝑅 /𝑎𝑅𝐶 /𝑎𝑅𝑅
𝑏𝑀𝐶 /𝑏𝑀𝑅 /𝑏𝑅𝐶 /𝑏𝑅𝑅
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level. The Newey-West
corrested standard errors are presented in parentheses.
The analysis of systematic relationship between generated rents demonstrated surprising
results, which are opposite to those gained in the previous section. In contrast to earlier findings
(Duso, T., Gugler, K. and Yurtoglu, B., 2011), we observed a systematic and positive dependence
between the CAR generated around the merger announcement and the CAR appeared around the
announcement of outright approval: the higher is the initially generated rent, the high is the rent
around the clearance. These findings can mean that clearance only encourages Russian investors, so
they really see greater opportunities for merging companies to increase their market power. As for
the remedies, the situation is even more interesting and complicated. The combination of significant
negative intercept and significant positive slope reveals that remedies do really imply extra costs to
merging companies; however, these remedies are not able to destroy systematically the abnormal
rents, i.e. to restore the pre-merger competition level. Therefore, we can interpret earlier obtained
negative reaction to the remedy-decision announcements as investors’ instant response to the onetime costs imposed by the remedy. This conclusion is reinforced by the existence of positive and
negative slope of rivals, which means that the FAS did not manage to regulate the anticompetitive
mergers that created opportunity to increase profitability for both merging firms and their rivals.
Taking into account the high frequency of behavioral remedies relative to structural ones in Russia,
the results indicate an ineffective use of behavioral remedies, which should act on a permanent basis
rather than on one-shot.
35
In order to illustrate the possible ineffectiveness of given remedies, let us consider several
examples. On the 11th of September 2012, the FAS approved the combination of pharmaceutical
companies “Pharmstandart” and “LEKKO” and developed five remedies for this deal1:
1. To guarantee the fulfillment of all existing contracts;
2. To develop and publish on the company’s official Website a document stating the
requirements for future customers and terms and conditions of possible partnerships
between the company and its customers;
3. Not to reduce the production of those goods, which are still demanded by the consumers;
4. Inform the FAS about the abidance by the first three remedies on an yearly basis;
5. Inform the FAS about the completion of the merger within 20 days.
Three of these conditions (№№ 2, 4, 5) do not relate to the idea of restoring the competition
and reducing possible negative effects of a merger. They only imply sending notifications to the FAS
and placing certain information on the official website. Another two remedies may be considered as
attempts to influence competition. However, the Federal Law № 145-FZ “On Protection of
Competition” prohibits such behavior for any company, regardless of whether a transaction occurred
or not. It means that remedies imposed to this case just partly replicate the law. Therefore,
development of such remedies would not reduce the risks of a merger. Finding matches the
observations made by Russian researchers Avdasheva, S. and Kalinina, M. (2012), who asserted that
remedies in Russia often have no novelty compared to the antitrust law. Thus, only placing
information on the website and sending notifications might be considered as actions imposing some
administrative and temporal costs, which were reflected in the negative intercept term.
Another example may be the deal between “Generating Company № 5” and “Heat Supplying
Company of Kirov City”2 that was cleared with remedies on the 22th of June 2010. Three remedies
given then:
1. To provide a nondiscriminatory access to the heat network for companies producing heat
energy;
2. To provide a nondiscriminatory access to the services for customers;
3. Not to impose terms and conditions that are unfavorable to consumers.
On the one hand, these remedies seem to be reasonable and able to make demanded effect.
However, we face again the same problem mentioned above. If any company discriminated
1
2
Direct access to the text of the case: http://solutions.fas.gov.ru/documents/8341.
Direct access to the text of the case: http://solutions.fas.gov.ru/documents/3211.
36
consumers, the FAS would bring an action against this company because this behavior violates the
law.
To sum up this portion of results, we have revealed a reason for possible ineffectiveness of
merging imposition. The majority of mergers do not differ significantly from the law, so more
profound economic analysis is required when developing any remedies.
6.4 Endogeneity and Self-Selectivity
In this section, we will discuss the results of additional analysis for possible endogeneity and
self-selectivity mentioned in the Methodology part. First of all, we estimated the regression of the
CAR generated around the merger announcement on the set of variables and obtained the predicted
values of the CAR to use them as instrument. The linear correlation coefficient between actual and
predicted CAR is 𝑐𝑜𝑟 = 0,58; so the predicted CAR may be considered as a relevant instrument.
Results of this step are presented in the Table 9. The bootstrapped standard errors are in the
parentheses.
Table 9
Regression Results for the Actual CAR
Name
Coefficient
0,009
Intercept
(0,015)
-0,167***
Electro
(0,062)
0,039
Banking
(0,037)
0,007
Assets
(0,009)
-0,305***
Media
(0,015)
Adj. R-squared
0,285
0,006
𝑝𝑟𝑜𝑏 > 𝐹
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level. The bootstrapped
standard errors are presented in parentheses.
As we can see, industry dummies are significant at 1%-level. Particularly, investors react
negatively when electricity producing companies and media holding intend to get involved in
business combinations.
37
After that, we estimated the relationship between the decision type and the estimated CAR
controlling for other variables. Both probit- and logit-models were employed, but only logit-model’s
coefficients are reported due to slightly higher model significance and bigger area under the ROCcurve (0,788 vs. 0,795). The estimation was performed using the Maximum Likelihood procedure in
the STATA12 package. The model predicts correctly 85,1% of outcomes. Appendix 5 provides a
more detailed model comparison.
Table 10
FAS’s Decisions Determinants
Name
Coefficient
-1,501***
Intercept
(0,400)
2,147
CAR_predicted
(5,377)
-0,019
DealValue
(0,013)
0,018*
DealValue2
(0,007)
0,364
Foreign
(0,462)
0,067**
Revenue
(0,032)
-0,007
Revenue2
(0,004)
Pseudo R-squared
0,171
2
0,045
𝑝𝑟𝑜𝑏 > 𝜒
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level. The bootstrapped
standard errors are presented in parentheses.
Insignificance of coefficient of predicted CAR demonstrates that the FAS does not take into
account the initially generated rents, but does pay attention to the estimated value of the deal and
target’s revenue, which corresponds with the results of other studies (e.g. Bergman, M., Jakobsson,
M. and Razo, C. 2005; Bougette, P. and Turolla, S. 2006).
Table 11 presents results of the last step. The relationship between actual CAR and the
predicted probability of giving remedies was estimated. Moreover, the truncated regression was
employed to account for possible self-selection bias.
38
Table 11
Relationship between CAR and Probability of Intervention
Coefficients
(Ordinary Regression)
-0,046
(0,029)
-0,120
(0,113)
0,075**
(0,036)
0,066
(0,055)
0,098***
(0,030)
0,002
(0,015)
-0,363***
(0,031)
Name
Intercept
probability_predicted
DealValue
Vertical
Foreign
Assets
Media
Coefficients
(Truncated Regression)
-0,047
(0,028)
-0,125
(0,105)
0,076**
(0,033)
0,067
(0,041)
0,099***
(0,036)
0,003
(0,006)
Omitted
0,095***
(0,011)
Adj. R-squared
0,325
0,003
0,002
𝑝𝑟𝑜𝑏 > 𝐹 / 𝑝𝑟𝑜𝑏 > 𝜒 2
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level. The bootstrapped
σ
-
standard errors are presented in parentheses.
Since the probability’s coefficient is insignificant, we can conclude that investors do not take
into account the probability of intervention. Together with the previous regression, it means that there
is no endogeneity in the analyzed data. Thus, we can rely on the results obtained in the previous
sections. As for the self-selectivity bias, the second set of estimated coefficients presented in Table
11 demonstrates that all the estimates keep their signs and levels of significance. Particularly, the
coefficient of the probability of giving remedies is still insignificant, so verifying the fact that
investors initially do not consider that probability of government intervention, whereas coefficient of
a deal’s value remain positive and significant at the 5%-level and effect of foreign companies does
not change its positive sign and 1%-significance level as well. The stability of the signs and
significance levels means the absence of self-selectivity bias in the analyzed dataset.
6.5 Robustness Check
In this section, the results of the robustness check are presented. We analyzed the sensitivity
of the estimated abnormal returns to the selected model of the normal return. While the main results
are based on the market model, now we use the constant return model:
39
𝑅𝑖,𝑡 = 𝛼𝑖 + 𝜀𝑖,𝑡 , 𝜀𝑖,𝑡 ~𝑖𝑖𝑑 (0; 𝜎𝜀2 ).
(13)
Results are presented on Tables 12 and 13.
Table 12
Reaction to the Merger Announcements
Merging Firms
Short Window
Long Window
(-5;+5)
(-20;+20)
-0,0113
0,0123
Competitors
Short Window
Long Window
(-5;+5)
(-20;+20)
-0,0137
0,0139
CAAR
bootstrapped
0,388
0,456
0,376
0,459
p-value
* Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level
Table 13
Reaction to the Decision Announcements
Merging Firms
Outright Approval
Remedies
(-5;+5) (-20;+20)
(-5;+5)
(-20;+20)
Competitors
Outright Approval
Remedies
(-5;+5) (-20;+20)
(-5;+5)
(-20;+20)
CAAR
0,0195
0,0531**
-0,0464**
-0,0477
0,0159
-0,0237
-0,0371*
-0,0335
bootstrapped
p-value
0,116
0,044
0,029
0,461
0,187
0,364
0,095
0,496
Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level
The additional analysis showed completely the same results in terms of signs and their
significance as were obtained initially. Therefore, collected estimates are robust to the choice of the
normal return model. This fact increases the reliability of the results.
40
7. Discussion of Results
The results of our research show that reaction of Russian stock market to merger events differ
remarkably from findings made by researchers in the European Union or Canada and the United
States. On the whole, we have obtained several surprising and nontrivial findings.
Overall, three most significant conclusions can be derived from this study. Firstly, our
observations provide additional suspicion of existence of insider trading and poor informational
environments, which is quite common for all developing markets (Griffin, J., Hirschey, N. and Kelly,
P. 2008). The absence of stock market reaction to initial merger announcement also raises a question
about the accuracy of merger proposals dates.
Secondly, the results of the third step of our research partly confirm previous findings of
Russian researchers (Avdasheva, S. and Kalinina, M., 2012) and contributes additional evidence,
which suggests ineffectiveness of the remedies given by the FAS during the observed period.
Thirdly, the analysis of possible endogeneity has demonstrated that the stock market does not
takes into account the probability of the FAS’s intervention. Moreover, the FAS does not consider
the initial investors’ response to merger announcementOn the one hand, the absence of investors’
anticipations about future FAS’s actions may again indicate investors’ inattentiveness, but at the same
time it can be interpreted as a consequences of intransparency of FAS’s regulation procedure.
Moreover, the finding that the FAS does not take into consideration the initial investors’ reaction may
indicate that FAS does not pay attention to important economic phenomena that are worth being
examined.
The last two findings of our study may have a number of important implications for future
practice of merger regulation in Russia. Currently, given remedies seem to be relatively formal and
not specific for particular merger. The reason for that can be associated with sort of education gained
by officers working at the FAS. Most of them are lawyers, rather than economists. Therefore, the
FAS needs to enhance its procedure of merger control with more profound economic reasoning. An
obvious way to put this recommendation into practice is to hire specialists with economic background.
Finally, a number of important limitations of our research need to be considered. First of all,
with a small sample size, caution must be applied, as the findings might vary considerably over
different samples. For example, the study of Tsytsulina, D. (2012) demonstrated stronger stock
market response using sample of about 60 transactions. Moreover, when using the external databases,
such as Zephyr or Thompson Reuters, no one can guarantee the accuracy of initial merger
41
announcements. Secondly, the current research was not specifically designed to check the
applicability of the event study methodology to Russian stock market data. However, our results
highlighted that this problem is worth addressing. Furthermore, we did not pay attention to the
problem of heteroscedasticity of abnormal returns when analyzed their significance due to complexity
of this question. Several authors analyzed the ways to deal with this issue (e.g. Boehmer, E.,
Musumeci, J. and Poulsen, A., 1979), however, there is still no agreement about the most effective
method of accounting for heteroscedasticity in the event studies.
42
Conclusion
An econometric appraisal of merger regulation has been increasingly popular during the recent
years. Researchers has developed several approaches to tackle this problem. Each of these techniques
allows for making different sorts of inferences about the state control of mergers and acquisitions.
Specifically, the approach based on the discrete choice models analysis reveals the factors influencing
the decision-making process of an antimonopoly body, so providing opportunity to analyze whether
the antimonopoly body’s activity corresponds with its primary antimonopoly goals. On the other
hand, the event study gives insights about the effectiveness of regulation by observing the reaction of
the (relatively) efficient and independent stock market.
The present study focused on the assessment of the Russian merger regulation by examining
the stock market response to such merger events, as initial merger announcement and following
publication of the FAS’s decisions. To our knowledge, this research is the first attempt to
quantitatively assess the merger control in Russia. It combines both the event study and discrete
choice models approaches. However, the main emphasis has been made to the event study technique
because its major advantage is the the objectivity of the data provided by the stock market. The
discrete models act as additional tool enabling testing of reliability of the results.
The data for empirical analysis were collected using the Zephyr database and the open FAS’s
decisions database. The whole sample contains 230 transactions that occurred between 2005 and 2014
in Russia. We also used the Finam in order to obtain companies’ quotations for the event study
investigation.
The empirical analysis comprised four major stages. The first one was to analyze the stock
market reaction to initial merger proposals. Then, the examination of the response to the FAS’s
decisions announcements was performed. Third, we looked at the systematic relationship between
the rents generated around these two event. Finally, we addressed the problem of endogeneity and
self-selectivity using the discrete choice model approach.
The author gained mixed results. First of all, using the sub-sample of 126 cases with available
merger announcements, we found that the stock market did not react to the initial merger
announcement. Moreover, we also found that investors of large merging companies reacted
negatively to the announcement of vertical mergers. At the same time, rivals did not react to any
initial announcements regardless of controlling for horizontal and vertical combinations. These
findings contradict results of many existing studies.
43
Talking about decision announcements available for 200 transactions, we observed positive
and significant reaction to the outright clearance by the merging companies. However, this fact may
also indicate the type II errors made by the FAS, i.e. accepting the anticompetitive mergers. Both
merging firms and competitors reacted negatively to the decision of imposing remedies, so the market
reacted extra costs, which corresponds to the basic goals of the remedies.
On the other hand, the analysis of systematic relationship between CAR generated around
merger and decision announcements refused the previously mentioned assumption about an effective
use of remedies, because this examination revealed the positive systematic dependence between them
for both merging firms and rivals in case of giving remedies, while the intercept for merging
companies was negative and significant. This finding can mean the ineffectiveness of remedies,
because they do impose some costs on the merging firms, but do not lead to the rent reversion, so
saving the opportunity for increasing the market power and profitability.
We also concluded that there is no endogeneity problem in the dataset, because neither
investors take into account the FAS’s probability to intervene nor the FAS pays attention to the
initially generated abnormal profits around the merger announcements. We also concluded the our
sample does not suffer from the self-selectivity.
The author believes that results of this research may be valuable and interesting for the Federal
Antimonopoly Service because they identified the shortcomings in the current merger control
procedure. Furthermore, the statistical outcomes resulted from this study may be useful for future
studies devoted to the assessment of the merger regulation in Russia.
The most fruitful directions of further research might be as follows. First of all, collection of
a larger sample of transactions is highly recommended. This may be done manually by analyzing
various news sources. Furthermore, an additional examination of different estimation windows may
be performed in order to analyze the sensitivity of Russian stock market data to the estimation period.
These refinements would provide more reliable and robust results. In addition, the obtained results
can be used in order to identify type I and type II errors. This would allow for analysis of factors
effecting the frequency of occurrence of these errors can be investigated and developing further
recommendation for the antimonopoly body.
44
References
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2. Order N108 «On Approval of the Proceedings of Analysis and Assessment of Competition
Environment on Goods Markets». Adopted by the FAS on October 6, 2006.
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49
Appendices
Appendix 1
Binary Choice Models
In this paper, we employed the binary choice models on order to identify the factors taken into
account by the Federal Antimonopoly Service. Within the framework of the present study, the
dependent variable takes two values: 𝑦 = 0 in case of outright approval and 𝑦 = 1, if remedies were
given. In this Appendix, we provide some theoretical foundations of binary choice modelling.
The binary choice models investigate the dependence of probability of positive outcome 𝑦 =
1 on the set of explanatory variables and unknown coefficients:
𝑃(𝑦𝑖 = 1) = 𝐹(𝑥́𝑖 𝛽),
(1)
where: 𝐹(∙) is a function describing the relationship.
The domain of this function lies between zero and one. This allows one to interpret the
predicted probability values. Usually, the function 𝐹(∙) is the distribution function of a latent
continues variable 𝑦𝑖∗ :
𝑦𝑖∗ = 𝑥́𝑖 𝛽 + 𝜀𝑖 , 𝜀𝑖 ~𝑖𝑖𝑑(0, 𝜎𝜀2 ).
(2)
The introduction of this latent variable enables one to analyze the behavior of the dependent
variable 𝑦. So, 𝑦 = 1 when the value of the latent variable exceeds a certain threshold value 𝛾. It can
be written as (Verbeek, M. 2012):
{
𝑦𝑖 = 1, 𝑖𝑓 𝑦𝑖∗ ≥ 𝛾,
𝑦𝑖 = 0, 𝑖𝑓 𝑦𝑖∗ < 𝛾 .
(3)
Assuming the identical and symmetric distribution of an error term, we can obtain the
following expression:
50
𝑃(𝑦𝑖 = 1) = 𝑃(𝑦𝑖∗ ≥ 0) = 𝑃(𝑥𝑖́ 𝛽 + 𝜀𝑖 ≥ 0) = 𝑃(𝜀𝑖 ≤ 𝑥𝑖́ 𝛽) = 𝐹(
𝑥𝑖́ 𝛽
𝜎
).
(4)
Since parameters 𝛽 and 𝜎 cannot be identified separately, a common solution is to assume
𝜎 = 1.
Usually, the function 𝐹(∙) is either the function of standard normal distribution: 𝐹(𝑢) =
Φ(𝑢) =
−𝑧
𝑢
2
𝑒
∫
√2𝜋 −∞
1
2
𝑒𝑢
𝑑𝑧, or the function of logistic distribution: 𝐹(𝑢) = Φ(𝑢) = 1+𝑒 𝑢. In the first
case we deal with a probit-model, whereas in the second one – with a logit-model.
The estimation of these models is performed using the Maximum Likelihood Method. The
obtained estimates are biased but asymptotically consistent, asymptotically efficient and
asymptotically normally distributed. As a result, the direct interpretation of the coefficients is not
possible. To quantitatively interpret results, the marginal effects can be found. These reflect the
sensitivity of probability of a positive outcome to changes in one of the explanatory variables:
𝜕𝑃(𝑦=1)
𝜕𝑥
́ 𝑝(𝑥́𝑖 𝛽)𝛽,
= 𝐹(𝑥́𝑖 𝛽)𝛽 =
(5)
where: 𝑝(∙) is the density function.
51
Appendix 2
Supplementary Data Description
The Table 1 contains the descriptive statistics of the variables, Table 2 presents the industries
description, and the Figure 1 demonstrates the industry structure. Table 1 serves for only 126
transactions that have the stated date of merger announcement (see the discussion of the endogeneity
problem in the methodology section), while Figure 1 describes the industry distribution of the whole
sample.
Table 1
Summary Statistics1
Variable’s
Name
Description
Mean
Standard
Deviation
Min
Max
Decision
1 - remedy, 0 – outright
clearance
14,8%
35,7%
0
1
Vertical
1 – vertical merger, 0 horizontal
12,5%
33,2%
0
1
Foreign
1 – non-Russian bidder, 0
- otherwise
28,1%
45,1%
0
1
DealValue
Estimated value of a deal,
thousand Euro
570 904
986 158
30,32
21 300 000
Revenue
Pre-deal target’s revenue,
thousand Euro
205 823,2
515 566,2
0
2 990 118
Assets
Pre-deal target’s total
assets, thousand Euro
670 197,1
1 956 893
5,7
10 900 000
1
This table serves for only 126 observations with available initial merger announcements dates
52
Table 2
Description of Industry Dummies
Name in the Database
Description
Banking
Banking and Insurance Services
Metal
Steel, Precious and Other Metals Mining
Oil
Oil and Petroleum Extraction and Distribution
Electro
Electric Power Generation and Distribution
Machinery
Engines, Electrical Equipment, and Other Machines
Construction
Gas
Natural Gas Extraction and Distribution
Transportation
Water Transportation Services
Buildings, Bridges, and Roads Construction, Property
Letting Services,
Motor Vehicles and Automobiles Construction
Publishing and TV Broadcasting Activity
Telecommunication Services
Fertilizers and Chemical Products Manufacturing
Pharmaceutical Products Manufacturing
Coal Mining
Various Foodstuff Production
Supermarkets Chains
Construction
Car
Media
Telecommunication
Chemic
Pharma
Coal
Food
Supermarket
53
1.74%
1.74%
Electro
3.04%
0.87%
2.17%
11.30%
3.04%
Oil
Machinery
1.74%
Telecommunication
3.04%
Banking
3.91%
Metal
Coal
16.96%
1.30%
Chemic
Pharma
8.26%
Construction
Food
Supermarket
8.26%
Car
Gas
Media
20.00%
Transportation
12.61%
Figure 1. Industries distribution of the whole sample
54
Appendix 3
Tests for Normality of Cumulative Abnormal Returns
This appendix provides results of tests for normality of CARs. We used the Jarque-Bera test
and histograms of distributions. The null hypothesis of the Jarque-Bera test is normality of
observations, hence low p-values mean rejection of the null hypothesis and nonnormality of data.
Merging Companies: Merger Announcement
Short Window
Long Window
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
55
Competitors: Merger Announcement
Short Window
Long Window
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,002
Merging Companies: Decision Announcement/ Unconditional Approval
Short Window
Long Window
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
56
Merging Companies: Decision Announcement/ Remedies Imposition
Short Window
Long Window
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,003
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,001
Competitors: Decision Announcement/ Unconditional Approval
Short Window
Long Window
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,001
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,002
57
Competitors: Decision Announcement/ Remedies Imposition
Short Window
Long Window
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0,000
58
Appendix 4
Table 1
List of Vertical Mergers with Available Merger Announcements Dates
Deal
Number
Bidder Company
1
1601440149
GAZPROMBANK OAO
2
1601419578
MOSTOTREST OAO
3
1601235124
4
1601222050
5
1601213386
GORNOMETALLURGICHESKAYA
KOMPANIYA NORILSKII
NIKEL OAO
SISTEMA AKTSIONERNAYA
FINANSOVAYA
KORPORATSIYA OAO
X5 RETAIL GROUP NV
6
1601176513
SOGAZ OAO
7
1601152297
SISTEMA AKTSIONERNAYA
FINANSOVAYA
KORPORATSIYA OAO
8
1601120293
X5 RETAIL GROUP NV
9
1601119434
10
1601046238
MAGNITOGORSKII
METALLURGICHESKII
KOMBINAT OAO
SISTEMA AKTSIONERNAYA
FINANSOVAYA
KORPORATSIYA OAO
11
1601046243
SISTEMA AKTSIONERNAYA
FINANSOVAYA
KORPORATSIYA OAO
12
615235
ARCELORMITTAL SA
13
566569
SALAVATNEFTEORGSINTEZ
OAO
14
580941
SISTEMA AKTSIONERNAYA
FINANSOVAYA
KORPORATSIYA OAO
15
305212
GAZPROMBANK OAO
Target Company
OBYEDINENNYE
MASHINOSTROITELNYE
ZAVODY (GRUPPA
URALMASH-IZHORA)
OAO
OBYEDINENNYE
SISTEMY SBORA PLATY
OOO
NORDAVIA REGIONALNYE
AVIALINII ZAO
NAVIGATSIONNOINFORMATSIONNYE
SISTEMY OAO
OSTROV-INVEST ZAO
GRUPPA KOMPANII
VIDEO INTERNESHNL
ZAO
NEFTEGAZOVAYA
KOMPANIYA RUSSNEFT
OAO
PATERSON-INVEST
OOO
Date of Announcement
27.12.2012
12.11.2012
17.12.2010
10.10.2010
24.08.2010
09.04.2010
19.01.2010
05.10.2009
ONARBAY
ENTERPRISES LTD
01.10.2009
UFAORGSINTEZ OAO
13.11.2008
AKTSIONERNAYA
NEFTYANAYA
KOMPANIYA
BASHNEFT OAO
SHAKHTA
PERVOMAISKAYA OAO
MELEUZOVSKIYE
MINERALNYE
UDOBRENIYA OAO
DALNEVOSTOCHNYI
KOMMERCHESKII
BANK DALKOMBANK
OAO
SEVMORNEFTEGAZ
ZAO
13.11.2008
01.02.2008
02.08.2007
19.09.2007
29.12.2004
59
Appendix 5
Logit- and Probit-Models Postestimation
Usually, logit- and probit-models give similar results (see Table 1 for basic comparison based
on the Akaike and Bayesian information criteria and the log-likelohood value), so it is quite difficult
to choose one of these models. A common way to make the decision is to use the ROC (Receiver
Operation Characteristic)-curve. It reflects the relationship between correctly and incorrectly
predictions of the dependent variable. The Y-axis of the graph depicts the fraction of correctly
classified values 𝑦 = 1. The X-axis shows the fraction of incorrectly specified 𝑦 = 0 values as the
threshold value varies (Cameron, A. and Trivedi, P., 2005). The area under the ROC-curve
characterizes the quality of model. So, the higher is the area, the better the model is. Figure 1 below
demonstrate the ROC-curve for our probit-model, Figure 2 – for logit-model. The area under the
second curve is higher, so we prefer the logit-model.
Table 1
Comparison Criteria for Binary Choice Models Estimated
Criteria
AIC
BIC
Log-likelihood
Probit
57,974
69,145
-23,987
Logit
57,327
68,498
-23,663
60
Figure 1. ROC-curve for probit
Figure 2. ROC-curve for logit
61
Appendix 6
Example of the Script to Conduct an Event Study in R Software
#activate the package allowing downloading Russian stock market data from the Finam
library(rusquant)
#Function to use lagged variables
shift<-function(x,shift_by){
stopifnot(is.numeric(shift_by))
stopifnot(is.numeric(x))
if (length(shift_by)>1)
return(sapply(shift_by,shift, x=x))
out<-NULL
abs_shift_by=abs(shift_by)
if (shift_by > 0 )
out<-c(tail(x,-abs_shift_by),rep(NA,abs_shift_by))
else if (shift_by < 0 )
out<-c(rep(NA,abs_shift_by), head(x,-abs_shift_by))
else
out<-x
out
}
#Download quotation of “Megafon” to estimate beta
getSymbols(Symbols="MFON", from="2013-05-10", to="2013-11-26", src="Finam", period="day")
#Transform stock prices into log-returns and set them as time-series data
MFON.lr<-diff(log(MFON[,4]))
MFON.lr<-ts(MFON.lr)
62
#To do the same for the MICEX Index
MICEX.lr<-diff(log(MICEX[,4]))
MICEX.lr<-ts(MICEX.lr)
#Estimate consistent beta
lm1=lm(MFON.lr~MICEX.lr)
lm2=lm(MFON.lr~shift(MICEX.lr,-1))
lm3=lm(MFON.lr~shift(MICEX.lr,+1))
cor=acf(MICEX.lr, na.action=na.pass,plot=F)$acf[2] #extract the first-order autocorrelation coefficient
beta=(coef(lm1)[2]+coef(lm2)[2]+coef(lm3)[2])/(1+2*cor)
#Additional variables indicating the length of time-series
n=length(MFON.lr)
nn=length(MICEX.lr)
#Estimate consistent alpha
a=mean(MFON.lr[2:(n-1)], na.rm=T)-beta*mean(MICEX.lr [2:(nn-1)], na.rm=T)
#Obtain quotations for the short event window
getSymbols(Symbols="MFON", from="2014-01-08", to="2014-01-22", src="Finam", period="day")
MFON.lr<-diff(log(MFON[,4]))
MFON.lr<-ts(MFON.lr)
getSymbols(Symbols="MICEX", from="2014-01-08", to="2014-01-22", src="Finam", period="day")
MICEX.lr<-diff(log(MICEX[,4]))
MICEX.lr<-ts(MICEX.lr)
#Calculate the abnormal returns
ar1=MFON.lr-a-beta*MICEX.lr
#Calculate the cumulative abnormal returns
car1=sum(ar1,na.rm = TRUE)
#Create an array of CARs
CAR=c(car1,car2,car3,car4,car5,car6,…)
63
#Test of significance of CARs
t.test(CAR)
64