The regulatory process of merger control

Table 2

The Timing Distribution of the Sample

Year	2014	2013	2012	2011	2010	2009	2008	2007	2006
Number of deals	3	32	48	22	41	39	18	16	8

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.

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 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.

Table 4

Reaction to the Merger Announcements

	Merging Firms	Competitors
	Short Window (-5;+5)	Long Window (-20;+20)	Short Window (-5;+5)	Long Window (-20;+20)
CAAR	-0,0039	-0,0017	0,0010	0,0100
bootstrapped p_value	0,458	0,489	0,464	0,459

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.

Table 5

Reaction to the Horizontal and Vertical Mergers

	Merging Firms	Competitors
	Horizontal	Vertical	Horizontal	Vertical
	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)
CAAR	-0,0005	-0,0029	-0,0319*	0,0340	-0,0086	-0,0134	0,0730	0,2001
bootstrapped p_value	0,504	0,483	0,090	0,425	0,283	0,398	0,331	0,302

Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level

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.

Table 6

Reaction to the Decision Announcements

	Merging Firms	Competitors
	Outright Approval	Remedies	Outright Approval	Remedies
	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)
CAAR	0,0090	0,0339**	-0,0331*	-0,0368	0,0032	-0,0444	-0,0280*	-0,0242
bootstrapped p_value	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.

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	Rivals
	Outright Clearance	Remedies	Outright Clearance	Remedies
/	0,007 (0,013)	-0,006 (0,005)	-0,115 (0,108)	0,002 (0,003)
/	0,462*** (0,144)	0,453*** (0,116)	0,049 (0,048)	0,455*** (0,140)
Adj. R-squared	0,243	0,473	0,001	0,578
	0,001	0,000	0,998	0,000

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.

Table 8

Relationship between Generated Rents for the Short Window

	Merging Firms	Rivals
	Outright Clearance	Remedies	Outright Clearance	Remedies
/	0,014 (0,009)	-0,006** (0,003)	0,012 (0,010)	-0,001 (0,004)
/	0,104 (0,157)	0,502*** (0,078)	-0,049 (0,018)	0,226* (0,129)
Adj. R-squared	0,001	0,437	0,014	0,175
	0,997	0,000	0,998	0,023

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 one-time 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.

In order to illustrate the possible ineffectiveness of given remedies, let us consider several examples. On the 11^th of September 2012, the FAS approved the combination of pharmaceutical companies “Pharmstandart” and “LEKKO” and developed five remedies for this deal:

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 (¹¹ 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 ¹ 145-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 ¹ 5” and “Heat Supplying Company of Kirov City” 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 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 ; 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
Intercept	0,009 (0,015)
Electro	-0,167*** (0,062)
Banking	0,039 (0,037)
Assets	0,007 (0,009)
Media	-0,305*** (0,015)
Adj. R-squared	0,285
	0,006

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.

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 ROC-curve (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
Intercept	-1,501*** (0,400)
CAR_predicted	2,147 (5,377)
DealValue	-0,019 (0,013)
DealValue2	0,018* (0,007)
Foreign	0,364 (0,462)
Revenue	0,067** (0,032)
Revenue2	-0,007 (0,004)
Pseudo R-squared	0,171
	0,045

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 tru

Table 11

Relationship between CAR and Probability of Intervention

Name	Coefficients (Ordinary Regression)	Coefficients (Truncated Regression)
Intercept	-0,046 (0,029)	-0,047 (0,028)
probability_predicted	-0,120 (0,113)	-0,125 (0,105)
DealValue	0,075** (0,036)	0,076** (0,033)
Vertical	0,066 (0,055)	0,067 (0,041)
Foreign	0,098*** (0,030)	0,099*** (0,036)
Assets	0,002 (0,015)	0,003 (0,006)
Media	-0,363*** (0,031)	Omitted
ó	-	0,095*** (0,011)
Adj. R-squared	0,325	-
/	0,003	0,002

Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level. The bootstrapped 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:

. (13)

Results are presented on Tables 12 and 13.

Table 12

Reaction to the Merger Announcements

	Merging Firms	Competitors
	Short Window (-5;+5)	Long Window (-20;+20)	Short Window (-5;+5)	Long Window (-20;+20)
CAAR	-0,0113	0,0123	-0,0137	0,0139
bootstrapped p_value	0,388	0,456	0,376	0,459

* Notes: *Significant at 1%-level **Significant at 5%-level ***Significant at 10%-level



Table 13

Reaction to the Decision Announcements

	Merging Firms	Competitors
	Outright Approval	Remedies	Outright Approval	Remedies
	(-5;+5)	(-20;+20)	(-5;+5)	(-20;+20)	(-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.



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 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.



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.

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.



References

Legislation

1. Federal Law ¹135-FZ of July 16th, 2006 "On Protection of Competition". Adopted by the State Duma on July 8, 2006.

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.

Special Literature

3. Aktas, E. and Derbaix, A. (2003) “Horizontal, downstream and upstream effects of merger & acquisition operations in the car industry”, Banque et Marché, Vol. 69, pp. 40-49.

4. Aktas, N., Bodt, E. and Roll, R. (2004) “Market Response to European Regulation of Business Combinations”, The Journal of Financial and Quantitative Analysis, Vol. 39 No. 4, pp. 731-757.

5. Amihud, Y. (1986) “Conglomerate Mergers, Managerial Motives and Stockholder Wealth”, Journal of Banking & Finance, Vol. 10 No. 3, pp. 401-410.

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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: in case of outright approval and , 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 on the set of explanatory variables and unknown coefficients:

, (1)

The domain of this function lies between zero and one. This allows one to interpret the predicted probability values. Usually, the function is the distribution function of a latent continues variable :

. (2)

The introduction of this latent variable enables one to analyze the behavior of the dependent variable . So, when the value of the latent variable exceeds a certain threshold value . It can be written as (Verbeek, M. 2012):

(3)

Assuming the identical and symmetric distribution of an error term, we can obtain the following expression:

. (4)

Since parameters and cannot be identified separately, a common solution is to assume .

Usually, the function is either the function of standard normal distribution: , or the function of logistic distribution: . 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:

, (5)

where: is the density function.

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

¹This table serves for only 126 observations with available initial merger announcements dates

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
Construction	Buildings, Bridges, and Roads Construction, Property Letting Services,

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