Determinants of premium for control of the emerging markets

Figure 10. Relationship MB with dummy variables

The hypothesis was confirmed. For sample, we received a negative relationship between CP and MB. Tobin `s q reflects a couple of variables, such as: (i) the carrying amount of the company's assets; (ii) "mood" of the market, expressed, for example, the opinion of analysts, with respect to the prospects of the company, or various speculations in the form of high-profile hearings; (iii) the intellectual capital of the company. We can say that in the case of high MB ratio, the company is likely to have an efficient management; it has a high proportion of the Intellectual Capital. The controlling investor has less opportunity to extract private benefits. Thus, the investor does not want to pay a premium for this control. It should be noted that China stands out on the chart. CP elasticity by MB is significantly lower than in other models. We can assume that this is due to a possible lack of transparency of China's economy in the context of the protection investors' rights, in other words there is the extraction of private benefits.

Figure 11. Relationship LEVERAGE with dummy variables

We checked leverage impact on control premium. Among researchers, there is no consensus about the impact of financial leverage on the size of the control premium. The debt limits access of free cash flow, indicating a negative impact on the private benefits of control (Albuquerque and Schroth (2010), Barak and Lauterbach (2012)). On the other hand, debt allows the company to expand company's assets, which undoubtedly has a positive effect on the size of control premium (Barclay and Holderness (1989); Nicodano and Sembenelli (2004)). We received a negative relationship, investors in emerging markets have a more short-term investment views, and, respectively, leverage will limit the possibility of obtaining private benefits.

Figure 12. Relationship T_MCAP with dummy variables

This result is consistent with other studies: Barclay and Holderness(1989), Nenova (2003), Gaspar et al. (2005).. We can also differentiate that the effect size of the company is weaker for China by 5 points and for residents during the crisis. But as we noted above, the last dummy variable was significant.

Figure 13. Relationship T_EQUITY with dummy variables

It is obvious that the company acquires tangible assets, fixed assets at the expense of equity. This is because the useful life of such assets fall is often longer than the issuance of any loan. Thus the purchase of fixed assets financed by equity. As expected that high level of fixed assets in the total assets of the company negatively effect on size of control premium. This is because for these assets require more time for their sale or removal of assets of the company. (Sonia P. Jurfest et al (2015)). On the graph, we can see the constant tendency for the coefficient by different dummy, except China. For this case, we have a positive relationship. Again, it is difficult to explain, because I had never seen before. We can assume that there is some kind of administrative features.

Our analysis of control premium has weaknesses and limitations:

(i) We have considered only a part of variables. Of course, other factors can affect the size of the control premium.

(ii) All variables are quantitative in models. We understand that there are quality features such as the Intellectual Capital and many others who are also define premium. It is important to include them in the analysis. It will further study in this direction;

(iii) The limited data. Databases provide incomplete data gaps and other defects. In this case, the researcher forced to abandon most of the samples.

Concluding Comments

In this work, we investigated the determinants of control premium on emerging markets. The control premium plays very important role in corporate finance and business valuation. A large number of researchers are working to determine the fair value of control premium. In many developed countries, this problem solved by creating a unified database for transactions and premiums for control. However, for emerging markets there is no single approach to determining the size of control premium.

In the first part of our research, we had conducted an analysis of existing approaches and determinants to determine the control premium both for developed and emerging markets. We found that the studies on the determination of the control premium in emerging markets is not so much as in developed countries. Also among researchers, there is no consensus in the process of determining a control premium. Many of the works choose the percentage difference between the market price of the block and the transaction price: control premium, acquisition premium, voting premium, takeover premium, and premium bidding, as the dependent variable. Thus the identification of different concepts, by the choice of the dependent variable, which has equally calculated for different terms.

The authors of the studies used a variety of different factors as explanatory variables. Traditionally used characteristics of the buyer and company goals. However, there are works in which as the variables chosen variables from financial architecture, as well as some indexes that estimate the quality parameters of the business environment, for example, investor protection, takeover regulation and other. In our work, all variables has arranged in groups such as explanatory variables and dummy variables, which represent a quality characteristics of control premium. control premium determinant

We divided the concept of control premium for the premium for financial control and the award for strategic control. This separation also allows you to highlight the investment value as a separate category. Since the control premium occurs at 50-100% acquisition, here, obviously we are talking about strategic plans for the company, but not short-term extraction of private benefits. To detect this difference, we introduce a dummy variable for buying blocks of 50-100%. This variable allows us to understand which variables will influence the size of strategic control premium, and which will be not statistically significant.

For further analysis, we have put forward seven hypotheses. They can be divided into several groups. The first groups refers to the explanatory variables, and the last testing some quality and country differences in the size of control premium. We also tested control premium for crisis period expect to get a result that is different from the sample in a stable period. It is possible that some factors will be more significant in the crisis period. Also interesting to see what amount of differences of control premium is between the two periods. In addition, we expect that some industries will have different set of significant variables as it has in other studies. However, it is important to understand what factors effect on control premium in different industries and why a control premium is different in industries.

To test these hypotheses we will use a linear regression model for panel data, for two data matrices. In the future we plan to vary the number of variables depending on what quality of data we can collect, and whether all the data for selected variables. In addition, we expect that there may be endogeneity in the model and multicollinearity. In this case, we have to change the model specification.

For future research directions, we plan to include another group of variables, but in this case, they will already be high quality. The task is to evaluate the influence of investor protection on the size of the control premium. The difficulty is that you need to build synthetic indicators that will reflect the level of this protection, and to collect relevant data.

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Appendix

Table XI

Descriptive Statistics for Brazil

Table XII

Descriptive Statistics for China

Table XIII

Descriptive Statistics for India

Table XIV

Descriptive Statistics for South Africa

Table XV

Descriptive Statistics in General

Table XVI

Correlation Matrix

Figure 14. Graphic analysis of determinants

Figure 15. Scatterplot for all variables

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