It is also worth drawing attention to the fact that in both models there are other variables which are statistically significant: company's size, income growth, ROA, dummy-variables 2010 year. In the first model dummy-variable on Energy sector is also statistically significant. And in the second model ratio of company capital expenditures to its assets, dummy-variables on Service and Trade sectors also turned to be statistically significant.

Conclusion

The main aim of the present work was to determine the effect of a capital structure deviation from an optimal level on value of Russian companies. The goal was realized by conducting a detailed study.

The first step was the profound analysis of crucial Capital Structure Theories and the Value Based Management concept. Not only the theoretical foundations and principles of the theories were studied, but also numerous empirical investigations devoted to the problem of testing these theoretical hypotheses on the data from developed and developing markets, which arouses greatest interest among a modern academic society. Despite of the fact that interrelations between capital structure and company's value has been studying since the end of the previous century there is not a clear vision of all possible changes of company's value which are caused by a capital structure deviation from an optimal level.

That is why this issue has been examined in considerable detail. Firstly, two most relevant approaches of calculation of an optimal capital structure were utilized: the minimum WACC and regression equation methods. In this study the database compiled by the International Laboratory of Intangible-driven Economy of the Higher School of Economics-Perm was used.

At the first step, in order to apply the WACC method a cost of borrowed capital was computed by the means of a synthetic credit raiting. Simultaneously, it was necessary to calculate cost of equity with the help of the CAPM model which also included the Hamada's equation. The second variant of defining an optimal capital structure was a regression equation. It included the most crucial and commonly used determinants of company's capital structure: company's profitability, tangibility, liquidity, value and size. Then, the initial database was divided into five sub-samples (based on the industry of each company) in order to acquire five various equations of an optimal debt-equity ratio for each particular sector of Russian economy. In that way, the optimum was defined again. After that, the difference between both found optimal points and current financial leverage indicator was found twice (because a deviation from an optimal level was calculated twice: from the WACC optimum and from the regression optimum).

At the second stage, another multiple regression model was developed in order to investigate the impact of capital structure deviations on companies' value. It was applied twice with a different value of a deviation. The equation entailed basic determinants of company's value which were selected based on previous scientific researches. Three variables describing a capital structure deviation were introduced into the model: a module of capital structure deviation itself; a dummy-variable which indicated if a certain deviation was negative or positive (if the current financial leverage was greater or less than the optimal one); a module of capital structure deviation multiplied by a dummy-variable in order to find their joint effect.

As a result, rather unexpected and controversial results were obtained. They suggest that in case of applying the WACC method an increase of a positive deviation cause an increase of firm's value, while a rise of a negative one has an opposite effect. It means that the Capital Structure Theory works only when an optimal financial leverage indicator is greater than a current one.

With regard to the regression equation method, it provides not a maximum but a minimum value for a company which is absolutely opposite to the premise of the Theory which works on in the small area where a deviation is smaller than a firm's optimum by 1.54 units.

These results are quite astonishing and disputable because they allow to suggest that the Value Based Management concept, with the use of the WACC and regression equation methods of calculation of an optimal capital structure works abnormally under the Russian economic conditions. This is due to the violation of the Capital Structure Theory regardless the method of computing an optimal level. Thereby, they require a further more scrutinized examination, and, perhaps, improvements in the used calculation methods and models. However, so far, it can be assumed, that in modern Russian economic conditions, this procedure cannot be used by companies to select their capital structure.

Therefore, the acquired results partly confirm the conclusions made in the number of works (Lemmon, Lins, 2003; Vo, Ellis, 2017; Ramli, 2018) studying the examined issue in the countries with the emerging financial markets. In such states, economy is still developing, thus, all necessary institutions and mechanisms have not been built yet, the shift of economic view and profound reverse of the economic system have not happen too. All this constraints distorted the way of function of the theories, and, consequently, companies' behavior. In particular, Capital Structure Theory provides not a maximum but a minimum value for Russian companies in a total contradiction to the experience of developed economies.

However, there are some new and rather peculiar conclusions concerning Russian companies. In this regard, it is important to understand the reasons of such unconventional functioning of the Capital Structure Theory in our country. At a first glance, there are several possible reasons.

Firstly, the analysis of preliminary results revealed that almost no Russian company had a debt-equity ratio conforming its optimal level defined by means of both chosen methods. This observation justified the assumption that the VBM concept is not widely spread among Russian enterprises. In turn, it implicitly confirms the assumption that today the majority of Russian companies' managers are still focused on increasing profitability of a business which, consistently, is absolutely opposite to the VBM's main goal - company's value maximization.

Secondly, Russian economy has been functioning under the market economic concept only since 1991 year. Due to this fact, in the comprehension of the majority of modern Russian businessmen and managers borrowed capital associated not with an opportunity of development through cheaper sources of money but with the risk, bad loans and deplorable perspective. So, company's CEO is not inclined to borrow. At the same time, it should be taken into account that shares of the large part of companies from the sample are traded on the Moscow Stock Exchange and Russian investors on it also has such type of business mentality. Thereby, a rise of a financial leverage indicator is perceived by investors as a negative signal of feasible problems in a company, consequently, increase of debt in company's capital structure facilitates plunge of value of a business instead of an increase assumed by the theory. Unfortunately, nowadays management of a great number of Russian companies is not ready for a radical change of their business mindset and understanding of company's success. Therefore, they are not able to apply the VBM and follow its approach. However, this can be the explanation of the defined correlation only in the area of a positive deviation.

Thirdly, debt is a more expensive source of funding in Russia in comparison with the EU and the USA. Western companies can get loans at relatively low interest rates (about 3.6%) while in Russia it is four times higher (approximately 15.7%) (baki.ru, 2009) Information for 2009 is relevant for this research because the sample used included observations over the period from 2010 to 2014. That is the reason why it is not beneficial for Russian firms to finance their activity by loans because in such a case cost of debt tremendously exceeds cost of equity. Due to these constraints - a significantly high cost of borrowed funds - the VBM concept developed for Western economy cannot be directly applied under modern Russian economic conditions.

Finally, there were government representatives in boards of directors of some companies. Russian government is not a very effective manager because it is aimed at a maximization of profit in order to raise tax payments to the budget. However, it is a shareholder at the same time and also has an interest in the rise of its shares prices. Thereby, there is a conflict between this two opposite aims and so it may impose some serious constraints on a company and hamper its functioning in accordance with the theory. This aspect can also be one of reasons explaining the found breakage of the Capital Structure Theory. Though, the percentage of companies with governmental representatives is quite small and may not have a significant impact on the overall results.

To conclude, it is possible to consider this study as an attempt to provide a relatively firm ground for Russian companies in questions of managing a capital structure. As long as firm's managers take into account all possible consequences of deviations from an optimal debt-equity ratio they will be able to devise an approach which ensures realization of the VBM.

In addition, the methods utilized in this study might have some practical implications in real domestic business sector. In case a management calculates an optimal capital structure of its company by means of the used approaches, this study will enable them to be absolutely conscious about peculiarities of obtained results. Subsequently, they will be able to capitalize on this information and maximize company's value. Thereby, the baseline findings of the present investigation will elucidate the issue of influence exerted by capital structure deviations of Russian companies from their optimal levels on their value.

Also these results may be interesting for foreign investors who have an intention to invest in Russian companies. The main aim of all investors is to maximize their welfare by raising value of companies they invest in. Thus, the main goal is to maximize companies' value which is most commonly achieved by a capital structure optimization. If foreign investors are aware of such an abnormal behavior of Russian companies, peculiarities of their functioning and public trading they will apply not conventional approaches but specially developed strategies. As a result, it will facilitate growth of company's value which is advantageous not only for investors but also for companies and, in turn, the whole Russian economy.

It is worth mentioning that this study has a number of significant constraints. First of all, the interest rate of debts of each particular company was computed by a quite rough method which gave only an approximate estimation. That is why, it might skew the results. Secondly, there emerged a heteroscedasticity problem and the errors of both models are not subjected to the Normal distribution. Besides, Ramsey Regression Equation Specification Error Test (RESET) revealed that there were specification errors in both models. Probably, not all the necessary variables were included into the equation or there were some excess regressors. Also, the wrong functional form might be chosen. Furthermore, due to a great number of missing data it was not possible to use panel data which was substituted for the pooled regression. Finally, the possibility of the wrong use of the chosen methods of defining an optimal capital structure, which might have occurred owing to a number of serious assumptions, can also be one of the constraints of the present work.

Therefore, an attempt to improve model can be made in the future. Furthermore, the emerged heteroscedasticity problem can be eliminated by applying more advanced methods than the used Weighted least squares approach which did not give a positive result. Moreover, other crucial determinants of company's value can be included into the regression model. For instance, it may be brand value (which is relevant for companies from Services and Trade sectors), phase of a business life cycle or growth rate variable with a one year lag which should assist to take into account the fact that results of firm's activity in a previous period may affect its current state and, consequently, capital structure. In addition, another functional form can be applied which will better describe relations between company's value and its main determinants.

It is also worth noticing that some Western scholars believe that impact of a capital structure on companies' value in various economic conditions is different. In this regard, it is possible to continue this study and examine whether an effect of capital structure deviations from optimal levels depends on a country where a firm carries out its economic activity. Furthermore, comparison with the results of countries with the developed market economies might help to identify pivotal peculiarities of Russian economic reality which are the possible reasons explaining the obtained conclusions.



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Appendices

Number of observations by year for each sector (1st step)

	2010	2011	2012	2013	2014
Construction	28	31	30	21	8
Manufacturing	120	132	131	108	44
Energy	99	101	95	82	34
Service	23	29	29	27	7
Trade	14	20	16	13	5

Number of observations by industry and year (2nd step)

	Construction	Manufacturing	Energy	Service	Trade
Number of observations	96	462	332	22	61
	2010	2011	2012	2013	2014
Number of observations	209	242	238	204	80

Descriptive statistics for the five sub-samples (1st step)

		Profitability	Tangibility	Liquidity	Value	Size	Observ.
Construction	Min	-0.26	0.00	0.59	0.05	-0.17	118
	Max	0.26	0.97	11609.68	10.99	8.14
	Mean	0.04	0.29	202.42	1.19	5.01
Manufacturing	Min	-0.24	0.02	0.22	0.00	1.05	535
	Max	0.85	0.90	120.69	10.28	9.79
	Mean	0.09	0.45	3.08	1.03	5.52
Energy	Min	-0.14	0.02	0.04	0.01	0.61	411
	Max	0.97	0.98	14.39	9.90	12.62
	Mean	0.13	0.63	2.05	0.92	6.12
Service	Min	-0.08	0.06	0.25	0.00	2.21	115
	Max	0.34	0.97	54.97	7.50	10.82
	Mean	0.08	0.62	3.60	1.36	5.80
Trade	Min	-0.33	0.00	0.53	0.09	0.71	68
	Max	0.31	0.86	18.40	6.88	8.66
	Mean	0.09	0.46	2.31	1.35	5.15
Total			1247

Regression model results (1st step, OLS model, Constriction)

Variable	Coefficients
Profitability	-5.789 (4.410)
Tangibility	-2.711* (1.558)
Liquidity	-0.0002 (0.0002)
Company's value	1.502*** (0.207)
Company's size	0.545** (0.208)
Controls on a year	Included
Constant	-1.198 (1.080)
Observations	118
R2	0.487
Adjusted R2	0.445
Residual Std. Error	3.146 (df = 108)
F statistic	11.404*** (df = 9; 108)
*p<0.1; **p<0.05; ***p<0.01

Regression model results (1st step, OLS model, Manufacturing, robust)

Variable	Coefficients
Profitability	-11.890*** (1.782)
Tangibility	-5.426*** (1.060)
Liquidity	-0.051 (0.039)
Company's value	1.609*** (0.286)
Company's size	0.313*** (0.100)
Controls on a year	Included
Constant	2.645*** (0.556)
Observations	535
R2	0.370
Adjusted R2	0.360
Residual Std. Error	3.443 (df = 525)
F statistic	34.315*** (df = 9; 525)
*p<0.1; **p<0.05; ***p<0.01

Regression model results (1st step, OLS model, Energy, robust)

Variable	Coefficients
Profitability	-2.006*** (0.600)
Tangibility	-3.786*** (0.465)
Liquidity	-0.291*** (0.046)
Company's value	0.231 (0.166)
Company's size	0.046* (0.024)
Controls on a year	Included
Constant	3.783*** (0.442)
Observations	411
R2	0.398
Adjusted R2	0.384
Residual Std. Error	1.177 (df = 401)
F statistic	29.399*** (df = 9; 401)
*p<0.1; **p<0.05; ***p<0.01

Regression model results (1st step, OLS model, Service)

Variable	Coefficients
Profitability	-7.466*** (1.826)
Tangibility	-2.065*** (0.549)
Liquidity	-0.017 (0.017)
Company's value	0.318*** (0.083)
Company's size	0.182*** (0.067)
Controls on a year	Included
Constant	1.665*** (0.519)
Observations	115
R2	0.289
Adjusted R2	0.228
Residual Std. Error	1.262 (df = 105)
F statistic	4.733*** (df = 9; 105)
*p<0.1; **p<0.05; ***p<0.01

Regression model results (1st step, OLS model, Trade)



Variable	Coefficients
Profitability	-1.450 (1.321)
Tangibility	-2.197*** (0.563)
Liquidity	-0.156*** (0.051)
Company's value	-0.036 (0.094)
Company's size	0.288*** (0.093)
Controls on a year	Included
Constant	1.291** (0.496)
Observations	68
R2	0.467
Adjusted R2	0.385
Residual Std. Error	0.892 (df = 58)
F statistic	5.653*** (df = 9; 58)
*p<0.1; **p<0.05; ***p<0.01

Auxiliary calculations for the second step results

1. First model

· Positive deviation:

· Negative deviation:

2. Second model

· Positive deviation:

· Negative deviation:

Threshold value:

Comparison of various regression models (2nd step, OLS model, robust)

Variable	Coefficients (example model)	Coefficients (used model)
Module of a capital structure deviation	X	0.027* (0.015)
Sign of a deviation	X	-0.783*** (0.107)
Interaction term	X	0.476*** (0.040)
Capital structure deviation	0.010 (0.011)	X
Controls	Included
Constant	0.490** (0.237)	0.315 (0.196)
Observations	974	974
R2	0.079	0.317
Adjusted R2	0.062	0.304
Residual Std. Error	1.389 (df=956)	1.196 (df=955)
F statistic	4.798*** (df=17; 956)	24.633*** (df=18; 955)
*p<0.1; **p<0.05; ***p<0.01

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