To answer this question, it will be analyzed the impact of intangible assets on company market capitalization.

There should be considered the fact, that only intangible assets, which are recognized on balance sheet are being to be used.

Data

As research main goal is to analyze the impact of intangibles recognized on company market value, it obviously needs to collect information about companies from two types of source: market (for market value) and company (for intangibles and other relevant variables). To make the process of data collection more productive in terms of collection more data for less time, it was a decision to use Thomson Reuters terminal. It has all relevant information as for market value, as for companies' financial statements. Accounting standards chosen is the IFRS and this decision has such reasons:

· IFRS financial statements are more popular among investors and creditors for investments decisions

· Consolidation. IFRS gives an ability to create financial statements for all company's affiliated companies and present full company financial performance.

Coming back to Thomson Reuters, to make sure the data was imported correctly, there were chosen some companies to check the match between imported data and companies' financial statements, thus it can be concluded that it is correct. There were chosen Russian companies, which are being publicly traded on MOEX (Moscow Exchange) for 2014-2015 years. There were 76 companies for 2014 and 83 companies for 2015 years. This period was chosen because of interest to take a look on effect of Crimea crisis in 2014 and sanction burden of Russian economy. The main criterion for sample compilation was the data availability for all needed financial indicators. There also all financial firms were excluded from the sample, because they have other specific structure of financial statements. Total amount of companies can vary per year from 72 to 84. Also, the fact of industries combination in sample was not controlled and it appeared in random way. Total amount of companies per each year can be seen on Figure 1.

Method of analysis

It was considered to use multiple regression for this work. This type of analysis was used in such works with common approach like (Volkov & Garanina, 2008) work, (Garcнa, 2018), (Belikova, 2017). In mentioned works were chosen different variations of OLS (Ordinary Least Squares). In this working paper is used OLS regression with dummy variables. To be more concrete it is used a LSDV (Least Squares Dummy Variables) model to analyze the impact of intangible assets value on market capitalization in 2014 and 2015 and in such industries: oil & gas, retail, logistics, construction, electric energy, engineering, extraction, food and beverages, telecommunications. More details about variables is in variables section further. Heij et al. (2004) stated that OLS should be the first step in valuation of relations in economic sphere, because it can suggest crucial insights into the description of variables relationship. Therefore, it is also an argument to use such method.

For applying this method were chosen these software programs:

· Microsoft Excel

It was used for data preparation as on input as for output.

· RStudio (programming language R)

The biggest part of work was completed in R. Here the data was prepared for analysis, variables were transformed into needed format. Analysis was completed in R and it was obtained a final model. There also were tests of data and model. Final code can be viewed in Appendix A.

· STATA

This software was attracted to create a table of descriptive statistics. This program was preferable because of final design of descriptive statistics.

Variables

Concerning about variables chosen it was compiled a table with variables chosen by other researchers in their works. All variables were taken in the same moment of time (in the end of the year). The variable market capitalization is a company market value. This variable is dependent one. It is calculated by multiplying amount of stocks on price per share on the end of the year. For model it was taken a logarithm for market capitalization. To take logarithm is normal practice in scientific works. In some cases, it is simpler to interpret, while independent variable is also a logarithm. Moreover, logarithm of variable help to transform its distribution to normal and also to make linear relationship stronger. For example, the same practice was chosen by (Buюinskienл, 2017). The intangible assets value is the independent variable. This value was calculated by takin the sum of recognized intangible assets in balance sheet. It is also a logarithm of such value in final model. There also were chosen control variables like financial leverage (calculated by dividing company's total debt on equity), net profit and Return on Assets (ROA). These control variables were also used in works, mentioned in Table 2. For final model were also used dummy variables (with binary choice: 1 or 0) for taking into account such points: the industry, in which company operates and the year (2014 or 2015). So, the rule for dummy variables is to be: k - 1, where k is an amount of characteristics. It appears because of simple conclusion: if the number of dummies will be equal to amount of characteristics, it will appear strong linear relationship between such variables, that will distort a final model result. Thus, having 9 economic sectors there are 8 dummies for each one, excluding telecommunications (it can be an author choice which one of dummies will not appear in list). It means, that if beta coefficient before 8 mentioned dummies is qual to 0, there is an observation from Telecommunications sector. Using the same logic was implemented a dummy variable for 2014 year. If observation is from 2014, the dummy value is "1", otherwise it is "0".

Dependent variable
Market capitalization	(Tahat, Ahmed, & Alhadab, 2017) (Buюinskienл, 2017) (Voblaia, 2017) (Belem & Marques, 2012)
Independent variable
Intangible assets value	(Belikova, 2017) (Bakar, Salamudin, Hassan, & Ibrakim, 2010) (Buюinskienл, 2017)
Control variables
Financial leverage	(Tahat, Ahmed, & Alhadab, 2017) (Belikova, 2017) (Garcнa, 2018) (Bhatia & Aggarwal, 2018)
Net profit	(Buюinskienл, 2017) (Castro & Benetti, 2013) (Behname, Pajoohi, & Ghahramanizady, 2012)
ROA	(Gamayuni, 2015) (Tahat, Ahmed, & Alhadab, 2017)

Table 2 Variables chosen by authors. Source: Made by author.

Results

To start with, it is be presented descriptive statistics of variables used in the model (Table 3).

Variable	Obs	Mean	Sd	Min	Max
logmcap	159,00	23,28	2,50	17,60	28,62
logias	159,00	19,69	3,48	9,21	25,95
Nprof(bln)	159,00	16.1	58,40	-46,2	394,00
roa	159,00	0,03	0,08	-0,31	0,35
fl	159,00	3,40	11,59	0,00	107,48

Table 3 Decriptive statistics of variables

The results of Pearson correlation between dependent and independent variables is presented below on Table 4.

	logias	roa	fl	nprof
logmcap	0,64	0,27	0,13	0,49

Table 4 Pearson correaltion

On the next table (Table 5) there are calculated results of regression analysis. The first column shows the variables included into analysis, the second column shows beta coefficients for each variable, the third column represents standard deviation, the fourth column shows the t-statistics number and the last one column represents the p-value of each variable.

Generally, obtained model can be presented by this formula:

Equation 1

The first thing, that should be pointed out is that R-squared adjusted is equal to 0,59 what shows that model is able to explain 59% of change in market capitalization number. Also, the quality of model is considered to be acceptable. Moreover, the p-value of model is <0.05 which confirms a significance of this model, what also a relevant indicator of model quality.

The next important point of the model is results of logias variable. It could be concluded that the influence of such factor on company market capitalization is positive (Beta coefficient >0) and significant (p-value<0.05). Therefore, it is a reason to reject null hypothesis, which stated that intangible assets value does not significantly affect the company market price, and opposite hypothesis is accepted. There also should be paid an attention to interpretation of obtained coefficient of logias. As dependent variable is a logarithm of company market capitalization and independent variable logias is also a logarithm of intangible assets value, so change of logias number for 1 percent will lead to change in logmcap for 0,44 percent in case of other variables are do not being changed.

Variable	Beta	Sd	t	p-value
(Intercept)	13,07	0,99	13,21	0,00	***
logias	0,44	0,04	10,49	0,00	***
Nprof(bln)	0,00	0,00	2,65	0,01	**
roa	5,68	1,71	3,33	0,00	**
fl	0,02	0,01	1,71	0,09
oilgas (1/0)	2,24	0,73	3,08	0,00	**
retail (1/0)	1,93	0,81	2,38	0,02	*
logistics (1/0)	2,35	0,69	3,38	0,00	***
constr (1/0)	2,14	0,63	3,40	0,00	***
electr (1/0)	0,86	0,49	1,75	0,08
engeneer (1/0)	0,37	0,52	0,72	0,47
extract (1/0)	1,62	0,44	3,65	0,00	***
foodbev (1/0)	1,15	0,72	1,60	0,11
t2014 (1/0)	- 0,02	0,26	0,07	0,94
R^2	0,62
R^2 adj	0,59			P<0.05	*
p-value	0,00	***		P<0.01	**
obs,	159			P<0.001	***

Table 5 Regression model results. Made by Author

There was also paid attention to tests of obtained model to understand its quality deeper. Firstly, it will be introduced the results of VIF (Variance Inflation Factor).

logias	nprof	roa	fl	oilgas	retail	logistics
1,32	2,03	1,20	1,16	2,30	1,25	1,43
constr	electr	engeneer	extract	foodbev	t2014
1,58	2,29	1,98	2,48	1,35	1,06

Table 5 VIF. Made by author

This table provides information about existence a fact of strong multicollinearity of independent variables. If the each number from the table above is smaller that 10, it means that there is no problem of multicollinearity between the independent variables. Thus, according to data from table, there in no fact of multicollinearity existence.

One more approval for the fact of multicollinearity non-existence is a correlation matrix table which is represented in Appendix B. The next test is Darbin-Watson test of autocorrelation. It is stated that D-W number can be from -4 to 4, and if the number is near to 2 it means that there is no autocorrelation problem in model. The null hypothesis of this test is that there is no autocorrelation. Thus, if p-value is less than 0,05, null hypothesis is being rejected and it is approval of fact that there is autocorrelation problem. For this research's model (see Table 6) the Darbin-Watson test showed the 1,78 D-W statistics and p-value is 0,11 that proves, that there is no problem with autocorrelation in this model.

Autocorrelation	D-W	p-value
0,10	1,78	0,11

Table 6 D-W test

And the last test is the Shapiro-Wilk test for normal distribution of model's residuals. Shapiro-Wilk test represented p-value = 0.1056, which is more than 0,05 and does not give the basis to reject the null hypothesis of normal distribution of residuals. The distribution of residuals is represented graphically on Figure 1.



Figure 1 The distribution of model's residuals.

From the management point of view, it is practical approval of positive relationship between intangible assets value and company market value in Russian companies. It can mean that investors of company appreciate its intangible assets value. Therefore, it should be paid an attention by companies' management to managing intangible assets in terms of creating more value for company. However, fundamentally there is a low probability of that having a big amount of intangible assets will create value for company, because it is crucial to accurately manage them and maximally use their potential to make benefits. It is not stated that results of this analysis cannot be interpreted in other ways - it is a filed for opinions of different experts, so it is kind of one more theoretical contribution to intangible assets research sphere.

Conclusion

The main purpose of this Bachelor thesis was explore the influence of intangible assets on Russian public company market capitalization. The research question was: "Does the influence of intangible assets book value on company market capitalization is significant?" Through regression analysis there was a model obtained. This model showed that intangible assets value has significant and positive relationship with company market value. As a result it was rejected the null hypothesis about intangible insignificance and accepted opposite hypothesis which stated that such relationship is significant. Thus, the answer on research question is that intangibles have a significant relationship with company market value. Especially it was stated that change of logias number for 1 percent will lead to change in logmcap for 0,44 percent in case of other variables are do not being changed.

According to previously observed literature it was defined, that the influence of intangible assets takes an interest of other researchers. There were results, which showed that intangible assets have positive relationship with company market value and its financial conditions in works of (Sinclane & Keller, 2014), (Makrominas, 2017) and also in works of (Behname, Pajoohi, & Ghahramanizady, 2012) and (Jaara & Khalid, 2016). However, there also were pointed out some negative effects of investing in intangibles on stockholders' return on short-term perspective (Tahat, Ahmed, & Alhadab, 2017), but in long-term it was defined as a positive factor for company slightly, but sustainable growth.

Coming back to value-based management, it is crucial to mention that according to this concept that the main task for owner or top manager of business is development of company liquid value and also creation of system of managing company's value with the long-run objective of its maximization. If this main goal will be linked with observed works, it can be concluded, that paying attention to managing intangible assets will be the right strategic decision. Current work emphasizes again, that companies should to work with intangible assets, because such assets have positive and significant influence on company market value growth. Thus, intangible assets can also serve as a driver of company market capitalization growth.

Furthermore, limitations should be taken in attention. This research has such limitations: it covers only 2014-2015 years data, it is a period of Crimea crisis in 2014 and sanction burden of Russian economy further. There was met such limitation like data availability, in terms of access toto big amount of information (there could be met NA numbers in some observations, that lead to decreasing sample size). Also, the observed companies' financial statements and information from these statements were presented according IFRS standards and results of current research can be not relevant for countries with their specific accounting standards. Moreover, it is also just about Russian market of publicly traded companies. Future works can add as more time periods, as more geographic positions of companies. There also could be created more complex models, which can take into account such factors like accounting standards. Thus, such research can become more useful for wider range of companies over the world.

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Appendix A.

Code in RStudio Bachelor Thesis.R

library(car)

## Loading required package: carData

library(readxl)

VYBORKA <- read_excel("VYBORKA.xlsx", sheet = "after crisis")

logmcap <- log(VYBORKA$mcap)

logias <- log(VYBORKA$ias)

roa <- VYBORKA$roa

fl <- VYBORKA$fl

nprof <- VYBORKA$nprof

oilgas <- as.factor(VYBORKA$oilgas)

retail <- as.factor(VYBORKA$retail)

logistics <- as.factor(VYBORKA$logistics)

constr <- as.factor(VYBORKA$constr)

electr <- as.factor(VYBORKA$electr)

engeneer <- as.factor(VYBORKA$engeener)

extract <- as.factor(VYBORKA$exract)

foodbev <- as.factor(VYBORKA$foodbev)

t2014 <- as.factor(VYBORKA$y)

model <- lm(logmcap~logias+nprof+roa+fl+oilgas+retail+

logistics+constr+electr+engeneer+extract+

foodbev+t2014, data = VYBORKA)

summary(model)

##

## Call:

## lm(formula = logmcap ~ logias + nprof + roa + fl + oilgas + retail +

## logistics + constr + electr + engeneer + extract + foodbev +

## t2014, data = VYBORKA)

##

## Residuals:

## Min 1Q Median 3Q Max

## -4.1161 -0.9043 -0.0898 1.1670 3.8596

##

## Coefficients:

## Estimate Std. Error t value Pr(>|t|)

## (Intercept) 1.307e+01 9.894e-01 13.205 < 2e-16 ***

## logias 4.400e-01 4.196e-02 10.486 < 2e-16 ***

## nprof 8.205e-12 3.101e-12 2.646 0.009051 **

## roa 5.675e+00 1.706e+00 3.327 0.001113 **

## fl 2.019e-02 1.182e-02 1.709 0.089624 .

## oilgas 2.243e+00 7.291e-01 3.076 0.002507 **

## retail 1.934e+00 8.116e-01 2.383 0.018478 *

## logistics 2.346e+00 6.943e-01 3.380 0.000933 ***

## constr 2.137e+00 6.278e-01 3.404 0.000858 ***

## electr 8.564e-01 4.909e-01 1.745 0.083175 .

## engeneer1 3.719e-01 5.169e-01 0.719 0.473038

## extract1 1.619e+00 4.436e-01 3.650 0.000366 ***

## foodbev 1.145e+00 7.177e-01 1.595 0.112802

## t20142015 -1.894e-02 2.622e-01 -0.072 0.942497

## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##

## Residual standard error: 1.6 on 145 degrees of freedom

## Multiple R-squared: 0.6243, Adjusted R-squared: 0.5906

## F-statistic: 18.53 on 13 and 145 DF, p-value: < 2.2e-16

vif(model)

## logias nprof roa fl oilgas retail logistics

## 1.316543 2.026775 1.201877 1.157404 2.303946 1.246052 1.430467

## constr electr engeneer extract foodbev t2014

## 1.576291 2.291009 1.978618 2.480472 1.346526 1.063643

dwt(model)

## lag Autocorrelation D-W Statistic p-value

## 1 0.1044053 1.781516 0.104

## Alternative hypothesis: rho != 0

qqnorm(model$residuals)

shapiro.test(model$residuals)

##

## Shapiro-Wilk normality test

##

## data: model$residuals

## W = 0.98583, p-value = 0.105

Appendix B.

Correlation matrix

	nprof	fl	roa	logias	oilgas	retail	logist~s	constr	electr	engeener	exract	foodbev	t2014
nprof	1,00
fl	0,07	1,00
roa	0,25	0,16	1,00
logias	0,29	0,24	0,06	1,00
oilgas	0,66	0,05	0,04	0,22	1,00
retail	0,03	0,02	0,14	0,12	0,05	1,00
logistics	0,07	0,02	0,06	0,14	0,07	0,04	1,00
constr	0,05	0,07	0,01	0,14	0,08	0,05	0,06	1,00
electr	0,13	0,05	0,06	0,21	0,14	0,09	0,11	0,13	1,00
engeener	0,12	0,23	0,09	0,02	0,11	0,07	0,09	0,11	0,19	1,00
exract	0,08	0,07	0,01	0,03	0,18	0,11	0,14	0,17	0,30	0,25	1,00
foodbev	0,05	0,05	0,11	0,03	0,06	0,04	0,05	0,06	0,10	0,09	0,13	1,00
t2014	0,03	0,05	0,20	0,03	0,06	0,03	0,01	0,06	0,07	0,01	0,05	0,02	1,00

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