The effect of production diversification on the investment attractiveness of Russian companies

-0. 051

(0. 068)

Debt-to-equity

0. 339***

(0. 061)

ROE

0. 137

(0. 225)

Log(assets)

-33.599

(11.375)

Time varying exogenous

Birth stage

5.480

(23.836)

Growth stage

-50.832

(69.338)

Turning stage

3.687

(17.239)

Recession stage

25.173*

(34.247)

Time invariant exogenous

Industry1

-90.720

(45.003)

Industry2

-35.739

(37.241)

Industry4

4.039

(43.004)

Industry3

-3.391

(40.168)

Const

643.063

(194.678)

Number of observations

384

Notes: *** significant at 1%. ** significant at 5%. *significant at 10%. Robust standard errors in parentheses.

During the estimation of the panel data the necessity to take into account the individual characteristics of the units leads to the following issue: the most effective methods of assessment turn out to be inconsistent and the consistent approaches give ineffective coefficient estimates in the regression model. In particular the “within” technique allows to eliminate the individual effects; however it leads to the considerable loss in efficiency. The latter is referred to fixed effects model and Hausman-Taylor method.

Moreover, the impact of the Hausman-Taylor model is quite weak on the applied researches due to the difficulties to find truly exogenous variables, which can be for sure taken as uncorrelated with the individual effects.

Consequently, the most appropriate model will be chosen between two standard specifications: random and fixed effects models. As it was denoted above the Hausman test can not be applied here, so another approach of selection will be applied in a form of the test of overidentifying restrictions, which says that:

a) The fixed effects assessment employs the orthogonality conditions where the independent variables are not correlated with the idiosyncratic error eit,

b) The random effects assessment employs the additional orthogonality conditions where the independent variables are not correlated with the group-specific error ui

The overidentifying restrictions imply these additional orthogonality conditions. The test is applied through the artificial regression method, where the random effects model is re-estimated with additional regressors, which consist of the original variables altered into the form of deviations from the mean value. The rejection of the null hypothesis determines that the fixed effect model is more preferable. Under the condition of homoscedasticity, this test can be used as an equivalent to the Hausman test.

Table 10 Test of overidentifying restrictions: fixed vs random effects

t-statistics	(Prob > t- statistics)
Sargan-Hansen statistic 64.485 Chi-sq(9)	P-value = 0.0000

The test of overidentifying restrictions shows that the fixed effect model is more preferable for our research. However, from this model estimation it can be seen that several variables are not significant in determining the investment attractiveness. Subsequently we eliminate the current ratio and ROE variables from the model. Moreover, it seems reasonable to test whether the variables, which are related to the industry, jointly affect the investment attractiveness. For this, the Wald test is conducted.



Table 11 The Wald test

H0	Statistics
ind1 = 0 ind2 = 0 ind3 = 0 ind4 = 0	chi2( 4) = 7.34 Prob > chi2 = 0.1189

The Wald test indicated that the dummy variables concerning industry do not jointly affect the investment attractiveness and can be deleted from the model.

The results of the new regression assessments are presented in the table:

Table 12 Model estimation

	Pooled regression	Fixed effects	Random effects
Herfindahl	0.684*** (0.201)	0. 418* (0. 214)	0. 645** (0. 261)
Debt-to-equity	0. 523*** (0. 095)	0. 327** (0. 138)	0. 431*** (0. 124)
Birth stage	-10.617* (51.096)	6.683 (30.117)	1.545 (26.807)
Growth stage	-177.000*** (33.605)	-49.186* (27.661)	-96.166*** (32.087)
Turning stage	-39.114 (27.933)	7.198 (12.949)	-7.586 (17.002)
Recession stage	34.507** (29.162)	36.924** (16.071)	36.037** (15.929)
Log(assets)	2.476 (2.120)	-36.174* (17.991)	-0.329 (3.138)
Const	-1.425 (43.982)	645.138 (299.222)	28.238 (56.903)
Adjusted R2	0.360	0.103	0.352
Number of observations	384	384	384

Notes: *** significant at 1%. ** significant at 5%. *significant at 10%. Robust standard errors in parentheses.

All three types of models are compared with each other to choose the most appropriate for this research.

d) The fixed effect model and the pooled regression are compared with the Wald test, where the null hypothesis is the equality of all individual effects to zero.

e) Random effects model and the pooled regression are compared with the Breusch-Pagan test, where the null hypothesis is the presence of random individual effect.

f) Fixed and random effects models are compared with the Hausman test, where the null hypothesis is that the residuals can be taken as random effects.

Table 13 Choosing the most appropriate model

	H0	t-statistics	(Prob > t- statistics)
Wald test	u =0	F(127, 249) = 6.07	0,0000
Breusch-Pagan test	var(u) = 0	chibar2(01) = 135.43	0,0000

The Wald test indicated that the null hypothesis is rejected at all the significance levels, consequently the fixed effects model is better than pooled regression, as the presence of individual effects is observable.

The Breusch-Pagan test revealed the rejection of the null hypothesis at all the significance levels, therefore the random effects model is more preferable than pooled regression.

Comparing random and fixed effects models the test of overidentifying restrictions is applied (the reasons for this were introduced earlier).

Table 14 Test of overidentifying restrictions

t-statistics	(Prob > t- statistics)
Sargan-Hansen statistic 32.038 Chi-sq(7)	P-value = 0.0000

The test indicates the appropriateness of the fixed effects model interpretation, which is given in the next section. However, we need to introduce some peculiarities regarding the fixed effects model.

The unpleasant limitation of fixed effects type of regression is the inability to identify the coefficients, which are related to the time-constant dummy variables. The Hausman test is accomplished only with the estimates which are used both in fixed and random effects models. This means that the time-invariant variables are not considered at all (though our second model did not include this type of variables).

Generally speaking the random effects model is fully appropriate when the regressors and random effects are not correlated. However, this requirement is often violated. Yet, the random effects model can be considered as a compromise between the pooled regression, which imposes a restriction of a homogeneity on all the coefficients and the fixed effects model, that captures the existing in reality but unobserved heterogeneity. The incentives for this compromise are related to the following reasons:

a) Despite the fact that the estimates in the fixed effects model are consistent provided the lack of endogeneity, they are likely to be not effective. In other words, it is probable that that the coefficients for the most variables of great importance in question might be insignificant.

b) The fixed effects model does not allow to assess the coefficients for time-invariant regressors, as they are eliminated from the model during the “within conversion”. The pooled regression does not face such disadvantages, yet its estimates are inconsistent as they do not take into consideration the individual heterogeneity.

c) Another drawback of the fixed effects model is the need to estimate a large number of parameters.

Random effects model is based on the following approach of the variable interpretation. It is assumed that individual distinctions have random nature. Random effects model is applied in case if N objects are randomly chosen from the population, for example during the examination of panel data concerning households, individuals and firms.

The standard situation is the random effects application; the fixed effects are only necessitated when the former are not pure and are correlated with regressors. When the research is based on the assessment of the whole population behavior but not on the particular objects, the random effects model is more preferable, and at the same time it is possible to get not only consistent and unbiased but also effective estimates.

So both types of the models have their own drawbacks and should not be avoided.

However, the obtained results will be interpreted and compared to the common sense expectations (based on the assessments from the fixed effects model). Due to the fact that the determinants of the investment attractiveness and their influence vary across different countries, industries and firms, it is essential to give reasonable economic interpretation of the estimation. Nevertheless, the derived effects will be likely coherent with the hypotheses in question.



4. Empirical Results

The research was conducted to understand how the product diversification affects the investment attractiveness of a company. The panel data was collected for 174 Russian enterprises from 2012 to 2014. Market-to-book value was taken as a proxy to measure the investment attractiveness and two model specifications were estimated.

Table 12 reveals the figures for the assessment of model after excluding the insignificant variables and time-constant dummies of the industries. The fixed effects model was chosen for the interpretation. Herfindahl index turned out to have positive effect on investment attractiveness, which means that one-product oriented (or less diversified firms) are more attractive for the potential investors than the companies, operating in several segments. These results are coherent with the previous findings in this area. Concurrently the significant impact of debt-to-equity, life cycle stage and size of a company on market-to-book value was found.

Table 15 Confirmation of the hypothesis

Hypotheses	Results
More product diversified company is less attractive for potential investors	Confirmed
ROA has a positive effect on the investment attractiveness	-
D/E ratio has a negative effect on the investment attractiveness	Not confirmed
Current ratio has a positive impact on the investment attractiveness	-
There is a positive relationship between size and the investment attractiveness	Not confirmed
The relationship between ROE and the investment attractiveness is positive	-
The life cycle of a company affects the investment attractiveness	Confirmed
The industry affects the investment attractiveness	-

The initial hypotheses about the influence of debt-to-equity did not confirm. The results are opposite due to the fact, that market-to-book ratio was taken as a proxy to measure the investment attractiveness. These coefficients have slightly the same denominators, which in turn gave contrary signs in the model. As for other variables, it was confirmed that the companies at the stage of recession seem more investment attractive than mature firms, whereas the companies at the growth stage are less attractive. The coefficient for the birth and recession stages are insignificant, consequently there is no sense to interpret them. The size of the company is negatively related to the investment attractiveness due to the fact that investors seek for small enterprises which will increase their performance in the nearest future. These firms might become the leaders in their sector and in long term period it may have higher revenues and other ratios.

However, our main concern was to find the effect of diversification. The significant effect implies that the costs (in a form of huge cross-subsidies from better performing segments to poor ones, the increase in additional resources and their allocation, opposing incentives of managers from different lines, high costs of control and coordination, information asymmetry etc.) exceed the benefits of product diversification for Russian companies, resulting in lower demand from the investors side.



Conclusion

The aim of this paper was to assess the influence of diversification on the investment attractiveness of a company. Currently this issue is quite relevant due to the presence of investment processes in the country's economy. Besides every potential investor faces the question of appropriateness of investing in this or that enterprise. In other words, this is the issue of selecting the most attractive company to gain the future return.

In the first section the definition of the investment attractiveness, the approaches to assess it and diversification effect based on literature review were considered. In the practice part the panel data was described, the models were specified, identified and verified. Several models were regressed, in which the market-to-book value was taken as a proxy variable for the investment attractiveness.

The independent variables comprised Herfindahl index to measure the diversification of the company as well as ROE, debt-to-equity ratio, current ratio, size of a firm, life cycle stage and industry.

Herfindahl index turned out to have positive effect on investment attractiveness, which means that firms which diversify more are less attractive for the potential investors than the one-product oriented companies. These results are coherent with the previous findings in this area. Moreover, the significant impact of debt-to-equity, the size of the firm and the life cycle stage on market-to-book value was found.

Speaking about the production diversification the risks might rise if the new product takes the sales away from the major production or if the costs are too high to enter the market. In these cases, the benefits for the firm might not compensate the risks.

Production diversification implies a high level of risk and needs careful investigation. Entering the unfamiliar markets with unfamiliar product implies the lack of experience in particular techniques, which are required. What is more the diversification might call for substantial expansion of financial and human resources, which in turn distracts from core business. Perhaps a firm would need experts or business partners to work for it to succeed in new and unknown areas. Therefore, this option can be applied in case when, for instance, the current product or current market orientation is not able to give more growth opportunities.

As for the estimation of the investment attractiveness and other variables, the current research faces the following limitation. Russia is a developing country, which in turn means that there exist a lack of financial market level, low efficiency of the stock market, a lack of transparency in transactions, the manipulations with the financial statements. Consequently, this peculiarity should be taken into consideration while studying interdependence of various categories in the Russian economy.

The further research in this sphere may be conducted by using more explanatory variables, which are related to the topic. Moreover, there are plenty of approaches to measure the investment attractiveness and other proxy variables can be used instead of M/B value. Using more or other variables can change the model specification, when it is economically justified. Besides the effect of diversification can be examined in a different way by taking its other appropriate measures.

All in all, the investment attractiveness is not only a matter to manage but also a criteria or evaluation for making the decision from the investors' point of view. And the production diversification is one of the significant categories which should be considered while studying these questions.



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Appendix

Application 1

Application 1 The determination of a life cycle stage

Stage	CFO	CFF	CFI
Formation(birth)	-	+	-
Growth	+	+	-
Maturity	+	-	-
Turning	+	+	+
	-	-	-
	+	-	+
Decline	-	+	+
	-	-	+

Boxplots after deleting the outliers

Application 2

Quantile graphs and histograms



Application 3

The description of variables used in the study

Variable	Description
Dependent variable
M/B	Market-to-book value
Explanatory variables
HERF	Herfindahl index (diversification)
ROA	Return on assets
ROE	Return on equity
CURRENT_RATIO	Liquidity ratio
QUICK_RATIO	Liquidity ratio
D/E	Debt-to-equity ratio
LOGASSETS	Size of a firm taken as logarithm of assets (proxy variable - total assets)
Dummy variables of a firm's life cycle
BIRTH	Takes the value 1 - if the company is at the stage of formation, 0 - otherwise.
GROWTH	Takes the value 1 - if the company is at the stage of growth, 0 - otherwise.
MATURE	Takes the value 1 - if the company is at the stage of maturity, 0 - otherwise.
TURNING	Takes the value 1 - if the company is at the stage of turbulence, 0 - otherwise..
RECESSION	Takes the value 1 - if the company is at the stage of decline, 0 - otherwise.
Dummy variables of industry
IND1	Takes the value 1 - if the company is related to manufacturing industry, 0- otherwise.
IND2	Takes the value 1 - if the company is related to energy sector, 0 - otherwise.
IND3	Takes the value 1 - if the company is related to oil and gas industry, 0 -otherwise.
IND4	Takes the value 1 - if the company is related to metallurgical industry, 0 - otherwise.
IND5	Takes the value 1 - if the company is related to chemical industry, 0 - otherwise.

Application 4

The companies in the sample

ABAKANVAGONMASH	KOMI ENERGOSBYTOVAYA	REZINOTEKHNIKA
ACRON	KOMIGAZ	RN HOLDING
AGREGAT OAO	KORSHYNOV MINING PLANT	ROSNFT-SMOLENSK
ALUZHSKIJ DVIGATEL	KOSAYA GORA IRON WORKS	ROSSETI
ANZHEROMASH	KOSTROMA RETAIL	RUSHYDRO
AO MOSENERGO (LON)	KOVROV MECHANICAL PLANT	RUSPOLIMET
ASHINSKIY MMTLWKS.	KRASNOYARSKNEFTEPROD	RYAZAN EN.DISTRIBUTING
ASTRAKHAN ENERGY RETAIL	KRASNOKAMSK MTME.WORKS	SAIANOG ALUMIN
AVTODIZEL	KRASNOYARSK HYPW.STI.	SAKHALINENERGO
BALTYSKY ZAVOD	KRASNOYARSKENERGOSBYT	SALAVATNEFTEORGSINTEZ
BASHNEFT	KRASNY KOTELSCHIK	SAMARAENERGO
BELORET MET KOMB	KUBANENERGO	SARATOVENERGO
BOGOSLOV RUDOUPR	KUBANENERGOSBYT	SARATOVNEFTEGAS
BOROVICHI REFRCT	KUIBYSHEVAZOT BRD	SARATOVNEFTEPROFUCT
BRYANSKOBLGAZ	KURSKGAZ	SARATOVOBLGAZ
TSENTRENERGOGAZ OAO	KUZBASSENERGO	SATURN NPO
CHELYABENERGOSBYT	LAZ	SEVERYA VERPH
CHELYABINSK FORGE-AND-PRESS FACTORY	LENENERGO	SEVERSTAL
CHELYABINSK TUBE ROLLING PLANT	LEPSETOK	SLAVNEFT
CHELYABINSK ZINC PLANT	LIPETSK EN.RETAIL CO.	SLAVNEFT MEGIONNEFTEGAZ
CHERNORGORNEFT	MAGADANENERGO	SLAVNEFT-YAROSL
CLYB.METALLURG.PLANT	MAGNITOGORSK IOSTL.WORKS	SOLIKAMSK MAGNESIUM
CRYOGENMASH	MARIENERGOSBYT	STAVROPOLENERGOSBYT
DAGESTAN EN.RETAIL CO.	MECHEL OAO	STAVROPOLKRAYGAZ
DOK KRASN OCTOB	METAFRAX	SURGUTNEFTEGAS
DOROGOBUZH	METALURG SEROVA	TAMBOV ENERGY RETAIL
E ON RUSSIA	MIKHAILOVSKII GOK	TATNEFT
ELECTROZINC	MMC NORILSK NICKEL	TERNEYLES OAO
ENERGOSBYT ROSTOVENERGO	MMK-METIZ	TERRITORIAL GNRT.CO.NO1
FAR EASTERN ENERGY	MORDOVIA EN.DISTRIBUTING	TERRITORIAL GNRT.CO.NO2
FED.GRID CO.OF UNG.SY.	MOSCOW INTEG.ELTY.DS.	TGC-14
GAZ-SERV BASHK	MOSCOW OIL REFINERY	THE LIKHACHOV PLANT
GAZPROM	MOSENERGO	TMK OAO
GAZPROM GAZOR.ROSTOV	NEFTEKAMSK AUTO PLANT	TOLYATTIAZOT
GAZPROM GAZORASPRED SMOLENSK	NIZHEGORODOBLGAZ	TOMSK DISTRIBUTING
GAZPROM NEFT	NIZHMASHZAVOD	TRANSMASH
GMZ	NIZHNEKAMSKNEFTEKHIM	TUIMAZINSKIY ZAVOD ABOZ.
IDGC OF CENTER AND VOLGAREGION	NIZHNY NOVGOROD RET.CO.	TULACHERMET
IDGC OF CENTRE	NOVATEK	TVZ
IDGC OF NORTH-WEST	NOVOLIPETSK STEEL	UDMURTHEFT
IDGC OF SIBERIA	NOVOSIBIRSKENERGO	UFALEYNICKEL
IDGC OF URALS	OBNYEFTEGASGEOLOGIA	ULIANOVSK ENERGO
IDGC OF VOLGA	OC ROSNEFT	URALEKTROMED
INTER RAO UES	OGK-2	URALKALI
IRG.DS.GY.OF NTHN.CUC.	OIL COMPANY LUKOIL	URALKUZ
IRG.DS.GY.OF SOUTH	OJSC ENEL OGK-5	VAREGANNEFT
IRKUTSKENERGO	OMSK KAUCHUCK	VARYEGANNEFTEGAZ
KALUGA RETAIL	OMZ URALMASH IZHORA GP.	VLADIMIR CHEMICAL PLANT
KALUGAOBLGAS	ORSKNEFTEORG	VLADIMIR EN.DISTRIBUTING
KALUZHSK TURB ZV	PAPER MILL KOMMUR	VOLGA TGC
KAMAZ	PERM EN.DISTRIBUTING CO.	VOLGOGRADENERGOSBYT
KAMCHATSKENERGO	PHOSAGRO	VOLGOGRADNEFTEM
KAZANORGSINTEZ	PIGMENT OAO	VORONEZH ENERGY SALING
KHABAROVSK NPZ	PJSC RUSHYDRO SPN.	VORONEZHOBLGAZ
KHABAROVSKNEFT	PJSC TATNEFT ADR	VSMPO
KHIMPROM	PROMGAZ	VYBORGSKIY SUDOSTROI
KIROV NON-FERROUS METALPROCESSING PLANT	QUADRA POWER GENERATION	YAKUTSENERGO
KIROVENERGOSBYT	RAO ENERGY SY.OF EAST	YAROSL SHIPBUILD



Abstract

This research will attempt to illuminate the effect of production diversification on the companies' investment attractiveness. Being one of the most important characteristics of enterprise's performance, the investment attractiveness implies a straightforward influence on the perspectives of a firm for future development, competitive strength, and financial stability. “Investment attractiveness” is defined as a group of economic indicators, which determine the maximum return on money invested. In this research, we consider market-to-book ratio as a proxy variable for the investment attractiveness, due to the fact that a higher coefficient is a signal for the investor how much a company is worth. This paper examines the effect of diversification as well as profitability, size, financial leverage, liquidity, life cycle stage and industry on the investment attractiveness. A sample of 174 Russian firms from the largest industries for the period from 2012 to 2014 has been chosen for the study. The research documents fixed and random effects models to determine the appropriate functional form and statistical significance. We propose and support the hypothesis that the firms, which operate in various directions in gaining earnings (diversification) seem less attractive for the potential investors. The estimation of the investment attractiveness can be applied both by managers and investors either to raise funds, to understand clearly the companies' performance or to gain return.

Keywords: production diversification, investment attractiveness, market value, life cycle, panel data.

Данное исследование направлено на изучение эффекта диверсификации производства на инвестиционную привлекательность компании. Будучи одной из основных характеристик деятельности любой компании, инвестиционная привлекательность оказывает значительное влияние на перспективы предприятия в будущем, его конкурентоспособность и финансовую стабильность. Под «инвестиционной привлекательностью» обычно подразумевают набор экономических показателей, которые определяют максимальную отдачу от инвестированных средств. В данной работе в качестве прокси показателя инвестиционной привлекательности будет использован коэффициент соотношения рыночной и балансовой стоимости компании, так как более высокое его значение говорит инвестору о ценности предприятия. В исследовании приводится изучение эффекта диверсификации, а также доходности, размера, финансового рычага, ликвидности, стадии жизненного цикла и индустрии на инвестиционную привлекательность. Выборка составляет 174 российские компании из крупнейших отраслей за период с 2012 по 2014 год. В работе применяются модели панельных данных со случайными и фиксированными эффектами для определения правильной функциональной формы и статистической значимости. Выдвигается гипотеза о том, что компании, которые диверсифицируют свое производство, менее привлекательны для инвесторов. Оценка инвестиционной привлекательность может быть использована как менеджерами, так и инвесторами для привлечения средств, либо для оценки результатов деятельности компании, либо для получения отдачи от инвестирования.

Ключевые слова: диверсификация производства, инвестиционная привлекательность, рыночная стоимость, жизненный цикл, панельные данные.

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