CC BY
1
УДК 330.43
Solovyev K.
the first year master student "International Finance"
Faculty Financial University under the Government of the Russian Federation
Moscow, Russia
ECONOMETRIC ANALYSIS OF THE FACTORS IN THE REGRESSION MODEL TO PREDICT THE MARKET VALUE OF DIVERSIFIED
PORTFOLIO OF STOCKS ЭКОНОМЕТРИЧЕСКИЙ АНАЛИЗ ФАКТОРОВ В РЕГРЕССИОННОЙ МОДЕЛИ ДЛЯ ПРОГНОЗИРОВАНИЯ РЫНОЧНОЙ СТОИМОСТИ ДИВЕРСИФИЦИРОВАННОГО ПОРТФЕЛЯ АКЦИЙ.
Abstract: The article analyzes practical applicability of econometric approach on evaluation of variable factors used to forecast the market price of securities. Used macroeconomic data is tested to identify their adequacy for prediction ofprice of a diversified stock portfolio.
Key words: Dow Jones Industrial Average, Econometric model, regression, least square method, Brent crude, real GDP of USA, FDI, effective federal funds rate.
Аннотация: Статья посвящена эконометрическому подходу оценки переменных факторов, используемых для прогноза стоимости диверсифицированного портфеля акций. Использованные
макроэкономические данные подвергаются тестированию с целью выявления их адекватности для прогноза стоимости портфеля.
Ключевые слова: Промышленный индекс Доу - Джонса, эконометрическая модель, регрессия, метод наименьших квадратов, нефть марки Brent, реальный ВВП США, прямые иностранные инвестиции, ставка ФРС.
Nowadays it is more and more important for a company to efficiently allocate and invest its cash flows, when increasing competition and expertise of its rivals lead to a necessity for a more efficient and more reliable forecasting methods.
A company's cash flow is divided into three categories, operating, financing and investment cash flows. Most of multinational corporations diversify its activities and hedge their future cash flows, whether they are inflows or outflows. Moreover, a company should provide liquidity in order to be able to repay its debts, financial instruments are also used for liquidity creation.
In order to effectively diversify a company's cash flows and increase funds trading on stock exchanges is widely used. But to achieve a successful trading strategy it is needed to forecast future shifts of price, for which a technical analysis and econometrics are used. Econometrics uses statistical data in order to establish connection with changes in several factors which influence a stock price. For a successful strategy, historical values of certain indicators should be analyzed such as:
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1. Financial statements of a company;
2. Central bank key rate;
3. Macroeconomic rates (GDP, FDI, inflation).
Any of the factors could positively or negatively influence the stock price, statistical approach helps to dentify the most significant of them and determine its impact on share price.
Dow Jones Industrial Average is index, which reflects momentum of the whole US economy and can be used to analyze trends delivering broad historical data. The index includes 30 companies from diversified corporations. Therefore, we could estimate the index dependence in some of the US macroeconomic indicators, such as:
Table 1 - indicators definition
Y Dow Jones Industrial Average
X1 Global price of brent crude, u.s. dollars per barrel
X2 Real gross domestic product, percent change from preceding period, quarterly, USA
X3 National currency unit per troy ounce gold price in us dollar, end of period
X4 Foreign direct investment in u.s.
X5 Effective federal funds rate, percent, not seasonally adjusted
Even though 5 indicators do not provide the full picture of the economy well
being, they could be used to increase a company's efficiency if their significance is high.
For the analysis a least square method is used - one of the most common methods for estimation unknown parameters. The model involves previous (historical) prices, in which should be minimized the sum of deviations [1]. The analysis is commonly used by investment institution to predict stocks and commodities prices.
Hence, the variables could be used to conduct analysis of Dow Jones Industrial Average index dependence on them and their applicability for the index momentum forecasting.
Table 2 - Least squares regression results
Svstem m
FstlmaSonMetood eastSquaies Date 02/28r17 Time 2?13 Sample: 1 37 induded nbser.gtioos 37
•Coefficient Std Error 1-Statistic Prob
CO) -1287.065 1934223 •5.235629 00000
C<2> 3 08723 0 465343 3 874682 0 0217
C(3) 34 56456 8.324534 3 928373 0 0081
C(4) -0 31979 0 211214 -2 934772 0 0271
C<5> 0 00011 5 11E-05 19 024372 0 0000
C<6) 57 34324 16.87293 4.153636 00011
Determinant residual »variance 9215 082
Equation Y«C(1)-C<2)"X1 *C(3),X2*C(4>'X3'C(5rX4tC(6>,X5
Ofcseivatons 37
R squared 0913242 Mean dependent var 1498 421
«dusted-¡squared 0 902387 SD dependent var 370 1 024
S F af regression 101 4231 Su1^ squared resid 379930 9
DurbiivWatson slat 1128382
The regression analysis shows that R - squared = 91.3242%, which implies
high significance of the link between Y and X. Probability of variables (Prob.) less than 5% which means high level of significance, moreover t - statistics also confirms it. Durbin - Watson test equals = 1.1283, which implies that our model is in the area of uncertainty, because some of the variables may depend on our final results (correlation).
In order to confirm that the model is useful, a test on heteroscedasticity has to be conducted. It analyzes whether the variance of the random error is constant or focused. We cannot accept the model if errors distributed without similarity. Heteroscedasticity is random distribution of errors, which we cannot use to predict share price. Provided two type of tests on heteroscedasticity.
__Table 3 - Glejser tes t
X1 = 29.37% Prevails heteroscedasticity.
X2 = 90.21% Prevails homoscedasticity.
X3 = 37.4% Prevails heteroscedasticity.
X4 = 19.73% Prevails heteroscedasticity.
X5 = 58.02% Prevails homoscedasticity.
Breusch - Pagan - Godfrey test equals 27.41%, that means we can accept appearance of heteroscedasticity with 72,59%. Two variables, demonstrate focused distribution of errors - x2 - real GDP and x5 - effective federal fund rate.
Table 3 - Correlation
y
x1
x2
x3
x4
x5
y
x1 x2 x3 x4 x5
1
0,245234238 0,382342316 0,155634522 0,782057327 0,139832656
1
0,148032743 0,430823125 0,229210012 0,043209742
1
0,320973946 0,369824052 0,100349724
1
0,292142131 0,472139023
1
0,440912801
We can draft the equation of Dow Jones Industrial Average:
Dow Jones Industrial Average = -1287.065 + x1 * 3.087 + x2 * 34.564 + x3 * (-0.319) + x4 * 0.001+x5 * 57.343
The equation could be interpreted that Dow Jones Industrial Average will change if one of the «x» factors is changed, most of them has positive impact, the one (x4) has negative. The most significant factors are: real GDP and effective federal fund rate.
The analysis of factors which could affect the Dow Jones Industrial Average index lead us to a conclusion that only two of them have passed all of the tests and could be used for statistical analysis - GDP growth and the effective federal funds will increase Dow Jones Industrial Average stocks' price. This model is suitable for forecasting, but for the more precise and reliable analysis additional factors should be added.
References:
1.Suslov M., Tregub I., Modeling the currency exchange rate. Methods and principles // Economics - 2015 № 1 p. 67 -70.
2.The World Bank Database [Электронный ресурс] URL: http://data.worldbank.org/
УДК 338.24
Talalai M. master student department of economics Southern Federal University Russia, Taganrog
Andrienko A. S., Associate Professor, Candidate of Pedagogical Sciences
department of foreign languages Southern Federal University Makarenya T.A., Associate Professor, Doctor of Economics
department of economics Southern Federal University DEVELOPMENT PERSPECTIVE OF IMPORT SUBSTITUTION IN THE DOMESTIC IT MARKET
Abstract: article is analyses the current research in the sphere of import substitution development in the Russian IT market. The main segments of the market are considered. The review of competitiveness of the Russian products is carried out. The obstacles for the import substitution program development are touched upon.
Keywords: import substitution, IT market, Software, Hardware, IT services.
IT market is one of the most dynamically grown markets in the Russian Federation. This market traditionally includes Hardware, computer peripherals, Software and IT-services.
Due the last political events, Government of the Russian Federation declared the path of "IT-sovereignty" like an import substitution of IT production. At the
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