CC BY
6
УДК 330.43
Sarkisov V.
The first year master course 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 SHARES ЭКОНОМЕТРИЧЕСКИЙ АНАЛИЗ ФАКТОРОВ В
РЕГРЕССИОННОЙ МОДЕЛИ ДЛЯ ПРОГНОЗИРОВАНИЯ РЫНОЧНОЙ СТОИМОСТИ АКЦИИ.
Abstract: The article is devoted to the econometric approach evaluation of variable factors used to forecast the value of securities. Used macroeconomic data are tested to identify their adequacy for prediction.
Key words: SnP500, Econometric model, regression, least square method, Brent crude, real GDP of USA, troy ounce gold, FDI, effective federal funds rate.
Аннотация: Статья посвящена эконометрическому подходу оценки переменных факторов, используемых для прогноза стоимости ценной бумаги. Использованные макроэкономические данные подвергаются тестированию с целью выявления их адекватности для прогноза.
Ключевые слова: Индекс SnP 500, эконометрическая модель, регрессия, метод наименьших квадратов, нефть марки Brent, реальный ВВП США, тройская унция золота, прямые иностранные инвестиции, ставка ФРС.
Nowadays not so many companies can effectively manage their cash flows. Annually appears new methods of company's management, all of them aimed on increase effectivity to satisfy growing consumers demand. Organization has three type of activities: operational, financial, investing activity. International companies diversify their activity - investing in shares, bonds and other financial instruments. Nevertheless not so many companies can effectively diversify cash flow to increase their funds. Stock exchange is not stable market and most amount of traders, companies face difficulties, to increase value of portfolio investment. Specialists use different instruments for trade and forecast share price, one of methods which can be applied - statistical. Analyzing historical values and factors, which influence on stock price, can be found events, which positively or negatively influence on price. Firstly, it may be annual reports about multinational company's activity. Secondly, key rate of central bank influence on companies (shortage or increase of money supply). A lot of factors may influence on stock price, but statistical approach helps to specialist identify most significant of them and determine positive or negative impact on share price.
SnP 500 is index, which describes American economy, from the one side it's difficult to describe it with only 5 variables. On another if it's macroeconomic factors, it may influence on productivity of companies. Using econometric model analyzed macroeconomic data and commodity prices (For research were taken
quarterly data from July of 2007 to July of 2016).
Table 1 - indicators definition
Y SnP500index
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
One stage least square is one of the common methods of regression analysis for estimation unknown parameters. In model involves previous (historical) prices, in which should be minimized the sum of deviations [1]. This kind of analysis popular among investment funds for predicting shares, commodity prices on the market.
In addition other variables may influence on SnP 500 index and this model can be modified to identify better coefficients for forecasting stock price. In our econometric model mistake of approximation may reflect other factors, not only commodity prices, operational activity or macroeconomic indicators. Investor's or political performance can change trend on stock price. Crisis 2008 year leave a strong imprint on institutional investors, mutual and pension funds. Pprejudices of many investors about the coming crisis may prompt withdraw investments from securities.
Table 1 - regression analysis
System: M
Estimation Method: Least Squares
Date: 02/10/17 Time: 10:32
Sample: 1 37
Included observations: 37
Total system (balanced) observations 37
Coefficient Std. Error t-Statistic Prob.
C(1) -1017.825 178.6778 -5.696429 0.0000
C(2) 2.040564 0.868108 2.350589 0.0253
C(3) 20.56217 7.455613 2.757945 0.0097
C(4) -0.274223 0.105522 -2.598714 0.0142
C(5) 0.000900 4.89E-05 18.41613 0.0000
C(6) 71.15476 18.26835 3.894975 0.0005
Determinant residual covariance 9690.026
Equation: Y=C(1 )+C(2)*X1 +C(3)*X2+C(4)*X3+C(5)*X4+C(6)*X5 Observations: 37_
R-squared 0.938374 Mean dependentvar 1508.641
Adjusted R-squared 0.928434 S.D. dependent var 402.0037
S.E. of regression 107.5431 Sum squared resid 358530.9
Durbin-Watson stat 1.191417
Based on regression analysis have been identified, that R - squared 93.83% (demonstrates high tightness of the link between Y and X). Probability of
variables (Prob.) less than 5% which means high level of significance, moreover it confirms t - statistic. Durbin - Watson test equals = 1.19, it means that our model in the area of uncertainty, it means that some of the variables may reflects on our final results (correlation).
To accept model should be analyzed test on heteroscedasticity, it means that variance of the random error should constant or focused. If errors distributed without similarity it means that we can't accept model. Heteroscedasticity is random distribution of errors, in which we can't predict share price. Provided two type of tests on heteroscedasticity.
Table 2 - Glej ser test
X1 = 31.75% Prevails heteroscedasticity.
X2 = 81.3% Prevails homoscedasticity.
X3 = 43.04% Prevails heteroscedasticity.
X4 = 22.17% Prevails heteroscedasticity.
X5 = 61.66% Prevails homoscedasticity.
Breusch - Pagan - Godfrey test demonstrates that variables from equation equals 38.64% (it means that with 61.36% we can accept appearance of heteroscedasticity). Nevertheless we have two variables, which demonstrates focused distribution of errors - x2(real GDP), x5(effective federal fund rate).
_Table 3 - correlation_
y_xl__x2__x3_x4__x5
y 1
x1 0,220106478 1
x2 0,40737528 0,158247195 1
x3 0,129127296 0,413278319 0,338141942 1
x4 0,909713811 0,298352752 0,328064357 0,31987138 1
x5 0,116124171 0,0393729 0,165729026 0,575617426 0,410052529 1
Equation of SnP 500:
SnP500 = -1017.82 + x1 * 2.04 +x2 * 20.56 + x3 * (-0.274)+x4 *
0.0009+x5 * 71.15
Let's give explanation of calculated equation. It means that SnP500 will change if one of the "x" factors vary, some of them has positive impact, the others negative. The most significant factors are: real GDP; effective federal fund rate.
Thus, analysis of factors affecting on the SNP 500 index demonstrated that only two of the five factors have passed all the tests, unlike the other three, in which were found correlation and heteroscedasticity. Growth of GDP and effective federal funds should increase stock price of SnP500. This model can be used to forecast, but for a more accurate analysis, it can be used in a more extensive analysis, which will more accurately reflect the influences and to predict the price.
References:
1. Suslov M., Tregub I., Modeling the currency exchange rate. Methods and principles // Economics - 2015 № 1 p. 67 -70.
2. Federal reserve bank of St. Louis [Электронный ресурс] URL: https://fred.stlouisfed.org
УДК 336.64
Uralova D.Zh. Master's student 1st course, the faculty of "International Finance" Financial University under the government of Russian Federation
Russia, Moscow
MODERN TENDENCIES IN THE PORTFOLIO FORMATION OF
RUSSIAN SECURITIES
Keywords: securities, investment portfolio, liquidity, Black-Litterman model, Sharpe ratio, Sortino Ratio, modern tendencies
The most well-known and widely used model for the formation of the securities portfolio is a model described in the classical works and G.Markovits A.Roy [6; 10]. This model generally involves maximizing investor's utility function, defined by the expected return and risk of the securities portfolio. As a general rule, to assess the expected return and risk the historical data is used.
At the same time, the Russian stock market's historical data can be an extremely unreliable source of information, especially in times of change in the market trend. As a result, there is a need for incorporation of predictive and analytical information into the model, which is not included in classical approaches.
The main features of Russian economy that should be remembered are as follows:
• Russian financial market is relatively undeveloped;
• Crediting rates are set too high;
• The peculiarities of inflation in Russia. The inflation is rather high, irregular, heterogeneous and poorly forecast;
• Several currencies are actually used in Russian economy simultaneously;
• Complexity of tax structure in Russia;
• The difference between Russian accounting standards and International Financial Reporting Standards (IFRS);
• Lack of government financing of the investment projects;
• Fluctuations in paying capacity of population and contracting parties;
• Legislation instability. [1]
Also it should be noted that one of the peculiarities of the Russian securities market is its relatively low liquidity, which is expressed in the high level of the spread between the lowest and the highest sales price of the purchase price (bid-ask spread).
In addition, analysis of Russian investors' preferences indicates that the most adequate behavior is not described by utility functions, but by the
