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ПРОБЛЕМЫ ЭКОНОМИКИ
УДК 336 DOI: 10.22394/2079-1690-2018-1-4-67-71
AN EXAMINATION OF THE DYNAMICS IN THE MOSCOW EXCHANGE: SOME EMPIRICAL TESTS FOR SECTORIAL MARKET EFFICIENCY
PhD in Economics, Associate Professor, ESC Rennes School of Business (Rennes, France), Specialist in Financial Math, Theory of Stock Market and Exchanges. Author of more than 40 articles indexed in IDB Scopus with hi-6 in Scopus. E-mail: [email protected]
Doctor of Economic Science, Professor, Head of Cathedra of Economic Theory and Entrepreneurship; South-Russia Institute of Management - branch of Russian Presidential Academy of National Economy and Public Administration. (70/54, Pushkinskaya St., Rostov-on-Don, 344002, Russian Federation). E-mail: [email protected]
Abstract
The purpose of this paper is to investigate the possibility of short run dynamics among sectorial stock markets of the Moscow Exchange (MOEX). "Causality" tests provide a statistical framework of testing the extent of possible links among equity indices. In addition, we examine the degree of autocorrelation of the indices in order to examine the speed of adjustment to news. Our results indicated that the degree of autocorrelation is close to zero giving support that the Moscow Exchange is an efficient market in the weak form. The results of the Granger "causalities" indicated that there are no "causalities" between the examined indices supporting again the view that the Moscow Exchange is an efficient market.
Keywords: Moscow Exchange (MOEX), informational efficiency, "causality", Fair Game model, stock market, sectorial indices, econometric analysis, stock indexes, securities market.
1. Theory and Methodology
A Fair Game model is derived from the Martingale model: E(Pt/It-1 )=Pt-1. According to the Martingale model, if the price of a stock is a Martingale the best forecast of price Pt that could be constructed based on the available information set It-1, would just equal Pt-1, assuming that Pt-1 is in It-1.
In an Efficient Market the Fair Game model holds for stock price changes:
E[ Pt-(P*t/It-1 )]=0 (1)
where It-1 is the information set available at time t-1, Pt is the actual price at time t, P*t is the expected price which is based on the information set It-1, and Pt-P*t is the forecast error which is uncorrelated with variables in the information set It-1. Obviously the same model holds for stock returns (r) as returns are a transformation of price changes.
Fama (1970), rejected the hypothesis that returns themselves are a Fair Game and proposed the following definition of market efficiency, which makes the EMH a joint hypothesis:
zt= r t -E(rt /It-1 ) (2)
with
E(zt )=E[r t-E(rt/It-1 )]=0 (3)
In economic terms zt is the return at time t, in excess of the equilibrium expected return projected at time t, on the basis of the information set It-1. With the additional assumption that the equilibrium return is constant through time then returns themselves are uncorrelated with variables in past information sets. The assumption that the equilibrium return is constant through time is crucial for empirical tests because as Leroy (1989) noted, "On Fama's definition any capital market is efficient and no empirical evidence can possibly bear the question of market efficiency."
Most of the empirical tests for market efficiency usually examine whether known information exists which could help to predict profitably stock returns. Most of the empirical tests for market efficiency usually examine whether known information exists which could help to predict profitably stock returns, Osborne (1959), Muth (1961), Osborne (1962), Cootner (1962), Fama (1965, 1970 and 1991), LeRoy (1989), Cutler,
Alexakis Cristos
Ignatova
Tatiana
Vladimirovna
Poterba & Summers (1989 and 1991). While initial studies could not reject the Random Walk hypothesis, later findings are mixed.
In this study, we will test the market efficiency for the Moscow Stock Exchange. Analytically, we will test for the possibility of predictive statistical relationships between the sectorial indices of the Moscow Stock Exchange in a univariate and a bivariate analysis.
The structure of this paper includes a brief description of the Moscow Stock Exchange and the methodology used, the statistical results and finally conclusions and policy implications.
2. Moscow Exchange (MOEX)
Moscow Exchange is the 22 nd largest exchange by total capitalisation of shares traded on international stock markets. It is a result of a merger of Moscow Interbank Currency Exchange and Russian Trade System in 2011. Moscow Exchange went public in February 2013 and is traded on its own trading platform under the ticker 'MOEX'.
Moscow Exchange hosts trading in equities, bonds, derivatives, currencies, money market instruments and commodities. The Group also includes Russia's central securities depository «the National Settlement Depository» and the National Clearing Centre, which performs the function of central counterparty. Moscow Exchange ranks among the 10 largest exchange platforms for bonds and derivatives trading. Securities of over 700 issuers are admitted to trading on the equity and bond markets of Moscow Exchange.
Its' market capitalization in March, 2018 was 646,85 bln adjusted US dollars. With range of capitalization from 949 bln adjusted US dollars in 2010 to 583 bln adjusted US dollars in 2014. A primary currency used by MOEX is Russian Ruble and its' fluctuations result in capitalization assessment and undervaluation of Russian stock market. Market capitalization to GDP ratio is 30,61% and in comparison with other developed national stock markets it is undervalued.
The Equity & Bond Market is a key platform for Russian businesses to raise capital and for domestic and international investors to access equity and debt investment opportunities. The marketplace is the main trading venue for Russian stocks as well as government, municipal and corporate bonds. More information and daily trade data could be found on moex.com.
3. The model employed
A popular method to examine the existence of a temporal statistical relationship with predictive value between two time series is the Granger "causality" test. Granger's definition for "causality" is in terms of pr e-dictability: A variable X causes another variable Y, with respect to a given information set that includes X and Y, if present Y can be better forecasted by using past values of X than by not doing so.
Granger's "causality" tests are based on the following statistical reasoning: if we consider two time series as Yt and Xt, the series Xt fails to Granger "cause" Yt, if in a regression of Yt on lagged Y's and lagged X's the coefficients of the latter are zero.
That is, consider equations 3 and 4:
AXt =±a ■ AXt , + ¿£ -AYt , +£lJ
j=1 j=1 (3)
AYt =¿-AYt_, + ¿A, -AXt , +s2J
j=1 j=1 (4)
If in the above equations, |3i=0 for i=1,2,....n in equation (3) we can conclude that Yt fails to Granger
cause Xt. If also Ai=0 for i=1,2,3.....n in equation (4) then Xt fails to "Granger cause" Yt. Then we can conclude
that the two series are temporally uncorrelated.
If pi^0 for i=1,2,3...n in (3) and Ai=0 for i=1,2,3....n in (4) then Yt "Granger cause" Xt. Also if |3i=0 i=1,2,3....n in (3) and i=1,2,3.....n in (4) then Xt "Granger cause" Yt.
Finally, if pi and Ai are different from zero in equations (3) and (4) then we conclude that between Xt and Yt there is a bi-directional "causality". Note that in all the above regressions £1,t and £2,t should be white noise and uncorrelated at any lag other than t. It is obvious from the above that the presence of "causality" between two stock price histories implies market inefficiency since one stock price series can be forecasted by the use of another stock price series.
4. Data and Results
In our statistical analysis we used a dataset of daily closing prices of sectorial indices of the Moscow Stock Exchange (moex.com).
The time period used is from September 15, 2015 to December 29, 2018 with a total of 599 observations for every stock market index. To perform the above analysis, we used the logarithmic transformation of the original closing values series.
The order of integration of a series may be ascertained by the application of a set of tests, commonly known as test for unit roots. We performed the Augmented Dickey-Fuller in order to ensure uncorrelated and
homoscedastic residuals in the test regression, Dickey and Fuller (1979), Dickey and Fuller (1981). Table I presents the Augmented Dickey-Fuller test statistics for the series under examination. The results suggest that each of the series is integrated of order one, I~(1). Thus, econometric analysis will be performed on the first difference transformation of the original series.
Table 1. Unit root tests
Variables Basic Material Energy Gas Metals Minerals Oil
Level -2.06 -2.38 -2.39 -1.57 -1.68 -2.39
Difference -23.50** -24.33** -24.23** -23.46** -23.52** -24.23**
Double star(**) denotes significance at 99% confidence interval
A visual of the series under investigation can be found in Diagrams 1 to 6. The series are quite volatile from period to period but the picture is close to the picture of a stock price index.
Diagrams 1 - 6. Sectorial returns of the Moscow stock Exchange
Diagram 1 Basic Material D(LBASIC_MATER)
Diagram 2 Energy D(LENERGY)
50 100 150 200 250 300 350 400 450 500 550
50 100 150 200 250 300 350 400 450 500 550
Diagram 3 Gas D(LGAS)
Diagram 4 Metals D(LMETALS)
50 100 150 200 250 300 350 400 450 500 550
50 100 150 200 250 300 350 400 450 500 550
Diagram 5 Minerals
D(LMINERALS)
Diagram 6 Oil D(LOIL)
50 100 150 200 250 300 350 400 450 500 550
50 100 150 200 250 300 350 400 450 500 550
-.02
-.04
-.06
-.06
0
0
Next we estimated the autocorrelation function of the series under investigation. The relevant Q statistics indicate that there is no significant autocorrelation. Based on the above statistical results we may say that the examined stock market indices and for the examined period support the Efficient Market Hypothesis.
Table 2. Autocorrelation function
Basic Material Energy Gas Metals Minerals Oil
Lag Q statistic Q statistic Q statistic Q statistic Q statistic Q statistic
1 0.7927 0.0017 0.0195 0.8639 0.8730 0.0195
2 0.7937 1.2945 1.4981 0.8880 0.8967 1.4979
3 2.0524 1.9462 2.2496 1.6608 1.6674 2.2488
4 2.5436 1.9737 2.3158 1.9843 1.9896 2.3149
5 7.0713 2.0531 2.4114 5.8507 5.8552 2.4103
As a next step, in order to find any possible "causalities" between the examined stock markets we performed the standard Granger tests. As suggested by the unit roots tests the Granger tests will be performed on the difference transformation of the original series. Table 3 presents the Granger "causality" results. Note, that in order to perform the tests we included lagged terms sufficient to ensure white noise residuals in the regressions but also we took in to account model selection information criteria. In addition, we estimated the models by specifying the residuals to take into account ARCH effects.
Table 3. "Causality" tests
Variable Y Variable X F statistic F statistic "causality" direction
Basic Material Energy 1.47 0.84 No "causality"
Basic Material Gas 1.41 0.84 No "causality"
Basic Material Metals 0.74 1.24 No "causality"
Basic Material Minerals 0.75 1.26 No "causality"
Basic Material Oil 1.41 0.84 No "causality"
Energy Gas 3.46** 3.34** Bidirectional
Energy Metals 1.46 1.77 No "causality"
Energy Minerals 1.46 1.78 No "causality"
Energy Oil 3.47** 3.34** Bidirectional
Gas Metals 1.46 1.68 No "causality"
Gas Minerals 1.46 1.69 No "causality"
Gas Oil 0.14 0.15 No "causality"
Metals Minerals 1.79 1.75 No "causality"
Metals Oil 1.68 1.46 No "causality"
Minerals Oil 1.69 1.46 No "causality"
Double star(**) denotes significance at 99% confidence interval
From the above table, the statistical evidence suggests that between sectorial indices of the Moscow Stock Exchange there are no price linkages, at least in the short run, except the cases, energy-gas and energy-oil. 5. Conclusions and Policy Implications
In this study we examined statistically the short run dynamics between sectorial indices of the Moscow stock exchanges. We performed univariate and bivariate analysis, i.e autocorrelation analysis and standard Granger "causality" tests. According to our results the Moscow stock exchange seems to be an efficient market for the period under examination. Efficiency is an element that attracts international investors. Thus, we believe that the Russian Authorities should keep the focus on the Moscow Stock Exchange since it can be proved a pillar for the financial development of the Russian economy.
References:
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Dickey D. A. and Fuller W. A. (1981), "The Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root", Econometrica, 49, 1057-1072.
Fama E. (1965), "The Behaviour of Stock Market Prices", Journal of Business, 38, 34-105.
Fama E. (1970), "Efficient Capital Markets: A Review of the Theory and Empirical Work", Journal of Finance, 25,383-416.
Fama E. (1991), "Efficient Capital Markets II", Journal of Finance, XLVI, 1575-1617.
LeRoy S. F. (1989) "Efficient Capital Markets and Martingales" Journal of Economic Literature, XXVII, 15831621.
Muth J. F. (1961), "Rational Expectations and the Theory of Price Movements", Econometrica, 29,315-335. Osborne M. F. M. (1959), "Brownian Motion in the Stock Market", Operations Research, 7,145-173. Osborne M. F. M. (1962), "Periodic Structure in the Brownian Motion of Stock Prices", Operations Research, 10, 345-379.
ИССЛЕДОВАНИЕ ДИНАМИКИ МОСКОВСКОЙ БИРЖИ: НЕКОТОРОЕ ЭМПИРИЧЕСКОЕ ТЕСТИРОВАНИЕ СЕКТОРАЛЬНОЙ РЫНОЧНОЙ ЭФФЕКТИВНОСТИ Алексакис Христос, PhD, ассоциированный профессор, Школа бизнеса Ренна, Ренн, Франция. Специалист в области финансовой математики, теории фондового рынка и биржевой торговли. Автор более 40 статей, проиндексированных в МБ Scopus. Индекс Хирша в МБ Scopus: 6. E-mail: [email protected]
Игнатова Татьяна Владимировна, доктор экономических наук, профессор, зав. кафедрой экономической теории и предпринимательства, Южно-Российский институт управления - филиал Российской академии народного хозяйства и государственной службы при Президенте РФ (344002, Россия, г. Ростов-на-Дону, ул. Пушкинская, 70/54). E-mail: [email protected]
Аннотация
В данной статье исследуется краткосрочная динамика секторальных рынков ценных бумаг на Московской бирже. Тесты причинности («каузальности») обеспечивают статистические рамки тестирования тесноты возможных связей фондовых индексов. В дополнение изучена степень автокорреляции индексов в целях исследования скорости восприятия новостей. Результаты, полученные авторами, показывают, что степень автокорреляции близка к нулю, и Московская биржа является слабоэффективным рынком. Результаты тестирования причинной связи («каузальности») по Грейнджеру также не выявили совпадений изученных индексов, что подтверждает вывод о слабой эффективности биржевого рынка.
Ключевые слова: Московская биржа, информационная эффективность, «каузальность», модель честной игры, фондовый рынок, секторальные индексы, эконометрический анализ, фондовые индексы, рынок ценных бумаг.
УДК 336.221.262 DOI: 10.22394/2079-1690-2018-1-4-71-75
РОЛЬ И ЗНАЧЕНИЕ ОЦЕНКИ НАЛОГОВОГО ПОТЕНЦИАЛА СТРАНЫ В СОВРЕМЕННЫХ УСЛОВИЯХ
Александрова кандидат экономических наук, доцент кафедры экономики, Маргарита Российский государственный университет правосудия (117418, Россия, Валерьевна г. Москва, ул. Новочеремушкинская, 69). E-mail: [email protected]
Маслюкова кандидат экономических наук, доцент кафедры экономики,
Екатерина Российский государственный университет правосудия (117418, Россия,
Александровна г. Москва, ул. Новочеремушкинская, 69). E-mail: [email protected]
Аннотация
В статье проведен анализ теоретических подходов к сущности налогового потенциала и методов его оценки с учетом различных факторов, с последующим выявлением преимуществ и недостатков их практического использования. Базируясь на выполненном исследовании, в работе представлена авторская формулировка дефиниции «налоговый потенциал», а также показана оценка налогового потенциала России за период 2014-2016 гг., свидетельствующая о низком уровне реализации налоговых возможностей Российской Федерации за рассматриваемый период.
Ключевые слова: налоговый потенциал, бюджетный потенциал, бюджетная система РФ, межбюджетные отношения, налоговые поступления, фискальный подход, ресурсный подход, налоговая политика, государственное управление экономикой.
В современных условиях хозяйствования налоговый потенциал является одним из важнейших инструментов государственного регулирования экономики.
Верное использование того или иного вида бюджетно-налоговой политики позволяет достичь следующих целей: борьба с инфляцией, ликвидация безработицы, стабилизация или стимулирование экономического роста, антициклическое регулирование экономики, достижение внешнеторгового баланса. Как правило, первичными инструментами государственного управления экономикой выступают мероприятия фискальной политики, причиной этому служит прямое подчинение этих мер правительству [1].
