Review of Business and Economics Studies, 2015, том 3, № 3

Review of 
Business and
Economics 
Studies

EDITOR-IN-CHIEF
Prof. Alexander Ilyinsky
Dean, International Finance Faculty, 
Financial University, Moscow, Russia
ailyinsky@fa.ru 

EXECUTIVE EDITOR
Dr. Alexander Kaffka

EDITORIAL BOARD

Dr. Mark Aleksanyan
Adam Smith Business School, 
The Business School, University 
of Glasgow, UK

Prof. Edoardo Croci
Research Director, IEFE Centre for 
Research on Energy and Environmental 
Economics and Policy, Università 
Bocconi, Italy

Prof. Moorad Choudhry
Dept.of Mathematical Sciences, Brunel 
University, UK

Prof. David Dickinson 
Department of Economics, Birmingham 
Business School, University of 
Birmingham, UK

Prof. Chien-Te Fan
Institute of Law for Science and 
Technology, National Tsing Hua 
University, Taiwan

Prof. Wing M. Fok
Director, Asia Business Studies, College 
of Business, Loyola University New 
Orleans, USA

Prof. Konstantin P. Gluschenko
Faculty of Economics, Novosibirsk State 
University, Russia

Prof. George E. Halkos
Associate Editor in Environment and 
Development Economics, Cambridge 
University Press; Director of Operations 
Research Laboratory, University of 
Thessaly, Greece

Dr. Christopher A. Hartwell
President, CASE - Center for Social and 
Economic Research, Warsaw, Poland

Prof. S. Jaimungal
Associate Chair of Graduate 
Studies, Dept. Statistical Sciences 
& Mathematical Finance Program, 
University of Toronto, Canada

Prof. Bartlomiej Kaminski
University of Maryland, USA; 

Rzeszow University of Information 
Technology and Management, 
Poland

Prof. Vladimir Kvint 
Chair of Financial Strategy, Moscow 
School of Economics, Moscow State 
University, Russia

Prof. Alexander Melnikov 
Department of Mathematical and 
Statistical Sciences, University of 
Alberta, Canada

Prof. George Kleiner
Deputy Director, Central Economics and 
Mathematics Institute, Russian Academy 
of Sciences, Russia

Prof. Kwok Kwong
Director, Asian Pacifi c Business 
Institute, California State University, Los 
Angeles, USA

Prof. Dimitrios Mavrakis
Director, Energy Policy and 
Development Centre, National and 
Kapodistrian University of Athens, 
Greece

Prof. Steve McGuire
Director, Entrepreneurship Institute, 
California State University, Los Angeles, 
USA

Prof. Rustem Nureev
Head of the Department of Economic 
Theory, Financial University, 
Russia

Dr. Oleg V. Pavlov
Associate Professor of Economics and 
System Dynamics, Department of Social 
Science and Policy Studies, Worcester 
Polytechnic Institute, USA

Prof. Boris Porfi riev
Deputy Director, Institute of Economic 
Forecasting, Russian Academy of 
Sciences, Russia

Prof. Svetlozar T. Rachev
Professor of Finance, College of 
Business, Stony Brook University, USA

Prof. Boris Rubtsov
Chair of Financial Markets and 
Financial Engineering, Financial 
University, Russia

Dr. Minghao Shen
Dean, Center for Cantonese Merchants 
Research, Guangdong University of 
Foreign Studies, China

Prof. Dmitry Sorokin
Deputy Rector for Research, Financial 
University, Russia

Prof. Robert L. Tang
Vice Chancellor for Academic, De La 
Salle College of Saint Benilde, Manila, 
The Philippines

Dr. Dimitrios Tsomocos 
Saïd Business School, Fellow in 
Management, University of Oxford; 
Senior Research Associate, Financial 
Markets Group, London School 
of Economics, UK

Prof. Sun Xiaoqin
Dean, Graduate School of Business, 
Guangdong University of Foreign 
Studies, China

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ISSN 2308-944X

Вестник
исследований
бизнеса 
и экономики

ГЛАВНЫЙ РЕДАКТОР
А.И. Ильинский, профессор, декан 
Международного финансо вого факультета Финансового университета 

ВЫПУСКАЮЩИЙ РЕДАКТОР
А.В. Каффка

РЕДАКЦИОННЫЙ СОВЕТ

М.М. Алексанян, профессор Бизнесшколы им. Адама Смита, Университет 
Глазго (Великобритания)

К. Вонг, профессор, директор Института азиатско-тихоокеанского бизнеса 
Университета штата Калифорния, 
Лос-Анджелес (США)

К.П. Глущенко, профессор экономического факультета Новосибирского 
госуниверситета

С. Джеимангал, профессор Департамента статистики и математических финансов Университета Торонто 
(Канада)

Д. Дикинсон, профессор Департамента экономики Бирмингемской бизнесшколы, Бирмингемский университет 
(Великобритания)

Б. Каминский, профессор, 
Мэрилендский университет (США); 
Университет информационных 
технологий и менеджмента в Жешуве 
(Польша)

В.Л. Квинт, заведующий кафедрой 
финансовой стратегии Московской 
школы экономики МГУ, профессор 
Школы бизнеса Лассальского университета (США)

Г. Б. Клейнер, профессор, член-корреспондент РАН, заместитель директора Центрального экономико-математического института РАН

Э. Крочи, профессор, директор по 
научной работе Центра исследований 
в области энергетики и экономики 
окружающей среды Университета 
Боккони (Италия)

Д. Мавракис, профессор, 
директор Центра политики 
и развития энергетики 
Национального университета 
Афин (Греция)

С. Макгвайр, профессор, директор Института предпринимательства 
Университета штата Калифорния, 
Лос-Анджелес (США)

А. Мельников, профессор 
Депар та мента математических 
и ста тистических исследований 
Университета провинции Альберта 
(Канада)

Р.М. Нуреев, профессор, заведующий 
кафедрой "Экономическая теория" 
Финансового университета

О.В. Павлов, профессор 
Депар та мента по литологии 
и полити ческих исследований 
Ворчестерского политехнического 
института (США) 

Б. Н. Порфирьев, профессор, 
член-корреспондент РАН, заместитель директора Института 
народнохозяйственного прогнозирования РАН

С. Рачев, профессор Бизнес-колледжа Университета Стони Брук 
(США) 

Б.Б. Рубцов, профессор, заведующий 
кафедрой "Финансовые рынки и финансовый инжиниринг" Финансового 
университета

Д.Е. Сорокин, профессор, членкорреспондент РАН, проректор 
Финансового университета 
по научной работе

Р. Тан, профессор, проректор 
Колледжа Де Ла Саль Св. Бенильды 
(Филиппины) 

Д. Тсомокос, Оксфордский университет, старший научный сотрудник 
Лондонской школы экономики 
(Великобритания)

Ч.Т. Фан, профессор, Институт 
права в области науки и технологии, 
национальный университет Цин Хуа 
(Тайвань)

В. Фок, профессор, директор по 
исследованиям азиатского бизнеса Бизнес-колледжа Университета 
Лойола (США)

Д.Е. Халкос, профессор, Университет 
Фессалии (Греция)

К.А. Хартвелл, президент Центра 
социальных и экономических исследований CASE (Польша)

М. Чудри, профессор, Университет 
Брунеля (Великобритания)

Сун Цяокин, профессор, декан Высшей школы бизнеса Гуандунского 
университета зарубежных исследований (КНР)

М. Шен, декан Центра кантонских 
рыночных исследований Гуандунского университета (КНР)

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CONTENTS

An Empirical Analysis of the Russian Financial 

Markets’ Liquidity and Returns

Karina Lebedeva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The Routes to Chaos in the Bitcoins Market

Hammad Siddiqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Exchange Rate Modeling: The Case of Ruble

Anton Kuzmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

World Islamic Banking: Growth and Challenges Ahead

Ahmad Shakib Mahmud  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Internal Control, Peculiarities of Application of the Requirements 

of the Sarbanes-Oxley Act and COSO Model

Ekaterina Tofeluk  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Review of 
Business and
Economics 
Studies

Volume 3, Number 3, 2015

Вестник
исследований
бизнеса 
и экономики

№ 3, 2015

CОДЕРЖАНИЕ

Эмпирический анализ ликвидности 

и доходности российского фондового рынка

Карина Лебедева . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Пути к хаосу на рынке биткоинов

Хамад Сиддики  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Моделирование курса рубля

Антон Кузьмин . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Мировой исламский банкинг: 

развитие и будущие вызовы

Ахмад Шахиб Махмуд . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Внутренний контроль, особенности применения 

требований закона Сарбейнса – Оксли и модели COSO на практике

Екатерина Тофелюк  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Review of Business and Economics Studies  
 
Volume 3, Number 3, 2015

INTRODUCTION

Every time a crisis happens, analysts address the 
questions of market efficiency, asset pricing or 
corporate fi nance. In the recent years liquidity has 

gained an enormous importance in each of these 
areas. In times of globalization and well-developed 
electronic trading platforms investors may quickly 
transfer their funds between different jurisdictions, 
and negative political or economic news may have 

An Empirical Analysis of the Russian Financial 
Markets’ Liquidity and Returns*

Karina LEBEDEVA
MS in Global Finance (RANEPA), BA (Hons) Finance and Investment Management (Northumbria University), BS in World 
Economics (Financial University, Moscow)
kmlebede@gmail.com

Abstract. The study aims to identify whether illiquidity and returns in the Russian stock and bond markets 
may be forecasted with the help of local macroeconomic variables, internet queries, global factors as well as 
the fundamental asset classes’ characteristics. To address these questions we use the correlation analysis, the 
VAR analysis and Granger causality tests. Despite the structural instability of the Russian financial markets, 
the market microstructure variables influence each other and are affected by the characteristics of other asset 
types. In highly volatile markets dynamic models should be applied. Stock and bond returns may be used for 
forecasting liquidity and volatility in the Russian market. Stock illiquidity is not useful for forecasting returns in 
the Russian market as opposed to the US and UK markets. In the Russian market investors rely on risk factors 
rather than on illiquidity measures in decision-making process. Bond maturity in the Russian market has a 
significant impact on the bonds’ characteristics and implicitly on switching between different asset classes 
similarly to the US market. Increase in the number of internet queries may serve as an indicator of higher 
volatility and illiquidity in the Russian stock market in the future, but Google Trends should be used only in 
combination with other forecasting tools such as macroeconomic measures and political situation analysis.

Аннотация. Целью работы является исследование возможностей прогнозирования неликвидности и 
доходности на российских рынках акций и облигаций с помощью макроэкономических переменных, 
данных по запросам в сети Интернет, глобальных факторов, а также фундаментальных характеристик 
различных классов активов. Для изучения данного вопроса используются корреляционный анализ, система 
векторных авторегрессий и тест причинности Грейнджера. Несмотря на структурную нестабильность 
российских финансовых рынков, переменные микроструктуры рынка влияют друг на друга и подвержены 
влиянию характеристик других классов активов. Для анализа рынков с высокой степенью волатильности 
необходимо использовать динамические модели. Доходность акций и облигаций может быть использована 
для прогнозирования ликвидности и волатильности на российском рынке. В отличие от рынков США 
и Великобритании фактор неликвидности акций не эффективен для прогнозирования доходности на 
российском рынке. В процессе принятия решений инвесторы на российском рынке в большей степени 
руководствуются факторами риска, чем показателями индикаторов неликвидности. Срок погашения 
облигаций на российском рынке имеет значительное влияние на характеристики облигаций и косвенно на 
переключение инвесторов между классами активов, что соответствует ситуации на рынке США. Увеличение 
количества интернет-запросов по российскому фондовому рынку может служить индикатором повышения 
волатильности и неликвидности в будущем, но Google Trends может быть использован только в комбинации 
с другими инструментами прогнозирования, такими как макроэкономические индикаторы и анализ 
политической ситуации.

Key words: Russia, fi nancial market microstructure, Google Trends as a forecasting tool, illiquidity spillovers, 
macroeconomic indicators, dynamic modeling.

* Эмпирический анализ ликвидности и доходности российского фондового рынка.

Review of Business and Economics Studies  
 
Volume 3, Number 3, 2015

a signifi cant impact on stock and bond markets’ liquidity and returns.
This study focuses on returns, liquidity that is calculated with the help of quotes and volumes as well 
as on the trading behavior. As such the research may 
be attributed to the field of market microstructure 
that focuses on the process and outcomes of trading 
assets under certain rules. Many economic studies describe the mechanics of trading, whereas microstructure theory explains how specifi c trading mechanisms 
infl uence the price formation process (O’Hara, 1995). 
In other words, the research in the given area examines factors infl uencing transaction costs, prices, 
quotes, volume, trading behavior, insider trading and 
market manipulation.
Financial crises of the last two decades have 
demonstrated that in unfavorable economic conditions liquidity may decrease significantly or even 
completely disappear. This fact may serve as an explanation of how liquidity shocks affect asset prices. 
There is a discussion in the contemporary literature 
on the causes of liquidity shortages and its contribution to fi nancial crises. Brunnermeier (2008), Brunnermeier and Pedersen (2009) explain the concept 
of "liquidity spiral" that is a consequence of mutual 
reinforcing of market liquidity and funding liquidity 
that occurred during subprime mortgage crisis in the 
USA, and after that took place in many countries all 
over the world. The process of liquidity spiral starts 
when asset prices drop, which deteriorates financial institutions’ capital. This results in tightening 
lending standards and margins. Both effects cause 
fi re — sales and additional wave of price decreases. 
Adrian and Shin (2009) state that in the marketbased fi nancial systems the banking sector and capital markets are interconnected, and a contraction of 
broker-dealer balance sheets may be an indicator of 
a negative trend in economic growth. The description of the mechanism of liquidity shocks’ infl uence 
on asset prices is presented in the studies of Amihud and Mendelson (1986) and Jacoby, Fowler and 
Gottesman (2000). Pastor and Stambaugh (2003) 
demonstrate that expected stock returns are linked 
to liquidity. Jones (2001) and Amihud (2002) state 
that liquidity is useful for expected returns prediction, however in their research liquidity is viewed in 
the context of transaction costs. Additional market 
microstructure elements examined in our research 
are return and volatility. Volatility or risk of the asset, typically measured as a standard deviation of 
returns is one of the factors that infl uence the willingness of investor to transfer funds between asset 
classes or assets. Returns are calculated on the basis 
of asset prices, either as differences or differenced 
logged prices.

Technological development has a growing infl uence on the society’s everyday life. People rely on 
the online information sources not only in such life 
aspects as health and entertainment, but also in the 
personal fi nance area. Internet search tools help investors get information for free and in a timely manner. This information is likely to affect traders’ decision making. According to MICEX (2015), individuals 
account for 53 per cent of all investors in the total 
shares trading turnover on the exchange. The abovementioned dominance of individual investors in Russia to some extent supports the usefulness of internet searches for investment decision-making. The 
rationale behind the internet search infl uence on the 
fi nancial markets’ liquidity is based on the fact that 
investors have limited cognitive resources, because of 
the information tracking and processing costs (Grossman & Stigliz, 1980; Merton, 1987). Due to these constraints market participants are likely to limit their 
choice to assets that attract their attention fi rst. Information on the assets, which investors search in 
the internet, may serve as a proxy for macroeconomic 
announcements as well as company-specifi c or assetspecifi c news considered in the investment decisionmaking. Thus, it is probable that people tend to trade 
heavily relying on the news available online.
Effi cient fi nancial market concept has been introduced in Fama (1970) seminal paper and defined as 
"one in which prices fully reflect available information". Following Fama (1970) this issue has been addressed by dozens of scholars: Basu (1977), Rosenberg, 
Reid, and Lanstein (1985). This study explores the infl uence of publicly available online information on the 
fundamental characteristics of assets or asset classes. 
As such, it relies on weak-form market effi ciency that 
assumes that "fundamental analysis may still provide 
excess returns". The Mixture of Distributions Hypothesis states that price volatility and trading volume are 
determined by the same information arrival rate (Luu 
& Martens, 2002). Renowned examples of MDH investigations are due to Clark, (1973), Epps and Epps 
(1976), Tauchen and Pitts (1983) and Andersen (1996). 
A common result of the Mixture of Distributions Hypothesis is that certain market activity patterns such as 
volatility persistence are determined by the same type 
of information fl ow (Vlastakis & Markellos, 2012).
One of the possible consequences of the economic 
news online availability for the international investment community decisions is an almost 250 percent 
net capital outfl ow increase which Russia experienced 
in 2014 as compared with 2013 (Bank of Russia, 2015). 
The Ministry of Economic Development of Russia 
(2015) forecasts that in 2015 investment is expected to 
fall by 13 percent. The initial forecast for the net capital outfl ow has been also increased by approximately 

Review of Business and Economics Studies  
 
Volume 3, Number 3, 2015

30 percent. Probable additional reasons for the investment outfl ow from Russia are economic slowdown and 
unfavorable environment of economic and political 
sanctions. At the moment the stock market experiences gradual recovery due to wider choice of investment 
contracts as well as market infrastructure improvement. Bond market suffered more from the sanctions, 
but the situation is likely to become better in the near 
future, because of the expansionary monetary policy 
of the Bank of Russia (Vedomosti, 2015). Dynamically changing patterns mentioned above as well as the 
unique character of the Russian market environment 
represent a particular interest for research.
The Russian market has been examined before with 
the focus and approach different from those in the given study. There are some similarities in the techniques 
employed, but no research exists, where particular 
models and tools are applied to the main research 
objects of the given master thesis with the same focus. It is necessary to mention that there are studies 
analyzing the relationship of stock and bond markets’ 
microstructure parameters, research focusing on stock 
market parameters and Google Trends, but, to the best 
of our knowledge, there is no study that would have 
provided dynamic models for stock and bond market 
microstructure parameters with the participation of 
internet search query factors for the emerging market, and there is no research, where Granger causality 
test is performed on the recent data for the individual 
assets or asset classes characteristics, internet search 
parameters and macroeconomic variables for the Russian Federation. These models will be an innovation 
introduced in the given research. This study contributes to the literature by building and interpreting such 
models as well as by testing the effectiveness of modern forecasting tools that may be used by investment 
community in the future.

1. LITERATURE REVIEW

The studies of stock and bond markets illiquidity 
have developed in separate literature strands. According to Chorida, Sarkar, and Subrahmanyam (2005), 
the early studies of liquidity focus solely on the stock 
market due to the data availability issues. Among the 
earliest research in the given fi eld one could mention 
Benston and Hagerman (1974), Glosten and Milgrom 
(1985), Seyhun (1986) and Amihud and Mendelson 
(1986). Glosten and Milgrom (1985) analyze the informational properties of transaction prices and the 
formation of bid-ask spreads adopting the adverse 
selection view to the insider trading phenomenon. 
Seyhun (1986) investigates the effect of insider trading on stock prices behavior and abnormal returns of 
informed traders. Both studies emphasize that insider 

trading signifi cantly infl uences stock market illiquidity. Butler, Grullon, and Weston (2005) is an example 
of a more recent work examining the stock market 
illiquidity from a perspective of the trading environment and frictions. The authors fi nd that investment 
banks’ fees are lower for companies whose stocks are 
liquid. In contrast to studies focusing mainly on the 
trading environment and institutional agreements, 
Naes, Skjeltorp and Odegaard (2011) examine bidirectional impact of the economic stance on the stock 
market liquidity. They compare the case of the USA 
and Norway and establish that stock market liquidity infl uences not only current, but also future state 
of the economy in the USA and Norway. The results 
received by the authors are robust to different liquidity proxies. Naes, Skjeltorp and Odegaard also show 
that there is Granger causality between liquidity and 
macroeconomic parameters in the given markets. 
Extending their idea, we investigate the bidirectional 
impact of the economic stance on the stock and bond 
market liquidity, volatility and returns in Russia. 
The research in this area was also performed by Kim 
(2013), who outlines that stock market illiquidity, in 
particular Amihud ratio, is an effective predictor of 
economic growth in Korea.
The idea of a joint analysis of volatility, liquidity 
and returns is not new. For instance, Andrikoupolos 
and Angelidis (2008) offer a pre-crisis analysis of the 
relations between volatility, illiquidity and returns 
on exchanges in advanced economies. The authors 
also conclude that there are volatility spillovers from 
large capitalization stocks to those with small capitalization and vice versa in London Stock Exchange. 
They establish that volatility shocks may be predicted by illiquidity shocks and return shocks. The authors also discuss illiquidity spillovers between large 
capitalization stocks and small capitalization stocks. 
Large capitalization stocks capture the effect first, 
while small capitalization stocks follow the pattern. 
Andrikoupolos, Angelidis, and Skintzi (2012) state 
that there are Granger-causal associations between 
volatility, illiquidity and returns of G-7 countries and 
within each country. The authors document that illiquidity and returns are negatively related in the 
majority of cases, and causal relationship between 
illiquidity and volatility is valid only for American 
market. Chang, Faff, and Hwang (2011) examine the 
dependency of liquidity, stock returns and the business cycle phase in Japan. The authors report that 
there is solely negative relationship between liquidity proxies and stock returns in Japanese market during the business cycle expansionary phase, while for 
the contractionary phase the results are ambiguous. 
Overconfi dence hypothesis is likely to explain turnover/return relationship in Japan.

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Volume 3, Number 3, 2015

Stocks and bonds’ trading activities follow completely different trading patterns due to the assets’ 
specific features and suitability of the given assets 
for various strategies. Among other things, the latter yields, different speed of responsiveness of bond 
and stock market liquidity to changes in macroeconomic situation. For both types of assets the effect 
of macroeconomic variables and announcements on 
the market liquidity has been extensively analyzed. 
Brandt and Kavajecz (2002) study the dependence 
of liquidity, order fl ow and yield curve and make the 
conclusion that order fl ow imbalances explain 26 % of 
the yield curve variation, and the impact of order fl ow 
on yields is the most evident in times of low liquidity. Fleming and Remolona (1999) and Balduzzi, Elton, and Green (2001) examine returns, spreads, and 
trading volume in the fi xed income markets around 
financial announcements. Fleming and Remolona 
(1999) fi nd that macroeconomic announcements have 
greater effect on expected future interest rate than on 
current short-term interest rates, and various types of 
announcements result in different expectations about 
the target rate. Balduzzi, Elton, and Green (2001) 
mention that adjustment of price volatility to news 
occurs within a minute, while bid-ask spreads widen 
and adjust to normal values only in 15 minutes after 
announcements. In addition, the authors state that 
the effect of macroeconomic announcements on bond 
market differs signifi cantly depending on the assets’ 
maturity; the statement is also supported by Beber, 
Brandt, and Kavajecz (2009), Longstaff (2004) and 
Goyenko and Ukhov (2009). Therefore, the analysis in 
this research also focuses on different bond maturities. Goyenko, Subrahmanyam and Ukhov (2011) outline that bond illiquidity infl uences the asset allocation effi ciency and interest rate discovery. Moreover, 
dynamics of the bond markets’ trading costs is very 
important for understanding investors’ cost optimization. Interestingly, illiquidity becomes higher during 
recession periods across all maturities. However, the 
effect is stronger for short-term bonds. The difference 
between spreads of various maturity fi xed income instruments also becomes more signifi cant during the 
times of economic downturn for both on-the-run and 
off-the-run issues. The macroeconomic parameters’ 
impact on the dealer costs has more importance in 
the less liquid off-the-run sector. On-the-run illiquidity is heavily infl uenced only by volatility, while offthe-run illiquidity is affected by infl ation, monetary 
policy surprises, bond returns, and volatility. Offthe-run illiquidity is a key determinant for returns 
forecasting, and thus the liquidity premium, in the 
Treasury market. Nowadays, the studies of stock and 
bond markets illiquidity have developed in separate 
strands. However, there are also papers that provide 

combined analysis of stock and bond markets illiquidity and describe the intuition behind their comovement — Chrorida, Sarkar, Subrahmanyam (2005), 
Goyenko and Uhov (2009). These papers apply vector 
autoregression analysis for the US market.
Although the studies of stock and bond markets illiquidity to some extent still constitute two separate 
literature strands, some researches have attempted to 
bridge the gap between them and provide a combined 
analysis of stock and bond markets illiquidity. Chrorida, Sarkar, Subrahmanyam (2005), Goyenko and Uhov 
(2009) model a joint dynamics of the US stock and 
bond markets within a vector autoregression framework and provide the intuition behind these markets’ 
comovement. Various authors establish the existence 
of an illiquidity spillover between the stock and bond 
market (see for instance: Chorida, Sarkar, & Subrahmanyam, 2005; Fleming, Kirby, & Ostdiek, 1998; 
Ho & Stoll, 1993; O’Hara & Oldfi eld, 1996). According 
to Goyenko and Ukhov (2009), there is mutual Granger causality between illiquidity of stock and Treasury 
bonds markets in the United States. Trading activity 
may result in the interaction between stock and fi xed 
income market illiquidity (Fox, 1999; Swensen, 2000; 
Longstaff, 2004; Goetzman & Mazza, 2002; Agnew & 
Balduzzi, 2005). The impact of stock market illiquidity 
on those of the bond market is consistent with fl ightto-quality and fl ight-to-liquidity episodes. At the same 
time, illiquidity of short-term bonds has a stronger effect on the stock market (Goyenko & Ukhov, 2009). 
The choice of the instruments by market participants 
depends heavily on the stage of economic cycle, bond 
maturity and date of the fi xed income instrument issue 
(Goyenko, Subrahmanyam, & Ukhov, 2011). Amihud 
and Mendelson (1986) report that market participants 
are willing to pay for liquidity. Since illiquidity is a systematic risk factor, therefore illiquidity in one market 
may affect illiquidity in another market (Chorida, Roll, 
& Subrahmanyam, 2000; Hasbrouck & Seppi, 2001; 
Huberman & Halka, 2001; Amihud, 2002; Pastor & 
Stambaugh, 2003; Amihud & Mendelson, 1986, 1989; 
Brennan & Subrahmanyam, 1996; Warga, 1992; Boudoukh & Whitelaw, 1993; Kamara, 1994; Krishnamurthy, 2002; Goldreich, Hanke & Nath, 2005; Goyenko & 
Ukhov, 2009; Brunnermeier & Pedersen, 2009). Vayanos (2004) outlines that illiquid assets become riskier 
whereas investors’ risk aversion increases over time. 
Interestingly, Brunnermeier and Pedersen (2009) indicate that Federal Reserve can improve market liquidity 
by monetary policy actions. Fleming, and Remolona 
(1997) and Fair (2002) report that monetary shocks 
are accompanied by signifi cant changes in stock and 
bond prices. Lesmond (2005) mentions that weak political institutions and legal enforcement system have 
a negative impact on the markets’ liquidity. Chorida, 

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Sarkar, and Subrahmanyam (2005) show that expansionary monetary policy results in higher stock market 
liquidity during recessions, and unexpected increases 
(decreases) in the federal funds rate lead to increases 
(decreases) in stock and bond volatility. In addition, 
the authors state that the fl ows in the stock and government bonds sectors are useful for stock and fi xed 
income markets liquidity prediction thus establishing 
the link between "macro" liquidity, or money fl ows, and 
"micro", or transaction-based, liquidity in the American market.
Actually, Chorida, Sarkar, and Subrahmanyam 
(2005) find that volatility is an important driver of 
liquidity. Innovation in the spreads in one market 
affects the spreads in another market; therefore it is 
possible to conclude that liquidity and volatility are 
driven by the common factors.
This study focuses on the liquidity, not on volatility, because liquidity belongs to a more complex fi eld 
of research. Various authors offer different measures 
of liquidity and its explanatory factors. There is also 
no consensus on the best liquidity indicator. Moreover, suitability of the indicators is determined by the 
asset type and data frequency. In addition, there is 
less data available for liquidity measures’ computation. The following sections provide a discussion of 
the most commonly employed measures of liquidity 
and its drivers, behavior of the Russian fi nancial market as well as the modern fi nancial markets’ forecasting techniques based on the information available 
online.

1.1 LIQUIDITY MEASURES

Liquidity is a key notion in financial markets studies, but as it was mentioned above, there are some 
difficulties with its measurement. Low-frequency 
price impact proxies described by Goyenko, Holden 
and Trzcinka (2009) include return-to-volume ratio 
of Amihud (2002), Pastor and Stambaugh (2003) and 
Amivest Liquidity (Amihud, Mendelson & Lauterbach, 1997). Goyenko, Holden and Trzcinka (2009) 
outline that Amihud (2002) is effective for capturing 
price impact and high-frequency transaction costs 
benchmarks in NYSE. Florackis, Gregoriou and Kostakis (2011) introduce another low-frequency liquidity measure not mentioned by Goyenko, Holden and 
Trzcinka (2009) that is the return-to-turnover ratio. 
Florackis, Gregoriou and Kostakis (2011) notice that 
asset pricing is signifi cantly infl uenced by trading frequency and transaction costs — the above-mentioned 
factors are not considered in isolation, and emphasize 
that return -to-turnover ratio separates size effect 
from illiquidity effect as compared to Amihud (2002) 
thus being a more accurate measure. Lesmond (2005) 
reports that volume and turnover-based measures 

are downward-biased for low-liquidity markets. This 
research uses low frequency price impact benchmark 
for stock illiquidity measurement similar to those 
presented by Florackis, Gregoriou and Kostakis (2011) 
and simplified low frequency spread benchmark as 
bond illiquidity proxy. The formula for the bond illiquidity proxy is provided in Methodology section.

1.2 FACTORS INFLUENCING THE RUSSIAN STOCK 
MARKET BEHAVIOR

Apparently, the first econometric study modeling 
the Russian stock market is due to Rockinger and 
Urga (2000) who state that the Russian market has 
a tendency to exhibit the market efficiency. Initially, most research has concentrated on market 
returns and volatility and employed models ranging from GARCH (Hayo & Kutan, 2005; Goriaev & 
Sonin, 2005), EGARCH (Jalolov & Miyakoshi, 2005), 
TGARCH (Hayo & Kutan, 2005) to non-parametric 
approach to event studies (Chesney, Reshetar, & 
Karaman, 2011). Generalized Autoregressive Conditional Heteroskedasticity or GARCH framework, an 
extension of ARCH model, is typically used to model 
time series variance (Engle, 1982; Bollerslev, 1986). 
EGARCH and TGARCH are examples of asymmetric 
GARCH models introduced by Nelson (1991) and 
Zakoian (1994) respectively. Goriaev and Zabotkin 
(2006) report high infl uence of "corporate governance, political risk and macroeconomic risk factors 
such as emerging markets performance, oil prices 
and exchange rates on the Russian stock market". 
They stress that signifi cant sensitivity of developing markets to political events may jeopardize the 
growth prospects, and macroeconomic factors that 
have significant impact in the developed markets 
become signifi cant in the volatile emerging markets 
only after corporate governance reaches the proper 
level of quality and transparency. Furthermore, investors’ over-reaction or under-reaction to certain 
events in highly volatile markets additionally contributes to the risk of the assets in addition to country- and fi rm-specifi c risks. Therefore, static models 
are not suitable for markets with high level of risk, 
and dynamic models should be applied. Anatolyev 
(2005) emphasizes a structural — not depending 
on the fi nancial crises — instability of the Russian 
market, and a growing importance for the Russian 
market of such explanatory factors as the US stock 
prices as well as the US and Russian interest rates. 
Nevertheless, according to Anatolyev the infl uence 
of the exchange rates, oil prices and monetary aggregates on the Russian stock market returns diminished in years 2003 and 2004. Interestingly, Jalolov 
and Miyakoshi (2005) suggest that German market is 
more effi cient predictor for the Russian stock mar

Review of Business and Economics Studies  
 
Volume 3, Number 3, 2015

ket monthly returns. In their view, this fact could 
be attributed to relatively close trade and investment relations between Germany and the Russian 
Federation. Surprisingly, the authors do not report 
a strong dependence between oil and gas prices and 
the Russian stock market returns. In contrast, Hayo 
and Kutan (2005) fi nd that the Russian market returns may be explained by their own lagged values 
as well as the S&P 500 return and oil index return 
and thus reject the EMH for the Russian stock market. The authors also establish a direct volatility link 
between the Russian and US markets.

1.3 GOOGLE TRENDS AND OTHER TYPES
OF ONLINE INFORMATION AS MODERN 
FORECASTING TOOLS

In the world of advanced technological development people tend to resort to the online information 
sources in many aspects of their life, including investment decision making. With a growing role of internet 
searches a valid research question is whether internet 
searches can help predict market behavior and what 
would be the rationale behind their forecasting capacity. Preis, Moat, and Stanley (2013) argue that Google 
Trends data may refl ect the current state of the economy and provide some insights to the future behavior 
of the economic actors. The authors state that there 
is an increase in the search for key words connected 
with the fi nancial market before the fi nancial market 
falls, so it is possible to construct trading strategies 
based on the volume of internet queries. Financial 
relevance of each term is calculated as a frequency 
of each term in the online edition of Financial Times 
newspaper normalized by the number of Google hits. 
In addition, Preis, Moat, and Stanley (2013) determine 
that Google search volume in the US is a better predictor for the US market price dynamics as compared 
with global Google search volume. Vlastakis and 
Markellos (2012) use Google Trends as information 
demand quantifi cation and empirically confi rm that 
information demand is positively related to investors’ 
risk aversion. The authors also obtain that demand for 
idiosyncratic information infl uences individual stock 
trading volume and excess stock returns. The usefulness of the Google search volume is not solely confi ned to the US market. Arouri et al. (2013) indicate 
that Google Trends tool is useful for the liquidity forecasting in French stock market. Adding information 
demand variables to their model helps improve it. In 
addition to Google Trends variables the authors use 
the following parameters as liquidity forecasting factors: absolute returns, fi rm size, information supply, 
risk and trading costs.
Apart from the liquidity forecasting, the Google 
search volumes have been examined with respect to 

their applicability in the market volatility and price 
dynamics prediction. Da et al. (2011), Dzielinski 
(2011) outline that internet search volume data may 
be effectively used for stock market volatility forecasting. Dimpfl and Jank (2011) state that Google 
Trends may be effi ciently employed for forecasting 
volatility in the UK, US, French and German markets. They show that adding internet search queries variables to the model leads to more precise 
in- and out-of-samples forecasts. Moreover, Dimpfl  
and Jank fi nd strong co-movement of stock indexes’ 
volatilities and internet queries for their names. In 
their models volatility is an exogenous factor owing 
to the fact that fi rst and subsequent internet queries are considered as a consequence of the strong 
primary fundamental volatility shock following 
the logic of Lux and Marchesi (1999). Our empirical strategy relies on all the market microstructure 
variables being endogenous and the global factors 
being exogenous both for microstructure variables 
and internet queries.

2. METHODOLOGY AND MODEL SELECTION

This study aims to assess the impact of the asset 
characteristics and internet searches on the returns 
and liquidity in the Russian stock and bond markets. 
The purpose of the research is to determine whether 
market microstructure parameters are useful for forecasting liquidity, volatility and return and if internet 
searches may be successfully employed to forecast 
the market microstructure characteristics. In particular, the following hypotheses are examined:
Ho (1): Individual asset or asset classes’ characteristics are irrelevant for the Russian fi nancial markets’ liquidity and returns forecasting.
Ho (2): Internet search time series is irrelevant for 
the Russian stock market liquidity and returns forecasting.
Ho (3): Changes in macroeconomic variables do 
not influence the Russian stock and bonds’ market 
return, liquidity and volatility.
Our empirical strategy involves the correlation 
analysis, the Granger causality tests and the vector 
autoregression models built for daily, weekly and 
monthly data. For correlation analysis Spearman 
method is used, because the data might not be normally distributed which is typical for the given type 
of research. In order to demonstrate non-normal 
distribution of data the Empirical Distribution Function Test for Normality is performed. The testing procedure is based on the statistics of Lilliefors (1967, 
1969), Cramer — von Mises (1928) and Anderson and 
Darling (1952, 1954). The null hypothesis is that data 
is normally distributed.

Review of Business and Economics Studies  
 
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3. DATA DESCRIPTION

This study uses daily, weekly and monthly data for the period between 2006 and 2015. The data has been obtained from Bloomberg database, MICEX offi cial website, Google, Yahoo! Finance, Bank of Russia and Federal 
Service of State Statistics web sites.

4. CHARACTERISTICS OF INDIVIDUAL ASSETS OR ASSET CLASSES

In weekly data model the following variables are analyzed: stock market return measure (RETURN), stock 
market illiquidity measure -the higher the factor is, the less liquid the market is (LIQUIDITY), stock market 
volatility measure (VOLATILITY). RETURN, VOLATILITY and LIQUIDITY are calculated based on time series 
for MICEX closing prices from the 21st of April 2006 to the 27th of February 2015. The data sources are MICEX 
offi cial web site and Bloomberg.
In daily data analysis the following variables are used: stock market return measure (RETS), short-term 
bonds return (RETBS), medium-term bonds return (RETBM), long-term bonds return (RETBL), stock market 
volatility (VOLS), short-term bonds volatility (VOLBS), medium-term bonds volatility (VOLBM), long- term 
bonds volatility (VOLBL), stock illiquidity (ILLIQS), short-term bonds illiquidity (ILLIQBS), medium-term 
bonds illiquidity (ILLIQBM), long-term bonds illiquidity (ILLIQBL).
RETS, VOLS, ILLIQS are calculated based on the MICEX time series closing prices for the period from 
the 1st of August 2012 to the 27th of February 2015. The data sources are MICEX official website and 
Bloomberg.
The period for bond microstructure parameters is from the 1st of August 2012 to the 27th of February 
2015. RETBS, VOLBS, ILLIQBS are calculated for closing prices time series for 7.5 % federal loan bonds 
(OFZ) with maturity on the 15th of March 2018 (approximately 3 years to maturity). The data source is 
Bloomberg. RETBM, VOLBM, ILLIQBM are calculated for closing prices time series for 7.6 % federal loan 
bonds (OFZ) with maturity on the 20th of July 2022 (approximately 7 years to maturity). The data source 
is Bloomberg. RETBL, VOLBL, ILLIQBL are calculated for closing prices time series for 10 % federal loan 
bonds (OFZ) with maturity on the 20th of August 2025 (approximately 10 years to maturity). The data 
source is Bloomberg.
In monthly data analysis the following market microstructure parameters are used: stock return (RETS), 
stock volatility (VOLS), stock illiquidity (ILLIQS) that are calculated based on the MICEX closing prices time 
series from April 2011 to February 2015. The data sources are MICEX offi cial web site and Bloomberg data 
base.
Short term bonds return (RETBS), short term bonds volatility (VOLBS), short term bonds illiquidity (ILLIQBS) are calculated for closing prices time series for 7.5 % federal loan bonds (OFZ) with maturity on the 15th 
of March 2018 (approximately 3 years to maturity). The data source is Bloomberg data base.
As in Goyenko and Ukhov (2009) the bond illiquidity measure is calculated as:

(
)
0.5 (
)
ASK
BID
ASK
BID



Following Amihud (2002), Florackis, Gregoriou and Kostakis (2011), the stock illiquidity measure is defi ned as:

1
wh
*
,
e
er
Absolutevaueof return
number of valid observationdays
Turnover
Total number of sharestraded during the period
Turnover
Averagenumber of sharesoutstanding during the period


 

The stock volatility and bond volatility are measured as standard deviation of their returns. For the convenience of work with data natural log of turnover time series is taken (return data is expressed in percentage terms, and turnover in 8-digit numbers). Volatility is calculated as a standard deviation for the previous 
22 observations for daily data (number of working days per month), and as a standard deviation for the previous 4 observations for weekly data. Return for monthly data is calculated as averages of daily returns for a 
specifi ed month. For volatility and liquidity the last observations for a specifi ed month are taken.

Review of Business and Economics Studies  
 
Volume 3, Number 3, 2015

4.1 INTERNET SEARCH PARAMETERS

Internet search measures included in the weekly 
data model are stock market internet queries in 
English language (GOOGLE_MICEX) and stock market internet queries in Russian language (GOOGLE_MMVB). GOOGLE_MICEX gives the number of 
searches done for a term "MICEX" relative to the 
total number of searches done on Google over time 
from the 21st of April 2006 to the 27th of February 
2015. GOOGLE_MMVB gives the number of searches 
done for a term "ММВБ" (MICEX name in the Russian language) in the relative to the total number 
of searches done on Google over time from the 21st 
of April 2006 to the 27th of February 2015. The data 
source is Google Trends — the statistics available 
online for weekly data. Unfortunately, there is no 
open access to daily data. Monthly GOOGLE_MMVB 
and GOOGLE_MICEX are calculated as monthly av
erage of weekly time series for the period from April 
2011 to February 2015.
Google Trends shows a percentage of Google 
searches to define the number of queries made for 
selected terms as compared to the total quantity of 
Google searches done during that period. The data 
is normalized with respect to total searches in order to avoid variable’s effect and to allow comparisons across regions. Therefore it is expressed in relative terms. Data is presented on a scale from 0–100 
(Google, 2015).
From the given chart it is possible to make the 
conclusion that the query "ММВБ" was more common 
in Google than the query "MICEX".
The interest for the Russian stock market is demonstrated not only in Moscow and Saint Petersburg, 
but also in top two global fi nancial centers London 
and New York (Z/Yen, 2015). The absence of interest 

Figure 4.2.1. Interest over Time — MICEX Query (Lower graph) vs. ММВБ Query (Upper graph) in Google Trends.

Source: Google (2015).

Figure 4.2.2. Regional Interest for "MICEX" by Country (End of April 2015).

Source: Google (2015).