moex_thesis_final

580 Research Project Report

Stock exchange mergers and market efficiency: MOEX

case

Evgeny Konov

7841876

Supervisors:

Ülkü, N.

Premachandra, I.M.

Submitted in partial fulfilment of the requirements of the

Master of Finance program

Department of Accountancy and Finance

February 5, 2015

Stock exchange mergers and market efficiency: MOEX case

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Abstract

The aim of this paper was to examine the impact of MICEX and RTS stock exchanges merger

on the informational efficiency of the integrated equity market. I used the serial correlation

test, the unit root test and the mean variance ratio test to investigate whether the daily

stocks price changes represented a random walk. Furthermore, I tested the profitability of

the technical trading strategies and explored the determinants of the trading volume and

value. All tests were performed in the pre-merger and the post-merger periods for three

various samples of stocks in order to identify any changes between the periods and across

the samples of stocks.

Large-cap stocks experienced an overall improvement of the market efficiency in the post-

merger period. The result for the mid-cap stocks was a decline in the price efficiency, while

the autocorrelation test indicated an improvement. Two of the four employed tests

suggested a deterioration in the market efficiency for the small-cap stocks. Two other tests

produced inconclusive results. Investigation of the determinants of the trading volume and

value identified the volatility of the global market is the main explanatory variable. Dummy

variable that measured whether trading volume became higher in the post-merger period

turned out to be insignificant. The unit root test and the variance ratio test produced

contradictory results.


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Contents Abstract ............................................................................................................................................................ i

Contents .......................................................................................................................................................... ii

List of the tables ............................................................................................................................................. iv

Chapter 1: Introduction .................................................................................................................................. 1

1.1 Background ........................................................................................................................................... 1

1.2 Motivation ............................................................................................................................................ 2

1.3 Research question................................................................................................................................. 5

1.4 Contribution .......................................................................................................................................... 5

1.5 Organization of the research ................................................................................................................ 5

Chapter 2: Review of the literature, methodology and the MOEX case study ............................................... 7

2.1 Literature review................................................................................................................................... 7

2.1.1 Informational efficiency ................................................................................................................. 7

2.1.2 Indirect measures of efficiency ...................................................................................................... 9

2.2 Expectations ........................................................................................................................................10

2.3 Methodology review ...........................................................................................................................11

2.3.1 Time series OLS regression ..........................................................................................................11

2.3.2 Tests of serial independence .......................................................................................................12

2.3.3 Runs test ......................................................................................................................................12

2.3.4 Unit root rest ...............................................................................................................................13

2.3.5 Multiple variance ratio test..........................................................................................................13

2.3.6 Generalized spectral shape test ...................................................................................................13

2.4 Case study under the review ..............................................................................................................13

2.4.1 Overview of MOEX .......................................................................................................................13

2.4.2 MICEX and RTS merger ................................................................................................................14

Chapter 3: Sample data, sample period and methodology ..........................................................................16

3.1 Sample data ........................................................................................................................................16

3.2 Sample period .....................................................................................................................................18

3.3 Methodology .......................................................................................................................................22

3.3.1 Effect of the post-merger infrastructure innovations on the trading volume and value ............23

3.3.2 Serial correlation test ...................................................................................................................24

3.3.3 Unit root test................................................................................................................................25

3.3.4 Variance ratio test ........................................................................................................................27

3.3.5 Moving average technical trading rules .......................................................................................27

Chapter 4: Results .........................................................................................................................................32

4.1 Analysis of the determinants of the trading volume and value..........................................................32


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4.2 Serial correlation test’s results ...........................................................................................................34

4.3 Unit root test’s results ........................................................................................................................38

4.4 Variance ratio test’s results ................................................................................................................42

4.5 Moving average technical trading rules’ results .................................................................................46

Chapter 5: Conclusion ...................................................................................................................................49

Acknowledgements ......................................................................................................................................51

Bibliography ..................................................................................................................................................52

Annex ............................................................................................................................................................54

Exhibit 1: Summary of the methodology ..................................................................................................54

Exhibit 2: MOEX equity indices map .........................................................................................................55

Exhibit 3: MOEX indices sector diversification ..........................................................................................55

Exhibit 4: Blue chip index constituents .....................................................................................................56

Exhibit 5: MICEX index constituents .........................................................................................................56

Exhibit 6: Second-tier index constituents .................................................................................................57

Exhibit 7: MOEX merger timeline .............................................................................................................58

Exhibit 8: Integration of MICEX and RTS markets .....................................................................................59

Exhibit 9: MICEX and RTS KPI before the merger .....................................................................................59

Exhibit 10: MICEX and RTS shares of the local trading volume (by number of trades) ............................60

Exhibit 11: MOEX vs LSE IOB (2014) ..........................................................................................................60

Exhibit 12: MOEX and RTS shares of listed companies (by number) ........................................................61

Exhibit 13: T+0 and T+2 markets’ shares of trading volume (RUB) ..........................................................61

Exhibit 14: Direct Market Access (DMA) ...................................................................................................62

Exhibit 15: MOEX – vertically integrated platform ...................................................................................62

Exhibit 16: Breakdown of the equity market participants (by trading volume) .......................................63

Exhibit 17: Cash equities ADTV by type of settlement (RUB bln) .............................................................63

Exhibit 18: MOEX vs LSE ADTV growth .....................................................................................................63

Exhibit 19: Foreign investors’ trading volumes in equity ..........................................................................64

Exhibit 20: Trading volume of top-10 brokers, 11m 2014 (RUB) ..............................................................64

Exhibit 21: Large-cap stocks: availability from brokers for a short position .............................................64

Exhibit 22: Mid-cap stocks: availability from brokers for a short position ...............................................65

Exhibit 23: Stata code ...............................................................................................................................65

23.1 Moving average trading rules .......................................................................................................65

23.2 Variance ratio test .........................................................................................................................65

23.3 Unit root test.................................................................................................................................65

23.4 Serial correlation test ....................................................................................................................66

23.5 OLS regression ..............................................................................................................................66


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List of the tables Table 1: Sample refinement ..................................................................................................... 18

Table 2: Determinants of the trading volume ......................................................................... 32

Table 3: Determinants of the trading value ............................................................................. 33

Table 4: Results of the serial correlation test .......................................................................... 35

Table 5: Results of the unit root tests ...................................................................................... 39

Table 6: Results of the variance ratio tests .............................................................................. 43

Table 7: Results of applying the moving average technical trading rules ............................... 47

Table 8: Summary of the results .............................................................................................. 49


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Chapter 1: Introduction

1.1 Background

International and domestic mergers of stock exchanges became the mainstream

development in the financial industry during the last two decades (Kokkoris & Olivares-

Caminal, 2008). Demutualization1 of the stock exchanges in the early 1990s was the main

impetus for the initial wave of consolidation at the end of the decade. An array of recent

merger drivers have been identified in the academic literature. They are expected to keep

the consolidation rate high for the upcoming years due to the pressure to increase

competitiveness and to cut costs, the harmonization of regulatory environments for capital

markets in Europe, and the technological advances to name a few (Hellstrom, Liu, &

Sjogren)2.

A crucial question that arises from this activity is whether such consolidations have any

impact on market microstructure and its quality, in particular on information efficiency3.

Market microstructure theory is concerned with the mechanism that translates investor’s

latent demands into prices and volumes. This field of knowledge has been rapidly

developing since the late 1980s, with numerous research dedicated to a wide array of topics

spanning from price formation process (how prices incorporate information) and market

structure and design (how trading protocols affect prices), to transparency issues (the ability

of market participants to observe information about the trading process) and applications to

other finance areas (for example asset pricing) (Madhavan, 2000). “A central idea in the

theory of market microstructure is that asset prices need not equal full-information

expectations of value because of a variety of frictions” (Madhavan, 2000, p. 207). Market

microstructure theory identified critical components of the trading process such as order

flow, trading costs, levels of inventory, market makers and dealers, bid-ask spread,

asymmetric information between market participants, degree of continuity of trading,

order- versus quote-driven markets, degree of trading automation, order forms (for

example market, limit, and stop), protocols (rules regarding program trading), and

1 The process of transformation of the trading venues from member-owned non-profit organizations into investor-owned for-profit firms. 2 The year of the publication is not identified in the paper. 3 A metric that gauges how precisely the stock prices reflect the fair values given the available information.


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anonymity versus trader identity to name a few. One of the questions posed by this theory

is whether competition among trading venues diminishes informational efficiency by

fragmenting the order flow. This paper intends to investigate the overall impact of stock

exchange mergers on the market efficiency.

1.2 Motivation

Stock price efficiency is of the utmost importance for a number of reasons:

1. It enables an efficient allocation of savings in the economy and higher risk-adjusted

returns: if asset prices duly incorporate all information, equity capital flows to its

highest-valued use.4

2. The price efficiency is critical for the investors’ and the firm owners’ trust in the stock

market as a trading venue and a source of new equity capital respectively.

3. It is essential for an efficient construction of management compensation: incentive

packages based on stock prices that poorly reflect fair values create misaligned

incentives (and amplify the principal-agent problem).

4. It is pivotal for asset returns predictability that enables investors to identify excess

return yielding investment strategies (Hellstrom et al.).

5. It provides access to a wider range of financial products at fair prices (Ho, Lean, Vieito,

& Wong, 2013).

There are three distinct groups of financial market observers that are interested in exploring

the relationship between stock exchange mergers and subsequent stock returns

performance:

1. Scholars seek to gain insights into time-variation of asset returns under various

conditions and a given impact of certain events.

2. Practitioners and investors who are trying to capitalize on the market inefficiencies.

3. In contrast, market regulators are established in order to maintain and enhance

informational efficiency (i.e. pricing fairness) of the securities markets (Khan & Vieito,

2012).

4 Tobin, 1982; Wurgler, 2000; Durnev et al., 2004.


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Most recent academic literature on the informational efficiency recognizes time-variation in

efficiency. These papers acknowledge that various financial markets can exhibit varying

degrees of weak-form market efficiency over time5 (Hellstrom, Liu, & Sjogren). A significant

number of researchers are dedicated to identifying conditions that promote market

efficiency. “Potential factors leading to departures from market efficiency include the

characteristics of the market microstructure, limitations to arbitrage, psychological biases

among investors, noise trading, and market imperfections. The degree of market efficiency

is further likely to evolve over time due to changes in market conditions, macro institutions,

market regulations, and information technologies” (Hellstrom, Liu, & Sjogren, p. 5).

Theoretical studies of the post-merger information efficiency and empirical research on the

metrics believed to be correlated with the efficiency identify both positive and negative

effects, while the net result is uncertain.

Potential positive impacts of consolidation on market efficiency include:

1. Mergers result in compatible trading systems that are likely to be an efficiency-

enhancing arrangement: financial institutions face lower access costs to maintain

connections to fewer standardized trading platforms (McAndrews & Stefanidis, 2002).

2. Harmonized trading forms reduce costs of cross-border trading, luring in new capital

that generates higher trading volumes, which are associated with deeper markets and,

consequently, higher liquidity. High levels of liquidity prevent large price fluctuations

in the absence of new market information (Pagano, 1989).

3. A parallel quotation of the same security on different domestic stock exchanges is

often observed in fragmented financial markets. Concentrated order flow of the

unified trading platform can enhance price stability and provide better price discovery

(McAndrews & Stefanidis, 2002).

4. Mergers that stem from high competition result in lower bid-ask spreads as evidenced

by the consolidation trend of the regional stock exchanges in the U.S.A.

5 See the following references: Gu & Finnerty (2002); Lagoarde-Segot (2009); Kim, Shamsuddin, & Lim (2011);

Chuluun, Eun, & Kilic (2011).


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5. A common trading venue and a clearing system give rise to economies of scale6 effect.

Consolidated stock exchange might pass the resulting saving on to investors,

decreasing their direct trading costs. “Indirect costs are likely to decrease since the

ease of trading is expected to improve due to uniform trading and clearing systems”

(Hellstrom, Liu, & Sjogren, p. 5). This reasoning supports the network argument7:

lower trading costs lead to a higher concentration of market participants in one

trading venue. Growing numbers of trading parties results in a bigger pool of potential

investors for firms (e.g. for an IPO/SPO), smoother pricing, and a higher scrutiny of

valuation and, hence, lower likelihood of deviations from the intrinsic value. Thus,

market efficiency is expected to improve.

6. Gains from merger can also come from economies of scope8 when stock exchange

mergers with a derivatives or commodity exchange.

7. Build-up of the vertical integration via acquisition by the stock exchange of financial

service companies (for example brokers and post-trade service providers) could

increase profit margins of the exchange, leading to reduced trading costs9.

Potential negative impacts of consolidation on market efficiency include:

1. Consolidation may result in higher direct fees for market participants owing to

reduced competition. In this instance, the unified stock exchange exerts its monopoly

power (Hellstrom, Liu, & Sjogren).

2. Reduced competition may also lower the degree of innovation and improvement of

exchange services (Ho, Lean, Vieito, & Wong, 2013).

It appears that theoretical studies and empirical inferences about variables closely

associated with market efficiency (such as bid-ask spread and liquidity) favour the

proposition of improved efficiency in the post-merger period. However, the following

6 The cost advantage that arises after the merger of two companies due to an inverse relation between per-unit fixed production costs and produced quantity. Economies of scale are achieved when two similar trading venues merge (for example two stocks exchanges, two derivatives exchanges, two commodity exchanges). 7 See the following reference: Mendelson (1987). 8 The cost advantage that arises from the concept of diminishing average production costs as a result of an increase in the number of different goods produced. Economies of scope are achieved when two dissimilar trading venues merge (for example stocks and derivative exchanges, derivatives and commodity exchanges). 9 Goldberg et al. (2002) explain how the consolidation of stock exchanges and clearing or settlement agencies in Europe could allow to increase the stock liquidity and decrease the fees charged by the stock exchanges.


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literature review demonstrates that the evidence of an actual impact of stock exchange

mergers on market efficiency is mixed, as there is a limited number of theoretical studies

and the amount of empirical research has just began picking up in recent years.

1.3 Research question

The main research question that I investigated in this study is “Has the merger of MICEX and

RTS resulted in a change in the equity market efficiency?”.

1.4 Contribution

All previous research on the effect of the trading venues’ mergers on the market efficiency

focused solely on the official merger date. Consequently, researchers broke down the full

sample period into the pre-merger and the post-merger periods based on the date of the

legal merger of the two entities. Such an approach is unrealistic and inflexible: all

developments that are theoretically responsible for the changes in the market efficiency are

assumed to occur concurrently after the legal merger and no adjustment period is

provisioned for these developments to gain full effect, implying immediate repercussions

for the informational efficiency. This research contributes to the existing literature by

relaxing these assumptions: the focus of this paper is not the legal merger of the venues,

but the subsequent infrastructural changes and precise timing of their implementation. The

most influential developments identified in this study are integration of the markets,

implementation of the T+2 settlement cycle, and introduction of the Central Securities

Depository (CSD).

Furthermore, I extend the methodology used for examining the change of the market

efficiency in the stock exchange mergers. I explore the profitability of the trading strategies

based on the moving average rules and examine the determinants of the trading volume

and value.

1.5 Organization of the research

The rest of the research is organized as follows. Chapters 2 discusses the existing literature

on the topic in question. I outline the expectations regarding the results of this study given

the empirical evidence of the previous research. The methodology used in the previous

studies is examined in order to identify the most relevant tests for this paper and find the


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gaps which could serve as a basis for contribution of this study. The final section of the

Chapter 2 is devoted to the overview of MOEX and the details of the merger.

Chapter 3 presents the data used in the study and its sources. I discuss the reasons for data

segmentation into three samples and provide a summary characteristics of every group. The

next section offers an extensive overview of the infrastructural changes introduced by

MOEX after the merger. This overview plays a critical role in the study as it provides

guidelines for specifying the appropriate sample period. Finally, methodology of the

research is presented.

Chapter 4 discusses the main findings of the research. Chapter 5 concludes and outlines the

avenues for the future study.


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Chapter 2: Review of the literature, methodology and the MOEX case study

2.1 Literature review

In this section I review literature that investigates empirical evidence of the impact of the

stock exchange mergers on the market efficiency. As becomes evident, the amount of

research dedicated solely to this relationship is rather scarce. One of the reasons is that

researchers require sufficient data for robust conclusions. Thus, sufficient time should pass

since the recent mergers to allow for them to be studied. It is important to keep in mind

that it takes time for exchanges to become fully integrated after the merger by forming

standardized trading platforms, phasing in uniform governance, and harmonizing trading

and listing rules. Studying the changes in the market efficiency before all these innovations

are completed is inconsequential. Studies that examine different aspects of the market

efficiency, such as liquidity, volatility and a bid-ask spread are more ubiquitous.

There are a number of metrics that are found to be correlated with the market efficiency

and, thus, offer some indirect guidance. Liquidity is generally positively correlated with

information efficiency10. Under the efficient market hypothesis (EMH), lower volatility is

associated with smoother price discovery and, thus, higher efficiency. Several other metrics

are also known for contributing to market efficiency by diminishing the impact of market

imperfections: for example, narrower bid-ask spreads, higher trading volumes, a larger

number of market participants and lower trading costs. Consequently, I gathered some

evidence on these metrics to broaden the review.

2.1.1 Informational efficiency

Charles, Darné, Kim, and Redor (2014) conducted the most comprehensive analysis to

examine the positive and negative impacts of stock exchange mergers on the informational

efficiency of the markets: their sample included 31 mergers of 37 stock exchanges. They

found significant evolution of the efficiency of stock prices: most observations revealed a

high impact of the merger on the efficiency. Evidence tended to be consistent with the

market power theory, as increases in efficiency were less frequently observed than the

opposite effect. Furthermore, they found that any positive effects of the mergers on

10 See Chordia, Roll, & Subrahmanyam (2008) and Chung & Hrazdil (2010). See Tetlock (2007) for the evidence of the reverse relationship.


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efficiency were short-lived and decayed over time. The authors concluded that there is a

ground for the concerns of the critics of the trading venue mergers.

Charles et al. (2014) also examined cross-sectional relationships between specific types of

mergers and the impact on the market efficiency. The research concludes the following:

1. Increases in the efficiency of stock markets are more prevalent in developed countries

than in developing countries.

2. Small stock exchange mergers may be too small to significantly increase the efficiency,

while large stock exchange mergers show a strong tendency to increase the efficiency.

3. Domestic mergers between two stock exchanges tend to have a negative impact on

efficiency.

4. Cross-border pure stock exchange mergers lead to lower efficiency and this is

observed more often than for comparable domestic mergers.

5. In the merger of two exchanges in which the degrees of efficiency are distinctively

different, there is a spill-over of inefficiency: both targets’ and bidders’ markets seem

to be less efficient after the consolidation takes place.

6. Evidence from the cross-border diversifying stock exchange mergers indicates lower

informational efficiency. However, domestic diversifying mergers produce higher

efficiency in more cases than domestic focusing mergers.

Khan and Vieito’s (2012) exploratory paper was among the first to examine the impact of

stock exchange mergers on informational market efficiency. They explored the merger of

the Portuguese stock exchange and Euronext that gave rise to Euronext Lisbon exchange in

2002. The authors maintained that the Portuguese equity market was weak-form inefficient

in a pre-merger period, and that the findings indicated a mixed evidence of improvements in

the post-merger period. It appeared that the merger had a significant effect on the

efficiency only in the long-run.

Hellstrom et al. observed the effects of the OMX merger that unified trading platforms of

Nordic and Baltic stock exchanges on weak-form market efficiency. Their findings suggested

that the average firm on the merger market enjoyed higher information efficiency. The

distribution of the consolidation effects was, however, asymmetrical across the firms:


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1. There was evidence of a flight to the liquidity effect: relatively larger firms located on

relatively larger markets appreciated improved efficiency, while the opposite was true

for relatively smaller firms located on smaller markets. Also, relative positions of firms

in the firm size distribution pre- and post-merger mattered.

2. Firms that were more visible to (i.e. traded by) foreign investors received a relatively

lower improvement in the efficiency than firms that were less visible in the pre-

merger period.

Ho et al. (2013) examined the effect of Euronext stock exchange merger with Amsterdam

Stock Exchange, Brussels Stock Exchange, Paris Bourse, and Bolsa de Valores de Lisboa e

Porto, in terms of market efficiency improvement. They found that “the Bolsa de Valores de

Lisboa e Porto is more efficient in the post merger period. For the rest of stock exchanges,

the merger process did not change the inefficient markets to efficient but we can see some

improvements of efficiency” (Ho, Lean, Vieito, & Wong, 2013, p. 20).

Jazepcikait (2008) conducted event-studies to analyze time-varying market efficiency for the

OMX Group merger with three Baltic stock exchanges (Vilnius, Riga, Tallinn). The author

drew a conclusion that there was no material improvement in the market efficiency: the

markets stayed weak-form inefficient in the post-merger period.

2.1.2 Indirect measures of efficiency

Dorodnykh and Youssef (2012) explored the effects of the stock exchange consolidation on

volatility of domestic asset returns, and highlighted the impact of the degree of the pre- and

post-merger integration between the trading venues on this relationship. They focused on

three business cases: Euronext, Bolsasy Mercados Espanoles (BME), and Swedish-Finnish

financial services company (OMX). The findings indicate progressively diminishing volatility

in each of the integrated stock markets, while the rate of the decline is a function of

economic characteristics of the engaged markets and their degree of integration. The

authors maintain that the stock operators established integration links via cross-

membership and cross-listing schemes before the consolidation. Hence, “the mergers

among stock exchanges can be considered as the rational consequences of the high internal

co-movements between involved markets” (Dorodnykh & Youssef, 2012, p. 1). They also

presented evidence of an immediate drop of volatility in the mergers that are based on pre-


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merger integration links. It takes several years before the volatility decreases for

unintegrated markets as it takes time to reach full integration.

Nielsson (2009) evaluated the effects of the Euronext stock exchange merger on listed firms.

Euronext was formed as a result of consolidation of stock market operators in Amsterdam,

Brussels, Lisbon and Paris. Specifically, he looked into the repercussions for the liquidity of

the combined market. The evidence revealed assymetric liquidity benefits from the merger:

most gains accumulate to big firms with foreign sales. “There is no clear evidence that the

Euronext merger led to decreased liquidity for any types of firms, which suggests that the

merger may still be Pareto improving” (Nielsson, 2009, p. 37).

Pagano and Padilla (2005)11 found that consolidation of market operators produces

significant direct and indirect efficiency benefits. Their exploratory paper inspected the

Euronext stock exchange formed in 2002 by consolidating French, Dutch, Portuguese and

Belgian markets. The evidence indicates:

1. Decreased trading fees on two venues.

2. Narrower bid-ask spreads for three venues.

3. Increased trading volumes in three venues.

4. Volatility of the largest firms diminished across all markets.

The authors did not test the hypothesis of increased market efficiency directly, but their

findings provide an indication of possible improvement.

Kokkoris and Olivares-Caminal (2008) support the trend of mergers in European financial

markets as they argue that the consolidation gives rise to lower trading costs due to

economies of scale and synergies. One of the by-products of these effects is higher

accessibility of financing capital to corporates.

2.2 Expectations

Based on the presented literature review I had mixed expectations regarding the outcome

of the tests of the informational efficiency of the Moscow Exchange stock market. While

Charles et al. (2014) and Jazepcikait (2008) found a reduction and no change in efficiency

respectively, evidence from Khan and Vieito (2012), Hellstrom et al. and Ho et al. (2013)

11 See also (Steil, 2001)


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indicates an increase in efficiency, despite the fact that it manifests itself only in the long-

run and is asymmetrically distributed between the traded companies and the merged

venues. Indirect studies of market efficiency tend to indicate higher levels of efficiency via

lower volatility, higher liquidity, lower fees, thiner spreads, and higher trading volumes.

Ambiguous empirical evidence warranted further research.

2.3 Methodology review

In Exhibit 1 I summarised the methodology used in the previous studies of the relationship

between the stock exchange mergers and the market efficiency. It becomes evident that

tests put forward by Khan & Vieito (2012) dominate the methodology. Hellstrom et al.

referred directly to the methodology in Khan & Vieito (2012) and suggested an extension by

conditioning tests on other covariates. This enabled them to study asymmetric merger

effects on information efficiency. The study by Ho et al. (2013) has a significant overlap with

Khan & Vieito (2012), while Charles, Darné, Kim, & Redor (2014) and Jazepcikait (2008)

chose to focus on one, rather than a set of tests. The following section discusses the tests

that are most widely used in the existing literature and serves as a basis for forming

methodology for this research.

2.3.1 Time series OLS regression

The efficient market hypothesis (EMH) relies on the premise that assets are priced fairly

since prices incorporate all available information. This implies that investors can only earn

risk-adjusted rates of return; they cannot systematically earn abnormal returns as securities

are traded at fundamental intrinsic levels (Khan & Vieito, 2012). Thus, systematic abnormal

returns can be interpreted as market inefficiency.

Most asset pricing models (APM) are single factor models in which a broad market portfolio

of assets is the only factor which explains the variation in stock returns. Hence, the APM can

be used to generate expected returns. The difference between expected and actually

realized returns produces abnormal returns. Consequently, if market returns fully explain

the variation in the stock return, there are no abnormal returns and the efficient market

hypothesis (EMH) holds, and thus the market is efficient. The null hypothesis is that the

market returns fully explain the variation in daily stock returns (Khan & Vieito, 2012).


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The OLS12 regression is used under this null hypothesis to test the broad market index

explanatory power in terms of variance in stock returns. The explanatory power of a single-

factor APM is investigated by running a time series regression for each stock in the index

individually, using daily returns as the dependant variable, and market index returns as the

independent variable. Next, model generated expected normal returns are compared with

the realized returns to estimate abnormal returns. Khan & Vieito (2012) also run the Chow

test to explore the presence of any structural breaks in the post-merger period: “whether

the impact of market returns (magnitude of regression coefficients) is significantly different

after the merger” (Khan & Vieito, 2012, p. 178). The null hypothesis in the Chow test is the

stability of the regression coefficients.

2.3.2 Tests of serial independence

Under the EMH, “stock prices move in a random manner, lacking any systematic patterns or

dependencies” (Khan & Vieito, 2012, p. 173). Price is an unbiased estimate of the intrinsic

value, errors in pricing are random and, hence, the likelihood of the asset being under-

priced or over-priced is equal. Such price behaviour precludes investors from constructing

trading strategies that earn abnormal returns, as there is no mechanism of predicting future

prices. That is, the price movements are random and are said to follow a random walk. Any

systematic patterns and dependencies in the time-series of the prices refute market

efficiency under the EMH (Khan & Vieito, 2012). The null hypothesis that stems from this

theoretical development is that the successive price changes are independent.

One of the most widely recognized tests used to analyse the random walk theory is a serial

correlation test. In most studies 5-day, 10-day and 20-day lags were used to investigate

whether any dependence exists in the stock returns.

2.3.3 Runs test

The runs test is a non-parametric statistical procedure that examines whether returns are

from a random process. The run is defined as a series of increasing prices or a series of

decreasing prices. This is a test for independence between consecutive price changes. In a

random process, the likelihood that the price at time t+1 is larger or smaller than the price

at time t follows a binominal distribution, which provides the expected number of runs. The

12 Ordinary least squares.


13

test compares the observed and expected number of runs: if prices follow a random walk, a

necessary EMH condition, then successive changes in price must be independent.

2.3.4 Unit root rest

The unit root test employs an autoregressive model to explore non-stationarity of a time

series variable. The null hypothesis is defined as the presence of the unit root in the data

series. There are a number of unit root tests that can be performed. Following Khan & Vieito

(2012), I used the Kwiatkowski, Philips, Schmidt, & Shin (1992) test.

2.3.5 Multiple variance ratio test

The RWH implies that the variance ratios should be equal to the unity. If that is the case,

than there is no systematic pattern and price changes are independent. Accordingly,

successive stock returns follow a martingale difference sequence (i.e. random walk). Khan &

Vieito (2012) used the multiple variance ratio test performed by Chow and Denning (1993)

since they argued it was superior to the single variance ratio test used by Lo and MacKinlay

(1988). “Single variance ratio tests (both parametric and nonparametric ones) fail to control

the joint-size and are associated with a large probability of Type-1 error13” (Khan & Vieito,

2012, p. 183).

2.3.6 Generalized spectral shape test

An additional test for return predictability can be drawn from the paper by Charles et al.

(2014), who used a statistical test that detects both linear and nonlinear dependence

present in financial time series – the generalized spectral shape (GSS) test. “The GSS test is a

generalized version of the spectral shape test of Durlauf (1991), constructed based on the

observation that the spectral density of a martingale difference sequence (MDS) is flat”

(Charles, Darné, Kim, & Redor, 2014, p. 13). One of the authors of this research had

previously demonstrated that this test exhibits desirable properties in small samples14.

2.4 Case study under the review

2.4.1 Overview of MOEX

Moscow Exchange (MOEX) runs Russia’s dominant public markets in all trading segments:

equity, fixed income, derivatives, foreign exchange (FX), and money market. It also owns

13 Incorrect rejection of a true null hypothesis. 14 Charles, A., Darné, O., Kim, J.H. (2011). Small sample properties of alternative tests for martingale difference hypothesis. Economics Letters, 110, 151-154.


14

and operates the central securities depository (CSD) and the largest clearing house.

Additional services rendered include provision of the market data, software solutions and

technology services. The exchange is ranked among the top 20 global exchanges for cash

equity trading by the market capitalization and top 10 for fixed income trading by trading

value. It is also one of the leading trading venues for the exchange-traded derivative

contracts by the number of contracts traded.

MOEX was formed in December 2011 following the merger between two leading exchanges

MICEX and RTS. MICEX historically developed as a FX trading platform and later branched

out into the securities and commodities niches. RTS was developed as derivatives-focused

trading venue. The merger gave rise to a vertically integrated. “This combination created a

vertically integrated public trading market across most major asset classes” (Moscow

Exchange, 2).

MOEX plays a fundamental role in the advancement of the Russian financial sector. It

stimulates a transparent price discovery for the assets, provides a full range of post-trading

services. Legal entities and individuals, residents, and non-residents have an access to the

markets; market data dis distributed globally. Market participants also have an access to the

standardized OTC derivatives market with a central counterparty trade (CCP) execution

system. The clearing house that acts as a CPP enables trading participants to utilize their

assets more efficiently (Moscow Exchange, 1).

2.4.2 MICEX and RTS merger

The merger was complete in December 2011. Upon consummation of the merger, the

company became an open joint stock company (OJSC Moscow Exchange MICEX-RTS, 2013).

A number of key deal drivers can be identified:

1. The merger was contemplated in order to complement existing businesses of the

individual exchanges. The combined company was expected to benefit from the

operational and financial synergies arising from the economies of scale and scope.

2. The combination of two incumbent players and formation of the dominant trading

venue was a cornerstone of the Russia government’s initiative to turn Moscow into a


15

global financial centre15. The Central Bank of Russia (CBR), a financial sector regulator

and a major shareholder in MICEX, advocated the deal.

3. The combined company could afford large infrastructure innovations that required

considerable investments. Furthermore, MOEX increased its capital ratios that were

critical for the regulatory and risk-management perspectives.

4. The merged company was perfectly positioned for an IPO, which was favoured by

multiple financial investors of the exchanges, who were seeking to exit an investment.

The IPO was consummated in February 2013.

The total consideration paid was USD 1.04 billion16. USD 276 million17 was paid in cash to

the RTS shareholders who sold 35% of their holdings to MICEX. The balance of the holdings

were converted into ordinary shares of MICEX at a fixed ratio. In the merger press release,

MOEX declared that one of the merger’s motives was to expand the market efficiency:

“Through the Merger, the Companies have sought to create a vertically integrated public

trading market across all major asset classes that provides investors and other market

participants with greater efficiencies. These efficiencies are being achieved, for example,

through the integration of complementary business models and of liquidity pools, the

merger of settlement depositaries facilitating the creation of the CSD, the formation of a

unified clearing and collateral management system for market participants, and establishing

common indices and tariffs” (OJSC Moscow Exchange MICEX-RTS, 2013). The next chapter

examines the methodology that was employed in order to find whether MOEX succeeded in

its initiative to increase the market efficiency.

15 http://www.mfc-moscow.com/index.php?id=41 16 RUB 33.54 billion as of December 19, 2011, www.oanda.com 17 RUB 8.88 billion as of December 19, 2011, www.oanda.com


16

Chapter 3: Sample data, sample period and methodology

3.1 Sample data

Stocks’ daily closing prices were obtained from the Thomson Reuters DataStream

database18 and were denominated in rubles. A proxy for a local Russian risk-free rate, a 3-

month MosPrime rate, was obtained from the Central Bank of Russia (CBR)19. The MICEX

index, denominated in RUB; the MSCI world price index, denominated in the local

currencies; the price of the crude oil dated Brent, US$/bbl.20; the RUB/USD FX rate; and the

Reuters commodities price index were also sourced from the Thomson Reuters DataStream

database using the monthly frequency. Trading value in RUB, and trading volume in trades

were obtained from the World Federation of Exchanges21 (WFE) database using the monthly

frequency.

Hellstrom et al. documented an uneven distribution of the merger effects among various

stocks. While larger firms appreciated improved efficiency, smaller ones saw a reduction in

efficiency. Furthermore, previously less traded stocks posted higher rates of improvement in

efficiency. Nielsson (2009) provided evidence that most liquidity gains accumulated to big

firms with foreign sales. Due to these findings that indicate that various types of stocks

experience merger effects on the market efficiency to a various extent, the sample of this

research was broken down into three groups in order to test the hypothesis of asymmetrical

distribution. Segmentation was based on the equity indices constructed by MOEX (see

Exhibit 2) and reflected inherent characteristics that were critical for this research such as

liquidity, capitalization, free-float, and rate of foreign investors’ participation.

The first group, hereafter referred to as the large-cap stocks, constituted the Blue Chip

Index22, which was an indicator of the market for the most liquid and capitalized issues of

the Russian stock market. The calculations were based on 16 stocks (consisting of 15 issuers

with one preferred issue admitted to the list). This sample was heavily dominated by the oil

and gas stocks (approximately 59% as measured by the capitalization) followed by the

financial stocks (approximately 18%) as evident from the Exhibit 3. From the Exhibit 4 it

18 https://forms.thomsonreuters.com/datastream/ 19 http://www.cbr.ru/eng/hd_base/?PrtId=mosprime 20 Oil barrel 21 http://www.world-exchanges.org/ 22 http://moex.com/s919


17

follows that an average free-float factor was 42% and top 5 constituents dominated the

index with a 62% weight. Blue chips contributed approximately 63% of the Russian equities

market capitalization, and top 10 most traded companies generated 83%23 of the total

trading volume.

The second group, hereafter referred to as the mid-cap stocks, constituted the MICEX and

RTS indices, which were calculated in rubles and US dollars respectively. This group included

“the 50 most liquid Russian stocks of the largest dynamically developing Russian issuers with

economic activities related to the main sectors of the Russian economy presented on the

Exchange24”. These 50 stocks included 15 blue chip stocks. Thus, the size of the mid-cap

stocks sample boiled down to 34 stocks or 32 issuers. Sector diversification among these

stocks increased: oil & gas industry accounted for approximately 22% weight in the index,

on par with the metals & mining sector, followed by the financial stocks with approximately

15%. From the Exhibit 5 it follows that average free-float factor decreased to 34% and top-5

constituents contributed a 40% weight. Mid-cap stocks contributed an approximately 20%

of the Russian equities market capitalization.

The third group, hereafter referred to as the small-cap stocks, constituted the Second-Tier

Index, which was based on the prices of the next 50 stocks (45 issuers) that followed the top

50 list. In terms of sector diversification, this index was heavily dominated by electric

utilities that accounted for approximately 47%, followed by metals & mining with an

approximately 12% weight (see Exhibit 3). As illustrated by the Exhibit 6, the average free-

float factor was down to 27% and the concentration among the top 5 constituents was 25%.

Mid-cap stocks contributed approximately 3% of the Russian equities market capitalization.

An important distinction between the small-cap stocks and the top 50 stocks was that the

former were traded with full prefunding, whilst the later required only partial collateral25.

Partial prefunding allows for more flexibility in cash management and decreases

opportunity costs of the traders.

23 World Federation of Exchanges 24 http://moex.com/a1577 25 Partial collateral enables market participants to execute transactions with cash balances of their trading accounts below the transaction consideration and deliver the full amount of the payment at the end of the second day (T+2 settlement cycle).


18

All three samples were refined by excluding stocks without trading history over the entire

sample period, stocks with discrete price changes (non-continuous, stepwise price changes),

and stocks with anomalous price changes (spikes). Furthermore, due to the methodology

(discussed in the corresponding section) applied to the large-cap stocks, one stock was

excluded from the original sample since it was not possible to take a short position in it

(MOEX: AFKS). The final sample included 14 large-cap stocks (out of 16), 23 mid-cap stocks

(out of 34), and 41 small-cap stocks (out of 50). The total number of stocks traded on MOEX

is 317,26 including common stocks, preferred stocks, and depository receipts (DRs). The

aggregate sample included 78 stocks or approximately 25% of all traded equities.

Table 1: Sample refinement

Sample Total number of stocks

Number of excluded stocks

Number of stocks used in the tests

% of stocks excluded

Large-cap stocks 16 2 14 13%

Mid-cap stocks 34 11 23 32%

Small-cap stocks 50 9 41 18%

Total 100 22 78 22%

MosPrime rate (Moscow Prime offered rate) is fixed by the National Foreign Exchange

Association (NFEA) in partnership with Thomson Reuters. MosPrime is a reference rate

based on the offer rates of Russian ruble deposits, as quoted by the leading participants of

the Russian money market to the first class financial institutions27. Following Lagoarde-Segot

& Lucey (2006), who used the average yield of the 3-month US Treasury bill as a proxy for

the risk-free interest rate, this paper used a 3-month MosPrime rate.

3.2 Sample period

The exact timing of the introduction of the most critical infrastructural changes was

identified. This information served as a basis for construction of the merger timeline. The

timeline (see Exhibit 7) enabled the sample period to be chosen precisely. MICEX, a stock

exchange with a focus on equities, fixed income securities, and foreign exchange trading,

merged with RTS, a derivatives trading venue, in December 2011. The IPO of the combined

firm was scheduled for the first half of 2013, which served as a catalyst for an introduction

of innovative infrastructure elements that were bound to increase the accessibility of

26 http://moex.com/s797 27 http://www.cbr.ru/eng/hd_base/mosprime.asp


19

Russian securities to the market participants, particularly to the foreign institutional

investors.

Exhibit 8 illustrates how MICEX and RTS markets were integrated under a single MOEX roof.

RTS was dominant in derivatives trading (see Exhibit 9) and had an approximately 10% share

of the local28 equities trading volume by the number of trades (see Exhibit 10). The chart in

the Exhibit 12 shows that before the merger almost all equities were traded concurrently on

both venues as they had a similar number of listed stocks. Thus, the merger of the

exchanges and the subsequent integration of the markets created unified liquidity pools for

the traded securities. MICEX Main Market29 was complemented by Standard30 and Classic31

markets from RTS, while RTS System of Guaranteed Quotes (SGQ) was discontinued as it

duplicated MICEX Main Market. Following this, MICEX derivatives platform was

discontinued in September 2012. Unification of the trading platforms is considered to be the

cornerstone of the merger as it is deemed to have the most profound effect on the

efficiency via eliminating fragmentation and increasing liquidity.

Furthermore, the ripple effect from the unification of markets propagated through the stock

exchange, prompting corresponding changes in the post-trade infrastructure. This

integration process of the trading platforms was mirrored by the similar developments in

the back-office, which is accountable for the post-trade services such as clearing and

settlement. Each trading venue had a clearinghouse and a depository that were also

merged. These changes could result in the economies of scale and scope, prompting better

services at lower costs for the market participants, or in the case of the monopoly power,

produce contrary results.

Post-trade infrastructure unification enabled MOEX to introduce long-awaited changes. The

T+2 settlement cycle was launched for the top 15 stocks (large-cap stocks or blue chips) in

March 2013 after the IPO of MOEX took place in February 2013. MOEX defined the objective

28 It is important to note that a significant share of Russian equities is traded offshore on LSE IOB (see the Exhibit 11). 29 MICEX Main Market accounted for approximately 97% of the cash equities total trading volume (RUB) in 2014. 30 Standard market was discontinued in May 2014. 31 Classic market was later renamed into Classica. Overview: the market does not require full advance depositing of assets; market participants may choose settlement day and terms and settle in a foreign currency. The trading volume became insignificant in 2014 (approximately 0.01% of the total).


20

of this project as follows: “a trading system with partial collateral and deferred settlement

of trades on T+2 will make Russian assets more accessible for foreign investors, enhance

transaction efficiency for domestic brokers due to lower funding costs, and create

opportunities for improving cash management and streamlining their business processes

(HFT)32.” In July 2013 additional 35 stocks (mid-cap stocks) were transferred from the

incumbent T+0 settlement cycle. Finally, in September 2013, T+2 was fully operational for all

equities. Whilst the T+2 was phased-in stepwise for different categories of stocks, it was in a

dry run until becoming fully operational in September 2013 when most of the trading

volume started to flow through this settlement cycle (see Exhibit 13). Thus, the starting

point of the post-merger period sample is September 2013. The end of the post-merger

sample period is November 2014 and the sample includes 325 daily observations (see

Exhibit 7). Following this logic, the pre-merger period includes 325 daily observations before

the legal merger in December 2011, starting in October 2010. Thus, this research makes a

provision for a transition period between the pre-merger and the post-merger sample

periods that span from January 2012 to August 2013. This is the period when most of the

innovations bound to have an impact on efficiency were introduced. T+2 and CSD (discussed

next) were the two main factors shaping the sample period. The idea behind the exclusion

of the transition period from the main pre- and post-merger samples is to make sure that

the factors responsible for varying efficiency fully manifest themselves.

The second most critical innovation after the T+2 was the introduction of the Central

Securities Depository (CSD). Before the CSD was launched in March 2013, global custodians,

and not their clients, were recognized as the ultimate beneficiaries under the then

prevailing legal framework. That created an inefficient ‘spaghetti’ system with multiple

layers of intermediaries that slowed the settlement process and increased transacting costs.

Launching the CSD enabled foreign investors and global custodians to open nominee

accounts with local custodians in Russia and yielded the following benefits:

1. Transacting costs decreased due to new fixed settlement fees (previously registrars

charged high bps33 fees).

32 HFT stands for high frequency trading. Source: http://moex.com/s688 33 Basis points.


21

2. Settlement cycles shortened due to the defragmentation of the post-trade

environment.

3. Settlement became a more straightforward process with elimination of the ‘spaghetti’

system.

4. Centralized location of settlements decreased risks of market participants.

5. Finality of settlement was guaranteed.

6. Foreign nominee accounts enabled investors to participate directly in corporate

events.

7. Electronic Data Interchange (EDI) decreased the time required for the settlement cycle

and the probability of errors.

8. Securities ownership became more transparent.

One particular benefit of the CSD introduction warrants a separate note. “Rule 17f-7 of the

Investment Company Act of 1940 provides that registered investment companies may place

or maintain its foreign assets with an eligible securities depository.”34 This rule effectively

prevented US institutional investors (for example hedge funds, pension funds and asset

managers) from investing directly into local equities, since Russia had no eligible securities

depository. US funds used to gain exposure to local equities via DRs, equities listed on LSE,

and ETFs. CSD met the requirements of the Rule 17f-7 allowing US investors to trade local

equities directly and at a lower cost. Over the longer term, the World Federation of

Exchanges expects CSD to enhance liquidity and lower settlement costs.35 The post-merger

sample period should reflect the benefits of the CSD as it started in September 2013, while

CSD had been operational since March 2013.

There are important developments that occurred after the starting point of the sample

period. At the end of September 2013, an array of global banks started offering Direct

Market Access (DMA) to the Russian market (see Exhibit 14). This innovation was intended

to decrease foreign investors’ costs and provide a better market access for algorithmic and

high frequency traders (HFT). These investors were expected to employ trading strategies

that previously were not exploitable, which could result in a higher market efficiency. The

National Clearing Centre, a subsidiary of MOEX, became qualified as the Central

34 https://www.nsd.ru/en/about/rule_17f_7/ 35 http://www.world-exchanges.org/news-views/russia-establishes-central-securities-depository-csd


22

Counterparty (CCP) in October 2013 and was further capitalized in January 2014. A well-

capitalized CCP considerably reduced a market-wide counterparty default risk and released

capital otherwise allocated to provisions and reserves (for example Russian banks were able

to use reduced risk ratios required of CCP in calculating their capital adequacy ratios and the

credit requirements to the qualified CCP are calculated with a 5% risk ratio instead of 20% or

100% as for other counterparties36). The full list of innovations introduced by MOEX is

presented in the Exhibit 15.

All of these innovations were expected to streamline the workings of the markets and lower

transaction costs. Specifically, MOEX targeted foreign institutional investors when

introducing the aforementioned infrastructural changes. MOEX reported an approximately

10% foreign institutional participation in trading in the first half of 2011 and approximately

45% in the first half of 2014 (see Exhibit 16). Average daily trading volume (ADTV) of the

post-merger period (September 2013 – November 2014, 15 months) reflected the growing

presence of the foreign investors, posting a 12% growth versus a 15-month period prior to

September 2013 (see Exhibit 17). Furthermore, following the transition to T+2, trading

volumes growth on MOEX exceeded the growth of DRs traded on LSE by approximately 2-

fold in 9 months 2014 (Exhibit 18). Trading volume growth and higher foreign investors’

participation rate were achieved on the back of the international sanctions37 imposed on

Russia in March 2014. The Exhibit 19 provides evidence that foreign investors38 generally

purchase only liquid, well-known stocks with high trading volumes, while their purchases of

second-tier stocks are irregular.

3.3 Methodology

The concept of the random walk is the foundation of the market efficiency theory. Firstly, a

linear regression was employed to investigate whether there was a significant change in the

trading volume and value in the post-merger period. Next, three tests of the random walk

hypothesis (RWH) were performed: the serial correlation analysis, the unit root test and the

36 http://moex.com/n4168/?nt=201 37 The spike in the ADTV in March 2014 is a large sell-off that marks the first round of sanctions (Exhibit 17). 38 MOEX statistics in Exhibit 19 distinguish between Cypriot and non-Cypriot foreign investors. Most of the Cypriot investors are effectively considered to be local investors as many of them prefer to set up trading accounts in Cyprus due to favorable taxation regime and other benefits. The statistics reflect this: Cypriot investors trade a wider range of stocks compared to other foreign investors, being better informed about the second-tier stocks as local investors would.


23

variance ratio test. Finally, the profitability of the trading strategies based on the moving

average rules was tested in order to document any changes in the weak-form efficiency of

the equity market.

3.3.1 Effect of the post-merger infrastructure innovations on the trading volume and value

As it follows from the previous dicussion, most of the innovations introduced by MOEX were

intented to increase investability of the local assets by streamlining the access to the

markets. Infrastructure advancements were intended to increase the investor base.

Specifically, efforts were focused on harmonizing the local trading and post-trading

environment with the global standards in order to bring in large foreign instititutional

investors. The total equity market trading volume and value are the metrics that would have

been affected should foreign investors had decided to participate in the Russian equity

market more actively. Higher equity trading volume and an associated increase in the

liquidity could mark an improvement in the market efficiency. It could have benefited

further from the arrival of new, diverse, and global professional investors. Thus, I examined

the impact of the infrastructure innovaitons on the equity market trading value (RUB) and

volume (trades) controlling for other factors using a regression model.

The analysis was structured as an OLS regression. An alternative bias correction for the

robust variance calculation was specified39. The trading value (rubwfe) and volume (num), as

reported by the WFE, served as the dependent variables. The explanatory variable took a

form of a dummy (dum) with a value of 1 in the post-merger period and 0 otherwise. The

sample period included monthly observations from January 2009 to November 2014. The

following control variables were included in the analysis: the MICEX index (r), the MSCI

world index (w), the price of the crude oil dated Brent (brent), the FX rate of the RUB/USD

currency pair (fx), and the Reuters commodities index (com). Two additional control

variables were estimated: the volatility of the MICEX index (volat) and the volatility of the

MSCI world index (wvolat). The first objective was to investigate the dymmy coefficient,

controling for external factors. The second objecive was to establish whether any of the

39 Stata: vce(hc3). Davidson, R., and J. G. MacKinnon. 1993. Estimation and Inference in Econometrics. New York: Oxford University Press.


24

control variables significantly affect the trading value and volume in the absence of the

dummy.

Logarithmic returns of every time series were used in the regression. Volatility was

approximated as the absolute value of the daily logarithmic return.

The following regression models were fitted:

(1) 𝑛𝑢𝑚 = 𝛽0 + 𝛽1𝑑𝑢𝑚 + 𝛽2𝑟 + 𝛽3𝑤 + 𝛽4𝑏𝑟𝑒𝑛𝑡 + 𝛽5𝑓𝑥 + 𝛽6𝑐𝑜𝑚 + 𝛽7𝑣𝑜𝑙𝑎𝑡 + 𝛽8𝑤𝑣𝑜𝑙𝑎𝑡

(2) 𝑟𝑢𝑏𝑤𝑓𝑒 = 𝛽0 + 𝛽1𝑑𝑢𝑚 + 𝛽2𝑟 + 𝛽3𝑤 + 𝛽4𝑏𝑟𝑒𝑛𝑡 + 𝛽5𝑓𝑥 + 𝛽6𝑐𝑜𝑚 + 𝛽7𝑣𝑜𝑙𝑎𝑡 +

𝛽8𝑤𝑣𝑜𝑙𝑎𝑡

where

(3) 𝑑𝑢𝑚 = {0 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑟𝑒 − 𝑚𝑒𝑟𝑔𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑: 𝐽𝑎𝑛𝑢𝑎𝑟𝑦 2009 − 𝐴𝑢𝑔𝑢𝑠𝑡 2013

1 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑜𝑠𝑡 − 𝑚𝑒𝑟𝑔𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑: 𝑆𝑒𝑝𝑡𝑒𝑟𝑚𝑏𝑒𝑟 2013 − 𝑁𝑜𝑣𝑒𝑟𝑚𝑏𝑒𝑟 2014

(4) 𝑣𝑜𝑙𝑎𝑡 = |𝑟|

(5) 𝑤𝑣𝑜𝑙𝑎𝑡 = |𝑤|

3.3.2 Serial correlation test

The test of the serial independence of the stocks returns is the most basic and presumably

widely used test to examine the RWH. Under the RWH, successive stock returns are

independent of each other. Thus, the autocorrelation test verifies whether this assumption

holds. The absence of independence in returns implies that “investors can predict the future

stock price changes and devise trading rules to earn abnormal returns” (Khan & Vieito,

2012).

This paper employed Breusch–Godfrey (BG) test (alternatively known as the Lagrange

multiplier (LM) test) for higher-order serial correlation in the disturbance. It is a likelihood-

based two-sided test. The test was performed for 50 lag orders (𝑝). The BG test examines

autocorrelation in the errors in a regression model. The test statistic is derived from the

residuals of the model. BG test was preferred to the Durbin–Watson (DW) test, which is only

able to test an 𝐴𝑅(1) model, while BG examines the time series up to order 𝑝. The BG test is

deemed to be more powerful than the DW test. The null hypothesis is that no

autocorrelation of the stocks returns of any order up to 𝑝 is present.

(6) 𝐻0: {𝜌𝑝 = 0 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑝}


25

(7) 𝐻𝑎: 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 𝜌𝑝 𝑖𝑠 𝑛𝑜𝑡 𝑧𝑒𝑟𝑜

where 𝜌𝑝 is a serial correlation in the stock returns of order 𝑝.

The test statistic (LM) is 𝑛𝑅2 and it is asymptotically distributed as chi-square (𝜒2) with 𝑝

degrees of freedom. 𝑛 is the number of observations and 𝑅2 is the R-squared metric from

the regression.

3.3.3 Unit root test

Unit root test is another approach used to examine the random walk nature of the stock

prices. The presence of the unit root in the stock returns supports the RWH, implying

informational efficiency. 𝐴𝑅(1) process for a stock price at time 𝑡 (𝑋𝑡) with a liner time

trend 𝑐 has the following form:

(8) 𝑋𝑡 = 𝑐 + 𝛼𝑋𝑡−1 + 𝜀𝑡

where 𝜀𝑡 is a random error at time 𝑡 and 𝑐 is a constant. Given that |𝛼| < 1, the process is

stationary when

(9) 𝐸[𝑋0] =𝑐

1−𝛼

Otherwise, when 𝛼 = 1, the stock price follows a random walk (RW) which is non-

stationary. Unit root tests of the RW investigate the value of 𝛼 to draw conclusions whether

the stock returns are stationary or non-stationary.

The most widely known unit root analysis procedures are the augmented Dickey–Fuller test

and the Phillips–Perron test that examine whether the time series is non-stationary by

employing an 𝐴𝑅(𝑝) model. However, following Khan & Vieito (2012) and Lagoarde-Segot &

Lucey (2006), this paper used Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test. This test

differs from the aforementioned tests since it examines a null hypothesis of stationary stock

returns (unit root in the stock returns is the alternative hypothesis). KPSS test distinguishes

between two approaches: the trend stationary model or the level stationary model of the

stock returns process.

The regression model for the KPSS test has the following form:

(10) 𝑋𝑡 = 𝑐 + 𝜇𝑡 + 𝑘 ∑ 𝜉𝑖 + 𝜂𝑡𝑡𝑖=1


26

where 𝜇𝑡 = 0 for the level stationary model and is different from zero for the trend

stationary model; 𝜂𝑡 is stationary and 𝜉𝑡 are i.i.d.40 (0,1).

The following null and alternative hypotheses are testes:

(11) 𝐻0: 𝑘 = 0 (Trend stationary process)

(12) 𝐻𝑎: 𝑘 ≠ 0 (Integrated process)

The residual partial sum of the residuals 𝜂𝑡 is estimated as follows:

(13) 𝑆𝑡 = ∑ 𝜂𝑡𝑡𝑖=1

The test statistic is

(14) 𝐾𝑃𝑆𝑆𝑇 =∑ 𝑆𝑡

2𝑛𝑡=1

𝑛2𝜛𝑇2

There are two ways to estimate the test’s denominator – an estimate of the long-run

variance, computed from the empirical autocorrelation function (ACF). The original paper

made use of the Bartlett kernel, while later examinations proved that Quadratic Spectral

kernel yields more “accurate estimates of sigma-squared than other kernels in finite

samples” (Baum, 2006). Thus, the variance of the stock returns was weighted by the

Quadratic Spectral kernel. The automatic bandwidth selection procedure41 was used to

determine the maximum lag order for the test, which produced a single test statistic at the

optimal bandwidth. The alternative was to set the maximum lag order (either manually or

using a rule42) and perform the test for each lag. However, the automatic bandwidth

selection routine rendered it unnecessary to assess an array of the output statistics for

various lags. Furthermore, there is evidence that the combination of the Quadratic Spectral

kernel and the automatic bandwidth selection procedure produces the best results in the

small sample tests that employ the Monte Carlo simulation methodology (Baum, 2006).

40 Independent and identically distributed. 41 Newey, W.K. and K.D. West. Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61, 1994, 631-653. Hobijn, Bart, Franses, Philip Hans, and Marius Ooms. 1998. Generalizations of the KPSS-test for Stationarity. Econometric Institute Report 9802/A, Econometric Institute, Erasmus University Rotterdam. 42 Schwert, G.W. Tests for Unit Roots: A Monte Carlo Investigation. Journal of Business and Economic Statistics, 7, 1989, 147-160.


27

3.3.4 Variance ratio test

“Studies have shown that unit root tests do not uniformly detect departures from a random

walk, and are consequently insufficient in testing the weak form of the efficient market

hypothesis” (Lagoarde-Segot & Lucey, 2006). Individual variance ratio test of log of stock

prices is useful as an alternative tool to test the predictability of stock price changes. Lo and

MacKinlay (1988)43 suggested the heteroscedasticity-adjusted test that has favorable finite-

sample properties and is sensitive to autocorrelation. The single variance ratio test

investigates the independence of successive stock returns. It is based on the concept that if

the log of the stock price log (𝑝𝑡) follows a random walk, then the variance (𝜎2) of the stock

log return over the 𝑘 period is equal to 𝑘𝜎2 (Lagoarde-Segot & Lucey, 2006).

The null hypothesis for the stock log return 𝑟𝑡 = ln(𝑝𝑡) − ln(𝑝𝑡−1) is as follows:

(15) 𝐻0: 𝑟𝑡 = 𝜇 + 𝜀𝑡; 𝜀𝑡~𝑁(0, 𝜎2)

and the alternative hypothesis is 𝑟𝑡 is stationary and auto-correlated.

The sum of the returns is estimated for specific aggregation intervals 𝑞:

(16) 𝑟𝑡(𝑞) = 𝑟𝑡 + 𝑟𝑡−1 + ⋯ + 𝑟𝑡−𝑞+1

Therefore, under 𝐻0 it holds that

(17) 𝑉𝑎𝑟{𝑟𝑡(𝑞)}

𝑞𝑉𝑎𝑟(𝑟𝑡)= 1

The test statistic is

(18) 𝑉𝑄(𝑞) =𝛾0(𝑞)

𝛾0− 1

where 𝛾0 is a consistent estimator for 𝑉𝑎𝑟(𝑟𝑡) and 𝛾0(𝑞) for 𝑉𝑎𝑟{𝑟𝑡(𝑞)}

𝑞.

This research runs the variance ratio test using 10, 20 and 50 aggregation intervals (𝑞). The

test statistics are robust to arbitrary heteroskedasticity.

3.3.5 Moving average technical trading rules

Following Lagoarde-Segot & Lucey (2006), this paper employs technical trading rules based

on a Moving Average (MA) of the stock price to test whether the merger of two trading

43 Lo, A. and MacKinlay, A. C., "Stock market prices do not follow random walks: evidence from a simple specification test", Review of Financial Studies 1:1, 1988.


28

venues has an effect on the market efficiency. “The rationale for using technical trading

simulations in efficiency analysis is that the derived rules may pick up some of the hidden

patterns that are not detected by the linear models” (Lagoarde-Segot & Lucey, 2006).

Brock, Lakonishock, & LeBaron’s (1992) seminal paper on Techical Analysis (TA) ignited

academic interest in testing strategies widely used by practitioners. TA is based on the

assumption of trend-following. While there are numrous TA indicators designed to generate

trading signals, most of the signals conincide and only have minor timing differences (Ulku &

Prodan, 2013). Hence, this paper focuses on the most popular TA rule that proxies a pool of

short-term trend-following indicators – a Moving Average (MA) rule, which is very popular

among technical traders. This paper tests four technical trading strategies denoted as

MA(1,22), MA(1,50), MA(5,22) and MA(5,50). A trading strategy based on the moving

average is generally defined as 𝑀𝐴(𝑠, 𝑙, 𝑓) where 𝑠 and 𝑙 are estimates of the short and long

moving averages (𝑠 < 𝑙), respectively, and 𝑓 is the filter that produces noise-dampening

effect. 𝑀𝐴(𝑠) estimates a moving average for the stock price over 𝑠 days, while 𝑀𝐴(𝑙)

estimates a moving average for the same stock price over 𝑙 days. The interaction between

𝑀𝐴(𝑠) and 𝑀𝐴(𝑙) produces trading signals. Parameter 𝑠 that is set to 1 signifies the current

price of the stock, 𝑃𝑡.

MA(1,50) strategy was used in the Brock, Lakonishock, & LeBaron (1992) paper, while

MA(1,22) was used in the Ulku & Prodan (2013) paper. 22 days MA was chosen for three

reasons: firstly, it is widely used; secondly, it produces a sufficient number of signals for the

limited sample size employed in this study; thirdly, it has a documented out-of-sample

profitability track record (Ulku & Prodan, 2013). Two more variations of these trading rules

are included in order to test whether the results are sensitive to the choice of the specific

strategy. Longer time horizons of 150 and 200 days (Brock, Lakonishock, & LeBaron, 1992)

are impractical in this study due to the sample size of 325 observations. Furthermore, no

band around the MA is used so that noisy signals are not filtered out. “MA(n) is definded as

a simple (equal-weighted) linear moving average of a specified number (n) of past closing

prices” (Ulku & Prodan, 2013). 22 and 50 days are considered by traders to be the

approximate numer of trading days in one and two months respectively. Thus, they

represent the mean level over the last one and two months respectively.This paper follows

Brock, Lakonishock, & LeBaron’s (1992) suggestion to use bootstrap simulation to obtain t-


29

statistics in order to account for documented significant deviaton of daily stock returns from

the normal distribution, which renders standard t-tests biased in assessing statistical

significance.

The trading signals generated by the MA rule and the positions taken are described as

follows:

1. The long position is established when the short-term 𝑀𝐴(𝑠) crosses the long-term

𝑀𝐴(𝑙) from below.

2. The short position is established when the 𝑀𝐴(𝑠) crosses the 𝑀𝐴(𝑙) down from

above.

3. Hence, a particular strategy is

long when 𝑀𝐴(𝑠) > 𝑀𝐴(𝑙);

short when 𝑀𝐴(𝑠) < 𝑀𝐴(𝑙).

Long and short positions are assumed to be taken at the closing price of the day on which

the signal is generated. Thus, returns to the position begin to accrue on the following day.

The methodolgy of simulating returns of the short position vary between large-cap and two

other samples. Under MOEX rules44 only top-50 stocks are shortable – those that comprise

the large- and medim-cap samples of this study. However, the clearance from MOEX to

establish short positions in these stocks does not imply the technical feasibility to do it. The

MSCI ‘Global Market Accessibility Review’ (2013)45 reports that “stock lending is allowed but

is not an established market practice due to the absence of a formal stock lending

regulation; short selling is allowed, but with some restrictions and it is not yet a common

practice”. This is in line with the comments received from the several market participants46

who who were interviewed for this research. They maintained that shortsellling is a viable

and widely used strategy for the top-15 stocks. This is due to the fact that the market

liquidity is concentrated in these stocks and they have sufficiently high free-float levels

44 http://nkcbank.com/fondMarketRates.do 45 http://www.msci.com/resources/products/indexes/global_equity_indexes/gimi/stdindex/MSCI_Global_Market_Accessibility_Review_June2013.pdf 46 Evgeny Avrakhov, CEO, IFC Option, algo-trader/HFT, http://www.option.ru/; Nikolai Dontsov, Member of the Board, ITinvest, broker for algo-traders/HFT, http://www.itinvest.ru/; anonymous private algo-trader/HFT.


30

(there is enough inventory). Next, margin lending policies of the top-5 local brokers47 were

examined in order to identify stocks that are loaned. These brokerage houses accounted for

an approximately 55% of the total trading volume in RUB terms over 11 months of 2014 (see

Exhibit 20). The findings confirmed that the large-cap stocks can be borrowed from the

brokers to establish a short position with the only exception of AFK Sistema (MOEX: AFKS).

Thus, this stock was exluded from the large-cap stocks sample in order to keep the

methodology applied to this sample consistent across all stocks (see Exhibit 21). Therefore,

the simulation assumes a short position upon a sell signal for the large-cap stocks. By

construction of the simulation, the trader always has either a long or a short position in the

market, and a neutral position is not permitted (Ulku & Prodan, 2013).

The Exhibit 22 illustrates that despite the fact that the mid-cap stocks are shortable under

MOEX rules, their availability from the brokers is very limited. BCS, the largest broker, lends

most of the stocks from this sample. However, overall, it is prudent to conclude that

establishing a short position in these stocks is challenging. Small-cap stocks are not

shortable under MOEX rules. Thus, following Lagoarde-Segot & Lucey (2006), an alternative

methodlogy construction is adopted for these two samples – the ‘double or out’ strategy

framework. “When a buy signal is generated, the investor borrows at the risk-free interest

rate, and doubles equity investment in the market. In response to a sell signal, the investor

sells the stocks, and invests in the risk-free interest rate” (Lagoarde-Segot & Lucey, 2006).

The underpinning of such a framework is that the borrowing and lending rates are equal and

that the risk of the exposure to the the long and short positions is the same. The 3-month

MosPrime rate is used as a proxy for the risk-free interest rate.

The daily returns generated by employing technical trading rules that allow for short-selling

are computed as follows:

(19) 𝑅𝑡𝑠ℎ𝑜𝑟𝑡𝑎𝑏𝑙𝑒 𝑇𝑇𝑅 = 𝐼𝑡 ∗ 𝑅𝑡

where

(20) 𝑅𝑡 = ln (𝑃𝑡

𝑃𝑡−1)

47 BCS, http://broker.ru/; FINAM, http://www.finam.ru/; OTKRITIE, http://open-broker.ru/; Renaissance Capital, http://www.rencap.com/; Sberbank, http://www.sberbank.ru/moscow/ru/person/investments/broker_service/marketsandservices/


31

and

(21) 𝐼𝑡 = {1, 𝑖𝑓

1

𝑠∑ 𝑃𝑡−𝑠 ≥

1

𝑙∑ 𝑃𝑡−𝑙

𝑙𝑠=1

𝑠𝑠=1

−1, 𝑖𝑓 1

𝑠∑ 𝑃𝑡−𝑠 ≤

1

𝑙∑ 𝑃𝑡−𝑙

𝑙𝑠=1

𝑠𝑠=1

The daily returns generated by employing technical trading rules that do not allow for short-

selling are computed as follows:

(22) 𝑅𝑡𝑛𝑜𝑛−𝑠ℎ𝑜𝑟𝑡𝑎𝑏𝑙𝑒 𝑇𝑇𝑅 = {

2 ∗ 𝑅𝑡 −𝑅𝑡

𝑓

𝑑, 𝑖𝑓 𝐼𝑡 = 1

𝑅𝑡𝑓

𝑑, 𝑖𝑓 𝐼𝑡 = −1

where

𝑅𝑡𝑓

is a risk-free interest rate;

𝑑 – number of days in a year, assumed to be 365.

The null hypothesis tested is

(23) 𝐻0: 𝑟𝑡 = 0

where 𝑟𝑡 is an average daily stock return over the sample period. The alternative hypothesis

is that 𝑟𝑡 ≠ 0 in which case it could be positive or negative. If the null hypothesis is rejected

and the stock return if found significantly negative, then the stock is considered weak-form

efficient as the trading rule based on the past data is not profitable. If the null hypothesis is

rejected and the stock return if found significantly positive, then the weak-form EMH for the

stock is refuted.


32

Chapter 4: Results

4.1 Analysis of the determinants of the trading volume and value

The OLS regression with the equity market trading volume measured by the number of

trades (num) as a dependent variable, dummy variable (dum) taking value of 1 in the post-

merger period and 0 otherwise as an explanatory variable, and the MICEX index (r), the

MSCI world index (w), the price of the Brent (brent), the RUB/USD FX rate (fx), the Reuters

commodities index (com), the volatility of the MICEX index (volat), and the volatility of the

MSCI world index (wvolat) produced the following result:

𝑛𝑢𝑚 = 𝛽0 + 𝛽1𝑑𝑢𝑚 + 𝛽2𝑟 + 𝛽3𝑤 + 𝛽4𝑏𝑟𝑒𝑛𝑡 + 𝛽5𝑓𝑥 + 𝛽6𝑐𝑜𝑚 + 𝛽7𝑣𝑜𝑙𝑎𝑡 + 𝛽8𝑤𝑣𝑜𝑙𝑎𝑡

Table 2: Determinants of the trading volume

Note: * denote statistical significance at 10% level; ** at 5%; *** at 1%.

The only variable that had a significant coefficient is the volatility of the MSCI world index

(wvolat), significant at the 10% level. The coefficient of the variable was positive, as

expected, implying that higher volatility caused higher trading volume. The other volatility

measure, the volatility of the MICEX index (volat), turned insignificant in this regression due

to its significant correlation with the volatility of the MSCI world index (wvolat) –

approximately 0.54. The log returns of the two indices were even more correlated – 0.63.

MICEX index is the main local equity index. The same regression with only the volatility of

*** *


33

the MICEX index (volat) included returned the positive coefficient for this variable,

significant at the 10% level. The same regression with only the volatility of the MSCI world

index (wvolat) included returned the positive coefficient for this variable, significant at the

5% level. Thus, I concluded that the volatility of the global market is the main determinant

of the local trading volume.

Dummy variable that measured whether trading volume became higher in the post-merger

period due to the higher degree of the local assets accessibility for the foreign investors,

turned out to be insignificant. This result was robust to various model specifications.

Furthermore, the result was robust to various cutoff dates. The default regression was run

with a post-merger period from September 2013. This is when most of the critical

innovations had been fully operational. Next, the regression was performed with a dummy

variable taking value of 1 after the legal merger of the two exchanges was completed,

December 2011, and 0 otherwise. This specification did not change the presented results –

the coefficient of the dummy variable stayed insignificant.

The regression that investigated the determinants of the trading value measured in RUB

(rubwfe) produced similar results. The dummy variable and the control variables stayed

insignificant. Both volatility measures were significant at the 10% level when one of them

was included in the model and the other was excluded (simultaneous presence rendered

them both insignificant).

𝑟𝑢𝑏𝑤𝑓𝑒 = 𝛽0 + 𝛽1𝑑𝑢𝑚 + 𝛽2𝑟 + 𝛽3𝑤 + 𝛽4𝑏𝑟𝑒𝑛𝑡 + 𝛽5𝑓𝑥 + 𝛽6𝑐𝑜𝑚 + 𝛽7𝑣𝑜𝑙𝑎𝑡 + 𝛽8𝑤𝑣𝑜𝑙𝑎𝑡

Table 3: Determinants of the trading value

*


34

Note: * denote statistical significance at 10% level; ** at 5%; *** at 1%.

Therefore, there was no evidence indicating that the recent MOEX infrastructure

advancements helped to secure higher market activity of the market participants.

4.2 Serial correlation test’s results

The results of the serial correlation test are presented in the Table 1. The large-cap stocks

sample demonstrated an increase in the efficiency: the test rejected the absence of the

serial correlation for 4 stocks in the pre-merger period and only for 1 stock in the post-

merger period (Panel A). The average of the chi-square statistic decreased in the post-

merger period, manifesting higher overall efficiency. The number of large-cap stocks that

revealed a presence of the serial correlation in the returns decreased from 29% to 7% of the

total sample in the post-merger period.

The absence of the serial dependence of prices was consistently rejected for the 2 mid-cap

stocks in both sample periods (Panel B). However, there were 5 stocks which rejected the

null hypothesis in the pre-merger period, while failed to reject it in the post-merger period.

These stocks experienced price efficiency improvement and only one provided evidence of

the deterioration. The averages for the sample behaved in the similar way as for the large-

cap stocks and their decline indicated a higher efficiency in the post-merger period for the

mid-cap stocks sample. The number of stocks with auto-correlated returns diminished from

30% to 13% in the post-merger period.

There were 5 stocks in the small-cap stocks sample that consistently rejected the null

hypothesis in both periods (Panel C). 7 stocks demonstrated an enhancement in the

efficiency as they progressed from the rejection of the absence of the serial correlation in

the pre-merger period to failure to reject it in the post-merger sample. 12 stocks revealed a

diminishment in the market efficiency as they rejected the null hypothesis in the post-

merger period. The average of the test statistic for the small-cap stocks sample increased in

the post-merger period, contrary to the statistics of two other samples of stocks. I

concluded that the market efficiency improved for the large- and mid-cap stocks, while

stagnated for the small-cap stocks. The number of stocks with auto-correlated returns

increased from 32% to 44% in the post-merger period.


35

Table 4: Results of the serial correlation test

Note: no autocorrelation is the null hypothesis.


36


37


38

4.3 Unit root test’s results

The results of the unit root test are presented in the Table 2. The null hypothesis of

stationary stock returns was rejected for 1 stock in the pre-merger period and for 2 stocks in

the post-merger period in the large-cap stocks sample (Panel A). The test results for this

group detected a marginal improvement in the efficiency. However, overall, the null

hypothesis was accepted. Only 7% of the total number of stocks in the sample in the pre-

merger period and 14% in the post-merger rejected stationarity. This constituted a

preliminary evidence of a rejection of the RWH.

There was one mid-cap stock that rejected the null hypothesis consistently for both sample

periods across both KPSS procedures (Panel B). 3 stocks were in line with the RWH

assumption of the stationary returns in the pre-merger period, while only 1 in the post-

merger period. I concluded that a marginal decline in the efficiency was observed in the

mid-cap stocks sample as the number of stocks rejecting stationarity declined from 17% to

9% in the post-merger period.

10 small-cap stocks manifested weak form of the market efficiency in the pre-merger period

(Panel C). The same number of the small-cap stocks rejected the stationary hypothesis in

the post-merger period. These two sets of stocks were mutually exclusive, which leaded to a

conclusion that no material change in the efficiency was detected. The number of stocks

rejecting stationarity declined from 22% to 15% in the post-merger period.

Overall, the KPSS test did not identify any significant change in the market efficiency

between two sample periods and provided no support for the weak form of the efficient

market hypothesis.


39

Table 5: Results of the unit root tests

Note: stationary return is the null hypothesis.


40


41


42

4.4 Variance ratio test’s results

The results of the variance ratio test are presented in the Table 3. The test failed to reject

the RWH of the stock prices for all large-cap stocks in both sample periods with only one

exception (Panel A). The price of one large-cap stock was did not follow the random walk in

the post-merger periods as its variance ratio for 50-day return was significantly different

from the unity. While the averages of the variance ratios for all three tested lag orders were

above one in the pre-merger period, they became consistently below one in the post-

merger period. Furthermore, the average of the test statistics turned from positive in the

pre-merger periods to negative in the post-merger period for all 𝑞-day returns. Therefore,

there is an evidence of a structural shift in the results of the test between the two sample

periods. However, the change in the price efficiency was not registered.

There were 4 mid-cap stocks that consistently rejected the RWH in the pre-merger for at

least two out of three estimated 𝑞-day returns and failed to reject the null hypothesis in the

post-merger period (Panel B). This is an evidence of the improved market efficiency. On the

other hand, there were 7 mid-cap stocks that consistently rejected the RWH in the post-

merger period for at least two out of three estimated 𝑞-day returns, while failed to reject

the null hypothesis in the pre-merger periods. This serves as an indication of the decline in

the market efficiency. Given the fact that more stocks ceased to follow the random walk in

the post-merger period than the number of stocks that began to adhere to the RWH, I

concluded that overall the mid-cap stocks sample became less efficient in the post-merger

period. The means of variance ratio and the test statistics did not provide any additional

inferences.

The test identified only 1 small-cap stock that consistently rejected the unity of variance in

both sample periods across all estimated lags (Panel C). There were 6 stocks that became

more efficient in the post-merger period in which they failed to reject the previously

rejected RWH. The contrary was true for 5 small-cap stocks that progressed from higher

efficiency to lower efficiency as indicated by the rejection of the null hypothesis in the post-

merger period. As previously, the means of the variance ratio and the test statistics were

not informative. Overall, the results of the variance ratio test of the market efficiency of the

small-cap stocks were inconclusive.


43

Table 6: Results of the variance ratio tests

Note: random walk is the null hypothesis.


44


45


46

4.5 Moving average technical trading rules’ results

The results of employing the moving average technical trading rules are presented in the

Table 4. There was one large-cap stock for which one of the MA trading rules generated a

return significantly different from zero in the pre-merger period and two of the MA trading

rules generated significant returns in the post-merger period (Panel A). There was one more

large-cap stock with significant returns generated by one trading rule in the post-merger

period. However, in all cases the returns were negative, indicating that stock prices were

efficient under the EMH in both periods.

While the mid-cap stocks sample had more significant coefficients, most of them were

negative (Panel B). No trading strategy generated a significantly positive return in the pre-

merger period. RS:PIK stock stood out in the post-merger period due to the fact that all four

trading rules generated significantly positive daily returns ranging from 0.35% to 0.66%.

MA(1,50) and MA(1,22) returns were significant at the 1% level. MA(5,22) generated a

positive return of 0.27% at the 10% significance level for the RS:ACR stock. Averages of the

returns (reported at the bottom of the tables) became consistently positive in the post-

merger period, indicating higher profitability of the trading strategies and lower degree of

the efficiency for the mid-cap stocks.

As previously, the sample of the small-cap stocks contained a significant number of the

statistically significant return coefficients most of which were negative (Panel C). Only

MA(1,22) trading rule revealed a significantly positive trading return in the pre-merger

period for the RS:NKK stock. There were three stocks in the post-merger period for which

one trading strategy generated a significantly positive return and one stock for which all

trading rules produced a positive return.

I concluded that the results for the MA technical trading rules indicated no change in the

informational efficiency for the large-cap stocks, while provided some evidence of the

marginal retreat of the efficiency in the mid- and small-cap stock samples. Overall, the

findings were not very conclusive with respect to any changes in the market efficiency, but

rather provided support for the EMH by demonstrating the inefficiency of the simple trading

rules based on the past information.


47

Table 7: Results of applying the moving average technical trading rules

Note: the null hypothesis is that the profitability of the strategy is zero. The returns presented in the table are the averages of the daily returns generated by the

corresponding trading strategy.


48


49

Chapter 5: Conclusion

The summary of the results is presented in the following table.

Table 8: Summary of the results

Autocorrelation Unit root Variance ratio

Moving average

Overall

Large-cap ↑ ↑ m - - ↑

Mid-cap ↑ ↓ m ↓ ↓ m ↓ m

Small-cap ↓ - - ↓ ↓ Note: ‘↑’ indicates an improvement in the market efficiency; ‘↓’ indicates a deterioration in the market

efficiency; ‘-‘ indicates that the results were inconclusive; ‘m’ stands for marginal.

Large-cap stocks experienced an overall improvement of the market efficiency in the post-

merger period. Serial correlation and unit root tests indicated an increase in the efficiency,

while two other tests produced neutral results. The variance ratio test provided an evidence

of a structural shift in the results of the test between the two sample periods: The ratio

changed from being consistently above the unity in the pre-merger period to consistently

below the unity in the post-merger period. However, the change in the price efficiency was

not registered. Trading rules did not produce any significantly profitable strategies.

An overall result for the mid-cap stocks was a decline in the price efficiency, while the

autocorrelation test indicated an improvement. Results of the unit root test and the

technical trading rule test provided evidence for only marginal deterioration in the

efficiency. Averages of the trading rules’ returns became consistently positive in the post-

merger period, indicating higher profitability of the trading strategies and lower degree of

the efficiency. Contradictory results of the autocorrelation test and variance ratio test were

more reliable. Thus, in general it was prudent to conclude a marginal diminishment in the

efficiency for this sample.

Two of the four employed tests suggested a deterioration in the market efficiency for the

small-cap stocks. Two other tests produced inconclusive results. The average of the serial

correlation statistic for this sample increased in the post-merger period, contrary to the

statistics of two other samples. The unit root test produced neutral results since the same

number of stocks experienced a decline and an increase in the efficiency. There were three

stocks in the post-merger period for which one trading strategy generated a significantly

positive return and one stock for which all trading rules produced a positive return.


50

Investigation of the determinants of the trading volume and value identified the volatility of

the global market is the main explanatory variable. Dummy variable that measured whether

trading volume became higher in the post-merger period turned out to be insignificant.

It is worth noting that the unit root test and the variance ratio test produced contradictory

results. Overall, the KPSS unit root test largely accepted the null hypothesis of stationary

returns, which implies the returns do not follow the random walk. However, the variance

ratio tests mostly failed to reject the null hypothesis of the random walk.

The presented evidence suggests that overall the large-cap stocks enjoyed a higher degree

of information efficiency in the post-merger period, while the contrary is true for the mid-

and small-cap stocks which experienced a deterioration. The large-cap stocks contributed

approximately 63% of the Russian equities market capitalization, while the top 10 most

traded companies generated 83%48 of the total trading volume. In the light of these data,

there are two hypotheses that could provide an interpretation of the results. The first one

suggests that since the dominant part of the market became more efficient, it is prudent to

conclude that overall the market became more efficient. The second hypothesis suggests a

mere flight of the efficiency from the mid- and small-cap stocks to the large-cap stocks.

Thus, there was no improvement, mere reallocation of the benefits of the market efficiency.

Further tests are required in order to confirm or falsify these hypotheses. These tests are

beyond the scope of this paper.

The findings of this study are in line with the evidence documented by Khan and Vieito

(2012). They reported a mixed evidence of improvements in the post-merger period and

suggested that the merger had a significant effect on the efficiency only in the long-run. It is

plausible that the sample period was not long enough for the change in the efficiency to

manifest itself clearly.

Hellstrom et al. reported asymmetrical distribution of the consolidation effects, favouring

smaller firms that were less visible in the pre-merger period. The average firm on the

merger market enjoyed higher information efficiency. However, the firms that were more

actvely traded by the foreign investors received a relatively lower improvement in the

48 World Federation of Exchanges


51

efficiency. The opposite is reported in this study: The relative improvement favoured larger

firms, while smaller firms expirienced a decline in the efficiency.

There are two points I would like to raise in the conclusion. Firstly, the post-merger period

contained only 325 daily price observations. A limited testing horizon could have affected

the results. Secondly, the Russian equity market was under a considerable pressure that

stemmed from the several rounds of financial and trade sanctions. The sanctions were

imposed by the EU and the USA as a result of the Ukrainian conflict. They triggered a

significant capital flight49 and rendered the efforts of MOEX, that were focused on brining

the foreign investment funds into the local equity market, fruitless.

I suggest to revisit the MOEX case later when more data are available and the effects of the

external factors are not significantly inhibitive. Furthermore, merger’s impact on the indirect

measures of the market efficiency such as the bid-ask spread, the volatility of the returns,

and the liquidity could be studied.

Acknowledgements

The author gratefully acknowledges a helpful guidance from the supervisors.

49 http://www.bloomberg.com/news/articles/2014-10-09/russia-capital-outflows-slowed-to-13-billion-last-quarter


52

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54

Annex

Exhibit 1: Summary of the methodology Summary of the methodology used in papers evaluating stock exchange merges and market efficiency.

Khan & Vieito (2012) Hellstrom, Liu, & Sjogren

Ho, Lean, Vieito, & Wong (2013)

Charles, Darné, Kim, & Redor (2014)

Jazepcikait (2008)

Serial correlation test Chow & Denning (1993)

Runs test Yes Yes

Unit root test Kwiatkowski, Philips, Schmidt, & Shin (1992) test

Multiple variance ratio test

Chow & Denning (1993)

Chow & Denning (1993)

Campbell et al. (1997), Lo and MacKinlay (1988), Chow & Denning (1993)

Ranks and signs test Wright (2000) Multiple version, Kim and Shamsuddin (2008)

Wright (2000)

Mean-variance criterion test

CAPM statistics, Sharpe, Treynor, Jensen ratios.

Stochastic dominance test

Hadar and Russell (1969), Hanoch and Levy (1969)

Generalized spectral shape test

Escanciano and Velasco (2006), Durlauf (1991)

Event-study Patell (1976)


55

Exhibit 2: MOEX equity indices map

Source: MOEX50

Exhibit 3: MOEX indices sector diversification

Source: MOEX51

50 http://moex.com/s929 51 http://moex.com/s929


56

Exhibit 4: Blue chip index constituents

Source: MOEX52

Exhibit 5: MICEX index constituents

Source: MOEX53

52 http://moex.com/a840 53 http://moex.com/s777


57

Exhibit 6: Second-tier index constituents

Source: MOEX54

54 http://www.moex.com/a843


58

Exhibit 7: MOEX merger timeline


59

Exhibit 8: Integration of MICEX and RTS markets

Source: Da Vinci Capital Management

Exhibit 9: MICEX and RTS KPI before the merger

Source: MOEX


60

Exhibit 10: MICEX and RTS shares of the local trading volume (by number of trades)

Source: World Federation of Exchanges55

Exhibit 11: MOEX vs LSE IOB56 (2014)

Source: MOEX

55 http://www.world-exchanges.org/ 56 IOB – international order book. EOB – electronic order book.


61

Exhibit 12: MOEX and RTS shares of listed companies (by number)

Source: World Federation of Exchanges

Exhibit 13: T+0 and T+2 markets’ shares of trading volume (RUB)

Source: World Federation of Exchanges

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

RTS Exchange MICEX Moscow Exchange

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jan

-12

Mar

-12

May

-12

Jul-

12

Sep

-12

No

v-1

2

Jan

-13

Mar

-13

May

-13

Jul-

13

Sep

-13

No

v-1

3

Jan

-14

Mar

-14

May

-14

Jul-

14

Sep

-14

No

v-1

4

T+0 Market T+2 Market


62

Exhibit 14: Direct Market Access (DMA)

Source: MOEX

Exhibit 15: MOEX – vertically integrated platform

Source: MOEX


63

Exhibit 16: Breakdown of the equity market participants (by trading volume)

Source: MOEX

Exhibit 17: Cash equities ADTV by type of settlement (RUB bln)

Source: MOEX

Exhibit 18: MOEX vs LSE ADTV growth

Source: MOEX

0

10

20

30

40

50

60

70

80

Jan

-12

Mar

-12

May

-12

Jul-

12

Sep

-12

No

v-1

2

Jan

-13

Mar

-13

May

-13

Jul-

13

Sep

-13

No

v-1

3

Jan

-14

Mar

-14

May

-14

Jul-

14

Sep

-14

No

v-1

4

T+0 Market T+2 Market


64

Exhibit 19: Foreign investors’ trading volumes in equity

Source: MOEX

Exhibit 20: Trading volume of top-10 brokers, 11m 2014 (RUB)

Source: MOEX

Exhibit 21: Large-cap stocks: availability from brokers for a short position


65

Exhibit 22: Mid-cap stocks: availability from brokers for a short position

Exhibit 23: Stata code

23.1 Moving average trading rules foreach x of newlist b c d e f g h i j k l m n o p q r s t u v w x y z{ regress stock`x' , vce(bootstrap, reps(1000)) putexcel `x'1=r* using "G:\Thesis\results.xls", modify }

23.2 Variance ratio test generate date2 = date(date1, "MDY") format %td date2 tsset date2, daily foreach x of newlist b c d e f g h i j k l m n o p q r s t u v w x y z{ lomackinlay stock`x', q(10 20 50) gaps robust putexcel `x'1 = r* using "G:\Thesis\results.xls", modify }

23.3 Unit root test generate time=_n tsset time foreach x of newlist b c d e f g h i j k l m n o p q r s t u v w x y z{ kpss stock`x', qs auto notrend putexcel `x'1 = r* using "G:\Thesis\results.xls", modify }


66

generate time=_n tsset time foreach x of newlist b c d e f g h i j k l m n o p q r s t u v w x y z{ kpss stock`x', qs auto putexcel `x'1 = r* using "G:\Thesis\results.xls", modify }

23.4 Serial correlation test generate time=_n tsset time foreach x of newlist b c d e f g h i j k l m n o p q r s t u v w x y z{ regress stock`x' estat bgodfrey, lags (50) putexcel `x'1 = r* using "G:\Thesis\results.xls", modify }

23.5 OLS regression generate t=_n tsset t generate volat=abs(r) generate wvolat=abs(w) reg num dum r w brent fx com volat wvolat, vce(hc3) reg rubwfe dum r w brent fx com volat wvolat, vce(hc3)

moex_thesis_final

Documents

market efficiency

price efficiency

informational efficiency

global market

autocorrelation test

trading volume

unit root test

postmerger periods