Equity market fragmentation and liquidity: the
impact of MiFID
(Preliminary)
Hans Degryse
Frank de Jongy
Vincent van Kervelz
January 2011
Abstract
Changes in European …nancial regulation (the Markets in Financial Instruments Directive) in November 2007 allow new trading venues to emerge and
compete with traditional stock exchanges (regulated markets). As a result,
currently, many stocks are traded on a multitude of trading platforms, i.e. the
market has become fragmented. This paper evaluates the impact of fragmentation on liquidity for a sample of 52 Dutch stocks. We consider global liquidity
by aggregating order book information over all European trading venues and
incorporating the depth of the entire order books, and local liquidity by considering the regulated market only. Controlling for stock characteristics and time
e¤ects, we …nd that global liquidity increases with fragmentation, but these
bene…ts are not enjoyed by all investors as local liquidity reduces.
JEL Codes: G10; G14; G15;
Keywords: Market microstructure, Market fragmentation, Liquidity, MiFID
z
CentER, EBC, TILEC, Tilburg University. TILEC-AFM Chair on …nancial market regulation.
CentER, Tilburg University
z
Corresponding author, TILEC, CentER, Tilburg University, Email: [email protected].
The authors thank Mark Van Achter and Gunther Wuyts, and participants at the 2010 Erasmus
liquidity conference (Rotterdam), the K.U. Leuven, the 2010 AFM workshop on MiFID (Amsterdam)
and Tilburg University for helpful comments and suggestions.
z
1
I
Introduction
Following the developments in the U.S., European equity markets have seen a proliferation of new trading venues. While traditional stock exchanges had a near monopoly
on trading until the beginning of 2007, recent changes in …nancial regulation, in
particular the Markets in Financial Instruments Directive (MiFID), allow new trading venues to compete for order ‡ow. Consequently, trading has become dispersed
over many trading venues, creating a fragmented market place. How fragmentation of
trading a¤ects market quality, and liquidity in particular, has since long interested researchers, regulators, investors and trading institutions. This question needs further
reconsideration as technological progress nowadays creates possibilities to connect
with di¤erent trading venues. In this paper, we shed new light on the matter and
improve upon previous research by employing a dataset that covers the relevant universe of trading platforms, provides stronger identi…cation and allows for improved
liquidity metrics.
In Europe, the fragmentation of trading is largely a consequence of the implementation of MiFID in November 2007. MiFID’s main goal is to improve market
quality through two channels; by introducing competition between trading venues
and by imposing similar degrees of transparency across most trading venues. First,
di¤erent types of trading venues are allowed to compete for order ‡ow with the traditional market. This is expected to reduce the monopoly power of the traditional
market, to lower transaction costs (Biais, Martimort, and Rochet, 2000) and to foster
technological innovation (Stoll, 2003). However, there is also evidence that fragmentation might reduce liquidity. When order ‡ow becomes fragmented, the probability
of …nding a counterparty diminishes. Consequently, execution probabilities are lowered, which might cause some investors to leave the market or informed investors to
leverage their informational advantage (e.g. Chowdhry and Nanda (1991)). Moreover, a single, consolidated market may enjoy economies of scale resulting in lower
processing costs. Second, in order to create a fair level playing …eld, most trading
venues have to comply with similar enhanced transparency requirements. In particular, trading venues are requested to report continuously on executed transactions and
on the quotes they o¤er.1 When transparency enhances, information can be accessed
1
An exception of the pre-trade transparency regulation is granted to so-called dark pools, as
such rules would con‡ict with their business model.
2
faster and cheaper, hence improving e¢ ciency (e.g. Boehmer, Saar, and Yu (2005)).
This paper adds to the literature by investigating the impact of fragmentation on
liquidity. Foucault and Menkveld (2008) study competition between the LSE and Euronext, using data from 2004, and …nd that fragmentation over these two traditional
stock markets improves liquidity. O’Hara and Ye (2009) …nd that fragmentation lowers transaction costs and increases execution speeds for NYSE and NASDAQ stocks.
In this paper, we investigate the impact of competition by new trading venues on
market liquidity for a sample of European stocks in the period after the introduction
of MiFID.
In contrast to the U.S., the impact in Europe may be a¤ected by di¤erent regulatory requirements on executed trades. Speci…cally, European brokers are required to
execute trades against the best available conditions with respect to price, liquidity,
transaction costs and likelihood and speed of execution.2 A consequence of this broad
de…nition of execution quality is that European brokers can de…ne the best-execution
benchmark themselves, and may even choose to trade on one trading venue only,
typically the traditional exchange. As such, price violations can occur, i.e. a trade
is executed at a price worse than the best price available in the market. In the U.S.
however, trading venues are obliged to execute trades against the best price available
in the market (the National Best Bid or O¤er, NBBO). Stoll (2006) argues that this
rule is controversial as forcing …rms to compete primarily on prices undermines the
other dimensions of execution quality. An advantage of the trade-through rule is that
new entrants are guaranteed to attract order ‡ow when they have the best price in
the market, which is not necessarily the case in Europe. This seemingly small deviation in regulation can have substantial e¤ects on the competitive nature of new
entrants (Gomber and Gsell, 2006). The current work con…rms that fragmentation
has substantially di¤erent consequences for global traders (with access to all trading
venues) and local traders (with access to the regulated market only).
We address the impact of fragmentation on market liquidity by creating per …rm
daily proxies of fragmentation and liquidity, employing information from all relevant
trading venues. Speci…cally, we study 52 Dutch stocks in a period before the start
that fragmentation has set in, until the end of 2009. For each stock, we construct
a consolidated limit order book (i.e., the limit order books of all trading venues
2
Directive 2004/39 on markets in …nancial instruments [2004] O.J. L145/1.
3
combined) to get a complete picture of the global liquidity available in the market.
Based on the consolidated order book, we analyze liquidity at the best price levels,
but also deeper in the order book. This is important, as the e¤ect of fragmentation
on liquidity may not be identical at di¤erent price levels in the limit order book. Next
to global liquidity, we also address the impact of fragmentation on liquidity at the
traditional market, which represents local liquidity. Global liquidity is relevant for
global traders, i.e. traders that have access to all markets, whereas local liquidity is
relevant for local traders, i.e. traders that tap the traditional market only.
The panel dataset aims to identify an exogenous relation between liquidity and
fragmentation by means of …rm*quarter …xed e¤ects and instrumental variables regressions. That is, the …rm*quarter dummies only allow for variation in fragmentation
and liquidity within a …rm-quarter. Hence, they provide robustness to self selection
issues, for example that competition might be higher for high volume and more liquid
stocks. In addition, the …rm*quarter dummies, at least partially, control for dynamic
interactions between market structure, competition in the market, the degree of fragmentation and liquidity. Finally, the results based on the …rm*quarter dummies are
more robust to time e¤ects such as the …nancial crisis. In the instrumental variables
regressions we use the daily average order size of the venues that trade the stock as
instruments for the degree of fragmentation. This approach solves self selection issues
if we assume that the variation in fragmentation generated by the instruments is not
related to …rm size and initial level of liquidity, given the control variables already in
the regression.
Our main …nding is that there exists an inverted U-shape in the e¤ect of fragmentation on global liquidity, where the optimal level of fragmentation employing our
most conservative estimates improves depth with approximately 32% compared with
a completely concentrated market. Interestingly, this improvement holds for liquidity
close to the midpoint, i.e. at relatively good price levels, but to a much lesser extent
for liquidity deeper in the order book, which improves by only 12%. This result suggests that new entrants mainly improve liquidity close to the midpoint, but do not
provide much liquidity deeper in the order book. While global liquidity bene…ts from
fragmentation, the traditional stock exchange is worse o¤ as local liquidity at good
price levels reduces by approximately 10%. This is in contrast to the result of Weston
(2002), who …nds that the introduction of ECNs improves liquidity on the primary
exchange. Finally, competition between trading venues is …ercer for large stocks then
4
for small stocks, as large stocks are more fragmented and bene…t twice as much from
fragmentation. In addition, the negative e¤ect of fragmentation on local liquidity is
only experienced by the small stocks.
These results suggest that local traders who only trade at the traditional stock
market, i.e. do not use Smart Order Routing Technology, can be worse o¤ in a
fragmented market, especially for relatively small orders. This conclusion adds to the
policy debate on the bene…ts and drawbacks of stock market fragmentation, where
fragmentation appears to have a bene…cial e¤ect on total market quality, but is not
equally enjoyed by all stock market participants.
The remainder of this paper is structured as follows. Section II describes the European …nancial market after the introduction of MiFID. Section III discusses related
literature and challenges in estimating fragmentation and liquidity. The dataset and
liquidity measures are described in sections IV and V, while section VI explains the
methodology and results. Finally, the conclusion is presented in section VII.
II
Background on European …nancial markets after MiFID
This section gives a brief discussion on the contents of the Markets in Financial
Instruments Directive (MiFID), e¤ective November 1, 2007. By implementing a single
legislation for the European Economic Area, MiFID aims to create a level playing …eld
for trading venues and investors, which would ultimately improve market quality. The
regulation entails three major changes to achieve this goal.
First, competition between trading venues is introduced by abolishing the “concentration rule”3 and allowing three types of trading systems to compete for order
‡ow. These are regulated markets (RMs), Multilateral Trading Facilities (MTFs) and
Systematic Internalisers (SIs). RMs are the traditional exchanges, matching buyers
and sellers through an order book or through dealers. A …rm chooses on which RM
3
The “concentration rule”, adopted by some EU members, obliges transactions to be executed
at the primary market as opposed to internal settlement. This creates a single and fair market on
which all investors post their trades, according to a time and price priority. The repeal of the rule
however allows markets to become fragmented and increases competition between di¤erent trading
venues (Ferrarini and Recine, 2006).
5
to list, and once listed, MTFs may decide to organize trading in that …rm as well.
MTFs are similar in matching third party investors, but have di¤erent regulatory
requirements and ‘rules of the game’. For example, MTFs and RMs can decide upon
the type of orders that can be placed, and the structure of fees, i.e. …xed fees,
variable fees as well as make or take fees.4 Typically, MTFs receive order ‡ow from
their owners or founders, generating some liquidity. But in order to survive, they
should attract order ‡ow from outside investors as well. In terms of traded volume,
the most important MTFs with visible liquidity are Chi-X, Bats Europe, NASDAQ
OMX and Turquoise. While SIX Swiss exchange is a regulated market, it took over
the MTF Virt-X in March 2008, which traded Eurostoxx 50 constituents.
Lastly, SIs are organized by investment banks where customers trade against the
inventory of the SI or with other clients (Davies, Dufour, and Scott-Quinn, 2006).
MiFID allows for new trading platforms and MiFID has at least partially succeeded
here. For example, market shares of the primary exchanges in Eurostoxx 50 stocks
have reduced from 100% in January 2008 to 78% in December 2009 (Gomber and
Pierron, 2010).
MiFIDs second keystone refers to transparency and guarantees the ‡ow of information in the market. As the number of trading venues increases, information about
available prices and quantities in the order books becomes dispersed. However, in
order for investors to decide on the optimal venue and to evaluate order execution, a
su¢ cient degree of pre-trade and post-trade transparency is required. In particular,
to allow for comparisons between trading venues, pre-trade transparency rules are
set in, where trading venues are required to make (part of) their order books public
and to continuously update this information. A number of waivers exist regarding
pre-trade transparency. In particular, there is the “large-in-scale orders waiver”, the
“reference price waiver”, the “negiotated-trade waiver”, and the “order management
facility waiver”. These waivers are used for example by MTFs with completely hidden
liquidity, known as dark pools, who only have to report executed trades. Also, SIs do
not have to report their quotes for illiquid stocks.5
4
Make and take fees are costs charged to investors supplying and removing liquidity, respectively.
Make fees can be negative, such that providers of liquidity receive a rebate for o¤ering liquidity.
5
A liquid stock is de…ned by the European Commission as having a free ‡oat market capitalization exceeding e500 million and an average daily trading activity of more than 500 trades Ferrarini
and Recine (2006). The stock has to be listed on a regulated markets as well.
6
The third and …nal pillar of MiFID is the introduction of the best-execution rule,
which obliges investment …rms to execute orders against the best available conditions
with respect to price, liquidity, transaction costs and likelihood and speed of execution
(Aubry and McKee, 2007). However, such a broad de…nition of best-execution policy
allows investment …rms to decide themselves where to route their orders to. For
example, an investment …rm may stipulate an execution policy that compares prices
with one market only. In absence of a clear benchmark, it becomes di¢ cult for an
investor to evaluate the quality of executed trades and the overall performance of an
investment …rm (Gomber and Gsell, 2006). This is the main di¤erence between MiFID
and its U.S. counterparty, Reg NMS, which only focusses on execution prices.6 For an
extensive summary of the implementation process of MiFID we refer the interested
reader to Ferrarini and Recine (2006).
III
A
Fragmentation and market quality
Related literature
The following subsections summarize the literature on order ‡ow fragmentation and
competition. There is a trade-o¤ between a single consolidated market and a fragmented market. A single market bene…ts from lower costs, compared with a fragmented market. These consist of the …xed costs to set up a new trading venue; …xed
costs for clearing and settlement; costs of monitoring several trading venues simultaneously; and advanced technological infrastructure to aggregate dispersed information
in the market and connect to several trading venues. Also, a single market that is
already liquid will attract even more liquidity due to positive network externalities
(e.g. Pagano (1989a), Pagano (1989b) and Admati, Amihud, and P‡eiderer (1991)).
Each additional trader reduces the stock’s execution risk for other potential traders,
attracting more traders. This positive feedback should cause all trades to be executed
at a single market, obtaining the highest degree of liquidity.
However, while network externalities are still relevant, nowadays they may be
realized even when several trading venues coexist. This happens to the extent that the
6
In the U.S., the price of every trade is reported to the consolidated tape, such that the performance of a broker can clearly be evaluated.
7
technological infrastructure seamlessly links the individual trading venues, creating
e¤ectively one market. From a broker’s point of view, the market is then virtually
not fragmented, which alleviates the drawbacks of fragmentation (Stoll, 2006). In
addition, fragmentation might also enhance market quality, as increased competition
among liquidity suppliers forces them to improve their prices, narrowing the bid-ask
spreads (e.g. Biais, Martimort, and Rochet (2000) and Battalio (1997)). Con…rming a
competition e¤ect, Conrad, Johnson, and Wahal (2003) …nd that Alternative Trading
Systems in general have lower execution costs compared with traditional brokers.
Furthermore, Biais, Bisiere, and Spatt (2010) investigate the competition induced by
ECN activity on NASDAQ stocks. They …nd that ECNs with smaller tick sizes, tend
to undercut the NASDAQ quotes and reduce overall quoted spreads.
Di¤erences between trading venues may arise to cater to the needs of heterogeneous clientele, as investors di¤er in their preferences for trading speed, order sizes,
anonymity and likelihood of execution (Petrella, 2009; Harris, 1993). In the U.S.,
Boehmer (2005) stresses the trade-o¤ between speed of execution and execution costs
on NASDAQ and NYSE, where NASDAQ is more expensive but also faster. In order
to attract more investors, new trading venues may apply aggressive pricing schedules,
such as make and take fees. Foucault, Kadan, and Kandel (2009) model that asymmetric make and take fees (i.e. liquidity takers pay a higher take fee than liquidity
makers receive as make fee) enhance trading activity. The fact that some investors
prefer a particular trading venue can also lead to varying degrees of informed trading
at each exchange. For instance, the NYSE has been found to attract more informed
order ‡ow than the regional dealers (Easley, Kiefer, and O’Hara, 1996) and NASDAQ market makers (Bessembinder and Kaufman (1997) and A- eck-Graves, Hedge,
and Miller (1994)). Furthermore, Barclay, Hendershott, and McCormick (2003) …nd
that ECNs attract more informed order ‡ow than NASDAQ market makers, as ECN
trades have a larger price impact.
Stoll (2003) argues that competition fosters innovation and e¢ ciency, but priority
rules may not be maintained. Speci…cally, time priority is often violated in fragmented
markets, and sometimes also price priority.7 Foucault and Menkveld (2008) study the
competition between an order book run by the LSE (EuroSETS) and Euronext Ams7
Time priority is violated when two limit orders with the same price are placed on two venues
and the order placed last is executed …rst. Price priority is violated, i.e. a trade-through, when
an order gets executed against a price worse than the best quoted price in the market. A partial
trade-through means that only part of the order could have been executed against a better price.
8
terdam for AEX …rms in 2004, and …nd a trade-through rate of 73%. They call for a
prohibition of trade-throughs as it discourages liquidity provision. More recently, this
has been studied by Ende, Gomber, and Lutat (2010) as well, who combine the order
books of ten trading venues for Eurostoxx 50 stocks in December 2007 and January
2008. They …nd that 6.7% of all transactions are full trade-throughs and an additional 6.5% are partial trade-throughs. The authors provide two explanations for this
result. First, brokers might economize on monitoring costs and discard information
on available prices in some trading venues, i.e. do not use Smart Order Routing Technology. Second, trading charges and other fees such as clearing and settlement can
either make it more expensive to trade at the market o¤ering higher visible liquidity,
or make it expensive to split up an order among several venues (Degryse, van Achter,
and Wuyts, 2010). These explicit transaction costs as well as make and take fees
are not taken into account in the combined order book, while they do contribute to
the investors execution costs. If the role of explicit transaction costs is large enough,
investor could not have done better and in a broad sense, there is no trade-through.
Finally, this paper is related to the literature on algorithmic trading,8 i.e. the use
of computer programs to manage and execute trades in electronic limit order books.
Algorithmic trading has strongly increased over time, and has drastically a¤ected the
trading environment (Hendershott and Riordan, 2009). In particular, it a¤ects the
level of market fragmentation analyzed in our sample, as computer programs and
Smart Order Routing Technology (SORT) allow investors to …nd the best liquidity in
the market by comparing the order books of individual venues.9 Moreover, algorithmic
trading is related to liquidity as it reduces implicit transaction costs by splitting
up a large order into many smaller ones (Hendershott, Jones, and Menkveld, 2010).
Programs are also used to identify deviations from the e¢ cient share price, by quickly
trading on information in the market or from other securities. Furthermore, programs
may provide liquidity when quoted spreads are large, e.g. when it is pro…table to do
so (Hendershott and Riordan, 2009). Hasbrouck and Saar (2009) describe “‡eeting
orders”, a relatively new phenomenon in Europe and the U.S., where limit orders are
placed and cancelled within two seconds if they are not executed. The authors argue
that ‡eeting orders are part of an active search for liquidity and a consequence of
improved technology, more hidden liquidity and fragmented markets; three elements
8
9
Algorithmic trading is also know as High Frequency Trading.
See e.g. Gomber and Gsell (2006) for a discussion on SORT and algorithmic trading in Europe.
9
that are strongly developing in the European trading landscape.
B
Challenges in analyzing the impact of fragmentation
In estimating the impact of fragmentation on liquidity, several data and methodological issues have to be solved. New solutions to these problems will place the current
paper within the closest related literature.
First, it is crucial to correctly measure the extent of fragmentation on a stock-bystock basis. We employ a Her…ndahl-Hirsmann Index (HHI; the sum of the squared
market shares) to measure how concentrated trading is over the di¤erent markets.
Our measure of fragmentation then equals 1-HHI which is zero in the absence of
fragmentation. We compute the HHI based on executed trades on all visible trading
venues. Furthermore, we control for the aggregate trading activity on all non-visible
order books such as dark pools, SIs and OTC.10 Our measure of fragmentation is
more accurate than that of O’Hara and Ye (2009), where the origin of trades are
classi…ed as either NASDAQ, NYSE or external. The main bene…ts of competition
in their paper arise from the external venues, but the degree of fragmentation among
those venues is unknown.
We create a panel dataset containing daily indicators of fragmentation and liquidity on a stock-by-stock basis over a time period of four years. A panel dataset allows
us to control for …xed …rm, time and …rm*time e¤ects, and provides an accurate
identi…cation of the impact of fragmentation and liquidity. Such a setup improves
on O’Hara and Ye (2009), who do a cross sectional regression, ignoring the time series variation in liquidity and fragmentation. Moreover, this setup allows for more
‡exibility than related papers such as Foucault and Menkveld (2008), Chlistalla and
Lutat (2009) and Hengelbroc (2010), who study the introduction of a new trading
venue (EuroSETS, Chi-X and Turquoise respectively). Speci…cally, these articles use
a binary variable, i.e. a dummy that equals one after the introduction of the new
venue, to estimate the e¤ect of fragmentation on liquidity. In our setting however,
this approach would su¤er from three problems. First, it assumes that the level of
fragmentation on other trading venues has no confounding e¤ects on the liquidity of
10
This aggregate non-visible orderbook activity does not provide information on which platform
the trade took place. We therefore do not include it in our fragmentation indicator as it may
represent trades on one venue only or on a panoply of platforms.
10
the venues in consideration. Second, this approach ignores the cross sectional variation in fragmentation, where some …rms are more heavily traded on the new venue
than others. And third, the binary variable does not take into account that a new
trading venue might need time to grow and attract order ‡ow. Furthermore, the
market as a whole might need time to adjust to a new trading equilibrium, so time
series variation in fragmentation is also ignored. Fortunately, the setup of our panel
dataset allows for cross sectional and time series variation and analyzes all visible
trading venues.
A second challenge is to use an appropriate measure of liquidity. We employ
two di¤erent views on liquidity representing the view from a global trader or local
trader, respectively. First consider the view from a global trader. We capture global
liquidity by aggregating the limit order books of all individual trading venues to
obtain a consolidated order book. This is important, as a global trader can use
Smart Order Routing Technology (SORT) to access the liquidity of all trading venues
simultaneously, so the consolidated order book re‡ects her real trading opportunities.
Based on the consolidated order book, we can construct the traditional liquidity
measures, such as the quoted, realized and e¤ective spreads. In addition, we improve
on the work of e.g. O’Hara and Ye (2009), Gresse (2010) and Hengelbroc (2010) by
estimating a liquidity measure based on the entire depth of the consolidated order
book. That is, our data set also includes limit orders further away from the best
quotes,11 which are used to construct a new depth measure. Analyzing depth beyond
the best quotes is important, as the e¤ect of fragmentation might be di¤erent for
liquidity close to the midpoint, compared with liquidity deeper in the order book.
Second, we also consider liquidity for a local trader. This represents the view of a
trader without access to SORT who trades on the traditional stock market. In that
case, we measure liquidity only based upon the order book of Euronext.
A third challenge is identi…cation: is the observed relation between fragmentation
and liquidity causal, or is fragmentation endogenous? Over the years, there may
exist dynamic interactions between market structure, competition in the market, the
degree of fragmentation and liquidity. For example, changes in market structure
will a¤ect competition and fragmentation over time, which in turn will a¤ect market
structure. However, long-term correlations between these forces will be absorbed by
11
The data contains the ten highest bid and lowest ask prices and their associated quantities, for
each trading venue.
11
the aforementioned …rm*quarter dummies. In addition, the …rm*quarter dummies
provide additional robustness to time e¤ects such as the …nancial crisis, which a¤ected
some stocks more than others. Furthermore, we execute an instrumental variables
regression, as large and liquid stocks may endogenously self select to becoming more
fragmented. As instruments we use the average daily order size of the seven visible
trading venues that trade the stock. This approach assumes that, after controlling
for stock characteristics and …rm*quarter dummies, the average daily order size is
uncorrelated to any self selection e¤ects. For exactly this issue, O’Hara and Ye (2009)
execute a Heckman correction model to account for a potential bias. However, self
selection issues are arguably less severe in Europe than in the United States. That
is, a new trading venue in Europe typically starts with a test phase in which only
a few liquid …rms are traded; but will allow trading in all stocks of a certain index
simultaneously when it goes live, i.e. there is no selection bias. For example, ChiX had a two week test phase where the …ve largest AEX25 and DAX30 …rms were
traded, while Turquoise had a …ve week test phase where …ve large European stocks
where traded. This is in strong contrast to the U.S., where some …rms may self select
to be traded on many di¤erent platforms.
IV
Market description, dataset and descriptive statistics
A
Market description
Our dataset contains 52 Dutch stocks forming the constituents of the AEX Large and
Mid cap indices. Over time, all of them are traded on several trading platforms. In
broad terms, we can summarize the most important trading venues for these stocks
into three groups, which we describe shortly.
First, there are regulated markets (RMs), such as NYSE Euronext, LSE and
Deutsche Boerse. All these markets have an opening and closing auction, and in
between there is continuous and anonymous trading through the limit order book.
Since Euronext merged with NYSE in April 2007, the order books in Amsterdam,
Paris, Brussels and Lisbon act as a fully integrated and single market (Davies, 2008).
In our sample, the LSE and Deutsche Boerse are not very important as they attract
12
less than 1% of total order ‡ow.
Second, there are the new MTFs with visible liquidity, such as Chi-X, Bats Europe, NASDAQ OMX and Turquoise. Chi-X started trading AEX …rms in April
2007; Turquoise in August 2008 and Nasdaq OMX and Bats Europe in October 2008.
Whether these MTFs will survive depends on the current level of liquidity, but also
on the quality of the trading technology (e.g. the speed of execution), the number of
securities traded, make and take fees and clearing and settlement costs.
The third group contains MTFs with completely hidden liquidity (e.g. dark
pools), SIs and the Over The Counter market. Dark pools are waived from the
pre-trade transparency rules set out by the MiFID due to the nature of their business model. Most dark pools employ a limit order book with similar rules as those
at Euronext for example. Other MTFs act as crossing networks, where trades are
executed against the midpoint on the primary market, and do not contribute to price
discovery. Gomber and Pierron (2010) report that the activity on dark pools and
OTC has been fairly constant for European equities in 2008 - 2009, where they execute approximately 40% of total traded volume. We refer the interested reader to
Davies (2008) for a description of some of the individual trading venues.
B
Dataset
Our dataset covers the AEX Large and Midcap constituents from 2006 to 2009, which
currently have 25 and 23 stocks respectively. An advantage of using both indices is
that we are able to follow stocks that switch. We remove stocks that are in the
sample for less than six months or do not have observations in 2008 and 2009, such
as Getronics, Pharming, Stork, Numico and ABN Amro. Due to some leavers and
joiners, our …nal sample has 52 stocks.
The data for the 52 AEX Large and Midcap constituents stem from the Thomson
Reuters Tick History Data base. This data source covers the seven most relevant
European trading venues in Dutch stocks, which have executed more than 99% of
the visible order ‡ow: Euronext, Chi-X, Deutsche Boerse, Turquoise, Bats trading,
NASDAQ OMX and SIX Swiss exchange (formerly known as Virt-X).12 We employ
12
The order books of the LSE are discarded as those stocks are denoted in pennies instead of
Euros; which in essence are di¤erent assets. The remaining trading venues with visible liquidity
13
data from all these venues but collect them only during the trading hours of the
continuous auction of Euronext Amsterdam, i.e. between 09.00 to 17.30, Amsterdam
time. Therefore, data of the opening and closing auctions at these venues are not
included.13
Each stock-venue combination is reported in a separate …le and represents a single
order book. Every order book contains the ten best quotes at both sides of the market,
i.e. the ten highest bid and lowest ask prices and their associated quantities, summing
to 40 variables per observation.14 A new “state” of a stock-venue limit order book
is created when a limit order arrives, gets cancelled or when a trade takes place.
A trade is immediately reported and we observe its associated price and quantity,
as well as an update of the order book. Price and time priority rules apply within
each stock-venue order book, but not between venues. Furthermore, visible orders
have time priority over hidden orders. Hidden orders are not directly observed in the
dataset but are detected upon execution. Therefore, we have the same information
set as traders have, i.e. the visible part of the order book in a continuous fashion.
Our dataset also provides information on “dark trades”, i.e. trades at dark pools,
SIs and Over The Counter (including trades executed over telephone). These dark
trades are part of the Thomson Reuters dataset and reported by Markit Boat, a
MiFID-compliant trade reporting company. Although these dark data have several
issues (e.g. double reporting and missing or double corrections), we use it only as a
control variable in our empirical analysis.15 In addition, we also add the OTC and SI
trades reported separately in the MiFID post trade …les from Euronext, Xetra, LSE,
Chi-X and Stockholm. While we have information regarding price, quantity and time
of execution, we do not observe the identity of the underlying trading venue. We
nevertheless can include aggregate information on “dark trading”in our analysis.
attract extremely little order‡ow for the …rms in our sample (e.g. NYSE, Milan stock exchange,
PLUS group and some smaller MTFs).
13
Unscheduled intra-day auctions are not identi…ed in our dataset. These auctions, triggered by
transactions that would cause extreme price movements, act as a safety measure and typically last
for a few minutes. Given that we will work with daily averages of quote-by-quote liquidity measures,
we assume that these auctions will not a¤ect our results.
14
Part of the sample only has the best …ve price levels: Euronext before January 2008. Only
liquidity deep in the order book is a¤ected. In section B.5 we execute the analysis on a yearly basis:
the results are una¤ected.
15
To solve these data issues, the Committee of European Securities Regulators (CESR) proposes
to standardise the trade reporting rules in their technical report of October 2010 on post trade
transparency. http://www.cesr-eu.org/data/document/10_882.pdf
14
C
Descriptive statistics
Figure 1 shows the evolution of the daily traded volume, aggregated over all AEX
Large and Mid cap constituents. The graph shows a steady increase in total trading
activity, which peaks around the beginning of 2008. Moreover, the dominance of
Euronext over its challengers is strong, but slowly decreasing over time. This pattern is representative for all regulated markets trading European blue chip stocks, as
analyzed in Gomber and Pierron (2010). Finally, while Chi-X started trading AEX
…rms in April 2007, all MTFs together have attracted signi…cant order ‡ow only as of
August 2008 (4,5%). The slow start up shows that these venues need time to generate
trading activity.
In Table I, the characteristics of the di¤erent stocks and some descriptive statistics
are presented. There is considerable variation in …rm size (market capitalization),
price and trading volume. In the sample, 40 stocks have a market capitalization
exceeding one billion Euro, while the 12 remaining stocks have values above 100
million Euro. In order to estimate a stock’s volatility, we divide the trading day
into 34 …fteen-minute periods, and calculate the stock return of each period, based
on the spread midpoint at the beginning and end of that period. The standard
deviation of these stock returns are daily estimates of realized volatility.16 The table
also shows the average market share of Euronext and dark trades, which covers all
trades reported by Markit Boat and OTC from the regulated markets. The market
shares are percentages of executed trades as of November 2007 onwards, the period
for which Markit Boat data have become available in the dataset. According to our
data, 25% of all trades are dark; which can be split up into 29.4% for AEX large cap
…rms and 18.9% for mid cap …rms.
V
A
Liquidity and fragmentation
The consolidated order book
The goal of this paper is to analyze the impact of equity market fragmentation on
liquidity. We follow the approach of Gresse (2010) and distinguish between “global
16
The use of realized volatility is well established, see e.g. Andersen, Bollerslev, Diebold, and
Ebens (2001).
15
traders” and “local traders”. For global traders, global liquidity applies as they
employ Smart Order Routing Technology (SORT) to access all trading venues simultaneously; so liquidity should be aggregated over these venues. However, for local
traders, representing small investors, SORT might be too expensive because of …xed
trading charges and costs of adopting this trading technology. We therefore also
compute local liquidity and assume that those local traders only have access to the
primary market. Empirically, SORT is not used by all investors (e.g. Ende, Gomber,
and Lutat (2010) and Foucault and Menkveld (2008)). To analyze the impact of
fragmentation for local and global investors, we relate fragmentation to liquidity of
the traditional and aggregated market, respectively. Here, Euronext Amsterdam is
the primary market and the consolidated order book the aggregated market.
To construct the consolidated order book, we follow the methodology of Chlistalla
and Lutat (2009) and Foucault and Menkveld (2008), based on snapshots of the limit
order book. A snapshot contains the ten best bid and ask prices and associated
quantities, for each stock-venue combination. Every minute we take snapshots of all
venues and “sum”the liquidity to obtain a stock’s consolidated order book. Therefore,
each stock has 510 daily observations (8.5 hours times 60 minutes), containing the
order books of the separate trading venues and the consolidated one.
B
Depth(X) liquidity measure
Our rich dataset allows to construct a liquidity measure that incorporates the limit
orders beyond the best price levels; which we will refer to as the Depth(X). The
measure aggregates the Euro value of the number of shares o¤ered within a …xed
interval around the midpoint. Speci…cally, the midpoint is the average of the best bid
and ask price of the consolidated order book and the interval is an amount X = {10,
20, ..., 60} basis points relative to the midpoint.17 The measure is expressed in Euros
and calculated every minute. Equation 1 shows the calculation for the bid and ask side
separately, which are summed to obtain Depth(X). Depth(X) is constructed for the
consolidated order book and the traditional stock market (i.e., Euronext Amsterdam)
separately. De…ne price level j = f1; 2; :::; Jg on the pricing grid and the midpoint of
17
Foucault and Menkveld (2008) aggregate liquidity from one up to four ticks away from the
best quotes. This measure only looks at the depth dimension, but does not incorporate the price
dimension, like ours. Furthermore, we use basis points as tick sizes vary over time.
16
the consolidated order book as M, then
Depth Ask(X) =
J
X
j=1
Depth Bid(X) =
J
X
j=1
PjAsk
QAsk
j PjAsk < M
j
PjBid QBid
j PjBid > M
j
(1 + X) ;
(1a)
(1
(1b)
X) ;
Depth (X) = Depth Bid(X) + Depth Ask(X):
(1c)
Figure 2 gives a graphical representation of the depth measure, where liquidity between the horizontal dashed lines is aggregated to obtain Depth(20 bps) and Depth(40
bps). The Depth(X) is calculated every minute and then averaged over the trading
day. This gives a proxy for a stock’s liquidity on a certain day, where Depth(10 bps)
represents liquidity close to the midpoint and Depth(60 bps) also includes liquidity
deeper in the order book. Comparing di¤erent price levels (X) reveals the shape of
the order book. For example, if the depth measure increases rapidly in X, the order
book is deep while if it increases only slowly, the order book is relatively thin.
The Depth(X) is very similar to the Exchange Liquidity Measure, XLM(V), which
also analyzes liquidity deeper in the order book (used by e.g. Gomber, Schweickert,
and Theissen (2004)). More speci…cally, the XLM(V) …xes the quantity V of a potential trade, i.e. V equals e100.000, and analyzes the impact on price; while the
Depth(X) …xes the price, i.e. X equals ten basis points around the midpoint, and
analyzes the available quantity. Although both measures estimate the depth and
slope of the order book, our approach solves two rather technical issues. First, the
impact on price cannot be calculated when a stocks order book has insu¢ cient liquidity to trade e100.000, such that the XLM(V) becomes missing. In contrast, if no
additional shares are o¤ered within the range of X and X + " basis points from the
midpoint, then the Depth(X) has a zero increment and Depth(X) = Depth(X + ").
Second, the XLM(V) may become negative when the consolidated spread is negative,
i.e. when the best ask price of Chi-X is lower than the best bid price of Euronext
or vice versa.18 While negative transaction costs cannot be interpreted meaningfully,
18
Technically, a negative consolidated spread is an arbitrage opportunity, which might not be
exploited because of explicit trading costs for example.
17
the midpoint and Depth(X) are perfectly identi…ed and re‡ect the available liquidity
in a meaningful fashion.
Figure 3 shows monthly averages of the Depth measure for 10 and 60 basis points
of Euronext Amsterdam and the consolidated order book, over the period 2006 to
2009. As expected, these measures covary strongly over time. However, some shocks
a¤ect liquidity close to the midpoint more than liquidity deep in the order book.
For example, when comparing the peak of liquidity in October 2007 to the low of
February 2008, the ratio of Depth(60 bps) to Depth(10 bps) doubles from 2.0 to 3.9.
This variation is present at the aggregate level and is also observed for most individual
…rms.
Finally, the upper panel of Figure 4 shows the shape of the order book by plotting
the median of the depth measure against the number of basis points around the
midpoint. The di¤erences between the medians reported here and the means of
Figure 3 are very large, a factor 3 to 5, which is in line with high levels of skewness and
kurtosis (not reported). Next to substantial variation over time (see Figure 3), there
also appear to be large di¤erences between …rms. For example, the 90th percentile of
Depth(10 bps) is e920.000, while the 10th percentile of Depth(60 bps) is e70.000. In
the regression analysis we will work with the logarithm of the depth measures, where
the 10, 50 and 90th percentiles are shown in the lower panel of Figure 4. Table II
contains the 25, 50 and 75th percentile of the Depth(X) measure on a yearly basis,
along with other liquidity measures discussed in the next section.
C
Other liquidity measures
This section compares our Depth(X) liquidity measure to the more traditional liquidity measures. These are the price impact, e¤ective and realized spread, based on
executed transactions, and the quoted spread and quoted depth, based on quotes
in the local and global order books. The quoted depth sums the Euro amount of
shares o¤ered at the best bid and ask price, whereas the quoted spread looks at the
associated prices. The appendix (section VIII) contains a formal description of these
measures.
The 25, 50 and 75th percentiles of the liquidity measures are reported in Table II, based on daily observations and calculated yearly, for the consolidated order
18
book (upper panel) and Euronext Amsterdam (lower panel). The table shows several
interesting results.
Liquidity close to the midpoint has reduced strongly over time, while liquidity
deeper in the order book only marginally. That is, the median of Depth(10 bps) has
decreased by 35% from 2006 to 2009, while Depth(60 bps) by only 12%. In addition,
the yearly standard deviation of the depth measures has decreased by approximately
50% over the years (not reported).
Strikingly, between 2006 and 2009 the median quoted spread has improved by
10%, while the quoted depth has worsened by 68%. This result is in strong contrast
with the decrease of 35% in the Depth(10 bps) measure, and shows a shortcoming of
the quoted depth and spread. That is, based on the quoted depth and spread alone,
one cannot state whether an investor is better o¤ in 2006 or 2009, as this depends on
the size of his trade. For a small investor mainly the quoted spread matters, while
for a larger one the depth dimension becomes more important. To interpret these
numbers, one can argue that a trader who places an order smaller than the median
quoted depth in 2009, e30.000, is very likely to be better o¤ with on average 10%
(an argument similarly to that made in Hendershott, Jones, and Menkveld (2010)).
An additional advantage of the Depth(X) measure is that it is not sensitive to
small, price improving orders. For example, the strong reduction in quoted depth over
time might be explained by lowered tick sizes,19 or by increased activity of algorithmic
traders, who evidently place many small and price improving limit orders. The impact
of these phenomena on the quoted spread and depth also hinges on the degree of
fragmentation and may therefore vary over time, which makes comparisons between
periods troublesome. Finally, the Depth(X) measure can easily be aggregated over
several trading venues, while the quoted depth and spread are only aggregated if the
best bid and ask prices of the trading venues in consideration are identical.
Turning to the liquidity measures based on executed trades, we observe that the
median realized spread has reduced from 2.46 bps in 2006 to 0.02 bps in 2009. In this
period, the price impact went up with a 2.9 bps while the e¤ective spread reduced with
0.9 bps. Note that the price impact and realized spread together equal the e¤ective
spread. However, when calculating percentiles, each measure is ranked separately
19
The e¤ect of the tick size on quoted depth and spread have been subject of analysis in several
papers, e.g. Huang and Stoll (2001), Goldstein and Kavajecz (2000).
19
and as such they do not add up exactly. In addition, we have an unbalanced panel
because of …rms joining and leaving the sample.
The lower panel in Table II shows the same liquidity measures for the order book
of the traditional stock exchange, which represents the liquidity to a local investor.
While in 2006 and 2007 the numbers are highly similar to those of the consolidated
order book, in 2009 the Depth(X) on Euronext represents only about 50% of the
consolidated depth. In the same year, the quoted depth and spread on Euronext
Amsterdam are 20% and 9% worse compared to the consolidated order book.
Despite the reduction in Depth(X), the price impact, realized and e¤ective spread
are almost identical to that of the consolidated order book. This …nding might be
in line with “market tipping”, where Euronext switches between periods of relatively
high liquidity, in which it attracts all trading, and periods of low liquidity, in which
trading takes place at competing trading venues. As the price impact, e¤ective and
realized spread are based on trades, relatively liquid periods receive a larger weight
in the calculation.
D
Equity market fragmentation
To proxy for the level of fragmentation in each stock, we construct a daily Her…ndahlHirschman Index (HHI) based on the number of shares traded on each venue, similar
P
2
to Bennett and Wei (2006) and Weston (2002). HHIit = N
v=1 M Sv;it , or the squared
market share of venue v, summed over all N venues for …rm i on day t. A single
dominant market has an HHI close to one whereas HHI goes to zero in case of complete
fragmentation. In 2009, the average HHI of the sample …rms is 0.70, which is in line
with other European stocks analyzed by Gomber and Pierron (2010). The U.S. is more
fragmented, as NASDAQ and NYSE combined have approximately 65% of market
share in 2008, resulting in an HHI lower than 0.50 (O’Hara and Ye, 2009). Table
III shows the yearly mean, quartiles and standard deviation of HHI, based on the
sample …rms. As expected, the HHI declines over time, i.e. fragmentation increases.
Moreover, the standard deviation of HHI is fairly small in 2006 and 2007, as only few
sample …rms where traded on Virt-X, LSE and Deutsche Boerse.
The variation in HHI mainly stems from changes over time and to a lesser extent of
changes between …rms; i.e. the time series variation is larger than the cross sectional
20
variation. This result is shown in the lower part of Table III, where the standard
deviation of HHI is demeaned by …rm, by quarter and by …rm and quarter. That
is, from each observation we subtract o¤ the …rm average, or the quarterly average
or both, and report the standard deviation. As the table shows, demeaning by …rm
0:149
reduces the standard deviation with only 9% ( 0:135
), while demeaning per quarter
0:149
reduces variation with 30%, so cross sectional variation is smaller than variation in
the time series.20 Finally, 43% of the total variation in fragmentation is captured by
adding both …rm and quarter dummies.
Figure 5 shows the 10, 50 and 90th percentile of HHI over time, calculated on
a monthly basis and covering all …rms. The sharp increase in fragmentation refers
to the period where Chi-X and Turquoise started to attract substantial order ‡ow,
September 2008. In the next section, we estimate the e¤ect of fragmentation on
various liquidity measures in a regression framework.
VI
The impact of fragmentation on global and local liquidity
A
Methodology
We employ multivariate regression analysis to study the impact of fragmentation on
liquidity. We have a panel data set with N = 52 …rms and T = 1022 days (2006 –
2009), which contains the liquidity and fragmentation measures discussed in section
V. The Depth(X) is calculated for X = f10; 20; :::; 60g basis points. As the e¤ect
of fragmentation on liquidity might not be linear, we add a quadratic term. To
ease the interpretation of the quadratic term, we will use F ragit = 1
HHIit and
F ragit2 to measure fragmentation, where F ragit = 0 if trading in a …rm is completely
concentrated.
Control variables commonly used in this literature are the …rm characteristics described in Table I.21 Furthermore, we control for algorithmic activity as this has been
20
Indeed, as we have daily observations, demeaning per quarter still leaves some variation in the
time series. However, demeaning per day reduces the standard deviation with only 1% compared to
demeaning per quarter.
21
Weston (2000) and O’Hara and Ye (2009), among others, use similar controls.
21
found to improve liquidity (e.g. Hendershott and Riordan (2009)), where we construct
a measure similar to Hendershott, Jones, and Menkveld (2010). On average, algorithmic traders place and cancel many limit orders, so the daily number of electronic
messages proxies for their activity, i.e. placement and cancellations of limit orders
and market orders. However, over time there is an increase in trading volume which
would lead to more electronic messages even in the absence of algorithmic traders.
To account for increasing trading volumes, the algorithmic trading proxy, Algoit ; is
de…ned as the number of electronic messages divided by trading volume (in e100),
for …rm i on day t. Some descriptives of Algoit , and the additional control variables
Ln(V olatility)it ; Darkit , Ln(P rice)it ; Ln(Size)it and Ln(V olume)it are presented in
Table IV. The regression equation becomes:
Liq M easureit = F irmi +
4 Darkit
7 Ln(V
1 F ragit
+
+
2
2 F ragit
5 Ln(P rice)it
olume)it +
+
8 Algoit
+
3 Ln(V
olatility)it +
6 Ln(Size)it
+
(2)
+ "it :
In our base speci…cation, …rm …xed e¤ects and quarter dummies are included22
and in all regressions we use heteroskedasticity and autocorrelation robust standard
errors (Newey-West for panel datasets), based on …ve lags.
B
Results
This subsection …rst presents our regression results for the base case (i.e., where we
include …rm …xed e¤ects and quarter dummies) for both the consolidated order book
and the traditional stock market. Later, we control for potential endogeneity issues
by introducing …rm*quarter e¤ects and two instrumental variables (IV) regressions.
The …rm*quarter dummies are aimed to control for the simultaneous interactions
between market structure, the degree of fragmentation, liquidity and competition in
the market. In the …rst instrumental variables regression we instrument the degree
of fragmentation of day t with that of t
1, such that we remove the unexpected
daily shocks from the variation in fragmentation. In the second IV regression we
instrument the degree of fragmentation by the average daily order size of each visible
venue that trades the stock. This approach may control for potential self selection
22
The results are almost identical when using day or month dummies instead of quarter dummies.
22
issues, e.g. that competition tends to be higher for high volume and more liquid
stocks (Cantillon and Yin, 2010). We conclude by analyzing large and small …rms
separately, along with some additional robustness checks.
B.1
Regression analysis: base speci…cation
The regression results for the liquidity measures employing the consolidated order
book are reported in Table V. In Table VI, the same regressions are executed for
liquidity measures of the traditional stock market, Euronext Amsterdam. The latter
represents liquidity available to local investors, while the former liquidity to global
investors using SORT.
The coe¢ cients of F ragit and F ragit2 (models (1) to (6), Table V) show that
liquidity …rst strongly increases with fragmentation and then starts to decrease as
the linear term has a positive coe¢ cient and the quadratic a negative one. The
results are easier to interpret when shown in a …gure where we display the implied
results for the six models representing di¤erent levels of depth. These are displayed
in Figure 6, with depth on the vertical axis and the level of fragmentation on the
horizontal axis.
The …gure clearly reveals a trade-o¤ in the bene…ts and downsides of fragmentation, where maximum liquidity is obtained at F rag = 0:35. This level of fragmentation is fairly close to the actual level observed in 2009 (where F rag is around 0:30).
The patterns are highly similar for all depth levels, although liquidity close to the
midpoint bene…ts more from fragmentation. The economic magnitudes of the variables are large, where the maximum e¤ect on Ln Depth(10 bps) is 0:50, meaning that
observations here have exp(0:50) = 1:65 or 65% more liquidity than observations in a
completely concentrated market. For Depth(60 bps), liquidity improves with 50% at
the maximum. The standard deviation of fragmentation is 0.15 in the entire sample
(Table III), so variation in fragmentation has a large impact on liquidity throughout
the entire order book.
We now investigate the impact of fragmentation on the other liquidity indicators
as reported in the remaining models from Table V. We observe the following in models
(7) to (9). The optimal degree of fragmentation (F rag = 0:35) reduces the price
impact and the e¤ective spread with 6:4 and 6:8 basis points in comparison with a
23
completely concentrated market. This is large, considering that the median e¤ective
spread in 2009 is 13:3 basis points. The economic impact of the optimal degree of
fragmentation on the e¤ective spread in our analysis is larger than the one estimated
in O’Hara and Ye (2009), where the bene…t is approximately three basis points for
NYSE and NASDAQ …rms.23 The e¤ect on the realized spread is much smaller with
a reduction of only 0.5 basis points.
In line with the other liquidity measures, the quoted spread is minimized at
F rag = 0:37 and is eight basis points lower compared with a completely concentrated
market (model (10)). This result is in stark contrast to the Ln quoted depth (model
(11)), which reduces with 27% at F rag = 0:37. The results on the quoted depth
point in the opposite direction of those of all other liquidity measures. Moreover,
considering the low correlation between the quoted depth and Depth(X) in Table II,
it appears that the quoted depth does not perform well in estimating liquidity over
longer periods of time.
Turning to the control variables of the regressions, we …nd that the economic
magnitude of Algoit is fairly small and negative. For example, a one standard deviation increase (s = 0:36), lowers the Depth(X) measures with 4%. However, as Algoit
might be indirectly related to fragmentation, we want to be careful in interpreting
this result. The remaining control variables in the regressions have the expected signs.
Larger …rms tend to be more liquid, so the coe¢ cient on Ln size is positive and fairly
large. The e¤ect of price is marginally positive and economically very small. As expected, increased trading volumes are associated to better liquidity. Also, volatility
has a negative impact on liquidity; especially for liquidity close to the midpoint. Not
surprisingly, the price impact strongly increases in volatility, which proxies for the
amount of information in the market. The coe¢ cients on Darkit are strongly negative, where a one standard deviation increase (s = 0:18) reduces Depth(10 bps) with
17%. Possibly, dark trading venues gain liquidity at the expense of lit markets, as
they are substitutes. In addition, dark trades seem to be correlated to more informed
trading, as the coe¢ cient on the price impact is positive (4.13 bps).
The impact of fragmentation on liquidity for an investor not using SORT is shown
in Table VI. This table contains the regression results where our liquidity measures
23
O’Hara and Ye (2009) …nd a linear coe¢ cient on “marketshare outside the primary markets”of
9 basis points, while the average level is 0.35, resulting in a bene…t of approximately 3 basis points.
24
are based on the order book of Euronext Amsterdam only. To interpret the results,
we plot depth against fragmentation in Figure 7. Interestingly, Depth(10 bps) …rst
slightly improves with fragmentation, where the maximum lies at +10% at F rag =
0:17, but afterwards quickly reduces to -10% at F rag = 0:4. This reduction is in line
with the theory of Foucault and Menkveld (2008), where the execution probability
on Euronext diminishes as competing venues take away order ‡ow. This side e¤ect of
competition makes Euronext less attractive to liquidity providers, resulting in lower
depth.
Consequently, small investors, who mainly care for Depth(10 bps) and are limited
to trading on Euronext only, are worse o¤. This result is in contrast to Weston (2002)
for instance, who …nds that the liquidity on NASDAQ improves when ECNs enter
the market and compete for order ‡ow. Likely, the di¤erence is due to the market
structure in the U.S., where NASDAQ market makers lost their oligopolistic rents
after the entry of ECNs.
The di¤erence between the global and local order book represents the liquidity
available at the competing trading venues, who mainly o¤er liquidity at Depth(10 bps)
and to a much lesser extent at Depth(60). That is, at F rag = 0:40, the MTFs o¤er
44% of Depth(10 bps) and only 26% of Depth(60 bps).24 It appears that competition
mainly takes place at liquidity close to the midpoint.
We now turn to the regressions of the remaining liquidity measures in Table VI,
columns (7) - (11). Surprisingly, these are not adversely a¤ected by fragmentation.
That is, despite the reduction in Depth(10 bps), the price impact and e¤ective and
realized spread still improve with fragmentation. It might be the case that Euronext
is very liquid on some parts of the day, while relatively illiquid during other parts. As
the e¤ective spread is based on trades, more liquid periods with many trades receive
a larger weight in the calculation. In addition, order splitting behavior and smaller
average order sizes may also generate lower average e¤ective spreads.
Finally, the quoted spread on Euronext improves with fragmentation, while the
quoted depth reduces with 30% at F rag = 0:35. This means that the gains of
improved prices are more than o¤set by the lower quantities o¤ered, such that the
24
At F rag = 0:40, the consolidated Depth(10 bps) is 63% higher than that of a completely
concentrated market, while the local Depth(10 bps) is 8% lower. Therefore, the MTFs o¤er 16316392 =
44% of the global Depth(10 bps).
25
Depth(10 bps) has reduced.
B.2
Regression analysis: …rm*time e¤ects
In this section we do the regressions speci…ed in (2), but add …rm*quarter dummies.
Instead of a single dummy for a period of four years, we add 16 dummies per …rm.
This is similar to Chaboud, Chiquoine, Hjalmarsson, and Vega (2009), who analyze
the e¤ect of algorithmic trading on volatility for currencies, and add separate quarter
dummies for each currency pair. This procedure is aimed to solve the following issues.
First, the …rm*quarter dummies provide additional robustness to the impact of
the …nancial crisis and industry speci…c shocks. For example, if the …nancial crisis
speci…cally a¤ects certain …rms or industries (e.g. the …nancial sector), and a¤ects
both liquidity and fragmentation, then the previous analysis would su¤er from an
omitted variables problem, leading to a bias in the coe¢ cients on fragmentation.
However, the …rm*quarter dummies will capture industry shocks and time-varying
…rm speci…c shocks.
Second, the …rm*quarter dummies can control for potential self selection problems. For example, Cantillon and Yin (2010) raise the issue that competition might
be higher for high volume and more liquid stocks; an e¤ect that will be absorbed by
the …rm*quarter dummies.
Third, the …rm*quarter dummies can, at least partially, control for dynamic interactions between market structure, competition in the market, the degree of fragmentation and liquidity. Speci…cally, such interactions are dynamic as, for example,
a change in the current market structure will a¤ect the level of competition in the
future, which, in turn, will a¤ect the market structure in the future. Our approach
controls for the long-term interactions of these forces by only allowing for variation in
liquidity and fragmentation within a …rm-quarter. Accordingly, the dummy variables
absorb the variation between quarters, which is likely to be more prone to endogeneity
issues.
The results for global liquidity reveal a similar pattern as those presented in the
original regressions, as shown in panel A of Table VII and displayed in Figure 8. For
the sake of brevity, the table does not report the coe¢ cients of the control variables
as these have the same signs and magnitudes as those reported in Tables V and VI,
26
but are available upon request.
In the …rst regression, we observe that Depth(10 bps) almost monotonically increases with fragmentation. That is, the maximum of the parabola lies outside the
relevant domain, at F rag = 0:67, so there appears to be no harmful e¤ect of fragmentation on liquidity close to the midpoint. This is not the case for the other depth
levels, as the maximum lies around F rag = 0:40, implying a trade-o¤ in the bene…ts
and drawbacks of fragmentation.
In addition, two other …ndings emerge from the …gure. First, the magnitudes
of the coe¢ cients are about 45% to 75% lower, as Depth(10 bps) equals 0:28 at
F rag = 0:40; compared with 0:50 in Table V, while Depth(60 bps) is 0:10 compared with the original 0:40 (but still highly signi…cant). This is likely because the
…rm*quarter dummies absorb long-term trends in the measure of fragmentation, while
the more noisy day-to-day ‡uctuations remain. From the regression results, it appears
that removing the long-term variation dampens the estimated daily e¤ects. Second,
liquidity deeper in the order book bene…ts less from fragmentation than liquidity close
to the midpoint does. This …nding was also con…rmed in Figure 6, but becomes more
pronounced. The fact that Depth(10 bps) still improves strongly in fragmentation is
in line with the earlier …nding that competition of new trading venues mainly takes
place at liquidity close to the midpoint.
Turning to the other liquidity measures in Table VII, columns (7) - (11), we
observe that the magnitudes of the coe¢ cients on fragmentation have diminished,
but in unreported plots all show a pattern similar to that of the base case.
The impact of fragmentation on local liquidity, including …rm*quarter e¤ects, is
shown in panel B of Table VII and Figure 9. The …gure shows that all the depth
measures reduce with fragmentation, while in the base speci…cation this only held
for Depth(10). However, not all coe¢ cients are signi…cant because the magnitude is
fairly close to zero while the 750 …rm*quarter dummies greatly reduce the statistical
power of the regressions. The fact that the coe¢ cients are close to zero can again
be attributed to the dummies, which absorb the long-term trends in fragmentation.
Similar to the previous subsection, we con…rm that the MTFs mainly o¤er liquidity at
Depth(10 bps), and to a much lesser extent at Depth(60). That is, following previous
calculations,24 at F rag = 0:40, the MTFs o¤er 30% of Depth(10 bps) and only 15%
of Depth(60 bps). This is in line with MTFs who speci…cally compete on the price
27
dimension of liquidity.
B.3
An instrumental variables approach
2
in equation (2),
The main focus of interest are the coe¢ cients of F ragi;t and F ragi;t
but these are potentially biased, as discussed earlier. In order to further alleviate
potential endogeneity issues, we employ two instrumental variables speci…cations.
Formally, the set of instruments needs to be uncorrelated with the error term "it
(exogeneity of the instrument), and correlated to F ragi;t (a su¢ ciently strong instrument).
2
with their lagged
In the …rst speci…cation, we instrument F ragi;t and F ragi;t
values F ragi;t
1
2
and F ragi;t
1 . The intuition is that potentially, an unexpected daily
shock can a¤ect liquidity and fragmentation simultaneously, resulting in a biased
coe¢ cient of fragmentation. Using F ragi;t
1
2
and F ragi;t
1
as instruments removes
the unanticipated daily ‡uctuations in fragmentation.
2
In the second speci…cation, we instrument F ragi;t and F ragi;t
with the logarithm
of the average daily order size of the seven venues that trade the stock, resulting in
seven instruments. This speci…cation is aimed to control for a selection bias, where
for example large or highly traded …rms are more likely to become fragmented. For
this reason, O’Hara and Ye (2009) estimate a Heckman correction model. Here, the
seven instruments are only weakly related to liquidity, but can strongly predict fragmentation.25 As such, the instruments generate su¢ cient variation in fragmentation,
which, we argue, is unrelated to any self selection e¤ect. In both speci…cations, we
again add …rm*quarter dummies.
The results of the …rst speci…cation are shown in panel A (global liquidity) and
B (local liquidity) of Table VIII. Again, for the sake of brevity we do not report the
coe¢ cients of the control variables, as these have the same signs and magnitudes as
those reported in the base speci…cations.
First we notice that the coe¢ cients are highly similar to those reported in Table
V. At the optimal level of fragmentation, F rag = 0:35, global Depth(10 bps) improves by 65% compared with a fully concentrated market. This is a consequence
25
In all IV regressions the weak identi…cation test (or Paap-Kleibergen Wald test), is strongly
rejected.
28
of two o¤setting forces that are at play here. On the one hand, the addition of the
…rm*quarter dummies removes long-term trends in fragmentation, which are more
strongly related to liquidity and would lower the estimated coe¢ cients (as in the
previous section). On the other hand, by using F ragi;t
1
as instrument, we remove
the noisy day-to-day ‡uctuations in fragmentation, which are only weakly related to
liquidity. The two forces combined are o¤ setting, as plots of the results are almost
identical to those of the base speci…cation regressions (not reported). The coe¢ cients
in panel B are also very similar to the original ones, but not statistically di¤erent
from zero as the standard errors are strongly increased by the IV procedure. The
fact that the IV coe¢ cients are of the same sign and magnitude as the original ones,
shows that the part of fragmentation that is known in advance (i.e. the day before)
can explain the bene…cial e¤ect of fragmentation on liquidity. The coe¢ cients of the
remaining regressions of panel A and B point in the same direction as the original
ones, but are lower in terns of magnitude and signi…cance.
Turning to the second set of IV regressions, reported in panel C and D of Table
VIII, we observe the following. In line with earlier results, at F rag = 0:35 global
Depth(10 bps) and Depth(60 bps) improve with 110% and 6% respectively, although
the level of signi…cance has substantially reduced because of the instruments. In
addition, the e¤ect on local liquidity is slightly positive, if anything, but plots do not
show a consistent trend. This hints at a relatively poor estimation of the coe¢ cients
for local liquidity, and we want to be careful in interpreting these.
The second IV approach uses the variation in fragmentation that can be predicted
by the average order sizes of the seven venues that trade the stock, after partialling
out the e¤ects of the control variables and …rm*quarter dummies. Given these control
variables, intuitively this variation in fragmentation is likely not related to …rm size or
initial level of liquidity, so the instruments seem valid. However, the overidentifying
restrictions test (Hansen J statistics) is rejected, meaning that the instruments are not
only related to liquidity via fragmentation, but also directly. One reason for rejection
is the large number of observations (N = 47:000), as closer inspection of the results
reveals that the order sizes are only marginally related to global Depth(10 bps). That
is, after we add the logarithm of the average daily order sizes to the main regression,
a one standard deviation increase in the order size of Chi-X improves global Depth(10
bps) with only 2%. This is small, and from an economic point of view we argue that
the average order sizes are still valid instruments.
29
B.4
Small versus large stocks
The bene…ts and drawbacks of competition on liquidity might hinge on certain stock
characteristics, such as …rm size. We pursue the point in question by executing the
base speci…cation regressions for large stocks, with an average market cap exceeding
ten billion Euro, and small stocks, with an average market cap below 100 million Euro
(as shown in Table I). The results for the global and local order books of 15 large
and 14 small sample stocks are reported in Table IX, panel A to D. The coe¢ cients
for the consolidated order book are plotted in Figure 10, and show two interesting
results.
First, the bene…ts of competition are higher for large stocks than for small stocks.
For large …rms, the Depth(10 bps) is 64% higher at F rag = 0:35, while for small …rms
the maximum, at F rag = 0:18; has 30% more liquidity compared with a completely
concentrated market.
Second, the …gure shows that the bene…t of competition for large stocks is monotonically positive, meaning there are no harmful e¤ects of fragmentation. By contrast,
the liquidity of small stocks is negatively a¤ected for levels of fragmentation exceeding
0:36. This suggests that the bene…ts of fragmentation strongly depend on …rm size.
Both …ndings are also con…rmed by the other liquidity measures, column (7) to (11)
of panel A and B.
Turning to the regressions in panel C and D of Table IX, we …nd that the local
liquidity of large stocks also increases with fragmentation, while that of small stocks
strongly decreases. That is, at F rag = 0:35 the Depth(10 bps) of large stocks improves by 12%, while that of small stocks reduces with 38%. Again, this con…rms that
the drawbacks of a fragmented market place mainly hold for relatively small stocks.
The fact that large stocks bene…t more from fragmentation is in line with their actual
levels of fragmentation, which is 0:41 in 2009, while for small stocks only 0:21.
B.5
Robustness checks
To investigate the sensitivity of our results, we perform a number of robustness checks.
First, we execute the regressions with …rm*quarter dummies based on observations
in 2008 and 2009 only. The results do not change (not reported), likely because
30
fragmentation especially took place in 2008 and 2009. This provides an additional
robustness to potential time e¤ects (e.g. the …nancial crisis), as the coe¢ cients on
fragmentation are estimated within a smaller time window. In addition, this covers for
the fact that our dataset contains the ten best price levels on Euronext Amsterdam as
of January 2008, while before only the best …ve price levels (as mentioned in footnote
14). Finally, this solves the issue that the data by Markit Boat on dark trades is
available only as of November 2007.
Second, we execute the regressions in …rst di¤erences, i.e. use the daily changes
instead of the daily levels. By analyzing the day-to-day changes, we remove the longterm trends in the data. The results (currently not reported), are very similar to
using …rm*quarter dummies.
Third, instead of using F rag to measure fragmentation, we use the market share
of the traditional market (Euronext Amsterdam), and the qualitative results do not
change. Finally, we have plotted higher order polynomials of F rag, and the inverted
U-shapes remain: there is indeed on optimal level of fragmentation.
VII
Conclusion
Changes in …nancial regulation (the Markets in Financial Instruments Directive) implemented in November 2007 allow for the proliferation of new trading platforms in
European equity markets. Currently, many stocks are traded on a variety of trading
venues, i.e. the market has become fragmented. How fragmentation a¤ects liquidity
is an important question to investors, …nancial institutions and the regulator. This
topic is very relevant as technological advances have drastically altered the …nancial
playing …eld. In addition, regulatory di¤erences between the U.S. and Europe may
cause unanticipated side e¤ects of competition. An example of such a di¤erence is
the best execution rule, which in the U.S. requires brokers to execute trades against
the best price available in the market, while European brokers also have to take other
dimensions of execution quality into account. As a consequence, European trading
platforms can compete on transaction costs, speed of execution and anonymity for
example, but price violations may occur.
This paper analyzes the e¤ect of fragmentation on liquidity for a large set of
31
Dutch stocks which are representative for the European arena in terms of degree of
fragmentation. We explicitly di¤erentiate between the impact of fragmentation on
global and local liquidity, where global liquidity takes all relevant trading venues into
account while local liquidity only the traditional stock market. Global liquidity is
appropriate for an investor using Smart Order Routing Technology (SORT) whereas
local liquidity is relevant when trading fees and costs of technological infrastructure
prevent investors from using SORT.
The main result is that the e¤ect of fragmentation on global liquidity shows an
inverted U-shape. Maximum liquidity is obtained when the market is partially fragmented, at HHI = 0:65. Based on our most conservative measure, global liquidity
is then improved by approximately 35% compared to the the case of a fully concentrated market. However, too much fragmentation lowers global liquidity, as there is
a trade-o¤ between the bene…ts and downsides.
Interestingly, while global liquidity generally improves in fragmentation, local
liquidity does not. That is, the competing trading platforms take away liquidity,
such that an investor who only has access to the traditional market is worse o¤. The
reduction in liquidity close to the midpoint, i.e. at relatively good prices, can be more
than 10% compared to the case of no fragmentation. This suggests that the overall
bene…ts of fragmentation are not shared by all investors.
These results follow from an approach that is aimed to identify an exogenous
relation between liquidity and fragmentation by means of …rm*quarter …xed e¤ects
and instrumental variables regressions. That is, the …rm*quarter dummies can control for potential self selection problems, e.g. that competition might be higher for
high volume and more liquid stocks. In addition, the …rm*quarter dummies provide robustness to dynamic interactions between market structure, competition in
the market, the degree of fragmentation and liquidity. Furthermore, the results based
on the …rm*quarter dummies are more robust to time e¤ects such as the …nancial
crisis. In the instrumental variables regressions we use the daily average order size of
the venues that trade the stock as instruments for the degree of fragmentation. This
approach solves self selection issues if we assume that the variation in fragmentation
generated by the instruments is not related to …rm size and initial level of liquidity,
given the control variables and …rm*quarter dummies already in the regression.
As additional results, we …nd that competition between trading venues is …ercer
32
for larger stocks, as these are more fragmented and have a higher marginal bene…t
of fragmentation. Moreover, large stocks do not face the drawbacks of fragmentation
like small stocks do. These results add to the policy discussion on competition in
…nancial markets, and suggest that overall market quality has improved, although
some types of stocks and investors are worse o¤.
We argue that the improved liquidity is due to competition between trading venues
and suppliers and demanders of liquidity. However, it might be the case that competition makes the market place more e¢ cient, which forces investors to generate
improved trading strategies and algorithms, or trading venues to update technological infrastructure. In our view, these would be indirect consequences of a more
competitive market place.
The current analysis su¤ers from an obvious shortcoming which is not incorporating the liquidity at dark pools, which could over or underestimate our results.
A potential avenue for future research is to incorporate explicit transaction costs
in the analysis, which would truly represent the cost of trading to an investor.
VIII
Appendix: Liquidity measures
The liquidity measures other than the Depth(X) are explained in this section. We
calculate the price impact and the e¤ective and realized spreads based on trades and
weighted over all trades per day. In contrast, the Depth(X), quoted spread and quoted
depth, are liquidity measures based on quotes o¤ered in the limit order book and time
weighted over the trading day. The e¤ective spread measures direct execution costs
while the realized spread takes the order’s price impact into account. The realized
spread is often considered to be the compensation for the liquidity supplier. Denote
M Qo as the quoted midpoint before an order takes place and M Qo+5 the quoted
midpoint, but …ve minutes later and D = [1,-1] for a buy and a sell order respectively,
then
P rice M Qo
M Qo
D 10:000;
(3)
P rice M Qo+5
M Qo
D 10:000;
(4)
Ef f ective half spread =
Realized half spread =
33
P rice impact =
M Qo+5 M Qo
M Qo
D 10:000:
(5)
The price impact, realized and e¤ective spread are …rst calculated per trade, based
on the midpoint of that trading venue. Then, all calculations are averaged over
the trading day, weighted by traded volume. Next, we average over trading venues,
again weighted by trading venue. This approach gives the average spread in the
whole market. Limited computer power is the reason we use the midpoint of the
trading venue where the trade took place instead of the consolidated midpoint. That
is, creating a consolidated midpoint quote-by-qoute, as is required for the e¤ective
and realized spreads, is computationally much more burdensome than creating a
consolidated order book using one-minute snapshots.26 The price impact and realized
spread are calculated between 09.00 - 16.25, while the e¤ective spread on 9.00 - 16.30.
Therefore, Ef f ective spread
Realized spread + P rice impact. The global quoted
spread is based on the best price in the consolidated order book (based on the oneminute snapshot data, see Section A) and expressed in basis points, while the local
quoted spread is based on the order book of Euronext. In a similar fashion, the quoted
depth aggregates the number of shares times their prices, expressed in Euros, or
P ASK P BID
Quoted spread =
M idpoint
Quoted depth = P ASK
10:000;
QASK + P BID
P BID :
(6)
(7)
Note that the quoted depth on Euronext can be larger than that of the consolidated
order book, for example when Chi-X o¤ers a better price but with a lower quantity.
The quoted spread of the consolidated order book is always equal or better than that
of Euronext. Finally, the quoted depth is identical to the Depth(10 bps) when the
quoted spread equals 20 basis points.
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38
Table (I)
Descriptive statistics of sample …rms: cross section
The dataset covers daily observations for 52 AEX large and mid cap constituents, from 2006 to
2009. All variables in the table are averages. Firm size and traded volume are expressed in millions
of Euros. Stdev re‡ects the daily standard deviation of 15 minute returns on the midpoint and is
multiplied by 100. Euronext represents the market share of executed trades on Euronext Amsterdam.
O¤ exchange is the market share of Over The Counter trades, Systematic Internalisers and dark
pools; this number is available as of November 2007.
Firm
Size
Price
Aalberts
Adv. Metal. Group
Aegon
Ahold
Air France
Akzo nobel
Arcadis
Arcellor Mittal
Asm Int.
ASML
Bamn Group
Binckbank
Boskalis
Corio
Crucell
CSMN
Draka Hold.
DSM
Eurocomm. Prop
Fortis
Fugro
Hagemeyer
Heijmans
Heineken
Imtech
ING
Nutreco
Oce
Ordina
Philips
R. Dutch Shell
R. KPN
R. ten cate
R. Wessanen
Randstad
Reed Elsevier
SBM O¤shore
Smit Int.
Sns Reaal
Tele Atlas
Tnt
Tomtom
Unibail Rodamco
Unilever
Usg People
Vastned
Vdr Moolen
Vedior
Vopak Int.
Wavin
Wereldhave
Wolters Kluwers
1.3
0.6
16.6
11.4
5.6
12.3
0.9
3.3
0.8
8.1
1.7
0.6
2.2
3.5
1.0
1.5
0.5
6.1
1.2
34.5
2.8
2.0
0.6
16.7
1.2
50.0
1.5
0.9
0.4
28.4
88.5
20.4
0.5
0.6
4.5
8.1
2.8
10.7
2.9
1.8
10.7
3.0
11.9
32.2
0.6
1.0
0.2
2.9
2.3
0.7
1.6
5.5
29:01
21:22
10:25
8:52
19:94
44:93
31:39
35:68
14:47
17:78
18:83
10:60
39:49
51:12
15:49
20:92
13:01
32:02
32:47
22:83
38:99
3:76
25:07
34:06
15:04
22:75
42:41
9:94
10:95
25:00
24:22
10:93
26:68
8:48
35:17
11:31
26:38
48:09
11:65
20:06
24:95
25:32
143:64
23:62
9:27
55:99
4:74
16:70
36:52
7:94
78:77
18:10
Volume
7:4
8:6
161:0
120:0
63:9
147:0
3:3
388:0
7:0
144:0
14:8
4:2
13:5
26:9
9:2
8:0
3:6
73:0
5:8
437:0
25:0
43:4
3:8
100:0
9:2
904:0
15:1
8:5
2:8
301:0
529:0
220:0
2:3
4:3
38:3
74:1
35:6
4:6
11:5
37:0
99:3
47:7
172:0
327:0
8:5
5:0
2:2
67:7
8:9
5:8
16:0
42:1
39
Stdev
O¤ Exchange
Euronext
0:39
0:78
0:46
0:28
0:40
0:30
0:41
0:50
0:44
0:39
0:41
0:36
0:42
0:36
0:36
0:29
0:55
0:29
0:37
0:38
0:34
0:31
0:40
0:28
0:40
0:44
0:28
0:40
0:41
0:32
0:27
0:26
0:40
0:32
0:39
0:27
0:36
0:38
0:42
0:33
0:33
0:54
0:36
0:26
0:71
0:32
0:35
0:27
0:30
0:49
0:28
0:30
7:95
17:94
15:21
18:61
15:06
19:59
10:78
24:17
10:23
16:75
11:81
10:53
12:73
14:99
8:49
11:99
16:18
16:89
11:14
13:37
10:30
0:00
8:30
18:86
18:02
14:23
12:35
10:54
7:30
21:05
21:57
23:25
10:11
9:10
14:21
20:51
13:18
15:30
12:94
8:24
19:65
10:44
34:67
17:58
19:01
10:50
2:12
2:99
12:24
11:83
13:56
13:15
89:98
78:89
76:92
74:93
78:04
73:42
87:51
70:14
86:31
75:52
83:46
88:51
83:65
79:03
89:80
86:03
77:54
76:86
86:99
83:51
84:83
99:28
90:56
74:15
77:45
81:24
85:27
86:81
89:97
71:25
69:54
69:79
87:63
87:49
79:11
71:97
80:12
80:02
85:50
68:20
73:59
83:14
57:59
73:85
68:01
87:41
97:45
96:48
84:75
86:88
81:87
78:09
Table (II) Descriptive statistics of the various liquidity measures for global and local traders.
The table shows the 25, 50 and 75th percentiles of the liquidity measures on a yearly basis, for the consolidated order book
(upper panel) and Euronext Amsterdam (lower panel). The quantiles are based on 52 …rms and 250 trading days per year
(11.250 obs). The Depth(X) is expressed in e1000s and represents the o¤ered liquidity within (X) basis points around the
midpoint. The quoted depth is the amount of shares, in e1000s, o¤ered at the best bid and ask price of the global and local
order book. The price impact and the e¤ective, realized and quoted spreads are measured in basis points. The price impact
and realized spread are based on a 5 minute timewindow.
40
Year
Percentile
2006
25
2006
50
2006
75
2007
25
2007
50
2007
75
2008
25
2008
50
2008
75
2009
25
2009
50
2009
75
Global
Depth10
Depth20
Depth30
Depth40
Depth50
Depth60
Quoted Spread
Quoted Depth
Realized
Price Impact
E¤ective
28
95
158
208
245
271
8.34
55
-0.60
6.38
9.20
102
263
367
441
488
517
13.26
101
2.46
10.39
14.10
383
938
1255
1404
1456
1476
22.39
212
7.40
17.57
23.28
33
89
140
180
207
226
6.90
42
-1.58
5.98
7.32
134
299
404
463
505
531
10.89
82
1.08
9.44
11.22
464
1143
1372
1465
1527
1542
18.35
201
4.78
15.57
18.12
11
33
54
73
91
107
7.40
26
-3.61
8.30
8.89
50
125
183
228
258
281
14.52
41
-0.06
14.33
15.08
231
511
748
899
995
1064
26.45
70
3.59
25.51
25.71
12
42
72
98
121
147
7.36
20
-3.05
7.51
7.73
66
187
291
367
420
455
12.01
32
0.03
13.32
13.17
249
646
934
1092
1171
1210
25.42
58
4.04
24.01
27.32
Local
Depth10
Depth20
Depth30
Depth40
Depth50
Depth60
Quoted Spread
Quoted Depth
Realized
Price Impact
E¤ective
27
92
152
200
235
260
8.76
55
-0.65
6.46
9.09
101
261
359
422
463
486
13.54
102
2.41
10.36
13.84
339
735
971
1129
1175
1192
23.13
212
7.22
17.23
23.13
33
86
136
177
204
222
7.64
43
-1.67
6.15
7.42
127
279
366
406
426
437
11.48
85
1.10
9.49
11.09
394
859
1053
1140
1203
1212
18.67
205
4.68
15.33
17.83
10
30
49
67
83
97
10.38
25
-3.78
8.54
9.04
39
94
141
178
205
225
16.81
40
-0.15
14.16
14.51
136
308
428
487
523
557
28.11
78
3.60
24.68
24.94
9
27
45
61
77
95
9.52
18
-3.56
7.85
8.13
36
93
155
206
244
267
14.69
30
0.10
13.54
13.08
110
319
500
589
628
641
28.54
53
4.45
23.77
26.68
Table (III) Descriptive statistics of fragmentation.
The yearly standard deviation, mean and quartiles of market concentration, HHI, are
reported, where HHI is based on the market share of visible traded volume. The lower
panel shows the standard deviation of HHI demeaned per …rm, per quarter and per
…rm and quarter. This is done by subtracting the …rm average, the quarterly average,
and …rst …rm, and then quarterly average from HHI.
Year
Stdev Mean
25th
50th
75th
2006
2007
2008
2009
Total
0.080
0.066
0.119
0.153
0.150
0.991
0.983
0.832
0.597
0.820
1.000
0.999
0.956
0.709
0.985
1.000
1.000
1.000
0.857
1.000
0.974
0.974
0.904
0.725
0.894
Stdev
HHI demeaned per Firm
HHI demeaned per Quarter
HHI demeaned per Firm - Quarter
0.135
0.104
0.085
Table (IV) Descriptive statistics: time series.
The yearly mean, median and standard deviation are reported for the following variables: Ln …rm size, Ln traded volume, algorithmic trading, Ln stdev and market share
of dark trades. Algo represents the number of electronic messages in the market divided by total traded volume (per e10.000). An electronic message occurs when a
limit order in the order book is executed, changed or cancelled. Volatility, Ln Stdev
here, is de…ned as the natural logarithm of the daily standard deviation of 15 minute
returns on the midpoint. Typically, the standard deviation is lower than one, so
the natural logarithm becomes negative. Dark share is the percentage of order ‡ow
executed at dark pools, SIs and Over The Counter.
Ln Size
Ln Volume
Algo (10.000)
Ln Stdev
Dark share
Ln Size
Ln Volume
Algo (10.000)
Ln Stdev
Dark share
2006
Mean Median Stdev
2007
Mean Median Stdev
15.1
16.7
2.6
-6.2
0.2
15.2
17.1
3.6
-6.1
4.2
14.7
16.7
1.9
-6.2
0.0
1.4
1.8
2.5
0.5
1.2
15.0
17.1
2.6
-6.1
0.0
1.4
1.8
3.9
0.5
11.6
2008
Mean Median Stdev
2009
Mean Median Stdev
14.8
17.1
12.3
-5.4
25.5
14.5
16.7
51.2
-5.6
25.0
14.7
17.0
6.6
-5.5
22.5
1.4
1.9
17.1
0.6
17.3
41
14.4
16.5
28.4
-5.6
22.2
1.5
1.8
57.1
0.5
16.9
Table (V)
The e¤ect of fragmentation on global liquidity.
The dependent variable in models (1) - (6) is the logarithm of the Depth(X) measure based on the consolidated order book. The Depth(X)
is expressed in Euros and represents the o¤ered liquidity within (X) basis points around the midpoint. The quoted, e¤ective, and realized
spread and price impact, (7) - (10), are measured in basis points. Ln quoted depth is the logarithm of the quoted depth in Euros (11).
Frag is the degree of market fragmentation, de…ned as 1 - HHI. Algo represents the number of electronic messages divided by traded
volume in the market (per e100); the other variables are explained in the descriptive statistics and Table I. The regressions are based on
1022 trading days for 52 stocks, and have …rm …xed e¤ects and quarter dummies. T-stats are shown below the coe¢ cients, calculated
using Newey-West (HAC) standard errors (based on 5 day lags). ***, ** and * denote signi…cance at the 1, 5 and 10 percent levels,
respectively.
42
Frag
Frag2
Ln Size
Ln Price
Ln Vol
Ln Stdev
Dark
Algo
Obs
R2
(1)
Ln
Depth10
(2)
Ln
Depth20
(3)
Ln
Depth30
(4)
Ln
Depth40
(5)
Ln
Depth50
(6)
Ln
Depth60
(7)
E¤ective
(8)
Realized
(9)
Price Impact
(10)
Quoted
Spread
(11)
Ln Depth
Quoted
2.844***
(15.9)
-4.069***
(-13.4)
1.008***
(24.2)
-0.0117
(-0.5)
0.576***
(40.9)
-0.619***
(-40.0)
-0.914***
(-20.2)
-0.116***
(-5.4)
46879
0.461
2.080***
(21.0)
-2.875***
(-15.7)
0.623***
(24.8)
0.0616***
(3.8)
0.429***
(45.7)
-0.537***
(-52.2)
-0.685***
(-23.7)
-0.106***
(-7.1)
46879
0.663
2.188***
(26.4)
-3.081***
(-19.2)
0.491***
(24.6)
0.0688***
(4.7)
0.385***
(47.0)
-0.466***
(-54.0)
-0.587***
(-24.0)
-0.094***
(-7.6)
46879
0.681
2.334***
(28.5)
-3.381***
(-21.1)
0.427***
(22.5)
0.0693***
(4.8)
0.353***
(47.4)
-0.420***
(-52.5)
-0.540***
(-22.9)
-0.097***
(-8.4)
46879
0.659
2.420***
(29.7)
-3.616***
(-22.7)
0.387***
(20.9)
0.0668***
(4.5)
0.327***
(46.9)
-0.384***
(-50.7)
-0.503***
(-21.8)
-0.097***
(-8.8)
46879
0.641
2.468***
(30.5)
-3.808***
(-24.2)
0.359***
(19.8)
0.0603***
(4.0)
0.309***
(46.5)
-0.360***
(-49.5)
-0.483***
(-21.3)
-0.092***
(-8.6)
46879
0.621
-31.32***
(-16.0)
33.94***
(9.8)
-6.996***
(-15.8)
-0.137
(-0.4)
-2.304***
(-11.3)
7.312***
(31.9)
2.960***
(3.7)
4.565***
(14.6)
46879
0.236
1.047
(0.5)
-6.615**
(-2.0)
-3.220***
(-7.6)
-0.207
(-0.7)
0.380**
(2.0)
-4.963***
(-23.6)
-1.147
(-1.4)
0.0336
(0.1)
46879
0.042
-32.40***
(-17.2)
40.61***
(12.8)
-3.779***
(-8.6)
0.0728
(0.3)
-2.682***
(-17.0)
12.28***
(53.0)
4.101***
(7.8)
4.527***
(13.7)
46879
0.331
-41.94***
(-24.8)
55.72***
(19.0)
-4.906***
(-9.4)
1.759***
(4.3)
-3.724***
(-29.4)
5.733***
(33.5)
4.476***
(9.8)
4.514***
(15.5)
46879
0.352
-0.888***
(-11.6)
0.305**
(2.0)
0.279***
(17.7)
-0.0561***
(-4.2)
0.233***
(43.2)
-0.223***
(-33.7)
-0.544***
(-26.8)
-0.0074
(-0.8)
46879
0.673
Table (VI)
The e¤ect of fragmentation on local liquidity.
The dependent variable in models (1) - (6) is the logarithm of the Depth(X) measure based on the order book of Euronext Amsterdam.
The Depth(X) is expressed in Euros and represents the o¤ered liquidity within (X) basis points around the midpoint. The quoted,
e¤ective, and realized spread and price impact, (7) - (10), are measured in basis points. Ln quoted depth is the logarithm of the quoted
depth in Euros (11). Frag is the degree of market fragmentation, de…ned as 1 - HHI. Algo represents the number of electronic messages
divided by traded volume in the market (per e100); the other variables are explained in the descriptive statistics and Table I. The
regressions are based on 1022 trading days for 52 stocks, and have …rm …xed e¤ects and quarter dummies. T-stats are shown below the
coe¢ cients, calculated using Newey-West (HAC) standard errors (based on 5 day lags). ***, ** and * denote signi…cance at the 1, 5 and
10 percent levels, respectively.
43
Frag
Frag2
Ln Size
Ln Price
Ln Vol
Ln Stdev
Dark
Algo
Obs
R2
(1)
Ln
Depth10
(2)
Ln
Depth20
(3)
Ln
Depth30
(4)
Ln
Depth40
(5)
Ln
Depth50
(6)
Ln
Depth60
(7)
E¤ective
Spread
(8)
Realized
Spread
(9)
Price Impact
(10)
Quoted
Spread
(11)
Ln Depth
Quoted
1.025***
(5.7)
-2.942***
(-9.6)
0.958***
(22.5)
-0.0520**
(-2.1)
0.578***
(40.1)
-0.609***
(-38.4)
-0.947***
(-20.8)
-0.128***
(-5.9)
46879
0.498
0.00555
(0.1)
-0.427**
(-2.2)
0.542***
(20.6)
0.0438**
(2.6)
0.426***
(43.1)
-0.534***
(-48.0)
-0.722***
(-23.5)
-0.187***
(-13.0)
46879
0.677
0.162*
(1.8)
-0.294
(-1.6)
0.407***
(18.9)
0.0596***
(3.7)
0.382***
(43.1)
-0.469***
(-47.7)
-0.647***
(-23.3)
-0.206***
(-16.2)
46879
0.671
0.411***
(4.5)
-0.624***
(-3.3)
0.344***
(16.1)
0.0600***
(3.6)
0.351***
(42.6)
-0.425***
(-45.3)
-0.621***
(-22.3)
-0.216***
(-17.4)
46879
0.636
0.589***
(6.4)
-0.960***
(-5.0)
0.307***
(14.5)
0.0533***
(3.2)
0.326***
(41.7)
-0.391***
(-43.2)
-0.596***
(-21.6)
-0.214***
(-17.5)
46879
0.607
0.687***
(7.4)
-1.220***
(-6.3)
0.284***
(13.5)
0.0407**
(2.4)
0.310***
(41.0)
-0.369***
(-41.6)
-0.584***
(-21.2)
-0.204***
(-16.8)
46879
0.581
-31.07***
(-14.7)
36.40***
(9.9)
-7.414***
(-16.5)
0.0694
(0.2)
-2.081***
(-9.4)
7.312***
(30.8)
2.348***
(2.9)
3.869***
(12.4)
46879
0.208
0.679
(0.3)
-6.349*
(-1.7)
-3.364***
(-7.7)
-0.163
(-0.5)
0.598***
(2.9)
-5.057***
(-22.9)
-1.784**
(-2.1)
0.108
(0.4)
46879
0.039
-31.79***
(-17.7)
42.82***
(14.2)
-4.054***
(-9.3)
0.236
(0.8)
-2.676***
(-17.4)
12.37***
(53.6)
4.127***
(8.0)
3.756***
(11.9)
46879
0.335
-35.21***
(-18.3)
48.65***
(17.0)
-4.471**
(-2.6)
1.894***
(4.7)
-4.066***
(-6.6)
8.337***
(6.5)
4.043***
(8.3)
6.010***
(18.4)
46858
0.121
-0.416***
(-5.8)
-1.248***
(-9.1)
0.224***
(14.0)
-0.0490***
(-3.5)
0.244***
(45.4)
-0.223***
(-34.4)
-0.541***
(-27.5)
0.0560***
(6.7)
46858
0.717
Table (VII)
The e¤ect of fragmentation on liquidity: …rm*quarter dummies.
The dependent variable in models (1) - (6) is the logarithm of the Depth(X) measure based on the consolidated order book (panel A)
and order book of Euronext Amsterdam (panel B). The Depth(X) is expressed in Euros and represents the o¤ered liquidity within (X)
basis points around the midpoint. The quoted, e¤ective, and realized spread and price impact, (7) - (10), are measured in basis points.
Ln quoted depth is the logarithm of the quoted depth in Euros (11). Frag is the degree of market fragmentation, de…ned as 1 - HHI.
For the sake of brevity, the coe¢ cients on the control variables are not reported, as they are very similar to those of Tables V and VI.
The control variables are Ln size, Ln price, Ln volume, Ln volatility, dark and algo, as explained in Table I. The regressions are based
on 1022 trading days for 52 stocks. T-stats are shown below the coe¢ cients, calculated using Newey-West (HAC) standard errors (based
on 5 day lags). ***, ** and * denote signi…cance at the 1, 5 and 10 percent levels, respectively.
44
(1)
Ln
Depth10
(2)
Ln
Depth20
(3)
Ln
Depth30
(4)
Ln
Depth40
(5)
Ln
Depth50
(6)
Ln
Depth60
(7)
E¤ective
Spread
(8)
Realized
Spread
(9)
Price Impact
(10)
Quoted
Spread
(11)
Ln Depth
Quoted
-5.996**
(-2.5)
4.206
(1.0)
0.084
-3.592*
(-1.7)
5.839
(1.5)
0.417
-3.939***
(-2.6)
0.842
(0.3)
0.547
0.0103
(0.2)
-0.185
(-1.6)
0.836
-6.544**
(-2.5)
2.452
(0.6)
0.082
-4.382**
(-2.3)
11.51***
(3.2)
0.411
-2.373
(-1.5)
7.060**
(2.6)
0.184
0.0241
(0.5)
-0.603***
(-6.3)
0.851
Panel A: Global, Firm * Quarter dummies
Frag
Frag2
R2
0.970***
(6.3)
-0.725***
(-2.7)
0.668
0.744***
(9.4)
-0.679***
(-4.6)
0.777
0.690***
(10.2)
-0.854***
(-6.7)
0.820
0.649***
(10.4)
-0.908***
(-7.7)
0.822
0.585***
(9.9)
-0.860***
(-7.6)
0.824
0.534***
(9.4)
-0.802***
(-7.3)
0.823
-9.609***
(-4.4)
10.05**
(2.6)
0.345
Panel B: Local, Firm * Quarter dummies
Frag
Frag2
R2
0.225
(1.5)
-0.974***
(-3.7)
0.683
-0.0682
(-0.8)
-0.332**
(-2.1)
0.755
-0.0993
(-1.3)
-0.324**
(-2.2)
0.768
-0.101
(-1.4)
-0.335**
(-2.3)
0.754
-0.113
(-1.5)
-0.311**
(-2.1)
0.742
-0.114
(-1.5)
-0.298**
(-2.1)
0.729
-10.95***
(-4.5)
13.97***
(3.3)
0.300
Table (VIII)
Instrumental Variables regressions of fragmentation on liquidity.
Fragi;t is the degree of market fragmentation based on the market shares of all trading venues, de…ned as 1 - HHI. Panel A and B show the regressions
for global and local depth respectively, where Fragi;t and Frag2i;t are instrumented by Fragi;t 1 and Frag2i;t 1 . Panel C and D show the regressions for
global and local depth, where the average daily order size of each of the seven venues that trade the stock are used as instruments. The dependent
variable in models (1) - (6) is the logarithm of the Depth(X) measure, based on the global order book (panel A and C) or the local order book (panel
B and D). The Depth(X) represents the o¤ered liquidity within (X) basis points around the midpoint, expressed in Euros. The quoted, e¤ective, and
realized spread and price impact, (7) - (10), are measured in basis points. Ln quoted depth is the logarithm of the quoted depth in Euros (11). The
regressions have …rm*quarter dummies, t-stats are shown below the coe¢ cients, calculated using Newey-West (HAC) standard errors (based on 5 day
lags). For the sake of brevity, we only report the coe¢ cients and t-stats of Frag and Frag2 ; the signs and magnitudes of the control variables are very
similar to those reported in Table V and VI. ***, ** and * denote signi…cance at the 1, 5 and 10 percent levels, respectively.
(1)
Ln
Depth10
(2)
Ln
Depth20
(3)
Ln
Depth30
(4)
Ln
Depth40
(5)
Ln
Depth50
(6)
Ln
Depth60
45
Panel A: Global, IV Fragi;t
Frag
Frag2
R2
2.407***
(5.0)
-2.746***
(-3.2)
0.668
1.861***
(0.251)
-2.490***
(0.466)
0.776
1.775***
(0.212)
-2.803***
(0.397)
0.819
1.719***
(0.197)
-2.939***
(0.370)
0.820
1.601***
(0.187)
-2.873***
(0.353)
0.823
Frag2
R2
0.458
(0.9)
-1.705*
(-1.8)
0.684
-0.234
(-0.8)
-0.407
(-0.7)
0.755
-0.282
(-1.1)
-0.405
(-0.8)
0.768
-0.249
(-1.0)
-0.503
(-1.1)
0.754
-0.225
(-0.9)
-0.560
(-1.2)
0.742
and Frag2i;t
1.453***
(0.181)
-2.662***
(0.342)
0.821
Panel B: Local, IV Fragi;t
Frag
1
1
(7)
E¤ective
Spread
-0.223
(-0.9)
-0.533
(-1.2)
0.728
(9)
Price Impact
(10)
Quoted
Spread
(11)
Ln Depth
Quoted
-9.175
(-1.3)
16.08
(1.3)
0.084
-0.925
(-0.1)
2.696
(0.2)
0.417
-3.302
(-0.7)
-2.276
(-0.3)
0.547
0.301
(1.6)
-1.204***
(-3.6)
0.835
-12.07
(-1.4)
23.28
(1.4)
0.081
-8.385
(-1.3)
16.21
(1.3)
0.411
-1.670
(-0.1)
9.240
(0.4)
0.185
-0.0607
(-0.4)
-0.916***
(-2.9)
0.851
48.99***
(4.3)
-250.9***
(-6.0)
-0.038
31.53***
(4.6)
-140.8***
(-5.7)
0.382
37.95***
(7.0)
-199.9***
(-11.7)
0.412
3.315***
(15.2)
-12.20***
(-16.6)
0.747
67.44***
(5.5)
-324.9***
(-7.1)
-0.095
30.58***
(4.7)
-130.6***
(-5.4)
0.377
65.08***
(3.8)
-283.2***
(-3.3)
0.125
3.401***
(15.0)
-13.89***
(-18.1)
0.736
1
-10.18
(-1.6)
18.89
(1.6)
0.345
and Frag2i;t
(8)
Realized
Spread
1
-20.53***
(-2.8)
39.57***
(2.8)
0.299
Panel C: Global, IV average order sizes
Frag
Frag2
R2
1.257**
(2.0)
2.541
(1.4)
0.661
1.037***
(3.3)
0.225
(0.2)
0.774
1.145***
(4.9)
-1.426*
(-1.8)
0.820
1.384***
(6.3)
-2.801***
(-3.7)
0.820
1.374***
(6.6)
-3.201***
(-4.5)
0.822
1.440***
(7.2)
-3.659***
(-5.3)
0.818
80.41***
(7.3)
-391.3***
(-9.4)
0.061
Panel D: Local, IV average order sizes
Frag
Frag2
R2
-0.518
(-0.7)
5.337**
(2.2)
0.672
-0.997*
(-1.8)
4.365**
(2.1)
0.746
-0.879*
(-1.7)
3.080
(1.5)
0.762
-0.609
(-1.1)
1.943
(0.9)
0.751
-0.577
(-1.1)
1.655
(0.8)
0.740
-0.495
(-0.9)
1.331
(0.6)
0.727
97.93***
(8.1)
-455.2***
(-10.0)
-0.055
Table (IX)
The e¤ect of fragmentation on liquidity: large and small …rms.
The regressions are executed separately for the 15 smallest stocks (avg market cap < 100 million) and the 14 largest stocks (avg market cap > 10
billion); for the global and local order books. The dependent variable in models (1) - (6) is the logarithm of the Depth(X) measure. The Depth(X) is
expressed in Euros and represents the o¤ered liquidity within (X) basis points around the midpoint. The quoted, e¤ective, and realized spread and
price impact, (7) - (10), are measured in basis points. Ln quoted depth is the logarithm of the quoted depth in Euros (11). Frag is the degree of
market fragmentation, de…ned as 1 - HHI. For the sake of brevity, the coe¢ cients on the control variables are not reported, as they are very similar
to those of Tables V and VI. The control variables are Ln size, Ln price, Ln volume, Ln volatility, dark and algo, as explained in Table I. T-stats are
shown below the coe¢ cients and calculated using Newey-West (HAC) standard errors (based on 5 day lags). ***, ** and * denote signi…cance at the
1, 5 and 10 percent levels, respectively.
(1)
Ln
Depth10
(2)
Ln
Depth20
(3)
Ln
Depth30
(4)
Ln
Depth40
(5)
Ln
Depth50
(6)
Ln
Depth60
(7)
E¤ective
Spread
(8)
Realized
Spread
(9)
Price Impact
(10)
Quoted
Spread
(11)
Ln Depth
Quoted
-5.731**
(-2.2)
7.478
(1.6)
0.042
-10.33***
(-6.5)
14.04***
(4.3)
0.317
-7.922***
(-3.3)
10.41**
(2.4)
0.105
-0.688***
(-4.5)
2.472***
(7.7)
0.743
-9.174
(-1.2)
29.72**
-2.000
0.059
-9.875
(-1.3)
29.33*
-1.900
0.425
-51.29***
(-9.8)
127.5***
-11.500
0.588
-0.073
(-0.5)
-0.114
(-0.3)
0.661
-7.828**
(-2.4)
7.706
(1.6)
0.037
-6.472***
(-3.9)
10.30***
(3.2)
0.303
-7.057***
(-5.4)
14.99***
(5.3)
0.573
0.392***
(2.9)
-0.502*
(-1.9)
0.801
-4.063
(-0.5)
15.41
(1.0)
0.058
-33.43***
(-4.6)
83.61***
(5.9)
0.414
-35.21***
(-6.5)
105.5***
(9.2)
0.604
-0.141
(-0.9)
-0.354
(-1.1)
0.663
Panel A: Global, large …rms
46
Frag
Frag2
R2
1.458***
(10.1)
-0.555*
(-2.0)
0.776
1.150***
(9.1)
-0.463*
(-1.8)
0.781
1.072***
(8.1)
-0.334
(-1.2)
0.710
1.052***
(7.4)
-0.374
(-1.3)
0.629
1.058***
(7.1)
-0.438
(-1.4)
0.574
1.071***
(7.1)
-0.452
(-1.5)
0.536
-16.06***
(-6.0)
21.52***
(4.5)
0.121
Panel B: Global, small …rms
Frag
Frag2
R2
2.992***
-4.900
-8.300***
(-6.9)
0.469
2.086***
-6.800
-5.373***
(-8.4)
0.654
1.805***
-7.400
-4.706***
(-8.9)
0.747
1.649***
-7.500
-4.290***
(-9.1)
0.764
1.443***
-6.900
-3.825***
(-8.7)
0.769
1.299***
-6.400
-3.507***
(-8.3)
0.767
-19.08***
(-2.7)
59.00***
-4.500
0.371
Panel C: Local, large …rms
Frag
Frag2
R2
0.640***
(5.1)
-0.869***
(-3.5)
0.855
0.478***
(3.9)
-0.484*
(-1.9)
0.829
0.355**
(2.6)
0.0768
(0.3)
0.755
0.352**
(2.3)
0.194
(0.6)
0.685
0.393**
(2.4)
0.141
(0.4)
0.638
0.405**
(2.5)
0.166
(0.5)
0.610
-14.30***
(-4.6)
18.01***
(3.6)
0.105
Panel D: Local, small …rms
Frag
Frag2
R2
1.330**
(2.1)
-6.230***
(-5.0)
0.466
0.380
(1.2)
-3.045***
(-4.5)
0.616
0.173
(0.6)
-2.406***
(-4.0)
0.679
0.139
(0.5)
-2.144***
(-3.7)
0.679
0.0487
(0.2)
-1.844***
(-3.2)
0.670
-0.00442
(-0.0)
-1.633***
(-2.9)
0.653
-37.52***
(-5.3)
98.94***
(7.3)
0.348
200
150
100
Millions of Euros
50
0
01 2006
01 2007
01 2008
Euronext
Germany
Other
Figure (1)
01 2009
12 2009
Offexchange
ChiX
Traded Volume in millions of Euros.
The graph displays the daily traded volume in millions, aggregated over the 52 AEX Large
and Mid cap constituents. Euronext consists of Amsterdam, Brussels, Paris and Lisbon.
Germany combines all the German cities while Other represents Bats Europe, NASDAQ
OMX Europe, Virt-x and Turquoise combined. Finally, O¤ exchange represents the order‡ow executed Over The Counter, at Systematic Inernalisers and dark pools; however, these
numbers are not available prior to November 2007.
47
Figure (2) Snapshot of a hypothetical limit order book.
The Depth(20) aggregates liquidity o¤ered within the interval of (M - 20 bps, M +
20 bps), which are 2500 shares on the ask side and 800 on the bidside. Depth(40)
contains 4100 and 2800 shares on the ask and bid side respectively. The number of
shares o¤ered are converted to a Euro amount.
48
1
0
.5
Millions of Euros
1.5
2
Depth Euronext and Consolidated
2006m1
2007m1
2008m1
2009m1
2010m1
Month
Con_10
Euro_10
Con_60
Euro_60
Figure (3) Local and global depth over time.
Monthly averages of Depth(10 bps) and Depth(60 bps) are shown, for Euronext Amsterdam (local) and the consolidated order book (global). The consolidated order
book aggregates the order books of Euronext Amsterdam, Deutsche Boerse, Chi-X,
Virt-X, Turquoise, Nasdaq OMX Europe and Bats Europe. Depth(10 bps) represents
liquidity o¤ered within 10 basis points around the midpoint, i.e. at good price levels.
Depth(60 bps) includes liquidity deeper in the order books as well, o¤ered up to 60
basis points around the midpoint. The values are averaged over the 52 AEX large
and mid cap constituents on a monthly basis and expressed in Euros.
49
Liquidity Consolidated orderbook
Euro in 1000s
500
400
300
200
100
10
20
30
40
50
60
40
50
60
16
Log Depth
14
12
10
8
10
20
30
Basis points around midpoint
log_con_10
log_con_50
log_con_90
Figure (4) Depth in the consolidated order book.
The upper panel shows the median of the Depth(X) measure, expressed in e1000s.
The measure aggregates the Euro value of the shares o¤ered within a …xed amount
of basis points around the midpoint, shown on the horizontal axes. The consolidated
order book represents liquidity to a global investor, where the order books of Euronext
Amsterdam, Deutsche Boerse, Chi-X, Virt-X, Turquoise, Nasdaq OMX Europe and
Bats Europe are aggregated. The median is based on the 52 AEX large and mid
cap constituents between 2006 - 2009. The lower panel displays the 10, 50 and 90 th
percentiles of the logarithm of the depth measure.
50
.5
.6
.7
HHI
.8
.9
1
HHI: 10, 50 and 90 percentile
2006m1
2007m1
2008m1
2009m1
2010m1
Months
hhi_10
hhi_90
hhi_50
Figure (5) Fragmentation of AEX large and Mid cap …rms.
The monthly 10, 50 and 90th percentiles of HHI are shown, for the 52 AEX large
and mid cap stocks between 2006 - 2009. HHI is based on the number of shares
traded at the following trading venues: Euronext (Amsterdam, Brussels, Paris and
Lisbon together), Deutsche Boerse, Chi-X, Virt-X, Turquoise, Nasdaq OMX Europe
and Bats Europe. Trades executed OTC, on SIs or dark pools are not taken into
account, as we analyze the degree of market fragmentation of visible liquidity.
51
0.5
Ln Depth
0.4
0.3
0.2
0.1
0.0
0
.2
.4
.6
Fragmentation
Ln Depth10
Ln Depth30
Ln Depth50
Ln Depth20
Ln Depth40
Ln Depth60
Figure (6) The e¤ect of fragmentation on global liquidity.
The coe¢ cients of regressions (1) - (6) of Table V are plotted. The vertical axis
displays the logarithm of the depth(X), while the horizontal axis shows the level of
fragmentation, de…ned as (1 - HHI).
52
0.1
Ln Depth
0.0
-0.1
-0.2
-0.3
-0.4
0
.2
.4
.6
Fragmentation
Depth10
Depth30
Depth50
Depth 20
Depth40
Depth60
Figure (7) The e¤ect of fragmentation on local liquidity.
The fragmentation coe¢ cients for Euronext Amsterdam are plotted (regressions (1)
- (6) of Table VI). The vertical axis shows the logarithm of Depth(X), while the
horizontal axis shows the level of fragmentation, de…ned as (1 - HHI).
53
Ln Depth
0.3
0.2
0.1
0.0
0
.2
.4
.6
Fragmentation
Ln Depth10
Ln Depth30
Ln Depth50
Ln Depth20
Ln Depth40
Ln Depth60
Figure (8) Fragmentation and global liquidity: …rm*quarter dummies.
The fragmentation coe¢ cients for the consolidated order book are plotted, where
the regressions have …rm*quarter dummies (regressions (1) - (6) of Table VII). The
vertical axis displays the logarithm of the depth(X), while the horizontal axis shows
the level of fragmentation, de…ned as (1 - HHI).
54
0.00
Ln Depth
-0.05
-0.10
-0.15
-0.20
0
.2
.4
.6
Fragmentation
Depth10
Depth30
Depth50
Depth 20
Depth40
Depth60
Figure (9) Fragmentation and local liquidity: …rm*quarter dummies.
The fragmentation coe¢ cients for the order book of Euronext Amsterdam are plotted,
where the regressions have …rm*quarter dummies (regressions (1) - (6) of Table VII).
The vertical axis displays the logarithm of the depth(X), while the horizontal axis
shows the level of fragmentation, de…ned as (1 - HHI).
55
Large stocks
Ln Depth
1.0
0.5
0.0
-0.5
-1.0
0
.2
.4
.6
.4
.6
Small stocks
Ln Depth
1.0
0.5
0.0
-0.5
-1.0
0
.2
Fragmentation
Depth10
Ln Depth20
Ln Depth30
Ln Depth40
Ln Depth50
Ln Depth60
Figure (10) Fragmentation and global liquidity: small versus large stocks.
The fragmentation coe¢ cients for the consolidated order book are plotted, for large
and small stocks (regressions (1) - (6) in panel A and B, Table IX). The 14 large
stocks have an average market cap exceeding ten billion Euro, while the 15 small
caps Large stocks consist of the 14 stocks with an average market cap exceeding ten
billion Euro, while the 15 small stocks have a market cap smaller than 100 million
Euro. The regressions include …rm*quarter dummies. The vertical axis displays the
logarithm of the depth(X), while the horizontal axis shows the level of fragmentation,
de…ned as (1 - HHI).
56