1. Trading Costs Analysis:
A Comparison of Euronext Paris and the London Stock Exchange ♣
Université de Paris-12, IRG
Université de Paris-Nanterre & CEREG
JEL classification: G19
Keywords: transaction costs, bid-ask spreads, dealer market, liquidity, order-
driven market, quote-driven market
First version: December 2002
Not to be quoted
This study has been conducted with the collaboration of Didier Davydoff, chairman of IEM-Finance and
Laurent Grillet-Aubert, research analyst at IEM-Finance. The data from the London Stock Exchange was provided
by the CEREG (Paris-Dauphine University) and the data from Euronext Paris was provided by IEM-Finance.
2. Trading Costs Analysis:
A Comparison of Euronext Paris and the London Stock Exchange
This article aims at comparing the cost of trading on the London Stock Exchange (LSE) and
Euronext Paris. Based on two stock samples paired according to economic sectors, free-float
capitalisations and trading volumes, our research shows that quoted spreads are lower on
the French market than in London, but that the London market records higher depth
indicators, in spite of the similarities in trading mechanisms of both exchanges. The Parisian
market is cheaper for small trades while the London market offers lower immediacy costs for
large trades, although the LSE has adopted an order-driven system for Blue Chips since 1997.
Thus, the differences in liquidity can only be assigned to differences in intermediary
practices like internalisation and order splits before execution, or to differences in the
characteristics of the final demand.
3. Trading Costs Analysis:
A Comparison of Euronext Paris and the London Stock Exchange
In practice, investors do, when they send orders, take the existence of trading costs into
account in their strategies. A financial asset that offers a low return, may, if returns net of
trading costs are considered as a selection criterion, remain an attractive investment
provided that trading costs are low. On equity markets, trading costs have two components,
explicit and implicit costs. Explicit trading costs include both brokerage fees paid to
intermediaries that process the order, and taxes. Implicit costs relate to the difference that
may appear at a given point of time between the buy and sell prices of a specific asset. The
existence of implicit costs has an effect on a market liquidity. An asset is liquid if it may be
bought and sold quickly without significant price change. One may measure this easiness of
trading either by the time necessary to realise the transaction at a reasonable price, or by the
cost of transforming an asset into cash immediately.
A large number of articles have already compared the liquidity of the two main market
models: quote-driven markets, as on most fixed income markets or on the NASDAQ, where
market makers commit themselves to post continuously bid and ask prices for minimal
quantities of assets, and order-driven markets, as most equity markets, where buy and sell
orders originating from final clients are directly matched against one another. On quote-
driven (or dealer) markets, all trades are decentralised and processed by intermediaries
acting on their own account that ensure that the market remains liquid. Conversely, on
order-driven (or auction) markets, all orders are centralised in an order book and investors
themselves provide liquidity. Intermediaries are, at least in theory, only brokers in charge of
transmitting final clients’ orders.
In reality, given the gains resulting from the adoption of each of these types of organisations,
most markets have adopted a mixed structure: Deutsche Börse and Euronext, for example,
organise the competition between market makers for small- and mid-caps1 and let investors’
limit orders compete against each other. At the London Stock Exchange (LSE), Blue Chips are
traded on an order-driven market (the SETS system2) but orders from individual investors
relating to the same securities are generally processed by Retail Service Providers (RSP) acting
as market makers.
1 They are called Liquidity Providers on Euronext, and Designated Sponsors (former Betreuer) on Deutsche Börse.
4. The trading costs of an order on a financial market thus depend on the characteristics of
security, the market structure on which it is traded and on the order placement strategies of
Against this background, the aim of this research consists in analysing trading costs on the
LSE and Euronext Paris, two stock markets that present well-identified differences and
similarities, as far as market models are concerned.
Our methodology consists in constructing samples of securities, paired according to their
economic sectors, free float market capitalisations and trading volumes. This methodology
compares to that of Huang and Stoll (1996) and Venkataraman (2001). It ensures that
empirical results can be assigned to differences in market structures and not to corporate
differences in compared stocks.
The first part aims at explaining the factors influencing trading costs on markets showing
structures such as that of the LSE or Euronext. The second part aims at proposing various
measures of trading cost and at characterising these trading costs by breaking them down. In
a third stage, these measures are applied to the Paris and the London exchanges so as to
compare trading costs, on the basis of intra-day market data (both orders and trades).
This research presents two advantages. It enables firstly to compare trading costs on two
differently organised markets. Secondly, it enables to determine how trading costs evolve as
a function of free float market capitalisation, trade size and volatility. Section 1 provides
information about European stock market structures and trading costs according to previous
studies. The liquidity measures used in the paper are presented in Section 2. Section 3
describes the data and the methodology. Empirical results are given in Section 4 and Section
1. The impact of the market organisation on trading costs
1.1. A comparison of trading costs on the various financial markets
Two sources of data may be used to measure implicit trading costs: one may either consider
relating data, for a given group of investors, to trade execution conditions or use the market
databases of the stock exchanges.
2 The Stock Exchange Electronic Trading Service opens from 8.00 am to 16.30 pm. It replaced the quote-driven
market system for Blue Chips in October 1997.
5. Some consultants sell measurements of their trading costs to institutional investors, be they
asset managers or brokers. The Elkins/McSherry consultancy, now a branch of the State
Street bank measures, for example, direct and indirect (or implicit) trading costs. Indirect
costs are measured by the spread between a market participant’s trading prices and a
reference price calculated as an average of four indicators: the day high, low, opening and
closing quotes. Basing their research on this database, Domowitz and al. (2001) study the
range of, and the factors determining, trading costs, and analyse the interactions between
costs, liquidity and volatility in 42 countries, between September 1996 and December 1998.
They offer evidence of a high degree of variability of trading costs across countries, which
might limit the gains from international diversification. Trading costs appear to be higher in
developing than in developed countries. According to this database, Euronext Paris ranks
among the cheapest (30 bp), whereas implicit costs on the LSE locate this exchange among
the most expensive.3 Table 1 shows a comparison of total costs, including both explicit and
implicit costs, across several countries.
Domowitz and al. also show that trading costs tend to decrease over time. Implicit costs
mostly contributes to this reduction, even though it still accounts for 1/3 of total trading
costs. From September 1996 to September 1998, implicit trading costs have decreased three
times as fast as explicit trading costs. The authors relate this downward trend to the market
technological modernisation, the introduction of sophisticated trading systems, and the
increased easiness of accessing information.
Other studies use exchange market data for measuring implicit costs, without trying to
distinguish the actors who bear these implicit costs from those who draw benefit from them.
Several research papers have made two-market liquidity comparisons, in order to estimate
the impact of the market structure and organisation. Huang and Stoll (1996) calculate
liquidity indicators for the NASDAQ and the NYSE. Their sample is made of pairs of
securities from both markets, formed by considering the company sector, long-term debt,
share price criteria, as well as number of admitted shares and book value. Accordingly,
quoted spreads, effective spreads and realised spreads were, in 1991, twice as high on
NASDAQ than on the NYSE. The authors attribute most of this difference to various market
features: internalisation, the ability for a broker to trade with its clients like a market maker,
and preferencing -the fact that an order collector may transmit its orders preferably to a
These measures relate to a period before the SETS electronic system was introduced on the LSE.
6. specific market maker. Market structure and the order matching process thus seem to have
an impact on the cost of trading.
Trading costs from September 1996 to December 1998
Total costs Explicit costs Implicit costs
Belgium 35.0 25.4 9.6
Canada 52.4 25.3 27.1
France 29.5 22.8 6.7
Germany 37.7 24.3 13.4
Italy 34.8 26.3 8.5
Netherlands 42.2 23.0 19.3
Spain 41.9 32.5 9.5
Sweden 35.8 26.2 9.6
Switzerland 38.5 29.8 8.7
United Kingdom 54.5 39.3 15.2
United States 35.0 25.4 9.6
This table shows costs observed in basis points throughout the period from September 1996 to
December 1998. Explicit costs include both commissions and taxes and implicit costs enable to compare
trading prices to a benchmark calculated on the day of the trade (average of the highest, the lowest, and
the opening and closing quotes). Source: Elkins/McSherry
Venkataraman (2001) compares liquidity indicators based on shares listed on the New York
Stock Exchange (a market where a floor still operates) to indicators based on companies
listed on Euronext Paris (a fully automated market). He constructs two samples using a
method similar to that of Huang and Stoll (1996) and pairs companies according to their
price, trading volume, market capitalisation and to the sector to which they belong. Before
taking the tick size into account, quoted spreads at Euronext Paris are on average lower than
that of the NYSE.
Research papers providing two stock exchanges comparisons thus achieved their aim of
producing relevant measurement indicators. But once all explanatory factors relating to
individual securities are taken into account, the differences between liquidity indicators are
intuitively attributed to behavioural or institutional factors, without true demonstration of a
causality. Against this background, Jain (2001), analyses, in a high number of exchanges, the
impact of institutional factors on market performance, measured by quoted, effective and
realised spreads,4 volatility and turnover ratios. The factors identified are: market
4 The interpretation of realised spreads proposed in the research, that relate the day’s closing prices to the next
day, seems difficult to interpret and is closer to a daily return: the market takes obviously the informational
content of a trade much quicker into account, and the opportunity cost increases in the following minutes, not the
7. organisation, trading mechanism, trading system, market transparency, degree of
fragmentation, share ownership structure and their variability over time. This research is
based upon the 15 biggest Blue Chips of 51 stock exchanges. The period ranges from January
to April 2000 and the liquidity indicators are averages of daily closing data. Globally, Jain
(2001) evidences that markets with a mixed structure record lower spreads and volatility
than markets with order-driven market structures. The latter, in turn, record lower costs and
volatility than quote-driven markets.
Spreads on 12 financial markets
Quoted spread Effective
Belgium 1,00% 1,40%
Canada 0,54% 0,50%
France 0,40% 0,49%
Germany 0,91% 0,77%
Italy 0,72% 0,92%
Netherlands 0,48% 0,55%
Spain 0,51% 0,50%
Sweden 0,68% 0,68%
Switzerland 0,43% 0,43%
United Kingdom 0,88% 0,83%
NASDAQ 0,41% 0,51%
NYSE 0,20% 0,10%
In Table 2, the lowest spreads are observed for the New York Stock Exchange (NYSE). This
NYSE characteristics explain its ranking, as well as the size of the companies in the sample:
spreads are indeed inversely correlated to firm size expressed either in terms of market
capitalisation, of trading volume, or of number of trades. In Europe, Euronext Paris and the
Swiss Stock Exchange show the lowest spreads.
A report of the London Economics (2002) confirms previously established results (those, for
example, of Domowitz and al. (2001)) and evidences a negative relation between trading
costs and market capitalisation, and a positive relation between trading costs and volatility.
Costs = 0.1893 − 0.012 × capitalisation + 0.3506 × volatility (1).
Basing on this equation, the report then attempts to demonstrate how financial markets
integration in Europe may improve liquidity (and thus reduce the cost of capital for firms)
thanks to a reduction in trading costs. Assuming that volatility on an integrated European
market is equal to the average of volatilities actually observed on the various European
8. markets, the authors use the above equation for estimating the reduction in trading costs for
each country as a result of the concentration of all European listed shares on a single market.
The table of statistics describing the samples used in this report mentions higher quoted
spreads in Paris than in London. Paradoxically, in the same table, the Spanish and Greek
markets appear to have the lowest quoted spreads along with the NYSE. Beyond the fact that
this research is based on daily closing data, the sample selection and data extraction may
explain the sort of results obtained. On the London market, for example, a group of only 530
companies was considered, which accounts for a small fraction of the total number of listed
companies, whereas all companies listed in Paris were taken into account. Moreover, among
the latter, approximately half are quoted at daily call auctions, which raises interrogations on
the method used for calculating effective spreads (in such a case either best bid and ask
prices are equal, or there is no trade).
As an example, the research of the London Economics concludes to the fact that quoted
spreads on Euronext Paris reach 5.931 %, whereas, in Hazart (2001), calculations based on
continuously traded shares and intra-day data set this spread at 0.18 % to 0.20 % depending
on the quarter in 2001.
1.2. Comparison of the organisations of the LSE and of Euronext Paris
1.2.1. Common features
The London and Paris markets have similar fundamental characteristics: intermediaries have
multiple capacities, which means that they may act either on their own account, possibly in
the context of market making, or on account of their clients.
The 1986 reform of the LSE enabled to remove the distinction between brokers and market
makers (all financial institutions could from then on, play the role of a market maker), and
liberalised pricing regulations. At present, the LSE admits only a single type of
intermediaries, the so-called broker-dealers, which have a dual capacity. This reform enabled
to introduce SEAQ, a single display system for the quotes of all London listed shares. The
second important reform happened in 1997 and led to the creation of SETS, an electronic,
order-driven and continuous market system for Blue Chips.
Euronext Paris, for its part, implemented two significant market reforms in 1986 and 1988:
– the “agents de change” were replaced by the “sociétés de bourse”, entitled, like
British intermediaries, to act both as brokers or on their own account;
9. – an electronic trading system was created (now called NSC) in order to organise a
centralised, order-driven market;5 Only Exchange members are allowed to
transmit and process orders on the system.
On order-driven market systems like NSC or SETS, buy and sell orders coming from final
clients are centralised in the order book, where they are automatically matched. Unlike on
quote-driven markets, investors themselves provide liquidity. Broadly speaking, investors
may use two types of orders: limit orders and market orders. Limit orders are characterised
by buying or selling quantities and a price limit. A buyer (resp. seller) sending a buy (resp.
sell) limit order wishes to buy (resp. sell) securities at a price lower (resp. higher) than or
equal to the order’s specified limit. Securities may be traded either through a call auction, or
continuously. In the case of a call auction, orders are accumulated and matched at a specified
hour of the day. The trading price is the one that maximises the trading volume. In the case
of continuous auctions, a trade happens each time an order matches with one or more orders
in the opposite direction, order execution priority being set by the price in the first place and
according to time in the second place.
Market members are likely to act either for third parties or on their own account. But nothing
tells whether they are liquidity providers, or act on their own account.
1.2.1. Main organisational differences between the LSE and Euronext
Though both markets have identical central functionalities, some differences remain.
At Euronext Paris, most orders from retail clients are routed towards the order book and
participate to the general matching of buy and sell orders.
In London, orders from retail clients are mostly processed outside the SETS system: Retail
Service Providers (RSP) act as a counterpart to the orders from private brokers. Those orders
are in general market orders and are processed at a price at least as favourable as the SETS
order book’s best limit.
On both markets, it is possible to trade blocks without using the automatic order matching
system. Moreover, Euronext adds a frequently used functionality, called cross trades. A cross
5 This system was later adopted by all of Euronext equity markets.
10. trade (applications) is an already matched trade entered by an exchange member who found
a buyer and a seller (he can act on his own account as a counterpart of a client, but can also
execute client orders against one another). Cross trades have to be processed at a price in the
range between the order book buy and sell best limits at the time of the trade. Once entered
into the trading system cross trades become immediately visible by all market members.
Small and mid-caps
The SETS system actually quotes only the 179 biggest listed companies: other securities are
negotiated over the phone using SEAQ, which displays market makers’ prices. On this
system, market makers have to post continuously both bid and ask prices for minimum
quantities. On this type of market, all trades are decentralised and processed by market
makers who provide the liquidity to the market.
Conversely, Euronext lists small and mid caps on the same trading system as Blue Chips. It
must however be noted that 394 of these companies are traded at call auctions, and, in
addition, that a « liquidity provision » mechanism was introduced. Thereby, a « liquidity
provider » (animateur) is an exchange member acting as a market maker or a specialist, who
commits to display continuously prices for minimal quantities. Unlike on traditional market
making exchanges, quoted prices are however competing with the order flow in the central
Table 3 synthesises the differences between the LSE and Euronext Paris.
2. Measurement and components of implicit trading costs
A financial market participant bears two types of trading costs: explicit and implicit trading
costs, the latter resulting from the difference between bid and ask prices. The study only
focuses on implicit costs by looking at diverse measures of spreads and depth.
2.1. Quoted spreads
On agency markets, where quotes appear in the order book, quoted spreads refer to the best
buy and sell limits. On the reverse, on dealer markets, each market maker has to display a
bid-ask spread, for a minimum quantity. The inside bid-ask spread is thus made up of the
best bid and the best ask prices, these prices coming generally from different market makers.
11. Table 3
Description of Euronext and the LSE market structures
Euronext Paris LSE
Trading mechanism – Automated, order-driven and continuous – Automated, order-driven and continuous
market system market system for Blue Chips (SETS)
– Automated, order-driven and batch – Quote-driven and continuous market
auctions for smaller companies system for small and mid caps (SEAQ)
– Mixed market system for micro-
capitalisations and growth companies (not
in this research’s field)
Liquidity providers – Patient investors (limit orders) – Patient investors (limit orders) for the
– « Specialists » (Animateurs) provide biggest market capitalisations on SETS
liquidity for small and mid caps and – RSPs for retail orders, and broker-dealers
compete with the order book’s limit outside the order book
orders – Market makers for small and mid caps
Liquidity consumers Urgent investors – Urgent investors on SETS
– All retail investors for which orders are
routed towards RSPs
– All SEAQ investors
Most frequent types – Limit orders On SETS
of orders – Market orders –Limit orders
–At best orders
Orders transmitted to RSPs: in general
On the SEAQ, market orders only
Priority rules – Price – Price
– Time – Time
Trading system Matching of orders in the order book – Matching of orders in the order book for
Blue Chips (SETS)
– Bilateral negotiations with market makers
for small and mid-caps (SEAQ)
– Processing of client orders by RSPs, at a
price at least as favourable than the order
book’s best limit in SETS
Block market Ability to process block trades at a price Ability to process protected block trades, that
between the volume weighted averages of is of benefiting from a longer delay for
bid and ask quotes and longer delay for reporting the trade
Exchange opening Accumulation of orders in the order book Accumulation of orders in the order book
and matching of orders at a price that and matching of orders at a price that
maximises the trading volume maximises the trading volume for Blue Chips
Tick size – Price lower than 50 € : 0,01 € On SETS
– Price between 50 and 100 € : 0,05 € – Price lower than 5£: 0,25 p
– Price between 100 and 500 € : 0,1 € – Price between 5 and 10£: 0,5 p
– Price over 500 € : 0,5 € – Price over 10£: 1 p
Quoted spreads measure the cost that would be borne by an investor that would buy and
immediately sell a security whose fundamental value remains unchanged, for a quantity
lower or equal to the quantities available at the order book best limits.
Quoted spread =
(Best selling limit − Best buying limit ) (2).
Middle of the spread
12. 2.2. Effective spreads
Effective spreads measure the cost borne by an investor consumer of liquidity that trades
immediately and “reaches” a limit posted on the opposite side.6
The effective spread is expressed as a difference between the price of a trade and the mid-
price of the best bid and ask prices, just before the trade happens.
Trading price − Mid (3).
Effective spread = 2 ×
2.3. Realised spreads
The realised spread measures the opportunity cost of an investor. It is expressed as a
difference between the price of a trade and the « true value » of the asset after the trade has
happened. Like Venkataraman (2001), one may represent the « true economic value » of an
asset by the mid price of the spread measured 30 minutes after the transaction, or possibly by
the daily closing price. The advantage of this indicator consists in providing an estimate of
the cost of information asymmetries borne by an agent that wishes to trade immediately. The
difference between effective realised spreads enables to estimate the cost of adverse selection
borne by a non-informed agent. Given the difficulty to identify the « true value » of a
security, this measure was not considered in the present research.
Trading price − Asset " true value" (4).
Realised spread = 2 ×
2.4. Effective depth
Effective depth measures the marginal effective cost of a share unit. One may measure this
cost as a difference between trading prices and the mid price as a percentage of the mid price
and divided by the size of the trade (in number of shares).
Trading price − Mid
Effective depth = Mid (5).
Cross trades on Euronext can only be made at prices comprised between the order book best buy an sell prices at
the time of the trade. Thus, for this category of trades, the effective spread is necessarily lower than the quoted
spread. Both in London and in Paris, for trades processed in the order book, on the reverse, a new order is at best,
matched only at the best limit on the other market side, and possibly by other less favourable limits if the quantity
offered for the best bid price is not sufficient: for these transactions, the effective spread is thus necessarily bigger
than the quoted spread.
13. 2.5. Quantities available at the best limits
The quantities available at the best limits measure the significance of quoted spreads. When
they are low, the order book does only enable to absorb small trades without price shift.
When high, they reflect the market liquidity. Unfortunately this information is rarely
available in market databases and may not be calculated here.7
3. Data and methodology
3.1. Sample selection
The present research thus considers all spreads and trades during the month of April 2002.
Trading data and quotes were extracted from the BDM market database as far as Euronext
Paris is concerned, and from the intra-day database of the LSE (Transaction Data Service).
Global statistics are calculated using total samples. In a second step, paired samples are
analysed, where the pairs are formed by considering security sectoral characteristics, free
float market capitalisation and global trading volume. Following securities were excluded
from our sample:
– securities for which the first closing price is lower than 1 €,
– securities listed less than 20 days in the month,
– non traded securities,
– securities traded at daily call auctions only.
To pair the samples from Euronext Paris and the LSE, a pairing algorithm similar to that of
Huang and Stoll (1996) and Venkataraman (2002) was used, which relates securities
– their Dow Jones Economic Sector,8
– their free float on 2 April 2002, according to Dow Jones Indexes free float
– their total trading volumes in euros during the month of April 2002.
7 On Euronext, quantities available at the best limits in 2001 were of 67,000 euros for AEX-shares, 60,000 euros for
CAC 40-shares, and 31,000 euros for BEL 20-shares. This should be compared to the fact that, on Euronext Paris,
85 % of the trades have a size of less than 50,000 euros and account for 31 % of the total traded volume.
8 Dow Jones indexes classification considers ten economic sectors: Basic Materials, Consumer Cyclical, Consumer Non-cyclical,
Energy, Financial, Healthcare, Industrial, Technology, Telecommunications, Utilities.
9 The free float of a security is equal to its market capitalisation minus cross-participations of 5% or more held by public
organisations or individuals.
14. For each company listed at Euronext Paris (105 companies) all possible pairs are calculated,
with firms of the same sector listed on the LSE (British sample of 173 firms). Among the
latter, the London security that minimises the difference between the two characteristics
otherwise considered, is retained:
X Paris − X London
Min ∑ 2 (6).
( X Paris + X London ) 2
3.2. Econometric method
In the study of the London Economics, authors consider a bivariate autoregressive model of
the securities spread and turnover. Their model shows that the spread at time t has a
simultaneous impact on the turnover and vice versa. The fact that both effects occur
simultaneously raises interrogations. Like in the model of Hasbrouck (1991), the volume at
time t has an impact on the revision of the price, but it depends of the revision of price at the
In their investigation, some explanatory variables may have a very strong correlation. This is
in particular the case of the “market capitalisation” and LARGE variables, the latter being a
dummy variable, which takes the value 1 when market capitalisation is above its median.
Stoll (2000), for its part, finds empirically, on the basis of a sample of companies listed on the
NYSE and on the NASDAQ, that quoted spreads depend linearly on the daily trading
volume, the variance of daily returns, market capitalisation, the closing price and on the
number of trades. With a determination coefficient exceeding 60%, securities characteristics
appear clearly to have an influence on trading costs.
The following cross-section equation, based on securities listed on Euronext Paris and on the
LSE, controls for free float, trading volume and volatility.
Spread indicator = intercept + α × free float + β × volatility + ε (7),
Spread indicator = intercept + α × volume + β × volatility + ε (8).
As in previous literature, trading costs decrease as a function of free float or trading volume
and rise as a function of volatility.
15. 4. A comparative analysis of market liquidity on the LSE and Euronext Paris
4.1. Descriptive statistics
Tables 5 shows that quoted and effective spreads estimated on global samples are lower on
Euronext Paris than on SETS in London. On both exchanges, the market structure for Blue
Chips is the same (order-driven market structure); yet, best limit spreads are smaller in the
Euronext Paris order than in the SETS order book. All the same, effective spreads are smaller
in Paris than in London. As a result, an investor asking for liquidity on the Parisian market
pays lower bid-ask spreads than an investor asking for liquidity in London.
However, the difference observed between average quoted spreads in Paris and in London is
bigger than that between average effective spreads. In London, investors may often improve
the price of a trade compared to the best limits. In Paris, prices displayed at the best limits
most frequently relate to trading prices. Best limits thus appear in Paris as a better indicator
of the actual value at which a share can effectively be traded. In so, NSC appears to be more
transparent about the prices at which an investor cam potentially trade.
Conversely, effective depth observations on the French and British markets show a deeper
British market. Consistently, the average trade size is lower on the French market than on the
British one. In short, the French market is thinner as spreads are globally lower in Paris than
in London, but trades in Paris are smaller and the British market is deeper.
The comparison of SETS and SEAQ shows, for its part, that the order-driven market (SETS)
has lower quoted and effective spreads than the order-driven market (SEAQ). The
comparison of both structures should however be put into perspective, as SETS lists Blue
Chips, whereas market makers on the SEAQ trade small and medium capitalisations. To
compare appropriately the two market structures, one should thus correct for the size effect.
16. Table 5
Global sample descriptive statistics
Euronext Paris LSE (SETS) LSE (SEAQ)
Number of securities 462 179 880
Total trading volume (€) 201 804 622.58 1 006 424 375.65 8 791 990.33
(767 238 472.87) (1 799 895 357.09) (14 999 477.46)
[3 033 853.5] [451 880 393.7] [2 821 264.89]
Min = 1 672 Min = 14 794 467.54 Min=1 856.93
Max = 8 660 058 152.6 Max = 15 011 434 062 Max=128 442 129.9
Average number of trades 7 094.27 12 556.53 214.42
(20629.58) (13 424.01) (268.27)
 [8 702] 
Min = 2 Min = 430 Min = 1
Max = 209 282 Max = 98 425 Max = 2 098
Average Trade size (€) 28 446.12 80 151.47 41 004.16
Quoted spread (%) 0.1635 0.2611 2.0285
(0.23118) (0.25313) (1.36495)
[0.1183] [0.2040] [1.6342]
Min = 0.07 Min = 0.09 Min = 0.48
Max = 22.1 Max = 7.25 Max = 56.46
Effective spread (%) 0.1607 0.1868 1.3251
(0.14819) (0.11064) (0.96477)
[0.1203] [0.1478] [1.0702]
Min = 0.08 Min = 0.08 Min = 0.38
Max = 15.91 Max = 2.29 Max = 56
Effective depth (%) 0.003369 0.0003 0.0023
(0.0082794) (0.00027) (0.0046)
[0.00215] [0.0002] [0.0012]
Min = 0.0012 Min = 0 Min = 0
Max = 0.0015332 Max = 0.01 Max = 0.33
Average quote number 13750 347.4 6.82
(27 372.6) (231.89) (3.18)
[1 378] [303.57] [6.19]
Min = 52 Min = 38.71 Min = 2
Max = 159114 Max = 1465.38 Max = 24.48
Volatility (%) 1.831 1.838
Min = 0.1 Min = 0
Max = 6.82 Max = 35.04
Statistics computed for April 2002 for both the French and British markets. The number of securities corresponds to the global sample
size on the French and the British markets. Shares traded at call auctions were not taken into account. Call auction trades were not taken
into account. The trading is expressed in € and relates to trade numbers during the month of April 2002 multiplied by the respective
trading prices. Average trade numbers are averages over securities of monthly trade averages for the month of April 2002. The average
trade size is April 2002 total trading volume (in €) divided by the total number of trades. For each security, quoted spreads are firstly
calculated by weighting each spread by its time of duration. Finally, quoted and effective spreads are calculated as averages weighted by
daily trading volume (in €). Volatility stands for volatilities calculated on the basis of daily closing price returns. The variable was
subsequently computed as a non-biased standard error of daily closing price returns. For each variable, standard error values are
between brackets and median between square brackets. The minimum and maximum have also been mentioned.
17. Table 6
Spreads on Euronext Paris and the LSE
Euronext Paris LSE (SETS)
Outside the order
Order book Cross trades Order book
Average trade size 23 672.16 1 582 616.48 95 789.47 167 211.54
Effective spread 0.1587 0.0007 0.1636 0.2324
(0.14275) (0.00656) (0.09402) (0.13343)
[0.1201] [0.0003] [0.1323] [0.1913]
Effective depth 0.003185 0.000024 0.0003 0.0004
(0.00555402) (0.0009315) (0.00029) (0.00026)
[0.00215] [0.000001] [0.0002] [0.0003]
The spread and effective depth were calculated by distinguishing order book trades from other trades. Call auction trades
were not taken into account. Spreads and depths were weighted by April 2002 average trading volumes expressed in
euros. Average trade size corresponds to the total trading volume (in €) for the month of April 2002 divided by the total
number of trades in the sample.
Table 7 shows the differences between trading costs in London and Paris basing on two
samples paired by considering each security economic sector, free float and total trading
volume. These results show that spreads on the Paris market are significantly lower than in
London. The difference between quoted spreads in Paris and in London is higher than the
difference between average effective spreads. Compared to quoted spreads, trading prices
are more often improved in London than in Paris. It should nevertheless be noted that
London quoted spreads do not necessarily reveal the best prices at which trading may
happen, contrarily to what can be observed on the Parisian market. In other terms, the
spreads between best limit prices in Paris are closer to the actual trading value, and more
informative about the best price an investor may get.
Trading costs measures based on paired samples
Euronext LSE Difference
Quoted spread 0.1483 0.2958 -0.1475***
Effective spread 0.1445 0.1881 -0.0436***
Effective depth 0.0028 0.0004 0.0024***
The quoted spread is a free float weighted average of quoted spreads over securities.
Effective spreads and depth are averages weighted by daily trading volumes (€). For each
variable, the standard error appears between brackets and the median between square
brackets. The two samples (100 securities) have been paired by considering the sector, the
free float and the trading volume. *** means that the difference is significant at a 1%
threshold. Results are gathered for two paired samples of 105 securities.
18. 4.2. Econometric analysis
Econometric regressions were conducted by considering successively quoted spreads and
effective spreads as the dependent variable. Explanatory variables were selected according to
results established by the literature. Variables determining the spread are the following:
– average daily trading volume in euros, expressed in logarithm,
– the free float market capitalisation, expressed in logarithm,
– the volatility of daily closing price returns.
Taking the correlation between daily trading volume and free float into account, the
econometric regression first uses the free float and then the trading volume. The results
gathered for Paris were in line with those for London. On both markets, quoted spread -as
well as effective spreads- decrease when trading volume rises. Similarly, quoted and
effective spreads decrease when free float rises. These results are consistent with those
obtained in the literature and mean that trading costs borne by an investor are all the weaker
than a security liquidity is high. The third element determining trading costs is volatility.
Trading costs rise significantly with volatility. Indeed, the higher the volatility, the higher the
risk borne by market actors who post prices. Taking risk aversion into account, they thus
request an additional compensation, which explains a rise in trading costs.
19. Table 8
Determination of the spread in Paris and London
London Stock Exchange Euronext Paris
Variable Quoted Quoted Effective Effective Quoted Quoted Effective Effective
spread spread spread spread spread spread spread spread
Number of 308 308 308 308 113 113 113 113
Intercept 4.79*** 4.29*** 2.34*** 2.14*** 3.5*** 2.8*** 3.18*** 2.66***
[36.05] [45.23] [30.25] [37.89] [7.81] [12.68] [6.8] [11.19]
Lnvolq -0.23*** -0.12*** -0.14*** -0.13***
[-45.14] [-37.6] [-12.09] [-10.92]
Lnflot -0.19*** -0.0094*** -0.15*** -0.14***
[-35.79] [-29.88] [-7.39] [-6.55]
Volat 0.12*** 0.17*** 0.009*** 0.11*** 0.00593 0.00954*** 0.00893*** 0.12***
[10.69] [16.47] [13.81] [18.78] [1.55] [3.145] [2.21] [3.769]
Adj R² 0.4 0.51 0.34 0.44 0.35 0.58 0.31 0.54
Lnvolq is computed as the logarithm of the daily trading volume expressed in euros. Lnflot stands for the logarithm of the free float. Volat stands for volatilities
calculated on the basis of daily closing price returns. The variable was subsequently computed as a non-biased standard error of daily closing price returns.
20. 5. Conclusion
This research aimed at identifying the factors determining cost of liquidity on equity
We show that the cost of liquidity for a given security depends on this security trading
volume free float and volatility. As a result, rigorous trading cost comparisons across
different markets should be based, in the first place, on samples of comparable securities.
By constituting « paired » samples using the securities of the Dow Jones TMI France and
United Kingdom stock indexes, spreads appear to be lower on the French market than in
London. The London market, however, records higher depth indicators.
The Paris market is cheaper for small trades , while the London market offers lower
immediacy costs for large trades. Liquidity consumption, with respect to spreads, is less
costly on the Parisian market, mainly because trades are smaller on average. This could be
due either to smaller liquidity needs from French investors, or, more likely, to order split
strategies conducted by Euronext members. Consequently, two directions for further research
appear useful at this stage.
– One should compare liquidity and depth indicators based on paired samples but
also broken down into homogeneous trade size categories. This would enable, in
the first place, to compare processing conditions for small orders, with regard to
the fact that they are processed by specialised market makers (RSPs in London) or
participate in the general matching of supply and demand.
– Explanations for the bigger trade size in London should be looked for, in order to
understand the respective role of investors’ characteristics on each of the two
markets -and thus assess the relative weight of the retail market. In addition, the
market members’ behaviour, and particularly the way they split or do not split
the orders submitted by institutional clients, should be precisely analysed. The
splitting of orders in London should also be analysed over time, given that the
introduction of SETS is still recent.
Domowitz I., Glen J. and Madhavan A., 2001, “Liquidity, Volatility and Equity Trading Costs
Across Countries and Over Time”, International Finance, v4(2), p. 221-255.
Economides, E. and B. Schwartz, 1995, “Equity Trading practices end Market Structure :
Assessing Asset Managers Demand for Immediacy”, in Financial Markets, Institutions
and Instruments, Volume 4, Number 4, Blackwell Publishers.
Gresse, C., 2002, “Crossing network trading and the liquidity of a dealer market: cream-
skimming or risk sharing”, papier de recherche, Université de Paris-Nanterre.
Hamon, J. and B. Jacquillat , 1992, Le marché français des actions: études empiriques 1977-1991,
Presses Universitaires de France.
Hasbrouck, J. and Schwartz R.A., 1988, “Liquidity and execution costs in equity markets”,
Journal of Portfolio Management, v14(3), p. 10-17.
Hazart, P., 2002, « Evolution des fourchettes sur Euronext, 1999-2002 »
Huang, R and Stoll H., 1996, “Dealer versus auction markets: A paired comparison of
execution costs on NASDAQ and the NYSE”, Journal of Financial Economics 41 313-357
Jain P., 2001, “Institutional Design and Liquidity on Stock Exchanges”, Research Paper,
Kelley School of Business – Indiana University.
London Economics, 2002, “European Financial Integration and equity markets”.
McInish T.H. and Wood R.A., 1992, “An analysis of intraday patterns in bid-ask spreads for
NYSE stocks”, Journal of Finance, v47(2), p. 753-764.
Schwartz, R.A. and B. Steil, 1996, „Equity Trading III: institutional Investor Trading Practices
and Preferences“, in The European Equity Markets: the State of the Union and an Agenda for
the Millenium, Benn Steil Ed., the Royal Institute of International Affairs,1996, pp. 81-
Stoll H., 2000, “Friction”, Journal of Finance, v55(4), p. 1479-1514.
Venkatamaran K., 2001, “Automated Versus Floor Trading: an Analysis of Execution Costs
on the Paris and New York Exchanges”, Journal of Finance, v56(4), p. 1445-1485.