Trading Costs Analysis:

A Comparison of Euronext Paris and the London Stock Exchange ♣

Jean-François Gajewski Université de Paris-12, IRG e-mail: gajewski@univ-paris12.fr

Carole Gresse Université de Paris-Nanterre & CEREG e-mail: carolegresse@hotmail.com http://www.carolegresse.com

JEL classification: G19

Keywords: transaction costs, bid-ask spreads, dealer market, liquidity, order-driven market, quote-driven market

First version: December 2002 Unfinished work 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.

Trading Costs Analysis:

A Comparison of Euronext Paris and the London Stock Exchange

Abstract

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.

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.

3

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

market participants.

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

5 concludes.

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.

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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

3 These measures relate to a period before the SETS electronic system was introduced on the LSE.

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specific market maker. Market structure and the order matching process thus seem to have

an impact on the cost of trading.

Table 1 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 day after.

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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.

Table 2 Spreads on 12 financial markets

Quoted spread Effective spread

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.

volatility0.3506tioncapitalisa0.0120.1893Costs ×+×−= (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

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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;

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– 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.

Retail market

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.

Large trades

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.

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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

order book.

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.

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Table 3 Description of Euronext and the LSE market structures

Euronext Paris LSE Trading mechanism – Automated, order-driven and continuous

market system – Automated, order-driven and batch

auctions for smaller companies

– Automated, order-driven and continuous market system for Blue Chips (SETS)

– Quote-driven and continuous market 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) – « Specialists » (Animateurs) provide

liquidity for small and mid caps and compete with the order book’s limit orders

– Patient investors (limit orders) for the biggest market capitalisations on SETS

– RSPs for retail orders, and broker-dealers outside the order book

– Market makers for small and mid caps (SEAQ)

Liquidity consumers Urgent investors – Urgent investors on SETS – All retail investors for which orders are

routed towards RSPs – All SEAQ investors

Most frequent types of orders

– Limit orders – Market orders

On SETS – Limit orders – Market orders – At best orders

Orders transmitted to RSPs: in general market orders. On the SEAQ, market orders only

Priority rules – Price – Time

– Price – 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 between the volume weighted averages of bid and ask quotes and longer delay for trade reporting

Ability to process protected block trades, that is of benefiting from a longer delay for reporting the trade

Exchange opening Accumulation of orders in the order book and matching of orders at a price that maximises the trading volume

Accumulation of orders in the order book and matching of orders at a price that maximises the trading volume for Blue Chips on SETS

Tick size – Price lower than 50 € : 0,01 € – Price between 50 and 100 € : 0,05 € – Price between 100 and 500 € : 0,1 € – Price over 500 € : 0,5 €

On SETS – Price lower than 5£: 0,25 p – Price between 5 and 10£: 0,5 p – 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.

( )spreadtheofMiddle

limitbuyingBestlimitsellingBestspreadQuoted −= (2).

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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.

MidMidpriceTrading

2spreadEffective−

×= (3).

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.

Midvalue"true"AssetpriceTrading

2spreadRealised−

×= (4).

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).

sizeTradeMid

MidpriceTrading

depthEffective

−

= (5).

6 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.

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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

according to:

– their Dow Jones Economic Sector,8

– their free float on 2 April 2002, according to Dow Jones Indexes free float

definition,9

– 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.

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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:

( ) 22XX

XXMin

LondonParis

LondonParis∑

+−

(6).

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

time t-1.

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.

εvolatilityβfloatfreeαinterceptindicatorSpread +×+×+= (7),

εvolatilityβvolumeαinterceptindicatorSpread +×+×+= (8).

As in previous literature, trading costs decrease as a function of free float or trading volume

and rise as a function of volatility.

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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.

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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

(767 238 472.87) [3 033 853.5] Min = 1 672 Max = 8 660 058 152.6

1 006 424 375.65 (1 799 895 357.09) [451 880 393.7] Min = 14 794 467.54 Max = 15 011 434 062

8 791 990.33 (14 999 477.46) [2 821 264.89] Min=1 856.93 Max=128 442 129.9

Average number of trades 7 094.27 (20629.58) [563] Min = 2 Max = 209 282

12 556.53 (13 424.01) [8 702] Min = 430 Max = 98 425

214.42 (268.27) [112] Min = 1 Max = 2 098

Average Trade size (€) 28 446.12 80 151.47 41 004.16 Quoted spread (%) 0.1635

(0.23118) [0.1183] Min = 0.07 Max = 22.1

0.2611 (0.25313) [0.2040] Min = 0.09 Max = 7.25

2.0285 (1.36495) [1.6342] Min = 0.48 Max = 56.46

Effective spread (%) 0.1607 (0.14819) [0.1203] Min = 0.08 Max = 15.91

0.1868 (0.11064) [0.1478] Min = 0.08 Max = 2.29

1.3251 (0.96477) [1.0702] Min = 0.38 Max = 56

Effective depth (%) 0.003369 (0.0082794) [0.00215] Min = 0.0012 Max = 0.0015332

0.0003 (0.00027) [0.0002] Min = 0 Max = 0.01

0.0023 (0.0046) [0.0012] Min = 0 Max = 0.33

Average quote number 13750 (27 372.6) [1 378] Min = 52 Max = 159114

347.4 (231.89) [303.57] Min = 38.71 Max = 1465.38

6.82 (3.18) [6.19] Min = 2 Max = 24.48

Volatility (%) 1.831 (0.88488) [1.6161] Min = 0.1 Max = 6.82

1.838 (2.0093) [1.3836] Min = 0 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.

16

Table 6 Spreads on Euronext Paris and the LSE

Euronext Paris LSE (SETS) Order book Cross trades Order book Outside the order

book Average trade size 23 672.16 1 582 616.48 95 789.47 167 211.54 Effective spread 0.1587

(0.14275) [0.1201]

0.0007 (0.00656) [0.0003]

0.1636 (0.09402) [0.1323]

0.2324 (0.13343) [0.1913]

Effective depth 0.003185 (0.00555402) [0.00215]

0.000024 (0.0009315) [0.000001]

0.0003 (0.00029) [0.0002]

0.0004 (0.00026) [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.

Table 7 Trading costs measures based on paired samples

Euronext Paris

LSE Difference

Quoted spread 0.1483 (0.15959)

0.2958 (0.1877)

-0.1475***

Effective spread 0.1445 (0.07542)

0.1881 (0.11498)

-0.0436***

Effective depth 0.0028 (0.00228)

0.0004 (0.00031)

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.

17

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.

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Table 8 Determination of the spread in Paris and London

London Stock Exchange Euronext Paris Variable Quoted

spread Quoted spread

Effective spread

Effective spread

Quoted spread

Quoted spread

Effective spread

Effective spread

Number of observations

308 308 308 308 113 113 113 113

Intercept 4.79*** [36.05]

4.29*** [45.23]

2.34*** [30.25]

2.14*** [37.89]

3.5*** [7.81]

2.8*** [12.68]

3.18*** [6.8]

2.66*** [11.19]

Lnvolq -0.23*** [-45.14]

-0.12*** [-37.6]

-0.14*** [-12.09]

-0.13***[-10.92]

Lnflot -0.19*** [-35.79]

-0.0094***[-29.88]

-0.15*** [-7.39]

-0.14*** [-6.55]

Volat 0.12*** [10.69]

0.17*** [16.47]

0.009*** [13.81]

0.11*** [18.78]

0.00593 [1.55]

0.00954*** [3.145]

0.00893*** [2.21]

0.12*** [3.769]

Adj R² 0.4 0.51 0.34 0.44 0.35 0.58 0.31 0.54Lnvolq 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.

19

5. Conclusion

This research aimed at identifying the factors determining cost of liquidity on equity

markets.

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.

20

21

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