Foucault And Lescourret-Information Sharing, Liquidity And Transaction Costs In Floor-Based Trading Syst~0.pdf

Information Sharing, Liquidity and Transaction Costs

in Floor-Based Trading Systems. 1

Thierry Foucault

HEC and CEPR

1, rue de la Liberation

Laurence Lescourret

CREST and Doctorat HEC

15, Boulevard Gabriel Peri

78351 Jouy en Josas, France.

Email:foucault@hec.fr

92245 Malako®, France.

Email:lescourr@ensae.fr

November, 2001

1 We thank Giovanni Cespa, Asani Sarkar and seminar participants at CREST, Laval Uni-

versity, the AFFI2000 conference, the EEA2000 conference, the FMA2001 Meetings and the

International Finance Conference Tunisie 2001. All errors are ours.

Abstract

Information Sharing, Liquidity and Transaction Costs in Floor-Based Trading

Systems.

We consider information sharing between traders (\°oor brokers") who possess di®erent

types of information, namely information on the payo® of a risky security or information on

the volume of liquidity trading in this security. We interpret these traders as dual-capacity

brokers on the °oor of an exchange. We identify conditions under which the traders are

better o® sharing information. We also show that information sharing improves price

discovery, reduces volatility and lowers expected trading costs. Information sharing can

improve or impair the depth of the market, depending on the values of the parameters.

Overall our analysis suggests that information sharing among °oor brokers improves the

performance of °oor-based trading systems.

Keywords : Market Microstructure, Floor-Based Trading Systems, Open Outcry, Infor-

mation Sharing, Information Sales.

JEL Classi¯cation Numbers : G10, D82.

1 Introduction

The organization of trading on the NYSE has been remarkably stable since its ¯rst con-

stitution in 1817. Trading is conducted through open outcry of bids and o®ers of brokers

acting on behalf of their clients or for their own account. 1 This trading mechanism is not

unique to the NYSE. Equity markets like the Frankfurt Stock Exchange and the AMEX

or derivatives markets like the CBOT and the CBOE are °oor markets. 2 However °oor-

based trading mechanisms are endangered species as they are progressively replaced by

fully automated trading systems 3 . Given this trend toward automation, it is natural to

ask whether °oor-based trading systems can provide greater liquidity and lower execu-

tion costs than automated trading systems. This question is of paramount importance for

market organizers and traders. In fact, it has been hotly debated between members of

Exchanges who considered switching from °oor to electronic trading 4 . In order to survive

°oor-based trading mechanisms must outperform automated trading systems along some

dimensions.

Automated trading systems dominate °oor-based trading systems in many respects.

First °oor markets are more expensive to operate (see Domowitz and Steil (1999)). Sec-

ond physical space limits the number of participants in °oor markets but not in automated

trading systems. Finally traders without an access to the °oor are at an informational dis-

advantage compared with the traders on the°oor. This disadvantage is likely to exacerbate

agency problems between investors and their brokers (Sarkar and Wu (1999)).

By design, °oor-based markets foster person-to-person contacts. Hence the ability of

market participants to share information is greater in these markets. This feature is often

viewed as being one advantage, if not the unique one, of °oor-based trading systems. 5 For

instance Harris (2000), p.8, points out that

1 Of course, many trading rules have been changed since the creation of the NYSE. But it has always

been a °oor market. See Hasbrouck, So¯anos and Sosebee (1993) for a detailed description of the trading

rules on the NYSE.

2 In Frankfurt, the °oor operates in parallel with an electronic trading system.

3 The Marche a Terme International de France (MATIF), the Toronto Stock Exchange and The London

International Financial Futures and Options Exchange (LIFFE) shut down their °oor in 1997, 1998 and

2000, respectively.

4 See the Economist (July 31st 1999):\A home grown revolutionary" and the Economist (August 26th

2000): \Out of the pits".

5 Coval and Shumway (1998) show that the level of noise on the °oor of CBOT's 30 year Treasury

Bond futures a®ects price volatility. This also suggests that person to person contacts on the °oor have

an impact on price formation.

`Floor-based trading systems dominate electronic trading systems when brokers

need to exchange information about their clients to arrange their trades.'

Information sharing is a function of the °oor which is di±cult to replicate in electronic

trading systems. These systems usually restrict the set of messages that can be sent by

users (generally traders can only post prices and quantities). Furthermore trading in these

systems is in most cases anonymous. This feature prevents traders from developing the

reputation of honestly sharing information through enduring relationships.

Information sharing on the °oor can take place between two types of participants. First

°oor-brokers can exchange information on their trading motivations with market-makers.

Benveniste, Marcus and Whilelm (1992) model this type of information sharing and show

that it mitigates adverse selection. Second °oor-brokers can communicate with other °oor-

brokers. For instance, So¯anos and Werner (1997), p.6 notice that

`In addition, by standing in the crowd, °oor brokers may learn about additional

broker-represented liquidity that is not re°ected in the specialist quotes: °oor

brokers will often exchange information on their intentions and capabilities,

especially with competitors with whom they have good working relationships.'

Our purpose in this paper is to analyze this type of information sharing. At ¯rst glance,

information sharing among °oor brokers is puzzling. In fact standard models with asym-

metric information (e.g. Kyle (1985)) show that informed traders want to hide their infor-

mation rather than disclose it to potential competitors. Furthermore, information sharing

reinforces informational asymetries between those who share information and those who

do not. It is therefore not obvious that it should improve market quality. Hence we ad-

dress two questions. First, is it optimal for °oor brokers to share information with their

competitors? Second, what is the e®ect of information sharing among °oor brokers on

the overall performance of the market? In particular we study the impact of inter-°oor

brokers communication on standard measures of market quality, namely price volatility,

price discovery, market liquidity and trading costs.

Wemodel°oortradingandinformationsharingusingKyle(1985)'smodelasa workhorse.

As in Roell (1990), we assume that traders (°oor brokers) have access to two types of in-

formation: (i) fundamental information which is information on the payo® of the security

and (ii) non-fundamental information which is information on the volume of liquidity (non-

informed) trading. We consider the possibility for two °oor brokers endowed with di®erent

types of information (one has fundamental information and the other has non-fundamental

information) to share information. More speci¯cally we assume that °oor brokers have in-

formation sharing agreements (they form a \clique"). An agreement speci¯es the precision

with which each broker reports his or her information to the other broker. After receiving

fundamentalor non-fundamentalinformation, thebrokersina cliquepooltheir information

according to the terms of their agreement just before submitting their orders for execution.

We establish the following results.

² There is a wide range of parameters for which it is optimal for °oor brokers to share

their information (i.e. their expected pro¯ts are larger with information sharing).

² Information sharing can improve or impair the depth of the market, depending on

the values of the parameters.

² Information sharing always reduces the aggregate trading costs for liquidity traders.

However when information sharing impairs market depth, some liquidity traders are

hurt.

² Information sharing occurs at the expense of the °oor brokers who are not part to

the information sharing agreement.

² Information sharing improves price discovery and reduces market volatility.

Intuitively information sharing intensi¯es competition between °oor brokers and in this

way it lowers the total expected pro¯ts of all °oor brokers (reduces the aggregate trading

costs). Information sharing also changes the allocation of trading pro¯ts among °oor

brokers. More speci¯cally the °oor brokers who share information capture a larger part of

the total expected pro¯ts, at the expense of °oor brokers who do not share information.

These two e®ects explain why information sharing can simultaneously bene¯t liquidity

traders and the °oor brokers who share their information. Overall information sharing

between °oor brokers is an advantage for °oor-based trading systems since it results in (a)

lower trading costs, (b) faster price discovery and (c) lower price volatility. Interestingly,

in line with our result, Venkataraman (2000) ¯nds that trading costs on the NYSE are

lower than on the Paris Bourse (an automated trading system), controlling for di®erences

in stocks characteristics. 6

6 Theissen (1999) compares e®ective bid-ask spreads in an automated trading system (Xetra) and the

°oor of the Frankfurt Stock Exchange for stocks that trade in both systems. He ¯nds that the average

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