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Adoption of electronic trading at the International

Securities Exchange

Bruce W. Weber*

Sainsbury 332, London Business School, Regents Park, London NW1 4SA, United Kingdom

Available online 19 November 2004

Abstract

Information technology is transforming financial trading, lowering costs, and increasing market transparency. Yet, new

electronic trading ventures often fail to attract sufficient activity levels, and close down. Optimark, Tradepoint, Jiway, and

BondConnect did not develop sufficient trading volume to survive. In contrast, the International Securities Exchange (ISE), an all-

electronic options trading platform has gained trading volumes in the United States in competition with four incumbent markets,

including the Chicago Board Options Exchange (CBOE). Compared with floor exchanges, electronic options markets offer

immediate trading, direct user access to the market, and reduced costs. The paper describes the ISE and examines newly available

data from brokerage firms to comply with the Securities and Exchange Commissions (SEC) Rule 11Ac1-6. The order routing

disclosures show that brokerage firms differ widely in the extent of their use of the ISE. Based on a sample of 188 quarterlydisclosures from 20 major brokerage firms, OLS, Tobit, and fixed-effects models of ISE use are estimated to explain individual

firms adoption levels. Significant factors are whether the firm is an online discount broker, the firms membership role in the ISE,

and the network externality effect of the ISE markets growth. Firm-specific factors are shown to account for about 60% of ISE

adoption explained by the model, with the remaining 40% accounted for by the network effects of growing market liquidity.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Electronic markets; Options exchange trading systems and technology; Exchange memberships; Brokerage firm order routing;

Market share models; Adoption models

1. Introduction

This paper examines the adoption patterns of U.S.

securities brokerage firms for electronic equity

options trading after the launch of the International

Securities Exchange (ISE), an all-electronic tradingplatform on May 26, 2000. In the first quarter of 2004,

the ISE handled 29.2% of all U.S. equity options

contracts traded and 33.2% of equity options trans-

actions, with the four incumbent options exchanges

accounting for the remainder (source: Options Clear-

ing). Quarterly data for a sample of 20 brokerage

firms from 3Q 2001 to 1Q 2004, however, reveal wide

variation in the extent of ISE use, from 0% to as high

0167-9236/$ - see front matterD 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.dss.2004.10.006

* Tel.: +44 20 7262 5050x3538; fax: +44 20 7724 7875.

E-mail address: [email protected].

Decision Support Systems 41 (2006) 728746

www.elsevier.com/locate/dsw

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as 61% of a firms options orders in a quarter.

Understanding what influences potential users to

adopt a new electronic market has research value

and practical implications for developers of newtrading platforms. We look at how broker-specific

and network-effect variables impact ISE use by

brokerage firms.

In the United States, the ISE is a competitor of four

established floor-based exchanges in Chicago, New

York (American Stock Exchange), Philadelphia, and

San Francisco (Pacific Exchange). The largest of

these, the Chicago Board Options Exchange (CBOE),

began operating in 1973, and has a competing market

maker structure with a floor trading crowd of 1437

that can provide for price and size improvement, andcomplex, linked transactions such as spreads and

straddles in which several options are purchased and

sold simultaneously.

The ISEs electronic market offers first infirst out

(FIFO) time priority among orders at a particular

price, and initially undercut the trading fees charged

by the floor options exchanges. Transactions on the

ISE are free to the brokerage firm and its customer.

ISE market maker members are charged about 20

cents per contract traded, and the turnaround time on

many orders to the ISE is less than 1 s. Before the ISE

launch, floor option exchanges were charging fees

about 50% higher than the ISE, but have since

lowered fees to match those charged by the ISE.

Floor orders can take anywhere from 15 s to several

minutes to execute and report to the client, depending

on the order and market conditions at the time.

At the time of its launch, the prospects for the ISE

were unclear. James Marks, an analyst with Credit

Suisse First Boston commented in the October 1,

2000 edition of CIO Magazine: bIts a bit of a

chicken-and-egg situation for the ISE. To get order

flow, they need liquidity-willing buyers and sellersbut to get liquidity they need order flow. Better,

cheaper, faster wont mean much if they dont get the

critical mass of order flow they need to keep their

market makers and the brokerages happy.Q Research

into the factors that determine whether an electronic

market will succeed is inconclusive. Kambil and Van

Heck [14] describe the few examples of online

financial and commercial B2B markets that have

succeeded. The authors contend that success results

largely from integrating product transactions with

information and services, such as logistics and pay-

ment support, and providing value, not just lower

prices, to all market participants. Hendershott [13]

examines the uneven adoption of electronic financialtrading, and uses Electronic Communication Net-

works (ECNs) for Nasdaq stocks and currency dealing

systems as examples of electronic trading successes.

Bond markets though remain largely dependent on

telephone contact for trading. Barclay et al. [1]

examine competition between Electronic Communi-

cation Networks (ECNs) and Nasdaq market makers

for trading, and conclude that multimarket trading

offers benefits and that ECNs are not a complete

substitute for trading with a traditional market maker.

Well-designed trading automation is beneficial toinvestors and traders in markets[16,17].For example,

the introduction of the Nasdaq screen market in 1971

to replace the OTC bpink sheetsQled to a reduction of

the average bid-ask spread (an important transactions

cost in financial markets) in a 174 stock sample to

40.3 cents from 48.7 cents [12]. The introduction of

the SEAQ screen-based market system as part of the

London Stock Exchanges 1986 Big Bang market

reforms improved the quality of the LSE market [4],

and played a part in trading volumes increasing from

$280 million a day in 1985, to $4.1 billion a day in

1994. Comparing SEAQ to the floor, Londons

electronic market proved to be more open and

competitive than the floor market, and led to lower

transactions costs for investors. In spite of advantages,

however, many new electronic trading platforms fail

to attract sufficient market activity to survive.

Researchers have recently identified further oppor-

tunities for exploiting IT, and specifically the Internet,

for financial trading. Established order routing prac-

tices in many brokerage firms, though, can hinder the

adoption of the most efficient trading practices, and

thus reduce the incentive to introduce trading systeminnovations. As Fan et al.[9] points out bThe vertical

relationships between the brokers and the market

centers adversely affect investors interest and under-

mine the competition at the exchange markets. These

relationships also reduce the incentive for market

centers to innovate to offer more efficient trading

services.Q An obstacle facing a new market, such as

the ISE, is how to attract sufficient order flows when

many brokers have existing relationships with floor

exchanges [11].

B.W. Weber / Decision Support Systems 41 (2006) 728746 729


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This paper will describe recent developments in

U.S. options markets, and then analyze U.S. broker-

age firms regulatory order routing disclosures. Table

1indicates that during the sample period, the 20 majorU.S. brokerage firms adopted the new ISE market at a

rate roughly equal to the overall volume growth for

the ISE. Rule 6 disclosures were mandated beginning

in 3Q 2001. The sample firms usage, however, has

lagged the ISEs market share somewhat (seeTable 1).

The dependent variable in the analyses is the

percentage of a firms options orders it routes to the

ISE in a quarter. The adoption differences across firms

will be examined to determine what influences the

extent that brokers adopt the ISE market. Rule 11Ac1-

6 disclosures were first required in 3Q 2001, whenjust two of the firms in the sample had adopted the

ISE. By 1Q 2004, 19 of 20 firms in the sample had

reported routing orders to the ISE (see Tables A1 and

A2 in Appendix A).

Three model specifications are considered: OLS,

Tobit, and a firm fixed-effects model. The Tobit

model is estimated since it corrects for limited

dependent variable problems since the dependent

variable is zero ( left-censored) in 58 of 188

observations [3,10]. All of the models estimated

are statistically significant, and have fairly consistent

coefficient values. The fixed-effects model has the

greatest explanatory power, but all three show that

both firm-specific factorsUincluding firms ISE

membership categoryUand network effects in the

form of the prior periods aggregate ISE share

influence ISE use. The paper concludes withimplications for electronic market governance and

success factors for new trading systems.

2. Intermarket competition and origins of the ISE

Innovative trading systems launched to compete

with established markets often fail to attract sufficient

activity and are later closed down. Launched in

January 1999, Optimark sought to win block trading

volume from the NYSE and Nasdaq Stock Markets,but closed in late 2000[6].Another electronic trading

system, Tradepoint, competed with the London Stock

Exchange, and the screen-based Cantor Exchange

sought to capture U.S. Treasury futures contract

trading from the Chicago Board of Trades vast floor

trading pits [19]. Jiway was launched in 2000 as an

online platform for European stock trading with

backing from Morgan Stanley and Swedens OM

Group. Unable to develop sufficient trading volume,

each of these entrants later suspended operations.

In the late 1990s, the serial entrepreneur and founder

of E-Trade, Bill Porter, conceived of a fully electronic

options exchange to reduce the cost of options trading

Table 1

Comparison of sample firms use of ISE with overall ISE market share

Brokerage firm sample (n=20) ISE overall market share

Average ISE market share (%) High (%) Low Contracts traded (%) Transactions (%)

2Q00 Rule 11Ac1-6 disclosures began 3Q 2001 0.1 0.1

3Q00 0.5 0.6

4Q00 1.0 1.5

1Q01 4.0 5.9

2Q01 7.1 8.13Q01 4.4 59 0% (for 12 firms) 10.8 11.8

4Q01 6.2 59 0% (9) 12.5 12.1

1Q02 9.4 51 0% (8) 16.5 15.7

2Q02 12.9 61 0% (6) 19.7 20.4

3Q02 13.5 47 0% (6) 21.4 23.4

4Q02 15.5 41 0% (3) 20.4 23.2

1Q03 16.0 45 0% (2) 23.9 26.6

2Q03 19.4 51 0% (3) 27.2 28.9

3Q03 17.9 43 0% (4) 28.1 30.1

4Q03 19.3 44 0% (4) 28.1 31.1

1Q04 20.6 43 0% (3) 29.2 33.2

Sources: Rule 11Ac1-6 reports, Options Clearing data.

B.W. Weber / Decision Support Systems 41 (2006) 728746730


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and challenge the entrenched options floor commun-

ities. The $80 million venture to launch the ISE was

largely backed by a broker/dealer consortium named

Adirondack Trading Partners, whose investorsincluded E*Trade, Herzog Heine Geduld (bought by

Merrill Lynch in mid-2000), Ameritrade, Knight

Financial Products, Scottrade, and Deutsche Bank.

The founders announced their plan for the ISE in

November 1998, and it received regulatory approval

from the Securities and Exchange Commission (SEC)

on February 24, 2000 to operate as an all-electronic

options exchange. It was the first exchange to be

registered by the SEC since the CBOE was approved in

1973.

At the time of its launch in May 2000, the ISEfaced the challenge of attracting order flow in

competition with four established options exchanges

in the United states [20]. The oldest and largest U.S.

options market, the Chicago Board Options Exchange

(CBOE), was established in 1973, and reached its

peak average daily volume of 1.5 million contracts (an

equity option contract is for 100 shares of the

underlying stock) in 2000. Prior to the arrival of

option bmultiple listingQ in August 1999, options

exchanges chose not to list options that were already

traded at another exchange.1 Exclusive listings were

ended when the CBOE announced that it would begin

trading options on Dell Computer, which previously

had been listed only on Philadelphia Stock Exchange.

The other exchanges soon followed, listing each

others options and triggering a competitive war for

options order flow that the ISE soon jumped into.

Launch day volumes were modest; ISE trading

volume on May 26, 2000 totaled 5032 contracts,

earning it just 0.3% of the days total equity options

volume. The ISE began with calls and puts on just three

stocks, and only three Primary Market Makers

(PMMs), eight Competitive Market Makers (CMMs),and 17 Electronic Access Members (EAMs). In 1Q

2004, options on 646 stocks were traded on the ISE,

and there were eight PMMs, 23 CMMs, and 126 EAMs

operating. Without the space constraints of a market

floor, the ISE had more flexibility in deciding what, if

any, membership categories to have. The ISE founders,

however, chose a structure similar to floor markets,

where trading firms have designated market maker or

competing market maker roles, and have obligations tomaintain bid and ask quotes. Firms that purchase ISE

market making memberships receive certain privileges

and accept responsibilities in ISE trading.

The market has grown steadily in volume and

membership. Table 2shows that, in 2003, in its third

full year of operations, the ISE became the second

largest options market in the United States.

Competition from the ISE for options orders has

forced the other markets to reduce their transaction

fees and to develop more advanced electronic trading

functions [2,17]. On June 12, 2003, for instance,

CBOE launched CBOEdirect HyTS, a system for

access to both screen-based and floor-based trading

environments. Along with reduced volumes at the

existing exchanges, the competitive effects are seen in

falling seat prices at the f loor-based options

exchanges. Seats provide the holder or leaser with

access to that exchanges trading floor, and lower,

member-only transaction processing and service fees.

The CBOE has a fixed number (1485) of seats, and

these will change hands at prices determined by

market forces (Table 3). The record price for a CBOE

seat was $735,000 set in February 1998, but droppedto $150,000 in August 2002.

3. Options order handling and the ISEs market

structure

In options markets, customer participants have

accounts with brokerage firms, who handle their

transactions, and bclearQ their trades by backing them

financially. Prior to 1999, brokers sent investors

Table 2

Volumes on five SEC-registered exchanges for listed options trading

(Options contracts in millions) 2000 2001 2002 2003

Chicago Board Options Exchange 326.3 306.7 267.6 283.9International Securities Exchange 7.6 65.4 152.4 245.0

American Stock Exchange 207.7 205.1 186.1 180.1

Philadelphia Stock Exchange 76.0 100.9 88.5 112.4

Pacific Exchange 108.5 102.7 85.4 86.2

Total 726.2 780.7 780.0 907.6

Trading of options on individual stocks and equity indexes (e.g.,

S&P500) is included.

Source: Options Clearing.

1 Several papers have examined the market microstructure

impacts of the 1999 multiple listing change. See de Fontnouvelle

et al.[7] and Battalio et al. [2].



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the Designated Primary Market-Maker (DPM) role

that is assigned at the CBOE, the PMMs at the ISE are

the single points of contact for the options in their bin,

and are responsible for maintaining orderly marketsand answering trading questions.

The second ISE membership category is Compet-

itive Market Maker. As of May 2004, 23 firms

operated as ISE CMMs, with 4 firms, including

Credit Suisse First Boston and Lehman Brothers,

operating as CMMs in all 10 bins (see Appendix A for

a list of ISE PMM and CMM firms). A CMM must

purchase or lease one of 160 CMM trading rights,

entitling them to enter quotations in the options in a

bin. CMMs add depth and liquidity to the market by

providing continuous quotations in at least 60% of the

options classes in their bin(s). Each CMM is required

to quote independently, and 16 CMMs are appointed

to each of the 10 groups of options traded on the

Exchange. CMM rights are sold by the ISE and can be

resold or leased. For instance, CMM trading priv-

ileges for Bin 3 were bought for $1.5 million each on

December 18, 2003. On September 29, 2003, the

PMM trading privileges in Bin 7 sold for $7.5 million.

PMMs have greater obligations, but also greater

privileges in ISE trading than CMMs. When a customer

market order arrives at the ISE, and any limit orders

have been filled, the ISE allocation rules entitle PMMsto trade a greater number of contracts than a CMM with

an identical quote. For instance, a market order to buy

100 contracts arriving when one PMM and one CMM

each are offering 100 contracts for sale would result in

60 being sold by the PMM and 40 by the CMM. This is

because ISE rules specify that the PMM receive the

greater of 60% or his bproportionate size interestQ, 50%

in this case of the 200 contracts offered at the quoted.

Had two CMMs also been offering the lowest price, the

split would be 40% to the PMM, and 30% each to the

CMMs. With three CMMs and the PMM matching on

price and size, the split of an arriving order would be

30% to the PMM, and the remaining 70% split evenly

over the three CMMs. An exception is made for thehandling of orders for five contracts or less. In this case,

the customer limit orders are traded first and the

remainder goes exclusively to the PMM provided he is

on the best bid or offer (BBO). If the PMM is not on the

BBO, the order follows normal size-based, pro-rata

allocation among the CMMs. For instance, if one

CMM is offering to sell 30 contracts and another is

offering 10, an order to buy 20 would be split 15 and 5.

The third ISE membership type is an bElectronic

Access MemberQ(EAM). An EAM is a broker/dealer

that acts as an order flow provider, andunlike PMMs

and CMMsis not required to purchase membership.

There are no limits on the number of EAMs, who pay a

monthly access fee to send orders in all of the options

traded on the ISE. EAMs cannot enter quotations or

otherwise engage in market making activities on the

Exchange. As of May 20, 2004, there were 126 EAMs

(Source:http://www.iseoptions.com).

Given its membership and market structure, the ISE

faced technical challenges in launching its trading

system. To receive approval from the SEC and potential

users, its fully electronic market needed fast response

times and redundancy in the case of hardware orsoftware component failures. Importantly, ISE tech-

nology had to integrate the best bids and offers from

other exchanges so that ISE member firms can keep

their prices current, and know, for regulatory bbest

executionQ purposes, if there are better quotes in

another market for an ISE traded option. The quote

data from the other four options markets comes from

the Options Prices Reporting Authority (OPRA),

which updated about 3500 quotes per second in mid-

2003, and distributes its feed through the major market

Fig. 2. ISE market flows: a customers order is entered online or phoned to a broker. The order (at the brokerage firms discretion) is routed to

the ISE, and for 10 s, other participants can react and trade with it at better than the quoted bid or ask prices. A layer of interaction and

information transmission that occurs with the floor broker and trading crowd are eliminated.

http://www.iseoptions.com/http://www.iseoptions.com/http://www.iseoptions.com/http://www.iseoptions.com/


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data vendors, such as Reuters and Bloomberg. The best

bids and offers, the quote sizes, and last trade prices on

the ISE are sent to OPRA, which consolidates them into

a National Best Bid and Offer (NBBO), which keeps allmarkets and market participants appraised of prices and

quote movements. OPRA also requires that trades be

reported in the correct sequence within 2 min of the

trade execution. A trade reported to OPRA after 2 min

must be marked as delayed by inserting a bDLYQinto

the OPRA trade message field.

4. Rule 11Ac1-6 and analysis of ISE order routing

To assess adoption of the ISE as a new market, Icollected options order routing data across 11 quarters

(3Q 20011Q 2004) for a sample of 20 brokerage

firms. Beginning in the third quarter of 2001, the

SECs Rule 11Ac1-6 (Rule 6) required brokers to

disclose on a quarterly basis their order routing

practices in U.S. equities and listed options. The bro-

kers in the sample were chosen from Smart Moneys

2002 broker rankings (http://www.smartmoney.com/

brokers/), whose bDelegatorQ and bDo-it-YourselferQ

rankings included 29 firms. The two categories

correspond to the Full-Service and Discount Online

groups below. For some of the firms, quarterly order

routing reports were not available for 2001, which left

a sample of 20 firms (Table 4).

While the data sample includes large and midsize

firms that differ in the volume of option orders, the

SEC disclosures only provide data in percent of the

firms total orders, and do not include contract volume

data. Hence, the 11Ac1-6 data only allow for

unweighted market shares to be compared. As a

result, we use the ISEs share of options trades, rather

than contract volumes for comparability.

4.1. The Securities and Exchange Commissions

(SEC) Rule 11Ac1-6

In November 2000, the SEC adopted Rule 11Ac1-

6 (Rule 6) which became effective on July 2, 2001. In

few other industries, such detailed information on

transactional arrangements and behind-the-scenes

market usage volumes is disclosed. Rule 6 requires

tracking of all customer orders that are bnondirectedQ

orders. These orders without specific customer

instructions on where they are to be routed make up

99.6% of customer orders for brokers in our sample

according to their disclosures. Beginning in the third

quarter of 2001, the following information is now

disclosed by brokerage firms:

(1) The identity of the market centers that receive 5%

or more of customers orders for four categories

of securities: (i) New York Stock Exchange-

Listed Securities, (ii) Nasdaq-Listed Securities,

(iii) American Stock Exchange-listed and

Regional Exchange-listed securities, and (iv)

Exchange-Listed Options. The actual disclosures

are the bPercentage of Customer Orders Having a

Market Value Less Than $200,000Qfor securities,

and for listed options, the bPercentage of

Customer Orders Having a Market Value Less

Than $50,000.Q

(2) Material aspects of the order-routing relationship

between the broker and the market center. Thatis, indications of ownership in trading firms or

trading systems, and payment for order flow

arrangements.

(3) The percentage of orders in the following four

categories: (i) all orders, (ii) market orders, (iii)

limit orders, and (iv) other orders (stop orders,

short sales, not held/discretionary orders, etc.).

Since the average options trade in 2003 was for

19.3 contracts with a value of $5887, the upper limit

Table 4

Brokers in the sample fit into two categories

Discount online brokers (OLBs) Full-service brokers (FSBs)

AMTD Ameritrade AGE A.G. EdwardsBRWN BrownCo.

(online unit of

J.P. Morgan Chase)

BOFA

BSC

Banc of America

Securities

Bear Stearns

DATK Dateka CSFB Credit Suisse

ETRD

FID

E-Trade

Fidelity DBAB

First Boston

Deutsche Bank

JBOX

SCH

J.B. Oxford

Charles Schwab GS

Alex Brown

Goldman Sachs

SCO Scott Trade LEHM Lehman Bros.

TDW T.D. Waterhouse MER Merrill Lynch

MS Morgan Stanley

PRU Prudential Securities

SSB Salomon Smith Barney

a Acquired by Ameritrade in September 2002.

http://www.smartmoney.com/brokers/http://www.smartmoney.com/brokers/


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of $50,000 indicated in #1 above screens out few

transactions from the Rule 6 disclosures. Below are

several examples of the disclosures bmaterial aspects

of the order-routing relationshipQ(#2 above) sections:

! Credit Suisse First Boston (CSFB)CSFB is a

Competitive Market Maker in certain options

traded on the International Securities Exchange

(ISE). Consequently, CSFB may incur any gains or

losses that are generated by acting in a market

making capacity.

! T.D. WaterhouseNISC (T.D. Waterhouses clear-

ing affiliate) directs customer orders to designated

Primary Market Makers (PMM) that trade listed

option classes on the ISE. Not all PMM firms payNISC for directing option orders. NISC receives

payment on approximately 30% of all option

trades routed to the ISE. Rates range from 40

cents to 1 dollar per contract. The average payment

per contract received by NISC for this period was

26 cents.

! Goldman, SachsSLK-Hull Derivatives LLC

(bSHDQ), an affiliate of Goldman, Sachs is a

specialist, primary market maker or market maker

on AMEX, CBOE, ISE, and PHLX. As an

affiliate, Goldman Sachs stands to share indirectly

in any profits that SHD or SLK generates from the

execution of customer orders.

! ScottradeScottrade may receive payment for

order flow ranging from $0.00 to $0.60 per option

contract for orders routed to Knight for execution

on the CBOE, AMEX, PHLX, ISE, and PSE.

Scottrades principal shareholder and President is a

director and shareholder of Knight Trading Group,

Scottrade may receive payment for order flow of

$0.75 per eligible options contract for ordersrouted to ABN AMRO for execution on the

CBOE, AMEX, PHLX, ISE, and PSE. Scottrade

may direct orders to Adirondack Trading Partners

for execution on the International Securities

Exchange. Scottrade may receive payment for

order flow of $0.75 per options contract. Scottrade

maintains an approximate 2.8% ownership interest

in Adirondack Trading Partners. Scottrade main-

tains an inactive seat on the International Secur-

ities Exchange.

A plot of the SEC Rule 6 data for the sample firms

along with the ISEs overall market share (Graph 1)

indicates that online brokers were initially quicker to

adopt the ISE market. However, both groups

increased their routing to the ISE at roughly the same

rate as the ISE share grew. Also notable is that the

sample firms have slightly lower average ISE use

relative to ISE overall market share. I was not able to

get an explanation for the shortfall.

Three categories of nondirected orders are reported

in Rule 6 disclosures:

Market OrdersAny order in which a customer

does not specify an execution price. Under normal

market conditions, the order is filled immediately

at the consolidated best bid or offer at the time of

receipt by the market center.

Graph 1. A comparison of the ISEs overall market share of options trading volume with the percentage of all orders routed to the ISE by the two

groups. For comparability over the 11 quarters, only the seven Online Brokers and seven Full-Service Brokers with all 11 quarters of Rule 6 data

are included. Sources: rule 11Ac1-6 reports, Securities Industry News, "Quarterly Statistical Reports", 2001 2003.



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Limit OrdersAny order in which a customer

specifies a limit to the price that they are willing to

buy or sell a security. The order is usually filled if

the consolidated best bid or offer btouchesQ thelimit price of the order under normal market

conditions.

Other OrdersOdd lots, market opening and

closing orders, orders submitted with stop prices,

all-or-none orders, orders that must be executed on

a particular tick or bid (such as nonexempt short

sale orders), and bnot heldQorders.

4.2. ISE use by broker type and order type

For the full sample, limit orders were 37% of allcustomer options orders, while market and other

orders accounted for the remaining 63% (Table 5).

Customer orders from Online Brokers were far more

likely to be limit orders than market or other orders.

Section 5 examines whether the ISE attracts limit

orders disproportionately.

Tables A3 and A4 in Appendix A show how the

two samples of firms increased their use of the ISE

market over the 11 quarters examined. The tables

include percentages of limit order, market and other

orders, and all orders routed to the ISE. Since Rule

11Ac1-6 exempts broker/dealers from identifying

execution venues that received less than 5% of a

firms nondirected orders provided that 90% of the

nondirected orders are identified, individual brokers

overall market share totals across the five options

exchange did not necessarily sum to 100%. I do not,

however, adjust the reported ISE data to reflect the

possibility of ISE shares that were positive but less

than 5%.

The next section describes four research hypoth-

eses, and estimates several multivariate regression

models of firms use of the ISE in a given quarter. Themodels are developed from the quarterly disclosures

from 20 major brokerage firms in the sample. The

independent variables used to predict ISE use are: the

firms ISE membership categories, if any, the type of

firm (online or not), and the lagged (prior quarter)overall market share of the ISE, which rose from 8.1%

in 2Q 2001 to 31.1% in 4Q 2003.

5. Hypotheses and multivariate analyses of ISE use

The electronic markets literature identifies many

broad factors that can contribute to a new markets

success. These include the need for mutual benefits

for participants including users and market makers

[14],for a critical mass of trading activity to develop[5], for adequate incentives to exist for traders to

realize the cost-savings from online markets [8], and

for characteristics of the traded instrument to be suited

to screen-based trading[13].

We examine the specific factors that explain an

individual brokerage firms level of use of a new

market, such as the ISE. The factors that contribute to

ISE use fall into two categories. First, the network

effects from the prior periods ISE market share

among all options exchanges. Secondly, we include a

number of measured, firm-specific factors including

its membership categories in a particular quarter and

whether it is an online broker or not. Finally, a firm

fixed-effects model is estimated to account for

unmeasured factors at the firm level that influence

ISE use.

The first hypothesis concerns the order types that

the ISE attracts. Before developing an explanatory

model of ISE use, it is important to examine whether

the limit and market orders routed to the ISE need to

be treated separately. Due to its market systems, it is

possible that the ISE will attract proportionately more

limit orders. The ability of the ISEs electronic marketto hold limit orders in price and time priority could

attract proportionately more limit orders to the ISE.

Hypothesis 1.The ISE will attract a higher proportion

of brokerage firms customer limit orders than market

orders.

For the sample of online brokers, an average of

16.2% of limit orders were routed to the ISE, while

market orders sent to the ISE averaged 15.6% over the

11 quarters. For the full service brokers, limit orders to

Table 5

Online brokers in the sample reported routing more limit orders to

options exchanges than full service brokers (n=188)

Online

brokers (9)

Full service

brokers (11)

All (20)

Limit orders 77.8% 50.7% 62.9%

Market/other orders 22.1% 49.3% 37.1%

100% 100% 100%



10/19

the ISE averaged 11.2%, and market orders to the ISE

averaged 11.4% of the total number of option orders.

t-Tests conducted on the individual brokers 11

quarterly disclosures indicated no significant differ-ences in 18 of the 20 sample firms between the

percentage of a firms market orders and the percent-

age of limit orders routed to the ISE in a quarter. Only

Goldman Sachs routed a significantly larger propor-

tion of limit orders to the ISE than market orders

(34.0% and 22.0%, t=2.88, p=1.6%). The Lehman

Brothers disclosures show it has routed no limit

orders to the ISE, but on average has routed 6.6% of

its market and other orders to the ISE (0.0% and 6.6%,

t=2.85, p=1.7%).

Since only one of the 20 firms routed a signifi-cantly greater percentage of limit orders to the ISE,

Hypothesis 1 is not supported. While the electronic

market system of the ISE maintains limit orders in

price and time priority in a way that does not occur on

exchange floors, this feature does not affect order

routing to the ISE by order type.

The analyses from hereon will only consider

the percentage of sample firms total options

orders routed to the ISE. Hypothesis 2a,b, 3, and

4 will be tested by developing three different model

specifications. Each model has as its dependent

variable the individual brokerage firms quarterly

use of the ISE as a percentage of its total customer

options order flow. The first model is an OLS

regression with robust standard errors to account for

the heteroskedasticity caused by the limited dependent

variable (lower limit of 0.0 and upper limit of 1.0).

The second model is a Tobit model that specifically

ensures the models predicted values fall between 0

and 1. The third model is a fixed effect model that

tests for unmeasured, firm-specific factors influencing

their use of the ISE.

Hypothesis 2a. The ISE will attract a higher

proportion of brokerage firms customer orders when

they have a membership affiliation in the ISE.

Hypothesis 2b.The type of membership affiliation a

broker has in the ISE (PMM, CMM, and EAM) will

affect the brokers level of ISE use.

There are three membership types in the ISE:

PMM, CMM, and EAM. Firms can have no ISE

membership, or participate in one, two, or all three

types of membership. Even if a broker is not an ISE

member, its orders can still be routed to the ISE if it

directs customer orders to a clearing broker that is an

ISE member.The ISE membership types for brokers in the

sample are detailed in Tables A3 and A4 in Appendix

A. Membership is coded as three indicator variables,

one for each of the three membership categories. The

indicator is set to one if the firm is that type of

member in the quarter, or zero if it is not. In the cases

where a firm becomes an ISE member during a

quarter, the indicator variable is set to the approximate

fraction of the quarter remaining in which it will

operate on the ISE in that capacity. For instance,

Ameritrade became an EAM on December 13, 2001with only about a sixth (2 of 13 weeks) of 4Q 2001

remaining. Thus, the indicator is set to 0.167 for 4Q

2001, and 1 thereafter.

Hypothesis 3. Online discount brokers will be more

active participants in the ISE market and have greater

ISE adoption rates (as evidenced by their Rule 11Ac1-

6 order routing practices submissions) than full-

service brokers.

An indicator variable (OLB) is set to 1 for online

discount brokers in the sample and 0 for the full service

brokers. Support for Hypothesis 3 is evident if the

OLB coefficient in the regression model is positive

and significant. Its value will reflect the incremental

percentage of orders routed to the ISE by an online

broker. It is possible that online firms technology,

flexibility, and drive to lower cost result in greater

adoption by online brokers of the ISE. In addition, the

sample of full-service brokers includes firms with

dedicated f loor brokers at the major options

exchanges, which could make them more likely to

use in-house resources to trade options rather than the

ISE.

Hypothesis 4. Network externalities will draw more

order flow to the ISE market as its volumes and

liquidity grow.

A trader adage states that bliquidity begets liquid-

ity.Q At some point, it becomes disadvantageous to

ignore a market that has attracted a significantly

volume of trading. With the exception of E-Trade, all

firms in the sample increased the fraction of options

order sent to the ISE over the 11 quarter period 3Q



11/19

20011Q 2004. Tables A1 and A2 in Appendix A

indicate that while only two of the sample firms

routed orders to the ISE in 3Q 2001, all but one of 20

had routed some of its orders to the ISE by 2Q 2003,the eighth quarter of the sample. Another factor

determining a brokers use of the ISE may simply

be the growing level of activity make its market more

liquid and attractive to participate in. Of the firms in

the sample, only A.G. Edwards, a St. Louis-based

full-service firm, did not route orders to the ISE in any

of the 11 quarters. E-Trade and Goldman Sachs were

active, lead users, and could have been responsible for

the ISE growing its market share and attracting

additional user firms. A significant positive coefficient

on the prior quarters ISE market share (PrQtr)indicates that the ISEs lagged market share is related

to individual brokers ISE use. Alternatively, if the

coefficient is not significant, then the ISEs growth

comes from new users, rather than more volumes

from existing ISE users.

The OLS and Tobit models are developed with

five independent variables. The first OLS model

estimated is:

ISE SHAREBroker i; Quarter j

Constant b1 Online Broker Indicator i

b2 PMM indicator i;j b3 CMM Indicator i;j

b4 EAM Indicator i;j

b5 ISE Overall Market Share Quarter j1 eij

Additional specifications are considered for the

OLS and Tobit models that leave out the ISEs lagged

market share. This is done to isolate the effect of the

liquidity externality created by the ISEs growing

share of trading in the sample period. Finally, a firmfixed-effects specification is estimated that controls

for unobserved, but salient features of the individual

brokers in the sample (seeTable 6).

The estimated models that follow are the result of

regressing the brokers use of the ISE in a quarter

against the indicated independent variables (Table 7).

The coefficients in the OLS and Tobit models are

fairly consistent. The first OLS model explains 57%

of the variance in ISE use over the brokerage firms,

and has four significant, positive coefficients at the

0.05 level. Being an online broker, a PMM, or an

EAM are significantly related to the percent of orders

routed to the ISE. Only the CMM indicator fails

(slightly) to be significant at the 0.05 level in a two-

sided test. It would be significant in a one-sided test.

In the first Tobit model, all five explanatoryvariables are positive and significant at the 0.05 level.

The second OLS and Tobit models leave out the

lagged ISE market share as an independent variable,

which reduces the explanatory power of the models.

The reduction in the models significance indicates

that firms use of the ISE is related to the exchanges

prior quarter market share. That is, liquidity external-

ities are evident at the broker level as the ISE has

grown over the nearly 3-year study period.

The third model specification is a firm fixed-effects

model with the 20 broker identities as the categorical

factor. Including fixed effects for each firm, and

dropping the OLB indicator (redundant with the firm

categorical variable), gives the fixed effects model

greater explanatory power. Based on the R2, unob-

served factors at the firm level account for an additional

2627% of the variance in ISE use compared to the first

OLS model. This is evidence of systematic differences

in order routing to the ISE beyond the five measured

variables. Notably in the firm fixed-effects model,

CMM drops out of significance, and the PMM

coefficient is now only marginally significant at the

0.10 level, and its value, 0.097, is about half what it wasin the OLS and Tobit specifications. The EAM

coefficient remains significant at the 0.05 level and is

a similar size, 0.076, to that in the OLS (0.046) and

Tobit (0.081) models. Apparently, fixed-effects pick up

much of the influence of the PMM and CMM variables,

but not the EAM indicator.

The individual firm coefficients in the fixed-effects

specification show that E-Trade has the largest positive

coefficient, perhaps reflecting the fact that its founder,

Bill Porter, is also the founder of the ISE. As of March

Table 6

Descriptive statistics for the variables in the model

Variable Mean (n=188)

ISE share of brokeri in quarterj (dependent) 14.7%OLB indicator 46.8%

PMM indicator 29.8%

CMM indicator 45.1%

EAM indicator 81.2%

Prior quarter ISE share of options transactions 21.8%



12/19

2004, E-Trade Group owned approximately 4.6% of

the ISE. The five most negative firm coefficients

(DBAB, SSB, LEHM, PRU, MER) are all for full-

service brokers that have had trading operations on the

floor option exchanges or long-standing relationships

with the other four U.S. options exchanges.

Hypothesis Tests. Overall, the results show support

for Hypothesis 2a,b, 3, and 4. Hypothesis 2a,b is

supported since members of the ISE routed more

orders to the ISE. The coefficients of the PMM,

CMM, and EAM indicator variables are positive and

at least marginally significant in all but the case of the

CMM variable in the fixed-effects model. The PMM

indicator variable has the largest coefficient in all

three models. A brokers choice to become an ISE

PMM is associated with greater activity level on the

ISE. The EAM coefficient is smaller than the PMM

coefficient, but is significant in all of the three models.

Table 7

Estimation results

Variable OLS with robust standard errors Tobit model Firm fixed-effects model

Coefficient(t-statistic)

p-value


p-value


p-value


p-value

Coefficient

Online broker (01) 0.0620218

(4.14) 0.000

0.0580603

(3.72) 0.000

0.111404

(5.11) 0.000

0.1088379

(4.70) 0.000

PMM 0.1832548

(6.38) 0.000

0.160528

(5.38) 0.000

0.1968406

(6.29) 0.000

0.1605806

(4.94) 0.000

0.0974612

(1.75) 0.082

CMM 0.0498074

(1.97) 0.051

0.0780087

(3.17) 0.002

0.08797

(2.80) 0.006

0.1301359

(4.00) 0.000

0.0265343(0.93) 0.356

EAM 0.0460201

(2.80) 0.006

0.0610617

(3.18) 0.002

0.081016

(2.47) 0.014

0.1100216

(3.20) 0.002

0.0761969

(2.36) 0.020

Prior Qtr ISE share 0.4778999

(3.86) 0.000

0.7691195

(4.97) 0.000

0.5616605

(7.22) 0.000

ETRD 0.252577BSC 0.137863

CSFB 0.098843

JBOX 0.097463

BOFA 0.081644

AMTD 0.079567

DATK 0.073466

GS 0.067213

MS 0.029577

AGE 0.008299

SCH 0.01FID 0.04142SCOT 0.04799BRWN 0.06586

TDW 0.08704MER 0.08746PRU 0.11421LEHM 0.11488SSB 0.13573DBAB 0.15729Constant 0.1010812

(4.16) 0.0000.0132062(0.86) 0.393

0.2760581(6.05) 0.000

0.1380609(3.90) 0.000

0.0548137(1.86) 0.065

Model (F-stat)

p-value

(60.25) p =0.0000 (51.82) p =0.0000 (21.32) p =0.000

R2 0.5667 0.5217 0.8422

AdjustedR 2 0.5548 0.5113 0.8200

(Likelihood ratio v2)

p-value

(156.90)

p=0.0000

(132.47)

p=0.0000



13/19

The use of 01 indicator variables allows inter-

pretations to be drawn from the ISE membership

coefficient values. From the first OLS and Tobit

models, for instance, a firm that is a PMM isexpected to route an incremental 18.3% and

19.7% of its customer options orders to the ISE

in any given quarter. Being an EAM leads to

4.6% and 8.1% more orders being routed to the

ISE.

Hypothesis 3, which proposes that online brokers

will use the ISE more, is strongly supported. The OLB

indicator variables coefficient is significantly positive

in all of the models it is included in. Its value of 0.062

in the OLS model and 0.111 in the Tobit model

indicates that all else the same, an online broker canbe expected to route an added 611% of its order flow

to the ISE.

Hypothesis 4 argues that more ISE market share in

the prior quarter attracts greater ISE use, and is also

supported. The positive and significant coefficients

demonstrate that the prior quarters overall market

share for the ISE is related to the sample brokers use

of the ISE in a quarter. The OLS, Tobit, and fixed-

effects coefficients of 0.478, 0.769, and 0.562 indicate

that each additional percent of ISE market share in a

quarter leads individual brokers to route an additional

half to three-quarter percent of its orders to the ISE in

the subsequent quarter.

Additional insights can be drawn from the models

by examining the relative con tribut ion of the

explanatory factors to the average ISE market share

of the broker sample to be examined. Taking the

Tobit and OLS model coefficients and multiplying

by the mean values of the independent variables

gives insight into the relative contribution to the

models estimated ISE share of firms options orders.

Table 8 shows that brokers use of the ISE in the

model is about 60% apportionable to broker-specific

characteristics (PMM, EAM, OLB), and 40%

accounted for by the network effects represented

by the prior quarters ISE overall market share. Of

the firm-specific factors, membership types (PMMand EAM) are about three times as influential as

whether or not a firm is an online broker: summing

to 42.8% vs. 15.1% in OLS, and 39.2% vs. 17.1% in

Tobit.

6. Discussion and conclusions

Understanding the mixed record of success of

electronic financial markets is a challenge for I.S.

researchers [6,13,18]. Unlike many new computer-ized markets, the ISE has succeeded in attracting a

critical mass of volume and liquidity. A requirement

of any new market is to attract use broadly or from a

narrow set of active participants, and the newly

available SEC Rule 6 disclosures provide a way to

assess market adoption at the level of individual

brokerage firms.

The order routing patterns studied showed that

online brokers overall and ISE member brokers in

particular were rapid early adopters of the ISE. Even

in electronic markets, exchange memberships are

important, and were shown to influence brokers use

of the ISE. The analysis shows that ISE members, in

particular PMMs, direct substantially more order

flow to the exchange than nonmembers. While

technology can improve the functioning of a market

and reduce costs, membership structures remain an

important element in attracting order flow to an

exchange.

In addition to broker-specific factors, the network

effects generated by the ISEs growth served to draw

additional orders from brokers. The liquidity external-

ity is evident as individual brokers use of the ISE is

Table 8

Relative influence of the explanatory variables on the models estimated level of ISE use for the 20 sample firms

Mean value across

169 observations

Coefficient in

OLS model

Proportional influence on

estimated ISE use (%)

Coefficient in

Tobit model

Proportional influence on

estimated ISE use (%)

OLB 0.468 0.0620 11.7 0.1114 13.6

PMM 0.298 0.1833 22.0 0.1968 15.3

CMM 0.451 0.0498 9.1 0.0880 10.3

EAM 0.812 0.0460 15.1 0.0810 17.1

LagISE% 21.8% 0.4779 42.1 0.7691 43.7



14/19

significantly related to the lagged ISE market share of

options trading in the prior quarter. Once the market

began to grow, its adopters raise their usage. Liquidity

does attract liquidity.The implications are that a well-designed member-

ship structure leads to committed early users that

contribute to the positive network effect from market

liquidity. We conclude that the ISE benefited from its

early efforts to attract members, who in turn con-

tributed order flow to the market and attracted

additional usage and new adopters. In particular, the

activity in late 2001 from early lead users, such as E-

Trade and Goldman Sachs, established a liquidity base

that encouraged other firms to use the ISE in

competition with established open outcry markets.For market developers and exchange officials, the

implications of this study of the ISEs success

factors are:

(a) early adoption of new markets across brokers is

likely to be uneven, but the sustained use of early

adopters can generate network externalities that

draw in more participation

(b) brokers and traders with membership positions

in an electronic exchange use its screen-based

market more actively, and membership advan-

tages are helpful in attracting order flow that

might otherwise be directed to the incumbent

exchanges

(c) segments of brokers, in the ISEs case online

brokers, are likely to adopt electronic trading

more rapidly than other segments such as full

service brokers

(d) electronic market making and access can attract

the order flow from securities firms that would

not join a floor-based exchange that required a

large staff.2

Using SEC Rule 11Ac1-6 disclosures from 20 major

brokerage firms, a number of significant influences

on firms use of a new electronic options market

were identified: the firms ISE membership catego-ries, if any, whether it is an online broker or not,

lagged ISE market share, and firm fixed-effects are

important determinants of ISE adoption at the

brokerage firm level. The work illustrates the many

nontechnological aspects of new trading systems that

influence their adoption. It shows how online

exchanges governance structures can create incen-

tives for user firms and order providers to benefit as

the liquidity and activity levels of the market

increase. Creating business value from electronic

markets and exchange is an important area of I.S.and economics research, and the work here shows

that governance structures and membership affilia-

tions can catalyze usage and liquidity in a new

market.

Although online markets do not have physical

space constraints, ISE PMM and CMM members

receive trading privileges in return for buying the

membership and adhering to certain obligations to

post quotes and trade. Evidence from the ISE suggests

these member firms helped build initial liquidity in the

critical early stages of the market. Other e-markets

however are based on more open structures without

costly memberships. Valuable future work on the

adoption of new electronic markets will come from

further study of membership privileges and obliga-

tions, new e-markets examples, and comparison of e-

market order types and functionality.

The new Boston Options Exchange (BOX)

launched its electronic trading platform and became

the sixth U.S. options exchange on February 6, 2004.

In contrast to the ISE, BOX provides open access to

all brokerage firms without expensive memberships,

up-front costs, or formal market maker designations.The ISEs member firms formed a committed user

group. BOXs ability to attract to volume will be a test

whether electronic markets can succeed with non-

exclusive memberless market structures.

Complex orders, such as spreads and straddles,

have traditionally been more suited to floor trading,

but are now being introduced into electronic markets.

The ability to accommodate more sophisticated orders

may determine the future growth of the ISE. Further

insight into the intermarket competition could come

2bMorgan Stanley was not in the [options trading] business

pre-ISE because we didnt deem it financially appropriate to

maintain a staff of many brokers and floor traders in various

exchanges.QUQuote in Business Week from Thomas R. Cardello,

a managing director at Morgan Stanley. The article continued:

bUsing ISEs automatic trading system and algorithms derived from

the stock prices, volatilities, and interest rates, Cardello says he can

quote 50,000 options at any given time.QFrom: bBest Little Options

Exchange in America?The arrival of the ISE broke up a cozy

cartel,QBusiness Week, September 2, 2002.



15/19

from more detailed analysis of the bmaterial aspects of

the order-routing relationshipQ disclosures from the

Rule 6 filings. These could show how ownership in

trading firms or trading systems, and payment fororder flow arrangements influence brokers in choos-

ing among competing securities and options market

centers. While it faced significant competitive

obstacles, the ISE caught on and can now demonstrate

what factors contribute to brokerage firms use of anew market and the markets eventual success.

AMTD (%) BRWN (%) DATK (%) ETRD (%) FID (%) JBOX (%) SCH (%) SCOT (%) TDW (%)Limit orders

3Q01 0 0 60 0 0 0 0 0

4Q01 4 5 60 0 0 0 1 0

1Q02 33 6 50 0 0 0 4 0

2Q02 40 5 62 0 6 8 4 0

3Q02 36 6 47 0 9 16 4 1

4Q02 38 16 41 4 9 16 15 2

1Q03 34 7 46 4 6 18 4 3

2Q03 40 8 52 6 25 18 5 17

3Q03 40 8 44 9 14 20 9 15

4Q03 44 12 40 11 17 19 21 13

1Q04 38 22 43 14 21 23 16 12

Market/other orders

3Q01 0 0 58 0 0 0 0 0

4Q01 5 5 58 0 0 0 21 0

1Q02 18 5 53 0 0 0 6 0

2Q02 25 5 60 0 4 10 8 0

3Q02 24 4 43 0 3 17 8 0

4Q02 37 38 39 6 4 18 17 1

1Q03 27 5 42 6 3 24 7 2

2Q03 32 6 42 9 19 24 6 15

3Q03 32 7 38 11 9 22 9 14

4Q03 28 9 47 12 14 20 20 13

1Q04 42 22 50 15 20 24 17 12

Total nondirected3Q01 0 0 59 0 0 0 0 0

4Q01 5 5 59 0 0 0 11 0

1Q02 29 6 51 0 0 0 5 0

2Q02 35 5 61 0 6 8 5 0

3Q02 30 6 47 0 7 16 5 1

4Q02 38 17 41 5 8 16 16 1

1Q03 33 6 45 4 5 19 5 3

2Q03 40 8 51 6 24 19 5 17

3Q03 40 8 43 9 12 20 9 15

4Q03 36 11 41 11 16 19 20 13

1Q04 39 22 44 14 21 23 17 12

AMTD acquired DATK in September 2002 for $1.29 billion. Combined data first reported in 1Q03. NA: not available.

Appendix A

Table A1. Online brokers in the sample routed a greater percentage of customer orders than the sample of full-

service brokers. Online brokers use of the ISE did not exceed the ISEs overall market share among the five U.S.

options exchanges.



16/19

AGE (%) BOFA (%) BSC (%) CSFB (%) DBAB (%) GS (%) LEHM (%) MER (%) MS (%) PRU (%) SSB (%)

Limit orders

3Q01 0 6 14 0 0 0

4Q01 0 15 0 0 0 1

1Q02 0 0 30 0 0 5 0 0

2Q02 0 0 41 0 0 23 0 0

3Q02 0 25 22 46 0 0 32 0 0

4Q02 0 0 23 41 0 11 32 6 0

1Q03 0 41 0 13 40 0 6 36 9 0

2Q03 0 34 37 0 0 42 0 7 39 11 0

3Q03 0 26 32 0 0 40 0 15 43 0 0

4Q03 0 31 35 0 0 38 0 15 46 0 0

1Q04 0 8 37 0 6 35 0 23 41 0 0

Market/other orders

3Q01 0 6 0 0 0 0

4Q01 0 7 0 0 0 2

1Q02 0 26 18 0 0 7 0 0

2Q02 0 26 22 0 0 29 0 0

3Q02 0 23 14 31 0 0 33 0 0

4Q02 0 23 16 26 0 5 34 3 0

1Q03 0 30 28 9 25 12 6 44 3 0

2Q03 0 14 28 32 0 27 16 6 39 5 0

3Q03 0 14 25 30 0 24 13 13 41 0 0

4Q03 0 10 36 25 0 27 16 15 39 0 0

1Q04 0 35 38 23 9 28 16 24 45 0 0

Total nondirected

3Q01 0 7 0 0 0 0

4Q01 0 13 0 0 0 2

1Q02 0 26 27 0 0 7 0 0

2Q02 0 26 36 0 0 23 0 0

3Q02 0 24 19 42 0 0 32 0 0

4Q02 0 23 20 37 0 5 32 5 0

1Q03 0 37 28 11 35 7 6 37 7 0

2Q03 0 29 34 32 0 38 10 7 40 9 0

3Q03 0 23 30 30 0 35 9 14 42 0 0

4Q03 0 25 35 25 0 35 10 15 44 0 0

1Q04 0 26 37 23 8 33 10 24 42 0 0

NA: Not available.

Table A2. ISE market share of full service brokers limit orders, and market and other orders, and total nondirected

orders.



17/19

Broker Membership

type

Date Notes

AMTD EAM 13-Dec-01 Indicator variable for EAM set to 0 for 3Q01, 1/6=0.167 for 4Q01,

and 1 thereafter. PMM and CMM via ownership interest in Adirondack

trading partners wholly owned by JP Morgan-Chase, whose JP Morgan

Securities unit was an EAM and later became a CMM.

CMM As of 31-Dec-00

PMM As of 31-Dec-00

BRWN EAM As of 31-Dec-00

CMM 20-Aug-01

DATK None

ETRD EAM As of 31-Dec-00 E*Trade is an EAM

CMM As of 31-Dec-00 PMM and CMM via ownership interest in Adirondack Electronic

Markets/KAP GroupPMM As of 31-Dec-00

FID EAM 18-Jun-02 Indicator variable for EAM set to 0 for 1Q02 and before, 1/6=0.167

for 2Q02, and 1 thereafter.JBOX None

SCH EAM As of 31-Dec-00

SCO EAM As of 31-Dec-00

TDW EAM 28-Nov-01 Indicator variable for EAM set to

0 for 1Q02 and before, 1/6=0.167 for 2Q02, and 1 thereafter.CMM 01-Apr-02

Table A3. Online brokers and their ISE membership and date.

Broker Membership

type

Date Notes

AGE None

BOFA EAM As of 31-Dec-00

BSC EAM As of 31-Dec-00 Sold its CMM and PMM memberships Nov 10, 2003. Indicator

variable for PMM and CMM set to 0 for 3Q03, 4/9=0.44

for 4Q03, and 0 thereafter.

CMM As of 31-Dec-00

PMM As of 31-Dec-00

CSFB EAM As of 31-Dec-00

CMM 29-Aug-02

DBAB EAM As of 31-Dec-00 PMM, CMM, and EAM via Deutsche Bank Securities

CMM As of 31-Dec-00

PMM As of 31-Dec-00

GS EAM As of 31-Dec-00 PMM and via Hull Trading Co. subsidiary, EAM viaGoldman Sachs and Co. and Spear, Leeds and Kellogg subsidiaryCMM As of 31-Dec-00

PMM As of 31-Dec-00

LEHM EAM As of 31-Dec-00 Indicator variable for CMM set to 0 for 3Q02, 1/2=0.5

for 4Q02, and 1 thereafter.CMM 15-Nov-02

MER EAM As of 31-Dec-00

CMM 10-Nov-03 Indicator variable for PMM and CMM set to 0 for 3Q03, 5/9=0.56

PMM 10-Nov-03 for 4Q03, and 1 thereafter.

MS EAM As of 31-Dec-00

CMM As of 31-Dec-00

PMM As of 31-Dec-00

PRU EAM As of 31-Dec-00

SSB EAM As of 31-Dec-00

Table A4. Full-service brokers and their ISE membership and date.



18/19

Primary Market Makers, as of May 2004

(1) Knight Financial Products LLC Bin 1

(2) SLK-Hull Derivatives LLC Bin 2

(3) Adirondack Electronic

Markets LLC

Bins 3 and 4

(4) Citadel Derivatives Group LLC Bins 5 and 8

(5) UBS Securities LLC Bin 6

(6) Timber Hill LLC Bin 7

(7) Deutsche Bank Securities Inc. Bin 9

(8) Morgan Stanley & Co.,

Incorporated

Bin 10

Competitive market makers As of May 2004

(1) Adirondack ElectronicMarkets LLC

Bins 1, 2, 5, 6, 10

(2) Archelon LLC Bins 2, 3, 4, 5, 7

(3) Bear Wagner Specialists LLC Bins 2, 6, 7, 9

(4) BNP Paribas Securitie Bins 1, 2, 3, 4, 5,

6, 7, 8, 9, 10

(5) Citadel Derivatives Group LLC Bins 1, 2, 3, 4, 6,

7, 9, 10

(6) Credit Suisse First Boston LLC Bins 1, 2, 3, 4, 5,

6, 7, 8, 9, 10

(7) Cutler Group, LP Bin 1

(8) Deutsche Bank Securities Bins 1, 2, 3, 4, 5,

6, 7, 8, 10

(9) Geneva Trading LLC Bin 10

(10) Group One Trading, L.P. Bins 8, 10

(11) J.P. Morgan Securities Bin 9

(12) Knight Financial

Products LLC

Bins 2, 3, 4, 5, 6,

7, 8, 9, 10

(13) Lehman Brothers Bins 1, 2, 3, 4, 5,

6, 7, 8, 9, 10

(14) MAKO Global

Derivatives LLC

Bin 9

(15) Merrill Lynch Professional

Clearing Corporation

Bins 1, 2, 3, 4, 7,

8, 9, 10

(16) Morgan Stanley & Co.

Incorporated

Bins 1, 2, 3, 4, 5,

6, 7, 8, 9

(17) Optiver US, LLC Bin 3

(18) PEAK6 CapitalManagement LLC

Bin 5

(19) SLK-Hull Derivatives LLC Bins 1, 3, 4, 5, 6,

7, 8, 9, 10

(20) TD Options LLC Bins 1, 3, 4, 5, 6,

7, 8

(21) Timber Hill LLC Bins 1, 2, 3, 4, 5,

6, 8, 9, 10

(22) UBS Securities LLC Bins 1, 2, 3, 4, 5,

7, 8, 9, 10

(23) Wolverine Trading, LLC Bins 1, 2, 3, 4, 5,

6, 7, 8, 9, 10

Source:http://www.iseoptions.com.

Table A5. ISE market makers. References

[1] M. Barclay, T. Hendershott, T. McCormick, Competition

among trading venues: information trading on electroniccommunications networks, Journal of Finance 58 (2003)

26372665.

[2] R. Battalio, B. Hatch, R. Jennings, Toward a national market

system for U.S. Exchange-listed equity options, Journal of

Finance 59 (2004) 933962.

[3] E. Bridges, Y.C. Kin, R. Briesch, A high-tech product market

share model with customer expectations, Marketing Science

14 (1) (1995 Winter) 61 81.

[4] E. Clemons, B. Weber, Londons big bang: a case study of

information technology, competitive impact, and organiza-

tional change, Journal of Management Information Systems 6

(4) (1990) 4160.

[5] E. Clemons, B. Weber, Alternative securities trading systems:

tests and regulatory implications of the adoption of technol-

ogy, Information Systems Research (1996 June) 163188.

[6] E. Clemons, B. Weber, The Optimark experience, in: R.A.

Schwartz (Ed.), Building A Better Stock Market: The Call

Market Alternative, Kluwer Academic Publishers, 2001,

pp. 353 364. Chapter 22.

[7] P. De Fontnouvelle, R.P.H. Fishe, J.H. Harris, The behavior of

bid-ask spreads and volume in options markets during the

competition for listings in 1999, Journal of Finance 58 (2003)

24372464.

[8] I. Domowitz, B. Steil, Innovation in equity trading systems:

the impact on transaction costs and the cost of capital, in: B.

Steil, D. Victor, R. Nelson (Eds.), Technological Innovation

and Economic Performance, Princeton University Press,2002.

[9] M. Fan, J. Stallaert, A.B. Whinston, The Internet and the

future of financial markets, Communications of the ACM 43

(11) (2000) 82 88.

[10] W.H. Greene, Econometric Analysis, 3rd Edition, Prentice-

Hall, Upper Saddle River, NJ, 1997.

[11] A. Grunbichler, F. Longstaff, E. Schwartz, Electronic screen

trading and the transmission of information: an empirical

examination, Journal of Financial Intermediation 3 (1994)

166187.

[12] J. Hamilton, Electronic market linkages and the distribution of

order flow: the case of off-board trading of NYSE-listed

stocks, in: H. Lucas, R. Schwartz (Eds.), The Challenge of

Information Technology for the Securities Markets: Liquidity,Volatility, and Global Trading, Dow Jones-Irwin, 1989.

[13] T. Hendershott, Technological innovations and electronic

trading systems in financial markets, IEEE-IT Professional

(2003 JulyAugust) 1014.

[14] A. Kambil, E. Van Heck, Making Markets: How Firms Can

Design and Profit from Online Auctions and Exchanges,

Harvard Business School Press, 2002.

[15] M. Massimb, B. Phelps, Electronic trading, market structure

and liquidity, Financial Analysts Journal (1994 January

February) 39 50.

[16] R.A. Schwartz, Reshaping the Equities Markets: A Guide for

the 1990s, Business One Irwin, Chicago, 1993.

http://www.iseoptions.com/http://www.iseoptions.com/http://www.iseoptions.com/


19/19

[17] R.A. Schwartz, B.W. Weber, Next-generation securities market

systems: an experimental investigation of quote-driven and

order-driven trading, Journal of Management Information

Systems 14 (2) (1997 Fall) 5779.

[18] B. Weber, Elements of market structure for on-line commerce,

in: Chris F. Kemerer (Ed.), Information Technology and

Industrial Competitiveness: How Information Technology

Shapes Competition, Kluwer Academic Publishers, Boston,

1998, pp. 15 32. Chapter 2.

[19] B. Weber, Next-generation trading in futures markets: a

comparison of open outcryand order matching systems, Journal

of Management Information Systems 16 (2) (1999 Fall) 29 45.

[20] B. Weber, Growing market liquidity at the international

securities exchange, IT Professional, IEEE Computer Society

5 (4) (2003 July/August) 2229.

Bruce W. Weber is Associate Professor

of Information Management at the Lon-don Business School, where he teaches

bInformation ManagementQand bTrading

and Market StructuresQ in the MBA

programme. His research examines elec-

tronic market systems and, in particular,

IT-driven competition in online financial

services and securities markets. He has an

A.B. in Applied Mathematics from Har-

vard University and a Ph.D. in Decision

Sciences from the Wharton School of the University of Pennsylva-

nia. Prior to joining the London Business School in 2003, he was on

the faculty of the Stern School of Business, New York University,

and Baruch College of the City University of New York, where he

was founding director of the Subotnick Financial Services Center. He

is on the editorial boards of Information Systems Research, Journal

of MIS and Decision Support Systems. His articles have appeared in

Management Science, Information Systems Research, Journal of

Management Information Systems, Journal of Organizational

Computing, and The London Stock Exchange Quarterly. His work

has been cited in the Financial Times, the Wall Street Journal, and the

New York Times, and he has been an invited speaker at regulatory

hearings and at industry conferences. He is co-developer with Robert

A.Schwartz of the NASD HeadTrader simulation, which is available

at http://www.nasd.com/HeadTrader/BYP-main.htm. He has con-

sulted on e-finance issues for several major financial services firms

and the Nasdaq Stock Market and London Stock Exchange and has

presented executive training programs in decision analysis and

technology strategy to groups from U.S. and European firms.

http://www.nasd.com/HeadTrader/BYP-main.htmhttp://www.nasd.com/HeadTrader/BYP-main.htmhttp://www.nasd.com/HeadTrader/BYP-main.htmhttp://www.nasd.com/HeadTrader/BYP-main.htmhttp://www.nasd.com/HeadTrader/BYP-main.htm

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