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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].
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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.
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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.
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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.
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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%
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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
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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%
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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
Coefficient(t-statistic)
p-value
Coefficient(t-statistic)
p-value
Coefficient(t-statistic)
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
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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
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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.
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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.
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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.
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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.
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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.
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[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.
B.W. Weber / Decision Support Systems 41 (2006) 728746746
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