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MARCH 2014
	 NUMBER 320

Chicag­ Fed Letter
Market structure, incentives, and fragility
by Carol L. Clark, senior policy advisor

The factors that have contributed to the adoption of high-speed trading and affected
market structure in recent years include competition, technology, and regulation. The
unexpected ways in which these dynamic forces are coming together raise a number
of important policy issues. 1

There have been profound changes in

trading in recent years as the use of technology has taken on greater importance
and introduced new risks. Trading speeds
have increased from seconds to milliseconds (thousandth
of a second) to micro1. U.S. equities average daily share volume (in billions)
seconds (millionth of
a second) and are
migrating to nano12
seconds (billionth of
a second). Such high10
speed trading (HST),
which includes automated, algorithmic,
and high-frequency
trading, has received
considerable media
attention, largely due
to high-profile market
events that have been
characterized by the
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
coding or deployment
Source: TABB Group.
of trading algorithms,
market breakdowns,
and market manipulation.
Certainly, HST poses operational risks to
the market due to the rate at which large,
unintended positions can accumulate.
There is also the possibility HST may result in positive or negative feedback loops
caused by a runaway algorithm triggering
other algorithms or by numerous HST
firms utilizing trading models that do not
accurately assess and respond to changing market conditions. The myriad of

technologies that support HST also result
in “systems that are robust yet fragile.”2
Failure in one of many parts may have
unexpected knock-on effects in others.
At the same time, institutional investors
who often complained of being front run3
by their trade intermediaries in a floorbased trading environment have welcomed
the anonymity electronic trading provides.
Various studies also indicate HST has
brought substantial benefits, including
lower costs, reduced volatility, and narrower bid-offer spreads. Some challenge
these studies, however, because many
were conducted by HST firms, exchanges
that have HST customers, or academics
sponsored by HST firms or exchanges.4
One may also argue that the movement
toward HST is part of a natural, evolutionary outcome within financial markets,
not unlike the adoption of automated or
computer-based systems in every other
major industry. Nevertheless, it is difficult
to find examples of firms in other industries that were rapidly brought to the
brink of bankruptcy due to technological
malfunctions like the 40 minutes it took
Knight Capital to lose $460 million.5
As we look at the profound changes that
have occurred in trading and in market
structure in recent years, we need to
examine some of the dynamic forces
that have influenced this transformation
and brought about the current HST
environment. These include regulatory

initiatives in some markets, competition
for order flow, and new ways technology is
used in that competition, all of which
may be coming together in unexpected
ways and raising a number of public policy
questions. Some of these relate to operational risk, pricing, and manipulation.
At times, problems associated with HST
can be resolved without undue market
disruption. One example is the successful bailout of Knight Capital by a group
of investors whose interest in the firm

This failure was substantially smaller than
that of Knight Capital. The event is noteworthy, however, because it could happen
in any jurisdiction if a failing firm does
not have assets that are of interest to
potential buyers and losses have to be
covered by the reserve fund. Moreover,
we cannot anticipate in advance how
large a loss may be if operational risk
controls are inadequate.
Importantly, regulators have made strides
in addressing operational risks. Early

Order flow is critical to a trading venue’s profitability because
most have revenue models that are dependent, in part, on the
number, and sometimes the value, of executed trades.
was likely related to Knight’s valuable
business as a wholesaler for retail order
flow for firms like Fidelity, TD Ameritrade,
Scottrade, and E*TRADE. Wholesalers
pay retail broker-dealers to route orders
to them and then match these orders
against each other or against orders from
the wholesaler’s proprietary trading desk.
Such orders are referred to as uninformed
order flow because retail investors generally hold a longer-term view of the
markets and are not concerned with
exploiting intraday, short-term price
swings. The profits wholesalers make on
trading against uninformed order flow
exceed the fees they have to pay to retail
broker-dealers because they are able to
capture the spread between buy and sell
orders and save on transaction costs that
would have to be paid to trading venues
if orders were routed there. One of the
investors in the bailout, the HST firm
GETCO, later merged with Knight.6
In contrast, the recent failure of a brokerage firm on the Korea Exchange (KRX)
ended less satisfactorily for market participants. After erroneously placing automated buy and sell options orders, HanMag
Securities lost $43.8 million, which exceeded the firm’s capital of $18.8 million.7
One report indicates KRX had to use cash
from an emergency reserve fund set up
by the exchange’s brokerage firms to
cover the loss.8 If HanMag files for bankruptcy and doesn’t find a buyer, the exchange’s brokers will be required to
replenish the money used from the fund.

in 2013, the Securities and Exchange
Commission (SEC) issued a proposal
to require key market participants to
have comprehensive policies and procedures in place for their technological
systems. In September 2013, the chair of
the SEC met with the heads of the major
U.S. exchanges to develop an action
plan to address recent technology outages.9 The Commodity Futures Trading
Commission (CFTC) issued a “Concept
Release on Risk Controls and System
Safeguards for Automated Trading
Environments” in September.10
Regulation, competition, and
technology intersect

In U.S. markets, stocks trade simultaneously at numerous trading venues. One
way the SEC promotes fairness and competition among these trading venues is
through the Regulation National Market
System (Reg NMS), which was fully implemented in 2007. Among other things,
Reg NMS prohibits one trading venue
from executing a trade at an inferior
price to another.
New trading venues began to enter the
market following the implementation
of Reg NMS in order to capture order
flow from liquidity providers, who post
buy and sell orders in the order book for
a specific stock at a specific price. The
more buy and sell orders in the order
book, the more liquid the trading venue
and the more likely it is that other market participants will find a price they
are willing to trade against.

Order flow is critical to a trading venue’s
profitability because most have revenue
models that are based, in part, on the
number, and sometimes the value, of executed trades. Competition for order flow
among trading venues in U.S. equities
markets has likely increased over the past
few years due to decreased trade volumes.
Even though the number of U.S. equities
trades increased dramatically (60%)
during the recent financial crisis from
6.1 billion in 2007 to a peak of 9.8 billion
in 2009 (see figure 1), current trade
volumes are closer to pre-crisis levels
(6.2 billion in 2013). This means trading
venues are competing for fewer transactions at the same time that exchanges
are losing volumes to off-exchange trading in broker-dealer internalizers and
dark pools,11 where an estimated 38%
of all trades now take place.12
Revenue models based on the values and
volumes of trades provide trading venues
with an incentive to attract HST firms,
which bring large numbers of orders.
Trading venues use a variety of strategies
to do this, including pricing structures,
order-matching algorithms, order types,
and technology products and services.
Some of these provide HST firms with
time, place, or informational advantages.
Pricing structures

BATS, a trading venue that entered the
market following the implementation of
Reg NMS, doubled its market share in
NYSE-listed stocks by offering a fast execution platform and a “maker-taker”
pricing structure to attract order flow.13
Maker-taker pricing structures pay liquidity providers a rebate to post buy and
sell orders to the order book and charge
other market participants (including
retail and institutional investors) to execute against these resting orders. Such
a design encourages liquidity providers
to send orders to the trading venue
with the highest rebates.
Conversely, “taker-maker” pricing structures pay brokers for retail and institutional orders that remove liquidity and
charge liquidity providers to execute
against those orders. If two or more
trading venues have the same price, this
pricing structure encourages brokers to
route orders to the trading venue offering the highest rebate.

While not every trading venue uses rebates, most have tiered pricing structures
whereby firms that execute larger numbers, and sometimes larger values, of
trades are charged lower fees than firms
with smaller trading volumes and values.
Order matching and special order types

Trading venues may use other means to
attract liquidity, such as the way they
match buy and sell orders. First-in-firstout (FIFO) matching algorithms compare
buy and sell orders based on the best
price and the time the order arrived in
the queue, providing a competitive advantage to firms with the best prices and
fastest systems. To level the playing field
between high-speed and other traders,
some trading venues have moved away
from using FIFO algorithms.14
In addition, some trading venues may try
to attract order flow by offering special order types. According to a Wall Street Journal
report, one such order type offered by the
exchange Direct Edge allowed some HST
firms to trade ahead of other investors.15
Technology products and services

Trading venues have also adjusted their
technology and product offerings to draw
HST firms. For example, many offer colocation services, which allow trading
firms to place their computer servers within the same data center that houses the
trading venue’s servers, thereby ensuring
the shortest transmission times between
the systems. Co-location enables HST
firms to access detailed price and other
information, view buy and sell orders in
the order book, and send their orders
to the trading venue’s matching engine
ahead of firms that do not co-locate.
Other technology advantages offered
by some trading venues include social
media feeds HST firms can incorporate
into their trading strategies and faster
trading routes via fiber-optic cables and
microwave technology. Technology products and services can provide time, place,
or informational advantages to firms
willing to pay for them.
Time, place, informational advantages

Of course, traders have always sought
time, place, and informational advantages. However, their means for doing

so have changed over time. In the past,
traders physically jostled for a better
position on the trading floor and used
their voices to drown each other out.
Placing a firm’s telephone closer to the
trading floor also provided an edge by
shortening the distance a runner had
to go to hand an order into the pit.
Going back to 1815, there is the account
of Nathan Rothschild receiving advance
news of the outcome of the Battle of
Waterloo via carrier pigeon or boat.16
Today, we might think of this as a technological edge or an early release of data.
One version of the story says Rothschild
made a fortune buying British government bonds based on the knowledge that
Napoleon had been defeated. Another
account claims that Rothschild was aware
that other traders knew he had excellent
communication systems so he started
selling British bonds, which prompted
others to sell. Rothschild then bought
back the bonds at a cheaper price.17
We are able to recount the Rothschild
tale because the identities of traders were
known in a traditional trading environment. Such knowledge could facilitate
retribution and may have prevented some,
but by no means all, bad behaviors. In
contrast, electronic markets, which have
been lauded for their transparency, actually have an element of opacity because trading firms do not know each
other’s identity.
Market manipulation and
operational risk

While market manipulation has always
been a concern in trading, it may be more
difficult to detect today because HST
firms trade correlated products across
multiple asset classes and trading venues
around the world. Moreover, trading
venues may be monitored by different
regulators using different technologies,
such as the CFTC’s Trade Surveillance
System (TSS)18 and the SEC’s Market
Information Data Analytics System
(MIDAS).19 In addition to concerns about
the interoperability of these systems, it
is questionable whether firm-level data
can be easily exchanged among regulators
because MIDAS does not provide attribution information about the brokers
or customers behind the orders.20

A recent example of regulators and trading venues working together to discipline
firms engaged in market manipulation is
the case of the HST firm, Panther Energy
Trading. In July 2013, the CFTC fined
Panther for manipulating U.S. commodities markets and the United Kingdom’s
Financial Conduct Authority fined the
owner of Panther for manipulating
markets there.
Public policy questions

Among the many questions HST raises
for policymakers and regulators are
the following:
•	 Are market participants underpricing
the risks of HST?
•	 Do they have the real-time controls
they need to manage these risks?
•	 Should trading venues evaluate alternative revenue models?
•	 Do regulators and trading venues
have the proper incentives and tools
to identify and control market manipulation? If not, why not?
•	 And since trading firms are trading
across asset classes globally, who has
the authority to implement an international approach to promptly monitor, respond to, and discipline firms
for market manipulation?
Charles L. Evans, President  Daniel G. Sullivan,
Executive Vice President and Director of Research;
Spencer Krane, Senior Vice President and Economic
Advisor ; David Marshall, Senior Vice President, financial
markets group  Daniel Aaronson, Vice President,
microeconomic policy research; Jonas D. M. Fisher,
Vice President, macroeconomic policy research; Richard
Heckinger,Vice President, markets team; Anna L.
Paulson, Vice President, finance team; William A. Testa,
Vice President, regional programs, and Economics Editor ;
Helen O’D. Koshy and Han Y. Choi, Editors  ;
Rita Molloy and Julia Baker, Production Editors 
Sheila A. Mangler, Editorial Assistant.
Chicago Fed Letter is published by the Economic
Research Department of the Federal Reserve Bank
of Chicago. The views expressed are the authors’
and do not necessarily reflect the views of the
Federal Reserve Bank of Chicago or the Federal
Reserve System.
© 2014 Federal Reserve Bank of Chicago
Chicago Fed Letter articles may be reproduced in
whole or in part, provided the articles are not
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Prior written permission must be obtained for
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ISSN 0895-0164

1 	The author gratefully acknowledges the

helpful comments on this article by Richard
Steiner, RBC Capital Markets, as well as
the following Federal Reserve Bank of
Chicago staff: David Marshall, Richard
Heckinger, and John McPartland.

	 Sam Mamudi, 2013, “Getco posts loss as


merger partner Knight’s trading improves,”
Bloomberg, August 7.

	 Kanga Kong, 2013, “Trading error leaves

Korean broker scrambling,” Korea Real
Time, blog, Wall Street Journal, December 18,
available at

	 Exchange clearing houses generally require


clearing members to contribute to a losssharing pool that can be drawn upon in the
event a member becomes insolvent. See
Carol L. Clark, 2010, “Controlling risk in
a lightning-speed trading environment,”
Chicago Fed Letter, Federal Reserve Bank of
Chicago, No. 272, March.

3	 Front-running is an illegal practice, where-

by a broker uses advance knowledge of
customer orders to gain an unfair trading
advantage. For example, if a customer has
a large order to buy, the broker buys the
same stock in advance of placing the customer’s large order. As a large order will
naturally drive up the price of the stock, the
broker then sells the shares at the higher
price, generating a profit at the expense
of the customer.

	 Dave Lauer, 2013, “2013 top stories: HFT—


In search of the truth,” TabbFORUM,
December 27.

	 Alexandra Stevenson, 2013, “Knight Capital


to pay $12 million fine on trading violations,”
New York Times DealBook, October 16.

and competition across trading venues in
Europe and the USA,” Journal of Financial
Regulation and Compliance, Vol. 18, No. 3,
pp. 257–271.


2	 The term was coined by Andrew Haldane.
See Andrew Haldane, 2013, “Why institutions matter (more than ever),” speech
given at the Centre for Research on SocioCultural Change Annual Conference,
University of London, School of Oriental
and African Studies, London, September 4.

	Giovanni Petrella, 2010, “MiFID, Reg NMS


	 Sarah N. Lynch and Herbert Lash, 2013, “U.S.


exchanges to create kill switches following
Nasdaq outage,” Reuters, September 12.



	Another common matching algorithm is


pro rata, where orders for the same price
are filled based on some percentage related
to the size of the order.

	Scott Patterson and Jenny Strasburg, 2012,


“For superfast stock traders, a way to jump
ahead in line,” Wall Street Journal,
September 19, available at http://online.



article/1401561, p. 4.

	John Kay, 2013, “Enduring lessons from the


legend of Rothschild’s carrier pigeon,”
Financial Times, May 28.



11	 Dark pools minimize the risk of large orders
moving the market price by allowing buyers
and sellers to submit orders anonymously
and report price and size information only
after the trade has been completed.

12 	John McCrank and Sarah N. Lynch, 2013,

“Exchange CEOs meet regulators on dark
pools, internalizers,” Reuters, April 9.



	Dave Michaels and Sam Mamudi, 2013,


“SEC once slowed by data gap to report
high-speed trader research,” Bloomberg,
October 1.

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