TECHNOLOGY 

ADVISORY 

COMMITTEE

TECHNOLOGICAL 

TRADING IN THE MARKETS

JULY 14, 2010

U.S. Commodity Futures Trading Commission

1155 21st Street, N.W.

Washington, DC 20581
Commodity Futures Trading Commission

Technology Advisory Committee 

July 14, 2010 Meeting

TAB DESCRIPTION

1. Agenda for Meeting, Wednesday, July 14, 2010

2. Press Release: CFTC Announces Members of the CFTC’s Technology Advisory Committee

3. Technology Advisory Committee Members

4. J. Bates, Algorithmic Trading and High Frequency Trading: Experiences from the Market and Thoughts on 

Regulatory Requirements (July 2010). 

5. B. Boultwood, HFT and Algorithmic Trading Issues and Regulatory Considerations (July 2010). 

6. J. Castura, R. Litzenberger, and Richard Gorelick, RGM Advisors, LLC, Market Efficiency and 

Microstructure Evolution in U.S. Equity Markets: A High Frequency Perspective (April 22, 2010). 

7. A. Chaboud, B. Chiquoine, E. Hjalmarsson, and C. Vega, Rise of the Machines: Algorithmic Trading in the 

Foreign Exchange Market, BD. OF GOVERNORS OF THE FED’L RES. SYS. INT’L FINANCE DISCUSSION 

PAPERS, No. 980 (October 2009). 

8. J. Cvitanić and A. Kirilenko, High Frequency Trading and Asset Prices (March 11, 2010).

9. B. Durkin, The Impact of Algorithmic and High Frequency Trading on CME Group, Inc. Markets (July 2010). 

10. Futures Industry Association, Market Access Risk Management Recommendations (April 2010). 
TAB 1
CFTC’s Technology Advisory Committee

1155 21st Street, N.W.

Washington, DC 20581

July 14, 2010

Agenda

11:45 a.m. to 12:00 p.m. Check-in with CFTC Reception – Lobby Level

12:00 p.m. to 12:50 p.m. Lunch with CFTC Commissioners and Technology Advisory Committee 

Members, 9th Floor Conference Room

1:00 p.m. to 5:00 p.m. TAC Meeting, Hearing Room

1:00 p.m. to 1:10 p.m. Opening Remarks: Commissioner Scott D. O’Malia, Chairman, TAC

1:10 p.m. to 1:30 p.m. Opening Remarks of CFTC Commissioners: Chairman Gary Gensler, 

Commissioner Michael Dunn, Commissioner Jill Sommers, and 

Commissioner Bart Chilton

1:30 p.m. to 1:40 p.m. Overview of Meeting and Introduction of Presenters

1:40 p.m. to 2:00 p.m. FIA’s Market Access Risk Management Recommendations, Mary Ann Burns, 

Executive Vice President, Futures Industry Association

2:00 p.m. to 3:00 p.m. Discussion of FIA’s Market Access Risk Management Recommendations

3:00 p.m. to 3:15 p.m. Break (Restrooms are located on the Mall Level)

3:15 p.m. to 3:35 p.m. A Perspective on High Frequency Trading (HFT) from RGM Advisors, LLC, 

Richard Gorelick 

3:35 p.m. to 4:00 p.m. High Frequency Traders and Asset Prices, Andrei Kirilenko, Senior Financial 

Economist, CFTC Office of the Chief Economist

4:00 p.m. to 4:45 p.m. Discussion of HFT presentations, debate on need for HFT best practices, 

and next steps for the TAC. 

4:45 p.m. to 5:00 p.m. Concluding Remarks of CFTC Commissioners: Chairman Gary Gensler, 

Commissioner Michael Dunn, Commissioner Jill Sommers, Commissioner 

Bart Chilton and Commissioner Scott D. O’Malia, Chairman, TAC
TAB 2


TAB 3
1

Commodity Futures Trading Commission

Technology Advisory Committee Members

Dr. John Bates

Senior Vice President, Chief Technology Officer and Head of Corporate Development

Progress Software

Dr. Bates is Senior Vice President, Chief Technology 

Officer (CTO) and head of Corporate Development for Progress Software. Dr. Bates is 

recognized as a driving force behind the emergence of complex event processing (CEP) and 

the commercial use of event processing applications in business solutions, including capital 

markets trading, risk and compliance, telecommunications, fraud prevention, and smart 

logistics. Prior to joining Progress Software, Dr. Bates was the co-founder, president and 

CTO of Apama (acquired by Progress Software in April 2005). Before Apama, Dr. Bates 

was a tenured academic at Cambridge University, where he directed the research into 

distributed computing systems.

Brenda Boultwood

Chief Risk Officer

Constellation Energy

Ms. Boultwood is Senior Vice President and Chief Risk 

Officer for Constellation Energy. She leads risk management activities for Constellation 

Energy and its businesses, including defining and assessing enterprise-wide business risks 

and facilitating proactive decision-making to effectively manage the risks associated with 

each business line. 

Prior to joining Constellation Energy, Ms. Boultwood most recently served as global 

head of strategy, Alternative Investment Services for J.P. Morgan Chase & Company, where 

she was responsible for developing strategy for the company’s Hedge Fund Services, Private 

Equity Fund Services, Leveraged Loan Services and Global Derivative Services business 

lines. During her tenure at J.P. Morgan Chase, she also served as global head, strategic risk 

management for its Treasury Services group and as global business head, Global Derivative 

Services of its Alternative Investment Services group. Ms. Boultwood joined J.P. Morgan 

Chase when it acquired Bank One Corporation in 2003. Prior to the merger, she held risk 

management positions with Bank One Corporation, having served as head, corporate market 
2

risk management and head, corporate operational risk management and then advancing to 

head, global risk management for its Global Treasury Services group. 

Ms. Boultwood also worked with PricewaterhouseCoopers as a senior manager in its 

Financial Risk Management Consulting Practice and was employed with Chemical Bank 

Corporation as a financial engineering associate. In addition, she spent six years teaching in 

the University of Maryland’s Master of Business Administration program.

Ms. Boultwood graduated with honors from the University of South Carolina with a 

bachelor’s degree in international relations. She also earned a Ph.D. in economics from the 

City University of New York. 

John Breyault

Vice President, Telecommunications and Fraud Public Policy

National Consumers League

 Mr. Breyault joined the National Consumers League in 

September 2008. Mr. Breyault’s focus at NCL is on advocating for stronger consumer 

protections before Congress and federal agencies on issues related to telecommunications, 

fraud, technology, and other consumer concerns. In addition, Mr. Breyault manages NCL’s 

Fraud Center and coordinates the Alliance Against Fraud coalition. Mr. Breyault is also 

Research Director for the Telecommunications Research and Action Center (TRAC), a 

project of NCL. In his role with TRAC, Mr. Breyault advocates on behalf of residential 

consumers of wireline, wireless, VoIP, and other IP-enabled communications services.

Prior to coming to NCL, Mr. Breyault spent five years as director of research at Amplify 

Public Affairs, where he helped launch the firm’s Web 2.0-based public affairs practice and 

focused on producing actionable public policy research. Earlier in his career, Mr. Breyault

worked at Sprint in its International Carrier Services Division and at the American Center 

for Polish Culture in Washington, DC.

Mr. Breyault was a member of the FCC’s Consumer Advisory Committee from 2005 to 2007 

and served on the Board of the Arlington-Alexandria Coalition for the Homeless. He is a 

graduate of George Mason University, where he received a bachelor’s degree in International 

Relations.
3

Dr. Peter Carr

Global Head of Market Modeling

MorganStanley

Dr. Carr is a Managing Director at Morgan Stanley in New 

York. He is also the Executive Director of the Masters in Math Finance program at NYU’s 

Courant Institute. Prior to his current positions, Dr. Carr headed quantitative research 

groups at Bloomberg LP and at Banc of America Securities. His prior academic positions 

include 4 years as an adjunct professor at Columbia University and 8 years as a finance 

professor at Cornell University. Dr. Carr is currently the treasurer of the Bachelier Finance 

Society and an associate editor for 8 journals related to mathematical finance and derivatives. 

He is also credited with numerous contributions to quantitative finance including: coinventing the variance gamma model, inventing static and semi-static hedging of exotic 

options, and popularizing variance swaps and corridor variance swaps. Dr. Carr received his 

Ph.D. in Finance from UCLA. 

Michael Cosgrove

Managing Director-Head of Commodities & Energy Brokerage, North America

GFI Group

Mr. Cosgrove started his career with Amerex Oil 

Associates in 1981 as a broker of international crude oil. In 1986, Mr. Cosgrove became a 

partner and Managing Director of Amerex’s European operations. During the next 20 years 

Mr. Cosgrove expanded Amerex’s business globally until; in 2006 it comprised 250 

employees serving a broad range of international energy and petroleum markets from 

offices. In 2006 Mr. Cosgrove joined GFI in connection with GFI’s purchase of the 

Amerex North American businesses. In the following year the Amerex U.K. and European 

businesses were sold in a management buyout and subsequently acquired by Tullet Prebon. 

In 2004, Mr. Cosgrove was named in the Energy Risk Hall of Fame. Mr. Cosgrove became 

Managing Director and Head of Commodities & Energy Brokerage for GFI in July 2008 and 

is responsible for all of GFI’s commodities & energy business in North America, including 

its Amerex and StarSupply divisions.
4

Gary A. DeWaal

Senior Managing Director and General Counsel

Newedge USA, LLC

Mr. DeWaal is a Senior Managing Director and the 

General Counsel of Newedge, as well as a member of Newedge’s governing Executive 

Committee. Newedge was created on 2 January 2008 from the merger of the Fimat and 

Calyon Financial Groups (Newedge refers to Newedge Group and all its branches and 

worldwide subsidiaries. Newedge Group is jointly owned by Société Générale and Calyon). 

Newedge’s worldwide Legal, Compliance and Financial Crimes Prevention (including AML) 

departments report to Mr. Dewaal.

Mr. Dewaal joined Fimat in March 1995 from Brody White & Company where he served, at 

various times, as President, General Counsel, Head of Operations and Head of Internal 

Audit since 1986. Previously Mr. Dewaal worked for the U.S. Commodity Futures Trading 

Commission’s Division of Enforcement in NYC, and Mudge Rose Guthrie & Alexander, a 

Wall Street corporate law firm whose principal partner was once Richard Nixon.

Mr. Dewaal also taught a course entitled “Trading Derivatives: Practice and Law” for most 

years from 1996 through 2006, and has also recently been an annual guest lecturer for the 

International Finance and Law program in New York City of the State University of New 

York at Buffalo School of Law.

Mr. Dewaal graduated in 1980 with JD and MBA degrees from the SUNY Buffalo and in 

1976 from the State University of New York at Stony Brook where he received a BA degree 

in English and economics; was elected to Phi Beta Kappa and Omicron Delta Epsilon 

(international economics honors society); and co-received awards as the University’s top 

overall graduating senior and junior.

Mr. Dewaal has published numerous articles on futures and securities industry issues, and 

frequently lectures or appears as a speaker at futures and securities industry conferences or in 

training sessions for regulators. His most recent articles are “Time to Clean up after the 

Party” (The Financial Times, October 14, 2008), “America’s Financial Regulation Needs an 

Overhaul (The Financial Times, October 31, 2007), “Chicago’s Merger Has to Protect the 

Users’ Interest,” (The Financial Times, November 15, 2006), “Streamlining Regulation” (The 

Washington Times, August 2, 2005) and “America Must Create a Single Financial Regulator 

(The Financial Times, May 19, 2005). He most recently participated as a panelist or speaker 

on the following panels: Moderator: “Soup to Nuts: General Futures Overview,” Futures 

Industry Association, Annual Law and Compliance Conference (May 2009); Panelist: “Crisis 

Management: What Happens When Global Firms Fail,” International Regulators 

Symposium and Training Conference, Commodity Futures Trading Commission (March 
5

2009); Moderator: “How Have Current Market Conditions Impact Opportunities in Asia,” 

Futures Industry Association, Annual Futures Industry Conference (March 2009); 

Moderator: “Lessons Learned from the Fall (2008) that Was,” Annual International 

Regulators Symposium and Training Conference, Commodity Futures Trading Commission 

(October 2008); Lecturer: “Observations on Kerviel: A Broker’s Perspective,” Australian 

Securities and Investment Commission (September 2008); Panelist: “Managing Risk in 

Volatile Markets, Financial Industry Regulatory Authority (September 2008); Moderator: 

“International Transactions” and panelist “Internal Controls post Kerviel:” Futures Industry 

Association, Annual Futures Industry Conference (March 2008); Panelist: “Introduction to 

Futures Regulation,” New York City Bar Association Continuing Legal Education Program 

(December 2007); “Crisis Response,” Annual International Regulators Symposium and 

Training Conference, Commodity Futures Trading Commission (October 2007); and 

Moderator: “Asian Regulatory Issues,” Taiwan Financial Securities Commission (October 

2007) and FIA Asia Conference (October 2007).

Mr. Dewaal lives in Brooklyn, New York with his wife, Myrna Chao, and two daughters, 

Justi and Nyasia. In his spare time, Mr. Dewaal is an avid photographer, and bicycles and 

cooks for fun.

Donald F. Donahue

President and Chief Executive Officer

The Depository Trust & Clearing Corporation

Mr. Donahue is Chairman and Chief Executive Officer for 

The Depository Trust & Clearing Corporation and for three of DTCC’s operating 

subsidiaries, The Depository Trust Company, Fixed Income Clearing Corporation and 

National Securities Clearing Corporation. He took this position in 2007, following on one 

year as President and Chief Executive Officer for DTCC, DTC, FICC and NSCC, and three 

years as Chief Operating Officer for DTCC and as President and Chief Operating Officer 

for DTC and NSCC.

Mr. Donahue has been with DTCC and its predecessor organizations since 1986. During his 

time at DTCC Mr. Donahue has held positions in a variety of areas, serving as head of the 

depository’s Operations Division from 1995 until 1997, as the depository’s Chief 

Information Officer from 1997 until 2000, and as head of DTCC’s Customer Marketing and 

Development Division, with responsibility for strategic planning, product development, IT 

applications development, and technology infrastructure support and telecommunications, 

from 2000 to 2003.

Prior to joining the depository, Mr. Donahue worked for five years for Barr Brothers & Co., 

Inc., a broker/dealer specializing in municipal securities. He worked for the Municipal 
6

Securities Rulemaking Board, the self-regulatory organization governing the U. S. municipal 

securities markets, from 1977 to 1985, first as Assistant Executive Director and then as 

Deputy Executive Director. From 1985 to 1986 he was President of two affiliated 

companies that developed and marketed secondary market credit enhancements for 

municipal securities.

From May 2004 to June 2006, Mr. Donahue served, under an appointment by Secretary John 

W. Snow of the United States Department of the Treasury, as Sector Coordinator for the 

U.S. Banking and Finance Sector in connection with the Treasury Department’s 

responsibilities as lead agency for the Sector under Homeland Security Presidential Directive 

7. In that capacity Mr. Donahue served as Chairman of the Financial Services Sector 

Coordinating Council for Critical Infrastructure Protection and Homeland Security, a private 

sector group that interacts with the Treasury Department and Federal and State regulators 

on infrastructure protection and homeland security issues. From April 2005 to April 2006, 

Mr. Donahue also served as Chairman of the Partnership for Critical Infrastructure Security, 

Inc., an organization of all of the Sector Coordinators appointed under HSPD-7 that works 

with the U.S. Department of Homeland Security on critical infrastructure protection matters.

Mr. Donahue has participated in a variety of financial services industry committees and task 

forces. He currently serves on the Board of Directors of the United Way of New York City, 

and on the Board of Directors of XBRL US, the nonprofit consortium for XML business 

reporting standards in the U.S. financial markets.

Mr. Donahue has a B.A. degree in History from Columbia University.

Bryan T. Durkin

Chief Operating Officer and Managing Director, Products & Services

CME Group Inc.

Mr. Durkin has served as Chief Operating Officer and 

Managing Director, Products & Services of CME Group since February 2010. He is 

responsible for the company’s Products & Services, Global Operations, Technology and 

Enterprise Computing, and Enterprise Solutions Divisions. Previously, he served as 

Managing Director and Chief Operating Officer since July 2007. He also led the global 

integrations following CME’s merger with the Chicago Board of Trade (CBOT) in 2007 and 

CME Group’s acquisition of the New York Mercantile Exchange (NYMEX) in 2008. He is 

a member of the COMEX Governors Committee and a Director of the CME Foundation. 

Before joining CME Group, Durkin served as Executive Vice President and Chief Operating 

Officer of the Chicago Board of Trade (CBOT). Prior to that role, he was in charge of the 

CBOT’s Office of Investigations and Audits where he oversaw the audits, financial 

surveillance, trade practice and market surveillance self-regulatory and enforcement divisions 

for the exchange. His career with both CME Group and CBOT spans more than 25 years. 

Durkin holds a bachelor’s degree in business administration and an MBA from Lewis 
7

University. He has been an adjunct faculty member of Lewis University’s MBA program, 

teaching courses in organizational behavior and management.

Richard B. Gorelick

Chief Executive Officer

RGM Advisors, LLC

Mr. Gorelick is the Chief Executive Officer of RGM 

Advisors, LLC, an automated trading firm that he co-founded in 2001. The company 

applies scientific approaches and computing power to automated trading strategies in 

multiple asset classes around the world. The company is headquartered in Austin, Texas, 

and, through a subsidiary, maintains a London office. 

Prior to founding RGM, Mr. Gorelick was the Chief Strategy Officer of Deja.com, Inc., 

which he joined in 1999, as the company's General Counsel. Prior to Deja, Mr. Gorelick 

was a corporate attorney in Coudert Brothers' New York office. 

Mr. Gorelick received a B.A. in international relations from the University of Pennsylvania 

and a J.D. from the Georgetown University Law Center.

Dr. Michael Gorham

Industry Professor of Finance and Director

IIT Center for Financial Markets

Illinois Institute of Technology

IIT Industry Professor Michael Gorham served for more 

than three decades as a research economist at the Federal Reserve Bank of San Francisco. 

Additionally, Prof. Gorham served as vice president of product development, commodity 

marketing, education and international marketing spanning 18 years at the Chicago 

Mercantile Exchange. He also has academic and research experience at IIT’s Center for Law 

and Financial Markets, has been editor of the Journal of Global Financial Markets and 

provides consulting services to international exchanges and regulators. Most recently, Prof. 

Gorham served as the first director of the Division of Market Oversight for the Commodity 

Futures Trading Commission. 
8

Simon Grensted

Managing Director, Business Development

LCH.Clearnet

Mr. Grensted is Managing Director, Business 

Development. He joined the company in 1997 to design OTC Clearing services. The first 

of these was the OTC interest rate SwapClear service. Since going live in 1999 the service 

has grown to include, as users, the majority of the global interbank market makers.

The team has gone on to develop OTC services in repo and bonds, energy, freight and 

equity derivatives. More recently, he has been involved in developing clearing services for 

Nodal Exchange, the new US power market as well as the newly formed Hong Kong 

Mercantile Exchange (HKMEx). 

Prior to joining LCH.Clearnet, Mr. Grensted was Director of IT and Operations at 

EuroBrokers, one of London’s largest brokers in emerging markets, money markets and 

derivative instruments.

Previously, for eleven years from 1982, Mr. Grensted was Managing Director of a software 

and systems vendor designing and implementing financial systems for banks, analytical 

products for information vendors and image processing systems for space and defence. 

During this time he was responsible for the development of a number of market leading 

products. He has also worked for Reuters and Datastream during his career in financial 

systems and product design.

Mr. Grensted has a BSc from University College London in Electrical and Electronic 

Engineering and has been a visiting lecturer at Leeds and Sheffield universities.
9

Jill L. Harlan

Corporate Risk Manager

Caterpillar, Inc.

Ms. Harlan is currently the Corporate Risk Manager 

responsible for managing the risk associated with Caterpillar Inc.’s foreign exchange 

exposures related to the Machine & Engine business. 

Ms. Harlan joined Caterpillar as a Finance Analyst in 1988. She is a graduate of 

Western Illinois University with a Bachelors of Business degree in Finance. She has 

had a diverse career with assignments focused on both core treasury operations and 

in marketing business units. Prior to her current position, she served as a Region 

Finance Manager for North American Commercial Division, Enterprise Risk 

Manager in Corporate Auditing, Treasurer for Asia Pacific Division, Finance Services 

Manager for Caterpillar of Australia and Foreign Exchange Administrator for 

Caterpillar EAME in Geneva. Other assignments in Corporate Treasury include 

Human Resources Manager, Risk Administrator, Machine Orders Analyst and a 

variety of Finance Analyst positions.

Douglas E. Harris

Managing Director

Promontory Financial Group, L.L.C. 

Mr. Harris is a Managing Director in the New York office 

of Promontory Financial Group, L.L.C., a financial services consulting and advisory firm, 

where he advises clients in regulatory matters involving risk management, compliance, 

investment products, derivatives, capital markets and complex structured transactions, and 

on general corporate governance, internal controls, and strategic advisory matters. 

. 

Formerly, he was the General Counsel and Chief Operating Officer of BrokerTec Futures 

Exchange, L.L.C. and BrokerTec Clearing Company, L.L.C. Before joining BrokerTec, Mr. 

Harris was a partner in the Regulatory Risk Services Group at Arthur Andersen LLP, where 

he advised commercial and investment banks, hedge funds and futures commission 
10

merchants on regulatory, compliance, risk management, derivatives and capital markets 

issues.

From 1993 to 1996, he held the position of Senior Deputy Comptroller for Capital Markets 

at the Office of the Comptroller of the Currency. He was responsible for the regulation and 

supervision of national bank capital markets activities, including trading, dealing and 

investing in derivatives and emerging markets instruments, as well as the development of risk 

management policies and guidelines. He also served on the Inter-Agency Task Force on 

Bank-Related Derivatives Activities and as senior staff member of the President’s Working 

Group on Financial Markets.

Previously, Mr. Harris served as Assistant General Counsel of JPMorgan and General 

Counsel of JPMorgan Futures, Inc. 

Mr. Harris is a Director of the National Futures Association, the self-regulatory organization 

for the U.S. futures industry, where he is a member of both the Compliance Consultative 

Committee and the Audit Committee. He is also a member of the Bar Association of the 

City of New York, the New York State Bar Association (Structured Products and 

Derivatives Law Committee), the American Bar Association (Committee on the Regulation 

of Futures and Derivatives), the Law & Compliance Division of the Futures Industry 

Association, and the Financial Markets Association.

Mr. Harris received his AB from Harvard College and his JD from Harvard Law School.

Christopher K. Hehmeyer 

Vice-Chairman of the Board 

National Futures Association

As CEO of HTG Capital Partners, Mr. Hehmeyer 

provides the strategic direction and leadership for HTG Capital Partners. Having starter his 

career as a runner on the floor of the Chicago Board of Trade in 1978, Mr. Hehmeyer 

became a full member of the CBOT in 1981 where he was a floor broker, floor trader, 

member of the board of directors, and chaired, vice chaired or served on over 40 

committees at the exchange. Most recently he was the CEO of Penson GHCO and 

continues to serve as its non-executive chairman.

Mr. Hehmeyer was one of the founding partners of Goldenberg, Hehmeyer & Co and 

worked as a managing director of the Virginia Trading Corporation beginning in 1981 and 

prior to establishment of the GHCO partnership in 1984.

In addition to his duties as CEO, Mr. Hehmeyer serves as vice chairman of the board of 

directors of the National Futures Association and as vice chairman of the board of the 

Futures Industry Association.
11

He is a member of the advisory board for the Master of Science of Financial Engineering 

Program at Kent State University, the Economics Club of Chicago, and the World 

Presidents' Organization. 

Mr. Hehmeyer has served on a variety of local charity boards including chairman of the 

exchange chapter of Ducks Unlimited and chairman of the annual LaSalle Street Dinner 

Dance for the Chicago Area Council of the Boy Scouts of America.

Steven A. Joachim

Executive Vice President of Transparency Services 

Financial Industry Regulatory Authority (FINRA)

Mr. Joachim is the Executive Vice President of 

Transparency Services at FINRA. His responsibilities include the Alternative Display 

Facility, FINRA’s listed equity quote and trade reporting vehicle; the Trade Reporting 

Facilities, FINRA’s joint ventures with Exchanges for printing listed equity trades; and the 

Over The Counter Equity transparency facilities, including OTC Bulletin Board and 

TRACE, the FINRA facility for reporting corporate bond trades. 

Prior to his arrival at FINRA in 2002, Mr. Joachim was the Senior Vice President, Chief 

Operating Officer, Chief Strategy Officer and General Manager for Plural from 1997 to 

2001. Plural was a custom interactive software development and strategy firm and is now 

owned by Dell Professional Services. In 1983, he began a nearly 15-year stint with Merrill 

Lynch. During his career at Merrill Lynch he served as head of Institutional Marketing, First 

Vice President, Business Architect for Capital Markets and Chief Technology Officer for 

Global Equity Markets, Director, Floor Brokerage Services and Business Manager, Global 

Equity Trading. Throughout his career at Merrill, he has managed operations in Asia, 

Europe and the U.S. From 1981 to 1983, Mr. Joachim worked for Bankers Trust Company 

as Vice President, Area Consultant for Lending and Money Transfer Operations. He also 

served as a Managing Consultant with Cresap McCormick and Paget, Inc. 

Mr. Joachim is the current Chairman of the International Forum for Investor Education and 

has served as a member of the Philadelphia Stock Exchange Board of Governors, Board of 

Directors for Merrill Lynch Specialists, Inc. and Board of Directors for Wilco, Inc. He has 

also been a member of the Nasdaq Industry Advisory Committee and the American Stock 

Exchange Upstairs Member Advisory Committee.

Mr. Joachim has an MA in Political Science from Duquesne University in Pittsburgh, PA, 

and an MS with distinction in Public Management and a BS in Mathematics from Carnegie 

Mellon University in Pittsburgh, PA.
12

Peter G. Johnson

Managing Director of Futures & Options

J.P. Morgan

Mr. Johnson is the Managing Director and Global Co-Head of Futures and Options and 

OTC Clearing for J.P. Morgan Futures Inc. In March 2010, Mr. Johnson was elected 

treasurer of the Futures Industry Association (FIA). Mr. Johnson also serves as Chairman of 

the FIA Market Access Working Group.

Dr. Albert S. Kyle

Charles E. Smith Chair Professor of Finance 

University of Maryland

Professor Kyle joined the University of Maryland faculty 

as the Charles E. Smith Chair Professor of Finance at the Robert H. Smith School of 

Business in August 2006. He earned his B.S. degree in mathematics from Davidson College 

in 1974, studied philosophy and economics at Oxford University as a Rhodes Scholar from 

Texas (1974), and completed his Ph.D. in economics at the University of Chicago in 1981. 

He has been a professor at Princeton University’s Woodrow Wilson School (1981-1987), at 

the University of California’s Haas Business School in Berkeley (1987-1992), and at Duke 

University (1992-2006). 

Professor Kyle’s research focuses on market microstructure. His research includes topics 

such as informed speculative trading, market manipulation, price volatility, and the 

information content of market prices, market liquidity, and contagion. His current research 

also deals with concepts from industrial organization to model the valuation dynamics of 

growth stocks and value stocks by applying techniques used to value real options. 

His teaching interests include market microstructure, institutional asset management, venture 

capital and private equity, corporate finance, option pricing, and asset pricing. 

He was elected Fellow of the Econometric Society in 2002. He was a board member of the 

American Finance Association from 2004-2006. He served as a staff member of the 

Presidential Task Force on Market Mechanisms (Brady Commission), after the stock market 

crash of 1987. He has been a member of the NASDAQ economic advisory board and the 

FINRA economic advisory board.
13

Garry N. O'Connor

Chief Executive Officer

International Derivatives Clearing Group, LLC

Mr. O'Connor is the Chief Executive Officer of IDCG. 

Prior to joining IDCG, Mr. O'Connor spent seventeen years in the Investment Banking 

industry, pricing and managing interest rate derivative portfolios. He has held senior 

positions in Sydney, Tokyo, Hong Kong and New York with Bankers Trust and then Merrill 

Lynch. During his time at Merrill Lynch, Mr. O'Connor held a number of roles managing 

interest rate derivatives risk including leading the Australasian interest rate derivatives trading 

operation out of Sydney, leading the Japanese Yen swaps desk out of Tokyo, and 

establishing and managing a US Dollar interest rate trading business in Hong Kong.

Most recently he was charged with establishing a North American presence in the European 

derivatives markets. At Bankers Trust, Mr. O'Connor managed interest rate, foreign 

exchange, and commodities risk in Auckland and in Sydney. He was also responsible for 

price making and risk management activities in Australian and New Zealand interest rate 

derivatives. 

As the CEO of IDCG, Mr. O'Connor has testified on Capitol Hill, met with government 

regulators and spoken at numerous industry forums on the need for central counterparty 

clearing and the benefits of extending clearing to all markets participants. He previously 

served as IDCG's Chief Product Officer and remains responsible for designing and 

implementing IDCG's cleared interest rate derivative products. He has used his experience 

as an interest rate trader to design IDCG's product specifications to be economically 

equivalent to over the counter market. 

Mr. O'Connor received a BCom (Hons) from Otago University in 1992. 

Michael Ricks

Merchandising Manager, North America

Cargill Inc.

From 1999 to present, Mr. Ricks has been the Merchandising Manager, North America for 

Cargill Incorporated located in Minneapolis, MN. Prior to that, from 1986-1999 Mr. Ricks 

was at Continental Grain dealing with grain merchandising. Mr. Ricks received his MS 

Degree, Agricultural Economics, North Dakota State University, Fargo ND in 1986.
14

Matt Schatzman

Senior Vice President, Energy Marketing

BG Americas & Global LNG

Mr. Schatzman is responsible for marketing BG’s global 

LNG supply, BG’s gas, power and NGL products in North America and BG’s oil 

production in Brazil. Prior to joining BG, Schatzman worked at Dynegy where his last 

position was president and chief executive officer of Dynegy’s energy marketing and power 

generation business. 

Mr. Schatzman holds a Bachelor of Arts degree in political science from Yale University.

Thomas Secunda

Chief Technology Officer

Bloomberg LP

Mr. Secunda, one of the founding partners of Bloomberg, 

has been with the company since its creation in 1982. Since Bloomberg’s inception, Mr. 

Secunda has served as Director of Research and Development, Director of Worldwide Sales 

and now Director of Financial Products which includes core terminals, trading systems, 

tradebook, and portfolio, analytics and risk.

Prior to joining Bloomberg, Mr. Secunda was a fixed-income trader at Morgan Stanley from 

1981-1982. Before that he worked in systems research at Salomon Brothers.

Mr. Secunda holds both undergraduate and graduate degrees in mathematics from SUNY 

Binghamton. He is currently on the Board of Directors of Bloomberg, the National Parks 

Conservation Association both its national board and it’s NYC Council, The Nature 

Conservancy, the Intrepid Museum Foundation and the Westchester County Parks, 

Recreation and Conservation Board.
15

Charles A. Vice

President and Chief Operating Officer

IntercontinentalExchange

 Mr. Vice is a founding member of IntercontinentalExchange 

(NYSE: ICE). He has served as Chief Operating Officer since July 2001 and President since 

October 2005. Mr. Vice works with the executive management team in setting corporate 

objectives and strategies and has day-to-day responsibility for technology, operations, and 

product development. Mr. Vice has been a leader in the management and application of 

information technology in the energy industry for nearly two decades. Prior to the 

formation of ICE in 2000, Mr. Vice was a Director at Continental Power Exchange (CPEX), 

an electronic spot market for electric power. Before joining the CPEX startup in 1994, he 

was a Principal at Energy Management Associates, where he provided consulting services to 

the electric power and natural gas industries. From 1985 to 1988, Mr. Vice was a Systems 

Analyst with Electronic Data Systems (General Motors) where he designed and marketed 

management information systems for auto, airline and financial service industry clients. 

Mr. Vice earned a Bachelor of Science degree in Mechanical Engineering from the University 

of Alabama and a Master of Business Administration from the Owen Graduate School of 

Management at Vanderbilt University.

Dr. Matthew White

Senior Economist

ISO New England, Inc.

Dr. White is the Senior Economist at ISO New England. 

His responsibilities include market design and development for ISO New England’s $12 

billion suite of auction-based electricity markets.

Prior to joining the ISO, Dr. White held faculty appointments at the Stanford University 

Graduate School of Business, the University of Chicago (Visiting), and the University of 

Pennsylvania’s Wharton School of Business. There he received numerous outstanding 

teaching awards for his lectures on how markets work. Dr. White’s public service includes 

appointments as a senior staff economist to the U.S Federal Trade Commission and the U.S. 

Federal Energy Regulatory Commission. 
16

Dr. White was a Faculty Research Fellow for twelve years at the National Bureau of 

Economic Research, the nation’s premier economic think tank. Dr. White’s expertise 

centers on energy markets and electricity market design, including market microstructure, 

pricing practices, and demand behavior. His research studies appear in leading academic 

journals, including the Review of Economic Studies, the RAND Journal of Economics, the Review of 

Economics and Statistics, and the Brookings Papers on Economic Activity. He has served as an 

evaluator and referee for more than 25 peer-reviewed scholarly journals spanning 

economics, engineering, and political science. He received his Ph.D. in Economics from the 

University of California, Berkeley in 1995.

Charles F. Whitman

Chief Executive Officer

Infinium Capital Management

Mr. Whitman is a founding partner and Chief Executive 

Officer of Infinium Capital Management, a proprietary trading firm based in Chicago with 

additional operations in New York and London. 

Mr. Whitman has been involved in the trading industry since 1987 when, at the young age of 

17, he was a runner for Produce Grain Inc. From 1987 to 1992 he attended DePaul 

University where he double majored in Accounting and Finance. While at DePaul, in 1988 

he became a clerk in the soybean options pit for Hanley Group. Mr. Whitman then 

successfully traded full time in the soybean options pit for several years and in 1996 became 

a partner at Hanley Group. Starting in 1994, while still trading full time and anticipating the 

rise in electronic trading, Mr. Whitman focused on developing methods of trading away 

from the exchange floor. From 1999-2001 Mr. Whitman conducted in depth market and 

business research for what would become Infinium Capital Management. During this 

research period, Mr. Whitman also taught options seminars for Dr. Van Tharp and 

mentored and trained several traders that went on to become extremely successful. In June 

of 2000 Mr. Whitman became a partner at Blink Trading, LLC, which was sold to GETCO 

in 2002. Since the launch of Infinium 9 years ago, Mr. Whitman has served the firm 

simultaneously as CEO and Head of Macro Trading. Infinium is widely recognized for its 

integrity and multi asset class presence, is an established leader in working with exchanges to 

develop new products and in 2008 was voted the 4th best place to work in Chicago by 

Crain’s Chicago Business.

Mr. Whitman has been designated a “Super Trader” by Dr. Van Tharp who was featured as 

the premier psychologist in the trading arena in the book Market Wizards. Mr. Whitman 

authored the foreword in the current edition of Dr. Tharp’s book Trade Your Way to 

Financial Freedom. Charles Whitman is a longtime member of Chicago Mercantile 

Exchange, Chicago Board of Options Exchange, Kansas City Board of Trade and 

Minneapolis Grain Exchange. Furthermore, he is a member of the Economic Club of 
17

Chicago, the Cato Institute and the Chairman’s Circle of the Chicago Council on Global 

Affairs. Mr. Whitman was highly influential in the development of a charitable inner-city 

ministry, GRIP Outreach for Youth, where he also served as Chairman of the Board. He is 

devoted to helping at-risk children and has mentored many middle school and high school 

kids through coaching basketball, one of his lifelong passions. Mr. Whitman is a substantial 

donor and supporter of several charities including Willow Creek Association, Robin Hood 

Foundation, Christian Heritage Academy, Caris Pregnancy Centers, World Vision and Direct 

Relief International. 
TAB 4
Algorithmic+Trading+and+High+Frequency+Trading

Experiences+from+the+Market+and+Thoughts+on+Regulatory+Requirements

Submission)to)CFTC)TAC)on)Algo)and)HFT

Dr.+John+Bates

CTO,+Progress+Software+and+Founder,+Apama

Background

This!document!is!divided!into!2!sections:!firstly!it!reviews!the!drivers!and!trends!in!

Algo!and!HFT;!secondly!it!discusses!the!topic!of!regulation!with!specific!regard!to!

the!CFTC,!Algo!and!HFT.!I’ve!been!very!fortune,!as!the!Founder!of!Apama!(one!of!the!

leading!platforms!for!Algo!and!HFT,!liquidity!aggregation,!smart!order!routing,!preE

trade!risk!and!market!surveillance)!to!be!involved!in!working!for!the!last!10!years!

with!leading!sellEside!and!buyEside!firms,!trading!venues!and,!more!recently,!

regulators.!I’ve!seen!Algo!and!HFT!evolve!in!many!interesting!ways!and!I!wanted!to!

try!to!capture!some!of!the!trends,!which!also!motivate!my!views!of!the!regulatory!

requirements!going!forward.

Algorithmic+Trading+Terminology

The!term!algorithmic!trading!is!not!used!consistently!in!the!industry!(sometimes!it!

is!used!generally!and!sometimes!to!describe!executionEonly!strategies!or!broker!

algorithms!–!see!below).!An!algorithm!is!“a!sequence!of!steps!to!achieve!a!goal”!–

and!the!general!case!of!algorithmic!trading!is!“using!a!computer!to!automate!a!

trading!strategy”.!In!almost!all!cases,!algorithms!encode!what!traders!could!do!by!

watching!the!market!and!manually!placing!orders.!The!difference!is!that!algos!don’t!

need!a!lunch!break!or!a!paycheck! It!takes!tens!of!milliseconds!for!a!trader’s!eye!to!

take!in!information,!communicate!with!the!brain,!a!decision!to!be!made!and!the!

brain!impulse!to!go!from!a!trader’s!brain!to!operate!his/her!fingers!to!trade.!In!that!

time!algorithms!can!have!made!and!executed!thousands!of!trading!decisions.

There!are!2!main!ways!in!which!algorithms!are!used!to!automate!trading:!

algorithms!for!execution!and!algorithms!for!HFT.!

Execution+Algorithms

Execution!algorithms!are!used!to!break!down!large!orders!and!slice!them!into!the

market!over!a!period!of!time.!The!goal!is!to!minimize!the!impact!that!a!large!order!

has!in!the!market!and!to!achieve!a!benchmarked!price.!Examples!of!this!include!the!

VWAP!(Volume!Weighted!Average!Price)!and!Market!Participation!algos.!These!

algorithms!use!metrics!to!determine!how!to!slice!a!large!order;!for!example,!VWAP!

uses!the!historic!volume!distribution!for!a!particular!symbol!over!the!course!of!a!

day!and!divides!the!order!into!slices,!proportioned!to!this!distribution.
The!typical!use!of!an!execution!algorithm!is!the!buyside!sending!an!order!to!be!

executed!algorithmically!into!a!broker.!This!can!be!done!either!by!phone!or!in!an!

automatic!way!from!a buyside!Execution!Management!System!(EMS)!as!a!FIX!order.!

The!buyside!provides!all!the!information,!such!as!instrument,!side,!quantity!and!the!

algorithm!to!use.!An!instance!of!the!execution!algorithm!is!then!instantiated!within!

the!broker!environment!to!trade!the!order.!It!is!also!possible!to!run!these!algorithms!

within!the!buyside!and!just!send!the!child&orders!straight!to!the!market!through!

DMA!(direct!market!access).!To!achieve!this!some!EMS!systems!have!builtEin!

algorithms!and!some!institutions!have!built!their!own!algorithms!using!technologies!

such!as!Complex!Event!Processing!(CEP!(described!later)).

High+Frequency+Trading+Algorithms

While!execution!algorithms!are!about!automating!“how!to!trade”!–!i.e.!how!to!place!

orders!in!the!market,!HFT!algorithms!add!to!this!“when!to!trade”!and!even!

sometimes!“what!to!trade”.!!Execution!algorithms!are!about!minimizing!market!

impact!and!trying!to!ensure!a!fair!price,!whereas!HFT!algorithms!are!about!profit.

The!“high!frequency”!refers!to!being!able!to!keep!up!with!the!high!frequency!

streams!of!data,!make!decisions!based!on!patterns!in!that!data!indicating!possible

trading!opportunities,!and!automatically!place!and!manage!orders!in!the!market!to!

capitalize.!

A!term!commonly!associated!with!HFT!is!statistical&arbitrage!(or!“statarb”) –

monitoring!instruments!that!are!known!to!be!statistically!correlated,!with!the!goal!

of!detecting!breaks!in!the!correlation E!indicating!trading!opportunities.!For!

example,!consider!the!relationship!(called!the!delta!1:1)!between!a!bond,!such!as!the!

10Eyear govvie!on!ICAP!(Brokertec),!and!a!derivative!of!it!on!CBOT.!These!

instruments!tend!to!move!together!–!but!if!that!relationship!breaks!for!a!few!

milliseconds!then!there!is!an!opportunity!to!buy!one!and!sell!the!other!at!a!profit.!

There!are!a!variety!of!types!of!HFT!algorithms!for!statarb,!including the!following:

• Pairs+trading E looking!for!breaks!in!the!correlated!relationships!between!pairs!of!

instruments.

• Index+arbitrage E monitoring!for!breaks!in!the!correlated!relationships!between!

instruments!and!the!index!of!its!sector,!e.g.!Ford!against!the!automotive!sector,!

or!a!stock!index!future!against!one!or!more!of!its!underlying!component!

elements.

• Basket+trading E!in!which!statarb!techniques!are!applied!not!with!individual!

instruments!but!with!custom!baskets!of!instruments.

• Spread+trading E!a!related!form!of!statarb!that!is!particularly!popular!in!the!

futures!market.!In!spread!trading,!trading!is!based!on!taking!positions,!usually!

one!long!and!one!short,!on!instruments!with!profitability!being!determined!by!
the!spread!(difference)!between!two.!Examples!include!the!purchase!of!July!Corn!

and!the!sale!of!December!Corn!(intraEmarket!spread),!the!purchase!of!February!

Lean!Hogs!and!the!sale!of!February!Live!Cattle!(interEmarket!spread),!and!the!

purchase!of!March!Kansas!City!Wheat!and!the!sale!of!March!Chicago!Wheat!

(interEexchange!spread).!More!complex!interEexchange!multiElegged!spreads!

include!crack&spreads:!trading!the!differential!between!the!price!of!crude!oil!and!

petroleum!products,!spark&spreads: trading!the!theoretical!gross!margin!of!a!gasE

fired!power!plant!from!selling!a!unit!of!electricity,!having!bought!the!fuel!

required!to!produce!this!unit!of!electricity!(and!including!all!other!costs!

operation!and!maintenance,!capital!and!other!financial!costs) and!crush&spreads:!

involving!the!purchase!of!soybean!futures and!the!sale!of!soybean!oil!and!

soybean!meal!futures.

In!multiEinstrument!HFT!strategies,!low!latency!is!very!important!–!in!order!to!see!

the!patterns!in!the!market!and!execute!trades!before!competitors.!This!is!

particularly!relevant!in!placing!multiple!trades!as!part!of!a!statarb!scenario!–!a!soE

called!multi3legged!trade!–!where!each!trade!is!a!leg.!Firstly!it!is!important!to!act!on!

the!liquidity!opportunity!seen!in!the!market;!thus!fast!reaction!is!important!before!a!

competitor!takes!the!opportunity.!And!secondly,!it!is!important!not!to!get!“legged!

out”!–!where!one!leg!of!the!strategy!executes!but!other!legs!find!the!market!has!

moved!and!the!opportunity!is!lost.!There!are!of!course!mitigating!actions!that!can!be!

taken!here,!either!automatically!or!manually.

HFT!algos!are!typically!used!in!bank!proprietary!trading!groups,!hedge!funds!and!

proprietary!trading!firms.!Instances!of!specific!trading!strategies!can!be!instantiated!

by!providing!key!parameters!E!for!example:!a!new!pairs!trading!strategy!needs!to!

know!the!instruments!and!specific!trading!thresholds.!Once!initiated,!HFT!algos!

often!run!with!little!human!intervention.!Often!traders!monitor!the!status,!P&L!and!

other!key!parameters!on!realEtime!dashboards!and!can!intervene!when/if!they!feel!

it!is!necessary.!In!the!case!of!spread!trading,!specialized!tools!called!Spreaders!are!

often!used!by!traders!to!instantiate!and!manage!spread!trading.

The!above!HFT!algo!types!are!a!subset!of!the!algos!in!the!market!but!illustrate!many!

of!the!principles.!A!selection!of!other!areas!in!which!high!frequency!algorithmic!

techniques!are!used!include!the!following:

• Liquidity+aggregation+and+smart+order+routing E As!market!fragmentation!has!

continued,!algorithmic!techniques!have!been!employed!to!aggregate!liquidity!

and!use!smart!order!routing!to!send!orders!to!the!venues!with!the!best!price!and!

liquidity.!These!techniques!(described!below!in!more!detail)!can!be!used!by!HFT!

algos!to!operate!more!effectively!in!a!fragmented!environment.!

• RealKtime+pricing+of+instruments E Algorithmic!techniques!have!also!been!used!in!

the!realEtime!pricing!of!instruments,!such!as!bonds,!options!and!foreign!

exchange.!Traditional!pricing!techniques!use!slowerEmoving!pricing!analytics!
and!fundamentals!to!price!instruments.!However,!now!higher!frequency!

algorithmic!techniques!can!enhance!these!pricing!algorithms!based!on!what!is!

happening!in!the!aggregated!market!(i.e.!how!can!we!make!money!by!increasing!

the!spread!on!liquidity!available)!and!the!tier!and!history!of!the!customer!for!

whom!we!are!publishing!the!price!(i.e.!how!should!be!adjust!the!spread!based!on!

how!important!the!customer!is).!High!frequency!pricing!can!thus!skew!prices!

and!spreads!based!on!the!upEtoEmillisecond!view!from!the!market!and!the!tier!of!

the!customer.

• Trading+on+news E In!the!last!couple!of!years,!there!has!been!increasing!interest!

from!HFT!firms!in!incorporating!news!into!HFT!algos.!The!idea!here!is!that!firms!

can!trade!automatically!on!news!sentiment!before!a!human!trader!can!react,!e.g.!

economic!releases,!news!of!a!war,!unexpected!weather!events!etc.!They!can!also!

correlate!and!respond!to!patterns,!e.g.!the!way!that!news!impacts!price!

movements.!For!a!number!of!years!a!handful!of!highly!innovative!firms!have!

been!experimenting!with!news!in!HFT.!Now,!however,!this!interest!is!growing!

due!to!new!types!of!structured!high!frequency!news!feeds.!News!providers,!such!

as!ThomsonEReuters!and!DowEJones!are!including!tags!in!the!feeds!that!enable!

algos!to!quickly!extract!key!information,!such!as!data!associated!with!an!

economic!release.

• Genetic+tuning E Another!interesting!technique!is!genetic&tuning –!in!which!many!

thousands!of!permutations!of!algorithms!are!run!in!parallel!and!fed!with!real!

market!data!but!are!not!necessarily!trading!live!in!the!market.!The!algorithms!

that!have!the!most!profitable!theoretical!P&L!can!be!put!into!the!market!to!trade!

live.!Over!time!live!algos!may!become!less!profitable!and!can!be!deactivated.!The!

branches!of!profitable!algorithms!can!be!grown!and!the!less!profitable!branches!

killed!off.!This!model!of!Darwinian&trading!allows!selfEevolving!systems!to!

discover!profitable!opportunities!through!evolutionary!processes,!with!some!

seeding!and!guidance!by!human!experts.!These!techniques!are!still!exploratory!

and!are!still!only!used!in!a!few!advanced!firms.

The!Holy!Grail!of!Algo!and!HFT!is!the!“money!machine”!–!an!algorithm!that!figures!

out!what!to!trade!and!the!strategy!to!trade!it,!and!then!continuously!selfEevolves!to!

remain!profitable!and!outwit!competitors.!While!there!are!many!semiEsmart!

algorithms!out!there,!most!still!require!human!expertise!and!oversight.!We!are!not!

yet!at!the!stage!of!truly!intelligent!algorithms,!although!it!is!inevitable!this!is!where!

the!market!is!aiming!for!and!we!must!be!prepared!for!this.

The+Latency+War

In!all!forms!of!algo!trading!–!but!particularly!in!HFT,!minimizing!latency!is!a!key!

factor!in!success.!Specifically,!trading!groups!are!concerned!with!end3to3end&latency

–!the!total!delay!from!the!market!data!being!generated!at!the!trading!venue(s),!

being!delivered!to!an!algo,!a!decision!being!taken!by!an!algo!and!the!necessary!

orders!being!placed!and!filled!in!the!venue(s).!When!several!firms!are!competing!for!
the!same!opportunity,!the!one!with!the!lowest!latency!wins.!There!is!a!lot!more!to!

Algo!and!HFT!than!just!latency –!as!described!below!–!but!clearly!latency!is!very!

important.!There!are!several!components!in the!low!latency!value!chain!including:

• Market+data –!traditionally!firms!like!ThomsonEReuters!were!the!preferred!oneE

stopEshop!way!of!delivering!market!data.!However,!market!data!intermediaries!

can!add!significant!latency!and!firms!focused!on!HFT!are!interested!in!

connecting!directly!to!the!trading!venues!through!their!market!data!APIs!

(Application!Programming!Interfaces).!Market!data!firms!have!responded!by!

creating!lower!latency!versions!of!their!products!and!new!vendors!have!

emerged!such!as!Wombat!(acquired!by!NYSE)!and!ActivFinancial.

• Algo+&+HFT+Engine –!the!traditional!approach!in!top!tier!firms!was!to!hire!the!top!

talent!and!handEbuild!algorithms!inEhouse!using!a!traditional!programming!

language,!such!as!C++.!These!algorithms!would!be!tuned!to!minimize!latency!in!

response!to!patterns!in!market!data.!However,!with!the!requirement!for!quicker!

timeEtoEmarket!of!new!algorithms,!new!technologies,!such!as!Complex!Event!

Processing!(CEP)!–!which!combine!rapid!development!with!low!latency!response!

to!complex!patterns!in!market!data!have!become!popular.

• Order+execution –!In!recent!years,!many!trading!venues!have!adopted!the!FIX!

protocol!as!the!standard!way!to!place!orders.!In!order!to!minimize!latency!many!

institutions!connect!directly!to!the!venues!and!place!and!manage!order!over!FIX.!

• Physical+connection –!Some!firms!have!become!focused!on!the!physics!of!

reducing!latency!–!making!the!wire!connection!over!which!market!data!and!

orders!are!transmitted!as!short!as!possible.!There!are!a!number!of!suppliers,!

such!as!BTERadianz,!that!can!provide!a!dedicated!network!that!is!already!wired!

into!trading!venues!around!the!world.

• CoKlocation –!At!the!extremes!of!reducing!the!latency!physics!is!coElocation, in!

which!algorithms!are!actually!installed!next!to!or!in!the!facilities!of!a!trading!

venue.!Several!hosting!companies!have!built!businesses!around!providing!

hosting!platforms!to!allow!trading!firms!to!install!their!software!in!these!co3lo

facilities.!The!challenge!with!coElo!comes!for!firms!that!run!crossEmarket,!crossE

asset!or!crossEborder!algorithms!–!which!might!involve!trading!with!multiple!

trading!venues!that!are!not!geographically!coElocated.!Where!does!one!put!these!

algorithms?!Usually!at!a!location!with!fast!interEconnect!to!all!the!necessary!

venues.

Rapid+Alpha+Discovery,+Authoring+and+Customization

In!general,!customization!of!algorithms!has!been!a!key!differentiator!to!both!brokers!

that!offer!execution!algorithms!and!to!HFT!shops.!The!principle!behind!this!is!that!if!

everyone!has!the!same!algorithms!then!there!is!no!competitive!advantage.!In!

practice!there!isn’t!a!tremendous!difference!between!different!broker’s!VWAP!
algorithms!or!different!prop!shop’s!pairs!trading!algorithm!–!but!each!firm!usually!

has!their!own!“secret!sauce”!that!makes!the!algorithm!slightly!different.!There!are!

also!obscure algo!approaches!that!are!unique!to!individual!firms!and!are!closely!

guarded!secrets.

A!key!statistic!to!note,!from!research!by!the!analyst!firm!Aite!Group,!is!that!the!

average!lifespan!of!an!algorithm!is!3!months.!This!indicates!the!pace!of!change!in!the!

market.!In!fact,!during!the!highly!volatile!markets!of!late!2008,!some!firms!changed

their!algorithms!on!a!daily!basis!–!to!anticipate!and!respond!to!daily!opportunities.

In!addition!to!the!runEtime!concerns!around!minimizing!latency!(described!above)

there!are!also!equally!important!concerns!around!rapid!research!and!development!

of!new!algorithms!and!customization!of!existing!algorithms.!The!reasons!for!this!are!

as!follows:

• FirstKmover+advantage E The!markets!change!all!the!time!and!new!patterns!that!

offer!potential!to!build!algorithms!around!emerge.!It’s!key!to!be!able!to!build,!

test!and!deploy!a!new!algorithm!quickly!because!competitors!may!have!spotted!

the!same!opportunity!and!be!trying!to!trade!on!it!first.

• Adapting+to+change E!Changes!in!the!market!can!also!impact!the!effectiveness!of!

existing!algorithms.!For!example,!a!HFT!algo!that!was!trading!on!a!phenomenon!

that!only!one!firm!had!spotted!initially,!may!not!be!effective!any!more!because!

various!competitors!have!spotted!the!pattern!and!are!mining!it!more!effectively.!

Thus the!original!HFT!algo!might!now!be!ineffective!or!even!loss!making.!In!this!

case!it’s!important!to!be!able!to!either!detect!this!quickly!and!then!switch!it!off!

or!customize!the!algo!to!improve!it.

• Reverse+engineering –!There!is!a!fear!in!the!market!that!competitors!can!watch!

the!pattern!of!orders!from!a!particular!market!participant,!figure!out!how!their!

algorithms!work!(soEcalled!reverse&engineering)!and!then!create!algorithms!to!

outEperform!them.!An!occasional!fear!that!the!buyEside!firms!have!is!that!their!

brokers’!prop!desks!are!reverse!engineering!and!then!frontErunning!their!orders.!

Of!course!this!would!be!a!breach!of!regulation!and!brokers!are!careful!to!ensure!

it!doesn’t!happen!–!but!nonetheless!the!fear!remains.

The!requirement!for!customization!and!continuous!innovation!is!why!there!is!not!a!

large!market!for!vendors!of!shrinkEwrapped!preEbuilt!algorithms.!However,!there!is!

a!market!for!techniques!to!assist!in!the!rapid!creation!and!customization!of

algorithms.!Trading!is!an!intellectual!property!business!and!often!the!differences!in!

algorithms!can!be!the!competitive!advantage!of!one!firm!over!another.

The!main!areas!of!interest!in!rapid!development!and!customization!of!algorithms!

are!as!follows:
• Alpha+Discovery –!Looking!for!new!patterns!in!the!market!that!might!be!viable!to!

trade!on.!Commercial!tools!exist!to!assist!with!this!process!but!also!many!

homegrown!tools!and!techniques!are!used.

• Algorithm+Authoring and+Customization –!Turning!a!discovered!pattern!into!an!

algorithm!that!can!trade!in!the!market!and!then!being!able!to!evolve!that!

algorithm!over!time.!There!are!a!number!of!approaches!here.!A!traditional!

approach!of!using!an!army!of!developers!to!code!a!strategy.!This!has!a!number!of!

problems,!including!slow!timeEtoEmarket,!frequently!not!coming!up!with!the!

strategy!the!business!wants!and!creating!a!spaghetti&code!maintenance!

nightmare!that!can!only!be!understood!by!certain!people!who!then!may!leave!

and!create!a!potential!hazard!in!the!market.!The!term!black&box!describes!an!

algorithm!the!workings!of!which!are!hidden.!InEhouse!build!often!creates!a!black!

box!–!only!understood!by!a!few!technical!wizards.!Becoming!popular!is!the!

concept!of!a!white&box!algorithm!–!which!is!built!on!a!model,!the!logic!of!which!

can!be!designed!by!and!is!clearly!visible!to!the!business!and!can!be!easily!

changed.!Modeling!tools!enable!a!strategy!to!be!laid!out!in!terms!of!state!flow,!

rules!and!analytics.!Such!tools!can!generate!an!executable!strategy!that!can!be!

loaded!into!an!algo!engine.!The!model!can!be!easily!changed!at!any!point.

• Backtesting+and+Simulation – Backtesting!involves!using!recorded!historic!data!to!

determine!how!an!algorithm!performs!under!certain!market!conditions.!This!can!

range!from!a!bull!market!to!a!bear!market!and!can!use!test!data!from!days!with!

certain!known!phenomena,!such!as!a!nonEfarm!payrolls!release!(or!other!

economic!releases)!or!even!the!flashcrash.!Simulation!involves!providing!

simulated!markets!for!the!algorithms!to!put!their!orders!into.!Simulators!can!be!

designed!to!work!handEinEhand!with!backtesting!environments!to,!for!example,!

simulate!the!market!impact!of!trades!(because!of!course!we!are!replaying!

historic!information!when!the!trades!we’re!generating!didn’t!really!happen).!

Different!firms!vary!in!their!approaches!to!simulation!and!backtesting.!Some!use!

complex!backtesting!and!simulation;!some!use!simulated!markets!as!provided!by!

trading!venues!before!going!live;!some!prefer!iterative!testing!in!live!markets.

• Production – Putting!an!algorithm!into!production!involves!making!it!live!–!so!

that!it!is!receiving!real!market!data,!making!trading!decisions!and!placing!actual!

orders!in!the!market.!Usually!algorithms!need!to!be!certified,!based!on!a!firm’s!

internal!certification!procedures,!before!they!are!put!into!production.!This!often!

involves!extensive!backtesting!and!simulation!and!often!some!trader!user&

acceptance&testing!with!some!selected!earlyEadopter!traders.!Most!trading!

groups!feel!backtesting!and!simulation!are!not!a!substitute!for!real!usage!–!and!

often!reality!presents!scenarios!that!were!not!considered!in!backtesting.!This!is!

why!realEtime!preEtrade!risk!precautions!should!be!built!into!all!algorithmic!

platforms!to!provide!additional!protection!against!unforeseen!circumstances.
• Analysis+and+Tuning –!Once!an!algorithm!has!been!running!live,!its!performance!

can!be!analyzed!to!detect!ways!in!which!it!can!be!optimized!to!be!made!more!

profitable,!more!efficient!or!respond!more!intelligently!to!certain!risk!scenarios.

This!continuous!analysis!may!also!discover!that!an!HFT!algo!is!no!longer!

profitable!enough!and!should!be!modified!or!discontinued.

The+Continuing+Evolution+of+Algo+and+HFT

One!can!liken!Algo!and!HFT!to!gold!mining.!When!gold!is!discovered!in!a!new!

territory!it’s!often!lying!around!on!the!surface.!When!more!people!hear!about!the!

gold,!a!goldrush!ensues!and!everyone!descends!upon!the!territory.!Then!one!has!to!

pan!for!gold!in!rivers!or!dig!to!find!the!hidden!seams!of!gold.!In!Algo!and!particularly!

HFT,!trading!firms!are!always!seeking!out!new!opportunities!and!trying!to!mine!

them!before!others!descend!upon!them.

Some!key!drivers!in!the!evolution!of!Algo!and!HFT!are!as!follows:

• Asset+Class –!Initially!exchangeEtraded!equities!and!futures!markets!were!the!

focus.!However,!as!FX!and!bond!markets!have!become!increasingly!electronic,!

open!and!fragmentation, so!Algo!and!HFT!have!grown!there.!More!recently,!HFT!

involving!energy!trading!has!been!becoming!more!popular.!Throughout!this!

evolution,!some!firms!have!employed!algos!that!incorporate!crossEasset!class!

trading!for!statistical!arbitrage!and!hedging!purposes.

• Fragmentation –!As!new!markets!have!emerged,!so!algorithmic!techniques!have!

evolved!to!capitalize.!Many!asset!classes!have!experienced!fragmentation!and!

this!trend!across!all!asset!classes!is!likely!to!continue.!Algorithmic!techniques!to!

manage!fragmentation!involve!liquidity&aggregation!and!smart&order&routing.!

Liquidity!aggregators!create!a!“super!book”!that!combines!liquidity!on!a!per!

symbol!or!currency!pair!basis.!This!offers!a!global!ordered!view!of!market!depth!

for!each!instrument!regardless!of!which!trading!venue!the!liquidity!is!on.!For!

example,!the!best!bid!for!a!Eurodollar!future!may!be!on!CME,!the!second!best!

may!be!ELX. If!human!traders!or!algorithms!trade,!then!smart!order!routing!

sends!the!order!to!the!relevant!market(s)!on!which!the!quote!is!displayed.!Low

latency!and!rapid!update!are!clearly!important!here!to!avoid!dealing!with!stale!

liquidity!information.

FX!is!becoming!of!increasing!relevance!to!the!futures!community!as!currency!

futures!and!futures!equivalents!are!being!aggregated!in!FX!liquidity!aggregators,!

combined!with!FXEspecific!trading!venues!and!bank!liquidity.!Also!FX!is!often!

used!as!an!important!component!of!crossEborder!futures!strategies.

• Geography – Algo!and!HFT!started!predominantly!in!the!US!and!UK!markets!but!

have!spread!geographically!over!time.!Firstly!the!spread!was!to!other!major!

trading!centers!such!as!Tokyo,!Sydney,!Hong!Kong,!Toronto!and!across!Europe.!

Then!to!locations!such!as!Korea,!Singapore!and!one!of!the!hottest!new!markets!–
Brazil,!where!both!futures!and!equities!are!now!widely!traded!algorithmically!on!

BMFEBovespa.!At!each!stage, algos!have!to!be!specialized!to!the!characteristics!of!

the!local!markets.!Each!new!market!presents!new!trading!opportunities.

Algo+and+HFT+Platforms+and+Technologies

Many!firms!still!use!inEhouse!development!for!the!custom!creation!of!algos.!

However,!due!to!the!need!to!create,!evolve,!backtest!and!tune!algorithms!rapidly,!as!

well!as!keep!up!with!connections!to!new!trading!venues,!an!increasing!number!of!

firms!are!using!thirdEparty!products!to!help!accelerate!their!trading!lifecycle.!Some!

key!technologies!include!the!following:

• Execution+Management+Systems –!FrontEend!trading!systems!that!allow!access!to!

broker!algorithms!as!well!as!access!to!custom!algorithms!integrated!with!the!

EMS.!Leading!providers!include!FlexTrade,!Portware!and!Orc.

• Complex+Event+Processing –!A!platform!specifically!designed!for!complex!analysis!

and!response!to!high!frequency!data.!CEP!platforms,!such!as!Progress!Apama,

incorporate!graphical!modeling!tools!that!can!rapidly!capture!and!customize!

strategies!and!a!trading!engine!connected!to!any!combination!of!crossEasset!

market!data!and!trading!venues.!CEP!is!used!widely!for!algo!trading,!HFT,!

liquidity!aggregation,!smart!order!routing,!preEtrade!risk!and!market!

surveillance.

• Tick+Databases –!A!realEtime!timeEseries!database!designed!to!capture!and!store!

high!frequency!data!for!analysis!and!backtesting.!Providers!include!ThomsonE

Reuters!and!KX!Systems.

Algo+and+HFT+Safety+Net

HFT!can!scale!the!capabilities!of!a!trader!hundreds!or!thousands!of!times.!However,!

this!can!of!course!increase!trading!risk!too.!To!complement!high!frequency!trading,!

high!frequency!preEtrade!risk!capabilities!are!needed.!Many!firms!embraced!this!

concept!some!time!ago.!However,!certain!groups!used!to!turn!off!their!preEtrade!risk!

management!as!it!“slowed!them!down”!and!any!potential!downside!was!overE

balanced!by!the!potential!upside!of!trading!first.!That!situation!has!changed!after!the!

2008!market!and!the!flashcrash,!and!with!increased!regulator!scrutiny.

Two!approaches!being!successfully!used!to!mitigate!trading!risk!are:

• RealKtime+PreKtrade+Risk+Firewall –!It!is!possible!to!continuously!recalculate!risk!

exposures!on!positions!whilst!monitoring!trades!as!they!go!to!market!and!

determining!what!impact!they!would!have!on!preEdefined!risk!limits.!In!the!

event!of!a!threshold!breach,!trades!can!be!blocked!from!going!to!market.!It!is!

also!possible!to!monitor!for!erroneous!trades,!such!as!“fat!finger!trades”!and!
block!them.!This!facility!is!not!just!useful!for!trading!groups!but!also!for!brokers!

offering!sponsored!access,!to!monitor!on!a!per!client!basis.!Using!the!latest!

technology!platforms,!such!as!CEP,!enables!preEtrade!checks!to!be!performed!

with!minimal!latency.

• Backtesting+and+Market+Simulation –!As!introduced!above,!before!putting!algos!

live,!it!is!highly!beneficial!to!test!them!with!a!variety!of!real!historical!and!preE

canned!scenarios!to!see!how!they!would!perform!if!live.!This!can!be!done!in!

conjunction!with!realistic!and!tunable!market!simulators.

RealKtime+Market+Monitoring+and+Surveillance

Several!regulators!around!the!world!have!recognized!that!realEtime!market!

monitoring!and!surveillance!allows!more!rapid!response!to!potential!crises!and!

market!abuse!–!potentially!allowing!rapid!action to!prevent!or!minimize!any!market!

impact.!The!FSA!–!the!UK!regulator!E!was!one!of!the!first!to!speak!up!on!this!and!

specify!a!system!to!achieve!more!realEtime!monitoring!using!Complex!Event!

Processing.!Now!other!regulators!around!the!world!are!looking!at!similar!

approaches.!Many!trading!venues!have!long!had!realEtime!surveillance!technologies!

but!there!is!a!lack!of!consistency!across!the!market.!Brokers!can!also!benefits!from!

this!kind!of!technology!to!prevent!abuse!in!their!trading!operations!and!ensure!their!

good!reputation.

The!goal!of!realEtime!monitoring!is!to!detect!anomalous!market!movements,!e.g.!

price!or!volume!spikes!for!a!particular!symbol!on!one!or!more!exchanges.!This!

provides!an!early!warning!system!to!potential!market!problems!and!enables!rapid!

response.

In!the!case!of!realEtime!market!surveillance,!the!goal!is!to!detect!potential!market!

abuse!while!it!is!happening.!The!FSA!drew!an!analogy!that!“traders!are!driving!

Ferraris!and!regulators!are!trying!to!catch!them!on!bicycles”.!Utilizing!the!same!

technology!used!in!HFT!for!realEtime!surveillance!and!monitoring!gives!regulators!

“Ferraris!as!police!cars”,!to!be!able!to!keep!up!with!the!high!frequency!markets.!The!

kind!of!patterns!that!can!be!detected!include:

• Insider+trading,!e.g.!detection!of!an!unusually!large!trading!pattern!followed!

closely!by!a!news!event!that!moves!the!market.

• Front+running+of+orders,!e.g.!detection!of!unusual!and!coincidental!orders!from!a!

prop!desk!just!prior!to!an!event!that!moves!the!market,!such!as!a!large!client!

order!being!placed!by!the!broker!in!the!same!firm!or!some!research!being!

published!by!an!analyst!in!the!same.

• Painting+the+tape,!i.e.!continuously!taking!the!best!offer!in!the!market!to!drive!the!

price!up.

• Fictitious+orders!to!manipulate!the!price!and!try!to!get!algos!to!respond.
• Trader+collusion,!in!which!traders!cooperate!to!deliberately!inflate!instrument!

volume!and!price, such!as!in wash&trading.

Keeping!an!audit!trail!of!market!data!and!potential!abuse!cases!is!also!important.!

Tick!databases!can!be!used!here.!Surveillance!systems!also!involve!researching!new!

abuse!patterns,!using!business!analytics!platforms!(from!providers!such!as!SAS).

Will Algo+and+HFT+replace+the+trader?

The!evolution!to!Algo!and!HFT!is!somewhat!analogous!to!the!markets!moving!from!

open!outcry!to!electronic!trading.!When!the!Liffe!floor!in!London!went!electronic,!

half!of!the!traders!evolved!to!the!new!way!of!doing!things!and!the!other!half!went!to!

drive!London!cabs.!The!same!is!true!with!Algo!and!HFT.!Traders!who!are!more!

involved!with!simple!order!entry!will!inevitably!be!replaced!by!technology.!!

However,!Algo!and!HFT!is!an!intellectual!property!business!–!so!those!with!the!right!

expertise!become!the!creative!minds!and!the!high!level!coordinators!of!armies!of!

algorithms.!Humans!are!here!to!stay.

Are Algo+and+HFT+out+of+the+price+range+of+small+firms?

Much!has!been!made!in!the!mainstream!press!of!the!unfair!advantage!of!HFT!

compared!with!techniques!available!to!the!ordinary!investor.!Actually!fund!

managers!will!use!algos!on!behalf!of!the!ordinary!investor. And!HFT!helps!keep!

markets!more!efficient!and!trading!more!cost!effective!at!the!benefit!of!the!ordinary!

investor.

With!regard!to!setting!up!an!HFT!shop,!everything!described!in!this!document!is!

available!to!any!firm.!The!question!is!can!they!afford!it? HFT!is!like!motor!racing.!

There!are!some!firms!that!compete!in!Formula&1 –!with!huge!budgets!and!the!

world’s!top!talent;!others!compete!in!national!championships;!others!in!club!racing;!

all!can!potentially!win!their!tier!and!be!successful.!For!$200,000!or!less!per!year!a!

firm!can!run!a!small!HFT!operation.!I!believe!the!costs!are!going!to!fall!as!hosted!

services!offering!customizable!Algo!and!HFT!capabilities!emerge.!CQG!and!Ffastfill,!

both!hosted!providers!of!trading!platforms!to!the!commodities!and!futures!markets,!

have!already!started!offering!customizable!algorithms!and!spreaders!at!a!lower!cost.!

It!is!even!possible!to!offer!hosted!modeling!tools!that!allow!totally!custom!algos!to!

be!created!and!deployed!into!the!cloud.!In!this!kind!of!scenario!small!trading!firms!

can!concentrate!on!their!own!IP!and!don’t!need!to!create!inEpremise!IT!shops!with!

skilled!IT!people,!hardware,!software,!dedicated!networks!etc.!This!will!inevitably!

continue!to!evolve!with!new!entrants,!lowering!the!barriers!to!entry!for!HFT.
Thoughts+on+Regulation+of+Algo+and+HFT

Market+Impact

What+are+the+positive+or+negative+impacts+of+Algo+and+HFT+on+the+futures+markets+and+

market+structure+(e.g.+liquidity,+volatility;+impact+of+fundamentals,+commercials+or+

hedgers;+other+issues)?

There!are!a!number!of!positive!impacts!of!Algo!and!HFT!–!but!there!are!also!a!

number!of!potential!negative!impacts.!However,!all!of!the!negative!impacts!can!be!

mitigated!by!a!combination!of!good!policing!and!best!practices!from!regulators,!

trading!venues!and!market!participants.

The!positive!impacts!of!Algo!and!HFT!include!the!following:

• Minimize+market+impact+of+large+trades –!As!already!described,!algorithmic!

trading!provides!an!automated!and!intelligence!way!to!break!down!large!orders!

into!smaller!chunks!to!minimize!their!impact!on!the!market,!while!achieving!a!

benchmarked!price.!The!market!statistics!illustrate!the!impact:!for!all!global!

markets!the!average!order!size!has!fallen,!while!the!number!of!order!has!risen!

significantly.

• Lower+cost+of+execution –!Execution!algorithms!put!capabilities!previously!only!

available!to!the!elite!into!the!hands!of!the!mainstream!buyEside.!The!use!of!algos!

rather!than!more!expensive!traders!and!the!competition!between!brokers!

continues!to!drive!down!margins!and!help!the!buyside!achieve!a!significantly!

reduced!cost!of!execution.

• More+efficient+markets – Most!emerging!statistical!arbitrage!opportunities!will!

be!quickly!identified!by!firms!and!algorithms!created!to!mine!the!seams!of!gold.!

Thus!markets!are!continuously!evolving!and!becoming!more!efficient.

• More+open+and+competitive+trading+markets – Contrary!to!some!popular!opinion,!

there!is!less!of!a!monopoly!in!the!market!generally.!Although!the!top!tier!firms!

can!still!hire!top!talent!and!are!continuously!seen!as!mysterious!controllers!of!

the!market,!the!reality!is!that!one!or!two!individuals!can!set!up!a!firm!that!can!

have!access!to!the!same!kind!of!technology!as!the!large!players.!Technologies!

like!CEP,!widely!available!low!latency!market!connectivity!and!hosting!

environments!enable!“Fred!and!Ed!in!a!shed”!to!run!an!advanced!quant!trading!

operation.
• Faster+evolving+trading+venues –!Market!fragmentation!has!caused!increased!

competition!for!liquidity!between!trading!venues.!This!is!putting!pressure!on!

exchange!costs.!It!is!also!accelerating!the!level!of!technological!advancement!

provides!by!trading!venues!–!for!example,!lower!matching!latency,!improved!

order!throughput!and!more!valueEadded!services,!such!as!coElo.

• Encouraging+entrepreneurship –!HFT!is!the!ultimate!form!of!capitalism.!It!enables!

intellectual!property!to!be!turned!into!profit!(or!loss)!rapidly,!whether!within!a!

large!firm!or!as!part!of!a!smaller!firm.

• Increasing+productivity –!One!trader!can!manage!a!handful!of!instruments!and!

can!manage a!few trading!strategies!manually.!A!trader!watches!the!market!and!

responds!by!entering!orders!when!(s)he!instinctively!spots!patterns!in!the!

market.!In!an!algoEenabled!world,!a!single!trader!can!be!the!initiator!and!

coordinator!of!hundreds!or!thousands!of!instances!of!algorithms.!The!trader!can!

see!P&L!and!status!for!all!algorithmic!instances!on!realEtime!dashboards!and!can!

manually!intervene!when!required.!In!this!way,!the!productivity!of!a!single!

trader!can!be!scaled!hundreds!or!thousands!of!times.

• Increasing+US+dominance+in+the+global+economy – Many!media!commentators!

have!portrayed!Algo!and!HFT!as!some!dark,!mysterious,!unfair!and!elite!practice.!

Actually!none!of!these!is!true!or!fair.!Commentators!also!speak!of!the!danger!of!

derivatives!and!we!should!just!go!back!to!owning!a!share!or!a!commodity!–

because!you!know!where!you!stood.!In!fact!the!capital!markets!are!a!major!part!

of!the!US!economy!and!a!key!part!of!its!economic!leadership!in!the!world!–!and!

central!to!this!is!the!growing!area!of!Algo!and!HFT.!The!recent!economic!

downturn!was!not!caused!by!Algo!and!HFT!but!by!more!fundamental!factors.!We!

can!take!some!lessons!from!the!recent!flashcrash!to!enhance!the!safety!system!of!

Algo!and!HFT,!given!their!importance!to!the!economy.!We!must!be!careful!not!to!

overEregulate!and!damage!this!important!economic!engine.

Possible!negative!issues!that!can!arise!from!Algo!and!HFT!include!the!following:

• Accelerating and+accentuating+market+movements –!While!algorithms!didn’t

cause!the!flashcrash,!it!is!likely!they!accelerated!and!accentuated!it.!Algos!have!

no!emotion;!they!are!looking!for!preEprogrammed!opportunities!and!will!

ruthlessly!execute!against!them.!Some!market!panic!in!particular!instruments,!as!

is!suspected!to!have!happened!in!the!flashcrash,!which!then!trigger!stopElosses!

and!a!radical!market!trend!downwards!might!lead!to!algos!shorting!those!

instruments!and!then,!at!an!appropriate!instant,!buying!them!back!at!a!profit.!

This!is!of!course!true!for!all!market!movements!every!day!–!and!flashcrashEstyle!

incidents!are!infrequent.!Although!there!are!a!number!of!mitigating!measures!

that!regulators,!trading!venues!and!trading!institutions!can!take!around!realE

time!market!monitoring!and!response!(see!below).
• Easier+to+game the+market+– With!millions!of!autonomous!algorithms!looking!for!

opportunities,!it!is!easier!to!spoof!the!market,!for!example!by!sending!in!

anomalous!quotes!to!try!to!trigger!certain!behavior!in!algorithms.!It’s!also!easier!

to!carry!out!potential!market!abuse,!such!as!wash&trades!or!painting&the&tape,!

because!finding!that!abuse!in!a!high!frequency,!fragmented!world!is!challenging.

Again!here,!regulators,!trading!venues!and!trading!institutions!can!employ!realE

time!surveillance!and!response!to!mitigate!these!risks.

• Increased+risk+profile+–!As!stated!above,!algorithms!can!make!a!trader!hundreds!

or!thousands!of!times!more!productive.!This!can!also!increase!the!risk!profile!

hundreds!or!thousands!of!times.!In!addition,!algorithms!are!moving!very!fast!and!

without!proper!preEtrade!risk!precautions,!critical!exposure!levels!can!be!

quickly!exceeded!or!errors,!such!as!fat&finger&trades,!can!be!quickly!accentuated.

• Algos+can+go+wild – Different!trading!firms!have!different!standards!of!

certification!for!algorithms!before!putting!them!live!–!and!in!some!cases!some!

logic!may!be!incorrect!or!missing.!Also,!the!phenomenon!of!the!black&swan

means!that!algorithms!may!meet!scenarios!they!have!never!been!prepared!for.!

For!these!reasons,!algorithms!can!go!wrong!or!behave!against!their!intended!

specification. This!can!result!in!incorrect!orders!being!placed!into!the!market!

and!a!large!potential!loss.!Worse!still,!it!can!result!in!a!stream!of!spurious!orders

being!placed!into!the!market.!There!have!been!a!number!of!such!cases!covered!in!

the!press!in!the!last!year.!The!problem!with!algorithms!is!that!they!are!running!

at!very!high!speed!and!detecting!these!problems!can!be!challenging.!One!way!to!

catch!this!is!that!a!trader!needs!to!be!watching!positions!and!behavior!on!realE

time!dashboard.!Ideally!algorithmic!platforms!have!a!“big!red!button”!to!pull!one!

or!all!algorithms!from!the!market.!Often!traders!then!prefer!to!hedge!the!

undesired!positions!manually.!A!more!effective!approach!to!algosEgoneEwild!is!to!

have!a!realEtime!preEtrade!risk!firewall!capability!–!that!can!block!incorrect!or!

spurious!trades!going!to!market!if!they!fall!outside!a!particular!behavior,!break!

policies!or!exceed!particular!risk!exposures.

• Potential+for+market+denial)of)serviceKstyle attacks –!There!have!been!a!number!of!

incidences!when!out!of!control!algorithms!have!fired!streams!of!orders!into!the!

market!in!quick!succession.!This!can!act!in!the!same!way!as!a!network!“denial!of!

service”!attack!–!in!which!a!network!firewall!spends!all!its!time!rejecting!

fraudulent!packets!and!thus!cannot!accept!any!real!data!packets.!The!market!can!

be!taken!up!with!handling!these!orders!and!thus!slowed!down!significantly.

• Additional+load+on+trading+venues+–!Further!to!the!above!point,!many!algorithms!

adjust!their!bids!and!offers!in!the!markets!as!the!market!changes!–!cancelling!

current!orders!and!replacing!them!with!modified!orders.!If!this!increases!it!will!

also!start!to!slow!down!the!markets.!Many!trading!venues!have!considered!
charging!for!excessive!order!cancellations!due!to!the!additional!load!that!it!puts!

on.

• Increased+difficulty+of+policing+the+market – Millions!of!high!frequency!algorithms!

combined!with!market!fragmentation,!crossEasset!trading,!dark!liquidity!and!the!

challenges!identifying!which!clients!of!member!firms!are!doing!what!–!all!

combine!to!make!the!job!of!the!regulator!very!challenging.!New!technologies!and!

techniques,!such!as!CEPEpowered!realEtime!surveillance!have!been!shown!to!

help!here!–!but!the!situation!is!still!complex.

• Potentially+easier+for+terrorists+to+manipulate+markets –!A!homeland!security!

issue!is!that!if!errors,!panic!and!wild!algorithms!can!influence!market!behavior!

then!it!may!be!possible!for!terrorists!to!initiate!such!behaviors.!We!need!to!

ensure!there!are!precautions!in!place!to!initiate!circuit!breakers!consistently!in!

such!a!circumstance.!

• Popular+fear+of+“big+brother” –!The!last!year!has!demonstrated!that!the!media!

and!the!general!populace!have!taken!a!negative!attitude!to!banks!and!also!to!

HFT.!There!has!been!a!lot!of!coverage!of!certain!Senators!implying!that!HFT!

gives!firms!an!unfair!advantage.!Unfairly,!HFT!has!been!linked!to!the!economic!

downturn.!The!media!has!portrayed&big&brother!style!algorithms!taking!

advantage!of!the!ordinary!investor.!Clearly!HFT!needs!a!Public!Relations!

makeover!

Regulation+and+Best+Practices

Should+the+Commission+adopt+regulations+and+best+practices (e.g.+trading,+oversight,+

surveillance+and+risk+management)+related+to+Algo+and+HFT?

The!CFTC!should!not!restrict!Algo!and!HFT.!Rather!they!should!improve!the!policing!

of!the!markets!in!the!form!of!market!monitoring!and!surveillance,!and!encourage!

best!practices!around!preEtrade!risk!for!market!participants.

What+should+the+role+of+the+Commission,+exchanges,+clearing+organizations+and+the+

NFA+be+with+regard+to+any+oversight+of+Algo+or+HFT?

The!CFTC!should!take!on!the!role!of!God’s&eye!oversight!of!the!market.!In!other!

words!the!CFTC!should!be!empowered!to!do!the!following:

• RealKtime+visibility E See!in!realEtime!what!is!happening!on!all!of!the!markets!the!

CFTC!supervise!(and!potentially!ones!they!don’t).!This!involves!connecting!to!

those!markets!and!getting!a!realEtime!feed!of!full!market!depth!and!trade!
information!for!each!market.!Using!this!information,!market!monitoring!and!

surveillance!should!be!provided!and!an!audit!trail!recorded.

• RealKtime+market+monitoring E Be!able!to!detect!patterns!that!indicate!potentially!

dangerous!market!movements,!such!as!price!or!volume!spikes!in!a!particular!

instrument.!This!involves!parallel!monitoring!of!all!instruments!on!all!trading!

venues.!This!gives!the!CFTC!an!early!warning!system!against!potential!problems!

and!the!ability!to!immediately!communication!with!trading!venues.

• RealKtime+market+surveillance E Be!able!to!detect!patterns!that!indicate!potential!

market!abuse,!such!as!insider!trading!or!market!manipulation!in!realEtime.!

These!incidents!should!be!used!to!create!cases,!crossEreferenced!against!past!

cases!and!possibly!acted!upon!immediately!or!later.!RealEtime!visualization!is!

required!to!show!CFTC!surveillance!staff!what!is!happening!in!the!market!and!

where!potential!abuse!hotspots!are!occurring.

• Audit+Trail+and+offline+investigation E Be!able!to!record!market!and!trade!data!as!

an!audit!trail!and!for!additional!offline!analysis!and!pattern!discovery.!One!use!is!

to!research!into!the!causes!of!flashcrashElike!incidents.!Another!is!to!analyze!and!

collect!additional!evidence!for!a!particular!investigation.!A!further!use!is!to!

discover!new!patterns!of!market!abuse!so!they!can!be!added!to!the!database!of!

patterns!and!can!be!looked!for!in!realEtime.

• Improved+reporting+of+OTC+products –!Although!less!realEtime,!it!would!be!

beneficial!to!improve!the!accuracy!and!timeliness!of!reporting!of!OTC!products.!

This!reporting!could!then!be!incorporated!into!surveillance!and!analysis!

operations.

• InterKregulator+visibility E In!addition,!it!would!be!ideal!to!have!cooperation!and!

information!sharing!between!other!regulators,!both!within!the!US!and!

internationally.!For!example,!there!are!several!trading!strategies!that!may!look!

like!market!abuse!until!you!see!that!they!are!part!of!a!crossEasset!strategy!

involving!equities!and!futures.!But!the!SEC!and!CFTC!in!isolation!might!only!see!

a!subset!of!the!trades.

The!Exchanges!and!CFTC!should!agree!and!ensure!each!Exchange!has!implemented!

the!following:

• Consistent+realKtime+surveillance+and+monitoring E!A!suitable!and!consistent!level!

of!realEtime!market!surveillance!and!monitoring!for!each!venue.!An!Exchange

should!be!able!to!detect!unusual!market!patterns,!such!as!price!and!volume!

spikes,!for!all!instruments.!They!should!also!be!able!to!detect!gaming!and!

potentially!abusive!trading!patterns!in!realEtime!and!followEup!with!the!member!

firms!involved.
• A+consistent+definition+of+when+to+invoke+circuit+breakers E During!a!market!

incident!(such!as!a!flashcrash),!while!circuit!breakers!might!work!for!some!

instruments!on!some!trading!venues,!liquidity!could!just!transfer!to!other!

venues!if!they!don’t!have!a!consistent!definition!of!when!to!invoke!the!circuit!

breakers.!Also,!circuit!breakers!should!ideally!work!for!all!instruments!at!all!

times!of!the!day.!Additionally!the!circumstances!under!which!circuit!breakers!

are!initiated!should!be!frequently!reviewed.!In!the!equities!markets!there!have!

been!a!number!of!false!positives!since!the!flashcrash,!which!has!disrupted!

trading!–!and!the!CFTC!should!consider!this!in!the!markets!they!regulate.

In!addition,!brokers!should!implement!the!following:

• PreKtrade+risk firewalls –!It!is!highly!desirable!to!analyze!each!trade!in!realEtime!

before!it!hits!the!market!and!if!necessary!block!the!trade!if!it!is!dangerous!or!

erroneous.!One!use!is!to!ensure!a!trade!does!not!push!exposure!beyond!key!

thresholds!levels.!Another!use!is!to!ensure!that!the!trade!is!not!erroneous,!such!

as!a!“fat!finger”!trade.!A!further!use!is!to!ensure!that!an!algorithm!hasn’t!gone!

wild,!such!as!getting!stuck!in!an!infinite&loop!whilst!sending!out!trading!signals.!

Such!risk!firewalls!are!not!just!useful!for!internal!users!but!also!for!clients!in!a!

sponsored!access!model,!to!ensure!on!a!clientEbyEclient!basis!that!their!preEtrade!

risk!is!under!control.!Clearly!it!is!key!that!this!preEtrade!analysis!does!not!slow!

down!HFT!algos.

• Internal+market+surveillance+and+monitoring+–!It!should!be!possible!to!detect!

unusual!market!movements!in!particular!instruments!as!an!early!warning!

system.!Also,!each!institution!should!monitor!its!own!trading!groups!and!

customers!to!ensure!that!the!regulator!could!not!perceive!any of!its!trading!

activity!as!market!abuse.

All!of!the!capabilities!mentioned!above!are!possible!technologically!now.!They!can!

be!achieved!without!disrupting!current!market!operations!and!without!putting!

additional!load!on!the!market.

Data+Availability+

What+types+of+data+(i.e.+raw+feeds)+are+Algos+and+HFT+receiving+from+exchanges+and+

news+organizations+versus+what+is+available+to+other+market+participants?

It!is!not!that!Algo!and!HFT!traders!have!privileged!access!to!certain!market!access!

methods!compared!to!what!is!available!to!other!market!participants;!it!is!rather!that!

they!often!need!as!fast!access!as!possible!and!choose!to!pay!for!it.!As!already!

described,!HFT!shops!want!to!get!endEtoEend!latency!as!low!as!possible!–!so!they!

will!want!low!latency!market!data!as!well!as!low!latency!market!access.!A!number!of!
market!data!vendors!specialize!in!low!latency!market!data,!and!some!trading!

platforms!offer!direct!connectivity!to!venues,!e.g.!CBOT!or!ICE.!This!access!may!cost!

more!than!less!high!performing!access!routes.

Similarly!for!news!providers,!there!are!now!news!offerings!from!firms!such!as!

ThomsonEReuters!and!DowEJones!that!offer!tagged!news!for!use!in!HFT.!Example!

uses!for!news!within!algos include!correlating!news!announcements!with!futures!

movements,!or!trading!on!news!before!the!markets,!e.g.!modifying!positions!on!

news!of!a!war,!economic!event!or!weather!event.!Again,!these!feeds!cost!extra!and!

are!only!useful!if!you!are!in!the!HFT!space.!In!fact!trading!on!news!in!HFT!is!still!a!

fairly!obscure!practice!that!still!only!a!handful!of!firms!are!involved!in.

Less!high!frequency!participants!may!gain!access!to!the!market!through!analysis!

and!trading!tools,!such!as!Bloomberg!or!CQG!–!which!have!their!own!integrated!

market!data!and!news!delivery.

It!is!“horses!for!courses”!when!deciding!your!needs!in!terms!of!market!data!and!

news.!It!is!certainly!not!some!secret!monopoly!by!Algo!and!HFT!firms.!But!there!is!a

cost!differential!in!terms!of!being!able!to!acquire!the!highest!frequency!feeds.

Technological+Challenges

What+are+the+technological+challenges+or+limitations+to+Algo+and+HFT?

When!describing!the!cutting!edge!of!technology,!the!phrase!“rocket!science”!is!

commonly!used.!Ironically!rocket!science!hasn’t!evolved!much!in!60!years,!whereas!

algorithmic!trading!technology!evolves!daily. In!Algo!and!HFT!the!barriers!of!today,!

described!below,!will!quickly!be!breached!and!new!barriers!will!emerge.

Some!of!the!barriers!that!are!being!hit!or!approached!by!current!generation!Algo!

and!HFT!technology!include!the!following:

• Transmissions+speed+(speed+of+light) E For!those!engaged!in!pure!latency!

arbitrage,!the!barriers!of!physics!are!providing!restrictions!in!terms!of!their!

ability!to!access!and!respond!to!market!data.!Some!firms!are!drilling!holes!

through!walls!just!to!shorten!the!piece!of!wire!that!connects!them!to!certain!

trading!markets.!Other!firms!are!turning!to!custom!hardware!to!assist!them!in!

pumping!the!data!in!quickly.!Einstein!proved!we!cannot!exceed!the!speed!of!light!

in!transmission!speeds!but!in!terms!of!data!throughput,!data!and!processing!

parallelism!offer!potential!for!increasing!throughput!considerably.

• Analysis+speed – Further!to!the!previous!point,!keeping!realEtime!analysis!up!

with!the!rates!of!data!throughput!is!also!becoming!challenging.!The!pattern!

analysis!within!algorithms!is!more!complex!than!just!getting!the!data!in!–
because!complex!analytics!are!being!done!rather!than!just!shipping!data.!The

OPRA!feed,!one!leading!benchmark!of!analysis!requirements,!has!exceeded!1!

million!events!per!second.!Technologies!like!Complex!Event!Processing,!

combined!with!parallelization!techniques,!such!as!grid!computing,!need!to!

continue!to!evolve!to!keep!up.

• Trading+venue+performance –!While!trading!venues!continue!to!consider!

performance!enhancements,!increased!loads!can!hinder!their!performance.!We!

have!already!seen!performance!hits!at!major!exchanges!under!high!load.!If!algos

are!allowed!to!continue!to!modify!orders!without!restriction!then!the!load!on!

trading!venues!will!continue!to!increase,!requiring!continued!technology!

enhancement.

• Keeping+up+with+global+market+fragmentation –!For!a!global!crossEasset!trading!

organization,!keeping!up!with!new!markets!is!an!expensive,!complex!and!timeE

consuming!endeavor.!However,!the!promise!of!“mining!new!seams!of!gold!before!

other!prospectors!arrive”!is!enticing.!

• Finding+new+trading+opportunities – Continuously!evolving!trading!operations!

and!looking!for!new!trading!opportunities!is!challenging.!Complex!research!tools!

and!human!processes!to!help!discover!new!trading!opportunities!must!continue!

to!evolve.

• Skills+shortage –!In!order!to!compete,!many!trading!strategies!are!becoming!more!

and!more!complex.!For!example,!a!HFT!algo!that!trades!on!crossEasset,!crossE

border!aggregated!liquidity.!The!human!intellectual!property!and!skills!in!

putting!together!such!scenarios!and!the!integrated!technology!to!support!them!

is!in!short!supply.

• Cost+barriers – To!compete!at!the!highest!level!in!Algo!and!HFT!is!costly.!

However,!as!already!described!it!is!possible!to!get!in!the!game!for!lower!cost.

Conclusions

Algo!and!HFT!are!highly!beneficial!to!the!US!and!global!economy.!Restricting!their!

usage!is!dangerous.!However,!best!practices!and!guidance!to!trading!institutions!is!

needed.!Mandated!preEtrade!risk!practices,!along!with!market!surveillance!and!

monitoring!are!needed!to!protect!against!the!potential!of!further!flash!crashes!and!

algos!going!wild!E!both!of!which negatively!influence!market!performance!and!

reputation.
TAB 5




TAB 6
Market Efficiency and Microstructure Evolution

in U.S. Equity Markets: A High-Frequency

Perspective

Jeff Castura, Robert Litzenberger, Richard Gorelick

RGM Advisors, LLC

April 22, 2010

1 Introduction

The impact of high frequency trading (HFT) on the U.S. equity markets has

received considerable attention in the wake of the financial crisis of 2008. It

has been asked whether the increase in the amount of automated trading as

a percentage of overall trading activity over the past several years has been

accompanied by degraded measures of market health such as liquidity, trading

costs, volatility, etc. Uninformed answers to these important questions have the

potential to influence policy makers toward actions that are not beneficial to

the vitality and efficient functioning of financial markets in the U.S.

This work presents some evidence showing that the U.S. equity markets

appear to have become more efficient with tighter spreads and greater liquidity

over the past several years; a period that has seen a sizable increase in the

prevalence of HFT, and a period during which there has been coincident growth

in automation and speed on many exchanges. It has been suggested that HFT

now accounts for over half of U.S. equity share volume [1]. With such a large

presence in the market, it is important to understand if there are any adverse

effects caused by such activity. While the existence of a causal relationship is

not proven, evidence is presented which suggests that the U.S. markets have

improved in several respects as HFT activity has grown.

One measure of efficiency investigated is the bid-ask spread and it is expected

that the presence of more participants, algorithmic and otherwise, will drive

spreads down due to competition, thereby decreasing costs to other investors.

The results presented in this paper confirm the results of many other studies,

showing that bid-ask spreads have come down over time for a broad range of

stocks.

Another measure of efficiency is liquidity, representing the ability of investors

to obtain their desired inventories with minimal price impact. Again, it is expected that more participants implies a greater amount of liquidity in the markets, a benefit to investors. This appears to be the case as this paper confirms

1
the results of other papers demonstrating an increase in available liquidity over

time.

It was shown by Samuelson in [2] that if a stock price is efficient, i.e., the

price is fairly valued with all public information, then it must follow a martingale

process. As a consequence, an efficient price exhibits no serial autocorrelation,

either positive (momentum) or negative (mean-reversion). Measurements are

made in this paper that test how closely stock prices resemble a random walk,

and improvements are seen for all segments.

A variance ratio test was developed by Lo and Mackinlay in [3] which makes

use of the fact that in an efficient market, the variance per unit time of the

price of a stock should be constant. This allows ratios of variances over different

time horizons to be taken and compared with theoretical expectations where,

in an efficient market, these tests would show that there is little or no serial

autocorrelation in prices. Another advantage of this type of test is that it

does not depend on a particular order of serial autocorrelation, only whether

any such autocorrelation is present. These tests, a novel contribution of this

paper, demonstrate that for all the data-sets investigated, there is an overall

improvement in efficiency in prices over time.

The data-sets used in this study are the Russell 1000 components, consisting

of 1000 large-cap and mid-cap stocks, and the Russell 2000 components, consisting of 2000 small-cap stocks. The set of components are taken as of Q4 2009,

and no attempt is made to correct for survivor bias, though it may be argued

that the nature of this study is not sensitive to such effects.

Additionally, each index is partitioned into two sets; NYSE-listed stocks and

NASDAQ-listed stocks. For much of the time period studied, NASDAQ-listed

stocks traded primarily on automated, electronic exchanges while NYSE-listed

stocks have transitioned from being primarily traded manually on the NYSE to

being traded on a more competitive, automated group of electronic exchanges.

Therefore the data essentially represents four distinct subsets of stocks, at least

from an historical context: large-cap stocks largely traded automatically (approximately 200 NASDAQ-listed stocks in the Russell 1000), large-cap stocks

largely traded manually (approximately 800 NYSE-listed stocks in the Russell 1000), small-cap stocks largely traded automatically (approximately 1300

NASDAQ-listed stocks in the Russell 2000), and small-cap stocks largely traded

manually (approximately 700 NYSE-listed stocks in the Russell 2000). This partition allows comparisons to be made that help more clearly identify the impact

of automation and technology advances on the health of the market.

The raw data is sampled at 1 second intervals for each stock during the

period Jan 1, 2006 to Dec 31, 2009 inclusive, representing 16 quarters of data.

The first 10 minutes and last 10 minutes of each day are omitted to prevent

opening and closing activities from influencing the results. Inside values are

used across the NASDAQ, NYSE, NYSE ARCA and BATS exchanges. This

represents a significant fraction of all shares traded in the U.S. and so is taken

to be representative of overall market activity.

With this data-set a series of statistical tests and measurements are run,

designed to reflect the health of the market. Spreads, available liquidity, and

2
transient volatility in the form of variance ratio tests are presented here as these

are commonly cited metrics of market efficiency and market quality.

2 Bid-Ask Spreads

Spreads are a cost to trading and, all else being equal, smaller spreads are

evidence of a better cost structure for investors. Conversely, market makers

and other liquidity providers earn profits through the spread. To that extent

smaller spreads imply not only smaller revenues for market makers but also that

these participants, by quoting smaller spreads, are more competitive; a sign of

a healthy market.

Bid-ask spreads are presented as the mean absolute spread of each of the

components of the index, where the absolute spread is defined as the best ask

price less the best bid price. There are other common ways to present bid-ask

spread data including the use of relative spreads. This formulation is meant

to more directly reflect transaction costs for investors caused by the bid-ask

spread. Market makers and other liquidity providers commonly adjust their

quotes based on market volatility in order to compensate for their increased

risk of holding inventory [4]. Therefore a volatility adjustment is commonly

done to remove the impact of volatility from spreads, typically making it easier

to spot trends in spreads over time. Dollar-value weighting is also sometimes

used in an effort to better reflect costs of the spread paid by investors. Equal

weighting is chosen here because many of the largest and most liquid stocks are

pinned at a spread of one penny.

Each of these adjustments will alter the results to some degree though overall trends are expected to remain, and this is confirmed in the appendix which

contains some results with these adjustments made. Also available in the appendix are some bid-ask spread results for the NASDAQ-100 index, consisting

of many of the largest stocks listed on the NASDAQ.

Figure 1 presents the mean of the absolute spread over time for the Russell

1000 stocks partitioned into its NYSE-listed and NASDAQ-listed components.

This is done to try to isolate differences in behavior over the period studied that

may be attributable to structural changes on each of these exchanges. Both

groups have seen a reduction in spreads over the period investigated, dropping

by about 1.5 pennies for the NYSE-listed stocks and about 1 penny for the

NASDAQ-listed stocks. By the end of 2009 it appears the the mean spread of

the two groups has converged to approximately the same value, something that

could not be said previously.

It is known that the rate of adoption of automated trading on NYSE-listed

stocks lagged behind that of NASDAQ-listed stocks. As the NYSE moved to an

electronic system to catch up technologically with the NASDAQ, and as other

electronic venues began taking market share from the NYSE, spreads in the

Russell 1000 dropped more dramatically for the NYSE-listed stocks than the

NASDAQ-listed stocks. This also suggests a relationship between the entrance

of algorithmic trading with a reduction in spreads, something that is noted for

3
Figure 1: Mean bid-ask spread for Russell 1000

Figure 2: Mean bid-ask spread for Russell 2000

4
the German DAX in [5].

The same information for the Russell 2000 index is presented in Figure 2.

Like the Russell 1000, these stocks have seen a reduction in mean spreads by

about a penny, with the NYSE-listed symbols showing a more dramatic reduction than the NASDAQ-listed symbols.

3 Available Liquidity

Liquidity is an important part of a vital market. It is often loosely defined

as the ability of participants to trade the amount that they wish at the time

they wish. One measure of liquidity is the amount of size offered for sale or

for purchase by market makers and other liquidity providers at a given point in

time. If more shares are available to be bought and sold at any given time, then

market participants have a greater ability to get into or out of positions based

on their needs or desires and are less dependent on either waiting for sufficient

size to become available or to seek an alternative execution venue.

Available liquidity is measured as the dollar value available to buy or sell at

any instant in time at the inside bid and ask, and time averages over an entire

quarter are taken. Each stock in an index is weighted by its capitalization

reported for the quarter to produce a single capitalization-adjusted available

liquidity metric. The motivation for weighting by capitalization is that it more

closely reflects the available fraction of a company’s total value that can be

transacted at any given time which may be more representative of the limitations

to investors. Additional available liquidity data is presented in the appendix,

including results for the NASDAQ-100.

Figure 3 presents the adjusted available liquidity for the Russell 1000 components partitioned into NYSE-listed and NASDAQ-listed stocks. Between 2006

and the end of 2009, the available liquidity of both groups of stocks increased

significantly, by about a factor of two, though all of that gain appears to have

taken place in 2009. Similar results are seen for the two groups of stocks in the

Russell 2000 which is shown in Figure 4.

It is plausible that the increase in liquidity can be explained, at least in part,

by the presence of HFT participants. Since the data used in this work is sampled

at a high rate, one can also claim that this liquidity measure is representative

of the immediacy that is available to market participants. This immediacy is

a type of option that is available to market participants providing them with

more flexibility than may otherwise be available.

5
Figure 3: Mean available liquidity for Russell 1000

Figure 4: Mean available liquidity for Russell 2000

6
4 Market Efficiency Tests

There exists a large body of research devoted to tests of market efficiency. In this

context, efficiency typically refers to the degree to which the price time-series of a

stock resembles a random walk. The theoretical foundation for this was laid out

by Samuelson in [2] which proves that a properly anticipated stock price should

fluctuate randomly with no serial autocorrelation. Pioneering work in this area

in the form of a variance ratio test was presented by Lo and Mackinlay in [3], in

which they show with some level of statistical confidence that the NYSE stockprice time-series do not appear similar to a random walk, suggesting inefficiency

in the markets. The data used in their paper is sampled daily and ends in 1988,

prior to a significant number of structural and regulatory changes that have

dramatically changed the nature of U.S. equity markets.

If stock price time-series truly followed random walks, it is expected that the

variance ratio computations would have values close to one. A variance ratio’s

deviation from unity can then be considered to be proportional to the amount

of inefficiency present in that stock or index. Values greater than one imply a

momentum process, equivalently a positive serial autocorrelation, while values

less than one imply mean-reversion, or negative serial autocorrelation.

Subsequent research extended the variance ratio tests in [3] to provide alternative methods to test market efficiency. In particular Chow and Denning

in [6] extend the work of [3] to provide a more statistically powerful test procedure and it is this “Chow-Denning” test that is used as a metric of market

efficiency in this section. To the best of the authors’ knowledge, such tests have

not previously been applied to data sampled at a high rate as is done here.

It is important to note that at this sampling rate micro-structural effects are

expected to be present. In particular, bid-ask bounce and statistical influences

caused by the discrete nature of price values will tend to skew the results toward

appearing mean-reverting. These effects are expected at high sampling rates and

are expected to decay as the sampling rate is decreased. However, for a given

sampling interval, the effect is expected to be roughly constant over time, and

thus the interesting aspect of the results is how they have changed over time

and whether they have converged toward a value of one. An attempt has been

made in the variance calculations to account for the discrete price values and

midpoint prices are used rather than last trade prices to minimize the effect of

bid-ask bounce. More details are available in the appendix, along with some

results based on last trade prices.

Raw variance ratio tests are applied to the Russell 1000 and Russell 2000,

partitioned into NYSE-listed and NASDAQ-listed stocks. Three ratios are chosen to be representative of what may be typical HFT holding periods; 10 seconds

over 1 second, 60 seconds over 10 seconds, and 600 seconds over 10 seconds.

Figures 5 and 6 show the raw variance ratios of 10 seconds over 1 second

for midpoint price data from the Russell 1000 and 2000, respectively. These

indexes are partitioned into NYSE-listed and NASDAQ-listed stocks. At this

high frequency, it is seen that the Russell 1000, NASDAQ-listed stocks show a

high degree of efficiency, and have been relatively efficient throughout the entire

7
period investigated, with some improvement seen over time. As these stocks

have largely been traded electronically for the entire period, such results are

expected. The NYSE-listed components, by contrast, show a relatively large

amount of inefficiency in 2006, but have increased to over 0.95 by 2009 and now

appear to be at least as efficient as the NASDAQ-listed stocks.

The Russell 2000 index in Figure 6 shows the same general trends, though the

overall efficiency is lower than the Russell 1000. This is to be expected since the

smaller-cap stocks of the Russell 2000 do not have the same amount of trading

activity as large-cap stocks. The NYSE-listed symbols show a greater degree of

improvement in efficiency than the NASDAQ-listed symbols, again coinciding

with improvements in automation and increased participation in these stocks

by automated trading firms.

The same results are presented for the variance ratios of 1 minute over 10

seconds in Figures 7 and 8 for the Russell 1000 and Russell 2000, respectively.

Similar conclusions hold when comparing these results with the 10 seconds over

1 second variance ratios. The degree to which the variance ratio of NYSE-listed

stocks in the Russell 1000 has improved of the period studied is dramatic, and

has largely converged to be identical to the NASDAQ-listed components of the

index. A similar trend is seen with the Russell 2000 components.

A large variance ratio of 10 minutes over 10 seconds is presented to provide

a picture of market efficiency over larger time-scales. Figures 9 and 10 show the

results for the Russell 1000 and Russell 2000, respectively, and the same general

trends seen in the previous plots of variance ratios are present in these figures.

The Chow-Denning method tests the null hypothesis that a price time-series

is drawn from a random walk, and produces a single test statistic. This value

can be compared to a threshold for a certain significance level. In this study

5% was chosen as the significance level.

The test was applied over each of the 16 quarters, individually to each stock

in the data-set with the input to the test being the logarithm of the midpoint

price. Sampling was done at 10 minute intervals and 10 second intervals. At

5% significance, if this test were run on 100 truly random time-series, one would

expect to see about 5 test outcomes reject the null hypothesis. That is to say,

due to the statistical nature of this test, it may produce false positives about

5% of the time.

Results for the 10 minute sampling Chow-Denning tests are presented in

Figures 11 and 12 for the Russell 1000 and 2000 data-sets, respectively. These

figures show the fraction of stocks in the index that the Chow-Denning test

reported as not being drawn from a random walk at a 5%-significance level.

Figure 11 shows that at 10 minute sampling, the number of such occurrences

has dropped over time and has largely been below 5% since the beginning of

2009, suggesting that there is no statistically significant inefficiencies at this

sampling interval that this test detects. The NYSE-listed stocks appear to have

a more dramatic improvement, in agreement with the variance ratio results

presented above.

Similar results are seen for the Russell 2000 in Figure 12 with a general

improvement in efficiency over the time period investigated although it appears

8
Figure 5: Variance Ratios, Russell 1000, 10 seconds / 1 second

Figure 6: Variance Ratios, Russell 2000, 10 seconds / 1 second

9
Figure 7: Variance Ratios, Russell 1000, 1 minute / 10 seconds

Figure 8: Variance Ratios, Russell 2000, 1 minute / 10 seconds

10
Figure 9: Variance Ratios, Russell 1000, 10 minute / 10 seconds

Figure 10: Variance Ratios, Russell 2000, 10 minute / 10 seconds

11
Figure 11: Chow-Denning test results for the Russell 1000, 10 minute sampling

Figure 12: Chow-Denning test results for the Russell 2000, 10 minute sampling

12
that there remains some degree of inefficiency at this time scale that the ChowDenning test is detecting. As expected, the large-cap stocks in the Russell 1000

exhibit a smaller number of significant events than the Russell 2000.

A smaller sampling interval of 10 seconds is also used for the Chow-Denning

tests, and the results of these computations are presented in Figures 13 and 14

for the Russell 1000 and Russell 2000, respectively. At this sampling rate the

impact of microstructural noise is expected to have a more significant impact

than at 10 minute sampling. Despite a higher degree of apparent inefficiency,

Figure 13 demonstrates that even at such fine sampling, the Russell 1000 appears

to have improved over the four years studied, and that the NYSE-listed symbols

have shown a more dramatic improvement in that time, largely converging with

the NASDAQ-listed symbols. Similar observations are made for the Russell

2000 index in Figure 14.

An alternative interpretation of these results is that of an increase in the

speed of mean-reversion over time. As mentioned, mean-reversion is present in

this data due in part to micro-structural effects, and as the rate of trading and

market activity increases, the impact of such noise on these variance ratio-based

tests become less prevalent. Therefore one can conjecture that the decrease in

the Chow-Denning test statistics may be as a result of an increased rate of

reversion of prices to their mean. This is also an indication of an increasing

competitive landscape and increasing efficiency in the market.

13
Figure 13: Chow-Denning test results for the Russell 1000, 10 second sampling

Figure 14: Chow-Denning test results for the Russell 2000, 10 second sampling

14
5 Summary

The presented data is suggestive that the U.S. equity markets have become more

liquid and efficient over the past four years, despite macro-economic shocks. As

the ratio of HFT activity to total market activity has grown, there appears

to be no evidence that short-term volatility, liquidity or spreads have risen

for the bulk of market participants. To the contrary, the evidence presented

here suggests a continued improvement in each of these factors, implying a

sympathetic relationship between HFT and the health of the overall markets.

The partitioning of data into the Russell 1000 and Russell 2000 shows that

there has generally been a larger degree of improvement in efficiency metrics in

the Russell 1000. The difference in trends observed between NYSE-listed and

NASDAQ-listed stocks also supports the hypothesis that increased automation

and the presence of HFT that has come with it has improved the market quality

metrics investigated in this paper.

References

[1 ] The TABB Group, “US equity high frequency trading: Strategies, sizing

and market structure”, TABB Group, http://www.tabbgroup.com

[2 ] Samuelson, P.A, “Proof that properly anticipated prices fluctuate randomly”, Industrial Management Review, 1965

[3 ] Lo, A.W. and Mackinlay A.C., “Stock market prices do not follow random

walks: Evidence from a simple specification test”, Review of Financial

Studies, 1988

[4 ] Glosten, L.R. and Harris, L., “Estimating the components of the bid-ask

spread”, Journal of Financial Economics, 1988

[5 ] Hendershott, T. and Riordan, R., “Algorithmic trading and information”, http://econpapers.repec.org/paper/netwpaper/0908.htm, 2009

[6 ] Chow, K.V. and Denning, K.C., “A simple multiple variance ratio test”,

Journal of Econometrics, 1993

[7 ] Weisstein, E. W., ”Sheppard’s Correction”, From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/SheppardsCorrection.html

15
6 Appendix

6.1 Bid-Ask Spreads

Absolute spreads are computed as follows. An individual stock i has a spread

at time t of Si(t) = ai(t) − bi(t). The spread over a quarter q is defined as

hSi(q)i =

P

t∈q Si(t)

P

t∈q

1

.

The spread S

N

q over an index N is the weighted average over all components,

where wi represents the weighting of stock i. The spread is then

S

N

q =

P

i∈N wi hSi(q)i

P

i∈N wi

.

The choice of equal weighting sets all wi = 1. Dollar value weighting is determined by setting the weight for each stock to the total dollar value of all

transactions for that stock in the quarter.

Relative spread can be computed in a similar manner, with the relative

spread SR(t)i =

a(t)i−b(t)i

p(t)i

replacing the absolute spread above, and where pi(t)

represents price. A common adjustment made to bid-ask spreads is a volatility

adjustment [4]. The VIX is used for this purpose and its value relative to the

mean of its value over the time period studied is chosen as the deflator. The

value of the VIX over the period studied is given in Figure 15.

VIX-adjusted spread data is presented in Figures 16 and 17 showing the

Russell 1000 and Russell 2000 relative spreads over time. Similar to the results

presented in the main body of this paper, the relative spreads have been stable

or falling over time, with a much larger reduction seen when adjusting for the

VIX.

For comparison, spread data is also presented for the NASDAQ-100 index.

Absolute spreads, both unadjusted and VIX-adjusted are given in Figure 18.

The trend for this index is consistent with that seen in the Russell data-sets.

Relative spreads are presented in a number of ways in Figure 19 and these

adjustments do not change the overall trends presented in the body of the text.

6.2 Available Liquidity

The available liquidity for a stock i at time t is given as

Li(t) = pi(t)



s

a

i

(t) + s

b

i

(t)),

where s

a

i

(t) and s

b

i

(t) are the inside size at the ask and bid, respectively. In a

quarter q, the average available liquidity of a stock is

hLi(q)i =

P

t∈q Li(t)

P

t∈q

1

.

16
Figure 15: Quarterly VIX prices

Figure 16: Mean bid-ask spread for Russell 1000, VIX-adjusted

17
Figure 17: Mean bid-ask spread for Russell 2000, VIX-adjusted

Figure 18: Absolute equal-weighted bid-ask spread for NASDAQ 100

18
Figure 19: Bid-ask spread for NASDAQ 100

The available liquidity over an index N is the weighted average over all components, such that

L

N

q =

P

i∈N wi hLi(q)i

P

i∈N wi

,

where wiis the weighting for stock i. A common adjustment made is a capitalization adjustment, which is done by setting wi to the market capitalization of

a stock i in quarter q.

The main body of this paper presents results for the Russell 1000 and Russell

2000. For comparison, the available liquidity for the NASDAQ-100 is presented

in Figure 20, showing both a capitalization-weighting and an equal-weighting.

In both cases, the general trend of increasing available liquidity over the period

studied is seen.

6.3 Market Efficiency

The methodology used to compute the variance ratio values follows that presented in [3]. In particular, equations (12a) and (12b) are used. Sheppard’s

correction [7] is applied to the variance estimates in order to reduce the discrete

values of prices (log-midpoint prices) used in the computation.

The raw variance ratio ri for a stock i with time-ratio D is given by

ri =

v

s1

i

Dvs2

i

,

where v

s1

is the variance for sampling rate s1 and v

s2

is the variance for sampling

rate s2 and by convention, s1

s2

= D > 1.

19
Figure 20: Mean available liquidity for NASDAQ 100

In order to gain a sense of the impact of bid-ask bounce and spreads on

variance ratios, Figure 21 presents the raw variance ratios for the NASDAQ 100

using the last traded price and the midpoint price in the same figure. From

the left panel, showing a fine sampling rate, it is seen that the impact of the

bid-ask bounce on last trade prices results in a smaller variance ratio than

when midpoint prices are used. As the sampling rate is decreased to longer

time periods, the impact of bid-ask bounce becomes less pronounced. This is

demonstrated in the right panel of Figure 21, where the difference between the

variance ratios using trade prices and midpoint prices is much smaller.

20
Figure 21: Mean Variance Ratios of Midpoint Prices vs. Trade Prices, NASDAQ

100. Left: 10 seconds / 1 second. Right: 1 minute / 10 seconds

21
TAB 7
Board of Governors of the Federal Reserve System

International Finance Discussion Papers

Number 980

October 2009

Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market

Alain Chaboud, Benjamin Chiquoine, Erik Hjalmarsson, Clara Vega

NOTE: International Finance Discussion Papers are preliminary materials circulated to 

stimulate discussion and critical comment. References in publications to International 

Finance Discussion Papers (other than an acknowledgment that the writer has had access 

to unpublished material) should be cleared with the author or authors. Recent IFDPs are 

available on the Web at www.federalreserve.gov/pubs/ifdp/. This paper can be 

downloaded without charge from Social Science Research Network electronic library at

http://www.ssrn.com/.
Rise of the Machines: Algorithmic Trading in the Foreign

Exchange Market

Alain Chaboud Benjamin Chiquoine Erik Hjalmarsson Clara Vega

September 29, 2009

Abstract

We study the impact that algorithmic trading, computers directly interfacing at high frequency with

trading platforms, has had on price discovery and volatility in the foreign exchange market. Our dataset

represents a majority of global interdealer trading in three major currency pairs in 2006 and 2007. Importantly, it contains precise observations of the size and the direction of the computer-generated and

human-generated trades each minute. The empirical analysis provides several important insights. First,

we Önd evidence that algorithmic trades tend to be correlated, suggesting that the algorithmic strategies

used in the market are not as diverse as those used by non-algorithmic traders. Second, we Önd that,

despite the apparent correlation of algorithmic trades, there is no evident causal relationship between

algorithmic trading and increased exchange rate volatility. If anything, the presence of more algorithmic

trading is associated with lower volatility. Third, we show that even though some algorithmic traders

appear to restrict their activity in the minute following macroeconomic data releases, algorithmic traders

increase their provision of liquidity over the hour following each release. Fourth, we Önd that nonalgorithmic order áow accounts for a larger share of the variance in exchange rate returns than does

algorithmic order áow. Fifth, we Önd evidence that supports the recent literature that proposes to depart

from the prevalent assumption that liquidity providers in limit order books are passive.

JEL ClassiÖcation: F3, G12, G14, G15.

Keywords: Algorithmic trading; Volatility; Liquidity provision; Private information.

Chaboud, Hjalmarsson, and Vega are with the Division of International Finance, Federal Reserve Board, Mail Stop 20,

Washington, DC 20551, USA. Chiquoine is with the Investment Fund for Foundations, 97 Mount Auburn Street, Cambridge MA 02138, USA. Please address comments to the authors via e-mail at alain.p.chaboud@frb.gov, bchiquoine@ti§.org,

erik.hjalmarsson@frb.gov and clara.vega@frb.gov. We are grateful to Terrence Hendershott and Albert Menkveld for their valuable insights, to EBS/ICAP for providing the data, and to Nicholas Klagge and James S. Hebden for their excellent research

assistance. We also beneÖted from the comments of Gordon Bodnar, Charles Jones, Luis Marques, DagÖnn Rime, Alec Schmidt,

John Schoen, Noah Sto§man, and of participants in the Spring 2009 Market Microstructure NBER conference, San Francisco

AEA 2009 meetings, the SAIS International Economics Seminar, the SITE 2009 conference at Stanford, and the Barcelona EEA

2009 meetings. The views in this paper are solely the responsibility of the authors and should not be interpreted as reáecting

the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve

System.
1 Introduction

The use of algorithmic trading, where computer algorithms directly manage the trading process at high

frequency, has become common in major Önancial markets in recent years, beginning in the U.S. equity

market more than 15 years ago. There has been widespread interest in understanding the potential impact

of algorithmic trading on market dynamics, as some analysts have highlighted the potential for improved

liquidity and more e¢ cient price discovery while others have expressed concern that it may be a source of

increased volatility and reduced liquidity, particularly in times of market stress. A number of articles and

opinion pieces on the topic have recently appeared in the press, with most decrying practices used by some

algorithmic traders in the equity market, and there have been calls for regulatory agencies in the United States

and Europe to begin investigations.1 Despite this interest, there has been very little formal empirical research

on algorithmic trading, primarily because of a lack of data where algorithmic trades are clearly identiÖed.

A notable exception is a recent paper by Hendershott, Jones, and Menkveld (2007), who get around the

data constraint by using the áow of electronic messages on the NYSE as a proxy for algorithmic trading.

They conclude that algorithmic trading on the NYSE, contrary to the pessimistsí concerns, likely causes

an improvement in market liquidity.2In the foreign exchange market, there has been no formal empirical

research on the subject. The adoption of algorithmic trading in the foreign exchange market is a far more

recent phenomenon than in the equity market, as the two major interdealer electronic trading platforms

only began to allow algorithmic trades a few years ago. Growth in algorithmic trading has been very rapid,

however, and a majority of foreign exchange transactions in the interdealer market currently involve at least

one algorithmic counterparty.

In algorithmic trading (AT), computers directly interface with trading platforms, placing orders without

immediate human intervention. The computers observe market data and possibly other information at very

high frequency, and, based on a built-in algorithm, send back trading instructions, often within milliseconds.

A variety of algorithms are used: for example, some look for arbitrage opportunities, including small discrepancies in the exchange rates between three currencies; some seek optimal execution of large orders at

the minimum cost; and some seek to implement longer-term trading strategies in search of proÖts. Among

the most recent developments in algorithmic trading, some algorithms now automatically read and interpret

economic data releases, generating trading orders before economists have begun to read the Örst line.

1 See, for instance, ìRewarding Bad Actors,î by Paul Krugman, New York Times, August 3, 2009, ìHigh-Frequency Trading

Grows, Shrouded in Secrecy,î Time, August 5, 2009, and ìDonít Set Speed Limits on Trading,î by Arthur Levitt Jr., Wall

Street Journal, August 18, 2009.

2We also note a paper by Hasbrouck (1996) on program trading, where he analyzes 3 months of data where program trades

can be separately identiÖed from other trades. He concludes that both types of orders have an approximately equivalent impact

on prices. Algorithmic trading is not exactly equivalent to program trading, though it is a close cousin. In principle, a program

trade could be generated by a traderís computer and then the trade conducted manually by a human trader. Our deÖnition of

AT refers to the direct interaction of a traderís computer with an electronic trading platform, that is the automated placement

of a trade order on the platform.

1
The extreme speed of execution that AT allows and the potential that algorithmic trades may be highly

correlated, perhaps as many institutions use similar algorithms, have been cited as reasons for concerns that

AT may generate large price swings and market instability. On the other hand, the fact that some algorithms

aim for optimal execution at a minimal price impact may be expected to lower volatility. In this paper, we

investigate whether algorithmic (ìcomputerî) trades and non-algorithmic (ìhumanî) trades have di§erent

e§ects on the foreign exchange market. We Örst ask whether the presence of computer trades causes higher or

lower volatility and whether computers increase or reduce liquidity during periods of market stress. We then

study the relative importance of human and computer trades in the process of price discovery and re-visit

the assumption that liquidity providers are ìuninformed.î

We formally investigate these issues using a novel dataset consisting of two years (2006 and 2007) of

minute-by-minute trading data from EBS in three currency pairs: the euro-dollar, dollar-yen, and euro-yen.

The data represent the vast majority of global spot interdealer transactions in these exchange rates. An

important feature of the data is that the volume and direction of human and computer trades each minute

are explicitly identiÖed, allowing us to measure their respective impacts.

We Örst show some evidence that computer trades are more highly correlated with each other than human

trades, suggesting that the strategies used by computers are not as diverse as those used by humans. But

the high correlation of computer trades does not necessarily translate into higher volatility. In fact, we Önd

next that there is no evident causal relationship between AT and increased market volatility. If anything,

the presence of more algorithmic trading appears to lead to lower market volatility, although the economic

magnitude of the e§ect is small. In order to account for the potential endogeneity of algorithmic trading with

regards to volatility, we instrument for the actual level of algorithmic trading with the installed capacity for

algorithmic trading in the EBS system at a given time.

Next, we study the relative provision of market liquidity by computers and humans at the times of the

most ináuential U.S. macroeconomic data release, the nonfarm payroll report. We Önd that, as a share of

total market-making activity, computers tend to pull back slightly at the precise time of the release but then

increase their presence in the following hour. This result suggests that computers do provide liquidity during

periods of market stress.

Finally, we estimate return-order áow dynamics using a structural VAR framework in the tradition of

Hasbrouck (1991a). The VAR estimation provides two important insights. First, we Önd that human order

áow accounts for much of the long-run variance in exchange rate returns in the euro-dollar and dollar-yen

exchange rate markets, i.e., humans appear to be the ìinformedîtraders in these markets. In contrast, in the

euro-yen exchange rate market, computers and humans appear to be equally ìinformed.î In this cross-rate,

we believe that computers have a clear advantage over humans in detecting and reacting more quickly to

2
triangular arbitrage opportunities, where the euro-yen price is brieáy out of line with prices in the euro-dollar

and dollar-yen markets. Second, we Önd that, on average, computers or humans that trade on a price posted

by a computer do not impact prices quite as much as they do when they trade on a price posted by a human.

One possible interpretation of this result is that computers tend to place limit orders more strategically

than humans do. This empirical evidence supports the literature that proposes to depart from the prevalent

assumption that liquidity providers in limit order books are passive.3

The paper proceeds as follows. In Section 2 we introduce the EBS exchange rate data, describing the

evolution over time of algorithmic trading and the pattern of interaction between human and algorithmic

traders. In Section 3 we study the correlation of algorithmic trades. In Section 4 we analyze the relationship

between algorithmic trading and exchange rate volatility. In Section 5 we discuss the provision of liquidity

by computers and humans at the time of a major data release. In Section 6 we report the results of the

high-frequency VAR analysis. We conclude in Section 7. Some robustness results are presented in the

Appendix.

2 Data description

Today, two electronic platforms process the vast majority of global interdealer spot trading in the major

currency pairs, one o§ered by Reuters, and one o§ered by EBS.4 These platforms, which are both electronic

limit order books, have become essential utilities for the foreign exchange market. Importantly, trading in

each major currency pair has over time become very highly concentrated on only one of the two systems. Of

the most traded currency pairs, the top two, euro-dollar and dollar-yen, trade primarily on EBS, while the

third, sterling-dollar, trades primarily on Reuters. As a result, the reference price at any moment for, say,

spot euro-dollar, is the current price on the EBS system, and all dealers across the globe base their customer

and derivative quotes on that price. EBS controls the network and each of the terminals on which the trading

is conducted. Traders can enter trading instructions manually, using an EBS keyboard, or, upon approval by

EBS, via a computer directly interfacing with the system. The type of trader (human or computer) behind

each trading instruction is recorded by EBS, allowing for our study.5

We have access to AT data from EBS from 2003 through 2007. We focus on the sample from 2006 and

2007, because, as we will show, algorithmic trades were a very small portion of total trades in the earlier years.

3For example, Chakravarty and Holden (1995), Kumar and Seppi (1994), Kaniel and Liu (2006), and Goettler, Parlour and

Ra jan (2007) allow informed investors to use both limit and market orders. BloomÖeld, OíHara and Saar (2005) argue that

informed traders are natural liquidity providers, and Angel (1994) and Harris (1998) show that informed investors can optimally

use limit orders when private information is su¢ ciently persistent.

4EBS has been part of the ICAP group since 2006.

5EBS uses the name ìautomated interfaceî (AI) to describe trading activity directly generated by a computer, activity we

call AT.

3
In addition to the full 2006-2007 sample, we also consider a sub-sample covering the months of September,

October, and November of 2007, when algorithmic trading played an even more important role than earlier

in the sample.6 We study the three most-traded currency pairs on the EBS system: euro-dollar, dollar-yen,

and euro-yen.

The quote data, at the one-second frequency, consist of the highest bid quote and the lowest ask quote on

the EBS system in these currency pairs, from which we construct one-second mid-quote series and compute

one-minute exchange rate returns; all the quotes are executable and therefore represent the true price at

that moment. The transactions data are at the one-minute frequency and provide detailed information on

the volume and direction of trades that can be attributed to computers and humans in each currency pair.

SpeciÖcally, the transactions volume data are broken down into categories specifying the ìmakerîand ìtakerî

of the trades (i.e., human or computer), and the direction of the trades (i.e., buy or sell the base currency),

for a total of eight di§erent combinations. That is, the Örst transaction category may specify, say, the minuteby-minute volume of trade that results from a human taker buying the base currency by ìhittingî a quote

posted by a human maker. We would record this activity as the human-human buy volume, with the aggressor

(taker) of the trade buying the base currency. The human-human sell volume is deÖned analogously, as are

the other six buy and sell volumes that arise from the remaining combinations of computers and humans

acting as makers and takers.

From these eight types of buy and sell volumes, we can construct, for each minute, trading volume and

order áow measures for each of the four possible pairs of human and computer makers and takers: humanmaker/human-taker (HH), computer-maker/human-taker (CH), human-maker/computer-taker (HC), and

computer-maker/computer-taker (CC).

7 That is, the sum of the buy and sell volumes for each pair gives

the volume of trade attributable to that particular combination of maker and taker (which we symbolize as,

V ol(HH) or V ol(HC), for example). The di§erence between the buy and sell volume for each pair gives

us the order áow attributable to that maker-taker combination (which we symbolize simply as HH or HC,

for example). The sum of the four volumes, V ol(HH + CH + HC + CC), gives the total volume of trade

in the market. The sum of the four order áows, HH + CH + HC + CC, gives the total (market-wide)

order áow.8 Throughout the paper, we will use the expression ìorder áowîto refer both to the market-wide

order áow and to the order áows from other possible decompositions, with the distinction clearly indicated.

Importantly, the data allow us to consider order áow broken down by the type of trader who initiated the

6We do not use December 2007 in the sub-sample to avoid the ináuence of year-end e§ects.

7 The naming convention for ìmakerî and ìtakerî reáects the fact that the ìmakerî posts quotes before the ìtakerî chooses

to trade at that price. Posting quotes is, of course, the traditional role of the market-ìmaker.î

8 There is a very high correlation in this market between trading volume per unit of time and the number of transactions

per unit of time, and the ratio between the two does not vary much over our sample. Order áow measures based on amounts

transacted and those based on number of trades are therefore very similar.

4
trade, human-taker order áow (HH + CH) and computer-taker order áow (HC + CC).

The main goal of this paper is to analyze the e§ect algorithmic trading has on price discovery and

volatility in the foreign exchange market. In our exchange rate data as in other Önancial data, the net of

signed trades from the point of view of the takers (the market-wide order áow) is highly positively correlated

with exchange rate returns, so that the takers are considered to be more ìinformedîthan the makers. Thus,

in our analysis of the relative e§ects of human and computer trades in the market, we consider prominently

the order áow decomposition into human-taker order áow and computer-taker order áow. However, we also

consider two other decompositions in our work. We consider the most disaggregated decomposition of order

áow (HH; CH; HC; CC), as this decomposition allows us to study whether the liquidity suppliers, who are

traditionally assumed to be ìuninformedî, are posting quotes strategically. This situation is more likely to

arise in our data, which comes from a pure limit order book market, than in data from a hybrid market

like the NYSE, because, as Parlour and Seppi (2008) point out, the distinction between liquidity supply

and liquidity demand in limit order books is blurry.9 We also decompose the data by maker type (human

or computer) in order to study whether computers or humans are providing liquidity during the release of

public information, which are periods of high exchange rate volatility and, often, market stress.

In our analysis, we exclude data collected from Friday 17:00 through Sunday 17:00 New York time from

our sample, as activity on the system during these ìnon-standardî hours is minimal and not encouraged

by the foreign exchange community. We also drop certain holidays and days of unusually light volume:

December 24-December 26, December 31-January 2, Good Friday, Easter Monday, Memorial Day, Labor

Day, Thanksgiving and the following day, and July 4 (or, if this is on a weekend, the day on which the U.S.

Independence Day holiday is observed).

We show summary statistics for the one-minute returns and order áow data in Table 1. This table contains

a number of noteworthy features. First, order áow, whether in total, broken down by human and computer

takers, or broken down into the 4 possible pairs of makers and takers, is serially positively correlated, which

is consistent with some informed trading models. For example, Easley and OíHara (1987) model a situation

where sequences of large purchases (sales) arise when insiders with positive (negative) signals are present in

the market. He and Wang (1995) also show that insiders with good (bad) news tend to buy (sell) repeatedly

until their private information is revealed in the prices. The positive serial correlation in order áow is also

consistent with strategic order splitting, i.e. a trader willing to buy for informational or non-informational

reasons and splitting his order to reduce market impact. Second, the standard deviations of the various order

áows di§er by exchange rates, by type of taker and across maker/taker pairs. These di§erences will be

9Parlour and Seppi (2008) note that in a limit order book investors with active trading motives, some of which are ìinformedî

traders, may choose to post limit orders that are more aggresive than those a disinterested liquidity provider would use but less

aggresive than market orders.

5
important in the interpretation of the upcoming VAR analysis and variance decompositions.

We show in Figure 1, from 2003 through 2007 for our three major currency pairs, the fraction of trading

volume where at least one of the two counterparties was an algorithmic trader, i.e. V ol(CH + HC + CC) as

a fraction of total volume.10 From its beginning in 2003, the fraction of trading volume involving AT grew by

the end of 2007 to near 60% for euro-dollar, and dollar-yen trading, and to about 80% for euro-yen. Figure 2

shows, for our three currency pairs, the evolution over time of the four di§erent possible types of trades (i.e.

V ol(HH), V ol(CH), V ol(HC), and V ol(CC); as fractions of the total volume). By the end of 2007, in the

euro-dollar and dollar-yen markets, human to human trades, in black, accounted for slightly less than half

of the volume, and computer to computer trades, in green, for about ten to Öfteen percent. In euro-dollar

and dollar-yen, we note that V ol(HC) and V ol(CH) are about equal to each other, i.e. computers ìtakeî

prices posted by humans, in red, about as often as humans take prices posted by market-making computers,

in blue. The story is di§erent for the cross-rate, the euro-yen currency pair. By the end of 2007, there

were more computer to computer trades than human to human trades. But the most common type of trade

was computers trading on prices posted by humans. We believe this reáects computers taking advantage

of short-lived triangular arbitrage opportunities, where prices set in the euro-dollar and dollar-yen markets

are very brieáy out of line with the euro-yen cross rate. In interpreting our results later in the paper, we

will keep in mind that trading volume is largest in the euro-dollar and dollar-yen markets, and that price

discovery happens mostly in those markets, not in the cross-rate. Our conclusions based on the euro-dollar

and dollar-yen markets will then be more easily generalized than those based on the euro-yen market. Table

2 tabulates the averages of the volume fractions shown in Figures 1 and 2, both for the full 2006-2007 sample

and the shorter three-month sub-sample.

3 How Correlated Are Algorithmic Trades and Strategies?

We Örst investigate the proposition that computers tend to have trading strategies that are more correlated

than those of humans. Since the outset of the Önancial turmoil in the summer of 2007, articles in the Önancial

press have suggested that AT programs tend to be similarly designed, leading them to take the same side of

the market in times of high volatility and potentially exaggerating market movements.11

One such instance may have happened on August 16, 2007, a day of very high volatility in the dollar-yen

market. On that day, the Japanese yen appreciated sharply against the U.S. dollar around 6:00 a.m. and 12:00

p.m. (NY time), as shown in Figure 3. The Ögure also shows, for each 30-minute interval in the day, computertaker order áow (HC +CC) in the top panel and human-taker order áow (HH + CH) in the lower panel.

1 0 The data in Figures 1 and 2 are 50-day moving averages of daily values, highlighting the broad trends over time.

1 1 See, for instance, ìAlgorithmic Trades Produce Snowball E§ects on Volatility,î Financial Times, December 5, 2008.

6
The two sharp exchange rate movements mentioned happened when computers, as a group, aggressively

sold dollars and bought yen. We note that computers, during these episodes, mainly traded with humans,

not with other computers. Human order áow at those times was, in contrast, quite small, even though the

overall trading volume initiated by humans (not shown) was well above that initiated by computers (human

takers were therefore selling and buying dollars in almost equal amounts). The ìtakingî orders generated

by computers during those time intervals were far more correlated than the taking orders generated by

humans. After 12:00 p.m., human traders, as a whole, then began to buy dollars fairly aggressively, and the

appreciation of the yen against the dollar was partially reversed. This is only a single example, of course,

but it leads us to ask how correlated computer trades and strategies have tended to be overall.

We do not know precisely the exact mix of the various strategies used by algorithmic traders on EBS.

Traders keep the information about their own strategies conÖdential, including, to some extent, from EBS,

and EBS also keeps what they know conÖdential.12 However, one can get a general sense of the market

and of the strategies in conversations with market participants. About half of the algorithmic trading

volume on EBS is believed to come from what is often known as the ìprofessional trading community,î

which primarily refers to hedge funds and commodity trading advisors (CTAs). These participants, until

very recently, could not trade manually on EBS, so all their trades were algorithmic. Some hedge funds

and CTAs seek to exploit short-lived arbitrage opportunities, including triangular arbitrage, often accessing

several trading platforms. Others implement lower-frequency strategies, often grouped under the statistical

arbitrage appellation, including carry trades, momentum trades, and strategies spanning several asset classes.

Only a very small fraction of the trading volume in our sample period is believed to have been generated

by algorithms designed to quickly react to data releases. The other half (approximately) of the algorithmic

trading volume comes from foreign exchange dealing banks, the only participants allowed on the EBS system

until 2003. Some of the banksí algorithmic trading is clearly related to activity on their own customer-todealer platforms, to automate hedging activity, and to minimizing the impact of the execution of large orders.

But a sizable fraction is believed to be proprietary trading implemented algorithmically, likely using a mix

of strategies similar to those employed by hedge funds and CTAs. Overall, market participants generally

believe that the mix of algorithmic strategies used in the foreign exchange market di§ers from that seen in

the equity market, where optimal execution algorithms are thought to be relatively more prevalent.

The August 16, 2007 episode shown above was widely viewed as the result of a sudden unwinding of the

yen-carry trade, with hedge funds and proprietary trading desks at banks rushing to close risky positions

and buying yen to pay back low-interest loans. The evidence in this case raises the possibility that many

1 2EBS requires that new algorithmic traders on its system Örst test their algorithms in simulated conditions. EBS then routinely monitors the trading practices of its customers. A high number of excessively short-lived quotes (áashing) is discouraged,

as is a very low ratio of trades to quotes.

7
algorithmic traders were using fairly similar carry trade and momentum strategies at the time, leading to

the high correlation of algorithmic orders and to sharp exchange rate movements. Of course, this is only

one episode in our two-year sample. Furthermore, episodes of very sharp appreciation of the yen due to the

rapid unwinding of yen carry trades have occurred on several occasions since the late 1990s, some obviously

before algorithmic trading was allowed in the market. The sharp move of the yen in October 1998, including

a 1-day appreciation of the yen against the dollar of about 7 percent, is the best-known example of such an

episode. Next, we investigate whether there is evidence that, over the entire sample, the strategies used by

algorithmic traders have tended to be more correlated than those used by human traders.

If computers and humans are indi§erent between taking or making liquidity at a given point in time,

then we should observe that computers and humans trade with each other in proportion to their relative

presence in the market. If, on the other hand, computers tend to have more homogeneous trading strategies,

we should observe computers trading less among themselves and more with humans. At the extreme, if all

computers used the very same algorithms and had the exact same speed of execution, we would observe no

trading volume among computers. Therefore, the fraction of trades conducted between computers contains

information on how correlated their strategies are.13

To investigate the proposition that computers tend to have trading strategies that are more correlated

than those of humans we pursue the following approach. We Örst consider a simple benchmark model that

assumes random and independent matching of traders. This model allows us to determine the theoretical probabilities of the four possible trades: Human-maker/human-taker, computer-maker/human-taker,

human-maker/computer-taker and computer-maker/computer-taker. We then make inferences regarding the

diversity of computer trading strategies based on how the trading pairs we observe compare to those the

benchmark model predicts.

In the benchmark model there are Hm potential human-makers (the number of humans that are standing

ready to provide liquidity), Ht potential human-takers, Cm potential computer-makers, and Ct potential

computer-takers. For a given period of time, the probability of a computer providing liquidity to a trader

is equal to P rob(computer  make) = Cm

Cm+Hm

, which we label for simplicity as m, and the probability

of a computer taking liquidity from the market is P rob(computer  take) = Ct

Ct+Ht

= t. The remaining

makers and takers are humans, in proportions (1  m) and (1  t), respectively. Assuming that these

events are independent, the probabilities of the four possible trades, human-maker/human-taker, computer1 3 Sto§man (2007) uses a similar method to estimate how correlated individual investor strategies are compared to institutional

investor strategies.

8
maker/human-taker, human-maker/computer-taker and computer-maker/computer taker, are:

P rob(HH) = (1  m)(1  t)

P rob(HC) = (1  m)t

P rob(CH) = m(1  t)

P rob(CC) = mt:

These probabilities yield the following identity,

P rob(HH) P rob(CC)  P rob(HC) P rob(CH);

which can be re-written as,

P rob(HH)

P rob(CH)



P rob(HC)

P rob(CC)

:

We label the Örst ratio, RH 

P rob(HH)

P rob(CH)

, the ìhuman-takerîratio and the second ratio, RC 

P rob(HC)

P rob(CC)

,

the ìcomputer-takerî ratio. In a world with more human traders (both makers and takers) than computer

traders, each of these ratios will be greater than one, because P rob(HH) > P rob(CH) and P rob(HC) >

P rob(CC) i.e., computers take liquidity more from humans than from other computers, and humans take

liquidity more from humans than from computers. However, under the baseline assumptions of our randommatching model, the identity shown above states that the ratio of ratios, R 

RC

RH , will be equal to one.

In other words, humans will take liquidity from other humans in a similar proportion that computers take

liquidity from humans.

Turning to the data, under the assumption that potential human-takers are randomly matched with

potential human-makers, i.e., that the probability of a human-maker/human-taker trade is equal to the one

predicted by our model, P rob(HH) = HmHt

(Hm+Cm)(Ht+Ct)

, we can now derive implications from observations of

R, our ratio of ratios. In particular, Önding R > 1 must imply that algorithmic strategies are more correlated

than what our random matching model implies. In other words, for R > 1 we must observe that either

computers trade with each other less than expected (P rob(CC) <

CmCt

(Hm+Cm)(Ht+Ct)

) or that computers trade

with humans more than expected (either P rob(CH) >

CmHt

(Hm+Cm)(Ht+Ct)

or P rob(HC) >

HmCt

(Hm+Cm)(Ht+Ct)

).

Our dataset allows us to estimate an ex-post proxy for R. Namely, for each trading day we estimate

RHd =

V ol(HH)

V ol(CH)

and RCd =

V ol(HC)

V ol(CC)

, where V ol(HH) is the daily trading volume between human makers

and human takers, and so forth. In Table 3 we show the mean of the daily ratio of ratios, Rb =

RCd

RHd ; for

each currency pair for the full sample and the three-month sub-sample. In contrast to the above theoretical

9
prediction that R 

RC

RH = 1, we Önd that for all currency pairs Rb is statistically greater than one. This

result is very robust: in euro-dollar, all daily observations of Rb are above one, and only a very small fraction

of the daily observations are below one for the other currency pairs. The results thus show that computers

do not trade with each other as much as random matching would predict. We take this as evidence that

algorithmic strategies are likely less diverse than the trading strategies used by human traders.

This Önding, combined with the observed growth in algorithmic trading over time, may raise some concerns

about the impact of AT on volatility in the foreign exchange market. As mentioned previously, some analysts

have pointed to the possible danger of having many algorithmic traders take the same side of the market at

the same moment. However, it is not a foregone conclusion that a high correlation of algorithmic strategies

should necessarily lead to higher volatility or large swings in exchange rates. Both the high correlation

of trading strategies and the widespread use of de-stabilizing strategies may need to be present to cause

higher volatility. For instance, if many algorithmic traders use similar triangular arbitrage strategies, the

high correlation of those strategies should have little impact on volatility, and may even lower volatility as

it improves the e¢ ciency of the price discovery process. Strategies designed to minimize the price impact of

trades should also, a priori, not be expected to increase volatility. In contrast, if the high correlation reáects

a large number of algorithmic traders using the same carry trade or momentum strategies, as in the August

2007 example shown at the beginning of this section, then there may be some reasons for concern. However,

as noted earlier, episodes of sharp movements in exchange rates similar to that example have occurred in

the past on several occasions, including well before the introduction of algorithmic trading in the foreign

exchange market, suggesting that such episodes are a result of the dramatic unwinding of certain trading

strategies, regardless of whether these strategies are implemented through algorithmic trading or not. In

the next section, we explicitly investigate the relationship between the presence of algorithmic trading and

market volatility.

4 The impact of algorithmic trading on volatility

In this section, we study whether the presence of algorithmic trading is associated with disruptive market

behavior in the form of increased volatility. In particular, taking into account the potential endogeneity of

algorithmic trading activity, we test for a causal relationship between the fraction of daily algorithmic trading

relative to the overall daily volume, and daily realized volatility.

10
4.1 A Örst look

We Örst take an informal look at the data. Figure 4 shows monthly observations of annualized realized

volatility (based on 1-minute returns) and of the fraction of algorithmic trading (the fraction of total trading

volume involving at least one computer trader) for each of our currency pairs. As discussed earlier, there is a

clear upward trend in the fraction of AT in the three currency pairs over 2006 and 2007. Realized volatility

in euro-dollar, dollar-yen, and euro-yen declines slightly until mid-2007, and then rises in the second half of

2007, particularly sharply in the yen exchange rates, as the Önancial crisis begins.

In Figure 5, we study whether days with high market volatility are also days with a higher-than-usual

fraction of algorithmic trading, and vice-versa. Using daily observations, we Örst sort the data into increasing

deciles of realized volatility (the decile means are shown as bars in the graphs on the left).14 We then calculate

the mean fraction of AT for the days in each of these deciles (shown as lines in the same graphs). To account

for the sharp upward trend in algorithmic participation over our sample, the daily fraction of algorithmic

trading is normalized: we divide it by a 20-day moving average centered on the chosen observation (a moving

average from day t  10 through day t + 10, excluding day t). Next, we repeat the exercise, now sorting

the daily data into increasing deciles of the normalized fraction of AT (the decile means are shown as bars

in the graphs on the right) and calculating mean realized volatility for the days in each of these deciles

(shown as lines in the same graphs). The results in Figure 5 (both the graphs on the left and the graphs on

the right) show little or no relationship between the level of realized volatility on a particular day and the

normalized fraction of AT on that same day. The highest decile in the euro-dollar currency pair may be the

only possible exception, with a slight uptick evident in both volatility and AT activity. Finally, we note that,

in untabulated results, for each of the three currency pairs, not one of the top 10 days in realized volatility

is associated with a top ten day in the share of (normalized) AT.

The simple analysis in Figure 5 does not point to any substantial systematic link between AT activity

and volatility. However, this analysis ignores the possible, and likely, endogeneity of algorithmic activity with

regards to volatility, and therefore does not address the question of whether there is a causal relationship

between algorithmic trading and volatility. In the remainder of this section, we attempt to answer this

question through an instrumental variable analysis.

4.2 IdentiÖcation

The main challenge in identifying a causal relationship between algorithmic trading and volatility is the

potential endogeneity of algorithmic trading. That is, although one may conjecture that algorithmic trading

1 4With 498 daily observations, the Örst 9 deciles each include 50 observations, and the highest decile contains 48 observations.

11
impacts volatility, it is also plausible that algorithmic trading activity may be a function of the level of

volatility. For instance, highly volatile markets may present comparative advantages to automated trading

algorithms relative to human traders, which might increase the fraction of algorithmic trading during volatile

periods. In contrast, however, one could also argue that a high level of volatility might reduce the informativeness of historical price patterns on which some trading algorithms are likely to base their decisions,

and thus reduce the e§ectiveness of the algorithms and lead them to trade less. Thus, one can not easily

determine in what direction the bias will go in an OLS regression of volatility on the fraction of algorithmic

trading. To deal with the endogeneity issue, we adopt an instrumental variable (IV) approach as outlined

below.

We are interested in estimating the following regression equation,

RVit = i + iATit + 


0

i

 it +

X

22

k=1

iRVitk + it; (1)

where i = 1; 2; 3 represents currency pairs and t = 1; :::; T, represents time. RVit is (log) realized daily

volatility, ATit is the fraction of algorithmic trading at time t in currency pair i,  it is either a time trend or

a set of time dummies that control for secular trends in the data, and it is an error term that is assumed

to be uncorrelated with RVitk, k  1, but not necessarily with ATit. The large number of lags of volatility,

which covers the business days of the past month, is included to control for the strong serial correlation in

volatility (e.g. Andersen, Bollerslev, Diebold, and Labys, 2003 and Bollerslev and Wright, 2000). The exact

deÖnitions of RVit, ATit, and  it are given below.

The main focus of interest is the parameter i, which measures the impact of algorithmic trading on

volatility in currency pair i. However, since ATit and it may be correlated, due to the potential endogeneity

discussed above, the OLS estimator of i may be biased. In order to obtain an unbiased estimate, we

will therefore consider an instrumental variable approach. Formally, we need to Önd a variable, or set of

variables, zit, that is uncorrelated with it (validity of the instrument) and correlated with ATit (relevance

of the instrument).

The instrument we propose to use is the fraction of trading áoors equipped to trade algorithmically on

EBS relative to the total number of trading áoors linked to the EBS system.15 That is, in order to place

algorithmic trades on EBS, a special user interface is required, and the total number of trading áoors with

such user interfaces thus provides a measure of the overall algorithmic trading ìcapacityî in the market.

The ratio of these algorithmic trading áoors to the total number of trading áoors provides a measure of the

1 5More precisely, we actually observe a time series of the number of EBS ìdeal codesî of each type over our sample period.

Generally speaking, EBS assigns a deal code to each trading áoor equipped with at least one of its terminals, and records

whether they are equipped to trade algorithmically or not. These data are conÖdential.

12
potential fraction of algorithmic trading. Since setting up an algorithmic trading operation likely takes several

months, the number of trading áoors of each type is clearly exogenous with regards to daily market volatility;

the fraction of AT trading áoors is therefore a valid instrument. In addition, it is positively correlated with

the fraction of algorithmic trading, and it provides a relevant instrument as seen from the tests for weak

instruments discussed below.

Under the breakdown provided by EBS, there are three types of trading áoors linked to the EBS system:

purely algorithmic trading áoors, purely manual trading áoors, and dual trading áoors, those equipped to

handle both manual and algorithmic trades. We consider two natural instrumental variables: the fraction of

pure AT trading áoors over the total number of trading áoors (including pure AT, manual, and dual ones),

and the fraction of the sum of pure AT and dual trading áoors over the total number. Since it is not obvious

which variable is the better instrument, we use both simultaneously.16

The data on AT trading áoors are provided on a monthly basis, whereas the data on realized volatility and

algorithmic trading are sampled on a daily frequency. We therefore transform the trading áoor data to daily

data by repeating the monthly value each day of the month. Although this leads to a dataset of two years

of daily data, the number of daily observations (498) overstates the e§ective number of observations, since

the coe¢ cient on AT participation will be identiÖed from monthly variations in the instrumental variables.

Transforming the instruments to a daily frequency is, however, more e¢ cient than transforming all data to

a monthly frequency, since the daily data help to identify the monthly shifts.

The instrumental variable regressions are estimated using Limited Information Maximum Likelihood

(LIML), and we test for weak instruments by comparing the Örst stage Fstatistic for the excluded instruments to the critical values of Stock and Yogoís (2005) test of weak instruments. We use LIML rather than

two-stage least squares since Stock and Yogo (2005) show that the former is much less sensitive to weak

instruments than the latter (see also Stock et al., 2002).

4.3 Variable deÖnitions

4.3.1 Realized Volatility

Volatility is measured as the daily realized volatility obtained from one minute returns; that is, the volatility

measure is equal to the square root of the daily sum of squared one minute log-price changes. The use of

realized volatility, based on high-frequency intra-daily returns, as an estimate of ex-post volatility is now

well established and generally considered the most precise and robust way of measuring volatility. Although

1 6Regressions not reported here show that using the fraction of pure AT trading áoors as a single instrument gives qualitatively

similar results to those presented below based on both instruments. Using the fraction of the sum of both pure and dual AT

trading áoors as a single instrument also leads to the same qualititative conclusion, but with more signs of weak instruments.

13
many older studies relied on Öve minute returns in order to avoid contamination by market microstructure

noise (e.g. Andersen et al., 2001), recent work shows that sampling at the one-minute frequency, or even

higher frequencies, does not lead to biases in liquid markets (see, for instance, the results for liquid stocks

in Bandi and Russel, 2006, and the study by Chaboud et al., 2007, who explicitly examine EBS data on the

euro-dollar exchange rate during 2005 and Önds that sampling frequencies upwards of once every 20 seconds

does not lead to noticeable biases). Here, we restrict ourselves to using minute-by-minute data.17 Following

the common conventions in the literature on volatility modelling (e.g. Andersen, Bollerslev, Diebold, and

Labys, 2003), the realized volatility is log-transformed to obtain a more well behaved time-series.

4.3.2 Algorithmic trading

We consider two measures of the fraction of algorithmic trading, ATit, in a given currency pair: the computerparticipation fraction and the computer-taker fraction. The Örst is simply the percent of the overall trading

volume that includes an algorithmic trader as either a maker or a taker (V ol(CH + HC + CC)); that is,

the percent of trading volume where a computer is involved in at least one side of the trade. In addition,

we also consider an alternative measure deÖned as the fraction of overall trading volume that is due to a

computer-taker (V ol(HC + CC)).

4.3.3 Time controls

As seen in Figure 4, there is a clear secular trend in the computer-participation fraction,18 which is not present

in realized volatility. Euro-dollar, dollar-yen, and euro-yen volatility is trending down at the beginning of the

period and starts to trend up in the summer of 2007. In order to control for the trend in algorithmic trading

in the regression, we include either a ìlinear quarterlyîtime trend or a full set of year-quarter dummies, one

for each year-quarter pair in the data (8 dummies). That is, the linear quarterly time trend stays constant

within each quarter and increases by the same amount each quarter, whereas the year-quarter dummies allows

for a more áexible trend speciÖcation that can shift in arbitrary fashion from year-quarter to year-quarter.

Both secular trend speciÖcations are thus Öxed within each quarter. This restriction is imposed in order to

preserve the identiÖcation coming from the monthly instrumental variables. Using monthly, or Öner, time

dummies would eliminate the variation in the instrument and render the model unidentiÖed. Although it is

theoretically possible to include a monthly time trend, this would lead to very weak identiÖcation empirically.

1 7Using realized volatility based on Öve-minute returns leads to results that are very similar to those reported below for the

one-minute returns, and the qualitative conclusions are identical.

1 8 The same is true for the computer-taker fraction, not shown in the Ögure.

14
4.4 Empirical results

The regression results are presented in Table 4. We present OLS and LIML-IV results, with either the

quarterly trend or the year-quarter dummies included. We show in Panels A and B the results for the

computer-participation volume, and in Panels C and D the results for computer-taker volume. We report

results for the sample starting in January 2006 and ending in December 2007. In order to save space, we

only show the estimates of the coe¢ cients in front of the fraction of algorithmic trading volume variables.

The OLS results, which are likely to be biased due to the aforementioned endogeneity issues, show a fairly

clear pattern of a positive correlation between volatility and AT participation, with several positive and

statistically signiÖcant coe¢ cients. The R2s are fairly large, reáecting the strong serial correlation in realized

volatility, which is picked up by the lagged regressors. There are also no systematic di§erences between the

quarterly trend and quarterly dummies speciÖcations.

Turning to the more interesting IV results, which control for the endogeneity bias, the coe¢ cient estimates

change fairly dramatically. All point estimates are now negative and some of them are statistically signiÖcant.

Thus, if there is a causal relationship between the fraction of algorithmic trading and the level of volatility, all

evidence suggests that it is negative, such that increased AT participation lowers the volatility in the market.

The stark di§erence between the IV and OLS results shows the importance of controlling for endogeneity

when estimating the causal e§ect of AT on volatility; the opposite conclusion would have been reached if

one ignored the endogeneity issue. The evidence of a statistically signiÖcant relationship is fairly weak,

however, with most coe¢ cients statistically indistinguishable from zero. The more restrictive quarterly trend

speciÖcation suggests a signiÖcant relationship for the euro-dollar and dollar-yen, but this no longer holds if

one allows for year-quarter dummies.

To the extent that the estimated coe¢ cients are statistically signiÖcant, it is important to discuss the

economic magnitude of the estimated relationship between AT and volatility. The regression is run with log

volatility rather than actual volatility, which makes it a little less straightforward to interpret the size of the

coe¢ cients. However, some back-of-the-envelope calculations can provide a rough idea. Suppose that the

coe¢ cient on computer participation is about 0:01, which is in line with the coe¢ cient estimates for the

euro-dollar. The average monthly shift in computer participation in the euro-dollar is about 1.5 percentage

points and the average log-volatility in the euro-dollar is about 3:76 (with returns calculated in basis points),

which implies an annualized volatility of about 6:82 percent. Increasing the computer participation fraction

by 1.5 percentage points decreases log-volatility by 0:015 and results in an annualized volatility of about 6:72.

Thus, a typical change in computer participation might change volatility by about a tenth of a percentage

point in annualized terms, a small e§ect.

15
The Örst stage Fstatistics for the excluded instruments in the IV regressions are also reported in Panels

B and D. Stock and Yogo (2005) show that this Fstatistic can be used to test for weak instruments.

Rejection of the null of weak instruments indicates that standard inference on the IV-estimated coe¢ cients

can be performed, whereas a failure to reject indicates possible size distortions in the tests of the LIML

coe¢ cients. The critical values of Stock and Yogo (2005) are designed such that they indicate a maximal

actual size for a nominal sized Öve percent test on the coe¢ cient. Thus, in the case considered here with two

excluded instruments and one endogenous regressor, a value greater than 8:68 for this Fstatistic indicates

that the maximal size of a nominal 5 percent test will be no greater than 10 percent, which might be deemed

acceptable; a value greater than 5:33 for the Fstatistic indicates a maximal size of 15 percent for a nominal

5 percent test. In general, the larger the Fstatistic, the stronger the instruments. As is evident from the

table, there are no signs of weak instruments in the speciÖcation with a quarterly trend. There are, however,

signs of weak instruments in the case with year-quarter dummies, for the euro-yen. This is not too surprising

given that the instruments only change on a monthly frequency, and the year-quarter dummies therefore put

a great deal of strain on the identiÖcation mechanism. Importantly, though, the results for the two major

currency pairs are robust to any weak-instrument problems and the reported coe¢ cients and standard errors

are unbiased.

To sum up, the evidence of any causal e§ect of algorithmic trading on volatility is not strong, but what

evidence there is points fairly consistently towards a negative relationship. There is thus no systematic

statistical evidence to back the often-voiced opinion that AT leads to increased levels of market volatility. If

anything, the contrary appears to be true.

5 Who provides liquidity during the release of public announcements?

In the previous section we discuss one of the major concerns regarding algorithmic trading, namely, whether

AT causes exchange rate volatility. We now examine another major concern, whether AT improves or

reduces liquidity during stress periods, when it is arguably needed the most. To answer this question, we

cannot simply regress computer-maker volume, a proxy for liquidity provided by computers, on exchange

rate volatility, a proxy for stress periods, because, as we discussed in the previous section, algorithmic

volume and volatility are endogenous variables. In contrast to the previous section we do not estimate an IV

regression, as there are no obvious instruments for volatility.19 Instead, we follow the event study literature

1 9One could consider macroeconomic news announcements as potential instruments for volatility. However, macroeconomic

news announcements are exogeneous variables that cause both foreign exchange rate volatility and liquidity changes. Since we

cannot assume that the e§ect macroeconomic news announcements have on liquidity is only due to the e§ect macroeconomic

16
and compare the liquidity provision by humans and computers during U.S. nonfarm payroll announcements,

a period of exogenously heightened volatility, to the liquidity provision by both types of agents during nonannouncement days. This comparison will help us determine who provides relatively more liquidity during

stress periods. We note that, when we consider liquidity provision by humans and computers following other

important macroeconomic news announcements, the results are qualitatively similar. However, we focus in

this section on the nonfarm payroll announcement only, as it routinely generates the highest volatility of all

US macroeconomic announcements.20

We consider two liquidity provision estimates: a one-minute estimate and a one-hour estimate. The oneminute estimate is calculated using volume observations from 8:30 a.m. to 8.31 a.m. ET (when U.S. nonfarm

payroll is released), while the one-hour estimate is calculated using observations from 8:25 am to 9:24 am

ET. We deÖne the one-minute (one-hour) liquidity provision by humans, LH, as the sum of human-maker

volume, V ol(HH +HC), divided by total volume during that period, and the one-minute (one-hour) liquidity

provision by computers, LC, as the sum of computer-maker volume, V ol(CC +CH), divided by total volume

during that period. Similar to the liquidity provision measures, we deÖne the one-minute volatility as the

squared 1-minute return from 8:30 a.m. to 8.31 a.m. ET and the one-hour volatility as the sum of squared

1-minute returns from 8:25 am to 9:24 am ET.

To compare liquidity provision by humans and computers during announcement times to liquidity provision during (more tranquil) non-announcement times, we could estimate the average liquidity provision during

announcement times and compare it to the average liquidity provision during non-announcement times, with

both means taken over the entire sample period. However, as we discussed previously, exchange rate trading

volumes and the shares of liquidity provision by humans and computers exhibit clear trends over our sample,

making the comparison of the two di§erent means problematic. Alternatively, and this is the methodology

we follow, on each announcement day we estimate the ratio of liquidity provision on that day relative to the

liquidity provision on days surrounding the announcement. This amounts to using a non-parametric approach

to detrend the data. The time series of these ratios will be stationary, and we can then test the hypothesis that the ratio is greater than one. SpeciÖcally, we divide the one-minute (one-hour) liquidity provision

by humans, LHa, and computers, LCa, estimated on announcement day t by the one-minute (one-hour)

liquidity provision by humans, LHn, and computers, LCn, respectively, estimated during the surrounding

non-announcement day period, deÖned as 10 business days before and after a nonfarm payroll release date

t. The liquidity provision measures on the non-announcement days are calculated in the same manner as

on the announcement days, using data only for the periods 8:30 a.m. to 8.31 a.m. ET or 8:25 am to 9:24

news announcements have on volatility, the exclusion restriction required by IV estimation is violated.

2 0Andersen and Bollerslev (1998), among others, refer to the nonfarm payroll report as the ìkingî of announcements, because

of the signiÖcant sensitivity of most asset markets to its release.

17
am ET, for the one-minute and one-hour measures, respectively.21 We follow the same procedure with our

one-minute and one-hour volatility estimates.

Consistent with previous studies, we show in Table 5 Panel A that the one-hour volatility on nonfarm

payroll announcement days is 3 to 6 times larger than during non-announcement days. The one-minute

volatility is 15 to 30 times larger during announcement days compared to non-announcement days. As

expected, given the fact that we focus on a U.S. data release, the volatility increase is smaller in the crossrate, the euro-yen exchange rate, than in the euro-dollar and yen-dollar exchange rates. Focusing on the

statistically signiÖcant estimates, we show in Table 5 Panel B that, as a share of total volume, humanmaker volume tends to increase during the minute of the announcement (the one-minute ratio LHa

LHn

is greater

than one), while computer-maker volume tends to decrease (the one-minute ratio LCa

LCn

is less than one).

Interestingly, this pattern is reversed when we focus on the one-hour volume estimates for the euro-dollar

and euro-yen exchange rate markets. In relative terms, computers do not increase their provision of liquidity

as much as humans do during the minute following the announcement. However, computers increase their

provision of liquidity relatively more than humans do over the entire hour following the announcement, a

period when market volatility remains quite elevated.

We note that, over our sample period, the U.S. nonfarm payroll data releases were clearly the most

anticipated and most ináuential U.S. macroeconomic data releases. They often generated a large initial

sharp movement in exchange rates, followed by an extended period of volatility. The behavior of computer

traders observed in the Örst minute could reáect the fact that many algorithms are not designed to react

to the sharp, almost discrete, moves in exchange rates that often come at the precise moment of the data

release. Some algorithmic traders may then prefer to pull back from the market a few seconds before 8:30

a.m. ET on days of nonfarm payroll announcements, resuming trading once the risk of a sharp initial price

movement has passed. But the data show that algorithmic traders, as a whole, do not shrink back from

providing liquidity during the extended period of volatility that follows the data releases.

6 Price Discovery

In the previous three sections, we analyze questions that are primarily motivated by practical concerns

regarding algorithmic trading, such as whether computer traders induce volatility or reduce liquidity. In this

section we turn to questions that are driven more by the market microstructure literature, but that also lead

2 1For simplicity, we label the 10 business days before and after the nonfarm payroll announcement as non-announcement days.

However, during this 20-day period there are both days with no macroeconomic news and days with news. For instance, every

Thursday, including the day before the monthly nonfarm payroll number is released, initial jobless claims are released. Thus,

our estimation will likely be biased towards not Önding statistically di§erent behavior across the two periods. As we show in

Table 5, volatility is, on average, much lower during this 20-day period than on nonfarm payroll days, and therefore the period

still serves as a good benchmark.

18
to interesting practical insights regarding the e§ects and nature of algorithmic trading. In particular, we

study price discovery within a vector autoregressive framework, which enables us to evaluate to what extent

humans or computers represent the ìinformedîtraders in the market. Our Öndings reveal several interesting

features regarding the impact of algorithmic trades and the order placement behavior of computer traders.

6.1 Who are the ìinformedî traders, humans or computers?

We Örst investigate whether human or computer trades have a more ìpermanentîimpact on prices. To this

end, we estimate return-order áow dynamics in a structural vector autoregressive (VAR) framework in the

tradition of Hasbrouck (1991a), where returns are contemporaneously a§ected by order áow, but order áow

is not contemporaneously a§ected by returns. Similar to Hasbrouckís (1996) decomposition of program and

nonprogram order áow, we decompose order áow into two components: human-taker OF(ht) = HH + CH

and computer-taker OF(ct) = HC + CC, and thus we estimate for each currency i one return equation

and two order áow equations. In light of Evans and Lyons (2008) Öndings, we estimate the structural VAR

with U.S. macroeconomic news surprises as exogenous variables that a§ect both returns and order áow.

SpeciÖcally, we estimate the following system of equations for each currency i,

rit = 

r +

X

J

j=1



r

ij ritj +

X

J

j=0




rct

ij OF(ct)

itj +

X

J

j=0




rht

ij OF(ht)

itj +

X

K

k=1



r

ikSkt + "

r

it; (2)

OF(ht)

it = 

OF

ht +

X

J

j=1



OF

ijhtritj +

X

J

j=1




OF (ht)

ijht OF(ht)

itj +

X

J

j=1




OF (ct)

ijht OF(ct)

itj +

X

K

k=1



OF

ikhtSkt + "

OF (ht)

it ;

OF(ct)

it = 

OF

ct +

X

J

j=1



OF

ijctritj +

X

J

j=1




OF (ct)

ijct OF(ct)

itj +

X

J

j=1




OF (ht)

ijct OF(ht)

itj +

X

K

k=1



OF

ikctSkt + "

OF (ct)

it :

Here rit is the 1-minute exchange rate return for currency i at time t; OF ht

it is the currency i human-taker order

áow at time t; OFct

it is the currency i computer-taker order áow at time t; and Skt is the macroeconomic news

announcement surprise for announcement k at time t deÖned as the di§erence between the announcement

realization and its corresponding market expectation. We use Bloombergís real-time data on the expectations

and realizations of K = 28 U.S. macroeconomic fundamentals to calculate Skt. The 28 announcements we

consider are similar to those in Andersen et al. (2003, 2007) and Pasquariello and Vega (2007).22 Since units

of measurement vary across macroeconomic variables, we standardize the resulting surprises by dividing each

2 2Our list of U.S. macroeconomic news announcements is the same as the list of announcements in Andersen et al. (2007) and

Pasquariello and Vega (2007) with the addition of three announcements: unemployment rate, core PPI and core CPI. Andersen

et al. (2007) and Pasquariello and Vega (2007) use International Money Market Services (MMS) data on the expectations of

U.S. macroeconomic fundamentals. In contrast, we use Bloomberg data because the MMS data are no longer available after

2003. Bloomberg provides survey data similar to those MMS previously provided.

19
of them by their sample standard deviation. Economic theory suggests that we should also include foreign

macroeconomic news announcements in equation (2). However, previous studies Önd that exchange rates do

not respond much to non-U.S. macroeconomic announcements, even at high frequencies (e.g. Andersen et

al., 2003), so we expect the omitted variable bias in our speciÖcation to be small.

The underlying economic model is based on continuous time, and we thus estimate the VAR using the

highest sample frequency available to us, minute-by-minute data. The estimation period is restricted to the

20062007 sample, and the total number of observations for each currency pair is 717; 120 in the full sample

and 89; 280 in the three-month sub-sample (September, October and November of 2007). In both samples,

20 lags are included in the estimated VARs, i.e. J = 20.

Our speciÖcation in equation (2) does not allow human-taker order áow to contemporaneously a§ect

computer-taker order áow or vice-versa. The advantage of this approach is that we can estimate the impulse

response functions without giving more importance to a particular type of order áow, i.e., we do not need to

assume a particular ordering of the human-taker and computer-taker order áow in the VAR. The disadvantage

is that the human-taker and computer-taker order áow shocks may not be orthogonal. However, in our

estimation this does not appear to be a problem, as our residuals are found to be approximately orthogonal

(the correlation between the human-taker and computer-taker equation residuals are -0.001, -0.1 and -0.1 for

the euro-dollar, yen-dollar, and euro-yen exchange rates respectively). As a robustness check, we also estimate

the VAR with two di§erent orderings. We Örst assume human-taker order áow a§ects computer-taker order

áow contemporaneously, and then assume the opposite ordering. This latter approach allows us to compute

upper and lower bound impulse responses. These results are presented in the Appendix, and show that the

results presented here are not sensitive to alternative identiÖcation schemes in the VAR.

Before considering the impulse response functions and the variance decompositions, we brieáy summarize

the main lessons from the estimated coe¢ cients in the VAR. Focusing on the return equation, we Önd that

minute-by-minute returns tend to be negatively serially correlated, with the coe¢ cient on the Örst own lag

varying between 0:08 and 0:15; there is thus some evidence of mean reversion in the exchange rates at

these high frequencies, which is a well-know empirical Önding. Both order áows are signiÖcant predictors of

returns. The price impact of the lagged order áows range from around 4 to 18 basis points per billion units

of order áow (denominated in the base currency), as compared to a range of approximately 28  100 basis

points in the contemporaneous order áow. As theory would predict, we Önd that U.S. macroeconomic news

announcements a§ect less the euro-yen exchange rate (i.e., the R2 of regressing the euro-yen exchange rate on

macroeconomic news surprises and restricting the sample to announcement-only observations is 23%) than

the euro-dollar and dollar-yen exchange rates (i.e., the R2s of an announcement-only sample are 60% and

59%, respectively). However, U.S. macroeconomic news announcements still have an e§ect on the cross-rate

20
to the extent that the U.S. economy is more or less correlated with the Japanese or the Euro-area economy.

Focusing on the order-áow equations, we Önd that the Örst own lag in both order áow equations is

always highly signiÖcant, and typically around 0:1 for all currency pairs. There is thus a sizeable Örst-order

autocorrelation in the human-taker and computer-taker order áows. The coe¢ cients on the Örst order crosslags in the order áow regressions are most often substantially smaller than the coe¢ cient on the own lag and

vary in signs. Lagged returns have a small but positive impact on order áow, suggestive of a form of trend

chasing by both computers and humans in their order placement.

We note that despite the strongly signiÖcant estimates that are recorded in the VAR estimations, the

amount of variation in the order áow and return variables that is captured by their lagged values is very

limited. The R2for the estimated equations with only lagged variables are typically around three to ten

percent for the order áow equations, and between one and three percent for the return equations. This can

be compared to an R2 of 20 to 30 percent when one includes contemporaneous order áow.

6.2 Impulse Response Function and Variance Decomposition Results

As originally suggested by Hasbrouck (1991b), we use the impulse response functions to assess the price

impact of various order áow types, and the variance decompositions to measure the relative importance of

the variables driving foreign exchange returns. In Table 6 Panel A, we show the results from the impulse

response analysis based on the estimation of equation (2), using the full sample for 2006-2007 and the threemonth sub-sample, when the size of the shock is the same across the di§erent types of order áow: a one billion

base currency shock to order áow. We also show the results when the size of the shock varies according to

the average size shock: a one standard deviation base currency shock to order áow (Table 6 Panel B).

We show both the short-run (instantaneous) impulse responses, the long-run cumulative responses, and the

di§erence between the two responses. The long-run statistics are calculated after 30-minutes, at which point

the cumulative impulse responses have converged and can thus be interpreted as the long-run total impact

of the shock. All the responses are measured in basis points. The standard errors reported in the tables are

calculated by bootstrapping, using 200 repetitions.

Starting with a hypothetical shock of one billion base currency order áow, the results in Table 6 Panel

A, show that the immediate response of prices to human-taker order áow is often larger than the immediate

response to computer-taker order áow. This may partially be attributed to the fact that some of the algorithmic trading is used for the optimal execution of large orders at a minimum cost. Algorithmic trades appear

to be successful in that endeavor, with computers likely breaking up the larger orders and timing the smaller

trades to minimize the impact on prices. We emphasize, though, that the di§erences in price impact, which

21
range from 1 to 8 basis points, are not very large in economic terms. Furthermore, we Önd that the result can

be reversed in the long-run and in the three-month sub-sample. For example, the euro-dollar human-taker

price impact is larger than the computer-taker price impact in the short-run, but the opposite is true in the

long-run and in the three-month sub-sample.

In contrast to these results, the response to a hypothetical one standard deviation shock to the di§erent

order áows (Table 6 Panel B) consistently shows that in the euro-dollar and dollar-yen markets, humans

have a bigger impact on prices than computers and the di§erences are relatively large. For example, a one

standard deviation shock to human-taker order áow in the yen-dollar exchange rate market has an average

long-run e§ect of 0.9 basis points compared to an average e§ect of 0.3 basis points for computer-taker order

áow. Interestingly, the di§erence in price impact in the cross-rate, the euro-yen exchange rate, is very

small. In this market, computers have a clear advantage over humans in detecting and reacting more quickly

to triangular arbitrage opportunities so that a large proportion of algorithmic trading contributes to more

e¢ cient price discovery. It is then not so surprising that in this market computers and humans, on average,

appear to be equally ìinformed.î

In Table 7 we report the fraction of the total (long-run) variance in returns that can be attributed to

innovations in human-taker order áow and computer-taker order áow.23 Following Hasbrouck (1991b), we

interpret this variance decomposition as a summary measure of the informativeness of trades, and thus, in the

current context, a comparison of the relative informativeness of the di§erent types of order áow. Consistent

with the results from the impulse response functions based on a one standard deviation shock to order áow, we

Önd that in the euro-dollar and dollar-yen exchange rate markets human-taker order áow explains much more

of the total variance in returns than computer-taker order áow. SpeciÖcally, human-taker order áow explains

about 30 percent of the total variance in returns compared to only 4 percent explained by computer-taker

order áow.

The fact that human-taker order áow explains a bigger portion of total variance in returns is not surprising

because human-taker volume is about 75 percent of total volume in these two markets in the full sample period

and about 65 percent of total volume in the three-month sub-sample (see Table 2). Moreover large buy (sell)

orders tend to be human-taker orders, i.e. we show in Table 1 that the standard deviation of human-taker

order áow is twice as big as that of the computer-taker order áow. But, do computers tend to contribute

ìdisproportionatelyîlittle to the long-run variance in returns relative to their trading volume? To answer this

question we do a back-of-the-envelope calculation. We compute the relative share of the explained variance

that is due to computer-taker order áow as the percent of total variation in returns explained by computer2 3 The variance decompositions are virtually identical in the short- and long-run and thus we only show the long-run decomposition results.

22
taker order áow divided by the percent of total variation in returns explained jointly by both human-taker

and computer-taker order áow. For example, this relative share is 14% = 100 

4:74

34 (Table 7) in the eurodollar market. We can then compare this relative share to the fraction of overall trading volume that is due

to computer-taker volume, which we show in Table 2. In the full 2006-2007 sample for the euro-dollar and

the dollar-yen currency pairs, the fraction of volume due to computer-takers is about twice as large as the

fraction of the explained long-run variance that is due to computer-taker order áow. In the euro-yen, the

fractions are approximately equal. These results are fairly similar in the three-month sub-sample, although

the fraction of explained variance has increased somewhat compared to the volume fraction. Thus, in the

two major currency pairs, there is evidence that computer-taker order áow contributes relatively less to the

variation in returns than one would infer from just looking at the proportion of computer-taker volume.

6.3 Are liquidity providers ìuninformedî?

We now turn to examine whether liquidity providers post quotes strategically. To this end we augment

equation (2) and decompose order áow into four components. SpeciÖcally, we estimate the following system

of equations for each currency i;

rit = 

r +

X

J

j=1



r

ij ritj +

X

L

l=1

X

J

j=0




rl

ijOF(l)

itj +

X

K

k=1



r

ikSkt + "

r

it; (3)

OF(l)

it = 

OF

l +

X

J

j=1



OF

ijl ritj +

X

L

l=1

X

J

j=1




OF (l)

ijl OF(l)

itj +

X

K

k=1



OF

ikl Skt + "

OF (l)

it :

where rit is the 1-minute exchange rate return for currency i at time t; L = 4, OF(1)

it = OF HH

it is the

currency i human-maker/human-taker order áow at time t; OF(2)

it = OF CH

it is the currency i computermaker/human-taker order áow at time t; OF(3)

it = OF HC

it is the currency i human-maker/computer-taker

order áow at time t; OF(4)

it = OF CC

it is the currency i computer-maker/computer-taker order áow at time t;

Skt is the macroeconomic news announcement surprise for announcement k at time t.

24

In addition to identifying whether traders, on average, have a more permanent impact on prices when

trading with humans than with computers, this speciÖcation also allows us to observe the e§ect order áow

has on prices when, for instance, no party has a speed advantage, i.e. both parties are humans or both parties

are computers, and when either the maker has a speed advantage, CH, or the taker has a speed advantage,

HC. This distinction may be particularly useful when analyzing the cross-rate, where computers likely have

2 4 In the Appendix, we analyze the robustness of this structural VAR by also estimating impulse responses and variance

decompositions from all possible triangular identiÖcation schemes, only imposing that returns are ordered last in the VAR.

23
a clear advantage over humans in detecting short-lived triangular arbitrage opportunities.

Starting with a hypothetical shock of one billion base currency order áow, the results in Table 8 Panel A

show that there is no clear pattern in which order áow impacts prices the most. However, the dynamics of the

VAR system help reveal an interesting Önding: There is a consistent and often large short-run over-reaction

to CC and CH shocks. That is, as seen in Table 8, the short run response to a CC or CH order áow shock

is always larger than the long-run response, and sometimes substantially so. The euro-dollar in the sample

covering September, October, and November of 2007 provides an extreme case where the initial reaction to

a one billion dollar CC shock is a 22 basis point move, but the long-run cumulative reaction is just 6 basis

points. Interestingly, the opposite pattern is true for the HH order áow shocks, where there is always an

initial under -reaction in returns. To the extent that an over-reaction of prices to order áow is suggestive of

the presence of liquidity traders, these impulse response patterns suggest that computers provide liquidity

when the probability of trading with an informed trader is low.25

The response to a hypothetical one standard deviation shock to the di§erent order áows consistently

shows that HH order áow has a bigger impact on prices than CC order áow (Table 8 Panel B) and that

the di§erences are large. In particular, a one standard deviation shock to HH order áow has an average

long-run e§ect of 0.6 basis points across currencies compared to a one standard deviation shock to CC order

áow, which has an average e§ect of 0.1 basis points. Interestingly, we observe that when humans trade with

other humans they ináuence prices more than when they trade with computers (the impact of HH on prices

is bigger than the impact of CH on prices), and when computers trade with other computers they ináuence

prices less than when they trade with humans (the impact of CC on prices is smaller than the impact of HC

on prices). Our interpretation is that computers provide liquidity more strategically than humans, so that the

counterparty cannot a§ect prices as much. This interpretation is consistent with the over-reaction of prices

to CC and CH order áow described above: Computers appear to provide liquidity when adverse selection

costs are low. This Önding relates to the literature that proposes to depart from the prevalent assumption

that liquidity providers in limit order books are passive.26

We also Önd that the price response to order áow varies across currencies as these markets di§er along

several dimensions. Trading volume is largest in the euro-dollar and dollar-yen markets, compared to the

euro-yen market, and price discovery clearly happens mostly in the two largest markets. In the cross-rate

2 5Dynamic learning models with informed and uninformed investors predict that prices will temporarily over-react to uninformed order áow and under-react to informed order áow (e.g., Albuquerque and Miao, 2008). We note that the over- and

under-reaction of prices to a particular type of order áow is di§erent from the over- and under-reaction of prices to public news,

which are both considered a sign of market ine¢ ciency. Order áow types are not public knowledge, so that agents cannot trade

on this information.

2 6For example, Chakravarty and Holden (1995), Kumar and Seppi (1994), Kaniel and Liu (2006), and Goettler, Parlour and

Ra jan (2007) allow informed investors to use both limit and market orders. BloomÖeld, OíHara and Saar (2005) argue that

informed traders are natural liquidity providers and Angel (1994) and Harris (1998) show that informed investors can optimally

use limit orders when private information is su¢ ciently persistent.

24
market, the euro-yen, computers have a speed advantage over humans in proÖting from triangular arbitrage

opportunities, where prices set in the euro-dollar and dollar-yen markets are very brieáy out of line with the

euro-yen rate. Consistent with this speed advantage we observe that human-maker/computer-taker order

áow has a larger price impact in the cross-rate market than in the other two markets.

In addition to the impulse response functions, we also report the long-run forecast variance decomposition

of returns in Table 9 for both the full sample and the three-month sub-sample. Consistent with the impulse

response functions to a one standard deviation shock to order áow, the HH order áow makes up the dominant

part of the variance share in the euro-dollar and dollar-yen exchange rate markets. In the last three months

of the sample, this share has generally decreased. The share of variance in returns that can be attributed

to the CC order áow is surprisingly small, especially in the latter sub-sample, given that this category of

trades represents a sizeable fraction of overall volume of trade during the last months of 2007, as seen in

Table 2. The mixed order áow (CH and HC order áow) typically contributes with about the same share to

the explained variance in the euro-dollar and dollar-yen exchange rate markets. In contrast, in the euro-yen

exchange rate market HC order áow makes up the dominant part of the variance share, which is consistent

with our discussion of computers taking advantage of triangular arbitrage opportunities in this market.

Overall, about 15 to 35 percent of the total variation in returns can be attributed to shocks to the four

order áows. However, in most currency pairs, very little of this ultimate long-run price discovery that occurs

via order áow does so via the CC order áow. Similar to Table 7, we also report in Table 9 the fraction of

the explained share of the return variance that can be attributed to the di§erent order áow combinations.

Comparing these to the fraction of overall volume that is due to these combinations of computers and humans,

reported in Table 2, gives an idea of whether the di§erent order áow combinations contribute proportionately

to the variance in returns. It is clear that CC order áow tends to contribute disproportionately little to the

long-run variance of returns, and that HH order áow often contributes disproportionately much.

7 Conclusion

Using highly-detailed high-frequency trading data for three major exchange rates over 2006 and 2007, we

analyze the impact of the growth of algorithmic trading on the spot interdealer foreign exchange market. We

focus on the following questions: (i) Are the algorithms underlying the computer-generated trades similar

enough to result in highly correlated strategies, which some fear may cause disruptive market behavior?

(ii) Does algorithmic trading increase volatility in the market, perhaps as a result of the previous concern?

(iii) Do algorithmic traders improve or reduce market liquidity at times of market stress? (iv) Are human

or computer traders the more ìinformedî traders in the market, i.e. who has the most impact on price

25
discovery? (v) Is there evidence in this market that the liquidity providers (the ìmakersî) and not just the

liquidity ìtakersî, are informed, and do computer makers post orders more strategically than human makers?

The Örst three questions directly address concerns that have been raised recently in the Önancial press,

especially in conjunction with the current crisis, while the last two questions relate more to the empirical

market microstructure literature on price discovery and order placement. Together, the analysis of these

questions brings new and interesting results to the table, both from a practical and academic perspective, in

an area where almost no formal research has been available.

Our empirical results provide evidence that algorithmic trades are more correlated than non-algorithmic

trades, suggesting that the trading strategies used by the computer traders are less diverse than those

of their human counterparts. Although this may cause some concerns regarding the disruptive potential of

computer-generated trades, we do not Önd evidence of a positive causal relationship between the proportion of

algorithmic trading in the market and the level of volatility; if anything, the evidence points towards a negative

relationship, suggesting that the presence of algorithmic trading reduces volatility. As for the provision of

market liquidity, we Önd evidence that, compared to non-algorithmic traders, algorithmic traders reduce their

share of liquidity provision in the minute following major data announcements, when the probability of a

price jump is very high. However, they increase their share of liquidity provision to the market over the entire

hour following these announcements, which is almost always a period of elevated volatility. This empirical

evidence thus suggests that computers do provide liquidity during periods of market stress.

To address the last two questions (price discovery and order placement), we use a high-frequency VAR

framework in the tradition of Hasbrouck (1991a). We Önd that non-algorithmic trades account for a substantially larger share of the price movements in the euro-dollar and yen-dollar exchange rate markets than

would be expected given the sizable fraction of algorithmic trades. Non-algorithmic traders are the ìinformedî traders in these two markets, driving price discovery. In the cross-rate, the euro-yen exchange rate

market, we Önd that computers and humans are equally ìinformed,î likely because of the large proportion

of algorithmic trades dedicated to search for triangular arbitrage opportunities. Finally, we Önd that, on

average, computer takers or human takers that trade on prices posted by computers do not impact prices

as much as they do when they trade on prices posted by humans. One interpretation of this result is that

computers place limit orders more strategically than humans do. This Önding dovetails with the literature

on limit order books that relaxes the common modeling assumption that liquidity providers are passive.

Overall, this study therefore provides essentially no evidence to bolster the widespread concerns about

the e§ect of algorithmic trading on the functioning of Önancial markets. The lesson we take from our analysis

of algorithmic trading in the interdealer foreign exchange market is that it is more how algorithmic trading

is used and what it is predominantly designed to achieve that determines its impact on the market, and not

26
primarily whether or not the order áow reaching the market is generated at high frequency by computers.

In the global interdealer foreign exchange market, the rapid growth of algorithmic trading has not come at

the cost of lower market quality, at least not in the data we have seen so far. Given the constant search

for execution speed in Önancial markets and the increasing availability of algorithmic trading technology, it

is likely that, absent regulatory intervention, the share of algorithmic trading across most Önancial markets

will continue to grow. Our study o§ers hope that the growing presence of algorithmic trading will not have

a negative impact on global Önancial markets.

Appendix: Robustness check of the VAR results

The impulse responses and variance decompositions in the above VAR analyses are derived under the assumption that there are no contemporaneous interactions between the di§erent order áow components. This

identifying assumption is appealing because it treats the order áow components symmetrically and ensures

that the results are not driven by the ordering of the order áows in the VAR. On the other hand, it cannot

be ruled out that one order áow component a§ects another one contemporaneously within the one-minute

timespan over which each observation is sampled. If this is the case, the VAR speciÖcation that we use above

would be too restrictive and the resulting impulse responses and variance decompositions would likely be

biased. As discussed above, given the fairly low correlation that we observe in the VAR residuals for the

di§erent order áow equations, this does not appear to be a major concern, but since these correlations are

not identical to zero it is still possible that other identiÖcation schemes would lead to di§erent conclusions.

In this section we therefore perform a comprehensive robustness check of the VAR results by calculating

upper and lower bounds on the impulse responses and variance decompositions. In particular, we consider

all possible orderings of the order áows in the VARs, while imposing a triangular structure. That is, we still

assume that returns are ordered last in the VAR and are thus a§ected contemporaneously by all order áow

components, but we then consider all possible orderings for the di§erent order áows. In the case where we

split order áow into human and computer order áow, this results in just two di§erent speciÖcationsó one

where computer order áow a§ects human order áow contemporaneously but contemporaneous human order

áow has no impact on computer order áow, and the opposite speciÖcation where human order áow a§ects

computer order áow contemporaneously. In the case with four di§erent order áows, there are 24 di§erent

orderings, when one allows for all possible triangular identiÖcation schemes, only imposing that returns are

ordered last. From each of these speciÖcations, we calculate impulse responses and variance decompositions.

The minimum and maximum of these over all speciÖcations are reported in Tables A1 and A2 for the two

order áow case and in Tables A3 and A4 in the four order áow case.

27
Starting with the simpler case with order áow split up into human or computer order áow, Tables A1

and A2 conÖrm our conjecture that the low correlation in the VAR residuals render the VAR speciÖcation

very robust to the ordering of the order áows. The min-max intervals shown in the two tables are generally

very tight and all of our earlier qualitative conclusions that we draw from our preferred structural VAR

speciÖcation holds also under these alternative orderings.

Turning to the VAR analysis with four separate order áow, the number of possible orderings increases

dramatically to 24. This large number of possible speciÖcations inevitably results in wider min-max intervals,

even though the correlations in the VAR residuals are generally small. In order to usefully interpret these

results, we check whether our main qualitative conclusions from our preferred structural speciÖcation analyzed

above also holds up, in a min-max sense, under all possible orderings. Our Örst main result in the above

analysis was that there is an initial over-reaction to CC and CH shocks and an initial under-reaction to HH

shocks. As seen in Table A3, these Öndings are mostly supported by the min-max results as well. The only

exceptions recorded are for the euro-yen cross rate, where the under-reaction to CC and CH shocks is also

much weaker in the original results in Table 8. It is also evident from Table A3, Panel B, that the min-max

results support the Önding that a one standard deviation shock to HH has a substantially bigger impact on

returns than a CC shock. In addition, Table A3, Panel B, also shows that the impact of the HH shock tends

to be larger than the CH impact, and the CC impact tends to be smaller than the HC impact. Finally,

the results in Table A3 also mostly support the Önding that the reactions to HC order áow are greater in

the euro-yen cross currency than in the two main currency pairs, although some overlap is seen for the one

standard deviation shock in Panel B.

Table A4 shows the corresponding min-max results for the variance decomposition. Again, our main

conclusions are mostly supported in a min-max sense. HH makes up the largest share of the explained

variance in the two main currency pairs in the full sample, although in the three-month sub-sample there

is some overlap between the min-max intervals for the HH order áow and the HC order áow. CC always

contributes a very small share of the explained variance and HC always contributes a fairly substantial share

in the cross currency.

In summary, these robustness checks show that our main VAR used for examining price discovery (equation

(2)), using human and computer order áows, is not particularly sensitive to the exact identiÖcation scheme

that is used. The results presented in Tables 6 and 7 thus appear to be robust to alternative orderings in the

VAR. Our second VAR speciÖcation (equation (3)), which we use to analyze strategic liquidity provision, is

a little more sensitive to the exact identiÖcation scheme used, but the min-max results are still overall very

supportive of our main conclusions.

28
Table A1: Min-max impulse responses from the VAR speciÖcation with human-taker and computer-taker

order áow. The table shows the minimum and maximum triangular impulse responses for returns as a result

of shocks to the human-taker order áow (HH + CH) or computer-taker (CC + HC) order áow, denoted

H-taker and C-taker in the table headings, respectively. In Panel A we show the return response, in basis

points, to a one-billion base-currency shock to one of the order áows. In Panel B we show the return response,

in basis points, to a one standard deviation shock to one of the order áows. We show the results for the

full 2006-2007 sample and for the three-month sub-sample, which only uses data from September, October,

and November of 2007. For each currency pair we show the short-run (immediate) response of returns; the

corresponding cumulative long-run response of returns, calculated as the cumulative impact of the shock after

30 minutes; and the di§erence between the cumulative long-run response in returns minus the immediate

response of returns, i.e., we provide the extent of over-reaction or under-reaction to an order áow shock. There

are a total of 717; 120 minute-by-minute observations in the full two-year sample and 89; 280 observations in

the three-month sub-sample.

Full 2006-2007 sample 3-month sub-sample

H-taker C-taker H-taker C-taker

Panel A: One billion base-currency shock

USD/EUR

Short run [28:05; 28:06] [26:84; 26:94] [23:11; 23:20] [24:89; 25:22]

Long run [27:85; 27:87] [32:26; 32:35] [24:06; 24:16] [31:04; 31:38]

Di§erence [0:20; 0:20] [5:42; 5:42] [0:94; 0:96] [6:14; 6:16]

JPY/USD

Short run [44:96; 46:76] [28:92; 39:81] [44:99; 48:02] [33:45; 44:88]

Long run [45:50; 47:50] [33:21; 44:27] [46:80; 49:54] [28:83; 40:63]

Di§erence [0:54; 0:74] [4:29; 4:46] [1:52; 1:81] [4:62; 4:25]

JPY/EUR

Short run [90:18; 99:32] [90:50; 102:71] [109:04; 124:02] [101:74; 115:52]

Long run [98:30; 108:07] [96:57; 109:85] [116:54; 132:53] [108:54; 123:26]

Di§erence [8:12; 8:75] [6:07; 7:14] [7:50; 8:51] [6:79; 7:74]

Panel B: One standard deviation shock

USD/EUR

Short run [0:6613; 0:6616] [0:2630; 0:2639] [0:6023; 0:6045] [0:3139; 0:3180]

Long run [0:6566; 0:6570] [0:3161; 0:3170] [0:6269; 0:6296] [0:3914; 0:3957]

Di§erence [0:0047; 0:0046] [0:0531; 0:0531] [0:0246; 0:0251] [0:0775; 0:0777]

JPY/USD

Short run [0:8370; 0:8660] [0:2375; 0:3251] [0:9594; 1:0158] [0:3798; 0:5056]

Long run [0:8470; 0:8796] [0:2727; 0:3616] [0:9980; 1:0480] [0:3274; 0:4577]

Di§erence [0:0100; 0:0137] [0:0352; 0:0364] [0:0322; 0:0386] [0:0524; 0:0479]

JPY/EUR

Short run [0:5060; 0:5541] [0:4318; 0:4874] [0:6671; 0:7532] [0:6725; 0:7581]

Long run [0:5515; 0:6030] [0:4608; 0:5213] [0:7130; 0:8049] [0:7174; 0:8089]

Di§erence [0:0455; 0:0488] [0:0289; 0:0339] [0:0459; 0:0517] [0:0449; 0:0508]

29
Table A2: Min-max variance decompositions from the VAR speciÖcation with human-taker and computertaker order áow. The table shows the minimum and maximum triangular long-run variance decomposition

of returns, expressed in percent and calculated at the 30 minute horizon. That is, the table shows the

proportion of the long-run variation in returns that can be attributed to shocks to the human-taker order

áow (HH + CH) and the computer-taker (CC + HC) order áow, denoted H-taker and C-taker in the table

headings, respectively. For each currency pair we show the actual variance decomposition, and the proportion

of the explained variance in returns that can be attributed to each order áow type. That is, we re-scale the

variance decompositions so that they add up to 100 percent. We show results for the full 2006-2007 sample

and for the three-month sub-sample, which only uses data from September, October, and November of

2007. There are a total of 717; 120 minute-by-minute observations in the full two-year sample and 89; 280

observations in the three-month sub-sample.

Full 2006-2007 sample 3-month sub-sample

H-taker C-taker H-taker C-taker

USD/EUR

Variance decomposition [29:25; 29:28] [4:71; 4:74] [25:78; 25:96] [7:08; 7:26]

Proportion of explained share [86:04; 86:14] [13:86; 13:96] [78:02; 78:58] [21:42; 21:98]

JPY/USD

Variance decomposition [27:71; 29:67] [2:31; 4:28] [26:03; 29:19] [4:21; 7:37]

Proportion of explained share [86:63; 92:77] [7:23; 13:37] [77:94; 87:40] [12:60; 22:06]

JPY/EUR

Variance decomposition [10:15; 12:16] [7:37; 9:39] [9:94; 12:67] [10:15; 12:88]

Proportion of explained share [51:93; 62:27] [37:73; 48:07] [43:55; 55:53] [44:47; 56:45]

30
Table A3: Min-max impulse responses from the VAR speciÖcation with all four human/computer-maker/taker order áow combinations. The table

shows the minimum and maximum triangular impulse responses for returns as a result of shocks to the human-maker/human-taker order áow (HH),

computer-maker/human-taker order áow (CH), human-maker/computer-taker order áow (HC), or computer-maker/computer-taker order áow (CC),

denoted in obvious notation in the table headings. The results are based on estimation of equation (3), using minute-by-minute data. In Panel A weshow the return response, in basis points, to a one-billion base-currency shock to one of the order áows. In Panel B we show the return response, inbasis points, to a one standard deviation shock to one of the order áows. We report the results for the full 2006-2007 sample and for the three-monthsub-sample, which only uses data from September, October, and November of 2007. For each currency pair we show the short-run (immediate) responseof returns; the corresponding cumulative long-run response of returns, calculated as the cumulative impact of the shock after 30 minutes; and thedi§erence between the cumulative long-run response in returns minus the immediate response of returns, i.e., we provide the extent of over-reaction orunder-reaction to an order áow shock. There are a total of 717; 120 minute-by-minute observations in the full two-year sample and 89; 280 observationsin the three-month sub-sample.

Full 2006-2007 sample 3-month sub-sample

H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/

H-taker H-taker C-taker C-taker H-taker H-taker C-taker C-taker

Panel A: One billion base-currency shock

USD/EUR

Short run [27:33; 33:32] [18:63; 53:71] [15:26; 45:85] [20:49; 52:41] [20:21; 27:69] [17:49; 47:57] [17:01; 42:58] [12:66; 38:58]

Long run [29:89; 34:83] [8:21; 46:56] [22:08; 50:08] [12:17; 47:64] [24:08; 30:77] [7:50; 42:68] [25:64; 49:51] [

4:72; 26:10]

Di§erence [1:05; 2:88] [

10:48; 7:05] [2:69; 7:20] [

9:13; 3:56] [2:26; 4:55] [

10:15; 4:71] [4:31; 9:43] [

17:81; 11:64]

JPY/USD

Short run [43:11; 51:88] [39:54; 95:65] [19:41; 60:20] [51:93; 83:64] [41:51; 54:04] [40:20; 94:60] [23:08; 64:84] [61:16; 89:94]

Long run [46:68; 54:24] [29:22; 89:09] [25:02; 63:23] [43:96; 77:69] [46:49; 57:52] [36:14; 90:68] [19:96; 59:37] [44:83; 71:65]

Di§erence [2:25; 3:71] [

10:42; 6:47] [1:75; 5:81] [

8:45; 4:72] [3:48; 4:98] [

7:41; 1:59] [

7:05; 2:82] [

18:43; 15:85]

JPY/EUR

Short run [102:33; 123:80] [30:23; 132:09] [78:32; 121:12] [99:99; 113:19] [138:95; 178:40] [30:01; 147:48] [90:10; 137:21] [86:01; 101:70]

Long run [115:86; 137:80] [25:52; 136:33] [84:82; 129:72] [91:10; 105:26] [159:08; 198:13] [20:15; 146:71] [96:18; 144:85] [86:58; 102:72]

Di§erence [13:37; 14:59] [

4:75; 4:27] [6:14; 8:73] [

8:89; 7:87] [18:60; 21:43] [

9:86; 0:77] [4:46; 7:81] [0:53; 1:67]

Panel B: One standard deviation shock

USD/EUR

Short run [0:4906; 0:6497] [0:1545; 0:4421] [0:1276; 0:3793] [0:0551; 0:1454] [0:3895; 0:5839] [0:1739; 0:4644] [0:1823; 0:4469] [0:0481; 0:1498]

Long run [0:5365; 0:6791] [0:0681; 0:3832] [0:1847; 0:4143] [0:0327; 0:1321] [0:4641; 0:6489] [0:0746; 0:4164] [0:2747; 0:5157] [

0:0179; 0:1014]

Di§erence [0:0202; 0:0537] [

0:0868; 0:0580] [0:0214; 0:0609] [

0:0246; 0:0099] [0:0468; 0:0919] [

0:1008; 0:0460] [0:0432; 0:1029] [

0:0677; 0:0452]

JPY/USD

Short run [0:6273; 0:8020] [0:2540; 0:5984] [0:1395; 0:4108] [0:1244; 0:2043] [0:6484; 0:9038] [0:3427; 0:7866] [0:2224; 0:5924] [0:2306; 0:3453]

Long run [0:6793; 0:8384] [0:1877; 0:5574] [0:1799; 0:4315] [0:1053; 0:1898] [0:7261; 0:9619] [0:3080; 0:7540] [0:1923; 0:5424] [0:1690; 0:2751]

Di§erence [0:0347; 0:0547] [

0:0669; 0:0405] [0:0118; 0:0418] [

0:0203; 0:0116] [0:0581; 0:0777] [

0:0576; 0:0143] [

0:0633; 0:0272] [

0:0708; 0:0597]

JPY/EUR

Short run [0:4281; 0:5356] [0:0847; 0:3515] [0:3459; 0:5018] [0:1549; 0:1755] [0:5595; 0:7502] [0:1084; 0:5103] [0:5330; 0:7700] [0:2191; 0:2590]

Long run [0:4847; 0:5963] [0:0715; 0:3628] [0:3746; 0:5374] [0:1412; 0:1632] [0:6405; 0:8332] [0:0728; 0:5076] [0:5690; 0:8128] [0:2206; 0:2616]

Di§erence [0:0565; 0:0620] [

0:0133; 0:0114] [0:0250; 0:0362] [

0:0138; 0:0122] [0:0775; 0:0884] [

0:0356; 0:0027] [0:0244; 0:0466] [0:0014; 0:0043]

31
Table A4: Min-max variance decompositions from the VAR speciÖcation with all four human/computer-maker/taker order áow combinations. The

table shows the minimum and maximum triangular long-run variance decomposition of returns, expressed in percent and calculated at the 30 minutehorizon. That is, the table shows the proportion of the long-run variation in returns that can be attributed to shocks to the human-maker/human-takerorder áow (HH), computer-maker/human-taker order áow (CH), human-maker/computer-taker order áow (HC), and computer-maker/computertaker order áow (CC), denoted in obvious notation in the table headings. We show the actual variance decomposition, and the proportions of the

explained variance in returns that can be attributed to each order áow type. That is, we re-scale the variance decompositions so that they add up to100 percent. We present results for the full 2006-2007 sample and for the three-month sub-sample, which only uses data from September, October, andNovember of 2007. There are a total of 717; 120 minute-by-minute observations in the full two-year sample and 89; 280 observations in the three-monthsub-sample.

Full 2006-2007 sample 3-month sub-sample

H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/

H-taker H-taker C-taker C-taker H-taker H-taker C-taker C-taker

USD/EUR

Variance decomp. [16:15; 28:28] [1:64; 13:06] [1:16; 9:71] [0:21; 1:42] [10:90; 24:29] [2:22; 15:27] [2:50; 14:24] [0:23; 1:63]

Proportion [47:42; 83:05] [4:80; 38:36] [3:42; 28:51] [0:63; 4:17] [32:50; 72:41] [6:61; 45:52] [7:46; 42:43] [0:69; 4:85]

JPY/USD

Variance decomp. [15:61; 25:47] [2:57; 14:16] [0:85; 6:77] [0:61; 1:65] [12:00; 23:18] [3:34; 17:45] [1:51; 10:04] [1:52; 3:40]

Proportion [48:22; 78:70] [7:94; 43:74] [2:64; 20:92] [1:90; 5:10] [35:04; 67:65] [9:74; 50:94] [4:40; 29:29] [4:44; 9:91]

JPY/EUR

Variance decomp. [7:29; 11:38] [0:29; 4:89] [4:73; 9:95] [0:95; 1:22] [7:02; 12:58] [0:29; 5:83] [6:41; 13:31] [1:07; 1:50]

Proportion [37:25; 58:18] [1:49; 24:98] [24:19; 50:87] [4:87; 6:24] [30:40; 54:54] [1:26; 25:27] [27:79; 57:67] [4:66; 6:49]

32
References

[1] Albuquerque, R., and Miao, J., 2008, Advanced Information and Asset Prices, Working Paper Boston

University.

[2] Andersen, T.G., and T. Bollerslev, 1998, Deutsche Mark-Dollar Volatility: Intraday activity patterns,

macroeconomic announcements, and longer run dependencies, Journal of Finance 53, 219-265.

[3] Andersen, T.G., T. Bollerslev, F.X. Diebold, and H. Ebens, 2001. The distribution of realized stock

return volatility, Journal of Financial Economics 61, 43ñ76.

[4] Andersen, T.G., T. Bollerslev, F.X. Diebold, and P. Labys, 2003. Modeling and Forecasting Realized

Volatility, Econometrica 71, 579-625.

[5] Andersen, T.G., T. Bollerslev, F.X. Diebold, and C. Vega, 2003. Micro E§ects of Macro Announcements:

Real-Time Price Discovery in Foreign Exchange, American Economic Review 93, 38-62.

[6] Andersen, T.G., T. Bollerslev, F.X. Diebold, and C. Vega, 2007. Real-time price discovery in global

stock, bond, and foreign exchange markets, Journal of International Economics 73, 251-277.

[7] Angel, J., 1992, Limit versus Market Orders, working paper, Georgetown University.

[8] Bandi, F.M., and J.R. Russel, 2006. Separating microstructure noise from volatility, Journal of Financial

Economics 79, 655-692.

[9] Berger, D.W., A.P. Chaboud, S.V. Chernenko, E. Howorka, and J.H. Wright, 2008. Order Flow and

Exchange Rate Dynamics in Electronic Brokerage System Data, Journal of International Economics 75,

93-109.

[10] Biais, B., Hillion, P., and Spatt, C., 1995, An empirical analysis of the limit order book and the order

áow in the Paris Bourse, Journal of Finance, 50, 1655-1689.

[11] Bollerslev T., and J.H. Wright, 2000. Semiparametric estimation of long-memory volatility dependencies:

The role of high-frequency data, Journal of Econometrics 98, 81-106.

[12] BloomÖeld, R., M. OíHara and G. Saar, 2005, The ìMake or Takeî Decision in an Electronic Market:

Evidence on the Evolution of Liquidity, Journal of Financial Economics 75, 165ñ199.

[13] Chaboud, A., B. Chiquoine, E. Hjalmarsson, and M. Loretan, 2007. Frequency of Observation and

the Estimation of Integrated Volatility in Deep and Liquid Markets, Journal of Empirical Finance,

Forthcoming.

33
[14] Chakravarty, S., and C. Holden, 1995, An Integrated Model of Market and Limit Orders, Journal of

Financial Intermediation 4, 213ñ241.

[15] Easley, D., and OíHara, M., 1987, Price, trade size, and information in securities markets, Journal of

Financial Economics, 19, 69-90.

[16] Evans, M., and R. Lyons, 2002. Order Flow and Exchange Rate Dynamics, Journal of Political Economy

110, 170-180.

[17] Evans, M., and R. Lyons, 2008. How is Macro News Transmitted to Exchange Rates?, Journal of

Financial Economics 88, 26-50.

[18] Goettler, R., C. Parlour and U. Rajan, 2007, Microstructure e§ects and asset pricing, working paper,

UC Berkeley.

[19] Harris, L., 1998, Optimal Dynamic Order Submission Strategies in Some Stylized Trading Problems,

Financial Markets, Institutions & Instruments 7, 1ñ76.

[20] Hasbrouck, J., 1991a. Measuring the information content of stock trades, Journal of Finance 46, 179-207.

[21] Hasbrouck, J., 1991b. The summary informativeness of stock trades: An econometric analysis, Review

of Financial Studies 4, 571-595.

[22] Hasbrouck, J., 1996. Order characteristics and stock price evolution: An application to program trading,

Journal of Financial Economics 41, 129-149.

[23] He, H., and J. Wang, 1995, Di§erential information and dynamic behavior of stock trading volume,

Review of Financial Studies, 8, 919-972.

[24] Hendershott, T., C.M. Jones, and A.J. Menkveld, 2007. Does algorithmic trading improve liquidity?,

working paper, University of California, Berkeley.

[25] Kaniel, R., and H. Liu, 2006, So What Orders do Informed Traders Use?, Journal of Business 79,

1867ñ1913.

[26] Kumar, P., and D. Seppi, 1994, Limit and Market Orders with Optimizing Traders, working paper,

Carnegie Mellon University.

[27] Madhavan, A., Richardson, M., and Roomans, M., 1997, Why do security prices change? A transactionlevel analysis of NYSE stocks, Review of Financial Studies, 10, 1035-1064.

34
[28] Milgrom, P., and Stokey, N., 1982, Information, trade and common knowledge, Journal of Economic

Theory, 26, 17-27.

[29] Parlour, Christine, and Seppi, Duane, 2008, Limit Order Markets: A Survey, Handbook of Financial

Intermediation & Banking, ed. A.W.A. Boot and A.V. Thakor, forthcoming.

[30] Pasquariello, Paolo, and Vega, Clara, 2007, Informed and Strategic Order Flow in the Bond Markets,

Review of Financial Studies, 20, 1975-2019.

[31] Stock, J.H., J.H. Wright, and M. Yogo, 2005. A Survey of Weak Instruments and Weak IdentiÖcation in

Generalized Method of Moments, Journal of Business and Economic Statistics, 20, 518-529.

[32] Stock, J.H., and M. Yogo, 2005. Testing for Weak Instruments in Linear IV Regression, in D.W.K.

Andrews and J.H. Stock, eds., IdentiÖcation and Inference for Econometric Models: Essays in Honor of

Thomas Rothenberg, Cambridge: Cambridge University Press, 80ñ108.

[33] Sto§man, Noah, 2007. Who Trades With Whom? Individuals, Institutions, and Returns. Unpublished

Manuscript

35
Table 1: Summary statistics for the one-minute return and order áow data. The mean and standard deviation,

as well as the Örst-order autocorrelation, , are shown for each variable and currency pair. The returns are

expressed in basis points and the order áows in millions of the base currency. The summary statistics are given

for both the full 2006-2007 sample, as well as for the three-month sub-sample, which only uses observations

from September, October, and November of 2007. The Örst two rows for each currency show the summary

statistics for returns and the total market-wide order áow. The following two rows give the results for the

order áows broken down into human-takers and computer-takers and the last four rows show the results for

the order áow decomposed into each maker-taker pair. There are a total of 717; 120 observations in the full

two-year sample and 89; 280 observations in the three-month sub sample. We show the statistical signiÖcance

of the Örst order autocorrelation. The ,

, and 

represent signiÖcance at the 1, 5, and 10 percent level,

respectively.

Full 2006-2007 Sample 3-month sub sample

Variable Mean Std. dev.  Mean Std. dev. 

USD/EUR

Returns 0:0030 1:2398 0:005 0:0080 1:2057 0:007

Total order áow (HH + CH + HC + CC) 0:0315 25:9455 0:150 0:0937 29:7065 0:174

H-taker (HH + CH) 0:0413 23:977 0:155 0:0796 26:8096 0:189

C-taker (HC + CC) 0:0099 9:9363 0:127 0:0140 12:8900 0:115

H-maker/H-taker (HH) 0:1425 19:9614 0:177 0:0327 21:9211 0:209

C-maker/H-taker (CH) 0:1012 8:8970 0:166 0:1123 10:7649 0:215

H-maker/C-taker (HC) 0:0123 8:9232 0:152 0:0483 11:5856 0:150

C-maker/C-taker (CC) 0:0222 2:7939 0:053 0:0623 3:9477 0:072

JPY/USD

Returns 0:0007 1:6038 0:010 0:0045 1:9110 0:007

Total order áow (HH + CH + HC + CC) 0:1061 20:0980 0:189 0:3439 23:6359 0:211

H-taker (HH + CH) 0:0853 19:1127 0:190 0:2088 22:0344 0:204

C-taker (HC + CC) 0:0209 8:3941 0:170 0:1351 11:5877 0:158

H-maker/H-taker (HH) 0:1037 15:9972 0:209 0:1203 17:4612 0:226

C-maker/H-taker (CH) 0:0184 6:9030 0:172 0:0885 9:1773 0:162

H-maker/C-taker (HC) 0:0198 7:5686 0:198 0:0901 10:1673 0:191

C-maker/C-taker (CC) 0:0011 2:4556 0:032 0:045 3:8751 0:026

JPY/EUR

Returns 0:0024 1:5976 0:053 0:0036 2:1398 0:017

Total order áow (HH + CH + HC + CC) 0:0648 7:0941 0:152 0:1574 8:5978 0:147

H-taker (HH + CH) 0:0497 5:7006 0:150 0:1216 6:2074 0:125

C-taker (HC + CC) 0:0151 4:8409 0:146 0:0358 6:7000 0:131

H-maker/H-taker (HH) 0:0172 4:4203 0:159 0:0600 4:3106 0:157

C-maker/H-taker (CH) 0:0325 2:8912 0:129 0:0616 3:7197 0:092

H-maker/C-taker (HC) 0:0095 4:5331 0:173 0:0264 6:0968 0:161

C-maker/C-taker (CC) 0:0056 1:5558 0:023 0:0095 2:5621 0:001

36
Table 2: Summary statistics for the fractions of trade volume attributable to di§erent trader combinations.

The table shows the fraction of the total volume of trade that is attributable to di§erent combinations of

makers and takers. Results for the full 2006-2007 sample as well as for the three-month sub-sample, which

only uses data from September, October, and November of 2007, are shown. We show the average of the

daily fractions, calculated by summing up across all minutes within a day, and the standard deviations of

those daily fractions. For each currency, the Örst row shows the fraction of the total volume of trade where

a computer was involved on at least one side of the trade (i.e. as a maker or a taker). The second row shows

the fraction of the total volume where a human acted as a taker, the third row shows the fraction of the total

volume where a computer acted as a taker, and the following four rows shows the fractions broken down by

each maker-taker pair.

Full 2006-2007 Sample 3-month sub sample

Variable Mean Std. dev. Mean Std. dev.

USD/EUR

C-participation (V ol (CH + HC + CC)) 0:4163 0:1135 0:5386 0:0355

H-taker (V ol (CH + HH)) 0:7810 0:0791 0:6864 0:0331

C-taker (V ol (HC + CC)) 0:2190 0:0791 0:3136 0:0331

H-maker/H-taker (V ol (HH)) 0:5837 0:1135 0:4614 0:0355

C-maker/H-taker (V ol (CH)) 0:1973 0:0398 0:2251 0:0144

H-maker/C-taker (V ol (HC)) 0:1710 0:0514 0:2304 0:0205

C-maker/C-taker (V ol (CC)) 0:0480 0:0290 0:0831 0:0150

JPY/USD

C-participation (V ol (CH + HC + CC)) 0:4242 0:1065 0:5652 0:0364

H-taker (V ol (CH + HH)) 0:7585 0:0805 0:6461 0:0311

C-taker (V ol (HC + CC)) 0:2415 0:0805 0:3539 0:0311

H-maker/H-taker (V ol (HH)) 0:5758 0:1065 0:4348 0:0364

C-maker/H-taker (V ol (CH)) 0:1827 0:0304 0:2114 0:0126

H-maker/C-taker (V ol (HC)) 0:1860 0:0498 0:2486 0:0154

C-maker/C-taker (V ol (CC)) 0:0555 0:0321 0:1052 0:0193

JPY/EUR

C-involved (V ol (CH + HC + CC)) 0:6186 0:1154 0:7907 0:0410

H-taker (V ol (CH + HH)) 0:5557 0:1018 0:4037 0:0467

C-taker (V ol (HC + CC)) 0:4443 0:1018 0:5963 0:0467

H-maker/H-taker (V ol (HH)) 0:3814 0:1154 0:2093 0:0410

C-maker/H-taker (V ol (CH)) 0:1743 0:0360 0:1944 0:0164

H-maker/C-taker (V ol (HC)) 0:3337 0:0473 0:3734 0:0193

C-maker/C-taker (V ol (CC)) 0:1106 0:0673 0:2229 0:0464

37
Table 3: Estimates of the ratio R = RC=RH. The table reports the mean estimates of the ratio R = RC=RH,

where RC = V ol(HC)=V ol(CC) and RH = V ol(HH)=V ol(CH). V ol(HH) is the daily trading volume

between human-makers and human-takers, V ol(HC) is the daily trading volume between human-makers and

computer-takers, V ol(CH) is the daily trading volume between computer-makers and human-takers, and

V ol(CC) is the daily trading volume between computer-makers and computer-takers. We report the mean

of the daily ratio R and the standard errors are shown in parantheses below the estimate. We also show the

number of days that had a ratio that was less than one. We report the results for the full 2006-2007 sample

and the three-month sub-sample, which only uses data from September, October, and November of 2007.

The ,

, and 

represent a statistically signiÖcant deviation from one at the 1, 5, and 10 percent level,

respectively.

Full 2006-2007 sample 3-month sub sample

USD/EUR

Mean of daily R = RC/ RH 1:4463 1:3721

Standard Error (0:0063) (0:0099)

No. of days with R < 1 0 0

No. of obs 498 62

JPY/USD

Mean of daily R = RC/ RH 1:2619 1:1719

Standard Error (0:0074) (0:0142)

No. of days with R < 1 15 4

No. of obs 498 62

JPY/EUR

Mean of daily R = RC/ RH 1:6886 1:6242

Standard Error (0:0154) (0:0250)

No. of days with R < 1 4 0

No. of obs 498 62

38
Table 4: Regressions of realized volatility on the fraction of algorithmic trading. The table shows the results

from estimating the relationship between daily realized volatility and the fraction of algorithmic trading,

using daily data from 2006 and 2007. Robust standard errors are given in parentheses below the coe¢ cient

estimates. The left hand side of the table shows the results with a quarterly time trend included in the

regressions and the right hand side of the table shows the results with year-quarter time dummies (i.e., eight

time dummies, one for each quarter in the two years of data) included in the regressions. Panels A and

B show the results when the fraction of algorithmic trading is measured as the fraction of the total trade

volume that has a computer involved on at least one side of the trade (i.e. as a maker or a taker). Panels C

and D show the results when only the fraction of volume with computer taking is used. In addition to the

fraction of algorithmic trading and the control(s) for secular trends, 22 lags of volatility are also included

in every speciÖcation. In all cases, only the coe¢ cient on the fraction of algorithmic trading is displayed.

Panels A and C show the results from a standard OLS estimation, along with the R2. Panels B and D show

the results from the IV speciÖcation estimated with Limited Information Maximum Likelihood (LIML). In

Panels B and D, the Stock and Yogo (2005) Ftest of weak instruments are also shown. The critical values

for Stock and Yogoís (2005) F-test are designed such that they indicate a maximal actual size for a nominal

sized Öve percent test on the coe¢ cient in the LIML estimation. Thus, in order for the actual size of the

LIML test to be no greater than 10% (15%), the F-statistic should exceed 8:68 (5:33). There are a total of

498 daily observations in the data. The ,

, and 

represent signiÖcance at the 1, 5, and 10 percent level,

respectively.

With quarterly time trend With year-quarter time dummies

USD/EUR JPY/USD JPY/EUR USD/EUR JPY/USD JPY/EUR

Panel A. Fraction of volume with any computer participation, OLS estimation

Coe§. on AT 0:0029 0:0018 0:0034 0:0078 0:0030 0:0065

(0:0024) (0:0021) (0:0012) (0:0027) (0:0024) (0:0016)

R

2

(%) 53:44% 61:13% 71:90% 56:73% 62:57% 73:33%

Panel B. Fraction of volume with any computer participation, IV estimation

Coe§. on AT 0:0121 0:0186 0:0022 0:0078 0:0101 0:0128

(0:0062) (0:0089) (0:0039) (0:0061) (0:0069) (0:0175)

F-Stat 29:58 19:46 32:18 38:17 20:89 2:25

Panel C. Fraction of volume with computer-taking, OLS estimation

Coe§. on AT 0:0037 0:0027 0:0015 0:0094 0:0034 0:0032

(0:0036) (0:0024) (0:0012) (0:0038) (0:0027) (0:0016)

R

2

(%) 53:39% 61:17% 71:56% 56:43% 62:55% 72:66%

Panel D. Fraction of volume with computer-taking, IV estimation

Coe§. on AT 0:0160 0:0215 0:0007 0:0072 0:0122 0:0182

(0:0080) (0:0109) (0:0028) (0:0070) (0:0082) (0:0291)

F-Stat 39:99 17:63 64:81 55:45 21:20 1:04

39
Table 5: We report the mean ratio of the exchange rate volatility (Panel A) and liquidity provision by

humans and by computers (Panel B) estimated during announcement days relative to that estimated during

non-announcement days. The one-hour measure is estimated using observations from 8:25 am to 9:24 am ET

and the one-minute measure is estimated using 8:30 am to 8:31 am ET observations. Announcement days are

deÖned as nonfarm payroll announcement days and non-announcement days are deÖned as 10 business days

before and after the nonfarm payroll announcement. In each panel, we report the chi-squared and p-value

of the Wald test that the ratio is equal to 1. In Panel C we report the chi-squared and p-value of the Wald

test that the liquidity provision of humans during announcement days relative to non-announcement days

is similar to the liquidity provision of computers. The statistics are estimated using data in the full sample

from 2006 to 2007 and there are 23 observations (April 6, 2007 nonfarm payroll announcement is missing

because it falls on Good Friday, when trading in the foreign exchange market is limited). Human liquidity

provision, LH, is deÖned as the sum of human-maker/human-taker volume plus human-maker/human-taker

volume divided by total volume. Computer liquidity provision, LC, is deÖned as the sum of computermaker/computer-taker volume plus computer-maker/human-taker volume divided by total volume. The 

,

, and 

represent signiÖcance at the 1, 5, and 10 percent level, respectively.

USD/EUR JPY/USD JPY/EUR

Hour Minute Hour Minute Hour Minute

Panel A: Volatility

a

n

6:236 21:704 5:595 24:812 3:697 14:403



2

(H0 : a = n) 69:86 18:76 33:34 15:45 19:37 5:96

p-value 0:0000 0:0003 0:0000 0:0008 0:0002 0:0235

Panel B: Liquidity Provision

Liquidity provision by humans, LHa

LHn

0:964 1:062 1:023 1:183 0:888 0:980

Liquidity provision by computers, LCa

LCn

1:132 0:871 0:974 0:652 1:227 1:151



2

(H0 : LHa = LHnorLCa = LCn) 16:56 9:04 2:71 31:91 25:19 0:5

p-value 0:0005 0:0067 0:1143 0 0:0001 0:487

Panel C: Comparison of Liquidity Provision between Humans and Computers

LHa

LHn



LCa

LCn

0:168 0:191 0:049 0:532 0:339 0:171



2



H0 :

LHa

LHn

=

LCa

LCn



19:24 5:91 1:50 36:07 25:21 0:66

p-value 0:0003 0:0241 0:2339 0:0000 0:0001 0:4245

40
Table 6: Impulse responses from the VAR speciÖcation with human-taker and computer-taker order áow.

The table shows the impulse responses for returns as a result of shocks to the human-taker order áow

(HH + CH) or computer-taker (CC + HC) order áow, denoted H-taker and C-taker in the table headings,

respectively. The results are based on estimation of equation (2), using minute-by-minute data. In Panel A

we show the return response, in basis points, to a one-billion base-currency shock to one of the order áows.

In Panel B we show the return response, in basis points, to a one standard deviation shock to one of the order

áows. We show the results for the full 2006-2007 sample and for the three-month sub-sample, which only

uses data from September, October, and November of 2007. For each currency pair we show the short-run

(immediate) response of returns; the corresponding cumulative long-run response of returns, calculated as

the cumulative impact of the shock after 30 minutes; and the di§erence between the cumulative long-run

response in returns minus the immediate response of returns, i.e., we provide the extent of over-reaction or

under-reaction to an order áow shock. There are a total of 717; 120 minute-by-minute observations in the

full two-year sample and 89; 280 observations in the three-month sub-sample. We show in parenthesis the

standard errors of the di§erence between the short-run and long-run response. These standard errors are

calculated by bootstrapping, using 200 repetitions.

Full 2006-2007 sample 3-month sub-sample

H-taker C-taker H-taker C-taker

Panel A: One billion base-currency shock

USD/EUR

Short run 28:06 26:94 23:20 25:22

Long run 27:87 32:35 24:16 31:38

Di§erence 0:20 5:42 0:96 6:16

(0:29) (0:67) (0:72) (1:36)

JPY/USD

Short run 46:77 39:81 48:02 44:89

Long run 47:50 44:27 49:54 40:63

Di§erence 0:74 4:46 1:52 4:26

(0:48) (1:08) (1:36) (2:35)

JPY/EUR

Short run 99:32 102:71 124:02 115:52

Long run 108:07 109:85 132:53 123:26

Di§erence 8:75 7:14 8:51 7:74

(1:50) (1:67) (4:79) (4:76)

Panel B: One standard deviation shock

USD/EUR

Short run 0:6617 0:2639 0:6045 0:3181

Long run 0:6570 0:3170 0:6296 0:3957

Di§erence 0:0046 0:0531 0:0251 0:0777

(0:0068) (0:0065) (0:0189) (0:0172)

JPY/USD

Short run 0:8706 0:3269 1:0241 0:5098

Long run 0:8843 0:3635 1:0565 0:4614

Di§erence 0:0137 0:0366 0:0324 0:0483

(0:0090) (0:0089) (0:0289) (0:0267)

JPY/EUR

Short run 0:5572 0:4901 0:7587 0:7636

Long run 0:6063 0:5242 0:8108 0:8148

Di§erence 0:0491 0:0341 0:0520 0:0512

(0:0085) (0:0080) (0:0294) (0:0314)

41
Table 7: Variance decompositions from the VAR speciÖcation with human-taker and computer-taker order

áow. The table provides the long-run variance decomposition of returns, expressed in percent and calculated

at the 30 minute horizon, based on estimation of equation (2), using minute-by-minute data. That is, the

table shows the proportion of the long-run variation in returns that can be attributed to shocks to the humantaker order áow (HH + CH) and the computer-taker (CC + HC) order áow, denoted H-taker and C-taker

in the table headings, respectively. For each currency pair we show the actual variance decomposition, and

the proportion of the explained variance in returns that can be attributed to each order áow type. That

is, we re-scale the variance decompositions so that they add up to 100 percent. We show results for the

full 2006-2007 sample and for the three-month sub-sample, which only uses data from September, October,

and November of 2007. There are a total of 717; 120 minute-by-minute observations in the full two-year

sample and 89; 280 observations in the three-month sub-sample. We show in parenthesis the standard errors

calculated by bootstrapping, using 200 repetitions.

Full 2006-2007 sample 3-month sub-sample

H-taker C-taker H-taker C-taker

USD/EUR

Variance decomposition 29:27 4:74 25:92 7:25

(0:95) (0:19) (0:79) (0:42)

Proportion of explained share 86:06 13:94 78:14 21:86

(2:79) (0:56) (2:38) (1:27)

JPY/USD

Variance decomposition 29:31 4:22 28:59 7:22

(0:35) (0:11) (0:50) (0:33)

Proportion of explained share 87:41 12:59 79:84 20:16

(1:04) (0:33) (1:40) (0:92)

JPY/EUR

Variance decomposition 12:03 9:28 12:47 12:67

(0:21) (0:20) (0:38) (0:38)

Proportion of explained share 56:45 43:55 49:60 50:40

(0:99) (0:94) (1:51) (1:51)

42
Table 8: Impulse responses from the VAR speciÖcation with all four human/computer-maker/taker order áow combinations. The table shows the impulse responses for returns as a result of shocks to

the human-maker/human-taker order áow (HH), computer-maker/human-taker order áow (CH), humanmaker/computer-taker order áow (HC), or computer-maker/computer-taker order áow (CC), denoted in

obvious notation in the table headings. The results are based on estimation of equation (3), using minute-byminute data. In Panel A we show the return response, in basis points, to a one-billion base-currency shock to

one of the order áows. In Panel B we show the return response, in basis points, to a one standard deviation

shock to one of the order áows. We report the results for the full 2006-2007 sample and for the three-month

sub-sample, which only uses data from September, October, and November of 2007. For each currency pair

we show the short-run (immediate) response of returns; the corresponding cumulative long-run response of

returns, calculated as the cumulative impact of the shock after 30 minutes; and the di§erence between the

cumulative long-run response in returns minus the immediate response of returns, i.e., we provide the extent

of over-reaction or under-reaction to an order áow shock. There are a total of 717; 120 minute-by-minute

observations in the full two-year sample and 89; 280 observations in the three-month sub-sample. We show

in parenthesis the standard errors of the di§erence between the short-run and the long-run response. These

standard errors are calculated by bootstrapping, using 200 repetitions.

Full 2006-2007 sample 3-month sub-sample

H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/

H-taker H-taker C-taker C-taker H-taker H-taker C-taker C-taker

Panel A: One billion base-currency shock

USD/EUR

Short run 27:64 29:66 26:57 32:19 20:58 30:94 28:94 21:74

Long run 30:13 20:47 29:89 24:92 24:18 23:35 34:64 5:94

Di§erence 2:49 9:19 3:32 7:26 3:60 7:59 5:70 15:80

(0:35) (0:88) (0:83) (2:42) (0:97) (2:03) (1:79) (4:64)

JPY/USD

Short run 43:48 58:94 40:34 61:57 41:96 64:63 46:08 67:65

Long run 47:01 49:53 42:61 54:37 46:83 57:24 40:33 51:81

Di§erence 3:53 9:41 2:27 7:20 4:87 7:39 5:75 15:85

(0:59) (1:57) (1:30) (3:38) (1:62) (3:43) (2:89) (7:39)

JPY/EUR

Short run 102:61 92:16 100:91 102:04 139:33 103:92 114:01 94:47

Long run 116:12 91:24 107:18 93:41 159:46 96:85 118:47 95:20

Di§erence 13:51 0:92 6:27 8:63 20:13 7:07 4:46 0:74

(1:98) (3:18) (1:94) (4:98) (7:25) (9:35) (5:78) (10:70)

Panel B: One standard deviation shock

USD/EUR

Short run 0:5389 0:2575 0:2318 0:0893 0:4342 0:3211 0:3228 0:0845

Long run 0:5875 0:1777 0:2608 0:0692 0:5101 0:2424 0:3864 0:0231

Di§erence 0:0486 0:0798 0:0290 0:0202 0:0760 0:0788 0:0636 0:0614

(0:0069) (0:0076) (0:0072) (0:0067) (0:0203) (0:0211) (0:0200) (0:0180)

JPY/USD

Short run 0:6721 0:3968 0:2962 0:1506 0:7019 0:5801 0:4544 0:2607

Long run 0:7267 0:3334 0:3129 0:1330 0:7834 0:5137 0:3976 0:1997

Di§erence 0:0546 0:0634 0:0167 0:0176 0:0815 0:0663 0:0567 0:0611

(0:0091) (0:0106) (0:0096) (0:0083) (0:0274) (0:0307) (0:0284) (0:0284)

JPY/EUR

Short run 0:4440 0:2629 0:4481 0:1583 0:5859 0:3829 0:6809 0:2409

Long run 0:5024 0:2603 0:4760 0:1449 0:6706 0:3568 0:7076 0:2428

Di§erence 0:0584 0:0026 0:0279 0:0134 0:0847 0:0260 0:0266 0:0019

(0:0086) (0:0091) (0:0086) (0:0077) (0:0306) (0:0344) (0:0345) (0:0273)

43
Table 9: Variance decompositions from the VAR speciÖcation with all four human/computer-maker/taker

order áow combinations. The table provides the long-run variance decomposition of returns, expressed

in percent and calculated at the 30 minute horizon, based on estimation of equation (3), using minuteby-minute data. That is, the table shows the proportion of the long-run variation in returns that can be

attributed to shocks to the human-maker/human-taker order áow (HH), computer-maker/human-taker order

áow (CH), human-maker/computer-taker order áow (HC), and computer-maker/computer-taker order áow

(CC), denoted in obvious notation in the table headings. We show the actual variance decomposition, and

the proportions of the explained variance in returns that can be attributed to each order áow type. That is,

we re-scale the variance decompositions so that they add up to 100 percent. We present results for the full

2006-2007 sample and for the three-month sub-sample, which only uses data from September, October, and

November of 2007. There are a total of 717; 120 minute-by-minute observations in the full two-year sample

and 89; 280 observations in the three-month sub-sample. We show in parenthesis the standard errors, which

are calculated by bootstrapping, using 200 repetitions.

Full 2006-2007 sample 3-month sub-sample

H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/ H-maker/ C-maker/

H-taker H-taker C-taker C-taker H-taker H-taker C-taker C-taker

USD/EUR

Variance decomp. 20:71 4:73 3:89 0:58 14:19 7:68 7:86 0:59

(0:89) (0:24) (0:21) (0:04) (0:75) (0:48) (0:43) (0:09)

Proportion 69:24 15:81 13:01 1:94 46:80 25:33 25:92 1:95

(2:98) (0:80) (0:70) (0:13) (2:47) (1:58) (1:42) (0:30)

JPY/USD

Variance decomp. 18:62 6:48 3:70 0:93 14:47 9:78 6:12 2:00

(0:33) (0:15) (0:11) (0:04) (0:46) (0:41) (0:31) (0:13)

Proportion 62:63 21:80 12:45 3:13 44:70 30:21 18:91 6:18

(1:11) (0:50) (0:37) (0:13) (1:42) (1:27) (0:96) (0:40)

JPY/EUR

Variance decomp. 7:84 2:74 7:94 0:99 7:72 3:32 10:47 1:30

(0:16) (0:12) (0:19) (0:06) (0:29) (0:20) (0:42) (0:11)

Proportion 40:18 14:04 40:70 5:07 33:84 14:56 45:90 5:70

(0:82) (0:61) (0:97) (0:31) (1:27) (0:88) (1:84) (0:48)

44
Figure 1: 50-day moving averages of participation rates of algorithmic traders 

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

0

20

40

60

80

100

Participation (Percent)

USD/EUR JPY/USD JPY/EUR
Figure 2: 50-day moving averages of participation rates broken down into four 

maker-taker pairs

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

0

20

40

60

80

100

JPY/EUR

Participation (Percent)

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

0

20

40

60

80

100

USD/EUR

Participation (Percent)

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

0

20

40

60

80

100

JPY/USD

Participation (Percent)

H-Maker/H-Taker

H-Maker/C-Taker

C-Maker/H-Taker

C-Maker/C-Taker
Figure 3: Dollar-Yen Market on August 16, 2007 

6

PM

12

AM

6

AM

12

PM

6

PM

12

AM

6

AM

12

PM

-2500

-2000

-1500

-1000

-500

0

500

1000

1500

111

112

113

114

115

116

117

$ Millions Yen/$

Computer-Taker Order Flow

Order Flow

Dollar-Yen

6

PM

12

AM

6

AM

12

PM

6

PM

12

AM

6

AM

12

PM

-2500

-2000

-1500

-1000

-500

0

500

1000

1500

111

112

113

114

115

116

117

$ Millions Yen/$

Human-Taker Order Flow

Order Flow

Dollar-Yen
Figure 4: Volatility and Algorithmic Market Participation 

*

Daily realized volatility is based on 1-minute returns. We show monthly observations **Share of algorithmic trading is at a monthly frequency

0

3

6

9

12

15

18

0

10

20

30

40

50

60

70

80

90

2006 2007

Percent Percent USD/EUR

Share of Algorithmic Trading*

Realized Volatility**

0

3

6

9

12

15

18

0

10

20

30

40

50

60

70

80

90

2006 2007

Percent Percent JPY/USD

0

3

6

9

12

15

18

0

10

20

30

40

50

60

70

80

90

2006 2007

Percent Percent JPY/EUR
Figure 5: Deciles of Realized Volatility and AT Participation 

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

4%

6%

8%

10%

12%

14%

16%

18%

1 2 3 4 5 6 7 8 9 10

DECILES OF REALIZED VOLATILITY

USD/EUR

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

4%

6%

8%

10%

12%

14%

16%

18%

1 2 3 4 5 6 7 8 9 10

DECILES OF NORMALIZED AT PARTICIPATION

USD/EUR

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

4%

6%

8%

10%

12%

14%

16%

18%

1 2 3 4 5 6 7 8 9 10

DECILES OF REALIZED VOLATILITY

JPY/USD

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

4%

6%

8%

10%

12%

14%

16%

18%

1 2 3 4 5 6 7 8 9 10

DECILES OF NORMALIZED AT PARTICIPATION

JPY/USD

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

4%

6%

8%

10%

12%

14%

16%

18%

1 2 3 4 5 6 7 8 9 10

DECILES OF REALIZED VOLATILITY

JPY/EUR

annualized volatility normalized AT participation

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

4%

6%

8%

10%

12%

14%

16%

18%

1 2 3 4 5 6 7 8 9 10

DECILES OF NORMALIZED AT PARTICIPATION

JPY/EUR

normalized AT participation annualized volatility
TAB 8
High Frequency Traders and Asset Prices ∗

Jakˇsa Cvitani ́c†

Andrei Kirilenko‡

March 11, 2010

Abstract

Do high frequency traders affect transaction prices? In this paper we derive distributions of transaction prices in limit order markets populated by low frequency traders

(humans) before and after the entrance of a high frequency trader (machine). We find

that the presence of a machine is likely to change the average transaction price, even in

the absence of new information. We also find that in a market with a high frequency

trader, the distribution of transaction prices has more mass around the center and

thinner far tails. With a machine, mean intertrade duration decreases in proportion to

the increase in the ratio of the human order arrival rates with and without the presence

of the machine; trading volume goes up by the same rate. We show that the machine

makes positive expected profits by “sniping” out human orders somewhat away from

the front of the book. This explains the shape of the transaction price density. In fact,

we show that in a special case, the faster humans submit and vary their orders, the

more profits the machine makes.

Keywords: high-frequency trading, electronic trading, asset prices, limit orders

JEL Classification: D4, G1

∗The research of J. Cvitani ́c was supported in part by NSF grant DMS 06-31298. We thank Doug Adams

for useful comments on the paper. The views presented here are our own and do not represent the views of

the Commodity Futures Trading Commission, its Commissioners or staff. All errors are our own.

†Caltech, Humanities and Social Sciences, M/C 228-77, 1200 E. California Blvd. Pasadena, CA 91125.

Ph: (626) 395-1784. E-mail: cvitanic@hss.caltech.edu.

‡Commodity Futures Trading Commission, 1155 21st Street, NW, Washington, DC 20581. Ph: (202)

418-5587. E-mail: akirilenko@cftc.gov.

1
1 Introduction

High frequency trading typically refers to trading activity that employs extremely fast automated programs for generating, routing, canceling, and executing orders in electronic

markets. High frequency traders submit and cancel a massive number of orders and execute

a large number of trades, trade in and out of positions very quickly, and finish each trading

day without a significant open position. High frequency trading is estimated to account for

at least half of the trading volume on equity and derivatives exchanges.

High frequency traders are very fast, but what valuable service do they provide to the

markets? Do they make prices more informative? Do they increase market liquidity? How

do they make money?

In this paper we study the distribution of transaction prices generated in an electronic

limit order market populated by orders from high frequency traders (machines) and low

frequency traders (humans). We focus on the period between two human transactions - a

very short period of time in a liquid market. We posit that during such a short horizon,

the impact of changes in the fundamentals is negligible. Therefore, we model the incoming

human buy order prices and sell order prices during the period as two iid sequences, arriving

according to exogenous Poisson processes. For tractability, we assume that the submitted

orders are of unit size and at infinitely divisible prices.1 We justify this simplification as

being appropriate for inter-trade intervals of relative homogeneity, in which the demand of

all the traders on the buy side is approximately the same, and also close to the quantity

that the individual traders on the sell side are willing to supply in a single trade.

Machines are assumed to be strategic uninformed liquidity providers. They have only

one advantage over the humans - the speed with which they can submit or cancel their

orders. Because of this advantage, machines dominate the trading within each period by

undercutting slow humans at the front of the book. This is only one of the strategies used

by actual high-frequency traders in real markets, and the only one we focus on.2In the

language of the industry, machines aim to “pick-off” or “snipe out” incoming human orders.

However, we assume that machines do not carry their submitted orders across time, for the

fear of being picked off themselves. Thus, we assume that machines submit deterministic

orders that get immediately canceled if not executed, and then get resubmitted again. With

these actions, they shape the front of the limit order book.

When we model the optimization of the machine during the intra-trade period, we assume

that it knows the process that governs the arrivals of human orders, the distribution of

incoming human limit orders, and the values of existing orders in the book. In reality, the

machine needs to estimate these quantities by “pinging the book” - sending quick trial orders

and canceling them immediately. We do not model the estimation procedure, but assume it

has been done before the beginning of the interval.

Our findings are as follows. First, we derive formulas for the distributions of transaction

1

In the actual limit order book environment, traders submit orders of different quantities at discrete price

intervals - ticks. At each tick, quantities get stacked up in accordance with a priority rule, e.g., time priority

or order size and then time priority. Our idealized model, with the order prices coming from a continuous

distribution and for one order only, can be thought of as taking the actual orders for multiple units stacked

up at each tick and “spreading” them between ticks.

2Other known high frequency trading strategies include (i) the collection of rebates offered by exchanges

for liquidity provision, (ii) cross-market arbitrage, and (iii) “spoofing” - triggering other traders to act.

2
prices and transaction times for a given intra-trade period both with and without the machines. We find that the presence of a machine is likely to change the average transaction

price, even in the absence of new information. We also find that in the presence of a machine,

the shape of the transaction price density remains the same in the middle, between the bid

and the ask of the machine, the far tails of the density get thinner, while the parts of the

tails closer to the bid and the ask of the machine get fatter. In the presence of the machine,

mean intertrade duration decreases in proportion to the increase in the ratio of the human

order arrival rates with and without the presence of the machine. Trading volume goes up

by the same rate. In other words, if the humans submit orders ten times faster when the

machine is present, intertrade duration falls and trading volume increases by a factor of ten.

Second, we compute the optimal bid and ask prices for the machine that optimizes

expected profits subject to an inventory constraint. The inventory constraint prevents the

machine from carrying a significant open position to the next intra-human-trade period.

The optimal bid and the ask for the machine are close to being symmetric around the mean

value of the human orders, with the distance from the middle value being determined by

the inventory constraint–the less concerned the machine is about the size of the remaining

inventory, the closer its bid and the ask prices are to each other. The expected profit of an

optimizing machine is increasing in both the variance and the arrival frequency of human

orders.

Our two findings are interrelated; one the one hand, an optimizing machine is able to

make positive expected profits by “sniping” out human orders somewhat away from the

front of the book. On the other hand, execution of the “sniping” order submission strategy

results in a transaction price density with bulges near the front and thinner outer tails. In

fact, in a special case, the faster humans submit and vary their orders, the more profits the

machine makes.

Our model has a number of limitations. First, it is not an equilibrium model of a limit

order market like those of Parlour (1998), Foucault (1999), Biais, Martimor and Rochet

(2000), Parlour and Seppi (2003), Foucault, Kadan and Kandel (2005), Goettler, Parlour,

and Rajan (2005), Back and Baruch (2007), and Biais and Weill (2009), among others.

These papers aim to derive the equilibrium price formation process. In order to cope with

the large dimensionality of the state and action spaces of limit order markets, these studies

use stylized models with many simplifying assumptions. In contrast to the equilibrium

considerations, we study the formation of transaction prices given the distribution of orders,

which we take to be exogenous over very short periods of time.

Second, our model is not a dynamic expected utility maximization model like those of

Avellaneda and Stoikov (2008), Kuhn and Stroh (2009), and Rosu (2009). Those studies

assume specific functional forms that govern the traders’ preferences. We take the approach

of modeling the order submission process over a very short period of time without specifying

the traders’ preferences or their optimization problems. Our model is essentially a stationary

sequence of one-period models, and all that matters is what happens during the interval

between two trades.

While these limitations make our results less satisfactory from the equilibrium analysis

point of view, our approach is more pragmatic. Our results work for any possible (continuous) distribution of orders - equilibrium or otherwise. Thus, if a model comes up with the

description of an equilibrium order submission process, we can plug it into our analysis and

3
get the distributions of transaction prices and transaction times. Moreover, our results can

be easily applied to the transaction-level data. We make no assumptions about the (unobservable) objectives of traders; we only make assumptions about their order submission

processes.

Finally, to our knowledge, this is the first model to formally investigate the impact of

high frequency trading on transaction prices, trading volume, and intertrade duration, as

well as to characterize the profits of a high frequency trader in terms of the properties of

low frequency traders.3

Our paper proceeds as follows. Section 2 studies the benchmark model without machines.

Section 3 compares the benchmark model to the model in which an infinitely fast machine

is present, and solves the optimization problem of the machine. Section 4 presents some

empirical implcations of our results. Section 5 concludes.

2 Benchmark Model: Identical (Slow) Traders

2.1 A Single Intra-Trade Period

The model setup is as follows. There are infinitely many (slow) traders who submit limit

orders into an electronic limit order book with the intent to buy or sell a single asset.

We make the following simplifying assumption:

Assumption 2.1 Each order is for one unit of the traded asset only.

We focus on a single intra-trade period, during which new buy and sell orders arrive into

the limit order book, where t = 0 represents the beginning of the period.

Buy order prices are assumed to be represented by a sequence of random variables Btn,

where tn are Poisson arrival times of the buy orders, with intensity γB. Similarly, Ssm

represent sell order prices, and they arrive with intensity γS. We denote by μB, μS the

maximum buy order price and the minimum sell order price, respectively, among those that

are already resting in the book at the beginning of the interval, i.e, at t = 0.

The orders go out of the book either if they are executed or if they are canceled.4 We

denote by MB

t

the maximum of existing buy order prices and by mS

t

the minimum of existing

sell order prices at time t ≥ 0. Orders at time t > 0 consist of the resting orders as of t = 0

and the newly arrived orders.

We define the execution time of the next trade as

τ := inf{t : MB

t ≥ mSt }

At execution time τ , the transaction price Pτ is set at the maximum buy price MB

τ

if the

trade was triggered by the sell order that came in at time τ ; otherwise, the transaction price

is set at the minimum sell price mS

τ

.

We introduce the following assumption, which presents a simple framework for studying

the randomness of the buy and sell orders:

3Cont, Stoikov, and Talreja (2008) present a stochastic stochastic model for the continuous-time dynamics

of a limit order book, but do not explicitly model high and low frequency traders.

4We will essentially assume away cancelations in what follows.

4
Assumption 2.2 (i) Incoming buy orders Btn are iid with distribution FB, conditionally

on the information available by time t = 0. Similarly, incoming sell orders, Stn, are iid with

distribution FS. (ii) FB and FS have densities, and the densities are strictly positive for all

x for which 0 < Fi(x) < 1, i = B, S. (iii) Sell orders are independent from the buy orders.

(iv) Within the intra-trade interval, the maximum buy order, MB, and the minimum sell

order, mS, do not get canceled.

We begin by computing the the distributions of bid and ask prices, and the distribution

of the intra-trade time. We have the following result.

Proposition 2.1 Under our standing assumptions, we have the following:

(i) The distribution of the minimum sell order price among those that arrived by time t

is given by

FmS

t

(x) = 1 − 1{x<μS}e

−tγSFS(x)

(ii) The distribution of the maximum buy order price among those that arrived by time

t is given by

FMB

t

(x) = 1{x≥μB}e

−tγB(1−FB(x))

(iii) Distribution of the time of trade is given by

P(τ > t) = P(MB

t < μS)P(mSt = μS) + Z μS

μB

P(MB

t ≤ x)dFmS

t

(x)

= e

−tγB(1−FB(μS))e−tγSFS(μS) +

Z μS

μB

e

−tγB(1−FB(x))tγSe−tγSFS(x)

dFS(x)

In particular, if the distributions of buy and sell order prices are the same, FB = FS = F,

and γB is different from γS, then the distribution of the time of trade is given by

P(τ > t) = e

−t(γB[1−F(μS)]+γSF(μS)) +

γS

γB − γS

e

−tγB



e

t(γB−γS)F(μS) − et(γB−γS)F(μB)



If, moreover, the new orders take values only inside the initial bid-ask spread, that is,

F(μB) = 0, F(μS) = 1, then the distribution is that of the sum of two independent exponentials:

P(τ > t) = γB

γB − γS

e

−tγS −

γS

γB − γS

e

−tγB

If FB = FS = F and γB = γS = γ, we get

P(τ > t) = e

−tγ(1 + tγ[F(μS) − F(μB)])

with the mean

E[τ ] = 1

γ

[1 + F(μS) − F(μB)]

This proposition gives us the full description of the distributions of bid and ask prices,

as well as that of the intra-trade time, as a functional of the distributions of buy and sell

orders and their frequency. Thus, it also gives us information about the volume in a given

interval of time. Interestingly, we see that in the symmetric case FB = FS = F the time

5
of trade distribution depends on F only through its values F(μB) and F(μS) evaluated at

the initial bid and ask. Moreover, if also the new orders take values only inside the initial

bid-ask spread, the expected time to trade is the sum of the expected buy and sell arrival

times. Otherwise, the latter sum is the upper bound for the expected time to trade.

To illustrate this and subsequent results, we denote the range of buy order prices in the

limit order book by [A, B], and the range of sell order prices by [C, D] where B and D can

be infinite. In order to exclude uninteresting cases, we assume that

A ≤ C ≤ B ≤ D , μB ∈ [A, B] , μS ∈ [C, D]

Corollary 2.1 Assume that FB is uniform on [A, B] and FS is uniform on [C, D], that

γB(D − C) 6= γS(B − A), and that the initial bid and ask can be ignored, that is, μB ≤ C,

μS ≥ B. Then,

P(τ > t) = e

−tγS

B−C

D−C +

γS

(D − C)[ γB

B−A −

γS

D−C

]

h

e

−tγS

B−C

D−C − e

−tγB

B−C

B−A

i

with the mean

E[τ ] = γB(D − C) + γS(B − A)

γSγB(B − C)

The above corollary presents the expression for the expected time to trade in a special

case when the distributions of buy and sell orders are assumed to be uniform. From the

corrolary, the expected time to trade, E[τ ], is large when B is close to C, so there is a small

overlap between the possible values of buy and sell orders. Moreover, the expected time

to trade is large if either the frequency for the arrival of buy orders or the frequency for

the arrival of sell orders (or both) is low. In contrast, the expected time to trade can get

shorter if the small overlap between the possible values for buy and sell orders can be made

up for by an increase in the buy or sell frequency or if low order arrival frequency can be

compensated by an increase in the buy-sell order overlap.

Next, we compute the distribution of transaction prices at a given time of trade, τ . We

first introduce the probability that, conditional on an order arriving, it was a buy order:

p :=

γB

γB + γS

Denote by A(τ ) the event that the order that just came in and triggered the transaction

was a sell order, and by Ac(τ ) the event that the transaction was triggered by an incoming

buy order. The transaction price is defined as

Pτ := MB

τ 1A(τ) + mSτ 1Ac(τ)

Proposition 2.2 Under our standing assumptions, we have the following:

(i) The distribution of the maximum buy order price at the time of trade is given by, for

x ∈ [μB, B],

P(MB

τ ≤ x) = p(1 − p)×

Z x∧μS

μB

FB(x) − FB(y)

[(1 − p)FS(y) + p(1 − FB(y))]2

dFS(y) + Z x∧μS

μB

FS(y)

[(1 − p)FS(y) + p(1 − FB(y))]2

dFB(y)



6
+[FB(x) − FB(μS)] p

p + (1 − p)FS(μS) − pFB(μS)]

+FS(μB)

1 − p

p + (1 − p)FS(μB) − pFB(μB)

(ii) The distribution of the minimum sell price at the time of trade is given by, for x ≥ C,

P(mS

τ ≤ x) = p(1 − p)×

Z x∧μS

μB

1 − FB(y)

[(1 − p)FS(y) + p(1 − FB(y))]2

dFS(y) + Z x∧μS

μB

FS(y ∧ x)

[(1 − p)FS(y) + p(1 − FB(y))]2

dFB(y)



+1{x>μS}[1 − FB(μS)] p

p + (1 − p)FS(μS) − pFB(μS)

+1{x>μB}FS(μB ∧ x)

1 − p

p + (1 − p)FS(μB) − pFB(μB)

(iii) The distribution of the transaction price is given by, for x ∈ [μB ∨ C, D],

P(Pτ ≤ x) = p(1 − p) ×

Z x∧μS

μB

FS(y)

[(1 − p)FS(y) + p(1 − FB(y))]2

dFB(y)

+FS(μB)

1 − p

p + (1 − p)FS(μB) − pFB(μB)

+p(1 − p) ×

Z x∧μS

μB

1 − FB(y)

[(1 − p)FS(y) + p(1 − FB(y))]2

dFS(y)

+1{x>μS}[1 − FB(μS)] p

p + (1 − p)FS(μS) − pFB(μS)

If FB = FS = F and p 6= 1/2, this becomes

P(Pτ ≤ x) = 1{x>μB}

p(1 − p)

1 − 2p



1

p + (1 − 2p)F(μB)

−

1

p + F(x ∧ μS)(1 − 2p)



+F(μB)

1 − p

p + (1 − 2p)F(μB)

+1{x>μS}[1 − F(μS)] p

p + (1 − 2p)F(μS)

If, in addition to FB = FS = F, we have p = 1/2, then we get

P(Pτ ≤ x) = 1{x>μB}F(x ∧ μS) + 1{x>μS}[1 − F(μS)]

that is, FP = F in the interval (μB, μS).

7
The four terms in the price distribution given in (iii) are due to the following: the first two

terms correspond to an incoming sell order being the new minimum and triggering the sale,

where the second, non-integral term corresponds to the states of the world in which none

of the buy orders that have arrived since the last trade is higher than the initial maximum

buy order μB (so that the transaction price equals μB); the last two terms correspond to an

incoming buy order being the new maximum and triggering the sale, where the very last,

non-integral term corresponds to the states of the world in which none of the sell orders that

have arrived since the last trade is lower than the initial minimum sell order μS (so that the

transaction price equals μS).

In the case FS = FB = F, denoting by f the density of F, the density of the transaction

price in the interval (μB, μS) is given by

fPτ

(x) = p(1 − p)

[p + F(x)(1 − 2p)]2

f(x)

The factor multiplying f(x) is increasing in F(x) for p > 0.5. That is, if the buy orders are

more likely, then the density f of order prices is distorted in favor of high transaction prices.

The opposite is true if the sell orders are more likely.

Similarly, for a fixed and small value of F(x), the factor multiplying f(x) is decreasing

in p – increasing p means less sell orders and more buy orders, so that the probability of the

transaction price being small becomes lower. The opposte is true for a fixed and high value

of F(x).

Corollary 2.2 Assume now that FB is uniform on [A, B] and FS is uniform on [C, D], that

γB(D − C) 6= γS(B − A), and that the initial bid and ask can be ignored, that is, μB ≤ C,

μS ≥ B. Then, we have, for x ∈ [C, B],

P(Pτ ≤ x) = p(1 − p)(B − C)

p(D − C) − (1 − p)(B − A)

×

"

1

p

B

B−A + (p − 1) CD−C + x



1−p

D−C −

p

B−A

 −

B − A

p(B − C)

#

with the density

fP (x) = p(1 − p)

(B − C)(B − A)(D − C)

{pB(D − C) + (p − 1)C[B − A] + x[(1 − p)(B − A) − p(D − C)]}

2

If, in addition, D − C = B − A, the expected value of the price is, in terms of the liquidity

variable z =

p

1−p =

γB

γS

,

E[Pτ ] = zB − C

z − 1

−

z

(z − 1)2

(B − C) log(z)

and the variance is

V ar[Pτ ] = z(B − C)

2

(z − 1)2



1 −

z

(z − 1)2

(log z)

2



8
We see that with a loss of liquidity on the buy side (as z → 0), the expected price tends

to its lowest possible value C, and with the loss of liquidity on the sell side the expected

price tends to its highest possible value B. In either case, the variance tends to zero. It

can also be verified that the expected price is an increasing concave function of z, while the

variance is a concave function of z with a maximum at z = 1.5

Remark 2.1 A digression on equilibrium order submission processes. We must

caution the reader that although results obtained under the assumptions of symmetric distributions of buy and sell orders, FB = FS = F, are elegant and tractable, such symmetric

order distributions may not arise in a full information equilibrium. In fact, we show in

the appendix that, if the buyers are all identical, if they believe that the sellers follow the

same distribution FS = FB for their orders, and if they are risk-neutral, then the necessary

condition for the buyers leads to the distribution of the form

FB(x) = cB(vB − x)

−2/3

where cB is a constant, and vB is the value the buyers assign to holding one unit of the asset.

6 However, under the analogous assumptions on the sellers, the necessary condition for the

sellers leads to the distribution of the form

FS(x) = 1 − cS(x − vS)

−2/3

Thus, the assumption of the form FB = FS = F is actually not tenable in such a full

information equilibrium.

2.2 Multiple Periods

In order to extend our model to a multi-period setting, we need to specify how the orders

change from one intra-trade period to another. We consider the case which will keep the

setup as stationary as possible. We assume that, conditional on the last transaction price,

P(k), buy order Bi(k + 1) in the next intra-trade period is given by

Bi(k + 1) = P(k) × Bi

where Bi are iid with distribution FB. In other words, the orders are equal to the previous

price randomly distorted by a multiplicative random factor (which means the log-order is

the previous log-price plus a random term). Set, without loss of generality, P(0) = 1 and

denote P(1) = P. The (conditional) distribution of the buy orders in the (k + 1)-st period

is

FB,k+1(x) = FB(x/P(k))

and similarly for sell orders. Assume now that the book is emptied after the previous trade.

It is then easily verified from Proposition 2.2 (iii) that in this model

FP(k+1)|P(k)(x) = FP (x/P(k)) (2.1)

5The variable z is one measure of liquidity - the difference between the arrival frequencies of buy and

sell orders. More broadly, liquidity reflects the ease with which an asset can be bought or sold without a

significant effect on its price. Thus, liquidity has a number of other dimensions that are not being captured

by z.

6For this to be a distribution function, x should take values less than a constant v < vB.

9
where FP(k+1)|P(k)

is the conditional distribution. In other words, we can write

P(k + 1) = P × P(k)

This means that the log-price is a random walk: it is obtained as a sum of iid random

variables each with the distribution of P = P(1). Thus, the distribution of the relative

return is

P(

P(k + 1)

P(k)

− 1) ≤ x) = FP (x + 1)

So, under the assumptions of this section, in order to study the qualitative properties of

the returns distribution, it suffices to study the price distribution.

Moreover, we also see that, denoting by τk the times of trade,

Fτk+1|P(k)(t) = Fτk

(t)

and thus the intra-trade distribution is stationary.

3 A Model With A Machine Trader

The setup of the model is the same as in the benchmark model with the addition of one

infinitely fast (from the point of view of other traders) high frequency trader. The high

frequency trader–the machine–is assumed to keep issuing the same buy order b and sell

order s, b < s, until a trade occurs. The orders b and s get immediately canceled if not

executed right away. This mimics the so-called “sniping” strategy – a strategy designed to

discover liquidity in the limit order book, or to “pick-off” orders already in the book.

We assume that the machine is so fast that it will always pick off a human sell order, Si,

before any other human trader whenever b ≥ Si, (machine buys for Si), unless Siis less than

the existing maximum buy order μB in the book, in which case the transaction is executed

at price μB. The assumption for the human buy orders, Bi, is similar.

Our objective is to compare a market with a machine to the one without it. Our comparison is non-strategic: in both setups, we maintain the same assumptions about the distributions and the arrival frequencies of the buy and sell orders. In a strategic setting, it is

quite possible that the presence of a machine would affect order human submission processes

and frequencies. However, we show that as in the benchmark case, the distribution of the

transaction prices depends on the arrival rates only through the ratio p = γB/(γB + γS).

Thus, if the arrival rates change by the same factor because of the machine presence, p will

not change, and there will be no effect on the price distribution. (On the other hand, there

might be effects from the changes in the orders distributions.)

We now proceed to examine the distributions of execution times and transaction prices.

Proposition 3.1 Assume μB < b < s < μS. The distribution of the time until next trade

is given by

P(τ > t) = P(MB

t < s)P(mSt ≥ s) + Z s

b

P(MB

t ≤ x)dFmS

t

(x)

10
= e

−tγSFS(s)

e

−tγB(1−FB(s)) +

Z s

b

tγSe

−tγSFS(x)

e

−tγB(1−FB(x))dFS(x)

In particular, if FB = FS = F and γB = γS = γ, we get

P(τ > t) = e

−tγ[1 + tγ(F(s) − F(b))]

with the mean equal to

E[τ ] = 1

γ

[1 + F(s) − F(b)]

As we can see from the last expression, when μB < b < s < μS, FB = FS = F and γB =

γS = γ, denoting by γ0 the arrival rates in the benchmark case with no machine, the ratio of

the mean time between transactions with and without the machine is γ

γ0

1+F(s)−F(b)

1+F(μS)−F(μB)

, which

is less than γ

γ0

, but not less than half thereof. If the order arrival rates with the machine and

without the machine are the same, i.e., γ = γ0, then the presence of the machine speeds up

the trades, but not more than by a factor of two, on average. By construction, the volume

goes up, but not more than double.

If, however, the ratio γ

γ0

, is large, then the presence of the machine will speed up the

trades (lower inter-trade duration) in proportion to this ratio. The volume will also increase

by the same proportion.

We now present the result for the uniform distribution.

Corollary 3.1 Assume μB < C < b < s < B < μS, that FB is uniform on [A, B] and FS

is uniform on [C, D], and that γB(D − C) 6= γS(B − A). Then, we have

P(τ > t) = e

−t[γB

B−s

B−A +γS

s−C

D−C

] +

γS

D − C

e

−t[

γBB

B−A −

γSC

D−C

]

γB

B−A −

γS

D−C

h

e

ts[

γB

B−A −

γS

D−C

] − etb[

γB

B−A −

γS

D−C

]

i

with the mean

E[τ ] = (B − A)(D − C)

γB(D − C) − γS(B − A)

×



γB(D − C)

γB(D − C)(B − s) + γS(B − A)(s − C)

−

γS(B − A)

γB(D − C)(B − b) + γS(B − A)(b − C)



In Figure 1 we plot the density of the intra-trade time with and without the machine

for the uniform distribution of orders. In the presence of the machine, this density is more

concentrated on low values.

11
 Time-to-trade density

0

1

2

3

4

5

6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

t

Density without HFT Density with HFT

Figure 1: Density of intratrade time with and without the high-frequency trader.

Figure 2 shows the mean intra-trade times with and without the machine, as the supports

of the buy and sell orders distributions have decreasing overlap. Average intertrade duration

increases with less overlap, but the increase is steeper without machine.

 Mean time-to-trade

0

0.2

0.4

0.6

0.8

1

1.2

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009

 Sell-orders mean minus buy-orders mean 

Mean time to trade without HFT Mean time to trade with HFT

Figure 2: Mean intratrade time as the sell-buy gap increases.

The following is the main technical result of this section, while its economics consequences

are given in the corollary below.

Proposition 3.2 Assume μB < b < s < μS. The distribution of the transaction price Pτ is

given by:

P(Pτ ≤ x) = p(1 − p)×

h Z x∧s

μB

FS(y)

[(1 − p)FS(y ∨ b) + p(1 − FB(y))]2

dFB(y)+Z x∧μS

b

1 − FB(y)

[(1 − p)FS(y) + p(1 − FB(y ∧ s))]2

dFS(y)

i

+1{x>μB}FS(μB)

1 − p

p + (1 − p)FS(b) − pFB(μB)

12
+1{x>μS}[1 − FB(μS)] p

p + (1 − p)FS(μS) − pFB(s)

+p × 1{x>s}

Z x∧μS

s

1

(1 − p)FS(y) + p(1 − FB(s))dFB(y)

+(1 − p) ×

Z x∧b

μB

1

(1 − p)FS(b) + p(1 − FB(y))dFS(y)

In particular, in case FB = FS = F, the price density on the interval (μB, μS) is given by

dFP (x) = dF(x)



1{μB<x<b}

(1 − p)[p + (1 − p)F(b)]

[(1 − p)F(b) + p(1 − F(x))]2

+ 1{b≤x<s}

p(1 − p)

[(1 − 2p)F(x) + p]

2

+1{s≤x<μS}

p[1 − p + p(1 − F(s))]

[(1 − p)F(x) + p(1 − F(s))]2



and, when in addition p = 1/2, on the interval (μB, μS) we have

dFP (x) = dF(x)



1{μB<x<b}

1 + F(b)

[F(b) + 1 − F(x)]2

+ 1{b≤x<s} + 1{s≤x≤μS}

2 − F(s)

[F(x) + 1 − F(s)]2



The following is the main economic result of this section, and it is obtained by direct

examination of the price distribution given in the previous proposition, and the analogous

result for the benchmark case of no machine. Here, we assume that the order distributions

FB, FS and the probability of a buy order p are the same in the markets without and with

the machine.

Corollary 3.2 (i) Inside the interval [b, s] the density of the transacted price remains the

same as in the benchmark case. The far tails are more narrow, that is, the probabilities of

the price being equal to μB and μS are lower, and, if μB is low enough, the density is lower

for x greater than but close to μB, and analogously for x close to μS. The values of the

density are higher at values less than but close to b and at values larger than but close to s.

(ii) For a fixed price value μB < x < b1 < b2, its density fP (x) is higher if the machine

uses lower bid b1 than if it uses higher bid b2, and analogously for s1 < s2 < x < μS, the

density is higher if the machine uses the higher ask s2.

(iii) Assume now FB = FS = F where F is symmetric, and p = 1/2. If b and s are

chosen symmetrically so that F(b) = 1 − F(s), and the same is true for μB and μS, then

the mean value of the transacted price is the same as the mean value of the incoming human

orders, hence the same as the mean of the transacted price when there is no machine.

The intuition behind (i) is the following. The density remains the same on the interval

(b, s) because the transaction price will take a value in that interval if and only if the

transaction was between two human traders. Outside of this interval, but close to it, the

density is higher relative to the benchmark case, as now the orders outside the interval [b, s]

get picked off by the machine. To compensate, the density has to go down in the far tails.

From (i) we see that the effect on the variance is complex – the thinning of the far tails

would reduce the variance, but the fattening of the nearer parts of the tails has the opposite

effect. Whether the variance goes up or down will depend on the actual values of b, s, μB,

13
μS, and on the distributions FB, FS. However, the higher even moments are likely to go

down, because of the thinning of the far tails. Furthermore, it can be verified that, if the

ratio fS(x)/fB(x) of the sell vs. buy order densities is bounded from above and away from

zero, then also bounded is the ratio of the density of the transaction price with machine

vs. that density without machine. Also, what we have just discussed is the variance of a

single transaction price. Let us recall that the time between transactions goes down in the

presence of the machine (at most by a factor of two). Thus, even if the variance of the single

transaction price goes up, the variance of the average transaction price per unit time may

go down.

The first part of item (ii) holds because there is higher density for values between b1 and

b2 if the machine uses b2, as it picks off those values, too. Thus, to compensate for this,

the density has to go down for values of x below b1 (the machine picks off fewer of those).

Similarly on the ask side.

Item (iii) gives conditions under which the mean price does not change. Perhaps more

interestingly, if these conditions are not satisfied, the mean price is likely to change, in

general. Thus, the presence of the sniping machine is likely to change the average transaction

price, even in the absence of new information, if the distributions of the sell orders and buy

orders are not symmetric, or if the machine’s bid and ask are not symmetric with respect

to the orders distribution.

In the case of the uniform distribution we get

Corollary 3.3 Assume μB < C < b < s < B < μS, that FB is uniform on [A, B] and FS

is uniform on [C, D], and that γB(D − C) 6= γS(B − A). Then, the density of the price for

x ∈ [C, B] is given by

fP (x) = 1{x<b}

p(1 − p)(B − C)(B − A)(D − C) + (1 − p)

2

(B − A)

2

(b − C)

[(1 − p)(b − C)(B − A) + p(B − x)(D − C)]2

+1{b<x<s}

p(1 − p)(B − C)(B − A)(D − C)

[(1 − p)(x − C)(B − A) + p(B − x)(D − C)]2

+1{x>s}

p(1 − p)(B − C)(B − A)(D − C) + p

2

(D − C)

2

(B − s)

[(1 − p)(x − C)(B − A) + p(B − s)(D − C)]2

Figure 3 illustrates the conclusions of Corollary 3.2, showing the thinning of the far

tails of the density, the fattening for the values moderately away from the middle of the

distribution, and no change in the middle.

14
 Price density

0

20

40

60

80

100

120

140

0.994 0.996 0.998 1 1.002 1.004 1.006

x

f(x)

Density without HFT Density with HFT

Figure 3: Price density when orders are uniform. 

Figures 4 and 5 show the means and the variances of the price with and without machine

presence, as the supports of the uniform distributions of orders have less and less overlap.7

 Mean price

0.99982

0.99984

0.99986

0.99988

0.9999

0.99992

0.99994

0.99996

0.99998

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009

 Sell-orders mean minus buy-orders mean 

Mean time to trade Mean time to trade with HFT

Figure 4: Mean price as the sell-buy gap increases.

7We decrease the overlap by moving to the right the support interval for the sell orders and keeping the

same the distribution of the buy orders.

15
 Price Variance

0

0.000001

0.000002

0.000003

0.000004

0.000005

0.000006

0.000007

0.000008

0.000009

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009

 Sell-orders mean minus buy-orders mean 

Variance without HFT Variance with HFT

Figure 5: Price variance as the sell-buy gap increases.

The average values are almost identical in the two cases, while the variance with machine

is somewhat lower than without it, but the difference vanishes as the supports of the buy

and sell orders diverge.

3.1 Machine Optimization

Up to now, we assumed that the machine submits very fast buy and sell orders and cancels them if they are not executed. Under this assumption, machine does not learn from

transaction prices or the execution of orders.

Let us now assume that the machine will be issuing the same orders b and s until a

random time τ , which is less or equal to the first time a human order “steals” from the

machine a human sell order Si < b or a human buy order Bi > s. The machine interprets

the time τ as the first time some new information arrives in the market. For simplicity, we

assume that over very short intervals of time that we focus on, the machine models τ as a

random time independent of everything else, having exponential distribution with intensity

λ.

8 Also for simplicity, we set μB = 0, μS = ∞, that is, the book is initially empty.

Denote by Nb (Ns) the number of buys (sells) of the machine during the random period

[0, τ ]. Also denote

pS = P(b ≥ Si), pB = P(s ≤ Bi)

rS = E[Si1b≥Si] , rB = E[Bi1s≤Bi]

Note that Nb, Ns are conditionally binomial with probability pS, pB, and the number of

trials being Poisson with intensity γS, γB.

8

If we require that τ is less or equal to the first “stealing time”, then it is not really independent of

everything, but we assume that the machine uses independence as an approximating assumption.

16
Lemma 3.1 We have

E[Nb] = 1

λ

pSγS

E[Ns] = 1

λ

pBγB

and the expected profit from buying and selling, ignoring the value of inventory, is

E[P] = 1

λ

rBγB −

1

λ

rSγS

Moreover, we have

E[N

2

b

] = 1

λ

γSpS +

2

λ

2

γ

2

S

p

2

S

E[N

2

s

] = 1

λ

γBpB +

2

λ

2

γ

2

Bp

2

B

E[NbNs] = 2

λ

2

γSpSγBpB

We suppose that the machine trader maximizes expected profit/loss during the interval,

but penalized by the size of the inventory, and adjusted by the value of the remaining

inventory. More precisely, the machine maximizes

E[G] := E[P] − ρE[(Ns − Nb)

2

] + vE[Nb − Ns]

where ρ is a penalization parameter, or a Lagrange multiplier for the inventory constraint,

and v can be thought of as proportional to the estimated future value of the asset.

This problem is hard in general, and we only consider the case when the human orders

are uniformly distributed.

3.1.1 Uniformly distributed orders

Let us assume uniform distributions

FB(x) = x − A

B − A

, FS(x) = x − C

D − C

that is, Bi, Si are respectively uniform on [A, B], [C, D].

Lemma 3.2 If FB is uniform on [A, B] and FS is uniform on [C, D], then we have

pS = FS(b) = b − C

D − C

pB = 1 − FB(s) = B − s

B − A

rS = FS(b)[C + FS(b)(D − C)/2] = b

2 − C2

2(D − C)

rB = (1 − FB(s))[B − (1 − FB(s))(B − A)/2] = B2 − s

2

2(B − A)

17
Proposition 3.3 For maximizing E[G], the interior first order condition with respect to s

is

s[1 + ρ

4

λ

γB

1

B − A

] = ρ + 4ρ

γB

λ

B

B − A

− 4ρ

γS

λ

b − C

(D − C)

+ v

The interior first order condition with respect to b is

b[1 + ρ

4

λ

γS

1

D − C

] = −ρ + 4ρ

γS

λ

C

D − C

+ 4ρ

γB

λ

B − s

(B − A)

+ v

In particular, if

A = C, B = D, γB = γS = γ

then, the interior solutions are

s =

4ργ(A + B) + vλ(B − A)

λ(B − A) + 8ργ

+ ρ

b =

4ργ(A + B) + vλ(B − A)

λ(B − A) + 8ργ

− ρ

Introducing the mean and the variance of the human orders,

μ = (A + B)/2 , σ2 = (B − A)

2

/12

we get

s =

8ργμ + v

√

12λσ

√

12λσ + 8ργ

+ ρ

b =

8ργμ + v

√

12λσ

√

12λσ + 8ργ

− ρ

From this proposition we find that (assuming interior solutions) the machine places

orders centered around the mid-price 8ργμ+v

√

12λσ

√

12λσ+8ργ

adjusted for the inventory penalty ρ. This

mid-price is less than the mean value of the incoming orders μ when the weight v given to

the expected future asset value is small, and is otherwise larger than μ. In addition, when

ρ = 0, then the optimal orders are simply b = s = v. Furthermore, when the trading interval

until the time of new information gets longer (λ closer to zero), then the machine orders

get closer to μ ± ρ. The same happens when the frequency γ of human orders gets large, or

when the variance σ

2 of human orders gets small. When γ gets small, the orders get close

to v ± ρ.

3.1.2 Orders symmetric around the mean

We again assume A = C, B = D, γb = γS. Everything simplifies if we only allow the orders

of the form

b = μ − x , s = μ + x

As discussed above, this is close to optimal if the product λσ is small relative to the product

ργ. Moreover, as stated below, with this choice the expected inventory size is zero, E[Nb −

18
Ns] = 0. Thus, the machine does not have to worry, in expected value sense, about the

future value of the asset.

If we optimize over x, it is easily seen that it is optimal to take

x = ρ.

Interpreting now ρ as a Lagrange multiplier, assume now we impose a constraint on the

inventory size as follows:

E[(Ns − Nb)

2

] ≤ K (3.1)

The following result is easy to verify.

Proposition 3.4 Under our assumptions, we have

0 = pS − pB = E[Nb − Ns]

and thus

E[(Ns − Nb)

2

] = γ

λ

(pS + pB)

Moreover, the equality in (3.1) will be attained for ρ given by

ρ =

1

2

[1 −

λ

γ

K](B − A)

In particular,

b = μ − ρ ≥ A

Furthermore, the expected profit can be computed as

E[P] =

√

3σ

2

K(2 −

λ

γ

K).

The highest inventory is attained for ρ = 0 which gives K = γ/λ. Thus, it suffices to

consider the values K < γ/λ. For K, it may be reasonable to take

K =

N2

λ

2

where N is a given constant that represents the maximal allowed inventory size per unit

time.

The proposition above states that the machine’s profit is a linear increasing function of

the human orders’ volatility σ (in the domain K < γ/λ). In addition, the machine’s profit

is increasing in the frequency of human orders γ.

The machine’s profit is bounded by

√

3σ

2

γ

λ

. Thus, if there were increasingly many machines, as the total profit would have to be shared, the profit for each one would be decreasing.

If we constrain the absolute size of inventory rather than its size per unit time, that is,

if K is kept fixed in a way that it does not depend on λ, the machine’s profit is increasing

in the mean length of the trading interval 1/λ. This is because more trades are likely to

be executed. This is also the case if we limit the inventory size per unit time, that is, K

19
is proportional to 1/λ2

, but only in the domain consisting of 1/λ small enough. For large

enough 1/λ, if the inventory per unit time is limited, then the machine’s profit is decreasing

in 1/λ.

From the expression for ρ we conclude that the machine provides less liquidity, in the

sense that ρ is larger, in the following cases: 1) the market is more volatile so that the

volatility of the orders is larger (that is, B − A is larger); 2) the humans are trying harder

to change their positions, that is, frequency γ is higher; 3) the value of K does not depend

on λ and there is less new information coming in, that is, λ is lower; 4) the value of K is

proportional to 1/λafor a > 2 and there is more new information coming in, that is λ is

higher. Note that item 1) implies, supposing that in the time of crisis the volatility and the

frequency of orders are higher, and supposing λ does not change much, that the machine

will provide less liquidity (wider bid-ask spread) when there is crisis.

Finally, we remark that if humans had perfect knowledge about the machine’s strategy,

then the humans would submit only orders with values inside the interval [b, s]. If they did

this by choosing values from a continuous distribution on [b, s], the machine would not be

able to make any trades, and would have zero profit. On the other hand, with this knowledge

it might be optimal for humans to submit orders with values b or s with positive probability,

which would place us outside of the assumptions of our model. However, because b and s

can change from one intra-trade interval to another, it is unlikely that humans would be

able to know their exact values.

4 Empirical Implications

Our results have a number of empirical implications. First, the distribution of transaction

prices (and returns) in markets with high frequency traders can be represented as a “mixture”

of the distributions of human-human and machine-human transaction prices (plus machinemachine prices, if there is more than one machine). With the knowledge of counterparties

for each transaction, one can reconstruct the mixture. In addition, if machine strategies

can, indeed, be closely approximated by a deterministic process (e.g., bracketing the last

human transaction price), then the component of the price distribution attributed to the

machines should be forecastable. As the proportion of transactions with the machines grows,

forecastability of transaction prices should improve.9

Second, trading volume and intertrade duration, as well as measures of market liquidity

based on them, should increase in direct proportion to how much humans change the speed

of their orders when the machine is present. To the extent that it is known how many order

per unit time have been submitted (modified or canceled) by machines and humans, this

implication can be verified in the data.

Third, profits of a high frequency trader should increase in both the variance and the

arrival frequency of human orders. Again, to the extent that both the arrival frequency and

the variance of human orders can be estimated, they can be empirically compared to the

profits and losses of a high frequency trader, as well as these traders as a group.

9Even if that component of the prices is forecastable, this does not mean that one can trade on it. It

would take a machine that’s faster than the fastest current machine to take advantage of this empirical

regularity.

20
5 Conclusion

We model an electronic limit order market populated by low frequency traders and then add

a high frequency trader. We postulate that low frequency traders (humans) follow certain

order submission strategies and then derive the distributions of transaction prices with and

without a high frequency trader (the machine).

We find that the presence of a machine is likely to change the average transaction price,

even in the absence of new information. We also find that in a market with a high frequency

trader, the distribution of transaction prices has more mass around the center and thinner

far tails. With a machine, mean intertrade duration decreases in proportion to the increase

in the ratio of the human order arrival rates with and without the presence of the machine;

trading volume goes up by the same rate.

We also find that a machine that optimizes expected profits subject to an inventory

constraint submits orders that are essentially symmetric around the mean value of the human

orders. The distance between the machine’s bid and ask prices increases with its concern

about the size of the remaining inventory. The expected profit of an optimizing machine

increases in both the variance and the arrival frequency of human orders.

Our model has two serious limitations. First, we do not solve for mutually best responses

of all parties; in other words, the order submission strategy that we postulate for the humans

may or may not be supported as an equilibrium strategy under general conditions. Second,

our model is static; we focus on a stationary sequence of one-period models–intervals between

two human trades.

Having said that, to our knowledge, this is the first model to formally investigate the

impact of high frequency trading on transaction prices, trading volume, and intertrade

duration, as well as to characterize the profits of a high frequency trader as a function of

the properties of low frequency traders.

21
References

[1] Avellaneda, M. and Stoikov,S. (2008), “High-frequency trading in a limit order book”.

Quantitative Finance, 8, 217-224.

[2] Back, K. and Baruch, S. (2007), “Working Orders in Limit-Order Markets and Floor

Exchanges”. forthcoming in the Journal of Finance, .

[3] Biais, B., Martimort, D. and Rochet, J.-C. (2000), “Competing mechanisms in a common value environment”. Econometrica, 68, 799837.

[4] Biais, B. and Weill, P.-O. (2009), “Liquidity shocks and order book dynamics”. Working

paper.

[5] Cont, R., Stoikov, S. and Talreja, R. (2009), “A stochastic model for order book dynamics”. Working paper.

[6] Foucault, T. (1999), “Order flow composition and trading costs in a dynamic limit order

market”. Journal of Financial Markets, 2, 99134.

[7] Foucault, T., Kadan, O. and Kandel, E. (2005), “Limit order book as a market for

liquidity”. Review of Financial Studies, 18, 1171-1217.

[8] Kuhn, C. and Stroh, M. (2009), “Optimal portfolios of a small investor in a limit order

market a shadow price approach”. Working paper.

[9] Rosu, I. (2009), “A dynamic model of the limit order book”. Forthcoming in The Review

of Financial Studies”.

[10] Parlour, C. (1998), “ Price dynamics in limit order markets”. Review of Financial

Studies, 11, 789-816.

[11] Parlour, A.S. and Seppi, D.J. (2003), “Liquidity-based competition for order flow”.

Review of Financial Studies, 16, 301-343.

[12] Goettler, R., Parlour, C., and U. Rajan (2005), “Equilibrium in a dynamic limit order

market”, Journal of Finance, 60, 2149-2192.

22
Appendix

Proofs for Section 2

Proof of Proposition 2.1: Conditioning on the number of sell orders, we get

FmS

t

(x) = X∞

k=o

[1 − 1{x<μS}(1 − FS(x))k]

(tγS)

k

k!

e

−tγS

which proves the result. Similarly for FMB

t

.

Next,

P(τ > t) = P(MB

t < mS

t

)

which gives the desired expression.

If FS = FB = F, then the integral can be easily computed explicitly to get the result.

Proof of Proposition 2.2: Denote by KB

τ

, KS

τ

the number of newly arrived buy and

sell orders in the book at the time of trade, by MB(r), mS(q) the maximum of r buy orders

and μB, and the minimum of q sell orders and μS, and by B(r), S(q) the r−th incoming

buy order and the q−th incoming sell order. Let us also denote B(r, q) the event that, given

that there are r buy orders (plus μB) in the buy side of the book, and q sell orders (plus μS)

in the sell side of the book, the last order was a buy order. Similarly for S(r, q), except the

sell order was the last. Then, we have

P(B(r, q)) = 

r − 1 + q

q



p

r

(1 − p)

q

P(S(r, q)) = 

r − 1 + q

r



p

r

(1 − p)

q

Notice that we have

X∞

q=0



r − 1 + q

q



(1 − p)

q = p−r

X∞

r=0



r − 1 + q

r



p

r = (1 − p)−q

Also note that we have

P(mS(q) ≤ x) = 1 − 1{x<μS}(1 − FS(x))q

so that

dFmS(q)(x) = 1{x<μS}q(1 − FS(x))q−1dFS(x)

and similarly

P(MB(r) ≤ x) = 1{x≥μB}(FB(x))r

dFMB(r)(x) = 1{x≥μB}r(FB(x))r−1dFB(x)

23
Using the above, we have

P(MB

τ ≤ x) = X

r,q

P(MB

τ ≤ x, KB

τ = r, KB

τ = q)

=

X

r≥1,q≥0

P(MB

τ ≤ x, KB

τ = r, KB

τ = q, B(r, q)) + X

r≥0,q≥1

P(MB

τ ≤ x, KB

τ = r, KB

τ = q, S(r, q))

=

X

r≥1,q≥0

P(B(r, q))P(MB(r) ≤ x, MB(r − 1) ≤ mS(q) ≤ B(r))

+

X

r≥0,q≥1

P(S(r, q))P(S(q) < MB(r) ≤ min{x, mS(q − 1)})

Conditioning on mS(q) in the first term and on MB(r) in the second term, we get

P(MB

τ ≤ x) = 1{x>μB}

X

r≥1,q≥0

Z x∧μS

μB

P(B(r, q))F

r−1

B

(y)[FB(x) − FB(y)]q(1 − FS(y))q−1

dFS(y)

+1{x>μS}

X

r≥1,q≥0

P(B(r, q))P(mS(q) = μS)F

r−1

B

(μS)[FB(x) − FB(μS)]

+

X

r≥0,q≥1

Z x∧μS

μB

P(S(r, q))FS(y)[1 − FS(y)]q−1rFr−1

B

(y)dFB(y)

+1{x>μB}

X

r≥0,q≥1

P(S(r, q))P(MB(r) = μB)[1 − FS(μB)]q−1FS(μB)

Inside the first integral we have a sum of the form

X

r,q



r − 1 + q

q



qxq−1y

r−1

This is a derivative with respect to x of the sum

X

r,q



r − 1 + q

q



x

q

y

r−1 =

X

r

y

r−1

(1 − x)

r

Thus, taking the derivative, we get the sum

1

(1 − x)

2

X

r

r



y

1 − x

r−1

=

1

(1 − x)

2

1

(1 −

y

1−x

)

2

Setting

x = (1 − p)(1 − FS(y)) , y = pFB(y)

we get the result for the first integral in the distribution of MB. The second integral is

obtained in a similar manner, and the second and fourth non-integral terms are obtained by

direct summation, taking into account that

P(MB(r) = μB) = F

r

B(μB) , P(mS

(q) = μS) = [1 − FS(μS)]q

24
Similarly, we have

P(mS

τ ≤ x) = X

r,q

P(mS

τ ≤ x, KB

τ = r, KB

τ = q)

=

X

r≥1,q≥0

P(B(r, q), MB(r − 1) ≤ mS(q) ≤ x ∧ B(r))

+1{x>μS}

X

r≥1,q≥0

P(B(r, q))P(mS(q) = μS)F

r−1

B

(μS)[1 − FB(μS)]

+

X

r≥0,q≥1

P(S(q) ≤ x ∧ MB(r), MB(r) ≤ ms(q − 1), S(r, q))

+

X

r≥0,q≥1

P(S(r, q))P(MB(r) = μB)[1 − FS(μB)]q−1FS(x ∧ μB)]

Similarly as above, conditioning on mS(q) in the first two terms and on MB(r) in the last

two terms, and by summation, we get the result.

The distribution of the transaction prices is now easily determined as above, from its

definition.

Derivations related to Remark 2.1

Let x be a submitted buy order. Denote

p

B

1

(x) = P(x executed at arrival)

p

B

2

(x) = P(x executed after arrival)

and similarly p

S

i

(x) if x is a sell order.

Let us suppose that a buy trader has the value v for the asset, at the time of the next

trade. Given that the trader submits a buy order x, and his utility function is U, denoting

by τx the time of his arrival and recalling that mS

t denotes the minimum sell order in the

book at time t, his expected profit is

p1E[U(v − mS

τx

) | x executed at arrival] + p2U(v − x)

Let us now change the variables to

u := FB(x)

and denote by π(u) the corresponding expected profit. Then, u is uniformly distributed,

and a necessary condition for a symmetric equilibrium is that π

0

(u) = 0.

Proposition 5.5 We have

p

B

1

(x) = Z x

0

dFS(y)

γSγB

[2γB + γSFS(y) − γBFB(y)]2

p

B

2

(x) = γBγSFS(x)

{2γB + γSFS(x) − γBFB(x)}{γB[1 − FB(x)] + γSFS(x)}

25
In case FS = FB, γS 6= γB, and in terms of variable u, we can write (with a slight abuse of

notation p1),

p

B

1

(u) = γSγB

γS − γB

(

1

2γB

−

1

2γB + (γS − γB)u

)

We also have

p

S

1

(x) = Z ∞

x

dFB(y)

γSγB

[γB + γS + γSFS(y) − γBFB(y)]2

p

S

2

(x) = γBγS[1 − FB(x)]

{γB + γS + γSFS(x) − γBFB(x)}{γB[1 − FB(x)] + γSFS(x)}

Moreover, the density of the minimum of sell orders at time of arrival of buy order x, denoted

f

m

x

, lives on (0, x), and is given by

f

m

x

(z) = 1{x>z}

(p

B

1

)

0

(z)

p

B

1

(x)

Similarly, the density of the maximum of sell orders at time of arrival of sell order x, denoted

f

M

x

, lives on (x, ∞), and is given by

f

M

x

(z) = −1{z>x}

(p

S

1

)

0

(z)

p

S

1

(x)

Let us assume linear utility for the buyer. Denote by A1 the event that buy order x is

executed at arrival. Then, the trader’s utility is

p

B

1

(x)E[v − mS

τx

|A1] + (vB − x)p

B

2

(x) = p

B

1

(x)vB −

Z x

0

z(p

B

1

)

0

(z)dz + (vB − x)p

B

2

(x)

If FB = FS, we get

p

B

1

(x) = F(x)/4 , pB

2

(x) = F(x)/2

In terms of the variable F(x) = u, denoting by β the inverse of F, this then becomes

1

4

[vBu −

Z β(u)

0

zF0(z)dz] + 1

2

u[vB − β(u)]

Taking derivative, setting it equal to zero, solving the obtained ODE for β(u) and inverting,

we get

F(x) = c(vB − x)

−2/3

A similar computation for the seller gives

F(x) = 1 − c(x − vS)

−2/3

Thus, the assumption of FS = FB is not tenable in this equilibrium.

Proof of Proposition 5.5: The execution probability can be decomposed over the

event that the order was executed as as soon as it arrived, and over the event that it was

executed later, but before any other order was executed. The probability over the first event,

by conditioning over the time t of the arrival of x, over the number r of buy orders by time

26
t and the number q of sell orders by time t, and over the minimum y of those q sell orders,

is given by

p1 := P(x executed at arrival)

=

X

r≥0,q≥1

Z ∞

0

γBe

−γBt

dt Z x

0

q(1 − FS(y))q−1dFS(y)e

−γBt

(γBt)

r

r!

e

−γSt

(γSt)

q

q!

F

r

B(y)

=

X

q≥1

Z ∞

0

γBe

−γBt

dt Z x

0

q(1 − FS(y))q−1dFS(y)e

−γBt[1−FB(y)]e−γSt

(γSt)

q

q!

=

Z ∞

0

γSγBte−γBtdt Z x

0

dFS(y)e

−γBt[1−FB(y)]e−γStFS(y)

=

Z x

0

dFS(y)

γSγB

[γB(2 − FB(y)) + γSFS(y)]2

Denote now, similarly as before, by S(r, q) the event that at the execution of order x,

there were r buy orders and q sell orders that arrived after x, which necessarily implies that

q ≥ 1, and that the last order that arrived was a sell order. The probability of execution of

x at some time after it arrived can be obtained by conditioning over the time t of the arrival

of x, over the number k of buy orders by time t and the number l of sell orders by time t,

and on the number r of buy orders and the number q of sell orders that arrived between

time t and the time of execution. This probability is given by

p2 := P(x executed after arrival)

=

X

k≥0,l≥0

X

r≥0,q≥1

Z ∞

0

γBe

−γBt

dte−γBt

(γBt)

k

k!

e

−γSt

(γSt)

l

l!

×P(MB(k) < x < mS(l), S(r, q), MB(r) ∨ S(q) ≤ x < mS(q − 1))

=

X

r≥0,q≥1

Z ∞

0

dtγBe

−γBt[2−FB(x)]e−γStFS(x)P(S(r, q), MB

(r) ∨ S(q) ≤ x < mS(q − 1))

=

γB

γB(2 − FB(x)) + γSFS(x)

X

r≥0,q≥1



r − 1 + q

r



p

r

(1 − p)

qFr

B(x)FS(x)[1 − FS(x)]q−1

=

γB

γB(2 − FB(x)) + γSFS(x)

X

q≥1

(1 − p)

qFS(x)[1 − FS(x)]q−1

[1 − pFB(x)]q

=

γB

γB[2 − FB(x)] + γSFS(x)

(1 − p)FS(x)

p[1 − FB(x)] + (1 − p)FS(x)

For p

S

i

the proof is similar.

Recall that A1 is the event that buy order x is executed at arrival. Let us now compute

P(mS

τx ≤ z|A1) by computing

P(mS

τx ≤ z, A1)

= 1{x≤z}P(A1)+1{x>z}

X

r≥0,q≥1

Z ∞

0

γBe

−γBt

dt Z z

0

q(1−FS(y))q−1dFS(y)e

−γBt

(γBt)

r

r!

e

−γSt

(γSt)

q

q!

F

r

B(y)

27
= 1{x≤z}p1(x) + 1{x>z}

Z z

0

dFS(y)

γSγB

[γB(2 − FB(y)) + γSFS(y)]2

Thus, we get

P(mS

τx ≤ z|A1) = 1{x≤z} + 1{x>z}

p1(z)

p1(x)

Therefore, the corresponding (conditional) density, denoted f

m

x

, lives on (0, x), and is given

by

f

m

x

(z) = 1{x>z}

p

0

1

(z)

p1(x)

Proof is similar for the sell side.

Proofs for Section 3

Proof of Proposition 3.1: Similarly as with no machine, using the expressions for the

distributions of MB

t

and mS

t

.

Proof of Proposition 3.2: We have

P(Pτ ≤ x) = X

r≥0,q≥1

P(S(r, q))P(S(q) < MB(r) ≤ min{x, s, mS(q − 1)}, mS(q − 1) > b)

+

X

r≥1,q≥0

P(B(r, q))P(MB(r − 1) < s, MB(r − 1) ∨ b ≤ mS(q) ≤ B(r) ∧ x)

+

X

r≥1,q≥0

P(B(r, q))P(s ∨ MB(r − 1) ≤ B(r) ≤ mS(q) ∧ x, MB(r − 1) < s, mS(q) > b)

+

X

r≥0,q≥1

P(S(r, q))P(MB(r) ≤ S(q) ≤ b ∧ x, MB(r) < s, mS(q − 1) > b)

The first term comes from the last order being a human sell order and trading with a

human buy order in the book, and the second term from the last order being a human buy

order and trading with a human sell order in the book. The third term comes from an

incoming buy order trading with the machine, and the fourth term comes from an incoming

sell order trading with the machine. The first two terms are computed similarly as with no

machine. Conditioning on B(r) in the third term and on S(q) in the fourth, and computing

the summations similarly as with no machine, we get the result.

Proof of Corollary 3.3: From the proposition, we have

P(Pτ ≤ x) = 1{x<b}

p(1 − p)

(B − A)(D − C)

Z x

C

y − C

[(1 − p)

b−C

D−C + p

B−y

B−A

]

2

dy

+1{x>b}

p(1 − p)

(B − A)(D − C)

Z x∧s

b

y − C

[(1 − p)

y−C

D−C + p

B−y

B−A

]

2

dy

28
+1{x<s}

p(1 − p)

(B − A)(D − C)

Z x

b

B − y

[(1 − p)

y−C

D−C + p

B−y

B−A

]

2

dy

+1{x>s}

p(1 − p)

(B − A)(D − C)

Z x∧B

s

B − y

[(1 − p)

y−C

D−C + p

B−s

B−A

]

2

dy

+1{x>s}

p

B − A

Z x∧B

s

1

(1 − p)

y−C

D−C + p

B−s

B−A

dy

+

1 − p

D − C

Z x∧b

C

1

(1 − p)

b−C

D−C + p

B−y

B−A

dy

We then get the density by differentiating.

Proof of Lemma 3.1: We have

E[N

2

b

|τ ] = E





X∞

n=1 Xn

i=0

1b>Si

!2

e

−γSτ

(γSτ )

n

n!







τ





=

X∞

n=1

[npS(1 − pS) + n

2

p

2

S

]e

−γSτ

(γSτ )

n

n!

= γSτpS + γ

2

S

τ

2

p

2

S

After integrating over τ , we get

E[N

2

b

] = 1

λ

γSpS +

2

λ

2

γ

2

S

p

2

S

and analogously

E[N

2

s

] = 1

λ

γBpB +

2

λ

2

γ

2

Bp

2

B

Similarly, we have

E[NbNs|τ ]

= E[Nb|τ ]E[Ns|τ ]

= τ

2

pSγSpBγB

so that

E[NbNs] = 2

λ

2

γSpSγBpB

The other expressions are proved in a similar fashion.

Proof of Proposition 3.3:

Since with uniform distribution we have

rS =

b

2 − C2

2(D − C)

29
rB =

B2 − sˆ

2

2(B − A)

we need to maximize

1

λ

γB[

B2 − s

2

2(B − A)

] −

1

λ

γS

b

2 − C2

2(D − C)

−ρ

"

1

λ

γB

B − s

B − A

+

1

λ

γS

b − C

D − C

+

2

λ

2



γB

B − s

B − A

− γS

b − C

D − C

2

#

The interior first order condition with respect to s is

s[1 + ρ

4

λ

γB

1

B − A

] = ρ + 4ρ

γB

λ

B

B − A

− 4ρ

γS

λ

b − C

(D − C)

The interior first order condition with respect to b is

b[1 + ρ

4

λ

γS

1

D − C

] = −ρ + 4ρ

γS

λ

C

D − C

+ 4ρ

γB

λ

B − s

(B − A)

If A = C, B = D, γB = γS = γ, then, if we add the two conditions we get

(s + b)α = β

where

α = [1 + ργ

8

λ

1

B − A

]

and

β = 8ρ

γ

λ

A + B

B − A

Subtracting we get

(s − b)κ = δ

where

κ = 1

and

δ = 2ρ

Solving this we get

s =

κβ + αδ

2κα

b =

κβ − αδ

2κα

and substituting we get

s =

4ργ(A + B)

λ(B − A) + 8ργ

+ ρ

b =

4ργ(A + B)

λ(B − A) + 8ργ

− ρ

30
TAB 9








TAB 10
Market Access

Risk Management Recommendations
2	 April 2010

Market Access Risk Management Recommendations

April 2010

On behalf of the Futures Industry Association Market Access Working Group, we are pleased

to present recommendations for managing the risk of direct access trading. Recognizing

the importance of promoting best practices in this area, the FIA board of directors in

January 2010 agreed to assemble a committee to formulate best practices for direct access

to exchanges. The group includes representatives from clearing firms, trading firms, and

exchanges. The scope of their work includes pre-trade order checks, post-trade checks, colocation policies, conformance testing, and error trade policies.

The study will be shared with futures and options exchanges around the world. Later this

year, FIA plans to survey exchanges that offer direct access to determine what types of risk

controls are in place and publish the results of the survey.

We appreciate the time and resources the members of the Market Access Working Gro

contributed to the creation of this document. This is not the first group FIA has convened

to address risk management practices. In 2004, FIA published a series of recommendations

on error trade polices. In 2007, FIA published the results of a survey on risk controls at key

exchanges. In 2009, the FIA/FOA Clearing Risk Study included recommendations for preand post-trade risk controls.

We expect the need for risk controls to continue to evolve as the industry evolves and FIA is

committed to monitoring and supporting practices and procedures that improve the integrity

of the markets.

Yours truly,

Peter Johnson

Chairman

Market Access Working Group

FIA Market Access Working Group

The following organizations participated in the development of the FIA Market Access Risk

Management Recommendations:

•	Bank of America Merrill Lynch

•	Barclays Capital

•	CME Group

•	Credit Suisse

•	DRW Trading

•	Eurex

•	Geneva Trading

•	IntercontinentalExchange

•	J.P. Morgan Futures

•	Newedge Group

•	Nico Trading

•	NYSE Liffe

•	XR Trading

The FIA is the U.S.-based international trade association which acts as a principal spokesman for 

the futures and options industry. Its membership includes the world’s largest futures brokers as well as 

derivatives exchanges from more than 20 countries.
3	 April 2010

Market Access Risk Management Recommendations

Managing the risk of providing direct access to an exchange’s network is a critically

important responsibility of all parties involved in the process—clearing firms, exchanges, and

the direct access firms themselves. However, managing such risk must be done in a manner

that does not disadvantage one direct access firm over another solely because it, or its clearing

firm, endeavors to act more responsibly. This can only be done if exchanges themselves

provide basic risk management tools, and construct them in such a manner that latency is

identical to all direct access firms, no matter how clearing firms utilize such tools. Indeed, this

will encourage the clearing firm to employ such tools in the most responsible fashion, without

fear that it will lose business to other clearing firms that do not act so responsibly.

Recognizing the importance of promoting best practices in risk management of direct access

trading, the FIA board of directors in January 2010 established a Market Access Working

Group to identify risk-specific controls that are already in place at exchanges, clearing and

trading firms and recommend controls that should be in place as a matter of best practice

before allowing direct access. The MAWG consists of representatives from clearing firms,

exchanges, and trading firms. The group has been meeting since January to agree on

recommendations for pre- and post-trade risk controls, co-location, conformance testing, and

error trade policies.

Latency-sensitive traders, which rely on direct access, can play a vital role in the marketplace,

bringing liquidity to the markets, reducing volatility, tightening bid-ask spreads, and

contributing to price discovery1

. The recommendations presented here represent anoth

step in improving the way direct access risk is managed. The industry has been worki

together for several years to ensure risk management practices reflect the realities of the

current trading environment. In 2004, FIA published a series of recommendations with

respect to exchange error trade policies and procedures. In 2007, FIA published a “Profile

of Exchange and FCM Risk Management Practices for Direct Access Customers,” which

identified issues with this type of trading and enumerated the results of a survey of risk

controls at key exchanges. The FIA/FOA Clearing Risk Study, released in February 2009,

included recommendations for exchanges to implement pre-defined authorizations, position

limits, and monitoring and intervention capabilities.

The current project establishes principles the industry should consider when allowing

direct access to exchanges. Although the guidelines contained in this document are more

generally suited to futures and options markets, many of the principles and recommended

implementations are applicable to other types of markets. The MAWG recognizes that

market structures vary and exchanges need to implement risk controls across multiple product

lines. For example, some exchanges offer both equities and futures on the same trading

platform. The MAWG also acknowledges that exchanges are in varying stages of permitting

direct access and therefore these recommendations may not be immediately achievable.

Instead, these recommendations are put forth as agreed-upon principles that the global

futures industry needs to work toward implementing. In addition, the MAWG recognizes

that these recommendations must be considered in the context of the regulatory structures in

which markets operate.

1 See Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market by Alain Chaboud, Benjamin

Chiquoine, Erik Hjalmarsson, Clara Vega, in which the empirical data examined by the authors suggested that, in

the spot interdealer foreign exchange market, “the presence of algorithmic trading reduces volatility” and “computers

do provide liquidity during periods of market stress.” (International Finance Discussion Paper, Board of Govern

of the Federal Reserve System, dated October 2009, p. 26.)

Introduction
4	 April 2010

Market Access Risk Management Recommendations

This document is designed to serve as a framework for developing risk controls. It attempts to

strike the right balance between guiding principles and prescriptive mandates. Accordingly,

this document reflects two types of recommendations: principles and implementation

recommendations. The first type is a guiding principle that describes the type of control and

what should be achieved by implementing the control. The principles, in some cases, are

followed by implementation recommendations.

The document includes a section on co-location and proximity hosting. Co-location and

proximity hosting have often been included in discussions related to risks associated with

high-frequency trading, but the MAWG does not believe this is a risk management issue.

Co-location and proximity hosting refer to data centers that offer an alternative method to

brokerage and trading firms seeking the fastest possible access to an exchange’s network and

are not inherently risky. Co-location takes place when the exchange provides connectivity

and hosting in its own data center via its own network. Proximity sites are data centers

offered by an exchange or a third-party vendor for low-latency access to an exchange’s

network via a third-party network connection.

Direct access firms either join the exchanges as non-clearing members (NCMs) or access

the exchanges in the name of their clearing member. While there is no distinction between

a direct access firm that becomes a non-clearing member of an exchange and one that

does not when it comes to risk and credit controls, NCMs are subject to an exchange

membership approval and vetting process. NCMs also are subject to exchange rules such as

market manipulation, wash trades and message limit violations. In either case, these firms’

transactions must be financially guaranteed by a clearing member before the exchange grants

direct access to these firms. The clearing firm guarantees the trades pursuant to an agreement

with the trading firm and retains administrative and risk control over orders submitted to the

exchange trading engine.

There are three ways a non-clearing firm can access the exchange network directly:

a.	 Direct access via a clearing firm (DA-C)—trading firm orders pass through the

clearing member’s system prior to reaching the exchange trading engine.

b.	 Direct access via vendor (DA-V)—trading firm routes orders through a vendor

controlled by the clearing firm or other third-party infrastructure to the exchange

trading engine.

c.	 Direct access to the exchange (DA-E)—trading firm routes orders directly to the

exchange trading engine without passing through the clearing member or a thirdparty infrastructure.

Risk management of direct access market participants is not the exclusive responsibility of

exchanges, clearing firms or even the direct access firms themselves. Rather, exchanges, clearing

firms, and direct access firms each have a role in ensuring that appropriate risk controls are

in place for this type of market access. Clearing firms that frequently manage many exchange

interfaces would benefit greatly from standardization of risk management controls across

exchanges. The more standardization of risk controls, the more efficiently and effectively

clearing firms are able to monitor and manage the risks associated with direct access clients.

Background
5	 April 2010

Market Access Risk Management Recommendations

Trading firms typically have risk controls in place to monitor and risk-manage their trading

systems. These protections operate within their risk model and include pre-trade ri

controls e.g. order size limits. Below is a sample of risk controls frequently employed by

trading firms. Although these controls represent good practice, they are not uniform

enforceable by exchanges or clearing firms.

•	 Conformance Testing. Trading firms are required to pass conformance testing with

the party providing access when implementing a new direct access system or when

the exchange deems it necessary because of a fundamental change in functionality

on the exchange side. The onus is on the trading firm to determine when it mu

recertify due to a change in logic within its system.

•	 Heartbeating with the Exchange. Trading systems can monitor “heartbeats” with the

exchange to identify when connectivity to the exchange is lost. If connectivity 

lost, the system is disabled and working orders are cancelled.

•	 Kill Button. Trading systems can have a manual “kill button” that, when activated,

disables the system’s ability to trade and cancels all resting orders.

•	 Pre-Trade Risk Limits. Trading firms can establish and automatically enforce

pre-trade risk limits that are appropriate for the firms’ capital base, clearing

arrangements, trading style, experience, and risk tolerance. These risk limits c

include a variety of hard limits, such as position size and order size. Depending 

the trading strategy, these limits may be set at several levels of aggregation. The

risk limits can be implemented in multiple independent pre-trade components of a

trading system.

•	 Post-Trade Risk Limits. Trading firms can also establish and automatically enforce

post-trade risk limits that are appropriate for the firm’s capital base, clearing

arrangements, trading style, experience, and risk tolerance. For example,

trading firm can set daily loss-limits by instrument, asset class, and strategy and

automatically close out or reduce positions if those limits are breached.

•	 Fat-Finger Quantity Limits. Trading systems can have upper limits on the size of the

orders they can send, configurable by product. They can prevent any order for

quantity larger than the fat-finger limit from leaving the system.

•	 Repeated Automated Execution Throttle. Automated trading systems can have

functionality in place that monitors the number of times a strategy is filled and then

re-enters the market without human intervention. After a configurable number 

repeated executions the system will be disabled until a human re-enables it.

•	 Near-Time Reconciliation. Trading systems can have functionality in place that

accepts drop-copies from exchanges and clearing firms. Drop copies are duplicate

copies of orders that allow a firm to compare the exchange or clearing firm view

of trades and positions with the firm’s internal view. This helps to assure that a

systems are performing as expected and maintaining accurate and consistent views of

trades and positions.

•	 Reasonability Checks. Trading systems can have “reasonability checks” on incoming

market data as well as on generated values.

Role of

Direct Access Participant
6	 April 2010

Market Access Risk Management Recommendations

The management of client risk by clearing firms, and of clearing member risk by

clearinghouses, has evolved as trading has moved from exchange floors to computer screens.

In most respects, risk controls have strengthened.

Clearing firms direct significant resources toward managing and monitoring risk and refining

approaches to assessing clients’ risk exposure. Clearing firms frequently employ the following

risk management controls with direct access clients:

•	 Most exchanges and self-regulatory organizations (SROs) require the clearing firm to

ensure that the trading firm has pre-trade risk controls in place. Clearing firms may

require the trading firm to provide network access to the trading firm’s pre-trade risk

controls to allow a clearing firm to set various risk limits and, if appropriate, stop the

trading firm’s trading. Network access is technically difficult to achieve, however,

and trading firms can override risk controls set by clearing firms.

•	 The clearing firm will conduct substantial due diligence on prospective direct access

clients and will grant direct access rights only to those clients who are deemed

sufficiently creditworthy and whose internal controls are deemed sufficiently strong

that pre-trade monitoring by the clearing firm is less essential. A clearing firm may

also require additional collateral to provide further certainty that the trading firm

will be able to meet any obligations that might arise from trading. In addition, the

clearing firm will monitor the trading firm’s account to determine whether margin

requirements are being met.

•	 Trading firms are judged on their willingness to share information with their clearing

firm. The more transparent a client is willing to be, the more likely the clearing firm

is to grant direct access.

•	 Clearing firms have risk controls built into order entry systems they offer trading

firms. These risk controls include many of the controls described later in this

document.

•	 Increasingly, clearing firms are depending on the exchanges to provide pre-trade risk

controls. Often, limits on the exchange systems can be configured and monitored

by the clearing firms. This ensures that risk controls do not become a source 

competition between clearing firms.

•	 Finally, clearing firms have agreements with trading firms that require the trading

firms to have specified risk controls in place, restrict access to authorized personnel,

and comply with relevant rules. Clearing firms monitor and enforce compliance with

these agreements on an ongoing basis.

Role of

Clearing Firm
7	 April 2010

Market Access Risk Management Recommendations

The primary business and function of exchanges is matching and clearing trades, regulating

their market, and ensuring that the market operates safely with minimal systemic risk in order

to sustain the overall viability of the market. The default or failure of the client of a clearing

member has no immediate risk consequences for the clearinghouse unless it causes losses that

lead to the default or failure of the clearing member. However, the provision of controls to

help avoid such events must be regarded as a priority of any exchange in order to protect the

overall integrity of its marketplace, and in recognition and support of the risk management

role undertaken by clearing members.

Exchanges have in place well-defined policies and procedures describing the responsibilities

of clearing firms and direct access firms.

•	 Exchange rules may require that clearing firms implement specified risk management

standards with regard to direct access clients. The exchange’s requirements a

onboarding processes for clearing firms and their direct access customers encompass

and support the risk management standards. The exchange processes may includ

legal paperwork, system certifications, and permissioning security.

•	 Clearing firms for directly connected entities must follow recommended exchange

guidelines for direct access, including in many cases requirements that clearing firms

configure and monitor automatic risk limits and that they maintain the ability to

halt a client’s trading system, if appropriate.

•	 Exchanges have the ability to establish an error trade policy that provides a uniform

set of policies and procedures that are followed in the event of an error.

•	 Exchanges have the ability to enable or restrict access per established rules.

•	 Exchanges establish rules surrounding processes to ensure that direct connections are

guaranteed by clearing firms.

•	 Exchanges make non-clearing entities and system providers aware of exchange

rules and responsibilities in the processes surrounding connectivity and electronic

trading and ask them to certify to the exchange and clearing firm their capabilities

to provide risk management functionality.

Role of

Exchange
8	 April 2010

Market Access Risk Management Recommendations

1. Execution Risk Tools 

Pre-trade order checks are risk controls put in place to prevent execution of a trade because of

error or “fat-finger” problems, or a client trading beyond authorized trading limits. Pre-trade

risk controls can be put in place at the trading firm, clearing firm, or exchange level. Pre-trade

risk controls have become a point of negotiation between trading firms and clearing members

because they can add latency to a trade. To avoid such negotiations, the MAWG believes

that certain risk controls should reside at the exchange level and be required for all trading

to ensure a level playing field. The right to set and manage, or authorize a trading firm to set

and manage, any pre- or post-trade order checks at the exchange’s matching engine, however,

should reside with the clearing firm.

Recommended Implementation: 

•	 To reduce the inevitable errors that occur with manual data entry, exchanges should

work towards providing a standard communication protocol that would allow firms

to automate setting and updating risk parameters for individual trading entities.

This would also give clearing firm risk managers the ability to more efficiently

disable a client from multiple exchanges simultaneously. An API based on 

agreed standard protocol such as FIX would be the preferred method for entering and

updating limits.

•	 Unless otherwise indicated, exchange risk control systems should provide clearing

firms with the ability to define risk controls by product. All limits should be set by

positive permissioning. The auto-default should be set to zero (i.e. clearing firm will

set limits only for the products that they are allowing the trading firm to trade).

a. Order Size

Quantity-per-order limits are the most basic types of pre-trade risk management tools to

help prevent accidental “fat-finger” incidents. This type of limit sets a maximum number of

contracts that can be bought or sold per order.

Principle: 

Quantity-per-order limits should be mandatory:

(a) The clearing firm should establish limits with the trading firm to avoid generating and

sending erroneously-sized orders to the market. Occasionally, larger-sized orders are legitimate.

In such cases, the trading firm needs to contact the clearing firm to adjust their limits.

(b) The exchange should provide default limits to protect the integrity of its market.

Recommended Implementation: 

A clearing firm providing direct access to a market should have visibility to the limits and the

ability to set appropriate limits for the trading firm’s activity, regardless of whether the trading

firm accesses the market directly (DA-E), through the clearing member system (DA-C) or

through a third-party system (DA-V).

•	 Risk controls need to be sophisticated enough to allow the clearing firm to set pretrade limits per product for each client and prevent trading beyond established

limits. Different sized limits are required for more liquid versus less liquid

instruments (e.g., front month versus back month futures or options, in-the-money

versus out-of-the-money options).

•	 Trading firm access to products should be blocked until limits are established by the

clearing firm. Default limits should not allow “unlimited” trading. In addition, the

clearing firm would like to have the ability to set controls for multiple products at

one time.

Market Access

Recommendations
9	 April 2010

Market Access Risk Management Recommendations

b. Intraday Position Limits

Intraday position limits give the clearing firm the ability to block a trading firm from

increasing its positions beyond a set threshold. Limits placed at the exchange level, rather

than the order-entry system, allow centralization and standardization of risk controls. Position

limits, however, are intended as “speed bumps on trading” and not as actual credit controls.

These limits include start-of-day positions, cash in account, and cross-asset margining.

Position limits provide the ability to automatically halt errant algorithms before credit

limits are exceeded. Once a trader is blocked, the risk department has time to perform a risk

evaluation before allowing further trading.

Principle: 

The exchange should make available the ability to set pre-trade intraday position limits.

Once the trading entity has reached these limits, only risk-reducing trades would be allowed.

Recommended Implementation for Futures:

The position limit capability should have the following characteristics:

•	 Set by trader, account, or firm and with the ability to set by groups of traders or

accounts.

•	 Set maximum cumulative long positions and maximum cumulative short positions.

•	 Include working orders in maximum long/maximum short position calculations.

•	 Set by product level.

•	 Provide the ability to raise or lower limits intraday.

•	 Be configurable by open API, preferably FIX API.

•	 Be mandatory for all participants so that latency is the same for all.

Recommended Implementation for Options:

•	 Recognizing that options have a lower delta than futures, position limit capability

must include the ability to differentiate limits by product type.

c. Cancel-On-Disconnect

When a system unintentionally disconnects from the exchange network, it creates

uncertainty about the status of working orders. Automatic cancellation of orders upon

disconnect provides certainty to the trading firm and risk manager whether orders have been

filled or cancelled. Some users, however, may not want to have their orders automatically

pulled from a market as the working order may be part of a hedged position or a crossexchange strategy trade.

Principle: 

Exchanges should implement a flexible system that allows a user to determine whether their

orders should be left in the market upon disconnection. This should only be implemented if

the clearing firm’s risk manager has the ability to cancel working orders for the trader if the

trading system is disconnected. The exchange should establish a policy whether the default

setting for all market participants should be to maintain or cancel all working orders.

d. Kill Button 

A “kill” button provides clearing firms with a fast and efficient way to halt trading activity at

the exchange level when a trading firm breaches its obligations vis-a-vis the clearer (e.g. by

exceeding credit limits due to erroneous activity of an automated trading application). T

trading firm will be excluded from trading until the clearing firm explicitly reinstates it.
10 April 2010

Market Access Risk Management Recommendations

Principle:

Exchanges should provide clearing firms with the ability to: 1) delete all open orders a

quotes and 2) reject entry of new orders and quotes.

Recommended Implementation: 

•	 The exchange should have a registration system that requires firms to specify which

staff members are authorized to use the kill button.

•	 The system itself should have explicit warnings informing authorized users of the

consequences of activating the kill button.

•	 Similar functionality could be implemented to allow a trading firm to halt trading

activity on a firm-wide, trading group or individual trader basis.

e. Order Cancel Capabilities

Principle: 

Exchanges should provide to clearing members an order management tool that allows realtime access to information on working and filled electronic orders. The tool should provi

risk mitigation functionality in the event of an electronic trading system failure.

Recommended Implementation: 

The clearing member and trading firm should have the ability to view and cancel orders via

this tool. Clearing members should be able to delegate and permission the tool for individu

traders or firms at granular levels.

The tool should provide view capabilities for:

	 • current ord

	 • fill information, including part

	 • cancel and replac

	 • order t

The tool should provide cancel capabilities for:

	 • individu

	 • groups 

	 • all working orders via a singl

f. Price Banding/Dynamic Price Limits

Price banding or dynamic price limits are an automated order-entry screening process

designed to prevent entry of buy or sell orders priced substantially through the contra side of

the market. It reduces the number of error trades that take place in the market by preventing

bids from being entered too far above current market prices and offers from being entered too

far below current market prices.

Principle: 

The exchange should have the ability to set price limits on a dynamic basis, continuously

adjusting throughout the day to account for current market conditions.
11 April 2010

Market Access Risk Management Recommendations

Recommended Implementation:

Exchanges should have the ability to widen price bands throughout the trading day when

necessary to account for additional volatility in the market. The width of the price limits

should be determined by product. Price banding occasionally can be too strict for less liquid

markets and may need manual intervention to facilitate trading if the current range is

deemed unsuitable.

Price banding for options requires a different approach because options are more dynamic.

Price banding may be too restrictive for less liquid options contracts because of wider bid-ask

spreads.

g. Market Maker/Sweep Protections

Sweep protections are designed for firms with specific market-marketing obligations to quote

options en masse. Although these protections are most frequently used in options markets,

they can be applied to other markets. Market-maker protections are parameters set by market

makers and implemented by the exchange to provide a degree of risk protection by limiting

the market maker’s quote execution exposure.

Principle:

Exchanges should allow a level of protection for market makers who quote simultaneously on

both sides of the market.

Recommended Implementation: 

Protection parameters should be optional and should allow values to be set by each market

maker or market-making entity. When market maker-defined protection values are met or

exceeded within certain time intervals, the protections should be triggered. When triggered,

the electronic trading system would initiate the market-maker protection functionality,

which rejects new messages and/or cancels resting quotes associated with the market maker.

h. Internal Trade Crossing 

It is common for multiple independent trading strategies to be active at the same time within

a single firm. The strategies may interact on the market by taking opposite sides, occasionally

generating inadvertent wash trades. This is a common situation with direct access and the

increasing use of broker execution algorithms that may stretch orders over a period of time,

micro-manage slices that may interact with another order placed by the same legal entity, or

run as an auto-hedging facility with no intention upfront to create a wash trade.

The MAWG considered whether technology could assist risk managers in identifying wash

trades. The group concluded that it is impossible for exchanges to implement such risk

controls because account ownership information is not available at the matching engine.

While clearing members have the ownership information and can confirm whether a client

resides in the same profit center of the firm, algorithms may be producing orders that interact

with accounts within the same legal entity. Further, customers can use multiple systems

within a legal entity that don’t necessarily interact with each other on a pre-trade basis. The

MAWG concluded that there was no way to design a rule that would prevent wash trades

without preventing legitimate trades.
12 April 2010

Market Access Risk Management Recommendations

Principle: 

Wash trades are prohibited to prevent manipulating the market by artificially distorting

market price or volume. Inadvertent crosses do not have the intent to mislead the public.

Exchanges, working within the framework provided by their respective regulators, should set

guidelines for vendors, customers, and clearing members, defining what would be acceptable

reasons for inadvertent cross trades. Existing rules should be re-examined in the context of

today’s trading environment.

2. Post-Trade Checks 

In addition to pre-trade risk controls, post-trade checks allow clearing and trading firm risk

managers to track all working/open orders and all executed and cleared orders. “Drop copy”

functionality gives clearing firms the ability to monitor orders on a near real-time basis

without adding latency to the order flow. Drop-copy functionality allows clearing members

to receive duplicate copies of client working/executed orders as they enter the exchange

network and/or are matched at the clearinghouse.

Principle: 

Exchanges should make drop copies available to clearing and trading firms.

•	 Trade capture drop copy: Exchanges should provide clearing firms with drop copies

of orders and executed trades. This allows clearing firms to get their current set of

trades and positions from a secondary channel independent of the primary trading

system.

•	 Post-clearing drop copy: Exchanges should provide clearing firms net position per

maturity per contract as soon as the trade is matched at the clearinghouse. This

functionality needs to be as close to real-time as possible.

•	 Exchange drop-copy functionality should allow clearing firms to enable trading firms

to receive trade capture and post-clearing drop copies.

Recommended Implementation: 

The post-clearing drop copy feed should contain all messages including acknowledgements,

fills, amendments and cancellations. Exchanges need to work toward an industry standard of

delivering cleared information in a maximum of two-three minutes after a trade is executed.

This data needs to be delivered via a standard protocol, preferably via FIX API.

3. Co-Location Policies 

When considering co-location, exchanges should recognize that one of the main benefits of

such a service is that it creates a level playing field for firms that want low-latency access to the

exchange. It provides firms, both large and small, with low-latency connectivity for a reasonable

cost made possible by the exchange sharing the costs of the required technical infrastructure

with interested participants. When co-location and proximity sites are not available, 

encourages firms to seek confidential knowledge about matching engine locations and compete

for building space closest to those engines so they can build their own private data centers. This

exacerbates the differences in the ability of market participants to obtain market access.

Principle:

Steps should be taken to ensure that access to co-location is available to every firm that is

interested in such a service and that the terms of the co-location service remain transparent

to all market participants.
13 April 2010

Market Access Risk Management Recommendations

4. Conformance/Certification Testing

Principle:

•	 All trading firms that wish to write directly to the order entry or market data

interfaces of an exchange should be required to pass an initial set of conformance

tests for execution and market data that highlight basic functionality of the trading

system that will be making the direct connection. All ISVs and proprietary syste

should be required to pass the same conformance tests, so the proprietary system

client using the ISV should not be required to pass conformance.

•	 The exchange should be required to provide a conformance environment ondemand for re-certification requirements.

Recommended Implementation: 

A representative of the exchange should interview the proprietary system client to determine

which functionality should be tested. Exchanges should test the ability of a direct access firm t

•	 Send a request for and process the exchange’s response for the following: Log On,

Log Off, New Order, Cancel, Order Modify, Sequence Reset, Instrument Definition

Requests, and Market Snapshot requests.

•	 Process the following exchange messages: Business Reject, Session Reject, Complete

Fills, Partial Fills, Exchange Open/Close, Market Data Updates, Trade Updates.

•	 Properly handle the exchange recovery mechanism provided when messages are sent

from the exchange to a proprietary system participant, but the client isn’t actively

connected.

•	 Recertification should be required whenever core functionality has changed at the

exchange. It should be up to the exchange to decide what functionality needs 

be recertified as well as to notify each proprietary system participant of the need to

recertify.

•	 Recertification should be required whenever a participant’s core functionality has

changed. It is up to the proprietary system participant to notify the exchange wh

this happens as well as to schedule the conformance test.
14 April 2010

Market Access Risk Management Recommendations

5. Error Trade Policy 

The potential for trading errors by direct access traders causing significant market disruptions

is of utmost concern to all market participants and regulators. Although traders and tradi

system engineers have an incentive to build robust systems and safeguards to avoid potential

error trade situations and the substantial costs associated with them, the potential for error

trades still exists. Robust pre-trade risk controls such as price banding significantly reduce the

potential for erroneous trades but exchanges still need to enforce a strict error trade policy.

A robust error trade policy minimizes systemic risk by affording market participants

confidence that when an error trade occurs, it will be evaluated and resolved according to a

uniform set of policies and procedures. Conversely, subjectivity or ambiguity in an error trade

policy amplifies risk through uncertainty. The objective of an error trade policy should be to

remove the uncertainty of open-ended market exposure and allow traders to expeditiously

resume normal trading activity. This is critical for maintaining market confidence and

continuity.

a. Trade Certainty

An important aspect of market integrity is the confidence that, once executed, transactions

will stand and will not be subject to arbitrary cancellation.

Principle:

Exchanges should adopt a “Preferred Adjust-Only Policy” to ensure absolute trade certainty

to all parties to an error trade. In a Preferred Adjust-Only Policy all trades inside of

product-specific “no-adjust” range are ineligible for adjustment. All trades outside of the

no-adjust range potentially could be adjusted to the edge of the no-adjust range from

the prevailing market at the time of execution. The Preferred Adjust-Only Policy would

not eliminate the authority of an exchange to cancel or correct trades under extreme

circumstances.

b. Contingency Orders

The most challenging aspect of an error trade policy is the appropriate way to handle a

contingency or stop order triggered by an erroneous transaction. The MAWG recognizes that

a clearing firm could incur losses on contingency orders their customers placed which were

filled as the result of an erroneous trade but cannot be passed on to the customer since the

adjusted price does not indicate that the order should have been filled.

Principle:

In keeping with the objective of the Preferred Adjust-Only Policy, contingent or stop orders

executed as a result of an error trade should be eligible for compensation from the party that

made the error. An exchange’s authority to cancel orders under extreme circumstances should

not be invoked merely because an order is a contingent order.
15 April 2010

Market Access Risk Management Recommendations

c. Notification

Markets continue to trade while the parties to a trade and the exchange determine whether

a trade is erroneous. The identification of a possibly erroneous trade well after it has be

executed and its later cancellation can create even more uncertainty in the market. Market

integrity, therefore, demands that exchange policies and procedures establish strict, narrow

time frames in which a request to cancel a trade is made.

Principle: 

The exchange should establish a minimal reporting time of less than five minutes for firms to

notify the exchange that an error has occurred.

The exchange should announce a potential adjust-or-bust situation immediately upon

notification and the adjust decision should be disseminated to the marketplace within a

reasonable timeframe via a specific market data message, email and/or other established mode

of communication on a best efforts basis.
Futures Industry Association

2001 Pennsylvania Avenue N.W., Suite 600

Washington, DC 20006-1823

202.466.5460 • www.futuresindustry.org