1

Staff Report on Algorithmic Trading in U.S. Capital Markets

As Required by Section 502 of the 

Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018

This is a report by the Staff of the U.S. Securities and Exchange Commission. The 

Commission has expressed no view regarding the analysis, findings, or conclusions 

contained herein.

August 5, 2020
2

Table of Contents

I. Introduction...................................................................................................................................................3

A. Congressional Mandate .........................................................................................................................3

B. Overview .....................................................................................................................................................4

C. Algorithmic Trading and Markets .....................................................................................................5

II. Overview of Equity Market Structure..................................................................................................7

A. Trading Centers ........................................................................................................................................9

B. Market Data............................................................................................................................................. 19

III. Overview of Debt Market Structure .................................................................................................. 23

A. Types of Debt Securities or Instruments ..................................................................................... 23

B. Data and Communications ................................................................................................................ 28

IV. Benefits and Risks of Algorithmic Trading in Equities .............................................................. 30

A. Investors................................................................................................................................................... 30

B. Brokers...................................................................................................................................................... 36

C. Principal Trading .................................................................................................................................. 37

D. Operational Risks to Firms and the Market................................................................................ 42

E. Studies of Effects on Market Quality and Provision of Liquidity........................................ 44

F. Effects of the COVID-19 Pandemic ................................................................................................. 47

V. Benefits and Risks of Algorithmic Trading in Corporate and Municipal Bonds............... 51

A. Liquidity Search and Trade Execution.......................................................................................... 51

B. ETF Market Making and Arbitrage................................................................................................. 53

C. Studies of Effects on Market Quality and Provision of Liquidity........................................ 53

VI. Regulatory Responses to Market Complexity, Volatility, and Instability ........................... 55

A. Improving Market Transparency.................................................................................................... 55

B. Mitigating Price Volatility.................................................................................................................. 60

C. Facilitating Market Stability and Security................................................................................... 63

D. Additional Ongoing and Potential Commission and Staff Actions ..................................... 67

VII. Summary of Studies on Algorithmic Trading ................................................................................ 69

A. Equities ..................................................................................................................................................... 69

B. Debt Securities ....................................................................................................................................... 82

VIII. Conclusion................................................................................................................................................... 83

IX. Bibliography to Summary of Academic Studies ........................................................................... 85

X. Appendix: Market Participants, Roles, and Obligations ............................................................ 92
3

I. Introduction

A. Congressional Mandate

The Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018 requires 

the staff of the U.S. Securities and Exchange Commission (the “SEC” or “Commission”) to 

submit to Congress a report on the risks and benefits of algorithmic trading in the U.S. 

capital markets.1 Specifically, § 502 provides:

(a) In General. Not later than 18 months after the date of enactment of this Act, the staff of 

the Securities and Exchange Commission shall submit to the Committee on Banking, 

Housing, and Urban Affairs of the Senate and the Committee on Financial Services of 

the House of Representatives a report on the risks and benefits of algorithmic trading 

in capital markets in the United States.

(b) Matters Required To Be Included. The matters covered by the report required by 

subsection (a) shall include the following:

(1) An assessment of the effect of algorithmic trading in equity and debt markets in 

the United States on the provision of liquidity in stressed and normal market 

conditions.

(2) An assessment of the benefits and risks to equity and debt markets in the 

United States by algorithmic trading.

(3) An analysis of whether the activity of algorithmic trading and entities that 

engage in algorithmic trading are subject to appropriate Federal supervision 

and regulation.

(4) A recommendation of whether

(A) based on the analysis described in paragraphs (1), (2), and (3), any 

changes should be made to regulations; and

(B) the Securities and Exchange Commission needs additional legal 

authorities or resources to effect the changes described in subparagraph 

(A).

 

1 Economic Growth, Regulatory Relief, and Consumer Protection Act, Pub. L. No. 115-174, 

§ 502, 132 Stat. 1296, 1361-62 (2018). 
4

B. Overview

As required by § 502 of the Economic Growth, Regulatory Relief, and Consumer Protection 

Act of 2018, this staff report describes the benefits and risks of algorithmic trading in the 

U.S. equity and debt markets. 

Broadly speaking, and as more fully discussed below, algorithmic trading in the equities—

and to a lesser extent—in the debt market, has improved many measures of market quality 

and liquidity provision during normal market conditions, though studies have also shown 

that some types of algorithmic trading may exacerbate periods of unusual market stress or 

volatility. Advances in technology and communications have enabled many market 

participants to more efficiently provide liquidity, more efficiently access market liquidity, 

implement new trading services, and more effectively manage risk across a range of 

markets. 

Furthermore, commenters have observed that the increasing complexity of multiple 

interconnected markets may have increased the risk that operational or systems failures at 

trading firms, platforms, or infrastructure may have broad, potentially unexpected, 

detrimental effects on the markets and investors. A number of observers have noted that

even as some uses of algorithms may contribute to market complexity, algorithms 

generally help market participants navigate market complexity. A common theme echoed 

by nearly all market professionals, academic researchers, and other students of the

securities markets is that that algorithmic trading, in one form or another, is an integral 

and permanent part of our modern capital markets. 

Several variations of algorithmic trading strategies have developed and expanded over the 

last several decades. These developments have been driven, in pertinent part, by the 

growth in available market data generated by and consumed by market professionals, 

major advances in computational power and the speed of data transmission, and the 

exponential increase in the speed of securities trading. Enhancements in algorithmic 

trading strategies have also been driven by investor demands for execution quality, the 

search for alpha and trading profits, and the application of sophisticated quantitative 

analytics. The Commission and other regulators have responded with a range of tools 

intended to mitigate risks to investors and to help ensure fair, efficient, and orderly 

markets. Commission staff will continue to monitor technological change and its influence 

on investment, trading, and the capital markets, and will continue to assess the need for 

additional regulation, resources, or legal authority.2

 

2 The significant and rapid economic impact precipitated by the COVID-19 pandemic was 

reflected in extraordinary trading in the U.S. secondary markets for equity and debt during 

the spring of 2020. While this report briefly discusses recent market events, including 

certain significant impacts on trading as market participants reacted to the effects of 

COVID-19, the report is focused on the broader questions raised in § 502. In April 2020, 
5

C. Algorithmic Trading and Markets

The use of algorithms in trading is pervasive in today’s markets. Any analysis of the role 

that algorithmic trading plays in the US equity and debt markets requires an understanding 

of equity and debt market structure,3 the role played by different participants in those 

markets, and the extent to which algorithmic trading is used by market professionals. 4 

In describing the uses of algorithms in trading, it is useful to first define an algorithm. At its 

most general level, an algorithm is a finite, deterministic, and effective problem-solving 

 

the Commission announced the formation of an internal, cross-divisional COVID-19 Market 

Monitoring Group to assist with Commission and staff actions and analysis related to the 

effects of COVID-19 on markets, issuers, and investors, and with responding to requests for 

information, analysis and assistance from fellow regulators and other public sector 

partners. See “SEC Forms Cross-Divisional COVID-19 Market Monitoring Group,” Press 

Release 2020-95 (Apr. 24, 2020); see also “COVID-19 Market Monitoring Group — Update 

and Current Efforts,” Statement of Chairman Jay Clayton and S.P. Kothari (May 13, 2020), 

available at: https://www.sec.gov/news/public-statement/statement-clayton-kotharicovid-19-2020-05-13 (describing some of the initial work of the COVID-19 Market 

Monitoring Group); COVID-19 Market Monitoring Group, “Credit Ratings, Procyclicality and 

Related Financial Stability Issues: Select Observations” (Jul. 15, 2020), available at:

https://www.sec.gov/news/public-statement/covid-19-monitoring-group-2020-07-15.

3 The section of this staff report on equity market structure echoes aspects of the 

Commission’s 2010 Concept Release on Equity Market Structure. See Concept Release on 

Equity Market Structure, Exch. Act. Rel. No. 61358, 75 Fed. Reg. 3594 (Jan. 21, 2010) 

(“Concept Release”). That Concept Release described the transition of modern equity 

trading markets away from a largely centralized, manual structure to the dispersed, 

automated structure that exists today. The Concept Release provided many useful 

institutional details; this report updates some of these details, and describes important 

developments that have occurred since 2010. When discussing debt markets, this report 

focuses on corporate and municipal bonds. While the markets for U.S. Treasury securities 

are described briefly, they are not a focus of this report.

4 The main body of this staff report presumes familiarity with core concepts in securities 

market structure, such as the distinction between acting as a broker and trading as 

principal, key differences between types of trading venues such as national securities 

exchanges and alternative trading systems, the difference between providing and 

demanding liquidity, and legal obligations such as best execution. Background on these 

concepts may be found in the appendix to this report, which provides a more general

orientation to market participants, roles, and obligations. 
6

method suitable for implementation as a computer program.5 In modern equity and debt 

markets, many problems are solved and decisions made in this computational, algorithmic 

manner. Today, algorithms address many of the problems and decisions that have long 

been central to the business of trading. What instrument(s) should be invested in or 

traded? What price should be bid or offered? What order size is optimal? What should be 

the response to a request for a quotation? What risk will be taken on by facilitating a 

trade? How does that risk change with the size of the trade? Is the risk of a trade 

appropriate to a firm’s available capital? What is the relationship between the price of 

different but related securities or financial products? To what market should an order be 

sent? Is it more effective to provide liquidity or demand liquidity? Should an order be 

displayed or non-displayed? To which broker should an order be sent? When should an 

order be submitted to a trading center? In general, algorithms utilize a rich array of market 

information to very quickly assess the state of the market, to determine when, where, and 

how to trade, and then to implement the resulting trading decision(s) in the marketplace.6

As described in more detail below, algorithms are broadly used in contemporary securities 

markets, and the range of uses differs across asset classes and across the roles and 

investment objectives of market participants. In light of the wide diversity of algorithms in 

modern trading, it is not a goal of this report to define a single type of trading or activity as 

uniquely algorithmic. Rather, this staff report attempts to describe many dimensions of the 

contemporary secondary markets for equity and debt securities that operate 

algorithmically. The types of trading described in more detail below each fundamentally 

depend upon computerized algorithms, and the data and technological infrastructure 

through which they operate, to address the types of problems and tasks described above. 

The staff’s approach differs from the more narrow approaches taken in much of the 

literature on algorithmic trading, which generally seek to examine a specific type of 

algorithmic activity. For example, one study defines algorithmic trading as “a tool for 

professional traders that may observe market parameters or other information in real-time 

and automatically generates/carries out trading decisions without human intervention.”7 

Other approaches, for example, characterize algorithmic trading as the use of programmed 

 

5 See, e.g., Robert Sedgewick & Kevin Wayne, Algorithms, 4 (4th Ed. 2011) (“The term 

algorithm is used in computer science to describe a finite, deterministic, and effective 

problem-solving method suitable for implementation as a computer program”).

6 These are just a few of the questions and decisions that algorithms address in today’s 

markets and the scope as well as the granularity of issues that algorithms address is 

virtually unbounded.

7 Peter Gomber, Björn Arndt, Marco Lutat, Tim Uhle, High Frequency Trading, 14 (Goethe 

Univ. Frankfurt Am. Main, Working Paper, 2011) (available at 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1858626).
7

trading instructions to execute small portions of larger orders over time.8 While activity 

meeting these definitions is encompassed in the approach taken here, this staff report’s 

coverage is broader, reaching areas where algorithms are used and may be important, but 

in some cases may not be used as exclusively or extensively as in the activities described in 

these examples.9

II. Overview of Equity Market Structure

Today’s equity market structure is highly fragmented, consisting of fifteen national 

securities exchanges, over thirty alternative trading systems, multiple single-dealer 

platforms within broker-dealers, and other forms of order matching. The equity markets 

are also highly complex, with dozens of different order types, a multitude of market 

connectivity options, and a rich array of market information products providing data in 

speeds often measured in microseconds. This data is the key input into the wide variety of 

algorithmic trading strategies that rapidly submit orders across venues, creating and 

moving the prices of securities, which, in turn, generate more data that drives the next set 

of algorithmic trading decisions. 

In Section 11A of the Exchange Act,10 Congress directed the Commission to facilitate the 

establishment of a national market system. The Commission is required to do so in 

accordance with the findings and objectives Congress outlined in the Exchange Act:

• The securities markets are an important national asset which must be preserved and 

strengthened;

• New data processing and communications techniques create the opportunity for more 

efficient and effective market operations;

• It is in the public interest and appropriate for the protection of investors and the 

maintenance of fair and orderly markets to assure–

– Economically efficient execution of securities transactions;

– Fair competition among brokers and dealers, among exchange markets, and 

between exchange markets and markets other than exchange markets;

 

8 See Katie Kolchin, Electronic Trading Market Structure Primer, SIFMA Insights, pp. 15-16 

(Oct. 10, 2019), available at: https://www.sifma.org/resources/research/electronictrading-market-structure-primer/.

9 For a more detailed discussion of some of the methodological issues involved with trying 

to precisely define algorithmic trading and its subsets, see Staff of Division of Trading and 

Markets, U.S. Securities & Exchange Commission, Equity Market Structure Literature Review 

Part II: High Frequency Trading, 4-11 (Mar. 18, 2014) (“HFT Literature Review”).

10 15 U.S.C. 78k-1.
8

– The availability to brokers, dealers, and investors of information with respect 

to quotations for and transactions in securities;

– The practicability of brokers executing investors’ orders in the best market; 

and

– An opportunity, consistent with economically efficient execution and the ability 

to execute orders in the best market, for investors’ orders to be executed 

without the participation of a dealer; and

• The linking of all markets for qualified securities through communication and data 

processing facilities will foster efficiency, enhance competition, increase the 

information available to brokers, dealers, and investors, facilitate the offsetting of 

investors’ orders, and contribute to best execution of such orders.

These findings and objectives give a paramount place to the interests of investors, and 

conclude that the interests of investors are best served by a market structure that is 

designed to promote and maintain both (1) an opportunity for interaction of all buying and 

selling interest and (2) fair competition among all types of market centers.11 As the 

Commission has noted, these objectives can be difficult to reconcile.12 For example, 

maximizing order interaction in individual securities may be in tension with market center 

competition for order flow, and market center competition for order flow may lead to 

fragmentation in the order flow for individual securities.13 As the Commission has stated, 

its “task has been to facilitate an appropriately balanced market structure that promotes 

competition among markets, while minimizing the potentially adverse effects of 

fragmentation on efficiency, price transparency, best execution of investor orders, and 

order interaction.”14

The secondary market for U.S.-listed equity securities that has developed within this 

structure is now primarily automated.15 The process of trading has changed dramatically 

 

11 Notice of Filing of Proposed Rule Change by the New York Stock Exchange, Inc. to Rescind 

Exchange Rule 390; Commission Request for Comment on Issues Relating to Market 

Fragmentation, Exch. Act Rel. No. 42450, 65 Fed. Reg. 10577, 10580 (Feb. 28, 2000) 

(“Fragmentation Release”).

12 See id. (“although the objectives of vigorous competition on price and fair market center 

competition may not always be entirely congruous, they both serve to further the interests 

of investors and therefore must be reconciled in the structure of the national market 

system”); see also Concept Release at 3597.

13 See, e.g., Concept Release at 3597.

14 Id.

15 See, e.g., id. at 3594.
9

primarily as a result of developments in technologies for generating, routing, and executing 

orders, as well as by the requirements imposed by law and regulation.16 Today, equity 

trading volume generally is dispersed among many automated trading centers that 

compete for order flow in the same stocks, principally by offering execution services

designed to fill the needs of the wide variety of market participants.17 Maintaining fair,

efficient, and orderly markets requires an understanding of the dependence of modern 

markets on algorithms used, among other things, for order routing, handling, and 

execution.

The following overview summarizes elements of the market structure most salient to 

algorithmic trading, including the various types of equity trading centers and the market 

data that facilitates communication among trading centers and participants. 

A. Trading Centers

A reasonable place to start in describing current equity market structure is an overview of 

the major types of trading centers and their share of volume in NMS stocks.18 Broadly 

speaking, the market can be divided into registered national securities exchanges and offexchange trading venues, which include alternative trading systems (ATSs) and several 

types of broker-dealer internalization platforms.19 Nearly all of these trading centers 

depend on automated systems and algorithms to perform their important role in the 

market structure for U.S. equities.

 

16 Id.

17 Id.

18 See, e.g., id. at 3597-3600. “NMS stock” means any security or class of securities, other 

than an option, for which transaction reports are collected, processed, and made available 

pursuant to an effective transaction reporting plan. See 17 CFR 242.600(b)(48) (defining 

“NMS stock” as “any NMS security other than an option”), 17 CFR 242.600(b)(47) (defining 

“NMS security” as “any security or class of securities for which transaction reports are 

collected, processed, and made available pursuant to an effective transaction reporting 

plan, or an effective national market system plan for reporting transactions in listed 

options”). In general, NMS stocks are those listed on a national securities exchange. See

Concept Release at 3597 n.20. 

19 A broker-dealer internalizes an order when it executes the order out of its own inventory 

of securities, rather than routing it to an exchange or other platform, or matches buyers 

and sellers together outside of an ATS or exchange. See, e.g., U.S. Securities & Exchange 

Commission Investor Publications, Trade Execution: What Every Investor Should Know

(Jan. 16, 2013), available at https://www.sec.gov/reportspubs/investorpublications/investorpubstradexechtm.html; Concept Release at 3599-3600.
10

Table 1 summarizes, for all NMS stocks in 2019, the percentage of trades, share volume, 

and dollar volume executed on each registered exchange or reported to each trade 

reporting facility.20 As summarized in Table 2, approximately 78% of all trades were 

executed on registered exchanges, and 22% off-exchange; 63% of all shares traded were 

executed on-exchange, and 37% off-exchange; and 65% of dollar-volume was executed onexchange, and 35% off-exchange.

Table 1: Percentage of All Trades, Shares, and Dollar Volume in 2019 at 

National Securities Exchanges or Reported to Trade Reporting Facilities (TRFs)

Venue/TRF Trades Shares $ Vol.

Cboe BYX 6.2% 3.8% 3.0%

Cboe BZX 8.7% 5.5% 6.4%

Cboe EDGA 4.3% 2.2% 2.1%

Cboe EDGX 6.4% 4.8% 4.7%

IEX 3.8% 2.7% 2.9%

Nasdaq 24.1% 17.2% 19.7%

Nasdaq BX 3.1% 1.8% 1.8%

Nasdaq PSX 0.9% 0.7% 0.9%

NYSE 8.5% 13.5% 12.4%

NYSE American 0.4% 0.3% 0.2%

NYSE Arca 9.4% 8.4% 9.3%

NYSE Chicago <0.01% 0.4% 0.8%

NYSE National 2.1% 1.4% 0.8%

TRF Nasdaq Carteret 18.6% 29.7% 29.3%

TRF Nasdaq Chicago 0.1% 0.1% 0.1%

TRF NYSE 3.5% 7.5% 5.6%

Source: NYSE TAQ

 

20 Trades executed otherwise than on a national securities exchange must be reported in a 

timely manner to a trade-reporting facility. See, e.g., FINRA Rules 6300A - 6380B, 7200A -

7280B. Currently there are three Trade Reporting Facilities.
11

Table 2: Percentage of All NMS Stock Trades, Shares, and Dollar Volume in 

2018 at All Registered Exchanges or Reported to TRFs

Venue Trades Shares $ Vol.

Exchanges 78% 63% 65%

Off-Exchange 22% 37% 35%

Source: NYSE TAQ

Currently, only national securities exchanges display quotations in the consolidated 

quotation data widely distributed to the public.21 Trades executed off-exchange (i.e., about 

35% of equity dollar volume, as shown in Table 2) take place on ATSs and dealer platforms 

where quotes are not publicly displayed. Because they do not publicly display quotes, 

these venues are commonly referred to as “dark pools” of liquidity.

1. National Securities Exchanges

In 2019, national securities exchanges together executed approximately 78% of trades, 

63% of share volume, and 65% of dollar volume in NMS stocks. In 2019, no single 

exchange accounted for more than 24% of all NMS stock trades, 17% of all NMS stock share 

volume and 20% of NMS stock dollar volume. Figure 1 compares the percentages of trades, 

share volume, and dollar volume across all registered exchanges in 2019.

 

21 These consolidated market data plans are discussed more fully below. See infra Section 

III.B. 
12

Figure 1: % of Trades, Shares, and Dollar Volume in 2019

While there are now fifteen registered national securities exchanges for equities, and 

thirteen equities exchanges operating,

22 twelve are owned by three corporate entities, 

commonly known as “exchange families.”23 Figure 2 shows the percentage of trades, share 

volume, and dollar volume executed at each exchange family during all of 2019.24

 

22 As of the date of publication of this staff report, Long-Term Stock Exchange, Inc. and 

MEMX LLC have not begun trading operations.

23 The exchange families are (1) CBOE Global Markets, Inc., which owns CBOE BYX 

Exchange, Inc., CBOE BZX Exchange, Inc., CBOE EDGA Exchange, Inc., and CBOE EDGX 

Exchange, Inc.; (2) Nasdaq, Inc., which owns Nasdaq BX, Inc., Nasdaq PHLX LLC, and The 

Nasdaq Stock Market LLC; and (3) Intercontinental Exchange, Inc., which owns New York 

Stock Exchange LLC, NYSE Arca, Inc., NYSE American LLC, NYSE Chicago, Inc., and NYSE 

National, Inc.

24 Long-Term Stock Exchange is not reflected in the 2019 data because it was not yet 

executing trades as a national securities exchange.
13

Figure 2: % of Trades, Shares, and Dollar Volume in 2019, by Exchange Family

Trading and communication at national securities exchanges are now almost entirely 

automated. Order entry, message acknowledgement, matching algorithms, trade 

confirmations, and market data systems all operate at microsecond or nanosecond 

timescales. 

To reduce time delay, or “latency,” between exchange systems and market participants, and 

to otherwise facilitate order entry and trade execution, exchanges offer data and 

connectivity services to market participants, including for example, allowing participants to 

place their servers close to exchange matching engines and data feeds to minimize data 

transmission time. Exchanges also offer market participants a variety of services for (1)

receiving and processing data, and (2) moving data between data centers around the 

country (such as fiber-optic cables, millimeter waves, and microwaves). Put simply, 

computers running sophisticated algorithms consume and analyze this data to help market 

participants respond to market developments.

In addition to offering various data services, national securities exchanges generally offer 

an extensive range of order types that facilitate automated trading. These order types 

provide market participants with a multitude of options for interacting with other market 

participants, including, for example, (1) providing liquidity by posting orders to a central 
14

limit order book, (2) removing liquidity by matching with an order already resting on the 

book, (3) displaying quotes to the market, (4) providing non-displayed liquidity, (5) 

accessing liquidity within the quoted spread, (6) accessing non-displayed liquidity, or (7) 

repricing orders based on changing market conditions or to meet certain regulatory 

obligations. Market participants often use algorithms to pursue more than one of these or 

other order options simultaneously. Because most exchange matching algorithms use a 

system based upon price-time priority, many order types are oriented towards helping 

participants achieve or retain priority in an order book queue.25 Two exchanges also offer 

order types that automatically reprice orders based on predicted changes in prices derived 

from activity at other markets.26 One registered exchange offers a “speedbump,” or 

intentionally-implemented delays in executions, intended to mitigate the advantages that 

some market participants may have in receiving and processing market data and rapidly 

taking liquidity.27

 

25 Generally, under a system of price-time priority, better priced orders are at the top of the 

order queue, with ties at the same price resolved in favor of the order first to arrive in time. 

Ties in arrival time (rare given the granularity of current timestamps) are sometimes 

resolved in favor of the order with the largest size. The New York Stock Exchange has a 

“parity” model, in which each floor broker, a stock’s designated market maker, and the 

central limit order book receive parity in the execution of orders, with allocations for 

smaller orders determined through a “parity wheel.” Nasdaq PSX operates on a “pricesetter pro rata” model, under which resting orders that set the PSX BBO are guaranteed a 

certain proportion of an execution against incoming marketable orders, with other resting 

orders at the same price filled on a size pro-rata basis out of the remaining shares. Cboe 

EDGX uses price-retail-time priority, in which displayed limit orders from or on behalf of 

individual retail investors are given priority over other orders at the same price. 

26 See IEX Rule 11.190(g); NYSE American Rule 7.31E(h)(3)(D).

27 See IEX Rule 11.510(a). NYSE American recently eliminated its delay mechanism. See

NYSE American LLC; Notice of Filing and Immediate Effectiveness of Proposed Rule Change To 

Amend Exchange Rules 1.1E and 7.29E To Eliminate the Delay Mechanism and Amend 

Exchange Rule 7.31E and Related Exchange Rules To Re-Introduce Previously-Approved Order 

Types and Modifiers, Exch. Act Rel. No. 87550, 84 Fed. Reg. 64359 (Nov. 21, 2019). 

Recently, several exchanges have proposed “asymmetrical” speedbumps that would 

intentionally delay incoming marketable orders, but allowing resting orders to be cancelled 

or modified without delay. One was withdrawn, and one was disapproved. See Exch. Act 

Rel. No. 84337, 83 Fed. Reg. 50720 (Oct. 9, 2018) (setting aside approval order under 

delegated authority after filing was withdrawn); “Order Disapproving Proposed Rule 

Change to Introduce a Liquidity Provider Protection Delay Mechanism on EDGA,” Exch. Act 

Rel. No. 88261, 85 Fed. Reg. 11426 (Feb. 27, 2020). 
15

In addition to providing continuous trading through their limit order books throughout the 

day, national securities exchanges may perform opening and closing auctions in their listed 

securities.28 These auctions have increased in importance in recent years. This increase is 

correlated with, and to at least some meaningful extent has been driven by, the increase in 

popularity of investment products that incorporate exchange closing prices in their 

operations, including index mutual funds.29 The listing exchanges vary in the percentage of 

their volume executed in auctions. Table 3 shows, for example, the average daily 

percentage of share volume in auctions for the listing exchanges in 2019. 

Table 3: Average Daily Percentage of Share Volume in Auctions Per Listing Exchange in 2019

VENUE Auc. %

Cboe BZX 0.4%

Nasdaq 12.3%

NYSE 33.8%

NYSE American 14.6%

NYSE Arca 8.5%

Source: NYSE TAQ

Most exchanges have adopted fee schedules that differentiate between the providers of 

liquidity and the takers of liquidity.30 Most exchanges use a “maker-taker” model, paying 

rebates to providers of liquidity, charging a fee to takers of liquidity, with the exchanges

keeping any difference between (1) the amount paid to the exchange by takers of liquidity 

and (2) the amount paid by the exchange to providers of liquidity, as revenue on each 

 

28 As needed, the listing exchanges also perform intraday re-opening auctions. See, e.g., Plan 

to Address Extraordinary Market Volatility, Section VII(B). Some exchanges may also 

perform auctions in securities that they do not list, see, e.g., NYSE Arca Rule 7.35-E(a)(1), 

and some may match orders at the listing market closing auction price, see Order Setting 

Aside Action by Delegated Authority and Approving a Proposed Rule Change, as Modified by 

Amendments No. 1 and 2, to Introduce Cboe Market Close, a Closing Match Process for NonBZX Listed Securities under New Exchange Rule 11.28, Exch. Act Rel. No. 88008 (Jan. 21, 

2020). 

29 See, e.g., Robin Wigglesworth, “The 30 minutes that have an outsized role in US stock 

trading,” Financial Times (Apr. 24, 2018); Corrie Driebusch, Alexander Osipovich and 

Gregory Zuckerman, “What’s the Biggest Trade on the New York Stock Exchange? The Last 

One,” The Wall Street Journal (Mar. 14, 2018). 

30 Rule 610(c) of Regulation NMS caps the access fee for executions against the best 

displayed prices of a national securities exchange at $0.0003 per share for stocks price at 

or above $1.00. See 17 CFR § 610(c)(1). 
16

trade. A smaller number of exchanges employ a taker-maker model, paying rebates to the 

consumers of liquidity, charging the providers, and again keeping the difference as 

revenue. An even smaller number of exchanges charge a flat fee for all orders. Because of 

the substantial amount of money rebated back to trading participants, some order types 

may be oriented towards helping participants capture these rebates.31 

2. Alternative Trading Systems

In 2019, thirty-three ATSs executed trades in NMS stocks.32 That year, these ATSs executed 

approximately 10.2% of share volume in NMS stocks. The top two ATSs each executed 

approximately 1-2% of share volume in NMS stocks, with most ATSs executing under 1% of 

share volume.

Table 4: 2019 Top Ten ATSs by Share Volume: Percentage of ATS Volume, OffExchange Volume, and NMS Stock Volume

ATS % ATS % Off-Exch. % NMS

UBS ATS 19.3% 5.3% 2.0%

CROSSFINDER 9.8% 2.7% 1.0%

JPM-X 7.1% 1.9% 0.7%

MS POOL (ATS-4) 7.1% 1.9% 0.7%

SIGMA X2 6.7% 1.8% 0.7%

LEVEL ATS 6.6% 1.8% 0.7%

THE BARCLAYS ATS 6.0% 1.6% 0.6%

BIDS ATS 5.1% 1.4% 0.5%

SUPERX ATS 3.9% 1.1% 0.4%

MS TRAJECTORY CROSS (ATS-1) 3.1% 0.8% 0.3%

Source: FINRA OTC/ATS Transparency

 

31 Such order types may include, for example, non-routable post-only orders that seek only 

to provide liquidity (on venues providing rebates for the provision of liquidity) and will 

not, upon order entry, execute against a resting order on the other side of the market 

(either by be re-priced automatically or cancelled) or be routed to a different trading 

center. 

32 As reflected in the FINRA OTC/ATS Transparency data. FINRA OTC/ATS Transparency 

Data is provided via http://www.finra.org/industry/OTC-Transparency and is copyrighted 

by FINRA.
17

Currently, no NMS stock ATS publishes quotation data in the consolidated data feed. 33 In 

other words, at the moment, all NMS stock ATSs are operating as “dark pools.” ATSs could 

publish their quotation data to operate as electronic communications networks, or 

“ECNs.”34 One ATS, IntelligentCross, currently publishes its own data feed of quotations 

and executions on the ATS.35

ATSs offer many different types of services and cater to different trading objectives. Some 

offer block order36 crossing networks, others match smaller customer orders with other 

customers and/or with broker-dealer or bank inventory, and some allow for order 

matching to be segmented by specific categories of market participants. In many cases, the 

same market makers that provide liquidity on exchanges also provide liquidity on ATSs. 

Frequently ATS functionality is intended to mitigate the effect of trading on subsequent 

prices for an instrument.37 Some offer unique order types not available on exchanges, such 

as conditional orders, intended to facilitate the search for larger blocks of liquidity. Many 

ATSs are operated by multi-service broker-dealers, while some are operated by 

independent firms or consortiums. A number of ATSs have developed trading models that 

are alternatives to the more prevalent price-time priority matching engines: for example, 

 

33 As noted above, supra n. 20, however, trades in these off-exchange venues are publicly 

reported. 

34 See 17 CFR § 600(b)(24); see also Concept Release at 3599 (“The key characteristic of an 

ECN is that it provides its best-priced orders for inclusion in the consolidated quotation 

data, whether voluntarily or as required by Rule 301(b)(3) of Regulation ATS”). In the past, 

some ECNs with displayed quotations have, at times, represented a significant amount of 

market share, and some eventually evolved into registered national securities exchanges 

(including all four CBOE equities exchanges, IEX, NYSE Arca, Island, Instinet, and BRUT). 

35 See, e.g., IntelligentCross Form ATS-N, Item 15 (filing of Jan. 16, 2020) (available at:

https://www.sec.gov/divisions/marketreg/form-ats-n-filings.htm). 

36 Generally speaking, a block order is a particularly large order for a given market, and its 

precise meaning changes in different contexts. For example, Regulation NMS defines an 

order of block size as an order of at least 10,000 shares or for a quantity of stock having a 

market value of at least $200,000. 17 CFR § 242.600(b)(10).

37 The importance of managing the informational and price impact of order entry and 

trading activity is described in more detail in the Appendix. See infra n. 320 and 

accompanying text.
18

one ATS uses machine learning to execute orders at a time intended to minimize 

subsequent impacts on prices,38 and others allow for periodic auctions.39

3. Broker-Dealer Internalizers

A third major category of venue for equity trading is broker-dealer internalization. Like 

national securities exchanges and ATSs, internalizers are heavily dependent on 

sophisticated algorithms to conduct their core functions. As described in more detail

below, there are several types of internalization, including wholesale market makers, 

single-dealer platforms, and central risk book trading and block positioning. Internalized 

trades of broker-dealers reflect liquidity that is not included in public quotation data.40 In 

2019, approximately 27% of NMS stock share volume was executed by broker-dealer 

internalizers.41

While hundreds of broker-dealers internalize trades, much of this volume is handled by a 

relatively small number of large firms. Table 5 illustrates this concentration.42 In 2019, 

 

38 See Form ATS-N filings and information for IntelligentCross ATS, available at 

https://www.sec.gov/divisions/marketreg/form-ats-n-filings.htm. 

39 See Form ATS-N filings and information for CODA ATS, available at 

https://www.sec.gov/divisions/marketreg/form-ats-n-filings.htm. 

40 See, e.g., Concept Release at 3599.

41 This figure was calculated by subtracting the percentage of share volume in NMS stocks 

executed on ATSs from the total percentage of share volume in NMS stocks executed offexchange, as reflected in NYSE TAQ data and FINRA OTC/ATS Transparency data. Cf. also

Concept Release at 3599, noting that in September 2009 approximately 17.5% of NMS 

stock share volume was executed by broker-dealer internalization. 

42 This list should be read as a rough illustration of the distribution of share volume in the 

non-ATS off-exchange market. This list is derived from the OTC Transparency data FINRA 

makes available to the public on its website. FINRA’s public data reflects firms with the 

obligation to report each off-exchange trade to a TRF (generally the executing brokerdealer), so only shows the firm involved on one side of each trade. Moreover, trading by 

broker-dealers that are not registered with FINRA (but that are registered with another 

SRO) is not reflected in this data, regardless of the volume of such off-exchange trading, 

because non-FINRA-members never have obligations to act as the reporting party under 

FINRA’s trade reporting rules. See FINRA Rule 6110(b). Currently, a list of OATS Reporting 

Non-FINRA Member firms is available at https://www.finra.org/industry/oats/oatsreporting-non-finra-member-firm-list. Finally, FINRA’s data for the period covered here 

groups all firms executing a number of trades below certain thresholds into a single de 

minimis category (though more recent FINRA data no longer includes a de minimis
19

each of the two largest internalizers executed more share volume off-exchange than was 

executed in any individual ATS, and more share volume than was executed on each of eight

national securities exchanges.

43 Internalizers can be significant sources of liquidity in 

today’s markets.

Table 5: 2019 Top Ten Internalizing Broker-Dealers by Share Volume: Percentage of 

Internalized Volume, Off-Exchange Volume, and NMS Stock Volume

Firm % Intern. % Off-Exch. % NMS

CITADEL SECURITIES LLC 24.3% 16.7% 6.2%

VIRTU AMERICAS LLC 13.2% 9.0% 3.4%

G1 EXECUTION SERVICES, LLC 6.9% 4.8% 1.8%

TWO SIGMA SECURITIES, LLC 1.5% 1.0% 0.4%

WOLVERINE SECURITIES, LLC 0.7% 0.5% 0.2%

JANE STREET CAPITAL LLC 0.5% 0.4% 0.1%

UBS SECURITIES LLC 0.4% 0.3% 0.1%

VIRTU FINANCIAL BD LLC 0.3% 0.2% 0.1%

GOLDMAN SACHS & CO. LLC 0.3% 0.2% 0.1%

ACS EXECUTION SERVICES, LLC 0.1% 0.0% 0.0%

Source: FINRA OTC/ATS Transparency

B. Market Data

Modern equity markets are connected in part by the data flowing between market centers. 

An enormous volume of data is available to market participants. In recent years, there has 

been an exponential growth in the amount of market data that is available, the speed with 

which it is disseminated, and the computer power used to analyze and react to price 

movements. As discussed below, for different types of investors and market professionals, 

the speed with which information can be acquired, analyzed, and acted upon is valued to 

 

category). See FINRA Rule 6110(b); Order Approving Proposed Rule Change To Expand OTC 

Equity Trading Volume Data Published on FINRA's Website, Exch. Act Rel. No. 86706, 84 Fed. 

Reg. 44341 (Aug. 23, 2019) (change to FINRA rules that would, among other things, 

eliminate the de minimis exception to public disclosures as of December 2, 2019); FINRA 

Regulatory Notice 19-29 (Sept. 13, 2019). In other words, it is likely that the attributions of 

volume in this data source understate the volume of some market participants, and omit 

entirely other market participants.

43 Both of these broker-dealers offer a range of internalization services, including for both 

retail and institutional investors, and, as reflected in retail broker order routing public 

disclosures, are particularly significant internalizers of retail investor trades. 
20

varying degrees. Most equity market participants use, in some respect, quotation data44

and last-sale transaction data.45 Market centers and securities information processors 

(SIPs), described below, also communicate important regulatory and administrative 

messages, such as when trading in a particular security is unexpectedly halted or paused. 

Some data feeds carry additional information about market dynamics, such as messages 

providing updates on order imbalances at regular intervals leading up to the daily closing 

auction.46

This wide range of market data provided to market participants by exchanges is distributed

through two broad categories of data feeds: (1) consolidated data feeds, and (2) 

proprietary data feeds.

The consolidated data feeds are operated by the self-regulatory organizations (SROs) via 

National Market System (NMS) plans pursuant to Commission regulation and oversight.47

The consolidated data feeds include top of book quotations, last sale information, and 

 

44 Quotation data can include information about both the best available prices at a given 

market (often called the “top of book”) and quotes resting in the order book at prices 

higher (for sell orders) or lower (for buy orders) (often called “depth of book” data). For 

some market participants information about the cancellation or modification of individual 

quotations is also important. 

45 As noted above, the Exchange Act includes a Congressional finding that it is in the public 

interest and appropriate for the protection of investors and maintenance of fair and 

orderly markets to assure the availability to brokers, dealers, and investors of information 

with respect to quotations for and transactions in securities. See 15 U.S.C. 78k1(a)(1)(C)(iii).

46 Ahead of each auction, each listing exchange gathers market and limit orders to execute 

in the closing auction. The number of buy and number of sell orders may not align. In 

order to attract liquidity and potentially improve the quality of the closing auction, listing 

exchanges disseminate messages providing the side and size of an order imbalance as the 

auction approaches.

47 This section describes the currently prevailing structure for the provision of 

consolidated equity data feeds. In February 2020, the Commission proposed rules that 

would update and expand the content of NMS market data and that would introduce a 

decentralized consolidation model under which competing consolidators, rather than the 

existing exclusive securities information processors, would collect, consolidate, and 

disseminate certain NMS information. See Market Data Infrastructure, Exch. Act Rel. No. 

88216, 85 Fed. Reg. 16726 (Mar. 24, 2020) (“Market Data Infrastructure Proposal”). 
21

important regulatory messages from exchanges.

48 Currently, there are three equity market 

data plans: the CQ plan (for quotations in securities not listed on Nasdaq), the CTA plan (for 

transaction reports in securities not listed on Nasdaq), and the UTP Plan (for both 

quotation and transaction reports in Nasdaq-listed equities).49 These plans together are 

often referred to as the “SIPs.”50

Proprietary data feeds offer additional and different market information from the SIPs. For 

example, some proprietary data feeds provide all displayed order messages at an exchange, 

including individual odd-lot orders, as well as order modifications and cancellations; others 

may not provide message-by-message data, but summarize the total displayed shares 

available at each level in the order book; others provide only the top-of-book across an 

exchange family’s related markets; and some offer detailed auction imbalance information. 

For various reasons, including because they do not need to go through a consolidation 

process at a separate geographic location, proprietary data feeds often reach market 

participants sooner than the SIPs.

51

 

48 Best-priced quotation and last sale data is often referred to as “core data.” See, e.g., Order 

Setting Aside Action by Delegated Authority and Approving Proposed Rule Change Relating to 

NYSE Arca Data, Exch. Act. Rel. No. 59039, 73 Fed. Reg. 74770, 74779 (Dec. 9, 2008) (“Core 

data is the best-priced quotations and comprehensive last sale reports of all markets that 

the Commission, pursuant to Rule 603(b), requires a central processor to consolidate and 

distribute to the public pursuant to joint-SRO plans”); Order Directing the Exchanges and 

the Financial Industry Regulatory Authority to Submit a New National Market System Plan 

Regarding Consolidated Equity Market Data, Exch. Act Rel. No. 88827, 85 Fed. Reg. 28702, 

28703 (May 13, 2020) (“SIP Governance Order”) (noting that “core data” consists of “(1) 

The price, size, and exchange of the last sale; (2) each exchange’s current highest bid and 

lowest offer, and the shares available at those prices; and (3) the national best bid and offer 

(‘‘NBBO’’) (i.e., the highest bid and lowest offer currently available on any exchange)”). 

49 In May 2020, the Commission issued an order requiring the national securities exchanges 

and FINRA to consolidate the three current equity market data plans into a new single 

equity market data plan and to implement specific governance provisions within that plan. 

See SIP Governance Order. Certain SROs have petitioned for review of this order in the D.C. 

Circuit.

50 “SIP” is an acronym for “securities information processor,” which is defined in Exchange 

Act Section 3(a)(22)(A), 15 U.S.C. 78c(a)(22)(A); see also Exchange Act Section 11A(b), 15 

U.S.C. 78k-1(b).

51 However, data distributed over the consolidated feeds cannot be transmitted by 

proprietary feed to a vendor or user any sooner than it is transmitted to a consolidated 

data processor. See 17 CFR 603(a); Regulation NMS, Exch. Act Rel. No. 51808, 70 Fed. Reg. 

37495, 37567 (June 25, 2005) (“Reg NMS Adopting Release”) (“adopted rule 603(a) 
22

Equity market data is physically disseminated and moved between data centers in a range 

of ways, including by fiber-optic cable and wirelessly via microwave towers. Moreover, the 

data can be physically accessed in a variety of ways, including through servers co-located in 

an exchange’s data center, ports and wires with various capacities and bandwidths, as well 

as through hardware that can process data directly, rather than relying on the often slower 

process of using software to process the data following receipt. The methods used to 

physically access and process data affect the speed and efficiency with which market 

participants are able to transact in the markets.52

The availability of different levels of market data and different access speeds to both 

markets and market data can advantage some market participants over others. For 

example, by accessing more granular data from proprietary market data feeds at higher 

speed, some users may be able to react to market events more strategically and more 

quickly than participants relying only on SIP data.53 Similarly, brokers and other market 

participants using advanced connectivity tools, such as microwave data transmissions and 

high-bandwidth connections, can process data and enter orders more rapidly than other 

market participants, particularly during periods of high volume when message traffic, and 

therefore network latency, may be at its highest. 

These market data considerations—including differing levels of both content and speed of 

access—extend beyond the cash equity markets. Many market participants make trading 

decisions and control risk using information from trading venues for other types of 

instruments. Accordingly, market data from those trading venues can be very important to

 

prohibits an SRO or broker-dealer from transmitting data to a vendor or user any sooner 

than it transmits the data to a Network processor”). For additional discussion of 

differences between the current consolidated data feeds and proprietary feeds, see, e.g., 

Market Data Infrastructure Proposal at 20-25. 

52 For example, “co-location is a service that enables exchange customers to place their 

servers in close proximity to an exchange’s matching engine in order to help minimize 

network and other types of latencies between the matching engine of the exchange and the 

servers of market participants.” Notice of Proposed Order Directing the Exchanges and the 

Financial Industry Regulatory Authority to Submit a New National Market System Plan 

Regarding Consolidated Equity Market Data, Exch. Act Rel. No. 87906, 85 Fed. Reg. 2164, 

2169 n.55 (Jan. 14, 2020) (“SIP Governance Proposed Order”). Similarly, “[d]ata 

connections that use fiber optic cable transmit data more slowly than data connections that 

use wireless microwave transmissions, though microwave connections are susceptible to 

interruption by weather conditions and are therefore less reliable than fiber connections.” 

Id. 

53 See, e.g., SIP Governance Proposed Order at 2169-70 (discussing potential implications 

for competitiveness between the consolidated data feeds and exchange proprietary feeds). 
23

the algorithmic trading strategies of equity market participants. Common examples of this 

cross-market data use include access to options market data and futures market data.54

III. Overview of Debt Market Structure

Cash debt markets have historically operated as over-the-counter principal markets, in 

which participants trade for their own accounts. Historically, the market operated largely

by “voice,” with trades negotiated and effected bilaterally between counterparties. In the 

last several decades, a range of dealers and platforms have implemented several models of 

electronic trading. Some of these models have automated aspects of the bilateral 

communication and trading process, while others have provided alternative trading models 

such as central limit order books. The development of automated tools and platforms has 

differed across the Treasury, corporate, and municipal bond markets, reflecting significant

underlying differences in the terms of these instruments and the structure of the markets 

in which they trade.

A. Types of Debt Securities or Instruments

Unlike equity securities, debt securities are not standardized, even by issuer. A given 

issuer might have tens, hundreds, or more than a thousand different types or “series” of

debt securities outstanding, each with a different notional value, maturity date, and interest 

rate. When a particular issuer has multiple series of bonds outstanding, secondary market 

liquidity is generally concentrated in the more recently-issued bonds (and, among the more 

recently-issued bonds, in the series with the greater amount outstanding).

1. U.S. Treasury Securities

The market for U.S. Treasury securities is the deepest and most liquid government 

securities market in the world.55 The U.S. Treasury issues bills, nominal fixed-rate coupon 

securities, nominal floating rate securities, and inflation-indexed securities (TIPS). Most 

secondary trading in Treasuries occurs across the most-recently issued (or, “on-the-run”) 

nominal coupon securities.56

 

54 For example, in the futures market, the E-Mini S&P 500 Futures contract traded on the 

CME is often regarded as a central focal point for price formation in the equities market. 

See, e.g., Joel Hasbrouck, “Intraday Price Formation in U.S. Equity Index Markets,” 58 

Journal of Finance 58: 2375-2399 (Dec. 2003). 

55 See, e.g., Joint Staff Report: The U.S. Treasury Market on October 15, 2014, at 1 (July 13, 

2015) (“Treasury Market Report”).

56 Id. at 11.
24

Treasury securities are traded on multiple venues.57 Interdealer trading of on-the-run

Treasury securities occurs mainly on centralized electronic trading platforms using a 

central limit order book. These venues are the primary points of price discovery for the onthe-run securities. This market has evolved considerably over the past decade. Where 

participation on interdealer platforms was once limited to primary dealers, principal 

trading firms now account for more than half of the trading activity in the electronic 

interdealer markets.58 Dealer-to-customer trading, in both on-the-run and off-the-run 

securities, is usually bilateral, either through voice or through an electronic platform, using 

for example, a request-for-quote (RFQ) process or streaming quotes.59 Some dealers and 

electronic market makers now provide their customers with a direct stream of continuous 

prices and sizes at which they are willing to trade in a range of issues.60 With price 

discovery increasingly occurring in electronic order books, trading in off-the-run securities 

occurs primarily through voice channels.61

 

57 Id.

58 See, e.g., id. at 36. More specifically, the Report notes that 56% of the volume in the onthe-run 10-year note is handled by principal trading firms, with about 35% handled by 

bank-dealers, and the remaining 9% being split among non-bank dealers, hedge funds, and 

asset managers. More recent data show that, as of 2019, “PTFs account for around 60 

percent of electronic IDB volumes.” See Remarks of Deputy Secretary Justin Muzinich at the 

2019 U.S. Treasury Market Structure Conference, Sept. 23, 2019 (available at:

https://home.treasury.gov/news/press-releases/sm782). 

59 Id. at 11.

60 See, e.g., Kevin McPartland, U.S. Treasury Trading No Longer a Divided Market, Greenwich 

Associates (Dec. 12, 2018); Kevin McPartland, How Bilateral Streams for U.S. Treasuries 

Really Work, And What They Mean for the Market, Greenwich Associates (June 18, 2019), 

available at: https://www.greenwich.com/blog/how-bilateral-streams-us-treasuriesreally-work.

61 Treasury Market Report at 35. Recently, non-central limit order book electronic 

platforms, such as OpenDoor Trading, have begun providing electronic venues for trading 

in off-the-run Treasuries and TIPS. See, e.g., John McCrank, OpenDoor opens up trading in 

illiquid U.S. Treasuries, Reuters (June 14, 2017), available at:

https://www.reuters.com/article/us-usa-treasuries-opendoor-idUSKBN1952DN.
25

2. Corporate Bonds

Corporate bond trading predominantly occurs over voice. Much of this voice activity is 

concentrated in the largest dealers—for example, one recent study estimates that 56% of 

buy-side volume in U.S. investment-grade corporate bonds is with the five largest dealers.62

In the past decade, a number of venues have developed to trade corporate bonds 

electronically. A recent estimate is that 26% of corporate bond volume was traded 

electronically in the third quarter of 2018.63 As described more fully below, much of this 

activity can be described as the automation of bilateral voice trading or RFQ trading. All-toall trading, in which any participant can provide quotes to and trade with any other 

participant, is increasingly a meaningful proportion of the electronic corporate bond 

market; for example, approximately 8% of volume in investment grade corporate bond 

may be executed on all-to-all platforms.64 Roughly 70% of corporate bond trades are for 

fewer than one hundred bonds, and 90% of these small trades are effected electronically; 

however, these small trades represent approximately 3-4% of the total value of corporate 

bonds traded on any given day.65 The electronic trading venues are regulated in a range of 

ways, with some as ATSs, some as broker-dealers, and others outside of either of those two 

regulatory structures.66 One national securities exchange, NYSE, provides the capability to 

trade corporate bonds.

 

62 Kevin McPartland, Corporate Bond Trading in 2019: Competition is Good, Complexity is 

Not, Greenwich Associates, p. 5 (Jan. 8, 2019).

63 Id. at 2, 4.

64 Id. at 8

65 Kevin McPartland, “The Challenge of Trading Corporate Bonds Electronically,” 1, 

Greenwich Associates (May 13, 2019) available at: 

https://www.greenwich.com/blog/challenge-trading-corporate-bonds-electronically.

66 See, e.g. FIMSAC Electronic Trading Subcommittee Recommendation on Oversight of 

Electronic Trading Platforms for Corporate and Municipal Bonds (July 16, 2018). For a 

more detailed discussion of quoting and trading on a recent sample of corporate bond 

activity on ATSs, see Staff of the Division of Economic and Risk Analysis of the U.S. 

Securities and Trading Commission, Access to Capital and Market Liquidity, pp. 178-90 

(Aug. 8, 2017) (“DERA Bond Study”), available at: https://www.sec.gov/files/access-tocapital-and-market-liquidity-study-2017.pdf
26

Average daily volume traded in corporate bonds has grown from a recent low of $14.3 

billion in 2008 to $31.2 billion in 2018.

67 Over the same period, the amount of corporate 

debt issued annually grew from approximately $717 billion in 2008 (from 946 new issues) 

to more than $1.3 trillion in 2018 (from 1,270 new issues) with a high of more than $1.65

trillion in 2017 (in 1,671 new issues).68 As discussed above, most corporate bond trading 

is concentrated in new issues, so the increase in ADV may be related to the increase in 

corporate bond issuance.69

Generally, corporate bonds are held by investors, with trades facilitated by broker-dealers 

and other intermediaries. Demand for intermediation is driven by various factors, 

including the general difficulty of finding natural counterparties for specific bonds due to 

the idiosyncratic nature of different series of bonds.70 This intermediation requires capital, 

which, at least at the dealer level, has been relatively more limited since the credit crisis ten 

years ago.71 Technology has helped mitigate the drop in dealer liquidity, and other types of

liquidity providers have entered the corporate debt markets. The trading of corporate 

bond ETFs, in particular, has introduced new liquidity suppliers, including those who may 

not be able to meet the capital requirements imposed on large dealers, including principal 

trading firms and quantitative hedge funds.72

 

67 Capital Markets Fact Book 2019, 20, SIFMA, available at: 

https://www.sifma.org/resources/research/fact-book/.

68 Id. at 7-8.

69 See McPartland, Corporate Bond Trading in 2019 at 3.

70 McPartland, The Challenge of Trading Corporate Bonds Electronically, at p. 2.

71 See, e.g., Tobias Adrian, Nina Boyarchenko, & Or Shachar, “Dealer Balance Sheets and 

Bond Liquidity Provision,” Federal Reserve Bank of New York Staff Report No. 803 (Mar. 

2017). For a more detailed discussion of research on recent trends in corporate bond 

market liquidity and potential relationships to regulatory changes, see DERA Bond Study at 

109-119.

72 McPartland, Corporate Bond Trading in 2019 at 5. For a more detailed discussion of 

studies on the evolution of electronic trading in corporate bond markets, see DERA Bond 

Study at 119-123.
27

3. Municipal Bonds

Municipal bond investors predominantly hold municipal securities for the long-term,73 and 

a significant percentage of municipal bonds are held by retail investors.74 Trading in 

municipal bonds is concentrated in the period after issuance, and becomes infrequent 

afterwards.75 Of the approximately one million outstanding series of municipal securities, 

on average slightly more than one percent trade on any given day.76

Municipal bonds are predominantly traded over-the-counter by voice, either between

dealers and customers or between dealers.

77 The market is fragmented, given, among 

other things, the number of unique municipal securities, the number of issuers, and low 

trading volume for most bonds.78 Largely because of their tax treatment, shorting of 

municipal bonds is difficult and rare.79 In recent years, several platforms have developed 

that facilitate the electronic trading of municipal bonds. Several of these are ATSs. One 

 

73 Simon Z. Wu, John Bagley & Marcelo Vieira, Staff of the Municipal Securities Rulemaking 

Board, Analysis of Municipal Securities Pre-Trade Data from Alternative Trading Systems, at 

4 (Oct. 2018), available at: http://www.msrb.org/~/media/Files/Resources/Analysis-ofMunicipal-Securities-Pre-Trade-Data.ashx?la=en (“MSRB ATS Study”).

74 See, e.g., Municipal Securities Rulemaking Board, “Muni Facts,” (Mar. 2019), available at:

http://www.msrb.org/msrb1/pdfs/MSRB-Muni-Facts.pdf (noting that nearly two-thirds of 

municipal securities are held by individual investors either directly or through mutual 

funds).

75 Id.

76 Id.; 2018 Fact Book, 34, Municipal Securities Rulemaking Board, available at:

http://www.msrb.org/Market-Transparency/Market-Data-Publications/MSRB-FactBook.aspx (average of 15,588 unique securities traded per day in 2018).

77 MSRB ATS Study at 4.

78 Id.

79 The interest on most municipal securities is exempt from federal income tax and, in some 

cases, state and local taxes. The Internal Revenue Service does not allow both the 

borrower and lender of a municipal security to claim a tax exemption, so in effect the 

lender of a municipal security would be trading tax-exempt interest for taxable interest. 

See Exch. Act Rel. No. 34-33743 (Mar. 9, 1994), 59 FR 12767, 12769 n.24 (Mar. 17, 1994) 

citing Internal Revenue Code, Sec. 6045(d); see also FINRA Notice 15-27 (July 2015).
28

recent study estimates that 12-15% of municipal bond trading is electronic.80 While some 

municipal bond ETFs exist, they are still a relatively small, but growing, proportion of fixed 

income ETFs.81

Among fixed income securities, municipal bonds have a uniquely high percentage of direct 

retail investors. For example, one recent study estimates that retail investors directly hold 

42% of municipal bonds by value, compared to 13% of Treasuries and 8% of corporate 

bonds.82 Retail investors may purchase municipal bonds through broker-dealers or 

investment advisers, who in turn source liquidity from banks or nonbank dealers and other

liquidity providers.83 The high volume of retail participation results in a large number of 

small trades, which may make aspects of the municipal bond market suited to electronic 

trading.84

B. Data and Communications

In debt markets, market data collection and distribution is uneven and fragmented. Pretrade transparency information on quotes or pricing generally can only be purchased from 

individual platforms or arranged through bilateral relationships. Post-trade transparency, 

in the form of transaction reports, generally is available for corporate and municipal bonds.

1. Transaction Reports in Corporate Bonds: TRACE

Transactions in corporate bonds must be reported to the Trade Reporting and Compliance 

Engine (TRACE) operated by FINRA.85 TRACE data is disseminated by FINRA immediately 

 

80 Kevin McPartland, The Modernization of Municipal Bond Trading, 2, Greenwich Associates 

(May 6, 2019).

81 See id. at 5, estimating that municipal bonds make up approximately 6% of fixed income 

ETF assets under management; see also Simon Z. Wu and Meghan Burns, Staff of the 

Municipal Securities Rulemaking Board, Municipal Bond ETFs: Impact on the Municipal 

Bond Market, at 5-6 (Apr. 2018), available at: 

http://www.msrb.org/~/media/Files/Resources/MSRB-Municipal-Bond-ETFsReport.ashx.

82 McPartland, The Modernization of Municipal Bond Trading, at 3.

83 Id. at 5.

84 Id. at 7.

85 See FINRA Rule 6730.
29

upon receipt.86 Each FINRA member that is party to a transaction in a TRACE-eligible 

security must report the trade as soon as practicable, but generally no later than within 

fifteen minutes of the execution of the trade.87 Detailed data is available through

subscription data feeds, and the FINRA website makes available for free aggregate statistics 

and details about individual trades, bonds, and issuers. The transaction data disseminated 

by FINRA includes, among other things, the issuer, CUSIP number for the bond, the entity 

type of the reporting and contra parties,88 execution time, quantity, price, side (buy or sell), 

size,89 and whether the trade was executed on an ATS.

FINRA has proposed publishing aggregate trade count and volume statistics for each 

corporate bond ATS, by CUSIP.90 The stated purpose of this proposal is to provide the 

market with more readily available information about potential sources of liquidity. FINRA

currently makes similar data available for equity ATSs.

2. Transaction Reports in Municipal Bonds: EMMA

Transactions in municipal bonds must be reported to the Municipal Securities Rulemaking 

Board’s (MSRB) Real-time Transaction Reporting System (RTRS).91 The MSRB 

disseminates the data upon receipt through subscription data feeds, and the MSRB’s

 

86 Following a recommendation by the Commission’s Fixed Income Market Structure 

Advisory Committee, FINRA has proposed a pilot program to study potential changes to 

corporate block trade dissemination. See FINRA Notice 19-12 (Apr. 12, 2019). Under the 

proposed pilot, the cap for disclosing the exact size of a trade would be raised to $10 

million for investment-grade corporate bonds and to $5 million for non-investment grade, 

and delay by 48 hours dissemination of reports for trades above those caps.

87 See FINRA Rule 6730(a).

88 These entity types can be broker-dealer, customer, non-member affiliate, and alternative 

trading system. Six months after a trade, the FINRA-member parties to each trade are 

identified.

89 For trades in investment grade bonds up to $5 million, the exact size of the trade is 

disseminated, but for trades above $5 million, the trade size is listed as “5MM+”; the 

analogous cap for non-investment-grade corporate bonds is $1 million. After six months, 

the exact size of capped reports is revealed. These rules mean that, for example, for several 

months, a $6 million dollar trade and a $100 million trade in an investment grade bond 

would both be reported as “5MM+”.

90 See FINRA Notice 19-22 (July 9, 2019).

91 Reports can be sent either directly to the MSRB or through the NSCC. See MSRB Rule G14 RTRS Procedures(a)(i). Most trades must be reported within fifteen minutes. See MSRB 

Rule G-14 RTS Procedures(a).
30

Electronic Municipal Market Access (EMMA) website makes available the trade data 

together with free aggregate statistics about individual trades, bonds, and issuers. The 

transaction data disseminated includes, among other things, the issuer, CUSIP number of 

the bond, trade date and time, price, size, trade type (i.e., inter-dealer or dealer-customer), 

and whether the trade was executed on an ATS.

IV. Benefits and Risks of Algorithmic Trading in Equities

As described above, the current markets for secondary trading in NMS stocks are 

predominantly electronic. While some pockets of activity can be described as manual, most 

of the lifecycle of trading is automated. Algorithms now facilitate the provision of and 

search for liquidity by a broad range of participants in the equities market across a diverse 

set of trading venues. This pervasive automation has also created new operational risks for 

firms and the market infrastructure more generally. Broadly speaking, studies have shown 

that algorithmic trading in equities has improved many measures of market quality and 

liquidity provision during normal market conditions, though other studies have also shown 

that some types of algorithmic trading may exacerbate periods of unusual market stress or 

volatility. 

A. Investors

For many investors, both retail and institutional, algorithms play a significant role in the 

investment and trading process. While investor orders are generally routed and executed 

through broker algorithms of various types, the ability to use routing and execution 

algorithms directly is also becoming more accessible to investors, as are the data feeds and 

processing tools that are essential to the use of algorithms. Some investors, both retail and 

institutional, also use algorithms to actively make investment and trading decisions 

through the rapid analysis of potentially voluminous amounts of market data. In other 

cases, investors track an outside reference, such as an index, and investment and trading 

decisions may be informed by algorithmically-determined decisions about composition

implicit in a benchmark index or other standard or set of rules.92 

 

92 This may be the case for some index mutual funds and ETFs, and investors holding ETNs 

may have a similar investment experience.
31

1. Retail Investors

As has been the case for many years,93 retail brokers largely route marketable retail 

investor orders (i.e., orders that can be executed immediately) to internalizing brokerdealers known as “wholesale” market makers for execution.94 Wholesale market-makers 

can frequently provide retail orders with some degree of price improvement, meaning they 

can execute the orders inside the spread of the national best bid and national best offer.95

Wholesale market-makers may also be able to provide retail orders with size improvement 

(i.e., more shares at a single price point than may be available at the national best bid or 

national best offer quoted on national securities exchanges). Some retail brokers receive 

payment for order flow in exchange for routing orders to these market-makers.96 

Wholesale market makers are willing to provide price improvement to retail investors and 

purchase order flow from brokers because access to the orders provides wholesalers, who 

 

93 See, e.g., Concept Release at 3600 (“OTC market makers, for example, appear to handle a 

very large percentage of marketable (immediately executable) order flow of individual 

investors that is routed by retail brokerage firms”).

94 See, e.g., CFA Institute, Dark Pools, Internalization, and Equity Market Quality, at 16 

(2012) (“Internalization is also thought to account for almost 100% of retail marketable 

order flow”).

95 In recent years, several firms, coordinated by the Financial Information Forum, have 

voluntarily disclosed retail execution quality statistics that include information on the 

average percentage of retail orders given price improvement and the average price 

improvement on each order. See, e.g. https://fif.com/tools/retail-execution-qualitystatistics. These summary statistics are distinct from the disclosures required by SEC Rule 

605. A staff review of these voluntary statistics from several retail brokers and wholesale 

market-makers for Q1 of 2019 indicates that some wholesale market-makers provide price 

improvement to more than approximately 85% of retail orders from a range of order sizes. 

In some cases, such as for smaller orders in more frequently-traded stocks, on average 

more than 95% of retail orders received price improvement. The monthly disclosures 

required by SEC Rule 605 also include information about each market center’s price 

improvement, but do not specifically break out statistics for retail orders. Because 

wholesale market-makers do not engage in quoting activity as part of their internalization 

activities, they can execute trades at prices more granular than the one-penny increment 

required for quotes in most equity securities.

96 For a more thorough description of payment for order flow practices, their historical 

development and regulation, and some potential concerns about the practice, see Staff of 

the Division of Trading and Markets, U.S. Securities & Exchange Commission, Memorandum 

to Equity Market Structure Advisory Committee on Certain Issues Affecting Customers in the 

Current Equity Market Structure, 5-11 (Jan. 26, 2016).
32

generally have more information and processing power than retail traders and brokers,

with a relatively low risk of adverse selection97 and a potential opportunity to profit from 

the informational content in aggregate retail order flow. For example, because wholesale 

market-makers are allowed to choose whether to execute any given order based on their 

own preferences and views of market conditions, they may, for example, trade against

incoming retail orders or route relatively risky or unfavorable orders to exchanges or other 

market centers.98

This discussion demonstrates the significant extent to which fast, effective processing of 

market data is central to the business of wholesale market-making in the cash equity 

markets: the market maker algorithm’s data-driven assessment of the market is not only 

central to its obligations with respect to best execution and compliance with the order 

protection rule, but allows it to make order-handling decisions, and provides the standards 

for evaluating price improvement. Wholesale market-makers also, over the long term, 

acquire voluminous amounts of market data, including information about retail order 

flows, which can be used in future modeling for order-handling, trading, and risk decisions.

Some specialized retail brokers allow individual retail customers to use more sophisticated 

broker algorithms that operate in a manner that is generally otherwise available only to 

institutional investors as described below, or allow retail customers to use their own 

algorithms. These specialized retail brokers typically operate on a smaller scale than the 

retail brokers described above, and may not rely as heavily on wholesaler market-makers 

as more traditional retail brokers.

2. Institutional Investors

The broad category of “institutional investors” encompasses a diverse range of market 

participants.99 This category includes, among others, registered investment companies, 

pension funds, insurance companies, endowments, and private investment funds such as

hedge funds, all of which employ a wide variety of trading strategies. While their needs and 

approaches to trading vary, they generally share a common focus on achieving high 

execution quality, which requires them to effectively manage the explicit and implicit costs 

of trading. The diversity of approaches to trading among institutions is reflected in the 

 

97 Id. at 6; see also Concept Release at 3612 (“Liquidity providers generally consider the 

orders of individual investors very attractive to trade with because such investors are 

presumed on average to not be as informed about short-term price movements as are 

professional traders”).

98 A broker serving retail customers may also operate an affiliated alternative trading 

system to which the retail orders may be routed. 

99 See infra Section X.A for additional detail on the composition of this category of investors. 
33

range of ways that they may use algorithms to (1) decide what to trade, (2) manage trade 

execution and (3) assess trading performance.

A number of institutional entities, such as mutual funds, track indices, and others may

invest in products, such as ETFs, that do so, and, as a result, they generally trade in 

response to market movements and other factors with the objective of keeping their 

underlying investments in line with a benchmark index.100 Similarly, some institutions 

may have targeted or fixed-weight proportions of equities within their investment 

portfolios, and so trade into or out of positions when market changes cause a portfolio to 

deviate too far from this target. For these institutions and investments, the decision 

algorithms built into their products or strategies affect which instruments to trade and 

when.

Index-oriented trading is not the only form of algorithmic trading that is driven by market 

movements. For example, some systematic institutional equity trading is algorithmically 

connected to other asset classes or indicators. This type of linkage is present in, for 

example, index option delta and gamma101 hedging strategies. These strategies generally

drive increased selling in equities markets when measures of volatility (such as the VIX) 

increase. Systematic volatility targeting and risk-parity strategies may similarly adjust 

their portfolio holdings depending on movements in some measure of volatility.102

Volatility-oriented strategies may have a momentum effect on stock prices: because 

volatility often rises with declining prices, strategies that drive increased selling of equities 

 

100 Such trading is pronounced and apparent on days when commonly-used benchmark 

indices are rebalanced. 

101 In options trading, “delta” measures the amount the cost of an option is expected to 

change given a change in the cost of the underlying asset. Delta is a proportion between 0 

and 1, or 0 and -1, depending on whether the option is a long or short put or call. For 

example, the cost of an option with a delta of .50 would be expected to move $0.50 for 

every $1 price change in the underlying stock. “Gamma” is an estimate of how much an 

option’s delta is expected to change given a change in the cost of the underlying asset. 

Gamma is a proportion between 0 and 1 for long options, or, for short options, 0 and -1. To 

extend the previous example, if an option has a delta of .50 and a gamma of .15, with a one 

dollar increase in the cost of the underlying stock, the cost of the option will increase by 

$0.50, and the option’s delta will increase from .50 to .65. For a more thorough summary 

introduction, see, e.g., the “Advanced Concepts” section at 

https://www.optionseducation.org/.

102 Generally speaking, volatility targeting strategies increase or decrease leverage in a 

portfolio as volatility moves above or below a target level. In risk-parity strategies, a 

portfolio is determined by the proportion of risk contributed to the portfolio by each asset, 

rather than by the proportion of capital allocated to each asset.
34

as volatility rises may, at least in the near term, further contribute to or exacerbate price 

declines.103

Institutional investors use a variety of approaches when executing trades. Some firms have 

their own trader or traders who implement investment decisions made by portfolio 

managers. At long-only mutual funds, for example, institutional traders typically utilize 

algorithms provided to them by brokers, although some create their own algorithms that 

determine when, where and how to execute an order, and then use brokers to execute 

orders in the marketplace. Other institutional firms may not have a dedicated trading staff 

but may employ professionals who create, analyze and execute algorithmic trading models. 

At firms where trading is highly automated, trading staff may perform a more monitor-like 

function, ensuring that systems are operating properly, and that trading is occurring as

intended and within risk limits. Some institutional investors may design and operate their 

own trading algorithms, while others may purchase firm-specific algorithmic trading 

services from third parties. The technical expertise, infrastructure, and resources required 

to design and manage algorithmic trading systems directly may be outside the abilities of 

many institutional investors, or they may prefer to outsource trade execution (as well as 

other aspects of their investment strategy) to other market participants. Notably, some 

technology providers are beginning to offer predictive analytics products that operate on 

real-time market data and incorporate machine learning. These types of products 

potentially could provide a more generally available tool that is analogous to and 

competitive with the low-latency data access, processing, and execution tools used by some 

of the fastest market participants.104

Institutions that do not create their own algorithms generally use algorithms provided to 

them by institutional brokers. Over the past decade, the “manual handling of institutional 

orders is increasingly rare, and has been replaced by sophisticated institutional order 

execution algorithms and smart order routing systems.”105 Institutional firms may send a 

single large “parent” order to a broker that will generally divide it into many smaller “child” 

orders to be executed in the market. Institutions may also send several larger orders to 

multiple brokers for similar treatment. In some cases, institutional investors may also send 

orders directly to specific broker algorithms or suites of algorithms.106 An institutional 

firm may have a core group of brokers used in most securities or market conditions, but 

 

103 See, e.g., Campbell R. Harvey et al., The Impact of Volatility Targeting, J. Portfolio Mgmt, 

14, 30-31 (Vol. 45, Fall 2018).

104 See, e.g., the “Signum” product offered by Exegy (https://www.exegy-signum.com).

105 Disclosure of Order Handling Information, Exch. Act Rel. No. 78309, 81 Fed. Reg. 49432, 

49436 (July 27, 2016) (“Order Handling Proposing Release”).

106 Institutional broker algorithms are more fully described below.
35

generally will be able to call on a larger pool of specialized brokers. However, use of broker 

algorithms appears to be highly concentrated; for example, one recent study estimates that 

approximately two-thirds of institutional order flow is sent through the three largest 

broker algorithm suites.107

Institutional firms are focused on costs of trading, with such costs being characterized as

“explicit” and “implicit.”108 Because institutions generally trade in substantial size, both 

explicit transaction costs—e.g., commissions paid to brokers—and implicit costs—e.g., 

information leakage and resulting adverse market impact—can be significant. However, 

the broker routing and execution process can often be opaque to institutional investors.109

For example, an institutional investor may not know the number or identity of venues to 

which its orders have been routed, whether and how extensively a broker employs 

actionable indications of interest, or whether there are compensation arrangements 

between brokers and market centers that may affect broker routing decisions. Institutional 

investors generally conduct, either on their own or through a third-party provider, 

transaction cost analysis in order to assess the quality of executions received from different 

brokers, different algorithms, or in different market centers. The availability of data is 

central to this cost assessment process, as is the capacity to effectively analyze it and

incorporate the results into future execution decisions. As described in more detail below, 

the Commission recently took steps to improve the scope and consistency of data available 

to investors about broker order handling.110

Increasingly, institutional firms route to brokers and assess their performance using a tool 

called an “algo wheel.”111 An algo wheel, which can be operated by an investor or provided 

by a third-party, connects investors into multiple broker algorithm offerings, and chooses 

brokers and individual algorithms based on specified constraints or preferences. An algo 

wheel allows a firm to closely track the performance of broker algorithms under different 

market conditions, and can enable a firm to switch between different brokers without input 

from a human trader. One recent study estimates that about a quarter of institutional “buy-

 

107 Richard Johnson, Trends in Global Equity Electronic Execution, 7, Greenwich Associates 

(Apr. 23, 2019).

108 See, e.g., 81 Fed. Reg. at 49436 (“Institutional customers have long focused on the 

execution quality of their large orders, and the potential impacts from information leakage 

and conflicts of interest faced by their broker-dealers”).

109 Id.

110 See Disclosure of Order Handling Information, 83 Fed. Reg. 58338 (Nov. 19, 2018) 

(“Order Handling Adopting Release”). See also Order Handling Proposing Release for 

additional background on institutional order routing practices.

111 See, e.g., Johnson, Trends in Global Equity Execution, at 5-7.
36

side” investors use algo-wheels,

112 while another recent study estimates about 14% of such 

investors use algo wheels.113 Much of the performance measurement built into algo wheels 

is associated with transaction cost analysis, but algo wheels can also inform the evaluation 

of best execution, since they can facilitate assessments of which brokers route most

effectively under different circumstances.

114

B. Brokers

Brokers are tasked by their customers with finding liquidity in a complex, fragmented 

market, achieving best execution, and minimizing information leakage and other implicit 

costs. To try to meet these goals, brokers use, and offer to their customers, a wide range of 

execution algorithms.

While brokers tend to offer a large suite of algorithms, many of the core types of algorithms 

are more or less similar across brokers. For example, many brokers offer algorithms that

provide their customers with a volume-weighted average price (VWAP), a time-weighted 

average price (TWAP), a minimum implementation shortfall (i.e., minimizing the total costs 

of trading relative to the market price at the time a trading decision is made), or trading at 

a specified percentage of market volume (PVOL or POV). Broker algorithms may seek to 

take liquidity resting at trading venues, to provide liquidity at venues, or some combination 

of the two.115 Broker algorithms take into consideration the diversity of venues available, 

including exchanges, ATSs, single dealer platforms, and central risk books. Moreover, 

algorithms account for the increasingly wide and complex range of order types available at 

venues such as exchanges and ATSs, which may have different effects on how orders are 

handled.

Brokers generally allow some degree of customization for their algorithms to suit customer 

needs. However, at some firms this process can be highly manual, and so may be available 

 

112 Id. at 6.

113 Campbell Peters, “Technology and the Buy-Side Liquidity Chase”, TabbFORUM (July 19, 

2019).

114 Id. at 7; see also Larry Tabb, Algo Wheels: Best Execution, Workflow Solution, or Both?, 

TabbFORUM (Oct. 15, 2019).

115 See, e.g, Order Handling Proposing Release at 49436.
37

only to large customers.116 Improvements in software used in the customization process 

may make precise customization more widely available.117

In searching for liquidity, many brokers turn initially to off-exchange venues. In some 

cases, this may include in-house sources of liquidity, such as alternative trading systems, 

single-dealer platforms, or central risk books operated by the broker or an affiliate.118 

Some studies have raised questions, however, about whether execution quality may suffer 

when brokers prefer their own or an affiliated ATS.119

An increasingly common tool used across multiple dark pools, particularly for large block 

size orders, is the so-called “conditional order.” Conditional orders allow investors to

algorithmically search for liquidity in multiple venues simultaneously, but require the user 

of a conditional order to affirmatively execute the order or begin negotiations for a trade 

when a response is received.120 This additional step can allow a search for liquidity to 

minimize risks of being executed in a size or on other terms that are different than 

anticipated or desired, but also may create risks of information leakage.121

C. Principal Trading

Equities markets have long included firms trading their own principal acting as market 

intermediaries, transferring risk between other market participants and attempting to 

profit directly from this intermediation. On exchanges, historically such participants 

included, for example, specialists, registered market makers, and floor traders;122 off 

 

116 Larry Tabb, Fragmentation vs. Liquidity: Can Technology Resolve the Debate?, 

TabbFORUM (Aug. 4, 2019), available at: https://tabbforum.com/opinions/fragmentationvs-liquidity-can-technology-resolve-the-debate/.

117 Id.

118 Single dealer platforms and central risk books are described more fully below.

119 Amber Anand, Mehrdad Samadi, Jonathan Sokobin, & Kumar Ventkataraman, FINRA 

Office of the Chief Economist, Institutional Order Handling and Broker-Affiliated Trading 

Venues (Feb. 22, 2019), available at:

https://www.finra.org/sites/default/files/OCE_WP_jan2019.pdf.

120 Campbell Peters, Conditional Orders: The Great Liquidity Aggregator, TabbFORUM (May 

30, 2019).

121 Tabb, “Fragmentation vs. Liquidity: Can Technology Resolve the Debate?”

122 See, e.g., Stock Exchange Practices, Senate Report No. 1455, 73rd Congress 2d Session 

(June 16, 1934).
38

exchanges, OTC market making and block positioning were often performed by large

investment banks and the broker-dealer affiliates of large banks.

123 In modern electronic 

equity markets, much of this activity is handled by a range of technologically and 

quantitatively sophisticated firms trading as principal, who rely on algorithms in many 

aspects of their business in order to trade competitively.

1. High Frequency Trading

For more than ten years, most activity in the U.S. equity markets has been conducted by 

professional traders using short-term strategies that place a high number of orders, and 

generate a large number of trades, on a daily basis.124 This activity has commonly been 

called high frequency trading, or “HFT,” though there is no statutory or regulatory 

definition of this term.125 The 2010 Concept Release noted five characteristics typical of 

principal trading firms engaged in HFT:

1. The use of extraordinarily high-speed and sophisticated computer programs for 

generating, routing, and executing orders;

2. Use of co-location services and individual data feeds offered by exchanges and others 

to minimize network and other types of latencies;

3. Very short time-frames for establishing and liquidating positions;

4. The submission of numerous orders that are cancelled shortly after submission; and

5. Ending the trading day in as close to a flat position as possible (that is, not carrying 

significant, unhedged positions overnight).126

Not all of these characteristics must be present for a firm to properly be described as 

engaging in HFT.127 Broadly speaking, it is essential for firms engaged in these strategies to 

have the information technology infrastructure and computational sophistication to 

quickly and accurately process massive volumes of data from a wide range of sources, 

implement trading and risk decisions based on that data, and quickly enter orders based on 

those decisions before identified trading opportunities pass. To engage in high frequency 

trading, nearly all aspects of trading must be implemented algorithmically.

 

123 See, e.g. Larry Tabb, The Future of Liquidity: Risk Transformation, TabbFORUM (July 22, 

2019).

124 See, e.g., Concept Release at 3606.

125 Traders using these strategies may also be organized in a variety of ways, including as 

market-making desks within multi-service broker-dealers or as hedge funds. Id.

126 Id.

127 See HFT Literature Review at 4.
39

High frequency trading is not a singular, monolithic type of activity.128 It includes a range 

of strategies, which, as described more fully below, may have different effects on market 

quality, particularly under different types of market conditions. The distinction between 

strategies that primarily provide liquidity and those that primarily demand liquidity may 

be particularly important when analyzing potential effects on market quality.129 The 2010 

Equity Market Structure Concept Release described four broad types of short-term high 

frequency trading strategies: passive market-making, arbitrage, structural, and 

directional.130

a. Passive Market-Making

Passive market-making involves submitting non-marketable orders on both sides (buy or 

“bid,” and sell or “offer”) of the marketplace. Profits are earned primarily by earning the 

spread between bids and offers, supplemented by liquidity rebates offered by many 

exchanges for offering resting liquidity.131 Passive market makers may trade aggressively, 

sometimes rapidly demanding liquidity, in order to quickly liquidate positions accumulated 

through providing liquidity. Passive orders are generally not executed immediately and 

rest on an order book, and so must be updated as conditions change. Passive marketmakers are vulnerable to “adverse selection,” or prices moving quickly in one direction 

against their bids or offers, which can make it difficult to profitably trade out of a 

position.132 As part of managing this risk, these strategies can produce enormous volumes 

of modification and cancellation messages.

 

128 See, e.g. HFT Literature Review at 9.

129 See, e.g. Donald MacKenzie, ‘Making’, ‘taking’ and the material political economy of 

algorithmic trading, Economy and Society, 47:4, 501-23 (2018) at 502 (describing the 

distinction between liquidity providing and liquidity demanding algorithms as “the single 

most important divide within HFT” (emphasis in original)); id. at 511 (noting that while 

HFT strategies at times necessarily blend passive and aggressive activity, it is more 

common to predominantly specialize in one or the other); HFT Literature Review at 9-10.

130 See Concept Release at 3607-10. The summary here largely follows the more detailed 

discussion in the Concept Release.

131 In order to trade as a market maker, the market participant must be able to consistently 

move their orders to the order book in each venue in which it trades. This effort requires 

fast, high-quality market data, as well as technology capable of quickly processing it. Many 

exchanges also offer order types that may assist resting orders in achieving or maintaining 

queue priority as conditions change.

132 Some exchanges, such as IEX and NYSE American, offer order types that will 

automatically reprice certain resting orders based on algorithmically-generated 

predictions of when market-wide prices may be moving against the order. For a more 
40

b. Arbitrage

Arbitrage strategies generally seek to capture pricing discrepancies between related 

products or markets, such as between an ETF and its underlying basket of stocks, or 

between futures contracts on the S&P 500 index and ETFs on that index.133 Arbitrage 

strategies are likely to demand liquidity and involve substantial hedging of positions across 

products and markets. These strategies do not depend on directional price moves in a 

single product, but on the divergence and convergence of prices between products.134

c. Structural

Structural strategies attempt to exploit structural vulnerabilities in the market or in certain 

market participants. For example, traders with the lowest-latency market data and 

processing tools may be able to profit by trading with market participants who receive and 

process data more slowly and, as a result, have not yet updated their prices to reflect the 

most recent events.135

d. Directional

Directional strategies generally involve establishing a short-term long or short position in 

anticipation of a price move up or down. These strategies generally require demanding 

liquidity to build such a position.136 Some directional strategies may focus on predicting 

price movements faster than other market participants, which requires sophisticated 

analytics and rapid processing abilities. For example, order anticipation strategies may 

attempt to predict or infer the existence of a large buyer or seller in the market, in order to 

 

general discussion of adverse selection and its role in market-making and quote-setting, see

Merritt B. Fox, Lawrence R. Glosten, & Gabriel V. Rauterberg, The New Stock Market: Law, 

Economics, and Policy, ch. 3 (2019) (“The New Stock Market”). 

133 Some ETF market-makers also act as Authorized Participants in ETFs, though the roles 

are distinct. Managing the ETF create-redeem process requires technological 

sophistication additional to that required for market-making. 

134 See HFT Literature Review at 8.

135 See, e.g., MacKenzie supra note 129 at 512-13 (noting that the response times for this 

type of strategy, as reported in interviews with high frequency traders, has declined from 

about 5 microseconds in 2011 to 300 nanoseconds in 2018).

136 See, e.g., id. at 512.
41

buy or sell ahead of the large order.137 Trading on such predictions may often contribute to 

the process of price discovery in a stock.138

2. Single Dealer Platforms

On single dealer platforms, an individual dealer stands ready to trade with other market 

participants, generally offering some mode of indicative pricing.139 Some single dealer 

platforms may execute block trades, and others may focus on smaller trades. Some single 

dealer platforms account for meaningful percentages of consolidated volume. For example, 

recent estimates indicate that some single dealer platforms may account for as much as 1% 

of consolidated average daily share volume.140

Like other participants in equities markets, single dealer platforms must rapidly process 

large volumes of market data in order to make trading and risk decisions, as well as 

effectively handle the routing and communications necessary to managing a large number 

of electronic orders.

3. Central Risk Books

Central risk books have become important sources of block liquidity for many market 

participants.141 Central risk books, generally offered as a type of principal trading by large, 

 

137 As the Concept Release notes, such order anticipation is an old strategy to which 

investors have long been vulnerable. See Concept Release at 3609. This vulnerability 

makes information leakage a central concern for institutional investors.

138 The Equity Market Structure Concept Release also noted that another type of directional 

strategy, momentum ignition, “may raise concerns.” Id. Momentum ignition strategies 

involve initiating a series of orders and trades in order to attempt to ignite a rapid price 

move up or down. As the Commission noted in the Concept Release, any market 

participant that manipulates the market has engaged in prohibited conduct. See id.

139 In a recent request for comment on a proposed disclosure rule, FINRA proposed a 

definition of “single dealer platform”: “an electronic trading platform owned and operated 

by a member on which the member trades solely for its own account when executing 

orders routed to the [single dealer platform] and represents either the buy or sell side of 

each trade on a proprietary basis.” See FINRA Regulatory Notice 18-28.

140 See Rosenblatt Securities, Let There Be Light: Rosenblatt’s Monthly US Dark Liquidity 

Tracker (Dec. 18, 2019).

141 See, e.g., Campbell Peters, Technology and the Buy-side Liquidity Chase, TabbFORUM (July 

19, 2019); Valerie Bogard, Justin Schack, & Anish Puaar, Central Risk Books: What the Buy 

Side Needs to Know, Rosenblatt Securities Trading Talk (Oct. 11, 2018) (“Rosenblatt CRBs”).
42

global firms, aggregate firm-wide risk across desks, regions, and asset classes, seeking to 

maximize firms’ capital while staying within overall risk limits.142 This process can reduce 

hedging costs and optimize resources across a firm. This data aggregation and analysis 

requires significant quantitative and technological sophistication, including the ability to 

reconcile the cross-market risk profiles of different instruments with potentially very 

different types of market and product data.143 This process can allow central risk books to 

provide liquidity for large orders, often with favorable pricing, and generally depends upon 

having sufficient capital to take on positions and hedge risk.144

Central risk book liquidity can be accessed through a variety of channels, including through

broker trading desks and algorithms, and may be reflected in ATSs.145 Some firms generate 

streaming indications of interest, including actionable indications of interest, available 

through information services such as Bloomberg. One survey notes that the liquidity 

offered by central risk books is “most likely smaller blocks of blue-chip stocks, rather than 

large blocks of small- or mid-cap stocks.”146

D. Operational Risks to Firms and the Market

The electronic, automated, and interconnected nature of modern equity markets has 

created operational risks for both individual firms and the markets as a whole. As 

illustrated by the types of events described below, operational failures can have 

detrimental effects throughout the market system. As multiple regulators have now 

emphasized, it is essential for a range of market participants to have in place policies, 

procedures, and practices to ensure the robust operation and resilience of technological 

systems.147

 

142 Rosenblatt CRBs at 1. Firms other than large, global banks may also offer central risk 

books or employ central risk management structures. See, e.g., Larry Tabb, The Central Risk 

Book: Rethink Risk, Rethink Trading (Dec. 5, 2017).

143 Rosenblatt CRBs at 2; Tabb, The Central Risk Book. 

144 Rosenblatt CRBs at 2.

145 Id. at 3-4.

146 Peters, Technology and the Buy-Side Liquidity Chase, supra note 141.

147 See, e.g., Risk Management Controls for Brokers or Dealers With Market Access, Exch. Act 

Rel. No. 63241, 75 Fed. Reg. 69791 (Nov. 15, 2010) (“Market Access Rule Adopting 

Release”); Regulation Systems Compliance and Integrity, Exch. Act Rel. No. 73639, 79 Fed. 

Reg. 72252 (Dec. 5, 2014) (“SCI Adopting Release”); FINRA Regulatory Notice 15-09 

(Mar. 2015).
43

Errors from improper technology development, testing, and implementation at individual 

firms can have severe effects on those firms. For example, in 2012, a broker-dealer 

experienced a significant error in its equity order routing system following a systems 

update, erroneously sending millions of orders into the market over a forty-five minute 

period, and ultimately costing the firm more than $460 million in losses.148 Similarly, 

improper controls at individual firms can negatively impact markets. For example,

between 2011 and 2013, a firm improperly allowed essentially anonymous non-U.S. 

traders to enter billions of orders into U.S. markets, and did so without implementing risk 

management controls reasonably designed to ensure compliance with applicable 

regulatory requirements, which also resulted in the firm violating other regulatory 

requirements.

149 Another firm, as a result of a coding change and series of changes to 

routing logic, and a failure to impose adequate post-trade surveillance, between 2010 and 

2014 erroneously allowed millions of orders with a notional value of approximately $116 

billion to be sent in violation of Rule 611 of Regulation NMS.150

It also is important for algorithmic trading platforms and other core infrastructure systems 

to maintain proper controls and data integrity.151 During the last decade, for example, 

inadequate policies and procedures and systems errors at exchanges resulted in violations 

of the securities laws as well as trading disruptions;152 systems failures have interrupted 

initial public offerings;153 capacity failures at one of the equity consolidated data feeds 

caused one SIP provider to fail, leading to a trading halt in all securities listed on one 

 

148 See Knight Capital Americas LLC, Exch. Act Rel. No. 70694 (Oct. 16, 2013) (settled 

matter).

149 Wedbush Securities Inc., Jeffrey Bell, and Christina Fillhart, Exch. Act Rel. No. 73652 (Nov. 

20, 2014) (settled matter).

150 Latour Trading LLC, Exch. Act Rel. No. 76029 (Sept. 30, 2015) (settled matter).

151 For more comprehensive discussions of systems events throughout the securities 

markets, see Regulation Systems Compliance and Integrity, Exch. Act Rel. No. 69077, 78 Fed. 

Reg. 18084 (Mar. 25, 2013) (“SCI Proposing Release”) at 18089-90; SCI Adopting Release at 

72254-56.

152 EDGX Exchange, Inc., EDGA Exchange, Inc., and Direct Edge ECN LLC, Exch. Act Rel. No. 

65556 (Oct. 13, 2011) (settled matter); New York Stock Exchange LLC, NYSE Arca, Inc., NYSE 

MKT LLC f/k/a NYSE Amex LLC, and Archipelago Securities, L.L.C., Exch. Act Rel. No. 72065 

(May 1, 2014) (settled matter).

153 SCI Proposing Release at 18089; The Nasdaq Stock Market, LLC and Nasdaq Execution 

Services, LLC, Exch. Act Rel. No. 69655 (May 29, 2013) (settled matter).
44

exchange;154 and opening auctions have been delayed as a result of high volumes and 

unusual volatility.155 A previous Commission staff report concluded that the interaction 

between automated execution programs and algorithmic trading strategies can quickly 

erode liquidity and result in disorderly markets, and that concerns about data integrity, 

especially those that involve the publication of trades and quotes to the consolidated tape

(SIP), can contribute to pauses or halts in many automated trading systems and in turn lead 

to a reduction in general market liquidity.156

E. Studies of Effects on Market Quality and Provision of Liquidity

As illustrated by much of the preceding discussion, algorithms are used in a diverse range 

of trading activities and, across the various activities and market participants in the cash 

equity market, are virtually ubiquitous. This ubiquity and diversity has, understandably, 

meant that studies on “algorithmic trading” are not always focused on the same activity. 

For example, much of the literature on algorithmic trading focuses on high frequency 

trading, either through proxy measurements of HFT activity, or using datasets that 

specifically identify high frequency trading firms or accounts. The methodology used in 

any given case for identifying the relevant firms or accounts can shape the results found, as 

can decisions about whether to focus on metrics relevant to primarily passive liquidityproviding activity, aggressive liquidity-taking activity, or all trading without a distinction 

between liquidity providing and taking.157 It is unsurprising that academic studies 

generally are narrowly focused, as the amount of data, computing power and sophistication 

necessary to engage in broader study are daunting and costly, and relevant data may not be

widely available or easily accessible. As a result, Commission staff notes that using a single 

or just a few studies as a basis for broad market conclusions entails risk and that it is likely 

that greater insight will be provided by viewing academic literature as a whole, recognizing 

 

154 SCI Adopting Release at 72255

155 Staff of the Office of Analytics and Research, Division and Trading and Markets, Equity 

Market Volatility on August 24, 2015 (Dec. 2015) (available at:

https://www.sec.gov/marketstructure/research/equity_market_volatility.pdf) (“Aug. 24, 

2015 Report”).

156 Findings Regarding the Market Events of May 6, 2010: Report of the Staffs of the CFTC and 

SEC to the Joint Advisory Committee on Emerging Regulatory Issues (Sept. 30, 2010), 

available at: https://www.sec.gov/news/studies/2010/marketevents-report.pdf (“Flash 

Crash Report”), at p. 8; SCI Proposing Release at 18089.

157 For a more fulsome discussion of these methodological issues, see HFT Literature 

Review at 4-11.
45

the general and individual limitations of the work, as opposed to viewing studies in 

isolation. 

The following summary attempts to distill a range of studies from the academic literature, 

many of which have focused on high frequency trading. Generally, studies on this type of 

algorithmic trading indicate that some dimensions and activities can have positive effects 

on market quality and efficiency, while others may impose costs on other market 

participants or pose risks during periods of unusual market stress. More detailed 

discussions of individual academic studies are available in a later section of this staff 

report, as well as in literature reviews previously published by Commission staff.158

Studies have generally concluded that high frequency trading may have improved standard 

measures of market quality during normal market periods.159 For example, passive 

market-making activity is generally viewed as reducing spreads, through competition to

both narrow spreads and achieve queue priority,160 and through the improved risk 

management that is possible with automated systems.161 In addition, liquidity-demanding 

strategies may help to improve price efficiency. Some studies have concluded that HFT 

activity can reduce intraday volatility,162 though results are mixed on this point.163

Some studies have concluded that high frequency trading activity may also contribute to 

increased costs for other market participants.164 For example, the ability of HFT algorithms

 

158 See HFT Literature Review.

159 For example, “primarily passive HFT strategies appear to have beneficial effects on 

market quality, such as by reducing spreads and reducing intraday volatility on average” 

(See HFT Literature Review at 9) and “aggressive HFT strategies can improve certain 

dimensions of price discovery, at least across very short time-frames” (HFT Literature 

Review at 10).

160 J. Hasbrouck, High frequency quoting: Short-term volatility in bids and offers, 53 J. Fin.and 

Quantitative Analysis 613 (2018); J. Brogaard & C. Garriott, High-Frequency Trading 

Competition, 54 J. Fin. and Quantitative Analysis 1469 (2019).

161 T. Hendershott, C.M. Jones & A.J. Menkveld, Does Algorithmic Trading Improve Liquidity?, 

66 J. Finance 1 (2011).

162 J. Hasbrouck & G. Saar, Low-Latency Trading, 16 J. Financ. Mark. 646 (2013).

163 HFT Literature Review at 10.

164 Id. at 10-11; A. Shkilko & K. Sokolov, Every Cloud Has a Silver Lining: Fast Trading, 

Microwave Connectivity and Trading Costs (Working Paper 2016), available at:

https://ssrn.com/abstract=2848562 (arguing that bad weather affecting microwave 

communications improves outcomes for other market participants).
46

to achieve queue priority can make it difficult for even marginally slower firms to 

successfully provide liquidity.165 Some strategies may also involve trading against stale 

orders that have not been updated to incorporate information available to participants 

with the fast data processing technology.166 Studies have also found evidence that HFT 

firms engage in order anticipation and momentum ignition strategies. 167 HFT firms can 

exploit information asymmetries derived from the speed with which they can access and 

process trading information as compared to other market participants.

Several studies have concluded that improvements in speed are valuable primarily on a 

relative basis, and that they do not necessarily provide more fundamental value.168 Some 

have argued that the technological “arms race” may therefore be socially wasteful.169

Various studies conclude that during periods of unusually high volatility or market stress 

the use of algorithms may exacerbate price movements. There is evidence that during 

periods of market stress, market participants self-impose trading halts or otherwise slow 

their activity in order to minimize their market risk.170 The withdrawal of liquidity caused 

by such a pause may cause prices to move further and more rapidly than they otherwise 

would due to a sudden absence of countervailing trading pressure. There is also evidence 

that some algorithmic trading firms aggressively trade into rapid price movements, 

 

165 C. Yao & M. Ye, Why Trading Speed Matters: A Tale of Queue Rationing under Price 

Controls, 31 Rev. Fin. Stud. 2157 (2018).

166 E. Budish, P. Cramton &J. Shim, The High-Frequency Trading Arms Race: Frequent batch 

Auctions as a Market Design Response, 130 Q. J. of Econ. 1547 (2015); J. Brogaard, T. 

Hendershott & R. Riordan, High Frequency Trading and the 2008 Short-Sale Ban, 124 J. Fin.

Econ. 22 (2016); M. Aquilina, E. Budish, and P. O’Neill, Quantifying the High-Frequency 

Trading “Arms Race”: A Simple New Methodology and Estimates, UK Financial Conduct 

Authority Occasional Working Paper No. 50 (2020) (available at:

https://www.fca.org.uk/publications/occasional-papers/occasional-paper-no-50-

quantifying-high-frequency-trading-arms-race-new-methodology).

167 HFT Literature Review at 10.

168 M. Gai, C. Yao & J. Ye, The Externalities of High Frequency Trading (Working Paper 2013), 

available at: https://ssrn.com/abstract=2066839; Budish et al., supra note 167; M. Baron et 

al., Risk and Return in High-Frequency Trading, 54 J. Fin. And Quantitative Analysis 

993(2019).

169 Budish et al., supra note 166.

170 Flash Crash Report at 6; HFT Literature Review at 34. 
47

exacerbating price movements by quickly exhausting available resting liquidity.171 Some 

researchers have argued, however, that quantitative investors may ultimately act as shock 

absorbers, since quantitative models will be able to signal when prices are so low that 

profit potential is worth the risk of trading during periods of extreme stress.172

F. Effects of the COVID-19 Pandemic

Beginning in late February 2020, world markets came under severe stress as a result of the 

global COVID-19 pandemic. Volatility, trading volumes, and message traffic increased 

significantly above their recent averages, and remained at elevated levels for several 

weeks. For example, the VIX, a widely-used measure of market volatility, peaked at an 

intraday value of 83.56 on March 16th, which is about five times higher than its average 

value for 2019.173

At the same time, many market participants and SROs were forced to alter their 

operational, supervisory, and compliance protocols to accommodate their trading and 

support personnel working from home or back-up facilities. Despite these challenges, U.S. 

equity markets functioned without significant technical, or logistical, disruption. 

Research on market activity and the actions of market participants, as well as the role of 

algorithmic trading during the initial stages of the pandemic, is ongoing and developing. It 

is beyond the scope of this report to provide a comprehensive overview of COVID-19’s 

impact on U.S. capital markets. However, the following initial observations may be relevant 

to this report. 

1. NYSE Floor Closure

The New York Stock Exchange (NYSE) operates a hybrid market model unique to U.S.

equity markets. It combines electronic trading with human presence and participation in 

the matching process on a physical trading floor. Designated Market Makers (DMMs, 

formerly known as “specialists”) have obligations to provide fair and orderly markets in 

their designated securities. In addition to DMMs, a number of Floor Brokers maintain 

 

171 See, e.g., Flash Crash Report at 48.

172 Kevin McPartland, Benefits and Future of Quantitative Investing, Greenwich Associates, 

pp. 7-8 (May 17, 2018).

173 See also, e.g., SIFMA, COVID-19 Related Market Turmoil Recap: Part I - Equities, ETFs, 

Listed Options & Capital Formation, p. 6 (Jun. 2020) (noting a record closing VIX value of 

82.69 on Mar. 16, 2020). 
48

physical presence on the floor of NYSE.

174 While most intraday trading on NYSE happens 

electronically, DMMs and Floor Brokers typically play a significant role during the opening 

and closing auctions. Specifically, they fulfill three main functions in the NYSE hybrid 

market model:

• Provide access to “D Orders,” a unique order type only available through floor 

personnel.

175 While D Orders are available throughout the trading session, most 

executions occur in the closing auction.

• Provide access to NYSE’s “Parity and Priority” structure, which allows orders entered 

via DMMs and Floor Brokers to have the same priority on the NYSE book as electronic 

orders which arrived earlier.176 As noted above, this structure differentiates NYSE’s 

model from the price-time priority available on most of the other exchanges.

• Serve as a source of information for off-floor traders, especially around auctions and 

significant market events.

On March 23, 2020, in response to the spread of COVID-19 in the New York metropolitan 

area and in the interests of its employees’ safety, NYSE moved to fully-electronic trading 

and temporarily closed its main physical trading floor.177 According to NYSE’s filing with 

the Commission of March 20, 2020:

Because the Trading Floor facilities will be closed, Floor brokers will not be 

able to enter orders on the Trading Floor. As a result, there will not be any 

Floor Broker Participants in allocations and there will not be any order types 

unique to Floor brokers, such as D Orders. In addition, because DMMs will not 

be on the Trading Floor, DMMs will not engage in any manual actions, such as 

 

174 See, e.g., 2020 NYSE Trading Floor Broker Directory, available at:

https://www.nyse.com/publicdocs/nyse/NYSE_Trading_Floor_Broker_Directory.pdf.

175 See, e.g., NYSE, “The Floor Broker’s Modern Trading Tool,” available at: 

https://www.nyse.com/article/trading/d-order.

176 See, e.g., NYSE, “Parity & Priority Fact Sheet,” available at: 

https://www.nyse.com/publicdocs/nyse/markets/nyse/Parity_and_Priority_Fact_Sheet.pd

f.

177 See Notice of Filing and Immediate Effectiveness of Proposed Rule Change To Amend Rules

7.35A, 7.35B, and 7.35C for a Temporary Period, Exch. Act Rel. No. 88,444 (Mar. 20, 2020), 

85 Fed. Reg. 17141 (Mar. 26, 2020). During this period, several options exchanges also 

closed their trading floors. These closures are not discussed here because options trading 

is not the focus of this report. 
49

facilitating an Auction manually or publishing pre-opening indications before 

a Core Open or Trading Halt Auction.178

On May 26, 2020, NYSE commenced “Phase I” of the reopening process, allowing a number 

of Floor Brokers to return to the floor of the exchange.

179 It was followed on June 17th by

“Phase II,” in which certain DMMs were allowed to be physically present on the floor and 

resume their main functions in a limited number of stocks.180 

The move by NYSE to fully electronic trading did not result in any significant market 

interruption or system-related issues. However, there are differing anecdotal views and 

limited research on how the floor closure affected certain metrics of market quality, such as 

liquidity and price formation during the opening and closing auctions, as well as effective 

spreads and displayed liquidity during the trading day. For example, Brogaard, 

Ringgenberg, and Roesch (2020) compared changes in intraday market quality metrics on 

NYSE with a control group comprising NASDAQ stocks.181 They show a relatively larger 

increase in effective spreads in NYSE stocks as compared to the control group following the 

floor closure, as well as more significant degradation in other metrics, such as volatility and 

“pricing errors.” The authors subsequently conclude that the NYSE hybrid model benefits 

overall intraday trading quality, with most of the benefit concentrated around opening and 

closing, when volatility is at its highest. 

2. Volatility Controls

A later section of this report describes various measures the Commission and other 

regulators have implemented to modulate extreme price swings in individual securities 

 

178 Id. at 17142 (internal citations omitted). 

179 See, e.g., Notice of Filing and Immediate Effectiveness of Proposed Rule Change To Extend 

the Temporary Period for Specified Commentaries to Rules 7.35, 7.35A, 7.35B, and 7.35C, 

Exch. Act Rel. No. 88,933 (May 22, 2020), 85 Fed. Reg. 32059 (May 28, 2020). 

180 See, e.g., Notice of Filing and Immediate Effectiveness of Proposed Rule Change To Add, for 

a Temporary Period That Begins on June 17, 2020, Commentary .06 to Rule 7.35A; 

Commentary .03 to Rule 7.35B; Supplementary Material .20 to Rule 76; and an Amendment to 

Supplementary Material .30 to Rule 36 To Support the Partial Return of Designated Market 

Makers to the Trading Floor, Exch. Act Rel. No. 89,086 (Jun. 17, 2020), 85 Fed. Reg. 37712 

(Jun. 23, 2020). 

181 Jonathan Brogaard, Matthew Ringgenberg, & Dominick Rösch, Does Floor Trading 

Matter? (Jun. 2020), available at: 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3609007.
50

and equity markets as a whole.182 Their implementation and usefulness have been 

extensively tested during the period of market volatility caused by the COVID-19 pandemic. 

For example, Market Wide Circuit Breakers (MWCB) were triggered four times during 

March 2020. Notably, on March 9th, the MWCB was triggered for the first time since both 

the reference index for the MWCB (from the DJIA to S&P500) and the thresholds for 

different CB levels were changed in 2012. All four MWCBs were Level 1 circuit breakers, 

i.e., triggered by a 7% drop in the index. The MWCBs appeared to have operated as 

intended, with generally issue-free pausing and re-opening processes, as well as orderly

trading following re-opening of the market. The incidence of individual stock volatility 

halts also significantly increased in March. For example, on March 18, 2020, there were 

1,475 limit-up limit-down volatility halts in 643 unique symbols, compared to a typical 

daily median count of approximately ten halts in approximately seven unique symbols.183

3. Liquidity and Spreads

Periods of heightened volatility normally lead to a degradation in market quality and 

increased implicit execution costs for investors. The period of severe market volatility 

caused by COVID-19 has resulted in increased effective spread measures and market 

impact costs across the board. Mittal, Saraiya, and Berkow (2020)184 compare various 

market characteristics during the period of heightened volatility with the period of relative 

calm in January 2020. They find that the normalized spread costs during the crisis period 

increased by 7.2 times for S&P 500 stocks and 4.1 times for Russel 2000 stocks. They also 

find that the realized market impact (in addition to spread costs) of trading a number of 

shares equivalent to 2% of the daily volume for an S&P 500 stock during the crisis is 

comparable to that of 10% of the daily volume during the “normal” period. 

4. General Observations on Initial Months of COVID-19 

During the initial months of the COVID-19 pandemic, the fully electronic, complex, and 

interconnected U.S. equity markets operated without significant disruption. Notably, this 

continuity was accomplished with most brokers, buy-side traders, exchange personnel, and

regulators working from home. Even on days when markets were paused due to sharp 

drops, re-openings resulted in orderly resumption of trading. Impact cost and spreads 

measures have responded, as they always do, to heightened uncertainty, as liquidity 

providers re-priced their risk and investors’ demand for liquidity has increased. And while 

 

182 See infra Section VI.B. 

183 Source: NYSE TAQ. To find typical daily values, the distribution of limit-up limit-down 

halts was calculated from January 1, 2019 through June 30, 2020. 

184 Hitesh Mittal, Nigam Saraiya, and Kathryn Berkow, US Equity Liquidity in the COVID-19 

Crisis (Mar. 31, 2020), available at: https://bestexresearch.com/wpcontent/uploads/2020/04/BestEx-Research-Market-Impact-Analysis-20200331.pdf
51

the markets came under severe stress, with unprecedented volatility and sharp daily 

fluctuations, they have proved to be resilient, efficient, and transparent.

V. Benefits and Risks of Algorithmic Trading in Corporate and 

Municipal Bonds

In the secondary markets for corporate and municipal debt securities, algorithms have 

begun to address a range of long-identified information issues, including the distribution 

and gathering of quotations, pricing, and trade matching and execution. These changes 

have been accompanied by the growth of liquidity provision by participants other than 

traditional dealers and an expansion of portfolio trading and bond exchange-traded 

products. 

A. Liquidity Search and Trade Execution

In some portions of the debt markets, algorithms are reshaping the problems of finding and 

providing liquidity. The most notable developments are the automation of the request-forquote process and streaming quotations directly between counterparties.185 The relative 

openness of many RFQ platforms and streaming quotation tools has allowed 

technologically-sophisticated non-dealer liquidity providers to move into the corporate 

and municipal bond markets.

In the corporate bond market, algorithms are central to the process of automating the RFQ 

process.186 An automated RFQ process may look something like the following stylized 

example.187 A platform may allow parties to identify or restrict the specific types of 

counterparties with whom they may communicate, using factors such as whether a party 

underwrote a new issue, has traded recently in a particular security, or has expressed

interest in trading a similar bond. A platform may also allow the party posting an RFQ to 

 

185 While not widely adopted across all types of debt securities, limit order books on 

platforms such as Brokertec and Nasdaq Fixed Income have become a key locus of trading 

in the interdealer market for benchmark, on-the-run U.S. Treasury securities. Some of the 

risks associated with electronic central limit order book trading in the U.S. Treasury market 

are described in the Treasury Market Report on the events of October 15, 2014. See

Treasury Market Report. Beyond Treasury securities, and even outside of the interdealer 

market in on-the-run Treasury securities, central limit order books have not seen 

widespread adoption in the debt markets. See, e.g., Kevin McPartland, Treasury Traders Shy 

Away from Order Books, Greenwich Associates (Jan. 30, 2018).

186 McPartland, Corporate Bond Trading in 2019, at 7.

187 See id.
52

set parameters, including, among other things, limiting pricing responses to a specified 

deviation from a pricing estimate, defining the time during which pricing responses must 

be received, and identifying a minimum number of pricing responses needed. Then, with 

these parameters set, when a party posts an RFQ, it is automatically sent to a dealer or 

counterparty list. Counterparties then respond automatically, generally based on their own 

pricing logic and algorithms. The platform then automatically confirms the best price, 

consistent with parameters set by the party posting the RFQ. Some platforms may allow the

party posting to an RFQ to review RFQ results and affirmatively confirm a trade rather than 

executing the trade automatically.

In the corporate bond market, $1 million is currently a rough upper bound for trades that 

can consistently be executed in an automated manner.188 The use of automated trading 

declines above that size, with very little adoption for block trades.

In many bonds, particularly bonds issued in smaller sizes and bonds that have been 

outstanding, pricing is a difficult task because each instrument trades relatively 

infrequently, and can do so at inconsistent sizes and under different market conditions. A 

variety of algorithms are designed to address this issue. Many models use so-called “matrix 

pricing” to estimate the price of a particular bond by looking at data for similar bonds, with, 

for example, comparable issuers, maturities, coupons, or credit ratings. A number of 

platforms now use machine learning algorithms to generate a price or spread for specific 

instruments.189

For an increasing number of bonds, market participants now stream to counterparties 

continuous prices or quote continuous prices on a platform. Dealers, principal traders, and 

customers are able to stream prices. These streams are generally bilateral, allowing a user 

to tailor liquidity sources across the market. This data can be supplemented by data from 

RFQ platforms, providing seekers of liquidity with an increasingly broader view of the 

market.190 Streaming or quoting continuous prices has become significantly more common 

in municipal bonds, where the typically small trade sizes may be amenable to electronic 

trading.

 

188 Id. at 7.

189 See, e.g., MarketAxess Research, Composite+: Algorithmic Pricing in the Corporate Bond 

Market, available at https://content.marketaxess.com/sites/default/files/2018-

08/MKTX_Composite%2B_whitepaper.pdf.

190 See, e.g., McPartland, How Bilateral Streams for U.S. Treasuries Really Work, And What 

They Mean for the Market (discussing use of streaming quotes in U.S. Treasury market); 

SIFMA, SIFMA Electronic Bond Trading Report: US Corporate & Municipal Securities, p. 8 

(Feb. 2016) (noting growing adoption of streaming price protocols from dealers in 

corporate and municipal securities).
53

B. ETF Market Making and Arbitrage

The share of global assets under management in fixed income ETFs has increased 

significantly over the past decade.191 A recent estimate is that approximately 1.6% of 

global fixed income assets under management are in fixed-income ETFs.192 ETFs have 

made it possible for investors to indirectly take positions on cash bond markets by trading 

intraday in the more liquid and transparent equity market. ETFs also provide investors 

with a generally more efficient means of accessing a diversified exposure to bonds as 

compared with directly assembling a bond portfolio.

The growth in ETFs has presented arbitrage opportunities to firms willing to trade 

between the ETF market and the underlying bonds. A market maker that is an Authorized 

Participant of an ETF can use the create-and-redeem process to manage the risk of taking 

on positions in either the equity market (for the ETF) or cash bond market (for the 

underlying bonds). This cross-market trading and risk-management activity depends on 

the effective and rapid processing of data on potentially hundreds of individual securities. 

Like the expansion of RFQ platforms, this arbitrage opportunity has attracted non-dealer 

liquidity providers to be active in the corporate and municipal cash bond markets.193

Developments in the technological infrastructure to conduct ETF arbitrage and marketmaking have also facilitated the expansion of portfolio trading, where investors can request 

a single price for a list of bonds, as opposed to trading them individually.194

C. Studies of Effects on Market Quality and Provision of Liquidity

Academic research on the effects of algorithmic trading on secondary debt markets is 

relatively limited. Lack of available data is an important constraint. Order level data is 

usually only available in the most liquid and “electronified” markets, such as on-the-run 

Treasuries. 195 Order level data is usually not available in less liquid debt markets. 

 

191 FIMSAC Subcommittee on ETFs and Bond Funds, “Report”, at 6 (Apr. 10, 2019).

192 Id.

193 McPartland, The Challenge of Trading Corporate Bonds Electronically, at 5-6. (“the profit 

opportunity presented by fixed-income ETF arbitrage strategies has brought a number of 

principal trading firms and some quantitative hedge funds into the corporate bond 

market”).

194 Id.

195 Most of the academic research on algorithmic trading in the fixed income markets 

comes from studying the on-the-run Treasuries market. Fleming (2016) estimates that 

trading in on-the-run securities accounts for roughly 85% of total trading volume across 

nominal Treasury securities. The majority of trading in on-the-run Treasuries occurs 
54

Although transaction data is available through TRACE, there is no attribute on TRACEdisseminated reports that identifies trades executed by algorithms.

Overall, research shows that algorithmic trading is prevalent in already liquid debt markets 

(e.g., on-the-run Treasuries). Studies of these markets generally find an overall positive 

effect of algorithmic trading on liquidity and price discovery during “normal” times. They 

also find that “electronification” lowers trading costs, since less intermediation is required 

for transactions to be executed. There is, however, some evidence of algorithmic trading 

being associated with increased volatility, but such evidence is not prevalent, and it 

generally is present during special market conditions, such as periods of unusually high 

volatility.196 However, there are very few studies focusing on algorithmic trading in the 

corporate and municipal bond markets.197

The Treasury Market Report on the extraordinary volatility of October 15, 2014 

highlighted several areas of risk related to the use of automated trading.198 These risks are 

similar to those that others have described with respect to automated trading in equities 

markets, including: operational risks from malfunctioning or incorrectly deployed 

algorithms; market liquidity risks from abrupt changes in trading strategies; market 

integrity risks from acts of manipulation; transmission risks from interconnected markets 

with closely related instruments; clearing and settlement risks from firms clearing outside 

a central counterparty structure; and risks to the effectiveness of risk management from 

the speed at which markets and risk positions can change.

 

electronically on the BrokerTec platform. Fleming, Mizrach, and Nguyen (2018) analyze 

the microstructure of the BrokerTec electronic platform, and report that it accounts for 

60% of electronic interdealer trading for each of the on-the-run 2-, 5- and 10-year notes. 

See also the discussion of potential limitations of use of academic research, supra Section 

IV.E.

196 For a more detailed discussion of studies analyzing electronic markets for U.S. Treasury 

securities, see infra Section VII.B.

197 See, e.g., Bank of International Settlement, Electronic trading in fixed income markets, 

at 23 (Jan. 2016) (noting that “[e]mpirical works on the impact of AT and particularly HFT 

on market quality are numerous, but unfortunately relatively few studies focus specifically 

on bond markets due to a lack of data”).

198 See Treasury Market Report Appendix C.
55

VI. Regulatory Responses to Market Complexity, Volatility, and 

Instability

Algorithmic trading, including trading that relies on rapid access to and processing of large 

amounts of market data, is ubiquitous in our equity markets and is increasingly important 

in our debt markets. Algorithmic trading has brought secondary market participants 

important benefits such as increased liquidity, cost reductions, and improvements in other 

measures of market quality. But advances in technology, and related developments in the 

provision of and access to market data, have also contributed to the growth of complexity 

in markets, arguably have contributed to episodes of volatility and dislocation, and have 

changed (and in some cases increased) the firm-level and market risks stemming from

system errors and operational failures.

Over the last decade, the Commission and self-regulatory organizations have taken various

steps to address these developments, including the evolving firm-level and market risks. 

Many of these steps are outlined below and Commission staff will continue to monitor 

these developments and, as may be necessary or appropriate, provide advice and make 

recommendations to the Commission, including whether the Commission may or may not 

need additional statutory authority to address market developments and emerging risks.

A. Improving Market Transparency

To promote a better understanding of the operation of our algorithm-driven and 

increasingly complex equity markets as well as our evolving debt markets, recently the 

Commission, SROs, and Commission staff have sought to expand transparency into several 

aspects of modern markets with an eye toward various regulatory objectives, including 

facilitating further analysis of market efficiency and integrity and fostering competition.

199

1. Large Trader Reporting

In 2011, the Commission adopted rules to assist the Commission in identifying and 

obtaining trading information on market participants that conduct a substantial amount of 

 

199 A number of these initiatives require the Commission or Commission staff to collect, 

store, or access sensitive market and participant data and information. See, e.g., Chairman 

Jay Clayton, “Statement on Cybersecurity” (Sept. 20, 2017) (available at:

https://www.sec.gov/news/public-statement/statement-clayton-2017-09-20). The 

Commission and Commission staff review these various data sets with the perspective that 

data should only be collected and accessed to the extent that it is necessary to further the 

agency’s mission and that it can reasonably be protected. Id.
56

trading activity in U.S. securities markets.200 This rule improves the Commission’s ability 

to identify large market participants, and collect and analyze information on their trading 

activity.201 Firms began reporting required information in 2012.

2. Consolidated Audit Trail

In July 2012, the Commission approved Rule 613 of Regulation NMS, which requires the 

self-regulatory organizations to submit and implement a national market system plan to 

create a consolidated audit trail (CAT) that would allow regulators to efficiently and 

accurately track virtually all activity in U.S. equity and options markets.202 The Commission 

approved a National Market System Plan for implementing the CAT in November 2016.203

In September 2019, the Commission proposed amendments to the NMS Plan designed to 

improve transparency and financial accountability of the development of the CAT.204 In 

March 2020, the Commission granted conditional exemptive relief to, among other things, 

reduce the amount personally identifiable information in the CAT database.205 Additional 

details on Plan implementation and proposed timelines are available on the Plan 

website.206 

 

200 See 17 CFR § 240.13h-1, 249.327; Large Trader Reporting, Exch. Act Rel. No. 64976, 76 

Fed. Reg. 46959 (Aug. 3, 2011).

201 76 Fed. Reg. at 46961, 46963 (the system of large trader reporting “represents an 

important enhancement to the Commission’s capabilities to uniformly identify large 

traders and quickly obtain information on their trading activity in a manner that can be 

implemented expeditiously by leveraging an existing reporting system”).

202 See 17 CFR § 242.613; Consolidated Audit Trail, Exch. Act Rel. No. 67457, 77 Fed. Reg. 

45721 (Aug. 1, 2012).

203 See Order Approving the National Market System Plan Governing the Consolidated Audit 

Trail, Exch. Act. Rel. No. 79318, 81 Fed. Reg. 84696 (Nov. 23, 2016).

204 See Proposed Amendments to the National Market System Plan Governing the 

Consolidated Audit Trail, Exch. Act Rel. No. 86901, 84 Fed. Reg. 48458 (Sep. 13, 2019). 

205 See Order Granting Conditional Exemptive Relief, Pursuant to Section 36 and Rule 608(e) 

of the Securities Exchange Act of 1934, from Section 6.4(d)(ii)(C) and Appendix D Sections 

4.1.6, 6.2, 8.1.1, 8.2, 9.1, 9.2, 9.4, 10.1, and 10.3 of the National Market System Plan Governing 

the Consolidated Audit Trail, Exch. Act Rel. No. 88393 (Mar. 17, 2020). 

206 See Consolidated Audit Trail, LLC, https://www.catnmsplan.com/.
57

3. FINRA ATS and OTC Transparency

As discussed above, FINRA makes publicly available statistics on off-exchange equity 

executions, both in alternative trading systems and at non-ATS OTC venues.207 FINRA

began collecting statistics on volume and number of transactions from ATSs in 2014, and

then made the statistics publicly available on an aggregated basis.208 In 2015, FINRA 

expanded the scope of publicly disseminated data to include non-ATS equity volume 

executed over-the-counter.209 FINRA also expanded its public disclosures to include 

information about equity block-size transactions on ATSs210 and in non-ATS over-thecounter transactions.211 FINRA has also proposed including in its public disclosures data 

on ATS transactions in corporate and agency debt securities.212 While FINRA at one point 

charged for professional or vendor access to the data discussed above, this data is now 

widely and freely available for public use.213

4. MSRB ATS Trade Indicator

In 2016, the MSRB began requiring a specific indicator on trade reports for trades executed 

on ATSs.214 This indicator is included both on trades where an ATS takes a principal 

position between buyer and seller, and where an ATS connects a buyer and seller but does 

not take a principal or agency position between the parties. The ATS indicator is included 

on transaction data disseminated publicly.

 

207 See, e.g., FINRA Rule 6110(b); see also File No. SR-FINRA-2013-042, 79 Fed. Reg. 4213 

(Jan. 17, 2014) (approving FINRA’s collection and public dissemination of ATS statistics); 

File No. SR-FINRA-2015-020, 80 Fed. Reg. 61246 (Oct. 9, 2015) (approving expansion to 

OTC data generally).

208 See, e.g. FINRA Regulatory Notice 14-07 (Feb. 2014).

209 See, e.g., FINRA Regulatory Notice 15-48 (Nov. 2015).

210 See FINRA Regulatory Notice 16-14 (Apr. 2016).

211 See FINRA Regulatory Notice 18-28 (Sept. 11, 2018).

212 See FINRA Regulatory Notice 19-22 (July 9, 2019).

213 See File No. SR-FINRA-2015-023, 80 Fed. Reg. 39811 (July 10, 2015).

214 See MSRB Regulatory Notice 2015-07 (May 26, 2015); MSRB Rule G-14 RTRS 

Procedures(b).
58

5. TRACE for U.S. Treasury Securities

Beginning in July 2017, FINRA began requiring member firms to report to TRACE 

transactions executed in U.S. Treasury securities.215 This requirement was, in part, a 

response to the unusual market volatility of October 15, 2014, which highlighted, among 

other things, the need for official-sector access to data regarding the cash market for 

Treasury securities.

216 Treasury securities are traded by broker-dealers that are FINRA 

members, as well as by market participants who are not registered broker-dealers, such as 

commercial banks and principal trading firms.217 To expand the scope of its data collection 

in Treasury securities, in 2019 FINRA began requiring certain large ATSs to report to 

TRACE the identities of non-FINRA member counterparties.218 Currently, the data 

submitted to TRACE is available only to regulators, including the Department of the 

Treasury. However, in 2020 FINRA will begin publishing weekly aggregated transaction 

information and statistics on U.S. Treasury Securities.219

6. Rule ATS-N

In August 2018, the Commission expanded the disclosure requirements for NMS Stock 

ATSs and required ATSs to implement safeguards to protect subscribers’ confidential 

trading information.220 On new Form ATS-N, ATSs must disclose key information about 

their manner of operations and the ATS-related activities of their broker-dealer operators

and affiliates. These disclosures are intended to allow market participants to better 

 

215 See FINRA Regulatory Notice 16-39 (Oct. 2016); SR-FINRA-2016-027, 81 Fed. Reg. 

73167 (Oct. 24, 2016) (SEC approval of FINRA rules requiring reporting). The reporting 

requirement was effective July 10, 2017.

216 See FINRA Notice 16-39 at 2.

217 Id.

218 See FINRA Regulatory Notice 18-34 (Oct. 4, 2018); File No. SR-FINRA-2018-023, 83 Fed. 

Reg. 40601 (Aug. 15, 2018). The requirement was effective April 1, 2019. The requirement 

will not include trades between two non-FINRA member firms. 

219 See, e.g., Financial Industry Regulatory Authority, Inc.; Order Approving Proposed Rule 

Change To Allow FINRA To Publish or Distribute Aggregated Transaction Information and 

Statistics on U.S. Treasury Securities, Exch. Act Rel. No. 87837, 84 FR 71986 (Dec. 30, 2019); 

see also U.S. Department of the Treasury, Quarterly Refunding Statement (Feb. 5, 2020) 

(noting that “the public report of weekly aggregated transactions will provide the most 

comprehensive account of how much, in what security types, and in what segments of the 

market Treasury securities are traded”). 

220 See ATS-N Adopting Release, 83 Fed. Reg. 38768 (Aug. 7, 2018).
59

understand how their orders will interact and be executed inside each ATS, and to help 

market participants understand differences, if any, in the treatment in the ATS between 

subscribers, on the one hand, and the broker-dealer operator and its affiliates, on the other 

hand. The disclosure is also intended to facilitate analysis of potential conflicts of interest

more generally as well as risks of information leakage. In addition, the disclosures are 

intended to make NMS Stock ATSs more comparable with one another, and to help market 

participants compare these venues with other market centers in the national market 

system.

7. Disclosure of Order Handling Information

In November 2018, the Commission amended its requirements with respect to order 

handling and routing disclosures.221 These amendments enhanced the quarterly public 

reports that broker-dealers were already required to publish, by mandating disclosure of, 

among other things, payment for order flow arrangements and profit-sharing relationships. 

The amendments also require broker-dealers, upon request by a customer who places a 

“not held” order,222 to provide a customer with a standardized set of individualized 

disclosures about the firm’s handling of the customer’s orders, including average rebates 

received from (or fees paid to) trading venues, and information about orders that provided 

or removed liquidity.223

8. Staff Reports on Episodes of Extreme Volatility

To facilitate market understanding of the dynamics of complex markets during periods of 

extreme volatility, Commission staff, in some cases working alongside the staff of other 

financial regulators, have published reports describing and analyzing market events. 

Specifically, reports were published following the Flash Crash of May 6, 2010,224 the 

unusual volatility in the U.S. Treasury market on October 15, 2014,225 and the equity 

market volatility of August 24, 2015.226 These reports discuss in detail the market 

 

221 See 17 CFR § 242.605-606; Order Handling Adopting Release.

222 A not-held order generally gives a broker-dealer price and time discretion in the 

handling of that order.

223 See 17 CFR § 242.606(b)(3).

224 See Flash Crash Report.

225 See Treasury Market Report.

226 August 24, 2015 Report; Austin Gerig and Keegan Murphy, The Determinants of ETF 

Trading Pauses on August 24th, 2015 (Feb. 2016), available at: 

https://www.sec.gov/marketstructure/research/determinants_eft_trading_pauses.pdf.
60

dynamics during these periods of unusual volatility. They also provide insight into the 

complexity and data-driven nature of markets as well as some of the limits of regulatory 

oversight and analysis as a result of data limitations. Some of these limitations have been 

or are expected to be addressed, including when the CAT is more fully operational. 

9. Market Structure Statistics and Research

The SEC website publishes market data statistics and research on market structure issues, 

and makes available tools for the public to visualize changes in market structure data.227

The statistics available on this website are derived from the Commission’s Market 

Information Data Analytics System (MIDAS), which provides Commission staff with market 

data comparable to that used by some of the more sophisticated market participants, 

including the equity and options SIPs, equity exchange proprietary data, fixed-income data,

futures market data, and cryptocurrency data.

B. Mitigating Price Volatility

As algorithmic and electronic trading have become more prevalent in today’s markets, 

several notable events and other considerations have lent support to the concern that 

algorithmic markets may be increasingly prone to quick, large market moves unrelated to 

fundamental economic information about the underlying companies or the broader 

economy. To help mitigate the negative effects of algorithmic price swings that may occur 

too rapidly for human detection and engagement and may unduly destabilize markets, the 

Commission and other regulators have implemented several controls to modulate large, 

rapid price moves in individual equity securities and the equity markets more generally.

1. Regulation SHO (Short Selling) Circuit Breaker

In 2010, the Commission approved rules requiring trading centers to have in place policies 

and procedures to restrict short selling in NMS stocks when a stock has declined 10% or 

more relative to the previous day’s closing price.228 Once this short-sale circuit breaker has 

been triggered, for the remainder of the day and the following day, short sale orders may 

generally, subject to certain exemptions, not be executed or displayed at a price that is less 

than or equal to the current national best bid.229 This rule is intended to prevent short 

 

227 See Market Structure, https://www.sec.gov/marketstructure/. Members of the public 

may also email Commission staff about this website and market structure issues at 

marketstructure@sec.gov.

228 See 17 CFR § 242.201; Amendments to Regulation SHO, Exch. Act Rel. No. 61595, 75 Fed. 

Reg. 11231 (Mar. 10, 2010).

229 17 CFR § 242.201(b).
61

selling, including potentially manipulative or abusive short selling, from driving down 

further the price of a security that has already experienced a significant intra-day price 

decline, and to facilitate the ability of long sellers to sell first upon such a decline.230

2. Single-Stock Circuit Breakers

One response to the volatility in equity markets on May 6, 2010 was the introduction of a 

single-stock circuit breaker pilot program.231 This program was implemented through 

three stages of rule filings by the exchanges and FINRA, beginning in June 2010.232 In the 

first stage, the Commission approved rules to pause trading during periods of 

extraordinary market volatility in stocks included in the S&P 500.233 The second stage 

added to the pilot securities in the Russell 1000 index, as well as specified exchange traded 

products.234 The third stage added all remaining NMS stocks to the pilot.235 All rights and 

warrants were later exempted from the pilot.236 The single-stock circuit breaker pilot 

expired at the end of July 31, 2012, and was replaced by the “limit-up, limit-down” plan, 

described below.

3. Limit-Up, Limit-Down Plan

To replace the expiring single-stock circuit breaker pilot, in 2012 the SEC approved on a 

pilot basis, and in 2013 the SROs implemented, the Plan to Address Extraordinary Market 

Volatility, more frequently called the “limit-up, limit-down” plan.237 The Plan has since 

been amended eighteen times, and has been made permanent.238 Under the Plan, the SIPs 

 

230 75 Fed. Reg. at 11231.

231 77 Fed. Reg. 33499-500 (June 6, 2012).

232 Id.

233 See Exch. Act Rel. Nos. 62252 (June 10, 2010), 75 Fed. Reg. 34186 (June 16, 2010); 

62251 (June 10, 2010), 75 Fed. Reg. 34183 (June 16, 2010).

234 See Exch. Act Rel. Nos. 62884 (Sept. 10, 2010), 75 Fed. Reg. 56618 (Sept. 16, 2010)); and 

Securities Exch. Act Rel. No. 62883 (Sept. 10, 2010), 75 Fed. Reg. 56608 (Sept. 16, 2010).

235 See Exch. Act Rel. No. 64735 (June 23, 2011), 76 Fed. Reg. 38243 (June 29, 2011).

236 See, e.g., Exch. Act Rel. No. 65810 (Nov. 23, 2011) 76 Fed. Reg. 74080 (Nov. 30, 2011) 

(SR-NYSE-2011-57).

237 See Exch. Act Rel. No. 67091, 77 Fed. Reg. 33498 (June 6, 2012).

238 See, e.g., Exch. Act Rel. No. 85623, 84 Fed. Reg. 16086 (Apr. 4, 2019) (approving the 

eighteenth amendment).
62

distributing consolidated data for individual stocks calculate price bands for each stock 

above and below a reference price. If the national best bid or national best offer of a stock 

equals or falls outside the upper or lower limits of one of these bands, the stock enters a 

“limit state.” If a stock remains in a limit state for fifteen seconds, trading in the stock is 

paused for five minutes. Trading in the stock then reopens with an auction at the stock’s 

primary listing exchange. The Plan is intended to pause trading when rapid price moves 

result from, for example, erroneous trades or gaps in liquidity, while not inappropriately 

restricting more fundamental price moves.239

Securities are divided into tiers, with each tier having a different threshold for the 

applicable price bands. Generally more liquid stocks have tighter price bands, and lessliquid stocks have wider price bands. The price bands are also wider for stocks in the more 

liquid tier in the minutes leading up to the closing auction, to avoid entering a pause during 

the price movement that may accompany the close of each trading day.240

All trading centers are required to establish, maintain, and enforce written policies and 

procedures designed to comply with the Plan.

4. Market-Wide Circuit Breakers

Also following the extraordinary volatility on May 6, 2010, the national securities 

exchanges and FINRA, in 2012, updated their rules providing for market-wide circuit 

breakers in the event of severe, market-wide downturns.241 The market-wide circuit 

breaker is intended to pause, and, if needed, halt all trading in the event that the broad 

market is declining rapidly.

Generally, if the S&P 500 index declines 7% since the end of the previous day’s close (Level 

1), trading in all equity stocks and options is paused for fifteen minutes. If it declines to 

13% from the prior day’s close (Level 2), the market pauses again for fifteen minutes. If the 

index declines 20% from the prior day’s close (Level 3), then all trading is paused until the 

next trading day. After 3:25pm, pauses will not occur at the 7% and 13% level, though a 

halt will occur for the remainder of the day if a decline reaches the 20% level.

A market-wide circuit breaker has been triggered four times. On both March 9, 2020 and 

March 12, 2020, several minutes after the market opened, the S&P 500 index declined 7% 

from the prior trading day’s closing price, triggering fifteen-minute Level 1 halts in all 

 

239 77 Fed. Reg. at 33500.

240 See 84 Fed. Reg. at 16092 (approving proposal to eliminate the doubling of price bands 

for Tier 2 stocks at the end of the trading day).

241 See Exch. Act Rel. No. 67090, 77 Fed. Reg. 33531 (June 6, 2012) (approving, on a pilot 

basis, the SRO market-wide circuit breaker rules adjusting limits and using the S&P 500 

index rather than the Dow Jones Industrial Average).
63

equities and options trading. On March 16, 2020, the Level 1 halt was triggered nearly 

immediately after the market opening, when the S&P 500 index rapidly declined more than 

7% compared with the prior trading day’s close. On March 18, 2020, the Level 1 was 

triggered early in the afternoon, several hours after the opening of regular continuous 

trading. In each case, trading resumed in an orderly fashion following the halts. Level 2 

and Level 3 market-wide circuit breakers have not been triggered to date.

C. Facilitating Market Stability and Security

Due to the complexity and interconnection of modern markets, algorithmic trading 

presents significant operational and related risks to market participants, investors and our 

economy more broadly, and the Commission, SROs, and Commission staff have focused on 

matters related to risk management, operational controls, resilience, and security.

1. Market Access Rule

In response to operational risks posed by the growth and expansion of algorithmic trading, 

and the risks posed by sponsored and direct access specifically, in 2010, the Commission 

adopted a rule requiring broker-dealers with direct access, or who provide sponsored

market access to others, to adopt a system of risk management controls and supervisory 

procedures reasonably designed to manage financial, regulatory, and other risks of that 

access.242 These requirements apply to broker-dealers with access to trading directly on 

exchanges or ATSs, including broker-dealers providing sponsored or direct access.243 They 

also apply to broker-dealer operators of ATSs that provide access to trading on their ATSs 

to a person other than a broker-dealer.244

The required financial risk management controls and supervisory procedures must be 

reasonably designed to prevent the entry of orders that exceed appropriate pre-set credit 

or capital thresholds, or that appear to be erroneous.245 The regulatory risk management 

controls and supervisory procedures must also be reasonably designed to prevent the 

entry of orders unless there has been compliance with all regulatory requirements that (1) 

must be satisfied on a pre-order entry basis, (2) are designed to prevent the entry of orders 

that the broker or dealer or customer is restricted from trading, (3) restrict market access 

 

242 See 17 CFR § 240.15c3-5; Market Access Rule Adopting Release.

243 17 CFR §240.15c3-5(a)(1).

244 Id.

245 17 CFR § 240.15c3-5(c)(1). 
64

technology and systems to authorized persons, and (4) assure appropriate surveillance 

personnel receive immediate post-trade execution reports.246

The risk management controls and supervisory procedures required by the Market Access 

Rule must be reviewed for effectiveness at least annually, and the broker-dealer’s chief 

executive officer must certify annually that the broker-dealer’s controls and procedures 

comply with the requirements.247 FINRA and the Commission’s examination staff inspect 

broker-dealer compliance with the Market Access Rule.

2. Regulation SCI

To help manage and mitigate operational risks in the markets,248 the Commission in 2014 

adopted Regulation Systems Compliance and Integrity (“Regulation SCI”), which requires 

classes of important market participants (“SCI entities”) to implement comprehensive 

policies and procedures to help ensure the resilience and robustness of their information 

technology systems, and that those systems operate in compliance with the federal 

securities laws and applicable (e.g., SRO) rules. Regulation SCI also requires SCI entities to

report to the Commission on certain events to facilitate Commission oversight of market 

infrastructure.249 Covered entities include most SROs, high-volume ATSs, NMS plan 

processors, and certain clearing agencies.

SCI entities must mandate participation by members or participants in scheduled testing of 

business continuity and disaster recovery plans, and coordinate with each other on an 

industry- or sector-wide basis.250 In addition to requiring notification of certain events to 

the Commission, the rules also require SCI entities to provide information about events to 

affected members or participants, or, for major events, to all members or participants of 

 

246 17 CFR § 240.15c3-5(c)(2).

247 17 CFR § 240.15c3-5(e).

248 See, e.g., 79 Fed. Reg. at 72253 (“At the same time, these technological advances have 

generated an increased risk of operational problems with automated systems, including 

failures, disruptions, delays, and intrusions. Given the speed and interconnected nature of 

the U.S. securities markets, a seemingly minor systems problem at a single entity can 

quickly create losses and liability for market participants, and spread rapidly across the 

national market system, potentially creating widespread damage and harm to market 

participants, including investors”). Commission staff continues to analyze and assess 

changes in market operational and cybersecurity risks, and whether to recommend to the 

Commission related regulatory action.

249 See 17 CFR § 242.1000-1007; SCI Adopting Release.

250 17 CFR § 242.1004.
65

the entity.251 SCI entities must also review their systems annually, submit quarterly reports 

on material systems changes to the Commission, and maintain appropriate books and 

records.252 The Commission’s examination staff inspects SCI entities for compliance with 

Regulation SCI, generally on an annual basis. 

3. FINRA Guidance on the Supervision of Algorithmic Trading

Recognizing the potential for algorithmic trading strategies to adversely impact market and 

firm stability, FINRA in 2015 provided guidance to its broker-dealer members on effective 

supervision and control practices for member firms and market participants that use 

algorithmic strategies.253 FINRA’s guidance is intended to complement Regulation SCI, and 

to emphasize to broker-dealers the importance of robust policies and procedures designed 

to protect against some of the risks addressed by Regulation SCI for SCI entities.254

At a general level, FINRA’s guidance suggests that firms: undertake a holistic review of 

their trading activity and consider implementing a cross-disciplinary committee to assess 

and react to the evolving risks associated with algorithmic strategies; focus efforts on the 

development of algorithmic strategies and on how those strategies are tested and 

implemented; test algorithmic strategies prior to being put into production; develop their 

policies and procedures to include review of trading activity after an algorithmic strategy is 

in place or has been changed; and ensure that there is effective communication between 

compliance staff and the staff responsible for algorithmic strategy development.255

4. FINRA Registration Requirement for Developers of Algorithms

In 2016, FINRA implemented a rule requiring registration as a Securities Trader by each 

associated person who is primarily responsible for the design, development, or significant 

modification of an algorithmic trading strategy or the day-to-day supervision or direction 

 

251 17 CFR § 242.1002.

252 17 CFR § 242.1003.

253 See FINRA Regulatory Notice 15-09 (Mar. 2015).

254 Id. at 4.

255 Id. at 5-7.
66

of these activities.256 To register as a Securities Trader, associated persons must pass 

qualification exams.257

5. Inspections and Examinations for Controls on Algorithmic Trading

In its 2020 Examination Priorities, the staff of the Commission’s Office of Compliance 

Inspections and Examinations (OCIE) stated that it will focus on, among other things, 

controls around the use of automated trading algorithms by broker-dealers. 258 Noting that 

“[p]oorly designed trading algorithms have the potential to adversely impact market and 

broker-dealer stability,” staff stated that OCIE may “examine how broker-dealers supervise 

algorithmic trading activities, including the development, testing, implementation, 

maintenance, and modification of the computer programs that support their automated 

trading activities and controls around access to computer code.” 259 

6. Participation in Financial Stability Oversight Council

The Chairman of the Commission is one of the voting members of the Financial Stability 

Oversight Council (FSOC).260 FSOC has also taken notice of the important recent 

technological and structural changes in financial markets. For example, in its 2019 annual 

report, FSOC noted that: 

The evolution of financial markets has been driven by technological advances 

and regulatory developments. While new technologies have reduced 

 

256 See FINRA Regulatory Notice 16-21 (June 2016); Exch. Act Rel. No. 77551, 81 Fed. Reg. 

21914 (Apr. 13, 2016) (Order Approving File No. SR-FINRA-2016-007).

257 FINRA Rule 1220(b)(4) (requiring passage of the Securities Industry Essentials exam 

and the Series 57 Securities Trader Representative Exam).

258 See Staff of the Office of Compliance Inspections and Examinations, 2020 Examination 

Priorities, p. 17 (Jan. 7, 2020) (“2020 Exam Priorities”) (available at:

https://www.sec.gov/about/offices/ocie/national-examination-program-priorities2020.pdf).

259 Id.

260 The other voting members of the Council are the Secretary of the Treasury (who chairs 

the Council), the Chairman of the Board of Governors of the Federal Reserve System, the 

Comptroller of the Currency, the Director of the Consumer Financial Protection Bureau, the 

Chairperson of the Federal Deposit Insurance Corporation, the Chairperson of the 

Commodity Futures Trading Commission, the Director of the Federal Housing Finance 

Agency, the Chairman of the National Credit Union Administration, and a presidentiallyappointed independent member with insurance expertise. 
67

transaction costs and made financial data more widely available, the 

increased use of technology and the entry of new types of market 

participants have created new types of risks. The increased use of automated 

trading systems and the ability to quote and execute transactions at higher 

speeds increase the potential for severe market disruptions from operational 

events at market makers or other participants. In some markets, economies 

of scale associated with new technologies have led to higher concentration 

and greater dependency for liquidity on a small number of participants. The 

emergence of new trading venues has fragmented trading and required the 

implementation of technological solutions to connect markets. The Council 

recommends that regulators continue to evaluate structural changes in 

financial markets and consider their impact on the efficiency and stability of 

the financial system. Regulators should also assess the complex linkages 

among markets, examine factors that could cause stress to propagate across 

markets, and consider potential ways to mitigate these risks. 261

FSOC is also responsible for, among other things, designating key financial market utilities 

as “systemically important” (“SIFMUs”). These utilities perform a variety of functions in the 

market, including the clearance and settlement of cash, securities, and derivatives 

transactions; many of them are central counterparties and are responsible for clearing a 

large majority of trades in their respective markets.262

D. Additional Ongoing and Potential Commission and Staff Actions

In addition to the actions discussed above that are focused on improving transparency, 

mitigating volatility and enhancing the stability and security of our trading infrastructure, 

on an ongoing basis, Commission staff monitors and assesses market integrity, efficiency,

and resiliency. In particular, Commission staff currently is monitoring and assessing

changes driven by advances in technology, the virtual ubiquity of algorithmic trading in 

listed equities, the increasing reliance on algorithmic trading in debt securities, and the 

firm-specific and general dependence on electronic systems as well as the risks created by 

these developments. The Commission has taken various actions at the recommendation of 

the staff in response to these developments. In addition, as indicated in the Commission’s 

published rulemaking agenda, the Commission and staff are contemplating further relevant 

measures. Ongoing and potential actions include: 

 

261 Financial Stability Oversight Council, 2019 Annual Report, pp. 5-6 (Dec. 4, 2019) 

(available at: https://home.treasury.gov/system/files/261/FSOC2019AnnualReport.pdf). 

262 For a list of the designated SIFMUs, as well as the bases for the Council’s designations, 

see https://home.treasury.gov/policy-issues/financial-markets-financial-institutions-andfiscal-service/fsoc/designations.
68

• In January 2020, the Commission proposed an order with respect to the governance 

of the consolidated equity market data plans,263 and in May 2020, the Commission 

approved an order directing the SROs to submit a new National Market System Plan 

for consolidated equity market data.264 Certain SROs have petitioned for review of 

this order in the D.C. Circuit.

• In February 2020, the Commission proposed rules related to equity market data 

infrastructure.265 Commission staff will continue to consider the issues raised in, as 

well as public feedback on, these proposals. 

• The Limit-Up, Limit-Down Plan provides that the Operating Committee of the Plan 

will annually provide the Commission and make publicly available a report 

concerning the Plan’s performance during the preceding year, which will include an 

update on Plan operations, an analysis of any amendments implemented during the 

period covered by the report, and analysis of potential material emerging issues that 

may directly impact the operation of the Plan.266 The Division of Trading and 

Markets and the Division of Economic and Risk Analysis will continue to analyze this 

information and make recommendations as appropriate. 

• OCIE staff will examine firms with respect to their controls around automated

trading algorithms.267

• OCIE staff will continue to evaluate whether SCI entities have established, 

maintained, and enforced SCI policies and procedures as required, and will continue 

to perform examinations to review whether SCI entities have taken appropriate 

action in response to past examinations.268 

• The Division of Trading and Markets is considering recommending that the 

Commission propose amendments to the National Market System Plan Governing 

the Consolidated Audit Trail regarding data security.269

 

263 See SIP Governance Proposed Order. 

264 See SIP Governance Order.

265 See Market Data Infrastructure Proposal.

266 See Plan to Address Extraordinary Market Volatility, Appendix B. 

267 2020 Exam Priorities, at 16-17.

268 Id. at 20. 

269 See Unified Agenda of Regulatory and Deregulatory Actions, RIN 3235-AM62 (Fall 2019) 

(available at:
69

VII. Summary of Studies on Algorithmic Trading

A. Equities

This section summarizes some of the analysis and conclusions from the academic literature

that focuses on the market effects of algorithmic trading and high-frequency trading, 

including effects on liquidity, price efficiency, and volatility. This section directly 

references some of the relevant academic studies.270 In addition, it also discusses the 

analysis and conclusions from regulatory studies as well as the academic literature that 

focuses on the market effects of some of the market and regulatory initiatives discussed 

above.

A number of the academic studies discussed below examine the effects of algorithmic 

trading, which encompasses the activity of a broad set of market participants, including

high-frequency traders (“HFTs”).271 Since HFTs, at least historically, have accounted for a 

large portion of algorithmic trading activity in the U.S. equity markets, many studies 

specifically focus on examining the effects of HFTs. As discussed above, high-frequency 

trading is classified as a subcategory of algorithmic trading and generally refers to 

professional traders who use extremely fast data access and processing capabilities to 

execute short-term strategies. HFTs generally trade frequently intra-daily and avoid 

carrying a position overnight.272 

The literature on algorithmic trading by HFTs in the secondary markets for U.S. equities is 

extensive. Commission staff previously published literature reviews on the related topics 

 

https://www.reginfo.gov/public/do/eAgendaViewRule?pubId=201910&RIN=3235-

AM62).

270 The studies discussed in this section use data from both the US and foreign markets 

(mainly Canada and Europe) to examine the effects of algorithmic trading and highfrequency trading. While studies of foreign markets do not directly examine US markets, 

they can provide insight into the effects of algorithmic trading and high-frequency trading 

that could be applicable to US markets. Some of the studies of foreign markets discussed 

here use detailed data or market structure changes as identification in order to study some 

effects of algorithmic trading and high-frequency trading that might be difficult to examine 

using the available data on US markets. 

271 See supra Section IV.

272 See supra Section IV.C.1.
70

of market fragmentation273 and high-frequency trading.274 In addition to summarizing the 

economic literature available at the time, these reviews discuss some of the methodological 

issues associated with studying algorithmic trading and HFTs, such as the difficulty of 

identifying relevant activity, particularly in publicly available datasets that do not explicitly 

flag algorithmic trading or high frequency trading.275 Additionally, many articles survey 

the academic literature related to algorithmic trading and HFTs.276

Overall, most academic studies find that algorithmic trading and HFTs have improved 

market quality and helped reduce transaction costs.277 There is ample evidence suggesting 

that, under normal market conditions, algorithmic trading and HFTs improve liquidity and 

price efficiency and reduce short term volatility. However, there is some evidence, mostly 

from the Flash Crash, that in certain instances algorithmic trading and HFTs may 

exacerbate price movements during periods of high volatility or market stress.

1. Liquidity

The academic literature has provided some important insight into questions associated 

with algorithmic trading and high-frequency trading. Although the results are not without 

exceptions or limitations, the literature has generally established that algorithmic trading 

and high-frequency trading improve liquidity, at least under normal market conditions. 

Some academic studies that examine different types of HFTs find that the results vary 

 

273 See Staff of the Division of Trading and Markets, Equity Market Structure Literature 

Review Part I: Market Fragmentation (Oct. 7, 2013) (available at 

https://www.sec.gov/marketstructure/research/fragmentation-lit-review-100713.pdf).

274 See HFT Literature Review.

275 See, e.g. HFT Literature Review, pp. 4-11.

276 See Biais and Woolley (2011), Chordia, Goyal, Lehmann and Saar (2013), Easley, L ́opez 

de Prado and O’Hara (2013), Gomber, Arndt, Lutat and Uhle (2011), Goldstein, Kumar and

Graves (2014), Jones (2013), Kirilenko and Lo (2013), Biais and Foucault (2014), O’Hara 

(2015), Chung and Lee (2016), Menkveld (2016), Davies and Sirri (2018), and Gomber and 

Zimmerman (2018).

277 It also should be recognized that, both in discrete market segments and more generally, 

sophisticated and well-resourced market participants, including exchanges, dealers and 

proprietary trading firms, have data access and computing capabilities that significantly 

exceed those of most academics. Further, it also should be recognized that because the 

market has been subject to rapid change as a result of technological, regulatory and other 

developments, the efficacy of period-to-period comparisons may be limited. See also the 

discussion above with respect to potential limitations on the use of academic studies, supra

Section IV.E. 
71

based on the type of HFT. Most studies that examine HFT market makers find that they 

improve liquidity and reduce spreads. Other studies find that HFTs who “pick off” stale 

orders can worsen liquidity by increasing “adverse selection” costs.278 The rest of this 

section discusses in more detail the different effects that studies believe algorithmic 

trading and high-frequency trading have or may have on liquidity, including the effects of 

liquidity supply by algorithmic trading and HFTs, the effects of HFTs activities that may 

increase adverse selection, the effects of HFTs competition, and the effects of changes in 

HFTs speed.

a. Algorithmic Trading and HFT Liquidity Supply

A number of academic studies find that algorithmic trading and high-frequency trading 

reduces bid-ask spreads. Most of these studies argue that faster speeds or an improved 

ability to monitor the market allow algorithmic traders and HFT liquidity suppliers to 

reduce their adverse selection costs, which allows them to quote tighter spreads. For 

example, Hendershott, Jones, and Menkveld (2011) examine the introduction of 

algorithmic trading on the NYSE and find that it reduces the bid-ask spread, which they 

attribute to algorithmic trader price quotes experiencing lower adverse-selection cost. 

Additionally, Brogaard, Hagströmer, Nordén, and Riordan (2015) examine an increase in 

the speed of HFT market makers and find it reduces their adverse selection costs, which 

allows them to quote tighter markets. 279

Other academic studies find that algorithmic trading and HFTs improve liquidity by 

smoothing it over time. For example, Hendershott and Riordan (2013) find that 

algorithmic traders inter-temporally smooth liquidity by providing liquidity when bid-ask 

spreads are wide and taking liquidity when spreads are sufficiently narrow. Carrion 

(2013) finds a similar result for HFTs.280

 

278 In this context, “adverse selection” refers to the ability of HFTs to react faster than other 

participants, such as passive market makers, and trade against resting orders that have not 

been updated for market movements. One strategy for passive market makers to avoid or 

minimize “adverse selection” costs resulting from rapid market movements, would be to 

widen their bid-ask spread. The effectiveness of such a strategy would depend on, among 

other factors, whether other market makers are willing to quote a narrower spread. 

279 See, e.g., Boehmer, Fong, and Wu (2018) , Carrion (2013), Hendershott and Riordan 

(2011), Hendershott and Riordan (2013), Korajczyk and Murphy (2019), Malinova, Park 

and Riordan (2018), and Riordan and Storkenmaier (2012)

280 See also Hagstroomer, Norden and Zhang (2014), Hendershott and Riordan (2011), and 

Jarnecic and Snape (2014).
72

Most academic studies conclude that the tighter quotes supplied by algorithmic trading and 

HFTs improve effective spreads and reduce the trading costs of retail investors.281 

However, academic studies find mixed results on the impact HFTs have on institutional 

trading costs.

282 Brogaard et al. (2014b) finds no clear evidence that HFTs impact 

institutional execution costs, with institutional traders’ costs remaining unchanged when 

HFT activity increases. Korajczyk and Murphy (2019) find evidence that HFTs only 

increase the costs of large institutional trades, i.e. above $2 million, and that they decrease 

the transaction costs of smaller institutional trades.283 Tong (2015) finds that an increase 

in HFT activity causes an increase in the price impact of institutional orders, which 

increases their implementation shortfall costs.284

Menkveld (2016) argues that an additional benefit of HFTs is they helped the market as a 

whole migrate quickly to electronic trading, which, in turn, yielded lower transaction costs

and more volume. He argues that there is a symbiotic relationship between new electronic 

venues and HFTs. New venues need HFTs to insert aggressively priced bid and ask quotes, 

and HFTs need the new venues to satisfy their requirements in terms of automation, speed, 

and low fees.

Although HFT market makers are the primary liquidity suppliers in equity markets, they 

usually do not have obligations to provide liquidity. One concern is that this could cause 

them to scale back from supplying liquidity when market conditions are uncertain or 

 

281 See, e.g., Conrad, Wahal, and Xiang (2015), Korajczyk and Murphy (2019), Malinova, K., 

A. Park, and R. Riordan (2018), Riordan and Storkenmaier (2012), and Bershova and 

Rakhlin (2013).

282 As discussed above, in order to reduce their price impact, large institutional “parent” 

orders are divided by algorithms into many smaller “child” orders to be executed in the 

market. See supra Section IV.A.2. Beason and Wahal (2019) provide a detailed examination 

of the child orders produced by four widely used standardized institutional trading 

algorithms.

283 They find the increased price impact caused by HFTs increases the costs of large trades, 

but the lower effective spread caused by HFTs lowers the costs of small trades. Van Kervel 

and Menkveld (2019) also find that HFTs increase the execution costs of large institutional 

trades. Malinova, Park and Riordan (2018) examine the average overall effects on 

institutional trades in the same setting as Korajczyk and Murphy (2019) and find that 

increased HFT activity causes them to decline.

284 Tong (2015) also finds that an increase in HFT activity causes the effective spread of 

institutional orders to decline, but the increase in the price impact dominates the decrease 

in the effective spread and causes the implementation shortfall costs of institutional 

investors to increase.
73

otherwise unfavorable. Boehmer, Li and Saar (2018) find that the strategies of HFTs are 

correlated and Malceniece et al. (2019) find that HFTs cause significant increases in comovement in the returns and liquidity of stocks. This correlation in price movements and 

the supply of liquidity could be the result of HFT market makers withdrawing from (and/or 

cause HFT market makers to withdraw from) the market at the same time. Anand and 

Venkataraman (2016) find evidence that market makers scale back in unison when market 

conditions are unfavorable, which contributes to covariation in liquidity supply, both 

within and across stocks.285 

Market making quoting obligations could improve liquidity in these circumstances. Anand 

and Venkataraman (2016) compare HFTs without quoting obligations to designated 

market makers (DMMs), i.e. HFTs who have quoting obligations. They find that DMMs 

continued to participate at times when the other HFTs scale back, which reduces execution 

uncertainty. Clark-Joseph, Ye, and Zi (2017) also look at how DMM obligations on NYSE 

affect liquidity and find evidence that is consistent with the idea that DMMs cause 

meaningful improvements in liquidity. 

b. HFT Activities and Increased Adverse Selection

This subsection discusses certain HFT activities that could increase the adverse selection 

costs of some traders, including: “stale quote arbitrage,” “order anticipation,” “quote 

stuffing,” and “spoofing.”

Stale Quote Arbitrage

Some HFTs can use their speed advantage to pick off stale limit orders. This can raise the 

adverse selection costs of market makers and lead to them quoting wider spreads. 

Academic studies have found evidence of HFTs being able to trade against stale quotes. For 

example, Brogaard, Hendershott and Riordan (2016) find that HFTs adversely select limit 

orders and this affects liquidity negatively.286 Aquilina, Budish, and O’Neill (2020) attempt 

to empirically estimate the costs they believe these arbitrage opportunities impose on 

investors and other market participants. 

Even with their speed advantage, HFT market makers cannot completely avoid being 

adversely selected on their limit orders. Menkveld (2013) and Brogaard, Hendershott and

Riordan (2014) find that HFT market makers are adversely selected on their quotes.287 

Budish, Cramton and Shim (2015) argue that this creates incentives for HFTs to overinvest 

 

285 Anand and Venkataraman (2016) find that HFT liquidity suppliers scale back when 

there are large order imbalances. They also find that HFTs earn higher profits and supply 

more liquidity during periods of higher stock volatility.

286 See also Brogaard, Hendershott and Riordan (2014) and Van Kervel (2015).

287 See also Biais, DeClerck and Moinas (2016).
74

in speed to be able to react the fastest. This competitive dynamic, which the authors refer 

to as a technological “arms race,” may not benefit market participants or market efficiency.

Order Anticipation

A number of academic studies show that HFTs are able to predict the order flow of other 

traders. For example, Hirschey (2018) finds that the aggressive buying and selling of HFTs 

is correlated with the aggressive buying and selling of non-HFTs in the next 30 seconds. He 

interprets this finding as anticipatory trading by HFTs. Similarly, Clark-Joseph (2013) also 

suggests that HFTs employ order anticipation strategies in the E-mini S&P 500 futures 

market by submitting small aggressive marketable orders and observing the responses.288

Yang and Zhu (2019) provide a model of “back-running,” where strategic traders use order 

anticipation strategies based on past order flows to predict the order flow of institutional 

investors. Empirical evidence suggests that HFTs are able to back-run on the long-lasting 

informed orders of institutional investors, which may increase institutional investors’ 

transaction costs. For example, Van Kervel and Menkveld (2019) find that HFTs initially 

trade in the opposite direction of large institutional orders, but eventually change direction 

and take positions in the same direction for the most informed institutional orders, which 

increases the execution costs for these orders.289

Quote Stuffing

One harmful market strategy HFTs may engage in is “quote stuffing,” which refers to an 

HFT strategy in which a very large number of orders to buy or sell securities are placed in 

quick succession and then canceled almost immediately.290 Davies and Sirri (2018) discuss

that this type of activity can be used to take advantage of orders that are based on the best 

bid and offer and can also impact market integrity by clogging message traffic and delaying 

other traders with slower connections from updating or submitting their orders.291 

 

288 See also Breckenfelder (2019) and Raman, Robe and Yadav (2014).

289 Korajczyk and Murphy (2019) find that during larger institutional trade executions, 

HFTs submit more same-direction orders. They find this leads to higher transaction costs 

for large, informed trades and lower transaction costs for small, uninformed trades.

290 The Concept Release discusses other forms of HFT directional strategies that may 

adversely affect some market participants, including order anticipation and momentum 

ignition strategies.

291 Quote stuffing can be difficult to detect. Periods of excessive quoting could be a part of a 

manipulative trading strategy. Alternatively, Hasbrouck (2018) also discusses that periods 

of excessive quoting could also be the result of HFTs competing with each other to 

undercut prices. Gai, Yao, and Ye (2013) identify potential quote stuffing in NASDAQ by
75

Egginton, Van Ness, and Van Ness (2016) look at intense episodic spikes in quoting activity 

and find that they have a negative impact on market quality, with targeted stocks suffering 

decreased liquidity, higher trading costs, and increased short-term volatility. 

Spoofing

Another harmful strategy HFTs may engage in is “spoofing,” which involves the submission 

and cancellation of buy and sell orders without the intention to trade in order to 

manipulate other traders.292 Lee, Eom, and Park (2013) examine spoofing in the Korea 

Exchange. They find that investors strategically placed spoofing orders to create the 

impression of substantial order book imbalances in order to manipulate subsequent prices. 

They find that the stocks targeted for spoofing had higher return volatility, lower market 

capitalization, lower price level, and lower managerial transparency.

c. HFT Competition

Academic studies provide mixed evidence about how competition among HFTs affects 

liquidity. A number of these studies generally find that more competition between HFTs 

seems to decrease spreads and improve price resiliency, i.e., a quicker narrowing of the 

spread after it widens. For example, Brogaard and Garriott (2019) examine how the entry 

of new HFTs affects competition among HFTs and find that bid–ask spreads decrease and 

effective and realized spreads for non-HFTs narrow when new HFTs enter the market.293

In contrast, Yao and Ye (2018) find that competition among HFTs can increase the costs of 

 

examining abnormally high levels of co-movement of message flows for stocks in the same 

data channel.

292 It should be noted that to the extent that any trading activity, including order activity, is 

manipulative it is subject to legal and regulatory sanction. The Commission has brought 

several actions based on alleged “spoofing” and the inspections and enforcement staff of 

the Commission review trading activity for possible violations of anti-manipulation laws 

and regulations. See, e.g., “SEC Charges Firms Involved in Layering, Manipulation Schemes” 

(Mar. 10, 2017) (noting filing of charges against individuals and a securities firm for 

engaging in and facilitating layering and other market manipulation) (available at: 

https://www.sec.gov/news/pressrelease/2017-63.html); “SEC Charges 18 Traders in $31 

Million Stock Manipulation Scheme” (Oct. 16, 2019) (noting an emergency action and asset 

freeze against defendants allegedly engaged in a market manipulation scheme creating the 

false appearance of trading interest and activity) (available at: 

https://www.sec.gov/news/press-release/2019-216).

293 See also Hasbrouck (2018).
76

non-HFTs.

294 They find that when the relative tick size, i.e. the minimum tick size 

increment divided by the stock price, is larger, HFTs compete more intensely to be the first 

one to the front of the limit order book queue in order to supply liquidity. This increases 

the difficulty of establishing time priority for non-HFT limit orders and compels them to 

submit more market orders as the relative tick size increases, which increases their trading 

costs.295

d. HFT Speed

Academic studies provide mixed evidence regarding how an increase in the speed of HFTs 

affects liquidity.296 Brogaard, Hagströmer, Nordén, and Riordan (2015) find that HFT 

market makers were most likely to take advantage of an optional speed upgrade offered by 

an exchange. When they did, it improved liquidity because it allowed them to reduce their 

adverse selection costs, which allowed them to quote tighter markets. In contrast, Shkilko 

and Sokolov (2016) examine instances of bad weather disrupting microwave trading 

networks between Chicago and New York and reducing the speed advantages of certain

HFTs who rely on these networks. When this occurs, they find that adverse selection risk 

imposed by HFTs falls, which causes trading costs to decline and liquidity to improve.

297

More speed heterogeneity among HFTs can lead to intermediation chains that improve 

liquidity. Menkveld (2016) discusses how differences in the trading speed and inventory 

holding periods of market makers can lead to the creation of intermediation chains, with a 

series of financial intermediaries passing along shares between end users. He argues that 

intermediation chains can be useful in completing trades between end users, either by 

forcing intermediaries to line up in a productive sequence, or by having intermediaries 

 

294 Breckenfelder (2019) also examines HFT competition and finds that when HFTs 

compete, their speculative, i.e. directional, trading increases, which causes market liquidity 

to deteriorate.

295 They find that a smaller relative tick size makes it easier for non-HFTs to execute their 

limit orders.

296 Baron et al. (2019) also investigate how differences in latency effect competition 

between HFTs. They find that there are differences in relative speed across HFT firms and 

that the fastest firms tend to earn the largest trading revenues and remain in the market 

longer. New HFT entrants tend to be slower, underperform, and more likely to exit the 

market.

297 Gai, Yao and Ye (2013) examine the impact of two speed upgrades on NASDAQ and find 

that the speed improvements led to substantial increases in the number of cancelled orders 

but did not lead to improvements in quoted spreads, effective spreads, trading volume or 

price efficiency. The authors argue that these results indicate that only relative speed 

matters.
77

effectively share the burden of an information asymmetry, or by having faster 

intermediaries trading more aggressively, thereby revealing information early and thus 

reducing information asymmetry later. Weller (2013) presents direct evidence on 

intermediation chains in commodity futures trading, where high-frequency market makers 

provide rapid execution to investors and effectively consume inventory risk-bearing 

services from slower market makers. Brogaard, Hagstroomer, Nordoen and Riordan

(2015) show that after an exchange offered a richer menu of colocation services, HFTs 

sorted themselves across the various options (not all bought the fastest service). Bid-ask 

spreads declined and depth improved after the event, consistent with intermediation 

chains benefiting liquidity.

2. Price Efficiency

Academic papers also examine how algorithmic trading and high frequency trading affect 

the price discovery process and price efficiency.298 The majority of these studies find that 

algorithmic trading and high-frequency trading improve price efficiency and decrease the 

time it takes for prices to incorporate new information. For example, Brogaard, 

Hendershott and Riordan (2014) find that, overall, HFTs facilitate price efficiency by 

trading in the direction of permanent price changes and in the opposite direction of 

transitory pricing errors.299 

On the other hand, Weller (2018) finds evidence that algorithmic trading may reduce price

efficiency by reducing the incentives for market participants to engage in costly research to 

learn more about companies. He finds that increased algorithmic trading leads to greater 

price jumps on earnings announcements. This implies increased algorithmic trading 

decreases the research market participants conduct to predict company earnings, which 

results in more surprise when earnings announcements are released. 

Academic papers also generally find that algorithmic trading and high frequency trading

cause quotes to contribute more to the price discovery process, as opposed to trades. For 

example, Hendershott, Jones, and Menkveld (2011) find that an increase in algorithmic 

trading caused quotes to become more informative and contribute more to price discovery 

as opposed to trades.300 Brogaard, Hendershott and Riordan (2019) more closely examine 

 

298 Price efficiency refers to the degree to which the price of a security incorporates all 

available information about the security. Price discovery refers to the process through 

which new information is incorporated into the price of a security. 

299 See also Benos, Brugler, Hjalmarsson and Zikes (2017), Boehmer, Fong, and Wu (2018), 

Brogaard, Hendershott and Riordan (2019), Carrion (2013), Conrad, Wahal, and Xiang 

(2015), Hendershott, Jones, and Menkveld (2011), Hendershott and Riordan (2011), and 

Riordan and Storkenmaier (2012).

300 See also Hendershott and Riordan (2011) and Riordan and Storkenmaier (2012).
78

the contribution of HFT and non-HFT limit and market orders to price discovery and find 

that the majority of price discovery occurs predominately through limit orders submitted 

by HFTs. They also find that the contribution to price discovery from limit orders 

decreases and the contribution from market orders increases when volatility increases and 

that this change is correlated with and may be due to changes in HFT behavior.

A number of academic papers look at the speed at which HFTs process public information 

and how quickly they incorporate it into stock prices. Hu, Pan and Wang (2017) examine a 

small group of fee-paying HFTs who receive the Michigan Index of Consumer Sentiment 

two seconds before its broader release and find that most of the index-futures price 

discovery happens within 0.2 seconds after HFTs had their early peek. Chordia, Green, and 

Kottimukkalur (2018) examine trading around macroeconomic announcement surprises 

and find that prices respond to the surprises within 5 milliseconds, but that profits from 

trading quickly around the announcements are relatively small.

3. Volatility

Drawing connections between algorithmic trading and high-frequency trading and rapid 

changes in prices would seem to be a straightforward argument given the immense speed 

at which they trade. It would also appear a small step further to conclude that rapid and 

significant changes (i.e., increased price volatility) would be driven by algorithmic trading. 

However, academic research has cast some doubt on this conclusion. Although some 

studies argue otherwise, a number of academic papers study the effects of algorithmic 

trading and high-frequency trading on volatility in equity markets and find evidence that,

under normal market conditions, they reduce short term volatility. However, there is some 

evidence, mostly from the Flash Crash, that in certain instances algorithmic trading and 

high-frequency trading may exacerbate price movements during periods of uncertainty or 

market stress.

a. Short Term Volatility

Most academic studies find that algorithmic trading and high-frequency trading reduce

short term volatility. For example, Brogaard, Hendershott and Riordan (2014) find that 

HFTs trade against transitory price movements, which can reduce volatility.

301 

Additionally, Boehmer, Li and Saar (2018) find evidence that increased competition 

between HFT market makers contributes to lower volatility.

However, in contrast to the previous studies, Boehmer, Fong, and Wu (2018) find that 

algorithmic trading increases short-term volatility and that the effects are stronger in small 

 

301 See also Groth (2011) Hagströmer and Nordén (2013), Hasbrouck and Saar (2013) 

Hendershott and Riordan (2011), and Hendershott and Riordan (2013).
79

stocks.302 The authors note that the increase in volatility cannot be attributed to faster 

price discovery or to the penchant of algorithmic traders for entering volatile markets. 

Some academic studies also find that algorithmic trading and high-frequency trading 

continue to reduce volatility during periods of heightened volatility. For example, 

Hendershott and Riordan (2013) find that algorithmic trading contributes to volatility 

dampening in turbulent market phases because algorithmic traders do not retreat from or 

attenuate trading during these times.

303 Brogaard, Carrion, Moyaert, Riordan, Shkilko and

Sokolov (2018) also find that HFTs trade against extreme price movements and thus 

stabilize prices during periods of heightened volatility. However, as discussed below in the 

section on the Flash Crash, there is evidence that in certain periods of market stress HFTs 

can exacerbate volatility, including because they may withdraw from the market en masse.

b. The Flash Crash

Generally, it appears algorithmic trading and HFTs improve market quality and reduce 

volatility during “normal” market periods. However, it is possible that such strategies and 

market participants may impact the market differently during periods of uncertainty and 

market stress. One area of concern has been whether high-frequency trading promotes

sudden and unexpected price dislocations. Some researchers suggest that the ability of 

HFTs to leave the market rapidly has made the markets more fragile and could exacerbate 

periods of market stress. Kirilenko and Lo (2013) suggest that, under certain market 

conditions, the automated execution of large orders can create a “feedback-loop” or vicious 

cycle effects. These could in turn generate systemic destabilizing market events, such as 

the May 2010 “Flash Crash.”

The “Flash Crash” occurred on May 6, 2010, when an algorithm rapidly sold 75,000 S&P500 

e-mini futures contracts. Major equity indices in both the futures and securities markets 

were already down over 4% from their prior-day close by the time the large sell order hit 

at 2:30 PM. Indices moved down a further 5-6% in a matter of minutes before rebounding 

almost as quickly. The CFTC and SEC (2010) joint report finds that many of the almost 

8,000 individual equity securities and ETFs traded that day suffered similar price declines 

and reversals within a short period of time, falling 5%, 10% or even 15% before recovering 

most, if not all, of their losses. However, some equities experienced even more severe price 

moves, both up and down. Over 20,000 trades across more than 300 securities were 

executed at prices more than 60% away from their values just moments before.

The CFTC and SEC (2010) joint report attributes the price declines to a sequence of events,

including the exhaustion of the liquidity supply by HFTs, traditional buyers, and cross 

market arbitrageurs who spread the price pressure to other markets. Eventually a “hot 

potato” effect developed where blocks of futures contracts rapidly moved among the same 

 

302 See also Bershova and Rakhlin (2013) and Malceniece et al. (2019).

303 See also Groth (2011).
80

set of HFTs. Equity market prices declined and some algorithms withdrew their orders on 

the bid side in the equity market. These same algorithms, along with other algorithms,

also drove demand for short positions in futures contracts and similarly withdrew from the 

futures market. This withdrawal from the equity market and of short demand in the 

futures market created a negative feedback loop that caused the bid side to be exhausted 

and virtually fall away so that sell orders were executed at distressed prices during a

period of several minutes until a sufficient number of market participants recalibrated

their algorithms or otherwise. When a five-second pause was triggered on the CME, prices 

began to recover and within minutes they had risen to almost their previous levels.

A number of academic studies examine the “Flash Crash” and the role that algorithmic 

trading and HFTs played in it. Most studies are consistent with the CFTC and SEC joint 

report and conclude that HFTs did not cause the 2010 Flash Crash, but their withdrawal 

from the market may have exacerbated the rapid price declines. For example, Easley, 

López de Prado, and O’Hara (2012) attribute the 2010 Flash Crash to the combination of 

automated market makers and increased order flow “toxicity,” which combined to cause 

market makers to withdraw their quotes and liquidate positions.304

Aldrich, Grundfest, and Laughlin (2017) find that instability of the market data 

infrastructure also contributed to the May 2010 Flash Crash. A lag in the data feed caused 

stale prices for the SPY ETF to be disseminated to the market. The authors argue this 

caused uncertainty among algorithmic traders, and that uncertainty rationally caused them 

to withdraw liquidity, which contributed to the price collapses. 

4. Regulatory Studies and Speed Bumps

a. Single-Stock Circuit Breaker Pilot and Limit-Up Limit-Down Plan 

A number of studies look at the effects of the single-stock circuit breaker pilot and “limitup, limit-down” plan.

305 Brogaard and Roshak (2016) find that the introduction of singlestock circuit breakers enhances market stability by reducing extreme events; however, this 

comes at the cost of reduced price efficiency in the market. Hautsch and Horvath (2019) 

examine trading pauses during the single-stock circuit breaker pilot and “limit-up, limitdown” plan and find that, on average, trading pauses enhance price discovery during the 

break but increase volatility and widen bid-ask spreads after the break. They argue there is 

a trade-off between the benefits of trading pauses in terms of their function as safeguards 

and protectors from extreme price movements and their adverse effects on volatility, price 

stability, and transaction costs.

 

304 See also Kirilenko, Kyle, Samadi, and Tuzun (2017), McInish, Upson and Wood (2014), 

and Menkveld and Yueshen (2018).

305 See supra Section VI.B.2 and Section VI.B.3 for discussions of the single-stock circuit 

breaker pilot and “limit-up, limit-down” plan.
81

As part of the “limit-up, limit-down” plan, the Participants to the Plan were required to 

submit an analysis of the “limit-up, limit-down” plan’s performance. The Supplemental 

Joint Assessment by the Participants finds that the number of trades that were cancelled 

decreased under the “limit-up, limit-down” plan and that the “limit-up, limit-down” plan’s 

parameters were successful in preventing trades from occurring outside of the “limit-up, 

limit-down” price bands, thus avoiding the types of mispriced trades that resulted in the 

Flash Crash. Hughes, Ritter, and Zhang (2017) examines how the “limit-up, limit-down” 

plan affects extraordinary transitory volatility, as measured by the frequency of large, 

short-term trade-price reversals. They find evidence that is consistent with the “limit-up, 

limit-down” mechanisms reducing extraordinary transitory volatility. They also find some 

evidence that is consistent with the “limit-up, limit-down” mechanisms reducing 

extraordinary transitory volatility relative to the single-stock circuit breaker mechanism 

that was in place prior to the “limit-up, limit-down” mechanism.306

b. 15c3-5

In November 2011, the SEC implemented the final provision of Rule 15c3-5 curbing 

unfiltered market access. The provision mandated that brokers verify their clients’ order 

flow for compliance with credit and capital thresholds before routing to market centers. 

Chakrabarty et al. (2019b) find that the new checks introduce latency to order flow and 

force some latency-sensitive strategies out of the market. As a result, liquidity providers 

are better able to revise their quotes in response to new information, which causes adverse 

selection to decline and liquidity to improve. Consistent with the notion that the market 

for liquidity provision is competitive, they argue that their results show that the benefit of 

lower adverse selection is transferred entirely to liquidity demanders in the form of lower 

trading costs.

c. Speed bumps

One solution that has been incorporated by some exchanges to reduce the adverse 

selection costs imposed by HFTs is the “speed bump,” an intentional delay that slows down 

access and messaging to the market center. The most well-known instance of this occurs 

on the Investors Exchange (IEX), which was previously organized as an ATS. IEX creates a 

350-microsecond delay by running all external communications through a coil of fiberoptic cable. 

Hu (2019) examines the effects of IEX’s speed bump and shows that when stocks are 

affected by IEX’s speed bump, their trading costs decline on average, with larger decreases 

for stocks with higher historical average trading activity on IEX.307 He also finds that 

adverse selection costs decrease and that the speed bump decreases the chances that 

 

306 Hughes, Ritter, and Zhang (2017) note that these results vary depending on the specific 

methodology employed.

307 See also Chakrabarty et al. (2019a).
82

stocks are exposed to “sweep risk,” the risk of trading against large informed or “toxic” 

orders that may trade through multiple levels of an order book or across multiple venues. 

The effects of speed bumps can vary based on whether they are applied equally to all 

traders or if they only slow down the orders of some traders but not others, i.e., an 

asymmetric speed bump. Chen et al. (2017) examines the introduction of an asymmetric, 

randomized speed bump on the Canadian exchange TSX Alpha that slows down liquidity 

demanding orders. This feature is designed to allow low-latency liquidity providers to 

avoid order-flow driven adverse selection by reacting to activity on other venues. They

observe that spreads and order cancellations increase when the exchange adds the speed 

bump and that it segments order flow and increases profits for fast liquidity providers on 

that venue at the expense of other liquidity providers and aggregate market quality.

B. Debt Securities

As noted above, academic research on algorithmic trading in debt securities is limited.308

The literature that exists focuses largely on instruments with readily-available order-level 

data, such as on-the-run Treasury securities. Due to a lack of order level data, there is little 

literature on algorithmic trading in corporate and municipal bonds. 

Similar to HFTs in the equity markets, principal trading firms (PTFs) play a prominent role 

as intermediaries in the interdealer on-the-run Treasuries market.309 Using data on trades 

in Treasury securities reported to TRACE, Brain et al. (2018) estimate that PTFs account 

for about 62 percent of trading volume in the electronic/automated interdealer-broker 

Treasury market. The majority of electronic trading in the on-the-run interdealer Treasury 

market occurs on the electronic BrokerTec platform. Fleming et al. (2018) examine 

liquidity and price discovery in on-the-run Treasuries traded in the central limit order 

book on this platform. In general, they find that the BrokerTec platform offers more 

liquidity, greater market depth, more trading activity, and narrower bid-ask spreads than 

the comparable voice-assisted broker systems. Similar to equities markets, they find that 

order cancellation rates are high and that more price discovery occurs via quotes than 

executed trades. 

Jiang et al. (2014) examines the effects of high-frequency trading activity, which would 

include PTFs, on liquidity and price efficiency in on-the-run Treasuries around prescheduled macroeconomic announcements. They find that high-frequency trading has a 

negative effect on liquidity (wider spreads) in the pre-announcement period, and that high-

 

308 See supra Section V.C.

309 As discussed above, principle trading firms (PTFs) are technologically and 

quantitatively sophisticated firms trading as principal. In the on-the-run Treasuries 

market, PTFs would exhibit similar characteristics to HFTS in the equities market. See infra

Section X.D, for a more general discussion of principal trading firms.
83

frequency trading lowers the depth of the order book post-announcement. However, they 

also conclude that price efficiency is improved both pre- and post-announcement.

In comparison to the Flash Crash discussion above, there is evidence to suggest that some 

PTFs in the on-the-run Treasury market may remain in the market and reduce the liquidity 

they supply, rather than withdrawal from the market, during periods of market stress. The 

Joint Staff Report (2015) examines the behavior of PTFs during the unusual Treasury 

market volatility of October 15, 2014, when there was a surge in trading activity in both the 

cash Treasuries and futures markets. The report finds that during the event PTFs

continued to provide the majority of order book depth, and maintained a tight spread 

between bid and ask prices, but decisively cut back their limit order quantities. In contrast, 

bank-dealers widened their bid-ask spreads so that limit orders were only met at a

substantial distance from the top of the book. Despite the surge in trading volume during 

the event window, there was no noticeable change in net positions of PTFs or bank-dealers. 

However, the report also finds evidence that some PTFs and bank-dealers may have 

contributed to the volatility. The report finds that nearly all of the large imbalance in 

aggressive buy orders during the early portion of the event window was attributable to 

PTFs and bank-dealers.310 

VIII. Conclusion

Today’s equity markets are highly interconnected and substantially, data-driven. 

Electronic trading and algorithmic trading (however defined) are both widespread and 

integral to the operation of our capital markets. We expect current trends to continue and 

that equity markets and various segments of our credit markets will remain highly

electronic, highly reliant on computer-driven algorithms, and subject to more-rapid 

dissemination of information into prices and trading activity. 

This increase in algorithmic trading brings with it both benefits and risks to our capital 

markets, including new and emerging risks. This means that continued vigilance in 

monitoring these advances in technology and trading, and updating of systems and 

expertise will be necessary in order to help ensure that our capital markets remain fair, 

deep, and liquid.

The Commission has undertaken various measures, and is evaluating other actions, to 

increase transparency, mitigate volatility, enhance stability and security and otherwise 

improve market integrity as summarized above. The Commission has sought input from 

market participants and the public more generally on these measures and potential actions. 

The Commission’s work benefits from this input and engagement and we encourage 

 

310 See The Joint Staff Report (2015) at 23-25.
84

interested parties to comment on these matters and to bring any other matters that may 

warrant analysis or action to our attention. 
85

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X. Appendix: Market Participants, Roles, and Obligations

This summary describes broad categories of investors in U.S. capital markets, along with 

the central problem of liquidity they face in trading; market intermediaries and 

professionals, including brokers and principal trading firms; trading platforms; and some 

basic legal standards central to modern trading.311

A. Investors

As the Commission has stated, “[t]he secondary securities markets exist to facilitate the 

transactions of investors.”312 Investors bear the risk of holding securities for long periods 

of time.313 The Commission has, for some purposes, distinguished between investors, who 

are the long-term bearers of investment risk, and market professionals, who bear risks of 

trading but may not share the same long-term risks as investors.314 This report adopts this 

general approach. It is common to further classify investors as either “retail” or 

“institutional.”315

The term “retail” investor is typically used to refer to natural persons. Individuals might 

invest in, for example, stocks, mutual funds, exchange-traded funds (ETFs), corporate 

bonds, Treasury bonds, municipal bonds, or options. Individuals who satisfy the 

 

311 This summary is necessarily simplified, and so may not precisely describe the 

operations of particular firms; in a similar vein, many of the activities described in this 

summary may be undertaken by individual entities or groups of affiliated entities. Because 

of this simplification, it may be most useful to think of this section as a summary of central 

roles in U.S. capital markets rather than as a description of specific firms. This point 

applies to the schematic diagram of market participant and venue types below, infra Figure 

3.

312 Fragmentation Release at 10580.

313 See also Concept Release at 3603 (Long-term investors “are the market participants who 

provide investment capital and are willing to accept the risk of ownership...for an extended 

period of time”).

314 See id.; see also the discussion of this distinction and its applicability in Reg NMS 

Adopting Release at 37499-501, where the Commission concluded that, “when the interests 

of long-term investors and short-term traders conflict in this context, the Commission 

believes that its clear responsibility is to uphold the interests of long-term investors”).

315 See, e.g., “Order Handling Proposing Release” at 49435-41 (discussing order handling 

and disclosures for institutional and retail orders).
93

requirements of being an “accredited investor” may also have access to private investments 

not otherwise advertised to the public.316

The term “institutional” investor generally refers to a diverse range of market 

participants—generally organizations which may be investing on their own behalf or on 

behalf of others. These investors may include, for example, the registered investment 

companies (e.g., mutual funds and ETFs) through which many Americans invest, pension

funds, insurance companies, endowments, and private investment funds such as hedge 

funds.317

B. The Problem of Liquidity

When an investor wants to trade a security, they need to find a counterparty to trade 

with—for a trade to be effected, someone needs to take the other side of the trade.

Finding an investor who wants to trade the same security, in a similar volume, at a 

mutually-agreeable price, but on the opposite side of the market, is not, in many cases, a 

straightforward task.318 Moreover, investors seeking to trade generally aim to do so 

 

316 See 17 CFR § 230.501(a) (for individuals, defining an accredited investor as someone 

who, either alone or with a spouse, has a net worth over $1 million (excluding the value of a 

primary residence), or who had an income of over $200,000 (or, with a spouse, over 

$300,000) in the preceding two years and reasonably expects the same for the current 

year).

317 See, e.g., Order Handling Proposing Release at 49433 (“An institutional customer 

includes, for example, pension funds, mutual funds, investment advisers, insurance 

companies, investment banks, and hedge funds”). Certain regulations and SRO rules define 

thresholds and requirements to be considered an “institution” or “institutional account” for 

the purposes of those regulations and rules. See, e.g., 17 CFR § 230.144A(a)(1) (defining 

“qualified institutional buyer” for the purpose of private resales of securities); Financial 

Industry Regulatory Authority (FINRA) Rule 4512(c) (defining “institutional account”—

including some accounts held by natural persons—for the purposes of certain customer 

recordkeeping requirements); FINRA Rule 2210(a)(4) (defining “institutional investor” for 

the purposes of requirements with respect to communications with the public); FINRA 

Rule 2242(a)(7) (defining “institutional investor” for the purposes of an exemption to 

certain requirements related to the distribution of debt research reports). In this report, 

however, the term “institutional investor” is used more generically, and does not 

necessarily align with any of these more specific definitions.

318 There may be, for example, practical impediments to and legal restrictions on investors’ 

abilities to find one another for the purposes of trading. For example, only broker-dealers 

may be members of national securities exchanges and submit orders to exchanges; this 

restriction means that any investor seeking to trade on an exchange must do so, in some 
94

without unfavorably impacting the price of the security in which they seek to trade.319 If a 

long-term investor counterparty cannot be found or is not readily available, an investor 

seeking to trade will likely need to do so with an intermediary willing to bear the shorterterm risk of holding the security until another counterparty can eventually be found. This 

intermediation can be potentially risky, and, therefore, potentially costly to investors.320

Put another way, investors who wish to trade immediately may need to pay some premium 

in order to do so.

A market is often said to be liquid if participants can trade immediately, even in substantial 

volume, without affecting the price of the instrument traded.321 Solutions to the challenge

of creating and finding liquidity reflect the nature of the securities traded, law and 

regulation, and the historical evolution of markets. Consequently, the markets for different 

 

manner, through a broker-dealer. In markets with limited publicly-available information, 

infrequently-traded securities, or limited centralized trading activity, investors may not 

know, and may have difficulty discovering, where to find an interested counterparty for a 

given security. An investor who trades infrequently or in small volumes may not, on their 

own, have an incentive to develop favorable market relationships or obtain the information 

necessary to effectively trade.

319 If others in the market know that a particular security is being purchased or sold in 

substantial size, they are likely to adjust their ask or bid prices in response to the perceived 

demand, or seek to trade ahead in order to profit from rising or falling prices. Information 

about trading interest can be conveyed by, for example, inquiries about potential trading 

interest, order placement around a market, or effected trades. Public information enables 

participants to understand the state of a market and thereby improve investment and 

trading decisions, but the publication of an investor’s own order or trading activity can, 

from the investor’s perspective, negatively impact prices.

320 “Microstructure theory broadly applied therefore implies that the costs of demanding 

immediate trade execution in both equity and fixed-income markets should depend on 

return volatility, customer arrival rates, and the likelihood of information asymmetries 

adverse to the intermediaries who effectively supply liquidity.” Hendrik Bessembinder et 

al., A Survey of the Microstructure of Fixed Income Markets at 3 (forthcoming, J. Fin. & Quant. 

Anal.) (available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3307502). See 

also The New Stock Market, ch. 3 (describing, for modern equity markets, the role that 

trader information plays in shaping the provision and demand for liquidity and the 

management of trading risk).

321 For a summary of approaches to evaluating liquidity, see, e.g., Treasury Market Report at 

8.
95

types of securities have developed unique (but, in some cases, linked) ecosystems for 

addressing this challenge.

The following sections describe the major types of market participants and infrastructure 

that have developed around this challenge, highlighting attributes that are generally 

common across markets. Specific activities and differences between the equity and debt 

markets are detailed more fully below.

Figure 3: Schematic of Key Market Role and Venue Types

C. Brokers

Generally speaking, a broker is a person engaged in the business of effecting transactions in 

securities for the account of others.322 A broker effects transactions as an agent for another 

person—they do not trade with the order themselves, but find someone else for an order to 

trade with, and generally earn a commission for doing so.

Brokers may specialize in serving different types of investors, or in different types of 

securities. For example, a number of large retail brokers serve retail investors, and handle 

millions of accounts. Institutional agency brokers, on the other hand, serve the different 

needs of institutions, which often trade in substantial size and are particularly focused on 

minimizing information leakage and price impact. Institutional agency brokers may be 

specialized firms, or desks or departments within large, more diversified broker-dealers323

or banks.

 

322 See Exchange Act Section 3(a)(4), 15 U.S.C 78c(a)(4).

323 Many firms engage in both broker and dealer activities. A “dealer” is generally defined

as any person engaged in the business of buying and selling securities for their own 
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D. Principal Trading Firms

Many participants in securities markets trade with their own principal (“principal trading 

firms” or “PTFs”).

324 Principal trading firms trade in a wide variety of ways. They may, for 

example, act as liquidity-providing market makers, publicly displaying quotes on both sides 

of a market seeking to earn the difference between the bid and the ask price.325 They may 

trade on trends in the markets, seeking to profit from price increases or price declines over 

relatively short timeframes. They may execute trades with retail orders, or hold 

themselves out as prepared to facilitate large block trades for institutions. Or they may 

perform arbitrage across products or types of securities, seeking to profit from price 

differentials in related instruments. In all cases, firms are using their own capital to take on 

market risk and seek to earn profits from their trading activity, often facilitated by the 

market information they are able to gather through their activities.

E. Trading Platforms

A range of platforms have developed to allow investors, broker-dealers, and traders to 

meet. Some platforms allow all types of market participants to meet each other on either 

side of a trade; others allow smaller groups of participants to meet on either side; some 

allow individual market participants to seek liquidity from multiple potential 

counterparties; and others allow a single dealer to offer liquidity to multiple potential 

counterparties. Each type of platform may provide a solution to the general trading 

problem of finding potential counterparties and liquidity under different market 

conditions.

 

account but not as part of a regular business. See Exchange Act Section 3(a)(5)(A) and (B), 

15 U.S.C 78c(a)(5)(A) and (B). These firms are generally known as broker-dealers. Section 

15(a)(1) of the Exchange Act makes it illegal for a broker or dealer to use the mails or any 

means or instrumentality of interstate commerce to effect any transaction in, or to induce 

or attempt to induce the purchase or sale of, any security unless the broker or dealer, 

absent an applicable exemption, is either registered with the Commission or a natural 

person associated with a registered broker or dealer. For a helpful summary of the types of 

functions that bring a person or entity within the regulatory category of a “broker-dealer,” 

see, e.g., The New Stock Market, ch. 9.

324 Just as a brokerage function may be embedded within a more diversified broker-dealer, 

see supra Note 323, some firms that engage in other activities (such as brokerage) may also 

trade as principal. 

325 On some venues such as exchanges, “market maker” can designate a specific category of 

registration, with attendant privileges and responsibilities.
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National securities exchanges are likely the most well-known type of platform. As 

described below, the exchanges are the predominant site of trading in equity markets.326

Exchanges must register with the Commission,327 which requires them to meet certain 

standards and to comply with certain obligations, and they must undertake self-regulatory 

responsibilities with respect to their members.328 Importantly, exchanges must comply 

with and enforce standards of fair access for their members—any broker-dealer meeting 

standard qualification requirements must be allowed to trade, and exchanges cannot 

unfairly discriminate between members.

329 Exchanges generally operate through a model 

known as a “limit order book.” A limit order is an order to buy a certain quantity of a 

security at a specified price or below, or to sell a certain quantity at a specified price or 

above. Limit orders that cannot be immediately executed upon receipt (because of a lack of 

orders on the opposite side meeting the price criteria) remain in the trading system and 

may be displayed to the broader market as quotes to indicate a willingness to trade at a 

given price. Together, the totality of these resting limit orders makes up the limit order 

“book.”330

A number of trading venues that otherwise meet the statutory definition of an exchange 

operate as alternative trading systems (ATSs), through an exemption from the definition of 

an “exchange.”331 These venues must be operated by a registered broker-dealer, and do 

not undertake self-regulatory obligations over their participants or file proposed rule 

changes with the Commission. An ATS must comply with a range of disclosure 

requirements to the public and the SEC with respect to its operations and the ATS-related 

activities of its broker-dealer operator and affiliates.332 Both equities and bonds trade on 

 

326 Into the 20th century, bonds were actively traded on exchanges such as the New York 

Stock Exchange; while bonds are still traded on some exchanges, such as the New York 

Stock Exchange, this activity is a relatively small component of the bond market.

327 See Sections 5, 6(a) of the Exchange Act, 15 U.S.C. 78e, 78f(a).

328 National securities exchanges register with the Commission by submitting and keeping 

current a “Form 1” (see https://www.sec.gov/files/form1.pdf). Forms 1 and updates 

thereto are publicly available.

329 See Exchange Act Section 6(b), 15 U.S.C 78f(b).

330 Before exchange automation, specialists or market makers in each stock wrote down the 

collection of outstanding limit orders in a physical book.

331 See Exchange Act Section 3(a)(1), 15 U.S.C. 78c(1); 17 CFR § 240.3a1-1; 17 CFR § 

240.3b-16(a); 17 CFR § 242.300(a).

332 See Regulation of NMS Stock Alternative Trading Systems, 83 Fed. Reg. 38768 (Aug. 7, 

2018).
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ATSs, and ATS subscribers do not need to be other broker-dealers. Importantly, an ATS is 

not required to enforce standards of fair access unless its trading exceeds certain volume 

thresholds.333 In practice, this means that individual ATSs are not necessarily available to 

all market participants, and that ATSs are held to less stringent standards than exchanges 

with respect to how they treat different market participants within the platforms. 

In so-called single dealer platforms, an individual dealer holds itself out to the market to 

trade a universe of securities off-exchange, usually at or better than the prevailing market 

price. Single-dealer platforms are carved out of the definition of an exchange, and so are 

not subject to regulation as either national securities exchanges or alternative trading 

systems.334

In markets where individual securities may be infrequently traded, request-for-quote 

(RFQ) platforms are common. In the U.S., RFQ platforms are used primarily in debt and 

swaps markets. On these platforms, a participant posts a request for dealers or other 

participants to provide quotes for a given security at a stated size; the participant can then 

elect to trade with the most favorable response received, either on or off the platform. 

Historically, the RFQ markets for many products have been bifurcated into customer-dealer 

platforms (where a customer requests quotes from multiple dealers) and inter-dealer 

platforms (where dealers can request quotes from each other). The registration status of 

RFQ platforms is largely determined by the facts and circumstances surrounding the 

activity of the platform.

F. Best Execution

A broker-dealer has a legal duty to seek best execution of customer orders. The duty of best 

execution derives from common law principles of agency and fiduciary duties, and is 

incorporated in rules of self-regulatory organizations and, through judicial and 

Commission decisions, the antifraud provisions of the federal securities laws.335 Generally, 

under the rules of self-regulatory organizations, a broker-dealer must use reasonable 

diligence to ascertain the best market for a security, and buy or sell in that market so that 

 

333 See 17 CFR § 242.301(b)(5).

334 See 17 CFR § 240.3b-16(b).

335 See, e.g. Reg NMS Adopting Release at 37538 (discussing cases and background); FINRA 

Rule 5310; Municipal Securities Rulemaking Board (MSRB) Rule G-18.
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the resultant price to the customer is as favorable as possible under prevailing market 

conditions.336 A range of factors should be considered in making this assessment.337

G. Providing and Demanding Liquidity

An important distinction in understanding some of the activities and trading strategies 

described in this staff report is that between providing liquidity and demanding liquidity. 

Providing liquidity refers to placing orders on a limit order book, whether displayed or not, 

or a participant otherwise indicating a willingness to trade a given instrument. A typical 

example of liquidity providing activity is market making, where a participant places orders 

on both sides of a market seeking to earn the spread between them. Demanding, or taking, 

liquidity is placing marketable orders to execute immediately against orders or other 

liquidity available at a given venue. A marketable order is an order that can be executed at 

current market prices—i.e., either a market order intended to execute immediately at the 

prevailing market price or a limit order with a limit price at or better than the prevailing 

market price.338 The liquidity providing party to a trade is often called the passive side, 

and the liquidity demanding party often called the aggressive side. Most, if not all, firms 

must balance providing liquidity and demanding liquidity at some point. However, some 

trading strategies may specialize predominantly in one or the other.

 

336 See FINRA Rule 5310; MSRB Rule G-18.

337 Specified factors include: the character of the market for the security, such as the price, 

volatility, and relative liquidity; the size and type of transaction; the number of markets 

checked; the accessibility of quotations; the terms and conditions of the order which 

results in the transaction; and, in the case of municipal securities, the information reviewed 

to determine the current market for the subject security or similar securities. FINRA Rule 

5310(a)(1); MSRB Rule G-18(a).

338 A “non-marketable” order is therefore an order that cannot be immediately executed at 

prevailing market prices. Depending on the venue, the instructions in an order, or the type 

of order used, the order may be, among other things, placed on a limit order book or 

cancelled.