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Recommendations for Equitable
Allocation of Trades in High Frequency
Trading Environments1
John McPartland
July 10, 20142

Executive Summary
Most industry observers and much of the academic research in this area have
concluded that high frequency trading (HFT) is generally beneficial. Many
institutional investors, however, argue that HFT places them at a competitive
disadvantage.3 Digital computers will always have some structural (speed)
advantages over human traders. This is inevitable.
This paper 1) acknowledges and summarizes much of the relevant published
research,4 2) discusses some of the HFT strategies that likely run counter to
good public policy, and 3) makes nine recommendations that, if implemented,
would likely restore the perception of fairness and balance to market
The author is a senior policy advisor in the Financial Markets Group of the Federal Reserve
Bank of Chicago. He wishes to acknowledge the very significant contributions that David
Marshall and Rajeev Ranjan made to this paper. He also wishes to thank the many industry
professionals who reviewed the document and its predecessor prior to publication. Any opinions
expressed in this paper are those of the author, and those opinions do not necessarily reflect the
opinions of the Federal Reserve Bank of Chicago or the opinions of the Federal Reserve
System.
2
This policy paper expands upon the identically titled prior work, dated July 25, 2013.
3
Andrew M. Brooks, 2012, “Computerized trading: What should the rules of the road be?,”
testimony of vice president and head of U.S. equity trading, T. Rowe Price Associates, Inc.,
before the United States Senate, Committee on Banking, Housing, and Urban Affairs,
Subcommittee on Securities, Insurance, and Investment, September 20, available at
www.banking.senate.gov/public/index.cfm?FuseAction=Files.View&FileStore_id=4ce0eb65ae54-45ab-82fa-072c3ee7236f. See also Charles Schwab Corporation, 2014, “High-frequency
trading is a growing cancer that needs to be addressed,” company statement, San Francisco,
April 3, available at
www.aboutschwab.com/press/issues/statement_on_high_frequency_trading
4
See, for example, Anton Golub, 2011, “Overview of high frequency trading,” Manchester
Business School, April 15, and Investment Industry Regulatory Organization of Canada, 2012,
“The HOT Study: Phases I and II of IIROC’s study of high frequency trading activity on Canadian
equity marketplaces,” report, Toronto, December 12.
1

1

participants that would be willing to expose their resting orders to market risk
for more than fleeting milliseconds.
Readers should avoid the tendency to review this working paper only within the
framework of their own nationality and market domain. The paper is meant to
be global in scope. Some HFT practices that may be inappropriate (or banned)
in some markets in some countries are alive and well in other markets in other
countries.
An exceptionally abbreviated summary of the nine recommendations follows.
1. Where appropriate, utilize a new trade allocation formula that is
intermediate between the Pro Rata trade allocation formula and the
Price/Time or FIFO (First In, First Out) trade allocation formula.
2. Create a new, optional, term limit order type that, as part of the trade
allocation process, would reward traders for the time that their orders
are committed to be resting in the order book.
3. Completely dark orders or the hidden portion of resting orders that are
not fully displayed (lit) in the order book should go to the very end of the
queue (within limit price) with respect to trade allocation.
4. Prior to trade allocation, multiple small orders from the same legal entity
entered contemporaneously for the sole purpose of exploiting the
rounding conventions of a trading venue should first be aggregated as a
single order and should carry the lowest allocation priority time stamp of
all of the orders so aggregated.
5. Rather than running a continuous trade match, trading venues should
divide their trading sessions into discrete periods of one half second. At
a completely random time within each half second period, the singleprice market-opening trade match and trade allocation algorithms
should be run once.
6. Visibility into the order book should be no more granular than aggregate
size at each price point. Market participants should not be able to view
the size of individual orders or any other identifiers of any orders of
others. This more granular information is not information that any
market participant needs to make a fully informed economic decision as
to the instantaneous value of the financial instrument being traded.
7. Under normal operating conditions, no market participant should be
permitted to cancel an order before first obtaining an acknowledgement

2

from the trading venue that the original order was received.5 We can
envision no legitimate trading strategy where the practice of cancelling
an order in this way would be necessary and any number of intentionally
deceptive trading strategies where it would.
8. Each automated trading system (each individual algorithm) that has the
capacity to generate, modify, or cancel orders without human
intervention should have a unique identifier. That unique identifier must
be known to every trading venue where the trading system can direct,
modify, or cancel an order. Trading venues must ascribe the unique
identifier as a critical information element of all relevant orders and
matched trades throughout the audit trail.
9. Relevant authorities should assess and, if appropriate, seek public
comment on precisely when trade information becomes generally
available to the public at large. Organizations that colocate in the data
centers of trading venues should not be receiving trade information from
the trade match engines but should be receiving such information from
the same ticker plants from which the general public receives trade
information. The issue is whether some firms have access to—and can
trade on—information that has not yet reached the public domain.

Background
Some twentieth-century financial markets had their origins in physical trading
halls. The design of the physical trading floors and the rules of these
exchanges provided the exchange members with a time, place, and
informational advantage over the order flow. In turn, members, specialists, or
market makers were expected to maintain continuous auction markets
(presumptive responsibility6). By the 1990s, open outcry markets had largely
given way to modern screen-based electronic markets—so-called click trading.
Before click trading had largely given way to today’s automated markets, no
single class of market participants had a time, place, or informational
advantage over all other classes of market participants. All market participants

This assumes that the trading venue is not experiencing technical difficulties that would prevent
it from promptly sending drop copy confirmations to market participants, confirming receipt of
orders.
6
U.S. Commodity Futures Trading Commission, Technology Advisory Committee, Market
Access Subcommittee, 2002, “Best practices for organized electronic markets,” final report,
Washington, DC, April, p. 4.
5

3

enjoyed an equal opportunity to buy at the bid and sell at the offer—and to do
so anonymously.
As algorithmic trading became far more prevalent, investment managers
increasingly discovered that the market neutrality of the click trading era had
been lost; that today’s algorithmic traders, or algo traders had assumed a
dominant market making role; and that role and its twenty-first-century
version of presumptive responsibilities came with a time, place, and
informational advantage.
While some investment managers might have
thought that the phenomenon of market neutrality had been taken from them,
market neutrality was never theirs in the first place.
Algorithmic trading is quite simply more competitive, and it has changed the
landscape and structure (and the public perception) of today’s modern financial
markets. In some sense, today’s perception that today’s markets may be unfair
seems to be associated with the “loss” of the market neutrality that was present
during the click trading era.
Many industry observers seem to believe that HFT offers many benefits to
organized financial markets and to society, including improved liquidity,
tightened bid/ask spreads, and a decrease in intraday price volatility. This
working paper describes some of the HFT techniques that have developed in
electronic markets around the world, as well as their effects.
Different financial centers have different rules and regulations regarding the
appropriateness of some HFT techniques. This working paper is intended to be
global in its scope and in its recommendations.
All of its nine
recommendations might not be appropriate for every electronic trading venue
in every financial center. Throughout the working paper, when discussing
different trade allocation methodologies, we refer to “shares,” “futures,” and
“lots,” which are three terms we use interchangeably.

Review of the Academic Literature7
Brogaard, Hendershott, and Riordan (2013) analyzed NASDAQ and NYSE high
frequency trading data8 that show high frequency traders increase price
See Investment Industry Regulatory Organization of Canada (2012, appendix A, pp. 51–56).
The HFT data represent a sample of 120 randomly selected stocks listed on NASDAQ and
NYSE for all of 2008 and 2009. Trades are time-stamped to the millisecond and identify the
liquidity demander and supplier as a high frequency trader or non-high-frequency trader.
7

8

4

efficiency by trading in the same direction of permanent price changes and
trading in the opposite direction of transitory pricing errors on normal trading
days and on days with the highest price volatility. In contrast, liquiditysupplying nonmarketable orders executed via HFT are adversely selected in
terms of the permanent and transitory components as these trades are in the
direction opposite to permanent price changes and in the same direction as
transitory pricing errors. HFT predicts price changes in the overall market over
short horizons measured in seconds.
HFT is correlated with public
information, such as macro news announcements, marketwide price
movements, and limit order book imbalances.9
Jones (2013) notes that the volume of HFT has increased sharply over the past
several years, has reduced trading costs, and has steadily improved liquidity.
The main positive is that HFT can intermediate trades at lower cost. However,
HFT speed could disadvantage other investors, and the resulting adverse
selection could reduce market quality. Ideally, research in this area should
attempt to determine the incremental effect of HFT beyond other structural and
technological changes in equity markets. The best papers for this purpose
attempt to isolate market structure changes that facilitate HFT. Virtually every
time a market structure change results in more HFT, liquidity and market
quality have improved because liquidity suppliers are better able to adjust their
quotes in response to new information. Jones cites the concern that HFT may
not help to stabilize prices during unusually volatile periods and notes that
there is a potential for an unproductive arms race among HFT firms for
speed.10
Cartea and Panalva (2012) conclude that the presence of high frequency
traders increases the price impact of liquidity trades and that this price impact
increases as the size of the trades increase. High frequency traders increase
microstructure noise of prices and increase trading volume. High frequency
traders and non-high-frequency professional traders coexist as competition
drives down profits for new HFT entrants while the presence of high frequency
traders does not drive out traditional professional traders. Finally, the paper
concludes that high frequency traders clearly generate costs, but they also

9

Jonathan Brogaard, Terrence Hendershott, and Ryan Riordan, 2013, “High frequency trading
and price discovery,” University of Washington, University of California, Berkeley and University
of Ontario Institute of Technology, working paper, April 22.
10
Charles M. Jones, 2013, “What do we know about high-frequency trading?,” Columbia
Business School, research paper, No. 13-11, March 20.
5

generate benefits, and that the net effect requires more precise empirical
analysis.11
The Litzenberger et al. (2010) paper concludes that overall market quality has
improved significantly, including bid/ask spreads, liquidity, and transitory
price impacts (measured by short-term variance ratios).
Studies using
proprietary, exchange-provided data that identify the trades of high frequency
trading firms show that HFT firms contributed directly to narrowing bid/ask
spreads, increasing liquidity, and reducing intraday transitory pricing errors
and intraday volatility.12
Wah and Wellman (2013) evaluate allocative efficiency and market liquidity
arising from simulated order streams in fragmented financial markets. They
find that market fragmentation and the presence of a latency arbitrageur
reduce total surplus and impact liquidity negatively. By replacing continuous
trade matching with periodic batch auctions or call markets, latency arbitrage
opportunities are eliminated and further efficiencies are achieved by
aggregating orders over short time periods.13
Budish, Cramton, and Shim (2013) use actual millisecond quotation data to
show that the prices of related financial instruments are highly correlated at
human-scale time horizons but that these correlations break down completely
at the single-digit millisecond level. The lack of price correlation at the
millisecond level can be arbitraged away profitably if a market participant can
act faster than other market participants similarly engaged in latency
arbitrage. Their theoretical model shows that that quest for speed is not only
wasteful but can lead to wider bid/ask spreads and thinner markets for
fundamental investors than would be otherwise. They then use their model to
show that frequent batch auctions can reduce the value of tiny speed
advantages because it forces completion that was previously based on speed
into competition to be based on price instead. They conclude that frequent

11

Álvaro Cartea and José Penalva, 2012, “Where is the value in high frequency trading?,”
University College London and Universidad Carlos III, Madrid, working paper, February 17.
12
Robert Litzenberger, Jeff Castura, Richard Gorelick, and Yogesh Dwivedi, 2010, “Market
efficiency and microstructure evolution in U.S. equity markets: A high-frequency perspective,”
RGM Advisors LLC, working paper, August 30.
13
Elaine Wah and Michael P. Wellman, 2013, “Latency arbitrage, market fragmentation, and
efficiency: A two-market model,” EC ’13: Proceedings of the 14th ACM Conference on Electronic
Commerce, New York: ACM, Inc., pp. 855–872.
6

batch auctions can lead to narrower bid/ask spreads, deeper markets, and
greater social welfare 14

Questionable HFT Techniques
Notwithstanding the evident benefits of HFT in electronic markets, many
market participants have argued that some HFT practitioners utilize trading
techniques that are detrimental to the well-functioning of financial markets.15
Some of the trading techniques generally considered to be detrimental and not
capital formative are spoofing, layering, and quote stuffing.16
Spoofing and layering are not at all unique to HFT. Both almost always involve
feigning to be a buyer when one is really a seller or vice versa. Algorithmic HFT
has, however, allowed these two strategies to be taken to new levels. FINRA
states the following about spoofing and layering:
Generally, spoofing is a form of market manipulation which involves placing certain nonbona fide order(s), usually inside the existing National Best Bid or Offer (NBBO), with the
intention of triggering another market participant(s) to join or improve the NBBO,
followed by canceling the non-bona fide order, and entering an order on the opposite
side of the market. Layering involves the placement of multiple, non-bona fide, limit
orders on one side of the market at various price levels at or away from the NBBO to
create the appearance of a change in the levels of supply and demand, thereby
artificially moving the price of the security. An order is then executed on the opposite
side of the market at the artificially created price, and the non-bona fide orders are
immediately canceled.17

Eric Budish, Peter Cramton, and John Shim, 2013, “The high-frequency trading arms race:
Frequent batch auctions as a market design response,” University of Chicago Booth School of
Business and University of Maryland, working paper, December 23.
15
See, for example, German Federal Ministry of Finance, “Speed limit for high-frequency
trading—Federal government adopts legislation to avoid risks and prevent abuse in highfrequency
trading,”
press
release,
Berlin,
September
26,
available
at
www.bundesfinanzministerium.de/Content/EN/Pressemitteilungen/2012/2012-09-26-speedlimit-for-high-frequency-trading.html, and Australian Securities & Investments Commission
(ASIC), 2012, “Australian market structure: Draft market integrity rules and guidance on
automated trading,” consultation paper, No. 84, Victoria, Australia, available at
www.asic.gov.au/asic/pdflib.nsf/LookupByFileName/cp184-published-13-August2012.pdf/$file/cp184-published-13-August-2012.pdf.
16
See the proposed amendments to the ASIC Market Integrity Rules (ASX Market) to preclude
market misconduct, manipulation or false trading in Australian Securities & Investments
Commission, 2013, “Dark liquidity and high-frequency trading,” report, No. 331, Victoria,
Australia, March, p. 10.
17
Financial Industry Regulatory Authority (FINRA), 2012, “FINRA joins exchanges and the SEC
in fining Hold Brothers more than $5.9 million for manipulative trading, anti-money laundering,
14

7

Quote stuffing is unique to algorithmic HFT. Regarding this third dubious
technique, Egginton, Van Ness, and Van Ness (2012) state the following:
Quote stuffing is a practice in which a large number of orders to buy or sell securities are
placed and then canceled almost immediately. During periods of intense quoting
activity stocks experience decreased liquidity, higher trading costs, and increased short
term volatility.18

Imagine that you are bidding at an art auction, and the serious bidders are
now reduced to two or three. One of the persons pretending to be an interested
bidder is really the owner of the art piece currently being auctioned off. It is to
their advantage to get the bona fide bidders to pay as much as possible for
their art piece. Bidders indicate their willingness to bid to the auctioneer by
raising the bidder numbers assigned to them by the auction house. The
spoofing equivalent in this physical environment would be if the seller of the art
piece, pretending to be a buyer, raised his or her bidder number one last time,
solely to get the last remaining buyer to pay more than they otherwise would be
willing to pay. Granted, the spoofer in this case is absolutely at risk of buying
their own art piece unless their spoofing strategy is successful and a bona fide
bidder betters the spoofer’s bid. While the practice of allowing sellers to
masquerade as buyers is probably not allowed at proper art auctions, its
electronic equivalent is permitted and well practiced among some HFT
practitioners. Make no mistake, HFT spoofers’ bids and offers are exposed to
market risk as much as the bids and offers of click traders, even if they are
often so exposed for only milliseconds. Spoofing is intentionally designed to be
deceptive and, at a minimum, frustrates fair value investors’ ability to
determine the true market value of the instruments that are being traded.
Layering is only a slightly different technique, designed to similarly deceive
market participants’ perception of the aggregate size of the bids and offers in
the order book. By entering thousands of bids or offers, and then cancelling
them virtually immediately, but only after they have been acknowledged as
having been present in the order book, HFT practitioners can successfully
create the illusion of greater size at the bid (or offer) than is realistically
executable. Investment managers often refer to this phenomenon as “phantom
liquidity” as the visible liquidity is often not there when one goes to hit the bid
and
other
violations,”
press
release,
Washington,
DC,
available
at
www.finra.org/Newsroom/NewsReleases/2012/P178687.
18
Jared Egginton, Bonnie Van Ness, and Robert Van Ness, 2012, “Quote stuffing,” Louisiana
Tech University and University of Mississippi, March 15.
8

or lift the offer. Not unlike the massive white clouds in the sky, they are
actually nothing more than thin water vapor that simply gives the illusion of
being huge, massive objects. Frequently, high frequency traders layer quotes
on the bid side of the market, in an attempt to attract other bidders, and then
hit the bid side of the market as a seller in size. Layering is designed to be
intentionally deceptive and similarly frustrates fair value investors’ ability to
ascertain the fair market value of the instruments traded. It also intentionally
and unduly complicates order execution.
Quote stuffing is roughly equivalent to driving a race car at 190 miles per hour,
but preventing the other drivers from exceeding 160 miles per hour. By
clogging a trading venue’s outbound quotation system (or inbound order entry
systems) with near worthless quotes, astute HFT practitioners can execute
trades on another trading venue or on the same trading venue with some
degree of confidence that at least a plurality of market participants (including
many other high frequency traders) will, at best, be reacting to delayed quotes,
creating an arguably unfair trading advantage for these HFT practitioners that
can “slow down” the other traders by relatively increasing their own reaction
times.19 As CNBC noted in 2012, “the ultimate goal of many of these programs
is to gum up the system so it slows down the quote feed to others and allows
the computer traders (with their colocated servers at the exchanges) to gain a
money-making arbitrage opportunity.”20 Price transparency is considered a
public benefit of organized financial markets. It is difficult to envision that the
practice of intentionally slowing down the dissemination of trade prices to the
public is an activity that serves the public interest.
The thesis of this paper is that, rather than attempting to ban these techniques
(which could likely be difficult to enforce in practice), one could change the
character and economics of the trading environment so as to disincentivize
these and similar undesirable trading techniques.
Rather than propose
solutions that might preclude specific HFT strategies, we propose to simply
change the economics of the trading environment by modifying the criteria of
order allocation priority and by discouraging certain questionable industry

19

Quote stuffing is an offensive tool that high speed traders most typically use to gain a
competitive advantage over other high speed traders. Click traders would not likely be
adversely affected if outbound quotations were intentionally delayed by, say, 200 milliseconds,
nor would they likely even be able to detect any such delay.
20
John Melloy, 2012, “Mysterious algorithm was 4% of trading activity last week," CNBC,
October 8, available at www.cnbc.com/id/49333454.
9

practices to strike a more equitable balance between the high frequency trading
community and the investment management community.

Recommendations
The proposal consists of nine recommendations that should be deemed as one
complete set that should be considered and implemented as a whole, where
appropriate. Several of the recommendations are admittedly rather complex,
but so are the current electronic market structures in which we find ourselves.
Our recommendations follow.

1. Trade Allocation with Cardinal Weighting of Time in the Order Book
The ideal trade allocation algorithm should be a combination of the Pro Rata
trade allocation algorithm and the Price/Time or FIFO trade allocation
algorithm.21 Descriptions of the Price/Time and Pro Rata algorithms would
seem to be in order.

Price/Time or FIFO
The Price/Time trade allocation algorithm is also known as the FIFO algorithm.
The Price/Time trade allocation algorithm first prioritizes all bids and orders
based on price, and within price, prioritizes orders (in an ordinal ranking)
based on the time that each order was received. An order can always gain
priority by bettering its price, while keeping its original time stamp. Within the
best price, the Price/Time algorithm attempts to completely fill the order with
the oldest time stamp, (the lowest ordinal ranking) with any residual contracts
or shares subsequently allocated to the next oldest bid or offer, until the
appropriate contracts or shares have been fully allocated.
The Price/Time trade allocation algorithm was the first algorithm utilized when
the era of electronic trading was ushered in. Some market participants
erroneously think that electronic markets still utilize the Price/Time trade
21

There are at least half a dozen other trade allocation algorithms currently in use but not
specifically referenced in this section. While it might be quite valuable for interested market
participants to have a detailed treatise on the various trade allocation algorithms currently in
use, that is not the objective of this section.
10

allocation exclusively—that is, that there are no other trade allocation
algorithms. While it is equitable, some trading venues have diversified away
from the Price/Time trade allocation as market participants tend to feel
disconnected when they join the bid or offer, but are not senior enough to
participate in any trade allocation. If there is a valid criticism of the Price/Time
trade allocation algorithm, it is that it allocates trades based only on a simple
ordinal ranking of bids or offers based on their respective time stamps. Basing
the allocation of trades on a cardinal weighting (ranking) of trades based on
their actual time stamps would seem to be a superior approach.

Pro Rata
In the Pro Rata trade allocation algorithm, all bids are allocated their pro rata
share of the allocation of a matched trade based solely upon the lot size of their
respective resting bid relative to the aggregate sum of all of the resting bids at
the same price. For example, if there are a total of 2200 lots bid for at 12 and
220 offers hit the bid, each resting bid would be allocated 10% of the lot size for
which they were bidding.22
If there is a criticism of the Pro Rata trade allocation logic, it is that many
market participants are constantly bidding or offering unrealistically large
quantities, often far greater than they could likely realistically absorb.

NYSE/Liffe
NYSE/Liffe has a hybrid trade allocation algorithm that assigns resting bids
and offers with an ordinal ranking (based on their time stamp) and then
allocates trades based on a combination of the Pro Rata approach and the
ordinal ranking of the bids and offers.
In the formula that is equation (1), the first bracketed expression simply says
that a market participant should be allocated the lesser of 1) the full amount of
the quantity of his order or 2) a lesser quantity based upon where his
respective order ranks in the order book, based on its time stamp, relative to
22

This trade allocation process has been shown to be prone to the apparently unavoidable
rounding error chicanery that occurs when dozens and dozens of one lot orders are intentionally
entered by a single market participant, in the hope of being unjustly enriched by the trading
system rounding of what would have been anything greater than 51/100ths of a futures contract
or share to one full contract or share. See recommendation 4.
11

the time stamps of the other orders in the order book. The second bracketed
expression determines the pro rata quantity of any given order relative to the
aggregate quantity of orders at the same price. The third bracketed expression
determines the ordinal ranking (by time stamp) of any given order relative to
the time stamps of all of the other orders at the same price in the order book.
It is this third bracketed expression that we believe could be improved.
NYSE/Liffe Time Pro-Rata algorithm23




 vn, fn * L 
An  Min
N


  fr

 r 1


where,


 

 vn   ( N  1)  n 

fn   N  * 
N
 vr  
r 
   

 r 1   r 1


(1)

N - Total number of resting buy (sell) orders sorted by time, n =
1(oldest) to N (newest)
n - Individual order being considered
r - Ascending sequence, 1 to N
An - Allocation for resting buy (sell) order, n
vn - Volume of resting order being considered, n
fn - ‘Time Pro Rata Factor’ calculated for resting buy (sell) order being
considered, n
L - Incoming sell (buy) order volume

Recommended Trade Allocation Algorithm
When allocating trades, the instant proposal places a greater weighting on the
time that an order is exposed to market risk. We extrapolate from the
NYSE/Liffe model and assign a cardinal ranking, rather than an ordinal one,24
23

Fractional allocations are rounded down to the nearest integer for all allocations greater than
1 and rounded up to 1 for all fractional allocations less than 1. For equally sized fractional
allocations, priority is granted to the oldest order. If any volume remains unallocated following
this sequence (for instance, as a result of rounding or when the calculated allocation for an order
is constrained by the Min function in the NYSE/Liffe Time Pro-Rata algorithm), then a further
pass of the sequence will occur.
24
Ordinal ranking of resting orders involves creating a simple ranking, not unlike athletes who
finish first, second or third in an athletic contest. That is, no consideration is given to the
difference in athletic performance between the first and second finisher and the second and third
finisher. A cardinal ranking would involve assigning a numeric value to the performances of the
athletes, not unlike in gymnastics, where there is a quantitative evaluation of individual
performances. In the instant proposal, our recommendation is to allocate matched trades based
on the actual time that the orders have been resting in the order book relative to the times that
12

to resting bids and offers based on the actual length of time that bids and
offers have been resting in the order book, relative to the time that all of the
other orders have been resting in the order book. This is accomplished by
raising “time in the order book” (Tau) to a low but effective exponential power



 



 vn     
f 'n
An  Min  vn, N
* L  where, f ' n   N  *  N n 


 vr     
  f 'r

   r 
 r 1   r 1

 r 1


(2)

 - Time duration (in milliseconds) for every resting order (time
difference between the time of trade match and an incoming order’s time
stamp)
N - Total number of resting buy (sell) orders sorted by time, n =
1(oldest) to N (newest)
n - Individual order being considered
- A constant parameter set by the trading venue
An - Allocation for resting buy (sell) order, n
vn - Volume of resting order being considered, n
f ' n - ‘Proposed Time Pro Rata Factor’ calculated for resting buy (sell)
order being considered, n
L - Incoming sell (buy) order volume

Note that equation (2) is identical to equation (1) except for the third bracketed
expression. Whereas the third bracketed expression in (1) is based on a simple
ordinal ranking of time in the order book, the third bracketed expression in (2)
raises time in the order book
causes a nonlinear marginal increase in the number of lots a longer-duration
order is allocated, compared with a shorter-duration order, based on the time
that the order has been exposed to market risk. This additional weighting
(resulting in a greater allocation of trades) could be set by the trading venue by
Figure 1 illustrates the progressive trade allocation results when is set equal
to or greater than zero and less than or equal to 2.3. As is set to increasingly
higher values, the weight associated with Tau (time in the order book)
other orders have been resting in the order book—and not based on the ordinal ranking of the
respective time stamps of resting orders. Thus, in a cardinal ranking structure, an order that has
been resting in the order book for four hours would be entitled to a far greater allocation of
trades than an order that has been resting in the order book for only four seconds. In an ordinal
ranking system, those orders would simply be ranked as #1 and #2 in priority.
13

increases exponentially and the actual time that an order has been resting in
the order book becomes an increasingly dominant component when the
algorithm allocates trades. It may be helpful to think of this recommendation
as the introduction of perfect gradient shades of gray that lie between black
(Pro Rata) and white (Price/Time).
Market View – Top of Book

Limit Orders – Listing all Bids at price of 99.69

Allocations

Figure – 1 (α: 0 – 2.3)

The horizontal axis reflects the value of . The vertical axis reflects the
quantities (lots) that would be allocated to resting orders based upon their
respective time in the order book.
set to zero, each of the five resting
bids would be allocated 60 lots, that is, an exact Pro Rata trade allocation
(where time in the order book means nothing). As
time in the order book receives more and more weighting. If
14

value higher than 1.9, the recommended trade allocation algorithm closely
approximates the Price/Time or FIFO trade allocation algorithm (where time in
the order book means everything). This continuum approach would allow
trading venues to select allocation outcomes of varying degrees between the Pro
Rata and the Price/Time trade allocation outcomes.25
In the example in figure 1, there are 900 lots offered at 99.70 and 600 lots bid
for at 99.69. The 600 lots on the bid side are comprised of five individual bids,
each for 120 lots, all at a price of 99.69 but with different time stamps. There
is a new incoming order to sell 300 lots at a price of 99.65, well below the
resting bids at 99.69. The incoming order of 300 lots is therefore going to take
out half of the 600 lots bid for at 99.69. The graphic demonstrates how 300
were set to various values between zero and 2.3.
Note that in this example, the oldest bid (in blue) resting in the order book is
750 milliseconds older than the second oldest resting bid (orange) but the
remaining bids are separated by only about 10 milliseconds. The effect of this
time differential can be d
that is selected
by the trading venue.
were set to 0.40, the oldest resting bid would be allocated 100% of its bid
quantity (120). This is largely due to the 750-millisecond time differential
between the oldest bid and the
to 0.40, the
remaining resting bids would be allocated 72, 59, 38, and 11 lots, respectively.
It is anticipated that, for every relevant instrument, a trading venue would
select a fairly permanent value of
that strikes an equitable balance that
rewards both liquidity providers and institutional market participants. When
making such a determination, trading venues will undoubtedly consider the
current preferences of market participants and the business risks and costs of
changing trade allocation algorithms for legacy products.

25

It is assumed that trading venues would change the value of very infrequently, if at all, as
market participants would need to have a clear understanding and expectation of the trade
allocation process in order to correctly size the quantities of their bids and offers. Setting to a
value between zero and 2.3 would allow trading venues a continuum from which they could
select the optimal trade allocation result for any financial instrument or product family.
15

2. Term Limit Order Type
Create a new, entirely optional noncancellable term limit order type—for
example, Buy at 12, good for 4 seconds or Buy at 12, good for 4/10ths of a
second. The order may not be cancelled during its stated term (see footnote 26)
and would be displayed in the order book just as any other resting bid at 12.
The term of the order is the minimum amount of time that the order would be
exposed to the market. Like any other non-term-limit orders, a term limit
order remains open until it is either filled (either during or after its stated term)
or cancelled after its term has expired.26 Importantly, the instant a term limit
order enters the order book, it has the trade match and trade allocation
priorities of having already been in the order book for the stated term of the
order. For example, the instant that an order to Buy at 12, good for 4 seconds
enters the order book, it would have the trade match priority and trade
allocation priority identical to an order that has already been resting in the
order book for 4 seconds.
This order type should have the potential to provide more balance between the
interests of institutional market participants and HFT practitioners, whose
orders are often in the order book for only a few milliseconds. When combined
with recommendation 1, allocation of matched trades should be directly related
to how long a resting order was exposed (or committed to be exposed) to market
risk. Orders that are resting (or committed to be) in the order book for a
material amount of time are exposed to market risk, provide tangible price
transparency to the public, and deserve a higher trade match priority and trade
allocation priority than orders that have barely been in the order book for a few
milliseconds and have provided only marginal (if not intentionally deceptive)
pricing information to the public. The combined effect of recommendations 1
and 2 should increase the likely allocation of lots27 to orders that are exposed
to market risk for a greater period of time, at the expense of orders that are
exposed to market risk for only a few fleeting milliseconds.

26

A trading venue might elect to allow a term limit order to be amended to a price better than the
original price of the term limit order during the period in which the order could not otherwise be
cancelled. Lot size could not be amended. This should only be allowed if the limit price of the
original order was initially entered to join the best bid or offer (the “top of the book”) and
remained at the top of the book, should the market move. Allowing such a trade (with the better
price) to keep its original time stamp would seem equitable and consistent with public policy
objectives.
27
Lots could mean shares, options, futures, swaps or any other specialized descriptor of traded
financial instruments. Our intention is not to limit the scope of the applicability of the
recommendations.
16

There is no apparent reason this optional order type could not be used in a
fragmented market structure where any number of trade allocation methods
are in use.28 Where financial markets are fragmented into multiple trading
venues, different trade execution venues could have different trade allocation
formulas. This proposed new order type would have no effect at all on trades
allocated on trading venues that use the Pro Rata trade allocation method, as
time in the order book would still be given no weight. It could, however, have a
material effect on trades allocated on trading venues that use the Price/Time
algorithm or which might adopt the trade allocation formula that we are
recommending.
Implementing any new order type would, of necessity, involve implementation
costs for trading venues, trade intermediaries, and, to a lesser degree, some
end-user market participants. It may be advantageous for one or more trading
venues to inaugurate pilot implementation programs for a limited number of
traded products to better gauge potential commercial acceptance of this
concept and to make a more informed business decision regarding wider (or
universal) implementation of the proposed new term limit order type.

3. Time Stamp Conventions of Dark or Unlit Orders
According to the Australian Securities & Investments Commission, “dark
liquidity refers to orders that are not known to the rest of the market before the
orders are matched as executed trades. Such trades, known as ‘dark trades,’
can occur on exchange markets … and in venues other than exchange
markets.”29 Orders of all types (except those noted in recommendation 2)
should have a time stamp reflecting the start of the period during which that
order was continuously visible in the order book to all market participants.
Said another way, an order originally entered as an unlit order should have no
valid time stamp as long as that order remains unlit and not visible in the
order book to all market participants. Without a time stamp, all unlit orders at
a limit price should stand behind all lit orders at the same limit price with
respect to trade allocation.

28

In the fragmented U.S. equities market, Regulation NMS would still require that an order
initially be routed to the trading venue with the best price. That trading venue may or may not be
utilizing a trade allocation method that places a value on the time an order has been resting in
the order book.
29
Australian Securities & Investments Commission (2013, p. 12).
17

If the offer were to go through all valid lit bids in the order book at the best bid
limit price and should there remain unlit orders resting in the order book at
the same limit price, the trading venue should allocate the residual amount of
lots to the unlit orders under either one of two protocols, both of which appear
to be equitable. The first protocol would simply be to allocate the residual
amount of unlit orders on a Pro Rata basis. The second protocol involves
ranking the unlit orders by the time stamp of the lit portion of their respective
orders. Once in the proper sequence, lots are allocated to those orders but
only for the quantity specified in the lit portion of each order. This process
would continue iteratively until all of the lots had been allocated or until there
were no longer any remaining unlit orders that had not been fully allocated.
Following either of these protocols should be consistent with principle 3 of the
of the 2011 report on dealing with dark liquidity by the Technical Committee of
the International Organization of Securities Commissions:
Principle 3: In those jurisdictions where dark trading is generally
permitted, regulators should take steps to support the use of transparent
orders rather than dark orders executed on transparent markets or
orders submitted into dark pools. Transparent orders should have
priority over dark orders at the same price within a trading venue.30
It is unfortunate that some trading venues allow traders to submit orders that
are not visible to others and then modify the order while retaining its original
time stamp. This practice runs counter to all principles of fairness. While this
recommendation would not ban dark or unlit orders, it should provide an
appropriate economic disincentive to utilize them to any great extent.

4. Aggregation of Consecutive Small Orders from the Same Legal Entity
Some traders have exploited trade allocation formulas that round fractional lot
allocations up to the next integer. For example, a buyer of one 100 lot order
might be entitled to an allocation of 62 futures or options contracts based on
the applicable trade allocation formula. If the buyer were to have entered his
100 lot order as 100 one lot buy orders and if the trading venue rounds
fractional allocations up to the next integer, the trade allocation formula would
allocate 62/100ths of a futures or options contract to each one lot order. Since
the trading venue cannot allocate 62/100ths of a futures or options contract,
30

Technical Committee of the International Organization of Securities Commissions, 2011
“Principles for dark liquidity: Final report,” Madrid, Spain, May, pp. 28–29.
18

the trader may be unjustly enriched by a rounding convention (that rounds up
fractions greater than 1/2) only because the trader entered a 100 lot order as
100 one lot orders. Taken to the extreme, the trader could be allocated as
many as 100 lots (one for each one lot order) while really only being entitled to
62 lots.
Prior to allocating trades, trading venues should first aggregate all matched
trades submitted by the same legal entity to mitigate the potential for
gamesmanship due to rounding conventions of one lot orders.31
The
aggregation routine should run once, every time that the trade allocation
algorithm is run and would involve only orders that would be entitled to a fill or
partial fill and only orders that appear to have been intentionally entered
sequentially, by their respective time stamps. This should leave unaffected the
bona fide trades of unequal quantities entered minutes apart by the same legal
entity. For example, it should be easy enough to discern between the two or
three unequal resting buy orders from a grain elevator, all entered within five
minutes32 and 100 one lot orders from an algorithmic trader, all entered three
milliseconds apart.
The aggregated order should be assigned the worst time stamp of all of the
multiple orders that it comprises.33 There seems to be no reason to run the
aggregation routine continuously or prior to running the trade allocation
algorithm (note recommendation 5). This recommended procedure should
eliminate much of the potential for gamesmanship, provided that market
participants do not then violate other rules of trading venues by lying about
their true identity.

5. Random Timing of the Trade Match Algorithm
Trading venues should divide their trading sessions into time periods of one
half of one second. At a completely random time during each one half second
trading period, the trading venue should run its trade match algorithm
(allocating trades utilizing the cardinal ranking of resting bids and offers as
31

In the alternative, trading venues may elect to round down, rather than round up, allocating
one lot trades that would otherwise be allocated a fractional lot, nothing, as at least one trading
venue already does.
32
The orders from the grain elevator should not be affected and should each severally retain
their own original time stamp.
33
This is not intended to affect all trades from the same legal entity. This recommendation is
intended only to mitigate the unjust enrichment associated with multiple and sequential one lot
and, perhaps, two lot trades.
19

described in recommendation 1 earlier) once.34 This procedure has several
advantages. Because high frequency traders would never know when the trade
match algorithm would be run during any one half second time interval, the
value of entering thousands of quotes for only a few milliseconds should be at
least partially diminished. Additionally, as many of those quotes are typically
not actually intended to be matched, they would occasionally, under this
proposal, become swept up and executed in the trade match algorithm,
because of its random timing within each half second period. Under this
proposal, high frequency traders could continue the practice of entering
thousands of quotes per second, only with more substantial potential financial
implications.
There is some anecdotal evidence that suggests that most humans can read,
recognize, and process two to three numerical quantities per second.35
Dividing the trading session into half second periods should provide human
institutional traders with useful visual information on their trading screens as
fast as that information can reasonably be comprehended. It may be helpful to
imagine that if such an electronic market had an audio attribute, the market
would trade and outbound quotations would be disseminated about as fast as
a professional auctioneer can speak. Budish, Cramton, and Shim (2014) have
suggested that such rapid-fire quotation dissemination (for matched trades)
would constitute sufficient pretrade price discovery. That is, if the market were
34

The practice of trading venues to display the probable single opening price (as the market is
about to open) usually comes with a policy that precludes the cancellation of orders within half of
a second prior to the opening time. Market opening trade match algorithms determine a single
price at which the maximum number of trades would optimally be matched. From a technical
perspective, it will likely then be impossible to run the market opening trade match algorithm at a
random time within every half second trading interval and display such a single price before the
fact. The instant proposal is simply to run the trade match and trade allocation algorithms once,
not necessarily with all of the bells and whistles that come with the single-price market-opening
trade match algorithm. HFT market participants and click traders should be able to deduce
whether the trade match algorithm will match trades at the highest bid or the lowest offer by
watching the size at the bid and offer.
Alternatively, trading venues could elect to have the next subsequent one half second
trading interval begin as soon as practicable after the prior trade match and trade allocation
algorithms have been run for the prior trading interval. Doing so would compound the
randomness of running the trade match and trade allocation algorithms. One potential downside
of doing so might be that occasionally, the trade match and trade allocation algorithms could be
running in excess of twice per second. Most human click traders would not be able to read,
digest, and respond to price and quantity information at such a fast pace.
35
See Kimron L. Shapiro, Karen M. Arnell, and Jane E. Raymond, 1997, “The attentional blink,”
Trends in Cognitive Sciences, Vol. 1, No. 8, November, pp. 291–296, and Jane E. Raymond,
Kimron L. Shapiro and Karen M. Arnell, 1992, “Temporary suppression of visual processing in
an RSVP task: An attentional blink?,” Journal of Experimental Psychology: Human Perception
and Performance, Vol. 18, No. 3, August, pp. 849–860.
20

to disseminate the single auction price as fast as an auctioneer can speak,
market participants would likely have sufficient price discovery information to
make informed economic decisions on the fair market value of the financial
instrument being traded and would no longer need a view into the order
book.36
Derivatives exchanges often group related product types—for example, equities,
interest rates, foreign exchange, agriculture, energy, and precious metals—on
their trade match engines. Under this proposal, derivatives exchanges would
run the trade match and trade allocation cycle, by product type (on each
server) at a completely random time, once during each half second time period.
Doing so should preserve the so-called implied functionality execution
functionality—for example, the soybean crush spread, the crude oil crack
spread, or simple calendar spreads. Again, if performed properly, this should
also randomize the sequence in which the servers run their product specific
trade match and trade allocation cycles.
Equity trading venues also typically run multiple trade match engines—for
example, all stocks that begin with the letters A through F might trade on one
server. It is envisioned that such a trading venue that has n number of servers
would run one trade match and trade allocation cycle at random times during
each half second period on each server. If done properly, this would also
randomize the sequence in which each of the n number of servers would run
their respective trade match and trade allocation algorithms.
The additional computational processing that would likely be required to
assimilate both the new term limit order (recommendation 2) and the allocation
of lots based on a cardinal ranking rather than a simple ordinal ranking
(recommendation 1) should be partially, if not totally, ameliorated by only
having to run the trade match and trade allocation algorithms once every half
second. Doing so should allow the trading venues to conserve significantly on
network bandwidth as outbound quotations would only be disseminated once
every half second—and then, only in batches.37 One firm that was invited to
comment on a prior draft of this paper indicated that implementing this
recommendation could potentially free up much of its annual information
See Eric Budish, Peter Cramton, and John Shim, 2014, “Implementation details for frequent
batch auctions: Slowing down markets to the blink of an eye,” American Economic Review, Vol.
104, No. 5, pp. 418–424.
37
In theory, disseminating outbound quotations in batches might also thwart the practice of
quote stuffing, as market venues would push quotation data out in data “packages,” which might
make quote stuffing a less effective strategy to intentionally clog up trading venues’ outbound
quotation systems.
36

21

technology (IT) budget—currently dedicated to buying more servers to handle
the tsunami of price and quantity (and related) data newly available by the
millisecond and to retransmit them just as quickly to their customers who
could not possibly react to them—to instead develop truly value added features
to their client user interface. It is entirely possible that these potential cost
savings could similarly be realized by a broader section of market participants.
Moving modern electronic markets away from continuous trade matching to
discrete auction processing might also improve the technological framework
within which national supervisory authorities will be held responsible for
providing supervisory market oversight now and for many years to come.
Implementation of this recommendation would materially reduce the amount of
quotation and trade match data that make up the audit trail for today’s
modern electronic financial markets.38 Common sense would argue that the
probability of achieving some success in this regulatory area might be greater if
the challenges of doing so could be made less formidable.
Millisecond-by-millisecond trade matching has also driven the average lot size
of equity and futures transactions down dramatically. Maintaining the IT
infrastructure to process a plethora of small lot fills does greatly increase the
operating expenses of trading venues, clearing organizations, and trade
intermediaries because the relevant operating expenses are driven by the
number of transactions, not the number of shares or futures contracts of those
transactions. Processing a small lot order consumes just as much bandwidth
and just as many IT resources as processing one 10,000 lot order that is
matched and clears as one 10,000 lot order. A serendipitous result of
implementing this recommendation could be a material reduction in the
operating expenses of trading venues, clearing organizations, and trade
intermediaries that must scale their infrastructure to handle the number of
transactions that they process or retransmit.
At least two leading electronic foreign exchange trading markets have
implemented processes to slow down or randomize incoming orders. ParFX
currently imposes randomized pauses on incoming orders.39 EBS accumulates
orders by institution, and, after waiting for either one, two, or three
Theoretically, if markets currently trade every millisecond, moving to a batch auction once
every half second would reduce the volume of outbound quotation data by a factor of 500.
39
Stephen Foley, 2013, “High-frequency traders face speed limits,” Financial Times, April 28,
available by subscription at www.ft.com/cms/s/0/d5b42402-aea3-11e2-8316-00144feabdc0.html; and
Nicola Tavendale and Joy Macknight, 2014, “Will latency floors do the trick?,” Profit & Loss in
the Currency & Derivatives Markets, Vol. 15, No. 151, May, pp. 52, 54.
38

22

milliseconds, randomizes the order of the institutions. This creates a matrix of
orders within an institution. After randomizing the ranking of the institutions,
the trade allocation algorithm then allocates the highest priority order from
each institution (one from each institution) until sufficient orders have been
allocated. This process effectively diminishes the value of any speed greater
than several milliseconds.40
Some lawmakers and regulators have suggested that quotations must be
exposed to the market for a minimum amount of time.41 Fifty to 500
milliseconds42 is an eternity to a proprietary algorithmic trader. The likely (and
logical) reaction would be for market makers to widen their respective bid/ask
spreads to compensate themselves for the additional market risk to which their
quotes would be exposed under any such minimum cancellation time regimes.
We would argue that rather than dampen the quest for speed, a minimum
cancellation time would have exactly the opposite effect. If market participants
could not cancel their quotes for 500 milliseconds, a persuasive argument
could be made that high-speed automated traders would be willing to pay huge
sums of money to ensure that they were the very fastest, thus enabling them to
pillage the quotes of slower traders who could not cancel their quotes (for 500
milliseconds) before being plundered.
Running the single-price market-opening trade match algorithm at a random
time within every half second time period would provide the trading venue with
the option of either providing or not providing market participants with a view
into the order book prior to the trade match. A cogent argument can be made
that providing single-price match trade information to the general public every
half second is more than sufficient for market participants to make a fully
informed economic decision as to the current fair market value of the financial
instrument being traded, and is as fast as a human can comprehend such
information. A suggestion that would have the equivalent effect would be to
run the single-price trade match algorithm once every half second (on the half
second) but not provide any visibility into the order book, before the fact.
Either approach would eliminate much of the time, place, and informational
advantage that high speed automated traders currently enjoy over all other
classes of market participants.

40

Ibid.
This provision was ultimately not included in the German High Frequency Trading Act,
referenced later in this paper.
42
Australian Securities & Investments Commission (2013, p. 10).
41

23

Our recommendation 5 would still allow algorithmic traders to cancel their
orders at any time and should thus render moot any potential arguments
about having market makers’ quotes unduly exposed to market risk. There is
some potential that implementation of this recommendation would
considerably dampen or possibly even end the incessant low latency arms
race.43
Lastly, some algorithmic trading firms have called into question how
interexchange arbitrage trades could be executed if some or all of the trading
venues ran their single-price trade match algorithm at random times within
one half second periods. Under those circumstances, interexchange trades
would be executed the same way they were executed for decades before trading
became dominated by algorithms and before trading became a millisecond-bymillisecond phenomenon. One leg of an arbitrage trade might be delayed,
perhaps up to the blink of an eye before being filled.
Algorithmic trading firms seem to be of the view that the market structure of
the future can only evolve from the (arguably remedial) market structure that
we have today. We reject this premise. If the current public focus over the
issue of “fairness” is generally reflective of the consensus of a democratic
society, then why would a free and educated society knowingly limit the design
for the structure of the financial markets of the future to a starting place that
is considered remedial? Interexchange trading will survive and flourish as long
as there is a profit motive driving it.

6. Granularity of Information in the Order Book
In general, all market participants should have access to the same information,
as well as the same level of granularity of information, from the order book.
Market participants should only have access to information that they
legitimately need to make an informed economic decision on market depth,
price, and liquidity. Market participants that have the ability to query the
order book should ideally only be able to see the aggregate size at each bid and
offer price points.
No market participants should be able to see any other identifying data in the
order book that would reveal the identity or origin of the other market
participants that have entered orders. No market participant should be able to
See Chris Sparrow, 2011, “The failure of continuous markets,” Market Data Authority, Tabb
Forum, December 5.
43

24

see or otherwise ascertain the time stamps or the individual lot sizes of orders
entered other than their own.44 Such granular data is not information that any
market participant legitimately needs to make an informed economic trading
decision.
It should be sufficient for trading venues to provide market
participants with a graphic representation of where they stand in the order
queue.
Many trading venues currently provide veritably all changes in the order book
(new orders and cancelled orders) via the User Datagram Protocol (UDP). Any
reasonably sophisticated trader can recreate the order book with precise detail
by monitoring which trades were recently entered, their lot sizes, and their time
stamps. By also capturing those orders that were cancelled, their lot sizes, and
their time stamps, one can not only recreate a granular order book, but also
determine the priority of their own orders and the priority and size of the
orders of other market participants. Some equity venues provide some market
participants with such granular information.45 At least two major futures
exchange tout their provision of such granular order book information as being
fully transparent. We contend that this practice is fundamentally wrong.
One might attempt to argue that this recommendation goes against the
following principle: Transparency in organized financial markets is beneficial
and consistent with good public policy. We disagree with this conclusion about
our recommendation. Dissemination of granular data from the order book
allows algorithmic traders to gain inappropriate insights into the trading
patterns of both algorithmic traders and click traders. That said, we have no
qualms about HFT firms using aggregated data from the order book: High
frequency traders should continue to have the unfettered ability to attempt to
reverse engineer aggregated data and reach any conclusions that they may care
to reach.
We note that there is an abundance of research that demonstrates a trade-off
between market liquidity and transparency.

This is based on the premise that traders join resting prices, not resting times. It may be
helpful to approach the issue from the perspective of a click trader, rather than from the
perspective of an algo trader.
45
See Sal Arnuk and Joseph Saluzzi, 2012, Broken Markets: How High Frequency Trading and
Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio,
Upper Saddle River, NJ: FT Press, pp. 102–103, and Charles Duhigg, 2009, “Stock traders find
speed
pays,
in
milliseconds,”
New York Times, July 23, available at
www.nytimes.com/2009/07/24/business/24trading.html.
44

25

For instance, Lee (1998, p. 98–99) states:
The choice by an exchange of what price and quote information to release is a central element
of the wider decision as to what market architecture to adopt. Not only are there substantial
differences between the types of data about prices and quotes that trading systems choose to
release, there are also differences in the types of information that trading systems are able to
deliver. … In no trading system are all these categories of price and quote information
published. Indeed, the strategic non-disclosure of some types of price and quote information is
a central element of all market architectures. For some of the information categories, the
reason is a matter of confidentiality. In most markets, for example, investors are unwilling to
countenance releasing information about what their trading policies have been or will be. The
identities of market participants submitting quotes and participating in trades are therefore
normally not publicly released. Sometimes, however, identities are concealed for commercial
reasons. For example, although the identities of traders on Instinet were initially released,
Instinet later decided against allowing this.46

And Pirrong (2010) notes:
It is well known that transparency has costs as well as benefits. … Moreover, transparency isn’t
the only thing that matters to market participants. Other aspects of execution affect their costs
and benefits as well. A myopic focus on transparency alone ignores these other relevant
dimensions.47

Additionally, Madhavan, Porter, and Weaver (2005, pp. 286) state:
Our findings are consistent with theoretical models in which traders adjust their trading
strategies based on the level of transparency. Too much transparency increases the
‘‘free option’’ cost of limit-order providers, resulting in order withdrawal and a
reduction in market depth. Thinner limit order books imply larger transitory price
movements associated with order flows, increasing volatility and execution costs.48

Ruben Lee, 1998, What Is an exchange? The Automation, Management, and Regulation of
Financial Markets, Oxford, UK: Oxford University Press.
46

Craig Pirrong, 2010, “What is a swap execution facility?,” Streetwise Professor, July 1,
available at http://streetwiseprofessor.com/?p=3964.
47

Ananth Madhavan, David Porter, and Daniel Weaver, 2005, “Should securities markets be
transparent?,” Journal of Financial Markets, Vol. 8, No. 3, August, pp. 265–288, available at
www.sciencedirect.com/science/article/pii/S1386418105000145.
48

26

It should become obvious that displaying the both the order sizes and the time
stamps of all other orders in the order book can only have a detrimental impact
on market liquidity. The granularity of order book information currently being
provided has now exceeded all bounds of propriety, confidentiality, and
common sense.

7. Improper Premature Cancellation of Orders
Unless the relevant trading venue is experiencing technical problems, market
participants should always be prohibited from cancelling orders before they
have received notification from the trading venue that the original order was
properly received by the order book. It is particularly difficult to envision a
legitimate trading strategy where one would need to cancel an order before
receipt of the order was even acknowledged by the trading venue. Engaging in
such a practice, however, would almost certainly enhance the effectiveness of
both layering and quote stuffing—behaviors that are intentionally deceptive.
Detecting this practice would arguably be made easier by having unique
identifiers for each algorithm that can generate orders (recommendation 8).
Some might attempt to make the case that one should be able to cancel an
order that was determined to have been sent in error. We agree. We also know
of no algorithm that can 1) detect that an order was sent in error, 2) generate a
cancellation message, and 3) release it faster than the time it takes for the
trade match engine to acknowledge receipt of the original message.
Others argue that Regulation NMS (National Market System) or the fragmented
U.S. equity markets somehow require market participants to send identical
bids to multiple equity trading venues. Then, the argument goes, that if the
order gets filled on trading venue #8, the algorithm might have to cancel the
identical bids that were sent to all other trading venues and that, on occasion,
that might entail sending a cancellation message to the other trading venues
before receiving the acknowledgement messages back from those venues that
the original orders were received. This argument requires one to believe that
trading venue #8 can 1) receive an order, 2) send an acknowledgement message
back that the order was received, 3) match that order against one or more
resting orders, 4) send matched trade drop copy messages to all of the affected
parties, and 5) send the information on the matched trade to its ticker plant
faster than the other trading venues can acknowledge receipt of their respective
original orders. This argument contradicts all common sense.
27

8. Unique Identifiers for Trading Systems that Generate Orders
Automatically
Every automated trading system that is capable of generating, modifying, or
cancelling orders without human intervention should have a unique identifier
that distinguishes it to the trading venues where it has the capability to send
orders. The process of designing, standardizing, and/or implementing a
framework for assigning such identifiers should involve the proprietary trading
companies and the relevant trading venues. We see no additional benefit
associated with the involvement of the relevant supervisory authorities, which,
nonetheless, could benefit from the establishment of such a framework.
Currently trading venues establish session IDs. Session IDs are like pipes
through which orders flow into the order book. Dozens of different algorithms
can be sending orders through the same session ID. This practice makes it
difficult, if not impossible, for supervisory authorities to detect intentional or
unintentional misbehavior of individual algorithms. This recommendation
would provide trading venues with the ability to identify the firm associated
with each session ID and within each session ID, each algorithm that is
currently operating. If trading venues had such granular information, they
could also potentially alert the trading firms of instances where their individual
algorithms might be behaving out of pattern. This capability could be quite
helpful to all concerned. It is truly astonishing with all of the human intellect
and sophisticated technology that this industry has marshalled, that individual
algorithms to this day are not individually identified at most trading venues.

9. Private Access to Trade Information before it is Generally Available to the
Public at Large
Trading venues typically provide optional services that allow market
participants and trade intermediaries to colocate their respective servers in the
data centers of the trading venues. We take no issue with this practice
provided that these colocation services are openly available and uniformly
priced. Nor do we take issue with the ability of market participants and trade
intermediaries to have a latency advantage when entering their orders, because
of their colocation in the trading venue’s data center. Decreasing the physical
distance between one’s servers and the trading venue’s trade match engine
reduces latency to the minimum dictated by the laws of physics.

28

The matter at issue is whether trading venues are providing trade information
to firms that colocate in their data centers before such information is generally
made available to the public at large and, if so, whether such a practice is
appropriate from a public policy perspective. For risk-management purposes,
after a trade is executed, the buyer(s) and sellers(s) that are direct parties to
that trade should be so advised as promptly as twenty-first-century technology
can.49 All other market participants, including those that colocate (but are not
direct parties to the trade) should be advised that this trade occurred at the
same time as the public at large, regardless of whether they subscribe to the
colocation services of the trading venue or not.
When is information on submitted orders and/or information on executed
orders generally available to the public at large? Are firms that subscribe to
colocation services currently gaining access to such information before it is
generally available to the public at large? Public policy issues of fairness would
seem to be mollified if firms that colocate were to receive trade information at
the same time that such information were made available to the public at large.
This would be accomplished if trading venues provided trade information from
their respective ticker plants, rather than from their trade match engines, to
firms that colocate in their data centers. Doing so would not only serve the
public interest, but also likely encourage the operators of industry utility ticker
plants to upgrade the technology of their ticker plants to current industry
standards.

Implications
Recommendation 5 (random trade match within half second time intervals)
may have the greatest potential to disincentivize all three questionable
behaviors—namely, spoofing, layering, and quote stuffing. If you don’t know
when the next trade match is going to occur, the downside risk of pretending to
be a seller when you are really a buyer could leave a trader with a position that
is exactly the opposite of his desired position. This could even more so act
against the interests of a trader that engages in a combination of spoofing and
layering, creating the illusion that there is size building on the bid side of the
market, when the trader is really a seller. The trader could get stuck with a
substantial position completely the opposite of what he actually wants.

Ideally, this notification would be received via the User Datagram Protocol, which should only
be sent to the actual parties to the trade. Please refer to recommendation 6.
49

29

One important potential implication of recommendations 5 and 7 is the
possible elimination of quote stuffing as a strategy to slow down other
algorithmic traders. As no one will know when the trade match and trade
allocation algorithms will actually run, one would either have to abandon this
strategy (simply considering it as being no longer effective) or attempt to clog
the outbound quotation system continuously. Trading venues are reasonably
adept at identifying and penalizing traders that have an exceedingly high ratio
of quotes to trades (as continuous quote stuffing would undoubtedly require).
Recommendation 8 would help do exactly that.
Perhaps most importantly, there is some possibility that recommendation 5
could dampen or even end the incessant low latency arms race. If the trade
match engine only runs once every half second, and (assuming some trading
venues might adopt recommendations 1 and 2) the allocation of orders would
increasingly become a function of time in the order book, the so-called real
money resting orders would receive increasing allocations and very short-term
traders would receive decreasing allocations. As very short-term traders get
allocated fewer and fewer lots, their respective quote-to-trade ratios would
logically increase, which almost always carries penalties assessed by the
relevant trading venues.
If trading venues only matched trades and
disseminated price and quantity information once every half second, there
would arguably be considerably less financial incentive for all concerned to
invest increasingly large sums in an effort to shave one or two milliseconds off
a process that only occurs once every 500 milliseconds.
Recommendations 3, 4, and 6 would likely only indirectly disincentivize
spoofing, layering, and quote stuffing. But those three trading strategies are
not the only behaviors that should arguably be discouraged. The questionable
behaviors addressed by recommendations 3 and 4 are obvious: using dark
orders and gaming rounding conventions.
Recommendation 6 (providing only aggregated pretrade information from the
order book) has more complex implications. Recent research papers by Weller
(2012)50 and by Baron, Brogaard, and Kirilenko (2012)51 indicate that the
fastest high frequency traders 1) are the most profitable and 2) tend not to have
Brian Weller, 2012, “Liquidity and high frequency trading,” University of Chicago Booth School
of Business and University of Chicago, Department of Economics, working paper, November 10,
pp. 42–43.
51
Matthew Baron, Jonathan Brogaard, and Andrei Kirilenko, 2012, “The trading profits of high
frequency traders,” Princeton University, University of Washington, and Commodity Futures
Trading Commission, working paper, November, pp. 20–21.
50

30

their trades match opposite other fast high frequency traders. So, at present,
some high frequency traders seem to be taking advantage of their advance
access to granular pretrade information from the order book to make large
profits and to avoid each other as trading partners. While this phenomenon
has been detected in futures contracts, where trades are completely
anonymous, it is undoubtedly occurring in the equity markets, because some
equity trading venues currently provide more specific trade identifiers—more
than only the aggregate quantity bid or offered at each price point—in pretrade
information made available to certain market participants.52
As this
information changes millisecond by millisecond, it cannot be of much value to
human click traders; it can only be of value to high frequency traders. By
obtaining this granular pretrade information, high frequency traders can 1)
more efficiently reverse engineer the trading algorithms of their competitors
and 2) more effectively discriminate among the counterparties whose resting
orders are in the order book. It is difficult to see how either of these activities
serves the public interest.
We continue to be of the opinion that trading decisions should be based on
economic fundamentals. We appreciate that the decision to buy on one
exchange and sell on another may in large part be based on the probability of
being allocated lots on the first exchange—and that that will likely be a
function of an order’s priority in the order book. However, by making
recommendation 6, we are challenging the entire premise that our modern
electronic financial markets need to be necessarily synchronized to the
millisecond. Financial markets have functioned reasonably well in the past at
human-scale time horizons.
Recommendation 9 may be the most important of all. If, after a public debate
of the issue, it is the consensus that all market participants should get access
to trade information at the same time, then automated trading firms that
colocate should no longer receive (and make trading decisions based on) trade
information before it is generally available to the public at large. In other
words, should this consensus arise, trading venues would need to cease
providing colocating firms with trade information from their trade match
engines and commence providing these firms with trade information from their
ticker plants. A welcomed byproduct of this would be the likely deployment of
better technology on trading venues’ ticker plants.

52

Arnuk and Saluzzi (2012, pp. 102–103).
31

Regulatory Initiatives
Germany
On May 15, 2013, the German High Frequency Trading Act
(Hochfrequenzhandelsgesetz) became effective.
It places a number of
requirements on exchanges and proprietary trading companies that operate in
Germany.
HFT market participants53 that trade for their own account need to have or
obtain
a
license
to
do
so
from
BaFin
(Bundesanstalt
für
Finanzdienstleistungsaufsicht), the German financial supervisory authority.
Firms that are located within the European Economic Area (EEA) can passport
their MiFID54 license to BaFin via their national authority. Non-German firms
that are not located within the EEA must create a subsidiary or branch office
in Germany in order to obtain a suitable license from BaFIN in order to trade
for their own account.
Market participants are subject to maximum order-to-trade ratios that are
calculated monthly by product. Violations can result in suspension from
trading and/or fines not to exceed €250,000.
Exchanges are required to levy fees on market participants for excessive use of
exchange systems, according to the German High Frequency Trading Act.
Importantly, HFT market participants are required to have each of their
algorithms labeled with a unique identifier to enable the Trading Surveillance
Office to identify manipulative or erroneous algorithms.
Exchanges are subject to a broad “orderly” requirement that would necessitate
kill switches, volatility interruptions, and many other protections that are
largely already in place.
Exchanges have the affirmative obligation to determine appropriate tick sizes to
avoid negative implication to market integrity and market liquidity, according
to the German law.

53

BaFIN has established four criteria to clarify HFT proprietary trading: proprietary trading, a
latency minimizing infrastructure, no human intervention, and high intraday message rates.
54
MiFID is the European Commission’s Markets in Financial Instruments Directive.
32

United States
On September 9, 2013, the U.S. Commodity Futures Trading Commission
published its “Concept release on risk controls and system safeguards for
automated trading environments.”55 This 137-page document covers a very
broad range of suggestions for risk controls and asks whether rulemaking
would be appropriate action for the commission to take. Dozens of public
comment letters (some over 80 pages in length) are being reviewed by staff. It
is not currently clear what ultimate regulatory actions might be forthcoming.
Canada
The Canadian Securities Administrators recently expressed their concern that
the payment of trading rebates may be incentivizing behavior that may not
serve the public interest. The director of market regulation at the Ontario
Securities Commission indicated that “a pilot study of how portions of the
market perform without maker-taker incentives will be conducted.”56

Conclusion
Using term limit orders and running the trade match algorithm at random
times within half second intervals would seem to provide an equitable balance
between human institutional traders and automated liquidity providers and
could drastically reduce the current tsunami of data disseminated by trading
venues. Allocating trades based on the actual time that orders have been
exposed (or committed to be exposed) to market risk would appear to be a more
equitable approach than some trade allocation algorithms currently in use.
Implementing both term limit orders and the new trade allocation formula
could return some equitability that some argue may have been lost.
Recommendations for establishing appropriate rounding conventions,
preventing orders from being cancelled before they are even confirmed,
appropriately treating invisible orders, tagging algorithms, and significantly
reducing or eliminating the granularity of available pretrade information visible

55

The release is available at
www.cftc.gov/ucm/groups/public/@newsroom/documents/file/federalregister090913.pdf.
56
Barbara Schechter, 2014, “Canadian regulators look at scrapping controversial fee model
linked to high-frequency trading,” Financial Post, May 15, available at
http://business.financialpost.com/2014/05/15/canadian-regulators-look-at-scrappingcontroversial-fee-model-linked-to-high-frequency-trading/.
33

in the order book are all approaches not inconsistent with sound and
defensible public policy with respect to HFT.
Relevant authorities should assess and, if deemed appropriate, solicit public
comment on when trade information should be deemed to be generally
available to the public at large. National authorities and purveyors of modern
electronic trading venues should consider these recommendations and the
informed comments of interested market participants.

34


Federal Reserve Bank of St. Louis, One Federal Reserve Bank Plaza, St. Louis, MO 63102