This draft: December 13, 2013
Can Brokers Have it all? On the Relation between Make Take Fees
& Limit Order Execution Quality*
Robert Battalio Mendoza College of Business
University of Notre Dame [email protected] (574) 631-9428
Shane Corwin
Mendoza College of Business University of Notre Dame
[email protected] (574) 631-6026
Robert Jennings Kelley School of Business
Indiana University [email protected]
(812) 855-2696
Extended Abstract: Today, every U.S. equity exchange utilizes a rebate-fee schedule (see Cardella, et al. (2013)), typically charging traders taking liquidity (e.g., marketable orders) and paying traders making liquidity (e.g., nonmarketable limit orders). Using publicly available data, we document that several large retail brokerages route their order flow in a manner that appears to maximize order flow payments: they sell marketable orders and they route limit orders to venues that offer the highest liquidity rebates. As rebates are funded by take fees, the venues with the highest liquidity rebates also have the highest take fees. If limit orders displayed on venues with low take fees trade before a similarly priced limit orders displayed on venues with high take fees, routing orders to maximize rebates might be inconsistent with a broker’s responsibility to obtain best execution. For a set of proprietary limit orders we obtain from a major broker, we document a negative relation between several measures of limit order execution quality and the relative level of liquidity fees. Among other things, we show that when ‘identical’ limit orders are routed to two venues at the same time, orders routed to the venue with the lower fee execute more frequently and earn higher realized spreads. We conclude our analysis by using the NYSE’s Trade and Quote data to show the negative relation between take fees and limit order execution quality extends beyond the limitations of our proprietary dataset. Thus, brokers cannot have it all. Routing limit orders in a manner that maximizes make rebates reduces fill rates and produces less profitable limit order executions.
*The authors gratefully acknowledge research support from the Q-Group. We thank Jeff Bacidore, Peter Bottini, Colin Clark, Joe Gawronski, Larry Harris, Steve Poser, Paul Schultz, Jamie Selway, Jeff Smith, John Standerfer, brownbag participants at Indiana University and at the University of Notre Dame, and seminar participants at Goldman Sachs, the University of Arizona and the SEC for their comments.
Today, every U.S. stock exchange levies fees or pays rebates based on order characteristics (see
Cardella, et al. (2013)). In the ‘traditional’ model, exchanges charge liquidity demanding orders (e.g.,
marketable orders) a fee that exceeds the rebate they offer liquidity supplying orders (e.g., nonmarketable
limit orders). More recently, a few ‘inverted’ exchanges began charging liquidity suppliers a fee that
exceeds the rebate they pay to liquidity demanders.1 Although these differential fee schedules give traders
increased flexibility, they are controversial. As noted by the Investment Company Institute in their April
2010 letter to the Securities and Exchange Commission (SEC), “brokers may refrain from posting limit
orders on a particular exchange because it offers lower liquidity rebates than other markets, even though
that exchange offers the best possibility of an execution for those limit orders.”2
Although the SEC’s Order Protection Rule establishes price priority in U.S. equity markets, the
rule does not specify who trades first when multiple venues have the best posted price. Angel et al. (2010)
note that traditional and inverted make-take fee schedules allow market participants to adjust the priority
of their orders at a given or stated limit price. All else equal, if two venues offer the national best bid, one
expects liquidity-demanding sellers arriving in the marketplace to route their orders to the venue with the
lower take fee. If sufficient selling demand arrives, sellers walk down the limit order books and the limit
order traders who purchased the stock at the bid price suffer a short-term loss. However, if the stock price
rises before liquidity is exhausted at the national best bid, limit orders on the venue with the higher take
fee (and thus, the higher make rebate) become isolated and miss out on profitable trading opportunities.
Thus, limit orders routed to venues with high take fees suffer greater adverse selection costs – they are
more likely to trade when the price moves against them and are less likely to trade when prices move in
their favor. This suggests that brokers routing limit orders to venues with the highest take fees (and make
rebates) might not be obtaining best execution for their clients.
Why might brokers’ and clients’ interests diverge? Angel et al. (2010) note that if clients
paid/received fees/rebates, brokers generally would send limit orders to the venue that maximizes the
1
The difference between the fee and the rebate is an important source of revenue for exchanges. Given the vigorous competition between U.S. exchanges, there is a high correlation between the level of an exchange’s fee and rebate.
likelihood of execution as brokers receive commissions only when orders execute. The typical situation,
however, is for a brokerage firm to offer a fixed commission that is set assuming it earns (pays) exchange
rebates (fees). All else equal, in a competitive market, brokers that pay the least to exchanges offer the
lowest commission. If investors choose brokers based primarily on commissions (perhaps because they
lack the sophistication and/or the necessary information to evaluate limit order execution quality), it may
be profit maximizing for brokers to maximize liquidity rebates rather than the profitability and probability
of limit order executions.3
Take fees are not the only factor that influences the frequency with which limit orders execute. In
stocks with sufficient intraday volatility, the limit order routing decision might have little or no impact on
fill rates as limit orders are likely to become marketable at some point in the trading day.4 In this
situation, there is little cost associated with routing limit orders to venues with high take fees and high
liquidity rebates. Moreover, as suggested by UBS’s best execution statement,5 brokers can find it optimal
to route marketable orders to venues with high take fees if those venues offer sufficient price and/or depth
improvement.6 It might also be the case that venues with high take fees attract marketable orders by
offering competitively priced co-location, which reduce execution price risk by making it easier for
brokers to trade at the quotes they see, or other services to brokers.
Thus, the relation between exchange fee schedules and limit order execution quality is an
empirical issue. Are marketable orders allocated to venues quoting the best price on the basis of fees (e.g.,
net price priority)? If so, then limit orders routed to exchanges with high take fees might suffer increased
adverse selection costs. Alternatively, do liquidity supply queues adjust in a way such that limit order
3 Indeed, the former head of order routing for the retail brokerage Ameritrade stated that by routing their market
orders to wholesalers and their nonmarketable limit orders to exchanges, Ameritrade could improve its revenue. See “Wall Street’s ‘Most Sophisticated Order Sender’” in the September 2010 issue of Trader’s Magazine.
4 Fill rates are one measure of execution quality for non-marketable limit orders. For example, in its August 2004
comment letter to the SEC, Morgan Stanley notes that “best execution obligations require consideration of the limit order fill rates of each OEF (order executing facility) in determining to which market it should send that limit order.”
5 See UBS’s Best Execution Statement at http://www.ubs.com/content/dam/static/wmamericas/bestexecution.pdf. 6 Price improvement occurs when a marketable order trades at a price that is better than the posted quote. Depth
improvement occurs when a marketable order trades more shares than are advertised in the posted quote. Price and depth improvement occur when a marketable order trades against non-displayed liquidity, which is typically provided by non-displayed limit orders on exchanges.
execution quality is unrelated to the venues’ rebate-fee structures? In this paper, we investigate these
issues using a proprietary dataset to examine limit order execution quality across a limited number of
venues with different fee structures and with NYSE TAQ data to evaluate the location and the
profitability of (apparent) limit order executions when multiple venues post the best bid or ask price.
If all brokers employ smart routers that accurately assess the likelihood of limit order execution,
then the policy implications of our study might be minimal. If, however, some brokers follow order
routing strategies designed to maximize maker rebates, our work may raise best execution questions.
Thus, we begin by investigating data that describe where eleven national brokerages route orders in the
fourth quarter of 2012. We interpret the routing decisions of four brokers, Ameritrade, E*Trade, Fidelity
and Scott Trade, as being consistent with the objective of harvesting liquidity rebates. Three of these
brokers sell market orders to market makers and route limit orders either to market makers or to EDGX,
an exchange offering the largest per share liquidity rebate during our sample period.
In an effort to understand whether routing nonmarketable limit orders to venues with high fees
results in diminished execution quality metrics, as hypothesized by Angel et al. (2010), we examine the
relation between take fees and limit order execution quality. We begin by analyzing proprietary limit
order data obtained from a major investment bank that uses a sophisticated algorithm to route orders.
Univariate limit order execution quality statistics are suggestive of a negative relationship between take
fees and limit order execution quality. However, it is difficult to use univariate statistics to make
assessments of relative limit order execution quality across exchanges, as orders may be routed to
different venues under different types of market conditions.
We address the endogeneity of the order routing decision by analyzing the association between
take fees and the probability that a limit order executes and the relation between take fees and execution
speed for executed limit orders in a multivariate setting. All else equal, we find that fill rates for displayed
limit orders are lower on exchanges with higher fees. In addition, limit orders executed on venues with
high fees take longer to execute than those executed on venues with low fees. These results confirm the
As the decision to route limit orders to different venues is likely not random, our findings may be
affected by a selection bias (see Peterson and Sirri (2002)). We utilize a unique feature of our data to
address this issue. Frequently, identically priced limit orders to trade shares of the same stock are routed
simultaneously to multiple venues. For these ‘identical’ orders, differences in fill rates, execution speeds,
and adverse selection costs can be linked directly to exchange characteristics such as the make-take fee
schedule, hidden liquidity, latency and queue length. Perhaps most important, an analysis of these orders
allows us to hold market conditions constant. Where there are sufficient order-pair observations, we
conduct ‘horseraces’ between different exchanges to assess whether the limit order routed to the venue
with the lower take fee provides the faster execution and lower adverse selection costs. In nearly every
comparison we make, the venue with the lower take fee wins a higher percentage of horseraces and has
less adverse selection. Interestingly, all of the winning venues and even most of the losing venues have
positive realized spreads, which suggests our data provider manages the adverse selection risk associated
with placing non-marketable limit orders.
For brokers that do not pass fees/rebates directly through to their customers, the results of our
analysis suggest that the decision to route limit orders to a single exchange that offers the highest liquidity
rebates is inconsistent with maximizing limit order execution quality. One may, however, question the
generalizability of our results as our order data are from a single broker, constitute only 1.5% of average
daily trading volume, and cover only about one-half of the U.S. equity trading venues. For this reason, we
next use the NYSE’s TAQ database to make inferences regarding the across-venue execution quality of
at-the-quote limit orders. If we assume that the quotes displayed on each of the traditional and inverted
venues are set by limit orders, we can examine whether take fees influence where limit orders tend to
execute when multiple venues are at the inside quote and the adverse selection associated with these
executions. Using average realized spreads as a proxy for adverse selection costs, we find that adverse
selection costs are generally increasing in make rebates/take fees. For at-the-quote limit order executions,
the inverted venues tend to have the least adverse selection and the high rebate/take fee venues tend to
better than expected given their fee structures. This suggests that fees are not the only determinant of
cross-venue differences in limit order execution quality.
Using a multivariate framework, we find a negative relation between the average realized spread
and take fees for at-the-quote trades in Nasdaq-listed securities. This negative relation also holds for
NYSE-listed securities, with the exception of limit orders executed on the NYSE. Surprisingly, we find
the average realized spread generated by at-the-quote trades on the NYSE, which has the smallest positive
take fee, is statistically indistinguishable from the average realized spread for limit orders executed on the
venue with the smallest overall take fee. Harris (2013) suggests the negative association between limit
order execution quality and take fees will be strongest in low priced stocks and in stocks with large depths
at the inside quotes. For both NYSE- and Nasdaq-listed securities, we find the negative relation between
fees and realized spreads is more pronounced in low priced stocks and for limit orders executed when
there are at least 20,000 shares offered at the relevant quote. The strong negative relationship between
take fees and the realized spreads earned by executed limit orders suggests the decision to route
nonmarketable limit orders to a single exchange paying the highest rebate is not consistent with a broker’s
responsibility to obtain best execution for customer orders.
Although it is not a common practice, some brokers pass fees/rebates directly through to their
customers. For these investors, realized spreads adjusted for fees are the appropriate executed limit order
quality metric. For NYSE-listed securities, the inverted venues, the NYSE, and EDGX offer positive
average realized spreads net of liquidity rebates/fees for quote limit order executions. For
at-the-quote limit order executions in Nasdaq stocks, only the inverted venues have average net realized spreads
that are positive. These findings suggest that brokers seeking to make limit order routing decisions to
optimize execution quality may make different routing decisions depending upon whether or not they pass
fees/rebates on to their customers.
The remainder of this paper is as follows. In Section II, we provide a brief literature review. In
Section III, we use publicly available data to investigate broker routing decisions. In Section IV, we use
quality. In Section V, we use the NYSE’s TAQ database to investigate the relationship between take fees
and limit order execution quality when multiple venues are at the inside quote. Section VI concludes.
II. Related Literature
When an asset’s trading volume is concentrated on one venue, the (limit) order routing decision is
trivial. In the United States, the order routing decision became more substantive after the Consolidated
Tape was introduced in 1982. By allowing trading venues to benchmark their trades against each other’s
quotes, competition for order flow became more intense. According to the SEC, when making its order
routing decisions “a broker-dealer must consider several factors affecting the quality of execution,
including, for example, the opportunity for price improvement, the likelihood of execution (which is
particularly important for customer limit orders), the speed of execution, and the trading characteristics of
the security, together with other non-price factors such as reliability and service.”7
The SEC’s 1997 “Report on the Practice of Preferencing,” contains one of the first cross-venue
analyses of retail limit order execution quality. In their study, the SEC documents fill rates,
time-to-execution statistics, and adverse selection costs for limit orders that execute on various U.S. exchanges.
Their analysis does not, however, control for the endogeneity associated with the order routing decision.
In order to ensure that routing retail limit orders away from the NYSE did not degrade execution
quality, regional exchanges implemented rules that benchmarked their limit order executions to NYSE
limit order executions. Using a methodology that allows them to control for market conditions and order
submission strategies, Battalio et al. (2002) find that these rules were effective at producing limit order
execution quality that was competitive with the NYSE’s.
Given the fear that at least some brokers were maximizing order flow payments rather than
execution quality, the SEC passed Rule 11ac1-5 (now Rule 605) and Rule 11ac1-6 (now Rule 606) in
2001. Together, these rules are intended to bring sufficient transparency to the market so that investors
can determine whether their brokers are making optimal order routing decisions. Rule 605 requires
exchanges to produce execution quality statistics on a monthly basis. Rule 606 requires brokers to reveal
on a quarterly basis the destinations to which they route orders and whether they receive compensation for
their routing choices. Boehmer, Jennings and Wei (2007) find that the routing of marketable order flow
became more sensitive to cross-venue changes in execution quality after Rule 605 execution quality
statistics were available. However, the Rule 605 limit order execution quality statistics are similar to those
used in the SEC’s 1997 study in that they do not control for potential endogeneity issues. We apply
alternative metrics to publicly available trade and quote data to gain insights as to the differences in limit
order execution quality across exchanges.
Foucault and Menkveld (2008) examine how market fragmentation and fee differentials affect
limit order execution quality when price priority is not enforced across markets. When the two markets
they examine are both at the inside quote, they find that smart routers predominately send orders to the
market with the lowest fee. They also find evidence that violations of price priority across the two
markets adversely affect liquidity provision. Consistent with the idea that marketable order flow is
sensitive to take fees, Cardella, Hao and Kalcheva (2013) find that reductions in relative take fees in U.S.
equity markets are associated with increased market share.
Colliard and Foucault (2013) and Foucault, Kadan and Kandel (2013) construct theoretical
models to investigate how make-take fees affect liquidity supply. Colliard and Foucault consider how an
exchange competing with a dealer market should optimally set its make-take fee schedule. They conclude
that the net fee (fee minus rebate) has an ambiguous effect on the execution probability for limit orders
and the speed with which the limit order queue moves. Foucault, Kadan, and Kandel (FKK) examine
whether it is the net fee, or the relative levels of the make and take fees that matter. They argue that
exchanges can maximize their trading volume by differentiating their make and take fees. If there is not
enough liquidity demand (supply), the venue can decrease (increase) its take fee and its make rebate. We
contribute to this literature by providing the first empirical evidence as to the relationship between take
Perhaps due to the availability of superior data, the relation between limit order execution quality
and make-take fees has received more attention in the practitioner arena than in academic research.8
Sofianos et al. (2010) use nonmarketable limit orders placed on six exchanges by the Goldman Sachs
smart router, SIGMA, to examine the relation between take fees and limit order execution quality. Using
only those limit orders split between two exchanges, Sofianos et al. make pairwise comparisons of
execution quality across exchanges. As all non-venue specific factors are the same, they note that
differences in execution quality are either due to adverse selection risk or differences in fill rates. They
find the venue utilizing an inverted make-take schedule has lower adverse selection costs, faster fills, and
higher fill rates than the other five exchanges. Ignoring fees, these results suggest brokers routing
nonmarketable limit orders to venues with high take fees disadvantage their clients. In addition to
replicating this analysis, we use both proprietary and publicly available data to examine these issues in
more general settings. We also examine whether the conclusions of Sofianos et al. change when
fees/rebates are passed through to limit order traders.
Using a proprietary dataset of orders generated from one of Goldman’s execution algorithms,
Bacidore, Otero and Vasa (2011) attempt to quantify the benefits of smart routing by evaluating five order
routing strategies. When routing limit orders, one strategy follows the ‘naïve’ approach of maximizing
rebates and two strategies attempt to maximize fill rates. When routing marketable order flow, one
algorithm minimizes take fees while two consider both take fees and hidden liquidity. They present
evidence that the naïve strategy delivers inferior limit order execution quality, even after accounting for
maker rebates. For marketable orders, they find smaller differences in execution quality across
algorithms, which they argue is “intuitive given the Reg. NMS protections on market orders.” This said,
in large capitalization stocks they find some benefit to routing on the basis of both fees and the prospect
of hidden liquidity. We complement this analysis by identifying retail brokerages whose order routing
8 One exception is Yim and Brzezinski (2012), who construct an empirical model that uses publicly available trade
and quote data to estimate average limit and market order arrival rates, limit order cancelation rates, and limit order queue lengths for three stocks in April of 2011. For these stocks, the authors find the inverted venues generally have shorter times-to-execution.
decisions appear consistent with the objective of maximizing rebates and then examining whether this
type of order routing disadvantages limit order traders.
III. Broker routing decisions.
Market makers profit by selectively purchasing and executing market orders and marketable limit
orders. One constraint on a market maker’s ability to interact with purchased order flow is FINRA Rule
5320, which states that a market maker holding a nonmarketable limit order “is prohibited from trading
that security on the same side of the market for its own account at a price that would satisfy the customer
order.”9 Thus, brokers can increase the revenue generated from their order flow by routing marketable
orders to purchasers and non-marketable limit orders to venues offering high make rebates. Indeed, if all
brokers routed orders in this fashion, order flow purchasers could interact with 100% of their marketable
order flow.
Whether or not the decision to route non-marketable limit orders to exchanges with high rebates
influences limit order execution quality, however, is an empirical question. If aspects of limit order
execution quality are inversely related to the level of maker rebates, a broker routing most of its
non-marketable limit orders to an exchange with the highest maker rebate might not fulfill its obligation to
obtain best execution for customer orders. In this section, we use Rule 606 data to present evidence that
some U.S. retail brokerages make order routing decisions that appear designed to capture rebates. How
these routing decisions affect limit order execution quality is addressed in subsequent sections of the
paper.
We begin by identifying brokers appearing in either Barron’s or Smart Money’s 2012 Broker
surveys. We next use broker websites to collect Rule 606 reports for the fourth quarter of 2012.10 Rule
606 requires that brokers reveal the fraction of their orders that were non-directed (e.g., the customer did
not choose the routing destination) and to report the percentage of non-directed orders that were market
9
See FINRA’s May 2011 Regulatory Notice 11-24.
orders, limit orders, and other orders. A broker must also report the percentage of market, limit, and other
orders routed to any venue receiving at least 5% of the broker’s non-directed orders.
Overall, nine of our brokers route at least a portion of their orders to market makers that pay for
the marketable orders they interact with. For brokers that do not want to delegate the handling of
customer limit orders in NYSE-listed securities to market makers (and potentially reduce payments for
marketable orders) there are several options. There were twelve ‘traditional’ and four ‘inverted’ trading
venues in 4Q2012.11 The three venues charging the SEC’s maximum permissible take fee of $0.30 per
hundred shares were DirectEdge X (EDGX), Nasdaq Stock Market (NDAQ), and the NYSE-Arca
Exchange (ARCA). Of these venues, EDGX offered the highest liquidity rebates ($0.0023 per share).
Conversely, the Nasdaq OMX BX (BSX) funded payments of $0.14 per hundred shares to liquidity
demanders by charging liquidity supplying limit orders a make fee of $0.18 per hundred shares
[Insert Table I about here.]
In Table I, we document the venues to which ten top retail brokerages route non-directed orders
in NYSE-listed securities during the 4Q2012.12 We also report the range of liquidity rebates/fees on each
of these venues. As our broker sample size is small, we conduct no statistical tests. Our goal in this
section is simply to examine the order routing decisions of popular retail brokers.
Of our sample brokers, Charles Schwab, Morgan Stanley, Edward Jones, Just2Trade, and
LowTrade route 100% of their non-directed orders to market makers that purchase order flow. By routing
all of their orders to market makers, a broker ensures that its limit orders can trade against the purchaser’s
marketable orders. Whether or not this is preferable to routing limit orders in a different fashion is an
empirical question that we cannot address with our data.
Four sample brokerages, Ameritrade, E*Trade, Fidelity, and Scott Trade, route orders in a way
that suggests they may be focused on liquidity rebates. Three of these brokers sell the vast majority of
market orders and route limit orders either to market makers or to a venue offering the most lucrative
11
Fees are obtained from Traders Magazine and from www.sec.gov.
12
For brevity, we do not include order routing decisions for Nasdaq-listed securities. These results, which are quite similar to those presented in Table I, can be obtained from the authors upon request.
liquidity rebates, EDGX. In addition to these venues, Scott Trade also routed some of its limit orders to
the Lava ATS, a venue with high make rebates. Our Rule 606 data do not distinguish between marketable
and non-marketable limit orders. Thus, we cannot determine whether the bulk of the limit orders routed to
market makers (EDGX and Lava) are marketable (non-marketable). However, given these brokers choose
to sell their market orders rather than route them to venues with take fees, it is likely that the majority of
limit orders routed to EDGX and Lava are non-marketable.
The evidence presented in Table I suggests that there is heterogeneity in order routing decisions.
The routing of five of brokerages suggests that they delegate the handing of their limit orders to market
makers. Interactive Brokers’ routing suggests that it found the NYSE, the venue with the lowest
non-negative make rebate, to be an attractive venue for its limit orders. However, for four of the brokerages
we examine, it appears that fees are a significant determinant in where orders are routed.
IV. Make-take fees and limit order execution quality: Proprietary order data.
A. Data.
To examine limit order execution quality, we obtain order data from a major broker-dealer’s
smart order routing system for October and November 2012. The data include 28,627,467 orders from the
broker-dealer’s algorithmic trading system and orders entered directly by customers.13
Each record contains information about the order, the destination execution venue, a
time-stamped order history, and information about the order outcome. The order is defined by the ticker
symbol, the order side (buy, sell, and short sell), the order size, the amount of that size to be displayed, the
time in force (all orders are day orders), the order type (market, market on open, market on close, limit,
limit or better, limit on open, and limit on close), and limit price if applicable. The broker-dealer uses
seven destination venues; the New York Stock Exchange (NYSE), ARCA, BATS Z Exchange (BZX),
Nasdaq OMX BX (BSX), DirectEdge A (EDGA), EDGX, and NADQ. Each order is dated and events in
the order’s life are time-stamped in microseconds (µs - millionths of a second). Recorded events are order
13
We treat each order routed to a venue as an independent order. Although it is likely that many of these orders were generated by the same ‘parent’ order, our data do not allow us to link parent and children orders.
time and, if applicable, reject time, first fill time, last fill time, replace time, and cancel time. Should an
order receive a full or partial fill, the order history contains the quantity of shares done and the average fill
price.
We focus on orders arriving during regular market hours (9:30am through 4:00pm) and, for filled
orders, filling by 4:02pm. These restrictions reduce the sample size to 28,456,733 orders (99.4% of the
original sample). The data appear to be very high quality. Checking for obvious data errors (e.g., outcome
time before order time, negative size or quantity done, or quantity done exceeding order size), we exclude
only 29 orders. Because we plan to focus our analysis on limit orders, we remove the 125,565 (0.4% of
the remaining sample) orders that are not simple limit orders. We also discard orders with an average
trade price worse (higher for buy orders or lower for sell orders) than the limit price.
We use Daily TAQ data to match quote data to order arrival time (adjusting the data’s µs time
stamps to TAQ’s millisecond time stamps) using the Holden and Jacobsen (2013) corrections. In order to
eliminate possible erroneous orders, we subjectively discard orders with limit prices more than 10 percent
from the relevant quoted price (ask price for sell orders and bid price for buy orders). We begin our
analysis with 28,221,514 orders involving 6,594 unique ticker symbols.
[Insert Table II about here.]
Table II provides some descriptive statistics about order characteristics. From Panel A, we see
that the average order arrives just prior to 1:00pm, but the open and close are quite busy. Five percent of
our orders are placed in the first ten minutes of the trading day and another five percent arrive in the final
two minutes. Typical order size is small (mean of 635 shares), but we have some large orders (the 95th
percentile is 2,100 shares). Very little of the typical order is displayed. The mean display size is 85 shares,
only about 13% of mean order size, and the majority of orders are non-displayed (more details on display
are provided in Panels C and D). Although the typical order displays little size, there is an order with
nearly three-quarters of a million shares displayed. We also have considerable variation on share price
As shown in Panel B of Table II, about 52% of the our broker’s limit orders are buy orders, 27%
are sell orders and 21% are short sell orders. In Panel C, we provide additional detail on display choice.
Overall, about 63% of orders are non-displayed, about 28% are fully displayed, and 9% are partially
displayed. Much of the subsequent analysis separates displayed and non-displayed orders.
Finally, Panel D of Table II describes where our (at least partially) displayed and hidden orders
were routed.14 EDGX received the most displayed orders (29.7%), while both NDAQ and the NYSE each
received more than 23%. NDAQ received the most hidden orders (39.8%), while EDGX received 32%.
Overall, five (four) venues received at least 5% of our broker’s displayed (hidden) limit orders. Panel D
also exposes a limitation of our data in that our broker routed very few orders to inverted venues.
[Insert Table III about here.]
In Table III, we describe order outcomes. There are four possibilities noted in the data; cancel,
fill, replace and reject (the venue rejects the order). Orders might also expire as all are day orders. A given
order might have more than one outcome. For example, it is common to have an order fill partially and
then have the remainder cancel or expire.
Just over 38% of the orders receive at least a partial fill and the average limit order in our sample
executes 103 shares (around 16% of average order size). When an order fills at least one share, the typical
order is completely filled and the average order trading any shares is 97% completed. The distribution of
average execution price differs considerably from the limit price distribution (see Table II) and suggests
that limit orders in higher priced stocks are more likely to execute. A majority (58%) of orders are
cancelled, and the average (median) time to cancellation is 194 (28) seconds. Ignoring the 116,352 orders
that are not actively cancelled lowers the average time to cancel to 158 seconds.15 The average (median)
time it takes an order to execute at least one share is 98 (15) seconds. For orders with multiple executions,
the average (median) time until the last fill is 130 (20) seconds. A very small number of orders are
replaced or are rejected by the destination venue.
14
Unless stated otherwise, we refer to an order as displayed if at least part of the order is displayed.
Panel B of Table III provides a bit more detail on order outcomes conditional on whether limit
orders are unfilled, partially filled, or completely filled. Of the 17,455,297 orders receiving no fills, over
90% are cancelled and 8.5% expire. Over 36% of the orders fill completely and another 2% fill partially.
Of the 10,766,217 orders receiving at least a partial fill, about 77% have a single execution. There are a
non-trivial number of completely filled orders with a cancellation or replacement time stamp. These
apparently are instances where cancellation/replacement notice is too late to be effective. The majority of
partially filled orders are cancelled.
We use Daily TAQ to determine the positioning of each buy (sell) order’s limit price relative to
the National Best Bid (Offer). Following Rule 605, we assign limit orders to four categories: marketable,
inside-the-quote, at-the-quote, and behind-the-quote. A limit order to buy (sell) shares at a price that is
equal to or greater (less than or equal to) the National Best Offer (Bid) is marketable. Inside-the-quote
limit orders have limit prices that improve prevailing quotes. A limit order to buy (sell) shares at the
National Best Bid (Offer) is at-the-quote, and a limit order seeking to buy (sell) shares at prices that are
outside the prevailing quotes are behind-the-quote. Given the difference in time stamp precision, (ms for
TAQ and µs for the proprietary data) we anticipate that our categorization of limit orders is imperfect, but
not biased in any particular direction.
[Insert Table IV about here.]
Columns two and three in Panel A of Table IV suggest that there is little difference in the
aggressiveness of buy versus sell limit orders. Approximately 11% of the limit orders establish a new
National Best Bid or Offer while another 60% join the NBBO. In columns four and five of Panel A, we
categorize the aggressiveness of sample limit orders as a function of display choice. Consistent with the
idea that investors can use quote improving orders to gain price priority (guaranteed by Reg. NMS), a
higher fraction of fully and partially displayed orders are inside-the-quote compared to hidden orders.
Conversely, non-displayed orders are more frequently behind-the-quote.
Panel B of Table IV, describes order aggressiveness and display choice conditional on destination
aggressiveness as a fraction of all hidden (displayed) orders sent to that venue. There is a clear difference
in the types of limit orders sent to traditional versus inverted venues. Consistent with the idea that the
inverted venues can be used to gain priority at a price, over 99% of the displayed orders routed to the
inverted venues are at-the-quote. Conversely, quote improving orders are much more frequently routed to
the traditional venues that offer make rebates. With the exception of the NYSE, most of the displayed
limit orders received by these venues are at-the-quote (anywhere from 60.4% to 81.8%). The NYSE
receives more behind-the-quote displayed orders (39.8%) than it does at-the-quote displayed orders
(39.2%). Overall, at-the-quote orders are the most popular type of hidden order. As is the case with
displayed orders, the NYSE receives a mix of hidden orders that differs from the other three venues
offering make rebates. Over 76% of the hidden orders routed to the NYSE are behind-the-quote while
17.4% are at-the-quote. Arca has the next highest concentration of behind-the-quote hidden orders at
37.8%.
We posit that venues’ take fees influence limit order execution quality. Specifically, the higher
the take fee (and, correspondingly the higher the make rebate), the less attractive it is for liquidity
demanders to use the venue. In the sample period, ARCA, EDGX, and NDAQ charged a take fee of $0.30
per hundred shares, BATS charged $0.29, and NYSE charged $0.23. The inverted venues, BSX and
EDGA, paid a rebate to takers. Thus, we examine whether our measures of limit order execution quality
are correlated with take fees/rebates in the remainder of this section.
B. Univariate Analysis.
We first provide several univariate execution-quality statistics separately for displayed (Panel A)
and for hidden (Panel B) at-the-quote limit orders by venue in Table V. We focus on at-the-quote orders
as it is difficult to control for the actual pricing aggressiveness of behind- or inside-the-quote limit orders
in our univariate analysis. We present fill rates, execution speeds, realized spreads, and good fill ratios by
venue. Fill rates are order weighted. An order is considered filled if any of the order fills, but recall that
97% of orders that fill at least one share fill the entire order. Execution speeds are in seconds from the
perspective of liquidity suppliers. Thus, for executed limit buy orders, the realized spread is equal to twice
the difference between the midpoint of the bid ask spread prevailing five minutes after the limit order
executes and the executed order’s limit price. For sell orders, we multiply by -1. Comparisons of realized
spreads across venues illustrate whether the short-term gross revenue earned by liquidity providers varies
as a function of take fees. If limit orders on venues with low take fees routinely execute prior to those on
venues with high take fees, then we expect to find that both fill rates and realized spreads are higher on
venues with lower take fees. Introduced by Sofianos and Yousefi (2010), the good fill ratio classifies limit
order executions relative to the sign of the realized spread. A limit order execution is classified as a good
fill if the order’s realized spread is positive (e.g., prices moved in the limit order trader’s favor after the
limit order executes).
[Insert Table V about here.]
Focusing on fill rates for displayed at-the-quote orders, we find that orders on the BSX are most
frequently filled and orders on the high take venues (EDGX, NDAQ and ARCA) are least frequently
filled. These results suggest that fill rates are negatively correlated with take fees. Likewise, the mean
realized spread and the good fill ratio for displayed orders are negatively correlated with take fees. Only
the inverted venues have positive mean realized spreads.
As hidden orders are by definition not displayed, it is difficult to predict the impact of take fees
on hidden limit order execution quality. Consistent with this observation, we find that the correlation
between take fees and fill rates is weaker for at-the-quote non-displayed orders than for at-the-quote
displayed orders. However, even for the hidden orders, the two inverted venues have the highest fraction
of good fills.
Overall, for at-the-quote limit orders, we find that several measures of limit order execution
quality are decreasing in take fee, especially for displayed orders. This suggests that order routing
C. Multivariate Analysis.
It is unlikely that our sample limit orders are distributed randomly across trading venues.
Presumably, limit order routing decisions are made conditional on several factors including stock-specific
characteristics, market conditions, and liquidity fees. As a result, it is potentially misleading to draw
conclusions from our unconditional statistics. To address this concern, we conduct multivariate analyses
to investigate whether a given order fills and, for filled orders, the time required to fill.
For each analysis, we conjecture that the relevant independent variables are those dealing with
order characteristics, stock attributes, the time of day, and venue traits. We distinguish between orders
based on the side the order takes (distinguishing between sell and short sell orders), whether the order is
displayed, the size of the order relative to the relevant quoted size (bid size for sell orders and ask size for
buy orders), and the limit price in relation to the relevant quoted price (for buy orders the limit price is
compared to the bid price and for sell orders the limit price is compared to ask price). Stock
characteristics include proxies for the intensity of trading, the price level, and the volatility of the stock
price. Given the frequency with which trading variables are characterized by extremes at open and close,
we compute a seconds from mid-day variable to represent time of day. The two venue variables of interest
are the average response time for cancellations (a proxy for the technological connectivity of the broker to
the exchange) and, of direct interest to our work, the take fee. Thus, we use probit to estimate equation (1)
and OLS to estimate equation (2). Both equations are presented below:
Fill = β0 + β1 Sell+ β2 Short Sell+ β3 Display+ β4 Moneyness + β5 Trading Intensity + β6 Price+ β7 Volatility+ β8 Seconds from Mid-day+ β9 Mean Response Time + (1) β10 Order Size + β11 Take Fee+ β12 (Display)(Take Fee) + ε and
Speed= β0 + β1 Sell+ β2 Short Sell+ β3 Display+ β4 Moneyness + β5 Trading Intensity + β6 Price+ β7 Volatility+ β8 Seconds from Mid-day+ β9 Mean Response Time + (2) β10 Order Size + β11 Take Fee+ β12 (Display)(Take Fee) + ε
where Fill equals 1 if the order at least partially fills and 0 otherwise; Speed is the number of seconds
between Order Time and First Fill Time conditional on at least a partial fill; Sell equals 1 if the order side
is “Sell” and 0 otherwise; Short Sell equals 1 if the order side is “Short Sell” and 0 otherwise; Display
equals 1 if the order is at least partially displayed and 0 otherwise; Moneyness equals (Limit Price/Ask
Price) – 1 for sell orders and (Bid Price/Limit Price) – 1 for buy orders; Trading Intensity equals the
number of orders in the proprietary dataset for this stock symbol; Stock Price equals the mean trade price
in the proprietary data set for this stock symbol; Volatility equals the standard deviation of the trade price
in the proprietary data set for this stock symbol; Seconds from Mid-Day equals the absolute value of
Order Time (in seconds since 12:00am) – 45900; Mean Response Time equals Out Time from the venue –
Cancel Time from the broker; Order Size is the number of shares specified in the order; and Take Fee
equals the venue’s take fee during the sample period.
We present the results from performing these regressions in Table VI. Given the sample size, all
coefficients are statistically significant at beyond the .0001 level.
[Insert Table VI about here.]
As can be seen in the second column of Table VI, sell orders are more likely to fill than are buy
orders while short sell orders are less likely to fill than buy orders. Consistent with the univariate results,
displayed orders, small orders, and orders more aggressively priced are more likely to fill. Orders
submitted early or late in the trading day are more likely to fill and orders in stocks with greater trading
intensity and higher volatilities are less likely to fill. Orders routed to venues with faster links to the
broker are more likely to fill. Of particular interest is the association between take fees and the likelihood
of filling. For displayed orders, the higher the take fee, the less likely the order is to fill (the sum of the
estimate of β11 and β12 is reliably negative). However, for non-displayed orders, the higher the take fee the more likely the order is to fill. The relationship between fill rates and take fees are broadly consistent with
the univariate results presented in Panels A and B of Table V.
Fill times are examined in the third column of Table VI. Sell orders fill slower and short sell
fill faster than non-displayed, large and passive orders. Stocks with larger share prices, higher trading
intensity and more volatility have faster fills. Fills occur more quickly away from mid-day. Perhaps not
surprisingly, venues with quick connections fill orders in less time than those with slower connections.
Again, focusing on take fees, we find that orders sent to venues with higher take fees fill more slowly
(regardless of whether they are or are not displayed).
Overall, we document that the execution quality of limit orders is related to the destination
venue’s take fee. We find that displayed orders on venues with high take fees fill less often than similar
orders on venues with low take fees. Non-displayed orders fill more frequently on venues with high take
fees. Conditional on filling, orders fill more quickly on venues with low take fees. Together, these results
suggest that routing all limit orders to an exchange with the highest make rebate (and correspondingly
high take fee) is not consistent with best execution.
D. Horseraces.
Our proprietary data allow another approach to examining differences in across-venue limit order
execution quality while controlling for other factors. We frequently find that pairs of identical orders are
simultaneously sent either by the broker’s order routing algorithm or directly by customers to different
venues. For each pair of identical orders, we define the venue filling the order first while the competing
venue still holds the paired order to be winner of the horserace. To shed light on the economic
implications of winning a set of horseraces, we examine the percentage of limit order executions that
produce positive realized spreads on each of the paired venues. Assuming that at a given price, limit
orders that execute first incur lower adverse selection costs, we expect a larger proportion of the
‘identical’ limit orders executed on the low fee venue to have a positive realized spreads. In other words,
the low fee venue should have the higher good fill ratio.
To begin this analysis we identify order-pairs that have the same stock symbol, same order date,
same order side (buy or sell), same limit price, same order time (to within one ms), but different
destination venues. These paired orders, by construction, control for stock characteristics and market
at least one of the two orders in the order-pair fill (at least partially) and that both orders in the pair be
outstanding at the time of the first fill. Should both orders fill, a venue wins if it fills the order first with at
least 500 µs before the second order executes.16 Should one order in the pair fill and the other be
cancelled or replaced, the venue filling the order wins. If one order in the order-pair is rejected, replaced,
or cancelled prior to its paired order filling, then we eliminate that pair from our sample. This allows us to
focus on order pairs where the losing order retained an apparent trading interest when the competing
venue filled the order.
We report detailed results of our horseraces in the Appendix conditional on which venue receives
the identical order in an order pair first. Figure 1 summarizes the outcomes of our pair-wise horseraces
between venues with different take fees using order pairs constructed from all orders. There are sufficient
data to make eight pairwise comparisons.
[Insert Figure 1 about here.]
For each venue-pair, Panel A of Figure 1 presents the number of order pairs routed to and the
percentage of order pairs for which both orders execute. Panel A also describes the difference in take fees
on the paired venues. The number of identical order pairs routed to different venue pairs varies
significantly. For example, the NYSE and NDAQ receive 79,159 identical order pairs while the NYSE
and EDGX receive only 719. Take fee differentials range from $0.0001 per share for BZX vs. ARCA,
BZX vs. NDAQ, and BZX vs. EDGX to $0.0044 per share for BSX vs. NDAQ. The primary concern for
a limit order trader might well be whether the order does or does not fill. Over 60% (less than 10%) of
the identical order pairs routed to the NYSE and ARCA (BSX and NDAQ) execute on both venues. Thus,
it is not generally the case that both exchanges get the trade done.
For each venue pair, Panel B of Figure 1 presents the percentage of horseraces won by the venue
16
Our data provider considers orders filled within 500 µs of one another to be ties. Our results are not sensitive to the exact definition of a tie.
with the lower take fee. A venue wins a horserace if it is first to execute the ‘identical’ order.17 Panel B
reveals that for seven of the eight sets of horseraces, the venue with the lower take fee won more than
50% of the horseraces. The largest margin of victory involves the venue pair with the largest fee
differential: BSX vs. NDAQ. BSX wins nearly 100% of the horseraces that do not end in a tie. In the
venue pair with the second highest fee differential, the NYSE wins 93.79% of its horseraces with EDGX.
The only set of horseraces in which the venue with the higher take fee executes a larger percentage of
identical orders first is BZX vs. ARCA. Despite having a take fee that is $0.0001 per share higher than the
BZX’s, ARCA wins over 76% of the horseraces. Overall, the data presented in Panel B suggest limit
order execution quality, as we measure it, is lower on venue with higher take fees.
We present good fill ratios for each of the eight venue pairs in Panel C of Figure 1. The results
are consistent with those in Panel B in that the venue winning a higher percentage of its horseraces has
the larger good fill ratio. In particular, the set of horseraces between venues with the largest fee
differential, NDAQ and BSX, also produces the largest difference in good fill ratios. Nearly 53% of the
identical limit orders executed on the BSX had positive realized spreads compared to just under 42% on
NDAQ. In addition, although ARCA won 76% of its horseraces with the BZX (see Panel B), ARCA’s
good fill ratio is only 0.39% higher than the BZX’s.
Overall, the results presented in Panels B and C of Figure 1 are consistent with the claim that, for
fee differences larger than one penny per 100 shares, limit order execution quality is inversely related to
take fees. In only one set of horseraces does the venue with the lower take fee fail to win a majority of its
contests. These results confirm our earlier finding that measured limit order execution quality is inversely
related to the relative level of make/take fees. Our results also suggest that the commonly used strategy of
using a high-rebate exchange as the primary venue to display limit orders (see Table I) results in
diminished execution quality along several important dimensions.
[Insert Figure 2 about here.]
17
The number of horseraces resulting in a tie for each venue pair can be inferred by comparing the number of order-pairs in Panels A and B. For example, there were 1,698 – 1,433 = 265 ‘identical’ orders routed to the BZX and ARCA that were executed within 500 µs of one another.
Because our data provider aggressively uses non-displayed orders and the mix of displayed and
hidden orders differs systematically across venues, the execution quality differences we identify may
partially reflect the display choice. To determine whether the decision to display an order affects the
results, we repeat the analysis for only those order pairs in which portions of both orders are displayed.
The results are Figure 2. Given the limited number of horseraces, the results in Panels B should be
interpreted with caution and are presented to serve as a benchmark against which to evaluate the results
we obtain using all orders.
There are six different sets of horseraces presented in Panel B. For five of the venue pairs, the
venue with lower fee clearly wins a higher percentage of the horseraces. In the sixth, between the NYSE
and the BZX, the lower priced NYSE only wins 51.27% of its horseraces. Further, when we focus on
displayed orders, the BZX vs. ARCA horserace goes in the predicted direction with BZX winning 69% of
the horseraces.
Restricting our analysis to displayed order pairs produces results that are largely consistent with
our analysis of order pairs constructed from all orders. In most cases, we find that when identical
displayed orders are routed to venues with different take fees, the venue with the lower take fee tends to
execute more quickly. Thus, our results appear to be robust to the order display decision.
E. Caveats.
Overall, the results of this section suggest that the decision to route the bulk of one’s limit orders
to a single venue paying the maximum permissible rebate (and, correspondingly charging a high take fee),
might well be inconsistent with a broker’s responsibility to obtain best execution. Certainly, the metrics
we use to measure execution quality are negatively correlated with the take fee. While suggestive, there
are a few reasons why our results may not generalize. First, the executed limit orders in our dataset are
from a single broker and they comprise only 1.5% of average daily volume. Second, our data do not span
the thirteen U.S. stock venues that utilize the traditional make-take or inverted make-take fee schedules.
Specifically, our data provider uses inverted venues somewhat sparingly. Finally, our proprietary data do
relatively high take fees is most (least) harmful to limit order execution quality. In the next section, we
use the NYSE’s TAQ database to address these concerns.
V. Make-take fees and limit order execution quality: NYSE TAQ data.
The NYSE’s TAQ database contains a time-stamped recording of each instance that a trading
venue’s best bid or offer price changes or the depth at these prices changes. TAQ also contains a
time-stamped recording of every trade. Thus, TAQ data can be used to determine whether fees influence where
marketable orders (as evidenced by trades) execute when multiple venues have the best posted quote.18
Based on the analysis in the prior section, we expect that venues with lower or negative take fees receive
a larger share of marketable orders than venues with high take fees when all venues display the best
quote. Unless a venue charging the maximum take fee always has the shortest queue when it is at the best
quote or the market always exhausts the aggregate depth at the best quote, such a finding would suggest
that brokers seeking to obtain best execution for customer limit orders should not route 100% of their
non-marketable limit orders to a venue offering the highest rebate (and charging the largest take fee).
Since exchanges today predominately operate as electronic limit order books, marketable orders
typically execute against standing limit orders.19 As a result, TAQ data are well suited for making
inferences as to the execution quality of executed limit orders. By examining the realized spreads of
executions that take place at the quoted price when each of the relevant trading venues is posting the best
quote, we can examine whether executed limit orders on venues with high take fees face higher adverse
selection risk than those executed on venues with low or negative take fees. By examining the percentage
of limit order executions for which the five-minute realized spread is positive (e.g., the good fill ratio), we
can examine whether the average limit order on each venue encounters short-term ex post regret. To
investigate how the routing decisions might be influenced by the decision to pass on make-take fees to the
18
Bessembinder (2003) examines quote-based competition for orders in NYSE-listed securities in June 2000. During this time period, virtually no trading volume in NYSE-listed securities was executed on venues offering make-take pricing. Among other things, he finds that venues realize increased market share when their quote has time priority and when the depth at their quote is large.
19
On venues such as NDAQ, NSX, and the NYSE, some of the reported trades be the result of a liquidity demander interacting with a dealer’s non-displayed trading interest. For example, internalized trades may be reported to the NSX and to NDAQ.
investor, we also assess realized spreads net of make-take fees. If the marginal investor is responsible for
the fees and rebates her trades generate, one might expect the adjusted realized spreads to equilibrate
across venues.
A. Summary Statistics.
We currently examine the first five trading days of October 2012 for both NYSE- and
Nasdaq-listed stocks.20 For each stock, we determine the best bid and offer for each trading venue in TAQ
throughout each trading day. Using these quotes, for each stock we compute the National Best Bid and
Offer (NBBO) at each point in time during the trading day.21 Using the NBBO, we employ the Lee-Ready
algorithm to determine whether trades are initiated by liquidity demanders or liquidity suppliers. As we
are interested in differences in limit order execution quality across exchanges, we eliminate sweep trades
since they are designed to trade against all orders posted at the best quote.22 Finally, we eliminate the
Nasdaq TRF from our analysis since most of its trades are internalized. Because of the expanded data, we
can conduct separate analyses for stocks listed on the NYSE and for stocks listed on Nasdaq. Since the
NYSE does not facilitate trades in stocks listed on Nasdaq, the relationship between fees and limit order
execution quality may depend on where a stock is listed. Panel A (Panel B) of Table VII presents
summary statistics for trading activity, trading costs, and adverse selection costs in NYSE-listed
(Nasdaq-listed) stocks.
[Insert Table VII about here.]
The first four rows of each panel describe the percentage of the trading day that each venue’s
quotes are equal to the NBB, the NBO, either the NBB or the NBO, and the NBB and the NBO,
respectively. Blume and Goldstein (1997) document that, for NYSE-listed stocks, the NYSE is at either
the NBB or the NBO an average of 99.9% of the trading day and executes 83% of the trades in
20 We are currently expanding the TAQ analysis to include the entirety of October and November 2012. 21 These screens are similar to those used by Corwin and Schultz (2012).
22 Regulation NMS allows an investor to walk up (down) a venue’s limit order book if the investor first sends an
intermarket sweep order (ISO) to each venue whose bid (offer) is equal to or better than the venue’s best bid (offer). The execution quality of at-the-quote limit orders that provide liquidity to ISOs is identical since ISOs consume all of the liquidity at a price point. For an in-depth analysis of ISOs, see Chakravarty et al. (2012).
listed securities during the twelve months ending in June 1995. Despite the fact that it executes less than
20% of all trades during our sample period, the NYSE is at either the NBB or the NBO an average of
91.37% of the trading day in October 2012. Moving along the first row of Panel A, we see that four
traditional exchanges are at one side of the NBBO at least half of the trading day. The percentage of time
spent by inverted venues at either the NBB or the NBO ranges from a low of 25.18% on EDGA to a high
of 46.01% on BYX.
The next four rows of Panel A describe the percentage of all trades, all shares, non-sweep trades,
and non-sweep shares in NYSE-listed securities executed by each trading venue. In the analysis to follow,
we exclude venues with market shares of less than 1%. Excluding sweep trades, three venues execute
more than 10% of trades in NYSE-listed securities: the NYSE, ARCA, and NDAQ. Of the remaining
traditional venues, BZX executes around 8% of non-sweep trades and EDGX has a market share of 6%.
Market shares on the inverted venues range from 1.6% on EDGA to 3.3% on BYX. Overall, the venues
with the larger market share of NYSE-listed trades are also the venues whose quotes are most frequently
at the NBBO.
The final six rows of Panel A present unconditional execution quality statistics. On average,
relative effective spreads are lower on the traditional exchanges than they are on the inverted venues. For
example, average effective spreads on the traditional venues range from a low of 6.76 bps on the BZX to
a high of 7.68 bps on the NYSE. EDGA’s average effective spread is 8.5 bps is the lowest of the inverted
venues. The fact that, on average, dollar effective spreads are lower on inverted venues suggests that this
result may be due to differences in the average price of the stocks traded on inverted and traditional
venues.
Unconditional realized spreads provide an estimate of the gross revenue earned by liquidity
providers on the various exchanges. Consistent with Chakravarty et al. (2012), who find that ISO orders
tend to be informed, average realized spreads on each venue are higher when we exclude ISO trades.
More importantly, we find that both dollar and percentage average realized spreads are monotonically
of $0.0068, while the three venues charging the maximum permissible take fee have average realized
spreads ranging between -$0.0032 and -$0.0055. With one exception, average realized spreads are
negative on each of the traditional venues and are positive on each of the inverted venues. For executed
limit orders in NYSE-listed securities, these results suggest that limit order execution quality is
decreasing in the size of the take fee. More important, of the seven relevant make-take venues we
examine, liquidity providers on EDGX earn the lowest realized spreads.23 This again suggests that routing
limit orders to a single venue charging the maximum permissible take fee may be inconsistent with the
pursuit of best execution.
We present these results separately for Nasdaq-listed stocks in Panel B since the NYSE does not
quote or trade these securities. Consistent with the results for NYSE-listed securities, the three venues
charging the maximum take fee are most frequently at either the NBB or the NBB while the three inverted
venues spend the least time at the best quote. The traditional (inverted) venues execute nearly half (just
over 7%) of non-sweep trades. As is the case in NYSE-listed stocks, average realized spreads for trades in
Nasdaq stocks are positive on the three inverted venues and are negative on the four traditional venues.
On average, the BSX again has the highest realized spreads and EDGX has the lowest. Overall, the results
presented in Panels A and B suggest that, on average, limit orders executed on venues with high take fees
generate negative short-term gross returns.
B. Across-venue quality of limit order executions when all venues are at the inside quote. We next examine whether fees influence which venue receives a marketable order (evidenced by
trades) when multiple venues are at the best quote. Since marketable orders have the choice of obtaining
liquidity on one of multiple venues, we expect the limit order routing decision to be more important in
these situations. How often does this situation arise? In untabulated results, we find that 12.25% of all
non-sweep trades in NYSE-listed stocks occur at the quote when each of the relevant trading venues is at
the inside quote. The average (median) NYSE stock has 3.97% (0.24%) of its non-sweep trades executing
23
The relevant trading venues include Nasdaq BSX, EDGE-A, BATS-Y, NYSE, BATS-Z, Nasdaq, NYSE ARCA, and EDGE-X. Amex, NSX, Chicago, PSX, and CBOE have less than one percent average market share and are excluded from the analysis.
at the quote when all venues are at that quote. The 90th percentile (maximum) NYSE-listed stock has
15.90% (42.30%) of its non-sweep trades executing at the quote when each of the relevant venues is at the
quote.24
For trades executed at the NBB (NBO) when all venues are at the NBB (NBO), we compute the
following statistics: each venue’s market share of trades, each venue’s average depth at the relevant quote
when a trade executes at that quote, each venue’s average depth at the relevant quote when it executes a
trade at that quote, each venue’s average realized spread, each venue’s average realized spread net of the
make rebate or fee, and each venue’s good fill ratio. Assuming the quotes on each venue represent limit
order trading interest, these statistics allow us to evaluate limit order execution quality for executed
at-the-quote limit orders. We can then examine the extent to which limit order execution quality is
associated with take fees.
[Insert Table VIII about here.]
In Table VIII we present execution quality statistics for at-the-quote limit orders executed when
all venues are at the relevant quote separately for NYSE-listed and Nasdaq-listed securities. As noted in
the bottom two rows, the requirement that trades in NYSE-listed (Nasdaq-listed) stocks occur when eight
(seven) venues are at the inside quote reduces the number of stocks in our sample from 1,210 to 850
(from 1,960 to 831). On average, each NYSE-listed (Nasdaq-listed) stock remaining in our sample has
4,254 (2,586) trades executed at the inside quote when all venues are at that quote.
While the BSX’s market share of all non-sweep trades is 2.80% (see Panel A of Table VII), it
climbs to just over 9% when we focus on trades executed when all venues are at the inside quote. Indeed,
the aggregate market share of trades executed by inverted venues increases from 9.86% to 22.82% when
we restrict our analysis to those non-sweep trades executed when all are at the inside quote. Conversely,
the market share of each of the traditional venues falls when this restriction is imposed. Consistently with
Cardella et al. (2012), these results suggest that fees influence the routing of marketable orders.