Can Brokers Have it all? On the Relation between Make Take Fees & Limit Order Execution Quality by...

8/13/2019 Can Brokers Have it all? On the Relation between Make Take Fees & Limit Order Execution Quality by Battalio Corwin Jennings

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This draft: October 29, 2013

Can Brokers Have it all? On the Relation between Make Take Fees& Limit Order Execution Quality*

by

Robert BattalioMendoza College of Business

University of Notre [email protected](574) 631-9428

Shane CorwinMendoza College of Business

University of Notre [email protected](574) 631-6026

Robert JenningsKelley School of Business

Indiana [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 makingliquidity (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


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sell marketable orders and they route limit orders to venues that offer the highest liquidity rebates. As

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.1Although 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 SECsOrder 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 loss. However, if the stock price rises


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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 the

frequency 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. 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 UBSs best execution statement,4algorithms can find it optimal to

route marketable orders to venues with high take fees if those venues offer sufficient price and/or depth

improvement.5 It might also be the case that venues with high take fees attract marketable orders by

offering competitively priced co-location services, which make it easier for algorithms to trade at the

quotes they see thereby reducing execution price risk.


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structures and with NYSE TAQ data to evaluate the location and the profitability of 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 fees, 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

sells market orders to market makers and routes limit orders either to market makers or to EDGX, an

exchange offering the largest per share liquidity rebate.

In an effort to understand whether routing nonmarketable limit orders to venues with high fees

results in diminished execution quality, 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


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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 fastest 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 paper trading profits after five minutes, which suggests our data provider does a nice job

managing 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 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


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For limit order traders that pay/receive liquidity fees/rebates, 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 at-the-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.

We plan to complete this section by using cross sectional regressions to better understand the

types of situations in which the routing of at-the-quote limit orders is most/least important.

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

proprietary order data to examine the relationship between make-take fees and limit order execution

quality. In Section V, we use the NYSEs 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 assetstrading volume is concentrated on one venue, the (limit) order routing decision is


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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 NYSEs.

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 across-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


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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

fees and multiple dimensions of limit order execution quality.

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.

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


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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 nave 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

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 makers 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.7Thus, 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


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We begin by identifying brokers appearing in either Barrons or Smart Moneys 2012 Broker

surveys. We next use broker websites to collect Rule 606 reports for the fourth quarter of 2012.

8

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

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 brokers non-directed orders.

Overall, nine of our brokers route at least a portion of their orders to market makers that provide

rebates 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 andfour inverted trading

venues in 4Q2012.9The three venues charging the 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. 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.]


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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 the venue offering the most lucrative

liquidity rebates, EDGX. In addition to these venues, Scott Trade also routed some of its limit orders to

the Lava ATS. The Lava ATS offers relatively high make rebates ($0.0028/share) and charges a take fee

that is more attractive than EDGX. 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) 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 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 its smart router 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


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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 95

th

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 Panel 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 that is

skewed toward low-priced shares.

As shown in Panel B of Table II, about 52% of the our brokers 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 B of Table II describes where our (at least partially) displayed and hidden orders

were routed.12EDGX 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 brokers displayed (hidden) limit orders. Panel B

also exposes a limitation of our data in that our broker routed very few orders to inverted venues.


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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.13The 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.

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) orders 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


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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

venue. We report the percentage of hidden (displayed) orders sent to a venue at a given pricing

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


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outperforms ARCA, EDGX, and NDAQ, and that ARCA, EDGX, and NDAQ perform similarly. We

examine these questions in the remainder of this section.

B. Univariate Analysis.

We first provide 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

order submission time until first fill time for filled orders. Realized spreads are presented 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 orders limit price. For sell orders, we multiply by -1. Comparisons of realized

spreads across venues illustrate whether the 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


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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 for at-the-quote non-displayed orders is much lower than the correlation

between take fees and fill rates 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 execution quality is decreasing in take fee,

especially for displayed orders. This suggests that order routing decisions may have an impact on limit

order execution quality.

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


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Fill = 0+ 1 Sell+ 2Short Sell+ 3Display+ 4Moneyness+ 5Trading Intensity+

6Price+ 7Volatility+ 8Seconds from Mid-day+ 9Mean Response Time+ (1)

10Order Size+ 11Take Fee+ 12(Display)(Take Fee) +

and

Speed= 0+ 1 Sell+ 2Short Sell+ 3Display+ 4Moneyness+ 5Trading Intensity+

6Price+ 7Volatility+ 8Seconds from Mid-day+ 9Mean Response Time+ (2)

10Order Size+ 11Take Fee+ 12(Display)(Take Fee) +

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; Sellequals 1 if the order side

is Sell and 0 otherwise;Short Sellequals 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; Moneynessequals (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 Priceequals the mean trade price

in the proprietary data set for this stock symbol; Volatilityequals 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 Timeequals Out Time from the venue


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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 ofthe estimate of 11

and 12is 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

orders fill faster than buy orders. Displayed orders, smaller orders, and orders more aggressively priced

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

venues 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


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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

conditions as we attempted to do above statistically. For a horserace to produce a winner, we require that

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 at least

500 s before the second order executes.14Should 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


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BZX vs. NDAQ, and BZX vs. EDGX to $0.0044 per share for BSX vs. NDAQ. Over 60% (less than

10%) of the identical order pairs routed to the NYSE and ARCA (BSX and NDAQ) execute on both

venues.

For each venue pair, Panel B of Figure 1 presents the percentage of horseraces won by the venue

with the lower take fee. A venue wins a horserace if it is first to execute the identical order.15Panel 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

BZXs, ARCA wins over 76% of the horseraces. Overall, the data presented in Panel B suggest limit

order execution quality 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


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relatively high take fees is most (least) harmful to limit order execution quality. In the next section, we

use the NYSEs TAQ database to address these concerns.

V. Make-take fees and limit order execution quality: NYSE TAQ data.

The NYSEs TAQ database contains a time-stamped recording of each instance that a trading

venues 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. 16

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

limit orders to an exchange charging the maximum take fee.

Since exchanges today predominately operate as electronic limit order books, marketable orders

typically execute against standing limit orders.17

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


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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.18 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.19Using 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.20Finally, 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-


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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


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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.21This 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


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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

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.22

For trades executed at the NBB (NBO) when allvenues are at the NBB (NBO), we compute the

following statistics: each venues market share of trades, each venues average depth at the relevant quote

when a trade executes at that quote, each venues average depth at the relevant quote when itexecutes a

trade at that quote, each venues average realized spread, each venues average realized spread net of the

make rebate or fee, and each venues 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


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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.

A comparison of columns three and four of Table VIII reveals that the inverted (traditional)

venues tend to have more (less) depth when they execute trades than they do when trades execute on other

venues at the best quote. Overall, average depths at the inside quote on traditional venues are two to three

times the size of the average depths on the inverted exchanges. Ranking the average depth when a venue

executes a trade while all are at the inside quote, we find that only the queue length on the NYSE is larger

than that on EDGX. Thus, even if marketable orders are randomly allocated across venues at the inside

quote, the results suggests brokers seeking to maximize limit order execution quality should not route

100% of their orders to an exchange charging the maximum permissible take fee.

A comparison of the realized spreads presented in column six of Panel A of Table VIII with those

presented in Table VII reveals that restricting our analysis to non-sweep trades executed when all are at

the inside quote increases the average realized spread on each trading venue. The across-venue range in

average realized spreads increases from 6.35 bps when we examine all non-sweep trades to 9.5 bps when

we examine non-sweep trades executed when all are at the inside quote. This suggests that when prices

become locally less volatile, order routing has a greater impact on the quality of limit order executions.


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determining which venues executed the trades in our sample. To address the latter concern, we examine

limit order execution quality statics for trades executed when each venue is at the inside quote and has a

depth of at least 2,000 shares (results not reported). With the exception of the NYSE, the inverted venues

execution quality for executed at-the-quote limit orders in NYSE-listed securities improves relative to the

traditional venues as we impose more stringent depth requirements. Surprisingly, the NYSE has the

highest average realized spread and good fill ratio when the 2,000 share depth requirement is imposed.

For at-the-quote trades in Nasdaq-listed securities when all are at the inside quote with increased depth,

the difference between good fill ratios and average realized spreads on the inverted and the traditional

venues becomes more pronounced.

Overall, with the exception of trades executed by the NYSE in NYSE-listed securities, the results

in Table VIII suggest there is a monotonic relationship between take fees and the execution quality of at-

the-quote limit order executions when all venues are at the inside quote. For at-the-quote limit order

executions in NYSE-listed securities, the NYSE (which charges a take fee of $0.23 per hundred) routinely

outperforms the inverted venues and the BZX underperforms venues with higher take fees. We might

expect this result if some brokers/market participants do not find it economical to connect to the inverted

venues. For these participants, the NYSE may be the venue with the lowest take fee when deciding where


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C. Multivariate analysis of take fees and realized spreads for at-the-quote trades.

In the prior section we attempted to hold all else equal by examining at-the-quote limit order

execution quality when all venues were at the inside quote. Admittedly, this sample selection process tilts

our analysis toward instances when fees matter mostprice volatility is relatively low and depth at inside

quote is relatively high when all venues are required to be at the inside quote. It is exactly in these

instances that we expect (and find) fee differentials to have the biggest impact on limit order execution

quality. How does the quality of limit order executions as a function of take fees in more general settings?

Are across-venue differences in execution quality greater for low priced stocks, where the minimum

variation is a bigger percentage of the stock price? Does the routing decision affect the execution quality

of limit orders in volatile stocks? In this section, we use pooled regressions of realized spreads on stock

and trade characteristics to examine these questions.

We ultimately present the results of four realized spread regressions for all at-the-quote trades and

for at-the-quote trades executed when all venues are at the relevant inside quote separately for NYSE-

listed and for Nasdaq-listed securities. In each of our analyses, a trade is the unit of observation. In each

specification, we include intraday time period dummies and controls for average daily volume, trade

price, trade size, and daily volatility as measured by the log ratio of the high-to-low stock price. We


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depth at the relevant quote is at least 20,000 shares and equals zero otherwise. We expect the benefit of

having a limit order executed on a venue with low take fees to be greater when there is more depth at the

relevant quote. In a third model, we include both the take fee of the venue executing the trade and an

interactive variable that is equal to the take fee of the venue executing the trade if the trade price is

between $1.00 and $6.00 inclusive. We include this variable as the relation between fees and stock price

may not be linear.

In our final specification, we include five dummy variables that equal one if the trade executes on

a venue with a take fee of $x and zero otherwise. We include dummies for take fees of -$0.04, -$0.02,

+$0.23, +$0.29, and +$0.30. In this specification, the intercept captures the impact of trades executing on

the venue with a take fee of -$0.14. We expect average realized spreads to be decreasing in these take fee

dummies.

[Insert Table IX about here.]

Panel A (Panel B) of Table IX presents our regression results for trades in NYSE-listed (Nasdaq-

listed) securities. Regardless of the number of venues at the relevant quote and the exact specification of

the regression, realized spreads for both NYSE and Nasdaq-listed stocks tend to be higher for trades that

are smaller, in lower priced stocks, in more actively traded securities, and in stocks with higher daily


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In the fifth and the ninth columns of Table IX, we present regression results for the model that

includes individual take fee dummy variables. Focusing on Panel A, the results presented in column five

suggest that when we examine all at-the-quote trades in NYSE-listed securities, trades executed on the

BSX generate realized spreads that are, on average, significantly higher than the realized spreads

generated on venues with higher take fees. Indeed, with the exception of the -$0.04 and the -$0.02 fee

regimes, statistical tests (not reported) reveal the average realized spread generated by at-the-quote trades

is decreasing in take fee. When we restrict our focus to trades in NYSE-listed securities executed when all

venues are at the relevant quote (see column nine), our inferences change as realized spreads executed

under the -$0.14 and the +$0.23 fee regimes are statistically indistinguishable. Likewise, average realized

spreads executed under the -$0.04 and the -$0.02 take fee regimes are also statistically indistinguishable.

For at-the-quote trades when all venues are at the relevant quote, the statistical relation between take fees

and average realized spreads is as follows: average realized spreads are lowest on the venue with the take

fee of $0.29, are higher on the venues with a take fee of $0.30, are even higher on venues with take fees

of -$0.02 and $-0.04, and are highest on the venues with take fees of +$0.23 and -$0.14.

The fifth and the ninth columns of Panel B present results for trades in Nasdaq-listed securities.

Regardless of whether the focus is on all at-the-quote trades or is only on at-the-quote trades when all


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average at-the-quote limit order investor need to compensate her for executing on the high take fee venue

rather than the low take fee venue? The trade-weighted average at-the-quote trade price in NYSE-

(Nasdaq-) listed securities when all venues are at the relevant quote is $14.98 ($11.80) per share. Thus,

the average at-the-quote limit order execution would have to earn a make fee of $1.16 per hundred shares

($14.98*0.000771*100) for NYSE-listed securities and a make rebate of $1.17 per hundred shares

($11.80*0.000992*100) for Nasdaq-listed securities to compensate for the lower realized spreads

generated by at-the-quote trades on the venues charging a take fee of $0.30 per hundred shares. Moreover,

the make fee would have to be directly passed through to investors.

VI. Conclusion.

We present evidence that Ameritrade, E*Trade, Scott Trade, and Fidelity make order routing

decisions in the 4thquarter of 2012 that appear to maximize the liquidity rebates generated from limit

order executions. To the best of our knowledge, none of these brokers makes it a practice to pass

exchange fees/rebates through to their customers. As a result, limit order execution quality, not liquidity

rebates, should be the primary (only) factor in determining where limit orders are routed. Angel et al.

(2010) hypothesize that, when multiple venues are displaying the best quote, limit orders resting on

venues that pay low/negative liquidity rebates should execute before those on venues that offer high


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References

Angel, James, Lawrence Harris, and Chester Spatt, 2010, Equity trading in the 21 st century, USCMarshall School of Business working paper 09-10.

Bacidore, Jeff, Hernan Otero, and Alak Vasa, 2011, Does smart routing matter?, Journal of Trading 6, 32-37.

Battalio, Robert, Jason Greene, and Robert Jennings, 2002, Does the limit order routing decision matter?,Review of Financial Studies 15, 159-194.

Bessembinder, Hendrick, 2003, Quote-based competition and trade execution costs in NYSE-listedstocks, Journal of Financial Economics 70, 385-422

Blume, Marshall and Michael Goldstein, 1997, Quotes, order flow, and price discovery, Journal ofFiannce, 221-244.

Boehmer, Ekkehart, Robert Jennings, and Li Wei, 2007, Public disclosure and private decisions: Equitymarket execution quality and order routing, Review of Financial Studies 20, 315-358.

Cardella, Laura, Jia Hao, and Ivalina Kalcheva, 2013, Make and take fees in the U.S. equity market,University of Arizona working paper.

Chakravarty, Sugato, Pankaj Jain, James Upson, and Robert Wood, 2012, Clean sweep: Informed tradingthrough intermarket sweep orders.

Colliard, Jean-Edouard, and Thierry Foucault, 2012, Trading fees and efficiency in limit order markets,Review of Financial Studies 25, 3389-3421.

Corwin, Shane and Paul Schultz, 2012, A simple way to estimate bid-ask spreads from daily high and lowprices, Journal of Finance 67, 719-760.


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Sofianos, George and Ali Yousefi, 2010, Smart routing: Good fills, bad fills and venue toxicity, GoldmanSachs Equity Execution Strats Street Smart 40, 1-9.

Sofianos, George, Juan Juan Xiang, and Ali Yousefi, 2010, Smart routing going to the races: Venuetoxicity comparisons, Goldman Sachs Equity Execution Strats Street Smart 41, 1-8.

Weber, Bruce, 2013, What holds online markets back? An examination of U.S. stockbrokers Rule11Ac1-6 reports, University of Delaware working paper.


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Table I

The Order Routing Decisions of Ten Retail Brokers in NYSE-Listed Securities.

VenueMake/Take

OrderMix

Charles Schwab,Morgan Stanley,

Just2Trade, EdwardJones, & LowTrade

Ameritrade E*Trade FidelityScottTrade

InteractiveBrokers

EDGX+$0.23/-$0.30+$0.32/-$0.29

% Mkt 0% 0% 0% 0%

% Lmt 49% 46% 28% 28%

NDAQ+$0.20/-$0.30+$0.29/-$0.30

% Mkt 3%

% Lmt 7%

Arca+$0.21/-$0.30+$0.30/-$0.30

% Mkt 2%

% Lmt 23%

BZX+$0.25/-$0.29

+$0.29/-$0.29

% Mkt 5%

% Lmt 14%

Lava+$0.24/-$0.28+$0.27/-$0.28

% Mkt 0%

% Lmt 51%

NYSE+$0.15/-$0.23+$0.21/-$0.23

% Mkt 23%

% Lmt 47%

Purchasers$0.0/


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Table II

Descriptive StatisticsProprietary Limit Order Data

Panel A. Order time, size, and price.

Variable Average Minimum 25th Median 75th Maximum

Order Time 12:52pm 9:30am 10:52am 12:51pm 3:01pm 4:00pm

Order Size (shares) 635 1 100 100 300 999,000

Display Size (shares) 85 0 0 0 100 745,547

Limit Price (dollars) 15.20 0.0435 14.80 26.89 46.09 2,398.97

Panel B. Distribution of buys, sells, and short sells.

Order Side % of Orders

Buy 51.71%

Sell 26.88%

Short Sell 21.41%

Panel C. Order display characteristics.

% of Orders

Full Display 28.07%

Partial Display 8.75%

No Display 63.18%

Panel D. Order destination.

Di l d O d Hidd O d% of Displayed % of Hidden


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Table III

Order Outcomes

Panel A. Summary statistics.

# of Orders Average Min. 25th Median 75th Max.

# Shares Executed:All Orders

28,221,514 103 0 0 0 100 745,547

# Shares Executed:(Partially) Filled Orders

10,766,217 270 1 100 100 200 745,547

Average ExecutionPrice

10,766,217 $39.85 $0.05 $16.53 $31.08 $50.39 $2,378.1

Seconds to FirstExecution

10,766,217 98 < 1 3 15 56 23,399

Seconds to LastExecution ( > 1 Fills)

2,452,149 130 < 1 4 20 66 23,399

Seconds to Cancellation:All Cancels

16,431,681 194 < 1 5 28 76 30,499

Seconds to Cancellation:Actively Cancels

16,315,329 158 < 1 5 28 74 2,339

Seconds to OrderRejection

1,682 27 < 1 < 1 < 1 < 1 12,877

Seconds until Order is

Replaced 118,010 26 < 1 1 4 14 22,279

Ratio of Shares Executedto Order Size

10,766,217 0.97 0.00 1.00 1.00 1.00 1.00


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Table IV

Order Aggressiveness

Panel A. Order aggressiveness conditional on order side and display choice.

Limit Order Type

Order Side Display Choice

Buy Orders Sell Orders HiddenPartially or Fully

Displayed

Behind-the-quote 14.90% 12.32% 32.21% 18.56%

At-the-Quote 30.74% 29.76% 58.84% 63.34%

Inside-the-Quote 5.48% 5.24% 7.19% 16.77%

Marketable 0.65% 1.04% 1.88% 1.38%

Panel B. Order aggressiveness of orders conditional on destination venue and display status.

Destination Venue

Traditional InvertedEDGX$0.30 NDAQ

$0.30 ARCA

$0.30 NYSE

$0.23 BZX

$0.29 EDGA

-$0.04 BSX

-$0.14

Behind-the-quote

Displayed 5.90% 14.91% 18.33% 39.76% 7.71% 0.10% 0.22%

Hidden 14.13% 29.72% 37.80% 76.73% 25.50% 15.19% 18.46%

At-the-QuoteDisplayed 81.77% 60.56% 60.37% 39.19% 88.97% 99.87% 99.44%

Hidden 79.37% 59.65% 51.30% 17.35% 59.86% 84.17% 41.28%

Inside-the-Quote Displayed 11.48% 22.42% 19.78% 20.45% 0.96% 0.00% 0.22%

Hidden 5.82% 8.55% 7.17% 5.33% 12.88% 0.00% 21.56%

Di l d 0 92% 2 40% 1 73% 0 68% 2 50% 0 24% 0 27%


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Table V

Limit Order Execution Quality Statistics for At-the-Quote Limit Orders

Panel A: Displayed orders.

VenueTake

Fee/Rebate# Orders Fill Rate

Fill Speed(seconds)

RealizedSpread

Good FillRatio

EDGX $0.30 2,525,513 53.96% 111 -$0.0021 49.32%

NDAQ $0.30 1,500,240 47.96% 59 -$0.0020 49.19%

ARCA $0.30 791,087 53.11% 61 -$0.0019 49.44%

BZX $0.29 486,375 54.66% 76 -$0.0028 50.42%

NYSE $0.23 1,088,393 56.75% 40 -$0.0011 50.39%

EDGA -$0.04 20,529 56.85% 105 $0.0011 57.36%

BSX -$0.14 170,736 74.51% 33 $0.0011 54.81%

Panel A: Hidden orders.

VenueTake

Fee/Rebate# Orders Fill Rate

Fill Speed(seconds)

RealizedSpread

Good FillRatio

EDGX $0.30 4,523,538 50.92% 102 $0.0192 46.92%

NDAQ $0.30 4,228,990 35.64% 127 -$0.0045 47.88%

ARCA $0.30 1,038,667 35.92% 66 -$0.0057 47.12%

BZX $0.29 229,131 44.10% 71 -$0.0046 48.65%


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Table VI

Multivariate Analysis of Limit Order Execution Quality

We use Probit to examine whether fees are associated with the probability that a limit order executes. We

use Ordinary Least Squares to examine the relationship between execution speed and fees.

Probability of Limit OrderExecution

(N = 28,221,392)

Execution Speed:Filled Orders

(N = 10,766,180)

Long Sales 0.0912 1.0520

Short Sales - 0.1143 - 16.5468

Order Displayed? 2.1318 - 73.2609

Average Trading Volume x 100 - 0.0004 - 0.1880

Stock Price 0.0486 - 0.5916

Stock Price Volatility - 0.0405 - 0.4178

Order Placement Time x 100 0.0008 - 0.4730

Venue Response Time - 4.4538 - 390.29

Moneyness - 797.3 59193.0

Order Size - 0.0109 1.7543

Take Fee 498.2 4828.90

Order Display * Take Fee - 597.8 13353.0

c/R2 0.737 0.0419


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42

Table VIITime at the Inside Quotes, Market Share, and Spread Decomposition by Trading Venue

% Time at Inside/Market Share

NYSE AMEX BSX NSX CHX ARCA NDAQ PSX BZX BYX EDGA EDGX CBOE

Panel A - NYSE Stocks (N=1210)

Time at the Inside:At Bid 74.39 0.00 20.74 11.26 0.03 56.69 52.98 13.09 44.43 28.26 14.62 38.59 21.85

At Ask 73.50 0.00 20.88 11.61 0.02 57.20 53.72 13.36 44.61 29.27 15.14 39.42 22.32

At One Side 91.37 0.00 35.99 18.58 0.05 76.57 70.64 21.43 61.79 46.01 25.18 55.64 38.53

At Both Sides 56.52 0.00 5.63 4.28 0.00 37.31 36.05 4.93 27.25 11.52 4.58 22.37 5.64

Market Share:

% All Trades 19.66 0.00 3.52 0.31 0.00 13.57 14.05 0.42 9.60 4.65 1.92 7.26 0.71

% All Share Volume 22.28 0.00 2.65 0.34 0.05 12.00 11.90 0.42 7.72 3.44 1.41 7.00 0.51

% No Sweep Trades 17.12 0.00 2.80 0.15 0.00 10.56 11.25 0.31 8.16 3.30 1.62 6.05 0.61

% No Sweep Volume 18.81 0.00 2.07 0.17 0.06 9.34 9.15 0.28 6.31 2.33 1.17 5.64 0.43

Bid-Ask SpreadsAll Trades:

Total Trades (000) 5,789 - 1,667 130 - 6,241 8,343 360 5,467 2,849 1,279 3,727 381

Realized Spread () -0.32 - 0.50 -0.62 - -0.60 -0.47 -0.21 -0.36 0.24 0.17 -0.70 0.85

Realized Spread (bps) -0.47 - 3.13 -2.57 - -1.86 -1.60 -0.37 -1.40 1.72 1.79 -2.50 5.59

Bid-Ask Spreads - No Sweep Trades:

Total Trades (000) 2,999 - 749 36 - 2,642 3,826 153 2,631 1,205 675 1,937 204

Effective Spread () 2.01 - 1.88 1.89 - 1.85 1.62 1.36 1.55 1.53 1.41 1.48 1.64

Price Impact () 2.11 - 1.20 2.46 - 2.35 1.94 1.49 1.78 1.20 1.14 2.02 0.76

Realized Spread () -0.10 - 0.68 -0.57 - -0.50 -0.32 -0.13 -0.23 0.33 0.27 -0.55 0.88Effective Spread (bps) 7.68 - 9.57 7.60 - 7.29 6.84 7.85 6.76 8.57 8.73 7.16 8.42

Price Impact (bps) 7.42 - 5.50 10.41 - 8.99 8.12 8.02 7.81 6.20 6.28 9.44 3.16

Realized Spread (bps) 0.27 - 4.07 -2.81 - -1.70 -1.29 -0.16 -1.05 2.37 2.45 -2.28 5.26


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43

Table VII (continued)

% Time at Inside/Market Share

NYSE AMEX BSX NSX CHX ARCA NDAQ PSX BZX BYX EDGA EDGX CBOE

Panel BNasdaq Stocks (N=1960)

Time at the Inside:

At Bid 0.00 2.22 14.62 9.12 0.14 42.69 68.24 7.14 32.15 20.40 9.85 35.62 18.05At Ask 0.00 2.32 15.03 9.13 0.07 42.77 69.08 7.65 33.37 21.82 10.48 36.24 18.67

At One Side 0.00 2.97 25.00 15.14 0.20 62.50 87.52 11.48 48.87 34.45 17.33 54.21 32.20

At Both Sides 0.00 1.57 4.66 3.11 0.01 22.97 49.81 3.31 16.65 7.78 3.00 17.66 4.52

Market Share:

% All Trades 0.00 0.03 3.06 0.64 0.07 12.23 29.48 0.32 8.79 3.70 1.76 10.77 0.89

% All Share Volume 0.00 0.02 2.19 0.59 0.13 10.76 27.42 0.33 6.96 2.64 1.25 11.77 0.64

% No Sweep Trades 0.00 0.01 2.65 0.24 0.02 9.83 20.68 0.22 7.42 2.86 1.71 8.05 0.59

% No Sweep Volume 0.00 0.01 1.84 0.22 0.05 8.68 18.17 0.20 5.65 1.97 1.19 8.38 0.41

Bid-Ask Spreads - All Trades:Total Trades (000) - 43 1,010 88 - 3,328 5,993 253 3,388 1,430 646 2,155 209

Realized Spread () - -0.25 0.57 -0.45 - -0.37 -0.55 -0.12 -0.33 0.32 0.31 -0.72 0.98

Realized Spread (bps) - -1.45 4.81 -4.49 - -1.89 -2.45 -0.50 -1.55 2.31 3.14 -3.66 9.14

Bid-Ask Spreads - No Sweep Trades:

Total Trades (000) - 6 470 22 - 1,605 2,838 96 1,739 629 356 1,124 104

Effective Spread () - 1.09 2.56 2.47 - 2.33 2.17 1.46 1.85 1.93 1.96 2.01 1.83

Price Impact () - 1.32 1.76 2.58 - 2.54 2.56 1.48 2.05 1.52 1.40 2.61 0.88

Realized Spread () - -0.22 0.80 -1.06 - -0.21 -0.39 -0.02 -0.20 0.40 0.56 -0.60 0.94

Effective Spread (bps) - 15.64 15.07 21.13 - 11.38 11.52 12.35 10.01 13.13 15.10 13.04 13.61Price Impact (bps) - 17.22 9.02 18.55 - 12.92 13.50 12.22 10.84 9.99 10.67 16.51 5.17

Realized Spread (bps) - -1.58 6.05 2.57 - -1.54 -1.97 0.14 -0.83 3.15 4.44 -3.47 8.44

Notes:The tables provides summary statistics for time at the inside quote, market share, and effective spread decomposition by trading venue.Time at the inside is computed for each stock-day as the proportion of seconds during the day for which the venue is equal to either the NBBO bid,the NBBO ask, or both. Market share is computed for each stock-day as the proportion of trades (volume) executed on each venue, both includingand excluding intermarket sweep orders. For each stock, time at the inside and market share are averaged across all trading days during the sample

period. The table then lists the cross-sectional average of these stock-specific means. For each venue, Total Quoted Depth is a trade-weightedaverage of bid plus ask depth across all non-sweep trades and spread components are calculated as an equal-weighted average across all non-sweep trades executed on that venue. Trade direction is determined based on the Lee-Ready algorithm. For buy (sell) trades, the Effective Spread


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45

Table VIII

Market Share of at-the-quote Trades when all Venues are at the Inside

NYSE Stocks Nasdaq Stocks

TakeFee ($)

MarketShare(%)

QtdDepth(000)

QtdDepthOwn

Trades(000)

RealizedSpread(bps)

RealizedSpreadNet of

Fee(bps)

GoodFill

Ratio(%)

MarketShare(%)

QtdDepth(000)

QtdDepthOwn

Trades(000)

RealizedSpread(bps)

RealizedSpreadNet of

Fee(bps)

GoodFill

Ratio(%)

BSX (B) -0.14 9.04 2.32 2.91 6.18 4.19 59.16 9.16 2.57 3.21 7.37 4.71 60.68

EDGA (J) -0.04 6.67 2.34 3.42 3.70 3.07 56.31 6.03 2.63 5.17 4.52 3.63 57.81

BYX (Y) -0.02 7.11 2.73 2.91 3.93 3.59 55.49 6.83 2.87 2.82 3.38 2.97 55.81

NYSE (N) 0.23 6.43 11.93 10.21 4.33 5.81 55.48 NA NA NA NA NA NA

BZX (Z) 0.29 6.92 8.28 6.02 -3.32 -1.06 46.85 9.34 11.08 8.37 -2.81 -0.09 47.90

NDAQ (T/Q) 0.30 11.23 12.15 7.99 -3.08 -1.43 46.52 13.85 21.63 13.07 -3.75 -1.58 46.49ARCA (P) 0.30 7.17 11.08 7.82 -3.25 -1.42 46.39 8.22 11.28 9.00 -2.43 -0.06 47.50

EDGX (K) 0.30 7.30 9.86 8.81 -1.26 1.00 49.15 6.37 11.06 7.88 -3.52 -0.57 46.78

Number of Stocks 850 831

Avg. Trades perStock

4,254 2,586

Notes:The table provides summary statistics for market share of at-the-quote trades and trade outcomes when all venues are simultaneously at the

inside quote. The analysis is limited to trades that occur at the NBBO bid (ask) when all relevant venues are simultaneously posting quotes equal

to the NBBO bid (ask). The relevant exchanges considered 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.Intermarket sweep trades are excluded. Market Share is defined across all trades on all venues and Quoted Depth is defined as an equal weighted

average across all trades. Quoted Depth on Own Trades, Realized Spread, and Good Fill Ratio are defined as equal weighted averages across all

relevant trades on a particular venue. Quoted depth is a venues posted depth on the relevant side of the market. Good Fill ratio is defined as the

proportion of trades with realized spread greater than zero. For trades at the NBO (NBB), the percentage Realized Spread is defined as two

(negative two) times the difference between the trade price and the NBBO midpoint five minutes after the trade, divided by the NBBO midpoint at

the time of the trade. Realized Spread Net of Fee is defined as the Realized Spread net of any Take Fee or Rebate. Results are reported separately

for stocks listed on the NYSE and Nasdaq.


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Table IX

Realized Spread Regressions

Panel ANYSE Stocks

At-the-Quote TradesAt-the-Quote Trades

with All Venues at the Inside

Intercept 0.0538(0.000)

0.0654(0.000)

0.0238(0.000)

0.0706(0.000)

0.1153(0.000)

0.0613(0.000)

0.0506(0.000)

0.1106(0.000)

Ln(Avg. Volume) 0.0038(0.000)

0.0033(0.000)

0.0031(0.000)

0.0043(0.000)

0.0046(0.000)

0.0073(0.000)

0.0049(0.000)

0.0067(0.000)

Ln(Trade Price) -0.0083

(0.000)

-0.0108

(0.000)

-0.0081

(0.000)

-0.0084

(0.000)

-0.0356

(0.000)

-0.0324

(0.000)

-0.0290

(0.000)

-0.0291

(0.000)

Ln(Trade Size) -0.0112(0.000)

-0.0108(0.000)

-0.0105(0.000)

-0.0127(0.000)

-0.0141(0.000)

-0.0134(0.000)

-0.0141(0.000)

-0.0183(0.000)

Ln(Daily High/Low) -0.3632(0.000)

-0.3582(0.000)

-0.3644(0.000)

-0.3569(0.000)

-0.2208(0.000)

-0.2718(0.000)

-0.2447(0.000)

-0.2228(0.000)

Take Fee -0.1327(0.000)

-0.1212(0.000)

- - -0.1382(0.000)

-0.1268(0.000)

- -

Take Fee * Min_Fee 0.0159

(0.000)

- - - - - - -

Take Fee * Low_Price - -0.0974(0.000)

- - -0.1794(0.000)

- - -

Take Fee * High_Depth - - - - - -0.0767(0.000)

- -

Inverted Dummy - - 0.0406(0.000)

- - - 0.0546(0.000)

-

Take Fee =-$0.04 - - - -0.0172

(0.000)

- - - -0.0242

(0.000)

Take Fee =-$0.02 - - - -0.0187(0.000)

- - - -0.0205(0.000)


48/58


49/58


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49

Figure 1

Horseraces: All Order Pairs

Panel A. Percentage of order pairs in which both orders fill. For each pair of venues, the venue with the lower take fee is listed first.

Panel B. Percentage of horseraces involving all orders won by venue with lower fee (excluding ties). For each pair of venues, the venue with the

lower take fee is listed first.

22.61%

34.69%39.22%

35.27%

64.73%

54.75%

11.13% 9.34%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%90.00%

100.00%

BZX vs ARCAFee Diff = $0.01

N = 1,698

BZX vs NDAQFee Diff = $0.01

N = 13,559

BZX vs EDGXFee Diff = $0.01

N = 32,300

NYSE vs BZXFee Diff = $0.06

N = 3,368

NYSE vs ARCAFee Diff = $0.07

N = 743

NYSE vs NDAQFee Diff = $0.07

N = 79,157

NYSE vs EDGXFee Diff = $0.07

N = 719

BSX vs NDAQFee Diff = $0.44

N = 3,374

23.59%

68.35%64.02%

54.45%60.01%

68.78%

93.79%99.74%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


N = 1,433


N = 9,811


N = 24,309


N = 2,637


N = 550


N = 51,240

NYSE vs EDGXFee Diff = $0.07

N = 676

BSX vs NDAQFee Diff = $0.44

N = 3,128


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50

Figure 1 (continued)

Panel C. Good fill ratios for orders executed in horseraces.

Notes:An Order Pair (e.g., a horserace) involves a pair of limit orders 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. Take fee is the cost of accessing 100 shares of

liquidity on the given venue. The Good Fill Ratio is the percentage of executed limit orders with positive realized spreads. Take fees are expressed

in cents per 100 shares.

55.91% 56.30%52.13% 50.98%

53.24% 52.98% 52.57%49.65%

57.15%54.42%

49.83%47.11%

63.23%

54.08% 52.91%

41.88%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BZX$0.29

ARCA$0.30

BZX$0.29

NDAQ$0.30

BZX$0.29

EDGX$0.30

NYSE$0.23

BZX$0.29

NYSE$0.23

ARCA$0.30

NYSE$0.23

NDAQ$0.30

NYSE$0.23

EDGX$0.30

BSX-$0.14

NDAQ$0.30


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51

Figure 2

Horseraces: Displayed Order Pairs

Panel A. Percentage of displayed order pairs in which both orders fill. For each pair of venues, the venue with the lower take fee is listed first.

Panel B. Percentage of horseraces involving displayed orders won by venue with lower fee (excluding ties). For each pair of venues, the venue

with the lower take fee is listed first.

33.80%

51.24%44.90% 46.68%

63.96%57.78%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%90.00%

100.00%


N = 213


N = 564


N = 4,493


N = 407


N = 197


N = 2,307

85.30%81.04%

71.95%

51.27%

65.77%69.88%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


N = 170


N = 327


N = 2,934


N = 310


N = 149


N = 1,604


53/58

52

Figure 2 (continued)

Notes:An Order Pair (e.g., a horserace) involves a pair of limit orders 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. Take fee is the cost of accessing 100 shares of

liquidity on the given venue. The Good Fill Ratio is the percentage of executed limit orders with positive realized spreads. Take fees are expressed

in cents per 100 shares.


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Appendix: Details on the horserace analysis.

In the first two columns, we denote the venue pair, with the ordering consistent with the ordering in

the data. That is, ARCA-NYSE indicates that the first identical order in these order pairs went to ARCA and

the second order in these order pairs went to the NYSE. Conversely, NYSE-ARCA indicates that the first

order went to the NYSE and the second went to ARCA. The third column indicates the number of pairs we

find for the indicated venue pair. We see that for some venue pairs, the ordering appears to be roughly equal

(e.g., there are 336 pairs in which ARCA receives the order before the NYSE and 407 pairs in which the

NYSE receives the order before ARCA) while in others one venue is clearly favored (e.g., there are 30,637

pairs in which EDGX receives the order before BZX and only 1,633 pairs in which BZX receives the order

before EDGX). In the fourth column, we report the percentage of order pairs in which both orders execute. In

the fifth column, for the order pairs where both orders execute, we present the difference in times to execution

for each of the two orders in the order pair. If the order resting at the first venue executes before (after) the

order on the second venues order book fills, this difference is negative (posit ive). We present the outcomes of

our horseraces in columns 6, 7 and 8. We declare the outcome of a horserace a tie if both orders in an order

pair execute within 500 s of one another. Otherwise, the order filling first is declared the winner. Finally, in

columns 9 (column 10), we document the percentage of orders executed on the first venue (second venue) that


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order, it wins 44.96% of the horseraces compared to 28.01% for ARCA. For limit orders that execute in

contests between the NYSE and ARCA, the NYSEs good fill ratio exceeds ARCAs good fill ratio.

Together, we interpret the data in the first two rows of Panel A as suggesting that, all else equal, our sample

limit orders receive better execution quality on the NYSE than on ARCA.

Rows three through eight of Panel A contain the remainder of the contests involving the NYSE. Over

79,000 pairs of identical orders are routed to the NYSE and NDAQ, which has a take fee of $0.30 per hundred

shares. Both orders execute in 63% of the order pairs when NDAQ receives the first order. When the NYSE

receives the first order, both orders execute only 39% of the time. Regardless of who receives the first order,

the average time to execution is shorter on the NYSE. Nearly 40% of the 51,474 order pairs in which NDAQ

receives the first order execute on both the NYSE and on NDAQ within 500 s of each another. The NYSE

wins 64.41% (38.83/(38.83+21.46)) of the remaining horseraces and it has a higher good fill ratio for its

executed limit orders. Excluding ties, the NYSEs advantage over NDAQ improves to 75. 48% when it

receives the identical order prior to NDAQ. Of the horseraces between the NYSE and EDGX in which there is

a clear winner, the NYSE wins 89.12% of the time when EDGX receives the order first and the NYSE wins

96.34% of the time when the NYSE receives the first order. In addition winning a large majority of the

horseraces, the NYSE also has a good fill ratio that is 799 basis points higher than EDGXs when EDGX


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given the closeness of the take fees, the execution quality differences here are much less definitive. The lower-

fee BZX provides better executions (as we measure execution quality) when compared to EDGX. However,

ARCA wins more of the horseraces than does the lower-fee BZX and it is difficult to pick a winner comparing

BZX and NDAQ. Finally, in the only set of contests pairing an inverted and a traditional venue, BSX wins

nearly all of the NDAQ contests. In the 3,256 cases in which BSX receives the first order, the BSX wins

99.77% contests that do not result in a tie.

In Panel B of Appendix Table I we require both members of the order pair to display at least part of

the orders size. There are six different horseraces presented in Panel B. In five of the horseraces, independent

of who receives the first order, the venue with the lowest take fee wins. Only in the comparison of order pairs

routed to the BZX and the NYSE, with the BZX receiving the first order, does the venue with the higher take

fee prevail. However, in this instance the margin of victory is smallBZX wins 50.21% of the horseraces.


57/58

56

Appendix Table I

Limit Order Horseraces

Panel A. All order pairs when venues have different take fees.

Order Pair# of Order

Pairs

% Both

Fill

Difference in

Time to 1stFill

% 1stVenue

Trades First

% 2ndVenue

Trades First % Tie

Good Fill Ratio

1stVenue(take fee)

2n Venue(take fee)

1stVenue 2ndVenue

Arca ($0.30) NYSE ($0.23) 336 62.50 13.24 31.85 43.75 24.40 58.62 61.67

NYSE ($0.23) Arca ($0.30) 407 66.58 -12.40 44.96 28.01 27.30 53.42 50.96

NDAQ ($0.30) NYSE ($0.23) 51,474 63.10 0.86 21.46 38.83 39.72 47.86 49.81

NYSE ($0.23) NDAQ ($0.30) 27,683 39.21 -3.98 55.11 17.90 26.99 49.87 45.72

EDGX ($0.30) NYSE ($0.23) 258 14.34 1.01 10.08 82.56 7.36 51.79 58.78

NYSE ($0.23) EDGX ($0.30) 461 9.33 57.91 91.32 3.47 5.21 65.72 55.36

BZX ($0.29) NYSE($0.23) 2,205 32.47 -3.78 37.19 41.54 21.27 49.33 53.40

NYSE ($0.23) BZX ($0.29) 1,163 40.58 -1.06 44.71 32.76 22.53 51.00 50.26

Arca ($0.30) BZX ($0.29) 386 20.98 -105.14 68.13 16.32 15.54 56.18 53.38

BZX ($0.29) Arca ($0.30) 1,312 23.09 -7.56 20.96 63.41 15.63 56.65 56.33

EDGX ($0.30) BZX ($0.29) 30,637 39.33 12.19 27.13 48.08 24.79 53.08 53.31

BZX ($0.29) EDGX ($0.30) 1,663 37.16 -20.67 50.03 26.16 23.81 51.98 51.05

NDAQ ($0.30) BZX ($0.29) 4,530 49.69 -2.34 34.22 27.55 38.04 50.93 50.82

BZX ($0.29) NDAQ ($0.30) 9,029 27.16 0.27 60.44 17.13 22.43 52.83 50.40

NDAQ ($0.30) BSX (-$0.14) 118 11.02 16.53 0.85 93.22 5.93 60.00 59.32

BSX ($0.14) NDAQ ($0.30) 3,256 9.28 -3.18 92.44 0.21 7.34 52.68 41.22


58/58

57

Appendix Table I (Continued)

Panel B.

Displayed order pairs when venues have different take fees.

Order Pair# of Order

Pairs

% Both

Fill

Difference in

Time to 1stFill

% 1stVenue

Trades First

% 2ndVenue

Trades First % Tie

Good Fill Ratio

1stVenue(take fee)

2n Venue(take fee)

1stVenue 2ndVenue

Arca ($0.30) NYSE ($0.23) 73 61.64 0.47 26.03 53.42 20.55

NYSE($0.23) Arca ($0.30) 124 65.32 -19.37 47.58 25.81 26.61

NDAQ ($0.30) NYSE ($0.23) 1,715 56.27 -8.31 20.47 50.20 29.33

NYSE ($0.23) NDAQ ($0.30) 592 62.16 -35.17 43.92 22.30 33.78

BZX ($0.29) NYSE ($0.23) 336 45.24 -7.48 38.10 37.78 24.11

NYSE ($0.23) BZX ($0.29) 71 53.52 -9.53 45.07 32.39 22.54

Arca ($0.30) BZX ($0.29) 9 44.44 24.79 22.22 55.56 22.22

BZX ($0.29) Arca ($0.30) 204 33.33 -52.09 68.63 11.27 20.10

EDGX ($0.30) BZX($0.29) 4,318 44.91 0.05 18.32 46.99 34.69

BZX ($0.29) EDGX ($0.30) 175 44.57 0.11 46.86 18.29 34.86

NDAQ ($0.30) BZX ($0.29) 185 69.19 61.93 12.43 27.03 60.54

BZX ($0.29) NDAQ ($0.30) 379 42.48 -12.43 56.73 10.29 32.98

Notes: An Order Pair (e.g., a horserace) involves a pair of limit orders 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. The 1st(2nd) venue receives the first (second) order of

the order pair. Take fee is the cost of accessing 100 shares of liquidity on the given venue. % Both Fill refers to the percentage of order pairs in

which both orders receive at least a partial execution. For the order pairs where both orders execute, Difference in Time to 1stFill is the difference

in times to execution for each of the two orders in the order pair. If the order resting at the first venue executes before (after) the order on the

second venues order book fills, this difference is negative (positive). % 1stVenue Trades First (% 2ndVenue Trades First) is the percentage of

horseraces won by the first (second) venue. We declare a horserace a tie if both orders execute within 500 s of one another. The Good Fill Ratio

is the percentage of executed limit orders with positive realized spreads. Take fees are expressed in cents per 100 shares.

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