6.................  No..................  On..................  Odd................  Penny..............            11.5            16.9             4.8
7.................  No..................  On..................  Round..............  Sub-Penny..........             1.1             1.6             7.1
8.................  No..................  On..................  Round..............  Penny..............            10.7            15.8             7.0
9.................  No..................  Off.................  Odd................  Sub-Penny..........             1.4             2.0             3.1
10................  No..................  Off.................  Odd................  Penny..............             1.7             2.5             6.2
11................  No..................  Off.................  Round..............  Sub-Penny..........             9.2            13.6             3.7
12................  No..................  Off.................  Round..............  Penny..............             8.0            11.8             8.9
                                                                                -----------------------------------------------

    Total PI......  ....................  ....................  ...................  ...................            67.8             100  ..............
--------------------------------------------------------------------------------------------------------------------------------------------------------
\a\ This table provides estimates of the average daily volume of trades receiving some form of price improvement from Jan. 2022 to June 2022, as well as
  estimates of the magnitude of the price improvement received. For purposes of this table, a trade is determined to have received price improvement if
  it occurred within the prevailing NBBO at the time of the trade. The numbers in the table represent daily averages. Panel A provides statistics for
  the total number of trades as well as the total share and dollar volume of trades that receive some form of price improvement while Panel B provides
  estimates of the total dollar value of the price improvement that is received. Price improvement statistics are computed for 12 categories of trade
  representing a unique combination of trading volume associated with midpoint, on versus off exchange, round versus odd-lot, and sub-penny versus penny
  transactions. The analysis includes all normal trades that execute during normal trading hours from TAQ. Normal trades are identified in TAQ data by
  sale conditions ``blank, @, E, F, I, S, Y'' which correspond to regular trades, intermarket sweep orders, odd lot trades, split trades, and yellow
  flag regular trades. A trade receives price improvement if it executes inside the prevailing NBBO. Price improvement for trades that occur above the
  prevailing midpoint is equal to the (NBO-price)*number of shares transacted, for trades that occur below the prevailing midpoint price improvement is
  equal to the (price-NBB)*number of shares transacted. For trades that occur at the midpoint price improvement is equal to one half the spread between
  the NBO and NBB multiplied by the number of shares transacted. Estimates are provided based on midpoint, location of the trade, odd-lot status, and
  sub-penny status. A trade is defined as a midpoint trade if it occurs at exactly the midpoint of the NBB and NBO at the time of the trade. Off
  exchange trades are those with exchange code `D' attached to the trade. Odd-lot trades are trades in sizes other than multiples of 100, and sub penny
  trades are those executing in prices with three or more decimal places.
\b\ See MDI Adopting Release, supra note 5. The effect of amending the definition of odd-lot information to include the best odd-lot quotes and
  accelerating the inclusion of odd-lot information might be marginal should the proposal described in a Mar. 2022 request for comment by the SIPs, CTA/
  UTP SIP Odd-Lot Request for Comment (available at https://www.ctaplan.com/publicdocs/ctaplan/CTA_Odd_Lots_Proposal_2022.pdf), be filed with the
  Commission as a proposed Plan amendment and approved. Even if that proposal were filed with the Commission and approved, however, it would result in
  the provision of less odd-lot information than would become available under this proposed rule. See supra notes 374, 386.

c. Tick Sizes and Quoted Spreads
    Table 4 presents data on trading volume in the first five months of
2022 based on average time weighted quoted spreads. The analysis breaks
trading volume each day into one of 16 average quoted spread buckets.
The first bucket is for tick-constrained stocks, which we define
empirically as stocks that have time weighted quoted spreads less than
or equal to $0.011 on a given day.\473\ The second bucket is for near-
tick-constrained stocks with quoted spreads less than $0.02. Each
succeeding bin increases the spread by $0.01 until the last bin which
contains all stocks with quoted spreads greater than $0.15. This
analysis indicates that 56% of share trading volume (23% of dollar
volume) occurs in stocks that are tick-constrained. That is, in the
absence of a one cent tick, these stocks would likely have quoted
spreads that are narrower than what they currently experience. Table 4
also reports that 15% of share volume (22% of dollar volume) occurs in
stocks that are near-tick-constrained. Table 4 also reports the daily
average number of stocks in each bin.
---------------------------------------------------------------------------

    \473\ Other studies may define tick-constrained stocks
differently. See also supra note 17 and supra note 448 and
accompanying text defining and discussing ``tick-constrained'' for
the release.

                           Table 4--Share Volume by Quoted Spread Jan. to May 2022 \a\
----------------------------------------------------------------------------------------------------------------
                                                                   Share volume    Dollar volume     Average #
                          Quoted spread                                 (%)             (%)           stocks
----------------------------------------------------------------------------------------------------------------
Quoted Spread < $0.011..........................................            56.1            23.2           1,337
$0.011< Quoted Spread <= $0.02..................................            15.5            21.7           1,093
$0.02 < Quoted Spread <= $0.03..................................             7.8             9.8           1,170
$0.03 < Quoted Spread <= $0.04..................................             4.2             5.7             946
$0.04 < Quoted Spread <= $0.05..................................             2.5             3.2             762
$0.05 < Quoted Spread <= $0.06..................................             1.8             2.5             629
$0.06 < Quoted Spread <= $0.07..................................             1.2             1.8             531
$0.07 < Quoted Spread <= $0.08..................................             1.2             2.1             468
$0.08 < Quoted Spread <= $0.09..................................             1.0             1.7             426
$0.09 < Quoted Spread <= $0.10..................................             0.9             1.5             383
$0.10 < Quoted Spread <= $0.11..................................             0.8             1.3             337
$0.11 < Quoted Spread <= $0.12..................................             0.7             1.1             279
$0.12 < Quoted Spread <= $0.13..................................             0.6             1.0             243
$0.13 < Quoted Spread <= $0.14..................................             0.5             0.9             214
$0.14 < Quoted Spread <= $0.15..................................             0.5             0.8             190
$0.15 < Quoted Spread...........................................             4.8            21.6           2,500
----------------------------------------------------------------------------------------------------------------
\a\ This table provides share volume by stocks with different quoted spread profiles. To create this table, for
  each day the universe of stocks covered in the WRDS Intra-Day Indicators data are assigned into one of the 16
  quoted spread bins based on that days' time-weighted quoted spread as computed by WRDS Intra-Day Indicators.
  Then all share and dollar trading volume across all trading days in Jan.-May 2022 is aggregated for each of
  the 16 quoted spread bins. Percentages based on these totals are then computed. This table also presents the
  daily average number of stocks in each bin. To compute this variable, for each trading day in Jan.-May 2022
  the number of stocks in each bin is tabulated, then the average across all trading days is presented here.
  Certain items in this Table 4 may also be affected by the MDI Rules once they are fully implemented. See Table
  3 note b.

    For tick-constrained stocks, spreads are potentially wider than
they would otherwise be. Wider quoted spreads mean greater cost to
liquidity demanders and greater revenue to liquidity suppliers.\474\ An
artificially wide spread due to the tick constraint subsidizes
liquidity provision. Because the compensation is above what would exist
in a competitive market there is an increased incentive to provide
liquidity via limit orders, so queues of limit orders tend to be
longer, wait times to get a limit order executed also tend to be
longer, and, thus the likelihood that the market moves away from an
investor's limit order increases, leading to lower overall fill rates
for limit orders.\475\ These dynamics mean that some investors who
might originally have wanted to trade using a limit order and earn the
quoted spread instead trade using a marketable order and pay the quoted
spread.\476\ There is also evidence that when tick sizes are too wide
volatility can increase.\477\
---------------------------------------------------------------------------

    \474\ Market participants can use inverted exchanges or ISO
orders to help ameliorate some of the negative effects of tick size
constraints.
    \475\ See e.g., Barbara Rindi and Ingrid M. Werner, U.S. Tick
Size Pilot (working paper Mar. 4, 2019), available at https://ssrn.com/abstract=3041644 (retrieved from SSRN Elsevier Database);
Mao Ye and Chen Yao, Tick Size Constraints, Market Structure and
Liquidity (working paper Dec. 26, 2019), available at https://ssrn.com/abstract=2359000 (retrieved from SSRN Elsevier database);
Phil Mackintosh, Why Ticks Matter, Nasdaq (May 19, 2022), available
at https://www.nasdaq.com/articles/why-ticks-matter; and MEMX
Report, supra note 105.
    \476\ See, e.g., Roberto Ricc[oacute] et al., Optimal Market
Asset Pricing (working paper Feb. 4, 2021), available at https://ssrn.com/abstract=3779195 (retrieved from SSRN Elsevier database)
(showing in a theoretical model that rebates can be optimal for
exchanges because they both induce and attract high-frequency
trading activity).
    \477\ See, e.g., Edwin Hu, et al., Tick Size Pilot Plan and
Market Quality (DERA White Paper, Jan. 31, 2018), available at
https://www.sec.gov/files/dera_wp_tick_size-market_quality.pdf;
Hendrick Bessembinder, Trade Execution Costs and Market Quality
After Decimalization, 38 J. Fin. & Quantitative Analysis 747 (2003);
and Tavy Ronen and Daniel G. Weaver, Teenies Anyone?, 4 J. Fin. Mkt.
231 (2001).
---------------------------------------------------------------------------

    When a stock is tick-constrained or near-tick-constrained it is
particularly important for a liquidity provider to get its quote to the
front of the queue (i.e., establish price/time priority on an order
book). Stock exchange priority rules give greater priority to better
priced orders and generally factor order entry time into the priority
of limit orders at the same price. Because liquidity providers cannot
establish price priority in when the NBBO spread is one tick,
establishing time priority becomes more important.\478\ Consequently,
an environment where stocks are tick-constrained with artificially
wider spreads and longer order queues tends to favor traders who are
better able to establish positions more quickly so they can be at the
front of the queue. Often the key differentiator to get to the front of
the queue, and to avoid unfavorable executions once there, is
speed.\479\ If a liquidity provider is too slow to establish a new
quote, then that quote could be buried in the queue. Conversely, even
with a favorable position in the queue, if the market moves in an
economically disadvantageous manner to a liquidity provider, it will
seek to avoid an adverse execution (e.g., by canceling an order) and
adjust its order to account for the new prevailing price of the NBB
(NBO). Liquidity providers that are too slow run the risk of having
their now-stale quote ``sniped.'' \480\ Sniping is costly to those
liquidity providers who get sniped and, so, effectively adds to adverse
selection risk for slower liquidity providers.\481\
---------------------------------------------------------------------------

    \478\ An order with time priority is executed first when
multiple orders are at the best price, regardless of how many orders
are at the best price. In longer order queues, liquidity-providing
orders deeper in the queue, which do not have time priority, are
less likely to be filled in a timely manner and, conditional on
being filled, the probability of the order having been adversely
selected tends to be greater compared to orders with greater fill
priority. Typically, liquidity providers compete to gain priority
over other resting orders by quoting a better price but tick-
constraints make doing so difficult. In the case when the spread is
constrained to a single tick, it would be impossible to improve on
the displayed price without locking markets. Even for near-tick-
constrained stocks, when the quoted spread may be greater than a
single tick, improving the price by an entire tick may be too much
in the sense that doing so may narrow the spread beyond what the
liquidity providers could tolerate. A narrower tick de-emphasizes
time priority on a stock exchange by making it easier to compete on
price. See Hu, et al. (2018), supra note 477; and Todd G. Griffith
and Brian S. Roseman, Making Cents of Tick Sizes: The Effect of the
2016 U.S. SEC Tick Size Pilot on Limit Order Book Liquidity, 101 J.
Banking Fin. 104 (2019).
    \479\ See, e.g., Chen Yao and Mao Ye, Why Trading Speed Matters:
A Tale of Queue Rationing Under Price Controls, 31 Rev. Fin. Stud.
2157 (2018).
    \480\ Sniping occurs when prices move against a quote and a very
fast market participant executes the now stale quote before the
quote submitter can cancel the now stale quote. See Sida Li, et al.,
Who Provides Liquidity, and When?, 141 J. Fin. Econ. 968 (2021).
    \481\ Id.
---------------------------------------------------------------------------

    Trading quality among stocks that are near-tick-constrained, can
also be significantly affected by the tick size.\482\ For example,
consider a stock that would otherwise trade at an offer price of
$10.015 and a bid of $10.005 absent the $0.01 tick. This stock would
have a spread of $0.01. However, due to tick constraints the stock will
quote at the best feasible ask price above $10.015 \483\ which is
$10.02 and the best feasible bid price below $10.005 which is
$10.00.\484\ Consequently, due to the tick constraint, the stock's
actual quoted spread is $0.02 instead of $0.01, or 100% wider than the
spread would otherwise be. According to the analysis in Table 4
approximately 16% of trading volume occurs in stocks that are near-
tick-constrained. Combining this volume with the 56% of trading volume
that occurs in tick-constrained stocks means that approximately 72% of
share trading volume in current markets occurs in stocks that are tick-
constrained or near-tick-constrained.
---------------------------------------------------------------------------

    \482\ See supra notes 449 and 458 for the empirical definition
and discussion of near-tick-constrained stocks. Near-tick-
constrained stocks are those with time average quoted spreads
greater than $0.011 and less than $0.02.
    \483\ This assumes that stock prices are expected to revert to
the next worse level. This may occur because standard economic
theory suggests that in a competitive market liquidity providers
will compete to provide liquidity until the spread--i.e., their
compensation for providing liquidity--is equal to the break-even
point for liquidity provision. See also Jonathan Brogaard and Corey
Garriott, High-frequency Trading Competition, 54 J. Fin. &
Quantitative Analysis 1469 (2019) (documenting that as more high-
frequency liquidity providers enter the market, spreads decrease
until they converge to competitive levels).
    \484\ The range of infeasible quoting prices narrows somewhat in
the presence of rebates for liquidity providers. Section V.C.2
discusses these effects.
---------------------------------------------------------------------------

2. Access Fees
    The market for trading services in NMS stocks where traders either
demand or supply liquidity is primarily served by the national equity
exchanges and ATSs along with 6 wholesalers who internalize large
portions of individual investor order flow. Exchanges and ATSs charge
an access fee or pay a rebate to either those demanding liquidity or to
those supplying it. Liquidity is typically provided through the
provision of passive limit orders, which commit to execute against
marketable orders that take liquidity. Rebates are typically captured
by traders, such as market makers and some high-frequency traders,
which specialize in the provision of liquidity and access fees are
typically paid by demanders of liquidity.
    Rule 610(c) limits exchange fees for accessing protected quotations
with prices of $1.00 per share or greater to $0.0030 per share (or 30
cents per 100 shares). This level is commonly referred to as 30 mils.
The rule also prohibits access fees in excess of 0.3% of the price for
stocks priced less than $1.00 per share. The 30 mil fee cap was adopted
as a part of Regulation NMS in conjunction with the order protection
rule and was implemented to prevent exchanges from charging excessive
fees to orders that were required to trade with a protected quote. The
30 mil fee

cap was also determined based on existing market practices.\485\ Rule
610(c) only regulates fees to access protected quotes; it does not
regulate fees to access non-protected quotes, nor does it regulate
rebates that exchanges can offer. However, the 30 mil fee cap has
become a central component of the structure of fees and rebates as
access fees for non-protected quotes generally do not exceed the 30 mil
fee cap, nor do average rebates.
---------------------------------------------------------------------------

    \485\ See Regulation NMS Adopting Release, supra note 16, at
section I.C.2 (page 28 in the SEC version) which states that the
selection of the access fee cap was chosen because ``it will not
seriously interfere with current business practices'' and ``[i]n the
absence of a fee limitation, some `outlier' trading centers might
take advantage of the requirement to protect displayed quotations by
charging exorbitant fees to those required to access the outlier's
quotations.''
---------------------------------------------------------------------------

    Fee/rebate schedules can be quite complex, and the fee schedules
change frequently.\486\ The actual fee or rebate that an investor is
assessed on most exchanges also generally depends on which tier a
market participant falls into based on trading volume in that month,
with higher volume market participants receiving a higher rebate or a
lower fee.\487\ Exchanges file their fee and rebate schedules with the
Commission and post them on their websites, which means that the rebate
and fee rates associated with each volume based tier can be known at
the time a market participant trades. However, market participants may
not know which volume based tier they would fall under at the time of
the trade (and thus the fee or rebate rate that would apply to their
particular trade) because the volume tier they would fall under is
determined based on their trading volume during the current month,
which is not finalized until the end of the month.\488\ More
specifically, the volume based fees or rebates a market participant
receives from an exchange are often determined by a market
participant's average total daily traded share volume on the exchange
during the month as a percentage of either the average total daily
market volume reported by one of the consolidated tapes during the
month or as a percentage of the average total daily market volume
reported by all consolidated tapes during the month.\489\
---------------------------------------------------------------------------

    \486\ See Table 5 for information on how often exchanges amend
their fees.
    \487\ See Letter from Richard Steiner, Electronic Trading
Strategist, RBC Capital Markets, to Brent Fields, Secretary,
Commission (Oct. 16, 2018), available at https://www.sec.gov/comments/s7-05-18/s70518-4527261-176048.pdf (commenting on the
transaction fee pilot).
    \488\ See Chester Spatt, Is Equity Market Exchange Structure
Anti-Competitive?, (Dec. 28, 2020) (unpublished manuscript),
available at https://www.cmu.edu/tepper/faculty-and-research/assets/docs/anti-competitive-rebates.pdf. However, not all exchanges offer
volume-based tiers in their fee structures. For example, LTSE does
not charge fees to transact and IEX does not offer volume based
tiering. For exchanges like these, it is possible to determine with
certainty the cost to transact prior to executing a trade.
    \489\ The Equity Data Plans disseminate SIP data over three
separate networks: (1) Tape A for securities listed on the New York
Stock Exchange (``NYSE''); (2) Tape B for securities listed on
exchanges other than NYSE and Nasdaq; and (3) Tape C for securities
listed on Nasdaq. These tapes are referred to as the ``consolidated
tapes.'' The CTA Plan governs the collection, consolidation,
processing, and dissemination of last sale information for Tape A
and Tape B securities. The CQ Plan governs the collection,
consolidation, processing, and dissemination of quotation
information for Tape A and Tape B securities. Finally, the UTP Plan
governs the collection, consolidation, processing, and dissemination
of last sale and quotation information for Tape C securities. For
details on exchange volume based fees and rebates, see, e.g., Add
and Remove Rates, NASDAQ, available at https://www.nasdaqtrader.com/Trader.aspx?id=PriceListTrading2; New York Stock Exchange Price List
2022, NYSE, available at https://www.nyse.com/publicdocs/nyse/markets/nyse/NYSE_Price_List.pdf; and Cboe U.S. Equities Fee
Schedules EDGX Equities, Chi. Bd. Options Exch., available at
https://www.cboe.com/us/equities/membership/fee_schedule/edgx/.
---------------------------------------------------------------------------

    Some information on average exchange fees and rebates is also
available through reports available under rule 606. With respect to
held orders, rule 606(a)(1) requires broker-dealers to produce
quarterly public reports regarding their routing of non-directed orders
\490\ in NMS stocks that are submitted on a held basis. Along with
other information, these reports require the broker-dealer to report
both the total dollar amount and per share average of net transaction
fees paid and net transaction rebates received for different order
types for each trading venue to which the broker-dealer reports routing
orders.\491\ Additionally, rule 606(b)(3) requires broker-dealers to
produce reports pertaining to order handling upon the request of a
customer that places, directly or indirectly, one or more orders in NMS
stocks that are submitted on a not held basis, subject to a de minimis
exception.\492\ For each venue to which the broker-dealer routed the
customer's orders, these reports require the broker-dealer to disclose,
among other things, the average net execution rebate or fee for shares
of orders providing liquidity and the average net execution rebate or
fee for shares of order s removing liquidity.\493\ However, these
reports only provide market participants with information on historical
average transaction fees and rebates and may not accurately reflect the
current exchange fees and rebates a market participate would encounter
at the time of its transaction.\494\
---------------------------------------------------------------------------

    \490\ A ``non-directed order'' means any order from a customer
other than a directed order. See 17 CFR 242.600(b)(56). A ``directed
order'' means an order from a customer that the customer
specifically instructed the broker or dealer to route to a
particular venue for execution. See 17 CFR 242.600(b)(27).
    \491\ Rule 606(a)(1) requires broker-dealers to report separate
information for market orders, marketable limit orders, non-
marketable limit order, and other orders. See 17 CFR 242.606(a)(1)
for the items that need to be disclosed in reports under rule
606(a)(1).
    \492\ See 17 CFR 242.606(b)(3). In addition, under rule
606(b)(5)'s customer-level de minimis exception, broker-dealers need
not provide upon request execution quality reports for customers
that traded on average each month for the prior six months less than
$1,000,000 of notional value of not held orders in NMS stocks
through the broker-dealer. See 17 CFR 242.606(b)(5).
    \493\ See 17 CFR 242.606(b)(3)(iii) and (iv).
    \494\ Reports under rule 606(a)(1) are produced by broker-
dealers at the end of the quarter and disclose information on
average fees and rebates for each month in that quarter. Reports
issued by broker-dealers to their customers under rule 606(b)(3)
disclose summarized information on the handling of the customer's
orders for each calendar month over the prior six months. The
broker-dealer must issue these reports to the customer within seven
business days of receiving the customer's request.
---------------------------------------------------------------------------

    The fee structure on an exchange can take one of three forms. The
most common is maker-taker, in which liquidity demanders (i.e., takers)
are assessed the access fee and liquidity providers (i.e., makers) are
offered a rebate. Exchanges can also be inverted (also known as taker-
maker), in which liquidity demanders are offered a rebate and liquidity
providers are assessed an access fee. The last form of fee structure is
flat; a flat exchange either charges one or both sides a fee but does
not offer rebates. While the exchanges are free to subsidize rebates
beyond what they earn through collecting access fees, in practice this
does not appear to happen.\495\ The difference between the average
access fee charged and the average rebate paid is the net capture
earned by the exchanges for facilitating a transaction.
---------------------------------------------------------------------------

    \495\ See infra section V.D.1 for more discussion on why
exchanges may not subsidize rebates from other sources of revenue.
See also Eric Budish, et al., A Theory of Stock Exchange Competition
and Innovation: Will the Market Fix the Market? (working paper May
22, 2019) available at https://ssrn.com/abstract=3391008 (retrieved
from SSRN Elsevier database).
---------------------------------------------------------------------------

    The regulatory access fee cap is most relevant for maker-taker
markets where the trader accessing a protected quote must pay the
access fee. This is because the access fee cap applies only to fees for
accessing protected quotations and does not apply to fees for posting
quotations. Therefore, on an inverted venue the exchange is not
restricted by rule 610 in terms of the rebate that it can offer to
access a protected quote or the fee to post a protected quote.\496\
Flat rate

venues, which do not offer rebates, do not appear to be economically
constrained by rule 610(c) as their fees for both taking and adding
liquidity are significantly lower than the 30 mil fee cap.
---------------------------------------------------------------------------

    \496\ As can be seen from Table 5, which presents information on
access fees and rebates for the 16 operating exchanges, in practice
the fee that is charged on an inverted fee venue to post liquidity
is generally very close to the 30 mil access fee cap even though not
constrained by rule 610.
---------------------------------------------------------------------------

    Table 5 provides an analysis of current fee and rebate schedules
based on rule 19b-4 filings with the Commission for each of the equity
exchanges operating in the United States as of June 1, 2022 as well as
a review of the transaction prices that each exchange posts.\497\ What
becomes apparent from this analysis is that the current structure of
fees and rebates is complex and consistently changing. On average, each
exchange filed 11.4 rule 19b-4 filings per year with the Commission.
Market participants interacting with all exchanges had to adjust to an
average of 155 fee changes per year across all exchanges. Exchanges
also tend to have numerous fee and rebate categories. The effect of the
30 mil fee cap as an anchor point is also apparent. For most exchanges
the maximum fee assessed, presumably for non-protected quotes, is close
to the 30 mil fee cap for protected quotes. The maximum rebate is
generally in the vicinity of 30 mils, further suggesting the 30 mil
access fee cap effectively limits what the exchanges offer as rebates.
---------------------------------------------------------------------------

    \497\ Panel A of Table 5 provides the category of exchange,
maker-taker, inverted, or flat/free, the number of fee revisions
since Jan. 2018 as indicated by the number of transaction fee
specific rule 19b-4 filings that the exchange has filed with the
Commission, the date that each exchange's website states that the
fee schedule posted there is effective and the range of fees and
rebates along with the number of categories of fees and rebates for
transactions priced equal to, or greater than, $1.00 per share.
---------------------------------------------------------------------------

    Panel B provides information on the exchange's fee schedules for
stocks priced lower than $1.00. For these transactions the fee
schedules tend to be simpler. Most exchanges do not offer a rebate for
transactions lower than $1.00 even if the exchange offers rebates for
other transactions--only two exchanges offer any sort of baseline
rebate.\498\ Additionally, the exchanges tend to charge the maximum
access fee of 0.3% of the share price. A few exchanges charge a fee to
both sides to transact with one exchange charging 0.3% to both sides of
a transaction.
---------------------------------------------------------------------------

    \498\ The two are Cboe EDGX and Members MEMX.

                             Table 5--Summary of Transaction-Based Fee Schedules for U.S. National Equities Exchanges as of
                                                                      May 2022 \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------------------------------------------------------------------------------------
                                              Panel A: Fees and Rebates for Transactions Greater than $1.00
--------------------------------------------------------------------------------------------------------------------------------------------------------
Exchange                                    Fee model....................  Number of revisions     Date of fee                 Fees              Rebates
                                                                            Jan 2018-June 2022        schedule           (number of           (number of
                                                                                (per year)                          categories)          categories)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Cboe BZX \b\..............................  Maker-Taker..................                   98        4/1/2022              $0.0030    [$0.0000-$0.0032]
                                                                                (21.8)                                  (5)                 (11)
Cboe BYX \c\..............................  Inverted.....................                   42        5/2/2022    [$0.0010-$0.0030]    [$0.0000-$0.0015]
                                                                                (9.3)                                 (10)                  (6)
Cboe EDGA \d\.............................  Inverted.....................                   26        4/1/2022    [$0.0008-$0.0030]    [$0.0000-$0.0024]
                                                                                (5.8)                                 (12)                  (9)
Cboe EDGX \e\.............................  Maker-Taker..................                   71        5/2/2022    [$0.0000-$0.0030]    [$0.0000-$0.0032]
                                                                                (15.8)                                  (7)                 (14)
BX \f\....................................  Inverted.....................                   45      10/12/2021    [$0.0010-$0.0030]    [$0.0000-$0.0021]
                                                                                (10.0)                                  (9)                 (15)
Phlx (PSX) \g\............................  Maker-Taker..................                   48        1/2/2022              $0.0030    [$0.0005-$0.0020]
                                                                                (10.7)                                  (1)                  (5)
Nasdaq \h\................................  Maker-Taker..................                   83       4/12/2022  [$0.0004-$-$0.0030]   [$0.0000-$0.00325]
                                                                                (18.4)                                  (3)                  (7)
NYSE Arca \i\.............................  Maker-Taker..................                   77        5/1/2022    [$0.0000-$0.0030]    [$0.0000-$0.0032]
                                                                                (17.1)                                 (15)                 (21)
NYSE American.............................  Maker-Taker..................                   11        5/1/2022    [$0.0010-$0.0030]    [$0.0020-$0.0030]
                                                                                (2.4)                                  (9)                 (10)
NYSE......................................  Maker-Taker..................                   82        5/1/2022    [$0.0000-$0.0030]    [$0.0000-$0.0030]
                                                                                (18.2)                                 (50)                 (69)
NYSE National.............................  Inverted.....................                   27        1/2/2022    [$0.0022-$0.0029]              $0.0000
                                                                                (6.0)                                 (16)                  (1)
NYSE Chicago..............................  Maker-Taker..................                    7        5/1/2022    [$0.0010-$0.0010]              $0.0010
                                                                                (1.6)                                  (5)                  (6)
IEX \j\...................................  Flat.........................                   19        4/1/2022    [$0.0006-$0.0010]              [$0.000
                                                                                (4.2)                                  (2)                  (1)
Members MEMX \k\..........................  Maker-Taker..................                   NA        6/1/2022    [$0.0000-$0.0030]    [$0.0018-$0.0035]
                                                                                (3)                  (4)
Miami MIAX \l\............................  Maker-Taker..................                   NA       9/24/2020              $0.0028    [$0.0022-$0.0028]
                                                                                (1)                  (2)
Long Term LTSE \m\........................  Free.........................                   NA             N/A              $0.0000              $0.0000
                                                                                (1)                  (1)
--------------------------------------------------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
                             Panel B: Fees and Rebates for Transactions Under $1.00
----------------------------------------------------------------------------------------------------------------
Exchange                           Fee model..........  Rebate.............          Fee  Charged
                                                                                both sides
----------------------------------------------------------------------------------------------------------------
Cboe BZX.........................  Maker-Taker........  0..................         0.30
Cboe BYX.........................  Inverted...........  0..................         0.10
Cboe EDGA........................  Inverted...........  0..................         0.30
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Exchange                           Fee model..........  Rebate.............          Fee  Charged
                                                                                both sides
----------------------------------------------------------------------------------------------------------------
Cboe EDGX........................  Maker-Taker........  0.00009 (per share)         0.30
BX...............................  Inverted...........  0..................         0.30
Phlx (PSX).......................  Maker-Taker........  0..................         0.20
Nasdaq...........................  Maker-Taker........  0..................         0.30
NYSE Arca........................  Maker-Taker........  0..................         0.30
NYSE American....................  Maker-Taker........  0..................         0.25
NYSE.............................  Maker-Taker........  0..................         0.30
NYSE National....................  Inverted...........  0..................            0
NYSE Chicago.....................  Maker-Taker........  0..................         0.10  Yes.
IEX..............................  Flat...............  0..................         0.30  Yes.
Members MEMX.....................  Maker-Taker........  0.10% (of value)...         0.25
Miami (MIAX).....................  Maker-Taker........  0..................         0.30
Long Term (LTSE).................  Free...............  0..................         0.30
----------------------------------------------------------------------------------------------------------------
\a\ The number of fee revisions is obtained by counting each rule 19b-4 filing for each exchange that is not
  clearly marked for a non-transaction fee related purpose such as connectivity fees, listing fees, options
  fees, etc. To determine the fee and rebate information each exchange's webpage was searched for its current
  posted access fee and rebate schedule and collected information only on access fees and rebates pertaining to
  non-auction trading in stocks priced equal to, or greater than, $1.00 per share. Sources for Current Access
  Fee Data were effective on the dates shown in Panel A of Table 5, and were accessed during May 2022 at the
  websites shown beneath the table.
\b\ https://www.cboe.com/us/equities/membership/fee_schedule/bzx/.
\c\ https://www.cboe.com/us/equities/membership/fee_schedule/byx/.
\d\ https://www.cboe.com/us/equities/membership/fee_schedule/edga/.
\e\ https://www.cboe.com/us/equities/membership/fee_schedule/edgx/.
\f\ https://listingcenter.nasdaq.com/rulebook/bx/rules/BX%20Equity%207.
\g\ https://listingcenter.nasdaq.com/rulebook/phlx/rules/phlx-equity-7.
\h\ https://listingcenter.nasdaq.com/rulebook/nasdaq/rules/Nasdaq%20Equity%207.
\i\ All NYSE Exchange Family fees: https://www.nyse.com/markets/fees.
\j\ https://exchange.iex.io/resources/trading/fee-schedule/.
\k\ https://info.memxtrading.com/fee-schedule/.
\l\ https://www.miaxoptions.com/sites/default/files/alert-files/MIAX_PEARL_Equities__Initial_FS_09242020.pdf.
\m\ https://ltse.com/trading/faqs.

    Complex fee schedules and volume based tiers mean that it is
difficult to determine the net capture on a given exchange (the
difference between average fees levied and rebates paid). Additionally,
financial statements for exchange groups generally do not break down
performance on a per venue level and they generally combine auction
access fees collected with regular trading access fees. Furthermore,
some exchanges are privately held and thus do not release the same
financial statements that public exchanges do. Using information from
the financial statements of the three major exchange groups which
collectively account for the overwhelming majority of trading volume on
exchanges, the Commission estimates that the average total net capture
is around 4 mils for all trading types.\499\ However, the Commission
understands based on Staff conversations with industry members that the
net capture for non-auction trading in stocks that have a price equal
to or greater than $1.00 is likely close to 2 mils, and in further
analysis where the net capture needs to be assumed, we use 2 mils. This
analysis suggests that the primary reason that access fees remain near
30 mils on most exchanges is to fund rebates. For stocks trading below
$1.00 the Commission estimates an average net capture of around 0.28%
of the transaction volume.\500\ This amount is very close to the 0.30%
access fee cap and arises because, as seen in Panel B of Table 5, most
exchanges set their baseline fee at 0.30% but do not offer baseline
rebates for transactions under $1.00 and some charge fees to both sides
of the transaction leading to more than 0.30% per trade earned by the
exchange.
---------------------------------------------------------------------------

    \499\ Intercontinental Exchange, the parent firm of NYSE,
reports on page 51 of its 2021 Form 10-K filing that their net
capture for U.S. equity transactions was approximately 4.2 mils in
2021. Nasdaq did not report its net capture in its Form 10-K filing,
however Nasdaq provides information on its investor relations web
page which, when we average the relevant 2021 volumes, indicates
that the average net capture across all Nasdaq platforms for U.S.
equity transactions was 5.9 mils (see Nasdaq 2022/2021 Monthly
Volumes, NASDAQ, available at https://ir.nasdaq.com/static-files/465d2157-c476-4546-a9f7-8d7ad0c9be77). Cboe reports in its Form 10-K
filing that its net capture for U.S. equity transactions was
approximately 2 mils.
    \500\ The estimate for the 0.28% net capture is obtained by
taking the estimated net transaction fee for each exchange and
multiplying it by the dollar trading volume presented in Panel B of
Table 6 below.
---------------------------------------------------------------------------

    Table 6 presents tabulations of the total share (Panel A) and
dollar (Panel B) trading volume executed on the 16 exchanges in the
first six months of 2022. This table provides estimates for the total
volume that executed below $1.00 and that which executed above $1.00.
These numbers represent an estimate of the total number of shares that
would have been subject to the access fees and rebates discussed in
this release.

          Table 6--Trading Volume by Exchange, Exchange Type, and Exchange Group Jan. to June 2022 \a\
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
                                              Panel A: Share Volume
----------------------------------------------------------------------------------------------------------------
Exchange name                   Exchange type...      <$1 Volume     >=$1 Volume     >=$1 Volume            % of
                                                      (billions)           tick-       non-tick-        exchange
                                                                     constrained     constrained          volume
                                                                      (billions)      (billions)
----------------------------------------------------------------------------------------------------------------
Off Exchange..................  ................            67.6           258.1           296.6  ..............
Nasdaq........................  Maker-Taker.....            13.3           111.8           169.2            26.5
----------------------------------------------------------------------------------------------------------------

     Table 6--Trading Volume by Exchange, Exchange Type, and Exchange Group Jan. to June 2022 \a\--Continued
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Exchange name                   Exchange type...      <$1 Volume     >=$1 Volume     >=$1 Volume            % of
                                                      (billions)           tick-       non-tick-        exchange
                                                                     constrained     constrained          volume
                                                                      (billions)      (billions)
----------------------------------------------------------------------------------------------------------------
NYSE Arca.....................  Maker-Taker.....             8.7            62.2            53.5            14.1
NYSE..........................  Maker-Taker.....             1.2            48.8            54.9            12.9
Cboe BZX......................  Maker-Taker.....             2.8            45.3            38.3            10.6
EDGX..........................  Maker-Taker.....             7.1            38.8            40.7            10.3
MEMX..........................  Maker-Taker.....             1.9            36.8            22.9             7.6
IEX...........................  Flat............             0.5            14.7            26.0             5.2
EDGA..........................  Inverted........             0.7            13.1             9.6             2.9
Cboe BYX......................  Inverted........             0.7            14.1            6,.5             2.6
MIAX Pearl....................  Maker-Taker.....             0.4            10.6             4.0             1.9
NYSE National.................  Inverted........             0.2            10.9             3.0             1.8
Nasdaq OMX PSX................  Maker-Taker.....            0.08             9.3             3.9             1.6
Nasdaq OMX BX.................  Inverted........             0.2             4.2             4.1             1.0
NYSE American.................  Maker-Taker.....             0.7             3.8             2.5             0.9
NYSE Chicago..................  Maker-Taker.....            0.02             0.5             1.9             0.1
LTSE..........................  Free............           0.002           0.003            0.01             0.0
                                                 ---------------------------------------------------------------
    Total.....................  ................           106.0           683.1           737.6  ..............
    Exchange Total............  ................            38.4           425.1           441.0  ..............
----------------------------------------------------------------------------------------------------------------
                                             Panel B: Dollar Volume
----------------------------------------------------------------------------------------------------------------
Exchange name                   Exchange type...      <$1 Volume     >=$1 Volume  >=$1 Non-tick-            % of
                                                      (billions)           tick-     constrained        exchange
                                                                     constrained      (billions)          volume
                                                                      (billions)
----------------------------------------------------------------------------------------------------------------
Off Exchange..................  ................           $31.5        $5,947.1       $23,715.1  ..............
Nasdaq........................  Maker-Taker.....             6.6         2,896.6        15,518.6            30.2
NYSE Arca.....................  Maker-Taker.....             4.0         1,953.9         5,274.7            15.1
NYSE..........................  Maker-Taker.....             0.6         1,080.5         4,034.4            11.5
Cboe BZX......................  Maker-Taker.....             1.2         1,278.2         3,807.3            11.3
EDGX..........................  Maker-Taker.....             3.5           857.5         3,213.9             8.9
MEMX..........................  Maker-Taker.....             1.0           841.3         1,588.8             5.5
IEX...........................  Flat............             0.2           387.7         2,356.1             6.3
EDGA..........................  Inverted........             0.4           373.0           802.4             2.7
Cboe BYX......................  Inverted........             0.4           319.8           626.2             2.1
MIAX Pearl....................  Maker-Taker.....             0.2           285.2           424.8             1.6
NYSE National.................  Inverted........            0.09           280.4           209.4             1.1
Nasdaq OMX PSX................  Maker-Taker.....            0.04           270.7           411.5             1.5
Nasdaq OMX BX.................  Inverted........            0.09           139.1           438.9             1.3
NYSE American.................  Maker-Taker.....             0.3            90.0           222.9             0.7
NYSE Chicago..................  Maker-Taker.....            0.01            20.3           351.5             0.3
LTSE..........................  Free............           0.001             0.1             1.0             0.0
                                                 ---------------------------------------------------------------
    Total.....................  ................            50.1        17,021.7        62,997.5  ..............
    Exchange Total............  ................            18.6        11,074.6        39,282.4  ..............
----------------------------------------------------------------------------------------------------------------
\a\ This table is created by aggregating all trade information from the TAQ database for every trading day in
  Jan. to June 2022. Only trading volume reflecting normal trades during regular trading. Normal trades are
  identified in TAQ data by sale conditions ``blank, @, E, F, I, S, Y'' which correspond to regular trades,
  intermarket sweep orders, odd lot trades, split trades, and yellow flag regular trades. We aggregate total
  remaining share volume by exchange, exchange type (maker-taker, inverted, flat, free), and exchange family
  (NYSE, Nasdaq, CBOE, Independent). We combine volume from exchange codes T and Q into `Nasdaq.' Panel A
  presents share volume totals and Panel B presents dollar volume totals. Certain items in this Table 6 may also
  be affected by the MDI Rules once they are fully implemented. See Table 3 note b.

    Transaction fees for trades in stocks priced equal to or greater
than $1.00 are generally levied per share transacted. From Table 6 we
see that in the first half of 2022, there were approximately 1.4
trillion shares transacted at prices equal to or greater than $1.00 per
share across all venues, 59% of which (866 billion shares) were
executed on a registered exchange.\501\ Of these on-exchange
transactions priced equal to or greater than $1.00 per share,
approximately half were in tick-constrained securities while the other
half were not. These numbers provide the basis for estimating the total
amount of access fees and rebates collected and distributed in
transactions priced equal to, or greater than, $1.00 per share. For
transactions less than $1.00 per share the access fee is generally
levied as a percent of the transaction share price. In Panel B we see
that in the first half of 2022 there was approximately $18 billion
transacted on exchanges in shares priced less than $1.00 per share.
---------------------------------------------------------------------------

    \501\ 1.4T shares 683 billion tick-constrained shares + 737
billion non-tick-constrained shares. Also, off exchange trading
volume has increased in recent years. See, e.g., Jonathan Brogaard
and Jing Pan, Dark Pool Trading and Information Acquisition, 35 Rev.
Fin. Studies 2625 (2022).
---------------------------------------------------------------------------

    Panels A and B of Table 7 break down the share and dollar volume
statistics presented in Table 5 by venue type: maker-taker, inverted,
and flat/free. The overwhelming majority of both dollar and share
exchange trading volume occurs on maker-taker venues with approximately
88% of both dollar and share volume executing on maker-taker

venues. Inverted exchanges capture about 6% of both dollar and share
volume, and the remaining share volume transact on flat/free exchanges.

        Table 7--Volume by Exchange Type and Estimated Access Fee/Rebate Estimates Jan. to June 2022 \a\
----------------------------------------------------------------------------------------------------------------
                                                          Price >$1 tick-     Price >$1 non-
                                          Price <$1         constrained     tick- constrained       % Total
                                          (billions)         (billions)         (billions)
----------------------------------------------------------------------------------------------------------------
                                  Panel A: Exchange Share Volume By Venue Type
----------------------------------------------------------------------------------------------------------------
Maker-Taker.........................               35.5              364.1              389.3               88.7
Inverted............................                1.9               38.4               18.9                6.6
Flat/Free...........................                0.5               14.7               26.1                4.6
----------------------------------------------------------------------------------------------------------------
                                  Panel B: Exchange Dollar Volume by Venue Type
----------------------------------------------------------------------------------------------------------------
Maker-Taker.........................               17.1            9,484.4           34,625.4               88.4
Inverted............................                1.0              986.1            2,036.9                6.1
Flat/Free...........................                0.2              387.8            2,357.1                5.5
----------------------------------------------------------------------------------------------------------------
                      Panel C: Estimated Fees Collected and Rebates Distributed (Billions)
----------------------------------------------------------------------------------------------------------------
Fees Collected......................  .................              $2.55  .................  .................
Rebates Distributed.................  .................               2.31  .................  .................
Exchange Capture....................  .................               0.24  .................  .................
----------------------------------------------------------------------------------------------------------------
                         Panel D: Total Estimated Net Fees by Liquidity Type (Billions)
----------------------------------------------------------------------------------------------------------------
Demander............................  .................              $2.13  .................  .................
Provider............................  .................              -1.89  .................  .................
Exchange Capture....................  .................               0.24  .................  .................
----------------------------------------------------------------------------------------------------------------
\a\ Certain items in this Table 7 may also be affected by the amendments in the MDI Rules once they are fully
  implemented. See Table 3 note b.

    Panel C provides an estimate of the total amount of access fees
collected and rebates distributed.\502\ In the first 6 months of 2022
there were an estimated $2.55 billion in access fees collected across
all exchanges and $2.31 billion in rebates distributed, resulting in a
net capture to all exchanges of $242 million.
---------------------------------------------------------------------------

    \502\ These estimates are computed by assuming a 30 mil access
fee and 28 mil rebate on all transactions that occur on maker-taker
or inverted exchanges and an 8 mil access fee (and no rebate) on the
volume priced equal to, or greater than, $1.00 per share that occurs
on IEX. For trading in sub $1.00 transactions, the various access
fees and rebates for each exchange presented in Panel B of Table 5
are multiplied by the corresponding dollar volume of trade in
transactions priced less than $1.00 per share to compute the total
access fees collected and rebates distributed for this volume. The
figures are summed together to provide the estimates of total access
fees collected and rebates distributed.
---------------------------------------------------------------------------

    Panel D of Table 7 provides estimates of the net access fee paid by
liquidity demanders and liquidity suppliers.\503\ In the first 6 months
of 2022 liquidity demanders paid an estimated $2.1 billion in net
access fees and liquidity providers received an estimated $1.89 billion
in rebates. With the difference of $242 million being the exchanges'
estimated net capture.
---------------------------------------------------------------------------

    \503\ This estimate presumes that for shares transacted in
prices equal to or greater than $1.00 per share on maker-taker
venues the liquidity demander pays a 30 mil access fee and the
liquidity provider receives a 28 mil rebate. On inverted exchanges
the opposite occurs. On IEX it is presumed that liquidity demanders
pay an 8 mil access fee and liquidity providers receive no rebate.
For trading in sub $1.00 transactions the various access fees and
rebates for liquidity suppliers and demanders are computed by taking
the respective fees and rebates for sub $1.00 transactions for each
exchange presented in Panel B of Table 5 and multiplying them by the
corresponding dollar volume of trade in transactions priced less
than $1.00 to compute the total access fees collected and rebates
distributed for liquidity-providing and demanding trades. The
figures are summed together to provide the estimates of total access
fees collected and rebates distributed.
---------------------------------------------------------------------------

    Although not subject to rule 610(c), because they do not post
protected quotes, ATSs also often assess transaction fees.\504\ As of
the second quarter of 2022 there were 32 ATSs that reported trading
volume to FINRA transacting a total of 81 billion shares.\505\ Unlike
exchanges, the fees that ATSs charge generally do not have a standard
structure and are often negotiated between the ATS and the customer.
Based on a review of item 19 in form ATS-N, ATSs generally do not
provide rebates, and when transaction fees are explicitly discussed,
they are often in the range of 10 mils.
---------------------------------------------------------------------------

    \504\ IntelligentCross ATS, for example, offers matching
processes for all NMS stocks eligible for trading, and disseminates
bids and offers in real-time to subscribers to the ATS's proprietary
data feed, but these are not protected quotes. See IntelligentCross,
Form ATS-N, Item 15 (Display) (dated Apr. 11, 2022) available at
https://www.sec.gov/Archives/edgar/data/1708826/000170882622000002/xslATS-N_X01/primary_doc.xml.
    \505\ See ATS Transparency Data Quarterly Statistics, available
at https://www.finra.org/filing-reporting/otc-transparency/ats-quarterly-statistics.
---------------------------------------------------------------------------

3. Round Lots and Market Data Infrastructure
    Currently, information on odd-lots inside the NBBO is only
available to investors who subscribe to proprietary data feeds, and
comprehensive odd-lot information is only available to market
participants who subscribe to the proprietary data feeds of all the
exchanges.\506\ The implementation of the MDI Rules will include odd-
lot information inside the NBBO.\507\ The MDI Rules will also change
the definition of a round lot. Specifically, the MDI Rules will lower
the round lot size to 40 shares for stocks priced greater than $250 and
less than $1,000. The round lot definition will become 10 shares for
stocks priced greater than $1,000 and less than $10,000. The round lot
definition will become 1 share for stocks priced greater than $10,000.
---------------------------------------------------------------------------

    \506\ See supra note 371 and accompanying text for a discussion
of a current request for comment by the exclusive SIPs about the
potential to amend the plans to include some odd-lot information in
the SIP data.
    \507\ See supra section IV.A.1 for a discussion on the expected
time of the implementation of the MDI Rules.

---------------------------------------------------------------------------

    When adopting the MDI Rules, the Commission enumerated numerous
economic effects specifically related to changing the round lot
definition and including odd-lot information as a part of core data.
For the change in the definition of round lots, these effects include:
(1) a mechanically tighter NBBO for higher priced stocks due to the
redefinition of the round lot sizes,\508\ (2) increased transparency
and better order execution,\509\ and (3) potentially more orders for
high priced stocks being routed to exchanges instead of ATSs.\510\ The
costs of changing the round lot definition are also discussed and
include upgrading systems to account for additional message traffic and
modifying and reprogramming systems.\511\ The Commission also discussed
the expected effect that changing the round lot definition would have
on other rules and regulations.\512\
---------------------------------------------------------------------------

    \508\ See MDI Adopting Release, supra note 5, section
V.C.1.(b).(i) for the full discussion of the effect of changing the
round lot size on the NBBO.
    \509\ See MDI Adopting Release, supra note 5, sections
V.C.1.b.(ii) and V.C.i.b.(iii) for the full discussion of the effect
of changing the round lot size on transparency and execution
quality.
    \510\ See MDI Adopting Release, supra note 5, section
V.C.1.b.(iv) for the full discussion of the effect of changing the
round lot size on exchange competition and order routing.
    \511\ See MDI Adopting Release, supra note 5, section
V.C.1.b.(vi) for the full discussion of the expected costs of
changing the round lot size.
    \512\ See MDI Adopting Release, supra note 5, section
V.C.1.b.(vii) for the full discussion of the effect of changing the
round lot size on other rules and regulations.
---------------------------------------------------------------------------

    For the inclusion of odd-lot information inside the NBBO to core
data,\513\ these effects include reducing information asymmetries
between investors who currently have access to odd-lot information
through proprietary data feeds and those who do not, leading to better
order execution and price efficiency.\514\ Providing a reasonable
alternative to some market participants to proprietary data will allow
some market participants to reduce data expenses required for
trading.\515\ The costs of including odd-lot information inside the
NBBO include: \516\ the cost of upgrading existing infrastructure and
software to handle the dissemination of additional core data message
traffic, the cost to SROs to implement system changes required in order
to make regulatory data and other data needed to generate consolidated
market data available to competing consolidators, the cost of
technological investments market participants might have to make in
order to receive the new core message traffic, and lastly, the cost to
users of proprietary data whose information advantage would dissipate
somewhat.\517\
---------------------------------------------------------------------------

    \513\ See MDI Adopting Release, supra note 5, section
V.C.1.c.(i) for the full discussion of the effect of including odd-
lot information inside the NBBO in its definition of core data.
    \514\ Id.
    \515\ Id.
    \516\ See MDI Adopting Release, supra note 5, sections
V.C.1.c.(iv), for the full discussion of the costs associated with
expanding core data to include odd-lot information inside the NBBO.
    \517\ Id.
---------------------------------------------------------------------------

    The MDI Rules do not require the competing consolidators to
disseminate odd-lot information. However, the Commission estimated that
at least one competing consolidator will disseminate the odd-lot
information because it believed that there will be demand for the
data.\518\
---------------------------------------------------------------------------

    \518\ See MDI Adopting Release, supra note 5, at footnote 1945
and surrounding text.
---------------------------------------------------------------------------

4. Affected Entities and Markets
    The proposal would affect trading in NMS Stocks, particularly on
exchanges that charge high access fees and in stocks with lower quoted
spreads, many odd-lots inside the spread, or higher prices. Therefore,
the proposal would affect a wide variety of market participants,
including national securities exchanges, other trading venues,
exclusive SIPs and their data users, competing consolidators
(eventually), broker-dealers operating order entry and order routing
systems, and others who engage in the trading of NMS Stocks, including
investors.
    There are 16 national securities exchanges on which NMS Stocks are
traded that would be affected by the proposal. The exchanges compete
with each other and other trading venues to attract order flow.
Exchanges compete with each other in how they set the rules that
dictate how orders routed to it interact given the broader requirements
of the Exchange Act and rules thereunder. Such rules are coded into the
systems of exchanges that match buy and sell orders. Exchanges also
differentiate themselves with the access fees they charge or the
rebates they pay out for particular order types.\519\ As a subset of
national securities exchanges, the five listing exchanges, set rules
for listing standards for securities and are responsible for tracking
certain regulatory information regarding their listed stocks.
---------------------------------------------------------------------------

    \519\ Exchanges can also facilitate the routing of orders to
other exchanges.
---------------------------------------------------------------------------

    Other trading venues, including 33 ATSs and 238 other FINRA
members, including OTC market makers, also compete with exchanges and
each other to attract order flow in NMS Stocks and can route orders to
the various trading venues. The order flow they attract depends on a
number of factors such as fees and price improvement over the NBBO
amongst other aspects of execution quality.
    Pending the full implementation of the MDI rules, the market for
market data is serviced by the two exclusive SIPs and exchange
proprietary feeds. The two exclusive SIPs collect trade, quote, and
regulatory data from the 16 exchanges and three trade reporting
facilities,\520\ consolidate those data, determine an NBBO, and
disseminate those data directly to users or through vendors and broker-
dealers. The exclusive SIPs can also collect information from the
alternative display facility (``ADF'') operated by FINRA though no one
currently uses the ADF to display quotes. Upon full implementation of
the MDI Rules, the exclusive SIPs will be retired and an unknown number
of competing consolidators will take over the collection,
consolidation, estimation, and dissemination. The volume of data to be
processed through these competing consolidators will be greater than
that currently processed through exclusive SIPs, but competing
consolidators will have flexibility to design data products tailored to
different user types. In addition to the exclusive SIPs, the exchanges
also disseminate market data to paying subscribers via proprietary data
feeds. These proprietary data feeds provide more data than the
exclusive SIPs at a lower latency.\521\ Following the transition to a
competing consolidator model for market data, the Commission expects
total fees for market data are likely to decline.\522\
---------------------------------------------------------------------------

    \520\ Trade Reporting Facilities (TRFs) are facilities through
which FINRA members report off-exchange transactions in NMS stocks,
as defined in SEC Rule 600(b)(47) of Regulation NMS.
    \521\ See supra note 330 and infra note 612 and associated text
for a further discussion on the nature of proprietary data feeds.
    \522\ See MDI Adopting Release, supra note 5, at 18773-64.
---------------------------------------------------------------------------

    Broker-dealers typically route their own orders or their customers'
orders for execution to trading venues. There were 3,564 registered
broker-dealers as of the end of calendar year 2021. A portion of these
broker-dealers focus their business on individual and/or institutional
investors in the market for NMS stocks.\523\ According to CAT data,

there were approximately 1,037 broker-dealers that originated NMS
stocks orders on behalf of individual investors and approximately 909
broker-dealers originated NMS stocks orders on behalf of institutional
investors.\524\ Institutional investor orders are typically ``not
held'' orders, which provides the broker-dealer with more time and
price discretion to execute the order or to minimize price impact.\525\
In contrast, broker-dealers must attempt to execute a marketable held
order immediately, which typically better suits individual investors
who tend to seek immediate executions and rely less on broker-dealer
order handling discretion since their orders typically have much lower
price impacts.\526\ Brokers-dealers serving individual investors often
distinguish themselves by the customer service and financial advice
they provide and the accessibility and functionality of their trading
platforms.
---------------------------------------------------------------------------

    \523\ Based on information from broker-dealers' 2021 FOCUS
Report Form X-17A-5 Schedule I. This includes both carrying broker-
dealers, who maintain custody of customer funds and securities, and
introducing broker-dealers, who accept customer orders and introduce
their customers to a carrying broker-dealer that will hold the
customers' securities and cash.
    \524\ Customer accounts are identified in CAT as accounts
belonging to either the ``Institutional Customer'' account type,
defined as accounts that meet the definition in FINRA Rule 4512(c),
or the ``Individual Customer'' account holder type, defined as
accounts that do not meet the definition of FINRA Rule 4512(c) and
are also not a proprietary account.
    \525\ See Disclosure of Order Handling Information, supra note 4
at nn.59-60 and corresponding text. Meanwhile, a broker-dealer must
attempt to execute a held order immediately, which typically better
suits individual investors who seek immediate executions and rely
less on broker-dealer order handling discretion.
    \526\ FINRA's best execution obligation requires that, ``A
member must make every effort to execute a marketable customer order
that it receives fully and promptly.'' See FINRA Rule 5310,
Supplementary Material paragraph .01.
---------------------------------------------------------------------------

    Many broker-dealers that handle customer accounts do not directly
access national securities exchanges or ATSs for their orders and rely
on other broker-dealers to facilitate market access for them through
those broker-dealers' order entry systems. The Commission estimates
that there are 1,192 broker-dealers with order entry systems that
submit orders in NMS stocks to exchanges in the minimum pricing
increments.\527\ Of these broker-dealers, an estimated 282 broker-
dealers operate smart order routers to facilitate order routing.
---------------------------------------------------------------------------

    \527\ See infra note 625.
---------------------------------------------------------------------------

D. Economic Effects

    The Commission expects the proposal to lead to lower transaction
costs for liquidity demanders on exchanges trading in stocks with
narrow spreads. The proposal for a minimum increment to trading would
improve exchanges' and ATSs' abilities to potentially innovate in ways
that could potentially increase competition for retail order flow. A
lower access fee cap would reduce the transaction costs of liquidity
demanders in the predominant maker-taker structure. Making fees and
rebates determinable at the time of trade may enhance broker-dealer
order routing by helping mitigate a potential conflict of interest and
providing clarity in terms of all in execution costs. Accelerating the
inclusion of odd-lot information into the exclusive SIPs, updating the
definition of a round lot, and providing the best odd-lot order, would
accelerate some of the benefits of the MDI Rules, and could also lead
to better order-execution by enhancing benchmarking. The proposed rule
would also impose compliance costs on various market participants.
1. Modification of Rule 612 To Create a Tiered Tick Structure
    The proposal would create a smaller tick size for some NMS stocks.
A smaller tick, as proposed, would have two competing effects on
transaction costs. First, a smaller tick leads to pricing that more
effectively balances liquidity supply and demand--which, all else
equal, can lower transaction costs. Second, a smaller tick fragments
liquidity in the order book into more price levels, which can increase
complexity and the incidence of pennying \528\--which could harm
liquidity. The primary mechanism by which a smaller tick would lead to
improved market quality is by reducing the tick size constraints that
prevent spreads from narrowing. The proposal would not change the tick
for NMS stocks when their prices drop below $1.00 or for stocks with
Time-Weighted Average Quoted Spread greater than $0.04 during an
evaluation month. The Commission expects that the trading environment
for stocks with these characteristics is unlikely to be significantly
affected.\529\
---------------------------------------------------------------------------

    \528\ See supra note 459 for the definition of pennying.
    \529\ It is possible that changes in the stocks priced greater
than $1.00 with quoted spreads of less than $0.04 could have spill-
over effects in these stocks, but the net effect of such is
uncertain and likely insignificant. For instance, tighter spreads in
stocks receiving a tick size reduction could potentially result in
the withdrawal of some liquidity providers from all markets (which
would harm liquidity in stocks with no tick size adjustment) and/or
lead some liquidity providers to shift their activity to stocks with
no tick size adjustment (improving liquidity in those stocks).
---------------------------------------------------------------------------

    The proposal assigns each NMS stock priced equal to, or greater
than, $1.00 to one of 4 tick sizes: $0.001, $0.002, $0.005, and $0.01
depending on the stock's Time-Weighted Average Quoted Spread during an
evaluation month. Table 8 presents estimates of the amount of share
trading volume that would have been associated with each of the four
tick sizes.

                               Table 8--Proposed Tick Sizes and Quoted Spread \a\
----------------------------------------------------------------------------------------------------------------
                                                                     Number of      Estimated %     Estimated %
             Average quoted spread                     Tick           stocks       share volume    dollar volume
----------------------------------------------------------------------------------------------------------------
Spread <=$0.016................................   $0.001, 0.002            1,707            64.0            37.9
$0.016 https://www.nasdaq.com/articles/looking-for-the-perfect-stock-price-2019-09-19.
---------------------------------------------------------------------------

    All else equal, reducing the tick size could narrow the spread. In
a competitive market, and in the absence of rebates or other price
distortions, the prevailing bid or ask price would be the feasible
price equal to just worse than the price that equates liquidity supply
and demand--as any price better than this would lead to an excess of
liquidity demand which would induce more liquidity providers. This tick
size effect diminishes as spreads widen. As a conceptual example,
consider a stock that, should the tick size be $0.0005, would trade at
an ask of $10.0065 and a bid of $10.0045. If the minimum tick were at a
penny, absent other effects, it is reasonable to believe that the ask
would be $10.01 and the bid $10.00. In contrast, were the tick size to
be $0.001, it would be feasible to have an ask of $10.007 and a bid of
$10.004. Rather than $0.01, the spread would be $0.003. In this
conceptual example that abstracts from other effects described below,
the stock would receive a 70% reduction in its quoted spread if the
tick size decreased from $0.01 to $0.001.
    As spreads widen, tick-size-induced distortions attenuate. To see
this, consider the same example above, but two orders of magnitude
larger where the prices that equate liquidity supply and demand are an
ask price of $1000.65 and a bid price of $1000.45. In the current one-
cent tick regime, the prevailing ask and bid would equal the spread
that equates liquidity supply and demand and there would be no tick-
size induced distortion. Consequently, reducing the tick size could
have a significant effect for stocks with narrow spreads, and this
effect may attenuate as the stock's spread widens. A risk of a smaller
tick is that it spreads liquidity over more price levels, which may
potentially create adverse effects--particularly for larger orders.
    Additionally, a smaller tick could increase the incidence of
pennying, which occurs when limit order providers get to the front of
the queue by providing economically trivial price improvement, and
could reduce the importance of time priority. The risk of being pennied
could discourage liquidity provision, particularly by market
participants that are slower to respond to changes in market
conditions, and could increase trading costs for these investors.\532\
To compensate for additional costs associated with a fragmented order
book, liquidity providers may post less aggressive quotes leading to
worse market quality. The reduction in the importance of time priority
would lower the risk of sniping and increase the opportunity for slower
traders (non-high-frequency traders) to fill orders using liquidity-
providing instead of liquidity-demanding transactions.\533\ This could
reduce adverse selection costs for these traders, countering the
effects of pennying.
---------------------------------------------------------------------------

    \532\ See Dyhrberg, et al., supra note 454, studying the effects
of imposing a tick size on a crypto exchange that previously did not
have a tick size. The authors report an improvement in market
quality due largely to a reduction in pennying behavior.
    \533\ See supra note 478 and related text.
---------------------------------------------------------------------------

    In contrast to the pricing effect discussed above, the pennying
effect would be most pronounced for stocks with wide spreads because
there are more intra-spread price levels and the cost of gaining
priority over other liquidity providers, by updating the best price by
a single tick, is lower.\534\ For example, a stock with a spread of one
cent, and a $0.001 tick, would have 10 price levels within the spread,
whereas a stock with a $0.10 spread would have 100. Because price has
first priority in order execution, a primary way to gain priority for a
trader providing liquidity is to price-improve over existing orders.
Without a small tick size relative to the spread, getting to the front
of the queue via price improvement would be more costly, requiring
larger relative price concessions.\535\
---------------------------------------------------------------------------

    \534\ The pennying effect would be particularly acute for wide
spread stocks with lower stock prices because a lower stock price
reduces the amount of capital needed to supply a round-lot quote and
hence make pennying less capital intensive.
    \535\ For example, if a stock has a spread of one cent and a
$0.001 tick, gaining priority through price improvement would
require narrowing the half-spread (i.e., the distance between the
current quote and the midpoint) by 20%. If instead a stock has a
spread of $0.10 with a $0.001 tick, a market participant would only
need to improve the half spread by 2% to get to the front of the
queue.

---------------------------------------------------------------------------

    In the presence of an NBBO and a differentiation between pricing
feeds that disseminate top of book versus depth of book data, there may
be informational consequences of a change in the tick size. As
mentioned above, fragmenting of the order book reduces the displayed
liquidity at the NBBO. This would temporarily reduce the information
about liquidity available in the market for market participants who do
not receive depth of book information from proprietary data feeds.
Having less information about available liquidity could make it more
difficult, more complex, and more expensive to locate shares for larger
trades and to manage liquidity provision strategies.\536\
---------------------------------------------------------------------------

    \536\ However, the inclusion of odd lot information helps to
mitigate this effect, and the eventual inclusion of depth of book
information in consolidated market data due to the implementation of
the MDI rules would render this effect temporary. At that point in
time, consolidated market data is expected to contain depth
information at many more price points, which would largely
counteract the effects of a reduction in displayed depth from a
reduction in tick size.
---------------------------------------------------------------------------

    For those who do not currently receive depth of book data or those
who would otherwise not purchase depth of book data from competing
consolidators, the proposal could increase the demand to purchase depth
of book data. Before the full implementation of the MDI Rules, this
could result in more market participants purchasing data from exchange
depth of book proprietary data feeds than do currently. Afterward, this
could result in more market participants purchasing depth of book data
from either competing consolidators or exchanges than in the absence of
the proposal.
    The expectation that a smaller tick size would lead to tighter
spreads for stocks that currently have narrow spreads finds empirical
support. The academic literature examining the effect of tick sizes on
financial markets largely studies two events: decimalization, which
occurred in 2001 \537\ and reduced the tick from 1/16th ($0.0625) to
$0.01; and the TSP which ran from October 2016 to October 2018 and
temporarily increased the minimum tick increment to $0.05 for a sample
of small cap stocks.\538\ Most of the literature surrounding
decimalization suggests that, on average, decimalization was associated
with a decline in spreads consistent with the notion that, on average,
and during this time period, lowering the tick relieved distortions
related to having a tick size that is too wide.\539\ Industry studies
show examples of reverse splits leading to large reductions in spreads
and hence trading costs.\540\ When a stock undergoes a reverse split
its share price goes up, the current penny tick is lower as a fraction
of the share price, implying that in economic terms, the stock could go
from being tick-constrained to non-tick-constrained.
---------------------------------------------------------------------------

    \537\ See, e.g., Exchange Act Release No. 42914 (June 8, 2000),
65 FR 38010 (June 19, 2000) (``Decimal Pricing Release'');
Commission Notice: Decimals Implementation Plan for the Equities and
Options Markets, SEC (July 24, 2000), available at https://www.sec.gov/rules/other/decimalp.htm.
    \538\ See supra note 84.
    \539\ See Bessembinder (2003) supra note 477. See also Michael
A. Goldstein and Kenneth A. Kavajecz, Eighths, Sixteenths and Market
Depth: Changes in Tick Size and Liquidity Provision on the NYSE, 56
J. Fin. Econ. 125 (2000) and Charles M. Jones and Marc L. Lipson,
Sixteenths: Direct Evidence on Institutional Execution Costs, 59 J.
Fin. Econ. 253 (2001), both examining the earlier tick size change
from \1/8\ to \1/16\ of a dollar. See also Sugato Chakravarty,
Venkatesh Panchapagesan, and Robert A. Wood, Did Decimalization Hurt
Institutional Investors?, 8 J. Fin. Mkts. 400 (Nov. 2005) and Sugato
Chakravarty, Bonnie F. Van Ness, and Robert A. Van Ness, The Effect
of Decimalization on Trade Size and Adverse Selection Costs, 32 J.
Bus. Fin. & Acc. 1063 (June/July 2005), both suggesting that large
institutional trades may have become more costly following
decimalization.
    \540\ See MEMX Report, supra note 105; see also Adrian
Griffiths, The Tick Size Debate Revisited, MEMX (Jan. 2022),
available at https://memx.com/wp-content/uploads/MEMX_MSR_Tick-Constrained-Securities-2_03b.pdf.
---------------------------------------------------------------------------

    The Commission supplements existing analysis with its own analysis
on the TSP. Focusing on the TSP, as opposed to decimalization, has
several advantages. Additionally, the Commission relies more on its own
analysis and the existing literature on the TSP than that for
decimalization for this purpose because market dynamics have changed
dramatically in the more than two decades since decimalization. Most
notably over that period, electronic, algorithmic, and high-frequency
trading have come to dominate the trading landscape today, whereas they
were much less prominent in 2001.\541\
---------------------------------------------------------------------------

    \541\ See, e.g., Terrence Hendershott, Charles M. Jones, and
Albert J. Menkveld, Does Algorithmic Trading Improve Liquidity?, 66
J. Fin. 1 (Feb. 2011).
---------------------------------------------------------------------------

    Using the TSP for analysis also has limitations because the TSP
affected a subset of small cap stocks and primarily focused on changes
in tick size \542\--it did not affect access fee caps for instance. The
TSP also did not contain ETPs. Nonetheless, the fact that it concluded
relatively recently suggests that its outcomes may be more
generalizable to current markets than decimalization.
---------------------------------------------------------------------------

    \542\ See Barardehi, et al. (2022) supra note 85.
---------------------------------------------------------------------------

    The academic literature studying the TSP shows that for stocks with
average quoted spreads close to $0.05 prior to the TSP (namely, stocks
likely to fall under our conceptual definition of tick-constrained),
the TSP led to an increase in effective and quoted spreads.\543\ That
is, these stocks tended to trade better with a $0.01 tick than with a
$0.05 tick. These results suggest that a tick that is too wide
increases the cost of transacting small and average sized orders.
Mechanically wider spreads in some stocks could mean that relatively
small orders that do not need to take advantage of any additional NBBO
depth may execute at a higher cost.\544\
---------------------------------------------------------------------------

    \543\ See Hu, et al. (2018), supra note 477; Kee H. Chung, et
al., Tick Size Liquidity for Small and Large Orders, and Price
Informativeness: Evidence From the Tick Size Pilot Program, 136 J.
Fin. Econ. 879 (2020); Rindi and Werner (2019) supra note 475;
Griffith and Roseman (2019) supra note 478; Barardehi, et al.
(2022), supra note 85. Part of this effect may be mechanical.
    \544\ See, e.g., Chung, et al. (2020) supra note 543; Rindi and
Werner (2019) supra note 475; and Maureen O'Hara, et al., Relative
Tick Size and the Trading Environment, 9 Rev. Asset Pricing Stud. 47
(2019).
---------------------------------------------------------------------------

    The TSP literature provides mixed results with regards to the
trading costs for large orders.\545\ Multiple academic studies have
found that a wider tick increases depth at the NBBO.\546\ This finding
is intuitive because all the quotes that would have been placed within
the spread with a $0.01 tick prior to the TSP, congregated at the next
best available prices under the $0.05 tick. In addition, any wider
spreads and greater pennying costs associated with a larger tick would
also serve to attract more liquidity to the NBBO. More depth at the
NBBO would mean that a larger order could execute without having to go
deeper into the book--potentially decreasing the cost to executing a
larger order if the added depth at the NBBO is sufficient to overcome
costs from the wider tick. However, one empirical study using different
data for the TSP found evidence suggesting that the TSP led to a
decrease in cumulative liquidity beyond the NBBO for test group stocks
with an average pre-pilot quoted spread less than $0.05 suggesting an
increase in the cost to transact a large order--although the authors do
not articulate a clear mechanism for this result.\547\
---------------------------------------------------------------------------

    \545\ Griffith and Roseman (2019) supra note 478 find evidence
that the imposition of the TSP had either no effect on or slightly
increased the cost of trading large trades for treatment stocks with
an average pre-pilot quoted spread greater than $0.05, though the
results were not statistically significant. They found statistically
significant evidence that trading costs for large trades increased
for treatment stocks with an average pre-pilot quoted spread less
than $0.05. Barardehi, et al. (2022), supra note 85, and Chung, et
al. (2020), supra note 543, both document that depth increases and
the cost of executing large trades decreases.
    \546\ See, e.g., sources cited supra note 543.
    \547\ Griffith and Roseman (2019), supra note 478, use the order
book data from (NASDAQ) and find that cumulative depth away from the
BBO is lower and larger trades became more costly to execute for
treatment stocks with an average pre-pilot quoted spread less than
$0.05 and hence became tick-constrained by the TSP. By focusing on a
single exchange the paper does not take into consideration depth
available on other exchanges, which could affect the paper's measure
of trading cost.

---------------------------------------------------------------------------

    The current literature offers little guidance regarding the
expected effect of a tick size change for stocks with wider spreads.
For stocks that were not tick-constrained by the $0.05 tick, i.e.,
those with quoted spreads wider than $0.05 prior to the TSP, the
literature examining the TSP is much less uniform in its assessment of
the effect of a wider tick on market quality for stocks with wider
spreads.\548\ The fact that this literature does not provide consistent
results on how a wider tick affects stocks with wider spreads is likely
the result of the different researchers using different definitions of
tick-constrained, and by virtually all studies simply bifurcating
stocks into either tick or non-tick-constrained stocks to perform
comparisons and assuming that all non-tick-constrained stocks will be
affected in a similar manner by the tick size change.\549\ In contrast,
the theoretical discussion at the beginning of this section suggests
that a simple bifurcation might not be the proper way to study the
effect of tick size on stocks with various quoted spreads, since the
relation between market quality and tick size is unlikely to be a
binary function of whether or not the stock is tick-constrained, but
rather depends on the number of ticks within the spread. That is, if
there were to be negative effects on spreads when ticks are made too
narrow, theory suggests that these would be most likely to be observed
in stocks for which spreads are especially wide, with many ticks within
the spread.
---------------------------------------------------------------------------

    \548\ Existing studies do not to agree on the overall impact of
a wider tick for stocks that were not tick-constrained by the $0.05
tick. For example, Rindi and Werner (2019), supra note 475, document
that while quoted spreads increased among non-tick-constrained
stocks, effective spreads decreased for these same stocks suggesting
that while displayed prices were worse with a $0.05 spread, the
actual transaction prices that investors received improved. Chung,
et al., (2020), supra note 543 find that in general, transaction
costs among stocks that were not tick-constrained decreased with a
$0.05 tick. Griffith and Roseman (2019), supra note 478 find that
the $0.05 tick was not associated with any change in order book
depth for non-tick-constrained stocks. Additionally, DERA White
Paper (2018), supra note 477 finds that for non-tick-constrained
stocks, the imposition of a $0.05 spread led to no change in quoted
spreads and very little change in effective spreads.
    \549\ The exact threshold, in terms of the time weighted quoted
spread at which a stock is considered tick-constrained, is subject
to debate and researchers use various thresholds. The Commission's
review of the literature and of industry publications suggests that
a time-weighted quoted spread of $0.011 is the most commonly used.
That spread is used for the analysis herein.
---------------------------------------------------------------------------

    The Proposal would reduce the tick size for some stocks with
narrower spreads that do not meet the definition of tick-constrained.
To provide greater insight into the impact of tick sizes on various
aspects of market quality across the quoted spread spectrum, Table 9
provides additional analysis that examines the impact of the TSP on a
wider range of quoted spread profiles than simply tick-constrained or
not. This analysis focuses on the end of the TSP, when the tick size
was reduced from $0.05 back to $0.01, because that event more closely
matches the proposal, which considers a tick size reduction.
    The analysis presented in Table 9 uses a difference-in-difference
methodology to study the effect of lowering the tick size from $0.05 to
$0.01 on TSP stocks at the end of the TSP.\550\ TSP treated and control
stocks are assigned near the end of the TSP into one of four bins
ranging from the most tick-constrained in the first bin to the least
constrained in the fourth bin.\551\ Key variables such as quoted depth
and spreads were measured before and after the tick size was lowered
and difference in difference estimation methods were used to examine
how these variables reacted to the tick size change. The analysis uses
ordinary least squares \552\ and quantile (median) regressions \553\ to
estimate the following regression model: \554\
---------------------------------------------------------------------------

    \550\ Difference-in-differences is a statistical technique in
which the effect that a treatment has on some response variable is
estimated by comparing the average change in the response over time
in the treatment group to the average change in the control group.
    \551\ Bin assignments are calculated according to the stock's
average quoted spreads for May and June of 2018, near the end of the
TSP. Specifically, we use WRDS Intra-day indicators to collect the
time-weighted quoted spread for all TSP and control stocks for each
trading day in May and June 2018. Then for each stock we calculate
the equally-weighted average quoted spread across all trading days.
Based on this average, TSP and control stocks are sorted into one of
four bins. The first bin is for stocks with quoted spreads ($0.00,
$0.06). Empirically, for stocks in the TSP, this bin is said to
include those stocks that were tick-constrained by the $0.05 tick
increment during the pilot. The second bin is for stocks with quoted
spreads in the range ($0.06, $0.09]. For stocks in the TSP, this bin
is said to include those stocks that were near-tick-constrained by
the $0.05 tick increment during the pilot. The third bin is for
stocks that had quoted spreads of ($0.09, $0.15) or approximately 2-
3 ticks intra at a $0.05 tick increment. The fourth bin is for
stocks with quoted spreads greater than $0.15. The TSP had three
test groups: the first group applied the $0.05 tick only to quoting,
the second group applied the $0.05 tick to quoting and trading (with
exceptions for benchmark and midpoint trades and for certain retail
price improvement trades), and the third group applied the $0.05
tick to trades and quotes the same as the second group but also had
a trade at rule applied. Because the proposal would apply the tick
size to both trading and quoting, the analysis presented here
includes only stocks in the latter two groups--i.e., test groups two
and three. Barardehi, et al. (2022), supra note 85 provide similar
analysis, and also expand the analysis in many dimensions and find
evidence that all key results presented here are robust to the test
group analyzed and to many other factors.
    \552\ Ordinary least squares (OLS) regression refers to a
statistical technique for estimating the linear relationship between
an independent variable and dependent variables by minimizing the
sum of squared errors between the estimate and the observed
independent variable. The use of OLS and quantile regressions is
common in the literature on the TSP pilot.
    \553\ The primary advantage to quantile regressions is that they
are less sensitive to outliers that can affect mean inference in
OLS. Thus median regressions provide additional robustness to the
analysis and ensure that results are not driven by outliers.
    \554\ In this equation the variable Y denotes the response
variable of interest such as quoted spread and depth. The subscripts
j and t serve to index stocks and days respectively. [alpha]0,
[alpha]p, [alpha]e, and [beta] are coefficients (to be estimated),
and uj,t is the error term. Pilotj is an indicator variable that
equals 1 if stock j was in the treatment group, or 0 if stock j was
in the control group. Eventt is a indicator variable which is equal
to 1 if the day t was post the treatment event and equals 0
otherwise. Table 9 reports the difference-in-difference estimator of
[beta] for a different response variable Y across the different
spread bins.

Yj,t = [alpha]0 + [alpha]PPilotj + [alpha]EEventt
---------------------------------------------------------------------------
+ [beta](Pilotj x Eventt) + uj,t

where the quantile regression optimizes: \555\
---------------------------------------------------------------------------

    \555\ In this equation uj,t is the error term from the previous
regression specification equation, supra note 554, and the loss
function is defined as: [rho]t(u) = [tau] max(u, 0) + (1-[tau])
max(-u, 0) ; where 0 < [tau] <1.
[GRAPHIC] [TIFF OMITTED] TP29DE22.000

                                    Table 9--Effects of a Reduction in Tick Size on Quoting and Trading Outcomes \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                          OLS                                    Quantile (median) regression
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                           Quoted spread ($) May & June 2018                   Quoted spread ($) May & June 2018
--------------------------------------------------------------------------------------------------------------------------------------------------------
                  Spread bin #                        1st          2nd          3rd          4th          1st          2nd          3rd          4th
--------------------------------------------------------------------------------------------------------------------------------------------------------
Depth (100 shares)..............................    *** -22.5    *** -5.30    *** -1.55        -0.51    *** -11.8    *** -3.16    *** -0.96    *** -0.21
                                                     [-12.02]      [-7.09]      [-4.40]      [-1.30]     [-16.99]     [-23.52]     [-17.81]      [-4.30]
Depth ($1,000)..................................    *** -16.7    *** -8.41    *** -4.67    *** -2.06    *** -11.2    *** -7.27    *** -3.96    *** -1.48
                                                     [-14.58]     [-10.94]      [-7.82]      [-3.66]     [-22.04]     [-20.70]     [-12.58]      [-4.14]
Quoted Spread ($)...............................   *** -0.033   *** -0.027    *** 0.023     *** 0.12   *** -0.034   *** -0.031    *** 0.012     *** 0.12
                                                     [-18.71]      [-6.46]       [2.99]       [5.51]     [-35.41]     [-10.31]       [2.03]       [6.80]
Relative quoted Spread..........................  *** -0.0049   * -0.00097      0.00034   *** 0.0046  *** -0.0041  *** -0.0014      0.00021   *** 0.0034
                                                      [-9.59]      [-1.80]       [0.53]       [3.30]      [-8.54]      [-6.89]       [0.74]       [4.66]
Effective spread ($)............................   *** -0.027       -0.026    *** 0.029     ** 0.038   *** -0.026   *** -0.021      -0.0018    *** 0.051
                                                      [-4.97]      [-1.43]       [5.17]       [2.16]     [-58.10]     [-12.81]      [-0.63]       [4.81]
Relative eff. spread............................  *** -0.0039      0.00043       0.0055   *** 0.0028  *** -0.0030  *** -0.0010     -0.00013   *** 0.0016
                                                      [-3.12]       [0.17]       [1.36]       [4.42]     [-10.78]      [-9.58]      [-1.09]       [3.23]
Cancel-to-trade.................................     *** 5.10     *** 6.69     *** 7.56     *** 18.8     *** 4.56     *** 5.49     *** 6.87     *** 12.3
                                                       [5.99]       [6.38]       [6.79]       [8.44]       [7.75]       [7.79]      [10.44]      [10.61]
Odd-lot rate (%)................................     *** 4.89     *** 5.61     *** 2.85      ** 1.49     *** 5.59     *** 6.39     *** 3.29      ** 1.85
                                                       [9.62]       [8.04]       [4.35]       [2.15]       [8.02]       [8.99]       [4.72]       [2.51]
Realized spread ($).............................   *** -0.014   *** -.0099       .00037    *** 0.040   *** -0.014   *** -0.013  *** -0.0068    *** 0.038
                                                     [-27.94]      [-7.43]       [0.12]       [4.45]     [-48.36]     [-17.96]      [-5.13]       [5.64]
Relative real. spread...........................   *** -.0024      -.00032      -.00039     ** .0014   *** -.0014  *** -.00054  *** -.00013    *** .0012
                                                     [-11.82]      [-1.36]      [-1.25]       [2.37]     [-14.08]     [-12.52]      [-2.77]       [3.65]
Volume (1,000 shares)...........................         26.5      ** 30.3         12.5        -5.41         19.1         3.35         0.20     ** -3.25
                                                       [1.30]       [2.13]       [1.32]      [-1.07]       [1.42]       [0.40]       [0.04]      [-2.44]
Cum Depth 10c from mdpt.........................    *** -0.17    *** -0.26     ** -0.27     ** -0.34    *** -0.49    *** -0.54    *** -0.45     ** -0.63
                                                      [-3.93]      [-5.00]      [-2.59]      [-2.37]      [-5.51]      [-6.29]      [-4.91]      [-3.15]
Cum Depth -10c from mdpt........................    *** -0.22    *** -0.19    *** -0.37     ** -0.45    *** -0.49    *** -0.42    *** -0.50     ** -0.79
                                                      [-5.28]      [-3.74]      [-3.44]      [-2.83]      [-6.33]      [-5.21]      [-5.68]      [-2.75]
CRT 10 round lots...............................   *** -0.026       -0.001    *** 0.035     *** 0.14   *** -0.037    *** 0.085    *** 0.035     ** 0.075
                                                     [-19.56]      [-0.19]       [3.99]       [1.03]      [-2.72]       [2.75]       [4.20]       [2.37]
--------------------------------------------------------------------------------------------------------------------------------------------------------
\a\ This table presents the effects of a reduction in minimum tick size from $0.05 to $0.01 cent on various quoting and trading outcome variables. The
  1st bin is most tick-constrained and the 4th bin is least tick-constrained. See supra note 554 for bin descriptions. A difference in difference
  regression with no control variables is estimated using data covering Control, Test Group 2, and Test Group 3 TSP stocks from 08/01/2018-11/30/2018.
  All observations are at the stock day level. The same model is used for all outcome variables. See supra note 554 for a discussion of why Test Group 2
  and Test Group 3 were selected. For each outcome variable Yjt, the table presents only the difference in difference coefficient estimates that
  indicate the effect of the TSP on the dependent variable. Estimates are performed by past quoted spread subsamples that decompose the sample based on
  average quoted spreads during June and July of 2018. Each regression is estimated using both OLS and quantile (median) regressions. The first four
  columns present the result from OLS regression results, the last four columns present the results from quantile regression results. Column titles 1st,
  2nd, 3rd, and 4th represent results estimated for bin 1, bin 2, bin 3 and bin 4 stocks respectively. The quoted spread refers to the distance between
  the NBBO midpoint and the NBBO quote. The effective spread is the distance between the NBBO midpoint and the realized trade price; the realized spread
  is the distance between a future NBBO midpoint (5-minutes ahead) and the trade price. Relative spread measures are calculated as the spread scaled by
  the NBBO midpoint. The cancel-to-trade ratio is the daily number of order cancellations divided by the number of trades, for displayed orders. The odd-
  lot rate is the percentage of trades in a day which executed against an odd-lot quote. CRT 10, or the cost of a round-trip trade of 10 round lots,
  measures the cumulative transaction costs from buying and then immediately selling 10 round lots. The CRT assumes that an order that is larger than
  the displayed depth at the best price will not execute in full at that price. Instead, the assumed unfilled portion will execute at worse prices until
  completely filled with displayed depth. All data are Winsorized at the 1% and 99% level. The numbers in the [ ] brackets reflect t-statistics that are
  based on two-way stock-and-date clustered standard errors. Symbols *, **, and *** reflect statistical significance at 10%, 5%, and 1% type-1 error
  levels.

    This analysis provides evidence of a fundamental tradeoff between
accurate pricing on one hand and incentives for liquidity provision on
the other. Across all specifications, the end of the TSP was associated
with a decrease in depth at the NBBO, when the tick size was reduced
from $0.05 to $0.01, as signified by the negative and, in most cases,
statistically significant coefficients reported. The magnitude of the
coefficients suggest that the reduction in shares available at the NBBO
was the greatest for stocks with tighter spreads and smaller for stocks
with wider spreads. The finding that tighter spread stocks experience
the greatest decline in depth at the NBBO is consistent with the -idea
that, for these stocks, the $0.05 tick was the most constraining, and
so liquidity that would have naturally spread out within the quoted
spread given a smaller tick, bunched at the wider tick increments, and
that once the tick-constraint was relaxed this liquidity naturally
spread out over the additional price levels. For less tick-constrained
stocks, the bunching was less severe since liquidity already had some
room to spread out.
    For stocks in the first or second bins, we find that lowering the
tick to $0.01 leads to significantly lower quoted spreads. These stocks
went from having approximately 1-2 ticks inside the spread, with a
$0.05 tick, to having 1-10 ticks inside the spread, with a $0.01 tick.
For these bins, relaxing the tick size served to narrow the spread.
This finding is consistent with the idea that for stocks that are tick-
constrained, or near-tick-constrained, the effect of decreasing the
tick size will narrow spreads by improving competition. For the stocks
in the third and fourth bins, the story is different, as the reduction
in the tick size was associated with a widening of the quoted spread.
These stocks went from having more than two or more ticks within the
spread, with a $0.05 tick, to having more than 10 ticks within the
spread, with a $0.01 tick. This result is consistent with the idea that
for wider spread stocks, the prevailing effect of reducing the tick
size was to increase fragmentation of liquidity and the risk of
pennying which made trading more costly leading to wider spreads. This
pattern of results--namely narrower spreads for the first and second
bins and wider spreads for the fourth--holds regardless of whether
dollar spreads, relative spreads, OLS, or quantile regressions are
used, suggesting this as a robust outcome of the end of the TSP.
    The pattern for effective spreads is similar to that observed for
quoted spreads. Effective spreads measure the average realized
transaction cost for trades as it measures the absolute distance
between the realized trade price and the NBBO midpoint at the time of
the trade. Effective spreads do not always equal quoted spreads because
trades can execute inside the NBBO for numerous reasons, such as odd-
lot trades, midpoint trades, and

hidden orders. For stocks in bin one--i.e., stocks for which the $0.05
tick was the most restrictive--all specifications suggest that reducing
the tick size was associated with a decrease in realized transaction
costs as measured by effective spreads. For stocks in bin four, those
with the widest spreads prior to the tick size reduction, all
specifications suggest that the reduction in the tick size lead to an
increase in transaction costs, measured by effective spreads. For
stocks in between these extremes in bins two and three, the results are
not as uniform. For stocks in bin two, the sign of the coefficients for
all estimates (dollar effective spreads, relative effective spreads,
OLS, and quartile regressions) suggest that lowering the tick size
decreased effective spreads, although not all specifications agree as
to statistical significance. The OLS regressions suggest that the
effect was statistically insignificant, while the quantile regressions
found a statistically significant effect and suggest that effective
spreads decreased. For stocks in the third bin, the analysis did not
find a consistent, statistically significant change in effective
spreads, or in other words, lowering the tick size did not appear to
reliably help or harm transaction costs as measured by effective
spreads.
    These results, like the results for quoted spread, suggest that for
stocks for which the narrowing of the spread meant that the stock went
from having less than 2 ticks within the spread to 1-10 ticks within
the spread, the effect of reducing the tick was beneficial in terms of
reducing transaction costs. For stocks with very wide spreads, reducing
the tick size appeared to harm liquidity, which is consistent with
fragmentation and pennying being the prevailing effect.
    The theoretical discussion above suggests that executing an order
may become more complex with a smaller tick size--meaning it may take
visiting more venues as well as across more price levels to execute an
order with a smaller tick size. This potential outcome is explored
using the ``cancel-to-trade'' ratio. A higher ratio indicates more
frequent canceling of orders per the amount of trading volume, and is
an indication that market participants are more active in managing
their quotes and their order strategies. In this analysis, both the OLS
and the quantile regressions confirm that a smaller tick resulted in a
statistically significant increase in the cancel to trade ratio,
suggesting more complexity. Additionally, the magnitude of the effect
is increasing in the quoted spread, with wider quoted spreads having
larger coefficients, suggesting a larger effect in the cancel-to-trade
ratio than stocks with narrower spreads. This pattern is consistent
with pennying and increased complexity having a greater impact on
stocks with wider spreads.
    The analysis also looks at the effect of lowering the tick size at
the end of the TSP on the usage of odd-lot orders. Across all quoted
spread bins, the usage of odd-lot orders increases when the tick size
decreases. This finding is consistent with the notion that liquidity
would be spread out over more levels and liquidity providers would be
willing to offer less liquidity at a given price level--leading to an
increased use of odd-lot orders to allow liquidity providers to offer
smaller levels of liquidity at finer price increments. This result also
suggests that a lower tick size increases the need for market
participants to have ready access to odd-lot information given that the
lower tick size can be expected to increase the usage of odd-lot
quotes.
    Effective spreads provide a measure of liquidity providers' revenue
and the contrasting economic effects also have implications for how
liquidity providers' revenue would be affected by a lower tick. The
effective spread captures the liquidity premium, paid by those
submitting orders for immediate execution, and can theoretically be
decomposed into two components: Effective Spread = Realized Spread +
Price Impact.\556\ One component of the effective spread is the price
impact or adverse selection component. It is the change in the NBBO
midpoint at the time of trade to some point in the future. This
component of the spread captures the portion of the spread liquidity
providers lose from trading with investors who are more informed than
they are, and is also referred to as the adverse selection component of
the bid ask spread. The remainder of the effective spread, after
removing the adverse selection component, is the realized spread. This
portion of the spread acts as a proxy \557\ for the compensation to the
liquidity provider for its non-adverse selection costs. If a smaller
tick decreases revenue for liquidity providers, by allowing bid and ask
prices to more accurately reflect supply and demand, then this effect
should manifest as a decrease in realized spreads for liquidity
providers. However, if increased order book fragmentation and pennying
risk increase the cost of providing liquidity, then liquidity providers
would need to be compensated for these costs in order to provide
liquidity and, thus, realized spreads would increase. To the extent
that the two effects offset one another, realized spreads might not
change.
---------------------------------------------------------------------------

    \556\ Effective spreads can be interpreted as what liquidity
providers expect to earn from providing liquidity, assuming that
prices do not change before the liquidity provider is able to unwind
its position and realize their profit. Under this interpretation,
realized spreads would proxy for what they actually earn, taking
into account that the market price may have moved against the
liquidity provider before it could unwind its position. Effective
Spread = Realized Spread + Price Impact. For a full mathematical
decomposition of effective spreads into realized spread and price
impact components see Peter N. Dixon, Why Do Short Selling Bans
Increase Adverse Selection and Decrease Price Efficiency, 11 Rev.
Asset Pricing Stud. 122 app. at 165 (2021).
    \557\ Realized spreads do not measure the actual trading profits
that market makers earn from supplying liquidity. In order to
estimate the trading profits that market makers earn, we would need
to know at what times and prices the market maker executed the off-
setting position for a trade in which it supplied liquidity (e.g.,
the price at which the market maker later sold shares that it bought
when it was supplying liquidity). If market makers offset their
positions at a price and time that is different from the NBBO
midpoint at the time lag used to compute the realized spread measure
(Rule 605 realized spread statistics are measured against the NBBO
midpoint 5 minutes after the execution takes place), then the
realized spread measure is an imprecise proxy for the profits market
makers earn supplying liquidity.
---------------------------------------------------------------------------

    For tick-constrained stocks in bin one, the analysis indicates a
decrease in realized spreads across all specifications, and when using
dollar or relative realized spreads when the tick size was reduced from
$0.05 to $0.01. This result is consistent with the notion that
liquidity providers' non-adverse selection revenues would decrease due
to bid and ask prices being more reflective of supply and demand with a
smaller tick. The opposite occurs for stocks with wide spreads in bin
four, where realized spreads increase significantly--consistent with
liquidity providers needing to be compensated for the increased cost
and complexity associated with trading a wide spread stock in a small
tick environment. For stocks in the middle two bins, the effect of
lowering the tick size on realized spreads is unclear, as about half of
the specifications indicate no change in realized spreads while the
other half indicate lower effective spreads. The specifications often
do not agree between relative and effective spread specifications and
between OLS and quantile regressions.
    The analysis also uses MIDAS data to study how the tick size change
affected liquidity deeper in the book. Analyzing liquidity deeper in
the book is valuable because it gives an indication of how trading
larger orders that must go deeper in the book to be fulfilled may be
affected by a change in the tick size. This analysis uses MIDAS data to
calculate the daily average cumulative

shares available at $0.10 above and below the midpoint for control and
treated stocks, and uses the same difference-in-difference analysis to
examine the effect of reducing the tick size on cumulative depth.\558\
Our analysis suggests that reducing the tick size also reduced the
total depth available deeper in the book with the coefficient for bin
4--i.e., those with the widest spreads--being the largest in magnitude.
This finding is consistent with a smaller tick discouraging the posting
of displayed liquidity due to pennying concerns for stocks with wide
spreads.
---------------------------------------------------------------------------

    \558\ In the regressions we take the natural log of shares
available. This conversion helps standardize shares available for
stocks with different prices by making the interpretation in terms
of percentage changes.
---------------------------------------------------------------------------

    These depth of book findings do not directly imply that trading
deeper in the book became more expensive for two reasons. First,
research suggests the use of non-displayed quotations increases
significantly when the tick size is reduced.\559\ Thus the decline in
liquidity that we document is only a decline in displayed liquidity.
Second, quotes tend to congregate at the price just worse than the
quoter's desired price so that the quoter does not lose money on a
transaction. When a wider tick is tightened, quotes that were
previously congregated at the wide tick will spread out at prices
better than the previous tick allowed. Thus, a market participant
taking liquidity from multiple price layers in the order book to
fulfill an order would have some shares that transact at superior
prices than it would have with the wider tick.\560\
---------------------------------------------------------------------------

    \559\ See analysis presented in Nasdaq Intelligent Tick
Proposal, supra note 180; see also Justin Cox, et al., Increasing
the Tick: Examining the Impact of the Tick Size Change on Maker-
Taker and Taker-Maker Market Models, 54 Fin. Rev. 417 (2019); Amy K.
Edwards, Paul Hughes, John Ritter, Patti Vegella, and Hao Zhang, The
Effect of Hidden Liquidity: Evidence from an Exogenous Shock
(working paper Mar. 1, 2021), available at https://ssrn.com/abstract=3766512 (2021) (retrieved from SSRN Elsevier database).
    \560\ Consider a numeric example. A market with a $0.05 tick is
quoting asks of 500 shares at $10.05 and 500 shares at $10.10. An
investor wishing to purchase 700 shares would purchase 500 at $10.05
and 200 at $10.10 for a total price of $7,045. If the tick shrinks
to $0.01 and cumulative shares posted decline by 20%--for example--
but those shares are spread evenly over the finer grid then there
would be 80 shares at each price level from $10.01 to $10.10. An
investor wishing to buy 700 shares would need to purchase 80 shares
at each price level from $10.01 to $10.08 and 60 shares at $10.09
for a total purchase price of $7,034. So even though total depth
declined, the cost to execute a 500 share trade would decrease due
to more efficiently spreading liquidity across more price levels.
---------------------------------------------------------------------------

    Table 9 also presents the effect of the TSP conclusion on the
round-trip cost to transact a trade for 10 round lots (1,000
shares).\561\ This analysis suggests mixed results for the effect of
the tick size reduction on the cost of executing a 10 round lot trade.
For pilot stocks that were tick-constrained by the Pilot with a $0.05
tick, the total round-trip cost of a 10 round lot trade decreased when
the tick size was lowered--suggesting an improvement in liquidity
deeper in the book. For near-tick-constrained stocks, the effect was
not clear. The OLS regressions suggested no effect, while the quantile