datasets,\762\ it is likely that the sample of securities lending data
that is reported to commercial data vendors is, on average, different
from the sample that is not reported. As such, there are well-
established theoretical reasons as to why these data are likely biased.
These reasons are largely based on the fact that market participants
that voluntarily contribute data to commercial datasets ``self-select''
the data that they would like to be included in the dataset. The
practice of having entities under study ``self-select'' into the
dataset used to study them is widely acknowledged in the empirical
economics literature to be very likely to lead to biased data.\763\
This is because the incentives to provide data are likely connected to
the incentives that drive the behavior under study, such that the
behavior of those market participants that do contribute data are
likely to be systematically different from the behavior of those that
do not contribute. Trying to draw inferences from such a dataset about
the entire population of market participants would thus result in a
biased view of the market.
---------------------------------------------------------------------------

    \762\ See infra of this part for a discussion of investors'
incentives related to voluntarily contributing data to commercial
datasets, and why this may result in systematic differences between
those that choose to contribute as compared to those that do not.
    \763\ See Heckman (1990), supra note 761.
---------------------------------------------------------------------------

    The Commission has identified examples of such incentives that
would likely introduce such ``self-selection biases'' into commercially
available securities lending datasets. First, there may be competitive
reasons for market participants to selectively choose which loans to
contribute to a securities lending dataset. For example, to attract
more beneficial owner customers, lending programs would like their
average terms to appear better than the benchmark average terms. Thus,
they are incentivized to voluntarily contribute information about their
lending activity for loans with higher-than-average borrowing costs--
skewing the average cost to borrow in the resulting dataset up and
making the lender's performance appear better than it actually
was.\764\ This could lead the reported borrowing costs in commercial
datasets to be systematically higher than the actual borrowing costs in
the securities lending market.\765\
---------------------------------------------------------------------------

    \764\ One commenter stated that lending agents frequently
provide their clients with benchmark reports based on market data,
and that beneficial owners currently benefit from substantial
information obtained from lending agents. See RMA Letter, at 2; see
also supra note 721 and infra note 772 and corresponding text.
However, even if this is the case, many beneficial owners do not
have access to securities lending data, particularly give-to-get
Wholesale data, and so could likely not independently benchmark
transactions.
    \765\ Note that the reverse could also be true, as lenders, in
order to attract more customers in the market for borrowers, may be
incentivized to voluntarily contribute information about loans with
lower-than-average borrowing costs, which would skew the average
cost to borrow downwards. Ultimately, the Commission is not able to
determine the potential direction of the self-selection bias, which
could also vary over time. This would complicate any attempt to
adjust for the bias when performing analyses on the data.
---------------------------------------------------------------------------

    Similarly, some market participants may choose not to voluntarily
contribute data to securities lending datasets for their own strategic
reasons, such as a desire to keep their activity private. The decision
to not to participate is, in itself, indicative that such market
participants are likely not reflective of those who do choose to
participate. For example, market participants that choose not to
contribute data may be more likely to be sophisticated investors with
greater incentives to protect their trading and hedging strategies.
This matters because the terms of securities loans are influenced by
the characteristics of the parties involved,\766\ and so it follows
that the borrowing costs for market participants that do not
voluntarily contribute data are systematically different than the
borrowing costs for market participants that do contribute data.
---------------------------------------------------------------------------

    \766\ See infra Part IX.B.3 for further discussion of the
factors that influence the prices of securities loans.
---------------------------------------------------------------------------

    In addition to the issue of self-selection bias, currently there is
no securities lending dataset that contains information about both the
Customer and Wholesale segments of the market. The give-to-get datasets
lack significant coverage of the Customer segment of the market, while
the Customer market survey data lack significant coverage of the
Wholesale segment of the market.\767\ While some, but not all, market
participants could potentially gain access to data on both the
Wholesale markets and Customer markets by subscribing to both give-to-
get and Customer market survey datasets, this would generally be
possible only for market participants that contribute data (and thus
have access) to give-to-get datasets and would also not surmount the
problems of incomplete or potentially biased data. Different types of
data may also be cumbersome to combine.\768\ There are also limitations
to using the Customer market survey data to assess conditions in the
Wholesale market.\769\ Furthermore, the need to subscribe to multiple
datasets increases market participants' overall costs of data
access.\770\
---------------------------------------------------------------------------

    \767\ One industry publication noted in an interview with a
market participant engaged in Wholesale market lending that they
feel unable to benchmark the performance of their lending programs
using commercial data because they have very little insight into the
Customer market. See, e.g., Bob Currie, The Power of Reinvention,
Sec. Fin. Times (Aug. 31, 2021), at 20, available at https://www.securitiesfinancetimes.com/sltimes/SFT_issue_285.pdf,
interviewing Matthew Chessum, who stated that ``as a lender, we
monitor fees paid to us by the agent, but we only see one side of
the trade. We have no sight of the pricing paid by a hedge fund or
prime broker, for example, when they borrow those securities.'' One
commenter questioned the applicability of this citation and stated
their opinion that, ``[t]hat a single person would like to see
certain data is not evidence that requiring the disclosure of that
data is worth the massive cost,'' the commenter also states that
``the Commission neglects to estimate the number of market
participants that are seeking the same disclosure'' (see Citadel
Letter, at 10). The Commission received numerous comment letters
from industry members and organizations expressing support for the
public disclosure of securities lending data. See, e.g., Nasdaq
Letter, at 1-2; AFREF Letter 1, at 1; Better Markets Letter, at 1;
NYSE Letter 1, at 1-2; ASA Letter, at 1; and Morningstar Letter, at
2; The Commission also received letters from academics and
individuals supporting additional transparency. See, e.g., James J.
Angel Letter, at 1; Letter from Carl Nyborg (Dec. 15, 2021), at 1;
Letter from Eric Olsen (Jan. 24, 2022); Letter from Scott Clark
(Jan. 22, 2022).
    \768\ For example, commercial data vendors often convert rebate
rates (for cash collateralized loans) into fees, in order to combine
with information about lending fees for non-cash collateralized
loans and cash collateralized loans; see, e.g., infra note 786.
Different commercial data vendors may use different methodologies
for this conversion, which would make it difficult to combine and
compare information about borrowing costs across different datasets.
    \769\ Borrowing costs in the Customer market are often
contractually negotiated in the brokerage agreement between a
customer and their broker-dealer. It is common for these brokerage
agreements to stipulate a fixed rate for general collateral stocks
and some predetermined process to determine rates for stocks on
special, such as referencing Wholesale market rates plus some mark-
up. For this reason, there is uncertainty regarding how strong the
correlation might be between Wholesale and Customer borrowing costs,
particularly for general collateral stocks.
    \770\ One commenter stated that, ``while the Proposal stated
that purchasing multiple vendor systems is expensive, there was no
quantification of actual vendor costs. Nor was there data provided
on how many firms actually use multiple vendors'' (see S3 Partners
Letter, at 4). The Commission cannot provide such analysis because
we do not have, and commenters did not provide, typical costs paid
to vendors for such data. The Commission understands that such
pricing is dynamic and individualized, and is generally not aware of
publicly available price lists or client lists.
---------------------------------------------------------------------------

    As an additional source of securities lending data, some market
participants that have many connections within the securities lending
market, such as large broker-dealers and lending programs, can query
other market participants directly about current conditions. These
centrally connected market participants are also likely to have access
to both give-to-get Wholesale data and customer

market survey data. Consequently, the largest and most centrally
connected broker-dealers and lending programs likely have access to
better information about the current state of the lending market than
other participants, including their customers, the beneficial owners,
and end borrowers. This asymmetric information between those on the
periphery of the lending market and those in the center, who have
access to both Wholesale and Customer market commercial datasets as
well as queried data from their connections, may lead to inferior terms
for those on the periphery in the form of lower performance and less
favorable prices for beneficial owners and end borrowers.\771\ Because
of the above-described issues with the comprehensiveness and
accessibility of existing commercial datasets, the availability of
commercial data products for the securities lending market does not
alleviate this information asymmetry.
---------------------------------------------------------------------------

    \771\ For example, broker-dealers acting on behalf of customers
have an incentive to lend from their inventory, even if lower cost
borrowing options exists, because they keep the whole lending fee in
such a transaction. The limited data available to the end borrower
about the state of the lending market make it difficult for the end
borrower to monitor the performance of its broker-dealer for
situations like this.
---------------------------------------------------------------------------

    One commenter disagreed with the Commission's view that there are
information asymmetries between those in the center of the lending
markets and beneficial owners and borrowers on the periphery, because
beneficial owners benefit from the information obtained by their
lending agents.\772\ However, the Commission believes that, even if
lending agents provide beneficial owners with benchmark reports based
on commercial securities lending data, this would not eliminate all
information asymmetries. The lending agent, or other centrally located
entity, has access to the raw data, as well as the potential to
influence that data through their own contributions of data to
commercial data vendors, whereas the beneficial owner would need to
rely on a subset of the processed data that their lending agent chooses
to provide to them, if any.
---------------------------------------------------------------------------

    \772\ See RMA Letter, at 6 (stating ``[w]hile the Proposing
Release repeatedly asserts that information asymmetries exist
between those `in the center' of the lending markets and beneficial
owners and borrowers on the `periphery,' in point of fact beneficial
owners currently benefit from substantial information obtained by
Lending Agents acting on their behalf and pricing remains highly
competitive'').
---------------------------------------------------------------------------

    In addition to the specific problem of information asymmetry
between various market participants, the lack of comprehensive and
widely available data on securities lending activity likely means that
the prices at which securities loans take place are not efficient,
relative to the hypothetical case in which complete information about
securities lending activity were widely available. Asymmetric
information in general deters outsiders from entering the market, as
they anticipate not being able to transact on the same terms. This
limits both liquidity (because fewer participants enter to transact)
and price discovery (because not all information enters prices).
Moreover, even lenders with many connections in the securities lending
market lack a complete picture of the lending market, implying that the
loan prices that they quote may not be completely efficient.
    Commenters stated that the lending market for U.S. Government
securities is already transparent ``because there is sufficient
liquidity and demand for loans of these types of securities on
platforms and venues that have a high degree of transparency.'' \773\
However, the commenters did not provide additional explanation as to
why a high degree of liquidity would entail a transparent market, or
additional details regarding the transparency of the platforms and
venues that trade in U.S. Government securities.
---------------------------------------------------------------------------

    \773\ See, e.g., RMA Letter, at 16; IIB Letter, at 6.
---------------------------------------------------------------------------

    The Commission believes that information asymmetries may still be
present in the lending market for U.S. Government securities. Some
information about U.S. Government securities loans is available from
the commercial securities lending datasets described above. In
addition, the Commission understands that information about loans of
U.S. Government securities that are awarded to primary dealers through
the Federal Reserve Bank of New York's System Open Market Account
(SOMA) portfolio is available from the Federal Reserve Bank of New
York.\774\ While volume and rate information from these auctions may be
informative to participants about benchmark lending rates, these data
do not contain information about activity in the broader U.S.
Government securities lending market. Furthermore, certain aggregate
data from the Federal Reserve's FR 2004 reports, which collect weekly
and daily position, transaction, financing, and fails data of primary
dealers in U.S. Government securities and other selected fixed income
securities, are published in the Federal Reserve Bank of New York's
press release, ``Weekly Release of Primary Dealer Transactions.'' \775\
This dataset contains aggregate volume information about securities
lent and borrowed by primary dealers. It does not contain information
about lending rates, and also does not contain information about the
U.S. Government loans of market participants other than primary
dealers.
---------------------------------------------------------------------------

    \774\ Through its System Open Market Account (SOMA) portfolio,
the Federal Reserve Bank of New York awards U.S. Government
securities loans to primary dealers (i.e., financial institutions
that are permitted to trade directly with the Federal Reserve
System) that have elected to participate in the program based on
competitive bidding in a multiple price auction held each business
day at noon. Participation by primary dealers is entirely voluntary
and summary results are released to the public following each day's
auction. See Fed. Res. Bank of N.Y., ``Securities Lending,''
available at https://www.newyorkfed.org/markets/domestic-market-operations/monetary-policy-implementation/securities-lending, last
visited Aug. 23, 2023. For a list of current primary dealers, see
https://www.newyorkfed.org/markets/primarydealers. These data
include both daily and historical data on aggregate volumes and
rates, as well as transaction-by-transaction data disseminated with
a two-year lag. The latter dataset contains identifying information
for the security lent (e.g., CUSIP), as well as the lending fee,
collateral amount, and the identity of the counterparty, see Fed.
Res. Bank of N.Y., ``Historical Transaction Data,'' available at
https://www.newyorkfed.org/markets/omo_transaction_data, last
visited Aug. 23, 2023.
    \775\ The Federal Reserve's weekly Form FR 2004 provides weekly
volume statistics about primary dealers' financing activity in U.S.
Government securities, including aggregated volumes in securities
lending and repo. See Fed. Res. Bank of N.Y., ``Primary Dealer
Statistics,'' available at https://www.newyorkfed.org/markets/counterparties/primary-dealers-statistics, last visited Aug. 23,
2023.
---------------------------------------------------------------------------

    Furthermore, some market participants may be able to infer some
information about the securities lending market from the market for
repos; \776\ this is particularly the case for securities with a very
active repo market, such as U.S. Government securities. A fixed-

term cash collateralized securities loan is economically similar to a
repo. Consequently, an investor wishing to gather information about
fixed-term loans in the securities lending market could use prevailing
terms in the repo market to infer information about borrowing costs for
the same securities. However, as majority of securities loans do not
have a fixed term,\777\ information from the repo market would only be
useful for a small subset of securities loans.
---------------------------------------------------------------------------

    \776\ In a repo, one party sells an asset, usually a Treasury
security or other fixed-income security, to another party with an
agreement to repurchase the asset at a later date at a slightly
higher price. Repos are a common form of short-term corporate
financing. In a repo, the party selling the security is similar to
the lender in a securities lending agreement; the party purchasing
the security is similar to a borrower in cash collateralized
securities lending. In both cases, the transaction is facilitated by
cash transferring from the purchaser (borrower) to the seller
(lender). In a securities loan, the cash is in the form of
collateral while in a repo transaction the cash is payment for the
security. In both cases, the purchaser or borrower becomes the legal
owner of the security. To unwind the repo or securities loan, cash
transfers back to the purchaser in terms of the repurchase cost for
a repo or in the form of returned collateral in a securities loan.
Repos and securities loans differ in that repos typically are
primarily used for short-term financing while securities loans
typically are used to gain access to the security itself. Also loans
generally allow the lender to recall the security on demand while
repos do not. Additionally, the cash received by the seller of a
repo is often not re-invested but is used to finance the operations
of a company whereas the cash received in a securities loan is
generally re-invested in low risk fixed-income securities for the
life of the loan. See, e.g., Gary Gorton & Andrew Metrick,
Securitized Banking and the Run on Repo, 104 J. Fin. Econ 425
(2012).
    \777\ The OFR Pilot Survey (supra note 136, at 9) estimates that
81% of the loans in their data were open loans for which no maturity
date is specified.
---------------------------------------------------------------------------

3. Characteristics of the Securities Lending Market
    The value of securities available to be loaned generally far
exceeds the total value on loan. The OFR Pilot Survey documents that in
2015 only about 10 percent of the value of securities available for
lending were on loan.\778\ However, for a specific security it is not
always the case that shares available to loan far exceeds shares on
loan. For some securities, particularly highly shorted securities, it
can be extremely difficult and expensive to find securities to borrow.
Securities that are difficult to borrow are said to be ``on special''
and can have average borrowing costs many times higher than a security
that is not on special.\779\
---------------------------------------------------------------------------

    \778\ See OFR Pilot Survey, supra note 136. Note that the number
of shares available for loan must be interpreted carefully. The
Commission understands that some beneficial owners may report a
supply of shares available that, if borrowed, would exceed the total
amount of securities lending they are willing to engage in, so that
not all shares reported as available could in fact be borrowed at
once. Investment companies that engage in securities lending
consistent with SEC staff's current guidance generally limit
securities lending to no more than one third of the value of their
portfolio on loan at a given point in time. Some investment
companies may set individual portfolio limits lower.
    \779\ In contrast, easy-to-borrow stocks are often referred to
as ``general collateral'' stocks, which tend to have lower borrowing
costs.
---------------------------------------------------------------------------

    Securities loans also exhibit a wide range of borrowing costs for
the same security on the same day. The range of borrowing costs for the
same security can be influenced by a number of characteristics, such as
the creditworthiness of the borrower, the type of collateral used, and
the terms of the loan.\780\ As discussed in further detail below, the
range of borrowing costs likely also represents asymmetric information
between the parties to the loan negotiation, such that one party is
able to charge a higher price than would be possible if the other party
had better information regarding the current loan prices for that
security.\781\ It may also represent a general lack of price efficiency
stemming from incomplete information, as market participants operate
without a clear view of the market as a whole.
---------------------------------------------------------------------------

    \780\ See supra note 732 for additional factors pointed out by
commenters that could be important drivers of borrowing costs.
    \781\ See supra Part IX.B.2 for a discussion of information
asymmetries in the securities lending market, specifically between
end borrowers and broker-dealers and between beneficial owners and
lending programs. See also supra note 771 for an example.
---------------------------------------------------------------------------

    Table 1 provides descriptive statistics illustrating these
characteristics of the securities lending market. The data come from
Fidelity National Information Services, Inc. (FIS) and reflect
conditions in the Wholesale market for U.S. equity loans on the same
days as the OFR Pilot Survey,\782\ for the sample of lenders that
provide data to FIS.\783\
---------------------------------------------------------------------------

    \782\ We limited our sample to the same period of time as the
OFR Pilot Survey (Oct. 9, 2015, Nov. 10, 2015, and Dec. 31, 2015)
for ease of comparison. Additionally, while the data presented here
is limited to equity loans, final Rule 10c-1a applies to loans of
both equity and fixed-income securities. The Commission believes
that there is a similar lack of transparency for fixed-income loans
and equity loans, and thus the same economic structure likely
applies to both the fixed-income and equity lending markets.
    \783\ FIS data are collected using a give-to-get model and thus
is unavailable for many entities other than those that provide their
data to FIS.
---------------------------------------------------------------------------

    Panel A of Table 1 provides the distribution of utilization rates
(defined as the percent of shares currently on loan relative to the
total number of shares available for lending). This panel highlights
that utilization rates are highly positively skewed. For most stocks
supply significantly outstrips demand with median utilization rates of
approximately 12 percent. For stocks at the 90th percentile,
utilization rates are near 70 percent, implying that an investor
seeking to find shares of such a stock to borrow may have a difficult
time doing so.
    Table 1 only presents utilization rates for equity lending
transactions. However, at least one commenter states that utilization
rates vary significantly across asset classes, stating that data from
the OFR Pilot Survey show that utilization rates are lower for more
individualized securities, such as corporate bonds.\784\ Meanwhile,
referencing the OFR Pilot Survey, the commenter pointed out that the
utilization rate for U.S. Treasuries and agencies is higher than that
of equities.\785\
---------------------------------------------------------------------------

    \784\ The commenter states that ``the corporate and asset-backed
debt markets in particular are characterized by relatively small
issuance sizes and substantial differences in instrument
characteristics,'' and that ``while the number of publicly traded
equity securities of U.S. issuers is around 3,600, there are over
two million unique issuances of corporate and government bonds and
asset-backed securities in circulation.'' See RMA Letter, at 16.
    \785\ See RMA Letter, at 16.
---------------------------------------------------------------------------

    Panel B of Table 1, which presents borrowing costs in term of
lending fees,\786\ shows that the lending fees paid for equity loans
exhibit a wide range.\787\ Stocks that are on special can have fees
many times higher than the median stock. Specifically, stocks at the
90th percentile of lending fees have an average lending fee of 7
percent per year while the median stock has a lending fee of about 0.6
percent per year. Even when loans involve the same stock, and on the
same day, there can be a significant range in fees paid to borrow
securities.
---------------------------------------------------------------------------

    \786\ FIS converts rebate rates to fees using by subtracting the
rebate rate from the Federal funds rate. See supra note 731 and the
footnote of Table 1.
    \787\ This result is consistent with the academic literature.
See, e.g., Dixon, et al., (2021), supra note 746. Also consistent
with the academic literature, average fees for each stock each day
are computed by FIS as the share weighted average fee across all
loans outstanding reported to FIS for a given stock on a given day.
Stocks are sorted by average fee and percentiles are determined.
---------------------------------------------------------------------------

    Panel C of Table 1 highlights the range of lending fees charged for
the same stock on the same day. The range in fees is defined as the
difference in the maximum and minimum fees reported to FIS for loans of
the same stock on the same day. This range can be quite substantial.
For the median stock the range is about three percentage points, or
approximately five times the median fee charged for securities lending
transactions.

                                             Table 1--Distribution of Lending Fees for U.S. Common Stocks *
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                         p10      p20      p30      p40     Median    Mean     p60      p70      p80      p90       N
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                       Panel A: Distribution of Utilization Rates
--------------------------------------------------------------------------------------------------------------------------------------------------------
9-Oct-15.............................................     1.02     2.94     5.42     8.28    12.06    22.70    17.21    24.83    39.35    68.98    3,638

10-Nov-15............................................     0.94     2.82     5.18     8.19    11.72    22.51    16.87    24.59    38.59    68.10    3,638
31-Dec-15............................................     0.75     2.35     4.52     7.31    11.17    22.25    16.49    25.02    40.42    67.64    3,639
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                      Panel B: Distribution of Average Lending Fees
--------------------------------------------------------------------------------------------------------------------------------------------------------
9-Oct-15.............................................     0.48     0.56     0.58      0.6     0.65     3.76     0.76     1.24     2.62     7.07    3,727
10-Nov-15............................................     0.47     0.56     0.59     0.61     0.66     3.77     0.77     1.32     2.76     7.36    3,725
31-Dec-15............................................     0.37     0.46      0.5     0.54     0.58     3.86     0.66     1.12     2.77     7.51    3,725
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                     Panel C: Distribution of Range of Lending Fees
--------------------------------------------------------------------------------------------------------------------------------------------------------
9-Oct-15.............................................     1.01     1.35     1.85     2.27     2.85     8.42     3.57     5.21     7.76    10.41    3,727
10-Nov-15............................................     0.93     1.31     1.81     2.36     2.98     8.39     3.78     5.51     7.73    11.16    3,725
31-Dec-15............................................     1.15     1.48     1.84     2.25     2.68     8.20     3.43     4.20     6.36    11.41    3,725
--------------------------------------------------------------------------------------------------------------------------------------------------------
* This table provides descriptive statistics using data from FIS on securities lending fees and utilization rates for U.S. Common stocks during sample
  period that matches the OFR Pilot Survey's sample dates (Oct. 9, 2015, Nov. 10, 2015, and Dec. 31, 2015). For loans collateralized by cash, rebate
  rates are converted to fees using the conventional method of subtracting the rebate rate from the Federal funds rate. Fees are converted to annual
  percent. N reports the number of observations from which FIS has reported the relevant statistics. Panel A shows the distribution of utilization
  rates, where the utilization rate is computed as the percent of shares on loan relative to total shares available for lending. Panel B provides
  estimates of the distribution of average lending fees for each stock and provides percentile thresholds for lending fees. Since there is a
  distribution of fees levied for the same stock on the same day, the average fee is computed as the value weighted average fee across all loans for a
  given stock on a given day. Panel C shows statistics for the range of fees levied for the same stock on the same day defined as the maximum fee minus
  the minimum fee. In all panels `Mean' is equal-weighted.

    One commenter argued that the Proposing Release offered ``no
evidence to suggest that lending fees broker-dealers charge are so
meaningfully different (after controlling for various commercial
factors).'' \788\ As the Commission acknowledged in the Proposing
Release and continues to acknowledge here, there are a number of
factors that can drive variations in borrowing costs including, e.g.,
the creditworthiness of the borrower.\789\ However, the Commission is
unaware of the existence of data that would allow for a full analysis
of all possible factors contributing to borrowing costs, and the
commenter did not provide any roadmap for performing such an analysis
accurately. That the observed range in borrowing costs is likely
driven, at least in part, by asymmetric information and a general lack
of price efficiency in the securities lending market is supported by
academic literature, which finds that asymmetric information drives
dispersion in prices, particularly in opaque markets with limited
transparency.\790\ One implication of the results from this literature
is that price dispersion should be higher when transparency is lower.
Indeed, there is evidence that the dispersion in borrowing costs is
higher in securities lending markets with less transparency, in which
information asymmetry is expected to be higher. For example, the OFR
Pilot Survey shows that the range of lending fees is higher in the U.S.
equity lending market than it is in the U.S. Treasury/Agency lending
market, where transparency is arguably higher.\791\
---------------------------------------------------------------------------

    \788\ See Citadel Letter, at 10.
    \789\ See Proposing Release, 86 FR 69833.
    \790\ See, e.g., Richard C. Green, Presidential Address:
Issuers, Underwriter Syndicates, and Aftermarket Transparency, 62 J.
Fin. 1529 (2007), whose theoretical model shows that, in opaque
dealer markets, if the costs of gathering price information are high
for some investors, this can result in price dispersion. One key
feature of this model is that ``the dealings of a particular
customer with a dealer are not transparent to other customers,'' at
1542. See also Richard C. Green, et al., Dealer Intermediation and
Price Behavior in the Aftermarket for New Bond Issues (EFA 2006
Zurich Meetings Paper, June 17, 2006) 86 J. Fin. Econ 643, available
at https://ssrn.com/abstract=909352 (retrieved from SSRN Elsevier
database), who argue that differences in investors' access to
information about municipal bond prices leads to a high level of
price dispersion.
    \791\ Specifically, using data from Table 8 of the OFR Pilot
Survey and defining the range in lending fees as the difference
between the 95th percentile and the 5th percentile, the range in
lending fees, averaged across the three days in the OFR Pilot Survey
sample, is around 303% of the mean lending fee in the equity lending
market, and 234% of the mean lending fee in the Treasury lending
market. See supra Part IX.B.2 for further discussion of why
transparency may be higher in the Treasury lending market.
---------------------------------------------------------------------------

    Furthermore, the Commission views it as unlikely that one of the
pricing factors frequently mentioned by commenters, namely the
creditworthiness or counterparty risk of the borrower,\792\ can fully
explain the large range in borrowing costs. Table 1 shows that the
median range of fees is about 400 percent larger than the median fee
itself--implying that some investors were paying at least four times as
much to borrow the same security on the same day as other
investors.\793\ Based on staff experience, securities loans are
virtually always fully collateralized, and so counterparty risk is
unlikely to be sufficient to explain such a large dispersion in
borrowing costs.
---------------------------------------------------------------------------

    \792\ See, e.g., HMA Letter at 2, Overdahl Letter at 6, and ISLA
Letter at 2.
    \793\ The OFR Pilot Survey at Table 8 provides similar
statistics and finds similar dispersion for U.S. Equities.
---------------------------------------------------------------------------

    Other commenters expressed support for the notion that price
dispersions in the securities lending market were due to information
asymmetries and a lack of informational efficiency.\794\ While at least
one commenter questioned the need to improve transparency in the
corporate bond and U.S. Treasury/Agency lending market due to
structural differences between those markets and the equity
market,\795\ there is evidence that there is a dispersion of borrowing
costs within these asset classes as well. Information from the OFR
Pilot Survey indicate a large dispersion of lending fees and rebates
across all asset classes, including for U.S. Treasury/Agency and
corporate bond securities.\796\ For the reasons discussed above, the
Commission believes that the observed variation in borrowing costs in
these

markets is likely also at least partially driven by asymmetric
information and a general lack of price efficiency in the securities
lending market.
---------------------------------------------------------------------------

    \794\ See, e.g., James J. Angel Letter, at 2 (expressing his
belief that the increased transparency of the rule would reduce
price dispersion in the securities lending market implying that a
lack of transparency currently causes some of the currently
experienced dispersion in fees).
    \795\ See RMA Letter, at 16.
    \796\ See Table 8 and Table 9 in the OFR Pilot Survey which
shows, for example that on October 9, 2015, the dispersion between
the 5th and 95th percentile fee for treasuries was 200% of the mean
fee, for corporate bonds it was 111%, and for equities it was 319%.
---------------------------------------------------------------------------

4. Structure of the Securities Lending Market
    The securities lending market is made up of borrowers, lenders, and
intermediaries that can facilitate a transaction. End borrowers can
borrow securities either through their broker-dealers, or by themselves
if they maintain their own relationships with lending programs. If they
borrow through their broker-dealer, then they transact in the Customer
market. If they maintain their own relationships and borrow directly
from lending programs, then they transact in the Wholesale market.\797\
Beneficial owners can either supply shares to the lending market by
contracting with a lending program, or they can run their own lending
program and lend directly to entities such as large hedge funds with
which they maintain relationships. In either case, such a transaction
occurs in the Wholesale market. Lenders can also be broker-dealers who
lend to end borrowers. These lenders transact in the Customer market.
---------------------------------------------------------------------------

    \797\ See supra note 708 for further discussion of the
distinction between Wholesale and Customer loans.
---------------------------------------------------------------------------

    A market participant such as a short seller wishing to borrow
shares usually does so through its broker-dealer, who offers to find
shares to borrow as part of its suite of services offered to customers.
A broker-dealer may start by providing a security loan to its customer
with shares from its own inventory or out of another customer's margin
account.\798\ The Commission understands that in order to facilitate
the amount of borrowing customers wish to do, a broker-dealer will
sometimes have to find other sources of shares. To that end, broker-
dealers maintain securities lending relationships with customers in
fully paid accounts as well as relationships with various external
lending programs.
---------------------------------------------------------------------------

    \798\ The notion that broker-dealers will first source shares
from internal inventory before accessing lending markets was
mentioned in the Proposing Release (see Proposing Release, 86 FR
69834) and was supported by at least one comment. See, e.g., James
J. Angel Letter, at 4 (stating that if ``the broker has shares
available for lending from its customer margin accounts, it does not
need to go out and borrow any more shares.'').
---------------------------------------------------------------------------

    Additionally, some large institutions, such as banks, credit
unions, pension funds, and hedge funds, choose to maintain their own
relationships with lending programs. These entities bypass broker-
dealers to search for borrowable shares themselves. This option is not
feasible for smaller institutions, who lack both the scale to make it
cost effective, and the creditworthiness to be an acceptable
counterparty for the lending programs in the absence of an
intermediary, e.g., a broker-dealer.
    The OFR Pilot Survey estimated that there were approximately $1
trillion of shares on loan.\799\ The OFR primarily focused on the
Wholesale market, and consequently the overwhelming majority of
borrowers were broker-dealers; the OFR Pilot Survey does not provide
much insight into who the end borrowers are for securities lent by
broker-dealers. Figure 1 provides the fraction of total securities on
loan by type of borrower based on the OFR Pilot Survey across three
trading days in 2015.\800\
---------------------------------------------------------------------------

    \799\ See OFR Pilot Survey at Table 1.
    \800\ These dates are Oct. 9, 2015, Nov. 10, 2015, and Dec. 31,
2015.
[GRAPHIC] [TIFF OMITTED] TR03NO23.000

---------------------------------------------------------------------------
Source: OFR Pilot Survey of Agent Securities Lending Activity

    There is currently no common source that those seeking security
loans can use to find shares available to lend, which is why broker-
dealers rely on relationships with lending programs to secure loans.
This situation has contributed to high search costs in this
market.\801\ High search costs imply that transactions cannot take
place without a costly effort to find a favorable counterparty. The
need for such costly effort can inhibit market efficiency.
---------------------------------------------------------------------------

    \801\ See, e.g., Kolasinski (2013), supra note 736.
---------------------------------------------------------------------------

    One commenter stated that the level of opacity in the securities
lending market does not lead to high search cost or inhibit the
efficiency of the securities lending market, because the level of
detail in data provided by data vendors has improved drastically, the
number of data vendors in the market has been growing, and the vast
majority of parties in securities lending markets have

access to one or more sources of data.\802\ While these facts are
generally associated with declining search costs and increased
efficiency, they do not, by themselves, imply low search costs or an
efficient market. As discussed above, the Commission continues to
believe that the datasets provided by commercial data vendors are
characterized by incomplete and potentially biased data.\803\ As a
result, market participants that want information on whether a given
loan price or quote is competitive, or how a given lending agent or
broker-dealer is performing relative to a benchmark, may need to
consult multiple datasets, if they have access to such data, or request
and receive multiple quotes in order to confirm that a given quote is
consistent with market conditions. A need to consult multiple sources
of information implies high search costs. When the cost of acquiring
additional information is high, it may not be economical to acquire the
information, resulting in less efficient terms for loans. The Proposing
Release also cited academic research characterizing the securities
lending market as having high search costs, which was not rebutted by
the commenter.\804\ Consequently, the Commission continues to believe
that, despite the availability of commercial datasets, opacity in the
securities lending market contributes to high search costs and
inefficiencies in that market.\805\
---------------------------------------------------------------------------

    \802\ See IHS Markit Letter, at 13.
    \803\ See supra Part IX.B.2 for further discussion of issues
related to the current availability and comprehensiveness of
securities lending data.
    \804\ See Kolasinski (2013), supra note 736, cited in the
Proposing Release with regard to search costs. See Proposing
Release, 86 FR 69805, 69831, and 69832.
    \805\ See discussion on this point in supra Parts IX.B.2 and
IX.B.3.
---------------------------------------------------------------------------

    Broker-dealers possess some market power over their customers.
Generally, broker-dealers assist investors in finding shares to borrow
as part of a suite of services and the cost of switching to a new
broker-dealer can be high. This relationship can make it difficult for
investors to change broker-dealers if they underperform in one area
because it is not just a securities lending relationship that would be
changed, but the whole suite of broker-dealer services that would be
affected.\806\
---------------------------------------------------------------------------

    \806\ Some entities, such as some hedge funds, have multiple
prime brokers. For such institutions it would be less difficult to
switch between broker-dealers if one is performing poorly as they
could redirect securities lending business to their top performing
prime-broker.
---------------------------------------------------------------------------

    Additionally, the relationship nature of the lending market favors
larger broker-dealers who can maintain high-volume relationships with
more lending programs. The limited availability of comprehensive data
makes it difficult for customers to evaluate the performance of broker-
dealers.\807\ Customers as well as lenders thus rely on relationships
and reputation, a situation that also tends to favor larger broker-
dealers.
---------------------------------------------------------------------------

    \807\ See supra Part IX.B.2 for further discussion of issues
related to the current availability and comprehensiveness of
securities lending data.
---------------------------------------------------------------------------

    The primary sources of shares to loan in the Wholesale market are
long-term investors such as investment firms, pension and endowment
funds, governmental entities, and insurance companies. These entities
generally make their shares available to lend either through a lending
program run by a lending agent or by running their own lending program.
Broker-dealers borrow shares from lending programs in order to
facilitate clearing and settlement on a net-basis, though they may
additionally source shares for this purpose from their own inventory,
from fully paid shares, and from customer margin accounts.\808\
---------------------------------------------------------------------------

    \808\ See supra note 798 and corresponding text for further
discussion of broker-dealers' sources of shares for securities
loans.
---------------------------------------------------------------------------

    As described above, a beneficial owner seeking to lend shares will
generally provide those shares to a lending agent, who runs a lending
program. There are two broad categories of lending programs: custodian
banks and third-party lending programs. In the case of custodian banks,
the lending program is generally offered as part of their general
custodian services.
    Both types of lending programs will generally pool shares across
accounts with which they have lending agreements to create a common
pool of shares available to lend. As shares are lent out, the revenue
earned from the pool of shares is generally distributed across all
accounts contributing shares to the pool of shares on loan on a pro-
rata basis. Pooled lending programs generally split the fees generated
from lending with the beneficial owners. Based on the staff's
experience, the Commission believes that the lending program will
usually take about a third of the fees earned. In the case of custodian
banks, the custodian bank may, rather than return the lending revenue
directly to the beneficial owner, instead apply the beneficial owner's
portion of the lending revenue to other fees charged by the custodian
bank for other services.
    Lending programs typically indemnify the beneficial owner from
default by the borrower. This indemnity gives the lending program an
incentive to ensure the creditworthiness of the borrower, and a lending
program may assess higher fees to borrowers it deems as less
creditworthy.
    Over the past two decades, auction-based security lending has
become an alternative for lender-borrower interactions. In this
setting, unlike with the directed lending programs, positions of
different beneficial owners are not pooled to cater to security-
specific demand from borrowers. Instead, after determining the desired
income streams, the lender's entire portfolio, or its segments, are
offered via blind single-bid auctions.
    In some cases, a beneficial owner may choose to set up its own
lending program. This course is more common among very large funds that
have the resources to build up the expertise necessary to operate a
lending program.\809\
---------------------------------------------------------------------------

    \809\ Based on a review of N-CEN reports, the Commission
estimates that 226 (out of 684) lenders do not employ a lending
agent. See infra Part X. Most of these lenders are likely to operate
their own lending programs.
---------------------------------------------------------------------------

    The current relationship and network structure of lending programs
and broker-dealers favors larger lending programs that have the
resources to maintain relationships with more and larger broker-
dealers. Thus, the Commission believes that the market for lending
services is likely dominated by a few large lending programs, including
those run by the large custodian banks.
    The OFR Pilot Survey estimated that as of the latter part of 2015
there were approximately $9.5 trillion worth of shares available for
lending.\810\ Figure 2 provides a breakout of the percent of shares
available for lending provided by the various entities.
---------------------------------------------------------------------------

    \810\ Commercial vendors typically report a value for securities
available to loan that is larger than what is reported in the OFR
Pilot Survey. This difference is likely due to sample construction.
The commercial vendors likely have a larger sample of lending
programs to draw from, particularly the lending programs based
outside of the U.S. See also supra note 778 for a discussion of
issues related to estimating the number of shares available for
lending.

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

[GRAPHIC] [TIFF OMITTED] TR03NO23.001

Source: OFR Pilot Survey of Agent Securities Lending Activity
5. Structure of the Market for Securities Lending Data and Analytics
    The market to collect and disseminate securities lending data is an
outgrowth of the market for securities lending market analytics.\811\
This market consists of a few established vendors that specialize in
geographic areas (i.e., U.S. and non-U.S.) but seek to compete in all
geographic areas. Most vendors collect the data to support the analysis
business in which they provide data-based service to institutions and
other lending programs. Others collect data through their facilitation
of security loans. As such, the data vendor business is often an
outgrowth of another business. The Commission understands that the
current practice by commercial data vendors is to provide preliminary
statistics on the same day based on the intraday data collected by the
vendors, while the main data are disseminated one day later.
---------------------------------------------------------------------------

    \811\ See the business model descriptions in IHS Markit's 2021
comment letter responding to FINRA's Regulatory Notice 21-19,
available at https://www.finra.org/sites/default/files/NoticeComment/IHS%20Markit_Paul%20Wilson_21-19_9.30.2021%20-%20IHSM%20Cmt%20Ltr%20re%20FINRA%20RFC%20Short%20Interest%20Position%20Reporting.pdf.
---------------------------------------------------------------------------

    The Commission believes that the data provided by the various data
vendors operating in the give-to-get Wholesale data market are largely
comparable.\812\ However, the entities providing data to the vendors
are also their customers. This relationship limits the market power of
the vendors with respect to their clients who provide data but results
in the clients' incentives limiting the competitiveness of the
market.\813\ This results in the market being largely inaccessible for
many entities that could use the data for their own benefit or the
benefit of the market as a whole.\814\
---------------------------------------------------------------------------

    \812\ See Truong X. Duong, et al., The Information Value of
Stock Lending Fees: Are Lenders Price Takers?, 21 Rev. Fin. 2353,
2353-2377 (2017) (``Duong, et al., (2017)''),who provide a
comparative analysis of the datasets of two of the main commercial
data vendors and find very high correlations between the values
presented in the different datasets.
    \813\ See supra Part IX.B.2 for further discussions of data
providers' incentives when providing data to data vendors under a
give-to-get model.
    \814\ See supra Part IX.B.2 for further discussion of the
current limits to transparency in the securities lending market.
---------------------------------------------------------------------------

    The give-to-get model for securities lending data is a significant
barrier to entry to any firm seeking to provide analytics services
regarding Wholesale loans. Firms cannot provide analytics services
without data, and the biggest three data vendors have established
relationships with data contributors to collect data. Such data
contributors have an incentive to also control who can access that
data. Consequently, the Commission understands that the market for
securities lending data and securities lending analytics in the
Wholesale space is largely concentrated among the three biggest data
vendors.
    One commenter stated that the give-to-get model is ``is not the
industry standard'' in the securities lending data market and pointed
out that there are some commercial securities lending datasets that are
available to all subscribers.\815\ The Commission understands that the
collection of these data largely relies on surveying Customer market
participants about their borrowing experiences. Data vendors then
aggregate and sell these data, or derivative products that combine the
survey data with other data sources to provide derived metrics in areas
such as short selling. For example, the Commission staff understands
that, based on interactions with vendors of such data, that the
customer market survey data are used largely as an input to proprietary
algorithms that combine information from multiple data sources--such as
FINRA's bimonthly short interest--to produce a short selling metric
that is then used by consumers.\816\ Building relationships with a
large number of Customer market participants is a significant barrier
to entry and thus the Commission understands that data provision and
analytics regarding customer market survey data is concentrated among
the top two biggest data vendors.
---------------------------------------------------------------------------

    \815\ See S3 Partners Letter, at 5.
    \816\ See infra Part IX.B.6 for further discussion of FINRA's
bimonthly short interest data.
---------------------------------------------------------------------------

6. Short Selling Transparency
    Multiple commenters stated that granular securities lending data
generally provide information about investors' short selling positions
and strategies.\817\ This section provides

information about existing sources of short selling data to facilitate
an analysis of the impact of the Rule on short selling transparency.
---------------------------------------------------------------------------

    \817\ See, e.g., Overdahl Letter, at 4, 9; Citadel Letter, at 5-
6. One commenter stated their belief that the rule would not provide
further transparency into the short interest market because ``not
all securities loan trades facilitate a short sale, and that the
securities lending market is not a mirror of the short interest
market''; See IHS Markit Letter, at 3. See supra Part IX.B.1 for a
discussion of the potential reasons for borrowing and lending
securities apart from to facilitate a short trade.
---------------------------------------------------------------------------

Securities Lending Data
    Because of the need to borrow securities to facilitate a short
sale, securities lending data can be used to gain insight into short
interest and short sentiment, and market participants use both give-to-
get Wholesale data and customer market survey data on Customer loans to
gain insights into short selling. For example, some academics have used
securities lending data to study short selling in published
research.\818\ Additionally, while access to Wholesale market data is
generally limited,\819\ the Commission understands that some third-
party data providers combine Wholesale market data with other sources
of short selling data, such as bimonthly short interest data, to
provide retail clients with estimated short selling metrics. The
Commission also understands that commercial data vendors use securities
lending data as an input to proprietary algorithms that combine
information from multiple data sources--such as biweekly short
interest--to produce a short selling metric that is then used by
consumers.
---------------------------------------------------------------------------

    \818\ See, e.g., Steven Lecce, et al., The Impact of Naked Short
Selling on the Securities Lending and Equity Market (Macquarie
Business School Research Paper, Feb. 1, 2012) at 83, available at
https://ssrn.com/abstract=3591994 (retrieved from SSRN Elsevier
database) stating that ``securities lending is a commonly used proxy
for the level of covered short selling''; see also, e.g., Gene
D'avolio, The Market for Borrowing Stock, 66 J. Fin. Econ. 271
(2002).
    \819\ See supra Part IX.B.2 for further discussion of the give-
to-get model.
---------------------------------------------------------------------------

    Data on Wholesale securities loans provide only a noisy
approximation of short interest at the security level. This is because
Wholesale loans are made largely to facilitate clearing and settlement
on a net basis at a clearing broker, rather than by transaction or
position. Thus, Wholesale loans are not traceable to individual short
sellers. Further, the Commission understands that broker-dealers will
usually source shares to meet their net clearing and settlement
requirements from other sources before engaging in Wholesale
loans.\820\
---------------------------------------------------------------------------

    \820\ These sources include, e.g., their own inventory, customer
margin accounts, and fully paid accounts at the broker-dealer with
lending agreements in place. See supra note 798 and corresponding
text for further discussion. Also, the fact that give-to-get
Wholesale market data are not comprehensive (see supra Part IX.B.2
for further discussion), means that there is additional noise with
regards to using Wholesale market data to estimate short sale
information.
---------------------------------------------------------------------------

    The Commission believes that information about Customer loans is
more likely to be informative about short selling activity than
information about Wholesale loans.\821\ This is because, unlike
Wholesale loans, Customer loans are used to facilitate individual short
positions. The size of a Customer loan when first created is the same
as the size of a short position as of the end of the day on which the
short position was created. Should the customer increase or decrease
the size of their short position, the Commission understands that the
original loan would be modified accordingly. Thus, individual Customer
loans can be tracked to reveal the size and changes in individual short
positions. However, existing Customer market datasets, like Wholesale
market datasets, lack comprehensiveness.\822\
---------------------------------------------------------------------------

    \821\ This is also supported by one commenter, who states that,
``there is a perfect correlation between a short sale and the
associated Short Sale Linked Activity, which facilitates the
customer fulfilling the delivery obligations arising from the short
sale.'' See Citadel Letter, at 5. The commenter states that by
``Short Sale Linked Activity,'' they are referring to what the
Proposing Release calls the ``retail market.'' See Citadel Letter,
at 1. This is equivalent to what is referred to as the Customer
market in this release.
    \822\ See supra Part IX.B.2 for further discussion of why the
Commission believes that Customer market survey datasets are not
comprehensive.
---------------------------------------------------------------------------

    Most commercially available datasets are currently produced with a
one-day lag. As a result of the difference in settlement cycles between
the equity and securities lending market, there is therefore typically
a three-day lag between the availability of the securities lending data
and the change in short interest that corresponds to the securities
loans.\823\
---------------------------------------------------------------------------

    \823\ See supra note 738 and corresponding text. This lag will
decrease to two days when equity settlement moves to T+1 on May 28,
2024.
---------------------------------------------------------------------------

Bimonthly Short Interest Data From FINRA
    One of the primary data sources for information about aggregate
short positions is the bimonthly short interest data collected by
FINRA.\824\ FINRA collects aggregate short interest information in
individual securities on a bimonthly basis as the total number of
shares sold short in a given stock as of the middle and end of each
month.\825\ In the case of U.S. exchange-listed stocks, FINRA shares
the data with the listing exchange.\826\ FINRA then publishes short
interest data for all exchange-listed and OTC equity securities on its
Equity Short Interest Data page free for the investing public.\827\
Specifically, aggregate positions in each security are provided for
publication on the seventh business day after the reporting settlement
date.\828\
---------------------------------------------------------------------------

    \824\ See, e.g., Division of Economic and Risk Analysis, Short
Sale Position and Transaction Reporting 6-7, (June 5, 2014), at 17-
18, available at https://www.sec.gov/files/short-sale-position-and-transaction-reporting0.pdf. This is a study of the Staff of the U.S.
Securities and Exchange Commission, which represents the views of
Commission staff, and is not a rule, regulation, or statement of the
Commission. The Commission has neither approved nor disapproved the
content of this study and, like all staff statements, it has no
legal force or effect, does not alter or amend applicable law, and
creates no new or additional obligations for any person.
    \825\ Specifically, the mid-month short interest report is based
on short positions held on the settlement date of the 15th of each
month or, if the 15th falls on a weekend or holiday, the previous
business day. The end-of-month short interest report is based on
short positions held on the last business day of the month on which
transactions settle. See FINRA, Equity Short Interest Data Catalog,
available at https://www.finra.org/finra-data/browse-catalog/equity-short-interest, last visited Aug. 23, 2023.
    \826\ See FINRA, Short Interest--What It Is, What It Is Not
(Jan. 25, 2023), available at http://www.finra.org/investors/insights/short-interest.
    \827\ See id. See also FINRA, Equity Short Interest Data,
available at https://www.finra.org/finra-data/browse-catalog/equity-short-interest/data, last visited Aug. 24, 2023.
    \828\ See FINRA Equity Short Interest Data Catalog, supra note
825. See also FINRA, Short Interest Reporting, available at https://www.finra.org/filing-reporting/regulatory-filing-systems/short-interest.
---------------------------------------------------------------------------

    FINRA computes short interest using information it receives from
its broker-dealer members pursuant to FINRA Rule 4560 reflecting all
trades cleared through clearing broker-dealers.\829\ FINRA Rule 4560
generally requires that broker-dealers that are FINRA members report
``short positions'' in customer and proprietary firm accounts in all
equity securities twice a month through FINRA's web-based Regulation
Filing Applications (RFA) system.\830\ FINRA defines ``short
positions'' for this purpose simply as those resulting from ``short
sales'' as defined in Rule 200(a) of Regulation SHO under the Exchange
Act.\831\ Member firms must report their short positions to FINRA
regardless of position size.\832\
---------------------------------------------------------------------------

    \829\ Id. (Short interest for a listed security at any date
reported by FINRA is ``a snapshot of the total open short positions
in a security existing on the books and records of brokerage firms
on a given date.'').
    \830\ FINRA Rule 4560 excludes short sales in ``restricted
equity securities,'' as defined in Rule 144, from the reporting
requirement.
    \831\ See FINRA Rule 4560(b)(1). See supra note 301 for the
definition of a short sale.
    \832\ See FINRA, Short Interest Reporting Instructions
(reporting instructions to FINRA member firms), available at https://www.finra.org/filing-reporting/short-interest/regulation-filing-applications-instructions, last visited Aug. 23, 2023.
---------------------------------------------------------------------------

    These short interest data are widely available and are used by
academics and other market participants. These short interest data are
found to predict future stock and market returns over the

monthly and annual horizons, suggesting that the bimonthly short
interest data capture short selling strategies based on fundamental
research.\833\ However, the transparency offered by these data is
limited in at least two ways. First, the information content is delayed
by at least seven business days, and also does not provide insight into
the timing with which short positions are established or covered within
the two-week reporting period. This precludes the possibility of
understanding the behavior of aggregate short selling in the two weeks
leading up to the reporting date of the positions. Second, given that
the data are aggregated at the security level, they prevent the public
from understanding certain aspects of the underlying proprietary short
selling strategies. For example, the data can't inform on whether short
positions are distributed across a large or small number of market
participants.
---------------------------------------------------------------------------

    \833\ See, e.g., Peter N. Dixon & Eric K. Kelley, Business Cycle
Variation in Short Selling Strategies: Picking During Expansions and
Timing During Recessions, 57 J. Fin. Quantitative Analysis 3018
(2022); see also Ekkehart Boehmer, et al., The Good News in Short
Interest, 96 J. Fin. Econ., 80-97 (2010); Stephen Figlewski, The
Informational Effects of Restrictions on Short Sales: Some Empirical
Evidence, 16 J. Fin. Quantitative Analysis 463 (1981).
---------------------------------------------------------------------------

Short Selling Volume and Transaction Data From SROs
    This section discusses data that exist on daily and intraday short
volume data, which provide different information relative to the
bimonthly short interest data discussed above.\834\ The bimonthly short
interest data provides an aggregate measure of outstanding short
interest whereas the data discussed in this section provides
information about the flow of short sales in the market. It is less
useful in determining total short interest outstanding because it is
not accompanied by data regarding when short sales are covered but
allows market participants to observe the actual trades of short
sellers.
---------------------------------------------------------------------------

    \834\ See supra Part IX.B.6 for more information about FINRA's
bimonthly short interest data.
---------------------------------------------------------------------------

    Since 2009, many SROs have been publishing two short selling
datasets, including same-day publication of daily aggregated short sale
volume in individual securities \835\ and publication of short sale
transaction information with a delay of up to two months.\836\ Some
SROs make the historical daily short volume data available to market
participants at a fee.\837\
---------------------------------------------------------------------------

    \835\ See Division of Economic Research and Analysis, Securities
and Exchange Commission (DERA), Short Sale Volume and Transaction
Data (June 5, 2014), available at https://www.sec.gov/answers/shortsalevolume.htm; (showing hyperlinks to the websites where SROs
publish these data). See also FINRA, Daily Short Sale Volume Files,
available at https://www.finra.org/finra-data/browse-catalog/short-sale-volume-data/daily-short-sale-volume-files (providing aggregated
volume by security on all short sale trades executed and reported to
a FINRA reporting facility during normal market hours) and FINRA,
Information Notice: Publication of Daily and Monthly Short Sale
Reports (Sept. 29, 2009), available at http://www.finra.org/web/groups/industry/@ip/<EMAIL_ADDRESS>    \836\ See FINRA, Monthly Short Sale Transaction Files, available
at https://www.finra.org/finra-data/browse-catalog/short-sale-volume-data/monthly-short-sale-volume-files (providing detailed
trade activity of all short sale trades reported to a consolidated
tape), last visited Aug. 24, 2023. See also Investor.gov, Short Sale
Volume and Transaction Data, available at https://www.sec.gov/answers/shortsalevolume.htm. Additional transaction data have been
available at various times, including transaction data from the
Regulation SHO Pilot, which have been discontinued by most exchanges
in July 2007 when the uptick rule was removed. See Release No. 34-
55970 (June 28, 2007), 72 FR 36348 (July 3, 2007), available at
https://www.sec.gov/rules/final/2007/34-55970.pdf. The Pilot data
comprised short selling records available from each of nine markets:
American Stock Exchange, Archipelago Exchange, Boston Stock
Exchange, Chicago Stock Exchange, NASD, Nasdaq Stock Market, New
York Stock Exchange, National Stock Exchange, and the Philadelphia
Stock Exchange. See SEC Division of Trading and Markets, Regulation
SHO Pilot Data FAQ, available at https://www.sec.gov/spotlight/shopilot.htm#pilotfaq.
    \837\ See, e.g., NYSE.com, TAQ Group Short Sale & Short Volume,
available at https://www.nyse.com/market-data/historical/taq-nyse-group-short-sales (for short sale data relating to all NYSE owned
exchanges). See Nasdaqtrader.com, Short Sale Volume and Transaction
Reports, available at https://nasdaqtrader.com/Trader.aspx?id=shortsale (for short sale data for Nasdaq exchanges);
See also Chicago Board Options Exchange (Cboe.com), U.S. Equity
Short Volume and Trades (for Cboe exchanges), available at https://datashop.cboe.com/us-equity-short-volume-and-trades.
---------------------------------------------------------------------------

    Despite offering higher granularity, there is a concern that the
SRO short volume data may over-represent the total volume of short
sales occurring in the market. This is because Regulation SHO provides
specific criteria regarding what is a long sale.\838\ If a market
participant is unclear whether their trade would meet all the
requirements at settlement to be marked a long sale, then they may
choose to mark the trade as short to not run afoul of Regulation SHO
requirements, even if the trade is likely economically equivalent to a
long sale.\839\ For instance, the market participant may be deemed to
own the security but does not reasonably believe they can deliver the
security in time for settlement, and thereby must mark the order as
short regardless of their ownership of the security. Aggregate short
selling volume data and short selling transactions data typically have
different lags with which they are available, though aggregate short
selling volume data can be disseminated as early as the same day of the
short sale.\840\
---------------------------------------------------------------------------

    \838\ Rule 200(g) of Regulation SHO specifies when an order can
be marked as long. See also Section III.B (adopting release of
Regulation SHO), available at https://www.sec.gov/rules/final/34-50103.htm#VI.
    \839\ See Proposed rule: Short Position and Short Activity
Reporting by Institutional Investment Managers, Release No. 34-94313
(Feb. 25, 2022) 87 FR 14950, 14989 (Mar. 16, 2022) at note 230
(citing 2009 Letter from SIFMA commenting on an alternative short
sale price test, and expressing concern that compliance with
Regulation SHO short selling marking requirements ``will result in a
substantial over-marking of orders as ``short'' in situations where
firms are, in fact, ``long'' the securities being sold'').
    \840\ For example, FINRA posts daily aggregate short volume data
for each security no later than 6:00 p.m. ET of the same day on the
relevant trade date; see FINRA, Daily Short Sale Volume Files, supra
note 835. Meanwhile, FINRA posts files including the transaction
time, price, and number of shares for every off-exchange short sale
transaction in an exchange-listed stock only on a monthly basis; see
FINRA, Monthly Short Sale Volume Files, supra note 836. See also
FINRA, Information Notice 5/10/19: Understanding Short Sale Volume
Data on FINRA's website (May 10, 2019), available at https://www.finra.org/rules-guidance/notices/information-notice-051019.
---------------------------------------------------------------------------

B. Economic Effects of the Final Rule

1. Benefits of Increased Transparency in the Securities Lending Market
    The Commission believes that the primary impact of final Rule 10c-
1a will be to increase transparency in the securities lending market
through improvements to the comprehensiveness, breadth, accuracy,\841\
and accessibility of securities lending data. These impacts will reduce
information asymmetries in the securities lending market and improve
informational efficiency leading to a more efficient securities lending
market.\842\
---------------------------------------------------------------------------

    \841\ Accuracy in this sense refers to less potential bias in
the loans reported and thus in the statistics obtained from the
loans reported. See supra Part IX.B.2 for a discussion of bias in
existing securities lending datasets and how this can lead to
inaccurate inference.
    \842\ The Commission expects that the benefits of final Rule
10c-1a will be similar for all reportable securities lending markets
that have a similar need for additional market transparency. It is
possible that some markets may be more transparent than others. See
infra note 958 for further discussion.
---------------------------------------------------------------------------

    Final Rule 10c-1a will reduce information asymmetries and improve
informational efficiency in three ways. First, the data provided by
final Rule 10c-1a will be more comprehensive than the data currently
offered by commercial data vendors because it will not rely on
voluntary data submissions but will result from mandated disclosure
from both the Wholesale and Customer segments of the market.\843\
Second, Rule 10c-1a data will improve

informational efficiency by including certain data fields that are not
currently offered by commercial data vendors, contributing to the
breadth of available securities lending data. In addition, Rule 10c-1a
data will contain detailed security loan modification information.
Third, the final rule will expand the accessibility of the data by
allowing all market participants to access Rule 10c-1a data, possibly
subject to a fee.\844\
---------------------------------------------------------------------------

    \843\ See supra Part IX.B.2 for further discussion of selection
issues in commercial securities lending databases based on voluntary
data submissions.
    \844\ See infra Part IX.C.3 for a discussion of the fees that an
RNSA could charge to data consumers. See also supra Part VII.K.3 for
further discussion of final Rule 10c-1a(i), which allows an RNSA to
``establish and collect reasonable fees, pursuant to rules that are
promulgated pursuant to section 19(b) and Rule 19b-4 of the Exchange
Act.''
---------------------------------------------------------------------------

    The increased transparency from final Rule 10c-1a will result in
several notable economic benefits. First, the final rule will reduce
information asymmetries, which will benefit investors by reducing
borrowing costs, increasing price efficiency, and increasing
competition between broker-dealers and lending programs.\845\ Second,
by reducing the borrowing costs for some securities, the final rule
will lower the cost of short selling these securities and improve
market efficiency. Third, improvements in the information available to
various market participants will lead to a variety of benefits for
these participants, including increased profits and reduced costs of
business due to easier access to information and more informed
decisions.
---------------------------------------------------------------------------

    \845\ Consequently, some market participants may see returns
decrease due to more competitive fee pricing, which may lower
securities lending revenues for some lenders. See infra Part IX.C.4
for further discussion.
---------------------------------------------------------------------------

Reduction in Information Asymmetry
    The Commission believes that the transparency created by final Rule
10c-1a will reduce information asymmetries between more centrally
connected securities lending market participants and those on the
periphery. Specifically, it will reduce the information asymmetries
between broker-dealers and end borrowers, and between beneficial owners
and lending programs. This will result in increased competition between
dealers and between lending programs, ultimately leading to better loan
terms for some securities.\846\
---------------------------------------------------------------------------

    \846\ A reduction in the cost to borrow may result in lower
securities lending revenues for some lenders. See infra Part IX.C.4
for further discussion.
---------------------------------------------------------------------------

    The Commission believes that the transparency created by the final
rule will benefit end borrowers by reducing the information
disadvantage they have with their broker-dealers when borrowing shares.
Most security loans obtained by end borrowers are facilitated through
broker-dealers.\847\ Rule 10c-1a data will allow end borrowers to
determine the extent to which their broker-dealers are obtaining terms
that are better, worse, or consistent with current market conditions
for loans with similar characteristics. It will facilitate this
comparison by providing comprehensive transaction-by-transaction
information about the cost to borrow and other loan characteristics
that are currently mostly unavailable to end borrowers.\848\ For
example, end borrowers will be able to compare the lending terms
provided by their broker-dealers to the terms of similar loans, such as
loans with the same collateral and borrower types. End borrowers will
also be able to compare loans of similar sizes, though with a delay of
20 business days. The Commission believes that, despite the delay in
dissemination, loan size information will still be useful for end
borrowers, who can use this information to compare the prices of their
loans to other loans with similar terms (e.g., size, collateral type,
loan type, etc.), that were effected approximately one month prior.
Additionally, to the extent that an RNSA publicly disseminates daily
volume-weighted cost-to-borrow statistics as part of its requirement to
disseminate daily aggregate information, these statistics will provide
end borrowers with access to information about loan prices that
incorporates information about loan size information even prior to the
public dissemination of loan sizes.\849\
---------------------------------------------------------------------------

    \847\ See supra Part IX.B.4 for further information about the
market for borrowing services.
    \848\ See supra Part IX.B.2 discussing the lack of
comprehensiveness of both Customer market data collected through
Customer market surveys and Wholesale market data collected using a
give-to-get model, the latter of which is also often unavailable to
end borrowers due to usage restrictions.
    \849\ See final Rule 10c-1a(g)(5), requiring an RNSA to
disseminate aggregated daily information. In general, the ability of
market participants to use the aggregate information provided by an
RNSA to compare loan rates across similar loans may depend on the
specific form of loan rate information that an RNSA chooses to
publish. For example, if an RNSA chooses to publish cost to borrow
statistics that are very granular (e.g., it provides separate
statistics for Customer and Wholesale loans and/or according to
other loan characteristics), then end borrowers would be able to
benchmark their transactions to these statistics with increased
accuracy. To the extent that an RNSA chooses to publish less
granular statistics, then the end borrower could still benchmark
relative to the cost to borrow statistics provided by an RNSA, but
the benchmark would be noisier. See also infra Part IX.C.1 for
further discussion of the Commission's uncertainty related to an
RNSA's discretion.
---------------------------------------------------------------------------

    Similarly, the Commission believes that the final rule will benefit
beneficial owners by reducing their information disadvantage with
respect to their lending programs. By allowing beneficial owners to
more easily benchmark their lending programs through access to data on
loan prices and other characteristics of recently transacted security
loans, as well as the statistics provided by an RNSA on the next
business day, the final rule will provide beneficial owners with an
improved ability to determine the quality of the loans that their
lending program executes on their behalf relative to other loans with
similar characteristics. The ability to view the cost to borrow in both
the Customer markets and Wholesale markets will also provide beneficial
owners with additional information that they can use to benchmark the
performance of their lending programs.\850\
---------------------------------------------------------------------------

    \850\ See, e.g., supra note 767 and corresponding text
describing how a market participant engaged in Wholesale lending
stated in an interview that they feel unable to benchmark the
performance of their lending programs using commercial data because
they have very little insight into the Customer market.
---------------------------------------------------------------------------

    Financial institutions such as banks and broker-dealers use the
securities lending market in order to manage collateral needed for
other transactions.\851\ These entities can face the same asymmetric
information concerns as do end borrowers and beneficial owners, and
thus an increase in market transparency may lead financial institutions
to receive better loan terms, and thus improve their ability to manage
collateral. Banks also borrow securities to manage their balance
sheets,\852\ and the Commission believes that this too will become
easier as a result of the final Rule 10c-1a due to a more competitive
lending market, leading to the benefit of improved balance sheet
management by banks.
---------------------------------------------------------------------------

    \851\ See supra Part IX.B.1 for further discussion of financial
institutions' use of securities loans for collateral and balance
sheet management.
    \852\ See id.
---------------------------------------------------------------------------

    At the same time, as access to securities lending data increases,
competition for securities lending analytics may increase as well.\853\
Increased competition for and availability of securities lending
analytics would further reduce information asymmetries by reducing the
cost of and increasing access to securities lending analytics for both
end borrowers and beneficial owners.
---------------------------------------------------------------------------

    \853\ See infra Part IX.D.2 for a discussion of the expected
impact of the final rule on competition for securities lending data
analytics services.
---------------------------------------------------------------------------

    If an end borrower or beneficial owner believes that a particular
broker-dealer or lending agent is consistently underperforming, the
final rule will provide that market participant with the

tools to identify such underperformance and address it with their
broker-dealer or lending agent, or to find a new broker-dealer or
lending agent.\854\ For example, a beneficial owner or end borrower
could use the transaction-by-transaction Rule 10c-1a data to create a
bespoke benchmark of recently transacted loans that are similar to a
loan they wish to effect, and compare this benchmark to the terms
offered by their lending agent or broker dealer for that loan. A
beneficial owner or end borrower could also use the daily information
disseminated by an RNSA about the distribution of loan prices to assess
how the terms offered by their broker-dealer or lending agent compare
more generally to the distribution of recently transacted loans in the
same security. An increased ability to compare loan performance will
increase competition between broker-dealers and between lending agents,
which will ultimately improve lending terms for both end borrowers and
beneficial owners.\855\
---------------------------------------------------------------------------

    \854\ One commenter questioned the usefulness of transaction-by-
transaction reporting and asserted that the Proposing Release did
not explain how an investor would make use of these data (see
Citadel Letter, at 9). This part, along with Part VI.C.1.a in the
Proposing Release, discuss the ways in which investors could use the
data. The Commission acknowledges that not every factor affecting
prices, such as counterparty risk, is accounted for in the data
provided by the final rule. See infra in this part for further
discussion. Furthermore, the ability of end borrowers to switch
broker-dealers may be limited by switching costs; see infra in this
part for further discussion on switching costs. At least one
commenter expressed concern that the proposed rule may result in
increased costs when switching lending agents. See CSFME Letter 1,
at 5. See infra Part IX.C.4 for further discussion.
    \855\ See infra Part IX.D.2 for a discussion of the expected
impact of the final rule on competition between broker-dealers and
between lending programs.
---------------------------------------------------------------------------

    The Commission expects improved terms to be concentrated among
securities that are ``on special'' and have higher borrowing costs.
This is because general collateral securities lending is a low margin
business and lending supply for these securities far outstrips lending
demand.\856\ Combined, these two factors mean that there is likely not
much room for fees to improve for general collateral securities.\857\
As securities become on special (i.e., harder to borrow), lending rates
and utilization rates increase. Higher utilization rates mean that
there are fewer shares available to borrow and lend. In this
environment, high search costs and asymmetric information are
significant contributors to both the level and dispersion of fees, and
reducing asymmetric information has a better chance of leading to
better terms.\858\ Beneficial owners that currently lend shares of
hard-to-borrow securities for rates that are consistently below the
market average could receive higher lending rates for these securities
as their lending agents both have better access to information and
become more accountable for their performance due to beneficial owners
also having access to the same information. Similarly, end borrowers
whose borrowing costs are currently higher than the market average for
hard-to-borrow securities may see their borrowing costs decrease as
both their broker-dealers' information access and their ability to
monitor to their broker-dealers improve. These two effects suggest that
the dispersion of borrowing costs for hard-to-borrow securities will
diminish.
---------------------------------------------------------------------------

    \856\ See, e.g., State Street Letter, at 5; See also RMA Letter,
at 5, 8 (discussing low margins in securities lending more
generally); See also infra Part IX.C.4 for additional discussion.
    \857\ See Panel A of Table 1 in supra Part IX.B.3, showing that
for most stocks the lending supply significantly outstrips demand
with median utilization rates of approximately 12%.
    \858\ See, e.g., Kolasinski (2013) (finding that when demand for
lendable shares outstrips supply, lending fees increase and the
dispersion of lending fees is high, particularly when search costs
are high).
---------------------------------------------------------------------------

    That transparency can result in improved market quality is
consistent with the experience in other markets. The implementation of
TRACE improved transparency in the corporate bond market, and research
has shown that TRACE lowered the average cost of transacting and
increased competition between dealers in this market.\859\
Additionally, recent research from Brazil has shown that improving
securities lending transparency led to lower fees, increased liquidity,
and increased price efficiency in that country.\860\ In both cases,
researchers have identified reduced information asymmetry as a key
mechanism leading to improved market outcomes.\861\
---------------------------------------------------------------------------

    \859\ See, e.g., Amy K. Edwards, et al., Corporate Bond Market
Transaction Costs and Transparency, 62 J. Fin. 1421, 1421-1451
(2007), Michael Goldstein, et al., Transparency and Liquidity: A
Controlled Experiment on Corporate Bonds, 20 Rev. Fin. Stud. 235
(2007), Hendrik Bessembinder, et al., Market Transparency, Liquidity
Externalities, and Institutional Trading Costs in Corporate Bonds,
82 J. Fin. Econ. 251 (2006), and Hendrik Bessembinder & William
Maxwell, Markets: Transparency and the Corporate Bond Market, 22 J.
Econ. Perspectives 217(2008) (``Bessembinder & Maxwell (2008)''). In
addition to these papers, the Proposing Release also cited a paper
showing a lower dispersion of transaction costs in the corporate
bond market following the introduction of TRACE; see Proposing
Release, 86 FR 69837 note 222 and corresponding text. Since the
Proposing Release the Commission has become aware that this result
has been removed from a more recent version of the paper after
results suggested ``that reduced price dispersion is more likely
attributable to market changes other than transparency''; see
Michael A. Goldstein, et al, Dealer Behavior and the Trading of
Newly Issued Corporate Bonds, (June 21, 2021), at 19 n.21, available
at https://ssrn.com/abstract=1022356 (retrieved from SSRN Elsevier
database). Nonetheless, the general result that information
asymmetries can lead to price dispersion in fixed-income and dealer
markets has been well-documented in other literature; see, e.g., the
papers cited in supra note 790.
    \860\ See F[aacute]bio Cereda, et al., Price Transparency in OTC
Equity Lending Markets: Evidence from a Loan Fee Benchmark, 143 J.
Fin. Econ. 569, 569-592 (2022) (``Cereda (2022)'').
    \861\ See, e.g., Bessembinder & Maxwell (2008), at 226, stating
that the results from the academic literature on the introduction of
TRACE shows that it ``reduced dealers' information advantage
relative to customers,'' and that, ``with transaction reporting,
customers are able to assess the competitiveness of their own trade
price by comparing it to recent and subsequent transactions in the
same and similar issues.'' See also Cereda (2022), at 587, stating
that their results are consistent with the idea that ``[g]ood
benchmarks mitigate search frictions by lowering the informational
asymmetry among market participants.''
---------------------------------------------------------------------------

    While some commenters supported the applicability of evidence from
TRACE,\862\ other commenters questioned the applicability of evidence
from TRACE \863\ and from the Brazilian policy change.\864\
Specifically, commenters argued that, because bond transactions are
irrevocable and fungible, the identity and characteristics of the
counterparty are less relevant to prices in bond transactions than in
securities loans, and thus the Commission's reliance on TRACE studies
to draw inferences about the securities lending market is
misplaced.\865\ At least one commenter disputed the Commission's use of
the Brazilian policy change to draw inferences about the potential
benefits of proposed Rule 10c-1 because the Brazilian policy ``did not
involve the trade-by-trade disclosure of stock lending terms'' and is
thus not comparable to the Rule.\866\ The Commission continues to
believe that both events provide meaningful information about the
potential impacts of the final Rule 10c-1, as evidence from both events
illustrates how a reduction in asymmetric information between market
participants has the effect of improving market quality.
---------------------------------------------------------------------------

    \862\ See James J. Angel Letter, at 2.
    \863\ See, e.g., Overdahl Letter, at 6; Citadel Letter, at 8. At
least one commenter stated that the evidence from TRACE was
applicable.
    \864\ See Overdahl Letter, at 5-7.
    \865\ See Overdahl Letter, at 6.
    \866\ See Overdahl Letter, at 7.
---------------------------------------------------------------------------

    First, the introduction of TRACE, which introduced transaction-by-
transaction transparency into a market that previously did not have
such transparency, represents a policy change that is markedly similar
to that under the final Rule 10c-1a. The Commission acknowledges that
the market setting for TRACE is different from that of final Rule 10c-
1a, for example, because of the

importance of counterparty risk in securities loans prices as compared
to prices of corporate bonds.\867\ However, the fact that corporate
bond prices are less sensitive to the identities of the counterparties
than securities loans does not invalidate using TRACE to illustrate the
effect of decreasing information asymmetry. Instead, it simply implies
that the baseline dispersion in corporate bond prices prior to TRACE
was likely less than it currently is in the securities lending
market.\868\
---------------------------------------------------------------------------

    \867\ See, e.g., discussions in supra Part IX.B.3, acknowledging
that a variety of factors can drive the pricing of securities loans,
including counterparty risk. The Commission also acknowledged this
in the Proposing Release by noting that counterparty characteristics
played a role in pricing loans; See Proposing Release, 86 FR 69831.
See also Proposing Release, 86 FR 69837, stating that ``the data
would allow end borrowers to determine the extent to which their
broker-dealer is obtaining terms that are better, worse, or
consistent for current market conditions for loans with similar
characteristics'' (emphasis added).
    \868\ See supra note 790 for a discussion of theoretical and
empirical evidence that information asymmetry drives dispersion in
prices. It could also be the case that securities lending
transactions are more sensitive to idiosyncratic factors, such as
``the identities of the transacting parties'' as mentioned by a
commenter (see Overdahl Letter, at 6), because there is a lack of
transparency in the market about what a standardized price for a
given set of loan characteristics should be, which could drive
idiosyncrasies in loan prices. If prevailing market prices become
more generally available, it could be the case that an increase in
competition lessens the importance of idiosyncratic factors for the
pricing of securities loans. In this case, final Rule 10c-1a may
make the securities lending market behave more similarly to more
fungible markets such as the corporate bond market, making an
analysis of TRACE more applicable to the securities lending market.
---------------------------------------------------------------------------

    Second, while the Brazilian study takes place in a similar market
setting as Rule 10c-1a (i.e., the securities lending market) it is true
that the policy details differ. In particular, the Brazilian policy
change involved an increase in transparency not through the creation of
a transaction-by-transaction report, but through an increase in the
informativeness of an available loan fee ``benchmark.'' \869\ However,
despite the differences in details, the policy had a similar target to
that of Rule 10c-1a: namely, an improvement in the transparency of the
securities lending market.\870\ The Brazilian study shows that this
increase in transparency improved market quality through the economic
channel of reduced information asymmetry. This study is thus directly
relevant for final Rule 10c-1a, as the Commission expects that many of
the economic effects of the rule will be realized through the same
economic channel (i.e., a reduction in information asymmetry).
---------------------------------------------------------------------------

    \869\ According to the authors, this loan fee benchmark is
publicly reported by a centralized platform maintained by the
Brazilian Stock Exchange, on which all securities lending
transactions are registered by brokers. See Cereda (2020), at 570.
    \870\ See Cereda (2020), at 570, referencing statements from the
Brazilian stock exchange that `` . . . the purpose of this change is
to make the securities lending service ever more transparent, in
order to attract more securities lenders and borrowers and to meet
the demand of institutional investors.''
---------------------------------------------------------------------------

    Additionally, in support of their concern over the applicability of
TRACE research, one commenter stated that, ``while there is no reason
for the supply of corporate bonds to decrease in response to increased
trade transparency, greater transparency in the securities lending
market may well reduce the lending supply.'' \871\ This commenter cited
industry research showing that increased short selling transparency can
decrease beneficial owners' willingness to lend shares,\872\ as well as
academic research calling into question the applicability of TRACE to
securities lending ``since greater loan fee transparency could reduce
the lending supply.'' \873\
---------------------------------------------------------------------------

    \871\ See Overdahl Letter, at 6.
    \872\ See Overdahl Letter, at 6, citing The Effects of Short-
Selling Public Disclosure Regimes on Equity Markets (2010),
OliverWyman.com, at 4, 16-17, available at https://www.oliverwyman.com/our-expertise/insights/2010/feb/the-effects-of-short-selling-public-disclosure-regimes-on-equity.html.
    \873\ See Overdahl Letter, at 9, citing Cereda et al. (2022).
---------------------------------------------------------------------------

    The Commission believes that the industry study cited by the
commenter has empirical limitations that make it difficult to extract
robust empirical conclusions from the study. These limitations are
acknowledged by the author of the study; specifically, the author
states that, during the sample period, which occurs during the height
of the 2008 financial crisis, ``equity markets were in a relatively
disordered state,'' and that ``such extreme change in markets has the
consequence of making it complicated to establish a control group of
stocks such that all variables, except regulatory variables, remain
constant. This, in effect, makes it difficult to attribute causality
solely to the regulatory variables.'' \874\ The Commission agrees that
the extreme market volatility during 2008-2009 increases the likelihood
that the decrease in beneficial owners' appetite to lend out shares
documented by the author may have been driven by other market events
concurrent to but distinct from changes to regulatory regime for short
selling and, as a result, ``causality is still difficult to prove.''
\875\
---------------------------------------------------------------------------

    \874\ See Oliver Wyman, supra note 872, at 11. Specifically, the
report examines a short selling ban that took place in the UK
between Sept. 18, 2008, and Jan. 16, 2009, and compares this to a
``pre-ban'' period between Jan. 1, 2008, and Sept. 17, 2008, and a
``post-ban'' period beginning on Jan. 17, 2009.
    \875\ See id.
---------------------------------------------------------------------------

    The Brazilian study, also cited by the commenters, occurred during
a less volatile period rendering potentially cleaner empirical results.
The authors of that study indeed suggest that the loan supply could
decrease ``if the lower loan fees received by lenders were not
sufficient to cover the potential losses from not selling a stock when
shorting activity increases.'' \876\ However, later in that same
paragraph, the authors reject this hypothesis and conclude that their
results were not consistent with increased transparency affecting
liquidity in the lending market.\877\
---------------------------------------------------------------------------

    \876\ See Cereda (2022), at 571. Specifically, the authors state
that their ``findings, however, indicate that the increased
transparency had positive effects overall; it reduced loan fees,
increased lending volume, did not affect lenders' total revenue, and
favored more efficient lenders.''
    \877\ See id.
---------------------------------------------------------------------------

Improved Market Quality Due to Lower Short Selling Costs
    The Commission expects that final Rule 10c-1a will produce
countervailing effects through its impact on short selling, but
ultimately that the net result will be positive. On one hand, the final
rule will benefit short sellers by lower borrowing costs for securities
that are hard to borrow, which will improve market quality through
increased price efficiency, managerial oversight, and liquidity.\878\
On the other hand, the final rule could potentially harm market quality
by making it easier for other investors to discern short sellers'
trading strategies, thereby discouraging the costly fundamental
research that underlies some short selling strategies.\879\ On balance,
the Commission expects that the final rule, which delays the
dissemination of loan volume information by 20 business days, is not
likely to significantly expand market participants' abilities to
discern short selling strategies.
---------------------------------------------------------------------------

    \878\ See supra in this part for a discussion on why the
Commission expects the economic effects of the final rule to be
concentrated among hard-to-borrow stocks.
    \879\ See, e.g., Overdahl Letter, at 3; Citadel Letter, at 5-7,
11; MFA Letter 3, at 3-4, 8.
---------------------------------------------------------------------------

    The Commission expects that final Rule 10c-1a will lower short
selling costs by decreasing borrowing costs, especially for hard-to-
borrow securities.\880\ Academic research

indicates that, when short selling costs diminish, investors increase
their fundamental research because it is easier to trade on any
negative information that they uncover (e.g., poor earnings).\881\ This
increase in fundamental research may in turn lead to better investment
decisions by these investors.\882\ Additionally, by facilitating more
research, the final rule will benefit market participants by improving
price discovery. Academic research shows that short sellers, through
their research, contribute to price efficiency by gathering and trading
on relevant private information.\883\
---------------------------------------------------------------------------

    \880\ See discussions in above and below in this part regarding
the impact of the final rule on borrowing costs. This effect may be
concentrated among stocks that are ``on special'' and have higher
borrowing costs, in which there are more opportunities for an
increase in price efficiency to lower fees. See supra in this part
for a discussion on why the Commission expects the economic effects
of the final rule to be concentrated among hard-to-borrow stocks.
    \881\ See Dixon, et al., (2021), supra note 746 and Peter Dixon,
Why Do Short Selling Bans Increase Adverse Selection and Decrease
Price Efficiency? Rev. Asset Pricing Stud. 122 (2021) (``Dixon
(2021)''). It is not necessary that the information uncovered by
this research be negative in nature for this to be true. The
possibility of easier securities borrowing ensures that if the
information happens to be negative, it will still be profitable.
Thus, the risk of engaging in costly research decreases and more
information, both positive and negative, is uncovered as a result.
    \882\ See infra this part for further discussion on how
increased information for participants in the securities lending
market may increase the profitability of some investors' trading
strategies more generally.
    \883\ See, e.g., Jesse Blocher, et al., Connecting Two Markets:
An Equilibrium Framework for Shorts, Longs and Stock Loans, 108 J.
Fin. Econ, 302, 302-22 (2021) and Dixon (2021) supra note 881.
---------------------------------------------------------------------------

    Short sellers also serve as valuable monitors of management. Extant
research has demonstrated that when management knows that short sellers
may be studying their firms, they are less likely to engage in
inappropriate or value-destroying behavior.\884\ Research also
indicates that when short selling becomes easier the effectiveness of
short sellers as monitors increases.\885\
---------------------------------------------------------------------------

    \884\ See, e.g., Eric C. Chang, et al., Does Short-Selling
Threat Discipline Managers in Mergers and Acquisitions Decisions?,
J. Acct. & Econ. 101223 (2019). See also Massimo Massa, et al., The
Invisible Hand of Short Selling: Does Short Selling Discipline
Earnings Management?, 28 Rev. Fin. Stud. 1701 (2015).
    \885\ See, e.g., Vivian W. Fang, et al., Short Selling and
Earnings Management: A Controlled Experiment, 71 J. Fin. 1251
(2016).
---------------------------------------------------------------------------

    Reducing the costs of short selling may also have the benefit of
increasing liquidity in the underlying securities markets. Short
sellers are key contributors to liquidity in both equity and options
markets and existing research shows that, when short selling is
constrained by tightness in the securities lending market, the stock
market is less liquid.\886\ Lower short selling costs could have
potential benefits for options market liquidity as well. Securities
lending affects liquidity in the options market through its impact on
how easily options market makers can delta hedge.\887\ Less costly
delta hedging may therefore increase liquidity in the options market.
Also, since some price discovery occurs in the options market, to the
extent that the final rule increases the ease with which investors can
trade in options, the proposal may further enhance price efficiency in
the options market.\888\
---------------------------------------------------------------------------

    \886\ See Dixon, et al. (2021), supra note 746.
    \887\ See supra Part IX.B.1 for a discussion of option market
makers' use of securities lending markets to engage in delta
hedging.
    \888\ See, e.g., David Easley, et al., Option Volume and Stock
Prices: Evidence on Where Informed Traders Trade, 53 J. Fin. 431,
431-65 (1998); Jun Pan & Allen M. Poteshman, The Information in
Option Volume for Future Stock Prices, 19 Rev. Fin. Stud. 871
(2006); Sophie Ni, et al., Stock Price Clustering on Option
Expiration Dates, 78 J. Fin. Econ. 49 (2005).
---------------------------------------------------------------------------

    Commenters to the proposed Rule 10c-1, which did not include a
delay in the dissemination of any information that would be collected
under the rule, expressed concern that the dissemination of Rule 10c-1a
data could harm short sellers, and thus U.S. financial and capital
markets in general, and that this outcome was not analyzed in the
Proposing Release.\889\ Some commenters expressed concern that
information about Customer loans collected under the rule in particular
could be used to determine individual short positions with a high
degree of accuracy,\890\ and that this could result in harm to short
sellers,\891\ resulting in negative outcomes for market quality, such
as reductions in liquidity, price efficiency and shareholder
engagement.\892\
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    \889\ See, e.g., Overdahl Letter, at 9-11; Citadel Letter, at 5-
7; AIMA Letter 2, at 2; CCMR Letter, at 1; AIMA Letter 3, at 2; MFA
Letter 3, at 3-4, 8. The Proposing Release acknowledged that the
Rule could diminish the value of collecting and trading on negative
information by revealing some new short selling information to the
market, and that these dynamics could mitigate some of the benefits
discussed. See Proposing Release, Part VI.C.1.c.
    \890\ See, e.g., Citadel Letter, at 5; Overdahl Letter, at 4;
SIFMA AMG Letter, at 5. Other commenters expressed concern that the
data provided under proposed Rule 10c-1 could allow for reverse
engineering of trading strategies more generally; see, e.g., MFA
Letter 1, at 8; MFA Letter 2, at 5; RMA Letter, at 18.
    \891\ Commenters pointed to a number of ways that short sellers
could be harmed, including increased costs to establishing short
positions, particularly large positions that require time to build
up (see, e.g., Citadel at 6), and increased risks of copycat
strategies (see, e.g., Overdahl Letter, at 9-10; Citadel Letter, at
6; MFA Letter 1, at 7; AIMA Letter 2, at 2-3), frontrunning (see,
e.g., SBAI Letter, at 2), issuer retaliation (see, e.g., Citadel
Letter, at 6), negative impacts on activities that rely on short
selling for hedging purposes (see, e.g., Citadel Letter, at 6), and
short squeezes (see, e.g., Citadel Letter, at 6; Overdahl Letter, at
11; MFA Letter 1, at 7; S3 Partners Letter, at 7). One commenter
cites a number of academic studies supporting the notion that
disclosure of short selling positions can be harmful to markets,
including Truong X. Duong et al., The Costs and Benefits of Short
Sale Disclosure, 53 J. Banking & Fin. 124 (2015) and Stephan Jank,
et al., Flying Under the Radar: The Effects of Short-Sale Disclosure
Rules on Investor Behavior and Stock Prices, J. Fin. Econ. 209
(2021) (see Overdahl Letter, at 8).
    \892\ See, e.g., Citadel Letter, at 6; MFA Letter 1, at 2 and 7;
SIFMA AMG Letter, at 5 and 7; SIFMA Letter 1, at 12, 15; AIMA Letter
1, at 2; Overdahl Letter, at 4; CCMR Letter, at 1. One commenter
referred to increased short selling disclosure as a wealth transfer
that is not necessarily welfare enhancing. See Overdahl Letter, at
3.
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    The Commission acknowledges these commenters' concerns about the
effect of proposed Rule 10c-1 on short sellers, and believes that these
concerns are mitigated by the final rule's inclusion of a delay in
disseminating loan size information.\893\ Specifically, the Commission
acknowledges that activity in certain segments of the securities
lending market are tightly linked to short selling positions, in
particular the market for Customer loans.\894\ Thus, the sum of loans
identified in Rule 10c-1a data as being to ``a customer (if the person
lending securities is a broker or dealer)'' \895\ could give a strong
indication of aggregate short interest. However, under the final rule,
this information will only be directly available to Rule 10c-1a data
consumers after a delay of 20 business days,\896\ which significantly
reduces the novelty of this information compared to the proposed rule.
In particular, this delay means that the loan size information, which
is the portion of the data most

directly related to short selling activity, disseminated under final
Rule 10c-1a will generally be less timely than pre-existing sources of
short selling transparency, such as FINRA's bimonthly short interest
data.\897\
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    \893\ The proposed rule, which commenters expressed concern,
would have required market participants to report transactions to an
RNSA within 15 minutes of the terms being settled with an RNSA
disseminating the data to the public as soon as practicable
thereafter.
    \894\ See supra Part IX.B.6 for further discussion of why the
market for Customer loans is tightly linked to short selling
positions.
    \895\ See 17 CFR 240.10c-1a(c)(7) (``final Rule 10c-1a(c)(7)''),
which requires a covered person to report, for a covered securities
loan, whether the borrower is a broker or dealer, a customer (if the
person lending securities is a broker or dealer), a clearing agency,
a bank, a custodian, or other person. While most loans that
facilitate short sales will likely be associated the category of
borrowers that are ``a customer (if the person lending securities is
a broker or dealer),'' not all will. Some large market participants
do not use broker-dealers as an intermediary when sourcing loans;
instead, they maintain relationships directly with lending programs
to source shares when they wish to sell short. These transactions
would show up in the data as a loan to ``other person.'' Lastly, to
the extent that a broker-dealer borrows shares to facilitate their
own short selling, the loan would show up in the data as a loan to a
``broker or dealer.'' However, by summing up all loans to ``a
customer (if the person lending securities is a broker or dealer)''
and ``other person,'' market participants could likely estimate
outstanding short interest with considerable accuracy.
    \896\ Note that, the difference in settlement cycles between the
equity and lending markets means that the size of equity loans that
facilitate short sales won't be made public until approximately 22
business days after the short sale occurs in the stock market. See
supra note 738 and corresponding text.
    \897\ See supra Part IX.B.6 for a discussion of the FINRA
bimonthly short interest data which are made available for
publication on the seventh business day after the reporting
settlement dates, which occurs bimonthly.
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    Additionally, the Commission does not believe that market
participants will have more timely access to indicators of aggregate
short interest from the daily information disseminated by an RNSA
pertaining to the aggregate transaction activity for each reportable
security.\898\ This is because the term ``aggregate transaction
activity'' refers to information pertaining to the absolute value of
transactions, and excludes information that could reveal net
transaction activity information.\899\ Therefore, it would not be
possible to use this information to discern information about, for
example, changes in net short sale positions.
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    \898\ See final Rule 10c-1a(g)(5), requiring an RNSA to
disseminate aggregated daily information.
    \899\ See supra Part VII.J for further discussion.
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    The loan amount data disseminated under final Rule 10c-1a will
provide some novel information about short positions, although only on
a look-back basis after the 20-business-day dissemination delay has
lapsed. In particular, since each loan likely relates to a unique
market participant, the public dissemination of such transaction-by-
transaction volume data under final Rule 10c-1a, albeit delayed, will
provide an indication of the distribution of short sentiment, i.e.,
whether short interest is concentrated among a few short sellers with
large positions, or whether it is spread out over many short
sellers.\900\ It can also give an indication about when individual
market participants increased or decreased their short positions by
examining the change in the size of a loan from the reported data. As
most currently available sources of short selling information only
contain information about aggregated short selling activity,\901\ this
could represent an increase in the granularity of market participants'
information about past short positions and, in this way, could provide
information about short sellers' strategies. Specifically, market
participants could examine the historical securities lending data to
try to identify factors that may be indicative of short selling
activity. However, it is not clear that such an analysis, which would
be inherently noisy, would provide actionable insights into future
short selling activity that could harm short sellers' abilities to
profit from negative information.
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    \900\ Note that this may only be possible with noise to the
extent that some borrowers, such as institutional investors, break
their loans up across multiple prime brokers. See supra note 806 for
more information about the use of multiple prime brokers by some
institutional investors.
    \901\ See discussion of the FINRA bimonthly short interest data
and SRO short selling volume and transaction data above in Part
IX.B.6.
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Economic Effects From Improved Securities Lending Data Quality
    The Commission believes that final Rule 10c-1a will increase the
information about the state of the securities lending markets that is
generally available to market participants. As discussed in this
section, increased information will result in benefits in the form of
better decision-making by investors, beneficial owners and other market
participants, reduced costs of business for broker-dealers, improved
performance and reduced costs for lending programs, new business
opportunities for data vendors, improvements to shareholder monitoring
and, ultimately, improved market stability and price discovery both in
the securities lending market and the market for the underlying
security.\902\
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    \902\ One commenter expressed concern that the Proposing Release
did not explain how market participants would use the data (See
Citadel Letter, at 9). However, the Proposing Release provided
specific examples of how the data could be used (See Proposing
Release, 86 FR 69829, 69836, 69840). These examples, and more, are
also provided throughout the current Economic Analysis and
particularly in this part. Similarly, some commenters expressed
concern that the Proposing Release did not identify who, other than
regulators, would benefit from certain data (see, e.g., ICI Letter
1, at 9). However, the same discussions of how market participants
would use the data also provide discussions of how various market
participants would benefit from the data. In this vein, this part
discusses a variety of uses and benefits of Rule 10c-1a data.
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    Overall, the data provided by the final rule will significantly
improve market participants' views into lending rates for various
securities. It does so first through the comprehensiveness of the data.
By mandating the disclosure of certain information about all securities
loans, the data provided by the Rule will be comprehensive and thus not
prone to biases that occur due to non-random observations.\903\
Comprehensive reporting also means that the data provided by the final
rule covers both the Wholesale and Customer segments of the market,
allowing market participants to compare trends in both markets
simultaneously. The data will also be more granular than existing data.
In addition to information about the size of the loan and the loan fee,
the data provided by the final rule contains a number of other data
fields which allow market participants to parse the data to analyze the
subset of transactions that are most relevant to their needs.
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    \903\ See supra Part IX.B.2 for additional discussion of bias in
existing securities lending data.
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    There are a few dimensions where data provided by commercial data
providers may be similar to or provide information not provided by the
Rule. The data provided by the final rule is disseminated next day,
which is similar to the timeliness of much of the data provided by the
commercial data providers except that some commercial data providers
offer subsets of intraday transaction data that is timelier than the
data provided by the final rule. Additionally, the data provided by the
Rule masks loan size for 20 business days. Consequently, existing data
providers' information regarding shares on loan will be considerably
timelier than the data provided by the Rule. Lastly, the Rule does not
provide information about utilization rates, and so market participants
wishing to observe utilization rates will need to maintain access to
commercial datasets.
    First, the improvement in securities lending rate information and
the ability to create bespoke benchmarks will improve the quality of
information that market participants rely on to make decisions
regarding investment strategies that require borrowing securities and
the cost of those strategies.\904\ An increase in the quality of
information regarding the costs of borrowing a security may decrease
risk and thereby increase the risk-adjusted profits of pursuing
investment strategies that require borrowing securities, such as short
sales.\905\ For example, prior to a short sale transaction, the end
borrower will be able to get a better sense of the likely costs
associated with such an investment strategy by examining the data
regarding recently transacted securities loans, as well as information
provided by an RNSA about aggregate transaction activity and cost to
borrow.\906\
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    \904\ See supra in this part for a discussion of how the final
rule will improve transparency through increased comprehensiveness,
breadth, and accessibility of securities lending data.
    \905\ See supra this part for further discussion of the expected
economic effects of the final rule through its impact on short
selling.
    \906\ See infra this part for a discussion of the Commission's
uncertainty regarding how the aggregated information disseminated by
an RNSA will compare to currently available data and the range of
analyses that market participants will be able to perform using this
information.
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    Access to Rule 10c-1a data may also benefit investors by enabling
them to make more informed decisions about whether to buy, hold, or
sell a given

security. This will occur as they will have increased certainty
regarding lending rates and which securities are most likely to be
profitably lent or not, leading to better risk allocations, investment
decisions, and ultimately risk-adjusted returns. Extant research has
demonstrated that securities lending data have information relevant to
the prices of the underlying security.\907\ By making securities