Stock Lending from Lenders’ Perspective:
Are Lenders Price Takers?
Zsuzsa R. Huszár, Ruth Tan Seow Kuan, and Weina Zhang
*
This Draft: June 18, 2014
Preliminary: Please do not cite without authors’ permission
Abstract
This study provides new insights about the source of short sale constraints, by showing that lending fees predict
future returns beyond shorting demand in recent years. Focusing on lenders’ perspective, we reveal that lending fees
are on average significantly higher for stocks with large active intuitional ownership. For stocks with high active
institutional ownership lending fees not only respond to shorting demand but are raised in anticipation of new future
shorting demand. Specifically, fees are about 8% higher before earnings announcements and 15% higher before
dividend declaration dates for stocks with 50% active institutional ownership than for stocks without active
institutional ownership. Lastly, we find that the negative relationship between future returns and lending fees
strengthens after the Lehman Brothers collapse as active institutions’ likely more proactive in capitalizing lending
fee revenues in the newly transparent and automated stock lending market.
Keywords: Institutional ownership, Securities lending market, Short sales, Short sale constraints.
* All authors are from NUS Business School, at the National University of Singapore (NUS), Mochtar Riady
Building, 15 Kent Ridge Drive, Singapore 119245. Huszár and Zhang are also affiliated with the Risk
Management Institute (RMI) and the Institute of Real Estate Studies (IRES) at NUS. Contact author’s: email:
[email protected], phone: +(65) 6516-8017, fax: +(65) 6779 2083. Huszár thanks Sungard for the invaluable
Sungard Astec Analytics data and support from Sungard’s sales and analytics team.
The authors would like to thank Ekkehart Boehmer, Burton Hollifield, Charles Lee, Alexander Ljungqvist, and
David Reeb for comments and suggestions.
JEL classification: G10, G12, G14
Abstract
This study provides new insights about the source of short sale constraints, by showing that lending fees predict
future returns beyond shorting demand in recent years. Focusing on lenders’ perspective, we reveal that lending fees
are on average significantly higher for stocks with large active intuitional ownership. For stocks with high active
institutional ownership lending fees not only respond to shorting demand but are raised in anticipation of new future
shorting demand. Specifically, fees are about 8% higher before earnings announcements and 15% higher before
dividend declaration dates for stocks with 50% active institutional ownership than for stocks without active
institutional ownership. Lastly, we find that the negative relationship between future returns and lending fees
strengthens after the Lehman Brothers collapse as active institutions’ likely more proactive in capitalizing lending
fee revenues in the newly transparent and automated stock lending market.
Keywords: Institutional ownership, Securities lending market, Short sales, Short sale constraints.
JEL classification: G10, G12, G14
In recent years, the hedge fund industry has experienced unprecedented growth, with assets
under management reaching $2 Trillion by 2011/2012 (Forbes, 2012). Hedge fund managers
generally actively manage the funds and aggressively participate in short selling either to create
market neutral portfolios and/or to exploit negative firm specific information. While regulators
generally support shorting, most exchanges have introduced some form of locate requirement
(e.g., Regulation SHO, 2004) or naked short bans. These restrictions requiring short sellers
and/or their brokers to ensure that shares are available before shorting have stimulated the
demand in the securities lending market. On the supply side, institutional lenders (e.g.,
custodians, mutual funds and pension funds) eagerly responded, looking for additional income
from their passive portfolio. 2
Large institutional investors, such as mutual funds, trusts, as beneficial owners of enormous
portfolios of securities, increasingly rely on securities lending as an important source of revenue
from their ―passive‖ holdings. Even pension funds pay close attention to their income from
securities lending, as suggested by the recent lawsuit filed against Blackrock for ―systematically
looting securities lending revenues from investors‖ (Bloomberg, 2013; Financial Times, 2013).
With the failure of Lehman Brothers, institutional investors suddenly become aware of risks as
well as potentially realisable returns on well managed and indemnified lendable assets. In
response, major infrastructure and data providers have improved the transparency and timeliness
2 Selling a stock short without fulfilling the locate requirement is considered a violation of Reg. SHO except for
shorts by market makers for bona fide market making. This locate requirement mandates that traders’ prime
brokers must ensure in advance that the stock sold (where the short sellers does not initially own the security) will
be available (can be borrowed) to fulfill the trade settlement.
1
of information about shorting demands and lending fees (e.g., SunGard provide daily and even
intraday data since 2012).
The recent securities lending market development also reinvigorated the short sale literature
by providing new high frequency data. The earliest studies using securities lending data
(D’Avolio, 2002; Boehmer et al. 2006) show that borrowing costs or lending fees can be very
high for some small, illiquid stocks with high shorting demand and that these high fees have
important asset pricing implications. More recently, Kolasinski et al. (2012) examine the
applicability of Duffie et al. (2002) search model in the traditionally oligopolistic and opaque
securities lending market and show that borrowing costs (measured by loan fees) are insensitive
to moderate changes in demand. However, large demand shocks move costs significantly,
especially in the presence of high dispersion of opinion. These latter findings are formalized by
Blocher et al. (2013) in a reduced-form framework. In disentangling the supply and demand
effects on loan fees, Kaplan et al. (2013) find that an exogenous shock to lending supply
significantly raises the equity loan fees. On the other hand, they also find that supply shifts
convey no information about future returns. As shown by Cohen et al. (2007), supply shocks are
only informative about future returns when combined with demand shocks. While the existing
studies imply equity lenders play mostly a passive role, it is important to note the security
lending market has experienced significant changes in the last decade and become significantly
more transparent (See appendix for details).
In this study, we complement existing studies on the US stock lending market to provide
insight on the source of direct shorting costs, i.e., lending fees. We strictly examine lending fees,
the income to beneficial owners, and not shorting or borrowing costs that include agents’ or
prime brokers’ fee. While traditionally 40/60 cut applied between lender and agent on the fees
2
received from borrower, in recent years the agents’ cut has been generally shrinking, resulting in
20/80 or even 10/90 cuts with the increased transparency and sophistication of lenders (GFOA,
2009, Clearstream, 2014).
Unlike earlier studies that focus on short sellers or borrowers, we focus on the role of
institutional lenders in the now transparent and automated securities lending market. We
specifically consider the role of institutional ownership by distinguishing across relatively active
institutions, such as asset management companies and investment companies, and passive
institutions, such as trusts, insurance firms and pension funds. We revisit the conventional
wisdom that lenders are passive price takers in the equity lending market by studying lenders’
response to new information, such as those contained in the shorting activity. More importantly,
do lenders increase fees and/or reduce inventory in anticipation of new information? Lastly, we
consider the impact of the global financial crisis and examine the change in lenders’ lending fee
pricing behavior after Lehman Brothers collapse.
Our findings are summarized as follows. First, we show that lending fees, fraction of the
shorting cost that lenders receive, are informative about future returns. Lending fees increase
with new shorting demand, suggesting that lenders (or their agents) respond to the information
implied by short sellers and market demand. Stocks with recently high shorting demand (from
the top decile based on new stock borrowing in the last day relative to shares outstanding) are
associated with approximately 15% higher fees.3 We also show that stocks with high active
institutional ownership on average command higher fees, contradictory to Saffi and Sturgess’s
3 The coefficient estimate of 0.139 on the high newSIR dummy in a log-liner model, implies that stock from the
treatment group (with high SIR) are associated with 100(e0.139-1)≈15% higher lending fees.
3
(2012) findings that institutional ownership relaxes short sale constraints. Our results are
somewhat consistent with Porras-Prado et al. (2013) results which argue that ownership
concentration is a major factor for short sale constraints. Unlike their study which focuses on
ownership structure and fees, we examine lending fee dynamics around corporate events while
considering the active role of some institutions as lenders.
Next, we examine whether lenders respond to and/or anticipate new information. We focus
on earnings announcements and dividend declaration dates, which are major information events.
Prior studies find that short sellers tend to be active around these events (Christophe et al. 2004;
Boehmer et al. 2012; Engelberg et al. 2012). We find evidence of proactive behavior, in that
lending fees are raised prior to earnings announcements and dividend declaration date in stocks
with significant active institutional ownership (IO). Specifically before earnings announcements,
stocks with 50% active IO have 8% (0.5x16%) higher lending fees than stocks without any
active IO.4 Similarly, in the 10-day window before dividend declaration date, stocks with 50%
active IO are associated with 15% (0.5x30%) higher lending fees than stocks with no active IO.
Last, we examine changes in lenders’ behavior from Jan. 2007 to Dec. 2010, during which
period the securities lending market become more transparent. Moreover the financial crisis
likely stimulated institutional investors to act as responsible lenders and more actively monitor
and more actively price lending to maximize revenues at a relatively low risk. Overall, our
results suggest that equity lenders (and/or their agents) not only become more responsive to
4 The 16% effect for a unit change in the underlying variable is inferred from the 15.2% coefficient estimate from
the log-liner model, as 16% ≈ 100(e0.152-1). Similarly, the coefficient estimate of 26.5% in Table 6 from the loglinear model implies that a unit change in the underlying is associated with a 30% increase (30%≈100*e0.265-1) in the
dependent variable, the log lending fees.
4
market demand and news but are also more preemptive to important informational (earnings) and
corporate (dividend) events.
We proceed as follows. In Section 1, we introduce the equity lending market and review
recent U.S. developments and relevant studies. Next, we describe our data and research methods
in Section 2. Our empirical analyses with the full sample and with subsamples on lending market
dynamics are presented in Sections 3 and 4 respectively. Finally, Section 5 concludes.
1. Review of the Equity Lending Market
1.1. Equity Lending Market Background
In the simplest equity lending transaction, a stock borrower (e.g., short seller) pays loan fees (i.e.,
percentage of the closing day stock price) directly to the lender or borrowing costs to the
agent/broker of the lender. Although there are no fixed rules on the division of income,
institutional lenders generally receive about 70% (or more) of the borrowing cost as lending fee
(or loan fee). In this study, we refer to lending fees strictly as the income lenders receive after the
broker cut, or net fee. The borrowers also post 102-105% of the short sale proceeds as collateral
and a 50% margin (Regulation T) with the broker until the trade is closed out. Institutional
lenders can also obtain additional income by reinvesting the collateral, which is generally cash in
the U.S. Nontrivial income creation from reinvestment is common but not guaranteed because it
depends on the lenders’ investment guidelines and the brokers’ or agents’ skill.
In recent years, institutional investors increasingly rely on securities lending of treasuries,
bonds and equities as an important source of revenue. According to Sungard Astec Analytics, the
5
annual stock lending revenue for mutual funds tripled from $300 million to more than $1 billion
from 2003 to 2007.5 In particular, non-actively managed funds, such as index funds and pension
funds, realized that fees on their idle assets through fully-collateralized loans is almost
universally profitable (used to cover administrative costs and complement portfolio returns) even
during the market contraction in late 2008. Besides mutual funds and index funds, many ERISA
funds, state and local treasuries, insurance companies, central banks and other institutional
investors are active in the securities lending market globally.
In the new millennium, the global securities lending market has grown beyond expectations.
It is estimated that the stock lending market encompasses more than $1.8 trillion securities
worldwide in 2012, fuelled by increased institutional trading (FSB, 2012). Responding to
industry demand, securities lending data and solution providers, such as Data Explorers (DE) and
SunGard increased the number of products and frequency of information provision. Today, these
stock lending market data and solutions are essential not only for borrowers but also for lenders
who want to actively manage their lending programs to maximize the potential of their portfolios
in the increasingly competitive stock lending market (see Appendix I for summary on industry
products that facilitate the transparency of lending market).
1.2. Literature Review of the Equity Lending Market
5 Our data with monthly average $50 million lending fee suggests about $600 million annualized income for 2007
based on our stock coverage. Given that our sample is only includes stocks with valid, institutional ownership,
earnings news etc., thus our estimated lending fee income is clearly just a lower bound of the real life aggregate
income.
6
In one of the first securities lending market study, Jones and Lamont (2002) use data from the socalled ―lending pit‖ on US stocks from 1926 to 1933 and show that stocks with high loan fees
significantly underperformed once the short sale constraints (high fees) are relaxed. D’Avolio
(2002) and Geczy et al. (2002) use more recent data and find that on average loan fees are
relatively small compared to the abnormal returns associated with well-known trading strategies,
such as momentum trading. Theoretically, Duffie et al. (2002) model endogenous short-sale
constraints in the equity lending market with a search model where lenders rationally set high
loan fees for stocks with investor disagreement. D’Avolio (2002) confirms empirically that loan
fees can be significant especially when both investor disagreements and shorting demands are
high. Overall, lending fees may have an economic impact on pricing if institutional lenders
rationally price the stock above fundamental value because of expected material future lending
income (Porras-Prado, 2013). However, Duffie et al. (2002) argue that the loan fees may impact
prices only when loan fees and/or shorting demand can be anticipated, while Evans, Geczy et al.
(2009) suggest that traders may find ways to get around high fees using failure (on delivery) as
an option.
Institutions become increasingly active in short selling in recent years. Not only traditionally
active short sellers, such as hedge funds, but also mutual funds actively short in nontrival
amount. Chen, Desai, and Krishnamurthy (2012) show that over 300 US mutual funds actively
short, on average $34.7 million or 15.6% of the total fund assets. These institutional changes
likely drive demand and lenders and/or their agent respond to large positive shifts in shorting
demand loan fees increase (Cohen et al. 2007). Building on these findings, Kolasinski et al.
(2012) suggest that lending market frictions allow lenders to charge higher loan fees in the
presence of dispersion of opinion and fees increase sharply in response to demand shocks when
7
the existing shorting demand is already high. With increasing activity by institutional traders
around dividend events, Blocher et al (2013) show stocks that are withdrawn from the lending pit
around dividend dates experience significant increase in loan fees as different institutional
clientele aims to exploit dividend arbitrage. More recently, Chuprinin and Massa (2012) find that
fund managers may be influenced by past performance in their lending decision, and that stocks
with recent reduced lending supply in the securities lending market subsequently underperform.
2. Data and Summary Statistics
2.1. Data
We use several data sources to build our dataset. We obtain data on daily returns, daily trading
volumes, prices, and number of shares outstanding from the Center for Research in Security
Prices (CRSP) daily files for domestic common stocks (stocks with CRSP share codes of 10 and
11). Dividend announcement and declaration dates are also extracted from CRSP daily files. This
return dataset is complemented with company-specific financial information, such as book value
of equity from Compustat’s annual industrial files. Last, we combine our data with earnings
announcement dates and earnings news from Institutional Brokers' Estimate System (I/B/E/S).
Ownership data are obtained from Factset, where there are five major types of institutional
ownerships, namely (1) banks, (2) insurance companies, (3) investment companies and their
managers, (4) investment advisors, and (5) all others including pension funds, university
endowments, and foundations. We combine all five ownership types to get aggregate
institutional ownership. Following Abarbanell et al. (2003) and Hameed et al. (2014), we
combine two ownership types, namely investment companies and investment advisory firms’
ownership, to create an active institutional ownership proxy. We merge the ownership
8
information at the quarterly frequency, based on quarterly reporting, and exclude all stocks
without coverage from Factset or without securities lending information from Sungard Astec
Analytics. 6
While a number of recent studies (e.g., Kolasinski et al. 2012; Saffi and Sigurdsson, 2010)
use securities lending market data, their primary data source is DataExplorer (now Markit
Securities). To our knowledge, we are the first to explore Sungard Astec Analytics data in an
academic study. It is slightly different than that of DataExplorer which focuses on the supply
side. Sungard Astec Analytics provides daily (and more recently intraday) data on securities
borrowing volume, lending fees, differentiating across the type of collateral and the type of loan
(overnight versus term loans). However, for earlier years, supply and utilisation rates data were
not available, providing thus only demand side information. Today, Sungard Astec Analytics
delivers global intraday rate and volume information on securities lending transactions as well as
analytics and benchmarking tools for trading, performance measurement and program
management to institutions involved in investment management and securities finance.
While DataExplorer was established in Europe, SunGard started in the U.S. and has
extensive daily coverage for most US listed equities.7 SunGard’s US market coverage is well
established by the mid 2000 as reflected by the relatively stable number of stocks over time (in
Figure 1). On the other hand, DataExplorer’s coverage grew over time, as the company expanded
beyond the European market. Since the dataset is new, screens were implemented to check for
6 We have to assume zero institutional ownership because of missing data less than 5% of the time. Excluding these
observations from the sample do not change the economic implications of our results.
7 More recently, SunGard established a new datafeed which provides real time coverage.
9
data errors, such as lending in excess of supply, or misreported fees. Manually all extreme fee
observations (fees in excess of annual 50%) have been validated.
Our final sample, from the
overlap of SunGard data with Compustat and CRSP files, includes 2,347,779 stock-day
observations for 3,766 unique stocks listed on major US equity markets (Amex, NYSE, and
NASDAQ). Our sample is restricted to stocks which have at least one earning news
announcement from IBES during our sample period.
Each day, we calculate the total lending fee income as the number of shares out on loan
times the share price times the relevant daily lending fee. Panel A of Figure 1 clearly show that
the lending fee income has a peak around the Lehman Brothers collapse as securities lenders
become exceedingly worried that their assets will not be returned if their prime broker or
securities lending agent should fail. But the aggregate fee income as a % of the total value
sharply declined thereafter as the stock prices declined. The aggregate fee income has another
significant peak in early 2009, coinciding with the lowest point of major US market indices, in
March 2009. The aggregate lending fee significantly dropped after mid 2009 as the markets
began to recover. However, we can observe an upward trend, which could be due to the
increased activity from the supply side as demand remained relatively stable.
[Figure 1 is about here]
Panel A of Figure 1 shows the time-series of average shorting demand, measured by the short
interest ratio (SIR) and the number of stocks in the sample (on the right axis). Overall, the
number of unique is 3776 is the sample stocks, on average about 3000 stocks daily. The average
3% AggSIR (reported 2.98% in Table 1) over our sample period indicates that the shorting
demand did not vary significantly over time, where AggSIR is calculated as the total number of
shares out on loan on the last trading day relative to the total shares outstanding. However, in
10
panel B, we show that the aggregate lending income significantly varied, suggesting that lending
fees varied and potentially the supply side become active since the demand side (SIR) is
relatively stable.
2.2. Variable Construction
Following Boehmer et al. (2008), we use control variables associated with long-term and
short-term return predictability. Our long-term controls are size (LogSize), book-to-market ratio
(BtoM), turnover (DailyTurn-1m), and past-month return volatility (RetStd-1m). We also use
alternative liquidity and volatility controls such as the average high-low price spread (HLspread1m),
and bid-ask spread (BAspread-1m) during the previous month. The high-low price spread is
the difference between the daily highest and lowest prices divided by the highest price of the
day, while the daily bid-ask spread is the difference between the ask price and the bid price
relative to the average bid-ask price. In robustness analysis, we also control for extreme stock
performance, using dummy variables for winners (WinnerD) and losers (LoserD), defined as
stocks from the top and the bottom ten percentile of the return distribution in the last week.
Unlike recent short sale studies that use Reg. SHO data trade level information, our measures
of shorting demand are obtained from securities lending market data. Consistent with other
studies that use securities lending data (e.g., Kolasinski et al., 2012; Berkman and McKenzie,
2012), we approximate new shorting demand with new borrowings (i.e., recently lent out shares)
and the outstanding short interest as the total number of shares out on loan at a specific snapshot
in time (generally the day before we start measuring the new shorting demand). Although stocks
are borrowed for a number of reasons, such as voting, dividend arbitrage, funding trade, and
collateral swaps, the primary reason for stock borrowings is still known to be short selling in the
U.S. (Clearstream, 2014).
11
Our primary shorting demand measure the HighSIR dummy variable takes on the value one
for stocks with AggSIR from the top decile of the distribution during the previous day. We use a
dummy variable for high shorting demand instead of the continuous SIR measure because earlier
studies (e.g., Desai et al., 2002; Asquith et al., 2005) show that the information content in short
selling is concentrated in the extremes. We also use two new shorting demand measures, where
HighSIRnew and HighSIVnew take on the value of one for stocks with high new shorting demand
from the top decile relative to total shares outstanding, and relative to daily trading volume
respectively.
Our main securities lending market measure is the value weighted average securities lending
fee. In general, we refer to lending fee (income to lender) as the generic loan rate or the premium
on non-cash loans, or the rebate rate on cash-collateralized loan. This rate is inferred from lender
– broker transaction, so called wholesale market not from short seller (borrower) broker
transaction, retail market (SunGard, 2009). This measure reflects both the supply and the demand
side and we try to disentangle whether lending fees are raised because of demand or because of
supply side anticipation. In our analysis, we use the natural logarithm of the annualized fees to
address the extreme skewness of the measure (Gagnon, 2012), where fees are reported in
percentages in our data.
2.3. Summary Statistics
The summary statistics in Table 1 are calculated as the time series averages of the daily summary
statistics. Panel A is based on all daily stock observations from July 5, 2006 to Aug 13, 2010,
while Panel B splits the results into before and after crisis periods. During the entire sample
period, the mean five-day cumulative returns are zero as expected. The AggSIR on average is
about 2.98% (median 1.86%) comparable with that reported by Boehmer et al. (2010).
12
[Table 1 about here]
The approximately 0.15% average SIRnew-1 indicates that about the 0.15% of the total shares are
borrowed each day, anew. We note that the average SIVnew-1 is 0.29, suggesting that daily new
borrowings are equivalent to about 29% of the daily turnover. This value is consistent with
Diether et al (2009) who report that 24% of NYSE trading volume is associated with short
selling. The average aggregate institutional ownership is 58.4%, while the active institutional
ownership is about 23.5%, suggesting that overall in our sample short sale constraints should not
be binding if institutional ownership relaxes short sale constraints (Asquith, Pathak, Ritter,
2005).
[Table 2 about here]
In Panel A and B of Table 2, we review our sample before and after the Lehman Brothers
collapse. We report and over decrease in shorting demand (i.e., the average SIR declined from
3.42% to 2.66%) while the average lending fees increased from 0.85% to 0.99%. While we are
not using loan supply in the analysis as that information is not available for the full sample
period, in subsample, we review that lending supply increased over time as reported by Markit
Securities and Sungard Astec Analytics. Taking these changes in the lending market together,
lenders are more likely to be actively involved in stock lending because they are able to charge
higher fees despite lower demand and greater supply. In Table 2, we also observe an increase in
daily stock return volatility and decrease in liquidity after the Lehman Brother’s collapse. These
results are indicative of the changing world, characterized by increased risk aversion and
uncertainty where lenders (e.g., institutional investors and wealthy individuals) are likely looking
for ―safe‖ returns from fully collateralized securities lending.
13
3. Empirical Analysis
Our empirical analysis is structured in two steps. In the first part, we confirm that our securities
loan demand measure provides consistent results for cross-sectional stock returns as the realized
shorting demand shows that lending fees on average respond to shorting demand. The second
part focuses on understanding the return implications of loan fees in relation with institutional
ownership. Specifically, we examine lending fee dynamics around corporate events that are
known in advance, specifically quarterly earnings announcements and dividend declaration
dates.8
3.1. Cross-sectional Returns and Shorting Demand and Lending Fees
In our regression framework, we follow Diether et al’s (2009) approach and adopt FamaMacBeth (1973) regressions for our primary analysis. In addition to the short term return
predictors used by Diether et al (2009), we also control for longer term return and stock return
volatility as in Boehmer et al (2008). Table 3 displays the cross-sectional return results from
Fama-MacBeth regressions with Newey-West (1987) standard errors. Unlike prior short-sale
studies (Boehmer et al. 2008 and Diether et al. 2009) which show the significant negative
relation between future return and high shorting, we also consider lending fees in explaining
cross-sectional returns.
[Table 3 about here]
8
Recent studies find that short selling is concentrated around earnings announcements (Boehmer et al., 2012),
suggesting that lenders can anticipate and price in greater expected shorting demands before announcements.
14
In Model 1 of Table 3, the negative coefficient on the HighSIR variable provide results
consistent with prior studies that high aggregate shorting is associated with about 69 bps lower
over a 20-day horizon future returns where aggregate shorting is proxied by the total number of
shares out on loan relative to the total shares outstanding. We also report positive relationship
between future returns and institutional ownership. Specifically a company with 10% higher
level of institutional ownership is expected to earn 36.1 bp higher returns in the next 20 trading
days (based on the coefficient estimate of 3.614 in model 3).
More importantly, we report significant negative coefficient on the Logfees measure across
Models 2-5. Specifically, a 50% higher lending fees imply about 12 bp lower returns, while a
100% returns suggest 21 bp lower returns.9 Since, lending fees are highly skewed, it is feasible
to have stocks with 0.1% and 10% annualized lending fees, suggesting that the economic effect
of fees is non-negligible. This negative coefficient on our lending fee measure suggests that
higher fees are associated with lower future return. These fee results are robust across alternative
specifications, even after controlling for high new shorting relative to total shares and relative to
trading volume with the HighSIRnew and HighSIVnew variables in Models 4 and 5.
With regards to the control variables, we also note that larger stocks and high volatility
stocks underperform on average, consistent with the well document volatility anomaly (Amihud,
2002; Ang, Xing and Zhang, 2006). In addition, we also find that in the cross-section, stocks
with more institutional ownership tend to better perform.
9 In the liner-log model of returns and lending fees, the coefficient estimate of -0.297 is the expected change in the
next 20-day returns when lending fees increase by 172% (exponential -1). Thus, we calculate the impact of a 50%
higher lending fees as -0.297(log(1+%change in fees))= - 0.297(log1.5)= 0.12. Similarly a 100% higher lending fee
effect is -0.297(log(1+1))=0.206.
15
3.2. Lending Fee Dynamics and Institutional Ownership
Our results show that lending fees do provide additional insights about future stock returns
beyond shorting demand. To shed light on understanding lending fees and why potentially they
may have information content, we need to consider the changing role of the lender. Traditionally,
stock lending is a passive activity where pension fund and institutions auction off their holdings
and are unlikely to influence lending fees in a systematic way. This is mainly because
information on borrowing demand and recent pricing information was not readily available in the
nontransparent OTC lending market. However, in recent years SunGard has made significant
improvement in providing daily and intraday information on the thousands of equity and debt
securities, enable borrows to faster locate securities while enable lenders to observe demand and
charge the most competitive price on the securities.
[Table 4 about here]
In Models 1 and 2 of Table 4, we show that lending fees respond to high new shorting
demand and fees are generally higher for stocks with active institutional ownership. On average,
the coefficient estimate of 1.325 implies that a stock with a unit increase in active IO (a stock
with 100% active IO versus a stock no active IO) have 276% higher fees (100(e1.325-1)≈276).
However active institutional ownership is not associated with higher fees in conjunction with
higher new shorting demand. This suggests that institutions in general still do not know exactly
the same information as short sellers, and may not be able to anticipate the shorting demand on
average. Therefore, in Tables 5 and 6, we turn our attention to two corporate events, namely
earnings announcements and dividend declaration where the dates are known in advance and
lenders can expect greater shorting or borrowing demand in advance.
16
Dividend arbitrage is a well-known strategy where institutions in the higher tax brackets lend
out their shares to other institutions in the lower tax brackets, thereby benefitting from lending
income and the reconstructed dividends. While some countries, such as Taiwan, tax the
replacement income that the borrower provides for the missed dividend to the lender, in most
countries, dividend arbitrage is still profitable and practiced. Prior studies (e.g., Boehmer and
Wu, 2012) also show that short selling significantly increase before earnings news, suggesting
that lender seek to raise fees in expectation of greater demand. To test these hypotheses, we
examine whether higher lending fee is associated with active institutional ownership around
earnings new dates and dividend declaration dates in Tables 5 and 6.
[Table 5 about here]
Table 5 Model 1shows that lending fees are somewhat lower before and after earnings news
on average, but the positive significant coefficient on the active institutional ownership (IOact)
variable still suggests that institutions may play an important role in determining lending fees.
Indeed, the positive significant coefficient on our interaction variables namely active institution
ownership and the earnings window (BefAN*IOact and AftAN*IOact) for Models 2 through 4,
imply a 16% higher lending fees in the 10 days before earnings announcements. These results are
robust, even after controlling for new shorting demand with HighSIRnew-1 and HighSIVnew-1 in
Models 3 and 4 respectively.
[Table 6 about here]
Table 6 Model 1 shows that lending fees are somewhat lower before dividend declaration on
average. More importantly, the positive coefficient on the interaction variable namely active
institutional ownership and the dummy variable for the 10-day window prior to dividend
17
declaration, BefDivD*IOact, is significantly positive. The coefficient estimate of 26.5 implies
that the lending fees before dividend declaration are 30% (i.e., 100(e0.265-1)≈30) higher for a
stock that has 100% active institutional ownership versus a stock that is held only by passive
institutional investors and/or individuals. Alternatively, we can say that stocks with 50% higher
active institutional ownership have on average 16% (0.5x0.262) higher lending fees before
dividend declaration dates.
3.3. Subsample Results: Before and After Lehman Brothers collapse
Last, we examine lending fee dynamics around earnings announcements before and after the
financial crisis separately. After the financial crisis, naked short selling is outright prohibited in
an effort to eliminate aggressive short selling and to support the market in the midst of negative
sentiment. Short sale restrictions may have reduced shorting demand, as noted in the summary
statistics shown on Table 2 Panel B, but the loan supply has been building up as institutions are
keen to generate income from their investment holdings whenever possible. Institutions assume
relatively low risk in lending because the assets can be recalled and are fully collateralized
(generally about 150%).
[Table 7 about here]
In Panel A of Table 7, we revisit the return predictability relation of the lending fee by
dividing the data into subsamples, before and after the Lehman Brothers collapse. Similar to the
results from Tables 3-6, we find that stocks with high aggregate IO experience higher future
returns. We also show that smaller stocks are associated with significantly higher returns in the
latter part of the sample, which is consistent with the recent US market recovery.
18
More
importantly, we report a greater coefficient on our lending fees variable (Logallfees) for the latter
part of our sample period. Overall our results imply that lending fees become an important
determinant of cross-sectional returns in recent years.
4. Robustness Analyses
To confirm the robustness of our results, we carried out numerous additional analyses. We
consider various alternative model specifications for our main analyses. Specifically in our return
regressions, we consider including short term return controls, either the lagged 5 days cumulative
return in continuous form or dummy variables for winner and loser stocks. We also include the
winner and loser dummies in robustness analysis for fee dynamics because market sentiment
may play an important role since shorting stocks with high exuberance can be very costly
(Lamont and Stein, 2004). In addition, we consider excluding penny stocks to confirm that our
results are not driven by small stocks, but we dismiss the idea because our sample has very few
small stocks since we examine stocks for which there is extensive coverage in numerous data
sources (e.g., Factset, CRSP, Compustat, I/B/E/S). Lastly, we consider excluding stocks with
extreme high lending fees to check that our results are not driven by a handful of outliers.
Overall, the results with alternative model specifications and alternative shorting proxies are
consistent with the results in Tables 3 through 7.
5. Conclusion
Numerous studies show that short sale constraints (e.g., stock borrowing costs) have important
asset pricing implications. The more recent literature examining the sources of short sale costs
finds that shorting demand plays an important role while lending supply is relatively inelastic
19
and lenders are passive. In this study, we challenge that view and investigate whether lenders are
price takers. Specifically, we consider recent developments in the securities lending market
where improved timeliness of information and greater automation enable lenders to take on more
active roles. Although lenders may be unable to predict shorting demand consistently, they can
anticipate greater shorting around large public information events, such as earnings
announcements.
In our empirical analysis, we first show that lending fees (i.e., lenders income from stock
borrowing) are informative about future returns beyond existing and new high shorting demand.
More importantly, when we examine lending fees dynamics, we find that lending fees not only
react to new information, such as past returns and new borrowing demand but are also higher in
anticipation of certain corporate events, such as dividend declaration and earnings
announcements. As not all institutions are in the position (e.g., have lending desks set up and/or
legally permitted) to actively participate in securities lending, we define active IO as ownership
by investment companies and their managers and investment advisors. In examining lending fees
before dividend declaration dates and earnings announcement dates, we find that lending fees are
significantly higher for stocks with significant active IO, suggesting evidence of proactive
behavior by active institutions in the stock lending market. Specifically, before earnings
announcements, stocks with 50% active IO (compared to stocks with passive IO) have 8% higher
lending fees. Similarly, in the 10-day window before dividend declaration dates, stocks with 50%
active IO command 15% higher lending fees.
Overall, we provide new insights about the functioning of equity lending markets from the
perspective of lenders. We show that lenders (and/or their agents) actively respond to new
market information as well as changes in shorting demand, likely supported by better access to
20
information in recent years. In the aftermath of the financial crisis, sensitivity of equity fees to
information increases as lenders aim to maximize income from their investment holdings. An
implication of our findings is that higher lending fees and greater income from securities lending,
while beneficial for lenders, can potentially be an impediment to market efficiency. This is
especially relevant if available stock supply restricted while fees are raised in the lending market
before news events just when short sellers are particularly active in supporting price discovery.
21
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25
Table 1 .
Summary statistics of NYSE-Amex and NASDAQ stocks from April 3rd of 2007 to July 20th of 2012
The table below shows the summary statistics of key variables, based on the time series averages. AggSIR-1 (in %) and SIRnew-1
are the total number of shares out on loan as of the last trading day and the number of shares newly borrowed on the last trading
day relative to shares outstanding, respectively. SIV-1 and SIVnew-1 are the total number of shares out on loan as of the last trading
day and the number of shares newly borrowed on the last trading day relative to daily trading volume, respectively. Allfee (in %)
is the value weighted average loan fee on all loan currently outstanding as of yesterday, where the weights are the number of
shares in the lending contract, irrespective of the loan age or loan term (whether overnight or term loan). RelSupply (in %) is the
number of shares available for borrowing relative to the total number of shares outstanding in percentage, while Utilisation (in
%) is the number of shares out on loan relative to the number of shares available for borrowing in percentage. IOall (IOact)
aggregate institutional ownership (number of shares owned by asset management and advisory firms) relative to total shares from
Factset. LogSize is the natural logarithm of the number of common shares in millions times the month-end price. BtoM is the
ratio of book value of equity to market value of equity following Fama and French’s (1993) definition. BAspread is the average
bid-ask spread for last month, where the bid-ask spread is the difference of the daily ask price and bid price relative to the
average of the daily bid and ask prices. HLspread-1m is the average daily pricespread during last month, where the pricespread is
the difference between the highest and lowest price of the day relative to the highest price of the day. DailyTurn-1m is the average
daily turnover in percentage during the past month. Retstd-1m is the standard deviation of daily return during the past month.
CumRet1,5 and CumRet1,20 are the future 5-day and 20-day holding period returns in percentage. The summary statistics are
calculated based on pooled observations, for the entire sample period from Jan 1st of 2007 to Dec 31st of 2010.
Variables
AggSIR-1(in %)
SIRnew-1 (in %)
SIV-1
SIVnew-1
Allfee (in %)
IO (in %)
IOact (in %)
LogSize
BtoM
BAspread-1m (in %)
HLspread-1m (in %)
DailyTurn-1m (in %)
RetStd-1m (in %)
CumRet1,5 (in %)
CumRet1,20 (in %)
Obs
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
2,347,779
Mean
2.978
0.155
7.466
0.293
0.946
0.584
0.235
6.212
1.020
0.683
4.727
0.940
3.458
0.209
0.698
26
25th percentile
0.613
0.000
1.207
0.000
0.066
0.367
0.136
4.941
0.317
0.110
2.799
0.324
1.877
-3.650
-7.550
Median
1.865
0.043
3.152
0.067
0.120
0.635
0.233
6.015
0.578
0.217
3.983
0.654
2.752
0.000
0.236
75th percentile
4.035
0.157
6.858
0.216
0.293
0.821
0.326
7.389
0.984
0.574
5.751
1.156
4.125
3.604
7.711
Table 2.
Subsample summary statistics of NYSE-Amex and NASDAQ stocks from April 3rd of 2007 to July 20th of
2012
The table below shows the summary statistics of key variables, based on the time series averages. Panel A shows summary
statistics for all stock observations based on the sample period before the Lehman Brother’s collapse, from Jan. 1, 2007 until
Aug. 31, 2008. Panel B shows summary statistics for all stocks based on the sample period after the Lehman Brother’s collapse,
from Oct. 1, 2008 until Dec. 31, 2010. The variable definitions are the same as in Table 1.
Panel A. Summary statistics of stocks characteristics before the Lehman Brother Collapse, before Sept 2008.
AggSIR-1(in %)
SIRnew-1 (in %)
SIV-1
SIVnew-1
Allfee (in %)
IO (in %)
IOact (in %)
LogSize
BtoM
BAspread-1m (in %)
HLspread-1m (in %)
DailyTurn-1m (in %)
RetStd-1m (in %)
CumRet1,5 (in %)
CumRet1,20(in %)
Obs
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
931,589
Mean
3.417
0.171
9.237
0.332
0.850
0.618
0.255
6.585
0.656
0.424
3.926
0.939
2.789
-0.174
25th percentile
0.732
0.000
1.359
0.000
0.104
0.422
0.159
5.311
0.260
0.117
2.485
0.373
1.660
-3.461
Median
2.342
0.040
3.595
0.056
0.137
0.673
0.253
6.285
0.461
0.196
3.515
0.693
2.395
-0.199
75th percentile
4.696
0.161
7.604
0.201
0.346
0.845
0.347
7.743
0.746
0.405
4.878
1.170
3.415
2.920
931,589
-0.942
-7.944
-0.891
5.654
Panel B. Summary statistics of stocks characteristics after the Lehman Brother Collapse, after Oct. 2008
AggSIR-1(in %)
SIRnew-1 (in %)
SIV-1
SIVnew-1
Allfee (in %)
IO (in %)
IOact (in %)
LogSize
BtoM
BAspread-1m (in %)
HLspread-1m (in %)
DailyTurn-1m (in %)
RetStd-1m (in %)
CumRet1,5(in %)
CumRet1,20(in %)
Obs
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
1,363,695
Mean
2.660
0.143
6.204
0.263
0.999
0.560
0.221
5.956
1.268
0.864
5.268
0.942
3.915
0.643
25th percentile
0.551
0.003
1.118
0.006
0.051
0.332
0.119
4.664
0.374
0.103
3.033
0.295
2.060
-3.593
Median
1.598
0.044
2.873
0.075
0.090
0.606
0.218
5.801
0.687
0.241
4.341
0.625
3.045
0.273
75th percentile
3.511
0.152
6.324
0.225
0.252
0.804
0.311
7.147
1.169
0.789
6.484
1.145
4.713
4.182
1,363,695
2.762
-6.250
1.550
9.524
27
Table 3.
Regression analysis of return cross sections in relation to new shorting demand
The dependent variable is the future 20-day cumulative holding period returns with a one day skipping from time
t+1 to t+20 in percentages. HighSIR-1 is a dummy variable that takes the value of one for stocks that are in the top
decile of the distribution of the SIR one day ago, where SIR is the total number shares out on loan as percentage of
the total shares. Logfees is the natural logarithm of the annualized lending fees in percentage. IOall (IOact)
aggregate institutional ownership (ownership by active institutions) as ratio to total shares from Factset.
HighSIRnew-1 and HighSIVnew-1 are dummy variables that take on the value for stocks from the top decile of the
distribution for the number newly borrowed (shorted) shares in the previous relative to the total share outstanding or
relative to the daily trading volume. LogSize is the natural logarithm of the number of common shares in millions
times the month-end price. BtoM is the ratio of book value of equity to market value of equity following Fama and
French’s (1993) definition. DailyTurn-1m and Retstd-1m are the average daily turnover and the daily standard
deviation of returns in percentage during the past month. BAspread-1m is the average bid-ask spread during the past
month, where the bid-ask spread is the difference of the daily ask price and bid price relative to the average of the
daily bid and ask prices. HLspread-1m is the average daily pricespread during the past month, where pricespread is
the difference between the highest and lowest price of the day relative to the highest price of the day. The sample
includes 1005 trading days from Jan. 2007 to Dec. 2010, with 3,776 unique stocks. The coefficient estimates from
Fama-MacBeth analyses are shown with Newey West (1987) t-stats (with 21 lags) in parentheses.
HighSIR-1
CumRet1,20
(1)
-0.692**
(-2.57)
Logfees-1
CumRet1,20
(2)
-0.397
(-1.43)
-0.297***
(-4.92)
IOall
IOact
CumRet1,20
(3)
-0.756***
(-2.65)
-0.117**
(-2.15)
3.614***
(3.76)
-1.772*
(-1.93)
CumRet1,20
(4)
-0.884***
(-3.19)
3.866***
(4.23)
-1.707*
(-1.82)
HighSIRnew-1
CumRet1,20
(5)
-0.691**
(-2.49)
-0.112**
(-2.06)
3.642***
(3.79)
-1.779*
(-1.94)
-0.269***
(-2.67)
HighSIVnew-1
LogSize
BtoM
BAspread-1m
HLspread-1m
DailyTurn-1m
Retstd-1m
Intercept
AdjRsq
Obs
-0.218
(-1.47)
0.030
(0.21)
0.034
(0.15)
0.199
(1.37)
0.263*
(1.69)
-0.204***
(-3.43)
0.982
(0.91)
0.055
2,347,779
-0.310**
(-2.10)
0.013
(0.09)
0.091
(0.40)
0.259*
(1.76)
0.274*
(1.77)
-0.211***
(-3.57)
0.655
(0.60)
0.057
2,347,779
-0.416**
(-2.42)
-0.004
(-0.03)
0.307
(1.25)
0.229
(1.57)
0.160
(1.14)
-0.183***
(-3.17)
0.136
(0.13)
0.062
2,347,779
28
-0.391**
(-2.23)
-0.000
(-0.00)
0.310
(1.27)
0.208
(1.42)
0.149
(1.05)
-0.180***
(-3.13)
0.144
(0.13)
0.061
2,347,779
-0.413**
(-2.40)
-0.003
(-0.02)
0.304
(1.24)
0.229
(1.57)
0.174
(1.24)
-0.184***
(-3.19)
0.132
(0.12)
0.063
2,347,779
CumRet1,20
(6)
-0.687**
(-2.49)
-0.106*
(-1.95)
3.636***
(3.76)
-1.769*
(-1.92)
-0.458***
(-3.60)
-0.411**
(-2.41)
-0.003
(-0.02)
0.298
(1.22)
0.226
(1.55)
0.145
(1.05)
-0.181***
(-3.14)
0.178
(0.17)
0.063
2,347,779
Table 4.
Regression analysis of lending fees in relation to IO in general
The dependent variable is the natural logarithm of the next day annualized average lending fee in percentage. IOall
(IOact) aggregate institutional ownership (number of shares owned by asset management and advisory firms)
relative to total shares from Factset. HighSIR-1 is a dummy variable that takes the value of one for stocks that are in
the top decile of the distribution of the SIR one day ago, where SIR is the total number shares out on loan as
percentage of the total shares. HighSIRnew-1 (HighSIVnew-1) dummy variable takes on the value one for stocks from
the top decile of the distribution with newly borrowed (shorted) shares relative to the total share outstanding
(relative to the daily trading volume). HighSIRnew*IOall (HighSIRnew*IOact) is interaction variable of IOall
(IOact) with the HighSIRnew-1 variable. HighSIVnew*IOall (HighSIVnew*IOact) is interaction variable IOall
(IOact) with the HighSIVnew-1 variable. Control variables, such as LogSize, BtoM, DailyTurn-1m, Retstd-1m,
BAspread-1m, and HLspread-1m are the same as defined in Table 3. The sample includes 1005 trading days from Jan.
2007 to Dec. 2010, with 3,776 unique stocks. The coefficient estimates with the corresponding t-stats in parentheses
are from panel regression with firm and day fixed effect, with firm level clustering of the standard errors.
IOall
IOact
HighSIR-1
Logallfee
(1)
-0.711***
(-5.03)
1.325***
(7.64)
0.632***
(18.42)
HighSIRnew-1
Logallfee
(2)
-0.735***
(-5.21)
1.325***
(7.65)
0.602***
(17.83)
0.139***
(15.68)
HighSIRnew*IOall
HighSIRnew*IOact
Logallfee
(3)
-0.668***
(-4.72)
1.287***
(7.49)
0.603***
(17.89)
0.422***
(9.19)
-0.530***
(-6.90)
0.334**
(2.44)
HighSIVnew-1
Logallfee
(4)
-0.722***
(-5.13)
1.321***
(7.64)
0.614***
(18.08)
Logallfee
(5)
-0.705***
(-5.02)
1.314***
(7.66)
0.616***
(18.09)
0.162***
(20.93)
0.236***
(9.34)
-0.162***
(-3.18)
0.095
(0.95)
0.097***
(3.34)
0.037***
(7.19)
-0.028***
(-3.72)
0.026***
(5.51)
0.086***
(10.30)
-0.017***
(-5.40)
-2.541***
(-13.66)
0.040
2,347,779
HighSIVnew*IOall
HighSIVnew*IOact
LogSize
BtoM
BAspread-1m
HLspread-1m
DailyTurn-1m
Retstd-1m
Constant
AdjRsq
Obs
0.096***
(3.33)
0.037***
(7.20)
-0.029***
(-3.80)
0.026***
(5.49)
0.085***
(10.17)
-0.017***
(-5.38)
-2.523***
(-13.54)
0.037
2,347,779
0.096***
(3.33)
0.037***
(7.18)
-0.029***
(-3.80)
0.025***
(5.47)
0.081***
(9.88)
-0.017***
(-5.38)
-2.516***
(-13.51)
0.039
2,347,779
29
0.095***
(3.28)
0.037***
(7.15)
-0.028***
(-3.68)
0.025***
(5.47)
0.080***
(9.82)
-0.017***
(-5.40)
-2.535***
(-13.65)
0.040
2,347,779
0.096***
(3.33)
0.037***
(7.19)
-0.028***
(-3.75)
0.026***
(5.51)
0.086***
(10.30)
-0.017***
(-5.41)
-2.532***
(-13.61)
0.040
2,347,779
Table 5.
Regression analysis of lending fees in relation to IO around earnings events
The dependent variable is the natural logarithm of the next day annualized average lending fee in percentage. IOall
(IOact) aggregate institutional ownership (number of shares owned by asset management and advisory firms)
relative to total shares from Factset. BefAN (AftAN) dummy variable takes on the value 1 for the 10-day window
before (after) the earnings announcement. BefAN*IOall and BefAN*IOact are interaction variables with the before
event window (10-days before announcement) and the aggregate and active institutional ownership. AftAN*IOall
and AftAN*IOact are interaction variables with the before after window (10-days after announcement)and the
aggregate and active institutional ownership. HighSIR-1 is a dummy variable that takes the value of one for stocks
that are in the top decile of the distribution of the SIR one day ago, where SIR is the total number shares out on loan
as percentage of the total shares. HighSIRnew-1 (HighSIVnew-1) dummy variable takes on the value one for stocks
from the top decile of the distribution with newly borrowed (shorted) shares relative to the total share outstanding
(relative to the daily trading volume). Control variables, such as LogSize, BtoM, DailyTurn-1m, Retstd-1m, BAspread1m, and HLspread-1m are the same as defined in Table 3. To save space the coefficient estimates on the latter control
variables are not shown in this table but available in the Appendix. The sample includes 1005 trading days from Jan.
2007 to Dec. 2010, with 3,776 unique stocks. The coefficient estimates with the corresponding t-stats in parentheses
are from panel regression with firm and day fixed effect, with firm level clustering of the standard errors.
30
IOall
IOact
BefAN
AftAN
Logallfee
(1)
-0.710***
(-5.03)
1.325***
(7.64)
0.000
(0.04)
-0.015***
(-4.12)
BefAN*IOall
BefAN*IOact
AftAN*IOall
AftAN*IOact
HighSIR-1
0.632***
(18.42)
Logallfee
(2)
-0.715***
(-5.05)
1.301***
(7.45)
-0.037***
(-2.91)
-0.042***
(-3.31)
-0.001
(-0.02)
0.152***
(3.68)
0.041*
(1.78)
0.006
(0.16)
0.632***
(18.42)
HighSIRnew-1
Logallfee
(3)
-0.739***
(-5.23)
1.301***
(7.46)
-0.037***
(-2.89)
-0.041***
(-3.27)
0.000
(0.02)
0.152***
(3.68)
0.035
(1.56)
0.009
(0.21)
0.602***
(17.83)
0.140***
(15.71)
HighSIVnew-1
LogSize
BtoM
BAspread-1m
HLspread-1m
DailyTurn-1m
Retstd-1m
Constant
Observations
R-squared
Number of stocks
0.096***
(3.33)
0.037***
(7.20)
-0.029***
(-3.81)
0.026***
(5.49)
0.084***
(10.16)
-0.017***
(-5.38)
-2.521***
(-13.52)
2,347,779
0.037
3,776
0.096***
(3.33)
0.037***
(7.20)
-0.029***
(-3.84)
0.026***
(5.49)
0.084***
(10.16)
-0.017***
(-5.38)
-2.513***
(-13.46)
2,347,779
0.037
3,776
31
0.096***
(3.33)
0.037***
(7.19)
-0.029***
(-3.84)
0.025***
(5.47)
0.081***
(9.88)
-0.017***
(-5.38)
-2.506***
(-13.43)
2,347,779
0.039
3,776
Logallfee
(4)
-0.727***
(-5.15)
1.297***
(7.45)
-0.038***
(-2.97)
-0.041***
(-3.30)
0.000
(0.00)
0.154***
(3.73)
0.042*
(1.84)
0.008
(0.19)
0.614***
(18.08)
0.162***
(20.92)
0.097***
(3.34)
0.037***
(7.19)
-0.029***
(-3.79)
0.026***
(5.51)
0.086***
(10.30)
-0.017***
(-5.41)
-2.522***
(-13.53)
2,347,779
0.040
3,776
Table 6.
Regression analysis of lending fees in relation to IO around dividend declaration dates
The dependent variable is the natural logarithm of the next day annualized average lending fee in percentage.
HighSIR-1 is a dummy variable that takes the value of one for stocks that are in the top decile of the distribution of
the SIR one day ago, where SIR is the total number shares out on loan as percentage of the total shares. IOall
(IOact) aggregate institutional ownership (number of shares owned by asset management and advisory firms)
relative to total shares from Factset. BefDivD dummy variable takes on the value of one for the 10 trading days prior
to dividend declaration date. BefDIvD*IOall (BefDivD*IOact) is an interaction variable of the BefDivD and IOall
(IOact) variables. HighSIRnew-1 (HighSIVnew-1) dummy variable takes on the value one for stocks from the top
decile of the distribution with newly borrowed (shorted) shares relative to the total share outstanding (relative to the
daily trading volume). Control variables, such as LogSize, BtoM, DailyTurn-1m, Retstd-1m, BAspread-1m, and
HLspread-1m are the same as defined in Table 3. To save space the coefficient estimates on the latter control
variables are not shown in this table but available in the Appendix. The sample includes 1005 trading days from Jan.
2007 to Dec. 2010, with 3,776 unique stocks. The coefficient estimates with the corresponding t-stats in parentheses
are from panel regression with firm and day fixed effect, with firm level clustering of the standard errors.
IOall
IOact
BefDivD
Logallfee
(1)
-0.710***
(-5.03)
1.326***
(7.64)
-0.054***
(-8.64)
BefDivD*IOall
BefDivD*IOact
HighSIR-1
0.632***
(18.42)
Logallfee
(2)
-0.714***
(-5.06)
1.313***
(7.56)
-0.152***
(-6.10)
0.055
(1.20)
0.265***
(2.83)
0.632***
(18.42)
HighSIRnew
Logallfee
(3)
-0.738***
(-5.24)
1.313***
(7.57)
-0.151***
(-6.09)
0.059
(1.29)
0.261***
(2.79)
0.602***
(17.84)
0.139***
(15.63)
HighSIVnew
LogSize
BtoM
BAspread-1m
HLspread-1m
DailyTurn-1m
RetStd-1m
Constant
Observations
R-squared
Number of stocks
0.097***
(3.35)
0.037***
(7.20)
-0.029***
(-3.79)
0.026***
(5.48)
0.084***
(10.16)
-0.017***
(-5.38)
-2.524***
(-13.56)
2,347,779
0.037
3,776
0.098***
(3.39)
0.037***
(7.20)
-0.029***
(-3.80)
0.026***
(5.49)
0.084***
(10.16)
-0.017***
(-5.38)
-2.524***
(-13.57)
2,347,779
0.038
3,776
32
0.098***
(3.39)
0.037***
(7.18)
-0.029***
(-3.80)
0.025***
(5.48)
0.081***
(9.88)
-0.017***
(-5.38)
-2.517***
(-13.53)
2,347,779
0.039
3,776
Logallfee
(4)
-0.725***
(-5.15)
1.309***
(7.56)
-0.150***
(-6.04)
0.055
(1.21)
0.266***
(2.85)
0.614***
(18.08)
0.162***
(20.88)
0.098***
(3.39)
0.037***
(7.19)
-0.028***
(-3.76)
0.026***
(5.52)
0.086***
(10.30)
-0.017***
(-5.41)
-2.533***
(-13.63)
2,347,779
0.040
3,776
Table 7.
Subsample results: Before and after Lehman collapse
The dependent variable is the future 20-day cumulative holding period return in percentage (in Panel A) and the
natural logarithm of the next day annualized average lending fee in percentage (in Panel B). HighSIR-1 is a dummy
variable that takes the value of one for stocks that are in the top decile of the distribution of the SIR one day ago,
where SIR is the total number shares out on loan as percentage of the total shares. Logfees is the natural logarithm of
the annualized lending fees in percentage. IOall (IOact) aggregate institutional ownership (number of shares owned
by asset management and advisory firms) relative to total shares from Factset. BefAN (AftAN) dummy variable takes
on the value of one for the 10 trading days prior (after) earnings announcement. BefAN*IOall (AftAN*IOall)
interaction variable of the BefAN (AftAN) and aggregate IO (IOall) variables. BefAN*IOact (AftAN*IOact)
interaction variable of the BefAN (AftAN) and active IO (IOactive) variables. HighSIRnew-1 and HighSIVnew-1
dummy variables take on the value one for stocks from the top decile of the distribution for the number newly
borrowed (shorted) shares in the previous relative to the total share outstanding or relative to the daily trading
volume. Control variables, such as LogSize, BtoM, DailyTurn-1m, Retstd-1m, BAspread-1m, and HLspread-1m are the
same as defined in Table 3. The sample includes 567 (417) trading days before (after) Sept. 2008. The month of
Sept. 2008 is excluded from the analysis. The coefficient estimates with the corresponding t-stats in parentheses are
from panel regression with firm and day fixed effect, with firm level clustering of the standard errors.
Panel A. Subsample results for return regression
Variables
HighSIR
Logallfee
IOall
IOact
HighSIVnew-1
LogSize
BtoM
BAspread-1m
HLspread-1m
DailyTurn-1m
RetStd-1m
Intercept
AdjRsq
Obs
Before Lehman (Jan. 2007 – Aug. 2008)
CumRet1,20
CumRet1,20
CumRet1,20
(1)
(2)
(3)
-0.356
-0.612
-0.584
(-0.94)
(-1.53)
(-1.49)
-0.194***
-0.041
-0.039
(-3.13)
(-0.73)
(-0.69)
2.232***
2.237***
(2.73)
(2.72)
-0.005
-0.005
(-0.01)
(-0.01)
-0.181
(-1.18)
0.113
0.082
0.083
(1.47)
(0.94)
(0.97)
-0.287
-0.322
-0.319
(-1.08)
(-1.20)
(-1.19)
0.294
0.678
0.664
(0.67)
(1.37)
(1.35)
-0.007
-0.050
-0.053
(-0.06)
(-0.38)
(-0.40)
0.033
-0.084
-0.089
(0.19)
(-0.51)
(-0.54)
-0.120
-0.077
-0.074
(-1.10)
(-0.71)
(-0.69)
-1.542*
-2.410***
-2.393***
(-1.77)
(-2.75)
(-2.76)
0.054
0.056
0.056
722,834
722,834
722,834
33
After Lehman (Oct. 2008-Dec. 2010)
CumRet1,20
CumRet1,20
CumRet1,20
(4)
(5)
(6)
-0.450
-0.954**
-0.847**
(-1.18)
(-2.51)
(-2.30)
-0.426***
-0.194**
-0.176**
(-6.04)
(-2.40)
(-2.14)
5.306***
5.342***
(3.93)
(3.94)
-3.482***
-3.475***
(-2.60)
(-2.60)
-0.737***
(-4.61)
-0.640***
-0.818***
-0.811***
(-2.74)
(-3.02)
(-3.02)
0.218
0.213
0.213
(1.58)
(1.56)
(1.56)
-0.123
-0.004
-0.011
(-0.55)
(-0.02)
(-0.05)
0.534**
0.507**
0.504**
(2.46)
(2.35)
(2.34)
0.474**
0.338*
0.313
(2.08)
(1.65)
(1.55)
-0.271***
-0.246***
-0.245***
(-4.03)
(-3.88)
(-3.86)
2.821*
2.441
2.515
(1.75)
(1.52)
(1.57)
0.057
0.059
0.06
1,674,135
1,674,135
1,674,135
Panel B. Subsample results for lending fee dynamics around earnings announcement
HighSIR
IOall
IOact
BefAN
AftAN
BefAN*IOall
AftAN*IOall
BefAN*IOact
AftAN*IOact
HighSIVnew-1
LogSize
BtoM
BAspread-1m
HLspread-1m
DailyTurn-1m
RetStd-1m
Constant
Observations
R-squared
Number of permno
Before Lehman (Jan. 2007 – Aug. 2008)
Logallfee
Logallfee Logallfee Logallfee
(1)
(2)
(3)
(4)
0.311***
0.311*** 0.311*** 0.305***
(10.88)
(10.89)
(10.88)
(10.69)
0.121
0.123
0.138
0.132
(0.82)
(0.83)
(0.93)
(0.89)
-1.189*** -1.194*** -1.206*** -1.205***
(-5.67)
(-5.69)
(-5.75)
(-5.75)
-0.026***
-0.008
-0.008
(-5.77)
(-0.65)
(-0.65)
-0.017***
0.008
0.008
(-3.84)
(0.71)
(0.71)
-0.064** -0.063**
(-2.34)
(-2.33)
-0.048* -0.047*
(-1.72)
(-1.70)
0.087*
0.086*
(1.75)
(1.74)
0.022
0.023
(0.42)
(0.43)
0.064***
(11.63)
-0.248*** -0.247*** -0.247*** -0.248***
(-7.80)
(-7.77)
(-7.79)
(-7.80)
0.057***
0.057*** 0.057*** 0.057***
(3.69)
(3.68)
(3.69)
(3.69)
-0.099*** -0.099*** -0.099*** -0.099***
(-6.58)
(-6.60)
(-6.59)
(-6.59)
0.007
0.007
0.007
0.007
(1.29)
(1.32)
(1.30)
(1.30)
0.034***
0.035*** 0.034*** 0.035***
(3.97)
(3.97)
(3.96)
(3.99)
-0.002
-0.002
-0.002
-0.002
(-0.66)
(-0.60)
(-0.65)
(-0.67)
0.243
0.230
0.235
0.235
(1.11)
(1.05)
(1.08)
(1.08)
931,589
931,589 931,589 931,589
0.032
0.032
0.032
0.032
3,272
3,272
3,272
3,272
34
After Lehman (Oct. 2008-Dec. 2010)
Logallfee Logallfee Logallfee Logallfee
(1)
(2)
(3)
(4)
0.637*** 0.637*** 0.637*** 0.618***
(16.92)
(16.92)
(16.92)
(16.59)
-0.855*** -0.854*** -0.856*** -0.864***
(-6.08)
(-6.08)
(-6.07)
(-6.15)
0.548*** 0.552*** 0.504** 0.498**
(2.82)
(2.84)
(2.57)
(2.55)
0.038***
-0.011
-0.012
(9.29)
(-0.92)
(-1.00)
0.002
-0.022* -0.021*
(0.59)
(-1.85)
(-1.80)
-0.032
-0.032
(-1.26)
(-1.28)
0.043*
0.043*
(1.79)
(1.81)
0.292*** 0.297***
(5.66)
(5.76)
-0.005
-0.004
(-0.11)
(-0.07)
0.173***
(21.28)
-0.120*** -0.121*** -0.120*** -0.119***
(-3.82)
(-3.83)
(-3.80)
(-3.79)
0.002
0.002
0.002
0.002
(0.35)
(0.34)
(0.35)
(0.33)
-0.046*** -0.046*** -0.046*** -0.045***
(-5.34)
(-5.35)
(-5.37)
(-5.28)
0.038*** 0.037*** 0.038*** 0.038***
(7.34)
(7.33)
(7.35)
(7.38)
0.074*** 0.074*** 0.074*** 0.075***
(8.01)
(8.00)
(8.01)
(8.12)
-0.014*** -0.014*** -0.014*** -0.014***
(-4.05)
(-4.06)
(-4.05)
(-4.05)
-1.180*** -1.173*** -1.173*** -1.189***
(-5.77)
(-5.74)
(-5.73)
(-5.82)
1,363,695 1,363,695 1,363,695 1,363,695
0.036
0.036
0.037
0.039
3,659
3,659
3,659
3,659
Panel A. of Figure 1. Time series of average relative shares out on loan (SIR) and firm coverage
5.0
5000
4.0
4000
3.0
3000
2.0
2000
1.0
1000
0.0
0
Average number of stocks with stock lending information on right axis
Average AggSIR (% of agg borrowed shares/total shares)
Panel B of Figure 1. Time series of aggregate value of stocks on loan and monthly lending income
250
200
200
160
150
120
100
80
50
40
0
0
Total monthly income from stock lending ($Mill) on right axis
Total value of stocks currently borrowed (out on loan) in $Bill
Figure 1.
Time series of daily lending income, stock borrowing demand and number of reporting firms
Monthly lending income is the aggregated monthly lending fees across all stocks for all trading day in the month,
where the daily lending income for a stock is the sum of shares on loans times share price times lending fee. The
total value of stocks on loan is the aggregated across stocks as the total number of shares out on loan times shares
price of the specific stock.
35
Appendix I.
Summary of products/databases of securities lending markets that support transparency
Source: Securities Lending Market Guide, 2010;
Sungard Astec Analytics (Sungard: http://admin.sungard.com/financialsystems/brands/astec.aspx) 1




Lending pit is an ASP-based system used traders desks, giving rebate rate/fee and loan volume data for
users to maximize spreads in the market. Provides borrower based market information, with red flags for
stocks with extreme rebate rates.
Lenders review provides an analysis of lending programs comparing them to the whole market. A
comprehensive analysis of one’s securities lending program, prepared by independent experts.
Securities Lending Market Research Reports provides analysis of the size, profitability and risks in the
securities lending market.
Short Side (shortside.com) is an ASP application that provides short selling market color, via securities
lending analytics, to investment management professionals.
DataExplorer (now Markit Securities) (http://www.markit.com/product/pricing-data-securities-finance)

Markit securities’ securities finance dataset covers more than $13 trillion of global securities in the lending
programmes of more than 20,000 institutional funds. It includes more than ten years of history with over
three million intraday transactions. Data is sourced directly from leading industry practitioners, including
prime brokers, custodians, asset managers and hedge funds. Number of tools offered, such as:
o Index Explorer enables investment managers to discover where professional short sellers think
future price weakness will occur
o Risk Explorer reveals potential lending, collateral and cash reinvestment risk
o Evaluation Explorer
o Transaction Explorer
1 A system offered using ASP model is also sometimes called on-demand software or software on service.
36