DOES PASSIVE INVESTING HELP RELAX SHORT-SALE

CONSTRAINTS?

Darius Paliaa,b and Stanislav Sokolinskia`

August 2019

Abstract

Prior literature argues that passive investing mainly introduces price inefficiencies. This article

presents a channel through which passive investing leads to more informative prices. We study the

impact of passive investors’ security lending on short-sale constraints. Stocks with higher passive

ownership exhibit larger short positions, lower lending fees and longer loan durations. This effect

is significantly larger for passive than for active investors. Higher passive ownership is associated

with lower cross-autocorrelations with negative market returns, less skewness, and a smaller value

premium, especially among hard-to-borrow stocks. These results suggest that passive investing

contributes to price efficiency by relaxing short-sale constraints.

_____________________________________________________________________________________ a Rutgers Business School and b Columbia Law School, respectively. We thank Azi Ben-Rephael, Nittai Bergman,

Menahem Brenner, Lauren Cohen, Liyuan Cui, Valentin Dimitrov, Todd Gormley, Ron Kaniel, Pradeep Yadav, David

Yermack and seminar participants at NYU, Oklahoma, Rutgers, IDC Summer Finance Conference, CUHK

International Finance Conference, and the Triple Crown Conference for helpful discussions and comments. We are

grateful to the Whitcomb Center for Research in Financial Services for providing funds to obtain data. All errors

remain our responsibility. Corresponding author: Darius Palia; dpalia@business.rutgers.edu

1

“Fidelity Investments is cutting out its middleman – Goldman Sachs – when dealing with

Wall Street short sellers. … The move comes as Fidelity and its rivals compete to cut fees on index

funds, luring assets that can be used for more profitable businesses like securities lending”.

Bloomberg, May 2019

“Large funds with large passive portfolios, such as ETFs and index funds, are more likely than

active funds to lend securities. The nature of their portfolios enables these funds to lend more

securities for longer periods, making them preferred counterparties for their loans”.

Callan: Institutional Investor Consulting

1. Introduction

Modern portfolio theory and the efficient market paradigm 1 has resulted in a large

increase in assets managed by passive investors (index mutual funds and ETFs) when compared

to actively managed assets. For example, 15% of total assets in the U.S. mutual funds were

managed passively in 2007, which went up to 25% by the end of 2018.2 The shift to passive

management was especially dramatic in the U.S. equity markets wherein the proportion of mutual

fund assets managed passively was over 40% in 2017.3 One of the potential reasons for this shift

is that investors in index funds pay significantly smaller fees, and many active mutual funds do

not generate significantly higher net-of-fee returns for their investors than comparable passive

funds.4

Recent literature argues that the shift to passive investing mainly introduces price

inefficiencies. The existing theoretical work shows that passive investing can generating price

pressure and increase volatility. Since passive investors do not actively seek security-level

1 See Fama (1970). 2 See 2018 Investment Company Fact Book available at www.icifactbook.org. 3 See Cremers, Fulkerson and Riley (2018) 4 Jensen (1968), Carhart (1997), Sharpe (1991), French (2008), Fama and French (2010) and Lewellen (2011) find

that the average active manager cannot outperform her benchmark net of fees. Some papers have found positive returns

to “conditional skill,” i.e., response to news events, industry specialization, education, etc. (see Daniel, Grinblatt,

Titman and Wermers (1997), Kosowski, Timmermann, Wermers and White (2006), Kacpercsyk, Sialm and Zheng

(2005), Kacperczyk, Van Nieuwerburgh and Veldkamp (2014), Pastor, Stambaugh and Taylor (2017)).

2

information, the shift to passive investing can also reduce the amount of information incorporated

in stock prices.5 Some of these concerns have found empirical support. In particular, passive

investing has been shown to increase volatility, auto-correlations, return co-movement, and

transaction costs as well as to weaken stock earnings response.6 While the existing literature has

not reached an overall consensus on the effects of passive investing, the prevailing view suggests

that an increased share of index funds and ETFs mostly “make markets dumb”.

In this paper, we propose and analyze a specific channel through which a shift to passive

investing leads to more information being embedded in prices and contributes to price efficiency.

In particular, we study of the impact of passive investors on short-sale constraints through their

security lending activities.7 In doing so, we bring together and extend two strands of literature. The

first strand of literature suggests that short-sale constraints represent a crucial limit to arbitrage

restricting incorporation of negative information into security prices and generating short-term

overvaluation (Miller (1977), Hong and Stein (2003)).8 The second strand of literature argues that

institutional investors are an important source of supply of lendable securities to short-sellers

(D'Avolio (2002), Geczy, Musto and Reed (2002), Nagel (2005), Asquith, Pathak and Ritter

(2005)). If passive investing helps relax short-sales constraints through the supply of lendable

securities, it can facilitate the incorporation of information in prices and improve efficiency. While

5 See, for example, Basak and Pavlova (2013), Brown and Davies (2017), Bond and Garcia (2017), Baruch and

Zhang (2018) and Garleanu and Pedersen (2018). 6 See Israeli, Lee and Sridharan (2017), Ben-David, Franzoni and Moussawi (2018), Coles, Heath and Ringgenberg

(2018) and Glosten, Nallareddy and Zou (2019). We provide a detailed literature review in Section 2. 7 We refer to index mutual funds and index ETFs as passive funds throughout this paper. We refer to passive ownership

as the combined ownership of index mutual funds and index ETFs. 8 Many empirical papers have shown that short selling helps predict stock returns (see Desai, Hemang, Thiagarajan

and Balachandran (2002), Jones and Lamont (2002), Ofek, Richardson and Whitelaw (2004), Asquith, Pathak and

Ritter (2005), Cohen, Diether and Malloy (2007), Diether, Lee and Werner (2009), Boehmer, Huszar and Jordan

(2010), Engelberg, Reed and Ringgenberg (2012, 2018), and Muravyev, Pearson and Pollet (2018)).

3

passive investors do not directly conduct informed trading, they can complement information-

seeking efforts of active investors who employ short-selling strategies.

The extant literature suggests that passive funds participate significantly in stock lending

programs. Prado, Saffi and Sturgess (2016) document a positive relationship between the fraction

of stock held by index funds and lending supply, and Evans, Ferreira and Prado (2017) find that

indexers report to the SEC more frequent participation in security lending relative to active funds.

It seems reasonable that passive fund managers make more prominent security lenders due to the

lack of managerial discretion over the fund’s asset allocation as they only have to limit lending in

response to fund outflows or index reconstitutions. In contrast, actively managed funds might

prefer to retain stocks as a part of their trading strategy and the opportunity to sell shares in the

future. In addition, they are also more likely to recall shares due to the change in the market

conditions other than fund flows or change in index composition (D'Avolio (2002)).

Using a comprehensive dataset of stock lending outcomes in the U.S. over 2007-2017, we

analyze whether the increased supply of lendable stock due to passive investing helps relax short-

sale constraints and increases stock price efficiency. In doing so, we examine: i) the relationship

between passive ownership and security lending outcomes; ii) the economic impact of passive

indexers relative to other institutional investors (active and non-mutual fund institutional investors

such as pension funds, banks/insurance companies, and endowments), iii) the relationship between

passive investing and the price impact of short-sale constraints; and iv) investigate whether the

relationships are casual.

4

We present four principal findings. First, stocks with higher levels of passive fund

ownership exhibit higher levels of short interest9 accompanied by lower lending fees and longer

duration of securities loans. These effects are economically meaningful, namely, a one standard

deviation increase in passive fund ownership increases short interest by 0.8% relative to the

average short interest of 3%, and has similarly large economic effects on other security loan

outcomes such as lending fees and loan durations. We also find that that passive ownership

increases lending supply as in Prado, Saffi and Sturgess (2016).

Second, we document that the effect of passive funds is larger by a factor of two-to-three

relative to actively managed funds, and by a factor of two-to-six relative to non-mutual fund

lenders. A one percent increase in passive fund ownership leads to an increase of 0.7 percent in

lending supply, a reduction of four basis points in lending fees, and an increase of 1.4 days in loan

duration. On the other hand, a one percent increase in active fund ownership leads to an increase

of 0.25 percent in lending supply, a reduction of two basis points in lending fees and an increase

of 0.6 days in loan duration. These differences are statistically significant and establish a clear

hierarchy: passive indexers appear to participate the most in their custodian’s lending programs,

followed by actively managed mutual funds and least by other institutional investors such as

pension funds, banks/insurance companies, and endowments. Splitting the sample across sub-

periods reveals that passive investing produces an especially large effect on short interest and loan

duration earlier in the sample, when passive investing was less popular.

Third, we study how passive investing affects the price impact of short-sale constraints.

We employ three measures of price impact developed in the literature on short selling. Our first

9 Note that short interest proxies for the actual quantity of stock borrowed in equilibrium, whereas lending supply

represents the amount of stock that can be borrowed (i.e., borrowing capacity).

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measure is the cross-autocorrelation between lagged market returns and stock returns conditional

on market returns being negative (Bris, Goetzmann and Zhu (2007), Saffi and Sigurdsson (2011)).

Diamond and Verrecchia (1987) theorize that in the presence of short-sale constraints, stock prices

do not fully incorporate past negative information. Accordingly, we hypothesize that if stocks with

higher passive fund ownership benefit from relaxed short-sale constraints, then we can expect them

to exhibit lower cross-autocorrelations with lagged market returns conditional on market returns

being negative. Our second measure of price impact is the skewness of stock returns. The empirical

research on short-sale constraints have shown that when these constraints are relaxed, large

negative price movements become less likely, and stock returns exhibit less skewness (Chang,

Cheng and Yu (2007), Xu (2007)). Accordingly, we expect to observe a negative relationship

between skewness and passive fund ownership. Our third test is based on Nagel (2005). He finds

that stocks that are owned by two passive-oriented institutional investors, namely, the Vanguard

S&P 500 Index Fund and Dimensional Fund Advisors, exhibit a lower value premium. Nagel

(2005) suggests that this effect exists because security lending activities of these insitutional

investors allow short-sellers to trade on the value effect. Extending his argument, we hypothesize

that stocks with higher passive fund ownership will have a lower value premium. Finally, across

all the tests we expect to observe stronger price effects for stocks that are harder to borrow (i.e.,

with high lending fees or otherwise known as “specials”) where short-sale constraints are likely to

be severe (D'Avolio (2002) and Geczy, Musto and Reed (2002)).

All the tests indicate that the price impact of increased passive fund ownership is similar

to the effects of relaxing short-sale constraints, and this effect is stronger for special hard-to-

borrow stocks. The first series of tests show that increased passive fund ownership significantly

lowers downside cross-autocorrelation for specials but it has not effect on upside cross-

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autocorrelation. In addition, passive fund ownership has no effect on either upside or downside

cross-autocorrelations for stocks with low fees (“general collateral” stocks). The second set of tests

illustrates that passive fund ownership is associated with reduced skewness in stock returns and

this effect is significantly larger for specials. The third test extends Nagel’s (2005) findings by

showing that stocks with more passive institutional ownership exhibit a lower value premium.

Finally, we find that active mutual fund ownership and non-mutual fund ownership have no

significant price impact for the hard-to-borrow stocks or “specials”. The combined evidence on

price impact of passive fund ownership suggests that indexers relax short-sale constraints and

facilitate incorporation of negative information in stock prices contributing to market efficiency.

Our main results hold in a large sample of heterogeneous U.S. stocks across a variety of

specifications controlling for stock and quarter fixed effects as well as for a number of time-

varying variables that have been shown to affect both lending supply and lending demand. At the

same time, identification still remains a concern as certain time-varying factors that determine

supply and demand for shorting can be unobserved.10 To examine whether the effects passive

ownership are casual, we use instrumental variables methodologies that are based on the

reconstitution of Russell 1000 and Russell 2000 indices. While these methodologies help mitigate

identification concerns, they have two important shortcomings for the purposes of our analysis.

First, Russell-based methodologies do not provide any instruments for active and non-mutual fund

ownership which does not allow to compare the effects of different types of ownership within the

same stock. Second, these methodologies operate in a small restricted sample of large and liquid

Russell stocks which are less likely to face short-sale constraints.

10 For example, ownership by passive investors might be correlated with other factors such as the firm’s investment

opportunities -- that are observed by short-sellers but not by the econometrician -- and can directly affect security loan

characteristics.

7

We implement the procedures suggested by Appel, Gormely and Keim (2018) and by Coles,

Heath and Ringgenberg (2018). The main idea is that firms cannot control small variations in their

market capitalization, and therefore index assignment near Russell thresholds are plausibly

exogenous. This process leads to significant differences in index weights around the thresholds

resulting in substantial variation in index ownership and mitigating concerns related to unobserved

heterogeneity across stocks.11

For lending outcomes, we find that our results generally hold in a reasonably causal

framework which attempts to isolate the supply effect across two different samples and

methodologies. Instrumenting passive fund ownership by assignment to Russell 2000, we find

that passive fund ownership increases lending supply and short interest as well as reduces lending

fees. The relationship between loan duration and index fund ownership is positive but not

statistically significant.

When we examine the effects of passive fund ownership on price impact of short-sale

constraints in the sample of large and liquid Russell stocks, we are limited to a very small number

of specials. In particular, our large sample tests involve about 12,000 observations of specials,

whereas in the Russell reconstitution sample we have only 126 to 235 observations of specials.

However, we are still able to document that passive fund ownership lowers downside cross-

autocorrelations for specials. At the same time, we are unable to confirm our large sample results

on skewness and value premium. Finally, we present a battery of tests to show that our findings

11 Other studies that have used various index reassignment methodologies are Chang, Hong, Liskovich (2015), Boone

and White (2015), Crane, Michemaud, and Weston (2016), Schmidt and Fahlenbrach (2017), Wei and Young (2017)

and Heath, Macciocchi, Michaely and Ringgenberg (2019).

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across Russell reconstitution methodologies are unlikely to be driven by unobserved demand for

shorting.

This paper proceeds as follows. Section 2 explains our contributions to the related literature.

Section 3 describes our data and variables. Section 4 reports our main empirical results. Our

supplemental results based on the Russell index reconstitution are reported in Section 5, and

Section 6 presents our conclusions.

2. Relevant Literature and Our Contribution

Our primary contribution is to show that passive investors play an important role in

relaxing short-sale constraints relative to other institutional investors as measured by multiple

security lending and price impact outcomes across three different samples and methodologies. A

number of studies have analyzed supply and demand in the market for securities lending (D’Avolio

(2002), Asquith, Pathak and Ritter (2005), Cohen, Dietner and Malloy (2007), Blocher, Reed and

Wesep (2013)). These studies focus on an equilibrium framework and they also examine

differential effects of shorting supply and demand. Blocher and Whaley (2016) show that the

security lending by indexers is profitable to fund families and affects fund holdings while Johnson

and Weitzner (2018) argue that this practice leads to distortions in asset allocation impacting fund

returns. Prado, Saffi and Sturgess (2016) study the relationship between various characteristics of

institutional ownership structure and short-sale constraints documenting positive relationship

between holdings of indexers and lending supply among their other results. Our article solely

focuses on passive investing due to its rapidly growing importance and provides a comprehensive

apples-to-apples comparison between the effects of passive funds, active mutual funds and other

institutional investors on short-sale constraints. In addition, we present direct evidence on the

effects of passive investing on actual equilibrium lending outcomes such as short interest, lending

9

fees and loan durations, as well as on price impact of short-sale constraints. We also address

causality concerns implementing multiple Russell reconstitution methodologies.

Our second contribution is to propose a new channel through which a shift to passive

investing can affect securities prices. The theoretical literature in this area focuses on the effects

of passive investing on price pressure and volatility (Basak and Pavlova (2013)), on incorporation

of systemic information in prices (Cong and Xu (2019)), on effort exerted by active managers

(Brown and Davies (2017)), and on reduced informational content of prices due to reduced active

investing (Bond and Garcia (2017), Baruch and Zhang (2018), Garleanu and Pedersen (2018)).

Overall, the theories of asset management typically do not consider the implications of an increase

in passive investing for securities lending and short-sale constraints.

The empirical literature on the price impact of passive investors evolves around the price

pressure effects on volatility and autocorrelations (Ben-David, Franzoni and Moussawi (2018))

together with correlation with index prices movements and trading costs (Israeli, Lee and

Sridharan (2017), Glosten, Nallareddy and Zou (2019), Choi (2017), Coles, Heath and

Ringgenberg (2018)). Most of these papers focus on exchange-traded funds (ETF) with Coles,

Heath and Ringgenberg (2018) being an exception (who focus on all passive investors including

index mutual funds). Unlike these studies, we focus on a different channel through which passive

fund investors can affect security prices. As our study is organized around the effects of passive

fund ownership on the relaxation for short-sale constraints, we depart from the literature on price

pressure and focus on the specific measures of price impact as suggested by the literature on short-

sales constraints (see, for example, Hong and Stein (2003), Geszy, Musto and Reed (2002), Bris,

Goetzman and Zhu (2007), Chang, Cheng and Yu (2007), Xu (2007), and Saffi and Sigurdsson

(2011)).

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Our final contribution is to examine the causal effect of passive fund ownership on lending

outcomes and on stock prices. Methodologically, we employ the instrumental variables

frameworks suggested by Appel, Gormley and Keim (2018) and Coles, Heath and Ringgenberg

(2018).12 In doing so, we complement the nascent literature on the causal effects of passive

investing on stock prices as well as the literature that studies the effects of passive investing on

other outcomes such as firm value and CEO power (Schnidt and Fahlenbrach (2017)), corporate

governance (Appel, Gormley and Keim (2018)), and product market competition (Azar, Schmalz

and Tecu (2018)).

3. Data and Variables

We combine stock-level mutual fund ownership data together with security lending data

from Markit, accounting and pricing data from CRSP and Compustat as well as Russell index

membership. We describe the construction of the main sample and variables in this section. We

also create a significantly smaller Russell assignment sample that is described in Section 5.

3.1 Fund Holdings

We follow the procedure similar to Iliev and Lowry (2015) and Appel, Gormley and Keim

(2016, 2018). We begin with the CRSP Mutual Fund database and classify domestic equity funds

as passive if CRSP indicates that the fund is an index fund. All the rest of the funds are classified

as active. Next we match fund classification to the mutual fund quarterly holdings from Thomson

Reuters Mutual Fund Holdings S12 database. We calculate stock ownership within each category

by aggregating the holdings of all passive and active funds for each stock-quarter observation. The

12 Among the studies on causal effects of securities lending, Kolasinski, Reed and Ringgenberg (2013) use instruments

for loan demand, whereas Kaplan, Moskowitz and Sensoy (2013) study the effect of supply shocks in an experimental

setting.

11

fund holdings are defined as proportion of shares held by the fund relatively to the total number of

shares outstanding. The number of shares outstanding within each stock-quarter is calculated by

using the information on shares outstanding from CRSP stock data.

We next turn to Thomson Reuters Institutional Ownership S34 database to obtain the

holdings of all 13F institutional investors. We follow Frazzini (2006) and Brav, Jiang and Li (2018)

and drop all the observations where the number of shares held by institutions exceeds the number

of shares outstanding in CRSP. Having this information, we calculate non-mutual fund ownership

as the difference between total institutional ownership and the ownership of passive and active

mutual funds. This definition captures the ownership of other institutional investors such as

pension funds, banks/insurance companies, and endowments. Non-passive ownership is defined

as the difference between total institutional ownership and the ownership of passive mutual funds.

3.2 Security Lending Data

We obtain security lending data from Markit. This daily dataset includes the key security

lending indicators from the vast majority of the U.S. stocks over the period of 2007-2017. We

focus on four key variables: “Active Lendable Quantity” which is a measure of lending supply,

“Quantity on Loan” which is a measure of short interest, “Indicative Fee” which is a measure of

lending fees,13 and “Average Tenure” which measures the average loan duration. We merge Markit

dataset to daily CRSP stock file and keep only U.S. common stocks (share codes 10 and 11).

For each daily stock observation, we first calculate lending supply and short interest as a

proportion of shares reported by Markit relative to total number of outstanding shares from CRSP

13 As in Muravyev, Pearson, and Pollet (2018) we use “Indicative Fees” which are the fees paid by short sellers to

prime brokers. They show that these fees are much greater than fees received by either the custodian or the ultimate

lender, frequently used in the literature.

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stock data. We next average both quantity variables within each stock-quarter to match with

quarterly holdings data. Lending fees and loan maturity are computed in a similar manner by

averaging the daily Markit data within each stock-quarter.

3.3 Price Impact Measures and Accounting Data

We have hypothesized that passive fund investors help to relax short-sales constraints.

Accordingly, we employ measures of price impact suggested by the literature on shorting. In

particular, we hypothesize that ownership by passive fund investors affect stock prices in the same

manner as relaxing short-sale constraints.

Our first measure of price impact is the downside cross-autocorrelation between lagged

market returns and stock returns (Hou and Moskowitz (2005), Bris, Goetzman and Zhu (2007),

Saffi and Sigurdsson (2011)). For each stock-quarter we calculate the downside cross-auto

correlation using daily stock returns and lagged market return as follows:

𝜌𝑖,𝑡− = 𝑐𝑜𝑟𝑟(𝑟𝑖,𝑑,𝑡, 𝑟𝑑−1,𝑡

𝑀− ), (1)

where 𝑟𝑖,𝑑,𝑡 is the return on stock i in quarter t on day d, and 𝑟𝑑−1,𝑡𝑀− is market returns on

day d-1 in quarter t conditional on market returns being negative. We follow Hou and Moskowitz

(2005) by using the CRSP value-weighted stock market index to obtain daily market returns. The

larger is the correlation of stock returns with past negative market returns, the larger is the delay

in price response to negative information.

Using a similar approach, we also compute upside cross-autocorrelations using positive

market returns and the difference between the downside and the upside autocorrelations as follows:

𝜌𝑖,𝑡+ = 𝑐𝑜𝑟𝑟(𝑟𝑖,𝑑,𝑡, 𝑟𝑑−1,𝑡

𝑀+ ), 𝜌𝑖,𝑡𝐷𝑖𝑓𝑓

= 𝜌𝑖,𝑡− − 𝜌𝑖,𝑡

+ . (2)

13

These measures help to quantity the asymmetry in price adjustment. As short-sale

constraints are not expected to affect the incorporation of positive information in prices, it is useful

to separately analyze upside and downside autocorrelations as well as the difference between them.

As correlations are bounded by -1 and 1, we apply the ln [(1 + 𝜌)/(1 − 𝜌)] transformation to both

of measures of cross-autocorrelations.

Our second measure of price impact is skewness of stock returns. We follow Bris,

Goetzman and Zhu (2007) applying log-transformation to returns, and calculate the skewness of

daily returns within each stock-quarter observation.14 Bris, Goetzman and Zhu (2007), Xu (2007),

Chang, Cheng and Yu (2007) and Saffi and Sigurdsson (2011) find that relaxing short sales

constraints is associated with less skewness in stock returns. We adopt the positive association

between short-sales constraints and skewness when testing the effects of ownership by passive

investors on individual stock returns.

Finally, we merge holdings data to securities lending data as well as the pricing information

from CRSP and accounting variables from Compustat to obtain the final dataset. The definitions

of our variables are provided in the Appendix.

3.4. Summary statistics

Table 1 presents our summary statistics. We observe that passive fund investors own 6%

of shares outstanding for the average U.S. stock. At the same time, the average level of active fund

ownership is 18%, and the average level of non-mutual fund ownership is 45%. While passive

funds are becoming more popular, they still own significantly less shares of the average company

relative to other institutional investors.

14 Our results hold even if we do not log-transform the daily returns.

14

*** Table 1***

The security lending data implies that, for the average stock, much of the lending supply

is not utilized by short sellers; specifically, average supply of lendable shares equals to 19% while

the average aggregate short position equals to only 3%. However, lending fees exhibit a high

degree of variability, wherein the average fee is 2% but the median fee is only 0.05%.15 These

results are consistent with Asquith, Pathak and Ritter (2005) who suggest that borrowing is not too

difficult for most stocks, but there exists some hard-to-borrow-stocks. We also find that the

average loan duration is 80 days.

We observe that individual stock returns are positively skewed and exhibit negative

downside cross-autocorrelation. Finally, the average stock has a market-to-book ratio of three and

a bid-ask spread of 1%.

4. Empirical Results

4.1 Effect of Passive Fund Ownership on Security Lending Outcomes

We begin by investigating the relationship between the ownership of passive funds and

security lending outcomes. In order to do so, we create 20 bins of passive fund ownership and plot

our loan outcomes against these 20 bins. We residualize all the variables on a set of control

variables such as ln (market capitalization), ln (book value of assets), market-to-book, bid-ask

spread as well as stock and quarter fixed effects. Figure 1 illustrates the relationships between

passive fund ownership and security lending variables, i.e., lending supply, short interest, lending

fees and loan duration. We observe a strong positive correlation between passive fund ownership,

lending supply, short interest and loan durations as well as a substantial negative correlation

15 In the case of cash collateral, the lending fee is calculated as the difference between returns on reinvested collateral

(typically, the fed fund rate) and the rebate received by the borrower.

15

between passive fund ownership and lending fees. Our preliminary analysis suggests that stocks

with higher passive fund ownership are cheaper to borrow, exhibit larger aggregate short positions

and are borrowed for longer time periods.

***Figure 1***

We next conduct formal tests by regressing the security lending variables on passive fund

ownership using the following specification:

𝑦𝑖,𝑡 = 𝛼𝑖 + 𝛼𝑡 + 𝛽 ∙ 𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 + 𝛾𝑋𝑖,𝑡 + 휀𝑖,𝑡 (3)

where 𝑦𝑖,𝑡 is a security lending outcome for stock i in quarter t, 𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 is a level of

passive fund ownership of stock i in quarter t, 𝛼𝑖 are stock fixed effects, 𝛼𝑡 are quarter fixed effects

and 𝑋𝑖,𝑡 is a vector of stock-specific control variables (namely, ln (market capitalization), ln (book

value of assets), market-to-book and bid-ask spread). All the variables are defined in Appendix A.

Standard errors are clustered at the stock level.

Table 2 confirms the stylized facts previously shown in Figure 1. Panel A presents the

evidence for the quantity variables: lending supply and equilibrium short interest. Column (1)

presents the baseline specification only with quarter fixed-effects suggesting that an increase of

one percent in passive fund ownership is associated with a two percent increase in the equilibrium

level of short interest. Column (2) introduces the control variables and the estimated elasticity

slightly declines to 1.83. This column also shows that larger and more liquid stocks have higher

levels of lending supply. Adding stock fixed effects in column (3) and employing within stock

variation in passive fund ownership reduces the elasticity to 0.82. In column (4) we control for

ownership of non-passive funds (actively managed mutual funds and other 13F institutions) and

the estimated elasticity basically remains at the same level. The effect is economically sizable,

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namely, a one standard deviation increase in passive fund ownership is associated with an increase

in half-standard deviation in lending supply.

*** Table 2***

We hence examine whether the supply increase driven by passive fund ownership results

in a higher level of short interest. The results are reported in columns (5) – (9). As can be seen, the

coefficient on passive fund ownership is always positive and significant at the one percent level.

The most restrictive specification in column (8) indicates that a one percent increase in passive

fund ownership results into an increase of 18 basis points in the level of short interest. These results

indicate that for the average stock, 25% of the additional supply produced by passive investors (18

basis points out of 78 basis points) is utilized by short sellers.

Panel B repeats the analysis and studies the effects passive fund ownership on lending fees

and security loan durations. The baseline specification (column (1)) indicates that an increase of

one percent in passive fund ownership is associated with a reduction of 31 basis points in lending

fees. Introducing additional control variables as well as stock fixed-effects leads to a considerable

decline in the estimated coefficient to three basis points. However, this effect is still economically

meaningful as moving from the 25th percentile (one percentage point) to the 75th percentile (10

percentage points) of the passive fund ownership reduces lending fees by 27 basis points.

We then examine the effect of passive fund ownership on security loan duration. Columns

(5) – (8) of Panel B demonstrate that higher level of passive fund ownership results in longer

duration of stock loans. According to the most restrictive specification in column (8), a change of

one-standard deviation in passive fund ownership results in the increase of seven days in average

stock loan duration.

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In sum, the results presented in Figure 1 and Table 2 suggest that stocks with higher levels

of passive fund ownership face weaker short-sale constraints as measured by multiple security

lending outcomes.

4.2 Differential Impact of Passive Funds and Other Securities Lenders

Having established the baseline effects of passive funds on security lending, we hence

examine if passive funds have a larger economic impact on lending outcomes than other

institutional investors. The size of lendable assets by types of beneficial owners is not precisely

known (Balkanova, Copeland and McCaughrin (2015)). However, the typical security lender is a

large institutional investor managing a low-levered portfolio of securities. Mutual funds, pension

funds, endowments and insurance companies represent the majority of lenders (Balkanova, Caglio,

Keane and Porter (2016)).

To address this question, we split institutional ownership of a given stock into three

categories: ownership by passive funds, ownership by active mutual funds and ownership by non-

mutual fund 13F institutions such as pension funds, endowments, and banks/insurance companies.

We use the following regression model:

𝑦𝑖,𝑡 = 𝛼𝑖 + 𝛼𝑡 + 𝛽1 ∙ 𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 + 𝛽2 ∙ 𝐴𝑐𝑡𝑖𝑣𝑒𝑖,𝑡 + 𝛽3 ∙ 𝑁𝑜𝑛𝑀𝐹𝑖,𝑡 + 𝛾𝑋𝑖,𝑡 + 휀𝑖,𝑡 (4)

where 𝑦𝑖,𝑡 is a security lending outcome for stock i in quarter t, 𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 is the level of

passive fund ownership of stock i in quarter t, 𝐴𝑐𝑡𝑖𝑣𝑒𝑖,𝑡 is the level of active mutual fund

ownership of stock i in quarter t, 𝑁𝑜𝑛𝑀𝐹𝑖,𝑡 is the ownership by non-mutual fund institutions of

stock i in quarter t, 𝛼𝑖 are stock fixed effects, 𝛼𝑡 are quarter fixed effects and 𝑋𝑖,𝑡 is a vector of

stock-specific control variables (namely, ln( market capitalization), ln(book value of assets),

market-to-book- and bid-ask spreads).

18

Table 3, Panel A presents the results. The first two columns show that passive funds have

a significantly larger effect on lending supply relatively to both active funds and non-mutual funds.

Specifically, an increase of one percent in passive fund ownership results in an increase of 0.76%

in lending supply, while an increase of one percent in active fund ownership contributes 0.25% to

lending supply. Non-mutual funds have the smallest impact as an increase of one percent in their

ownership results in 0.17% increase in lending supply.

*** Table 3***

The dominating effects of passive investors on security lending can be seen throughout the

rest of the outcomes. Passive funds have twice the effects on lending fees and loan durations

relative to active mutual funds. The larger effect on loan durations suggest that short-sellers may

prefer passive investors as stock lenders who are less likely to recall the stock for their own needs.

Passive investors also have larger effects on equilibrium short interest relative to both actively

managed mutual funds and non-mutual funds. Panel B formally evaluates the differences in the

magnitude of the coefficients and confirms the importance of passive funds. In particular, the

difference between coefficients of passive versus active funds is statistically significant at one

percent level for lending supply and loan duration. It is also statistically significant at the 10

percent level for short interest and lending fees. Non-mutual funds appear to have the least impact,

having substantially smaller coefficients when compared to both passive and active funds.

Our sample spans over 2007-2017 when passive investing was consistently gaining market

share. In particular, in the early sample period index funds and ETFs represented a significantly

lower share of the U.S. mutual fund market. In Table 4, we examine the effects across sub-samples

by interacting the ownership variables with a dummy that equals one if the observation belongs to

the pre-2012 period. The main effects remain similar to Table 3 and passive investing still produces

19

the largest impact on all the lending outcomes. In addition, the interaction terms reveal that passive

investing had an especially large effects on short interest and loan duration earlier in the sample.

This result suggests that at times of relatively scarce passive ownership, it had an even stronger

impact on selected lending outcomes.

*** Table 4***

Overall, the findings establish a clear hierarchy among various institutional investors in

their impact on securities lending outcomes. Passive funds appear to participate the most in lending

programs, followed by active mutual funds, and lastly by other institutional asset managers.

4.3 Effect of Passive Fund Ownership on Price Impact of Short-Sale Constraints

Having established the effects of passive investors on securities lending outcomes, we turn

to pricing implications. Given the results in Tables 2 and 3, we hypothesize that the price impact

of the increased passive fund ownership is similar to the effects of relaxing short-sale constraints.

We test this hypothesis using three different approaches from the literature on short selling.

In addition, the relaxation of short-sale constraints will generate stronger effect on stock

prices when these constraints are initially more severe. Capitalizing on this idea, we expect to

observe larger price consequences for the stocks that are harder to borrow. We follow D’Avolio

(2002) and Gezcy, Musto and Reed (2003) using the lending fee as a proxy for the severity of the

short-sale constraints. In our regression analysis, we split the sample into the following two types

of stocks based on their lending fees: “general collateral” (GC) stocks with a fee of less than 2%,

and “specials” or hard-to-borrow stocks with a lending fee larger than 2%.16 Our hypothesis across

16 The 2% cutoff implies that roughly 10% of stocks are defined as specials consistent with D’Avolio (2002). Our

results are also robust to the cutoff fee of 1% from D’Avolio (2002) or to the cutoff fee of 3%.

20

all the tests is that the effect of passive fund ownership on price impact of short-sale constraints is

more pronounced for the hard-to-borrow stocks when compared to other stocks.

4.3.1 Cross-autocorrelations

Following Bris, Goetzmann and Zhu (2007), and Saffi and Sigurdsson (2011), we

hypothesize that if stocks with higher passive fund ownership benefit from relaxed short-sale

constraints, then they would be expected to exhibit lower cross-autocorrelations with lagged

market returns conditional on market returns being negative. The top graphs of Figure 2 provide

the initial descriptive evidence presenting the relationship between passive fund ownership and

downside cross-autocorrelations separately for special and general collateral stocks using an

approach similar to the construction of Figure 1. Consistent with the literature, the figure shows

that hard-to-borrow stocks have higher downside cross-autocorrelation in comparison with other

stocks. We can also see that when passive fund ownership within a stock increases, downside

cross-autocorrelation declines for specials at a much higher rate than for GC stocks. This

descriptive evidence suggests that specials with high level of passive fund ownership exhibit faster

price discovery conditional on negative information.

***Figure 2***

Table 5 presents the formal regression results using econometric specifications based on

equation (4) across “special” and “GC” stocks. Column (1) shows that a within stock increase in

passive fund ownership results in lower downside cross-autocorrelation for specials and this effect

is statistically significant at the five percent level. We also see that neither active fund ownership

nor non-mutual fund ownership affects downside cross-autocorrelation for specials. Column (2)

shows that there is no effect of passive fund ownership on downside cross-autocorrelation for

21

general collateral stocks. Columns (3) – (4) analyze the upside cross-autocorrelations and finds

that the impact of passive fund ownership on the speed of incorporation of positive information

into stock prices is statistically insignificant. Columns (5) - (6) report the results for the difference

between the upside and the downside cross-autocorrelations and find that the asymmetric effect is

especially pronounced for specials.

*** Table 5***

These results confirm that price discovery conditional on negative information is faster for

constrained stocks when passive fund ownership is higher. Consistent with the short-selling

literature, the effect of passive fund ownership on adjustment of prices to information is

asymmetric, namely, the speed of incorporation of positive information is not affected.

4.3.2 Skewness

We next analyze the effect of passive fund ownership on skewness of stock returns. The

bottom panel of Figure 2 presents the relationship between passive fund ownership and skewness

similarly to our analysis of cross-autocorrelations. Consistent with the predictions by Xu (2007),

we can see that hard-to-borrow stocks exhibit higher skewness relatively to other stocks. We also

observe that when passive fund ownership increases, skewness steadily declines and this

relationship is stronger for specials. The figure suggests that higher passive fund ownership is

associated with less skewness in individual stock returns - which is in accordant with the relaxation

of short-sale constraints.

Table 6 confirms the result through regression tests. Columns (1) and (2) present the effects

of passive fund ownership on the skewness of stock returns separately for special and GC stocks.

We can see that the coefficient on passive fund ownership is negative and the magnitude of the

22

effect is significantly larger for specials. Both coefficients are statistically significant at the one

percent level. In addition, the effect of active mutual fund ownership on skewness is more than

twice smaller while the effect of non-mutual fund investors is positive.

*** Table 6***

In sum, the evidence on the relationship between passive fund ownership and stock return

skewness is in line with the papers that document a negative relationship between relaxation of

short-sale constraints and skewness (Bris, Goetzman and Zhu (2007), Xu (2007), Chang, Cheng

and Yu (2007) and Saffi and Sigurdsson (2011)).

4.3.3 Value Premium

Our final test is based on Nagel (2005), who shows that ownership by two prominent

security lenders, namely, the Vanguard S&P 500 Index Fund, and Dimensional Fund Advisors, is

associated with a reduced value premium. He suggests that this effect exists because security

lending activities of these insitutional investors allow short-sellers to trade on known price

anomalies. Conequently, we hypothesize that stocks with higher ownership by passive indexers

might exhibit a reduced value premium.

We closely follow Nagle’s (2005) methodology. As in his paper, we transform all return

predictors into decile ranks each quarter and scale them such that their values fall into interval

between 0 and 1. Our dependent variable is the return over four quarters from t+1 to t+4, which

is regressed on quarter t stock characteristics. To be consistent with our previous results, we use

the same stock characteristics that we employed in the previous analysis.

Table 7 presents the results. Columns (1)-(2) show the results for the hard-to-borrow

special stocks, and columns (3)-(4) show the results for the general collateral stocks. In column

23

(1), the returns are regressed on the ownership variables, on market-to-book, controlling for other

stock characteristics as well as quarter and stock fixed-effects. The coefficient on market-to-book

is negative and significant which confirms the presence of the value premium. Consistently with

Nagel (2005) and Asquith, Pathak and Ritter (2005), we also observe that stocks with higher

institutional ownership of any type have lower future returns. In Column (2) we interact market-

to-book with the different types of institutional ownership. The coefficient on market-to-book

implies that if a given stock is moved into the lowest ownership decile across all the investor types,

the difference in returns between top and bottom market-to-book deciles becomes 56% per year.17

However, if we go to the highest decile of passive fund ownership, holding ownerships by other

funds fixed, the value effect is reduced by more than two-thirds (44%). We note that neither

ownership by active mutual funds nor by non-mutual fund institutional investors has a significant

effect on the value premium in the sample of hard-to-borrow stocks. Consistent with our results

on other measures of price impact of short-sale constraints, we observe that the effect of passive

fund ownership on the value premium is twice smaller for general collateral stocks.

*** Table 7***

Overall, our results in Tables 4-6 provide consistent evidence that increased passive fund

ownership generates price impact similar to the impact of relaxing short-sale constraints

documented by the literature. We confirm that passive investors improve the speed of

incorporation of negative information into stock prices, reduce the likelihood of large negative

returns as well as diminish value premium. These effects are more pronounced for the constrained,

17 Note that our regression specifications include stock fixed effects. Therefore, we interpret the economic magnitude

of the coefficient as moving the same stock from the extreme growth decile to the extreme value decile, and conversely.

24

hard-to-borrow, special stocks as suggested by D’Avolio (2002) and Gezcy, Musto and Reed

(2003).

5. Russell Indices Reconstitution Test

While our results are robust to the inclusion of a rich set of control variables as well as

quarter and stock fixed-effects, identification still remains a concern. Specifically, certain stock–

specific time-varying parameters that determine supply and demand for shorting, such as

valuations of marginal investors and short-sellers, are unobserved. For example, ownership by

passive investors can be correlated with other factors such as the firm’s investment opportunities

that might be observed by short-sellers but are not observed by econometrician, and can directly

affect security loan characteristics. In this section, we develop an identification strategy that draws

from the literature on Russell indices reconstitution and the effects of passive fund ownership.

Methodologically, we present the instrumental variables framework suggested by Appel, Gormley

and Keim (2018) who study the effects of passive investors on corporate governance.18 While

Russell-based methodologies help mitigate identification concerns, they have two important

shortcomings for the purposes of our analysis. First, Russell-based methodologies do not provide

any instruments for active and non-mutual fund ownership which does not allow to compare the

effects of different types of ownership within the same stock. Second, these methodologies operate

in a small restricted sample of large and liquid Russell stocks which are less likely to face short-

sale constraints.

18 See Appel, Gormley and Keim (2019) for a discussion of various methodologies based on Russell indices

reconstitutions.

25

5.1 Sample construction

Our sample construction procedure follows Appel, Gormely and Keim (2018). Markit

provides data on security lending starting in 2007, and in the same year Russell implemented a

new assignment regime known as “banding.” First, stocks are sorted based on May market

capitalizations and then two bands around the stock ranked 1000th are generated. Each band’s

width is equal to 2.5% of the total May market capitalization of the entire Russell 3000 index. The

stocks within the band do not change their index assignment from the last year.

Consider the following example. After the Russell banding procedure, the two following

thresholds around the 1000 rank were created.: an upper threshold of 875 and a lower threshold of

1100. In this example, all 225 stocks ranked in between 875-1100 are not predicted to change their

index assignment as they fall within the band (the variable Band equal to one and zero otherwise,

in Equation (5) below). The stocks ranked above 875 are predicted to be reassigned to Russell

1000 only if they were assigned to Russell 2000 in the previous year. The stocks ranked below

1100 are predicted to be reassigned to Russell 2000 only if they were included in Russell 1000 in

the previous year. Effectively, the banding procedure generates two cutoffs instead of one (a rank

of 1000) and creates an assignment process that is relatively difficult to manipulate.

Following Appel, Gormely and Keim (2018) we construct our sample selecting top 250

stocks in Russell 2000 and bottom 250 stocks in Russell 1000. Table 8 presents the descriptive

statistics for this sample. The variables of interest are calculated for the 3rd quarter in any given

year as this quarter exactly follows the annual June reconstitution.19 For the average stock, 9% are

owned by passive funds and 18% are owned by active funds. The overall level of institutional

19 Our results are also robust to calculating variables of interest over the 4th quarter.

26

ownership is 78%. All these variables are higher than at the larger sample of stocks used in the

analysis of the previous sections. These differences come from the fact that the cutoff sample

stocks are relatively large due to being highly ranked members of the Russell indices and therefore

exhibit much higher level of institutional ownership.

*** Table 8***

5.2 Methodology

We follow Appel, Gormley and Keim (2018) to identify the effects of passive fund

ownership on securities lending and price impact of short-sale constraints. In particular, we use

the inclusion into Russell 2000 as an instrument for ownership of passive funds. The assignment

into Russell 2000 is determined by the following factors: i) end-of May market capitalization of

the stock, ii) whether the stock is “banded” by Russell and does not switch indices in a given year,

iii) whether the stock was included in Russell 2000 during the last reconstitution year, iv) the

interaction between the two indicators. In addition, stock index weights are determined by end-of-

June float-adjusted market capitalization calculated by Russell. Therefore, we include the above

determinants of index assignment in our specifications. In particular, we estimate the following

first stage regression:

𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 = 𝛼 + 𝛽𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡 + ∑ 𝜃𝑛(ln(𝑀𝑎𝑟𝑘𝑒𝑡𝐶𝑎𝑝𝑖,𝑡))𝑛

𝑁

𝑛=1

+ 𝛾 ln(𝐹𝑙𝑜𝑎𝑡𝑖,𝑡)

+ 𝜇1𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡−1 + 𝜇2𝐵𝑎𝑛𝑑𝑖,𝑡 + 𝜇3𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡−1 × 𝐵𝑎𝑛𝑑𝑖,𝑡 + 𝜏𝑡+휀𝑖,𝑡 (5)

where 𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 is the amount of passive ownership for stock i in year t, 𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡 is an

indicator variable equal to one if the stock is included in Russell 2000 in year t, 𝑀𝑎𝑟𝑘𝑒𝑡𝐶𝑎𝑝𝑖,𝑡 is

the end-of-May market capitalization from CRSP, 𝐹𝑙𝑜𝑎𝑡𝑖,𝑡 is the end-of-June float-adjusted market

27

capitalization calculated by Russell, 𝐵𝑎𝑛𝑑𝑖,𝑡 is an indicator variable equals one if the stock i is

within the band in year t and 𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡−1 is an indicator variable equal to one if the stock is

included in Russell 2000 in year t-1. Finally, we cluster our standard errors at the individual stock

level.

Our second stage estimation mirrors the specification from the first-stage and estimates the

effects of passive fund ownership on security lending and price impact variables. In particular, we

implement the following regression model:

𝑦𝑖,𝑡 = 𝜗 + 𝛿𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡̂ + ∑ 𝜑𝑛(ln(𝑀𝑎𝑟𝑘𝑒𝑡𝐶𝑎𝑝𝑖,𝑡))

𝑛𝑁

𝑛=1

+ 𝜌 ln(𝐹𝑙𝑜𝑎𝑡𝑖,𝑡) + 𝜎1𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡−1

+ 𝜎2𝐵𝑎𝑛𝑑𝑖,𝑡 + 𝜎3𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡−1 × 𝐵𝑎𝑛𝑑𝑖,𝑡 + 𝜖𝑡 + 𝑢𝑖,𝑡 (6)

where 𝑦𝑖,𝑡 is an outcome of interest for stock i in year t and 𝑃𝑎𝑠𝑠𝑖𝑣𝑒̂𝑖,𝑡 is the predicted level of

passive fund ownership for stock i in year t from the first stage estimation.

Our methodology is based on two identification assumptions. First, inclusion in Russell

2000 should affect the level of passive fund ownership after controlling for the criteria used by

Russell when determining index assignment in any given year. This condition is verified below

through the first-stage estimation. Second, inclusion in Russell 2000 should not directly affect our

outcomes of interest except through its impact on ownership by passive funds. As we argue that

the effect of passive fund ownership operates only through the increase of supply of lendable

shares, our primary concern is the effect of inclusion on shorting demand in the next three months

following index reconstitution. Chang, Hong and Liskovich (2014) demonstrate that inclusion in

Russell 2000 generates predictable short-term price increase. If market participants act on this and

28

reduce their short positions around the inclusion, our results cannot be solely attributed to the

increased supply provided by passive investors.

A related concern is that short-selling activity can affect index inclusion rather than the

other way around. Stocks are ranked by their market capitalization at the end of May to determine

index assignments. If an increased demand for shorting reduces stock price and market

capitalization around the rank day, stocks with higher levels of short interest might be assigned to

Russell 2000 due to lower market capitalization.

To address the first concern and bolster our assumption regarding the exclusion restriction,

we will show in latter tests that our instrument is not related to other factors that have been shown

to affect shorting demand including stock characteristics such as the bid-ask spread, market-to-

book and the book value of assets. We will also present evidence that our instrument is not related

to long positions of other institutions suggesting that these investors do not actively trade on the

expected price increase. In addition, we will illustrate that the level of short-selling activity does

not exhibit any irregularities around the reconstitution days for stocks that switch indices.

With respect to the second concern, we control for stock market capitalization in May in

multiple ways using polynomials of various ranks. Additionally, we document the lack of

abnormal short-selling activity around the ranking days for stocks that move between the indices.

5.3. First-Stage

Table 8 presents the first-stage regressions using first-, second-, and third-order

polynomials when controlling for market capitalization. The results confirm that Russell 2000

membership is strongly associated with an increase in passive fund ownership. According to Panel

A, the inclusion in Russell 2000 is translated into an increase of 2% in ownership by passive funds

29

which amounts to half a standard deviation. This effect is consistent across the polynomials of

different orders.

The table also shows that the inclusion in Russell 2000 does not induce statistically

significant change in neither the level of active mutual fund ownership nor the level of non-mutual

fund ownership. These findings suggest that the increase in lending supply and the resultant

changes in fees and short interest are the most likely to come from the effect of increased passive

fund ownership as there is no change in holdings of other potential security lenders. In addition,

this evidence implies that following the inclusion in Russell 2000 actively trading institutions do

not increase their long positions suggesting that the average institutional investor does not actively

trade based on short-term price increase (as documented by Chang, Hong and Liskovich (2014)).

*** Table 9***

5.4 Effects of Passive Fund Ownership on Securities Lending Outcomes: Russell Sample

Table 10 presents the effects of passive fund ownership on security lending outcomes

employing the instrumental variables approach. Panel A focuses on quantities and columns (1)-(3)

show the effect of passive fund ownership on lending supply. Our identification strategy confirms

that higher ownership by passive investors results in greater supply of shares to short-sellers. The

effect is economically meaningful such that an increase in one standard deviation in passive fund

ownership is associated with an increase of one standard deviation in lending supply.20 Columns

20 At the first glance it appears than a 1% increase in passive ownership increases lending supply by more than 1%.

However, one should remember that passive ownership is reported by CRSP at the end of the 3rd quarter, while lending

supply is defined as an average lending supply over the third quarter. For a sake of clarification, consider a simple

example. Lending supply is reported three times per quarter: July 31st - 2%, August 31st - 3% and September 31st –

0.7%. Passive ownership is reported on September 31st only, and equals to 1% such that on this date the lending supply

is significantly smaller than the passive ownership. Due to mutual fund reporting, it is impossible to determine the

level of passive ownership on the rest of the dates. At the same time, the average lending supply over the 3rd quarter

equals to 1.9% and appears to be larger than the passive ownership.

30

(4)- (6) confirm the effects of passive fund ownership on the size of the short positions. This effect

is also both economically and statistically significant as one standard deviation increase in passive

fund ownership leads to one standard deviation increase in short interest.

*** Table 10***

The effects of passive fund ownership on lending fees and loan maturity are shown in Panel

B. Columns (1)-(3) show that passive ownership reduces lending fees with this effect being

statistically significant at the 10 percent level. This effect is economically large, a one standard

deviation increase in passive fund ownership is associated with one standard deviation reduction

in lending fees. Columns (4)-(6) presents the results for loan duration and documents negative

effect of passive fund ownership which is not statistically significant at the conventional levels.

In sum, our instrumental variables approach on the much smaller Russell reconstitution

sample yields results that are generally consistent with those obtained using the fixed-effects

methodology in our much larger sample.

5.5 Effects of Passive fund ownership On Price Impact of Short-Sale Constraints: Russell

Sample

We closely follow the methodology presented in Section 4 and split our sample into hard-

to-borrow specials as well as low-fee general collateral stocks. As our small sample consists of

large and liquid member of Russell indices, the number of stocks with lending fee above 2% per

year is highly limited. In particular, we are constrained to 126 stock-year observations while in the

large sample we use about 12,000 observations of specials. Accordingly, the smaller sample can

substantially restrict our ability to detect the casual effects of passive fund ownership on prices of

hard-to-borrow stocks.

31

Table 11 presents the results for the impact of passive fund ownership on stock prices.

Panel A shows the results for cross-autocorrelation variables and, despite very low number of

specials, confirms our previously documented findings. Column (1) shows that higher levels of

passive fund ownership result into faster incorporation of negative information in prices of special

stocks as measured by downside cross-autocorrelations with past market returns. Consistent with

our previous findings, column (2) confirms that there is no effect of passive fund ownership on

speed of price discovery in the sample of GC stocks that are less likely to experience short-sale

constraints. Columns (3)-(6) show the results are robust to different polynomial specifications.

The analysis of the effects of passive fund ownership on skewness (Panel B) and value premium

(Panel C) does not yield any significant results in our small sample of Russell specials. 21

*** Table 11***

In sum, we find similar results for downside cross-autocorrelations in a much smaller

Russell sample of special stocks, although the statistical significance is weaker relatively to our

initial large sample results.

5.6 Robustness Tests

In our main analysis, we follow Appel, Gormley and Keim (2018), and construct our

sample using 250 top stocks in Russell 2000 and 250 bottom stocks in Russell 1000. However, our

results are robust to using other than the 250 cutoff. Table B.1 in Appendix B examines the

robustness of our results to specifications using samples of 200, 300, 400 and 500 of top Russell

21 In the analysis of value premium, we face two endogenous variables – passive ownership and its interaction with

market-to-book. Consequently, we use two instrumental variables, Russell 2000 dummy and its interaction with

market-to-book in our econometric model.

32

2000 and bottom Russell 1000 stocks. We observe that our estimates as well as their significance

are generally not sensitive to the choice of bandwidth.

*** Table B1 in Appendix B***

To strengthen our assumption regarding the exclusion restriction, we study the relationship

between our instrument and other variables that have been shown to affect demand for shorting.

Table B.2 in Appendix B presents the result for three such variables: bid-ask-spreads, market-to-

book, and book value of assets, as they have been shown to be related to security lending outcomes

in Table 2. We observe that the relationship between inclusion in Russell 2000 and each of these

variables is not statistically significant, which allows us to gain additional confidence in our

identification strategy.

*** Table B2 in Appendix B***

To further support that our results are not contaminated by major variation in shorting

demand, we study the short-selling activity around reconstitution days and rank days. Figure B.1

in Appendix B presents the graphical results and show the lack of abnormal shorting activity

around both reconstitution and rank days. Table B.3 in Appendix B presents the results of

regression of daily short interest on dummy that equals one if a date falls into a 5-day window

around rank day (columns (1)-(2)) or reconstitution day (columns (3)-(4)). The regression results

confirm the previously shown graphical results illustrating the lack of irregular shorting activity

around these days.

*** Figure B1 and Table B3 in Appendix B***

We also implement an additional methodology developed by Coles, Heath and

Ringgenberg (2018) that utilizes Russell assignments thresholds in the post-2007 period similar to

33

that used by Appel, Gormley and Keim (2018). Appendix C briefly describes their approach as

well as presents the results which are generally similar to our previous findings.

Finally, we examine if the financial crisis interferes with our results. As in Fahlenbrach,

Prilmeier and Stulz (2012), we define the crisis period as 2007q3 to 2008q4, and exclude it from

our sample. None of our results changed significantly.

6. Conclusions

In this paper, we propose and analyze a security lending channel through which passive

investors can affect security prices. We find that passive funds operate as significant lenders of

shares to arbitrageurs and by doing so relax short-sale constraints. We empirically confirm the

effects of passive investors by showing that their security lending activities expand the supply of

lendable stock leading to larger short positions, lower lending fees and longer loan durations. As

a result, stocks with more passive fund ownership exhibit faster price discovery, lower likelihood

of large negative returns and a smaller value premium.

Our findings yield two main implications. First, recent research has argued the increase in

passive investing can make prices less efficient as these investors do not actively seek out and

utilize security-specific information when making investment decisions, and generate price

pressure. However, our study suggests that passive investors complement information-seeking

efforts of active investors who employ short-selling strategies. While our results do not resolve the

ongoing debate, they provide a channel by which passive investing increases amount of

information incorporated in prices. Specifically, the relaxation of short-sale constraints can lead to

more information being reflected in stock prices.

Second, our study argues for the inclusion of security lending activity in theoretical models

of passive and active investing. The recent advances in these area focuses on price pressure and

34

information acquisition, and do not take into account the effects of passive investing on short-sale

constraints. The incorporation of these effects into the theories of asset management can lead to a

better understanding of the aggregate effect of passive investing on financial markets.

35

References

Appel, Ian R, Todd A Gormley, and Donald B Keim. 2016. "Passive Investors, Not Passive

Owners." Journal of Financial Economics 121 (1): 111-141.

Appel, Ian R, Todd A Gormley, and Donald B Keim. 2018. "Standing on the Shoulders of

Giants: The Effects of Passive Investors on Activism." Review of Financial Studies.

Appel, Ian, Todd A. Gormley, and Donald B. Keim. 2019. "Identification Using Russell

1000/2000 Index Assignments: A Discussion of Methodologies." Working Paper.

Asquith, Paul, Parag A Pathak, and Jay R Ritter. 2005. "Short Interest, Institutional Ownership

and Stock Returns." Journal of Financial Economics 78 (2): 243-276.

Azar, Jose, Martin C Schmaltz, and Isabel Tecu. 2018. "Anticompetitive Effects of Common

Ownership." Journal of Finance 73 (4): 1513-1565.

Baklanova, Victoria, Cecilia Caglio, Frank Keane, and Burt Porter. 2016. A Pilot Survey of Agent

Securities Lending Activity. Staff Report, Federal Reserve Bank of New York.

Balklanova, Victoria, Adam Copeland, and Rebecca McCaughrin. 2015. Reference Guide to U.S.

Repo and Securities Lending Markets. Staff Report, Federal Reserve Bank of New York.

Baruch, Samuel, and Xiaodi Zhang. 2018. "Is Index Investing Benign?" Working Paper.

Basak, Suleyman, and Anna Pavlova. 2013. "Asset Prices and Institutional Investors." American

Economic Review 103 (5): 1728-1758.

Ben-David, Itzhak, Francesco Franzoni, and Rabih Moussawi. 2018. "Do ETFs Increase

Volatility?" Journal of Finance 73 (6): 2471-2535.

Blocher, Jesse, Adam V Reed, and Edward D Wesep. 2013. "Connecting Two Markets: An

Equilibrium Framework for Shorts, Longs, and Stock Loans." Journal of Financial

Economics 108 (2): 302-322.

Blocher, Jesse, and Bob Whaley. 2016. "Two-Sided Markets in Asset Management: Exchange-

traded Funds and Securities Lending ." Working Paper.

Boehmer, Ekkehart, Zsuzsa R Huszar, and Bradford J Jordan. 2010. "The Good News in Short

Interest." Journal of Financial Economics 96 (1): 80-97.

Bond, Philip, and Diego Garcia. 2017. "Informed Trading, Indexing, and Welfare." Working

Paper.

Boone, Audra, and Joshua T White. 2015. "The Effect of Institutional Ownership on Firm

Transparency and Information Production." Journal of Financial Economics 117 (3):

508-533.

36

Brav, Alon, Wei Jiang, and Tao Li. 2018. "Picking Friends Before Picking (Proxy) Fights: How

Mutual Fund Voting Shapes Proxy Contests." Working Paper.

Bris, Arturo, William N Goetzmann, and Ning Zhu. 2007. "Efficiency and the Bear: Short Sales

and Markets Around the World." Journal of Finance 62 (3): 1029-1079.

Brown, David, and Shaun William Davies. 2017. "Moral Hazard in Active Asset Management."

Journal of Financial Economics 125 (5): 311-325.

Carhart, Mike M. 1997. "On Persistence in Mutual Fund Performance." Journal of Finance 52

(1): 57-83.

Chang, Eric C, Joseph W Cheng, and Yinghiu Yu. 2007. "Short‐Sales Constraints and Price

Discovery: Evidence from the Hong Kong Market." Journal of Finance 62 (5): 2097-

2121.

Chang, Yen-Cheng, Harrison Hong, and Inessa Liskovich. 2015. "Regression Discontinutiy and

the Price Effects of Stock Market Indexing." Review of Financial Studies 28 (1): 212-

246.

Choi, Youngmin. 2017. "Complementarity of Passive and Active Investment on Stock Price

Efficiency." Working Paper.

Cohen, Lauren, Karl B Diether, and Christopher J Malloy. 2007. "Supply and Demand Shifts in

the Shorting Market." Journal of Finance 62 (5): 2061-2096.

Coles, Jeffrey L, Davidson Heath, and Matthew C Ringgerberg. 2018. "On Index Investing."

Working Paper.

Cong, Lin William, and Douglas Xu. 2019. Rise of Factor Investing: Asset Prices, Informational

Efficiency, and Security Design. Working Paper.

Crane, Alan D, Sebastien Michenaud, and James P Weston. 2016. "The Effect of Institutional

Ownership on Payout Policy: Evidence from Index Thresholds." Review of Finaical

Studies 29 (6): 1377-1408.

Cremers, Martijn, John A Fulkerson, and Timothy B Riley. 2018. "Benchmark Discrepancies

and Mutual Fund Performance Evaluation." Working Paper.

Daniel, Kent, Mark Grinblatt, Sheridan Titman, and Russ Wermers. 1997. "Measuring Mutual

Fund Performance with Characteristic‐Based Benchmarks." Journal of Finance 52 (3):

1035-1058.

D'Avolio, Gene. 2002. "The Market for Borrowing Stock." Journal of Financial Economics 66

(2): 271-306.

Desia, Hemang, Ramesh K, S. Ramu Thiagarajan, and Bala V Balachandran. 2002. "An

Investigation of the Informational Role of Short Interest in the Nasdaq Market." Journal

of Finance 57 (5): 2263-2287.

37

Diamond, Douglas W, and Robert E Verrecchia. 1987. "Constraints on Short-Selling and Asset

Price." Journal of Financia Economics 18 (2): 277-311.

Diether, Karl B, Kuan-Hui Lee, and Ingrid M Werner. 2009. "It's SHO Time! Short‐Sale Price

Tests and Market Quality." Journal of Finance 61 (1): 37-73.

Engelberg, Joseph E, Adam V Reed, and Matthew C Ringgenberg. 2018. "Short‐Selling Risk."

Journal of Finance 73 (2): 755-786.

Engelberg, Joseph E, Adam V Reed, and Matthew C. Ringgenberg. 2012. "How are Shorts

Informed?: Short Sellers, News, and Information Processing." Journal of Financial

Economics 105 (2): 260-278.

Eugene, Fama F, and Kenneth R French. 2010. "Luck versus Skill in the Cross‐Section of

Mutual Fund Returns." Journal of Finance 65 (5): 1915-1947.

Evans, Richard, Miguel A. Ferreira, and Melissa Porras Prado. 2017. "Fund Performance and

Equity Lending: Why Lend What You Can Sell?" Review of Finance 21 (3): 1093–1121.

Fahlenbrach, Rudiger, Robert Prilmeier, and Rene M. Stulz. 2012. "This Time Is the Same:

Using Bank Performance in 1998 to Explain Bank Performance during the Recent

Financial Crisis." Journal of Finance 67 (6): 2139-2185.

Fama, Eugene. 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work." The

Journal of Finance 25 (2): 383-417.

Frazzini, Andrea. 2006. "The Disposition Effect and Underreaction to News." Journal of

Finance 61 (4): 2017-2046.

French, Kenneth R. 2008. "Presidential Address: The Cost of Active Investing." Journal of

Finance 63 (4): 1537-1573.

Garleanu, Nicolae, and Lasse Heje Pedersen. 2018. "Active and Passive Investing." Working

Paper.

Garleanu, Nicolae, and Lasse Heje Pedersen. 2018. "Efficiently Inefficient Markets for Assets

and Asset Management." Journal of Finance 73 (4): 1663-1712.

Geczy, Christopher C, David K Musto, and Adam V Reed. 2002. "Stocks Are Special Too: An

Analysis of the Equity Lending Market." Journal of Financial Economics 66 (2): 241-

269.

Glosten, Lawrence, Suresh Nallareddy, and Yuan Zou. 2019. "ETF Activity and Informational

Efficiency of Underlying Securities." Working Paper.

Health, Davidson, Daniele Macciocchi, Roni Michaely, and Matthew C Ringgenberg. 2019. "Do

Index Funds Monitor?" Working Paper.

Hong, Harrison, and Jeremy C Stein. 2003. "Differences of Opinion, Short-Sales Constraints and

Market Crashes." Review of Financial Studies 16 (2): 487-525.

38

Hou, Kewei, and Tobias J Moskowitz. 2005. "Market Frictions, Price Delay, and the Cross-

Section of Expected Returns." Review of Financial Studies 18 (3): 981-1020.

Iliev, Peter, and Michelle Lowry. 2015. "Are Mutual Funds Active Voters?" Review of Financial

Studies 28 (2): 446-485.

Israeli, Doron, Charles M.C. Lee, and Suhas A Sridharan. 2017. "Is There a Dark Side to

Exchange Traded Funds? An Information Perspective." Review of Accounting Studies 22

(3): 1048-1083.

Jensen, Michael. 1968. "The Performance of Mutual Funds in the Period 1945-1964." Journal of

Finance 23 (3): 389-415.

Johnson, Travis, and Gregory Weitzner. 2018. "Distortions Caused by Lending Fee Retention."

Working Paper.

Jones, Charles M, and Owen A Lamont. 2002. "Short-Sale Constraints and Stock Returns."

Journal of Financial Economics 66 (2): 207-239.

Kacpercsyk, Marcin, Clemens Sialm, and Lu Zheng. 2005. "On the Industry Concentration of

Actively Managed Equity Mutual Funds." Journal of Finance 60 (4): 1983-2011.

Kacperczyk, Marcin, Stijn Van Nieuwerburgh, and Laura Veldkamp. 2014. "Time‐Varying Fund

Manager Skill." Journal of Finance 69 (4): 1455-1484.

Kaplan, Steven N, Tobias J Moskowitz, and Berk A Sensoy. 2013. "The Effects of Stock

Lending on Security Prices: An Experiment." Journal of Finance 68 (5): 1891-1936.

Kolasinski, Adam C, Adam V Reed, and Matthew C Ringgenberg. 2013. "A Multiple Lender

Approach to Understanding Supply and Search in the Equity Lending Market." Journal

of Finance 68 (2): 559-595.

Kosowski, Robert, Allan Timmermann, Russ Wermers, and Hal White. 2006. "Can Mutual Fund

“Stars” Really Pick Stocks? New Evidence from a Bootstrap Analysis." Journal of

Finance 61 (6): 2551-2595.

Lewellen, Jonathan. 2011. "Institutional Investors and the Limits of Arbitrage." Journal of

Financial Economics 102 (1): 62-80.

Miller, Edward M. 1977. "Risk, Uncertainty, and Divergence of Opinion." Journal of Finance 32

(4): 1151-1168.

Muravyev, Dmitriy, Neil D Pearson, and Joshua Matthew Pollet. 2018. "Understanding Returns

to Short Selling Using Option-Implied Stock Borrowing Fees." Working Paper.

Ofek, Eli, Matthew Richardson, and Robert F Whitelaw. 2004. "Limited Arbitrage and Short

Sales Restrictions: Evidence From the Options Markets." Journal of Financial

Economics 74 (2): 305-342.

39

Pastor, Lubos, Robert F Stambaugh, and Lucian A Taylor. 2017. "Do Funds Make More When

They Trade More?" Journal of Finance 72 (4): 1483-1528.

Prado, Melissa Porras, Pedro A. C. Saffi, and Jason Sturgess. 2016. "Ownership Structure,

Limits to Arbitrage, and Stock Returns: Evidence from Equity Lending Markets." Review

of Financial Studies 29 (12): 3211-3244.

Saffi, Pedro A C, and Kari Sigurdsson. 2011. "Price Efficiency and Short Selling." Review of

Financial Studies 24 (3): 821-852.

Schmidt, Cornelius, and Rudiger Fahlenbrach. 2017. "Do Exogenous Changes in Passive

Institutional Ownership Affect Corporate Governance and Firm Value?" Journal of

Financial Economics 124 (2): 285-306.

Sharpe, William F. 1991. "The Arithmetic of Active Management." Financial Analysts Journal

47 (1): 7-9.

Wei, Wei, and Alex Young. 2017. "Selection Bias or Treatment Effect? A Re-Examination of

Russell 1000/2000 Index Reconstitutions." Working Paper.

Xu, Jiangou. 2007. "Price Convexity and Skewness." Journal of Finance 62 (5): 2521-2552.

40

Figure 1

This figure presents the relationships between passive fund ownership and security lending

outcomes: lending supply, short interest, lending fee and loan duration. The figure uses binned-

scatter plots with 20 bins of passive fund ownership. We residualize all the variables on a set of

control variables such as ln (market capitalization), ln (book value of assets), market-to-book, bid-

ask spread as well as stock and quarter fixed effects. Detailed definitions of variables can be found

in the Appendix A.

41

Figure 2

This figure presents the relationships between passive fund ownership, downside cross-

autocorrelations and skewness of daily returns. The figure uses binned-scatter plots with 20 bins

of passive fund ownership. We residualize all the variables on a set of control variables such as ln

(market capitalization), ln (book value of assets), market-to-book, bid-ask spread as well as stock

and quarter fixed effects. Detailed definitions of variables can be found in the Appendix.

42

Table 1: Summary Statistics, Full Sample

This table presents summary statistics for our 2007-2017 quarterly panel of stocks. Detailed definitions of

variables can be found in the Appendix. Ownership variables are calculated using end-of-the-quarter ownership

as reported by Thomson Reuters Mutual Fund Holding database. The definitions of ownership types are based

on CRSP Mutual Fund database. The security lending variables are from Markit and represent daily averages

within each stock-quarter observation. The price impact and control variables are calculated using CRSP and

Compustat.

Stock-quarter level variables N Mean Std.

Dev.

Median Min. Max.

Ownership variables

Passive fund ownership (fraction) 121,405 0.06 0.05 0.06 0.00 0.55

Active fund ownership (fraction) 121,405 0.11 0.09 0.10 0.00 0.87

Total mutual fund ownership (fraction) 121,405 0.18 0.12 0.17 0.00 0.89

Non-mutual fund ownership (fraction) 121,109 0.45 0.20 0.48 0.00 0.99

Total institutional ownership (fraction) 121,405 0.62 0.27 0.69 0.00 1.00

Non-passive ownership (fraction) 121,109 0.56 0.25 0.61 0.00 1.00

Security lending variables

Lending supply (fraction) 121,383 0.19 0.11 0.20 0.00 0.42

Short interest (fraction) 121,326 0.03 0.04 0.02 0.00 0.24

Lending fee (fraction) 121,307 0.02 0.06 0.00 0.00 1.20

Loan duration (days) 121,326 80.53 67.86 63.52 3.17 463.86

Price impact variables

Downside cross-autocorrelation 121,232 -0.06 0.44 -0.05 -13.57 15.72

Upside cross-autocorrelation 121,237 0.00 0.38 -0.01 -9.57 10.46

Downside minus upside 121,209 -0.06 0.57 -0.05 -13.14 15.15

Skewness 121,289 0.24 1.32 0.19 -7.34 7.78

Control variables

Log(market value) 121,305 20.37 1.95 20.20 13.61 27.48

Log(book value) 112,549 19.71 1.83 19.53 6.91 26.59

Market-to-book 113,533 3.00 3.82 1.83 0.30 27.29

Bid-ask spreads (fraction) 121,305 0.01 0.01 0.00 0.00 0.31

43

Table 2: Effects of Passive Fund Ownership On Security Lending Outcomes

This table reports the results from regressing security lending outcomes on passive ownership and a set of control variables. Detailed

definitions of variables can be found in the Appendix. Panel A reports the results on quantity variables. Column (1) reports the baseline

specification for lending supply including quarter fixed-effects, column (2) adds control variables and column (3) adds stock fixed-

effects. Column (4) adds ownership of non-passive institutional investors as an additional control variable. Columns (5) - (8) repeat the

specifications from columns (1) - (4) using short interest as dependent variable. Panel B reports the results for fees (columns (1) - (4))

and loan duration (columns (5) - (8)). *, **, and *** denote statistical significance at 10%, 5% and 1% levels respectively. Standard

errors clustered by stock are in parentheses.

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A:Quantities

y = Lending supply y = Short interest

Passive fund ownership 2.12*** 1.83*** 0.82*** 0.78*** 0.29*** 0.35*** 0.20*** 0.18***

(0.02) (0.03) (0.02) (0.02) (0.01) (0.01) (0.02) (0.02)

Non-passive ownership 0.18*** 0.11***

(0.00) (0.00)

Log(market value) 0.01*** 0.01*** 0.00 0.00*** -0.01*** -0.01***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Log(book value) 0.00** 0.02*** 0.01*** -0.00*** 0.01*** 0.00***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Market-to-book -0.00*** 0.00*** 0.00*** -0.00*** 0.00*** 0.00***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Bid-ask spread -1.23*** -0.32*** -0.17*** -0.78*** -0.41*** -0.33***

(0.07) (0.04) (0.03) (0.03) (0.03) (0.02)

Observations 121,383 112,515 112,279 112,098 121,326 112,471 112,237 112,060

𝑅2 0.62 0.65 0.88 0.90 0.12 0.18 0.57 0.61

Quarter fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes

Stock fixed-effects No No Yes Yes No No Yes Yes

44

(1) (2) (3) (4) (5) (6) (7) (8)

Panel B:Fee and Loan

Maturity

y = Lending fee y = Loan duration

Passive fund ownership -0.31*** -0.20*** -0.04*** -0.03*** 159.52*** 325.77*** 146.03*** 140.21***

(0.01) (0.01) (0.01) (0.01) (14.30) (14.88) (21.51) (21.43)

Non-passive ownership -0.02*** 28.53***

(0.00) (4.48)

Log(Market value) 0.00*** -0.01** -0.00*** -7.67*** -18.63*** -20.24***

(0.00) (0.00) (0.00) (0.66) (1.15) (1.14)

Log(Book value) -0.00*** -0.00 -0.00 1.02 3.62*** 2.96***

(0.00) (0.00) (0.00) (0.65) (0.99) (0.98)

Market-to-book -0.00*** -0.00* -0.00* 0.16 0.22 0.21

(0.00) (0.00) (0.00) (0.21) (0.21) (0.20)

Bid-ask spread -0.06 -0.10** -0.12*** -396.53*** 129.94* 156.72**

(0.04) (0.04) (0.04) (67.78) (67.64) (68.54)

Observations 121,307 112,455 112,221 112,044 121,326 112,471 112,237 112,060

𝑅2 0.06 0.11 0.58 0.58 0.03 0.05 0.32 0.32

Quarter fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes

Stock fixed-effects No No Yes Yes No No Yes Yes

45

Table 3: Differential Impact of Institutional Investors on Security Lending Outcomes

This table reports the results from regressing security lending outcomes on ownership of various institutional investors. Detailed

definitions of variables can be found in the Appendix. Panel A reports the regression results. Column (1) reports the baseline

specification for lending supply including stock and quarter fixed-effects and column (2) adds ownership by non-mutual fund

institutional investors. Columns (3) and (4) repeat the specifications using short interest as the dependent variable. Columns (5) and (6)

report the results for lending fees and columns (7) and (8) report the results for loan duration. Panel B reports the p-values of test for the

differences between every pair of coefficients in Panel A. *, **, and *** denote statistical significance at 10%, 5% and 1% levels

respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A: Regressions

y = Lending supply y = Short interest y = Lending fee y = Loan duration

Passive fund ownership 0.77*** 0.76*** 0.16*** 0.16*** -0.04*** -0.04*** 138.39*** 137.98***

(0.02) (0.02) (0.02) (0.01) (0.01) (0.01) (19.69) (19.66)

Active fund ownership 0.19*** 0.25*** 0.11*** 0.14*** -0.01*** -0.02*** 58.75*** 65.27***

(0.01) (0.01) (0.01) (0.01) (0.00) (0.00) (6.99) (7.16)

Non-mutual fund ownership 0.17*** 0.10*** -0.02*** 20.56***

(0.00) (0.00) (0.00) (4.42)

Observations 112,279 112,098 112,237 112,060 112,221 112,044 112,237 112,060

𝑅2 0.89 0.90 0.59 0.62 0.58 0.58 0.34 0.35

Quarter fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes

Stock fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes

Control variables Yes Yes Yes Yes Yes Yes Yes Yes

Panel B: p-values of tests for differences between coefficients

𝐻𝑜: Active > Passive 0.00 0.07 0.07 0.00

𝐻𝑜: Non-mutual fund > Passive 0.00 0.00 0.09 0.00

𝐻𝑜: Non-mutual fund > Active 0.00 0.00 0.77 0.00

46

Table 4: Differential Impact of Institutional Investors on Security Lending Outcomes: Time-Series Analysis

This table reports the results from regressing security lending outcomes on ownership of various institutional investors. Detailed

definitions of variables can be found in the Appendix. The ownership variables are interacted with the dummy variable that equals one

if the year is 2011 or earlier. Columns (1) - (4) report the results for lending supply, short interest, lending fee and loan duration

respectively. *, **, and *** denote statistical significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock

are in parentheses.

(1) (2) (3) (4)

y = Lending supply y = Short interest y = Lending fee y = Loan duration

Passive fund ownership 0.81*** 0.16*** -0.03** 115.01***

(0.02) (0.02) (0.01) (22.13)

Active fund ownership 0.23*** 0.12*** -0.01** 75.11***

(0.01) (0.01) (0.01) (9.41)

Non-mutual fund ownership 0.14*** 0.08*** -0.02*** 22.07***

(0.01) (0.00) (0.00) (5.88)

Passive fund ownership × Before 2012 0.01 0.15*** 0.01 76.55***

(0.03) (0.02) (0.01) (25.17)

Active fund ownership × Before 2012 0.02** 0.02** -0.01* -24.92**

(0.01) (0.01) (0.01) (10.04)

Non-mutual fund ownership × Before 2012 0.04*** 0.01*** 0.00 0.40

(0.00) (0.00) (0.00) (5.77)

Observations 112,101 112,063 112,047 112,063

𝑅2 0.90 0.63 0.57 0.34

Quarter fixed-effects Yes Yes Yes Yes

Stock fixed-effects Yes Yes Yes Yes

Control variables Yes Yes Yes Yes

47

Table 5: Effect of Passive Fund Ownership on Cross-Autocorrelations

This table reports the results from regressing cross-autocorrelations on ownership of institutional investors. Detailed definitions of

variables can be found in the Appendix. Column (1) shows the results for specials (lending fee>2%) and column (2) reports the results

for general collateral stocks (lending fee <2%). Columns (3) - (4) repeat the specifications for upside cross-autocorrelations. Columns

(5) – (6) repeat the specification for the difference between the upside and the downside autocorrelations. *, **, and *** denote statistical

significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4) (5) (6)

Cross-Autocorrelation Downside Upside Downside Minus Upside

Special GC Special GC Special GC

Passive fund ownership -0.78** -0.12 0.01 -0.03 -0.79* -0.10

(0.31) (0.07) (0.26) (0.06) (0.41) (0.09)

Active fund ownership 0.04 0.09*** -0.07 0.00 0.11 0.08**

(0.13) (0.03) (0.12) (0.03) (0.17) (0.04)

Non-mutual fund ownership 0.06 -0.06*** -0.01 -0.01 0.07 -0.05**

(0.06) (0.02) (0.05) (0.02) (0.07) (0.02)

Observations 11,996 99,595 11,996 99,595 11,996 99,595

𝑅2 0.28 0.42 0.38 0.44 0.20 0.13

Controls Yes Yes Yes Yes Yes Yes

Quarter fixed effects Yes Yes Yes Yes Yes Yes

Stock fixed effects Yes Yes Yes Yes Yes Yes

48

Table 6: Effect of Passive Fund Ownership on Skewness

This table reports the results from regressing skewness on ownership of institutional investors. Detailed

definitions of variables can be found in the Appendix. Column (1) shows the results for specials (lending

fee>2%) and column (2) reports the results for general collateral stocks (lending fee <2%). *, **, and ***

denote statistical significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock are

in parentheses.

(1) (2)

Skewness

Special GC

Passive fund ownership -2.71*** -1.58***

(0.92) (0.24)

Active fund ownership 0.13 -0.77***

(0.46) (0.10)

Non-mutual fund ownership 0.13 0.25***

(0.19) (0.06)

Observations 12,012 99,616

𝑅2 0.19 0.08

Controls Yes Yes

Quarter fixed effects Yes Yes

Stock fixed effects Yes Yes

49

Table 7: Effects of Passive Fund Ownership on the Value Premium

This table reports the results from regressing annual future stock returns on ownership of institutional investors. In doing so, we closely

follow the methodology of Nagel (2005). Detailed definitions of variables can be found in the Appendix. Columns (1) and (2) show the

results for specials (lending fee>2%). Column (1) presents the baseline results and column (2) includes the interactions between market-

to-book and ownership variables. Columns (3) - (4) repeat the specifications for general collateral stocks (lending fee<2%). *, **, and

*** denote statistical significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4)

Special GC

Passive fund ownership -27.20*** -54.15*** -8.27*** -19.96***

(10.30) (18.72) (2.34) (5.13)

Active fund ownership -16.36* -12.10 -5.34** -10.10**

(8.86) (15.86) (2.08) (4.75)

Non-mutual fund ownership -22.27** -9.64 -3.37 -12.57**

(10.15) (14.42) (2.34) (5.35)

Market-to-Book -52.88*** -56.04*** -39.95*** -65.51***

(10.54) (13.63) (3.99) (8.74)

Passive fund ownership × Market-to-Book 44.13* 21.04***

(26.05) (7.57)

Active fund ownership × Market-to-Book -6.98 7.64

(24.09) (7.33)

Non-mutual fund ownership × Market-to-Book -21.97 16.89**

(23.85) (8.00)

Observations 12,005 12,005 99,606 99,606

𝑅2 0.49 0.49 0.38 0.38

Quarter fixed effects Yes Yes Yes Yes

Stock fixed effects Yes Yes Yes Yes

Controls Yes Yes Yes Yes

50

Table 8: Summary Statistics, Russell Sample

This table presents summary statistics for our 2007-2016 annual panel of stocks for Russell sample. For each

year the variables are calculated over the third quarter. Detailed definitions of variables can be found in the

Appendix. The ownership variables are calculated using end-of-the-quarter ownership as reported by Thomson

Reuters Mutual Fund Holding database. The definitions of ownership types are based on CRSP Mutual Fund

database. The security lending variables are from Markit and represent daily averages within each stock-quarter

observation. The price impact variables are calculated using CRSP stock database and Compustat.

Stock-3rd quarter level variables N Mean St Dev Median Min Max

Ownership variables

Passive fund ownership (fraction) 4,284 0.09 0.05 0.09 0.00 0.32

Active fund ownership (fraction) 4,283 0.18 0.09 0.17 0.00 0.63

Total mutual fund ownership (fraction) 4,283 0.27 0.12 0.28 0.00 0.81

Non-mutual fund ownership (fraction) 3,543 0.53 0.16 0.54 0.01 0.98

Total institutional ownership (fraction) 3,546 0.78 0.19 0.84 0.00 1.00

Non-passive ownership (fraction) 3,545 0.62 0.16 0.64 0.01 0.99

Security lending variables

Lending supply (fraction) 4,284 0.27 0.09 0.28 0.03 0.50

Short interest (fraction) 4,284 0.06 0.06 0.04 0.00 0.33

Lending fee (fraction) 4,284 0.01 0.02 0.00 0.00 0.51

Loan duration (days) 4,284 80.30 52.11 68.94 11.96 382.05

Price impact variables

Downside cross-autocorrelation 4,284 -0.11 0.43 -0.08 -1.92 1.39

Upside cross-autocorrelation 4,284 0.06 0.37 0.04 -1.38 1.95

Downside minus upside 4,284 -0.18 0.54 -0.15 -3.62 2.48

Skewness 4,284 0.11 1.34 0.05 -6.10 7.72

51

Table 9: Impact of Russell 2000 Membership on Institutional Ownership

This table reports the results from our first stage regressions for the relationship between Russell

2000 index assignment and ownership variables. Detailed definitions of variables can be found in

the Appendix. Panel A reports the result for ownership by passive funds. Column (1) reports the

results from the specification with first order polynomial. Columns (2) and (3) report the results

using the specification with second and third order polynomials. Panels B and C repeat the

specifications for active fund ownership and non-mutual fund ownership. *, **, and *** denote

statistical significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock

are in parentheses.

(1) (2) (3)

Panel A: y = Passive fund ownership

Russell 2000 0.02*** 0.02*** 0.02***

(0.00) (0.00) (0.00)

Observations 3,715 3,715 3,715

𝑅2 0.68 0.68 0.68

Banding controls Yes Yes Yes

Float controls Yes Yes Yes

Year fixed effects Yes Yes Yes

Polynomial Order, N 1 2 3

(1) (2) (3)

Panel B: y = Active fund ownership

Russell 2000 -0.01 -0.01 -0.01

(0.01) (0.01) (0.00)

Observations 3,714 3,714 3,714

𝑅2 0.23 0.23 0.23

Banding controls Yes Yes Yes

Float controls Yes Yes Yes

Year fixed effects Yes Yes Yes

Polynomial Order, N 1 2 3

(1) (2) (3)

Panel C: y = Non-mutual fund ownership

Russell 2000 -0.03 -0.03 -0.02

(0.02) (0.02) (0.00)

Observations 3,017 3,017 3,017

𝑅2 0.18 0.18 0.18

Banding controls Yes Yes Yes

Float controls Yes Yes Yes

Year fixed effects Yes Yes Yes

Polynomial Order, N 1 2 3

52

Table 10: Effect of Passive Fund Ownership on Security Lending Outcomes: 2SLS Regressions

This table reports the results from our instrumental variables regressions for the relationship between passive fund ownership and securities

lending outcomes. Detailed definitions of variables can be found in the Appendix. Panel A reports the results for quantities. Columns (1) – (3)

report the results for lending supply using polynomials of different orders. Columns (4) – (6) repeat the specifications for short interest. Panel B

presents the results for lending fees (columns (1) – (3)) and loan duration (columns (4) – (6)). *, **, and *** denote statistical significance at

10%, 5% and 1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4) (5) (6)

Panel A: Quantities

y = Lending Supply y = Short Interest

Passive fund ownership 2.26*** 2.26*** 1.83*** 1.03*** 1.04*** 0.80**

(0.38) (0.38) (0.35) (0.32) (0.34) (0.33)

Observations 3,715 3,715 3,715 3,715 3,715 3,715

Banding Controls Yes Yes Yes Yes Yes Yes

Float controls Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

Polynomial Order, N 1 2 3 1 2 3

(1) (2) (3) (4) (5) (6)

Panel B: Fee and Loan Maturity

y = Lending Fee y = Loan Duration

Passive fund ownership -0.18* -0.19* -0.19* 276.74 281.82 149.01

(0.10) (0.11) (0.11) (264.91) (264.07) (285.08)

Observations 3,715 3,715 3,715 3,715 3,715 3,715

Banding Controls Yes Yes Yes Yes Yes Yes

Float controls Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

Polynomial Order, N 1 2 3 1 2 3

53

Table 11: Effect of Passive Fund Ownership on Price Impact Variables: 2SLS Regressions

This table reports the results from our instrumental variables regressions for the relationship between passive fund ownership and securities prices.

Detailed definitions of variables can be found in the Appendix. Panel A reports the results for downside cross-autocorrelation. Column (1) report

the results for specials (lending fee>2%) and column (2) reports the results for general collateral stocks (lending fee <2%). Columns (3) – (4)

repeat the specification using second order polynomial and columns (5) – (6) use third order polynomial. Panel B repeats the splits and the

specifications for skewness. Panel C presents the results for future annual stock returns adding market-to-book and its interaction with passive

fund ownership as explanatory variables. *, **, and *** denote statistical significance at 10%, 5% and 1% levels respectively. Standard errors

clustered by stock are in parentheses.

(1) (2) (3) (4) (5) (6)

Panel A: Downside Cross-Autocorrelation Special GC Special GC Special GC

Passive fund ownership -17.03* -0.58 -14.72* -0.58 -14.74* -0.13

(10.19) (1.41) (7.92) (1.42) (8.03) (1.55)

Observations 126 3,589 126 3,589 126 3,589

Banding Controls Yes Yes Yes Yes Yes Yes

Float controls Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

Polynomial Order, N 1 1 2 2 3 3

(1) (2) (3) (4) (5) (6)

Panel B: Skewness Special GC Special GC Special GC

Passive fund ownership 41.33 -1.91 47.08 -1.90 47.19 -4.81

(47.00) (7.29) (47.87) (7.17) (48.06) (7.73)

Observations 126 3,589 126 3,589 126 3,589

Banding Controls Yes Yes Yes Yes Yes Yes

Float controls Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

Polynomial Order, N 1 1 2 2 3 3

54

(1) (2) (3) (4) (5) (6)

Panel C: Value Premium Special GC Special GC Special GC

Passive fund ownership -269.77 -23.40 -284.94 -23.49 -327.53 -25.27

(235.66) (14.61) (234.57) (14.62) (270.92) (16.01)

Market-to-Book 46.21 -4.11 54.67 -4.25 64.66 -3.41

(269.39) (12.88) (304.38) (12.87) (308.94) (13.14)

Passive fund ownership × Market-to-Book -159.21 -1.60 -178.38 -1.32 -209.12 -3.58

(585.12) (25.45) (663.22) (25.38) (678.31) (26.35)

Observations 111 3,403 111 3,403 111 3,403

Banding Controls Yes Yes Yes Yes Yes Yes

Float controls Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

Polynomial Order, N 1 1 2 2 3 3

55

Appendix A

Table A.1: Definition of Variables

Variable name Source Definition

Ownership variables:

Passive fund ownership Thomson Reuters S12

Mutual Fund Holdings

Percentage of shares outstanding held by

passively managed funds

Active fund ownership Thomson Reuters S12

Mutual Fund Holdings

Percentage of shares outstanding held by

actively managed funds

Total mutual fund

ownership

Thomson Reuters S12

Mutual Fund Holdings

Percentage of shares outstanding held by

mutual funds

Total institutional

ownership

Thomson Reuters S34

Institutional Holdings

Percentage of shares outstanding held by

institutional investors

Non-mutual fund ownership Thomson Reuters S34

Institutional Holdings

Difference between total institutional

ownership and total mutual fund ownership

Non-passive ownership Thomson Reuters S34

Institutional Holdings

Difference between total institutional

ownership and passive fund ownership

Security lending variables:

Lending supply Markit Percentage of shares actively available for

lending, as indicated by “Active Lending

Supply” in Markit

Short interest Markit Percentage of shares on loan as indicated by

“Quantity on Loan” in Markit

Lending fee Markit Lending fee as indicated by “Indicative Fee”

in Markit

Loan duration Markit Duration of the average loan for a given

security as indicated by “Average Tenure” in

Markit

Price impact variables:

Downside cross-

autocorrelation

CRSP Correlation between stock returns in time t

and CRSP value-weighted index returns in

time t-1, conditional on index return being

negative

56

Variable name Source Definition

Upside cross-

autocorrelation

CRSP Correlation between stock returns in time t

and CRSP value-weighted index returns in

time t-1, conditional on index return being

positive

Downside minus upside

cross-autocorrelation

CRSP Difference between downside cross-

autocorrelation and upside cross-

autocorrelation

Skewness CRSP Skewness of daily log-returns

Other variables:

Log(market value) CRSP Natural logarithm of market capitalization

Log(book value) Compustat Natural logarithm of book value of equity

Market-to-book CRSP, Compustat Ratio of market capitalization to book value

of equity

Bid-ask spread CRSP Closing daily bid-ask spread scaled by price

R2000 Russell Investments Indicator equals one if firm is in the Russell

2000, and zero otherwise

57

Appendix B

Table B.1: Impact of Passive Fund Ownership on Security Lending Outcomes: 2SLS Regressions - Different Sample Cutoffs

This table reports the results from our instrumental variables regressions for the relationship between passive fund ownership and securities

lending outcomes using different Russell cutoffs. Detailed definitions of variables can be found in the Appendix. In all the specifications, we

control for market capitalization using second order polynomials. Panel A reports the results for lending supply. Columns (1) shows the

estimates for a sample of 200 top stocks in Russell 2000 and 200 bottom stocks in Russell 1000. Columns (2) – (3) repeat the specifications

using 300, 400 and 500 cutoffs. The further panels repeat the specification for short interest, lending fees, loan duration as well as for downside

cross-autocorrelation and skewness in the samples of special stocks (lending fee>2%). *, **, and *** denote statistical significance at 10%,

5% and 1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4)

Panel A: Lending Supply 200 300 400 500

Passive fund ownership 2.27*** 2.17*** 2.07*** 2.03***

(0.53) (0.35) (0.31) (0.31)

Observations 2,977 4,457 5,927 7,428

(1) (2) (3) (4)

Panel B: Short Interest 200 300 400 500

Passive fund ownership 1.13** 0.82*** 1.25*** 1.47***

(0.50) (0.27) (0.25) (0.25)

Observations 2,977 4,457 3,715 7,428

(1) (2) (3) (4)

Panel C: Lending Fee 200 300 400 500

Passive fund ownership -0.11 -0.13* -0.14* -0.13*

(0.10) (0.07) (0.07) (0.07)

Observations 2,977 4,457 5,927 7,428

58

(1) (2) (3) (4)

Panel D: Loan Duration 200 300 400 500

Passive fund ownership 119.35 344.39 526.59*** 503.10***

(360.81) (220.88) (190.20) (182.78)

Observations 2,977 4,457 5,927 7,428

(1) (2) (3) (4)

Panel E: Downside Cross-

Autocorrelation-Specials

200 300 400 500

Passive fund ownership -6.79*** -12.53** -4.57* -6.87**

(1.81) (5.40) (2.50) (3.19)

Observations 112 142 192 235

(1) (2) (3) (4)

Panel F: Skewness-Specials 200 300 400 500

Passive fund ownership 4.35 49.93 33.71 35.85

(5.68) (40.08) (23.35) (25.12)

Observations 112 142 192 235

59

Table B.2: Impact of Russell 2000 Membership on Stock Characteristics

This table reports the results from our first stage regressions for the relationship between Russell 2000 index

assignment and stock characteristics. Detailed definitions of variables can be found in Appendix. Panel A reports the

result for bid-ask spread. Column (1) reports the results from the specification with first order polynomial. Columns

(2) and (3) report the results using the specification with second and third order polynomials. Panels B and C repeat

the specifications for market-to-book and for book value. *, **, and *** denote statistical significance at 10%, 5% and

1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3)

Panel A: y = Bid-ask spread

Russell 2000 -0.00 -0.00 -0.00

(0.00) (0.00) (0.00)

Observations 3,715 3,715 3,715

𝑅2 0.33 0.33 0.33

Banding controls Yes Yes Yes

Float controls Yes Yes Yes

Year fixed effects Yes Yes Yes

Polynomial Order, N 1 2 3

(1) (2) (3)

Panel B: y = Market-to-book

Russell 2000 -0.23 0.01 0.44

(0.84) (1.43) (0.81)

Observations 3,715 3,715 3,715

𝑅2 0.01 0.02 0.04

Banding controls Yes Yes Yes

Float controls Yes Yes Yes

Year fixed effects Yes Yes Yes

Polynomial Order, N 1 2 3

(1) (2) (3)

Panel B: y = Log(Book value)

Russell 2000 -0.16 -0.16 -0.19

(0.11) (0.11) (0.00)

Observations 3,715 3,715 3,715

𝑅2 0.19 0.19 0.19

Banding controls Yes Yes Yes

Float controls Yes Yes Yes

Year fixed effects Yes Yes Yes

Polynomial Order, N 1 2 3

60

Figure B.1: Short Interest Around Rank Days and Reconstitution Days

This figure shows the average daily short interest around rank days and reconstitution days. Detailed definitions of

variables can be found in the Appendix.

61

Table B.3: Short Interest Around Rank Days and Reconstitution Days

This table reports the results from regressing short interest on a dummy variable that equals one if a date falls into a

5-day window around Russell rank day or Russell reconstitution day in a given year. Detailed definitions of variables

can be found in the Appendix. Column (1) reports the baseline results for rank days and Column (2) adds stock fixed

effects. Columns (3) and (4) repeat the specifications for reconstitution days. *, **, and *** denote statistical

significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4)

Rank day Rank day Reconstitution

day

Reconstitution

day

y=Short Interest y=Short Interest y=Short Interest y=Short Interest

(0,1) In 5-day window -0.00 -0.00 -0.00 -0.00

(0.00) (0.00) (0.00) (0.00)

Observations 2,296 2,296 2,296 2,296

𝑅2 0.04 0.96 0.05 0.96

Year fixed effects Yes Yes Yes Yes

Stock fixed effects No Yes No Yes

62

Appendix C

Description of Methodology Based on Coles, Heath and Rinngenberg (2018)

1. Sample selection

We select our sample following the procedure described in Coles, Heath and Rinngenberg (2018; further

referred to as CHR). Table C.1 presents the descriptive statistics for this sample. The variables of interest are

calculated for the 3rd quarter in any given year as this quarter exactly follows the annual June reconstitution. For

the average stock, 9% are owned by passive funds and 19% are owned by active funds. The overall level of

institutional ownership is 78%.

2. Estimation

We use the inclusion into Russell 2000 as an instrument for ownership of passive funds. As the stocks are

ranked based on their market capitalization and the sampling is based on the two different cutoffs, the rank and the

cutoff (which changes from year to year) can directly affect the level of passive fund ownership irrespective of

index assignment. Therefore, we include both stock ranks and year fixed- effects interacted with the assignment

cutoffs in our specifications. In particular, we estimate the following first stage regression:

𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 = 𝛽 ∙ 𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡 + 𝑓(𝑅𝑎𝑛𝑘𝑖,𝑡) ∙ 𝑈𝑝𝑝𝑒𝑟𝐶𝑢𝑡𝑜𝑓𝑓𝑖,𝑡 + 𝛾𝑡 ∙ 𝑈𝑝𝑝𝑒𝑟𝐶𝑢𝑡𝑜𝑓𝑓𝑖,𝑡 + 휀𝑖,𝑡

where 𝑃𝑎𝑠𝑠𝑖𝑣𝑒𝑖,𝑡 is the amount of passive ownership for stock i in year t, 𝑅𝑢𝑠𝑠𝑒𝑙𝑙2000𝑖,𝑡 is an indicator

variable equal to one if the stock is included in Russell 2000 in year t, 𝑈𝑝𝑝𝑒𝑟𝐶𝑢𝑡𝑜𝑓𝑓𝑖,𝑡 is an indicator variable equal

to one if the stock i belongs to the upper cutoff sample in year t and 𝛾𝑡 is a year fixed effect. 𝑓(𝑅𝑎𝑛𝑘𝑖,𝑡) is a

polynomial control function of rank of stock i in year t. Finally, we cluster our standard errors at the individual

stock level.

63

Our second stage estimation mirrors the specification from the first stage and estimates the effects of

passive fund ownership on security lending and efficiency variables. In particular, we implement the following

regression model:

𝑦𝑖,𝑡 = 𝛽 ∙ 𝑃𝑎𝑠𝑠𝑖𝑣𝑒̂𝑖,𝑡 + 𝑓(𝑅𝑎𝑛𝑘𝑖,𝑡) ∙ 𝑈𝑝𝑝𝑒𝑟𝐶𝑢𝑡𝑜𝑓𝑓𝑖,𝑡 + 𝛾𝑡 ∙ 𝑈𝑝𝑝𝑒𝑟𝐶𝑢𝑡𝑜𝑓𝑓𝑖,𝑡 + 휀𝑖,𝑡

where 𝑦𝑖,𝑡 is an outcome of interest for stock i in year t and 𝑃𝑎𝑠𝑠𝑖𝑣𝑒̂𝑖,𝑡 is the predicted level of passive fund

ownership for stock i in year t from the first stage estimation.

Table C.2 presents the effects of passive fund ownership on security lending outcomes employing the CHR

approach. Panel A focuses on quantities and columns (1) - (3) show the effect of passive fund ownership on lending

supply. Our identification strategy confirms that more ownership by passive investors results in greater supply of

shares to short-sellers. The effect is economically meaningful such that an increase in one standard deviation in

passive fund ownership is associated with an increase of one standard deviation in lending supply. Columns (4) –

(6) confirm the effects of passive fund ownership on the size of the short positions. This effect is also both

economically and statistically significant.

The effect of passive fund ownership on lending fees and loan maturity are shown in Panel B. Columns (1)

- (3) show that more ownership by passive investors generally lowers the lending fees. While the coefficient is

economically sizable and is even larger than the coefficient obtained in the full sample, it is not statistically

significant at conventional levels. Columns (4) - (6) presents the results for loan duration but the coefficients are not

statistically significant.

64

Table C.1: Summary Statistics, Russell Sample, CHR Approach

This table presents summary statistics for our 2007-2016 annual panel of stocks for the cutoff sample using CHR

approach. For each year the variables are calculated over the third quarter. Detailed definitions of variables can be

found in the Appendix. The ownership variables are calculated using end-of-the-quarter ownership as reported by

Thomson Reuters Mutual Fund Holding database. The definitions of ownership types are based on CRSP Mutual

Fund database. The security lending variables are from Markit and represent daily averages within each stock-

quarter observation. The price impact variables are calculated using CRSP stock database and Compustat.

Stock-3rd quarter level variables N Mean Std.

Dev.

Median Min. Max.

Ownership variables

Passive fund ownership (fraction) 801 0.09 0.05 0.09 0.00 0.32

Active fund ownership (fraction) 800 0.19 0.10 0.18 0.00 0.63

Total mutual fund ownership (fraction) 800 0.28 0.12 0.28 0.00 0.76

Non-mutual fund ownership (fraction) 631 0.53 0.16 0.55 0.03 0.97

Total institutional ownership (fraction) 630 0.78 0.21 0.85 0.01 1.00

Non-passive ownership (fraction) 631 0.62 0.19 0.75 0.03 0.99

Security lending variables

Lending supply (fraction) 801 0.26 0.09 0.27 0.02 0.45

Short interest (fraction) 801 0.07 0.06 0.04 0.00 0.36

Lending fee (fraction) 801 0.01 0.03 0.00 0.00 0.51

Maturity (days) 801 76.68 48.87 65.88 11.17 270.26

Price impact variables

Downside cross-autocorrelation 801 -0.12 0.43 -0.10 -2.09 1.26

Upside cross-autocorrelation 801 0.04 0.38 0.04 -1.00 1.30

Downside minus upside 801 -0.18 0.60 -0.15 -4.53 2.48

Skewness 801 0.19 1.42 0.08 -5.44 6.93

65

Table C.2: Impact of Passive Fund Ownership on Security Lending Outcomes: 2SLS Regressions, CHR

Approach

This table reports the results from our instrumental variables regressions for the relationship between passive fund

ownership and securities lending outcomes using CHR approach. Detailed definitions of variables can be found in the

Appendix A. Panel A reports the results for quantities. Columns (1) – (3) report the results for lending supply using

polynomials of different orders. Columns (4) – (6) repeat the specifications for short interest. Panel B presents the

results for lending fees (columns (1) – (3)) and loan duration (columns (4) – (6)). *, **, and *** denote statistical

significance at 10%, 5% and 1% levels respectively. Standard errors clustered by stock are in parentheses.

(1) (2) (3) (4) (5) (6)

Panel A: Quantities

y = Lending Supply y = Short Interest

Passive fund ownership 2.05*** 2.23*** 2.23*** 0.89** 0.93** 0.94**

(0.37) (0.37) (0.40) (0.35) (0.37) (0.40)

Observations 797 797 797 797 797 797

Year x band fixed-effects Yes Yes Yes Yes Yes Yes

Polynomial order, N 1 2 3 1 2 3

(1) (2) (3) (4) (5) (6)

Panel B: Fee and Loan Maturity

y = Lending Fee y = Loan Duration

Passive fund ownership -0.12 -0.12 -0.11 -51.71 -76.39 -57.39

(0.11) (0.12) (0.12) (273.92) (272.35) (281.38)

Observations 797 797 797 797 797 797

Year x band fixed-effects Yes Yes Yes Yes Yes Yes

Polynomial order, N 1 2 3 1 2 3