Electronic copy available at: http://ssrn.com/abstract=1947503

1

Affiliated Agents, Boards of Directors, and Mutual Fund

Securities Lending Returns♣♣♣♣

John C. Adams, Sattar A. Mansi, and Takeshi Nishikawa

February 18, 2013

Abstract

Prior SEC inquiries concerning self-dealing in securities lending programs suggest potential conflicts of interest when funds employ lending agents that are affiliated with their sponsor. We posit that the level of self-dealing is potentially greater, and mutual funds’ securities lending returns are lower, when funds employ sponsor affiliated lending agents. Using a manually collected U.S. index mutual fund sample, we find that funds with sponsor affiliated lending agents have lower annual returns on lent securities and that securities lending returns are significantly higher when funds administer their own lending programs. We also find that mutual funds boards of directors provide a monitoring role in the securities lending market. Specifically, we document that multiple board of director appointments, more director fund ownership, higher board independence, and lower excess director compensation are associated with higher lending returns. Overall, the evidence has implications for mutual fund boards as they consider lending proposals from affiliated agents and for future regulatory actions.

Keywords: Securities Lending; Governance; Affiliated Agents; Mutual Fund Board Structure

JEL Classification: G34, G32, G20

♣ Adams is at University of Texas at Arlington, Mansi is at Virginia Tech, and Nishikawa is at the

University of Colorado at Denver. The authors would like to thank Jason Zweig from the Wall Street Journal for comments and suggestions that helped improve the paper. The remaining errors are ours.

Electronic copy available at: http://ssrn.com/abstract=1947503

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I. Introduction

In 2004, the SEC launched an investigation into the management of securities lending

programs for mutual funds administered by a number of investment houses including State

Street, Bank of New York-Mellon, Northern Trust, and JP Morgan. The investigation revealed

potential conflicts of interest when funds employed securities lending agents that were

affiliated with their sponsor.1 It also revealed numerous instances where sponsors manipulated

the bidding process so that mutual fund boards would select affiliated over non-affiliated

lending agents and that most boards failed to adequately oversee securities lending programs.

Further concerns were expressed about off the books side payments not being disclosed to fund

shareholders, lending proceeds not going back to lending funds, and that securities lending

may only be profitable to the fund’s sponsor or lending agent and not the shareholders.2 The

result is a wide-spread criticism and lawsuits alleging that many securities lending agents either

misled investors about the risks of lending securities or inappropriately invested lending

collateral in risky investments that resulted in significant losses during the market crash of

2008.3 Despite these concerns anecdotal evidence points to an increase in the use of securities

lending as a means to enhance returns and the market has grown to over $12 trillion as of 2012.4

In this paper, we examine whether affiliated lending agents and mutual fund boards affect

securities lending returns in a hand collected sample of U.S. index mutual funds. This issue is

important as securities lending income benefits shareholders by offsetting fund expenses. The

extant literature largely ignores agency considerations between beneficial owners and lending

agents. Conflicts of interests are potentially severe in securities lending arrangements because

fund sponsors (or their corporate parents) have financial incentives to favor affiliated lending

agents.5 Although it is customary for mutual funds and lending agents to split lending

1 See Tom Lauricella, SEC Discovers Breaches in Lending Securities, Wall Street Journal (Jan. 29, 2007). The term

sponsor refers to the investment company which manages the fund. The term affiliated refers to sponsors and lending agents that are part of the same corporate entity.

2 Robert Wittie, SEC Concerns With Securities Lending by Mutual Funds, 12th Annual Beneficial Owners’ Summit on Domestic and International Securities Lending and Repo (Feb. 2006).

3 See, for example, Jason Zweig, Is Your Fund Pawning Shares at Your Expense?, Wall Street Journal (May 30, 2009), Emily Lambert, Securities Lending Meltdown, Forbes (June 22, 2009), and Louise Story, Banks Shared Clients’ Profits, but Not Losses, New York Times (October 17, 2010).

4 See “Securities Lending Best Practices,” by eSEC Lending, 2012 report. 5 It is well documented that funds can increase investor cash inflows by increasing fund performance. One way

to increase fund performance is to maximize securities lending returns. However, what matters is the marginal effect to the corporate parent (i.e., collecting higher fees versus increased cash flows to the funds they manage).

3

proceeds, details on these fee splits are rarely disclosed to investors. This lack of transparency

likely exacerbates agency problems associated with self-dealing behavior.

We posit that the level of self-dealing is potentially greater, and mutual funds’ securities

lending returns are lower, when funds employ sponsor affiliated lending agents. Mutual funds

can select their custodian bank or a third party specialist (i.e., broker/dealer), both of which

may be affiliated with their sponsors, as their lending agent. Mutual funds can also act as their

own securities lending agents. They can utilize an auction service, grant a principal

intermediary or other third party specialist rights to all or portion of their portfolios, or lend

directly to borrowers (e.g., such as hedge funds, market makers, and broker/dealers).

Alternatively, mutual funds can use some combination of custodian, third party specialist, and

self-lending routes to the securities lending market (i.e., seeking competitive prices from various

agents and borrowers for each security to be loaned).6 Self-lending funds likely retain a greater

portion of the proceeds from lending and are able to mitigate the conflicts of interest inherent in

all, and especially affiliated agent, lending agreements. Therefore, we expect that securities

lending returns are higher when funds administer their own lending programs.7

We also examine whether mutual funds boards of directors play a role in monitoring

securities lending programs. The Investment Company Act of 1940 (ICA) charges independent

directors with protecting mutual fund investors from conflicts of interests. The SEC has also

interpreted Rule 38a-1 of the ICA to subject securities lending agents to the review and

oversight of the fund’s board of directors.8 Therefore, we hypothesize that the effectiveness of

board oversight is associated with increased securities lending returns. Tufano and Sevick

(1997) report that as boards become increasingly independent of managers, their monitoring

effectiveness increases. Del Guercio, Dann, and Partch (2003) and Adams, Mansi, and

Nishikawa (2010) find that board size is negatively related to overall performance. Chen,

Goldstein, and Jiang (2008) and Cremers et al. (2009) find that director ownership in the funds

they oversee is associated with improved shareholder value. Similarly, Khorana, Tufano, and

6 See the appendix for a detailed discussion on securities lending agreements. 7 We acknowledge that variation in securities lending returns across funds may be partly driven by collateral re-

investment restrictions that we cannot observe since funds typically do not disclose that information. 8 These guidelines state that boards must approve all components of a fund’s securities lending policies

including who will be the agent, the level of fees, to whom the securities will be lent, loan parameters, and lending agent compensation – especially how funds and lending agents split the fees earned by lending.

4

Wedge (2007) examine mergers in the mutual fund industry and find that sponsor interests

often conflict with shareholder interests when directors are highly compensated. Overall, the

evidence indicates that the opaque nature of securities lending programs provides an enhanced

role for boards of directors.

Finally, we examine the relation between lending attributes and the likelihood of operating

a securities lending program. Fabozzi and Mann (2005) note that lender characteristics such as

large portfolios, large holdings of desirable securities, low asset turnover, and organizational

support (e.g., resources to support efficient lending practices) are valued by borrowers.

Sponsors have an incentive to lend so that they (or an affiliated agent) can capture a portion of

lending proceeds. Sponsors favor lending since it enhances performance and this in turn attracts

future cash inflows (see Sirri and Tufano, 1998). Therefore, we expect the likelihood of operating

a securities lending program to increase when funds are large, funds have more desirable

securities, and when mutual fund investors are especially sensitive to performance.

Using manually collected securities lending data of 1,374 fund-year observations covering

the period from 2003 to 2010, we find that securities lending returns are 40% lower than the

sample means when funds use sponsor affiliated lending agents. We confirm these results by

examining securities lending returns during the 2008 financial crisis and documenting the

negative effect of affiliated agent choice is four times larger during the crises than in other years.

We also find that lending returns are negatively associated with excess director pay, and

positively associated with board independence, directors with multiple board appointments,

and director ownership. We further document that the probability of lending increases with

fund and sponsor size and with increased institutional ownership. The latter suggesting that

fund managers lend to increase fund performance and attract institutional cash flows.

Additional testing reveals that securities lending returns are significantly higher when funds

administer their own lending programs.

Our paper is directly related to recent research by Evans, Ferreira, and Prado (2013) on

securities lending in the mutual fund industry. Evans et al. find that lending funds

underperform non-lending funds and this underperformance is caused by investment

restrictions that prevent funds from selling stocks with high borrower demand. They also find

that restricted funds partially offset future stock underperformance by lending securities they

are unable to sell. The next closest studies are those of Kaplan, Moskowitz, and Sensoy (2011)

5

who examine supply shocks and lending fees, Geczy, Musto, and Reed (2002) who examine

borrower returns from numerous strategies that involve short-selling, and Kolasinski, Reed, and

Ringgenberg (2013) who document considerable variation in fees across lending agents and

conclude that lenders benefit from an opaque lending market. Our paper differs from theirs in

that we investigate securities lending returns to the beneficial owners of lent securities rather

than to borrowers and securities lending agents.9

This study makes three main contributions to the literature on the securities lending market

and mutual funds. First, we examine securities lending agency issues from the perspective of

mutual funds who are the actual owners of the lent securities. This issue is of importance

especially in light of the recent SEC investigation into the management of securities lending

programs by various investment banks. We focus on index funds since they are widely held and

provide for natural and active lenders as they often have large positions and follow buy and

hold investment strategies thereby reducing recall risk to borrowers (D’Avolio, 2002; Duffie et

al., 2002). Second, we provide evidence of wealth expropriation from shareholders when funds

employ sponsor affiliated lending agents. This evidence is consistent with recent studies

examining the agency effects of sponsor affiliations on shareholder wealth. Hao and Yan (2012)

find that investment bank affiliated funds underperform unaffiliated funds in part because they

are more likely to hold underperforming securities of affiliated investment banks’ clients.

Bhattacharya, Lee, and Pool (2013) report that affiliated funds of mutual funds provide an

uncompensated insurance benefit to other funds in the family. Third, our results have

implications for mutual fund boards as they consider proposals from affiliated agents, fund

management deliberations as to whether and how to implement a securities lending program,

and for possible future regulatory actions.

The remainder of the paper is organized as follows. Section II discusses the sample, variable

measurements, and descriptive statistics. Section III presents our results on securities lending

returns, securities lending program size, and incidence of securities lending in a multivariate

framework. Section IV concludes.

9 Only one of the twelve lenders in the sample used by Kolasinski, Reed, and Ringgenber (2013) is a mutual fund,

nine are either agent or broker dealers, and two are hedge funds.

6

II. Data and Variable Measures

A. Sample

We use information from the SEC’s EDGAR and Morningstar’s mutual fund databases to

compile the sample. We manually collect securities lending data of index mutual funds from the

annual and semiannual Certified Shareholder Reports, Forms N-CSR and N-CSRS, which are

located in the SEC’s EDGAR database. SEC regulations began requiring registered investment

advisors to certify financial statements and other information in Form N-CSR filings in 2003 in

response to the Sarbanes-Oxely Act of 2002. In addition, we manually gather data from EDGAR

on each fund’s board of trustees (directors) from the Statement of Additional Information (SAI),

which is located in Form 485 of each fund’s prospectus.

We identify index funds from the December 31 editions of the Morningstar Direct mutual

fund database for the period from 2003 to 2010. The Morningstar database contains monthly

class-level returns and total net assets as well as yearly information including investment

objective, 12b-1 fees, expense ratios, management fees, and purchase constraints (e.g.,

institutional share classes). We use the monthly total net assets and fund return information to

compute the monthly standard deviation of funds’ net cash flows from investors (i.e., cash flow

volatility). For each fund, we also gather sponsor-level information including the number of

funds managed and the total net assets from all funds under management. Since many mutual

funds have multiple share classes that differ primarily in expenses, loads, and clientele, we

combine the different classes into a single fund. Specifically, we compute the net asset value

weighted average of the class-level data items. Merging the data and applying these

requirements yields a dataset of 1,374 fund-year observations on 204 funds from 78 sponsors

covering the period from 2003 to 2010.10

B. Measuring securities lending activity

We manually collect securities lending information from the financial statements and

accompanying notes of each fund. For each year of the sample, we record the securities lending

income from each fund’s statement of operations, which is located in Form N-CSR. When

10 We track funds through sponsor level mergers and acquisitions.

7

earnings from securities lending are less than 5% of gross income, Regulation S-X permits funds

to combine securities lending income with the proceeds of other miscellaneous earnings in the

“other income” category. In these instances we record the income from securities lending as

zero.

Forms N-CSR and N-CSRS list the outstanding dollar value of funds’ securities loaned

inclusive of the collateral posted by borrowers as a liability in the statement of assets and

liabilities (Securities Lending Liabilities). Since the level of lending activity can vary throughout

the accounting period, we compute the average of securities on loan at the beginning (prior year

Form N-CSR), middle (Form N-CSRS), and end of each year (current year Form N-CSR).

Securities lending income increases performance by offsetting operating expenses. To illustrate

the overall economic effect of lending on fund performance, we compute the ratio of securities

lending income to total fund net assets (SL Income to Fund TNA). Securities lending income is

a function of several factors including the supply and demand for lendable securities, rebate

rates, fee splits, lending agent fees, and returns on collateral reinvestments. Collectively these

largely unobservable factors determine the size of a fund’s lending program and the return

earned by lending securities. To better understand how lending agent characteristics, fund and

family factors, and governance structures influence securities lending income in mutual funds,

we decompose the ratio of securities lending income to fund assets as follows:

FundAssets

iesSLLiabilit

iesSLLiabilit

SLIncome

FundAssets

SLIncome×= (1)

where SL Income is securities lending income, SL Liabilities is securities lending liabilities, and

FundAssets is total net assets. The ratio of securities lending income to securities lending

liabilities measures the return on lent securities (Return on Lent Securities), and the ratio of

securities lending liabilities to total assets captures the proportion of each fund’s portfolio that is

on loan (Securities on Loan).11 As Equation 1 shows, funds can increase securities lending income

by lending more securities at higher rates of return. Funds typically lend several securities per

year at quantities determined by borrower demand, SEC regulations, and fund lending policies.

11 We report the ratio of securities lending income to net assets (SL Income to Fund TNA) rather than gross assets

to facilitate comparisons with fund expense ratios. Because of differences in the reporting of cash vs. non-cash collateral we use fund gross assets to compute the proportion of fund assets on loan (Securities on Loan).

8

These loans are typically open ended (e.g., no fixed maturity date) but are generally settled

within a few days or weeks. The returns on the individual loans depend on rebate rates that

vary according to supply and borrower demand and the returns on the reinvestment of

borrowers’ collateral (typically 102%-105% of the market value of the securities). The returns on

collateral reinvestments can vary considerably depending on the level of risk and costs of the

collateral reinvestment choice. The return on lent securities is our primary variable of interest

since it captures the overall results of these loans and how lending proceeds are shared between

the lending agent and fund (e.g., the fee split).

We further collect information related to how the securities lending program is structured

from Forms N-CSR and N-CSRS. Specifically, the name of the lending agent, lending agent’s

affiliation to the fund sponsor, type of lending agent, and whether the collateral of loaned

securities is invested in a lending agent affiliated portfolio. It is a common practice for funds to

disclose the details of their securities lending programs in the notes to the financial statement

section of its annual report in Forms N-CSR and NCRS although the level of disclosure varies

greatly by funds. We are able to identify the securities lending agent for about 91% of the cases

using the name or information provided in the filing. The remaining cases either conduct

securities lending programs on their own,12 or we were unable to identify the securities lending

agents. In the latter case, we examine Form 485 in an attempt to identify the lending agents. If

we are unable to precisely identify the lending agent, we classify these funds as unreported.

Using the identity of the lending agent, we record whether the lending agent is affiliated with

the fund sponsor. We also classify lending agents into three types: custodian, broker, or self.

We next record if the collateral for loaned securities is invested in a lending agent affiliated

fund, an unnamed collateral reinvestment pool, another fund offered by the trust family, or in a

fund that is not affiliated with the agent or sponsor. To ensure that the accuracy of our data for

this item, along with the notes to financial statements section, we utilize the information listed

in the Statement of Net Assets for each fund in Forms N-CSR and N-CSRS.

12 For example, Vanguard funds conduct their own securities lending programs. Vanguard 500 index fund states

in its 2006 annual report: “The fund may lend its securities to qualified institutional borrowers to earn additional income. Securities loans are required to be secured at all times by collateral at least equal to the market value of securities loaned. The fund invests cash collateral received in Vanguard Market Liquidity Fund.”

9

C. Measuring boards and trustees

We manually collect calendar year end data on each fund’s board of directors from the SAI,

which is included in each fund’s prospectus (Form 485). These include: board size, board

independence, multiple boards of directors appointments held by each trustee, trustee fund

family ownership, and trustee compensation. Board Size is computed as the natural logarithm

of the number of trustees serving on each fund’s board.13 Board Independence is measured as

the proportion of independent or disinterested trustees to total directors, where director

independence is determined in accordance with SEC (2004) regulations. Under these

regulations, independence indicates that an outsider is not an employee, not an employee

family member, not an employee or a 5% shareholder of a registered broker-dealer, and is not

affiliated with any recent legal counsel to the fund. The All Independent Directors is a dummy

variable that equals one if all of the directors on the board are independent.14

Multiple boards of directors appointments (Outside Directorships) is the average number of

other directorships, excluding appointments at non-profits, held by each board member. The

SAI also lists the value of each trustee’s ownership, in specific dollar ranges, of funds in the

fund family. Family Ownership is the proportion of directors with a minimum of $100,000 in

shares of funds within the fund family. We also record the overall compensation received for all

of the funds overseen for each trustee. Our last board structure variable, Unexplained

Compensation, measures each independent trustee’s compensation and is computed as the

residual from the regression model of trustee compensation on the number of funds overseen

by a trustee and the total net assets overseen by a trustee as in Tufano and Sevick (1997).

D. Control Variables

We employ several fund and family specific characteristics as controls in the analysis. These

include performance, size, external monitoring, management and custodian fees, liquidity, and

dummy variables that capture different fund investment objectives. Portfolio return, a proxy for

13 We rerun the analysis computing board size as the number of independent directors and find similar results. 14 In unreported analysis we include a dummy variable that equals one when a board’s chair is listed as

independent or disinterested in the SAI document. However, this variable is insignificant and its inclusion does not substantially alter our findings.

10

the demand for lendable securities, is computed as a fund’s annual return net of operating

expenses.15 Fund and family size, proxies for the supply of lendable securities and pricing

power with lending agents, are computed as the natural log of total gross (inclusive of borrower

collateral) assets for the fund and total net assets for the fund family, respectively. Expense

ratios are included to control for each fund’s fee structure since lending income directly offsets

fund expenses and is computed as the ratio of net fund expenses (after lending income) to total

net assets. Management and custodian fees collected from Form N-CSR, proxy for the incentive

structure of funds and fund sponsors to engage in securities lending since fund managers and

sponsors can offset higher fees with securities lending programs. These fees are computed as

the proportion of each fee to total net assets.

Asset turnover and cash flow volatility are proxies for the risk that borrowers have to return

securities early. Asset turnover is computed as the lesser of sales or purchases divided by

average monthly total net assets and cash flow volatility is the standard deviation of monthly

investor net cash flows. Our external monitoring measure is computed as the proportion of each

fund’s total net assets that are in institutional share classes.16 We also include dummy variables

to denote whether funds are enhanced or managed since these funds are more likely to pursue

aggressive lending policies than purely passive indexers. Kolasinski, Reed, and Ringgenberg

(2013) note that the lending market is opaque which makes it costly for lenders and borrowers

to find each other in which case funds with investment objectives that hold high demand

securities may find it less costly to administer their own lending programs. Our analysis

includes 26 Morningstar investment category dummies to capture variations in lending costs as

well as the supply and demand for lendable securities that are due to investment objectives.17 A

brief description of the sample variables and the data sources used is provided in Table I.

[Insert Table I about here]

E. Sample description

15 We also consider each fund’s gross return before expenses and Jensen’s alpha and find similar results. 16 We also control for clientele effects by noting the presence of marketing costs, namely each fund’s average

12b-1 fees, front end loads, and deferred sales charges. Our results are robust to these alternative specifications. 17 Although not reported, we also employ prospectus investment objective and benchmark index dummies,

available from Morningstar, and find similar results.

11

Table II provides summary statistics for the securities lending practices in our sample. Panel

A reports the incidence of securities lending activities segmented by investment objective. The

data indicate that the largest category of index funds is US large blend funds (43%), followed by

US small blend (11%), foreign large blend (10%), US mid-cap blend (10%), US intermediate

bond (7%), and US large growth (4%). In terms of securities lending programs, funds with the

highest incidence of lending are concentrated mainly in the US mid-cap blend and US

intermediate bond categories (both at about 84%) and foreign large blend funds (76%), followed

by US large blend, US small blend, and all other categories (e.g. Asian and European growth,

technology, financial, real estate, government bonds, and balanced index funds) of about equal

amounts (in the range of 68% to 69%). For the overall sample of 1,374 fund-year observations,

about 70% report securities lending income.

[Insert Panel A of Table II about here]

Panel B reports the distribution of securities lending across fund TNA, sponsor TNA,

expense ratio, and portfolio return deciles. About 52% of funds in the smallest Fund size decile

report securities lending income while 100% of funds in the largest fund TNA decile report

lending income. In fact, each of the five largest size deciles has a greater incidence of securities

lending than do the smallest five deciles. The incidence of securities lending is similarly

distributed in the sponsor TNA deciles with the largest fund sponsors lending 86% of the time

and the smallest only about 38% of the time. Securities lending is more prevalent in funds with

low expense ratios, where 81% of funds in the lowest expense ratio decile have a securities

lending program than in funds with the highest expense ratios (in this case only 50% lend).

Panel B also reports a similar incidence of securities lending across high and low return funds.

[Insert Panel B of Table II about here]

Panel C of Table II shows the incidence and medians values for the percentage of portfolio

securities on loan and return on securities lent segmented by securities lending agent type,

whether the lending agent is affiliated with the fund and/or fund sponsor, and how the

collateral is invested. As expected, the majority of the index funds in the sample (about 52%)

use a custodian to implement a securities lending program. However, a large number of funds

12

(about 36%) are their own lending agent while only 11% of the sample has an independent

lending agent or broker to handle all of their lending.

Funds that use a custodian as their lending agent lend about 8.8% of fund assets (median

value) while funds that act as their own agent (self-lending agents) only lend about 2.9% of

fund assets. Interestingly, funds with custodian lending agents only earn about 27 basis points

per year (median value) on the securities they lend while self-lending agents earn a return that

is about four times higher (about 105 basis points). The increased levels of securities lending in

custodial versus self-lending agents are consistent with preferences to borrow from custodians

who are able to lend large quantities of securities since they already hold the securities of

several funds and fund families. The higher returns for funds who self-lend suggest they are

able to obtain better terms from borrowers and do not incur the level of securities lending agent

fees as do funds that employ outside lending agents. Alternatively, self-lending funds are more

likely to have a policy of only lending those securities that are especially profitable to lend (an

industry practice known as value lending) and custodial lending agents earn a lower average

lending return by lending both high and low profit securities.

[Insert Panel C of Table II about here]

Panel C also reports that 29% of the sample employs lending agents who are affiliated with

the fund sponsor. Funds that use affiliated lending agents lend more of their portfolio securities

than funds that use unaffiliated agents (8% vs. 5%) but report a median return on lent securities

lower than funds that use unaffiliated agents. When collateral for lent securities is invested

with a lending agent affiliated mutual fund or with an unnamed pool (the vast majority of

unnamed collateral reinvestment pools are controlled by lending agents) the percentage of

portfolio securities on loan is about 8% versus 4% when the collateral is invested with a money

market type mutual fund in the same family trust as the lending fund. The median percentage

return on lent securities is about 73 and 91 basis points when the collateral is invested with a

family or non-affiliated fund, respectively. However, when the collateral is invested with a

lending agent affiliated fund or reinvestment pool the returns are 37 and 22 basis points.

13

Overall, Panel C reports considerable differences in the amount of securities on loan and the

return on lent securities across lending agent types.18

Table II also reports securities lending income by investment objective for funds with

securities lending income. For ease of exposition, Panel D reports results for the six investment

objectives with the highest levels of lending activity and aggregates the remaining investment

objectives into an ‘other’ category. Consistent with Diether, Lee, and Werner (2011) who note

that stock characteristics explain much of the variation in lending fees, we report significant

differences in securities lending returns when segmenting by fund investment objective. Panel

D reports distributional statistics for the dollar amount of securities lending income, the

percentage of portfolio securities on loan, the return on lent securities, and percentage of

securities lending income to fund TNA. US mid-cap index funds had the highest mean dollar

value of securities lending income, $2.06 million, whereas foreign large blend funds had the

highest median value, $479 thousand. US small blend index mutual funds had the most

portfolio securities on loan with a mean value of 12.03%. In terms of median values, US mid-

cap and US intermediate bond funds had the most securities on loan at 11.31% and 11.02%,

respectively. Foreign large blend funds earned the highest median return from their lent

securities and US large blend funds received the lowest, perhaps reflecting better rebate rates

for more difficult to borrow international securities and the greater supply and consequently

higher rebate rates of large blend securities (e.g., S&P 500 index constituents). The net result of

the amount of portfolio securities on loan and the return of lent securities is the securities

lending income to TNA. US Small blend, US mid-cap and foreign large blend funds had the

highest levels of lending income to TNA with mean values of about 8-12 basis points.19

Panel A of Table III provides fund level summary statistics for the remaining variables used

in this analysis for securities lenders and non-lenders. Included are the mean, median, and

standard deviation values for the fund and governance characteristics. In terms of fund

characteristics, the mean and median fund size for securities lenders is $4.89 billion and $832

million, respectively. The non-lending funds are much smaller with mean and median fund

18 Note that the number of funds reported in collateral reinvestment section of panel C exceeds the overall

number of funds in the sample as funds can use multiple collateral reinvestment vehicles. 19 Panel D also reports the first and third quartile values and for each measure of securities lending considerable

interquartile ranges exists. This suggests that some funds are more aggressive in pursuing securities lending opportunities than others.

14

total net assets of $560 million and $232 million. A similar pattern holds for family size. The

larger fund and family sizes are consistent with larger funds and fund families having the

resources to manage a lending program and borrowers’ preferences for borrowing securities

from a single source.

[Insert Panel A of Table III about here]

Non-lenders have higher mean and median portfolio returns than lenders, however lenders

have higher measures of Jensen’s alpha. Securities lending funds charge lower expense ratios

and management fees than non-lenders. Median custodian fees are about 1 basis point for

lenders and 2 basis points for non-lenders. This is consistent with anecdotal evidence

suggesting that custodians offer lower fees in exchange for the opportunity to serve as lending

agents. This is also consistent with an economy of scale effect as lenders are on average larger

than non-lenders. Average asset turnover is higher for non-lenders, but median asset turnover

is lower than for lenders. The reverse pattern holds for cash flow volatility. However,

institutional ownership mean and median values are much greater in funds that lend. This is

consistent with institutional investors being more knowledgeable than retail investors and

seeking the lowest fees. Since securities lending income directly reduces expense ratios lenders

are more likely to charge lower fees and attract institutional investors than non-lenders.

In terms of fund governance, lenders and non-lenders have similar board sizes with about

eight directors each. Lenders appear to have slightly more independent directors than non-

lenders with median values of 86% and 80%, respectively. The average number of outside

directorships per trustee is greater for lenders than non-lenders. Family ownership levels are

higher in funds with securities lending income with mean and median values of 86% and 100%

versus non-lender values of 81% and 89%. The mean and median unexplained compensation

for directors is higher for lenders than non-lenders by about $24,000 and $8,800, respectively.

Overall, Panel A reports considerable variations in fund and governance characteristics of

lenders and non-lenders.

Panel B reports Pearson correlations among the variables in the sample. The correlation

between the return on lent securities and the amount on loan is negative and significant. The

return on lent securities is also negatively correlated with the presence of an affiliated lending

15

agent. The correlation between the return on lent securities and the dummy variable that

captures whether a fund acts as its own lending agent is positive and significant. The remaining

fund measures (fund and family TNA, independent directors, and director family ownership)

are also significantly positively correlated with the return on lent securities. In contrast, the

amount on loan is positively correlated with affiliated lending agents and unexplained

compensation and negatively correlated with fund and family TNA. However, the amount on

loan and the return on lent securities are both positively correlated with independent directors.

[Insert Panel B of Table III about here]

III. Empirical Results

We conduct cross-sectional analysis to examine relation between returns on lent securities

and lending related governance mechanisms while controlling for fund-specific measures as in

Adams, Mansi and Nishikawa (2010). To test the hypotheses that the returns to lending

programs are influenced by lending agent type, whether the agent is affiliated with the fund

sponsors, and that effective boards mitigate conflicts between agents and fund shareholders, we

apply the following specification using fund level clustered standard errors

SecuritiesLending ei,t = β0 +

3

1=Σj(Affiliatedi,t ) +

6

4=Σj

(Lending Agent Typei,t )

+12

7=Σj

(Board Characteristicsi,t ) +20

13=Σj

(Fund Characteristicsi,t )

+ 46

21=Σj

(Investment Objectivei,t )+ 64

46=Σj

(Timet ) + εi,t, (2)

where Securities Lending is measured as the return on lent securities. Note that securities lending

return calculations are vulnerable to measurement error as the amount of securities on loan is

only observable at the beginning, middle, and end of each fiscal year while loan durations

typically range from a few days to a few weeks. This measurement error inflates lending return

variance and biases the t-statistics downward. We mitigate this potential measurement problem

in two ways. First, we limit our sample to funds that report securities on loan at least twice

during the fiscal year (i.e., lending liabilities at either the beginning and end of the year, middle

16

and end of year, etc.). Second, we trim observations with securities lending returns that are

below or above the first and 99th percentiles (i.e., a 1% trim).20 Affiliated are dummy variables

that indicate whether the lending agent is affiliated with the fund sponsor and if securities

lending collateral is invested in an agent affiliated fund or an unnamed investment pool.

Lending Agent Type consists of dummy variables to indicate whether a fund self-lends, uses a

broker or dealer, or if lending agent type is not reported. Board Characteristics include board size,

independent directors, all independent directors dummy, outside directorships, director

ownership levels in the fund family, and director compensation. Fund Characteristics include

portfolio return, family TNA, fund TNA, institutional ownership, fund turnover ratio, cash flow

volatility, enhanced fund dummies, custody fees, and the percentage of portfolio securities on

loan. The variables Investment Objective and Time represent dummies for each fund’s

Morningstar investment objective category (large blend, large growth, large value, mid-cap

blend, foreign blend, etc.) and time (year and fiscal year end month) dummies, respectively.

A. Securities Lending Returns

Table IV presents the results from regressing securities lending returns on fund, family, and

lending program characteristics. We employ six models to minimize multicollinearity effects.

Model 1 reports regression coefficients for the specification that focuses on the effect of fund

characteristics on securities lending income. Model 2 is similar to Model 1 but adds custodian

fees. Since custodian fees are not uniformly reported the number of observations in Model 2 is

less than that reported for Model 1.21 Model 3 incorporates dummy variables to capture lending

agent type.22 Model 4 includes affiliated agent and collateral investment measures.23 Models 5

20 In unreported analysis we obtain similar results with 0%, 0.5%, and 2.5% sample trimming and from requiring

one or three lending observations during the fiscal year. 21 Custodian fees are often bundled with other services and not separately reported. 22 The Vanguard funds represent about 27% of the overall sample and about 48% of the self-lending sub-sample.

Because Vanguard does not retain any securities lending proceeds from the funds they sponsor and follows a value oriented policy of only lending high yielding securities, we repeat our analysis excluding Vanguard funds and find similar results.

23 Mutual fund managers are employees of sponsors and not fund shareholders so fund managers who administer their own lending programs are affiliated with fund sponsors. That is, all self-lending agents are affiliated with fund sponsors but not all sponsor affiliated agents are self-lending agents. Our use of the term affiliated lending agent applies to external agents (custodians, broker/dealers, etc.) and not internal agents (i.e. fund managers).

17

and 6 add lending program size (the percentage of each fund’s portfolio that is on loan) to the

affiliated agent and lending agent type specifications.

[Insert Table IV about here]

Model 1 reports a positive and significant (at the 1% level) relation between fund size and

securities lending returns, which suggests that larger funds are able to manage their portfolio

lending more effectively than smaller funds, possibly because managers of larger funds have

greater abilities to generate lending returns than managers of smaller funds. Alternatively,

borrowers may seek larger lending portfolios in order to reduce costs associated with locating

additional sources of borrowable securities. If so, they may be willing to pay a premium to gain

access to larger pools of securities. Our finding that family size is also positively and

significantly related to lending returns is consistent with both explanations. Cash flow volatility,

our proxy for the risk of early redemption, is positive and statistically significant (at the 1%

level) because the characteristics of the underlying fund assets that lead mutual fund

shareholders to make buy and sell decisions are also relevant to short sellers. Alternatively,

higher redemption risk is associated with higher returns simply since, on average, riskier

securities offer higher lending returns. The remaining fund characteristics are statistically

insignificant.

Model 2 reports the estimated coefficient for family size is similar in size, sign, and

significance to that reported in Model 1. Interestingly, Model 2 finds that none of the other

variables, including custodian fees, are statistically significant. Model 3 reports estimated

coefficients for three types of non-custodian lending agents. Funds who act as their own

lending agents (Self Lending Agent), funds that employ a 3rd party agent, a broker and/or a

dealer and funds that do not disclose their lending agent in the N-CSR or Form 485. The

coefficient for the self-lending agent dummy is economically large, positive, and statistically

significant at the 1% level. The results from Model 3 suggest that securities lending returns are

about 1.3% higher in funds that administer their own lending program rather than using their

custodian to lend securities. Furthermore, this value is almost equal to the overall sample

average return on lent securities of about 1.5%. Applying the sample mean values for the

amount of securities on loan and fund TNA, the estimated benefit of self-lending versus agent

18

lending is over $5 million per fund. Since most fund complexes offer several funds the

economic incentives to administering lending programs internally can become large. These

results suggest that funds can earn higher lending income and charge lower expenses to their

shareholders by effectively administering their own lending program. This finding also

indicates potential conflicts in securities lending arrangements between funds and affiliated

lending agents where arm’s length bargaining is potentially problematic.24

Model 4 provides results from including measures intended to capture potential agency

conflicts between fund shareholders and sponsors via sponsor affiliated lending agents. The

most interesting finding is that the estimated coefficient on the sponsor affiliated lending agent

dummy variable (Sponsor Affiliated Agent) is negative and highly statistically significant (at the

1% level). The result is also economically large since returns to lent securities are about 0.6%

lower when the lending agent is affiliated with the sponsor. Applying the sample average

annual return on lent securities of 1.5%, our results indicate that affiliated lending agents are, on

average, associated with a 40% reduction in lending returns. This finding suggests self-dealing

behavior by some affiliated lending agents. Model 4 also reports that investing the collateral

that borrowers commit to secure securities loans with lending agents (Collateral Invested with

Lending Agent Fund and Collateral Invested in Unnamed Pool) are associated with lower lending

returns (at the 1% and 5% levels). These results indicate that, on average, lending agents do not

provide competitive collateral reinvestment returns. These results also suggest that lending

funds should carefully consider whether the benefits of bundling lending and collateral

reinvestment services, a common industry practice, outweigh the costs.

Models 3 and 4 present considerable evidence that securities lending agent choice

influences the return mutual funds earn on lent securities. This is important as securities

lending returns increase mutual fund returns. However, lending returns are correlated with the

24 Self-lending funds likely incur additional staffing and related costs from administering their own lending

programs while funds that employ lending agents incur little additional lending expenses. That is, securities lending income for agent lending funds is generally reported net of expenses while the reported securities lending income for self-lending funds may not include all lending related expenses. If lending costs are not deducted from lending income but are included in overall fund expenses our findings could overstate the true benefits of self-lending. In unreported univariate and cross sectional analysis we examine fund gross expense ratios (e.g. what expenses ratios would have been without lending income) and do not find any evidence that self-lenders have higher operating expenses. In addition, the size of the self-lending benefit is much larger than we anticipate any additional staffing cost to be, especially when considering that lending staffing costs are likely spread over several funds in the complex.

19

portion of assets on loan since some funds follow a value oriented policy of only lending when

lending yields are relatively high while others follow a wholesale approach and lend as many

securities as possible (LaBarge, 2011). Funds that follow a value orientated lending policy earn

relatively more of their securities lending income through lending fees and less by reinvesting

borrowers’ collateral since they typically have fewer (but higher yielding) securities on loan. In

contrast, wholesale lenders focus on earning securities lending income via reinvesting borrower

collateral so they have incentives to lend more and invest the collateral in higher risk, higher

return investment vehicles. Panel C of Table II notes considerable variation in lending policies

across lending agent type (e.g. custodian vs. self-lending) and lending agent affiliation status

and suggests that self-lending funds, on average, follow a value lending policy while custodian

and affiliated lending agents tend to take a wholesale approach. In addition, funds that

administer their own lending programs may have fewer lending opportunities than funds

which employ custodian lending agents.

Models 5 and 6 repeat the analysis of Models 3 and 4 while controlling for the portion of

fund assets on loan in order to capture possible variations in securities lending returns due to

differences in lending strategies. As in the previous specifications, Models 5 and 6 include fund

investment objective category dummies to capture borrowing demand across different types of

securities. All of the control variable coefficients that were significant in Models 3 and 4 are also

significant in Models 5 and 6. The estimated coefficients on the percentage of portfolio loaned

are economically large, negative, and statistically significant at the 1% level. The estimated

coefficient on the self-lending agent dummy in Model 5 remains positive and statistically

significant at the 1% level. Similarly, Model 6 reports that affiliated lending agents are

significantly (at the 1% level) and negatively associated with lending returns after controlling

for the level of lending activity.

Overall, the results in Table IV suggest that securities lending returns are lower when funds

use sponsor affiliated lending agents. It also demonstrates how mutual funds that administer

their lending programs, either by acting as their own agent or employing their custodian, effects

securities lending returns. As such, the results provide compelling evidence that conflicts of

interests exist in securities lending programs. In addition, Table IV reports that securities

lending returns are negatively associated with lending agent affiliated collateral reinvestment

choices and to the percentage of fund assets on loan.

20

B. Additional Tests

B.1 The Financial Crisis

For robustness, we examine our results using an exogenous shock provided by the financial

crisis. The credit and liquidity crisis that reached a peak around the Lehman Brothers

bankruptcy of 2008 presents a unique opportunity to examine returns generated by self and

affiliated lending agents. Although often portrayed as a low risk strategy to enhance fund

returns, funds can lose money when lending securities. Funds face two main risks: counterparty

risk, the concern that the borrower may not return the lent securities, and collateral

reinvestment risk, the possibility that the value of the collateral investment is insufficient to

repay the borrower when the lent securities are returned. Both counterparty and collateral

reinvestment risks increase as funds lend more. When lenders need cash to repay borrowers’

collateral (or liquidity in general) they may be forced to sell the collateral investments at steep

discounts during a credit or liquidity crisis. The discounts will be greater for longer maturity

and riskier reinvestment pools. One way that lending agents can engage in self-dealing

behavior is to offer less favorable fee splits to mutual funds while lending more securities.

Although this strategy can result in overall lending revenues that are comparable to strategies

that feature more favorable fee splits to the funds with fewer (higher yielding) securities on

loan, it also exposes funds to greater risk. Lending agents are less concerned with securities

lending risks since, as agents, they typically do not share in lending losses. While some agents,

most notably custodial lending agents, do indemnify funds against counterparty risk it is rare

for agents to indemnify against collateral reinvestment risks. We hypothesize that agency

problems in lending programs are exacerbated during periods when counterparty and

reinvestment risks are particularly high.

Table V presents the results from repeating the specifications of Models 5 and 6 in Table

IV.25 The specifications include the amount of securities on loan (and by extension the collateral

reinvestment levels) to account for variations in securities lending returns due to value and

wholesale lending approaches and are segmented by the year 2008. Models 1 and 2 provide

25 For ease of exposition only selected variable coefficients are reported.

21

results on the impact of agent type (e.g., self-lending vs custodial lending). Models 3 and 4

report coefficients estimates for affiliated agent and collateral reinvestment status.

The estimated coefficients for the self-lending agent dummy variable are positive and

statistically significant (at the 1% level) in Models 1 and 2, suggesting that funds that administer

their own lending programs outperform agent lenders regardless of market conditions.

Furthermore, these results are consistent with those previously reported in Table IV and do not

appear to be driven by differences in value and wholesale lending strategies as both models

control for the amount of a fund’s portfolio on loan. The magnitude of the self-lending

estimated coefficient in Model 1 is about twice as large as the coefficient in Model 2 (a difference

that is statistically significant at the 1% level). This difference indicates that self-lending funds

were better able to manage the risks associated with the credit and liquidity crisis than were

agent lenders.

Models 3 and 4 report results for the affiliated agent lender specifications. The results for

the control variables are consistent with those reported in Models 1 and 2. Model 3 reports, for

2008, that sponsor affiliated agents are associated with a 0.8% decrease in securities lending

returns, results that are statistically significant at the 5% level. This is about twice the negative

estimated coefficient for affiliated agent lending reported in Model 4 (all other years). The

estimated coefficients for collateral reinvestment choices are statistically significant in both

models but there is considerable variation in economic significance. Specifically, Models 3 and

4 report that funds which elected to invest borrowers’ collateral in lending agent affiliated funds

experienced significantly negative securities lending returns in all years, but the negative effect

in the year 2008 was about four times that of other years (estimated coefficients of about -1.5%

vs-0.4%). Similarly, the negative relation between securities lending returns and unnamed

collateral reinvestment pool choices is much greater during the financial crisis (again about four

times). This is consistent with the industry practice of lending agents usually not indemnifying

funds against collateral reinvestment losses. Furthermore, as in Models 1 and 2, these results

account for the percentage of portfolio assets on loan so the results are unlikely to be driven by

differences in value and wholesale lending strategies.

Table V reports that when funds administer their own lending programs and collateral

reinvestments their securities lending returns are higher than the returns of funds that employ

agent lenders, especially during periods of increased lending market uncertainty. Table V also

22

reports that affiliated agents are associated with lower lending returns when collateral

reinvestment risk is particularly high. As such, the results presented in Table V are consistent

with the hypothesis that agency costs inherent in securities lending programs are elevated

during periods of heightened counterparty and collateral reinvestment risks. The results are

also consistent with conflicts of interest not only resulting in lower lending returns but also

subpar risk/return tradeoffs.

B.2 Endogeneity and Sample Selection Bias Tests

Our analysis suggests that the choice of securities lending agent (e.g., affiliated vs. non-

affiliated) causes lending returns to be relatively high or low. However, the opaque nature of

securities lending markets makes any determination of causality problematic. Rebate rates, fee

splits, and collateral reinvestment policies, are important but unobservable determinates of the

returns earned on any lent security. If agent choice is correlated with these unobservable

factors the affiliated and self-lending agent dummies are endogenous. While it is not possible

to completely mitigate endogeneity concerns, in addition to employing investment objective

category, year, and fiscal year end month fixed effects, we employ a two stage least squares

approach and use sponsor organization type (e.g., whether the investment company that

sponsors the fund is publicly traded or privately held) as an exogenous first stage instrument.26

Sponsor ownership structure is likely determined before funds are created and any

securities lending policies are set. Adams, Mansi, Nishikawa (2010) and Ferris and Yan (2009)

demonstrate that shareholders of mutual funds sponsored by publicly traded investment

companies suffer more from conflicts of interest than funds sponsored by privately held firms.

If so, we expect public sponsors to be more likely to engage in self-dealing behavior such as

favoring affiliated agents that don’t offer the best fee splits or execution and to avoid self-

lending where gains accrue to the mutual fund shareholders and not to the sponsor or its

affiliated agent. In addition, public sponsors are usually part of large corporate parents who are

26 In unreported analysis we use the number of share classes offered by each fund as a proxy for operational

complexity. Our rationale is that fund sponsors and/or managers with the ability to manage complex fund operations are more likely to internally manage lending programs (i.e., act as self-lending agents) and that any influence of operational complexity on securities lending returns is via the decision to self-lend. Our results are robust to the use of this alternative instrument.

23

more likely to have affiliated subsidiaries that offer lending services (e.g. custodial banks).

Rebate rates are market determined and U.S. offered index funds generally invest securities

lending collateral in money market type investment vehicles so sponsor organizational

structure only matters to securities lending returns via its role in influencing lending agent

choice. As such, sponsor organizational type is a plausible instrument since it satisfies both the

relevance (e.g., public sponsors are more likely to favor affiliated agents) and the untestable

exclusion (only influences returns via its effect on agent choice) conditions.

Table VI reports results from two stage instrumental variable regressions as in Wooldridge

(2002). Models 1 and 4 report the first stage estimated coefficients obtained from probit

regressions of the self-lending and affiliated agent dummy variables, respectively, on the public

sponsor dummy instrumental variable and fund characteristics. The remaining models report

second stage ordinary least squares regression results. The self-lending Models 2 and 3 are

comparable to Models 3 and 5 of Table IV and the affiliated agent Models 5 and 6 are

comparable to Models 4 and 6 of Table IV.

[Insert Table VI about here]

Consistent with our expectations, Model 1 of Table VI reports the estimated coefficient on

the public sponsor dummy is negative and statistically significant at the 5% level. Model 2

reports that the self–lending agent dummy is positive and statistically significantly related to

the return on lent securities. Model 3 of Table VI reports that the estimated coefficients for the

self-lending dummy and the percentage of portfolio loaned are both statistically significant,

results that similar to those reported in Table IV. Consistent with the requirement that

instrumental variables be relevant with regards to the endogenous variable, the public sponsor

dummy coefficient is positive and significant in Model 4. Similar to the results reported in

Table IV, the sponsor affiliated agent and collateral invested with unnamed pool dummy

variables are both negative and statistically significant in Models 5 and 6. However, the

estimated coefficient on the collateral invested with lending agent fund dummy is insignificant

in Models 5 and 6. Finally, similar to previous models the percentage of portfolio loaned is

significant in Model 6.

24

We also consider potential sample selection bias arising from selective disclosure of

securities lending income by mutual funds (e.g. window dressing). Regulation S-X permits

funds to put securities lending income in the “other income” category on income statements

when earnings from securities lending are less than 5% of gross fund income. This allowance

makes it difficult to identify funds with poorly performing lending programs. We address the

issue of sample selection bias by implementing the Heckman (1979) approach using our original

data set that includes funds that do not report securities lending income in the first stage to

calculate the inverse Mill’s ratio which we use as an additional explanatory variable to control

for sample selection bias in the OLS models. The results of this alternative specification are

reported in the appendix, Table A.1, and are similar to those reported in Table IV.

Overall, the results reported in Tables IV, V, VI, and Appendix A.1 provide strong evidence

that the choice of securities lending agent (e.g., affiliated vs. non-affiliated) is an important

driver of variation across securities lending programs and suggest conflicts of interest result in

lower securities lending returns for mutual fund shareholders. We next consider the impact of

boards of directors, who are responsible for awarding contracts to lending agents and

monitoring lending programs on behalf of mutual fund shareholders, on securities lending

returns.

C. Boards of Directors and Securities Lending

Tables IV and V strongly suggest that agency problems are potentially severe in securities

lending arrangements. We next examine whether mutual fund boards mitigate conflicts

between sponsors, lending agents, and fund shareholders. Table VII repeats the analysis of

Table IV while including mutual fund board and director characteristics. Models 1 and 2

provide results for regressions that include different measures of board independence: the

percentage of independent directors on a fund board (Independent Directors) and a dummy

variable that takes on a value of one if all of the directors on a board are classified as

independent (All Independent Directors). Models 3 and 4 include the agent type and affiliated

agent variables, respectively, to the specification presented in Model 1.

The significantly negative coefficients for the percentage of portfolio loaned in Table VII are

comparable to those reported in Table IV. Models 1 and 3 report that boards comprised of more

25

independent directors are associated with statistically significant (at the 10% level) positive

lending returns. These findings are consistent with Tufano and Sevick (1997), who report lower

expense ratios when boards have greater percentage of independent directors. Model 2 reports

higher securities lending income when mutual fund boards have all independent directors,

however the result is not statistically significant. In unreported analysis we find a negative and

insignificant independent chair coefficient which calls into question the efficacy of independent

board leadership. This finding is also consistent with Lutton et al. (2011) who report lower

index mutual fund fees for independent board chairs.

Table VII also considers individual director attributes. Directors with more outside

(multiple) board appointments (Outside Directors) are associated with higher lending returns

(results that are significant at the 1% level), perhaps because more in demand directors are

better monitors. Consistent with the findings of Chen, Goldstein, and Jiang (2008) that director

ownership in the funds they oversee is associated with improved shareholder value and/or

governance, Models 1 and 2 report that director mutual fund ownership (Family Ownership) is

positively associated with securities lending returns. In addition to being statistically significant

(at the 5% level), fund family ownership by directors is also economically significant. Our

analysis indicates that annual securities lending returns are, on average, about 1% higher when

directors own shares in the funds they monitor (which represents an increase of about 66% over

the sample mean securities lending return). Interestingly, Models 1 and 2 indicate that

relatively higher paid directors (Unexplained Compensation) are associated with lower returns

suggesting highly compensated directors may fear losing their positions and therefore may not

adequately monitor sponsor lending program proposals (see Khorana, Tufano, and Wedge,

2007).

The sign and significance on the self-lending dummy coefficient reported in Model 3 are

similar to those presented in Table IV, although the economic impact is slightly lower. Similarly,

Model 4 shows that sponsor affiliated agents are associated with lower securities lending

returns. However, the sponsor affiliated agent estimated coefficient is statistically insignificant;

suggesting that boards of directors can mitigate some of the agency problems in securities

lending programs. Similar to Table IV, the agent affiliated collateral investment choices (agent

fund and unnamed pool) are negative and significant in Model 6. Overall, the results presented

in Table VII show that lending agent collateral reinvestment choices are associated with lower

26

returns on loaned securities even after controlling for boards of directors. This finding is of

particular interest given the recent concerns that boards of directors may not be adequately

monitoring securities lending programs. Table VII also supports our earlier finding that funds

which administer their own lending programs is robust to the inclusion of board and director

characteristics. Finally, Table VII reports that the associations between board of director

attributes and securities lending returns are generally economically and statistically smaller

after controlling for lending agent choice. This finding is consistent with board influence on

lending returns is primarily related to lending agent choice.

D. Logistic Analysis on the Incidence of Securities Lending

We next provide multivariate logistic analysis to examine whether fund characteristics are

related to the incidence of securities lending. We apply the following logit model and compute

fund clustered errors as in Petersen (2009).27 That is

1

5536,3712

,117

1,610

,

))()(

(

)(exp(1

)(

−

−−

−

−−

−−

−

−−+

=

tti

ti

ti

ti

TimeObjectiveInvestment

cteristicsBoardChara

teristicsFundCharac

LendingSecuritiesE

ββ

β

ββ

(3)

Where SecuritiesLendingi,t is a dummy variable that takes on a value of one if a fund reports

securities lending income in its financial statements that are located in the annual report, if a

fund self lends, or if a fund employs an affiliated agent. All models include FundCharacteristics

such as portfolio return, family TNA, fund TNA, institutional ownership, asset turnover ratio,

cash flow volatility, and enhanced fund dummies.28 Board Characteristics include board size,

independent directors, outside (multiple) directorships, family fund ownership of directors, and

unexplained director compensation. As in the earlier models, the variables InvestmentObjective

27 Although we compute fund clustered standard errors as in Petersen (2009) in all of the cross-sectional

analyses, this methodology can be problematic when the number of observations within clusters is small (Donald and Lang, 2007). As such, we repeat the analysis using robust standard errors and find similar results. For completeness, we also compute fund family and time clustered standard errors. We report t-statistics using fund clustered standard errors since securities lending returns within family and time clusters are often negatively correlated (e.g., the securities of some family funds can be in high demand while the securities of other family funds may be in low demand) which in turn biases the t-statistics upward. In general, the t-statistics obtained from fund clustering are similar or smaller than those obtained from family and time clustering.

28 In unreported analysis we include expense and management fee ratios and find similar results.

27

and Time represent dummies for each fund’s benchmark investment objective category, year,

and month dummies.

Table VIII provides results for four model specifications. Model 1 reports estimated

coefficients for the primary specification that includes each fund’s annual portfolio return, fund

and family total net assets, institutional ownership, cash flow volatility, and asset turnover.

Model 2 incorporates board characteristics, Model 3 uses the self-lending dummy as the

dependent variable, and the dependent variable in Model 4 is the affiliated lending agent

dummy. Models 1 and 2 both report positive and significant relations between prior year

portfolio returns and the likelihood of reporting securities lending income. The results for

Models 1 and 2 indicate a positive and significant relation (at the 1% and 5% levels) between

fund size and the likelihood of a fund reporting securities lending income. This finding suggests

that economies of scale are important considerations in operating a securities lending program.

Similarly, the estimated coefficients on fund family size are positive and significant (at the 1%

level). This is consistent with larger sponsors being more able to provide the expertise to lend

securities than their smaller competitors.

[Insert Table VII about here]

Model 1 reports that institutional ownership is associated with an increased likelihood of

funds having securities lending programs (at the 1% level). This finding is consistent with

sophisticated investors seeking higher returns and lower expenses, which in turn provide

incentives for funds to engage in securities lending in order to improve fund performance.

Models 1 and 2 also report positive and marginally significant (at the 10% level) estimated

coefficients for cash flow volatility. None of the board characteristic variables are significant in

Model 2. The results presented in Models 1 and 2 suggest the decision to lend securities

depends on fund characteristics and not board oversight.

In contrast to Models 1 and 2, the estimated coefficient on portfolio return reported in

Model 3 is negative and statistically significant at the 1% level. The differences in the sign and

significance of the portfolio return coefficient may be due to lending strategies adopted by

funds after deciding to become lenders. However, since securities loans typically have

durations that vary from a few days to a few weeks and borrower demand for any securities

28

likely varies throughout any year it is difficult to interpret how annual returns are related with

lending agent choice. The estimated coefficients for family size is negative and significant (at the

10% level) in Model 3 but positive, perhaps because larger families are more likely to have an

affiliated custodian or other lending agent. The fund size coefficient in Model 3 is positive and

significant (at the 1% level) which is consistent with larger funds having the resources necessary

to administer their own lending programs. In terms of board structure, Model 3 reports funds

with larger boards and funds with relatively high director pay are less likely to self-lend, results

that are significant at the 1 and 5% levels. Model 3 also reports that self-lending is more likely

when directors own funds they oversee (Family Ownership) and when they have outside

(multiple) directorships, results that are statistically significant at the 1% level.

Similar to Models 1 and 2 and unlike Model 3, the estimated coefficient on portfolio returns

are positive and statistically significant (at the 10% level). Model 4 reports larger funds are less

likely to employ affiliated lending agents. This finding is consistent with the positive coefficient

reported for fund size in Model 3 for self-lending and suggests larger funds have the necessary

human capital to manage securities lending programs. Model 4 reports a statistically lower

likelihood of funds employing affiliated lending agents when directors have more outside

(multiple) appointments and when directors own funds in the family complex. In contrast,

Model 4 reports the estimated coefficient on unexplained director compensation is economically

and statistically significant (at the 1% level) indicating that relatively high paid directors are

more likely to approve affiliated lending agent service contracts.

IV. Conclusion

Recently, a great deal of attention has been given to the management of securities lending

programs and whether conflicts of interests exist when funds employ securities lending agents

that are affiliated with their sponsor. Central to this issue is the question of whether boards of

directors adequately oversee securities lending programs. In this paper, we attempt to shed

light on these issues by examining the relation between affiliated lending agents and securities

lending returns in a hand collected sample of U.S. index mutual funds.

Our analysis suggests that the SEC concerns of self-dealing in some securities lending

programs may be warranted. Specifically, we find that sponsor affiliated lending agents are

29

associated with lower annual returns on lent securities and that securities lending returns are

lower when borrower’s collateral is invested with sponsor affiliated agents. In terms of

governance, we find that excess director compensation is negatively associated with securities

lending returns, while director ownership in the funds they oversee is negatively related to the

incidence of sponsor affiliated lending agents. This suggests that conflicts of interest in the

mutual fund industry are an important consideration in the lending of securities.

We also provide a first look at the characteristics of the mutual fund securities lending

market. We document that about 70% of funds engage in some level of securities lending and

the typical fund lends about 8% of its portfolio. Fund and family TNA are also positively related

to the likelihood a fund having a securities lending program. Funds with institutional

ownership are also more likely to lend, suggesting that fund managers lend to increase fund

performance which in turn attracts institutional cash flows. Finally, we report significant

differences in securities lending returns when the data is segmented by fund investment

objective. Overall, the results have implications for mutual fund boards as they consider

lending proposals from affiliated agents and for possible future regulatory actions.

30

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32

Table I Variable Definitions

Variable Definition Data Source

Securities Lending Securities Lending Income Annual income from securities lending program as reported in statement of operations N-CSR Securities Lending Liability Two year average of borrowers’ collateral as reported in the statements of assets and

liabilities. N-CSR/S

Portfolio Securities on Loan Ratio of securities lending liability to fund assets (%) N-CSR/S Return on Lent Securities Ratio of securities lending income to securities lending liability (%) N-CSR/S Securities Lending Inc. to Assets Ratio of securities lending income to fund assets (%) N-CSR/S Custodian Lending Agent Dummy variable that equals 1 when lending agent is the fund’s custodian. N-CSR/Form 485 Self-Lending Agent Dummy variable that equals 1 when fund is its own lending agent. N-CSR/Form 485 3rd Party/Broker Agent Dummy variable that equal 1 when lending agent is 3rd party or broker N-CSR/Form 485 Unknown/Unreported Dummy variable that equals 1 when lending agent is not identified in Form N-CSR N-CSR/Form 485 Affiliated Lending Agent Dummy variable that equals 1 when the lending agent is affiliated with fund sponsor,

excludes self-lending agents N-CSR/Form 485

Collateral Inv. Lending Agent Fund

Dummy variable that equals 1 if borrower’s collateral is invested in a fund offered by lending agent

N-CSR/S

Collateral Invested in Unnamed Pool

Dummy variable that equals 1 if borrower’s collateral is invested in an unnamed collateral reinvestment pool

N-CSR/S

Fund Characteristics Portfolio Return Annual fund return net of expenses (%) Morningstar Jensen’s Alpha Annualized Jensen’s alpha computed from S&P 500 over 24 months (%) Morningstar Fund Assets Log of total assets of fund ($MM) at fiscal year end Morningstar Family TNA Log of total net assets for all funds managed by sponsor ($MM) Morningstar Expense Ratio Expense ratio of fund (%) Morningstar Management Fee Percentage of fund assets used to pay management and administrative services Morningstar Custodian Fee Percentage of fund assets used to pay custodian Morningstar Institutional Holding Percentage of institutional class holdings in fund Morningstar Enhanced Index Funds Dummy variables that equal 1 when fund attempts to outperform index using leverage

or other techniques or is managed to minimize taxes Morningstar

Cash Flow Volatility Standard deviation of monthly net cash flows computed over the observation year Morningstar Asset Turnover Trading activity/change in portfolio holdings computed as the lesser of sales or

purchases divided by average monthly total net assets in percentage Morningstar

33

Governance Characteristics Board Size Log of number of directors on fund board Form 485 Independent Directors Proportion of directors who are classified as outsiders (independent) Form 485 All Independent Dummy variable that equal 1 when all directors are independent Form 485 Outside Directorships Average number of outside/other directorships of directors Form 485 Family Ownership Proportion of directors who have ownership in funds in family Form 485 Unexplained Compensation Tufano and Sevick’s (1997) measure of relative independent director compensation Form 485 Note: This table provides variables definitions for the sample used in the analysis. The data covers the period from 2003 to 2010 for 204 funds comprising 1,374 fund-year observations. N-CSR/S is the annual/semiannual Certified Shareholder Report and Form 485 contains each fund’s prospectus and Statement of Additional Information. Both reports can be found in the SEC’s EDGAR database.

34

Table II Summary Statistics Panel A: Incidence of Securities Lending by Investment Objective

Note: Panel A reports the distribution of six investment objectives and categories of the funds in our sample. The sample consists of 1,374 fund-year observations, representing index mutual funds offered for sale in the U.S. market, covering the period from 2003 to 2010. The investment and category designations are obtained from Morningstar. Variable definitions are provided in Table I.

Panel B: Distribution of securities lending

Fund Assets

Sponsor TNA

Expense Ratio

Portfolio Return

Deciles (%) (%) (%) (%)

Overall Sample 69.65 69.65 69.65 69.65 Smallest 1 52.17 37.68 80.88 77.21 2 59.85 59.71 70.07 63.50 3 51.82 55.47 84.56 76.81 4 56.93 74.26 77.37 75.91 5 70.07 81.02 74.45 66.67 6 71.74 86.43 83.44 71.53 7 82.48 66.67 69.64 70.07 8 78.99 63.57 55.15 68.12 9 83.82 97.08 59.12 66.42 Largest 10 100.00 86.47 50.00 71.53

Note: Panel B lists the percentage of funds with material securities lending programs in the overall sample and in each decile. The data covers the period from 2003 to 2010.

Observations (%)

Funds with Securities Lending

Income (%)

US Large Blend 594 43.23 384 69.65 US Small Blend 157 11.43 109 69.43 Foreign Large Blend 142 10.33 108 76.06 US Mid-Cap Blend 132 9.61 111 84.09 US Intermediate Bond 90 6.55 76 84.44 US Large Growth 61 4.44 33 54.10 Other 198 14.41 136 68.69 Overall 1,374 957 69.65

35

Panel C: Securities Lending by Agent Type Note: Panel C reports the median distribution of securities lending agent type and affiliation status of the funds in the sample. The sample includes 957 fund-year observations with securities lending activity covering the period from 2003 to 2010. The lending agent type and affiliation status are obtained from the Certified Shareholder Report (Form N-CSR). Variable definitions are provided in Table I.

Obs. (%)

Portfolio Securities on Loan

(%)

Return on Securities

Lent (%)

Custodian 496 51.83 8.814 0.268 Self-Lending Agent 347 36.26 2.921 1.051 Broker/3rd Party 108 11.29 6.343 0.435 Unreported/Unknown Agent 6 0.63 11.650 0.138 Affiliated Agent 282 29.47 8.246 0.348 Nonaffiliated Agent 675 70.53 5.480 0.482 Is the Collateral Invested with Fund of Lending Agent 350

36.57 8.168 0.372

Unnamed Pool 384 40.13 8.854 0.215 Fund in Trust Family 474 49.53 4.268 0.729 Nonaffiliated Fund 31 3.24 4.019 0.912

36

Panel D: Securities Lending Income by Investment Objective

Obs. Mean Standard Deviation

First Quartile Median

Third Quartile

US Large Blend ($000) 384 1,571 7,009 32 132 497 Portfolio Securities on Loan (%) 6.327 5.669 1.843 5.214 9.575 Return on Lent Securities (%) 1.334 4.479 0.117 0.218 0.731 Securities Lending Income to TNA (%) 0.025 0.067 0.005 0.011 0.027 US Small Blend ($000) 109 1,609 5,173 52 139 528 Portfolio Securities on Loan (%) 12.031 8.689 4.785 10.061 18.376 Return on Lent Securities (%) 1.231 1.519 0.358 0.775 1.295 Securities Lending Income to TNA (%) 0.120 0.132 0.035 0.076 0.146 Foreign Large Blend ($000) 108 1,579 3,117 48 479 1,747 Portfolio Securities on Loan (%) 7.019 4.899 2.749 6.454 10.557 Return on Lent Securities (%) 1.771 2.440 0.579 1.056 1.965 Securities Lending Income to TNA (%) 0.076 0.074 0.026 0.059 0.090 US Mid-Cap Blend ($000) 111 2,058 4,719 54 263 1,082 Portfolio Securities on Loan (%) 11.341 7.464 4.243 11.309 16.809 Return on Lent Securities (%) 1.101 1.584 0.207 0.550 1.278 Securities Lending Income to TNA (%) 0.082 0.089 0.022 0.047 0.121 US Intermediate Bond ($000) 76 487 997 55 167 415 Portfolio Securities on Loan (%) 10.908 7.195 4.462 11.016 16.628 Return on Lent Securities (%) 0.520 1.030 0.128 0.229 0.526 Securities Lending Income to TNA (%) 0.035 0.055 0.004 0.017 0.039 US Large Growth ($000) 33 442 959 15 92 322 Portfolio Securities on Loan (%) 8.444 9.402 0.252 7.524 18.047 Return on Lent Securities (%) 2.205 3.371 0.110 0.590 2.985 Securities Lending Income to TNA (%) 0.021 0.030 0.004 0.009 0.025 Other ($000) 136 2,612 6,629 29 291 1,856 Portfolio Securities on Loan (%) 7.962 9.807 0.602 3.564 10.764 Return on Lent Securities (%) 2.887 5.371 0.156 0.898 3.111 Securities Lending Income to TNA (%) 0.051 0.071 0.010 0.024 0.068

37

Note: Panel D provides the securities lending income by investment objective. The data covers the period from 2003 through 2010. Variable definitions are provided in Table I.

Overall ($000) 957 1,656 5,686 40 166 772 Portfolio Securities on Loan (%) 8.306 7.487 2.200 6.526 12.719 Return on Lent Securities (%) 1.531 3.769 0.160 0.438 1.296 Securities Lending Income to TNA (%) 0.053 0.085 0.008 0.024 0.065

38

Table III Descriptive Statistics (Fund Level Data) Panel A: Securities Lenders vs Non-Lenders Funds with Securities Lending Income Funds w/o Securities Lending Income Differences Mean Median Std. Dev. Mean Median Std. Dev. Mean Median

Fund Characteristics Assets ($000,000) 4,889 832 14,355 560 232 792 4,329a 600a

Family TNA ($000,000) 270,170 39,971 430,364 95,364 11,534 252,529 174,806a 28,437a

Portfolio Return (%) 9.199 10.728 21.626 11.387 13.246 22.306 -2.188c -2.518 Jensen’s Alpha (%) 0.176 -0.710 4.240 -0.997 -1.556 5.336 1.173a 0.846a

Expense Ratio (%) 0.478 0.386 0.396 0.700 0.574 0.546 -0.222a -0.188a

Management Fee 0.213 0.200 0.199 0.319 0.250 0.255 -0.106a -0.050a

Custodian Fees (%) 0.024 0.010 0.056 0.038 0.020 0.116 -0.014a -0.010a

Enhanced (%) 9.269 0.000 29.000 21.891 0.000 41.402 -12.622a 0.000a

Asset Turnover (%) 42.542 16.000 104.838 78.125 12.000 247.887 -35.583a 4.000c

Institutional Ownership (%) 36.303 19.555 39.301 21.876 0.000 35.916 14.427a 19.555a

Cash Flow Volatility (%) 63.859 2.138 1,768 14.989 2.486 94.071 48.870 -0.348a

Fund Governance Board Size 8.378 8.000 2.169 7.803 8.000 2.368 0.575a 0.000a

Independent Directors(%) 84.559 85.714 8.803 80.880 80.000 9.747 3.679a 5.714a

All Independent 13.683 0.000 34.385 4.975 0.000 21.770 8.708a 0.000a

Outside Directorships 0.859 0.875 0.540 0.670 0.556 0.594 0.189a 0.319a

Family Ownership (%) 86.288 100.000 23.750 80.853 88.889 26.355 5.435a 11.111a

Unexplained Compensation($) 6,593 -8,257 55,872 -17,392 -17,065 62,379 23,985a 8,808a

No. of Observations 957 957 957 417 417 417 Note: This panel provides descriptive statistics for the sample segmented by securities lenders and non-lenders. The notations a, b, c denote statistical significance at the 1%, 5%, and 10% levels, respectively. The data covers the period from 2003 to 2010 for 204 mutual funds. Variables definitions are provided in Table I.

39

Panel B – Pearson Correlations

% Sec. on

Loan

% Return on

Loan

% Return to TNA

Fund Assets

Family TNA

Port. Return

Inst. Own.

Self Lend. Agent

Aff. Lend. Agent

Indep. Dir.

Outside Director

Family Own.

%Return on Loan -0.291

% Return to TNA 0.393 0.092

TNA -0.138 0.205 -0.027

Family TNA -0.230 0.346 0.026 0.486

Portfolio Return -0.025 -0.001 -0.255 0.008 0.036

Institutional Ownership 0.218 -0.029 0.088 -0.011 -0.113 -0.016

Self-Lending Agent -0.168 0.284 -0.046 0.277 0.511 0.032 -0.197

Aff. Lending Agent 0.137 -0.135 0.068 -0.094 -0.155 0.017 0.077 -0.458

Independent Directors 0.079 0.107 0.140 0.092 0.164 -0.068 -0.005 0.130 0.034

Outside Directorships -0.118 0.175 0.040 0.168 0.360 0.088 -0.025 0.362 -0.013 0.146

Family Ownership -0.037 0.155 0.053 0.156 0.322 -0.024 -0.130 0.335 -0.061 0.162 0.129

Unexp. Compensation 0.118 -0.002 0.106 0.005 0.017 0.009 0.024 0.012 0.292 0.244 0.517 0.253

Note: This panel provides Pearson correlations for selected variables used in the analysis. The data covers the period from 2003 to 2010 for 204 mutual funds. Variables definitions are provided in Table I. Correlations with significance at the 1% level are presented in bold.

40

Table IV Securities Lending Returns and Agents Dependent Variable = Return on Securities Loaned

Primary Specification

Custodian Fees

Lending Agent Type

Affiliated Agent

Lending Agent Type and Amount

Loaned

Affiliated Agent and Amount Loaned

(1) (2) (3) (4) (5) (6) Portfolio Return -0.003

(0.51) -0.001

(0.16) -0.002

(0.38) -0.002

(0.40) -0.002

(0.44) -0.002

(0.44) Fund Assets 0.179b

(2.12) 0.126

(1.35) 0.072

(0.92) 0.141c

(1.80) 0.023

(0.32) 0.095

(1.33) Family TNA 0.221a

(2.72) 0.257a

(3.09) 0.161b

(2.14) 0.154c

(1.86) 0.109

(1.61) 0.111

(1.45) Institutional Ownership 0.003

(0.98) 0.002

(0.45) 0.005

(1.27) 0.005

(1.51) 0.007b

(2.20) 0.006c

(1.97) Turnover Ratiox100 0.054

(0.55) 0.019 (0.16)

-0.011 (0.14)

0.059 (0.56)

-0.101 (1.17)

-0.033 (0.34)

Cash Flow Volatilityx1000 0.019a

(3.35) -0.477 (0.14)

0.021b

(2.55) 0.014c

(2.76) 0.007 (0.97)

0.001 (0.32)

Custody Fee Ratio 2.451

(1.42)

Self-Lending Agent 1.300a

(4.11) 1.246a

(4.50)

3rd Party Agent/Broker 0.124

(0.44) 0.133

(0.56)

Unreported/Unknown Agent -0.172 (1.07)

-0.165 (1.17)

Sponsor Affiliated Agent -0.557a

(3.14) -0.468a

(3.00) Collateral Invested in Lending Agent Fund

-0.679a

(2.76) -0.639a

(2.73) Collateral Invested in Unnamed Pool -0.475b

(2.57) -0.352b

(2.05) Percentage of Portfolio Loaned -0.081a

(6.24) -0.076a

(6.15)

41

Inv. Objective Dummies Y Y Y Y Y Y Year Dummies Y Y Y Y Y Y FYE Month Dummies Y Y Y Y Y Y Adjusted-R2 0.387 0.423 0.430 0.423 0.475 0.462 Model p-value 0.00 0.00 0.00 0.00 0.00 0.00 Observations 841 722 841 841 841 841

Note: This table provides results from regressing the percentage return on securities loaned on various fund characteristics. The data covers the period from 2003 to 2010 for 204 funds. Independent variables include: annual fund return (Portfolio Return), fund total net assets (Fund TNA), investment company total net assets (Family TNA), percentage of institutional class holdings in the fund (Institutional Ownership), standard deviation of monthly cash flows in and out of the fund computed over the prior twelve months (Cash Flow Volatility), percentage of fund assets used to pay custodian (Custodian Fees), dummy variables that equals 1 when the lending agent is a custodian, 3rd party/broker, and self-lending agents, dummy variable that equals 1 when the lending agent is affiliated with fund sponsor, excludes self-lending agents, and a dummy variable that equals 1 when borrowers’ collateral is invested with agent or unnamed pool. All models include year, FYE month, and investment objective fixed effects (including dummy variables for enhanced, tax managed, or leveraged funds). T-statistics derived from fund-level clustered robust standard errors are in parentheses. The notation a, b, c denotes statistical significance at the 1%, 5%, and 10%, respectively.

42

Table V Securities Lending Returns and Risk During the Financial Crisis

Dependent Variable = Return on Securities Loaned (%) Lending Agent Type Affiliated Agent

Year=2008

All Other Years Year=2008

All Other Years

(1) (2) (3) (4) Portfolio Return 0.117

(1.61) 0.005

(0.62) 0.133

(1.55) 0.005

(0.60) Fund Assets 0.106

(0.42) 0.002

(0.02) 0.219

(1.09) 0.064

(0.86) Family TNA 0.206

(1.19) 0.085

(1.21) 0.235

(1.25) 0.097

(1.27) Self-Lending Agent 2.426a

(2.79) 1.075a

(4.60)

3rd Party Agent/Broker 0.310 (0.51)

0.079 (0.35)

Unreported/Unknown Agent -0.196 (0.53)

-0.078 (-0.50)

Sponsor Affiliated Agent -0.833b

(2.18) -0.441a

(2.80) Collateral Invested in Lending Agent Fund

-1.549a

(2.98) -0.426b

(2.11) Collateral Invested in Unnamed Pool -1.052c

(1.66) -0.267c

(1.78) Percentage of Portfolio Loaned -0.086b

(2.44) -0.081a

(6.46) -0.094b

(2.42) -0.076a

(6.23) Inv. Objective Dummies Y Y Y Y Year Dummies N Y N Y FYE Month Dummies Y Y Y Y Adjusted-R2 0.547 0.441 0.579 0.423 Model p-value 0.00 0.00 0.00 0.00 Observations 121 720 121 720 Note: This table provides selected results from regressing the percentage return on securities loaned on various fund characteristics segmented by the year 2008. The data covers the period from 2003 to 2010 for 204 funds. Independent variables include: annual fund return (Portfolio Return), fund total net assets (Fund TNA), investment company total net assets (Family TNA), percentage of institutional class holdings in the fund (Institutional Ownership), standard deviation of monthly cash flows in and out of the fund computed over the prior twelve months (Cash Flow Volatility), percentage of fund assets used to pay custodian (Custodian Fees), dummy variables that equals 1 when the lending agent is a custodian, 3rd party/broker, and self-lending agents, dummy variable that equals 1 when the lending agent is affiliated with fund sponsor, excludes self-lending agents, and a dummy variable that equals 1 when borrowers’ collateral is invested with agent or an unnamed pool. Some variable coefficients are not reported to ease exposition. All models include year, FYE month, and investment objective fixed effects (including dummy variables for enhanced, tax managed, or leveraged funds). T-statistics derived from fund-level clustered robust standard errors are in parentheses. The notation a, b, c denotes statistical significance at the 1%, 5%, and 10%, respectively.

43

Table VI Securities Lending Returns and Agents: Tests of Endogeneity Lending Agent Type Affiliated Agent 1st Stage

Probit Dependent Variable =

Self Lending Dummy

2nd Stage OLS Dependent Variable = Return on Securities Loaned

2nd Stage OLS Dependent Variable = Return on Securities Loaned

1st Stage Probit

Dependent Variable = Affiliated Agent

2nd Stage OLS Dependent Variable = Return on Loaned Securities

2nd Stage OLS Dependent Variable = Return on Loaned Securities

(1) (2) (3) (4) (5) (6) Public Sponsor -0.486b

(2.01) 0.541b

(2.08)

Portfolio Return -0.002

(0.53) 0.010c

(1.78) 0.005

(0.94) 0.002

(0.56) 0.001

(0.09) -0.003

(0.39) Fund TNA 0.194b

(2.08) -0.104

(1.08) -0.146c

(1.70) -0.171c

(1.77) -0.052

(0.47) -0.060

(0.60) Family TNA 0.212a

(2.59) 0.226a

(2.67) 0.205a

(2.69) 0.216c

(1.93) 0.399b

(3.68) 0.304a

(2.79) Institutional Ownership -0.832a

(2.62) 0.515c

(1.75) 0.691a

(2.81) 0.444

(1.44) 0.572c

(1.68) 0.686b

(2.29) Turnover Ratiox100 -0.001

(0.01) -0.003c

(1.83) -0.001 (1.20)

-0.004

(1.10) -0.001 (0.83)

-0.001 (0.24)

Cash Flow Volatility -0.001 (0.53)

0.001c

(1.81) -0.001 (0.10)

-0.001a

(3.94) -0.001 (0.95)

-0.001b (2.03)

Self Lending Agent 2.344a

(3.56) 1.884a

(3.28)

3rd Party Agent/Broker 0.585

(1.59) 0.390

(1.22)

Unreported/Unknown Agent 0.054 (0.21)

-0.088 (0.40)

Sponsor Affiliated Agent -2.680b

(2.14) -2.197c

(1.91) Collateral Invested in Lending Agent Fund 0.414c

(1.71) 0.094

(0.34) -0.184

(0.72) Collateral Invested in Unnamed Pool -0.407c -0.637a -0.519b

44

(1.67) (2.65) (2.37)

Percentage of Portfolio Loaned -0.084a

(5.67) -0.070a

(3.62) Inv. Objective Dummies Y Y Y Y Y Y Year Dummies Y Y Y Y Y Y FYE Month Dummies Y Y Y Y Y R2 0.321 0.325 0.405 0.212 0.198 0.296 Model p-value 0.00 0.00 0.00 0.00 0.00 0.00 Observations 841 841 814 841 841 841

Note: This table provides results from two stage instrumental variable regressions. The data covers the period from 2003 to 2009 for 226 funds. Independent variables include: annual fund return (Portfolio Return), fund total net assets (Fund TNA), investment company total net assets (Family TNA), percentage of institutional class holdings in the fund (Institutional Ownership), standard deviation of monthly cash flows in and out of the fund computed over the prior twelve months (Cash Flow Volatility), percentage of fund assets used to pay custodian (Custodian Fees), dummy variables that equals 1 when the lending agent is a custodian, 3rd party/broker, and self-lending agents, dummy variable that equals 1 when the lending agent is affiliated with fund sponsor, excludes self-lending agents, and a dummy variables that equal 1 when borrowers’ collateral is invested with agent or in an unnamed pool. All models include year, FYE month, and investment objective fixed effects (including dummy variables for enhanced, tax managed, or leveraged funds). T-statistics derived from fund-level clustered robust standard errors are in parentheses. The notation a, b, c denotes statistical significance at the 1%, 5%, and 10%, respectively.

45

Table VII Securities Lending Returns and Boards of Directors

Independent Directors

All Independent

Type/Board Structure

Aff./Board Structure

(1) (2) (3) (4) Portfolio Return -0.001

(0.17) -0.001

(0.22) -0.001

(0.02) -0.001

(0.11) Fund TNA 0.063

(0.77) 0.061

(0.72) -0.010

(0.11) 0.067

(0.82) Family TNA 0.032

(0.22) 0.059

(0.42) 0.052

(0.38) -0.013

(0.09) Institutional Ownership 0.004

(1.25) 0.467

(1.31) 0.591

(1.63) 0.005

(1.55) Turnover Ratiox100 0.001

(0.43) 0.001 (0.65)

-0.001 (0.40)

0.001 (0.68)

Cash Flow Volatility -0.001 (0.17)

-0.001 (0.29)

-0.001 (0.26)

0.001 (0.29)

Percentage of Portfolio Loaned -0.092a

(4.34) -0.092a

(4.30) -0.089a

(4.34) -0.085a

(4.21) Self-Lending Agent 1.138a

(3.54)

3rd Party Agent/Broker 0.234

(0.84)

Unreported/Unknown Agent -0.849 (1.90)

Sponsor Affiliated Agent -0.233

(1.04) Collateral Invested in Lending Agent Fund

-0.625b

(2.15) Collateral Invested in Unnamed Pool -0.355c

(1.81) Board Size 0.620

(1.17) 0.587

(1.08) 0.611 (1.16)

0.535

(1.05) Independent Directors 2.364c

(1.68) 2.516c

(1.92) 1.258 (1.00)

All Independent Directors 0.368 (1.05)

Outside Directorships 0.724a

(3.19) 0.700a

(3.15) 0.361c

(1.84) 0.416c

(1.93) Family Ownership 1.028b

(2.05) 1.091b

(2.07) 0.299

(0.73) 0.733

(1.58) Unexplained Compensation (per $100,000)

-0.591a

(2.94) -0.602a

(3.07) -0.352

(1.61) -0.491b

(2.12) Inv. Objective Dummies Y Y Y Y Year Dummies Y Y Y Y FYE Month Dummies Y Y Y Y Adjusted-R2 0.465 0.460 0.480 0.474 Model p-value 0.00 0.00 0.00 0.00 Observations 700 700 700 700

46

Note: This table provides results from regressing the percentage return on securities loaned on various board, agent, and fund characteristics. The data covers the period from 2003 to 2010 for 204 funds. Independent variables include: annual fund return (Portfolio Return), fund total net assets (Fund TNA), investment company total net assets (Family TNA), percentage of institutional class holdings in the fund (Institutional Ownership), standard deviation of monthly cash flows in and out of the fund computed over the prior twelve months (Cash Flow Volatility), dummy variables that equals 1 when the lending agent is a custodian, 3rd party/broker, and self-lending agents, dummy variable that equals 1 when the lending agent is affiliated with fund sponsor, excludes self-lending agents, dummy variables that equal 1 when borrowers’ collateral is invested with agent or in unnamed pool, log of number of directors on fund board (Board Size), proportion of directors who are classified as outsiders (Independent Directors), proportion of directors with outside board appointments, proportion of directors who have ownership in funds in family (Family Ownership), Tufano and Sevick’s (1997) measure of relative independent director compensation (Unexplained Compensation). All models include year, FYE month, and investment objective fixed effects (including dummy variables for enhanced, tax managed, or leveraged funds).. T-statistics derived from fund-level clustered robust standard errors are in parentheses. The notation a, b, c denotes statistical significance at the 1%, 5%, and 10%, respectively.

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Table VIII Likelihood of Fund having a Securities Lending Program

Primary Specification: Dependent Variable=1 if Fund Lends

Boards: Dependent Variable=1 if Fund Lends

Self-Lending: Dependent Variable=1 if

Fund Self Lends

Affiliated Agent:

Dependent Variable=1 if Fund Employs

Affiliated Agent

(1) (2) (3) (4) Portfolio Returnt-1 0.013b

(2.02) 0.016c

(2.34) -0.018a

(2.74) 0.016c

(1.79) Fund TNAt-1 0.463a

(3.43) 0.339c

(2.27) 0.585a

(2.81) -0.431b

(2.32) Family TNAt-1 0.432a

(3.22) 0.700a

(3.01) -0.387c

(1.77) 0.546c

(1.89) Institutional Ownershipt-1 0.017a

(2.90) 0.018 (1.60)

-0.013c

(1.66) -0.008

(0.87) Cash Flow Volatilityt-1 0.001c

(1.83) -0.001c

(1.75) -0.006 (0.26)

0.005 (1.02)

Asset Turnovert-1 -0.033 (0.36)

-0.033 (0.37)

-0.011c

(1.73) -0.018b

(2.21) Board Size -0.566

(0.63) -2.413b

(2.56) 0.794 (0.72)

Independent Directors 3.156 (1.36)

-2.110 (0.70)

-0.715 (0.27)

Outside Directorships 0.053 (0.10)

1.580a

(3.00) -3.231a

(4.03) Family Ownership -0.851

(0.79) 4.897a

(3.54) -2.297c

(1.94) Unexplained Compensation (per $100,000)

-0.643 (1.63)

-1.780a

(4.66) 4.340a

(7.92)

Inv. Objective Dummies Y Y Y Y Year Dummies Y Y Y Y FYE Month Dummies Y Y Y Y Pseudo-R2 0.368 0.304 0.421 0.507 Model p-value 0.00 0.00 0.00 0.00 Observations 1,086 841 614 614 Note: This table presents logit regressions modeling impact of fund characteristics on the likelihood of a fund having a securities lending program. The data covers the period from 2003 through 2010 for 204 funds. Independent variables include: annual fund return (Portfolio Return), fund total net assets (Fund TNA), investment company total net assets (Family TNA), percentage of institutional class holdings in the fund (Institutional Ownership), standard deviation of monthly cash flows in and out of the fund computed over the prior twelve months (Cash Flow Volatility), and the percentage of fund holdings that changed over the preceding year (Asset Turnover), log of number of directors on fund board (Board Size), proportion of directors who are classified as outsiders (Independent Directors), proportion of directors with outside board appointments, proportion of directors with outside board appointments, proportion of directors who have ownership in funds in family (Family Ownership), Tufano and Sevick’s (1997) measure of relative independent director compensation (Unexplained Compensation). All models include year, FYE month, and investment objective fixed effects (including dummy variables for enhanced, tax

48

managed, or leveraged funds)and are lagged one period where so noted. T-statistics derived from fund-level clustered robust standard errors are in parentheses. The notation a, b, c denotes statistical significance at the 1%, 5%, and 10%, respectively.

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Appendix 1. The Market for Securities Lending

Subsequent to the 1940 Investment Company Act which allowed mutual funds to lend securities, the SEC developed operating guidelines to regulate securities lending practices of funds in a series of no-action letters.29,30 These guidelines address board oversight, loan collateral, termination of the securities loan, returns, fees, voting rights, and loan limits. For each securities loaned the mutual fund must receive eligible collateral from the borrower at the time of the loan (e.g., Nagel, 2005). The collateral must be marked to market daily to account for any increases in the values of the securities loaned and/or decreases collateral value. In practice, most lending agreements call for 102% collateralization for domestic loans and 105% collateralization for foreign securities.31 The higher collateral requirement for foreign securities is intended to protect funds from foreign currency fluctuations. In addition to the initial margin, the lending agreement specifies the minimum margin level to be maintained throughout the life of the loan. The overwhelming majority of securities loans in the U.S. are facilitated with cash collateral while other forms of collateral (e.g., corporate and government bonds, equities, letters of credit, warrants, and delivery by value) are more common in the U.K. and European markets.32

Mutual funds in the U.S. can terminate a securities loan at any time and if the borrower does not return the securities the mutual fund can use the borrower’s collateral to repurchase the securities (Evans et al., 2009). Likewise, it is common for securities borrowers in the U.S. to maintain the right to return securities early. In other jurisdictions, mutual funds can enter into fixed term lending agreements although loans are usually open (e.g., the maturity dates are non-binding and either party can terminate at any time) especially for equity loans. How mutual funds generate returns from securities lending depends on how much securities are on loan, the supply and demand for lendable securities, lending agent fees, and collateral type (cash vs. non-cash). Obviously, larger securities lending programs will generate higher expected lending income. The SEC has interpreted Section 18(f) of the Investment Company Act of 1940 as a limit on the size and scope of U.S. domiciled mutual funds’ securities lending programs. Specifically, mutual funds may not lend securities in excess of 1/3 of their TNA.

If a mutual fund receives cash as collateral in exchange for lending securities the lending fee is computed using a market reinvestment interest rate (typically an overnight rate). A portion of the reinvestment rate, the “rebate interest rate”, is paid by the lending fund to the borrower (similar to the repo rate in the bond lending market). If the fund decides to invest the collateral at the reinvestment rate, the lending fund’s profit is the reinvestment interest earned less the rebate interest paid to the borrower and lending agent fees. Lending funds can elect to invest borrowers’ collateral in more risky investments in order to receive higher expected

29 See Dechert on Point, March 2007, Issue 11 for a more complete discussion on the legal environment

surrounding securities lending by mutual funds, and Faulkner (2006) for details of the securities lending market. 30 Securities lending regulations in a number of jurisdictions are similar to those in the U.S. In addition, most

securities lending agreements utilize standardized contracts such as the U.S., Global, and European Master Securities Lending Agreements.

31 The overcollateralization increased by as much as 20% during the Fall of 2008 (Spitalfields Advisors, 2008. “The Impact of Recent Regulatory Changes and Economic Events on Beneficial Owners Participating in a Lending Programme.” Available at www.spitalfieldsadvisors.com/pdfs/Impact_on_beneficial_owners.pdf.)

32 U.S. rules limit securities lending collateral to cash, U.S. Treasuries, and letters of credit.

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lending returns.33 However, borrowers are entitled to receive the rebate interest based on the agreed upon reinvestment rate regardless of the actual returns on the invested collateral. U.S. mutual funds generally invest collateral at the market interest rate or similar low risk investment vehicle. For securities loans not collateralized with cash borrowers pay lenders an outright fee based on the length of the loan.

Securities loan fees are a function of borrower demand and lender supply and can vary dramatically (Cohen, Diether, and Malloy 2007).34 For example, D’Avolio (2002) finds that S&P 500 constituent stocks are in excess supply and lending fees are less than 1% per annum while stocks with less supply and greater borrower demand (commonly referred to as ‘special’ or ‘hot’) have significantly higher lending fees. When borrowing demand for a securities is high and the securities is not widely available to lend the laws of supply and demand drive lending fees up and for cash collateralized loans the rebate rate can become negative (e.g. the borrower pays the lender additional interest and does not receive a rebate). Finally, funds that employ lending agents or intermediaries compensate them by either splitting the lending fees (more common) or by making an outright payment. U.S. regulations require mutual funds to receive no less than 50% split of gross lending fees. However, it is difficult to observe fee splits in practice as mutual funds are not required to disclose this information and few do.

Mutual funds and other securities lenders have four general alternatives when it comes to selecting an agent or principle to facilitate their securities lending programs. They can select their custodian bank, a third party specialist such as a broker/dealer, lend directly to borrowers, or some combination of the aforementioned lending choices (e.g. acting as their own lending agent). Very often, the securities lending agent employed by a fund is affiliated with the firm that sponsors the fund.

Custodian banks are the most commonly selected lending agent type (about 51% of the funds in our sample use custodian lending agents) as the custodian provides an easy entry into the lending market since (i) presumably the mutual fund board has already performed due diligence on the custodian, (ii) the custodian already has custody of fund assets, (iii) are able to accommodate small portfolios by pooling their securities with other funds, and (iv) custodians are in a position to handle all of the operational details associated with lending (Fabozzi, 2008). In addition, as banks, custodial lending agents may (but not necessarily) provide lenders indemnities and manage borrowers’ collateral. Given the reduced levels of effort and risk associated with using custodial lending agents, revenues are usually lower than if the mutual fund selects another way to access the securities lending market (see Fabozzi and Mann, 2005).

Securities lenders may also opt to appoint a third party specialist agent or principle, typically a broker/dealer, to facilitate their lending programs. These third party intermediaries focus on securities lending and do not provide the wide range of services offered by custodial banks. Third party specialists are generally smaller than custodian agents and are not constrained by allocation rules that determine how much of each securities a fund can lend

33 We use the term lending return to describe the combined fee and collateral reinvestment income earned by the

mutual fund. 34 Motivations to borrow include the need to cover a short position (i.e. settlement coverage, pairs trading, naked

shorting, market making), a desire by borrowers to lend cash as part of a financing transaction (i.e. prime brokerage units supporting hedge fund activities may borrow to manage their inventory and balance sheets), and to facilitate arbitrage opportunities (e.g. tax, trading, and dividend arbitrage).

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when the supply of lendable funds exceeds demand. As such, third part specialists have the potential to increase the amount of securities on loan and offer higher lending returns. In addition, a principal third party intermediary allows lenders to effectively lend to organizations they would not lend to directly because the borrowers may not be well recognized, regulated, and/or do not have good credit ratings. An example of principal third party intermediation is a prime broker who borrows from mutual funds and lends to hedge funds The main disadvantage to using a principal third party instead of an agent third party or custodian is that principal lenders do not receive a portion of the earning from lending securities, but instead benefit by borrowing at the lowest possible cost. Therefore, mutual fund managers must have the expertise to negotiate lending fees with principal third parties. Figure 1 illustrates one of the many ways mutual funds can lend securities.

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Figure 1

Figure 1 illustrates one of many ways a mutual fund can access the securities lending market. A customer of the broker/dealer sells a particular security short to a long investor. The broker/dealer locates and borrows securities to facilitate the short sale using the proceeds of their customer’s short sale to collateralize the borrowed securities. For this example the reinvestment rate is 4.00% and the lending fund/agent and broker/dealer agree to a 1.00% rebate rate. The lending fund approves the lending agreement and instructs the lending agent to invest the collateral in the agent’s collateral pool to earn the reinvestment rate (for the sake of simplicity we assume the lending fund does not choose an alternative investment vehicle for the collateral). The lending agent and the mutual fund split the net interest profit of 3.00% (4.00%-1.00%) with at least 50% of the proceeds going to the lending mutual fund. The broker/dealer uses the borrowed securities to make settlement with the long investor. If the short selling customer earns 50 basis points on the cash proceeds from the short sale the broker/dealer earns the remaining 50 basis points. Mutual funds can increase their profits by searching for borrowers and/or agents who are willing to receive the lowest rebate rates and fee splits. Likewise, the remaining parties to the security lending arrangement seek the most favorable terms.

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Appendix Table A.1 Securities Lending Returns

Dependent Variable = Return on Loaned Securities (%)

(1) (2) Portfolio Return -0002

(0.38) -0.002

(0.39) Fund TNA -0.014

(0.09) 0.091

(0.59) Family TNA 0.144

(1.53) 0.140

(1.34) Institutional Ownership 0.516

(1.57) 0.527

(1.55) Turnover Ratiox100 -0.001

(0.28) 0.001 (0.45)

Cash Flow Volatility 0.001b (2.55)

0.001a

(2.87) Lambda —0.539

(0.54) -0.469 (0.42)

Self Lending Agent 1.316a

(3.63)

3rd Party Agent/Broker 0.136

(0.48)

Unreported/Unknown Agent --0.205 (1.17)

Sponsor Affiliated Agent? -0.562a

(2.82) Collateral Invested in Lending Agent Fund?

-0.691b

(2.48) Collateral Invested in Unnamed Pool?

-0.473b

(2.40) Inv. Objective Dummies Y Y Year Dummies Y Y Adjusted-R2 0.429 0.459 Model p-value 0.00 0.00 Observations 719 841

Note: This table provides results from regressing the percentage return on loaned securities on various fund characteristics using the Heckman (1979) approach, where we first model the dummy variable that takes a value of 1 if a fund reports the securities lending income and otherwise zero, using the probit model to calculate the inverse Mills ratio. The independent variables of this first probit model includes organizational status (public or private), fund total net assets (Fund TNA), investment company total net assets (Family TNA), investment objective dummy, and year dummy. The data covers the period from 2003 to 2010 for 204 funds. Independent variables include: annual fund return (Portfolio Return), fund total net assets (Fund TNA), investment company total net assets (Family TNA), percentage of institutional class holdings in the fund (Institutional Ownership), dummy variables that take a value of 1 when the fund is an enhanced, leveraged, or tax managed index fund, standard deviation of monthly cash flows in and out of the fund computed over the prior twelve months (Cash Flow Volatility), dummy variables that equals 1 when the lending agent is a self-lending agent, 3rd party/broker, and unknown, dummy variable that equals 1 when the lending agent is affiliated with fund sponsor, dummy variables that equal 1 when borrowers’ collateral is invested with a lending agent fund or an unnamed collateral reinvestment pool, and lambda that is estimated from the first probit model. T-statistics derived from fund-level clustered robust standard errors are in parentheses. The notation a, b, c denotes statistical significance at the 1%, 5%, and 10%, respectively.