Agency Conflicts in Delegated Portfolio Management: Evidence from Namesake Mutual Funds

by

Stephen P. Ferris Department of Finance

404 Cornell Hall University of Missouri – Columbia

Columbia, MO 65211-2600 Tel: (573) 882-9905

Email: Ferriss@missouri.edu

and Xuemin (Sterling) Yan*

Department of Finance 427 Cornell Hall

University of Missouri – Columbia Columbia, MO 65211-2600

Tel: (573) 884-9708 Email: YanX@missouri.edu

___________________________ * We thank W.D. Allen, John Howe, Mike Sykuta, John Zimmerman, George Wood, and seminar participants at the Contracting and Organizations Research Institute (CORI) of University of Missouri – Columbia for helpful comments. All remaining errors are ours.

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Agency Conflicts in Delegated Portfolio Management: Evidence from Namesake Mutual Funds

Abstract

Namesake funds provide a unique sample for studying the two agency conflicts existing

within a mutual fund. The first is between the fund company and its shareholders while the second occurs between the fund company and the portfolio manager. A typical namesake fund manager sits on his fund’s board, frequently as the chairman, is the majority owner of the fund company, and has significant investments in the fund he manages. We find that namesake funds are more tax-efficient, consistent with the idea that managerial ownership helps align the interests of managers with that of shareholders. Because of fewer career concerns, we observe that namesake fund managers herd less while assuming greater unsystematic risk. We find limited evidence that namesake fund managers outperform their benchmarks and peers. Namesake funds do not attract greater investor cash flows after controlling for performance. Finally, our results suggest that the boards of namesake funds are ineffective since these funds charge higher fees and display less consistency with their stated investment objectives. These results offer support for the SEC’s proposal of an independent board chairman accompanied by a three-quarters majority of independent directors.

Keywords: mutual funds; agency conflict; namesake funds JEL Codes: G23; G34

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Agency Conflicts in Delegated Portfolio Management: Evidence from Namesake Mutual Funds

1. Introduction

Over the last year, the numerous scandals in the mutual fund industry often accompanied

by large settlements resulting from legal and regulatory action have brought tremendous

attention to this industry.1 With nearly one of every two American households invested in mutual

funds and the aggregate value of the industry’s holdings over 7 trillion dollars (Donaldson,

2004), such attention is hardly unexpected. Although there are numerous instances of late

trading, market timing, self-dealing, and other abuses, the overarching issue facing the mutual

fund industry is one of mitigating and managing its agency conflicts. Indeed, we believe that the

well-publicized scandals and wrongdoing of mutual funds are the result of agency conflicts that

have been inadequately addressed by both the industry and its regulators.

There are two sets of agency conflicts operating within a mutual fund that generate the

problems currently challenging the industry. These conflicts are graphically presented in Figure

1. The first of these is the conflict between the fund company, which is interested in generating

fee income, and its shareholders, who desire to maximize the after-tax returns from their

investment. The second conflict within the industry is more subtle, and consequently has

received less attention from the media and regulators. This is the conflict between the fund

company and the fund manager. This occurs because of career concerns by the fund manager that

might incent them to make investment decisions inconsistent with shareholder wealth

maximization.

1 Morningstar at www.morningstar. com for instance, maintains a current listing of regulators’ charges, settlements and fund company statements in response to allegations.

3

This study will examine the nature and impact of these agency conflicts by using a

special type of mutual fund: namesake funds. A namesake fund is a fund who shares its name

with its managers. An example of a namesake fund is that of Baron Asset fund and the Baron

Growth fund, both of which are run by Ron Baron, a former equity analyst. Indeed, namesake

funds are typically founded and managed by former money managers or analysts. We select

namesake funds for our analysis because the nature of namesake funds allows a unique

examination of the various forces latent within the two agency conflicts that plague mutual

funds.

One such force is the board structure of namesake funds. The board structure of these

funds might not be aligned with shareholder interests. Unlike other mutual funds, the portfolio

manager of a namesake fund is almost always a director of the fund, and in many cases, also

serves as board chair. Indeed, for our sample of namesake funds, over 98% of the portfolio

managers serve as fund directors while 73% chair the board. This phenomenon of the portfolio

manager simultaneously serving as the board chair provides the individual with an unusual

ability to dominate both the fund’s strategic and investment decisions. These decisions might not

necessarily be consistent with the interests of shareholders. Further, our analysis of this feature of

namesake funds provides evidence regarding the usefulness of the recent proposal by the SEC to

require an independent board chair for mutual funds accompanied by a three-quarters majority of

independent directors. 2

Another force shaping the nature of this first agency conflict is that of managerial

ownership. In the case of namesake funds, however, portfolio managers have two ownership

interests. The first ownership interest of the portfolio manager is in the fund itself. Prior studies

2 William Donaldson, current chair of the SEC, argued in Congressional testimony (8 April 2004) that such independence was an important part of the SEC’s efforts “to restore overall accountability to the fund board”.

4

of corporate managers suggest that managerial ownership helps to align the interests of managers

with those of shareholders (see e.g., Morck, Shleifer, and Vishny (1988) and McConnel and

Servaes (1990)). Namesake fund managers have significant investments in the funds they

manage. Based on information gathered from the Securities and Exchange Commission (SEC),

nearly 70% of namesake fund managers own more than $100,000 in the funds they manage, and

over 90% of these managers own more than $100,000 in the funds belonging to their fund

family.3 Further, in an industry study, Sanders (1998) identifies twelve fund managers who

significantly invest with shareholders. Nine out of these twelve managers are namesake fund

managers.4

Complementing the portfolio manager’s direct ownership in the fund is his intangible

ownership attributable to reputation. Namesake fund managers have significant reputational

capital at risk because their names are directly associated with the fund. This combination of

tangible and intangible ownership interests in the fund, can help to align the interests of the

portfolio managers with those of shareholders.

The second ownership interest held by the portfolio manager is in the fund company.

Unlike other fund managers, namesake managers hold a majority interest in the fund company.

This moves the interests of the portfolio manager away from those of the fund’s shareholders and

towards those of the fund company.

Although the namesake manager has two ownership interests, these interests are not

typically comparable in magnitude. The portfolio manager’s investment in the fund company is

3 Starting from 2002, the SEC requires funds disclose the dollar range of director investments in the fund and the family of funds. The range runs from $1 to $10,000, $10,001 to $50,000, $50,001 to $100,000 and more than $100,000. 4 The nine namesake fund managers are Ron Baron, Michael Fasciano, George Mairs, Ron Muhlenkamp, David Schafer, Robert Torray, Roy Papp, David Dreman, and Chris Davis. The remaining three managers are Richard Lawson of Weitz Series Hickory, Martin Whitman of Third Avenue Value, and David Williams III of Excelsior Value and Restructuring.

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usually much larger than that in the fund he manages. Consequently, we believe that the net

incentive effect of the portfolio manager’s ownerships will be to align his interest with that of the

fund company rather than that of the shareholders.

Namesake funds also provide a unique opportunity to study the second agency conflict

within the mutual fund industry: that between fund managers and their employing fund

companies. More specifically, we use this sample of namesake funds to examine how career

concerns affect fund managers’ investment decisions. Chevalier and Ellison (1999a) analyze how

manager behavior might be affected by a manager’s desire to avoid losing his job. They show

that career concerns of younger portfolio managers cause them to be more risk averse in

selecting their fund’s portfolios. In particular, younger managers tend to take on lower

unsystematic risk and deviate less from the typical behavior than their older counterparts.

Namesake fund managers, however, are their own bosses. They will not be easily terminated

from their positions. Almost 80 percent of these managers are the founders of the fund

companies, and nearly all of them are Chief Executive Officers (CEO) and/or Chief Investment

Officers (CIO) of the firm.

These two agency conflicts manifest themselves in a variety of the fund’s characteristics

and operations. More specifically, we examine how agency conflicts effect the fees, style

consistency, tax efficiency, herding behavior, risk levels, return performance, and new investor

cash flow of namesake funds. An analysis of namesake funds on these dimensions provides us

with useful insights regarding the relative strength of factors that either mitigate or exacerbate

agency conflicts. Such evidence will be useful to the public debate on how best to reform the

mutual fund industry.

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The factors contributing to these agency conflicts exert different, and sometimes contrary

influences on the mutual fund. By analyzing the fee structure of these funds, we gain insight into

how the boards of these funds balance the desire for higher fee revenue against a shareholder

preference for lower fees and higher net returns. Further, this analysis will provide insight into

the relative strengths of the conflicting incentives resulting from the portfolio manager’s two

ownership interests. Our analysis of the style consistency of these funds allows us to determine

how a board chair that serves as portfolio manager balance individual investment desires and

style against the publicly stated investment strategy. Since the boards of namesake funds are

insider-dominated, we expect to find less effective governance manifested in greater style drift

and higher fees.

By examining the tax efficiency of namesake funds, we investigate the incentive effects

of ownership. Because the portfolio managers of namesake funds typically have significant

amounts of their own capital invested in these funds, we hypothesize that namesake funds will be

more tax efficient than other funds. Because of their majority ownership in the fund company,

these portfolio managers have less concern about career progression. Consequently, it is likely

that managers of namesake funds will herd less and assume higher levels of unsystematic risk

relative to other managers.

An important manifestation of the two agency conflicts present within the mutual fund

industry is the extent to which they influence fund performance. To examine this issue, we

estimate two measures of performance for namesake funds. The first is realized stock returns

while the second is risk-adjusted returns. The relative performance of namesake funds is unclear

because of the conflicting influences of high levels of managerial ownership and a board that

lacks independence. Finally, we examine the ability of the namesake fund to attract new investor

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cash flow. Because the managers of namesake funds have a history of success as analysts and

managers, such individuals have “star power”. Hence, we hypothesize that they have a greater

ability to attract new investment capital than the managers of other funds.

We organize our study as follows. In the following section, we describe our data and

method of sample construction. Section three examines board effectiveness by analyzing the fee

levels and style consistency of namesake funds. In section four, we discuss our findings

regarding the tax efficiency of namesake funds. In section five we present our results from an

analysis of the herding and portfolio risk profile of the portfolio managers for these funds.

Section six contains our results from an analysis of the return performance of our sample funds

while section seven presents our findings regarding investor cash flows. We conclude in section

eight with a summary and a brief commentary.

2. Data and Sample Construction

2.1 Sample Construction

Our primary data source for mutual funds is the Center for Research in Security Prices

(CRSP) Survivor Bias-Free Mutual Fund Database. The sample period is 1962-2001. Following

many prior studies of mutual funds, we focus on diversified domestic equity funds. In particular,

we exclude international funds, sector funds, and balanced funds. We also exclude index funds

because they are not actively managed.5 We use the ICDI fund objective code to assign each

fund one of three investment objectives: Aggressive Growth (AG), Long-term Growth (LG), and

Growth and Income (GI).

5 We identify index funds by searching for “index” in fund names. To ensure accuracy, We hand check all selected funds and delete spurious ones.

8

We identify namesake funds by comparing fund names and manager names. We first

obtain all funds whose fund name and manager name share at least one word. We then exclude

those funds where the common word is “investment”, “team”, “capital”, or “management”. To

focus on agency issues, we also exclude those funds whose managers are sub-advisors. The

managers of these funds are not employees of the fund companies; rather, they are hired by the

fund companies to manage the funds as “subcontractors”. To ensure accuracy, we hand check all

selected funds. In total, we identify 219 namesake funds.

For our sample of namesake funds, we also hand collect background information about

their managers and directors from various sources including fund prospectuses, statements of

additional information (SEC form 485B), and fund websites.

2.2 Characteristics of Namesake Funds

Table I presents the summary statistics for our sample of namesake funds. The second

column presents the mean values for a number of namesake fund characteristics. For

comparison, the third column presents the corresponding means for all other equity mutual

funds.

Namesake funds are on average smaller than other equity funds. An average namesake

fund has total net assets (TNA) of $219.20 million, while an average equity fund has $391.21

million in total assets. Namesake funds also reside in smaller fund families. The fund family to

which a namesake fund belongs has an average market share (by TNA) of 0.09%, compared to

that of other equity funds at 1.08%. The average turnover rate of namesake funds is 56.67%,

substantially lower than that of other equity funds, whose annual turnover is in excess of 76%.

The average expense ratio of namesake funds is 1.24%, which is 27 basis points higher

than that of other equity funds. This difference in expense ratios, however, can not be explained

9

by 12b-1 fees. The average 12b-1 fee of a namesake fund is 24 basis points, 12 basis points

lower than that of other equity funds.

The average total load charged by namesake funds is 1.16%, which is much lower than

the 2.72% charged by other equity funds. Only 31.8% of the namesake funds charge a load fee,

while over 54% of other equity funds require a load fee. This suggests an important difference in

the distributional channels between namesake funds and other equity funds. Namesake funds are

more likely to sell shares directly to investors while other equity funds are more likely to use

brokers.

The average tenure for namesake fund managers is 5.98 years, nearly 2 years longer than

managers of other equity funds.6 But this is to be expected. Namesake funds are usually family-

owned and family-managed, and hence their managers are less likely to leave the fund or lose

their jobs. We will examine the characteristics of namesake fund managers in greater detail in the

following subsection.

Namesake funds hold slightly more cash and less stock than other equity funds. On

average, namesake funds hold 8.93% of their assets in cash and 87.99% in stocks, compared to

6.95% cash and 89.71% stocks for other equity funds. Further, we note that a higher percentage

of namesake funds are Growth and Income funds compared to other equity funds.

2.3 Characteristics of Namesake Fund Managers

Table II presents the characteristics of namesake fund managers. Unlike their

counterparts in other equity funds, nearly all namesake fund managers (98.32%) are directors of

the funds that they manage. Almost three quarters of these managers are chairman of their

boards. Approximately 79% of the namesake fund managers are also founders of the fund

6 The average manager tenure (5.98 years) is less than the average fund age (8.05 years) for two reasons. First, some funds are managed by children or grandchildren of the founders of the funds. Second, the manager tenure data are missing for some older funds. These managers tend to have a longer-than-average tenure.

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companies. Managers of namesake funds also hold significant ownership in the fund companies,

and all of them are CEO and/or CIO of the fund companies.

Almost all of the namesake fund managers have relevant prior experience. Panel B

reports that approximately 65% of these managers were formerly portfolio managers. Another

20% or so were analysts or directors of research in money management firms. Approximately

12% of these managers were formerly vice presidents, managing directors, or CIOs of financial

institutions. Interestingly, 2.38% of these managers are former university professors.

Significantly, namesake fund managers have sizable investments in their funds and fund

families. For example, 68.22% of these managers invested $100,000 or more in the funds they

manage. Perhaps more revealing, 91.20% of these managers invested $100,000 or more in their

fund family. We collect the above ownership information from SEC form 485B. The SEC

requires that funds disclose director investments in the fund and fund family in terms of dollar

ranges.

2.4 Governance Characteristics of Namesake Funds

In this section, we describe the board composition of namesake funds. The boards of

directors for namesake funds lack independence because their managers sit on the boards, often

as the chairmen. Consequently, an analysis of these boards is central to our understanding of the

agency conflicts that exist within a mutual fund.

Panel A of Table III presents our findings concerning board composition. The average

number of directors in a namesake fund is 6.9. Approximately two thirds of the directors are

independent. Using a sample of funds in the 50 largest fund families, Tufano and Sevick (1997)

report that there are on average 8.7 directors in a board, and 6.2 of them are independent

directors. Compared to funds contained in Tufano and Sevick’s sample, namesake funds have

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smaller boards, but similar percentage of independent directors. The namesake funds have

smaller boards in large part because they are smaller funds than those in the Tufano and Sevick’s

sample.

3. Fund Governance: Fees and Style Consistency

The board of directors is central to effective mutual fund governance and the control of

agency conflict between the fund and its shareholders. Indeed, the primary responsibility of the

board of directors of mutual funds is to protect the interests of fund shareholders and thereby

mitigate the agency conflicts that result from the divergent interests of shareholders and the fund.

Specifically, the board decides whether to renew the investment advisory contract, votes on fee

changes, approves any merger or liquidation, and monitors the specific investments within the

portfolio to ensure that the adviser’s investments comply with the fund’s stated investment

objectives. As noted previously, the boards of namesake funds are not independent. Almost all of

the namesake fund managers sit on the board, and nearly three-quarters serve as chairman.

Because of these characteristics of namesake funds, we hypothesize that their boards are less

effective.

We use two measures to assess the quality of governance for namesake funds. The first

measure is fund fees. As argued by Tufano and Sevick (1997) and Del Guerico, Dann and Partch

(2003), higher fees reflect less effective fund governance since the investment advisory contract

which stipulates fund fees must be renewed annually by the board of directors. Consequently,

we hypothesize that namesake funds are more likely to charge higher fees. This hypothesis is

consistent with the presence of agency conflict despite ownership and reputational incentives to

the contrary.

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In Table I, we show that the average expense ratio of namesake funds is 27 basis points

higher than other equity funds. It is possible, however, that this higher fee charged by namesake

funds is due to the smaller size of these funds. Panel B of Table III presents our regression

analysis of fund expense ratios, controlled for size and other fund characteristics. Consistent with

casual empiricism, larger funds, older funds, and funds from larger families charge lower fees.

After controlling for these factors, we still find that namesake funds charge significantly higher

fees than other equity funds. Specifically, the coefficient of the namesake fund dummy variable

is above 0.2, suggesting that the average expense ratio of namesake funds is more than 20 basis

points higher than other equity funds.

Our second measure of board effectiveness is style consistency. One of the

responsibilities of the board is to monitor fund portfolios to ensure that they comply with the

stated investment objectives of the fund. If a fund has a weaker board, then it is more likely to

suffer style drift. Following Brown and Harlow (2002), we estimate style consistency using two

proxies: the R-square and the tracking error of the regression of fund returns on factor returns.

Panel C of Table III presents our results concerning style consistency. Consistent with the

view that namesake funds have less effective fund governance, we find that the average R-

squares are reliably lower, and the average tracking errors are reliably higher for namesake

funds, when compared to other equity funds. For instance, the average R-square is 0.82 for

namesake funds, and 0.89 for other equity funds. The average tracking error is 2.23% for

namesake funds, and 1.71% for other equity funds. The differences are statistically significant at

the 1 percent level.

These results for style consistency and annual expense ratios provide strong evidence

that the insider-dominated boards of namesake funds provide less effective oversight than those

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of other funds. These results further suggest that independent boards are necessary in controlling

the agency conflict that exists within a mutual fund between its shareholders and the fund. Our

findings are also consistent with the SEC’s claims that independent mutual fund boards will

improve governance of these funds and thereby benefit shareholders.

4. Tax Efficiency

Tax planning is an essential element of the investment process and will influence the

magnitude of the after-tax returns earned by investors. But in mutual funds, shareholders have no

control over the timing or the size of taxable distributions. Average fund managers have little

incentive to improve the fund’s tax efficiency because fund managers are typically evaluated

based on their funds’ pre-tax performance.7 But since the managers of namesake funds have

significant amounts of their own capital at risk, we expect that their portfolio decisions are more

tax efficient than those of other fund managers. Hence, an analysis of this issue will allow us to

assess the incentive effects provided by ownership in the fund.

To examine the tax-efficiency of mutual funds, we define a fund’s total pretax return in

the same manner as Bergstresser and Poterba (2002). Specifically, we express a fund’s total

pretax return as the sum of dividends, short-term capital gains, long-term capital gains, and

undistributed capital gains.

uggdR lsp +++= (2)

We define the fund’s after tax-return as:

( ) ( ) ( ) ( )uggdR ucglcgsdda ττττ −+−+−+−= 1111 (3)

7 The SEC now requires funds disclose after-tax returns based on assumed tax rates. However, the fund industry and media still focuses on pre-tax returns.

14

For our analysis, we assume that the marginal tax rate on dividends (τd) is 31%, and the

marginal tax rate on long term capital gains (τcg) is 20%. Consistent with SEC policy, we set the

tax rate on undistributed capital gains as zero (τucg=0) in calculating pre-liquidation after-tax

returns. Since we only have data on total capital gain distributions, we assume all capital gains

are long-term.8

We define tax efficiency as follows:

0000

0

10

=++<>++<

≥=

lsa

lsa

apa

ggdandRifggdandRif

RifRREfficiencyTax (4)

That is, we define tax efficiency as the ratio between the after-tax return and the pre-tax

return when the after-tax return is positive. When the after tax return is negative, the tax

efficiency is 1 if the fund does not have any taxable distributions and is 0 otherwise. Based on

the above definition, tax efficiency is always between 0 and 1, where 1 represents the highest tax

efficiency and 0 represents the lowest tax efficiency.

Table IV presents our results for an analysis of namesake tax efficiency. Panel A presents

the equally weighted and TNA-weighted average tax efficiency. Using either measure, we find

that namesake funds have significantly higher tax efficiency than other equity funds. For

example, using the equally weighted measure, the tax efficiency is 67.98% for namesake funds,

but only 61.64% for other equity funds. The difference of 6.34% is statistically significant at the

1 percent level. Results for value-weighted averages are similar.

To examine whether the difference in tax efficiency between namesake funds and other

equity funds can be explained by other fund characteristics, we present the results from a

regression analysis in Panel B. After controlling for fund size, age, and fund family size, we find 8 Our results are robust to alternative choices of marginal tax rates and alternative ways to separate short-term versus long term capital gains.

15

that namesake funds continue to remain more tax-efficient than other equity funds. The

coefficient for the namesake fund dummy variable is slightly more than 5% in all regressions,

only slightly smaller than the difference between average tax efficiencies of these two groups of

funds. This result suggests that most of the difference in tax efficiency can not be explained by

fund characteristics. We find that older funds are less tax-efficient, perhaps due to their large

accrued capital gains. Controlled for age, larger funds are more tax-efficient. The size of the fund

family is positively related to tax efficiency, but this relation is not statistically significant.

5. Career Concerns

Our sample of namesake funds also allows us to examine the agency conflict that exists

between the fund manager and the employing fund company. This conflict centers around how

managerial career concerns influence the portfolio decisions of fund managers. Chevalier and

Ellison (1999a) investigate how the behavior of mutual fund managers might be affected by their

desire to remain employed. Chevalier and Ellison find that the probability of termination is a

decreasing and convex function of performance. The probability of termination is sensitive to

differences at negative excess return levels, but insensitive to differences at positive excess

return levels. As a result, fund managers have an incentive to avoid unsystematic risk.

Furthermore, the probability of termination is higher as a manager deviates more from the herd.

This provides an implicit incentive for managers to herd. Consistent with the view that younger

managers have more career concerns, Chevalier and Ellison find that younger managers take on

less unsystematic risk than older managers, and are less likely to deviate from the herd than older

managers.

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But most namesake fund managers are founders and CEO/CIO of their fund companies.

This suggests that namesake fund managers have few career concerns. Consistent with Chevalier

and Ellison (1999a), we hypothesize that namesake fund managers assume greater unsystematic

risk and herd less than other fund managers.

5.1 Measurement and methodology

To estimate unsystematic risk and factor loadings, we estimate the following regression

for each fund using data for the past 24 months, depending upon data availability:

tttttt UMDuHMLhSMBsMKTmR εα +⋅+⋅+⋅+⋅+= (5)

Following Chevalier and Ellison (1999a), we measure unsystematic risk by using the

standard deviation of the error term εt. Table V shows that namesake funds, on average, have a

higher unsystematic risk. Consistent with Chevalier and Ellison (1999a), we measure the extent

of herding by using the following six deviation measures: return deviation, unsystematic risk

deviation, market beta deviation, SMB beta deviation, HML beta deviation, and UMD beta

deviation. For example, we calculate market beta deviation for each fund as the absolute

difference between fund market beta and the average market beta of all equity funds.

5.2 Findings for herding by namesake funds

Table V presents our findings regarding herding by the portfolio managers of namesake

funds. We find that namesake funds have significantly greater deviations from typical behavior

than other equity funds, regardless of the deviation measure used. For example, the average

return deviation is 2.33% for namesake funds, but only 1.84% for other equity funds. The

average market beta deviation is 0.18 for namesake funds, but only 0.11 for other equity funds.

These differences are statistically significant at the 10 percent level for return deviation, and at

the 1 percent level for all five other deviation measures.

17

Overall, our results are consistent with the proposition that namesake funds have fewer

career concerns, and hence take more unsystematic risk and herd less. These findings suggest

that the agency conflict between the fund and fund manager is less acute for namesake funds

relative to other funds.

6. Performance

This section examines the extent to which the agency conflicts inherent in a mutual fund

impact firm performance. In this sense, it answers the question: Do these agency conflicts affect

shareholder value? We use both raw and risk-adjusted returns to determine how the return

performance of namesake funds differs from those of other equity funds.

6.1 Raw return performance

In this section, we compare the raw returns of namesake funds with that of other equity

funds. We analyze both the net and gross raw return to namesake funds. Net return represents

after-expense return, and is reported in the CRSP mutual fund database. We calculate gross

return by adding back the expenses to net returns. We calculate excess returns by subtracting the

value-weighted market returns from net or gross returns. Panel A of Table VI presents the time-

series average gross excess returns and net excess returns of TNA-weighted portfolios.

The average gross return of namesake funds is 17.68 basis points per month, and the

average net excess return is 6.52 basis points. These results indicate that namesake funds

outperform the market by 2.12% per year before expenses, and by 0.78% after expenses.

Namesake funds also outperform other equity funds. The average gross excess return for other

equity funds is 0.33 basis points per month and the average net excess return is -5.97 basis points

per month. This finding that equity mutual funds underperform the market by about the expenses

18

is consistent with existing studies. For example, Chen, Hong, Huang, and Kubik (2003) report a

gross return of 1 basis point and net return of -8 basis points for equity funds during 1962-1999.

Using median returns reveals less impressive performance for namesake funds. The

median gross return is 4.92 basis points per month, and the median net excess return is negative

at -4.27 basis points. According to our estimates of median returns, namesake funds outperform

the market by about 0.59% per year before expenses, but underperform relative to the market by

about 0.5% per year after expenses. The median returns for all other equity funds are similar to

their mean values.

Examining fund performance across bull/bear markets reveals some interesting results.

We use low frequency bull/bear market cycles. Specifically, 1962-1965 and 1982-1999 are

classified as bull markets, and 1966-1981 and 2000-2001 are classified as bear markets. In total,

there are 22 years of bull markets and 18 years of bear markets.

Namesake funds produce superior returns in bear markets, but not in bull markets. Using

average net excess returns, we find that in bear markets namesake funds outperform the market

by 16.62 basis points per month, and outperform peer funds by 22.53 basis points per month. In

contrast, namesake funds underperform the market by 1.74 basis points per month in bull

markets, although they still outperform peer equity funds by 4.28 basis points per month. Using

medians, namesake funds still outperform both the market and peer equity funds in bear markets,

but by much narrower margins at 5.95 basis points and 10.31 basis points, respectively. In bull

markets, the median namesake funds not only underperform the market but also underperform

peer equity funds by 4.09 basis points.

6.2 Risk-adjusted return performance

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In the preceding section, we examine raw returns as a measure of fund performance. It is

possible that namesake funds outperform the market and peer funds because they hold more

risky portfolios. To account for this possibility, we evaluate fund performance using alpha, the

intercept term in the regression of fund returns on risk factors. We focus on the Carhart (1997)

four-factor model, which augments the Fama-French three-factor model with a momentum

factor. We also use the CAPM and the Fama-French three-factor model and they produce similar

results.

The regression model is as follows:

tttttt UMDuHMLhSMBsMKTmR εα +⋅+⋅+⋅+⋅+= (6)

where MKT is the market factor, SMB is the size factor, HML is the value factor, and UMD is

the momentum factor. We employ two different regression approaches: the portfolio regression

approach and the fund regression approach. In the portfolio regression approach, we compute

monthly TNA-weighted return of the namesake fund portfolio and then use it as the dependent

variable in the above regression. In the fund regression approach, we use fund returns in the

above regression and then report the average coefficient estimates.

Panel B of Table VI presents the results for the portfolio regression approach. We find

that namesake funds have an alpha of 3.51 basis points, outperforming both the market and other

equity funds, which have an alpha of -5.96 basis points. So after controlling for risk, we find that

namesake funds still outperform their benchmarks and peers, although by a smaller amount than

as measured by average returns.

Namesake funds have slightly different factor loadings than other equity funds.

Namesake funds have lower market beta than other equity funds (0.934 versus 0.976), consistent

with our previous finding that namesake funds hold more cash than other equity funds.

20

Additionally, namesake funds tend to hold more small stocks, as reflected in their higher SMB

beta. Namesake funds load slightly more on value stocks than other equity funds. Namesake

funds have a lower momentum beta, suggesting that they do not chase returns as much as other

equity funds.

Using the fund regression approach produces qualitatively similar results. Panel C of

Table VI summarizes the average coefficient estimates. We find that namesake funds have an

average alpha of 16.71 basis points, outperforming both their benchmarks and other equity funds,

which have an average alpha of -5.83 points. Namesake funds have smaller exposures to the

market factor, value factor and momentum factor, but have greater exposure to the size factor,

relative to other equity funds.

Overall, we find some evidence that namesake funds outperform the market and peer

funds. In particular, namesake funds demonstrate superior performance in bear markets. It should

be noted, however, that the above results are somewhat sensitive to whether we examine mean or

median returns. These results also mitigate our earlier findings of higher fees and greater style

drift for namesake funds.

7. Investor cash flows

It is possible that namesake funds are able to use their managers’ names as a marketing

device to attract new investor cash flow since many of these namesake fund managers are star

managers. Consequently, we hypothesize that namesake funds attract greater investor cash flows

because of the “star power” of their managers.

To test the ability of namesake funds to attract new cash flow, we compute annual fund

flow as follows:

21

( )t

ttttt TNA

MGTNARTNATNAFlowFund

−+−= + 11 (7)

where MGTNA is the total net asset acquired from mergers during the period. The above

calculation assumes that fund flows occur at the end of the year. Alternative assumptions about

the timing of the fund flow do not affect my results.

Consistent with previous researchers such as Barber, Odean and Zheng (2002) and Elton,

Griber and Blake (2003), we control for past returns, fund size, expenses, and 12b-1 fees in our

analysis of fund flows. To examine the flow-performance relation, we sort all funds based on

their past 1-year returns and then form five quintiles. Quintile 1 contains the worst performing

funds while quintile 5 contains the best performing funds.

Table VII presents the regression results. Fund flow is positively related to past

performance. This can be seen through all coefficients on quintiles 2 – 5 dummy variables being

positive and significant. Furthermore, within each model, these coefficients are increasing from

quintile 2 to quintile 5. Consistent with prior studies (e.g., Chevalier and Ellison (1997)), we find

that the flow/performance relationship is convex. For instance, in Model 1, we find that an

average fund in the third return quintile attracts 4.1% more cash flow than an average fund in

quintile 2. However, an average fund in quintile 5 generates 23.7% more cash flow than an

average fund in quintile 4. In other words, the fund attracts more flow by moving from quintile 4

to quintile 5 than from quintile 2 to quintile 3.

Consistent with Barber, Odean and Zheng (2002), we find that fund cash flows are

positively related to 12b-1 fees. This suggests that advertising is effective in attracting investor

cash flows. This is also consistent with the idea that investors pay more attention to direct fees

like loads than less obvious costs like the 12b-1 fee. We find that larger funds generate lower

cash flows, in part because we measure fund flows as percentages of the fund’s TNA.

22

Furthermore, funds in larger families attract more cash flows. We find only weak evidence that

namesake funds attract more cash flows. In three of the four regressions, the coefficient of the

namesake dummy is insignificant. In the fourth regression, the coefficient of the namesake

dummy is positive and statistically significant at the 10 percent level.

8. Conclusion

We argue in this study that the scandals currently plaguing the mutual fund industry are

symptomatic of the two agency conflicts that exist within the mutual fund industry. Our sample

of namesake funds represents a unique laboratory for studying these agency conflicts. They

allow us to examine the widely publicized conflict that exists between the fund and its

shareholders as well as the less prominent conflict that exists between the fund company and the

fund managers. Further, because these funds are characterized by insider-dominated boards, they

are very useful for testing the usefulness of the recent SEC proposal for independent boards and

chairs.

Our results suggest that the boards of these funds are ineffective and that agency conflict

between shareholders and the fund is not mitigated with the oversight provided by an insider-

dominated board. We find that average expense ratio of namesake funds is more than 20 basis

points higher than those of other equity funds while they simultaneous demonstrate greater drift

in their investment style. Theses results suggest that the arguments for greater board

independence and oversight by the SEC have a basis in fact.

But the characteristics of these funds that have resulted in weak boards and important

agency conflict between the fund and its fund managers, have also produced less career concerns

among portfolio managers. Because the fund managers have stature and standing in the

23

sponsoring firm, these managers are less inclined to herd less and to assume greater unsystematic

risk. We conclude that namesake managers do not include career considerations in their portfolio

investment decisions to the extent that managers of other funds are likely to do so.

Further complicating a facile description of the extent of agency conflict within mutual

funds are our results concerning performance. In many ways, performance is the measure by

which we assess how significant are these agency conflicts. We find some evidence that

namesake fund managers outperform their benchmarks and peers, especially in bear markets. We

do caution however that these results are somewhat sensitive to the choice of a mean or median

measure of average return. We also find that namesake funds are more tax-efficient, consistent

with the idea that managerial ownership helps to align the interests of managers with that of

shareholders. Namesake funds however do not attract greater investor cash flows after

controlling for performance.

24

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Agency Conflict #1

Between Fund Company and FundShareholders

Fund Company Interested in Maximizing fees

Fund ShareholdersInterested in Maximizing Risk-adjusted Returns

Agency Conflict #2

Between Fund Companyand Fund Managers

Managerial ownershipIn the Fund-Stock Ownership andReputation Capital at RiskHelp Align Interests

Board of Directors- Monitor Funds toProtect Interests ofFund Shareholders

• Namesake Managers Invest Significantly in Their Funds• Namesake Managers Have Significant Reputation Capital at Risk

• Boards of Namesake Fund lack independence• Manager Sits on Boardoften as the Chairman

Namesake Managers are Their Own Bosses and Have Minimal Career Concerns

Implications

• Higher fees• Less style consistency• Higher tax efficiency

Figure 1: Agency Conflicts in Namesake Mutual Funds

Fund CompanyInterested in MaximizingFees

Fund Managers HaveCareer Concerns

Implications

• Less Herding• Assume GreaterUnsystematic Risk

Indeterminate Impact on Fund Performance

Managerial ownershipIn the Fund Company- Exacerbate the Conflict Between Fund Company And Fund Shareholders

Namesake Managers are founders and Majority Owners of Fund Company

Table I Summary Statistics of Namesake Funds

The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, We exclude international funds, sector funds, balanced funds, and index funds. We use the ICDI fund objective code and assign each fund one of three investments objectives: Aggressive growth (AG), Long-term growth (LG), and Growth and Income (GI). We identify namesake funds by comparing fund names and manager names. Item Namesake Funds Other Equity FundsTotal Net Asset - $ million 219.20 391.21Fund Age – years 8.05 9.29Turnover - % 56.67 76.07Expenses - % 1.24 0.9712b-1 fee - % 0.24 0.36Total Load - % 1.16 2.72Percent of Funds with Load 31.80 54.57Manager Tenure – years 5.98 4.04Market Share of the Fund Family - % 0.09 1.08Percent Cash Holding 8.93 6.95Percent Stock Holding 87.99 89.71Percent of Aggressive Growth Fund 30.67 37.04Percent of Long-term Growth Fund 45.30 50.51Percent of Growth and Income Fund 24.03 12.45

Table II Characteristics of Namesake Fund Managers

The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, We exclude international funds, sector funds, balanced funds, and index funds. We identify namesake funds by comparing fund names and manager names. We hand collect the background information of namesake fund managers from fund websites, and SEC forms 485B and N-SAR. Panel A: Other Positions Held by Namesake Fund Managers

Item Percent Percent of managers also board member of the fund 98.32 Percent of managers also chairman of the board 73.05 Percent of managers also founder of the investment advisory firm 78.57 Percent of managers also significant owner of the investment advisory firm 98.80 Percent of managers also CEO or CIO of the investment advisory firm 100.00

Panel B: Prior Experience of Namesake Fund Managers Title Portfolio

Manager Analyst Director of

Research Managing Director

VP CIO Professor

Percent 65.08 10.32 10.32 5.55 2.38 3.97 2.38 Panel C: Manager Ownership in Fund and Fund Family Percent of Managers Amount None >$0 >$10,000 >$50,000 >$100,000Ownership in Fund 7.48 10.28 8.41 5.61 68.22Ownership in Fund Family 0.00 1.60 4.00 3.20 91.20

Table III The Nature and Effectiveness of Governance for Namesake Funds

The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, We exclude international funds, sector funds, balanced funds, and index funds. We identify namesake funds by comparing fund names and manager names. We collect the board information from the SEC form 485B. We collect the investment restrictions data from SEC form N-SAR. Tufano and Sevick sample refers to the sample used by Tufano and Sevick (1997). Almazan, Brown, Carlson, and Chapman sample refers to the sample used by Almazan, Brown, Carlson, and Chapman (2003). Panel C presents the R-square and Root Mean Square Error from fund-level regressions of returns on risk factors Namesake is a dummy variable that takes a value of 1 if the fund is a namesake fund and 0 otherwise. Family size is the market share of the fund family that a fund belongs to. The dependent variable is fund expense ratio in all regressions. In each regression, the first row reports the OLS coefficient estimates and the second row reports the White standard errors. ***, **, * denotes statistical significance at the 1, 5, and 10 percent levels, respectively. Panel A: Board Composition Namesake Funds Tufano and Sevick Sample Average Number of Directors 6.9 8.7 Average Number of Independent Directors 4.5 6.2 Average Percentage of Independent Directors 66% 71%

Panel B: Expense Analysis

Constant Log (TNA) Namesake Log (Age) Family Size R-square 1.827*** -0.112*** 0.220*** 0.118 (0.014) (0.003) (0.018)

1.945*** -0.089*** 0.225*** -0.116*** 0.134 (0.120) (0.004) (0.018) (0.005)

1.937*** -0.076*** 0.202*** -0.122*** -0.022*** 0.161 (0.010) (0.003) (0.018) (0.005) (0.002)

Panel C: Style Consistency Namesake

Funds Other Equity

Funds Difference (t-stat)

Average R-square - four-factor model 0.82 0.89 -0.07 (15.64) Average Tracking Error – four-factor model 2.23% 1.71% 0.53% (9.67)

Table IV Tax Efficiency of Namesake Funds

The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, We exclude international funds, sector funds, balanced funds, and index funds. We identify namesake funds by comparing fund names and manager names. We compute tax efficiency according to equations (2) through (4). Namesake is a dummy variable that takes a value of 1 if the fund is a namesake fund and 0 otherwise. In Panel B, the dependent variable for all regressions is tax efficiency. In each regression, the first row reports the OLS coefficient estimates and the second row reports the White standard errors. ***, **, * denotes statistical significance at the 1, 5, and 10 percent levels, respectively. Panel A: Summary Statistics Namesake Funds All Other Equity

Funds Difference

(t-statistics)Tax Efficiency – equally weighted (%) 67.98 61.64 6.34 (5.70)Tax Efficiency – TNA-weighted (%) 64.64 58.80 5.84

Pane B: Regression Analysis

Constant Log (TNA) Namesake Log (Age) Fund Family Market Share

R-square

66.152*** -1.031*** 5.119*** 0.01 (0.444) (0.101) (1.124)

72.285*** 0.328*** 5.515*** -6.500*** 0.02

(0.505) (0.116) (1.115) (0.272)

71.393*** 0.535*** 5.778*** -6.517*** 0.153 0.02 (0.527) (0.128) (1.123) (0.285) (0.118)

Table V

Do Namesake Fund Managers Herd Less? The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, We exclude international funds, sector funds, balanced funds, and index funds. We identify namesake funds by comparing fund names and manager names. We obtain unsystematic risk and risk factor loadings using the following procedure. At the start of each year, We run regressions of fund returns on the four factors using the previous 36 months of data. The coefficient of the market factor is then the market beta. SMB beta, HML beta, and UMD beta are defined similarly. The root-mean squared error is my measure of unsystematic risk. Namesake Funds All Other Equity

Funds Difference

(t-statistics)Average return deviation (%) 2.33 1.84 0.49 (1.84)Average unsystematic risk deviation (%) 0.82 0.55 0.27 (6.66)Average market beta deviation 0.18 0.11 0.07 (10.24)Average SMB beta deviation 0.23 0.21 0.02 (1.99)Average HML beta deviation 0.35 0.25 0.10 (8.57)Average UMD beta deviation 0.20 0.15 0.05 (6.53)

Table VI Performance of Namesake Funds

The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, we exclude international funds, sector funds, balanced funds, and index funds. We identify namesake funds by comparing fund names and manager names. The periods 1962-1965 and 1982-1999 are classified as bull markets. The periods 1966-1981 and 2000-2001 are classified as bear markets. In Panel A, the numbers in parentheses are medians. We employ a four-factor asset pricing model in Panel B and Panel C. The four factors are market factor, size factor, value factor and momentum factor. Alpha is the intercept of the regression of fund (portfolio) returns on the above factors. In Panel B, we employ a portfolio regression approach. In Panel C, we employ a fund regression approach. Panel A: Mean (median) Returns Namesake Funds All Other Equity

Funds Difference

All Periods Gross Excess Return - basis point 17.68 (4.92) 0.33 (0.11) 17.35 (4.81) Net Excess Return - basis point 6.52 (-4.27) -5.97 (-4.69) 12.49 (0.62)Bull Market Gross Excess Return - basis point 9.89 (0.45) 1.08 (0.87) 8.81 (-0.42) Net Excess Return - basis point -1.74 (-9.61) -6.02 (-5.52) 4.28 (-4.09)Bear Market Gross Excess Return - basis point 27.19 (13.47) -0.59 (-0.48) 26.60 (13.95) Net Excess Return - basis point 16.62 (5.95) -5.91 (-4.36) 22.53 (10.31) Panel B: Abnormal Returns and Factor Loadings – Portfolio Regression Namesake Funds All Other Equity FundsAlpha - basis point (t-statistics) 3.506 (0.29) -5.957 (-2.05)Market Beta (t-statistics) 0.934 (32.15) 0.976 (141.73)SMB beta (t-statistics) 0.280 (7.38) 0.078 (8.70)HML beta (t-statistics) 0.008 (0.18) -0.081 (-7.67)UMD beta (t-statistics) 0.007 (0.24) 0.037 (5.27) Panel C: Average Abnormal Returns and Factor Loadings – Fund Regression Namesake Funds All Other Equity FundsAlpha - basis point (t-statistics) 16.706 (4.18) -5.826 (-6.99)Market Beta (t-statistics) 0.927 (49.89) 1.015 (263.30)SMB beta (t-statistics) 0.268 (9.96) 0.223 (36.12)HML beta (t-statistics) -0.021 (-0.62) 0.057 (8.61)UMD beta (t-statistics) 0.007 (0.48) 0.027 (9.62)

Table VII

Investor Cash Flows of Namesake Funds

The sample period is 1962–2001. The primary data source for mutual funds is the CRSP Survivor Bias-Free Mutual Fund Database. We include only actively-managed diversified domestic equity funds in my sample. In particular, We exclude international funds, sector funds, balanced funds, and index funds. We identify namesake funds by comparing fund names and manager names. We compute fund cash flow using equation (5). Namesake is a dummy variable that takes a value of 1 if the fund is a namesake fund and 0 otherwise. Each year, we group returns into quintiles. Quintile 2 is a dummy variable that takes on a value of 1 is the fund return falls in the second lowest quintile last year. Quintiles 3 through 5 are similarly defined. The dependent variable is annual fund cash flow in all regressions. In each regression, the first row reports the OLS coefficient estimates and the second row reports the White standard errors. ***, **, * denotes statistical significance at the 1, 5, and 10 percent levels, respectively. Independent Variables Model 1 Model 2 Model 3 Model 4 Constant 0.022 -0.002 -0.080*** -0.090*** (0.014) (0.14) (0.023) (0.015) Quintile 2 0.164*** 0.163*** 0.182*** 0.182*** (0.019) (0.019) (0.019) (0.019) Quintile 3 0.205*** 0.202*** 0.216*** 0.212*** (0.019) (0.019) (0.019) (0.019) Quintile 4 0.332*** 0.329*** 0.349*** 0.345*** (0.019) (0.019) (0.019) (0.019) Quintile 5 0.569*** 0.565*** 0.582*** 0.579*** (0.019) (0.019) (0.019) (0.019) Lag TNA ( × 104 ) -0.192*** -0.259*** -0.168*** -0.237*** (0.022) (0.023) (0.022) (0.023) Namesake -0.008 0.019 0.017 0.048* (0.028) (0.028) (0.028) (0.028) Family Size 0.026*** 0.026*** (0.003) (0.003) Expenses 0.013 (0.016) 12b-1 0.215*** 0.228*** (0.022) (0.015) R-square 0.063 0.068 0.076 0.081