Intended Benefits and Unintended Consequences of Improved

Performance Disclosure

John C. Heater∗

john.heater@yale.edu

Yale University

November 7, 2017

Abstract

In response to the call for additional evidence of the economic effects of disclosure (Leuz andWysocki, 2016), I document significant unintended consequences of a seemingly innocuous, well-intentioned disclosure rule change. In 1998, the SEC required that all funds disclose a self-selected, primary benchmark. Benchmark reporting varied among actively managed mutualfunds: some compared performance to a single benchmark, but most mentioned multiple bench-marks or none at all. The new disclosures should be relatively uninformative for sophisticatedinvestors because they recognize that benchmark choice is strategic, and they observe indepen-dent "best fit" benchmarks from Morningstar. However, I find that fund flows for institutionalinvestors become more sensitive to performance relative to the disclosed benchmark, whereasretail investors—presumably the SEC’s focus—remain unaffected. Turning to fund manager re-sponses to the disclosure change, I document significant real effects (where disclosure changesfund behavior) and externalities (where strategic benchmark disclosure alters market share). Ifind that two-thirds of funds select an inaccurate benchmark, which they persistently outper-form by strategically increasing risk. The new disclosures also exacerbate the well-documentedtendency of underperforming fund managers to increase risk in the second half of the year.

JEL Classification: G14, G18, G30, G38, K20, M41, M48

Keywords: Disclosure Regulation, Mutual Funds, Managerial Incentives, Real Effects, Financial Reporting

∗I am grateful for comments from participants of the Trans-Atlantic Doctoral Conference 2017 at the London BusinessSchool, the AAA/Deloitte Foundation/J. Michael Cook Doctoral Consortium 2017, and the Society for the Advancement ofSocio-Economics Conference 2017, as well as Sriya Anbil, Rick Antle, Kyriakos Chousakos, Aytekin Ertan, Roger G. Ibbotson,William N. Goetzmann, Colin Haslam, Zeqiong Huang, Elisabeth Kempf, Kalin Kolev, Toomas Laarits, Alina Lerman, YukunLiu, Song Ma, Ben Matthies, Alan Moreira, Erik Olson, Cameron Peng, Matthew Spiegel, Andrew Sinclair, Robert Stoumbos,Shyam Sunder, Qin Tan, Jacob Thomas, Jun Wu, and X. Frank Zhang.

1 Introduction & Overview

Mutual fund financial reporting and disclosure has received little attention in the academic

literature. At the end of 2016, U.S.-domiciled mutual funds managed over $16.3 trillion in financial

assets, highlighting their importance to the economy. Leuz and Wysocki (2016) call for additional

research on the economic effects of disclosure, as well as considering novel settings where disclosure

change may facilitate research design. They describe our current understanding of the effects of

disclosure and financial reporting regulation as limited in three areas: 1) drawing causal inferences,

2) documenting real effects, and 3) evidence of market-wide effects and externalities. In this paper,

I attempt to contribute to all three areas by examining a regulation that changed disclosure require-

ments for mutual funds. I document important and unintended consequences that resulted from a

simple and seemingly innocuous rule change intended to standardize performance disclosures.

In 1998, the SEC promulgated Final Rule: S7-10-97, which required that all actively managed,

open-end mutual funds select and clearly disclose a primary third-party benchmark index for com-

parative purposes.1 In actively managed funds, performance relative to an appropriate benchmark

provides important information to investors. While different constituents argued strongly for the

rule change, it should be noted that fund and comparable index return data were widely avail-

able even before the promulgation of the new standard. Since the mid-1980s, Morningstar, Inc.,

as well as other independent information intermediaries, has provided substantial information to

investors about fund performance and investment objectives. However, standardized mutual fund

performance disclosures and benchmarking were not required in SEC filings prior to this regula-

tion. Some funds would voluntarily disclose a primary benchmark to investors. However, most

funds would either disclose multiple benchmarks, a composite benchmark, or omit a benchmark

completely.

The features of the mutual fund setting and this rule change offer an opportunity to pro-

vide strong identification and causal inference of the effects of disclosure mandates—the first area

highlighted by Leuz and Wysocki (2016). First, the performance measure sought by investors is

comprehensive and precise, unlike the multiple measures that are relevant for traditional firm per-1Final Rule: S7-10-97 (last accessed November 1, 2017): https://www.sec.gov/rules/final/33-7512r.htm

2

formance. Second, managerial compensation for funds is also relatively straightforward and easy

to measure. Managers are typically compensated based on a percentage of net asset value (NAV).

Third, impediments to causal inference are reduced because of the direct impact of the rule change

on investors and fund managers. Fourth, this rule change arose from SEC Chairman Arthur Levitt’s

Plain English Initiative for corporate and fund disclosures. I view this change as an exogenous shock

to fund disclosures and note that no other major regulatory changes affecting performance report-

ing for funds occurred within my sample period. Finally, I hand-collect data on fund benchmark

selection and voluntary benchmark disclosure prior to the rule change. This allows me to identify

how the disclosure mandate affects investors and managers.

The SEC’s benchmark requirement provides a clear shock to identify real effects and exter-

nalities from performance disclosure, the second and third areas emphasized in Leuz and Wysocki

(2016). I investigate whether the disclosure change had real effects on manager decision-making.

Specifically, I examine whether managers vary portfolio risk relative to the required benchmark.

Managers may change investment strategies in ways that expose investors to unexpected risk to

beat benchmarks, representing a real effect of disclosure.2 Additionally, externalities can arise if

managers strategically disclose a benchmark (e.g., Dye, 1990). Investor decision-making can be

influenced by the self-designated benchmark when choosing between funds. Funds that disclose an

inappropriate benchmark mislead investors about both expected risk and return. Investors, relying

on the disclosed benchmark, will shift capital away from non-strategic funds and toward strategic

funds based on perceived outperformance. Therefore, real externalities on non-strategic funds can

occur if investors make use of strategic funds’ benchmark disclosures.

Before examining investor behavior—fund flows in response to past performance—I first inves-

tigate the specific period over which investors evaluate fund performance. Prior literature assumes

that investors follow a calendar year model (e.g., Brown, Harlow, and Starks, 1996; Chevalier and

Ellison, 1997; Sensoy, 2009). Because SEC filings are updated every six months, investors might

reasonably follow a "semester model," where fund flows in the current half of the year (semester) are2As defined in Leuz and Wysocki (2016), footnote 9, a real effect is when the "...disclosing person or reportingentity changes its behavior in the real economy." Managers may respond to incentives arising from disclosure changeby altering fund/firm activities—also known as information inductance. Prakash and Rappaport (1977) defineinformation inductance as "...the process through which the behavior of an information sender is influenced by theinformation he is required to communicate."

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explained by performance in the prior semester.3 Using post-rule change data, I find evidence con-

sistent with investors following a calendar year model, but I also find that investors make mid-year

adjustments in response to performance in the first semester of each year. Therefore, I estimate

two sets of regressions: 1) investor fund flows in the first semester on performance in the prior

calendar year (calendar year model), and 2) fund flows in the second semester of the calendar year

on first-semester performance (mid-year adjustment model).

My findings regarding investor behavior suggest that they were, in aggregate, substantially

affected by the disclosure rule change. For a 1 percent increase in calendar year excess benchmark

returns, I find that the change in fund market share in the six months of the following calendar year

increases from 0.011 basis points to 0.026 basis points after the disclosure requirement. To validate

these aggregate findings, I estimate regressions separately for funds voluntarily and not voluntarily

disclosing a primary benchmark before the rule change. I find that investors respond to excess

benchmark returns prior to the rule change for funds voluntarily disclosing a primary benchmark,

while there is no significant change in fund flow sensitivity to performance after the rule change.

However, investors of funds not voluntarily disclosing a benchmark prior to the rule change have

no significant sensitivity to excess benchmark performance pre-1998 but respond significantly to

performance after the rule change.4

Contrary to my expectations, I find that unsophisticated (retail) investors—the subset of in-

vestors expected to benefit the most from the newly mandated information—are relatively unaffected

by the disclosure change. Retail investor fund flows before the rule change period are not sensitive

to excess benchmark returns, whether or not the benchmark was disclosed. The incremental effect

of the rule change is also insignificant for retail investors. Instead, the aggregate results described

above are driven mainly by sophisticated (institutional) investors.

I turn next to managerial behavior in response to the 1998 disclosure rule change. I expect

that managers’ behavior will be affected by the disclosure requirement, primarily because it will

affect the intensity of investor capital flows into and out of the fund. Prior literature provides3Funds in my sample were only required to file with the SEC semi-annually. The quarterly filing requirement formutual funds did not take effect until 2004.

4Test results for the mid-year adjustment model provide similar inferences.

4

evidence that managers vary risk in response to incentives created by investors’ fund flows related

to performance (e.g., Brown, Harlow, and Starks, 1996; Chevalier and Ellison, 1997). I assume that

managers understand how fund performance affects investors’ decision-making when choosing fund

investments. Managers may respond to the rule change along two dimensions: 1) by strategically

selecting a low-risk benchmark relative to their funds’ risk (when adopting the new rules), and 2)

by responding (each year after the rule change) to mid-year underperformance via increasing risk

relative to the benchmark in the second half of the year to "catch up." The first response is an ex

ante effect, designed to set a relatively higher mean level of risk to increase expected returns against

the benchmark. The second response is a dynamic effect that managers undertake to increase the

odds of a large positive second half-year return.

I find evidence that managers behaved strategically in response to the ex ante effect mentioned

above. The availability of alternative performance measures (e.g., Morningstar) allows me to identify

strategic managerial behavior. Morningstar publishes information on indexes that "best fit" the

risk and return profile of every open-end mutual fund using historical performance data.5 This

benchmark represents the most relevant independent index. I find that the funds most likely to

select a non-best fit index benchmark are also the funds most affected by the disclosure in terms of

the sensitivity of investors to performance. Consistent with the previous findings related to investors’

response to the rule change, funds not voluntarily disclosing a benchmark and institutional funds

are 7.9 percent and 7.1 percent more likely to select a non-best fit index benchmark, respectively.6

Furthermore, I find that non-best fit index benchmark selection is strategic in nature. I document

that non-best fit index benchmark funds outperform their benchmarks 8.0 percent more frequently

than best fit index benchmark funds after the rule change. I also find that non-best fit index

benchmark funds significantly increase risk relative to their selected benchmark, increasing the

likelihood of outperforming the disclosed benchmark. These results provide the first channel for

the real effects of disclosure: through strategic benchmark selection and elevated risk relative to a

benchmark, managers earn positive excess benchmark returns more often.

Strategic benchmark selection has real externalities in addition to real effects. As described5Morningstar identifies the best fit index of a fund as the index that has the highest R2 regression estimate of fundreturns on index returns in the preceding 36 months.

6In aggregate, I find that approximately 67 percent of funds select a non-best fit index benchmark.

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by Dye (1990), "A disclosure by one firm is said to create a real externality for other firms if the

disclosure alters those firms’ cash flows." Given that investors increase fund flows to funds with pos-

itive excess benchmark returns, funds that strategically disclose a benchmark impose externalities

on non-strategic funds. Non-strategic funds lose market share and capital to strategic managers

over time because strategic funds more frequently outperform their disclosed benchmark.

Turning to the dynamic response mentioned above, I find that managers’ mid-year risk shifting

behavior in response to underperformance becomes more intense after the rule change. I document

that, for funds underperforming their benchmark, a one standard deviation decrease in excess

benchmark returns in the first six months of the year results in an incremental increase in the

standard deviation of excess benchmark returns in the remaining six months of the year of 0.23

percent after the rule change. Moreover, I find that this increase in risk-taking is due to managers

shifting risk upward in the second semester. I measure risk shifting as the ratio of excess benchmark

return standard deviation in the second semester to that of the first semester. The risk shift ratio

for underperforming funds increases by about 12.3 percent for the average fund after the rule change.

This represents a significant increase in risk shifting by underperforming funds across the rule-change

period. These results provide the second channel for the real effects of disclosure.

The findings of this paper raise questions for future research. In analyses determining investor

sensitivity to excess benchmark performance, I find two unexpected results. First, retail investors

are not responsive to the disclosure change. One of the stated goals of the regulatory change

was "...to improve fund prospectus disclosure and to promote more effective communication of

information about funds to investors."7 Nevertheless, I find that the subset of investors expected to

benefit the most from standardized disclosures remain relatively unaffected. Second, institutional

investors appear to respond to the self-designated benchmark, but only when fund managers make

the benchmark known via disclosure. Institutional investors have more frequent discussions with

fund managers about funds’ objectives and strategies, which should make the disclosure change

uninformative. However, I find that institutional investors respond to the disclosed benchmarks

and that fund managers anticipate this by using strategic benchmark disclosure.7Final Rule: S7-10-97

6

I contribute to the disclosure literature in the three areas emphasized by Leuz and Wysocki

(2016). First, I exploit the simple interrelationships and incentives between investors and managers

that are unique to the mutual fund setting. This allows me to provide clean identification of effects

related to disclosure change. Second, I provide evidence that investors utilize benchmark disclosure

when making capital allocation decisions, and that managers experience information inductance

related to the benchmark disclosure requirements (Prakash and Rappaport, 1977), resulting in

real effects from disclosure change. Third, I document real externalities caused by the strategic

disclosure of inaccurate benchmarks for funds. The substantial discretion managers have to select

a primary benchmark allows them to misrepresent expected fund risk and return to investors. This

finding is consistent with the potential consequences of switching from allowable voluntary disclosure

to mandated disclosure raised by Dye (1990). I also contribute to the mutual fund literature by

demonstrating that investors engage in mid-year portfolio corrections in response to excess self-

designated benchmark returns.

The paper is organized as follows: Section 2 provides motivation and a literature review, Section

3 provides the underlying hypotheses developed for testing purposes, Section 4 describes the data

sources and summary statistics of the data used, Section 5 presents the empirical test design and

results, and Section 6 concludes the paper.

2 Motivation & Prior Literature

While mutual funds have received significant attention in the finance literature, mutual fund

financial reporting and disclosure remains relatively unexplored. The mutual fund setting provides

a novel environment to examine whether changes to disclosure provides net benefits to the economy.

Mutual funds are a large and important economic institution, with 44% of U.S. households holding

mutual fund investments and with $16.3 trillion in assets under management (AUM).8 Mutual funds

are subject to the Securities Act of 1933 and the Securities Exchange Act of 1934. However, the

Investment Company Act of 1940 is the primary body of legislation regulating mutual funds domi-

ciled in the U.S. This Act outlines permitted fund capital structures, audit requirements, and sets8Source: ICI 2017 Fact Book.

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out the basic fiduciary responsibilities of managers and directors. The N-1A registration statement

is a required filing under both the Securities Act of 1933 and the Investment Company Act of 1940,

and it contains the bulk of fund disclosures.

Theoretical literature suggests that disclosure has benefits to investors. Ripley (1927) and Berle

and Means (1932) argue that firm value increases with disclosure by disciplining managers. Non-

manager investors are protected when truthful disclosure about firm performance is increased. How-

ever, managers are likely to change their behavior in response to mandated disclosure requirements

when their incentives are affected. Prakash and Rappaport (1977) argue that "information induc-

tance" occurs if eliciting accounting information from managers distorts their behavior. Hermalin

and Weisbach (2012) provide an analytical model where information inductance affects governance,

and the agency issues at firms become worse when disclosure increases. Managers in this model

are exposed to higher termination risk when asked to provide more information about the firm,

and they respond by distorting that information. A natural tension arises: increasing disclosure

requirements may increase decision-relevant information, but managers may respond by changing

real operations in ways that make investors worse off (e.g., Kanodia and Sapra, 2016). Managers’

actions in response to mandated disclosure can also generate externalities on other firms when the

disclosed information misleads investors (e.g., Dye, 1990). However, Leuz and Wysocki (2016) ar-

gue that only limited empirical evidence of disclosure-related welfare effects exists. Evidence of

real effects and externalities directly arising from disclosure change would be an important welfare

implication. A goal of this paper is to utilize the mutual fund setting to better determine whether

these occur.

In 1998, the SEC mandated that mutual fund filings provide a clear and independent third-

party index to benchmark fund performance. The SEC’s stated goal with the 1998 rule change was

to increase decision-relevant information to investors. However, it is not clear that a fund-designated

benchmark will matter to investors. Prior to this rule change, the SEC required open-end funds to

report daily NAV per share. Therefore, investors had the ability to compare fund returns against

various index benchmarks well before the rule change occurred. Since the rule change, funds must

disclose a primary benchmark for comparative purposes, not just total returns. The self-designated

benchmark may still communicate information if fund investors have information constraints and

8

cannot unwind the relevant benchmark. Prior literature supports this notion, suggesting that

investors are subject to information processing constraints around reporting (e.g., Cready, 1988;

Bernard and Thomas, 1989, 1990; Bloomfield, 2002; Libby, Bloomfield, and Nelson, 2002; Hirshleifer

and Teoh, 2003). The SEC’s disclosure rule change in 1998 provides an opportunity to investigate

whether increased performance disclosure in mutual funds reduces processing constraints for fund

investors and, in particular, for unsophisticated investors.

Communicating performance is an important goal of fund reporting because many funds under-

perform passive investment vehicles after fund fees. Early work from Jensen (1968) documents fund

managers’ inability to beat broad market index returns, on average. Carhart (1997) and Wermers

(2000) find that funds persistently underperform the market after accounting for fund expenses and

management fees. Managers’ lack of performance persistence has been shown to hold in recent

periods as well, calling into question the usefulness of active fund management. However, Berk

and Green (2004) argue that this lack of persistence is not a failure of active management to gen-

erate alpha but rather indicates that fund investment strategies cannot replicate previous returns

with new capital inflows because positive alpha strategies have limited scalability. Another concern

about fund performance is that mutual fund returns do not communicate the market’s assessment

of manager skill. Gruber (1996) argues that because mutual funds are bought and sold at NAV,

the price mechanism fails to impound managerial ability. Mutual fund investors, therefore, cannot

differentiate managers’ ability based solely on fund returns. While the initiation of self-designated

benchmark disclosure provides an important signal of past performance to investors, the use of

this benchmark to intuit the ability of managers is questionable. Nonetheless, underperforming a

self-designated benchmark is a negative signal to investors.

Managers are highly sensitive to comparative performance. Performance benchmarking is a

significant concern of managers because of its wide use by investors. For example, Schrand and

Walther (2000) find that managers selectively choose benchmarks to condition how investors per-

ceive performance. For mutual funds, investors have many options for performance benchmarking.

The mutual fund tournament model, established in Brown, Harlow, and Starks (1996), describes

managers’ incentives to outperform competitors through total return performance. Top fund per-

formance attracts the majority of available fund flows from investors and rewards these managers

9

with increased fund fee revenues. Substantial research follows this line of literature and suggests

that managers specifically focus on year-end performance. Chevalier and Ellison (1997) document

managers’ dynamic response to excess market performance because of incentives generated by in-

vestors’ fund flows. Goriaev, Palomino, and Prat (2001) echo these findings, demonstrating that

managers increase risk taken in the second half of the year in response to performance in the first

half of the year. Taylor (2003) builds additional theoretical support for funds’ year-end risk shifting

after mid-year underperformance. The conclusion from this literature is clear: managers will use

risk to increase expected performance when incentivized with fund flows. Therefore, reporting re-

quirements increasing investors’ sensitivity to performance via disclosure may exacerbate managers’

incentives to increase risk-taking.

The specific weight that investors place on benchmarks is widely debated. One option investors

have is to use excess market returns (e.g., Ippolito, 1989; Chevalier and Ellison, 1997; Sirri and

Tufano, 1998). Other fund benchmarks utilize regression analysis to determine excess returns (e.g.,

Carhart, 1997; Goetzmann, Ingersoll, and Ivković, 2000). Regardless of which benchmark is chosen,

many practitioners and academics believe that the best signal of performance is persistence in

earning excess returns. This appears not to hold empirically (Carhart, 1997), and studies supporting

return persistence have been shown to be affected by survivorship bias (e.g., Grinblatt and Titman,

1989; Brown, Goetzmann, Ibbotson, and Ross, 1992; Carhart, 1996; Elton, Gruber, and Blake,

1996).9 Turning to the focus of this study, Sensoy and Kaplan (2007) and Sensoy (2009) examine

how self-designated benchmark performance and managers’ behavior relate. Sensoy and Kaplan

(2007) find mixed evidence of managers timing benchmark returns but suggest that managers may

vary market beta to increase risk and expected returns. Sensoy (2009) documents that disclosed

benchmarks do not line up with actual fund style and that mismatched benchmarks exist throughout

the fund industry. In this paper, I investigate whether the initiation of the self-designated benchmark

disclosure results in managers strategically changing their portfolio risk and disclosing a benchmark

that masks actual fund style.

Overall, the prior literature finds 1) that outperforming (underperforming) funds capture higher9The consensus in the literature is that long-window performance studies can produce spurious findings. The windowsutilized in this paper are intentionally short to avoid survivorship bias in performance analyses.

10

(lower) fund flows from investors, and 2) that managers utilize risk when responding to their own past

performance. Reporting requirements increasing the salience of comparative performance against

a benchmark, as well as investors’ sensitivity to that performance because of mandated disclosure,

may exacerbate managers’ incentives to increase risk-taking. The SEC rule change was intended

to address the voluminous and non-standard disclosures made by funds in the N-1A. Managerial

incentives, however, complicate the implementation of improved performance disclosure. Managers’

response to disclosure change may make investors worse off if unintended consequences result in

higher risk exposure. The exogenous shock provided by this rule change allows empirical explo-

ration of two important issues: 1) whether the information shock increases investor sensitivity to

the new performance metrics (e.g., reducing processing costs), and 2) whether managers respond

to mandated benchmarks with risk-taking and strategic benchmark choice (e.g., real effects and

externalities).

3 Hypothesis Development

Constraints on investors’ information processing have been documented in the literature. In-

vestors must spend time and resources analyzing many funds when making an investment decision.

The primary goal of the mutual fund disclosure change in 1998 was to reduce investors’ processing

costs by increasing decision-relevant information. The rule change sought to reduce costs associated

with seeking appropriate fund investments by increasing both new information (e.g., the relevant

fund benchmark) and comparability across funds (e.g., benchmark adjusted returns).

Observing how investors respond to fund excess benchmark returns after the disclosure change

will provide evidence on the effect that the regulation has had on reducing processing costs. Incre-

mental sensitivity of investor fund flows to excess benchmark returns would indicate that investors

were not widely aware of the fund benchmark in the pre-change period. However, revealing a self-

designated benchmark may have limited use to investors because other widely available risk-adjusted

return measures should provide sufficient information to gauge past fund performance.

I hypothesize that the impact of excess benchmark returns on net fund flows increases after the

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rule change.10 While the returns of all possible benchmarks are available prior to the rule change,

I expect that the disclosure change will increase investors’ attention to the benchmark and that

funds’ excess benchmark returns will incrementally and significantly predict fund flows.

H1: Fund flows respond more sensitively to excess benchmark returns after the rule

change.

Alternative hypotheses might argue that some other parameters of the disclosure change af-

fected investors’ sensitivity to fund performance. To rule out other explanations, I collect data

directly identifying whether funds voluntarily disclose a primary self-designated benchmark prior

to the rule change. I hypothesize that fund flows should be affected by excess benchmark returns

when the benchmark of the fund is disclosed. That is, I expect that fund flows are sensitive to

excess benchmark returns prior to 1998 if a fund voluntarily discloses its primary benchmark. I also

expect that funds not disclosing a benchmark before the rule change will experience a significant

increase in the sensitivity of fund flows to excess benchmark performance after the rule change.

H2a: Fund flows respond to excess benchmark returns in the pre-change period if the

self-designated benchmark is voluntarily disclosed.

H2b: Fund flows respond incrementally higher to excess benchmark returns in the

post-change period if the benchmark was not voluntarily disclosed prior to the rule

change.

Furthermore, the SEC intended to improve information flow to unsophisticated investors (prox-

ied for by retail funds). The hypothesized relationships of H2a and H2b should be driven by retail

fund investors if the SEC was successful in facilitating unsophisticated investors’ information pro-

cessing. Sophisticated investors (proxied for by institutional funds) should either be less affected or

unaffected by the disclosure.

H3: The sensitivity of fund flows to excess benchmark returns as predicted in H2a and

H2b is driven by unsophisticated investors.10I utilize funds’ market share changes to construct net fund flows, as outlined in Spiegel and Zhang (2013). See

Section 4 for details.

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Support for H3 implies that the SEC successfully reduced processing constraints for unsophis-

ticated investors via disclosure. However, I find no support for H3. Instead, I find that institutional

investors are sensitive to excess benchmark performance when the benchmark is disclosed. Addition-

ally, I find that retail investors are not sensitive to excess benchmark returns before the rule change

period and show no incremental sensitivity to excess benchmark returns after the rule change.

The failure to reject the null hypothesis for H3, and evidence supporting that institutional

investors drive the incremental performance-flow sensitivity after the rule change is unexpected.

However, it provides insight about how the disclosure was used. These findings suggest that the

SEC was unable to reach its presumed target audience, i.e., individual investors. There may be

a number of reasons for this. One reason may be that institutional investors utilize benchmark

identification for fund of funds investing as a heuristic of identifying "hot hands" funds (funds that

have a recent string of good performance). Alternatively, it may be that institutional investors are

the subset of investors likely to read and process these disclosures and consider it news. Both expla-

nations, however, require institutional investors to ignore the large body of evidence that returns

are not persistent, which is unlikely given their relative sophistication. Another explanation is that

institutional investors do not know the benchmark. However, this is also unlikely, as institutional

investors frequently interact with fund managers, making their response to excess benchmark re-

turns puzzling. I leave the reasons for institutional investor sensitivity to excess benchmark returns

as an open question for future research.

Next, I investigate managers’ response to the rule change. Prior literature indicates that

managers engage in strategic disclosure when desirable (e.g., Schrand and Walther, 2000). Strategic

selection of the self-designated benchmark is likely to occur when managers’ incentives are affected

by investors’ sensitivity to performance. Other literature indicates that fund managers will engage in

risk-taking to increase expected returns when incentivized to do so (e.g., Brown, Harlow, and Starks,

1996; Chevalier and Ellison, 1997). This opens the question as to whether the disclosure change

exacerbated managerial risk-taking for strategic managers. A clear and conservative benchmark is

the fund’s best fit index. A mutual fund’s best fit index is an independent third-party market-based

index that has the highest corresponding R2 in regressions explaining fund returns over a given

span of time. I hypothesize that a fund selecting a benchmark that differs from the best fit index

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will have done so strategically. Managers that choose a non-best fit index as their benchmark likely

do so to control the perception of comparative fund performance. This represents a channel for real

externalities through a misrepresentation of fund risk and return (Dye, 1990).

The above discussion suggests that institutional funds have a strong incentive to select bench-

marks strategically if institutional investors are sensitive to excess benchmark returns. I hypothesize

that funds serving institutional clients act more strategic than retail funds in benchmark selection

by selecting a non-best fit index more frequently.

H4a: Non-best fit index benchmark selection occurs more frequently for funds serving

institutional investors.

Fund benchmark selection is static over time. Sensoy and Kaplan (2007) document that changes

in the fund benchmark are rare and highly discouraged by the SEC. Funds are unable to change their

benchmark freely, locking them into the initial benchmark choice. Funds not voluntarily disclosing

a benchmark likely omit it for strategic reasons. One reason may be concerns about investors’

perception of fund performance. Underperformance relative to a benchmark communicates low

manager ability or effort to investors even if returns are not associated with ability. Managers

concerned about investors’ perception are likely not to disclose a benchmark in the pre-change

period. However, when compelled to disclose a benchmark, managers not previously disclosing a

benchmark have an incentive to choose one that is strategic in nature and not equal to the best fit

index.

H4b: Non-best fit index benchmark selection occurs more frequently for funds that did

not disclose a benchmark prior to the rule change.

I hypothesize that the strategic nature of selecting a non-best fit index arises from managers’

expectation to beat those benchmarks. I expect that funds with a strategic benchmark have positive

excess returns more frequently than non-strategic benchmark funds. Achieving positive excess

benchmark performance may be a good signal to investors and regulators. However, the costs

to do so may be higher. Increasing fund portfolio risk relative to the benchmark will result in

funds outperforming the benchmark more frequently in expectation, but exposes investors to higher

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risk—a real effect of disclosure.

H4c: Funds disclosing a non-best fit index benchmark will outperform their benchmark

more frequently than best fit index benchmark funds after the rule change.

H4d: Funds disclosing a non-best fit index benchmark will have higher excess bench-

mark return risk than best fit index benchmark funds after the rule change.

Managers have incentives to recoup early-year losses because of how investor fund flows re-

spond to performance (Brown, Harlow, and Starks, 1996; Chevalier and Ellison, 1997; Cashman,

Deli, Nardari, and Villupuram, 2012). Constraints on investors’ processing capabilities generate

incentives for managers to strategically respond to the information shock (Hirshleifer and Teoh,

2003). Investors relying only on excess benchmark returns for investing decisions are likely to pun-

ish underperforming managers. Varying fund investment risk is a method utilized by managers to

outperform benchmarks in expectation, which is a potential unintended consequence resulting from

this disclosure change. I expect that funds will be incentivized to take additional risk relative to

their benchmark in an attempt to recover from early-year losses. Managers can do this by shifting

risk upward through the second semester of the calendar year after underperforming in the first

semester. I also expect this behavior to be more intense after the rule change.

H5: Funds underperforming benchmark returns in the first half of the year will in-

crease risk relative to their benchmark in the second half of the year after the rule

change.

4 Data & Summary Statistics

4.1 Data Sources

I use the CRSP Survivorship Bias Free Mutual Fund database (CSBFMFD) to identify fund

share classes, dates when active, and other fund information. I use Morningstar Direct data for

mutual fund performance and primary benchmark information. I select all open-end U.S.-domiciled

15

mutual funds that have required benchmark disclosures available from the above databases, and

active during the years 1994 to 2001. For the pre-rule change period, I use the post-rule change

benchmark to calculate excess benchmark returns. This allows me to compare investor and manager

sensitivity to excess benchmark returns before and after the disclosure change.

All data from CSBFMFD and Morningstar Direct are first collected at the class level and

then aggregated up to the fund level.11 Consistent with prior literature, I use the existence of

an institutional share class to classify a fund as an institutional fund, while funds without an

institutional share class are classified as retail funds. I aggregate weekly fund returns for each share

class and benchmark returns to calculate excess benchmark returns. Fund flows are generated using

a fund’s market share change over the specified period. I utilize this measure of flows rather than

the fractional flow of NAV. Spiegel and Zhang (2013) find that the fractional share calculation of

net fund flows generates spurious results in linear regression estimates because of relative scaling

issues involved with fund size and performance. The market share of a fund is defined as fund total

NAV scaled by the aggregate NAV of all open-end U.S. funds in that period. I calculate the change

in fund market share as the ending period market share less the beginning period market share.

Control variables from CSBFMFD and Morningstar Direct include fund size, age, fund expense

ratio, fund expense waiver, portfolio turnover ratio, and market beta.12 I also use Fama-French

three-factor risk-adjusted returns as a control.

Data on pre-change period disclosure is hand-collected from SEC EDGAR mutual fund filings.13

If a fund reports a primary benchmark in any filing between 1994 and 1997, I classify the fund as

having a voluntary benchmark disclosure. Funds that do not disclose a benchmark, blend indexes

as a benchmark, or disclose multiple benchmarks without acknowledging one as a primary bench-

mark in the pre-change period are considered non-disclosers. I verify the accuracy of benchmark

identification from Morningstar Direct in the hand-collected data for filings from 1998 to 2001. I

also collect information on the number of funds within a fund family as a control in some regression11Morningstar Direct provides both class-level and fund-level identification. While some variables are easily aggregated,

such as class size, others, such as class-level returns, differ because of fee agreements. Mean values are used toaggregate fund returns; however, class size-weighted means do not significantly change any results throughout thepaper in robustness checks.

12Formal variable definition and data sources are in Appendix A.13I use directEDGAR to collect N-1A filings from 1994 to 2001 for fund families.

16

specifications. Morningstar Principia provides historical as-of annual data on mutual funds, which

I use to collect best fit index identification.14

I require that funds have 36 months of performance data available for prior-year returns calcu-

lation. I remove funds identified in CRSP as index funds or value funds, flagged as ETFs, missing

a CUSIP, missing a ticker symbol, or funds that have a turnover ratio less than or equal to zero

because these funds are described by CRSP as passively managed funds. Funds missing an iden-

tifiable benchmark after the merge with the Morningstar Direct database or missing performance

data during the period were also removed from the sample.

I use fund-calendar year observations for this study. Calendar year is appropriate because

the rule change required performance disclosures to be made based on the calendar year rather

than the fiscal year. The final sample after merging the CRSP and Morningstar Direct databases

includes 1,036 unique funds. Because multiple share classes within a fund share a portfolio, fund-

level performance is the relevant measure. I average the returns for each share class within a fund.

The variability in returns and other measures between share classes are typically related to the

fee structure of the particular share class. Additionally, I provide evidence that investors generally

follow a fund-year model when making investment decisions in Section 5.1.1.

I use two different six-year windows for testing the hypotheses outlined previously. Managers

were informed about the upcoming rule change by the initial proposal release in February 1997.

Managers were able to prepare for the rule change during 1997. Therefore, I omit observations in

1997 for tests involving managerial behavior because it is unclear whether fund-year 1997 should

be in the pre- or post-change period. Investors, however, were not informed of the self-designated

benchmark until 1999 for the vast majority of funds.15 Because I use the ex post self-designated

benchmark to assess the incremental effect of disclosure on investors and managers, I limit the

window on either side of the rule change to three years to avoid survivorship bias. I note a tradeoff

between test power and survivorship bias (e.g., Grinblatt and Titman, 1989; Brown, Goetzmann,

Ibbotson, and Ross, 1992; Carhart, 1996; Elton, Gruber, and Blake, 1996). However, three-year14I use the December 1998 Morningstar Principia best fit index throughout the sample.15SEC fund filings made after December 1, 1998 required by the new N-1A registration rules and format. For example,

a December 31 year-end fund filing would not occur until early 1999 and would include the required benchmarkdisclosure.

17

windows on either side of the rule change provide sufficient fund-year observations to perform

analyses while limiting the potential effects of survivorship bias. For investor (manager) focused

tests, the final data set provides a sample of 5,712 (5,208) fund-year observations for the pre- and

post-window years of 1996-1998 and 1999-2001 (1994-1996 and 1998-2000), respectively.

4.2 Descriptive Statistics

Summary statistics for all fund years are presented in Table 1. Panel A provides the descriptive

statistics for the investor fund flow regression variables for the 1996-1998 and 1999-2001 windows.

Panel B provides the descriptive statistics for the fund manager-based regression variables over the

1994-1996 and 1998-2000 windows. In addition to the summary statistics documented in Table 1, I

note that there are 1,036 unique funds and 139 unique benchmarks in this sample. Benchmarks are

selected an average of 7.45 times in the sample. The most frequently selected benchmark is the S&P

500 Index—chosen by 257 funds. Of the 139 unique benchmarks, 68 benchmarks are used by only

one fund. The average annual 12-month return (untabulated) for the mutual funds over 1996-2000

(1994-1996, 1998-2000) is 8.47% (9.68%), while the average 12-month return for the benchmarks

over the same period is 9.19% (10.94%).

The summary statistics for fund returns suggest that actively managed funds underperform

their comparative indexes by approximately 1% per year, on average, consistent with prior literature

and practitioner anecdotes. Fund size in Table 1 is measured by the natural log of the beginning year

NAV. The average fund size for the 1996-2000 (1994-1996, 1998-2000) period is approximately $490

million ($453 million). Expense fee ratios and fee waivers appear to be on par with typical averages

in practitioner journals of the time.16 After the rule change, 67% of funds choose a benchmark that

is different from the best fit index.17

Pearson’s and Spearman’s correlation tables are presented in Table 2 for fund flow and risk

regression variables in Panels A and B, respectively. Unsurprisingly, both excess benchmark returns

and Fama-French three-factor risk-adjusted returns are positively and highly correlated with fund16Morningstar’s Mutual Fund [Historical] Expense Ratio Trends – Publication June 201417I utilize the 1998 best fit index. Using other years results in quantitatively similar results because the best fit index

rarely changes over time in this window.

18

market share changes. Larger funds appear to take higher risk relative to the benchmark and higher

risk overall. Fund marketing fees appear to be negatively correlated with excess benchmark returns,

suggesting that managers may increase marketing expenses when returns are poor or that these fees

substantially drive down excess benchmark performance.

5 Methodology & Test Results

5.1 Investor Models, Performance Benchmarking, & Investors’ Response to the

Disclosure Change

The main goal of the SEC’s rule change was to provide more decision-relevant information

about fund performance to mutual fund investors. The SEC compelled managers to disclose a self-

designated benchmark to help investors understand the context of fund performance. Preferences

for risk, investment goals, and investment horizon make constructing useful benchmarks difficult

for investors. Compelling funds to provide a primary benchmark informs investors of what risk and

return they should expect from investing in the fund. However, if this information is already known

from other sources, then the new disclosure requirement should have little or no effect on investors.

Leuz and Wysocki (2016) suggest that researchers should utilize regulatory changes to study

the effectiveness of mandated disclosure. Researchers can provide regulators with evidence of the

costs imposed on firm stakeholders versus the benefits generated by new disclosures. In this study,

if investors are inattentive to fund performance or already know this information, the disclosure

mandate will not provide new information. In this case, the regulatory change will have no effect

on investor sensitivity to performance and only impose a net cost on both investment managers and

investors from increased filing preparation costs. However, if investors do respond more sensitively to

excess benchmark returns after the benchmark requirement, then the SEC’s stated goal to increase

decision-relevant information was met.

19

5.1.1 Investment Model of Mutual Fund Investors

I first determine the performance windows used by fund investors. Performance-flow studies

have historically used a calendar year window for determining investor behavior. Additionally, the

SEC rule change requires the disclosure to be based on calendar year performance. However, I

am not aware of any study that establishes what performance model investors use when making

investment decisions.

Fractional net fund flows have typically been used to measure changes in fund investment.18

However, the construction of fractional flows suffers from misspecification in OLS models. As

described in Spiegel and Zhang (2013), fractional net fund flows in performance studies can result

in spurious inferences because of the convexity of fund flows between "hot" and "cold" money funds.

Following their methodology, I instead use changes in fund market share as the dependent variable

for fund flows. The main independent variable of interest is excess benchmark returns. Excess

benchmark returns are fund returns less the benchmark returns over the specified period.

For this test, I use post-rule change data to ensure that the benchmark is clearly disclosed

for all funds. I examine the investor fund flows’ response to performance using both a semester

(half-year) and calendar year model. Additionally, I check whether investors adjust their holdings

based on mid-year performance. I then compare sensitivity to performance across these models. I

model these using the following OLS specifications:

∆Mkt. Share: 13-18mit = β1Return F-BM: 0-12mit + εit (1)

∆Mkt. Share: 13-18mit = γ1Return F-BM: 7-12mit + εit (2)

∆Mkt. Share: 7-12mit = δ1Return F-BM: 0-6mit + εit (3)

Table 3 provides chi-squared tests comparing the sensitivity of fund flows to performance under18Fractional net fund flows are typically calculated as follows: [NAVt − NAVt−1] ∗ [1 + Returnt]/NAVt−1.

20

a calendar year model versus a semester model—Models (1) and (2). Model (1) provides regression

estimates of fund market share changes in the following year on calendar year performance (i.e.,

the calendar year model). Model (2) provides regression estimates of fund market share changes

in the following year on second-semester performance (i.e., the semester model). Model (3) checks

whether investors also follow a calendar year model with mid-year adjustment (i.e., the mid-year

adjustment model). This model provides regression estimates of fund market share changes in the

second half of the year on excess benchmark performance in the first half of the year. In all three

regression models, fund market share changes are positively and significantly related to historical

excess benchmark performance at the 1% level.

Chi-squared tests confirm that investors respond to historical excess benchmark performance on

a calendar year basis. Model (1) suggests that for a 1% increase in calendar year excess benchmark

returns, the next-year fund market share increases by 0.031 basis points. In standardized terms,

a one standard deviation increase in annual excess benchmark returns leads to an increase that

is 23.71% of ∆ Mkt. Share: 13-18m’s standard deviation. This is a larger performance-to-flows

sensitivity compared to Model (2), significant at the 1% level. A chi-squared test comparing β1 to

γ1 rejects the hypothesis that investors follow a semester model. Results documented for Model (3)

suggest that investors also make mid-year corrections. A 1% increase in excess benchmark returns

in the first semester predicts a 0.031 basis point increase in the second-semester fund market share.

A one standard deviation increase in first-semester excess benchmark returns leads to an increase

that is 14.16% of ∆ Mkt. Share: 7-12m’s standard deviation. Comparing coefficients across models

suggests that δ1 is larger than γ1 at the 1% level, again rejecting the notion that investor flows

follow a semester performance window.

The results from Table 3 indicate that the hypothesis tests should use a calendar year model.

In the following sections on investor behavior (tests for H1-H3), I examine whether next-year market

share changes respond incrementally to calendar year excess benchmark performance after the rule

change. Additionally, because I find that investors make mid-year adjustments, I also check whether

second half-year market share changes respond incrementally to first half-year performance after

the rule change. Furthermore, since manager compensation and incentives are driven by NAV and

fund flows, manager behavior will also be assessed on a calendar year performance basis (tests for

21

H4-H5).

5.1.2 H1: Models and Tests of Net Fund Flows Response to the Initiation of Self-

designated Benchmark Disclosure

In this section, I test whether investor sensitivity to the self-designated benchmark increases

after the rule change. I define the pre-change period benchmark as the fund’s chosen benchmark

after the rule change. This allows me to assess whether the disclosure had an incremental effect

on investor decision-making. If there is an incremental increase in the sensitivity of fund flows

to excess benchmark performance after the rule change, it would suggest that the SEC increased

decision-relevant information.

Investors may respond to the introduction of a performance benchmark in two ways: 1) no in-

cremental sensitivity of flows to excess benchmark returns, or 2) increased sensitivity of fund flows

to excess benchmark returns. Under 1), a sufficient amount of information on fund performance

in the pre-change period window exists, and investors do not view a required primary benchmark

disclosure as news. Under 2), investors become more informed of fund performance and respond to

excess fund benchmark performance more sensitively. I adopt the first response as the null hypoth-

esis for H1—that the mandated disclosure requirement for a performance benchmark provided no

incremental information for investment decision-making. I model this as follows:

∆Mkt. Share: 13-18mit = α + β1Return F-BM: 0-12mit

+ β2Return F-BM: 0-12mit × Postit

+ β3Postit +∑

βkControlskit + εit

(4)

∆Mkt. Share: 7-12mit = α + γ1Return F-BM: 0-6mit

+ γ2Return F-BM: 0-6mit × Postit

+ γ3Postit +∑

γkControlskit + εit

(5)

Results for the calendar year model—equation (4)—are in Table 4, Model (1). I find that

22

historical excess benchmark returns have a positive and significant effect on fund market share

change in the pre-change period. For a 1% increase in excess benchmark performance in the pre-

change period, fund market share increases by 0.011 basis points, statistically significant at the

5% level. In line with H1, I find a positive and significant incremental effect of historical excess

benchmark returns on fund market share changes. I find a 0.015 basis point increase in sensitivity to

a 1% increase in excess benchmark returns after the rule change. A one standard deviation increase

in annual excess benchmark returns before the rule change leads to an increase that is 7.92% of ∆

Mkt. Share: 13-18m’s standard deviation, which increases to 18.73% after the rule change. These

results support H1: the SEC was broadly successful in achieving its intended goal with the 1998

disclosure change. The conclusion here is that mutual fund investors became more sensitive to

excess benchmark performance.

Results for the mid-year adjustment model—equation (5)—are in Table 5, Model (1), and are

consistent with results for equation (4). In the pre-change period, I find that a 1% increase in

excess benchmark returns in the first six months of the year results in a 0.021 basis point increase

in market share over the next six months. After the rule change, I find that a 1% increase in

excess benchmark returns in the first six months of the year results in an incremental 0.028 basis

point increase in market share over the next six months. A one standard deviation increase in

first-semester excess benchmark returns before the rule change leads to an increase that is 5.15% of

∆ Mkt. Share: 7-12m’s standard deviation, which increases to 12.01% after the rule change. This

provides additional support for H1.

For both models, I control for Fama-French three-factor risk-adjusted returns, fund size (NAV

and market share), fund risk (fund variance and market beta), expense ratio, 12B-1 marketing fee,

and fund age. Interestingly, Fama-French three-factor risk-adjusted returns experience a decrease in

sensitivity of 0.030 basis points in the calendar year model, suggesting that investors substitute risk-

adjusted returns for benchmark-adjusted returns in the post-change period. Larger funds and higher-

risk funds experience declines in market share during this period. Investors appear to decrease

investment as expenses rise, consistent with the competitive nature of fund returns. Fund marketing

expense, however, appears to have a positive and significant effect on increasing fund market share.19

19Recently, mutual funds and institutional investors have come under criticism from the SEC for misappropriating

23

5.1.3 H2a/H2b: Investor Response to Voluntary and Non-voluntary Disclosure of the

Primary Fund Benchmark

Funds that disclose a primary benchmark prior to the rule change should drive the pre-change

period performance-flow sensitivity observed in Tables 4 and 5. I use hand-collected data from fund

N-1A filings to clearly identify when funds disclosed their primary benchmark. I classify a fund as

a voluntary discloser if the fund discloses a primary self-designated benchmark in any year between

1994 and 1997. If the fund discloses multiple benchmarks, blended benchmarks, or no benchmark, I

classify it as a non-voluntary discloser. I find that most funds do not disclose a primary benchmark

in the pre-change period.20 Only 25% of funds disclose a primary benchmark prior to the SEC’s

rule change, indicating that pre-change period primary benchmark disclosure was not widespread.

In this section, I test H2a/H2b by rerunning equations (4) and (5) separately based on whether

the fund voluntarily discloses a benchmark or does not disclose one until after the rule change.

I provide results for these regressions in Tables 4 and 5, Models (2) and (3), for voluntary and

non-voluntary disclosers, respectively. For equation (4), in Table 4, Model (2), I find a positive

and significant coefficient on pre-change period excess benchmark returns, whereas I find a non-

significant effect on excess benchmark returns after the rule change, supporting H2a. Furthermore,

in Model (3), I find that excess benchmark returns do not significantly affect market share changes

in the pre-change period for non-disclosing funds but have a significant and positive incremental

effect in the post-change period, supporting H2b. Results in Table 5 for equation (5) run separately

on voluntary/non-voluntary disclosers are consistent with the results in Table 4.

Taken together, the results in Models (2) and (3) from Tables 4 and 5 suggest that the SEC’s

rule change had an important impact on investors’ decision-making. Investors appear to be unable

to identify the fund’s primary benchmark until it is disclosed. Even with public information available

on potential benchmarks, investors do not appear to trade on excess benchmark performance unless

investors’ assets by spending too much on marketing. One recent example is the SEC’s suit versus SunTrust inFebruary 2017. The SEC alleges that SunTrust’s clients were inappropriately funneled into more expensive fundswith high 12B-1 marketing fees. The finding here suggests that fund marketing successfully captures fund flows,after controlling for fund performance. This is a potential source of dead-weight loss to investors.

20I provide results of a descriptive regression discussed in Section 5.2.1 and located in Table 6 on firm characteristicsrelated to pre-change period voluntary disclosure.

24

the benchmark is widely disclosed.

The type of investor using the information from this disclosure also matters. The main objec-

tive of the disclosure change was to improve information quality communicated to unsophisticated

investors. If unsophisticated investors do not respond to benchmark disclosures, then other potential

issues arise with this mandated disclosure change.

5.1.4 H3: Retail & Institutional Investor Response to the Primary Fund Benchmark

The results from tests of H1 and H2a/H2b support the notion that the SEC was successful

in increasing decision-relevant information for investors. However, to understand which subset of

investors was affected, I rerun the voluntary/non-voluntary regressions further split on funds that

serve sophisticated investors (institutional funds) and those that do not (retail funds). Institutional

investors manage other funds in pensions, hedge funds, and so on. They have a fiduciary duty to

appropriately allocate their client funds. In discussions with institutional fund managers, institu-

tional funds have direct and recurring contact with fund managers. The nature of these discussions

revolves around fund objectives, industry trends, and the outlook of certain asset classes. These

types of investors are not the primary target of the SEC’s rule change because they are informed

and able to discern appropriate fund benchmarks absent disclosure.

To test H3, I use the existence of an institutional share class at a fund as a proxy for sophis-

ticated investors. I classify a fund as "institutional" if it has a share class tailored to institutional

investors. These share classes typically have no front-end sales charges and require high up-front

commitments of capital. Institutional funds are not precluded from also having retail investors.

However, the presence of institutional investors creates differing incentives for managers because

institutional investors typically demand more direct information from, and have closer interactions

with, these fund managers. The retail/institutional investor split in this subsection will examine

whether the SEC’s rule change improved information transfer specifically for the intended audience:

unsophisticated investors.

Using the same models for H1 and H2a/b from equations (4) and (5), I further separate

25

regressions on retail and institutional fund classifications, as well as on voluntary/non-voluntary

pre-change period disclosure. Regression results for the partitions of retail–voluntary, retail–non-

voluntary, institutional–voluntary, and institutional–non-voluntary, funds are reported in Models

(4), (5), (6), and (7), respectively, in Tables 4 and 5.

The evidence here fails to reject the null hypothesis for H3. I find that retail fund investors are

unresponsive to excess benchmark performance in the pre-change period whether the benchmark

is disclosed or not. The incremental effect of the rule change on retail investors, while positive,

is insignificant at traditional levels. However, for regressions partitioned on institutional investors,

the corresponding effects of performance on flows identified in Models (2) and (3) occur when the

benchmark is disclosed. Institutional investors appear to respond to excess benchmark performance

in the pre-change period when disclosed (with no significant incremental effect in the post-change

period). When benchmarks at institutional funds are not disclosed, fund flows appear incrementally

sensitive to the excess benchmark performance in the post-change period. Overall, the results in

Models (2) and (3) appear to be driven by institutional investors, not retail investors.

Institutional investor sensitivity to the benchmark is puzzling and raises questions about why

these investors respond to disclosed benchmarks. One alternative hypothesis is that institutional

investors are the only subset of investors that use fund filings. However, it is unlikely that institu-

tional investors do not understand which benchmark is appropriate to contextualize fund returns.

Additionally, it is unlikely that institutional investors rationally chase returns, given the widespread

evidence on the lack of persistence in fund returns (e.g., Jensen, 1968; Carhart, 1997). However, I

observe that institutional investors are not sensitive to excess benchmark returns for non-disclosing

funds in the pre-change period, and are incrementally sensitive to the disclosure after 1998. This

suggests a strategic investment aspect on the part of institutional investors.

Another hypothesis that arises from these findings is that institutional investors likely trade

on disclosures revealing winner and loser funds. Clients of institutional investors will then observe

that their institutional fund managers selected winners during the year. Additionally, it allows

institutional fund investors to claim that their fiduciary duties are fulfilled while decreasing effort in

selecting quality funds. Using the disclosed benchmark as a simple "good" or "bad" indicator allows

26

them to rely on this disclosure and report positive outcomes to their clients. The findings in this

section support a hypothesis that institutional fund of funds investors window-dress their portfolios

(e.g., Lakonishok, Shleifer, Thaler, and Vishny, 1991; Agarwal, Gay, and Ling, 2014). Surprisingly,

this behavior may have been exacerbated by the SEC’s rule change and represents an unintended

consequence of increased performance disclosure.

5.2 Fund Manager Response to Increased Performance Disclosure

Fund managers do not operate in a vacuum, and changes to required disclosures can have

a significant effect on real operations and portfolio strategy. Given that managers focus on 1)

comparative return performance, and 2) net fund flows, any disclosure affecting either of these

can affect how managers operate. In this section, I explore how the disclosure change distorted

managerial behavior, encouraged strategic benchmark disclosure, and increased overall risk-taking.

5.2.1 The Choice to Voluntarily Disclose a Primary Benchmark

I run a descriptive regression of voluntary disclosure on fund characteristics during the pre-

change period. I provide these results in Table 6. Model (1) runs a LPM regression of an indicator

equal to 1 if the fund voluntarily discloses a primary benchmark in the pre-change period, and 0

otherwise, on only raw fund returns. I find a positive but insignificant coefficient on fund returns,

suggesting that the overall performance of a fund is not a main determinant of voluntary disclosure.

Model (2) runs the same regression, substituting raw performance for Fama-French three-factor

risk-adjusted returns. The results similarly suggest that risk-adjusted returns are not a significant

determinant of voluntary disclosure.

Including fund characteristics as additional determinants sheds some light on correlations be-

tween the decision to voluntarily disclose a primary benchmark and firm factors. Consistent with

results investigating investor behavior, institutional funds are less likely to voluntarily disclose.

Funds that advertise more are less likely to voluntarily disclose a benchmark, suggesting that ad-

vertising and performance benchmarking disclosure are substitutes. The number of funds in the

27

investment family also appears to be positively related to voluntary disclosure, potentially as an

effort to assist investors with allocation decisions. Finally, I find that funds with Big 5 (or Big

6 prior to 1998) auditors are significantly more likely to voluntarily disclose a benchmark (12.4%

more likely, significant at the 1% level). This suggests that benchmark disclosure may also act as a

governance mechanism. Other fund characteristics such as size, expense ratio, expense waiver, age,

and turnover ratio do not appear to be associated with voluntary disclosure.

5.2.2 H4a/H4b: Benchmark Selection—Funds Choosing a Benchmark Other Than

the Best Fit Index

The SEC’s guidelines for the selection of a self-designated benchmark are not restrictive. The

rule states that a fund must report its own performance against "the returns of an appropriate broad-

based securities market index," where an appropriate index is defined as "one that is administered

by an organization that is not an affiliated person of the Fund, its investment adviser or principal

underwriter, unless the index is widely recognized and used."21 Given the leeway in the selection of

a primary benchmark, managers are given ample discretion to act strategically.

Sensoy (2009) finds that fund returns relative to self-designated benchmark returns often indi-

cate a mismatched benchmark based on actual fund style. An open question is whether benchmark

selection is strategic. The rule change in 1998 provides managers an opportunity to strategically

select a benchmark that differs from the fund’s actual risk and return profile.

I hypothesize that a benchmark choice is strategic when funds choose a benchmark not equal to

the Morningstar best fit index in 1998 (H4c/H4d). The best fit index is the most relevant benchmark

for a fund, based on the fund’s historical risk and return profile. Strategic selection of a benchmark

will occur if managers wish to "game" the benchmark. If managers select a benchmark that deviates

from the most relevant index, it may represent a decision by management to optimistically portray

fund performance (H4c) but may result in higher risk to achieve higher expected returns (H4d).

Before testing whether non-best fit index selection is strategic, I identify which funds choose21Sample Form for the N-1A (as of November 1, 2017): https://www.sec.gov/rules/final/33-7512f.htm

28

non-best fit index benchmarks (H4a/H4b). I find widespread non-best fit index disclosure in funds.

About 66.8% of funds disclose a benchmark that is not equal to the best fit index. In the prior

section, I identify that institutional investors respond the most sensitively to excess benchmark

performance. Additionally, non-voluntary funds also drive the incremental sensitivity to excess

benchmark performance. Correspondingly, I test whether these two fund groups—institutional

funds and non-voluntary funds—have the highest likelihood of choosing a non-best fit index. In

Table 7, I run an LPM regression on the four categories of institutional versus retail investors and

voluntary versus non-voluntary disclosers.

BM , BF Indexi =β1Institutional & Non-voluntaryi + β2Institutional & Voluntaryi

+ β3Retail & Non-voluntaryi + β4Retail & Non-voluntaryi + εi

(6)

Institutional funds that did not previously disclose a benchmark subsequently choose a bench-

mark not equal to the best fit index 74.5% of the time, the highest among the pairings. Comparing

institutional/non-voluntary non-best fit index benchmark selection to the retail/non-voluntary sub-

sample suggests that this is a 14.7% increase in non-best fit index benchmark selection, significant

at the 1% level (Table 7, Panel B). Comparing the institutional and non-voluntary group against

retail funds with voluntary disclosure of a benchmark in the pre-change period reveals the largest

difference, 21.9%, significant at the 1% level. Within the voluntary subgroup, institutional funds

select a non-best fit index benchmark 9.9% more frequently, significant at the 10% level.22

Panel C of Table 7 provides the cross-sectional regressions with controls. I use the following

LPM regression in the pre-change period to assess support for H4a and H4b:

BM , BF Indexit =α + γ1Institutional Fundit + γ2Non-voluntary BM +∑

γkControlskit + εit

(7)

Model (1) suggests that non-voluntary disclosers versus voluntary disclosers select a benchmark22In a one-tail test, this is significant at the conventional 5% level.

29

not equal to the best fit index 9.1% more frequently, significant at the 1% level, supporting H4a.

Furthermore, Model (2) suggests that institutional funds select a benchmark not equal to the best

fit index 8.3% more frequently, significant at the 1% level, supporting H4b. Model (3) includes

indicators for both non-voluntary disclosers and institutional funds. The combined effect of being

an institutional and non-voluntary discloser in the pre-change period suggests a 14.9% increase in

the probability of selecting a non-best fit index benchmark. Model (4) provides estimates within

the non-voluntary subsample and again finds that institutional funds are 8.6% more likely to select

a non-best fit index benchmark.

The evidence suggests that institutional funds and funds not voluntarily disclosing a benchmark

potentially select a strategic benchmark more frequently. This is consistent with incentives related

to the attention that institutional investors give to excess benchmark performance, as identified in

Section 5.1.4. To provide evidence that the choice of a benchmark not equal to the best fit index

is indeed strategic, I check the differential probability of beating benchmark returns across the rule

change period. Additionally, I check whether that outperformance is due to increased risk-taking

relative to the benchmark.

5.2.3 H4c: Strategic Benchmark Selection—Outperforming Benchmarks

I first establish whether non-best fit index benchmark funds are more likely to outperform

their selected benchmarks (H4c). If benchmarks are strategically chosen, managers must do so

rationally. Strategic benchmark selection would imply that managers believe they can outperform

these benchmarks in expectation. To test this hypothesis, I estimate the following regression:

I(F-BM Return: 0-12m>0)it =α + β1BM , BF Indexit + β2BM , BF Indexit × Postit

+ β3Postit +∑

βkControlskit + εit

(8)

where I(F-BM Return: 0-12m>0) is an indicator equal to 1 if the fund beats its self-designated

benchmark during the year, and 0 otherwise.

I report regression estimates for equation (8) in Table 8. In Model (1), I find that, before the

30

rule change, firms not selecting the best fit index as the benchmark are 4.2% more likely to beat it

than firms selecting their benchmark as the best fit index, significant at the 10% level. This is not

unexpected because funds voluntarily disclosing may also have also been doing so strategically even

before the mandated requirement. However, I expect the strongest incentive to act strategically

occurs when the disclosure becomes widespread, mandated, and easily comparable across funds. In

Model (2), for the post-rule change subsample, I find the probability that a fund beats its benchmark

when selecting a non-best fit index is 8.0%, significant at the 1% level. Model (3) confirms that the

incremental effect of the rule change is 5.1%, significant at the 1% level, supporting H4c.

These results alone do not suggest that managers acted strategically. An alternative hypothesis

is that managers just exerted more effort and began to outperform benchmarks. However, another

way to achieve higher performance against the benchmark is by increasing relative risk. That is,

funds may increase risk relative to the self-designated benchmark to outperform that benchmark in

expectation—a real effect of disclosure change. I explore this in tests of H4d.

5.2.4 H4d: Risk-taking Relative to the Benchmark

Strategic benchmark selection that results in higher risk exposure to investors would be a

significant unintended consequence of disclosure. Support for H4c suggests that managers beat non-

best fit index benchmarks more frequently. This may be a boon to investors. If managers perform

better because of more effort and similar risk-taking, then the disclosure is beneficial to investors.

If managers instead just take higher risk to increase expected returns, then investors are exposed to

unexpected risk levels with respect to the benchmark disclosed. H4d hypothesizes that managers

strategically increase risk relative to a non-best fit index benchmark after the rule change. I model

the pre- versus post-rule change excess benchmark return risk by regressing the standard deviation

of excess benchmark risk during the year on an indicator equal to 1 if the best fit index is not the

self-designated benchmark, and 0 otherwise, and I interact this with an indicator separating the

31

rule change periods:

SD(F-BM Return: 0-12m)it =α + β1BM , BF Indexit + β2BM , BF Indexit × Postit

+ β3Postit +∑

βkControlskit + εit

(9)

I present regression estimates for equation (9) in Table 9. Model (1) presents the effect that

non-best fit index benchmark selection has on excess benchmark return risk. I find that non-

best fit index benchmark selection results in increased excess benchmark return risk, indicating

that managers selected a benchmark strategically. Model (2) shows that, in the three years after

the disclosure change, funds exhibit increased risk-taking overall. Model (3) provides a regression

estimate of excess benchmark risk on the interaction of the non-best fit index benchmark indicator

and an indicator for the post-change period. This regression estimate supports H4d: pre-change

period excess benchmark return risk is positive but not significant, while the incremental effect

of non-best fit index benchmark selection shows a positive and significant increase in risk-taking.

After the rule change, non-best fit index benchmark funds increase the standard deviation of excess

benchmark returns by 0.52% over best fit index benchmark funds, significant at the 1% level. Model

(4) includes controls and confirms that the findings are robust.

Support for H4c and H4d reveal that managers beat a strategically selected benchmark by

increasing risk. Mandated disclosure change that allows discretionary benchmark choice results

in increased risk exposure. This represents both a real effect and real externality of disclosure.

Managers choosing a strategic benchmark subsequently increase risk relative to that benchmark after

the rule change—a real effect. Investors increase fund flows into strategic funds more often because

these funds more frequently outperform their benchmark—a real externality on non-strategic funds

(Dye, 1990).

5.2.5 H5: Subsequent Risk Sensitivity Following Excess Benchmark Performance

Two prior studies establish that managers respond to partial-year underperformance. Brown,

Harlow, and Starks (1996) find that managers will increase risk at the end of the year when under-

32

performing competitors at mid-year because of "tournament-like" incentives. In their tournament

model, managers are driven to outperform rivals to capture fund flows. Chevalier and Ellison

(1997) demonstrate that managers dynamically respond to performance incentives from fund flows

by ratcheting up risk in the fourth quarter. I investigate whether risk-taking behavior demonstrated

in the extant literature intensifies after the rule change.

Based on this prior literature, I expect to find managerial risk-taking in response to under-

performing the self-designated benchmark even when it is not disclosed. However, I hypothesize

in H5 that risk-taking incentives increase because of information inductance. Therefore, managers

will have higher incentives to increase risk because of the disclosure requirement. Support for H5

would be an unintended consequence and a real effect of disclosure: managerial risk-taking intensi-

fies because of benchmark disclosure requirements. This would represent a trade-off for regulators

mandating disclosure. I model the actual fund risk relative to the benchmark in the second half of

the calendar year against first half-year excess benchmark returns, as follows:

SD(Return F-BM: 7-12m)it =α + β1Return F-BM: 0-6mit

+ β2I(F-BM Return: 0-6m<0)it + β3Postit

+ β4Return F-BM: 0-6mit × I(F-BM Return: 0-6m<0)it

+ β5I(F-BM Return: 0-6m<0)it × Postit

+ β6Return F-BM: 0-6mit × Postit

+ β7Return F-BM: 0-6mit × Postit

× I(F-BM Return: 0-6m<0)it +∑

βkControlskit + εit

(10)

SD(Return F-BM: 7-12m)it =α + γ1Return F-BM: 0-6mit

+ γ2Return F-BM: 0-6mit × Postit

+ γ3Postit +∑

γkControlskit + εit

(11)

where I(F-BM Return: 0-6m<0)it is an indicator equal to 1 for positive excess benchmark perfor-

mance in the first semester, and 0 otherwise, for firm i in year t.

33

I note that managers’ risk-taking relative to past historical performance is not strictly linear

(Brown, Harlow, and Starks, 1996; Chevalier and Ellison, 1997). Therefore, the above models provide

two estimations. First, I regress the second-semester standard deviation of excess benchmark returns

on the first-semester excess benchmark returns, including interaction terms for positive/negative

excess benchmark returns in the first semester and for post-rule change observations. This triple

interaction allows the slopes to differ for under/overperforming fund across the rule change pe-

riod within the same regression. Second, I run separate regressions for overperforming funds and

underperforming funds relative to benchmark returns in the first semester.

I find that most funds underperform their benchmark in the first semester of the year (63.7%),

consistent with prior literature.23 One major criticism of active fund management is the perva-

sive underperformance relative to related indexes. Regulators mandated the rule change to help

investors identify these managers. However, managers rationally respond to incentives induced by

performance benchmarking and are likely to take more risk relative to the benchmark when under-

performing. Support for H5 would be indicated by a negative and significant coefficient on β7 and

γ2 in equations (10) and (11), respectively.

I report results for these equations in Table 10. Model (1) reports regression estimates for

equation (10), before controls. In support of H5, I find a negative and significant coefficient for

β7, at the 1% level. Model (2) includes controls and reduces the magnitude of the coefficient but

continues to remain significant at the 1% level.

Model (3) reports the regression rerun for only the underperforming subsample to facilitate

the inference of magnitudes. Consistent with prior literature, I find the expected pre-rule change

relationship between second-semester fund risk and first-semester excess benchmark returns. For

underperforming funds prior to the rule change, a one standard deviation decrease in F-BM Returns:

0-6m results in an increase of 0.08 in SD(F-BM Returns: 7-12m). The incremental effect of the

rule change (γ2) is also negative and significant at the 1% level, consistent with H5. The impact of

the rule change is substantial: the incremental post-rule change effect of a one standard deviation

decrease in F-BM Returns: 0-6m results in an additional increase in SD(F-BM Returns: 7-12m) of23Most funds also underperform calendar year benchmark returns (66.5%).

34

0.23%, significant at the 1% level. Model (4) reports the outperforming subsample. After controls,

I find no incremental increase in risk-taking related to excess benchmark returns.

As a robustness check and to illustrate the effect of the rule change, I plot a local polynomial

regression using kernel density estimation of the relationship between SD(F-BM Returns: 7-12m)

and F-BM Returns: 0-6m in Figure 2. For funds underperforming their benchmark by mid-year,

I note a significant increase in the slope of risk sensitivity after the rule change. The separation

of the slope profile from pre- to post-rule change occurs at approximately negative 3.5-4.0% excess

benchmark performance (significant at the 5% level). For overperforming funds, I also note an

increased sensitivity of the performance-risk relationship but only between about 4-10% excess

benchmark performance. Funds in the upper tail of performance (>10%) do not statistically differ

from pre- to post-rule change. This is consistent with the tournament models outlined in Brown,

Harlow, and Starks (1996). Funds that have exceptional performance in the first half of the year

are relatively insensitive to the rule change because the self-designated benchmark is irrelevant.

I control for fund risk in the first half of the year in equations (10) and (11). However, to

rule out ex ante fund risk-taking driving these findings, I perform another analysis for robustness

to confirm that increased risk relative to underperformance is specifically caused by risk shift-

ing. I expect that risk shifting—defined as the ratio of SD(F-BM Returns: 7-12m)/SD(F-BM

Returns: 0-6m)—increases significantly after the rule change, consistent with managers responding

to underperformance by increasing risk. I estimate the following regression equation to model this

relationship:

Risk Shift: 7-12m/0-6mit =α + β1I(F-BM Return: 0-6m<0)it

+ β2I(F-BM Return: 0-6m<0)it × Postit

+ β3Postit +∑

γkControlskit + εit

(12)

I report the results of this regression in Table 11, Model (1). I confirm that the increased risk-

taking in response to negative excess benchmark returns in the first semester is due to risk shifting.

Estimates of β2 indicate that managers underperforming their benchmark incrementally increase

risk shifting by 0.13, a 12.3% increase over the mean, significant at the 1% level. Interestingly, before

35

the disclosure requirement, pre-change period risk shifting for funds underperforming benchmarks

is negative, suggesting that underperforming managers subsequently reduce risk-taking. To ensure

that the post-change period relationship is indeed positive and significant, I rerun the regression

for only underperforming funds to obtain a clear estimate of the average increase in risk shifting

for underperformers, presented in Model (2). In Model (3), I rerun the regression for only the post-

change period subsample. In Model (2), I find that the risk shift ratio increases by 0.30, an increase

of 27.5% over the mean, significant at the 1% level. When comparing under and overperformers in

the post-rule change period, I find that the risk shifting ratio is 0.051 higher for underperformers,

significant at the 5% level in Model (3).

Overall, I find substantial support for H5: mandated benchmark disclosure distorts manage-

rial incentives, resulting in portfolio risk shifting—a real effect of disclosure. Managers become

increasingly sensitive to underperforming benchmarks at mid-year and increase risk in an attempt

to recover. Increased risk shifting by underperformers after the disclosure mandate confirms that

managers were incentivized to take more risk to compensate for underperformance.

5.3 Robustness Checks

Below, I provide a summary of the robustness checks performed over the previously discussed

tests:

• I verify that fund benchmark switching is rare (Sensoy and Kaplan, 2007). In my sample,

only 111 fund-year observations from 41 unique funds changed their benchmark after the rule

change (less than 2% of the sample). Test results are robust when omitting these observations.

• For observations in the pre-change period, I run regressions of market share change on an

interaction of excess benchmark returns and an indicator for voluntary benchmark disclosure.

In untabulated results, I verify that the incremental effect of excess benchmark returns for

voluntary disclosers is positive and significant. This incremental effect is strongest for insti-

tutional funds, while retail funds have no difference between voluntary and non-voluntary

disclosers. This confirms that the pre-change period excess benchmark returns coefficients for

voluntary and non-voluntary disclosers in Tables 4 and 5—for Model (2) versus Model (3),

36

and Model (6) versus Model (7)—are significantly different.

• For tests of fund flow sensitivity (Tables 4 and 5), I substitute the control for risk, Fund

Variance: 0-12m, with SD(Fund Return: 0-12m), and note no significant changes in test

results. I also interact these measures of risk with the post-rule change indicator to confirm

that investors are responding to excess benchmark returns.

• In untabulated results, I rerun regressions in Tables 8 and 9 and plots in Figure 1, redefining the

independent variable as equal to 1 for the funds selecting a non-best fit index benchmark and

serving institutional clients, and 0 otherwise. I also redefine the independent variable as equal

to 1 for the funds selecting a non-best fit index benchmark and not disclosing a benchmark

in the pre-change period, and 0 otherwise. The results are robust to these redefinitions.

• For tests of excess benchmark return risk (Table 9), I substitute the dependent variable with

fund-only risk: SD(Fund Returns: 0-12m). I also substitute the dependent variable with the

difference of SD(Fund Returns: 0-12m) and SD(BM Returns: 0-12m). The results are robust

to dependent variable definition.

• In untabulated results, I substitute the dependent variable in Table 9 with the difference of

benchmark risk and best fit index risk. I confirm that mutual funds selecting a non-best fit

index as the benchmark choose one with lower risk than the best fit index on average, as

suggested by Table 9.

• All regressions utilizing a LPM were rerun using a logit regression. The results are robust to

regression specification.

5.4 The Impact of Disclosure Change on Managers’ Behavior and Regulatory

Change Implications

The implication of this paper is that there exists a trade-off between increasing decision-relevant

information and managerial risk-taking. The demand for information production is due to informa-

tion asymmetry between managers and investors (Healy and Palepu, 2001). Mandated disclosure

compels managers to reveal previously withheld private information. Managers, however, will ad-

just their behavior relative to the information they are required to provide to investors (Prakash

and Rappaport, 1977).

37

The evidence I provide implies that there are costs imposed on investors because of man-

dated disclosure. I find evidence in line with Hermalin and Weisbach (2012) and argue that better

disclosure can lead to increased agency issues. Specifically, managers in this setting experienced

information inductance because of the required benchmark disclosure. I find that increased dis-

closure of managerial performance leads to increased risk-taking by managers through strategic

responses to the benchmark. This supports the notion that there is a "double-edged sword" of

disclosure as outlined in Hermalin and Weisbach (2012). Specifically, when regulators mandated

performance benchmarks, managers responded by altering their real operations and strategy in

response. Managerial risk-taking increased in an effort to beat benchmark returns in expectation.

This risk increase is borne directly by investors and imposes a net loss on risk-averse investors.

Other concerns regarding mandated disclosure come from competitive and proprietary costs of

newly revealed information (e.g., Verrecchia, 1983; Darrough, 1993). In the fund setting, proprietary

costs are a significant concern because trading strategies are easily replicated by competitors (Frank,

Poterba, Shackelford, and Shoven, 2004). Allowing voluntary benchmark disclosure gave managers

the ability to choose a disclosure policy that attracted capital inflows, whereas the mandated re-

quirement induced managers to act strategically. Additionally, this paper shows that increasing

information for an intended audience may result in unintended use by another audience. Institu-

tional investors appear to be interested in benchmark performance only when it is publicly disclosed;

however, retail investors remain unresponsive. This is a puzzling relationship, and it may indicate

strategic use of the benchmark for institutional fund of funds investments. Institutional funds can

justify investment in funds that outperform their benchmark with low research costs because it is

a justifiable and verifiable choice to their investors, albeit inefficient.

6 Conclusion

Utilizing a rule change for mutual fund reporting requirements, I provide evidence on the real

effects and real externalities of mandated performance disclosure. Mutual fund financial reporting

provides a clean setting to study this type of disclosure. Mutual fund investors directly utilize past

performance when making capital allocation decisions. Mutual fund managers have simple incentive

38

structures corresponding mainly to generating more revenue for the management company via higher

fund flows. These features, along with a limited set of incentive inputs for managers, allow me to

exploit this setting with less concern about possible alternative explanations driving my findings.

First, I find that investor sensitivity to excess benchmark performance increased in response

to the 1998 mutual fund benchmark disclosure requirement. Using hand-collected data to identify

which funds voluntarily disclose a primary benchmark prior to the rule change, I provide evidence

that investors responded to funds’ relative benchmark returns only when they were informed of

the fund-designated benchmark. By documenting how investors behave differently depending on

when the benchmark is disclosed, I provide strong evidence that investors utilize this information

for decision-making. However, I find that only sophisticated investors significantly responded to the

rule change, while retail investors remained uninformed.

Next, I find that managers’ behavior became distorted as a result of the rule change. I find that

fund managers frequently select strategic benchmarks that misrepresent their funds’ underlying risk

and return profile. Investors relying on the disclosure of the primary fund benchmark for capital

allocation decisions will shift investment away from non-strategic funds and toward strategic funds—

a real externality. Fund managers of institutional funds and of funds not voluntarily disclosing a

benchmark more frequently select strategic benchmarks, consistent with how investors responded

to the rule change. Risk relative to the selected benchmark increases after the rule change—a real

effect of performance disclosure. Managers also responded more sensitively to underperforming their

benchmark’s performance with increased risk-taking. Risk shifting by managers occurs in response

to mid-year underperformance—an additional real effect of disclosure.

Disclosure is important for investors; however, regulatory agencies must assess possible re-

sponses by management to increased transparency and standardization. I document that disclosure

quality improvements that increase information flow to investors is not costless. Besides economic

costs related to changes in the preparation of reports, investors are exposed to increased managerial

risk-taking behavior. Mandated disclosure change must be subject to a rigorous cost-benefit analysis

by regulators and researchers. Such analyses should factor in how increased disclosure requirements

may affect managerial incentives.

39

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Appendix A: Variable Definitions

Variable Description

∆ Mkt. Share Calculated following Spiegel and Zhang (2013). Monthly market share is mea-

sured using fund NAV over cumulative total mutual fund NAV for that month,

which represents the percent of market share the fund possesses. The change in

market share is the difference between the fund’s market share at time t + 1 and

t.

Source: Morningstar Direct

F-BM Return The fund’s total return less the self-designated benchmark return over the speci-

fied period. Returns are calculated at the class level and averaged over all classes

within the fund.

Source: Morningstar Direct

SD(F-BM Return) The standard deviation of the fund’s weekly excess benchmark returns over the

specified period.

Source: Morningstar Direct

FF3F Return The Fama-French 3-factor risk-adjusted return of the fund over the specified

period. Weekly return data is used from Morningstar Direct. Factor data is

sourced from Kenneth French’s website.

Source: Morningstar Direct; Kenneth French’s Website

Fund Size The beginning period natural log of the fund’s net asset value (NAV). NAV is

aggregated from the class level to the fund level on a monthly basis.

Source: Morningstar Direct

Fund Share (%) Calculated following Spiegel and Zhang (2013). The beginning period total

market share of the fund. Monthly market share is measured using fund NAV

over cumulative total mutual fund NAV for that month, which represents the

percent of market share the fund possesses.

Source: Morningstar Direct

Fund Variance The variance of the fund’s weekly returns over the specified period.

Source: Morningstar Direct

Ln(Age) The natural log of the fund’s age.

Source: CRSP Survivorship Bias Free Database

12B-1 Marketing Fee The ratio of the total assets attributed to marketing and distribution costs.

Source: Morningstar Direct

Voluntary BM Disclosure An indicator equal to 1 if the fund disclosed a primary self-designated benchmark

between the years of 1994-1997, and 0 otherwise.

Source: SEC EDGAR, N-1A Filings

44

Variable Description

BM , Best Fit Index A variable equal to 1 if the self-designated benchmark selected by the fund

post-1998 is not the same index identified by Morningstar as the best fit index,

and 0 otherwise. Morningstar identifies the best fit index as the index that has

the highest R2 in a regression of fund returns on index returns over the past

36-months.

Source: Morningstar Principia

Institutional Fund An indicator equal to 1 if the fund has at least one share class that serves

institutional investors, and 0 otherwise.

Source: CRSP Survivorship Bias Free Database

F-BM Return:0-12m>0 An indicator equal to 1 if the fund outperformed its self-designated benchmark

during calendar year, and 0 otherwise.

Source: Morningstar Direct

F-BM Return:0-6m<0 An indicator equal to 1 if the fund underperformed its self-designated benchmark

in the first 6 months of the calendar year, and 0 otherwise.

Source: Morningstar Direct

Expense Ratio The fund’s expense ratio. The expense ratio represents the total percent of

assets used to pay for fund expenses. These expenses typically include 12B-

1 fees, admin fees, management fees, operating costs, and any other expenses

incurred through fund operations.

Source: Morningstar Direct

Turnover Ratio A measure of the fund’s trading activity. Morningstar Direct calculates this as

the lesser of purchases or sales over average monthly net assets.

Source: Morningstar Direct

N-1A Fund Family Size The number of funds within a fund family that are filed in the N-1A.

Source: SED EDGAR, N-1A Filings

Big 5/6 Auditor An indicator equal to 1 if the fund had a Big 6 (or 5, after Price Waterhouse

and Coopers & Lybrand merged in 1998) auditor, and 0 otherwise.

Source: Morningstar Principia

Market Beta The market beta of the fund at the start of the calendar year. The fund’s market

beta calculated over the prior 60-months that are available.

Source: Morningstar Direct

Risk Shift: 7-12m/0-6m The ratio of the standard deviation of second half-year excess benchmark re-

turn to the standard deviation of first half-year excess benchmark returns.

Risk Shift: 7-12m/0-6m = SD(F-BM Return: 7-12m)SD(F-BM Return: 0-6m)

Source: Morningstar Direct

45

Appendix B: TablesTable 1: Summary Statistics - Regression Variables over Fund Years 1994-2001

Panel A: Market Share Change Models - Descriptive Statistics for Fund Years 1996-2001Mean Median S.D. P25 P75

∆ Mkt. Share: 13-18m -0.00001 -0.00000 0.00013 -0.00003 0.00001∆ Mkt. Share: 7-12m 0.00000 -0.00000 0.00020 -0.00003 0.00002F-BM Return: 0-12m -0.00728 -0.00815 0.09750 -0.03827 0.01319F-BM Return: 0-6m -0.00637 -0.00361 0.04833 -0.01943 0.00571FF3F Return: 0-12m -0.00269 -0.00925 0.10682 -0.06063 0.03395FF3F Return 0-6m -0.00211 -0.01008 0.06125 -0.03347 0.02061Fund Size 20.00976 20.02093 1.66318 18.86730 21.14340Fund Share (bps) 3.79070 1.02082 8.45778 0.30709 3.23675Fund Variance: 0-12m 0.00042 0.00017 0.00060 0.00002 0.00059Fund Variance: 0-6m 0.00039 0.00015 0.00068 0.00002 0.00045Market Beta 0.53457 0.54314 0.39142 0.17316 0.87933Expense Ratio 1.10793 1.09000 0.44259 0.80000 1.4050012B-1 Marketing Fee 0.21032 0.00000 0.27542 0.00000 0.41667Ln(Age) 2.20134 2.19722 0.48880 1.94591 2.48491Observations 5,712

Panel B: Risk Models - Descriptive Statistics for Fund Years 1994-1996, 1998-2000Mean Median S.D. P25 P75

Voluntary BM Disclosure 0.25365 0.00000 0.43514 0.00000 1.00000BM , Best Fit Index 0.66820 1.00000 0.47090 0.00000 1.00000SD(F-BM Return): 0-12m 0.00998 0.00775 0.00885 0.00366 0.01306SD(F-BM Return): 7-12m 0.00982 0.00717 0.00899 0.00321 0.01318Risk Shift (7-12m/0-6m) 1.08741 0.99157 0.48822 0.75418 1.30470SD(Fund Return: 0-12mo) 0.01463 0.01062 0.01166 0.00549 0.02145SD(Fund Return: 0-6m) 0.01383 0.00976 0.01157 0.00565 0.01877Institutional Fund 0.63422 1.00000 0.48170 0.00000 1.00000F-BM Return: 0-6m -0.00649 -0.00410 0.04408 -0.01942 0.00629FF3F Return: 0-12m -0.00080 -0.01062 0.10623 -0.06729 0.03651FF3F Return 0-6m 0.00341 -0.00303 0.06163 -0.03561 0.03178I(F-BM Return: 0-12m>0) 0.33545 0.00000 0.47219 0.00000 1.00000I(F-BM Return: 0-6m<0) 0.63748 1.00000 0.48077 0.00000 1.00000Fund Size 19.93131 19.92524 1.63502 18.83335 21.07163Expense Ratio 1.09444 1.06000 0.44251 0.77000 1.3950012B-1 Marketing Fee 0.20947 0.01240 0.26775 0.00000 0.40000Expense Waiver 0.04365 0.00000 0.19162 0.00000 0.01691Ln(Age) 2.08758 2.19722 0.51336 1.79176 2.39790Turnover Ratio 0.76398 0.53000 0.78214 0.25440 1.00000N-1A Fund Family Size 4.65899 2.00000 6.73363 1.00000 5.00000Big 5/6 Auditor 0.92320 1.00000 0.26631 1.00000 1.00000Market Beta 0.54872 0.55112 0.39906 0.18168 0.90239Observations 5,208

Regression variable descriptive statistics are provided above. Panel A provides the descriptive statistics for the fund flow regression variables over the years 1996-2000. Panel Bprovides the descriptive statistics for the risk model regression variables over the years 1994-1996, 1998-2000. All continuous variables are winsorized at 1% and 99%. See AppendixA for definitions of all variables.

46

Table 2: Correlation Tables - Market Share Change & Risk Models

Panel A: Market Share Change Models - Correlation Table for Fund Years 1996-2001(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

(1) ∆ Mkt. Share: 13-18m 1.000 0.641*** 0.177*** 0.106*** 0.261*** 0.173*** -0.216*** -0.290*** 0.068*** 0.069*** 0.054*** 0.065*** -0.038** 0.094***(2) ∆ Mkt. Share: 7-12m 0.681*** 1.000 0.279*** 0.137*** 0.423*** 0.229*** -0.148*** -0.219*** 0.026* 0.044*** 0.049*** 0.036** -0.017 0.099***(3) F-BM Return: 0-12m 0.231*** 0.302*** 1.000 0.641*** 0.395*** 0.263*** 0.008 -0.040*** 0.007 0.036** -0.022 -0.093*** -0.072*** 0.013(4) F-BM Return: 0-6m 0.114*** 0.137*** 0.633*** 1.000 0.304*** 0.315*** 0.008 -0.023 -0.047*** -0.044*** -0.040*** -0.074*** -0.041*** -0.003(5) FF3F Return: 0-12m 0.172*** 0.269*** 0.499*** 0.397*** 1.000 0.730*** 0.002 -0.090*** 0.024* 0.045*** 0.070*** -0.034** -0.049*** 0.138***(6) FF3F Return 0-6m 0.115*** 0.157*** 0.361*** 0.430*** 0.784*** 1.000 0.048*** -0.015 0.141*** 0.126*** 0.133*** 0.010 -0.060*** 0.117***(7) Fund Size -0.100*** 0.004 -0.009 -0.005 0.027* 0.073*** 1.000 0.895*** 0.169*** 0.163*** 0.184*** -0.196*** -0.007 0.303***(8) Fund Share (bps) -0.108*** 0.038** -0.049*** -0.050*** -0.004 0.035** 0.611*** 1.000 0.106*** 0.109*** 0.229*** -0.217*** 0.036** 0.108***(9) Fund Variance: 0-12m -0.055*** -0.054*** 0.052*** 0.080*** 0.247*** 0.269*** 0.133*** 0.039** 1.000 0.975*** 0.812*** 0.323*** -0.151*** 0.209***

(10) Fund Variance: 0-6m -0.086*** -0.076*** 0.074*** 0.088*** 0.255*** 0.259*** 0.127*** 0.033* 0.933*** 1.000 0.808*** 0.314*** -0.155*** 0.203***(11) Market Beta 0.025 0.041** 0.040** -0.021 0.198*** 0.254*** 0.237*** 0.221*** 0.671*** 0.586*** 1.000 0.278*** -0.094*** 0.132***(12) Expense Ratio -0.005 -0.032* -0.007 -0.035** -0.013 0.025 -0.226*** -0.192*** 0.243*** 0.199*** 0.256*** 1.000 0.411*** 0.016(13) 12B-1 Marketing Fee -0.013 0.001 -0.048*** -0.018 -0.051*** -0.066*** -0.010 -0.035** -0.125*** -0.117*** -0.111*** 0.472*** 1.000 -0.046***(14) Ln(Age) 0.017 0.036** 0.043** 0.029* 0.088*** 0.100*** 0.260*** 0.171*** 0.142*** 0.140*** 0.124*** -0.008 -0.051*** 1.000

Panel B: Risk Models - Correlation Table for Fund Years 1994-1996, 1998-2000(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

(1) Voluntary BM Disclosure 1.000 -0.096*** 0.058*** -0.162*** 0.009 0.012 0.088*** 0.015 -0.003 -0.063*** -0.154*** -0.005 0.028* 0.137*** 0.054*** 0.093***(2) BM , Best Fit Index -0.096*** 1.000 0.242*** 0.149*** 0.074*** 0.059*** 0.207*** -0.015 -0.013 0.180*** -0.077*** -0.015 0.290*** -0.077*** -0.061*** 0.167***(3) SD(F-BM Returns: 0-12mo) 0.073*** 0.247*** 1.000 -0.011 -0.059*** 0.082*** 0.759*** 0.050*** 0.060*** 0.320*** -0.138*** 0.073*** 0.148*** -0.034* -0.048*** 0.587***(4) Risk Shift (7-12m/0-6m) 0.017 0.007 0.045** 1.000 -0.004 0.024 0.030* -0.001 0.210*** 0.127*** 0.227*** 0.061*** 0.098*** -0.067*** 0.050*** 0.046***(5) Institutional Fund -0.162*** 0.149*** -0.019 0.014 1.000 0.309*** -0.027 -0.044** 0.024 -0.063*** -0.029* -0.018 0.016 0.055*** 0.060*** 0.001(6) F-BM Return: 0-6m 0.010 0.068*** 0.015 -0.053*** -0.005 1.000 0.153*** -0.108*** 0.055*** 0.027 -0.088*** 0.206*** 0.061*** -0.019 0.018 0.189***(7) FF3F Return: 0-12m 0.017 0.083*** 0.217*** -0.065*** 0.018 0.398*** 1.000 0.116*** 0.145*** 0.321*** -0.152*** 0.195*** 0.170*** -0.061*** -0.037** 0.821***(8) SD(Fund Return: 0-12mo) 0.086*** 0.228*** 0.711*** 0.107*** 0.021 0.103*** 0.341*** 1.000 0.027 0.050*** 0.023 0.018 0.004 0.013 -0.011 0.091***(9) Fund Size 0.002 -0.028* 0.046** 0.054*** 0.217*** 0.016 0.077*** 0.138*** 1.000 -0.211*** -0.005 0.312*** -0.024 -0.169*** 0.141*** 0.159***

(10) Expense Ratio -0.050*** 0.192*** 0.314*** 0.029* 0.119*** -0.032* 0.043** 0.286*** -0.266*** 1.000 0.400*** 0.001 0.221*** 0.008 -0.059*** 0.291***(11) 12B-1 Marketing Fee -0.154*** -0.058*** -0.117*** 0.043** 0.247*** 0.004 -0.067*** -0.117*** 0.020 0.482*** 1.000 -0.043** -0.045** -0.049*** 0.003 -0.104***(12) Ln(Age) 0.007 -0.048*** 0.094*** 0.021 0.038** -0.015 0.172*** 0.196*** 0.291*** -0.009 -0.030* 1.000 0.050*** -0.085*** 0.008 0.159***(13) Turnover Ratio 0.053*** 0.240*** 0.083*** 0.007 0.079*** 0.056*** 0.066*** 0.100*** -0.042** 0.170*** 0.011 0.046*** 1.000 0.031* 0.072*** 0.129***(14) N-1A Fund Family Size 0.258*** -0.070*** 0.125*** -0.004 -0.167*** 0.070*** 0.029* 0.095*** -0.168*** 0.124*** -0.077*** 0.008 0.069*** 1.000 0.020 -0.090***(15) Big 5/6 Auditor 0.054*** -0.061*** -0.034* 0.000 0.050*** 0.054*** 0.022 -0.021 0.159*** -0.072*** 0.027* 0.014 0.072*** 0.063*** 1.000 -0.041**(16) Market Beta 0.089*** 0.192*** 0.489*** 0.071*** 0.049*** 0.056*** 0.286*** 0.736*** 0.157*** 0.288*** -0.092*** 0.157*** 0.050*** 0.047*** -0.048*** 1.000

See Appendix A for variable definitions. Pearson correlations are shown in the below diagonal and Spearman correlations above. Significance levels are as follows: *** p<0.01, ** p<0.05, * p<0.1.

47

Table 3: Mutual Fund Investor Model: Performance/Fund Share in the Post-rule Change Period

(1) (2) (3)VARIABLES ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 7-12mo

F-BM Return: 0-12m 0.0310***(14.99)

F-BM Return: 7-12m 0.0180***(13.86)

F-BM Return: 0-6m 0.0311***(8.293)

Observations 2,897 2,897 2,897Adjusted R-squared 0.072 0.062 0.023Chi-Sq Test 66.60*** 8.60***P-value (0.000) (0.005)

This table reports univariate regression estimates of fund investors’ response to excess benchmark re-turns in the three years following the 1998 rule change. Fund market share change is calculated followingSpiegel and Zhang (2013). Six-month excess benchmark returns are annualized for comparative pur-poses. Chi-squared tests reported in Model (1)—the calendar year model—and Model (3)—the mid-yearadjustment model—represent coefficient comparisons with Model (2)—the semester model. For this ta-ble, market share changes are reported in basis points and excess benchmark returns are reported intotal percent (i.e., X%) for ease of presentation. See Appendix A for variable definitions. T-statisticscalculated using clustered standard errors by fund are included in brackets. Two-tailed p-values arereported: *** p<0.01, ** p<0.05, * p<0.10.

48

Table 4: Fund Market Share Change in Response to Annual Performance, Pre- vs. Post-rule Change

Institutional/Retail Fund Pooled Pooled Pooled Retail Retail Institutional InstitutionalVoluntary BM Disclosure? Pooled Voluntary Non-voluntary Voluntary Non-voluntary Voluntary Non-voluntary

(1) (2) (3) (4) (5) (6) (7)VARIABLES ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo ∆ Mkt. Share: 13-18mo

F-BM Return: 0-12m (%) 0.011** 0.022*** 0.007 0.016 0.008 0.037** 0.005(2.57) (2.70) (1.30) (1.59) (1.00) (2.39) (0.74)

F-BM Return: 0-12m (%) × Post 0.015*** 0.013 0.015*** 0.005 0.015 0.015 0.016**(3.17) (1.48) (2.66) (0.52) (1.64) (0.92) (2.32)

FF3F Return: 0-12m (%) 0.037*** 0.042*** 0.033*** 0.026*** 0.028*** 0.066*** 0.035***(8.53) (5.31) (6.53) (3.11) (3.84) (3.76) (5.45)

FF3F Return: 0-12m (%) × Post -0.030*** -0.033*** -0.027*** -0.021*** -0.029*** -0.053*** -0.027***(-7.16) (-4.21) (-5.38) (-2.63) (-3.39) (-3.07) (-4.34)

Post -0.023 -0.301*** 0.073 -0.218 0.237*** -0.401** 0.006(-0.49) (-2.78) (1.40) (-1.62) (3.89) (-2.26) (0.08)

Fund Size -0.066*** -0.036 -0.078*** -0.044 -0.049* -0.045 -0.096***(-3.91) (-1.09) (-3.68) (-1.31) (-1.84) (-0.83) (-3.29)

Fund Share (bps) -0.015** -0.021** -0.013 0.001 -0.013 -0.024* -0.012(-2.02) (-2.00) (-1.23) (0.19) (-0.68) (-1.86) (-0.99)

Fund Variance: 0-12m -3.507*** -4.198*** -3.091*** -4.454*** -2.565*** -3.561** -3.293***(-6.91) (-3.86) (-5.66) (-2.91) (-3.71) (-2.03) (-4.80)

Market Beta -0.126 -0.586** -0.029 0.081 -0.121 -1.442*** 0.048(-1.02) (-2.42) (-0.20) (0.29) (-0.61) (-3.44) (0.25)

Expense Ratio -0.247*** -0.376*** -0.203*** -0.218* -0.193** -0.494** -0.134*(-4.62) (-3.11) (-3.30) (-1.74) (-2.33) (-2.08) (-1.71)

12B-1 Marketing Fee 0.240*** 0.283 0.259*** 0.263 -0.025 0.299 0.273**(2.78) (1.52) (2.61) (1.52) (-0.23) (1.20) (2.18)

Ln(Age) 0.077** 0.023 0.094** 0.062 -0.009 0.004 0.127**(2.04) (0.27) (2.24) (0.73) (-0.14) (0.03) (2.53)

Observations 5,712 1,435 4,277 707 1,353 728 2,924Adjusted R-squared 0.106 0.153 0.090 0.115 0.116 0.199 0.088Fund Category FE Yes Yes Yes Yes Yes Yes Yes

This table reports the incremental effect of increased performance disclosure on market share changes due to investor fund flows in the three years surrounding the rule change (1996-2001). Fund market share change iscalculated following Spiegel and Zhang (2013). Model (1) reports the pooled, cross-sectional regression estimate of changes in market share on annual excess benchmark performance, with controls included. Models (2)and (3) split the regression in Model (1) on whether the fund voluntarily discloses a primary benchmark in the pre-rule change period. Models (4)-(7) further split regressions on institutional and retail fund identification.Fund category fixed effects are included in all regressions, and are defined using "Global Fund Category" from Morningstar Direct. For this table, market share changes are reported in basis points and excess benchmarkreturns/Fama-French risk-adjusted returns are reported in total percent (i.e., X%) for ease of presentation. See Appendix A for variable definitions. T-statistics calculated using clustered standard errors by fund are includedin brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10.

49

Table 5: Fund Market Share Change in Response to Mid-year Performance, Pre- vs. Post-rule Change

Institutional/Retail Fund Pooled Pooled Pooled Retail Retail Institutional InstitutionalVoluntary BM Disclosure? Pooled Voluntary Non-voluntary Voluntary Non-voluntary Voluntary Non-voluntary

(1) (2) (3) (4) (5) (6) (7)VARIABLES ∆ Mkt. Share: 7-12mo ∆ Mkt. Share: 7-12mo ∆ Mkt. Share: 7-12mo ∆ Mkt. Share: 7-12mo ∆ Mkt. Share: 7-12mo ∆ Mkt. Share: 7-12mo ∆ Mkt. Share: 7-12mo

F-BM Return: 0-6m (%) 0.021** 0.061*** 0.009 0.030 0.026 0.102*** -0.001(2.16) (3.23) (0.75) (1.44) (1.53) (2.68) (-0.09)

F-BM Return: 0-6m (%) × Post 0.028** -0.005 0.039*** -0.003 0.006 -0.001 0.053***(2.25) (-0.16) (2.82) (-0.10) (0.33) (-0.02) (2.98)

FF3F Return 0-6m (%) 0.048*** 0.033** 0.050*** 0.025 0.033*** 0.039 0.059***(5.33) (2.00) (4.90) (1.37) (2.95) (1.05) (4.23)

FF3F Return: 0-6m (%) × Post -0.012 0.007 -0.015 0.009 -0.033** 0.004 -0.012(-1.21) (0.33) (-1.51) (0.43) (-2.32) (0.09) (-0.91)

Post 0.183*** 0.031 0.218*** 0.120 0.154*** 0.056 0.260***(3.02) (0.21) (3.17) (0.72) (2.75) (0.23) (2.74)

Fund Size -0.079*** -0.072 -0.069* -0.050 -0.048 -0.117 -0.081(-2.69) (-1.31) (-1.77) (-0.98) (-1.33) (-1.50) (-1.49)

Fund Share (bps) 0.009 0.017 0.002 0.072*** 0.007 0.010 0.001(0.71) (0.93) (0.09) (4.28) (0.26) (0.55) (0.03)

Fund Variance: 0-6m -5.729*** -7.433*** -4.902*** -8.834*** -2.120** -5.948** -5.862***(-6.85) (-4.16) (-5.39) (-3.17) (-2.53) (-2.42) (-4.87)

Market Beta 0.634*** 0.178 0.727*** 0.891* 0.305 -0.707 0.929***(3.42) (0.43) (3.52) (1.67) (1.37) (-1.01) (3.39)

Expense Ratio -0.299*** -0.309 -0.284*** 0.028 -0.266*** -0.503 -0.220*(-3.79) (-1.58) (-3.34) (0.15) (-3.23) (-1.23) (-1.89)

12B-1 Marketing Fee 0.336*** 0.085 0.417*** -0.196 0.019 0.262 0.502***(2.82) (0.33) (3.03) (-0.78) (0.14) (0.65) (2.83)

Ln(Age) 0.098* 0.008 0.126** 0.114 0.039 -0.051 0.157**(1.80) (0.07) (2.13) (0.83) (0.49) (-0.27) (2.19)

Observations 5,712 1,435 4,277 707 1,353 728 2,924Adjusted R-squared 0.056 0.064 0.054 0.120 0.023 0.057 0.068Fund Category FE Yes Yes Yes Yes Yes Yes Yes

This table reports the incremental effect of increased performance disclosure on market share changes from investor fund flows in the three years surrounding the rule change (1996-2001). Fund market share changeis calculated following Spiegel and Zhang (2013). Model (1) reports the pooled, cross-sectional regression estimate of changes in market share in the second half of the year on mid-year excess benchmark returns,with controls included. Models (2) and (3) split the regression in Model (1) on whether the fund voluntarily discloses a primary benchmark in the pre-rule change period. Models (4)-(7) further split regressions oninstitutional and retail fund identification. Fund category fixed effects are included in all regressions, and are defined using "Global Fund Category" from Morningstar Direct. For this table, market share changesare reported in basis points and excess benchmark returns/Fama-French risk-adjusted returns are reported in total percent (i.e., X%) for ease of presentation. See Appendix A for variable definitions. T-statisticscalculated using clustered standard errors by fund are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10.

50

Table 6: Determinants of Pre-change Period Voluntary Benchmark Disclosure

(1) (2) (3)VARIABLES Voluntary BM Disc. Voluntary BM Disc. Voluntary BM Disc.

Fund Returns: 0-12m 0.169 0.134(1.011) (1.000)

FF3F Return: 0-12m 0.0940 -0.119(0.516) (-0.836)

Institutional Fund -0.0989***(-2.923)

Fund Size 0.0142(1.212)

Expense Ratio 0.0456(0.882)

12B-1 Marketing Fee -0.215***(-2.609)

Expense Waiver 0.0176(0.409)

Ln(Age) 0.0136(0.479)

Turnover Ratio 0.0193(0.997)

N-1A Fund Family Size 0.0158***(7.517)

Big 5/6 Auditor 0.123***(2.658)

Observations 2,307 2,307 2,307Adjusted R-squared 0.009 0.008 0.118Year FE Yes Yes YesFund Category FE Yes Yes Yes

This table reports estimates of a LPM regression of fund voluntary benchmark disclosure on fundcharacteristics. A fund is defined as voluntarily disclosing a primary benchmark in the pre-changeperiod if a single, third-party index is presented in the N-1A filings during the filing years 1994-1997.Model (1) reports the effect of excess benchmark returns on the choice to report a primary benchmarkin the pre-change period. Model (2) reports the effect of Fama-French three-factor risk-adjusted returnson the choice to report a primary benchmark in the pre-change period. Model (3) includes bothexcess benchmark returns and Fama-French three-factor risk-adjusted returns, with various other firmcharacteristics. Year fixed effects are included in all regressions. Fund category fixed effects are alsoincluded in all regressions, and are defined using "Global Fund Category" from Morningstar Direct. SeeAppendix A for variable definitions. T-statistics calculated using clustered standard errors by fund areincluded in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10.

51

Table 7: Determinants of a Non-Best Fit Index as the Benchmark

Panel A: Probability Distribution of Inst./Retail andVol./Non-vol. Pairings

(1)VARIABLES Benchmark , Best Fit Idx.

Institutional & Non-voluntary 0.745***(36.94)

Institutional & Voluntary 0.626*** (A)(15.43)

Retail & Non-voluntary 0.598*** (A)(B)(20.13)

Retail & Voluntary 0.527*** (B)(12.98)

Observations 1,036Adjusted R-squared 0.677

Panel B: Mean Pairwise Differences Between Groups of Selecting a Benchmark , Best Fit Index

Institutional & Non-voluntary Institutional & Voluntary Retail & Non-voluntary Retail & VoluntaryInstitutional & Non-voluntary 0

Institutional & Voluntary 0.119** 0(2.63)

Retail & Non-voluntary 0.147*** -0.028 0(4.09) (0.55)

Retail & Voluntary 0.219*** 0.099* 0.072 0(4.82) (1.73) (1.42)

Observations 1,036

Panel C: Pre-period Cross-sectional Regression Model of Non-Best Fit Index Benchmark SelectionDisclosure Period

All Funds All Funds All Funds Non-voluntary(1) (2) (3) (4)

VARIABLES Benchmark , Best Fit Idx. Benchmark , Best Fit Idx. Benchmark , Best Fit Idx. Benchmark , Best Fit Idx.

Institutional Fund 0.0826*** 0.0706** 0.0864**(2.880) (2.371) (2.495)

Non-voluntary BM Disclosure 0.0907*** 0.0785**(3.002) (2.506)

Fund Returns: 0-12m -0.00504*** -0.00511*** -0.00499*** -0.00597***(-5.557) (-5.565) (-5.438) (-5.061)

SD(Fund Return: 0-12mo) 0.0941*** 0.0892*** 0.0963*** 0.0583**(4.435) (4.215) (4.523) (2.248)

Ln(Age) -0.171*** -0.172*** -0.170*** -0.164***(-5.757) (-5.823) (-5.736) (-4.916)

Fund Size -0.00654 -0.0119 -0.0118 -0.0119(-0.695) (-1.232) (-1.231) (-1.061)

Turnover Ratio 0.0178 0.0129 0.0161 0.00364(1.326) (0.964) (1.209) (0.253)

Observations 2,307 2,307 2,307 1,704Adjusted R-squared 0.398 0.398 0.403 0.383Year FE Yes Yes Yes YesFund Category FE Yes Yes Yes Yes

This table reports the distribution of non-best fit index benchmark selection based on fund pairings between institutional/retail funds and funds that voluntarily/non-voluntarily disclosed a benchmark in the pre-change period. Panel (A) reports a LPM regression of an indicator equal to 1 if the fund selected a non-best fit indexbenchmark, and 0 otherwise, on indicators for the four pairs of institutional/retail and voluntarily/non-voluntarily disclosing funds. Panel (B) calculates pairwisedifferences among all the pairings. Panels (A) & (B) are based on aggregate fund characteristics. Panel (C) provides estimates of LPM regressions over the three-yearsprior to the announcement of the rule change, with control variables included. Model (1) reports a LPM regression estimate of the choice to select a non-best fitindex benchmark on an indicator equal to 1 if the fund did not voluntarily disclose a benchmark in the pre-change period, and 0 otherwise. Model (2) reports a LPMregression estimate of the choice to select a non-best fit index benchmark on an indicator equal to 1 if the fund serves institutional clients, and 0 otherwise. Model(3) repeats the regression with both indicators for institutional fund classification and non-voluntary disclosure in the pre-change period. Model (4) reruns Model (2)for the non-voluntary subsample. Year fixed effects are included in all Panel (C) regressions. Fund category fixed effects are included in all Panel (C) regressions, andare defined using "Global Fund Category" from Morningstar Direct. See Appendix A for variable definitions. For this table, return variables are reported in totalpercent (i.e., X%) for ease of presentation. T-statistics calculated using clustered standard errors by fund are included in brackets. Two-tailed p-values are reported:*** p<0.01, ** p<0.05, * p<0.10.

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Table 8: Probability of Outperforming the Benchmark—Strategic vs. Non-strategic Managers

Sample Period1994-1996 1998-2000 1994-1996; 1998-2000

(1) (2) (3)VARIABLES F-BM Return: 0-12m>0 F-BM Return: 0-12m>0 F-BM Return: 0-12m>0

BM , Best Fit Index 0.0422* 0.0801*** 0.0394*(1.75) (3.60) (1.85)

BM , Best Fit Index × Post 0.0509**(2.10)

Post -0.1915***(-10.01)

FF3F Return: 0-12m -0.0028 0.0004 -0.0000(-1.49) (0.55) (-0.04)

SD(Fund Return: 0-12mo) 0.1186*** 0.1252*** 0.1048***(4.50) (11.08) (11.17)

Ln(Age) -0.1298*** -0.0200 -0.0742***(-6.82) (-0.86) (-4.91)

Fund Size 0.0222*** 0.0144*** 0.0157***(3.71) (2.74) (3.90)

Turnover Ratio -0.0234* -0.0255** -0.0216**(-1.93) (-2.26) (-2.51)

Observations 2,307 2,901 5,208Adjusted R-squared 0.033 0.087 0.066Fund Category FE Yes Yes Yes

This table reports a LPM regression of an indicator equal to 1 if the fund outperforms its benchmark during the calendaryear, and 0 otherwise, on an indicator equal to 1 if the fund selected a non-best fit index as their benchmark, and 0 otherwise(strategic benchmark selection). Model (1) reports the regression estimate of positive excess benchmark performance onstrategic benchmark selection, for the pre-change period, including controls. Model (2) reports the regression estimate ofpositive excess benchmark performance on strategic benchmark selection, for the post-change period, including controls.Model (3) reports the regression estimate of positive excess benchmark performance on the indicator for strategic bench-mark selection interacted with an indicator equal to 1 for post-rule change period observations, and 0 otherwise, includingcontrols. Fund category fixed effects are also included in all regressions, and are defined using "Global Fund Category" fromMorningstar Direct. See Appendix A for variable definitions. For this table, return variables are reported in total percent(i.e., X%) for ease of presentation. T-statistics calculated using clustered standard errors by fund are included in brackets.Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10.

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Table 9: Excess Benchmark Return Risk and Strategic Benchmark Selection

(1) (2) (3) (4)VARIABLES SD(F-BM Return: 0-12m) SD(F-BM Return: 0-12m) SD(F-BM Return: 0-12m) SD(F-BM Return: 0-12m)

BM , Best Fit Index 0.0035*** 0.0006 0.0001(9.19) (1.26) (0.18)

BM , Best Fit Index × Post 0.0052*** 0.0053***(10.36) (10.29)

Post 0.0023*** -0.0012*** -0.0012**(9.37) (-2.87) (-2.51)

Fund Size 0.0001(0.98)

Ln(Age) -0.0006(-1.55)

Turnover Ratio 0.0005***(2.65)

Market Beta -0.0003(-0.17)

Expense Ratio 0.0026***(6.04)

Observations 5,208 5,208 5,208 5,208Adjusted R-squared 0.338 0.331 0.373 0.390Fund Category FE Yes Yes Yes Yes

This table reports regression estimates of total fund excess benchmark return risk on the strategic selection of a benchmark. Model (1) reportsa univariate regression of total fund excess benchmark return risk on an indicator equal to 1 if the fund selected a non-best fit index as theirbenchmark, and 0 otherwise (strategic benchmark selection). Model (2) provides a regression estimate for total excess benchmark return risk on anindicator equal to 1 for post-rule change period observations, and 0 otherwise. Model (3) provides a regression estimate for total excess benchmarkreturn risk on the interaction of the indicator for strategic benchmark selection and the rule change period, to show the incremental effect of the rulechange. Model (4) adds control variables. Fund category fixed effects are included in all regressions, and are defined using "Global Fund Category"from Morningstar Direct. See Appendix A for variable definitions. T-statistics calculated using clustered standard errors by fund are included inbrackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10.

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Table 10: Fund Excess Benchmark Return Risk Sensitivity, Pre- vs. Post-rule Change

Performance SubsampleAll Funds All Funds F-BM Ret. 0-6m<0 F-BM Ret. 0-6m≥0

(1) (2) (3) (4)VARIABLES SD(F-BM Return: 7-12m) SD(F-BM Return: 7-12m) SD(F-BM Return: 7-12m) SD(F-BM Return: 7-12m)

F-BM Return: 0-6m 0.063*** 0.046*** -0.019*** 0.041***(5.81) (4.17) (-2.79) (3.68)

F-BM Return: 0-6m × Post 0.037*** 0.006 -0.052*** 0.014(3.50) (0.53) (-7.01) (1.13)

Post 0.001*** -0.000 -0.002*** -0.000(3.33) (-1.08) (-5.99) (-0.40)

I(F-BM Return: 0-6m<0) 0.000 -0.000(0.85) (-0.33)

F-BM Return: 0-6m × I(F-BM Return: 0-6m<0) -0.093*** -0.072***(-5.81) (-5.03)

Post × I(F-BM Return: 0-6m<0) -0.002*** -0.001**(-3.77) (-2.18)

F-BM Return: 0-6m × Post × I(F-BM Return: 0-6m<0) -0.109*** -0.060***(-7.52) (-3.93)

SD(Fund Return: 0-6m) 0.307*** 0.378*** 0.228***(13.87) (13.99) (7.72)

FF3F Return 0-6m 0.005** 0.001 0.010**(2.16) (0.19) (2.39)

Fund Size 0.000 0.000 0.000(0.54) (0.34) (0.62)

Market Beta -0.005*** -0.008*** -0.002(-4.55) (-6.81) (-1.26)

Expense Ratio 0.002*** 0.002*** 0.002***(4.84) (3.61) (4.09)

Turnover Ratio 0.000* 0.000* 0.000(1.93) (1.92) (1.11)

Observations 5,208 5,208 3,320 1,888Adjusted R-squared 0.423 0.493 0.511 0.464Firm Category FE Yes Yes Yes Yes

This table reports regression estimates of second half-year risk-taking on mid-year excess benchmark performance. Model (1) provides the regression estimate of the standarddeviation of excess benchmark returns in the second half of the calendar year on a three-way interaction of first half-year excess benchmark returns, an indicator equal to 1for post-rule change period observations, and 0 otherwise, and an indicator equal to 1 if the fund underperformed its benchmark in the first half of the year, and 0 otherwise.Model (2) includes controls for Model (1). Models (3) and (4) split the sample on whether funds underperformed or overperformed the self-designated benchmark, respectively.Fund category fixed effects are also included in all regressions, and are defined using "Global Fund Category" from Morningstar Direct. See Appendix A for variable definitions.T-statistics calculated using clustered standard errors by fund are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10.

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Table 11: Risk Shifting after Underperforming the Benchmark, Pre- vs. Post-rule Change

Performance Subsample All Funds F-BM Ret. 0-6m<0 All FundsSample Period 1994-1996; 1998-2000 1994-1996; 1998-2000 1998-2000

(1) (2) (3)VARIABLES Risk Shift: (7-12m/0-6m) Risk Shift: (7-12m/0-6m) Risk Shift: (7-12m/0-6m)

I(F-BM Return: 0-6m<0) -0.087*** 0.051**(-5.85) (2.35)

I(F-BM Return: 0-6m<0) × Post 0.133***(5.38)

Post 0.156*** 0.300***(6.95) (17.21)

Fund Size 0.011*** 0.006 0.022***(2.72) (1.09) (3.90)

FF3F Return: 13-24mo 0.873*** 0.715*** 1.090***(13.77) (7.76) (15.19)

FF3F Return: 0-12m -0.752*** -0.800*** -0.708***(-9.61) (-7.67) (-7.72)

Market Beta 0.125*** 0.050 0.221***(3.22) (1.26) (3.66)

Expense Ratio -0.001 -0.041** 0.013(-0.09) (-1.98) (0.59)

Ln(Age) -0.081*** -0.088*** -0.152***(-6.04) (-5.38) (-6.79)

Turnover Ratio -0.001 0.010 0.006(-0.11) (0.89) (0.45)

Observations 5,208 3,320 2,901Adjusted R-squared 0.129 0.141 0.119Firm Category FE Yes Yes Yes

This table reports regression estimates of fund risk shifting in response to underperformance of the benchmark at mid-year.Model (1) provides a regression estimate of the ratio of the standard deviation of excess benchmark returns in the secondhalf of the year to the first half of the year on the interaction of an indicator equal to 1 if the fund underperformed itsbenchmark at mid-year (negative excess benchmark returns), and 0 otherwise, and an indicator equal to 1 for post-rulechange period observations, and 0 otherwise. Model (2) reruns Model (1) for the underperforming fund subsample. Model(3) reruns Model (1) for the post-rule change subsample. Fund category fixed effects are also included in all regressions,and are defined using "Global Fund Category" from Morningstar Direct. See Appendix A for variable definitions. T-statistics calculated using clustered standard errors by fund are included in brackets. Two-tailed p-values are reported:*** p<0.01, ** p<0.05, * p<0.10.

56

Appendix C: Figures

.005

.01

.015

SD

(F−

BM

Ret

urn:

0−

12m

)

1994 1995 1996 1997 1998 1999 2000 2001Year

BM: Best Fit Index BM: Not Best Fit Index

Trend Lines: Strategic Benchmark Selection

Figure 1: Trend lines plotted for funds selecting the best fit index as their benchmark versus those that

did not. Aggregate mean values for total fund excess benchmark return standard deviation by year are

plotted across time. The red line represents managers not selecting the best fit index as their benchmark

(strategic), whereas the blue line represents managers selecting the best fit index as their benchmark

(non-strategic). The shaded regions around the line plots represent the 95% confidence interval.

57

.005

.01

.015

.02

.025

.03

SD

(F−

BM

Ret

urn:

7−

12m

)

−.14 −.12 −.1 −.08 −.06 −.04 −.02 0 .02 .04 .06 .08 .1 .12 .14 .16F−BM Return: 0−6m

Post−rule Change Pre−rule Change

Fund Risk Taking in Response to Mid−year Performance

Figure 2: Plot of a local polynomial regression using kernel density estimation of the excess benchmark

return standard deviation in the second half of the year on the mid-year excess benchmark performance.

The y-axis is the excess benchmark return standard deviation in the second half of the year. The x-axis is

mid-year excess benchmark returns. The red line represents the post-change period estimate, and the blue

line represents the pre-change period estimate. The shaded regions around the line plots represent the 95%

confidence interval.

58

Appendix D: Benchmark Presentation Example

Source: Appendix B, SEC Sample N-1A (03/23/1998)

59