Disclosure Obfuscation: Evidence from Index Funds

Ed deHaan edehaan@uw.edu

Foster School of Business University of Washington

Yang Song songy18@uw.edu

Foster School of Business University of Washington

Chloe Xie

chloex@stanford.edu Graduate School of Business

Stanford University

Christina Zhu chrzhu@wharton.upenn.edu

The Wharton School University of Pennsylvania

November 25, 2019

Working paper, incomplete, and subject to change.

Abstract Theory suggests that managers use complex disclosures to obfuscate unfavorable information. We examine disclosure obfuscation among S&P 500 index funds that provide investors largely identical risks and pre-expense returns but deliver different net returns due to differences in fees. Our tests use bespoke measures of discretionary disclosure complexity that we develop using SEC guidance on the characteristics of clear mutual fund disclosures. Our research setting and custom complexity measures allow us to hold constant most variation in non-discretionary complexity across funds. We find compelling evidence that high-fee fund managers attempt to obfuscate their poor net performance in unnecessarily complex disclosures. Our study improves our understanding of price dispersion among functionally identical securities and informs regulatory discussions about improving mutual fund disclosures. Our study also provides several insights for the broader literature on disclosure obfuscation in the corporate setting.

Thanks to Alper Darendeli, Thomas Gilbert, Zachary Kaplan, Olivia Mitchell, Frank Zhou, and workshop participants at Georgia Tech, Texas A&M, UT Austin, and Wharton for helpful comments. We are grateful for support from Foster School of Business, University of Washington, Stanford Graduate School of Business, and The Wharton School, University of Pennsylvania. We thank Dengsheng Chen, Adam Greene, Andri Hail, Shibao Liu, Claudia Peng, and Jia Teo for excellent research assistance. We thank Jun Wu (WRDS) and Douglas King (Wharton Research Computing) for programming assistance. We thank mutual fund professionals from Fidelity for sharing their institutional insight. A previous version of this manuscript was circulated under the title “Discretionary Disclosure Complexity: New Predictions and Evidence from Index Funds.”

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1. Introduction

Do managers attempt to obfuscate unfavorable information with complex financial

disclosures? An econometric challenge in addressing this important question is isolating

managers’ disclosure choices from complexity driven by the underlying firm and transactions

(Bloomfield 2008; Leuz and Wysocki 2016). Loughran and McDonald (2016) note that “much of

the prior literature has used rather crude measures to control for firm-specific complexity” (p37).

Moreover, the extant findings on disclosure obfuscation remain mixed (Blankespoor et al.

2019).1 We provide new evidence on this question by examining a unique setting that allows us

to examine discretionary disclosure choices across entities with largely homogenous non-

discretionary fundamentals: S&P 500 index mutual funds.

Examining the “manager obfuscation” hypothesis in the context of S&P 500 index funds

provides three advantages. First, although S&P 500 funds have largely homogenous non-

discretionary characteristics such as gross investment returns, risks, and regulations, they have

heterogeneous net performance due to differences in fees. For example, Schwab’s S&P 500 fund

charged investors 2 bps in 2017 while Deutsche charged up to 508 bps, despite earning nearly

identical pre-expense index returns. Thus, examining index funds allows us to assess how

disclosures vary across funds with stronger versus weaker net performance, while holding

constant many drivers of non-discretionary complexity. Second, the SEC provides explicit

guidance on the characteristics of clear versus complex fund disclosures, which allows us to

develop bespoke measures of discretionary disclosure complexity to use in addition to standard

measures such as filing length and Fog. Third, Carlin (2009) analytically models why high-fee

funds create complexity to obfuscate worse net performance, which provides well-grounded

1 See Loughran and McDonald (2016), Li (2010), and Blankespoor et al. (2019) for surveys of the textual analysis literature, including several studies of firm performance and disclosure complexity not specifically cited herein.

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empirical predictions as well as new insights that are relevant to both the mutual fund and

corporate disclosure literatures.

Carlin (2009) models the link between net performance and managerial obfuscation in the

context of homogenous securities such as index funds. In Carlin (2009), complex disclosures

make it difficult for retail investors to understand and compare fees across funds. Some investors

find learning to be too costly, so they remain uninformed and invest randomly across funds.2

Other investors become informed and purchase the cheapest fund. The fraction of uninformed

investors is determined endogenously by aggregate complexity across funds. As depicted in

Figure 1, a mixed-strategy Nash competitive equilibrium emerges in which some funds choose

high fees and complex disclosures, and other funds choose low fees and less complex

disclosures.

Carlin (2009, p279) describes two types of disclosure complexity that high-fee funds can

use to obfuscate their weak net performance. The first, which we label “narrative complexity,”

makes disclosures less readable. This prediction is equivalent to the hypothesis in the corporate

disclosure literature that managers use unclear reports to obfuscate weak performance. The

second, complementary method is to create unnecessarily complex intra-fund structures that

make it harder for investors to process fund disclosures. For example, low-fee funds might have

a single class with a simple annual fee, while high-fee funds have multiple share classes with a

combination of tiered fees and charges. This form of “structural complexity,” which is entirely

discretionary for index funds, makes it difficult for investors use disclosures to identify the fees

2 Uninformed investors may be rational and aware that they may over-pay for a fund, but expected cost savings from a cheaper fund do not exceed the requisite processing costs. Prior research finds empirical evidence of random allocation of capital by uninformed investors. For example, Huberman and Jiang (2006) find that pension plan participants allocate contributions evenly across the funds offered. This finding supports the “1/n strategy” of naïve diversification as described by Benartzi and Thaler (2001) and as assumed in Carlin (2009).

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they must pay.

Based on Carlin (2009), our Predictions 1 and 2 are that high-fee index funds have

greater narrative complexity and structural complexity, respectively. Importantly, these

predictions are not causal in that high fees cause disclosure complexity or vice-versa. Rather, as

shown in Figure 1, the associations between fees and complexity are both a function of the

manager’s choice about their fund strategy.

Data and proxies

Our sample includes S&P 500 index funds over 1994-2017. We restrict our sample to

funds tracking the same index to hold constant gross performance and risks. After eliminating

funds without retail classes and necessary data, our sample comprises 38 funds and 458 fund-

year observations. Since S&P 500 index funds have nearly identical gross returns within a year,

variation in net performance is determined by the fund’s expenses. Thus, we measure net

performance as the fund’s total cost of ownership including all fees and charges (variable

Expense).3 We measure structural complexity using discretionary characteristics including the

fund’s number of different share classes and types of fees, combined into a principal component

Structural_Complexity.

We proxy for narrative complexity using measures of readability, where low readability

impedes investors’ ability to understand disclosures (Loughran and McDonald 2016). Our first

two measures of narrative complexity are discretionary and specifically tailored to fund

disclosures. FundsinFiling is the number of unique funds included in the prospectus (e.g., S&P

500 fund, Russell 3000 fund, etc.). Fund management companies choose whether to combine

3 Our models hold constant annual gross performance. Funds also have small tracking errors due to imperfections in tracking the index. Tracking errors are immaterial for our sample of S&P 500 funds and have no impact on our results (see Section 5). High tracking errors reported in the popular press are more common for funds tracking indices with illiquid securities (WSJ 2012).

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multiple funds, and the number of funds to combine, in a single prospectus. Our second proxy,

Repetition, measures the degree to which the summary section of the prospectus, which is

intended to be short and only contain key information, repeats language from the rest of the

prospectus. As discussed in Section 3, practitioners and the SEC argue that FundsinFiling and

Repetition make it difficult for retail investors to understand fund prospectuses. Importantly,

FundsinFiling and Repetition are largely independent of the fund’s nondiscretionary

characteristics such as turnover, tracking error, and size. They are also unaffected by structural

complexity; for example, structural complexity resulting from multiple fund classes likely

necessitates a longer prospectus, but it should not affect FundsinFiling or Repetition.

We also examine narrative complexity using standard readability measures from the

corporate disclosure literature. Following Loughran and McDonald (2014), our third measure of

readability is document size (DocSize). Our fourth measure is based on average sentence length

and number of unique words, combined as Wordiness (Loughran and McDonald 2011). We

measure DocSize and Wordiness for funds’ entire prospectuses and then specifically in the

prospectuses’ summary section expense disclosure. Example summary expense disclosures are

provided in Appendix B. We expect the summary expense disclosures to provide the best-

specified tests using these standard measures because they discuss the specific information (i.e.,

fees) that managers of high-fee funds attempt to obfuscate. Despite regulation of fund

disclosures, we observe substantial variation in our narrative complexity measures across funds.

Findings

We find evidence consistent with both predictions about disclosure complexity. Our

analyses are based on OLS regressions with year fixed effects. Within-year comparisons

eliminate common temporal variation in index returns, risks, regulations, and many other non-

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discretionary fund characteristics. For Prediction 1, we find that higher Expense is associated

with less readable prospectuses. We find even stronger results when examining the summary

expense disclosures within prospectuses. For Prediction 2, we find that higher Expense is

strongly associated with greater structural complexity. These findings are consistent with fund

managers creating unnecessarily complex disclosures to obfuscate poor performance, particularly

in the fee disclosures.

In additional analyses, we provide further support for part of the mechanism in the Carlin

(2009) model: that at least part of the fees that fund managers aim to obfuscate are discretionary

and are set in excess of operating costs. This feature of Carlin (2009) is a departure from much of

the corporate disclosure literature, in which performance is often exogenous. We decompose fees

into a non-discretionary and discretionary component. We find variation in discretionary fees,

which provides evidence of managerial choice in setting fees. Then we find the associations

between Expense and complexity persist after isolating funds’ discretionary fees, consistent with

managers jointly choosing both high fees and complex disclosures.

Contribution to the mutual funds literature

Over 8,000 mutual funds holding $18.7 trillion in assets were traded on U.S. exchanges

during 2017. Mutual funds hold 31% of the total US equity market value and comprise 61% of

US investors’ retirement savings (Investment Company Institute 2018). Despite their popularity,

many studies find that, after fees, mutual funds under-perform relative to a passive diversified

portfolio (e.g., Jensen 1968; Malkiel 1995; Gruber 1996; Fama and French 2010).

Specific to index funds, research documents heterogeneity in S&P 500 index fund fees,

and that retail investors often choose high-fee funds (Elton et al. 2004, Choi et al. 2010).4

4 Index funds are economically important unto themselves as that they comprise roughly 50% of all U.S. equity fund assets (Morningstar 2019).

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Investment in high-fee index funds is attributed to frictions affecting retail investors including

high search costs and low financial literacy (Hortaçsu and Syverson 2004; Sirri and Tufano

1998; Alexander et al. 1998; Choi et al. 2010). We build on recent papers investigating whether

funds strategically exploit search costs and other frictions to steer investors into high-fee funds.

Adams et al. (2012) find that funds with weak governance have more share classes, higher fees,

and bigger differences in fees between share classes, consistent with high fees being driven by

agency conflicts. Carlin (2009) introduces the idea that funds strategically create complex

disclosures to make it more difficult for investors to comparison shop. We extend the literature

by empirically examining whether funds manipulate narrative and structural complexity to

obfuscate high fees.5 Our findings should also inform the SEC’s efforts to improve the

readability of fund disclosures to help retail investors (SEC 2009a; 2009b; 2014; 2018).

Contributions to the corporate disclosure literature

Index fund disclosures and performance are obviously not perfect analogues to their

counterparts in the corporate setting. However, information asymmetry between managers and

investors exists in both funds and corporations, and both fund and corporate managers have

discretion over disclosure complexity. In light of these similarities, our paper provides three

insights for the corporate disclosure literature.

First, we provide new evidence that at least some managers use narrative complexity to

obfuscate unfavorable information. As most recently reviewed by Blankespoor et al. (2019),

papers on disclosure obfuscation use a variety of approaches to address the fundamental

5 Although not specifically focused on index funds, studies provide mixed evidence on whether mutual fund advertisements and communications improve or impair investment decisions (Huhmann and Bhattacharya 2005; Gallagher et al. 2015; Sirri and Tufano 1998; Khorana and Servaes 2012). Again not specific to index funds, descriptive analyses in Philpot and Johnson (2007) find heterogeneity in Flesch reading scores across a small sample of 60 mutual fund disclosures. Our results conflict with the associations in Philpot and Johnson (2007) in some regards, likely due to our focus on homogenous index funds and better-specified research design.

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challenge of controlling for non-discretionary complexity, but no approach is perfect and

conclusions about the choice to obfuscate remain mixed. We examine the question using a novel

approach and different setting, and our findings provide compelling evidence of obfuscation

among index funds. Despite dissimilarities between index funds and corporations, if managers

use narrative complexity to obfuscate performance for entities as seemingly simple to understand

as index funds, it seems highly plausible that they use narrative complexity to obfuscate

corporate performance.

Second, our finding about structural complexity has implications for corporate disclosure

research. Linguistics research to date has treated structural complexity as non-discretionary,

aiming to control for it when analyzing narrative complexity. Our findings indicate that index

funds manipulate structural complexity to mislead investors. It seems plausible that corporate

managers also manipulate structural complexity to obfuscate unfavorable information. For

example, they can create complicated intra-company structures or reporting segments as in the

Enron fraud. While the possibility of discretionary structural complexity has been considered in

the earnings management and governance literatures (e.g., Desai and Dharmapala 2006; McVay

2006; Feng et al. 2009; Dechow et al. 2011), this idea has not yet been considered in the

literature on disclosure linguistics.

Third, our finding that performance and disclosure complexity can be joint choices has

implications for the corporate disclosure literature. Much of the literature is silent on the causes

of poor performance or finds evidence indicating that poor performance is non-discretionary.6

However, it is plausible that, similar to the index fund setting, corporate managers choose some

6 For example, Li (2008) is silent on whether performance is influenced by managers. Merkley (2014) finds that managers provide longer disclosures to help inform investors about poor performance, indicating that performance is non-discretionary. Guay et al. (2016) find that financial statement complexity is non-discretionary, implying that any associated poor performance is also non-discretionary. Performance is explicitly exogenous in Asay et al. (2018).

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aspects of the performance they aim to obfuscate with complex disclosures. For example, a

manager may choose to shirk or extract perquisites, and obfuscate the impact on firm

performance via unclear disclosures (e.g., Bebchuk and Fried 2003).7 Lo et al. (2017) find that

corporate managers use complexity to mask earnings management, providing some evidence that

performance and obfuscation are codetermined. Given potential simultaneity, studies should be

cautious in interpreting associations between performance and disclosure complexity as evidence

that one causes the other.

A final contribution to the broader literature is to raise researchers’ awareness that mutual

funds can be a useful setting to test existing and new hypotheses in the accounting literature.

Mutual funds are economically important and produce annual prospectuses and financial

statements, but have thus far received very little attention in the literature. Studying the types of

investors who invest in index funds is especially important given the rise in passive investing and

inter-investor differences in disclosure processing costs (Blankespoor et al. 2019).

Limitations

Naturally, our study has simplifications and limitations. Although our within-year tests

across index funds likely eliminate vastly more non-discretionary complexity and confounds

than do cross-sectional tests across corporations, unobservable variation across funds still exists

and there is a non-zero chance of correlated omitted variable bias. Section 5.4 discusses several

unobserved differences across S&P 500 funds. A second limitation is that untabulated tests find

only weak evidence of cross-sectional variation in complexity based on competition, as predicted

by Proposition 2 of Carlin (2009). A likely explanation is that 93% of our sample is from 2000

7 Studies have shown that disclosures can serve as a governance mechanism and reduce agency problems (Bushman and Smith 2001). Disclosure quality is related to effective governance and investment efficiency (Karamanou and Vafeas 2005; Biddle et al. 2009; Cheng et al. 2013).

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onward, which Carlin (2009) notes is after the increase in competition in the S&P 500 market

(also see Hortaçsu and Syverson 2004). Finally, Carlin (2019) is silent on how funds choose

between low- versus high-fee strategies and this question is beyond the scope of our study.

2. Sample construction

2.1 Sample of index funds

We examine S&P 500 index mutual funds, which are the original and most prevalent type

of index fund. We do not expand our sample to other types of index funds such as Russell 1000

funds for several reasons. First, to pool heterogeneous index funds would undermine the

identification strengths of analyzing funds with homogenous investments and risks. Second, the

process of identifying index funds and matching with SEC disclosures is largely manual and

extremely time consuming. Third, there is no theoretical reason why our findings based on S&P

500 funds would not generalize to other index funds.

We start with a sample of all mutual funds in the CRSP survivorship-bias-free mutual

fund database on a monthly basis from 1994 to 2017. We identify index equity mutual funds

using the indicator “index_fund_flag.” We identify potential S&P 500 funds by searching for

“500” in fund names within this set of index funds. Next, we manually review fund names and

information online to remove ETNs/ETFs, sector funds, non-domestic equity funds, and funds

that have “500” in the name but do not track the S&P 500 index (e.g., smart beta funds).

Funds can have multiple share classes (CRSP identifier “fundno”) that invest in the same

assets but have different fees. Our analyses require us to identify which share classes are

available to retail investors versus institutions. CRSP fund names typically combine both the

fund and class component separated by a backslash or semicolon; for example, “Nationwide

Variable Insurance Trust: NVIT S&P 500 Index Fund; Class Y Shares.” We identify institutional

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share classes as those that have “Inst” or a variation of “Class I” or “Class Y” in the share class

portion of the CRSP name (Gil-Bazo and Ruiz-Verdú 2009; Pastor et al. 2015; Berk and van

Binsbergen 2015). All non-institutional classes are designated as retail classes. We aggregate

retail share classes to the fund level using CRSP’s Fund-Portfolio Map and a mapping file from

Pastor et al. (2015) based on Morningstar.

2.2 Fund Prospectuses

Our narrative disclosure complexity measures use funds’ statutory prospectuses, Forms

485BPOS and 485APOS. Funds file prospectuses when there are modifications to the fund’s

registration statement. They file Form 485BPOS annually, which include up-to-date costs,

performance, and other financial information. Funds file Form 485APOS when there are non-

routine amendments. Funds typically have one prospectus that includes all share classes,

although a single prospectus can contain multiple funds.

Our procedure to obtain prospectuses consists of three steps. First, we match each of our

sample funds to CIKs and Series IDs on a monthly basis. CIK is the SEC identifier for entities

that submit filings (e.g., Deutsche Bank DWS, as in Example 2 of Appendix B), and Series ID is

the SEC identifier for each fund (e.g., Deutsche’s S&P 500 Index Fund). We construct this list

through a combination of ticker-matching, name-matching, and manual searches on the SEC

website. Second, we extract the CIK and Series ID from all Form 485BPOS and Form 485APOS

filings, downloaded from WRDS. Third, we match the extracted values from the second step to

our list of CIKs and Series IDs from the first step.8

After matching prospectuses with narrative complexity data discussed below and

8 Each prospectus is matched to the CRSP fund-month observation in the month after the filing, and retained for future months until a new prospectus is filed. Merging datasets using tickers or names is noisy, so research assistants manually verified each match of fund-month to CIK, Series, and filing.

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averaging monthly data to obtain calendar-year units of observation, our final sample includes

458 fund-years comprising 38 unique funds.9

2.3 Expense Summary Disclosures

Some of our tests specifically examine the discussion of expenses from the summary

section of the prospectus (hereafter, “expense disclosures”). We obtain expense disclosure text

and numeric information from the Mutual Fund Prospectus Risk/Return Summary Data Sets,

which are available from the SEC for filings after 2010. The contents and lengths of tagged

elements of the summary section of Form 485BPOS are structured data extracted from XBRL.

Each SEC filing has an accession number, which we use to match filing data to our sample.

Because summary sections were not required before 2010, our sample of expense disclosures

consists of 123 fund-years and 26 unique funds. Example expense disclosures are provided in

Appendix B.

3. Variable Construction

Appendix A provides detailed variable definitions. Table 1 provides summary statistics

and Table 2 provides correlations.

3.1 Fund Performance

Fund performance is defined as the post-expense return, which for our index funds is

equal to the S&P 500 index return minus fund fees.10 Because all of our analyses are performed

within-year, cross-sectional variation in post-expense performance is determined by differences

in funds’ fees. Thus, instead of using post-expense returns to measure performance, we

9 Our samples are aggregated to the fund-level so are smaller than prior studies of index funds in which analyses are performed at the class-level (e.g., Hortaçsu and Syverson 2004). We must aggregate to the fund-level because prospectuses are typically only available at the fund-level. 10 Tracking errors, defined as the standard deviation of the difference between gross-fee monthly fund returns and the monthly return of the S&P 500 index, cause small differences in pre-expense performance and average four basis points per year in our sample. We discuss tracking errors below. Section 5 discusses robustness tests that include inefficiencies in tracking the S&P 500 index as part of the cost of ownership.

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(inversely equivalently) measure performance as the fund’s fees: variable Expense. The

advantage of using Expense instead of post-expense returns is that the descriptive statistics more

clearly present the fees charged by funds, which is the interesting source of variation. Our

Expense measure includes only retail share classes because retail investors are the target of the

disclosure obfuscation strategy in Carlin (2009). However, Section 5 discusses robustness tests

calculating Expense across all share classes.

Expense includes the annual expense, the annualized rear load, and the annualized front

load.11 Following Gil-Bazo and Ruiz-Verdú (2009) we amortize loads over a seven-year

expected holding period. For funds with multiple classes, we use the maximum cost across retail

share classes.12 Winsorized at 1% and 99%, the mean (median) Expense in our sample is 69 (56)

basis points and the minimum (maximum) Expense is 9 (227) basis points.

3.2 Narrative Complexity

Our narrative complexity measures are based on fund prospectuses. Prospectuses are the

primary source of information supplied by funds to current and prospective investors. A 2012

SEC study on financial literacy found that a majority of survey respondents either always, very

frequently, or frequently read prospectuses, and 24.6% cited the prospectus as their primary

source of information in deciding whether or not to invest (SEC 2012). Many survey respondents

noted that they did not read prospectuses more frequently because they were too complicated

11 The annual expense ratio includes 12b-1 fees and may also include waivers and reimbursements. Front loads are often structured to change with the level of investment along specified breakpoints. We use the maximum front load, when there are multiple levels front loads specified, in our annual front load calculation. The rear load could be structured as a redemption fee, which is charged when investors redeem shares, or as a contingent deferred sales charge. A contingent deferred sales charge is paid when investors sell shares within a specified number of years. In our sample, this number of years is always fewer than our assumed holding period of seven years. Thus, we do not include any contingent deferred sales charges in the annual rear load calculation for our main tests. When we reduce the assumed holding period to three and five years, in Section 5, we include contingent deferred sales charges when their holding periods are less than three and five years, respectively. 12 See Section 5 for robustness tests assuming different holding periods and calculations of cost across classes.

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(57.3%) or too long (51.4%). Similarly, a survey of 737 fund owners by Investment Company

Institute (2006) finds that the primary reason for not reading the complete document is that

prospectuses are “too long and difficult to understand” (p25). At the same time, survey

respondents overwhelmingly agreed that the information in prospectuses (and particularly fee

information) is important to know before making a purchase, and express strong demand for

summary sections (which were not required until after 2010).13 These survey responses are

consistent with the underlying premise of Carlin (2009): fund investors want to become informed

but find it too costly to read complex disclosures. Once an investor determines that prospectuses

are too complex to understand, it less likely that they attempt to read other prospectuses and

compare funds to each other. Thus, the greater the aggregate complexity in the market, the more

costly the search environment, and thus the greater the fraction of uninformed investors.

We also calculate narrative complexity measures specifically for the fund’s discussion of

expenses provided in the summary section of the prospectus. The prospectus summary section is

meant to provide “plain English” summaries of key information about expenses, performance,

risk, and strategy so that investors can make informed decisions (SEC 2009a, p17). The

summary section information is also required to be provided on funds’ websites in interactive

data format, so investors are likely to see it even if they do not access the full prospectus. In

addition, the summary section typically contains the same content as the (voluntary) “Summary

Prospectus” which funds mail or email directly to investors. Darendeli (2019) finds that retail

investor flows are more sensitive to performance and risk metrics when fund summaries are

disseminated, even though the same information in the summary is available in the prospectus.

Given that the summary section is intended to be investors’ primary source of information, we

13 These findings are generally consistent with evidence in Alexander et al. (1998) and SEC (2009a).

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expect that managers aiming to obfuscate high expenses will do so by manipulating the text of

the expense disclosure in the summary section.

3.2.1 Full Prospectus Narrative Complexity Measures

We measure narrative complexity using four proxies for readability, under the

assumption that less readable prospectuses require greater processing efforts (Loughran and

McDonald 2016). We obtain readability data from the WRDS SEC Analytics Suite and original

SEC filings.

Our first readability proxy is the number of funds included in the prospectus

(FundsinFiling). Investment companies have a choice to file prospectuses for multiple funds in

the same prospectus or in separate prospectuses. We include this measure because the SEC has

noted that “multiple fund prospectuses contribute substantially to prospectus length and

complexity, which act as barriers to investor understanding” (SEC 2009a). The SEC decided to

address this issue by requiring summary information to be presented for each fund separately in a

prospectus, but it did not prohibit multiple fund prospectuses despite investor focus groups’

comments that this type of presentation made reviewing information for a single fund difficult.

This measure is only available post-2006, when the SEC required investment company filers to

electronically identify the separate funds in their filings (SEC 2005).

Our second readability proxy is Repetition between the summary section and the rest of

the prospectus. The summary section is supposed to summarize, not repeat, detailed information

that is available elsewhere in the prospectus. The SEC has noted: “When the staff observes a

Summary Section that is long, dense and complex… the staff will remind the fund that the

Summary Section is intended to summarize the key information that is important to an

investment decision, with more detailed information presented elsewhere” (SEC 2014). Instead

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of clear and concise summaries, “the repetition of substantially the same—or identical—

information […] often highlights that a fund has not provided a summary” (SEC 2014). We

calculate Repetition as the percent of sentences in the summary section that are duplicated from

the rest of the prospectus. Following Merkley (2014), we define a sentence as repeated if the

cosine similarity between it and any sentence in the non-summary part of the prospectus is at

least 90%.

Our third measure of narrative complexity, DocSize, is based on document size. The SEC

has repeatedly raised concerns that fund prospectuses are too long and thus difficult for investors

to understand, and has even considered imposing page limits (SEC 2009; 2014). In the academic

literature, Loughran and McDonald (2014; 2016) argue that document size is a simple measure

of financial document readability, has fewer subjective assumptions, has less measurement error,

and generates less confounded results than linguistic-based readability proxies such as Fog. As in

the corporate setting, it seems plausible that fund managers obscure value-relevant information

in large amounts of text and data.

Our fourth measure of narrative complexity, Wordiness, is the first principal component

of combining the average words per sentence and the number of unique words in the document.

Again, the SEC has raised concerns that mutual fund prospectuses use complex language, and in

2009 introduced regulations intending “to improve mutual fund disclosure by providing investors

with key information in plain English in a clear and concise format” (SEC 2009a). Following

Loughran and McDonald (2014), we use words per sentence as a readability measure, rather than

the Fog index, which is composed of words per sentence and the percent of words that are

complex. The number of unique words is the count of words in the filing that appear in the

Loughran-McDonald Master Dictionary. We combine words per sentence and number of unique

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words into a single principal component for parsimony and because they likely capture similar

aspects of readability.14 We also combine DocSize and Wordiness via principal component

analysis into WordySize.

Our four readability proxies have different strengths and weaknesses. Our first two

readability proxies, FundsinFiling and Repetition, are not as driven by non-discretionary fund

characteristics or structural complexity. That is, regardless of how large the fund is or how

complex the fee structures chosen by funds are, funds have discretion over how much

information to repeat versus summarize in their summary sections, and they can choose how

many funds to discuss in one filing. Also, FundsinFiling and Repetition have strong internal

validity given that they are specifically cited by the SEC as making it difficult for investors to

read and understand prospectuses (SEC 2009a; 2014). Our latter two proxies, DocSize and

Wordiness, are commonplace in the disclosure and linguistics literature. These measures are easy

to replicate and generalize to corporate disclosure. However, a limitation of these measures is

that managers might not manipulate DocSize and Wordiness per se, but rather document size and

wordiness are a byproduct of structural complexity. For example, setting up complex fee

structures might necessitate longer filings and more complex language to describe these fee

structures.

3.2.2 Expense Disclosure Narrative Complexity Measures

Our measures of expense disclosure complexity mimic those for the Prospectus.

FundsinFiling and Repetition are the same for the expense disclosure as they are for the

prospectus. We do not have a file size for the expense disclosure, so DocSize_ExpNarra is

defined as the text length. Wordiness_ExpNarra is the same as for the prospectus, but we

14 All principal component combinations herein generate one component with an eigenvalue greater than one.

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calculate this measure ourselves because the WRDS SEC Analytics Suite does not have

measures for subsections. DocSize_ExpNarra and Wordiness_ExpNarra are highly correlated in

the expense disclosure sample (see Table 2), so when used in the same model we combine them

via principal component analysis into WordySize_ExpNarra.

3.3 Structural Complexity

We define structural complexity as the complexity of the fund’s fee structure. We use five

characteristics to measure structural complexity and combine these characteristics into a

Structural_Complexity principal factor. The characteristics used in our Structural_Complexity

measure are, to the best of our knowledge, entirely discretionary; i.e., we are unable to document

any regulatory requirements for a fund to have these characteristics. Figure 2 illustrates an

example fund strategy of high versus low structural complexity.

The first characteristic is the number of share classes within a fund. Having multiple share

classes with different levels and structures of fees gained popularity in the 1990s (Nanda et al.

2009). In Appendix B, fund example 1 has just one class. Example 2a has multiple classes, and

example 2b is additional information necessary to understand the fee structures for those classes.

While funds argue that classes cater to different clienteles, note that all of the classes in example

2b charge substantially higher fees than the single class in example 1.15 Consistent with Carlin

(2009), multiple classes create additional complexity that makes it difficult for investors to

comparison shop. Thus, our first characteristic is the total number of fund classes

(ShareClasses).

Our next two characteristics are indicator variables for complex fee structures: front

loads and 12b-1 fees. Front loads are complex because they require that the investor understands

15 Example 1 shows a one-year total ownership cost of 2 basis points. Example 2a shows one-year total ownership costs ranging from 35 basis points to 508 basis points.

18

that he/she needs to pay this fee on top of the annual expenses. Only 21% of investors surveyed

in the NASD Investor Literacy Survey (2003) know what a “no-load mutual fund” is. Front loads

are identified with the indicator FrontLoad. The second characteristic is the 12b-1 marketing and

distribution fee. An example of structural complexity is having no load but charging high 12b-1

fees, which enables funds to advertise the “no load” aspect of their fees while still having high

fees (Carlin 2009, p279). Thus, the variable NoLoad_12b1 is equal to one for funds without a

load but that do have a 12b-1 fee. Charging loads and 12b-1 fees is controversial (Bousquin

1999; Barber et al. 2005), and fee transparency has been the focus of recent SEC investigations

(Wall Street Journal 2019).16

Our final two structural complexity measures relate to the specific structures of front and

rear loads. Front loads can have breakpoints that determine the level of the load. For example,

the front load is high for investments below $1,000, lower for investments between $1,000 and

$5,000, etc. The more breakpoints, the more complex the structure (variable FrontLoadBreaks).

Funds may also include breakpoints in rear load Contingent Deferred Sales Charges (CDSC),

which decrease as the investor holds the shares for longer. CDSCBreaks is the number of

breakpoints that determine the CDSC rear load.

4. Research design and results

As depicted in Figure 1, Carlin (2009) predicts that funds’ fees and disclosure complexity

are simultaneous outcomes of the manager’s choice of fund strategy. Thus, high fees do not

cause complex disclosures or vice versa, but both are outcome variables caused by the manager’s

strategic choice.

16 “There really is not enough difference among index funds to justify that kind of a load,” Mattes [Brian Mattes, a spokesman at the Vanguard Group] says. “You have no chance of outperforming the market, and you'll forever be behind the market because of the compounding effect of what you paid” (Bousquin 1999).

19

As we cannot observe manager’s strategic choices using archival data, we cannot perform

typical regressions in which outcome variables Y (in our case, high fees and disclosure

complexity) are regressed on independent variables X (in our case, the manager’s strategic

choice). Instead, our empirical strategy is to test whether the two simultaneous outcome variables

from Carlin (2009) are associated with one another in the way predicted by the model.

Specifically, if managers aim to obfuscate high fees with complex disclosures, then we should

observe that high fees and disclosure complexity are positively associated.

We test for empirical associations between fees and complexity using OLS regressions.17

Because fees and complexity are both outcome variables, either can be the right-side or left-side

variable in an OLS model. We include fees as the left-side variable so that we can investigate

multiple complexity measures on the right-side at the same time:

Expense = a + Sbb*Complexityb +Sby*Yeary + e (1)

Expense is the total annual cost of ownership and Complexityb is one or more of our complexity

variables. bb estimates the statistical association between complexity and expenses, which we

predict is positive. Yeary fixed effects eliminate common temporal variation in the S&P 500

return, risks, regulations, and other non-discretionary characteristics of the index funds, as well

as common temporal trends in disclosure complexity and the index fund market. 18 Standard

errors are clustered by fund to accommodate cross-sectional correlation. We do not cluster by

year because our tests include just seven (24) years of data for the expense disclosures (full

prospectus). Given our small sample sizes, winsorizing is potentially ineffective in mitigating the

effects of extreme observations. Thus, we also perform robust regressions instead of OLS (Leone

17 OLS regressions are frequently used to test for causal relations between X and Y, but econometrically there is nothing causal about OLS coefficient estimates. OLS simply tests for empirical associations between X and Y and is therefore appropriate for our purposes. 18 All fund-year observations are on a consistent calendar-year basis.

20

et al. 2019).

Model (1) is intentionally parsimonious so we explicitly discuss the possibility of

correlated omitted variables. Omitted variable bias will occur if model (1) excludes a covariate

that causes the positive association between Expense and Complexity for reasons other than

managerial obfuscation. As discussed and tested in Section 5.1, we do not control for fund

characteristics such as size or portfolio turnover because: i) while fund characteristics likely

affect Expense they are unlikely to also affect Complexity in the way necessary to cause

correlated omitted variable bias; and ii) some fund characteristics are endogenous outcomes of

the fund strategy and therefore over-control the model.19 Temporal trends that could drive

associations between Expense and Complexity are eliminated by the year fixed effects. Finally,

due to lack of data, our models exclude variables that capture potential complementary strategies

taken by high-fee funds such as aggressive marketing through investment advisors. Section 5.4

discusses why complementary strategies to sell high-fee funds are not an internal validity threat

to our interpretation of bb.

4.1 Prediction 1: Narrative Complexity

Table 3, Panel A presents results of estimating model (1) for narrative complexity in the

prospectus. Columns (i) through (v) separately examine each of FundsinFiling, Repetition,

DocSize, Wordiness, and WordySize (a single principal component for DocSize and Wordiness).

The sample size changes across columns due to data availability. FundsinFiling and Repetition

have significantly positive associations with Expense. Wordiness has a marginally significant and

positive association with Expense. In columns (vi) and (vii), we examine each of the readability

19 For example, fund size likely affects operating costs and thus affects the expenses fund managers must charge. However, to generate omitted variable bias, fund size must also causally affect narrative complexity. There is no reason to believe that fund size should affect fund managers’ choice to summarize versus repeat information in the summary section (i.e., Repetition). Thus, omitting fund size should not cause correlated omitted variable bias.

21

measures together and find that FundsinFiling and Repetition are positive and significant using

OLS and all readability measures are significant using robust regression.20 We interpret this

evidence as consistent with managers obfuscating weak performance with narrative complexity.

Table 3, Panel B presents similar results for the expense disclosures. Columns (i) through

(v) find that each of our complexity measures is positively associated with Expense. Column (vi)

and (vii) present results considering all readability measures simultaneously. We find that all

measures are significantly positive using both OLS and robust regression.

Overall, the results in Table 3 are consistent with Prediction 1: that managers use

narrative complexity to obfuscate high fees. In particular, the results indicate that managers

generate complexity through having overly long and wordy prospectuses that often combine

many funds into one filing and repeat information between the summary section and the rest of

the filing. Results for FundsinFiling and Repetition are especially compelling given that these

disclosure choices are entirely discretionary and not necessitated by complex fee structures.

4.2 Prediction 2: Structural Complexity

Results for structural complexity are tabulated in Table 4. Panel A reports results for the

prospectus sample and Panel B for the expense disclosure sample. All measures of structural

complexity are highly significant, as is the principal component Structural_Complexity. These

results continue to hold when we use robust regression in column (vii). For brevity we tabulate

robust regression results only for the combined structural complexity measure, but untabulated

robust regression results for each individual measure are qualitatively unchanged from the

tabulated regressions. These results are consistent with managers obfuscating weak performance

with structural complexity.

20 Untabulated tests assess the variance inflation factors of all regressions that include multiple complexity measures. Unless otherwise noted, all variance inflation factors are below the common threshold of 10.

22

5. Additional Analyses and Robustness Tests

5.1 Additional Analyses of Complexity and Discretionary Fees

Carlin (2009) models both fees and disclosure complexity as choice variables, which

assumes managers exercise discretion to set fees above operating costs to extract rents from

investors. This is a departure from many corporate disclosure studies in which poor performance

is predetermined (e.g., an earnings miss), and then managers respond by creating unnecessarily

complex disclosures. This section explores whether fund managers use complex disclosures to

obfuscate discretionarily high fees, as in Carlin (2009).

The null hypothesis is that high fund fees are a result of non-discretionary factors such as

high operating costs. While finding evidence of discretionarily high fees would further support

the validity of the Carlin (2009) model, failing to find evidence of discretionary fees would not

invalidate our previous inferences that fund managers aim to obfuscate high fees with complex

disclosures. That is, even if high index fund fees are non-discretionary, high narrative and

structural complexity are still discretionary and can be used by fund managers to obfuscate high

fees.21

First, we decompose Expense into its discretionary and non-discretionary components.

Finding variation in the discretionary component of fees provides some evidence that managers

choose at least a portion of fees. Next, we test the association between the discretionary

component and Complexity. If fund managers choose high complexity to obfuscate

discretionarily high fees, then we should continue to observe a positive association between

Complexity and discretionary Expense.

Our decomposition of fees starts with the assumption that funds do not operate at a loss.

21 If fund managers create complex disclosure to obfuscate exogenous high fees, then model (1) becomes a “reverse regression” and remains valid for simply testing whether high fees and high complexity are positively associated.

23

Managers must charge fees to at least cover the fund’s operating costs, so we define the non-

discretionary component of Expense as the portion driven by operating costs. For index funds,

we expect that the primary determinants of operating costs are fund size and turnover (Elton et

al. 2018), but we also include tracking error for completeness.22 Larger funds are able to spread

fixed costs over more assets under management and thus have lower average costs. A fund that

has to trade more in order to track the S&P 500 index (i.e., has higher turnover) has higher

operating costs. Consistent with these expectations, a simple regression of Expense on net assets,

tracking error, and turnover generates an r-squared of 31% (untabulated). Thus, we use net

assets, tracking error, and turnover to model funds’ nondiscretionary costs. This regression also

provides evidence of variation in discretionary fees in our sample.

We then test for the association between discretionary fees and complexity. To avoid bias

from a two-step procedure which first estimates a discretionary Expense component, we estimate

a single, equivalent regression (Chen et al. 2018):23

Expense = a + Sbb*Complexityb + bc*Size + bd*Turnover + be*TE +Sby*Yeary + e

(2)

A significant issue with model (2) is that fund size is endogenous to the fund’s pricing

strategy, so using size to model non-discretionary expenses over-controls the model.

Specifically, in Carlin (2009) the low-fee funds capture the entire share of informed investors

and its equal share of uninformed investors, while the high-fee funds capture only their equal

share of the uninformed investors. Thus, fund size is endogenous such that low-fee funds will be

22 Our Table 1, consistent with Elton et al. (2018), finds that tracking errors are small, but we include tracking errors to reduce any concerns that they are a material driver of net performance. Tracking errors are insignificant in all results. Removing tracking errors has an immaterial impact on our results (untabulated), and tracking errors are further investigated in robustness tests in Section 5.3. 23 A two-step procedure would first regress fees on turnover, size, and tracking error, and then use the first-stage residuals as our measure of discretionary fees in a second-stage regression. Chen et al (2018) show that a one-step procedure produces the same coefficient estimates but better-specified standard errors.

24

larger than high-fee funds. Including fund size in model (2) likely eliminates part of the effect we

are looking for and potentially biases bb towards zero. In untabulated results we obtain

qualitatively similar results if we exclude net assets as a determinant.24

Table 5 investigates discretionary fees and narrative complexity by extending the

regressions from Table 3 using model (2). As expected, fund size is negatively associated with

Expense and turnover is positively associated. Generally speaking, the narrative complexity

coefficient estimates in Panels A and B of Table 5 are smaller than those in Table 3, but most

remain significant and are quite strong in Panel B. The attenuated results are consistent with a

portion of complexity being driven by non-discretionary fees. Furthermore, including net assets

as a control likely over-controls the model. Table 6 investigates discretionary fees and structural

complexity by extending the regressions from Table 4, and again finds smaller but significant

coefficient estimates. Overall and subject to the earlier caveats, the results in Tables 5 and 6 are

consistent with the prediction in Carlin (2009) that funds use narrative and structural complexity

to obfuscate discretionarily chosen high fees.

5.2 Analyzing Narrative and Structural Complexity Together

For descriptive purposes, additional analyses in Table 7 run regressions including both

narrative and structural complexity in the same model. Columns (i) through (v) of Panels A and

B include Structural_Complexity with a single measure of narrative complexity. The last two

columns in each panel include Structural_Complexity with FundsinFiling,Repetition, and

WordySize. Overall, Structural_Complexity remains highly significant while only some narrative

complexity measures remain significant. Specifically, Wordiness remains significant for the full

prospectus and Repetition is positive and marginally significant in both the full prospectus

24 “Qualitatively similar” means that the sign and significance of the coefficient of interest remains unchanged at the 10% level.

25

sample and the expense disclosure sample.

We do not draw inferences from the results in Table 7 for three reasons. First,

Structural_Complexity is highly positively correlated with most of our narrative complexity

measures, even FundsinFiling and Repetition which are not mechanically driven by complex

structures (correlations of roughly 37% and 36%, respectively, as reported in Table 2). Second,

consistent with our observed high correlations, Carlin (2009) notes that narrative and structural

complexity are likely two aspects of a single obfuscation strategy. If these two aspects of

complexity are typically used simultaneously, then it would not be surprising that one is

insignificant when they are tested together. Third, there is likely considerably less measurement

error in estimating structural complexity relative to narrative complexity, meaning that

comparing coefficients between the two can be misleading. Given these concerns, we find it

descriptively interesting that most of our narrative complexity measures are insignificant when

combined with Structural_Complexity, but we do not draw inferences from these findings.

5.3 Robustness Tests

We run several tests to reduce concerns about error in identifying retail share classes.

First, we re-run our tests using all available funds and classes and calculate Expense as the

maximum total ownership cost across all share classes. This method increases our sample to 41

funds, but the sample now includes funds that are not available to small investors, and

funds/classes that are only available to institutions or through employers. Second, we re-run all

of our tests identifying retail funds using CRSP’s “retail_fund” indicator. We do not use the

CRSP indicator in our main analyses because it is known to be noisy and has less precedent in

26

the literature than using a names-based approach.25 Neither method materially alters our results.

We run several tests to alleviate other concerns about the measurement of our Expense

variable. First, we calculate Expense as the average ownership cost of all retail classes, rather

than the maximum cost. Second, we calculate Expense as the gross difference between the S&P

500 index and the net retail investor returns to holding the fund, which captures any

inefficiencies in funds’ tracking of the S&P 500 index. We do not include deviations from the

S&P 500 index in our main Expense variable because it is not known ex ante. Third, we

construct versions of Expense that amortize one-time loads and fees over three- and five-year

holding periods, as opposed to the seven-year period in our main tests. Our results are

qualitatively similar using these alternative measures of Expense.

5.4 Discussion of Other Variation across S&P 500 Index Funds

Our setting and within-year research design allows us to hold constant many non-

discretionary factors that likely contribute to complexity in index funds. Further, our two custom

complexity measures, FundsinFiling and Repetition, are likely unaffected by many unobservable

factors that could drive document length and wordiness. However, unobservable, within-year

differences across our sample index funds still exist.

One concern is that we cannot observe other price dispersion strategies in the index fund

market, including the effects of advisors recommending high-fee funds, market segmentation

whereby fund classes are only available from specific institutions, or the effects of advertising

and media coverage (e.g., Jain and Wu 2000; Hortaçsu and Syverson 2004; Cronqvist 2006;

25 Based on our communications with WRDS, noise in the “retail_fund” indicator stems from CRSP’s aggregation of multiple mutual fund data collectors over the last 20 years. As a result, data consistency is poor, and the “retail_fund” variable is missing for some funds.

27

Koehler and Mercer 2009; Solomon et al. 2014; Gallagher et al. 2015).26 Research finds these

frictions also contribute to price dispersion, and funds’ exploitation of these frictions may be a

complementary strategy to disclosure obfuscation. For example, high-fee funds may compensate

brokers for steering uninformed investors to invest in these funds (Elton et al. 2004; Del Guercio

and Reuter 2014).

High-fee funds’ complementary strategies such as broker incentives are not a validity

threat to our interpretation of model (1). Instead, high-fee funds’ complementary strategies such

as broker incentives would appear as additions within the rightmost boxes in Figure 1. For

example, the upper box would read “simple disclosures, low fees, and no broker incentives” and

the lower box would read “complex disclosures, high fees, and high broker incentives.” Thus,

complementary strategies are additional but unobservable outcomes of fund managers’ strategy

choices. There is little reason to believe that complementary strategies are correlated omitted

variables that cause the association between Expense and Complexity for reasons other than

managers’ strategy choices.

Another unobserved source of variation across index funds is the extent to which the fund

generates revenue from securities lending, but we again do not view lending as a likely validity

threat. Evans et al. (2014) find widespread securities lending among index funds but, unlike

active funds, finds no association between lending and index fund net performance. McCullough

(2018) finds that lending improves net returns for small- and mid-cap index funds, but earns just

1 basis point for large-cap funds because the large-cap lending market is highly liquid. Thus,

while it is conceivable that greater lending drives marginally lower Expense among our sample

of S&P 500 index funds, the effect is likely to be immaterial. Further, even if variation in lending

26 Prior literature does not find support for the hypothesis that high-fee funds offer extra benefits such as helpful investment advice or customer service (Anagol and Kim 2012).

28

affects both Expense and some measures of narrative complexity (e.g., DocSize if funds discuss

securities lending in their prospectuses), the association should be negative and therefore is

unlikely to explain the positive associations between complexity and fees that we document.

Finally, our tailored measures of complexity, (FundsinFiling and Repetition, should be

independent of securities lending.

6. Summary and Conclusions

We study whether managers obfuscate unfavorable information in the setting of S&P 500

index funds. Our examination of homogenous S&P 500 index funds and within-year research

design allow us to hold constant non-discretionary factors that have been challenging to control

for in prior literature on discretionary disclosure complexity. Consistent with theory in Carlin

(2009), we find that funds with higher fees have higher narrative complexity in their disclosures,

using both traditional complexity measures and proxies tailored to mutual funds. We also find

that funds obfuscate high fees by ex ante designing complex entity structures. Finally, we find

evidence in support of the assumption in Carlin (2009) that funds simultaneously choose high

fees and high disclosure complexity. Our results provide insights about intentional disclosure

obfuscation for both the mutual fund and corporate disclosure literatures.

29

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Securities and Exchange Commission (2014). “Guidance Update: Guidance Regarding Mutual Fund Enhanced Disclosure.” Available at: https://www.sec.gov/investment/im-guidance-2014-08.pdf. Last retrieved on March 11, 2019

32

Securities and Exchange Commission (2018). “SEC Modernizes the Delivery of Fund Reports and Seeks Public Feedback on Improving Fund Disclosure.” Available at: https://www.sec.gov/news/press-release/2018-103. Last retrieved on March 23, 2019

Sirri E.R., & Tufano, P. (1998). “Costly search and mutual fund flows,” Journal of Finance 53: 1589-1622.

Solomon, D., Soltes, H. E., & Sosyura, D. (2014). “Winners in the spotlight: Media coverage of fund holdings as a driver of flows.” Journal of Financial Economics 113: 53–72.

Wall Street Journal (2012). “Watch an Index Fund's 'Tracking Error.'” Available at: https://www.wsj.com/articles/SB10001424052702303734204577466453629079534. Last retrieved on March 21, 2019

Wall Street Journal (2019a). “Dozens of Advisers Face Claims of Overcharging for Mutual Funds.” Available at: http://www.wsj.com/articles/dozens-of-advisers-face-claims-of-overcharging-for-mutual-funds-11548936000. Last retrieved on March 15, 2019

Wall Street Journal (2019b). “Firms to Pay $125 Million to Clients Over Fee-Disclosure Practices.” Available at: http://www.wsj.com/articles/firms-to-pay-125-million-to-clients-over-fee-disclosure-practices-11552335611. Last retrieved on March 15, 2019

33

Appendix A: Variable Definitions All continuous variables are winsorized at 1% and 99%.

Variable Name Description Source

AvgWordsPerSentence Average number of words per sentence, calculated as the number of words in the document divided by the total number of sentence termination characters after removing those associated with headings and abbreviations.

SEC Analytics Suite

AvgWordsPerSentence_ExpNarra

Average number of words per sentence. Calculated at the annual level as the mean of this value for each expense narrative obtained from the tag “ExpenseNarrativeTextBlock” in the TXT files of the SEC Mutual Fund Prospectus Risk/Return Summary Data Sets for Form 485 filings related to the fund over the year.

SEC XBRL submissions

CDSCBreaks Number of breakpoints in the Contingent Deferred Sales Charge (CDSC) rear load. Calculated at the fund level as the maximum number of breakpoints across share classes.

CRSP

DictionaryWords The word count from the filing, based on words appearing in the Loughran–McDonald master dictionary. Calculated at the annual level as the mean of this word count for each Form 485 filing related to the fund over the year.

SEC Analytics Suite

DictionaryWords_ExpNarra

The word count of total Loughran-McDonald master dictionary words. Calculated at the annual level as the mean of this word count for each expense narrative obtained from the tag “ExpenseNarrativeTextBlock” in the TXT files of the SEC Mutual Fund Prospectus Risk/Return Summary Data Sets for Form 485 filings related to the fund over the year.

SEC XBRL submissions

DocSize Form 485 document size in megabytes. SEC Analytics Suite

DocSize_ExpNarra

The length of the white space-normalized expense disclosure in the Form 485 risk/return summary section. Obtained from the tag “ExpenseNarrativeTextBlock” in the TXT files of the SEC Mutual Fund Prospectus Risk/Return Summary Data Sets

SEC XBRL submissions

Expense

Total annual ownership cost charged to the fund's retail investors, in percentage points. Calculated first at the share class level, then we choose the highest total ownership cost calculated over all retail share classes to get the fund level Expense. We use the CRSP fund name to identify share classes that are not institutional share classes. Each share class's total annual ownership cost is the sum of the following: annual expense ratio, total maximum front load divided by seven, and the redemption fee divided by seven.

CRSP

FrontLoad Indicator variable set to 1 if any share class of the fund, in a given year, has a front load.

CRSP

FrontLoadBreaks Number of breakpoints in the front load. Calculated at the fund level as the maximum number of breakpoints across share classes.

CRSP

FundsinFiling Log of the number of funds in the Form 485 filing. SEC Edgar filings

Size

Log of the fund’s total net assets (i.e., assets under management) in millions, inclusive of all share classes. First, calculated at the monthly level as the summed net assets across all classes. Then, calculated at the annual level as the mean of the monthly sums. Missing share class level net assets are set to 0.

CRSP

NoLoad_12b1 Indicator variable set to 1 if any share class of the fund, in a given year, has no front load but has a 12b-1 fee.

CRSP

34

Repetition Fraction of sentences in the summary section of Form 485 that are repeated in the rest of the document. A sentence is coded as "repeated" if the cosine similarity between it and any of the sentences in the rest of the document is 90% or greater.

SEC XBRL submissions, SEC Edgar filings

ShareClasses The number of unique share classes of the fund. CRSP

Structural_Complexity First principal component of combining the following six variables: ShareClasses, FrontLoadBreaks, CDSCBreaks, FrontLoad, and NoLoad_12b1.

CRSP, SEC Analytics Suite

TE

Tracking error, in basis points. First, calculate the gross-fee monthly return (net -fee return+1/12 expense ratio). Then, for a given year, use the all available monthly gross-fee returns and calculate the standard deviation of the difference between gross-fee monthly fund returns and the monthly return of the S&P 500 index.

CRSP, Bloomberg

Turnover Minimum (of aggregated sales or aggregated purchases of securities), divided by the average 12-month Total Net Assets of the fund. Calculated at the annual level as the mean of the monthly turnover.

CRSP

Wordiness First principal component of combining the following two variables: WordsPerSentence and DictionaryWords.

SEC Analytics Suite

Wordiness_ExpNarra First principal component of combining the following two variables: WordsPerSentence_ExpNarra and DictionaryWords_ExpNarra.

SEC XBRL submissions

WordySize First principal component of combining the following three variables: DocSize, WordsPerSentence and DictionaryWords.

SEC Analytics Suite

WordySize_ExpNarra First principal component of combining the following three variables: DocSize_ExpNarra, WordsPerSentence_ExpNarra and DictionaryWords_ExpNarra.

SEC XBRL submissions

35

Appendix B: Mutual Fund Disclosure Examples Example 1 is Summary Expense Disclosure for a low-complexity S&P 500 Index Fund. This fund (Schwab) has a

single class, no loads, and no 12b-1 fees. Example 2a is a Summary Expense Disclosure for a high-complexity S&P

500 Index Fund. This fund (Deutsche) has multiple classes with various combinations of fees and expenses.

Example 2b is seven additional pages of information from Deutsche’s Prospectus that is needed to understand the

classes and expenses summarized in Deutsche’s Summary Expense Disclosure. Schwab and Deutsche earned

virtually identical pre-expense returns during 2017.

Example 1: Summary Expense Disclosure for a Low-Complexity Fund (Schwab)

Schwab® S&P 500 Index FundTicker Symbol: SWPPX

Investment Objective

The fund’s goal is to track the total return of the S&P 500® Index.

Fund Fees and Expenses

This table describes the fees and expenses you may pay if you buyand hold shares of the fund. This table does not reflect anybrokerage fees or commissions you may incur when buying orselling fund shares.

Shareholder Fees (fees paid directly from your investment)None

Annual Fund Operating Expenses (expenses that you pay each year as a %of the value of your investment)

Management fees 0.02Other expenses None

Total annual fund operating expenses1 0.02

1 The information in the table has been restated to reflect current fees andexpenses.

Example

This example is intended to help you compare the cost of investingin the fund with the cost of investing in other funds. The exampleassumes that you invest $10,000 in the fund for the time periodsindicated and then redeem all of your shares at the end of thosetime periods. The example also assumes that your investment hasa 5% return each year and that the fund’s operating expensesremain the same. The figures are based on total annual fundoperating expenses after any expense reduction. The example doesnot reflect any brokerage fees or commissions you may incur whenbuying or selling fund shares. Your actual costs may be higher orlower.

Expenses on a $10,000 Investment

1 Year 3 Years 5 Years 10 Years

$2 $6 $11 $26

Portfolio Turnover

The fund pays transaction costs, such as commissions, when itbuys and sells securities (or “turns over” its portfolio). A higherportfolio turnover may indicate higher transaction costs and mayresult in higher taxes when fund shares are held in a taxableaccount. These costs, which are not reflected in the annual fundoperating expenses or in the example, affect the fund’s

performance. During the most recent fiscal year, the fund’sportfolio turnover rate was 2% of the average value of its portfolio.

Principal Investment Strategies

To pursue its goal, the fund generally invests in stocks that are

included in the S&P 500 Index†. It is the fund’s policy that undernormal circumstances it will invest at least 80% of its net assets( including, for this purpose, any borrowings for investmentpurposes) in these stocks; typically, the actual percentage isconsiderably higher. The fund will notify its shareholders at least 60days before changing this policy.

The fund generally will seek to replicate the performance of theindex by giving the same weight to a given stock as the index does.However, when the investment adviser believes it is in the bestinterest of the fund, such as to avoid purchasing odd-lots ( i.e.,purchasing less than the usual number of shares traded for asecurity), for tax considerations, or to address liquidityconsiderations with respect to a stock, the investment adviser maycause the fund’s weighting of a stock to be more or less than theindex’s weighting of the stock. The fund may sell securities that arerepresented in the index in anticipation of their removal from theindex, or buy securities that are not yet represented in the index inanticipation of their addition to the index.

The S&P 500 Index includes the stocks of 500 leading U.S. publiclytraded companies from a broad range of industries. Standard &Poor’s, the company that maintains the index, uses a variety ofmeasures to determine which stocks are listed in the index. Eachstock is represented in the index in proportion to its marketcapitalization.

The fund may invest in derivatives, principally futures contracts,and lend its securities to minimize the gap in performance thatnaturally exists between any index fund and its correspondingindex. This gap occurs mainly because, unlike the index, the fundincurs expenses and must keep a small portion of its assets in cashfor business operations. By using futures, the fund potentially canoffset a portion of the gap attributable to its cash holdings. Inaddition, any income realized through securities lending may helpreduce the portion of the gap attributable to expenses.

The fund may concentrate its investments in an industry or groupof industries to the extent that the index is also so concentrated.

† Index ownership – “Standard & Poor’s®,” “S&P®,” and “S&P 500®” are registered trademarks of Standard & Poor’s Financial Services LLC (S&P), and “DowJones®” is a registeredtrademark of Dow Jones Trademark Holdings LLC (Dow Jones) and have been licensed for use by S&P Dow Jones Indices LLC and its affiliates and sublicensed for certainpurposes by Charles Schwab Investment Management, Inc. (CSIM). The “S&P 500® Index” is a product of S&P Dow Jones Indices LLC or its affiliates, and has been licensedfor use by CSIM. The Schwab® S&P 500 Index Fund is not sponsored, endorsed, sold or promoted by S&P Dow Jones Indices LLC, Dow Jones, S&P, or their respective affiliates,and neither S&P Dow Jones Indices LLC, Dow Jones, S&P, nor their respective affiliates make any representation regarding the advisability of investing in the fund.

Schwab S&P 500 Index Fund | Fund Summary 1

36

Example 2a: Summary Expense Disclosure for a High-Complexity Fund (Deutsche)

Deutsche S&P 500 Index Fund

INVESTMENT OBJECTIVEThe fund seeks to provide investment results that, beforeexpenses, correspond to the total return of commonstocks publicly traded in the United States, as representedby the Standard & Poor’s 500 Composite Stock Price Index(S&P 500

®Index).

The fund invests for capital appreciation, not income; anydividend and interest income is incidental to the pursuit ofits objective.

The fund is a feeder fund that invests substantially all of itsassets in a “master portfolio,” the Deutsche Equity 500Index Portfolio (the “Portfolio”), which will invest directly insecurities and other instruments. The Portfolio has thesame investment objective and strategies as the fund.References to investments by the fund may refer toactions undertaken by the Portfolio.

FEES AND EXPENSES OF THE FUNDThese are the fees and expenses you may pay when youbuy and hold shares. You may qualify for sales chargediscounts if you and your immediate family invest, or agreeto invest in the future, at least $100,000 in Class A sharesin Deutsche funds or if you invest at least $250,000 inClass T shares in the fund. More information about theseand other discounts and waivers is available from yourfinancial advisor and in Choosing a Share Class (p. 34), SalesCharge Waivers and Discounts Available Through Interme-diaries (Appendix B, p. 74) and Purchase and Redemptionof Shares in the fund’s Statement of Additional Information(SAI) (p. II-16).

SHAREHOLDER FEES (paid directly from your investment)

A T C R6 S

Maximum sales charge (load)imposed on purchases, as % ofoffering price 4.50 2.50 None None None

Maximum deferred sales charge(load), as % of redemption proceeds None None 1.00 None None

Account Maintenance Fee (annually,for fund account balances below$10,000 and subject to certainexceptions) $20 None $20 None $20

ANNUAL FUND OPERATING EXPENSES(expenses that you pay each year as a % of the value of your investment)

A T C R6 S

Management fee 0.05 0.05 0.05 0.05 0.05

Distribution/service (12b-1) fees 0.24 0.25 0.99 None None

Other expenses1 0.30 0.30 0.26 0.35 0.29

Total annual fund operatingexpenses2 0.59 0.60 1.30 0.40 0.34

Fee waiver/expense reimbursement 0.00 0.00 0.00 0.05 0.00

Total annual fund operatingexpenses after fee waiver/expensereimbursement 0.59 0.60 1.30 0.35 0.34

1 ”Other expenses“ for Class T are based on estimated amounts for thecurrent fiscal year.

2The table and Example below reflect the expenses of both the fund andthe Portfolio.

The Advisor has contractually agreed through April 30,2019 to waive its fees and/or reimburse fund expenses,including expenses of the Portfolio allocated to the fund, tothe extent necessary to maintain the fund’s total annualoperating expenses (excluding certain expenses such asextraordinary expenses, taxes, brokerage, interest andacquired fund fees and expenses) at a ratio no higher than0.35% for Class R6. The agreement may only be termi-nated with the consent of the fund’s Board.

6Prospectus May 1, 2018 Deutsche S&P 500 Index Fund

EXAMPLEThis Example is intended to help you compare the cost ofinvesting in the fund with the cost of investing in othermutual funds. The Example assumes that you invest$10,000 in the fund for the time periods indicated and thenredeem all of your shares at the end of those periods. TheExample also assumes that your investment has a 5%return each year and that the fund’s operating expenses(including one year of capped expenses in each period forClass R6) remain the same. Although your actual costsmay be higher or lower, based on these assumptions yourcosts would be:

Years A T C R6 S

1 $ 508 $310 $ 232 $ 36 $ 35

3 631 437 412 123 109

5 764 576 713 219 191

10 1,155 981 1,568 500 431

You would pay the following expenses if you did notredeem your shares:

Years A T C R6 S

1 $ 508 $310 $ 132 $ 36 $ 35

3 631 437 412 123 109

5 764 576 713 219 191

10 1,155 981 1,568 500 431

PORTFOLIO TURNOVERThe fund pays transaction costs, such as commissions,when it buys and sells securities (or “turns over” its port-folio). A higher portfolio turnover may indicate highertransaction costs and may mean higher taxes if you areinvesting in a taxable account. These costs are notreflected in annual fund operating expenses or in theexpense example, and can affect the fund’s performance.During the most recent fiscal year, the Portfolio’s portfolioturnover rate was 6% of the average value of its portfolio.

PRINCIPAL INVESTMENT STRATEGYMain investments. Under normal circumstances, thePortfolio intends to invest at least 80% of assets, deter-mined at the time of purchase, in stocks of companiesincluded in the S&P 500® Index and in derivative instru-ments, such as futures contracts and options, that provideexposure to the stocks of companies in the index. ThePortfolio’s securities are weighted to attempt to make thePortfolio’s total investment characteristics similar to thoseof the index as a whole. The Portfolio may also hold short-term debt securities and money market instruments.

The S&P 500® Index is a well-known stock market indexthat includes common stocks of 500 companies fromseveral industrial sectors representing a significant portionof the market value of all stocks publicly traded in the US.

Stocks in the S&P 500® Index are weighted according totheir total market value. The fund is not sponsored,endorsed, sold or promoted by the Standard & Poor’s(S&P) Division of The McGraw-Hill Companies, Inc. Whilethe market capitalization range of the S&P 500® Indexchanges throughout the year, as of February 28, 2018, themarket capitalization range of the S&P 500® Index wasbetween $2.6 billion and $905.1 billion. The S&P 500®

Index is rebalanced quarterly on the third Friday of March,June, September and December.

Management process. Portfolio management uses quan-titative analysis techniques to structure the Portfolio toseek to obtain a high correlation to the index while seekingto keep the Portfolio as fully invested as possible in allmarket environments. Portfolio management seeks a long-term correlation between Portfolio performance, beforeexpenses, and the index of 98% or better (perfect correla-tion being 100%). Portfolio management uses anoptimization strategy, buying the largest stocks in the indexin approximately the same proportion they represent inthe index, then investing in a statistically selected sampleof the smaller securities found in the index.

Portfolio management’s optimization process is intendedto produce a portfolio whose industry weightings, marketcapitalizations and fundamental characteristics (price-to-book ratios, price-to-earnings ratios, debt-to-asset ratiosand dividend yields) closely replicate those of the index.This approach attempts to maximize the Portfolio’s liquidityand returns while minimizing its costs.

Derivatives. Portfolio management generally may usefutures contracts, which are a type of derivative (a contractwhose value is based on, for example, indices, curren-cies or securities), to keep cash on hand to meetshareholder redemptions or for other needs while main-taining exposure to the stock market.

The fund may also use other types of derivatives (i) forhedging purposes; (ii) for risk management; (iii) fornon-hedging purposes to seek to enhance potential gains;or (iv) as a substitute for direct investment in a particularasset class or to keep cash on hand to meet shareholderredemptions.

Securities Lending. The Portfolio may lend securities (upto one-third of total assets) to approved institutions, suchas registered broker-dealers, banks and pooled investmentvehicles.

MAIN RISKSThere are several risk factors that could hurt the fund’sperformance, cause you to lose money or cause the fund’sperformance to trail that of other investments. The fundmay not achieve its investment objective, and is notintended to be a complete investment program. An invest-ment in the fund is not a deposit of a bank and is notinsured or guaranteed by the Federal Deposit Insurance

7Prospectus May 1, 2018 Deutsche S&P 500 Index Fund

37

Example 2b: Additional Prospectus Information Needed to Understand the Classes in 2a (Deutsche)

38

39

Figure 1: Carlin (2009) Fund Strategies

The figure below depicts index fund managers’ strategic choices in Carlin (2009). In Carlin (2009), index fund

managers choose one of two strategies within a mixed-strategy Nash equilibrium.27 Under the “simple” strategy, the

manager chooses low fees and simple disclosures. The “complex” strategy is to choose high fees and complex

disclosures.

Complex strategy funds receive investment only from uninformed investors who cannot understand disclosures and

therefore invest randomly across both simple and complex strategy funds. Simple strategy funds also receive

investment from informed investors who are able to understand disclosures and identify the cheapest funds. The

fraction of uninformed investors is determined endogenously by aggregate disclosure complexity across all funds.

The model is competitive in that all funds earn equal profits in equilibrium.

27 We have simplified this figure to include just two strategies for illustration purposes. In Carlin (2009), funds choose fees according to a distribution and choose complexity as an increasing function of the fees.

40

Table 1: Descriptive Statistics This table presents summary statistics for unlogged variables. All variables are defined in Appendix A.

N Mean Std. Dev. P25 Median P75

Fund variables

Expense 458 0.689 0.526 0.200 0.554 1.150

TE 452 455.719 233.662 271.839 387.188 630.415

Size 458 8,065.09 25,812.86 270.17 1,213.78 2,772.67

Turnover 458 0.114 0.235 0.040 0.060 0.100

Stuctural complexity variables

ShareClasses 458 2.578 1.925 1.000 2.000 3.000

FrontLoadBreaks 458 1.821 2.704 0.000 0.000 5.000

CDSCBreaks 458 1.283 1.787 0.000 1.000 1.000

FrontLoad 458 0.298 0.456 0.000 0.000 1.000

NoLoad_12b1 458 0.566 0.495 0.000 1.000 1.000

Structural_Complexity 458 0.000 1.879 -1.500 -0.788 1.937

Prospectus narrative variables

FundsinFiling 286 11.834 10.588 3.404 8.333 15.917

Repetition 123 0.311 0.259 0.074 0.229 0.511

DocSize 458 4.165 4.274 0.908 2.515 6.056

WordsPerSentence 458 0.025 0.003 0.024 0.026 0.027

DictionaryWords 458 0.150 0.109 0.068 0.122 0.193

Wordiness 458 0.000 1.031 -0.579 0.226 0.725

WordySize 458 0.000 1.021 -0.546 0.213 0.686

Expense narrative variables

FundsinFiling 123 13.641 11.223 4.000 10.833 17.576

Repetition 123 0.311 0.259 0.074 0.229 0.511

DocSize_ExpNarra 123 322.099 210.325 105.000 239.250 525.753

WordsPerSentence_ExpNarra 123 24.929 6.224 20.000 24.000 28.333

DictionaryWords_ExpNarra 123 51.574 31.107 19.182 42.000 82.038

Wordiness_ExpNarra 123 0.000 1.323 -1.300 -0.653 1.111

WordySize_ExpNarra 123 0.000 1.636 -1.613 -0.872 1.573

Table 2: Correlations This table presents Pearson (Spearman) correlations above (below) the diagonal. P-values are presented in parentheses. Panel A presents results for the full prospectus sample, and Panel B presents results for the expense disclosure sample. All variables are defined in Appendix A. To facilitate comparisons, all variables (except Expense) in this table and the remaining tables are standardized to have a mean (standard deviation) of zero (one).

Panel A: Pearson-Spearman correlations, Full Prospectus Sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 Expense -0.070 -0.268 0.428 0.678 0.842 0.644 0.861 0.618 0.874 0.389 0.446 0.254 -0.098 -0.097 (0.136) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.037) (0.037) 2 TE -0.059 -0.123 0.023 -0.113 -0.037 -0.090 -0.038 -0.116 -0.089 -0.038 0.092 -0.162 0.121 0.119 (0.214) (0.009) (0.619) (0.016) (0.428) (0.056) (0.425) (0.014) (0.060) (0.523) (0.312) (0.001) (0.010) (0.011) 3 Size -0.443 -0.103 -0.079 -0.043 -0.178 -0.169 -0.178 0.058 -0.132 -0.122 -0.226 -0.056 -0.129 -0.120 (0.000) (0.029) (0.091) (0.354) (0.000) (0.000) (0.000) (0.212) (0.005) (0.040) (0.012) (0.230) (0.006) (0.010) 4 Turnover 0.036 0.168 -0.219 0.021 0.151 0.044 0.207 0.165 0.142 0.196 0.247 0.032 -0.007 -0.006 (0.443) (0.000) (0.000) (0.654) (0.001) (0.347) (0.000) (0.000) (0.002) (0.001) (0.006) (0.491) (0.878) (0.898) 5 ShareClasses 0.735 -0.133 -0.145 -0.054 0.727 0.520 0.691 0.520 0.828 0.369 0.193 0.374 -0.273 -0.267 (0.000) (0.005) (0.002) (0.248) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.032) (0.000) (0.000) (0.000) 6 FrontLoadBreaks 0.699 0.010 -0.196 0.033 0.621 0.727 0.977 0.505 0.956 0.355 0.353 0.291 -0.133 -0.130 (0.000) (0.828) (0.000) (0.485) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.004) (0.005) 7 CDSCBreaks 0.648 -0.073 -0.198 -0.074 0.493 0.687 0.653 0.380 0.785 0.142 0.378 0.184 -0.080 -0.075 (0.000) (0.120) (0.000) (0.114) (0.000) (0.000) (0.000) (0.000) (0.000) (0.016) (0.000) (0.000) (0.086) (0.109) 8 FrontLoad 0.784 -0.030 -0.256 0.044 0.718 0.911 0.642 0.513 0.933 0.361 0.319 0.276 -0.129 -0.127 (0.000) (0.523) (0.000) (0.352) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.006) (0.007) 9 NoLoad_12b1 0.581 -0.102 -0.310 0.075 0.619 0.450 0.359 0.514 0.667 0.290 0.341 0.152 -0.141 -0.146 (0.000) (0.030) (0.000) (0.110) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.002) (0.002)

10 Structural_Complexity 0.773 -0.098 -0.249 -0.017 0.807 0.770 0.683 0.795 0.835 0.367 0.362 0.309 -0.179 -0.175 (0.000) (0.036) (0.000) (0.712) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

11 FundsinFiling 0.347 -0.033 -0.101 0.163 0.422 0.433 0.289 0.423 0.331 0.427 0.126 0.482 -0.488 -0.495 (0.000) (0.583) (0.087) (0.006) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.164) (0.000) (0.000) (0.000)

12 Repetition 0.363 0.036 -0.356 -0.123 0.112 0.277 0.319 0.237 0.329 0.288 0.163 0.025 0.231 0.199 (0.000) (0.692) (0.000) (0.176) (0.218) (0.002) (0.000) (0.008) (0.000) (0.001) (0.072) (0.783) (0.010) (0.028)

13 DocSize 0.221 -0.187 0.112 -0.056 0.319 0.150 0.175 0.264 0.193 0.247 0.509 -0.002 -0.644 -0.627 (0.000) (0.000) (0.017) (0.229) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.981) (0.000) (0.000)

14 Wordiness -0.073 0.123 -0.168 -0.027 -0.210 -0.080 -0.129 -0.142 -0.174 -0.193 -0.541 0.206 -0.653 0.989 (0.121) (0.009) (0.000) (0.570) (0.000) (0.088) (0.006) (0.002) (0.000) (0.000) (0.000) (0.022) (0.000) (0.000)

15 WordySize -0.072 0.122 -0.169 -0.026 -0.209 -0.079 -0.127 -0.142 -0.173 -0.191 -0.541 0.202 -0.652 1.000 (0.126) (0.009) (0.000) (0.575) (0.000) (0.091) (0.006) (0.002) (0.000) (0.000) (0.000) (0.025) (0.000) (0.000)

42

Panel B: Pearson-Spearman correlations, Expense Disclosure Sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 Expense -0.070 -0.268 0.428 0.678 0.842 0.644 0.861 0.618 0.874 0.389 0.446 -0.097 0.756 0.711 (0.136) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.037) (0.000) (0.000) 2 TE -0.059 -0.123 0.023 -0.113 -0.037 -0.090 -0.038 -0.116 -0.089 -0.038 0.092 0.119 0.105 0.114 (0.214) (0.009) (0.619) (0.016) (0.428) (0.056) (0.425) (0.014) (0.060) (0.523) (0.312) (0.011) (0.250) (0.211) 3 Size -0.443 -0.103 -0.079 -0.043 -0.178 -0.169 -0.178 0.058 -0.132 -0.122 -0.226 -0.120 -0.347 -0.263 (0.000) (0.029) (0.091) (0.354) (0.000) (0.000) (0.000) (0.212) (0.005) (0.040) (0.012) (0.010) (0.000) (0.003) 4 Turnover 0.036 0.168 -0.219 0.021 0.151 0.044 0.207 0.165 0.142 0.196 0.247 -0.006 0.264 0.247 (0.443) (0.000) (0.000) (0.654) (0.001) (0.347) (0.000) (0.000) (0.002) (0.001) (0.006) (0.898) (0.003) (0.006) 5 ShareClasses 0.735 -0.133 -0.145 -0.054 0.727 0.520 0.691 0.520 0.828 0.369 0.193 -0.267 0.527 0.576 (0.000) (0.005) (0.002) (0.248) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.032) (0.000) (0.000) (0.000) 6 FrontLoadBreaks 0.699 0.010 -0.196 0.033 0.621 0.727 0.977 0.505 0.956 0.355 0.353 -0.130 0.801 0.769 (0.000) (0.828) (0.000) (0.485) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.005) (0.000) (0.000) 7 CDSCBreaks 0.648 -0.073 -0.198 -0.074 0.493 0.687 0.653 0.380 0.785 0.142 0.378 -0.075 0.454 0.440 (0.000) (0.120) (0.000) (0.114) (0.000) (0.000) (0.000) (0.000) (0.000) (0.016) (0.000) (0.109) (0.000) (0.000) 8 FrontLoad 0.784 -0.030 -0.256 0.044 0.718 0.911 0.642 0.513 0.933 0.361 0.319 -0.127 0.845 0.815 (0.000) (0.523) (0.000) (0.352) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.007) (0.000) (0.000) 9 NoLoad_12b1 0.581 -0.102 -0.310 0.075 0.619 0.450 0.359 0.514 0.667 0.290 0.341 -0.146 0.660 0.633 (0.000) (0.030) (0.000) (0.110) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.002) (0.000) (0.000)

10 Structural_Complexity 0.773 -0.098 -0.249 -0.017 0.807 0.770 0.683 0.795 0.835 0.367 0.362 -0.175 0.772 0.760 (0.000) (0.036) (0.000) (0.712) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

11 Fundsin Filing 0.347 -0.033 -0.101 0.163 0.422 0.433 0.289 0.423 0.331 0.427 0.126 -0.495 0.258 0.179 (0.000) (0.583) (0.087) (0.006) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.164) (0.000) (0.004) (0.048)

12 Repetition 0.363 0.036 -0.356 -0.123 0.112 0.277 0.319 0.237 0.329 0.288 0.163 0.199 0.284 0.233 (0.000) (0.692) (0.000) (0.176) (0.218) (0.002) (0.000) (0.008) (0.000) (0.001) (0.072) (0.028) (0.001) (0.010)

13 DocSize_ExpNarra -0.072 0.122 -0.169 -0.026 -0.209 -0.079 -0.127 -0.142 -0.173 -0.191 -0.541 0.202 0.007 0.010 (0.126) (0.009) (0.000) (0.575) (0.000) (0.091) (0.006) (0.002) (0.000) (0.000) (0.000) (0.025) (0.940) (0.916)

14 Wordiness_ExpNarra 0.658 0.088 -0.468 0.209 0.630 0.717 0.361 0.791 0.658 0.672 0.299 0.175 -0.002 0.938 (0.000) (0.335) (0.000) (0.020) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.052) (0.982) (0.000)

15 WordySize_ExpNarra 0.617 0.107 -0.421 0.189 0.640 0.721 0.445 0.788 0.609 0.672 0.211 0.122 0.018 0.929 (0.000) (0.237) (0.000) (0.036) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.019) (0.178) (0.846) (0.000)

43

Table 3: Prediction 1 – Narrative Complexity This table presents results of regressing total ownership cost (Expense) on narrative complexity variables. Panel A presents results for the full prospectus, and Panel B presents results for the expense disclosure. Year fixed effects are included in all models. All variables are defined in Appendix A. T-statistics are in brackets. Standard errors are clustered by fund. *** indicates significance at 1%; ** at 5%; and * at 10%. Panel A: Full Prospectus

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

FundsinFiling 0.198*** 0.233** 0.155** (2.70) (2.54) (2.15)

Repetition 0.280*** 0.233*** 0.290*** (3.16) (3.06) (4.28)

DocSize 0.113* (1.66)

Wordiness -0.010 (-0.18)

WordySize -0.012 -0.002 0.130** (-0.23) (-0.03) (2.03)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.090 0.174 0.040 0.011 0.011 0.300 0.455 Observations 286 123 458 458 458 123 123

Panel B: Expense Disclosure

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

FundsinFiling 0.282*** 0.138*** 0.117*** (2.86) (3.44) (4.45)

Repetition 0.280*** 0.157*** 0.168*** (3.16) (3.72) (5.17)

DocSize_ExpNarra 0.453*** (5.99)

Wordiness_ExpNarra 0.426*** (5.36)

WordySize_ExpNarra 0.443*** 0.365*** 0.374*** (5.95) (6.05) (12.65)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.162 0.174 0.553 0.484 0.528 0.653 0.691 Observations 123 123 123 123 123 123 123

44

Table 4: Prediction 2 – Structural Complexity This table presents results of regressing total ownership cost (Expense) on structural complexity variables. Panel A presents results for the full prospectus, and Panel B presents results for expense disclosure. Year fixed effects are included in all models. All variables are defined in Appendix A. T-statistics are in brackets. Standard errors are clustered by fund. *** indicates significance at 1%; ** at 5%; and * at 10%. Panel A: Full Prospectus

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

ShareClasses 0.355*** (5.92)

FrontLoadBreaks 0.437*** (10.65)

CDSCBreaks 0.340*** (6.83)

FrontLoad 0.448*** (10.07)

NoLoad_12b1 0.316*** (4.64)

Structural_Complexity 0.454*** 0.436*** (10.53) (14.96)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.438 0.715 0.442 0.735 0.373 0.758 0.849 Observations 458 458 458 458 458 458 458

45

Panel B: Expense Disclosure

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

ShareClasses 0.377*** (6.31)

FrontLoadBreaks 0.509*** (8.85)

CDSCBreaks 0.349*** (4.24)

FrontLoad 0.521*** (8.66)

NoLoad_12b1 0.408*** (5.26)

Structural_Complexity 0.507*** 0.475*** (7.81) (10.95)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.363 0.717 0.310 0.755 0.443 0.711 0.842 Observations 123 123 123 123 123 123 123

46

Table 5: Additional Analysis of Narrative Complexity and Discretionary Fees This table presents results of regressing total ownership cost (Expense) on narrative complexity variables. Control variables are included in all columns to isolate the discretionary component of Expense. Year fixed effects are included in all models. All variables are defined in Appendix A. T-statistics are in brackets. Standard errors are clustered by fund. *** indicates significance at 1%; ** at 5%; and * at 10%. Panel A: Full Prospectus

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

FundsinFiling 0.127** 0.105 0.088 (1.98) (1.27) (1.28)

Repetition 0.128* 0.130** 0.155*** (1.94) (2.06) (2.82)

DocSize 0.098* (1.86)

Wordiness -0.046 (-1.09)

WordySize -0.045 -0.067 0.121** (-1.09) (-1.07) (2.08)

Size -0.216*** -0.249*** -0.202*** -0.215*** -0.214*** -0.228*** -0.208*** (-5.16) (-5.28) (-4.44) (-4.73) (-4.73) (-4.86) (-4.95)

Turnover 0.167*** 0.153*** 0.183*** 0.181*** 0.181*** 0.144*** 0.181*** (7.78) (6.42) (8.19) (7.46) (7.52) (5.85) (7.21)

TE 0.027 0.075 0.008 0.007 0.007 0.120 0.318*** (0.36) (0.81) (0.15) (0.13) (0.13) (1.30) (2.73)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.442 0.525 0.372 0.356 0.355 0.574 0.682 Observations 281 123 452 452 452 123 123

47

Panel B: Expense Disclosure

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

FundsinFiling 0.147* 0.091** 0.074** (1.93) (2.32) (2.54)

Repetition 0.128* 0.100** 0.106*** (1.94) (2.53) (3.14)

DocSize_ExpNarra 0.332*** (4.81)

Wordiness_ExpNarra 0.300*** (5.57)

WordySize_ExpNarra 0.320*** 0.295*** 0.317*** (5.43) (5.52) (8.16)

Size -0.278*** -0.276*** -0.152*** -0.190*** -0.170*** -0.128*** -0.134*** (-6.24) (-5.28) (-3.52) (-5.68) (-4.62) (-3.73) (-4.48)

Turnover 0.204*** 0.205*** 0.176*** 0.181*** 0.177*** 0.146*** 0.139*** (6.30) (6.42) (6.65) (7.12) (6.85) (5.70) (6.48)

TE 0.082 0.053 -0.062 -0.054 -0.062 -0.036 -0.023 (1.26) (0.81) (-1.37) (-1.11) (-1.31) (-0.83) (-0.61)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.537 0.525 0.705 0.683 0.700 0.743 0.842 Observations 123 123 123 123 123 123 123

48

Table 6: Additional Analysis of Structural Complexity and Discretionary Fees This table presents results of regressing total ownership cost (Expense) on structural complexity variables. Control variables are included in all columns to isolate the discretionary component of Expense. Year fixed effects are included in all models. All variables are defined in Appendix A. T-statistics are in brackets. Standard errors are clustered by fund. *** indicates significance at 1%; ** at 5%; and * at 10%. Panel A: Full Prospectus

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

ShareClasses 0.336*** (6.34)

FrontLoadBreaks 0.387*** (14.89)

CDSCBreaks 0.299*** (7.94)

FrontLoad 0.391*** (12.18)

NoLoad_12b1 0.251*** (3.93)

Structural_Complexity 0.405*** 0.407*** (13.94) (15.24)

Size -0.177*** -0.104*** -0.135*** -0.097*** -0.143** -0.097*** -0.100*** (-5.39) (-5.99) (-4.05) (-5.15) (-2.41) (-6.02) (-6.47)

Turnover 0.186*** 0.147*** 0.181*** 0.127*** 0.154*** 0.150*** 0.175*** (9.43) (16.60) (14.17) (10.36) (6.00) (13.45) (15.89)

TE 0.038 0.004 0.019 -0.003 0.049 0.030 0.006 (1.39) (0.16) (0.51) (-0.13) (1.03) (1.51) (0.97)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.732 0.847 0.664 0.834 0.559 0.890 0.905 Observations 452 452 452 452 452 452 452

49

Panel B: Expense Disclosure

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

ShareClasses 0.351*** (7.76)

FrontLoadBreaks 0.425*** (10.49)

CDSCBreaks 0.262*** (5.79)

FrontLoad 0.437*** (8.83)

NoLoad_12b1 0.289*** (3.76)

Structural_Complexity 0.421*** 0.414*** (9.72) (10.56)

Size -0.256*** -0.128*** -0.222*** -0.108** -0.189** -0.134*** -0.143*** (-6.98) (-3.16) (-5.22) (-2.18) (-2.27) (-4.57) (-5.69)

Turnover 0.258*** 0.202*** 0.239*** 0.170*** 0.211*** 0.215*** 0.218*** (21.52) (16.83) (15.81) (10.25) (8.88) (20.94) (23.91)

TE 0.060 -0.071* 0.051 -0.101** 0.051 -0.018 -0.005 (1.54) (-1.73) (0.80) (-2.38) (0.85) (-0.47) (-0.14)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.844 0.905 0.670 0.887 0.689 0.917 0.931 Observations 123 123 123 123 123 123 123

50

Table 7: Additional Analyses – Simultaneous Narrative and Structural Complexity This table presents results of regressing total ownership cost (Expense) on both narrative and structural complexity variables. Panel A presents results for the full prospectus, and Panel B presents results for the expense disclosures. Year fixed effects are included in all models. All variables are defined in Appendix A. T-statistics are in brackets. Standard errors are clustered by fund. *** indicates significance at 1%; ** at 5%; and * at 10%. Panel A: Full Prospectus

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

FundsinFiling 0.009 0.077 0.004 (0.27) (1.09) (0.11)

Repetition 0.100 0.087* 0.054 (1.65) (1.74) (1.48)

DocSize -0.030 (-0.98)

Wordiness 0.045** (2.05)

WordySize 0.043* 0.033 0.034 (1.92) (0.69) (1.14)

Structural_Complexity 0.454*** 0.422*** 0.460*** 0.459*** 0.459*** 0.403*** 0.399*** (8.71) (8.09) (11.12) (10.74) (10.71) (7.30) (9.37)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.716 0.734 0.760 0.764 0.763 0.740 0.847 Observations 286 123 458 458 458 123 123

Panel B: Expense Disclosure

Dependent variable: Expense (i) (ii) (iii) (iv) (v) (vi) (vii)

FundsinFiling 0.068 0.070 0.020 (1.07) (1.28) (0.49)

Repetition 0.100 0.102* 0.049 (1.65) (1.93) (1.53)

DocSize_ExpNarra 0.149 (1.24)

Wordiness_ExpNarra 0.092 (0.85)

WordySize_ExpNarra 0.121 0.138 0.045 (1.04) (1.21) (0.65)

Structural_Complexity 0.479*** 0.470*** 0.392*** 0.437*** 0.413*** 0.333*** 0.416*** (7.88) (8.09) (4.04) (4.98) (4.43) (2.83) (5.12)

Estimation OLS OLS OLS OLS OLS OLS Robust

Regression Year Fixed Effects Y Y Y Y Y Y Y Adjusted R2 0.719 0.734 0.735 0.719 0.725 0.760 0.849 Observations 123 123 123 123 123 123 123