1

How valuable is investor access to mutual fund portfolio disclosure?

Evidence from the EDGAR log file*

Natalya Bikmetova

September 2021

Abstract

Using web traffic data from the Securities and Exchange Commission's EDGAR mutual fund filings, I

document instant demand for mutual fund portfolio disclosures. The demand is high for large funds with

extremely good or bad performance. Stock returns and short-selling activity around filing date however do

not exhibit significant patterns of front-running or copycatting by other investors. I find that mutual fund

trading activities seem to carry over from the reporting quarter to the subsequent quarter prior to the filing

date and these trades generate abnormal returns. The results show the 60-day gap between the reporting

quarter and filing date allows funds to complete their information- and liquidity-driven trades before

disclosure. The evidence suggests that the substantial filing delay erodes the information value of portfolio

disclosure.

Keywords: Information acquisition, Portfolio Disclosure, Institutional Investors, EDGAR

JEL classification: G14, G2

* Bikmetova is a Finance Ph.D. Candidate in the College of Business Administration of University of Central Florida, PO Box 161400, Orlando, FL 32816-1400, natalya.bikmetova@ucf.edu. I thank Dr. Qinghai Wang for the useful insights and suggestions he provided on this research project and the seminar participants at the University of Central Florida for their helpful comments and discussions on issues examined in the paper.

2

1. Introduction

The Securities and Exchange Commission (SEC) mandates mutual funds to regularly

disclose their portfolio and investment information1, which entails both benefits and costs. While

protecting shareholders' interests and improving stocks' liquidity, frequent disclosure significantly

increases the reporting obligations and undermines fund performance (e.g., Agarwal et al. (2015)

and Parida and Teo (2010)). The Investment Company Institute (ICI), advancing the interests of

investment funds and their shareholders, provides extensive arguments against frequent portfolio

disclosures2. Two of the most pronounced costs funds face are free-riding on disclosed investment

opportunities (e.g., Frank et al. (2004) and Verbeek and Wang (2013)) and front-running using

disclosed liquidity information (e.g., Brunnermeier et al. (2005) and Shive and Yun (2013)). To

hide information, funds may need to maneuver their trades, which is also costly (Wang, 2011).

Despite the evidence on portfolio disclosure costs, the demand for disclosed information and its

timing mostly remain unexplored.

This paper explicitly estimates the demand for mutual fund disclosure and examines its

implications. I document instant demand for mutual fund portfolio disclosure, particularly strong

for large funds with extremely good or bad performance. I find that stock returns or short-selling

activity around filing date do not exhibit significant front-running or copycatting patterns by other

investors. Pre-disclosure stock returns and fund trades, however, indicate that mutual funds exploit

their information advantage before the market learns their positions. Mutual fund trading activities

seem to carry over from the reporting quarter to the subsequent quarter prior to the filing date, and

these trades generate abnormal returns. The 60-day gap between the reporting quarter and filing

1 In 2004, the filing frequency of mutual fund disclosures became quarterly instead of semi-annual; recently, the SEC adopted monthly portfolio reporting. 2 See, for example, “The Potential Effects of More Frequent Portfolio Disclosure on Mutual Fund Performance” by Russ Wermers (Investment Company Institute Perspective 7, 2001)

3

date allows funds to complete their information- and liquidity-driven trades before disclosure. In

sum, the evidence suggests that the substantial filing delay erodes the information value of

portfolio disclosure and mitigates mutual fund disclosure costs. Investors may recognize the

limited informativeness of disclosure and refrain from massively employing copycatting or front-

running strategies, albeit possibly using it for other purposes (e.g., defensive) as high information

demand suggests.

I measure the information acquisition activity and its timing using the novel SEC

EDGAR log data3. This dataset reveals internet search traffic for EDGAR filings on the SEC

website. It contains information about a specific form, time of its filing and accession, and Internet

Protocol (IP) address associated with the accession. Such a multiple terabyte log dataset provides

researchers with a direct measure of demand for financial information (see Ryans (2013)), aiming

to overcome its unobserved nature. By connecting the EDGAR log with mutual funds data, I

construct a unique 10-year sample of fund performance, characteristics, mandatory report filings,

and information access from January 2006 to June 2017.

First, I document an instant demand for mutual fund filings, despite funds taking on

average 60 days to disclose their positions. A sharp spike in the requests on the disclosure day

decreases by more than 50% the next day and dissipates afterward. The holdings' information

consumption keeps increasing with years (from 255,329 requests in 2006 to 12,178,677 requests

in 2016) and globally. Moreover, a brief look at the most active IP addresses reveals a number of

wealth management firms, consistent with sophisticated investors using disclosed information.

3 Digital technologies allowed the SEC to ensure efficient and prompt information dissemination by launching the publicly available EDGAR database in 1993. In compliance with the Freedom of Information Act, the SEC Division of Economic and Risk Analysis (DERA) has assembled internet search traffic for EDGAR filings through SEC.gov from 2003 to the first half of 2017 and made this information available to the public. With SEC filings being the most common source of holdings data for the outside investors, the current study identifies when investors access the portfolio information and explicitly measures such information's demand based on the filings' accessions.

4

Each information consumer typically focuses her search efforts on a specific subset of funds,

accessing, on average, 15 funds each quarter.

Second, the information demand is not homogeneous: filings by large funds that

performed poorly recently or exceptionally well in the long run experience high information

demand. Notably, various forms may attract a different audience. Beyond fund holdings, annual

and semiannual forms disclose performance-related information and letters to shareholders. The

quarterly form contains mainly information on fund portfolio positions, being a cleaner measure

of the demand for holdings information. The quarterly form subsample tests exhibit strong

relationship between information demand and extreme performance, confirming that filing

accessions are primarily motivated by the demand for portfolio information.

Third, I test if the market engages in trading using the disclosed information. I document

no abnormal returns or short-selling activity on the disclosed stocks around filing events on the

subsample of the largest decile funds, which ex-ante experiences high information demand.

Instead, pre-disclosure returns on stocks bought by past winners and stocks sold by past losers are

positive and significant. This result suggests that, before the market learns about these trades,

information about superior stocks incorporates into the market, while the depressed by liquidity-

driven trades stock prices rebound. Thus, a substantial reporting delay seems to provide funds

sufficient time to complete profit- and liquidity-driven trades before facing any disclosure costs.

Investors, in turn, may recognize the disclosures' limited informativeness and thus refrain from

massively exploiting them in their trades.

Finally, considering the potential value of disclosure delay, I investigate whether funds

trade strategically in response to the disclosure requirement. I find that mutual fund trading

activities seem to carry over from the reporting quarter to the subsequent quarter prior to the filing

5

date and these trades generate abnormal returns. The subsequent quarter trade patterns differ for

past winners and past losers', inducing some predictability investors could exploit if there were no

filing delay. Moreover, strong pre-disclosure return patterns on these trades suggest their

informativeness: funds can generate abnormal returns before filing events.

Overall, the evidence suggests that the substantial filing delay erodes the information value

of portfolio disclosure. The funds respond to the information demand and mitigate its negative

consequences by strategically delaying filings. This delay may discourage investors from

massively employing the disclosed investment and liquidity information for their copycat or front-

running trades. Nonetheless, the instant demand for portfolio information suggests that investors

could use this information for other (for instance, defending) purposes. Specifically, the portfolio

information allows other investors to assess the impact of potential future trades of the disclosing

funds on their own portfolios. Alternatively, the information could be more valuable and be traded

upon only during specific extreme market or fund conditions4. In both cases, the information

demand would not trigger an immediate reaction on average consistently with the documented

findings.

The paper contributes to the extensive literature on mandatory portfolio disclosure costs

and mutual funds' strategic responses. Agarwal et al. (2015) and Parida and Teo (2010) show that

frequent portfolio disclosure undermines fund performance. Free-riding on researched information

by copycatting disclosed portfolios at no cost (Frank et al., 2004) may be particularly profitable

after the SEC raised mandatory disclosure frequency from semi-annual to quarterly (Verbeek and

Wang (2013)). Consistent with front-running costs (Brunnermeier et al. (2005)), Shive and Yun

(2013) provide indirect evidence on hedge funds profiting from the predictable, flow-induced

4 However, the additional tests on the subsample crisis period (2007-2009) do not exhibit strong disclosure price reaction either, suggesting that economic downturn by itself does not induce trading on disclosed information.

6

mutual fund trades, while Brown and Schwarz (2013) find that mutual funds' holdings are met

with a significant adverse reaction by the market. To avoid valuable information disclosure, funds

may maneuver their trades within a quarter, which introduces another cost (see Wang (2011) and

Puckett and Yan (2011)) but conceals trading information (e.g., Lakonishok et al. (1991) and

Kacperczyk et al. (2008)). This study's main methodological contribution is an explicit estimation

of information demand using filings' and requests' exact timing. The paper further presents

evidence on the demand implications and highlights that in addition to disclosure frequency,

reporting delay plays an essential role in mitigating mutual fund disclosure costs by eroding

disclosure information value.

Additionally, the paper contributes to the novel strand of literature employing the SEC

EDGAR log file to estimate information demand and search patterns of economic agents. Such

research has focused on institutional investors' and analysts' information acquisition patterns and

its outcomes (e.g., Chen et al. (2020), Crane et al. (2018), Gibbons et al. (2019) and Boone (2019)),

as well as financial statements use with demographic characteristics (see Drake et al. 2017). As

most of these studies consider institutional investors as information acquires, this study

complements the existing literature by examining institutional investors as information acquisition

targets instead. To the best of my knowledge, the demand for mutual fund holdings' information

yet remains unexplored.

The following section explores the data and summary statistics. Section 3 examines the

variation of documented information demand. Section 4 tests the implications of this demand to

the market and funds' strategic response, section 5 investigates mutual funds' trade patterns around

disclosure, while section 6 concludes.

2. Data and descriptive statistics

7

This study employs multiple data sources to execute empirical tests. To measure the

demand for filings, I use the novel SEC log dataset, mutual fund information from Thomson

Reuters and CRSP Mutual fund databases, stock-related information from CRSP and Compustat,

and short-selling data from Markit.

2.1. Mutual funds’ data

The mutual fund data comes from merging two sources: Thompson Financial

CDA/Spectrum holdings database and the Center for Research in Security Prices (CRSP)

Survivorship Bias-Free Mutual Fund Database covering both holdings and funds' characteristics5.

The sample consists of active domestic equity mutual funds with non-missing previous

month's TNA and two-year returns history. The information disclosed by actively trading funds is

presumably more valuable and contains more exploitable investment and liquidity opportunities

than other mutual funds' disclosures. Target funds are excluded since their specific goals and

investment horizon considerably restrict their asset allocation.

2.2. Filings and Web traffic data from the SEC EDGAR server

The second source of data comes from the US Securities and Exchange Commission

(SEC). Firstly, the SEC provides the EDGAR daily Index of the disclosure forms filed to the portal

by organizations or individuals. The filing usually becomes available to the public almost

immediately after addition. Each filing contains the company (fund) name, the filing type, the date

5 I merge two holdings’ datasets using CRSP Mutual Fund Links. Occasional discrepancies between the links, fund numbers, and CRSP portfolio numbers are corrected manually using fund names, deviations in holdings data, and other characteristics. I use CRSP Holdings Data only for the 2008-2017 years; before these years, the data may not be complete and reliable enough (Schwarz and Potter, 2016). Additionally, I employ Thompson Financial CDA/Spectrum holdings database over the period from 2006 to 2017. In cases where both of the datasets offer holdings data, I retain only the Thompson data. Most of the sample funds report their holdings to SEC quarterly; some CRSP funds, however, have monthly reports not disclosed through SEC and sold to the private vendor. I exclude them from the sample as they were not available to the public. CRSP Mutual Funds data also covers fund returns, total net assets (TNA), expense ratio, and other fund characteristics on the share class level. I aggregate returns, expense ratios, and other attributes from multiple share classes to the fund level by weighing them using the previous month's TNA. I calculate fund age from the earliest initiation date among all share classes in a given fund.

8

of addition to EDGAR, registrant CIK number (the ID of organization logged in and filed the

report), and the filing accession link. The complete EDGAR Daily Index dataset with all the daily

filings combined into one table is available on the James Ryans' website6.

I employ two complementary fund identifiers (CIK and series CIK) to connect each of

these filings to the corresponding funds7. I add these identifiers to the Mutual Funds dataset using

the CRSP CIK map, which connects fund CIK and series CIK to each CRSP fund number.

Additionally, I merge remaining funds and filings if the fund name and series name from the form

match exactly the CRSP fund and series names.

Finally, I connect each fund-filing from the resulting dataset to this study's key data source.

The SEC log data are extensive daily time-series datasets of filings requests from the SEC website.

These big data contain millions of records on every individual accessing document via the SEC; it

allows the direct measurement of demand for the filings. The information provided includes the

time of accession, IP address of a user with a masked fourth octet to protect identity, Central Index

Key (CIK) of the form registrant, identifying corporations and individuals filing disclosures with

SEC, form's accession number.

To perform any statistical analysis, these multiple terabytes of data have to be appropriately

filtered and aggregated first. I use two demand measures: the number of raw requests (IPs) with

only unsuccessful downloads and indexes eliminated and the number of requests (IPs) after mass

download screening. This additional filtering excludes IP addresses accessing more than 3000 files

6 The data are available at the website: http://www.jamesryans.com 7 Although EDGAR Daily Index contains only Registrant CIK, in many cases, forms include multiple funds disclosing in the same document under one Registrant CIK. These additional fund-filers may have fund CIK different from the Registrant or several different series CIKs. To ensure each form's merge to all associated funds, I scrape each of the corresponding 207,186 filings from the SEC website to obtain all filers' CIKs, series CIKs (series IDs), fund and series names, and end- of-reporting period date recorded in the forms.

9

in a given day. The motivation behind such screening is to eliminate massive machine requests. In

most of the specifications, I use screened requests and IPs as a cleaner measure of immediate

attention. The same tests with raw demand measures show similar, albeit weaker, results.

Additionally, I connect each IP address with a geolocation database8, using the unmasked

first three octets of IP addresses. A region-specific location's precision level is at least 98%.

Although a small proportion of IPs could change their location even over this recent sample period,

the patterns can provide a crude estimation of the information demand origins.

This study's log-fund panel data covers the period from 2006 to the second quarter of 2017.

Although the log data is available since 2003, before 2006 funds filed only fund CIK, not a unique

fund identifier. Since 2006, all open-end mutual funds additionally provide Series CIK (Series ID)

in their filings as per SEC requirement. According to Hillert et al. (2016), in conjunction with CIK,

Series CIK correctly links each filing to the corresponding fund in more than 96% of the cases.

Only forms N-Q, N-CSR, and N-CSRS are included in the sample, as they are only

mandatory filings revealing the holdings' information. Form N-Q, disclosed at the end of the first

and third fiscal quarters, contains mainly portfolio holdings disclosure. In contrast, forms N-CSR

and N-CSRS are annual and semiannual shareholders reports, presenting performance and

explanatory information to their shareholders in addition to the holdings data. There are two other

disclosures not used in the analysis: semiannual mandatory form N-30D submitted before May

2004 switch to quarterly disclosures and a voluntary disclosure form N-30B-2. The number of

these two types in the sample is negligent.

8 The data are available at the website: https://www.ip2location.com

10

The first part of the analysis employs the intersection of CRSP Mutual fund data

(excluding holdings data), Edgar Daily Index with forms files, and associated log files9. The

sample consists of 4,038 distinct funds and 35,398 distinct forms filed. Since each form may

contain several filers, the sample consists of 104,576 fund-filing tuples; the regression analysis

employs only 76,007 observations with non-missing required fund characteristics.

The second part of the analysis additionally employs Mutual Funds holdings data;

matching the above data with holdings reduces the number of funds to 3,300, the number of forms

to 26,844, the number of funds-filings tuples to 71,122, and the number of fund-filing-stock

observations and inferred from them trades to 7,437,228. Sometimes, the mutual fund holdings

database and actual EDGAR filings have slightly different end of reporting period quarter; in this

case, I use the EDGAR reporting date since it is the original data source.

2.3. Descriptive statistic

How valuable is the information disclosed by the selected mutual funds? During the sample

period, the Investment Company Act requires each fund to transmit annual and semi-annual reports

to shareholders within 60 days after the reporting quarter end and submit forms with the SEC no

later than 10 days afterward10. Thus, the funds have a total of 70 days to file the annual and

semiannual reports, while a quarterly N-Q form submission deadline is 60 days after the first and

third fiscal quarter. Table 1 presents filing patterns of sample funds compared to other funds filing

the same forms. The patterns are very similar. Compared to Christoffersen et al., 2015, who

document that filing practices of institutional investors filing13-F forms vary greatly, domestic

9 In some cases, when duplicate forms with different filing dates cover the same reporting period, I keep the earliest filing and disregard the amendments assuming the newly issued holdings information release to have a more substantial effect on the market. If there are two disclosures filed for the same period on the same day, I count views and IPs on both of the filings to estimate the level of demand to a given disclosure. 10 In practice, the reports are submitted electronically (see https://www.sec.gov/info/edgar/regoverview.htm), and it is typical for funds to file the report both to the SEC and shareholders on the same day.

11

active equity mutual funds' strategies are somewhat more homogeneous. In general, most of them

tend to delay their filings right until the deadline (60 and 70 days after the reporting period end),

with some funds filing past the deadline.

Panel A of Table 2 indicates that this "last-minute disclosure" pattern holds across time,

with only a slight decrease in filings' delay across years. Funds tend to delay the release of holding

information, presumably to make it as stale as possible. If funds indeed strategically delay the

disclosure, the valid concern is whether market participants are interested in this delayed

information upon exposure.

Panel A of Table 2 documents the growth of the number of views for all selected forms:

N-Q, N-CSR, and N-CSRS for both domestic equity active and other funds. The steady increase

in attention from year to year indicates SEC filings' popularity as an information source on mutual

funds. The increasing number of funds can partially contribute to increased views. Figure 1

demonstrates the cumulative number of views by days after the form's filing. There is a dramatic

spike in the filing day (day 0) with a gradual decrease in the subsequent days, suggesting the high

and instant demand for the information disclosed. Surprisingly, even though the disclosed holding

information is significantly delayed, the market still tries to gain insight by researching it

immediately after the disclosure. The geography of requests also expands; Figure 2 demonstrates

the spatial patterns of the U.S. and worldwide information demand during 2005, 2010, and 2015

years. The location patterns are also not homogeneous: Table 10 in the Appendix presents the lists

of most active regions accessing the filings within two days after disclosures.

Since the demand for holdings information exists, the natural question to examine is the

market participants focus their search efforts on a particular subset of funds. Panel A of Table 3

examines the information demand intensity by each active IP-address; to qualify as active, IP-

12

address should make at least one request a given year. The table presents the distribution for the

average number of funds the IP-address accesses within 2 and 7 days after disclosure each quarter.

Only IPs accessing more than one filing per year are included. The prevailing number of IP-

addresses has low search activity: the distribution is highly skewed. The most active users,

however, instantly collect the information on multiple funds, resulting in an average of 14.99 funds

instantly examined by the users. Such a diversification suggests the information employment is

more complicated than just imitating a specific fund’s holdings. The information activity within

7 days is less intensive than the instant demand within 2 days.

A large number of money managers searching the disclosed information further indicate

that the market is interested in some mutual funds' disclosures. Table 11 in the Appendix presents

the lists of most active IP addresses accessing the filings after disclosures. As Bowles (2019) noted,

despite the masked fourth octet of IP addresses, it is possible to unmask relatively large

organizations as they typically register blocks of IP addresses, and the most common block

contains 256 IP numbers. A brief look at the most active IP addresses reveals a number of wealth

management firms and banks suggesting sophisticated investors actively processing the holdings

information.

3. Information demand and fund characteristics

As the previous section demonstrates that the mutual funds’ holding information still

matters for market participants, the next question is how homogeneous the demand for this

information. If filing accessions are spread randomly across funds, then some trivial interest, as

opposed to the profit-driven intent, motivates this demand.

Panel B of Table 3 presents the distribution of information demand as proxied by the

number of requests per day accessed the fund’s filing within the first 2 and 7 days by years. In all

13

further tests, including this one, I screen out mass downloads from the total number of requests.

These statistics indicate that 1 percent of the funds receive 0-6 requests, while 50 percent of the

funds attract just 7 to 27 requests during the first seven days after the disclosure. The numbers

gradually increase during the more recent periods. The top 20 percent experience much greater

demand: the maximum number of requests often exceeds 500 within seven days after the

information becomes publicly available. The demand for disclosures is certainly non-

homogeneous and most likely is driven by specific funds’ features. Using the number of IP

addresses as a proxy for information demand yields similar results (Table 3 B in the Appendix).

If funds attract the attention of free riders' who access the forms to learn about profitable

investment opportunities without incurring the research costs, funds' high past performance must

matter. Conversely, poorly performing funds may become front-running targets, and we would

expect their holding disclosures also to drag attention. Additionally, larger funds may attract more

attention due to a substantial number of investors and potentially higher impact of their trades on

the market. Indeed, the data confirm these conjectures.

Table 4 presents the demand level proxied by the number of requests for the first two days

after the fund's disclosure, for funds sorted independently by past performance deciles and

previous to the reporting month TNA quintiles. To estimate past performance, I compound the

fund's net returns and style-benchmark return (average return for a given CRSP objective code)

over the previous 3, 6, and 12 months before the current report period and find their difference.

On average, funds receive a moderate number of requests with demand increasing in fuds' size.

Many funds, however, do not receive any information demand and thus bring these averages down.

Dividing funds in size deciles instead of quintiles (available upon request) provides a stronger size

contrast. Within two days after disclosure, top-size decile funds in the lowest past 3-months

14

performance group receive 19.7 requests, while top-decile best past 12-months performers get 19.4

requests on average. The largest funds, on average, receive 2-3 more requests. The extreme

performance also matters the most for the largest funds' quintile. If a large fund is among top

performers decile, its longer-term success of 12 months triggers higher information demand than

outperformance based on shorter windows; the difference is statistically significant. The opposite

holds for failures – large funds underperforming over the 12-month window receive less attention

than the most recent past three months failures.

This evidence supports the story of copycatting and predatory trading practices. To imitate

someone's portfolio, one needs to distinguish skill from luck. Therefore, a long-term record of

returns matters more. Conversely, to exploit portfolio disclosure for front-running, one needs the

most recent loser's holdings data to predict which positions will be reduced by the distressed fund.

To summarize, there is a non-linear relation between large funds' past performance and attention

to their mandatory disclosures, with low and top past performers receiving more attention than

average ones. Using IP-addresses accessed the information as a proxy for information demand

yields similar results (Table 4 B in the Appendix). Although the magnitude of these differences

seems to be very moderate, it is essential to note that the decile averages include multiple funds

receiving 0 accessions and thus no information demand. In fact, many funds may experience

extreme levels of attention, as Table 3 demonstrates.

The double-sort results are on the fund level; therefore, each form can contain several filers.

The variation within size can be driven by disclosures containing multiple filers receiving more

requests. To address this concern, I also calculate the mean number of filers in a form within each

decile (the results are available upon request). The relation between size and attention is not

mechanical as the number of filers does not necessarily increase with size or performance.

15

Apart from fund size, many other fund features can correlate with the market’s demand for

the fund’s disclosure. To find the determinants of this interest and ensure fund size and

performance relation holds in a more formal test, I employ the pooled OLS regression with a

dependent variable number of requests as a proxy for information demand:

(1) logRequests𝑖𝑖𝑖𝑖 = 𝛽𝛽0 +𝛽𝛽1 logAge𝑖𝑖𝑖𝑖 +𝛽𝛽2 logTNA𝑖𝑖𝑖𝑖-1

+𝛽𝛽3 PastPerformance𝑖𝑖𝑖𝑖+𝛽𝛽4 exp_ratio𝑖𝑖𝑖𝑖 +𝛽𝛽5 turn_ratio𝑖𝑖 +𝛽𝛽6 volatility12M𝑖𝑖𝑖𝑖

+𝛽𝛽7 logNumOtherFilers𝑖𝑖𝑖𝑖-1+𝛽𝛽7 logNumDAEFilers𝑖𝑖𝑖𝑖 + 𝛽𝛽8 EarlyFiling𝑖𝑖𝑖𝑖+𝛽𝛽9 LateFiling𝑖𝑖𝑖𝑖

+𝛽𝛽12 TOPSIZE𝑖𝑖𝑖𝑖 + 𝛽𝛽 13 TOPSIZExHIGHPERF𝑖𝑖𝑖𝑖 + 𝛽𝛽 14 TOPSIZExLOWPERF𝑖𝑖𝑖𝑖 +𝜀𝜀𝑖𝑖𝑖𝑖

This multivariate model helps examine the factors that could increase the demand for the

funds' disclosure. Requests is the variable counting of form accessions within the first two days

after the filing, with multiple requests from the same IP address same day counted only

once. logRequests is the regression transformation and represents the natural logarithm of one

plus the dependent variable. As the attention patterns are very similar for

both Requests and IPs count measures, I present the results only using requests count.

Requests and IPs, however, are highly correlated, and the regression with IPs as dependent

variable yields to similar results (available upon requests). To make certain outlier variables do

not drive the results, I winsorize Requests and IPs at a 1% level.

The main explanatory variables are the fund-level characteristics and filing

patterns. LogAge is the log of one plus fund age in quarters, logTNA is the log of one plus fund's

total net assets in the previous to the end of the report period month. PastPerformance is a find’s

benchmark-adjusted return, calculated as in Table 4. Exp_ratio and turn_ratio are annual expense

and turnover ratios from the CRSP dataset, volatility12M is the 12-month standard deviation of

16

the fund's net returns. In many cases, several funds disclose their information in the same filings;

therefore, I include the log of one plus the number of domestic equity active funds-

filers logNumDAEFilers, and the log of one plus the number of other filers logNumOtherFilers.

First, there can be a mechanical relation since more filers may automatically attract more viewers

seeking information on different funds in one form. Second, these variables could proxy for the

size of the fund family. EarlyFiling and LateFiling are dummy variables equal to one if a filing is

submitted before or after the deadline accordingly. TOPSIZE is an indicator of the largest decile

funds, size estimated as the previous-month

TNA. TOPSIZExHIGHPERF and TOPSIZExLOWPERF are the interactions of the described

indicators to capture the additional attention effect that large funds receive if they end up in the

extreme performance deciles. With that, LOWPERF and HIGHPERF are indicators for funds in

the top and bottom past performance decile to capture the non-linear relation, with past

performance windows of 3, 6, 12, and 24 months in the four model's specifications.

Panel A of Table 5 sheds some light on the information access for the whole panel of

funds-disclosures. FormNQ and formNCSRS are indicators for quarterly and semiannual filings,

respectively, accounting for possible differences on the form level. While N-Q contains only

holdings information, N-CSR and N-CSRS additionally include the information for shareholders.

Certain potential funds' investors could exhibit the interest in this fund-specific information, such

as performance figures and letters to shareholders, provided in the funds' semiannual and annual

reports. Thus, we can anticipate the heterogeneity of attention on funds and forms level, with the

demand for N-Q disclosures being a cleaner indicator of holdings information acquisition activity.

Panel B of Table 5 presents the results on the three subsamples by form type to account for possible

differences in information demand.

17

Consistent with previous results, large funds (as per logTNA) experience a high number

of views, with top size funds experiencing the most intense information demand. As the fund's

TNA reflects its skill (Berk and Green (2004)), high attention may be driven by rivals trying to the

strategies researched by such fund exploit at no cost. Large funds' trades may also substantially

impact the stocks' prices; therefore, holding research may be beneficial for predatory traders to

understand which holdings distressed funds may redeem.

PastPerformance attempts to capture linear relation between fund's returns and

information demand. The coefficient lacks significance in both the main and the subsample

analyses. A positive and significant coefficient for TOPSIZExLOWPERF (0.093 meaning over 1

percent increase in requests if TOPSIZExLOWPERF=1) indicates that the market pays some

attention to the large funds' recent poor performance. On one side, investors may want to learn

about the reasons for such a poor performance. On another side, rival funds may try to exacerbate

this past loser's financial distress caused by the capital outflows by getting its holdings data and

attempting to front-run the fund (Brunnermeier et al. (2005)). In contrast, large top-performing

funds experience more extra demand if they have a long track of high past performance:

interaction TOPSIZExHIGHPERF is positive and gains most significance as the performance

evaluation window lengthens (the coefficient of 0.053 for past 24-month performance, implying

almost 1 percent increase in requests if TOPSIZExHIGHPERF=1). This extreme performance

relationship is the most pronounced for the N-Q form subsample. As N-Q mainly focuses on fund

holdings information, this evidence confirms that the portfolio positions, not shareholder reports

or other information, attract other investors.

The longer the fund delays the filing of holdings information, the more time passes

between the holdings snapshot and disclosure date. Thus, a fund possessing some valuable

18

investment ideas may be reluctant to share them with the market and can strategically delay the

disclosure to make the information as stale as possible or take further advantage of it before it

becomes publicly available. If the market expects the fund to have some valuable opportunities to

disclose, we can anticipate a higher number of views on this fund's disclosure even after delays.

The results confirm this hypothesis. LateFiling, the indicator for fund delays of the filing, is

positively and significantly associated with high attention in all specifications, suggesting that the

market participants are still interested in the disclosed information despite the delays. Surprisingly,

in the subsample analysis, the relation doesn’t hold for the semiannual form filings.

On the other side, as time passes by, holding information becomes more outdated and

perhaps less attractive to competitors. Therefore, early disclosure may also invoke more attention

due to the actuality of the information it contains. The evidence, however, is mixed. The coefficient

on EarlyFiling, an indicator variable for disclosure taking place before the deadline, is positive

and significant in the full sample. In the subsample analysis, however, EarlyFiling is negatively

associated with attention for semiannual and annual filings.

As the conducted analysis is on the fund-level, there may be a mechanical relation

between the number of funds in the form and attention. Two variables address this issue: the

number of domestic equity active funds filing in the same form (logNumDAEFilers) and the

number of other funds calculated as the difference between the total number of filers and the

domestic equity ones (logNumOtherFilers). Both of the measures are positively and significantly

associated with the number of form accessors. Apart from the mechanical relation described above,

these variables can proxy for the fund family size as filings tend to contain the same family funds;

in this case, we observe that larger families get significant attention.

19

To conclude, this analysis indicates the heterogeneity in attention, with multiple funds'

characteristics contributing to the level of interest their filings receive. The results suggest that

large funds with recent bad or long-term good performance are the primary targets for information

acquisition activity, consistent with the existing research about copycatting and predatory trading

practices.

4. Does the market react to mutual fund disclosure?

The demand for disclosures varies with the funds' size and past performance, indicating

that only certain information matters for the market. How do other investors use the accessed

information? As a fund's outstanding performance can induce free-riding, or other funds can front-

run the liquidity-driven trades of an underperforming fund, the portfolio holdings information

should be actively exploited immediately upon disclosure. The SEC reports are the timeliest public

source of holdings' data. Suppose market participants indeed trade on this information as soon as

disclosure becomes accessible through the SEC portal. In that case, investors' excess demand

should push the associated stocks' prices up or down depending on the transaction side.

The following analysis tests whether high information demand affects disclosed stock

returns using the subsample of funds in the top size decile as they ante receive high attention. To

exploit the disclosed information, investors have: to access the specific information and then trade

on it, perhaps even selecting within several conflicting strategies. Large funds' strategies are likely

to be followed more intensively, possibly leading to a sizable market reaction. As flows chase

performance (Sirri and Tufano, 1998), other investors may attribute skill to the fund's with large

TNA (Berk and Green, 2004) and mimic these funds more intensively. Alternatively, large funds'

trades may substantially impact stocks' prices in the case of liquidity-driven trades.

20

I re-rank the subsample of funds with non-missing holdings information. The ranking

criteria are fund past 12-month performance quintiles and previous month TNA deciles, with the

only top decile included in the analysis. The results based on past 6-month performance and the

results employing the full sample of funds are available in the Appendix. As this relatively short

sample (45 quarters from 2006 till 2017) includes the global financial crisis, I run the tests on the

subsample of 2010-2017 to ensure this economic downturn does not affect the results. The full

sample results are similar.

The portfolios are based on funds' disclosed trades. Suppose a fund files the disclosure on

May 31 for the period ending on March 31. As March 31 is the holdings snapshot's date, I calculate

the change in stock positions for each stock in the portfolio, comparing this report to the previous

one. If the prior disclosure is more than four months away from the March report, I consider the

inferred trade information stale and exclude it from the analysis. I assume transactions occur at the

end of the reporting period and multiply each change in shares adjusted by the stock's price as of

March 31 to get a dollar value of the trade. If the price on March 31 is missing, I employ the

previous price available, going back no longer than three months.

The abnormal benchmark-adjusted returns calculation for each disclosed stock follows the

Daniel, Grinblatt, Titman, and Wermers (1997) technique of momentum, size, and book-to-market

return adjustment (DGTW)11. I calculate the cumulative abnormal returns (CARs) over the

reporting quarter, the period between the end of the reporting quarter and disclosure, at disclosure

11 First, stocks are conditionally sorted into quintiles based on market value, book-to-market ratio, and momentum factor. These groups form 125 benchmark portfolios with corresponding daily value-weighted returns, with the stocks' previous June's market caps as weights. The benchmark is assigned at the end of the prior report. For the current example, is it assigned on December 31 of the previous year to calculate trades as of March 31. Every day I subtract the benchmark return from the stock's return to calculate the daily abnormal return and then sum these returns over the corresponding window to get the cumulative abnormal return (CARs).

21

and following two trading days, and the subsequent after-disclosure period starting three days after

disclosure until the end of the following reporting quarter.

For each fund, trades' information splits into several portfolios. "Buy" portfolio includes

all the stocks with an increased position, "Sell" consists of all stocks with the reduced position.

"Hold" portfolio includes only stocks with no change in the positions since the prior report, while

"Positions" portfolio includes all fund holdings. The long-short "Buy minus Sell " portfolio return

for each fund-disclosure case is the difference between "Buys" and "Sell".

The fund portfolio return is calculated two ways – with equal weights (EW) and weights

based on the trades' dollar value for "Buy" and "Sell" and on position's value for "Hold" and

"Position" portfolios (VW). For weighting, the reporting period relies on the beginning of the

reporting quarter positions, while before, around, and after disclosure periods employ the end of

the reporting quarter positions. If the investment opportunities, on average, are profitable for

copycatters, the EW CARs would indicate so. If rival investors front-run the funds' trades

immediately after accessing holdings reports, the VW CARs would indicate the price pressure

induced by the excess demand or supply induced by the front-runners.

After constructing the multiple portfolios for each fund-disclosure tuple, I divide funds by

past performance quintiles to calculate a cross-sectional average of each return quintile and

quarter. After calculating CARs for each portfolio-quarter, I find the time-series average along

with the t-statistic for each performance group and each portfolio.

Panels A of Tables 6, 6 B, and 6 C present the VW CARs around disclosures. Upon

disclosure, there are slightly negative disclosure returns on the stocks bought by the funds with

poor 6-month performance (- 11.1 basis points) and a slightly positive reaction on the past 12-

month winners' positions (5.2 basis points); both of the effects are not sizable. Panels B of Tables

22

6, 6 B, and 6 C demonstrate that EW returns are not significant around and after disclosure.

Overall, even accounting for the fact that not all reports are relevant for traders and employing the

subsample of largest funds, there is no evidence of significant price reactions around the portfolio

disclosures.

The period between the report date and the disclosure date takes on average two months,

allowing the funds some time to maneuver. Thus, VW returns on positions and bought stocks

increase with past performance, with the largest funds’ top performance quintile portfolio earning

54 and 72 basis points, respectively. Such a pattern is consistent with high performers making

informative trades during the reporting period. Suppose the manager buys undervalued assets and

the information is not fully released at a stock’s price during the reporting quarter. In that case, it

keeps releasing into the stock prices afterward, generating some abnormal returns. Conversely, the

returns on sold stocks decrease with past performance, with the highest return of 58 basis points

for the lowest performance quintile of funds. Poor performing funds are likely to face outflows

and be forced to conduct liquidity-driven sales, which could depress stock prices. Prices rebound

during the pre-disclosure period, which on average takes two months. This time seems to be

enough for funds to complete their information- and liquidity-driven trades until the market even

learns about them and thus before facing any disclosure costs. EW returns demonstrate similar

pattern12.

Notably, pre-disclosure VW returns on funds holdings are positive for all performance

quintiles suggesting that funds outperform for two-thirds of the quarter. These results differ from

the previous literature (see, for instance, Wermers (2000)). Different samples and timing could

12 To address the concern that some funds could distribute the holdings information to their shareholders before filing date, I run the same tests on the subsample of quarterly N-Q filings, which are not distributed to shareholders. The return patterns are similar (the results are available upon request).

23

drive such results. An examination of the quarterly distribution of funds within each performance

quintile reveals that during the sample period, some funds earned extreme positive returns during

certain quarters, thus positively affecting the equal-weighted mean calculation across quintiles and

quarters. EW results do not show such an effect.

The return tests are indirect: even if some investors trade in a certain way, the price impact

could still be small. Short-selling activity is a cleaner measure of potential market reaction: daily

data helps to test directly whether some investors exploit the disclosed holdings information. If

investors engage in predatory trading activity without holding the stock, a short interest ratio (SIR)

change around the disclosures will indicate so. While the change in long positions needs to be

inferred from the stock price movements, the Markit data explicitly indicates the number of

borrowed stocks. For each stock in the portfolio, I calculate the SIR in percent as the number of

stocks borrowed divided by the number of shares outstanding and multiplied by 100. I estimate

the change in SIR as the difference between SIR at the end of the window and SIR at the beginning

of the window and construct portfolios following the methodology from Table 6, this time

computing SIR change for different portfolios.

Table 7 presents the results, with Panel A reporting the VW SIR change and Panel B

presenting the EW SIR change for the portfolios based on the past 12-month performance of the

largest funds' decile. There is no evidence of abnormal short-selling activity before, around, and

after the disclosures in either specification. This analysis of the trading activity suggests that

sophisticated investors do not use disclosures to front-run the funds by shorting the stocks ahead

of these funds' sales. Tables 7 B and 7 C replicate the analysis sorting funds by past 6-month

performance and by employing all funds sample regardless of size and yield similar results.

24

The results in Table 6 and Table 7 indicate that despite the documented high information

demand, the investors refrain from the widespread usage of the trading strategies reflected in

mandatory disclosures. Stock returns and short-selling activity do not exhibit significant patterns

of front-running or copycatting by other investors around filing date. The 60-day gap between the

reporting quarter and filing date allows funds to complete reporting quarter information- and

liquidity-driven trades before disclosure.

5. Mutual funds' trading around disclosure

The previous section suggests that the market may anticipate mutual funds mitigating

disclosure adverse effects by delaying filings strategically. This strategic delay mitigates fund

disclosure costs by providing sufficient time for their reporting quarter trades' completion. This

section tests whether funds resume trading on the stocks listed in disclosures but yet undiscovered

by the public during the sufficient pre-disclosure time.

Several reasons may motivate mutual fund trades. First, superior information about specific

investment opportunities will induce funds to buy outperforming stocks and sell underperforming

stocks. Second, funds may conduct liquidity-driven trades when experiencing extreme inflows or

outflows. The third reason could be the manipulation of portfolio positions to make them less

informative. Although this strategy is not value-maximizing, it can mitigate the disclosure costs.

Together these factors may lead to different trading patterns based on the fund performance. The

following analysis attempts to uncover these patterns and examine whether such trades can yield

abnormal returns. First, I compare funds' trades on the disclosed stocks in the subsequent quarter

with the reporting quarter's trades. Second, I examine the returns on these disclosed stocks' trades

before and after disclosure.

25

As before, the analysis includes only large funds as ex-ante they have the most substantial

impact on the market. Based on the subsequent period, I compute funds' subsequent quarter trades

(i.e., the quarter when disclosure occurs) only on the disclosed stocks. The trades are based on a

change in holdings between quarterly public disclosures. For each disclosure, I count the number

of transactions in the following report by their trade direction. For instance, if the fund sells stock

in a given period but repurchases it in the next one, the direction is "sell-buy"; if the fund buys

stock it held with no trade in the previous period, it is "no trade - buy". For each direction, I divide

the number of such trades by the total number of transactions to obtain the percentage. Each

quarter, I calculate the average within the performance quintile. Finally, I average these

percentages for each group across all quarters and calculate the difference between the top and

bottom past performance quintiles and a t-statistic.

Table 8 presents the analysis of the large funds' trading patterns based on 12-month

performance quintile. This summary indicates that large top-performing funds trade somewhat

differently than large funds with poor performance. Large winners buy the same shares they bought

earlier 9 percent more often and are 1.8 percent less prone to sell them than losers. As for the

previously sold stocks, these funds are 6 percent less likely to continue selling and are 2 percent

less likely to keep the positions unchanged for the previously sold stocks. Instead, they buy

previously sold shares 1 percent more often than losers.

Tables 8 B and 8 C present similar statistics for quintiles based on past 6-month

performance and the full sample of funds. The results are weaker for the full sample, albeit the

patterns are still the same. The liquidity needs are likely to induce poor-performing funds to sell

more often. Regardless of the reasons, such patterns can potentially induce predictability and allow

other investors to profit on the disclosed trades if it was not too late. Investors need to know the

26

reporting period trades to forecast the subsequent moves. The reporting quarter's disclosure,

however, occurs when most of the subsequent quarter is gone along with funds' trades, thus leaving

little opportunity profit on this predictability.

Given the sufficient pre-disclosure gap, do the funds still profit on such subsequent quarter

trades? How valuable is the disclosure delay for funds? The funds have more than a half of the

subsequent quarter until the reporting quarter information gets public, potentially taking further

advantage of the undisclosed investment opportunities.

Table 9 addresses this question and reports large funds’ returns on the above trade patterns,

following the same methodology as Table 7. As in Table 8, only the stocks reflected in the holding

report are included in the calculation to test if the funds trade on their own disclosed information

or take advantage of the pre-disclosure period when they still can exploit yet undisclosed

investment strategies. Table 9 B in the Appendix presents the results based on the sorting by the

past 6-months performance, and Table 9 C employs the full sample of funds regardless of size.

Panel A presents value-weighted returns. Disclosed stocks bought in the subsequent period

typically underperform during the pre-disclosure period and perform well after the disclosure; this

pattern is particularly strong for the previously sold stocks. For instance, losers sell previously

bought stocks 1.85 percent more often than winners; such stocks pre-disclosure returns are 1.25

percent (t=2.97), after disclosure, they lose significance. Conversely, stocks sold by funds in the

subsequent period exhibit stronger return patterns before the disclosure; afterward, the abnormal

returns weaken. The strongest effect is for the stocks fund bought or sold during the reporting

period. Thus, large winners buy previously sold stocks 1.3 percent more often than losers, and

such stocks pre-disclosure returns are -1 percent (t=2.81).

27

The equally weighted returns from Panel B demonstrate similar patterns, further

suggesting the results are not merely driven by large trades' price pressure. There is no substantial

difference between the patterns of large funds and the whole sample, supporting the idea that

regardless of size, funds may take advantage of the concealed investment opportunities before they

get public.

As the examined above trade patterns are calculated from the quarterly change in holdings,

there is no information on the trades' exact timing. Thus, the caveat is that funds could employ the

strategies during the post-disclosure period and earn no abnormal returns. However, since the pre-

disclosure period constitutes about two-thirds of the quarter, a substantial proportion of such trades

may yield outperformance based on yet undisclosed information.

In sum, this section sheds some light on the funds’ strategic reaction to mandatory

disclosures. Mutual fund trading activities seem to carry over from the reporting quarter to the

subsequent quarter before filing. These trades can be informative and generate abnormal returns if

funds buy the undervalued stocks and dispose of overvalued assets before the filing date. Albeit

past poor and high performers seem to trade differently, strategic disclosure delay seems to protect

funds from exploiting this predictability. Funds can keep trading on the yet undisclosed stocks and

earn superior returns before the filing date.

Conclusion

This paper studies the impact of information acquisition activity with mutual funds as

targets. Using SEC EDGAR log data, I document instant demand for mutual fund disclosure

information. The intensity of requests expands globally; money managers searching the disclosed

information indicate the sophisticated investors' interest in specific mutual funds' disclosures.

28

Investors tend to focus on a limited subsample of funds; the largest funds with recent poor or long-

term high performance attract high number of requests upon disclosure.

Given the revealed demand, I refer to its potential implications. Other investors may

access the information promptly to imitate the disclosed positions of well-performing funds at no

cost. Conversely, a rival can front-run liquidity-driven trades of a poorly performing fund. Stock

price movement and short-selling activity patterns, however, provide no supportive evidence of

investors massively trading on the disclosed information. Moreover, pre-disclosure return patterns

suggest that, before disclosure occurs, the information about superior stocks bought by winners

impounds in stock prices while depressed by losers’ liquidity-driven sales prices rebound.

Therefore, the results show the 60-day gap between the reporting quarter and filing date allows

funds to complete their information- and liquidity-driven trades before disclosure.

Next, I examine if funds adjust their trades on the stocks reflected in disclosures in

response to the demand for mandatory filing. Mutual fund trading activities seem to carry over

from the reporting quarter to the subsequent quarter prior to filing. Return patterns suggest these

trades generate abnormal returns. The evidence indicates that the past winners and past losers trade

around disclosures somewhat differently, which could potentially induce some predictability for

outside investors. This predictability, however, cannot be timely observed by the market as the

funds disclose trades with a 60-day delay.

Nonetheless, the instant demand for portfolio information with its low copycat or front-

running value suggests that investors still may use it for other purposes. For instance, the portfolio

information allows other investors to assess the impact of potential future trades of the disclosing

funds on their portfolio for defensive reasons. Alternatively, the information could be traded upon

29

during more extreme market or fund conditions and thus, on average, would not trigger an

immediate reaction.

To conclude, the documented evidence suggests that portfolio information usage has

limitations. Filing delay may degrade the opportunities to earn superior returns by mimicking fund

holdings or efficiently front-run its trades. Information loss leads to lowered disclosure value, and

investors recognize it. These findings highlight that in addition to disclosure frequency, reporting

delay also plays an essential role in mitigating mutual fund disclosure costs by reducing the

information value.

30

References

Agarwal, V., Mullally, K.A., Tang, Y. and Yang, B., 2015. Mandatory portfolio disclosure, stock

liquidity, and mutual fund performance. The Journal of Finance, 70(6), pp.2733-2776.

Barber, B.M. and Lyon, J.D., 1997. Detecting long-run abnormal stock returns: The empirical

power and specification of test statistics. Journal of financial economics, 43(3), pp.341-372.

Berk, J.B. and Green, R.C., 2004. Mutual fund flows and performance in rational markets. Journal

of political economy, 112(6), pp.1269-1295.

Bowles, B., 2019. Constrained Information Acquisition., Working paper, University of North

Carolina

Brown, S.J. and Schwarz, C., 2013. Do market participants care about portfolio disclosure?

Evidence from hedge funds’ 13F filings, Working paper, New York University, University of

California at Irvine

Brunnermeier, M.K. and Pedersen, L.H., 2005. Predatory trading. The Journal of Finance, 60(4),

pp.1825-1863.

Chen, H., Cohen, L., Gurun, U., Lou, D. and Malloy, C., 2020. IQ from IP: Simplifying search in

portfolio choice. Journal of Financial Economics, 138(1), pp.118-137.

Christoffersen, S.K., Danesh, E. and Musto, D.K., 2015. Why do institutions delay reporting their

shareholdings? Evidence from form 13F. Rotman School of Management Working Paper,

(2661535).

Crane, A., Crotty, K. and Umar, T., 2018. Do hedge funds profit from public information. Working

paper, Rice University.

Daniel, K., Grinblatt, M., Titman, S. and Wermers, R., 1997. Measuring mutual fund performance

with characteristic‐based benchmarks. The Journal of finance, 52(3), pp.1035-1058.

Drake, M.S., Quinn, P.J. and Thornock, J.R., 2017. Who uses financial statements? A demographic

analysis of financial statement downloads from EDGAR. Accounting Horizons, 31(3), pp.55-68.

31

Frank, M.M., Poterba, J.M., Shackelford, D.A. and Shoven, J.B., 2004. Copycat funds:

Information disclosure regulation and the returns to active management in the mutual fund

industry. The Journal of Law and Economics, 47(2), pp.515-541.

Gibbons, B., Iliev, P. and Kalodimos, J., 2020. Analyst information acquisition via

EDGAR. Management Science.

Hillert, A., Niessen-Ruenzi, A. and Ruenzi, S., 2016. Mutual fund shareholder letters: flows,

performance, and managerial behavior, Working Paper, Goethe University Frankfurt

Kacperczyk, M., Sialm, C. and Zheng, L., 2008. Unobserved actions of mutual funds. The Review

of Financial Studies, 21(6), pp.2379-2416.

Lakonishok, J., Shleifer, A., Thaler, R. and Vishny, R., 1991. Window dressing by pension fund

managers (No. w3617). National Bureau of Economic Research.

Puckett, A. and Yan, X., 2011. The interim trading skills of institutional investors. The Journal of

Finance, 66(2), pp.601-633.

Ryans, J., 2017. Using the EDGAR log file data set, Working paper, London Business School

Schwarz, C.G. and Potter, M.E., 2016. Revisiting mutual fund portfolio disclosure. The Review of

Financial Studies, 29(12), pp.3519-3544.

Sirri, E.R. and Tufano, P., 1998. Costly search and mutual fund flows. The journal of

finance, 53(5), pp.1589-1622.

Wang, Q., 2011. How does Portfolio Disclosure affect Institutional Trading? Evidence from their

Daily Trades, Working paper, Georgia Institute of Technology

Wermers, R., 2000. Mutual fund performance: An empirical decomposition into stock‐picking

talent, style, transactions costs, and expenses. The Journal of Finance, 55(4), pp.1655-1695.

Wermers, R. “The Potential Effects of More Frequent Portfolio Disclosure on Mutual Fund

Performance.” Investment Company Institute Perspective, June 2001, pp. 1–12.

32

Figure 1 - Raw requests after publications for all N-Q, N-CSR, and N-CSRS forms' filings

This graph depicts the number of requests for all N-Q, N-CSR, and N-CSRS forms' filings by days after disclosure. Mass downloads are not excluded. The x-axis presents the day relative to the filing event. The y-axis depicts the total number of requests for each of these days.

0

500000

1000000

1500000

2000000

2500000

3000000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

33

Figure 2 – Geolocation of the requests within 2 days after disclosures by years, worldwide and within the U.S.

This figure depicts the geolocational patterns of requests for all N-Q, N-CSR, and N-CSRS forms' filings within two days after the disclosure for 2005, 2015, and 2015 years for the U.S. and globally. Mass downloads are not excluded.

2005 2010 2015

2005 2010 2015

34

Table 1 – Mandatory disclosure delay

This table shows the distribution of filing delay (the number of days between the end of reporting period and disclosure date) by forms for the sample of domestic equity mutual funds compared to all other funds. The results are on fund level - there are duplicate fund-filing observations for the filings containing several funds.

Form Fund type

Days between the end of the reporting period and the disclosure date

N Mean Min Max

P1 P10 P20 P30 P40 P50 P60 P70 P80 P90 P99

N-CSR Other 117239 64.62 0 3713 50 57 59 62 64 66 67 68 69 70 72

N-CSR Domestic equity active 33337 64.15 0 1164 47 57 59 61 63 65 66 68 69 70 72

N-CSRS Other 110993 64.19 0 1160 49 57 59 61 63 65 66 68 69 70 72

N-CSRS Domestic equity active 31900 63.50 0 3715 46 56 58 60 62 64 66 67 68 69 72

N-Q Other 226024 57.54 0 3709 39 53 55 57 58 58 59 60 60 60 62

N-Q Domestic equity active 64680 56.86 0 881 32 52 55 56 57 58 59 59 60 60 62

35

Table 2 - Mandatory disclosure delay and information access This table presents the number of filings, the number of days between the end of the reporting period, and the disclosure for active equity domestic funds' N-Q, N-CSR, and N-CSRS filings by years and the total number of all N-Q, N-CSR, and N-CSRS forms' requests by years. The raw data on requests includes observations explicitly marked as successful, non-index requests. The screened data additionally excludes IP addresses with more than 3000 requests in a given day.

Year Filed # of fund-filing observations

Mean days between end of the reporting period and disclosure date

for active domestic equity funds

Total # of Requests for all N-Q, N-CSR, and N-CSRS forms'

Raw requests After removing mass requests

2006 8947 61.57 255329 171776 2007 10130 62.54 507154 298183 2008 10810 60.81 626544 427908 2009 11039 60.68 1284783 679912 2010 10735 60.15 2817867 803260 2011 10868 60.29 2158393 747397 2012 10860 60.26 4331929 852547 2013 10884 59.77 7438178 1644438 2014 11266 59.99 6073861 1755464 2015 11467 59.77 8678770 2046995 2016 11496 59.54 12178677 2875967

2017 (6 months) 11415 59.51 7718596 1955778

36

Table 3 - Information demand intensity Panel A demonstrates the distribution of search intensity for all N-Q, N-CSR, and N-CSRS forms' requests by active IP-addresses within 2 and 7 days after disclosure, respectively. IP-address is considered active if it made more than 1 request during selected year. Panel B presents the distribution of the number of requests by all IP-addresses for each of the domestic equity mutual funds' filings, by years. Each of the two specifications includes requests from IPs within the first 2 and 7 days after disclosure, respectively. Requests from IP addresses accessing more than 3000 files in a given day are excluded from both panels.

Panel A: Information demand intensity, by users

Active IP addresses Mean Max Min P20 P50 P80 P90 P99

# of CIKs accessed by IP-address within 2 days after disclosure, per

quarter 23443 14.99 1928.93 1 2 2.67 7 17 291

# of CIKs accessed by IP-address within 7 days after disclosure, per

quarter 34501 12.51 2123.60 1 1.67 2 6 14 226.75

Panel B: Information demand intensity, by years

Information demand measure

Year Fund-filings observations Mean Max Min P1 P20 P50 P80 P99

# of distinct

form requests within 2

days after disclosure

2006 5542 5.32 67 0 0 2 5 8 18

2007 6087 7.07 82 0 1 4 6 9 20

2008 7609 13.66 259 0 2 7 12 18 50

2009 9841 13.15 438 1 3 7 12 17 40

2010 9739 16.40 319 2 4 10 16 22 41

2011 9854 13.33 444 0 1 6 13 19 37

2012 9921 13.63 338 0 1 8 13 19 31

2013 9892 17.41 745 2 5 11 16 23 40

2014 10208 20.96 182 3 8 14 19 27 45

2015 10429 26.00 177 4 9 16 23 35 59

2016 10356 29.50 206 4 9 18 28 39 72

2017 (6 months) 4653 18.04 106 0 0 0 17 34 55

# of distinct

form requests within 7

days after disclosure

2006 5542 7.59 113 0 0 3 7 11 27

2007 6087 9.70 144 0 2 5 9 13 33

2008 7609 18.92 349 0 3 10 16 25 76

2009 9841 19.94 517 2 5 12 17 26 58

2010 9739 23.96 606 2 7 16 22 30 54

2011 9854 19.28 735 0 2 10 18 27 64

2012 9921 18.98 412 0 3 11 18 26 49

2013 9892 23.55 852 3 8 15 22 30 53

2014 10208 29.17 248 6 11 19 27 37 72

2015 10429 36.18 602 5 13 23 33 47 116

2016 10356 39.37 296 5 13 25 36 49 132

2017 (6 months) 4653 23.82 212 0 0 0 25 41 92

37

Table 4 – Information access by fund size and performance The table presents the mean number of requests for the domestic equity mutual funds' filings by performance and fund size. Each quarter, I independently ranked funds by their previous month's TNA and past performance. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding periods are 3, 6, and 12 months before the end of the reporting period in Panels A, B, and C, respectively. Each quarter, I average requests within the size-performance group. After that, I calculate the time-series average for each rank along with the t-statistic. I exclude requests from IPs accessed more than3000 filings in a given day and keep only requests within the first two days after the form filing. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Fund size

quintile

Panel A: Deciles based on past 3-months performance

1 (Low) 2 3 4 5 6 7 8 9 10 (High)

Low - Mid

High - Mid

Mean number of views

1 14.76 15.21 15.06 15.03 15.18 15.07 15.42 15.10 14.87 15.34 -0.38 0.20 (-1.59) (0.76)

2 15.63 15.54 15.41 15.69 15.14 15.60 15.66 15.22 15.23 15.36 0.28 0.01 (1.27) (0.05)

3 15.95 15.89 16.09 16.22 15.86 15.58 16.20 16.10 15.85 15.81 0.21 0.07 (0.79) (0.26)

4 16.15 16.28 16.60 16.29 16.20 16.44 16.31 16.40 16.14 16.33 -0.18 0.00 (-0.68) (0)

5 18.49 17.04 17.12 16.96 17.40 16.86 17.64 17.48 17.03 17.96 1.37** 0.84* (2.4) (1.91)

High - Low

3.73*** 1.83*** 2.06*** 1.94*** 2.22*** 1.79*** 2.22*** 2.39*** 2.16*** 2.62*** (8.14) (5.26) (4.95) (4.95) (7.37) (4.78) (5.1) (6.82) (4.29) (6.15)

Fund size

quintile

Panel B: Deciles based on past 6-months performance

1 (Low) 2 3 4 5 6 7 8 9 10 (High)

Low - Mid

High - Mid

Mean number of views

1 14.765 14.828 15.128 15.048 15.202 15.468 15.270 15.259 15.271 15.132 -0.55* -0.19 (-1.95) (-0.71)

2 15.410 15.612 15.128 15.642 15.187 15.493 15.560 15.609 15.513 15.298 0.07 -0.05 (0.28) (-0.2)

3 15.766 15.971 16.287 15.923 15.945 16.000 16.007 15.692 15.865 15.928 -0.21 -0.05 (-0.83) (-0.17)

4 15.943 16.195 16.745 16.778 16.565 16.265 16.601 15.952 15.985 16.086 -0.46 -0.32 (-1.4) (-0.99)

5 18.154 17.266 17.239 16.820 17.191 17.130 17.433 17.625 17.192 17.661 0.99* 0.490 (1.82) (1.16)

High - Low

3.39*** 2.44*** 2.11*** 1.77*** 1.99*** 1.66*** 2.16*** 2.37*** 1.92*** 2.53*** (7.29) (6.81) (5.02) (6.1) (6.37) (4.87) (5.49) (5.44) (5.83) (6.33)

Fund size

quintile

Panel C: Deciles based on past 12-months performance

1 (Low) 2 3 4 5 6 7 8 9 10 (High)

Low - Mid

High - Mid

Mean number of views

1 14.843 14.837 15.091 15.260 15.521 15.415 15.239 14.931 15.241 15.083 -0.63** -0.39 (-2.6) (-1.25)

2 15.639 15.431 15.432 15.612 15.385 15.558 15.354 15.154 15.198 15.511 0.17 0.04 (0.76) (0.17)

3 16.244 16.172 15.791 15.693 15.855 16.155 16.006 15.773 15.734 16.071 0.25 0.08 (1.02) (0.3)

4 16.151 16.512 16.393 16.754 16.343 16.511 16.324 15.954 15.795 16.222 -0.29 -0.21 (-1.09) (-0.87)

5 18.335 17.375 17.157 17.040 17.154 17.115 17.194 16.841 17.532 18.077 1.19** 0.93* (2.33) (1.98)

High - Low

3.49*** 2.54*** 2.07*** 1.78*** 1.63*** 1.7*** 1.96*** 1.91*** 2.29*** 3*** (6.98) (6.92) (8.27) (4.7) (4.58) (5.23) (5.08) (5.14) (5.45) (6.75)

38

Table 5– Fund size, performance and information access For each fund-filing, I estimate pooled OLS regression with the natural log of the number of requests within two days after disclosure as the dependent variable. Panel A presents the full sample results, while Panel B presents the coefficients by the form type subsamples. LogAge is the log of 1plus fund's age in quarters, logTNA is the log of the previous month's fund TNA, TOPSIZE is a dummy variable for funds in the top decile by the previous month's fund TNA. exp_ratio is the annual expense ratio, turn_ratio is turnover ratio, volatility12M is the standard deviation of fund previous 12-month returns. logNumOtherFilers is the log of 1 plus the number of other than domestic equity active funds filing in the same form, logNumDAEFilers is the log of 1 plus the number of domestic equity funds filing in the same form. EarlyFiling is an indicator for a form submitted earlier than the deadline. LateFilingDays is an indicator for a form submitted past the deadline. The deadlines are 60 days for N-Q and 70 days for N-CSR, N-CSRS. PastPerformance is the difference between the compounded fund return and the compounded style benchmark return (as defined by the CRSP objective code). The compounding periods are 3, 6, 12, and 24 months prior to the end of the reporting period. LOWPERF and HIGHPERF are dummy variables for funds in the bottom and top deciles by the past performance. FormNQ and formNCSRS are forms' dummies. The dataset is on fund level – forms can have duplicates if they have several filers in one form. Standard errors are clustered by fund. Year-quarter fixed effects are included in the regressions. t-statistics are in parenthesis. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Fund size, performance and information access: full sample Log (1 + Requests)

Past performance window 3 months 6 months 12 months 24 months

Intercept 1.019*** 1.018*** 1.018*** 1.018*** (33.52) (33.51) (33.53) (33.46)

logAge -0.009* -0.009* -0.009* -0.009* (-2.04) (-2.04) (-2.04) (-2.03)

logTNA 0.017*** 0.017*** 0.017*** 0.017*** (8.05) (8.03) (8.04) (8.03)

Past Performance -0.017 0.007 -0.003 -0.005 (-0.31) (0.18) (-0.1) (-0.25)

exp_ratio 1.397* 1.419* 1.41* 1.415* (2.03) (2.06) (2.05) (2.06)

turn_ratio 0.002 0.002 0.002 0.002 (1.13) (1.14) (1.13) (1.11)

volatility12M 0.426* 0.434* 0.432* 0.424* (1.66) (1.69) (1.68) (1.66)

logNumOtherFilers 0.054*** 0.054*** 0.054*** 0.054*** (12.97) (12.95) (12.98) (12.99)

logNumDAEFilers 0.043*** 0.043*** 0.043*** 0.042*** (6.11) (6.11) (6.11) (6.09)

EarlyFiling 0.029*** 0.03*** 0.03*** 0.03*** (4.82) (4.84) (4.85) (4.88)

LateFiling 0.126*** 0.126*** 0.126*** 0.126*** (13.59) (13.6) (13.61) (13.59)

TOPSIZE 0.021* 0.026* 0.022* 0.020 (1.67) (1.98) (1.74) (1.6)

TOPSIZExHIGHPERF 0.036* 0.026 0.05** 0.053** (1.72) (1.28) (2.39) (2.51)

TOPSIZExLOWPERF 0.093*** 0.054* 0.071* 0.082*** (3.34) (2.18) (2.31) (2.73)

formNQ -0.314*** -0.314*** -0.314*** -0.314*** (-45.61) (-45.54) (-45.56) (-45.53)

formNCSRS -0.083*** -0.083*** -0.083*** -0.083*** (-15.48) (-15.44) (-15.48) (-15.45)

Observations 76007 76007 76007 76007 Adjusted R-Square 0.633 0.633 0.633 0.633

39

Table 5 – Continued Panel B: Fund size, performance and information access by form type

Log (1 + Requests) Past performance

window 3 months 6 months 12 months 24 months

Form N-CSR N-CSRS N-Q N-CSR N-CSRS N-Q N-CSR N-CSRS N-Q N-CSR N-CSRS N-Q

Intercept 0.567*** 1.145*** 0.907*** 0.567*** 1.144*** 0.907*** 0.567*** 1.144*** 0.907*** 0.567*** 1.145*** 0.906*** (13.95) (24.54) (18.01) (13.94) (24.54) (18.01) (13.95) (24.53) (18.02) (13.95) (24.52) (17.98)

logAge -0.001 -0.016*** -0.011** -0.001 -0.016*** -0.011** -0.001 -0.016*** -0.011** -0.001 -0.016*** -0.011** (-0.14) (-2.7) (-1.96) (-0.15) (-2.69) (-1.96) (-0.17) (-2.7) (-1.96) (-0.14) (-2.69) (-1.96)

logTNA 0.018*** 0.025*** 0.015*** 0.018*** 0.025*** 0.015*** 0.018*** 0.025*** 0.015*** 0.018*** 0.025*** 0.015*** (5.74) (8.64) (5.48) (5.72) (8.63) (5.47) (5.64) (8.67) (5.51) (5.57) (8.56) (5.58)

Past Performance 0.094 -0.007 -0.036 0.080 -0.008 0.008 0.101* -0.039 -0.024 0.053 0.025 -0.041 (0.93) (-0.07) (-0.47) (1.09) (-0.12) (0.15) (1.87) (-0.77) (-0.6) (1.54) (0.78) (-1.53)

exp_ratio 3.181*** 2.626*** -1.015 3.214*** 2.633*** -0.996 3.237*** 2.596*** -1.029 3.221*** 2.687*** -1.051 (3.59) (3.52) (-1.22) (3.63) (3.52) (-1.19) (3.67) (3.47) (-1.23) (3.65) (3.6) (-1.26)

turn_ratio 0.003 0.000 0.003* 0.003 0.000 0.003* 0.003 0.000 0.003* 0.003 0.001 0.003* (1.47) (0.19) (1.87) (1.49) (0.19) (1.89) (1.52) (0.17) (1.85) (1.55) (0.22) (1.79)

volatility12M 0.182 0.296 0.704** 0.176 0.299 0.711** 0.223 0.290 0.7** 0.215 0.270 0.699** (0.5) (0.83) (2.11) (0.48) (0.84) (2.13) (0.61) (0.82) (2.09) (0.59) (0.76) (2.1)

logNumOtherFilers 0.063*** 0.061*** 0.041*** 0.063*** 0.061*** 0.041*** 0.063*** 0.061*** 0.041*** 0.063*** 0.061*** 0.041*** (10.71) (11.85) (8.23) (10.7) (11.86) (8.22) (10.75) (11.85) (8.23) (10.75) (11.91) (8.23)

logNumDAEFilers 0.067*** 0.016** 0.054*** 0.067*** 0.017** 0.054*** 0.067*** 0.016** 0.054*** 0.067*** 0.016** 0.053*** (6.94) (1.98) (6.55) (6.96) (1.99) (6.53) (6.95) (1.98) (6.54) (6.96) (1.98) (6.53)

EarlyFiling -0.177*** -0.162*** 0.114*** -0.177*** -0.162*** 0.114*** -0.177*** -0.162*** 0.114*** -0.177*** -0.162*** 0.115*** (-15.97) (-12.35) (16.32) (-15.97) (-12.35) (16.31) (-15.96) (-12.35) (16.33) (-15.96) (-12.32) (16.34)

LateFiling 0.076*** 0.025 0.127*** 0.076*** 0.025 0.128*** 0.077*** 0.025 0.128*** 0.076*** 0.026 0.127*** (4.93) (1.2) (11.69) (4.93) (1.2) (11.72) (4.97) (1.19) (11.73) (4.93) (1.22) (11.66)

TOPSIZE 0.028 0.032* 0.020 0.033* 0.034* 0.024 0.036* 0.029* 0.018 0.035* 0.029 0.016 (1.41) (1.76) (1.32) (1.66) (1.92) (1.52) (1.83) (1.71) (1.14) (1.8) (1.63) (1.03)

TOPSIZExHIGHPERF 0.061* 0.026 0.025 0.035 0.019 0.017 0.040 0.042 0.053* 0.034 0.032 0.067** (1.82) (0.74) (1) (1.21) (0.53) (0.62) (1.42) (1.19) (1.85) (1.15) (1.09) (2.26)

TOPSIZExLOWPERF 0.111** 0.055 0.09** 0.065* 0.039 0.061* 0.034 0.065 0.099*** 0.038 0.109* 0.084** (2.53) (1.63) (2.39) (1.73) (0.98) (1.72) (0.74) (1.17) (2.62) (0.87) (1.87) (2.08)

Observations 19171 18831 38005 19171 18831 38005 19171 18831 38005 19171 18831 38005 Adjusted R-Square 0.633 0.618 0.652 0.632 0.618 0.652 0.632 0.618 0.652 0.632 0.618 0.652

40

Table 6 – Stock returns around disclosure This table presents cumulative abnormal returns on the hypothetical portfolios of stocks traded by the reporting funds in the top-size decile. I compute returns over several windows: the period between the end of the reporting period and the disclosure, the three days starting at disclosure, the period starting three days after the disclosure, and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's previous report multiplied by the stock price at the end of the current period's date. I exclude trades for funds' first and last reports and reports with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate daily DGTW-adjusted returns and sum them up over the relevant window. Then calculate the average for each fund-disclosure portfolio. Include buying and selling trades in "Buy" and "Sell" portfolios, respectively, the stocks with unchanged positions in "Hold" portfolio, and all fund's positions in "Positions" portfolio. For VW returns in Panel A, I weigh the stocks' returns by a dollar value of trade to construct "Buy" and "Sell" portfolios and a dollar value of the position to construct "Hold" and "Positions" portfolios. For EW returns in Panel B, I average the returns with equal weights in all four portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

41

Table 6 – Continued Panel A – Value-weighted returns

Return Window Side VW returns

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.039 0.021 0.016 0.008 0.052* 0.013

t-statistic (0.91) (0.91) (0.82) (0.38) (1.8) (0.26)

Buy mean 0.013 -0.020 -0.018 -0.002 -0.001 -0.014

t-statistic (0.23) (-0.65) (-0.38) (-0.08) (-0.03) (-0.23)

Hold mean 0.060 0.035 -0.012 -0.004 0.113 0.053

t-statistic (0.83) (0.97) (-0.22) (-0.08) (1.45) (0.56)

Sell mean 0.036 0.004 -0.019 0.010 0.069 0.033

t-statistic (0.52) (0.11) (-0.37) (0.3) (1.62) (0.37)

Buy-Sell mean -0.025 -0.032 -0.004 -0.017 -0.073 -0.048

t-statistic (-0.32) (-0.62) (-0.08) (-0.45) (-1.47) (-0.46)

Between end of report

period and disclosure

Positions mean 0.361** 0.374*** 0.327*** 0.417*** 0.542*** 0.180

t-statistic (2.17) (3.64) (2.97) (3.57) (2.84) (0.83)

Buy mean 0.279 0.268** 0.303** 0.484*** 0.726*** 0.447

t-statistic (1.17) (2.16) (2.17) (4.12) (3.96) (1.37)

Hold mean -0.120 0.204 -0.016 0.189 0.072 0.191

t-statistic (-0.41) (1.11) (-0.08) (1.23) (0.26) (0.41)

Sell mean 0.587** 0.529*** 0.683** 0.211 0.267 -0.320

t-statistic (2.66) (3.93) (2.56) (1.29) (1.45) (-1.17)

Buy-Sell mean -0.279 -0.266* -0.389 0.277 0.42* 0.699

t-statistic (-0.84) (-1.71) (-1.12) (1.33) (1.76) (1.64)

After disclosure until next

end-of period date

Positions mean 0.18** 0.068 0.134* 0.115 0.068 -0.112 t-statistic (2.23) (0.99) (1.99) (1.53) (0.71) (-1.31)

Buy mean 0.097 -0.037 0.050 0.072 0.130 0.033 t-statistic (0.62) (-0.3) (0.72) (0.87) (1.4) (0.2)

Hold mean -0.106 0.082 0.011 0.066 0.120 0.226 t-statistic (-0.48) (0.5) (0.09) (0.55) (0.84) (1.08)

Sell mean 0.187 -0.053 0.027 0.036 0.140 -0.047 t-statistic (1.48) (-0.57) (0.38) (0.53) (1.43) (-0.31)

Buy-Sell mean -0.116 0.004 -0.007 0.021 0.013 0.129

t-statistic (-0.54) (0.03) (-0.07) (0.24) (0.11) (0.65)

42

Table 6 – Continued Panel B – Equal-weighted returns

Return Window Side EW returns

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.024 -0.004 -0.014 -0.011 -0.003 -0.027

t-statistic (0.74) (-0.27) (-0.86) (-0.87) (-0.13) (-0.76)

Buy mean 0.007 -0.019 -0.014 -0.027 -0.029 -0.036

t-statistic (0.13) (-0.65) (-0.44) (-1.44) (-0.95) (-0.65)

Hold mean 0.087 0.018 -0.027 -0.023 0.045 -0.043

t-statistic (1.27) (0.33) (-0.55) (-0.74) (0.59) (-0.47)

Sell mean 0.014 -0.007 -0.005 0.005 0.033 0.019

t-statistic (0.29) (-0.27) (-0.26) (0.23) (1.25) (0.34)

Buy-Sell mean -0.001 -0.012 -0.008 -0.026 -0.071** -0.070

t-statistic (-0.02) (-0.29) (-0.26) (-1.08) (-2.32) (-1.03)

Between end of report

period and disclosure

Positions mean 0.004 0.162* 0.113 0.127* 0.161 0.156

t-statistic (0.03) (1.75) (1.64) (1.72) (1.33) (0.75)

Buy mean 0.135 0.169 0.170 0.21** 0.305* 0.171

t-statistic (0.62) (1.15) (1.52) (2.19) (1.92) (0.58)

Hold mean -0.588* -0.118 -0.081 0.182 -0.187 0.401

t-statistic (-1.87) (-0.56) (-0.44) (1.16) (-0.81) (1.03)

Sell mean 0.209 0.175 0.093 -0.079 -0.048 -0.257

t-statistic (1.56) (1.57) (0.87) (-0.67) (-0.38) (-1.37)

Buy-Sell mean -0.125 -0.012 0.078 0.289*** 0.293* 0.417

t-statistic (-0.56) (-0.08) (0.6) (2.79) (2.04) (1.55)

After disclosure until next

end-of period date

Positions mean 0.122** -0.011 0.031 0.031 0.024 -0.098 t-statistic (2.15) (-0.19) (0.59) (0.86) (0.45) (-1.31)

Buy mean -0.032 -0.084 0.027 0.025 0.058 0.090 t-statistic (-0.24) (-0.66) (0.37) (0.34) (0.81) (0.68)

Hold mean 0.098 -0.002 -0.096 0.014 0.081 -0.017 t-statistic (0.57) (-0.01) (-0.79) (0.13) (0.69) (-0.09)

Sell mean 0.171* 0.008 0.070 -0.062 0.080 -0.091 t-statistic (1.78) (0.08) (1.01) (-0.87) (1.32) (-0.92)

Buy-Sell mean -0.240 -0.093 -0.061 0.083 -0.017 0.223

t-statistic (-1.43) (-0.8) (-1.06) (0.95) (-0.2) (1.38)

43

Table 7 - Short selling around disclosure This table presents the change in short interest ratio (SIR) on the hypothetical portfolios of stocks traded by the reporting funds in the top-size decile. I calculate the SIR in percent as the number of stocks borrowed divided by the number of shares outstanding and multiplied by 100. I compute change in SIR over several windows: the period between the end of the reporting period and the disclosure, the three days starting at disclosure, the period starting three days after the disclosure, and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's previous report multiplied by the stock price at the end of the current period's date. I exclude trades for funds' first and last reports and reports with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate change in SI as the difference between SIR at the end and the beginning of the relevant window. Then calculate the average for each fund-disclosure portfolio. Include buying and selling trades in "Buy" and "Sell" portfolios, respectively, the stocks with unchanged positions in "Hold" portfolio, and all fund's positions in "Positions" portfolio. For VW computation in Panel A, I weigh change in SIR by a dollar value of trade to construct "Buy" and "Sell" portfolios and a dollar value of the position to construct "Hold" and "Positions" portfolios. For EW returns in Panel B, I average change in SIR with equal weights in all four portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Value-weighted SIR

Return Window Side VW ∆SIR, %

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean -0.002 -0.002 0.002 0.002 0.008** -0.002

t-statistic (-0.54) (-0.5) (0.67) (0.42) (2.4) (-0.54)

Buy mean 0.006 0.003 0.007 0.001 0.004 0.006

t-statistic (0.66) (0.67) (1.05) (0.14) (0.72) (0.66)

Hold mean -0.002 0.013 0.002 -0.001 0.013 -0.002

t-statistic (-0.21) (1.35) (0.21) (-0.16) (1.4) (-0.21)

Sell mean -0.006 0.003 0.004 -0.005 0.005 -0.006

t-statistic (-0.79) (0.74) (0.68) (-0.97) (0.61) (-0.79)

Between end of report

period and disclosure

Positions mean -0.045 -0.01 -0.023 -0.022 -0.034 -0.045

t-statistic (-1.4) (-0.37) (-0.87) (-0.82) (-1.08) (-1.4)

Buy mean -0.024 0.01 -0.008 -0.027 0.008 -0.024

t-statistic (-0.38) (0.33) (-0.24) (-0.95) (0.17) (-0.38)

Hold mean -0.104* -0.072 -0.08* -0.013 -0.114 -0.104*

t-statistic (-1.81) (-1.45) (-1.78) (-0.3) (-1.66) (-1.81)

Sell mean 0.01 -0.021 -0.011 -0.04 -0.055 0.01

t-statistic (0.25) (-0.54) (-0.3) (-1.06) (-1.05) (0.25)

After disclosure until next

end-of period date

Positions mean -0.034* -0.02 -0.03* -0.023 -0.013 -0.034* t-statistic (-1.7) (-1.43) (-2.03) (-1.51) (-0.67) (-1.7)

Buy mean -0.025 -0.014 -0.025 -0.005 0.017 -0.025 t-statistic (-0.6) (-0.7) (-1.24) (-0.26) (0.59) (-0.6)

Hold mean -0.02 -0.017 -0.052** 0.002 -0.021 -0.02 t-statistic (-0.4) (-0.61) (-2.23) (0.06) (-0.52) (-0.4)

Sell mean -0.012 -0.02 -0.033* -0.032 -0.036 -0.012 t-statistic (-0.53) (-0.77) (-1.88) (-1.56) (-0.99) (-0.53)

44

Table 7 – Continued Panel B: Equal-weighted SIR

Return Window Side EW ∆SIR, %

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean -0.001 0 -0.002 0.001 0.007** -0.001

t-statistic (-0.2) (-0.15) (-0.37) (0.17) (2.11) (-0.2)

Buy mean 0.026 0.002 0.006 0.004 0.01** 0.026

t-statistic (0.94) (0.56) (1.01) (0.68) (2.53) (0.94)

Hold mean -0.002 0.014** 0.007 0.005 0.015* -0.002

t-statistic (-0.2) (2.13) (1.07) (1.06) (1.76) (-0.2)

Sell mean -0.009 0.002 -0.006 -0.002 0 -0.009

t-statistic (-1.7) (0.81) (-0.65) (-0.48) (0.05) (-1.7)

Between end of report

period and disclosure

Positions mean -0.045 -0.002 -0.016 -0.013 -0.025 -0.045

t-statistic (-1.22) (-0.06) (-0.51) (-0.45) (-0.7) (-1.22)

Buy mean -0.009 0.021 0.011 -0.016 0.021 -0.009

t-statistic (-0.17) (0.6) (0.3) (-0.53) (0.52) (-0.17)

Hold mean -0.115* -0.048 -0.075 0.043 -0.103 -0.115*

t-statistic (-1.83) (-1.09) (-1.42) (1.12) (-1.52) (-1.83)

Sell mean -0.018 -0.05 0.014 -0.02 -0.06 -0.018

t-statistic (-0.53) (-1.32) (0.42) (-0.57) (-1.33) (-0.53)

After disclosure until next

end-of period date

Positions mean -0.025 -0.023 -0.022 -0.014 -0.006 -0.025 t-statistic (-1.14) (-1.35) (-1.3) (-0.86) (-0.25) (-1.14)

Buy mean -0.014 -0.011 -0.013 0 0.002 -0.014 t-statistic (-0.4) (-0.51) (-0.62) (0.01) (0.07) (-0.4)

Hold mean -0.004 -0.006 -0.048** -0.007 0.005 -0.004 t-statistic (-0.07) (-0.2) (-2.07) (-0.31) (0.13) (-0.07)

Sell mean -0.013 -0.009 -0.024 -0.021 -0.023 -0.013 t-statistic (-0.56) (-0.43) (-1.23) (-1.07) (-0.71) (-0.56)

45

Table 8 – Mutual funds' trading patterns around disclosure I calculate funds' trade direction on the disclosed stocks in the subsequent reporting period based on past performance. Only the top-size decile funds are included. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. I exclude trades for funds' reports with previous holdings data older than four months. The trades are changes in positions from a fund's current report until its subsequent report; only stocks disclosed in the preceding period are included. "Strategy" presents the comparison of the fund's actions: for example, 'SALE BUY' indicates that during the current period fund purchases the stock it was selling in the reporting period. I use equally weighted trades to calculate the percentage of each strategy within current fund-disclosure trades. After that, each quarter, I average the report-level percentages within the past performance groups with equal weights. I also compute the High minus low performers' portfolio each quarter. Finally, I calculate time-series averages across quarters for each group along with a t-statistic. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Past performance

quintile Statistic

Strategy as percentage of # of trades in the disclosure SELL SELL

SELL BUY

SELL NO TRADE

BUY BUY

BUY SELL

BUY NO TRADE

NO TRADE BUY

NO TRADE SELL

NO TRADE NO TRADE

1 (Low) mean 21.40 3.88 15.96 11.18 13.31 10.80 2.39 7.37 13.71 2 mean 17.66 4.54 15.54 15.43 12.53 10.44 2.95 7.13 13.78 3 mean 17.90 5.37 14.86 16.90 12.69 10.38 3.16 5.91 12.83 4 mean 16.22 5.06 14.28 18.91 12.74 9.63 3.35 6.34 13.47

5 (High) mean 14.95 5.12 13.77 20.81 11.46 10.48 3.65 6.13 13.63

(High-Low) mean -6.46*** 1.25*** -2.19*** 9.63*** -1.85** -0.32 1.27*** -1.25** -0.08 t stats (-6.08) (3.23) (-3.2) (6.81) (-2.36) (-0.33) (4.46) (-2.37) (-0.07)

46

Table 9 – Mutual funds' trading patterns and returns This table presents cumulative abnormal returns on the hypothetical portfolios of stocks traded by the reporting funds in the top-size decile. I compute returns over two windows: the period between the end of the reporting period and disclosure, and the period starting at disclosure and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's current report until its subsequent report, multiplied by the stock price at the end of the subsequent period. Only stocks disclosed in the preceding period are included. I exclude trades for funds with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate daily DGTW-adjusted returns and sum them up over the relevant window. Then calculate the average for each fund-disclosure portfolio. The portfolios are defined based on trade direction in Table8. For VW returns in Panel A, I weigh the stocks' returns by a dollar value of trade direction. For EW returns in Panel B, I average the returns with equal weights in all portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

47

Table 9 – Continued Panel A – Value-weighted returns

Between end of period and

disclosure

Side VW returns Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

BUY-BUY mean -0.865** 0.003 0.042 0.128 -0.335 0.530

t-statistic (-2.74) (0.02) (0.28) (0.78) (-1.66) (1.45)

SELL-BUY mean -1.294* -1.165*** -0.491* -0.450 -1.008*** 0.286

t-statistic (-1.95) (-2.94) (-1.75) (-1.69) (-2.81) (0.38)

HOLD-BUY mean -1.578*** -0.911** 0.177 0.202 -0.915** 0.663

t-statistic (-2.87) (-2.13) (0.35) (0.68) (-2.61) (1.05)

BUY-HOLD mean 0.817 0.201 0.751*** 0.596** 1.209*** 0.392

t-statistic (1.5) (0.55) (3.45) (2.08) (4.27) (0.63)

HOLD-HOLD mean 0.225 -0.031 -0.112 0.148 0.473** 0.248

t-statistic (0.41) (-0.13) (-0.54) (0.42) (2.27) (0.45)

SELL-HOLD mean -0.464 -0.545 -0.181 -0.241 -0.665** -0.201

t-statistic (-1.63) (-1.6) (-0.81) (-1.1) (-2.43) (-0.52)

BUY-SELL mean 1.245*** 1.277*** 0.669** 0.733*** 1.035** -0.211

t-statistic (2.97) (3.6) (2.35) (3.55) (2.66) (-0.36)

HOLD-SELL mean 0.655* 1.314*** 0.607* 0.871*** 1.169*** 0.514

t-statistic (1.89) (3.32) (1.99) (3.29) (3.02) (1.03)

SELL-SELL mean 1.404*** 1.217*** 0.726*** 0.784*** 0.812*** -0.593*

t-statistic (5.4) (8.11) (4.14) (3.91) (3.85) (-1.7)

After disclosure until next

end-of-period date

BUY-BUY mean 0.376 0.339** 0.363*** 0.26** 0.140 -0.236

t-statistic (1.63) (2.24) (3.17) (2.08) (0.83) (-0.79)

SELL-BUY mean -0.152 -0.073 0.045 0.338*** 0.235 0.387

t-statistic (-0.42) (-0.36) (0.25) (3) (0.9) (0.87)

HOLD-BUY mean 0.368 0.137 0.089 0.272 0.091 -0.277

t-statistic (0.94) (0.55) (0.29) (1.28) (0.43) (-0.63)

BUY-HOLD mean 0.015 -0.211 0.164 0.216 0.323* 0.307

t-statistic (0.05) (-1.3) (0.85) (1.18) (1.97) (1.05)

HOLD-HOLD mean 0.303 0.170 -0.175 0.045 0.264 -0.039

t-statistic (1.54) (0.75) (-1.2) (0.29) (1.38) (-0.13)

SELL-HOLD mean 0.104 -0.034 -0.005 0.058 -0.097 -0.201

t-statistic (0.37) (-0.15) (-0.03) (0.37) (-0.54) (-0.55)

BUY-SELL mean 0.280 0.073 0.203 0.189 0.138 -0.142

t-statistic (0.64) (0.4) (1.36) (1.26) (0.83) (-0.29)

HOLD-SELL mean 0.394* 0.233 0.226 0.444** 0.668*** 0.275

t-statistic (1.77) (1.16) (1.27) (2.07) (3.25) (0.87)

SELL-SELL mean 0.575*** 0.423*** 0.393** 0.333*** 0.301** -0.273

t-statistic (3.23) (4.28) (2.44) (3.05) (2.67) (-1.36)

48

Table 9 – Continued Panel B – Equal-weighted returns

Return Window

Side VW returns Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

BUY-BUY mean -1.033*** -0.238 -0.191 -0.017 -0.351** 0.682*

t-statistic (-3.29) (-1.38) (-1.24) (-0.13) (-2.06) (1.99)

SELL-BUY mean -1.091* -1.358*** -0.797*** -0.644** -1.073*** 0.018

t-statistic (-1.89) (-4.15) (-3.01) (-2.71) (-5) (0.03)

HOLD-BUY mean -1.433*** -0.966* -0.397 -0.257 -0.948*** 0.485

t-statistic (-3.22) (-2.03) (-0.97) (-0.88) (-3.66) (0.84)

BUY-HOLD mean 1.008* 0.249 0.718*** 0.501* 0.905*** -0.103

t-statistic (1.93) (0.72) (3.32) (1.94) (3.47) (-0.17)

HOLD-HOLD mean -0.121 -0.117 -0.060 0.194 0.329* 0.449

t-statistic (-0.26) (-0.55) (-0.35) (0.57) (1.8) (0.91)

SELL-HOLD mean -0.310 -0.579* -0.298 -0.413** -0.904*** -0.593

t-statistic (-0.99) (-1.83) (-1.33) (-2.17) (-3.48) (-1.58)

BUY-SELL mean 0.642* 0.756*** 0.455* 0.486** 0.645** 0.003

t-statistic (1.86) (2.96) (2.03) (2.43) (2.09) (0.01)

HOLD-SELL mean 0.003 0.276 0.145 0.250 0.330 0.327

t-statistic (0.01) (0.86) (0.66) (1.09) (0.98) (0.79)

SELL-SELL mean 0.602*** 0.465*** 0.162 0.151 0.207 -0.396

t-statistic (3.02) (3.71) (0.97) (0.94) (1.35) (-1.62)

BUY-BUY mean 0.159 0.063 -0.021 -0.057 0.081 -0.078

t-statistic (0.61) (0.46) (-0.22) (-0.54) (0.67) (-0.28)

SELL-BUY mean -0.259 -0.146 -0.010 0.150 -0.018 0.241

t-statistic (-0.79) (-0.74) (-0.06) (1.27) (-0.1) (0.67)

HOLD-BUY mean 0.224 -0.018 -0.114 0.060 -0.038 -0.262

t-statistic (0.65) (-0.07) (-0.4) (0.3) (-0.19) (-0.64)

BUY-HOLD mean 0.166 -0.182 0.054 0.090 0.251* 0.085

t-statistic (0.65) (-1.18) (0.28) (0.48) (1.85) (0.31)

HOLD-HOLD mean 0.366** 0.125 -0.233* -0.033 0.222 -0.144

t-statistic (2.05) (0.67) (-1.77) (-0.2) (1.28) (-0.54)

SELL-HOLD mean 0.007 0.007 -0.214 -0.028 0.019 0.013

t-statistic (0.02) (0.03) (-1.21) (-0.16) (0.12) (0.04)

BUY-SELL mean -0.069 -0.199 -0.141 -0.056 -0.110 -0.041

t-statistic (-0.29) (-1.45) (-1.07) (-0.45) (-0.74) (-0.13)

HOLD-SELL mean 0.233 -0.100 -0.236 0.231 0.159 -0.074

t-statistic (1.1) (-0.53) (-1.43) (1.26) (0.97) (-0.28)

SELL-SELL mean 0.218* 0.156 0.045 0.024 0.010 -0.207

t-statistic (1.94) (1.6) (0.41) (0.25) (0.12) (-1.68)

49

Appendix Table 4 B – Information access by size and performance (# of IPs)

The table presents the mean number of IP-addresses accessed the domestic equity mutual funds' filings by performance and fund size. Each quarter, I independently ranked funds by their previous month's TNA and past performance. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding periods are 3, 6, and 12 months before the end of the reporting period in Panels A, B, and C, respectively. Each quarter, I average requests within the size-performance group. After that, I calculate the time-series average for each rank along with the t-statistic. I exclude requests from IPs accessed more than 3000 filings in a given day and keep only requests within the first two days after the form filing. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Fund size

quintile

Panel A: Deciles based on past 3-months performance

1 (Low) 2 3 4 5 6 7 8 9 10 (High)

Low - Mid

High - Mid

Mean number of IPs

1 14.178 14.475 14.306 14.259 14.411 14.321 14.633 14.267 14.159 14.668 -0.20 0.29 (-0.94) (1.27)

2 14.861 14.746 14.647 14.871 14.358 14.819 14.865 14.462 14.477 14.642 0.29 0.07 (1.49) (0.39)

3 15.080 15.013 15.136 15.305 15.078 14.800 15.316 15.249 14.990 14.945 0.13 -0.01 (0.49) (-0.03)

4 15.281 15.355 15.561 15.417 15.189 15.471 15.296 15.464 15.293 15.519 -0.05 0.19 (-0.22) (0.79)

5 17.239 15.782 16.050 15.787 16.168 15.659 16.425 16.238 15.947 16.606 1.33** 0.7* (2.65) (1.77)

High - Low

3.06*** 1.31*** 1.75*** 1.53*** 1.76*** 1.34*** 1.79*** 1.97*** 1.79*** 1.94*** (7.24) (4.26) (4.91) (4.49) (7.2) (4.54) (4.25) (7.05) (4.14) (5.12)

Fund size

quintile

Panel B: Deciles based on past 6-months performance

1 (Low) 2 3 4 5 6 7 8 9 10 (High)

Low - Mid

High - Mid

Mean number of IPs

1 14.197 14.176 14.399 14.194 14.380 14.667 14.470 14.492 14.573 14.466 -0.30 -0.03 (-1.27) (-0.15)

2 14.653 14.857 14.335 14.785 14.434 14.791 14.790 14.725 14.777 14.585 0.04 -0.03 (0.18) (-0.14)

3 14.855 15.086 15.336 14.999 15.104 15.181 15.134 14.909 15.060 15.030 -0.30 -0.12 (-1.32) (-0.5)

4 15.147 15.334 15.628 15.709 15.584 15.361 15.523 15.170 15.152 15.217 -0.32 -0.25 (-1.08) (-0.86)

5 17.108 15.974 15.994 15.609 15.972 15.967 16.160 16.459 16.041 16.386 1.13** 0.410 (2.33) (1.16)

High - Low

2.91*** 1.8*** 1.6*** 1.42*** 1.59*** 1.3*** 1.69*** 1.97*** 1.47*** 1.92*** (6.76) (6.1) (4.56) (6.15) (6.34) (4.85) (5.3) (4.74) (5.61) (5.73)

Fund size

quintile

Panel C: Deciles based on past 12-months performance

1 (Low) 2 3 4 5 6 7 8 9 10 (High)

Low - Mid

High - Mid

Mean number of IPs

1 14.249 14.176 14.370 14.372 14.568 14.638 14.550 14.233 14.526 14.401 -0.35* -0.20 (-1.8) (-0.76)

2 14.865 14.676 14.660 14.737 14.576 14.803 14.609 14.443 14.516 14.730 0.18 0.04 (0.85) (0.19)

3 15.233 15.284 14.974 14.812 14.929 15.285 15.131 15.009 15.000 15.140 0.13 0.04 (0.62) (0.17)

4 15.325 15.535 15.471 15.632 15.365 15.516 15.353 15.220 14.971 15.304 -0.12 -0.14 (-0.56) (-0.61)

5 17.269 16.094 15.897 15.768 15.950 15.830 16.015 15.848 16.309 16.785 1.37*** 0.89** (2.91) (2.05)

High - Low

3.02*** 1.92*** 1.53*** 1.4*** 1.38*** 1.19*** 1.47*** 1.62*** 1.78*** 2.38*** (6.56) (6.1) (6.68) (4.37) (5.16) (4.28) (4.71) (5.15) (5.23) (6.05)

50

Table 6 B – Stock returns around disclosure, by 6-month performance This table presents cumulative abnormal returns on the hypothetical portfolios of stocks traded by the reporting funds in the top-size decile. I compute returns over several windows: the period between the end of the reporting period and the disclosure, the three days starting at disclosure, the period starting three days after the disclosure, and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 6 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's previous report multiplied by the stock price at the end of the current period's date. I exclude trades for funds' first and last reports and reports with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate daily DGTW-adjusted returns and sum them up over the relevant window. Then calculate the average for each fund-disclosure portfolio. Include buying and selling trades in "Buy" and "Sell" portfolios, respectively, the stocks with unchanged positions in "Hold" portfolio, and all fund's positions in "Positions" portfolio. For VW returns in Panel A, I weigh the stocks' returns by a dollar value of trade to construct "Buy" and "Sell" portfolios and a dollar value of the position to construct "Hold" and "Positions" portfolios. For EW returns in Panel B, I average the returns with equal weights in all four portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Value-weighted returns

Return Window Side VW returns

Past 6-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean -0.003 0.014 0.037* 0.043** 0.021 0.019 t-statistic (-0.08) (0.66) (1.93) (2.68) (0.74) (0.63)

Buy mean -0.111* -0.024 0.025 0.046 -0.011 0.091* t-statistic (-2.03) (-0.73) (0.93) (1.63) (-0.28) (1.74)

Hold mean 0.000 0.041 -0.010 0.031 0.111 0.100 t-statistic (0) (1.13) (-0.28) (0.83) (1.37) (1.04)

Sell mean 0.015 -0.019 -0.005 0.044* 0.051 0.044

t-statistic (0.37) (-0.6) (-0.17) (1.94) (1.31) (0.84)

Buy-Sell mean -0.11* -0.029 0.035 -0.017 -0.042 0.039

t-statistic (-1.89) (-0.61) (0.95) (-0.48) (-0.71) (0.48)

Between end of report

period and disclosure

Positions mean 0.655*** 0.333*** 0.396*** 0.412*** 0.453** -0.355 t-statistic (3.67) (3.08) (3.23) (3.98) (2.2) (-1.63)

Buy mean 0.541** 0.455*** 0.46*** 0.4*** 0.586** -0.102 t-statistic (2.14) (4.47) (3.7) (2.9) (2.42) (-0.29)

Hold mean 0.403* -0.080 -0.034 0.395*** -0.068 -0.675* t-statistic (1.96) (-0.39) (-0.18) (2.81) (-0.19) (-1.89)

Sell mean 0.813*** 0.329** 0.314** 0.31** 0.349* -0.437* t-statistic (4.45) (2.05) (2.31) (2.09) (2.01) (-1.9)

Buy-Sell mean -0.356 0.161 0.157 0.079 0.080 0.457 t-statistic (-1.35) (0.97) (0.93) (0.35) (0.4) (1.38)

After disclosure until next

end-of period date

Positions mean 0.083 0.153** 0.129** 0.105 0.002 -0.029 t-statistic (1.1) (2.49) (2.17) (1.34) (0.02) (-0.27)

Buy mean -0.002 0.202* -0.035 0.098 -0.014 0.055

t-statistic (-0.01) (1.88) (-0.38) (1.07) (-0.12) (0.31)

Hold mean -0.008 0.068 0.008 0.109 -0.092 0.067 t-statistic (-0.05) (0.51) (0.07) (0.94) (-0.41) (0.29)

Sell mean 0.174 0.046 -0.006 0.076 0.096 -0.136 t-statistic (1.54) (0.42) (-0.07) (1.27) (0.93) (-1.15)

Buy-Sell mean -0.185 0.125 -0.039 0.018 -0.097 0.189

t-statistic (-0.9) (0.85) (-0.36) (0.16) (-0.6) (0.82)

51

Table 6 B – Continued Panel B: Equal-weighted returns

Return Window Side EW returns

Past 6-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean -0.029 0.001 0.002 0.001 0.001 0.016

t-statistic (-1.13) (0.06) (0.14) (0.06) (0.05) (0.71)

Buy mean -0.062 -0.014 -0.004 0.001 -0.015 0.028 t-statistic (-1.16) (-0.54) (-0.16) (0.04) (-0.4) (0.52)

Hold mean -0.001 0.036 -0.015 -0.030 0.089 0.090

t-statistic (-0.01) (0.91) (-0.4) (-0.71) (1.16) (1.02)

Sell mean -0.007 -0.031 0.014 0.007 0.019 0.026

t-statistic (-0.21) (-1.03) (0.7) (0.4) (0.73) (0.71)

Buy-Sell mean -0.033 -0.003 -0.010 -0.013 -0.023 -0.016

t-statistic (-0.58) (-0.08) (-0.37) (-0.49) (-0.5) (-0.27)

Between end of report

period and disclosure

Positions mean 0.368** 0.142* 0.115 0.142** 0.083 -0.444** t-statistic (2.63) (1.73) (1.58) (2.21) (0.42) (-2.27)

Buy mean 0.399* 0.242* 0.247** 0.194* 0.204 -0.336

t-statistic (1.93) (1.98) (2.55) (1.82) (0.91) (-1.23)

Hold mean 0.198 -0.250 -0.197 0.165 -0.235 -0.583

t-statistic (0.74) (-1.17) (-1.1) (1.01) (-0.75) (-1.45)

Sell mean 0.402*** 0.001 0.032 0.019 -0.069 -0.467**

t-statistic (3.14) (0.01) (0.22) (0.19) (-0.52) (-2.45)

Buy-Sell mean -0.074 0.245** 0.214* 0.158 0.091 0.225 t-statistic (-0.48) (2.25) (1.77) (0.99) (0.63) (1.24)

After disclosure until next

end-of period date

Positions mean 0.010 0.080 0.012 0.032 -0.044 -0.007

t-statistic (0.17) (1.54) (0.26) (0.62) (-0.66) (-0.1)

Buy mean -0.173 0.130 -0.027 0.008 -0.006 0.225

t-statistic (-1.16) (1.14) (-0.34) (0.1) (-0.07) (1.63)

Hold mean 0.076 0.064 -0.124 0.041 -0.143 -0.072 t-statistic (0.5) (0.48) (-1.18) (0.33) (-0.7) (-0.37)

Sell mean 0.162** 0.076 -0.017 0.060 0.009 -0.167** t-statistic (2.07) (0.76) (-0.23) (1) (0.14) (-2.25)

Buy-Sell mean -0.342** 0.040 -0.003 -0.070 -0.010 0.385**

t-statistic (-2.1) (0.31) (-0.05) (-0.87) (-0.11) (2.34)

52

Table 6 C – Stock returns around disclosure, full sample This table presents cumulative abnormal returns on the hypothetical portfolios of stocks traded by the full sample of reporting funds. I compute returns over several windows: the period between the end of the reporting period and the disclosure, the three days starting at disclosure, the period starting three days after the disclosure, and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's previous report multiplied by the stock price at the end of the current period's date. I exclude trades for funds' first and last reports and reports with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate daily DGTW-adjusted returns and sum them up over the relevant window. Then calculate the average for each fund-disclosure portfolio. Include buying and selling trades in "Buy" and "Sell" portfolios, respectively, the stocks with unchanged positions in "Hold" portfolio, and all fund's positions in "Positions" portfolio. For VW returns in Panel A, I weigh the stocks' returns by a dollar value of trade to construct "Buy" and "Sell" portfolios and a dollar value of the position to construct "Hold" and "Positions" portfolios. For EW returns in Panel B, I average the returns with equal weights in all four portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Value-weighted returns

Return Window Side VW returns

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.019 0.028** 0.034*** 0.022 0.036** 0.017 t-statistic (1.51) (2.39) (3.22) (1.57) (2.21) (1.01)

Buy mean 0.033* 0.035** 0.007 0.013 0.008 -0.025 t-statistic (1.81) (2.17) (0.45) (0.79) (0.41) (-0.86)

Hold mean -0.078 0.012 0.002 -0.005 0.003 0.081 t-statistic (-1.26) (0.67) (0.1) (-0.33) (0.11) (1.16)

Sell mean 0.018 -0.003 0.012 0.016 0.000 -0.019 t-statistic (0.85) (-0.17) (0.79) (1.19) (-0.02) (-0.59)

Buy-Sell mean 0.012 0.036 -0.005 -0.004 0.005 -0.007 t-statistic (0.5) (1.52) (-0.29) (-0.2) (0.26) (-0.22)

Between end of report

period and disclosure

Positions mean 0.398*** 0.324*** 0.336*** 0.359*** 0.354** -0.045 t-statistic (3.68) (3.59) (3.49) (3.17) (2.34) (-0.32)

Buy mean 0.363*** 0.386*** 0.33*** 0.459*** 0.457*** 0.094

t-statistic (3.25) (4.49) (3.97) (4.93) (3.9) (0.6)

Hold mean 0.252** 0.081 0.218** 0.286*** 0.226 -0.026

t-statistic (2.68) (1.11) (2.17) (3.13) (1.63) (-0.13)

Sell mean 0.416*** 0.362*** 0.375*** 0.283*** 0.3** -0.115

t-statistic (3.77) (4.42) (4.37) (2.95) (2.33) (-0.88)

Buy-Sell mean -0.074 0.058 -0.056 0.198** 0.168 0.242* t-statistic (-0.62) (0.62) (-0.63) (2.42) (1.51) (1.9)

After disclosure until next

end-of period date

Positions mean 0.035 0.071 0.077 0.074 0.073 0.039 t-statistic (0.64) (1.37) (1.34) (1.13) (0.94) (0.57)

Buy mean -0.015 0.013 0.040 0.048 0.043 0.058

t-statistic (-0.21) (0.24) (0.85) (1.31) (0.69) (0.67)

Hold mean 0.004 0.094 0.046 0.114 0.084 0.081 t-statistic (0.05) (1.6) (0.83) (1.62) (1.64) (0.9)

Sell mean -0.060 0.022 0.015 -0.022 0.004 0.063 t-statistic (-0.91) (0.44) (0.29) (-0.46) (0.05) (0.66)

Buy-Sell mean 0.059 -0.006 0.022 0.077* 0.043 -0.016

t-statistic (0.82) (-0.13) (0.47) (1.72) (0.65) (-0.15)

53

Table 6 C – Continued Panel B: Equal-weighted returns

Return Window Side EW returns

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.007 0.012 0.006 0.002 0.008 0.002

t-statistic (0.58) (1.29) (0.76) (0.26) (0.61) (0.09)

Buy mean 0.034* 0.03* -0.007 0.006 -0.005 -0.040 t-statistic (1.75) (1.93) (-0.53) (0.55) (-0.36) (-1.49)

Hold mean -0.089 -0.012 -0.009 -0.017 -0.021 0.068

t-statistic (-1.47) (-0.63) (-0.49) (-1.27) (-0.88) (1.06)

Sell mean -0.001 -0.007 0.013 0.010 0.000 0.001

t-statistic (-0.05) (-0.69) (1.1) (0.98) (0.02) (0.06)

Buy-Sell mean 0.031* 0.038** -0.021 -0.003 -0.011 -0.043

t-statistic (1.7) (2.11) (-1.57) (-0.26) (-0.76) (-1.61)

Between end of report

period and disclosure

Positions mean 0.131* 0.085 0.090 0.088 0.046 -0.085 t-statistic (1.72) (1.37) (1.59) (1.48) (0.52) (-0.69)

Buy mean 0.138 0.129 0.108 0.164* 0.138 0.001

t-statistic (1.12) (1.37) (1.24) (1.83) (1.24) (0.01)

Hold mean 0.054 -0.086 0.098 0.162* -0.014 -0.067

t-statistic (0.55) (-1.04) (1.1) (1.86) (-0.12) (-0.37)

Sell mean 0.212** 0.106* 0.093 0.037 0.007 -0.205

t-statistic (2.43) (1.73) (1.39) (0.61) (0.08) (-1.59)

Buy-Sell mean -0.099 0.051 0.003 0.139** 0.123 0.223** t-statistic (-1.04) (0.71) (0.05) (2.23) (1.58) (2.24)

After disclosure until next

end-of period date

Positions mean -0.052 -0.016 -0.006 0.006 0.023 0.075

t-statistic (-1.33) (-0.43) (-0.16) (0.2) (0.5) (1.24)

Buy mean -0.090 -0.029 -0.012 0.018 0.005 0.095

t-statistic (-1.44) (-0.53) (-0.25) (0.46) (0.1) (1.3)

Hold mean -0.052 0.036 -0.006 0.052 0.047 0.099 t-statistic (-0.73) (0.63) (-0.14) (0.92) (0.91) (0.98)

Sell mean -0.046 -0.042 -0.033 -0.055* -0.050 -0.004 t-statistic (-0.8) (-0.88) (-0.79) (-1.7) (-0.95) (-0.05)

Buy-Sell mean -0.035 0.012 0.021 0.082** 0.049 0.084

t-statistic (-0.64) (0.28) (0.61) (2.32) (1.35) (1.17)

54

Table 7 B - Short selling around disclosure, by 6-month performance This table presents the change in short interest ratio (SIR) on the hypothetical portfolios of stocks traded by the reporting funds in the top-size decile. I calculate the SIR in percent as the number of stocks borrowed divided by the number of shares outstanding and multiplied by 100. I compute change in SIR over several windows: the period between the end of the reporting period and the disclosure, the three days starting at disclosure, the period starting three days after the disclosure, and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 6 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's previous report multiplied by the stock price at the end of the current period's date. I exclude trades for funds' first and last reports and reports with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate change in SI as the difference between SIR at the end and the beginning of the relevant window. Then calculate the average for each fund-disclosure portfolio. Include buying and selling trades in "Buy" and "Sell" portfolios, respectively, the stocks with unchanged positions in "Hold" portfolio, and all fund's positions in "Positions" portfolio. For VW computation in Panel A, I weigh change in SIR by a dollar value of trade to construct "Buy" and "Sell" portfolios and a dollar value of the position to construct "Hold" and "Positions" portfolios. For EW returns in Panel B, I average change in SIR with equal weights in all four portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Value-weighted SIR

Return Window Side VW ∆SIR, %

Past 6-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean -0.003 0 0.004* 0.002 0.007* -0.003

t-statistic (-0.53) (0.01) (1.71) (0.51) (1.98) (-0.53)

Buy mean 0.009 -0.006 0.006 -0.001 0.006 0.009

t-statistic (1.03) (-1.35) (1.07) (-0.12) (1.02) (1.03)

Hold mean 0.001 0.014 0.008 -0.005 0.011 0.001

t-statistic (0.07) (1.05) (1.2) (-0.88) (1) (0.07)

Sell mean 0.001 -0.005 0.004 -0.005 0.008 0.001

t-statistic (0.33) (-0.73) (0.63) (-0.97) (1.18) (0.33)

Between end of report

period and disclosure

Positions mean -0.019 -0.036 -0.031 -0.029 -0.021 -0.019

t-statistic (-0.59) (-1.41) (-1.28) (-1.08) (-0.63) (-0.59)

Buy mean -0.045 0.013 -0.01 -0.044 0.025 -0.045

t-statistic (-0.93) (0.34) (-0.35) (-1.17) (0.54) (-0.93)

Hold mean -0.028 -0.064* -0.047 -0.122** -0.032 -0.028

t-statistic (-0.3) (-2.03) (-1.17) (-2.62) (-0.47) (-0.3)

Sell mean -0.04 -0.043 0.003 -0.03 -0.041 -0.04

t-statistic (-1.21) (-0.92) (0.06) (-0.94) (-0.83) (-1.21)

After disclosure until next

end-of period date

Positions mean -0.037* -0.021 -0.021 -0.026* -0.015 -0.037* t-statistic (-2.03) (-1.31) (-1.45) (-1.73) (-0.73) (-2.03)

Buy mean 0.023 -0.021 -0.025 -0.015 0.015 0.023 t-statistic (0.5) (-0.99) (-1.15) (-0.74) (0.45) (0.5)

Hold mean -0.051 0.015 -0.015 -0.03 0.018 -0.051 t-statistic (-1.23) (0.45) (-0.5) (-0.95) (0.33) (-1.23)

Sell mean -0.03 -0.036 -0.017 -0.024 -0.03 -0.03 t-statistic (-1.18) (-1.42) (-0.79) (-1.34) (-0.88) (-1.18)

55

Table 7 B – Continued Panel B: Equal-weighted SIR

Return Window Side EW ∆SIR, %

Past 6-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.004 -0.001 0.003 -0.002 0.006 0.004

t-statistic (0.97) (-0.16) (0.86) (-0.47) (1.53) (0.97)

Buy mean 0.027 -0.005 0.003 0.003 0.008* 0.027

t-statistic (1.39) (-1.29) (0.71) (0.62) (1.79) (1.39)

Hold mean 0.008 0.015* 0.013** -0.005 0.016 0.008

t-statistic (1.15) (1.77) (2.42) (-0.72) (1.57) (1.15)

Sell mean 0.003 -0.003 0.002 -0.002 0 0.003

t-statistic (0.73) (-0.63) (0.41) (-0.43) (-0.06) (0.73)

Between end of report

period and disclosure

Positions mean -0.024 -0.023 -0.021 -0.021 -0.02 -0.024

t-statistic (-0.68) (-0.77) (-0.72) (-0.63) (-0.56) (-0.68)

Buy mean -0.02 0.017 0.003 -0.026 0.022 -0.02

t-statistic (-0.44) (0.46) (0.08) (-0.68) (0.5) (-0.44)

Hold mean 0.012 -0.074** 0.004 -0.095* -0.023 0.012

t-statistic (0.14) (-2.16) (0.13) (-1.93) (-0.34) (0.14)

Sell mean -0.032 -0.042 -0.012 -0.007 -0.065 -0.032

t-statistic (-0.94) (-1.08) (-0.36) (-0.21) (-1.43) (-0.94)

After disclosure until next

end-of period date

Positions mean -0.023 -0.014 -0.021 -0.022 -0.013 -0.023 t-statistic (-1) (-0.75) (-1.18) (-1.26) (-0.67) (-1)

Buy mean 0.027 -0.008 -0.016 -0.019 0.005 0.027 t-statistic (0.73) (-0.31) (-0.78) (-0.9) (0.17) (0.73)

Hold mean -0.026 0.021 -0.031 -0.017 0.023 -0.026 t-statistic (-0.6) (0.7) (-1.38) (-0.5) (0.55) (-0.6)

Sell mean -0.015 -0.035 -0.019 -0.007 -0.025 -0.015 t-statistic (-0.62) (-1.52) (-0.88) (-0.34) (-1.09) (-0.62)

56

Table 7 C - Short selling around disclosure, full sample This table presents the change in short interest ratio (SIR) on the hypothetical portfolios of stocks traded by the full sample of reporting funds. I calculate the SIR in percent as the number of stocks borrowed divided by the number of shares outstanding and multiplied by 100. I compute change in SIR over several windows: the period between the end of the reporting period and the disclosure, the three days starting at disclosure, the period starting three days after the disclosure, and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's previous report multiplied by the stock price at the end of the current period's date. I exclude trades for funds' first and last reports and reports with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate change in SI as the difference between SIR at the end and the beginning of the relevant window. Then calculate the average for each fund-disclosure portfolio. Include buying and selling trades in "Buy" and "Sell" portfolios, respectively, the stocks with unchanged positions in "Hold" portfolio, and all fund's positions in "Positions" portfolio. For VW computation in Panel A, I weigh change in SIR by a dollar value of trade to construct "Buy" and "Sell" portfolios and a dollar value of the position to construct "Hold" and "Positions" portfolios. For EW returns in Panel B, I average change in SIR with equal weights in all four portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Value-weighted SIR

Return Window Side VW ∆SIR, %

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.003 0.002 0.002 0 0.003 0.003

t-statistic (1.11) (0.51) (0.56) (0.03) (1.04) (1.11)

Buy mean 0.001 -0.002 0.001 -0.001 0.006 0.001

t-statistic (0.13) (-0.55) (0.31) (-0.43) (1.4) (0.13)

Hold mean 0.015 0.003 0.002 -0.004 0.001 0.015

t-statistic (1.41) (0.81) (0.46) (-1.04) (0.31) (1.41)

Sell mean -0.002 0.003 0.002 -0.001 0 -0.002

t-statistic (-0.52) (0.69) (0.56) (-0.26) (0.03) (-0.52)

Between end of report

period and disclosure

Positions mean -0.055 -0.019 -0.011 -0.012 -0.009 -0.055

t-statistic (-1.52) (-0.71) (-0.4) (-0.44) (-0.28) (-1.52)

Buy mean -0.04 0.008 0.005 0.001 0.015 -0.04

t-statistic (-0.95) (0.24) (0.17) (0.03) (0.39) (-0.95)

Hold mean -0.036 -0.027 -0.03 -0.003 -0.02 -0.036

t-statistic (-1.08) (-0.87) (-1.07) (-0.1) (-0.57) (-1.08)

Sell mean -0.032 -0.015 -0.018 -0.015 -0.013 -0.032

t-statistic (-0.9) (-0.49) (-0.63) (-0.46) (-0.33) (-0.9)

After disclosure until next

end-of period date

Positions mean -0.02 -0.019 -0.021 -0.019 -0.018 -0.02 t-statistic (-1.11) (-1.21) (-1.32) (-1.23) (-1.03) (-1.11)

Buy mean -0.007 -0.013 -0.015 -0.013 -0.013 -0.007 t-statistic (-0.33) (-0.65) (-0.71) (-0.7) (-0.55) (-0.33)

Hold mean -0.012 -0.021 -0.013 -0.015 -0.003 -0.012 t-statistic (-0.61) (-1.25) (-0.89) (-0.87) (-0.14) (-0.61)

Sell mean -0.023 -0.029 -0.019 -0.026 -0.015 -0.023 t-statistic (-1.1) (-1.67) (-1.08) (-1.51) (-0.73) (-1.1)

57

Table 7 C – Continued Panel B: Equal-weighted SIR

Return Window Side VW ∆SIR, %

Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

[0:2]

Positions mean 0.002 0 0.001 -0.001 0.002 0.002

t-statistic (0.71) (0.08) (0.25) (-0.3) (0.61) (0.71)

Buy mean 0 -0.001 0.002 -0.001 0.006* 0

t-statistic (0.07) (-0.27) (0.56) (-0.33) (1.86) (0.07)

Hold mean 0.013 -0.001 0.003 -0.002 0.002 0.013

t-statistic (1.45) (-0.34) (1.07) (-0.47) (0.37) (1.45)

Sell mean -0.003 0.002 0 -0.001 -0.001 -0.003

t-statistic (-0.74) (0.43) (-0.03) (-0.27) (-0.21) (-0.74)

Between end of report

period and disclosure

Positions mean -0.041 -0.008 -0.001 0.003 0.005 -0.041

t-statistic (-1.19) (-0.26) (-0.03) (0.09) (0.15) (-1.19)

Buy mean -0.017 0.018 0.013 0.019 0.034 -0.017

t-statistic (-0.41) (0.54) (0.43) (0.65) (0.92) (-0.41)

Hold mean -0.026 -0.007 -0.025 0.008 -0.014 -0.026

t-statistic (-0.79) (-0.2) (-0.84) (0.24) (-0.4) (-0.79)

Sell mean -0.027 -0.015 0.002 -0.007 -0.017 -0.027

t-statistic (-0.86) (-0.5) (0.08) (-0.21) (-0.43) (-0.86)

After disclosure until next

end-of period date

Positions mean -0.018 -0.016 -0.018 -0.017 -0.012 -0.018 t-statistic (-0.91) (-0.92) (-1.01) (-0.98) (-0.55) (-0.91)

Buy mean -0.002 -0.002 -0.014 -0.007 -0.017 -0.002 t-statistic (-0.08) (-0.12) (-0.72) (-0.36) (-0.68) (-0.08)

Hold mean -0.006 -0.016 -0.012 -0.02 0.001 -0.006 t-statistic (-0.27) (-0.88) (-0.69) (-1.13) (0.03) (-0.27)

Sell mean -0.028 -0.021 -0.017 -0.02 -0.013 -0.028 t-statistic (-1.31) (-1.24) (-0.97) (-1.09) (-0.65) (-1.31)

58

Table 8 B – Mutual funds' trading patterns around disclosure, by 6-month performance I calculate funds' trade direction on the disclosed stocks in the subsequent reporting period based on past performance. Only the top-size decile funds are included. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 6 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. I exclude trades for funds' reports with previous holdings data older than four months. The trades are changes in positions from a fund's current report until its subsequent report; only stocks disclosed in the preceding period are included. "Strategy" presents the comparison of the fund's actions: for example, 'SALE BUY' indicates that during the current period fund purchases the stock it was selling in the reporting period. I use equally weighted trades to calculate the percentage of each strategy within current fund-disclosure trades. After that, each quarter, I average the report-level percentages within the past performance groups with equal weights. I also compute the High minus low performers' portfolio each quarter. Finally, I calculate time-series averages across quarters for each group along with a t-statistic. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Past performance

quintile Statistic

Strategy as percentage of # of trades in the disclosure SELL SELL

SELL BUY

SELL NO TRADE

BUY BUY

BUY SELL

BUY NO TRADE

NO TRADE BUY

NO TRADE SELL

NO TRADE NO TRADE

1 (Low) mean 20.28 4.17 15.48 13.62 13.45 10.24 2.72 7.10 12.93 2 mean 17.43 4.88 14.89 16.07 12.89 10.62 2.94 6.41 13.87 3 mean 16.86 5.00 15.17 17.99 13.53 10.20 3.07 6.31 11.87 4 mean 16.29 5.09 14.41 18.24 11.43 10.38 3.45 6.43 14.29

5 (High) mean 16.16 5.24 13.94 19.35 11.79 10.06 3.45 6.20 13.80

(High-Low) mean -4.12*** 1.07*** -1.54*** 5.73*** -1.66** -0.19 0.73*** -0.9** 0.87 t stats (-4.79) (2.73) (-2.51) (4.6) (-2) (-0.21) (3.19) (-2.14) (0.65)

59

Table 8 C – Mutual funds' trading patterns around disclosure, full sample I calculate funds' trade direction on the disclosed stocks in the subsequent reporting period based on past performance. The full sample of reporting funds is included. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. I exclude trades for funds' reports with previous holdings data older than four months. The trades are changes in positions from a fund's current report until its subsequent report; only stocks disclosed in the preceding period are included. "Strategy" presents the comparison of the fund's actions: for example, 'SALE BUY' indicates that during the current period fund purchases the stock it was selling in the reporting period. I use equally weighted trades to calculate the percentage of each strategy within current fund-disclosure trades. After that, each quarter, I average the report-level percentages within the past performance groups with equal weights. I also compute the High minus low performers' portfolio each quarter. Finally, I calculate time-series averages across quarters for each group along with a t-statistic. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

Past performance

quintile Statistic

Strategy as percentage of # of trades in the disclosure SELL SELL

SELL BUY

SELL NO TRADE

BUY BUY

BUY SELL

BUY NO TRADE

NO TRADE BUY

NO TRADE SELL

NO TRADE NO TRADE

1 (Low) mean 16.04 4.25 18.94 11.84 14.49 11.83 2.48 6.73 13.40 2 mean 16.02 4.97 17.40 13.98 14.18 11.17 3.12 6.49 12.66 3 mean 16.12 5.34 17.05 14.93 13.95 11.25 3.07 6.08 12.21 4 mean 15.95 5.56 16.36 16.02 14.43 10.82 3.17 6.03 11.66

5 (High) mean 14.62 5.48 15.54 18.91 13.95 11.04 3.38 5.59 11.48

(High-Low) mean -1.42*** 1.23*** -3.4*** 7.07*** -0.53 -0.79** 0.9*** -1.14*** -1.93*** t stats (-2.94) (6.61) (-10.82) (12.12) (-0.95) (-2.35) (6.07) (-5.95) (-3.02)

60

Table 9 B – Mutual funds' trading patterns and returns, by 6-month performance This table presents cumulative abnormal returns on the hypothetical portfolios of stocks traded by the reporting funds in the top-size decile. I compute returns over two windows: the period between the end of the reporting period and disclosure, and the period starting at disclosure and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 6 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's current report until its subsequent report, multiplied by the stock price at the end of the subsequent period. Only stocks disclosed in the preceding period are included. I exclude trades for funds with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate daily DGTW-adjusted returns and sum them up over the relevant window. Then calculate the average for each fund-disclosure portfolio. The portfolios are defined based on trade direction in Table8. For VW returns in Panel A, I weigh the stocks' returns by a dollar value of trade direction. For EW returns in Panel B, I average the returns with equal weights in all portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

61

Panel A – Value-weighted returns

Between end of period and

disclosure

Side VW returns Past 6-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

BUY-BUY mean -0.433 0.014 0.367** -0.112 -0.567** -0.134

t-statistic (-1.42) (0.08) (2.31) (-0.72) (-2.43) (-0.32)

SELL-BUY mean -1.322** -0.749* -0.658** -0.409 -1.399*** -0.077

t-statistic (-2.54) (-1.85) (-2.14) (-1.51) (-4.06) (-0.12)

HOLD-BUY mean -0.407 -0.120 -0.253 -0.220 -1.013** -0.605

t-statistic (-0.77) (-0.25) (-0.72) (-0.73) (-2.35) (-0.88)

BUY-HOLD mean 0.717 0.788** 0.504* 0.704*** 0.872*** 0.155

t-statistic (1.48) (2.51) (1.93) (3.67) (3.04) (0.26)

HOLD-HOLD mean 0.268 -0.071 -0.028 0.261 0.311 0.043

t-statistic (0.59) (-0.28) (-0.13) (1.18) (1.03) (0.09)

SELL-HOLD mean -0.319 -0.482 -0.092 -0.499* -0.439 -0.120

t-statistic (-1.04) (-1.5) (-0.34) (-1.99) (-1.67) (-0.27)

BUY-SELL mean 1.284*** 0.707** 1.181*** 0.7** 1.198*** -0.086

t-statistic (2.86) (2.33) (3.53) (2.57) (2.96) (-0.15)

HOLD-SELL mean 1.62*** 0.864** 0.388 1.2*** 1.051** -0.569

t-statistic (3.15) (2.64) (1.43) (3.41) (2.71) (-0.93)

SELL-SELL mean 1.824*** 0.687*** 0.751*** 0.789*** 0.977*** -0.848**

t-statistic (7.02) (4.07) (4.46) (3.58) (3.38) (-2.28)

After disclosure until next

end-of-period date

BUY-BUY mean 0.232 0.334** 0.523*** 0.326** -0.073 -0.305

t-statistic (1.31) (2.35) (4.36) (2.46) (-0.49) (-1.42)

SELL-BUY mean 0.206 0.031 0.086 0.141 0.026 -0.180

t-statistic (0.72) (0.17) (0.5) (0.76) (0.12) (-0.54)

HOLD-BUY mean 0.517 0.130 0.268 -0.149 -0.120 -0.637

t-statistic (1.46) (0.46) (1.25) (-0.62) (-0.46) (-1.59)

BUY-HOLD mean -0.375 0.125 -0.022 0.31* 0.221 0.597**

t-statistic (-1.42) (0.85) (-0.13) (2) (1.05) (2.42)

HOLD-HOLD mean 0.212 0.023 -0.220 0.240 -0.034 -0.246

t-statistic (1.09) (0.14) (-1.48) (1.69) (-0.19) (-0.98)

SELL-HOLD mean 0.044 -0.004 0.106 -0.151 0.070 0.025

t-statistic (0.2) (-0.02) (0.46) (-0.68) (0.38) (0.1)

BUY-SELL mean 0.258 0.216 0.063 0.068 0.290 0.032

t-statistic (0.85) (1.07) (0.41) (0.48) (1.66) (0.09)

HOLD-SELL mean 0.276 0.455** 0.188 0.44*** 0.668*** 0.392

t-statistic (1.05) (2.48) (0.74) (2.95) (3.14) (1.31)

SELL-SELL mean 0.434** 0.434*** 0.378*** 0.434*** 0.234 -0.201

t-statistic (2.21) (3.82) (3.1) (4.11) (1.64) (-0.95)

62

Table 9 B – Continued Panel B – Equal-weighted returns

Between end of period and

disclosure

Side VW returns Past 6-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

BUY-BUY mean -0.463 -0.238 -0.074 -0.165 -0.541*** -0.078

t-statistic (-1.48) (-1.43) (-0.79) (-1.11) (-2.88) (-0.21)

SELL-BUY mean -1.196*** -0.974*** -0.837*** -0.654** -1.446*** -0.250

t-statistic (-2.77) (-3.2) (-3.28) (-2.51) (-4.58) (-0.41)

HOLD-BUY mean -0.536 -0.299 -0.805** -0.421 -1.114*** -0.579

t-statistic (-1.16) (-0.72) (-2.39) (-1.49) (-3.33) (-0.92)

BUY-HOLD mean 0.907* 0.784*** 0.405 0.612*** 0.696** -0.211

t-statistic (1.91) (2.79) (1.53) (4.03) (2.4) (-0.36)

HOLD-HOLD mean 0.203 -0.070 -0.113 0.187 0.268 0.065

t-statistic (0.45) (-0.31) (-0.55) (0.93) (0.9) (0.12)

SELL-HOLD mean -0.309 -0.557** -0.207 -0.57** -0.666*** -0.356

t-statistic (-1.17) (-2.08) (-0.75) (-2.13) (-2.86) (-0.98)

BUY-SELL mean 0.717** 0.541** 0.694*** 0.497** 0.739** 0.022

t-statistic (2.31) (2.46) (3.03) (2.1) (2.28) (0.05)

HOLD-SELL mean 0.817** 0.122 -0.083 0.375 0.281 -0.536

t-statistic (2.19) (0.45) (-0.3) (1.38) (0.86) (-1.12)

SELL-SELL mean 0.845*** 0.222 0.189 0.189 0.237 -0.607**

t-statistic (5.13) (1.27) (1.2) (1.3) (1.21) (-2.4)

After disclosure until next

end-of-period date

BUY-BUY mean 0.005 0.047 0.131 0.018 -0.094 -0.099

t-statistic (0.03) (0.3) (1.35) (0.2) (-0.71) (-0.51)

SELL-BUY mean -0.046 -0.005 0.064 -0.035 -0.151 -0.105

t-statistic (-0.2) (-0.03) (0.48) (-0.21) (-0.85) (-0.43)

HOLD-BUY mean 0.141 0.127 0.044 -0.200 -0.267 -0.409

t-statistic (0.46) (0.53) (0.25) (-0.81) (-1.16) (-1.27)

BUY-HOLD mean -0.429* 0.105 -0.107 0.222 0.244 0.673**

t-statistic (-1.91) (0.72) (-0.63) (1.42) (1.17) (2.49)

HOLD-HOLD mean 0.211 0.075 -0.334** 0.128 -0.061 -0.272

t-statistic (1.06) (0.49) (-2.08) (0.84) (-0.32) (-1.01)

SELL-HOLD mean -0.096 -0.056 0.086 -0.220 0.097 0.192

t-statistic (-0.5) (-0.32) (0.38) (-0.98) (0.54) (0.82)

BUY-SELL mean -0.261 -0.086 -0.150 -0.148 0.013 0.273

t-statistic (-1.19) (-0.63) (-1.22) (-1.07) (0.07) (0.98)

HOLD-SELL mean 0.040 0.165 -0.003 0.014 0.202 0.161

t-statistic (0.15) (1.14) (-0.01) (0.08) (1.29) (0.61)

SELL-SELL mean 0.118 0.22** 0.025 0.068 -0.048 -0.167

t-statistic (1.09) (2.05) (0.3) (0.72) (-0.46) (-1.29)

63

Table 9 C – Mutual funds' trading patterns and returns, full sample This table presents cumulative abnormal returns on the hypothetical portfolios of stocks traded by the full sample of reporting funds. I compute returns over two windows: the period between the end of the reporting period and disclosure, and the period starting at disclosure and ending at the end of the subsequent reporting period. Each quarter, I divide the funds by their past performance quintiles. The past performance measure is the difference between the compounded fund return and the compounded style benchmark return (average for a given CRSP objective code). The compounding period is 12 months before the end of the reporting period. To ensure that the holdings data is publicly available immediately after the filing, I include only holdings data disclosed in SEC reports. The trades are changes in positions from a fund's current report until its subsequent report, multiplied by the stock price at the end of the subsequent period. Only stocks disclosed in the preceding period are included. I exclude trades for funds with previous holdings data older than four months. I form portfolios as follows: for each stock, calculate daily DGTW-adjusted returns and sum them up over the relevant window. Then calculate the average for each fund-disclosure portfolio. The portfolios are defined based on trade direction in Table8. For VW returns in Panel A, I weigh the stocks' returns by a dollar value of trade direction. For EW returns in Panel B, I average the returns with equal weights in all portfolios. When Buy or Sell returns data is missing, I still include the other side in the calculation. After, for both VW and EW specifications, I average the returns with equal weights within each quarter-performance quintile. Finally, I calculate the fund-disclosure time-series average along with a t-statistic for each performance quintile. Statistical significance is denoted by ∗∗∗, ∗∗ and ∗ for significance at the 1%, 5%, and 10% levels, respectively.

64

Panel A – Value-weighted returns

Between end of period and

disclosure

Side VW returns Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

BUY-BUY mean -0.716*** -0.369*** -0.117 -0.055 -0.32* 0.396

t-statistic (-4.84) (-3.47) (-1.13) (-0.54) (-1.85) (1.43)

SELL-BUY mean -1.064*** -0.781*** -0.544*** -0.762*** -1.177*** -0.113

t-statistic (-4.02) (-3.78) (-4.68) (-5.39) (-6.16) (-0.29)

HOLD-BUY mean -0.976*** -0.945*** -0.315** -0.355** -1.11*** -0.135

t-statistic (-4.56) (-4.25) (-2.26) (-2.65) (-5.95) (-0.39)

BUY-HOLD mean 0.491*** 0.442*** 0.382*** 0.591*** 0.724*** 0.233

t-statistic (3.25) (3.9) (4.54) (7.1) (5.62) (1.19)

HOLD-HOLD mean 0.074 -0.060 0.241** 0.162 0.211* 0.137

t-statistic (0.62) (-0.57) (2.66) (1.4) (1.84) (0.76)

SELL-HOLD mean -0.146 -0.133 -0.154 -0.176 -0.232 -0.086

t-statistic (-0.89) (-1.01) (-1.45) (-1.34) (-1.39) (-0.39)

BUY-SELL mean 1.486*** 1.164*** 1.093*** 1.131*** 1.209*** -0.277

t-statistic (7.4) (7.13) (6.49) (7.02) (5.08) (-0.87)

HOLD-SELL mean 1.308*** 1.106*** 1.105*** 1.078*** 1.088*** -0.219

t-statistic (7.84) (7.45) (8.14) (8.83) (4.79) (-0.84)

SELL-SELL mean 1.417*** 1.164*** 1.104*** 1.007*** 1.101*** -0.316

t-statistic (7.84) (9.01) (9.62) (7.19) (4.98) (-1.29)

After disclosure until next

end-of-period date

BUY-BUY mean 0.206** 0.224** 0.224*** 0.275*** 0.204** -0.002

t-statistic (2.27) (2.63) (3.5) (4.08) (2.05) (-0.01)

SELL-BUY mean -0.021 0.119 0.121 0.028 -0.004 0.017

t-statistic (-0.15) (1.08) (1.47) (0.35) (-0.04) (0.11)

HOLD-BUY mean 0.026 0.104 0.068 0.081 0.053 0.027

t-statistic (0.2) (1.16) (0.99) (0.81) (0.59) (0.15)

BUY-HOLD mean 0.150 0.067 0.132** 0.181** 0.292*** 0.142

t-statistic (1.69) (1.02) (2.12) (2.43) (3.44) (1.38)

HOLD-HOLD mean 0.008 0.176** 0.068 0.030 0.119 0.111

t-statistic (0.09) (2.12) (0.83) (0.43) (1.34) (0.85)

SELL-HOLD mean 0.005 -0.010 0.059 -0.001 0.107 0.102

t-statistic (0.06) (-0.14) (0.86) (-0.02) (1.35) (0.81)

BUY-SELL mean 0.413*** 0.347*** 0.309*** 0.375*** 0.352*** -0.061

t-statistic (3.38) (4) (3.97) (4.84) (3.15) (-0.45)

HOLD-SELL mean 0.458*** 0.372*** 0.402*** 0.485*** 0.435*** -0.023

t-statistic (4.85) (5.12) (3.82) (5.55) (4.42) (-0.17)

SELL-SELL mean 0.416*** 0.37*** 0.461*** 0.394*** 0.367*** -0.049

t-statistic (6.15) (6.31) (5.57) (5.55) (3.55) (-0.43)

65

Table 9 C – Continued Panel B – Equal-weighted returns

Between end of period and

disclosure

Side VW returns Past 12-month fund style-benchmark-adjusted return rank

1 (Low) 2 3 4 5 (High) High-Low

BUY-BUY mean -0.778*** -0.54*** -0.417*** -0.318*** -0.476*** 0.301

t-statistic (-5.6) (-4.98) (-4.35) (-3.32) (-3.39) (1.24)

SELL-BUY mean -1.112*** -1.012*** -0.769*** -0.87*** -1.17*** -0.058

t-statistic (-4.57) (-5.38) (-6.48) (-6.48) (-6.89) (-0.16)

HOLD-BUY mean -1.092*** -1.131*** -0.626*** -0.58*** -1.207*** -0.115

t-statistic (-5.43) (-5.35) (-4.78) (-4.3) (-6.84) (-0.36)

BUY-HOLD mean 0.477*** 0.421*** 0.389*** 0.53*** 0.624*** 0.147

t-statistic (3.1) (3.31) (3.57) (7.41) (4.94) (0.76)

HOLD-HOLD mean 0.103 0.007 0.199** 0.111 0.115 0.011

t-statistic (0.89) (0.08) (2.13) (1.03) (0.9) (0.06)

SELL-HOLD mean -0.134 -0.150 -0.132 -0.170 -0.296* -0.162

t-statistic (-0.86) (-1.15) (-1.3) (-1.63) (-2.04) (-0.75)

BUY-SELL mean 0.758*** 0.562*** 0.489*** 0.506*** 0.49** -0.268

t-statistic (4.38) (4.04) (3.16) (3.41) (2.39) (-1.06)

HOLD-SELL mean 0.535*** 0.403*** 0.45*** 0.41*** 0.397** -0.138

t-statistic (4.36) (3.79) (4.66) (3.73) (2.29) (-0.71)

SELL-SELL mean 0.589*** 0.436*** 0.407*** 0.33*** 0.397*** -0.192

t-statistic (4.84) (4.83) (4.42) (3.58) (2.83) (-1.09)

After disclosure until next

end-of-period date

BUY-BUY mean -0.098 -0.060 -0.055 -0.032 -0.018 0.080

t-statistic (-1.13) (-0.74) (-0.81) (-0.58) (-0.23) (0.65)

SELL-BUY mean -0.155 -0.079 -0.081 -0.166** -0.181* -0.026

t-statistic (-1.15) (-0.76) (-0.98) (-2.35) (-1.99) (-0.19)

HOLD-BUY mean -0.160 -0.108 -0.157** -0.110 -0.103 0.058

t-statistic (-1.21) (-1.27) (-2.18) (-1.12) (-1.17) (0.32)

BUY-HOLD mean 0.111 0.070 0.075 0.154** 0.225*** 0.114

t-statistic (1.31) (1.16) (1.27) (2.14) (2.97) (1.15)

HOLD-HOLD mean -0.031 0.126 -0.001 0.021 0.063 0.094

t-statistic (-0.38) (1.66) (-0.01) (0.37) (0.7) (0.76)

SELL-HOLD mean -0.009 -0.040 -0.034 -0.015 0.108 0.117

t-statistic (-0.11) (-0.58) (-0.54) (-0.22) (1.27) (0.93)

BUY-SELL mean -0.068 -0.042 -0.045 -0.023 -0.011 0.057

t-statistic (-0.55) (-0.53) (-0.59) (-0.35) (-0.1) (0.48)

HOLD-SELL mean 0.043 0.026 0.044 0.109 0.050 0.008

t-statistic (0.42) (0.38) (0.59) (1.49) (0.59) (0.06)

SELL-SELL mean 0.044 0.058 0.048 0.093* 0.063 0.019

t-statistic (0.77) (1.11) (0.86) (1.81) (0.72) (0.18)

66

Table 10 - The most active locations by the information access within 2 days after disclosures

For each location, I count the number of requests and IP-addresses associated with these requests throughout all sample years. I list the most active locations (by # of requests) in descending order. I exclude requests from IPs accessed more than 3000 filings in a given day and keep only requests within the first two days after the form filing.

# of Requests # of IPs Country Region City

264162 4452 United States of America New York New York City 233121 8718 United States of America Virginia Ashburn 73798 572 United States of America Oregon Portland 67360 176 United States of America North Carolina Charlotte 61593 759 United States of America Pennsylvania Philadelphia 56368 709 United States of America Illinois Chicago 55782 10 United States of America Pennsylvania Audubon 45253 5 United States of America Texas Spring 42602 1937 United States of America California Mountain View 42301 728 United States of America Texas Dallas 41525 62 United States of America Indiana Fort Wayne 39947 819 United States of America California San Francisco 35734 693 United States of America District of Columbia Washington 32868 185 United States of America California San Jose 26406 11 United States of America New Jersey Carlstadt 25727 233 India Maharashtra Mumbai 24054 650 United States of America Massachusetts Boston 22576 44 United States of America Colorado Boulder 22159 100 India Telangana Hyderabad 22018 24 India Uttar Pradesh Noida 21422 10 United States of America Massachusetts Fall River 20785 34 United States of America California Long Beach 20590 18 Bahamas New Providence Nassau 19387 365 China Beijing Beijing 18678 101 United States of America Utah Salt Lake City 18316 1187 United States of America Georgia Atlanta 17908 119 United States of America Nebraska Omaha 17639 28 United States of America Massachusetts Chicopee 17478 730 United States of America Louisiana Monroe 17361 9 United States of America Colorado Littleton

67

Table 11 - The most active users by the information access within 2 days after disclosures For each IP-address, I count the number of requests throughout all sample years. I list the most active IP-addresses in descending order. I exclude IPs accessed more than 3000 filings in a given day and keep only requests within the first two days after the form filing. I manually search for the IP-addresses to locate the firms that registered these IP-addresses. Investment firms, (such as hedge-funds, advising firms) or financial-related firms are in bold font. (ISP) denotes the cases when an IP-address is registered under the internet service provider name, and there is no open information about the actual user. It does not mean, however, that users under such IP-addresses are not involved in investment activity.

# of Requests Company Country Region City

76137 Amazon Data Services United States of America Virginia Ashburn 51443 United States of America North Carolina Charlotte 42157 Network Transit Holdings LLC (ISP) United States of America Texas Spring 31781 Two Sigma Investments LP United States of America New York New York City 31629 Symphony Asset Management United States of America California San Francisco 31491 Two Sigma Investments LP United States of America New York New York City

26244 SunGard Availability Network Solutions Inc.

(investments) United States of America New Jersey Carlstadt 25378 Atlantic Bank of New York United States of America New York New York City

24800 SunGard Availability Network Solutions Inc.

(investments) United States of America Pennsylvania Philadelphia 23602 Mediacom Communications Corp (ISP) United States of America Indiana Fort Wayne 21215 United States of America New York New York City 20784 Co-Location.com Inc. (ISP) United States of America Massachusetts Boston 20559 TTN Global Operations Limited Bahamas New Providence Nassau 19796 Amazon Technologies Inc. United States of America Oregon Portland

19711 The Washington Service (provider of trading

data and analytics) United States of America District of Columbia Washington

19117 India Telangana Hyderabad 18633 High Availability Inc. (data centers) United States of America Pennsylvania Audubon 17126 Markit On Demand Inc. (financial research) United States of America Colorado Littleton 16991 Amazon.com Inc. United States of America Virginia Ashburn

16505 Linode LLC (cloud data centers) United States of America California Fremont 16434 High Availability Inc. (data centers) United States of America Pennsylvania Audubon 16208 Meganet Communications (ISP) United States of America Massachusetts Fall River 16061 DediPath (ISP) United States of America Delaware Hockessin 15452 Cox Communications (ISP) United States of America California Santa Barbara 14574 SoftLayer Technologies Inc. (cloud data centers) United States of America Texas Dallas 14269 Megapath Corporation (ISP) United States of America Pennsylvania Philadelphia 14090 Amazon.com Inc. United States of America Virginia Ashburn 13609 R.R. Donnelley & Sons Company (ISP) United States of America Georgia Stone Mountain 13365 Two Sigma Investments LP United States of America New York New York City