Bok Baik Jin-Mo Kim Kyonghee Kim Sukesh PatroWe thank Bruce Billings, Simi Kedia, David Koo, Jongha...

Hedge Fund Ownership and Voluntary Disclosure*

Bok Baik

Seoul National University

Email: [email protected]

Jin-Mo Kim

Rutgers University


Kyonghee Kim

University of Missouri at Columbia


Sukesh Patro

Northern Illinois University


March, 2014

*We thank Bruce Billings, Simi Kedia, David Koo, Jongha Lim, Donald Monk, Rick Morton, Vikram Nanda, Oded

Palmon, Bharat Sarath, Isabel Wang, Ben Whipple, Holly Yang (discussant), and participants at the 2014 FARS

mid-year meeting, Florida State University, Korea University, and Rutgers University for their helpful comments

and suggestions.

Hedge Fund Ownership and Voluntary Disclosure

Abstract

Using 13F filings from 1996 to 2011, we examine the association between hedge fund ownership

and a firm’s voluntary disclosure. We find that hedge fund holdings are negatively associated with

the subsequent frequency of voluntary disclosure. This is opposite to the positive association

documented in earlier studies for overall institutional ownership as well as for non-hedge fund

ownership in our sample. The negative association is more pronounced in firms held mainly by

hedge funds with short-term investment horizons. We also find that the stocks that decrease their

voluntary disclosure subsequent to increases in hedge fund holdings earn positive abnormal

returns. Overall, our findings suggest that hedge fund influence on firm voluntary disclosure

policy differs from that of other institutional investors and potentially contributes to hedge fund

profitability.

JEL classification: G14, M41

Keywords: Institutional investors, Hedge fund ownership, Management forecasts, Analyst

forecasts

1

1. Introduction

Hedge funds are important participants in the financial market and the importance of hedge

funds’ equity investments in the U.S. has increased substantially over the past two decades.1 A

growing body of work documents that hedge funds often take an active role in the management

of portfolio firms (Brav et al. [2008], Klein and Zur [2009]; Bebchuk, Brav, and Jiang [2013]).

Increases in hedge fund ownership are often associated with significant firm-level responses. For

example, in response to an increase in hedge fund ownership to 4% in 2012, Procter and Gamble

accelerated its job-cutting program, produced quarterly results that exceeded expectations, and

increased its share repurchases.2 In this study, we examine the relation between hedge fund

holdings and firms’ voluntary disclosure policy. While much research has been conducted on the

impact of hedge funds on corporate decisions and firm performance, we are unaware of research

that examines the relation between hedge fund holdings and a firm’s voluntary disclosure policy.

Given the evidence of the far-reaching impacts of hedge fund ownership on corporate behavior,

it is reasonable to expect that hedge fund ownership is also likely to impact the voluntary

disclosure policy of firms.

Stylized facts from prior research suggest that institutional investors prefer firms with

transparent information environments and improved disclosure quality (O’Brien and Bhushan

[1990], Das, Levine, and Sivaramakrishnan [1998], Bushee and Noe [2000]). Often the extent of

institutional holdings is used as a proxy for the degree of firm transparency (Healy, Hutton, and

Palepu [1999], Bartov, Radhakrishnan, and Krinsky [2000]). These findings, however, are based

on aggregate measures of ownership that implicitly treat institutions as a homogeneous group.

1 According to Hedge Fund Research, Inc., the market value of assets managed by hedge funds increased from $50 billion

in 1990 to over $1.65 trillion in 2010. Hedge funds now represent almost half of all trading on the New York Stock

Exchange (Anderson [2006]). 2 USA Today, December 15, 2012. Recent business media is replete with such examples. For example, FedEx responded in

a very similar manner to ownership increases by three of its top investors (New York Times, November 14, 2013).

2

While prior work has examined the impact of aggregate institutional ownership on a firm’s

disclosure practices, there are several reasons why one might expect hedge fund ownership to

have different implications for their portfolio firms’ voluntary disclosure policy relative to other

institutional investors.

First, hedge fund managers have strong incentives to acquire information about their

portfolio firms to generate excess returns because their pay is closely tied to their fund

performance.3 Moreover, the high fee structure of hedge funds allows them to spend more

resources on equity research than other institutions, suggesting that they are likely to have less

need of voluntary disclosures than non-hedge funds.

Second, regulatory constraints may contribute to differences between the investment

behavior of hedge and non-hedge funds. Non-hedge fund institutional investors, such as banks

and mutual funds, face extensive limitations on the type and extent of investments they can make

(Shive and Yun [2013], Teo [2011]).4 Because hedge funds cater to more sophisticated investors

and are exempt from such regulations, they have greater flexibility with regard to their

investments (Stulz [2007]). Further, in addition to written regulations governing institutional

investment behavior, broad standards such as the “prudent man” rule may also lead to

differences in investment behavior.5 For example, non-hedge funds, like mutual funds, which are

open to the general public and subject to several federal securities laws, may demand more

3 For instance, hedge fund managers typically earn 20% of fund profits as part of their compensation, a very different

compensation structure from other types of institutional investors, such as mutual funds and pension funds. Grossman [2005]

argues that unlike mutual funds, which hold diversified portfolios, a hedge fund is a vehicle for acquiring the specialized

talent that its manager possesses to capture profits from a unique strategy. 4 In the U.S., investment companies that must register with the SEC are subject to regulation under four federal laws:

the Securities Act of 1933, the Securities Exchange Act of 1934, the Investment Company Act of 1940, and the Investment Advisers Act. Hedge funds are not considered investment companies and thus are not subject to the

federal securities laws (Eichengreen et al. [1998]). 5 Although banks are the only type of institution strictly governed by the common-law “prudent-man rule”, previous

studies show that it also affects the investment behavior of non-bank institutions (Longstreth [1986], Badrinath, Gay,

and Kale [1989], Del Guercio [1996], Gompers and Metrick [2001]).

3

voluntary disclosures and transparent information environments to avoid investments that carry

greater litigation risk.6

Third, there is increasing evidence that hedge funds outperform other institutions.7 Several

studies find that hedge funds deliver significant abnormal returns, suggesting the existence of

information advantages for hedge funds (Ackerman, McEnally, and Ravenscraft [1999], Brown,

Goetzmann, and Ibbotson [1999], Kosowski, Naik, and Teo [2007]).8 To the extent that informed

investors seek confidentiality of information, which is also necessary for the preservation of

incentives to collect and process information (Grossman and Stiglitz [1980]), hedge funds that

are better informed might have different incentives concerning their portfolio firms’ voluntary

disclosure practices from other institutions that prefer more voluntary disclosures and transparent

information environments.

Overall, these differences between hedge and non-hedge funds suggest that a link between

hedge fund ownership and the information environment of firms could be markedly different

from related evidence on overall institutional ownership in prior literature, making the

investigation of the impact of hedge fund holdings on firms’ voluntary disclosure policy

interesting.

Using hedge funds’ 13F stock holdings for the sample period 1996-2011, we find that hedge

fund ownership is negatively and significantly associated with the frequency of management

earnings forecasts (MEFs) in subsequent quarters. This evidence is in stark contrast with that in

Ajinkya, Bhojraj, and Sengupta [2005] of a positive relation between aggregate institutional

6 Cohen, Polk, and Silli [2010] argue that the “prudent man” rule makes mutual funds more diversified. For example,

mutual fund managers tend to diversify their investments because they may feel that they are more likely to face investor

litigation when they manage more concentrated funds that perform poorly. 7 Several studies, however, fail to find positive abnormal returns from hedge fund investments (Asness, Krail, and

Liew [2001], Amin and Kat [2003], Malkiel and Saha [2005], Kat and Palaro [2006], Griffin and Xu [2009]). 8 Several studies, including Brunnermeier and Nagel [2004], Aragon and Martin [2012], and Agarwal et al. [2013], also

examine the performance of stocks on hedge funds’ 13F filings and show that hedge funds’ stock positions earn abnormal

returns.

4

holdings and MEF frequency. However, similar to Ajinkya, Bhojraj, and Sengupta [2005], we

find that the corresponding associations for non-hedge fund institutional ownership are positive

and statistically significant. These findings suggest that the effect of institutional ownership on a

firm’s information environment is different across hedge funds and non-hedge funds. Further, to

the extent that institutional investors’ preference or management’s perception of investors’

preference influences firm policy (Bushee [1998], Bushee and Noe [2000], Bushee [2004]), these

results suggest that hedge funds influence managers to decrease voluntary disclosure, possibly to

maintain their information advantage over other investors.

Testing the effect of investment horizons, we find that the negative association between

hedge fund ownership and the frequency of MEFs is more pronounced in firms where hedge fund

ownership is predominantly characterized by short-term investment horizons. To the extent that

short-term institutional investors are better informed than long-term institutional investors (Ke

and Ramalingegowda [2005], Ke, Ramalingegowda, and Yu [2006], Yan and Zhang [2009]), our

results on investment horizon further support the view that informed investors are more likely to

discourage their portfolio firms from disclosing information to exploit their information

advantage.

To gain further insight into the negative relation between hedge fund ownership and a firm’s

voluntary disclosure, we examine whether this relation contributes to the patterns in the stock

performance of hedge funds’ portfolio firms. We find that changes in MEF frequency are

significant in explaining portfolio firms’ stock return patterns associated with hedge fund trades.

Specifically, changes in hedge fund ownership are positively and significantly associated with

firms’ risk-adjusted abnormal returns (Daniel et al. [1998]) in up to two subsequent quarters.

Pertaining to the information environment of firms, this positive association varies significantly

5

with the subsequent changes in MEFs. The mean abnormal return for firms with an increase in

hedge fund ownership and increased opacity is 4.06% (annualized) higher than for firms with an

increase in hedge fund ownership and increased transparency (p-value < 0.01). This result

indicates that hedge fund holdings predict future stock returns, and that the predictability is more

evident in firms that decrease their voluntary disclosure subsequent to increases in hedge fund

holdings, consistent with the view that informed hedge funds adversely affect firms’ voluntary

disclosure to exploit their information advantages. This finding is also consistent with the notion

that the return predictability of institutional investors is more pronounced in firms with high

information asymmetry (Coval and Moskowitz [1999], Ivkovic and Weisbenner [2005], Malloy

[2005]).

We run a battery of robustness tests, including a change specification, and find that our inference

on the effect of hedge fund holdings on firms’ information environment remains intact. We re-

estimate the relation between hedge fund ownership and voluntary disclosure using the frequency of

accurate MEFs, i.e., management earnings forecasts that are the same as or close to the reported

earnings ex post. Next, to examine whether the association between hedge fund ownership and the

frequency of MEFs holds also for non-earnings-related voluntary disclosure, we relate hedge fund

ownership to managerial guidance on future capital expenditures or dividends (Non-Earnings

Guidance). Both tests yield results that are consistent with our main findings. We also document

that an increase in hedge fund holdings is associated with greater analyst forecast dispersion and

inaccuracy in the following two quarters, suggesting that firms become more opaque subsequent to

increases in hedge fund holdings.

Our study extends the literature on the relation between institutional investors and the

information environment of firms (O’Brien and Bhushan [1990], Healy, Hutton, and Palepu [1999];

6

Ajinkya, Bhojraj, and Sengupta [2005]). To the best of our knowledge, this paper is the first to

examine the association between hedge funds holdings and management earnings forecasts. Our

study adds to Bushee and Noe [2000], which finds a positive relation between institutions with

short-term investment horizons and voluntary disclosure activity, by providing evidence that hedge

funds that are classified as short-term investors negatively affect a firm’s voluntary disclosure

activity. This result suggests that, unlike transient institutions that rely on firms’ investor relations

activities, such as management forecasts and conference calls (Bushee and Noe [2000], Bushee

[2004]), high-turnover hedge funds trade on their own information. The difference in the impact of

hedge and non-hedge fund institutions on management earnings forecasts suggests caution in the

use of overall institutional ownership as a homogeneous proxy for firm information transparency.

Our paper is also closely related to studies that examine institutional investors’ ability to

predict future firm profitability and returns (Bennett, Sias, and Starks [2003], Ali et al. [2004],

Ke and Petroni [2004], Ke and Ramaligegowda [2005], Bushee and Goodman [2007], Yan and

Zhang [2009]). Our results extend this line of literature by showing that subsequent changes in

the portfolio firm’s information environment are significant in explaining the investment

performance of institutional investors – the findings suggest that increases in firms’ information

opacity are a potential source of hedge funds’ outperformance, and also extend the literature on

hedge funds’ performance (Ackerman, McEnally, and Ravenscraft [1999], Brown, Goetzmann,

and Ibbotson [1999], Agarwal and Naik [2004], Brunnermeir and Nagel [2004], Ibbotson and

Chen [2005], Kosowski, Naik, and Teo [2007], Agarwal et al. [2013]).

Caveats are in order. First, we acknowledge that it is difficult to parse out the precise combination

of managers’ voluntary response to hedge funds’ preference for disclosure policy on the one hand, and

hedge funds’ explicit influence on a firm’s disclosure policy on the other, which leads to decreased

7

voluntary disclosure activities. Second, our conclusions are subject to the potential endogeneity of

hedge fund ownership to the voluntary disclosure policy of firms. We attempt to address this issue by

using a firm-fixed effects model and a change specification. We also examine the impact of prior

changes in MEF frequency on subsequent changes in hedge fund ownership and find no association,

suggesting that our inferences are unlikely to be driven by the reverse causality explanation. Finally,

while it is impossible to eliminate the coverage biases in First Call completely (Chuk, Matsumoto, and

Miller [2013]), additional tests indicate that our results are less likely subject to issues arising from

such biases.

The rest of the paper is organized as follows. Section 2 presents key arguments for our

hypothesis that hedge fund ownership affects firms’ voluntary disclosure practices. Section 3

describes the data and research design. Sections 4 and 5 provide the empirical results and

additional analyses, respectively. Section 6 concludes.

2. Hypothesis Development: Effect of Hedge Fund Ownership on Voluntary Disclosure

Prior research suggests that institutional investors’ preference or management’s perception of

the investors’ preference influences firm policies, such as investment and disclosure. For

example, Bushee [1998] shows that the short-term focus of transient institutions creates

incentives for managers to cut R&D to meet or beat short-term earnings benchmarks. Several

studies show that institutional investors have a strong preference for firms with more disclosure,

and that such preference for a transparent information environment generates pressure on

managers to increase disclosure (O’Brien and Bhushan [1990], Das, Levine, and

Sivaramakrishnan [1998], Ajinkya, Bhojraj, and Sengupta [2005]). In particular, Bushee and Noe

[2000] classify institutional investors into three different groups based on their trading behavior

8

and show that different types of institutional investors have different preferences for disclosure

policy. These findings suggest that the composition of a firm’s institutional investor base can

affect its disclosure practices, since company managers respond to the pressures created by

different types of institutional investors (Bushee and Noe [2000], Bushee [2004]).

Unlike other institutional investors, hedge funds might have lesser demand for firms’

voluntary disclosure. Given hedge funds’ strong incentives to generate profits, they are more

likely to engage in information search activities and may have superior information about a

firm’s prospects relative to its other investors. In addition, due to their high fee structure, hedge

funds can spend more resources to analyze their portfolio stocks than other institutions. Thus, if

hedge funds substitute a firm’s voluntary disclosure with their own research and trade based on

their own information, voluntary disclosures would be relatively less important to them.

Prior research suggests that information opacity is important to protect investors’ private

information advantages (Fishman and Hagerty [1992], Huddart, Hughes, and Levine [2001],

Agarwal et al. [2013]). In fact, several studies show that the information advantages of certain

groups of institutional investors are more pronounced in firms with high information asymmetry

(Coval and Moskowitz [1999], Ivkovic and Weisbenner [2005], Malloy [2005], Kang and Kim

[2008]). More importantly, Agarwal et al. [2013] show that hedge funds use confidential filings

of Form 13F disclosures to hide private information on their portfolio stocks. Related to this,

Ben-David et al. [2013] provide evidence that hedge funds manipulate the stock prices of their

portfolio firms on critical reporting dates. These findings suggest that hedge funds that possess

an informational advantage over other investors have incentives to maintain the opacity of their

target firms’ information environment to protect private information.

9

Therefore, if hedge funds can exert significant influence on firms’ corporate decisions, they

may discourage their portfolio firms from disclosing information to outside investors, thereby

undermining firms’ information environments. Alternatively, to the extent that company

managers know their investor base and perceive differences in the preference for disclosure

policy between hedge and non-hedge funds, managers faced with a high proportion of hedge

fund ownership are likely to decrease their voluntary disclosure to respond to the different

informational demands of hedge funds (Bushee and Noe [2000], Bushee [2004]). To the extent

that delayed disclosure preserves incentives to collect and process information (Grossman and

Stiglitz [1980]), hedge funds have incentives to reduce MEFs to delay the disclosure of quarterly

earnings information. Therefore, we expect hedge fund ownership to be negatively related to a

firm’s voluntary disclosure activity.9 Furthermore, if hedge funds better exploit their information

advantages in more opaque information environments, we expect them to earn positive abnormal

returns from firms that decrease their voluntary disclosure subsequent to increases in hedge fund

holdings.

3. Data and Research Design

3.1. Data

Our initial sample of 699,712 firm quarters includes observations for U.S. firms listed in the

CDA/Spectrum Institutional (13F) Holdings data set for the period 1995-2011. The

CDA/Spectrum data are based on Form 13F disclosures made by institutional owners to the

9 It is also plausible that firm managers reduce or limit corporate communication to preempt or avoid hedge funds’

influence on firm policies. For example, organizations such as the Conference Board (2008) have evolved guidelines on such preemptory steps for management to lower the likelihood of hedge funds’ intervention. In contrast, hedge

funds could potentially improve the quality of corporate communication. A growing literature presents evidence on

hedge fund activism and a subset of this literature suggests significant improvements in operating performance and

stock performance in the period following an increase in hedge fund holdings (Brav et al. [2008], Klein and Zur

[2009], Bebchuk, Brav, and Jiang [2013]).

10

Securities and Exchange Commission (SEC). The SEC specifies disclosure requirements for

investment managers who manage more than $100 million in equity. Such managers are required

to file a quarterly report with the SEC of all equity holdings greater than 10,000 shares or

$200,000 in market value. To identify and estimate hedge funds’ stock holdings, we use multiple

data sources. Following Aragon and Martin [2012] and Agarwal, Fos, and Jiang [2013], we first

identify candidate hedge fund holding companies from the Trading Advisor Selection System

(TASS) and the Bloomberg Financial Markets databases. To ensure that the main business of the

identified companies is hedge fund management, we apply the filtering process of Brunnermeier

and Nagel [2004]. We check whether a candidate hedge fund company is registered as an

investment advisor with the SEC. Because non-hedge fund companies, such as mutual funds and

pension plans, are required to register with the SEC, we only consider an investment company

for potential inclusion in the list of hedge fund holding companies if it is not registered with the

SEC. If an investment company is registered, it is required to complete Form ADV.10

We

manually check the ADV forms and require the following two criteria for a registered investment

company to be eligible for inclusion in our sample of hedge fund holding companies: (1) at least

50% of its clients are “other pooled investment vehicles” (such as private equity and hedge funds)

or “high net worth individuals,” and (2) it charges performance-based fees. Previous studies of

hedge fund performance mostly use commercial hedge fund return databases, in which hedge

funds voluntarily participate to report returns. It is, however, well documented that these

databases are subject to various sample biases, including sample selection, survival, and back-fill

(Fung and Hsieh [2000, 2001, 2009], Liang [2000], Stulz [2007]). By using equity holdings of

10 Form ADV is a required submission to the SEC by all professional investment advisors, which specifies the

investment style, assets under management, and key officers of the firm.

11

hedge funds obtained from mandatory 13F filings, we avoid the issues that arise from using self-

reported data.

Table 1 about here.

We limit this initial sample to firms that we can properly identify in COMPUSTAT and

CRSP, reducing the sample size to 415,250 quarters. Management earnings forecasts are

obtained from the First Call CIG database. We start with 112,817 management earnings forecasts

of annual and quarterly earnings per share issued between 1996 and 2011 for firms included in

both COMPUSTAT and CRSP. We use MEFs issued from 1996 because coverage in the CIG

database is scant prior to 1996. We summarize MEFs by calendar quarter for each firm, starting

from the first quarter in 1996 or the firm’s initial MEF, whichever is later, and ending in the last

quarter in 2011. This step yields 295,146 firm quarters, including quarters in which the firm did

not issue an MEF (for such quarters, MEF frequency is coded as zero). We next intersect the

hedge fund ownership data with the MEF data and limit the sample to quarters with at least one

MEF during the four quarters (t-4 to t-1) prior to the quarter in which hedge fund ownership is

measured (quarter t). This requirement ensures that firms that make management forecasts

sporadically do not bias the sample. These steps further reduce the sample size to 95,774 quarters.

Finally, we require that key quarterly financial data required for our baseline regression

estimation (see Table 3) are available in COMPUSTAT, and trading and return data are available

in CRSP. Our final sample consists of 92,941 firm quarters. Table 1 summarizes the sample

formation process. Appendix 1 summarizes the detailed timeline of the quarterly MEF frequency

and ownership variable measurement. For analysis that requires non-EPS forecasts and analyst

forecasts, we use the I/B/E/S Guidance database and the I/B/E/S database, respectively.

3.2. Research Design

12

To test the impact of hedge fund and non-hedge fund ownership on the frequency of MEFs, we

estimate the following equation:

β β

β

β

( ) β

β β

β

β

β

ε ( )

MEF Frequency is measured in quarters subsequent to that in which institutional ownership

is measured (quarter t). Specifically, we measure MEF Frequency in each quarter over the

subsequent four quarters, i.e., quarters t+1 through t+4. Based on our identification of hedge

funds outlined earlier, we separate the total institutional ownership of the firm into two types,

hedge and non-hedge funds. We also include firm characteristics that are known to affect

managers’ voluntary disclosure, including firm size, leverage, the market-to-book ratio of equity

(MTB), changes in ROA, and stock return volatility (Rogers and Stocken [2005], Bergman and

Roychowdhury [2008]).

To account for the impact of market-level risk on managers’ choice to make earnings

forecasts, we include the level of the Chicago Board of Options Exchange volatility index (VIX)

(Kim, Pandit, and Wasley [2013]). We also include a dummy variable, Inside Trade, indicating

whether the firm’s management traded the stock during quarter t (Li, Wasley, and Zimmerman

[2012]). The inclusion of firm-fixed effects helps account for across-firm variation in the

stickiness of voluntary disclosure choice — it is well-documented that some firms make

voluntary disclosure more routinely while others make sporadic forecasts. Firm-fixed effects also

help account for differences in the amount of litigation risk faced by firms and other time-

invariant unobservable firm characteristics. Finally, to account for secular changes in MEF

frequency, the model includes year-fixed effects.

13

3.3. Descriptive Statistics

Table 2 reports hedge fund ownership on a yearly basis as well as key descriptive statistics for

the base sample of 92,941 firm quarters. Panel A of Table 2 shows that the average hedge fund

ownership increases steadily from 1.87% in 1996 to a peak of 10.07% in 2007. Thereafter, it

remains steady at the 7.50% to 8.00% level and accounts for about 11-13% of the overall

institutional ownership. The mean value of non-hedge fund institutional ownership increases

steadily from around 44% in 1996 to over 68% in 2011. The more rapid growth in hedge fund

ownership during the sample period is consistent with the growing importance accorded to hedge

funds in academic research and the financial press.

Table 2 Panel A and Panel B about here.

Panel B of Table 2 presents descriptive statistics for the variables used in our analysis. The

mean (median) of hedge fund ownership in the sample is 5.77% (3.90%), while the mean

(median) of non-hedge fund ownership is 56.33% (60.78%). The mean ownership of high-

turnover (low-turnover) hedge funds is 1.19% (2.23%). The average ownership held by non-

hedge institutions with short-term horizons (15.52%) is similar to that of non-hedge institutions

with long-term horizons (14.18%). Mean quarterly changes for both types of ownership are

rather small, 0.31% for non-hedge funds and 0.11% for hedge funds. However, there is

substantial variation in these changes, with the standard deviation of ownership changes at 2.48%

for hedge funds and 6.10% for non-hedge funds.

The sample firms issue, on average, 1.14 MEFs per quarter in the four-quarter period prior to

the quarter in which hedge fund ownership is measured. This frequency decreases to 0.96 (0.94)

in the subsequent two-quarter (four-quarter) period. The average change in management forecast

frequency measured over these periods is -0.17 (p-value < 0.01). Panel B also reports descriptive

14

statistics of firm characteristics. Total assets averaged over the past four quarters has a mean of

$4,740 million. Similarly estimated, leverage has a mean of 0.177. The market-to-book ratio of

equity has a mean (median) of 3.003 (2.174). The seasonally adjusted change in ROA has a

mean of -0.3%. Risk-adjusted returns following Daniel et al. [1997] show a mean (median) of

0.2% (-2.10%) for the two-quarter period after the quarter in which ownership is estimated.

About 34% of firm quarters have insider trades in quarter t. Appendix 2 contains detailed

descriptions of all the variables used in the analyses that follow.

4. Results

4.1. Management Earnings Forecasts and Hedge Fund Ownership

We start our analysis by examining whether hedge fund ownership is related to MEF frequency in

subsequent quarters. Table 3 reports panel estimation results for the level of MEF frequency in each

quarter, t+1 to t+4 (equation 1). The results show that greater hedge fund ownership in quarter t is

negatively associated with MEF frequency in quarter t+1 (t = -2.36), t+2 (t = -2.44), t+3 (t = -2.14),

and t+4 (t = -1.90).11

These results are in contrast to the corresponding association for non-hedge fund

institutional ownership. Consistent with Ajinkya, Bhojraj, and Sengupta [2005], non-hedge fund

ownership is positively associated with MEF frequency in each of quarters t+1 to t+4 (t > 6.00). The

coefficients on the control variables are largely consistent with prior research. Columns 1-4 show that

MEF frequency is positively related to firm size (up to quarter t+3), the market-to-book ratio of equity

(t > 2.00), and firm performance measured by changes in ROA (t > 6.00), while it is negatively related

to firm leverage (t < -1.90) and stock volatility (t < -7.00).12

Consistent with recent research, the

results also show that MEF frequency is positively related to insider trading (t > 3.00) (Li, Wasley,

11 T-stats are based on standard errors robust to heteroscedasticity and clustering at the firm level. 12 Untabulated OLS estimation results show that, consistent with the stickiness of disclosure frequency, the frequency of

MEFs is significantly positively associated with prior MEF frequency (t > 36.0).

15

and Zimmerman [2012]). Market-level uncertainty, measured by the VIX, has a negative impact on

MEF frequency in quarters t+1 to t+3 (t < -7.00) (Kim, Pandit, and Wasley [2013]). However, in

quarter t+4 the association is positive (t = 3.79).

Table 3 about here.

The results in Table 3 indicate a negative (positive) effect of hedge fund (non-hedge fund)

ownership on firms’ voluntary disclosure activity. The evidence suggests that hedge fund

institutions decrease corporate communication while non-hedge fund institutions increase

voluntary disclosure activity.

4.2. Management Earnings Forecasts and Hedge Fund Ownership by Turnover Type

To examine how the impact of hedge fund ownership on voluntary disclosure varies with

their investment horizons, we decompose the total hedge fund ownership of a firm into high-,

mid-, and low-turnover types. Following Brown and Schwarz [2011], we measure the portfolio

turnover of hedge funds and non-hedge fund institutional investors as the average of total dollar

amounts of buys and sells, scaled by the portfolio value each quarter as disclosed in 13F filings.

In each quarter, hedge funds (non-hedge funds) are ranked into high-, mid-, and low-turnover

types if they belong to the top, middle, and bottom tercile of portfolio turnover, respectively.

Using this classification, we partition a firm’s total hedge fund ownership into high-, mid-, and

low-turnover hedge fund ownership and re-estimate the regressions in Table 3 after replacing the

level of hedge fund ownership with the level of each type of hedge fund ownership.

Table 4 about here.

Table 4 reports results for the level (Model 1) of MEF frequency averaged across quarters

t+1 and t+2 and across quarters t+3 and t+4. While we do not report individual quarters for the

sake of brevity, we have similar inferences in each quarter. Model 1 shows that the level of high-

16

turnover hedge fund ownership is significantly negatively associated with MEF frequency in the

subsequent two quarters (coefficient = -1.119, t = -3.69). The association for mid-turnover

ownership is less strong (coefficient = -0.354, t = -1.97) and it is insignificant for low-turnover

hedge fund ownership (coefficient = -0.107, t = -0.45). The impact of the high-turnover type is

significantly stronger than that of the low-turnover type (p-value < 0.01). This evidence is

different from Bushee and Noe (2000), who find that transient institutions, which trade

aggressively based on short-term trading strategies, prefer firms with higher disclosure rankings.

Model 1 also shows that the MEF frequency averaged across quarters t+3 and t+4 yields a

qualitatively similar pattern. However, we find that the difference between the high- and low-

turnover types is not statistically significant (p = 0.46).

To examine the impact of investment horizons for non-hedge fund ownership on MEF

frequency, we also decompose the total non-hedge fund ownership of the firm into high-, mid-,

and low-turnover types. Similar to our conditioning of hedge fund ownership, we compute

portfolio turnover for non-hedge funds following Brown and Schwarz [2011], and use the top,

middle, and bottom tercile turnover rates to partition the non-hedge fund institutional ownership

of the firm into high-, mid-, and low-turnover types. We include these variables in equation (1)

and re-estimate the regressions. Model 2 of Table 4 shows that for quarters t+1 to t+2, the

positive impact of non-hedge fund institutional ownership obtains for all three types and is

significant for the mid-level and high-turnover types (p-value < 0.01). This result is consistent

with that of Bushee and Noe [2000], who show that firms’ disclosure quality is positively

associated with ownership by both transient institutions and quasi-indexers. Bushee [2004]

argues that transient institutions have a preference for firms with investor relations activities

geared toward forward-looking information, like management forecasts, which presents

17

opportunities for speculative trading, suggesting that transient institutions trade on information

from firms’ voluntary disclosures. This sharp difference of trading behavior between high-

turnover hedge and non-hedge funds suggests that while high-turnover non-hedge funds trade on

firms’ investor relations activities, such as management forecasts and conference calls (Bushee and

Noe [2000], Bushee [2004]), high-turnover hedge funds trade actively based on their own research

on portfolio stocks. Consistent with Bushee and Noe [2000], the results also show that both hedge

funds and non-hedge funds appear to be insensitive to management forecasts when they trade

with long-term investment horizons.

Also, Model 2 shows that partitioning non-hedge institutional ownership based on turnover

does not affect the impact of hedge fund ownership as documented in Model 1. This is important

because it helps clarify that the differential impact of hedge funds versus non-hedge funds on

MEF frequency is not solely an artifact of the investment horizon of institutional ownership.

Finally, Model 2 also shows that for quarters t+3 to t+4 the impact of non-hedge institutional

ownership based on turnover type is qualitatively very similar to the impact in quarters t+1 to

t+2.

The results of Table 4 indicate that the negative relation between hedge fund holdings and

management’s voluntary disclosure activity is more evident for hedge funds with short-term

investment horizons. To the extent that institutional investors’ investment horizons reflect their

information advantage, and short-term investors are better informed than long-term investors

(Grinblatt and Titman [1989], Ke and Ramalingegowda [2005], Ke, Ramalingegowda, and Yu

[2006], Yan and Zhang [2009]), these results suggest that hedge funds with high portfolio

turnover are more likely to affect firms’ voluntary disclosure adversely to exploit their

information advantages. These results also suggest that while non-hedge funds, especially transient

18

institutions, trade on the information obtained from their portfolio firms’ voluntary disclosures

(Bushee and Noe [2000], Bushee [2004]), hedge funds, especially those with short-term investment

horizons, trade on information from their own research.

Taken together, the results of Tables 3 and 4 suggest that the positive relation between

institutional ownership and management forecasts documented in Ajinkya, Bhojraj, and

Sengupta [2005] does not extend to hedge funds.

4.3. Changes Specification

To investigate the robustness of our results, we perform additional analysis examining the

association between changes in hedge fund ownership and changes in the frequency of MEFs.

Specifically, we estimate a model where changes in MEF Frequency (ΔMEF Frequency) are

regressed on prior changes in hedge fund (non-hedge fund) institutional ownership and changes

in control variables. ΔMEF Frequency in quarter t+n is the mean-adjusted MEF frequency in

quarter t+n, i.e., MEF Frequency in t+n – lag Mean MEF Frequency, where lag Mean MEF

Frequency is the average quarterly MEF frequency in the prior four calendar quarters, i.e.,

quarters t-1 through t-4 (see Appendix 1 for a detailed timeline). For the change model, we drop

the firm-fixed effects and include an indicator variable for industries with high litigation risk.

( )

ε

( )

Panel A of Table 5 reports the results. The coefficient on the change in hedge fund ownership is

negative and statistically significant in the subsequent two quarters (p-values < 0.01), which indicates

that firms with increased hedge fund ownership have a subsequent decrease in voluntary disclosure.

19

We observe that the relation becomes insignificant in quarters t+3 and t+4. By comparison, the

coefficient on the change of non-hedge fund ownership is positive and significant for all four

subsequent quarters (p-values < 0.01).

Table 5 Panels A and B about here.

Next, we consider the portfolio turnover of hedge funds and examine whether it impacts the

relation between changes in hedge fund ownership and the change in MEF frequency. As

discussed earlier, we compute portfolio turnover following Brown and Schwarz (2011) for hedge

and non-hedge funds and partition the change in hedge and non-hedge fund institutional

ownership into high-, mid-, and low-turnover types. We then re-estimate equation (2). Panel B of

Table 5 shows that the changes in high-turnover hedge fund ownership have a negative and

significant impact on MEF frequency in the subsequent two quarters (p-value < 0.01). The

impact decreases monotonically with turnover and the impact of changes in high-turnover hedge

fund ownership is significantly stronger than that of low-turnover hedge ownership (p-value <

0.01). The results for quarters t+3 to t+4 are qualitatively similar but overall much weaker. We

also find that the change in non-hedge fund institutional ownership is positively associated with

the change in MEF frequency (p-value < 0.01). Overall, the results suggest that our inferences

with respect to hedge fund ownership remain largely unaltered under the change specification.

4.4. Information Opacity and Hedge Fund Performance

Thus far, we have shown that hedge fund ownership is negatively associated with the

frequency of portfolio firms’ voluntary disclosure. In this section, we examine whether the

positive relation between increases in hedge fund ownership and future returns documented in

20

prior studies obtains in our sample, and if this relation is significantly different between firms

with decreased voluntary disclosure activity and those without.13

Table 6 about here.

In each quarter, we sort stocks into four groups on the basis of the change in hedge fund

ownership (decrease versus increase) and the change in the frequency of management forecasts

(decrease versus non-decrease) and compare equally-weighted abnormal returns across the four

groups. Abnormal returns are calculated following Daniel et al. [1997] for the period covering

quarter t+1 (alternatively, t+1 to t+2).14

Panel A of Table 6 shows that for the overall sample the

returns vary significantly across the four types. The average abnormal returns are highest for the

subgroup in which hedge funds increase their ownership and which undergoes increases in

information opacity in the subsequent quarter (i.e., MEF frequency decreases). The average

abnormal return of 0.81% (3.28% annualized) for these firms is significantly higher (p-value <

0.01) than the average abnormal return of -0.19% for the subgroup of firms in which hedge funds

increase their ownership and which does not have subsequent increases in information opacity

(i.e., MEF frequency does not decrease). Among stocks that increase information opacity, the

average abnormal return is higher when hedge funds increase their ownership of stocks than

when they decrease ownership (p-value < 0.01). Finally, the abnormal return for the group with

increased hedge fund ownership (0.42%) is significantly larger (p-value < 0.01) than that for the

group with decreased hedge fund ownership (-0.32%). Overall, the results in Panel A of Table 6

13 While it is possible that hedge funds can also profit from selling/shorting firms that increase transparency in

subsequent quarters, data limitations do not permit us to test this possibility fully. 14 Results for quarters t+1 to t+2 are similar to those discussed here. We do not tabulate the result for the sake of brevity.

21

suggest that hedge funds earn statistically and economically significant excess returns in a

subsample of firms with a more opaque information environment.15

Given the significant variation in the information environment associated with hedge funds’

investment horizons, we also examine portfolios based on changes in each type of ownership

(low- and high-turnover) interacted with subsequent changes in MEF frequency (increase versus

decrease). The results are reported in Panel B (high-turnover) and Panel C (low-turnover) of

Table 6. We find that the results are qualitatively similar for portfolios formed on the basis of

changes in high-turnover hedge fund ownership. Specifically, firms with increases in high-

turnover hedge fund ownership paired with subsequent decreases in MEF frequency outperform

the remaining portfolios (p < 0.01). However, we do not find such a pattern for low-turnover

hedge funds.

5. Additional Tests

We conduct additional tests (a) to measure the related impact of hedge fund ownership on

other aspects of a firm’s information environment, and (b) to establish the robustness of our main

results.

5.1. Other Measures of Firm Information Environment

Frequency of Accurate Management Forecasts: To the extent that MEFs with more accurate

information are more likely to improve firms’ information environment, a test that relates hedge fund

holdings to the frequency of accurate MEFs will provide more direct evidence on how hedge funds

15 Prior research provides mixed evidence on the relation between firm performance and voluntary disclosure. Skinner

[1994, 1997] and Kasznik and Lev [1995] suggest that firms with bad news are more likely to disclose their forecasts to

reduce litigation risk, whereas Miller [2002] shows an increase in disclosure for firms experiencing earnings increases. Our

evidence is consistent with the view that firms with good news decrease forecasts in response to hedge fund holdings to

target their investor base (Bushee [2004]).

22

influence firms’ information environment. To conduct this test, we define accurate MEFs as forecasts

that are within one cent of actual reported EPS and examine the association between hedge fund

ownership and the frequency of accurate MEFs.16

Table 7 presents the results. Similar to the findings

in Tables 3 and 4, we find that hedge fund ownership is negatively associated with the frequency of

accurate MEFs in subsequent quarters at the 5% level. When we decompose hedge fund ownership

according to investment horizons, we find that the negative association is more pronounced for firms

with high-turnover institutional ownership.17

Interestingly, however, we do not find that non-hedge

institutional ownership has a positive impact on the frequency of accurate MEFs.

Table 7 about here.

Management Non-Earnings Guidance: So far, our main findings are based on management

forecasts of earnings per share. To check if our results can be generalized to voluntary disclosure of

other types of information, we use management guidance on future capital expenditures and dividends

per share (Non-Earnings Guidance) from the I/B/E/S Historical Detail Guidance database and re-

estimate equation 1. The results are reported in Table 8.

Table 8 about here.

We find weaker but similar inferences using Non-Earnings Guidance. While the overall level

of hedge fund ownership is insignificantly associated with the frequency of management

guidance on capital expenditures and dividends, we find that the level of high-turnover hedge

fund ownership is negatively associated with the frequency of Non-Earnings Guidance in the

subsequent two quarters (p < 0.01). By contrast, we find a positive and significant relation (p <

16 As an alternative measure of accurate MEFs, we calculate the scaled absolute value of management earnings forecast

error, i.e., |reported EPS minus MEF|/lagged stock price. We define an MEF as an accurate MEF if the scaled management

forecast error is no greater than 0.001. Our results are robust to using this alternative definition of accurate MEFs. 17 We also estimate logit regressions where the dependent variable is an indicator variable coded as 1 if a firm issued at least

one accurate MEFs during the quarters t+1 and t+2, and 0 otherwise, and find similar results.

23

0.01) between the level of non-hedge fund ownership and the frequency of Non-Earnings

Guidance.

Analyst Forecast Dispersion and Inaccuracy: To study the information environment of firms

more broadly, we turn to analyst forecast attributes as additional proxies for the firm’s

information environment and examine how hedge fund ownership impacts such attributes.

Specifically, we examine whether hedge fund ownership is associated with analyst forecast

dispersion and inaccuracy in subsequent quarters. As shown in Table 9, we find a significant and

positive relation between hedge fund ownership and analyst forecast dispersion in the subsequent

two quarters (i.e., t+1 and t+2) (p-values < 0.01). When we decompose total hedge fund

ownership into high-, mid-, and low-turnover types, we find that the positive association is more

pronounced in firms held by hedge funds with short-term investment horizons. Table 9 also

shows that hedge fund ownership is strongly positively related to analyst forecast inaccuracy in

the subsequent two quarters (p-values < 0.01). Further, this positive relation is evident only for

the mid- and high-turnover types of ownership. This evidence further corroborates our findings

that firms with high hedge fund ownership have a more opaque information environment.

Table 9 about here.

5.2. Reverse Causality

Prior studies suggest that institutional investors may select their portfolio firms based on the

firms’ information environment, implying that hedge funds may self-select into firms with a

more opaque information environment. While we control for time-invariant characteristics of the

firm’s information environment via the firm-fixed effect model and test the robustness of our

results employing the change model, it may not be sufficient to completely rule out the reverse

causality explanation. In other words, hedge funds may change their investment in a firm in

24

anticipation of changes in the firm’s voluntary disclosure. To address this concern directly, we

examine whether changes in MEF frequency in the current quarter can explain changes in hedge

fund ownership in the subsequent quarter. Table 10 shows that the coefficient on the change in

MEF frequency is both statistically and economically insignificant. This is true when it is used to

predict both changes in overall hedge fund ownership and changes in low- or high-turnover types

of hedge fund ownership – in each case the coefficient is quite close to zero (the magnitude of

the coefficient is around 0.000 across the different models). By comparison, the rightmost

column shows that when we replace changes in hedge fund ownership with changes in non-

hedge institutional ownership, the coefficient is positive and significant (p < 0.01). This evidence

suggests that non-hedge fund institutions tend to self-select into firms with a more transparent

information environment, consistent with the view that non-hedge funds prefer increased

transparency in their portfolio firms (Bushee and Noe [2000]).

Further, we re-estimate the firm-fixed effects model in Table 3 after including the frequency

of MEFs in subsequent quarters (Lag Mean MEF Frequency) using a pooled OLS model, and

find our results are robust to the inclusion of prior MEF frequency (untabulated). In sum, the

results do not seem to be driven by the reverse causality explanation.

Table 10 about here

5.3. Robustness Tests

Incomplete Coverage of Voluntary Disclosure in First Call: Recent work shows that the First

Call database has incomplete coverage of management forecasts and that coverage of firms

varies in systematic ways (Chuk, Matsumoto, and Miller [2013]). To address this issue, we

conduct additional tests. First, we limit our sample period to 1998-2011, since First Call’s data

coverage bias is particularly severe prior to 1998, and find that our results are robust to this

25

sample restriction. Second, we restrict our sample to those firms with at least three analysts

following them since incomplete coverage by First Call is more pronounced for firms with low

analyst coverage. We find that our inferences are unaltered. Further, our main analysis utilizes

EPS forecasts of First Call’s CIG database, which is less likely subject to coverage biases.

Finally, by research design, our study is conditioned on those firms that are covered by First Call

and examines whether the extent of voluntary disclosure varies with hedge fund ownership.

Combined, these results suggest that it is unlikely that our findings are impacted by systematic

coverage biases in the First Call database.

Alternative Regression Specification: Because the MEF variable has three natural categories

(i.e., an increase, no change, and a decrease in MEFs) and the values have a meaningful

sequential order, we use an ordered logit model. We define a value of 3 for an increase in MEF

frequency, 2 for no change, and 1 for a decrease in MEF frequency, and re-estimate the

regressions in Table 5. Similar to the results based on continuous values, we find a significant

negative relation between the change in hedge fund ownership and subsequent MEF frequency

and a positive association between the change in non-hedge fund ownership and subsequent

MEF frequency documented earlier (untabulated).

We also examine whether our results are sensitive to the presence of additional control

variables (i.e., earnings volatility, analyst following, and analyst forecast dispersion) and find

that inclusion of the variables does not yield any significant changes in the results.

6. Conclusion

Despite the growing importance of hedge funds, much work remains to be done for a full

understanding of hedge funds’ impact upon corporate decisions and stock prices. In particular, we are

26

unaware of evidence linking hedge fund holdings to the extent of voluntary disclosure about firm

performance by management.

Using 13F filings from 1996 to 2011, we find that hedge fund ownership is negatively associated

with the frequency of managers’ voluntary disclosures in subsequent periods. In contrast, non-hedge

fund institutional ownership is positively associated with the subsequent frequency of management

forecasts. Furthermore, the negative association for hedge funds is more pronounced in firms held by

hedge funds with short-term investment horizons. We also find that hedge funds earn significant

abnormal returns in subsequent quarters and they are more likely to outperform when firms

experience a decrease in voluntary disclosure. These findings are consistent with the notion that hedge

funds have an adverse effect on firms’ information environments, which in turn enables them to

exploit their superior information about future earnings.

27

Appendix 1

Timeline for Measurement of the Level and Change in Institutional Ownership and

Management Forecast Frequency

t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4

Change in Hedge Fund

and Non-Hedge Fund

Institutional Ownership

Quarterly Frequency of

Management Earnings

Forecasts in Each Calendar

Quarter t+n (n = 1~4) (MEF

Frequency)

Average Quarterly

Frequency of Management

Earnings Forecasts over

Calendar Quarters t-1

through t-4 (Lag Mean MEF

Frequency)

MEF Frequency t+n = MEF Frequency t+n – Lag Mean MEF Frequency

28

Appendix 2

Variable Description

Ownership Variables

Hedge Overall hedge fund institutional ownership in quarter t

Non-Hedge Overall non-hedge institutional ownership in quarter t

Hedge (Non-Hedge) High Turnover Holdings of hedge funds (Non-Hedge Institutions) with top-tercile

portfolio turnover in quarter t

Hedge (Non-Hedge) Mid Turnover Holdings of hedge funds (Non-Hedge Institutions) with mid-

tercile portfolio turnover in quarter t

Hedge (Non-Hedge) Low Turnover Holdings of hedge funds (Non-Hedge Institutions) with bottom-

tercile portfolio turnover in quarter t

Hedge Change in hedge fund ownership (Hedge t – Hedge t-1)

Non-Hedge Change in non-hedge institutional ownership (Non-Hedge

Institution t – Non-Hedge Institution t-1)

Hedge High (Mid or Low)

Turnover

Change in high-turnover hedge fund ownership is measured as

Hedge High Turnover in quarter t minus Hedge High Turnover in

quarter t-1. Change in mid- or low-turnover hedge fund ownership

is measured similarly

Management Earnings Forecast Variables

Lag Mean MEF Frequency Average quarterly frequency of MEFs over quarters t-1 through t-

4

Mean MEF Frequency Average quarterly frequency of MEFs over quarters t+1 through

t+2 or quarters t+1 through t+4

MEF Frequency MEF frequency t+n – Lag Mean MEF Frequency

Frequency of Accurate MEF

Average quarterly frequency of accurate MEFs over quarters t+1

through t+2. MEFs are classified as accurate if the magnitude of

the difference between reported earnings per share (Actual EPS)

and the MEF is less than one cent

Non-Earnings Guidance

Average quarterly frequency of management guidance on capital

expenditure (CPX) or dividends per share (DPS) over quarters t+1

and t+2. The data is from the I/B/E/S Historical Detail Guidance

database

Others

Assets ($ million) Average book value of assets over quarters t-1 through t-4

(Compustat Quarterly)

Assets ($ million) Book value of assets in quarter t – Average book value of assets

over quarters t-1 through t-4

Analyst Forecast Dispersion

Average analyst forecast dispersion over quarters t+1 and t+2.

Analyst forecast dispersion is measured as standard deviation of

consensus analyst forecasts divided by lagged book value of

assets. We transform the measure into percentile rank form, i.e., it

has a value in the range of 0 to 1 (Diether, Malloy and Scherbina

[2002], Johnson [2004])

29

Analyst Forecast Inaccuracy

Average analyst forecast inaccuracy over quarters t+1 and t+2.

Analyst forecast inaccuracy is measured as the absolute value of

the difference between reported quarterly earnings per share

(actual EPS) and the median analyst forecasts of EPS (O’Brian

[1990], Sinha, Brown, and Das [1997]). We scale the absolute

value by the lagged book value of assets and transform it into a

percentile rank variable, i.e., it has a value between 0 and 1

DGTW Ret Buy-and-hold returns based on Daniel et al. [1997] over quarter

t+1 or quarters t+1 through t+2

High Litigation Industry Dummy

Indicator variable for high litigation risk industries (SIC: 2833-

2836, 3570-3577, 3600-3674, 5200-5961, 7371-7379) measured

in quarter t based on Matsumoto [2002]

Inside Trade

Indicator variable that takes a value of 1 if inside trading by the

firm’s management is filed with the SEC in quarter t, and 0

otherwise. Inside trading data is from Thompson Reuters Inside

Trading database

Inside Trade Indicator variable for Inside Trade in quarter t minus indicator

variable for inside trade in quarter t-1

Leverage

Average ratio of long-term debt to book value of assets (Leverage)

over quarters t-1 through t-4. To test returns, we use Leverage at

the beginning of the quarter t (Compustat Quarterly)

Leverage Leverage over quarter t – Average Leverage over quarters t-1

through t-4

MTB Average ratio of the market value of equity to book value of

equity over quarters t-1 through t-4 (Compustat Quarterly)

MTB Market-to-book in quarter t – Average market-to-book over

quarters t-1 through t-4

Momentum Firm-level price momentum measured as cumulative monthly

return over quarters t-1 through t-3

RetStd Standard deviation of daily returns in quarter t-1

RetStd Standard deviation of daily returns in quarter t minus standard

deviation of daily returns in quarter t-1

Lag ROA

Average seasonal change in return on assets (ROA) over quarters

t-1 through t-4. ROA is measured as the ratio of income before

extraordinary items to book value of assets. Seasonal change in

ROA for quarter t-1 is measured as ROA t-1 minus ROA t-5

(Compustat Quarterly)

Share Turnover

Share Turnover is the average ratio of daily trading volume to

daily shares outstanding over quarter t-1. Share Turnover is

Share Turnover in quarter t minus Share Turnover in quarter t-2

VIX Average monthly value of the Chicago Board of Options

Exchange (CBOE) volatility index during quarter t

VIX VIX in quarter t – VIX in quarter t-1

30

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34

Table 1

Sample Formation

This table presents the sample selection procedure. The final sample consists of firm quarters with institutional

ownership from CDA/Spectrum Institutional (13F) Holdings and voluntary guidance data from the First Call

database for the period 1996 to 2011.

No. of Management

Earnings Forecasts

(MEFs)

No. of Calendar

Quarters

13F(1995-2011) 699,712

Merge 13F with CRSP and COMPUSTAT annual with firm ID and fiscal year information

415,250

Unique MEFs of annual or quarterly earnings with

COMPUSTAT firm information (Firm ID and fiscal year

information)

112,817

Firm calendar quarters that are on/between the firm’s first

MEF (or 1996, whichever is later) and the firm’s last calendar quarter (or last calendar quarter of 2011,

whichever is earlier)

295,146

Calendar firm quarters with hedge fund ownership and MEF

95,774

Final sample with additional CRSP and COMPUSTAT

data requirements 92,941

35

Table 2

Descriptive Statistics

The following tables present institutional ownership by year during our sample period (Panel A) and descriptive

statistics for the variables used in the regression analysis (Panel B). Detailed definitions of variables are included in

Appendix 2.

Panel A: Institutional Ownership (Non-Hedge versus Hedge Funds) by Year

Year N Non-Hedge Institution Hedge Funds

Mean Median Mean Median

1996 2,960 44.05% 44.29% 1.87% 0.47%

1997 3,754 44.71% 46.89% 2.09% 0.65%

1998 4,694 46.58% 48.72% 2.20% 0.87%

1999 6,440 44.61% 45.71% 2.65% 1.20%

2000 5,909 44.04% 45.47% 3.00% 1.47%

2001 7,653 48.18% 50.47% 3.97% 2.41%

2002 8,243 51.58% 54.99% 4.59% 3.06%

2003 7,682 55.70% 59.65% 5.05% 3.61%

2004 7,463 60.26% 65.05% 6.04% 4.46%

2005 6,919 62.06% 67.27% 7.17% 5.47%

2006 6,630 63.51% 68.80% 8.66% 6.86%

2007 6,290 65.57% 71.67% 10.07% 8.08%

2008 5,592 66.45% 71.08% 8.13% 6.76%

2009 5,088 66.45% 71.08% 8.13% 6.76%

2010 4,444 68.85% 73.65% 7.96% 6.48%

2011 3,180 68.37% 73.51% 7.58% 5.99%

Total 92,941 56.33% 60.78% 5.77% 3.90%

36

Table 2 – Continued

Panel B: Descriptive Statistics

Obs Mean Median 25th 75th Std

Institutional Ownership Hedge 92,941 5.77% 3.90% 1.27% 8.22% 6.26%

Hedge High Turnover 92,941 1.19% 0.43% 0.04% 1.43% 1.88%

Hedge Mid Turnover 92,941 2.35% 0.94% 0.14% 2.97% 3.77%

Hedge Low Turnover 92,941 2.23% 0.93% 0.10% 2.99% 3.13%

Hedge 92,941 0.11% 0.00% -0.55% 0.69% 2.48%

Hedge High Turnover 92,941 0.02% 0.00% -0.24% 0.27% 1.53%

Hedge Mid Turnover 92,941 0.05% 0.00% -0.46% 0.55% 2.65%

Hedge Low Turnover 92,941 0.04% 0.00% -0.33% 0.38% 2.30%

Non-Hedge 92,941 56.33% 60.78% 37.84% 76.77% 25.16%

Non-Hedge High Turnover 92,941 15.52% 14.03% 7.32% 22.19% 10.65%

Non-Hedge Mid Turnover 92,941 26.63% 27.00% 16.00% 36.86% 14.25%

Non-Hedge Low Turnover 92,941 14.18% 13.58% 7.48% 19.83% 8.68%

Non-Hedge 92,941 0.31% 0.25% -1.73% 2.44% 6.10%

Management Earnings Forecasts (MEFs) Lag Mean MEF Frequency (t-4, t-1) 92,941 1.137 1.000 0.500 1.500 0.925

Mean MEF Frequency (t+1, t+2) 91,934 0.961 0.500 0.000 1.500 1.113

Mean MEF Frequency (t+1, t+4) 89,761 0.941 0.750 0.000 1.500 1.020

Mean MEF Frequency (t+1, t+2) 91,934 -0.173 -0.250 -0.500 0.250 0.923

Frequency of Accurate MEF (t+1, t+2) 33,941 0.256 0 0 0.500 0.459

Non-Earnings Guidance (t+1, t+2) 23,118 0.456 0 0 1.000 0.661

Returns

DGTW Return (t+1, t+2) 74,450 0.002 -0.021 -0.178 0.142 0.320

Firm Characteristics

Analyst Forecast Dispersion (t+1, t+2) 46,945 0.477 0.455 0.259 0.686 0.258

Analyst Forecast Inaccuracy (t+1, t+2) 60,217 0.496 0.495 0.247 0.745 0.288

Assets ($ million) 92,941 4,740 733 206 2,813 12,656

Leverage 92,941 0.177 0.139 0.012 0.286 0.175

MTB 92,941 3.003 2.174 1.417 3.504 3.246

Lag ROA 92,941 -0.003 -0.001 -0.008 0.004 0.029

RetStd 92,941 0.033 0.029 0.020 0.041 0.018

VIX 92,941 22.054 21.589 16.368 25.726 7.870

Inside Trade 92,941 0.336 0.000 0.000 1.000 0.472

Share Turnover 92,941 8.709 6.365 3.533 11.068 8.326

Momentum 82,711 0.032 0.030 -0.100 0.157 0.288

37

Table 3

Hedge Fund Ownership and Frequency of Management Earnings Forecasts

This table reports regression results for the association between institutional ownership (hedge fund and non-hedge

fund) and the frequency of management earnings forecasts in the subsequent quarters (t+1 through t+4). Detailed

definitions of variables are included in Appendix 2. t-statistics (in parentheses) are based on standard errors

clustered at the firm level. *** p < 0.01; ** p < 0.05; * p < 0.10 (two-tailed).

Dependent Variable = Quarterly Frequency of MEFs

Qtr t+1 Qtr t+2 Qtr t+3 Qtr t+4

Hedge -0.386** -0.404** -0.363** -0.322*

(-2.36) (-2.44) (-2.14) (-1.90)

Non-Hedge 0.535*** 0.494*** 0.433*** 0.420***

(8.30) (7.57) (6.43) (6.17)

Log(Assets) 0.139*** 0.088*** 0.043** 0.024

(6.64) (4.18) (2.02) (1.09)

Leverage -0.291*** -0.265** -0.251** -0.223**

(-2.91) (-2.56) (-2.32) (-1.96)

MTB 0.012*** 0.010*** 0.008** 0.007**

(3.69) (3.10) (2.43) (2.20)

Lag ROA 1.612*** 1.588*** 1.371*** 1.160***

(9.43) (8.99) (7.69) (6.99)

RetStd -6.524*** -6.444*** -5.680*** -4.687***

(-10.47) (-10.48) (-9.17) (-7.61)

VIX -0.005*** -0.005*** -0.004*** 0.002***

(-7.86) (-7.98) (-7.04) (3.79)

Inside Trade 0.038*** 0.045*** 0.057*** 0.049***

(3.69) (4.39) (5.54) (4.58)

Constant -0.406*** -0.106 0.042 0.102

(-2.78) (-0.73) (0.27) (0.66)

Firm-Fixed Effect Yes Yes Yes Yes

Year-Fixed Effect Yes Yes Yes Yes

Observations 92,941 91,934 90,841 89,761

Adjusted R2 0.3348 0.3374 0.3427 0.3396

38

Table 4

Hedge Fund Ownership and Frequency of Management Earnings Forecasts

– by Investment Horizons

This table reports regression results for the association between hedge fund ownership and the frequency of

management earnings forecasts in subsequent quarters by hedge fund type – top tercile portfolio turnover (Hedge

High Turnover), middle tercile portfolio turnover (Hedge Mid Turnover), and bottom tercile portfolio turnover

(Hedge Low Turnover). Detailed definitions of variables are included in Appendix 2. t-statistics (in parentheses) are

based on standard errors clustered at the firm level. *** p < 0.01; ** p < 0.05; * p < 0.10 (two-tailed).

Mean Quarterly Frequency of MEFs

Model 1 Model 2

Qtrs t+1, t+2 Qtrs t+3, t+4 Qtrs t+1, t+2 Qtrs t+3, t+4

Hedge Low Turnover -0.107 -0.286 -0.064 -0.239

(-0.45) (-1.18) (-0.27) (-0.99)

Hedge Mid Turnover -0.354** -0.323* -0.420** -0.380**

(-1.97) (-1.72) (-2.35) (-2.03)

Hedge High Turnover -1.119*** -0.549* -1.358*** -0.762**

(-3.69) (-1.71) (-4.48) (-2.36)

Non-Hedge Low Turnover

0.156 -0.006

(1.58) (-0.06)

Non-Hedge Mid Turnover

0.408*** 0.346***

(5.53) (4.44)

Non-Hedge High Turnover

0.841*** 0.732***

(10.43) (8.81)

Non-Hedge 0.518*** 0.425***

(8.07) (6.33)

Constant -0.306** 0.061 -0.359** 0.010

(-2.19) (0.41) (-2.56) (0.07)

Controls Yes Yes Yes Yes

Controls No No No No

Firm-Fixed Effect Yes Yes Yes Yes


Observations 91,934 89,761 91,934 89,761

Adjusted R2 0.4538 0.4499 0.4546 0.4508

Difference Test (Low Turnover = High Turnover)

Model 1 Model 2

Qtrs t+1, t+2 Qtrs t+3, t+4 Qtrs t+1, t+2 Qtrs t+3, t+4

Hedge p < 0.01 p = 0.46 p < 0.01 p = 0.15

Non-Hedge

p < 0.01 p < 0.01

39

Table 5

Change Model

This table reports regression results for the association between changes in hedge fund ownership and changes in the

frequency of management earnings forecasts in subsequent quarters. We examine changes in overall hedge fund

ownership in Panel A. In Panel B, we examine changes in hedge fund ownership by their investment horizons – top

tercile portfolio turnover (Hedge High Turnover), middle tercile portfolio turnover (Hedge Mid Turnover), and

bottom tercile portfolio turnover (Hedge Low Turnover). Detailed definitions of variables are included in Appendix

2. t-statistics (in parentheses) are based on standard errors clustered at the firm level. *** p < 0.01; ** p < 0.05; * p

< 0.10 (two-tailed).

Panel A: Hedge Fund Ownership and MEF Frequency

Dependent Variable = Quarterly Frequency of MEFs


Hedge -0.517*** -0.522*** -0.203 -0.103

(-2.93) (-3.43) (-1.33) (-0.68)

Non-Hedge 0.309*** 0.401*** 0.419*** 0.566***

(4.64) (6.00) (6.42) (8.81)

Log(Assets) 0.346*** 0.359*** 0.354*** 0.342***

(13.38) (12.72) (11.78) (10.97)

Leverage -0.204*** -0.235*** -0.232*** -0.312***

(-3.00) (-3.15) (-2.88) (-3.68)

MTB 0.006*** 0.010*** 0.012*** 0.014***

(3.61) (5.44) (6.34) (6.49)

ROA 0.494*** 0.832*** 0.936*** 0.946***

(6.09) (10.15) (10.91) (10.65)

RetStd -0.970*** -1.099*** -1.132*** -0.689***

(-4.00) (-4.25) (-4.43) (-2.75)

VIX -0.001** -0.000 -0.002*** 0.002***

(-2.34) (-0.39) (-4.66) (3.72)

Inside Trade 0.010* 0.006 0.014** 0.012**

(1.70) (1.02) (2.37) (2.02)

High Litigation Risk 0.031*** 0.031*** 0.032*** 0.033***

(4.66) (3.94) (3.49) (3.17)

Constant -0.193*** -0.212*** -0.228*** -0.241***

(-46.81) (-44.19) (-41.52) (-39.07)


Observations 91,369 90,373 89,288 88,221

Adjusted R2 0.0203 0.0256 0.0314 0.024

40

Table 5 – Continued

Panel B: Hedge Fund Ownership and MEF Frequency –

by Investment Horizons

Qtrs t+1, t+2 Qtrs t+3, t+4

Hedge Low Turnover -0.315* 0.176

(-1.87) (1.09)

Hedge Mid Turnover -0.456*** -0.181

(-3.10) (-1.29)

Hedge High Turnover -0.857*** -0.432**

(-4.72) (-2.37)

Non-Hedge 0.358*** 0.504***

(6.42) (8.91)

Constant -0.196*** -0.227***

(-43.86) (-38.63)

Controls Yes Yes

Firm-Fixed Effect No No

Year-Fixed Effect Yes Yes

Observations 90,373 88,221

Adjusted R2 0.026 0.027

Difference Test Qtrs t+1, t+2 Qtrs t+3, t+4

Δ Hedge t (Low Turnover = High Turnover) F = 9.34 (p < 0.01) F = 11.11 (p < 0.01)

41

Table 6

Risk-Adjusted Returns on Portfolios Sorted by Changes in Hedge Fund Ownership and

Changes in MEF Frequency

This table (Panels A through C) reports the average risk-adjusted buy-and-hold returns over quarter t+1 for

portfolios based on changes in hedge fund ownership and changes in quarterly frequency of MEFs. Risk-adjusted

returns are benchmark-adjusted returns based on Daniel, Grinblatt, Titman, and Wermers (1997). The sample used

for the following test does not include the observations where hedge fund ownership does not change in quarter t.

Panel A: Overall Hedge Funds

MEF Frequency t+1 Hedge t

Increase (c) Decrease (d) N Diff (c) - (d) p-value

Decrease (a) 0.81% 0.20% 45190 0.61% p < 0.01

Not-Decrease (b) -0.19% -1.11% 29641 0.92% p < 0.01 Overall 0.42% -0.32% 74831 0.74% p < 0.01

N 38900 35931

Diff: (a) - (b) 1.00% 1.31%

p-value p < 0.01 p < 0.01

Panel B: High Turnover (Top Tercile) Hedge Funds

MEF Frequency t+1 Hedge High Turnover t


Decrease (a) 0.88% 0.40% 39,292 0.48% p = 0.03

Not-Decrease (b) -0.22% -0.82% 27,608 0.60% p = 0.01

Overall 0.43% -0.11% 66,900 0.54% p < 0.01

N 34,045 32,855

Diff: (a) - (b) 1.10% 1.22%

p-value p < 0.01 p < 0.01

Panel C: Low Turnover (Bottom Tercile) Hedge Funds

MEF Frequency t+1 Hedge Low Turnover


Decrease (a) 0.61% 0.39% 40,028 0.22% p = 0.32

Not-Decrease (b) -0.39% -0.57% 27,829 0.18% p = 0.46

Overall 0.21% -0.01% 67,857 0.22% p = 0.20

N 34,552 33,305

Diff: (a) - (b) 1.00% 0.96%

p-value p < 0.01 p < 0.01

42

Table 7

Hedge Fund Ownership and Frequency of Accurate Management Forecasts

This table examines the association between hedge fund ownership and the quarterly frequency of accurate

management forecasts. We classify a management earnings forecast as accurate (Accurate MEFs) if the magnitude

of the difference between reported earnings per share (Actual EPS) and the MEF is less than one cent. The

dependent variable is average quarterly frequency of Accurate MEFs in quarter t+1 and quarter t+2, where quarter t

is the quarter when hedge fund ownership is measured. MEF Horizon measures the number of days between fiscal

period-end and the MEF issuance date. Detailed definitions of variables are included in Appendix 2. t-statistics (in

parentheses) are based on standard errors clustered at the firm level. *** p < 0.01; ** p < 0.05; * p < 0.10 (two-

tailed).

Quarterly Frequency of Accurate MEFs (Qtrs t+1, t+2)

Model 1 Model 2

Hedge -0.182**

(-1.97)

Hedge Low Turnover

0.063

(0.48)

Hedge Mid Turnover

-0.195*

(-1.72)

Hedge High Turnover

-0.614***

(-3.38)

Non-Hedge -0.036 -0.036

(-0.89) (-0.89)

Log(Assets) 0.064*** 0.063***

(3.92) (3.86)

MTB 0.008*** 0.008***

(4.22) (4.22)

Lag ROA -0.036 -0.024

(-0.21) (-0.14)

ROAStd -0.230 -0.227

(-0.71) (-0.70)

RetStd 0.131 0.135

(0.27) (0.28)

VIX -0.000 -0.001

(-1.06) (-1.39)

MEF Horizons -0.001*** -0.001***

(-14.64) (-14.43)

Constant -0.208 -0.201

(-0.45) (-0.44)

Year Fixed Effect Yes Yes

Firm Fixed Effect Yes Yes


Adjusted R2 0.1505 0.1506

Difference Test Model 2

Hedge Low Turnover = Hedge High Turnover p < 0.01

43

Table 8

Non-Earnings Guidance by Management

This table reports results for the association between hedge fund ownership and the average quarterly frequency of

non-earnings management guidance (e.g., capital expenditures or dividends per share) in subsequent quarters (t+1

and t+2). The data used for this test is from the I/B/E/S Historical Detail Guidance database. Due to the availability of I/B/E/S guidance data, the sample period for this test is limited to 2007-2011 – the guidance variables are not

populated in 2003-2004 and sparsely populated in 2005-2006. t-statistics in parentheses are based on standard errors


Average Quarterly Non-Earnings Guidance (Qtrs t+1, t+2)

Model 1 Model 2

Hedge 0.108

(0.83) Hedge Low Turnover

0.591***

(3.17)

Hedge Mid Turnover

0.183

(1.21)

Hedge High Turnover

-1.276***

(-4.62)

Non-Hedge t 0.217*** 0.209***

(3.33) (3.13)

Log(Assets) 0.070** 0.132***

(2.14) (4.57)

Leverage 0.301*** 0.318***

(2.77) (2.89)

MTB 0.003 0.000

(0.93) (0.09)

Lag ROA -0.041 0.269

(-0.22) (1.40)

RetStd 0.512 1.356***

(1.02) (3.34)

VIX 0.003*** 0.004***

(7.81) (9.91)

Inside Trade 0.011 0.008

(1.27) (0.92)

Constant -0.476** -0.830***

(-2.07) (-3.98)

Firm Fixed Effect Yes Yes

Year Fixed Effect Yes Yes


Adjusted R2 0.5495 0.5439

Difference Test Model 2

Hedge Low Turnover = Hedge High Turnover p < 0.01

44

Table 9

Analyst Forecast Attributes

This table reports results for the association between hedge fund ownership and the average analyst forecast

dispersion and inaccuracy in subsequent quarters (t+1 and t+2). Analyst forecast dispersion is standard deviation of

analyst forecasts divided by the firm’s book value of assets in quarter t. We transform the measure into percentile

rank form, i.e., it has a value in the range 0 to 1. Analyst forecast inaccuracy is the absolute value of the difference

between reported quarterly earnings per share (EPS) and the median individual analyst forecasts of EPS. Similar to

analyst forecast dispersion, we scale the absolute value by the book value of assets and transform it into a percentile

rank variable. A larger value of this measure indicates lower accuracy of individual analyst forecasts. t-statistics in

parentheses are based on standard errors clustered at the firm level. *** p < 0.01; ** p < 0.05; * p < 0.10 (two-

tailed).

Analyst Forecast Dispersion Analyst Forecast Inaccuracy

(Qtrs t+1, t+2) (Qtrs t+1, t+2)

Model 1 Model 2 Model 1 Model 2

Hedge 0.127***

0.129***

(4.00)

(4.02)

Hedge Low Turnover

0.090**

-0.007

(2.08)

(-0.16)

Hedge Mid Turnover

0.119***

0.173***

(2.99)

(4.51)

Hedge High Turnover

0.229***

0.278***

(3.58)

(4.71)

Non-Hedge t 0.000 -0.000 -0.032*** -0.032***

(0.02) (-0.00) (-2.62) (-2.63)

Log(Assets) -0.075*** -0.075*** -0.112*** -0.112***

(-15.58) (-15.57) (-25.50) (-25.46)

MTB -0.001** -0.001** -0.004*** -0.003***

(-2.19) (-2.18) (-6.23) (-6.21)

Lag ROA -0.359*** -0.360*** -0.335*** -0.337***

(-6.47) (-6.48) (-7.21) (-7.26)

ROAStd 0.601*** 0.599*** 0.669*** 0.666***

(4.96) (4.94) (6.13) (6.11)

RetStd 0.675*** 0.674*** 1.060*** 1.063***

(4.46) (4.46) (7.57) (7.61)

VIX 0.001*** 0.001*** 0.001*** 0.001***

(11.90) (12.04) (10.25) (10.71)

Constant 0.871*** 0.871*** 1.088*** 1.087***

(15.39) (15.38) (20.55) (20.44)

Firm Fixed Effect Yes Yes Yes Yes

Year Fixed Effect Yes Yes Yes Yes

Observations 46,945 46,945 60,217 60,217

Adjusted R2 0.7567 0.7568 0.7391 0.7394

Difference Test

Analyst Forecast Dispersion Analyst Forecast Inaccuracy

Low Turnover = High Turnover p = 0.04 p < 0.01

45

Table 10

Reverse Causality This table examines whether changes in management earnings forecast frequency ( MEF) explain changes in

hedge fund ownership in subsequent quarters. MEF is measured as Quarterly Frequency of MEFs in quarter t

minus average quarterly frequency of MEFs in quarters t-1 through t-4. Hedge (Non-Hedge) is measured as hedge

fund (non-hedge fund) ownership in quarter t+1 minus hedge fund (non-hedge fund) ownership in quarter t.

Detailed definitions of variables are included in Appendix 2. t-statistics (in parentheses) are based on standard errors


Hedge Hedge Hedge Non-Hedge

Overall High Turnover Low Turnover Overall


MEF 0.000 -0.000 0.000 0.001***

(0.06) (-0.19) (0.26) (3.59)

Log(Assets) -0.003*** -0.002*** -0.001 0.005***

(-5.56) (-5.26) (-1.47) (3.18)

Leverage 0.004** 0.003** 0.001 -0.000

(2.41) (1.97) (0.64) (-0.02)

MTB -0.000 0.000 -0.000 0.000***

(-0.58) (0.60) (-0.97) (3.04)

ROA 0.002 0.000 0.002 0.049***

(0.87) (0.13) (0.69) (8.77)

RetStd -0.002 0.014 -0.016 -0.194***

(-0.16) (1.39) (-1.63) (-10.59)

VIX 0.000*** 0.000*** -0.000*** -0.000

(8.34) (13.70) (-5.32) (-0.46)

Inside Trade -0.000** -0.000* -0.000 0.001*

(-2.54) (-1.94) (-0.18) (1.92)

Share Turnover -0.000** -0.000*** 0.000** 0.000

(-2.28) (-4.41) (2.32) (0.28)

Momentum 0.159*** 0.026 0.133*** 1.433***

(5.30) (0.89) (4.58) (22.07)

High Litigation Risk 0.000 -0.000 0.000 -0.001**

(0.42) (-0.16) (0.64) (-2.28)

Constant 0.001*** 0.001*** 0.001*** 0.002***

(18.87) (12.78) (10.86) (12.64)

Year fixed effect Yes Yes Yes Yes

Observations 82,400 82,400 82,400 89,389

Adjusted R2 0.0092 0.016 0.0092 0.0211

Date post:	12-May-2018
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Bok Baik Jin-Mo Kim Kyonghee Kim Sukesh PatroWe thank Bruce Billings, Simi Kedia, David Koo, Jongha...

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