1

Access to Management and the Informativeness of Analyst Research

T. Clifton Green, Russell Jame, Stanimir Markov, and Musa Subasi*

September 2012

Abstract

We study the effects of broker-hosted investor conferences on the informativeness of analyst research.

We find analysts’ stock recommendations have significantly larger price impacts when the broker has a

conference-hosting relationship with the firm. The incremental effect is most pronounced in the quarter

following the conference and remains significant for three quarters. The post-conference effect is stronger

for small, volatile stocks and when the analyst has more experience covering the firm. Analysts at brokers

with a conference-hosting relation also issue more accurate earnings forecasts than non-hosts in the post-

conference period. Our findings suggest access to management remains an important source of analysts’

informational advantage following the passage of Regulation Fair Disclosure.

JEL: G14

* Green is from Goizueta Business School, Emory University. Jame is from School of Banking and Finance,

University of New South Wales. Markov is from the School of Management, University of Texas at Dallas. Subasi

is from Trulaske College of Business, University of Missouri.

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1. Introduction

A large literature establishes the important informational role that brokerage research

analysts play in financial markets. Analysts’ earnings forecasts have been documented to be

generally more accurate than statistical models (Brown and Rozeff, 1978; Bradshaw et al. 2012),

and another line of research shows that analysts’ stock recommendations tend to be profitable

(Womack 1996, Barber et al. 2001, Jegadeesh et al. 2004). Although analysts’ expertise could

arise from skilled processing of public information, a common explanation for analysts'

forecasting skill relies on superior access to management. Brokerage analysts place considerable

emphasis on interacting with firm management through visits to company headquarters, investor

office meetings, and broker-hosted investor conferences. Despite the widespread nature of these

costly activities, relatively little is known about the extent to which access to management

provides analysts with value-relevant information.

The enactment of Regulation Fair Disclosure (Reg FD) in 2000 requires that management

disclose material information to all investors at the same time, which would seem to diminish the

value of private meetings with management. Indeed, several studies find evidence that Reg FD

largely eliminates the benefits of access to management. For example, Cohen, Frazzini, and

Malloy (2010) document that analysts with educational ties to managers issue more informative

research than other analysts, but only in the pre-Reg FD period, which suggests value-relevant

information may no longer flow along social networks. Chen and Matsumoto (2006) find that

analysts providing optimistic recommendations issue more accurate forecasts, but exclusively in

the pre-regulation period, suggesting analysts providing favorable research may no longer be

rewarded with value-relevant information (see also Gintschel and Markov, 2004).

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On the other hand, existing work on management access relies on relatively noisy proxies

based on geographic proximity (Malloy, 2004), the timing of earnings announcements (Ivkovic

and Jegadeesh, 2004), analyst optimism (Chen and Matsumoto, 2006), or educational ties

(Cohen, Frazzini, and Malloy 2010). In this article, we analyze a direct measure of management

access using a large sample of broker-hosted investor conferences and examine whether analysts

with access to management produce more informative stock recommendations and earnings

forecasts.

Broker-hosted investor conferences are organized to provide select investing clients with

opportunities to interact with corporate managers, yet the analyst host also may reap

informational benefits. The typical conference format includes formal company presentations

followed by Q&A sessions, often led by the analyst-host, and series of one-on-one meetings

between management and select clients, also often led by the analyst-host (see Bushee, Jung, and

Miller, 2011; and Green et al. 2012 for institutional evidence). With other analysts excluded

from these private interactions, investor conferences present an ideal opportunity for measuring

and evaluating the informational benefits of management access.

We hypothesize that interaction with management at investor conferences provides

analysts with an informational advantage that leads to more informative research. Research

published immediately following a conference may be particularly informative, and examining

contemporaneous research from non-host analysts provides a control for any public information

releases. We measure the information content of analyst research as the buy-and-hold abnormal

return following stock recommendation changes.1Our methodology involves regressing the

market reaction to recommendation changes on indicator variables related to the timing and

1 Our emphasis is on recommendation changes since they generally produce larger market reactions than earnings

forecast revisions, although we find similar evidence using both measures of analyst research.

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source (host or non-host) of the recommendation, as well as various firm, analyst, and broker

characteristics to control for factors influencing the informativeness of analyst research (Loh and

Stulz, 2010).

Our analysis of 2,749 investor conferences hosted by 107 brokerages reveals convincing

evidence that investor conferences provide their analysts hosts with value-relevant information.

We find analysts at brokerages with a hosting relation with a firm issue more informative

recommendations than non-hosts. The results are robust to a variety of controls and hold with

analyst and firm fixed effects. Brokerage analysts hosting firms at conferences issue especially

informative research in the post-conference period. In particular, recommendation changes in the

three months following conferences induce incremental abnormal returns of 30 to 50 basis points

depending on the specification estimated. The post-conference effect is particularly strong for

small, volatile stocks and when the analyst has more experience covering the firm. We find no

evidence that recommendation changes for conference stocks by non-hosts induce incremental

abnormal returns during this period, which suggests that only the host obtains value-relevant

information during the conference.

The informational benefits of access to management at investor conferences likely

decrease over time, and we explore this conjecture by partitioning the post-conference period

into six sub-periods. Relative to pre-conference recommendations, intuitively we find that

recommendation revisions in the first three months following the conference induce the largest

market reaction, yet recommendation changes produce significantly larger market responses for

up to nine months following the conference. Moreover, after controlling for known determinants

of research informativeness, we find brokers that host a firm at any point during the sample

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period issue more informative research than non-hosts, which suggests that conference

attendance may signal a more general connection to management.

A potential alternative explanation for the market impact findings is that hosts do not

obtain value-relevant information on conference days, but that market participants nevertheless

perceive their post-conference research to be more informative. We address this concern by also

studying the effects of management access on analysts’ earnings forecast accuracy. Consistent

with the market impact results, we find evidence of increased forecast accuracy for conference

hosts but not for other analysts in the post-conference period. Specifically, in the three months

following the conference, the hosting analyst issues forecasts that are 8% to 13% more accurate

than non-hosts. Together, the improved forecast accuracy and larger market response to

recommendation changes in period following investor conferences provides compelling evidence

that access to management is an important determinant of analysts’ information advantage.2

Although analysts spend significant resources meeting with management, our findings

provide some of the first direct evidence that such meetings lead to more informative research,

particularly following the enactment of Regulation Fair Disclosure. Soltes (2012) examines the

private interactions between sell-side analysts and the senior management of a single large-cap

NYSE firm over a one year period and finds no evidence that private interactions leads to more

informed research. In contrast to Soltes (2012), we examine analyst research for over 3000

different companies over a seven year period and find that management access leads to more

informative recommendation changes and more accurate earnings forecasts. Our results also

2We caution against concluding that analysts obtain material nonpublic information at investor conferences in

violation of Regulation FD. Analysts may have the ability to produce value-relevant information by piecing together

public information and nonmaterial information from management (i.e. the mosaic theory), and Regulation FD

allows the transfer of nonmaterial information. While the issue of whether analysts specifically obtain material

nonpublic information from management is important, this level of analysis is beyond the scope of the data.

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compliment recent work by Bushee, Jung and Miller (2012), and Soltes and Solomon (2012),

who find that institutional investors benefit from private interactions with firm management.

Our findings suggest broker-hosted investor conferences provide a measure of access to

management that is more effective than indirect measures based on the timing of earnings

announcements (Ivkovic and Jegadeesh, 2004), analyst optimism (Chen and Matsumoto, 2006),

or educational ties (Cohen, Frazzini, and Malloy, 2010). Moreover, the evidence that analysts at

brokers with a hosting relation with a firm issue more informative research prior to conferences

suggests conference attendance may proxy for other forms of management access such as

company visits or investor office meetings

The remainder of the paper is organized as follows. Section 2 describes the investor

conference and analyst research data and presents descriptive statistics; Section 3 examines the

effects of investor conferences on the informativeness of analyst research; and Section 4

concludes.

2. Data and Descriptive Statistics

2.1 Brokerage Research Reports

Our sample consists of data on brokerage research reports and broker-hosted investor

conferences. We obtain data on stock recommendations from the Institutional Broker Estimate

(I/B/E/S) Recommendation History dataset. The recommendation history file contains the

recommendations of individual analysts with ratings ranging from 1 (strong buy) to 5 (strong

sell). We focus on recommendation revisions since prior research finds that recommendation

changes are more informative than levels (see e.g. Jegadeesh et al., 2004). Recommendation

changes are computed as the current rating minus the prior rating by the same analyst. We limit

the sample to recommendation revisions made between 2004 and 2010 to match the sample of

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investor conferences. We remove analysts coded as anonymous by I/B/ES since it is not possible

to track their recommendation revisions. We also exclude reiterations of earlier

recommendations. Our initial sample consists of 82,849 recommendation changes.

Altinkilic and Hansen (2009) highlight that recommendation revisions often react to

recent corporate news. In assessing the informativeness of analyst research, it is therefore

important to remove revisions that are merely responding to firm-specific news releases. To

control for firm-specific news, we follow Loh and Stulz (2010) and exclude revisions that fall in

the three day window [-1,1] around quarterly earnings announcement dates (obtained from

Compustat) or management earnings guidance days (as reported in Fall Call's Company Issued

Guidelines Database). We also exclude recommendation revisions where multiple analysts

issued a recommendation on the same day. After these filters, 46,903 recommendation changes

remain.

We next merge our recommendation revision sample with CRSP and Compustat. For

each firm, we collect data on share price, stock returns and volume from CRSP and we obtain

data on book value of equity from Compustat. We drop any firms that have missing return or

volume date over the prior year, as well as any firms with missing or negative book values of

equity. Our final sample includes 45,840 recommendation changes.

Prior research finds that recommendation revisions have a greater impact on stock prices

than revisions of earnings forecasts (see e.g. Loh and Stulz (2010)). As a result, our primary

focus is on recommendation revisions. Nevertheless, as an additional test, we also examine

earnings forecast revisions. We obtain data on individual analyst’s earnings forecasts from the

(I/B/E/S) Detail History dataset. Forecast revisions are computed as the current forecast for one-

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year ahead earnings minus the prior forecast by the same analyst.3 We impose all the same filters

that were applied to our recommendation revisions sample. Our initial sample consists of

371,059 forecast revisions. This number is reduced to 171,402 after excluding firm-specific news

days, and 168,285 after dropping firms with missing data in CRSP and Compustat.

2.2 Broker-Hosted Conferences

We obtain data on broker-hosted investor conferences for the period January 2004 to

December 2010 from the Bloomberg Corporate Events Database. The database includes

information on the conference name, date, and hosting organization, as well as the presenting

company name. We eliminate conferences that are not hosted by I/B/E/S-listed equity research

providers which employ at least 5 analysts in a given year. We then match companies attending

investor conferences by name or ticker with the CRSP and COMPUSTAT databases. Our final

sample consists of 68,194 presentations, by 4,394 companies, at 2,749 conferences, hosted by

107 I/B/E/S-listed brokers.

We merge our revision samples with our conference data by both broker and stock. For

each revision we create four conference indicator variables:

Host: An indicator variable equal to one if the recommendation revision is for a firm

that attended an investor conference hosted by the analyst's brokerage house at any

point over the sample period.4

Non-Host: An indicator variable equal to one if the recommendation revision is for a

firm that has never attended a conference hosted by the analyst's brokerage house at

any point over the sample period.

Host_Post-Conf: An indicator variable equal to one if the recommendation revision is

issued in the 60 trading days following an investor conference, and the report is

authored by the conference host.5

3 We also examine forecasts of quarterly earnings and find very similar results.

4 We define Host at the broker level rather than the analyst level since the broker’s resources are required to host the

conference and therefore the hosting relation may not travel with analysts across brokers. We find similar results if

we define Host at the analyst level. 5 Table 5 explores horizons beyond 60 days.

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Non-Host_Post-Conf: An indicator variable equal to one if the recommendation

revision is issued in the 60 trading days following an investor conference, and the

report is authored by a Non-Host.

We conjecture that firms attending broker-hosted investor conferences will have a closer ongoing

relationship with the hosting analyst than non-hosts, resulting in more private interactions (e.g.,

more company visits and meetings with management), and a continual flow of value-relevant

information throughout the sample period. We therefore hypothesize that analysts generally issue

more informative research for firms that attend their conferences, that is, Host revisions are more

informative than Non-Host revisions.6

In addition to providing a signal of access to management, investor conferences also

provide a specific opportunity for the transfer of value-relevant information. We therefore

predict that analysts issue unusually informative research for firms that attend their conference in

the post-conference period, i.e. Host_Post-Conf revisions are more informative than Host

revisions. We note that Host_Post-Conf revisions are a subset of Host revisions.

Bushee, Jung, and Miller (2011) find evidence that firms disclose more information

publicly on conference days, which raises the concern that the increase in the informativeness of

the host’s research in the post-conference period is due to the analyst’s ability to interpret

publicly disclosed information. If this concern is valid, then Non-Host_Post-Conf revisions

should also be as informative as Host_Post-Conf revisions, and including this dummy variable

provides an effective control.

Panel A of Table 1 describes the sample of recommendation changes. Of the 45,840

recommendation changes in our sample, 30,520 are classified as Non-Host recommendation

changes, while the remaining 15,320 are Host recommendation changes. Our sample includes

6 We acknowledge the potential for endogeneity (e.g. firms may be more likely to attend conferences hosted by

analysts who issue more informative research), and thus we primarily focus on the post-conference period.

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1938 Host_Post-Conf recommendation changes, 916 (1022) of which are upgrades

(downgrades). Panel B of Table 1 presents results for earnings forecast revisions. Our sample

includes 111,396 Non-Host forecast revisions and 56,889 Host forecast revisions, of which 6,997

are Host_Post-Conf forecast revisions.

2.3 Other Variable Construction and Descriptive Statistics

For each revision, we compute a number of characteristics about the revision, the analyst

and brokerage firm making the revision, and the firm for which the revision is being made. In

this section we discuss these characteristics and motivate their inclusion as controls. The details

of the variable construction are presented in the Appendix.

We first examine characteristics of the revision itself. We include an Upgrade dummy to

control for the fact that upgrades and downgrades may have a differential effect on prices. We

also include Abs(Rec Change) and Abs(Revision)/Price as measures of the magnitude of the

recommendation and forecast revision, respectively. Kesckes, Michaely, and Womack (2011)

find that stock recommendations accompanied by earnings forecast revisions lead to larger price

reactions. Thus for recommendation revisions we include a Concurrent Earnings Forecast

dummy, and for forecast revisions we include a Concurrent Recommendation dummy. Ivkovic

and Jegadeesh (2004) show that revisions prior to (after) an earnings announcement lead to

greater (weaker) price responses. We control for these effects by including a Pre_Earnings

(Post_Earnings) dummy variables which equals one if the revision was made in the two weeks

prior to (after) an earnings announcement. Earnings forecast and recommendation revisions that

go away from the consensus lead to larger price impacts (see e.g. Gleason and Lee, 2003 and

Jegadeesh and Kim, 2010). To capture this effect, we include an Away from Consensus dummy.

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Lastly, we include an Affiliation dummy since the presence of an investment banking

relationship with the firm may influence the informativeness of analyst research (Malloy, 2005).

We next include analyst characteristics. Stickel (1995) finds that recommendation

changes made by all-star analysts have greater price effects, so we create an All-Star dummy

variable. We also include prior Forecast Accuracy since Loh and Mian (2006) show that analysts

who possess more accurate earnings forecasts also issue more profitable recommendations.

Mikhail, Walther, and Willis (1997) highlight the importance of analyst experience as a forecast

accuracy determinant. We include two measures of experience: Total Experience which

measures the number of years since the analyst issued research on any stock, and Firm

Experience which measures the number of years the analyst has covered that specific firm minus

the average experience for all other analysts covering the firm.

Finally, we include Broker Size, which reflects resources available to the analyst

(Clement, 1999), and several firm characteristics: Book-to-Market (BM), Size, Turnover,

Volatility, Momentum, Analyst Coverage, and Conference Attendance.

Panel A of Table 2 presents descriptive statistics for our sample of recommendation

changes. Columns 1 and 2 reveal a number of substantial differences between Host and Non-

Host recommendation changes. First, we observe that affiliated analysts account for 3% of Host

revisions and 1% of Non-Host revisions. Since affiliated brokers tend have a closer relationship

with firm management, this finding is consistent with the view that brokers are more likely to

invite a firm to its conference if they have a close relationship with the firm's management. We

also find that Host revisions are more likely to be made by All-Stars, analysts with greater firm-

specific experience, and analysts who work at larger brokerage firms. In addition, Host revisions

are more likely to be made for smaller firms and firms with low BM. This is consistent with

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Green et al. (2012) who find that, relative to published research, investor conferences tend to

overweight smaller firms with high intangible assets.

We find similar, but generally amplified differences, when we compare Host_Post-Conf

revisions to Non-Host_Post-Conf revisions. In particular, relative to Non-Host_Post-Conf

revisions, Host_Post-Conf revisions are significantly more likely to be made by affiliated

analysts, all-star analysts, analysts with greater firm-specific experience, and analysts working

for larger broker houses.7 They are less likely to be made immediately after an earnings

announcement and also more likely to be bold recommendations (i.e. move away from the

consensus), on smaller stocks with less analyst coverage.

Panel B of Table 2 presents analogous results for our sample of earnings forecast

revisions. Although our forecast revision sample consists of larger stocks with greater analyst

coverage (relative to our recommendation revision sample), the patterns across the two samples

are very similar. Overall, the findings from Table 2 suggest analysts hosting investor conferences

have characteristics associated with more informative research.

3. Empirical Analyses

3.1 Informativeness of Analyst Revisions: Univarite Results

We measure the informativeness of analyst revision (recommendation or forecast) as the

stock-price reaction in the two-day event window [0,1], where day 0 is the announcement date of

the revision. Following Loh and Stulz (2010), we compute the two-day cumulative buy-and-hold

abnormal return (CAR) for revision i as:

∏ ( ) ∏ (

) (1)

7 Statistical significance is estimated based on standard errors clustered by analyst and firm.

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Rit is the raw return of stock i on day t and is the return on day t of a benchmark portfolio

with the same size, book-to-market, and momentum characteristics as the stock.8 We winsorize

CARi at the 99th

and 1st percentile for upgrades and downgrades separately.

9

We begin by looking at the two-day abnormal returns around recommendation upgrades

and downgrades for our four conference variables. The results are presented in Panel A of Figure

1. Consistent with analysts obtaining value-relevant information at investor conferences, we find

that Host_Post-Conf upgrades generate the largest two-day abnormal returns (333 bps), and

Host_Post-Conf downgrades generate the most negative two-day abnormal returns (-319 bps).

We also find that Host upgrades generate larger returns than Non-Host Upgrades (287 bps vs.

185 bps), and we find similar patterns for downgrades. This is consistent with the view that

hosting brokers have closer relationship with the firms they invite to conferences and are thus

able to issue more informative research. Lastly we see that Non-Host_Post-Conf revisions are

less informative than Non-Host revisions, which is inconsistent with non-hosting analysts

obtaining valuable information from conference presentations.

Panel B of Figure 1 presents analogous results around forecast revisions. Consistent with

prior literature, the price effects associated with forecast revisions are significantly smaller than

recommendation changes. Nevertheless, a similar pattern emerges in relative informativeness

across our four conference variables. In particular, Host_Post-Conf upgrades are associated with

the largest two day returns (96 bps), followed by Host, Non-Host, and Non-Host_Post-Conf (50

bps). A nearly identical pattern emerges for downgrades.

3.2 Informativeness of Analyst Revisions: Regression Evidence

8 See Daniel, Grinblatt, Titman and Wermers (1997) for a more detailed discussion of the construction of the DGTW

benchmark portfolio. 9 Winsorzing helps reduce the impact of extreme 2 day returns that are likely driven by firm-specific news but are

not captured by our filters. Nevertheless, our results are qualitatively similar if we use non-winsorized returns.

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The results from Figure 1 suggest access to management at investor conferences

determines the informativeness of analyst research. In this section, we more formally investigate

this idea using a regression framework. Given the largely symmetric patterns for upgrades and

downgrades in Figure 1, we estimate regressions on the full sample of revision (regardless of the

direction of the revision) and create a new dependent variable: CAR_INDi equal to CARi

multiplied by an indicator variable equal to 1 (-1) when the revision is an upgrade (downgrade).10

We begin by estimating the panel regression:

1 2 3_ _ _ .i i i iCAR IND Host Host Post Conf Non Host Post Conf - - -

(2)

Specification 1 of Panel A of Table 3 presents the results of the regression for our sample of

recommendation revisions. The results confirm the findings of Figure 1 and also verify that the

estimates are highly significant, where statistical significance is computed from standard errors

clustered by analyst and firm.

The intercept of 189 bps reflects the average two-day abnormal return for Non-Host

recommendation revisions. Host revisions are associated with an incremental 80 bps return

relative to Non-Host revisions. Host_Post-Conf revisions are associated with an additional 56

bps return relative to Host revisions. Thus, Host_Post-Conf revisions outperform Non-Host

revisions by 136 bps and are substantially more informative. There is no evidence that non-

hosting analysts issues more informative research around conference presentations. In fact, the

coefficient on Non-Host_Post-Conf revisions is significantly negative (perhaps non-hosts are at

an informational disadvantage following conferences).

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Estimating the regression separately for upgrades and downgrades yields very similar coefficients, but statistical

significance is reduced due to the smaller sample size.

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In the bottom row, we report our estimate of 1 2 3 . A positive estimate indicates

that the increase in the informativeness of host’s research following conferences exceeds that of

non-hosts’ research. This estimate is positive and statistically significant.11

In specification 2 we add the recommendation, analyst, and broker characteristics

discussed in section 2.3. To ease the interpretation of the continuous variables, we scale Total

Experience, Firm Experience¸ and Broker Size to have a standard deviation of 1. To reduce

skewness, Broker Size is reported in natural logs. The coefficients on the controls variables are in

line with prior literature. For example, revisions that are issued with concurrent earnings

forecasts, revisions that move away from the consensus, and revisions that are made by analysts

working at larger brokerage houses are more informative, while revisions made immediately

after earnings are less informative. However, these controls have very little impact on our

conference variables. Host and Host_Post-Conf remain positive and highly significant, while

Non-Host_Post-Conf remains significantly negative.

In specification 3 we add firm characteristics. All firm characteristics are scaled by their

standard deviation, and all variables except the two momentum variables, are reported in natural

logs. Not surprisingly, recommendations for smaller firms, firm with less analyst coverage, more

volatile firms, and growth firms have larger price responses. After including firm characteristics,

the coefficients on Host, Host_Post-Conf, and Post-Conf_Diff remain positive and highly

significant.

In specification 4 we augment specification 3 by including analyst fixed effects. This

helps control for time-invariant analyst characteristics, such as persistent analyst skill (e.g.

Mikhail, Walther, and Willis, 2004). Intuitively, the coefficient on Host tests whether a given

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An alternative test is that the incremental benefits of a firm attending an analyst hosted conference is greater for

the hosting analyst than the non-hosting analyst (i.e. β2 > β3). In unreported results we formally test β2 > β3 and find

statistically significant differences across all specifications.

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analyst issues more informative revisions for stocks that attend conferences hosted by the

analyst’s broker relative to firms that do not attend the broker’s conferences.12

Similarly, the

coefficient on Host_Post-Conf now tests whether a given analyst issues more informative

revisions for stocks that recently attended a conference hosted by the analyst’s broker relative to

firms that have attended (or will attend) conferences hosted by the broker, but not in the past 60

trading days. The coefficients on Host, Host_Post-Conf, and Post-Conf_Diff remain

economically and statistically significant.

In specification 5 we replace analyst fixed effects with firm fixed effects, with minimal

impact on the coefficients of interest. Lastly, in specification 6 we include both analyst and firm

fixed effects. The only source of variation in Host is the relatively small sample of analysts who

switch brokerage houses. The coefficient on Host_Post-Conf now tests whether a given analyst

issues more informative revisions for a specific stock in the 60 days after that stock attended a

conference hosted by the analysts' broker. Despite the relatively low power of this test, the

coefficient on Host and Host_Post-Conf remain positive and statistically significant. Moreover

Host_Post-Conf revisions generate two-day abnormal returns that are 96 bps higher than Non-

Host_Post-Conf revisions.

To further explore the robustness of our Table 3 finings, we re-estimate specification 3

each year from 2004-2010. Figure 2 reports the coefficients on Host, Host_Post-Conf, Non-

Host_Post-Conf, and Post-Conf_Diff for each year. The figure indicates that our results are stable

over time. Host is positive in all 7 years, Host_Post-Conf is positive in 6 of 7 years, and Post-

Conf_Diff is positive in all seven years, ranging from a low of 49 bps in 2007 to a high of 127

bps in 2008.

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For a small subset of analysts who switch brokerage firms, the effects also pick up whether the analyst issues

more informative research for a given firm when the analyst is a working at a broker that has invited the firm to one

of its conferences. Results are nearly identical after removing analysts who switch brokerage houses.

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Panel B of Table 3 repeats the analysis of Panel A, but substitutes earnings forecasts

revisions for recommendation revisions. The coefficients on Host is positive in all six

specifications, and is statistically significant in four specifications. The coefficient on Host_Post-

Conf is also positive in all specifications and statistically significant in two specifications. Post-

Conf_Diff ranges from 14 to 46 bps and is significant in all specifications except specification

six, which suffers from relatively low power. In sum, the forecast revision results yields similar

but weaker results than the recommendation changes. The weaker results for forecast revisions is

not surprising in light of prior research finding relatively small price reactions to forecast

revisions. In light of the generally weak market response to earnings forecast revisions, our

remaining tests focus on recommendation revisions.

3.3 Event-Time Analysis

The positive and significant coefficient on Host_Post-Conf in Table 3 supports our

prediction that recent access to management allows analysts to issue more informative research.

We proxy for recent access to management by identifying cases where analysts meet with

management in the prior 60 days, but the benefits of access to management may persist for

longer periods. For example, interactions with firms' management may allow analysts to better

interpret information released by the company one or two years after the meeting. However, if

much of the information is time-sensitive, then the hosting analyst’s information will likely

decline over time.

We further explore the dynamics of the benefits of access to management by introducing

additional indicator variables based on the timing of recommendation changes relative to

investor conferences. As before, recommendation revisions are classified as Host_Post-Conf if

the issuing analyst works for a broker that hosted the firm at a conference in the past quarter (60

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trading days). For clarity in Table 4 we label this as Host_Post-Conf_Q1, and add indicator

variables for Quarter 2 (days 61-120), Quarter 3 (days 121-180), Quarter 4 (days 181-240),

Year 2 (days 241-480), and Beyond Year 2 (days >480). Note that by including indicators for the

entire post-conference period, the coefficient on Host now captures the abnormal returns around

recommendation changes issued by analysts on firms who have not yet attended a conference

hosted by the analysts' brokerage firm (as of the beginning of our sample in 2004), but who will

do so by the end of our sample period in 2010).

Specification 1 of Table 4 presents the results of the regressions (3). Relative to Non-Host

revisions, Host revisions outperform by 60 bps. The larger market response to host analyst’s

research prior to the conference is consistent with investor conferences signaling a more general

relationship between the analyst and firm management. For example, conference attendance may

proxy for other forms of access to management such as company visits or investor office

meetings.13

However, even after controlling for this general effect, revisions made by the hosting

analyst in the first quarter after the firm attends its conference outperform by an additional 76

bps. This difference falls slightly to 56 and 63 bps for revisions made 2 and 3 quarters after the

conference. The informational advantage falls further to 28 bps in the fourth quarter after the

conference, 16 bps in the 2nd year after the conference, and 10 bps for revisions made on firms

that attended the hosting brokers' conference over two years ago.14

Specifications 2 through 6, which are analogous to the specifications reported in Table 3

except they now include the additional Post_Conf variables, yield largely similar results. The

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Future conference attendance may also signal the existence of past conference attendance before our sample

begins in 2004. Consistent with this view, the coefficient on conference Host is largest in 2004 (82 bps). However,

the coefficient on Host exceeds 35 bps in each year of the sample, which suggests past conference attendance is at

most a partial explanation. 14

In unreported results, we also split the first quarter into two six-week periods (days 1-30 and 31-60). The

coefficient for recommendations released by the host analyst within six weeks of the conference is 82 bps, and the

coefficient for the second six week period is 69 bps.

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coefficient on Host_Post-Conf is the largest in the first quarter following the conference, and

generally remains statistically significant for three quarters following the conference. Overall,

the results are largely consistent with access to management providing analysts an immediate

informational advantage that decays over time.

3.4 Cross-Sectional Determinants

In this section we examine cross-sectional variation in the informational advantage of the

hosting analyst in the period immediately following the conference. More specifically, we

estimate the following panel regression:

1 2 3_ _ _

.

i i i i

i i i

CAR IND Host Host Post Conf Non Host Post Conf

4 5

- - -

β X β Host_Post-Conf *Z (3)

Xi is vector that contains all of the recommendation, analyst, broker, and firm characteristics

included as controls in specification 3 of Table 3. Zi is also a vector of recommendation, analyst,

broker, and firm characteristics and is a subset of Xi.

We include a number of variable in Zi that we believe may influence the magnitude of

the Host_Post-Conf coefficient. First, we examine whether such effects are significantly larger

(or smaller) for Upgrades. Managers may be more willing to disclose positive information,

suggesting that upgrades may be more informative. On the other hand, the market may discount

upward revisions if they believe analysts are simply rewarding management for attending the

conference. Recommendations that deviate from the consensus or recommendations

accompanied by earnings forecast revisions may also be particularly informative. In addition,

analysts with more skill, more experience, and better resources may disproportionately benefit

from meeting with management. We include All-Star and Prior Forecast Accuracy as measures

of skill, Firm Experience and Total Experience as measures of experience, and Broker Size as a

20

measure of resources. Lastly, meetings with management may be particularly valuable for hard-

to-value firms such as growth firms, small firms, and more volatile firms.

Table 5 reports the results of Equation (3). For brevity, we report only the coefficient on

Host_Post-Conf and the interaction terms (i.e. β5). Most of the coefficients are small and

statistically insignificant. However, there is some evidence that Host_Post-Conf revisions are

more informative when the hosting analyst also makes a concurrent earnings forecast. This is

consistent with part of the hosting analyst's information advantage being driven by their ability to

better forecast subsequent earnings, a possibility we more formally examine in Section 3.6. We

also find that revisions are significantly more informative for smaller stocks and more volatile

stocks. Intuitively, access to management is more valuable for harder-to-value firms.

The sample of Host_Post-Conf recommendation changes is relatively small at 1,938. We

also consider a longer post-conference period to increase the power of the test. Motivated by the

long-horizon results in Table 4, we redefine Host_Post-Conf to be equal to 1 if the analyst issues

a recommendation change for a firm that attended a conference hosted by the analyst’s broker

during the past 180 trading days (instead of 60). Lengthening the post-conference period

increases the number of Host_Post-Conf recommendation changes to 5,173 and generally

produces stronger interaction effects. The incremental informativeness of post-conference

recommendation changes remain significantly stronger for smaller and more volatile stocks, and

the effects become statistically stronger for revisions made around concurrent earnings forecasts.

We also now find that the information advantage is larger for analysts with greater firm-specific

experience. As firm-specific experience increases, analysts may obtain better access to

management or may better interpret information revealed by management.

3.5 Longer-Horizon Returns

21

Our analysis relies on two-day returns around the release of revisions as a measure of the

informativeness of the analysts' research. Such a measure is reasonable if markets are efficient.

However, it is possible that the market incorrectly believes that the hosting analyst has more

information and overreacts to revisions released by the hosting analyst. Alternatively, it is

possible that market underreacts to the hosting analysts information, in which case two-day

returns understate the true benefits of access to management.

If the larger abnormal returns around Host and Host_Post-Conf revisions effects are

driven by overreaction we would expect such revisions to exhibit reversals over longer horizons.

To explore this possibility, we re-estimate the equation in Table 3, but now the dependent

variable is the cumulative buy-and-hold abnormal returns over the subsequent month

(CAR21_INDi), subsequent six months (CAR126_INDi), or subsequent year (CAR252_INDi).15

We

continue to winsorize all returns at the 99th and 1st percentile for upgrades and downgrades,

respectively, although non-winsorized results are similar.

Figure 3 plots the coefficients on Host, Host_Post-Conf, Non-Host_Post-Conf, and Post-

Conf_Diff for the three longer-horizon returns, as well as the original two-day return. The

coefficients on Host is relatively flat across all time-horizons. The coefficient on Host_Post-Conf

declines slightly over the first month (from 43 bps to 14 bps) and then increases to a statistically

significant 203 bps (t=2.06) over the subsequent year. Similarly, Non-Host_Post-Conf increases

from -9 bps over the two-day window to 90 bps over the subsequent year, however none of the

estimates for Non-Host_Post-Conf are significantly different from zero. Overall, the return

results are more consistent with underreaction than overreaction; however it is difficult to draw

any definitive conclusions given the imprecision in estimating longer-horizon returns. In the next

15

The figure results use controls as in Specification 3 of Table 3. We find similar results using any of the other

specifications in Table 3.

22

section, we present a more powerful test to preclude the possibility that the higher market

reaction to recommendations by conference hosts is driven by overreaction.

3.6 Forecast Accuracy

The results from Table 5 suggest that recommendation changes in the post conference

period are particularly informative when the hosting analysts also issues an earnings forecast

revision. This result is consistent with the view that access to management provides information

that allows analysts to better estimate future earnings. We specifically test this conjecture by

examining whether analysts issue more accurate earnings estimates for firms that recently

attended a conference hosted by their brokers. Such a finding would also provide evidence that

the larger market response to post-conference recommendation changes reflects information

regarding fundamentals rather than overreaction by market participants.

We estimate forecast accuracy using annual earnings forecasts, although quarterly

earnings forecasts generate similar results. If an analyst issues more than one forecast for the

firm-year, we use the most recent forecast prior to the earnings announcement date.16

Following

Clement (1999) we measure forecast accuracy as the proportional mean forecast error (PMAFE),

which is calculated as:

, , , ,i j t j t j tAFE AFE AFE . (5)

, ,i j tAFE is the absolute forecast error for analyst i's forecast of firm j for year t earnings, and

,j tAFE is the mean absolute forecast error for firm j in year t. We demean by firm and year to

help control for differences in forecast difficulty that vary by firm-year.

16

We rely on the most recent estimate to make the results more comparable to prior literature on forecast accuracy

(e.g. Mikhail, Walther, and Wallis, 1997; Jacob, Lys, and Neale, 1999; and Clement, 1999). Our findings are robust

to using all forecast estimates.

23

We next estimate the following panel regression:

1 2 3_ _ .ijt i i iPMAFE dHost dHost Post Conf dNon Host Post Conf - - - (6)

dHost is the firm-year demeaned value of Host as defined in section 3.2, but modified for

earnings estimates. Specifically, Host now equals 1 if the analyst is issuing an earnings estimate

for a firm that attended a conferences hosted by the analyst’s broker at any point during the

2004-2010 sample period. dHost_Post-Conf and dNon-Host_Post-Conf are defined analogously.

Since all dependent and independent variables are demeaned by firm-year averages, the

regression becomes equivalent to a firm-year fixed effects regression. Consequently, the

regression is estimated without an intercept.

The results of regression (6) are presented in Table 6. We find evidence that Host and

Host_Post-Conf earnings estimates are significantly more accurate. Specifically, Host_Post-Conf

earnings estimates are 4.7% more accurate relative to Host estimates and 13% more accurate

than Non-Host estimates. The findings indicate that access to management at broker conferences

improves analysts’ forecast accuracy. The coefficient on Non-Host_Post-Conf is insignificantly

different from zero, suggesting that the information advantage immediately after conferences

accrues only to the hosting broker.

In specification 2, we include the following control variables: Total Experience, Firm

Experience, Broker Size, Forecast Age, Forecast Frequency, and Firms Followed. The

construction of the control variables is presented in the Appendix. Again, all control variables are

demeaned by firm-year averages. The coefficients on the controls are in line with prior literature

(Clement, 1999). For example older forecasts are less accurate while forecasts made by analysts

with greater firm-specific experience are more accurate. Adding the controls eliminates the

24

statistical significance of Host, but Host_Post-Conf and Post-Conf_Diff remain highly

significant.

In specification 3 we add analyst fixed effects to control for differences in innate ability.

The coefficient on Host_Post-Conf remains highly significant. Similarly, the coefficient on Post-

Conf_Diff is largely unchanged at 8.8%, an estimate that is both statistically and economically

significant. Overall, the results suggest the access to management at investor conferences allows

the hosting analyst to obtain better estimates of future earnings. This finding clarifies the nature

of the value-relevant information transmitted, and precludes the alternative explanation that the

market incorrectly perceives hosts’ research on conference stocks to be more informative.

4. Conclusion

Broker-hosted investor conferences provide analysts with private opportunities for

interactions with firm management. With other market participants excluded from these

interactions, brokerage-hosted conferences provide an excellent opportunity for studying whether

analysts obtain superior information in periods when they have greater access to management.

Our analysis of 2,749 investor conferences hosted by 107 brokerages reveals convincing

evidence that investor conferences provide their analysts hosts with value-relevant information.

We find analysts at brokerages with a hosting relation with a firm issue more informative

recommendations changes than non-hosts, and that this difference is the largest in the post-

conference period. In particular, recommendation revisions in the three months following

conferences induce incremental abnormal returns of 30 to 50 basis points depending on the

estimated specification.

We consider an alternative explanation for our results, which is that market participants

wrongly perceive hosts’ post-conference research to be more informative. We find no evidence

25

of return reversal in the months subsequent to the recommendation change. More importantly,

we find evidence of increased forecast accuracy for conference hosts but not for other analysts in

the post-conference period. These findings support our hypothesis that access to management at

investor conferences generates informational advantages for the hosting analyst.

While investor conferences appear to be an important mechanism through which analysts

obtain management access, there are many other ways analysts interact with management. For

example, analysts routinely take clients to meet management at company headquarters. Analysts

also spend significant amount of time communicating with management over the phone and via

email. The importance of management access as a source of analysts’ information advantage is

therefore likely to be greater than what our evidence suggests. Our finding that analysts with a

hosting relationship with the firm generally issue more informative research than non-hosts

throughout the sample period suggests that investor conferences may serve as a more general

proxy for access to management.

26

Appendix: Description of Control Variables

This appendix describes the construction of a number of variables describing the characteristics

of the recommendation revision, earnings forecast revision, or earnings forecast. The

characteristics are partitioned into three groups: Revision Characteristics, Analyst and Broker

Characteristics, and Firm Characteristics.

Revision Characteristics:

Host_Post-Conf - a dummy variable equal to one if the revision is for a firm that attended

a conference hosted by the analysts' brokerage house over the past 60 trading days.

Host - a dummy variable equal to one if the revision is for a firm that attended a

conference hosted by the analysts' brokerage house at any point over the sample period.

Non-Host_Post-Conf - a dummy variable equal to one if the revision is for a firms that

has never attended a conference hosted by the analysts' brokerage house, but who has

attended a conference hosted by a different brokerage house over the past 60 trading

days.

Non-Host - a dummy variable equal to one if the revision is for a firm that has never

attended a conference hosted by the analysts' brokerage house.

Upgrade - a dummy variable equal to one if the revision is favorable (e.g. a

recommendation change from hold to buy or an upward revised earnings forecast)

Abs(Rec Change) – the absolute value of the magnitude of the recommendation change.

For example, going from a hold (=3) to a strong buy (=1), would have a value of 2.

Abs(Revision)/Price – the absolute value of the forecast revision change scaled by the

price of the stock two days prior to the revision change. This value is winsorized at the

99%.

Concurrent Forecast - a dummy variable equal to one if the recommending analyst

issued an earnings forecast for the stock in the 3 days surrounding the recommendation [-

1,1] and the forecast was in the same direction as the revision.

Concurrent Recommendation - a dummy variable equal to one if the analyst issuing a

forecast revision also issued a recommendation change for the stock in the 3 days

surrounding the forecast revision [-1,1] and the recommendation change was in the same

direction as the revision.

27

Pre-earnings - a dummy variable equal to one if the recommendation (or forecast

revision) was issued in the two weeks prior to an earnings announcement

Post-earnings - equals one if the recommendation (or forecast revision) was issued in the

two weeks after an earnings announcement [Motivation: Ivkovic and Jegadeesh (2004).

Away from Consensus - a dummy variable equal to one if the absolute deviation of the

new recommendation (or new earnings forecast) from the consensus is larger than the

absolute deviation of the prior recommendation (or prior earnings forecast) from the

consensus. If the firm has less than 3 outstanding recommendations (forecast revisions),

this value is set equal to 0, and we include an indicator variable (not reported) that equals

one when there is a missing value, and zero otherwise

Affiliated Broker = a dummy variable equal to one if the analyst works for a brokerage

firm that was a lead underwriter for the firm in an IPO or SEO in the past 3 years. For

2009-2010. affiliation is determined based on data available in 2008.

Forecast Age - the number of calendar days between the forecast issue date and the

earnings announcement date

Forecast Frequency - the number of forecasts issued by an analyst, for a firm, during the

fiscal year.

Analyst and Broker Characteristics:

All-Star Analyst = a dummy variable equal to one if the analyst is ranked as an All-

American (first, second, third, or runner-up teams) in the annual polls in the Institutional

Investor magazine in the year prior to the recommendation (or revision) change. For

2009-2010, All-Star is determined based on data available in 2008.

Past Forecast Accuracy Quintile - Analysts are ranked into quintiles based on their prior

one year forecast accuracy in the stock, with quintile 1 being the most accurate and five

being the least accurate. If fewer than five analysts are covering the stock, the value is set

equal to 0, and we include an indicator variable (not reported) that equals one when there

is a missing value, and zero otherwise

Firm Experience - The number of years the analysts has covered the firm minus the

average number of years all other analysts have been covering the firm.

Total Experience - The number of years since the analyst first issued an earnings forecast

(for any firm)

28

Broker Size - the total number of analysts working at the brokerage firm of the

recommending analysts.

Firms Followed - the total number of firms followed by an analyst in a given year.

Firm Characteristics:

Book-to-Market - book to market ratio computed as the book value of equity for the year

ended before the most recent June 30th, divided by market capitalization on December

31st of the same fiscal year. Negative values are excluded. Positive values are winsorized

at the 99%.

Size - the market capitalization computed as share price times total shares outstanding as

of the end of June in the year prior to the recommendation change (in $Millions).

Turnover - the average daily turnover (i.e. share volume scaled by shares outstanding)

over the 63 days prior to the recommendation change.

Volatility - the standard deviation of daily returns over the 63 days prior to the

recommendation change.

Momentum21 - the stock return over the 21 trading days prior to the recommendation.

Momentum21_252 - the stock return over the prior 252 trading days prior to the

recommendation, excluding the 21 trading days prior to the recommendation.

Analyst Coverage - the total number of analysts covering the firm in the year of the

recommendation change.

Conference Attendance - the total number of broker-hosted conferences the firm attended

during the year of the recommendation change.

29

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30

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31

Table 1

Summary Statistics

This table presents summary statistics on recommendation changes and earnings forecast revisions from the I/B/E/S dataset for the period January 2004 to

December 2010. Recommendation changes are computed as the current rating minus the prior rating by the same analysts. Analysts initiations or

recommendations with no prior outstanding ratings are excluded. Anonymous analysts are also excluded. Forecast revisions are computed as the current forecast

for one-year ahead earnings minus the prior forecast by the same analyst. Excluding news drops revisions that occur in the three-day window around the firm's

quarterly earnings announcement date, the three-day window around the release of earnings guidance by the firm's management, and days where multiple

analysts issued recommendations for the same firm. Non-missing data further excludes revisions for stocks with missing return or volume data over the prior

year, as well as firms with negative or missing book-value of equity. The non-missing data sample is partitioned into Non-Host and Host revisions. Non-Host

revisions are revisions made by analyst for a firm who has never attended a conference hosted by the analysts' brokerage firm. Host revisions are revisions made

by analyst for a firm who has attended a conference hosted by the analysts' brokerage firm. Host_Post-Conf revisions are revisions made by analysts for firms

that attended a conference hosted by the analyst’s broker over the past 60 trading days. Non-Host_Post-Conf revisions are revisions made by analysts for firms

that have never attended a conference hosted by the analyst’s broker, but who have attended a conference hosted by a different brokerage firm over the past 60

trading days. Excluding News & Non-Missing Data

Full Sample Excluding News

Non-Missing

Data Non-Host Host

Host

Post-Conference

Non-Host

Post-Conference

Recommendation Changes

-4 435 219 214 179 35 5 75

-3 236 129 125 104 21 2 48

-2 17,891 9,657 9,454 6,610 2,844 399 2,642

-1 24,474 13,362 13,058 8,526 4,532 616 3,317

1 23,119 13,735 13,404 8,501 4,903 580 3,298

2 16,171 9,499 9,296 6,346 2,950 335 2,573

3 174 99 95 84 11 0 34

4 349 203 194 170 24 1 60

Upgrades 39,813 23,536 22,989 15,101 7,888 916 5,965

Downgrades 43,036 23,367 22,851 15,419 7,432 1,022 6,082

All 82,849 46,903 45,840 30,520 15,320 1,938 12,047

Earnings Forecast Revisions

Upward 187,421 77,734 76,225 51,356 24,869 3,022 21,400

Downward 183,638 93,668 92,060 60,040 32,020 3,975 25,533

All 371,059 171,402 168,285 111,396 56,889 6,997 46,933

32

Table 2

Characteristics of Analyst Research by Revision Type

This tables reports a number of characteristics about the revision, the analyst and brokerage firm making the revision, and the firm for which the revision is

being made. The details of the variable construction are presented in the Appendix. The sample includes revision changes over the 2004-2010 sample period

with non-missing data. This includes 45,840 recommendation changes in Panel A and 168,285 earnings forecasts revisions in Panel B. Columns 1-4 present the

results for Non-Host, Host,Host_Post-Conf, and Non-Host_Post-Conf revisions as defined in Table 1. The 5th column, Post-Conf_Diff, reports the difference

between Host_Post-Conf and Non-Host_Post-Conf revisions. The 6th column reports the t-statistic testing whether the difference in column 5 is significantly

different from zero. The t-statistic is based on standard errors clustered by analyst and firm.

Panel A: Recommendation Changes

Non-Host Host

Host

Post-Conference

Non-Host

Post-Conference

Post-Conference

Difference

t(Post-Conference

Difference)

[1] [2] [3] [4] [3] – [4]

Upgrade 0.49 0.51 0.48 0.50 -0.02 (-1.66)

Abs(Rec Change) 1.47 1.40 1.39 1.48 -0.10 (-2.18)

All Star Analyst 0.05 0.14 0.15 0.06 0.10 (6.09)

Affiliated Broker 0.01 0.03 0.03 0.01 0.03 (6.11)

Concurrent Forecast 0.16 0.17 0.16 0.16 -0.01 (-0.62)

Pre-Earnings 0.07 0.07 0.06 0.08 -0.01 (-2.25)

Post-Earnings 0.12 0.10 0.09 0.12 -0.03 (-3.08)

Away from Consensus 0.52 0.53 0.56 0.52 0.04 (3.02)

Past Forecast Accuracy 2.56 2.47 2.50 2.53 -0.05 (-0.97)

Firm Experience 0.17 0.56 0.53 0.13 0.41 (4.84)

Total Experience 6.83 7.51 7.42 6.97 0.45 (1.84)

Broker Size 38.62 68.77 66.84 33.68 33.17 (11.43)

Book-to-Market 1.11 0.48 0.45 0.46 -0.01 (-0.60)

Size ($Millions) 9.58 7.26 5.35 12.59 -7.22 (-11.50)

Turnover (%) 13.22 13.71 13.93 14.67 -0.74 (-1.90)

Volatility (%) 2.70 2.77 2.80 2.72 0.08 (1.46)

Momentum1 (%) 1.11 1.01 1.38 0.74 0.64 (1.47)

Momentum2_12 (%) 18.25 16.69 13.91 16.02 -2.11 (-0.85)

Total Coverage 15.06 15.05 14.36 18.30 -3.93 (-7.55)

Total Conferences 2.95 4.88 6.23 5.57 0.66 (3.95)

33

Table 2 (continued)

Panel B: Earnings Forecast Revisions

Non-Host Host

Host

Post-Conference

Non-Host

Post-Conference

Post-Conference

Difference

t(Post-Conference

Difference)

[1] [2] [3] [4] [3] – [4]

Upgrade 0.46 0.44 0.43 0.46 -0.02 (-2.70)

Abs(Revision)/Price 0.82 0.83 0.81 0.77 0.04 (0.86)

All Star Analyst 0.11 0.15 0.16 0.11 0.05 (2.47)

Affiliated Broker 0.01 0.03 0.04 0.01 0.03 (6.62)

Concurrent Rec. 0.05 0.05 0.05 0.05 -0.01 (-2.03)

Pre-Earnings 0.17 0.17 0.18 0.18 0.00 (-0.47)

Post-Earnings 0.24 0.21 0.20 0.23 -0.04 (-4.31)

Away from Consensus 0.43 0.46 0.47 0.43 0.04 (4.72)

Past Forecast Accuracy 2.48 2.44 2.44 2.47 -0.03 (-1.28)

Firm Experience 0.24 0.40 0.53 0.19 0.34 (2.65)

Total Experience 7.41 7.58 7.76 7.51 0.25 (1.02)

Broker Size 51.86 64.81 64.66 50.32 14.34 (6.51)

Book-to-Market 0.84 0.56 0.55 0.57 -0.01 (-0.55)

Size (millions) 12.90 10.32 9.98 15.79 -5.81 (-7.77)

Turnover (%) 13.74 13.86 13.99 14.86 -0.87 (-2.61)

Volatility (%) 2.84 2.99 3.04 2.93 0.11 (2.38)

Momentum1 (%) 0.41 0.17 0.07 -0.1 0.17 (0.60)

Momentum2_12 (%) 10.08 9.12 6.48 8.07 -1.59 (-1.51)

Total Coverage 17.30 16.32 15.70 20.26 -4.56 (-13.11)

Total Conferences 3.26 4.99 6.39 5.61 0.75 (6.04)

34

Table 3

Access to Management and the Market Response to Analyst Research

Specification [1] of this table reports the results of the following panel regression:

1 2 3_ _ _ .i i i iCAR IND Host Host Post Conf Non Host Post Conf - - -

CAR_INDi equals the two-day abnormal return around a revision multiplied by an indicator variable which equals 1

(-1) if the revision is an upgrade (downgrade). Host, Host_Post-Conf, and Non-Host_Post-Conf are defined as in the

Appendix. Post-Conf_Diff, below the main regression estimates, tests for whether Host_Post-Conf revisions are

significantly more informative than Non-Host_Post-Conf revisions (i.e. β1 + β2--β3>0). Specification [2] adds a

number of revision, analyst, and broker characteristics, and specification [3] adds firm characteristics. All

characteristics are defined in the Appendix. Specification 4 adds analyst fixed effects, specification 5 adds firm-

fixed effects, and specification 6 includes both analyst and firm-fixed effects. The sample includes revision changes

over the 2004-2010 sample period with non-missing data. This includes 45,840 recommendation changes in Panel A

and 168,285 earnings forecasts revisions in Panel B. Standard errors are clustered by analyst and firm, and t-

statistics are reported below each estimate.

35

Panel A: Recommendation Changes (Table 3 continued)

[1] [2] [3] [4] [5] [6]

Intercept 189.41 37.97 903.52 0.00 0.00 0.00

(37.74) (2.33) (22.57) (0.00) (0.00) (0.00)

Host 79.67 68.83 42.71 14.77 23.49 41.26

(11.71) (10.13) (6.49) (2.01) (3.49) (1.96)

Host_Post-Conference 56.12 54.49 43.19 31.59 40.41 41.39

(4.76) (4.65) (3.79) (2.72) (3.49) (2.48)

Non-Host_Post-Conference -29.04 -19.39 -8.63 -5.59 -11.56 -13.54

(-5.14) (-3.53) (-1.47) (-0.92) (-1.92) (-1.58)

Upgrade

21.75 24.31 24.47 21.35 22.99

(4.96) (5.42) (5.31) (4.75) (4.25)

Abs(Rec Change)

39.52 36.87 52.32 34.47 50.86

(7.70) (7.77) (7.06) (7.73) (4.71)

All-Star Analyst

-19.10 21.40 8.10 20.04 15.34

(-1.84) (2.37) (0.47) (2.47) (0.62)

Affiliated Broker

54.28 15.73 24.45 28.77 100.34

(2.34) (0.70) (1.07) (1.18) (1.66)

Concurrent Earnings Forecast

66.73 68.04 63.09 66.30 70.34

(9.80) (11.05) (10.16) (10.63) (8.20)

Pre-Earnings

-4.10 -1.20 -4.92 -2.19 -4.68

(-0.70) (-0.21) (-0.84) (-0.39) (-0.63)

Post-Earnings

-44.87 -48.87 -35.94 -43.50 -31.61

(-8.41) (-9.32) (-6.56) (-8.01) (-4.23)

Away from Consensus

41.85 39.73 39.43 39.09 39.63

(10.04) (9.76) (9.31) (9.38) (7.56)

Past Forecast Accuracy Quintile

-8.61 -8.68 -5.63 -7.45 -3.40

(-4.04) (-4.56) (-2.69) (-3.96) (-0.98)

Firm Experience

6.54 1.59 3.20 5.50 7.77

(2.35) (0.66) (1.12) (2.25) (0.73)

Total Experience

2.99 6.26 -20.57 1.13 -5.36

(0.76) (1.91) (-2.57) (0.37) (-0.31)

Log (Broker Size)

22.17 35.98 31.75 46.21 34.38

(5.60) (8.66) (4.66) (15.71) (3.41)

Log (Book-to-Market)

-22.16 -11.03 -3.68 -13.45

(-8.09) (-3.43) (-0.61) (-1.45)

Log (Size)

-56.11 -48.39 -53.63 -67.03

(-14.78) (-10.64) (-4.21) (-3.36)

Log (Turnover)

4.73 4.78 3.49 -1.93

(1.40) (1.31) (0.54) (-0.20)

Log (Volatility)

48.56 47.76 31.18 32.35

(13.74) (12.42) (6.83) (5.53)

Momentum1

-8.24 -7.39 -6.11 1.41

(-2.33) (-2.02) (-1.70) (0.31)

Momentum2_12

-13.52 -14.36 -16.88 -13.43

(-3.96) (-4.29) (-4.78) (-2.88)

Log (Total Coverage)

-26.61 -39.32 -34.08 -16.08

(-6.33) (-6.81) (-4.13) (-1.20)

Log (Total Conferences)

4.53 4.23 -6.03 -10.67

(1.55) (1.37) (-1.32) (-1.55)

Analyst Fixed Effects No No No Yes No Yes

Firm Fixed Effects No No No No Yes Yes

R2 1.28% 2.69% 7.20% 19.79% 21.14% 60.51%

Post-Conference Difference 164.83 142.70 94.53 51.96 75.46 96.20

(12.90) (11.28) (7.96) (4.08) (6.22) (3.72)

36

Panel B: Earnings Forecast Revisions (Table 3 continued)

[1] [2] [3] [4] [5] [6]

Intercept 65.07 71.40 321.53 0.00 0.00 0.00

(24.84) (9.84) (13.36) (0.00) (0.00) (0.00)

Host 17.11 16.82 13.09 5.00 8.03 5.95

(4.63) (4.57) (3.84) (1.33) (2.46) (0.62)

Host_Post-Conference 13.69 12.76 10.57 8.24 8.79 5.71

(2.37) (2.23) (1.87) (1.46) (1.59) (0.93)

Non-Host_Post-Conference -15.50 -13.47 -4.85 -4.34 -4.62 -2.49

(-4.89) (-4.27) (-1.36) (-1.21) (-1.30) (-0.60)

Upgrade

5.62 10.04 12.09 11.48 12.31

(1.86) (3.26) (3.99) (3.78) (3.78)

Log [Abs(Revision)/Price]

23.61 17.27 21.66 19.69 20.65

(12.04) (9.75) (11.49) (9.96) (9.23)

All-Star Analyst

-7.08 1.81 0.19 -2.39 -0.53

(-1.55) (0.43) (0.03) (-0.64) (-0.07)

Affiliated Broker

12.67 -0.78 11.36 7.03 -13.89

(1.44) (-0.09) (1.28) (0.78) (-0.73)

Concurrent Recommendation

180.56 178.11 178.92 174.53 178.08

(26.22) (26.25) (26.29) (26.16) (24.58)

Pre-Earnings

-14.61 -11.97 -13.20 -13.26 -14.69

(-4.14) (-3.43) (-3.75) (-3.94) (-3.87)

Post-Earnings

-39.25 -42.79 -36.53 -37.22 -32.17

(-10.91) (-12.11) (-10.72) (-11.21) (-8.64)

Away from Consensus

12.36 12.02 11.09 12.84 7.12

(4.72) (4.65) (4.42) (5.20) (2.70)

Past Forecast Accuracy Quintile

-2.21 -2.40 -1.67 -2.09 -1.66

(-2.53) (-2.78) (-1.85) (-2.43) (-1.36)

Firm Experience

1.98 0.05 1.54 1.58 -5.31

(1.28) (0.03) (1.07) (1.21) (-0.76)

Total Experience

-0.75 0.68 -6.79 -0.12 14.18

(-0.44) (0.44) (-1.50) (-0.08) (1.27)

Log (Broker Size)

3.31 6.95 10.26 9.42 10.52

(2.22) (5.14) (3.05) (7.24) (2.78)

Log (Book-to-Market)

-10.27 -2.82 -0.15 -0.24

(-5.69) (-1.40) (-0.04) (-0.05)

Log (Size)

-22.57 -13.71 -4.25 -3.86

(-8.79) (-5.15) (-0.47) (-0.34)

Log (Turnover)

-1.02 -4.11 -13.44 -13.96

(-0.49) (-1.83) (-3.49) (-2.92)

Log (Volatility)

6.52 6.48 7.92 4.22

(2.73) (2.52) (2.66) (1.29)

Momentum1

-4.08 -2.99 -2.08 -2.88

(-1.74) (-1.27) (-0.87) (-1.09)

Momentum2_12

-1.12 0.71 0.42 -0.97

(-0.64) (0.40) (0.21) (-0.44)

Log (Total Coverage)

-8.95 -11.18 -17.01 -11.84

(-3.12) (-3.78) (-3.59) (-2.00)

Log (Total Conferences)

-2.20 -1.59 -2.12 -1.26

(-1.09) (-0.77) (-0.80) (-0.38)

Analyst Fixed Effects No No No Yes No Yes

Firm Fixed Effects No No No No Yes Yes R

2 0.12% 1.69% 2.16% 6.35% 6.38% 24.23%

Post-Conference Difference 46.30 43.05 28.51 17.57 21.44 14.16

(7.51) (7.02) (4.89) (2.94) (3.88) (1.40)

37

Table 4

Access to Management and the Market Response to Recommendation Changes: Event-Time Analysis

This table reports the results of regressing two-day cumulative abnormal returns following recommendation changes

on indicator variables related to the source and timing of report. Cumulative abnormal returns are multiplied by an

indicator variable equal to 1 (-1) for recommendation upgrades (downgrades). Host is an indicator variable equal to

1 if the issuing analyst works at a broker that hosted the recommended firm at a conference at some point during the

sample period. For conference hosts, the Post-Conference period is categorized (using indicator variables) into

Quarter 1 (trading days 1-60 following the conference), Quarter 2 (trading days 61-120), Quarter 3 (days 121-180),

Quarter 4 (days 181-240), Year 2 (241-480) and > Year 2 (days >481). Non-Host_Post-Conference,Quarter 1 is an

indicator variable equal to one if the recommendation revision is issued in the 60 trading days following an investor

conference, and the report is authored by a Non-Host. Specification [2] adds a number of revision, analyst, and

broker characteristics, and specification [3] adds firm characteristics (as in Table 3). All characteristics are defined

in the Appendix. For brevity, the coefficients on the characteristics are not reported. Specification 4 adds analyst

fixed effects, specification 5 adds firm-fixed effects, and specification 6 includes both analyst and firm-fixed effects.

The sample includes 45,840 recommendation changes over the 2004-2010 sample period. Standard errors are

clustered by analyst and firm, and t-statistics are reported below each estimate.

[1] [2] [3] [4] [5] [6]

Host 60.06 49.30 34.76 8.89 16.50 42.03

(7.73) (6.49) (4.82) (1.08) (2.20) (1.91)

Host_Post-Conference, Quarter 1 75.72 74.37 53.31 40.72 49.67 43.68

(6.01) (5.94) (4.39) (3.25) (4.00) (2.28)

Host_Post-Conference, Quarter 2 56.44 54.99 31.98 20.73 20.62 10.18

(4.12) (4.03) (2.44) (1.48) (1.53) (0.46)

Host_Post-Conference, Quarter 3 63.13 62.97 38.42 34.46 40.48 28.90

(4.33) (4.32) (2.74) (2.44) (2.91) (1.27)

Host_Post-Conference, Quarter 4 28.24 26.88 6.30 -0.13 6.35 -16.45

(1.88) (1.81) (0.44) (-0.01) (0.42) (-0.72)

Host_Post-Conference, Year 2 16.72 17.78 3.27 14.54 -3.15 -16.55

(1.15) (1.24) (0.24) (1.01) (-0.23) (-0.77)

Host_Post-Conference, > Year 2 9.45 14.07 2.45 -4.33 9.81 -17.09

(0.62) (0.94) (0.17) (-0.28) (0.69) (-0.63)

Non-Host_Post-Conf, Quarter 1 -29.04 -19.38 -7.13 -4.54 -10.32 -13.18

(-5.14) (-3.53) (-1.21) (-0.75) (-1.70) (-1.54)

Revision, Analyst, and Broker

Controls No Yes Yes Yes Yes Yes

Firm Controls No No Yes Yes Yes Yes

Analyst Fixed Effects No No No Yes No Yes

Firm Fixed Effects No No No No Yes Yes

R2 1.37% 2.77% 7.23% 19.92% 21.20% 60.63%

38

Table 5

Cross-Sectional Determinants of the Informational Benefits of Access to Management

This table reports the estimates from the following panel regression:

1 2 3_ _ _ .i i i i i iCAR IND Host Host Post Conf Non Host Post Conf 4 5

- - - β X β Host_Post-Conf *Zi

CAR_INDi equals the two-day abnormal return around a revision multiplied by an indicator variable which equals 1

(-1) if the revision is an upgrade (downgrade). Returns are winsorized at the 99th and 1st percentiles for upgrades

and downgrades, separately. Host, and Non-Host_Post-Conf are defined as in the Appendix. In specification 1 (2),

Host_Post-Conf is a dummy variable equal to one if the revision is for a firm that attended a conference hosted by

the analyst's brokerage house over the past 60 (180) trading days. X is a vector that contains all of the

recommendation, analyst, broker, and firm characteristics included as controls in specification 3 of Table 3. Z is a

vector of recommendation, analyst, broker, and firm characteristics and is a subset of X. For brevity, we report only

the coefficients on Host_Post-Conf and the interaction terms (i.e. β5). The sample includes 45,840 recommendation

changes over the 2004-2010 sample period. Standard errors are clustered by analyst and firm, and t-statistics are

reported below each estimate.

[1] [2]

Host_Post-Conference 572.12 513.52

(3.78) (5.73)

Post1 * Upgrade 1.97 10.76

(0.09) (0.77)

Post1 * All-Star Analyst -14.97 -1.51

(-0.48) (-0.08)

Post1 * Affiliated Broker -41.36 48.36

(-0.58) (0.98)

Post1 * Concurrent Earnings Forecast 61.27 46.93

(1.83) (2.31)

Post1 * Away from Consensus -8.48 -12.14

(-0.38) (-0.86)

Post1* Past Forecast Accuracy Quintile -10.52 -8.05

(-0.98) (-1.17)

Post1 * Firm Experience 14.55 16.77

(1.28) (2.20)

Post1 * Total Experience -8.65 -9.93

(-0.70) (-1.18)

Post1 * Log (Broker Size) 9.20 -5.92

(0.47) (-0.46)

Post1 * Log (Book-to-Market) -7.45 -1.52

(0.50) (-0.17)

Post1 * Log (Size) -33.03 -32.50

(-2.17) (-3.41)

Post1 * Log (Volatility) 34.68 21.60

(2.30) (2.36)

Post-Conference Window 60 Days 180 Days

R2 7.27% 7.35%

39

Table 6

Access to Management and Forecast Accuracy

Specification [1] of this table reports the results of the following panel regression:

, , 1 , , 2 , , 3 , ,_ _ .i j t i j t i j t i t tPMAFE dHost dHost Post Conf dNon Host Post Conf - - -

PMAFE is the proportional mean forecast accuracy defined as the Absolute Forecast Error for analyst i's forecast of

firm j for year t earnings less the mean absolute forecast error for firm j in year t across all analysts, scaled by the

mean absolute forecast error for firm j in year t across all analysts. dHost is the firm-year demeaned value of Host as

defined in the Appendix, but modified for earnings estimates. dHost_Post-Conf and dNon-Host_Post-Conf are

defined analogously. dPost-Conf_Diff, below the main regression estimates, tests for whether dHost_Post-Conf

earnings estimates are significantly more accurate than dNon-Host_Post-Conf earnings estimates (i.e. β1 + β2 - β3<0).

Specification [2] adds a number of earnings estimate, analyst, and broker characteristics, and specification [3] adds

analysts fixed effects. All characteristics are defined in the Appendix, except that all characteristics are now

demeaned by firm and year. If an analyst issues more than one forecast for the firm-year, we use the most recent

forecast. The sample includes 188,337 earnings forecasts over the 2004-2010 sample period. Standard errors are

clustered by analyst and firm, and t-statistics are reported below each estimate.

[1] [2] [3]

dHost -4.69 -0.15 0.34

(-5.86) (-0.21) (0.42)

dHost_Post-Conf -8.84 -8.03 -8.21

(-6.84) (-6.72) (-6.61)

dNon-Host_Post-Conf -0.41 0.56 1.19

(-0.37) (0.57) (1.23)

dFirm Experience

-0.68 -0.46

(-6.83) (-4.31)

dTotal Experience

-0.11 0.09

(-1.92) (-0.86)

dlog( Broker Size)

-0.71 0.64

(-2.21) (1.32)

dAge

0.55 0.53

(108.51) (101.46)

dForecast_Freq

-1.23 -0.82

(-9.76) (-6.10)

dFirms_Followed

0.11 -0.05

(2.51) (-0.82)

Analyst Fixed Effects No No Yes

R2 0.10% 15.80% 21.00%

dPost-Conf_Diff -13.125 -8.738 -9.061

(-10.10) (-7.13) (-6.81)

40

Figure 1

Access to Management and Two-Day Abnormal Returns Around Revisions: Univariate Evidence

This figure plots the two-day cumulative buy-and-hold abnormal returns for recommendation changes (Panel A) and

earnings forecast revisions (Panel B). The first group of four columns reports 2-day abnormal returns for upgrades

while the second group of four columns reports 2-day abnormal returns for downgrades. The four columns

correspond to (in order) Host_Post-Conf revisions, Host revisions, Non-Host revisions, and Non-Host_Post-Conf

revisions, as defined in the Appendix. Abnormal returns are measured as the raw return less the return on a Size-BM-

Momentum matched portfolio. The sample spans 2004-2010 sample period. This includes 45,840 recommendation

changes in Panel A and 168,285 earnings forecasts revisions in Panel B.

-350

-250

-150

-50

50

150

250

350

Upgrades

Ret

urn

(in

bp

s)

Panel A: Recommendation Changes

Host_Post-Conf Host Non-Host Non-Host_Post-Conf

-100

-75

-50

-25

0

25

50

75

100

Upward Revisions

Ret

urn

(in

bp

s)

Panel B: Forecast Revisions

Host_Post-Conf Host Non-Host Non-Host_Post-Conf

Downgrades

Downward Revisions

41

Figure 2

Access to Management and Two-Day Abnormal Returns Around Recommendation Changes: Time-Series

This figure plots the estimates of the following panel regression:

CAR_INDit =α + β1 Hosti +β2 Host_Post-Confi + β3 Non-Host_Post-Confi + β4 Controlsi+ ε.

CAR_INDi equals the two-day abnormal return around a revision multiplied by an indicator variable which equals 1

(-1) if the revision is an upgrade (downgrade). Host, Host_Post-Conf, and Non-Host_Post-Conf are defined as in the

Appendix. Controlsi is a vector that contains all of the recommendation, analyst, broker, and firm characteristics

included as controls in specification 3 of Table 3. The regression is estimated each year, from 2004-2010. The figure

plots the coefficients on Host, Host_Post-Conf, and Non-Host_Post-Conf each year. It also plots year-by-year

estimates of Post-Conf_Diff, (i.e. β1 + β2--β3). The sample includes 45,840 recommendation changes over the 2004-

2010 sample period.

-50

-25

0

25

50

75

100

125

150

Host Host_Post-Conf Non-Host_Post-Conf Post-Conf_Diff

Ret

urn

(in

bp

s)

2004 2005 2006 2007 2008 2009 2010

42

Figure 3

Access to Management and Longer-Horizon Abnormal Returns Around Recommendation Changes

This figure plots the estimates of the following panel regression:

CART_INDi =α + β1Hosti +β2Host_Post-Confi + β3Non-Host_Post-Confi + β4Controlsi+ ε.

CART_INDi equals the T-day abnormal return around a revision multiplied by an indicator variable which equals 1

(-1) if the revision is an upgrade (downgrade). Returns are winsorized at the 99th and 1st percentiles for upgrades

and downgrades, separately. We estimate the regression for four different values of T: 2, 21, 126, and 252. All

holding periods compute returns starting on day 0 (the day of the recommendation change) and are held until day T-

1. Host, Host_Post-Conf, and Non-Host_Post-Conf are defined as in the appendix. Controls is a vector that contains

the recommendation, analyst, broker, and firm characteristics included as controls in specification 3 of Table 3. The

figure plots the coefficients on Host, Host_Post-Conf, and Non-Host_Post-Conf for each holding period. It also plots

estimates of Post-Conf_Diff, (β1 + β2 - β3) for each holding period. The sample includes 45,840 recommendation

changes over the 2004-2010 time period.

-25

25

75

125

175

225

Host Host_Post-Conf Non-Host_Post-Conf Post-Conf_Diff

Ret

urn

(in

bp

s)

Trading Days 0-1 Trading Days 0-20 Trading Days 0-125 Trading Days 0-251