Ties that bind: Professional connections and sell-side analysts
Daniel Bradleya, Sinan Gokkayab, and Xi Liuc
a
Department of Finance, University of South Florida, Tampa, FL 33620, 813.974.6326, danbradley@usf.edu
b
Department of Finance, Ohio University, Athens, OH 45701,740.593.0514 , gokkaya@ohio.edu
c
Department of Finance, Ohio University, Athens, OH 45701, 740.593.0019, liux4@ohio.edu
Current version: May 29, 2015
______________________________________________________________________________
Abstract
We examine professional connections among executives and analysts formed through common
historical employment. We find that professional connections not only benefit analysts, but also
connected executives’ firms and analysts’ brokerage houses. Analysts with professional connections
to firms have more accurate earnings forecasts, informative stock recommendations and are more
likely to become all-star analysts. Connected analysts are more likely to follow departing executives
and initiate research coverage on their new firms. Finally, buy-side investors allocate greater trade
commissions to brokers employing professionally connected analysts. The evidence is robust postRegulation FD indicating that management access remains important to capital market participants.
Keywords: Analyst forecasts, analyst recommendations, professional networks, capital market
relationships, management access, analyst coverage, Regulation FD, broker commissions
JEL classifications: G10, G20, G23, G24
______________________________________________________________________________
We appreciate comments from Ying Duan, Andrew Fodor, Mark Huson, Russell Jame, Randall
Morck, John Stowe, Fei Xie, Mengxin Zhao and seminar participants at the University of Alberta.
We are solely responsible for all errors.
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Ties that bind: Professional connections and sell-side analysts
1. Introduction
Sell-side equity analysts collect, decipher and disseminate complex information to their buyside and retail clientele. The extant literature indicates that analysts are important information
intermediaries in capital markets as their research output are used by investors in generating earnings
expectations, evaluating firm performance and forming investment decisions (Francis, Lafond,
Olsson and Schipper, 2005; Loh and Mian, 2006). While analysts rely on a wide array of information
channels in developing insights about their coverage firms, superior access to management has long
been recognized as one of the most important and useful conduits of information available to
analysts (Chen and Matsumoto, 2006; Soltes, 2014). Survey evidence from analysts in Brown, Call,
Clement and Sharpe (2014) and Institutional Investor’s annual poll of buy-side institutions suggest that
maintaining strong relationships with firm management is a critical component of their job. 1
In this paper, our focus is on management access obtained through a distinct and
economically important information channel—professional connections. Considerable evidence
illustrates that professional connections are among the most frequently used relationships in
financial markets and have a significant impact on the transmission of business-related information
and decision making (e.g. Kuhnen, 2009; Stuart and Yim, 2010; Engelberg, Gao and Parsons, 2012;
Duchin and Sosyura, 2013). Business ties do not mandate homophily between two parties, are
developed in a professional setting and considered more formal compared to social connections (i.e.,
educational ties, religion, nonprofessional organizations) representing a unique aspect of capital
market relationships (Hwang and Kim, 2009; Fracassi and Tate, 2012). However, the literature is
silent on the impact of business ties to analysts. 2 We conjecture that analysts exploit professional
networks within their coverage portfolio to cultivate superior access to management thereby
providing them a competitive advantage in acquiring and/or processing information. We also
investigate the benefits of professional connections from the perspective of connected executives’
A 2013 global survey of 693 CEOs and CFOs conducted by the Bank of New York likewise suggests that of the total
time spent with the investment community about 20% is with sell-side analysts. See Bank of New York Mellon, Global
Trends in Investor Relations: A Survey Analysis of IR Practices Worldwide, 9th edition, 2013.
2 Conversations with several Directors of equity research (DOR) and sell-side analysts confirm the view that professional
connections are an important information channel. In fact, one DOR indicated that analyst connections are a factor they
take into consideration when making hiring decisions.
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firms and analysts’ brokerage houses providing a more complete picture of the importance of these
connections in the capital markets. 3
Our sample consists of a large and unique dataset of hand-collected pre-analyst employment
backgrounds merged with biographical information for senior officers and directors obtained from
Boardex of Management Diagnostic Limited (Boardex). A significant advantage of pre-existing professional
connections is that they have been formed ex ante, therefore their formation is not related to
company-specific information being transferred eliminating reverse causality concerns. We define a
professional connection as one where an analyst and a member of a coverage firm’s management are
directly connected through overlapping employment at the same firm prior to the analyst becoming
a sell-side analyst or where an analyst is indirectly connected to management through “intransitive
triads” created by direct professional connections (e.g. Engelberg, Gao and Parsons, 2012; Ishii and
Yuan, 2014; Bramoullé, Djebbari and Fortin, 2009; Pool, Stoffman and Yonker, 2014).
In our sample of 94,185 forecasts on 3,413 firms by 2,467 analysts between 1993 and 2011,
we find that earnings forecasts issued by analysts with professional connections to management in
the coverage firm are more accurate than forecasts of analysts without such connections. The mean
relative accuracy of analysts with professional connections is 2.7% higher compared to analysts
lacking such ties after controlling for other factors shown in the analyst literature to explain crosssectional differences in earnings forecast accuracy. By way of comparison, all-star analysts generate
2.5% more accurate forecasts. In addition, the inclusion of alternative potential channels of
management access (i.e., investment bank affiliation, optimism, alumni networks, geographical
locality) leaves our results nearly unchanged, lending support to the notion that professional
connections represent an economically important and distinct information channel exploited by sellside analysts.
We examine the impact of Regulation Fair Disclosure (Reg FD), which was a regulatory
shock enacted in August 2000 prohibiting selective disclosure of private information. This regulation
has generated a tremendous amount of research with mixed findings. Our results are robust pre- and
post-Reg FD providing evidence in favor of analysts’ views that management access remains
important in a post-Reg FD world (Brown et al., 2014).
We acknowledge that business connections are only one of several forms of management access. Educational links
(Cohen, Frazzini and Malloy, 2010), optimistic research on client firms (Gintschel and Markov, 2004; Chen and
Matsumoto, 2006), investment banking affiliation and geographical proximity (Malloy, 2005) might be among other
means to get privileged access to management. However, as we show in Appendix A, the correlation between
professional connections and these alternative channels is very low. For instance, the correlation between alumni links
and professional connections is only 0.05.
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Akin to better earnings forecast accuracy, superior access to management through
professional connections may also allow an analyst to issue more informative stock
recommendations. We find stock upward revisions issued by analysts on coverage firms where
professional connections exist elicit a 0.44% greater abnormal market reaction compared to
upgrades issued by analysts without such connections. The impact of professional connections on
stock upgrades is smaller in magnitude in the post-Reg FD period, but remains significant. This
result is also in contrast to Cohen et al. (2010), suggesting that professional connections are indeed
driven by different mechanisms and incentives compared to informal social connections formed
through college alumni links. We do not find similar return differentials on analysts’ downgrades.
This asymmetric market reaction to upgrades and downgrades is consistent with the interpretation
that senior managers may be more reluctant to share insights about their firms during negative
business states (Mayew and Venkatachalam, 2012).
Our baseline results strongly suggest that professional connections aids analysts’
performance, but it is possible that such information advantage may be correlated with unobservable
characteristics. To address this potential issue, we use a natural experiment where there exists a
plausibly exogenous shock to an analyst’s professional connection stemming from executive
movements in the coverage firm. We identify a subsample of 2,267 (4,373) cases in which an analyst
loses (gains) professional connections in the coverage firm emanating from connected executives
changing employers. We find relative forecast accuracy of analysts in the coverage firm with
professional employment connections deteriorates by 4.8% following the departure of a connected
senior manager. Similarly, analysts improve their forecast accuracy by 4.5% when an insider with
professional ties to a sell-side analyst joins the followed firm lacking such linkages. Using the same
natural experiment of executive job movements employed for earnings forecasts, we find market
reactions to connected analysts’ recommendation upgrades are significantly lower (higher) after an
analyst loses (gains) a professional connection in the coverage firm.
Next, we examine if professional connections are related to analyst career outcomes and lead
to an increased probability of being included in Institutional Investor’s annual all-star team. Superior
management access of sell-side analysts is consistently ranked among the most important attributes
by buy-side institutions that rank the best analysts in each industry. Our findings are consistent with
this notion. We find that professional connections strongly increase the likelihood that an analyst
will become an all-star analyst. Specifically, a one-standard deviation increase in the log number of
professional connections increases the odds of becoming an all-star by 29% for an analyst after
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controlling for factors previously documented in the literature. Consistent with our other tests, this
result is robust in the pre- and post-Reg period.
Lastly, we shift our attention to the importance of professional connections for the
executive’s firm and analyst’s brokerage house. On the benefits accrued to firms, we focus on a
dynamic setting by analyzing analyst coverage initiation decisions in the context of executive
movements across firms (Brochet, Miller, and Srinivasan, 2014). For analysts covering departing
executives’ old firms, we find analysts with professional connections are significantly more likely to
initiate coverage on their new firms. In economic terms, the likelihood of analysts initiating new
coverage is 2.1 times more likely when analysts share professional connections with the departing
executive compared to cases where no such connections are present. The results hold in the preand post-Reg FD era, suggesting that firms secure research coverage from sell-side analysts through
their executives’ professional connections.
From the brokerage houses’ perspective, if sell-side analysts assist buy-side clients obtain
superior management access to their investment firms through exploitation of their professional
connections, then we expect these clients to reward brokers with greater trade allocations and hence
commissions on these firms. We empirically test this conjecture using institutional transaction data
obtained from Ancerno Ltd over 2000 to 2011 with a sample of 173 unique institutions transacting
in 2,968 investment firms covered by 1,947 analysts. The results indicate that the relative
commission share of brokers is 0.25% higher on firms covered by connected analysts compared to
firms followed by analysts lacking professional connections. This result illustrates the importance of
sell-side analyst professional connections in trade allocation decisions of fund managers across
brokerage houses.
Our paper contributes to several recent strands of literature and should be of interest to
both academicians and practitioners. First, our paper makes an important contribution to the large
body of research focused on the sources of systematic performance differences across analysts. We
document a previously unexplored and economically important source of management access
exploited by sell-side analysts through professional connections with executives in coverage firms.
Second, our paper sheds further light on the ongoing debate on whether Reg FD has
curtailed the benefits of superior access to management or firm management continues to be an
important source of information for analysts in the post Reg-FD period. Our findings are consistent
with the view among practitioners that acquisition of in-depth public insights on coverage firms
through professional connections aids analysts in filling out a “mosaic” of value-relevant
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information emphasizing the importance of cultivating access to management in the post-Reg FD
era.
Third, we provide empirical evidence that executives’ professional connections help their
firms attract analyst research coverage, furthering our understanding into the factors affecting
coverage decisions of sell-side analysts. Analysts’ professional connections to management in
coverage firms likewise aid brokers obtain higher trading commission from client buy-side funds.
Consequently, our paper is also relevant to the growing literature investigating the determinants of
buy-side investors’ commission and trade volume allocation decisions (e.g. broker size (Juergens and
Lindsey, 2009); affiliated equity underwriting business (Irvine, 200, 2004); analyst reputation and
industry knowledge (Jackson, 2005; Gokkaya, Liu, Xie and Zhang, 2015)).
Finally, we add to the emerging literature that highlights the importance of professional
networks for a wide range of parties in corporate and financial settings; e.g. investment banking
(Cornelli and Goldreich, 2001), venture capital (Hochberg, Ljungqvist and Lu, 2007), private equity
(Stuart and Yim, 2010), mutual funds (Kuhnen, 2009; Tang, 2013), mergers and acquisitions
(Engelberg, Gao and Parsons, 2012), and CEO compensation (Engelberg, Gao and Parsons, 2013).
Our paper is the first to illustrate the implications of professional connections in the context of sellside analyst research.
The remainder of the paper proceeds as follows. Section 2 provides a brief review of the
literature while section 3 describes the data and provides descriptive statistics. Section 4 presents the
main results on analyst output; forecasting accuracy and stock recommendations. Section 5 provides
an analysis on career outcomes. Section 6 presents an analysis on the impact of professional
connections to firms and brokerage houses. Section 7 provides robustness tests and section 8
concludes.
2. Related literature and motivation
Our study is related to at least three different branches of literature. First, our paper
contributes to an extensive body of work attempting to identify the main sources of performance
differences across sell-side analysts. Second, it sheds additional light on the ongoing debate about
whether passage of Reg FD has eliminated benefits accrued to analysts from better management
access. Third, our study is linked to research exploring the implications of professional connections
in various corporate and financial settings. In this section, we provide an overview of relevant
studies in each of these three strands.
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2.1. What analysts provide to market participants
A large component of a sell-side analyst’s job entails gathering and processing information
about her coverage portfolio and producing research reports that include forecasts on future
earnings prospects and stock recommendations on these firms. The general consensus is that
analysts bring valuable new information to market participants through their research as shown by
their ability to influence stock prices of coverage firms (e.g. Womack, 1996; Clement, 1999; Gleason
and Lee, 2003; Ivkovic and Jegadeesh, 2004; Bradley, Clarke, Lee and Orthanalai, 2014).
Academic research indicates that many firm- and analyst-specific attributes including, but not
limited to, analyst overall and firm-specific experience, portfolio complexity, forecast age, all-star
status, brokerage house reputation, and analyst industry expertise impact analysts’ performance
(Stickel, 1992, 1995; Clement, 1999; Jacob, Lys and Neale, 1999; Michaely and Womack, 1999,
Gilson et al., 2001; Clement, Rees and Swanson, 2003; Asquith et al. 2005; Ljungqvist et al., 2006;
Kadan et al., 2012; Bradley, Gokkaya and Liu, 2014).
While analysts gather information from a variety of resources in developing insights about
the firms in their coverage portfolio, management of the coverage firms represent one of the key
sources of information (Chen and Matsumoto, 2006). Brown et al. (2014) poll sell-side analysts and
they concur—management access is a critical component of producing more informative research.
This brings up a natural question. How does an analyst gain better access to management?
Malloy (2005) examines the role of distance between analysts and coverage firms and
documents that geographically proximate analysts provide more accurate forecasts. Gintschel and
Markov (2004) and Chen and Matsumoto (2006) find that analyst optimism improves the flow of
information between analysts and management. Malloy (2005) and Chen and Martin (2011)
investigate underwriting and lending relationships between firms and affiliated banks and find more
informative research produced by affiliated analysts suggestive of superior information generation by
these affiliated analysts. Cohen, Frazzini and Malloy (2010) show that analysts with college alumni
links to directors provide superior recommendations, albeit not more accurate earnings forecasts.
These social connections made by sharing common educational institutions indicate that even
informal social networks foster information flow between analysts and management. Mayew, Sharp
and Venkatachalam (2013) indicate that earnings conference calls help participating analysts receive
beneficial information from management. Green et al. (2014a), Bushee, Jung and Miller (2014) and
Kirk and Markov (2014) suggest that publicly observable interactions with firm management at
broker-hosted conferences, industry conferences and analyst/investor days provide analysts an
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opportunity to obtain superior information. Christensen, Mikhail, Walther and Wellman (2014)
show that sell-side analysts obtain value-relevant private information from their brokerage houses’
political connections. Our paper adds to this growing literature by focusing on professional
connections formed through an analyst’s work experience prior to becoming a sell-side analyst. We
conjecture that these connections represent a unique and economically important channel through
which analysts could get privileged access to management and obtain valuable information they
otherwise would not have.
2.2. Regulation Fair Disclosure (Reg FD) and management access
There is an ongoing debate in the literature regarding the impact of Reg FD on the benefits
of management access to analysts. The main premise of Reg FD is to prohibit selective disclosure of
information by management to analysts and institutional investors thereby putting all investors on
equal-footing with respect to firm-level information disclosure. Koch, Lefanowicz, and Robinson
(2013) summarize the literature and suggest that the evidence on its effects is mixed.
While transmission of private material information to analysts may have been eliminated and
access to management has become harder to get by the passage of Reg FD, new research suggests
that alternative forms of management access continue to help analysts improve their performance in
the post-Reg FD era. Perhaps one reason why management access remains important in a Reg-FD
world is because acquisition of in-depth public information from management can complement the
private information set of an analyst and help them piece together value-relevant information into a
mosaic leading to more informative research (e.g. Barron, Byard, and Kim, 2002; Mayew, 2008).
Mayew, Sharp and Venkatachalam (2013) and Green et al. (2014a) provide empirical evidence that
better management access obtained through participation in earnings conference calls and brokerhosted conferences continue to provide analysts with a competitive information advantage in the
post Reg-FD period. Brochet, Miller and Srinivasan (2014) analyze analyst coverage decisions and
CEO-CFO movements and find analysts are more likely to move with managers to their new
destination firms even following the passage of Reg FD. Our work complements this strand of
literature by examining the impact of Reg-FD on professional connections to the coverage firms.
2.3. The value of professional connections
An emerging literature in finance and accounting focuses on professional connections and
their ability to influence financial decision making and information transfers. Ljungqvist, Marston
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and Wilhelm (2009) show investment banks tend to co-underwrite stock offerings with banks with
which they have long-standing professional relationships. Hochberg, Ljungqvist and Lu (2007)
provide empirical evidence on the positive impact of venture capital networks for the quality of deal
flow and investment performance. Stuart and Yim (2010) show professional networks of board
members impact change-of-control transactions in the private equity market. Garmaise and
Moskowitz (2003) analyze informal financial networks of property brokers and show that these
networks have a significant impact on the availability of financing to brokers’ clients.
Kuhnen (2009) examines professional connections in the context of mutual funds and
documents directors of mutual funds and managing advisory firms hire each other conditional on
past business connections. Tang (2013) finds that mutual fund managers gain an information
advantage through business connections made during prior employment as sell-side analysts. Ishii
and Yuan (2014) examine cross-board connections in M&A transactions and illustrate professional
connections between boards of acquirer and target firms lead to lower acquirer returns. Engelberg,
Gao and Parsons (2012) examine interpersonal linkages between banks and firms, and find that
when respective management previously worked together, borrowing costs are substantially reduced.
Engelberg, Gao and Parsons (2013) investigate the impact of employment networks in the context
of CEO compensation and illustrate that professional connections to executives outside the firm
substantially increase CEO compensation. Duchin and Sosyura (2013) identify external professional
connections between CEOs and divisional managers and find that CEOs allocate more capital to
divisions where the manager and CEO are connected through previous employment experiences.
Bertrand, Bombardini and Trebbi (2014) analyze the impact of business connections for lobbyists
and documents that lobbyists are more likely to cover issues if they are connected to legislators. Our
paper adds to this growing literature by being the first to study the implications of professional
connections in the context of sell-side analyst research.
3. Data and descriptive statistics
We start our analysis by merging Institutional Broker Estimate System (I/B/E/S) with
CRSP/COMPUSTAT and identifying all sell-side analysts who provided at least one annual earnings
forecast between 1993 and 2011. We only retain the most recent forecast for each analyst firm year.
I/B/E/S provides only the analyst’s last name and the initial of his/her first name. We eliminate
observations if multiple analysts share the same first name initial and last name at the same
brokerage house. We also remove analyst teams since only the last names of analyst team members
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are provided by I/B/E/S. This initial screening yields 346,564 forecasts issued by 9,235 analysts on
6,330 unique firms.
We next search Zoominfo.com to obtain the full first name of each analyst. With the full first
and last name, we search for each analyst on LinkedIN.com to capture employment history.
LinkedIN.com is the world’s largest professional network. To ensure we have the right person, the
name on LinkedIN.com must reveal employment at the analyst’s employer on the date corresponding
to the forecast. 4 If the analyst is not found on LinkedIN.com, we use Zoominfo.com where applicable.
For each analyst, we gather past employment information about the names of their former
employers along with the corresponding time periods they were there. Appendix A provides a
detailed description of our data screening and collection process.
Employment history on executives and board members is provided by Boardex, which has
been extensively used to examine the role of networks in a variety of academic studies (e.g.
Engelberg, Gao and Parsons, 2012; Ishii and Yuan, 2014). We merge these two datasets, which
allows us to determine if professional connections exist between sell-side analysts and senior
directors in the coverage firm. Specifically, we define professional connections as those where an
analyst and a member of senior management at the followed firms are connected either directly
through overlapping past employment (primary professional connection) or indirectly through
intransitive triads created by primary professional connections (secondary professional connection)
(Bramoulle, Djebbari and Fortin, 2009; Pool, Stoffman and Yonker, 2014). We require the
formation of professional connections to predate analyst coverage on the connected firm by more
than 3 years to ensure that connections between the analyst and followed firm are formed at a
distant time eliminating concerns on reverse causality (Engelberg, Gao and Parsons, 2012). Further,
there must be a minimum of one year of employment overlap between executives and analysts. 5
Following prior work, we construct an aggregate measure of professional connectedness that sums
primary and secondary connections. However, as later shown in section 7, our results do not depend
In some cases, we manually match shortened names such as ‘Tom’ for ‘Thomas’ if the last name and brokerage house
match exactly and there is no other employee at the firm with a similar name. Otherwise, if the names are not an exact
match we purge them from the sample. Name changes from divorce or marriage may be eliminated from our sample if
we can’t find an exact match, but there is no reason to believe these situations would systematically bias our results.
5 As an example to the structure of primary professional connections, an analyst (Analyst A) in our sample worked for
the Gap Inc. as a Merchandise planner from July 1995 to August 1997. In August 2000 he became a sell-side equity
research analyst at Morgan Stanley and began covering Coach Inc. in 2005. An insider (Insider A) at Coach Inc. in 2005
also worked for the Gap Inc. as a VP of Merchandising between December 1994 and 2003 and thus overlapped with
Analyst A, creating a primary connection between Analyst A and Insider A. Furthermore, Insider A previously worked at
Macy’s Department Stores from 1987 to 1992 as a Divisional Merchandise Manager. Insider B also worked at Macy’s
from 1990 to 1997 as Chairman and CEO, overlapping with Insider A. Thus, Analyst A has a secondary professional
connection to Insider B via Insider A.
4
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on this aggregation and hold uniformly across both connection types. To better illustrate the
formation of professional connections between analysts and insiders at coverage firms, see figure 1.
***Insert Figure 1 here***
Table 1 reports descriptive statistics on our sample. Panel A presents summary statistics on
earnings forecasts. In our sample, 2,467 analysts issue 94,185 annual earnings forecasts on 3,413
unique firms. About 57.9% of coverage firms have at least 1 insider with a professional connection
to one of the coverage sell-side analysts. In addition, approximately 35.3% of analysts have a
professional connection to at least one of the firms in their coverage portfolios and 24.2% of
forecasts are issued on firms where an analyst has a professional connection to a senior member of
management in the covered firm. Our sample contains 59% of firms, 24.1% (25.7%) of the total
number of annual earnings forecasts (analysts) in I/B/E/S, and 89.1% of the market capitalization
of the universe of stocks covered in I/B/E/S. The percentage of professional connections increases
through time. Likewise, the percentage of firms and earnings forecasts covered by our sample rises
over time. This is most likely because the participation rate on LinkedIn.com has gained traction
among professionals coupled with the fact that biographical information on employment histories
from Boardex is relatively incomplete before 2000 (Ishii and Xuan, 2014). 6
***Insert Table 1 here***
Panel B reports analogous statistics on the recommendation revisions sample. With the
exception of lower values for the percentage of firms, all other statistics are roughly similar across
Panels A and B. In particular, approximately 35.5% of analysts have at least one professional
connection to one of their coverage firms and 24.3% of recommendation revisions are issued on
these professionally connected firms.
4. Professional connections and analyst research quality: Forecast accuracy and stock
recommendation informativeness
In this section we examine if better management access obtained through professional
connections aids in analysts’ ability to provide more accurate earnings estimates and/or more
informative recommendation revisions.
This could create a selection bias early in our sample. However, if anything, this bias would work against us finding any
results. Nonetheless, to deal with this potential issue we provide two tests. First, we provide results conditional on an
analyst having at least one connection with the inclusion of analyst fixed effects, such that we compare forecasts on
firms with and without professional connections by the same analyst (Section 7). Further, we examine our results
separately pre- and post- Reg FD, which was enacted in 2000. The results post-Reg FD should mostly be free of this
bias.
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4.1. Baseline regression models of accuracy
Our measure for earnings forecast accuracy is the widely adopted proportional mean
absolute forecast error, PMAFEi,j,t (e.g. Clement 1999; Malloy, 2005; Clement and Tse, 2005;
Clement, Koonce and Lopez, 2007). PMAFEi,j,t is defined as the difference between the absolute
forecast error, AFEi,j,t of analyst i for firm j at time t and the mean absolute forecast error for firm j
at time t. This difference is then scaled by the mean absolute forecast error for firm j at time t to
reduce heteroskedasticity. By construction, negative values of PMAFEi,j,t represent better than
average performance, while positive values indicate worse than average performance.
Following the extant literature, we estimate cross-sectional regressions of relative forecast
accuracy to control for characteristics that have been shown to impact earnings forecast
performance. The key variable of interest is Professional connection, which is an indicator variable that
captures if the analyst has a professional connection to management in the followed firm, zero
otherwise.
We control for analyst general (DGExp) and firm-specific experience (DFExp) as in Clement
(1999), Clement and Tse (2003, 2005) and others. DGExp is computed as the total number of years
that analyst i appeared in I/B/E/S (GExp) minus the average tenure of analysts following firm j at
time t appeared in I/B/E/S. DFExp is the total number of years since analyst’s i first earnings
forecast for firm j (FExp) minus the average number of years analysts following firm j has supplied
forecasts. Clement (1999) documents that relative forecast errors increase by 0.35% per day,
emphasizing the need to control for the forecast horizon. Therefore, we include DAge computed as
the age of analyst i’s forecast (Age) minus the age of the average analyst’s forecast following firm j at
time t, where age is defined as the age of forecasts in days at the minimum forecast horizon date.
Previous studies have shown that analysts covering more firms and industries and also those
employed at smaller brokerage houses are associated with lower forecast performance (Clement,
1999; Jacob, Lys, and Neale, 1999). Thus, we include controls for the mean-adjusted portfolio size
(DPortsize), number of two-digit SICs (DSic2) in the analyst’s portfolio, and top decile brokerage
houses (DTop10). Consistent with Stickel (1992), we further control for reputation of forecasting
analysts with all-star status (All-star). Standard errors are heteroskedastic-consistent and double
clustered at the analyst- and firm-level. Formally, our model is as follows:
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PMAFEi,j,t= β0 + β1(Professional connection) + β2(DGExp) + β3(DAge) + β4(DFExp) +
β5(DPortsize) + β6 (DSic2) + β7 (DTop10) + β8(All-star) + ε
(1)
***Insert Table 2 here***
Table 2 reports the regression results. Model 1 includes our variable of interest and controls.
The relative earnings forecast errors of analysts with professional senior management connections
are 2.68% lower compared to those of analysts without such ties. The sign and magnitudes of other
control variables are similar to previous work on analyst forecasting performance. For example,
analysts’ forecast accuracy improves with general and firm experience, brokerage resources and allstar status, but gets worse the more complex the analysts’ portfolio and the older the forecast. Note
that the economic importance of professional connections on forecasting performance is at least as
large as any of these other ubiquitous factors. To put this result in perspective, all-star analysts
produce 2.52% more accurate earnings forecasts than non-star analysts.
Bradley, Gokkaya and Liu (2014) document that sell-side analysts with related industry work
experience are associated with more accurate forecasts. They suggest this is primarily due to a better
understanding of industry fundamentals, such as the impact of macroeconomic and microeconomic
factors on the operations and financials of followed firms. Since professional connections may be
correlated with this effect, we include a control for Related experience which equals one if analyst i
makes forecasts on a covered firm related to her previous work experience, zero otherwise.
Specifically, related experience equals one if an analyst’s previous employer and covered firm shares
the same 4-digit Global Industry Classification System (GICS) code, zero otherwise. While the
coefficient on related industry experience behaves similar to that reported in Bradley, Gokkaya and
Liu (2014), our main results on professional connection remain intact.
Another legitimate concern with our measure of professional connection is that it could be
correlated with alternative channels of management access channels. To rule out this possibility,
models 3 and 4 explicitly control for these channels. We control for firm-analyst investment banking
affiliation (Affiliated), social connections between analysts and management through shared
education backgrounds (Education connection), analyst optimism (Optimism), and analyst locality (Local).
Because we lose 23% of observations when we consider the distance between the firm and analyst’s
location, we estimate this model separately to retain as many observations as possible. The economic
and statistical significance of professional connections continues to be strong, suggesting that we
have indeed identified an economically important and unique information channel.
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A related explanation for our results is that analysts might simply devote more effort towards
professionally connected firms to honor their relationships with connected managers, explaining the
superior earnings forecasts generated on such firms. We include controls for the number of forecast
revisions (No of forecasts) (Jacob et al., 1999) and issuance of no forecasts over the following fiscal
year (Drop coverage) (Chen and Matsumoto, 2006) as measures of analyst forecasting effort in model 5
of Table 2. While the accuracy of earnings forecasts are positively related to these proxies of analyst
effort put forth by the forecasting analysts, the marginal impact of professional connections on
analyst performance remains robust.
4.1.2. Professional connection mechanism
Having documented the impact of professional networks in capturing privileged access to
senior management, we next investigate the mechanism(s) through which information is transferred
along these networks. Professional connections may help an analyst obtain easier access to private
material information on coverage firms’ operations, and/or forthcoming earnings announcements.
On the other hand, an analyst may simply exploit her professional connections to collect valuerelevant in-depth public information that would complement her private information set leading to
more informative research. This may emanate from superior identification of coverage firm
managements’ quality or style-related to strategic decisions, operations and performance (Bertrand
and Schoar, 2003; Brochet, Miller and Srinivasan, 2014) and/or better interpretations of signals
gleaned from connected managers’ body language and vocal cues during one-on-one interactions
(Mayew and Venkatachalam, 2012).
In order to distinguish between these two main explanations, we split our sample into preand post- Reg-FD periods and analyze the extent to which the passage of Reg FD has impacted the
benefits accrued from professional networks. The enactment of Regulation Fair Disclosure (Reg
FD) in October 2000 materially impacted information flow from firm management to analysts by
prohibiting selective disclosure of material information. In the context of our paper, Reg FD
represents a significant regulatory shock presumably eliminating the flow of private information
through professional connections.
***Insert Table 3 here***
Comparing the coefficients on professional connections in model 1 (pre-Reg FD) to model 6
(post-Reg FD), the value of management access through these connections declined after Reg FD
passage, but has not been eliminated. For instance, analysts with professional connections are 8.84%
14
more accurate pre-FD and 1.95% more accurate post-FD. Other variables in the remaining models
behave roughly similarly across estimations. These results indicate that while Reg FD likely has
eliminated the benefits from private information flow to analysts through professional connections,
these connections still remain important for analysts’ performance in the post-Reg FD era,
suggesting that preferential access to private information is not the sole explanation of our results.
These results are also consistent with the conventional view among sell-side analysts that while
harder to obtain, management access remains important for analyst performance post-Reg FD.
4.2 Professional connections and analyst recommendations
Loh and Mian (2006) examine the relation between earnings forecast accuracy and stock
recommendations and document that superior forecast accuracy translates into more informative
stock research. Brown et al. (2014) reveal that analysts’ top motivation for issuing accurate forecasts
is to use these forecasts as inputs into their corresponding stock recommendations. Further, they
suggest that communication with management is also an important input for stock
recommendations.
Following the existing literature (e.g. Loh and Stulz, 2011; Green et al., 2014a), we measure
the informativeness of stock recommendations by the immediate market reactions to their release.
We use abnormal 3-day CRSP value-weighted index-adjusted stock returns over the [0, 2]-day
window around the announcement date of recommendation revision by analyst i for firm j at time t.
Because of the asymmetric market reactions across upgrades and downgrades, we consider these
separately. 7 We regress the market reaction on an indicator variable related to the presence of
professional connections between the revising analyst and senior management in the coverage firm
and also control for various firm, analyst and broker characteristics. Standard errors are
heteroskedastic-consistent and double clustered at the analyst and firm level. Calendar year dummies
are included to control for yearly fixed effects. Our model is as follows:
CAR(0,2)i,j,t = β1 (Professional connection) + β2 (Size) + β3 (BM) + β4 (All-star) + β5 (GExp) + β6 (FExp) +
β7 (Top10) + β8 (Past 6m Ret) + β9 (Rec Level) + β10(∆Recommendation) + β11 (Related
experience) + β12 (Affiliated) + β13 (Education connection) + β14 (Optimism) + β15 (Local) + β16
(No of forecasts) + β17 (Drop coverage) +Year dummies + ε
(2)
In 2002, many brokerage houses changed their rating scales from a 5-point scale to a 3-point scale. For cases where a
bank stopped coverage but then subsequently initiated coverage using a 3-point scale we use this scale going forward. To
ensure this rescaling does not impact our results, we remove these revisions that were mechanically induced as a result of
this rating change.
7
15
***Insert Table 4 here***
Panel A of Table 4 presents results for upgrades. The first model indicates that professional
connections lead to more informative stock recommendations. Market reactions are 0.35% higher
for analysts possessing professional connections to executives in coverage firms. Models 2 and 3
include controls for alternative management access channels such as investment banking affiliation,
educational ties, optimism and analyst locality. We confirm Cohen, Frazzini, and Malloy (2010)’s
result that social connections to management through shared alumni networks leads to more
informative upgrade recommendations. Optimism is discounted by market participants while analyst
locality is not significant. Consistent with Stickel (1992) and others, all-star analysts generate higher
market reactions as do upgrade revisions from analysts with higher general experience, industry
experience, affiliated analysts and analysts at top 10 brokerage houses. The results are robust to the
inclusion of analyst effort in model 4. 8
Models 5-12 focus on differences before and after the passage of Reg FD. The market
reaction to analyst upgrade revisions with professional connections are stronger in the pre-Reg FD
period, but remain economically and statistically important in the post-Reg FD period. For instance,
model 5 indicates that professionally connected analysts generate 1.01% higher market reactions to
upgrades in the pre-Reg FD period compared to 0.22% in the post-reg FD period (model 9). These
results mirror the earnings forecast evidence.
In Panel B for downgrade revisions, we do not find that analysts with professional
connections lead to significantly more negative market returns. This is consistent with the notion
that insiders may be less forthcoming about value relevant information on bad news to their analyst
colleagues (Mayew and Venkatachalam, 2012). 9
4.3. Natural experiments of executive turnover
We consider some other potentially omitted variables. Malloy (2005) suggests that analysts that focus in a single
industry have better forecasts. Guan, Wong and Zhang (2014) illustrate that analysts following covered firm’s main
customers outperform other analysts’ forecasts issued on supplier firms. Therefore, we include binary indicators for
industry specialists and analysts following along the supply-chain to mitigate the potential concern that professional
connections proxy for these effects. The results are robust in Table 5 and also in the earnings forecast performance
specifications in Table 2.
9 As an additional way to assess the extent to which professional connections help analysts with their research outputs,
we also examine target prices issued by professionally connected and unconnected analysts using a framework similar to
Brav and Lehavy (2003). In untabulated analysis, we find that analysts with professional connections issue significantly
more informative upward target price revisions (but not downward revisions) after controlling for the magnitude of
target price revisions, as well as stock recommendations and earnings forecasts issued in conjunction with these price
estimates. These results suggest that professional connections also help analysts issue more informative target price
estimates.
8
16
It is plausible that unobservable analyst characteristics may drive our results on earnings
forecasts and recommendations. To help address this issue, we examine changes in professional
connections as a natural experiment. These are caused by connected senior managers in an analysts’
coverage portfolio switching firms.
We identify 2,267 cases where a sell-side analyst loses her professional connection and 4,373
cases where she gains one. If the improved performance we find is indeed mainly driven by
professional connections, we expect to see a decrease in relative forecast accuracy following the
departure of connected insiders. Likewise, if losing connections caused by executives switching jobs
reduces the information content of analysts’ stock recommendation revisions, we expect the
market’s reaction to their upgrades to be muted. On the other hand, forecasting performance and
information content of stock upgrades are expected to improve when a connected insider joins an
existing followed firm.
In this specification, Lose professional connection equals one if the forecast/recommendation is
issued in the period following the departure of a professionally-connected insider, zero if issued in
any of years prior to the connected executive leaving the firm. Gain professional connection likewise
equals one if the forecast/recommendation is issued after the connected insider joins a firm lacking
such connections, zero if issued before. 10 Therefore, we compare analysts’ research quality before
and after executive employment switches holding the analyst constant. The models are similar to
those in Table 2 and 4, but the connection variables are replaced with losing/gaining connections. 11
***Insert Table 5 here***
Panel A of Table 5 documents that analysts’ relative forecast accuracy deteriorates by 4.77%
following the departure of a professionally-connected insider in the coverage firm. Model 2 repeats
the same analysis for analysts gaining a professional connection. Consistent with expectations,
professionally-connected insiders joining the coverage firm positively impacts forecasting
performance in the post-gain years. Accuracy improves by 4.53% when a professionally-connected
insider joins the coverage firm.
To further ensure that professionally-connected analysts do not already have a competitive
advantage relative to other analysts before (for gaining) or after (for losing) employment switches, we
The event year in which a connected insider leaves (joins) the firm is excluded from the sample since we cannot
identify whether the change to an analyst’s professional connection takes place before or following the earnings forecast
in the event year. Further, we require at least one earnings forecast before and after the gain/loss of a connected insider
to capture the impact of changes in professional connections on analyst performance.
11 We exclude the variable Local because of lack of data.
10
17
compare the forecast accuracy of connected analysts impacted by the switch relative to other
analysts following the same firms, however lacking connections. Model 3 of Panel A in Table 5
presents the results of analyst forecast accuracy in the period following the switch for analysts that
lose their connection to management (Post-losing professional connection). The results suggest that
analysts that lose their professional connection do not have significantly different forecast accuracy
than other analysts. Model 4 provides a similar analysis for analysts that gain a connection (Pre-gaining
professional connection). In the period before an analyst gains a connection, earnings forecast
performance is not different from analysts that also do not have a professional connection.
Panel B of Table 5 presents analogous analysis for stock upgrades. We find that when a
connected analyst loses a professional link in the coverage firm, the market reaction is lower (0.49%) for upgrades compared to when the analyst had the link. Likewise, when an analyst gains a
professional connection, the market reaction to upgrades is 0.42% higher in the period when the
analyst has a connection relative to the period when no professional connections exist. Our results
also document that analysts do not have competitive advantage in these firms relative to other
unconnected analysts following the departure of their connected executives (model 3) or before such
connected executives join these coverage firms (model 4). These results are similar to those reported
for earnings forecast performance, underlining the importance of professional connections for the
production of higher quality investment research.
Coupled with the empirical evidence provided in Table 2 and 4, the results from this section
point to a causal effect of professional ties on analysts’ forecast performance. By examining a natural
experiment caused by executives’ employment switching, which disrupts professional ties to an
analyst’s coverage portfolio, the evidence suggests that professional connections are important for
analysts’ performance metrics.
5. Professional connections and all-star status
Our evidence suggests that professional ties result in better accuracy and more informative
upgrade stock recommendation revisions. As discussed previously, superior management access is
among one of the most sought after qualities in analysts that buy-side institutions rank for the survey
conducted by II magazine. In this section, we examine if professional connections lead to favorable
career concerns. Actual compensation of analysts is very difficult to ascertain because of data
constraints, but it is widely known that all-star status is a level of recognition that all analysts strive
for and is widely adopted as a proxy for the level of compensation (Groysberg, Healy and Maber,
18
2011). Therefore, we test if analysts with professional connections are more likely to be named
Institutional Investor all-star analysts.
We estimate logistic regressions and control for a set of analyst characteristics that might be
related to the probability of favorable career outcomes. These include general analyst experience
(GExp), portfolio complexity (Portfolio size, Port sic2), brokerage size (Brokerage size), the mean annual
forecast accuracy of analysts (Average PMAFE), average firm market capitalization in analysts’
portfolios (Average firm size) and pre-analyst industry work experience related to coverage firms
(Analyst with related experience). Management access acquired through alternative channels are explicitly
accounted for by the natural log of one plus social ties through alumni networks (Ln education
connections), the number of stocks with optimistic views (Ln optimistic stocks), and the number of
affiliated (Ln affiliated stocks) and local stocks (Ln local stocks) in an analyst’s portfolio in a given year.
We also proxy for the level of analyst effort with average number of forecast revisions (Average no of
forecasts) and stocks with no forecasts over the following fiscal year (Average drop coverage). Finally,
because all-star status is likely subject to a high degree of autocorrelation, we also control for all-star
status in year t-1 (All-star prior year). We control for year fixed effects and report heteroskedasticconsistent standard errors clustered by analyst. To mitigate concerns on reverse causality, in addition
to All-star prior year, we lag all control variables by one year. Our variable of interest is the natural log
of one plus the number of professionally connected stocks for a given analyst (Ln professionally
connected stocks). Our formal model is as follows:
(All-star = 1)i,t = β1 (Ln professionally connected stocks )t-1 + β2 (Average firm size) t-1 + β3 (GExp) t-1
+ β4(Portfolio size) t-1 + β5 (Port sic2) t-1 +β6 (Brokerage size) t-1 + β7 (Average PMAFE) t-1 + β8 (Allstar prior year) + β9 (Analyst with related experience) t-1+β10 (Ln affiliated stocks) t-1 + β11 (Ln education
connections) t-1 + β12 (Ln optimistic stocks) t-1+ β13 (Ln local stocks) t-1 + β14 (Average no of forecasts) t-1 +
β15(Average drop coverage) t-1 + Year Dummies + ε
(3)
***Insert Table 6 here***
Model 1 of Table 6 suggests that professional connections increase the likelihood of being
selected an all-star analyst. Specifically, a one-standard deviation increase in the log number of
professionally connected stocks increases the likelihood of becoming an all-star by 29%. To put this
in perspective, the likelihood of becoming an all-star is 18% (14%) higher as the log number of
optimistic stocks (general forecasting experience) is increased by one standard deviation. Consistent
with Bradley, Gokkaya and Liu (2014), analysts with related pre-analyst industry experience are more
likely to become all-star analysts. Other control variables are signed as expected. Earnings forecast
19
performance is related to being selected an all-star analyst, but professional connections are still
important. This finding is consistent with buy-side investors’ claim that management access is an
important factor that is incremental to earnings forecast quality when selecting all-star analysts.
Decomposing the sample into pre- and post-Reg FD periods in model 5 through 12, our findings
document the value of professional connections influencing the probability of star status remains
important under the new regulatory regime.
As an alternative way of examining the importance of professional connections to an
analysts’ career, we consider career outcomes of analysts that work at brokerage firms involved in a
merger. Typically, when brokerage houses merge, some analysts are let go to avoid duplication of
effort. We estimate the probability of analyst i being retained by the newly merged firm using a
logistic regression. In untabulated results, we find that the likelihood of being retained increases by
24.7% for a one standard deviation increase in the number of professional links to senior
management in their coverage portfolios after the inclusion of the other control variables in
equation 3.
6. Professional connections and impact on firms and brokerage commissions
Our results to this point provide pervasive evidence that professional connections aid
analysts’ performance and career outcomes. As an additional way to gauge the importance of
analyst-executive connections, we explore its impact on some of the other players involved—
coverage firms and brokerage houses employing connected analysts. Analyst coverage is beneficial to
firms (e.g. Kelly and Ljundqvist, 2012), so our results can indirectly be interpreted such that
professional connections provide value to firms in that they get coverage. However, it is not clear
that professional connections drive the coverage decision. Thus, we test this conjecture. We next
investigate the benefits accrued to brokerage houses from employing analysts with professional
connections. If sell-side analysts provide superior corporate access to buy-side institutions through
their professional connections, then we expect brokers employing such analysts benefit from this
important client service through increased trading commission allocations.
6.1. Analyst Coverage Initiations
We examine initiation decisions in the context of executive movements across firms in a
similar spirit to Brochet, Miller and Srinivasan (2014). They find that analysts covering a firm that
experiences a top-level executive departure are more likely to follow the executive to the destination
20
firm and conclude that this behavior demonstrates the importance of professional relationships
developed between the analyst and executive for the firms. An important difference between our
approach and theirs is that in our setting, we are able to pinpoint such professional relationships. We
posit that analysts with professional connections to the executive should be more likely to initiate
coverage on the executive’s new firm (relative to analysts lacking such connections) to maintain this
relationship and competitive advantage. Note that this test has the added benefit that it is largely
independent of performance measures.
To test this conjecture, we consider a large sample of job changes (N=5,051) involving
executives and board members and identify companies insiders leave (old firm) and firms they join
(new firm). We estimate a logistic regression and include controls for structural barriers and
perceived benefits related to coverage decisions across old and new firms. We consider the
differences between old and new firms with respect to their primary industries (Same industry), size
(Size diff (new-old)), book-to-market (BM diff (new-old)) and stock performance (Annual ret diff (new-old).
We further consider the degree of analyst overlaps between these two firms (Analyst overlap),
competition from other analysts (No of analysts (old), No of analysts (new)), and other analyst
characteristic variables. Our primary variable of interest, Professional connection to migrated manager, is a
binary indicator that takes the value of one if the analyst has a professional connection formed
through common pre-analyst employment to the migrated manager, zero otherwise. The dependent
variable (Migrated) equals one if the analyst covering the old firm initiates coverage on the executive’s
new firm by the end of the first full fiscal year after the migrating executive joins the firm. Year fixed
effects are included and standard errors are heteroskedastic-consistent that are clustered at the
analyst level. Our model is as follows:
(Migrated=1)i,t = β1 (Professional connection to migrated manager)t-1 + β2 (Same industry) t-1 + β3
(Analyst overlap) t-1 + β4(No of analysts (old firm)) t-1 + β5 (No of analysts (new firm)) t-1 +β6 (Size diff
(new-old)) t-1 + β7 (BM diff (new-old)) t-1 + β8 (Annual ret diff (new-old)) + β9 (All-star)t-1+β10 (GExp) t-1
+ β11 (FExp) t-1 + β12 (Affiliated) t-1+ β13 (Top 10) t-1 + β14 (Related experience) t-1 + β15(Education
connection to migrated manager) t-1 + Year Dummies + ε
(4)
***Insert Table 7 here***
Table 7 presents the results from the logistic regression. Model 1 suggests that analysts with
professional connections to the departing executives are more likely to initiate coverage on their new
firms. In economic terms, the odds of initiating coverage on the executive’s new firm is 2.1 times
higher when analysts share professional connections with the executive compared to when no such
21
connections exist. Other controls are also signed consistent with expectations. For example,
initiations of coverage are more likely when the new firm shares the same industry as the old firm. In
models 2 and 3, we split the sample into pre and post-Reg FD periods and continue to find similar
results. Taken as a whole, these results further our understanding of analyst coverage decisions and
emphasize the binding nature of professional connections in financial markets. Also consistent with
the evidence presented earlier, these new results highlight the importance of analyst-executive
relationships even after the passage of Reg FD. 12
6.2 Brokerage commission allocations
In a similar spirit to previous research (i.e. Irvine 2000; Goldstein et al. 2009), we measure
relative firm-specific broker trading commission share as total commissions allocated to broker i on
firm j by fund k during period t scaled by total broker commissions allocated for firm j by the same
fund k at the same point in time across all brokers. Broker trading commissions are obtained over
1999 and 2011 period from Ancerno Ltd., a proprietary institutional transaction dataset. 13 We
estimate OLS regressions of fund-firm level relative commission allocations across brokerage houses
for transaction firms with sell-side analyst coverage. We control for a comprehensive set of broker
and analyst characteristics that may also be related to buy-side funds’ trade allocations decisions.
These controls include broker size (Top 10), analyst characteristics (i.e. GExp, FExp, Portfolio size,
Port sic2, All-star), quality of earnings estimates (PMAFE) (Brown et al., 2014), related industry work
experience (Related experience) (Gokkaya, Liu, Xie and Zhang, 2015) and analyst effort (no of forecasts,
drop coverage). Potential analyst management access through alternative channels are controlled with
investment banking affiliation (Affiliated), social connections via shared alumni links (Education
connection), optimistic forecasting behavior (Optimism) and analyst locality (Local). Conrad, Johnson
and Wahal (2001) suggest that buy-side institutions’ commission allocations across brokers display
persistent behavior over time, and prior-period broker commission share is the most important
determinant of current period broker allocations. Therefore, we control for one-year lagged firmlevel relative broker commission market share (% Lag Broker share).
In unreported analyses we find that analysts sharing professional connections with migrated insiders continue to
generate more informative earnings forecasts on these new firms relative to other analysts lacking professional
connections. This suggests that the benefits obtained through professional connections are manager specific resulting in
analysts maintaining their competitive advantages across different firms. On the other hand, migrating analysts lacking
professional connections to the departing executive do not generate significantly more informative earnings forecasts in
new firms.
13 Prior studies using Ancerno data include Goldstein et al. (2009); Chemmanur, He and Hu (2009); Puckett and Yan
(2011), and Green et al., (2014b).
12
22
Our econometric model includes year fixed effects and report heteroskedasticity robust
standard errors clustered at analyst and firm level. In addition to % Lag Broker share, we also lag all
other control variables to mitigate reverse causality concerns. Our variable of interest is the binary
indicator that equals one if analyst has professional connections to buy-side institutions’ transaction
firm, zero otherwise. Our model is as follows:
Broker share% i,j,t = β1 (Professional connection) t-1 + β2 (GExp) t-1 + β3 (FExp) t-1 + β4(Portfolio size) t-1 + β5(Port
sic2) t-1 + β6 (All-star) t-1 + β7 (PMAFE) t-1 + β8 (Related experience) t-1 + β9(No of forecasts) t-1 +
β10 (Drop coverage) t-1 + β11 (Education connection) t-1 + β12(Optimism) t-1 + β13 (Local) t-1 +
β14(Top10) t-1 + β15 (Affiliated) t-1 + β16( % Lag Broker share) +Year dummies + ε
(2)
***Insert Table 8 here***
Consistent with our conjecture, model 1 of Table 8 suggests that buy-side investors indeed
allocate higher trading commissions to brokers on firms covered by sell-side analysts with
professional connections. In economic terms, relative firm-level trading commission share of an
average broker is 0.25% higher if the coverage analyst has privileged access to corporate
management through professional connections. Other controls also have signs consistent with
expectations. For instance, lagged commission share is the most important variable that factors into
current year commission allocation decision. Our findings also reveal that analysts with all-star
status, related industry work experience, higher firm specific forecasting experience and larger
portfolio size are more influential in attracting broker trading commissions on their coverage stocks
relative to analysts lacking such traits.
Model 2 and 3 focus on a dynamic setting arising from executive employment changes. If
gaining (losing) professional connections improves (mitigates) analysts’ ability to provide corporate
access to their buy-side clients, we expect broker commission allocations to be increased (decreased)
following such natural experiments. In line with our expectations, analysts losing professional
connections experience 0.31% lower relative broker commissions on their coverage firms relative to
when connections existed. Likewise, we find that commission allocations are 0.53% higher when an
analyst gains a professionally connected executive in her coverage firm compared to the period when
he lacked such a connection. Model 4 and 5 present the results of commission allocations in the
period before (after) an analyst gains (loses) connections and document that analysts do not have
any competitive advantage in attracting commissions in these period relative to their peers. Overall,
the analysis from this section paints a very clear picture that sell-side analyst professional
connections help employing brokerage houses attract higher trading commissions from investors.
23
7. Additional analyses and robustness tests
In this section we provide some additional analyses and robustness tests. First, we investigate
cross-sectional variation in professional connections for earnings forecasts, stock upgrades, and
analyst career outcomes. Second, we consider alternative definitions of professional connections as
well as forecast performance. Finally, we perform some robustness tests to mitigate concerns on
omitted variables and provide an additional analysis on analyst boldness behavior.
7.1. Cross-sectional analysis in professional connections
We first investigate whether professional connections exhibits cross-sectional variation with
respect to the quality and types of connections to shed further light on our main findings. We first
consider the directness of professional connections. The more direct the link, the stronger should be
the information flow. We differentiate between primary (Primary professional connection) and secondary
professional connections (Secondary professional connection) forged through employment networks and
re-estimate equation 1, 2 and 3.
***Insert Table 9 here***
Table 9 presents these results. For brevity, only the coefficient estimates on primary
variables of interest are presented; all other explanatory variables are suppressed. Model 1 shows that
the relative accuracy of earnings forecast on coverage firms with primary professional connections
are 4.9% more accurate compared to those of forecasts for firms where no such connections exists.
Likewise, forecast errors are 2.1% more accurate for analysts with secondary connections with
management. Models 4 and 7 replicate the analysis on upgrade stock recommendation revisions and
the probability of becoming all-star analysts, respectively. The results mirror the evidence for
earnings forecast performance. The differences in estimated coefficients between primary and
secondary professional connections are statistically and economically significant in all cases
presented in Table 9. Thus, we conclude that the more direct the link, the more valuable and precise
the information flow is.
Next, we consider the identity of professionally-connected executives to capture the quality
of such connections. We distinguish between professional connections to CEOs/CFOs (Professional
connection to CEO/CFO) and other insiders (Professional connection to non CEO/CFO). Top executives
spend more time with analysts and they also have more insights on firms’ strategies and financial
targets compared to other insiders. Therefore, we expect professional connections with
24
CEOs/CFOs to provide sell-side analysts superior information advantages. Splitting professional
connections into CEO/CFO and other executives, we indeed find more profound benefits accrue to
analysts with connections to these top executives. For instance, analysts professionally linked to
CEOs/CFOs issue 5.54% (1.05%) more informative earnings forecasts (upward stock revisions) and
are 87.4% more likely to become all-star analysts compared to their peers possessing professional
connections only to other executives.
Finally, we investigate the implications of professional connections to executives employed
in customers and suppliers of coverage firms. Superior information obtained from insiders in the
coverage firm’s customer-supply chain might provide professionally-connected analysts incremental
information complementariness, resulting in more informative research (Groysberg and Healy, 2013;
Guan, Wong and Zhang, 2014). In model 3 of Table 9, we partition professional connections to the
coverage firm into two new explanatory variables based on the presence of professional connections
also to the executives in the coverage firms’ supply chain (Professional connection- firm and CS,
Professional connection- only firm) and re-estimate equation 1. 14 Consistent with our conjecture, the
marginal impact of professional connection is higher when the same analyst also possesses
professional connections to insiders in the customers-suppliers of the coverage firms. The results are
also similar for stock recommendations and career outcomes as indicated in model 6 and 9. The
results are also consistent pre- and post-Reg FD for each cross-sectional test in Table 9 (not
reported).
7.2. Alternative professional connection measures and forecast performance
A plausible concern with our analysis is that professional connections may proxy for
superior forecasting by analysts on their former employers since professional ties are still likely to
exist between the analyst and former employer. While the analysis on executive turnovers should
alleviate this concern, for completeness we partition professional connections into two variables,
Cover same firm and Professional connection (not-same-firm) that measures professional connections in the
same firm and in firms other than the analysts’ former employers, respectively. Model 1 and 2 of
Supplier-customer pairs are obtained from the Compustat Customer Segments database. For customer names that are
abbreviated, we follow Cohen and Frazzini (2008) and manually match the customer name with the company name as
indicated in CRSP. We eliminate firms where there is not a perfect match after inspecting the firm’s name, segment, and
industry.
14
25
Table 10 provides these results for earnings forecast performance and stock upgrades, respectively.
We find that both are economically and statistically important.
***Insert Table 10 here***
Another reasonable concern with our analysis is that we cannot precisely identify the
location of overlapping employment. In other words, analysts and executives that we define as
connected may have worked at different facilities and thus never communicated. However, if
anything, this will increase noise and introduce bias against us finding anything. Nonetheless, to at
least partially address this issue we relax the constraint that employment must be overlapping during
the same time period. This, of course, would further lower the probability that the analyst and
executive had a professional relationship. As a result, we would expect the results to weaken. By
introducing more noise, this is exactly what we find. While the coefficient on professional
connection remains negative and largely economically significant for forecast performance, they lose
their statistical significance as shown in model 3. The same is true for stock upgrades in model 4
(positive coefficient, but insignificant).
To make our results comparable to related work, we employ Clement’s (1999) commonly
adopted measure of analyst relative earnings forecast performance throughout the paper. To ensure
the results are also robust to alternative performance specifications, we follow Clement and Tse
(2005) and Bae, Stulz and Tan (2008) and measure relative accuracy of earnings forecasts by a pricescaled range metric (Range). In particular, Range is defined as the ratio of the difference between the
highest price-scaled analyst absolute forecast error for firm j in year t and forecasting analyst i’s
price-scaled absolute forecast error for the same firm j in year t. This difference is then scaled by the
range of price-scaled analyst absolute forecast errors for firm j for year t. We re-estimate equation 1
substituting this forecast performance measure in lieu of PMAFE for our dependent variable. Model
5 of Table 10 indicates that our results remain intact to this measure as well.
7.3. Additional analysis
To control for unobservable time invariant analysts’ characteristics which could affect
forecast accuracy, we next conduct an analysis with only a subsample of analysts making forecasts
on both professionally-connected and non-connected firms and include analyst fixed effects. In
other words, we compare the accuracy of forecasts issued by the same analysts on firms which she
has professional connections relative to firms where no such connections exist. These findings are
reported in Model 6 of Table 10. Forecasts of analysts are, on average, 4.1% more accurate when
26
professional connections exists to the coverage firm compared to other firms’ lacking such
connections in the same analyst’s coverage portfolio, which is even greater in economic magnitude
relative to the results in Table 2. Model 7 present a similar analysis on upgrades. Holding the analyst
constant, we find that having professional connections to the coverage firm results in 0.65% larger
abnormal market reactions to upgrade revisions. These point estimates are also larger in economic
magnitude than those presented in Panel A of Table 4.
As a final test, we consider if analysts that are connected are more likely to provide bold
forecasts that go against the consensus. The literature suggests that bold forecasts reflect superior
analyst information on the coverage firms (Hong, Kubik and Solomon, 2000; Clement and Tse,
2005). It follows that analysts with connections are more likely to deviate from the herd if they
receive value-added insights from their superior management access. To test this conjecture, we
estimate a logistic regression model where the dependent variable takes a value of one if a bold
forecast is released, zero otherwise. Model 8 presents these results. Consistent with our expectations,
analysts with professional connections are more likely to issue bold forecasts.
8. Conclusion
Access to management is deemed one of the most important sources of information by sellside analysts and also buy-side institutions that rank the top analysts in their industry. This begs an
important question—how does one get access to management? In this paper, we consider one such
unique channel formed through overlapping employments. We tie the pre-analyst employment
backgrounds of financial analysts with the senior management of firms in which these analysts cover
creating professional connections. We conjecture that these professional connections should be
related to analysts’ performance and coverage decisions by giving connected analysts a competitive
advantage in acquiring and/or interpreting information.
Using a large panel of employment-connection links and analysts’ earnings forecasts between
1993 and 2011, we find the relative accuracy of earnings forecasts issued on professionally
connected firms is better than forecasts on firms lacking such connections. Professional connections
also translate into more informative stock recommendations. These results are robust to controlling
for other analyst characteristics, analyst effort and also alternative channels of management access.
Regulation FD has diminished, but not eliminated the information advantage provided by
professional connections suggesting that preferential access to private information is not solely
27
driving our results. This provides additional support for the continued emphasis on management
access by sell-side analysts and buy-side institutions in the post-Reg era.
Perhaps most importantly, when we consider natural experiments to exploit changes in
professional connections caused by executives’ job movements, we find that accuracy improves
(worsens) when a professional connection is gained (lost). Likewise, market reactions to upgrades
are lower (higher) when a connection is lost (gained) as a result of an executive switch. Similarly,
connected analysts affected by executive job movements do not have better performance relative to
their peers before (after) connected executive movements (departures), further emphasizing that
analysts possess a comparative advantage through professional connections.
In addition, we find that having professional connections to coverage firms increases the
likelihood of being selected to Institutional Investor’s all-star team. Focusing on executive job changes,
we also examine the impact of professional connections on coverage initiation decisions. Analysts
with professional connections to executives that move firms are more likely to initiate coverage on
the executive’s new firm. Finally, our results illustrate that buy-side investors reward brokerage
houses employing analysts with professional connections to their transaction firms through greater
allocation of trading commissions. Overall, our paper provides pervasive evidence that access to
management via professional connections is a unique and economically important information
channel that aids sell-side analysts in providing more informative research, leads to more favorable
career outcomes and is related to their coverage decisions. Professional connections also factor into
buy-side investors’ commission allocation decisions across brokerage houses.
28
References
Asquith, P., Mikhail, M., and Au A., 2005, Information content of equity analyst reports. Journal of
Financial Economics , 75, 245-282.
Bae, K., Stulz, R., and Tan, H., 2008, Do local analysts know more? A cross-country study of the
performance of local analysts and foreign analysts. Journal of Financial Economics, 8, 581–606.
Bamber, L. S., Jiang, J., & Wang, I. Y., 2010, What's my style? The influence of top managers on
voluntary corporate financial disclosure, The Accounting Review, 85, 1131-1162.
Barron, O. E., Byard, D. and Kim, O., 2002, Changes in analysts' information around earnings
announcements. The Accounting Review, 77, 821-846.
Bertrand, M., Schoar, A., 2003, Managing with style: The effect of managers on firm policies. The
Quarterly Journal of Economics, 118, 1169-1208.
Bertrand, M., Bombardini, M., and Trebbi, F., 2014, Is it whom you know or what you know? An
empirical assessment of the lobbying process, American Economic Review, forthcoming.
Bradley, D., Clarke, J., Lee, S., and Ornthanalai, C., 2014, Information disclosure and intraday price
discovery: Evidence from jumps. Journal of Finance, 69, 645-674
Bradley, D., Gokkaya, S., and Liu, X., 2014, Before an analyst becomes an analyst: Does industry
experience matter. Available at SSRN 2375262.
Brav, A. and Lehavy, R., 2003, An empirical analysis of analysts' target prices: Short‐term
informativeness and long‐term dynamics, The Journal of Finance, 58, 1933-1968.
Bramoullé, Y., Djebbari, H., and Fortin, B., 2009, Identification of peer effects through social
networks. Journal of Econometrics, 150, 41-55.
Brochet, F., Miller, G. S. and Srinivasan, S., 2014, Do analysts follow managers who switch
companies? An analysis of relationships in the capital markets. The Accounting Review, 89, 451482.
Brown, L., Call, A., Clement, M., and Sharp, N., 2014, Inside the 'Black Box' of sell-side financial
analysts. Journal of Accounting Research, forthcoming.
Cai, Y., and Sevilir, M., 2012, Board connections and M&A transactions. Journal of Financial Economics,
103, 327-349.
Chemmanur, T. J., He, S., and Hu, G., 2009, The role of institutional investors in seasoned equity
offerings. Journal of Financial Economics, 94, 384-411.
Chen, T., and Martin, X., 2011, Do bank-affiliated analysts benefit from lending relationships?
Journal of Accounting Research, 49, 633-675.
29
Chen, S., and Matsumoto, D. A., 2006, Favorable versus unfavorable recommendations: The impact
on analyst access to management‐provided information. Journal of Accounting Research, 44, 657689.
Christensen, D. M., Mikhail, M. B., Walther, B. R., & Wellman, L., 2014, From K Street to Wall
Street: Politically Connected Analysts and Stock Recommendations. Available at SSRN
2402411.
Clement, M., 1999, Analyst forecast accuracy: Do ability, resources and portfolio complexity matter?
Journal of Accounting and Economics 27, 285-303.
Clement, M. B., Rees, L., and Swanson, E. P., 2003, The influence of culture and corporate
governance on the characteristics that distinguish superior analysts. Journal of Accounting,
Auditing & Finance, 18, 593-618.
Clement, M. B., Koonce, L., and Lopez, T. J., 2007, The roles of task-specific forecasting experience
and innate ability in understanding analyst forecasting performance. Journal of Accounting and
Economics, 44, 378-398.
Clement, M. B., and Tse, S. Y., 2003, Do investors respond to analysts' forecast revisions as if
forecast accuracy is all that matters? The Accounting Review , 78, 227-249.
Clement, M., and Tse, S., 2005, Financial analyst characteristics and herding behavior in forecasting,
Journal of Finance, 60, 307-341.
Cohen, L., Frazzini, A. and Malloy, C., 2008, The small world of investing: Board connections and
mutual fund returns, Journal of Political Economy, 116, 951–979.
Cohen, L., Frazzini, A., and Malloy, C., 2010, Sell-Side school ties. Journal of Finance, 65, 1409-1437.
Conrad, J. S., Johnson, K. M., and Wahal, S., 2001, Institutional trading and soft dollars. The Journal
of Finance, 56, 397-4
Cornelli, F. and Goldreich, D., 2001, Bookbuilding and strategic allocation, The Journal of Finance, 56,
2337-2369.
De Franco, G., and Zhou, Y., 2009, The Performance of analysts with a CFA® designation: The
role of human-capital and signaling theories. The Accounting Review, 84, 383-404.
Duchin, R., and Sosyura, D., 2013, Divisional managers and internal capital markets. Journal of
Finance, 68, 387-429.
Engelberg, J., Gao, P., and Parsons, C. A., 2012, Friends with money. Journal of Financial
Economics, 103, 169-188.
30
Engelberg, J., Gao, P., and Parsons, C. A., 2013, The Price of a CEO's rolodex. Review of Financial
Studies, 26, 79-114.
Fracassi, C. and Tate, G., 2012, External networking and internal firm governance. Journal of Finance,
67, 153-194.
Francis, J., LaFond, R., Olsson, P., and Schipper, K., 2005, The market pricing of accruals
quality. Journal of Accounting and Economics, 39, 295-327.
Garmaise, M. J., and Moskowitz, T. J., 2003, Informal financial networks: Theory and
evidence. Review of Financial Studies, 16, 1007-1040.
Gilson, S. C., Healy, P. M., Noe, C. F., and Palepu, K. G., 2001, Analyst specialization and
conglomerate stock breakups. Journal of Accounting Research, 39, 565-582.
Gintschel, A., and Markov, S., 2004, The effectiveness of Regulation FD. Journal of Accounting and
Economics, 37, 293-314.
Gleason, C. A., and Lee, C. M., 2003, Analyst forecast revisions and market price discovery. The
Accounting Review, 78, 193-225.
Gokkaya, S., Liu, X. , Xie, F. and Zhang, J. , 2015, Who Benefits from Industry Knowledge of SellSide Analysts? Evidence from Broker Commission Payments and Client Fund Performance,
Goldstein, M. A., Irvine, P., Kandel, E., and Wiener, Z., 2009, Brokerage commissions and
institutional trading patterns. Review of Financial Studies, hhp083.
Green, T. C., Jame, R., Markov, S., and Subasi, M., 2014a, Access to management and the
informativeness of analyst research. Journal of Financial Economics, 2, 239-255.
Green, T. C., Jame, R., Markov, S., and Subasi, M., 2014b, Broker-Hosted Conferences. Journal of
Accounting and Economics, 58, 142-166.
Groysberg, B., Healy, P. M., and Maber, D. A., 2011, What drives sell‐side analyst compensation at
high‐status investment banks? Journal of Accounting Research, 49, 969-1000.
Groysberg, B. and Healy, P., 2013, Wall Street Research: Past, Present and Future, Stanford
University Press.
Guan, Y, Wong, M. F., and Zhang, Y. 2014, Analyst following along the supply chain. Review of
Accounting Studies, June, 1-32.
Hochberg, Y. V., Ljungqvist, A., and Lu, Y., 2007, Whom you know matters: Venture capital
networks and investment performance. Journal of Finance, 62, 251-301.
Hong, H., Kubik, J. D., and Stein, J. C., 2005, Thy neighbor's portfolio: Word of mouth effects in
the holdings and trades of money managers. Journal of Finance, 60, 2801-2824.
31
Hwang, B. H., and Kim, S., 2009, It pays to have friends. Journal of Financial Economics, 93, 138-158.
Irvine, P. J., 2000, Do analysts generate trade for their firms? Evidence from the Toronto stock
exchange. Journal of Accounting and Economics, 30(2), 209-226.
Irvine, P. J., 2004, Analysts' forecasts and brokerage-firm trading. The Accounting Review, 79(1), 125149.
Ishii, J., and Xuan, Y., 2014, Acquirer-target social ties and merger outcomes. Journal of Financial
Economics, 112, 344-363.
Ivković, Z., & Jegadeesh, N., 2004, The timing and value of forecast and recommendation
revisions. Journal of Financial Economics, 73, 433-463.
Jackson, A. R., 2005, Trade generation, reputation, and sell‐side analysts. The Journal of Finance, 60(2),
673-717.
Jacob, J., Lys, T. Z., and Neale, M. A., 1999, Expertise in forecasting performance of security
analysts. Journal of Accounting and Economics, 28, 51-82.
Juergens, J. L., and Lindsey, L., 2009, Getting out early: an analysis of market making activity at the
recommending analyst's firm. The Journal of Finance, 64, 2327-2359
Kadan, O., Madureira, L., Wang, R., and Zach, T., 2012, Analysts' industry expertise, Journal of
Accounting and Economics , 54, 95-120.
Koch, A., Lefanowicz, C. E., and Robinson, J. R., 2013, Regulation FD: A review and synthesis of
the academic literature. Accounting Horizons, 27, 619-646.
Kuhnen, C. M., 2009, Business networks, corporate governance, and contracting in the mutual fund
industry. Journal of Finance, 64, 2185-2220.
Ljungqvist, A., Marston, F., and Wilhelm W. J. 2006, Competing for securities underwriting
mandates: Banking relationships and analyst recommendations. Journal of Finance, 61, 301340.
Loh, R. K., and Mian, G. M., 2006, Do accurate earnings forecasts facilitate superior investment
recommendations? Journal of Financial Economics, 80, 455-483.
Loh, R., and Stulz, R., 2011, When are analyst recommendation changes influential? Review of
Financial Studies, 24, 593-627.
Malloy, C. J., 2005, The Geography of equity analysis. Journal of Finance, 60, 719-755.
Mayew, W. J., 2008, Evidence of management discrimination among analysts during earnings
conference calls. Journal of Accounting Research, 46, 627-659.
32
Mayew, W., and Venkatachalam, M., 2012, The power of voice: Managerial affective states and
future firm performance. Journal of Finance, 67, 1-43.
Mayew, W. J., Sharp, N. Y., and Venkatachalam, M., 2013, Using earnings conference calls to
identify analysts with superior private information. Review of Accounting Studies, 18, 386-413.
Michaely, R., and Womack, K., 1999, Conflict of interest and the credibility of underwriter analyst
recommendations. Review of Financial Studies 12, 653-686.
Pool, K. P., Stoffman, N., and Yonker, S., 2014, The people in your neighborhood: Social
interactions and mutual fund portfolios, Journal of Finance, forthcoming.
Puckett, A., and Yan, X. S., 2011, The interim trading skills of institutional investors. The Journal of
Finance, 66(2), 601-633
Stickel, S. E., 1992, Reputation and performance among security analysts. Journal of Finance, 47, 18111836.
Stickel, S. E., 1995, The anatomy of the performance of buy and sell recommendations. Financial
Analysts Journal , 51, 25-39.
Stuart, T. E., and Yim, S., 2010, Board interlocks and the propensity to be targeted in private equity
transactions. Journal of Financial Economics, 97, 174-189.
Tang, Y., 2013, Business connections and informed trading of mutual fund managers. Available at
SSRN 1517476.
Valentine, J. J., 2011, Best Practices for Equity Research Analysts. McGraw-Hill
Womack, K. L., 1996, Do brokerage analysts' recommendations have investment value? Journal of
Finance, 51, 137-167.
33
Table 1. Summary statistics
This table reports summary statistics of the sample. Panel A (Panel B) presents summary statistics for annual earnings forecasts (stock recommendation revisions)
between 1993 and 2011. % Analysts with professional connections is the percentage of analysts with at least one professional connection to the firms in the analyst’s
coverage portfolio. % Firms with Professional connections is the percentage of coverage firms that have at last 1 insider with a professional connection to one of the
coverage sell-side analysts. % Analysts with Professional connections is the percentage of analysts that have a professional connection to at least one of the firms in
their coverage portfolios. % Forecasts (% Rec) on Professional Connections is the percentage of forecasts (recommendations) issued on firms with professional
connections. % Firm, % Analysts, % Forecasts, and % Market Cap are the percentage of firms, analysts forecasts, and market capitalization representing the ‘clean’
I/B/E/S universe of US firms, respectively. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S. Employment history of sellside analysts is from LinkedIn.com and supplemented with Zoominfo.com. Biographical information for senior officers and directors is provided by Boardex of Management
Diagnostic Limited. Stock price data are obtained from CRSP.
Panel A: Earnings forecasts
Year
N Firms
N Analysts
N Forecasts
% Firms
with
professional
connections
All
199301-199612
199712-200010
200011-200412
200501-200812
200901-201112
Average
3,413
712
1,244
2,042
2,750
2,123
1,774.2
2,467
339
676
1,453
1,670
1,383
1,104.2
94,185
3,693
7,291
24,462
35,661
23,078
18,837.0
57.90%
25.28%
34.73%
49.17%
55.20%
60.06%
44.89%
35.31%
23.60%
26.63%
30.49%
35.57%
40.01%
31.26%
24.17%
16.08%
19.89%
21.83%
25.16%
27.78%
22.15%
58.88%
23.53%
35.90%
66.13%
87.52%
90.53%
60.72%
25.65%
10.52%
16.29%
25.33%
35.17%
37.76%
25.01%
N Rec
% Firms
with
professional
connections
% Analysts
with
professional
connections
% Rec on
professional
connections
% Firms
% Analysts
Panel B: Recommendation Revisions
Year
All
199301-199612
199712-200010
200011-200412
200501-200812
200901-201112
Average
N Firms
2,793
426
906
1,582
2,135
1,607
1331.2
N Analysts
1,777
207
524
1,014
1,163
936
768.8
31,287
1,222
3,060
9,065
11,243
6,697
6257.4
48.48%
21.13%
27.37%
39.95%
41.92%
45.49%
35.17%
% Analysts
with
professional
connections
% Forecasts on
professional
connections
% Firms
% Analysts
33.60%
22.22%
25.38%
29.49%
33.19%
38.03%
29.66%
34
23.47%
15.55%
19.84%
22.22%
24.12%
27.16%
21.78%
29.87%
10.86%
16.83%
35.78%
45.51%
41.61%
30.12%
20.61%
8.65%
13.48%
22.76%
26.92%
26.04%
19.57%
% Forecasts
% Market Cap
24.14%
5.09%
10.45%
25.22%
37.26%
41.79%
23.96%
89.07%
59.24%
73.30%
85.12%
99.75%
102.17%
83.92%
% Rec
% Market Cap
10.83%
3.14%
5.61%
12.20%
16.46%
12.69%
10.02%
75.77%
50.65%
66.89%
66.13%
91.79%
83.43%
71.78%
Table 2. Analyst earnings forecast performance and professional connections
This table presents OLS regression results for analyst earnings forecasts between 1993 and 2011. The dependent variable is the
proportional mean absolute forecast error (PMAFE) defined as the difference between the absolute forecast error for analyst i
for firm j and the mean absolute forecast error at time t scaled by the mean absolute forecast error for firm j at time t. Refer to
Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. Employment history of sell-side analysts is
from LinkedIn.com and supplemented with Zoominfo.com. Biographical information for senior officers and directors is provided
by Boardex of Management Diagnostic Limited (Boardex). T-statistics are in parentheses with heteroskedastic-consistent standard
errors double clustered at the firm and analyst level. For brevity we do not report intercepts. *, **, and *** indicate statistical
significance at the 10%, 5%, and 1%, respectively.
Professional connection
DGExp
DAge
DFExp
DPortsize
DSic2
DTop10
All-star
Related experience
(1)
-2.682***
(-5.132)
-0.169***
(-3.126)
0.495***
(89.212)
-0.223**
(-2.361)
0.043
(0.762)
0.305*
(1.916)
-1.063**
(-2.098)
-2.523***
(-3.656)
(2)
-2.241***
(-4.231)
-0.183***
(-3.381)
0.494***
(89.160)
-0.219**
(-2.321)
0.045
(0.802)
0.301*
(1.889)
-1.177**
(-2.321)
-2.740***
(-3.965)
-5.252***
(-6.089)
(3)
-2.256***
(-4.260)
-0.179***
(-3.315)
0.494***
(89.135)
-0.227**
(-2.403)
0.040
(0.717)
0.308*
(1.931)
-1.294**
(-2.535)
-2.771***
(-4.013)
-5.232***
(-6.067)
0.087
(0.121)
-0.107
(-0.216)
-2.072***
(-3.687)
(4)
-2.480***
(-4.116)
-0.206***
(-3.297)
0.506***
(79.956)
-0.169
(-1.522)
0.046
(0.710)
0.276
(1.493)
-0.823
(-1.402)
-1.927**
(-2.341)
-5.108***
(-5.311)
0.307
(0.392)
0.111
(0.192)
-1.939***
(-2.983)
-0.401
(-0.469)
12.90%
94,185
12.93%
94,185
12.94%
94,185
13.50%
72,553
Affiliated
Education connection
Optimism
Local
No of forecasts
Drop coverage
R2
N
35
(5)
-2.279***
(-3.781)
-0.227***
(-3.618)
0.496***
(77.404)
-0.144
(-1.299)
0.040
(0.625)
0.242
(1.304)
-0.796
(-1.358)
-1.569*
(-1.904)
-4.885***
(-5.075)
0.292
(0.372)
0.192
(0.331)
-1.519**
(-2.320)
-0.486
(-0.569)
-0.417***
(-4.466)
3.820***
(5.514)
13.57%
72,553
Table 3. Analyst earnings forecast performance and professional connections: Pre- vs Post-Reg FD
This table presents OLS regression results for analyst earnings forecasts in the pre and post-Reg FD period. The dependent variable is the proportional mean absolute
forecast error (PMAFE) defined as the difference between the absolute forecast error for analyst i for firm j and the mean absolute forecast error at time t scaled by the
mean absolute forecast error for firm j at time t. Refer to Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. Employment history of
sell-side analysts is from LinkedIn.com and supplemented with Zoominfo.com. Biographical information for senior officers and directors is provided by Boardex of
Management Diagnostic Limited. T-statistics are in parentheses with heteroskedastic-consistent standard errors double clustered at the firm and analyst level. For brevity
we do not report intercepts. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Pre-Reg FD Period
Post-Reg FD Period
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Professional connection
-8.835***
-8.565***
-8.609***
-9.384***
-8.554***
-1.951***
-1.495***
-1.512***
-1.729***
-1.604**
(-6.935)
(-6.677)
(-6.704)
(-6.013)
(-5.447)
(-3.447)
(-2.602)
(-2.633)
(-2.678)
(-2.483)
DGExp
-0.470***
-0.478***
-0.470***
-0.657***
-0.671***
-0.138**
-0.153***
-0.150***
-0.164**
-0.180***
(-2.923)
(-2.975)
(-2.927)
(-3.329)
(-3.425)
(-2.421)
(-2.674)
(-2.629)
(-2.494)
(-2.734)
DAge
0.414***
0.414***
0.413***
0.432***
0.411***
0.510***
0.510***
0.510***
0.518***
0.510***
(31.422)
(31.347)
(31.345)
(26.672)
(24.745)
(83.720)
(83.681)
(83.614)
(75.377)
(73.315)
DFExp
0.323
0.324
0.327
0.451
0.553*
-0.306***
-0.301***
-0.309***
-0.244**
-0.230*
(1.280)
(1.285)
(1.298)
(1.448)
(1.793)
(-2.998)
(-2.961)
(-3.034)
(-2.057)
(-1.930)
DPortsize
0.042
0.023
0.029
0.291*
0.302*
0.047
0.053
0.047
0.004
-0.003
(0.326)
(0.177)
(0.224)
(1.869)
(1.949)
(0.755)
(0.845)
(0.757)
(0.061)
(-0.037)
DSic2
0.462
0.516
0.494
-0.013
-0.232
0.244
0.230
0.238
0.314
0.290
(1.303)
(1.447)
(1.387)
(-0.027)
(-0.496)
(1.371)
(1.291)
(1.336)
(1.556)
(1.438)
DTop10
-5.590***
-5.560***
-5.493***
-5.114***
-5.280***
-0.327
-0.462
-0.590
-0.054
-0.055
(-4.308)
(-4.280)
(-4.223)
(-3.332)
(-3.458)
(-0.597)
(-0.842)
(-1.065)
(-0.085)
(-0.087)
All-star
-4.689***
-4.782***
-4.608***
-5.041***
-4.578***
-1.458*
-1.700**
-1.747**
-0.809
-0.465
(-3.575)
(-3.635)
(-3.511)
(-3.073)
(-2.793)
(-1.788)
(-2.083)
(-2.142)
(-0.855)
(-0.491)
Related experience
-6.118**
-6.046**
-6.354**
-5.525*
-5.167***
-5.165***
-4.959***
-4.823***
(-2.565)
(-2.523)
(-2.217)
(-1.921)
(-5.604)
(-5.602)
(-4.849)
(-4.712)
Affiliated
-2.080
-2.018
-2.354
0.526
0.763
0.773
(-1.256)
(-1.063)
(-1.246)
(0.658)
(0.886)
(0.898)
Education connection
-1.081
-0.109
0.394
0.030
0.090
0.153
(-0.918)
(-0.075)
(0.274)
(0.055)
(0.143)
(0.243)
Optimism
-4.929***
-5.948***
-4.524**
-1.548**
-1.274*
-0.971
(-3.208)
(-3.196)
(-2.417)
(-2.556)
(-1.827)
(-1.384)
Local
-1.174
-1.719
-0.211
-0.253
(-0.542)
(-0.799)
(-0.229)
(-0.274)
No of forecasts
-1.628***
-0.276***
(-5.908)
(-2.784)
Drop coverage
6.935***
3.385***
(3.319)
(4.602)
R2
12.37%
12.40%
12.49%
13.39%
13.87%
13.07%
13.11%
13.11%
13.60%
13.65%
N
13,269
13,269
13,269
8,981
8,981
80,916
80,916
80,916
63,572
63,572
36
Table 4. Market reactions to stock recommendation revisions and professional connections
This table reports immediate market reactions to analysts’ recommendation revisions. The dependent variable is the CRSP-VW index-adjusted abnormal returns over
the [0, 2]-day window around the announcement date of upward/downward revision by analyst i for firm j at time t. Models 5 and 11 report results pre-Reg FD and 6
and 12 post-Reg FD. Refer to Appendix C for a detailed description of variables. Stock price data are obtained from CRSP. T-statistics are in parentheses with
heteroskedastic-consistent standard errors double clustered at the firm and analyst level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%,
respectively.
(1)
Professional connection 0.344***
(2.803)
Size
-0.712***
(-22.223)
BM
-0.004
(-0.343)
All-star
0.429**
(2.307)
GExp
0.035**
(1.998)
FExp
-0.028
(-1.262)
Top10
0.643***
(6.012)
Past 6m Ret
-2.326*
(-1.797)
Rec Level
-0.685***
(-5.013)
∆Recommendation
0.337**
(2.546)
Related experience
0.507***
(2.713)
Affiliated
Education connection
Optimism
Local
(2)
0.343***
(2.801)
-0.713***
(-21.667)
-0.003
(-0.277)
0.384**
(2.068)
0.032*
(1.823)
-0.025
(-1.109)
0.605***
(5.644)
-2.326*
(-1.801)
-0.607***
(-4.295)
0.289**
(2.126)
0.508***
(2.725)
0.560***
(2.665)
0.241**
(2.214)
-0.666**
(-2.139)
No of forecasts
Drop coverage
Year Fixed Effects
R2
n
Y
4.88%
15,005
Y
5.00%
15,005
Stock Upgrades
(3)
(4)
0.440***
0.439***
(3.233)
(3.223)
-0.749*** -0.750***
(-20.427)
(-20.461)
-0.010
-0.009
(-1.170)
(-1.083)
0.483**
0.478**
(2.364)
(2.339)
0.029
0.029
(1.477)
(1.469)
-0.021
-0.021
(-0.846)
(-0.829)
0.473***
0.475***
(3.941)
(3.949)
-2.885**
-2.910**
(-2.076)
(-2.094)
-0.636*** -0.639***
(-4.005)
(-4.026)
0.387**
0.389**
(2.478)
(2.493)
0.402**
0.403**
(2.037)
(2.033)
0.555**
0.552**
(2.466)
(2.452)
0.317***
0.318***
(2.636)
(2.646)
-0.957*** -0.960***
(-2.593)
(-2.596)
-0.013
-0.010
(-0.074)
(-0.059)
0.000
(0.003)
-0.186
(-1.041)
Y
Y
5.17%
5.18%
12,517
12,517
(5)
1.030**
(2.284)
-0.536***
(-5.137)
-0.238*
(-1.871)
0.637
(1.322)
0.023
(0.178)
0.146
(1.172)
1.021***
(2.839)
-2.668
(-0.863)
0.393
(1.136)
0.342
(0.972)
0.299
(0.368)
0.352
(0.610)
0.974***
(2.730)
-0.019
(-0.020)
0.449
(0.833)
0.026
(0.463)
-0.648
(-1.353)
Y
5.48%
1,920
(6)
0.353**
(2.495)
-0.803***
(-20.959)
-0.006
(-0.635)
0.201
(0.915)
0.031
(1.570)
-0.027
(-1.044)
0.326**
(2.558)
-3.237**
(-2.114)
-0.979***
(-5.429)
0.534***
(3.024)
0.382*
(1.902)
0.569**
(2.374)
0.155
(1.238)
-1.005**
(-2.516)
-0.091
(-0.491)
0.001
(0.039)
-0.068
(-0.349)
Y
5.59%
10,597
37
(7)
-0.110
(-0.784)
0.651***
(16.322)
0.425**
(2.187)
-0.449**
(-2.334)
-0.068***
(-3.571)
0.121***
(4.972)
-0.381***
(-3.100)
3.171**
(2.238)
0.059
(0.594)
-0.169
(-1.501)
-0.677***
(-3.078)
(8)
-0.089
(-0.632)
0.620***
(15.283)
0.418**
(2.174)
-0.398**
(-2.077)
-0.064***
(-3.361)
0.119***
(4.873)
-0.310**
(-2.511)
3.194**
(2.253)
0.029
(0.271)
-0.239**
(-2.060)
-0.675***
(-3.067)
-1.199***
(-4.719)
-0.054
(-0.434)
0.167
(1.311)
Y
3.40%
16,282
Y
3.60%
16,282
Stock Downgrades
(9)
(10)
-0.136
-0.127
(-0.872)
(-0.814)
0.626***
0.631***
(13.608)
(13.733)
0.385**
0.386**
(2.092)
(2.097)
-0.271
-0.256
(-1.303)
(-1.233)
-0.072*** -0.073***
(-3.394)
(-3.429)
0.127***
0.128***
(4.473)
(4.498)
-0.239*
-0.235*
(-1.728)
(-1.697)
2.089
2.136
(1.384)
(1.413)
-0.029
-0.036
(-0.242)
(-0.299)
-0.274**
-0.275**
(-2.117)
(-2.121)
-0.543**
-0.528**
(-2.300)
(-2.243)
-1.237*** -1.239***
(-4.622)
(-4.626)
-0.070
-0.064
(-0.503)
(-0.464)
0.283**
0.292**
(2.009)
(2.065)
-0.143
-0.145
(-0.669)
(-0.679)
-0.012
(-0.523)
0.318*
(1.654)
Y
Y
3.20%
3.22%
13,673
13,673
(11)
-0.580
(-0.940)
0.515***
(3.378)
0.805**
(2.417)
-0.203
(-0.287)
-0.340*
(-1.725)
0.442**
(2.560)
-1.921***
(-3.761)
2.842
(0.613)
-1.016
(-1.611)
-0.982*
(-1.954)
-1.482
(-1.483)
-2.605***
(-2.818)
-0.072
(-0.150)
1.159**
(2.245)
-1.018
(-1.376)
-0.014
(-0.159)
0.302
(0.508)
Y
6.84%
1,424
(12)
-0.077
(-0.479)
0.649***
(13.758)
0.359**
(2.039)
-0.122
(-0.578)
-0.067***
(-3.107)
0.111***
(3.875)
-0.032
(-0.225)
2.085
(1.312)
0.031
(0.248)
-0.183
(-1.368)
-0.421*
(-1.781)
-1.083***
(-3.850)
-0.040
(-0.277)
0.198
(1.360)
-0.025
(-0.113)
-0.016
(-0.651)
0.280
(1.371)
Y
3.02%
12,249
Table 5. Change in earnings forecast performance and market reactions to upward recommendation
revisions: Losing/ gaining professional connections
Panel A (B) presents OLS regression results for changes in analyst earnings forecasts (market reactions to stock upgrades) for
a subsample of analysts losing or gaining professional connections in the covered firm. The dependent variable is proportional
mean absolute forecast error (PMAFE) for analyst i on firm j in Panel A, CRSP-VW index-adjusted abnormal returns over the
[0, 2]-day window around the announcement date of upward recommendation revision by analyst i for firm j at time t in Panel
B. Refer to Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. Employment history of sellside analysts is from LinkedIn.com and supplemented with Zoominfo.com. Biographical information for senior officers and
directors is provided by Boardex of Management Diagnostic Limited. T-statistics are in parentheses with heteroskedastic-consistent
standard errors double clustered at the firm and analyst level. For brevity we do not report intercepts. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1%, respectively.
Panel A: Earnings Forecast Performance
Lose professional connection
Gain professional connection
(1)
4.778***
(2.894)
(2)
-4.537***
(-3.259)
Post-losing professional connection
Pre-gaining professional connection
DGExp
DAge
DFExp
DPortsize
DSic2
DTop10
All-star
Related experience
Affiliated
Education connection
Optimism
Post-Reg FD
No of forecasts
Drop coverage
R2
N
-0.085
(-0.301)
0.437***
(28.845)
-0.318
(-0.787)
0.119
(0.566)
0.185
(0.257)
-1.398
(-0.648)
-4.487**
(-2.078)
-5.223***
(-2.933)
2.510
(1.052)
0.484
(0.285)
-3.554
(-1.564)
2.005
(0.795)
-0.660**
(-2.446)
2.248
(1.022)
19.45%
7,240
-0.229
(-1.634)
0.423***
(38.814)
-0.268
(-1.163)
0.082
(0.593)
0.290
(0.617)
-1.594
(-1.185)
-2.599*
(-1.698)
-4.463***
(-2.839)
1.882
(1.159)
-0.852
(-0.733)
-2.340
(-1.623)
-0.275
(-0.187)
-0.574***
(-2.863)
3.821**
(2.535)
17.63%
15,272
38
(3)
4.827
(0.955)
-0.121
(-0.819)
0.493***
(29.638)
-0.234
(-1.034)
0.167
(1.016)
-0.016
(-0.034)
-1.975
(-1.423)
-1.853
(-0.827)
-5.767**
(-2.242)
4.637
(1.600)
-1.937
(-1.553)
-1.632
(-1.028)
-2.104
(-0.644)
-0.449**
(-2.161)
2.973*
(1.869)
13.49%
13,634
(4)
3.006
(0.837)
-0.114
(-0.663)
0.364***
(23.092)
-0.048
(-0.178)
0.054
(0.341)
0.244
(0.569)
-3.051**
(-2.155)
-2.010
(-1.112)
-8.363***
(-2.700)
3.071
(1.127)
-2.009
(-1.452)
-2.845*
(-1.717)
-3.854***
(-2.810)
-1.060***
(-4.401)
14.682***
(6.716)
12.10%
11,333
Panel B: Stock Upgrades
Lose professional connection
Gain professional connection
(1)
-0.490***
(-2.674)
Post-losing professional connection
(2)
0.422**
(1.984)
Pre-gaining professional connection
Size
BM
All-star
GExp
FExp
Top10
Past 6m Ret
Rec Level
∆Recommendation
Related experience
Affiliated
Education connection
Optimism
Post Reg-FD
No of forecasts
Drop coverage
R2
N
-0.554***
(-9.050)
-0.117
(-0.771)
0.390
(1.555)
-0.004
(-0.143)
-0.016
(-0.417)
0.982***
(2.635)
0.834***
(3.828)
-5.655**
(-2.113)
0.257
(1.567)
0.314
(1.627)
0.775***
(2.736)
0.309
(1.507)
-0.656
(-0.944)
0.825**
(2.303)
0.017
(0.460)
-0.203
(-0.628)
7.02%
6,146
-0.320***
(-9.439)
0.031
(0.171)
0.177
(0.690)
0.006
(0.173)
-0.009
(-0.191)
0.981***
(2.650)
0.494**
(2.017)
-5.486**
(-2.191)
0.075
(0.502)
0.401*
(1.844)
0.186
(0.675)
0.047
(0.233)
-0.996
(-1.357)
-2.068**
(-2.128)
-0.006
(-0.216)
-0.162
(-0.588)
4.80%
5,427
39
(3)
0.127
(0.214)
-0.698***
(-13.658)
0.005
(0.063)
0.847***
(2.673)
0.032
(1.316)
-0.003
(-0.113)
0.427***
(2.847)
-1.835
(-0.983)
-0.635***
(-3.113)
0.213
(1.064)
0.206
(0.663)
0.881**
(2.572)
0.406**
(2.569)
-1.464***
(-2.993)
-0.262
(-1.191)
0.014
(0.533)
-0.409*
(-1.795)
5.49%
6,056
(4)
0.235
(0.546)
-0.631***
(-11.450)
-0.127
(-1.197)
0.888***
(2.767)
0.018
(0.623)
-0.023
(-0.608)
0.575***
(3.466)
-2.239
(-1.167)
-0.476**
(-2.168)
0.073
(0.337)
0.538
(1.548)
0.816**
(2.333)
0.398**
(2.343)
-1.123**
(-2.081)
-0.054
(-0.234)
0.005
(0.188)
-0.302
(-1.099)
4.79%
5,681
Table 6. Becoming all-star analyst and professional connections
This table reports logistic regression results on the probability of becoming an all-star analyst. The dependent variable in each model is indicator binary variable for allstar status in year t, which equals 1 if the analyst was voted American all-star analyst in the October issue of Institutional Investor magazine, 0 otherwise. All control
variables are lagged by one year. Refer to Appendix C for a detailed description of variables. T-statistics are in parentheses with heteroskedastic-consistent standard
errors clustered at the analyst level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Ln professionally connected stocks
Average firm size
GExp
Portfolio size
Port sic2
Brokerage size
Average PMAFE
All-star prior year
Analyst with related experience
Ln education connections
Ln affiliated stocks
Ln optimistic stocks
Ln local stocks
Average no of forecasts
Average drop coverage
Year Fixed Effects
N
Full Sample
(1)
(3)
(3)
(4)
26.890*** 25.740*** 25.670*** 24.680***
(5.421)
(5.077)
(5.073)
(4.792)
8.830*** 9.010*** 8.920*** 10.080***
(3.003)
(3.013)
(2.983)
(3.231)
2.580** 2.580** 2.580** 3.410***
(2.505)
(2.481)
(2.481)
(3.128)
4.600*** 2.580** 2.490** 2.130*
(4.600)
(2.132)
(2.041)
(1.664)
-8.000** -8.040** -8.120** -4.130
(-2.439) (-2.474) (-2.506) (-1.180)
1.590*** 1.610*** 1.610*** 1.660***
(13.826) (13.644) (13.529) (13.607)
-30.200*** -29.970*** -30.030*** -22.460***
(-3.687) (-3.637) (-3.636) (-2.612)
474.970*** 476.580*** 476.200*** 475.170***
(40.665) (40.457) (40.597) (39.044)
57.060*** 60.320*** 60.720*** 56.760***
(4.815)
(5.108)
(5.154)
(4.706)
9.520
13.920
14.260
(1.254)
(1.418)
(1.435)
15.030
9.080
4.670
(1.545)
(1.195)
(0.614)
28.590*** 28.690*** 13.400
(2.762)
(2.777)
(1.286)
7.590
10.590
(0.726)
(0.986)
30.130***
(9.475)
-142.660***
(-4.947)
Y
Y
Y
Y
14,513
14,513
14,513
14,513
Pre-Reg FD Period
(5)
(6)
(7)
(8)
58.670*** 57.150*** 56.690*** 47.720***
(4.224)
(3.955)
(3.934)
(3.370)
5.220
5.900
5.800
8.530
(1.132)
(1.239)
(1.221)
(1.619)
3.230*
3.330*
3.280*
3.900*
(1.765)
(1.771)
(1.745)
(1.806)
5.490*** 2.330
2.210
2.370
(3.192)
(1.104)
(1.047)
(0.940)
-13.360** -14.980*** -14.750** -8.990
(-2.348) (-2.587) (-2.547) (-1.360)
2.080*** 2.040*** 2.050*** 2.150***
(9.163)
(8.644)
(8.686)
(8.532)
-31.990** -28.410** -28.990** -20.860
(-2.420) (-2.095) (-2.111) (-1.375)
455.380*** 457.310*** 456.720*** 444.110***
(22.510) (22.583) (22.588) (20.196)
42.360** 53.760*** 54.240*** 53.750***
(2.316)
(2.908)
(2.956)
(2.814)
38.390** 36.180** 33.130**
(2.517)
(2.342)
(2.081)
20.370
19.190
5.640
(1.555)
(1.457)
(0.421)
35.910** 35.550** 12.220
(2.204)
(2.177)
(0.739)
15.350
26.100
(0.912)
(1.526)
58.040***
(5.205)
-137.390**
(-2.514)
Y
Y
Y
Y
3,011
3,011
3,011
3,011
40
Post-Reg FD Period
(9)
(10)
(11)
(12)
23.080*** 22.930*** 22.930*** 22.660***
(4.282)
(4.192)
(4.184)
(4.068)
10.000** 10.050** 10.040** 10.790**
(2.500)
(2.457)
(2.449)
(2.569)
2.450*
2.480** 2.480** 3.040**
(1.944)
(1.968)
(1.968)
(2.375)
3.930*** 2.980*
2.960*
2.260
(2.848)
(1.828)
(1.794)
(1.299)
-4.660
-4.730
-4.750
-1.570
(-1.192) (-1.216) (-1.221) (-0.383)
1.390*** 1.420*** 1.420*** 1.450***
(11.032) (10.923) (10.840) (10.821)
-28.200*** -28.060*** -28.050*** -22.260**
(-2.730) (-2.693) (-2.692) (-2.100)
489.780*** 491.660*** 491.590*** 491.830***
(34.298) (34.167) (34.186) (33.412)
71.420*** 71.650*** 71.720*** 67.570***
(4.517)
(4.526)
(4.491)
(4.231)
-1.220
-1.390
1.110
(-0.102) (-0.113)
(0.089)
3.950
3.890
1.920
(0.418)
(0.414)
(0.206)
22.170* 22.200* 10.350
(1.725)
(1.734)
(0.801)
1.120
3.060
(0.086)
(0.228)
23.660***
(7.258)
-118.460***
(-3.529)
Y
Y
Y
Y
11,502
11,502
11,502
11,502
Table 7. Coverage initiations on migrating insiders’ new firms and professional connections
This table reports logistic regression results on the probability of coverage initiations on the job changing executives’ new
destination firms for analysts already covering the origin firms. The dependent variable in each model is indicator binary
variable (Migrated) that equals 1 if the analyst covering the old firm initiates coverage on the executive’s new firm for the first
time by the end of the first full fiscal year after the migrating executive joins the firm, 0 otherwise. Refer to Appendix C for a
detailed description of variables. T-statistics are in parentheses with standard errors double clustered at the firm and analyst
level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Professional connection to migrated manager
Same industry
Analyst overlap
No of analysts (old firm)
No of analysts (new firm)
Size diff (new-old)
BM diff (new-old)
Annual ret diff (new-old)
All-star
GExp
FExp
Affiliated
Top 10
Related experience
Education connection to migrated manager
R2
N
Full Sample
114.160***
(7.900)
147.410***
(12.123)
19.960***
(13.578)
-0.343
(-0.570)
-2.360***
(-3.882)
21.850***
(8.636)
-1.020
(-0.397)
4.420
(0.959)
-14.060
(-0.871)
-2.470*
(-1.930)
0.857
(0.446)
-2.940
(-0.139)
5.220
(0.459)
24.830
(1.385)
-0.096
(-0.003)
71.78%
32,930
Pre-Reg FD Period
89.420**
(2.211)
175.000***
(6.581)
17.210***
(4.357)
-0.832
(-0.626)
0.035
(0.031)
13.110**
(2.324)
-0.736
(-0.212)
5.990
(0.775)
-22.370
(-0.660)
-0.386
(-0.120)
-5.800
(-1.343)
70.020*
(1.743)
0.988
(0.037)
54.930
(1.345)
-84.830
(-0.772)
70.82%
5,097
41
Post-Reg FD Period
118.980***
(7.627)
141.050***
(10.503)
20.570***
(13.019)
-0.115
(-0.171)
-3.090***
(-4.280)
24.610***
(8.575)
-3.460
(-0.911)
4.280
(0.719)
-14.000
(-0.752)
-2.760*
(-1.957)
2.300
(1.075)
-23.590
(-0.955)
6.730
(0.532)
19.900
(1.002)
15.570
(0.440)
71.98%
27,833
Table 8. Trading commission allocations and analysts’ professional connections
This table presents OLS regression results for relative firm-specific broker trading commission allocations of buy-side clients
across brokerage houses over time. The dependent variable is relative firm-specific broker trading commission share as total
commissions allocated to broker i on firm j by fund k during period t scaled by total broker commissions allocated for firm j
by the same fund k at the same point in time across all brokers. Refer to Appendix C for a detailed description of variables.
Broker trading commissions are obtained over 1999 and 2011 period from Ancerno Ltd. Analyst data are from I/B/E/S.
Employment history of sell-side analysts is from LinkedIn.com and supplemented with Zoominfo.com. Biographical information
for senior officers and directors is provided by Boardex of Management Diagnostic Limited. Stock price data are obtained from
CRSP. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at the firm and analyst level. *,
**, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Model 1
Model 2
Model 3
Model 4
Model 5
0.248***
Professional connection
(11.520)
Lose professional connection
-0.313**
(-2.456)
Gain professional connection
0.529**
(2.169)
0.092
Post-losing professional connection
(0.502)
Pre-gaining professional connection
0.066
(0.274)
GExp
0.000
-0.023
-0.075
-0.008
-0.003
(0.123)
(-1.632)
(-1.180)
(-1.150)
(-0.439)
FExp
0.014***
0.015
0.087
0.005
-0.001
(4.729)
(0.896)
(1.393)
(0.460)
(-0.112)
Portsize
0.006***
-0.002
0.039
0.000
0.003
(3.636)
(-0.218)
(1.537)
(0.024)
(0.403)
0.005
0.016
-0.055
0.035**
0.054***
PortSic2
(1.354)
(0.598)
(-0.723)
(2.174)
(2.838)
All-star
0.249***
0.307***
0.807**
0.421***
0.879***
(10.379)
(2.642)
(2.080)
(3.734)
(7.286)
Pmafe
-0.096***
-0.156***
-0.369***
-0.116***
-0.077**
(-13.402)
(-2.686)
(-3.516)
(-3.464)
(-2.099)
Related experience
0.266***
0.041
-0.242
0.106
0.756**
(5.520)
(0.365)
(-0.805)
(0.716)
(2.506)
No of forecasts
0.003
-0.013
-0.058**
0.005
0.008
(1.218)
(-0.964)
(-2.263)
(0.567)
(0.702)
Drop coverage
-0.048***
-0.223*
0.181
-0.026
-0.135*
(-2.997)
(-1.868)
(0.508)
(-0.390)
(-1.700)
Education connection
-0.005
-0.284
-0.132
-0.204
-0.269
(-0.067)
(-0.846)
(-0.404)
(-1.352)
(-1.488)
Optimism
-0.015
0.114
-0.083
0.042
0.127
(-0.775)
(0.802)
(-0.201)
(0.699)
(1.463)
Local
0.125***
0.675***
1.109**
-0.011
0.043
(3.744)
(4.014)
(2.226)
(-0.086)
(0.379)
0.283***
1.111***
0.856*
0.448***
0.271***
Top 10
(21.179)
(11.289)
(1.768)
(7.992)
(4.615)
Affiliated
0.254***
0.068
0.185
0.304
0.739
(5.655)
(0.348)
(0.370)
(0.823)
(1.480)
Broker share%
9.733***
12.944***
15.438***
20.716***
15.584***
(57.105)
(18.926)
(7.803)
(17.392)
(12.283)
5.10%
7.69%
10.16%
9.66%
7.94%
R2
n
4,801,780
257,483
28,880
282,358
169,084
42
Table 9: Cross-sectional variation: Forecast performance, stock recommendations and career outcomes
This table presents robustness test results for analyst earnings forecasts, recommendation revisions and all-star status between 1993 and 2011. For models (1) (2) and
(3) the dependent variable is the proportional mean absolute forecast error (PMAFE) defined as the difference between the absolute forecast error for analyst i for
firm j and the mean absolute forecast error at time t scaled by the mean absolute forecast error for firm j at time t. For models (4) (5) and (6) the dependent variable is
the CRSP-VW index-adjusted abnormal returns over the [0, 2]-day window around the announcement date of upward recommendation revision by analyst i for firm j
at time t, and the models include year fixed effects. For models (7) (8) and (9) the dependent variable in each model is indicator binary variable for all-star status in year
t, which equals 1 if the analyst was voted American all-star analyst in the October issue of Institutional Investor magazine, 0 otherwise. These models likewise include
year fixed effects. For brevity, only the coefficient estimates on key variables are presented; all other explanatory variables are suppressed. Refer to Appendix C for a
detailed description of variables.. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Primary professional connection
Secondary professional connection
Professional connection to CEO/CFO
(1)
-4.938***
(-2.979)
-2.111***
(-3.397)
Professional connection to non CEO/CFO
Professional connection- firm and CS
Earnings Forecasts
(2)
-5.540***
(-3.203)
-1.998***
(-3.226)
Professional connection-only firm
Difference
Control Variables
R2
N
-2.888*
(-1.702)
Y
13.57%
72,553
-3.542**
(-2.008)
Y
13.57%
72,553
(3)
(4)
0.969***
(3.145)
0.413***
(2.980)
-4.204***
(-3.843)
-1.702**
(-2.568)
-2.502**
(-2.106)
Y
13.57%
72,553
0.640**
(2.017)
Y
5.21%
12,517
43
Stock Upgrades
(5)
1.050***
(2.776)
0.396***
(2.852)
0.655*
(1.723)
Y
5.19%
12,517
(6)
(7)
54.780***
(3.913)
17.800***
(3.207)
0.860***
(3.285)
0.313**
(2.156)
0.548**
(1.987)
Y
5.21%
12,517
36.980**
(0.079)
Y
37.10%
14,513
All-star Selection
(8)
(9)
62.840***
(2.923)
15.990***
(2.683)
46.850*
(0.138)
Y
37.10%
14,513
45.950***
(3.924)
15.620***
(3.093)
30.330**
(0.110)
Y
37.69%
14,513
Table 10. Robustness tests
This table presents robustness test results for analyst earnings forecasts and recommendation revisions between 1993 and 2011. For models (1) (3) and (6), the
dependent variable is the proportional mean absolute forecast error (PMAFE) defined as the difference between the absolute forecast error for analyst i for firm j and
the mean absolute forecast error at time t scaled by the mean absolute forecast error for firm j at time t. For model (5), the dependent variable is proportional absolute
forecast error range (Range). For models (2) (4) and (7) the dependent variable is the CRSP-VW index-adjusted abnormal returns over the [0, 2]-day window around the
announcement date of upward recommendation revision by analyst i for firm j at time t, and the models include year fixed effects. For model (8) the dependent
variable is the indicator for analyst boldness which equals 1 if the forecast is classified as bold and 0 otherwise. (H) denotes non-demeaned variables as in models (2)
(4) and (7). Refer to Appendix C for a detailed description of variables. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Professional connection
Cover same firm
Professional connection (not-same-firm)
DGExp(H)
DAge
DFExp(H)
DPortsize
DSic2
DTop10(H)
All-star(H)
Related experience
Affiliated
Education connection
Optimism
Local
No of forecasts
(1)
-7.740*
(-1.908)
-2.264***
(-3.746)
-0.227***
(-3.617)
0.496***
(77.384)
-0.144
(-1.298)
0.040
(0.619)
0.242
(1.305)
-0.802
(-1.367)
-1.577*
(-1.913)
-4.797***
(-4.967)
0.287
(0.366)
0.184
(0.318)
-1.521**
(-2.323)
-0.469
(-0.548)
-0.417***
(2)
1.320*
(1.893)
0.433***
(3.167)
0.029
(1.463)
-0.021
(-0.831)
0.473***
(3.940)
0.481**
(2.351)
0.389*
(1.956)
0.554**
(2.461)
0.320***
(2.659)
-0.957***
(-2.589)
-0.013
(-0.074)
0.000
(3)
-0.690
(-1.172)
(4)
0.117
(0.882)
(5)
2.000***
(6.928)
(6)
-4.108***
(-4.784)
(7)
0.658***
(2.700)
(8)
5.980**
(2.327)
-0.223***
(-3.558)
0.497***
(77.342)
-0.143
(-1.288)
0.038
(0.594)
0.255
(1.379)
-0.973*
(-1.661)
-1.919**
(-2.337)
-5.239***
(-5.426)
0.291
(0.372)
0.213
(0.370)
-1.494**
(-2.281)
-0.550
(-0.644)
-0.429***
0.027
(1.392)
0.148***
(5.023)
-0.186***
(-70.645)
0.028
(0.555)
0.023
(0.773)
-0.097
(-1.137)
0.247
(0.898)
0.772**
(2.039)
2.213***
(4.773)
-2.399***
(-5.959)
0.122
(0.432)
0.753**
(2.496)
-0.279
(-0.685)
0.271***
-0.214
(-1.396)
0.377***
(47.696)
0.119
(0.733)
0.257**
(2.291)
0.048
(1.534)
-2.251
(-1.629)
-2.372
(-1.605)
-11.828***
(-4.672)
0.373
(0.335)
0.984
(1.018)
-1.181
(-1.167)
-0.994
(-0.784)
-0.440***
2.275***
(2.582)
0.465
(1.545)
0.053**
(2.283)
0.169
(0.384)
-0.609
(-1.646)
-0.259
(-0.305)
5.530**
(1.996)
-1.330
(-0.393)
5.020
(1.204)
-4.720
(-1.573)
-1.220
(-0.542)
1.630
(0.629)
4.240
(1.346)
-2.270***
44
-0.020
(-0.808)
0.505***
(4.203)
0.542***
(2.664)
0.487**
(2.450)
0.562**
(2.503)
0.312***
(2.595)
-0.968***
(-2.617)
-0.001
(-0.004)
0.001
-0.043
(-0.872)
1.225***
(3.328)
0.840*
(1.874)
0.660**
(2.262)
0.839**
(2.176)
0.217
(0.811)
-0.644
(-0.958)
0.424
(1.073)
-0.068
Drop coverage
Size
(-4.466)
3.820***
(5.513)
BM
Past 6m Ret
Rec Level
∆Recommendation
R2
N
13.57%
72,553
(0.014)
-0.186
(-1.039)
-0.750***
(-20.464)
-0.009
(-1.085)
-2.903**
(-2.089)
-0.640***
(-4.033)
0.391**
(2.502)
5.18%
12,517
(-4.585)
3.880***
(5.598)
13.55%
72,553
45
(0.041)
-0.192
(-1.070)
-0.733***
(-20.140)
-0.009
(-1.107)
-2.950**
(-2.121)
-0.650***
(-4.095)
0.387**
(2.480)
5.10%
12,517
(6.182)
-1.059***
(-3.370)
9.53%
72,553
(-2.805)
3.990***
(3.974)
16.95%
25,625
(-1.620)
-0.383
(-1.107)
-0.713***
(-9.162)
-0.030
(-1.094)
-4.708**
(-2.315)
-0.544*
(-1.779)
0.278
(0.871)
20.68%
3,446
(-5.605)
-10.220***
(-3.799)
0.26%
72,553
Figure 1. Construction of professional connections between analysts and insiders
This figure illustrates how professional connections are formed and defined. Analyst A covers firms X and Y.
Analyst A overlapped with insider A at the same firm, forming a primary professional connection. Insider A
worked with Insider B at some point at the same firm. Analyst A has an indirect or secondary professional
connection to Insider B through her primary professional connection with Insider A. Professional connection is an
aggregate connectedness measure that sums primary and secondary professional connections between each
analysts and executive.
Analyst A
Firm X
Firm Y
Primary connection
Insider B
Insider A
46
Appendix A. Pearson correlation coefficients between management access channels
This table presents correlation coefficients between main channels of management access. All values are multiplied by 100. See Appendix C for a
description of these variables. Analyst data are from I/B/E/S from 1993 to 2011, stock price data are from CRSP, and firm characteristics are obtained
from Compustat. Employment history is collected from LinkedIn.com and supplemented with Zoominfo.com
Professional connection
Affiliated
Education connection
Optimism
Local
Professional connection
Affiliated
100
1.92
4.87
-0.26
1.95
100
-2.30
-1.84
2.54
Education connection
100
0.79
4.99
47
Optimism
100
0.73
Local
100
Appendix B. Data screening and collection process
Forecasts
Firms
Analysts
All analysts’ annual EPS forecasts between 1993 to 2011
from I/B/E/S.
2,218,597
15,943
15,038
Merge with CRSP/COMPUSTAT for stock price data and
firm characteristics.
1,589,996
8,969
11,700
Retain the last annual earnings forecast with a horizon
between 1 and 12 months.
365,091
6,330
11,109
Merge our sample with the I/B/E/S recommendation file
to get analyst last name, first initial and brokerage firm
estimid to identify brokerage firm names. Remove analysts
without a first initial, last name or brokerage estimid, analyst
teams (those analyst names recorded as “research
department” or contain two analyst last names). This is our
‘clean’ I/B/E/S sample.
346,564
6,330
9,235
Manually search zoominfo.com for analysts’ first names by
matching their brokerage firm, last name and first initial.
237,243
6,109
4,837
Collect analysts’ employment history on LinkedIn.com.
103,387
5,116
2,499
94,185
3,413
2,467
Merge with Boardex and remove firms not covered by
Boardex.
48
Appendix C. Variable definitions
Variable
Definition
PMAFE
The proportional mean absolute forecast error calculated as the difference between
the absolute forecast error (AFE) for analyst i on firm j and the mean absolute
forecast error (MAFE) for firm j at time t scaled by the mean absolute forecast error
for firm j at time t.
Professional connection Indicator variable is one if an analyst and a member of a coverage firm’s
management overlapped at the same firm prior to the analyst becoming a sell-side
analyst (primary professional connection) and/or if an analyst is indirectly connected
(secondary professional connection) to the management through “intransitive triads”
created by primary connections.
DGExp
The total number of years that analyst’s i appeared in I/B/E/S (GExp) minus the
average tenure of analysts supplying earnings forecasts for firm j at time t.
DAge
The age of analyst’s i forecast (Age) minus the average age of forecasts issued by
analysts following firm j at time t, where age is defined as the age of forecasts in days
at the minimum forecast horizon date.
DFExp
The total number of years since analyst’s i first earnings forecast for firm j (FExp)
minus the average number of years I/B/E/S analysts supplying earnings forecasts
for firm j at time t.
DPortsize
The number of firms followed by analyst i for firm j at time t (Portsize) minus the
average number of firms followed by analysts supplying earnings forecasts for firm j
at time t.
DSic2
The number of 2 digit SICs followed by analyst i at time t (PortSic2) minus the
average number of 2-digit SICs followed by analysts following firm j at time t.
DTop10
Indicator variable is one if analyst works at a top decile brokerage house (Top10)
minus the mean value of top decline brokerage house indicators for analysts
following firm j at time t.
All-star
Indicator variable is one if the analyst is named to Institutional Investor’s all-star team
in current year, and zero otherwise.
All-star prior year
Indicator variable is one if the analyst is named to Institutional Investor’s all-star team
in previous year, and zero otherwise.
Affiliated
Indicator variable is one if analyst’s brokerage house was the underwriter/ advisor
of the covered firm’s IPO/SEO/MA deal during the past 3 years, and zero
otherwise.
Related experience
Indicator variable is one if the industry of the forecasted firm is related to the
49
analyst’s prior industry work experience industry defined with 4 digit Global
Industry Classification System (GICS), zero otherwise.
Education connection
Indicator variable is one if the forecast is issued by an analyst connected to the
covered firm through educational links, and zero otherwise. Education connection is
defined similar to Cohen, Frazzini and Malloy (2010).
Optimism
Indicator variable is one if the most recent recommendation issued by the analyst
within 12 months is above the median consensus recommendation, and zero
otherwise.
Local
Indicator variable is one if the analyst is located within 100 kilometer of firm j’s
headquarter in fiscal year t, and zero otherwise.
Lose professional
connection
Indicator variable is one if the forecast is issued in the period following the
departure of a professionally connected insider, zero if issued in any of years prior to
the connected executive leaving the firm.
Gain professional
connection
Indicator variable is one if the forecast is issued in the period after the Professional
connections insider joins the firm lacking such connections, zero if issued before.
Post-losing
professional connection
Indicator variable is one if the forecast is issued by an analyst who used to be
connected to the covered firm through professional connections, and zero if issued
prior to professionally connected insider departure.
Pre-gaining
professional connection
Indicator variable is one if the forecast is issued by an analyst who will be connected
to the covered firm through professional primary (secondary) links in the future, and
zero otherwise.
Post-Reg FD
Indicator variable is one if the forecast is issued following the passage of Regulation
Fair Disclosure, zero otherwise.
CAR (0,2)
CRSP-VW index-adjusted abnormal returns over the [0, 2]-day window around the
announcement date of recommendation revision.
Size
The natural log of market capitalization of the covered firm (in $thousands) at the
end of the month prior to the earnings forecast.
BM
Book value of equity in the fiscal year prior to the earnings forecast divided by the
current market value of equity.
Past 6m ret
CRSP VW-index-adjusted buy-and hold abnormal returns (BHARs) over six months
prior to the announcement date of the recommendation revision.
Rec level
Inverted analyst recommendation code where 1 stands for strong sell and 5 stands
for strong buy.
50
∆Recommendation
The magnitude of the recommendation revision, calculated as the absolute value of
the change in recommendation level.
Analyst with related
experience
Indicator variable is one if the analyst’s prior industry work experience industry is
related to at least one of covered firms’ industry defined with 4 digit GICS, and zero
otherwise.
Brokerage size
The total number of analysts working at a given analyst i’s brokerage house.
Average PMAFE
The mean annual PMAFE of forecasts issued by analyst i at time t.
Ln Affiliated stocks
The number of affiliated stocks in an analyst’s portfolio, measured as the natural
logarithm of 1 plus the number of affiliations.
Ln Education
connections
The number of educationally connected stocks in an analyst’s portfolio, measured as
the natural logarithm of 1 plus the number of education connections.
Ln Optimistic stocks
The number of optimistic stocks in an analyst’s portfolio, measured as the natural
logarithm of 1 plus the number of optimistic recommendations.
Ln Local stocks
The number of local stocks in an analyst’s portfolio, measured as the natural
logarithm of 1 plus the number of local stocks.
Ln Professional
connections
The number of stocks in an analyst’s portfolio with professional connections,
measured as the natural logarithm of 1 plus the number of professionally connected
stocks.
%Broker share
Measured as relative firm-specific broker trading commission share as total
commissions allocated to broker i on firm j by fund k during period t scaled by total
broker commissions allocated for firm j by the same fund k at the same point in
time across all brokers.
Professional connection
(not same firm)
Indicator variable is one if the forecast is issued by an analyst connected to the
covered firm through professional primary links excluding his former employers,
and zero otherwise.
Cover same firm
Indicator variable is one if the forecast is issued by an analyst on his former
employer, and zero otherwise.
Professional connection
to CEO/CFO
Indicator variable is one if an analyst and coverage firm’s CEOs or CFOs share
professional connections, zero otherwise.
Professional connection
to non CEO/CFO
Indicator variable is one if an analyst is professionally connected to coverage firms’
insiders other than CEOs and CFOs, zero otherwise.
51
Professional connection- Indicator variable is one if an analyst has professional connections to the insiders in
firm and CS
coverage firms as well as their suppliers or customers, zero otherwise.
Professional connection- Indicator variable is one if an analyst has professional connections to only the
only Firm
coverage firm (no connections to insiders in their suppliers or customers), zero
otherwise.
Professional connection
to migrated manager
Indicator variable is one if an analyst and the migrating manager share professional
connections, zero otherwise.
Same industry
Indicator variable that equals 1 if the migrating insider’s origin firm and destination
firm are in the same industry defined with 2 digit SIC codes, zero otherwise.
Analyst overlap
Number of analysts covering the origin and the destination firms in the year prior to the
executive’s appointment in the destination firm.
No of analysts (old firm) Number of analysts covering the origin firm in the year prior to the migrating insider’s
appointment in the new destination firm.
No of analysts (new
firm)
Number of analysts covering the destination firm in the year prior to the migrating insider’s
appointment in the new destination firm.
Size diff (new-old)
The difference in the natural logarithm transformed firm size of the origin and the
destination firms as of the end of the year migrating insider’s appointment in the new
destination firm.
BM diff (new-old)
The difference in the origin and the destination firms’ book-to-market ratio
measured as of the end of the year migrating insider’s appointment in the new destination
firm.
Annual ret diff (newold)
The difference in the origin and the destination firms’ cumulative market-adjusted
return over the most recent calendar year preceding the migrating insider’s
appointment in the new destination firm.
Education connection to Indicator variable is one if an analyst and the migrating manager share connections,
migrated manager
zero otherwise.
Range
The ratio of the difference between the highest price-scaled analyst absolute forecast
error for firm j in year t and forecasting analyst i’ s price-scaled absolute forecast
error for the same firm j in year t. This difference is then scaled by the range of
price-scaled analyst absolute forecast errors for firm j for year t.
Bold forecast
Indicator variable is one if earnings forecast revision is above/below both the
consensus and the previous earnings forecast issued by the same analyst on the same
firm, zero otherwise.
52