Download Directors` of Research, subordinate analysts and

The boss knows best: Directors’ of Research, subordinate analysts and brokerage houses
a
Daniel Bradleya, Sinan Gokkaya b, and Xi Liuc
Department of Finance, University of South Florida, Tampa, FL 33620, 813.974.6326, [email protected]
b
Department of Finance, Ohio University, Athens, OH 45701, 740.593.0514, [email protected]
c
Department of Finance, Ohio University, Athens, OH 45701, 740.593.2040, [email protected]
Current version: April 18, 2016
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Abstract
Equity research departments are managed by Directors of Research (DORs) whom are often former
sell-side analysts. Extrapolating DORs’ industry experience based on their previous analyst
experience, we find analysts supervised by industry-aligned DORs provide superior earnings
forecasts, investment recommendations, and are more likely to be named all-stars with higher ranks.
Further, subordinates’ research elicits stronger capital market reactions. DOR-analyst industry
alignment benefits brokers by driving more trading commissions. Overall, our paper identifies a
unique channel whereby industry experience of top management filters through to individual
subordinates and consequently organizational performance.
Keywords: Directors of research, analyst earnings forecasts, analyst recommendations, broker
resources, trading commissions, industry experience, industry knowledge, management access
JEL classifications: G20, G23
______________________________________________________________________________
We thank Lauren Cohen, Michael Cook, Laura Field, David Haushalter, Russell Jame, Tomas
Jandik, Yelena Larkin, Wayne Lee, Alexey Malakhov, Peter Park, Sam Radnor, Jay Ritter, Hongping
Tan, Jared Williams, Geoff Warren, seminar participants at Florida State University,
Pennsylvania State University, St John’s University, University of Arkansas, York University and the
2015 World Finance Conference for many helpful comments. We are responsible for all errors.
1. Introduction
What is the value of top management to their organizations? The extent to which executives
provide meaningful services to their organizations and improve financial performance remains an
unsettled question (e.g. Westphal, 1998; Mian, 2001; Bertrand and Schoar, 2003; Helland and Sykuta,
2004; Adams, Almedia and Ferreira, 2005; Guner, Malmendier and Tate, 2008; Kale, Reis and
Venkateswaran, 2009; Giannetti, Liao and Yu, 2015; Dittmar and Duchin, 2015). To shed fresh light
on this important question, we exploit a novel sample of Directors of Research (DORs) at brokerage
houses and examine the impact of these top executives on the research quality of their subordinate
sell-side analyst workforce. Thus, unlike the existing literature that solely focuses on inherently
difficult to measure firm-level outcomes, we are able to assess the importance of top management in
a direct way by measuring their effect at the individual subordinate level. The sell-side research market
provides a nice laboratory for this research as individual analyst outputs are readily available as are
well-established performance metrics. Given the knowledge-intensive nature of the securities
industry, the quality of broker employees also directly affects performance outcomes (Groysberg and
Lee, 2010). 1
Equity research divisions are comprised of a team of sell-side analysts whom directly report
to a Director or Head of Research (DOR). 2 DORs are often former analysts that move up to this
management position. 3 While the job description of this leadership role likely varies by firm, for the
most part they oversee equity analysts and manage day-to-day operations of the division. A typical
DOR assists subordinate analysts with formulating investment ideas and is responsible for the
hiring/retention decisions of the broker. Ultimately, DORs are responsible for improving reputation
and annual trading commissions of their research departments.
In this paper, we conjecture that the industry expertise of DORs could represent an
important managerial skill thereby improving the research quality and labor market outcomes of
their subordinate analysts. For instance, industry experienced DORs may help subordinate analysts
A large body of academic literature indicates that sell-side analysts provide superior information services to financial
market participants through their ability to process financial information, provide research ideas, and increase the speed
through which firm and industry information is incorporated into coverage firms’ stock prices (Womack, 1996; Ivkovic
and Jegadeesh, 2004; Piotroski and Roulstone, 2004; Boni and Womack, 2006; Chan and Hameed, 2006; Bradley, Clarke,
Lee and Orthanalai, 2014).
2 Following discussions with several former and current research executives at several brokerage firms, DORs defined as
executives holding titles “Head of Equity,” “Head of Equity Research,” “Head of Research,” “Director of Equity,”
“Director of Equity Research,” and “Director of Research.”
3 Approximately 48% of DORs in our sample have former sell-side analyst experience in I/B/E/S with an average
length of forecasting experience of 9.5 years.
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better interpret the products, customers and opportunities within an industry that are critical for
understanding coverage firms’ operations, financial outcomes, strategies and decisions proposed by
management. Second, DORs may identify and provide superior resources in their industry
experience area, including, but not limited to, more (and/or better) research associates, training
and/or talent development programs, back office research and administrative support. In addition,
subordinate analysts may exploit the networks of industry experienced DORs with management in
their coverage firms, providing them with competitive advantages in the acquisition and/or
interpretation of value-relevant firm and/or industry specific information. 4 DORs’ industry
experience may also translate into improved organizational performance if buy-side clients reward
brokers through increased equity trading commission allocations to recognize the quality of research
provided by these DORs’ research departments. 5
Using a novel and comprehensive sample of DORs from Nelson Information’s Directory of
Investment Research, we hand-collect the employment background of each DOR in our sample. We
extrapolate industry experience from pre-DOR sell-side analyst experience and then match this with
subordinate analysts’ portfolios. 6 We first examine the potential benefits of industry experience
overlaps between DORs and supervised analysts by focusing on earnings forecast accuracy. In our
1989 to 2008 sample of 199,559 forecasts on 6,707 firms, we find that the earnings forecasts of
subordinate analysts working under DORs possessing the same industry forecasting experience are
3.2% more accurate than those of analysts lacking such DORs. The economic impact of industryaligned DORs on forecasting performance is similar to that of forecasting analysts’ all-star status.
On the other hand, analysts do not benefit from DORs’ exposure to industries unrelated to
In the investment research world, industry knowledge and management access are crucial. Institutional Investor’s (II)’s
survey results consistently demonstrate that institutional clients deem these traits very important. Sell-side analysts
likewise view industry knowledge and access to management as one of the most useful factors for their forecasting
performance and career outcomes (Brown et al., 2015).
5 Based on our discussions with several DORs and sell-side analysts at various brokers, there seems to be wide variation
in the management style of DORs with respect to interactions with subordinate analysts. One DOR we spoke with
required subordinate analysts to run their research through them to ensure forecast and/or recommendation changes are
well-founded, consistent with survey evidence in Brown et al. (2015). At the other extreme, another DOR gave analysts
much freedom with regard to forecasts and recommendations as long as they were in line with the business strategy of
the firm.
6 We acknowledge that there are likely other types of pre-DOR industry experience we omit that could also be related to
analyst research quality. Further, some DORs may be former sell-side analysts, but are not covered in the I/B/E/S
universe. However, if anything, not considering these other types of experience would introduce noise and bias results
against us. Nevertheless, we collect information on pre-DOR non-analyst work experience and control for it in our
analysis. The results remain robust.
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subordinates’ coverage firms. Our results remain robust to inclusion of controls for analyst effort,
various measures of broker-level industry specialization and resources. 7
Our baseline results may bring up concerns about potential endogenous matching between
industry-experienced DOR and brokerage houses, which can bias our results. In particular, there
may be unobserved broker and DOR heterogeneities simultaneously explaining the assignment of
industry experienced DORs to brokers as well as the benefits accrued to subordinate analysts from
access to such DORs. 8 First, we consider a dynamic setting and examine two natural experiments
where there is a change in the availability of industry experienced DORs stemming from executive
movements in and out of research departments. That is, we identify 582 (549) cases in which a
subordinate analyst gains (losses) access to a DOR arising from changes in employment status of
research executives. Subordinates’ relative earnings forecast accuracy on the same coverage firm
improves (worsens) by 3.3% (4.9%) following a DOR with the same industry experience joins
(departs from) a broker. In a more restrictive setting, we focus only on DOR losses emanating from
retirements and deaths and find qualitatively similar results (e.g. Jenter, Lewellen, Warner, 2011;
Pan, Wang and Weisbach, 2015). We also employ a differences-in-difference (DID) framework and
compare changes in analyst forecast performance following changes in broker management to an
industry experienced DOR from an inexperienced DOR with a control sample of brokers associated
with inexperienced to inexperienced DOR transitions. Finally, we use a propensity score procedure
and match industry experienced DORs’ brokers to those lacking such executives. Control and
matched brokers are similar with the exception of DORs’ past professional experience. Our results
continue to indicate that industry experience possessed by DORs transform into improved research
quality for subordinate analysts.
We next seek to better understand the potential mechanisms through which industry
experienced DORs affect the research performance of supervised analysts. First, we examine the
extent to which analysts benefit from industry contacts of their experienced DORs. To do so, we
distinguish between DORs possessing social and professional connections to management in
Further investigation reveals that the quality of industry experience possessed by DORs is also important for
subordinate analyst performance. For instance, analyst performance is positively related to DORs’ own prior forecast
accuracy, length of forecasting experience, coverage portfolio size, and all-star status during their analyst years. These
results suggest that the higher quality of DOR industry experience, the more value they create for their subordinate
analysts. We also find that DORs’ industry experience provide greater benefits to subordinate analysts who are more
junior, lack pre-analyst industry work experience and are non-all-stars.
8 We address this concern, at least partially, through exploiting within-brokerage house variation by including brokerage
house and DOR fixed effects.
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subordinate analysts’ coverage firms and find economically stronger effects for analysts working
under connected DORs (Cohen et al., 2010; Bradley, Gokkaya and Liu, 2015b). However, DORs
without such connections continue to provide significant benefits to subordinate analysts, suggesting
that improved access to management through DOR industry contacts is unlikely the only
explanation for our results. Next, we attempt to differentiate between industry knowledge provided
by DORs on analyst research from more broker resources as a potential mechanism for improved
subordinate performance. We acknowledge that these two channels are not likely mutually exclusive
and thus we cannot completely rule one out. However, for cases where there are no significant
investments in industry-aligned broker resources since DORs have assumed their roles, resource
allocation would unlikely be a complete explanation for our results. In these cases, we continue to
find that DOR-industry alignment results in better subordinate analyst performance. 9
Our next set of tests examines investment recommendations. Comparing the investment
performance of portfolios constructed based on subordinate analysts’ recommendations, our
findings suggest that analysts produce significantly more profitable buy and sell recommendations
on firms for which they have access to their DORs’ industry experience. A calendar time portfolio
formed based on DOR industry experience on coverage stocks translates into Daniel, Grinblatt,
Titman and Wermers (1997) (DGTW) characteristic-adjusted monthly returns that are 0.35%
(0.34%) higher (lower) for buy (sell) recommendations.
We also investigate how information flows from DOR industry experience are reflected in
capital markets by focusing on short-term price impacts associated with subordinate analysts’
forecast revisions. Analysts with access to DORs possessing overlapping industry exposure evoke
more pronounced market reactions with their earnings revisions. For instance, such analysts elicit
0.29% (0.32%) higher (lower) abnormal market reactions with their upgraded (downgraded) earnings
revisions compared to analysts lacking industry experienced DORs. The results suggest market
participants assign greater importance to research produced by these subordinate analysts leading to
greater price reactions.
After documenting that DOR-industry alignment aids subordinate analysts’ performance and
translates into stronger information transfers to capital markets, we turn our attention to labor
market outcomes. We conjecture that DORs improve the likelihood that industry alignment propels
In the context of CEO turnover in conglomerate firms where the CEO has expertise in one division, Xuan (2009)
finds industry specialization negatively impacts resource allocation to this division. Instead, capital allocation is increased
to other divisions as a mechanism to enhance cooperation within these non-expertise areas. Thus, within our context it
is also plausible that industry alignment with the DOR may actually stifle resources to this industry.
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subordinate analysts’ careers by increasing the probability they get selected as Institutional Investor allstars. We find that a one standard deviation increase in the log number of coverage stocks where
DORs possess overlapping industry experience increases the likelihood of becoming an all-star by
41.8% for subordinate analysts. To put this in perspective, a one standard deviation increase in
analyst general forecasting experience increases the likelihood of being selected to the all-star team
by 9%. We also find such analysts obtain higher ranks within the all-star roster. 10 Conditional on
making the all-star team, DOR-industry alignment significantly reduces the time elapsed between
entering the analyst industry and obtaining star status. In sum, these results suggest that subordinate
analysts have strong monetary incentives to benefit from their bosses’ industry experience in light of
the strong correlation between analyst compensation and all-star rankings (Groysberg, Healy and
Maber, 2011).
Finally, we shift our attention to organizational-level benefits to equity research departments
from DOR industry experience. In particular, we examine if buy-side clients reward brokers
supervised by industry experienced DORs through increased equity trading commission revenues
using institutional level equity transaction information from Ancerno Ltd (formerly Abel Noser). We
find broker commission market shares are 0.16% higher in industries in which DORs possess
relevant industry experience relative to brokers lacking such DORs. In dollar terms, brokers
generate 7.7% relatively higher commissions in DORs’ background experience industries.
.
Irrelevant industry experience of DORs, on the other hand, does not translate into higher relative
commission market shares and dollar commissions.
Our paper cuts across several streams of literature. First, it adds to the growing strand of
research in finance and economics that attempts to estimate the impact of top managerial talent on
firm performance and outcomes (e.g. Bertrand and Schoar, 2003; Perez-Gonzalez, 2006;
Malmeinder and Tate, 2009; Mironov, 2014). Second, we contribute to the vast body of academic
research investigating the sources of performance differences across sell-side analysts (e.g. O’Brien,
1988; Stickel, 1995; Clement, 1999; Jacob, Lys and Neale, 1999; Clement and Tse, 2003; Malloy,
2005; Kadan, Madureira, Wang and Zach, 2012; Brown, et al., 2015). In-house research directors
represent a unique and economically important factor contributing to subordinate sell-side analyst
performance and labor market outcomes.
A legitimate concern is that DORs may attract all-star analysts in their former industries, leading to a significant
positive association between DOR experience and subordinate analyst career outcomes. Focusing only on a subsample
of analysts hired prior to experienced DOR’s transition into the brokerage house, we continue to find qualitatively similar
results.
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Third, we extend the literature focusing on variations of resources across research firms, e.g.
in-house peers and macroeconomists (Jacob, Lys and Neale, 1999; Hugon, Kumar and Lin, 2014)
and affiliated asset management and commercial lending divisions (Irvine, Simko and Nathan, 2004;
Chen and Martin, 2011). Fourth, we add to an emerging literature that highlights the importance of
industry experience in various settings, e.g. buy-side analysts (Brown et al., 2014), mutual funds
(Kacpercyzk, Sialm and Zheng, 2005; Cici, Trapp, Goricke and Kempf, 2014; Kempf, Manconi and
Spalt, 2014), institutional investors (Bushee and Goodman, 2007), investment banks (Stomper, 2006;
Liu and Ritter, 2011) and corporate management (Dass et al., 2013; Custódio and Mertzer, 2013;
Masulis et al., 2014).
Finally, our evidence on the benefits to research departments advances our understanding of
the functioning of investment research industry and also how buy-side clients reward brokers for
sell-side research services. As such, we contribute to the flourishing academic literature investigating
the factors critical to attracting order flow and commissions from institutional investors (e.g.
Conrad, Johnson and Wahal, 2001; Ellis, Michaely and O’Hara, 2002; Bushee and Miller, 2012;
Green, Jame, Markov, Subasi, 2014b, among others).
The remainder of the paper proceeds as follows. Section 2 describes the data and provides
descriptive statistics. Section 3 provides analysis on earnings forecast performance and section 4
presents results on the stock recommendations. Section 5 provides analysis on stock market impact
of DOR experience, section 6 presents evidence on analyst career outcomes. Section 7 presents
benefits to organizational level outcomes. Section 8 illustrates robustness tests and additional
analyses, and section 9 concludes.
2. Data and descriptive statistics
Our data come from several sources. Analyst data are from Institutional Broker Estimate
System (I/B/E/S). We retain only equity analysts who provided at least one annual earnings forecast
on US domestic stocks. We then merge this sample with stock returns and accounting data from
CRSP-COMPUSTAT. The next step involves manually matching the names of unique brokers with
those reported in Nelson’s Information Directory of Investment Research (NIDIR). NIDIR is a
directory of over 900 domestic and international research firms that list key research executives and
analysts. For each of the 252 of 343 unique brokerage houses hand-matched with NIDIR, we collect
information on the names of domestic equity research executives over 1989 and 2008. Per
discussions with former and current DORs at several brokerage firms, we obtain the names of
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executives holding titles “Head of Equity,” “Head of Equity Research,” “Head of Research,”
“Director of Equity,” “Director of Equity Research,” and “Director of Research.”
For each DOR, we obtain information on their former coverage portfolio during their
analyst years from I/B/E/S. Next, we extrapolate DOR industry experience based on their former
sell-side coverage portfolio using the Global Industry Classification System (GICS). We then match
this experience into “same industry” and “other industry” for the subordinate analysts’ portfolios
working under them. For instance, if a DORs former analyst experience is in retail (GICS 2550), we
then classify any analysts following retail firms (GICS 2550) at this brokerage house as “DOR with
same industry,” else “DOR with other industry.” Appendix B provides a detailed description of the
data collection and screening process.
Of the 782 unique DORs in our sample, 373 or 47.7% have past sell-side analyst experience.
Conditional on being a former sell-side analyst, the average length of DOR analyst experience is 9.5
years relative to the average experience of 6.4 years for subordinate sell-side analysts in our sample.
This suggests that the average DOR is more experienced than the average subordinate analyst before
transitioning into the DOR role. On average, they were good analysts—77.5% of DORs had relative
forecast performance above the average analyst and forecast accuracy 11.2% better than their peers
covering the same firms. Their coverage portfolios are roughly the same size as other analysts, but
19.2% of industry experienced DORs obtained all-star status during their pre-DOR employment as
sell-side analysts compared to 11.6% for subordinate analysts in our sample. Panel A of Table I
provides descriptive statistics of the distribution of earnings forecasts across time periods.
Subordinate analysts in our sample issue a total 199,559 annual earnings forecasts on 6,707 unique
firms. 41% of earnings forecasts are issued by analysts employed at brokers where the DOR had
sell-side analyst experience. Decomposing executive forecasting experience based on the relevance
to the industry of coverage firms followed by subordinate analysts, we find that 12.9% of earnings
forecasts are made on firms in which research executives have the same GICS industry experience.
The percentage of forecasts with experienced DORs rises over time. Our sample spans 72% of the
total analysts and 96% (55%) of the total firm market value (earnings forecasts) covered in the
I/B/E/S / CRSP-COMPUSTAT merged sample.
****Insert Table I here****
Panel B reports analogous summary statistics on stock recommendations. Similar to those
reported for earnings forecasts, 41.7% of recommendations are issued by analysts with access to an
experienced DOR at their brokers. Conditional on having these executives, we find that 14% of
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revisions are made on firms where DORs have the same past GICS forecasting experience. All other
statistics are roughly similar between Panel A and Panel B of Table I.
3. DOR industry experience and subordinate analysts’ earnings forecasts
We start our analysis by examining the relation between DORs and the accuracy of earnings
estimates. We hypothesize that in-house executives’ industry experience may improve the research
quality of their subordinate analysts, leading to better earnings forecasts compared to those issued by
analysts lacking such DORs.
3.1 Baseline models
To test our hypothesis and facilitate comparisons with a large literature examining analyst
earnings forecast performance, our main performance measure for earnings accuracy is the
commonly adopted proportional mean absolute forecast error (PMAFE i,j,t) (e.g. Clement, 1999;
Malloy, 2005; Bae, Stulz and Tan, 2008; Green, Jame, Markov and Subasi, 2014a; etc.). 11 PMAFE i,j,t is
defined as the difference between the absolute forecast error (AFE i,j,t) for analyst i for firm j in time t
and the mean absolute forecast error for firm j at time t. This difference is then divided by the mean
absolute forecast error for firm j at time t to reduce heteroskedasticity. As constructed, PMAFE i,j,t
implicitly controls for intertemporal variations in task difficulty, an important feature for crosssectional and time-series comparisons (Jacob, Lys and Neale, 1999). It is inversely related to
performance.
We first estimate multivariate OLS regressions to measure the incremental impact research
executives have on analyst research quality while controlling for characteristics that have been shown
to impact earnings forecasting performance. We have three key variables of interest. The first is
DOR with industry experience, which is a binary indicator variable equal to one if the DOR previously
worked as an analyst, zero otherwise. The other two variables capture the nature of industry
experience possessed by the DOR. DOR with same industry experience (DOR with other industry experience)
are indicator variables equal to one if the broker’s analyst covers a firm in the same (different)
industry as their DOR’s former forecasting experience based on 4-digit GICS industry codes, zero
otherwise.
We also experiment with Hong and Kubik’s (2003) relative accuracy score measure and absolute forecast errors (AFE)
to ensure our results are not sensitive to the measurement of analyst forecast performance. Discussion of this analysis is
provided in section 8.3.
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We control for analyst’s forecasting ability with general (DGexp) and firm specific forecasting
experience (DFexp). DGexp is computed as the total number of years that analyst i forecasted in
I/B/E/S (Gexp) minus the average general forecasting experience of analysts following firm j at time
t. Similarly, DFexp is the total number of years analyst i has been forecasting on firm j (Fexp) minus
the average number of years analysts following firm j has provided forecasts. We consider forecast
horizon and include a covariate for DAge computed as the age of analyst i’s forecast (Age) minus the
average age of forecasts issued by analysts following the same firm j at time t. Consistent with
Clement (1999) and Clement and Tse (2005), age is defined as the age of forecasts in days at the
minimum forecast horizon date. Portfolio complexity is captured by the mean adjusted portfolio size
(DPortsize) and portfolio complexity (DGics) of the forecasting analyst, computed as the number of
firms and industries followed by analyst i for firm j at time t minus the average number of firms and
industries followed by other analysts following firm j at time t, respectively. Prior academic work
shows that more reputable analysts, those working for larger brokerage houses, and those that have
an underwriting relationship with the firm may produce better forecasts (Clement, 1999; Malloy,
2005). Therefore, we control for all-star status (All-star), forecasts issued at top decile brokerage
houses (DTop10) and affiliated underwriters (Affiliated).
There may be unobserved time invariant broker heterogeneities that simultaneously explain
the matching between brokers and DORs as well as the benefits accrued to supervised analysts from
industry experienced DORs. Therefore, we mitigate this potential selection concern by exploiting
within-brokerage house variation and including broker fixed effects throughout our analyses.
Reported standard errors are heteroskedastic-robust. Formally, our model is as follows:
PMAFEi,j,t= β1(DOR with industry experience / DOR with same industry experience) + β2 (DOR with
other industry experience) + β3(DAge) + β4(DGexp) + β5(DFexp) + β6(DPortsize) + + β7 (DTop10)
β8 (DGics) + β9(All-star) + β10(Affiliated) +Broker Fixed Effects + ε
(1)
****Insert Table II here****
Table II reports the regression results. In model 1, we find that analysts that work for DORs
possessing former analyst experience (DOR with industry experience) issue significantly better forecasts
compared to analysts that work for non-analyst DORs. In model 2, we separate the DOR’s past
forecasting experience into same industry experience and other industry experience. We find that
forecasts issued by analysts having access to industry-aligned DORs are 3.8% more accurate than
those by analysts lacking access to such DORs. This suggests DORs’ relevant industry experience
helps their subordinate analysts’ forecasts. On the other hand, forecasts issued by analysts in
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industries unrelated to DORs’ industry experience are no different than those issued by analysts
lacking access to DORs with past industry experience. Looking at the coefficient estimates for our
controls, the economic impact of similar industry DOR experience on earnings accuracy is quite
large. To put this result in perspective, all-star analysts, a widely known select group of skilled
analysts, produce 3.0% better forecasts than non-star analysts. Other controls are generally
consistent with prior work. For instance, forecast accuracy gets better with analyst general and firm
specific forecasting experience, less complex portfolios, forecast age and broker size.
Jacob, Lys and Neale (1999) argue that industry specialization of brokers may allow analysts
to develop a better understanding of coverage firms within that industry, resulting in more accurate
forecasts. To ensure that DOR industry experience is not simply capturing broker-level industry
specialization, model 3 includes a set of controls for it. Broker industry expertise is measured by the
percentage of analysts following company i’s GICS industry j from the same broker (Broker Ind.
specialization). In model 4, we also control for changes in broker industry specialization (Jacob, Lys and
Neale, 1999). Specifically, we control for the proportion of new analysts following industry j that
recently joined the broker relative to the number of total analysts following industry j during the
calendar year the forecast was issued (Pin). This covariate represents an increase in broker
specialization in a particular industry. Model 4 likewise controls for a decrease in broker expertise as
captured by the proportion of analysts who were covering industry j, but left the broker during the
forecast calendar year, scaled by the total number of analysts following industry j at the same broker
(Pout). Model 3 and 4 document that these variables are signed as expected, however only Pin is
statistically significant. Again, it has no significant impact on the association between industry
experience of DORs and subordinate analyst performance.
Another reasonable concern with our analysis is that DORs may simply provide more
effective monitoring on coverage industries related to their professional experience partially
explaining the results documented in Table 2. To mitigate this concern, model 5 controls for the
number of forecasts (Ln (Freq)) as a proxy for analyst effort (Jacob, Lys and Neale, 1999). We
continue to find qualitative and quantitatively similar results. 12
3.2. Endogenous DOR-brokerage house matching and subordinate analyst performance
We also investigate additional controls for analyst effort such as portfolio turnover and coverage terminations (Chen
and Matsumoto, 2006). The results are robust.
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In this section, we consider several alternative specifications to mitigate remaining concerns
related to potentially endogenous DOR-broker matching biasing our results. First, we consider the
possibility that DOR industry experience effect on subordinate analyst performance is driven by
unobserved time-invariant DOR characteristics that may be related to endogenous matching
between DORs and brokerage houses. In order to address this concern, model 1 of Table III
includes DOR fixed effects along with broker fixed effects. For the sake of brevity, we only report
coefficients of interest in this table. We continue to find similar results.
****Insert Table III here****
Second, we explore a dynamic setting created when industry experienced DORs transfer into
and out of brokerage firms. Specifically, we identify 582 cases when an analyst gains access to a
DOR with the same industry experience as a result of a new DOR joining a brokerage lacking such
an experienced executive. Likewise, we identify 549 cases where an analyst loses access to the
industry experienced DORs following the departure of such an executive from the broker. We then
compare the earnings forecasts issued by the pre-DOR hired analyst for the same set of firms over
three years before and after the year of change. 13 In this specification, Gaining (Losing) DOR with same
industry experience equals one if the forecast is issued by the subordinate analyst in the years following
the corresponding DOR moving in (out of) the broker, zero if issued in the years before. The
econometric model is similar to that in Table II, but the main variable of interest is substituted with
gaining/losing industry experienced DORs.
Model 2 of Table III suggests that the earnings forecast accuracy of analysts improves by
3.3% after an executive with the same industry experience joins a brokerage house lacking such
industry experience. Likewise, model 3 shows that earnings forecasts following the departure of
industry experienced DORs are 4.9% less accurate when an analyst loses their DOR-industry expert.
In a more restrictive setting, model 4 focuses only on DOR losses resulting from executive
retirements (age 65 and over) and deaths unrelated to broker mergers or acquisitions to further
eliminate any potential concerns regarding the nature of DOR job changes (e.g. Jenter, Lewellen,
and Warner, 2011; Pan, Wang and Weisbach, 2015). The results are similar to model 3. In models 5
and 6, we consider the changes in earnings forecast quality of subordinate analysts experiencing
changes only in the availability of DORs with other industry experience. We find that gaining/losing
We exclude the event year in which the DOR leaves (joins) the brokerage house since we cannot identify whether the
employment change occurs before or after the earnings forecast in the event year. In this analysis, we focus only on
analysts hired before the DOR joins the firm. We also require analysts to issue at least one earnings forecast in the postand pre-event year to capture the impact of DOR job changes on performance of subordinate analysts.
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DORs with other industry experience does not have a significant impact on supervised analysts’
performance.
We next employ a differences-in-difference (DID) framework akin to Huang and Kisgen
(2013) and compare earnings forecast performance of subordinate analysts experiencing changes in
the availability of DOR experience to that of a control sample of analysts associated with
inexperienced to inexperienced DOR transitions at their research departments. The earnings
forecast accuracy of analysts increases (deteriorates) by 5.3% (4.3%) following the hire (departure) of
industry experienced DORs compared to forecasts issued on the same set of firms by analysts
employed at brokers going through top management transitions involving only inexperienced DORs.
Overall, these tests based on DOR movements are consistent with the baseline results.
Finally, we consider propensity score matching (Xuan, 2009; Huang and Kisgen, 2013;
Custódio and Metzger, 2014; Pan, Wang and Weisbach, 2015). First, we estimate a probit regression
of DOR same industry experience on observable broker characteristics including employed analysts’
average general and firm specific forecasting experience, portfolio size, number of industries
covered, all-star status and broker industry resources. Second, we match brokers using a nearestneighbor matching estimator with replacement using the propensity scores from this probit model.
This step allows us to match brokers supervised by industry experienced DORs to those managed
by similar DORs with the exception of their industry work experience (Abadie et al., 2004). We then
run regressions on the control and matched samples to examine the impact of DORs industry
experience on subordinate analysts’ forecasting performance. Model 9 of Table III presents these
results. The results again support our baseline estimations. Analysts benefit from having access to
DORs possessing overlapping industry experience in their coverage portfolios whereas analysts
working under DORs without analyst experience or only with unrelated industry forecasting
experience provides no significant benefits. In sum, analyses from this section suggest that any
potential endogeneity associated with DOR-broker matching is an unlikely concern for our analysis.
3.3. DOR industry experience mechanisms
Having documented the robustness of our main results, we next seek to understand the
mechanisms through which DORs’ previous industry experience transforms into superior
forecasting skills for their subordinate analyst workforce. One plausible channel whereby analysts
may benefit from industry-experienced DORs is through their social networks and human capital in
the industry. For instance, if analysts tap into DORs’ industry contacts, they may obtain value
12
relevant private and/or public information on the coverage firms and/or industries. To test whether
executive networks represent the dominant source of superior performance, we distinguish between
DORs with alumni and professional connections to firms covered by subordinate analysts (Cohen et
al., 2010; Bradley, Gokkaya and Liu, 2015b). If superior access to management through DOR
industry networks is the main channel for improved analyst performance, then we would expect the
results to be concentrated for analysts supervised by DORs possessing such connections.
****Insert Table IV here****
Model 1 of Table IV presents these results. The coefficient on connected DORs is stronger
(=-5.62, t-stats=-4.70), but DORs without connections continue to aid their subordinate analysts
suggesting exploitation of DOR industry contacts is an unlikely sole channel for our results. Next,
we attempt to distinguish between industry knowledge provided by DORs to their subordinate
analysts from better brokerage departmental resources in the experience industry as the dominant
mechanism for superior analyst performance. We acknowledge benefits accrued to analysts from
industry experienced DORs through these two mechanisms are likely not mutually exclusive and we
cannot completely rule one out. Nevertheless, better resource allocation would be an unlikely
complete explanation of our results for brokers where there is no significant increase in industryaligned resources coinciding with the time frame when DORs have assumed their roles. To test this
conjecture, Columns 2 through 4 of Table 4 stratifies the variable DOR with same industry experience at
the median based on changes in industry-aligned broker resources during DORs’ tenure (i.e. industry
broker specialization, Pin, Pout since DOR appointment). The marginal impact of DOR same industry
experience is lower, but continues to be economically and statistically significant for brokers with no
significant resource investments (e.g. change in Broker Specialization Low, change in Pin Low, change in Pout
High). Thus, identification of and investment in superior departmental resources related to DORs’
experience industries is also unlikely the only explanation of our results. Rather, mentoring and
aiding subordinate analysts interpret industry information appears to be a more plausible mechanism
for the benefits gained by subordinate analysts. This result also echoes our discussions with DORs
and analysts.
4. DOR industry experience and subordinate analysts’ investment recommendations
Brown et al. (2015)’s survey among sell-side analysts indicates that analysts themselves
consider industry knowledge and management access among important factors for their stock
13
recommendations. Thus, akin to earnings forecasts, supervised analysts can also benefit from the
industry experience of DORs and issue more profitable stock recommendations.
We examine the investment value of subordinate analyst recommendations through
construction of buy and sell recommendations portfolios using a standard calendar time portfolio
approach. Specifically, we include stocks upgraded (downgraded) relative to the prior outstanding
recommendation as well as stocks with Strong Buy (Strong Sell) and Buy (Sell) recommendation
reiterations/initiations/resumes in the Buy (Sell) recommendation portfolio (e.g. Barber, Lehavy,
McNichols, and Trueman, 2006). These portfolios are then rebalanced daily when analysts revise
their recommendations or when the recommendation becomes stale (no change for 1 year). We use
Daniel, Grinblatt, Titman and Wermers (1997) (DGTW) characteristic-adjusted returns to measure
abnormal investment returns (Cohen, Frazzini and Malloy, 2010). This method has the added
advantage of mimicking an investment strategy from following analyst recommendations as it
incorporates directional analyst advice as well as holding period returns. We regress DGTW returns
on indicator variables related to the type of DOR experience (same vs. other industry experience),
firm, analyst and broker characteristics as well as broker and year fixed effects. These regressions are
estimated daily and converted into monthly coefficients for ease of interpretation. Standard errors
are heteroskedasticity consistent. The econometric model is as follows:
Buy/Sell Recommendations (DGTW)i,j,t = β1(DOR with industry experience / DOR with same industry
experience) + β2 (DOR with other industry experience) + β3 (Size) + β4(BM)+ β5 (Gexp) +
β6(Fexp) + β7(Port size) + β8 (Top 10) + β9(Port Gics) + β10 (All-star) + β11 (Affiliated) + β12
(Past 6m ret) + β13 (Broker Ind. specialization) + β14 (Pin) + β15(Pout)+ β16 (Ln (Freq) +Broker
Fixed Effects+ Year Fixed Effects+ ε
(2)
****Insert Table V here****
Models 1 through 5 of Table V report results for buy recommendation portfolios. The
immediate takeaway from Model 2 is that analysts supervised by DORs with relevant industry
experience produce buy recommendations with higher abnormal returns. Buy recommendation
portfolio returns are 0.35% higher for analysts managed by DORs with the same industry
experience. This finding remains robust to inclusion of controls on industry expertise of brokers (i.e.
Broker Ind. specialization, Pin, Pout) and analyst effort in models 3 through 5. On the other hand, inhouse DOR industry experience not aligned with coverage firms does not translate to significantly
more profitable buy recommendations. Models 6 through 10 of Table V provide regressions for sell
recommendation portfolios. Similar to previous results, model 7 indicates that sell recommendations
from analysts at brokers with industry-aligned DORs are also associated with -0.34% lower returns
14
compared to those by analysts lacking access to industry experienced DORs. 14 In sum, this section
provides evidence that investment recommendation performance of subordinate analysts likewise
benefit from industry experience of their DORs.
5. DORs industry experience and price impact of subordinate analysts
This section investigates the information flows to capital markets from subordinate analysts’
research. We focus on short term abnormal market reactions to earnings revisions and examine if
stock market participants place greater weight on research of analysts supervised by industryexperienced DORs.
We follow prior work (e.g. Gleason and Lee, 2003; Malloy, 2005) and consider the direction
and magnitude of revisions to earnings forecasts. Upward (downward) revisions are defined as those
above (below) the same analyst’s previous earnings forecast and the consensus forecast on the same
firm. Abnormal price impacts are measured three days around EPS revisions [0,+2] with CRSP
value-weighted index-adjusted returns. The econometric model is as follows:
CAR(0,2)i,j,t =β1(DOR with industry experience / DOR with same industry experience) + β2 (DOR with other
industry experience) + β3 (EPS change) +β4 (DAGE) + β5 (DGExp) + β6 (DFExp) +
β7(DPortsize) + β8 (DTop10) + β9(DGics) + β10 (All-star) + β11 (Affiliated) + β12 (Broker Ind.
specialization) + β13 (Pin) + β14(Pout)+ β15 (Ln (Freq)) +Broker Fixed Effects+ Year Fixed
Effects+ ε (3)
***Insert Table VI here***
Models 1 through 5 of Table VI report the regression results for earnings upgrades. The
results suggest that short-term price reactions to revisions of analysts supervised by industry
experienced DORs are more prominent relative to those for peers lacking such DORs. For example,
model 2 of Table VI indicates upward EPS revisions evoke 0.34% more pronounced abnormal
market reactions if the revising analyst has access to a DOR with overlapping industry experience.
Alternatively, in-house DOR industry experience not aligned with coverage firms does not translate
to stronger market reactions. Other control variables are signed as expected. For example, we find
that all-star and top 10 broker analysts generate higher market reactions with their earnings forecasts.
Similar to section 3.2, we consider several alternative specifications to mitigate concerns related to potentially
endogenous DOR-broker matching biasing our results on stock recommendation portfolios. These include DOR fixed
effects, natural experiments resulting from DOR job changes (overall and retirements/deaths), DID estimations and
propensity score matching estimator methods. The results are robust and available upon request.
14
15
Models 6 through 10 replicate the analogous analyses for downward EPS revisions. The
results are similar. For instance, 7 illustrates that CARs to downgrade earnings forecast revisions are
0.40% lower when issued by an analyst managed by DORs with relevant industry experience. Other
industry experience of DORs is, again, not related to stronger price reactions, consistent with the
evidence throughout the paper. 15 In sum, our results suggest that DOR-subordinate analyst industry
alignments also transform into more prominent information flows into the stock markets through
subordinates’ research.
6. DORs industry experience and subordinate analyst career outcomes
In this section, we examine the extent to which DORs affect subordinate analysts’ labor
market outcomes. As discussed previously, buy-side institutions participating in Institutional Investor’s
annual survey deem industry knowledge and management access as among the most important
qualities an analyst can possess. Therefore, we hypothesize that analysts with access to industry
experienced DORs have a higher likelihood of being named to the all-star team roster. Given the
impact of all-star status for the level of analyst compensation (Groysberg, Healy and Maber, 2011),
this would also emphasize the monetary incentives subordinate analysts have in benefitting from the
industry experience of their bosses.
To test this conjecture, we first estimate logistic regressions where the dependent variable is
a binary variable that equals one if the analyst made the all-star team in year t, zero otherwise. The
primary variable of interest is the number of stocks in an analyst’s portfolio where an in-house DOR
possessing the same GICS industry experience is available (Ln stocks with DOR same industry
experience). To eliminate concerns on reverse causality, we lag all variables by one year. The model
includes a host of firm and analyst controls and also broker and year fixed effects. Formally, the
model:
Logit(All-star=1)i,t= β1 Ln (stocks with DOR industry experience / DOR same industry experience/ DOR same
(exclude new hires))+ β2 Ln (stock with DOR other industry experience) + β3 (Gexp) +
β4(Port Size) + β5 (Port Gics) +β6 (Brokerage size) + β7 (Average PMAFE) + β8 (Average
Buy Rec returns) + β9 (Average Sell Rec returns) + β10(Average Firm Size) + β11 (All-star
(prior year)) + β12 (Average Ln (Freq))+ β13 (Average Broker Ind. Specialization) + β14
(Average Pin)+ β15 (Average Pout)+Broker Fixed Effects+ Year Fixed Effects+ ε
(4)
Untabulated analyses follow empirical specifications introduced in Section 3.2 to mitigate endogeneity concerns. The
results are robust and available upon request.
15
16
***Insert Table VII here***
Model 1 of VII indicates that an analyst is more likely to become an all-star if her DOR has
industry experience. Model 2 shows that this effect only holds if the analyst’s stocks are aligned with
the industry experience of DORs. Specifically, a one standard deviation increase in the natural
logarithm of the number of coverage stocks with same DOR-industry experience increases the odds
of becoming an all-star analyst by 41.8%. In model 3, we control for other broker-level industry
resources and continue to find robust results.
A potential concern with this analysis is that industry experienced DORs may simply attract
all-star analysts to their brokers, especially in their background industries. Considering the likelihood
of becoming an all-star analyst in year t is strongly correlated with all-star status in year t-1, these new
hires may lead to a positive association between our key variable and all-star status in year t. To
alleviate this concern, in model 4, we eliminate all new analysts hired by experienced DORs and
focus only on a subsample of analysts who were hired prior to industry experienced DORs began
their term at the broker. Our results remain intact.
In model 5, we also investigate the ranking of the all-star team. Groysberg, Healy and Maber
(2011) find a significant step in analyst compensation between first, second, third and runner up
positions. We re-estimate equation (3) with an OLS model where the dependent variable ranges
from 1 to 4 based on the analyst’s rank in II polls, where 4 represents the highest rank. Analysts
supervised by industry experienced DORs also obtain higher all-star ranks. In model 6, we focus
only on a subsample of analysts who made the all-star roster and consider the years elapsed since
such analysts got into the equity research profession and the first time they made the team. The
results indicate that supervised analysts also make the team faster if they have access to the industry
experience of their bosses. The results in this section paint a very clear picture that brokerage house
DORs have economically and statistically significant effects on subordinate analysts’ labor market
outcomes.
7. Industry-aligned DORs and brokerage performance
In this section, we shift our attention to the organizational-level benefits to brokerage houses
that employ industry experienced DORs. In particular, we examine whether buy-side clients reward
brokers through increased equity trading commission allocations to recognize the quality of research
provided by industry experienced DORs’ research departments. To test the impact of DORs on
broker revenues, we obtain equity trading commission information from Ancerno Ltd. over 1999
17
and 2008 and measure relative industry-specific broker equity trading commission market share as
commissions allocated to broker j in industry k during time t relative to total commissions in
industry k. This relative measure has the added advantage that it removes variation in total dollar
commissions as potential source of bias (Green, Jame, Markov, Subasi, 2014b). We also consider the
level of total dollar commissions as measured by ln(1+dollar commissions) in industry k for broker j
during time t.
We estimate OLS regressions of these broker revenue measures after controlling for a set of
potential analyst and broker specific characteristics that may also be related to commissions. These
controls include average performance (Average PMAFE, Average Sell Rec Return, Average Buy Rec
Return), general experience (Average Gexp), portfolio size (Average Portsize), and forecast frequency
(Average Ln (Freq)) of employed analysts in industry k (e.g. Hong, Kubik and Solomon, 2003;
Juergens and Lindsey, 2009; Groysberg, Healy and Maber, 2011). We also consider the number of
all-star analysts (No of All-stars)) as suggested by Clarke, Khorana, Patel and Rau (2007) and broker
characteristics such as broker size (Broker size), broker industry specialization (Broker Ind.
Specialization, Pin, Pout), lagged relative broker market share (Lag (Broker share) (Goldstein et al., 2009)
. Broker and year fixed effects are included and heteroskedastic-robust standard errors are reported.
All control variables in addition to Lag (Broker share) are lagged by one year to alleviate concerns of
reverse causality. Our main variable of interest is DOR same industry experience, that equals one if DOR
possesses relevant industry work experience, zero otherwise. Our econometric model is as follows:
Broker Market Share j,k,t= β1 DOR Same industry experience+ β2 DOR Other industry experience + β3 Average
PMAFE +β4 Average GExp + β5 Average Port Size + β6 Average Ln (Freq) +β7 No of
All-star analysts+ β8 (Broker Ind. Specialization) + β9 Pin+ β10 Pout+ β11 Top 10
(Commissions)+ β12 No of SEOs + β13 No of Analysts+ β14 (Average Sell Portfolio Return)
+ β15 (Average Buy Portfolio Return) + β16 Broker Market Share (lagged)+ Broker Fixed
Effects+ Year Fixed Effects+ ε
(5)
***Insert Table VIII here***
The first model in Table VIII suggests that brokers supervised by DORs with relevant
industry experience attract higher trading commissions from buy-side clients relative to those lacking
such DORs. For instance, equity trading commission market share of brokers are 0.16% higher in
industries where the DOR has same GICS industry experience. Other control variables also have
expected directions. For example, we find higher experienced analysts and top 10 brokers generate
higher commissions from buy-side clients. Our results also indicate that the prior-period
18
commissions are economically the most important determinant of commission allocations for the
current year (Goldstein et al., 2009). In Model 2, we use total dollar commissions as the dependent
variable and find similar results. In dollar terms, brokers led by DORs generate 7.5% higher
commissions in DORs’ background experience industries. Conversely, DORs’ other industry
experience does not translate into higher equity trading commissions, consistent with results in
previous sections. The analyses from this section provide evidence consistent with the notion that
DORs industry experience also results in higher organizational performance as measured by higher
trading commissions allocated to their brokers.
8. Robustness tests and additional analyses
This section provides discussion of additional analyses and robustness tests. We first
examine the impact of new analyst hires on the main results. Next we consider cross-sectional
variation with respect to the quality of DOR industry experience, firm and industry specific nature
of DOR experience as well as alternative measures of earnings forecast performance. Finally, we
examine the impact of DOR innate talent as well as potentially unobserved analyst heterogeneities.
8.1. New hires and subordinate analyst performance
In this section we focus on a dynamic setting of analyst job changes across brokerage houses.
In particular, we examine the performance of newly hired analysts recruited by industry experienced
DORs and compute the changes in performance of these analysts across their new and old brokers.
To isolate the DOR’s impact on analyst performance, we require that job changing analysts did not
have an industry-aligned DOR at their former firms. Hired by DOR with (without) same industry
experience equals one for forecasts issued by industry-aligned (non-aligned) DOR hires in the posthire period, zero for pre-hire forecasts. With the exception of these variables, the econometric
model is similar to those in Table II and V.
***Insert Table IX here***
In the first model of Table IX, ‘Post vs Pre-hire performance’ compares the earnings
forecast performance of analysts hired by a DOR with (without) same industry experience at their
new broker relative to the performance at their old broker. Consistent with the theme throughout
this paper, analysts hired by industry-aligned DORs improve their earnings accuracy when they gain
access to industry experience of DORs at their new brokers. There is no significant improvement in
performance of other hires. The same positive benefits show up for the investment value of their
19
recommendations (models 3 and 5). Thus, performance indeed is boosted when boss-analyst
industry alignment occurs, even for new hires.
As discussed in section 5, DORs may hire better analysts or may be able to better identify
talent. One plausible concern with our main analysis is that a potential influx of industry-aligned new
hires may explain some of the results presented in the paper. That is, the DOR is not boosting
performance of their analyst workforce per se; they are simply recruiting talent making it appear as if
they have a positive impact on performance. The evidence for career outcomes was not consistent
with this explanation, but it is plausible that some of the analyst performance results in Sections 3
and 4 could be. 16
To shed further light on this point, we eliminate the impact of these new hires on our main
results in model 2, 4 and 6 of Table IX. ‘Exclude new hires’ purges the forecasts and
recommendations issued by these analysts at their new brokerage houses from our analyst universe.
We re-estimate equation 1 and 2 with this subsample of analysts and find our results mirror the full
sample evidence.
8.2. Cross-sectional analysis
Given that analysts can benefit from their supervisors, which DORs have the most to offer
and which analysts have the most to gain? In this section, we focus on this question by considering
cross-sectional variation within DORs and subordinate analysts. Specifically, we conjecture that
DORs that were better analysts can offer their subordinates more while junior subordinate analysts
lacking industry experience are more likely to benefit from their experienced DORs.
We first explore the quality of pre-DOR industry experience and partition the variable DOR
with same industry experience into two new explanatory variables at the median based on the DOR’s
analyst earnings forecast accuracy (DOR Low/High PMAFE), length of general forecasting
experience (DOR Long/Short General Experience), and all-star status (DOR All-star/Non-star). Panel A
of Table X re-estimates equation (1) and reports only the coefficients of interests to conserve space.
Across every proxy for the quality of DOR industry experience, we find that better pre-DOR analyst
performance of DORs result in better forecast performance for supervised analysts. For instance,
earnings forecasts are 5.3% (5.0)% more accurate for subordinate analysts having access to DORThese concerns should be mitigated because of our dynamic settings in Section 3.2 focused only on analysts working
for the broker before the DOR joined or exited. Nonetheless, for completeness, we conduct additional analysis on this
point.
16
20
industry experience with below (above) median forecast errors (general forecasting experience),
compared to 1.9% (1.8)% for those with access to executives with above (below) median forecast
errors (general forecasting experience). The differences in these coefficients, along with every
comparable pair in Panel A, are economically and statistically significant. Thus, the higher the quality
DOR’s industry experience, the more subordinates can benefit.
***Insert Table X here***
Panel B focuses on supervised analysts. We measure sell-side analyst industry experience by
their pre-analyst related industry work experience (Analyst with/without Related Work Experience),
length of general and firm specific forecasting experience (Analyst High/Low General & Firm
Experience) and all-star status (Analyst All-star/Non-star). 17 The findings from Panel B of Table X
suggest that the benefits from boss-analyst industry alignments are generally muted for those who
already possess such industry experience. For instance, DOR industry-aligned experience translates
into 5.4% more accurate forecasts for analysts without pre-analyst industry work experience
compared to 2.0% (insignificant) for those with related pre-analyst experience. Likewise, the impact
of DOR experience on forecast performance of subordinate analysts with shorter general/firm
forecasting experience and not having all-star status is higher relative to those with longer
general/firm forecasting experience or possessing all-star status. In all cases, the coefficients are
significantly different from each other. Thus, DOR-industry alignment favors analysts that have the
most potential to benefit from industry experience of their bosses.
8.3. Additional analyses and robustness
We have argued that subordinate analyst performance and career outcomes are related to the
industry experience of their DORs and buy-side clients likewise recognize equity research
departments led by such DORs with higher commission allocations. However, it may not be
industry-level, but rather firm-specific DOR experience driving these results. To isolate industry as
opposed to firm-specific benefits, we partition the DOR industry experience into experience
acquired at the same coverage firm (DOR with same firm experience) versus all other firms in the same
industry (DOR with same industry experience (not same firm)).
Pre-analyst industry work experience is obtained from LinkedIn.com, the world’s largest professional network.
Specifically, we first capture information on the names and years of employment for pre-analyst experience firms. Next,
we classify analyst work experience as “related” and “unrelated” at the coverage firm level. An analyst is defined to have
“related experience” in a coverage firm’s industry if the same analysts’ pre-analyst employer(s) and the coverage firm
share the same 4-digit GICS industry code (Bradley, Gokkaya and Liu, 2015a).
17
21
Model 1 of Appendix A provides results for earnings accuracy. Subordinate analysts indeed
issue 7.64% more accurate forecasts on firms that their DOR used to cover. However, these analysts
also issue 2.76% better forecasts on firms operating in the same industry but not previously covered
by their DORs. In models 2 and 3, we provide a similar analysis for investment value of buy and sell
recommendations. In both cases, the economic impact of industry experience (excluding same firm)
is similar to those reported in the main analysis. However, there seems to be an incremental effect
for same firm experience suggesting that analysts also receive some benefit from covering the firms
their boss previously covered. We find similar results for analyst career outcomes and broker
commissions in untabulated analysis.
Models 4 through 6 include analyst fixed effects to further control for any other
unobservable subordinate analyst characteristics which may affect both earnings forecasts and stock
recommendations. Our findings from model 4 indicate that earnings forecasts are 3.15% more
accurate when in-house DORs possess relevant experience. We further repeat this analysis on stock
recommendations in model 5 and 6. The results remain intact.
Next, we consider two alternative earnings forecast performance measures to ensure our
results are not driven by measurement of earnings accuracy as developed by Clement (1999). First,
we use performance metric of Hong and Kubik (2003) where analysts are ranked based on forecast
errors on coverage firms. Second, we re-estimate equation 1 with absolute forecast errors (AFE) and
include firm-year fixed effects. Models 7 and 8 present results from these analyses and suggest that
subordinate analysts’ earnings forecast performance is not sensitive to the measurement of earnings
forecast accuracy.
It is also plausible that industry experience of DORs may be correlated with the industry
experience of supervised analysts. Therefore, we include a control for it in our earnings forecasts
and recommendation specifications. Models 9 through 11 proxy for analyst industry knowledge with
related pre-analyst industry work experience (Analyst work experience). Consistent with Bradley et al.
(2015a), analysts with pre-analyst industry expertise issue better earnings and more profitable
investment recommendations. However, the inclusion of this control leaves the coefficient estimates
on DOR same industry experience relatively unchanged for our main results.
Finally, another plausible concern is that industry experience of DORs may be related to
these research executives’ innate talent. While we partially address this concern with the inclusion of
DOR fixed effects, we further check the robustness of our results by including a set of observable
DOR characteristics. To capture executive talent, we follow prior work on firm executives (e.g.
22
Oyer, 2008; Schoar and Zuo; 2011; Falato, Lu and Milbourn, 2012; Huang and Kisgen, 2013) and
add controls for DORs attending an Ivy League school at any academic level (DOR Ivy League),
DORs with first academic degrees awarded in a recession year defined by the National Bureau of
Economic Research (NBER) (DOR Recession Grad), fast track DORs being promoted to DOR
positions earlier in their careers (DOR Fast track). We also control for DORs holding MBA degrees
and also consider their non-analyst industry experience (DOR MBA, DOR Non-analyst Gexp). The
results for subordinate analyst earnings forecast performance and stock recommendations remain
robust as presented in model 12 through 14. 18
9. Conclusion
An evolving literature in finance and economics attempts to estimate the value of top
management for their organizations by focusing on firm-level outcomes. The novelty of this paper is
that we are able to directly identify the impact of executives on their subordinates. We exploit a
novel hand-collected sample of Directors of Research (DOR) managing equity research departments
and examine the implications of their industry experience on the performance and labor market
outcomes of their sell-side analyst workforce.
Over 1989 and 2008, we find subordinate analysts with access to DORs possessing relevant
industry experience are associated with more accurate earnings forecasts. In economic terms, the
positive impact of industry DOR-aligned bosses is large and holds to a battery of robustness tests
accounting for potential DOR-broker selection and other endogeneity concerns. Akin to better
forecast accuracy, our evidence suggests that analysts with industry-aligned DORs generate more
profitable buy and sell recommendations.
While we cannot precisely identify only one channel to explain these results because they are
not mutually exclusive, the evidence is most consistent with two views. First, the industry knowledge
of the DOR is transferred to their subordinates resulting in better performance. Second, exploitation
of DORs’ social and professional connections in the industry provide access to better information
flow and result in more pronounced improvements in subordinate performance.
Industry experience of DORs also results in stronger information flows to the capital
markets. In particular, subordinate analysts’ upward/downward earnings revisions evoke more
Untabulated results for career outcomes and broker performance persist after the inclusion of related pre-analyst
industry work experience and aforementioned DOR characteristics and are available upon request. Also, we use a
continuous variable of executive industry experience instead of a binary indicator. The results are robust.
18
23
pronounced price reactions when their boss possess the same industry experience, indicating market
participants likewise assign greater importance to research outputs of these analysts. Examining the
impact of DORs on analyst labor market outcomes, we find subordinate analysts with access to
DORs’ experience on coverage firms are more likely to be selected as All-stars in Institutional Investor’s
annual poll, obtain higher ranks in the roster and also make this team faster.
Finally, we consider the impact of DOR industry experience on their organizations through
examination of buy-side clients’ trading commission allocations. We find that brokerage houses
attract incrementally higher commissions in DORs’ professional background industries relative to
brokers lacking such directors. These results further highlight the importance of top management’s
industry experience for organizational level success.
24
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Table I. Summary statistics
This table reports summary statistics of the sample. Panel A presents summary statistics for annual earnings forecasts between 1989 and 2008. Panel B reports
descriptive statistics for analyst stock recommendation over the same time period. % Forecasts (Rec) DOR with industry experience is the percentage of forecasts
(recommendations) by analysts with access to at least one DOR with sell-side analyst forecasting experience. % Forecasts (Rec) DOR with same (other) industry experience is the
percentage of forecasts (recommendations) by analysts with access to at least one DOR with sell-side analyst forecasting experience in the same (different) GICS
industry of the followed firm %Analysts, % Market Cap, % Forecasts (% Rec) are the percentage of analysts, market capitalization, and forecasts (recommendations)
representing I/B/E/S universe of US firms, respectively. Information on DORs are obtained from Nelson Information’s Directory of Investment Research where
DORs are defined as domestic research executives holding titles Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity
Research, and Director of Research. Refer to Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. Stock price data are obtained from
CRSP.
Panel A: Earnings forecasts
Period
Overall
1989-1993
1994-1998
1999-2003
2004-2008
Average
N Analysts
7,609
1,012
2,737
4,189
3,804
2,936
N Firms
6,707
1,885
3,713
3,409
3,313
3,080
N Forecasts
199,559
15,406
51,463
58,517
74,173
49,890
Panel B: Recommendations
Period
Overall
1993
1994-1998
1999-2003
2004-2008
Average
N
Analysts
6,268
492
2,418
3,435
3,238
2,396
N Firms
5,477
1,095
3,057
2,878
2,981
2,503
N Rec
125,898
2,859
34,927
41,416
46,696
31,475
% Forecasts
DOR with industry
experience
41.01%
26.85%
28.14%
37.94%
55.31%
37.06%
% Rec
DOR with
industry experience
41.67%
26.65%
28.72%
38.14%
55.41%
37.23%
% Forecasts
DOR with same
industry experience
12.90%
7.73%
8.96%
13.37%
16.33%
11.60%
% Rec
DOR with same
industry experience
14.09%
10.00%
10.00%
14.76%
16.79%
12.89%
30
% Forecasts
DOR with other
industry experience
28.57%
19.12%
19.33%
25.27%
39.56%
25.82%
% Rec
DOR with other
industry experience
28.13%
16.65%
18.83%
24.18%
39.28%
24.74%
%Analysts
72.10%
34.98%
70.80%
79.20%
76.31%
65.32%
% Analysts
50.65%
39.68%
51.67%
53.60%
54.73%
49.92%
%Market
Cap
96.14%
90.46%
96.28%
96.90%
96.54%
95.04%
%Market
Cap
87.78%
82.27%
84.66%
86.48%
90.55%
85.99%
%Forecasts
55.05%
23.46%
60.66%
62.12%
62.98%
52.30%
% Rec
33.63%
27.92%
31.69%
32.48%
36.94%
32.26%
Table II. Subordinate analysts’ earnings forecasting performance and Director of Research
(DOR) industry experience
This table presents OLS regression results for subordinate analysts’ earnings forecasts between 1989 and 2008. 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. Information on DORs are obtained from Nelson Information’s Directory of
Investment Research where DORs are defined as domestic research executives holding titles Head of Equity, Head of
Equity Research, Head of Research, Director of Equity, Director of Equity Research, and Director of Research. Refer to
Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. T-statistics are in parentheses with
heteroskedastic-consistent standard errors. Broker fixed effects are included. *, **, and *** indicate statistical significance
at the 10%, 5%, and 1%, respectively.
DOR with industry experience
Model 2
Model 3
Model 4
Model 5
0.46***
(204.12)
-0.10**
(-2.51)
-0.25***
(-3.51)
0.11***
(3.54)
-5.59***
(-8.82)
0.53***
(4.26)
-3.04***
(-6.18)
0.27
(0.56)
-3.76***
(-6.280)
-0.12
(-0.26)
0.46***
(204.03)
-0.10***
(-2.58)
-0.25***
(-3.53)
0.11***
(3.60)
-5.52***
(-8.71)
0.51***
(4.06)
-2.96***
(-6.04)
0.26
(0.52)
-3.66***
(-6.03)
-0.16
(-0.37)
0.46***
(204.04)
-0.11***
(-2.60)
-0.25***
(-3.53)
0.11***
(3.63)
-5.51***
(-8.70)
0.51***
(4.04)
-2.97***
(-6.05)
0.27
(0.56)
-1.65
(-1.14)
-3.46***
(-5.51)
-0.01
(-0.02)
0.47***
(195.53)
-0.10**
(-2.27)
-0.29***
(-3.93)
0.12***
(3.45)
-5.27***
(-7.88)
0.52***
(3.92)
-3.20***
(-6.19)
0.31
(0.60)
-0.60
(-0.35)
-1.82**
(-2.28)
0.53
(0.63)
Yes
18.40%
199,559
Yes
18.42%
199,559
Yes
18.42%
199,559
Yes
18.55%
180,538
-3.15***
(-5.02)
0.44
(0.94)
0.45***
(170.37)
-0.12***
(-2.77)
-0.18**
(-2.47)
0.15***
(4.55)
-5.29***
(-7.92)
0.51***
(3.84)
-2.92***
(-5.65)
0.21
(0.42)
-1.11
(-0.64)
-2.80***
(-3.51)
0.54
(0.65)
-6.41***
(-16.25)
Yes
18.67%
180,538
Model 1
-1.04**
(-2.45)
DOR with same industry experience
DOR with other industry experience
DAge
DGexp
DFexp
DPortsize
DTop10
DGics
All-Star
Affiliated
Broker Ind. specialization
Pin
Pout
Ln(Freq)
Broker Fixed Effects
Adj R2
N
31
Table III. Subordinate analysts’ earnings forecasting performance and endogenous DOR-broker matching
This table presents OLS regression results for analyst earnings forecasts between 1989 and 2008. 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. Model 1 includes DOR FEs, model 2 (3) is for a sample of analysts gaining (losing) DORs with same industry expertise, and
gaining (model 5)/losing (model 6) DORs with only other industry expertise. Model 4 presents result on losing DORs with same industry expertise due to DOR
retirements and deaths. Model 7 (8) repeats DOR gain (loss) experiments with difference-in-difference (DID) approach, whereas Model 9 repeats baseline regressions
with propensity score (PS) matching procedure. For brevity, only the coefficient estimates on key variables are presented; all other explanatory variables are
suppressed. Information on DORs are obtained from Nelson Information’s Directory of Investment Research where DORs are defined as domestic research
executives holding titles Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity Research, and Director of Research.
Refer to Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. T-statistics are in parentheses. Broker fixed effects are included. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1%, respectively.
DOR Same Industry Experience
Gaining DOR with same industry experience
Model 1
-3.07***
(-4.578)
Losing DOR with same industry experience
Model 2
-3.31**
(-2.15)
Losing DOR with same industry experience(only
retirement/death)
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
4.88***
(3.06)
5.05**
(2.30)
Gaining DOR with only other industry experience
Losing DOR with only other industry experience
-2.08
(-1.51)
Gaining DOR with same industry experience (DID)
0.24
(0.17)
Losing DOR with same industry experience (DID)
-5.27***
(-2.93)
DOR Same Industry Experience (PS Match)
4.26**
(2.13)
DOR Other Industry Experience(PS Match)
Control Variables
Broker Fixed Effects
Adj R2
n
Yes
Yes
19.47%
74,647
Yes
Yes
19.01%
4,120
Model 9
Yes
Yes
24.10%
5,791
32
Yes
Yes
26.50%
2,318
Yes
Yes
22.85%
13,107
Yes
Yes
18.36%
10,628
Yes
Yes
21.70%
11,472
Yes
Yes
19.41%
9,587
-4.28***
(-8.77)
-0.66
(-1.59)
Yes
Yes
0.04%
150,118
Table IV. Subordinate analysts and DOR industry experience mechanisms
This table presents OLS regression results for analyst earnings forecasts between 1989 and 2008. 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. Information on DORs are obtained from Nelson Information’s Directory of Investment Research where DORs
are defined as domestic research executives holding titles Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity
Research, and Director of Research. Refer to Appendix C for a detailed description of variables. For brevity, only the coefficient estimates on key variables are
presented; all other explanatory variables are suppressed. Analyst data are from I/B/E/S. T-statistics are in parentheses. Broker fixed effects are included. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1%, respectively.
DOR with same industry experience (Connected)
DOR with same industry experience (Unconnected)
DOR with same industry experience (change in Broker Specialization High- since DOR appointment)
Model 1
-5.62***
(-4.70)
-2.62***
(-3.94)
DOR with same industry experience (change in Broker Specialization Low - since DOR appointment)
DOR with same industry experience (change in Pin High - since DOR appointment)
Model 2
-4.77***
(-5.30)
-2.26***
(-3.13)
DOR with same industry experience (change in Pin Low - since DOR appointment)
DOR with same industry experience (change in Pout High- since DOR appointment)
Model 3
-4.53***
(-5.27)
-2.27***
(-3.11)
DOR with same industry experience (change in Pout Low - since DOR appointment)
Difference
Control Variables
Broker Fixed Effects
Adj R2
N
-2.99**
Yes
Yes
18.78%
180,538
33
-2.51**
Yes
Yes
18.67%
180,538
-2.26**
Yes
Yes
18.67%
180,538
Model 4
-1.87**
(-2.08)
-4.15***
(-5.19)
2.28**
Yes
Yes
18.67%
180,538
Table V. Investment value of Subordinate analysts’ Buy/ Sell recommendation portfolios and DOR industry experience
This table presents panel regressions of returns to recommendations over 1993 and 2008. Buy (sell) recommendation calendar time portfolios include stocks upgraded
(downgraded) relative to the prior outstanding recommendation as well as stocks with Strong Buy (Strong Sell) and Buy (Sell) recommendation
reiterations/initiations/resumes in the Buy (Sell) recommendation portfolio. These portfolios are then rebalanced daily when analysts revise their recommendations,
drop coverage or when the recommendation becomes stale (i.e. no change for 1 year). The dependent variable is Daniel, Grinblatt, Titman and Wermers (1997)
(DGTW) characteristic adjusted returns stock returns. Regressions are run daily, but converted into monthly coefficients for ease of interpretation. Information on
DORs are obtained from Nelson Information’s Directory of Investment Research where DORs are defined as domestic research executives holding titles Head of
Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity Research, and Director of Research. Refer to Appendix C for a detailed
description of variables. Analyst data are from I/B/E/S. T-statistics are in parentheses. Broker and year fixed effects are included. *, **, and *** indicate statistical
significance at the 10%, 5%, and 1%, respectively.
DOR with industry experience
Model 1
0.09*
(1.782)
DOR with same industry experience
DOR with other industry experience
Size
BM
Gexp
Fexp
Port size
Top 10
Port Gics
All-star
-0.29***
(-24.96)
-0.05***
(-3.05)
0.00
(-0.09)
0.02***
(2.67)
0.00
(0.03)
0.14*
(1.68)
-0.05***
(-5.03)
0.10*
Buy Recommendation Portfolio
Model 2
Model 3
Model 4
0.35***
(5.34)
0.00
(-0.07)
-0.29***
(-24.90)
-0.05***
(-2.97)
0.00
(0.03)
0.02***
(2.62)
0.00
(0.02)
0.13
(1.52)
-0.05***
(-4.98)
0.09
0.33***
(4.99)
0.01
(0.19)
-0.29***
(-24.81)
-0.04***
(-2.91)
0.00
(0.14)
0.02***
(2.62)
0.00
(-0.04)
0.14
(1.64)
-0.04***
(-4.88)
0.09
0.32***
(4.72)
0.02
(0.38)
-0.29***
(-24.33)
-0.04**
(-2.48)
0.00
(0.10)
0.01**
(2.15)
0.00
(-0.07)
0.12
(1.39)
-0.04***
(-4.53)
0.10
34
Model 5
0.32***
(4.68)
0.02
(0.33)
-0.29***
(-24.35)
-0.04**
(-2.52)
0.00
(0.20)
0.01*
(1.89)
0.00
(-0.14)
0.12
(1.39)
-0.04***
(-4.52)
0.09
Model 6
-0.14***
(-2.665)
0.12***
(9.89)
-0.01
(-0.81)
-0.01*
(-1.70)
0.00
(0.59)
0.01**
(2.48)
-0.27***
(-2.99)
-0.01
(-1.42)
-0.25***
Sell Recommendation Portfolio
Model 7
Model 8
Model 9
Model 10
-0.34***
(-5.08)
-0.07
(-1.38)
0.12***
(9.88)
-0.01
(-0.82)
-0.01*
(-1.82)
0.00
(0.56)
0.01**
(2.46)
-0.27***
(-2.91)
-0.01
(-1.50)
-0.25***
-0.32***
(-4.59)
-0.06
(-1.12)
0.11***
(9.24)
0.00
(-0.29)
-0.01*
(-1.86)
0.00
(0.62)
0.01*
(1.69)
-0.31***
(-3.26)
-0.01
(-1.05)
-0.23***
-0.33***
(-4.80)
-0.08
(-1.49)
0.12***
(9.75)
-0.01
(-0.87)
-0.01*
(-1.90)
0.00
(0.56)
0.01**
(2.45)
-0.27***
(-3.00)
-0.01
(-1.55)
-0.25***
-0.33***
(-4.77)
-0.07
(-1.28)
0.11***
(8.94)
0.00
(-0.34)
-0.01*
(-1.66)
0.00
(0.00)
0.00
(1.57)
-0.30***
(-3.19)
-0.01
(-1.13)
-0.24***
Affiliated
Past 6m ret
(1.65)
0.10*
(1.80)
2.75***
(8.40)
(1.55)
0.10*
(1.82)
2.74***
(8.39)
(1.57)
0.10*
(1.76)
2.76***
(8.43)
0.47***
(2.86)
(1.64)
0.08
(1.51)
2.24***
(6.67)
0.25
(1.37)
0.09
(1.08)
-0.18**
(-1.97)
Yes
Yes
0.02%
Yes
Yes
0.02%
Yes
Yes
0.02%
Yes
Yes
0.02%
Broker Ind. specialization
Pin
Pout
Ln(Freq)
Broker Fixed Effects
Year Fixed Effects
Adj R2
35
(1.59)
0.08
(1.50)
2.25***
(6.71)
0.25
(1.38)
0.10
(1.19)
-0.18**
(-1.97)
0.07*
(1.76)
Yes
Yes
0.02%
(-4.50)
-0.08
(-1.25)
4.32***
(12.00)
(-4.40)
-0.08
(-1.30)
4.32***
(12.02)
(-4.39)
-0.08
(-1.27)
4.31***
(11.97)
-0.37*
(-1.95)
(-4.23)
-0.09
(-1.40)
3.49***
(9.50)
-0.86***
(-4.27)
-0.09
(-1.04)
-0.01
(-0.11)
Yes
Yes
0.02%
Yes
Yes
0.02%
Yes
Yes
0.02%
Yes
Yes
0.02%
(-3.95)
-0.08
(-1.30)
3.32***
(9.01)
-0.88***
(-4.39)
-0.12
(-1.32)
-0.02
(-0.18)
-0.23***
(-5.54)
Yes
Yes
0.02%
Table VI. Market reactions to Subordinate analysts’ EPS Forecast Revisions and DOR industry experience
This table reports market reactions to the analyst upward/downward earnings forecast revisions over 1989 and 2008. The dependent variable is the CRSP-VW index-adjusted
abnormal returns over 3 days (0, 2) around the announcement date of revision by analyst i for firm j at time t. Information on DORs are obtained from Nelson Information’s
Directory of Investment Research where DORs are defined as domestic research executives holding titles Head of Equity, Head of Equity Research, Head of Research,
Director of Equity, Director of Equity Research, and Director of Research. Refer to Appendix C for a detailed description of variables. Stock price data are obtained from
CRSP. T-statistics are in parentheses. Broker and year fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
DOR with industry experience
DOR with same industry experience
Model 1
0.15**
(2.173)
DAGE
DGExp
DFExp
Dportsize
Dtop10
Dgic
All Star
Affiliated
Ind spec
Upgrades
Model 3
6.01***
(11.17)
0.00
(0.50)
-0.01
(-0.94)
0.01
(0.70)
-0.01**
(-2.52)
0.38***
(3.19)
0.00
(0.07)
0.48***
(6.40)
-0.15
(-1.59)
0.26***
(2.85)
0.06
(0.83)
5.95***
(11.06)
0.00
(0.50)
0.00
(-0.81)
0.01
(0.64)
-0.01***
(-2.86)
0.37***
(3.10)
0.01
(0.35)
0.48***
(6.46)
-0.16*
(-1.67)
1.23***
(3.47)
0.29***
(3.10)
0.06
(0.78)
5.99***
(10.87)
0.00
(0.28)
-0.01
(-0.93)
0.00
(0.39)
-0.01**
(-2.53)
0.37***
(2.99)
0.01
(0.61)
0.51***
(6.55)
-0.13
(-1.30)
1.73***
(4.30)
0.12
(0.95)
0.19
(1.47)
Yes
Yes
14.46%
57,912
Yes
Yes
14.48%
57,912
Yes
Yes
14.52%
57,912
Yes
Yes
14.62%
53,959
Pin
Pout
Ln(Freq)
Broker Fixed Effects
Year Fixed Effects
Adj R2
N
Model 4
0.34***
(3.63)
0.03
(0.40)
6.01***
(11.17)
0.00
(0.50)
-0.01
(-0.92)
0.01
(0.72)
-0.01***
(-2.59)
0.38***
(3.13)
0.00
(0.20)
0.47***
(6.31)
-0.15
(-1.56)
DOR with other industry experience
EPS change
Model 2
36
Model 5
0.29***
(3.10)
0.05
(0.76)
5.98***
(10.84)
0.00
(0.50)
-0.01
(-0.89)
0.00
(0.31)
-0.01**
(-2.56)
0.37***
(2.99)
0.01
(0.61)
0.51***
(6.50)
-0.13
(-1.29)
1.74***
(4.32)
0.13
(0.99)
0.19
(1.49)
0.08
(0.74)
Yes
Yes
14.62%
53,959
Model 6
-0.14
(-1.618)
Model 7
Downgrades
Model 8
Model 9
Model 10
-0.32**
(-2.39)
-0.04
(-0.44)
-4.49***
(-11.42)
0.00
(-1.52)
-0.01
(-0.79)
0.01
(0.66)
0.01**
(2.02)
-0.32**
(-2.22)
0.04*
(1.66)
-0.45***
(-4.81)
-0.52***
(-3.73)
-1.66***
(-3.51)
-0.29*
(-1.80)
-0.27*
(-1.72)
-0.03
(-0.23)
Yes
Yes
11.04%
53,699
-4.43***
(-11.69)
0.00*
(-1.86)
-0.01
(-0.75)
0.01
(0.79)
0.01*
(1.78)
-0.32**
(-2.35)
0.04**
(1.99)
-0.43***
(-4.81)
-0.56***
(-4.22)
-0.40***
(-3.07)
-0.01
(-0.06)
-4.42***
(-11.67)
0.00*
(-1.89)
-0.01
(-0.78)
0.01
(0.80)
0.01*
(1.84)
-0.31**
(-2.28)
0.04*
(1.87)
-0.42***
(-4.73)
-0.56***
(-4.25)
-0.29**
(-2.23)
-0.04
(-0.50)
-4.39***
(-11.66)
0.00*
(-1.86)
-0.01
(-0.91)
0.01
(0.84)
0.01**
(2.08)
-0.31**
(-2.26)
0.04*
(1.73)
-0.43***
(-4.85)
-0.55***
(-4.13)
-1.56***
(-3.88)
-0.32**
(-2.40)
-0.04
(-0.45)
-4.50***
(-11.43)
0.00
(-1.56)
-0.01
(-0.79)
0.01
(0.64)
0.01**
(2.00)
-0.32**
(-2.21)
0.04*
(1.66)
-0.45***
(-4.82)
-0.52***
(-3.73)
-1.66***
(-3.51)
-0.29*
(-1.78)
-0.27*
(-1.72)
Yes
Yes
10.68%
58,584
Yes
Yes
10.69%
58,584
Yes
Yes
10.74%
58,584
Yes
Yes
11.04%
53,699
Table VII. Subordinate analyst labor market outcomes and DOR industry experience:
becoming All-Star analyst, ranks and time elapsed for All-star analysts
This table reports logistic regressions on the probability of subordinate analysts becoming an all-star analyst. 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 an all-star analyst in the October issue of Institutional Investor magazine, 0 otherwise. Model 4 excludes analysts
hired after DORs with same industry experience joined the broker. Model 5 presents OLS results for a subsample of Allstar analysts where the dependent variable ranges from 1 to 4 based on the analyst’s rank in II polls is the ranking of
analysts in the all-star roster, where 4 represents the highest rank. Model 6 presents OLS results for a subsample of
analysts who made the all-star roster where dependent variable is the years elapsed since such analysts got into the equity
research profession and the first time they made the team. Information on DORs are obtained from Nelson
Information’s Directory of Investment Research where DORs are defined as domestic research executives holding titles
Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity Research, and
Director of Research. Refer to Appendix C for a detailed description of variables. T-statistics are in parentheses with
standard errors clustered at the analyst level. Broker and Year fixed effects are also included. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1%, respectively.
Ln stocks with DOR industry experience
Ln stocks with DOR same industry experience
Model 1
15.39*
(1.65)
Model 2
Model 3
56.50***
(4.38)
56.02***
(4.31)
4.20***
(3.47)
8.40***
(7.37)
-0.76
(-0.15)
1.55***
(3.34)
-26.41
(-1.28)
3.04*
(1.82)
-0.70
(-0.36)
23.62***
(3.68)
456.89***
(28.70)
209.37***
(8.13)
2.29
(0.26)
4.41***
(3.61)
8.10***
(7.11)
-1.41
(-0.28)
1.30***
(2.80)
-25.98
(-1.26)
3.00*
(1.76)
-0.27
(-0.14)
21.47***
(3.34)
459.31***
(28.41)
212.84***
(8.36)
2.62
(0.29)
4.60***
(3.77)
8.19***
(7.00)
-1.53
(-0.30)
1.18**
(2.52)
-28.50
(-1.35)
3.02*
(1.81)
-0.29
(-0.15)
20.54***
(3.15)
459.67***
(28.73)
217.06***
(8.53)
-27.35
(-0.19)
100.48**
(2.56)
8.34
(0.19)
Yes
Yes
39.70%
12,574
Yes
Yes
39.80%
12,574
Yes
Yes
40.21%
12,285
Ln stocks with DOR same (exclude new hires)
Ln stocks with DOR other industry experience
Gexp
Port size
Port Gics
Brokerage size
Average PMAFE
Average Buy Rec return
Average Sell Rec return
Average Firm Size
All-star (prior year)
Average Ln(Freq)
Average Broker Ind. specialization
Average Pin
Average Pout
All-star rank (prior year)
Broker Fixed Effects
Year Fixed Effects
Adj R 2
N
37
Model 4
65.54***
(3.18)
-1.04
(-0.11)
5.24***
(4.03)
9.34***
(6.62)
-3.71
(-0.67)
0.89*
(1.71)
-25.66
(-1.13)
3.63**
(2.03)
-0.40
(-0.18)
25.98***
(3.81)
460.71***
(26.75)
212.71***
(8.26)
56.74
(0.33)
130.67***
(2.89)
20.25
(0.42)
Yes
Yes
42.40%
10,091
Model 5
Model 6
9.73**
(2.27)
-106.97**
(-2.42)
-3.96
(-1.63)
-0.79
(-1.47)
-0.18
(-0.28)
-2.15
(-0.98)
-0.07
(-0.72)
-1.83
(-0.20)
0.51
(0.61)
0.61
(0.89)
1.73
(0.63)
14.40
(0.48)
25.50**
(2.19)
-49.40
(-0.86)
-15.83
(-0.89)
-12.08
(-0.74)
55.35***
(23.57)
Yes
Yes
38.74%
1,289
74.96
(1.12)
386.83
(1.10)
3.03
(0.02)
29.41
(0.26)
26.33***
(5.12)
-1.21
(-0.07)
-2.08***
(-3.03)
48.17
(0.98)
0.60
(0.21)
5.09
(1.14)
37.73**
(2.03)
Yes
Yes
15.22%
463
Table VIII: DOR industry experience and brokers’ relative/dollar trading commissions
This table reports broker-industry specific trading relative commission market shares (Model 1) and level of total dollar
commissions as measured by Ln (1+dollar commissions) (Model 2). Information on DORs are obtained from Nelson
Information’s Directory of Investment Research where DORs are defined as domestic research executives holding titles
Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity Research, and
Director of Research. Refer to Appendix C for a detailed description of variables. Stock price data are obtained from
CRSP. T-statistics are in parentheses. Broker and year fixed effects are included. *, **, and *** indicate statistical
significance at the 10%, 5%, and 1%, respectively.
Model 1
0.16**
(2.42)
0.05
(1.30)
-0.03
(-0.85)
0.01*
(1.94)
0.01*
(1.82)
0.00
(0.09)
0.00
(0.45)
0.12
(0.65)
-0.10*
(-1.95)
0.01
(0.24)
0.19***
(3.11)
0.03***
(5.51)
0.00
(1.33)
-0.64
(-1.39)
0.21
(0.39)
25.81***
(139.42)
DOR Same Industry Experience
DOR Other Industry Experience
Average PMAFE
Average Gexp
Average Portsize
Average Ln(Freq)
No of All-star Analysts
Broker Ind. specialization
Pin
Pout
Top 10 (Commissions)
No of SEOs
No of Analysts
Average Sell Portfolio Return
Average Buy Portfolio Return
Lag (Broker Share)
Ln (1+dollar commissions) (lagged)
Broker Fixed Effects
Year Fixed Effects
Adj R2
n
Yes
Yes
16.17%
227,449
38
Model 2
7.47***
(3.60)
0.93
(0.79)
1.14
(1.19)
0.59***
(3.41)
0.84***
(6.25)
4.46***
(3.09)
0.09
(0.97)
10.69*
(1.79)
1.85
(1.13)
1.90
(1.12)
20.98***
(10.50)
5.42***
(33.03)
0.15***
(4.62)
-18.94
(-1.29)
18.74
(1.07)
42.53***
(222.96)
Yes
Yes
40.28%
227,449
Table IX: Newly hired Subordinates and DOR industry experience: earnings forecasts and Buy/Sell recommendations
This table focuses on a dynamic setting of analyst job changes across brokerage houses and compares the performance of newly hired analysts recruited by industry
experienced DORs across their new and old brokers, ‘Post vs pre-hire performance.’ Additionally, new hires are excluded from the analyses, ‘Exclude new hires.’ (R)
denotes non-demeaned variables. Information on DORs are obtained from Nelson Information’s Directory of Investment Research where DORs are defined as domestic
research executives holding titles Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity Research, and Director of Research.
Refer to Appendix C for a detailed description of variables. Analyst data are from I/B/E/S. Stock price data are obtained from CRSP. T-statistics are in parentheses.
Broker and year fixed effects are included *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Earnings forecasts
Post vs pre-hire
Exclude
performance
new hires
Hired by DOR with same industry experience
-7.27**
(-2.521)
Hired by DOR without same industry experience
-0.45
(-0.34)
DOR with same industry experience
-2.79***
(-3.880)
DOR with other industry experience
0.66
(1.39)
DAGE(R)
0.40***
0.45***
(34.30)
(164.97)
Size
Buy recommendations
Post vs pre-hire
Exclude
performance
new hires
0.69***
(2.585)
0.13
(1.04)
0.29***
(3.771)
0.01
(0.13)
-0.31***
(-8.77)
-0.16
(-1.49)
0.00
(-0.01)
0.02
(1.05)
-0.03**
(-2.49)
0.15
(0.81)
0.04
(0.98)
0.13
(0.85)
0.39*
(1.93)
BM
DGexp(R)
DFexp(R)
DPortsize(R)
DTop10(R)
DPortgics(R)
All-star
Affiliated
0.07
(0.39)
0.09
(0.34)
0.16
(1.03)
-6.20***
(-3.31)
-0.71
(-1.18)
-3.02*
(-1.74)
-3.49
(-1.55)
-0.12***
(-2.84)
-0.20***
(-2.66)
0.16***
(4.75)
-5.28***
(-7.59)
0.46***
(3.39)
-2.98***
(-5.69)
0.34
(0.64)
39
-0.28***
(-23.43)
-0.04**
(-2.54)
0.00
(-0.17)
0.01**
(1.97)
-0.01*
(-1.69)
0.17*
(1.85)
0.00
(-0.20)
0.07
(1.22)
0.06
(0.96)
Sell recommendations
Post vs pre-hire
Exclude
performance
new hires
-0.82***
(-2.724)
-0.14
(-1.01)
-0.34***
(-4.374)
-0.05
(-0.94)
0.07*
(1.76)
0.20**
(2.20)
-0.01
(-0.84)
0.00
(-0.20)
0.02
(1.27)
-0.22
(-1.12)
-0.05
(-1.06)
0.15
(0.86)
0.04
(0.18)
0.10***
(8.17)
-0.05**
(-2.13)
-0.01
(-1.36)
0.00
(0.48)
0.01***
(2.70)
-0.27***
(-2.77)
-0.04***
(-3.63)
-0.27***
(-4.69)
-0.07
(-1.00)
Past 6m ret
Broker Ind. specialization
Pin
Pout
Ln(Freq)
Broker Fixed Effects
Year Fixed Effects
Adj R 2
9.19
(1.48)
4.38*
(1.66)
0.84
(0.31)
-2.02
(-1.42)
Yes
No
13.80%
0.48
(0.49)
0.27
(0.47)
-0.01
(-0.03)
0.33
(1.25)
0.16
(1.19)
Yes
Yes
0.04%
-1.93
(-1.07)
-2.93***
(-3.55)
0.80
(0.93)
-6.41***
(-15.79)
Yes
No
18.71%
40
2.14***
(6.19)
0.15
(0.80)
0.11
(1.23)
-0.16*
(-1.67)
0.07*
(1.73)
Yes
Yes
0.02%
-1.13
(-1.02)
-0.39
(-0.59)
-0.56**
(-2.08)
0.72**
(2.49)
-0.18
(-1.23)
Yes
Yes
0.03%
3.03***
(7.88)
-0.83***
(-3.93)
-0.11
(-1.21)
0.00
(0.04)
-0.21***
(-4.94)
Yes
Yes
0.02%
Table X. Cross-sectional variation in DOR industry experience
This table presents OLS regression results for analyst earnings forecasts between 1989 and 2008. 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. Information on
DORs are obtained from Nelson Information’s Directory of Investment Research where DORs are defined as domestic research
executives holding titles Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity
Research, and Director of Research. Refer to Appendix C for a detailed description of variables. Panel A sorts on DOR
characteristics, panel B analyst characteristics. In both Panels, only industry-aligned DOR forecasts are examined. For brevity, only the
coefficient estimates on key variables are presented; all other explanatory variables are suppressed. Analyst data are from I/B/E/S. Tstatistics are in parentheses. Broker fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%,
respectively.
Panel A: Quality of DORs industry experience
(DOR-Low PMAFE)
Model 1
-5.27***
(-5.793)
-1.86**
(-2.49)
(DOR-High PMAFE)
(DOR-Long General Experience)
(DOR-Short General Experience)
(DOR-Large Portfolio)
Model 2
-4.97***
(-5.99)
-1.80**
(-2.41)
(DOR-Small Portfolio)
(DOR All-Star)
Model 3
-4.61***
(-5.20)
-2.34***
(-3.26)
(DOR Non-Star)
Differences
Control Variables
Broker Fixed Effects
Adj R 2
N
-3.41***
Yes
Yes
18.67%
180,538
Panel B: Quality of Subordinate Analysts’ industry experience
(Analyst with Related Work Experience)
(Analyst without Related Work Experience)
(Analyst-High General Experience)
Model 1
-1.96
(-1.336)
-5.40***
(-4.60)
(Analyst-Low General Experience)
(Analyst-High Firm Experience)
-3.17***
Yes
Yes
18.55%
180,538
Model 2
-0.07
(-0.08)
-5.90***
(-7.76)
(Analyst Low Firm Experience)
(Analyst-All-star)
-2.26**
Yes
Yes
18.55%
180,538
Model 3
-2.16***
(-2.75)
-4.07***
(-5.33)
(Analyst-Non-star)
Difference
Control Variables
Broker Fixed Effects
Adj R 2
N
3.44**
Yes
Yes
17.62%
62,574
41
5.83***
Yes
Yes
18.69%
180,538
1.90**
Yes
Yes
18.67%
180,538
Model 4
-5.30***
(-4.70)
-2.40***
(-3.38)
-2.90**
Yes
Yes
18.67%
180,538
Model 4
-0.11
(-0.06)
-3.48***
(-5.32)
3.37*
Yes
Yes
18.67%
180,538
Appendix A. Robustness tests
This table presents robustness test results for analyst earnings forecasts and buy/sell recommendation portfolios. For models 1, 4, 9 and 12 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 7(8), dependent variable is Hong and Kubik (2003)’s rank measure
(AFE). For models 2, 5, 10, and 13 (3, 6, 11 and 14) the dependent variable is returns to Buy (sell) recommendation calendar time portfolios. (R) denotes nondemeaned variables. Information on DORs are obtained from Nelson Information’s Directory of Investment Research where DORs are defined as domestic
research executives holding titles Head of Equity, Head of Equity Research, Head of Research, Director of Equity, Director of Equity Research, and Director of
Research. Refer to Appendix B for a detailed description of variables. T-statistics are in parentheses. Broker fixed effects are included in all models, and analyst fixed
effects are also included in model 4 through 6. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
DOR with same firm
experience
DOR with same industry
experience (not same firm)
DOR with same Industry
Experience
DOR with other Industry
Experience
DAGE (R)
Size
Model 1
Model 2
Model 3
-7.64***
(-4.94)
0.64***
(3.98)
-0.72***
(-4.04)
-2.76***
(-4.32)
0.37***
(5.38)
-0.39***
(-5.61)
0.42
(0.91)
0.45***
(170.36)
BM
DGexp(R)
DFexp(R)
DPortsize(R)
DTop10(R)
DPortgics(R)
All-star
-0.12***
(-2.84)
-0.19**
(-2.53)
0.15***
(4.56)
-5.31***
(-7.95)
0.50***
(3.80)
-2.91***
0.04
(0.78)
-0.09*
(-1.74)
-0.28***
(-24.13)
-0.04**
(-2.49)
0.00
(0.08)
0.01*
(1.70)
0.00
(-1.54)
0.13
(1.53)
-0.01
(-1.16)
0.09
0.11***
(9.40)
0.00
(-0.30)
-0.01*
(-1.76)
0.00
(0.56)
0.01***
(2.92)
-0.30***
(-3.21)
-0.04***
(-3.88)
-0.24***
Model 4
Model 5
Model 6
Model 7
Model 8
Model 9
Model 10
Model 11
Model 12
Model 13 Model 14
-3.15***
(-5.02)
0.31***
(4.60)
-0.32***
(-4.65)
59.91**
(2.41)
-0.39***
(-4.51)
-4.18***
(-4.09)
0.31***
(2.80)
-0.27**
(-2.39)
-3.29***
(-5.09)
0.30***
(4.22)
-0.34***
(-4.75)
0.44
(0.94)
0.45***
(170.37)
0.01
(0.25)
-0.06
(-1.12)
-17.13
(-0.93)
-5.91***
(-52.30)
-0.05
(-0.72)
0.04***
(96.71)
0.16
(0.21)
0.45***
(97.21)
-0.03
(-0.36)
0.03
(0.40)
0.31
(0.64)
0.45***
(170.27)
0.00
(-0.08)
-0.08
(-1.47)
-0.28***
(-24.09)
-0.04**
(-2.49)
0.00
(0.02)
0.01*
(1.75)
0.00
(-1.51)
0.13
(1.58)
-0.01
(-1.19)
0.09
0.11***
(9.31)
0.00
(-0.30)
-0.01*
-2.33
(-1.70)
(-1.38)
0.00
16.39***
(0.55)
(5.62)
0.01*** -10.49***
(2.86)
(-7.69)
-0.31*** 89.15***
(-3.27)
(3.36)
-0.04*** -14.47***
(-3.82)
(-2.73)
-0.24*** 88.10***
-0.35***
(-17.84)
-0.03
(-1.57)
0.00
(-0.14)
0.03**
(2.26)
-0.01
(-0.91)
0.25*
(1.82)
0.00
(-0.09)
0.25**
0.14***
(7.40)
0.00
(0.04)
0.00
(0.18)
0.01
(0.66)
0.03***
(5.58)
0.17
(1.13)
-0.09***
(-4.55)
-0.43***
-0.28***
(-24.07)
-0.04**
(-2.51)
0.00
(0.09)
0.01*
(1.73)
-0.01
(-1.56)
0.17*
(1.95)
-0.01
(-1.13)
0.08
0.11***
(9.27)
0.00
(-0.30)
-0.01*
(-1.66)
0.00
(0.56)
0.01***
(2.82)
-0.30***
(-3.18)
-0.04***
(-3.87)
-0.24***
-0.12***
(-2.77)
-0.18**
(-2.47)
0.15***
(4.55)
-5.29***
(-7.92)
0.51***
(3.84)
-2.92***
42
0.00
(-0.36)
0.00
(-0.40)
0.01*
(1.73)
-0.43***
(-6.23)
0.08***
(3.65)
-0.21***
-0.25***
(-3.40)
0.05
(0.41)
0.32***
(5.01)
-5.59***
(-4.92)
0.25
(1.05)
-3.56***
-0.12***
(-2.80)
-0.18**
(-2.45)
0.15***
(4.54)
-5.44***
(-8.09)
0.50***
(3.77)
-2.87***
Affiliated
Past 6m ret
Broker Ind. specialization
Pin
Pout
Ln(Freq)
(-5.63)
0.21
(0.41)
-1.12
(-0.64)
-2.80***
(-3.51)
0.54
(0.64)
-6.42***
(-16.28)
(1.50)
0.09
(1.57)
2.24***
(6.67)
0.25
(1.39)
0.11
(1.28)
-0.19**
(-2.06)
0.07*
(1.71)
(-4.23)
-0.08
(-1.30)
3.30***
(8.96)
-0.81***
(-4.03)
-0.13
(-1.45)
-0.01
(-0.07)
-0.23***
(-5.64)
(-5.65)
0.21
(0.42)
-1.11
(-0.64)
-2.80***
(-3.51)
0.54
(0.65)
-6.41***
(-16.25)
(1.47)
0.09
(1.59)
2.24***
(6.67)
0.27
(1.52)
0.10
(1.22)
-0.18**
(-1.96)
0.07*
(1.72)
(-4.18)
-0.08
(-1.31)
3.30***
(8.95)
-0.85***
(-4.20)
-0.13
(-1.40)
-0.01
(-0.10)
-0.23***
(-5.64)
(4.36)
23.12
(1.15)
(-2.76)
-0.05
(-0.46)
(-4.04)
0.84
(0.99)
34.97
(0.50)
-22.55
(-0.71)
-0.78
(-0.02)
21.59
(1.23)
-0.71***
(-3.28)
-0.30**
(-2.30)
0.02
(0.16)
-2.32***
(-28.87)
0.26
(0.08)
-1.24
(-0.89)
0.34
(0.23)
-5.69***
(-8.40)
-4.41***
(-7.35)
Analyst work Experience
(2.53)
0.04
(0.48)
2.16***
(3.94)
0.98***
(3.04)
0.04
(0.28)
0.32**
(2.05)
-0.04
(-0.54)
0.15**
(2.32)
(-4.63)
-0.23**
(-2.35)
3.87***
(6.72)
-1.96***
(-5.48)
-0.30**
(-2.01)
-0.02
(-0.09)
-0.35***
(-5.03)
-0.26***
(-4.16)
DOR Ivy league
DOR Recession grad
DOR Fast track
DOR MBA
DOR Non-analyst experience
Broker Fixed Effects
Year Fixed Effects
Analyst Fixed Effects
Firm-year Fixed Effects
Adj R2
Yes
No
No
No
Yes
Yes
No
No
Yes
Yes
No
No
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
No
No
No
Yes
Yes
No
No
Yes
Yes
No
No
18.78%
0.02%
0.02%
18.78%
0.02%
0.02%
3.19%
76.87%
17.95%
0.02%
0.03%
43
(-5.56)
0.21
(0.41)
-0.98
(-0.56)
-2.79***
(-3.48)
0.50
(0.59)
-6.42***
(-16.28)
(1.35)
0.09*
(1.65)
2.23***
(6.65)
0.26
(1.48)
0.11
(1.30)
-0.18**
(-1.98)
0.07*
(1.75)
(-4.18)
-0.08
(-1.32)
3.30***
(8.95)
-0.86***
(-4.25)
-0.13
(-1.41)
-0.01
(-0.08)
-0.23***
(-5.62)
0.55
(1.25)
0.36
(0.55)
-0.63*
(-1.86)
0.39
(1.10)
-0.13
(-0.42)
Yes
No
No
No
0.03
(0.59)
-0.12
(-1.48)
0.17***
(4.46)
-0.04
(-1.14)
0.01
(0.18)
Yes
Yes
No
No
-0.01
(-0.24)
0.00
(0.02)
0.00
(-0.05)
0.04
(0.93)
-0.05
(-1.42)
Yes
Yes
No
No
18.78%
0.02%
0.02%
Appendix B. Data screening and collection process
Forecasts
Firms
Analysts
All analysts’ annual EPS forecasts between 1989 and 2008 from
I/B/E/S.
2,167,053
16,441
14,964
Merge with CRSP/COMPUSTAT for stock price data and firm
characteristics.
1,608,569
9,981
11,903
Retain the last annual earnings forecast with a horizon between 1
and 12 months. This is our ‘clean’ I/B/E/S sample.
390,087
6,979
11,375
Merged above sample with Nelson Information’s Director of
Investment Research (NDIR) by hand-matching broker names.
242,092
6,739
8,215
We collect information on the names of domestic equity research
executives, merge these names with Clean IBES sample (that
includes first and last names of analysts) and require non-missing
value in all control variables
199,559
6,707
7,576
44
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.
DOR with industry
experience
An indicator variable equals to one if the DOR previously worked as a sell-side
analyst, zero otherwise.
DOR with same industry
experience
An indicator variable equals to one if the broker’s analyst covers a firm in the same
industry as their DORs’ former forecasting experience based on 4-digit Global
Industry Classification System (GICS), zero otherwise.
DOR with other industry
experience
An indicator variable equals to one if the broker’s analyst covers a firm in different
industries as their DORs’ former forecasting experience based on 4-digit Global
Industry Classification System (GICS), zero otherwise.
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.
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.
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.
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.
DGics
The number of GICS industries followed by analyst i at time t (Port Gics) minus the
average number of GICS industries followed by 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.
45
All-star (prior year)
Indicator variable is one if the analyst is named to Institutional Investor’s all-star team
in year t-1, 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.
Broker Ind. specialization
Percentage of total analysts in a broker that cover the underlying firm i’s GICS
industry.
Pin
The proportion of new analysts following industry j and recently joined the broker
relative to the number of total analysts following industry j during the calendar year
the forecast was issued
Pout
The proportion of analysts who were covering industry j, but left the broker during
the forecast calendar year, scaled by the total number of analysts following industry j
at the same broker
Ln(Freq)
The natural log transformed number of annual earnings forecasts that analyst i
made on firm j in year t.
Gaining (Losing) DOR
with same industry
experience
An indicator variable equals one if the forecast is issued in the years following the
corresponding DOR moving in (out of) the broker, zero if issued in the years
before.
Losing DOR with same
industry experience (only
retirement/death)
An indicator variable equals one if the forecast is issued in the years following the
corresponding DOR retiring from the broker/ death, zero if issued in the years
before.
Losing retired DOR with
same industry experience
An indicator variable equals one if the forecast is issued in the years following the
corresponding DOR retirement, zero if issued in the years before.
CAR (0,2)
CRSP-VW index-adjusted abnormal returns over 3 days (0, 2) 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.
EPS Change
The percentage change of the EPS forecast from most recent forecast.
Brokerage size
The total number of analysts working at a given analyst i’s brokerage house.
46
Average PMAFE
The mean annual PMAFE of forecasts issued by analyst i at time t.
Average Buy/Sell Rec
return
The mean monthly return following the analyst’s buy/sell recommendations
Average Firm Size
The mean size of firms in coverage analyst’s portfolio
All-star rank
Analyst’s rank in II polls between first, second, third and runner up
positions, where 1 represents the lowest rank and 4 represents the highest
rank.
Ln stocks with DOR
industry experience
The natural logarithm of 1 plus the number of firms that an analyst covers in
brokers where DORs has former analyst forecasting experience.
Ln stocks with DOR
same (other) industry
experience
The natural logarithm of 1 plus the number of firms that an analyst covers where inhouse DOR possess the same (other) GICS industry experience.
Average Broker Ind.
specialization
The average percentage of total analysts in a broker that cover the analyst i’s
portfolio GICS industries
Average Pin
The average Pin value of all the industries that analyst i covers in year t
Average Pout
The average Pout value of all the industries that analyst i covers in year t
Broker Market Share
Relative industry-specific broker equity trading commission market share,
which is measured as commissions allocated to broker j in industry k during
time t relative to total commissions in industry k.
Ln (1+dollar
commissions)
Total dollar commissions as measured by ln (1+dollar commissions) in
industry k for broker j during time t.
Hired by DOR with same
industry experience
An indicator variable equals to one if the newly hired analyst covers the same
industry as new broker’s DORs’ former forecasting experience based on 4-digit
Global Industry Classification System (GICS), zero otherwise.
Ln stocks with DOR
same/other industry
experience (no new hires)
The natural logarithm of 1 plus the number of firms that an analyst covers where inhouse DOR possess the same (other) GICS industry experience, conditional on that
analyst was hired before the incoming DORs with same industry experience.
DOR with same firm
experience
An indicator variable equals to one if the analyst covers the same firm in
DORs’ former forecasting portfolio, zero otherwise.
47
DOR with same industry
experience (not same firm)
An indicator variable equals to one if the analyst covers the same industry as
their DORs’ former forecasting experience based on 4-digit Global Industry
Classification System (GICS) excluding the same coverage firms, zero
otherwise.
DOR with same industry
experience (Connected/
Unconnected)
An indicator variable equals to one if the broker’s analyst covers a firm in the
same industry as their DORs’ former forecasting experience based on 4-digit
Global Industry Classification System (GICS), and the DORs possess social
and professional connections to management in subordinate analysts’
coverage firms, zero otherwise.
DOR with same industry
experience (change in Broker
Specialization High/LowRelated industry since DOR
appointment)
An indicator variable equals to one if the broker’s analyst covers a firm in the
same industry as their DORs’ former forecasting experience based on 4-digit
Global Industry Classification System (GICS), and the cumulative change in
broker specialization in the related industry since DOR appointment is
above/below median, zero otherwise.
DOR with same industry
experience (change in Pin
High/Low -Related industry
since DOR appointment)
An indicator variable equals to one if the broker’s analyst covers a firm in the
same industry as their DORs’ former forecasting experience based on 4-digit
Global Industry Classification System (GICS), and the cumulative change in
Pin in the related industry since DOR appointment is above/below median,
zero otherwise.
DOR with same industry
experience (change in Pout
High/Low -Related industry
since DOR appointment)
An indicator variable equals to one if the broker’s analyst covers a firm in the
same industry as their DORs’ former forecasting experience based on 4-digit
Global Industry Classification System (GICS), and the cumulative change in
Pout in the related industry since DOR appointment is above/below median,
zero otherwise.
48