The effect of industry co-location on analysts’ information acquisition costs
Jared Jennings
Olin Business School
Washington University in St. Louis
St. Louis, MO 63130-6431
[email protected]
Joshua Lee
Olin Business School
Washington University in St. Louis
St. Louis, MO 63130-6431
[email protected]
Dawn Matsumoto*
Foster Business School
University of Washington
Seattle, WA 98133
[email protected]
April 2014
*Corresponding author. We thank Kris Allee, Dave Burgstahler, Ted Christensen, Peter Demerjian, Weili Ge, Bret
Johnson, Sarah McVay, Rick Mergenthaler, Mike Minnis, Terry Shevlin, and Jake Thornock for helpful comments.
We also thank workshop participants at Santa Clara University, the University of Washington, the Nick Dopuch
Conference at Washington University, and the Accounting Research Symposium at Brigham Young University. All
errors are our own.
Electronic copy available at: http://ssrn.com/abstract=2308879
The effect of industry co-location on analysts’ information acquisition costs
ABSTRACT: We examine how the co-location of firms in the same industry affects analysts’ cost of
gathering and processing information. Prior research finds evidence consistent with firms being more
knowledgeable about other firms in the same geographical area. We argue that this improved knowledge
base affects the information set of financial analysts. Consistent with this conjecture, we find that analysts
include fewer firms in their portfolios when following firms located farther away from other firms in the
same industry. In addition, we find that firms located farther away from other firms in the same industry
have lower analyst following and less timely forecast revisions. We find that the additional costs that
analysts incur to follow distant firms are amplified when earnings are more difficult to forecast. Lastly,
we provide some evidence that managers communicate their knowledge about other firms in the same
geographic area to analysts and investors. Specifically, during conference calls, managers are more likely
to reference firms in their industry that are geographically closer. This paper provides additional evidence
that the co-location of firms in the same industry not only affects operating and strategic decisions (as
documented in the existing literature) but also analysts’ costs of gathering and analyzing information as
well as managers’ communication with external market participants.
Keywords: Geographic Location; Voluntary Disclosure; Security Analysts
JEL Classification: D83; M40; M41; R10; R12
Electronic copy available at: http://ssrn.com/abstract=2308879
I. INTRODUCTION
The co-location of firms in the same geographic area can affect the firm’s operations, strategy,
and information environment. Marshall (1920) suggests that co-locating near other firms in the same
industry results in several positive externalities, such as knowledge spillovers that facilitate innovation
and growth, specialized labor forces, and reduced costs from obtaining inputs or shipping products to
customers. The prior research suggests that these positive externalities increase managers’ knowledge and
awareness of other firms in the same geographic area (e.g., Lovely et al., 2005; Audretsch and Feldman,
1996). We add to the literature by examining how the firm’s location can affect its information
environment. Specifically, we examine whether financial analysts incur higher information gathering and
processing costs when following firms located farther away from other firms in the industry.
We predict that analysts’ information acquisition costs decrease when the firm is geographically
closer to other firms in the industry for three reasons. First, managers’ increased knowledge of other firms
in their geographical area, if communicated to analysts, is likely to improve analysts’ information set.
The prior research provides evidence consistent with executives and employees being more
knowledgeable about other firms in the same geographic area (Jaffe et al., 1993). Firms are also more
likely to co-locate when the benefits of knowledge sharing are higher (Audretsch and Feldman, 1996;
Lovely et al., 2005). We conjecture that managers communicate this information to analysts. For
example, managers may compare or benchmark their firm’s operations and performance with other firms
that are located in the same area. Second, analysts are more likely to visit firms that are located in close
proximity to other firms in the industry, which likely provides informational benefits by allowing analysts
1) to speak informally with managers as well as with lower level employees and 2) to quickly and
effectively compare the operations and facilities of the firms in the same geographic area. Third, local
media outlets are more likely to compare and contrast local firms (i.e., firms in the same geographic area)
reducing the analysts’ costs of performing similar analyses.
1
We conduct several tests to examine analysts’ costs of following firms that are located
geographically farther away from other firms in the industry.1 We first find that analysts follow fewer
firms when the average distance between the firms in the analyst’s portfolio and other firms in the same
industry increases. While this result implies that analysts bear higher costs when following firms located
farther from other firms in the industry, it does not necessarily imply that firms located farther from other
firms in their industry attract lower analyst attention or that analysts obtain less information about these
firms. Indeed, the benefits from following these distant firms might outweigh the greater information
acquisition costs such that analysts reduce their effort or coverage of other firms in the industry.
We specifically test whether the firm’s location in relation to other firms in the industry affects
analyst following and the quality of the analysts’ output. We find evidence that analyst following is lower
for firms that are located farther away from other firms in the same industry. We also find that analysts
revise their forecasts in a less timely manner when following geographically more distant firms,
suggesting that analysts incur higher information acquisition and processing costs when following these
firms. Interestingly, we do not find evidence in our main results that analyst forecast accuracy is lower, on
average, for firms located farther away from other firms in the industry.
We also examine when the geographical spacing of the firm relative to other firms in the industry
has a greater influence on analyst following, the timeliness of analyst revisions, and analyst forecast
accuracy. We expect the additional costs that analysts incur to follow firms farther from other firms in the
same industry are less relevant when earnings are relatively easy to forecast, potentially offsetting the
negative effects of being located farther away. First, we find that the negative association between the size
of the analyst’s portfolio and the average distance between the firms included in the analysts’ portfolio
and other firms in the same industry is magnified when earnings are more difficult to forecast. We also
1
While a firm’s choice of location is not exogenously determined, we do not believe that effects on analysts’ costs play a firstorder role in management’s decision on where to locate. Thus, we do not believe that reverse causality is a plausible alternative
explanation. It is possible that a third factor affects both the decision to locate outside an industry cluster as well as analyst costs
(i.e., a possible correlated omitted variable problem). As we discuss in our research design, we include numerous control
variables that prior research has identified as determinants of our dependent variables, as well as variables that capture potential
factors that determine the location decision. In addition, as discussed further below, our hypotheses include cross-sectional
analyses that reduce the likelihood that our results are driven by a correlated omitted variable.
2
find that analyst following, revision timeliness, and even forecast accuracy are more negatively associated
with the firm’s relative distance to other firms in the industry when earnings are inherently more difficult
to forecast. These results suggest that forecasting difficulty exacerbates the costs of acquiring and
analyzing information for firms that are geographically distant from other firms in the same industry.
Importantly, these results hold while controlling for several known determinants of the costs and
benefits of information acquisition, including size, growth, institutional ownership, performance, share
turnover, volatility, and business concentration. We also control for the co-movement of earnings, as
Engelberg et al. (2010) argue that the geographical clustering of firms within an industry leads to greater
co-movement of fundamentals. Our results suggest that the co-movement of fundamentals associated with
the firm’s geographic proximity to other firms in the industry is not the only mechanism through which
distance affects analysts’ information acquisition costs. Rather, managers’ knowledge of other firms in
the industry also likely plays a role.
Lastly, we provide some evidence to support our conjecture that managers communicate a portion
of their information about other firms in the same geographic area to analysts and other market
participants. Specifically, we examine the extent to which managers reference other firms during their
conference calls. We provide evidence that the probability of a manager mentioning a firm in the same
industry during a conference call decreases as the geographic distance between the two firms increases.
This evidence is consistent with managers having a greater knowledge of other firms in the same industry
and geographical area and with managers communicating this information to investors and analysts.
We contribute to both the analyst literature as well as the literature on firm geography. Beyer et
al. (2010) suggests that more research is needed to understand why firms are added to analyst portfolios
and what firm characteristics affect this decision. We directly add to the analyst literature by providing
evidence that the firm’s location can affect analyst information acquisition costs, thereby affecting analyst
portfolio composition decisions, the number of analysts following a firm, analysts’ ability to quickly
provide information to investors, and the accuracy of analyst outputs. We also provide evidence that the
additional analyst costs associated with locating farther away from other firms in the industry are
3
primarily driven by firms with earnings that are more difficult to forecast. This evidence is important to
investors and managers in understanding the costs that analysts incur to follow firms.
We also contribute to the growing literature on firms’ geographical location by examining the
effects of a firm’s location relative to other firms in its industry. Prior studies have examined an
individual analyst’s geographical location relative to the firm and the impact this has on that individual
analysts’ forecasting behavior (e.g., Orpurt, 2004; Malloy, 2005; Bae et al., 2008). In addition, Loughran
and Schultz (2005) find that fewer analysts follow firms located in rural areas, which are unlikely to be
areas of high analyst concentration. We believe that we make an important contribution to the literature
by documenting how the firm’s relative location to other firms in the industry affects the firm’s
information environment. Also, in additional robustness tests, we control for whether the firm is located
in a rural or urban area and find quantitatively similar results. Thus, our results do not seem to be
explained by the firm’s location in an area of high analyst concentration. Engelberg et al. (2010) also
examine the effects of a firm’s location relative to other firms in their industry. They demonstrate greater
co-movement in fundamentals among firms in an industry cluster, leading to higher analyst following. We
demonstrate that the effect of geographical clustering on analyst following go beyond the co-movement of
fundamentals, as the effect of distance relative to others in the industry exists even after controlling for
earnings co-movement. Moreover, our evidence based on our analysis of management’s communication
with analysts during conference calls supports our theory that managers have more information about
geographically closer firms in the industry and that they communicate this information to analysts,
improving the information set of analysts. Understanding how the firms’ location can influence the firm’s
information environment is likely of importance to managers, investors and other market participants.
The rest of the paper is organized as follows. In Section 2, we discuss the prior literature and
develop our hypotheses. In Section 3, we outline the empirical models, describe the sample and discuss
the results of the tests of our hypotheses. In Section 4, we examine the effect of co-location on managers’
voluntary disclosure policies. In Section 5, we conduct several robustness tests. We conclude this study
in Section 6.
4
II. THEORY AND HYPOTHESIS DEVELOPMENT
Co-location
Marshall (1920) identifies three positive externalities that arise from the localization of an
industry. First, the localization of an industry creates a demand for specialized labor. Second, the
geographic clustering of firms in an industry likely reduces the costs of obtaining inputs from suppliers
and shipping goods to customers. Third, the geographic clustering of firms within an industry allows for
knowledge spillovers that facilitate innovation and growth among firms in the industry. These positive
externalities likely improve managers’ knowledge of other firms in the same industry and geographical
area. For example, the demand for specialized labor can result in a shared labor pool and employees
moving between companies in the same industry. Prior research supports the notion that managers are
more knowledgeable of firms in close geographic proximity. For example, Jaffe et al. (1993) find
evidence that patent citations are attenuated when moving farther away from the original patent location.
Prior research also provides evidence that firms co-locate when the benefits to knowledge sharing are
greater. For example, Lovely et al. (2005) find evidence that firms dealing in high levels of foreign
market exports tend to co-locate in the same geographic location, especially when foreign market
information is difficult to obtain. Audretsch and Feldman (1996) find that more research and development
intensive firms are more likely to co-locate in similar geographic areas.
The idea that firms in the same industry, particularly competitors, would willingly share
information with each other might seem implausible. However, Stein (2008) provides a theoretical
framework for information spillovers among competitors. He argues that firms mutually benefit from
conversations with their competitors and analytically shows that honest knowledge sharing can occur
between firms even if the firms are in competition, as long as there are complementarities in idea
5
production – that is, that firms build on and refine an idea when exchanging information.2 Moreover,
knowledge sharing can occur not just with competitors but also with other firms in a firm’s supply chain –
i.e., customers and suppliers.
Analyst Behavior
We consider how the co-location of firms within an industry affects the information acquisition
and processing costs of an important set of information intermediaries, financial analysts. Investors use
analyst research (e.g., earnings forecasts, stock recommendations, target prices, and other qualitative and
quantitative analyses) to help establish earnings expectations and aid in the price discovery process (e.g.,
Brown and Rozeff, 1978; Givoly and Lakonishok, 1979; Brown et al, 1987; Fried and Givoly, 1982;
Asquith et al., 2005; Frankel et al., 2006). Therefore, understanding the factors that impact the quality of
analyst output is important.
Analysts gather data from many sources including firm disclosures, industry publications, market
trends, and informal communication with the firm’s employees. We anticipate that the geographic
location of the firm relative to other firms in the industry reduces analysts’ costs of obtaining and
analyzing useful and relevant information in three ways. First, as discussed above, executives and
employees likely know more about other firms in the same geographic area. To the extent managers
communicate this information to analysts (as hypothesized above) and this information is useful to
analysts, following firms located in close proximity to other firms in the industry will improve the
analysts’ information set. For example, managers or employees may use other firms as a benchmark or
reference point when talking about their firm. Managers can communicate this information to analysts
publicly (e.g., during a firm-sponsored conference call) as well as privately (e.g., during a site visit or a
2 Despite the benefits, not all firms choose to locate in close proximity to their competitors, suggesting additional costs or reduced
benefits to being located in an industry cluster. Shaver and Flyer (2000) argue that the costs of contributing to the industry cluster
outweigh the benefits of being part of the cluster for firms with the best technologies, training programs, suppliers, or
distributors. As a result, these firms are more likely to locate outside the industry cluster because they are less likely to benefit
from knowledge spillovers, a specialized labor force, and reduced transportation costs. Alcacer (2006) provides evidence
consistent with more-capable firms co-locating less often than less-capable firms in the cellular handset industry. Close
geographic locations can also increase competition in some industries. Baum and Haveman (1997) argue that hoteliers in
Manhattan locate close to each other when they are similar on one product dimension but different on other dimensions to avoid
local competition.
6
one-on-one phone call). While management cannot provide any private and material information that is
not also disclosed to the public (due to Regulation Fair Disclosure), analysts can ask clarifying and
follow-up questions about the firm’s current or future operations to improve the information content of
their recommendations or the accuracy of their forecasts.3
Second, when firms in the same industry are located in close proximity to one another, analysts
may be more likely to visit these firms and more likely to visit multiple firms in quick succession. While
out-of pocket transportation costs are admittedly trivial for most brokerage houses, time constraints are
likely real and, at the margin, could affect an analysts’ propensity to make a site visit. Physically visiting
a firm’s location likely provides additional informational benefits because it allows the analyst to talk
with managers informally and also allows them to speak with lower level employees. 4 In addition,
visiting multiple locations in quick succession likely enables analysts to more efficiently compare and
contrast the operations of those firms and their conversations with management and other employees,
potentially allowing them to identify subtle differences in the firms’ operations.
Finally, local information sources (e.g., local newspapers) may reduce analysts’ costs of gathering
and analyzing data about related firms located in the same geographical location. Local information
sources are more likely to compare and contrast the operations and performance of related firms in their
geographically area, reducing the amount of data gathering and analysis performed by the analyst.
Based on the above, we predict that the costs of gathering and processing information about a
firm are significantly higher for firms that are geographically more distant from other firms in the same
industry. We formally state our first hypothesis in alternative form below.
H1 -
Analysts incur greater costs to follow firms that are geographically farther away from other
firms in the same industry.
3
As an example, in a recent conference call held by Herbalife (January 10, 2013), management openly invited analysts and other
interested market participants to call management and visit the production facilities to obtain clarification on the firm’s business
model. In addition, Green et al. (2012) provide evidence that analysts who interact with corporate managers at broker-hosted
conferences have more informative stock recommendations and this persists in the post Reg FD era.
4 As evidence of the information advantage of visiting a firm’s physical location, Malloy (2005) finds that individual analysts
who are located closer to firms (presumably allowing them to visit the physical location more frequently) are relatively more
accurate than other analysts following the firm.
7
To the extent managers are unwilling to share information about other firms in the same
geographical area, site visits are ineffective, and the local information sources are uninformative, the
geographic distance of the firm to other firms in the same industry may not affect the information
acquisition costs of following a firm. It is possible that the vast majority of analysts’ information is
gathered through other channels such as mandatory disclosure, industry publications, and other industry
and firm specific data sources. Also, if firms operating farther away from other firms in the same industry
have earnings that are easier for analysts to forecasts, this could offset the increased costs and reduced
benefits from being located farther away. As a result, we examine how forecasting difficulty affects the
relation between the firm’s geographical spacing and analyst costs. We expect earnings that are easier to
forecast to at least partially offset the higher costs associated with following firms that are geographically
farther away from other firms in the same industry. Our formal hypothesis stated in alternative form is:
H2 -
Analysts’ information acquisition costs of following a firm that is geographically farther away
from other firms in the same industry increase with forecasting difficulty.
III. EMPIRICAL ANALYSIS
Sample Selection
We obtain our sample from the universe of U.S. firms with data from Compustat, CRSP, and
IBES between 1994 and 2011.5 We exclude firms in the financial services (SIC 6000 to 6999) and utilities
(SIC 4900 to 4999) industries due to differences in the information environment of these regulated
industries. We also require at least 20 observations per year for each two-digit SIC code to ensure that
firms have the opportunity to locate near other firms in the same industry.6,7 We also require there to be
sufficient data to calculate the dependent and independent variables in our regression analyses.
5 We choose 1994 as the initial start date of our sample because we collect address information from company reports issued to
the SEC which are readily available online through EDGAR beginning in 1994.
6 Our results are robust to alternative industry cutoffs of 10, 30, and 50 firms.
8
Following prior research, we use the firm’s headquarters as a proxy for its location.8 We collect
the addresses of firms’ headquarters from reports filed with the SEC for each firm/year.9 When missing,
we use the addresses provided by Compustat. We measure the distance of a firm relative to all other firms
in its industry using the universe of firms for which we have location data, regardless of the availability of
data to estimate our regression analyses. We calculate two variables to estimate the distance of a firm to
other firms within its industry.10 First, we define DIST 10i,t as the mean distance between the firm’s
headquarters and the headquarters of the 10 closest firms within the industry (two-digit SIC code) for
each year. Second, we define # FIRMS 100i,t as the number of firms in the industry located within 100
miles of the firm in each year.11,12 We multiply # FIRMS 100i,t by negative one so that DIST 10i,t and #
FIRMS 100i,t are both increasing in the geographic spacing of firms within the industry. We then
7
We define industry based on 2-digit SIC industry code classification to allow for the possibility of suppliers and customers
being included in the same industry, which we consider an important source of the information spillover effect (i.e., knowledge
about significant suppliers and customers that are communicated to analysts likely helps the analyst in forecasting for these
firms). An alternative measure of industry based on Hoberg and Phillips (2010) primarily identifies competitors, which is why
we do not choose to use their definitions for our main tests. However, we test the sensitivity of our results to defining our
distance variables using their industry definitions in Section 5.
8 Several finance and management papers use the firm’s headquarters as its location such as Coval and Moskowitz (1999), Zhu
(2002), Ivkovic and Weisbenner (2005), Loughran and Schultz (2004 and 2005), Malloy (2005) and Chhaochharia, Kumar, and
Niessen-Ruenzi (2012). We use the headquarters location rather than the state of incorporation because firms tend to incorporate
in states with favorable tax and bankruptcy laws and often do not incorporate in the state of their primary operations (Coval and
Moskowitz 1999). The headquarters is likely close to the firm’s principal operations and the headquarters is also where
management operates. For these reasons, information transfers are most likely to occur in the geographic area of the firm’s
headquarters. However, to the extent a firm operates in multiple geographic locations, information spillovers may occur in areas
outside the firm’s headquarters.
9 Compustat provides the most recent address of the firm. However, because our sample period covers such a long time span, we
manually collect the address of the firm’s headquarters as of each reporting date. The address is found in the firms’ annual report
and is potentially different than the business and mailing addresses provided in the header of each filing. We identify the
headquarters as the address the company specifically specifies as its “principal executive offices.”
10 Chhaochharia et al. (2012) use similar methods to measure the distance of the firm to institutional investors.
11 We choose to define our distance measures using the number rather than the percentage of firms in the industry located near
the firm’s headquarters. Information spillovers are likely to occur for firms located nearby, regardless of the number of other
firms in the industry that are not located nearby. For example, consider two firms, firm A and firm B. Firm A has 5 firms
located nearby and 20 firms in its industry. Firm B has 25 firms located nearby and 100 firms in its industry. An industry
concentration measure would indicate that 25 percent of firms are located nearby for both firms. However, firm B has greater
potential for information spillovers given the greater number of firms located nearby. For this reason, we use the number rather
than the percentage of firms located nearby in our main analysis. As a robustness test, we also examine distance measures using
the percentage of firms within an industry that are located nearby. See section 5 for additional detail.
12 As an additional robustness test, we perform all analyses using the mean distance between the firm and the 5 closest firms in its
industry and the number of firms located within 50 miles and find qualitatively similar results for all tests (untabulated). See
section 5.
9
standardize the distance measures to take values between 0 and 1 by placing them in deciles from 0 to 9
and dividing by 9.13
We next examine how several firm specific characteristics vary with our measures of distance. In
Table 1 we report the mean values of various firm characteristics for the full sample and for the partitions
of firms in the top and bottom halves of the distance distribution (computed using the DIST 10i,t variable).
In the final column, we report the p-values testing the difference in mean values between the firms in the
top and bottom partitions. We note no difference in market capitalization for firms that are farther away
from other firms in the same industry (MKT VALi,t). We find that firms that are farther away from other
firms in the industry have higher book-to-market ratios (BTMi,t), are more profitable (ROAi,t), have lower
stock turnover (TURNi,t), are less concentrated in a single industry (BUS CONCi,t), have lower earnings
volatility (EARN VOLi,t), lower return volatility (RET VOLi,t), are older (AGEi,t), and have earnings that
are more likely to co-move with the earnings of the ten geographically closest firms in the industry
(EARN COMOVEi,t) (see Appendix A for variable definitions).14, These significant differences highlight
the need to control for firm specific characteristics when examining our hypotheses. In untabulated results
we examine the differences between the bottom and top distance partition using the # FIRMS 100i,t
variable and find qualitatively similar results.
Table 1 also reports the average values of our distances measures for the full sample and for high
and low levels of DIST 10i,t. The values indicate that on average the firms in the top half of DIST 10i,t are
located approximately 229 miles from the ten closest firms in the same industry and have 4 industry firms
13
As examples, the following firms fell in the upper deciles of our distance measures. First, Key Tronic Corp, an electronics
manufacturing company located in Spokane, Washington is approximately 750 miles from the majority of the firms in its
industry (industrial and commercial machinery and computer equipment), which are located in San Jose, CA. Another example is
Tesla Motors Inc. located in Palo Alto, California which is over 2,000 miles from several major competitors in Detroit, Michigan
(e.g., Ford, GM, Spartan Motors). Finally, U.S. Energy Corp is located in Wyoming whereas the majority of the firms in its
industry (oil and gas extraction) are located in Texas.
14 The finding that firms located farther from firms in their industry have earnings that co-move more with the closest firms in
their industry is somewhat contrary to the findings in Engelberg et al. (2010). However, if we use industry-adjusted variables, we
find negative association between our distance measures and earnings co-movement, consistent with the findings in Engelberg et
al. (2010). Our main analysis includes industry fixed effects, which is equivalent to using industry mean-adjusted variables.
10
within 100 miles. In contrast, firms in the bottom half of the distribution are located approximately 10
miles from the ten closest firms in the same industry and have 56 firms within 100 miles.
Tests of Hypothesis 1
We use several tests to examine whether the analysts’ costs of gathering and analyzing
information about the firm increase when the firm is farther away from other firms in the industry. We
start by examining whether the number of firms included in the analyst’s portfolio decreases when the
firms in the analyst’s portfolio are farther away from other industry firms. We then move to firm specific
analyses and test whether analyst following, forecast revision timeliness, and accuracy are affected by the
location of the firm relative to other firms in the industry.
Analyst Portfolios
To examine whether the number of firms included in analysts’ portfolios is smaller when the
average distance of the firms in the portfolio is higher, we run the below regression by analyst/year:
ln(# FIRMS FOLLa,t) =
α0 + α1 MEAN DISTa,t + α2 BROKER SIZEa,t +
(1)
α3 ln(EXPERIENCEa,t) + α4 ln(# INDUSTRIESa,t) + α5 MEAN ln(MKT
VAL)a,t + α6 MEAN BTMa,t + α7 MEAN INST OWNa,t + α8 MEAN ROAa,t
+ α9 MEAN TURNa,t + α10 MEAN BUS CONCa,t + α11 MEAN EARN
VOLa,t + α12 MEAN RET VOLa,t + α13 MEAN ln(AGE)a,t + α14 MEAN
EARN COMOVEa,t + εa,t
The # FIRM FOLLa,t, variable is equal to the number of firms analyst a follows during year t. The
MEAN DISTa,t variable is the primary coefficient of interest and is either equal to the MEAN DIST 10a,t or
MEAN # FIRMS 100a,t variable. The MEAN DIST 10a,t (MEAN DIST 10a,t) variable is equal to the average
DIST 10i,t (# FIRMS 100i,t) variable for the firms that analyst a follows during year t. We then rank the
average distance variable into deciles from 0 to 9, and divide by 9.
We include several control variables that potentially explain the size of an analysts’ portfolio to
reduce the likelihood that correlated omitted variables are biasing our inferences. BROKER SIZEa,t is a
proxy for the size of the brokerage house for which analyst a works, measured using the number of firms
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followed by the brokerage house during year t. We standardize the BROKER SIZEa,t variable to take on
values between 0 and 1 by taking an ordinal ranking of the brokerage houses each year and dividing by
the total number of brokerage houses. We expect larger brokerage houses to have greater resource support
(e.g., subordinate analysts, databases, estimation techniques industry connections, etc.), which likely
allows for faster gathering and processing of information, resulting in larger portfolios. We also control
for the analyst’s experience (EXPERIENCEa,t), which is equal to the number of years that analyst a is
found on IBES as of the end of year t. More experienced analysts are potentially able to handle larger
portfolios. Finally, we control for the number of industries (# INDUSTRIESa,t) followed by the analyst,
defined as the number of unique industries (two-digit SIC) followed by analyst a during year t. To the
extent covering multiple industries is more difficult, we expect analysts to have smaller portfolios when
covering more industries.
We also control for certain characteristics of the firms in the analyst’s portfolio that are
potentially correlated with our distance measures. Using the variables identified in Table 1, we compute
the mean of each variable for all firms in analyst a’s portfolio at time t and re-label the variables with the
term MEAN in front to differentiate from the firm-specific variables reported in Table 1. For example, for
return on assets we compute the mean ROAi,t for all firms in analyst a’s portfolio at time t and call the
resulting variable MEAN ROAa,t. We include controls for the means of ln(MKT VAL)i,t, BTMi,t, INST
OWNi,t, ROAi,t, TURNi,t, BUS CONCi,t, EARN VOLi,t, RET VOLi,t, ln(AGE)i,t, and EARN COMOVEi,t. In
addition to the above, we include year indicator variables to control for unidentified time-variant
fluctuations in the size of analysts’ portfolios. We also cluster the standard errors by analyst to correct the
standard errors for potential serial-correlation.
Table 2 presents the results using the MEAN DIST 10i,t and MEAN # FIRMS 100a,t variables in
Column 1 and 2, respectively. We find that the coefficients on the MEAN DIST 10a,t (MEAN # FIRMS
100i,t) variable is equal to -0.191 (-0.455) and significant at the 1% level. Since we take the natural log of
the dependent variable, we are able to interpret the change from the 1st to 10th decile (values of 0 to 1) in
the MEAN DISTa,t variable as a percentage change in the dependent variable. As a result, the coefficient
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of -0.191 (-0.455) suggests a 19% (46%) decrease in the number of firms followed by the analyst when
the MEAN DIST 10a,t (MEAN # FIRMS 100a,t) variable moves from the 1st to 10th deciles, suggesting an
economically meaningful effect of distance on the size of an analyst’s portfolio.
We find that the coefficient on the BROKER SIZEa,t variable is positive and significant,
suggesting that analysts employed by larger brokerage houses have more resources and are able to follow
more firms. The coefficient on the EXPERIENCEa,t variable is positive and significant, suggesting that
experienced analysts follow more firms. The positive coefficient on the # INDUSTRIESa,t variable is
contrary to our expectations; however, it is possible that “generalist” analysts (i.e., analysts who do not
specialize in a particular industry) cover more firms than “specialist” analysts.
The coefficients on our average firm variables suggest that analysts cover more firms when the
firms in their portfolio have greater business segment concentration, lower return volatility, and greater
earnings co-movement with other firms in the geographic area and industry consistent with lower costs of
covering these firms. We also find a negative coefficient on the mean institutional ownership variable
suggesting that analysts follow fewer firms when the firms in their portfolios have a greater information
demand from large shareholders.
These results are consistent with the notion that covering more distant firms is more costly to
analysts, resulting in smaller portfolio sizes. Whether this additional cost results in distant firms having
lower coverage depends on whether the benefits of covering these firms are higher, which we explore in
the next sub-section.15
Analyst Following
Our first firm-level test examines whether analyst following is lower for geographically more
distant firms. The Pearson and Spearman correlations between the natural log of ANAL FOLLi,t and the
15
While the analysts’ portfolio test sheds light on whether analysts bear greater costs when covering distant firms, it does not
necessarily imply that more distant firms are covered by fewer analysts. Firm-specific benefits may offset the costs of following
more distant firms and analysts may incur the costs to follow these firms. This would imply lower overall analyst coverage in the
economy but does not necessarily imply that more distant firms will be followed less often. We, therefore, specifically test
whether firms that are farther away from other firms within the industry attract less analyst following.
13
distance measures (DIST 10i,t and # FIRMS 100i,t) are indeed negative and statistically significant, ranging
between -0.05 and -0.07 (untabulated). These correlations, while small, provide preliminary evidence that
analyst following is lower when the firm is located farther away from other firms in the industry.
We now turn our attention to the multivariate regression results. Our multivariate regression is
defined in Equation 2.
ln(ANAL FOLL)i,t =
α0 + α1 DISTANCEi,t + α2 ln(MKT VALi,t) + α3 BTMi,t + α4 INST OWNi,t + α5 (2)
ROAi,t + α6 TURNi,t + α7 BUS CONCi,t + α8 EARN VOLi,t + α9 RET VOLi,t +
α10 ln(AGEi,t) + α11 EARN COMOVEi,t + YEAR + INDUSTRY + εi,t
All dependent and independent variables are as previously defined or defined in Appendix A. In
addition, we include year and industry (two-digit SIC code) indicator variables to control for systematic
shifts in analyst following over time and across industries. We cluster the standard errors by firm due to
potential serial correlation in our dependent and independent variables (Petersen 2009). For all subsequent
tests, we include industry and year indicators variables and cluster the standard errors by firm unless
specified differently.
The DISTANCEi,t variable, defined as either DIST 10i,t or # FIRMS 100i,t, is our main variable of
interest. Column 1 (2) of Table 3 includes the results when the DISTANCEi,t variable is equal to the DIST
10i,t (# FIRMS 100i,t) variable. We find that the coefficients on the DIST 10i,t and # FIRMS 100i,t variables
are significant at the 1% level and 10% level, respectively, and equal to -0.075 and -0.042, which suggests
that moving from the 1st to 10th distance decile reduces analyst following by 4% to 8%.
Similar to Bhushan (1989), we find a positive coefficient on the ln(MKT VALi,t) variable,
suggesting that larger firms have higher analyst following. We find a positive coefficient on the book-tomarket ratio (BTMi,t), suggesting that lower growth or more established firms are more likely to have
higher analyst following. Similar to Bhushan (1989) and Alford and Berger (1999), we find a positive
coefficient on both the INST OWNi,t and TURNi,t variables, consistent with analysts’ responding to
14
investors’ demand for information. Contrary to Hayes (1998), we find a negative coefficient on ROAi,t.16
We find a positive coefficient on the business segment concentration variable (BUS CONCi,t), consistent
with analysts being more likely to follow firms that concentrate in a specific industry (Gilson et al., 2001).
We find a negative coefficient on the EARN VOLi,t and RET VOLi,t variables, suggesting that analysts are
attracted to firms with less volatile operating environments. We find a negative coefficient on AGEi,t
suggesting younger firms attract greater analyst following.17 Lastly, we find a positive coefficient on
EARN COMOVEi,t suggesting that firms that respond similarly to local information shocks as other
industry firms in their geographic area attract greater analyst following. This result is consistent with
findings in Engelberg et al. (2010). However, the fact that our distance measures are negative and
significant even after controlling for earnings co-movement suggests that the effect of distance on analyst
following is not solely because the co-movement of fundamentals lowers analysts’ information
acquisition costs.
Forecast Revision Timeliness
We next examine the timeliness of analyst revisions to further test whether analysts’ costs are
higher when following a firm farther away from other firms in the industry. Similar to Zhang (2008), we
assume that higher information acquisition and processing costs result in longer delays in issuing
forecasts following important news events such as earnings announcements. QUICK REVi,t is equal to the
percentage of analysts following firm i that issue a forecast revision within two days following the
quarterly earnings announcement.18 To be consistent with our annual tests, we compute the average of the
QUICK REVi,t variable over the four quarters during year t to obtain an annual measure of the timeliness
of forecast revisions. The Pearson and Spearman correlations between the QUICK REVi,t and distance
16 The univariate correlation between analyst following and ROA is positive (0.16 and 0.17 for Pearson and Spearman
correlations, respectively). Thus, the negative relation represents the marginal effect of performance on analyst following, after
controlling for our other determinants of analyst following.
17 This result runs counter to our expectation. However, the univariate correlation between firm age and analyst following is
positive (0.17). The reversed sign of the coefficient in the regression is likely due to the high correlation of firm age with other
firm characteristics (e.g., firm size, institutional ownership, and volatility).
18 In addition to using the percentage variable, we also use an indicator variable equal to 1 if any analyst following firm i during
year t issues a forecast revision within two days following the annual earnings announcement and find qualitatively similar
univariate and multivariate results (untabulated) as those reported in Table 4.
15
variables (DIST 10i,t and # FIRMS 100i,t) are significantly negative at the 1% level, ranging between 0.066 and -0.068. This provides preliminary evidence that analysts are less likely to revise forecasts
immediately following the annual earnings announcement when following firms farther away from other
firms in the industry.
In our multivariate analysis, we control for several firm characteristics, which are included in
Equation 2.
QUICK REVi,t =
β0 + β1 DISTANCEi,t + β2 ln(MKT VALi,t) + β3 BTMi,t + β4 INST OWNi,t + β5
(3)
ROAi,t + β6 TURNi,t + β7 BUS CONCi,t + β8 EARN VOLi,t + β9 RET VOLi,t + β10
ln(AGEi,t) + β11 EARN COMOVEi,t + β12 ln(ANAL FOLLi,t) + β13 BROKER
SIZEi,t + β14 ln(# FIRMS FOLLi,t) + β15 ln(EXPERIENCEi,t) + YEAR +
INDUSTRY + εi,t
In addition to the control variables previously discussed, we also include three analyst specific
variables: # FIRMS FOLLi,t, BROKER SIZEi,t, and EXPERIENCEi,t.19 # FIRMS FOLLi,t is the average
number of firms covered by the analysts following firm i during year t. BROKER SIZEi,t is the average
brokerage size for the analysts following firm i during year t. EXPERIENCEi,t is the average number of
years of experience held by the analysts following firm i during year t. We expect a negative coefficient
on # FIRMS FOLLi,t and positive coefficients on BROKER SIZEi,t and EXPERIENCEi,t.20
Table 4 reports the results using Equation 3. Similar to the preceding table, Column 1 (2) includes
the results using the DIST 10i,t (# FIRMS 100i,t) as the main variable of interest. We find that the
coefficients on the DIST 10i,t and # FIRMS 100i,t variables are equal to -0.022 and -0.023 and are
19
In previous tests, we show that analysts follow fewer firms when the firms in their portfolio are more distant from other firms
in the industry suggesting greater costs of covering distant firms. To the extent analysts cover fewer firms when covering more
distant firms, they would have more time to devote to the firms they do cover, potentially resulting in no difference in the
timeliness to revision. We specifically control for this possibility by including these analyst specific variables.
20 In additional robustness tests, we include control variables for the percentage of firms in the industry that announce earnings in
the [0,1] window surrounding the firm’s earnings announcement date and for the percentage of firms in the industry that have
announced earnings as of the day prior to the firm’s earnings announcement date. If firms that co-locate near other firms in the
industry are also more likely to time their announcements with other firms in the industry, our distance measures can potentially
be picking up an industry information effect rather than a distance effect. We find evidence that our distance measures are
negatively correlated with the percentage of concurrent announcements (correlation equal to -0.06) and positively correlated with
the percentage of prior announcements (correlation equal to 0.03). Including these variables in Equation 3 does not affect the
sign or significance of the coefficients on our distance variables.
16
significant at the 5% level. The coefficient estimate suggests moving from the lowest to the highest decile
of the DIST 10i,t (# FIRMS 100i,t) variable reduces the percentage of analysts that quickly revise their
forecasts by 5.1% (5.4%), relative to the mean of QUICK REVi,t. These results provide additional
evidence that analysts incur higher costs when following firms located farther away from other firms in
the industry.
With respect to our control variables, we find analysts revise more quickly when institutional
ownership (INST OWNi,t) and analyst following (ln(ANAL FOLLi,t)) are higher and performance (ROAi,t)
and firm age (ln(AGEi,t)) are lower, suggesting that an increase in the demand for timely information
increases with these factors. Analysts are also able to revise more quickly when firms have concentrated
operations (BUS CONCi,t), suggesting that the costs of covering such firms are lower. This result
corroborates Thomas (2002) who finds some evidence that analysts incur higher costs when following
firms that are less operationally concentrated by industry. We also include controls for growth (BTMi,t),
turnover (TURNi,t), earnings volatility (EARN VOLi,t), return volatility (RET VOLi,t), and earnings comovement (EARN COMOVEi,t); however, we find insignificant coefficients on each of these variables,
with the exception of a negative coefficient on return volatility, suggesting that analyst forecast revisions
are less timely when the firm’s operating environment is more volatile.
With respect to our analyst variables, we find a positive and significant coefficient on BROKER
SIZEi,t, suggesting that larger brokerage houses have more resources, which allow analysts to revise their
forecasts more quickly. We also find that the coefficient on EXPERIENCEi,t is positive and significant,
suggesting that more experienced analysts can more quickly process information and issue revisions. We
do not find a significant coefficient on # FIRMS FOLLi,t.
Analyst Accuracy
Our last test of Hypothesis 1 examines the effect of the firm’s location relative to other firms in
the industry on analysts’ forecast accuracy. To the extent analysts are unable to compensate for the lack of
information obtained from firms that are located geographically farther away from other industry firms,
analyst accuracy should suffer. We define ACCURACYi,t as the negative value of the absolute difference
17
between the last median quarterly analyst consensus forecast and actual EPS divided by share price for
firm i in year t. Similar to the QUICK REVi,t variable, we compute the average of the ACCURACYi,t
variable over the four quarters during year t to obtain an annual measure of accuracy. We find small but
statistically positive Pearson and Spearman correlations between ACCURACYi,t and our distance measures
(DIST 10i,t and # FIRMS 100i,t) of approximately 0.01, contrary to our expectations. However, we also
find that our distance and accuracy measures are highly negatively correlated with both earnings volatility
and return volatility. Thus, because more distant firms have lower volatility and lower volatility firms
have higher forecast accuracy, the positive univariate correlations between our distance measures and
accuracy might reflect a volatility effect rather than a distance effect. To examine whether this is the
case, we estimate the following multivariate regression:
ACCURACYi,t =
β0 + β1 DISTANCEi,t + β2 ln(MKT VALi,t) + β3 BTMi,t + β4 INST OWNi,t + β5 (4)
ROAi,t + β6 TURNi,t + β7 BUS CONCi,t + β8 EARN VOLi,t + β9 RET VOLi,t + β10
ln(AGEi,t) + β11 EARN COMOVEi,t + β12 ln(ANAL FOLLi,t) + β13 BROKER SIZEi,t
+ β14 ln(# FIRMS FOLLi,t) + β15 ln(EXPERIENCEi,t) + YEAR + INDUSTRY + εi,t
Our results using Equation 4 are found in Table 5. Both coefficients on the DIST10i,t and #
FIRMS 100i,t variables are insignificant, providing no evidence that analyst forecast accuracy is affected
by the location of the firm relative to other firms in the industry after controlling for important
characteristics associated with our distance measures. However, in the next sub-section we utilize
additional cross-sectional tests to examine whether the relation between analyst accuracy and the firms
location relative to other industry firms exists under certain conditions.
We include the same control variables from Equation 3 to control for possible alternative
explanations. Consistent with prior studies (e.g., Lang and Lundholm, 1996; Alford and Berger, 1999),
we find positive and significant coefficients on ln(MKT VALi,t), TURNi,t, INST OWNi,t, and ROAi,t. We
also find negative coefficients on EARN VOLi,t and RET VOLi,t, consistent with operating volatility
making forecasting more difficult and/or reducing analysts’ incentives to gather information about the
firm (Lang and Lundholm 1996). We find a negative coefficient on the BTMi,t, variable suggesting that
18
analysts are less accurate when following low growth firms. We also find a negative coefficient on the
ln(AGEi,t) variable suggesting forecasts are more accurate for younger firms.21
With respect to the analyst variables, we find a positive and significant coefficient on the ANAL
FOLLi,t variable (Alford and Berger, 1999), suggesting that higher analyst following results in a more
accurate consensus forecast due to increased analyst competition. The coefficient on the
ln(EXPERIENCEi,t) variable is negative and significant at the 10% level, which is contrary to our
expectation. The coefficients on the BROKER SIZEi,t and # FIRMS FOLLi,t variables are insignificant.
Test of Hypothesis 2
We now turn to our second hypothesis. We use the following regression to test whether the
geographic spacing of the firm relative to other firms in the industry has a more negative effect on
analysts’ information acquisition costs (Hypothesis 2).
ANAL BEHAVIORt =
η0 + η1 DISTANCEt + η2 HIGH EARN VOLt + η3 DISTANCEt * HIGH (5)
EARN VOLt + CONTROLSt + εt
We examine the four aspects of analyst behavior (ANAL BEHAVIORt) used to examine the first
hypothesis: size of the analyst’s portfolio (# FIRMS FOLLa,t), analyst following (ANAL FOLLi,t), forecast
revision timeliness (QUICK REVi,t), and forecast accuracy (ACCURACYi,t). Similar to the preceding
models, the DISTANCEi,t variable is equal to MEAN DIST 10a,t or MEAN # FIRMS 100a,t in the analyst
portfolio tests and DIST 10i,t or # FIRMS 100i,t in the analyst following, revision timeliness, and analyst
accuracy tests. We proxy for the difficulty in forecasting using the volatility in seasonally differenced
quarterly earnings over the prior 3 years.22 We then create an indicator variable equal to one if EARN
VOLi,t is above the median and 0 otherwise (HIGH EARN VOLi,t). For our analyst portfolio tests, the
partition is based on the mean EARN VOLi,t of the firms in the analysts’ portfolio (HIGH MEAN EARN
21
As in our analysis of analyst following, the univariate correlation between ln(AGEi,t) and ACCURACYi,t is positive (0.09)
suggesting the negative coefficient in the regression may be due to the high positive correlation between firm age and other
control variables (e.g., firm size, institutional ownership, and volatility).
22 We are attempting to capture forecasting difficulty that is inherent to the firm and not affected by voluntary disclosure choices,
such as the provision of guidance. The firm’s geographic distance to other firms in the industry may impact firms’ voluntary
disclosure choices. Therefore, we use earnings volatility to measure forecasting difficulty versus past forecast accuracy.
19
VOLa,t). We expect an insignificant coefficient on the DISTANCEi,t variable, suggesting that firms with
earnings that are easier to forecast are less likely to be affected by the additional costs associated with
being located farther away from other firms in the industry. We expect a negative coefficient on the
interaction between the DISTANCEi,t and HIGH EARN VOLi,t variables, suggesting that the firm’s
location relative to other firms in the industry is more likely to affect analysts’ cost of gathering and
analyzing information when earnings are more difficult to forecast. We also include all previously
discussed control variables in each specification.
We report the results of estimating Equation 5 in Table 6. Panel A presents the regression results
with the size of the analyst’s portfolio (# FIRMS FOLLa,t) as the dependent variable. The coefficient on
the MEAN DIST 10a,t variable is equal to 0.000 and insignificant, while the coefficient on the MEAN #
FIRMS 100a,t variable is equal to -0.297 and significant at the 1% level. This result provides some
evidence that the average distance between the firm and other firms in the industry decreases the number
of firms followed by the analyst even when earnings are relatively easy to forecast. More importantly,
both coefficients on the interactions between the average distance variables and the difficult to forecast
indicator variable are negative and significant at the 1% level, suggesting the effect of distance on
analysts’ costs of acquiring and analyzing information is more pronounced when earnings are more
difficult to forecast. The sum of the coefficients on the MEAN DIST 10a,t (MEAN # FIRMS 100a,t) variable
and the interaction term is equal to -0.298 (-0.558), suggesting that analysts who follow firms that are
geographically more distant to other firms in the industry are likely to follow 30% (56%) fewer firms. All
control variables have similar signs and magnitudes to those reported in Table 2.
In Panel B of Table 6, the dependent variable is analyst following (ln(ANAL FOLLi,t)). The
coefficients on DIST 10i,t and # FIRMS 100i,t are equal to -0.019 and 0.012 and are statistically
insignificant, suggesting that distance has no effect on analyst following for firms with low earnings
volatility. The coefficient on the interaction between the HIGH EARN VOLi,t and DIST 10i,t (# FIRMS
100i,t) variables is -0.088 (-0.087) and significant at the 1% level suggesting that firms with earnings that
are more difficult to forecast have lower analyst following when located farther away from other firms in
20
the industry. The sum of the DIST 10i,t (# FIRMS 100i,t) variable and the interaction term is equal to 0.107 (-0.075) and is significant at the 1% level, indicating that firms with high earnings volatility
experience a 11% (8%) decrease in analyst following when moving from the lowest to the highest deciles
of our distance variables. All other control variables are similar to those reported in Table 3. This
evidence suggests that distance is only an important detractor of analyst following when the firm has
earnings that are more difficult to forecast, which is consistent with our second hypothesis.
Panel C reports the results with analyst forecast timeliness (QUICK REVi,t) as the dependent
variable. We find insignificant coefficients of 0.004 and 0.006 on the DIST 10i,t and # FIRMS 100i,t
variables, suggesting that distance has no effect on forecast revision timeliness for firms with earnings
that are easier to forecast. We find negative and significant (1% level) coefficients of -0.045 and -0.053
on the interaction between the distance variables (DIST 10i,t and # FIRMS 100i,t) and the HIGH EARN
VOLi,t variable, suggesting that analyst forecast revisions are less timely for firms with high earnings
volatility. The sum of the coefficients on the DIST 10i,t (# FIRMS 100i,t) variable and the interaction term
is equal to -0.041 (-0.047) and is significant at the 1% level indicating that firms with high earnings
volatility experience a 9.6% (11.0%) decrease in the percentage of analysts revising forecasts (relative to
the mean of QUICK REVi,t) on the day of or the day following the annual earnings announcement. These
results are also consistent with our second hypothesis.
Finally, Panel D reports the results with analyst forecast accuracy (ACCURACYi,t) as the
dependent variable. Surprisingly, we find positive and significant (1% level) coefficients on the DIST 10i,t
and # FIRMS 100i,t variables of 0.224 and 0.205, respectively. These results suggest that distance
improves analyst forecasting ability for firms with low earnings volatility, which is contrary to
expectations. However, for firms with high earnings volatility, the effect reverses. The coefficient on the
interaction between the HIGH EARN VOLi,t and DIST 10i,t (# FIRMS 100i,t) variables is -0.347 (-0.293)
and significant at the 1% level, suggesting that firms with earnings that are more difficult to forecast have
lower forecast accuracy when located farther away from other firms in the industry. The sum of the
coefficients on the DIST 10i,t (# FIRMS 100i,t) variable and the interaction term is equal to -0.123 (-0.088)
21
and is significant at the 5% (10%) level, indicating that for firms with high earnings volatility moving
from the bottom to the top decile of the distance measure reduces analyst forecast accuracy by 19.5%
(13.9%), relative to the mean of ACCURACYi,t. These results contrast those reported in Table 5 and
suggest distance reduces forecast accuracy only for firms with earnings that are more difficult to forecast,
which is consistent with our second hypothesis.
Overall, we find largely consistent evidence that distance increases analysts’ gathering and
analyzing costs, primarily for firms with earnings that are more difficult to forecast.
IV. ANALYSIS OF MANAGEMENT COMMUNICATION
The prior evidence is consistent with our hypothesis that geographic proximity to other firms in
the industry produces information spillover effects that enhance managers’ knowledge of other firms in
the area. This enhanced knowledge reduces analysts’ information acquisition costs, assuming managers
communicate their knowledge to analysts. To provide some evidence on this issue, we examine the
likelihood that a manager references geographically close firms in their industry when communicating
with analysts, investors, and other market participants. Specifically, we examine whether firms are more
likely to mention, during their earnings conference calls, other firms in the same industry as the distance
between them decreases. We recognize that conference calls are just one mechanism through which
managers might convey their enhanced knowledge of other firms in their industry to analysts. As
discussed previously, informal meetings that occur during site visits as well as during one-on-one phone
calls also represent avenues through which managers can communicate their knowledge. However, these
modes of communication are not directly observable.
For our sample firms, we obtain conference call manuscripts from Factiva’s FD Wire from 2002
to 2011. We begin our analysis in 2002 because this is when Factiva began collecting conference calls.
We find 41,365 total conference calls for 13,251 of our sample firms-years. The mean (median) firm has
3.12 (3) calls each year, which is reasonable since firms typically have one conference call per quarter.
For each firm-year, we obtain the names of all other firms in the same industry from Compustat and
22
search the conference call transcripts for mentions of these firms’ names.23 We then create a dataset of all
firm i-j matches, where firm i is the firm holding the conference call and firm j is a firm in the same
industry as firm i. We do not require that firm j hold a conference call during the year. The resulting
dataset consists of 4.54 million unique observations.
We then create an indicator variable (MENTIONi,j,t) that is equal to one if the managers of firm i
or the analysts involved in the conference call mention firm j in any of firm i’s conference calls during
year t. We also create two additional indicator variables equal to one if the managers of the firm mention
the industry firm first (MENTION (FIRM FIRST)i,j,t) and if the analysts mention the industry firm first
(MENTION (ANAL FIRST)i,j,t). In these tests, we do not use the DIST 10i,t and # FIRM 100i,t variables (as
previously described) because we are looking at unique firm i-j combinations rather than average firm i-j
combinations for all firms in the industry. The DISTANCEi,j,t variable is our variable of interest and
represents the decile ranked distance between the headquarters of firm i and firm j during year t.24 We
find 16,594 total mentions of industry firms in our sample. While this may appear small relative to the
total number of paired observations in our sample, we find that 46% of our sample firms mention at least
one other firm in the same industry in their conference calls during the year.
Based on the significant differences in firm specific characteristics discussed in Section 3, we test
whether firms are less likely to mention more geographically distant industry firms by estimating the
following logistic regression.
CONF CALL MENTIONSi,j,t =
α0 + α1 DISTANCEi,j,t + α2 ln(MKT VALi,t) + α3 ln(MKT VALj,t) + α4 (6)
BTMi,t + α5 BTMj,t + α6 INST OWNi,t + α7 INST OWNj,t + α8 ROAi,t
+ α9 ROAj,t + α10 TURNi,t + α11 TURNj,t + α12 BUS CONCi,t + α13
23 A potential concern is the use of abbreviations such as “corp” instead of “corporation” or simply omitting the word
“corporation” when referencing another firm in the industry. We attempt to mitigate this problem by removing all instances of
the words “corporation” or “incorporated” (and their counterparts “corp” and “inc”) in our search. For firms in which the
company name is ambiguous, we include the word “corp” or “inc” in our search. For example, we include “corp” for “News
Corp” but exclude “corp” for “Microsoft Corp.” We also manually examine each company name to identify potential alternative
abbreviations likely used by analysts (e.g., Exxon for ExxonMobile).
24 The ranking process of the DISTANCE
i,j,t variable is similar to that for the DIST 10i,t and # FIRMS 100i,t variables. Using all
firm combinations, we decile rank the distance between firm i and j, subtract 1 and divide by 9 to obtain a variable ranking taking
on values between 0 and 1.
23
BUS CONCj,t + α14 EARN VOLi,t + α15 EARN VOLj,t + α16 RET
VOLi,t + α17 RET VOLj,t + + α18 ln(AGEi,t) + α19 ln(AGEj,t) + α20
ln(ANAL FOLLi,t) + α21 ln(ANAL FOLLj,t) + α22 COMPETITORi,j,t
+ α23 EARN COMOVEi,j,t + YEAR + INDUSTRY + εi,t
CONF CALL MENTIONSi,j,t equals either MENTIONSi,j,t, MENTIONS(FIRM FIRST)i,j,t, or
MENTIONS(ANAL FIRST)i,j,t. We expect a negative coefficient on the DISTANCEi,j,t variable. We include
control variables for both the mentioning firm (firm i) and the mentioned firm (firm j) since the firm
characteristics of either firm could influence the decision to mention a firm. We also include an indicator
variable equal to 1 if firm i and firm j are competitors and 0 otherwise (COMPETITORi,j,t). We define
competitors using the Hoberg and Phillips (2010) text-based network industry definitions, which use
contextual analysis of the product descriptions in the 10-K’s to identify product competitors. This variable
represents a potentially correlated omitted variable if firms locate closer to competitors and if firms are
more likely to mention competitors during conference calls. We also include the co-movement of
earnings between firm i and firm j (EARN COMOVEi,j,t), defined as the correlation between the quarterly
operating incomes scaled by lagged total assets of firm i and firm j over the twelve quarters prior to
period t, to control for the possibility that more similar firms (i.e., those that respond similarly to industry
shocks) locate more closely together and are more likely to mention each other in their conference calls.
We also include year and industry (two-digit SIC code) indicator variables to control for differences in
mentions over time and across industries. Finally, we cluster the standard errors by firm i-j combinations
due to potential serial correlation in our dependent and independent variables (Petersen, 2009).
We report the results of estimating Equation 6 in Table 7. We note that the coefficient on the
DISTANCEi,j,t variable is equal to -0.346 in Column 1, suggesting that managers and analysts are less
likely to mention other firms in the same industry when those firms are geographically farther away. We
also note that the odds ratio is equal to 0.71, suggesting that management and analysts are 29% less likely
to mention a firm in the same industry that is in the 10th distance decile compared to a firm in the 1st
distance decile. These results suggest that managers communicate their knowledge and awareness of other
24
industry firms in the same geographic area with analysts and investors during the earnings conference
call.25 We find similar results in Columns 2 and 3 when the managers of the firm are the first to mention
the industry firm and when the analysts are the first to mention the industry firm, respectively.
Specifically, the coefficient on DISTANCEi,j,t in Column 2 (Column 3) is equal to -0.308 (-0.409) and the
odds ratio is 0.73 (0.66). These results suggest that both managers and analysts are less likely to mention
industry firms that are located farther away from the firm.
We find that both characteristics of the mentioning firm as well as the mentioned firm influence
the decision to mention another firm. In particular, we note that firms are more likely to mention direct
competitors. We also note that managers are more likely to mention other industry firms that are older,
larger, have higher return volatility, have greater earnings co-movement, and have greater analyst
following. Interestingly, we find that higher institutional ownership and higher ROA reduces the
likelihood that managers mention another firm in the industry.
V. ROBUSTNESS TESTS
Changes Analysis
Our primary analysis is based on a “levels” specification because changes in firms’ headquarter
locations are relatively rare events. Moreover, the decision to move locations is not likely exogenous;
thus, the benefit of a changes specification relative to the costs associated with a much smaller sample
size is likely low. Nevertheless, we examine the effect of headquarter relocations on changes in analyst
characteristics surrounding these relocation events. We expect analysts’ information acquisition costs to
decrease when the firm relocates closer to other firms in the industry and to increase when the firm moves
farther away. We identify relocating firms as those in which the headquarters location in year t is greater
25
We also run an alternative specification at the firm/year level. The dependent variable is equal to one if firm i mentions another
firm in the same industry during year t, and our variable of interest is either DIST 10 or # FIRMS 100. The interpretation of these
results, however, is somewhat different. A significant coefficient on DIST 10 (#FIRMS 100) indicates that firms are more likely
to mention another firm in their industry when they are located closer to other firms in the industry (irrespective of the distance of
the specific firm mentioned). Results suggest this is the case, which is consistent with the results of our analysis reported here.
25
than or equal to 200 miles from the headquarters location in year t-1. We choose 200 miles to ensure the
firm is truly relocating away from industry firms in its previous geographical area. We also eliminate
observations where the move appears to be the result of a merger. We find a total of 192 total relocations
for our sample firms.
Using a multivariate analysis, we examine whether the changes (from year t-1 to t+1) in analyst
following, timeliness of revisions, and forecast accuracy decrease more for firms that move farther away
from other industry firms compared to firms who move closer. We identify firms that move farther away
by examining whether the change in DIST 10i,t and #FIRMS 100i,t increase from year t-1 to t+1. We also
include the changes in control variables from year t-1 to t+1 because changes in firm characteristics can
influence a firm’s decision to re-locate. The control variables include changes in market value, book to
market ratio, institutional ownership, return on assets, stock turnover, business concentration, earnings
volatility, returns volatility, and earnings co-movement within the industry. Finally, we include year and
industry fixed effects as in prior tests.
Using the change in the # FIRMS 100i,t variable, we find that firms that move farther away
relative to those firms that move closer to industry peers have lower analyst following, which is
marginally significant at the 10% level. We also find evidence that analysts’ forecast accuracy decreases
at the 1% and 10% significance level using the DIST 10i,t and #FIRMS 100i,t variables for firms that move
farther away compared to firms who move closer to industry peers. We do not find any evidence that the
percentage of analyst who revise their forecasts in a timely manner decreases more for firms that move
farther away relative to those firms who move closer to industry peers. Given the relatively small sample
size, we view the evidence based on this changes analysis as supportive of our prior results, suggesting
that proximity to other firms in the industry reduces analysts’ information acquisition costs.
Alternative Measurements and Controls
We also perform several robustness tests to examine the sensitivity of our analyses to various
measurement choices and additional control variables. First, we test the sensitivity of our industry
26
definitions. We use two-digit SIC codes throughout the main body of the paper because we want to use a
definition of industry that includes not only a firm’s direct competitors but also firms in the supply chain
(because information spillovers can occur across suppliers and customers as well). However, we also reperform each test using the Hoberg and Phillips (2010) industry definitions to identify firm competitors.26
Using the Hoberg and Phillips (2010) industry classifications, we find similar results (untabulated) for our
main analyses (Tables 3, 4, and 5). For the cross-sectional analysis based on forecasting difficulty (Table
6), we find similar results with the following exceptions: 1) analyst following is lower when the firm is
farther away from other firms within the industry regardless of whether earnings are more or less difficult
to forecast (i.e., the main effect of DIST 10i,t # FIRMS 100i,t is significantly negative but the interaction
terms with HIGH EARN VOLit are no longer significantly negative) and 2) in the forecast revision
timeliness regression, the interaction between HIGH_EARN_VOLit and #FIRMS100i,t is not significant at
conventional levels (t-stat of 1.60).. These tests suggest that our main results are not solely driven by the
co-location of significant suppliers and customers, who are more likely to share information because of
their shared operations, but also by competitors who locate in close proximity to the firm.
Next, our distance measures are potentially influenced by the size of the industry. For example, a
firm operating in an industry with 20 firms is less likely to have firms nearby than a firm operating in an
industry with 200 firms. In our main analysis, we control for this problem by including industry fixed
effects so that our distance measures are capturing a within-industry effect. However, we directly address
this issue using two additional sensitivity tests. First, we find qualitatively similar results (untabulated)
when we specifically control for the number of firms in the industry for each year. Second, we re-define
our distance measures to be relative to the size of the firm’s industry. We convert our two distance
variables into percentage variables: 1) DIST 10%i,t, defined as the distance between the firm and the
closest 10% of firms in the industry and 2) %FIRMS 100i,t, defined as the percentage of firms in the
industry within 100 miles of the firm. Our results (untabulated) are generally consistent with these
26
Hoberg and Phillips (2010) use product descriptions from mandatory disclosures to identify firms with the most similar
activities to identify text-based network industry classifications.
27
alternative definitions with three exceptions. First, we do not find significant coefficients on DIST 10%i,t
and %FIRMS 100i,t in our main analyst following tests (Table 3) However, if we instead define distance
as the percentage of firms within 50 miles of the firm (%FIRMS 50i,t), then we find a significantly
negative coefficient on the distance variable at the 5% level. Second, we do not find significantly negative
coefficients on either the main or interaction coefficients in the cross-sectional analyst following tests
(Table 6 Panel A) using %FIRMS 100i,t as our measure of distance; however, we do find a significantly
negative interaction coefficient on DIST 10%i,t and HIGH EARN VOLi,t, at the 10 percent level. Third, the
DIST 10%i,t variable is insignificant in the main analyst portfolio test (Table 2), but its interaction with
high earnings volatility is negative and significant in the cross-sectional test (Table 6, Panel A). In
general, our results, particularly with respect to analyst following, are somewhat sensitive to defining
distance based on the percentage of firms nearby versus defining distance based on the number of firms
nearby. However, because our results are robust to both including industry fixed effects as well as to
including the number of firms in the industry as an additional control variable, we do not believe the
sensitivity of our results to the use of percentages is the result of the size of the industry. Rather, we
interpret these results as evidence that it is the number of the firms in the industry rather than the
percentage of the firms in the area that influence analysts’ information acquisition costs.
Next, Loughran and Schulz (2005) examine whether firms in rural areas are followed by fewer
analysts than firms in urban areas. In untabulated analysis, we verify that our distance measures are not
merely capturing a “rural” or “urban” effect by including indicator variables for rural and urban firms in
the analyst following model. Specifically, following Loughran and Schulz (2005), we include a rural
indicator variable equal to 1 if the firm is located 100 miles or more from the center of any of the 49 U.S.
metropolitan statistical areas with one million or more people. We also include an urban indicator
variable equal to 1 if the firm is located in one of the ten most populated metropolitan statistical areas
during year t: New York, Los Angeles, Chicago, Washington-Baltimore, San Francisco, Dallas,
Philadelphia, Boston, Houston, or Detroit.
The sign and significance of the coefficients on the
DISTANCEi,t variables are unaffected by the inclusion of the rural and urban indicator variables.
28
Controlling for the mean effect of being located in an urban area also addresses the concern that our
results reflect the distance of the firm to the sell-side analyst. Malloy (2005) provides evidence that sellside analysts who are closer to the firm are likely to have more accurate recommendations. To the extent
that firms located in urban areas are geographically closer to a larger proportion of the analysts covering
the firm, our distance variable could be capturing the distance of the firm to the analyst (versus the
information-spillover effect associated with being closer to other firms in the industry). The inclusion of
the urban indicator variable addresses this concern.27
VI. CONCLUSION
We provide evidence that the co-location of firms within an industry affects analysts’ costs of
gathering and processing information. Based on the prior research, we argue that firm managers who are
located closer to other firms in the same industry and geographic area are more likely to know more about
these firms, increasing the level of industry specific information held by these managers. If managers
communicate this information to analysts, we expect analysts’ costs of gathering and processing
information to decrease as they follow firms that are located geographically closer to other firms in the
industry. We provide evidence consistent with this prediction. First, we find that analyst portfolios
contain fewer firms when the firms included in the portfolio are located farther away from other industry
firms. We also find that fewer analysts follow firms that are located farther away from other firms in the
industry and they are less likely to revise their forecasts immediately following the earnings
announcement, suggesting increased costs to follow these firms. In additional cross-sectional tests, we
find that the increased analyst costs that are associated with being farther away from other firms in the
same industry are less of a concern when earnings are easier to forecast: the negative impact of distance
on the size of analysts’ portfolios, the number of analysts following a firm, the timeliness of forecast
revisions, the accuracy of analysts’ forecasts is greater when earnings are more difficult to forecast.
27
Directly controlling for the average distance between the firm and the analysts covering the firm is problematic
because the location of the analyst is not readily measurable.
29
Lastly, we provide supporting evidence that managers are more knowledgeable about firms in the
same industry that are geographically closer by examining the location of those firms mentioned by
management during conference calls. We find that firms are less likely to mention other firms in the
industry during conference calls as they become more geographically distant, providing some evidence
that firms are more knowledgeable about firms that are geographically closer.
To the best of our knowledge, we are the first to thoroughly examine how the firm’s geographic
location affects analysts’ information acquisition costs. Our results are of potential interest to investors in
understanding how the location of the firm relative to other firms in the industry affects information
intermediaries’ costs of obtaining information about the firm. In this paper, we do not address the specific
mechanisms by which the information is transferred. We leave this research question open to future
research.
30
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32
APPENDIX A: VARIABLE DEFINITIONS
Variable
# FIRMS 100i,t
Definition
The decile rank (between 0 and 9 and divided by 9) of the number of
firm within 100 miles of firm i's headquarters during year t
multiplied by negative one
# FIRMS FOLLa,t
The number of firms followed by analyst a during year t.
# FIRMS FOLLi,t
The average # FIRMS FOLLa,t for all analysts following firm i in
year t.
# IND FIRMSi,t
# INDUSTRIESa,t
The number of firms in firm i's industry during year t.
The number of industries followed by analyst a during year t.
# INDUSTRIESi,t
The average # INDUSTRIESa,t for all analysts following firm i in
year t.
ACCURACYi,t
The average over the four quarters during year t of the absolute value
of the quarterly analyst consensus forecast less actual EPS divided
by price multiplied by -1 for firm i in year t.
AGEi,t
The number of years between the fiscal period end date and the date
the firm first appears in the Compustat database for firm i in year t.
ANAL FOLLi,t
The number of analysts following firm i in year t.
BROKER SIZEa,t
The normalized rank of analyst a's brokerage house based on the
number of firms followed by the broker during year t.
BROKER SIZEi,t
The average BROKER SIZEa,t for all analysts following firm i in year
t.
BTMi,t
The book value of equity divided by the market value of equity for
firm i in year t.
BUS CONCi,t
The firm's business segment concentration defined as the firmspecific Herfindahl-Hirschman Index computed for firm i's industry
business segment sales in year t.
COMPETITORi,j,t
An indicator variable equal to 1 if firm j is one of firm i's competitors
in year t based on the Hoberg and Phillips (2010) text-based network
industry classifications.
DIST 10i,t
The decile rank (between 0 and 9 and divided by 9) of the mean
number of miles from firm i to its 10 geographically closest industry
peers during year t.
DISTANCEi,j,t
The decile rank (between 0 and 9 and divided by 9) of the number of
miles between firm i and firm j in year t.
33
EARN COMOVEi,t
EARN VOLi,t
The average correlation between firm i’s operating income scaled by
lagged total assets and the operating incomes scaled by lagged total
assets of the ten geographically closest firms in the same industry
over the previous 12 quarters (requiring a minimum of 8 prior
quarters to compute this variable)
The standard deviation of seasonally-differenced earnings over the
last 12 quarters for firm i at the end of year t (requiring a minimum
of 8 prior quarters to compute this variable)
EXPERIENCEa,t
The number of years since analyst a first appears in the IBES
database as of year t.
EXPERIENCEi,t
The average EXPERIENCEa,t for all analysts following firm i in year
t.
HIGH EARN VOLi,t
An indicator variable equal to 1 if EARN VOLi,t is above the median
and 0 otherwise.
INST OWNi,t
The percentage of firm i's shares held by institutional investors in
year t.
MEAN # FIRMS 100a,t
The mean of # FIRM 100i,t for all firms followed by analyst a during
year t.
MEAN BTMa,t
The mean of BTMi,t for all firms followed by analyst a during year t.
MEAN BUS CONCa,t
The mean of BUS CONCi,t for all firms followed by analyst a during
year t.
MEAN DIST 10a,t
MEAN EARN COMOVEa,t
The mean of DIST 10i,t for all firms followed by analyst a during
year t.
The mean of EARN COMOVEi,t for all firms followed by analyst a
during year t.
MEAN EARN VOLa,t
The mean of EARN VOLi,t for all firms followed by analyst a during
year t.
MEAN INST OWNa,t
The mean of INST OWNi,t for all firms followed by analyst a during
year t.
MEAN ln(AGE)a,t
MEAN ln(MKT VAL)a,t
MEAN RET VOLa,t
The mean of ln(AGEi,t) for all firms followed by analyst a during
year t.
The mean of ln(MKT VALi,t) for all firms followed by analyst a
during year t.
The mean of RET VOLi,t for all firms followed by analyst a during
year t.
34
MEAN ROAa,t
The mean of ROAi,t for all firms followed by analyst a during year t.
MEAN TURNa,t
The mean of TURNi,t for all firms followed by analyst a during year
t.
MENTIONi,j,t
An indicator variable equal to 1 if firm i mentions firm j (a firm in
the same industry as firm i) in any of its conference calls during year
t.
MKT VALi,t
QUICK REVi,t
The market value of equity of firm i at the end of year t.
The average over the four quarters during year t of the percentage of
analyst forecasts revised on day 0 or day 1 following the quarterly
earnings announcements for firm i in year t.
RET VOLi,t
The standard deviation of monthly returns over the last 12 months
for firm i at the end of year t.
ROAi,t
The net income before extraordinary items divided by lagged total
assets for firm i in year t.
TURNi,t
The percentage of firm i's shares traded during year t.
35
TABLE 1
Descriptive Statistics
This table presents the means of firm-specific variables for the bottom and top halves of the DIST 10i,t
variable and for the full sample. We also report the p-value of the t-test of the difference in means
between the two groups. *, **, and *** represent significance at the 10%, 5%, and 1% levels,
respectively. All variables are defined in Appendix A. All continuous variables are winsorized at the
1st and 99th percentiles.
DIST 10i,t Partition
Bottom
N = 16,510
Top
N = 16,511
All
N = 33,021
Top v. Bottom
DIST 10i,t (UNRANKED)
10.933
232.461
121.707
0.00***
# FIRMS 100i,t (UNRANKED)
-54.168
-3.958
-29.061
0.00***
MKT VALi,t
3,447.3
2,854.8
3,151.0
0.10
BTMi,t
0.460
0.579
0.520
0.00***
INST OWNi,t
0.589
0.600
0.595
0.11
ROAi,t
-0.012
0.045
0.016
0.00***
TURNi,t
0.023
0.019
0.021
0.00***
BUS CONCi,t
0.939
0.903
0.921
0.00***
EARN VOLi,t
0.047
0.026
0.036
0.00***
RET VOLi,t
0.151
0.127
0.139
0.00***
AGEi,t
17.101
20.961
19.031
0.00***
EARN COMOVEi,t
0.061
0.071
0.066
0.00***
Variable
36
TABLE 2
Number of Firms Followed Regression Results.
This table includes all analyst/year observations from 1994 to 2011 with sufficient data to calculate the
dependent and independent variables. The dependent variable is the natural logarithm of the number of
firms followed by analyst a during year t (ln(# FIRMS FOLLa,t)). Columns 1 and 2 include the mean of
DIST 10i,t and # FIRMS 100i,t, respectively, for all firms followed by analyst a during year t as the
independent variables of interest (MEAN DIST 10a,t and MEAN # FIRMS 100a,t). All variables are
defined in Appendix A. Year fixed effects are included as additional independent variables. The
coefficients on the year indicator variables are suppressed. Standard errors are clustered by analyst. All
continuous variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at
the 10%, 5%, and 1% levels, respectively.
INTERCEPT
MEAN DIST 10a,t
[1]
ln(# FIRMS FOLLa,t)
[2]
ln(# FIRMS FOLLa,t)
-0.565***
(-7.007)
-0.191***
(-11.213)
-0.272***
(-3.391)
0.976***
(29.647)
0.372***
(68.294)
0.743***
(66.483)
0.019***
(3.847)
0.000
(1.374)
-0.361***
(-8.982)
-0.001
(-1.314)
0.163
(0.494)
1.069***
(18.404)
0.017
(1.311)
-0.716***
(-6.731)
-0.021
(-1.546)
0.402***
(10.396)
-0.455***
(-27.073)
0.978***
(30.350)
0.363***
(67.527)
0.737***
(68.378)
0.004
(0.895)
0.000
(1.179)
-0.351***
(-8.947)
-0.001
(-1.278)
-0.322
(-0.994)
0.954***
(16.520)
0.008
(0.851)
-1.023***
(-9.953)
0.014
(1.041)
0.468***
(12.437)
56,128
0.459
56,128
0.478
MEAN # FIRMS 100a,t
BROKER SIZEa,t
ln(EXPERIENCEa,t)
ln(# INDUSTRIESa,t)
MEAN ln(MKT VAL)a,t
MEAN BTMa,t
MEAN INST OWNa,t
MEAN ROAa,t
MEAN TURNa,t
MEAN BUS CONCa,t
MEAN EARN VOLa,t
MEAN RET VOLa,t
MEAN ln(AGE)a,t
MEAN EARN COMOVEa,t
#OBS
Adjusted R2
37
TABLE 3
Analyst Following Regression Results
This table includes all firm/year observations from 1994 to 2011 with sufficient data to calculate the
dependent and independent variables. The dependent variable is the natural logarithm of the number of
analysts following firm i during year t (ln(ANAL FOLLi,t)). Column 1 (2) includes DIST 10i,t (# FIRMS
100i,t) as the independent variable of interest. All variables are defined in Appendix A. Year and industry
(two-digit SIC code) fixed effects are included as additional independent variables. The coefficients on
the year and industry indicator variables are suppressed. Standard errors are clustered by firm. All
continuous variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at
the 10%, 5%, and 1% levels, respectively.
INTERCEPT
[1]
[2]
ln(ANAL FOLLi,t)
ln(ANAL FOLLi,t)
0.091
(0.789)
0.054
(0.470)
-0.075***
(-3.318)
DIST 10i,t
-0.042*
(-1.896)
# FIRMS 100i,t
ln(MKT VALi,t)
0.332***
(87.875)
0.333***
(88.076)
BTMi,t
0.159***
(13.454)
0.160***
(13.526)
INST OWNi,t
0.347***
(15.493)
0.347***
(15.475)
ROAi,t
-0.398***
(-14.586)
-0.403***
(-14.769)
TURNi,t
7.599***
(28.181)
7.628***
(28.262)
BUS CONCi,t
0.155***
(5.281)
0.159***
(5.400)
EARN VOLi,t
-0.250***
(-3.145)
-0.240***
(-3.012)
RET VOLi,t
-0.260***
(-4.427)
-0.256***
(-4.364)
ln(AGEi,t)
-0.108***
(-13.697)
-0.108***
(-13.821)
EARN COMOVEi,t
0.058***
(3.300)
0.060***
(3.405)
33,021
33,021
0.673
0.673
#OBS
2
Adjusted R
38
TABLE 4
Analyst Forecast Revision Timeliness Regression Results
This table includes all firm/year observations from 1994 to 2011 with sufficient data to calculate the
dependent and independent variables. The dependent variable is the average percentage of analyst forecasts
revised on day 0 or day 1 following the quarterly earnings announcement for firm i in year t (QUICK REVi,t).
Column 1 (2) includes DIST 10i,t (# FIRMS 100i,t) as the independent variable of interest. All variables are
defined in Appendix A. Year and industry (two-digit SIC code) fixed effects are included as additional
independent variables. The coefficients on the year and industry indicator variables are suppressed. Standard
errors are clustered by firm. All continuous variables are winsorized at the 1% and 99% levels. *, **, and ***
represent significance at the 10%, 5%, and 1% levels, respectively.
INTERCEPT
DIST 10i,t
[1]
QUICK REVi,t
[2]
QUICK REVi,t
-0.567***
(-12.342)
-0.022**
(-2.462)
-0.568***
(-12.399)
-0.002
(-0.976)
0.004
(0.699)
0.071***
(7.035)
-0.020*
(-1.687)
0.160
(1.393)
0.054***
(4.161)
0.044
(1.272)
-0.050*
(-1.840)
-0.017***
(-5.261)
0.008
(0.916)
0.063***
(15.366)
0.416***
(17.593)
0.006
(0.780)
0.027***
(5.611)
-0.023**
(-2.534)
-0.002
(-0.949)
0.004
(0.725)
0.071***
(7.020)
-0.020*
(-1.734)
0.157
(1.366)
0.055***
(4.205)
0.045
(1.279)
-0.050*
(-1.843)
-0.017***
(-5.314)
0.008
(0.961)
0.063***
(15.426)
0.415***
(17.524)
0.006
(0.789)
0.027***
(5.631)
33,021
0.521
33,021
0.521
# FIRMS 100i,t
ln(MKT VALi,t)
BTMi,t
INST OWNi,t
ROAi,t
TURNi,t
BUS CONCi,t
EARN VOLi,t
RET VOLi,t
ln(AGEi,t)
EARN COMOVEi,t
ln(ANAL FOLLi,t)
BROKER SIZEi,t
ln(# FIRMS FOLLi,t)
ln(EXPERIENCEi,t)
#OBS
Adjusted R2
39
TABLE 5
Analyst Forecast Accuracy Regression Results
This table includes all firm/year observations from 1994 to 2011 with sufficient data to calculate the
dependent and independent variables. The dependent variable is average quarterly analyst forecast
accuracy (ACCURACYi,t) for firm i in year t. Column 1 (2) includes DIST 10i,t (# FIRMS 100i,t) as the
independent variable of interest. All variables are defined in Appendix A. Year and industry (two-digit
SIC code) fixed effects are included as additional independent variables. The coefficients on the year
and industry indicator variables are suppressed. Standard errors are clustered by firm. All continuous
variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at the 10%, 5%,
and 1% levels, respectively.
INTERCEPT
DIST 10i,t
[1]
ACCURACYi,t
[2]
ACCURACYi,t
-0.575***
(-2.986)
0.028
(0.683)
-0.590***
(-3.088)
0.056***
(4.936)
-0.196***
(-5.373)
0.443***
(9.739)
0.742***
(9.397)
1.725**
(2.478)
-0.056
(-1.195)
-3.333***
(-12.880)
-3.944***
(-18.661)
-0.136***
(-9.365)
-0.005
(-0.118)
0.209***
(10.474)
0.168
(1.273)
-0.001
(-0.029)
-0.042*
(-1.734)
0.046
(1.232)
0.056***
(4.944)
-0.196***
(-5.379)
0.443***
(9.743)
0.741***
(9.409)
1.743**
(2.504)
-0.056
(-1.195)
-3.329***
(-12.870)
-3.942***
(-18.664)
-0.136***
(-9.346)
-0.005
(-0.123)
0.209***
(10.489)
0.171
(1.293)
-0.001
(-0.025)
-0.042*
(-1.748)
33,021
0.276
33,021
0.276
# FIRMS 100i,t
ln(MKT VALi,t)
BTMi,t
INST OWNi,t
ROAi,t
TURNi,t
BUS CONCi,t
EARN VOLi,t
RET VOLi,t
ln(AGEi,t)
EARN COMOVEi,t
ln(ANAL FOLLi,t)
BROKER SIZEi,t
ln(# FIRMS FOLLi,t)
ln(EXPERIENCEi,t)
#OBS
Adjusted R2
40
TABLE 6
Cross Sectional Regression Results for Analyst Properties
This table includes all firm/year observations from 1994 to 2011 with sufficient data to calculate the dependent and
independent variables. The dependent variables in Panels A through D are ln(# FIRMS FOLLa,t), ln(ANAL FOLLi,t),
QUICK REVi,t, and ACCURACYi,t, respectively. All variables are defined in Appendix A. Year fixed effects are
included as additional independent variables. Industry (two-digit SIC code) fixed effects are included in Panels B
through D. The coefficients on the year and industry indicator variables are suppressed. Standard errors are
clustered by firm. All continuous variables are winsorized at the 1% and 99% levels. *, **, and *** represent
significance at the 10%, 5%, and 1% levels, respectively.
Panel A. Number of Firms Followed by Analysts
INTERCEPT
MEAN DIST 10a,t
MEAN DIST 10a,t * HIGH MEAN EARN VOLa,t
[1]
ln(# FIRMS FOLLa,t)
[2]
ln(# FIRMS FOLLa,t)
-0.661***
(-8.105)
0.000
(0.016)
-0.298***
(-10.242)
-0.354***
(-4.340)
MEAN # FIRMS 100a,t
0.268***
(12.995)
0.977***
(29.925)
0.368***
(67.216)
0.737***
(66.369)
0.024***
(4.711)
0.000
(1.389)
-0.374***
(-9.239)
-0.000
(-0.762)
-0.634*
(-1.947)
1.042***
(17.885)
-1.077***
(-10.519)
-0.017
(-1.244)
0.405***
(10.649)
-0.297***
(-11.692)
-0.261***
(-8.772)
0.187***
(9.306)
0.972***
(30.252)
0.361***
(67.011)
0.739***
(68.916)
0.007
(1.456)
0.000
(1.179)
-0.364***
(-9.231)
-0.000
(-0.759)
-0.788**
(-2.424)
0.939***
(16.225)
-1.197***
(-11.998)
0.015
(1.117)
0.473***
(12.675)
56,128
0.464
56,128
0.481
MEAN # FIRMS 100a,t * HIGH MEAN EARN VOLa,t
HIGH MEAN EARN VOLa,t
BROKER SIZEa,t
ln(EXPERIENCEa,t)
ln(# INDUSTRIESa,t)
MEAN ln(MKT VAL)a,t
MEAN BTMa,t
MEAN INST OWNa,t
MEAN ROAa,t
MEAN TURNa,t
MEAN BUS CONCa,t
MEAN RET VOLa,t
MEAN ln(AGE)a,t
MEAN EARN COMOVEa,t
#OBS
Adjusted R2
F-tests:
DISTANCEi,t + DISTANCEi,t * HIGH MEAN EARN VOLi,t = 0
Values
-0.298***
-0.558***
41
Panel B. Analyst Following
INTERCEPT
DIST 10i,t
DIST 10i,t * HIGH EARN VOLi,t
[1]
ln(ANAL FOLLi,t)
[2]
ln(ANAL FOLLi,t)
0.023
(0.196)
-0.019
(-0.687)
-0.088***
(-3.379)
-0.012
(-0.100)
# FIRMS 100i,t
0.076***
(4.746)
0.334***
(88.421)
0.167***
(14.243)
0.355***
(15.964)
-0.356***
(-13.190)
7.401***
(27.518)
0.151***
(5.160)
-0.331***
(-5.688)
-0.106***
(-13.580)
0.054***
(3.067)
0.012
(0.440)
-0.087***
(-3.348)
0.077***
(4.825)
0.335***
(88.683)
0.167***
(14.299)
0.355***
(15.927)
-0.361***
(-13.371)
7.427***
(27.614)
0.154***
(5.251)
-0.329***
(-5.650)
-0.106***
(-13.671)
0.056***
(3.183)
33,021
0.673
33,021
0.673
# FIRMS 100i,t * HIGH EARN VOLi,t
HIGH EARN VOLi,t
ln(MKT VALi,t)
BTMi,t
INST OWNi,t
ROAi,t
TURNi,t
BUS CONCi,t
RET VOLi,t
ln(AGEi,t)
EARN COMOVEi,t
#OBS
Adjusted R2
F-tests:
DISTANCEi,t + DISTANCEi,t * HIGH EARN VOLi,t = 0
Values
-0.107***
-0.075***
42
Panel C. Forecast Revision Timeliness
INTERCEPT
DIST 10i,t
DIST 10i,t * HIGH EARN VOLi,t
[1]
QUICK REVi,t
[2]
QUICK REVi,t
-0.584***
(-12.729)
0.004
(0.348)
-0.045***
(-4.305)
-0.586***
(-12.815)
# FIRMS 100i,t
0.032***
(4.974)
-0.001
(-0.681)
0.004
(0.825)
0.072***
(7.141)
-0.015
(-1.313)
0.128
(1.119)
0.054***
(4.123)
-0.055**
(-2.065)
-0.016***
(-5.116)
0.006
(0.786)
0.062***
(15.149)
0.415***
(17.608)
0.006
(0.801)
0.027***
(5.734)
0.006
(0.533)
-0.053***
(-4.939)
0.036***
(5.523)
-0.001
(-0.655)
0.005
(0.871)
0.072***
(7.120)
-0.015
(-1.301)
0.122
(1.064)
0.054***
(4.126)
-0.056**
(-2.102)
-0.016***
(-5.130)
0.007
(0.847)
0.062***
(15.195)
0.414***
(17.575)
0.006
(0.761)
0.028***
(5.782)
33,021
0.522
33,021
0.522
# FIRMS 100i,t * HIGH EARN VOLi,t
HIGH EARN VOLi,t
ln(MKT VALi,t)
BTMi,t
INST OWNi,t
ROAi,t
TURNi,t
BUS CONCi,t
RET VOLi,t
ln(AGEi,t)
EARN COMOVEi,t
ln(ANAL FOLLi,t)
BROKER SIZEi,t
ln(# FIRMS FOLLi,t)
ln(EXPERIENCEi,t)
#OBS
Adjusted R2
F-tests:
DISTANCEi,t + DISTANCEi,t * HIGH EARN VOLi,t = 0
Values
-0.041***
-0.047***
43
Panel D. Accuracy
INTERCEPT
DIST 10i,t
DIST 10i,t * HIGH EARN VOLi,t
[1]
ACCURACYi,t
[2]
ACCURACYi,t
-0.618***
(-3.135)
0.224***
(5.722)
-0.347***
(-6.283)
-0.611***
(-3.129)
# FIRMS 100i,t
-0.111***
(-3.920)
0.048***
(4.315)
-0.158***
(-4.376)
0.480***
(10.446)
0.923***
(11.465)
1.606**
(2.313)
-0.065
(-1.395)
-4.019***
(-19.131)
-0.131***
(-9.125)
0.006
(0.153)
0.224***
(11.248)
0.186
(1.398)
-0.007
(-0.205)
-0.045*
(-1.864)
0.205***
(5.740)
-0.293***
(-5.545)
-0.138***
(-5.048)
0.048***
(4.286)
-0.159***
(-4.402)
0.479***
(10.410)
0.917***
(11.432)
1.639**
(2.359)
-0.067
(-1.436)
-4.022***
(-19.136)
-0.131***
(-9.108)
0.008
(0.190)
0.225***
(11.301)
0.194
(1.459)
-0.009
(-0.259)
-0.045*
(-1.875)
33,021
0.272
33,021
0.271
# FIRMS 100i,t * HIGH EARN VOLi,t
HIGH EARN VOLi,t
ln(MKT VALi,t)
BTMi,t
INST OWNi,t
ROAi,t
TURNi,t
BUS CONCi,t
RET VOLi,t
ln(AGEi,t)
EARN COMOVEi,t
ln(ANAL FOLLi,t)
BROKER SIZEi,t
ln(# FIRMS FOLLi,t)
ln(EXPERIENCEi,t)
#OBS
Adjusted R2
F-tests:
DISTANCEi,t + DISTANCEi,t * HIGH EARN VOLi,t = 0
Values
-0.123**
-0.088*
44
TABLE 7
Industry Firm Mentions in Conference Calls Regression Results
This table includes all firm/year observations from 2002 to 2011 with sufficient data to calculate the
dependent and independent variables. The dependent variable in Column 1 is an indicator variable equal
to 1 if firm i mentions firm j in any of its conference calls during year t (MENTIONi,j,t). In Column 2 (3),
the dependent variable is an indicator variable equal to 1 if the managers (analysts) mention firm j first
during any of firm i's conference calls during year t (MENTION (FIRM FIRSTi,j,t) and MENTION (ANAL
FIRSTi,j,t)). The independent variable of interest is the distance between firm i and firm j in year t
(DISTANCEi,j,t). All variables are defined in Appendix A. Year and industry (two-digit SIC code) fixed
effects are included as additional independent variables. The coefficients on the year and industry
indicator variables are suppressed. Standard errors are clustered by firm i-j combinations. All continuous
variables are winsorized at the 1% and 99% levels. *, **, and *** represent significance at the 10%, 5%,
and 1% levels, respectively.
[1]
[2]
[3]
MENTIONi,j,t
MENTION
(FIRM FIRST)i,j,t
MENTION
(ANAL FIRST)i,j,t
INTERCEPT
-12.119***
(-16.814)
-12.452***
(-15.634)
-13.730***
(-11.101)
DISTANCEi,j,t
-0.346***
(-8.233)
-0.308***
(-6.511)
-0.409***
(-5.831)
ln(MKT VALi,t)
0.063***
(5.280)
0.032**
(2.364)
0.137***
(6.324)
ln(MKT VALj,t)
0.869***
(66.549)
0.904***
(61.318)
0.745***
(33.535)
BTMi,t
0.022
(0.777)
-0.004
(-0.132)
0.040
(0.778)
BTMj,t
0.299***
(6.838)
0.314***
(6.089)
0.194**
(2.552)
INST OWNi,t
-0.201***
(-4.223)
-0.203***
(-3.686)
-0.077
(-0.951)
INST OWNj,t
-0.912***
(-21.127)
-0.954***
(-19.484)
-0.712***
(-9.260)
ROAi,t
-0.042
(-0.771)
-0.053
(-0.846)
-0.038
(-0.334)
ROAj,t
-0.589***
(-8.260)
-0.507***
(-6.095)
-0.740***
(-5.331)
TURNi,t
2.965***
(4.660)
3.397***
(4.784)
2.291**
(2.015)
TURNj,t
1.968***
(2.651)
1.445*
(1.668)
4.998***
(4.125)
BUS CONCi,t
0.423***
(4.548)
0.425***
(3.960)
0.489***
(3.237)
45
TABLE 7 (continued)
BUS CONCj,t
0.257***
(3.402)
0.201**
(2.393)
0.485***
(3.616)
EARN VOLi,t
0.592***
(3.586)
0.715***
(3.940)
-0.234
(-0.595)
EARN VOLj,t
0.106
(0.458)
0.465*
(1.780)
-1.203**
(-2.287)
RET VOLi,t
-0.104
(-0.697)
-0.085
(-0.500)
-0.151
(-0.472)
RET VOLj,t
1.210***
(6.369)
1.229***
(5.486)
1.080***
(2.801)
ln(AGEi,t)
-0.042*
(-1.760)
-0.074***
(-2.712)
0.038
(0.931)
ln(AGEj,t)
0.191***
(7.600)
0.194***
(6.892)
0.166***
(3.667)
ln(ANAL FOLLi,t)
0.266***
(12.122)
0.278***
(11.156)
0.205***
(4.806)
ln(ANAL FOLLj,t)
0.116***
(5.026)
0.082***
(3.198)
0.198***
(5.046)
COMPETITORi,j,t
1.686***
(59.804)
1.609***
(50.546)
1.850***
(36.230)
EARN COMOVEi,j,t
0.093***
(3.827)
0.061**
(2.218)
0.213***
(4.516)
#OBS
4,543,622
4,543,622
4,543,622
0.307
0.295
0.270
2
Adjusted R
46