Inter-Industry Network Structure and the Cross-Predictability of Earnings and
Stock Returns *
Daniel Aobdia
Northwestern University, Kellogg School of Management
Judson Caskey
University of Texas Austin, McCombs School of Business
N. Bugra Ozel
UCLA, Anderson School of Management
April 2013
*
We thank David Aboody, Sam Bonsall, Robert Freeman, Jack Hughes, Gil Sadka, Brett Trueman and seminar
participants at Columbia University for helpful comments. We also thank Sam Bonsall for providing data that
identify bellwether firms.
Inter-Industry Network Structure and the Cross-Predictability of Earnings and
Stock Returns
Abstract
We examine how the patterns of inter-industry trade flows impact the transfer of
information and economic shocks. We provide evidence that the intensity of transfers depends on
industries’ positions within the economy. In particular, some industries occupy central positions
in the flow of trade, serving as hubs. Consistent with a diversification effect, we find that these
industries have more exposure to aggregate risks than do non-central industries. Additionally,
earnings response coefficients of firms in central industries are lower than those of other firms,
consistent with investors placing less emphasis on the firm-specific information on account of
the relative importance of aggregate risk to central firms. Comparing central industries to noncentral industries, we find that the stock returns and accounting performance of central industries
better predict the performance of industries linked to them. This suggests that shocks to central
industries propagate more strongly than shocks to other industries. Our results highlight how
industries’ positions within the economy affect the transfer of information and economic shocks.
JEL classification: D57; G14; M41.
Keywords: Information transfer; inter-industry networks; aggregate risk; earnings; stock returns.
1
Introduction
The accounting literature has long recognized the role of information transfers among
firms in forming expectations about earnings and returns. Early studies in this area document
within-industry information transfers, consistent with the notion that similar companies face
similar economic shocks (e.g., Foster 1981; Clinch and Sinclair 1987). More recent studies
document information transfers via customer/supplier links (e.g., Pandit et al. 2011) and the
informativeness of earnings guidance about market returns (e.g., Anilowski et al. 2007; Bonsall
et al. 2012). We examine how inter-industry trade affects the transmission of information and
economic shocks. Our evidence suggests that transfers depend not only on industries’ immediate
trading partners but also on industries’ positions within the overall economy. 1
As an example of how an industry’s position can affect its impact on the economy,
consider the automobile industry. Based on the Bureau of Economic Analysis’ (BEA) data on
U.S. gross domestic product (GDP), consumer expenditures on ‘Motor vehicles and parts’
comprise around 3% of GDP, which is slightly more, but comparable to the contribution of
‘Clothing and footwear’ and consistently smaller than ‘Food services and accommodations.’ As
evidence that the auto industry occupies a relatively important position in the economy, consider
that a strike at a General Motors plant in Flint, Michigan – an idiosyncratic shock to the auto
industry – led to a measureable impact on GDP (Montgomery and Vames 1998). Following the
2007-2008 financial crisis, the auto industry received a federal aid package largely because “the
government believed it could not afford to let the [auto] industry fail” (Shepardson 2009). While
the economic shock of the financial crisis did not originate in the auto industry, politicians acted
on the belief that the industry had the potential to significantly amplify the crisis.
Our analysis views industries through the lens of the economy-wide trade between
1
Later in the introduction, we discuss why we conduct our analysis at the industry-level rather than the firm-level.
1
customers and suppliers. An industry’s role in propagating economic shocks depends not only on
its size but also on the extent to which it interacts with different economic sectors as both a
customer and a supplier (Acemoglu et al. 2012). We place particular attention on industries that
form hubs in the flow of trade (‘central industries’). An industry’s role as a hub reflects both its
direct trading relationships – its immediate customers and suppliers – and its indirect trading
relationships with customers-of-customers, suppliers-of-suppliers, and so forth. We provide
evidence that the performance of central industries depends more on aggregate risks than does
the performance of non-central industries. We also provide evidence that shocks to central
industries propagate more strongly than shocks to non-central industries.
Our first set of findings provides evidence that central industries obtain some
diversification by virtue of their exposure to wide swaths of the economy. We estimate the R2s
from one-factor (CAPM) and three-factor (Fama-French) regressions as gauges of the extent to
which aggregate risks explain stock returns. We then regress these R2s on explanatory factors,
including the industry’s centrality, market capitalization, average analyst coverage, and average
trading volume. The R2 tests indicate that aggregate risks explain a greater portion of central
industries’ returns than of non-central industries’ returns, consistent with a diversification effect.
We supplement our R2 analysis with an examination of firms’ earnings response
coefficients (ERCs). To the extent that the value of firms in central industries depends on
aggregate risks, investors have alternative sources of information to earnings releases. For
example, macroeconomic news may allow investors to better anticipate central industries’
earnings than non-central industries’ earnings. We find some evidence in support of this
prediction when we control for the timing of earnings announcements. Prior studies have shown
that the stock price reaction to earnings announcements is greater for early announcing firms
2
than for late-announcing firms (Foster 1980, 1981). 2 Among firms that announce their earnings
early in the quarter, we find that those in central industries have lower ERCs than those in noncentral industries. While late-announcing firms in non-central industries have lower ERCs than
early-announcing firms, such a difference is absent in central industries. This result is consistent
with central firms’ earnings being preempted primarily by macroeconomic news available prior
to the earnings release, and with late-announcing non-central firms’ earnings announcements
being preempted by other firms’ earnings announcements. Among late-announcing firms and in
the pooled sample, we find no statistical difference between the ERCs of central and non-central
firms.
Our second set of analyses examines how centrality impacts transfers of information and
economic shocks. For each industry, we examine the association between its monthly stock
returns and concurrent and one-month-ahead stock returns of the industries it directly trades with
(adjacent industries). We perform a similar analysis using seasonal changes in quarterly return on
assets (ROA). We find that, compared to non-central industries, central industries’ monthly
returns have a stronger association with adjacent industries’ concurrent and one-month-ahead
returns. Similarly, our ROA tests show that central industries’ ROA changes are more strongly
associated with the concurrent and one-quarter-ahead ROA changes of the adjacent industries. In
other words, while shocks transfer between any two industries that trade with each other, shocks
to central industries propagate more strongly than those to non-central industries. The returns and
ROA tests suggest that central industries’ earnings are informative about non-central industries’
earnings, while the ERC tests indicate that investors obtain relatively more information about
central industries’ earnings prior to their earnings announcements.
2
This is a different issue than the ‘good news early/bad news late’ phenomenon (e.g., Begley and Fischer 1998) that
examines late announcements vis-à-vis when a firm typically announces. See Ramnath (2002) for a study of intraindustry information transfers where the early-announcing earnings surprises are informative about subsequent
earnings surprises.
3
Our study relates to the literature on the interrelationship between market fundamentals
and portfolio performance. Hong, Torous, and Valkanov (2007) identify industries whose stock
returns have a high association with macroeconomic fundamentals and show that these industries’
returns can predict market returns. Along the same lines, Anilowski et al. (2007) and Bonsall et
al. (2012) find that ‘bellwether’ firms’ earnings guidance predicts market-wide returns, where
they identify bellwether firms based on size and the past relation between earnings and
macroeconomic variables, respectively. 3 Shivakumar’s (2007, 2010) discussions of Anilowski et
al. (2007) and Cready and Gurun (2010) call for research on the relation between earnings and
macroeconomic conditions and on inter-industry information transfers in the context of financial
reporting. Our study adds to this literature by identifying a specific mechanism – inter-industry
trade flows – that moderates the relation between macroeconomic fundamentals and industry
performance.
Our study also complements prior work, such as Cohen and Frazzini (2008) and Pandit,
Wasley, and Zach (2011), that examines firm-specific customer and supplier relations to identify
potential information transfers. On the one hand, the use of firm-level disclosures of customers
facilitates the examination of firm-level information transfers. On the other hand, the firm-level
data include a relatively small set of large customers and do not have sufficient coverage to
estimate firms’ roles in the overall economy. 4 The use of industry as the unit of measurement
inherently coarsens our data, but allows us to incorporate the large amount of economic activity
3
Our results are distinct from the bellwether effect documented by Bonsall et al. (2012). In untabulated analyses we
examine the distribution of bellwether firms as identified in Bonsall et al. (2012) over industries and find that the
correlation between an indicator variable for bellwether firms and our measure of centrality is insignificant.
4
For example, US firms are required to disclose only major customers that comprise at least 10% of revenues. This
limits disclosures to a fairly small number of large customers and provides limited ability to track the economy-wide
flow of trade. According to customer and supplier links data compiled from Compustat and available on Andrea
Frazzini’s website, there were 454 firms with usable sales data in 1997, the vintage of BEA data we utilize. This is a
small fraction of 12,000 firms on Compustat in that year and, according to Cohen and Frazzini (2008), the disclosed
customers tend to be larger than their suppliers.
4
that does not appear in the disclosures of sales to large customers. In particular, we utilize the
BEA input/output tables to estimate the trade flows across industries. These data provide
comprehensive coverage of the US economy. Our results suggest that prior findings on firmlevel cross-predictability of returns among economically linked firms (Cohen and Frazzini 2008;
Menzly and Ozbas 2010; Pandit et al. 2011) will be stronger when information flows from a firm
in a central industry to a firm in a non-central industry.
Our study is also related to the literature that examines the propagation of shocks
throughout the economy. Ahern and Harford (2012) examine how central industries drive waves
of merger activity. Acemoglu et al. (2012) examine a multi-sector setting to identify conditions
under which idiosyncratic shocks can lead to aggregate fluctuations. They show that shocks to
sectors that trade with a disproportionately large number of other sectors can be amplified into
aggregate fluctuations. 5 While we do not differentiate our analysis based on the origination point
of the shocks, our finding that central industries have stronger associations with aggregate
fluctuations than do non-central industries is consistent with this theory.
The paper proceeds as follows. Section 2 discusses the concept of centrality as well as
our empirical predictions. Section 3 describes our data. Section 4 provides evidence that
aggregate risks account for a relatively large portion of returns for firms in central industries.
Section 5 provides evidence on the propagation of shocks between industries and Section 6
concludes.
5
As Lucas (1977) and others argue, in highly disaggregated economies, idiosyncratic shocks will remain fairly
confined. While Dupor (1999) and Horvath (1998, 2000) debate on whether sectoral shocks can transfer into
aggregate fluctuations, Acemoglu et al. (2012) provide a more complete answer to this question by showing that
sectoral shocks can lead to aggregate fluctuations only when there is heterogeneity in the links between sectors.
With heterogeneous links, shocks to ‘hub’ sectors can lead to aggregate shocks. Gabaix (2011) provide a similar
model using firm-level shocks as a source of aggregate fluctuations.
5
2
Measure of Centrality and Empirical Predictions
2.1 Measure of Centrality
Our empirical predictions pertain to industries that serve as hubs in the economy-wide
flow of trade, so we must define what makes an industry a hub before proceeding to our
predictions. We identify hub industries with respect to their role in inter-industry trade flows.
Following Ahern and Harford (2012) and Anjos and Fracassi (2012), we measure trade flows
using the BEA’s 1997 “Use Table,” which documents economy-wide trade at the industry level. 6
We define industries using the four-digit IO industry codes provided by the BEA. We exclude
government, special industries, value added, and final users (industry definitions that start with
the letters S, V, or F). 7 Figure 1 plots the inter-industry links based on the BEA data. The black
circles denote industries that we classify as non-central and the white squares represent central
industries, which we define precisely in Section 3.2.
(Insert Figure 1 about here)
The inter-industry trade links can be represented in matrix form by a matrix A with
elements Aij = Aji = 1 if industries i and j trade with each other, and with diagonal elements Aii =
0. Measures of industries’ influence utilize the matrix A. Recognizing the heterogeneity in
trading relationships, similar to Ahern and Harford (2012), we represent the links in A with
6
Like these studies, we focus on trade flows in 1997 since this year is the approximate mid-point of our sample
period. The BEA had been publishing input-output tables every five years until 2002. However, inasmuch the
industry definitions vary in each version of these tables, it is not possible to form a consistent time series based on
different versions of input-output tables. Because there are relatively minor changes between 1997 and 2002
industry classifications, we recomputed our centrality measure using the 2002 data and find that the correlation
between the 1997 centrality and the 2002 centrality is high at 96%. As a robustness check we also replicate our
baseline specifications using the 2002 input-output table only, and using a combination of the 1997 and 2002 tables
for the pre- and post-1999 periods, respectively. These replications yield qualitatively similar results.
7
The BEA’s “special industries” category includes non-comparable imports (industry code S003), scrap, used, and
secondhand goods (S004), rest of world adjustment (S006), and inventory valuation adjustment (S007). We do not
include government/special industry/value added/final uses industries in the calculation of the strength measure
because of the difficulties in the interpretation of certain associations – e.g., negative sales values, changes in private
inventories etc. In the calculation of the strength measure, we include private consumption expenditures to calculate
total sales numbers so that our measures gauge importance relative to overall sales, rather than strictly business-tobusiness sales.
6
measures of the strength of trade between two industries. Specifically, we compute the elements
as:
A=
ij
sij
s ji
s ji
1  sij
+
+
+

4  ∑ k sik ∑ k skj ∑ k s jk ∑ k ski

,


(1)
where sij equals to the sales (in dollars) of goods and services by industry i to industry j. The first
(second) ratio measures the sales made by i to j as a percentage of i’s total sales (j’s total
purchases), gauging whether j is an important customer to i (i is an important supplier to j). The
last two ratios similarly measure whether i is an important customer to j, and j is an important
supplier to i, respectively. A larger Aij indicates a stronger link between industries i and j.
We measure an industry’s position in the economy using Bonacich’s (1972) eigenvector
centrality metric (hereafter ‘centrality’). 8 An industry rates as having high centrality if it has
strong trade links with industries that have high centrality. Specifically, industry i’s centrality is:
ci =
1
λ
∑ j Aij c j ,
(2)
where λ is a normalization factor. An industry’s centrality depends on both how many other
industries it trades with and those industries’ position within the overall economy. Table 1
provides a list of the most central industries. Appendix B briefly elaborates on the centrality
measure. 9
(Insert Table 1 about here)
8
In the interest of maintaining the paper’s focus, we briefly describe the eigenvector centrality measure here and
refer interested readers to textbook treatments (e.g., Jackson 2008 or Newman 2010) or Borgatti (2005) for
discussions of centrality metrics. The term eigenvector centrality arises because the vector c of centralities can be
defined from (2) as λc = Ac, which is an eigenvalue equation for the matrix A. Other notions of centrality, such as
betweenness and closeness, pertain to shortest paths between industries and do not seem particularly relevant to our
setting (See, e.g., Borgatti 2005). For example, the iron and steel industry’s economic importance likely stems more
from its use in a variety of outputs than it having a short route from production to final consumption.
9
We obtain similar results using an alternative measure, degree centrality, that is a strength-weighted count of an
industry’s neighbors, ((ΣjAij)/(# industries - 1)). We report results for the eigenvector measure because it is more
comprehensive, reflecting higher order links such as neighbors of neighbors, and because of its common use in the
literature on networks.
7
2.2 Empirical Predictions
In any economy, the patterns of inter-industry trade have an impact on the importance of
an industry and on the factors that affect that industry’s performance. Some industries, such as
wholesale trade, have strong customer/supplier interactions with several other industries and
therefore are in more central positions. Others, such as tobacco products manufacturing, are
relatively isolated.
An industry’s centrality, as we defined it in Section 2.1, reflects its influence on and
exposure to the overall economy. An industry’s centrality increases with the number of industries
it trades with, the importance of the industries it trades with, and the strength of those trading
relationships. By their nature, shocks to central industries will tend to be associated with
macroeconomic factors (Acemoglu et al. 2012). This can be either because a shock originates in
a central industry and impacts its trading partners, or because a widespread shock impacts
several of a central industry’s trading patterns.
An industry’s performance can be informative about the concurrent and/or future
performance of the industries with which it trades. The predictive ability depends on the extent
and nature of trade between the industries. For example, if a given industry’s trading partners
maintain large inventory buffers, shocks to that industry may transfer to its partners with a delay,
if at all. If the given industry represents only a small portion of its trading partners’ trade ties,
then shocks to that industry will have a small impact on its trading partners.
We hypothesize that a central industry’s performance has greater predictive ability for its
trading partners than does a non-central industry’s performance. Because centrality stems from
strong trade exposure to the overall economy, shocks to central industries should be experienced
by those industries’ trading partners. In contrast, a shock to a single trading partner of a central
industry should have relatively little effect on the central industry because the central industry’s
8
performance depends more on the overall economy than the performance of a single trading
partner.
We test the following two predictions. First, compared to the performance of the noncentral industries, the performance of the central industries associates more strongly with
aggregate fluctuations and risks. A related prediction is that investors can rely relatively more on
macroeconomic news than firm-specific news when pricing central firms, so that prices react less
to central firms’ earnings announcements. Second, shocks to the performance of a central
industry are more strongly associated with the concurrent and/or future performance of industries
to which the central industry is linked, than shocks to a non-central industry are associated with
its linked industries. When testing this second hypothesis, we control for the strength of trade
between industries in order to ensure that we estimate the effect of centrality, itself, rather than
the effect of strong trade ties on the transmission of shocks.
3
Data and Descriptive Statistics
3.1 Data
We obtain accounting and stock returns data from Compustat and CRSP, respectively.
Analyst-related data come from I/B/E/S. In all specifications except in the analysis of mean
reversion of earnings shocks, we require the availability of Compustat quarterly data and CRSP
monthly returns. 10
We require that the historical NAICS industry code be available in Compustat in order to
merge our firm-related data with our centrality measures, available at the BEA four-digit
industry level. This restricts our sample period to the fiscal years beginning in 1985, ending in
2011. We exclude regulated firms (NAICS 22) and financial institutions (NAICS 52) from the
10
We conduct the mean reversion analysis at the year level and, accordingly, use annual data. We also impose the
additional requirement that all control variables be available in both R2 and ERC tests. The ERC test is conducted
using the intersection of Compustat quarterly with CRSP daily and I/B/E/S.
9
sample because their earnings and returns depend relatively more on the regulatory environment
than those of other firms.
3.2 Descriptive Statistics
Figure 2 plots the centrality measure in descending order. The figure exhibits the
common phenomenon that a small number of nodes in the network, industries in our case,
exhibit high connectivity (e.g., Gabaix 2009). As the figure demonstrates, while most industries
exhibit some connections with the other industries, there are relatively few ‘hub’ industries. Four
industries – wholesale trade, construction, management of companies, and real estate – exhibit
the highest degree of centrality, followed by a number of other industries with still relatively
high centrality. We confirm these observations in Table 2. The centrality measure quickly
decreases from its maximum of 0.266 to 0.097 at the 75th percentile or a difference of 0.169. The
distribution then levels out with 70% of the observations clustered between the 0.036 values and
the 0.097 values, or a difference of 0.060.
(Insert Figure 2 and Table 2 about here)
There are four major discontinuities in the centrality values not counting the two cliffs at
the very beginning and very end of the distribution: the first is at 0.216, the second is at 0.137,
the third is at 0.125, and the last is at 0.111. The distribution of centrality values becomes visibly
smooth beyond 0.111 (the dotted line in Figure 2), suggesting that industries below this level are
relatively more comparable in terms of centrality. Accordingly, we choose to define central
industries as those that have centrality values greater than 0.111, which accounts for the top 17%
of the industries. This cutoff is conservative enough to ensure that all central industries are
indeed classified as central. Our results remain similar when we set the cutoff at 0.125 or when
10
we use different cutoffs around the baseline level. 11 The industries with a centrality measure
above the 0.111 threshold are indicated by square nodes in the network plot in Figure 1.
4
Are Central Industries More Exposed to Aggregate Risks?
4.1 R2 Tests
Research design
As we explain in Section 2.2, our first prediction is that central industries are more
exposed to aggregate risks than non-central industries. In order to test this prediction, we
estimate the ability of CAPM and Fama-French three-factor models to explain central industries’
returns. We expect that the R2s from these models will be higher for central industries than for
non-central industries.
We form value-weighted industry portfolios and estimate CAPM and Fama-French threefactor models with both daily and monthly returns data using rolling five-year windows
beginning in 1981 and ending in 2011. 12 Upon estimating the CAPM and the three-factor models
we test the association between centrality and the R2s from these models using the following
regression: 13
Rit2 =
α + β1Centralityi + β 2 Avg. Analyst Coverageit
+ β3 Avg . Proportion Tradedit + β 4 Avg . Log Market Valueit + ε it
(3)
Because R2s are bounded by zero and one, we use a fractional logit model (Papke and
11
In particular, our analyses remain qualitatively similar when using a cutoff of 0.12, or 12% of the distribution of
industries, when using a cutoff of 0.10 or 21% of the industries, and when using a cutoff of 0.137 or 8% of the
industries. Our results are sensitive to a more aggressive cutoff of 0.216. However, this cutoff eliminates over 80%
of industries (including industries like petroleum and coal products manufacturing, oil and gas extraction,
semiconductors manufacturing, retail trade and iron and steel mills) that are originally classified as central under the
0.111 cutoff. Our results also remain similar when we replace the dummy variable with the actual value of the
centrality score.
12
We use industry definitions as of the end of each estimation window. Because NAICS codes became available in
1985, our estimation starts with the 1981-1985 window and we estimate regressions for 1981-1985, 1982-1986, and
so on until 2007-2011. Table 3 reports double-clustered standard errors on year and industry to control for the
within-industry correlation of residuals caused by overlapping periods, and for the within-time correlation caused
by the regression potentially having a better fit in some years than in others (Thompson 2011).
13
Our approach of using R2s as the dependent variable parallels its use in studies of stock return synchronicity (e.g.,
Morck et al. 2000; Piotroski and Roulstone 2004).
11
Wooldridge, 1996) to estimate (3). A positive coefficient on Centrality, (β1) indicates that
aggregate risks have a greater impact on central industries than on non-central industries.
Because the R2s in the regressions could also depend on the information environment and the
liquidity of the stocks traded, we add three control variables following Piotroski and Roulstone
(2004) and Kelly (2005): Average Analyst Coverage is the value-weighted average of an
indicator variable that equals one for any stock in the portfolio that is covered by analysts at the
end of the estimation period; Average Proportion Traded is the average monthly trading volume
as a percentage of shares outstanding during the period (i.e., we compute the portfolio’s shares
traded/shares outstanding each period, and take the average over the periods). 14 Average Log
Market Value is the logarithm of the estimation-period average of the portfolio’s market
capitalization, where we compute the market values of the portfolios’ constituents as the CRSP
shares outstanding times market price at the beginning of each period in the regression (monthly
for monthly returns, and daily for daily returns). Based on Kelly’s (2005) finding that R2s from
market models increase with information availability and liquidity, we expect positive
coefficients on the control variables. Appendix A provides additional information on the
computation of control variables. Both the dependent variable and control variables are
winsorized at the 1st and 99th percentiles in order to reduce the impact of outliers in the
specifications.
Results
We present our results from the estimation of (3) in Table 3. From the 123 industries in
Table 2, this table excludes some industries (e.g., Religious organizations) that have insufficient
data in CRSP and Compustat, leaving 114 industries for which we conduct our analysis. Panel A
presents descriptive statistics for central and non-central industries. We test for the difference of
14
This controls for the fact that the market price of a stock will remain the same if the stock is not traded during a
given period, which would result in a low R2.
12
the means (medians) using a t-test (Wilcoxon-Mann-Whitney test). All of the R2 measures
indicate that central industries have higher R-squares than non-central industries. The mean test
shows no difference in analyst coverage between central and non-central industries, while the
median test shows that the median non-central industry has greater analyst coverage than the
median central industry. Non-central industries have a higher proportion of their shares traded
and a lower market capitalization, indicating the need to control for these variables.
(Insert Table 3 about here)
We present the results of the fractional logit estimations in Panel B. The first two
columns show the results using daily returns data. The coefficient on Centrality is positive in
both CAPM and Fama-French specifications at the 1% level. Untabulated results indicate that,
for the CAPM specification using daily returns, the marginal effect of the centrality variable
equals 0.853 at the mean of the control variables. This implies that an increase of centrality of
0.075, corresponding to the difference in average centrality between central and non-central
industries reported in Table 3, Panel A, would increase the average R2 by roughly
6.4%(=0.853×0.075), compared to the sample average non-central R2 of 35.9%. Consistent with
Piotroski and Roulstone’s (2004) and Kelly’s (2005) findings, our results indicate that industries
with high analyst coverage, and industries with a higher market capitalization have higher R2s,
indicating that larger industries are more exposed to aggregate risks than other industries. Our
analysis using monthly returns yields results that are similar to those using daily returns. For both
CAPM and Fama-French three factor models, we find that central industries have higher R2s.
Overall these findings are consistent with our prediction that central industries have greater
exposure to aggregate risks. 15
15
In untabulated analyses, we confirm that our results are qualitatively unchanged when using OLS regressions,
regressions where continuous variables are replaced with their ranks, and in an OLS regression of the log-odds ratio
13
4.2 ERC Tests
Research design
In this section we examine whether investors react less to earnings announcements of
firms in central industries (central firms) compared to firms in non-central industries (non-central
firms). Our prediction follows from our evidence that central industries are more exposed to
aggregate risks than non-central industries. Price reactions to earnings announcements diminish
when investors anticipate much of the announced earnings numbers (Kothari and Sloan 1989;
Kothari 2001). Because investors have access to a wide variety of information sources on
aggregate risks, and central industries are more exposed to aggregate risks, we predict that
central firms will have lower ERCs than non-central firms.
We estimate the following regression in order to investigate this hypothesis:
CARit =
α + β1Forecast Errorit + β 2Centrali + β3 Forecast Errorit × Centrali
+ β 4 X it + β5 Forecast Errorit × X it + ε it
(4)
where CARit is the cumulative abnormal returns for firm i and quarter t, defined over a three-day
window around the earnings announcement date reported by I/B/E/S. Cumulative abnormal
return is defined as cumulative return on a firm’s stock minus the value-weighted market return
over the same period; Centrali is an indicator variable equal to one for central firms, where we
specify central industries by the 0.111 centrality cutoff from the prior tests; Forecast Errorit is
equal to the actual earnings per share less the most recent analysts’ consensus earnings forecast
prior to the announcement window, both scaled by the stock price at the beginning of the firm’s
fiscal quarter. 16 Both actual earnings per share and the consensus earnings forecast are from
I/B/E/S. Following Bartov et al. (2002) we require the most recent consensus forecast to precede
log(R2/(1-R2)), similar to Piotroski and Roulstone (2004).
16
We obtain similar results when estimating (4) with raw returns as the dependent variable, which allows for the
possibility that the forecast error impacts concurrent market returns.
14
the actual announcement date by three or more days. 17 Because we use analyst forecasts to
calculate the surprise, our sample is restricted to firms that are covered by analysts. This skews
the sample towards larger companies. In order to conduct further analyses based on early and late
announcers, we also restrict our sample to firms with fiscal quarters coinciding with calendar
quarters. 18
We also include a vector of control variables (Xit) that prior research identifies as
determinants of earnings response coefficients (see for example Francis and Ke 2006; Lim and
Tan 2008; and Dechow and You 2012). Xit includes the following: TobinQ, the Tobin Q
coefficient defined as the market value of assets (i.e., total liabilities plus market value of equity
at the end of the fiscal quarter) divided by the book value of total assets, which serves to control
for growth; StdReturns, the 90 calendar day stock returns volatility ending three days prior to the
earnings announcement date, as a control for risk; Leverage, the firm’s book leverage (total debt
over total debt plus book value of equity); Logmkval, the natural logarithm of the firm fiscal
quarter end market value, as a control for size; Loss, an indicator variable equal to one when the
actual I/B/E/S EPS is negative, as a control for losses being less persistent; Specialitems, an
indicator variable equal to one when special items during the quarter are above 5% of total assets,
as an additional control for non-persistent items in earnings; Fourthquarter, an indicator variable
equal to one for the fourth fiscal quarter, as a control for end of fiscal-year adjustments and
potential impact of the auditor certification. In addition to our use of the Specialitems and Loss
variables as controls for persistence, we further examine the effects of earnings persistence in the
next subsection. We winsorize all continuous variables at the 1st and 99th percentiles. All control
variables are interacted with the forecast error in order to control for their impact on the ERCs. In
17
The purpose is to make sure that the surprise is a true surprise and not “contaminated” by actual knowledge of
earnings numbers by some analysts.
18
Results are qualitatively unchanged in our main specification when we also include the firms with fiscal quarters
not coinciding with calendar quarters.
15
order to present the uninteracted coefficient on Forecast Error at the mean of the continuous
variables interacted with Forecast Error, we subtract the sample mean from continuous
explanatory control variables. 19 A negative coefficient on the Centrality interaction term (β3) is
consistent with a diminished ERC due to investors’ ability to use macroeconomic news to
anticipate much of the earnings of firms in central industries.
Results
We present our results from the estimation of (4) in Table 4. Panel A presents descriptive
statistics that compare central firms with non-central firms. Similar to the full-sample results of
Table 3, the central firms’ larger Logmkval indicates that they are larger than non-central firms.
Central firms also have lower TobinQ, lower volatility, and higher leverage than non-central
firms. 17.8% of central firm-quarters have losses, compared to 26.6% of non-central firms. 20
Fewer of the central firm’s quarterly earnings include large special items. All of these differences
are significant at the 1% level when using a t-test of differences for the mean or a WilcoxonMann-Whitney test for the median. In untabulated tests, we also find that the betas of central
firms are slightly lower than the betas of non-central firms. Overall, these results suggest that
central firms are larger and more mature than non-central firms. This confirms the need to
control for these differences in our specifications on the ERCs. 21
(Insert Table 4 about here)
Column (1) of Table 4, Panel B presents the effects of centrality on ERCs. The
19
The reported coefficient on Forecast Error is affected by a change of centering of the continuous variables
because of the interaction coefficients. Centering the interacted continuous variables to zero allows us to present the
value of the uninteracted coefficient on Forecast Error at the mean of the continuous variables. See Aiken and West
(1991) for additional details. We also confirm in untabulated tests that our results are qualitatively similar when
excluding the continuous coefficients that we interact with the Forecast Error.
20
Because the central firms exhibit higher leverage, we conducted untabulated analyses where we replace earnings
variables with earnings plus interest (Compustat XINT) and find similar results.
21
In untabulated results, we confirm that these differences between central firms and non-central firms hold when
incorporating the entire sample of firms from Compustat quarterly merged with CRSP, and not only the sample
restricted to companies covered by analysts.
16
coefficient on the interaction Forecast Error × Central has a negative sign, consistent with our
prediction that central firms have lower ERCs; however, the coefficient is not statistically
significant at conventional levels. In Column (2), we distinguish between firms that announce
late in the announcement period from those that announce early using a Late Announcer
indicator variable. The variable equals one when firms announce after the median announcement
date within the quarter. 22 We interact Late Announcer with Forecast Error and with the Central
dummy. The coefficient on Forecast Error × Central is negative and significant, indicating that
the ERCs of early announcing central firms are lower than those of non-central firms. The
reduction in ERC for early-announcing central firms represents almost 12% (=0.135 negative
coefficient on Forecast Error × Central divided by the 1.139 coefficient on Forecast Error) of
the ERC of early announcing firms.
We also find a negative coefficient on the interaction Forecast Error × Late Announcer,
consistent with information relevant to late announcers in non-central industries being released
by the early announcers. However, we find that the interaction Forecast Error × Late Announcer
× Central is significantly positive, indicating that the reduction in ERC between early
announcers and late announcers is not as acute for central firms as it is for non-central firms. An
F-test of the sum (Forecast Error × Late Announcer) + (Forecast Error × Late Announcer ×
Central) is insignificant, which indicates that among central firms, there is no late announcement
effect. This result is consistent with the reduction in ERCs for central firms being primarily due
to preemption by the release of macroeconomic news and not by firm-specific news. An F-test of
the sum (Forecast Error × Central) + (Forecast Error × Late Announcer × Central) indicates
that the ERCs of late-announcing central firms are essentially the same as late-announcing non22
The median announcement date is computed from the entire universe of Compustat firms that have fiscal quarters
ending in calendar dates. Results are qualitatively unchanged when computing the median from our sample of firms
where analyst forecast data is available.
17
central firms. The lack of a difference among late-announcing firms explains why column (1)
shows that, on average, central firms’ ERCs are not significantly different from those of noncentral firms.
4.3 Mean Reversion in Earnings Shocks
Research design
The ERC tests in the preceding subsection are consistent with investors using news about
aggregate risks to anticipate the earnings of central firms; however, prior research has provided
evidence that low earnings persistence can also cause low ERCs (e.g., Kormendi and Lipe 1987;
Collins and Kothari 1989; Kothari 2001). Accordingly, we investigate whether central firms’ low
ERCs stem, at least partly, from having more transitory earnings shocks. In particular, since
central firms are more exposed to aggregate fluctuations, firm-specific shocks could be more
transitory for central firms which, in turn, would result in lower ERCs for central firms.
Following prior studies that focus on the time series properties of earnings (Bernard and Thomas
1990; Sloan 1996; Skinner and Soltes 2011), we test for this possibility using the following
specification:
Earnings Shocksi ,t +1 =
α + β1 Earnings Shocksit + β 2 Centrali + β3Centrali × Earnings Shocksit
+ β 4 GDP Shockit + β 4 GDP Shockit × Earnings Shocksit + ε it
(5)
Earnings Shocks is defined as the year-on-year difference of returns on assets (ROA), where
ROA equals earnings from continuing operations for the year divided by the average of the
assets at the beginning and at the end of the period. Central is defined similarly as in the ERC
analyses. In order to control for the aggregate component of shocks, we also include GDP
Growth, which we define as the annual growth in GDP. In order to present the uninteracted
coefficient on Earnings Shocks at the mean of the GDP growth variable, we subtract the sample
18
mean from GDP Growth. 23 Because earnings shocks are known to mean revert (e.g., Brooks and
Buckmaster 1976; Bernard and Thomas 1990), we expect that the coefficient on Earnings Shocks
is negative. We predict that the coefficient on the interaction Central × Earnings Shocks is
negative if shocks to central firms are more transitory than shocks to non-central firms.
Results
We report results in Table 5. Column (1) presents the results without any control
variables. Our findings indicate that annually-differenced earnings exhibit a negative
autocorrelation of 0.24, which is comparable to -0.24 in Bernard and Thomas (1990) for
seasonally-differenced quarterly earnings and slightly larger than -0.18 in Dechow (1994) for
changes in annual earnings. Column (2) includes the centrality measure, and the Central ×
Earnings Shock interaction has a negative coefficient, consistent with earnings shocks of central
firms being more transitory than earnings shocks of non-central firms. This potentially accounts
for the lower ERCs of central firms documented in Table 4. In particular, we find that the
negative autocorrelation is increased to -0.29 for central firms compared to -0.23 for non-central
firms. Because we are interested in the portion of the shock that is firm-specific, we introduce
GDP Growth in Column (3). We find a positive coefficient on the interaction GDP Growth ×
Earnings Shock, implying that shocks are more persistent when GDP growth is higher. Column
(4) includes all explanatory variables and we find that the coefficient on the interaction Central
× Earnings Shocks remains negative, consistent with the central firms’ firm-specific shocks
being more transitory than for non-central firms. Overall, these results provide an explanation for
our results of lower ERCs on central firms, in addition to the possibility that macroeconomic
news preempts their earnings announcements. 24
23
The rationale is similar to that discussed in footnote 19.
Untabulated analyses indicate that the results on the interaction coefficient Central × Earnings Shock are robust to
inclusion in the specification of additional variables proxying for aggregate shocks, including shocks on the
24
19
(Insert Table 5 about here)
5
Do Shocks to Central Industries Propagate More Strongly Than Shocks to
Non-Central Industries?
In this section, we test our second prediction that, compared to non-central industries, a
central industry’s performance has a stronger association with its trading partners’ performance
shocks. This could reflect two scenarios. First, aggregate shocks can originate in central
industries (Acemoglu et al. 2012), and then propagate to those industries’ trading partners.
Second, central industries tend to have trading partners in many other industries, so that many of
their trading partners must have a correlated shock (i.e., an aggregate shock) in order for it to
impact the central industry. Both scenarios lead to the prediction that a central industry’s
performance correlates relatively more strongly with the performance of the industries it trades
with. We test this prediction in terms of returns performance and accounting performance.
5.1 Returns Predictability
Research design
Our analysis of the predictability of returns is similar to that in Hong, Torous, and
Valkanov (2007). We test whether monthly return of an industry, which we denote as ‘source
industry,’ predicts the future returns of the industries to which it is linked (hereafter the ‘linked
industries’), and whether the effects are enhanced when the source is a central industry. In
particular, for each industry i and month t we compute the value-weighted return, based on the
beginning-of-month market values of firms in the industry. We subtract the value-weighted
NYSE/AMEX/NASDAQ return to obtain the industry’s abnormal return Rit. We estimate the
association between the returns of an industry and its trading partners by regressing the abnormal
returns of the portfolio of trading partners on the returns of the industry, as follows:
inflation, the bond spread, the dividend rate and stock market returns.
20
Linked Abn Returnit= α + β1 Source Abn Returnit
+ β 2 Centrali + β3Centrali × Source Abn Returnit + γ X it + ε it ,
(6)
Linked Abn Returni ,t +1 =
α + β1 Linked Abn Returnit + β 2 Source Abn Returnit
+ β3Centrali + β 4 Centrali × Source Abn Returnit + γ X it + ε i ,t +1 ,
where Source Abn Return is Rit and Linked Abn Return is the return on the portfolio of trading
partners. We weight the portfolio of trading partners by the strength of link to the source industry.
Specifically, denoting by Aij the strength of the link between source industry i and linked
industry j (See expression (1)), we compute Linked Abn Returnit as:
Linked Abn Returnit = ∑ j Aij R jt .
(7)
Strength-weighting the portfolios of linked industries controls for the trade ties between the
source and linked industries. We expect that the source industry returns will have a greater
association with the returns of industries with which it trades more. 25
Central is an indicator variable that equals one if the centrality of the industry is greater
than 0.111 and zero otherwise. Having controlled for the strength of trade between industries, the
interacted coefficient Centrality × Source Abn Return measures the impact of centrality, per se. X
is a vector of control variables defined as in Hong et al. (2007) and includes the following
variables: Spread, the default spread, defined as the difference between the Moody’s BAA-rated
and AAA-rated bond yields; Inflation, the seasonally adjusted monthly inflation, measured as the
growth rate of the consumer price index; Stdev, the daily market volatility estimated over a one
month period; Dividend rate, the one year market dividend yield, computed as the total dividend
An alternative weighting scheme is to normalize the sum in (7) by the sum of strength measures, ΣjAij, which
yields weights that sum to one. Untabulated results indicate that our results for both returns and ROA predictability
are qualitatively unchanged when using these alternative weights. The conceptual problem with the normalized
weights is that they fail to take into account the strength of inter-industry links. For example, suppose an industry
trades with two others, and the strength measure is 0.3 for both of the links. The linked return measure with
normalized weights would treat this the same as an industry that trades with two others, with strength measures of
0.02 for both links. The strength-weighting in (7) would distinguish between these two cases, with a larger linked
return where industries share stronger ties.
25
21
from the CRSP market portfolio divided by the current market level.
We predict a positive coefficient on Source Abn Returns if source industries’ returns are
positively correlated with their trading partners’ returns. A positive coefficient would be
consistent with the findings of Menzly and Ozbas (2010) who find that stocks of economically
related supplier and customer industries predict each other’s returns. We predict a positive
coefficient on the interaction term, Central × Source Abn Returns, if, compared to non-central
industries, central industries’ returns have a stronger association with their trading partners’
returns.
Results
We present our results from the estimation of in Table 6. In Panel A, we examine the
concurrent associations. In Column (1), we regress current period linked industries’ abnormal
returns on the control variables. We find that higher spreads between AAA and BAA bonds,
lower standard deviation of market returns and lower dividend rates are positively associated
with concurrent abnormal returns. In Column (2), we add the returns of the source industries and
their interactions with the central dummy. The positive coefficient of 0.061 on Source Abn
Returns indicates that the industries’ returns co-move with the returns of their trading partners
(Menzly and Ozbas 2010). The association almost quadruples when the source is a central
industry, as evidenced by the coefficient of 0.18 on the interaction Source Abn Returns × Central.
The R2s in the specification go from 0.02 in Column (1) to 0.15 in Column (2), indicating that
the returns of the source and their interaction with the central dummy have strong incremental
explanatory power in the regression. Overall, these results provide evidence that central
industries’ returns have a stronger association with their trading partners’ returns than do noncentral industries.
(Insert Table 6 about here)
22
Table 6, Panel B presents the results of the specifications using one-month-ahead returns
for the linked industries. Column (1) presents the results for baseline specifications with
inclusion of the abnormal returns for the linked industries at month t. Consistent with the
literature on momentum (see for example Moskowitz and Grinblatt, 1999), we find that the
returns of the linked industries at month t are positively associated with the returns of the linked
industries at month t+1. We introduce the returns of the source and their interactions with the
central dummy in Column (2). We find a positive coefficient on the returns of the source,
significant at the 1% level, indicating that industries predict the combined returns of their
suppliers and customers, consistent with Menzly and Ozbas (2010). The interaction Central ×
Source Abn Returns is positive and significant at 5%, providing evidence that central industries’
returns have more predictive power than non-central industries’ returns. The total coefficient on
Source Abn Returns goes from 0.005 for non-central industries to 0.026 for central industries, a
difference higher than the increase in coefficient for concurrent returns.
The results in Table 6 are qualitatively unchanged when using alternative specifications.
These include replacing the 0.111 threshold for centrality with thresholds at 0.136, 0.12 and 0.10
or replacing the central indicator variable with the continuous centrality variable, replacing our
centrality measure with a different measure, the degree centrality, and when using the BEA data
for 2002 instead of 1997 or a combination of the 1997 and 2002 data in the specifications.
5.2 Earnings Predictability
Research design
In this subsection we test our second prediction using accounting performance instead of
stock returns. Unlike predictability of future stock returns, predictability of accounting
performance does not require some sort of market friction or inefficiency. Hence we expect that
our findings for the cross-predictability of performance will be at least as strong when
23
accounting performance is used instead of stock returns.
We compute return on assets (ROA) as earnings from continuing operations (IBQ in
Compustat) divided by the average of the assets at the beginning and end of the period. We
compute industry-level ROA changes by weighting each firm’s ROA change by its beginning-ofquarter market capitalization. 26 We estimate the following regression at the industry level:
Linked ROA Changeit =
α + β1 Source ROA Changeit + β 2 Centrali
+ β3Centrali × Source ROA Changeit +
+ ∑ s =1 γ s Linked ROA Changei ,t − s + ε it
4
(8)
Linked ROA Changei ,t +1 =
α + β1 Source ROA Changeit + β 2 Centrali
+ β3Centrali × Source ROA Changeit +
+ ∑ s =0 γ s Linked ROA Changei ,t − s + ε it
4
where Source ROA Change is an industry’s ROA change and Linked ROA Change is the
weighted average ROA change of the source industry’s trading partners. As in the returns
regressions, we weight trading partners by the strength of trade as follows, where Aij denotes the
strength of the link between source industry i and linked industry j (See expression (1)):
Linked ROA Changeit = ∑ j Aij ROA Change jt .
(9)
We predict that the coefficient on β1 is positive if source industries’ ROA changes are
correlated with their trading partners’ ROA changes (Menzly and Ozbas 2010). We also predict
that the coefficient on β3 is positive if central industries’ earnings are more strongly associated
with the earnings of their trading partners than are the earnings of non-central industries. We
include lagged values of Linked ROA Change in order to control for future predictability of
within industry earnings changes, following Bernard and Thomas’ (1990) findings at the firmlevel. In particular, we predict that β4, β5 and β6 should be positive while β7 should be negative,
26
Our results are similar when defining industry ROA as the industry’s total earnings divided by the average of
beginning and ending total industry assets.
24
due to positive autocorrelation of earnings for the prior three quarters and negative
autocorrelation of earnings for the prior fourth quarter, as in Bernard and Thomas (1990). For the
purpose of consistency in our regressions, we restrict our sample to firms having fiscal quarters
ending in March, June, September and December.
Results
We present our results from the estimation of (8) in Table 7. The results in Panel A
present specifications using concurrent earnings change as the dependent variable. Column (1)
presents specifications using the control variables only. Consistent with Bernard and Thomas
(1990), we find that the first three lags of Linked Change ROA are positively associated with the
current value of this variable and two of the three associations are significant. Also consistent
with Bernard and Thomas (1990) the coefficient on the fourth lag of Linked Change ROA is
negative and significant.
We add Source Change ROA in Column (2), as well as its interaction with the Central
dummy variable. We find a significant positive association between source and linked industries’
concurrent year-on-year changes in ROA, evidenced by a positive coefficient on Source Change
ROA. We also find that this positive association is significantly enhanced when the source
industry is central. In particular, the coefficient more than quadruples from 0.027 to 0.110 (=
0.027+0.083).
(Insert Table 7 about here)
In Panel B, we present the results of the tests where the dependent variable is onequarter-ahead value of the Linked Change ROA. Results in Column (1), including control
variables only, are similar to those of Column (1) in Panel A. In Column (2), consistent with our
predictions, we find that Source Change ROA at quarter t positively predict Linked Change ROA
at quarter t+1, with a significant improvement of the predictive power when the source is a
25
central industry, as evidenced by a positive coefficient on the interaction Source Change ROA ×
Central. The predictive coefficient increases from 0.011 to 0.053 (= 0.011+0.042), comparable
to the results in Panel A and to the increase in predictability in the returns analysis presented in
Table 6.
The evidence from Table 7 indicates that, while all industries’ earnings predict their
trading partners’ earnings, the effect is much stronger for central industries. This supports our
hypothesis that information transfers and the propagation of shocks depend not only on trading
links between industries, but also on industries’ positions in the flow of trade. By definition,
central industries have direct or indirect trading links to large portions of the economy. Shocks to
these industries therefore are more likely to be either the origins of or reflective of aggregate
risks that affect other segments of the economy.
6
Conclusion
We investigate the role of inter-industry trade flows as a source of information transfers
among firms. We hypothesize that an industry’s position in the inter-industry network is an
important determinant of that industry’s exposure to aggregate shocks and the extent to which
idiosyncratic shocks to that industry affect the industries to which it is linked. Building on both a
diversification based argument and on prior theoretical findings on the propagation of industry
shocks, we predict and find that firms in central industries are more exposed to aggregate risks
compared to firms in non-central industries.
We also document that shocks to a central industry’s performance, as measured by stock
returns and accounting earnings, propagate more strongly than shocks to a non-central industry’s
performance. In particular, we find that, compared to a non-central industry, a central industry’s
performance is more strongly associated with the concurrent and future performance of the
26
industries to which it is linked. This suggests that central industries play a particularly important
role in the transmission of shocks.
Our study adds to the literature on the gradual diffusion of information across asset
markets via inter-industry trade flows. Prior research examines the cross-predictability of returns
across economically linked firms, and our findings suggest that such associations are stronger
when information is flowing from a central firm to a non-central firm than vice versa. The strong
relation between central industries and their trading partners calls attention to their usefulness in
forecasting the performance of industries with which they trade. Additionally, the relatively
stronger association of central industries’ performance with macroeconomic risks suggests that
the use of economic fundamentals, like GDP growth rate, as a basis for long-term growth
forecasts (e.g., Penman 2005, 2010 p.73) possibly yield more accurate intrinsic value estimates
in central industries than in non-central industries.
Our paper also contributes to the literature on the propagation of idiosyncratic shocks in
the economy. Consistent with Acemoglu et al.’s (2012) prediction that central industries play a
key role in amplification of idiosyncratic shocks into aggregate shocks, our empirical evidence
indicates that the shocks to central industries propagate more strongly than those to non-central
industries.
27
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Appendix A. Variable Descriptions
Variable
Average Analyst
Coverage
Average Proportion
Traded
Average Log Market
Value
CAR
Central
Centrality
Daily CAPM Rsquare
Daily FF R-square
DividendRate
Earnings Shock
Forecast Error
Fourthquarter
GDP Shock
Inflation
Late Announcer
Leverage
Linked Abn Returns
Linked ROA Change
Logmkval
Loss
Monthly CAPM Rsquare
Monthly FF R-square
Source Abn Returns
Source ROA Change
Description
The value-weighted average of an indicator variable that is set equal to one for any stock in the
portfolio that has analyst coverage at the end of the estimation period, and zero otherwise.
Average over a five year period of the monthly (daily) percentage of shares traded, defined as the
total monthly (daily) trading volume of the stocks in the portfolio divided by the total number of
shares outstanding for the stocks in the portfolio.
Average over a five-year period of the portfolio’s market capitalization, computed at the beginning
of each period (monthly for monthly returns and daily for daily returns) as shares outstanding
times market price in CRSP.
Cumulative returns for a given firm's stock for the three days around the earnings announcement
date, less the value weighted NYSE/NASDAQ/AMEX market return.
Indicator variable that is set equal to one if the centrality of the industry is greater than 0.111 and
zero otherwise.
The value of eigenvector centrality. The calculation is described in detail in Section 2.1. The
measure is computed based on the 1997 BEA "Use Tables".
R2 from a market model regression of daily portfolio returns on value weighted market returns,
estimated over five years.
R2 from a regression of daily portfolio returns on the Fama-French factors, estimated over five
years.
Total dividend from the CRSP market portfolio over the past twelve months divided by the current
market level.
The difference between current and prior year’s ROA, where ROA equals earnings from
continuing operations divided by the average of beginning and end-of-quarter assets.
I/B/E/S actual EPS less the most recent analyst consensus forecast (at least three days prior to the
earnings announcement), deflated by beginning-of-quarter price.
An indicator variable that equals one for the fourth fiscal quarter and zero otherwise.
Annual GDP growth less the average of the variable in the sample to normalize mean to zero.
Monthly change in the seasonally adjusted consumer price index.
Indicator variable that is set equal to one when the earnings announcement date occurs after the
median earnings announcement date during the quarter and zero otherwise.
Total debt (DLCQ plus DLTTQ) divided by itself plus book value of equity (SEQQ).
Strength weighted average of the monthly returns on linked industries less the value weighted
market return. The weights are the strength of the link between the source and the linked industries
and the monthly industry returns are equal to value weighted firm-level returns where the weights
are market capitalization at the beginning of the month.
Strength weighted average of year on year changes in ROA (IBQ/Average ATQ) of linked
industries. The weights are the strength of the link between source and linked industries. Industry
changes in ROA equal the market value weighted average of the firm-level change in ROA
(quarter t less quarter t-4).
Logarithm of the market capitalization of the firm at the end of the period (shares outstanding ×
share price at the end of the fiscal quarter (CSHOQ × PRCCQ)).
Indicator variable that equals one if the actual EPS as reported in I/B/E/S is negative.
The R2 from a market model regression of monthly stock returns on value weighted market returns
estimated over a five year window.
The R2 from a regression of monthly stock returns on the Fama-French factors estimated over a
five year window.
Market capitalization weighted average monthly returns of the firms in the source industry less
value weighted market return for the same month.
Market capitalization weighted average of the change in ROA (IBQ/Average ATQ) between
quarter t and quarter t-4 of firms in the industry.
32
Specialitems
Spread
Stdev
StdReturns
TobinQ
Indicator variable that equals one if special items (SPIQ) exceed 5% of total assets and zero
otherwise.
The difference between the yield of BAA rated and AAA rated bonds.
The standard deviation of market returns estimated over a period of one month.
Standard deviation of stock returns estimated over a 90 days period ending three days prior to the
earnings announcement date.
End-of-quarter book value of total liabilities plus market value of equity, divided by total assets
(ATQ-SEQQ+CSHOQ×PRCCQ)/ATQ.
33
Appendix B: Additional Details on Centrality Measure
This appendix provides additional discussion of the measurement of centrality. Figure B
replicates Figure 1 from the main body, with the exception that we use shapes other than squares
to denote industries included in the discussion of this appendix. The triangle denotes the Retail
Trade industry, the pentagon denotes Basic Chemicals, and the hexagon denotes Wholesale
Trade.
Figure B: Inter-industry network based on BEA input/output tables
This figure plots the inter-industry network based on the U.S. input-output matrix of 1997 provided by the Bureau of
Economic Analysis (BEA).Each node corresponds to an industry (based on BEA’s four-digit industry code) and
each edge corresponds to a link between industries with strength (Aij) of 3% or more. See Section 2.1 for the
definition of strength of the link between two industries. The triangle, pentagon, and hexagon show the Retail Trade,
Basic Chemical Manufacturing, and Wholesale Trade industries, respectively, which we refer to in the discussion in
this appendix. These three nodes, along with the square nodes, indicate industries with a centrality score above the
0.111 threshold that we use to indicate central industries, as we discuss in Section 3.2.
A basic measure of an industry’s centrality is the raw number of industries it trades with,
which is called the industry’s degree. For example, the Wholesale Trade industry is linked to 29
34
other industries, while Retail Trade and Basic Chemicals are each linked with nine. This metric
ranks Wholesale Trade as the most central industry, while Retail Trade and Basic Chemicals
share the rank of 11th most central. Representing the network by the matrix A, with elements Aij
= Aji = 1 if industry i trades with industry j, and Aii = 0, an industry’s degree is di = ΣjAij.
The simple count of links fails to distinguish the relative importance of an industry’s
trading partners. For example, visual inspection of Figure B suggests that Retail Trade, the
triangle, has more highly connected trading partners than Basic Chemicals, the pentagon. The
eigenvector centrality metric addresses this. Instead of counting links (di = ΣjAij), eigenvector
centrality weights the links by their importance, ci = ΣjAijcj. With this metric, Wholesale Trade,
the hexagon in Figure B, maintains the highest centrality. Retail Trade has the eighth highest
rank, which exceeds the degree-based rank of eleven on account of Retail Trade interacting with
relatively important sectors of the economy. Basic Chemicals ranks as 52nd in terms of
eigenvector centrality. Because Basic Chemicals plays a relatively peripheral role in this
example where we do not distinguish links by strength of trade, its importance in terms of
eigenvector centrality drops significantly relative to its rank in terms of degree.
The eigenvector centrality metric that we discuss in the preceding examples suffers from
a shortcoming that we remedy in the measure used in our main analysis. The examples fail to
account for heterogeneity in the amount of trade between industries. In our main analysis, we
allow the matrix A of trade links to reflect the strength of trade. This has substantial effects. For
example, Basic Chemicals’ eigenvector centrality ranks as 21st, rather than 52nd, when we
account for the amount of trade it conducts with other industries.
35
Appendix C: Table 4B regressions with full set of variables
This appendix presents Table 4, Panel B with the entire set of control variables listed,
which we omit because they are not the focus of our study and to make the table more compact.
Dependent variable: CARs
Forecast Error
(1)
1.069
(11.37)
-0.001
(-1.62)
-0.052
(-1.54)
Central
Forecast Error × Central
***
Late Announcer
Late Announcer × Central
Forecast Error × Late Announcer
Forecast Error × Late Announcer × Central
Tobinq
-0.001
(-2.94)
-0.007
(-1.01)
0.086
(1.69)
1.586
(2.26)
0.002
(1.51)
-0.087
(-2.66)
-0.001
(-2.74)
0.068
(4.85)
-0.018
(-19.50)
-0.719
(-9.58)
-0.007
(-4.79)
-0.103
(-3.21)
0.000
(0.42)
-0.072
(-2.54)
0.005
(6.44)
Forecast Error × Tobinq
StdReturns
Forecast Error × StdReturns
Leverage
Forecast Error × Leverage
Logmkval
Forecast Error × Logmkval
Loss Dummy
Forecast Error × Loss Dummy
Specialitems Dummy
Forecast Error × Specialitems Dummy
Fourthquarter Dummy
Forecast Error × Fourthquarter Dummy
Constant
36
***
*
**
***
***
***
***
***
***
***
**
***
(2)
1.139
(11.91)
0.000
(-0.46)
-0.135
(-2.06)
-0.004
(-3.34)
-0.001
(-1.26)
-0.185
(-6.11)
0.130
(1.80)
-0.001
(-2.79)
-0.003
(-0.41)
0.080
(1.56)
1.384
(1.93)
0.002
(2.02)
-0.067
(-2.07)
-0.001
(-3.91)
0.055
(3.88)
-0.018
(-18.93)
-0.705
(-9.42)
-0.007
(-4.82)
-0.100
(-3.02)
0.001
(1.40)
-0.043
(-1.47)
0.006
(6.56)
***
**
***
***
*
***
*
**
**
***
***
***
***
***
***
***
Table 1: List of the most central industries
Rank
Name of the Industry
1
Wholesale Trade
2
Construction
3
Management of Companies/Enterprises
4
Real Estate
5
Retail Trade
6
Administrative and Support Svc.
7
Money Authorities/Credit Intermediation
8
Power Generation and Supply
9
Advertising and Related Svc.
10
Plastics/Rubber Products Mfg.
11
Motor Vehicle Body/Trailer/Parts Mfg.
12
Other Fabricated Metal Product Mfg.
13
Petroleum and Coal Products Mfg.
14
Food Mfg.
15
Oil and Gas Extraction
16
Employment Svc.
17
Semiconductor and Electronic Eq. Mfg.
18
Iron and Steel Mills
19
Management and Tech. Consulting Svc.
20
Architectural and Engineering Svc.
37
Table 2: Distribution of the centrality variable
This table presents the distribution of the eigenvector centrality measure for the industries in the U.S. The measure is
calculated using the detailed use tables of 1997 published by the Bureau of Economic Analysis (BEA). Industries
are defined using the BEA’s four-digit industry codes and the total number of industries is 123.
Variable
Centrality
Minimum
0.021
5th Percentile
0.036
10th Percentile
0.045
25th Percentile
0.054
50th Percentile
0.074
75th Percentile
0.097
90th Percentile
0.127
95th Percentile
0.143
Maximum
0.266
Mean
0.081
StDev
0.040
N
123
38
Table 3: Analysis of the R2s from CAPM and Fama-French three-factor models
This table presents the analysis of the association between centrality and exposure to aggregate risk using a fractional logit model (see Papke and Wooldridge
1996). The dependent variable is the R2 from CAPM or Fama-French three-factor models, estimated using daily or monthly data, of value weighted industry
portfolios. Estimations are conducted using five-year windows. Centrality is the centrality measure defined in Section 2.1 and is the variable of interest. Average
Analyst Coverage is a value weighted measure of analyst coverage, based on whether there is a quarterly consensus EPS forecast provided in I/B/E/S for the last
quarter of the analysis period. Average Proportion Traded equals the average over a five year period of the value weighted monthly percentage of shares traded.
Average Log Market Value equals the average over the five-year period of the logarithm of the industry portfolio’s market value. Additional information is
provided in Appendix A. Panel A presents descriptive statistics for the dependent and control variables. There 2,920 observations: 2,434 for non-central
industries and 486 for central industries. Panel B presents the results of the fractional logit model. Standard errors in Panel B and for the difference in means in
Panel A are clustered at the industry and year levels. The R-square of the regressions in Panel B is computed as in OLS (1 – SSR/SST) following Papke and
Wooldridge 1996. *, **, and *** denote significance at a two-sided 10%, 5% and 1% level.
Panel A: Descriptive Statistics
Mean
Variable
Median
Test of Equality (Central - Non-Central)
39
Central
Non-Central
Central
Non-Central
Mean
Median
Daily CAPM R-square
0.485
0.359
0.485
0.343
(4.21)
***
(11.65)
***
Daily FF R-square
0.543
0.401
0.542
0.391
(5.08)
***
(13.37)
***
Monthly CAPM R-square
0.532
0.404
0.544
0.406
(4.42)
***
(11.53)
**
Monthly FF R-square
0.623
0.492
0.628
0.501
(5.03)
***
(13.12)
***
Centrality
0.143
0.068
0.127
0.067
(7.92)
***
(34.86)
***
Average Analyst Coverage
0.820
0.799
0.877
0.912
(0.69)
(-4.43)
***
Average Proportion Traded
4.718
5.146
3.480
3.868
(-1.16)
(-2.87)
***
Average Log Market Value
17.041
15.856
17.125
16.032
(3.34)
(11.97)
***
***
Table 3: Analysis of the R2s from CAPM and Fama-French three-factor models (continued)
Panel B: Fractional Logit Regressions
Daily R-squares
Dependent Variable:
R-square
Centrality
Average Analyst Coverage
Average Proportion Traded
Average Log Market Value
Constant
CAPM
3.737
(3.96)
0.611
(2.51)
0.041
(2.01)
0.239
(7.56)
-5.385
(-12.26)
Monthly R-squares
Fama French
***
**
**
***
***
4.257
(4.58)
0.512
(2.29)
0.038
(2.02)
0.228
(7.69)
-4.947
(-12.04)
CAPM
***
**
**
***
***
4.301
(4.74)
0.432
(1.97)
0.012
(0.11)
0.119
(3.69)
-2.934
(-5.81)
Fama French
***
**
***
***
4.461
(4.96)
0.361
(1.88)
-0.003
(-0.03)
0.112
(3.79)
-2.388
(-5.57)
***
*
***
***
40
Number Observations
Clustering
Number of Clusters
Chi-Square
R-Square
2,920
Industry/Year
114/27
142.01
0.85
***
2,920
Industry/Year
114/27
164.64
0.88
***
2,920
Industry/Year
114/27
2,920.00
0.82
***
2,920
Industry/Year
114/27
65.09
0.89
***
Table 4: Earnings response coefficients
Table 4 Panel A presents descriptive statistics for the sample of firms covered by analysts. There are 55,961
observations for central firms and 137,653 observations for non-central firms. Panel B presents the analysis of the
association between earnings response coefficients and centrality. In Panel B, cumulative market-adjusted return for
the three day announcement period [-1, 1] is regressed on the Forecast Error, an indicator variable for central firms,
and the interaction of the central dummy with the forecast error. Control variables, and their interactions with
Forecast Error are also included in the model. Control variables include TobinQ, the Tobin Q coefficient defined as
the market value of assets divided by the book value of total assets, StdReturns, the 90 calendar day stock returns
volatility, Leverage, the firm book leverage, Logmkval, the natural logarithm of the firm market value, Loss, an
indicator variable when the actual EPS is negative, Specialitems, an indicator variable when special items during the
quarter are above 5% of the total assets value, Fourthquarter, an indicator variable for the fourth fiscal quarter.
Continuous variable are normalized in Panel B so that their mean equals zero. Standard errors in Panel B and in the
mean test of equality in Panel A are double clustered at the firm and quarter level, the time unit in the analysis. For
brevity the control variables and their interactions with Forecast Error are not reported, but are available from the
authors upon request. The definitions of the variables are available in Appendix A. *, **, and *** denote
significance at a two sided 10%, 5% and 1% level.
Panel A: Descriptive Statistics
Mean
Variable
Test of Equality
(Central - Non-Central)
Median
Central
Non-Central
Central
Non-Central
Mean
CAR
0.002
0.000
0.001
0.000
Forecast Error
-0.004
-0.003
0.000
0.000
(-0.85)
Tobinq
1.815
2.287
1.425
1.689
StdReturns
0.032
0.035
0.027
Leverage
0.338
0.285
Logmkval
6.167
Loss
0.178
Specialitems
*
(1.81)
(-12.38)
***
(-63.05)
***
0.030
(-5.65)
***
(-30.13)
***
0.325
0.229
(7.23)
***
(47.84)
***
6.021
6.106
5.878
(2.58)
***
(18.25)
***
0.266
0.000
0.000
(-9.83)
***
(-40.80)
***
0.025
0.035
0.000
0.000
(-8.14)
***
(-11.99)
***
Fourthquarter
0.272
0.276
0.000
0.000
(-0.80)
(-1.64)
Late Announcer
0.321
0.309
0.000
0.000
(1.28)
(4.80)
41
(1.59)
Median
*
(-1.02)
***
Table 4: Earnings response coefficients (continued)
Panel B: Earnings Response Coefficients (ERCs)
Dependent variable: CARs
(1)
Forecast Error
1.069
(11.37)
-0.001
(-1.62)
-0.052
(-1.54)
Central
Forecast Error × Central
(2)
***
Late Announcer
Late Announcer × Central
Forecast Error × Late Announcer
Forecast Error × Late Announcer × Central
Constant
0.005
(6.44)
Yes
Yes
Control Variables
Control Variables × Forecast Error
Number Observations
Clustering
Number of Clusters
Adjusted R-square
F-statistic
193,614
Firm/Quarter
8,118/109
0.03
194.84
***
***
1.139
(11.91)
0.000
(-0.46)
-0.135
(-2.06)
-0.004
(-3.34)
-0.001
(-1.26)
-0.185
(-6.11)
0.130
(1.80)
0.006
(6.56)
Yes
Yes
193,614
Firm/Quarter
8,118/109
0.03
164.40
F-test: Late announcement effect on central firms
Forecast Error × Late Announcer + Forecast Error × Late Announcer × Central = 0
F-test
0.71
p-value
0.40
F-test: Late announcement effect on central firms vs. effect on non-central firms
Forecast Error × Central + Forecast Error × Late Announcer × Central = 0
F-test
0.02
p-value
0.89
42
***
**
***
***
*
***
***
Table 5: Mean reversion of earnings shocks
This table presents the analysis of the mean reversion of annual earnings shocks. Earnings Shock is calculated as the
difference between ROA for the current year and the ROA for the prior year. One year ahead earnings shocks are
regressed on current period earnings shocks, a central dummy, and its interaction with current period earnings
shocks. GDP Growth equals the year on year GDP growth less mean GDP growth and controls for the impact of the
overall economy on earnings persistence. The definitions of the variables are available in Appendix A. Standard
errors are double clustered at the firm and year level. *, **, and *** denote significance at a two sided 10%, 5% and
1% level.
Dependent variable:
Earnings Shock t+1
Earnings Shock t
(1)
-0.242
(-14.54)
(2)
***
-0.231
(-14.16)
0.000
(0.08)
-0.056
(-3.55)
Central
Central x Earnings Shock t
(3)
***
GDP Shock t × Earnings Shock t
Number observations
Clustering
Number of clusters
Adjusted R-square
F-statistic
-0.009
(-2.57)
89,379
Firm/Year
11,105/27
0.06
777.32
**
***
-0.010
(-2.63)
89,379
Firm/Year
11,105/27
0.06
287.25
43
***
***
GDP Shock t
Constant
-0.239
(-15.55)
(4)
***
***
-0.456
(-2.93)
1.457
(1.86)
-0.010
(-2.97)
89,379
Firm/Year
11,105/27
0.06
329.84
***
*
***
***
-0.229
(-15.10)
0.000
(-0.12)
-0.053
(-3.36)
-0.454
(-2.90)
1.431
(1.84)
-0.010
(-3.05)
89,379
Firm/Year
11,105/27
0.06
214.16
***
***
***
*
***
***
Table 6: Returns predictability
This table presents the analysis of the association between centrality and the predictability of stock returns at the
industry level. The dependent variable is equal to the weighted monthly returns of the linked industries of a given
industry i (i.e., the source industry) less the market returns. To calculate the weighted monthly returns of the linked
industries we first calculate value weighted returns of all firms in each linked industry then multiply these returns
with the strength of the link of between the industry and the source industry. Panel A presents results for the
concurrent association between the returns of the linked industries and the source and Panel B presents the results
where the dependent variable is one-month-ahead return of the linked industries. The definitions of the variables are
available in Appendix A. Standard errors are clustered at the industry and month level. *, **, and *** denote
significance at a two sided 10%, 5% and 1% level.
Panel A: Concurrent returns
Dependent Variable:
Concurrent returns linked
(1)
(2)
Returns source
Central dummy
Central × Returns Source
Spread
0.008
(3.37)
0.097
(0.55)
-0.344
(-2.35)
-0.320
(-3.44)
0.001
(0.41)
Inflation
Stdev
Dividend Rate
Constant
Number of observations
Clustering
Number of Clusters
Adjusted R-square
F-statistic
36,133
Industry/Month
114/335
0.02
177.76
F-test: Returns source + Central × Returns Source = 0
F-statistic
p-value
44
***
**
***
***
0.061
(8.62)
-0.001
(-3.01)
0.180
(5.82)
0.007
(3.23)
0.094
(0.62)
-0.238
(-1.89)
-0.260
(-3.23)
0.000
(0.19)
36,133
Industry/Month
114/335
0.15
559.91
61.11
0.00
***
***
***
***
*
***
***
Table 6: Returns predictability (continued)
Panel B: One-month-ahead returns
Dependent Variable:
Returns linked month t+1
(1)
Returns linked
0.071
(1.92)
(2)
*
Returns source
Central dummy
Central × Returns Source
Spread
0.006
(2.34)
0.132
(0.71)
-0.106
(-0.66)
-0.255
(-2.75)
0.000
(-0.20)
Inflation
Stdev
Dividend Rate
Constant
Number of observations
Clustering
Number of Clusters
Adjusted R-square
F-statistic
36,133
Industry/Month
114/335
0.02
124.42
F-test: Returns source + Central × Returns Source = 0
F-statistic
p-value
45
**
***
***
0.057
(1.59)
0.005
(2.42)
-0.001
(-2.55)
0.021
(2.56)
0.006
(2.33)
0.133
(0.72)
-0.101
(-0.63)
-0.254
(-2.73)
0.000
(-0.15)
**
**
**
**
***
36,133
Industry/Month
114/335
0.03
83.88
8.55
0.00
***
Table 7: Earnings predictability
This table presents the analysis of the association between centrality and the predictability of accounting
performance at the industry level. The dependent variable is equal to the weighted seasonally-differenced quarterly
return-on-assets of the linked industries of a given industry i (i.e., the source industry). The seasonally-differenced
quarterly ROA of the linked industries is computed as the average ROA change of each linked industry multiplied
by the strength of the link summed over all linked industries. Each industry’s average ROA is computed as the
market value weighted average of the ROA changes of the firms comprising the industry. Panel A presents results
for the concurrent association between the ROA of the linked industries and the source and Panel B presents the
results where the dependent variable is one-quarter-ahead ROA of the linked industries. The definitions of the
variables are available in Appendix A. Standard errors are double clustered at the industry and quarter level. *, **,
and *** denote significance at a two sided 10%, 5% and 1% level.
Panel A: Concurrent ROAs
Dependent variable:
Linked Change ROA t
(1)
(2)
Source Change ROA t
0.027
(2.65)
-0.001
(-3.83)
0.083
(4.19)
0.463
(9.83)
0.034
(0.67)
0.074
(1.71)
-0.220
(-3.52)
-0.001
(-4.64)
Central
Source Change ROA t × Central
Linked Change ROA t-1
Linked Change ROA t-2
Linked Change ROA t-3
Linked Change ROA t-4
Constant
Number Observations
Clustering
Number of Clusters
Adjusted R-square
F-statistic
0.500
(9.91)
0.044
(0.84)
0.081
(1.82)
-0.222
(-3.28)
-0.001
(-4.83)
11,374
Industry/Quarter
114/107
0.30
456.00
***
*
***
***
***
11,374
Industry/Quarter
114/107
0.34
334.78
F-test: Source Change ROA + Central × Source Change ROA = 0
F-statistic
p-value
46
20.11
0.00
***
***
***
***
*
***
***
***
Table 7: Earnings predictability (continued)
Panel B: One-quarter-ahead ROAs
Dependent variable:
Linked Change ROA t+1
(1)
(2)
Source Change ROA t
0.011
(3.84)
-0.001
(-3.55)
0.042
(3.89)
0.496
(9.46)
0.024
(0.46)
0.078
(1.73)
-0.282
(-3.45)
0.103
(1.88)
-0.001
(-3.80)
Central
Source Change ROA t × Central
Linked Change ROA t
Linked Change ROA t-1
Linked Change ROA t-2
Linked Change ROA t-3
Linked Change ROA t-4
Constant
Number Observations
Clustering
Number of Clusters
Adjusted R-square
F-statistic
0.521
(9.94)
0.032
(0.60)
0.083
(1.82)
-0.279
(-3.39)
0.111
(2.04)
-0.001
(-3.91)
11,374
Industry/Quarter
114/107
0.31
355.68
***
*
***
**
***
***
11,374
Industry/Quarter
114/107
0.32
265.22
F-test: Source Change ROA + Central × Source Change ROA = 0
F-statistic
p-value
47
23.13
0.00
***
***
***
***
*
***
*
***
***
Figure 1: Inter-industry network based on BEA input/output tables
This figure plots the inter-industry network based on the U.S. input-output matrix of 1997 provided by the Bureau of
Economic Analysis (BEA).Each node corresponds to an industry (based on BEA’s four-digit industry code) and
each edge corresponds to a link between industries with strength (Aij) of 3% or more. See Section 2.1 for the
definition of strength of the link between two industries. The square nodes indicate industries with a centrality score
above the 0.111 threshold that we use to indicate central industries, as we discuss in Section 3.2.
48
Figure 2: Distribution of centrality measure
0
.05
Eigenvector Centrality
.2
.1
.15
.25
This figure plots the distribution of the eigenvector centrality measure. Eigenvector centrality (defined formally in
Section 2.1) of an industry is as function of that industry’s direct or indirect ties to the other industries in the
economy as well as the strength of these ties. Larger values indicate more central industries. The dotted reference
line in the figure corresponds to 0.111, the cutoff value we use to differentiate central industries from the non-central
industries.
0
20
40
60
80
Centrality Rank
49
100
120