Download Industry Concentration and Average Stock Returns

Industry Concentration and Average Stock Returns ∗
Kewei Hou and David T. Robinson
Ohio State University, Fisher College of Business,
and Columbia University, Graduate School of Business
This Draft: May 28, 2003
ABSTRACT
This paper shows that differences in industry concentration help explain the crosssection of average stock returns. Firms in concentrated industries earn lower returns,
even after controlling for size, book-to-market and momentum. The premium for industry concentration exhibits systematic business cycle variation. In addition, the premium
on book-to-market is higher in more concentrated industries. These findings suggest that
barriers to entry in highly concentrated industries insulate firms from aggregate shocks
that lead to economic distress.
JEL Classification Codes: G12, G33, L10
∗ We
thank Judith Chevalier, Eugene Fama, Peter Hecht, Owen Lamont, Toby Moskowitz, Per Olsson, Per
Stromberg, and seminar participants at the University of Chicago and the Chicago Quantitative Alliance Academic Paper Competition for many helpful comments on earlier drafts. Any remaining errors are our own.
Correspondence to: Kewei Hou, Fisher College of Business, Ohio State University, 2100 Neil Avenue,
Columbus, OH 43210. E-mail: [email protected].
I. Introduction
Firms generate cash flows through their actions in product markets; these risky cash flows
are in turn priced in financial markets. Yet, the economic link between product markets and
asset prices remains relatively unexplored. Our goal is to explore this link by posing the
following questions: How do average stock returns vary with industry concentration? Do firms
in competitive industries earn higher or lower returns than their peers in more concentrated
industries? If so, why?
In this paper, we show that the characteristics of product markets are important determinants of the cross-sectional behavior of equity returns, even after controlling for other known
risk factors. In particular, we show that higher industry concentration leads to lower average
stock returns. Industry concentration measures whether total industry sales are concentrated
in the hands of a few firms (high concentration), or whether they are spread evenly across
many firms (low concentration). Thus, our finding indicates that firms operating in highly
competitive industries earn higher average returns. This finding is important for a number of
reasons.
First, different industries have experienced dramatically different average returns over
time. For example, returns for the drug industry (SIC 283) average 24.3% per year over
the 1973-2001 period, while the soft-drink industry (SIC 208) averaged only 13.5% over the
same period. The steelworks industry (SIC 337) was even worse: 8.34% per annum. Indeed,
Fama and French (1997) estimate annual costs of equity from the CAPM and 3-factor model
that differ by as much as a factor of two across industries.
Second, a growing literature in asset pricing explores the importance of industries for explaining empirical regularities in stock returns. For example, Asness and Stevens (1996) decomposes size and book-to-market factors into inter- and intra-industry components and finds
that intra-industry factors have greater explanatory power in the cross-section. Moskowitz
and Grinblatt (1999) document industry portfolios exhibit significant momentum. In addition,
1
Hou (2003) finds that the lead-lag effect in stock returns is due to an intra-industry component
which also drives the industry momentum effect. However, these papers stop short of testing a
detailed mechanism for why industry differences are important for understanding the behavior
of stock returns.
As suggested above, we argue in this paper that differences in industry concentration are an
important part of the answer to these questions. Our hypothesis is that barriers to entry, which
differ at the industry level, insulate firms from aggregate economic shocks, and therefore
affect the risk premia associated with particular shocks. High barriers to entry lead to lower
risk premia. At the same time, high barriers to entry also lead to high levels of industry
concentration. Thus, highly concentrated industries naturally earn lower stock returns than
highly competitive ones because their barriers to entry are higher.
Since the existing literature is largely silent on the link between industry structure and
stock returns, one empirical approach would be simply to measure barriers to entry at the industry level directly, and then relate these to the cross-section of stock returns. However, this
approach suffers from the fact that barriers to entry may be difficult to observe and may reflect strategic choices by incumbent firms, rather than exogenous production characteristics.1
Instead, we focus on industry concentration, since it is a natural outcome of barriers to entry,
regardless of how they arose, and relate it to the behavior of stock returns.
To test the hypothesis that industry concentration affects stock returns, we calculate salesbased Herfindahl indices and 5-firm ratios for 3-digit SIC code industries using COMPUSTAT
data. Linking these to CRSP monthly stock data, we find the following results.
First, industry concentration is correlated with firm characteristics that are known to describe the cross section of average stock returns. Firms in highly concentrated industries have
1 Early work in industrial organization, beginning with Bain (1954), argues that barriers to entry are exogenous
determinants of industry structure. Were this the only way in which barriers to entry arose, they would be
potentially easy to measure. However, more recent theories (Sutton (1991), (1998)) argue that barriers to entry
to an industry can be erected endogenously by early entrants who wish to keep out future competitors. These
barriers can take many different forms (such as large asset base, high research and development expenditure,
advertising outlays, brand differentiation, and product segmentation) and are therefore difficult to measure.
2
bigger market capitalization and higher book-to-market ratios. In addition, they tend to have
lower leverage ratios and higher βs than firms in highly competitive industries. But more importantly, the relation between stock returns and industry concentration demonstrate a clear
pattern: firms in highly concentrated industries earn lower returns, even after controlling for
size, book-to-market, momentum, and other known risk characteristics. This holds at both the
industry level as well as the firm level, and is robust to alternative empirical specifications.
Second, we examine how firm-level characteristics interact with industry concentration.
We find that the premium associated with the book-to-market ratio is larger in more concentrated industries.
Finally, we show that the premium to industry concentration exhibits sensible business cycle variations. The spread between the highest and lowest concentration portfolios co-moves
positively with inflation and negatively with the term spread. Since the average value of the
premium is negative, this indicates that the magnitude of the premium is highest at an economic trough.
These findings clearly demonstrate that industry concentration affects stock returns, and
lend support to our explanation for why this result holds. The fact that the magnitude of the
concentration premium is greatest at an economic trough suggests that the spread in returns
between firms that are insulated from economic distress and those that are not is greatest
when general economic conditions are weak. Moreover, the interaction effect suggest that
high book-to-market firms are likely to be in greater distress if they are operating in industries
dominated by a few firms with big market share, all else equal.
Of course, there are a number of potential alternative mechanisms linking industry concentration to expected returns. For example, industry characteristics could simply be correlated
with other known risk measures. If this were the case, our story linking economic fundamentals to risk premia could be correct without industry concentration playing an independent role
in determining asset prices. For example, this would be true if size and book-to-market ratio
3
completely determine the cross-section of expected stock returns, and industry concentration
simply described industry-level, cross-sectional differences in those characteristics.
This explanation could potentially account for the correlations between industry concentration, size, book-to-market and β. However, it falls short of explaining the relation between
stock returns and industry concentration controlling for these characteristics. The fact that we
find a significant concentration effect even after controlling for those known determinants of
average returns indicates that these correlations in characteristics are not driving our results.
Alternatively, industry concentration could affect expected returns through capital structure choice, by increasing or decreasing the amount of leverage that firms in an industry can
sustain. For example, if industry concentration results in lower operating risk, then managers
may optimally choose to increase leverage. This would raise equity returns through the linearity of equity returns in the leverage ratio. However, this effect should work against us,
since it implies that increases in industry concentration would be correlated with increases in
expected returns, all else equal. On the other hand, firms in concentrated industries may face
higher costs of financial distress. This could occur if the potential lost franchise value of the
firm is higher because firms earn positive economic profits, or because the threat of government intervention is higher. This would lead firms in more concentrated industries to have
lower leverage, lowering average stock returns.
We address this possibility by controlling for leverage in our empirical analysis. The
results show that, in fact, firms in more concentrated industries tend to have lower leverage.
But even after controlling for this effect, our industry concentration results still hold.
While it is difficult to control perfectly for every conceivable alternative, these results
support the hypothesis that industry concentration is a channel through which economy-wide
risk is amplified or diminished as it is transmitted to individual firms. Our findings are more
difficult to reconcile with competing explanations.
4
This paper is part of a larger literature that links industrial organization to issues in financial
economics. Earlier work such as Titman (1984) studies how capital structure and product
markets interact through the liquidation decision. A number of recent papers have examined
the link between capital structure and industry characteristics; see, for example, Mackay and
Phillips (2002) or Almazan and Molino (2001). To our knowledge, however, ours is the first
paper to link expected stock returns to industry product-market characteristics through the
channel we propose.
The remainder of the paper is structured as follows. Section II describes the data and how
we construct industry concentration measures. In addition, this section relate the industry
concentration measures to various industry-level characteristics. Section III examines how
industry concentration affects the cross-section of stock returns. Section IV studies the interaction between industry concentration and firm characteristics, while section V considers
business cycle variation in concentration premia. Section VI concludes.
II. Data and Measures of Industry Concentration
Our sample includes all listed companies that are contained in the intersection of NYSE/AMEX
and NASDAQ monthly return files from the Center for Research in Security Prices (CRSP)
and the COMPUSTAT industrial annual file between January, 1973 and December, 2001.
2
To ensure that accounting information is already impounded into stock prices, we match
CRSP stock return data from July of year t to June of year t + 1 with accounting information
for fiscal year ending in year t − 1 as in Fama and French (1992). In order to be included
in our return tests, a firm must have CRSP stock price, shares outstanding and 3-digit SIC
2 Our
main reason for focusing on the post-1973 period is to ensure that we have the widest possible industry
coverage. Prior to 1973, the CRSP sample includes NYSE and AMEX firms only, and NASDAQ firms are added
to the sample in 1973.
5
classification for June of year t.
3
It must also have return data from the previous three years
for market β estimates. In addition, it should have COMPUSTAT data on sales, book equity,
market equity and total asset for its fiscal year ending in year t − 1.4 Book equity is stockholder’s equity plus balanced deferred taxes and investment tax credit minus the book value of
preferred stock. COMPUSTAT market equity is stock price times shares outstanding at fiscal
year end. Book-to-market ratio is calculated by dividing book equity by COMPUSTAT market
equity. Leverage is defined as the ratio of book liabilities (total asset minus book equity) to
total market value of firm (COMPUSTAT market equity plus total asset minus book equity).
Size (CRSP market equity) is measured by multiplying shares outstanding by stock price for
June of year t. Finally, we follow Fama and French (1992) to estimate market β by computing
full-period βs for portfolios sorted by size and pre-ranking β and then assigning portfolios βs
to stocks in those portfolios. The pre-ranking β is estimated as the sum of the coefficients of
regressions of individual stock returns on contemporaneous and lagged market returns over
the past three years.
Throughout the paper, we use three-digit SIC classifications to define industry membership. This choice reflects the desire to balance two offsetting concerns. On the one hand, we
wish to use fine-grained industry classifications so that firms in unrelated lines of business
are not grouped together. On the other hand, using too fine an industry classification results
in portfolios that are statistically unreliable and firms being grouped into distinct industries
arbitrarily. Choosing 3-digit classifications strikes a balance between these two concerns.5
3 Kahle
and Walkling (1996) report problems between CRSP and COMPUSTAT with regard to SIC industry
classifications. To minimize any impact this may have on our results, and to maintain internal consistency with
our variable construction, we disregard COMPUSTAT SIC classifications.
4 The data requirement we impose here probably biases our sample towards larger firms.
5 Although all of the results in the paper are presented with 3-digit SIC classifications, we have also replicated
our findings for 2-digit and 4-digit SIC classifications. These results are available from the authors upon request.
6
We employ two commonly-used measures for industry concentration: the Herfindahl index
and the 5-Firm ratio. The Herfindahl index for industry j is defined as follows:
I
Herfindahl j = ∑ s2i j ,
(1)
i=1
where si j is the share of total industry sales generated by the ith firm in industry j (the ratio of
firm i’s sales to total industry sales). Thus, the Herfindahl measure uses the entire distribution
of industry sales information to obtain a complete picture of industry concentration. Small
values of the Herfindahl index imply that industry output is shared among many competing
firms but none owning a very large chunk of the market, while large values imply that industry
sales are concentrated in the hands of a few, large firms.6
Likewise, the 5-Firm ratio for industry j is the percentage of market share owned by the
largest five firms in the industry:
5
5 − Firm Ratio j = ∑ si j ,
(2)
i=1
for s1 j > . . . > si j > si+1 j > . . . > sI j .
Similar to the Herfindhal index, a low 5-Firm ratio would indicate a very competitive industry,
whereas a high 5-Firm ratio indicate a very concentrated industry. Unlike the Herfindahl index,
however, the 5-Firm ratio only uses data for the 5 largest firms in the industry, and sometimes
does not provide a complete picture of how industry sales are distributed across the entire
industry.
One concern with using Compustat data to generate measures of industry concentration is
that we do not have information on privately held firms. In principal, privately held firms are
included on Compustat if they have engaged in public debt issues, but in practice, the number
6 Many
papers studying the diversification discount use the Herfindahl index to measure the degree to which
internal investment opportunities of a single firm are spread across many projects (diversified firms) or only a
few projects (focussed firms). For example, see Berger and Ofek (1995), Rajan, Servaes, and Zingales (2000) or
others. Instead, our measure captures market shares across all firms in a given 3-digit industry.
7
of such firms is small. While we recognize that this may be a shortcoming of our measures,
we think that this shortcoming only makes it more difficult to establish our results. To the
extent that privately held firms tend to be small relative to their publicly traded peers, omitting
them probably has no effect at all on the 5-firm ratio, and only a small effect on the herfindahl
index. The fact that our results are robust to different measures of industry concentration and
different industry classifications suggests that this is unlikely to be a problem for our findings.
To calculate Herfindahl and 5-Firm ratios for the industries in our sample, we perform the
above calculations each year for each industry, and then average the values over the past three
years to smooth out noise in COMPUSTAT sales data. Panel A of Table I reports summary
statistics for the two industry concentration measures. The Herfindahl index has a mean of
0.524, a median of 0.471 and a standard deviation of 0.309. The mean and median values of the
5-Firm Ratio are much bigger (0.914 and 0.994, respectively) whereas the standard deviation
is smaller (0.138). Panel A also reports the quintile breakpoints for the two concentration
measures. One thing that is worth noting is that both the 60th and 80th percentile of the 5FIRM ratio are equal to one, which suggests that more than 40% of the 3-digit SIC industries
have 5 firms or fewer. Finally, the Herfindahl index and the 5-Firm ratio are highly correlated
with a correlation coefficient of 0.701.
Next we examine how industry concentration covaries with other industry-level characteristics. Panel B and C report the characteristics of quintile portfolios sorted by the Herfindhal
index and the 5-Firm ratio. In particular, we are interested in what types of industries/firms
fall into the two extreme quintiles. In June of each year between 1973 and 2001, we sort
industries into concentration quintiles based on their three-year average Herfindahl index or
5-Firm ratio. Panel B reports the time series means of the average industry median characteristics over the 1973 to 2001 period for portfolios formed on the Herfindahl index. The average
Herfindahl index for the most competitive quintile is .145. This increases to .985 for the most
concentrated quintile.
8
Consistent with the argument that industry concentration is associated with high barriers
to entry, firms in highly concentrated industries tend to be larger in total asset and spend more
in research and development. They also appears to be more profitable, as shown by their
higher levels of sales revenue and stronger earnings. Turning to variables that are known to
explain the cross-section of expected returns, we find firms in highly concentrated industries to
have larger market capitalization and higher book-to-market equity ratio. The market β, however, is mostly flat across the concentration quintiles.7 In addition, they appear to have lower
leverage than firms in highly competitive industries. This is inconsistent with the argument
that the link between concentration and stock returns stems from firms in highly concentrated
industries choosing high leverage levels. Thus, the empirical evidence favors an alternative
explanation along the lines of franchise value, whereby firms in concentrated industries face
higher expected costs of financial distress because their expected lost future profits are greater
than firms in highly competitive industries. We will address this in the next section.
Panel C reports the same statistics for concentration quintiles formed on the 5-Firm Ratio.
The same basic patterns hold for 5-Firm ratio quintiles. Quintiles 4 and 5 are grouped together
because slightly more than 40% of the 3-digit industries have 5 firms or fewer in a given
year. Not surprisingly, the spreads in industry characteristics are also attenuated for 5-Firm
Ratio quintiles. For this reason, we focus most of our discussion on the findings based on the
Herfindahl index, although the 5-Firm ratio results coincide throughout the paper.
To gain further insight into the correlations between concentration measures and industry
characteristics, Panel D reports Fama and MacBeth (1973) regressions of the cross-section of
industry concentration measures on industry median characteristics. Regressions are run every
year from 1973 to 2001, where the time-series mean of the annual cross-sectional coefficient
estimates are reported along with the time-series t-statistics. This procedure employs all industry observations without imposing quintile breakpoints, allows for multivariate analysis and is
7 As
we will demonstrate below, this is mainly a function of the fact that the reported numbers are first
averaged within each quintile portfolio and then averaged across time, smearing the relationship between the two
variables.
9
robust to cross-correlated error terms. The results from this panel sharpens the findings from
the previous two panels. In particular, we see that industry concentration varies positively with
industry median asset, research and development expenditure, sales, earnings, market capitalization, book-to-market equity ratio, and negatively with industry median leverage.8 Finally,
there is a positive and statistically significant relationship between industry concentration and
industry median market β.
III. Industry Concentration and the Cross-Section of Stock
Returns
This section relates industry concentration to the cross-section of average stock returns, measured both at the industry and firm level. Again, our hypothesis is that firms in concentrated industries earn lower returns because higher barriers to entry better insulate them from economywide shocks.
Table II provides support for this hypothesis. In June of each year, industries are sorted
into quintiles based on their Herfindahl index or 5-Firm ratio. The average monthly returns
and t-statistics of these portfolios as well as the difference between Quintile 5 (most concentrated) and 1 (least concentrated) are reported for the Herfindahl quintiles (Panel A) and
5-Firm Quintiles (Panel B).
The first row in each panel are raw average returns computed by equally weighting firms
within each concentration portfolio. Looking across Herfindahl quintiles, firms in the least
concentrated (most competitive) industries earn an average return of 1.316% per month. This
declines to 1.037% per month for firms in the most concentrated quintile. The spread between the two is -0.279% per month, which is statistically different from zero at the 5% level.
8 Because most of these variables are highly correlated with one another, when they are included simultaneously in the regressions, some of these variables lose their significance or change sign.
10
Sizeable return difference also exists for the 5-Firm ratio: the spread between the most concentrated and the least concentrated portfolio is -0.261% and statistically significant.
Since Table I shows that industry concentration is associated with a number of known
determinants of average returns, the second row of each panel reports characteristic-adjusted
average returns of the above quintile portfolios as well as the average spread between Quintile
5 and 1, where individual stock returns are adjusted for premia associated with size, bookto-market and momentum following the procedure in Daniel, Grinblatt, Titman, and Wermers
(1997). All firms in our sample are first sorted each month into size (CRSP market capitalization) quintiles, and then within each size quintile, further sorted into book-to-market quintiles.
Within each of these 25 portfolios, firms are again sorted into quintiles based on the firm’s past
12-month return, skipping the most recent month. Stocks are equally weighted within each of
these 125 portfolios to form an equally weighted benchmark that is subtracted from each individual stock’s return. The expected value of this excess return is zero if size, book-to-market
and past one-year return completely described the cross-section of expected returns.
Even after adjusting for these known premia, we still see a significant spread in average
returns across concentration quintiles. Interestingly, adjusted returns for the quintile of the
most competitive industries (Q1) are positive and statistically significant, and they decrease
to negative and statistically significant for the quintile of most concentrated industries (Q5).
However, the adjustments have little or no effect on the total spread in returns. The spread for
Herfindahl index is statistically significant at -0.267% per month, only a 1 basis point reduction
from the raw return figure. Similar results are obtained for 5-Firm ratio. Together, this suggests
that the return premium associated with industry concentration is independent from those
of size, book-to-market and momentum, and that controlling for industry concentration is
important for understanding the cross-section of stock returns.
The third and fourth row in each panel repeat the analysis above, but reports raw and adjusted returns calculated by first equally weighting firms into industry portfolios, and then
equally weighting industry returns within each concentration quintile. These industry-level
11
returns mirror the firm-level results. In each case, we see a large and statistically significant spread between the most concentrated and the most competitive quintile. The spreads
range from -0.171% to -0.318% per month depending on the concentration measure used and
whether raw or characteristic-adjusted returns are reported.
To further examine the relation between industry concentration and average stock returns,
we conduct Fama and MacBeth (1973) regressions of monthly stock returns on industry concentration measures and other characteristics. Table III reports industry-level results whereas
Table IV presents firm-level results. More specifically, for each month t, we estimate crosssectional regressions of the form:
N
R jt = αt + ∑ λnt X jnt + ε jt
(3)
n=1
where R jt is the return of industry (firm) j in month t, X jnt (from 1 to N) are industry-level
(firm-level) characteristics of industry (firm) j. The time series average of the cross-sectional
regression loadings λnt is reported along with its time-series t-statistic.9 These regressions
provide robustness check of the relationship between industry concentration and average returns without imposing quintile breakpoints and allow us to control for additional alternative
explanations.
The first two rows of Table III show that more concentrated industries earn lower average returns, consistent with our previous results from quintile portfolios. The cross-sectional
regression coefficients on the Herfindahl index and the 5-Firm Ratio are both negative and
statistically significant at the 5% level. The next five rows demonstrate that industry average returns are positively related to industry median book-to-market ratio, leverage and past
one year’s industry return, and insignificantly related to industry median size and market β.10
9 The
interpretation of a Fama-MacBeth regression coefficient is that it is the return to a zero-cost portfolio
with the weighted characteristic equal to one on the corresponding regressor and zero on all other regressors
(Fama (1976)).
10 In fact, the coefficient on industry median β has the wrong sign. We have also tried market β of the industry
portfolio and obtained nearly identical results.
12
Finally, the last two rows show that controlling for these variables does not drive out the significance of the industry concentration effect. Thus, while the results of Table I suggest that
industry concentration is correlated with other industry characteristics that describe average
returns, the results from Table III suggest that those correlations are not the driving forces
behind the inverse relationship between industry concentration and average stock returns.
Turning to firm-level evidence, the first two rows of Table IV show that the degree of
concentration of the industries to which individual firms belong is important even for understanding the cross-section of firm-level returns. Fama and MacBeth (1973) regressions of
individual stock returns on Herfindahl index alone produce an average slope coefficient of
-0.375% with a t-statistic of -2.65. Using the 5-Firm ratio in place of the Herfindahl index
generates a similar result: the average slope remain almost unchanged at -0.372% and the
t-statistic falls to -2.29 but still significant at the 5% level. The next two rows confirm the
standard results found in the literature that average individual stock returns are negatively related to size, positively related to book-to-market ratio and past one year return, and once they
are controlled for, leverage and market β are not priced in the cross-section. Accounting for
the premia associated with these variables does not alter our results on industry concentration.
On the contrary, it enhances the results. Introducing size, book-to-market ratio, past one year
return, leverage and market β to the cross-sectional regressions raise both the point estimates
as well as the t-statistics for our concentration measures.
The conclusion to take away from this section is that not only do industry returns vary
with industry concentration, but individual stock returns do as well: firms in concentrated
industries earn lower stock returns than firms in more competitive industries. The results hold
under different empirical methodologies employed, and whether or not we control for a variety
of characteristics such as size, book-to-market, and past returns at the firm and industry levels.
These controls suggest that the industry concentration effect we have identified are not being
driven by correlations with other determinants of expected returns, or through capital structure
choice.
13
IV. Industry Concentration and Firm Characteristics
The results thus far have shown an economically important and statistically significant link
between industry concentration and average stock returns. Our hypothesis for this result is
that industry concentration attenuates economy-wide shocks which would otherwise result in
distress. Based on this argument, this section explores the interaction between concentration
and firm-level distress proxies that have been identified in the previous literature.
If industry concentration is related to distress, then we expect high book-to market firms
to carry a higher premium in more concentrated industries. This coincides with Fama and
French (1992), who interpret book-to-market ratio as a risk proxy related to relative distress
in a multi-factor ICAPM or APT framework (see Merton (1973) or Ross (1976)).11 This interpretation is further supported by the evidence in ? that that low book-to-market firms have
persistently strong fundamental performance, while high book-to-market firms have persistently weak performance. Interpreting high book-to-market ratios as evidence of distress, we
postulate that distressed firms in highly concentrated industries which are dominated by a few
large firms with big market shares are likely to be in greater distress, and therefore will carry
a higher book-to-market premium.
We address this issue by including interaction terms between our industry concentration
measures and book-to-market ratio in the firm-level Fama and MacBeth (1973) cross-sectional
regressions. The last two rows of Table IV report the estimation results. The coefficients on
the interaction terms are positive and statistically significant, suggesting that the premium
associated with being a high book-to-market firm grows as industry concentration increases.
This lends support to the idea that book-to-market ratio is related to distress risk and industry
concentration is a mechanism through which this risk is propagated through the economy.12
11 Chan and Chen (1991) also propose the the existence of a risk factor in returns and expected returns that is
related to relative distress.
12 We have also examined interaction effect between industry concentration and size, and found evidence
that the size premium grow with industry concentration. This indicates that small firms operating in industries
characterized by a few, large firms may be in greater distress than small firms operating in more competitive
industries, since their size disparity may signal that they are at a disadvantage relative to their industry peers.
14
To get a sense of the economic magnitude involved here, next we examine returns to bookto-market portfolios for different levels of industry concentration. In June of each year, we
sort industries into concentration quintiles according to their Herfindahl index or 5-Firm ratio.
Then firms within each concentration quintile are further sorted into five portfolios according
to their book-to-market ratio. The equal-weighted returns on these double-sorted portfolios are
calculated over the following years from July to June. Table V reports the average monthly
returns of the five book-to-market portfolios as well as the difference in returns between quintile 5 and 1 for each Herfindahl (Panel A) or 5-Firm (Panel B) group. Each row demonstrate
the prevalence of the book-to-market effect within each concentration group.
As the table indicates, the spread in returns associated with book-to-market ratio is the
largest among the most concentrated industries. For example, high book-to-market stocks
outperform low book-to-market stocks by 1.072% per month in the lowest Herfindahl quintile, and this number grows to 1.814% per month for the highest Herfindahl quintile. A similar
pattern can be observed across the 5-Firm quintiles. These double-sorted portfolio results reinforce the findings from the cross-sectional regressions, which show that the book-to-market
premium grows as industry concentration increases.
These findings suggest that differences in industry concentration affect stock returns in two
ways. At the industry level, industry concentration lowers average stock returns. But within
industries, conditional on being in distress, industry concentration magnifies the distress premium.
V. Time Series Variation of Industry Concentration Premium
This section links changes in the premium associated with industry concentration to various
risk factors and business cycle indicators. This allows us to revisit the question of whether the
Unlike the book-to-market effect, however, the economic magnitude for the size interaction is rather small even
though it is statistically significant. Therefore, we choose to focus on the book-to-market interactions in this
paper.
15
concentration premium remains significant after controlling for existing risk factors, and also
enables us to further explore the link between concentration and distress in greater detail by
tracking the concentration premium along business cycles.
In Table VI, we report results from the following time-series regressions of monthly
spreads in concentration portfolios on risk factors and economic indicators:
I
J
R(5)t − R(1)t = α + ∑ βi Fit + ∑ γ j X jt + εt ,
i=1
(4)
j=1
where Fit are returns to the factor-mimicking portfolios in month t, and X jt are month t values
of the business cycle indicators. The dependent variable is the equally weighted, monthly
return spread between the portfolio of firms in the most concentrated quintile (R(5)), and the
portfolio of firms in the least concentrated quintile (R(1)). In Panel A, that spread is based on
the Herfindahl index, while in Panel B we use the 5-Firm ratio.13
In the first row of each panel, the CAPM model is estimated where the monthly concentration spreads are regressed against the market excess return. The next row employs the FamaFrench (1993) three factor model where two factor-mimicking portfolios that are associated
with the size effect (SMB) and book-to-market effect (HML) are added to the regression. The
following row adds a momentum factor-mimicking portfolio to the Fama-French factors as in
Carhart (1997) to estimate a four factor model.14 As the table indicates, the regression intercepts are both economically and statistically significant in the presence of various risk factors.
The spread for Herfindahl quintiles drops only slightly from -0.28% (Table II) to -0.27% per
month when regressed on the market excess return. The adjusted R2 from this regression is
close to zero. Controlling for the Fama and French (1993) factors actually increase the spread
to -0.33%, whereas the R2 remains low at 11%. Adding the momentum factor decrease the
13 Formally,
we use the difference in R(4&5) - R(1) for the 5-Firm Ratio spread, due to the distribution of the
5-Firm Ratio as discussed in Table I. Comparing the findings across the two panels shows that this is not an issue
for our results.
14 See Fama and French (1993) and Carhart (1997) for details on the construction of these factors. We thank
Kenneth French for making the factor data available on his website.
16
spread to -0.25% but still significant, and there is a slight increase in the R2 . Similar results
are obtained for the 5-Firm quintiles. To summarize, these three sets of regressions show that
the concentration premium cannot be explained by known risk factors, which reinforce the
finding in Section III that industry concentration contains independent information about the
cross-section of expected returns.
The following three rows of each panel regress concentration premia on the inflation rate
and the term spread.15 Inflation is measured by the growth rate of the consumer price index
(CPI). The term spread is the difference between ten-year and one-year treasury constant maturity rate. Both variables have been demonstrated by the literature to track business cycle
fluctuations.16 We see that both the Herfindahl spread and the 5-Firm ratio spread carry a
positive and statistically significant loading on the inflation rate. Since they both have a negative mean value, this means that the concentration premium is higher (in absolute value) at
business cycle trough - when the inflationary pressure is at a minimum - than it is at the preceding or following business cycle peaks, when the inflationary pressure is highest. They load
negatively on the term spread. Since the term spread tend to decrease as the business cycle
moves from trough to peak, this finding is consistent with the loading on the inflation rate. It
says that the concentration premium diminishes as the economy takes an upturn. This is again
consistent with a distress interpretation: the spread in returns between firms that are insulated
from economic distress and those that are not is greatest when the economy is at the bottom
of the business cycle.
The final row of each panel regresses concentration premia on risk factors and business
cycle variables. Controlling for factor returns in addition to business cycle movement raises
the regression intercept, but also weakens the loading on the term spread. Nevertheless, the
message remain largely unaltered: the premium associated with industry concentration is not
spanned by existing factors and it exhibits sensible business cycle variation. The fact that the
15 Both
series are computed using data obtained form the FRED database maintained by the Federal Reserve
Bank of St. Louis.
16 See, for example, Fama and French (1989).
17
concentration premium is highest in business cycle troughs, when economic distress is greatest, is further support for our hypothesis that industry concentration is a mechanism through
which aggregate shocks are propagated through the equity market.
VI. Conclusion
This paper explores the link between industry concentration and stock returns. We show that
firms in more concentrated industries earn lower returns on average than firms in more competitive industries, even after controlling for the usual suspects that affects the cross-section of
average returns such as size, book-to-market and momentum. This holds both at the industry
level and the firm level and is robust to alternative empirical specifications. Our interpretation
for these findings is that highly concentrated industries have higher barriers to entry, and that
these barriers to entry insulate firms from aggregate shocks that are associated with economic
distress.
To provide support for our interpretation, we investigate the time-series behavior of the
spreads in concentration portfolios. Looking across the business cycle, the absolute value of
the concentration premium is greatest at the bottom of a business cycle: this is when inflation
is lowest and the term spread is highest. Further evidence favoring the distress interpretation
is found by examine the interaction between the book-to-market effect and industry concentration. Consistent with the distress story, the spread in returns between high book-to-market
stocks and low book-to-market stocks is significantly higher in most concentrated industries
than in most competitive industries.
Our study control directly for a number of alternative explanations. Accounting for the
premia associated with size, book-to-market, and other average return determinants indicates
that our results are not being driven purely by correlations with those variables. Controlling
for leverage allows us to be sure that are results are not caused by the endogeneity of capital
structure choice.
18
There are other alternatives that are not addressed directly in this paper. One is regulation. If highly concentrated industries are more likely to be regulated industries, and regulated
industries face lower operating risk, then concentrated industries may have a lower cost of capital. In unreported results, we have found that excluding regulated industries has virtually no
impact on our findings. Thus, it seems unlikely that our results are being driven by regulation.
Yet another alternative mechanism through which industry concentration could potentially
affect stock returns is through information quality. For example, investors might demand
lower returns on firms in more concentrated industries because the cost of collecting and
processing information is lower in those industries. However, if this were the case, we would
expect information transmission to be faster in more concentrated industries. Hou (2003)
documents the opposite result: the speed of information diffusion between large and small
firms is slower in more concentrated industries, not faster. Thus, it seems unlikely that our
findings are attributable to information quality.
Our results suggest a number of fruitful areas for future research. First, our list of alternative explanations is far from exhaustive. Carefully examining other alternative explanations
may yield testable implications which can sharpen our understanding of the way in which
industry concentration impacts the cross-section of stock returns.
Second, this paper primarily focuses on the unconditional roles played by industry characteristics for understanding the equilibrium tradeoff between risk and return. However, much
of the recent literature in empirical asset pricing uses industry membership as conditioning
information, and explores whether certain asset-pricing phenomena are attributable to industry effects. A better understanding of how industry characteristics affect expected returns can
potentially yield insights into why many stylized facts about stock returns seem to contain
important industry components.
The findings in this paper ultimately raise more questions than they answer. What is the
exact mechanism by which barriers to entry insulate firms from aggregate shocks? How do
different types of barriers to entry affect stock returns? How do managerial choices impact
19
this link? What is the impact of this on firms’ investment and financing decisions? How
does this impact the diffusion of information in the market place? Is the geographic scope
of the industry important (national markets vs. local markets)? The story we propose in this
paper is a reduced form version of a more complicated analysis in which product markets
affect investment opportunities and decisions about investment and capital structure. A better
understanding of the precise mechanisms that link these phenomena is likely to lead to richer
predictions and empirical tests. We leave these issues for future work.
20
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21
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22
23
Mean
0.524
0.914
Herf.
0.145
0.291
0.479
0.752
0.985
Herf.
5-Firm
Rank
1
2
3
4
5
0.897
1.000
80th
Panel B: Characteristics of Industry Quintile Portfolios sorted by Herfindahl Index
Asset
R&D
Sale
Earnings
Size
B/M
Leverage
β
393.7
4.8
318.4
11.9
176.0
0.934
0.473
1.271
594.8
4.2
320.3
15.2
146.7
1.081
0.458
1.314
1092.0
7.4
426.6
20.2
219.9
1.558
0.464
1.301
517.6
10.1
291.5
12.8
218.6
2.677
0.456
1.301
2389.8
24.7
941.7
58.9
549.0
3.571
0.431
1.297
Panel A: Summary Statistics of Industry Concentration Measures
Percentiles
th
Median Std. Dev.
Max
Min
20
40th
60th
0.471
0.309
1.00
0.017
0.215
0.368
0.585
0.994
0.138
1.00
0.169
0.833
0.963
1.000
Firms
43.4
16.1
8.0
4.2
1.4
Industries
69.0
69.2
69.2
68.1
63.4
Correlation with
Herf.
5-Firm
1.000
0.701
0.701
1.000
The sample includes all firms at the intersection of the CRSP NYSE/AMEX/NASDAQ monthly stock files and the COMPUSTAT industrial annual file between Jannury, 1973 and December,
2001. Panel A reports summary statistics of industry concentration measures for 3-digit SIC industries. The Herfindahl index of an industry is formed by first calculating the sum of squared
sales-based market shares of all firms in that industry in a given year and then averaging over the past three years. The 5-Firm ratio is formed analogously, but is the percentage of market share
owned by the largest 5 firms in the industry. Panel B and C present the properties of industry concentration portfolios. In June of each year, industries are sorted into quintiles based on their
Herfindahl index (Panel B) or 5-Firm ratio (Panel C). Reported are industry median characteristics averaged first within each quintile and then across time. For 5-Firm quintiles, quintiles 4 and 5
are grouped together because slightly more than 40% of the 3-digit SIC industries have 5 firms or fewer in a given year. Asset is total asset. R&D is research and development expenses. Sale is
net sales. Earnings is income before extraordinary items. Market equity is number of shares outstanding times stock price. Book equity is stockholder’s equity plus balanced deferred taxes and
investment tax credit minus the book value of preferred stock. B/M is the ratio of book equity to market equity. Leverage is the ratio of book liabilities (total asset minus book equity) to total
market value of firm (market equity plus total asset minus book equity). All COMPUSTAT items are in millions of dollars measured at fiscal year end in year t − 1. Size is CRSP stock price
times number of shares outstanding in June of year t (in millions of dollars). β is post-ranking β as in Fama and French (1992). It is estimated by computing full-period βs for portfolios sorted
by size and pre-ranking β and then assigning portfolio βs to stocks in those portfolios. The pre-ranking β is estimated as the sum of the coefficients of regressions of individual stock returns on
contemporaneous and lagged market returns over the past three years. Firms is the number of firms in an industry. Industries is the number of industries within each concentration quintile. In
Panel D, univariate (simple) and multivariate (multiple) cross-sectional regressions are run every year for Herfindahl index and 5-Firm ratio on industry median characteristics. The time-series
mean of the regression coefficients and the time-series t-statistics (in parentheses) are reported. All regression coefficients on size, asset, sale, R&D and earnings are multiplied by 1,000.
Table I: Summary Statistics
24
5-Firm Ratio
Herfindahl
Rank
1
2
3
4&5
5-Firm
0.682
0.904
0.988
1.000
multiple
0.001
(10.50)
-0.015
(-2.03)
0.480
(7.05)
0.514
(8.41)
0.006
(5.99)
-0.030
(-3.28)
0.102
(4.85)
0.709
(3.29)
0.016
(4.94)
0.023
(1.88)
0.004
(3.87)
0.021
(4.44)
-0.037
(-8.25)
0.003
(0.28)
Leverage
-0.106
(-6.58)
0.037
(1.15)
Panel D: Cross-Sectional Determinants of Industry Concentration Measures
Independent Variable = Industry Median
Asset
R&D
Sale
Earnings
Size
B/M
simple
0.009
1.146
0.025
0.545
0.092
0.008
(6.90)
(7.57)
(6.30)
(5.68)
(5.55)
(3.45)
multiple
0.015
1.189
-0.098
0.325
0.174
0.050
(1.29)
(5.09)
(-7.40)
(1.16)
(4.00)
(4.17)
simple
Firms
46.6
16.0
7.0
2.1
Panel C: Characteristics of Industry Quintile Portfolios sorted by 5 Firm Ratio
Asset
R&D
Sale
Earnings
Size
B/M
Leverage
β
376.1
4.3
292.2
11.2
165.7
0.919
0.463
1.281
198.0
4.4
208.7
7.2
118.0
0.942
0.460
1.307
547.2
9.8
408.8
18.7
204.7
0.984
0.460
1.283
1822.7
14.3
663.4
38.9
388.4
3.332
0.451
1.305
Table I: Summary Statistics (continued)
0.050
(10.41)
0.027
(4.99)
β
0.039
(2.58)
0.054
(3.06)
Industries
69.1
68.9
58.7
142.2
Table II
Returns to Concentration Quintile Portfolios
In June of each year, industries are grouped into quintiles based on their Hefindahl index or 5-Firm ratio. The
average monthly returns and t-statistics (in parentheses) of the quintile portfolios as well as the difference between
Quintile 5 (most concentrated) and 1 (least concentrated) are reported for the Herfindahl quintiles (Panel A)
and 5-Firm Quintiles (Panel B). Firm-level raw returns are unadjusted returns, in percent per month, averaged
across firms within the same concentration quintile. Firm-level adjusted returns are calculated by subtracting the
return on a characteristics-based benchmark from each firm’s return, then averaging within the same concentration
quintile. Characteristics-based benchmarks are constructed following Daniel, Grinblatt, Titman, and Wermers
(1997) to account for the premia associated with size, book-to-market, and momentum. Industry-level raw and
adjusted returns are computed similarly, except that individual stock raw and adjusted returns are first averaged
within each industry and then averaged across industries within the same concentration quintile.
Returns
Firm-Level
Raw
Panel A: Herfindahl Quintiles
Quintiles
(1)
(2)
(3)
(4)
(5)
1.316 1.236 1.207 1.081 1.037
(4.24) (3.74) (3.88) (3.69) (3.39)
Spread:
(5)-(1)
-0.279
(-2.47)
Firm-Level
Adjusted
0.050
(2.16)
-0.027
(-0.57)
-0.007
(-0.12)
-0.162
(-2.65)
-0.218
(-2.03)
-0.267
(-2.30)
Industry-Level
Raw
1.327
(4.40)
1.312
(4.21)
1.271
(4.14)
1.191
(3.85)
1.092
(3.51)
-0.235
(-2.17)
Industry-Level
Adjusted
0.037
(1.88)
-0.063
(-1.34)
-0.039
(-0.64)
-0.145
(-2.30)
-0.281
(-2.47)
-0.318
(-2.65)
Returns
Firm-Level
Raw
(1)
1.479
(4.57)
Panel B: 5-Firm Quintiles
Quintiles:
(2)
(3)
(4&5)
1.351 1.295
1.218
(4.23) (3.72)
(3.90)
Firm-Level
Adjusted
0.033
(1.44)
0.038
(0.83)
-0.114
(-2.13)
-0.157
(-2.32)
-0.189
(-2.31)
Industry-Level
Raw
1.365
(4.37)
1.358
(4.30)
1.234
(4.01)
1.194
(3.85)
-0.171
(-2.11)
Industry-Level
Adjusted
0.019
(1.00)
0.001
(0.07)
-0.124
(-2.25)
-0.159
(-2.33)
-0.178
(-2.22)
25
Spread:
(4&5)-(1)
-0.261
(-2.05)
Table III
Industry-Level Fama-MacBeth Regressions
This table presents results from industry-level Fama and MacBeth (1973) cross-sectional regressions estimated monthly
between January, 1973 and December, 2001. Equally weighted industry portfolio returns are regressed on industry median
values of ln(Size), ln(B/M), leverage, and β, as well as industry Herfindahl index, 5-Firm ratio, and the past one-year return on
the industry portfolio, Ret(12:2). Time-series average values of the monthly regression coefficients, in percent, are reported
along with time-series t-statistics (in parentheses).
ln(Size)
ln(B/M)
Ret(12:2)
Leverage
β
Herfindahl
-0.278
(-2.41)
5-Firm Ratio
-0.440
(-2.90)
-0.073
(-1.24)
0.349
(4.24)
1.004
(3.96)
0.782
(3.03)
-0.458
(-1.29)
-0.137
(-2.50)
0.184
(2.49)
0.875
(3.86)
0.310
(1.30)
-0.614
(-2.01)
-0.136
(-2.46)
0.184
(2.49)
0.876
(3.86)
0.327
(1.38)
-0.606
(-1.98)
26
-0.241
(-2.19)
-0.332
(-2.26)
Table IV
Firm-level Fama-MacBeth Regressions
This table presents results from firm-level Fama and MacBeth (1973) cross-sectional regressions estimated monthly between
January, 1973 and December, 2001. Monthly individual stock returns are regressed on ln(Size), ln(B/M), the past one-year
stock return (Ret(12:2)), leverage, β, industry-level concentration measures (Herfindahl index and 5-Firm ratio), as well as
interaction terms between concentration measures and ln(B/M). Time-series average values of the cross-sectional regression
coefficients, in percent, are reported along with time-series t-statistics (in parentheses).
ln(Size)
ln(B/M)
Ret(12:2)
β
Leverage
Herf.
-0.375
(-2.65)
5-Firm
ln(B/M)*
Herf 5-firm
-0.372
(-2.29)
-0.142
(-2.55)
0.284
(3.88)
0.549
(3.02)
-0.155
(-3.08)
0.271
(5.03)
0.546
(3.34)
-0.144
(-2.58)
0.286
(3.93)
0.544
(3.00)
-0.143
(-2.59)
0.290
(4.04)
0.543
(3.00)
-0.156
(-3.11)
0.275
(5.16)
0.541
(3.32)
-0.105
(-0.48)
-0.173
(-0.62)
-0.154
(-3.09)
0.279
(5.31)
0.539
(3.31)
-0.111
(-0.51)
-0.157
(-0.56)
-0.142
(-2.55)
0.210
(2.43)
0.548
(3.02)
-0.141
(-2.54)
0.024
(0.17)
0.545
(3.01)
-0.081
(-0.38)
-0.177
(-0.63)
-0.425
(-3.37)
-0.446
(-2.66)
-0.418
(-3.02)
-0.414
(-2.37)
0.287
(2.64)
0.346
(2.61)
27
Table V
Interaction of Industry Concentration with Book-to-Market Effect
in June of each year, industries are sorted into concentration quintiles based on their Herfindahl index (Panel A)
or 5-Firm ratio (Panel B). Firms within each concentration group are further sorted into quintiles based on their
book-to-market ratio. This table reports the average monthly returns of the five book-to-market portfolios, their
t-statistics (in parentheses), as well as the difference in returns between quintile 5 and 1 for each concentration
group.
Panel A: First Sorted on Herfindahl Index, Then on B/M
Herfindahl
Book-to-Market Quintiles
Spread:
(1)
(2)
(3)
(4)
(5)
(5)-(1)
Quintile
1
0.828 1.189 1.457 1.602 1.900
1.072
(2.00) (3.63) (5.22) (5.92) (6.00)
(4.57)
0.565 1.163 1.393 1.540 1.735
1.170
2
(1.48) (3.41) (4.19) (4.81) (5.12)
(5.52)
3
0.720 1.104 1.395 1.507 1.688
0.968
(1.79) (3.34) (4.56) (5.10) (5.17)
(3.54)
4
0.471 0.951 1.316 1.455 1.747
1.276
(1.09) (2.82) (4.26) (5.07) (4.91)
(4.69)
-0.108 1.103 1.210 1.487 1.706
1.814
5
(-0.26) (2.93) (3.46) (3.75) (4.23)
(4.75)
Panel B: First Sorted on 5-Firm Ratio, Then on B/M
5-Firm
Book-to-Market Quintiles
Spread:
Quintile
(1)
(2)
(3)
(4)
(5)
(5)-(1)
1
0.907 1.146 1.428 1.590 1.903
0.996
(2.07) (3.46) (4.98) (5.78) (5.94)
(4.31)
2
0.671 1.241 1.469 1.564 1.757
1.086
(1.75) (3.73) (4.57) (4.97) (5.37)
(4.82)
3
0.537 0.993 1.469 1.443 1.684
1.147
(1.48) (3.12) (4.94) (4.92) (5.16)
(4.82)
4&5
0.495 1.099 1.121 1.502 1.809
1.313
(1.36) (3.39) (3.79) (4.88) (5.12)
(5.20)
28
Table VI
Time-Series Variation of Industry Concentration Premium
This table presents results from time-series regressions of concentration premia on various risk factors and business cycle variables. The
dependent variable is the firm-level raw return spread between concentration quintiles from Table II. In Panel A, the spread is based
on Herfindahl index quintiles, and in Panel B, 5-Firm ratio quintiles. The independent variables are the market excess return (Rm − r f );
returns on factor-mimicking portfolios associated with the size (SMB), book-to-market (HML), and momentum effect (MOM); and the
inflation and term spread which capture business cycle variation. Inflation is measured by the growth rate of the consumer price index
(CPI). The term spread is the difference between ten-year and one-year treasury constant maturity rate. The factor data was downloaded
from Kenneth French’s website. The business cycle variables are calculated using data obtained form the FRED database maintained by
the Federal Reserve Bank of St. Louis.
α
-0.27
(-2.49)
-0.33
(-3.09)
-0.25
(-2.28)
-0.64
(-3.49)
-0.29
(-2.11)
-0.56
(-2.26)
-0.49
(-2.03)
α
-0.32
(-2.68)
-0.44
(-3.85)
-0.28
(-2.36)
-0.60
(-3.05)
-0.29
(-2.17)
-0.87
(-3.25)
-0.92
(-3.7)
Rm − r f
-0.044
(-1.84)
0.018
(0.71)
0.014
(0.54)
SMB
Panel A: Herfindahl Index Spread
HML MOM Inflation Term Spread
-0.163 0.107
(-5.00) (2.70)
-0.163 0.079 -0.061
(-5.02) (1.91) (-2.14)
0.111
0.121
1.110
(2.84)
0.023
(0.88)
Rm − r f
0.057
(2.23)
0.139
(5.11)
0.129
(4.89)
-0.162
(-5.02)
SMB
0.081
(1.96)
-0.062
(-2.18)
0.961
(2.03)
1.065
(2.48)
0.021
-0.172
(-2.05)
-0.056
(-0.56)
-0.072
(-0.80)
Panel B: 5-Firm Ratio Spread
HML MOM Inflation Term Spread
-0.077 0.229
(-2.21) (5.41)
-0.077 0.168 -0.135
(-2.26) (3.88) (-4.50)
-0.075
(-2.22)
0.173
(3.99)
-0.135
(-4.54)
29
0.015
0.0105
0.129
Adj. R2
0.012
0.118
0.167
1.177
(3.03)
0.141
(5.30)
Adj. R2
0.007
1.145
(2.12)
1.680
(3.03)
0.024
-0.301
(-2.24)
-0.024
(-0.62)
-0.063
(-1.26)
0.015
0.023
0.207