THE JOURNAL OF FINANCE • VOL. LXVIII, NO. 5 • OCTOBER 2013
Corporate Diversification and the Cost of Capital
REBECCA N. HANN, MARIA OGNEVA, and OGUZHAN OZBAS∗
ABSTRACT
We examine whether organizational form matters for a firm’s cost of capital. Contrary to the conventional view, we argue that coinsurance among a firm’s business
units can reduce systematic risk through the avoidance of countercyclical deadweight
costs. We find that diversified firms have, on average, a lower cost of capital than
comparable portfolios of stand-alone firms. In addition, diversified firms with less
correlated segment cash flows have a lower cost of capital, consistent with a coinsurance effect. Holding cash flows constant, our estimates imply an average value gain of
approximately 5% when moving from the highest to the lowest cash flow correlation
quintile.
The conventional view among practitioners and researchers is that organizational form does not matter for a firm’s cost of capital because, while the
imperfect correlation of business unit cash flows may help reduce idiosyncratic
risk, this should have no effect on systematic risk. Long a part of mainstream
thought, the conventional view is widely disseminated through standard finance textbooks and classroom teaching. The notion that corporate diversification cannot affect systematic risk is usually covered explicitly in the mergers
and acquisitions chapter1 or implicitly through the stand-alone principle in the
capital budgeting chapter.
∗ Rebecca N. Hann is with University of Maryland Smith School of Business; Maria Ogneva
and Oguzhan Ozbas are with University of Southern California Marshall School of Business. We
thank an anonymous referee, an anonymous Associate Editor, Phil Berger, Harry DeAngelo, Paul
Fischer, Ilan Guedj, Cam Harvey (the Editor), Jerry Hoberg, Chris Jones, Simi Kedia, John Matsusaka, Berk Sensoy, and seminar participants at Baruch College, Chinese University of Hong
Kong, Columbia University, Hong Kong University of Science and Technology, London Business
School, Northwestern University, Penn State University, Purdue University, Sabancı University,
University of Chicago, University of Hong Kong, University of New South Wales, University of
Oregon, University of Southern California, 2011 AFA Meetings, DC Area Accounting Symposium,
21st Annual Conference on Financial Economics and Accounting, 2010 Harvard University Information, Markets, and Organizations Conference, 2010 Koç Finance Conference, 2010 Napa
Conference on Financial Markets Research, 2009 University of Minnesota Empirical Conference,
and 2010 University of Toronto Accounting Research Conference for helpful comments. We thank
Jieying Zhang for helping us with bond pricing data. We also thank the Rock Center for Corporate
Governance at Stanford University for providing access to the DealScan database and Yifeng Zhou
for his excellent research assistance. Financial support from the Marshall General Research Fund
and KPMG is gratefully acknowledged.
1 “Systematic variability cannot be eliminated by diversification, so mergers will not eliminate
this risk at all.” (Ross, Westerfield, and Jaffe (2008, p. 823)).
DOI: 10.1111/jofi.12067
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In this paper, we present evidence that is contrary to the conventional view.
We find that diversified firms have a lower cost of capital than comparable
portfolios of stand-alone firms. We also find that the reduction in cost of capital
is strongly related to the correlation of business unit cash flows, consistent with
a coinsurance effect.
We argue that organizational form can affect a firm’s cost of capital, and
in particular, coinsurance—the imperfect correlation of cash flows—among a
firm’s business units can reduce systematic risk through the avoidance of countercyclical deadweight costs. Using deadweight costs of financial distress as an
illustrative example, if coinsurance reduces default risk (Lewellen (1971)) and
enables a diversified firm to avoid countercyclical deadweight costs of financial distress (Elton et al. (2001) and Almeida and Philippon (2007)) that its
business units would have otherwise incurred as stand-alone firms, then coinsurance should lead to a reduction in the diversified firm’s systematic risk and
hence its cost of capital.
Costly financial distress is, of course, just one example of deadweight costs
faced by firms. Other examples include adverse selection and transaction costs
of external finance and resulting investment distortions, forgone business opportunities due to defections by important stakeholders such as suppliers, customers, or employees, and so on. Many of these costs tend to arise following low
cash flow realizations—making them countercyclical since low cash flow realizations are more likely during bad economic times. Amplification mechanisms
such as the credit channel or asset fire sales can also add to the countercyclical
nature of these costs.
Our general argument is that coinsurance should enable a diversified firm
to transfer resources from cash-rich units to cash-poor units in some states of
nature and thereby avoid some of the countercyclical deadweight costs that
stand-alone firms cannot avoid on their own. As a result, cash flows of diversified firms should contain less systematic risk than those of comparable
portfolios of stand-alone firms. In addition, the reduction in systematic risk
should depend on the extent of coinsurance among diversified firms’ business
units.
We test these predictions using a sample of single- and multi-segment firms
spanning the period 1988–2006. Our main cost of capital proxy is the weighted
average of cost of equity and cost of debt. We use ex ante measures of expected
returns for both components of financing: implied cost of equity constructed
from analyst forecasts to proxy for expected equity returns and yields from
the Barclays Capital Aggregate Bond Index to proxy for expected debt returns.
We estimate implied cost of equity based on the approach of Gebhardt, Lee,
and Swaminathan (2001), which has been recently employed in several asset
pricing contexts (Pástor, Sinha, and Swaminathan (2008) and Lee, Ng, and
Swaminathan (2009)).
We also use two alternative proxies for expected returns: ex post realized
returns and a hybrid proxy combining ex ante and ex post approaches (fitted
values from regressing ex post realized returns on a set of ex ante measures
of expected returns, which we refer to as instrumented returns). The hybrid
Corporate Diversification and the Cost of Capital
1963
approach filters out information shocks that contaminate realized returns and
make them noisy proxies for expected returns (Elton (1999)). Our empirical
analyses are based on “excess cost of capital” measures that benchmark the
cost of capital of a diversified firm against that of a comparable portfolio of
stand-alone firms.
Most of our findings, which we summarize below, are robustly significant
at conventional levels using ex ante measures of expected returns and instrumented returns but not realized returns. Our interpretation is that the added
level of noise in realized returns due to information shocks indeed makes realized returns poor proxies for expected returns.
Using ex ante measures of expected returns as well as instrumented returns,
we find that diversified firms, on average, have a significantly lower cost of capital than comparable portfolios of stand-alone firms, rejecting the conventional
view that organizational form does not matter for a firm’s cost of capital. We
consider cash flow and investment correlations among a firm’s segments as an
inverse measure of coinsurance. Consistent with a coinsurance effect, we find
a significant positive relation between excess cost of capital and cross-segment
correlations. In addition, we examine whether coinsurance effects are stronger
for firms facing greater financial constraints since such firms are more likely to
incur greater deadweight costs and thus benefit more from coinsurance. Using
proxies of financial constraints such as the Whited–Wu index, the Hadlock–
Pierce index, and S&P debt rating (speculative versus investment grade), we
find that coinsurance effects are, in general, stronger for more financially constrained firms.
These findings are robust to controlling for potential analyst forecast biases
and using alternative measures of (i) implied cost of equity (Claus and Thomas
(2001) and Easton (2004)), (ii) cost of equity not reliant on analyst forecasts,
(iii) cost of debt inferred from publicly traded bonds or private loans, and
(iv) coinsurance. They are also robust to controlling for selection effects in
a Heckman two-stage analysis and using changes in coinsurance over which
managers arguably have no control (Lamont and Polk (2002)).
Our findings are also economically significant. Our estimates imply an average percentage reduction of approximately 2%–3% in cost of capital and an
average value gain of approximately 5%–6% when moving from the highest to
the lowest cash flow correlation quintile.
The rest of the paper proceeds as follows. Section I provides a discussion
of the setting and related research. Section II outlines the valuation approach
that we use in estimating the implied cost of equity along with the construction
of excess cost of capital and coinsurance measures. Section III describes our
sample. Section IV presents our findings. Section V concludes.
I. The Setting
A. Systematic Risk and Cost of Capital in a Model of Coinsurance
Our hypotheses about organizational form and cost of capital are based on a
model of coinsurance in the spirit of Lewellen (1971). This section summarizes
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the model’s basics and outlines the assumptions under which the imperfect
correlation of business unit cash flows lowers a diversified firm’s cost of capital
relative to a comparable portfolio of stand-alone firms.2
To illustrate our main ideas, suppose that firms incur certain deadweight
losses when their projects experience low cash flow outcomes. Examples of
deadweight losses include forgone business opportunities due to defections by
important stakeholders such as suppliers, customers, or employees, financial
distress or external finance costs, and so on. A large body of research in finance
shows that the expected value of such deadweight losses is higher during worse
economic times, possibly due to the higher incidence of low cash flow outcomes,
or due to amplification mechanisms such as the credit channel or asset fire
sales. As a result, firms face deadweight losses that are partly countercyclical
and firms’ cash flows contain more systematic risk than they otherwise would
in a frictionless world. That is, countercyclical deadweight losses add to the
systematic risk of firms.
In such a setting, it is straightforward to show that a diversified firm’s systematic risk would be lower than that of a comparable portfolio of stand-alone
firms. The imperfect correlation of business unit cash flows allows resources
to be transferred from cash-rich units to cash-poor units in some states of nature to avoid some of the countercyclical deadweight losses that stand-alone
firms cannot avoid on their own. More generally, a diversified firm with less
correlated business unit cash flows, and hence greater coinsurance potential
would have less systematic risk. Only in the case of perfectly correlated business unit cash flows would a diversified firm’s systematic risk approach that of
a comparable portfolio of stand-alone firms.
For these results to hold, two further assumptions are needed. First, it must
be costly for stand-alone firms to enter into state-contingent financing contracts
with each other to replicate the extent of deadweight loss avoidance achieved by
diversified firms. Second, it must be costly for firms to hold first-best amounts
of financial slack to avoid all future deadweight losses. Both assumptions strike
us as accurate descriptions of the real world. Verifiability and enforcement frictions likely render state-contingent financing contracts expensive or infeasible.
In addition, tax and agency costs likely discourage firms from holding first-best
amounts of financial slack.
In the Internet Appendix, we consider two extensions of the basic model.3
First, we allow for the possibility of agency costs of diversification and the
possibility of inefficient internal capital markets to address a model prediction
that some might see as counterfactual—the basic model without any cost of
diversification predicts a diversification premium. We show that these costs
do not change the qualitative implications of the model about countercyclical
coinsurance. So, it is possible to observe both a diversification discount and a
2 We use the model to derive additional testable predictions, which we later state in this section.
The formal analysis can be found in the Internet Appendix.
3 The Internet Appendix may be found in the online version of this article.
Corporate Diversification and the Cost of Capital
1965
coinsurance effect at the same time. Second, we extend the model to include
debt alongside equity and show that the coinsurance results apply to both debt
and equity financing.
To summarize, the model setting outlined above has the following testable
predictions. First, diversified firms should have a lower cost of capital than comparable portfolios of stand-alone firms. Second, the reduction in cost of capital
should be related to expected coinsurance opportunities. Diversified firms with
less correlated business unit cash flows and thus greater coinsurance potential
should have a lower cost of capital.4 Third, diversified firms facing greater financial constraints and associated deadweight losses should benefit more from
coinsurance. Consequently, coinsurance effects should be more pronounced for
such firms.
B. Related Literature
The notion of coinsurance among a firm’s business units goes at least as far
back as Lewellen (1971). The ensuing stream of research studies coinsurance
in the context of conglomerate mergers (Higgins and Schall (1975) and Scott
(1977)) and examines whether such mergers lead to wealth transfers from
shareholders to bondholders (Kim and McConnell (1977)). Importantly, this
literature does not recognize the possibility that coinsurance can affect a firm’s
systematic risk. For example, standard textbooks emphasize the irrelevance
of corporate diversification and coinsurance when explaining the stand-alone
principle of capital budgeting by either implicitly following or explicitly citing
Schall’s (1972) analysis. To our knowledge, our study is the first to establish a
link between coinsurance and cost of capital.
Our study also complements the literature on corporate diversification and
firm value (Lang and Stulz (1994), Berger and Ofek (1995), Campa and
Kedia (2002), Graham, Lemmon, and Wolf (2002), Mansi and Reeb (2002), and
Villalonga (2004)) by exploring an important dimension that thus far has received little attention, namely, cost of capital. The discussion in this literature
revolves mostly around future cash flow differences between conglomerates
and stand-alone firms, and confounding selection effects. An exception is Lamont and Polk (2001), who raise the possibility that valuation differences may
arise due to differences in expected returns. They find a significant and negative relation between excess values and future returns for diversified firms,
suggesting that valuation differences are explained in part by differences in
expected returns. While their study introduces the important role of expected
returns in understanding the valuation of diversified firms, their main focus is
to explain the cross-sectional variation in excess value, and not how diversification affects a firm’s cost of capital. Our work deepens the foundations of this
4 It is worth noting that a model of contagion would generate the opposite predictions. For
instance, if the liquidity concerns of cash-poor units spread to other units of the firm and cause
deadweight losses that stand-alone firms would not incur on their own, then diversified firms would
incur greater deadweight losses than comparable portfolios of stand-alone firms.
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literature by exploring whether the cross-sectional variation in cost of capital
is due to coinsurance.
Our work is also related to an extensive literature on the deadweight costs of
external finance, and the ability of different organizational forms to avoid them.
Livdan, Sapriza, and Zhang (2009) show that more financially constrained
firms are riskier and earn higher expected stock returns than less financially
constrained firms. Dimitrov and Tice (2006) show that during recessions both
sales and inventory growth rates drop more for bank-dependent stand-alone
firms than they do for rival segments of bank-dependent diversified firms. Yan,
Yang, and Jiao (2010) show that stand-alone firms experience investment declines relative to diversified firms during periods of depressed conditions in external capital markets. Related work by Yan (2006) also shows that diversified
firms have higher valuations when external capital is more costly. Hovakimian
(2011) shows that more financially constrained diversified firms allocate capital more efficiently during recessions. Using the 2007–2009 financial crisis
as a natural experiment, Kuppuswamy and Villalonga (2010) show that the
value of diversified firms increased relative to stand-alone firms due to financing and investment advantages. Studying deadweight costs of asset fire sales,
Pulvino (1998) finds that financially constrained airlines receive lower prices
than their unconstrained rivals when selling used narrow-body aircraft. Consistent with deadweight costs of asset fire sales being countercyclical, OrtizMolina and Phillips (2009) find that firms with more liquid real assets have a
lower cost of capital. Finally, Duchin (2010) studies the relation between coinsurance and firms’ cash retention policies. Our paper combines with Duchin’s
to form a nascent literature examining the implications of coinsurance for corporate finance in general.
II. Empirical Design
The coinsurance hypothesis outlined in Section I.A relates a diversified firm’s
cost of capital to the extent of coinsurance among its business units. In this
section, we discuss our main proxies for these constructs.
A. Cost of Capital
Prior research in finance has generally used ex post realized returns to proxy
for expected returns and cost of capital (Fama and French (1997), Lamont
and Polk (2001)). However, realized returns are noisy proxies for expected
returns due to contamination by information shocks, which can lead to biased
inferences in finite samples (Elton (1999)). To address this concern, recent
literature in accounting and finance has developed an ex ante approach to
measuring expected returns by estimating the implied cost of equity (Claus
and Thomas (2001), Gebhardt, Lee, and Swaminathan (2001), Easton (2004)).
The implied cost of equity is the internal rate of return that equates the current
stock price to the present value of all expected future cash flows to equity. Thus,
Corporate Diversification and the Cost of Capital
1967
the value of the firm at time t can be expressed as
Pt =
∞
Et [FCFEt+i ]
,
(1 + re )i
(1)
i=1
where Pt is the market value of equity at time t, FCFEt+i is free cash flow to
equity at time t+i, and re is the implied cost of equity.
In constructing our primary measure of cost of capital, we follow the ex
ante approach of Gebhardt, Lee, and Swaminathan (2001) (hereafter, GLS) to
estimate the implied cost of equity. The GLS measure has been successfully
employed in several asset-pricing contexts (Pástor, Sinha, and Swaminathan
(2008), Lee, Ng, and Swaminathan (2009), Chava and Purnanandam (2010)).
The GLS measure uses I/B/E/S consensus analyst forecasts to proxy for future
earnings (see Appendix A for details).
The total cost of capital is computed as follows:
COCi,t = Di,t−1 YtBC + (1 − Di,t−1 )COECi,t ,
(2)
where COCi,t is cost of capital for firm i in year t, Yt BC is the aggregate bond
yield from the Barclays Capital Aggregate Bond Index (formerly, the Lehman
Brothers Aggregate Bond Index), COECi,t is the implied cost of equity (GLS),
and Di,t-1 is the firm’s book value of debt divided by total value (book value of
debt plus market value of common equity).5
To benchmark our results against those from prior research, we also report
results based on ex post realized stock returns. In particular, we follow an
approach similar to Lamont and Polk (2001) and define total cost of capital
as the weighted average of a firm’s realized equity return and the return on
the Barclays Capital Aggregate Bond Index. Realized equity returns are buyand-hold returns accumulated over 12 months starting in July of year t+1
(see Figure 1 for timing convention). To mitigate concerns about the noisy
nature of realized returns due to information shocks, we construct a third
measure of cost of capital that combines information from ex post and ex ante
approaches. Specifically, we regress ex post realized returns on a set of ex
ante measures of cost of capital and use the fitted value from the regression
as the proxy for expected returns. We include six ex ante measures of cost
of capital in the first stage regression: 1) GLS, 2) an alternative implied cost
of capital measure based on Claus and Thomas (2001) (hereafter, CT), 3) an
alternative implied cost of capital measure based on Easton (2004) (hereafter,
PEG), 4) expected returns from the Fama–French three-factor model (hereafter,
FF),6 5) the earnings yield (E/P), and 6) the earnings yield adjusted for growth
5 Book value of debt is long-term debt (Compustat Item #9) plus short-term debt (Compustat
Item #34); market value of equity is fiscal year-end stock price (Compustat Item #199) multiplied
by shares outstanding (Compustat Item #25).
6 To calculate FF expected returns, we estimate factor loadings using 24 months of prior excess
returns, multiply the loadings with corresponding historical risk premiums, and add the yield on
the 10-year Treasury note. We exclude observations with negative FF cost of equity estimates from
the analysis (about 8% of our sample).
End of
December
t-1
Coinsurance
End of
December
t
• Lagged 12month return
Beginning
of June
t
•
•
•
Book value of equity
and dividend payout
ratio for implied cost
of equity estimation
Market capitalization
Leverage
Book-to-market ratio
End of End of
May
June
t+1
t+1
•
•
•
•
End of
December
t+1
• Unexpected
forecast
error for
year t+1
• Realized 12month return
One- and twoyear-ahead
earnings forecasts
Stock price for
implied cost of
equity estimation
Earnings long-term
growth forecast
Forecast dispersion
Expected forecast
error for years t+1
and t+2
Bond yield
End of
June
t+2
Figure 1. Timeline of variable measurement for a year t observation (assuming December fiscal year end).
Beginning
of January
t-10
•
•
•
•
End of
December
t+2
• Unexpected
forecast
error for
year t+2
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Corporate Diversification and the Cost of Capital
1969
(E/P growth-adjusted).7 This measure of “instrumented” returns (hereafter,
INSTRET) is likely superior to realized returns as a proxy of expected returns if
the first-stage regression successfully purges the information shocks in realized
returns.
To compare a diversified firm’s cost of capital to the cost of capital that its
business units would have as stand-alone firms, we compute a measure of
“excess cost of capital.” For GLS and INSTRET, excess cost of capital is the
natural logarithm of the ratio of the firm’s cost of capital to its imputed cost
of capital. For realized returns, excess cost of capital is simply the difference
between the firm’s cost of capital and its imputed cost of capital. The imputed
cost of capital of the firm is a value-weighted average of the imputed cost of
capital of its segments:
iCOCi =
n
k=1
iMVik
n
iCOCik ,
k=1 iMVik
(3)
where n is the number of the firm’s segments, iCOCik is the imputed cost of
capital of segment k, which is equal to the median cost of capital of singlesegment firms in the segment’s industry, and iMVik is the imputed market
value of segment k, calculated as in Berger and Ofek (1995).
The procedure for estimating segments’ imputed market values is described
in detail in Berger and Ofek (1995). In short, the procedure consists of (1)
estimating the median ratio of enterprise value to sales for all single-segment
firms in the industry to which the segment belongs, and (2) multiplying the
segment’s sales by the median industry ratio. Industry definitions are based
on the narrowest SIC grouping that includes at least five single-segment firms
with at least $20 million in sales and has a non-missing cost of capital estimate.
B. Coinsurance
Measuring the level of coinsurance among a diversified firm’s business units
is empirically challenging because the joint distribution of future business unit
cash flows is not observable. Moreover, using the distribution of historical business unit cash flows is problematic because firm composition changes over time.
Accordingly, we construct coinsurance proxies using correlations of industrylevel cash flows based on single-segment firms.8 We define industries using the
7 Earnings yield is computed as the ratio of net income to beginning-of-year market value of
equity, using only observations with positive net income. Because earnings yield also contains information about growth opportunities, we include a last measure, E/P growth-adjusted, calculated
as the sum of earnings yield and growth in net income over the previous year, to incorporate the
effect of earnings growth.
8 We perform robustness tests using two alternative coinsurance measures based on firm-specific
segment cash flow and investment data. In particular, in order to provide a reasonable period for
estimating cross-segment correlations, the analysis is performed using a subset of firms whose
segment structures remain unchanged for 5 or 7 years. Results from these robustness tests are
presented in the Internet Appendix.
The Journal of FinanceR
1970
narrowest SIC grouping that includes at least five single-segment firms with
at least $20 million in sales over the last 10 years.9
To ensure that estimated pairwise industry correlations are not contaminated with systematic risk, we perform the computation in two stages. First, for
each industry in a given year, we compute idiosyncratic industry cash flows for
the prior 10 years as residuals from a regression of average industry cash flow
on average market-wide cash flow and two additional size and book-to-market
factors (Fama and French (1995)). Next, for each year in our sample, we estimate pairwise industry correlations using prior 10-year idiosyncratic industry
cash flows. As coinsurance of investment opportunities can also help firms
avoid deadweight costs of external finance (Matsusaka and Nanda (2002)), we
similarly estimate pairwise industry correlations using prior 10-year idiosyncratic industry investments.10 These estimated correlations serve as inputs to
our coinsurance measures described below.
As an inverse measure of coinsurance, we compute a sales-weighted portfolio
correlation measure ρ it(n) for firm i in year t with n business segments as
n
n wip( j) wiq(k) Corr[t−10,t−1] ( j, k),
(4)
p=1 q=1
where wip(j) is the sales share of segment p of firm i operating in industry
j (similarly for business segment q of firm i operating in industry k), and
Corr[t−10,t−1] ( j, k) is the estimated correlation of idiosyncratic industry cash
flows or investments between industries j and k over the 10-year period before
year t. We obtain similar results using an alternative coinsurance measure,
which also includes the standard deviation of industry cash flow and investment (Duchin (2010)).
Note that a single-segment firm’s sales-weighted cash flow or investment
correlation measure equals one by definition. This is also true for a multisegment firm whose segments operate in the same industry.
C. Financial Constraints
We use three measures of financial constraints to test whether coinsurance
helps firms avoid deadweight losses associated with financial constraints: the
Whited–Wu (WW) index (Whited and Wu (2006)), the size and age (SA) index
(Hadlock and Pierce (2010)), and S&P debt rating (speculative versus investment grade). The WW index and the SA index are robustly associated with
the degree of financial constraints in recent data samples (Hadlock and Pierce
9 We perform robustness tests using three alternative coinsurance measures based on the following industry definitions: Fama and French (1997) 48 industries, three-digit SIC codes, and
two-digit SIC codes. These robustness tests are presented in the Internet Appendix.
10 As is standard practice, we measure cash flow as operating income before depreciation (Compustat Item #13) scaled by total assets (Compustat Item #6) and investment as capital expenditures
(Compustat Item #128) scaled by total assets (Compustat Item #6).
Corporate Diversification and the Cost of Capital
1971
(2010)). The support for using debt ratings comes from Campello, Graham, and
Harvey (2010), who use CFO survey data to study the real effects of financial
constraints during the 2008 financial crisis. They find that, among various
archival measures of financial constraints, credit ratings are the most highly
correlated with their survey-based measure of financial constraints. Further,
of all the measures examined in their study, “credit ratings come closest to
replicating the patterns [they] find for the behavior of financially constrained
and unconstrained firms during the crisis” (p. 477).
III. Sample and Data
A. Sample Selection
We obtain our sample from the intersection of the Compustat and I/B/E/S
databases for the period 1988–2006.11 We construct cost of capital measures by
combining firm-level accounting information from the Compustat annual files
with analyst forecasts from I/B/E/S. The excess cost of capital measures and
the coinsurance measures require availability of segment disclosures from the
Compustat segment-level files.
Additionally, we impose the following sample restrictions. First, we follow
Berger and Ofek (1995) and require that (1) all firm-years have at least
$20 million in sales to avoid distorted valuation multiples, (2) the sum of segment sales be within 1% of the total sales of the firm to ensure the integrity
of segment data, (3) all of the firm’s segments for a given year have at least
five firms in the same two-digit SIC industry with non-missing firm value to
sales ratios and GLS cost of capital estimates, and (4) all firms with at least
one segment in the financial industry (SIC codes between 6000 and 6999) be
excluded from the sample. Second, we require the following data to estimate
the GLS cost of capital measure: (1) 1- and 2-year-ahead earnings forecasts, (2)
either a 3-year-ahead earnings forecast or the long-term growth earnings forecast and a positive 2-year-ahead earnings forecast, and (3) positive book value
of equity. The initial sample with available GLS excess cost of capital estimates
consists of 38,399 firm-year observations, of which 27,765 (10,634) are singlesegment (multi-segment) firms. With additional data requirements for the control variables (discussed in the next section), the final sample consists of 30,554
firm-year observations, of which 21,969 (8,585) observations pertain to singlesegment (multi-segment) firms. Some of the sensitivity analyses impose further
data restrictions, as discussed in the corresponding sections of the paper.
B. Control Variables
To ensure that our results on the relation between coinsurance and cost
of capital are distinct from the well-documented return patterns (Fama and
11 The start of our sample period is driven by our use of pairwise industry correlation estimates
based on prior 10-year single-segment data, which start in 1978.
The Journal of FinanceR
1972
French (1992) and Jegadeesh and Titman (1993)), we control for size, bookto-market, and momentum as proxied by the log of market capitalization, the
book-to-market ratio, and lagged buy-and-hold returns over the past 12 months,
respectively. Including a measure of momentum also controls for sluggishness
in analyst forecasts. Recent revisions in the stock market’s earnings expectations, although immediately reflected in stock prices, may not be incorporated
in analyst forecasts on a timely basis, which could induce a negative correlation
between past returns and implied cost of equity estimates.12
Recent research by Hughes, Liu, and Liu (2009) shows that, when discount
rates are stochastic, implied cost of equity estimates can deviate from expected
returns and these deviations can be related to the volatility of, as well as the
sample correlation among, expected returns and cash flows, expected growth in
cash flows, and leverage. They argue that the resulting “measurement error” in
implied cost of equity estimates may therefore be correlated with variables that
are traditionally not associated with systematic risk exposure, explaining the
significant correlation between implied cost of equity and leverage, expected
earnings growth, and forecast dispersion documented in prior research (Gode
and Mohanram (2003)). Therefore, we include these variables as additional
controls to avoid spurious results. All variables are winsorized at the top and
bottom 1%.
The timeline of variable measurement is depicted in Figure 1 and the definitions of control variables are summarized below (numbered items refer to the
Compustat annual database):
Log(market
capitalization)
Leverage
=
Book-to-market
=
=
Log(forecast
=
dispersion)
Long-term growth =
forecast
Lagged 12-month =
return
Natural logarithm of fiscal year-end stock price times shares
outstanding from Compustat (#199*#25)
Book value of debt divided by the sum of book value of debt and market
value of equity from Compustat (#9+#34)/(#9+#34+#199*#25)
Ratio of book value of equity to market value of equity from Compustat
(#60/(#199*#25))
Natural logarithm of the standard deviation in analysts’ 1-year-ahead
earnings forecasts from I/B/E/S
Consensus (median) long-term growth forecast from I/B/E/S
Buy-and-hold stock return from the beginning of June t until the end of
May of year t+1 from CRSP
IV. Empirical Results
A. Summary Statistics: Excess Cost of Capital
In Table I, we present summary statistics for three measures of excess cost
of capital (excess GLS, RET, and INSTRET in Panels A, B, and C, respectively)
12 It is possible that we are overcontrolling by including size and the book-to-market ratio in
our regressions. First, book-to-market may be associated with coinsurance-related forward-looking
betas in a conditional asset pricing model (Petkova and Zhang (2005)). Second, size may serve as
an alternative proxy for coinsurance. Larger firms are likely to have a greater number of unrelated
projects and thus experience greater coinsurance benefits.
Corporate Diversification and the Cost of Capital
1973
Table I
Summary Statistics: Excess Cost of Capital
This table reports summary statistics for three measures of excess cost of capital, GLS, RET, and
INSTRET in Panels A, B, and C, respectively. The statistics are computed over the period 1988–
2006 for a sample of single- and multi-segment firms. GLS, RET, and INSTRET are defined in
Appendix B. For GLS and INSTRET, excess cost of capital is defined as the natural logarithm of
the ratio of a firm’s cost of capital to its imputed cost of capital. For RET, excess cost of capital
is the difference between a firm’s cost of capital and its imputed cost of capital. The imputed cost
of capital of a firm is a value-weighted average of the imputed cost of capital of its segments.
Specifically,
iCOCi =
n
k=1
iMVik
n
iCOCik ,
k=1 iMVik
where n is the number of the firm’s segments, iCOCik is the imputed cost of capital of segment
k, which is equal to the median cost of capital of single-segment firms in the segment’s industry,
and iMVik is the imputed market value of segment k, calculated as in Berger and Ofek (1995). For
each segment, an industry is the narrowest SIC grouping that includes at least five single-segment
firms with non-missing cost of capital estimates. ***, **, or * indicate that the coefficient estimate
is significant at the 1%, 5%, or 10% level, respectively.
Obs.
Mean
Std. Dev. Lower Quartile
Median
Upper Quartile
−0.002***
−0.027***
−0.025***
0.093
0.090
−0.007***
−0.002
0.006***
0.105
0.104
0.000
−0.031***
−0.031***
0.099
0.077
Panel A. Excess GLS
Single-segment
Multi-segment
MS-SS
21,969
8,585
−0.038***
−0.048***
−0.010***
0.281
0.270
−0.127
−0.153
Panel B. Excess RET
Single-segment
Multi-segment
MS-SS
21,880
8,544
−0.006***
0.005**
0.012***
0.226
0.216
−0.118
−0.098
Panel C. Excess INSTRET
Single-segment
Multi-segment
MS-SS
12,897
5,260
0.000
−0.026***
−0.026***
0.198
0.198
−0.104
−0.137
for multi- and single-segment firms. Because the results for excess GLS and
INSTRET are qualitatively similar, we focus our discussion on the results
for excess GLS. For the multi-segment subsample, both mean and median
excess GLS are negative and significant (−0.048 and −0.027). For the singlesegment subsample, the median value of excess GLS is close to zero (−0.002),
although the estimate is still statistically significant.13 The mean value of
excess GLS is negative (−0.038) and significant, indicating that the distribution
13 Note that, for single-segment firms, the median values of all excess cost of capital measures
are zero by construction because the imputed values are calculated using the cost of capital of the
median single-segment firm in each industry. The reported median values differ slightly from zero
due to the elimination of observations with missing control variables.
1974
The Journal of FinanceR
is negatively skewed. The difference in means between the multi- and singlesegment subsamples is negative (−0.010) and different from zero at better
than the 1% level of statistical significance, rejecting the conventional view
that organizational form does not matter for a firm’s cost of capital.
In contrast, the mean value of excess RET is positive (0.005) and significant for multi-segment firms, and negative (−0.006) and significant for singlesegment firms. The difference in means between multi- and single-segment
firms is positive (0.012) and significant. It is worth noting that the results using excess RET are consistent with those using excess GLS and INSTRET when
we compare multi-segment firms with higher and lower levels of coinsurance
in the next section.
Recall that our excess GLS and INSTRET cost of capital measures are defined
as the natural logarithms of the ratio of the firm’s cost of capital to its imputed
cost of capital based on comparable single-segment firms. Hence, when we
discuss percentage differences in excess cost of capital, we imply logarithmic
percentage differences throughout the paper. Using the estimate for excess GLS
as an example—a logarithmic percentage difference of −1% (−0.010) between
multi- and single-segment firms—the cost of capital of a multi-segment firm
would be roughly 9.9% if the cost of capital of a single-segment firm were 10%.
The modest difference in cost of capital is likely due to the pooling of all multisegment firms, many of which operate within a single industry and enjoy little
cross-segment coinsurance.
B. Analysis of Excess Cost of Capital and Coinsurance
B.1. Nonparametric Results
In Table II, we sort our sample of multi-segment firms into quintiles based
on cross-segment cash flow and investment correlations (defined in Section
II.B), where the highest correlation quintile contains multi-segment firms with
correlations of one. We report the average excess GLS, RET, and INSTRET for
each quintile in panels A, B, and C, respectively.14 We also present the results
for single-segment firms. Note that single-segment firms can be viewed as limit
observations with respect to the degree of coinsurance—for these firms, cash
flow and investment correlations equal one by definition. Because the results
are qualitatively similar across the two correlation sorts and across the three
measures of excess cost of capital, we focus our discussion on the first sort based
on cross-segment cash flow correlations for excess GLS.15
Consistent with the coinsurance hypothesis, we observe a monotonic increase in excess GLS from the lowest correlation quintile (Q1) with the most
14
We maintain the same quintile break points across Panels A, B, and C. This stabilizes the
quintiles and makes them comparable across the different panels, but, due to missing observations,
leads to a slightly uneven number of observations in Panels B and C.
15 While the results across the three measures of excess cost of capital are qualitatively similar
within the multi-segment sample, the difference between Q1 and single-segment firms for excess
RET is markedly weaker (0.001 for both cash flow and investment correlation sorts).
Corporate Diversification and the Cost of Capital
1975
Table II
Excess Cost of Capital and Cross-Segment Correlations
This table presents excess cost of capital sorts based on cross-segment cash flow and investment
correlations. The sample period spans 1988–2006. Measures of excess cost of capital, GLS, RET,
and INSTRET are defined in Appendix B. Multi-segment firms are sorted into quintiles based on
their cross-segment cash flow and investment correlations, where the highest correlation quintile
contains multi-segment firms with correlations of one. Cash flow and investment correlations for
a firm are measured as the sales-weighted sum of pairwise segment correlations estimated using
idiosyncratic industry cash flow and investment based on single-segment firms over a prior 10year period. ***, **, or * indicate that the estimate is significant at the 1%, 5%, or 10% level,
respectively.
Firms Sorted by
Cash Flow Correlations
Obs.
Sort Variable Excess COC
Investment Correlations
Obs.
Sort Variable Excess COC
Panel A. Excess GLS
Multi-segment Firms
Q1 (Lowest correlation)
Q2
Q3
Q4
Q5 (Highest correlation)
Single-segment firms
Q1–Q5
Q1–Single-segment
1,495
1,496
1,496
1,496
2,602
21,969
0.414
0.734
0.911
0.998
1.000
1.000
−0.059
1,495
−0.054
1,496
−0.050
1,496
−0.046
1,496
−0.038
2,602
−0.038
21,969
−0.022***
−0.022***
0.430
0.760
0.929
0.999
1.000
1.000
−0.072
−0.056
−0.042
−0.039
−0.038
−0.038
−0.034***
−0.034***
0.429
0.760
0.929
0.999
1.000
1.000
−0.005
−0.014
0.020
0.009
0.012
−0.006
−0.016**
0.001
0.433
0.758
0.928
0.999
1.000
1.000
−0.046
−0.036
−0.035
−0.008
−0.013
0.000
−0.033**
−0.046**
Panel B. Excess RET
Multi-segment Firms
Q1 (Lowest correlation)
Q2
Q3
Q4
Q5 (Highest correlation)
Single-segment firms
Q1–Q5
Q1–Single-segment
1,489
1,490
1,487
1,487
2,591
21,880
0.414
0.733
0.911
0.998
1.000
1.000
−0.005
1,483
−0.004
1,489
0.014
1,494
0.007
1,487
0.012
2,591
−0.006
21,880
−0.017***
0.001
Panel C. Excess INSTRET
Multi-segment Firms
Q1 (Lowest correlation)
903
Q2
937
Q3
948
Q4
921
Q5 (Highest correlation) 1,551
Single-segment firms
12,897
Q1–Q5
Q1–Single-segment
0.413
0.736
0.911
0.998
1.000
1.000
−0.037
−0.038
−0.036
−0.014
−0.013
0.000
−0.024**
−0.037**
933
963
890
923
1,551
12,897
1976
The Journal of FinanceR
coinsurance to the highest correlation quintile (Q5) with the least coinsurance. The mean difference between Q1 and Q5 is a statistically significant
−0.022. Similarly, the mean difference between the cost of capital of multisegment firms in the lowest correlation quintile (Q1) and single-segment firms
is −0.022, consistent with a significant coinsurance effect. These results reject
the conventional view in favor of the coinsurance hypothesis—diversified firms
that consist of businesses with less correlated cash flows have a lower cost of
capital.
B.2. Main Regression Results
Next, we investigate whether the nonparametric evidence in Table II is robust to controlling for the set of firm characteristics discussed in Section III.B
The results of this analysis are presented in Table III with standard errors
block-bootstrapped by year reported in parentheses below corresponding coefficients.16
Panel A of Table III reports results for the full sample. Consistent with
the nonparametric results, the coefficient estimate on cross-segment cash flow
correlations is positive for all three measures of excess cost of capital and it
is different from zero at the 1% level of statistical significance for excess GLS
and INSTRET. Similarly, the coefficient estimate on cross-segment investment
correlations is positive and different from zero at the 1% level for excess GLS
and INSTRET and at the 10% level for excess RET.
Panel B of Table III reports regression results for the sample of multisegment firms. The results with excess GLS and INSTRET are similar to those
for the full sample. With excess RET, coinsurance estimates remain positive but
are no longer statistically significant, consistent with concern in the literature
that realized returns are noisy proxies of expected returns.
Overall, our results reject the conventional view in favor of the coinsurance
hypothesis. Firms with lower cross-segment cash flow correlations and hence
greater coinsurance potential have a lower cost of capital.
B.3. Financial Constraints
As discussed in Section I.A, one would expect the benefit of coinsurance and
its effect on cost of capital to be more pronounced for diversified firms facing
greater financial constraints and associated deadweight costs. We test this prediction using three measures of financial constraints: the WW index, the SA
index, and S&P debt rating (see Section II.C).17 The results for each measure
16 We report bootstrapped standard errors to account for the generated regressor problem due to
the inherent estimation uncertainty in our coinsurance measures. Our inferences are unchanged
using robust standard errors that are heteroskedasticity consistent and double clustered by firm
and year (Petersen (2009)).
17 The Internet Appendix contains additional results based on three other measures (net debt,
cash, and the KZ index), which Hadlock and Pierce (2010) argue rely on financial choices made by
managers and therefore may not have a straightforward relation to financial constraints.
Corporate Diversification and the Cost of Capital
1977
Table III
Regressions of Excess Cost of Capital on Cross-Segment Correlations
This table presents regressions of excess cost of capital on cross-segment correlations. The regressions are estimated over the period 1988–2006 for a sample of single- and multi-segment firms
(multi-segment firms) in Panel A (B). Cash flow and investment correlations for a firm are measured as the sales-weighted sum of pairwise segment correlations estimated using idiosyncratic
industry cash flow and investment based on single-segment firms over a prior 10-year period.
All other variables are defined in Appendix B. Standard errors block-bootstrapped by year are in
parentheses. ***, **, or * indicate that the coefficient estimate is significant at the 1%, 5%, or 10%
level, respectively.
GLS
Model 1
Model 2
RET
Model 1
Model 2
INSTRET
Model 1
Model 2
Panel A. Full Sample
Cash flow correlations
0.068***
(0.012)
Investment correlations
Number of segments
Logarithm of market
capitalization
Leverage
Book-to-market
Lagged 12-month return
Long-term growth forecast
Logarithm of forecast
dispersion
Constant
Observations
R2
0.008***
(0.003)
−0.029***
(0.005)
−0.157***
(0.022)
0.120***
(0.021)
−0.081***
(0.009)
−0.370***
(0.112)
0.009***
(0.002)
0.146***
(0.056)
30,554
0.121
0.020
(0.020)
0.088***
(0.015)
0.009***
(0.003)
−0.029***
(0.006)
−0.157***
(0.022)
0.120***
(0.021)
−0.081***
(0.009)
−0.371***
(0.113)
0.009***
(0.002)
0.126***
(0.049)
30,554
0.122
0.054***
(0.012)
0.036*
(0.022)
0.006
0.007
(0.005)
(0.005)
−0.005
−0.005
(0.005)
(0.005)
−0.102** −0.101**
(0.043)
(0.043)
0.025** 0.025**
(0.010)
(0.010)
0.005
0.005
(0.016)
(0.016)
0.090*
(0.047)
30,424
0.002
0.073*
(0.038)
30,424
0.002
0.007***
(0.002)
−0.025***
(0.004)
−0.123***
(0.014)
0.010
(0.012)
−0.112***
(0.008)
0.371***
(0.038)
0.028***
(0.002)
0.158***
(0.039)
18,157
0.156
0.066***
(0.017)
0.008***
(0.002)
−0.025***
(0.004)
−0.122***
(0.014)
0.009
(0.012)
−0.112***
(0.008)
0.371***
(0.038)
0.028***
(0.002)
0.146***
(0.033)
18,157
0.156
Panel B. Multi-segment Sample
Cash flow correlations
Investment correlations
Number of segments
Logarithm of market
capitalization
Leverage
0.052***
(0.011)
0.072***
(0.016)
0.017*** 0.017***
(0.003)
(0.003)
−0.033*** −0.033***
(0.006)
(0.006)
−0.191*** −0.190***
(0.040)
(0.040)
0.019
(0.020)
0.036
(0.022)
0.010** 0.011***
(0.004)
(0.004)
−0.006
−0.005
(0.005)
(0.005)
−0.064** −0.061**
(0.029)
(0.029)
0.050***
(0.011)
0.063***
(0.018)
0.009*** 0.009***
(0.003)
(0.003)
−0.026*** −0.026***
(0.006)
(0.006)
−0.117*** −0.116***
(0.025)
(0.024)
(Continued)
1978
The Journal of FinanceR
Table III—Continued
GLS
Model 1
Model 2
RET
Model 1
Model 2
INSTRET
Model 1
Model 2
Panel B. Multi-segment Sample
Book-to-market
Lagged 12-month return
Long-term growth forecast
Logarithm of forecast
dispersion
Constant
Observations
R2
0.141*** 0.140*** 0.031** 0.031** 0.015
0.014
(0.040)
(0.040)
(0.015)
(0.015)
(0.022)
(0.022)
−0.068*** −0.068*** −0.014
−0.014
−0.118*** −0.118***
(0.014)
(0.014)
(0.017)
(0.017)
(0.011)
(0.010)
−0.310** −0.315**
0.396*** 0.396***
(0.122)
(0.124)
(0.095)
(0.096)
0.006*
0.007*
0.023*** 0.024***
(0.003)
(0.004)
(0.005)
(0.005)
0.133*
0.116
0.069
0.053
0.141**
0.131**
(0.076)
(0.071)
(0.044)
(0.039)
(0.064)
(0.059)
8,585
8,585
8,544
8,544
5,260
5,260
0.111
0.112
0.002
0.002
0.132
0.134
are, respectively, presented in Panels A, B, and C of Table IV (nonparametric results) and Table V (regression results). Consistent with relatively weak results
using realized returns in the main analysis in Table III, we find no significant
interactions between financial constraints and coinsurance for excess RET. To
streamline the presentation, and, more importantly, to underscore that excess
GLS and INSTRET are likely superior measures of cost of capital compared
to ex post realized returns, we focus on those two measures in the rest of the
analyses.18
Table IV presents nonparametric results where we sequentially sort observations first on each measure of financial constraints, and then within each
financial constraint partition, on cash flow or investment correlations.19 For
the WW and SA index, we sort observations into high- and low-constraint subsamples using the median as a cutoff.20 For S&P debt rating, the sample is
partitioned based on whether the firm’s credit rating is lower than BBB (“Speculative Grade”) or BBB and higher (“Investment Grade”). Similar to Table II, we
18 As pointed out by Elton (1999), ex post realized returns can be noisy proxies for ex ante
expected returns and may lead to biased coefficient estimates in finite samples due to contamination
by cash flow shocks. Several recent papers (Campello, Chen, and Zhang (2008) and Chava and
Purnanandam (2010)) show that these biases can be substantial, and our analysis in the previous
section bears out a similar conclusion. For interested readers, the Internet Appendix contains
results on financing constraints for realized returns.
19 In all three panels, the number of observations for Q5 is higher than that in Q1–Q4 because
Q5 includes all multi-segment firms with cash flow and investment correlations equal to one.
20 The number of multi-segment observations is not evenly distributed across the high and
low partitions of WW and SA in Panels A and B because the sorting on financial constraints is
performed for the full sample of multi- and single-segment firms. A robustness test that performs
the financial constraint sort within only multi-segment firms yields qualitatively and statistically
similar results.
Corporate Diversification and the Cost of Capital
1979
Table IV
Excess Cost of Capital, Cross-Segment Correlations,
and Financial Constraints
This table presents two-way sorts based on cross-segment cash flow and investment correlations
and three measures of financial constraints, the WW index, the SA index, and S&P debt ratings,
in Panels A, B, and C, respectively. Observations are first sorted based on the degree of financial
constraints. Within each financial constraint partition, observations are sorted based on cash flow
and investment correlations. The sample period spans 1988–2006. Measures of excess cost of
capital, GLS and INSTRET, are defined in Appendix B. Cash flow and investment correlations for
a firm are measured as the sales-weighted sum of pairwise segment correlations estimated using
idiosyncratic industry cash flow and investment based on single-segment firms over a prior 10year period. Observations in Panel A (B) are partitioned into above- and below-median WW (SA)
index. Observations in Panel C are partitioned into speculative grade (below BBB) or investment
grade (BBB or above) credit ratings. ***, **, or * indicate significance at the 1%, 5%, or 10% level,
respectively.
Panel A. WW Index
Firms Sorted by
Cash Flow Correlations
Excess GLS
Obs. Low WW Obs.
Multi-segment Firms
Q1 (Lowest correlation) 293
Q2
294
Q3
294
Q4
294
Q5 (Highest correlation) 536
Single-segment firms
8,634
Q1–Q5
Q1–Single-segment
−0.024
−0.026
0.018
−0.011
−0.010
−0.025
−0.014
0.001
674
674
674
674
984
6,665
High WW
Investment Correlations
Obs. Low WW Obs.
−0.072
293
−0.064
294
−0.075
294
−0.045
294
−0.040
536
−0.035 8,634
−0.033***
−0.037***
−0.031
−0.011
0.003
−0.005
−0.010
−0.025
−0.021
−0.006
674
674
674
674
984
6,665
High WW
−0.087
−0.079
−0.055
−0.035
−0.040
−0.035
−0.047***
−0.052***
Firms Sorted by
Cash Flow Correlations
Excess INSTRET
Obs. Low WW Obs.
Multi-segment Firms
Q1 (Lowest correlation) 210
Q2
241
Q3
222
Q4
204
Q5 (Highest correlation) 378
Single-segment Firms
5,635
Q1–Q5
Q1–Single-segment
0.011
−0.012
0.040
0.054
0.035
0.031
−0.024
−0.020
447
473
507
525
745
5,192
High WW
Investment Correlations
Obs. Low WW Obs.
High WW
−0.056
224
0.008
462 −0.070
−0.058
232
0.004
496 −0.054
−0.070
215
0.021
469 −0.061
−0.040
206
0.058
525 −0.040
−0.031
378
0.035
745 −0.031
−0.032 5,635
0.031
5,192 −0.032
−0.025**
−0.026
−0.039***
−0.024***
−0.023*
−0.038***
(Continued)
present the difference between Q1 and Q5 and between Q1 and single segment
firms and examine whether coinsurance effects are more pronounced for the
subsample that faces greater financial constraints. We find that the “Q1–Q5”
and “Q1–Single-Segment Firms” differences tend to be more pronounced
The Journal of FinanceR
1980
Table IV—Continued
Panel B. SA Index
Firms Sorted by
Cash Flow Correlations
Excess GLS
Multi-segment Firms
Q1 (Lowest correlation)
Q2
Q3
Q4
Q5 (Highest correlation)
Single-segment firms
Q1–Q5
Q1–Single-segment
Obs.
598
598
598
598
951
7,033
Low SA
Obs.
Investment Correlations
High SA
Obs.
Low SA
−0.046
370
−0.077
598
−0.050
−0.048
371
−0.061
598
−0.053
−0.034
371
−0.063
598
−0.033
−0.031
371
−0.043
598
−0.023
−0.021
572
−0.043
951
−0.021
−0.034
8,322 −0.025 7,033 −0.034
−0.025**
−0.034**
−0.029***
−0.012
−0.052***
−0.016**
Obs.
High SA
370
371
371
371
572
8,322
−0.101
−0.075
−0.044
−0.023
−0.043
−0.025
−0.059***
−0.077***
Firms Sorted by
Cash Flow Correlations
Excess GLS
Multi-segment Firms
Q1 (Lowest correlation)
Q2
Q3
Q4
Q5 (Highest correlation)
Single-segment firms
Q1–Q5
Q1–Single-segment
Obs.
424
446
456
476
732
5,851
Low SA
−0.031
−0.039
−0.045
−0.017
−0.015
−0.019
−0.016
−0.012
Obs.
236
267
273
253
394
5,017
Investment Correlations
High SA
Obs.
Low SA
−0.042
427
−0.043
−0.050
459
−0.031
−0.026
434
−0.047
−0.003
482
−0.013
0.003
732
−0.015
0.024
5,851 −0.019
−0.045**
−0.027**
−0.067***
−0.024***
Obs.
High SA
260
268
251
250
394
5,017
−0.048
−0.047
−0.024
−0.001
0.003
0.024
−0.050***
−0.072***
Panel C. S&P Debt Rating
Firms Sorted by
Cash Flow Correlations
Excess GLS
Multi-segment Firms
Q1 (Lowest correlation)
Q2
Q3
Q4
Q5 (Highest correlation)
Single-segment firms
Q1–Q5
Q1–Single-segment
Investment Correlations
Obs. Inv. grade Obs. Spec. grade Obs. Inv. grade Obs. Spec. grade
506
506
507
506
663
3,151
−0.083
−0.085
−0.111
−0.103
−0.093
−0.093
0.010
0.010
295
296
296
296
527
2,271
−0.044
−0.039
−0.051
−0.016
−0.020
−0.036
−0.024
−0.008
506
506
507
506
663
2,271
−0.109
−0.100
−0.087
−0.086
−0.093
−0.093
−0.015
−0.016
295
296
296
296
527
3,151
−0.063
−0.042
−0.034
−0.012
−0.020
−0.036
−0.043***
−0.027**
(Continued)
Corporate Diversification and the Cost of Capital
1981
Table IV—Continued
Firms Sorted by
Cash Flow Correlations
Excess INSTRET
Multi-segment Firms
Q1 (Lowest correlation)
Q2
Q3
Q4
Q5 (Highest correlation)
Single-segment firms
Q1–Q5
Q1–Single-segment
Investment Correlations
Obs. Inv. grade Obs. Spec. grade Obs. Inv. grade Obs. Spec. grade
344
364
386
413
534
1,889
−0.046
−0.062
−0.074
−0.050
−0.043
−0.048
−0.003
0.002
113
124
121
137
245
1,658
−0.056
−0.021
−0.061
−0.015
−0.028
−0.027
−0.028
−0.029
349
390
372
396
534
1,889
−0.064
118
−0.056
122
−0.060
117
−0.054
138
−0.043
245
−0.048 1,658
−0.021*
−0.016*
−0.069
−0.043
−0.031
−0.010
−0.028
−0.027
−0.041*
−0.041**
for the high financial constraints subsample (firms with higher WW and SA
index or with speculative grade credit rating).
Table V presents regression results for the full sample as well as for the
subsample of multi-segment firms. The main coefficient of interest is the
interaction term between cross-segment correlations and measures of financial constraints. For Panels A and B, the WW and SA index are measured as
“quintile rank” that ranges from zero for firms in the lowest index quintile
(least financially constrained) to four for firms in the highest index quintile
(most financially constrained). For Panel C, “speculative grade” is an indicator
variable equal to one (zero) for firms with S&P credit rating below BBB (BBB
or above). The coefficient estimates on the interaction between cross-segment
correlations and financial constraints measures are all positive and significant,
except for excess INSTRET in the multi-segment sample in Panel C. Overall,
these results suggest that coinsurance effects are stronger for firms facing
greater financial constraints, consistent with the prediction that coinsurance
benefits are greater for these firms.
B.4. Controlling for Selection Effects
Our estimates of the coinsurance effect might be biased due to selection effects arising from firms’ decisions to diversify, an issue that has been
addressed extensively in the diversification discount literature. However, it is
unclear how a strong monotonic relation between our continuous coinsurance
measures and excess cost of capital would be driven by a dichotomous selection
mechanism that pushes some business units to conglomerate. In addition, one
might think a priori that high-risk business units, which have the most to gain
from coinsurance, are more likely to diversify than low-risk business units,
in which case the selection bias would work against us finding a coinsurance
effect.
Table V
Long-term growth
forecast
Logarithm of forecast
dispersion
Lagged 12-month return
Book-to-market
Logarithm of market
capitalization
Leverage
Investment correlations ×
WW quintile rank
Number of segments
Cash flow correlations ×
WW quintile rank
Investment correlations
Cash flow correlations
0.009***
(0.003)
−0.055***
(0.006)
−0.260***
(0.028)
0.143***
(0.023)
−0.092***
(0.007)
−0.042
(0.043)
0.025***
(0.003)
−0.028***
(0.008)
0.038***
(0.003)
GLS
−0.014
(0.011)
0.038***
(0.003)
0.010***
(0.003)
−0.055***
(0.006)
−0.261***
(0.028)
0.142***
(0.023)
−0.091***
(0.007)
−0.043
(0.043)
0.026***
(0.003)
INSTRET
0.009***
(0.003)
−0.033***
(0.006)
−0.183***
(0.017)
0.002
(0.014)
−0.115***
(0.008)
0.435***
(0.036)
0.032***
(0.002)
0.031**
(0.015)
0.006**
(0.002)
0.034*
(0.018)
0.006**
(0.002)
0.009***
(0.003)
−0.033***
(0.006)
−0.183***
(0.017)
0.002
(0.014)
−0.115***
(0.008)
0.434***
(0.036)
0.032***
(0.002)
Panel A. WW Index
Full Sample
0.016***
(0.003)
−0.060***
(0.008)
−0.330***
(0.040)
0.213***
(0.036)
−0.093***
(0.016)
0.028
(0.076)
0.016***
(0.005)
−0.029***
(0.010)
0.041***
(0.007)
GLS
−0.020
(0.012)
0.043***
(0.007)
0.016***
(0.003)
−0.061***
(0.009)
−0.333***
(0.040)
0.209***
(0.036)
−0.093***
(0.016)
0.022
(0.078)
0.017***
(0.005)
0.013***
(0.003)
−0.041***
(0.008)
−0.250***
(0.027)
0.005
(0.026)
−0.126***
(0.013)
0.553***
(0.099)
0.032***
(0.005)
0.000
(0.017)
0.015***
(0.004)
INSTRET
Multi-segment Sample
(Continued)
0.002
(0.021)
0.016***
(0.004)
0.013***
(0.003)
−0.041***
(0.008)
−0.250***
(0.026)
0.004
(0.026)
−0.125***
(0.013)
0.551***
(0.101)
0.032***
(0.006)
This table presents regressions of excess cost of capital on cross-segment correlations and interactions with three measures of financial constraints, the
WW index, the SA index, and S&P debt ratings, respectively, in Panels A, B, and C, for a sample of single- and multi-segment firms. The regressions
are estimated over the period 1988–2006. Measures of excess cost of capital, GLS and INSTRET, and control variables are defined in Appendix B. Cash
flow and investment correlations for a firm are measured as the sales-weighted sum of pairwise segment correlations estimated using idiosyncratic
industry cash flow and investment based on single-segment firms over a prior 10-year period. “WW quintile rank” (“SA quintile rank”) in Panel A (B)
is quintile rank of the WW (SA) index and ranges from zero for firms in the lowest quintile to four for firms in the highest quintile. “Speculative-grade”
is an indicator variable equal to one (zero) for firms with a S&P credit rating below BBB (BBB or above). Standard errors block-bootstrapped by year
are in parentheses. ***, **, or * indicate that the coefficient estimate is significant at the 1%, 5%, or 10% level, respectively.
Regressions of Excess Cost of Capital on Cross-Segment Correlations and Financial Constraints
1982
The Journal of FinanceR
Observations
R2
Long-term growth
forecast
Logarithm of forecast
dispersion
Constant
Lagged 12-month return
Book-to-market
Logarithm of market
capitalization
Leverage
Investment correlations ×
SA quintile rank
Number of segments
Cash flow correlations ×
SA quintile rank
Investment correlations
Cash flow correlations
Observations
R2
Constant
0.009***
(0.003)
−0.029***
(0.005)
−0.167***
(0.024)
0.181***
(0.025)
−0.092***
(0.008)
−0.081*
(0.046)
0.025***
(0.003)
0.123**
(0.052)
20,753
0.198
0.071***
(0.011)
0.003*
(0.002)
0.346***
(0.061)
20,690
0.213
GLS
0.087***
(0.018)
0.003*
(0.002)
0.010***
(0.003)
−0.029***
(0.005)
−0.166***
(0.024)
0.181***
(0.025)
−0.092***
(0.008)
−0.084*
(0.047)
0.025***
(0.003)
0.107**
(0.043)
20,753
0.199
0.334***
(0.052)
20,690
0.214
INSTRET
0.225***
(0.043)
14,779
0.180
0.009***
(0.003)
−0.029***
(0.005)
−0.170***
(0.015)
0.008
(0.014)
−0.115***
(0.008)
0.412***
(0.037)
0.032***
(0.002)
0.199***
(0.041)
14,825
0.181
0.034*
(0.017)
0.006***
(0.002)
0.038*
(0.020)
0.006***
(0.002)
0.009***
(0.003)
−0.029***
(0.005)
−0.169***
(0.015)
0.008
(0.014)
−0.115***
(0.008)
0.411***
(0.038)
0.032***
(0.003)
0.194***
(0.034)
14,825
0.181
Panel B. SA Index
0.228***
(0.049)
14,779
0.180
Panel A. WW Index
Full Sample
Table V—Continued
0.016***
(0.003)
−0.036***
(0.006)
−0.241***
(0.033)
0.237***
(0.037)
−0.092***
(0.016)
−0.014
(0.076)
0.017***
(0.005)
0.121*
(0.071)
5,398
0.209
0.039***
(0.014)
0.011***
(0.002)
0.315***
(0.088)
5,391
0.222
GLS
0.053**
(0.021)
0.011***
(0.002)
0.016***
(0.003)
−0.036***
(0.006)
−0.238***
(0.033)
0.235***
(0.037)
−0.092***
(0.016)
−0.023
(0.078)
0.017***
(0.005)
0.110*
(0.064)
5,398
0.211
0.317***
(0.086)
5,391
0.225
0.013***
(0.003)
−0.034***
(0.006)
−0.226***
(0.029)
0.014
(0.026)
−0.125***
(0.013)
0.520***
(0.096)
0.032***
(0.006)
0.220***
(0.060)
3,957
0.175
0.011
(0.017)
0.011***
(0.003)
0.275***
(0.074)
3,952
0.173
INSTRET
Multi-segment Sample
(Continued)
0.016
(0.023)
0.010***
(0.003)
0.013***
(0.004)
−0.033***
(0.006)
−0.224***
(0.029)
0.014
(0.026)
−0.125***
(0.013)
0.521***
(0.098)
0.032***
(0.006)
0.214***
(0.055)
3,957
0.174
0.275***
(0.071)
3,952
0.173
Corporate Diversification and the Cost of Capital
1983
Observations
R2
Long-term growth
forecast
Logarithm of forecast
dispersion
Constant
Lagged 12-month return
Book-to-market
Logarithm of market
capitalization
Leverage
Investment correlations ×
speculative grade
Number of segments
Cash flow correlations ×
speculative grade
Investment correlations
Cash flow correlations
0.005**
(0.002)
−0.017***
(0.006)
−0.156***
(0.035)
0.115***
(0.023)
−0.057***
(0.010)
−0.249***
(0.042)
0.012***
(0.003)
0.077
(0.058)
9,820
0.101
0.068***
(0.012)
0.052***
(0.005)
GLS
0.106***
(0.018)
0.049***
(0.005)
0.007***
(0.002)
−0.018***
(0.006)
−0.156***
(0.035)
0.114***
(0.023)
−0.056***
(0.010)
−0.247***
(0.042)
0.012***
(0.003)
0.043
(0.053)
9,820
0.103
INSTRET
0.004
(0.003)
−0.017***
(0.006)
−0.098***
(0.017)
−0.021
(0.015)
−0.091***
(0.008)
0.097
(0.082)
0.023***
(0.003)
0.138**
(0.059)
6,328
0.067
0.051***
(0.019)
0.012**
(0.005)
0.072***
(0.025)
0.011**
(0.005)
0.005*
(0.003)
−0.017***
(0.006)
−0.099***
(0.018)
−0.021
(0.015)
−0.091***
(0.008)
0.100
(0.082)
0.023***
(0.004)
0.118**
(0.054)
6,328
0.069
Panel C. S&P Debt Rating
Full Sample
Table V—Continued
0.012***
(0.002)
−0.023***
(0.007)
−0.185***
(0.048)
0.144***
(0.036)
−0.066***
(0.013)
−0.288***
(0.097)
0.017***
(0.004)
0.109
(0.078)
4,398
0.125
0.068***
(0.013)
0.052***
(0.010)
GLS
0.104***
(0.019)
0.044***
(0.009)
0.013***
(0.003)
−0.024***
(0.007)
−0.183***
(0.048)
0.142***
(0.036)
−0.064***
(0.013)
−0.279***
(0.095)
0.019***
(0.004)
0.085
(0.074)
4,398
0.128
0.008**
(0.004)
−0.019***
(0.006)
−0.080***
(0.025)
−0.020
(0.023)
−0.099***
(0.016)
0.062
(0.146)
0.023***
(0.005)
0.143*
(0.076)
2,781
0.067
0.046***
(0.016)
0.006
(0.008)
INSTRET
Multi-segment Sample
0.066***
(0.022)
0.005
(0.008)
0.009**
(0.004)
−0.020***
(0.007)
−0.081***
(0.027)
−0.020
(0.023)
−0.099***
(0.016)
0.071
(0.145)
0.023***
(0.006)
0.128*
(0.073)
2,781
0.071
1984
The Journal of FinanceR
Corporate Diversification and the Cost of Capital
1985
Nevertheless, we acknowledge that selection is an important concern and we
address this issue in two ways. First, we estimate Heckman two-stage regressions to correct for potential selection biases. Second, we follow an approach
that is similar in spirit to that of Lamont and Polk (2002) and examine the
relation between exogenous changes in coinsurance and changes in excess cost
of capital. As we describe below, the results from both analyses suggest that our
estimates of the coinsurance effect in Table III are unlikely to be contaminated
by selection effects or firms’ decisions to diversify.
Heckman’s Two-Stage Analysis. To control for potential selection biases using
Heckman’s two-stage procedure, we first estimate a first-stage probit model
for firms’ decisions to diversify. The dependent variable in the probit model is
equal to one for a multi-segment firm and zero for a single-segment firm. We
estimate two different first-stage models. The first model (“No Instrument”)
includes all of the control variables in our main regression model. The second
model (“With Instruments”) further includes two instruments used in Campa
and Kedia (2002), namely, PNDIV (the fraction of all firms in the industry
that are conglomerates) and PSDIV (the fraction of sales accounted for by
conglomerates). The second-stage regressions control for the inverse Mills ratio
estimated from these two first-stage models.
The results of the second-stage regressions for GLS and INSRET are, respectively, reported in Panels A and B of Table VI. The first two columns present
the results using the inverse Mills ratio from the “No Instrument” first-stage
probit model whereas the last two columns present the results using the inverse Mills ratio from the “With Instruments” first-stage probit model. In all
models, the estimated coefficients on cross-segment correlations are positive
and different from zero at the 1% level of statistical significance. Importantly,
the magnitudes of the coefficients are similar to those reported in Table III.
Exogenous Changes in Coinsurance and Changes in Excess Cost of Capital.
We also follow an approach that is similar to that of Lamont and Polk (2002)
to address the issue of selection effects. Specifically, we decompose changes in
cross-segment correlations into two components: an exogenous component that
reflects changes in pairwise industry correlations that are arguably outside
the control of managers, and an endogenous component that reflects changes
in firm segment structure that managers can control. Specifically
ρt = ρ(st , ct ) − ρ(st−1 , ct−1 )
= ρ(st , ct ) − ρ(st−1 , ct ) + ρ(st−1 , ct ) − ρ(st−1 , ct−1 ),
endogenous change in ρ
(5)
exogenous change in ρ
where st and ct represent the firm’s segment structure and estimates of pairwise
industry correlations in year t, respectively.
Next, we regress changes in excess cost of capital on exogenous and endogenous changes in cross-segment correlations as well as changes in the control
The Journal of FinanceR
1986
Table VI
Regressions of Excess Cost of Capital on Cross-Segment Correlations:
Controlling for Selection Effects
Panels A and B present second-stage excess cost of capital regressions that control for the inverse
Mills ratio from first-stage probit models explaining whether the firm is a multi-segment firm for
GLS and INSTRET, respectively. Under “No Instrument,” the inverse Mills ratio is from a firststage probit model with all of the control variables in the second stage. Under “With Instruments,”
the first-stage probit model further includes PNDIV and PSDIV (Campa and Kedia (2002)). PNDIV
measures the fraction of all firms in the industry that are conglomerates, and PSDIV measures the
fraction of sales accounted for by conglomerates. Panels C and D present regressions of changes
in excess cost of equity capital on exogenous and endogenous changes in cash flow and investment
correlations for GLS and INSTRET, respectively. Exogenous changes reflect changes solely due
to changes in pairwise industry correlations. All regressions are estimated over the period 1988–
2006. Measures of excess cost of capital, GLS and INSTRET, and control variables are defined
in Appendix B. Cash flow and investment correlations for a firm are measured as the salesweighted sum of pairwise segment correlations estimated using idiosyncratic industry cash flow
and investment based on single-segment firms over a prior 10-year period. Standard errors blockbootstrapped by year are in parentheses. ***, **, or * indicate that the coefficient estimate is
significant at the 1%, 5%, or 10% level, respectively.
No Instrument
With Instruments
Panel A. Heckman’s Second-Stage Regression: GLS
Cash flow correlations
0.060***
(0.012)
Investment correlations
Number of segments
Logarithm of market capitalization
Leverage
Book-to-market
Logarithm of forecast dispersion
Long-term growth forecast
Lagged 12-month return
Inverse Mills ratio
Constant
Observations
R2
0.014***
(0.003)
−0.030***
(0.005)
−0.160***
(0.021)
0.119***
(0.021)
0.009***
(0.002)
−0.362***
(0.113)
−0.081***
(0.009)
−0.012***
(0.004)
0.153***
(0.055)
30,554
0.122
0.063***
(0.011)
0.081***
(0.016)
0.014***
(0.003)
−0.030***
(0.005)
−0.160***
(0.021)
0.118***
(0.021)
0.009***
(0.002)
−0.364***
(0.113)
−0.081***
(0.009)
−0.011***
(0.004)
0.133***
(0.048)
30,554
0.122
0.011***
(0.004)
−0.030***
(0.005)
−0.159***
(0.021)
0.119***
(0.021)
0.009***
(0.002)
−0.366***
(0.113)
−0.081***
(0.009)
−0.007
(0.004)
0.151***
(0.055)
30,554
0.121
0.084***
(0.016)
0.012***
(0.004)
−0.030***
(0.005)
−0.158***
(0.021)
0.119***
(0.021)
0.009***
(0.002)
−0.367***
(0.113)
−0.081***
(0.009)
−0.007
(0.005)
0.130***
(0.047)
30,554
0.122
Panel B. Heckman’s Second-Stage Regression: INSTRET
Cash flow correlations
Investment correlations
0.052***
(0.011)
0.047***
(0.011)
0.064***
(0.018)
0.059***
(0.018)
(Continued)
Corporate Diversification and the Cost of Capital
1987
Table VI—Continued
No Instrument
With Instruments
Panel B. Heckman’s Second-Stage Regression: INSTRET
Number of segments
Logarithm of market capitalization
Leverage
Book-to-market
Logarithm of forecast dispersion
Long-term growth forecast
Lagged 12-month return
Inverse Mills ratio
Constant
Observations
R2
0.010***
(0.002)
−0.026***
(0.004)
−0.124***
(0.015)
0.010
(0.013)
0.028***
(0.002)
0.395***
(0.044)
−0.105***
(0.008)
−0.005
(0.004)
0.157***
(0.039)
18,157
0.151
0.010***
(0.003)
−0.026***
(0.004)
−0.123***
(0.015)
0.010
(0.013)
0.028***
(0.002)
0.395***
(0.045)
−0.105***
(0.008)
−0.004
(0.004)
0.145***
(0.034)
18,157
0.152
0.012***
(0.002)
−0.027***
(0.004)
−0.125***
(0.015)
0.010
(0.013)
0.028***
(0.002)
0.399***
(0.044)
−0.105***
(0.008)
−0.010***
(0.003)
0.161***
(0.039)
18,157
0.152
0.012***
(0.003)
−0.027***
(0.004)
−0.125***
(0.015)
0.010
(0.013)
0.028***
(0.002)
0.398***
(0.045)
−0.105***
(0.008)
−0.009***
(0.003)
0.149***
(0.033)
18,157
0.152
Panel C. Changes in Excess Cost of Capital and Exogenous Changes in Cross-Segment
Correlations: GLS
Model 1
Cash flow correlations
Model 2
Model 3
0.042
(0.030)
Cash flow correlations, exogenous
0.049**
(0.024)
0.029
(0.022)
Cash flow correlations, endogenous
Investment correlations
0.088***
(0.029)
Investment correlations, exogenous
Investment correlations, endogenous
Number of segments
Logarithm of market capitalization
Leverage
Book-to-market
Logarithm of forecast dispersion
Long-term growth forecast
Model 4
0.011**
(0.004)
0.024***
(0.006)
−0.286***
(0.020)
0.046***
(0.011)
0.010***
(0.002)
0.096**
(0.043)
0.011**
(0.004)
0.024***
(0.006)
−0.286***
(0.020)
0.045***
(0.011)
0.010***
(0.002)
0.097**
(0.043)
0.012***
(0.004)
0.024***
(0.006)
−0.285***
(0.020)
0.046***
(0.011)
0.010***
(0.002)
0.096**
(0.043)
0.080***
(0.023)
0.072***
(0.022)
0.012***
(0.004)
0.024***
(0.006)
−0.286***
(0.020)
0.046***
(0.011)
0.010***
(0.002)
0.096**
(0.043)
(Continued)
1988
The Journal of FinanceR
Table VI—Continued
Panel C. Changes in Excess Cost of Capital and Exogenous Changes in Cross-Segment
Correlations: GLS
Lagged 12-month return
Constant
Observations
R2
Model 1
Model 2
Model 3
Model 4
−0.093***
(0.003)
0.003
(0.002)
19,092
0.124
−0.093***
(0.003)
0.003
(0.002)
19,092
0.124
−0.093***
(0.003)
0.002
(0.002)
19,092
0.124
−0.093***
(0.003)
0.003
(0.002)
19,092
0.124
Panel D. Changes in Excess Cost of Capital and Exogenous Changes in Cross-Segment.
Correlations: INSTRET
Cash flow correlations
0.070*
(0.036)
Cash flow correlations, exogenous
0.079**
(0.032)
0.062**
(0.027)
Cash flow correlations, endogenous
Investment correlations
0.150***
(0.040)
Investment correlations, exogenous
Investment correlations, endogenous
Number of segments
Logarithm of market capitalization
Leverage
Book-to-market
Logarithm of forecast dispersion
Long-term growth forecast
Lagged 12-month return
Constant
Observations
R2
0.007
(0.005)
−0.031***
(0.008)
−0.324***
(0.029)
−0.081***
(0.018)
0.012***
(0.002)
1.006***
(0.066)
−0.126***
(0.003)
0.002
(0.002)
10,915
0.202
0.007
(0.005)
−0.031***
(0.008)
−0.325***
(0.029)
−0.082***
(0.018)
0.012***
(0.002)
1.007***
(0.066)
−0.126***
(0.003)
0.002
(0.002)
10,915
0.203
0.008
(0.005)
−0.030***
(0.008)
−0.324***
(0.029)
−0.081***
(0.018)
0.012***
(0.002)
1.004***
(0.066)
−0.126***
(0.003)
0.002
(0.002)
10,915
0.203
0.117***
(0.032)
0.108***
(0.031)
0.008
(0.005)
−0.031***
(0.008)
−0.325***
(0.029)
−0.082***
(0.018)
0.012***
(0.002)
1.005***
(0.066)
−0.126***
(0.003)
0.002
(0.002)
10,915
0.203
variables from Table III. The results for GLS and INSTRET are, respectively,
reported in Panels C and D of Table VI. Models 1 and 3 are analogous to
Models 1 and 2 in Table III, but in a first-differenced form, which effectively
controls for firm fixed effects. Models 2 and 4 decompose total changes in crosssegment correlations into exogenous and endogenous changes.
Corporate Diversification and the Cost of Capital
1989
Similar to the regression results in Table III, the coefficient estimates on
cross-segment correlations in Models 1 and 3 are all positive and significant in
three out of four specifications. In Models 2 and 4, the coefficient estimates on
exogenous changes in cross-segment correlations are also positive and significant, with magnitudes similar to those in Table III.
It is worth noting that, while our main focus is on exogenous changes in
cross-segment correlations, endogenous changes are also of interest as a firm’s
cost of capital should change in response to changes in its organizational structure. Consistent with this prediction, the coefficient estimates on endogenous
changes in cross-segment correlations are all positive and significant in three
out of four specifications.
B.5. Economic Significance
To evaluate the economic significance of our findings, we estimate the effect
of coinsurance-related reduction in cost of capital on firm value. In the simple
Gordon growth model, under a zero dividend growth assumption, a 1% decrease
in cost of capital from 10% to 9.9% approximately translates into a 1% increase
in firm value. However, the relation between cost of capital and firm value is,
in general, nonlinear and depends on other inputs in the valuation formula—
expected earnings and earnings growth.
Our analysis compares actual firm values to as-if firm values calculated
using imputed cost of capital (i.e., the cost of capital on a comparable portfolio
of single-segment firms) while holding cash flows constant in the GLS valuation
model (described in Appendix A). The “excess value” attributable to differences
in cost of capital is calculated as the natural logarithm of the ratio of actual
firm value to as-if firm value.
Using this approach, we find an economically significant 4.8% (6.4%) average
gain in total firm value when moving from the lowest to the highest coinsurance
quintile based on cross-segment cash flow (investment) correlations. We note
that these estimates might represent a lower bound for the coinsurance effect
on firm value because our proxies are limited to segment data and do not
capture coinsurance among different product lines or geographic areas.
C. Robustness Tests
C.1. Analyst Forecast Errors
A potential limitation of implied cost of equity measures is measurement errors arising from biases in analyst forecasts. We use two approaches to address
this concern. First, we control for 1- and 2-year-ahead unexpected and expected
forecast errors in our main regression models. In particular, we follow Ogneva,
Subramanyam, and Raghunandan (2007) and estimate expected forecast errors
using the prediction model in Liu and Su (2005). Our parsimonious version of
the model includes the following predictors that proxy for systematic biases in
analyst forecasts: (1) past stock returns, (2) recent analyst earnings forecast
1990
The Journal of FinanceR
revisions, and variables related to overreaction to past information, namely,
(3) forward earnings-to-price ratios, (4) long-term growth forecasts, and (5)
investments in property, plant, and equipment. Estimation of the predicted
forecast error is performed separately for 1- and 2-year-ahead forecast errors.
Unexpected forecast errors are computed as the difference between realized
errors and their predicted component. Because 1- and 2-year-ahead expected
errors are highly collinear, we use the average expected errors over the 2 years
as the control measure. The results for excess GLS and INSTRET reported
in Panel A of Table VII continue to show a positive and significant coefficient
on cross-segment cash flow and investment correlations, suggesting that our
main findings are unlikely driven by systematic differences in analyst forecast
biases between single- and multi-segment firms.
Second, Easton and Monahan (2005) find that the reliability of implied cost of
equity estimates increases as analyst forecast accuracy improves. Accordingly,
we partition our sample into terciles using absolute forecast errors in 1-yearahead earnings and estimate cost of capital regressions within each subsample.
The results for excess GLS and INSTRET are, respectively, reported in Panels
B and C of Table VII. The coinsurance effect is weakest in the subsample
with high absolute forecast errors. These results suggest that our findings are
unlikely driven by measurement errors in the implied cost of equity estimates
that are induced by biased forecasts. Rather, our results are weakened by
them.
C.2. Excess Cost of Debt
The model outlined in Section I.A predicts coinsurance effects for both equity
and debt. In this subsection, we investigate the effects of coinsurance on the
cost of debt. We first construct a firm-specific measure of cost of debt using corporate bond yields from Datastream and loan spreads from DealScan database
provided by Loan Pricing Corporation.21 Specifically, we use the weighted average of firm-specific bond yield spread and all-in-drawn loan spread when both
spreads are available, or the available spread when only one of the two spreads
is available.22 Similar to excess cost of equity, excess cost of debt is estimated
as the logarithm of the ratio of the firm’s cost of debt to its imputed cost of debt
based on similar single-segment firms. Using this firm-specific excess cost of
debt measure, we repeat our main analysis with two additional variables to
control for variation in months to maturity and default risk (Merton (1974)).
The results are reported in Table VIII. The first two columns report results for
excess cost of debt. The last four columns present results using excess cost of
21 We use the Compustat-DealScan link made publicly available by Michael Roberts (see Chava
and Roberts (2008)) to match the databases.
22 If a firm has more than one loan facility outstanding, we compute the firm-level all-in-drawn
spread as a weighted-average of loan facility spreads, with weights equal to the loan amounts.
Similarly, if a firm has more than one bond issue, we compute the firm-level corporate bond yield
spread as a weighted average of yield spreads, with weights equal to the bonds’ market values.
Corporate Diversification and the Cost of Capital
1991
Table VII
Regressions of Excess Cost of Capital on Cross-Segment Correlations:
Analyst Forecast Bias
This table presents regressions of excess cost of capital on cross-segment correlations, controlling
for effects of analyst forecast biases. Panel A reports regressions with expected and unexpected
forecast errors added as controls. Panels B and C report regressions for subsamples partitioned
on the magnitude of absolute forecast error for GLS and INSTRET, respectively. The regressions
are estimated over the period 1988–2006. Cash flow and investment correlations for a firm are
measured as the sales-weighted sum of pairwise segment correlations estimated using idiosyncratic
industry cash flow and investment based on single-segment firms over a prior 10-year period.
The construction of expected and unexpected analyst forecast errors follows Liu and Su (2005)
and Ogneva, Subramanyam, and Raghunandan (2007). Measures of excess cost of capital, GLS
and INSTRET, and other control variables are defined in Appendix B. Standard errors blockbootstrapped by year are in parentheses. ***, **, or * indicate that the coefficient estimate is
significant at the 1%, 5%, or 10% level, respectively.
Panel A. Controlling for Analyst Forecast Errors
GLS
Cash flow correlations
0.055***
(0.013)
Investment correlations
Number of segments
Logarithm of market capitalization
Leverage
Book-to-market
Lagged 12-month return
Long-term growth forecast
Logarithm of forecast dispersion
Unexpected analyst forecast error in
year +1
Unexpected analyst forecast error in
year +2
Average predicted analyst forecast
error in year +1 and +2
Constant
Observations
R2
0.009**
(0.003)
−0.026***
(0.005)
−0.172***
(0.024)
0.119***
(0.019)
−0.065***
(0.012)
−0.416***
(0.105)
0.005**
(0.002)
−0.135
(0.105)
−0.419***
(0.079)
−1.035***
(0.198)
0.131***
(0.044)
25,187
0.158
INSTRET
0.044***
(0.013)
0.077***
(0.017)
0.010***
(0.003)
−0.026***
(0.005)
−0.171***
(0.024)
0.118***
(0.019)
−0.065***
(0.012)
−0.418***
(0.105)
0.006**
(0.002)
−0.133
(0.105)
−0.417***
(0.079)
−1.032***
(0.197)
0.108***
(0.041)
25,187
0.158
0.006**
(0.003)
−0.023***
(0.004)
−0.136***
(0.016)
0.002
(0.016)
−0.084***
(0.009)
0.275***
(0.055)
0.023***
(0.002)
−0.102
(0.105)
−0.336***
(0.061)
−1.337***
(0.299)
0.154***
(0.039)
15,530
0.180
0.055***
(0.017)
0.006**
(0.003)
−0.023***
(0.004)
−0.136***
(0.016)
0.002
(0.016)
−0.085***
(0.009)
0.275***
(0.056)
0.023***
(0.003)
−0.102
(0.105)
−0.335***
(0.061)
−1.333***
(0.297)
0.142***
(0.035)
15,530
0.180
(Continued)
The Journal of FinanceR
1992
Table VII—Continued
Absolute Forecast Error
Low
Medium
High
Panel B. Partitions Based on Absolute Forecast Error: GLS
Cash flow correlations
0.106***
(0.018)
Investment correlations
Number of segments
Logarithm of market
capitalization
Leverage
Book-to-market
Lagged 12-month return
Long-term growth
forecast
Logarithm of forecast
dispersion
Constant
Observations
R2
0.010***
(0.004)
−0.031***
(0.006)
−0.082***
(0.031)
0.242***
(0.022)
−0.097***
(0.011)
−0.362***
(0.130)
0.008**
(0.003)
0.031
(0.062)
9,695
0.213
0.054**
(0.021)
0.126***
(0.021)
0.011***
(0.004)
−0.031***
(0.006)
−0.082***
(0.031)
0.241***
(0.022)
−0.097***
(0.011)
−0.363***
(0.130)
0.008**
(0.003)
0.012
(0.053)
9,695
0.214
0.002
(0.003)
−0.019***
(0.005)
−0.173***
(0.034)
0.202***
(0.019)
−0.066***
(0.009)
−0.378***
(0.136)
−0.000
(0.003)
0.043
(0.047)
9,689
0.132
0.026**
(0.012)
0.073***
(0.021)
0.003
(0.003)
−0.019***
(0.005)
−0.172***
(0.034)
0.201***
(0.019)
−0.066***
(0.009)
−0.380***
(0.137)
−0.000
(0.003)
0.025
(0.041)
9,689
0.132
0.004
(0.003)
−0.011***
(0.004)
−0.213***
(0.022)
0.075***
(0.022)
−0.038***
(0.013)
−0.257***
(0.089)
−0.008*
(0.004)
0.088**
(0.040)
9,683
0.037
0.035**
(0.017)
0.004
(0.003)
−0.011***
(0.004)
−0.213***
(0.022)
0.075***
(0.022)
−0.038***
(0.013)
−0.258***
(0.089)
−0.008*
(0.004)
0.079**
(0.038)
9,683
0.037
Panel C. Partitions Based on Absolute Forecast Error: INSTRET
Cash flow correlations
0.054***
(0.020)
Investment correlations
Number of segments
Logarithm of market
capitalization
Leverage
Book-to-market
Lagged 12-month return
Long-term growth
forecast
Logarithm of forecast
dispersion
Constant
Observations
R2
0.005*
(0.003)
−0.025***
(0.005)
−0.041**
(0.019)
0.050***
(0.019)
−0.104***
(0.011)
0.159***
(0.042)
0.024***
(0.003)
0.134***
(0.045)
6,832
0.143
0.050***
(0.013)
0.031
(0.026)
0.063**
0.068***
(0.025)
(0.018)
0.006*
0.005
0.006*
0.005
(0.003)
(0.003)
(0.003)
(0.003)
−0.025*** −0.018*** −0.018*** −0.012***
(0.005)
(0.003)
(0.003)
(0.003)
−0.041** −0.110*** −0.110*** −0.224***
(0.019)
(0.020)
(0.020)
(0.017)
0.051*** 0.030**
0.030** −0.029**
(0.019)
(0.015)
(0.015)
(0.011)
−0.104*** −0.101*** −0.101*** −0.086***
(0.011)
(0.007)
(0.007)
(0.010)
0.160*** 0.371*** 0.370*** 0.691***
(0.042)
(0.053)
(0.053)
(0.061)
0.025*** 0.013*** 0.013*** 0.014***
(0.003)
(0.003)
(0.003)
(0.004)
0.125*** 0.057*
0.040*
0.089*
(0.040)
(0.032)
(0.023)
(0.051)
6,832
6,039
6,039
4,561
0.143
0.122
0.123
0.183
0.036
(0.031)
0.005
(0.003)
−0.012***
(0.003)
−0.223***
(0.017)
−0.029**
(0.011)
−0.086***
(0.010)
0.691***
(0.062)
0.014***
(0.004)
0.085
(0.053)
4,561
0.183
Corporate Diversification and the Cost of Capital
1993
Table VIII
Regressions of Excess cost of Debt Capital on Cross-Segment
Correlations
This table presents regressions of excess cost of debt (COD) and excess cost of capital derived using
firm-specific cost of debt. The regressions are estimated over the period 1988–2006. Excess cost
of debt (capital) is defined as the natural logarithm of the ratio of a firm’s cost of debt (capital) to
its imputed cost of debt (capital) calculated using a portfolio of comparable single-segment firms.
Cost of capital is measured as the weighted average of cost of equity and cost of debt. Cost of
equity is measured as the implied cost of equity based on the approach of Gebhardt, Lee, and
Swaminathan (2001) (GLS) and instrumented equity returns (INSTRET) constructed similarly to
instrumented total returns. cost of debt is measured as the weighted average of the firm-specific
bond yield spread and all-in-drawn loan spread. Cash flow and investment correlations for a
firm are measured as the sales-weighted sum of pairwise segment correlations estimated using
idiosyncratic industry cash flow and investment based on single-segment firms over a prior 10-year
period. The control variables are defined in Appendix B. Standard errors block-bootstrapped by
year are in parentheses. ***, **, or * indicate that the coefficient estimate is significant at the 1%,
5%, or 10% level, respectively.
COD
Model 1
Cash flow correlations
0.046**
(0.020)
Investment correlations
Number of segments
Logarithm of market
capitalization
Leverage
Book-to-market
Lagged 12-month return
Long-term growth forecast
Logarithm of forecast
dispersion
Months to maturity
Distance to default
Constant
Observations
R2
Model 2
0.000
(0.002)
−0.033***
(0.004)
0.154***
(0.024)
−0.033***
(0.012)
0.012***
(0.004)
0.102**
(0.046)
0.006***
(0.002)
0.000***
(0.000)
0.064***
(0.018)
0.109***
(0.030)
4,792
0.199
GLS
Model 1
Model 2
0.110***
(0.028)
0.074***
(0.020)
0.001
(0.002)
−0.033***
(0.004)
0.154***
(0.024)
−0.033***
(0.012)
0.012***
(0.004)
0.102**
(0.046)
0.006***
(0.002)
0.000***
(0.000)
0.064***
(0.018)
0.081***
(0.029)
4,792
0.201
0.011***
(0.004)
−0.027***
(0.005)
0.046**
(0.020)
0.123***
(0.027)
−0.078***
(0.012)
0.052
(0.071)
0.015***
(0.003)
−0.000
(0.000)
0.075**
(0.035)
0.012
(0.058)
4,271
0.186
INSTRET
Model 1
Model 2
0.051
(0.038)
0.160***
(0.038)
0.012***
(0.004)
−0.027***
(0.005)
0.047**
(0.019)
0.123***
(0.027)
−0.078***
(0.012)
0.051
(0.072)
0.015***
(0.003)
−0.000
(0.000)
0.075**
(0.035)
−0.038
(0.057)
4,271
0.188
0.008**
(0.003)
−0.010**
(0.005)
−0.168***
(0.027)
0.067***
(0.024)
−0.062***
(0.014)
0.097
(0.071)
0.018***
(0.003)
0.000
(0.000)
0.070
(0.054)
0.066
(0.079)
2,240
0.094
0.102**
(0.045)
0.009***
(0.003)
−0.010**
(0.005)
−0.168***
(0.027)
0.066***
(0.024)
−0.062***
(0.014)
0.096
(0.071)
0.018***
(0.003)
0.000
(0.000)
0.073
(0.053)
0.014
(0.079)
2,240
0.097
capital that combines firm-specific cost of debt with the GLS implied cost of equity measure and instrumented equity return. The latter is a fitted value from
realized stock returns regressed on ex ante cost of capital measures described
in Section II.A.
1994
The Journal of FinanceR
As expected, the sample size is relatively small for this analysis—4,792 (4,271
and 2,240) firm-year observations for the cost of debt (GLS and INSTRET cost
of capital) analyses, compared to 30,554 firm-year observations in Table III.
For the cost of debt, the coefficient estimates on cash flow and investment
correlations are positive and significant. For GLS, the coefficient estimates on
cash flow and investment correlations are also positive and significant with
magnitudes about double those reported in Table III, suggesting that our main
estimates with index-level cost of debt might understate the importance of coinsurance. For INSTRET, the coefficient is positive and significant for investment
correlations, but it is insignificant for cash flow correlations, probably due to
the substantial drop in the number of observations. It is also worth noting that
firm-specific bond and loan yields reflect both systematic and idiosyncratic risk,
so the results from the excess cost of debt analysis should be interpreted with
some caution.
V. Conclusion
In this paper, we study the connection between organizational form and cost
of capital. We argue that, with countercyclical deadweight costs, combining
business units with imperfectly correlated cash flows can lead to a reduction
in systematic risk and hence the combined firm’s cost of capital. This coinsurance effect is decreasing in the cross-segment correlation of cash flows. Our
empirical analysis provides evidence consistent with these predictions. In particular, we find that diversified firms have, on average, a lower cost of capital
than comparable portfolios of single-segment firms. We also find a significant
and positive relation between excess cost of capital and cross-segment cash
flow correlations. Holding cash flows constant, these findings imply an economically significant 5%–6% value gain when moving from the highest to the
lowest cash flow correlation quintile. Further, we find that the positive relation between excess cost of capital and cross-segment cash flow correlations
is more pronounced for firms that face severe financial constraints, consistent
with a greater coinsurance effect when expected deadweight costs of external
financing are greater.
The core of our findings represents a major challenge to the conventional
view that corporate diversification reduces only idiosyncratic risk. In addition,
our evidence suggesting that coinsurance affects firms’ cost of capital has novel
implications for valuation and capital budgeting as ignoring coinsurance effects
may yield incorrect firm value and NPV estimates, particularly in the context
of diversifying mergers and acquisitions. Moreover, because the effects that
we find are economically significant, coinsurance is likely to affect optimal
financial policies. The role of coinsurance in shaping corporate financial policies
represents an exciting avenue for future research.
Initial submission: September 18, 2009; Final version received: April 25, 2013
Editor: Campbell Harvey
Corporate Diversification and the Cost of Capital
1995
Appendix A: Implied Cost of Equity Estimation
A.I. Gebhardt, Lee, and Swaminathan (2001) Measure (GLS)
The GLS measure is based on the residual income valuation model, which
is derived from the discounted dividend model with an additional assumption
of clean-surplus accounting.23 In the model, the value of the firm at time t is
equal to
Pt = Bt +
∞
Et [N It+i − re Bt+i−1 ]
,
(1 + re )i
(A1)
i=1
where Pt is the market value of equity at time t, Bt is the book value of equity
at time t, NIt+i is net income at time t+i, and re is the implied cost of equity.
We assume a flat term structure of interest rates.
GLS further restate the model in terms of ROE, and assume that ROE for
each firm reverts to its industry median over a specified horizon. Beyond that
horizon, the terminal value is calculated as an infinite annuity of residual ROE,
T
F ROEt+i − re
F ROEt+T − re
Bt+i−1 +
Bt+T −1 ,
Pt = Bt +
(1 + re )i
re (1 + re )T
(A2)
i=1
where Bt+i is book value per share estimated using a clean-surplus assumption
(Bt+i = Bt+i-1 − k*FEPSt+i + FEPSt+i , where k is the dividend payout ratio
and FEPSt+i is the analyst earnings per share forecast for year t+i); FROEt+i
is future expected return on equity, which is assumed to fade linearly to the
industry median from year 3 to year T; and all other variables are as defined
previously.
As in GLS, we assume that the forecast horizon, T, is equal to 12 years.
We use median consensus forecasts to proxy for the market’s future earnings
expectations and require that each observation has non-missing 1- and 2-yearahead consensus earnings forecasts (FEPSt+1 and FEPSt+2 ) and positive book
value of equity. We use 3-year-ahead forecasts for future earnings per share, if
they are available in I/B/E/S; otherwise, we estimate FEPSt+3 by applying the
long-term growth rate to FEPSt+2 . We use stock price per share and forecasts of
both EPS and long-term earnings growth from the I/B/E/S summary tape as of
the third Thursday in June of each year. Book value of equity and the dividend
payout ratio for the latest fiscal year-end prior to each June are obtained from
the Compustat annual database.24 We assume a constant dividend payout
ratio throughout the forecast period. For the first 3 years, expected ROE is
23
Under the clean-surplus assumption, book value of equity at t+1 is equal to book value of
equity at t plus net income earned during t+1 minus net dividends paid during t+1.
24 Book value of equity is Compustat Item #60; the dividend payout ratio is computed as dividends (Compustat Item #21) divided by earnings (Compustat Item #237). If earnings are negative,
then the dividend payout ratio is computed as dividends over 6% of total assets (Compustat Item
#6).
The Journal of FinanceR
1996
estimated as FROEt+i = FEPSt+i /Bt+i–1 . Thereafter, FROE is computed by
linear interpolation to the industry median ROE (where we use Fama and
French (1997) industry definitions). The cost of equity is calculated numerically
by employing the Newton–Raphson method. We set the initial value of the
cost of equity to 9% in the first iteration; the algorithm is considered to have
converged if the stock price obtained from the implied cost of equity deviates
from the actual stock price by no more than $0.005.
A.II. Claus and Thomas (2001) Measure (CT)
The CT expression for price per share at time t is
Pt = Bt +
5
F EP St+i − re Bt+i−1
F EP St+5 − re Bt+4
+
,
(1 + re )i
(re − g)(1 + re )5
(A3)
i=1
where Bt+i is the book value per share computed using the clean-surplus assumption, FEPSt+i is the i-period-ahead earnings per share forecast;25 g is the
terminal growth rate of residual earnings, which is equal to the expected inflation rate (nominal risk-free rate minus a real risk-free rate of 3%); and re
is the cost of equity capital. The implied cost of equity is estimated using the
iterative procedure described in detail above.
A.III. Easton (2004) Measure (PEG)
The model equates the price of one share to the sum of capitalized
1-year-ahead EPS and the capitalized abnormal growth in EPS. Easton
makes two simplifying assumptions, namely, zero future dividends and zero
growth in abnormal earnings changes beyond 2 years, to arrive at the PEG
model:
Pt =
F EP St+2 − F EP St+1
,
(re )2
(A4)
where all variables are as previously defined. From the above model, PEG cost
of equity is calculated as a function of the forward earnings-to-price ratio and
the expected earnings growth rate:
F EP St+1
,
(A5)
re = g ∗
Pt
where
g=
(F EP St+2 − F EP St+1 )
.
F EP St+1
(A6)
25 We use 3-, 4-, and 5-year-ahead forecasts for future earnings per share when available in
I/B/E/S. If any of these forecasts is unavailable, we estimate the corresponding value by applying
the long-term growth rate to the 2-year-ahead forecast.
Corporate Diversification and the Cost of Capital
1997
The PEG cost of equity can be estimated only for firms where 2-year-ahead
EPS forecasts exceed 1-year-ahead EPS forecasts. In addition, the estimation is
restricted to firms with forward earnings-to-price ratios greater than 0.5%. We
incorporate the predicted earnings long-term growth rate (ltg) in the estimation
by setting g equal to the average of 1-year-ahead earnings growth rate and ltg.
The additional winsorization procedures include restricting ltg to be less than
50%, restricting the 1-year-ahead growth rate to fall between ltg and one, and
restricting PEG cost of equity to be less than one.
Appendix B: Variable Definitions
Variable
GLS
RET
INSTRET
Market capitalization
Leverage
Book-to-market
Forecast dispersion
Long-term growth
forecast
Lagged 12-month
return
Definition
The weighted average of the implied cost of equity based on the
approach of Gebhardt, Lee, and Swaminathan (2001) and the yields
from the Barclays Capital Aggregate Bond Index.
The weighted average of a firm’s realized equity return and the
return from the Barclays Capital Aggregate Bond Index.
The fitted value from regressing RET on implied cost of capital
measures constructed based on Gebhardt, Lee, and Swaminathan
(2001), Claus and Thomas (2001), and Easton (2004); expected
returns from the Fama–French three-factor model; earnings yield;
and earnings yield adjusted for growth (see Section II.A for a more
detailed description of these measures).
The fiscal year-end stock price (#199) multiplied by shares
outstanding (#25).
The book value of debt (#9+#34) divided by the sum of book value of
debt and market capitalization.
The book value of equity (#60) divided by market capitalization.
The standard deviation of analysts’ 1-year-ahead earnings forecasts
from I/B/E/S.
The median long-term growth forecast from I/B/E/S.
The buy-and-hold return from the beginning of June of year t to the
end of May of year t+1.
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