Corporate Diversification and the Cost of Capital

Transcription

1 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 chapter 1 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, 21 st 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: /jofi

2 1962 The Journal of Finance R 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 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

3 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

4 1964 The Journal of Finance R 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.

5 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.

6 1966 The Journal of Finance R 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 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, Ortiz- Molina 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,

7 Corporate Diversification and the Cost of Capital 1967 the value of the firm at time t can be expressed as E t [FCFE t+i ] P t =, (1) (1 + r e ) i i=1 where P t is the market value of equity at time t, FCFE t+i is free cash flow to equity at time t+i, andr e 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: COC i,t = D i,t 1 Yt BC + (1 D i,t 1 )COEC i,t, (2) where COC i,t is cost of capital for firm i in year t, Y BC t is the aggregate bond yield from the Barclays Capital Aggregate Bond Index (formerly, the Lehman Brothers Aggregate Bond Index), COEC i,t is the implied cost of equity (GLS), and D i,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).

8 1968 The Journal of Finance R Coinsurance Book value of equity and dividend payout ratio for implied cost of equity estimation Market capitalization Leverage Book-to-market ratio 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 Unexpected forecast error for year t+1 Beginning of January t-10 End of December t-1 Beginning of June t End of December t End of May t+1 End of June t+1 End of December t+1 End of June t+2 Lagged 12- month return Realized 12- month return Figure 1. Timeline of variable measurement for a year t observation (assuming December fiscal year end). Unexpected forecast error for year t+2 End of December t+2

9 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: icoc i = n k=1 imv ik n k=1 imv icoc ik, (3) ik where n is the number of the firm s segments, icoc ik 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 imv ik 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.

10 1970 The Journal of Finance R 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 p=1 q=1 n w ip( j) w iq(k) Corr [t 10,t 1] ( j, k), (4) where w ip(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).

11 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 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.

12 1972 The Journal of Finance R 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) = Natural logarithm of fiscal year-end stock price times shares outstanding from Compustat (#199*#25) Leverage = 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) Book-to-market = Ratio of book value of equity to market value of equity from Compustat (#60/(#199*#25)) Log(forecast dispersion) = Natural logarithm of the standard deviation in analysts 1-year-ahead earnings forecasts from I/B/E/S Long-term growth forecast = Consensus (median) long-term growth forecast from I/B/E/S Lagged 12-month return = 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.

13 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 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, icoc i = n k=1 imv ik n k=1 imv icoc ik, ik where n is the number of the firm s segments, icoc ik 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 imv ik 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 Panel A. Excess GLS Single-segment 21, *** *** Multi-segment 8, *** *** MS-SS 0.010*** 0.025*** Panel B. Excess RET Single-segment 21, *** *** Multi-segment 8, ** MS-SS 0.012*** 0.006*** Panel C. Excess INSTRET Single-segment 12, Multi-segment 5, *** *** MS-SS 0.026*** 0.031*** 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 ( 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.

14 1974 The Journal of Finance R 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).

15 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 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 10- year period. ***, **, or * indicate that the estimate is significant at the 1%, 5%, or 10% level, respectively. Firms Sorted by Cash Flow Correlations Investment Correlations Obs. Sort Variable Excess COC Obs. Sort Variable Excess COC Panel A. Excess GLS Multi-segment Firms Q1 (Lowest correlation) 1, , Q2 1, , Q3 1, , Q4 1, , Q5 (Highest correlation) 2, , Single-segment firms 21, , Q1 Q *** 0.034*** Q1 Single-segment 0.022*** 0.034*** Panel B. Excess RET Multi-segment Firms Q1 (Lowest correlation) 1, , Q2 1, , Q3 1, , Q4 1, , Q5 (Highest correlation) 2, , Single-segment firms 21, , Q1 Q *** 0.016** Q1 Single-segment Panel C. Excess INSTRET Multi-segment Firms Q1 (Lowest correlation) Q Q Q Q5 (Highest correlation) 1, , Single-segment firms 12, , Q1 Q ** 0.033** Q1 Single-segment 0.037** 0.046**

16 1976 The Journal of Finance R coinsurance to the highest correlation quintile (Q5) with the least coinsurance. The mean difference between Q1 and Q5 is a statistically significant 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.

17 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 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 RET INSTRET Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Panel A. Full Sample Cash flow correlations 0.068*** *** (0.012) (0.020) (0.012) Investment correlations 0.088*** 0.036* 0.066*** (0.015) (0.022) (0.017) Number of segments 0.008*** 0.009*** *** 0.008*** (0.003) (0.003) (0.005) (0.005) (0.002) (0.002) Logarithm of market 0.029*** 0.029*** *** 0.025*** capitalization (0.005) (0.006) (0.005) (0.005) (0.004) (0.004) Leverage 0.157*** 0.157*** 0.102** 0.101** 0.123*** 0.122*** (0.022) (0.022) (0.043) (0.043) (0.014) (0.014) Book-to-market 0.120*** 0.120*** 0.025** 0.025** (0.021) (0.021) (0.010) (0.010) (0.012) (0.012) Lagged 12-month return 0.081*** 0.081*** *** 0.112*** (0.009) (0.009) (0.016) (0.016) (0.008) (0.008) Long-term growth forecast 0.370*** 0.371*** 0.371*** 0.371*** (0.112) (0.113) (0.038) (0.038) Logarithm of forecast 0.009*** 0.009*** 0.028*** 0.028*** dispersion (0.002) (0.002) (0.002) (0.002) Constant 0.146*** 0.126*** 0.090* 0.073* 0.158*** 0.146*** (0.056) (0.049) (0.047) (0.038) (0.039) (0.033) Observations 30,554 30,554 30,424 30,424 18,157 18,157 R Panel B. Multi-segment Sample Cash flow correlations 0.052*** *** (0.011) (0.020) (0.011) Investment correlations 0.072*** *** (0.016) (0.022) (0.018) Number of segments 0.017*** 0.017*** 0.010** 0.011*** 0.009*** 0.009*** (0.003) (0.003) (0.004) (0.004) (0.003) (0.003) Logarithm of market 0.033*** 0.033*** *** 0.026*** capitalization (0.006) (0.006) (0.005) (0.005) (0.006) (0.006) Leverage 0.191*** 0.190*** 0.064** 0.061** 0.117*** 0.116*** (0.040) (0.040) (0.029) (0.029) (0.025) (0.024) (Continued)

18 1978 The Journal of Finance R Table III Continued GLS RET INSTRET Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Panel B. Multi-segment Sample Book-to-market 0.141*** 0.140*** 0.031** 0.031** (0.040) (0.040) (0.015) (0.015) (0.022) (0.022) Lagged 12-month return 0.068*** 0.068*** *** 0.118*** (0.014) (0.014) (0.017) (0.017) (0.011) (0.010) Long-term growth forecast 0.310** 0.315** 0.396*** 0.396*** (0.122) (0.124) (0.095) (0.096) Logarithm of forecast 0.006* 0.007* 0.023*** 0.024*** dispersion (0.003) (0.004) (0.005) (0.005) Constant 0.133* ** 0.131** (0.076) (0.071) (0.044) (0.039) (0.064) (0.059) Observations 8,585 8,585 8,544 8,544 5,260 5,260 R 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.