Return Sensitivity to Industry Shocks: Evidence on the (In-)Efficient Use of Internal Capital Markets João F. Cocco and Jan Mahrt-Smith 1 This version: November 2001 First draft: December 2000 1 Both authors are from London Business School. We thank Francesca Cornelli, Julian Franks, David Goldreich, Dennis Gromb, Eric Powers, Henri Servaes and Jeremy Stein for helpful discussions and to seminar participants at Copenhagen Business School, Lancaster University and at London Business School for comments. Please address correspondence to Jan Mahrt-Smith, London Business School, Institute of Finance and Accounting, Regent s Park, London NW1 4SA, United Kingdom Tel: 44-20-7706-6888 Fax: 44-20-7724-3317. E-mail: jmahrt@london.edu. http://www.london.edu/faculty/jmahrtsmith. All errors are our own.
Return Sensitivity to Industry Shocks: Evidence on the (In-)Efficient Use of Internal Capital Markets Abstract If internal capital markets are valuable, diversified firms that use them efficiently should be able to take more advantage of positive industry shocks than similar focused firms, and insulate themselves from negative industry shocks. With this insight in mind, we examine the return sensitivity of high-valued and low-valued diversified firms to positive and negative industry return shocks. Our empiricalresults support the view that internalcapitalmarkets add to firm value when used correctly, and reduce value when used incorrectly. In addition, there are interesting asymmetries in the data. We find that internalcapitalmarkets are most valuable in allowing high-valued diversified firms to insulate themselves from negative industry shocks. For low-valued conglomerates the option to re-allocate resources internally is abused most often when there are high returns in the industries where the conglomerate operates. JEL Classification: G30, G32 Keywords: Conglomerates, Internal Capital Markets, Return Shocks, Conglomerate Discount
1 Introduction Does corporate diversification destroy value? Is a diversified firm anything other than a portfolio of similar stand-alone firms put together? A diversified firm differs from a comparable portfolio of focused firms in that it has the option to transfer resources from one division to the another internally. These resources may be capital, as in the internal capital markets literature, 2 managerialtalent, or information. 3 If this option is valuable, and if diversified firms exercise this option efficiently (as in Gertner, Scharfstein and Stein (1994)), 4 then corporate diversification should increase value. If on the other hand, diversified firms use the option inefficiently, say because the incentives of decision makers are distorted (as in Rajan, Servaes and Zingales (2000) and Scharfstein and Stein (2000)), 5 then diversification decreases value. In this paper we present evidence that the option to re-allocate resources internally is used efficiently (inefficiently) by high-valued (low-valued) conglomerates. The main idea of our approach is the following: if diversified firms are just like their stand-alone peers, industry specific news should affect stock prices of diversified firms and a comparable portfolio of focused firms in a similar fashion. If, on the other hand, the market expects that diversified firms willuse their internalcapitalmarkets to re-allocate funds among 2 See e.g. Lamont (1997), Shin and Stulz (1998). 3 We do not take a stand on which resources are most often re-allocated internally. We will simply refer to any internal re-allocation as an internal capital market. 4 Other papers which suggest that the internal capital market may lead to higher valuations are Williamson (1975), Stein (1997), Hubbard and Palia (1999) and Wulf (2000). Khanna and Tice (2001) provide some recent empirical evidence. 5 Another model is due to Meyer, Milgrom and Roberts (1992). Evidence that conglomerates may be abusing their internal capital market is also provided e.g. in Morck, Shleifer, and Vishny (1990), Comment and Jarrell (1995), Servaes (1996), Denis, Denis, and Sarin (1997), Gertner, Powers, and Scharfstein (2001) and Scharfstein (1998). 1
divisions, then a conglomerate s stock price will react differently than the price of a comparable portfolio of focused firms to news about a particular industry. More precisely, suppose that internalcapitalmarkets, properly used, have the potential to work better than externalcapitalmarkets in some states of the world. Then, there should be an asymmetric behavior of conglomerate stock prices to positive and negative industry shocks. Take a conglomerate which uses the internal capital market to its advantage: when there is negative news about some of the industries in which the conglomerate operates, it can use the internal capital market to re-allocate potentially scarce resources towards other divisions. This means that its industry betas should be lower than the industry betas of a matched portfolio of focused firms (the negative shocks are ameliorated for the conglomerate). On the other hand, when there is a positive industry shock, internalcapitalmarkets again allow the conglomerate to channel resources in the direction of the most deserving division, leading to a higher industry beta for the conglomerate than that of matched stand-alone firms (positive shocks are amplified). Overall, the existence of an efficiently utilized internal capital market implies that the pattern of conglomerate stock returns should be convex relative to their matched stand-alone counterparts, similar to the payoffs of an option that is exercised optimally. Now consider a conglomerate which uses its internal capital market in an inefficient manner, say by cross-subsidizing inefficient divisions. 6 The sensitivity of stock prices to positive industry shocks should be lower than that of matched stand alone firms, because the conglomerate allocates resources inefficiently. The overall pattern of returns should be concave. Figure 1 illustrates this reasoning. On the horizontal axis we measure industry return shocks. The verticalaxis measures the impact of the industry return shock on the portfolio return. The thin dashed line measures the return on a portfolio of focused firms for which 6 See Rajan, et al. (2000), Scharfstein and Stein (2001). 2
This figure illustrates our hypothesis about the response of conglomerate returns (measured in the vertical axis) to industry return shocks (measured in the horizontal axis). If internal capital markets are more efficient than external ones, then diversified firms which use them efficiently should be able to take more advantage of positive industry shocks and be less exposed to negative industry shocks, resulting in a convex return response to shocks. Inefficiently used internal capital markets lead to a concave return response pattern. The return response of single-segment firms (which do not have the internal capital market option) is the basis for comparison. It is normalized to be on the 45 line. Conglomerate Return Unused / valueless option Option to re-allocate funds valuable and used efficiently Industry Shock Option to re-allocate funds used inefficiently Unused / valueless option Figure 1: The response of conglomerate returns to industry shocks. the option to re-allocate resources internally is not available. We measure the impact on the diversified firm return relative to this value. The patterns in Figure 1 are indeed what our results show: returns of high-valued conglomerates exhibit a convex sensitivity to industry return shocks, while those of low-valued conglomerates show a concave pattern. Thus, we show that the relation between conglomerate valuations and the sensitivity to industry shocks is consistent with the efficient (inefficient) use of the internalcapitalmarket by good (bad) conglomerates. 3
Moreover, there are interesting asymmetries. Most of the convexity for good conglomerates is due to lower sensitivity to negative industry shocks, while the concavity for bad conglomerates mostly comes from their underperformance when there are positive industry news. In other words, our results show that the option to re-allocate resources internally is most valuable in allowing (good) conglomerates to insulate themselves from negative industry shocks in some of their divisions. For bad conglomerates, the option to re-allocate resources internally is abused most often when there is good news about the industries in which the conglomerate operates. A potentialconcern is that the patterns shown in Figure 1 are not a characteristic of good and bad diversified firms, but are a characteristic of good and bad firms. That is, it may be the case that a good firm is one that is able to insulate itself somewhat from negative industry shocks, whether or not it is a diversified firm. In the data, however, we find that the convex and concave patterns are present only for diversified firms, and not for good and bad focused firms. We also show that the differentialsensitivities are not driven by differences in leverage or taxes. Our paper is related to the extensive literature on the diversification discount. A large number of papers have documented that diversified firms are on average valued less than similar firms operating in only one industry. 7 However, recent papers have questioned whether the average discount arises because of diversification. Since the decision to diversify is endogenous, and low-valued firms are the ones who generally choose to diversify, an average diversification discount would appear in the data even if the act of diversification per se does not destroy value. 8 7 See Lang and Stulz (1994), Berger and Ofek (1995), Servaes (1996), Lins and Servaes (1999). 8 For models, see Matsusaka (1998), Fluck and Lynch (1999), Bernardo and Chowdry (2001), Burch, Nanda, and Narayanan (2000), and Maksimovic and Phillips (2001). For evidence, see Campa and Kedia (2000), Hyland and Diltz (2001), Graham, Lemmon and Wolf (1999), Maksimovic and Phillips (1999), Schoar (1999) 4
Whether the average diversification discount arises because of diversity or because of firms endogenous choices, there is substantialcross-sectionalvariation in diversified firms valuations, with a large proportion of them - often far more than a third - trading at a premium relative to their stand alone counterparts. 9 We explore this cross-sectional heterogeneity to show that the effective/ineffective use of the internalcapitalmarket by different conglomerates is correlated with their relative valuations. While previous studies have attempted to relate the conglomerate discount to firm level differences, the overall explanatory power has been low. One of the difficulties in these studies, which use mainly accounting data, is the inability to control effectively for investment opportunities (Tobin s Q) when it comes to predicting differences in corporate activities (e.g. investments). This is a difficult undertaking, and several papers have cast doubt on the ability to estimate Q well (Whited 2000, 2001). One of the advantages of our approach is that there is no need to estimate marginaltobin s Q to proxy for investment opportunities. Rather, we use monthly industry return shocks to estimate the changes in the fortunes of an industry. We then relate the overall monthly returns of the conglomerates and stand-alone firms directly to these industry shocks and estimate the firms sensitivity to the shocks. Our results support the view that the efficient (inefficient) use of diversification creates (destroys) value. The remainder of the paper proceeds as follows: Section 2 describes the data and the matching procedure that we use to construct the matching portfolios of similar focused firms. Section 3 describes our regression analysis. Section 4 presents the results. In Section 5 we show that our results are not driven by differences in leverage or taxes and perform several other robustness checks. Section 6 concludes. and Villalonga (2001a). 9 See Villalonga (2001b) for a paper that focuses on the diversification premium. Also, Lamont and Polk (2001) examine premium and discount conglomerates separately, but their focus is on long-run returns. 5
2 The Data We estimate the return sensitivity of high and low-valued conglomerates, and comparable portfolios of stand-alone firms, to positive and negative industry shocks. In this section we describe the data, the matching procedure, and our calculation of industry returns. 2.1 Description of thedata Our data and the data cleaning we do follows closely the existing literature. We use annual Compustat and monthly CRSP data for the years 1979 through 1998. From Compustat data, including the segment tapes, we obtain information on the industry composition of each conglomerate and the book value of the assets for both conglomerates and stand-alone firms, as well as leverage information. From CRSP data we obtain the return series. We drop from our sample diversified firms with exposure to the financialsector (SIC6000-6999), firm years which have unavailable or negative SIC codes, assets, or market values, firm years with non-available CRSP returns, and conglomerate years where the sum of the reported segment assets exceeds the reported firm assets. In line with the existing literature, for those conglomerates that report having common assets, we allocate these assets to each of the divisions of the conglomerate, in proportion to the relative size of the division in the conglomerate. We also eliminate firm years for which the conglomerate has more than 90% of its assets in a single industry. The rationale is that these are not true conglomerates. We classify each of the divsions of each conglomerate into one of 43 industry groups, following the industry classification in Fama and French (1997). 10 10 Fama and French (1997) consider 48 industries including the financial sector which we drop. Their industry classification is shown in the appendix. 6
2.2 The Mimicking Portfolio At the beginning of each year we match each of the segments of each conglomerate to a single-segment firm. The matching firm is the single-segment firm that operates in the same industry as the conglomerate division and that is most similar in terms of the book value of the assets. If returns for this single-segment firm cease to be available after a given month, we continue with the returns of the next closest match. In the vast majority of cases (more than 90%) we only need one matching single-segment firm per year. To assess the quality of our matches, Table 1 reports the proportion of matches that result in the assets of single segment firms differing from the corresponding conglomerate division by less than five, ten, twenty and thirty percent. The table shows that our asset matching procedure is fairly accurate. For over 56% of the cases the asset value of matching firm differs from the asset value of the division of the conglomerate by less than 5%. [TABLE 1 HERE] The matching procedure yields the monthly returns of the single-segment firms that mimic each conglomerate division. These returns are combined to obtain the weighted average mimicking portfolio return. The weights used in this average, ω ijt, are equalto the ratio of the date t value of the assets of conglomerate i in industry j, tothedatet value of the overall assets of conglomerate i. More precisely, the date t weight of diversified firm i in industry j, ω ijt, is given by: ω ijt = IndAss ijt T otass it (1) where IndAss ijt is the date t book value of the assets of conglomerate i in industry j, and T otass it is the date t book value of the total assets of conglomerate i. 7
Ideally we would like to be able to measure ω ijt at a monthly frequency, but asset information is not available at that frequency. Therefore we compute the weights at the beginning of each year and assume that they remain constant throughout the year. Note that we make the same assumption for both conglomerates and mimicking portfolios. We also eliminate firm years where the conglomerate added or divested a division. 11 We do so because we do not know when during the year the firm changed composition, and we do not want to compare a conglomerate to a mimicking portfolio that may differ in terms of industry distribution, potentially for several months of the year. 2.3 The Conglomerate Discount At the beginning of each year we compute the conglomerate discount as the percentage difference between the market-to-book ratio of the conglomerate and the mimicking portfolio of stand-alone firms. The market value is equal to share price times number of outstanding shares plus book value of total liabilities. Book value is total book asset value. More precisely, let i denote a given conglomerate and let j i =1,..,M i denote the singlesegment firms we use to construct the corresponding mimicking portfolio. The conglomerate discount for conglomerate i and date t is equalto the percentage difference between the market-to-book values for the conglomerate and its mimicking portfolio. The market-to-book ratio of the mimicking portfolio is equal to the weighted average of the market-to-book ratios of the M i single-segment firms in the mimicking portfolio, where the weights are equal to the weights of the divisions of the conglomerate, ω ijt. The date t discount for diversified firm i is computed as: 11 Using the Segment ID code as an identifier. 8
This figure plots the frequency distribution of the conglomerate discount in our sample. The sample period is from 1979 to 1998. The discount is the percentage difference between the market-to-book ratio of the conglomerate and the weighted average market to book ratio of a portfolio of industry and size matched single-segment firms. The median discount in the sample is 13.2%. frequency 300 250 200 150 100 50 0 less than 2.5 Premium -2.25-1.99-1.73-1.47-1.21-0.95-0.69-0.43-0.17 0.09 0.35 0.61 0.87 Discount (negative = premium) Discount Figure 2: The conglomerate discount distribution. Disc it = Mi Mi j i =1 ω ijt Market j Book ji,t i,t j i =1 ω ijt M arket j Market it Book it Book ji,t i,t. (2) The median conglomerate discount in our sample is 13.2%. Although we have used a different methodology to compute the discount (direct firm matching, rather than comparing conglomerates to weighted average industry medians), we obtain a median discount similar to the values reported by Berger and Ofek (1995) and Lang and Stulz (1994) among others. For each conglomerate we obtain the median discount over the years in which the conglomerate appears in our sample. Figure 2 shows this discount distribution. In order to test the patterns in figure 1 we explore the heterogeneity in diversified firms 9
valuations. While the median conglomerate has a lower market-to-book ratio than an industrymatched portfolio of stand-alone firms, a large proportion (just over one third of the firms in our sample) is actually trading at a premium relative to their stand alone counterparts. It is natural to think that for these firms the option to re-allocate resources internally is valuable and used correctly; that is why they trade at a premium. Similarly, we would guess that low valued diversified firms exercise the option to re-allocate resources incorrectly. With this in mind we classify conglomerates into good (top 1/3 of the valuations) and bad (bottom 1/3 of the valuations) based on this distribution. Thus, in our sample, good conglomerates are those that trade at a median discount smaller than - 1.2%, with the negative number meaning that they effectively trade at a premium, and bad conglomerates are those that trade at a median discount larger than 28.5%. Note that since we are using the discount only to classify conglomerates into good and bad, we are not worried about the skewness in the discount distribution. Also, since we split the sample using percentiles, we do not worry about outliers in the data. 2.4 Industry Returns To construct industry factors we use the equalweighted average of the returns of all singlesegment firms in industry j (obviously, conglomerate returns cannot be assigned to an individual industry). We use equal weighting because value weighted returns reflect (mostly) the return of a small number of very large firms. This would incorporate the idiosyncratic risk of a few large firms into our industry return estimate. As long as all firms returns contain on average similar amounts of information about the industry prospects, equal-weighting will lead to a better estimate. In addition, for each conglomerate, we eliminate from the industry return calculations the single-segment firms which are used to construct the mimicking portfolio of that conglomerate. We do so in order to avoid biases that could result from regressing a 10
mimicking portfolio return on an average which includes the return on a firm in the mimicking portfolio. The construction of industry factors from single-segment firm returns may give rise to some concerns. Suppose that for some reason single-segment firm returns move together, and that this co-movement is not captured by the other factors that we include in the regression. The industry factors that we construct will capture this co-movement. When we regress diversified firm and their mimicking portfolio returns on industry factors, the industry β of the mimicking portfolio of single-segment firms will tend to be higher than those of the diversified firms because the returns on all stand-alone firms move together. While this concern is legitimate, our results are not about the unconditional average industry β of conglomerates and standalone firms. Our results are about the differential sensitivity of good and bad conglomerates to positive and negative industry shocks. Therefore, the above concern does not arise. 12 We obtain the return distribution of industry shocks, and keep observations in the top third (good shocks) and bottom third (bad shocks). This leaves us with a finalsample of 55,648 firm-month observations (for both conglomerates and mimicking portfolios), evenly divided between good conglomerates/good shocks [GCGS], good conglomerates/bad shocks [GCBS], bad conglomerates/good shocks [BCGS] and bad conglomerates/bad shocks [BCBS]. 3 Methodology If diversified firms are similar to a portfolio of comparable stand-alone firms, then their return reaction to industry shocks, i.e. their industry betas, should be similar, provided that they 12 Unless one thinks that the extent of single-segment co-movement is correlated both with relative conglomerate valuations and the direction of industry shocks. This possibility appears remote. 11
are similar as far as leverage and taxes are concerned. 13 If on the other hand the option to re-allocate resources internally is valuable and exercised correctly by managers of diversified firms, we expect that this will be reflected in their industry betas. To obtain industry betas we assume a linear factor structure for returns, and estimate a multifactor model. We wish to estimate the impact of industry shocks on portfolio returns, so that in addition to industry factors, we controlfor the return on the market portfolio, the Fama-French size and book-to-market factors, and the momentum factor. The equation that we estimate is: R it = β 0 + β m R mkt + β smb R smb,t + β hml R hml,t + β mom R mom,t + 43 β j ω ijt R jt (3) j=1 where R m,t is the excess return on the market, R smb,t is the return on a portfolio of small firms minus the return on a portfolio of large firms, R hml,t is the return on a portfolio of high book-to-market firms minus the return on a portfolio of low book-to-market firms, R mom,t is the momentum factor, 43 is the number of industries, R jt are the industry factors, and ω ijt is the date t weight of diversified firm i in industry j. We estimate equation (3) both for conglomerate i and for i s mimicking portfolio. When we estimate the equation for conglomerate i, then R it is the date t conglomerate i return. When we estimate the equation for conglomerate i s mimicking portfolio, then R it is the weighted average of the returns of the mimicking single-segment firms given by: M i R it = ω ijt R jt (4) j=1 where R jt is the date t return on the mimicking single-segment firm j (the dependence of R jt on i is suppressed to economize on notation). 13 In the robustness checks below we show that this is indeed the case. 12
The estimation is done using seemingly unrelated regressions (SURE). Since we have the same right hand side variables in all equations, the estimated coefficients will be the same as if we had estimated each equation separately using ordinary least squares. However, SURE has the adavantage of estimating a single variance-covarince matrix for all regressions, which allows for hypothesis testing between regressions. In particular, this allows us to compare the estimated return sensitivity of good and bad diversified firms to good and bad industry shocks using Wald tests (F-Tests). 4 Results 4.1 Baseline Results Figure 3 and Table 2 summarize our results. Table 2 shows the estimated industry β s for good/bad conglomerates (CON) and good/bad shocks, and also for the mimicking portfolio (MIM). For each conglomerate and mimicking portfolio, the average industry β is equalto the weighted average of the β s of the industries where the firm operates, with the weights being asset weights in each of the industries. At the beginning of Table 2 we compute the weighted average industry β for both conglomerates and mimicking portfolios. The second row shows, for each of the four cases, the percentage difference between the average industry beta of conglomerates and mimicking porfolios. The third row shows the p-value for a Wald test of this difference. Figure 3 provides a graphicaldescription of the numbers in Table 2. This figure shows the estimated response of good and bad conglomerate returns to industry return shocks. Good (bad) conglomerates are those in the bottom (top) 1/3 of the discount distribution. The point estimates were obtained using regression (3) in the text, and are reported in Table 2. The return response of single-segment firms (which do not have the 13
This figure shows the estimated response of good and bad conglomerate returns to industry return shocks. Good (bad) conglomerates are those in the bottom (top) 1/3 of the discount distribution. The point estimates were obtained using regression (3) in the text, and are reported in Table 2. The return response of single-segment firms (which do not have the internal capital market option) is the basis for comparison. It is normalized to be on the 45 line. A positive industry shock that has a 1% impact on the mimicking portfolio return has a 0.70% impact on the return of bad conglomerates. An negative industry shock that has a -1% impact on the mimicking portfolio return has a -0.74% impact on the return of good conglomerates. Conglomerate Return Not Significant Good Conglomerates 1% 45 o Line Significant 0.70% Industry Shock Significant -0.74% -1% Bad Conglomerates Not Significant Figure 3: The estimated response of good and bad conglomerate returns to industry return shocks. internalcapitalmarket option) is the basis for comparison. It is normalized to be on the 45 line. A positive industry shock that has a 1% impact on the mimicking portfolio return has a 0.70% impact on the return of bad conglomerates. An negative industry shock that has a -1% impact on the mimicking portfolio return has a -0.74% impact on the return of good conglomerates. [TABLE 2 HERE] 14
Bad conglomerates are as sensitive to negative industry shocks as similar focused firms, but are significantly less sensitive to positive industry shocks. That is, bad conglomerates do not do as well as the industries in which they operate when the industry does well. On the other hand, good conglomerates are less sensitive to negative industry shocks than similar focused firms, but tend to benefit as much when there are positive industry shocks. These differences are both statistically significant and economically meaningful: a positive industry shock that has a 1% impact on the mimicking portfolio return, only has a 0.70% impact on a bad conglomerate s return. This difference is statistically significant at 1%. On the other hand, a negative industry shock that has a -1% impact on the mimicking portfolio return, only has a -0.74% impact on a good conglomerate s return. We can reject that this difference is statistically equal to zero with a 5% level of confidence. The differences between good conglomerates/good shocks and mimicking portfolio, and bad conglomerates/bad shocks and mimicking portfolio are not statistically different from zero, although our point estimates seem to suggest that there might also be some inefficient allocation of resources in good conglomerates when there are positive industry shocks. Figure 3 shows that high-valued conglomerates exhibit a convex sensitivity to industry return shocks, while low-valued conglomerates exhibit a concave pattern. A convex pattern arises in the presence of a valuable option that is exercised optimally, whereas if one uses the option incorrectly the payoffs will be concave. Table 2 and Figure 3 also show that the option to transfer resources internally is most valuable in allowing (good) diversified firms to insulate themselves from negative industry shocks in some of their divisions. For bad conglomerates, the option to transfer resources internally is abused most often when there is good news about the industries where the conglomerate operates. As a finaltest of whether good and bad conglomerates differ in the way that they use their internalcapitalmarkets, we compare their overall differentialsensitivity to good and bad shocks. In terms of Figure 3, this amounts to testing whether the convex pattern for good 15
conglomerates is not only graphically, but also statistically, different from the concave pattern for bad conglomerates. Using a Wald Test we can reject the hypothesis of equality at the 5% level of confidence. 4.2 Internal Capital Markets or Firm Effects? A potentialconcern is that the patterns shown in Figure 3 are not a characteristic of good and bad diversified firms, but are a characteristic of good and bad firms. That is, it may be the case that a good firm is one that is able to insulate itself somewhat from negative industry shocks, whether or not it is a diversified firm. If this were the case the convex and concave patterns in Figure 3 would not have anything to do with the internal reallocation of resources across divisions. In order to test this hypothesis we estimate industry betas using data on stand-alone firms only. Just as we did for conglomerates, for each single segment firm in Compustat we select a matching single segment firm in the same industry that is closest in size. We then compute the difference of market to book ratios of the stand-alone firm and its matched companion, or its relative valuation/discount. As before, good (bad) firms are those in the top (bottom) one third of the discount distribution. Again to avoid bias in the construction of the industry factors we drop both the original single-segment firm and the matched firm. Finally, we estimate equation (3) for good and bad stand-alone firms, for positive and negative industry shocks, and for their mimicking stand-alone firms. Table 3 shows the results. None of the differences between good firms and mimicking firms and bad firms and mimicking firms are statistically significant at conventional levels of confidence. The overall test for convexity/concavity similarly fails to be significant. This happens despite the fact that the sample is four times as large as our conglomerate sample. Thus the convex and concave patterns in Figure 3 are not a characteristic of good and bad 16
firms. They are a characteristic of good and bad diversified firms. [TABLE 3 HERE] 5 Robustness Checks 5.1 Good/Bad Conglomerates: Leverage and Taxes One concern is that the differences in β s that we emphasize may be driven by differential leverage between the conglomerates and the stand-alone firms in the mimicking portfolio. To investigate whether this is the case we have computed severalleverage measures for good and bad conglomerates, as well as for the mimicking portfolio. Table 4 reports the results for the mean of one of these measures, the ratio of total liabilities to total assets. The ratio of total liabilities to total assets for the mimicking portfolio is equal to the weighted average of the ratio of total assets to total liabilities of the firms that compose the mimicking portfolio. The weights are the asset weights of the different divisions of the conglomerate. 14 [TABLE 4 HERE] Table 4 shows that conglomerates and mimicking stand-alone firms have very similar leverage in our sample. The mean ratio of total liabilities to total assets is 53% for good conglomerates and 54% for the mimicking portfolio. For bad conglomerates the numbers are also similar: 54% for conglomerates and 53% for the mimicking portfolio. Thus, the differential return sensitivity is not driven by differentialleverage. One may also be concerned that the differences in estimated β s are driven by differences 14 The results are similar for other leverage measures. 17
in tax rates between conglomerates and the stand-alone firms in the mimicking portfolio. Therefore, as for leverage, we have compared average marginaltax rates between good and bad conglomerates and the mimicking portfolio. The marginal tax rate for the mimicking portfolio is equalto the weighted average of the marginaltax rates for the stand-alone firms in the mimicking portfolio. The data on marginal tax rates is the same used by Graham (2000). As for leverage, average marginaltax rates for good and bad firms and their mimicking portfolios are very similar. 5.2 Industry Composition of Good/Bad Conglomerates Do good and bad conglomerates operate in different industries? Are the patterns shown in Figure 3 driven by a few industries? To address these questions, Figure 4 shows the average industry asset weights of good and bad conglomerates. Good and bad conglomerates appear to operate in similar industries. The largest differences are in electronic equipment, where bad conglomerates predominate, and utilities where good conglomerates tend to be relatively more important. In order to see whether our results are driven by these industries, we have repeated our estimation dropping the conglomerates that have divisions in these industries. The results are shown in Table 5. None of our conclusions are affected. [TABLE 5 HERE] 5.3 Other Discount Measures Our discount measure is based on direct firm matching rather than comparing conglomerate valuations to weighted average industry median valuations as in Ofek (1995), Lang and Stulz (1994), among others. Although we have obtained a median discount similar to theirs, one may still be concerned about the extent to which our split between good and bad conglomerates 18
This figure shows the industry weights for good and bad conglomerates. The industry definitions are given in the appendix. Good (bad) conglomerates are those in the botoom (top) 1/3 of the discount distribution. The largest weight differences are in industries 31 (utilities) and 36 (electronic goods). Industries 5, 25 and 26 had too few observations to be included. 0.12 0.1 0.08 0.06 0.04 0.02 0 0000 0000 0000 0000 00 0000 0000 000 0 0000 00 0 0 0 0 000 00 0 000 00 00 0 000 00 0000 0 0 0 0 00 0000 0 0 0 0 0000 0000 000 000 00 00 00 00 00 0 000 0 0 0000 00 00 00 000 0 0 0 00 0 0 0 00 0 0 0 0 000 0 0 000 00 000 000 0 0 000 0 0 0 0 00 0000 Restaurants, Hotel, Motel Retail Wholesale Transportation Shipping Containers Business Supplies Measuring and Control Equipment Electronic Equipment Computers Business Services Personal Services Telecommunications Utilities Petroleum and Natural Gas Coal Nonmetallic Mining Precious Metals Defense Shipbuilding and Railroad Aircraft Automobiles and Trucks Electrical Equipment Machinery Fabricated Products Steel Works Etc Construction Construction Materials Textiles Rubber and Plastic Products Chemicals Pharmaceutical Products Medical Equipment Healthcare Apparel Consumer goods Printing and Publishing Entertainment Recreational Products Tabacco Products Alcoholic Beverages Candy and Soda Food Products Agriculture 00000 00000 Bad Conglomerates 00000 Good Conglomerates Figure 4: Industry weights for good and bad conglomerates. 19
would be different if instead we had computed the discount using weighted average industry median valuations. To answer this question we have computed the discount as in Ofek (1995) and Lang and Stulz (1994) and have calculated the correlation between the two discount measures. As expected, the correlataion is large, and equal to 81%. Furthermore, Table 6 shows the proportion of conglomerates that our discount measure classifies a conglomerate as good (bad) and according to the weighted average industry median would be classified as bad (good). This number is fairly low and equal to 2% (and 4%, respectively). [TABLE 6 HERE] 6 Conclusion If internal capital markets are valuable, diversified firms that use them efficiently should be able to take more advantage of positive industry shocks than similar focused firms, and insulate themselves somewhat from negative industry shocks. With this insight in mind, we have examined the return sensitivity of high-valued and low-valued diversified firms to positive and negative industry shocks. We have found that internalcapitalmarkets are most valuable in allowing high-valued diversified firms to insulate themselves from negative industry shocks in some of their divisions. For low-valued conglomerates the option to re-allocate resources internally is abused most often when there is good news about the industries where the conglomerate operates. Our results support the view that the efficient (inefficient) use of diversification creates (destroys) value. Our approach is valid whether the average discount is positive, negative or zero. In particular, since we are examining differences between good and bad conglomerates, we only need 20
that our ordering of diversified firms valuations is meaningful. Our paper compares conglomerates to other conglomerates using the relative distance to stand-alone firms to control for industry effects. In other words, the Market-to-Book ratio leads to a meaningful split between conglomerates that are likely to be well run and those that are not. Furthermore, our approach does not rely on estimating marginal Tobin s Q to proxy for investment opportunities, which is problematic because of measurement error (Whited 2000, 2001). 21
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Table 1: Accuracy of the Matching Procedure At the beginning of each year, each segment of each conglomerate is matched to the single-segment firm which operates in the same industry and is closest in size (assets). The industry definitions are shown in Appendix 1. The table reports the percentage of the sample that is matched to within 5% (10%, 20%, 30%) of the asset size of the conglomerate division. Percentage Difference Percentage of Greater Than Less than Sample 0 5% 56.5% 5% 10% 14.7% 10% 20% 11.2% 20% 30% 5.1% 30% 12.6%
Table 2: Industry Factor Loadings for Conglomerates and Matched Mimicking Firms The main regression (3) in the paper results in 43 industry factor loadings (betas). Three industries (5, 25 and 26) do not have enough observations available and are not included. The estimation is done using Seemingly Unrelated Regressions [SURE]. The sample period covers 1979-1998. There are 55,648 monthly observations, evenly spread over the sub-samples: Good Conglomerates - Good Shocks [GCGS], Good Conglomerates - Bad Shocks [GCBS], Bad Conglomerates - Good Shocks [BCGS], and Bad Conglomerates - Bad Shocks [BCBS]. Good (bad) conglomerates are those in the bottom (top) 1/3 of the discount distribution. Good (bad) shocks are those in the top (bottom) 1/3 of the distribution of industry shocks. The estimation is done for conglomerate returns [CON] and for the matching single-segment portfolio returns [MIM].The table reports the weighted average industry beta for each of the four sub-samples, where the weights are the industry asset weights of the conglomerates in each of the four sub-samples. The table also reports the percentage difference between the weighted average industry betas of conglomerates and mimicking portfolio and p-values for Wald tests of linear restrictions (equality of average betas). In addition the table reports individual industry betas. T-Statistics for all but the first regression are omitted for space reasons. Also estimated (but not reported) are a constant, and coefficients on the market, size, book-to-market and momentum factors. GCGS GCBS BCGS BCBS Conglomerate Mimick. Congl. Mimick. Congl. Mimick. Congl. Mimick. Weighted Average 0.35 0.42 0.43 0.58 0.37 0.53 0.48 0.47 Beta Difference 17% 26% 30% -2% P-Value 0.25 0.05 0.01 0.93 Overall Test for differences between coefficients: P-Value = 0.05 (1) Individual Betas Beta T-Stat Beta Beta Beta Beta Beta Beta Beta rete1-0.12-0.71 0.27-0.03 0.27 0.03 0.20 0.33 0.13 rete2 0.01 0.03 0.43 0.14 0.59 0.31 0.61 0.38 0.42 rete3 0.12 0.41 0.23 0.42 0.55-0.07 0.43-0.20 0.61 rete4-0.03-0.15 0.20-0.13 0.37 0.10 0.20 0.26 0.16 rete5 N/A N/A N/A N/A N/A N/A N/A N/A N/A rete6 0.05 0.22 0.32 0.46 0.93 0.22 1.03 0.52 0.58 rete7 0.34 2.13 0.36 0.73 0.76 1.01 0.55 0.86 0.35 rete8 0.17 1.06 0.26 0.33 0.37-0.07 0.72 0.53 0.32 rete9 0.20 1.66 0.37 0.54 0.68 0.52 0.63 0.47 0.64 rete10 0.32 1.73 0.61 0.59 0.64 1.31 0.74 1.20 0.48 rete11 0.86 5.55 0.36 0.65 0.88 0.87 0.73 0.55 0.67 rete12 1.19 8.45 0.62 0.65 0.84 0.64 0.73 0.69 0.75 rete13 0.12 1.47 0.44 0.10 0.38 0.49 0.85 0.45 0.76 rete14 0.30 1.31 0.64 0.30 0.62 0.28 0.31 0.32 0.39 rete15 0.23 1.53 0.31 0.37 0.59 0.57 0.30 0.47 0.47 rete16 0.90 3.80 0.52 0.48 0.73 0.35 0.51 0.55 0.73 rete17 0.41 3.82 0.45 0.55 0.42 0.49 0.53 0.58 0.70 rete18 0.34 2.63 1.01 0.62 0.49 0.30 0.59 0.41 0.32 rete19 0.47 4.22 0.60 0.59 0.89 0.33 0.93 0.58 0.68 rete20 0.19 1.59 0.08 0.40 0.62 0.30 0.40 0.73 0.46
rete21 0.47 4.95 0.55 0.70 0.71 0.60 0.72 0.60 0.68 rete22 0.30 2.02 0.43 0.45 0.81 0.33 0.61 0.51 0.53 rete23 0.51 3.42 0.47 0.42 0.75 0.41 0.52 0.63 0.65 rete24 0.22 1.74 0.60 0.37 0.49 0.14 0.17 0.50 0.22 rete25 N/A N/A N/A N/A N/A N/A N/A N/A N/A rete26 N/A N/A N/A N/A N/A N/A N/A N/A N/A rete27 0.73 6.41 0.74 0.87 0.82 0.22 0.83 0.18 0.96 rete28 0.34 2.71 0.25 0.39 0.41 0.21 0.49 0.43 0.71 rete29 0.51 1.48 0.04 0.91-0.13 0.61-0.26 0.71-0.40 rete30 0.73 10.92 0.78 0.93 0.88 0.70 0.91 0.85 0.92 rete31 0.28 1.07 0.58 0.03 0.28 0.51 0.91 0.20-0.34 rete32 0.37 3.41 0.57 0.57 0.68 0.41 0.52 0.48 0.61 rete33 0.40 1.83 0.60 0.24 0.56 0.10 0.60 0.12 0.29 rete34 0.46 4.43 0.62 0.56 0.79 0.43 0.85 0.50 0.58 rete35 1.04 8.49 0.60 0.88 0.89 0.77 0.69 0.83 0.62 rete36 0.60 5.25 0.54 0.84 0.82 0.54 0.56 0.50 0.70 rete37 0.24 1.50 0.30 0.37 0.71 0.67 0.71 0.61 0.56 rete38-0.03-0.17 0.41 0.45 0.63 0.31 0.58 0.56 0.43 rete39-0.13-0.81 0.03 0.13 0.24 0.10 0.18 0.33 0.74 rete40 0.44 1.87 0.17 0.39 0.79-0.27 0.64 0.44 0.53 rete41 0.21 1.94 0.36 0.52 0.77 0.41 0.54 0.49 0.62 rete42 1.02 7.41 0.54 0.55 0.87 0.32 0.77 0.17 0.67 rete43 0.37 2.01 0.72 0.20 0.59 0.47 0.25 0.80 0.56 Adjusted R-Squared (Conglomerates) 18% (2) Adjusted R-Squared (Mimicking) 21% (1) This is the p-value from a F-Test for whether the concavity of good conglomerates across shock (relative to focused firms) is statistically different from the convexity of bad conglomerates across shocks. (2) R-Squared is not well defined for SURE (it is a GLS regression). The number given here is merely the percentage of the variance explained by the regressors.
Table 3: Industry Factor Loadings for Stand-Alone Firms and Matched Mimicking Firms The main regression (3) in the paper results in 43 industry factor loadings (betas). Three industries (5, 25 and 26) do not have enough observations available and are not included. The estimation is done using Seemingly Unrelated Regressions [SURE]. The sample period covers 1979-1998. There are 259,496 monthly observations, evenly spread over the sub-samples: Good Firms - Good Shocks [GFGS], Good Firms - Bad Shocks [GCBS], Bad Firms - Good Shocks [BCGS], and Bad Firms - Bad Shocks [BCBS]. Good (bad) firms are those in the bottom (top) 1/3 of the valuation distribution. Good (bad) shocks are those in the top (bottom) 1/3 of the distribution of industry shocks.the estimation is done for firm returns [FRM] and for the matching single-segment returns [MIM]. The table reports the average industry beta for each of the four subsamples. The table also reports the percentage difference between the weighted average industry betas of the firms and mimicking firms and p-values for Wald Wald tests of linear restrictions (equality of average betas). In addition the table reports individual industry betas. T-Statistics for all but the first regression are omitted for space reasons. Also estimated (but not reported) are a constant, and coefficients on the market, size, book-to-market and momentum factors. GCGS GCBS BCGS BCBS Conglomerate Mimick. Congl. Mimick. Congl. Mimick. Congl. Mimick. Weighted Average 0.45 0.43 0.55 0.58 0.47 0.49 0.60 0.55 Beta Difference -5% 5% 4% -9% P-Value 0.63 0.61 0.52 0.24 Overall Test for differences between coefficients: P-Value = 0.17 (1) Individual Betas Beta T-Stat Beta Beta Beta Beta Beta Beta Beta rete1-0.10-1.14 0.04 0.51 0.43 0.12 0.03 0.36 0.49 rete2 0.24 2.08 0.43 0.48 0.81 0.50 0.35 0.61 0.58 rete3 0.24 1.64 0.27 0.35 0.49 0.38 0.28 0.62 0.37 rete4 0.28 1.34 0.10 0.45 0.38 0.13 0.06 0.55 0.39 rete5-0.11-0.22-0.34-1.13 0.23 0.04 0.91 0.74 0.34 rete6 0.57 6.29 0.42 0.65 0.78 0.35 0.45 0.70 0.60 rete7 0.52 7.29 0.32 0.56 0.58 0.41 0.73 0.72 0.55 rete8 0.11 0.85 0.15 0.29 0.39 0.31 0.20 0.39 0.53 rete9 0.46 5.93 0.52 0.73 0.65 0.51 0.44 0.61 0.64 rete10 0.59 6.87 0.37 0.84 0.73 0.48 0.63 0.60 0.64 rete11 0.63 10.14 0.51 0.69 0.83 0.60 0.62 0.76 0.65 rete12 0.68 14.53 0.69 0.71 0.72 0.54 0.51 0.75 0.73 rete13 0.74 19.43 0.75 0.79 0.76 0.71 0.85 0.76 0.79 rete14 0.36 3.32 0.42 0.66 0.48 0.41 0.40 0.48 0.47 rete15 0.36 3.89 0.37 0.59 0.66 0.49 0.41 0.56 0.58 rete16 0.40 2.98 0.54 0.62 0.62 0.55 0.46 0.67 0.62 rete17 0.41 4.60 0.48 0.52 0.53 0.59 0.49 0.62 0.56 rete18 0.48 5.27 0.71 0.50 0.58 0.84 0.74 0.71 0.69 rete19 0.57 6.58 0.64 0.59 0.57 0.58 0.57 0.71 0.71 rete20 0.36 3.38 0.16 0.46 0.31 0.19 0.24 0.42 0.50