Excess Value and Restructurings by Diversified Firms

Transcription

1 Excess Value and Restructurings by Diversified Firms Gayané Hovakimian Fordham University Schools of Business 1790 Broadway, 13 th floor New York, NY10019 Tel.: (212) Abstract I study the link between excess value and restructurings in diversified firms. Specifically, I examine whether excess value affects the likelihood of changing a diversified firm s existing level of diversification and the direction of change. I find that excess value is positively and significantly associated with change in diversification level, measured by change in number of segments, number of industries, or Herfindahl index. Further, lower levels of excess value increase the likelihood of focusing relative to the likelihood of not restructuring and higher levels of excess value increase the likelihood of further diversification relative to the likelihood of maintaining the current level of diversification. Overall, the evidence shows that excess value is significant determinant in diversified firms restructuring decisions. These findings are consistent with predictions of value-maximizing, catering, and market timing theories. JEL Classification Codes: G31, G34 Keywords: diversification, conglomerates, excess value, diversification discount, restructuring

2 1. Introduction Achieving the optimal level of diversification is one of the most important questions faced by management when crafting a company s corporate policy. In different periods during their lifetimes and under different management teams, firms may enter and exit business lines in search of the organizational structure and the level of diversification that seem optimal for them at the moment. For a diversified company, the possible paths to pursue are further diversification, refocusing, or maintaining its current business structure, and among the potential factors that may trigger a restructuring is its value relative to focused firms and relative to other diversified firms. According to a Barron s article from February, 2012, breakups are a big trend in corporate America, with ITT Industries (ITT), Fortune Brands (FO) and Marathon Oil (MRO) lately announcing splits as managements seek to eliminate so-called conglomerate discounts, appease restive shareholders and create the smaller, more focused companies that are favored by institutional investors. The current paper examines the role played by a diversified firm s value relative to focused firms or so-called excess value in deciding the path for further organizational development. Diversification and its correlation with firm value is a well explored area of financial economics. The two main strands of the existing literature on this issue discuss the value of conglomerates relative to specialized firms and the effect that a change in firm diversification level has on its value. The current study focuses on the opposite aspect of the diversificationvalue relationship the effect of a conglomerate s value relative to specialized firms on a firm s level of diversification. Specifically, the paper examines whether diversified firms restructure in response to their high or low excess values by refocusing or diversifying further. The value of diversified firms relative to focused firms has been found to be negative, on average, by most prior studies. In particular, Lang and Stulz (1994), Servaes (1996) find that 1

3 Tobin s q is negatively correlated with a firm s level of diversification. Berger and Ofek (1995) and Servaes and Lins (1999, 2002) find up to a 15% discount for diversified firms relative to portfolios of comparable stand-alone firms. However, several studies suggest that the average level of the excess value may be overstated. For example, Campa and Kedia (2002) and Villalonga (2004) find that the negative excess value disappears or turns into a premium after controlling for the selectivity bias in the diversification decision. Graham, Lemmon, and Wolf (2002) find that about half of the negative excess value can be explained by target segments being discounted prior to their acquisition. On the other hand, based on the evidence reported by other studies, it is hard to argue that the cross-sectional variation in excess value is meaningless. For example, Berger and Ofek (1996) find that firms with higher discounts are more likely to be taken over and broken up into pieces. The observed negative excess value has also been linked to various inefficiencies within the conglomerate form of organization. 1 These findings suggest that whatever the bias may be in the mean excess value, the cross-sectional variation is meaningful. In particular, the distribution of excess values of diversified firms suggests that a significant proportion of them may be successful diversifiers and a significant proportion may be unsuccessful diversifiers, even if the average diversified firm s excess value is zero. While the effect of diversifying and refocusing activities on firm value has been well examined, it is still an open question whether firms revise their current diversification policies based on the feedback received from the market participants. A significant body of literature has 1 Diversification discount has been most often explained by inefficiencies in internal capital allocation or subsidization of low-q segments by high-q segments (Shin and Stulz (1998), Rajan, Servaes, and Zingales (2000). Further, when firms increase focus, investment policies become more efficient and there are value gains: information asymmetries, Krishnaswami and Subramaniam (1999); analyst specialization, Gilson, Healy, Noe, and Palepu (2001); secular decrease in transaction costs of external funds, Matsusaka and Nanda (2002); market liquidity, Schlingemann, Stulz, Walkling (2002), efficient capital allocation, Dittmar and Shivdasani (2003). 2

4 examined the effect of restructuring activities on firm value. Many of them report that breaking up a diversified firm creates value. For example, John and Ofek (1995), Comment and Jarrel (1995), Desai and Jain (1999) show that most of the value gains in asset sales and corporate spinoffs are concentrated in transactions that increase parent firm focus. Event studies also examine the market s reaction to diversifying acquisitions, while reporting mixed evidence (Schipper and Thompson, 1983; Matsusaka, 1993; Hubbard and Palia, 1999). The current paper addresses the possibility of causality in the opposite direction. Specifically, it examines whether after observing the value of a conglomerate relative to specialized firms and relative to other conglomerates managers undertake any restructurings in a specific direction or not. It is important to note that the purpose of the study is not to address the existence of a negative excess value for diversified firms relative to specialized firms. However, any finding of a significant relationship between conglomerate excess value and change in diversification will support the notion that the cross-sectional variation of excess value is meaningful and is tied to the real or perceived success of diversification. The study contributes to the existing body of literature by providing evidence on the relationship between conglomerates value relative to specialized firms and other conglomerates and their restructuring actions, which, to the best of my knowledge, is a largely unexplored area. 2 I examine the diversification and focusing decisions by already diversified firms. I consider three restructuring decisions, which are refocusing, further diversifying or no restructuring and examine whether diversified firms restructure when their market valuations are high or low, relative to focused firms and relative to other conglomerates. The results of the analysis show that conglomerates excess values are significant predictors of their diversifying and focusing 2 In this paper, I do not take a position regarding the existence or the size of excess value. Instead, I focus on crosssectional differences in excess value. This approach is valid as long as measurement errors or sample bias, if any, do not differ systematically across firms or over time. 3

5 decisions. In particular, there is a significantly positive relationship between excess value and change in the level of firm diversification measured by the number of segments, the number of industries, or Herfindahl index. This evidence suggests that conglomerates with more favorable valuations increase their levels of diversification and/or those with less favorable valuations refocus. Firms with higher excess values also invest and grow at higher rates. Further, based on the results of multinomial logit regressions, I find that conglomerates valued favorably relative to other conglomerates are significantly more likely to diversify than not alter their existing level of diversification, while conglomerates valued unfavorably relative to other conglomerates are significantly more likely to focus rather than remain at their current level of diversification. Overall, the results strongly support the hypothesis that firms current market valuations affect their decision to restructure or not and the direction in which firms restructure. If the market approves the current diversification state of the firm, it expands and diversifies further, and if the market disapproves it, the firm contracts and refocuses. This evidence is consistent with value-maximization, assuming that variations in excess values reflect variations in firms fundamental values. It is also consistent with catering and market timing theories, assuming temporary misevaluation of some conglomerates or conglomerates, in general. The rest of the paper is organized as follows. Section 2 discusses the existing literature and its implications regarding the effect of conglomerates values on their restructuring decisions. Section 3 presents the sample and data. Section 4 examines the relationship between firm relative values and restructuring decisions. Section 5 conducts robustness tests. Section 6 concludes the paper. 2. Existing Literature and Empirical Predictions 4

6 The existing literature has various implications concerning the potential link between conglomerates excess values and their restructuring decisions. In particular, the expected relationship can be positive, negative, or insignificant depending on what the estimated excess values reflect and how managers use this information. 3 For example, it is possible that the variation in estimated excess values reflects diversification-related variation in firms fundamental values. Specifically, firms with higher excess values may be more successful diversifiers than those with lower excess values. 4 In this case, the relationship between excess value and restructuring is expected to be insignificant if managers either irrationally ignore market s feedback because they are hubris-driven or they are not compelled to act in shareholders interests due to entrenchment, improper incentive alignment or ineffective corporate governance mechanisms. However, if managers act in shareholders interests, their restructuring activities are expected to be correlated with the estimated excess values. Such managers will re-focus, diversify further or not engage in restructuring, depending on what the value-enhancing alternative is at the moment. 5 Some value-maximizing theories suggest that firms will determine whether to diversify or re-focus based on the trade-off between the costs and the benefits of conglomeration. 6 Specifically, according to Coase (1937), firms will expand until the marginal costs from diversification equal the marginal benefits. Thus firms that are not at their optimal level of diversification will likely pursue restructurings in an effort to reach the level of cost and benefit 3 Note that it is irrelevant whether firms have created or destroyed value by diversifying in the first place. For a potential relationship, it is sufficient if excess values are related to the current state of being diversified. 4 The location of the estimated mean excess value relative to its true mean is irrelevant in this case as long as its cross-sectional variation is unbiased. 5 For the correlation between excess value and restructuring to exist, it is irrelevant whether managers make inferences about the firm s fundamental value by observing the excess value or they know it based on other information. 6 See Montgomery (1994) and Stein (2001) for a survey of this literature. 5

7 equilibrium. Specifically, firms with lower excess values are expected to refocus and those with higher excess values, for which costs of diversification have not caught up with the benefits, are expected to diversify further. Matsusaka (2001) develops a value-maximizing theory, which implies that focus, as well as further diversification, can be a value-enhancing path to follow at current low levels of valuation. Specifically, diversification is perceived as a dynamic process through which firms enter and exit industries to find the best match for their organizational capabilities. A firm in search of a better match may actually diversify further when it is discounted. For example, while entering a new business in search of a better match, a firm may retain a current business and exit it slowly, rather than liquidate it, even if it is not be the best match for the firm s organizational capabilities. Thus this model predicts that a change in any direction is possible if a firm s excess value is lower. However, it is hard to predict whether firms with higher levels of excess value will diversify further or not restructure. It is also possible that the variation in estimated excess values reflects changes in the sentiment of investors, who are being irrational, towards a specific form of business organization. In this case, higher and lower excess values will reflect short-term overvaluation and undervaluation, respectively, which may be either idiosyncratic or reflect over- or undervaluation of all or a certain group of diversified firms. This possibility may trigger a relationship between excess values and restructurings for a number of reasons. Based on the catering argument (Baker and Wurgler, 2004), managers who are short-term value oriented will diversify and focus to cater to investors preferences for specific forms of organization. Specifically, they will diversify at higher excess value levels to satisfy the temporarily higher investor demand for conglomerates and focus at lower excess values to meet 6

8 investor preference for focused forms of organization. However, managers interested in longterm value creation will ignore short-term variations in excess value, in which case there should be no significant relationship between excess value and restructuring. Short-term overvaluation of conglomerates may lead to more diversification also in cases when managers are exploiting it through market timing. As shown by Shleifer and Vishny (2003), when sentimental demand is strong and a firm s equity is overvalued, acquisitions may occur to preserve some of the temporary overvaluation for long-run shareholders. In particular, over-valued bidders can exploit investors misvaluation and pay for comparatively less overvalued targets with overvalued equity. Although the direct motivation in this case is not to gain synergies, it is not excluded that there may be perceived synergies. Managers may also time the market to more easily follow empire building (Jensen, 1986) or decrease the risk of their human capital (Amihud and Lev, 1981) through further diversification. Thus to the extent that the proportion of overvalued conglomerates is higher among conglomerates with higher excess valuations, there will be more acquisitions, including diversifying acquisitions, by conglomerates with higher relative values. The market timing argument has no predictions regarding restructurings that increase firm focus. Higher excess values, whether they reflect fundamental value or investor sentiment, may also fuel managerial hubris and overconfidence and lead to further diversification. On the other hand, lower excess values, whether they reflect fundamental value or investor sentiment, may also lead to further diversification through defensive takeovers for purposes of managerial entrenchment (Shleifer and Vishny, 1989) either by pursuing defensive takeovers. They may also motivate managers to divest peripheral divisions to emphasize their strength in the core divisions. Thus, at lower relative valuations, conglomerates may diversify or focus. This 7

9 prediction is not affected by any assumptions made regarding the rationality of market participants, since managers are likely to feel pressured whether the valuations of their firms reflect fundamental value or short-term investor sentiment. Last, if estimated relative values are merely an artifact of measurement procedures, there should be no significant relationship between them and firm restructuring activities. Thus an extensive body of existing literature has implications suggesting that conglomerate managers may respond to the message conveyed by their companies' excess values by changing their diversification policy in a certain direction. The issue of how accurately the currently applied methods reflect the true value of diversified firms relative to focused firms remains unsettled. Therefore a lack of a significant relationship between conglomerate excess values and any restructuring action may be due to the caveats with measuring the excess value rather than the absence of such a relationship. On the other hand, the potential problems with excess value estimation make any possible findings even more convincing. Most of the discussed predictions are not easy to distinguish empirically. However, the primary focus of the current paper is to examine whether there is any significant link between valuations of diversified firms and restructuring activities. 3. Sample Selection The sample and data in this study come from the Compustat Industry Segment database and Compustat Annual database. I select all firms that are listed on both databases between 1978 and 2011, have annual sales of $20 million or more, positive assets, non-missing values for beginning-of-year total capital, and no financial segments (SIC codes between 6000 and 6999). Due to the use of lagged and positively lagged values for some key variables, in the analysis, the first restructuring occurs from 1979 to 1980 and the last one occurs from 2010 to

10 In 1997, the Statement of Financial Accounting Standards 131 (SFAS 131) changed the way public firms define segments, which makes it difficult to compare information obtained before and after To avoid any potential effect that this shift may have on the results, I control for it in the tests. I also exclude year 1997 from the analysis, since changes in firm structure between 1997 and 1998 may reflect not only restructuring activities but also changes due to reporting requirements. I impose the following further restrictions on the sample. To exclude discontinued operations, I remove segments with names discontinued or eliminations, segments with missing values simultaneously for SIC code, sales, and assets, and segments with a missing value for SIC code and an identification of 99. I also exclude firms that have at least one segment with a missing imputed value required for excess value calculation. This occurs when there are less than five firms with a valid value over sales or value over assets ratio in the segment s industry. Further, since the analysis focuses on restructuring decisions by diversified firms, I consider firms that at the time of the restructuring decision have multiple business segments. Firms are also required to have available data on their segment and industry composition in the following year, since restructuring decisions are assessed based on information from current to next year. I further follow the approach outlined in Berger and Ofek (1995) and require that the sum of segment sales for sample firms be within 1% of the firm total sales. Firms with the sum of segment identifiable assets deviating from firm total assets by more than 25% are excluded from tests including segment assets. I reallocate the unallocated portion of other data items based on item proportions in the consolidated item. I classify all firm-years into focusing, diversifying, and no restructuring subsamples. The classification is based on either the number of segments or number of industries (based on the 3-9

11 digit SIC code) reported by the conglomerate in the current and the following fiscal years. Specifically, in alternative specifications, a firm-year is classified as focusing if the number of segments (industries) reported by the conglomerate decreases in the following year and is classified as diversifying if the number of segments (industries) increases. If the number of reported segments (industries) is unchanged, a firm-year is classified as no restructuring. 7 The final sample includes 3,913 firms and 21,958 firm-years with 58,955 segment observations, totally. Depending on the availability of required data, the actual size of the sample used in some tests slightly varies. Table 1 presents the sample breakdown by years. Based on change in the number of segments, 2,134 firm-years (9.72%) are classified as focusing and 857 firm-years (3.9%) are classified as diversifying and based on change in number of industries, 1,820 firm-years (8.29%) are classified as focusing and 662 firm-years (3.01%) are classified as diversifying. Among focusing firms, 1,076 reduce the number of their segments down to one but no firm reduces the number of its 3-digit SIC code industries to one. Further, 1,776 (1,582) firms divest one segment (industry) only and 358 (300) firms divest two or more segments (industries). Among diversifying firms, 736 (582) add one segment (industry) only and 121 (80) firms add two or more segments (industries). The maximum number of divested segments or industries is seven and the maximum number of added segments or industries is also seven. Table 2 provides summary statistics for all conglomerates included in the final sample. 8 To asses a firm s degree of diversification, I use the number of segments, number of industries, 7 Some firms in this subsample do change the composition of their segments by divesting some and at the same time acquiring other segments or industries. Their classification as non-restructuring firms is likely to work against finding a relationship between firm value and restructuring, rather than in favor. 8 To minimize the influence of outliers, extreme observations of all ratio variables are winsorized. Extreme observations include values in the 99 th percentile and, for variables with negative values, also those in the 1 st percentile. 10

12 and sales-based Herfindahl Index. Herfindahl Index is calculated as the sum of squared ratios of segment assets over consolidated firm assets. Excess values (EV) are estimated using asset multiplier and sales multiplier, based on the approach outlined in Berger and Ofek (1995). The details of EV estimation are provided in the Appendix. 4. Linking Excess Value to Restructurings In this section, I examine whether excess value is related to the diversification decision, such as re-focusing, no change or further diversification, by a conglomerate. As discussed above, based on existing research, lower EV firms are expected to focus or diversify depending on what causes the lower valuation, while higher EV firms are expected to diversify further or not restructure. There may also be no link between the EV and diversification change for a number of reasons: (1) managers do not act in shareholders interests; (2) EV does not reflect fundamental value, but managers focus on long-term value, ignoring market sentiment; (3) EV is an artifact of measurement problems. 4.1 Univariate results Although I define a diversified firm as a firm that operates in multiple business segments, firms restructuring activities in response to excess value are assessed based on a number of alternative variables. It has been argued that firms may increase or decrease the number of their segments without really changing their operations. For example, Denis, Denis, Sarin (1997) find that about a quarter of all changes in number of segments are purely reporting changes, as opposed to real instances of restructurings. Thus as an alternative measure for assessing change in firm diversification, I include change in the number of industries based on 3-digit SIC code. Firms often have multiple segments operating in the same industry, which can be combined or separated to increase or decrease the number of reported segments. However, an elimination or 11

13 an addition of a new 3-digit SIC code line is less likely to occur without a real entry to or exit from an industry. Therefore changes in the number of industry lines, especially at 3-digit SIC code level, are significantly more likely to represent real changes in operations than changes in the number of segments. Table 3 presents the average levels for various variables that reflect the frequency and the direction in which a firm restructures, as well as firm investment and growth, by EV quartiles. Panels A and B represent the EV quartiles that are based on asset and sales multipliers, respectively. In the current and all upcoming tests, EV is estimated in the year prior to the restructuring or, in other words, as of the beginning of the restructuring period. The restructuring variables include change in the number of segments, change in the number of industries, dummy variables for increase and decrease in number of segments, dummy variables for increase and decrease in number of industries, and change in sales-based Herfindahl Index. The investment and growth variables include capital expenditures over sales and the growth rates in capital expenditures, assets, and sales. The results show dramatic changes in the average levels of these variables across EV quartiles. Specifically, as EV increases, the proportion of focusing firms significantly decreases, and the proportion of diversifying firms significantly increases, based on both number of segments and number of industries. Further, changes in Herfindahl Index, number of segments, and number of industries, as well as all investment and growth variables reflect increase in the level of diversification and expansion. Based on the results reported in Panel A, firms decreasing their number of segments and number of industries constitute, respectively, 12.2% and 10.8% of firms in the lowest EV quartile and 8.9% and 7.3% of firms in the highest EV quartile. In Panel B, these numbers are 11.2% and 12

14 9.5%, respectively, in the lowest EV quartile and 8.4% and 6.5% in the highest EV quartile. Further, the fraction of firms increasing their number of segments and industries is 3.3% and 2.8%, respectively, in the lowest EV quartile and 4.4% and 3.4%, respectively, in the highest EV quartile in Panel A. The corresponding numbers in Panel B are 3.3% and 2.6%, respectively, in the lowest EV quartile and 4.5% and 3.1%, respectively, in the highest EV quartile. The differences in all the mentioned statistics between the lowest and highest EV quartiles are statistically significant at the one percent level. While the average changes in the number of segments and industries are negative in all EV quartiles, they are more than two times higher in the highest quartile of EV, compared to the lowest quartile in both Panels A and B. The differences between the average levels of all variables between the highest and the lowest EV quartiles are all highly statistically significant, except for the fraction of firms increasing their number of segments and industries in Panel A. Overall, the results of this preliminary test indicate that there is a significant relationship between the level of conglomerates value relative to focused firms and the following change in their level of diversification. Specifically, at lower levels of EV, conglomerates are significantly more likely to focus and less likely to diversify and at higher levels of EV, they are more likely to diversify and less likely to focus. One piece of evidence worth noting is that low EV seems to be more strongly associated with focusing than higher EV is associated with diversifying. Even though higher EV firms are more likely to diversify, there seems to be significant expansion in the industry lines that they already are in than adding many more segments or lines of business. One possible explanation is that firms with higher excess values prefer to simply expand on what seems to be working for them rather than changing their organizational structure through further diversifying. Another 13

15 explanation is that EV may play a more significant role in triggering refocusing transactions, since managers are naturally less inclined to refocus unless they receive a strong signal from the market about their performance or preferences. Unlike focusing, diversification is an activity that managers may be more inclined to pursue more naturally, irrespective of a positive feedback from the market. Thus the statistical relationship between EV and focusing may be stronger. 4.2 Multivariate tests: EV and change in diversification level To test the relationship between EV and conglomerates restructuring decisions while controlling for other variables, I conduct two multivariate tests. The first one examines the relationship between EV and the magnitude of change in diversification based on a fixed firm effects regression model. The second one assesses how much more likely a firm is to focus or diversify than not restructure depending on its EV based on a multinomial logistic regression model. The two approaches are expected to yield results consistent with each other but they also complement each other. To test whether the magnitudes and directions of changes in diversification levels are related to excess value, I estimate the following empirical model: Diversification it = 1 EV it-1 + f (control variables) + i it. (1) Depending on the specification, in (1), Diversification it is gauged based on the change in firm i s number of segments, number of 3-digit SIC code industries, or Herfindahl Index, from year t to year t+1. All explanatory variables, including EV, are estimated in year t-1. 14

16 Of key importance is the coefficient of EV, 1. A significant coefficient is consistent with the hypothesis that conglomerates factor in market s feedback while crafting their diversification policies. However, the interpretation of the relationship will depend on the sign of the coefficient. An insignificant coefficient may be interpreted as either a lack of any relationship between conglomerate excess values and their diversification policies, for various reasons discussed earlier, or a misestimation of conglomerate values relative to focused firms. The list of control variables includes firm characteristics, industry characteristics, and macroeconomic variables. Among firm characteristics, I control for firm size, estimated as the natural logarithm of firm book value of assets, firm performance and liquidity, measured by cash flow (CF) over sales, investment and growth opportunities, measured by capital expenditures (CAPEX) over sales and R&D expense over sales, 9 and long-term leverage. I also control for financial distress. Financial distress is expected to play a disciplining role for managers who might otherwise be reluctant to change their strategies even if they are value-destroying. Firms are classified as financially distressed if their EBITDA is less than eighty percent of interest expense or if EBITDA has been less than the interest expense for two consecutive years. 10 To control for industry conditions, I include the market share of focused firms in the total 3-digit SIC code industry sales and the proportion of focused firms in industry. To estimate these variables for firms operating in multiple segments, I estimate the firm-level sales-weighted average. The conditions in the economy are controlled for by the GDP growth rate and the number of recession months in the year. The latter is taken from the National Bureau of Economic Research (NBER) Business Cycle Expansions and Contractions. The regressions are estimated with fixed firm effects ( i ), standard errors adjusted for heteroskedasticity and within 9 Missing values for R&D expense are replaced with zeros. 10 This definition of financial distress is the same as the one used in Asquith, Gertner, and Scharfstein (1994). 15

17 period clustering. To control for the shift in segment data reporting, I include a dummy variable, which is set to one for observations after Table 4 presents the estimation results for EV, estimated based on asset multiplier (Panel A), and EV, estimated based on sales multiplier (Panel B). In the three alternative specifications presented in each panel, the dependent variable is the change in the number of segments, change in the number of industries, or change in Herfindal Index. The results show that the coefficient of EV is positive and highly significant in all three models in Panel A and Panel B, indicating that firms changes in diversification are related to their excess values. Specifically, when the change in diversification is measured as change in the number of segments, the coefficients on EV are and for EV based on asset and sales multipliers, respectively. The results are quite similar when the change in diversification is measured as the change in the number of 3-digit SIC code industries in which a firm operates. Specifically, the EV coefficients are and in Panels A and B, respectively. When the dependent variable is the change in Herfindahl Index, the signs of the coefficients are negative, which is consistent with the results above, since Herfindahl Index is lower for a more diversified firm and, therefore, a positive relationship between change in diversification and EV is equivalent to a negative relationship between change in Herfindahl Index and EV. Specifically, the coefficients for EV are and when EV is based on asset and sales multipliers, respectively. The EV coefficients in all six specifications presented in Table 4 are statistically significant at better than one percent level. In all six specifications, the change in diversification level is also negatively correlated with size. All coefficients for size are statistically significant at the one percent level. In five out of six specifications, change in diversification level is positively correlated with cash flow over 16

18 sales, at various levels of statistical significance, indicating that firms with higher levels of liquidity are more likely to increase their levels of diversification and/or firms with liquidity constraints are more likely to divest operations. Among industry-related variables, the proportion of focused firms in industry is negatively correlated with change in diversification when the dependent variable is the change in Herfindahl Index. This result suggests that firms with segments in industries that are dominated by focused firms are more likely to become more focused and/or firms with segments in industries with a lower proportion of focused firms are more likely to diversify. This finding is consistent with earlier findings reported by Campa and Kedia (2002) and Villalonga (2004). These results are consistent with the univariate results and suggest that even after controlling for firm characteristics that are typically associated with a firm s diversification decision, EV is significantly correlated with the change in the diversification level and, particularly, higher levels of EV are associated with higher changes in diversification and/or vice versa. This evidence is consistent with several of the empirical predictions discussed earlier. In particular, the results are consistent with value maximizing, catering, and market timing hypotheses. The advantage of this test is that it allows assess the magnitude and the direction of the change in diversification that is correlated with EV. However, it is not clear what exactly drives the positive relationship. There are several alternative possibilities that are not mutually exclusive. Specifically, firms may either be focusing in response to relatively lower EVs, or diversifying in response to higher EVs, or both. However, we cannot detect which of these scenarios is likely to be happening. To explore these possibilities, I conduct further tests in the following section. 17

19 Although not reported in the paper to reserve space, tests conducted with change in diversification measured over the two-year period from t-1 to t+1, yield even stronger results in support of a positive relationship between EV and change in level of diversification. The choice to report the current results is done for the purpose of easier comparison with the results reported in previous studies most of which measure the change in diversification or the decision to diversify over a one year period. 4.3 Multivariate tests: EV and the likelihood to restructure Unlike focused firms, diversified firms have two directions to go if they decide to restructure further diversification or focus. To investigate whether the positive relationship between EV and the change in the level of diversification is due to firms focusing when their EVs are low and/or diversifying when it is high, I estimate a multinomial logistic regression of a conglomerate s propensity to restructure based on its EV. The set of available actions includes three possibilities: diversify, focus or not restructure. The vector of independent variables includes beginning-of-period EV and the control variables measuring firm characteristics, market conditions, and macroeconomic environment described in the earlier tests. 11 I estimate two specifications. In the first one, focusing firm-years are defined on the basis of change in the number of segments from current to following year. Specifically, if a conglomerate has decreased the number of segments, the observation is classified as focusing. If a conglomerate has increased the number of segments, the observation is classified as diversifying. All other observations are considered non-restructuring. In the second specification, I define focusing and diversifying observations based on the change in the number of 3-digit SIC 11 The results are similar when I estimate separate logistic regressions for diversification vs. no restructuring and focusing vs. no restructuring. 18

20 code industries in which a firm operates. Conglomerates decreasing the number of industries are categorized as focusing and those increasing the number of industries are categorized as diversifying. Some firms may actually restructure but incur no change in the number of their segments or 3-digit SIC code industries if they trade a segment or an industry with another one. These cases are treated as non-restructuring even though they represent restructurings primarily because of the difficulty to classify them into one of the other two categories. However, this approximation is expected to work against rather than in favor of finding any differences between focusing and diversifying decisions in terms of their relationship with EV. Table 5 reports the results of estimation of the first specification, for EV measured based on asset multiplier, in Panel A, and, for EV measured based on sales multiplier, in Panel B. Along with maximum likelihood estimates of coefficients, the table also reports the marginal effects of changes in explanatory variables, estimated at the sample means, as well as the estimated probabilities for focusing and diversifying when all explanatory variables are set equal to the sample mean. In particular, with EV based on the asset multiplier, the probability to focus and diversify are 9.4% and 3.7%, respectively, and with EV based on the sales multiplier, the probabilities to focus and diversify are 8.9% and 3.8%, respectively,. The results show that EV has a significant impact on restructuring probabilities in either direction. Specifically, the coefficient of EV is negative for the focusing versus no restructuring decision, indicating that, at lower levels of EV, the likelihood of focusing becomes higher than the likelihood of no restructuring. This result is significant at the 5% level for asset-based EV and at the 1% level for sales-based EV. In contrast, the coefficient of EV is positive for the diversifying further versus no restructuring decision, indicating that, at higher levels of EV, the likelihood of diversifying becomes higher than the likelihood of no restructuring. This result, like 19

21 the previous one, is significant at the 5% percent level for asset-based EV and at the one percent level for sales-based EV. Some other variables also have a significant impact on restructuring probabilities. Most notably, cash flow and financial distress, as well as leverage, have a significantly negative effect on the likelihood of focusing versus no restructuring. In particular, cash flow and the financial distress dummy are statistically significant at the one percent level in both panels of Table 5. Leverage is statistically significant at the one percent level in Panel A and at the 5% level in Panel B. These results suggest that firms with liquidity problems and heavier debt burden may divest operations that can generate quick liquidity for the firm or operations that dry up the firms liquid funds. This is consistent with earlier findings by Schlingemann, Stulz, Walkling (2002). The results reported in Table 5 also show that EV positively affects the likelihood of further diversifying versus no restructuring, indicating that at higher EV levels firms become more likely to diversify than not change their diversification level. The coefficient of asset-based EV in Panel A is significant at the 5% level and the coefficient of the sales-based EV in Panel B is significant at the one percent level. Further, the coefficient of capital expenditures, scaled by sales, is negative and statistically significant at the 5% percent level in both Panels A and B. The magnitudes of the coefficients from a multinomial logit regression are difficult to interpret because of the nonlinearity of the function. To assess the economic significance of EV, I estimate the changes in the likelihoods of focusing and diversifying compared to no restructuring by perturbing EV and leaving other regressors at the sample mean values. In particular, perturbing EV from one standard deviation below the mean to one standard deviation above the mean decreases the probability of focusing from 10.1% to 8.8% when using asset multiplier EV and from 9.6% to 8.2%, when using sales multiplier EV. It also increases the probability of 20

22 diversifying from 3.3% to 4.1% when using asset multiplier EV and from 3.4% to 4.3%, when using sales multiplier EV. Panels A and B of Table 6 present the results of the multinomial logit model where the restructuring choices are defined based on change in number of industries, for asset-based and sales-based EV, respectively. The results are qualitatively similar to those presented in Table 5. Specifically, the propensity for a diversified firm to decrease the number of industries in which it operates, compared to that of maintaining its current level of diversification, significantly increases as EV decreases. The propensity to increase the number of industries, compared to that of staying on the same level of diversification increases significantly as EV increases. The EV coefficient is statistically significant at the one percent level for the propensity of focusing versus not restructuring in both Panels A and B. For the propensity of diversifying versus no restructuring, the coefficient is statistically significant at the one percent level for asset multiplier-based EV and at the 5% level for sales multiplier-based EV. Further, perturbing EV from one standard deviation below the mean to one standard deviation above the mean decreases the probability of divesting a whole industry from 8.8% to 7.1% when using asset multiplier EV and from 7.7% to 6.4%, when using sales multiplier EV. With a similar perturbation, the probability of diversifying into a new industry increases from 2.5% to 3.3% and from 2.4% to 3.0%, when using asset-based and sales-based EV, respectively. If we assume that variations in EV reflect the relative efficiency of the diversification policy relative to other conglomerates, these results suggest that both superior and inferior conglomerates respond to their market valuations. Overall, the evidence is consistent with valuemaximizing intentions, as well as catering theories. The positive relationship between EV and further diversification is also consistent with market timing and some of the predictions of 21

23 agency theories. However, the reduction in diversification at low levels of EV is not consistent with these theories. The finding of a significant relationship between EV and the magnitude and propensity of change in diversification level further suggests that even though the mean level of EV may be misstated, its cross-sectional variation seems to be meaningful in terms reflecting of the real or perceived success of diversification. These results further clarify the results obtained in the previous section. In particular, they show that the relationship between EV and change in diversification level is driven by both higher propensity of focusing restructurings or negative changes in diversification level at lower EV levels and higher propensity of diversifying restructurings or positive changes in diversification at higher EV levels. 5. Robustness Tests In this section, I conduct robustness checks based on alternative definitions of conglomerate value relative to focused firms used in the diversification discount literature. Specifically, I use the EV measures developed by Lang and Stulz (1994) and, more recently, Santalo and Becerra (2008). Lang and Stulz (1994) estimate EV as the difference between a firm s q and the asset-weighted average of the imputed q s of its segments, where segment q is the industry average q based on the narrowest SIC classification with at least five focused firms. EV, estimated based on Santalo and Becerra (2008) is the natural logarithm of the ratio of a firm s value of total capital over the sum of the imputed values of the firm s segments as standalone firms. Segment imputed values are the product of segment assets or sales and the ratio of the value of total capital over the same variable for the median focused firm in the segment s industry. Industry matching is performed based on 3-digit SIC codes with at least one focused firm. Santalo and Becerra (2008) discuss the potential bias that may be induced by imposing a 22

24 restriction on the number of specialized firms operating in an industry when estimating segment imputed values as in Berger and Ofek (2005). They accept that placing no restrictions on the number of specialized firms introduces larger measurement errors whenever there are very few observations available for estimating the industry median. However, it is unlikely to affect the cross-sectional variation in EV, which is what is important for the current analysis. Table 7 presents the results of the regressions of change in diversification level on EV and the control variables, described in Equation (1), using the two alternative estimates of EV. The regression is estimated with fixed firm effects and standard errors adjusted for heteroskedasticity and within period clustering. To reserve space, I report only the coefficients of EV. The results show that, in all specifications, EV is positively correlated with the change in diversification based on all alternative EV measures. Moreover, all EV coefficients are statistically significant at the one percent level. Table 8 presents the EV coefficients from a multinomial logit regression of the propensity of focusing or diversifying versus not restructuring using the alternative EV measures discussed above. In Panel A, focusing and diversifying cases are defined based on the change in the number of segments, and, in Panel B, they are defined based on the change in the number of industries. For the likelihood of focusing relative to remaining on the same level of diversification, the three coefficients in Panel A have a negative sign indicating a higher likelihood of focusing at lower EV levels. However, they are not significant at the conventional levels of significance. The EV coefficients for focusing likelihood are negative and statistically significant when the decision is based on change in the number of industries (Panel B). The EV coefficients based on the Lang and Stulz (1994) approach and based on the Santalo and Becerra (2008) approach, 23

25 applying the sales multiplier are significant at the 5% level. The coefficient of EV, applying the asset multiplier, is significant at the one percent level. For the propensity to diversify versus not restructure, the EV coefficient is positive and significant in all specifications. It is significant at the one percent level when EV is estimated based on the Santalo and Becerra (2008) approach, applying the sales multiplier. The remaining five coefficients are significant at the 5% level. Overall, the results support the notion that at low EV levels, firms have a higher propensity to focus and at higher EV levels, they have a higher propensity to diversify than not restructure. These results are consistent with the main findings presented in the previous section and provide further support for them. 6. Conclusion This paper examines how further diversification and focusing activities by already diversified firms are related to their market values relative to focused firms. I find that relative market value has significant power in predicting the magnitude of change in the level of firm diversification, the propensity to change, and the directions of such a change. In particular, the magnitude of change in diversification is positively correlated with conglomerates relative value. Also, inferior conglomerates that are valued poorly relative to their peers are more likely to reduce their level of diversification, and superior conglomerates with better relative values are more likely to diversify further, both decisions made as opposed to maintaining their current levels of diversification. Although the results are highly significant for changes in both directions, the relationship is even stronger for focusing activities. Overall, the results are consistent with value-maximizing theories, which suggest that corporate diversifying and refocusing activities are driven by the trade-off between the costs and 24