Corporate diversification and asymmetric information: evidence from stock market trading characteristics

Transcription

1 Journal of Corporate Finance 10 (2004) Corporate diversification and asymmetric information: evidence from stock market trading characteristics Jonathan E. Clarke a, C. Edward Fee b, *, Shawn Thomas c a DuPree College of Management, Georgia Institute of Technology, Atlanta, GA , USA b Department of Finance, Michigan State University, East Lansing, MI , USA c Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA Received 14 August 2001; received in revised form 12 November 2001; accepted 16 July 2002 Abstract We examine the relation between firm diversification and asymmetric information empirically using metrics drawn from the market microstructure literature. We find that the average diversified firm in our sample has somewhat less severe asymmetric information problems than a similarly constructed portfolio of stand-alone firms chosen to approximate the segments of the conglomerate. We also find that the information asymmetry of diversified firms is very similar to that of individual focused firms that approximate the conglomerates along several dimensions not including industry composition. We conclude that greater diversification is not on average associated with increased asymmetric information. D 2002 Elsevier Science B.V. All rights reserved. JEL classification: G32 Keywords: Diversification; Asymmetric information; Adverse selection 1. Introduction While financial theory suggests that corporate diversification may have both costs and benefits, some executives and members of the financial press appear to have embraced * Corresponding author. Tel.: ; fax: address: fee@msu.edu (C.E. Fee) /$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. doi: /s (02)

2 106 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) corporate focus as something of a panacea. 1 A common motivation cited in favor of conglomerate breakups is that the resulting stand-alone firms are more transparent and, hence, subject to less information asymmetry. These firms may then benefit from easier access to external capital. For example, it was conjectured that ITT s proposed breakup in 1995 would be beneficial because, in part, separating out all those businesses should help each to gain access to the cheapest capital. 2 Similar arguments have been advanced in the academic finance literature, e.g., see Habib et al. (1997), Krishnaswami and Subramaniam (1999), and Nanda and Narayanan (1999). Despite the potential benefits of focus, many diversified firms choose to remain diversified rather than split along industry lines into separately traded firms. 3 Denis et al. (1997) and Berger and Ofek (1999) find evidence that problems associated with the separation of ownership and control may contribute to the seeming reluctance of firms to break up. Another possibility is that these diversified firms might be preserving valuable internal capital markets. This paper investigates a third possible explanation, namely, that diversification does not exacerbate asymmetric information problems between most diversified firms and the external capital markets as is often conjectured. This could occur if information asymmetries about firm-specific factors are diversified away when firms are held as segments of a conglomerate, an argument formalized in Subrahmanyan (1991), Gorton and Pennacchi (1993), and Hadlock et al. (2001). Given there is no clear theoretical consensus about the overall effect of firm diversification on the severity of asymmetric information problems, we examine the relation between firm diversification and asymmetric information empirically. We construct stock market-based measures of the severity of a firm s asymmetric information problems. Specifically, we draw on the market microstructure literature and estimate adverse selection components of bid ask spreads and the price impact of trading. To gauge the potential information consequences of diversification, we compare the information asymmetry measures of the conglomerates in our sample with those of stand-alone firms that approximate the conglomerates along various dimensions. We find that the average diversified firm in our sample has somewhat less severe asymmetric information problems than a similarly constructed portfolio of stand-alone firms chosen to approximate the segments of the conglomerate. This suggests that for most of the firms in our sample a breakup might be expected to exacerbate information problems for the individual segments. 4 Further, we document, as portfolio theory would predict, a negative relation between a firm s level of unrelated diversification and the relative severity of its information asymmetry problems. 1 For example, Sadtler et al. (1997) prescribe that at least half of all diversified firms should be broken up into separately operated and traded firms. Also, see Confessions of a Corporate-Spinoff Junkie by Roger Lowenstein in the Wall Street Journal, March 28, See the article The Death of the Geneen Machine in The Economist, June 17, Montgomery (1994) reports that the average level of diversification among the largest 500 US public firms increased between 1985 and Villalonga (2001) reports that between 1991 and 1997 diversified firms comprised about 60% of total firm assets reported on Compustat. 4 This could be due to the inability of the newly focused firms to attract industry specialist analysts as in Gilson et al. (2001) and Stein (in preparation).

3 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) We also compare the information asymmetry of diversified firms with that of individual focused firms that approximate conglomerates along several dimensions not including industry composition. One might view the individual focused firms that are the basis for this comparison as reflecting the level of information asymmetry that a segment of a conglomerate might exhibit if rather than becoming part of a diversified firm it had instead remained focused and developed other attributes (scale, stock price, etc.) approximating those of the conglomerate. While this analysis is silent as to the potential effects of breakups since, by definition, focused firms cannot breakup along industry lines, it does provide an interesting alternative test of the effect of diversification on information asymmetry. We find that diversified firms have information asymmetry measures that are very similar to those of their individual matching firms. While this finding is clearly inconsistent with corporate diversification reducing firm transparency on average, it does suggest that the information diversification benefits that we document in our other tests might be sensitive to the specific matching algorithm employed. For instance, finding that diversified firms have less information asymmetry than similarly constructed portfolios of focused firms but have similar information asymmetry to individual focused firms each as large as the conglomerates suggests that differences other than diversification between the conglomerates and their matching firms may explain our findings. While matching conglomerates against portfolios of focused firms that reflect segment characteristics (e.g., size) certainly best reflects the realities of a breakup, we are ultimately interested in the effects of diversification on information asymmetry independent of differences between conglomerates and their matching firms on dimensions other than diversification. Thus, we investigate the potential influence of these non-diversification differences on our results. We conclude that these differences are not likely the sole explanation of our results and that, on balance, greater diversification is not associated with greater information asymmetry for most firms. This paper contributes to the literature examining the effects of organizational form on information asymmetry. For instance, our results complement those of Hadlock et al. (2001) and Thomas (2002) which both find evidence of potential information benefits of diversification. While we do not examine breakups per se, this paper also has implications for the literature examining the effects of changes in organizational form on information asymmetry. For instance, Krishnaswami and Subramaniam (1999), Gilson et al. (2001), Bates et al. (1999), and Huson and MacKinnon (2001), all examine the relation between stock breakups and information asymmetry. These papers offer somewhat mixed evidence on the effects of a breakup on information asymmetry. Our findings suggest that those firms most likely to reap significant information benefits from a breakup are those that suffer from particularly severe diversification-related transparency problems. Finally, this paper also adds to the burgeoning literature relating market microstructure to corporate finance. This paper proceeds as follows. In Section 2, we further develop the hypotheses to be tested. In Section 3, we describe the sample and empirical design of the tests. In Section 4, we present the empirical results. In Section 5, we offer a concluding discussion.

4 108 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Hypotheses Much attention has been paid to the relationship between the headquarters of conglomerates and the business segments they oversee, especially in regards to the workings of internal capital markets. 5 This paper focuses on a second relationship created by forming a conglomerate, i.e., the relationship between headquarters and the external capital market. 6 The theoretical literature on corporate diversification offers conflicting predictions about how diversification affects the level of information asymmetry surrounding a firm. On the positive side, if the errors the market makes in valuing a conglomerate s segments are not perfectly positively correlated, then portfolio theory would suggest that the market s estimate of conglomerate value will be more precise than the separate estimates of the individual segments values, even if segment valuations are somewhat less precise than those for comparable focused firms. Further, the potential reduction in the severity of information problems should be greater when the factors affecting the segments values are less correlated. This idea is the basis of a model in Hadlock et al. (2001). Gorton and Pennacchi (1993) and Subrahmanyan (1991) draw on similar logic in developing an explanation for the growth of the market for index-linked securities, e.g., S&P 500 index futures. These authors suggest that combining individual securities into baskets may reduce the adverse selection costs of trading. The value of private information about the individual securities that constitute the basket is in effect diversified away, allowing market makers to set lower spreads and thereby reducing trading costs for uninformed traders. To the extent that a diversified firm represents a basket of securities, these theories would predict a reduction in trading costs attendant with greater corporate diversification. We refer to the possibility that diversification results in a net reduction of asymmetric information problems as the information diversification hypothesis. While the information diversification hypothesis predicts that diversified firms suffer from less severe information problems than separately traded focused firms, there are also compelling reasons to suspect that diversified firms might suffer from larger information asymmetry problems. For example, diversified firms are required to report only limited accounting information for their business segments. Further, managers may exercise considerable discretion in allocating revenues, costs, and assets across business segments. Habib et al. (1997) present a model in which splitting a firm along industry lines into separately traded firms leads to more informative stock prices. Similarly, Nanda and Narayanan (1999) present a model of optimal corporate scope in which managers trade the 5 Earlier theoretical literature focuses primarily on the benefits of internal capital markets, e.g., see Alchian (1969), Williamson (1975), Gertner et al. (1994), and Stein (1997). More recent theoretical work focuses on the possible negative effects of internal capital markets, e.g., see Scharfstein and Stein (2000), Scharfstein (1998), and Rajan et al. (2000). The available empirical evidence is somewhat mixed regarding the overall effect of diversification on firm value, e.g., see Lang and Stulz (1994), Berger and Ofek (1995), Whited (2001), Campa and Kedia (in preparation), and Maksimovic and Phillips (2002). 6 In theory, the importance of this relationship may be diminished for diversified firms since they can substitute internally generated funds for external capital. However, Comment and Jarrell (1995) find that diversified firms are no less reliant on external capital markets (both debt and equity) than are focused firms. Therefore, the question of how diversification affects a firm s access to external capital is an important one in practice.

5 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) benefits of internal capital markets against diversification-related asymmetric information costs. If a lack of transparency makes segment-level information asymmetries considerably larger than those of comparable stand-alone firms, then diversification may still result in a net increase in asymmetric information problems despite the potential benefits of conglomeration. Further, the magnitude of the increase in information asymmetry should be increasing in a firm s level of unrelated diversification. We term the possibility that diversification results in a net increase in asymmetric information problems the transparency hypothesis. 3. Sample selection, variable construction, and matching-firm portfolio approach 3.1. Sample selection Our initial sample is drawn from the universe of NYSE/Amex firms covered in the Compustat, CRSP, and New York Stock Exchange TAQ databases. 7 Of these, we restrict our attention to those firms with data available from Compustat on both a consolidated and industry segment basis (both research and active files). We also exclude foreign firms, ADRs, REITs, and those firms with segments that are regulated utilities (SIC codes and ) or financial service operations (SIC codes ). Our information asymmetry measures are calculated using the transactions level TAQ database. We obtain the date, time, price, and size of each transaction from the consolidated trade file and the posted bid and ask prices and quoted depth from the consolidated quote file. We merge the trade and quote file using the methodology of Lee and Ready (1991). Specifically, we consider quotes reported within 5 seconds prior to a trade as having arrived following the trade. 8 We define trades above the quote midpoint as buys and those below the quote midpoint as sales. We use the Lee and Ready (1991) tick rule to classify those trades that occur at the bid ask midpoint. We derive each of the information asymmetry measures separately for each firm for the first three calendar months of We then average the three monthly measures for each firm in a particular year to arrive at our variables for that firm-year. In constructing our measures, we apply several screens to assure the cleanliness and comparability of the data. First, we exclude firm-months in which a firm undertook either a stock split or stock dividend and those firm-months with less than 30 transactions. Second, we restrict our attention to companies whose average monthly stock prices were greater than US$3 a share. Finally, we also exclude those firm-years where the effective spread exceeded 35% of the 7 We restrict our attention to NYSE/Amex firms for two reasons. First, most Nasdaq firms report only one line of business each year, e.g., see Comment and Jarrell (1995). Second, various authors have documented significant differences in spreads and components of spreads across trading mechanisms, e.g., see Affleck-Graves et al. (1994) or Van Ness et al. (2001). 8 The appropriate lag time has been the subject of some debate. Hasbrouck et al. (1993), for example, report that the median trade delay on the NYSE is 14 seconds. Piwowar and Wei (2001) argue that trades should be matched to contemporaneous quotes.

6 110 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) stock price. The nature of these screens is similar to those in other studies examining crosssectional differences in firms trading characteristics, e.g., see Affleck-Graves et al. (1994) and Flannery et al. (2000). For firms meeting these screens, the TAQ variables are matched with fiscal year-end data from Compustat for the years For firms with December fiscal year-ends, information asymmetry is thereby measured in the 3 months immediately subsequent to fiscal year-end. For firms with a non-december fiscal year-end, the trading variables may lag or lead the fiscal year-end by up to several months. To investigate the sensitivity of our results to calendar-year timing, we repeated our tests on the subsample of firms with December fiscal year-ends. Since the results are very similar, we report only the full sample results. Table 1 details the sample. The frequency of observations by year and firm-type is reported in Panel A. The frequency of observations by firm, industry, and year is reported in Panel B. There are 1956 distinct firms represented, each of which is included an average (median) of 4.11 (5) times. These firms operated in 232 different industries (three-digit SIC codes) with a mean (median) industry representation of (50) firm-years per industry Measures of information asymmetry We use two measures of information asymmetry. The first is the adverse selection component of the bid ask spread as a percentage of the stock price calculated using the Table 1 Sample characteristics Panel A: frequency of observations by year and firm-type Year Multiple-segment Single-segment Total Totals Panel B: frequency of observations as a function of firm, industry, and year Number of times represented Total Minimum Maximum Mean Median Standard deviation Firm Industry Year This sample includes firms with transactions data available from the NYSE s TAQ Database for the first 3 months of Firms must be listed on the NYSE or Amex and also have data available from Compustat and CRSP. Firms with reported business segments that are regulated utilities (SIC codes between and ) or financial services operations (SIC codes between ) are excluded as are foreign firms, ADRs, and REITs. Additionally, firm-month observations are excluded if a firm experienced a stock split or stock dividend or the average monthly stock price was less than US$3 or less than 30 transactions occurred in the month. We also exclude those firm-years where the effective spread exceeded 35% of the stock price. Industries in Panel B are defined as three-digit SIC codes.

7 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) methodology of Lin et al. (1995). The second is the price impact of trading calculated using the methodology of Brennan and Subrahmanyam (1995). We chose these measures because they, or similar variants, are frequently used in studies investigating information-related trading costs, e.g., see Brennan and Subrahmanyam (1995) and Flannery et al. (2000) Calculating ASCOST When quoting a bid ask spread, a market maker faces the risk of trading against betterinformed traders. The portion of the spread which compensates the market maker for this risk is the adverse selection component of the bid ask spread. The greater the degree of information asymmetry, the greater the risk the market maker faces and the wider the spread she must quote. Thus, the adverse selection component of the bid ask spread is a natural measure of information asymmetry. We employ the methodology of Lin et al. (1995) in calculating this variable (ASCOST). The procedure to estimate ASCOST involves determining how much the fair stock price (the midpoint of the bid ask spread) moves (as a percentage of the effective bid ask spread) in response to a given transaction. This change is assumed to represent the market maker s revised assessment of the stock s value following the information contained in the transaction. ASCOST is thus an estimate of adverse selection costs as a percentage of the stock price and higher levels of ASCOST indicate greater information asymmetry Calculating LAMBDA ASCOST is a spread-based measure of adverse selection costs. As such, it does not distinguish between the impact of large and small trades. However, the price impact and, hence, actual cost of trading may be greater for large trades than for small trades. For these trades, market depth is as at least as important as the size of the spread. We therefore calculate LAMBDA, an estimate of how much a firm s stock price changes per unit of volume, as an alternative measure of asymmetric information costs. LAMBDA can be interpreted as an empirical estimate of the parameter k in the Kyle (1985) market microstructure model. As Kyle (1985) demonstrates, the magnitude of the price impact for a given size trade is increasing in the degree of unrevealed private information. This variable represents the percentage stock price change for each thousand shares bought or sold. 10 We calculate LAMBDA using the approach of Hasbrouck (1991) as modified by Foster and Viswanathan (1993) and implemented by Brennan and Subrahmanyam (1995). LAMBDA is calculated in a two-stage regression system. The first regression estimates the predictable component of trading volume based on the past price and volume history. The second regression then uses the residual from the first stage to measure the price 9 Recent studies have investigated the probability of informed trading (PI) as calculated by Easley et al. (1996) as another potential measure of information asymmetry. However, from the results of these studies it appears that the adverse selection component and price impact of trading measures are more reliably correlated with other measures of information asymmetry, and each other, than is PI, e.g., see Clarke and Shastri (2001) and Dennis and Weston (2001) who both find a negative correlation between PI and the adverse selection component of the spread. We have replicated all of our tests using PI. The results (unreported) were generally similar to but weaker than the results using adverse selection and market depth. 10 For example, stock prices would be expected to rise (fall) by 100*LAMBDA percent for a 100,000 share buy (sell) order.

8 112 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) impact of unexpected trading volume. Higher levels of LAMBDA are associated with lower market depth and greater information asymmetry Diversification We use the entropy measure of total diversification (ENTROPY). 11 The entropy measure is calculated as in Jacquemin and Berry (1979) as: ENTROPY ¼ Xn i¼1 P i lnð1=p i Þ ð1þ where P i is the percentage of firm assets (sales) employed in industry segment i (four-digit SIC code) and the summation is over the n industry segments in which the firm operates. Larger values for the entropy measure correspond to less concentration of assets among segments and, hence, greater total diversification. Single-segment firms all have entropy measures equal to zero. A key benefit of the entropy measure of diversification is that it can be used to distinguish between related and unrelated diversification. Jacquemin and Berry (1979) and Palepu (1985) show that the measure of total diversification, defined above, is the sum of an unrelated diversification component and a related diversification component. The unrelated diversification component captures the degree to which a firm s assets (sales) are allocated across unrelated (different two-digit SIC codes) industry segments. In contrast, the related diversification component captures the degree to which a firm s assets are allocated among related (same two-digit SIC codes) segments within industry groups (two-digit SIC codes). Suppose a firm with n industry segments operates in g industry groups (two-digit SIC codes) where n z g. Unrelated entropy is calculated as: UNRELATED ¼ Xg g¼1 P g lnð1=p g Þ ð2þ where P g is the percentage of a firm s assets employed in industry group g (two-digit SIC code) and the summation is over the g industry groups in which the firm operates. Thus, knowing a firm s diversification at the industry segment level (four-digit SIC code) and a firm s diversification at the industry group level (two-digit SIC code), a weighted average of a firm s industry diversification within each industry group is correspondingly defined. This diversification within industry groups is the RELATED component. Since it can be shown that the total measure is the sum of the unrelated and related components, we calculate the related component as the difference between the total measure and the unrelated component. For more detailed derivations, the interested reader is referred to Jacquemin and Berry (1979) and Palepu (1985). 11 As alternative measures of diversification, we used the number of segments with different two-digit SIC codes, the within-firm Herfindahl index, and the Caves et al. (1980) concentric index. The tenor of our results is similar regardless of which diversification measure we use. However, we chose to report results using the entropy measure and return correlation since many of the theoretical predictions we test depend directly on the degree of unrelatedness among the segments.

9 3.4. Sample description J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Table 2 reports descriptive statistics for multiple- and single-segment firms in the sample. Multiple-segment firms have lower adverse selection costs and smaller price impacts of trading than single-segment firms. These comparisons appear consistent with the information diversification hypothesis and inconsistent with the transparency hypothesis. However, simple univariate comparisons ignore many potential differences between diversified and focused firms that could also affect information asymmetry. Given recent studies exploring the correlations between various measures of information asymmetry (e.g., see Clarke and Shastri, 2001), we calculate the correlations of our measures of information asymmetry (among all firms and among multiple-segment and single-segment firms separately). ASCOST and LAMBDA are in all cases positively related to each other with Spearman s correlation coefficients of at least The multiple-segment firms in our sample exhibit varying levels of industry focus with a mean total entropy measure of This roughly corresponds to a firm with two segments (with different four-digit SIC codes) that comprise about 60% and 40%, respectively, of total firm assets. Given the mean of UNRELATED is 0.50 or about 75% of the mean of ENTROPY, much of the diversification pursued by the firms in our sample is of the unrelated (different two-digit SIC codes) variety. Table 2 Descriptive statistics by firm-type Multiple-segment (N = 2225) Single-segment (N = 4569) Differences Mean Median Mean Median Means Medians ASCOST (%) *** 0.06*** LAMBDAx10 (%) *** 0.05*** ENTROPY UNRELATED RELATED MVE *** *** VOLUME *** 15.16*** PRICE *** 6.34*** OWN (%) *** 6.97*** LEVG ** MB *** 0.10*** STDRET (%) *** 0.51*** ASCOST is the adverse selection component estimated as in Lin et al. (1995) expressed as a percentage of stock price. LAMBDA is the percentage stock price change for each thousand shares bought or sold. UNRELATED is the portion of total firm diversification attributed to unrelated diversification. RELATED is the portion of total firm diversification attributed to related diversification. MVE is the market value of equity in millions of dollars. VOLUME is average monthly dollar trading volume (in millions). PRICE is the average stock price. OWN is the ownership percentage of directors and officers. LEVG is the ratio of long-term debt and debt in current liabilities to total assets. MB is the ratio of the firm s market value (market value of equity plus the book value of total assets minus the book value of equity) to the firm s book value of total assets. STDRET is the standard deviation of daily returns. Differences in medians are assessed using a Wilcoxon Ranksum test. ** Significance at the 0.05 level. *** Significance at the 0.01 level.

10 114 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Multiple-segment firms are larger in terms of market capitalization (MVE) and have higher average monthly dollar trading volume (VOLUME). Further, multiple-segment firms have higher average share prices (PRICE) than single-segment firms. Since insider ownership has been investigated as a determinant of stock market liquidity, we include insider ownership as a control variable in our analyses below. 12 Our insider ownership variable (OWN) is the percentage of total shares held by all officers and directors as a group. Our basic source of ownership data was Compact Disclosure supplemented by proxy statements. Consistent with the findings of Denis et al. (1997), the percentage of shares held by insiders (OWN) is larger for the single-segment firms in our sample than for the multiple-segment firms. Diversified and focused firms have similar levels of leverage (LEVG), defined as the ratio of long-term debt and debt in current liabilities to total assets. Consistent with Lang and Stulz (1994), diversified firms have lower market-to-book ratios (MB), defined as the ratio of market value of equity plus the book value of total assets minus the book value of equity to the firm s book value of total assets. Finally, we report the standard deviation of daily stock returns (STDRET) over the previous year. As portfolio theory would predict, diversified firms have significantly less volatile returns than those of focused firms. This finding is also consistent with the idea that diversification reduces the ability of informed traders to profit from their information, e.g., see Gorton and Pennacchi (1993) Estimating matching-firm portfolio information asymmetry levels To examine the relation between firm diversification and the severity of asymmetric information problems, we compare the information asymmetry measures of each conglomerate in our sample with that of a portfolio of stand-alone firms that approximates the conglomerates segments along various dimensions. We refer to the procedure for choosing these matching-firm portfolios as Algorithm 1 and employ the following procedure to identify the firms that comprise the matching-firm portfolios. For each segment-year, we identify those focused firms that trade on the same exchange as the conglomerate, have the same three-digit SIC code as the segment, have a stock price between 0.5 and 1.5 times the price of the conglomerate, and have a market value of equity between 0.5 and 1.5 times the implied market value of equity of the segment (defined as the percentage of conglomerate assets the segment represents times the market capitalization of the conglomerate). From these firms, we choose as matches those firms that are closest in trading volume to the conglomerate. If such a match is not found at the three-digit (two-digit) level, we choose among the possible matching firms at the two-digit level (one-digit level). If there is no match at the one-digit level, then the segment goes unmatched. The 2225 multiple-segment firm-years in our sample included 6275 distinct segmentyears. Matching firms that met the screens above were identified for 4849 of these segmentyears. In our sample, 1390 segment-years had suitable matches at the three-digit level, 1865 segment-years had matches available at the two-digit level but not the three-digit level, and 12 Sarin et al. (2000) find a negative relation between insider ownership and measures of stock market liquidity.

11 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) segment-years had matches available only at the one-digit level. The mean (median) ratio of segment market value of equity to matching-firm market value of equity is 0.92 (0.97). A total of 1227 multiple-segment firm-years had each of its industry segments matched with a focused firm of similar size and industry affiliation. The matching-firm portfolio information asymmetry measures that we calculate for Algorithm 1 take the general form: Matching Firm Portfolio Information Asymmetry ¼ XN j¼1 w j *MFIA j ð3þ where w j is the ratio of segment j s sales (assets) to the sum of segment sales (assets), MFIA j is the matching-firm information asymmetry, and the summation is over the N segments of the diversified firm. Thus, a conglomerate s matching-firm portfolio information asymmetry is a weighted average of the information asymmetry measures of matching firms where the weights are the respective sales (assets) of the segments relative to the conglomerate. In essence, these weighted averages reflect the information asymmetry characteristics that diversified firms might be expected to exhibit if they were split into separately traded firms along industry lines. 13 Further, since our trading cost measure (ASCOST) is expressed as a percentage of the stock price, the weighted average of this variable can also be interpreted as the percentage adverse selection cost an investor would have to pay to replicate a stock position in a conglomerate by buying the shares of its segments subsequent to a breakup Results 4.1. Relative information asymmetry Table 3 contrasts the information asymmetry characteristics of conglomerates with their matching-firm portfolios. For each information asymmetry measure using both weighting schemes, conglomerates exhibit lower levels of information asymmetry than their matching-firm portfolios. These differences are all significant at the 1% confidence level using tests of both means and medians. Thus, breaking up diversified firms along industry lines would appear to exacerbate information asymmetry problems for the segments of the average diversified firm. For example, across the weighting schemes, between 69% and 71% of diversified firms have smaller adverse selection costs than those they could expect to exhibit as portfolios of separately traded pure-play firms. As is apparent in Table 3, the estimates of the information asymmetry measures are virtually identical across sales and assets weighting schemes. While we conduct all of the 13 Note that diversification is a corporate choice variable and, like the decision to merge or breakup, the decision to remain diversified is somewhat endogenous, e.g., see Campa and Kedia (in preparation). Thus, our measures likely understate the potential costs of breakups for our sample of diversified firms since we are comparing them against firms who have optimally chosen to remain focused. Similarly our measures likely overstate the benefits to pursuing diversifying mergers for those firms that are presently focused. 14 We explicitly do not include order processing, inventory, and brokerage costs in our measures. Thus, we understate the true trading costs for both buying the conglomerate stock and for replicating the conglomerate.

12 116 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Table 3 Comparisons using matching Algorithm 1 Conglomerate Matching-firm portfolio Differences Mean Median Mean Median Mean Median Sales Weights ASCOST (%) *** 0.05*** LAMBDAx10 (%) *** 0.06*** SEGMENT MVE *** 18.77*** MVE *** *** PRICE *** 3.64*** VOLUME *** 18.06*** Asset Weights ASCOST (%) *** 0.04*** LAMBDAx10 (%) *** 0.06*** SEGMENT MVE *** 18.77*** MVE *** *** PRICE *** 3.79*** VOLUME *** 17.56*** The sample includes 1227 multiple-segment firm-years with exchange, industry, price, size (segment market value of equity), and volume matched focused firms for each segment-year. ASCOST is the adverse selection component estimated as in Lin et al. (1995) expressed as a percentage of stock price. LAMBDA is the percentage stock price change for each thousand shares bought or sold. Matching-firm portfolio measures of information asymmetry are calculated as Aw j*mfia j, where w j is the ratio of segment j s sales (assets) to the sum of segment sales (assets), MFIA j is the matching-firm information asymmetry, and the summation is over all segments of the multiple-segment firm. SEGMENT MVE is the market value of equity of each segment defined as the percentage of conglomerate assets the segment represents times the market capitalization of the conglomerate. SEGMENT MVE for the matching-firm portfolio is the market value of equity in millions of dollars of the focused firms used as matches for the segments. MVE is the market value of equity in millions of dollars. MVE of the matching-firm portfolio is the average market value of equity for the firms comprising the matching-firm portfolio. VOLUME is average monthly dollar trading volume (in millions). VOLUME of the matching-firm portfolio is the average dollar trading volume for the firms comprising the matching-firm portfolio. PRICE is the average stock price. PRICE of the matching-firm portfolio is the average stock price for the firms comprising the matching-firm portfolio. Differences in medians are assessed using a sign test. *** Significance at the 0.01 level. tests described in the paper using both asset weights and sales weights, we report only the sales-weight results hereafter to conserve space. Not surprisingly, the asset-weight results are very similar. Also included in Table 3 are summary statistics for the variables used in the matching procedure. While the matching procedure attempts to minimize the differences between the segments of the conglomerates and focused firms chosen as matches, differences along several important dimensions persist. Specifically, the conglomerates are larger (at both the segment and firm level) and have higher stock prices and trading volume than the average focused firms comprising the matching-firm portfolios. 15 These differences could account 15 Differences in volume are generally larger than differences in stock price and segment size given that we require potential matches to be within certain ranges for price and size and then pick the firm that is closest in terms of volume.

13 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) for our finding that conglomerates have smaller information asymmetry. For this reason, we run cross-sectional regressions, the results of which are reported in Section 4.3, to investigate the ultimate sources of the measured differences in information asymmetry. The explanatory variables in these regressions include differences in market capitalization, stock price, volume, ownership, leverage, growth opportunities, and risk as well as the nature of corporate diversification, i.e., related vs. unrelated. The regression results provide an indication of the influence of firm diversification on asymmetric information after controlling for differences between conglomerates and focused firms still resulting after applying matching Algorithm 1. The matching results can thus be viewed as a gauge of the overall effect of diversification on asymmetric information problems including any effects of diversification on other factors that also influence asymmetric information. For instance, the matching algorithm explicitly recognizes that breaking up a conglomerate will result in a portfolio of generally smaller pureplay firms which will no longer benefit from any advantages that accompany being part of a larger firm (e.g., more press and/or analyst coverage). While breakups will result in market prices for narrow sets of assets that managers can use as additional guidance in investment decisions, as Stein (in preparation) points out, breakups may actually dampen the amount of stock market information if, given fixed costs of information acquisition, the resulting small firms fail to attract interest from market participants. Clearly, this is an important cost of breaking up that should not be overlooked. Perhaps consistent with firms viewing this size reduction as a significant cost of breaking up, Schlingemann et al. (2002) find that the probability a segment is divested is inversely related to its relative size within the firm. Similarly, practitioners and academics alike have argued that increased liquidity is an important motivation for undertaking mergers. 16 In an effort to shed further light on how the differences persisting after applying matching Algorithm 1 may affect the conclusions that we can draw, we also examine the results of an alternative matching algorithm (Algorithm 2) that is designed to further minimize these differences. We compare the information asymmetry of diversified firms with that of individual focused firms that approximate conglomerates along several dimensions not including industry composition. One might view the individual focused firms that are the basis for this comparison as reflecting the level of information asymmetry that a segment of a conglomerate might have exhibited if rather than becoming part of a diversified firm it had instead remained focused and developed other attributes (scale, stock price, etc.) approximating those of the conglomerate. While this analysis is silent as to the potential effects of breakups since, by definition, focused firms cannot breakup along industry lines, it does provide an interesting alternative test of the effect of diversification on information asymmetry. We employ the following procedure to identify the Algorithm 2 matching firms. For each conglomerate-year, we identify those focused firms that trade on the same exchange 16 Robert Johnson, chairman of First Savings Bancorp, indicated that one benefit of First Saving s merger with TriState Bancorp was that by combining shareholders and shares outstanding, the market for the stock of the merged holding company is... expected to be enhanced (italics added). See, First Savings Bancorp and Tristate Bancorp, Holding Companies of First Financial and Cottage Savings, Sign Merger Agreement, Business Wire, Feb. 27, See Chang and Yu (1999) for a formalization of this argument.

14 118 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) as the conglomerate, have a stock price between 0.5 and 1.5 times the price of the conglomerate, have a market value of equity between 0.5 and 1.5 times the market value of equity of the conglomerate, and have standard deviation of daily returns between 0.5 and 1.5 of that of the conglomerate. From these firms, we chose as a match the firm that is closest in trading volume to the conglomerate. Thus, each conglomerate is matched with an individual focused firm that is similar in terms of exchange listing, stock price, market capitalization, risk, and trading volume. Matches were available for 2194 firm-years. From Table 4 it is apparent that the information asymmetry measures of conglomerates are virtually identical to those of the individual stand-alone firms. While the median tests of the differences are slightly significant (10% confidence level), the mean tests are insignificant, and the economic magnitudes of the differences themselves are very small. It is also apparent from Table 4 that matching Algorithm 2 results in much smaller (though statistically significant) differences between diversified firms and the focused firms chosen for comparison. The results of Table 4 suggest that diversified firms are no less transparent than stand-alone firms that approximate conglomerates on important dimensions other that industry composition. Thus, in this sense, the results are consistent with those in Table 3. They also suggest that differences between conglomerates and the focused firms chosen as matches for each segment may be factors in the benefits we tentatively attribute to diversification in Table 3. The effect of these differences on our conclusions is explored further in the tests below Relative information asymmetry vs. diversification As our relative information asymmetry measures, we calculate ASDIFF and LAMB- DIFF. ASDIFF is the natural log of the ratio of a conglomerate s adverse selection cost to that of its matching-firm portfolio. Similarly, LAMBDIFF is the natural log of the ratio of a Table 4 Comparisons using matching Algorithm 2 Conglomerate Matching firm Differences Mean Median Mean Median Mean Median ASCOST (%) * LAMBDAx10 (%) * MVE *** 85.76*** PRICE *** 3.0*** VOLUME *** 0.46*** STDRET (%) *** 0.14*** The sample includes 2194 multiple-segment firm-years with exchange, price, size (conglomerate market value of equity), volatility, and volume matched focused firms for each firm-year. ASCOST is the adverse selection component estimated as in Lin et al. (1995) expressed as a percentage of stock price. LAMBDA is the percentage stock price change for each thousand shares bought or sold. MVE is the market value of equity in millions of dollars. VOLUME is average monthly dollar trading volume (in millions). PRICE is the average stock price. STDRET is the standard deviation of daily returns. Differences in medians are assessed using a sign test. * Significance at the 0.10 level. *** Significance at the 0.01 level.

15 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) conglomerate firm s LAMBDA to that of its matching-firm portfolio. ASIDFF and LAMBDIFF can be interpreted as the percentage difference in asymmetric information between conglomerates and their matching firms. A diversified firm with a positive (negative) ASDIFF or LAMBDIFF has more (less) severe information asymmetry problems than its matching-firm portfolio. To test the basic tenor of our hypotheses, we sort firms into quartiles based on their entropy measure of total diversification. Firms in Quartile 1 are the least diversified and firms in Quartile 4 are the most diversified. If greater diversification is associated with more information asymmetry, then we would expect Quartile 4 firms to have greater relative information asymmetry. Alternatively, if greater diversification is associated with less information asymmetry, then we would expect Quartile 1 firms to have greater relative information asymmetry. The results of these sorts are presented in Table 5. Matching Algorithm 1 results are reported in Panel A. For both measures of relative information asymmetry, Quartile 1 firms have significantly larger levels of relative information asymmetry than Quartile 4 firms. This is consistent with greater diversification reducing relative information asymmetry. Matching Algorithm 2 results are in Panel B. The results in Panel B reveal no differences across entropy quartiles in the measures of relative information asymmetry. Given that our matching Algorithm 1 may introduce size differences between conglomerates and their matching firms, greater entropy could be proxying for differences in firm size in the sorts above. We are, by definition, comparing the information asymmetry of a conglomerate (larger firm) with that of a weighted average of matching single-segment firms (smaller firms). Thus, by sorting on diversification, we may be in effect sorting on Table 5 Relative adverse selection and price impact of trading vs. entropy Sort variable Information asymmetry Quartile 1 Quartile 2 Quartile 3 Quartile 4 Q1 Q4 Panel A: matching Algorithm 1 ENTROPY ASDIFF mean (%) *** median (%) *** ENTROPY LAMBDIFF mean (%) *** median (%) *** Panel B: matching Algorithm 2 ENTROPY ASDIFF mean (%) median (%) ENTROPY LAMBDIFF mean (%) median (%) ASDIFF is the natural log of the ratio of a conglomerate firm s adverse selection cost to that of its matching portfolio of focused firms. LAMBDIFF is the natural log of the ratio of a conglomerate firm s LAMBDA to that of its matching portfolio of focused firms. LAMBDA is the percentage stock price change for each thousand shares bought or sold. ENTROPY is the entropy measure of total diversification. Matching Algorithm 1 sample includes 1227 multiple-segment firm-years with exchange, industry, price, size (segment market value of equity), and volume matched focused firms for each segment-year. Matching Algorithm 2 sample includes 2194 multiplesegment firm-years with exchange, price, size (conglomerate market value of equity), volatility, and volume matched focused firms for each year. Differences in medians are assessed using a Wilcoxon Ranksum test. *** Significance at the 0.01 level.

16 120 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Table 6 Relative adverse selection and price impact of trading vs. return correlation Sort variable Information asymmetry Quartile 1 Quartile 2 Quartile 3 Quartile 4 Q1 Q4 CORRELATION ASDIFF mean (%) * median (%) CORRELATION LAMBDIFF mean (%) *** median (%) *** The sample includes the 665 firm-years in which a multiple-segment firm reported two business segments and had exchange, industry, price, size (segment market value of equity), and volume matched focused firms for each segment-year. ASDIFF is the natural log of the ratio of a conglomerate firm s adverse selection cost to that of its matching portfolio of focused firms. LAMBDIFF is the natural log of the ratio of a conglomerate firm s LAMBDA to that of its matching portfolio of focused firms. LAMBDA is the percentage stock price change for each thousand shares bought or sold. CORRELATION is the correlation of equity returns for the single-segment firms chosen as matches for the respective segments of the conglomerates. Differences in medians are assessed using a Wilcoxon Ranksum test. * Significance at the 0.10 level. *** Significance at the 0.01 level. differences in size, i.e., bigger firms in entropy Quartile 4 and smaller firms in Quartile 1. Hence, the results of Table 5 could be interpreted to reflect these size differences. Indeed the Spearman s correlation between entropy and our measure of size differences (the natural logarithm of the ratio of conglomerate size to the average size of the matching firms) is 0.66, suggesting that such concerns are valid. To examine the importance of size differences for these results, we examine similar sorts using an alternative measure of diversification that a priori should be less correlated with size differences. Specifically, we define CORRELATION to be the correlation of monthly equity returns for the single-segment firms chosen as matches for the respective segments of the conglomerates. 17 The equity returns are for the 1-year period from January to December of year t 1, where year t is the year for which the information asymmetry measures are estimated. These correlations can only be estimated for the firm-years in which a multiplesegment firm reported two business segments. The Spearman s correlation between our returns-correlation measure and the entropy diversification measure is Also, the Spearman s correlation between our returns-correlation measure and our size difference measure is Table 6 reports the results of the returns correlation analysis. While firms with lower returns-correlation have lower relative adverse selection costs, only the difference in means is statistically significant. The results using the relative price impact of trading are quite strong. Both the mean and median differences in LAMBDIFF across Quartiles 1 and 4 of CORRELATION are significant at the 1% confidence level. Given that our returns correlation and our size difference measure are not highly correlated as documented above, these results suggest that the results in Table 3 are not likely entirely attributable to size differences introduced by matching Algorithm This analysis cannot be performed for matching Algorithm 2 since only one focused firm is used per conglomerate for comparison.

17 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Relative information asymmetry and diversified firm characteristics From Table 2 it is apparent that the multiple-segment firms in our sample differ from the single-segment firms not only in terms of the number of reported segments but also along several other dimensions. Thus, to better identify the sources of the asymmetric information differences documented in the previous tables, we run cross-sectional regressions of relative information asymmetry against differences in firm characteristics between conglomerates and the matching firms used for comparisons. For example, we control for the differences in market capitalization between conglomerates and the firms used to construct their matching portfolios by creating a size difference variable (MVEDIFF), which is equal to the natural logarithm of the ratio of conglomerate market value of equity to the average market value of equity of the firms in the matching portfolio. Similarly, we create control variables for differences in stock price (PRICEDIFF), insider ownership (OWNDIFF), trading volume (VOLDIFF), leverage (LEVGDIFF), market to book (MBDIFF), and return volatility (STDDIFF). Of the control variables, volatility merits additional mention. It is well established that measures of adverse selection are correlated with stock return volatility, e.g., see Brennan and Subrahmanyam (1995). Thus, volatility suggests itself as a potentially important control variable in our regressions. However, volatility can also be viewed as a measure of information asymmetry. Indeed, a reduction in volatility is a mechanism through which the information diversification hypothesis suggests that adverse selection costs may be reduced. If volatility is itself a function of asymmetric information, then including it in a regression explaining asymmetric information would be inappropriate. We therefore report specifications with and without a volatility control. We also report specifications with and without a stock price control because ASCOST is normalized by stock price. We regress firm level averages of the independent variables against firm level averages of the dependent variables. We choose this specification rather than simple pooled crosssectional, time series regressions because the firm-level observations are clearly not independent across time, e.g., the Spearman s correlation of adverse selection cost with lagged adverse selection cost for diversified firms is As a robustness check, we ran the specifications on a year-by-year basis and obtained similar results. 18 The results of cross-sectional regressions of ASDIFF calculated using matching Algorithm 1 against diversification are reported in Table 7. Columns 1 and 2 include as independent variables only the unrelated and related diversification measures, respectively. The negative and significant coefficient on UNRELATED is consistent with higher levels of unrelated diversification being associated with lower levels of relative information asymmetry. The insignificant coefficient on RELATED is consistent with no reliable net effect on relative information asymmetry pursuant to a strategy of diversifying into very closely related businesses. Taken together, these results are consistent with the predictions of the information diversification hypothesis and inconsistent with the predictions of the transparency hypothesis. 18 In all cases, the diversification variables with significant coefficients in the firm-average regressions (reported) also had significant coefficients in at least three of the five yearly regressions (not reported).

18 122 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) Table 7 Regressions of firm characteristics on relative adverse selection using matching Algorithm 1 Regression (1) (2) (3) (4) (5) (6) (7) (8) INTERCEPT ( 1.98)** ( 6.32)*** ( 0.39) ( 1.02) (0.69) (0.86) ( 0.35) ( 0.95) UNRELATED ( 3.06)*** ( 2.81)*** ( 2.80)*** ( 2.71)*** RELATED (0.06) (0.88) (0.86) (0.77) MVEDIFF (1.05) ( 0.47) (0.92) ( 0.61) (1.52) (0.18) PRICEDIFF ( 1.51) ( 1.51) ( 0.57) ( 0.54) OWNDIFF (0.39) (0.46) (0.31) (0.38) (0.02) (0.07) VOLDIFF ( 6.44)*** ( 6.18)*** ( 6.57)*** ( 6.31)*** ( 6.78)*** ( 6.58)*** LEVGDIFF (1.54) (1.59) (1.57) (1.62) (1.40) (1.45) MBDIFF (0.94) (1.06) (1.08) (1.20) (0.37) (0.46) STDDIFF (2.15)** (2.22)** R N The sample includes 1227 multiple-segment firm-years with exchange, industry, price, size (segment market value of equity), and volume matched focused firms for each segment-year. Regressions are on firm-level averages. The dependent variable is ASDIFF, the natural log of the ratio of a conglomerate firm s adverse selection cost to that of its matching portfolio of focused firms. UNRELATED is the portion of total firm diversification attributed to unrelated diversification. RELATED is the portion of total firm diversification attributed to related diversification. MVEDIFF is the natural logarithm of the ratio of conglomerate market value of equity to the average market value of equity of the focused firms in the matching portfolio. PRICEDIFF is the natural logarithm of the ratio of conglomerate stock price to the average stock price of the focused firms in the matching portfolios. OWNDIFF is the difference between insider (directors and officers) ownership percentage of a conglomerate and the average insider ownership of the focused firms in the matching portfolio. VOLDIFF is the natural logarithm of the ratio of conglomerate monthly dollar trading volume to the average monthly dollar trading volume of the focused firms in the matching portfolio. LEVGDIFF is the natural logarithm of the ratio of conglomerate leverage to the average leverage of the focused firms in the matching portfolios. MBDIFF is the natural logarithm of the ratio of conglomerate market-to-book to the average market-tobook of the focused firms in the matching portfolios. STDDIFF is the natural logarithm of the ratio of conglomerate return volatility to the average volatility of the focused firms in the matching portfolios. T-statistics are in parentheses. ** Significance at the 0.05 level. *** Significance at the 0.01 level. Columns 3 and 4 add all of the control variables except volatility. 19 Columns 5 and 6 remove the price control variable. Finally, columns 7 and 8 include all the control variables. In all cases the coefficient on UNRELATED remains negative and significant at the 1% level. Thus, the univariate results in support of the information diversification hypothesis appear robust to the addition of the control variables. In all specifications, the coefficient on RELATED remains insignificant. Of the control variables, only VOLDIFF 19 The difference in observations between columns 1 and 2 and the remaining specifications is due to either conglomerates or one of their matching firms missing the data needed to construct a control variable, e.g., insider ownership.

19 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) comes in significantly which might be expected given our matching algorithm is more restrictive in terms of size and stock price than trading volume. Notably, the size control, MVEDIFF, is not significant in any specifications. The regressions of ASDIFF calculated using matching Algorithm 2 against diversification are reported in Table 8. Neither measure of diversification is significantly related to ASDIFF estimated using Algorithm 2. Again, this could be interpreted as evidence that the Algorithm 1 results are driven by size differences between conglomerates and the matching Table 8 Regressions of firm characteristics on relative adverse selection using matching Algorithm 2 Regression (1) (2) (3) (4) (5) (6) (7) (8) INTERCEPT ( 0.78) ( 1.06) ( 0.09) ( 0.49) ( 0.35) ( 0.95) ( 0.08) ( 0.49) UNRELATED (0.39) (0.02) ( 0.06) (0.02) RELATED (0.73) (0.04) (0.87) (0.90) MVEDIFF ( 0.02) ( 0.04) (0.09) (0.08) ( 0.04) ( 0.05) PRICEDIFF ( 1.25) ( 1.27) ( 1.22) ( 1.25) OWNDIFF ( 0.76) ( 0.72) ( 0.86) ( 0.82) ( 0.75) ( 0.71) VOLDIFF ( 3.01)*** ( 3.02)*** ( 2.97)*** ( 2.97)*** ( 3.00)*** ( 3.01)*** LEVGDIFF ( 0.44) ( 0.45) ( 0.49) ( 0.50) ( 0.44) ( 0.45) MBDIFF (0.58) (0.60) (0.57) (0.60) (0.58) (0.61) STDDIFF ( 0.08) ( 0.11) R N The sample includes 2194 multiple-segment firm-years with exchange, price, size (conglomerate market value of equity), volatility, and volume matched focused firms for each year. Regressions are on firm-level averages. The dependent variable is ASDIFF, the natural log of the ratio of a conglomerate firm s adverse selection cost to that of its matching focused firm. UNRELATED is the portion of total firm diversification attributed to unrelated diversification. RELATED is the portion of total firm diversification attributed to related diversification. MVEDIFF is the natural logarithm of the ratio of conglomerate market value of equity to the market value of equity of its matching focused firm. PRICEDIFF is the natural logarithm of the ratio of conglomerate stock price to the stock price of its matching focused firm. OWNDIFF is the difference between insider (directors and officers) ownership percentage of a conglomerate and the insider ownership of its matching focused firm. VOLDIFF is the natural logarithm of the ratio of conglomerate monthly dollar trading volume to the average monthly dollar trading volume of its matching focused firm. LEVGDIFF is the natural logarithm of the ratio of conglomerate leverage to the leverage of its matching focused firm. MBDIFF is the natural logarithm of the ratio of conglomerate market-to-book to the market-to-book of its matching focused firm. STDDIFF is the natural logarithm of the ratio of conglomerate return volatility to the volatility of its matching focused firm. T-statistics are in parentheses. *** Significance at the 0.01 level.

20 124 J.E. Clarke et al. / Journal of Corporate Finance 10 (2004) firms that comprise the matching-firm portfolios. However, if the size-difference explanation is true, then it is difficult to explain the differing results for related and unrelated diversification observed in Table 7 as well as the insignificance of the size-difference control variable. The only variable that is significant in the regressions of Table 8 is VOLDIFF. The results of cross-sectional regressions of LAMBDIFF calculated using matching Algorithm 1 and Algorithm 2 are reported in Tables 9 and 10, respectively. The coefficients on unrelated diversification are negative and significant in all specifications using matching Algorithm 1 and never significant using matching Algorithm 2. In all cases, the coefficients Table 9 Regressions of firm characteristics on relative price impact of trading using matching Algorithm 1 Regression (1) (2) (3) (4) (5) (6) (7) (8) INTERCEPT ( 7.20)*** ( 13.45)*** ( 4.11)*** ( 4.64)*** ( 2.73)*** ( 3.24)*** ( 4.09)*** ( 4.60)*** UNRELATED ( 3.17)*** ( 1.98)** ( 1.90)* ( 1.90)* RELATED ( 0.58) (1.29) (1.33) (1.20) MVEDIFF ( 1.85)* ( 3.29)*** ( 1.19) ( 2.56)** ( 1.42) ( 2.69)*** PRICEDIFF (6.74)*** (6.71)*** (6.87)*** (6.85)*** OWNDIFF (1.42) (1.49) (1.69)* (1.76)* (1.12) (1.18) VOLDIFF ( 9.14)*** ( 8.99)*** ( 8.24)*** ( 8.10)*** ( 9.07)*** ( 8.94)*** LEVGDIFF ( 0.01) ( 0.02) ( 0.11) ( 0.14) ( 0.12) ( 0.14) MBDIFF (3.11)*** (3.22)*** (2.41)*** (2.52)** (2.60)*** (2.69)*** STDDIFF (1.66)* (1.69)* R N The sample includes 1227 multiple-segment firm-years with exchange, industry, price, size (segment market value of equity), and volume matched focused firms for each segment-year. Regressions are on firm-level averages. The dependent variable is LAMBDIFF, the natural log of the ratio of a conglomerate firm s LAMBDA to that of its matching portfolio of focused firms. LAMBDA is the percentage stock price change for each thousand shares bought or sold. UNRELATED is the portion of total firm diversification attributed to unrelated diversification. RELATED is the portion of total firm diversification attributed to related diversification. MVEDIFF is the natural logarithm of the ratio of conglomerate market value of equity to the average market value of equity of the focused firms in the matching portfolio. PRICEDIFF is the natural logarithm of the ratio of conglomerate stock price to the average stock price of the focused firms in the matching portfolios. OWNDIFF is the difference between insider (directors and officers) ownership percentage of a conglomerate and the average insider ownership of the focused firms in the matching portfolio. VOLDIFF is the natural logarithm of the ratio of conglomerate monthly dollar trading volume to the average monthly dollar trading volume of the focused firms in the matching portfolio. LEVGDIFF is the natural logarithm of the ratio of conglomerate leverage to the average leverage of the focused firms in the matching portfolios. MBDIFF is the natural logarithm of the ratio of conglomerate market-to-book to the average market-to-book of the focused firms in the matching portfolios. STDDIFF is the natural logarithm of the ratio of conglomerate return volatility to the average volatility of the focused firms in the matching portfolios. T-statistics are in parentheses. * Significance at the 0.10 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level.