Coordination Costs, Institutional Investors, and Firm Value

Transcription

1 Coordination Costs, Institutional Investors, and Firm Value Abstract Coordination costs among institutional investors have a signi cant impact on corporate governance and rm value. We use two measures, one based on the geographic distance among institutional shareholders and the other based on the correlation in their portfolio allocation decisions, to proxy for coordination costs. We nd that, after controlling for other factors, coordination costs are negatively associated with rm value as proxied by industry-adjusted Tobin s q. This e ect is robust to controlling for the endogeneity of the institutional ownership structure. Using the 1992 proxy reform as an exogenous shock that relaxes restrictions on communication and coordination among shareholders, we show that this e ect becomes signi cantly stronger after the reform. We further show that the ease of coordination among institutions is associated with fewer anti-takeover provisions adopted by the rm, higher equity-based pay for CEOs, and improved CEO turnover-performance sensitivities. Overall, these ndings suggest that the ease of coordination improves rm value by enhancing the governance role of institutional investors. JEL Classification: G23, G32, G34 Keywords: Coordination costs; Institutional investors; Corporate governance; Firm value

2 1 Introduction While institutional investors collectively hold the majority of the U.S. equity market, their in uence on corporate governance and corporate performance remains unclear. Theoretical work suggests that institutional investors, as large shareholders, can discipline corporate managers through active monitoring and intervention (Shleifer and Vishny, 1986; Maug, 1998; Kahn and Winton, 1998) as well as through the threat of exit (Admati and P eiderer, 2009; Edmans, 2009). Empirical research, however, suggests that there is little evidence of improvement in the long-term rm value from institutional monitoring. 1 One major limitation of institutional monitoring is the free-rider problem, because institutional equity ownership is widely dispersed. As Figure 1 shows, the median value of an institution s equity holdings in a rm as a fraction of the rm s outstanding shares is 0:07% during 1980 to 2009 and decreases over the years. The di used institutional ownership structure suggests that, in the absence of coordination, the classical free-rider problem can prevail (Grossman and Hart, 1980). It has been recognized that institutions can play a more e ective monitoring role through coordinated activities (see, e.g., Black, 1992). Recent survey evidence of McCahery, Sautner, and Starks (2010) shows that 59% of institutional investment managers consider coordinating their actions in disciplining corporate management. Of great importance, and so far largely unexplored, is the cost of coordinating a group of institutional investors, which includes information production costs (e.g., to identify trustworthy and cooperative peers), communication and other costs incurred to reach an agreement, as well as costs associated with monitoring and enforcement of the agreement. In this paper, we examine the impact of coordination costs on the role of institutional investors in improving corporate governance and rm value. We hypothesize that a low coordination cost improves rm value by facilitating a stronger governance role provided by institutional investors. On the one hand, coordination costs can impact the e ectiveness of institutional monitoring and intervention. Although it is not cost-e cient for a small shareholder to monitor managers because of the free-rider problem, low coordination costs enable dispersed institutional shareholders to conduct coordinated monitoring activities and mitigate managerial agency costs. For instance, 1 See, e.g., Gillan and Starks (2007) and Yermack (2010) for recent surveys of the literature. 1

3 institutions can form a shareholder coalition to sponsor proxy proposals to e ect changes in corporate governance (Gillan and Starks, 2000; Del Guercio, Seery, and Woidtke, 2008) as well as to engage in direct negotiation with corporate management seeking governance changes (Becht, Franks, Mayer, and Rossi, 2009). This predicts that a low coordination cost should enhance the monitoring role of institutions and lead to higher rm valuation. On the other hand, the ease of coordination can also intensify the threat of exit. Admati and P eiderer (2009) argue that the threat of exit by a large shareholder can have a disciplinary impact if the shareholder possesses private information about corporate managers extraction of private bene ts (and hence her trading can have an impact on the stock price on which managerial compensation is based). In the absence of coordination, institutions may be limited in using the threat of exit as a disciplinary device, because, as mentioned above, the individual equity stake by an institution is very small and because information production is costly. Thus a low coordination cost enables institutional investors to share information and to conduct coordinated selling, which can strengthen the disciplinary e ect of the threat of exit. This again predicts that the ease of coordination should be related to improved corporate governance and rm value. Coordination costs are hard to observe or quantify. In this paper, we use data on institutional shareholders and construct two measures to capture the ease with which they conduct coordinated actions (in monitoring and selling). The rst measure is the geographic distance among a rm s institutional shareholders. If a rm s institutional shareholders are geographically close to one another, they are more likely to communicate and thus coordinate their actions in major corporate events such as takeovers. This arises because geographic proximity facilitates word-of-mouth communication among professional money managers (Hong, Kubik, and Stein, 2005) and because geographic proximity can promote cooperation among agents through repeated interaction and mutual trust (Leamer and Storper, 2001). The second measure is the correlation in portfolio allocation decisions among institutional shareholders. Institutional asset managers with similar portfolio allocations are likely to form strong ties among themselves because of the homophily e ect. A high portfolio correlation can be the consequences of social connections as well, because institutions in the same social networks have access to the same information sources (Cohen, Frazzini, and Malloy, 2008) and because they engage in direct communication with one another (Hong, Kubik, 2

4 and Stein, 2005; Stein, 2008). Thus, a high portfolio correlation indicates greater homophily and stronger social ties among institutional asset managers, which should facilitate coordination. Using a comprehensive sample of stocks from 1980 to 2009, we nd that rm valuation (proxied by an industry-adjusted Tobin s q) decreases with coordination costs among institutional shareholders. The economic magnitude of this e ect is meaningful: Moving from the 10 th percentile in the geographic distance (portfolio correlation) among institutional shareholders to the 90 th percentile decreases (increases) the industry-adjusted Tobin s q by 0:055 (0:173), as compared to the median Tobin s q of 1:29. The e ect is robust to controls for other institutional shareholder characteristics (such as aggregate institutional ownership, institutional ownership concentration, investment horizons of institutional shareholders, and the distance between institutional shareholders and the rm), rm size, growth opportunities, diversi cation, nancial performance, managerial ownership, and rm-speci c e ects. Furthermore, this e ect is driven mainly by independent institutions and non-transient institutions, both of which are more likely to play an active governance role. These results are consistent with the hypothesis that the ease of coordination among institutional shareholders enhances rm value. Institutional investors do not randomly invest in rms, which suggests that institutional ownership structure and hence our coordination cost measures may be endogenous. For instance, institutions that are located nearby to one another may share similar preferences and invest in high-q stocks. This will result in a reverse causality from rm valuation to the geographic clustering of institutional shareholders. We use two approaches to address this potential endogeneity e ect. The rst is an instrumental variable approach. The instruments are based on whether or not the top institutional shareholder is from New York or Boston and on the inclusion of a rm s stock in the Standard & Poor s 500 index. If the top institutional shareholder of a rm is located in cities with highly concentrated institutional asset managers, the rm is likely to have a low coordination cost among the institutional shareholder. The addition of a stock to the S&P 500 index can attract institutions that are benchmarked against the index, thereby resulting in a more homogeneous institutional shareholder base and hence reduced coordination costs. On the other hand, it is reasonable that these instruments do not a ect our outcome variable through channels other than institutional ownership 3

5 structure. We nd that the above relation between coordination costs and rm value persists even after controlling for the endogeneity of coordination costs. The second is a di erence-in-di erences approach to gauge the impact of exogenous shocks on the relation between coordination costs and rm value. We use the proxy reform in 1992 as an exogenous shock that eases coordination among shareholders. We show that the e ect of coordination costs on rm value becomes signi cantly stronger after the reform. In addition, we use the decimalization in 2001 as an exogenous shock that reduces trading costs and hence strengthens the disciplinary impact of the threat of exit. We nd that the e ect becomes signi cantly stronger after decimalization, suggesting that the threat of exit is one of the channels through which coordination cost impact rm value. We then consider how the ease of coordination among institutional shareholders might add value by focusing on corporate governance mechanisms and governance outcomes. We nd that rms with low coordination costs are associated with better corporate governance, as proxied by the number of anti-takeover provisions (i.e., the G-index and the E-index). We also show that low coordination costs are associated with higher CEO equity-based pay and improved CEO turnoverperformance sensitivities. These results strengthen our interpretation that the ease of coordination enhances the role of institutional investors in corporate governance. This paper is related to two strands of empirical literature, of which the rst is the literature on institutional monitoring. A number of studies suggest that institutional investors in uence corporate policies through costly monitoring or intervention (see, e.g., Hartzell and Starks, 2003; Chen, Harford, and Li, 2007; Gillan and Starks, 2000; Del Guercio, Seery, and Woidtke, 2008) as well as through the threat of exit (see, e.g., Parrino, Sias, and Starks, 2003). Much of the literature, however, implicitly treats institutional investors (or certain types of institutions) as a monolithic entity. Our paper is the rst in the literature to study the impact of coordination costs on the role of institutional investors in improving corporate governance and rm value. This paper also connects to the growing body of nance literature on geography. Hong, Kubik, and Stein (2005) show that mutual fund managers located close by make similar portfolio decisions, suggesting that geographic proximity facilitates communication among professional money 4

6 managers. Coval and Moskowitz (1999, 2001) nd that mutual fund managers exhibit a strong bias towards locally headquartered rms and deliver superior returns on their local investments than distant investments, indicating an information transfer from rm managers to mutual fund managers located nearby. Gaspar and Massa (2007) show that mutual funds located near their portfolio companies play the role of informed monitors. Kang and Kim (2007) nd that, in partial block acquisitions, acquirer rms tend to pursue geographically proximate targets and play a strong monitoring role in such targets post-acquisition. Our paper adds to the literature by showing that the geographic proximity among shareholders matters by a ecting the governance role of shareholders. The rest of the paper is organized as follows. Section 2 describes the data and summary statistics. Section 3 presents the empirical results, and Section 4 concludes. 2 Data and Summary Statistics We retrieve the data for our study from the Center for Research in Stock Prices (CRSP) database, COMPUSTAT, and Thomson Reuters 13F institutional ownership database. Our sample includes all common stocks listed on the NYSE, AMEX or NASDAQ during the period from 1980 to 2009 for which su cient information is available in the three databases. There are 105; 454 rm-year observations in the sample. We construct two measures to capture the coordination cost among institutional shareholders of a rm. The rst measure is based on the geographic concentration of institutional ownership. The premise is that asset managers located close to one another are more likely to come into direct contact (Hong, Kubik, and Stein, 2005) and hence to take coordinated actions. Moreover, geographic proximity can promote cooperation among institutional asset managers by facilitating repeated interaction and cultivating trust (Leamer and Storper, 2001). To construct the geographic distance measure, we rst manually identify the location (zip code) of institutional investors using the Nelson s Directory of Investment Managers and by searching the lings by institutional investors on the SEC Edgar website. We then calculate, for each rm- 5

7 quarter, the weighted-average geographic distance among institutional shareholders of the rm. In particular, for each institutional shareholder in the rm, we calculate the geographic distance between the institution and all institutions in the rm, weighted by their respective fractional holdings in the rm. This measure captures the average distance between an institutional shareholder and its peers. We then calculate a weighted-average of these distances across all institutional shareholders of the rm, again weighted by their fractional holdings. This weighting scheme ensures that institutions that are likely to be more in uential, i.e., those with larger holdings in the rm, receive greater weights in determining the distance among shareholders. Last, we take a simple average of the geographic distance among shareholders for each rm over the four quarters in a year. Speci cally, Geographic distance among institutional shareholders for rm c = X 4 X w c;i;q X w c;j;q l Dist ij;q l A5, (1) 4 i2s j2s l=1 where S is the set of institutional shareholders in rm c, w c;i;t is the weight of institution i in the total percentage held by institutions in rm c at quarter q, and Dist ij;t is the geographic distance between institutions i and j at quarter q. To reduce the skewness of the variable, we use the logarithm of one plus the geographic distance among institutional shareholders, Log(1 + Shareholder distance), as an explanatory variable in the regressions. The second measure is based on the portfolio correlation among institutional shareholders of the rm. This variable is intended to capture the extent of homophily and social ties among institutional shareholders. A large body of literature on homophily in social networks suggests that individuals tend to build connections with others similar to themselves (see McPherson, Smith-Lovin, and Cook, 2001 for a review of research on homophily in social networks). Thus, institutional asset managers that share similar views about certain stocks, i.e., a high correlation in their portfolio allocations, are more likely to exhibit homophily and form strong ties among themselves, which should facilitate coordination. Moreover, a high portfolio correlation can be the consequences of social ties, because institutions in the same social networks have access to the same information sources, e.g., through shared educational ties (Cohen, Frazzini, and Malloy, 2008) and geographic 6

8 proximity (Coval and Moskowitz, 2001), and because they engage in direct communication with one another (Hong, Kubik, and Stein, 2005; Stein, 2008). To the extent that the portfolio correlation measure captures homophily and social connectedness among institutional asset managers, it should be negatively related to shareholder coordination costs. To construct the portfolio correlation measure, we retrieve the entire portfolio holdings of all institutional shareholders of our sample rms in each quarter. For each pair of institutional shareholders, we calculate the correlation coe cient of the excess portfolio weights on common holdings, i.e., stocks that are held by both institutions. 2 The excess portfolio weights are calculated as the actual portfolio weight assigned to a stock relative to the weight of the stock in the market portfolio. We use the excess portfolio weights, rather than the actual weights, to focus on active portfolio allocation decisions of institutional asset managers. Similar to the construction of the geographic distance variable, we rst calculate, for each institutional shareholder, the portfolio correlation between the institution and all institutions in the rm, weighted by their respective fractional holdings. We then calculate the weighted-average of these correlations across all institutional shareholders, again weighted by each institution s fractional holdings in the rm. We take a simple average of the institutional portfolio correlation for the stock over four quarters in a year. Speci cally, Portfolio correlation among institutional shareholders for rm c = X 4 X w c;i;q X w c;j;q l Corr ij;q l A5, (2) 4 i2s j2s l=1 where S is the set of institutional shareholders in rm c, w c;i;t is the weight of institution i in the total percentage held by institutions in rm c at quarter q, and Corr ij;t is the correlation coe cient of the excess portfolio weight (measured as the actual weight relative to the weight in the market portfolio) allocated to overlapping holdings between institutions i and j at quarter q. Panel A of Table 1 presents summary statistics for the two measures of shareholder coordination costs for all sample rms. The average geographic distance among institutional shareholders is 2 If two institutions have less than ve common holdings, we set the correlation to zero. The results are robust to using a di erent cuto or setting it to missing. 7

9 878:1 miles. 3 The average portfolio correlation among institutional shareholders is 0:30. Both of the coordination cost measures exhibit a fair degree of cross-sectional variation across rms. Panel A of Table 1 also presents summary statistics for other shareholder characteristics and rm characteristics. In particular, since institutions located close to their portfolio companies are likely to play a monitoring role (Gaspar and Massa, 2007; Chhaochharia, Kumar, and Niessen, 2009), we calculate the weighted-average geographic distance between institutional shareholders and rms, weighted by institutions fractional holdings. The average distance between institutional investors and the rm is 945:9 miles. Institutional shareholders, in aggregate, own 33% of the outstanding shares of the average rm. Following Hartzell and Starks (2003), we calculate institutional ownership concentration as a Her ndahl Index of institutional ownership concentration based on the percentages of institutional holdings by all 13F institutions. The average institutional ownership concentration for the rms is 0:012. Following Gaspar, Massa, and Matos (2005), we calculate shareholder turnover of a rm as the weighted-average of the average total portfolio turnover rate of the rm s institutional shareholders. The average shareholder turnover rate for the rms is 0:27. We calculate Tobin s q as the ratio of market value of assets to book value of assets, where market value of assets is measured as the market value of common equity plus the book value of preferred stock (carrying value) plus the book value of long-term debt minus deferred taxes and investment tax credit. 4 The industry-adjusted Tobin s q is calculated as the di erence between the rm s Tobin s q and its industry median using the three-digit SIC code (McConnell and Servaes, 1990). The mean Tobin s q is 1:83, and the mean industry-adjusted Tobin s q is 0:30. Panel B of Table 1 presents a correlation matrix of the main variables. The two coordination cost proxies are highly negatively correlated, with a correlation coe cient of 0:786, suggesting that institutional shareholders located close to one another tend to have correlated portfolio allocations. This is consistent with the word-of-mouth e ect documented by Hong, Kubik, and Stein 3 The magnitude of this distance appears large. However, it should be noted that it is measured across all institutions that hold shares in the rm. Consider a hypothetical rm with 50 institutional shareholders from the 50 states in the U.S. (assuming they are located in the state capitals), each holding 2% of the rm s outstanding shares. The shareholder distance for the rm would be 1; 822 miles. Thus, the seemingly large distance among institutions is driven mainly by the fact that the U.S. is geographically large. 4 A more sophisticated approach to estimating Tobin s q is to calculate the replacement cost of assets (Lindenberg and Ross, 1981). We use the simple approach, instead of the more sophisticated one because the latter requires arbitrary assumptions about depreciation and in ation rates and because the two approaches deliver highly correlated estimates of Tobin s q (Villalonga and Amit, 2006). 8

10 (2005). Furthermore, both Tobin s q and industry-adjusted Tobin s q are signi cantly negatively correlated with the geographic distance measure, and both are signi cantly positively correlated with the portfolio correlation measure. These results give a preliminary indication that the ease of coordination cost may improve rm value. In addition, we retrieve various corporate goverance-related variables, such as managerial ownership, board characteristics, and executive compensation, from RiskMetrics and ExecComp. We report the summary statistics as well as the correlation matrix for these variables in Table 1. [Insert Table 1 about here] 3 Empirical Results 3.1 Coordination Costs and Firm Value In this section, we rst examine the relation between rm value and coordination costs using rm- xed e ects regressions. We then address endogeneity concerns by using an instrumental variable approach and by using a di erence-in-di erences approach to gauge the impact of two exogenous shocks. Last, we conduct robustness checks of the regression results Firm- xed E ects Regressions To examine the e ects of coordination costs on rm valuation, we run rm- xed e ects regressions of industry-adjusted Tobin s q on our coordination costs proxies and control variables. We lag all our explanatory variables by one year to mitigate any confounding e ects due to contemporaneous measurement. Speci cally, q j;t = + j + Coordination Costs j;t 1 + X i x i;j;t 1 + " j;t, (3) where q j;t is rm j s industry-adjusted Tobin s q at the end of year t, j is rm- xed e ects, Coordination Costs j;t 1 is one of the two measures of coordination costs for rm j in year t 1, and 9

11 x i;j;t 1 includes standard control variables for Tobin s q such as rm size, pro tability, capital expenditure, leverage, R&D expenses, institutional ownership, institutional ownership concentration, investment horizons of institutional shareholders, the distance between institutional shareholders and the rm, and year dummies. We cluster the standard errors at the rm level (Petersen, 2009). As Panel B of Table 1 shows, the two coordination cost proxies are highly negatively correlated, we include them in the regressions one at a time. The regression results, shown in Panel A of Table 2, indicate that the ease of coordination has a positive e ect on rm value. The economic magnitude of this e ect is meaningful: Based on the full speci cation (i.e., the last two columns of Table 2, Panel A), moving from the 10 th percentile in the geographic distance (portfolio correlation) among institutional shareholders to the 90 th percentile decreases (increases) the industry-adjusted Tobin s q by 0:055 (0:173), as compared to the median Tobin s q of 1:29. Since institutions may di er in their incentives and abilities to play a governance role, we partition institutional investors into groups in two di erent ways. First, we classify institutions into independent institutions and grey institutions following Chen, Harford, and Li (2007). Independent institutions include investment companies, independent investment advisors, and public pension funds, which do not have business relationships with their portfolio companies and hence are more likely to engage in active monitoring. Grey institutions include insurance companies, banks, and private pension funds, which are less likely to play a governance role because of their business ties with the rms they invest in. Second, we divide institutions into transient and non-transient categories following Bushee (1998). Non-transient institutions are dedicated and quasi-indexer based on Bushee s de nition, which are likely to be more e ective monitors. We expect that the e ect of coordination costs on rm value should be driven mainly by independent institutions and non-transient institutions. We reconstruct the coordination cost measures separately for each category of institutions. We replace the aggregate coordination cost measures in Eq. (3) with separate coordination cost measures for each category of institutions, and re-estimate the regressions. Panel B of Table 2 reports the results. Consistent with our expectation, the negative e ects of coordination costs on rm value are driven mainly by independent institutions and by non-transient institutions. 10

12 We further add control variables related to managerial ownership and board structure in our rm- xed e ects regressions to examine whether the negative relation between Tobin s q and coordination costs are driven by these factors. In particular, we include managerial ownership, managerial ownership squared, indicator variables for small boards (board size less than 9), independent boards (independent outside directors account for more than 75% of the board), and CEO/Chairman duality. The sample size for these variables is 31; 559, about a third of our sample size in the baseline regressions. The results, reported in the last two columns of Table 2, Panel B, suggest that the negative e ects of coordination costs on rm value are robust to adding these controls. The coe cient on managerial ownership is positive and signi cant, whereas that on managerial ownership squared is negative and signi cant. These results are consistent with an inverted U-shaped relation between Tobin s q and managerial ownership (e.g., Morck, Shleifer, and Vishny, 1988). [Insert Table 2 about here] Addressing Endogeneity Concerns The panel regression results presented above may raise endogeneity concerns, because institutional investors do not invest randomly. For instance, institutions that are located nearby to one another may share similar preferences and invest in high-q stocks. This will result in a reverse causality from rm valuation to the geographic clustering of institutional shareholders. We use two approaches to address this potential endogeneity e ect. The rst is an instrumental variable approach, and the second is to exploit regulatory changes as exogenous shocks to shareholder coordination. A. Instrumental Variable Regressions. We use two instruments. The rst is an indicator variable for whether the largest institutional shareholder is headquartered in New York City and Boston. Intuitively, if the top institutional shareholder is from a city with highly concentrated institutional investment managers, the institution can more easily coordinate with other institutional shareholders, because they are likely to be located in the same city and hence share similar portfolio allocations (Hong, Kubik, and Stein, 2005). This should lead to a lower coordination cost for the rm. We use metropolitan statistical areas (MSAs) to de ne the location of institutional asset 11

13 managers. We choose New York and Boston, because these two cities dominate the institutional asset management landscape, representing 19:3% and 16:0% of the total dollar holdings by all 13F institutions, respectively. We indentify the largest institutional shareholder of a rm at the start of year t 1, i.e., 24 months prior to measuring Tobin s q, based on holdings in the rm s stock. The exclusion restriction for an instrument that it should not directly a ect or be directly a ected by the dependent variable is also satis ed, since the change in the fundamental value of a rm should not be directly related to whether or not the rm s the top institutional shareholder is located in New York or Boston. The second is an indicator variable for S&P 500 index inclusion that equals one if the stock is included in the S&P 500 index in year t 1 and zero otherwise. The addition of a stock to the S&P 500 index can attract institutions that are benchmarked against the index, thereby resulting in a more homogeneous institutional shareholder base and hence reduced coordination costs. The reverse is true for index deletions. On the other hand, the S&P500 inclusion or deletion seems to satisfy the exclusion restriction for a valid instrument, because a large literature contends that index inclusion is unrelated to any change in the fundamental performance of the included stock (see, e.g., Shleifer, 1986; Kaul, Mehrotra, and Morck, 2000). 5 We use the two-stage least square (2SLS) procedure to account for the endogeneity of coordination costs. In the rst stage, we regress coordination costs measures on the two instruments and other exogenous variables. In the second stage, we run a regression of the industry-adjusted Tobin s q on the tted values from the rst stage regression as the instrument for coordination costs. Speci cally, we estimate the following 2SLS model: First Stage: Coordination Costs j;t 1 = c+ j +NYC/Boston j;t 1 +S&P500 j;t 1 + P i=1;k # ix i;j;t 1 + j;t 1 Second Stage: q j;t = + j + Instrumented Coordination Costs j;t 1 + P i=1;k ix i;j;t 1 + " j;t (4) where NYC/Boston j;t 1 is an indicator variable that equals one if stock j s largest institutional shareholder as of the start of year t 1 is located in New York or Boston and zero otherwise; 5 Standard and Poor s explicitly states that the decision to include a company in the S&P 500 Index is not an opinion on that company s investment potential. 12

14 S&P500 j;t 1 is an indicator variable that equals one if stock j is included in the S&P 500 index in year t 1 and zero otherwise; Instrumented Coordination Costs j;t 1 is the tted value of the coordination costs measures from the rst-stage regressions, j and j are rm- xed e ects, x i;j;t 1 is the same set of control variables as in Eq. (3). Table 3 report the results from the 2SLS model. Columns 1 and 2 of Table 3 reports the results of the rst-stage regression with the dependent variable being one of the two measures of coordination costs. Consistent with economic intuition, rms whose largest institutional shareholder is from New York or Boston are associated with signi cantly lower coordination costs, i.e., a smaller geographic distance and a higher portfolio correlation, among the institutional shareholders. Moreover, the addition of a stock to the S&P 500 index has a signi cant negative e ect on the coordination costs among institutions. These e ects are economically signi cant as well. For instance, the geographic distance among institutions decreases by 5:7% if the stock s largest institutional shareholder switches from a non-new York/Boston institution to a New York/Boston one; the geographic distance among institutions decreases by 26:1% when the stock is added to the S&P 500. We conduct F -tests of joint signi cance of the two instruments. The F -statistics strongly reject the null hypothesis that our instruments are irrelevant in the rst-stage regressions. We also conduct the Stock and Yogo (2005) weak instrument test of the null hypothesis that the instruments are only weakly correlated with the endogenous variables. The test strongly rejects the null hypothesis that the instruments are weak. We report these test statistics at the bottom of Table 3. Columns 3 and 4 of Table 3 report the second-stage results with industry-adjusted Tobin s q as the dependent variable. Consistent with our baseline results from rm- xed e ects regressions, the coe cient estimates of the instrumented coordination costs measures remain signi cant (at the 5% level) and in the predicted directions. The absolute magnitude of these coe cient estimates appears greater than those obtained using rm- xed e ects regressions. Since we use two instruments for each of the coordination costs variables, we have an overidenti ed speci cation. We conduct the Hansen overidenti cation test. The Hansen J -statistics cannot reject the joint null hypothesis that the instruments are uncorrelated with the error term and are correctly excluded from the second-stage regressions. 13

15 Overall, the 2SLS regression results suggest that the impact of coordination costs on Tobin s q is not driven by the endogenous selection of high-q rms by coordinated institutions. [Insert Table 3 about here] B. The E ect of the 1992 Proxy Reform. We now exploit the 1992 proxy reform as an exogenous shock that reduced the barriers to shareholder coordination in corporate governance (Choi, 2000; Bradley, Brav, Goldstein, and Jiang, 2010). Prior to the October 1992 changes to the proxy rules, any communication among a group of 10 shareholders or more under circumstances reasonably calculated to a ect voting decisions would amount to proxy solicitation and was not allowed until a formal proxy statement was delivered to other shareholders. This communication restriction was eased with the 1992 proxy reform such that any communication by shareholders not directly seeking the power to vote as proxy for other shareholders was excluded from the de nition of what constitutes a solicitation. The reform thus signi cantly eased communication and coordination among shareholders. This predicts that the e ects of coordination costs on rm value should become stronger in the post-reform period. We use a di erence-in-di erences approach to examine the impact of the 1992 reform on the relation between coordination costs and rm value. We use a two-year window and de ne the scal year in which the reform occurred as year t. We choose year t 2 for the pre-reform period, and year t as the post-reform period. We discard the year immediately before the reform, i.e., year t 1, because the reform was widely discussed in the media before the nal adoption of the changes and, as such, rm value in year t 1 may have factored in the e ect associated with coordination costs. In addition, because we are interested in the e ect of proxy reform on coordination costs and rm value, we require that each stock be present in both windows around the reform. As a result, for every stock we note only two observations one in each window of the event. We divide the sample of stocks into quintiles based on each of the coordination costs proxies. Stocks in the bottom quintile of coordination costs constitute a treatment group that experiences an exogenous shock to shareholder coordination. Stocks in the top quintile constitute the control group. Intuitively, the reform signi cantly reduces the restrictions on shareholder coordination, thereby enabling institutions with low coordination costs to conduct coordinated monitoring activ- 14

16 ities. In contrast, the reform should have little, if any, impact on rms whose institutional shareholders face prohibitively high coordination costs, because the institutions are likely to remain passive post-reform due to the high coordination costs. By comparing the change in industry-adjusted Tobin s q after the reform for the treatment and control groups, we allow for both group-speci c and time-speci c e ects. Panels A and B of Table 4 present the results of univariate di erence-in-di erences comparisons in industry-adjusted Tobin s q between low- and high-coordination-cost rms before and after the proxy reform. The di erence-in-di erences estimator indicates a large increase in industry-adjusted Tobin s q for rms with low coordination costs relative to those with high coordination costs after the reform. In particular, Panel A shows that rms in the bottom quintile of the geographic distance among the institutional shareholders (the treatment sample) experience an increase of 0:11 in industry-adjusted Tobin s q, compared to a change of 0:01 for rms in the top quintile. The di erence in the change in industry-adjusted Tobin s q between the two groups, albeit not statistically signi cant, is economically large. Panel B shows that when the portfolio correlation measure is employed as the coordination cost proxy, the di erence-in-di erences estimator suggests an increase in industry-adjusted Tobin s q of 0:15 (signi cant at the 5% level) for the treatment rms relative to the control rms. To control for the e ect of other factors that may a ect rm value, we estimate multivariate di erence-in-di erences regressions on the two-year sample around the reform. In particular, we add an indicator variable, Post-reform, which equals one for observations after October 1992, and zero otherwise. We interact our coordination costs variables with the post-reform dummy; the coe cient on the interaction term captures the di erence-in-di erences e ect of the reform on rms with low coordination costs relative to those with high coordination costs. Panel C of Table 4 presents the results of the di erence-in-di erences regressions. In all four speci cations, the coe cient on the interaction between the coordination costs variables and the post-reform dummy is signi cant at the 1% level and in the predicted directions. For instance, Column 2 shows that, after controlling for other factors that a ect industry-adjusted Tobin s q, the e ect of the geographic distance among institutions on industry-adjusted Tobin s q is signi cantly more negative after the reform as compared to before. These results are consistent with our 15

17 univariate results, indicating a causal e ect of coordination costs on rm value. [Insert Table 4 about here] C. The E ect of Decimalization. Institutions can coordinate their selling behavior and use the threat of exit as a disciplinary device. Admati and P eiderer (2009) contend that a liquid stock market, i.e., lower transaction costs, can improve the e ectiveness of the threat of exit as a corporate governance mechanism. We use decimalization as an exogenous shock that increases stock market liquidity, which in turn can intensify the disciplinary e ect of the coordinated threat of exit. The stock markets in the U.S. converted to the decimal-pricing system and reduced the minimum tick size from a sixteenth of a dollar to one cent during the period between August 2000 and April This led to signi cant drops in bid-ask spreads following decimalization (Bessembinder, 2003; Fur ne, 2003). Institutional investors, due to their sizable holdings, are sensitive to transaction costs. 6 Other things equal, rms whose institutional shareholders face lower coordination costs should be more likely to coordinate and use the threat of exit to discipline corporate managers postdecimalization, compared to rms with widely dispersed institutional shareholders. This predicts that the e ects of coordination costs on rm value should become stronger following decimalization. We estimate multivariate di erence-in-di erences regressions on a two-year sample around decimalization. In particular, we de ne the scal year in which decimalization occurred as year t. We choose year t 1 for the pre-decimalization period, and year t as the post-decimalization period. We add an indicator variable, Post-decimalization, which equals one for observations after January 2001, and zero otherwise. We interact our coordination costs variables with the post-decimalization dummy; the coe cient on the interaction term captures the di erence-in-di erences e ect of decimalization on rms with low coordination costs relative to those with high coordination costs. Table 5 presents the results of the di erence-in-di erences regressions. The coe cients on the interaction between the coordination costs variables and the post-reform dummy are all in the predicted directions and generally signi cant. These results suggest that the threat of exit is one of the channels through which coordination costs a ect rm value. 6 For example, Wermers (2000) nds that 0:8% of the 2:3% performance di erence between mutual funds gross returns and net returns is due to transaction costs. 16

18 [Insert Table 5 about here] Robustness Checks In this section, we conduct a series of robustness checks on the relation between rm valuation and coordination costs. A. Controlling for local institutions. Investors located close to their investments are likely to have an informational advantage (Coval and Moskowitz, 2001; Baik, Kang, and Kim, 2010) as well as to provide a strong monitoring role (Gaspar and Massa, 2007; Kang and Kim, 2008). To test whether the ndings are driven by local institutional shareholders, we reconstruct the two measures of shareholder coordination costs by excluding institutional investors located within 100 kilometers of the rm s headquarter and re-estimate Eq. (3). The results, reported in the rst two columns of Table 6, show that the e ects of coordination costs on rm value are qualitatively unchanged, suggesting that the results are not driven by local institutions. B. Excluding cities with highly concentrated institutional investors. Institutional asset management is highly geographically concentrated. One concern is that the ndings are driven by a few cities with a high concentration of institutional investors. We thus repeat the analysis by excluding these cities. We use metropolitan statistical areas (MSAs) to de ne the location of institutional asset managers. For each MSA and each quarter, we calculate the total dollar value of equity holdings that are managed by institutions residing in that MSA. New York and Boston dominate the institutional asset management landscape, representing 19:3% and 16:0% of the total dollar holdings by all 13F institutions, respectively. We then construct the two measures of shareholder coordination costs by excluding the two MSAs and re-estimate Eq. (3). Columns 3 and 4 of Table 6 show that the results are again qualitatively unchanged. This nding suggests that the results are not driven by the two extreme cities per se. C. Excluding foreign institutions. The fraction of the total institutional equity holdings in the U.S. managed by foreign institutions has increased signi cantly from 3% in 1980 to 15% in On the one hand, the presence of foreign institutions can increase the geographic distance among shareholders and, to the extent that they have di erent investment objectives from domestic 17

19 institutions, decrease the portfolio correlation among the institutional shareholders of a rm. On the other hand, foreign institutions might be less e ective in monitoring management than domestic institutions due to geographic distance (Kang and Kim, 2008). To test whether the results are driven by foreign institutions, we reconstruct the two measures of shareholder coordination costs by excluding foreign institutions and re-estimate Eq. (3). The results, reported in Columns 5 and 6 of Table 6, are essentially unchanged compared to the baseline results reported in Table 2, Panel A, which suggests that foreign institutions do not drive the results. D. OLS regressions with lagged dependent variables. We estimate OLS regressions adding lagged industry-adjusted Tobin s q as a control variable. The last two columns of Table 6 report the results. As expected, the coe cient on the lagged industry-adjusted Tobin s q is positive and highly signi cant. The coe cients on our key variables, i.e., the coordination costs variables, remain signi cant and in the predicted directions. Furthermore, our results are robust to adding two or three lags of industry-adjusted Tobin s q in the OLS speci cation. [Insert Table 6 about here] 3.2 Coordination Costs and Corporate Governance We now consider how the ease of coordination among institutional shareholders might add value by focusing on corporate governance mechanisms and governance outcomes Anti-takeover Provisions A large literature in corporate governance suggests that anti-takeover provisions have a negative impact on rm value by insulating corporate managers from the external discipline of takeovers (e.g., Gompers, Ishii, and Metrick, 2003; Bebchuck, Cohen, and Ferrell, 2009). If coordination costs are low, a coalition of institutional shareholders can in uence the use of anti-takeover provisions by corporations by coordinating their actions. For example, institutional shareholders can jointly propose and vote on governance issues, such as removing anti-takeover provisions, in annual shareholder meetings. This predicts that the ease of coordination among institutions should be related 18

20 to a lower number of anti-takeover provisions. We use two indices to measure the level of external corporate governance. The rst is the G- index proposed by Gompers, Ishii, and Metrick (2003), which is based on 24 anti-takeover provisions. The second is the entrenchment index (E-index) proposed by Bebchuck, Cohen, and Ferrell (2009). The entrenchment index consists of six provisions, namely classi ed boards, limits to shareholder bylaw amendments, poison pills, golden parachutes, and supermajority requirements for mergers and charter amendments. For both indices, a low number indicates strong corporate governance. We estimate multivariate regressions of the corporate governance indices on coordination costs and control variables. Speci cally, Governance Index j;t = + $Coordination Costs j;t 1 + X i x i;j;t 1 + j;t, (5) where Governance Index j;t is one of the two corporate governance indices for rm j in year t; Coordination Costs j;t 1 is one of the two measures of coordination costs among institutional shareholders of rm j in year t 1; and x j;t 1 includes year and industry xed e ects, rm characteristics, and other ownership characteristics of rm j in year t 1. Table 7 reports the results. In three out of four speci cations, the coordination costs variables are signi cant and in the predicted directions. The economic magnitude is large as well: for instance, moving from the 10 th to the 90 th percentiles in the geographic distance (portfolio correlation) variable increases (decreases) the G-index by 0:71 (4:04), compared to the median G-index of 9. These results suggest that the ease of coordination enables institutional shareholders to play a stronger monitoring role by removing barriers to takeovers. [Insert Table 7 about here] Equity-based Incentives An extensive literature suggests that equity-based compensation for corporate managers can improve rm performance (e.g., Mehran, 1995). Hartzell and Starks (2003) suggests that institutional investors can enhance the pay-for-performance sensitivity of managers through increased monitor- 19

21 ing. We hypothesize that institutional investors, through coordinated monitoring, can improve corporate governance by increasing corporate managers equity-based incentives. We use two measures to capture the equity-based incentives of CEOs. The rst measure is the incentive ratio proposed by Bergstresser and Philippon (2006). This ratio employs the total holding of stock and options rather than annual grants, and is de ned as follows: Incentive ratio = Increase in value of CEO stock and options for a 1% increase in stock price Increase in value of CEO stock and options + annual salary + annual bonus (6) where the numerator is calculated as 0.01 multiplied by the product of the rm s share price and the number of shares and options held by the CEO. The second measure is the option fraction as in Mehran (1995), which is calculated as the percentage of total CEO annual compensation comprised of grants of new stock options, with the options valued by the Black-Scholes formula. Data on option grants, salary, bonus, and other compensation are available from Standard and Poor s ExecuComp database, available through Compustat. We estimate multivariate regressions of the equity-based incentives on coordination costs and control variables using a speci cation similar to Eq. (5). Table 8 reports the results. In all four speci cations, the coordination costs variables have the predicted signs, although only two are signi cant at the 1% level. These results suggest that a low coordination cost enables institutional shareholders to in uence compensation policies that enhance shareholder value. [Insert Table 8 about here] Turnover-performance Sensitivity A primary outcome of internal monitoring by shareholders and board of directors is CEO turnover (Huson, Parrino, and Starks, 2001). Coordinated monitoring by institutional investors can exert pressure on the rm s board of directors to identify and terminate incompetent CEOs. We explore this possibility by testing whether the ease of coordination enhances CEO turnover-performance sensitivity. 20