Distribution of institutional ownership and corporate firm performance

Journal of Banking & Finance 34 (2010) 606–620

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Distribution of institutional ownership and corporate firm performance Elyas Elyasiani a,*, Jingyi Jia b a b

Department of Finance, Fox School of Business and Management, Temple University, United States Department of Economics and Finance, School of Business, Southern Illinois University, Edwardsville, United States

a r t i c l e

i n f o

Article history: Received 3 December 2008 Accepted 26 August 2009 Available online 31 August 2009 JEL classification: G32 Keywords: Institutional ownership Monitoring Firm performance

a b s t r a c t We investigate the association between corporate firm performance and the level and stability of institutional ownership within a simultaneous equation model. Our main ownership stability measures include ownership persistence and the time-lengths over which investors hold non-zero shares or maintain their shareholding. We find that there is a positive relationship between firm performance and institutional ownership stability, accounting for the shareholding proportion. This relationship is robust to the employment of ownership turnover measures used in the literature and consistent with the view that stable institutional investors play an effective role in monitoring. When we disaggregate institutional investors into pressure-insensitive and pressure-sensitive categories, we find that stable shareholding of each group has a positive impact on performance, with the first group exerting a larger effect. The channels of the effect include, but are not limited to, decreased information asymmetry and increased incentive-based compensation. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction Since Berle and Means (1932) a central question on corporate governance has been how to solve the control problems associated with dispersed ownership. Ownership dispersion has, however, changed overtime as shares owned by individuals are increasingly managed by institutional investors such as mutual and pension funds (Chen et al., 2007). Indeed, in the most recent decades, institutional investors have become the largest owners of the US corporations (Gillan and Starks, 2000). In terms of shareholding size, expertise in information collection and monitoring the managers, institutional investors are very different from atomistic investors. Hence, a question arises as to whether and how institutional ownership influences corporate governance and firm performance. Three plausible scenarios can describe the role played by institutional investors: active monitoring, passive monitoring, and siding with managers to exploit smaller shareholders. In the first scenario, monitoring by institutional investors is likely to result in improved firm performance because, as large and sophisticated shareholders, institutional investors have the incentive and expertise to monitor the management, can do so at a lower cost than atomistic shareholders (Shleifer and Vishny, 1986), and are able to exert enough influence to alter the governance structure and the firm’s course of actions. The passive role scenario is based on the argument that institutional owners may be short-term investors acting like ‘‘traders”, holding or selling the stocks according * Corresponding author. Tel.: +1 215 204 5881; fax: +1 215 204 1697. E-mail addresses: [email protected] (E. Elyasiani), [email protected] (J. Jia). 0378-4266/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2009.08.018

to their portfolio rebalancing needs, instead of intervening in corporate governance. A special case of this scenario is when institutional investors are passive indexers; namely that they hold a stock just because it is a component of an index such as S&P 500. In this case, institutional ownership is not expected to correlate with firm performance but it will be more stable because the investors will continue to hold the S&P 500 firms.1 According to the third scenario, some institutional investors cooperate with the management in order to expropriate the dispersed small shareholders. For example, investment companies may support the managers at the expense of the shareholders in order to receive more investment banking business (Brickley et al., 1988; Cornett et al., 2007).2 These three scenarios are not mutually exclusive, though one may dominate the others as the main determinant of institutional investor behavior. Extant studies on the relationship between firm value and institutional ownership mainly use the shareholding proportion as the measure of institutional investors’ influence (Duggal and Millar, 1999; Woidtke, 2002). However, shareholding proportion alone 1 Fund managers pursue indexing to become indistinguishable from other fund managers and to avoid a reduction in their compensation in response to poor performance. Moreover, indexing may attract new investors and please the regulators since a stable portfolio allocation appears more ‘prudent’ to investors and regulators. 2 The exploitation scenario is less likely if the smaller investors are also institutions, and in the US, where the interests of smaller shareholders are better protected, than in the emerging markets. Pound (1988) has pointed out that, due to conflict-ofinterest pressures, institutional investors may vote with the management against their own fiduciary interests. Prudent man laws, however, purport to protect the beneficiaries from a fiduciary who fails to invest in their best interest (Del Guercio, 1996).

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may not fully capture the monitoring incentives of these investors. For example, for two firms with the same institutional ownership proportion, if institutional investors in one firm change frequently but those of the other remain the same, the latter are more likely to have an effect on firm value. In this study, we consider both the first moment (proportion) and the second moment (stability) of the institutional ownership distribution as determinants of firm performance. Three main indicators of institutional ownership stability are used; institutional ownership persistence (IOP), defined as institutional ownership proportion standardized by its standard deviation, and non-zero-points and maintain-stake-points durations, introduced by Bohren et al. (2005), as measures of the length of time over which an investor has non-zero holdings or maintains his/her stake. The advantages of using institutional ownership stability, in addition to the ownership level, as a proxy for institutional investors’ influence are threefold. First, given the durability of their ownership, stable institutional investors have ample opportunities to learn about the investee firms as well as bigger incentives to effectively monitor them on an ongoing basis. This is likely to reduce the agency costs, the information asymmetry between outsiders and insiders, and detrimental ‘‘activism”.3 Second, long-term institutional ownership is associated with reduced redemption pressures and reduced information asymmetry, making it possible for the managers to engage in longer-term investment, leading to better long-term performance (Jensen and Meckling, 1976). Given their sizable connection to financial markets, long-horizon institutional investors can also help the management to bring about increased Wall Street coverage of the company, increasing demand for and improving the liquidity of its shares and, thereby, reducing the firm’s transaction and financing costs.4 Third, stable institutional owners, especially those with large holdings, can improve firm governance by pressuring the management to change the executive compensation structure to better align the interests of the managers with those of the shareholders. This will inhibit the managers from opportunistic earnings management and will direct them to focus on long-term profitability instead (Hartzell and Starks, 2003). In addition, institutional investors may bring about an increase in the proportion of independent directors, known to improve governance (Gallagher et al., 2007). The main objectives of this study are to explore the association between firm performance and institutional ownership stability, to investigate whether this association strengthens when monitoring incentives are greater, to examine some of the channels through which institutional investor monitoring impacts firm performance, and to check the robustness of our results to the employment of alternative measures of ownership stability used in the literature. These objectives are described in more details in the hypotheses section. Our tests are conducted within a simultaneous equations model.5 3 Detrimental activism is driven by political reasons and is bolstered when the costs of such activism are small, i.e., when ownership is small and short-term. 4 ‘‘Smart companies think of institutional shareholders as partners, who stick with them in bad times and can help them raise large pools of capital in good times,” says David Liu, senior vice president of investment bank Broadview International, a division of Jefferies Group (Tech Firms Seek Stability in Arms Of Institutional Holders; Wall Street Journal, August 26, 2004). 5 The current study is distinct from Elyasiani and Jia (2008) and Elyasiani et al. (forthcoming) in several ways. The former study contrasts the differential impacts of institutional ownership stability on regulated and unregulated industries while the latter investigates its impact on bond yields. The current work instead focuses on the relationship between institutional ownership stability and non-regulated industrial firm performance. Moreover, the current work investigates the channels of association between institutional ownership stability and firm performance and employs a much larger sample, a much longer sample period, and a larger set of institutional ownership stability measures to check the robustness of the findings. See Sections 2.3 and 3.2.2 for a comparison of our measure with those commonly used in the extant studies.

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We find five main results. First, our findings, based on the aggregate sample, confirm that stable institutional ownership is indeed associated with better corporate firm performance. Second, while ownership stabilities of both pressure-insensitive and pressure-sensitive institutional investors are directly associated with firm performance, the association is stronger for the former group. Third, there is a negative association between firm turnover measures used by Gaspar et al. (2005) and Wahal and McConnell (2000) and firm performance, confirming that our results are robust to the employment of these measures. Fourth, higher levels of institutional ownership stability (IOP, non-zero-points, and maintain-stake-points duration) are associated with lower information asymmetry (stock residual volatility), thereby leading to a higher firm performance in the longer-run. Fifth, institutional ownership stability measures are positively associated with executive incentive–compensation ratio (stock-based compensation/total compensation). This suggests that stable institutional holding alters the managers’ compensation structure in favor of the incentive-component and, thereby, aligns the interests of the managers with those of the shareholders, resulting in reduced agency costs of dispersed ownership and improved firm performance. The remainder of this paper is organized as follows: Section 2 covers the literature review and hypotheses development. Section 3 discusses the data and methodology. Section 4 presents the results, examines the robustness of the findings and investigates some of the channels through which institutional ownership stability helps improve performance. Section 5 concludes. 2. Hypotheses development 2.1. Firm performance and institutional ownership The nature of the relationship between firm performance and institutional ownership is dependent on the dominance of one or the other of the three scenarios discussed earlier. If the active monitoring hypothesis is in effect, a positive and significant relationship should be expected while under the passive monitoring hypothesis, no relationship, or a weaker relationship, would be manifested. The less likely management-supportive scenario may produce a negative relationship if the management engages in activities detrimental to firm value.6 Extant studies on the relationship between firm value and institutional ownership are centered on the effect of ownership proportion. For example, McConnell and Servaes (1990) find a significant positive relationship between Tobin’s Q and the proportion of institutional ownership. Woidtke (2002) finds that Tobin’s Q is positively (negatively) related to ownership proportion of the private (public) pension funds. The explanation for the dissimilarity is that private pension fund managers align their interests with those of the shareholders, while managers of public pension funds are motivated more by political or social influences than by firm performance.7 Cornett et al. (2007) find a significant relation between 6 Institutional ownership may impair performance and increase risk, e.g., through an increase in fraud. Firms with high institutional shareholding are more likely to commit fraud because there is a higher likelihood for managers of these firms to be dismissed if the firm performs poorly (Denis et al., 2006). Institutional investors may also overlook management fraud if they benefit from the high stock price resulting from it. This argument may be valid for short-term but less so for long-term institutional investors because the latter are knowledgeable about the managers’ actions through ongoing monitoring (see Section 2.4). 7 Public pension funds, e.g., NY State & Local Retirement System, are regulated by their respective states in terms of the investments they can make, the composition of the board of directors, and the cap on the compensation of the fund managers. Private pension funds, e.g., the College Retirement Equities Fund (CREF), must adhere to the fiduciary responsibility rules under the Employee Retirement Income Security Act. The boards of these funds represent the beneficiaries. Moreover, for these funds, the compensation of the fund managers is not capped and it is considerably higher.

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a firm’s operating cash flow returns and both the shareholding proportion and the number of investors, for those institutional investors less likely to have a business relationship with the firms. In the international context, Yuan et al. (2008) find mutual fund ownership is positively associated with firm performance in China. However, Karpoff (2001) reports that although institutional shareholder activism does prompt small changes in target firms’ governance structure (e.g., it increases the number of independent directors on audit committees and changes managers’ compensation), it has negligible impacts on share values and earnings. Similarly, Duggal and Millar (1999), who investigate the takeover cases initiated during the 1984–1990 period, do not find a positive relationship between bidders’ gains and estimated institutional ownership proportion. Although some recent studies (e.g., Chen et al., 2007; Brav et al., 2008) do find significant positive abnormal stock returns upon announcement of activism by hedge funds, and a positive association between the holding of long-term independent institutions and post-merger performance, few studies investigate the effect of long-term monitoring by stable institutions. Aggregated or disaggregate proportions of institutional ownership are inadequate measures of institutional ownership influence because they overlook ownership dimensions other than the ownership level. As pointed out earlier, stability of shareholding is an important factor in capturing the effect of institutional ownership on firm performance because it strengthens the motivation of the institutional owners to monitor while providing them with ample opportunities to do so. Moreover, ownership stability allows the investee firms to focus on longer investment horizons and enhances firm’s governance and its coverage on Wall Street (Del Guercio, 1996; Woidtke, 2002; Bushee, 1998; Bushee and Noe, 2000; Chen et al., 2007). In addition, shareholding stability of institutional investors varies to a greater extent than that of the other block-holders such as founding families, because the former are likely to trade more frequently than the latter. The greater variation in institutional ownership stability will help capture the association between stability, monitoring, and firm performance more accurately (Chen et al., 2007). Under the active monitoring view, if institutional shareholding is significant and stable, institutional investors will monitor the management and improve firm performance. However, if the passive monitoring hypothesis better describes the reality, institutional ownership stability is not an indicator of more intense monitoring. In this case, there will be no relationship, or a weaker relationship, between firm performance and the stability measures. Accordingly, we propose the following hypothesis: Hypothesis 1a. There is a positive relationship between firm performance and institutional ownership stability, at a given level of institutional ownership (Active Monitoring Hypothesis) 2.2. Alternative stability measures Gaspar et al. (2005) and Wahal and McConnell (2000) use investor turnover (WM turnover) to measure institutional ownership stability. They measure the turnover of each institutional investor based on the turnover of that investor’s overall portfolio and then aggregate the turnover of all the institutional investors of a firm to calculate the firm’s investor turnover. These measures are based on the assumption that an institutional investor follows the same trading style (high or low turnover) in managing all the stocks in his/her portfolio, though in reality the trading styles of an institutional investor might be quite different when investing in different stocks, depending on his/her expertise. An advantage of our institutional ownership measure (IOP) is that it allows for style heterogeneity as detailed in Section 3.2.2.

We are interested in learning whether the relationship between firm performance and institutional ownership stability is robust to the employment of these turnover measures. Accordingly, we will also test whether the firm’s Gaspar turnover and the shareholding of the highest Wahal and McConnell (WM) turnover groups are negatively related to firm performance, at a given level of institutional ownership. Hypothesis 1b. The firm’s Gaspar turnover is negatively related to firm performance, at a given level of institutional ownership. Hypothesis 1c. The shareholding of the highest (the lowest) Wahal and McConnell (WM) turnover group is negatively (positively) related to firm performance, at a given level of institutional ownership. 2.3. Institutional ownership stability-performance link by investor type Brickley et al. (1988) classify institutional investors into three groups according to whether they have potential business relationships with the investee firms and, hence, their sensitivity to management pressure (Brickley et al., 1988; Del Guercio, 1996; Almazan et al., 2005; Chen et al., 2007; and Cornett et al., 2007). These groups include: pressure-insensitive (public pension funds, mutual funds), pressure-sensitive (insurers, banks, and nonblank trusts owning at least one percent of the firm’s stock), and pressure-indeterminate (corporate pension funds, brokerage houses, investment counsel firms, miscellaneous, plus institutions owning less than one percent of the firm’s stock). Brickley et al. (1988) find that only pressure-insensitive institutions, that are free of conflicts of interests, can be active monitors and more likely to vote against the management. Pressure-sensitive institutions act as passive investors because they do not want to risk losing their business relationships with the investee firms. The Brickley et al. classification is adapted by Almazan et al. (2005), Chen et al. (2007), and Cornett et al. (2007). Almazan et al. (2005) classify investment companies and advisors as potentially active monitors, and investors of other categories such as banks, insurers and others, as potentially passive monitors. Chen et al. (2007) refer to institutional investors with potential business relationship with the investees as ‘‘grey” and to the other institutional investors as ‘‘independent”. Cornett et al. (2007) divide institutional investors into pressure-insensitive and pressure-sensitive groups. They show that, for a sample of S&P100 firms, there is a significant relationship between the firms’ cash flow returns and both the shareholding size and the number of active institutional investors. We are interested in learning whether stability of active institutional investors has a greater impact on firm performance, than that of passive institutional investors. A similar question can be posed about the size of the share holding. According to Shleifer and Vishny (1986), investors with larger shares have higher monitoring incentives because they receive greater benefits for a given monitoring cost. Following Bhojraj and Sengupta (2003), we divide the institutions into two categories: investors owning 5% or more of the stocks and investors owning less than 5% of the stocks. We propose that the former have a greater effect on firm performance: Hypothesis 2. The positive association between firm performance and institutional ownership stability is stronger for institutional investors with greater monitoring incentives (i.e., pressure-insensitive and larger institutional investors)

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2.4. Channels of association

3. Data and methodology

An interesting question is if performance is associated with institutional ownership structure, through which channels the two variables are linked. Myers and Majluf (1984) demonstrate that ownership dispersion deepens information asymmetry between managers and outside investors, leading to a higher financing cost and a lower investment level. Further, Jensen and Meckling (1976) show that information asymmetry between investors and investee firms can induce agency costs because the self-interested entrepreneur has an incentive to make decisions that expropriate investors’ funds. The resulting lower investment and higher agency costs, in turn, harm firm performance.8 Aghion et al. (2009) show that institutional investor monitoring can reduce informational asymmetry between managers and shareholders and can also improve managers’ incentive to innovate and to focus on long-term investments. Their argument is that if a CEO innovates, he/she takes the risk that if innovation goes wrong for stochastic reasons (bad luck) he/she will be perceived as a bad manager by the owners. However, institutional investors will be knowledgeable about the managers’ skills through their monitoring and are less likely to fire such CEOs. They find that higher institutional ownership is indeed associated with greater innovation. Along the same lines, Wahal and McConnell (2000) argue that institutions possess an informational advantage over impatient individual investors and, thus, act as a buffer between individual investors and corporate managers. The involvement of these investors results in larger expenditures on projects with long-term payoff and improved performance. Moreover, Rubin and Smith (2009) show that institutional ownership reduces stock volatility in nondividend paying stocks, which are associated with high information asymmetry, because the skills of institutional investors in gathering and processing information makes prices more informative.9 Based on these arguments, we propose that stable institutional investors reduce information asymmetry cost of dispersed ownership by means of monitoring:

3.1. Data and sampling

Hypothesis 3. Stable institutional ownership is negatively related to information asymmetry. Stable institutional ownership may be also associated with value-enhancing corporate governance mechanisms such as higher incentive-based executive compensation, which aligns the interests of the managers with those of the shareholders, resulting in reduced agency costs and improved performance. In this context, Hartzell and Starks (2003) have shown that institutional ownership concentration is directly associated with the sensitivity of executive compensation to firm performance. Thus, we propose that stable institutional ownership potentially alters the wage structure of the CEOs in favor of stock-based compensation. We also propose that both reduced information asymmetry and increased incentive–compensation advance firm performance. Hypothesis 4. Institutional ownership stability is positively associated with executives’ stock-based incentive–compensation. Hypothesis 5. Reduced information asymmetry and increased incentive–compensation are positively associated with firm performance. 8 Akerlof (1970) also shows that information asymmetry, or ‘‘lemons” problem, induces capital markets to undervalue good investment projects and break down the capital flow from investors to firms. McConnell and Muscarella (1985) and Chan et al. (1990) find that lower investment, in turn, negatively affects corporate firm value. These effects provide a negative link between information asymmetry and firm value. 9 However, Rhee and Wang (2009) find that foreign institutional ownership has a negative impact on stock market liquidity in Indonesia, implying that foreign institutional ownership increases information asymmetry.

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The sample includes all the firms with complete data from the Center for Research in Equity Prices at the University of Chicago (CRSP), Thomson Financial, Compustat, and ExecuComp databases over 1992–2004.10 Financial and public utility firms (SIC codes = 60–69, 48–49) are excluded because of their special asset composition, high leverage, stricter government regulation, and the fact that, due to the uniqueness of these industries, the Tobin’s Q for these firms cannot be meaningfully compared to those of the firms in the other industries. This data selection process results in a set of rather homogenous firms, expected to display similar behavior. The data used to compute the firm performance measures and control variables are drawn from Compustat annual industrial files. Institutional ownership data are from Thomson Financial. To make sure that our results will not be affected by the outlier values of Tobin’s Q, we delete three firms whose industry-adjusted Tobin’s Q exceeds 10. We construct our institutional ownership stability measures over rolling 5-year periods. To make all variables in the model contemporaneous, we also use rolling 5-year averages of other variables in the empirical tests. Because of the noisiness of the short-term (annual) data and lags in the effect of ownership stability and monitoring on performance, this effect can be more reliably measured in this longer-term horizon. The long-term covariation examined in our model may be considered a ‘‘core” or ‘‘permanent” relationship.11 A total of 8370 firm-year observations from 1532 firms are in the final sample. There are 1807 observations from 295 S&P 500 firms, 1211 observation from 220 S&P Midcap Index firms, 1617 observations from 313 S&P Smallcap Index firms, and 3735 observations from 704 non-S&P index firms. 3.2. Model specification and variable construction Institutional ownership stability can enhance firm performance but good performance may also attract the investors and/or encourage them to hold onto the stock over a longer horizon demonstrating ownership stability. To account for endogeneity between performance and institutional ownership stability, we formulate a simultaneous equations model in which both of these variables are treated as endogenous variables.

Performance ¼ a0 þ a1 Stability þ a2 Prop þ /P þ kC þ Year Dummy18 þ e

ð1Þ

Stability ¼ b0 þ b1 Performance þ uS þ cC þ Year Dummy18 þ g

ð2Þ

Measures of performance and ownership stability will be defined below. To control for the shareholding proportion (Prop) effect, we include this variable as a regressor in the models with the duration measures. Vectors P and S refer to the instrumental variables, respectively, for performance and ownership stability, and Vector C and Year dummies contain the other control variables. 10 The CDA/Spectrum Institutional Holding database by Thomson Financial has quarterly information on the institutional investors’ common stock holdings of US securities. Pursuant to Section 13(F) of the Securities Exchange Act of 1934, any institutional investor managing $100 million or more is required to disclose this information by filing Form 13F to the Security Exchange Commission (SEC). In this database institutional investors are classified into bank trust departments (type 1), insurance companies (type 2), investment companies (type 3), independent investment advisors (type 4) and others (type 5). Special thanks are due to Mr. Gene Yhim for providing the Thomson Financial data. 11 This concept is similar to the concept of ‘‘core deposits” used in the banking literature and the concept of ‘‘permanent income” used in Milton Friedman’s permanent income theory of consumption.

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3.2.1. Firm performance and institutional ownership stability measures We use industry-adjusted return on assets (ROA, net income/ book value of assets) as the measure of firm performance. This measure is approximated as the firm’s ROA minus the median ROA of the firms with the same two-digit SIC code. The first institutional ownership stability measure used is institutional ownership persistence (IOP). For an institutional investor in a specific firm, IOP is defined as the ratio of the average ownership proportion to the standard deviation of the ownership proportion, over a 5-year period that includes the sample year and the preceding 4 years. For example, the IOP measure for 1996 is calculated using 20 quarters of data from the first fiscal quarter of 1992 to the fourth quarter of 1996. If an investor holds a large proportion of a company’s stock, and the holding proportion is stable during the 5-year sample period, its IOP measure will be high and vice versa. We calculate the IOP measure for a firm as the average IOP across all institutional investors in the firm. This measure is a unitless metric and can be considered the reciprocal of the coefficient of variation (standard deviation/absolute value of the mean). This measure may be termed the volatility-adjusted ownership proportion and has the advantage that it makes the second moment of the institutional ownership distribution an important dimension of the ownership influence. According to the statistics in Table 1, the mean (median) IOP is 0.622 (0.577). The second and third measures of institutional ownership stability are non-zero-points duration and maintain-stake-points duration (Bohren et al., 2005). Non-zero-points duration is the number of quarters in which an institutional investor has non-zero holdings out of the 20 quarters over the 5-year period defined above. Maintain-stake-points duration is the number of quarters in which an institutional investor maintains its stake (either keeps the same proportion or increases the holding) out of the 20 quarters. Thus, the higher the non-zeropoints or the maintain-stake-points duration, the higher the ownership stability will be. We calculate the average of each of these two measures across all institutional investors in a firm, and use them as duration measures for the firm. The mean (median) nonzero-points and maintain-stake-points durations are 5.965 (6.055) and 3.959 (4.029), respectively, suggesting that on average institutional investors hold a stock for about six quarters and maintain their stakes for about a year. 3.2.2. Alternative ownership stability measures used in the literature Two other measures of ownership stability are used in the existing literature. Gaspar et al. (2005) define a turnover measure to determine whether a firm is dominated by short-term or long-term investors (Gaspar Turnover). They first calculate a turnover measure called the ‘‘churn rate”, for each institutional investor, which measures how much the investor changes his/her positions on the stocks of his/her portfolio. The turnover measure for each firm is then calculated as the weighted average ‘‘churn rate” of all the investors in the firm, with the weight for each investor being the ratio of the holdings of the individual investor to the aggregate institutional ownership of the firm. A firm with high turnover measure is classified as a firm with short-term investors. With a similar idea, Wahal and McConnell (2000) use the shareholding proportions of investor turnover quintiles to measure the influence of stable versus unstable shareholders (WM turnover). To this end, they first compute the portfolio turnover (churn rate) for each institutional investor and then group the investors into quintiles according to their turnover from the highest-turnover group to the lowest-turnover group. The sum of the shareholding proportions of the institutional investors in each of the five groups is a proxy for the influence of that group on the firm. These shareholding proportions are used to measure the institutional owner-

ship stability at the firm-level. The sum of the shareholdings in the five quintiles is the total institutional ownership proportion. As pointed out earlier, a notable advantage of our main measure of ownership stability (IOP), over the turnover indicators used in most extant studies (e.g., Bushee 1998; Gaspar et al., 2005; Chen et al., 2007) is that we measure ownership stability with respect to a specific investor in a particular stock, assuming that an institutional investor may manifest different stabilities (or different trading styles) in different stocks. Gaspar et al. (2005) and Wahal and McConnell (2000) measure the trading style of each institutional investor based on the turnover of that investor’s overall portfolio, in effect assuming that an institutional investor has the same trading style for all the stocks in his/her portfolio. 3.2.3. Instrumental and control variables According to Angrist and Krueger (2001), the best instrumental variables are those highly correlated with the regressors they serve as instruments for (stability measure, or firm performance), but unrelated to the error terms in the equation those regressors are used. We follow these criteria in choosing our instruments. To select the appropriate instruments, we consider the variables associated with institutional ownership level in the literature (Del Guercio, 1996; Gompers and Metrick, 2001; Woidtke, 2002; Bhojraj and Sengupta, 2003; Bennett et al., 2003) and choose those variables which are unrelated to the error term, but highly related to the stability measures. Following Elyasiani and Jia (2008), the instrument (S) used here for institutional ownership stability measures is the logarithm of stock’s daily turnover (log (trading volume/number of shares outstanding)). Share volume turnover is not expected to be related to the error in the performance equation in a given direction. When stock performance improves, some investors buy in anticipation of an extrapolative trend while some others sell to cash in the gains. Similarly, when performance is poor and price is low some investors buy because they consider it a good buying opportunity and some investors sell to avoid further losses. It follows that both good and bad performance can potentially lead to a higher turnover. Lack of a strong link between performance and turnover in a specific direction helps qualify turnover as an instrumental variable for ownership stability. In the performance model using the alternative stability measures, the instruments include transaction costs, dividend yield, a dummy for positive earnings in the previous year, the number of analysts following, and the logarithm of stock’s daily turnover. These variables are based on the selection criteria described above and are helpful in tests of channels of association and tests of alternative stability measures.12 The instruments specific to firm performance (vector P) are leverage, insider ownership, and insider-ownership squared (Morck et al., 1988; McConnell and Servaes, 1990). Inclusion of ‘‘insider-ownership squared” accounts for the nonlinear impact of interest alignment between insiders and shareholders. The three variables chosen are not highly correlated with the error term of the stability equation. Leverage is total debt divided

12 The following variables have been used as instruments for institutional ownership level in the literature: standard deviation of daily stock return, number of analysts-following, log of shares outstanding (Bhojraj and Sengupta, 2003), positive earning in prior year dummy, transaction cost (Woidtke, 2002). Additionally, the following variables have been used as factors determining the level of institutional holdings: dividend yield and S&P index membership (Del Guercio, 1996), book-tomarket ratio, firm age (Gompers and Metrick, 2001) and lagged 6-month cumulative stock return (Bennett et al., 2003). Following Keim and Madhavan (1997), the transaction cost measure is calculated as cost = 0.687 + 0.239  D NASDAQ  0.076  Log (market value of equity) + 9.924 (1/calendar year-end closing stock price), where Dummy variable DNASDAQ equals 1 for the stocks listed on NASDAQ, and market value of equity is the market capitalization of the stock traded (thousands of dollars). The larger this measure, the higher the transaction costs will be.

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Table 1 Descriptive statistics. The data set is comprised of 8370 firm-year observation from 1532 firms over 1992–2004 in the ExecuComp database. All the institutional ownership stability measures are calculated based on institutional investors over a 5-year sample period. We compute the performance and control variables with 5-year averages of annual data to make them simultaneous to ownership stability measures. The firm performance measures are: industry-adjusted ROA and Industry Adjusted Tobin’s Q. Shareholding proportion (Prop) is the average aggregate institutional shareholding proportion. IOP is calculated as the average ratio of mean to standard deviation of shareholding proportions across all the institutional investors. Maintain-stake-points duration is the average number of quarters in which institutional investors maintain the stake (keep the same proportion or increase the holding). Non-zero-points duration is the average number of quarters in which institutional investors have non-zero holdings. The log of book value of total assets is used as the proxy for firm size. 3-Year sales growth rate is the 3-year least squares annual growth rate of sales. Share volume turnover is the ratio of total number of shares traded to the number of shares outstanding presented as one thousandth. Transaction cost measure is based on Keim and Madhavan (1997). It is calculated as 0.687 + 0.239  DNASDAQ  0.076  Log (market value of equity) + 9.924  (1/Calendar year-end Closing Stock Price). DNASDAQ is equal to 1 for stocks listed on NASDAQ. Dividend yield is dividends per share by ex-date divided by close price for the fiscal year. # Of analysts following is the number of analysts providing EPS forecasts in a specific year. Variable

Mean

Median

Minimum

Standard deviation

Maximum

Industry-adjusted ROA (net income/book value of assets) (%) Industry-adjusted Tobin’s Q Proportion of institutional ownership (Prop) (%) Institutional ownership persistence (IOP) Maintain-stake-points Non-zero-points Age (Years since on CRSP) CEO incentive ratio (total compensation–salary-bonus)/total compensation) Insider shareholding (%) (shares held by executives/shares outstanding) Return volatility (Standard deviation monthly returns) Leverage (total debt/total assets) Total assets ($ Million) 3-Year sales growth rate (%) Share volume turnover (one thousandth) Transaction cost measure Dividend yield (%) # Of analysts following

3.758 0.586 47.523 0.622 3.959 5.965 15.067 0.419 18.411 0.472 0.514 3846.760 22.223 8.023 0.454 0.923 12.829

3.571 0.167 49.935 0.577 4.029 6.055 13.000 0.404 8.823 0.423 0.519 842.853 10.824 5.601 0.153 0.000 10.200

214.000 2.644 0.871 0.224 0.971 1.000 0.333 0.000 0.000 0.131 0.028 15.696 69.326 0.144 0.700 0.000 2.000

13.165 1.383 19.993 0.222 1.024 1.720 10.096 0.236 22.693 0.229 0.219 17890.040 98.032 7.034 2.466 2.293 8.820

61.134 8.012 99.469 4.521 7.252 11.684 33.000 0.999 99.825 4.117 2.917 581017.200 5437.740 81.892 79.649 69.818 56.600

by total assets, and insider ownership is the percentage of company’s stocks held by all the executives. The control variables used in both equations are firm size, growth opportunities, measured by the 3-year sales growth rate, firm risk, firm age, CEO incentive–compensation ratio and S&P 500 dummy. Firm size is measured by the logarithm of the book value of total assets. Firm risk, measured by the standard deviation of monthly stock returns, and firm age, measured by the number of years a firm has been on the CRSP database (since 1925), have been used in previous studies as control variables in modeling firm performance and institutional and family ownership levels (Anderson and Reeb, 2003; Bennett et al., 2003). Moreover, there is considerable evidence that executive incentive–compensation is closely related to firm performance and institutional ownership (Jensen and Murphy, 1990; Hartzell and Starks, 2003). Thus, following Anderson and Reeb (2003), we include the CEO incentive–compensation ratio, and the above variables as control variables for both equations. CEO incentive–compensation ratio is defined as the fraction of total pay that the CEO receives in stocks and options. Empirical evidence shows that S&P 500 firms may show a better performance (Woidtke, 2002). Hence, institutional investors may include them in their portfolio based on the consideration of fiduciary duties (Del Guercio, 1996). To control for this effect, we also include an S&P 500 dummy (equal to 1 for the S&P 500 index firms and zero otherwise) in both equations.

4. Empirical results 4.1. Firm performance and institutional ownership Table 2 shows the system estimation results with industry-adjusted ROA as performance measure. Columns (1), (3), and (5) report the results on firm performance (Eq. (1)) with institutional ownership stability measures IOP, non-zero-points, and maintain-stake-points duration. Consistent with McConnell and Servaes (1990), the coefficients of the shareholding proportion are positive

and significant at the 1% level, demonstrating that higher institutional shareholding is associated with better firm performance. More interestingly, the coefficients of the institutional ownership stability measures are also positive and significant at the 1% level (columns (1), (3), and (5)), indicating that institutional ownership stability is a significant explanatory variable for firm performance, even after the shareholding level effect is accounted for. The above results support hypothesis (1a) which suggests a positive association between firm performance and institutional ownership stability. To determine whether the association between institutional ownership stability and performance is unilateral or mutual, we have to examine the stability equation (Eq. (2)) as well. The coefficients of the industry-adjusted ROA in the stability regressions are all significant (columns (2), (4) and (6)), suggesting that firm performance is indeed among the determinants of the holding decision by institutional investors and that the association between the two variables is bidirectional in nature. In terms of the magnitude of the effect, we find a one standard deviation increase in IOP (0.222) is associated with a 2.70% increase in industry-adjusted ROA (12.142  0.222 = 2.70). Similarly, one standard deviation increase in non-zero-points duration (1.720 quarters) and maintain-stake-points duration (1.024 quarters) are, respectively, associated with 7.34% and 5.67% increase in industry-adjusted ROA (4.266  1.720 = 7.34; 5.540  1.024 = 5.67). These figures indicate that the ownership stability effects show economic as well as statistical significance. The coefficient signs for most other control variables in the firm performance equation are as expected: stock return volatility, leverage, and age are all negatively and significantly related to industry-adjusted ROA. These findings, all in accord with Anderson and Reeb (2003), suggest that riskier, more highly leveraged, and older firms produce a smaller industry-adjusted ROA. The lack of significance of insider ownership effect in the performance equation, with ROA as performance measure, is consistent with Hu and Zhou (2008) who find the coefficients of the insider ownership and insider-ownership squared to be both insignificant. The

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Table 2 Firm performance and institutional ownership stability. This table displays estimation results from simultaneous equations model of firm performance (industry-adjusted ROA) and institutional ownership stability (IOP, non-zero-points and maintain-stake-points duration) described by Eqs. (1) and (2) below. The variables are as defined in Table 1. The dependent variables are listed on the top of the columns. T-statistics are in parentheses.

Performance ¼ a0 þ a1 Stability þ a2 Prop þ /P þ kC þ Year Dummy18 þ e Stability ¼ b0 þ b1 Performance þ uS þ cC þ Year Dummy18 þ g

Intercept

Industry-adjusted ROA (1)

IOP (2)

Industry-adjusted ROA (3)

Non-zero-points (4)

Industry-adjusted ROA (5)

Maintain-stake-points (6)

13.127*** (7.26)

0.558*** (30.19) 0.006*** (12.99)

4.788** (2.25)

4.474*** (29.71) 0.073*** (19.02)

6.008*** (2.67)

4.241*** (46.46) 0.040*** (17.10)

Industry-adjusted ROA

11.025*** (14.46)

Proportion/100 IOP

ð1Þ ð2Þ

11.538*** (16.36)

12.142*** (5.02) 4.266*** (12.26)

Non-zero-points Maintain-stake-points Log(Age)/100 CEO incentive pay ratio Insider Shareholding/100 (Insider shareholding/100)2 Return volatility Total debt/total assets Log(Total assets)/100 3-Year sales growth rate S&P 500 index dummy

16.011*** (7.72) 1.875*** (2.90) 0.623 (0.39) 0.287 (0.15) 16.418*** (17.95) 17.930*** (25.85) 29.230** (2.05) 0.008*** (5.83) 2.029*** (4.49)

0.599*** (28.56) 0.024** (2.59)

0.027* (1.78)

1.907*** (11.00) 0.00004* (1.76) 0.090*** (15.14) 0.088*** (27.22) Yes

Turnover/1000 Year dummies System weighted R2 * ** ***

Yes

29.541*** (12.49) 0.959 (1.45) 0.259 (0.22) 0.646 (0.46) 11.337*** (11.48) 12.884*** (19.06) 35.608** (2.27) 0.005*** (3.25) 0.142 (0.30)

5.423*** (31.08) 0.140* (1.78)

0.223* (1.84)

20.449*** (14.21) 0.00001 (0.06) 0.596*** (12.17) 0.453*** (16.85) Yes

Yes

0.4209

5.540*** (12.73) 24.409*** (12.39) 1.937*** (2.93) 0.708 (0.52) 0.495 (0.30) 11.849*** (12.42) 14.452*** (22.27) 34.923*** (2.67) 0.005*** (3.69) 1.050** (2.52)

Yes

0.4723

3.037*** (28.80) 0.063 (1.33)

0.014 (0.19)

1.447* (1.66) 0.00003 (0.33) 0.269*** (9.08) 0.406*** (24.98) Yes 0.4158

Indicate statistical significance at the 10% level, respectively. Indicate statistical significance at the 5% level, respectively. Indicate statistical significance at the 1% level, respectively.

significantly positive coefficient of the S&P 500 dummy suggests that S&P 500 firms demonstrate better firm performance than non-S&P 500 firms.13 In the stability equation (Eq. (2)), consistent with the findings of Del Guercio (1996) and Bennett et al. (2003), institutional ownership stability measures are positively related to firm age, CEO incentive–compensation, firm size, sales growth and S&P dummy, and negatively related to daily stock turnover measures. Institu-

tional investors prefer to hold stably the stocks issued by older well-established firms, large firms, and S&P 500 firms. The finding on the S&P 500 firms may be partially contributed to indexing objectives. In brief, we find that, when institutional ownership level and endogeneity of institutional ownership stability are accounted for, stability of institutional ownership does enhance performance and performance does encourage institutional owners to extend their shareholding over a longer horizon.

13 The model is also estimated using the data on S&P 500 firms alone. The positive association between performance and institutional ownership stability (IOP) remains in effect for this group of firms as well, indicating that the behavior of institutional investors is consistent with an active monitoring role for these firms also, and that their investment in these firms is not entirely, if at all, driven by indexing purposes. This result for the S&P 500 firms is robust to the employment of a single equation and a simultaneous equation system. Moreover, when we add an interaction term (slope shift) between SP500 dummy and ownership stability (IOP), the coefficient is positive and significant indicating that the effect of stability on performance is larger for SP500 firms than for other firms. Results based on industry-adjusted Tobin’s Q, used as a measure of performance, are qualitatively similar for SP500 firms during some sub-sample periods but vary across sub-samples. In additional models, we also added S&P Midcap and Smallcap index dummies to Eqs. (1) and (2). The coefficients of these dummies are positive and significant in both performance (ROA) and stability equations (IOP). In these models, the coefficient of SP500 is still positive and significant indicating that institutions prefer SP500, Midcap and Smallcap firms to non-SP firms.

4.2. Robustness of findings 4.2.1. Alternative turnover measures Table 3 shows the results for Gaspar and MW turnover measures. The coefficient of Gaspar turnover in the performance equation is negative and significant at 5% (column (1)), indicating that a higher investor turnover hurts firm performance. The negative coefficient of industry-adjusted ROA in column (2) shows that profitability does affect the decision by institutional investors to continue their ownership and establishes mutual interdependence between the two variables. Columns (3–8) show the results for the relationship between performance and MW turnover proportions. The coefficients of shareholding proportion for quintiles 4 and 5 (the two lowest-turnover quintiles) are positive while those for

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quintiles 1 and 2 (the two highest-turnover quintiles) are negative, with all coefficients being significant at 5% or 1%. The significantly positive (negative) coefficients for lower (higher) turnover quintile proportions indicate that shareholding proportion of stable (unstable) investors is positively (negatively) associated with firm performance. The results of turnover measures are by and large consistent with the findings based on our stability measures and demonstrate the latter’s robustness. Hypotheses 1b and 1c are supported. 4.2.2. Alternative procedures Tables A2 and A3 in the appendix report the robustness test results of alternative instruments, alternative rolling sample period, and alternative procedures. In the system model presented in columns (1 and 2) of Table A2, market capitalization (the product of number of shares and year-end stock price) is used as an alternative instrument for institutional ownership persistence (IOP), instead of turnover. Institutional investors prefer to hold large stocks stably because large capitalization is associated with high liquidity and investment safety (Del Guercio, 1996; Woidtke, 2002). Market capitalization is not highly related to industry-adjusted ROA because both large and small stocks can be associated with good accounting performance (the correlation coefficient is 0.14). The coefficient of IOP in column (1) of Table A2 is still positive and significant at the 1% level, indicating that our primary result is robust to the use of a new instrument for institutional ownership stability. Our stability measures are constructed based on 5-year rolling periods. To check whether our results are robust to the length of the rolling sample period, we construct these measures also based on 3-year rolling periods. The figures reported in column (3–4) of Table A2 show that IOP constructed over 3-year period is also positively related to performance and it is significant at the 10% level. Moreover, unreported results show that non-zero-points and maintain-stake-points durations, based on 3-year windows, are also positively related to industry-adjusted ROA at the 1% significance level, indicating that our results are insensitive to the length of the construction period for ownership stability measures. We also employ lagged regression models as another way to address the endogeneity problem. If industry-adjusted ROA is positively associated with lagged institutional ownership stability, it is more likely that the direction of the effect runs from institutional ownership stability to performance. Column (5) of Table A2 reports the results of a model regressing the industry-adjusted ROA on IOP constructed over lagged 3-years. The coefficient of lagged IOP and lagged maintain-stake-points duration are indeed positive and significant. The coefficient of lagged non-zero-points duration is also positive but statistically insignificant. These findings confirm the positive association between institutional ownership stability and performance while accounting for the endogeneity concerns. Finally, to further examine the endogeneity issue, we estimate a ‘‘treatment effect” model based on Heckman’s (1979) two-step procedure. The endogeneity argument is that the positive relationship between performance and ownership stability may be due to self-selection (reverse causality); institutional investors may hold a stock stably due to its good performance. Moreover, institutional investors may be influenced to hold the stocks stably by a set of omitted variables which affect both performance and stability. Following the Heckman procedure, in the first step we introduce a probit model whose left-hand side variable is a dummy variable (High Stability Dummy) and whose independent variables are similar to those in Eq. (2). The High Stability Dummy takes a unit value when a firm’s IOP is larger than the median IOP, and zero otherwise. Share volume turnover is assumed to affect the decision to invest in a firm stably, but not to influence firm performance. This variable serves as the identifying variable for the High Stability

613

Dummy and it is excluded from the performance model in the second step in order to satisfy the exclusion restriction necessary for identification in the procedure (Villalonga and Amit, 2006). The probit model produces the self-selection variable (k), or the inverse of the Mill’s ratio, which is included in the performance model, to control for the self-selection bias, in the second step of the estimation. Table A3 in the Appendix reports the estimation results. In the probit model, the coefficient of stock volume turnover is negative and significant, suggesting that high turnover stocks are less attractive to institutional investors. The coefficients of main interest in the second step performance model are those of the inverse Mill’s ratio, or the self-selection bias (k), and the High Stability Dummy. The coefficient of parameter (k) is negative and significant, supporting the presence of self-selection bias in the estimation of ownership stability effect and the bi-directional nature of the relationship between performance and ownership stability. Villalonga and Amit (2006) also use treatment effect models to address self-selection and reverse causality concerns. Similar to their results, the negative sign of (k) also shows that the omitted or unobservable factors that encourage institutions to hold the stock stably tend to be associated with lower firm performance (ROA), providing an indication that these factors have only weakened the observed effect of institutional ownership stability on performance. This gives further credence to our results. The coefficient of High Stability Dummy is positive and significant after the selfselection bias is controlled for, indicating the robustness of our findings. 4.3. Ownership stability of active versus passive institutional investors According to Brickley et al. (1988), Almazan et al. (2005) and Cornett et al. (2007), only pressure-insensitive (independent) institutional investors such as investment companies and investment advisors monitor the firm’s management effectively. Pressure-sensitive investors, e.g., insurance companies, which rely on firm managers to get pension fund management business, are less likely to monitor the management diligently or to vote against its decisions. The finding of these studies is that the shareholding level of pressure-insensitive investors does, and that of pressure-sensitive investors does not have a positive effect on firm performance. These findings, however, are based on shareholding levels of different categories of investors, with total disregard of the ownership stability effects. As proposed in Hypothesis 2, we are interested in learning whether the association between institutional ownership stability and firm performance is also dissimilar for different categories of investors. To this end, we construct our ownership stability measures for pressure-insensitive and pressure-sensitive institutional investors separately, and conduct the tests carried out earlier for each of the two groups. Table 4 reports the results for pressure-insensitive and pressure-sensitive, as well as investors with the largest and smaller shares in the investees. The pressure-insensitive group includes investment companies and independent investment advisors (types 3 and 4). The pressure-sensitive group includes bank trust departments, insurance companies, and others (types 1, 2, and 5). The investor types are defined by the CDA/Spectrum data source (see footnote 10). The largest and smaller investors are defined as those owning 5% or more, and less than 5%, respectively. In order to compare the magnitudes of the effects of the stability measures across investor groups, following Bennett et al. (2003), we standardize all the variables in the model by subtracting the cross sectional mean and dividing by the standard deviation of each year.14 14 Only the results based on maintain-stake points and non-zero points are reported in order to save space. The coefficients of IOP are insignificant.

Intercept

Industry-adjusted ROA (1)

Gaspar Turnover (2)

Industry-adjusted ROA (3)

Prop of quintile 1 (4)

Prop of quintile 2 (5)

Prop of quintile 3 (6)

Prop of quintile 4 (7)

Prop of quintile 5 (8)

34.849*** (3.85)

0.260*** (13.39) 0.003*** (3.40)

18.209 (0.67)

8.938*** (25.97) 0.067*** (2.78)

11.051*** (33.45) 0.006 (0.26)

14.908*** (34.72) 0.122*** (4.02)

17.913*** (37.97) 0.200*** (6.04)

15.492*** (44.42) 0.064*** (2.62)

2.803*** (4.35) 0.027 (0.10)

3.714*** (6.00) 0.974*** (3.50)

3.787*** (4.70) 2.261*** (6.24)

2.884*** (3.27) 0.965** (2.44)

1.204* (1.84) 0.034 (0.12)

1.906*** (4.69)

3.779*** (9.70)

7.014*** (14.01)

6.913*** (12.33)

2.687*** (6.52)

0.601*** (3.79) 0.087*** (11.36) 1.711*** (17.69) 0.500*** (8.92) 0.040 (1.46) 1.487*** (4.38) Yes

0.033 (0.22) 0.045*** (6.17) 1.926*** (21.07) 0.414*** (7.68) 0.109*** (4.16) 1.158*** (3.55) Yes

1.491*** (7.51) 0.009 (0.91) 2.049*** (18.6) 0.715*** (10.19) 0.016 (0.48) 3.344*** (7.88) Yes 0.3067

0.742*** (3.42) 0.020* (1.91) 2.594*** (18.86) 0.975*** (12.71) 0.089** (2.37) 4.156*** (8.95) Yes

0.085 (0.53) 0.022*** (2.89) 0.466*** (4.76) 0.528*** (9.29) 0.040 (1.45) 1.900*** (5.52) Yes

Industry-adjusted ROA

Proportion/100 Gaspar turnover

6.652*** (7.33) 84.060** (2.34)

Prop of quintile 1

Prop of quintile 3 Prop of quintile 4 Prop of quintile 5

CEO incentive ratio Insider holding/100 (Insider holding/100)2 Return volatility Debt/total assets Log(Total assets)/100 3-Year sales growth S&P 500 dummy

21.377*** (4.21) 1.953 (1.31) 1.476 (0.91) 2.742 (1.37) 13.879*** (7.22) 11.937*** (6.62) 20.952 (0.89) 0.006 (1.64) 1.460* (1.82)

0.139*** (4.28) 0.003 (0.17)

0.025 (1.20)

0.013 (1.57) 0.0002 (0.89) 0.012*** (4.65)

# Analysts following Turnover/1000

19.506** (2.55) 20.634*** (3.87) 0.412 (0.09) 17.727*** (3.12) 17.031*** (4.64) 133.973*** (4.45) 26.025*** (2.94) 8.979 (0.90) 10.167 (0.79) 14.724* (1.77) 7.463 (1.18) 2068.13*** (3.21) 0.006 (0.70) 2.666 (0.65)

Transaction costs Dividend yield

0.001 (1.06)

Lagged EPS dummy Year dummies System weighted R2 * ** ***

Yes

Yes 0.1238

Indicates statistical significance at the 10% level, respectively. Indicates statistical significance at the 5% level, respectively. Indicates statistical significance at the 1% level, respectively.

Yes

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Prop of quintile 2

Log(Age)/100

614

Table 3 Institutional ownership turnover measures. This table presents the estimation results for the model (Eqs. (1), (2)) with turnover measures used as ownership stability indicators. Columns (1 and 2) are for performance and Gaspar Turnover measure, and columns (3–8) are for performance and shareholding proportion of the five quintiles of investors specified in Wahal and McConnell (2000). The dependent variable in each equation is listed on the top the column. T-statistics are reported in parentheses.

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Table 4 Firm performance and institutional ownership stability by investor type. This table presents the standardized regressions of firm performance on maintain-stake-points and nonzero-points durations for different categories of institutional investors. Pressure-insensitive investors refer to investment companies and independent advisors. Pressure-sensitive investors include banks and insurance companies and other investors. Investors owning 5% or more are classified as largest investors. Investors owning less than 5% are classified as smaller investors. The simultaneous equation model described by Eqs. (1) and (2) is estimated with the two duration measures calculated separately for the each category of institutional investors (columns (1 and 2), (3 and 4), (5 and 6), and (7 and 8)). T-statistics are reported in parentheses.

Intercept

Pressure-insensitive

Pressure-sensitive

Investors owning 5% or more

Investors owning less than 5%

IndustryAdjusted ROA (1)

Maintainstake-points (2)

Industryadjusted ROA (3)

Maintainstake-points (4)

Industryadjusted ROA (5)

Non-zeropoints (6)

Industryadjusted ROA (7)

Non-zeropoints (8)

0.023 (0.80)

0.082*** (3.07) 0.575***

0.039 (1.32)

0.075*** (2.93) 0.338***

0.090** (2.22)

0.086*** (2.74) 0.537***

0.068*** (2.74)

0.015 (0.74) 0.497***

Industry-adjusted ROA

(18.57) Proportion/100 Non-zero or Maintain-stake Log(Age)/100 CEO incentive ratio Insider shareholding/ 100 (Insider shareholding/ 100)2 Return volatility Total debt/total assets Log(Total assets)/ 100 3-Year sales growth S&P 500 Index dummy

0.148*** (15.37) 0.428*** (11.73) 0.188*** (11.96) 0.018 (1.63) 0.016

0.293*** (25.45) 0.008 (0.74)

(0.70) 0.002

(0.10) 0.165*** (9.46) 0.234***

0.028* (1.69)

* ** ***

(20.07) 0.002 (0.24) 1.286***

(4.51) 0.179*** (6.85) 0.032*** (2.92) 0.038

0.331*** (29.85) 0.001 (0.05)

(1.38) 0.011

(12.55) 0.181*** (9.33) 0.048*** (3.12) 0.038*

0.122*** (8.79) 0.024* (1.94)

(1.91) 0.020

(0.40) 0.217*** (9.03) 0.292***

0.094*** (5.75)

(24.60) 0.011 (1.16) 0.198*** (3.82) 0.122*** (8.42) 0.028*** (2.79) 0.066***

0.190*** (20.08) 0.032*** (4.01)

(3.47) 0.049***

(1.05) 0.193*** (9.04) 0.146***

0.085*** (4.05)

(2.65) 0.311*** (14.97) 0.309***

0.033** (2.17)

(21.30) 0.045***

0.054***

(22.86) 0.070***

0.032**

(10.22) 0.176***

0.167***

(25.57) 0.023

0.300***

(2.97) 0.080***

(4.18) 0.042***

(4.61) 0.086***

(2.55) 0.006

(7.54) 0.050***

(10.23) 0.034***

(1.13) 0.071***

(28.99) 0.013

(7.70) 0.088**

(4.12) 0.316***

(7.66) 0.150***

(0.57) 0.287***

(3.19) 0.392***

(2.71) 0.371***

(5.44) 0.210***

(1.29) 0.412***

(2.64)

(10.44) 0.258*** (21.04) Yes

(3.76)

(9.80) 0.189*** (16.06) Yes

(5.29)

(10.10) 0.028* (1.90) Yes

(5.58)

(17.88) 0.235*** (23.97) Yes

Turnover/1000 Year dummies System weighted R2

(10.64) 0.022 (1.48) 0.320***

Yes

Yes

0.4144

0.3378

Yes 0.2619

Yes 0.4961

Indicates statistical significance at the 10% level, respectively. Indicates statistical significance at the 5% level, respectively. Indicates statistical significance at the 1% level, respectively.

In columns (1 and 3) of Table 4, we find that the coefficients of maintain-stake-points duration are positive and significant at the 1% level for pressure-sensitive and pressure-insensitive investors. This suggests that ownership stability has a positive impact on firm performance for both investor groups and, hence, it is likely that both groups do perform some monitoring. Comparing the magnitudes of these coefficients, however, we find that the coefficient of maintain-stake-points duration for pressure-insensitive institutional investors (0.428; column (1)) is larger than that of pressuresensitive investors (0.320; columns (3)). A cross-equation F-test of equality between these two coefficients in a simultaneous equations system model including maintain-stake-points durations of pressure-insensitive and pressure-sensitive institutions shows that they are statistically different. This finding indicates that a given increase in the maintain-stake-points duration induces a larger enhancement in firm performance for pressure-insensitive than pressure-sensitive investors. The implication is that the former

group monitors more frequently, and/or more effectively, than the latter group.15 Since these results are based on standardized regressions, the coefficients of the duration measures can be interpreted as the expected standard deviation change in industry-adjusted ROA, given one standard deviation change in the duration measures. In terms 15 It is notable that we have included the investors with indeterminate pressure sensitivity (containing some active investors) in the pressure-sensitive investor group. This can only have weakened the observed dissimilarity in monitoring effectiveness between the pressure-insensitive and pressure-sensitive groups. The diminishing distinction among the financial institutions, as a result of deregulation, technological change and financial innovation, has had a similar weakening effect. In addition, regardless of whether institutional investors are bank trust department, pension managers of insurance company, or managers of mutual fund or private or public pension funds, they are all the representatives of individual investors and their performance and prudence is under the scrutiny of both the investors and courts. Thus, it is possible that they all have a positive impact on firm performance through monitoring.

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of magnitude, one standard deviation increase in maintain-stakepoints duration is associated with 5.63% (0.428  13.165% = 5.63%, one standard deviation of industry-adjusted ROA is 13.165%) increase in industry-adjusted ROA for pressure-insensitive institutional investors but only 4.21% (0.320  13.165% = 4.21%) for pressure-sensitive institutional investors. Similarly, as shown in columns (5) and (7), the non-zero-points duration coefficients for investors with the largest shares (owning 5% or more stocks) and other investors are both positive. In addition, the coefficient of the investors owning 5% or more of the investee firms (1.286; column (5)) is larger than that of the investors with smaller ownerships (0.198; column (7)), indicating that the former have greater incentives to monitor and/or greater effectiveness in improving firm performance. Further, a cross-equation test of equality of coefficients within a model which includes the stability of both large and small investors, shows that these two coefficients are also statistically different. In terms of economic magnitudes, one standard deviation increase in non-zero-points duration is associated with 16.93% (1.286  13.165% = 16.93%) increase in industry-adjusted ROA for investors with larger holding, but only 2.61% (0.198  13.165% = 2.61%) increase in industry-adjusted ROA for investors with smaller ownerships. Our findings on pressure-sensitive versus pressure-insensitive investors, and largest versus other shareholders, support hypothesis 3. 4.4. Channels of association In Section 2.4, we hypothesized that reduced information asymmetry and increased CEO incentive-based compensation are two of the possible channels through which institutional ownership stability and firm performance are linked.16 These channels will be examined here as possible conduits for transmission of the impact. As discussed earlier, stable institutional ownership can reduce information asymmetry, and can, thereby, improve firm performance (Myers and Majluf, 1984). The lower level of information asymmetry and better performance may, in turn, induce more stable institutional ownership. It is even possible that institutional investors seek firms with high information asymmetry problems with the intention to reduce their information asymmetry and to enhance their performance. Similarly, institutional ownership stability may improve the performance of the investee firms through an increase in the incentivebased compensation ratio of the CEOs (stock and option-based compensation/total compensation). It is also possible that institutional investors seek firms with high incentive–compensation ratios on the grounds that this wage structure will lead to good performance or that they seek firms with low incentive–compensation ratios with plans to raise these ratios and to enhance their values. To account for the mutual interdependence of firm performance, information asymmetry, CEO incentive–compensation and ownership stability, we use a simultaneous equation system, described below, to examine the relationship between these variables:

Adjusted ROA ¼ A0 þ A1 Residual Volatility þ A2 Incentive Ratio þ A3 Return Volatility þ A4 LogAge þ A5 Insider þ A6 Insider

2

þ A7 LEV þ A8 Size þ A9 SGR þ A10 SP500 þ Year Dummies þ e

ð3:1Þ

16 Another possible channel for the effect of institutional ownership on performance is a change in the board structure, e.g., an increase in the proportion of independent directors (Gallagher et al., 2007). We find that institutional ownership stability is indeed positively associated with the latter variable. Admittedly, this specification overlooks some other channels through which ownership stability affects performance.

Residual Volatility ¼ B0 þ B1 Stability þ B2 MRET þ B3 Turnover þ B4 MV þ B5 LEV þ B6 EP þ B7 BP þ B8 DP þ B9 SGR þ B10 SP500 þ Year Dummies þ g

ð3:2Þ

Incentive Ratioi ¼ C 0 þ C 1 Stability þ C 2 Residual Volatility þ C 3 LogAge þ C 4 Size þ C 5 MRET þ C 6 Adjusted Tobin’ Q i þ C 7 Adjusted ROA þ Year Dummies þ d

ð3:3Þ

Stabilityi ¼ D0 þ D1 Adjusted ROA þ D2 Residual Volatility þ D3 Incentive Ratio þ D4 LogAge þ D5 MV þ D6 DP þ D7 SGR þ D8 Turnover þ D9 SP500 þ D10 Inv estors þ Year Dummies þ t

ð3:4Þ

In this model, the effect of institutional ownership stability is assumed to be transmitted to firm performance through reduced information asymmetry (Residual Volatility) and increased incentive-based compensation (Incentive-Ratio). Eq. (3.1) in this model is similar to Eq. (1), except that it includes Residual Volatility, a proxy for information asymmetry, rather than institutional ownership stability, as an explanatory variable. Residual Volatility is defined as the log of the standard deviation of the residuals from regression of daily stock returns on market return. Eq. (3.2) describes information asymmetry (Residual Volatility) and it is adopted from Bushee and Noe (2000). Eq. (3.3) follows Hartzell and Starks (2003) and Boumosleh and Reeb (2005). The annual market-adjusted return of the firm (MRET) appearing in Eqs. (3.2) and (3.3) is a proxy for stock performance and, as in Bushee and Noe (2000), is expected to be positively related to residual stock return volatility. Share volume turnover (Turnover) is included to control for the effect of stock liquidity on return volatility. Similarly, log of the market value of the firm (MV) controls for the effect of market capitalization, and Leverage (LEV; total debt/total assets) serves as a proxy for firm risk due to leverage. Some variables affecting the trading strategies of institutional investors are also included as control variables. These include earnings-price ratio (EP), book-price ratio (BP), dividend yield (DP), 3-year sales growth rate (SGR) and S&P 500 (SP500). The CEO incentive–compensation ratio, used in Eq. (3.3), is a measure of the executive compensation structure. Following Hartzell and Starks (2003) and Boumosleh and Reeb (2005), residual volatility, firm age, firm size, annual market-adjusted return (MRET), industry-adjusted Tobin’s Q and ROA are used as explanatory variables for the incentive–compensation ratio. Eq. (3.4) describes institutional ownership stability. Dividend yield (DP) and market capitalization (MV), used as explanatory variables for institutional ownership in the literature (Bennett et al., 2003; Del Guercio, 1996; Woidtke, 2002), are also included here as control variables. In addition, this equation includes firm age, 3-year sales growth rate (SGR), share volume turnover (Turnover), S&P 500 dummy (SP500) and the number of investors as explanatory variables. Because firm size (log total assets) and market capitalization are highly correlated (the correlation coefficient is 0.8353), we exclude the former from Eq. (3.4). Table 5 reports the estimation results for Eqs. (3.1)–(3.4). Institutional ownership stability is measured alternatively by IOP, non-zero-points and maintain-stake-points duration. Since the estimation results for the three stability measures are qualitatively similar, we only report those based on IOP. In column (1), the coefficient of residual volatility is negative and significant, suggesting that less severe (more) information asymmetry is beneficial

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Table 5 Channels of association between performance and institutional ownership stability: incentive executive compensation and information asymmetry. This table reports estimation results for a simultaneous equation model of firm performance, stock residual volatility, incentive–compensation ratio, institutional ownership stability specified in Eqs. (3.1)– (3.4). Dependent variables are listed at the top of each column. Other variables are as defined in Table 1. The model is estimated using the 3SLS procedure. T-statistics are reported in parentheses. Independent variable

Intercept

Dependent variables Industry-adjusted ROA (1)

Residual volatility (2)

Incentive-ratio (3)

IOP (4)

1.967 (0.47)

3.139*** (88.36)

1.034*** (26.35) 0.026 (1.45) 0.187*** (5.68) 0.036 (1.21)

0.971*** (18.52) 0.006*** (15.47)

0.322*** (21.69)

0.075*** (4.93)

Industry-adjusted ROA 0.421*** (10.17)

IOP Log(Age)/100 Incentive ratio Insider holding/100 (Insider holding/100)2 Residual volatility Total volatility Total debt/total assets Log(Total assets)/100 3-Year sales growth S&P 500 dummy

10.951*** (7.41) 5.968*** (9.26) 2.578 (1.47) 2.149 (1.02) 6.690*** (6.04) 15.508*** (12.02) 17.326*** (26.73) 42.038*** (2.85) 0.008*** (5.66) 3.370*** (8.94)

Stock return Turnover Market capitalization Earnings-price ratio Book-price ratio Dividend yield

0.090*** (5.65) 6.834*** (33.13) 0.0002*** (4.75) 0.019* (1.89) 0.062*** (6.33) 0.240*** (33.96) 0.086*** (25.27) 0.008*** (3.65) 0.006*** (5.16) 0.016*** (10.49)

0.00002 (1.37) 0.022*** (4.25) 0.012 (1.62) 0.112*** (24.68) 0.002 (0.69)

0.002*** (3.19) 0.040*** (20.81)

Tobin’s Q # Investors Year dummies System weighted R2 * ***

0.526*** (25.23) 0.082*** (9.59)

Yes

Yes

Yes

0.336*** (25.74) Yes

0.4942

Indicates statistical significance at the 10% level, respectively. Indicates statistical significance at the 1% level, respectively.

(harmful) for firm performance. The coefficient of CEO incentiveratio is positive and significant at the 1% level, indicating that stock-based compensation can increase the alignment between the interests of the shareholders with those of the managers, resulting in better firm performance. Overall, the results reported in Table 5 confirm information asymmetry and CEO incentive–compensation as two channels of association between firm performance and institutional ownership stability. According to the results shown in columns (1) and (2) of this table, IOP is negatively related to residual volatility and the latter is negatively related to ROA, jointly demonstrating a positive co-movement between IOP and performance. According to the results in columns (1) and (3), the coefficient of IOP in the incentive–compensation ratio equation and the coefficient of the incentive–compensation ratio in the ROA equation are both positive and significant, again jointly manifesting a positive association between IOP and performance. These results support hypothesis 3

and 4 indicating that ownership stability is associated with lower information asymmetry and a higher incentive–compensation ratio. According to column (1), both of these effects translate into better firm performance. This supports hypothesis 5. Based on the results reported in column (4), higher industry-adjusted ROA also induces institutional investors to hold the stocks for a longer period, establishing bidirectional interdependence between ownership stability and performance. In terms of economic significance of the channel effects, one standard deviation increase in IOP reduces residual volatility by 0.093 (0.421  0.222 = 0.093) and one standard deviation reduction in residual volatility (equivalent of 0.439) will increase industry-adjusted ROA by 2.94% (6.690  (0.439) = 2.94). In sum, one standard deviation increase in IOP will decrease residual volatility by 0.093 which is 0.21 of one standard deviation of residual volatility (0.093/0.439 = 0.21), resulting in an increase of industry-adjusted ROA by 0.62% (2.94%  0.21 = 0.62%). Following the

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same approach, one standard deviation increase in IOP increases the CEO incentive-ratio by 0.042 (0.187  0.222 = 0.042), resulting in an increase of 0.25% in the industry-adjusted ROA. These results

show that the two channels considered are both statistically and economically significant and their effects add up to 0.87% (0.62% + 0.25%). From Section 4.1, we know that one standard

Table A1 Correlation matrix of major variables.

Industry-adjusted ROA IOP

IOP

Prop

Non-zeropoints

Maintain-stakepoints

Log(Age)

Incentive ratio

Insider holding

Volatility

Size

Sales growth

SP dummy

0.187 <0.0001 1

0.212 <0.0001 0.427 <0.0001 1

0.220 <0.0001 0.873 <0.0001 0.622 <0.0001 1

0.221 <0.0001 0.842 <0.0001 0.596 <0.0001 0.957 <0.0001 1

0.086 <0.0001 0.514 <0.0001 0.257 <0.0001 0.555 <0.0001 0.514 <0.0001 1

0.047 <0.0001 0.031 0.0044 0.099 <0.0001 0.014 0.1999 0.087 <0.0001 0.050 <0.0001 1

0.018 0.0928 0.138 <0.0001 0.114 <0.0001 0.152 <0.0001 0.093 <0.0001 0.147 <0.0001 0.198 <0.0001 1

0.246 <0.0001 0.462 <0.0001 0.176 <0.0001 0.472 <0.0001 0.484 <0.0001 0.431 <0.0001 0.193 <0.0001 0.085 <0.0001 1

0.117 <0.0001 0.417 <0.0001 0.187 <0.0001 0.475 <0.0001 0.319 <0.0001 0.380 <0.0001 0.192 <0.0001 0.279 <0.0001 0.360 <0.0001 1

0.096 <0.0001 0.132 <0.0001 0.119 <0.0001 0.159 <0.0001 0.162 <0.0001 0.174 <0.0001 0.0001 0.9914 0.122 <0.0001 0.169 <0.0001 0.080 <0.0001 1

0.173 <0.0001 0.388 <0.0001 0.236 <0.0001 0.429 <0.0001 0.303 <0.0001 0.262 <0.0001 0.190 <0.0001 0.158 <0.0001 0.216 <0.0001 0.545 <0.0001 0.045 <0.0001

Prop Non-zero-points Maintain-stake-points Log(Age) Incentive ratio Insider holding Volatility Size Sales growth

Table A2 Robustness tests. This table reports the results of three robustness tests. In columns (1 and 2), market capitalization which is the product of number of shares and year-end stock price is used as an instrument of institutional ownership persistence (IOP). In columns (3 and 4), IOP is constructed on 3-year rolling sample period and share volume turnover is its instrument. In column (5), industry-adjusted ROA is regressed on IOP constructed over lagged 3-years. Other variables are as defined in Table 1. T-statistics are reported in parentheses.

Intercept

Market capitalization as instrument

Stability based on 3-year rolling period

Lagged regression

Industry-adjusted ROA (1)

IOP (2)

Industry-adjusted ROA (3)

IOP (4)

Industry-adjusted ROA (5)

55.828*** (9.00)

0.496*** (29.30) 0.001 (1.33)

20.018*** (11.53)

1.320*** (22.63) 0.012*** (8.06)

27.109*** (20.57)

Industry-adjusted ROA IOP Log(Age)/100 Equity-based pay Insider shareholding/100 (Insider shareholding/100)2 Return volatility Total debt/total assets Log(Total assets)/100 3-Year sales growth rate S&P 500 index dummy

128.776*** (12.67) 88.867*** (12.81) 3.723*** (2.94) 4.402*** (2.51) 3.400* (1.66) 10.307*** (3.77) 9.420*** (6.40) 234.036*** (6.65) 0.003 (1.00) 9.424*** (7.51)

0.654*** (32.60) 0.044*** (4.74)

0.193*** (10.91)

0.350 (0.70) 0.0001*** (2.78) 0.073*** (13.20) 0.032*** (5.50)

Market capitalization

1.608* (1.77) 11.054*** (5.22) 1.719*** (2.43) 2.518 (1.23) 3.655 (1.47) 19.717*** (20.39) 19.244*** (26.20) 36.261** (2.29) 0.005*** (2.99) 2.737*** (5.89)

5.376*** (9.61) 0.0001 (0.88) 0.213*** (11.68)

Yes

0.273*** (27.28) Yes

1.429*** (22.08) 0.102*** (3.53)

0.064 (1.31)

Turnover/1000 Year dummies System weighted R2 * ** ***

Yes

Yes 0.3960

Indicates statistical significance at the 10% level, respectively. Indicates statistical significance at the 5% level, respectively. Indicates statistical significance at the 1% level, respectively.

0.3718

0.388* (1.71) 11.026*** (6.22) 3.421*** (4.88) 2.239 (1.08) 4.416* (1.78) 26.482*** (28.60) 19.992*** (27.52) 8.766 (0.58) 0.016*** (4.76) 3.348*** (8.19)

Yes 0.2283

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Table A3 Treatment effect regression estimated with Heckman’s two-step sample selection procedure. This table reports the estimation results the treatment effect regression estimated with Heckman’s two-step procedure. Stability Dummy is equal to one if the stability measure of a specific firm-year observation is larger than the median, and zero otherwise. The first-step probit model include share volume turnover as a specification variable which is not included in second-step regression. In the second step, the inverse Mill’s ratio (k) constructed from the first step is included in the second step regression. Other variables are as defined in Table 1. T-statistics are reported in parentheses. IOP First-stage probit model (1)

Non-zero-points Second-stage structural model (2)

Proportion/100 Stability dummy Log(Age)/100 CEO incentive pay ratio

4.111*** (22.55) 0.302*** (3.53)

Insider shareholding/ 100

2.065*** (14.94)

Total debt/total assets Log(Total assets)/100 3-Year sales growth rate S&P 500 index dummy Turnover/1000

4.228** (2.69) 0.006*** (6.73) 0.945*** (18.25) 0.492*** (15.33)

Selection parameter (k) Year dummies ** ***

3.716*** (20.65) 0.439*** (5.16)

(0.31) 0.668

(Insider shareholding/ 100)2 Return volatility

5.806*** (5.84) 16.603*** (8.25) 2.085*** (3.20) 0.593

Yes

(0.29) 15.949*** (17.37) 18.947*** (28.08) 52.571*** (3.96) 0.008*** (6.06) 2.112*** (4.89)

2.676*** (4.39) Yes

Maintain-stake-points

First-stage probit model (3)

Second-stage structural model (4) 8.855*** (9.89) 8.835*** (7.52) 18.543*** (8.96) 1.019 (1.54) 0.655

First-stage probit model (5)

3.255*** (18.65) 0.133 (1.60)

(0.35) 1.113

2.243*** (16.23)

4.975*** (3.18) 0.006*** (6.40) 0.951*** (18.24) 0.299*** (9.53)

Yes

(0.49) 14.395*** (14.70) 18.678*** (27.88) 46.160*** (3.33) 0.008*** (5.53) 1.299*** (2.82)

4.569*** (6.39) Yes

Second-stage structural model (6) 8.659*** (9.59) 9.320*** (9.03) 18.842*** (9.92) 1.749** (2.62) 0.589 (0.31) 1.099

2.611*** (18.83)

8.023*** (5.26) 0.003*** (5.05) 0.699*** (14.25) 0.500*** (16.06)

Yes

(0.49) 13.049*** (12.80) 18.779*** (28.03) 62.298*** (4.65) 0.008*** (5.48) 1.576*** (3.71)

4.487*** (7.02) Yes

Indicates statistical significance at the 5% level, respectively. Indicates statistical significance at the 1% level, respectively.

deviation increase in IOP (0.222) is associated with a total of 2.70% increase in industry-adjusted ROA (12.142  0.222 = 2.70). The sum of the economic effect of the two channels considered here accounts for 32% (0.87%/2.7% = 32%) of institutional ownership stability effect on industry-adjusted ROA. 5. Conclusion We investigate the relationship between firm performance and institutional ownership level and stability. Our contribution includes the following: first, previous ownership studies focus on the proportion of institutional ownership, disregarding the volatility of ownership. We propose and empirically show that both shareholding proportion and the shareholding stability are important in capturing the monitoring incentives and monitoring effectiveness of institutional investors. The significance of institutional investor stability is due to the fact that institutional investors who hold the stock for a longer time period have more opportunities to learn about the firm and greater incentives to effectively monitor it. Our finding on the positive association between firm performance and institutional ownership stability is robust to alternative methodologies, disaggregation of investors into subgroups of pressure-insensitive versus pressure-sensitive, and investors with largest holdings versus other investors, as well as the choice of alternative turnover measures proposed by Gaspar et al. (2005) and Wahal and McConnell (2000). Our findings on the channels

of association show that long-term institutional ownership is associated with improved firm performance through decreasing information asymmetry and advancing the incentive-based component of executive compensation, among other channels. These findings imply that managers need to build and maintain long-term relationships with institutional investors in order to enhance firm performance, and that understanding this relationship can help investors to pick suitable stocks according to their investment horizons. Appendix See Tables A1–A3. References Aghion, P., Van Reenen, J., Zingales, L., 2009. Innovation and institutional ownership. Working Paper, Chicago Booth School of Business. Akerlof, G., 1970. The market for ‘lemons’: Quality uncertainty and the market mechanism. Quarterly Journal of Economics 84, 488–500. Almazan, A., Hartzell, J.C., Starks, L.T., 2005. Active institutional shareholders and cost of monitoring: Evidence from executive compensation. Financial Management 34, 5–34. Anderson, R.C., Reeb, D.M., 2003. Founding family ownership and firm performance. Evidence from the S&P 500. Journal of Finance 58, 1301–1328. Angrist, J.D., Krueger, A.B., 2001. Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives 15 (l), 69–85. Bennett, J.A., Sias, R.W., Starks, L.T., 2003. Greener pastures and the impact of dynamic institutional preferences. Review of Financial Studies 16, 1203–1238.

620

E. Elyasiani, J. Jia / Journal of Banking & Finance 34 (2010) 606–620

Berle, A.A., Means, G.C., 1932. The modern corporation and private property. New York, Macmillan Books. Bhojraj, S., Sengupta, P., 2003. Effect of corporate governance on bond ratings and yields: The role of institutional investors and outside directors. Journal of Business 76, 455–475. Bohren, O., Priestley, R., Odegaard, B.A., 2005. The duration of equity ownership. Working Paper, Norwegian School of Management. Boumosleh, A., Reeb, D., 2005. The Governance role of corporate insiders. Working Paper, Temple University. Brav, A., Jiang, W., Partnoy, F., Thomas, R. S., 2008. The returns to hedge fund activism. Working Paper, European Corporate Governance Institute (ECGI) – Law. Brickley, J.A., Lease, R.C., Smith, C.W., 1988. Ownership structure and voting on antitakeover amendments. Journal of Financial Economics 20, 267–291. Bushee, B., 1998. The influence of institutional investors on myopic R&D investment behavior. Accounting Review 73, 305–333. Bushee, B., Noe, C., 2000. Corporate disclosure practices, institutional investors, and stock return volatility. Journal of Accounting Research 38, 171–202. Chan, S.H., Martin, J., Kensinger, J., 1990. Corporate research and development expenditures and share value. Journal of Financial Economics 26, 255–276. Chen, X., Harford, J., Li, K., 2007. Monitoring: Which institutions matter? Journal of Financial Economics 86, 279–305. Cornett, M.M., Marcus, A.J., Saunders, A., Tehranian, H., 2007. The impact of institutional ownership on corporate operating performance. Journal of Banking and Finance 31, 1771–1794. Del Guercio, D., 1996. The distorting effect of the prudent-man laws on institutional equity investment. Journal of Financial Economics 40, 31–62. Denis, D.J., Hanouna, P., Sarin, A., 2006. Is there a dark side to incentive compensation? Journal of Corporate Finance 12, 467–488. Duggal, R., Millar, J.A., 1999. Institutional ownership and firm performance. The case of bidder returns. Journal of Corporate Finance 5, 103–117. Elyasiani, E., Jia, J., 2008. Institutional ownership stability and BHC performance. Journal of Banking and Finance 32, 1767–1781. Elyasiani, E., Jia, J., Mao, C.X., forthcoming. Institutional ownership stability and the cost of debt. Journal of Financial Markets. Gallagher, D.R., Smith, G., Swan, P.L., 2007. Institutional investor monitoring and the structure of corporate boards. SSRN Working Paper. Gaspar, J.M., Massa, M., Matos, P., 2005. Shareholder investment horizons and the market for corporate control. Journal of Financial Economics 76, 135–165. Gillan, S.L., Starks, L.T., 2000. Corporate governance proposals and shareholder activism: The role of institutional investors. Journal of Financial Economics 57, 275–305. Gompers, P., Metrick, A., 2001. Institutional investors and equity prices. Quarterly Journal of Economics 116, 229–259.

Hartzell, J.C., Starks, L.T., 2003. Institutional investors and executive compensation. Journal of Finance 58, 2351–2374. Heckman, J.J., 1979. Sample selection bias as a specification error. Econometrica 47, 153–161. Hu, Y., Zhou, X., 2008. The performance effect of managerial ownership: Evidence from China. Journal of Banking and Finance 32, 2099–2110. Jensen, M.C., Meckling, W.H., 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3, 305– 360. Jensen, M.C., Murphy, K.J., 1990. Performance pay and top-management incentives. Journal of Political Economy 98, 225–264. Karpoff, J.M., 2001. The impact of shareholder activism on target companies: A survey of empirical findings. SSRN Working Paper. Keim, D.B., Madhavan, A., 1997. Transactions costs and investment style: An interexchange analysis of institutional trades. Journal of Financial Economics 46, 265–292. McConnell, J.J., Servaes, H., 1990. Additional evidence on equity ownership and corporate value. Journal of Financial Economics 27, 595–612. McConnell, J., Muscarella, C., 1985. Corporate capital expenditure decisions and the market value of the firm. Journal of Financial Economics 14, 399–422. Morck, R., Shleifer, A., Vishny, R.W., 1988. Management ownership and market valuation: An empirical analysis. Journal of Financial Economics 20, 293– 315. Myers, S., Majluf, N., 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13, 187–221. Pound, J., 1988. Proxy contests and the efficiency of shareholder oversight. Journal of Financial Economics 20, 237–265. Rhee, S.G., Wang, J., 2009. Foreign institutional ownership and stock market liquidity: Evidence from Indonesia. Journal of Banking and Finance 33, 1312– 1324. Rubin, A., Smith, D.R., 2009. Institutional ownership, volatility and dividends. Journal of Banking and Finance 33, 627–639. Shleifer, A., Vishny, R.W., 1986. Large shareholders and corporate control. Journal of Political Economy 94, 461–488. Villalonga, B., Amit, R., 2006. How do family ownership, control and management affect firm value? Journal of Financial Economics 80, 385–417. Wahal, S., McConnell, J.J., 2000. Do institutional investors exacerbate managerial myopia? Journal of Corporate Finance 6, 307–329. Woidtke, T., 2002. Agents watching agents? Evidence from pension fund ownership and firm value. Journal of Financial Economics 63, 99–131. Yuan, R., Xiao, J.Z., Zou, H., 2008. Mutual funds’ ownership and firm performance. Evidence from China. Journal of Banking and Finance 32, 1552–1565.