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Institutional Investors, Risk/Performance and Corporate Governance Marion Hutchinsona ,⁎, Michael Seamer  b , Larelle (Ellie) Chapplea  a 

Queensland University of Technology, Australia  b University of Newcastle, Australia

Received 17 September 2013 Available online 9 January 2015

Abstract

Modern portfolio theory suggests that investo Modern investors rs minimize risk for a given level of expected return  by carefully choosing the proportions of various assets. This study sets out to determine the role of  the institutional investor in monitoring risk and � rm performance. Using a sample of Australian � rms from 2006 to 2008, our empirical study shows a positive association between � rm-speci�c risk, risk � rms with increasing institutional shareholdings. The study management policy, and performance for  � also �nds that the signi�cance of this association depends on the institutional investor's ability to in�uence management, which in turn depends on the size of ownership and whether the investee � rm doess not have pote doe potentia ntiall busi busines nesss dea dealing lingss with the inve investor stor.. We also �nd that when �rms are �nancially distressed, institutional investors engage in promoting short-term performance or exit  rather than support long-term value creation. The results are robust while controlling the potential for  endogeneity and using sensitivity tests to control for variants of performance and risk. These � ndings add to the gro growing wing body of lite literat rature ure exa examin mining ing inst institut itution ional al own owners ership hip and the importanc importancee of  understanding the role of risk-management in the risk and return relation. Crown Copyright © 2014 University of Illinois. All rights reserved.  JEL classification:  M40; G34  Keywords:  Institutional investors; Corporate governance; Risk and performance

1. Introd Introduction uction

Many researchers and practitioners have identified excessive risk-taking as the major  contributor to the global financial crisis (GFC) and highlight the importance of institutional ⁎

Corresponding author.

http://dx.doi.org/10.1016/j.intacc.2014.12 http://dx.doi.org/10.1016/j .intacc.2014.12.004 .004 0020-7063/Crown Copyright © 2014 University of Illinois. All rights reserved.

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M. Hutchin Hutchinson son et al. / The Inter Internatio national nal Journal of Account Accounting ing 50 (2015) 31 – 52 52

investorss in this investor this scen scenario ario (Callen & Fang, 2013; Gorter & Bikker, 2013). 2013). The GFC highlights the importance of an appropriate corporate governance structure for managing risk. The  purpose of this study is to determin determinee the role of institut institutional ional investor investorss in the risk –return relation in a period of increasing risk-taking, the GFC. The question is whether institutional investors actively engage or seek out information on the risk-management practices of their   portfolios  portfoli os or whether they pursue aggressive risk-taking and favor short-term profits. This question is particularly important in a country such as Australia, where employers in all sectors are required to contribute to a compulsory employee superannuation scheme.1 This means that Australia has the fourth-largest pension fund pool in the world, creating enormous investment opportunities.2 In addition, the global financial crisis provides a unique setting to determine the consequences of institutional investors' involvement in, and influence over, corporate management and board behavior in the period leading up to the crisis. The OECD countries that experienced the fastest economic growth in the 15 years prior to the global financial crisis –  including the US, the UK, Australia, and Ireland –  were also the countries with wi th th thee gr grea eate terr fi fina nanc nciial se sect ctor or de dere regu gula lati tion on (Pom Pomfr fret et,, 20 2009 09). ). How Howev ever er,, fi fina nanc ncial ial deregulation increases vulnerability to a financial crisis (Pomfret, (Pomfret, 2009). 2009). When the global financial crisis unfolded, it was regarded as unexpectedly sudden (Sidhu (Sidhu & Tan, 2011). 2011). Alth Al thou ough gh it is ge gene nera rally lly ac acce cepte pted d th that at th thee Au Aust stra rali lian an ma marke rkett su suff ffer ered ed le less ss of an economic downturn than other economies (Reserve (Reserve Bank of Australia, RBA, 2010), 2010), the downt do wntur urn n in incr crea ease sed d bu busi sine ness ss ri risk sk th thro rough ugh,, in inte terr al alia ia,, it itss im impac pactt on eq equi uity ty an and d cr cred edit  it  markets (Xu, (Xu, Jiang, Fargher, & Carson, 2011). 2011). While we can evaluate the impact of the global financial crisis in hindsight, there was a climate of uncertainty in the investment  community from the global effects, and it is in this environment of escalating uncertainty that we examine the risk-return relationship. In ou ourr stu study, dy, we mo model del fi firm rm pe perfo rform rmanc ancee as a fun funct ction ion of fi firmrm-sp spec ecifi ificc ri risk, sk, ri riskskmanagement manageme nt practices and the size of institutional ownership. ownership. While controllin controlling g for potentia potentiall endogeneity, we determine whether the size of institutional investor has any influence over  the association between risk and performance, measured as return on assets. The results of  the 3SLS regression show that increasing levels of firm-specific risk and a comprehensive risk-man risk -managem agement ent poli policy cy is asso associat ciated ed with incr increasi easing ng inst institut itutiona ionall owne ownershi rship p and firm  performance.  performa nce. Further investig investigation ation reveals that this associat association ion is only signific significant ant for   pressure-resista  pressure -resistant nt institut institutional ional investors. Research defines pressure pressure-resista -resistant nt investor investorss as those who do not have the scope for economic bonds and who are less averse to challenging manageme mana gement. nt. Pres Pressure sure-sen -sensiti sitive ve inst institut itutiona ionall inve investor storss have the pote potentia ntiall for busi business ness relationss with investee firms and are therefore less likely to challenge management for fear of  relation losing business (Brickley, (Brickley, Lease, & Smith, 1988). 1988). This study contributes to the literature relating to the influence of instit institutional utional investors investors on the relationship between risk and firm performance in several ways. First, our results demonst dem onstrat ratee tha thatt fir firm-s m-speci pecific fic ris risk k and ris risk-m k-mana anageme gement nt pra practic ctices es are an imp import ortant  ant  determinant of the size of institutional investment. Our study helps to explain previous conflicting results on whether institutional investors consider the governance practices of  1

Employers are required by law to pay an additional amount based on a proportion of an employee's salaries and wages (currently 9.25%) into a complying superannuation fund. 2 http://www.asx.com.au/documents/research/ �nancial_sector_factsheet.pdf .

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the firms in which they invest. This information can enable diversified investors to structure their portfolios in accordance with their risk preference. Second, we contribute to the question on whether large institutional investors are active in ways that improve firm  performance by investigating whether institutional investors monitor the risk and  performance relationship. Third, further analysis reveals that institutional investors exit  financially-distressed firms in the long term. The results of this study are important because they shed some light on the influence of  institutional investors in periods of increasing risk. Since the global financial crisis, researchers have called for more research into this area. Bebchuk and Weisbach (2010: 6) wrote: The financial crisis has intensified the ongoing debate about the role that   shareholders should play in corporate governance. To some, increasing shareholder   power and facilitating shareholder intervention when necessary is part of the necessary reforms. To others, activism by shareholders who potentially have shortterm interests is part of the problem, not a solution. To what extent (and when) can  shareholder activism improve firm value and performance? To what extent (and  when) can shareholder activism produce distortions that make matters worse?  Research by financial economists that seeks further light on these questions will   provide valuable input to the questions with which decision-makers are wrestling. 2. Background and hypothesis development

The traditional capital asset pricing model (CAPM) suggests that there is no economic gain to diversified investors from reducing firm-specific risk, because they will not receive a higher  risk premium on the asset. Only non-diversifiable (systematic or market) risk (beta) is rewarded. A sufficiently diversified portfolio limits the risk exposure to systematic risk only, and the level of systematic risk is not affected by the failure of any one firm. Therefore, excessive managerial risk-taking is not considered problematic to a diversified shareholder because one firm's failure will not affect a diversified investor's portfolio in any directional way. Gordon (2010: 4) explains diversified investor attitude to risk: “Competitors of the failed firm may do better; suppliers to the failed firm may do worse, but the consequences are ‘unbiased.’ If all firms are taking good bets, however, then on average the diversified investor  will be better off.”   Consequently, shareholders are risk neutral. Institutional investors can invest in different equities to diversify risk and maintain liquidity. We expect a positive association between the size of institutional ownership and firm risk, because research tells us that shareholders prefer more risk (Jensen & Meckling, 1976; Pathan, 2009) due to the anticipated higher return for greater risk. Subsequently, firms are likely to take on more highrisk projects to attract greater institutional investment. This leads to our first hypothesis: H1.  The level of firm risk is positively associated with institutional ownership.

2.1. The role of institutional investors and risk-management 

In contrast to the CAPM's assumption of a trade-off of higher risk for higher return, modern portfolio theory suggests that investors minimize risk for a given level of expected

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M. Hutchinson et al. / The International Journal of Accounting 50 (2015) 31 – 52

return by carefully choosing the proportions of various assets. While more investment risk  typically results in higher expected returns, it can also lead to a mismatch between portfolio assets and liabilities, thereby eroding beneficiaries' future returns as demonstrated by the recent global financial crises. Modern portfolio theory suggests that even large institutional investors might want to reduce firm-specific risk because large investors internalize, at least partially, the consequences of firm failure (systemic risk) and are wary of excessive risk-taking (Gordon & Muller, 2011). These firm-specific risks include such things as the risk created  by poor risk-management practices. Consequently, institutional investors are more likely to invest in firms with governance practices that concur with their fiduciary duties (Hawley & Williams, 2000) and reduce their costs of monitoring (Bushee & Noe, 2000). Institutional investors with a large investment in a firm have a direct incentive to seek  more comprehensive information on the risk-management practices of their portfolio firms and to respond through exit or engagement (Harper Ho, 2010). These large institutional investors influence the firm directly through ownership in the investee firm or indirectly by trading their shares in the firm (Gillan & Starks, 2003). Heavy selling by these investors can cause the share price to decline, or can be interpreted as bad news, thereby triggering sales by other investors, further contributing to a decline in share price (Baysinger, Kosnik, & Turk, 1991; Parrino, Sias, & Starks, 2003) and an ensuing increase in the cost of capital. While some research finds that institutional investors reduce agency costs (e.g., Gillan & Starks, 2000, 2007), others argue that they can also create agency costs (Stapledon, 1996; Webb, Beck, & McKinnon, 2003). They suggest that there are several disincentives for  institutional investors to actively participate in the governance of portfolio firms. Webb et al. (2003)   refer to: high transaction costs; the use of index-tracking funds; the inability of  investors to influence company strategy; and, the free-ride of other investors on the acquisition cost of the institutional investor. Agency theory suggests that the divergent risk preferences of risk-neutral (diversified) shareholders and risk-averse managers necessitate monitoring by the board (Jensen & Meckling, 1976; Subramaniam, McManus, & Zhang, 2009). Consequently, without  monitoring, risk-averse managers may reject profitable (risky) projects that are attractive to diversified investors. The challenge for diversified investors is to encourage managers to take all positive net present value investment opportunities despite the likelihood that some risky projects will turn out badly, thus increasing the probability of bankruptcy. Despite the assertion by some researchers that diversified investors are not concerned with corporate governance of individual firms (Callen & Fang, 2013), we suggest that how firms manage risk is important to all types of investors, particularly those with substantial “skin in the game.”   Firms design their governance practices to monitor managerial  behavior and to curb suboptimal risk-taking. Recent research shows that suboptimal risktaking is reduced by corporate governance, as firm-specific risk is negatively associated with the governance variables of board independence and director qualifications (Christy, Matolcsy, Wright, & Wyatt, 2013).3

3

Christy et al. (2013)  use stock return volatility as their measure of risk when testing the association between corporate governance variables and risk.

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Information regarding firm-level risk-management practices might increase the quantity and quality of information available to the market and better enable institutional investors to structure their portfolios in accordance with their risk preferences. Institutional investors rely on the information reflected in market prices and on financial agents' responses to those prices. If market efficiency is limited, the market may only imperfectly account for  risk. Incorporating risk-management factors into investment analysis may offer a way for  investors to achieve higher risk-adjusted returns. Firms' risk-management includes monitoring the level of risk the firm is exposed to while keeping in mind the desire to maximize returns. The firm may have a separate committee (a risk-management committee) that advises the board on the firm's management of the current risk exposure and future risk strategy (Walker, 2009), thus shifting the risk-monitoring costs from the institutional investor to the firm. Consequently, we anticipate that, given institutional investors are diversified investors with a propensity to invest in high-risk firms, they will increase their investment in firms with a comprehensive risk-management policy. We expect a positive association between the size of institutional ownership and comprehensive risk-management policy,4 whether due to institutional investor pressure to improve risk-management or from efforts of portfolio firms in improving risk-management practice to attract higher institutional investment. H2.   The comprehensiveness of firms' risk-management policy is positively associated with the size of institutional ownership.

2.2. Risk, performance and institutional investors

Institutional investors (and portfolio managers) are under pressure to show short-term returns, as they are rewarded and reviewed based on quarterly, or at most, annual  performance results (Aguilera, Rupp, Williams, & Ganapathi, 2007; Baysinger et al., 1991; Graves, 1988). As such, these investors are predisposed to supporting investments when there is an immediate association with profits, such as mergers and acquisitions, to maintain short-term competitiveness rather than taking a long-term view in their investment decisions (Graves, 1988). This study empirically determines whether the level of institutional investment is associated with a positive risk/performance relationship. Do institutional investors invest in high-risk firms in anticipation of making short-term returns? Firm risk, as a measure of the firm's information environment and the risk of its operating environment, is a potentially important determinant of firm performance. Bowman (1980)   finds that the relationship between risk and return could be negative when accounting measures are used as measures of return.  Bowman (1980) tested his hypothesis that prosperous firms will avoid high-risk investments, because the consequences of failure will affect their reputation, while poorly-performing firms will pursue risky investments in the hope that high returns will reverse their poor   performance. Bowman tested this hypothesis by examining firms' annual reports over 

4

Comprehensive risk-management is evidenced by written policies with separate committee oversight.

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a nine-year period for over 1500 companies in 85 different industries, and found that  higher returns (ROE) carried lower risk, and lower returns carried higher risk (Raynor, Ahmed, & Shulz, 2010). Raynor et al. (2010: 105)   state that  “risk is prospective—forward-looking and unavoidably based on subjective assessments of the probability of future outcomes—while results (the returns to the investment) are retrospective and much more nearly in the realm of facts.”  The objective of this study is not to prove or disprove Bowman's hypothesis, only to report what we have observed from the data. We posit that large institutional shareholders are more likely to influence the risk/   performance relation and are more likely to influence proxy voting if their demands are not  met (Johnson, Schnatterly, Johnson, & Chiu, 2010). Subsequently, we expect the size of  institutional investment to influence the association between risk and performance. The more “skin in the game”  institutional investors have, the greater the motivation to monitor  the risk/return of the investee firm. Recently, Callen and Fang (2013) find that institutional ownership by public pension funds is significantly negatively associated with the risk of  future stock price crash. Based on the preceding discussion, the following hypothesis is  posited: H3. A positive association between risk and performance depends on the size of  institutional investment. 3. Method

3.1. Sample selection

The sample period 2006–2008 is relevant, as the study looks at the association between risk and performance in a period of escalating risk (the global financial crisis). The corporate governance profile of the investee firms is examined from 2006, as this provides a sufficient time lag to account for the Australian Securities Exchange Corporate Governance Council best practice guidelines that were first introduced in 2004 (Australian Security Exchange Corporate Governance Council, 2007). The global financial crisis essentially impacted Australia by the end of January 2008, when the stock market dropped 17% with an ensuing bear market (Brown & Davis, 2008); accordingly, the sample period ends in 2008. The sample was constructed from the Top 400 firms in terms of market capitalization listed on the ASX, which was reduced to 316, as each firm is required to be in the Top 400 for each year (2006–2008) and to lag variables into 2005. The sample was further reduced due to the lack of institutional investor data for some companies. The final sample consists of an unbalanced panel data set of 256 firms listed on the Australian Securities Exchange for the years 2006 to 2008 (712 observations), based on the availability of the relevant data. Ownership data is collected from the Osiris database, and archival data on firms' corporate governance characteristics is hand collected from company annual reports. Financial variables are provided by Aspect FinAnalysis. Risk measures are obtained from the Centre for Research in Finance (CRIF) risk measurement service of the Australian School of  Business, University of New South Wales.

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3.2. Research model 

In this study we use instrumental variables in the form of 3SLS regression models to capture cross-equation effects, as error terms of individual equations in the system are assumed to be contemporaneously correlated. Also, the 3SLS estimation technique is more suitable for cross-sectional studies, where some of the institutional investors own multiple equity stakes in different firms across industries with different levels of risk. As a result, institutional ownership, risk, and performance issues can affect each other in various ways. These interactions can be captured through the 3SLS estimation technique. To eliminate the potential endogeneity problem or two-way causality, we endogenize risk, risk-management, and performance (ROA and Tobin's Q). The three equations are solved as a system of simultaneous equations using the three-stage least squares (3SLS) estimation method. The effect of institutional ownership and risk-management policy on firm-specific risk is specified by Eq. (1). The effect of firm-specific risk and institutional ownership on risk-management is specified in Eq. (2). Finally, the effect of firm-specific risk, risk-management, and institutional ownership on ROA is specified in Eq. (3). The equations set out below are used to test   H1, H2, and H3 simultaneously. To test hypothesis 1, the endogenous variable is the industry-adjusted measure of  firm-specific risk, with instrumental variables of net gearing,5 net interest coverage,6 and risk-management. The level of risk may be due to large institutional investors increasing  pressure on management to invest in risky projects, or it could be that institutional owners invest in firms with high risk. We use the following equation in the model to test  H1: INDADJSTD  ¼ β0  þ  β 1 ALLINST þ  β 2 NETGEARING þ  β 3 NETINTCOV þ β 3 RISKMGT þ  ε :

ð1Þ

 Next, we test the impact of increasing institutional ownership on firms' riskmanagement practices by considering the interaction between the size of institutional investment and firm-specific risk on the comprehensiveness of firms' risk-management   policy. The instrumental variable is firm size measured as the log of total assets. The regression equation for H2 is: RISKMGT  ¼ β0  þ  β 1 INDADJSTD þ  β 2 ALLINST þ  β 3 LNTA þ  ε :

ð2Þ

We test the impact of increasing institutional ownership on firms' performance by considering the interaction between the size of institutional investment and risk-management  for a given level of firm-specific risk. When institutional investors own a large proportion of  the firm's shares and the firm has a comprehensive risk-management policy, we expect a  positive association between risk and performance. With high ownership concentration, institutional investors have the incentive and ability to pressure management to monitor risk  5 6

(Short term debt + long term debt  −  cash) / shareholder's equity. Earnings before interest and tax / net interest (income −  expense).

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M. Hutchinson et al. / The International Journal of Accounting 50 (2015) 31 – 52

and performance more closely. The instrumental variables are prior-year performance, market capitalization, industry, and year. The model for testing H3  is: PERFORMt  ¼ β0  þ  β 1 PERFORMt −1 þ  β 2 INDADJSTD þ  β 3 RISKMGT þ  β 4 ALLINST þβ5 LNMKTCAP þ  β 6 INDY þ  β 7 YEAR  þ  ε :

ð3Þ 3.3. Endogenous variables 3.3.1. Firm performance As there are various measures of firm performance used in prior research, this study uses two measures of performance: accounting performance and firm value. Accounting  performance, the firm's return on assets (ROAt ), is likely to be influenced by the firm's managerial risk-taking behavior. ROAt  is an indication of the ability of the firm to produce accounting-based revenues in excess of actual expenses from a given portfolio of assets measured as amortized historical costs (Carter, D'Souza, Simkins, & Simpson, 2010). ROAt  is measured as net income plus interest expense multiplied by (1-corporate tax rate) and divided by total assets less outside equity interests at year end t. As a measure of firm value, we use Tobin's Q. Tobin's Q is measured as the market  value of the firm divided by replacement value of assets. If Tobin's Q is greater than one, the market value of shareholder and creditor investment is greater than the amortized historical cost of the assets. Because Tobin's Q measures the market value of shareholder  and creditor investment, it encompasses a market assessment of the investment opportunity set and future cash flows of the firm. This study adopts a simple measure of Tobin's Q as adopted by Agrawal and Knoeber  (1996). The market value of the firm is the market value of equity (total number of issued shares by the ordinary share price at year end) and debt (total of short and long-term debt). The replacement value of the firm's assets is the book value of total assets. This simple measure of Tobin's Q is adopted because it is highly correlated (0.93) with the traditionally inflation-adjusted figures and for ease of computation. This measure is particularly relevant, as Australian firms report the re-valued asset figures for property, plant, and equipment. 3.3.2. Risk  Risk is measured by the standard deviation of the firm's daily stock returns for each fiscal year (STDEV). It is measured as the standard deviation of the rate of return on equity for the company, and is expressed as a rate of return per month computed from the (continuously compounded) equity rates of return for the company's equity.7 The standard deviation is a measure of historical volatility, and is used by investors to gauge the amount  of expected volatility. This measure encompasses both systematic and unsystematic risk  7

It is measured over the four-year period ending at the companies' annual balance date. All measurable monthly returns in the four-year interval are included. Individual monthly returns measure total shareholder returns for the company, including the effects of various capitalization changes such as bonus issues, renounceable and nonrenounceable issues, share splits, consolidations, and dividend distributions.

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(Carr, 1997), and has been used extensively. We control for industry risk by using ∂ i net of  industry risk (INDADJSTD), which results in a measure closer to firm-specific risk.

3.3.3. Risk management  Companies are classified into one of three groups depending on the comprehensiveness of their Enterprise Risk Management (ERM) strategies, as determined from their  disclosures as required by ASX (measured as RISKMGT). Highest ranked are companies with formal ERM policies with oversight delegated to a dedicated committee separate from the board (ranked 2); next ranked are companies with formal ERM policies but no separate committee oversight (ranked 1); and companies with no formal ERM policies are ranked lowest (0).

3.4. Exogenous variable 3.4.1. Institutional ownership The Osiris database classifications are used to identify institutional ownership. The investor categories include banks, insurance companies, pension funds, other investment  firms,8 and hedge funds. For each category, institutional investment is computed as the  proportion of institutional investors' shares of total shares outstanding.

3.5. Instrumental variables

We use prior-year performance reported return on assets (ROAt  − 1) Tobin's Q (TOBQt  −  1), as it is likely to have an impact on current performance. Inclusion of the lag of the dependent variable is likely to mitigate concerns over reverse causality and omitted variables. To the extent that omitted correlated variables are relatively stable, their effects can be captured by lagged values of the dependent variable. Moreover, using a lagged dependent variable makes reverse causality less plausible, because if profitability determines the levels of institutional stockholding, why would institutional stockholding continue to explain future profitability even when contemporaneous profitability is controlled for? Firm size is often included as a control variable in previous corporate governance studies (e.g., Pathan, 2009), as an increase in firm size is likely to lead to greater monitoring and hence the greater need for corporate control mechanisms. We measure size as the log of market capitalization (LNMKTCAP), and the log of total assets (LNTA).  NETGEARING and NETINTCOV (net interest coverage) are included for their effect on risk. We include industry (IND) by a dummy variable of 1 if in the metals and mining industry, and 0 otherwise, as this industry is the most represented in the sample. Finally, we include year dummy variables to control for the effects of the GFC on the risk and  performance relation.

8

Similar to pension funds, these are large listed

�rms

that exist solely to invest in other  � rms.

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4. Results

Table 1   reports the descriptive statistics for the variables related to institutional investment, firm governance, and financial performance characteristics for the three years. The results show that there is very little change in the governance variables over time, and this is to be expected (see   Brown, Beekes, & Verhoeven, 2011). However, there is considerable growth in institutional ownership from 2006, with a mean of 44% of issued shares, to 2008, where institutional investment is 71% of issued shares on average. We also identify that the period under investigation is one of increasing total risk. Total risk  (STDEV) is 10.07 in 2006, growing to 13.16 in 2008. Industry adjusted risk is 5.55 in 2006 and 7.96 in 2008. The results of correlating the transformed variables are reported in Table 2. There are significant correlations between risk-management, risk, performance, and institutional ownership, providing some support for the hypotheses. Variance inflation statistics were run to test the issue of multicollinearity. The VIFs were all below 3, indicating that  multicollinearity is not an issue with the data. 4.1. GLS regression results

Table 3 provides the results of the 3SLS regression model. Column 1 of   Table 3 Panel A shows the effect of institutional ownership on firm-specific risk, as specified by Eq. (1). Column 2 shows the effect of risk and institutional ownership on risk-management (Eq. (2)). Column 3 shows the effect of firm-specific risk, risk-management, and institutional

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4. Results

Table 1   reports the descriptive statistics for the variables related to institutional investment, firm governance, and financial performance characteristics for the three years. The results show that there is very little change in the governance variables over time, and this is to be expected (see   Brown, Beekes, & Verhoeven, 2011). However, there is considerable growth in institutional ownership from 2006, with a mean of 44% of issued shares, to 2008, where institutional investment is 71% of issued shares on average. We also identify that the period under investigation is one of increasing total risk. Total risk  (STDEV) is 10.07 in 2006, growing to 13.16 in 2008. Industry adjusted risk is 5.55 in 2006 and 7.96 in 2008. The results of correlating the transformed variables are reported in Table 2. There are significant correlations between risk-management, risk, performance, and institutional ownership, providing some support for the hypotheses. Variance inflation statistics were run to test the issue of multicollinearity. The VIFs were all below 3, indicating that  multicollinearity is not an issue with the data. 4.1. GLS regression results

Table 3 provides the results of the 3SLS regression model. Column 1 of   Table 3 Panel A shows the effect of institutional ownership on firm-specific risk, as specified by Eq. (1). Column 2 shows the effect of risk and institutional ownership on risk-management (Eq. (2)). Column 3 shows the effect of firm-specific risk, risk-management, and institutional ownership on ROA (Eq. (3)). Panel B shows the same associations using Tobin's Q as the measure of firm performance. In Panel A and B of   Table 3 we find that firm-specific risk (INADJSTD) is positively associated with institutional ownership ( B  = 0.072; p b 0.001), thus supporting H1, indicating that institutional investors desire more risk-taking. While testing H2, we find risk-management policy is negatively associated with firm-specific risk ( B = − 0.059;  p b 0.001) and positively associated institutional ownership ( B  = 0.004; p b  0.001), suggesting that institutional investors increase their ownership in firms with a more comprehensive risk-management policy, because these firms bear the cost of monitoring suboptimal risk-taking. Column 3 shows the results from testing H3. Firm accounting performance (ROA) is  positively associated with institutional ownership ( B  = 0.001; p b   0.001). Thus, even while directly and simultaneously controlling for endogeneity with 3SLS, the study shows that increasing the size of institutional ownership is associated with increasing accounting  performance. We interpret this result as suggesting that large investors have sufficient  ownership (“skin in the game”) to encourage investee firms to monitor and increase short-term returns.  Next, we determine whether the increase in short term accounting performance is made to the detriment of long-term firm value (Tobin's Q), which includes the markets' subjective assessment of future performance. Table 3 Panel B shows the same association as Panel A when using Tobin's Q as the measure of performance. Tobin's Q is positively associated with the size of institutional ownership ( B  = 0.025; p b  0.01). This result is

4   2  

Table 2 Pair-wise correlations of variables. This table provides the correlation coefficients of variables. The tests are significant at 0.01***; 0.05**; 0.10* levels (N = 676).

ROAt TOBQt  INDADJSTD ALLINST RISKMGT  NETINTCOV  NETGEAR  LNMKTCAP INDY 2006 2007 2008

ROAt

TOBQt

1 − 0.052 − 0.406*** 0.177*** 0.223*** 0.001 − 0.003 0. 194*** − 0.230*** − 0.031 0.009 0.021

1 0.323*** − 0.108** − 0.113** − 0.003 − 0.208*** − 0.025 0.161** 0.001 0.094** − 0.094***

INDADJSTD

ALLINST

RISKMGT

NETINTCOV

NETGEAR

1 0.070* − 0.004 − 0.020 0.051 − 0.010 − 0.039

1 0.184*** − 0.073* − 0.003 0.005 − 0.002

LNMKTCAP

INDY

2006

2007

2008

1 − 0.141*** − 0.287***

0.004 − 0.145*** − 0.321*** 0.428*** − 0.102** − 0.080* 0.178***

1 0.337*** − 0.023 0.124*** 0.290*** 0.005 − 0.175*** − 0.128*** 0.296***

1 − 0.004

0.19.***7 0.312*** − 0.174*** − 0.111** − 0.004 0.111**

1 − 0.015 − 0.091**

0.109*** − 0.019

1 − 0.227***

0.119*** 0.102***

1 − 0.476*** − 0.495***

1 − 0.529***

1

Definitions: ROAt: current year ROA [Net Income + Interest Expense ∗ (1 −  Corporate Tax Rate)] / [Total Assets −  Outside Equity Interests; TOBQt: the market value of equity and debt divided by the book value of total assets in year t; INDADJSTD: total risk is calculated as the standard deviation of firm daily stock returns for each fiscal year, which is subtracted from the industry standard deviation of daily stock returns; ALLINST: institutional ownership as a percentage of total issued shares; RISKMGT: dummy variables 2 = rigorous RM policies with oversight of a dedicated (separate) committee, 1 = formal RM policies but no separate committee oversight (oversight by board), 0 = no formal RM policies (or dedicated committee oversight); LNMKTCAP: closing share price on the last day of the company's financial year  ∗  number of shares outstanding at the end of the period, logged; NETGEARING: (Short term debt + long term debt  −  cash) / shareholders' equity; NETINTCOV: earnings before interest and tax / net interest (income −  expense); INDY: dummy variable 1 for metals and mining industry, 0 otherwise; YEAR: dummy variable 1 for 2006, 2007, and 2008; 0 otherwise.

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Table 3 Determinants of firm performance (ROA and Tobin's Q). This table presents the instrumental variables in the form of 3SLS regression models. The first-stage model is not reported for parsimony. The results of simultaneously testing the three equations for the role of institutional investors on risk, risk management, and performance are  presented. Two-tailed Z statistics in parentheses significant at 0.01***; 0.05**; 0.10* levels. Panel A: ROAt INADJSTD CONS INDADJSTD RISKMGT ALLINST  NETGEARING  NETINTCOV LNMKTCAP LNTA ROAt  −  1 / TOBQt  − INDY

27.160 (21.32)***

RISKMGT

Panel B: TOBQt   ROAt

1.584 − 0.212 (13.47)*** (− 2.04)** − 0.059 0.003 (− 20.85)*** (0.72) − 16.478 − 0.147 (− 17.46)*** (− 2.37)*** 0.072 0.004 0.001 (6.73)*** (7.58)*** (3.08)*** 0.044 (0.40) − 0.00004 (− 0.14) 0.021 (3.33)*** 0.003 (0.52) 1 0.573 (13.07)*** − 0.119 ( 4.39)***

INADJSTD 25.921 (17.65)***

RISKMGT

TOBQt  

1.156 − 30.129 (8.48)*** (7.61)*** − 0.061 1.029 (− 15.67)*** (5.39)*** − 15.395 − 3.311 (− 13.43)*** (− 1.19) 0.064 0.004 0.025 (6.08)*** (7.27)*** (2.12)** 0.087 (0.58) − 0.0001 (− 0.58) 1.493 (6.96)*** 0.007 (0.85) 0.092 (0.78) − 6.912 ( 5.97)***

M  .H  u  t    c  h   i    n  s   o  n  e  t    a  l    . /   T   h   e I    n  t    er   n  a  t    i    o  n  a  l    J    o  ur   n  a  l    o  f   A   c  c  o  u  n  t    i    n  g  5   0   (   2   0  1   5   )    3  1  –  5  2 

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Table 3 Determinants of firm performance (ROA and Tobin's Q). This table presents the instrumental variables in the form of 3SLS regression models. The first-stage model is not reported for parsimony. The results of simultaneously testing the three equations for the role of institutional investors on risk, risk management, and performance are  presented. Two-tailed Z statistics in parentheses significant at 0.01***; 0.05**; 0.10* levels. Panel A: ROAt INADJSTD CONS INDADJSTD RISKMGT ALLINST  NETGEARING  NETINTCOV LNMKTCAP LNTA ROAt  −  1 / TOBQt  − INDY 2007 2008  N CH12  p

27.160 (21.32)***

RISKMGT

Panel B: TOBQt   ROAt

1.584 − 0.212 (13.47)*** (− 2.04)** − 0.059 0.003 (− 20.85)*** (0.72) − 16.478 − 0.147 (− 17.46)*** (− 2.37)*** 0.072 0.004 0.001 (6.73)*** (7.58)*** (3.08)*** 0.044 (0.40) − 0.00004 (− 0.14) 0.021 (3.33)*** 0.003 (0.52) 1 0.573 (13.07)*** − 0.119 (− 4.39)*** 0.005 (0.43) − 0.013 (− 0.71) 688 688 688 382.37 559.36 513.49 0.00 0.00 0.00

INADJSTD

RISKMGT

TOBQt  

25.921 (17.65)***

1.156 − 30.129 (8.48)*** (7.61)*** − 0.061 1.029 (− 15.67)*** (5.39)*** − 15.395 − 3.311 (− 13.43)*** (− 1.19) 0.064 0.004 0.025 (6.08)*** (7.27)*** (2.12)** 0.087 (0.58) − 0.0001 (− 0.58) 1.493 (6.96)*** 0.007 (0.85) 0.092 (0.78) − 6.912 (− 5.97)*** 0.955 (2.21)** − 1.764 (− 2.41)*** 675 675 675 222.08 406.06 195.62 0.00 0.00 0.00

Definitions: INDADJSTD: total risk is calculated as the standard deviation of firm daily stock returns for each fiscal year, which is subtracted from the industry standard deviation of daily stock returns; ROAt: current year ROA [Net  Income + Interest Expense ∗ (1 −  Corporate Tax Rate)] / [Total Assets −   Outside Equity Interests; ROAt  − 1:  prior year ROA; TOBQt: the market value of equity and debt divided by the book value of total assets in year t; TOBQt  −  1: prior year TOBQ; RISKMGT: dummy variables 2 = rigorous RM policies with oversight of a dedicated (separate) committee, 1 = formal RM policies but no separate committee oversight (oversight by  board), 0 = no formal RM policies (or dedicated committee oversight); ALLINST: institutional ownership as a  percentage of total issued shares; LNMKTCAP: closing share price on the last day of the company's financial year  ∗  number of shares outstanding at the end of the period, logged; NETGEARING: (Short term debt + long term debt  −  cash) / shareholders' equity; NETINTCOV: earnings before interest and tax / net interest (income − expense); LNTAt = Total assets, logged; INDY: dummy variable 1 for metals and mining industry, 0 otherwise; YEAR: dummy variable 1 for 2006, 2007, and 2008; 0 otherwise.

contrary to  Velury, Reisch, and O'Reilly's (2003)  prediction that institutional investors  prefer short term profit (accounting profit) over long-term future returns (Tobin's Q). Consequently, this paper provides some preliminary findings that show that institutional

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Table 4 Determinants of firm performance (ROA and Tobin's Q). This table presents the second-stage results of the Heckman's (1979) two-step procedure of the determinants of institutional ownership, which is a probit model that  determines when the dependent variable (institutional ownership) in the second stage is not missing for   performance. The first-stage is not reported for parsimony. This table presents the second-stage of testing the determinants of firm performance using random effects GLS with cluster robust standard errors. MILLS is the correction term computed from the probit model in the first stage. A three-way interaction term for: risk, risk-management, and institutional ownership test the three equations. Two-tailed Z statistics in parentheses significant at 0.01***, 0.05**, and 0.10* levels.

CONS ROAt-1/TOBQt-1 INDADJSTD ALLINST RISKMGT INDADJ ∗  RISKMGT LNMKTCAP INDY MILLS 2007 2008  N Firms Wald Rsq

∗

ALLINST

ROAt

TOBQt  

Coef.

Coef.

0.023 (0.42) 0.450 (6.01)*** − 0.003 (− 2.19)** 0.00003 (0.20) − 0.003 (− 0.46) 0.00002 (2.18)** 0.003 (0.76) − 0.040 (2.59)*** 0.025 (1.36) − 0.003 (− 0.36) − 0.009 (− 1.16) 710 255 352.18*** 0.775

− 1.62

(− 1.31) 0.372 (3.59)*** 0.154 (3.87)*** 0.008 (2.14)** 0.082 (0.47) − 0.001 (− 3.22)*** 0.115 (1.90)* 0.109 (0.31) 0.628 (1.36) 0.089 (0.57) − 0.755 (− 4.72)*** 697 250 100.16*** 0.638

Definitions: ROAt: current year ROA [Net Income + Interest Expense ∗  (1 −   Corporate Tax Rate)] / [Total Assets −  Outside Equity Interests]; ROAt  −  1: prior year ROA; TOBQt: the market value of equity and debt divided by the book value of  total assets in year t; TOBQt  −  1: prior year TOBQ; INDADJSTD: total risk is calculated as the standard deviation of  firm daily stock returns for each fiscal year, which is subtracted from the industry standard deviation of daily stock  returns; ALLINST: institutional ownership as a percentage of total issued shares; RISKMGT: dummy variables 2 = rigorous RM policies with oversight of a dedicated (separate) committee, 1 = formal RM policies but no separate committee oversight (oversight by board), 0 = no formal RM policies (or dedicated committee oversight); INDADJ ∗  RISKMGT ∗  ALLINST: interaction of risk, risk-management, and institutional ownership; LNMKTCAP: closing share price on the last day of the company's financial year  ∗  number of shares outstanding at the end of the  period, logged; INDY: dummy variable 1 for metals and mining industry, 0 otherwise; MILLS: Inverse Mills Ratio computed from the first stage regression; YEAR: dummy variable 1 for 2006, 2007, and 2008; 0 otherwise.

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shareholder activism is associated with increasing short-term accounting performance and has some influence in creating long-term firm value.

4.2. Robustness tests

To test the validity of our results, we use random effects generalized least square (GLS) regression, estimated with firm clustered-robust (also referred to as Huber –White) standard errors on firm to control for any serial dependence in the data (Gow, Ormazabal, & Taylor, 2010; Petersen, 2009). While testing the hypotheses, we control for the potential problem of selection bias with the two-stage Heckman (1976) procedure, because our sample may  be biased-based. We first compute the Inverse Mills Ratio (MILLS) (Heckman, 1976; Johnston & DiNardo, 1997) from the model that predicts the factors that are associated with institutional ownership. The dependent variable is   INSTDUM,   which is a dummy variable equal to 1 if the firm has a high institutional ownership concentration (greater than the median 52.4) and 0 otherwise. In the second stage we include the MILLS as a correction factor for potential selection  bias. We include a three-way interaction term in stage two and report the results in Table 4. After controlling for potential selection bias by testing factors that are likely to be associated with institutional ownership in stage one, the results show that ROA is positively associated with the size of institutional investors' investment, firm-specific risk, and comprehensive risk-management ( B  = 0.00002; p b  0.01). In contrast, Tobin's Q is negatively associated with the interaction term ( B = − 0.001;  p b  0.001). The random effects regression is likely to produce either non-significant coefficients or  coefficients that are statistically significant but of substantially lower magnitude, compared to 3SLS regression. This is because the random effects regressions will possibly produce  biased standard errors and suffer from Type I errors. In contrast, the 3SLS method, which takes into account covariances between the error terms of different equations, is more likely to provide unbiased and consistent standard errors, thus yielding more robust  coefficient results and valid tests of hypotheses (Setia-Atmaja, Tanewski, & Skully, 2009).9

4.3. Institutional investor heterogeneity

One issue that arises in measuring institutional influence is that not all institutions are willing or able to exert influence. A growing body of corporate governance literature refers to this as institutional investor heterogeneity (Agrawal, 2012). Using economic theories relating to incentives, we distinguish broadly between pressure-resistant investors, who have more economic incentive to monitor, and pressure-sensitive investors, who have less economic incentive to monitor, based on conjecture that they have competing economic incentives due to their potential business relationships with investee firms. The constructs of  9

The Durbin–Wu–Hausman (DWH) test, which determines whether there is no endogeneity in the equation (null hypotheses). The signi �cant DWH tests ( F (1, 681);  p  = 0.0000) indicate that endogeneity is present in the OLS estimates and the instruments have corrected for it.

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“activism”  (i.e.,

pressure-sensitive versus pressure-resistant) therefore are primarily driven  by theory, not by actual observed evidence of monitoring behavior of the sampled firms. This compelling distinction relies on recognizing significant business relationships that  investors have with their firms, based on the industry of the investor and the ordinary course of their business. While studies on institutional investors consider various methods of distinguishing institutional investor incentives to monitor  –   for example, political or  social influences conflicting with the objective of firm performance (Agrawal, 2012; Gillan & Starks, 2000; Woidtke, 2002) –   we follow   Brickley, Lease, and Smith (1988) and Almazan, Hartzell, and Starks (2005), who divide institutional investors into two groups,  pressure-sensitive and pressure-insensitive investors.10 Institutional investors are categorized into two groups based on their potential business relations with the firms in which they invest. The first group is defined as pressuresensitive owners as, due to the potential business relations with investee firms, they may be less likely to challenge management for fear of jeopardizing the relation. Osiris classifications are used with pressure-sensitive institutions, including banks and insurance companies. The second group is defined as pressure-resistant owners, due to the expectation that they will not have significant potential business relations with firms and are therefore more likely to directly challenge management. These institutions include pension funds, other investment  firms,11 and hedge funds. For each category, institutional investment is computed as the  proportion of institutional investors' shares of total shares outstanding. The proportion of   banks and insurance companies' ownership is added together (SENSITIVE), and the pro portion of pension funds, other investment firms, and hedge fund ownership is added together  (RESISTANT). The results reported in Table 5 show that firm accounting performance (ROA) and firm value (Tobin's Q) are positively associated with pressure-resistant institutional ownership ( B  = 0.001; p b  0.001; B  = 0.033; p b  0.001). Performance and value are not significantly associated with pressure-sensitive institutional ownership. This result suggests that it is only institutional investors without a possible business association with the investee firm that persuade the firm to monitor suboptimal risk-taking, indicated by a positive association between the size of pressure-resistant institutional ownership and performance. An alternative explanation is that pension funds (the major investor in this group) are more likely to invest for the long-term, which also leads to greater monitoring of performance by these funds. In contrast, pressure-sensitive institutional investors are less likely to directly  pressure firms and, in fear of losing their business, rely on firms' internal governance practices. Typically, insurance companies, the primary investor in this group, are likely to be replaced if they challenge management.12  Next, we use an alternate measure of firm-specific risk: the probability of bankruptcy. The most popular and robust measure of bankruptcy risk is the Altman Z score model, which uses discriminate analysis (DA) to combine five accounting ratios into a score that  represents the bankruptcy risk inherent in a firm (Altman, 1968). Although the model was introduced in the late 1960s, it is still relevant and used for financial research to proxy for  10 11 12

We prefer the term pressure-resistant. Similar to pension funds, these are large listed �rms that exist solely to invest in other  � rms. Running the analysis with pension funds and insurance funds provided the same results.

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Table 5 Determinants of firm performance (ROA and Tobin's Q). This table presents the instrumental variables in the form of 3SLS regression models. The first-stage model is not reported for parsimony. The results of simultaneously testing the three equations for the role of institutional investor type (pressure sensitive and pressure resistant) on risk, risk management, and performance are presented. Two-tailed Z statistics in parentheses significant at  0.01***, 0.05**, and 0.10* levels. Panel A: ROAt

CONS

INADJSTD

RISKMGT

ROAt

INADJSTD

RISKMGT

TOBQt  

27.609 (21.58)***

1.593 (14.13)*** − 0.059 (− 21.22)***

− 0.217

26.440 (17.87)***

1.537 (8.93)*** − 0.060 (− 16.02)***

− 30.483

INDADJSTD RISKMGT SENSITIVE RESISTENT  NETGEARING  NETINTCOV

Panel B: TOBQt  

− 16.739

(− 17.84)*** 0.156 (4.16)*** 0.050 (3.48)*** 0.040 (0.38) − 0.00004 (− 0.13)

0.009 (4.25)*** 0.003 (3.62)***

LNMKTCAP

− 15.717

(− 13.71)*** 0.148 (4.16)*** 0.043 (3.15)*** 0.083 (0.58) − 0.0002 (− 0.69)

0.009 (4.27) 0.003 (3.46)***

0.021 (3.33)***

LNTA

INDY 2007 2008 688 401.48 0.00

688 576.79 0.00

(7.61)*** 1.033 (5.44)*** − 3.110 (− 1.11) − 0.009 (− 0.32) 0.033 (3.02)***

1.491 (7.00)***

0.002 (0.48)

ROAt  − 1 / TOBQt-1

 N CH12  p

(− 2.06)** 0.003 (0.75) − 0.145 (− 2.31)** 0.0007 (1.18) 0.001 (3.51)***

0.006 (0.77) 0.573 (13.10)*** − 0.119 (− 4.40)*** 0.007 (0.57) − 0.013 (− 0.70) 688 516.61 0.00

675 232.35 0.00

675 419.42 0.00

0.092 (0.78) − 6.918 (− 6.02)*** 1.088 (2.64)*** − 1.736 (− 2.44)** 675 198.74 0.00

Definitions: INDADJSTD: total risk is calculated as the standard deviation of firm daily stock returns for each fiscal year, which is subtracted from the industry standard deviation of daily stock returns; ROAt: current year ROA [Net  Income + Interest Expense ∗ (1 −  Corporate Tax Rate)] / [Total Assets −   Outside Equity Interests; ROAt  − 1:  prior year ROA; TOBQt: the market value of equity and debt divided by the book value of total assets in year t; TOBQt  −  1: prior year TOBQ; RISKMGT: dummy variables 2 = rigorous RM policies with oversight of a dedicated (separate) committee, 1 = formal RM policies but no separate committee oversight (oversight by  board), 0 = no formal RM policies (or dedicated committee oversight); SENSITIVE: bank and insurance company ownership divided by total issued shares; RESISTANT: pension funds, investment firms, and hedge fund ownership divided by total issued shares; LNMKTCAP: closing share price on the last day of the company's financial year  ∗   number of shares outstanding at the end of the period, logged; NETGEARING: (Short term debt + long term debt  − cash) / shareholders' equity; NETINTCOV: earnings before interest and tax / net  interest (income −  expense); LNTAt = Total assets, logged; INDY: dummy variable 1 for metals and mining industry, 0 otherwise; YEAR: dummy variable 1 for 2006, 2007, and 2008; 0 otherwise.

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financial distress and default risk (Aslan & Kumar, 2012; Becker & Stromberg, 2012; Bryan et al., 2013). Altman (1968) derived a “cut-off ”  point, or optimum Z value, by observing firms that  were misclassified by the DA model in the initial sample. He concluded that all firms with a Z score of greater than 2.99 clearly fall into the “non-bankrupt ”  sector, while those firms with a Z below 1.81 are bankrupt. 13 Consequently, firms are classified as firms with a Z of  ≤ 1.81 as a high probability of bankruptcy and coded 1, and firms with a Z of  ≥ 2.99 as a low probability of bankruptcy and coded 0, thus excluding the “gray area”   due to the  propensity of misclassification. Altman (1968) suggests that the predictive model is useful for screening out undesirable investments, as investors tend to underestimate the extent of  financial difficulties of the firms that eventually go bankrupt. The results reported in Table 6 Panel A, where firm performance is measured as ROA, are consistent with the results using firm-specific risk. That is, only pressure-resistant  institutional investors monitor the short-term performance of potentially financiallydistressed firms as short-term performance increases with pressure-resistant institutional ownership. However, when using Tobin's Q, which measures firm value, while taking into consideration future cash flows, we find that Tobin's Q is negatively associated with the size of both types of institutional ownership. This result suggests that all types of institutional investors exit rather than monitor or pressure financially-distressed firms to increase long-term value. This result is consistent with  Frino, Jones, Lepone, and Wong (2014), who find that  following the announcement of financial distress, some institutional investors exit the stock. However, the withdrawal is gradual and is driven by active institutional investors reacting to the release of the financially-distressed firm's last publicly released financial report. Their sample of Australian firms (1995–2006) is before the GFC, while our sample is in the period leading up to and including the GFC. Interestingly, we find a particularly strong positive relationship between a comprehensive risk-management policy and Tobin's Q for distressed firms. This result suggests that in times of distress, stringent riskmanagement is needed to take into account long-term value creation.

5. Conclusion

The global financial crisis has focused attention on the consequences of short-term investment strategies and urges a reassessment of the balance between risk-taking and risk-management (Harper Ho, 2010). Kashyap, Rajan, and Stein (2008) suggest that the global financial crisis resulted from excessive risk-taking, highlighting the importance of  monitoring risk. In the period leading up to the global financial crisis, investors sanctioned high levels of risk in the pursuit of high returns. Consequently, it is important to determine whether institutional investors differ in their ability to influence managements' pursuit of  short-term returns or firm value. 13

In Altman's revised model (2000), analyzing  � rms up to 1999, he  � nds that the gray area is wider as the lower   boundary is 1.23. However, he suggests that the revised model is weaker than the original. Consequently, the original model is used in this study.

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Table 6 Determinants of firm performance (ROA and Tobin's Q). This table presents the instrumental variables in the form of 3SLS regression models. The first-stage model is not reported for parsimony. The results of simultaneously testing the three equations for the role of institutional investor type (pressure sensitive and pressure resistant) on the probability of bankruptcy risk (ZSCORE: Dummy variable 1 if ZScore b  1.81, 0 if  N 2.99), risk management, and performance are presented. Two-tailed Z statistics in parentheses significant at 0.01 ***; 0.05**; 0.10* levels. Panel A: ROAt

CONS

ZSCORE

RISKMGT

ROAt

ZSCORE

RISKMGT

TOBQt  

0.119 (1.34)

− 1.027

− 0.170

− 0.666

(− 3.13)*** − 0.301 (− 2.14)**

(− 1.22) − 0.033 (− 0.69) − 0.130 (− 1.00) 0.001 (1.27) 0.001 (2.21)**

0.031 (0.32)

107.47 (7.19)*** − 47.627 (− 12.98)*** 87.75 (10.51)*** − 0.410 (− 5.80)*** − 0.133 (− 5.29)***

ZSCORE RISKMGT SENSITIVE RESISTANT  NETGEARING  NETINTCOV

0.010 (0.14) 0.003 (1.69)* 0.001 (1.85)* 0.095 (6.94)*** 0.0002 (1.18)

0.008 (2.81)*** 0.003 (2.49)***

LNMKTCAP

0.080 (1.07) 0.02 (1.29) 0.001 (1.46)*** 0.088 (6.45)*** − 0.0002 (− 1.50)

(2.08)** − 0.040 (− 0.28)

0.007 (2.63)*** 0.002 (2.22)**

0.019 (1.37)

LNTA

− 10.199

(− 8.60)***

0.120 (6.78)***

ROAt-1/TOBQt-1 INDY 2007 2008  N CH12  p

Panel B: TOBQt  

550 81.74 0.00

550 100.22 0.00

0.099 (5.75)*** 0.516 (7.55)*** − 0.089 (− 1.91)* 0.004 (0.21) − 0.002 (− 0.08) 550 431.63 0.00

− 0.205

540 80.52 0.00

540 91.26 0.00

(− 1.80)* − 26.737 (− 8.00)*** − 9.956 (− 7.40)*** − 12.631 (− 8.09)*** 540 398.96 0.00

Definitions: ZSCORE: dummy variable 1 if ZScore b   1.81, 0 if  N 2.99; ROAt: current year ROA [Net Income + Interest  Expense ∗ (1 −  Corporate Tax Rate)] / [Total Assets −  Outside Equity Interests; ROAt  −   1: prior year ROA; TOBQt: the market value of equity and debt divided by the book value of total assets in year t; TOBQt  −  1: prior  year TOBQ; RISKMGT: dummy variables 2 = rigorous RM policies with oversight of a dedicated (separate) committee, 1 = formal RM policies but no separate committee oversight (oversight by board), 0 = no formal RM  policies (or dedicated committee oversight); SENSITIVE: bank and insurance company ownership divided by total issued shares; RESISTANT: pension funds, investment firms, and hedge fund ownership divided by total issued shares; LNMKTCAP: closing share price on the last day of the company's financial year  ∗  number of  shares outstanding at the end of the period, logged; NETGEARING: (Short term debt + long term debt  −  cash) /  shareholders' equity; NETINTCOV: earnings before interest and tax / net interest (income −  expense); LNTAt = Total assets, logged; INDY: dummy variable 1 for metals and mining industry, 0 otherwise; YEAR: dummy variable 1 for 2006, 2007, and 2008; 0 otherwise.

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Institutional investors are an important component of capital markets, especially in times of  increasing risk and risk-monitoring. However, there are significant differences among institutional investors and the institutional context in which they exist. First, using instrumental variables to control for potential endogeneity, this study shows that  firm-specific risk and a firm's comprehensive risk-management policy are associated with the size of institutional ownership. We find that institutional ownership increases with a comprehensive measure of risk-management, regardless of the type of institutional investor. Our results demonstrate that increasing institutional ownership is associated with increasing accounting performance and firm value. That is, large institutional investors have sufficient  “skin in the game” to pressure firms to monitor risk and increase both shortand long-term performance. However, institutional investors are not a homogeneous group. They have an overriding responsibility to their clients, but they have different investment objectives (Webb et al., 2003). We categorize institutional investors based on their ability to influence management. The ability to influence management depends on the size of the investment and whether the investee firm has or could have business dealings with the investor, classified as pressure sensitive, vis-a-vis pressure-resistant institutional investors, where there is no such potential. Our results highlight that the ability of market forces (institutional investors) to influence short-term accounting performance and long-term value depends on increasing pressure-resistant institutional ownership. The results also demonstrate the importance of risk-management policy for financiallydistressed firms. We find that when firms are financially distressed, institutional investors engage in promoting short-term performance or exit rather than support long-term value creation. The study of institutional investors and the incentive to have “skin in the game” can provide important insights into widespread perceptions that institutional investors have superior information, which in turn leads to engagement or exit of portfolio firms. These findings add to the growing body of literature examining institutional ownership and the importance of understanding the role of risk-management in the risk and return relation. This information can enable diversified investors to structure their portfolios in accordance with their risk preference. Acknowledgments

The authors are grateful for comments received from Tom Smith, Robert Faff, Francesco Bova, and from the participants at the Accounting and Finance Association of  Australia and New Zealand Conference, Melbourne Australia, July 2012, and the International Journal of Accounting Symposium, Zhongnan University of Economics and Law, China, May 2013. References Agrawal, A. (2012). Corporate governance objectives of labor union shareholders: Evidence from proxy voting.  Review of Financial Studies,  25 (1), 187–226. Agrawal, A., & Knoeber, C. R. (1996). Firm performance and mechanisms to control agency problems between managers and shareholders.  Journal of Financial and Quantitative Analysis,  31 (3), 377–397.

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