A View Inside Corporate Risk Management. This Draft: November 18, 2014

A View Inside Corporate Risk Management This Draft: vember 18, 2014

Introduction Why do firms hedge? It is very difficult to answer this basic question. Traditional economic theory suggests that firms rely on risk management to mitigate financial constraints or other market frictions due to informational asymmetries or agency problems. Yet, across the world, large, rated, dividend paying firms (arguably firms that are less affected by market imperfections) are significantly more likely to hedge than their small, unrated, non dividend paying counterparts (see Figure 1). 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 76% 63% 62% Sample Average: 52% 41% 42% 40% Large: vs. Rated: vs. Dividend Payer: vs. Figure 1 Risk Management and Firm Characteristics This figure reports the percentage of firms with a risk management program in place. The data are from our Corporate Risk Management Survey, which was conducted in the first quarter of 2010. The presented sample includes non financial firms from around the globe. Firms are defined as Large: if their sales are above $1 billion, Rated: if they have a credit rating for their debt, and Dividend Payer: if they pay regular dividends. These simple empirical facts motivate the central questions of our paper: What explains the wide variation in risk management practices across firms, and are these explanations captured by extant theories of risk management? The limited availability of data on corporate risk management has presented a major challenge to answering these questions. To assess current models of risk management, the empiricist needs detailed data on the firm s propensity to hedge, the extent of risk management, the motivation for hedging, the role played by executives in the decision to set up the risk management program, and managerial characteristics including executives attitudes towards risk. At an even more basic level, one also needs to identify whether the firm is facing any material hedgeable risks. This type of information is not available in standard archival data sources (such as COMPUSTAT), which only collect data on observed outcomes as recorded in annual reports or other corporate documents. To mitigate these limitations, we gather information from 681 CFOs from around the world. In this 2010 survey, we ask CFOs whether their companies have a risk management program in place, 1 their inside views on their firms growth prospects, and their views of the interconnected roles played by lines of credit and cash in hedging and liquidity management. We also ask the CFOs to reveal the motivations behind why their firms do (or do not) hedge; that is, we ask them to answer the central questions of our paper. For instance, we ask the CFOs about the extent to which risk management is 1 Throughout the study we use risk management and hedging interchangeably. 1

used as an instrument to reduce cash flow volatility or whether accounting standards might limit their firms usage of hedging instruments. We use the firm s ex ante hedging motivation to study the relation between market frictions (credit rationing, information asymmetry, or agency problems) and the firm s decision to hedge. There is an important advantage to studying hedging intentions rather than ex post hedge outcomes. A firm might intend to hedge but due to (potentially) extraordinary economic circumstances, not follow through on their intentions. We observe the intentions or ex ante plans, which should not be contaminated by ex post factors that impact the execution of risk management. By asking about motivations for hedging, our goal is to shed light on the relation between the type of frictions that the firm faces and the role of risk management in mitigating these frictions. In addition to evaluating existing theories, we administer a psychometric test to gauge managerial risk aversion. We combine this information with demographic data on executive characteristics related to compensation, age, experience, and education. This allows us to study the nexus between personal risk aversion and corporate risk management decisions. To our knowledge, ours is the first paper to empirically examine the link between personal risk aversion and corporate risk management. While the survey method can be used to gather unique information, there are limitations to any survey data based study. For instance, it is possible that the sample of respondents is not representative of the underlying population. Similarly, it is possible that some of the questions are misunderstood or otherwise produce noisy measures of risk management decisions, firm characteristics, or managerial traits. In addition, when interpreting field studies, one needs to consider that market participants do not necessarily have to understand the reason they do what they do in order to make (close to) optimal decisions. Finally, we are limited to a single cross section of firms. As a result, we cannot make any causal inference. To alleviate some of these concerns, we consulted with design experts, conducted focus groups, and performed beta tests in an attempt to minimize ambiguity in the questions. Further, we confirm that our sample is representative of data from standard archival databases in terms of basic demographics related to size, dividends, profitability, leverage, and cash holdings. While these findings are reassuring, they do not eliminate all the concerns about survey based research. To recap, our global survey is well suited to study why firms hedge. There are, however, limitations with this approach. In this sense, our approach complements existing empirical papers (such as Tufano, 1996; or more recently, Rampini, Sufi, and Viswanathan, 2014) that study risk management practices using detailed data for a limited number of firms from one industry. We find that 52% of the firms in our sample have a formal risk management program in place. 2 Our analysis also shows that there is significant variation in risk management practice across regions and ownership forms (e.g., 74% of public companies have a risk management program versus 39% of private firms). These differences persist when we control for firm characteristics such as size. We use information on the propensity to hedge to test existing theories of risk management. To the extent that large, rated, dividend paying companies are less financially constrained than their small, unrated, non dividend paying counterparts, evidence like that in Figure 1 is inconsistent with the 2 Bodnar et al. (2013) detail the results on many different types of risk management: credit risk, interest rate risk, foreign exchange risk, commodity risk, and geopolitical risk management. 2

credit rationing prediction that constrained firms hedge more (Froot, Scharfstein, and Stein, 1993). Importantly, we find that these patterns persist even if we focus on companies with high growth prospects, those more concerned with the effects of financial constraints on their ability to fund future investment. Theory also predicts that companies may substitute credit lines or cash holdings for risk management to deal with cash flow shortfalls (Froot, Scharfstein, and Stein, 1993; and Holmström and Tirole, 2000). If financially constrained companies were more likely to have access to credit lines or had more cash, this could explain why they are less likely to hedge. However, we find that based on our proxies financially constrained (small, unrated, non dividend paying) firms are significantly less likely to have a risk management program (than their unconstrained counterparts), even after we control for access to credit lines or the availability of cash. 3 Other studies have argued that economies of scale associated with setting up a risk management program (e.g., Mian, 1996; Bodnar, Hayt, and Marston, 1998) could explain why we do not find support for the credit rationing hypothesis. That is, while smaller firms might benefit from hedging, they often lack the scale to set up a risk management program or they may lack the financial expertise to use sophisticated financial instruments such as derivatives. To deal with this concern, we rely on a testing strategy that bypasses the effect of economies of scale. An important part of the credit rationing argument is that financially constrained companies hedge to reduce cash flow volatility. Thus, in this part of our analysis we focus only on firms with a risk management program in place and ask their CFOs to give us a qualitative assessment of the importance of risk management as a tool to reduce cash flow volatility. We find that CFOs indicate that reducing the volatility of cash flows is an important reason to implement a risk management program. However, we do not find that the desire to reduce the volatility of cash flows is more important for financially constrained firms; therefore, we do not find support for a key feature of the credit rationing hypothesis. The finding that, across the board, firms consider risk management an important tool to reduce cash flow volatility is consistent with Smith and Stulz (1985) and Stulz (2013). These authors argue that, independent from financial constraints, firms will hedge if their unhedged risks are sufficiently large that they could potentially lead to financial distress and undermine the very existence of the firm. That is, firms have incentive to hedge downside risk to reduced expected costs of financial distress. To the extent that small firms face higher information asymmetry, our finding that small firms are less likely to establish a risk management program (regardless of whether they have promising investment prospects) is also inconsistent with information asymmetry models of risk management such as DeMarzo and Duffie (1995). Their model predicts that managers rely on risk management to signal private information about the investment prospects of the firm. Rampini and Viswanathan (2010, 2013) build a model that explains why financially constrained firms hedge less than unconstrained firms. They argue that financially constrained (e.g., small) companies are less likely to hedge because they focus their resources directly on investment rather than hedging. Specifically, they model a collateral constraint as the friction that leads to financially 3 While these metrics are routinely used in empirical studies to assess financial constraints, they do not unambiguously identify whether a firm faces financial constraints. For example, a firm could be small but have large cash holdings, in which case it might be unclear whether to categorize the firm as financially constrained. For this reason, in our analysis we also partition the financially constrained firms on the basis of their cash holdings. 3

constrained firms hedging less. The collateral channel affects hedging as follows: Lenders require that firms pledge collateral to borrow and therefore, because they use collateral to borrow (and fund investment), these constrained firms lack the additional collateral that is required by hedging counterparties. Consistent with the models of Rampini and Viswanathan (2010, 2013) and previous empirical work (e.g., Nance, Smith, and Smithson, 1993; Mian, 1996; and Geczy, Minton, and Schrand, 1997), we find that financially constrained companies are less likely to hedge (see our Figure 1). Like Rampini and Viswanathan, we also find that the propensity to hedge increases with net worth. As to whether the collateral channel is the root cause of these effects, our evidence is inconclusive (with coefficients often having the correct sign but being insignificant). Relatedly, we do not detect a significant hedging role for cash holdings, even though cash is likely the primary form of hedging collateral (e.g., the International Swaps and Derivatives Association (ISDA) reports that in 2009 cash was the primary form of collateral for about 95% of OTC derivatives that might be used to hedge). 4 Moreover, we do not find a significant link between hedging and investment prospects. Next we study the human element of corporate risk management. Aside from Stulz (1984) and Smith and Stulz (1985), the human element is not explicitly considered in most theories of risk management and is also understudied empirically. We find that more risk averse managers are more likely to work at firms that have established a risk management program. Furthermore, we find that the effects of personal risk aversion vary conditional on other personal characteristics of the risk manager: Compensation, age, experience, and education. These findings are consistent with a core argument in cognitive psychology and economics that personal traits and aspects of human experience can alter the effect of manager risk aversion on corporate policies (e.g., Johnson and Tversky, 1983; Slovic, 1987). Recent finance theory has also embraced the implications of this argument for corporate decisions (e.g., Gervais, Heaton, and Odean, 2011; Palomino and Sadrieh, 2011). Our analysis suggests that ignoring the role of the individual manager might in part explain the limited ability of risk management theories to explain why firms hedge. To recap, we provide new insights regarding why firms hedge and test the predictions of modern risk management theories. Our results are consistent with aspects of some theories but inconsistent with others. 5 Importantly, our survey allows us to go beyond traditional predictions and allows us to examine the link between personal risk aversion, managerial traits, and corporate risk management decisions. Our analysis suggests that the human factor executive risk aversion in combination with personal characteristics related to compensation, age, experience, and education plays a crucial role in corporate risk management decisions. Future research could benefit from incorporating this important human element into risk management models. The paper is organized as follows. The next section provides a review of risk management theories and summarizes the empirical predictions. We review the empirical literature in the appendix. We describe the survey data in Section 2. Section 3 presents our findings. The final section offers some conclusions. 4 ISDA reports very similar figures for the years 2010 2013. 5 We discuss how our findings relate to previous empirical work in the appendix. 4

1. Theories of Risk Management 1.1. The Neoclassical View of Risk Management In the absence of transaction costs and other frictions, hedging can affect the variability of cash flows but not necessarily their expected value. In this case, risk neutral firms do not need to hedge because shareholders can hedge on their own without incurring any additional costs. This is known as the neoclassical view of risk management and is based on the same assumptions as Modigliani and Miller (1958). Several theories of risk management have been developed over the last 30 years. These theories depart from the neoclassical view by considering the effect of credit frictions and other market imperfections on the firm s decision to hedge. We summarize the key insights from these theories in the following subsections. To shorten the exposition, we review some of the main empirical studies in the appendix, where we also highlight when the empirical predictions from risk management models are not fully testable with archival data. 1.2. The Credit Rationing Hypothesis of Risk Management One of the classic motivations for corporate risk management is the necessity to mitigate the effects of credit rationing on the firm s ability to invest. This is known as the credit rationing hypothesis of risk management (Froot, Scharfstein, and Stein, 1993; Holmström and Tirole, 2000). 6 Risk management helps mitigate the effects of credit rationing because it reduces the volatility of cash flows that can be used to fund new investment projects in states when access to credit is limited or very costly. Froot, Scharfstein, and Stein (1993) and Holmström and Tirole (2000) also argue that prearranged lines of credit can function as a substitute for risk management in mitigating credit rationing. They argue that if companies have access to a credit line, they will be less compelled to rely on hedging as a tool to mitigate credit rationing because they could draw down from the credit facility to cover cash flow shortfalls. We examine three empirical predictions related to the credit rationing hypothesis. The first prediction is that firms are more likely to hedge if they face credit rationing. Given that the importance of risk management as an instrument to mitigate financial constraints is related to the firm s need to fund future investment, the hypothesis also predicts that credit rationed companies are more likely to rely on risk management if they have significant investment prospects that need funding. A related effect is that the survey responses of financially constrained firms should rank reduce the volatility of cash flows by hedging as more important compared to financially unconstrained firms. The third prediction is that companies hedge more if they do not have access to credit lines (or cash). Our data are well suited to test the predictions from the credit rationing hypothesis of risk management. We have information on the insiders views about the investment prospects of the firm, the degree of financial constraints, whether the firm has access to large credit lines or cash balances, and why the firm hedges. 6 Mello and Parsons (2000) develop a dynamic model to show that hedging mitigates financial constraints by reducing the costs of financial distress and increasing financial flexibility. 5

1.2.1. Financial Constraints, Access to Collateral and Hedging Rampini and Viswanathan (2010, 2013) build a model that explains why financially constrained (e.g., small, non dividend paying, unrated) firms hedge less than their unconstrained counterparts. In their model, the friction of limited availability of collateral leads to financially constrained firms hedging less. Financially constrained companies face a tradeoff. They can pledge collateral to lenders to borrow so they can increase (or maintain) spending on valuable capital and labor, or they can pledge collateral to hedging counterparties to set up a risk management program (e.g., a hedging program based on over the counter forward contracts). This tradeoff implies that when the firm needs to fund a new investment project, the financing need prevails over the hedging concern. Thus, all else equal, we should expect financially constrained firms with low collateral capacity to be less likely to hedge relative to financially constrained companies with high collateral capacity. We should also expect these patterns to be stronger for firms with good investment prospects. Testing the effects of the collateral channel is complicated by the difficulty of obtaining good measures of the amount of collateral usable in hedging agreements and of the companies growth prospects (e.g., Rampini, Sufi, and Viswanathan, 2014). In our study, we ask CFOs whether the size of cash holdings would influence the intensity of risk management activities at their firms. Similarly, rather than using market based measures to assess growth prospects, we directly ask CFOs to give us their personal views on the investment prospects for their firms. The richness of our data allows us to study the collateral channel of risk management in a way that is not possible with the ex post archival databases commonly used in risk management studies. 1.3. Risk Management and Agency Problems The key assumption of the agency models of risk management is that managers are risk averse (Smith and Stulz, 1985; Holmström and Ricart i Costa, 1986). This is an important difference from the other neoclassical theories of risk management, which assume that managers are aligned with shareholders and act as risk neutral agents. However, agency models of risk management do not explicitly account for heterogeneity in the degree of risk aversion across managers. The assumption of managerial risk aversion has important implications for risk management. Given that managerial claims to the firm are not easily diversifiable, risk averse managers can reduce the effect of the non diversifiable risk of their claims by hedging, even when this decision is not valuemaximizing from the perspective of well diversified shareholders. (For example, executives in the oil industry could reduce their personal exposure to oil price fluctuations by selling oil price forward contracts as a part of their firm s hedging program). The first prediction is that firms in which risk averse managers have a relatively large share of their wealth invested in their firm s shares will be more likely to manage risk to reduce the effect of low diversification. Relatedly, holding constant the percentage of their wealth invested in their firm s shares, more risk averse executives are more likely to manage risk. 7 7 Risk management has also implications for debt capacity. If hedging reduces the variability of expected cash flows, then all else equal lenders should be willing to provide more financing to firms with a risk management program in place (Mayers and Smith, 1982; Smith and Stulz, 1985; Leland, 1998; Graham and Rogers, 2002). 6

In our survey, we estimate managerial attitude towards risk using a psychometric test design following Graham, Harvey, and Puri (2013). This allows us to test the predictions from agency models of risk management using a direct measure of managerial risk aversion. Importantly, we go beyond solely testing the effect of personal risk aversion on corporate risk management. Building on recent developments in cognitive psychology and economics, recent finance theory (e.g., Gervais, Heaton, and Odean, 2011; Palomino and Sadrieh, 2011) embraces the argument that personal traits and aspects of personal experience can modify the effect of risk aversion on corporate policy. For example, high underlying CFO risk aversion may by itself lead to more corporate hedging; however, the pure risk aversion effect might be attenuated among CFOs who are highly educated or very experienced because of their familiarity with the instruments that are used to hedge and their ability to make accurate predictions of the future during difficult circumstances. We are able to gather detailed information on managerial risk aversion and other personal characteristics related to compensation structure, age, experience, and education. This allows us to study whether the effect of personal risk aversion on corporate risk management changes in relation to other personal characteristics. 1.4. Information Asymmetry and Risk Management Breeden and Viswanathan (1999), DeMarzo and Duffie (1991, 1995), and Raposo (1997) argue that when it is difficult for non controlling shareholders (outsiders) to assess the quality of the management, higher quality managers can signal their type by hedging. The premise of their argument is that firm performance depends on managerial ability and other contingencies that are not directly controllable by management (e.g., currency fluctuations). In the presence of information asymmetry, outsiders cannot separate managerial ability from external contingencies. Higher ability managers hedge to mitigate the effect of hedgeable risks on firm performance and signal their type. Lower ability managers do not hedge because having a risk management program is costly. The main prediction from this signaling argument is that firms are more likely to install a risk management program when information asymmetry is high. 1.5. Testing the Theories Table 1 presents a summary of the main empirical predictions from the current theories of risk management. Empirical evidence related to the predictions from existing models is limited (see the Appendix). 7

Table 1 Theories of Risk Management: Summary of Empirical Predictions Theories Main theory reference Key assumptions Role of managerial risk aversion Main predictions Testable w/archival data Testable w/ survey data Neoclassical View Modigliani and Miller (1958) Absence of frictions risk management Credit Rationing Froot, Scharfstein, and Stein (1993) Credit rationing; firm is risk neutral 1. Risk management more likely by credit rationed firms 2. Risk management more likely by credit rationed/growth firms Partially 2.1. Risk management more likely to reduce cash flow volatility by credit rationed/growth firms 3. Risk management more likely by firms w/out credit line Partially Access to Collateral Rampini and Viswanathan (2010, 2013) Credit rationing; limited collateral; firm is risk neutral Risk management more likely by credit rationed/growth firms w/ plentiful collateral Agency Problems Smith and Stulz (1985) Shareholder manager; shareholder bondholder conflicts; firm is risk neutral 1. Risk management more likely if risk averse manager has large stock ownership Partially 2. Risk management more likely if manager risk aversion is high (holding stock ownership constant) Information Asymmetry DeMarzo and Duffie (1995) Managerial ability unobservable; firm is risk neutral Risk management more likely by high quality firms, when information asymmetry is higher Partially 2. Data 2.1. The Data Gathering Process To obtain our data, we contacted members of the Duke CFO Magazine Survey panel, the International Swaps and Derivatives Association (ISDA), and the Global Association of Risk Professionals (GARP). We invited CFOs to take part in the survey via email in the last week of February 2010. Reminder emails were sent out throughout March 2010. The survey closed at the end of April 2010. We sent out about 29,000 email invitations to non financial companies in rth America, Europe, Asia, and other regions (including Australia, New Zealand, Latin America, the Middle East, and Africa). In total, we gathered 681 responses (from both public and private companies), which, to our knowledge constitutes the largest survey sample on risk management assembled to date. The response rate is 2.5% which seems low. However, the response rate is misleading. We are interested in surveying only the officers with detailed knowledge or who are in charge of risk management. Many of the emailed organizations, particularly for GARP and ISDA, had a very low proportion of 8

senior financial officers. In the end, our sample includes only senior financial officers and key risk managers. We refer to the survey sample as CFOs though some have titles such as Treasurer, Vice President of Finance, or Chief Risk Officer. We ask CFOs about their companies risk management practices and demographics, including sales, credit ratings, dividend policies, investment prospects, and several other characteristics. We also collect information about risk aversion and other personal traits. We use this information below to study the nexus between personal risk aversion and corporate risk management decisions. 2.2. Descriptive Statistics Table 2 reports descriptive statistics for the firms in our sample. About 52% of the companies report having a risk management program (Risk Management indicator variable). 8 Table 2 also shows significant regional variation in risk management practices. Risk management also varies significantly with respect to ownership form: 74% of the public companies have a risk management program compared to 39% of the private companies. 9 In our regressions, we control for these sources of heterogeneity by including region and ownership form dummies. Table 2 also reports descriptive statistics for several measures of firm characteristics. About 30% of the firms have revenues above $1 billion (Large). There is also some demographic variation across regions and ownership form. For instance, 15% of the Asian firms are classified as Large, compared to 35% and 42% of the rth American and European companies, respectively. We also report summary statistics on dividend policy, investment prospects, leverage, and whether the firm has a credit rating. [Table 2 Here] Table 2 shows that 36% of the firms are public, which indicates that our sample has a good balance of public and private companies. The table also provides information on the distribution of the sample across regions for the firms for which headquarters information is not missing. 53% of the firms in the sample are from rth America, 18% are from Europe, and 25% are from Asia. The remaining 4% of companies (25 observations) are headquartered in Australia, New Zealand, Latin America, the Middle East, and Africa. We categorize these companies as Other Region firms. 2.3. Comparing the Survey Sample to COMPUSTAT Table 3 compares our sample of public firms with data from the COMPUSTAT Global database (which only includes public firms). This allows us to assess whether our sample characteristics are similar to 8 All our findings are qualitatively very similar (albeit in some cases with lower statistical power) if we replace the Risk Management indicator variable with an indicator for whether the firm actively uses derivative instruments to manage financial risk (foreign exchange rate, interest rate, and commodity risk). 9 Overall, our risk management summary statistics align closely to those reported by previous studies. In a similar survey setting, Bodnar, Hayt, and Marston (1998) find that 50% of the firms in their sample hedge. Bartram, Brown, and Minton (2010) find that in a sample of public listed firms located for two thirds in rth America, 66% hedge. Our survey data show that 74% of the public companies have a risk management program. If we restrict the sample to public firms in rth America, the percentage of firms with a risk management program goes down to 67% (which is very close to the 66% in Bartram, Brown, and Minton, 2010). 9

standard databases used in corporate finance research. There are 244 non financial publicly listed firms in our survey sample, which we compare with a sample of about 22,700 non financial companies in COMPUSTAT with a fiscal year ending in May 2010 or the 11 months prior. [Table 3 Here] The evidence in Table 3 suggests that our sample is broadly comparable to the COMPUSTAT sample. About 45% of firms have annual sales of less than $1 billion ( small ) in our public sample versus 52% in COMPUSTAT. Our data indicate that about 33% of the survey firms do not pay dividends regularly, relative to 24% of the companies in Global COMPUSTAT. 10 The two samples are quite similar in terms of profitability. 3. Results 3.1. Why Firms Hedge We ask CFOs why their firms do (or do not) have a risk management program in place. We explore how risk management objectives relate to cash flow variability, access and cost of finance (debt and equity), ratings, firm value, and decision making. We tailor the questions to the theories of risk management that we test in this study. For example, the credit rationing hypothesis of risk management predicts that firms hedge to reduce the volatility of cash flows that can be used to fund new investment projects when they are financially constrained and funding access is limited. Therefore, it is important to know whether firms value risk management as a tool to reduce the volatility of cash flows, improve access to finance, and to enable investments, all conditional on the degree of financial constraints. Figure 2 summarizes the factors that CFOs rank as important or very important (a 3 or 4 respectively, on a scale from 1 to 4). The primary factors include a desire for lower unexpected losses, as well as decreased volatility and surprises. More than 80% of the CFOs in our sample say that Increase Expected Cash Flows and Decrease Unexpected Losses are important determinants of risk management policies. Relatedly, about 80% (75%) of the CFOs say that Reduce Cash Flow Volatility ( Improve Earnings Predictability ) is an important determinant of risk management. Overall, these findings suggest that predictability of cash flows and earnings are among the main reasons that firms hedge. [Figure 2 Here] Other factors also play an important role in corporate hedging decisions. For example, of the firms with credit ratings, almost 75% use hedging to Increase/Maintain Ratings. Given its importance in the theoretical risk management literature, it is worth noting that Improve Investment in Difficult Times is important, but less so than the reasons listed above. About two thirds of CFOs indicate that this investment based motive to hedge is important or very important. As Figure 2 shows, other factors such as decreasing the cost of equity or share price volatility are less important. In unreported tests, we find, for example, that earnings predictability/smoothing are significant determinants of why public firms hedge more than private firms. To the extent that public firms are 10 For U.S. firms in COMPUSTAT, the percentage of non dividend paying firms is about 70%. For rth America firms in our sample, the percentage of non dividend paying firms is 52%. 10

more compelled to hit earnings targets and they hedge to increase the predictability of earnings, these findings suggest that accounting considerations (e.g., whether a financial instrument qualifies for hedge accounting) might play a role in explaining hedging. In the following sections, we tie the CFOs responses to formal tests of risk management theories. While the predictions from some of these theories are intuitive, the empirical evidence does not always indicate that they play a primary role in explaining corporate risk management in practice. We also provide evidence on interactions between various theoretical influences on hedging. For instance, one theory predicts that financially constrained firms cannot hedge because they are required by lenders to pledge collateral to borrow and lack the additional collateral that is needed to enter into a hedging agreement. However, another theory predicts that hedging can increase debt capacity of financially constrained firms by reducing the volatility of their cash flows. The interplay of these channels is not part of any existing model. Therefore, it is important to test the relation between these channels empirically. 3.1.1. Why Firms Do t Hedge: The Role of Accounting Standards The accounting literature shows that managers have strong incentives to manage earnings. Earnings smoothing can be produced via accounting choices or real business decisions (such as the timing of capital projects, research and development, advertising and the decision to hedge). 11 There is an open debate as to whether accounting standards impede firm s usage of derivatives for hedging (see, e.g. Zhang, 2009). Our paper provides new insight on this issue. Hedge accounting is regulated under the International Accounting Standards (IAS) 39 (or the corresponding Financial Accounting Standards (FAS) 133 in the U.S.). Through hedge accounting, firms can offset the profit and loss volatility in the derivatives (arising from marking to market the derivatives) with the reciprocal profits and losses in the hedges. The most stringent requirement for hedge accounting to be applicable is for the hedging firm to show that there is an offsetting relation between the value of the derivatives and the value of the hedged item ( hedge effectiveness tests 12 ), and that the offset is not the mere consequence of chance. Yet, this is often difficult to prove in practice because the derivative instrument and the hedged item often do not match in terms of commodity type, notional amounts, payment dates, and other basic terms. The accounting treatment of derivatives as well as regulatory requirements may provide a strong disincentive for hedging. In our survey, we ask CFOs to tell us whether the hedge effectiveness tests mandated by IAS39 or FAS133 have led to changes in the intensity of corporate hedging with derivatives. In unreported results, we find that 19% of firms with a risk management program 11 Graham, Harvey, and Rajgopal (2005) find that managers have a preference for using real actions (like capital investment) to smooth earnings rather than accounting choices. 12 For example, consider the case of an airline entering a long futures contract to hedge against a possible increase in the price of jet fuel. Assume also that the minimum notional amount for standard futures contracts is 1 million gallons of jet fuel. Consider two scenarios: (1) The firm needs to hedge 1 million gallons; (2) The firm needs to hedge 500,000 gallons. If the fuel price decreases, marking to market the derivative requires the airline to report a loss. However, if the notional amount of the futures contract matches the firm s need for hedging as in (1), the firm meets the hedge effectiveness tests mandated by IAS39 or FAS133 and can offset the loss on the derivatives with the profit arising from a lower market price of jet fuel. On the other hand, the firm does not meet the hedge effectiveness tests in scenario (2) and must report the loss on the derivatives in the income statement without being able to offset it. 11

reduced the use of derivatives as a result of these regulations. Moreover, we find that 23% of public firms (versus 12% of private firms) respond to this hedging disincentive, indicating that public firms are more concerned about the possible adverse effect that the accounting treatment of derivatives use might have on earnings. 13 In the analysis that follows, we relate the CFOs descriptions of why firms hedge (or do not hedge) to both firm level characteristics and modern theories of risk management. 3.2. Evidence on the Credit Rationing and Information Asymmetry Models of Risk Management The credit rationing hypothesis of risk management predicts that the probability of risk management increases with financial constraints and investment growth prospects. To begin our credit rationing analysis, we categorize firms as financially constrained if their sales are below $1 billion, their public debt is unrated, or they do not pay dividends regularly. We follow the literature in this conditioning. 14 We classify firms as high (low) investment prospect firms if the CFO rates their investment prospects above (below) the sample median. 15 Table 4 presents mean difference tests on the percentage of companies that have a risk management program in place conditional on financial constraints, investment prospects, and the combination of financial constraints and investment prospects. Panel A shows that a significantly lower percentage of financially constrained firms have a risk management program in place. For example, small firms are much less likely to have risk management programs in place relative to large companies (41% vs. 76%). Panel A also reports that companies in the high investment prospect group are more likely to manage risk relative to their low investment prospect counterparts (55% vs. 48%). However, this difference is not statistically significant. [Table 4 Here] The cross tabulations in Panel A shows that a significantly lower percentage of financially constrained firms have a risk management program relative to their unconstrained counterparts for both the high and the low investment prospect groups. For instance, we find that only 45% of small firms with high investment prospects have a risk management program in place, relative to 76% of the large companies with the same investment prospects. Overall, these findings show that a firm s propensity to hedge varies with financial constraints, not investment prospects. 13 In unreported tests, we find that earnings predictability/smoothing are significant determinants of why public firms hedge more than private firms. To the extent that public firms are more compelled to hit earnings targets and they hedge to increase the predictability of earnings, these findings suggest that accounting considerations (e.g., whether a financial instrument qualifies for hedge accounting) play a role in explaining hedging. 14 Gilchrist and Himmelberg (1995) and Fama and French (2002) argue that small firms are typically young, less well known, and therefore more exposed to credit frictions. The argument that rated companies are less likely to be financially constrained is proposed by Faulkender and Petersen (2006). Related approaches for characterizing financing constraints are used by Gilchrist and Himmelberg (1995) and Almeida, Campello, and Weisbach (2004). Fazzari, Hubbard, and Petersen (1988) argue that firms are more likely to pay dividends if they are less susceptible to credit rationing. 15 We obtain very similar results if we classify firms as high/low investment prospects according to whether the firms investment prospects are above/below median investment prospects for the industry. 12

Panel A also shows that financially constrained companies (small, no rating, no dividend) are somewhat more likely to establish a risk management program when their investment prospects are high, although the numbers are generally not statistically different from low investment prospect firms. For instance, we find that 45% of small firms with high investment prospects have a risk management program relative to 38% of low investment prospect firms. We do not find any patterns in the propensity to hedge across investment prospect groups for financially unconstrained firms. 16 Panels B and C show similar patterns in risk management practice (to those documented in Panel A for the full sample) for different subsamples based on ownership form (public and private companies). To sum up, the evidence in Table 4 shows that financially unconstrained firms (large, rated, dividend paying) are significantly more likely to have a risk management program in place relative to their constrained counterparts. These patterns are very similar for firms with either high or low investment prospects. tably, these findings are inconsistent with the key prediction of the credit rationing hypothesis that financially constrained firms hedge to mitigate the effect of limited access to credit. However, in partial support of the credit rationing hypothesis, we find that within the financially constrained categories, companies with high investment prospects are somewhat more likely to hedge, although differences are only statistically significant in one of three categorizations (dividend paying status). It should also be noted that Froot, Scharfstein, and Stein (1993) argue that, beyond describing what firms do, their theory has important prescriptive implications. In relation to our findings, this implies that there could be frictions that limit the ability of financially constrained firms to hedge, even though it could be theoretically optimal for them to do so. The evidence in Table 4 can also be used to evaluate a prediction from the information asymmetry models of risk management (DeMarzo and Duffie, 1995). To the extent that information asymmetry is higher for small (or unrated or non dividend paying) firms, theory predicts these firms to hedge more than their large firm counterparts. The evidence in Table 4 suggests the opposite. Likewise, we compare public and private firms. Brennan and Subrahmanyam (1995), Easley, O Hara, and Paperman (1998), and Hong, Lim, and Stein (2000) suggest that information asymmetry is lower for public firms because they are followed by analysts, so we might expect less public firm hedging. In unreported tests, we again find the opposite, even when controlling for investment opportunities and firm size. Overall, our results are inconsistent with information asymmetry predictions. 17 Overall, the evidence in Table 4 is inconsistent with the credit rationing hypothesis of risk management. Previous empirical studies have reported evidence consistent with ours (e.g., Nance, Smith, and Smithson, 1993; Geczy, Minton, and Schrand, 1997). We now focus on a specific channel of the credit rationing hypothesis: The link between risk management and cash flow volatility. The volatility of cash flows is at the core of the credit rationing hypothesis, which predicts that risk management helps financially constrained firms fund new investment opportunities by reducing the volatility of cash flows, helping the firm to fund investment via internal funds in states when external credit is rationed. We discuss tests related to this prediction in the remaining part of this section. In these tests, we focus exclusively on companies with a risk management program in place whose 16 In unreported tests, we find very similar evidence in a regression framework after controlling for firm heterogeneity and regional variation. 17 We acknowledge that this conclusion hinges on the accuracy of our measures of information asymmetry. If, for example, large firms are complex and complexity leads to more information asymmetry, our evidence would then be consistent with DeMarzo and Duffie (1995). 13

CFOs rate on a scale from 1 to 4 the importance of risk management as an instrument to decrease cash flow volatility. Figure 2 (discussed above) indicates that almost 80% of the CFOs say that reducing cash flow volatility is either important or very important for the decision to hedge. But does the importance of risk management as a tool to reduce cash flow volatility vary by financial constraints and investment prospects as predicted by the credit rationing hypothesis? To answer this question, Table 5 reports mean difference tests on Decrease Cash Flow Volatility by financial constraints, investment prospects, and the combination of financial constraints and investment prospects. Table 5 shows that the average unconditional response on Decrease Cash Flow Volatility is 3.01 on a scale from 1 to 4. Perhaps more interesting, we examine whether the importance of decreasing cash flow volatility varies with other key variables. We find that the importance of cash flow volatility does not vary in relation to financial constraints, investment prospects, or the combination of both financial constraints and investment prospects. For instance, the mean response is 3.11 for the companies in the large category relative to 3.06 for firms in the small category (row 1, columns 2 and 3) and the difference is not statistically different from zero. The mean response is 2.95 for companies in the high investment prospect group relative to 3.05 for firms in the low investment prospect group (column 1, rows 2 and 3). Similarly, the mean responses for high versus low investment prospect large firms, and the same comparison for small firms, 18 are not significantly different from each other. 19 [Table 5 Here] Altogether, the findings in Table 5 are not consistent with the predictions of the credit rationing hypothesis of risk management. Our evidence suggests that the importance of risk management as an instrument to reduce cash flow volatility is uncorrelated with financial constraints, investment prospects, or the combination of financial constraints and investment prospects. These findings are consistent with a pragmatic view of risk management whereby firms, independently from size, ratings, or dividend policy, need to hedge when they are facing risks that could cause financial distress and undermine the very existence of the firm (Stulz, 2013). 3.2.1. Evidence on the Substitution between Risk Management, Credit Lines, and Cash Holdings Froot, Scharfstein, and Stein (1993) and Holmström and Tirole (2000) argue that lines of credit can function as a substitute for risk management in mitigating credit rationing. The implication of this argument for our tests is that if the majority of our financially constrained firms have access to untapped funds from credit lines, this could explain why we do not find (in Table 4) that financially constrained firms are more likely to rely on risk management. Table 6 presents tests related to this prediction. In Panel A, we report mean difference tests on the percentage of companies having a risk management program conditional on financial constraints, credit lines, and the combination of the two. To mitigate the concern that having access to a line of credit could depend on whether the firm is financially constrained, we focus exclusively on firms 18 We find very similar patterns if we partition the sample by continent (results available upon request). 19 As an alternative to Reduce Cash Flow Volatility, we ask CFOs to rate risk management as an instrument to Improve Earnings Predictability or Decrease Unexpected Losses and find patterns very similar to the ones documented in Table 5. 14

with a certain proportion of the credit line that is untapped (and only look at firms with access to a credit line). We categorize a firm as having a high ( low ) percentage of the credit line undrawn if the percentage of the credit line unutilized is above (below) the sample median. Arguably, firms with a high percentage of undrawn credit line are those that can substitute credit lines for risk management as predicted by the credit rationing hypothesis. As in previous tables, Panel A shows that a significantly lower percentage of financially constrained (small, unrated, non dividend paying) firms have a risk management program in place relative to their unconstrained counterparts. However, this relation does not appear to be driven by unused credit line capacity. In fact, opposite from the credit rationing prediction, within each financial constraint category, we find that firms with a high percentage of unused credit lines are generally more likely to have a risk management program in place (although differences are generally not statistically different from zero). For instance, 45% of the small firms with a high undrawn credit line capacity have a risk management program relative to 38% of the similar firms a low percentage undrawn. [Table 6 Here] The literature on liquidity management 20 also suggests that lines of credit and cash holdings might work as substitutes. Therefore, we reevaluate the evidence in Panel A by sorting companies on the basis on their cash holdings. We say that a firm has high ( low ) cash holdings if the ratio of cash and marketable securities to total assets is above (below) the sample median. The evidence in Panel B is very similar to the credit line findings in Panel A (i.e., opposite direction to the hypothesis but insignificant). For example, we find that 45% of the small firms with high cash holdings have a risk management program relative to 37% of the small companies with low cash holdings. Overall, we do not find evidence that unused credit line capacity or cash holdings affect whether firms have a risk management program. 21 3.2.2. The Role of Collateral We have shown above that financially constrained firms hedge less (rather than hedging more as predicted by the credit rationing hypothesis). Rampini and Viswanathan (2010, 2013) argue that this result is consistent with their model due to the friction of constrained firms having limited collateral and choosing to use that collateral to borrow (and invest) rather than as hedging collateral. In this part of our analysis we therefore focus on whether the observed financial constraint effects are driven by the collateral channel. ISDA (2009) reports that between 93% 98% of the collateral used for Over the Counter (OTC) derivatives is in the form of cash or cash like (government) securities. Given the frequency with which cash is used as collateral in hedging, we revisit the analysis in Panel B, Table 6 to discern the role played by cash (collateral) in hedging. 20 See Sufi (2009), Lins, Servaes, and Tufano (2010), and Campello, Giambona, Graham, and Harvey (2011). 21 Opler et al. (1999) find evidence that is consistent with the view that that cash and hedging are complements rather than substitutes. In contrast, Nance, Smith, and Smithson (1993) and Geczy, Minton, and Schrand (1997) find that cash holdings and hedging activities are negatively correlated. More recently, Bonaimé, Hankins, and Harford (2014) find that payout flexibility (i.e., favoring repurchases over dividends) and hedging are negatively correlated. 15

To the extent that our measure of cash holdings captures the availability of collateral to hedge, we would expect to find that high cash firms hedge more. Across each of our financial constraint proxies, the propensity to hedge is higher for the firms with more cash (collateral), consistent with the prediction of Rampini and Viswanathan. Yet, the differences are significant in only one of three categories (unrated firms). In unreported tests, we find very similar results for both high and low investment prospect firms. Overall, the evidence in Panel B that financially constrained companies are less likely to hedge is consistent with Rampini and Viswanathan (2010, 2013). However, the statistical evidence in our analysis is weak that the effects of financial constraints are driven by limited access to collateral (when collateral is measured by the most common form of collateral: cash and marketable securities). In addition to examining the existence of a risk management program, we also ask CFOs whether the intensity of risk management would change if access to cash holdings were high. For 77% of CFOs, increased access to cash either has no impact on the intensity of their hedging or leads to a decrease in hedging intensity. Relatedly, we do not find significant evidence that the effects of cash holdings depend on financial constraints or investment prospects. We also examine the role of cash collateral using archival data. As mentioned, while cash is an imperfect proxy for collateral, 22 in practice, it is the dominant form of collateral. 23 We focus on hedging intensity in the airline industry. Our analysis confirms the evidence in Rampini, Sufi, and Viswanathan (2014) that net worth is positively correlated with hedging. As above, we also find that the coefficient on cash (included as a proxy for available collateral) is positive as predicted but again it is insignificant. 24 This is consistent with the results in our overall survey sample (as reported above). 3.3. Risk Aversion, Executive Characteristics, and Risk Management 3.3.1. Measuring Risk Aversion To our knowledge, our survey contains for the first time a direct measure of risk aversion of risk managers. Following Barsky, Kimball, Juster, and Shapiro (1997), our questions involve choices over lifetime income from labor. One approach might be to ask CFOs whether they would choose to stay in their current job or move to an attractive new position. Barsky et al. warn that such a question might lead to a status quo bias in which individuals might choose the current job over the risky alternative because changing jobs is costly and not because they are risk averse. Our question design mitigates this problem. 25 In the questionnaire, we ask the CFOs to choose between two new jobs. Graham, Harvey, and Puri (2013) use this expedient to mitigate the status quo bias. We ask the CFOs the following sequence of questions: 22 For example, one constrained company might have low reported cash holdings but have the option to draw down a line of credit to produce cash when it is needed versus another constrained company that has already drawn down its credit line to increase its current cash holdings. 23 For example, we examined the annual reports for 23 airline firms in 2010. Cash is the only form of hedging collateral mentioned in any of the annual reports. (Nine airlines mention cash explicitly; the other 14 do not mention any form of collateral.) 24 We also find the coefficient on cash to be positive but insignificant for the transportation firms in our survey. 25 See Barsky, Kimball, Juster, and Shapiro (1997) and Graham, Harvey, and Puri (2013) for additional details on the design of questions aimed at measuring risk aversion. 16

Suppose you are the only income earner in your family. Your current income is $X. Your doctor recommends that you move because of allergies. Which of the following two job opportunities would you prefer? (1) 100% chance job pays $X for life; (2) 50% chance job pays $2X for life and 50% chance job pays $2/3 X for life. If the CFO chooses (1), then the respondent is asked to answer the following follow up question: Which of the following two job opportunities would you prefer? (3) 100% chance job pays $X for life; (4) 50% chance job pays $2X for life and 50% chance job pays $4/5 X for life. If the CFO chooses (2), then the respondent is asked to answer the following follow up question: Which of the following two job opportunities would you prefer? (5) 100% chance job pays $X for life; (6) 50% chance job pays $2X for life and 50% chance job pays $1/2 X for life. We categorize the CFOs that picked the sequence (1) and (3) as Highly Risk Averse, the CFOs that picked the sequence (1) and (4) as Moderately Risk Averse, and the CFOs that picked either the sequence (2) and (5) or (2) and (6) as Less Risk Averse. We combine our measures of managerial risk aversion with demographic data related to compensation, age, professional experience, and education. Table 7 shows that only 20% of the CFOs in our sample are Highly Risk Averse. Of the remaining group, 17% of the CFOs are Moderately Risk Averse, while a sizable 63% are Less Risk Averse. We find very similar patterns across rth America, Europe, and Asia. This finding suggests that a common trait of CFOs around the world is their tolerance of risk. Overall, the CFOs of public and private firms have similar risk tolerance: 32% of the CFOs of public firms are either highly or moderately risk averse, relative to 39% of the CFOs in private firms. [Table 7 Here] Our goal is to study the effect of risk aversion on hedging in relation to other personal characteristics. Therefore, it is important to discuss these characteristics before analyzing how they interplay with risk aversion in the risk management decision. Table 7 shows that CFOs in Europe and Asia are younger than executives in rth America and are less likely to be compensated with stocks and options. CFOs are comparable across regions in terms of years on the job and education. Almost 80% of the CFOs have been on the job for at least four years and more than 60% have an MBA or other master s degree. Executives in public firms are younger and more likely to be compensated with stocks and options than executives in private companies but are otherwise comparable in terms of job tenure and education. 3.3.2. The Effect of Risk Aversion and Executive Characteristics on the Risk Management Decision 17

We next examine the effect of managerial risk aversion on corporate risk management decisions. We also examine the interactive effects of compensation, age, experience, and education. The argument that other personal traits can modify the effect of risk aversion on corporate policies has been a source of considerable debate in cognitive psychology since the 1980s (e.g., Johnson and Tversky, 1983; Slovic, 1987), and has been recently embraced by finance theory (e.g., Gervais, Heaton, and Odean, 2011; Palomino and Sadrieh, 2011). We are not aware of any empirical studies that directly link executive risk aversion to corporate risk management. Perhaps this is not surprising given that good measures of managerial risk aversion are generally unavailable and data on managerial characteristics are difficult to obtain. 26 Crucially, this type of information is available from our survey. We use these data to estimate a probit model where the dependent variable is Risk Management (an indicator variable for firms with a risk management program), modeled as a function of control variables and our managerial risk aversion measure: Highly Risk Averse. The control variables include Large, Ratings, Dividend Payer, Investment Prospects, Profitable, Credit Line, Cash Holdings, Leverage, Public, and regional dummies (indicators for firms headquartered in Europe, Asia, and other regions, with the rth America firm indicator as the omitted indicator variable). We select our control variables in an attempt to hold constant the effects from other theories and important firm heterogeneity. All of our regressions are estimated with heteroskedasticity consistent errors clustered by region. Results from the estimation of the probit model are reported in Table 8. Column 1 reports results for the full sample. The key finding in column 1 is that the coefficient on Highly Risk Averse is positive and significant, indicating that risk averse executives are more likely to work at firms with a risk management program. The effect is economically important. The marginal effect of 0.101 implies that companies with highly risk averse CFOs are 10.1% more likely (which is 19.5% [= 0.101/0.52] of the sample average Risk Management of 0.52) to have a risk management program in place, relative to their more risk tolerant counterparts. This economic effect is comparable to or larger than the marginal effects of most of the control variables in (1) (see Figure 3). For instance, it is comparable to the marginal effect of 0.084 for Dividend Payer and almost three times bigger than the marginal effect of 0.036 for Cash Holdings. Only Large and Public have larger marginal effects. We note that we are limited to a single cross section of firms. Thus, we do not interpret our findings in terms of causality. [Table 8 Here] [Figure 3 Here] We next analyze the effect of managerial risk aversion on corporate risk management conditional on other managerial traits. The first step is to sort firms into different groups based on compensation structure, age, professional experience, and education. We then re estimate our probit model for each group. Estimation results are reported in columns 2 9, Table 8. We find that the Highly Risk Averse indicator is positive and significant for the sample in which CFOs receive stocks and options as part of their compensation package (column 2). The effect is also economically very sizable. The marginal effect of 0.205 implies that companies with highly risk 26 Tufano (1996) studies how managerial characteristics affect risk management decisions in the gold mining industry. See also Gay and Nam (1998) and Knopf, Nam, and Thornton (2002). However, these studies do not directly measure managerial risk aversion. 18

averse CFOs are 20.5% more likely (or 39% relative to the sample average Risk Management of 0.52) to have a risk management program in place. We do not find any effect of risk aversion on risk management among firms in which the CFO does not receive stock or options as part of the compensation package (column 3). To the extent that executive stocks and options can be used as a proxy for whether executives have a large stake of their wealth invested in the firm, the evidence in column 2 is consistent with agency models of risk management (i.e., Stulz, 1984; Smith and Stulz, 1985) that executives with a less diversified personal portfolio are more likely to act on their risk aversion by hedging. 27 These findings are also consistent with the argument in cognitive economics that the combination of personal risk aversion and other managerial traits has important implications for corporate decisions. For example, a manager who is very risk averse may choose a contract that consists of mainly fixed compensation (rather than variable) thereby modifying (reducing) the impact of the risk aversion on the way she performs her corporate duties. We also find that risk aversion is economically and statistically important for the risk management propensity of younger CFOs (less than 55 years old) and CFOs with MBAs. If young age and education are indicative of how exposed CFOs have been to innovative financial instruments, these findings are in line with cognitive models suggesting that younger and more educated executives could be more willing to engage in hedging because they are more likely to have been exposed (e.g., through education) to derivatives (Tufano, 1996). Thus, age and education modify the effects of risk aversion. We do not find any effect of risk aversion on risk management for the samples with older CFOs and CFOs without MBAs. Finally, we also find that risk aversion is important for the propensity to hedge among less experienced CFOs (four or less years on the job), but has no effect on the hedging practice of the more experienced CFOs. This combined finding suggests that experience can mitigate how humans react to their personal risk aversion. To the extent that less experienced CFOs are also younger, this finding is also consistent with the evidence discussed in the previous paragraph about younger CFOs. To recap, our analysis suggests that a manager s tolerance for risk is important for whether their organization manages risk. Our analysis also suggests that the channel through which risk attitude affects corporate policies interacts with personal traits related to compensation, age, experience, and education. In line with behavioral models (e.g., Johnson and Tversky, 1983; Slovic, 1987), these findings suggest that personal characteristics related to lifetime experience have implications for how people act on their degree of risk aversion. Surprisingly, the role of this human component has received limited attention in corporate risk management research, both theoretical and empirical. We hope our study will lead researchers to incorporate this important element in future research. 4. Conclusions 27 Agency models of risk management also predict that when compensated with stock options, executives may prefer not to hedge because higher stock price volatility has the effect of increasing the value of their stock options. However, our evidence suggests that risk averse executives are more likely to hedge if they are likely to have a significant portion of their portfolio invested into the firm. 19

Many theories of risk management predict that companies hedge to mitigate the effects of financial constraints, information asymmetry, or agency costs. The lack of data has limited the extent to which these theories have been empirically tested. To overcome some of these limitations, in early 2010 we conducted a survey with the objective of gathering new data on corporate risk management practices at both public and private companies around the globe. We find that financially constrained firms are significantly less likely to have a risk management program in place than their financially unconstrained peers. These patterns persist when we partition companies in high and low investment growth prospect firms and when we condition on access to lines of credit or the availability of cash. We also find no evidence that the CFOs of financially constrained firms consider hedging more important to reduce cash flow volatility than do the CFOs of unconstrained firms. These findings are inconsistent with the predictions of the credit rationing hypothesis of risk management (which predicts that constrained firms hedge more to mitigate the effect of credit rationing on a firm s ability to invest). To the extent that small firms face higher information asymmetry our evidence is also inconsistent with information asymmetry models of risk management that predicts small firms hedge more than their larger firm counterparts. We are also able to study the relation between managerial risk aversion and corporate risk management. In the agency theories of risk management, managerial risk aversion plays a key role in explaining corporate risk management decisions. However, in the past this relation has not been directly testable because managerial risk aversion is not observable from archival data sources. Our survey instrument overcomes this limitation. Our analysis suggests that the human element executive risk aversion as moderated by other personal characteristics related to compensation, age, experience, and education plays a crucial role in corporate decisions concerning risk management. Our analysis suggests that incorporating the role of the individual manager into theories and empirical tests of corporate risk management will improve our ability to explain the practice of corporate hedging. 20

Appendix Risk Management: Evidence from Archival Data Empirical evidence on risk management theories is scarce. The main limitation is that the data necessary to test these theories is not always available from standard archival databases. In this appendix, we review the main empirical studies, highlighting how using data from our survey instrument can help overcome some of the limitations of existing studies. Table A.1 summarizes the findings from empirical archival based studies. Nance, Smith, and Smithson (1993) and Graham and Rogers (2002) test the credit rationing hypothesis of risk management and find mixed evidence. The authors rely on Tobin s q as a proxy for growth prospects. The problem with this measure is that it does not capture information on growth opportunities that are unknown to outsiders. We directly ask the CFOs to give us their inside views on the investment growth prospects of the firm. To increase the chance that the executives will disclose their views, we maintain their identity as strictly anonymous. 28 Table A.1 Risk Management: Evidence from Archival Data Theories Main empirical study Summary empirical findings Consistent with prediction Main limitation of archival tests Related empirical studies Credit Rationing Nance, Smith, and Smithson (1993) Smaller firms hedge less Growth prospects/financial constraints are difficult to measure Graham and Rogers (2002) Geczy, Minton, and Schrand (1997) Gay and Nam (1998) High Tobin s q firms hedge less High R&D firms hedge more Partially Access to Collateral Rampini, Sufi, and Viswanathan (2014) High net worth firms hedge more Collateral/financial constraints are difficult to measure N.A. Agency Problems Tufano (1996) Firms w/ high executive stock ownership hedge more Measure of risk aversion is not available Geczy, Minton, and Schrand (1997) Berkman and Bradbury (1996) Mayers and Smith (1987) Bessembinder (1991) Information Asymmetry DeGeorge, Boaz, and Zechhauser (1996) Firms w/ high ROA hedge more Partially Information asymmetry on managerial ability is difficult to measure N.A. To our knowledge, there is no empirical study on whether firms substitute lines of credit for hedging. This likely is because there is no archival database that combines information on risk management with credit line data. Our survey database overcomes this limitation. Our data contain also information on why firms hedge. Tests based only on whether or not the firm hedges might confound other effects. For example, finding that smaller (arguably more constrained) firms hedge less is not necessarily evidence against the credit rationing hypothesis of risk management. In fact, smaller companies might not be able to hedge because setting up a risk management program is too costly (e.g., Mian, 1996), 29 they do not have the collateral required by the hedging counterparties (Rampini and Viswanathan, 2010, 2013), or more simply, they do not 28 See Petersen and Thiagarajan (2000) for additional discussion on the limitations of using information from financial reports or market data to measure investment prospects. 29 See also Block and Gallagher (1986), Booth, Smith, and Stolz (1984), and Bodnar, Hayt, and Marston (1998). 21

face significant hedgeable risks (Petersen and Thiagarajan, 2000). 30 By focusing on companies with a risk management program in place and asking their CFOs why they hedge, our study mitigates the effect of these confounding factors. The evidence on the relation between agency issues and hedging is scarce. Tufano (1996) is one notable exception. One of the core assumptions of the agency models of risk management is that managers are risk averse. The difficulty of obtaining an accurate measure of risk aversion might explain the limited number of empirical studies. To overcome this limitation, we estimate managerial attitude towards risk using a psychometric test. DeGeorge, Boaz, and Zeckhauser (1996) is the only paper that as far as we know has tested information asymmetry models of risk management. The authors use return on assets as a proxy for managerial ability. The problem with return on assets and other archival measures is that they are based on observed outcomes and therefore do not necessarily reflect uncertainty about managerial ability. In our study, we analyze on the relation between information asymmetry and risk management using the CFO inside assessment of the investment growth prospects as a proxy for managerial ability that is unknown to outsiders. 31 To summarize, testing the theories of risk management requires firm level data on whether the firm has a risk management program in place, on the extent of hedging, on CFO motivation for hedging, on the role played by the CFO in the decision to set up the risk management program, and on information on managerial characteristics, including executive attitude towards risk. At an even more basic level, one also needs to be able to identify whether the firm is facing any material hedgeable risks. This information is not generally available in standard archival databases. Our data potentially fills this void. 30 Petersen and Thiagarajan (2000) emphasize that without understanding the risk exposure of a firm, it is not possible to study whether the firm is managing risk according to theory. 31 There is a more recent strand of literature focusing on the real effects of risk management. Campello, Lin, Ma, and Zou (2011) show that hedging helps firms increase investment by lowering borrowing costs. Carnaggia (2013) argues that the introduction of a new crop insurance program in the agricultural industry had a positive effect on the productivity of firms that had access to the insurance. There is also a stream of literature, mostly in the accounting domain, focusing on the impact of derivative use on firm risk (Guay, 1999; Hentschel and Kothari, 2001; Zhang, 2009) or the role of hedging for stock liquidity (Minton and Schrand, 2014). 22

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Table 2 Descriptive Statistics This table reports summary statistics for the main variables used in the study. The data are from the Corporate Risk Management Survey, which was conducted in the first quarter of 2010. The sample includes non financial firms from around the globe. Risk Management is an indicator variable for firms with a risk management program in place. Large is an indicator variable for firms with sales of at least $1 billion. Ratings is an indicator variable for firms with a debt rating. Dividend Payer is an indicator variable for firms that pay regular dividends. Investment Prospects reflects the CFO s rating of the firm s long term investment and growth opportunities, ranging from 0 (no growth opportunities) to 100 (excellent growth opportunities). Profitable is an indicator variable for firms that reported accounting profits during the previous fiscal year. Credit Line is an indicator variable for firms with a line of credit. Cash Holdings is cash holdings and marketable securities as a percentage of total assets. Leverage is the ratio of total debt to total assets. Public is an indicator variable for firms listed on a stock exchange. rth America Firms are those headquartered either in the U.S. or Canada. European Firms are those headquartered either in the European Union or other European countries. Asian Firms are those headquartered in an Asian country. Other Region Firms are those headquartered in Australia, New Zealand, Latin America, the Middle East, and Africa. Variables Full Sample rth America Europe Asia Other Regions Public Private Mean Obs. Mean Panel A: Firm Characteristics Risk Management 0.52 646 0.44 0.71 0.53 0.54 0.74 0.39 Large 0.31 656 0.35 0.42 0.15 0.24 0.55 0.16 Ratings 0.45 656 0.47 0.50 0.39 0.44 0.64 0.34 Dividend Payer 0.52 656 0.41 0.62 0.69 0.60 0.67 0.43 Investment Prospects 65.87 405 68.08 63.91 61.68 66.33 67.05 65.07 Profitable 0.78 445 0.72 0.81 0.88 0.80 0.82 0.76 Credit Line 0.77 429 0.77 0.76 0.77 0.80 0.83 0.74 Cash Holdings 0.19 388 0.17 0.17 0.26 0.17 0.20 0.19 Leverage 0.27 394 0.28 0.29 0.24 0.22 0.29 0.26 Public 0.36 681 0.35 0.43 0.36 0.44 1.00 0.00 Panel B: Regional Distribution Obs. [%] rth America Firms 344 [53%] European Firms 122 [18%] Asian Firms 164 [25%] Other Region Firms 25 [4%] 26

Table 3 Representativeness of the Sample: Comparing to COMPUSTAT This table compares public firms in the Corporate Risk Management Survey global sample with active firms in the COMPUSTAT Global database as of fiscal year ending in May 2010. The samples include non financial firms. We report number of observations and percentages based on several firm characteristics. We also report basic descriptive statistics on leverage and cash holdings. Firms are defined as Small if their revenues are less than $1 billion, and Large otherwise. n Dividend Payer firms are firms that do not regularly pay a dividend. Dividend Payer firms pay a dividend regularly. Unprofitable firms are those that did not report accounting profits during the previous fiscal year. Profitable firms are defined as those reporting an accounting profit during the previous fiscal year. Leverage is the ratio of total debt to total assets. Cash Holdings is the ratio of cash holdings and marketable securities to total assets. Firm Types Survey Sample COMPUSTAT Global Sample Obs. (N) Freq. (%) Obs. (N) Freq. (%) Small 110 45% 11,825 52% Large 134 55% 10,915 48% n Dividend Payer 80 33% 2,491 24% Dividend Payer 162 67% 7,887 76% Unprofitable 30 18% 4,540 20% Profitable 134 82% 18,159 80% Mean Median Mean Median Leverage 29% 28% 34% 19% Cash Holdings 20% 15% 17% 12% 27

Table 4 Risk Management, Financial Constraints and Investment Prospects This table reports survey averages for the risk management indicator variable by firm characteristics and investment prospects. The data are from the Corporate Risk Management Survey in the first quarter of 2010. The sample includes non financial firms from around the globe. The evidence in Panel A is for the full sample. The evidence in Panel B is by ownership structure (public and private firms). Firms are defined as Large if their sales are equal to or larger than $1 billion, and Small otherwise. Ratings: firms are those with a credit rating for their debt. Ratings: firms are those without a credit rating for their debt. Dividend Payer: are those firms that pay dividends regularly. Dividend Payer: are those firms that do not pay dividends regularly. High Inv. Prospect firms are those with investment prospects above the sample median. Low Inv. Prospect firms are those with investment prospects below the sample median. Size Ratings Dividend Payer Mean Large Small Diff. Small Large Diff. Ratings Diff. Divs Panel A: Full Sample Risk Management 0.52 0.76 0.41 0.35*** 0.63 0.42 0.21*** 0.62 0.40 0.22*** High Inv. Prosp. 0.55 0.76 0.45 0.31*** 0.66 0.45 0.22*** 0.60 0.49 0.11 Low Inv. Prosp. 0.48 0.78 0.38 0.40*** 0.56 0.43 0.13* 0.61 0.35 0.26*** Diff. Low High Prosp. 0.07 0.02 0.07 0.10 0.02 0.01 0.14** Panel B: By Ownership Public Firms Risk Management 0.74 0.83 0.63 0.20*** 0.75 0.70 0.05 0.82 0.57 0.25*** High Inv. Prosp. 0.76 0.81 0.70 0.11 0.78 0.73 0.05 0.78 0.72 0.06 Low Inv. Prosp. 0.67 0.77 0.58 0.19 0.66 0.69 0.03 0.80 0.46 0.34*** Diff. Low High Prosp. 0.09 0.04 0.12 0.12 0.04 0.03 0.26* Private Firms Risk Management 0.39 0.63 0.34 0.29*** 0.49 0.33 0.16*** 0.45 0.33 0.12** High Inv. Prosp. 0.40 0.60 0.36 0.24* 0.49 0.35 0.14 0.39 0.40 0.01 Low Inv. Prosp. 0.39 0.80 0.32 0.48*** 0.48 0.35 0.13 0.48 0.32 0.16* Diff. Low High Prosp. 0.01 0.20 0.04 0.01 0.00 0.09 0.08 te: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% (two tail) test levels, respectively. 28

Table 5 Risk Management and Cash Flow Variability: By Financial Constraints and Investment Prospects This table reports mean survey responses on how important CFOs rate risk management to Decrease Cash Flow Volatility. The data are from the Corporate Risk Management Survey in the first quarter of 2010. The sample includes non financial firms from around the globe. Respondents are asked to rank the factor on a scale from 1 to 4 (where 1 means not important, and 4 means very important). Responses are reported by firm characteristics and investment prospects. Firms are defined as Large if their sales are equal to or larger than $1 billion, and Small otherwise. Ratings: firms are those with a credit rating for their debt. Ratings: firms are those without a credit rating for their debt. Dividend Payer: are those firms that pay dividends regularly. Dividend Payer: are those firms that do not pay dividends regularly. High Inv. Prospect firms are those with investment prospects above the sample median. Low Inv. Prospect firms are those with investment prospects below the sample median. Size Ratings Dividend Payer Mean Large Small Diff. Small Large Diff. Ratings Diff. Divs Full Sample Decrease Cash 3.01 3.11 3.06 0.05 3.07 3.08 0.01 3.06 3.10 0.04 Flow Volatility High Inv. Prosp. 2.95 2.97 2.94 0.03 2.97 2.93 0.04 2.94 2.96 0.02 Low Inv. Prosp. 3.05 3.20 3.01 0.19 3.13 3.01 0.12 3.04 3.06 0.02 Diff. Low High 0.10 0.23 0.07 0.16 0.08 0.10 0.10 Prosp. te: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% (two tail) test levels, respectively. 29

Table 6 Risk Management, Financial Constraints, Credit Lines, and Cash Holdings Panel A reports survey averages for the risk management dummy variable by firm characteristics, credit lines, and cash holdings. Panel B reports survey averages for the risk management indicator variable by firm characteristics and cash holdings. The data are from the Corporate Risk Management Survey in the first quarter of 2010. The sample includes non financial firms from around the globe. Firms are defined as Large if their sales are equal to or larger than $1 billion, and Small otherwise. Ratings: firms are those with a credit rating for their debt. Ratings: firms are those without a credit rating for their debt. Dividend Payer: are those firms that pay dividends regularly. Dividend Payer: are those firms that do not pay dividends regularly. High % Undrawn firms are those with a percentage of the credit line unutilized above the sample median. Low % Undrawn firms are those with a percentage of the credit line unutilized below the sample median. High Cash Holdings firms are those with the ratio of cash and marketable securities to total assets above the sample median. Low Cash Holdings firms are those with the ratio of cash and marketable securities to total assets below the sample median. Size Ratings Dividend Payer Mean Large Small Diff. Small Large Diff. Ratings Diff. Divs Panel A: By Credit Line Risk Management 0.52 0.76 0.41 0.35*** 0.63 0.42 0.21*** 0.62 0.40 0.22*** High % Undrawn 0.58 0.80 0.45 0.35*** 0.68 0.46 0.22*** 0.67 0.47 0.18** Low % Undrawn 0.44 0.67 0.38 0.29*** 0.60 0.34 0.26*** 0.53 0.35 0.20** Diff. Low High Undrawn Credit Line 0.14** 0.13 0.07 0.08 0.12 0.14* 0.12 Panel B: By Cash Holdings Risk Management 0.52 0.76 0.41 0.35*** 0.63 0.42 0.21*** 0.62 0.40 0.22*** High Cash Holdings 0.56 0.83 0.45 0.38*** 0.65 0.50 0.15** 0.62 0.48 0.14** Low Cash Holdings 0.47 0.72 0.37 0.35*** 0.61 0.36 0.25*** 0.69 0.39 0.20*** Diff. Low High 0.09* 0.11 0.08 0.04 0.14** 0.03 0.09 Cash te: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% (two tail) test levels, respectively. 30

Table 7 Risk Aversion and Executive Characteristics This table reports summary statistics for executive risk aversion and other managerial characteristics. The data are from the Corporate Risk Management Survey in the first quarter of 2010. The sample includes nonfinancial firms from around the globe. Risk Management is an indicator variable for firms with a risk management program in place. Highly Risk Averse is an indicator variable for executives that prefer their current salary to a job that pays twice their current salary with 50% probability or 80% of their current salary with 50% probability. Moderately Risk Averse is an indicator variable for executives that prefer their current salary to a job that pays twice their current salary with 50% probability or 66% of their current salary with 50% probability. Less Risk Averse is an indicator variable for executives that prefer a job that pays twice their current salary with 50% probability or 66% of their current salary with 50% probability to their current salary. Compensation w/ Stocks and Options is an indicator variable for CFOs with compensation package that includes stocks and options. 55 Years of Age or Older is an indicator variable for CFOs that are 55 years of age or older. 4 Years in the Job or More is an indicator variable for CFOs with at least 4 years on the job. MBA/Master s Degree is an indicator variable for CFOs with an MBA or master degree. rth America Firms are those with the headquarters either in the U.S. or Canada. European Firms are those with the headquarters either in the European Union or other European countries. Asian Firms are those with the headquarters in an Asian country. Public Firm is an indicator variable for publicly listed firms. Private Firm is an indicator variable for firms that are not listed in a public exchange. Variables Full Sample rth Europe Asia Public Private America Mean Obs. Mean Executive Traits Risk Management 0.52 646 0.44 0.71 0.53 0.74 0.39 Highly Risk Averse 0.20 89 0.20 0.20 0.21 0.15 0.23 Moderately Risk Averse 0.17 76 0.19 0.13 0.16 0.17 0.16 Less Risk Averse 0.63 281 0.61 0.67 0.63 0.68 0.61 Compensation with Stocks and Options 0.37 373 0.46 0.24 0.26 0.67 0.20 55 Years of Age or Older 0.25 451 0.31 0.12 0.22 0.13 0.32 4 Years on the Job or More 0.79 446 0.79 0.76 0.81 0.79 0.78 MBA/Master s Degree 0.63 448 0.64 0.68 0.61 0.70 0.59 31

Table 8 Risk Aversion, Executive Characteristics, and the Risk Management Decision This table reports probit estimation results from the risk management model. The dependent variable is Risk Management, which is an indicator variable for firms with a risk management program. The data are from the Corporate Risk Management Survey first quarter of 2010. The sample includes non financial firms from around the globe. Refer to Table 2 for detailed variable definitions. Standard errors reported in parentheses are estimated with heteroskedasticityconsistent errors clustered by region. Risk Aversion and Executive Traits Full Sample Compensation w/ Stocks and Options 55 Years of Age or Older 32 4 Years on the Job or More MBA/Master s Degree (1) (2) (3) (4) (5) (6) (7) (8) (9) Highly Risk Averse 0.256** 0.553*** 0.159 0.161 0.536*** 0.126 1.679*** 0.424** 0.116 (0.125) (0.173) (0.194) (0.259) (0.084) (0.133) (0.256) (0.191) (0.167) [Marginal Effects] [0.101]** [0.205]*** [0.063] [ 0.060] [0.201]*** [0.050] [0.537]*** [0.159]** [ 0.043] (0.049) (0.058) (0.078) (0.096) (0.029) (0.052) (0.050) (0.067) (0.062) Large 0.592*** 10.035*** 0.524** 0.519** 0.634** 0.471** 2.150*** 0.728*** 0.180 (0.151) (0.239) (0.266) (0.251) (0.293) (0.185) (0.543) (0.166) (0.191) Ratings 0.250 0.067 0.392 0.831*** 0.012 0.229 1.165*** 0.302 0.170 (0.161) (0.218) (0.252) (0.084) (0.205) (0.146) (0.203) (0.240) (0.363) Dividend Payer 0.213* 0.625* 0.002 0.069 0.272*** 0.178*** 0.290 0.258 0.150 (0.125) (0.374) (0.209) (0.079) (0.102) (0.065) (0.555) (0.220) (0.658) Inv. Prospects 0.238* 0.322* 0.063 0.208 0.174 0.079 2.404** 0.017 0.482 (0.128) (0.189) (0.270) (0.486) (0.248) (0.235) (1.170) (0.252) (0.333) Profitable 0.181 0.157 0.139 0.329* 0.375 0.171 0.676*** 0.023 0.294 (0.305) (0.311) (0.403) (0.197) (0.331) (0.323) (0.168) (0.275) (0.241) Credit Line 0.127 0.291 0.208 0.561** 0.045 0.254 1.318*** 0.132 0.493 (0.167) (0.186) (0.358) (0.272) (0.272) (0.270) (0.301) (0.133) (0.588) Cash Holdings 0.089 0.009 0.266 1.520*** 0.273 0.351 0.854 0.034 0.010 (0.576) (1.058) (0.762) (0.472) (0.715) (0.461) (1.458) (0.351) (1.171) Leverage 0.081 0.637** 0.159 0.006 0.146 0.108 0.050 0.175 0.775 (0.272) (0.303) (0.441) (0.536) (0.238) (0.406) (1.217) (0.485) (0.813) Public 0.642*** 0.446 0.913*** 0.625*** 0.688*** 0.928*** 1.029** 0.490** 1.189*** (0.152) (0.381) (0.307) (0.178) (0.230) (0.228) (0.408) (0.248) (0.194) Region Fixed Effects Obs. 344 115 199 90 251 269 67 216 122 Pseudo R 2 0.160 0.258 0.129 0.241 0.163 0.184 0.351 0.153 0.261 te: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% (two tail) test levels, respectively.

30% 40% 50% 60% 70% 80% 90% Increase Expected Cash Flows Decrease Unexpected Losses Satisfy Shareholders' Expectations Increase Firm Value Reduce Cash Flow Volatility Improve Decision Making Improve Earnings Predictability Improve Investment in Difficult Times Increase/Maintain Ratings Decrease Cost of Debt Decrease Cost of Equity Increase Borrowing Capacity Decrease Share Price Volatility Figure 2 Percentage of CFOs Indicating Factor as Important or Very Important for the Decision to Have a Risk Management Program The data are from our Corporate Risk Management Survey, which was conducted in the first quarter of 2010. The sample includes nonfinancial firms from around the globe. 33

Highly Risk Averse CFO 0.10 Large 0.23 Ratings 0.10 Dividend Payer 0.08 Investment Prospects 0.09 Profitable 0.07 Credit Line 0.05 Cash Holdings 0.04 Leverage 0.03 Public 0.25 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Figure 3 Marginal Effects from Probit Estimation Describing Why Firms Hedge This figure reports marginal effects relative to the probit estimation in Column 1, Table 8. The data are from our Corporate Risk Management Survey, which was conducted in the first quarter of 2010. The sample includes non financial firms from around the globe. 34