Financial distress and corporate risk management

behavior on the part of the firm; ex-post, shareholders of a levered firm may not find it optimal to engage inhedging activities due to their risk-shifting incentives Jensen and Meckling, 1

Journal of Financial Economics 87 (2008) 706–739

Financial distress and corporate risk management:

a positive (negative) relation between leverage and hedging for moderately (highly) leveraged firms Consistent with thetheory, empirically I find a non-monotonic relation between leverage and hedging Further, the effect of leverage onhedging is higher for firms in highly concentrated industries

r2008 Elsevier B.V All rights reserved

Tel.: +1 734 764 6886; fax: +1 734 936 8715.

E-mail address: amiyatos@umich.edu

1 Other motivations for corporate hedging include convexity of taxes, managerial risk-aversion ( Stulz, 1984; Smith and Stulz, 1985 ) underinvestment costs ( Froot, Scharfstein, and Stein, 1993 ), and information asymmetry ( DeMarzo and Duffie, 1991, 1995 ) See also

Breeden and Viswanathan (1996) and Stulz (1996)

behavior on the part of the firm; ex-post, shareholders of a levered firm may not find it optimal to engage inhedging activities due to their risk-shifting incentives (Jensen and Meckling, 1976).2 I extend the currentliterature by explaining the ex-post risk-management motivation of the firm.3I provide a simple model thatgenerates new cross-sectional predictions by relating firm characteristics such as leverage, financial distresscosts, and project maturity to risk-management incentives I test the key predictions of the model with hedgingdata of COMPUSTAT-CRSP firms meeting some reasonable sample selection criteria for fiscal years1996–1997 The empirical study presents the first large-sample evidence on the determinants of the extent offirms’ hedging activities and provides new findings.

The key assumption underlying my theory is the distinction between financial distress and insolvency

I assume that apart from the solvent and the insolvent states, a firm faces an intermediate state called financialdistress Financial Distress is defined as a low cash-flow state in which the firm incurs losses without beinginsolvent The notion that financial distress is a different state from insolvency has some precedence in theliterature Titman (1984) uses a similar assumption to study the effect of capital structure on a firm’sliquidation decisions

There are three important sources of financial distress costs First, a financially distressed firm may losecustomers, valuable suppliers, and key employees.4Opler and Titman (1994)provide empirical evidence thatfinancially distressed firms lose significant market share to their healthy counterparts in industry downturns.Using data from the supermarket industry, (Chevalier 1995a, b) finds evidence that debt weakens thecompetitive position of a firm Second, a financially distressed firm is more likely to violate its debt covenants5

or miss coupon/principal payments without being insolvent.6These violations impose deadweight losses in theform of financial penalties, accelerated debt repayment, operational inflexibility, and managerial time andresources spent on negotiations with the lenders.7 Finally, a financially distressed firm may have to forgopositive NPV projects due to costly external financing, as inFroot, Scharfstein, and Stein (1993) In this paper

I focus on the first of these costs, i.e., the product market-related costs of financial distress

I develop a dynamic model of a firm that issues equity capital and zero-coupon bonds to invest in a riskyasset The firm makes an initial investment with the consent of its bondholders At a later date, shareholderscan modify the firm’s investment risk by replacing the existing asset with a new one The firm’s asset valueevolves according to a stochastic process The firm is in financial distress if the asset value falls below somelower threshold during its life In this state, the firm loses market share to its competitors and therefore isunable to realize its full upside potential, even when the industry condition improves at a later date Insolvencyoccurs on the maturity date if terminal firm value is below the face value of debt, in which case debtholdersgain control of the firm Shareholders’ final payoffs depend on the terminal asset value as well as on the pathtaken by the firm’s asset over its life.8

2 Throughout the paper, I use the terms ex ante and ex post with respect to the time of borrowing.

3 Other papers analyzing shareholders’ ex-post risk-management decisions include Leland (1998) and Morellec and Smith (2003) Leland (1998) provides a justification for the firm’s ex-post hedging behavior in the presence of tax-benefits of debt In Morellec and Smith (2003) , the manager-shareholder conflict reduces shareholders’ ex-post asset-substitution incentives My model, in contrast, is based on the cost of financial distress and provides new empirical predictions.

4

For example, in the mid-1990s Apple Computers had financial difficulties leading to speculation about its long-term survival (see Business Week, January 29 and February 5, 1996) Software developers were reluctant to develop new application software for Mac-users, which led in part to a decline of 27% in the unit sales of Mac computers from 1996 to 1997 (see Apple’s 1998 10-K filings with the SEC) Similarly, when Chrysler faced financial difficulties in the early 1980s, Lee Iacocca (former CEO of the company) observed that ‘‘its share

of new car sales dropped nearly two percentage points because potential buyers feared the company would go bankrupt’’ (quoted from

7 For example, when Delta airlines violated a debt-to-equity ratio covenant in 2002, it was required by its lenders to maintain a minimum

of $1 billion in cash and cash equivalents at the end of every month from October 2002 until June 2003 See Delta’s 2002 10-K filings with the SEC.

8 This approach is similar (but not the same) to valuation of equity as a path-dependent (down-and-out call) option The equity value in

my model differs from the corresponding barrier option by the amount of losses incurred in financial distress Brockman and Turtle (2003)

provide some empirical evidence in support of equity’s valuation as a path-dependent option.

The optimal level of ex-post investment risk, from the shareholders’ perspective, is determined by the off between the costs of financial distress and value associated with the limited liability of the firm’s equity.9Unlike in the risk-shifting models such asJensen and Meckling (1976), equity value is not always an increasingfunction of firm risk in my model While a high risk project increases the value of equity’s limited liability, italso imposes a cost on shareholders by increasing the expected cost of financial distress Due to these losses,the shareholders find it optimal to implement a risk-management strategy ex-post even in the absence of anexplicit pre-commitment to do so.

trade-The optimal investment risk in my model depends on firm leverage, the financial distress boundary, the timehorizon of the project, and the costs of financial distress As in the extant models (Smith and Stulz, 1985),

I show that a firm with high leverage has a higher incentive to engage in hedging activities However, the management incentives disappear for firms with extremely high leverage The incentive to hedge arises fromthe product market-related financial distress costs and these costs are more likely to be present when a firm isvulnerable to losing market share to its competitors Empirical studies by Opler and Titman (1994) and

risk-Chevalier (1995a, b)show that debt weakens the competitive position of a firm in its industry Further, theadverse consequences of leverage are more pronounced in concentrated industries Motivated by these studies

my model argues that industry concentration provides a good proxy for financial distress costs Highlyleveraged firms in concentrated industries are more likely to experience a deterioration in their competitiveposition in the event of financial distress i.e., are expected to incur higher financial distress costs Thus, themodel predicts a stronger hedging incentive for highly levered firms in concentrated industries

The model shows that hedging incentives increase with project maturity because the likelihood ofexperiencing financial distress as well as the expected loss of default increases with the life of the asset Risk-management motivation in my model arises from costs incurred by the firm in states in which the firm hits thefinancial distress barrier but remains solvent on the maturity date If there are no financial distress costs, risk-management incentives disappear On the other hand, if these costs are very high, the distinction betweenfinancial distress and insolvency diminishes along with any ex-post risk-management motivations.Intermediate levels of losses create risk-management incentives within the firm Therefore, my model predicts

a U-shaped relation between financial distress costs and hedging

The predictions of my model have important implications for the empirical research To test the existingtheories, empirical studies regress some measure of financial distress (typically leverage) on firms’ risk-management activities If firms with extreme distress are less likely to hedge, these models may be misspecified.The bias can be particularly severe in small-sample studies It is not surprising that existing empirical studiesfind mixed evidence in support of the distress cost-based theories of hedging.10

I contribute to the empirical management literature by analyzing foreign currency and commodity management activities of a comprehensive sample of nonfinancial firms Since data on firms’ hedging activities(by means of derivatives) are not readily available, empirical studies in this area are based on small samples orinvestigate only the yes–no decision to hedge.11 This has created two major challenges First, our currentunderstanding is mostly based on analyses that treat firms with different hedging intensities as similar, whichlimits our ability to investigate firms’ hedging motivations Second, we have been able to gain only limitedinsight into the effect of industry-specific factors on hedging decisions

risk-I test the predictions of my model with data on the extent of hedging of more than 2,000 firms for the fiscalyear 1996–1997 Due to the large sample size drawn from different industries, I provide new empirical evidencerelating industry structure to hedging decisions Consistent with the theory, I find strong evidence that firmswith higher leverage hedge more, although the hedging incentives disappear for firms with very high leverage.Also in line with my theory, I find that financially distressed firms in highly concentrated industries hedge

9 In the context of swap markets, Mozumdar (2001) demonstrates the trade-off between risk-shifting and hedging incentives in the presence of information asymmetry about the firm type His model relates hedging incentives to firm type.

10 For example, while Haushalter (2000) and Graham and Rogers (2002) find a positive relation between the two variables, Nance, Smith and Smithson (1993) , Mian (1996) , and Tufano (1996) fail to find such evidence.

11 For example, Geczy, Minton, and Schrand (1997) use 372 firms with 154 hedgers; Graham and Rogers (2002) use about 400 firms with

158 hedgers Studies by Mian (1996) and Bartram, Brown, and Fehle (2003) use large samples to investigate the yes–no decision of hedging Tufano (1996) and Haushalter (2000) provide detailed evidence from gold and oil & gas industries, respectively Brown (2001)

provides evidence from a detailed case study Purnanandam (2007) investigates the risk-management decisions of commercial banks.

more My empirical results are robust to alternative proxies of financial distress (such as leverage, adjusted leverage and Altman Z-score), alternative ways of measuring the hedging activities (yes–no decision

industry-to hedge and industry-total notional amount of hedging) and various controls for nonderivative-based hedgingstrategies Further for a subsample of 200 manufacturing firms, I obtain data on the firms’ hedging activitiesfor fiscal years 1997–1998 and 1998–1999 and show that the basic results remain similar for a regression modelinvolving changes in hedging activities While firms with a moderate increase in leverage increase their hedgingactivities, firms with an extreme increase in leverage decrease their hedging positions As long as firms do notfrequently change their operational hedging strategies (such as opening plants in foreign countries to hedgetheir foreign currency risk), the analysis based on change regressions provides a robust control fornonderivative-based hedging strategies of the firm The change regressions also allow me to partiallydisentangle the effects of ex-ante and ex-post hedging incentives

The rest of the paper is organized as follows In Section 2, I provide the model description Section 3analyzes the optimal risk-management policy of the firm The empirical tests are provided in Section 4, andSection 5 concludes the paper Without any loss in continuity, readers mostly interested in the empirical part

of the paper can skip to Section 3.1, which provides a self-contained summary of the key features of thetheoretical model

2 Model

I consider a stylized model of a continuous trading economy with time horizon ½t0; T  There are threeimportant dates in the model discussed below Though a discrete time model can also be used to capture thekey feature of my model, the continuous time version allows for an easier analytical solution at the expense ofadditional mathematical overhead In addition, the continuous time model provides additional predictionrelating the time to maturity of the firm’s project to its hedging incentives

At t ¼ t0, the firm makes its capital structure decision and invests in risky asset Ai(i stands for the initialinvestment), which I refer to as an ‘‘EBIT-generating machine’’ (Goldstein, Ju and Leland, 2001) Thesedecisions may or may not be made with the consent of the firm’s debtholders The risky asset ðAiÞis acquired

at the market-determined price and financed through a mix of zero-coupon debt and equity capital Let L bethe face value of the zero-coupon debt, payable at time T, and Etbe the time t-value of the firm’s equity There

is a tax benefit of debt, which provides the incentive to issue debt in my model For simplicity the tax benefit isassumed to be a fraction t of the face value of debt L Optimal capital structure is determined by a trade-offbetween the tax benefit of debt and bankruptcy costs For simplicity, I do not endogenize the capital structuredecisions However, the key predictions of the model remain similar for a more general model (unreported)that solves for capital structure decisions as well The cash generated by the machine and its asset value Aitaredriven by a Brownian motion with the usual properties

At some later time t ¼ t1 ðt12 ðt0; TÞÞ, the shareholders (or managers acting on their behalf) make a management decision At this time, which can be an instant or days or months after the capital structuredecisions, they have an opportunity to change the asset’s risk without the bondholder’s approval To capturethe risk-shifting incentives, I assume that the bondholders are unable to recontract with the shareholders at

risk-t ¼ risk-t1 Further, I assume that the two parties cannot contract on the risk-management choice at time t0

through the use of bond covenants This latter assumption is what gives rise to the risk-shifting incentive in mymodel This assumption is in the spirit of a large literature on incomplete contracting in economics and finance(see for example,Bolton and Dewatripont, 2005) The premise here is that it is too costly to specify every state

of the world and write down debt covenants that will limit shareholders behavior with respect to firm risk ineach of those states Even if such covenants could be written to tie down the manager’s risk-managementbehavior, it would be too costly to implement them especially in very high leverage states when shareholdershave a large incentive to default on covenants.12

This assumption is in the spirit of Jensen and Meckling’s argument that ‘‘To completely protect thebondholders from the incentive effects, these provisions would have to be incredibly detailed and cover most

12 As long as there are nontrivial costs in writing, monitoring and enforcing these contracts, some residual risk-management decisions are always optimally left with the shareholders/managers, which is sufficient to generate the main results of my model.

operating aspects of the enterprise including limitations on the riskiness of the projects undertaken The costsinvolved in writing such provisions, the costs of enforcing them and the reduced profitability of the firm(induced because the covenants occasionally limit management’s ability to take optimal actions on certainissues) would likely be nontrivial In fact, since management is a continuous decision making process it will bealmost impossible to completely specify such conditions without having the bondholders actually perform themanagement function.’’

After the risk-management decisions have been made, the firm acquires a new EBIT-generating machine.This EBIT-generating machine generates cashflows dtforever that evolves according to a geometric Brownianmotion The value of this EBIT-generating machine, i.e., the value of a similar unlevered firm, is denoted by

At.13One can think of dt as the state vector representing the state of the firm’s industry I assume that thechange in the investment risk of the asset (from Aito AÞ has no cashflow impact on the firm at t ¼ t1 Thisprovides an initial boundary condition in the model, namely At1¼Ait1 Further, for analytical simplicity Iassume that the total payout (to debtholders and shareholders) by the firm is zero during ½t0; T Þ, with the finalpayoffs realized at t ¼ T The shareholders receive the terminal equity value of the firm.14The bondholdersreceive the face value of debt (L) if the firm remains solvent on the maturity date t ¼ T15; otherwise theyreceive the residual value of the firm The model can be represented by the following timeline:

t ¼ t0 t ¼ t1 t ¼ T

Capital structure Risk-management Payoffs

Initial investment decisions

This modeling framework allows me to address the issue of ex-ante vs ex-post risk-management behavior ofthe firm in the presence of the shareholders’ risk-shifting incentives I now discuss the main assumption of thepaper, namely, the distinction between financial distress and insolvency

2.1 Financial distress and insolvency

If during (t0; TÞ the firm’s asset value At falls below a boundary KðLÞ;16the firm is in the state of financialdistress Insolvency, on the other hand, occurs on the terminal date T if the terminal firm value ðVT) is lessthan the debt obligations Therefore, in the state of financial distress, control of the firm does not shift to thebondholders immediately, but the firm does incur costs that increase with leverage.Opler and Titman (1994)

show that financially distressed (highly leveraged) firms lose significant market share to their healthycompetitors during industry downturns The drop in sales faced by Apple Computers and Chrysler duringperiods of financial difficulty provide anecdotal evidence in support of such deadweight losses In a sample of

31 high-leveraged transactions (HLTs),Andrade and Kaplan (1998) isolate the effect of economic distressfrom financial distress and estimate the cost of financial distress as 10–20% of firm value.Asquith, Gertnerand Scharfstein (1994)show that on average financially distressed firms sell 12% of their assets as part of theirrestructuring plans

Chevalier (1995a, b)uses detailed information from the local supermarket industry to provide evidence insupport of predatory behavior in this market She shows that following supermarket leveraged buyouts

13

The value of the levered firm of my model differs from A t by the amount of the tax benefit of debt as well as the costs associated with financial distress and bankruptcy Throughout this paper I denote the value of the levered firm by V t and the value of its assets (EBIT- generating machine) by At.

14 For analytical simplicity I assume that the model’s terminal date corresponds to the maturity date of the firm’s debt, at t ¼ T This assumption should not be confused with the assumption that the firm’s life is finite It simply states that at time T initial shareholders sell the firm to some other investors at the fair market value of the firm as an ongoing concern.

15 Other maturity structures are possible To illustrate the main results of the paper in its simplest form, I prefer to work with zero coupon debts.

16 I refer to K as the distress barrier in the rest of this paper K is assumed to be an increasing function of leverage This definition of financial distress is equivalent to assuming that when industry conditions deteriorate, firms with high leverage become financially distressed.

(LBOs), prices fall in local markets in which rival firms have low leverage and are concentrated Further, theseprice drops are associated with LBO firms exiting the local market These findings suggest that rivals attempt

to prey on LBO chains.Phillips (1995)studies the interactions between product market and financial structurefor four industries and finds evidence consistent with debt weakening the competitive positions of firms (seealsoKovenock and Phillips, 1997; Arping, 2000) Using deregulation of the trucking industry as an exogenousshock,Zingales (1998)studies the interplay between financial structure and product market competition andprovides evidence that leverage reduces the probability of a firm’s survival after an increase in competition.The overall message from these papers is that financial distress may impose a real cost on firms by weakeningtheir competitive position in the product market

Motivated by the empirical findings of above papers and anecdotal evidence, I assume that a firm infinancial distress loses a fraction of its market share to its healthy competitors.17In my model, this is achieved

by assuming that the financially distressed firm’s EBIT-generating machine produces less cashflow resulting in

a lower value for the distressed firm If the firm does not experience financial distress during t 2 ½t1; T , theterminal firm value is VT However, if the distress boundary is hit, the terminal value falls to f ðVTÞ, where

f ðVTÞoVT (seeFig 1) The function f represents the losses caused by financial distress

2.2 Valuation of equity

The shareholders receive liquidating dividends at T Due to equity’s limited liability, the final payoff to theshareholders ðxTÞ is zero if the terminal firm value is below L Let us define inft1ptpTAt mT forthe minimum value of the asset during ½t1; T In the event of no distress (i.e., mT4K) and solvency on theterminal date (i.e., VT4L), the shareholders get a liquidating dividend of ðVTLÞ If financial distress isexperienced (i.e., mTpK), but on the terminal date the firm remains solvent (i.e., f ðVTÞ4L), the shareholders

f ðA T Þ), remains above the face value of debt (i.e., L) Thus, this is the state where f ðA T Þ4L or alternatively A T 4f1ðLÞ, as depicted in the figure Finally, the bottom-most path corresponds to the state of ‘Insolvency.’

17 In a more general industry equilibrium setting, firms can make strategic decisions about their leverage, investment risk, and hedging (see e.g., Adam, Dasgupta, and Titman, 2004 ; Nain, 2006 ) My model abstracts from such considerations and focuses on the firm’s decision, taking industry structure as given.

receive liquidating dividends of f ðVTÞ L In the event of insolvency, shareholders receive nothing and firmvalue drops by the fraction g 2 ½0; 1 The shareholders’ payoff under different states is summarized asState at t ¼ T Corresponding firm values Payoff to shareholders

The equity value, as shown in Proposition 1, has three components The first term ðEQ½VTLÞ representsthe equity value without the distress costs and the limited liability feature The second term ðEQ½ðVT

f ðVTÞÞ1f f ðV T Þ4L;mT pKgÞ represents the cost of financial distress Because the shareholders of a financiallydistressed but solvent firm bear this cost, the equity value decreases by this amount The risk avoidanceincentive results from this cost The third term ðEQ½ðL  VTÞf1f V T pLgþ1ff 1ðLÞ4V T 4L;mT pKggÞrepresents thesavings enjoyed by the shareholders of a levered firm due to the limited liability feature of equity This termcaptures shareholders’ risk-shifting incentives By increasing the asset’s risk, the shareholders can makethemselves better off by increasing the call option value (the third term) At the same time, however, theexpected loss in the event of financial distress also increases with an increase in asset risk The optimal level ofinvestment risk is determined by the trade-off between the two

2.2.1 Financial distress costs

Proposition 1 provides a general valuation formula in my model To proceed further I need to beexplicit about the form of financial distress cost that is borne by the shareholders of a financiallydistressed firm In addition, I make some simplifying assumptions for analytical tractability I assume that inthe event of distress (i.e., mTpK), the firm’s cashflows drop to ldt; l 2 ð0; 1 and never reach beyond somearbitrary upper bound Uo1 at time T, i.e., dTpU Therefore, the losses take the form of lost upsidepotential This representation of financial distress cost is motivated by existing empirical findings andanecdotal evidence, and captures the intuition that distressed firms lose cashflows due to lost sales tocompetitors If industry conditions improve in the future, the distressed firms continue to feel the negativeeffect of distress due to lost customers This representation of distress is also consistent with the view thatwhen financially distressed firms restructure themselves by selling assets (Asquith, Gertner and Scharfstein,

1994), their EBIT-generating machine produces lower contemporaneous cashflows and in addition it limitstheir ability to capitalize on very good industry conditions in the future To concentrate on the effect offinancial distress costs (as opposed to tax-motivated incentives of hedging as inLeland, 1998), in the rest of thepaper I set t ¼ 0.18Under this assumption and the assumption l ¼ 1, the distressed firm’s asset value can berepresented as19:

f ðATÞ ¼AT if fdTpUg; and M0 if fdT4U g for some constant M0 (2)

18 In unreported analysis, I solve the model with tax benefits and obtain the firm’s optimal capital structure However, to keep the focus

of this paper on risk-management decisions, I do not present these results in the paper With tax benefits, the firm’s payoffs increase by tL without qualitatively changing the results of the analysis.

19 If l o1, then financial distress costs are even higher and the results become stronger This assumption is made only for analytical simplicity.

Let us denote the asset value ðATÞ corresponding to dT ¼U by L þ M The shareholders’ liquidatingdividends are given as

States Payoff to shareholders Firm value

xt1¼erTEQ½ðATLÞ1fAT 4L;mT 4Kgþ ðATLÞ1fAT 4L;AT pLþM;mT pKg

Fig 2plots the equity value as a function of the terminal asset value of the firm As the diagram shows, theequity value is not a strictly convex function of the underlying firm value as in the classical approach whereequity is valued as a call option on firm value The deadweight loss of distress introduces a concavity in theequity value, which results in risk-management incentives for the firm

3 Optimal choice of investment risk

Without loss of generality, I set the risk-free interest rate to zero in the rest of the analysis At t ¼ t1, theshareholders make a decision about the optimal investment risk of the firm There are two possibilities forchanging the investment risk: (a) the firm can directly choose an optimal level of s at t ¼ t1, or (b) the asset’srisk, s, may be fixed and the firm can alter its risk profile by buying derivative contracts such as futures andoptions I analyze the problem of finding optimal s assuming that investment risks can be costlessly modified.Proposition 2 The shareholders have a well-founded incentive to engage in risk-management activities ex-post

At t ¼ t , the shareholders optimally choose a level of risk s in the interior of all possible risks

L

L

L+M 0

Equity Value In Healthy State

Equity Value in Financial Distress

Equity Value

in my model

Asset Value at T Fig 2 This figure plots the equity value as a function of the terminal asset value of the firm For illustrative purposes I set the tax rate to zero and g ¼ 1 for this diagram The equity value in my model is depicted by the solid line The upper dotted line represents the equity value for the Healthy state The lower dotted line depicts the equity value in the state of Financial Distress The equity value in my model is

a weighted average (weight is decided by the relative likelihood of the two states) of the equity value in these two states.

Proof As shown in Appendix A.2 and A.3, the optimum level of investment risk is obtained by the followingfirst-order condition:

where h1 ¼ ðlnðAt1=LÞ þ ðs2=2ÞT0Þ=s ffiffiffiffiffi

T0

p, h2¼h1s ffiffiffiffiffi

T0

p, T0¼T  t1, c1¼lnðK2=At1ðL þ MÞÞ þ ðs2=2ÞT0=

As a result of the trade-off between the risk-shifting and risk-avoidance incentives, an interior solution forthe optimal risk is obtained in the model This result differs from that of the earlier models In risk-shiftingmodels such asJensen and Meckling (1976), the shareholders take as much risk as possible, whereas in risk-management models such as Smith and Stulz (1985), the optimal level of risk is obtained at s ¼ 0 Byobtaining an interior solution for the optimal investment risk of the firm, my model provides insights into therisk-management policies of the firm, as discussed below.20

Proposition 3 The firm chooses a lower level of investment risk if (a) it faces a higher distress barrier (K), and (b) ithas a longer project maturity ðT0¼T  t1Þ The relation between the deadweight losses and the optimal investmentrisk is U-shaped Let Mc¼L exp2ð

The investment risk decreases (i.e., the risk-management incentive increases) with the distress boundary (K) Asexpected, a higher boundary increases the likelihood of financial distress Therefore, the shareholders optimallychoose a lower investment risk to avoid the financial distress costs The results show that the firm with a longeroperational horizon ðT0¼T  t1Þfinds it optimal to engage in increased risk-management activities With longertime-horizon, the probability of hitting the lower barrier increases Further, consequent to entering the state ofdistress expected losses increase with time to maturity because there is a higher probability of improvements inindustry conditions and the distressed firm will not be able to capitalize on these opportunities There isconsiderable empirical evidence that large firms hedge more than small firms The pursuit of economies of scale hasbeen suggested as one possible explanation for this empirical regularity My model suggests another explanation:the time horizon of operations If firms with longer time horizons grow larger over time, the researcher would find

a positive association between risk-management activities and firm size at any given point in time

Finally, I find a U-shaped relation between the risk management incentives and the cost of financial distress.Recall that the deadweight losses in my model are parameterized by M (losses are given by

ðATMÞ:1f AT 4LþM;mT pKg) In the event of financial distress, the firm loses its upside potential beyond

L þ M Thus, the higher the M, the lower the lost upside potential and therefore the lower the deadweight losses

If the deadweight losses are absent (i.e., M ¼ 1), the shareholders lose nothing in the state of financial distressand hence there is no risk-management incentive On the other hand, if deadweight losses are very high (i.e.,

M ¼ 0) the distinction between default and insolvency disappears along with the risk-management incentives.21It’s the intermediate cases that generate risk-management incentives in the model.Fig 3illustrates this relation

20 With nonzero tax rates (in unreported analysis), the optimal s is even lower The additional incentives for risk reduction, in the presence of the tax-benefit of debt, comes from the potential loss in the tax shield of debt for a bankrupt firm This additional effect generates ex-post hedging as in Leland (1998) See also Fehle and Tsyplakov (2005)

21 In this case, equity value becomes similar to a down-and-out barrier option Since the value of this option is increasing in the volatility

of the underlying assets, the shareholders do not have any risk-management incentives at t

Leverage and risk management: To study the relation between leverage and risk management, I differentiatethe optimal s with respect to firm leverage at time 1 ðlev ¼ L=AÞ The details are provided in Appendix A.5.After some simplification it can be shown that the optimal sigma decreases (i.e., risk-management incentivesincrease) with an increase in leverage for a wide range of specifications of the distress boundary and

2.78 2.8 2.82 2.84 2.86 2.88 2.9 2.92 2.94

Investment Risk vs Leverage

Fig 4 This figure plots the optimal investment risk of the firm against the debt-asset ratio For this graph I assume the following structure

on the distress boundary and deadweight losses: K ¼ 1  exp 0:1 lev and M ¼ 7  exp 2 lev Amount of debt raised at time zero (L) if fixed

at 1 lev equals L scaled by At1 T is set to one.

deadweight loss parameter This relation reverses when leverage is very high due to the risk-shifting incentives.

At very high leverage, the value associated with the call option of equity dominates the cost borne byshareholders and thus they lose risk-management incentive Using a parametric specification of K and M,

I solve for optimal risk as a function of leverage and report the results inFig 4 The relation is summarizedbelow:

Proposition 4 Risk-management incentives increase with leverage; this relation reverses for extremely high levels

andChevalier (1995a, b)such costs are more likely to be incurred by a firm in concentrated industries Thus, inthe context of my model industry concentration provides a good proxy for the financial distress costs.Accordingly, high leverage firms in concentrated industries are predicted to have greater hedging incentives.3.1 Summary of theoretical model

In this section I present a self-contained summary of the theoretical part of the paper that serves as the basisfor the empirical tests to follow In my stylized model, a firm starts with some mix of debt and equity at timezero and buys a productive asset At this time the capital structure of the firm is determined by trading off thetax benefit of debt against the expected financial distress and bankruptcy costs I do not solve for the optimalleverage policy in my theoretical model to keep the focus of my analysis on risk-management decisions.However, making capital structure decisions endogenous does not change the key results of the paper Inunreported analyses, I solve for optimal leverage and as expected show that the debt ratio increases with thetax benefits and decreases with bankruptcy and financial distress costs.22

Given a level of debt determined at time t0, the firm experiences some random shocks to its value till t1,which perturbs its leverage ratio At this point the shareholders make the key decision in the model, i.e., a risk-management decision so as to maximize equity value This modeling structure allows me to focus on the ex-post hedging incentives Subsequent to the risk-management decision at t1, the asset value evolves according to

a stochastic process from time t1to T in the model If the firm’s asset value breaches a lower threshold beforethe terminal date T, then the firm enters financial distress Financial distress imposes costs on the firm such aslost customers to the competitors, which in turn prohibits it from capitalizing on its full upside potential.Motivated by the earlier empirical finding, I assume that highly levered firms lose more when they enter thestate of financial distress

After the distress boundary is hit, the firm can either stay solvent on the terminal date or go bankrupt,depending on whether its value, net of distress costs, is above or below the debt value The state in which thefirm enters financial distress but remains solvent at time T imposes a real cost on shareholders In this statethey incur the financial distress costs without being able to use their limited liability option An increase in firmrisk increases the probability of financial distress and the associated deadweight losses that are borne by theshareholders, not the debtholders On the other hand, by increasing firm risk they benefit on account of theusual limited liability feature The optimal risk-management policy trades off these two incentives Formoderate levels of leverage, the risk-management incentive dominates But when leverage becomes too high at

22 In a rational expectation framework firm value at time t0should be maximized keeping in mind the expected level of risk that will be optimally undertaken by the shareholders at time t1 This expected sigma along with the tax benefit of debt and bankruptcy costs will determine the optimal amount of debt raised by the firm at time t0 Indeed the actual leverage at time t1will be different from the rationally expected value of leverage, depending on the shocks experienced by the firm in the intervening period Depending on the realizations of these shocks in the interim period, the firm’s leverage at time t1 will be different and shareholders may deviate from the rationally anticipated risk policy that is based on the expected level of leverage and not on realized leverage The main result that shareholders will have risk-management incentives as long as their leverage doesn’t go up too much follows When the asset value realization is too low (i.e., leverage is too high as compared to expectations) the risk-shifting incentive follows.

time t1, the value associated with the call option feature of equity dominates the expected financial distresscosts and shareholders find it optimal not to engage in risk-management activities Thus, the model predicts anonmonotonic relation between leverage and hedging Further, the positive relation between leverage andhedging is expected to be stronger for firms operating in industries with a higher incidence of predatorybehavior such as concentrated industries I test these predictions of the model in the rest of the paper.

of debt issues and hedging decisions, which unfortunately are not available Below I begin with discussing thesample and data collection procedure followed by econometric strategies used to account for the endogeneityproblem Empirical results follow these discussions I then discuss the issues related to ex-ante vs ex-postincentives in a later section

4.1 Sample selection and data

I test the key predictions of my model using the foreign currency and commodity derivatives holdings of alarge cross-section of firms during the fiscal years 1996 and 1997 I start with all firms in the intersection of theCRSP and COMPUSTAT with 10-Ks available on the SEC website I remove financials and utilities since therisk-management incentives of these firms are not necessarily comparable to other industrial firms From thissample, I exclude firms that fall in the last quartile of the size distribution based on total sales Earlierempirical studies and survey evidence suggest that such small firms are very unlikely to use derivative productsfor hedging purposes (Dolde, 1993), arguably due to the lack of economies of scale

For the remaining firms, I collect data on derivative usage from the 10-K filings In the first step I obtain allavailable 10-K filings of firms in the intersection of COMPUSTAT and CRSP from the SEC for the calendaryear 1997.23 I obtain data by searching the entire 10-K filings for the following text strings: ‘‘riskmanagement,’’ ‘‘hedg,’’ ‘‘derivative’’, and ‘‘swap.’’ If a reference is made to any of these key words, I read thesurrounding text to obtain data on foreign currency and commodity derivatives I obtain data on the notionalamount of foreign currency derivatives used for hedging purposes across various derivative instruments such

as swaps, forwards, futures, and options.24For commodity hedging I only obtain data on whether a firm usesderivatives for hedging or not, since the reporting requirement for commodity derivatives doesn’t allow for aneasy quantification in terms of dollar value If there are no references to the key words, the firm is classified as

a nonhedger I require that data on net sales, leverage and market capitalization be available for a firm to beincluded in the sample

In addition, to capture the dynamic behavior of a firm’s hedging and leverage decisions, I focus on a smallersubset of 200 manufacturing firms (one-digit SIC code 2) and collect data using the same procedure for twoadditional years, i.e., 1998 and 1999 This smaller subsample allows me to relate the changes in a firm’shedging activities to changes in financial conditions, which in turn allows me to draw sharper inferences asoutlined in the subsequent sections

I limit my analysis to only those firms that have well-defined exposures to foreign currency and commodityrisks I conduct my analysis for foreign currency derivatives on the subsample of firms with an exposure toforeign currency risk and similarly commodity derivatives on the subsample of firms with an exposure to

23 For some firms (most of the firms with a fiscal year ending in October, November, or December) this corresponds to fiscal year 1996, while for others this corresponds to the fiscal year 1997.

24 The break-up of the notional amount across various instrument types was not easy to obtain for some sample firms For these firms,

I collect data on the aggregate notional amount of derivatives only Since most of the analysis is conducted with the aggregate amount

of derivatives, this doesn’t create any bias in the study.

commodity price risk This sample selection criteria ensures that I can treat the lack of derivative usage as afirm’s choice variable to not hedge rather than an absence of exposure to the risk I identify a firm’s exposure

to these risks in the following manner

4.1.1 Exposure to foreign currency risk

I closely followGeczy, Minton, and Schrand (1997) to identify firms with pre-defined exposure to foreigncurrency risks A firm is classified as being exposed to foreign currency risk if any of the following criteria ismet: (a) it reports foreign currency sales in the COMPUSTAT geographical segment file in the fiscal year ofderivative usage or within þ= one year; (b) it reports foreign income taxes, deferred foreign currency taxes,

or pre-tax foreign income in its annual statements; (c) it reports foreign currency adjustments in its annualreport; or (d) it discloses an exposure hedged with foreign currency derivatives in its footnotes identified byhand-collected data

Based on these screens, I identify 1,781 firms as exposed to foreign currency risk.25 In the subsequentregression analysis I lose additional firms due to missing data on the explanatory variables used to estimate themultivariate models

4.1.2 Exposure to commodity price risk

Compared to foreign currency exposure, identifying firms with an exposure to fluctuations in commodityprices is harder to measure This arises because current accounting standards do not require firms todisclose much information with respect to their exposure to commodity price risk In the absence of anybalance sheet information, I identify a firm’s exposure to commodity price risk by estimating the sensitivity ofits earnings to movements in various indices of commodity prices As an alternative specification, one can use

a simpler approach and take the set of all commodity-producing industries as the sample of firms that areexposed to commodity price risk However, with such an approach it would be hard to detect firms that areexposed to commodity price risk on the input side (such as airline industry) Thus, for the sake ofcomprehensiveness, I adopt the more involved methodology of detecting firms with exposure to commodityprice risk

In particular, I regress the quarterly earnings before interest and taxes obtained from COMPUSTAT’squarterly files on the quarterly changes in several commodity price indices and classify a firm as having anexposure to commodity price risk if the resulting coefficient is significant at the 10% level or better I take datafrom the last 60 quarters (or the maximum available) to estimate this model Most of the effect of commodityprice movements is reflected in a firm’s sales or its cost of production, such as raw material or energy costs.Therefore, I take EBIT as the relevant measure of earnings for the purpose of sensitivity analysis.26

There are two important issues with this estimation methodology First, the use of derivatives can make afirm’s earnings less sensitive to movements in commodity prices, rendering my methodology ineffective forhedger firms However, I already have hand-collected data on whether these firms use commodity derivatives

to hedge a well-specified risk I, therefore, add the commodity hedgers to the set of firms that I detect as having

an exposure to commodity risk based on the above methodology

Second, firms may be exposed to various types of commodity risks, ranging from oil price shocks to metals

to farm produce Based on the firm’s disclosure in the footnotes of their annual statements as well as thecontract volume of various futures contracts on the futures exchanges, it is clear that the main sources ofcommodity risk facing U.S nonfinancial firms are the following: (a) crude oil and related products; (b) metalssuch as copper and iron; (c) farm products such as corn; and (d) various industrial chemicals Noting this,

I obtain data on the quarterly price changes for a basket of these commodities from the Bureau of Labor

25 Geczy, Minton, and Schrand (1997) also consider firms with a high concentration of foreign importers in the industry as exposed to foreign currency risk I don’t consider this screening criterion since they show that very few firms are identified as having a foreign currency exposure based solely on this criterion In their sample of about 370 firms, only three firms are identified as having exposure based solely

on this criterion.

26 I also repeat my analysis with other measures such as cashflows, EBIT/TA, NI/TA, the seasonally adjusted earnings, and obtain similar set of firms Note that scaling EBIT by total assets doesn’t make any qualitative difference because the regression is estimated on a firm-by-firm basis with fairly stable total asset values (as compared to EBIT) Therefore, I only present results with the EBIT-based sensitivity analysis to conserve space.

Studies Further, I obtain data on quarterly changes in the aggregate Producer Price Index (PPI), whichreflects price changes based on a basket of commodities including oil, farm products, industrial chemicals,metals, and other commonly used products by the industrial producers Thus I have five price indices (crudeoil, metals, farm products, chemicals, and all commodities) and I estimate a firm’s commodity price sensitivitywith respect to each of these indices separately Since my analysis doesn’t distinguish firms based on thespecific source of risk they face, I consider a firm as exposed to commodity price risk if I obtain a significantcoefficient in any of the five regressions This methodology identifies 1,238 firms as having an exposure tocommodity price risk in my sample When I merge this sample with the sample of firms with ex-ante exposure

to foreign exchange movements, I find that I have a total of 2,256 firms with an exposure to at least one ofthese sources of risk

4.1.3 Derivatives as a proxy for hedging

I use two definitions of hedging based on derivative usage The first definition is based on the firm’s binarydecision of whether to use derivatives for hedging purposes This specification uses both types of derivativecontracts—foreign currency and commodity In the second specification, I use the total notional amount offoreign currency derivatives The notional amount-based definition of hedging captures the firm’s totalownership of risk-management instruments and is thus able to distinguish between firms with differenthedging intensities

There are two important concerns associated with the use of derivatives as a proxy for hedging activities.First, though I obtain data on derivatives classified as risk-management tools, there may still be a concernabout their intended use—are firms indeed using these instruments for hedging purposes or not? Earlierempirical studies find strong evidence in support of risk-reducing (i.e., hedging) effects of derivatives onvarious measures of a firm’s risk.Guay (1999)finds that the new users of derivatives experience a decline intheir earnings and stock price volatility after the initiation of derivatives contracts SimilarlyAllayannis andOfek (2001)show that using derivatives reduces currency exposure, andHentschel and Kothari (2001)do notfind any evidence that derivatives are used for speculative purposes Thus, there is enough evidence in theliterature to suggest that the majority of firms use derivative instruments for hedging purposes and not forspeculative reasons

The second concern with the use of derivatives data relates to the importance of derivatives on theoverall cashflows of the firms Allayannis and Weston (2001) and Graham and Rogers (2002) find asignificant impact of derivative instruments on firm value and the firm’s debt capacity, respectively.These findings suggest that derivative instruments have a significant impact on firm performance andthus are good instruments for the firm’s risk-management activities Guay and Kothari (2003) showthat the median firm’s derivatives cashflow sensitivity (defined as the level of cashflows that derivativeinstruments can generate in extremely adverse scenarios of interest rate, foreign currency or commodityprices) is modest at only about 10% (mean of 45%) of the average year’s operating cash-flows of thefirms.27 At an extreme, if the median firm’s operating cash-flows drops to 25% of its normal level, theimpact of derivative instruments can be as high as 40% of a bad year’s operating cash-flows However,

at the same time the study by Guay and Kothari underscores the importance of nonderivativebased risk-management strategies for firm-value The study by Petersen and Thiagarajan (2000) illustratesthe importance of nonderivative based hedging strategies for a firm’s overall risk-management decisions

In my empirical study I provide various robustness tests to account for nonderivative-based methods ofhedging

4.1.4 Descriptive statistics of hedging variables

Table 1 provides the descriptive statistics of hedging activities In Panel A, I provide the frequencydistribution of hedgers of different risks Out of a total of 1,781 firms with an exposure to foreign currencyrisk, 497 (about 28% of the firms) use derivatives to hedge their exposure to movements in foreign exchangerates For commodity price risk, there are 211 hedgers (about 20% of the firms) out of a total sample size of1,238 firms If I consider exposure to either type of risk, I find a total of 645 hedgers from a sample of 2,256

27 The sensitivity varies from 9% to 39% depending on the scaling variable used (see Table 4 of Guay and Kothari, 2003 ).

firms Panel B provides the summary statistics for the aggregate notional amount of foreign currencyderivatives used for risk-management purposes The mean (median) notional amount of foreign currencyderivatives is $359.15 million ($40 million) The average level of derivatives holdings in my sample is smallerthan that of earlier studies such asGraham and Rogers (2002) This is not surprising, since these studies focusmostly on large firms, whereas my sample contains many medium and small firms as well The notional value

of derivatives scaled by the book value of the firm’s total assets (sales) amounts to 8.62% (10.74%) for theaverage firm in the sample These numbers are comparable to earlier studies

Table 1 (Panel C) also provides the break-up of foreign currency derivatives across instrument types.Forward and futures contracts are the most widely used instruments for managing foreign currency risk.Among the foreign currency hedgers, about 80% of firms use forward and futures contracts In unreportedanalyses, I find that there are comparable levels of transactions for both buying and selling in the foreigncurrency forward markets

My main tests are based on the relation between leverage and hedging In the next section, I briefly describethe control variable used in the analysis before turning to the issue of endogenous modeling of risk-management and leverage decisions

4.1.5 Control variables

Earlier theoretical and empirical work in this literature proposes several variables that can explain a firm’shedging incentives My control variables are motivated by these studies First, I control for firm size (size) asmeasured by log of total sales to capture the well-known size effects in derivative usage (seeDolde, 1993) I usethe ratio of research and development (R&D) expenses to total sales as a proxy for firm’s growthopportunities.Froot, Scharfstein, and Stein (1993) predict a positive relation between growth opportunitiesand hedging incentives since hedging can minimize the underinvestment problem in low cash-flow states of theworld I also use a firm’s market-to-book ratio as an additional control variable for growth opportunities andobtain similar results However, I do not include it in my base model since market-to-book has been taken as ameasure of firm-value in several studies in corporate finance and firm value may itself depend on derivativeusage Second, I model leverage in an endogenous setting, which requires regressing leverage on all

Table 1

Descriptive statistics—derivatives usage

This table provides the descriptive statistics of derivatives usage by sample firms Panel A provides the number of firms that use foreign currency (FX) or commodity (CM) derivatives for hedging purposes for the fiscal year ending between September 1996 to August 1997 The ‘Any’ column represents the number of firms that use either FX or CM (or both) derivatives for hedging purposes Panel B provides the details on the notional amount of FX derivatives Panel C provides the instrument-wise break-up of FX derivatives across swaps, forwards/futures, and options This panel is based on a smaller subsample of 435 foreign currency hedgers for which the instrument-wise break-up is available Statistics in Panel C are based on only those observations that have nonzero values for the respective hedging instrument.

Panel A

explanatory variable in the first stage regression Given the illusionary nature of relation between leverage andmarket-to-book ratio, this specification poses additional challenges.28The underinvestment problem of a firmcan be reduced by keeping more liquid assets I include the quick ratio of the firm (quick) as a measure of thefirm’s liquid assets The quick ratio is constructed as a ratio of cash and short-term investments to the currentliabilities of the firm.29

Motivated by earlier studies (seeGeczy, Minton, and Schrand, 1997;Graham and Rogers, 2002), I includeinstitutional shareholdings as an explanatory variable in the model to control for risk-management incentivesdue to information asymmetry between firm’s insiders and outsiders.DeMarzo and Duffie (1991)andBreeden

shareholders should hedge more Assuming that higher institutional share-holdings leads to lower informationasymmetry between the managers and shareholders of the firm, the coefficient on this variable should benegative as predicted by these theories The inst variable measures the fraction of common shares of the firmheld by institutional investors The data are obtained from the 13-F filings In an alternative unreportedspecification, I also use the number of analysts following the firm as a proxy of (inverse) informationasymmetry and obtain similar results

Next, I control for tax convexity-based hedging incentives If a firm faces a progressive tax structure, then itspost-tax value becomes a concave function of its pre-tax value The firm can lower its expected tax liability byengaging in hedging activities (Smith and Stulz, 1985) I use the methodology suggested byGraham and Smith(1999)to measure the tax-convexity incentive of hedging A brief description of their methodology is provided

in Appendix A.6 The tax-convexity variable measures the expected tax benefits (in dollars) from a 5%reduction in the firm’s income volatility I scale this measure by the total sales of the firm Since this variable isestimated by using other accounting variables of the firm, in my base-case analysis I do not control for the tax-convexity measure to ensure that my key results are not driven by the inclusion of this imputed variable.Subsequently, I control for this effect and show that the results with respect to the key variables of interestremain robust to the inclusion of this control variable in the model

In the foreign currency hedging model, I include foreign currency sales as a percentage of a firm’s total sales

as an additional control variable (fsale).Jorion (1991)shows that foreign currency sales is a good proxy of thefirm’s exchange rate risk exposure Thus, this variable controls for two effects First, it controls for the extent

of exposure faced by the sample firms, and second, it proxies for economies of scale that can be exploited inhedging foreign currency risks High exposure firms should have a lower cost of hedging if there are significanteconomies of scale in these activities

Firms can achieve significant reductions in their foreign currency risk exposure by operating in multiplegeographical locations around the world (see Allayannis, Ihrig, and Weston, 2001) A firm with morediversified geographical operations has a natural foreign currency hedge if currencies in different markets arenot highly correlated I control for these effects by including the number of geographical segments reported bysample firms as a control variable In an unreported analysis, I also control for the entropy of a firm’s foreignsales in diverse geographical regions and obtain similar results.30

28 In one of the unreported analyses, I also use the analyst growth forecast obtained from I/B/E/S as a proxy for the growth option of the firm Since my results remain qualitatively similar, I don’t report the results of this model.

29 See also Acharya, Almeida, and Campello (2004) , who argue that cash can serve as a hedge against future cash shortfalls for financially constrained firms.

4.2 Endogenous modeling of leverage and hedging

My theory predicts a positive relation between leverage and hedging for firms with moderate levels ofleverage, and a negative relation at extremely high levels of leverage In addition, the relation between leverageand hedging is expected to be stronger for firms operating in industries with greater likelihood of predatorybehavior such as concentrated industries Thus, my key tests are based on the relation between hedgingand leverage

For expositional simplicity and analytical tractability, at the time the hedging decision is made in thetheoretical model (i.e., at time t1in the model) the debt level is pre-determined However, we know from priortheoretical work that a firm’s debt capacity and hence leverage can itself increase due to hedging For example,consider a variant of my model where firms hedge first and obtain debt at a later date In the context of mymodel, hedging lowers the volatility of firm value, which in turn lowers the probability of bankruptcy and thusallows firms to borrow more for a given level of the tax benefit of debt This leads to endogeneity betweenleverage and hedging It, therefore, becomes important for my empirical study to explicitly account for thisendogeneity bias To do so, I need a structural model for the capital structure choice and hedging decisions ofthe firm In the absence of a consensus on an ideal model for debt choices, it is advantageous to have atheoretical model linking capital structure and hedging choices I keep the empirical estimation tightly linked

to the theoretical model In particular, I estimate the following structural model:

leverage ¼ b0þb1hedging þ Sg  Xiþei, (7)hedging ¼ a0þa1leverage þ a2leverage2þSy  Yiþi (8)This model is estimated in a two-stage instrumental variable (IV) regression framework The first-stageequation is an OLS model for the leverage decision, whereas the second equation models a firm’s hedging(derivative) decisions In the second stage, the risk-management equation is estimated using the predicted value

of the leverage ratio as the explanatory variable in the Logit or Tobit estimation I try alternative econometricspecifications to this model in later sections.31The leverage (leverage) of a firm is defined as the ratio of totaldebt (long-term debt plus debt included in the current liabilities) to the book value of total assets Toinvestigate the effect of extreme leverage on hedging, I include leverage2 as an additional explanatory variable

in the second equation I expect a positive sign on leverage and a negative sign on leverage2 in the regressioninvolving various measures of hedging as the dependent variable X and Y represent control variables affectingfirms’ leverage and hedging decisions, respectively

As argued earlier, industry concentration provides a good measure of financial distress costs in my model

In such industries, highly levered firms are more vulnerable to losing their competitive position in the industry

in the event of financial distress Opler and Titman (1994) provide empirical evidence in support of thisassumption Based on this argument, my model predicts a positive relation between hedging and industryconcentration for highly levered firms To capture this effect empirically, I include industry concentrationmeasure and its interaction with leverage in the hedging model This measure is constructed by summing themarket shares (based on sales in 1996) of the top four players in the firm’s three-digit SIC code Then I create adummy variable (concd) that equals one if the concentration ratio is above the median, and zero otherwise

4.2.1 Identification strategy

To estimate this model I need to find proper instrument(s) for the first-stage leverage regression A largeliterature studies corporations’ capital structure determinants (seeFrank and Goyal, 2003for a survey) andresearchers have proposed several determinants of a firm’s leverage such as size, tangible assets, the book-to-market ratio, earnings volatility, profitability, and marginal tax rates (seeBradley, Jarrell, and Kim, 1984;

others) For my identification strategy to work, one has to argue that one or more of these variables affect afirm’s hedging decision only through their impact on leverage and not independently by themselves Finding a

31 In particular, I estimate an alternative econometric model suggested by Wooldridge (2002) for IV estimations involving the presence of

a function of the endogenous variable (i.e., leverage2) in the second stage.