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Strategic Risk Managementand Product Market Competition

Tim R. AdamNational University of Singapore & RMI

Amrita NainMcGill University

Comments welcome!

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Theory of Corporate Risk Management

Firm-specific factors

• Taxes (Smith and Stulz, 1985)

• Financial distress costs (Smith and Stulz, 1985)

• Information asymmetries & agency costs (Froot, Scharfstein and Stein, 1993, DeMarzo and Duffie, 1991, …)

• Risk-aversion of stakeholders (Smith and Stulz, 1985)

Industry-specific factors

• Degree of competition, hedging decisions of competitors (Mello & Ruckes, 2006, Adam, Dasgupta and Titman, 2007)

• Derivatives decisions are not made in isolation but take the decisions of competitors into account.

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Empirical Literature

• Nance, Smith and Smithson (1993), Mian (1996), Dolde (1993) Geczy, Minton and Schrand (1997), Tufano (1996), Haushalter (2000), Allayannis and Ofek (2001), Brown (2001), Graham and Rogers (2002), Adam and Fernando (2006), Lel (2006), …

• Most variation in derivatives strategies cannot be explained by traditional models of hedging / firm-specific factors.

• Brown (2001) studies risk management at a major durable goods producer (HDG).– Earnings management and competitive concerns in the product

market motivate HDG’s FX risk management rather than the traditional models of hedging.

– HDG tracks the hedging programs of its major US-based competitors.

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Objective

• Are industry-specific factors likely to be important in determining a firm’s derivatives strategy?– Do the derivatives strategies of competitors matter?

– Does the degree of competition affect derivatives strategies?

• Derive testable hypotheses based on the models by Adam, Dasgupta and Titman (2007), and Mello and Ruckes (2006).

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The ADT Model

• Analyze firms’ hedging decisions within the context of an industry equilibrium.– n identical firms, Cournot competition– Common cash flow (cost) shocks

• Firms hedge their cash flows as in FSS (1993)– Cost effect: Hedging reduces expected costs– Flexibility (real option) effect: Volatility in cash flows is

beneficial because firms can choose output after observing their cash flows.

• Low cash flow → high marginal cost → reduce production• High cash flow → low marginal cost → increase production

– Shleifer and Vishney (1992) effect: Firms benefit if their cash flows are high when their competitors have low cash flows and vice versa.

• Low agg. cash flow → high price → high investment opportunities• High agg. cash flow → low price → low investment opportunities

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Why Symmetric Equilibria Don’t Exist

• Suppose all firms hedge their cash flows– Constant cash flows constant costs constant output

constant price– A financially constrained firm benefits from volatility in its cash

flow (marginal cost) because when its cash flow is high it produces more and when its cash flow is low it produces less. (Flexibility effect)

• Suppose no firm hedges– Variable cash flows variable costs variable output variable

price– Firms have high cash flows when prices are low and vice versa.– A financially constrained firm benefits from shifting cash from

states with low marginal productivity (high cash flow states) to those with high marginal productivity (low cash flow states). (Shleifer and Vishney (1992) effect)

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Testable Hypotheses

Do derivatives strategies of competitors matter?• Is the sensitivity of output prices (to FX shocks) affected by

aggregate hedging decisions?• Is a firm’s exposure affected by aggregate hedging decisions?

– Most firms hedge• Exposure of a hedged firm is low• Exposure of an unhedged firm is high

– Most firms do not hedge• Exposure of an unhedged firm is low• Exposure of a hedged firm is high

Degree of competition• Does the degree of competition affect aggregate hedging

decisions?

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Data

• Derivatives data– Search all SEC 10-K filings for year of 1999 for text strings such as

“hedg”, “swap”, “cap”, “forward”, etc.

– Match sample with Compustat firms. Exclude financial firms and utilities.

– Collect gross notional amounts of FX derivatives (forwards, swaps, options).

• Ex-ante exposure data– We classify firms as having ex-ante FX exposure if they disclose foreign

assets, foreign sales, foreign income, foreign taxes, exchange rate effect, or foreign currency adjustments.

FX exposure No FX exposure

FCD user 429 119 548

FCD non-user 2,377 3,461 5,838

2,806 3,580 6,386

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Firm Characteristics

Mean Med.Std. dev

Min Max Obs

Market value of assets(in millions of US$)

4,302 347.7 18,736 0.076 408,030 2,398

Tobin’s q 2.129 1.475 1.906 0.525 19.51 2,387

Debt-equity ratio 0.565 0.146 1.398 0 22.09 2,293

Quick ratio 1.820 1.283 1.688 0.053 16.54 2,713

Payout ratio 0.130 0 0.618 0 15 2,719

Foreign sales / net sales 0.357 0.293 0.273 0.000 1 2,398

FCD users (dummy variable) 0.153 0 0.360 0 1 2,806

Notional value of FX derivatives / market value of assets

0.079 0.028 0.213 0.000 2.96 417

Firms with ex-ante FX exposure only.

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Industry Characteristics (6-digit NAICS)

Mean Med.Std. dev

Min Max Obs

Number of public firms 9.5 3 1 802

Weighted fractionof exposed firms

0.576 0.797 0.433 0 1 766

Median exposure (exposed firms) 0.318 0.242 0.258 0.000 1 526

Market value weighted fraction of FCD users (exposed firms)

0.195 0 0.324 0 1 802

Industry weighted average hedge ratio (exposed firms)

0.010 0 0.041 0 0.481 787

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Estimating the Sensitivity of Producer Prices to FX shocks

• Following Feinberg (1989), we estimate the following model using monthly data from 1996 to 2000.

RPPIjt = real producer price index

EXCHt = real trade-weighted value of the U.S. dollar against its major trading partners

FRACTIONjt = market value-weighted fraction of FCD users

• Price sensitivity may be a function of FRACTION (endog.) Instrument: fraction of IR derivatives users (2SLS); model is estimated in log changes; Newey-West standard errors.

jtjttjtjjjt FRACTIONEXCHEXCHRPPI 1211

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Price Sensitivity to FX Shocks

Dependent variable = Δln RPPIt+1

Δln EXCHt -0.076* -0.077*

Δln EXCHt × Fraction of FCD users 0.433** 0.436**

Δln EXCHt × Foreign inputs -0.964*

Δln EXCHt × Exports 4.565** 5.949**

Δln EXCHt × Industry concentration -2.253* -2.180*

Δln EXCHt × Foreign competition -1.457**

Δln EXCHt × Capital intensity 1.154 0.994

Industry dummies & controls Yes Yes

Observations 5,211 5,211

F-statistic 3.46*** 3.55***

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Key Results

• When the USD depreciates (EXCH ↓) and the cost of imports rise, domestic producer prices increase.– A real depreciation of the US$ by 10% increases real domestic

producer prices by 0.77%.

• The price sensitivity (pass-through) is lower– in industries in which FX derivatives usage is more widespread

– in industries that use fewer foreign inputs

– in industries that export more

– in less concentrated (more competitive) industries

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• Is a firm’s exposure affected by aggregate hedging decisions?

• Estimate firms’ ex-post FX exposures.

• Analyze the exposures of FCD users and non-users.

Fraction of FCD users - high

FCD user low exposure

FCD non-user high exposure

Fraction of FCD users - low

FCD user high exposure

FCD non-user low exposure

itmtimtixiit rEXCHr 0

iiiiix FRACTIONFCDdumFCDdum 210

Determinants of Exposure

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Estimating the FX Exposure of Firms

• For each firm we estimate the following market model using monthly returns from 1996 to 2000.

rit = firm i’s stock return

rmt = value-weighted market return

ΔEXCHt = change in trade-weighted value of the U.S. dollar against its major trading partners

• The FX exposure estimates ßix range from -1.03 to 1.22. Out of 3,036 firms 344 firms have significant exposures to the trade-weighted value of the U.S. dollar.

itmtimtixiit rEXCHr 0

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Comparison of FX Exposures

All firms FCD users FCD non-users

Difference between users and non-users

abs(FX ex-posure) = |ßix|

0.010

0.001

0.020

0.004

-0.010***

FX exposure if ßix > 0 0.012

0.011

0.008

0.020

0.013

-0.018***

FX exposure if ßix < 0 -0.009

-0.010

-0.007

-0.016

-0.009

0.005**

Top figures denote means, bottom figures denote medians.

FCD users have lower exposures to the trade-weighted value ofthe U.S. dollar than FCD non-users.

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Distribution of Exposure Coefficients

010

20

30

40

50

Density

-.1 0 .1Exposure Coefficients of FCD Users

010

20

30

40

50

Density

-.1 0 .1Exposure Coefficients of FCD Non-Users

Avg. FRACTION of FCD Users = 0.35

Avg. FRACTION of FCD Users = 0.42

Avg. FRACTION of FCD Users = 0.39

Avg. FRACTION of FCD Users = 0.33

FCD USERS

FCD NON USERS

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Aggregate Hedging and FX Exposures

Dependent variable: |βix|

Intercept 1.535*** 1.240***

FCD user -0.122

FCD user × FRACTION -0.990**

FRACTION 0.816***

FCD non-user 1.111***

FCD non-user × (1-FRACTION) -0.990**

(1-FRACTION) 0.174

Control variables Yes Yes

Observations 2826 2826

F-statistic 10.83 10.83

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Aggregate Hedging and FX Exposures

Dependent variable: |βix|

FCD user -0.192

FCD user × FRACTION -0.966**

FRACTION 0.799***

FCD user × Pass-through coefficient -3.528**

Pass-through coefficient 0.747

FCD non-user 4.686***

FCD non-user × (1-FRACTION) -0.966**

1-FRACTION 0.167

FCD non-user × (1-Pass-through coeff.) -3.528**

(1-Pass-through coefficient) 2.782

Observations 2826 2826

F-statistic 12.67 12.67

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Key Results

• FCD users have lower ex-post FX exposures than FCD non-users.

• As the fraction of derivatives users increases, the exposure– of FCD users declines

– of FCD non-users increases.

FCD user FCD non-user

Fraction of FCD users - high

low exposure high exposure

Fraction of FCD users - low

high exposure low exposure

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Derivatives Usage and Competition

• Allayannis and Ihrig (2001)– Exposures increase as mark-ups fall.

Firms that operate in more competitive industries face larger exposures and therefore are more likely to hedge.

• Mello and Ruckes (2006)– Firms hedge less if competition is more intense in order to gain a

competitive advantage (market share) if prices move favorably.

• Adam, Dasgupta and Titman (2007)– Competition can have a positive or negative impact on the number

of firms that hedge in equilibrium, depending on whether hedging or not hedging is optimal in the absence of any competitive interaction between firms.

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Testable Hypotheses

Degree of competition

• Does the degree of competition affect aggregate hedging decisions?

• Do firms hedge less in more competitive industries?

# of firms (competition)

Fraction of FCD users

½

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Equilibrium

)(

)(

2

11

2

12wE

wEa

nbnn

mh

In equilibrium EΠh(w) – EΠu(w) 0

The proportion of firms that use derivatives is given by

0 ½ 1

Fractionof firmshedging

• Flexibility effect dominates cost reduction effect• Small market share (a - α)

• Cost reduction dominates flexibility effect• Large market share (a - α)

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Measuring the Degree of Competition

Mean Median Std.dev Min Max Obs.

PCM 0.324 0.305 0.163 0 1 701

PCMCensus 0.337 0.329 0.099 0.094 0.818 350

Herfindahl indexCensus 0.423 0.394 0.265 0.009 0.999 237

Concentration ratio(top 4 firms)

0.423 0.406 0.209 0.036 1 349

Concentration ratio(top 8 firms)

0.553 0.561 0.223 0.066 1 346

Herfindahl indexCensus

PCMCensus Below median Above median Total

Below median 74 54 128

Above median 45 64 109

Total 119 118 237

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Fraction of FCD Users

Intercept-0.703***(-5.73)

-0.671***(-4.07)

-0.385*(-1.89)

-0.480***(-3.28)

-0.545**(-2.60)

-0.565***(-3.81)

PCM0.665***(3.55)

PCMCensus1.296***(3.54)

Herfindahl indexCensus0.362**(2.01)

Concentration ratio (top 4 firms)

0.483***(2.67)

PCMCensus Herfindahl index

0.814***(3.66)

PCMCensus

Concentration ratio0.661***(4.07)

Weighted fraction ofexposed firms

0.495***(6.50)

0.260**(2.55)

0.367***(2.75)

0.306***(2.96)

0.324**(2.46)

0.279***(2.74)

ln(median firm size)0.050***(2.80)

0.049**(2.39)

0.019(0.60)

0.029(1.31)

0.019(0.63)

0.034(1.62)

Median Tobin’s q-0.128***(-2.95)

-0.086(-1.23)

-0.077(-0.87)

-0.005(-0.07)

-0.127(-1.41)

-0.051(-0.76)

Number of obs. 659 338 231 337 231 337

Pseudo R2 0.086 0.057 0.041 0.047 0.067 0.065

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Intercept-0.506**(-2.44)

-0.650***(-2.89)

-0.335(-1.36)

-0.360*(-1.80)

-0.525**(-2.01)

-0.476**(-2.31)

PCM0.679**(2.45)

PCMCensus1.083***(2.84)

Herfindahl indexCensus0.102(0.50)

Concentration ratio (top 4 firms)

0.066(0.27)

PCMCensus Herfindahl

index 0.571**(2.32)

PCMCensus

Concentration ratio0.433**(2.20)

Weighted fractionof exposed firms

0.415***(3.24)

0.374***(2.67)

0.574***(3.56)

0.441***(3.12)

0.508***(3.18)

0.391***(2.77)

ln(median firm size)0.001(0.04)

0.010(0.26)

0.024(0.50)

0.004(0.11)

0.021(0.45)

0.003(0.08)

Price sensitivity0.502(1.29)

0.962*(1.91)

1.273*(1.78)

0.809(1.59)

1.099(1.57)

0.837*(1.66)

Cost convexity0.459*(1.76)

0.272(0.89)

-0.102(-0.23)

0.252(0.80)

-0.117(-0.26)

0.213(0.69)

ln(market share)0.025(0.59)

0.028(0.59)

-0.011(-0.17)

0.034(0.68)

-0.011(-0.18)

0.027(0.55)

Fraction of firms with investment grade rating

-0.192(-0.59)

-0.373(-1.08)

-0.308(-0.52)

-0.217(-0.63)

-0.328(-0.57)

-0.272(-0.79)

Number of obs. 212 183 132 183 132 183

Pseudo R2 0.090 0.093 0.092 0.067 0.115 0.083

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Extent of FCD Usage

Intercept-0.197***(-8.96)

-0.193***(-6.24)

-0.167***(-5.44)

-0.184***(-6.71)

-0.182***(-5.57)

-0.194***(-6.79)

PCM-0.015(-0.54)

PCMCensus0.054(1.06)

Herfindahl indexCensus0.024(1.09)

Concentration ratio (top 4 firms)

0.032(1.11)

PCMCensus Herfindahl

index 0.053*(1.95)

PCMCensus

Concentration ratio0.045*(1.84)

Fraction ofexposed firms

0.126***(8.41)

0.127***(5.51)

0.113***(4.58)

0.130***(5.61)

0.109***(4.46)

0.127***(5.53)

ln(Median firm size)0.012***(4.40)

0.007**(2.28)

0.006*(1.66)

0.006*(1.75)

0.007*(1.74)

0.006*(1.91)

Number of obs. 663 340 232 339 232 339

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Summary

• Output prices are less sensitive to FX shocks (lower pass-through) if more firms use derivatives.

• Firms’ FX exposures appear to be a function of the prevalence of derivatives usage.– If derivatives usage is widespread, FCD users exhibit relatively

low exposures, while FCD non-users exhibit relatively high exposures.

– If derivatives usage is less common, FCD users exhibit relatively high exposures, while FCD non-users exhibit relatively low exposures.

• In more competitive industries fewer firms use derivatives.• In more competitive industries the average size of

derivatives positions is lower.