Transcript Financial Constraints, Competition and Hedging in Industry

Strategic Risk Management
and Product Market Competition
Tim R. Adam
National University of Singapore & RMI
Amrita Nain
McGill University
Comments welcome!
1
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.
2
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.
3
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).
4
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
5
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)
6
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?
7
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.
FCD user
FCD non-user
FX exposure
No FX exposure
429
119
548
2,377
3,461
5,838
2,806
3,580
6,386
8
Firm Characteristics
Mean
Med.
Std.
dev
Min
Max
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
0.028
0.213
0.000
2.96
417
Notional value of FX derivatives /
0.079
market value of assets
Firms with ex-ante FX exposure only.
Obs
9
Industry Characteristics (6-digit NAICS)
Std.
dev
Mean
Med.
Min
Max
Number of public firms
9.5
3
Weighted fraction
of 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
1
Obs
802
10
Estimating the Sensitivity of Producer Prices
to FX shocks
• Following Feinberg (1989), we estimate the following model
using monthly data from 1996 to 2000.
RPPI jt   j  1 j EXCHt 1   2 j EXCHt 1  FRACTION jt   jt
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.
11
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***
12
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
13
Determinants of Exposure
• Is a firm’s exposure affected by aggregate hedging
decisions?
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
• Estimate firms’ ex-post FX exposures.
rit  i 0  ixEXCHt  imrmt   it
• Analyze the exposures of FCD users and non-users.
ix  0  1FCDdumi  2 FCDdumi  FRACTION i   i
14
Estimating the FX Exposure of Firms
• For each firm we estimate the following market model
using monthly returns from 1996 to 2000.
rit  i 0  ixEXCHt  imrmt   it
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.
15
Comparison of FX Exposures
All firms
FCD users
FCD nonDifference
users between users
and non-users
abs(FX exposure) = |ßix|
0.010
0.001
0.020
0.004
-0.010***
FX exposure
if ßix > 0
0.020
0.013
-0.018***
0.012
0.011
0.008
-0.016
-0.009
0.005**
-0.009
-0.010
-0.007
FX exposure
if ßix < 0
Top figures denote means, bottom figures denote medians.
FCD users have lower exposures to the trade-weighted value of
the U.S. dollar than FCD non-users.
16
Distribution of Exposure Coefficients
Density
30
FCD USERS
40
50
Avg. FRACTION of
FCD Users = 0.42
0
10
20
Avg. FRACTION of
FCD Users = 0.35
0
Exposure Coefficients of FCD Users
.1
50
-.1
10
Avg. FRACTION of
FCD Users = 0.39
0
FCD NON
USERS
20
Density
30
40
Avg. FRACTION of
FCD Users = 0.33
-.1
0
Exposure Coefficients of FCD Non-Users
.1
17
Aggregate Hedging and FX Exposures
Dependent variable: |βix|
Intercept
1.535***
FCD user
-0.122
FCD user × FRACTION
-0.990**
FRACTION
0.816***
1.240***
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
18
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
19
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
20
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.
21
Testable Hypotheses
Fraction of
FCD users
½
# of firms (competition)
Degree of competition
• Does the degree of competition affect aggregate hedging
decisions?
• Do firms hedge less in more competitive industries?
22
Equilibrium
In equilibrium EΠh(w) – EΠu(w)  0
The proportion of firms that use derivatives is given by
mh 1  1    1
E ( w) 
        a   
n 2  n nb   2
E ( w2 ) 
• Flexibility effect dominates • Cost reduction dominates
cost reduction effect
flexibility effect
• Small market share (a - α)
• Large market share (a - α)
0
½
Fraction
of firms
1 hedging
23
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
24
Fraction of FCD Users
Intercept
-0.703***
(-5.73)
PCM
0.665***
(3.55)
-0.671***
(-4.07)
-0.385*
(-1.89)
-0.480***
(-3.28)
-0.545**
(-2.60)
-0.565***
(-3.81)
1.296***
(3.54)
PCMCensus
0.362**
(2.01)
Herfindahl indexCensus
Concentration ratio
(top 4 firms)
0.483***
(2.67)
PCMCensus 
Herfindahl index
0.814***
(3.66)
PCMCensus 
Concentration ratio
0.661***
(4.07)
Weighted fraction of
exposed 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)
659
338
231
337
231
0.086
0.057
0.041
0.047
0.067
337
25
0.065
Number of obs.
Pseudo R2
Intercept
-0.506**
(-2.44)
PCM
0.679**
(2.45)
-0.650***
(-2.89)
-0.335
(-1.36)
-0.360*
(-1.80)
-0.525**
(-2.01)
1.083***
(2.84)
PCMCensus
0.102
(0.50)
Herfindahl indexCensus
Concentration ratio (top
4 firms)
0.066
(0.27)
PCMCensus  Herfindahl
index
0.571**
(2.32)
PCMCensus 
Concentration ratio
Weighted fraction
of exposed firms
-0.476**
(-2.31)
0.433**
(2.20)
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 sensitivity
0.502
(1.29)
0.962*
(1.91)
1.273*
(1.78)
0.809
(1.59)
1.099
(1.57)
0.837*
(1.66)
Cost convexity
0.459*
(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)
212
183
132
183
132
183
0.090
0.093
0.092
0.067
0.115
0.083
Number of obs.
Pseudo R2
26
Extent of FCD Usage
Intercept
PCM
-0.197***
(-8.96)
-0.193***
(-6.24)
-0.167***
(-5.44)
-0.184***
(-6.71)
-0.182***
(-5.57)
-0.194***
(-6.79)
-0.015
(-0.54)
0.054
(1.06)
PCMCensus
0.024
(1.09)
Herfindahl indexCensus
Concentration ratio
(top 4 firms)
0.032
(1.11)
PCMCensus  Herfindahl
index
0.053*
(1.95)
PCMCensus 
Concentration ratio
0.045*
(1.84)
Fraction of
exposed 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)
663
340
232
339
232
339
Number of obs.
27
Summary
• Output prices are less sensitive to FX shocks (lower passthrough) 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.
28