Centre for Risk & Insurance Studies
enhancing the understanding of risk and insurance
The Management of Currency Risk:
Evidence from UK Company Disclosures
Tony Muff, Stephen Diacon and Margaret Woods
CRIS Discussion Paper Series – 2008.I
The Management of Currency Risk:
Evidence from UK Company Disclosures
Tony Muff*
University of Northampton
Stephen Diacon
Margaret Woods
University of Nottingham
January 2008
* Correspondence Details
Dr Tony Muff
Northampton Business School
University of Northampton
Park Campus
Northampton NN2 7AL
United Kingdom
1
T 01604 892356, F 01604 721214, [email protected]
2
The Management of Currency Risk: Evidence from UK Company Disclosures
Tony Muff, Stephen Diacon, Margaret Woods
ABSRACT
This paper examines the use of currency derivatives and/or currency borrowing in a sample of 277
non financial firms taken from the UK actuaries all share index that were reporting continual data for
the years 1995-2001. The results of the univariate and multivariate tests indicate those UK firms with
low profitability, high growth opportunities, and higher tax liabilities are more likely to use currency
derivatives. The important determinants of hedging using derivatives appear to have little to do with
the decision to raise currency debt.
Key words: currency risk, risk management, currency hedging
JEL Classification: F31, G32
We would like to thank Kevin Dowd, Paul Fenn, Christine Helliar and Steve Toms for
comments on earlier versions of this research. The paper also benefited from the suggestions of
participants at the European Risk Research Network at the University of Münster, September
2007.
3
The Management of Currency Risk: Evidence from UK Company Disclosures
1. Introduction and Research Issues
The foreign exchange rate exposure of non financial firms has been subject to extensive research
especially in the USA but remains a contentious issue. Many studies of firms’ sensitivity to
exchange rate volatility have failed to find any significant exposure. Several researchers have
suggested that key explanations for the lack of significance is the widespread use by companies of
financial and operational hedging to reduce exposure1.
This aspect of corporate risk management has attracted a lot of attention over recent years in
order to identify value maximising activities in the use of exchange rate derivatives. The hedging
of exchange risk and other financial risks may add to firm value because of imperfections in
neoclassical capital markets. Examples are the costs of financial distress, the underinvestment
problem and associated costs of external funding, agency conflicts between managers and
shareholders, and the convexity of the tax function.
Most of the studies on the determinants of hedging are based on comparisons between firms that
use derivative financial instruments (including forwards, options, futures and swaps) and those
that do not. However a study of these financial hedging instruments alone may not fully reflect
the full hedging strategy of the firm. Although some aspects of the hedge management strategy
may be unobservable (internal hedging for example), there is evidence to suggest that operational
hedging using currency borrowing is also widely used by firms. Therefore, any study that ignores
the potential to use currency borrowing may be excluding important instruments of hedging
activity. Recent empirical evidence supports the use of currency borrowing as a hedge
management tool and concludes that hedging is an important determinant of the currency of
denomination decision - since those firms for which international trade constitute a significant
proportion of turnover are the most likely to raise foreign currency debt2. In addition, a number of
For example, Guay (1999) shows that when firms start to use derivatives, their stock return volatility falls by 5%
and their foreign exchange rate exposure by 11%.
2 For example, see Joseph (2000), Bradley and Moles (1999), Kedia and Mozumdar (2003), and Keloharju and
Niskanen (2001)
1
4
studies report on firms’ use of currency borrowing as an operational hedge and suggest that the main
reason for foreign currency borrowing was to manage exchange rate risk3.
Motivated by these issues, this paper contributes to the empirical literature in three main ways. First,
the study takes advantage of changes to financial reporting requirements of derivative use in the
United Kingdom (Financial Reporting Standard FRS13) that requires all U.K. firms to provide
information on the use of derivative products to manage exchange rate exposure. Secondly, the
study undertakes a cross sectional analysis on a broad range of firm characteristics that provide
incentives for hedging that are not restricted to exporters. Thirdly, univariate and multivariate tests
are utilised to determine if foreign currency derivatives and foreign currency borrowing are motivated
by managerial risk aversion and/or the need to promote shareholder wealth maximisation.
The paper is organised as follows. Section 2 presents a brief review of the literature on corporate
hedging determinants and their measurement. The hypothesis and regression models are introduced
in section 3 and the data set described in section 4. Section 5 presents the empirical results and
section six concludes.
2. Hedging determinants and their measurement
The literature on derivative use provides two principal explanations for corporate hedging: the
maximisation of shareholder value and managerial risk aversion. In addition, Nance et al. (1993)
also identifies a category of hedging substitutes that may reduce the need for hedging.
2.1 Managerial Risk Aversion
Research on the reward systems for senior management suggests that deferred reward in the form
of beneficial shares encourages risk management to hedge and minimise the variability of cash flow.
Tufano (1996) and Schrand and Unal (1998) find evidence that hedging increases with managerial
shareholding and decreases with managerial option ownership. Graham and Rogers (2000) and
3
Bradley and Moles (2002) in their U.K. survey investigation, advise that foreign currency borrowing was the most
popular method of operational hedging.
5
Dunne et al. (2004) report evidence that hedging increases with managerial share holdings only.
These conclusions suggest that compensation programmes are important determinants of hedging
but the inconsistency in the reported results indicate they are difficult to interpret.
Fok et al. (1997) suggest that the relationship could be representative of entrenchment: as managers
increase their share holding in the firm, the ability of outside investors to monitor managerial nonvalue maximising activities decreases. Therefore as managerial shareholding increases, so too does
the incentive to consume perquisites at the expense of value maximising activities. This implies a
negative relationship between corporate hedging and managerial ownership. Amihud et al. (1983)
provide alternative evidence to suggest that higher managerial share ownership sends signals to
other shareholders that shareholding interests of both parties are aligned closely, and by implication,
that managers will make value-maximising decisions. Higher inside ownership signals a higher firm
value, reducing the incentive to signal by hedging. Thus, as managerial ownership increases, the
incentive to hedge decreases because managerial ownership is a substitute signal.
Perfect et al. (2000) provided evidence to suggest that managerial compensation in the form of
option contracts encourages hedging. They argue that if managers are long term value maximisers
they would act to reduce the probability of firm insolvency or hedge.
To test the hypothesis that managerial compensation packages influence risk management strategies,
two variables are introduced to measure executive remuneration: the log of the value of the
executives’ total beneficial share ownership (LOGDIRVAL) and bonus payments in the form of the
value of total share options (LOGOPTVAL). It is expected that there will be a positive relationship
between share ownership and hedging, and a negative relationship between option holdings and
hedging. A third alternative variable is used to control for size of the directors’ holding namely the
percentage beneficial share ownership (SHS). In order to control for the size of the management
team, alternative specifications of managerial share ownership are used, including the per capita
shareholding of the executive directors (AVGDIRVAL) and the per capita option holding
(AVGOPTVAL).
If a significant proportion of the directors’ share holding is concentrated in one director, her holding
might be more highly associated with risk management activity of the firm than the shareholdings of
6
other executive directors. If this is the case then using total management shareholdings and per capita
holding may fail to capture this effect. To test for this a power variable is introduced, defined as the
largest percentage ownership of a firm's shares beneficially owned by one director (DLSHS). This
variable is separated from the share ownership of the remaining directors (DRSHS). The result of
separation will be to test whether the risk management strategy of UK firms is determined by a group
of directors and not by one dominant figure. The separation will also test for evidence of the
entrenchment hypothesis and/or the signaling hypothesis.
Note however that if outside blockholders are better diversified than directors, they would
impose a countervailing pressure not to hedge (De Marzo and Duffie, 1995; Breeden and
Viswanathan, 1998; Tufano, 1996). There would therefore be a negative relationship between
hedging and the size of outside blockholders (Tufano, 1996). However these results are also
consistent with a clientele hypothesis that blockholders seek investment in firms with high levels
of exposure to maximize gain, so that the risk management policy actually reflects these
investment goals. In order to test for clientele effects a control variable is used to measure large
block holdings by investors who are not officers or directors of the firm. This variable is
measured by total share ownership of large outside block holders who report holdings of 3% or
more of the firm’s shares (BLOCK). It is expected that the degree of institutional ownership is
positively related to portfolio diversification and therefore negatively related to hedging activity.
If however block holders seek investment in firms to support value maximizing goals as opposed
to optimal risk management goals, this may create a clientele effect. A second dichotomous
variable is set equal to unity if the largest non manager shareholder owns more than 10% of the
total shares outstanding (BLOCKD).
2.2 Shareholder Value Maximisation
Financial Distress
Smith and Stulz (1985) suggest that highly-geared firms which have cash-flow problems or are
otherwise near to possible bankruptcy will have an incentive to reduce risk in order to reduce the
costs of this financial distress and hence increase shareholder value. Subsequent researchers have
used a number of measures to proxy for financial distress, primarily based on the borrowing capacity
of the firm or leverage.
7
The capital gearing ratio (CGR) can be used to measure financial distress, and net income to assets
(NETINCASSTS) to proxy for a profitability measure best reflective of net operations. Most studies
find a positive significant relationship between hedging and leverage and interpret this as evidence
that greater expected financial distress costs result in increased hedging4. As a firm’s profitability may
be inversely related to hedging, less profitable firms have a higher probability of financial distress.
Therefore an inverse relationship between net income and hedging is expected.
Underinvestment and the Expected Costs of External Financing
A firm which is highly-geared may find it difficult to obtain external financing for growth and may
therefore be forced to undertake sub-optimal investment strategies and forego profitable
investment opportunities – the so-called underinvestment problem (Mayers and Smith, 1987). This
agency cost creates a cost differential between internally generated funds and external costs of
finance. Exchange rate risk management may help to ensure that internally-generated cash is
available to fund value enhancing investment and resreach and development programmes and Froot
et al. (1993), Nance et al. (1993), Geczy (1997), and Howton and Perfect (1997) find evidence of a
positive relationship between external financing costs and the use of derivatives.
Two alternative measures are widely employed to reflect an underinvestment problem: the firm’s
book to market ratio, and research and development expenditure (R&D). However most studies
have failed to find support for the book to market ratio as a proxy for underinvestment. On the other
hand, a positive relationship between hedging activity and R& D expenditure was reported by Geczy
et al. (1997), Allayannis and Ofek (2001), and Gay and Nam (1998) all finding that hedging increases
with R&D expenditure.
Instead of using R&D directly, we allow for the combined effect of R&D and the firm’s cash
position by including an interaction between the ratio of research and development expenditures
(scaled by sales RDSLS) and the level of liquidity (the quick asset ratio QAR). Thus the variable
RDQAR tests the association between financial expenditure commitments and the level of liquidity
to fund these commitments. The firms most at risk will be those that have high levels of RDSLS
expenditure and low levels of liquidity (QAR) as this would reflect high growth options most at risk
8
of potential underinvestment.
Progressive Tax Rates
Smith and Stulz (1985) demonstrate that a convex tax schedule provides an incentive to hedge, as a
reduction in the volatility of taxable profits would increase firm value by reducing taxes paid. There
two major means by which firms’ tax curves become convex:
•
if the firm’s tax rate lies in the progressive tax curve (Haushalter, 2000)
•
if the firm has tax preference items such as investment tax credits or tax loss carry forwards
(net operating loss). Berkman and Bradbury (1996) use a tax dummy to measure tax loss
carry forwards and report a significant and positive association between tax loss and the use
of derivatives indicating that firms use derivatives to protect the tax loss.
This study uses three variables to reflect the tax incentive to hedge. It is hypothesized that in order to
maximise the use of tax shields and to minimise the extent of income volatility, a firm’s tax ratio
(TAX) is positively associated with the employment of currency hedging instruments. In addition, a
proxy variable measuring a tax loss carry forward is used and defined as a net operating loss (NOL):
this tax loss dummy variable equals one if the firm has tax losses carried forward and zero otherwise:
there should be a positive relationship between NOL and hedging. Finally a second dummy variable
(TAXD) reflects whether or not a firm’s marginal tax rate is less than 32.75%.
2.3 Hedging Substitutes
Nance et al. (1993) and Froot et al. (1993) argue that firms can mitigate the expected cost of
financial distress and agency cost by maintaining a larger short term liquidity position, or take
steps to reduce drains on cash flow by having a lower dividend payout. As a general strategy,
holding liquid assets will reduce financial distress. In effect holding liquidity can be seen as a
substitute for hedging activity in so far as the cost of holding liquid assets is lower than the cost
of entering into financial hedging contracts. Liquidity is measured by the quick asset ratio (QAR).
4
Allayannis and Ofek (2001) find a significant but negative association
9
Nance et al. (1993) suggest other substitutes such as dividend payout or dividend yield would be
inversely related to hedging. Dividend restriction would allow a firm to retain sufficient liquidity
to make hedging unnecessary. The variable dividend yield (DIVYIELD) is anticipated to have a
positive relationship with hedging.
2.4 Size and International Operations
Researchers including Nance et al. (1993), Smith and Stulz (1985), Geczy et al. (1997), Allayannis
and Weston (2001) and Dunne et al. (2004), report that currency risk management activity is
positively related to the size of the firm (represented by the log of market value LNMV) and the
extent of international operations (exports as % total sales EXPORT). Larger firms are not only
able to benefit from economies of scale in the use of derivatives, but also to take advantage of
cheaper borrowing costs on international financial markets. Larger firms are therefore able to lower
the cost of operations through economies of scale in hedging and borrowing and to position
themselves strategically to take maximum advantage of risk diversification. In addition, the size of
foreign assets held by firms (FORASTS) is also included as an additional variable to represent
international operations.
10
Table 1: Variable Definitions
Each financial variable (except BMCAT) is calculated for each firm as the average reported value in financial statements 2000 and 2001. The
Managerial Compensation variables are reported as at 2001
The Managerial risk aversion
hypothesis
Managerial
Compensation
Packages
LOGDIRVAL
Expected
sign
LOGOPTVAL
_
SHS
+
DLSHS
+
DRSHS
+
AVGDIRVAL
+
AVGOPTVAL
-
External share ownership
BLOCK
-
+
Directors Share Holding
Alternative explanatory variable
Log of the value of directors share holding (£). Based on average share
price for the year
Log of the value of total options outstanding (£), based on the average
share price for the year
Directors beneficial shareholding as a percentage of issued share capital
for 2001
Director with largest holding as a percentage of
issued share capital for 2001
Remaining directors share holding as a percentage
of issued share capital for 2001 (SHS-DLSHS)
Total value of executive shareholding divided by
the size of the executive team (log transformation)
Total value of the options held by executive
directors divided by the size of the executive team
(log transformation)
Block Holding
Total reported external holdings (other than directors) over 3% of
issued share capital. Recorded As a percentage
BLOCKD
Dummy variable equal to 1 if total reported holding
(other than directors) over 10% of largest block
holder holding of issued share capital.
11
Table 1: Variable Definitions (Continued)
Shareholder Value
Maximisation Hypothesis.
Expected
sign
Financial Distress
NETINCASSTS
CGR
+
Net income to total assets
Capital gearing ratio. Preference capital + total debt as a percent of
total capital employed +short term borrowing – intangibles
Underinvestment problem
BMCAT
-
Book to market rank. Book value of assets as a percent of market value
of equity and preference capital. Due to discontinuity at zero,
BMCAT’s are reported in ranks 1-10 representing BMCAT’s as a
percentage category BMCAT. BMCAT values above 100% are coded
11 and negative values 12.
RDSLS
+
RDQAR
Taxation
TAX
NOL
TAXD
-
Hedging substitutes
QAR
DIVYIELD
Other Variables
LNMV (Market value £m)
EXPORTS
FORASTS
Research and Development expenditure as a
percentage of sales
Interaction between RDSLS and QAR.
+
+/+/-
Total tax charge as a percentage of pre-tax profit
Dummy variable if firm has net operating losses in t and t-1
+
Cash and debtors as a percentage of current liabilities
Total dividend paid in proportion to share price
+
+
+
Market value reported at financial year end (log transformation)
Exports as a percentage of total sales
Dummy variable if firm has TAX% greater than 0
and ≤ 32.75%
Foreign assets as a percentage of total assets
12
Table 2: Summary of Financial Characteristics
Summary statistics for 277 non-financial U.K. firms. The variables are proxies for foreign currency
hedging. The reported values represent an average using two years of financial reports.
Variable 277
observations
LOGDIRVAL
LOGOPTVAL
SHS
DLSHS
DRSHS
AVGDIRVAL
AVGOPTVAL
BLOCK
BLOCKD
CGR
NETINCASSTS
BMCAT
RDSLS
RDQAR
TAX
TAXD
NOL
QAR
DIVYIELD
LNMV
EXPORTS
FORASTS
Mean
14.6179
13.5037
5.6887
3.7314
1.9572
13.2854
0.8936
30.9386
0.5595
44.3988
4.4401
7.2490
1.8145
2.5629
21.6534
0.8302
.1660
.9314
4.0660
12.4894
40.8229
32.2718
Standard
Deviatio
0.0805
0.9768
11.3075
8.3165
4.6421
1.9563
3.7429
18.0606
0.4973
35.6584
15.9390
3.1918
8.0569
13.1964
51.2708
0.3760
0.3728
0.5531
3.0491
1.9418
33.7806
32.1240
Min
9.4190
2.1972
0
0
0
7.8095
0
0
0
0
-71.5500
1
0
0
-333.0000
0
0
0.1000
0
8.0600
0
0
Max
20.6932
18.8783
59.3300
59.1000
33.1200
19.0328
17.4920
79.0000
1.0000
235.0600
72.2500
12.0000
117.9700
197.0000
285.0000
1.0000
1.0000
3.2000
23.6
18.6400
100.0000
100.0000
3. Data Sources and Methodology
To be included in the sample, a firm had to be a constituent of the F.T.S.E. all share index between
the years 1995 and 2001 and report risk management activity in their annual reports and accounts for
years 2000 and 2001. This restriction was deemed important to identify those firms that have a
trading history over a five year period and a consistent policy of risk management reporting. In
addition firms were screened using the following criteria as at January 1st 2002: all companies were
required to report financial data on DATASTREAM continually from January 1995 to December
13
2001, and all financial firms were excluded5
After eliminating financial firms and firms with missing data there were 277 representative firms in
the sample. Unlike studies whose sample set consists of large firms with extensive international
operations (Geczy, et al. 1997), firms are included irrespective of foreign sales or size provided they
are live and represented in the F.T.S.E. all share index - as they may still face currency exposure
through export or import competition despite having no foreign sales (Allayannis and Weston, 2001).
This criteria is important as survey and market based evidence suggests that many U.K. firms are
also exposed to exchange rate risk through the costs of fuel and imported factor inputs (Rees and
Unni, 1999; Bradley and Moles, 2002).
Two consecutive years of accounts were scrutinised to determine if firms in the sample conform to a
hedging strategy that is robust over time and not subject to year-by-year changes. For each firm, the
annual reports were screened for information on its hedge management strategy. Information was
gathered on a firm’s use of derivatives and foreign currency borrowing from reading the accounting
footnotes to annual reports in the year 2000 and 2001. A firm is recorded as a user of derivatives and
currency borrowing if it reported as such in the notes to the annual accounts for both 2000 and 2001:
thus a firm would need to have a consistent policy on hedging for the two consecutive years, and
report the use of derivative products (forward contracts, currency options, currency swaps contracts,
or currency borrowing) to manage exchange rate risk. Care was taken to ensure that the hedging
contracts were employed to manage currency exposure; in particular only currency swaps (and not
interest rate swaps) were included.
The models use two main dependent variables: the first reflects whether or not any currency
derivatives are employed, hence DER=1 or 0 if firms do or do not report using derivative products
respectively. The second defines BORROWING=1 when firms use foreign currency denominated
debt. Given the dichotomous nature of the dependent variables, the models were analysed using a
probit specification to estimate the probability that the firm will hedge currency risk with a particular
type of technique.
5 This is consistent with Allayannis and Ofek (2001). Financial firms motivation for using derivative products could
be very different from that of non financial firms.
14
The explanatory variables are those as described above to test for managerial risk-aversion, and
shareholder value maximisation. Table 2 summarises the financial characteristics of the variables. A
number of alternative model specifications were identified to allow for substitution of the regressors.
The definitions of the variables are given in Table 1.
4. The Use of Currency Derivatives
Table 3a reports the frequency of use of derivative hedging instruments and foreign currency
borrowings by size and industry. Firms are ranked by size quartiles.
The table shows that U.K. firms utilise derivative products to a greater extent than their U.S.
counterparts as reported by Geczy et al (1997): 74% against 41% despite the inclusion of firms that
report no foreign currency exposure. Forward currency contracts are the most widely used hedging
instrument, options were used less so and in limited currencies. The size quartile rankings indicate
that there is a significant scale economy in the use of derivatives: the percentage use of options and
swaps is more pronounced in larger companies indicating that they are more likely to use more
complex hedging combinations.
The results indicate that 51% of reporting firms use currency borrowing to manage exchange rate
risk and there useage increases with the size of the organisation. Narrative information reported in
annual reports suggest included firms use foreign currency debt as a natural hedge to protect against
volatility in balance sheet value of foreign currency assets. Approximately 90% of companies that
report holding foreign assets use currency borrowing as a hedging tool.
15
Table 3: Frequency of use of Derivative Hedging Instruments and Borrowing by Size and Industry
Summary information for the use of derivative instruments and currency borrowing for 277 U.K. firms for fiscal year ending 2001. Currency derivatives reported
by U.K. firms include forwards, options and swaps. Information is taken from the footnotes to the reports and accounts for the year 2000 and 2001. The 1st
quartile represents the smallest firms defined by market value; the 4th quartile represents the largest firms. Industry groupings are determined by the Financial
Times actuaries’ index.
Table 3a: Summary of Currency Derivative Use and Foreign Currency Borrowing by Size.
Number
All Firms
Quartile 1
2nd Quartile 2
3rd Quartile 3
4th Quartile 4
1st
277
70
69
69
69
Any
derivative
207
48
52
45
62
%
74
69
74
65
89
Currency
Forward
197
46
52
39
60
%
70
66
74
56
86
Currency
Option
35
3
8
5
19
16
%
13
4
11
7
27
Currency
Swaps
45
2
3
13
27
%
16
3
4
19
39
Currency
Borrowing
142
22
39
33
48
%
51
32
57
48
70
Table 3b: Summary of Currency Derivatives Use and the Use of Foreign Currency Denominated Debt identified by Industry
Number
All firms
Building and Construction
Engineering
Retail
Electronics
Transport and Distribution
Support
Textiles
Breweries
Food producers
Paper and packaging
Health
Media
Oil and Extraction
Chemicals
Diversified Industry
Manufacturing
277
36
43
37
17
30
14
10
10
9
9
8
8
8
8
7
7
Any
derivative
207
17
38
25
14
24
11
9
5
8
6
5
7
7
7
7
6
%
74
47
88
66
81
80
79
90
50
89
66
63
88
88
88
100
86
Forward
197
15
38
23
14
23
10
9
5
8
6
5
6
6
7
7
6
%
0ption
72
42
88
61
81
77
71
90
50
89
66
63
75
75
88
100
86
35
2
6
4
1
2
3
1
2
1
0
1
2
4
1
1
0
17
%
13
6
14
11
8
7
21
10
20
11
0
13
25
50
13
14
0
Swaps
45
5
8
2
1
10
0
0
0
1
1
1
1
5
2
2
0
%
17
14
19
5
8
33
0
0
0
11
11
13
13
63
25
29
0
Borrowing
142
16
30
15
9
14
6
3
4
4
6
4
5
3
6
4
4
%
51
44
70
41
53
47
43
30
40
44
67
50
63
38
75
57
57
Table.3b reports the use of derivative products by industry groupings. The most likely industries to
utilise derivatives are engineering, electronics, transport and distribution, textiles, food processing,
media, oil and extractive industry, chemical, diversified firms and manufacturing. The most likely
industries to utilise foreign currency borrowing are the traditional chemical, engineering and
electronic industries.
5. Univariate Analysis
Tests for differences in the means of derivative users and non-users utilising the explanatory variables
described above were undertaken using standard t tests (which assume a normal distribution) and
non-parametric Wilcoxon Mann-Whitney tests. Results of the Kolmogorov-Smirnov test below
(Table 4) indicate that there was evidence of non-normality in the explanatory variables and therefore
the results of the Wilcoxon Mann-Whitney test were preferred for analysis.
5.1 Univariate Analysis of Currency Derivatives
Table 5a shows descriptive statistics for derivative users and non-uses for any derivative product
(forwards, option or swaps), using both parametric and non-parametric tests. The results of both
tests are broadly similar. Differences do occur however for variables that measure managerial
compensation, underinvestment and hedging substitutes. The sign of the relationship remains the
same for both tests.
18
Table 4: Tests of Normality on explanatory variables for 277 non-financial firms
Variables
LOGDIRVAL
LOGOPTVAL
SHS
DLSHS
AVGDIRVAL
AVGOPTVAL
BLOCK
CGR
NETINCASSTS
BMCAT
RDQAR
RDSLS
TAX
NOL
QAR
DIVYIELD
LNMV
EXPORTS
FORASSTS
KolmogorovSmirnov Test
Statistic
Sig.
.056
.036
.240
.000
.307
.000
.327
.000
.067
.004
.211
.000
.063
.010
.127
.000
.165
.000
.162
.000
.423
.000
.411
.000
.241
.000
.506
.000
.148
.000
.107
.000
.048
.000
.145
.000
.167
.000
Managerial Risk Aversion Variables
As indicated by the t and z values user firms are statistically different from non user firms with
respect to those variables that are proxies for managerial compensation - namely the value of
directors’ beneficial ownership of shares (LOGDIRVAL AND SHS) and directors’ largest holding
(DLSHS) and average holding (AVGDIRVAL). The univariate tests indicate that non users of
derivatives have higher share and option holdings.
It was previously hypothesised that the larger the director beneficial holding (LOGDIRVAL) the
greater the incentive to hedge, but this is not born out in the results. Instead a negative relationship
exists (so that a higher the level of beneficial holdings is associated with a lower level of risk
management.) which is robust across all alternative shareholder specifications reported using both the
t-test and Wilcoxon Mann-Whitney test. This is also the case when the data is split into the largest
single director’s shareholding (DLSHS) and the remaining directors’ shareholding (DRSHS). As the
19
variable DLSHS is highly significant, this suggests that where management power is highly
concentrated, firms are prepared to accept higher levels of risk. This is consistent with the signaling
hypothesis or the managerial entrenchment hypothesis. There are no significant differences between
users and non-users of derivative products in terms of the value of executive share options
(LOGOPTVAL). There is also no evidence in these univariate tests to suggest that taking the per
capita values (AVGOPTVAL) have any significant effect on outcomes.
With reference to variables that are proxies for external shareholding (BLOCK), the univariate tests
suggest no significant association between users and non-users. An alternative substitute proxy
(BLOCKD), a dummy variable representing only the largest single block holder holding in excess of
10% of the firms’ shares is also insignificant.
Shareholder Maximisation Variables
The univariate tests suggest that users of currency derivatives are significantly different from non
users with respect to all three categories of hedging determinants, although not all proxy variables
are significant. The univariate results are broadly consistent with financial theory. The capital
gearing ratio (CGR) is significantly different for users of derivatives from non-users - consistent
with Smith and Stulz (1985). Hedgers have significantly lower values of the interaction term
RDQAR indicating that underinvestment is most acute for firms with growth opportunities but
experiencing low levels of liquidity. It would be expected that firms would protect their research
and development expenditure by hedging to reduce income volatility however the Wilcoxon
Mann-Whitney test suggests otherwise. The tests also suggest foreign currency hedging firms are
more likely to have higher tax liabilities (TAX). The liquidity variable (QAR) is significant and
indicates that derivative users have higher levels of liquidity that non-users but this is inconsistent
with the argument that liquidity is a substitute for hedging. The t test indicates that dividend yields
(DIVYIELD) are higher for derivative hedgers than for non-users, consistent with the Nance et al.
(1993) argument that dividend curtailment is a substitute for hedging.
In addition to cross sectional differences in hedging incentives, currency derivative users and
non-users differ with regards to size (LNMV), international operations (EXPORTS) and the scale
of international assets (FORASTS). Derivative users are larger and export more, and have larger
foreign currency assets.
20
Table 5a: Summary of Financial Characteristics of Currency Derivative Users and Non
Users
Summary statistics for 277 non-financial U.K. firms that disclose the use/non-use of pooled currency
derivatives, namely, forwards options and swaps. The variables are proxies for foreign exchange exposure.
The t-statistics are given for tests of the equality of means between currency derivative users and nonusers. T tests assume equal variances unless the null hypothesis of equal variances is rejected at the 10%
significance level. The z statistic presents the results of the Wilcoxon rank sum tests. Significant p-values at
the 10% level are highlighted in bold.
Derivatives
Currency Derivative
Users n =209
Mean
Std Dev
Variable
Currency Derivative
Non Users n = 68
Mean
Std Dev
t
p value
Higher
W score
Z stat
N-users
-1.8930
p value
The Managerial risk aversion hypothesis
Managerial Compensation Packages
LOGDIRVAL
14.4705
2.0160
15.0710
LOGOPTVAL
SHS
2.2220
-2.0800
13.4925
3.1030
4.6120
10.9000
.03840
0.0584
13.5380
2.5697
-0.1106
0.9110
N-users
-0.8460
0.3970
8.9978
11.9623
-2.8130
0.0050
N-users
-4.2650
0.0000
DLSHS
2.8770
7.5768
6.3560
9.8704
-3.0410
0.0030
N-users
-4.2850
0.0000
DRSHS
1.7350
4.7555
2.6410
4.2343
-1.4000
0.1630
N-users
-2.7540
0.0059
AVGDIRVAL
13.1470
1.9039
13.7110
2.0658
-2.0774
0.0386
N-users
-1.7767
0.0756
AVGOPTVAL
12.1689
3.0215
12.1780
2.5361
-0.0234
0.9813
N-users
-1.1659
0.2436
BLOCK
30.8000
18.3600
31.3800
17.1600
-.229
0.8190
N-users
-0.2440
0.8072
BLOCKD
0.5694
0.4960
0.5294
0.5028
.575
0.5660
users
-0.5760
0.5680
External share ownership
Shareholder Value Maximisation Hypothesis
Financial Distress
CGR
46.6820
35.2543
37.3793
36.2380
1.8770
0.0620
users
-2.375
0.0175
NETINCASSTS
3.3636
15.5624
7.7486
16.7317
-1.9810
0.0486
N-users
-2.008
0.0445
BMCAT
7.1005
3.2157
7.7059
3.0958
-1.3610
.1750
N-users
-1.2470
0.2124
RDQAR
1.7770
5.2600
4.9782
24.9732
-1.7439
0.0823
N-users
-4.0487
0.0000
RDSLS
1.4657
3.8072
2.8867
14.8813
-1.2630
0.2080
N-users
-4.0240
0.0000
TAX
26.5700
42.5200
6.5400
69.9800
2.8350
0.0050
users
-1.772
0.0763
Underinvestment Problem
Taxation
NOL
0.1483
.35620
0.2205
0.4177
-1.3907
0.1654
N-users
-1.388
0.1650
TAXD
0.5072
0.5011
0.6029
0.4929
-1.3740
0.1700
N-users
-1.372
0.1700
Hedging Substitutes
QAR
0.9497
0.5120
0.8753
DIVYIELD
4.2574
3.2581
3.4779
2.2085
Other Variables
0.6650
.9640
0.3360
users
-1.995
0.0461
1.8389
0.0670
Users
-1.420
0.1555
LNMV
12.6580
2.0427
11.9698
1.4900
2.5660
0.0110
users
-2.239
0.0251
EXPORTS
46.8180
32.5749
22.3971
30.8197
5.4400
0.0000
users
-5.692
0.0000
FORASTS
36.6840
32.0620
18.7100
28.4980
4.1220
0.0000
users
-4.816
0.0000
21
5.2 Univatiate Analysis of Foreign Currency Borrowing
Table 5b shows descriptive statistics for users and non-users of foreign currency borrowing. It would
be expected to find some overlapping of results with derivative use as companies can use currency
borrowing to substitute for managing transactions and translation risk and the results support this
inference.
Managerial Risk Aversion
Similar to the use of derivative hedging, there is consistent support to suggest that managerial
motivation to borrow increases the smaller the beneficial share holdings of managers, and this is
particularly pronounced when there is significant share holding by one individual (DLSHS). Again
this is consistent with the signaling hypothesis. In addition, the greater the managerial option
holding the greater the likelihood to borrow: this is inconsistent with the argument of Tufano
(1996) but managerial compensation may not be easy to unravel, and may be a highly complex
interrelationship of pay, compensation and power. There is no evidence to suggest that managerial
motivation to hedge using currency borrowing is influenced by external block holders.
Shareholder Maximisation
There are some differences in the univariate test for the use of currency borrowing and derivatives
and differences between the t-test and the Wilcoxon Mann-Whitney test. The non-parametric tests
indicate a very significant positive association between gearing (CGR) and the use of currency
borrowing. There is a significant negative relationship between RDQAR and RDSLS and the use of
currency borrowing. Firms that have high levels of research and development expenditure are most
likely to use currency borrowing when liquidity is low. The tests suggest a significant positive
association between dividend yield (DIVYIELD) and the use of currency borrowing. This may
suggest that firms are using borrowing as a substitute vehicle to raising finance internally and in
support of a firm’s known dividend policy. There is no significant difference between the users of
currency borrowing and non-users in relation to the underinvestment problem (BMCAT) and the tax
hypothesis (TAX and NOL). The remaining size variables indicate that large firms (LNMV) with
significant exports (EXPORTS) or foreign assets (FORASTS) are more likely to borrow foreign
currency.
22
Table 5b: Summary of Financial Characteristics of Currency Borrowing, Users and Non
Users
Summary statistics for 277 non-financial U.K. firms that disclose the use/non-use of currency borrowing.
The variables are proxies or foreign exchange exposure. The t-statistics are given for tests of the equality
of means between currency borrowing users and non-users. T tests assume equal variances unless the null
hypothesis of equal variances is rejected at the 10% significance level. The z statistic presents the results of
the Wilcoxon rank sum tests. Significant p-values at the 10% level are highlighted in bold.
Borrowing
Currency Borrowing
Users
n = 142
Mean
Standard
Deviatio
n
Variable
Currency Borrowing
Non Users n = 135
Mean
Standard
Deviation
t
p value
Higher W
score
Z stat
p value
The Managerial risk aversion hypothesis
Managerial Compensation Packages
LOGDIRVAL
14.5013
1.9810
14.7406
2.1810
-0.9567
0.3390
N-users
-0.726
0.4670
LOGOPTVA
13.9332
2.7003
13.0520
3.1903
2.4854
0.0130
users
-2.901
0.0030
SHS
3.7613
10.235
7.7160
12.042
-2.9500
0.0030
N-users
-4.603
0.0000
DLSHS
2.1928
6.8422
5.3494
9.3836
-3.2100
0.0010
N-users
-4.431
0.0000
DRSHS
1.5684
4.7949
2.3666
4.4567
-1.4330
0.1530
N-users
-3.738
0.0000
AVGDIRVAL
13.1777
1.8198
13.3987
2.0911
-0.9394
0.3480
N-users
-1.776
0.0750
AVGOPTVA
12.6096
2.5737
11.7101
3.1624
2.6022
0.0090
users
-1.165
0.2430
External share ownership
BLOCK
29.65
18.82
32.30
17.16
-1.225
.2220
N-users
-1.474
0.1410
BLOCKD
0.5775
0.4957
0.4507
0.5001
.614
.5400
N-users
-0.614
0.5390
Shareholder Value Maximisation Hypothesis
Financial Distress
CGR
52.1294
34.363
36.2674
35.3061
3.789
0.0000
users
-4.825
0.0000
NETINCASSTS
4.5417
13.008
4.33325
18.5794
0.10862
0.9130
users
-0.659
0.5090
Underinvestment Problem
BMCAT
7.0563
3.2129
7.4519
3.1687
-1.0310
0.3030
N-users
-0.972
0.3310
RDQAR
1.9556
6.3390
3.2017
17.7655
-0.7849
0.4332
N-users
-4.061
0.0000
RDSLS
1.5340
4.1524
2.1097
10.7549
-0.5930
0.5540
N-users
-4.011
0.0000
Taxation
TAX
24.5800
46.9100
18.5600
55.4400
0.9770
0.3300
users
-1.306
0.1910
NOL
0.1619
0.3697
0.1703
0.3773
-0.1870
0.8510
N-users
-0.187
0.8510
TAXD
0.4789
0.5013
0.5852
0.4943
-1.7760
0.0770
N-users
-3.864
0.0000
Hedging substitutes
QAR
0.9278
.4481
.9353
.6472
-0.1120
0.9110
users
-1.399
0.1620
DIVYIELD
4.4028
2.9108
3.71185
3.160272
1.8939
0.0590
users
-2.283
0.0220
Size variables
LNMV
12.954
1.9562
12.0001
1.8085
4.2120
.0000
users
-3.993
0.0000
EXPORTS
54.1070
30.2970
26.8501
31.6355
7.3250
.0000
users
-6.730
0.0000
FORASTS
45.6860
30.8400
18.1610
27.0812
7.8770
.0000
users
-7.772
0.0000
23
6. Multivariate Analysis of Corporate Hedging and Currency Borrowing
Table 6 shows a correlation matrix for all explanatory variables used in the study. The table indicates
there were high levels of correlation between variables that proxy for managerial risk aversion
(especially SHS, DLSHS and DRSHS), underinvestment (RDQAR and RDSLS) taxation, (TAX
NOL and TAXD) and international operations, (FORASSTS and EXPORTS).
Tables 7a and 7b present the results of probits models to explain the probability that firms use
derivatives and foreign currency borrowing6. Alternative models are used where explanatory
variable have been substituted.
6.1 Managerial Risk Aversion
There is some suggestion that management incentives in the form of share options (LOGOPTVAL)
have an effect on the probability of using currency derivatives, as the coefficients in Table 7a are
consistently negative across all five models (as suggested by Tufano (1996)) and are generally
significant at the 10% level. The results do not support the contention that poorly diversified
managers engage in hedging activity to maximise their personal utility by utilizing conventional
ownership measurements (Smith and Stulz, 1985). The results are also insignificant for all alternative
hedging instruments used to proxy for managerial incentives. However the sign of the variables
LOGDIRVAL and AVGDIRVAL are negative indicating that managers would hedge less the
greater their managerial share ownership. This negative relationship is consistent with the
entrenchment hypothesis. There is no significant power effect when directors share holdings are split
between the largest shareholder (DLSHS) and the remaining shareholders (DRSHS). When
considering per capita directors’ holdings, unlike Tufano (1996) these results do not find a significant
relationship.
The results in Table 7b do not support the use of currency borrowing to mitigate managerial risk
aversion. The results are consistent with financial hedging and indicate that managers holding
beneficial shares would borrow less.
6
That is, we model Prob(DER = 1|xj) = Ф(xjβ) where the explanatory variables xj reflect the managerial and
shareholder measues in Table 1. Similarly for Prob(BORROWING = 1|xj) = Ф(xjβ)
24
When considering the size of external ownership (BLOCK) there is no evidence to suggest that this
has any significant influence on the risk management decisions of the directors. The alternative
explanatory valuable (BLOCKD) is also insignificant indicating that there is no clientele effect
influencing the decision to hedge (the results are not reported). These results are consistent across
firms that use currency borrowing indicating that blockholding is not a determinant to encourage
exchange rate risk management.
6.2 Shareholder Maximisation
Cost of Financial Distress
The capital gearing ratio (CGR) is insignificant in Table 7a and contrary to the findings of Dunne et
al. (2004), Graham and Rogers (2002), Nguyen and Faff (2002), Gay and Nam (2000) and Berkman
and Bradbury (1996). The results provide no support for the role of gearing in stimulating derivative
hedging.
It would be expected that profitability would be inversely related to hedging if less profitable firms
have higher probability of encountering distress. The variable net income to assets (NETINCASSTS)
has a highly significant negative relationship with hedging in line with financial theory. This evidence
suggests that U.K. firms are sensitive to changes in income and profitability and not capital gearing.
Table 7b shows that there is no significant relationship between a firm’s foreign borrowing and
profitability (NETINCASSTS) but a significant positive relationship between the use of foreign
currency borrowing and financial distress (CGR).
Underinvestment
Three proxy variables are used to measure underinvestment: a rank variable to represent book to
market rank (BMCAT), the variable RDSLS and an interaction term between R&D scaled by sales
and the QAR (RDQAR). Table 7a demonstrates a significant negative relationship between
BMCAT, RDQAR and derivative hedging. Those firms that have low BMCATs are those more
likely to have high growth options and be most at risk of underinvestment. Those firms with low
interaction values would also be at risk due to the lack of liquidity to finance growth. The variable
25
RDSLS was found to be insignificant and negative.
Prior researchers including Geczy et al. (1997) and Graham and Rogers (2002) found a significant
relationship between R&D expenditure and derivative use, however the majority of recent
researchers have not found any significant relationship between BMCAT ratios and derivative
hedging. In this study there is a strong negative association between BMCAT, RDQAR and
derivative hedging indicating that hedging to protect the financing of growth options is likely for
U.K. firms. As gearing ratios in the U.K. are historically higher than in the U.S., it is not surprising
that firms with a greater degree of leverage may hedge more to mitigate the underinvestment
problem so that the cost of debt can be reduced. A further possible explanation for the significance
of these results is that U.K. firms are more concerned with the underinvestment problem in
managing the potential growth in the portfolio of assets that they hold.
Taxation
It would be expected that hedging would be positively correlated with the tax liability (TAX), the
net operating loss (NOL) and the progressive tax curve indicator variable (TAXD). Because these
variables are highly correlated, each of the variables was substituted into the main model. Each was
highly significant. Table 7a finds a significant positive relationship between the annual tax charge
(TAX) and currency-hedging indicating that corporate taxation is an important factor in the
decision to use derivatives. Substituting net operating loss (NOL) the results indicate a strong
negative significant association with the use of derivatives.
These results are inconsistent with Berkman and Bradbury (1996) who found strong support for
higher derivative use in firms with higher tax losses. However Graham and Rogers (2000, 2002)
argue that using existing NOL is too simple a measure to capture incentives that result from the
shape of the tax function. Graham and Smith (1999) document that existing NOLs provide a tax
disincentive to hedge for firms with small expected losses, especially if a firm expects to lose money
in future years. Further investigation into the sample reveals 46 firms had NOLs of which a
significant number had experienced persistent losses over the last 5 years, and all but 6 of the firms
were below the average size as measured by market value. These results offer some support for the
tax disincentive argument to hedge, and also as firms were below average size, an economy of scale
argument in the use of derivative hedging.
26
In order to test if firms hedge because the tax rate lies in the statutory progressive tax curve, the
dummy variable (TAXD) is used and equal to 1 if a firm lies in the progressive tax curve based on a
marginal tax rate of 32.75%. The results in Table 7a support a strong significant and positive
association between the TAXD variable and the likelihood of hedging confirming a positive
relationship between tax convexity and hedging.
There is no support to suggest that currency borrowing is used to hedge tax liabilities.
6.3
Hedging Substitutes
This analysis employs two hedging substitutes namely the quick asset ratio (QAR) and dividend yield
(DIVYIELD) both extensively used by most prior researchers. There is a weak positive association
between dividend yield and derivative hedging in Table 7a. These results are consistent with Froot et
al. (1993), Berkman and Bradbury (1996), and Howton and Perfect (1998) who predict that using
substitutes reduces the need for hedging activity. Interestingly there is a positive and significant
relationship between the dividend yield and the extent of currency borrowing in 7b. One explanation
for this significance is the potential complementary usage of derivatives and borrowing and the
likelihood that dividends are paid in multi currencies.
6.4
Size Variables
Tables 7a and 7b show that large firms with international operations are more likely to use both
derivative products and currency borrowing. This is consistent with Geczy et al. (1997), Allayannis
and Ofek (2001), Berkman and Bradbury (1996), Dunne et al. (2004) and Howton and Perfect (1998)
whereas derivative use increases with size and the extent of foreign sales, and the use of foreign debt
also with size and foreign sales.
27
Table 6: Pearson Correlation Coefficients for Explanatory Variables used in the Probit Regressions (expressed in percentages)
LOGDIR
VAL
LOGOPT
VAL
SHS
DLSHS
AVGDIR
VAL
DRSHS
AVGOPT
VAL
BLOCK
BLOCK
D
NETINC
ASSTS
CGR
BMCAT
RDQAR
RDSLS
TAX
LOGDIRVAL
1
LOGOPTVAL
0.20
1
SHS
0.54
-0.15
1
DLSHS
0.49
-0.14
0.93
1
DRSHS
0.45
-0.10
0.76
0.48
AVGDIRVAL
0.97
0.16
0.55
0.50
0.44
1
AVGOPTVAL
0.01
0.89
-0.17
-0.16
-0.12
0.01
1
BLOCK
-0.28
-0.20
-0.02
-0.02
-0.00
-0.25
-0.06
1
BLOCKD
-0.22
-0.07
-0.06
-0.12
0.06
-0.19
0.05
0.58
CGR
0.10
0.18
-0.11
-0.10
-0.07
0.10
0.15
-0.03
-0.01
1
NETINCASSTS
0.17
0.04
0.05
0.06
0.02
0.16
-0.10
-0.08
-0.12
-0.12
1
BMCAT
-0.37
-0.18
-0.09
-0.09
-0.06
-0.36
-0.01
0.22
0.16
-0.14
-0.25
1
RDQAR
0.06
0.02
0.21
0.29
0.00
0.06
0.04
-0.03
-0.05
-0.09
-0.19
-0.14
1
RDSLS
0.05
0.03
0.19
0.27
-0.01
0.05
0.04
-0.03
-0.05
-0.07
-0.19
-0.16
0.99
1
NOL
TAXD
0.00
0.06
-0.09
-0.03
-0.17
0.01
0.00
-0.09
-0.04
0.01
0.19
-0.00
-0.02
-0.00
1
-0.08
-0.08
0.02
0.01
0.03
-0.08
0.03
0.09
0.06
0.09
-0.50
0.11
0.20
0.20
-0.57
1
TAXD
0.09
0.08
-0.01
-0.01
-0.02
0.08
-0.04
-0.09
-0.07
-0.09
0.54
-0.09
-0.19
-0.19
0.57
-0.98
QAR
0.01
-0.11
0.07
0.07
0.04
0.04
-0.07
0.04
0.03
-0.25
-0.07
-0.02
0.23
0.19
0.01
0.09
EXPORTS
LNMV
FORASSTS
-0.11
-0.01
-0.04
LNMV
FOR
ASSTS
1
NOL
-0.22
EXPORTS
1
TAX
DIVYIELD
DIV
YIELD
QAR
0.03
-0.22
-0.07
0.06
0.06
0.03
0.03
0.28
-0.15
-0.16
0.00
-0.08
-0.17
0.11
-0.17
-0.16
-0.12
-0.14
0.14
-0.01
0.13
0.16
-0.13
-0.04
0.15
0.19
0.08
0.07
0.34
0.48
-0.20
-0.18
-0.17
0.29
0.20
-0.53
-0.30
0.21
0.25
-0.43
-0.08
-0.06
0.13
-0.18
-0.03
0.15
-0.16
-0.16
-0.09
-0.04
0.11
-0.11
0.00
0.26
-0.00
-0.09
0.05
0.06
0.03
0.05
28
1
0.07
0.10
-0.06
0.18
-0.04
1
-0.08
1
0.28
-0.00
1
-0.12
-0.18
0.12
1
0.20
0.05
0.72
0.29
1
Table 7: Probit Regression Results, Currency Derivatives and Currency Borrowing
Table 7a: Currency Derivatives
The probit regressions estimate the relationship between the probability that a firm uses currency derivatives
and proxies for the incentives to use derivatives in a framework of managerial risk aversion and shareholder
maximisation hypothesis. Models 1-6 use a full sample of 277 firms reporting continual financial information
on DATASTREAM. Models 2-6 show variable substitution. Models 2 & 3 show alternative variables to
proxy for managerial motivation. Models 4 and 5, Tax and model 6 RDSLS. Significant p values at the 10%
level are indicated in bold.
DER=1
Coef.
LOGDIRVAL
LOGOPTVAL
SHS
DLSHS
DRSHS
AVGDIRVAL
AVGOPTVAL
BLOCK
CGR
NETINCASSTS
BMCAT
RDQAR
RDSLS
TAX
NOL
TAXD
QAR
DIVYIELD
LNMV
EXPORTS
CONS
LR chi2 (13)
Probchi2
Pseudo R2
Model 1
Model 2
Model 3
Model 4
Z
P> ¦ z Coef. z
P> ¦ z Coef. Z
P> ¦ z Coef. z
P> ¦ z
-0.0807 -1.1800
-0.0616 -1.6700
0.0013 0.1200
0.0050
0.0004
-0.0312
-0.0801
-0.0327
0.8300
0.1300
-3.4500
-2.1800
-2.0800
-0.7099 -2.3600
0.1190
0.0718
0.2053
0.0137
0.1009
68.59
0.000
0.2221
0.6200
1.9400
2.5600
4.0800
0.0700
0.240 -0.0845 -1.23
0.096 -0.0625 -1.69
0.908
-0.0038 -0.26
0.0135 0.55
0.409
0.898
0.001
0.029
0.038
-0.0037 0.81
0.0004 0.16
-0.0312 -3.44
-0.0805 -2.19
-0.0327 -2.08
0.220
0.091
0.0009
0.08
-0.0900
-0.0498
0.0046
0.0004
-0.0312
-0.0808
-0.0337
-1.29
-1.46
0.76
0.15
-3.46
-2.21
-2.13
-0.093
-0.047
0.933 0.0038
-1.35
-1.4
0.34
0.178
0.160
0.734
0.199
0.144
0.446
0.878
0.001
0.027
0.033
0.0057
-0.0010
-0.0290
-0.0920
-0.0380
0.93
-0.23
-3.52
-2.5
-2.43
0.350
0.815
0.000
0.012
0.015
0.0060
2.82
0.005
0.1198
0.0895
0.1851
0.0134
0.0516
72.07
0.000
.2334
0.62
2.26
2.4
3.98
0.04
0.535
0.024
0.017
0.000
0.97
0.792
0.584
0.416
0.875
0.001
0.029
0.038
0.018 -0.7165 -2.37
0.018 -0.6876 -2.29
0.022
0.536
0.052
0.010
0.000
0.941
0.530
0.058
0.01
0.000
0.912
0.491
0.062
0.026
0.000
0.795
0.1208
0.0700
0.2073
0.0135
0.1497
68.9
0.000
.2232
0.63
1.89
2.58
4.04
0.11
29
0.1317
0.0689
0.1658
0.0140
0.3546
67.84
0.000
0.2197
0.69
1.87
2.23
4.18
0.26
Table 7a: Currency Derivatives (Continued)
Model 5
Coef.
LOGDIRVAL
LOGOPTVAL
SHS
DLSHS
DRSHS
AVGDIRVAL
AVGOPTVAL
BLOCK
CGR
NETINCASSTS
BMCAT
RDQAR
RDSLS
TAX
NOL
TAXD
QAR
DIVYIELD
LNMV
EXPORTS
CONS
LR chi2 (13)
Probchi2
Pseudo R2
z
Model 6
P> ¦ z Coef.
Z
P> ¦ z
-0.0808
-0.0615
0.0013
-1.18
-1.66
0.12
0.239
0.096
0.908
-0.0788
-0.0635
0.0013
-1.15
-1.72
0.12
0.249
0.085
0.904
0.0050
0.0004
-0.0315
-0.0807
-0.0329
0.83
0.13
-3.51
-2.21
-2.10
0.409
0.900
0.000
0.027
0.036
0.0054
0.0006
-0.0297
-0.0756
0.90
0.20
-3.35
-2.05
0.368
0.842
0.001
0.040
-0.0375
-1.55
0.122
-0.7083
-2.37
0.018
0.0672
0.0735
0.2135
0.0137
-0.0422
66.65
0.36
1.99
2.68
4.07
-0.03
0.719
0.046
0.007
0.000
0.975
0.7104
0.1183
0.0716
0.2052
0.0136
-0.5978
68.55
2.35
0.61
1.94
2.56
4.08
-0.45
0.019
0.539
0.053
0.011
0.000
0.654
0.000
0.2220
0.000
0.2159
30
Table 7b: Currency Borrowing
The probit regressions estimate the relationship between the probability that a firm uses currency borrowing
and proxies for the incentives to use borrowing in a framework of managerial risk aversion and shareholder
maximisation hypothesis. Models 1-6 use a full sample of 277 firms reporting continual financial information
on DATASTREAM. Models 2 & 3 show alternative variables to proxy for managerial motivation. Models 4
and 5 Tax and Model 6, RDSLS. Significant p values at the 10% level are indicated in bold.
BORROWING=1
Model 1
Model 2
Model 3
Model 4
Coef. z
P> ¦ z Coef. z
P> ¦ Coef.
Z
P> ¦ z Coef. z
P> ¦ z
LOGDIRVAL -0.0088 -0.1400 0.885 -0.0143 -0.23 0.816
-0.0099 -0.16 0.870
LOGOPTVAL -0.0036 -0.0110 0.912 -0.0035 -0.11 0.914
-0.0033 -0.11 0.915
SHS -0.0052 -0.4900 0.623
-0.0059 -0.57 0.571 -0.0051 -0.48 0.631
DLSHS
-0.0121 -0.89 0.375
DRSHS
0.0108 0.48 0.631
AVGDIRVAL
-0.0033 -0.05 0.958
AVGOPTVAL
-0.0031 -0.1 0.921
BLOCK 0.0033 0.5900 0.555 0.0032 0.57 0.57
0.0033 0.59 0.554 0.0033 0.59 0.553
CGR 0.0041 1.5800
0.011 0.0042 1.61 0.107
0.0040 1.56 0.120 0.0042 1.62 0.105
NETINCASSTS 0.0016 0.2500 0.806 0.0021 0.32 0.748
0.0016 0.25 0.806 0.0005 0.10 0.923
BMCAT -0.0019 -0.0600 0.953 -0.0008 -0.03 0.98
-0.0015 -0.05 0.962 -0.0018 -0.06 0.954
RDQAR -0.0047 -0.5400 0.586 -0.0034 -0.37 0.713
-0.0047 -0.54 0.588 -0.0045 -0.52 0.604
RDSLS
TAX
-0.0002 -0.01 0.989
NOL 0.9783 0.3600 0.720 0.0889 0.33 0.745
0.0998 0.37 0.714
TAXD
QAR -0.1208 -0.6700 0.502 -0.1217 -0.67
0.5
-0.1225 -0.68 0.496 -0.1173 -0.66 0.512
DIVYIELD 0.0692 2.2700 0.023 0.0675 2.21 0.027
0.0696 2.27 0.023 0.0679 2.24 0.025
LNMV 0.1659 2.2900 0.022 0.1679 2.32 0.02
0.1614
2.4 0.016 0.1630 2.36 0.018
EXPORTS 0.1595 5.5700 0.000 0.0157 5.48 0.000
0.0160 5.62 0.000 0.0159 5.54 0.000
CONS -2.9339 -2.4600 0.014 -2.885 -2.42 0.016
-2.9756 -2.46 0.014 -2.8761 -2.39 0.017
LR chi2 (13) 74.94
75.61
74.92
74.81
2
Probchi
0.000
0.000
0.000
0.000
Pseudo R2 0.1953
0.1970
0.1952
0.1949
31
Table 7b: Currency Borrowing (Continued)
BORROWING
Coef.
LOGDIRVAL
LOGOPTVAL
SHS
DLSHS
DRSHS
AVGDIRVAL
AVGOPTVAL
BLOCK
CGR
NETINCASSTS
BMCAT
RDQAR
RDSLS
TAX
NOL
TAXD
QAR
DIVYIELD
LNMV
EXPORTS
FORASSTS
CONS
LR chi2 (13)
Probchi2
Pseudo R2
Model 5
z
P> ¦ z Coef.
Model 6
z
P> ¦ z
-0.0087
-0.0039
-0.0052
0.06
0.03
0.01
-0.14
-0.12
-0.49
-0.0101 -0.17
-0.0031 -0.10
-0.0049 -0.46
0.868
0.923
0.643
0.0033
0.0041
0.0013
-0.0016
-0.0046
0.59
1.59
0.21
-0.05
-0.53
0.553
0.112
0.835
0.959
0.594
0.0033 0.59
0.0040 1.55
0.0013 0.21
-0.0038 -0.12
0.558
0.121
0.837
0.905
-0.0106 -0.69
0.489
0.1019
-0.0700
-0.1198
0.0689
0.1657
0.0159
-0.25
-0.67
2.26
2.29
5.57
0.801
0.506
0.024
0.022
0.000
-2.8576
74.88
-2.4
0.017
0.37
0.708
-0.1228 -0.69
0.0684 2.24
0.1650 2.28
0.0161 5.58
0.492
0.025
0.022
0.000
-2.8887 -2.42
75.20
0.016
0.000
0.000
0.1951
0.1959
32
7. Summary and Conclusions
This paper examines whether firms use currency derivatives or currency borrowing to manage their
foreign currency exposure using a sample of 277 non financial firms taken from the U.K. actuaries all
share index that were reporting continual data on DATASTREAM for the year 1995-2001. Derivatives
use is classified as a binary dependent variable to include firms that report the use of forwards, options
or swaps. Currency borrowing is also reported as a binary for firms that explicitly report using currency
borrowing as a hedging tool.
The footnotes to firms’ group reports and accounts for the years 2000 and 2001 were scrutinized for
information on exchange rate risk management strategy to provide empirical evidence on the relative
importance of factors that induce U.K. firms to use derivative products or foreign currency borrowing.
The results of the univariate and multivariate tests of the difference between currency derivative users
and non- users indicate those firms with compensation packages that favour share options may be less
likely to hedge. There is only a weak relationship between managerial motives to hedge using derivatives
and there is no support to suggest that currency borrowing is influenced by managerial ownership.
Alternative theories supporting managerial entrenchment and signaling as a motivation to hedge carries
no support.
There is however evidence to support the financial distress hypothesis, underinvestment hypothesis, and
tax hypothesis. Firms with low profitability, high growth opportunities, and higher tax liabilities are more
likely to use currency derivatives. There is also evidence to suggest that U.K. firms with tax loss carry
forwards may not hedge if it is expected that a return to profitability was not expected in the near future.
There is little support however to suggest that managerial compensation or shareholder maximisation
has anything to do with the decision to raise foreign currency debt. Consistent with prior studies, the use
of derivative products increases with the size of the organisation and the degree of international
operations. This economy of scale argument is also strongly supported in the use of currency borrowing;
larger firms and those with international operations are more likely to use currency borrowing
There are significant differences between the determinants deemed important for U.K. companies and
those of their U.S. counterparts. This is noticeable for the financial distress hypothesis and the
underinvestment hypothesis using the BMCAT rank, whereas most U.S. studies including Geczy et al
33
(1997) failed to find any significant relationship. There is greater consistency in these results and those
reported by Berkman and Bradbury (1996) and Berkman et al. (1997) - the only major non U.S. studies.
Although the reasons for these differences between U.S. and non-U.S. studies are subject to research,
cross sectional differences in currency exposure and capital structure differences in the use of currency
debt cannot be ruled out.
This study finds supports the findings of Keloharju and Niskanen (2001). Firms that are extensively
involved in international operations are most likely to raise currency debt. Consistent with Allayannis
and Ofek (2001) this study reports large firms and firms with international operations are likely to use
foreign debt to hedge exposures. However the variables deemed important determinants of hedging
using derivatives appear to have little to do with the decision to raise foreign currency debt.
A firm’s exposure through foreign sales and size is a very important factor that prompts the decision to
hedge. As expected, firms can equally use foreign currency borrowing to manage their exposures and the
use of foreign currency borrowing is very important for large firms engaged in significant foreign
operations with foreign assets.
In considering the choice of hedging instrument in relation to the exposure faced, the multivariate
results suggest that derivative products are the main hedging instrument to manage firm specific
determinants of hedging. There is evidence to suggest that derivative hedging is primarily used to
hedge short-term exposures and borrowing used to hedge longer-term exposures, in particular there
is support for the use of currency borrowing to manage foreign currency asset exposure.
Unfortunately a large number of firms within the study used multiple hedging techniques and this
may have created noise in isolating hedging determinants. Further analysis on a larger sample may be
able to isolate more effectively instrument choice and exposure type.
34
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