Corporate Hedging Strategy and Firm Value

International Research Journal of Finance and Economics

ISSN 1450-2887 Issue 44 (2010)

© EuroJournals Publishing, Inc. 2010

http://www.eurojournals.com/finance.htm

Corporate Hedging Strategy and Firm Value

Slim Mseddi

Institute of the Higher Business Studies (IHEC), University of Sfax

Route Sidi Mansour, Km 10, Sfax -Tunisia

E-mail: [email protected]

Fathi Abid

Faculty of Business and Economics (FSEG), University of Sfax

Route de l’aéroport, Km 4.5, Sfax-Tunisia

E-mail: [email protected]

Abstract

The purpose of this paper is to show the importance of hedging activities in context

of firm theory and information asymmetry. It tries, initially, to measure risk exposures of

interest rate, exchange rates and commodities on a sample of 403 non-financial US firms in

1999. The confrontation of these measures to information published in annual reports will

enable to bring a judgement on the exactitude of exposures. We find that the most common

combination of exposed hedged is of interest rate and currency exchange rates. Second, and

contrary to previous studies, our paper provides a distinctive methodological contribution

according to the determination of hedging variables. More precisely, the recourse to

performance models tested on two sub-samples: those which hedge risks and those which

not hedge, will enable to determine the distinctive and relevant variables for carrying out a

hedging strategy. The results show that the probability of hedging is positively related to

tax loss carry forwards. We find that firms with important research and development

activities are most likely to engage in hedging programs. The results also indicate that

larger firms are more likely to use derivatives to hedge risk exposures.

Keywords: Coordinated risk management, hedging and its determinants and firm value.

1. Introduction Risk management and hedging are two interesting activities for financial firms as well as non-financial

firms. Companies are exposed to a wide variety of such exchange rate risks, interest risks, commodity

risks, and supply-demand coordinated risks, that affect the firm value. Activity risks are mainly related

on investments and investment opportunities (in fact, with its reel assets) whereas financial risks are

strongly dependent on the financing mode of these investments (such as a high level of debts increase

financial risk of shareholders). Risk management is the process which makes possible to influence

these exposures to risks on firm value (i.e. hedging of an exposure reduce dependence of firm value to

this exposure, whereas speculation means the increase in dependence to this exposure).

The main objective of corporate risk management programs, such actions taken by the

management to control risk exposures through hedging, is to increase firm value and so shareholder

wealth. According to Modigliani and Miller (1958), under the same assumptions (perfect and complete

markets) that guarantee independence between the value of the firm and its capital structure, firm value

International Research Journal of Finance and Economics - Issue 44 (2010) 106

is also independent of corporate risk management programs. Hedging cannot reduce cost of capital,

since shareholders have access to same capital markets, to same information and risk management

tools as firms. Shareholder can manage all risks by themselves and so there is no value creation

through hedging. However, financial economics offers several motivations to explain why corporate

hedging can be rational or value enhancing, each of which relies on some form of market imperfection.

Smith and Stulz (1985) show that hedge can reduce the likelihood of default by increasing the income

it gets in the downside. Smith and Stulz state that hedging leads to lower expected bankruptcy cots and

higher firm value.

Stulz (1984), Smith and Stulz (1985), Francis and Stephan (1990), Froot, Scharfstein and Stein

(1993), Nance, Smith and Smithson (1993), Goldberg, Godwin, Kim and Tritschler (1993), DeMarzo

and Duffie (1995), Tufano (1996) and Geczy, Minton and Schrand (1997) provide useful information

on numerous valid reasons why firms should use a risk-management programs to maximize

shareholder wealth.

Our study has two primary advantages over previous studies. First, using the studies of French,

Ruback and Schwert (1983), Flannery and Jamest (1984), Scott and Peterson (1986), Sweeney and

Warga (1986) and Smith and Smithson (1998), we estimate a firm sensitivity to interest rates,

exchange rate, and the price of oil. Our main contribution consists to the confrontation of the outputs

(degrees of significant of coefficient sensitivities) of time serial regressions to the information

published in the annual reports according to the risk management activity during 1999. Second,

contrary to the same methodology used in studies conducted by Nance, Smith and Smithson (1993),

Tufano (1996), Mian (1996) Berkman and Bradbury (1996), Fok, Carrol and Chiou (1997) Géczy,

Minton and Schrand (1997), Guay (1999), Haushalter (2000), Graham and Rogers (2002) and Guay

and Kothari (2003), our paper provides a distinctive methodological contribution to the level of the

determination of the variables of hedging. More precisely, the recourse to performance models tested

on two sub-samples: those which hedge the risks and those which not hedge, will enable to determine

the distinctive and relevant variables for carrying out a well strategy of hedging.

The remaining of the paper is organized as follows. We review the theoretical motivations for

corporate hedging with derivatives in section 2. We describe the sample procedure, data characteristics

and sources in section 3, while section 4 presents the empirical results on the determinants of return of

the firm and corporate hedging strategy. We conclude with Section 5.

2. Theoretical Motivations for Corporate Hedging Strategy with Derivatives and

Hypothesis The classic paper of Modigliani and Miller (1958) suggests that, in the absence of market

imperfections, hedging should not add to firm value. If capital markets are perfect, shareholders have

access to the basic information about a firm’s risk exposures, and the necessary tools, to create their

desired diversified portfolio; therefore, there is no reason for a firm to hedge. When the financial

market is imperfect, corporate hedging can directly affect the cash flow of the firm. Exposures to

volatile interest rate, exchange rate and commodity prices are costly for corporations. Smith and Stulz

(1985), Bessembinder (1991), Nance, Smith and Smithson (1993) and Guay and Kothari (2003),

among others, show why market imperfections lead to an increase in firm value through risk

management activities.

Smith and Stulz (1985) argue that volatility is costly for firms with convex tax functions.

Hedging pre-tax income can increase firm value. This will only happen when the firm faces a

progressive effective marginal tax rate. More convex the effective tax schedule it, the greater is the

reduction in expected taxes. Stulz (1996) and Leland (1998) demonstrate that a decrease in cash flow

volatility through hedging activities can increase debt capacity, raise funds at a lower cost and generate

greater tax benefits. Using a sample of US firms in the period 1994-1995, Graham and Rogers (2002)

provide significant insights that tax convexity does not seem to be a factor in the hedging decision but

107 International Research Journal of Finance and Economics - Issue 44 (2010)

do find that firm hedge to increase debt capacity; the resultant tax benefits add about 1.1% to firm

value. Nance, Smith, and Smithson (1993) use three variables to measure a firm’s effective tax

function: investment tax credits, net operating loss carry forwards, and a binary variable that indicates

whether income is in progressive region.

Another motivation for hedging is related to situations in which there are expected costs of

financial distress. Firms engage in hedging activities to avoid the costs of financial distress. Financial

distress may affect the firm’s ability to retain its relationships with stockholders. Mayers and Smith

(1990), Smith and Stulz (1985), Froot, Scharfstein, and Stein (1993), and Nance, Smith, and Smithson

(1993) show that hedging via derivatives can increase firm value by reducing the expected costs of

financial distress. As a consequence, we expect that firms with low liquidity and high leverage should

have more incentives to hedge their risky positions.

Conflicts of interest between shareholders and bondholders give rise to underinvestment

problems. Myers (1977) and Myers and Majluf (1984) describe circumstances of information

asymmetries between existing shareholders and new investors in which a firm might reject positive net

present value projects. Bessembinder (1991) and Froot, Scharfstein, and Stein (1993) propose hedging

activities as a solution to avoid the underinvestment problem. Froot, Scharfstein and Stein (1993) show

that the greater a firm’s growth potential, the greater are the expected costs associated with variation in

cash flows. The ratio of book value of assets to market value of assets, research and development

expenditure and fixed assets deflated by total assets are used to proxy for the level of growth options.

Even in the absence of stockholder and bondholder conflicts, managers have incentives to use

derivatives for purposes based on managerial utility maximisation. Stulz (1984), and Smith and Stulz

(1985) show that, since the value of employee stock options is increasing in stock price volatility,

options provide incentives for managers to engage in activities that increase risk. Tufano (1996) argues

that managers whose human capital and wealth are poorly diversified engage strongly in risk

management and may hedge to protect their reputation (DeMarso and Duffie, 1995). Corporate risk

management will change the distribution of future cash flow or firm value and, thus, management’s

expected utility.

Theoretical literature provides substitutes for hedging. Nance, Smith, and Smithson (1993)

show that hedging substitutes can reduce the need for hedging programs. Dividend restrictions and

holding liquid assets might allow a firm to retain sufficient liquidity and, thus, may reduce incentives

to hedge. Firms can avoid bankruptcy and maintain tax benefits by issuing convertible debt. Thus,

convertible debt reduces the need to hedge.

Table 1 presents Summary of variables in previous empirical papers

Table 1: Hypothesis relations between Hedgers and Non-Hedgers for variables used as incentives to hedge

Variables Prediction NSS T M BB FCC GMS G H A0

Cost of

financia

l

distress

EBIT/ Interest Expense -

Long-Term Debt/ Market

Value of Firm

+

Total Debt/ Ln total Assets +

Total Debt/ Book Value of

Equity

+

Expense Charges +

Ln Sales -

Market value of Equity -

Total Assets +/-

Expecte

d Taxes

Simulated marginal tax rate -

Average Tax rate +

Tax Incentives dummy (1=

income in progressive

region (>34%), 0=

otherwise)

+


Loss Carry Forwards/ total

Assets

+

Dummy (1= Loss Carry

Forwards, 0= otherwise)

+

Investment Tax Credit/ total

Assets

+

Dummy (1= Investment Tax

Credit, 0= otherwise)

+

Investm

ent and

Financi

al

Decisio

ns

Market Value/Book Value

of the Firm

+

Dummy (1= listed firm, 0=

otherwise)

-

Dummy (1= if Debt

ratio.>average and Working

Capital Ratio <average, 0=

otherwise)

+

Operational Expenses/

Market Value of firm

+

Acquisition Activities/

Market Value of Firm

+

Ratio of Distribution of

Dividend

-

Share Return -

Working Capital ratio -

Market Value of firm +/-

Growth

opportu

nities

Market-to-Book Value +

R&D/ book value of Firm +

R&D/ Sales +

Price-Earning ratio -

Fixed Assets/ Market Value

of Firm

+

Dividen

d

distribu

tion

Dividend yield +

Ratio of distribution +

Liquidit

y

Working Capital ratio -

Current Ratio -

Current Assets/ Market

Value of Firm

-

Substitu

tes for

Hedgin

g

Convertible Debt/ Total

Assets

+/-

Preferred Stock / Total

Assets

+/-

(Convertible Debt +

Preferred Stock)/ Total

Assets

+/-

Risk Coefficient beta +

NSS: Empirical evidence by Nance, Smith and Smithson (1993) for 169 US corporations (Study year 1986).

T: Empirical evidence by Tufano (1996) for 48 US gold mining industry corporations (Study period 1991-1993).

M: Empirical evidence by Mian (1996) for 3 022 US corporations (Study year 1992)

BB: Empirical evidence by Berkman and Bradbury (1996) for 116 New Zealand corporations (Study year 1994).

FCC: Empirical evidence by Fok, Carrol and Chiou (1997) for 369 US corporations (Study period 1990-1992)

GMS: Empirical evidence by Géczy, Minton and Schrand (1997) for 372 US corporations (Study year 1996)

Guay: Empirical evidence by Guay (1999) for 353 US corporations (Study period 1990-1994).

H: Empirical evidence by Haushalter (2000) for 100 US gas and oil producers (Study period 1992-1994).

AO: Empirical evidence by Allayannis and Ofeck (2001) for 724 US corporations (Study period 1992-1993).


3. Data Description The analysis is conducted on a sample of 403 non-financial US corporations (SIC Code 2000 through

3999) for the period from 1995 to 1999. Financial statement data is from Zack Investment Research

Inc., and Edgar Scan and stock return from Market Guide Inc. To be included in the analysis sample,

firms are required to have a proxy statement for all years between 1995 and 1999. The initial sample

contains 542 firms; only 403 firms satisfy our sampling criteria every year. In our study firms are

classified hedgers and non-hedgers using disclosures in annual reports for the year 1999. Information

about price of oil, treasury bills and currency exchange rates such as Pound, Yen, Euro, Canadian

Dollar are collected for the year 1999. Table 2 summarizes the number of firms by industry.

Table 2: Summary of firms and industries in the sample

Industry name Two-digit SIC

code

Number of

sample firms

Percent of

sample firms

Food and kindred products 20 24 5.96%

Textile mill products 22 4 0.99%

Apparel and other finished products 23 5 1.24%

Lumber and wood products, expect furniture 24 10 2.48%

Furniture and fixtures 25 11 2.73%

Paper and allied products 26 13 3.23%

Printing, publishing and allied 27 24 5.96%

Chemicals and allied products 28 72 17.87%

Petroleum refining and related industries 29 12 2.98%

Rubber and miscellaneous plastic products 30 15 3.72%

Leather 31 2 0.50%

Stone, clay, glass, and concrete products 32 7 1.74%

Primary metal industries 33 15 3.72%

Fabricated metal, expected machinery, transportation equipment 34 8 1.99%

Machinery, expect electrical 35 69 17.12%

Electrical, electrical machinery, equipment, supplies 36 60 14.89%

Transportation equipment 37 20 4.96%

Measuring instruments; photographic goods; watches 38 28 6.95%

Miscellaneous manufacturing industries 39 4 0.99%

Total 403 100%

4. Empirical Results Initially, we try to highlight the idea of coordinated risk management. We compare exposures to risks

of interest rate, currency exchange rates, and oil prices (as measured by estimated coefficients in

regressions of return of firm on several risks), with information on hedging activities published in

annual reports in 1999.

Second, we have a methodological contribution according to variable’s selection of hedging.

This approach differs from previous papers since it makes possible to take only distinctive variables

that increase firm value. Such variables are obtained from regressions on performances models.

4.1. Measures of Strategic Exposures

Annual reports published in 1999 show that firms are exposed at various risks such as interest rate, raw

material prices, and some currencies. We retained rate of return of US treasury bills for three months

period as measure of interest risk, price of barrel of oil (since it constitutes the most used raw material)

as measure of commodity risk and rates of return of exchange rates. Annual reports provide list of


countries or currencies for US corporations that have import-export business with the world. Table 3,

presents the main destination of goods with US corporations (import or export):

Table 3: Classification of number of US firms that import or export by destination

Destination Numbers of corporations

Euro area 2511

England 219

Canada 210

Japan 137

Mexico 108

Australia 104

China 83

Taiwan 52

India 41

We have limited the present study at the companies which import from or export at the euro

area, and Japan, Canada and England, since they constitute the principal destinations of import-export

for US Corporations. The figures below show the evolution of the main currencies, treasury bills, and

oil price in 1999.

Figure 1: Oil price change in 1999

Figure 2: EUR/USD change in 1999

0

5

10

15

20

25

30

04

/01

/19

99

04

/02

/19

99

04

/03

/19

99

04

/04

/19

99

04

/05

/19

99

04

/06

/19

99

04

/07

/19

99

04

/08

/19

99

04

/09

/19

99

04

/10

/19

99

04

/11

/19

99

04

/12

/19

99

Time

Pri

ce i

n $

CRUDE OIL

0,9

0,95

1

1,05

1,1

1,15

1,2

Time

EU

RO

/US

D

EURO/ USD

Figure 3: CAD/USD change in 1999

Figure 4: YEN/USD change in 1999

1,4

1,42

1,44

1,46

1,48

1,5

1,52

1,54

Time

CAD/ USD

0

2040

60

80

100120

140

04

/01

/99

04

/02

/99

04

/03

/99

04

/04

/99

04

/05

/99

04

/06

/99

04

/07

/99

04

/08

/99

04

/09

/99

04

/10

/99

04

/11

/99

04

/12

/99

Time

YE

N/U

SD

YEN/USD

1 mostly France: 144, Germany: 141, Italy: 80, Spain: 67 and Belgium: 47


Figure 5: USD/Pound change in 1999

Figure 6: Treasury bills rate change in 1999

1,481,5

1,521,541,561,581,6

1,621,641,661,681,7

04

/01

/99

04

/02

/99

04

/03

/99

04

/04

/99

04

/05

/99

04

/06

/99

04

/07

/99

04

/08

/99

04

/09

/99

04

/10

/99

04

/11

/99

04

/12

/99

Time

US

D/P

ou

nd

USD/ Pound

0

1

2

3

4

5

6

Time

tre

as

ury

bil

ls r

ate

BT

Table 4: Descriptive statistics of risk exposures

Parameters (annual) T.B. Oil EUR/USD CAD/USD YEN/USD USD/POUND

low price 4.18% 11.38 1.0016 1.444 101.53 1.5515

high price 5.42% 28.03 1.1812 1.5302 124.45 1.6765

Mean 4.642% 19.37 1.0653 1.4857 113.6883 1.6174

Standard deviation 0.28% 4.55 0.0396 0.0201 7.0008 0.0240

Figures (from 6 to 11) and table 4 show significant volatilities of treasury bills, crude oil prices

and principal currencies, which stimulate corporations to hedge risks.

We have preceded to 403 regressions on time series daily data for year 1999. Indeed, to

estimate a firm’s sensitivity to interest rates, exchanges rates, and the price of oil, we would estimate

the following equation:

t

tOil

oil

tCAD

CAD

tYEN

YEN

tEur

Eur

ttTB

TB

tP

Pb

P

Pb

P

Pb

P

Pb

P

Pb

P

PbaR ε+

∆+

∆+

∆+

∆+

∆+

∆+= 6543

£

£

21

where

tR

: rate of return of firm i.

a : intercept

TBTB PP∆

: percentage change in three month treasury bills;

££ PP∆

: percentage change in dollar prices of pounds, euro, yen, and Canadian dollar;

Oiloil PP∆

: percentage change in crude oil price.

Coefficients b1, b2, b3, b4, b5, b6 provide measures of sensitivity of firm value to percentage

change in three month treasury bills, exchange rates, and crude oil, respectively.

Results show that, for significant level lower or equal to 10%, rates of return of US

corporations are sensitive to changes of:

• treasury bills for 59 cases (39 of whom are negatively correlated);

• crude oil price for 63 cases (10 of whom negatively correlated);

• euro price for 72 cases (67 of whom negatively correlated);

• Canadian dollar price for 88 cases (79 of whom negatively correlated);

• Japanese yen price for 43 cases (14 of whom negatively correlated);

• and pounds price for 57 cases (51 of whom negatively correlated).


It should be noted that risk exposures can take different signs depending on positions of firm:

net exporter or net importing (Muller and Verschoor, 2006).

Various risk exposures of US firms can be summarized in the following table:

Table 5: Risk Exposures of 403 non-financial US firms (according to the above regressions)

Exposures Number Percentage

Interest rate only 29 7.20%

Exchange rate only 131 32.51%

Crude oil only 24 5.96%

Interest rate and exchange rate 24 5.96%

Interest rate and crude oil 2 0.50%

Exchange rate and crude oil 33 8.19%

Interest rate, exchange rate, and crude oil 4 0.99%

Any risk 156 38.71%

Total 403 100%

Table 5 shows that among 403 firms, 247 firms (61.29%) are exposed at one or more risks.

Currency rate exchange constitutes most significant source of risk, in fact, 192 companies are exposed

to the exchange risk.

4.2. Measure of Hedging Activities

An endogenous variable has been created in the form of dummy measure and was coded “1” for those

firm that indicate they engage in hedging activities and “0” for those firms that disclosure they do not

hedge. Firms with no disclosure on hedging are not considered in our sample. Nance, Smith and

Smithson (1993), Mian (1996), Geczy, Minton and Schrand (1997), Cummins, Phillips and Smith

(2001) and Allayannis and Weston (2001) have defined the same measure of hedging activities.

Table 6 shows that 53.35% of sample firm use hedging programs (according to information

published in annual reports). 33.5% do not use any derivatives to hedge financial price exposures.

Firms that provide no disclosure on hedging represent 13.5%.

Table 6: Hedging programs disclosures by non-financial US firms

Number Percentage

Hedgers 215 53.35%

Not hedgers 135 33.5%

No disclosure on hedging 53 13.15%

Total 403 100%

Table 7 reports frequency of hedging by industry. It shows that firms in Sic code “34”

(Fabricated metal, expected machinery, transportation equipment) hedge more financial risk exposures

than others. Lumber and wood products, expect furniture and Printing, publishing and allied industries

show less incentives to hedge activities. The same result is confirmed by the studies conducted by

Geczy, MintonandSchrand (1997) and Dolde (1993).


Table 7: Frequency of hedging by industry

SIC Industry name All

firms

Disclosure

information hedgers Interest rate Exchange rate Crude oil

N N % N % N % N % N %

20 Food and kindred

products

24 19 79.2 16 84.2 15 78.9 13 68.4 12 63.2

22-23 Textile mill products,

and Apparel and other

finished products

9 7 77.8 3 42.9 3 42.9 2 28.6 2 28.6

24 Lumber and wood

products, expect

furniture

10 9 90.0 1 11.1 1 11.1 1 11.1

25 Furniture and fixtures 11 10 90.9 6 60.0 5 50.0 3 30.0 1 10.0

26 Paper and allied

products

13 11 84.6 6 54.5 6 54.5 3 27.3 2 18.2

27 Printing, publishing

and allied

24 20 83.3 4 20.0 4 20.0 2 10.0 0.0

28 Chemicals and allied

products

72 55 76.4 33 60.0 26 47.3 24 43.6 9 16.4

29 Petroleum refining and

related industries

12 7 58.3 6 85.7 3 42.9 6 85.7 6 85.7

30-31 Rubber and

miscellaneous plastic

products, and Leather

17 15 88.2 13 86.7 9 60.0 8 53.3 1 6.7

32 Stone, clay, glass, and

concrete products

7 5 71.4 2 40.0 1 20.0 2 40.0

33 Primary metal

industries

15 15 100 9 60.0 5 33.3 5 33.3 8 53.3

34 Fabricated metal,

expected machinery,

transportation

equipment

8 6 75.0 6 100 5 83.3 4 66.7 3 50.0

35 Machinery, expect

electrical

69 66 95.7 43 65.2 23 34.8 33 50.0 7 10.6

36 Electrical, electrical

machinery, equipment,

supplies

60 56 93.3 29 51.8 15 26.8 21 37.5 8 14.3

37 Transportation

equipment

20 18 90.0 12 66.7 10 55.6 6 33.3 2 11.1

38-39 Measuring

instruments;

photographic goods;

watches and

Miscellaneous

manufacturing

industries

32 31 96.9 26 83.9 10 32.3 12 38.7 2 6.5

Total of the sample 403 350 86.8 215 61.4 141 40.3 145 41.4 63 18.0

The next table provides combinations of exposures hedged by non-financial US firms.

Table 8: Combination of exposures hedged by hedging firms

Exposures Number Percentage

Interest rate only 18 8.37%

Exchange rate only 57 26.51%

Crude oil only 5 2.33%

Interest rate and exchange rate 77 35.81%

Interest rate and crude oil 3 1.40%

Exchange rate and crude oil 12 5.58%

Interest rate, exchange rate, and crude oil 43 20 %

Total 215 100%


Table 8 shows that the most hedged exposures is the exchange rate and the most combination of

exposed hedged is currency exchange and interest rate hedging. Only five firms among the sample that

hedge the position in crude oil. Our results seem to be confirmed by the recent study conducted by

Bartram, Brown and Fehle (2007). They provide evidence from international data (7292 non-financial

firms) by investigating derivative usage across 48 countries. The results show that 59.8% of companies

use derivatives. The companies use currency derivatives for 43.6%, interest rate for 32.5%, and only

10% that hedge raw materials exposures.

4.3. Estimated Coefficients Versus Information Published in Annual Reports: A Comparison

By referring to information on hedging activities published in annual reports, we assume that estimated

coefficients obtained by regressions of rate of returns of firms on several risks are insensitive to

changes of hedged exposures (estimated coefficients are not significant)

The following table compares information published in annual reports of firm’s sample with the

results obtained by regressions.

Table 9: Comparison between data provided by annual reports and the coefficients estimated by the

regressions of rate of returns on interest rate, exchange rate and crude oil.

Interest rate Exchange rates Crude oil

Exposures hedged (information in annual reports) 141 145 63

Number of estimated coefficient non significant 120 68 52

Percentage of similarity 85.11% 46.9% 82.54%

Number of estimated coefficient significant (negative) 16 77

1

Number of estimated coefficient significant (positive) 5 10

Exposures not hedged (according to annual reports) 209 205 287

Number of estimated coefficient non significant 166 112 235

Number of estimated coefficient significant (negative) 28

93

9

Percentage of firms exposed to risks 13.4% 3.83%

Number of estimated coefficient significant (positive) 15 43

With regard to interest rate, annual reports (tables 7 and 9) specify that the number of

corporations which hedge interest rate volatility is 141. Not significant coefficients number is about

120 confirming 85.11% the hypothesis that equity return rate is insensitive to hedged interest rate

exposure. We note that 5 firms profited from hedging (positive relation between return rate and

treasury bills exposure). The number of significant negative coefficient is 16 meaning interest rate

positions is weakly hedged, therefore the degree of assumed risk is rather high. Not hedged exposures

rise to 209 cases, according to published annual reports. 166 coefficients estimated are not significant;

these firms do not assume interest rate risk. We find that 15 firms profited from treasury bills return

changes (positive relation). Finally, 28 companies assume really interest rate risk (coefficients

estimated are significant and negative).

According to exchange rates, the number of firms which showed intention to hedge currency

exchanges exposures rises to 145. Firm’s return rates are insensitive to euro, yen, pounds and Canadian

dollar changes for 68 cases (46.9%) of hedged positions. We note that 77 hedged firms still incur

exchange rate risks (the majority of coefficients estimated are negative), which can be explained by a

partial hedging exposures. Annual reports indicate that 205 firms do not hedge currency exchange

exposures. We find that 112 cases haven’t significant coefficients. The results can be justified by

invoicing in national currency (US dollars) or weak exposures. An interesting number among firm

sample (93 cases) is exposed to currency exchange risk.


The data provided by annual reports show less hedging activities for raw material risks. The

main raw material risk for US corporations is oil barrel price changes. Firms that hedge the type of this

risk are 63. Results show 82.54% (52 among 63) of hedged firms haven’t significant coefficient.

Regression outputs show also 235 cases among 287 non-hedgers haven’t significant estimated

coefficients. Only 9 firms incur oil price risk; 3.83% of sample firms do not hedge oil price exposure.

4.4. Derivatives use and Performance

We have collect information about hedging variables from EDGARSAN and COMPUSTAT databases.

53 sample firms among 403 are eliminated given we haven’t information according to hedging

programs. We calculated the average for each explanatory variable during the period 1995 to 1999

(except variables relating to search and development and tax function: tax rate and tax loss carry

forwards). The endogenous dummy variable (hedge or not hedge) relates to the year 1999. The same

methodology is used by Dolde (1995) during the period 1989-1991 for US firm’s sample and Amrit

(2005) during the period 1990-1995 for Britannic firm’s sample.

In order to determine variables of hedging policy, we follow a new methodology different from

previous studies (Stulz, 1996; Dolde, 1995; Berkman and Bradbury, 1996; Sinkey and Carter, 1994;

Dolde, 1996; Geczy, Minton and Schrand, 1997; Gay and Nam, 1999; Howton and Perfect, 1999;

Haushalter, 2000; Graham and Rogers, 1999, 2002, Judge, 2004 and Albuquerque, 2007). It consists in

dividing initial sample into two sub-samples: the first includes hedgers, whereas the second includes

non-hedgers regarding information published in annual reports.

To highlight such approach, we consider in a first stage a performance accounting measurement

like Return on Asset (ROA) as dependent variable that can be explained by hedging variables. Two

regressions are used, the first one for sub-sample of hedgers and the second for sub-sample of non-

hedgers. In a second stage, a comparison between the two regressions results makes it possible to

determine the distinctive variables of performance. We assume that: hedgers have greater incentives

to control hedging variables likely to improve investments profitability.

4.4.1. Performance Models

The following models are estimated:

Model I: performance model for hedgers firms

i

àH

i

oductivitySalesinofitMBetaTAefCapTAConDebt

WKRLnDivTAsFixedAsseticeShareEPSRDBBM

sFixedAssetCAFLossBLnMVETAEquityIEEBITROA

εααααα

αααααα

αααααα

+++++

++++++

++++++==

Pr/argPr/Pr/

/Pr//

///

1615141312

11109876

543211

Model II: performance model for non-hedgers firms

i

àH

i

oductivitySalesinofitMBetaTAefCapTAConDebt

WKRLnDivTAsFixedAsseticeShareEPSRDBBM

sFixedAssetCAFLossBLnMVETAEquityIEEBITROA

εααααα

αααααα

αααααα

+++++

++++++

++++++==

Pr/argPr/Pr/

/Pr//

///

1615141312

11109876

543210


Where:

EBIT/IE : Interest cover

Equity/TA : Market value of equities/ Total Assets

LnMVE : Firm seize : natural logarithm of firm market value

Lossb : Tax function: dummy (1= tax loss carry forwards, 0= otherwise)

CAF/Fixed Assets : Funds from operations / Fixed Assets

M/B : Market-to-Book

RDB : Dummy (1= research and development activities, 0= otherwise)

EPS/Share Price : Share return

Fixed Assets /TA : Fixed Assets/ Total Assets

LnDiv : natural logarithm of distrbuted dividend

WCR : Working Capital Ratio (Short Terme Debt/Current Assets)

ConDebt/TA : Convertible Debt/Total Assets

PreCap/TA : Prefered Capital/Total Assets

Beta : Volatitlity coefficient

Profit Margin/Sales : Profit Margin/ Total Sales

Productivity : Value Added/ employees number

Table 10 presents correlations between exogenous variables. At a level equal or less to 5%, 66

correlations are significant. 33 correlations have a coefficient less than 0.2, and only eight correlations

have an absolute value more than 0.4. There is a problem of multicollinearity but not severe.


Table 10: Pearson Correlation Coefficients

Pearson correlation coefficients for 16 variables used in performance models and logit regressions. Table 10 include correlation

coefficient, observation number and the two-tailed significance level.

Variables EBIT/IE Equity/TA LnMVE Lossb CAF/Fix

As

M/B RDB EPS/Share

Price

Fixed

Assets /TA

LnDiv WCR ConDebt/T

A

PreCap/TA Beta Prof

Mar/Sales

Productivit

y

EBIT/IE 1

N 371

Equity/TA 0.258* 1

N 371 403

LNMVE -0.029 -0.256* 1

N 366 398 398

LOSSB -0.118** -0.079 0.00 1

N 320 349 347 349

CAF/Fixed Assets 0.472* 0.054 0.157* -0.218* 1

N 371 403 398 349 403

M/B -0.054 0.059 0.121** 0.062 -0.131* 1

N 367 399 397 348 399 399

RDB 0.012 0.09 0.087 0.141* -0.025 0.096 1

N 318 347 345 347 347 346 347

EPS/Share Price 0.055 -0.168* 0.118** -0.203* 0.331* -0.125** -0.045 1

N 365 397 397 346 397 396 344 397

Fixed Assets /TA -0.145* -0.482* 0.294* -0.031 0.057 -0.184* -0.242* 0.221* 1

N 371 403 398 349 403 399 347 397 403

LnDiv -0.134* -0.394* 0.721* -0.05 0.081 -0.041 -0.06 0.294* 0.462* 1

N 371 403 398 349 403 399 347 397 403 403

WCR -0.191* -0.629* 0.388* 0.002 0.065 -0.027 -0.219* 0.228* 0.875* 0.564* 1

N 371 403 398 349 403 399 347 397 403 403 403

ConDebt/TA -0.024 -0.133* -0.063 0.101 -0.203* 0.108** 0.09 -0.222* -0.064 -0.183* -0.099** 1

N 371 403 398 349 403 399 347 397 403 403 403 403

PreCap/TA 0.001 -0.113** 0.073 0.054 0.02 -0.008 0.023 -0.051 0.067 0.09 0.074 0.074 1

N 371 403 398 349 403 399 347 397 403 403 403 403 403

BETA 0.114** 0.217* 0.343* 0.059 0.036 0.205* 0.295* -0.140* -0.361* -0.075 -0.321* 0.137* 0.012 1

N 367 399 397 349 399 398 347 396 399 399 399 399 399 399

Profit

Margin/Sales

0.200* -0.092 0.144* -0.122** 0.613* -0.130* -0.057 0.239* 0.236* 0.143* 0.242* -0.308* -0.007 -0.025 1

N 371 403 398 349 403 399 347 397 403 403 403 403 403 399 403

Productivity 0.012 -0.104 0.153* 0.043 0.154* -0.067 -0.113** 0.164* 0.129** 0.176* 0.137** -0.043 0.004 -0.022 0.175* 1

N 313 342 340 342 342 341 340 339 342 342 342 342 342 342 342 342

* Significant at the 1% level

** Significant at the 5% level


Table 11 reports results of performance model regressions. Coefficients estimated by

performance models are in bold. The statistic below coefficients estimated is t-student value.

According to multicolliniearity test, we use “VIF” statistic (or variance inflation factor) and we find

that all variables included in models have a value less than 10 (thus, there is no problem of

multicollinearity).

Table 11: Results of linear regression of performance on hedging variables

Variables Hedgers Non-Hedgers

Intercept -0.042** -0.082**

-2.315 -2.185

EBIT/IE 0.016 0.018

0.433 0.433

Equity/TA 0.132* 0.163*

3.013 3.69

LNMVE -0.003 0.112**

-0.047 2.204

LOSSB -0.019 -0.055***

-0.586 -1.707

CAF/Fixed Assets 0.465* 0.506*

8.261 8.58

M/B 0.412* -0.207*

10.51 -5.267

RDB 0.07** -0.071**

2.153 -2.164

EPS/Share Price 0.313* 0.113***

8.755 1.656

Fixed Assets /TA 0.19* 0.065

3.122 0.658

LnDiv 0.071 -0.043

1.191 -0.833

WCR -0.139** -0.028

-2.076 -0.257

ConDebt/TA -0.075** -0.199*

-2.317 -5.502

PreCap/TA 0.038 -0.027

1.2 -0.83

BETA -0.144* -0.041

-3.141 -0.997

Profit Margin/Sales 0.225* 0.202*

4.487 3.266

Productivity 0.002 -0.011

0.078 -0.182

R Square 83.50% 92.56%

Adjusted R Square 82.00% 91.26%

Fischer 57.108* 71.516*

Observations Number 197 108

* Significant at the 1% level

** Significant at the 5% level

*** Significant at the 10% level.


Table 11 shows that independent variables explain more than 83% performance variation.

Fisher statistic is important and significant at the 1% level.

The performance of hedger firms can be explained by low debt level, recourse at self-financing

to finance fixed assets, positive growth opportunities, research and development activities, important

margin profits, low systematic risk, and less incentive to use hedging substitutes.

The financial performance of non-hedgers seems to be explained by low debt level, negative

impact of loss carry forwards, recourse at self-financing to financed fixed assets, worse use of research

and development activities and growth opportunities, and less incentives to recourse at convertible debt

(hedging substitute)

A comparison of these two regressions enables us to detect hedging determinant variables and

their importance in improvement of corporation’s performance. This comparison makes it possible to

release seven variables:

1. The variable natural logarithm of firm market value (LnMVE) which measures the size is

significant only for non-hedgers;

2. The dummy variable tax loss carry forwards (LossB) which indicate tax function convexity is

significant only for non-hedgers;

3. The variable M/B as measurement of the growth opportunities is significant for hedgers and

non-hedgers but with different sings;

4. The binary variable research and development (RDB) as a second determinant of growth

opportunities also presents different signs;

5. The variable Fixed Assets /Total Assets (Fixed Assets /TA), a measurement of the investment,

is significant only for hedgers;

6. The variable working capital ratio (WCR), measurement of equilibrium and liquidity, is

significant only for hedger’s corporations;

7. The variable Beta which is a measurement of risk is significant only for hedgers.

These seven variables will be introduced into logit regression models to show their influence on

the decisions to hedge or not risks.

It should be noted that financial distress costs variables do not reveal too much importance.

Indeed, the variable interest cover does not explain the performance of hedgers and non-hedgers, since

this variable is not significant in the two regression models. In the same way, the variable relating to

the debt level is significant for the two sub-samples. These variables are excluded in the logit

regression models since they do not represent a distinction between hedgers and non-hedgers. The

same result is obtained in the empirical study of Nance, Smith and Smithson (1993) which shows that

there is not a significant difference for the variable debt level between hedgers and non-hedgers.

4.4.2. Logit Models

Before proceeding to the logit regressions allowing determining hedging variables, we propose

univariate tests which confront theoretical assumptions with sample firms’ real data.

A. Univariate Analysis

The univariate tests verify endogenous variables heterogeneity or homogeneity between hedged firms

and non-hedged firms. Table 12 reports means and variances of our explanatory variables for

derivative users and non-users.


Table 12: Univariate tests of Derivatives Use

Univariate tests concern a sample of 350 US non-financial firms including 215 derivative

users and 135 non-users in 1999. The Fisher and t-student statistics are given for tests of

variance equality and means equality between hedgers and non-hedgers respectively.

Observations’ number is given in parentheses. We use the real values of loss carry forwards

and research and development expenditures (more significant) and not dummy variable

values. H0 and H1 indicate the assumption of equal variances and the assumption of

unequal variances, respectively.

Group Statistic

means

Levene test for equality of

variances

Test-t for equality of means

Variables Prediction Actual Hedgers Non-

Hedgers

Hyp. F Sig. t Sig Mean diff. Stand.

Dev. Dif

95% Conf. mean

lower upper

Tax convexity function

LossCarFor H>NH H>NH

85.558 21.895 H0 19.43 0

3.08 0 63.66 20.64 23.06 104.26

(215) (133) H1 3.7 0 63.66 17.21 29.79 97.54

Growth Opportunities

Fixed

Assets/TA H>NH H>NH 0.535 0.468 H0

7.67 0.01 3.36 0 0.07 0.02 0.03 0.11

(215) (133) H1 3.22 0 0.07 0.02 0.03 0.11

M/B H>NH H<NH

2.793 3.264 H0 2.03 0.16

-1.13 0.26 -0.47 0.42 -1.29 0.35

(215) (132) H1 -0.95 0.34 -0.47 0.5 -1.45 0.51

RD H>NH H>NH

295.102 69.814 H0 6.25 0.01

1.72 0.09 225.29 130.93 -32.25 482.82

(215) (131) H1 2.15 0.03 225.29 104.67 19.13 431.45

Firm seize

LNMVE H? NH H>NH

7.675 6.441 H0 6.82 0.01

7.88 0 1.23 0.16 0.93 1.54

(215) (131) H1 8.25 0 1.23 0.15 0.94 1.53

Hedging substitutes

WCR H>NH H>NH

0.732 0.594 H0 3.88 0.05

5.21 0 0.14 0.03 0.09 0.19

(215) (133) H1 5.11 0 0.14 0.03 0.09 0.19

Risk

BETA H>NH H>NH

0.761 0.756 H0 6.41 0.01

0.13 0.9 0.01 0.04 -0.07 0.08

(215) (133) H1 0.12 0.9 0.01 0.04 -0.08 0.09

Table 12 shows that hedgers are statistically different from non-hedgers with respect of all

variables except market-to-book variable. Consistent with tax function convexity hypotheses,

derivatives users have higher tax loss carry forwards. Both mean and variance tests are significant

supporting the idea that hedgers have more tax loss carry forwards. This evidence is confirmed by

Berkman and Bradbury (1996), Mian (1996) and Amrit (2005).

The univarite results do not generally support the underinvestment hypotheses as suggested by

Myers (1997) and Froot, Scharfstein and Stein (1993). Hedgers have higher fixed assets and more

research and development incentive but have lower market-to-book ratios. Parametric tests are

significant for fixed assets-to- total assets ratios and research and development, and not significant for

market-to-book ratios. Amrit (2005) uses four variables to test underinvestment hypotheses: fixed asset

expenditure, research and development/sales, market-to-book and price-earning-ratio. No one of these

variables explain hedging decision. Geczy, Minton and Schrand (1997) examine the interaction

between financial distress (measured by debt level) and four proxies of growth opportunities

(R&D/sales, fixed asset expenditure/sales, M/B and price-earning-ratio). Only the interaction between

debt level and M/B is significant. Nance, SmithandSmithson (1993) use R&D expenditures and M/B as

proxies of underinvestment, their finding is similar to our results. Mian (1996) finds a significant test

for M/B, but contradictory to the underinvestment theory.

We also examine the seize effect on hedging. Natural logarithm of firm market value is used as

proxy of firm seize. Both tests of mean and variance inequalities are significant. Larger firms have

more incentives to hedge risk exposures. The studies conducted by Nance, Smith and Smithson (1993),

Geczy, Minton and Schrand (1997), Mian (1996) confirm the hypothesis of seize. Dolde (1995) finds

that smaller firms hedge more than larger firms, but the result is not statistically significant.

The evidence regarding substitutes’ hedging, we use working capital ratio as measured by short

term debt/ current assets and we find evidence supporting the theory. Amrit (2005) and Mian (1996)


find a significantly relation between working capital ratio and incentives to hedging. Amrit (2005)

shows that univariate tests are not significant for convertible debt and preferred capital as proxies of

hedging substitutes. The same result is shown by Nance, Smith and Smithson (1993). In this study,

these two variables are not considered as real proxies of hedging because they not explain firm

performances.

We have also examined the relation between systematic risk and incentive to hedge. The results

of parametric tests show that hedgers have more systematic risk compared to the non-hedgers, but the

test is not significant.

B. Multivariate Analysis

In this study we use logit regression to determine the impact of exogenous variables on the firm’s

hedging decision. Nance, Smith and Smithson (1993), Francis and Stephan (1993), Mian (1996),

Colquit and Hoyt (1997), Géczy, Minton and Schrand (1997) and Haushalter (1998) examine the effect

of independent variables on hedging decision by using logit models. The model is following:

z

z

ie

eYob

+==

1)1(Pr

Or,

zie

Yob+

==1

1)0(Pr

With Z a linear combination which arises as follows:

PP XBXBXBBZ ++++= .......22110

Y = 1, firm engage in hedging program, and

Y = 0, firm does not engage in hedging program.

From the )1(Pr =iYob , we can determine the Odds ratio:

)0(Pr1

)1(Pr)1(

=−

===

Yob

YobYOdds

This equation can be transformed as follows:

...)1()2(var)1(var 210 ×××==

iableBiableBBeeeYodds

...1

1hedging ofy Probabilit

)2(var)1(var 210 ×××+=

−−− iableBiableBBeee

The transformation of odds ratio by natural logarithm is called Logit.

{ }

=−

====

)1(Pr1

)1(Pr)1()(

Yob

YobLogYoddsLogYLogit

We estimate the following model:

i

i

i

i

BetaWCRLnMVERDB

BMTAsFixedAssetLossBHEDGELogitYP

YPLogZ

εαααα

αααα

++++

++++==

=−

==

7654

3210 // )1(1

)1(

The following table shows the results of logit regressions.


Table 13: Logit regression estimates: determinants of derivative use

The sample includes 350 non-financial firms containing information about disclosure of

hedging activities in year end 1999. Hedgers that disclosure risk management programs to

hedge currency exchange rates, interest rate and oil exposures in their annual reports are

215 firms. The number of non-hedging firms is 135. The number of observations for the

regressions depends upon the variable included for the model.

Variables Pred. M. 1 M. 2 M. 3 M. 4 M. 5 M. 6 M. 7 M. 8 M. 9 M. 10 M. 11 M. 12

Intercept 0.450 0.590 0.251 -0.531 -0.279 -1.084 -1.831 -4.279 -4.570 -4.276 -5.221 -4.951

Wald 3.092 16.052 2.487 2.669 1.238 10.458 13.396 36.617 37.629 31.939 37.992 34.089

Sig. level 0.079 0.000 0.115 0.102 0.266 0.001 0.000 0.000 0.000 0.000 0.000 0.000

Tax convexity function

lossB + 0.434 0.512 0.469 0.390 0.426

Wald 3.819 4.239 3.482 2.284 2.667

Sig. level 0.051 0.040 0.062 0.131 0.102

Growth opportunities

Fixed As./TA + 2.014 2.673 -4.295 -3.663 -5.084

Wald 10.701 15.856 5.477 4.192 6.969

Sig. level 0.001 0.000 0.019 0.041 0.008

M/B + -0.034 -0.024 -0.121

Wald 1.083 0.601 3.166

Sig. level 0.298 0.438 0.075

RDB + 0.966 1.327 1.220 1.215

Wald 11.863 18.703 12.328 11.991

Sig. level 0.001 0.000 0.000 0.001

Firm seize

LNMVE +/- 0.682 0.725 0.732 0.725 0.743

Wald 44.608 33.100 31.726 29.871 29.083

Sig. level 0.000 0.000 0.000 0.000 0.000

Hedging substitutes

WCR + 2.354 0.516 3.471 3.383 4.221

Wald 23.769 0.666 5.429 5.477 7.298

Sig. level 0.000 0.414 0.020 0.019 0.007

Risk

BETA + 0.039 -0.830 -0.963 -1.225 -1.095

Wald 0.017 3.995 5.042 7.724 5.947

Sig. level 0.897 0.046 0.025 0.005 0.015

Statistics

Chi-square 0.017 1.267 3.836 11.050 12.019 25.973 30.058 60.560 72.999 79.387 90.885 95.449

Sig. level 0.897 0.260 0.050 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000

-2log likelihood 231.45 229.87 229.54 225.94 223.52 218.48 213.53 199.25 193.03 189.84 182.14 178.87

R2 of Cox & Snell 0.000 0.004 0.011 0.031 0.034 0.072 0.083 0.161 0.190 0.205 0.232 0.243

R2 of Nagelkerke 0.000 0.005 0.015 0.042 0.046 0.098 0.114 0.219 0.259 0.279 0.316 0.331

Percentage correct 61.782 62.248 61.782 62.644 64.740 67.241 66.957 69.653 72.543 70.231 74.128 73.178

Non-Hedgers 133 132 133 133 131 133 130 131 131 131 129 128

Hedgers 215 215 215 215 215 215 215 215 215 215 215 215

Sample seize 348 347 348 348 346 348 345 346 346 346 344 343

Table 13 shows the results of twelve logit regressions of determinants of hedging use. We

report wald statistic for significance of coefficients and same others statistics such as Chi-square, log

likelihood, R-square of Cox & Snell, R-square of Nagelkerke, and Percentage of good classification for

the significance of the model.

The first group models from 1 to 6 and 8 show the results of the contribution of each variable in

hedging decision process. Model 7 gives the unique importance of growth opportunities for hedging

strategy. The models from 9 to 11 present a combination between the various variables. Finally, model

12 considers all the variables resulting from the linear regressions of the performance on hedging


determinants. The estimated coefficients of independent variables correspond to natural logarithms of

the Odds.

Chi-square statistics are significant for all models except for the two first. More the Chi-square

value is high, more the model is powerful. According to table 13, Chi-square value tends to improve

from model 1 to 12. The highest statistic (95.5) is observed for the model 12 which include all hedging

variables. Log likelihood, R-square of Cox & Snell, R-square of Nagelkerke, and percentage of good

classification statistics; show that the last regression (model 12) is the best model.

The regression results permit as to identify factors that explain the probability to engage in

hedging programs.

)(1.095-)(4.221)(0.743

)(1.215)/(-0.121)/.(-5.084)(0.426-4.951)1(

BETAWCRLnMVE

RDBBMTAFixedAsLossB

eee

eeeeeYOdds

××

×××××==

)(1.095)(4.221-)(0.743-)(1.215-)/(0.121)/.(5.084)(0.426-4.9511

1

Pr

BETAWCRLnMVEWCRBMTAFixedAsLossB eeeeeeee

of hedgingobability

×××××××+

=

This real example (at random) illustrates the effect of independent variables variations on

hedging probability:

The data relative to non-Hedger firm is follow:

Lossb Fixed Assets/TA M/B RDB LnMVE WCR Beta

0 0.35 1.39 0 3.11 0.46 0.19

Thus, the probability of hedging risks is equal to 5.45% as shown by the following equation:

%45.51

1)1(

19.01.09546.04.221-11.30.743-01.215-39.10.12135.05.08400.426-4.951=

×××××××+==

××××××× eeeeeeeeYP

This probability is conforming to the information published in annual report. We tried to

simulate the rising effect of each variable on the increase or decrease of hedging probability. For

example, if this firm anticipates that will have a tax loss carry forwards (the dummy variable passes

from 0 to 1) keeping other items unchanged, probability will increase at 8.1%.

%1.81

1)1(

19.01.09546.04.221-11.30.743-01.215-39.10.12135.05.08410.426-4.951=

×××××××+==

×××××××eeeeeeee

YP

In order to test the tax convexity function effect on the hedging probability, Mian (1996) used

three independent variables such as tax loss carry forwards, tax credits and the incomes in progressive

area. Positive signs were predicted for these variables. The results show significant estimated

coefficients but only tax credit variable confirms theory. In our study, the variable tax loss carry

forwards is positive and significant. Our finding is conform to previous studies conducted by Berkman

and Bradbury (1996), Fok, Carroll and Chiou (1997), Géczy, Minton and Schrand (1997), and

Allayannis and ofek (2001). Nance, Smith and Smithson (1993) and Tufano (1996) studies does not

confirm the assumption of tax function convexity.

Fixed assets/ total assets, market-to-book and the dummy variable research and development

measure the importance of growth opportunities on hedging decision. According to model 12, the

coefficients associated with these ratios, are negative and significant indicating that sample firms show

less incentives for hedging, which contradicts theory. Whereas the coefficient associated with research

and development activity is significant and positive. Model 7 regresses hedging decision on the

underinvestment variables and shows that the ratio fixed assets/ total assets and R&D are significant

and positive. Amrit (2005) and Géczy, Minton and Schrand (1997) use, respectively, the ratios fixed

asset to sales and fixed assets to total assets as proxies of growth opportunity. They find positive

coefficients but statistically not significant. Nance, Smith and Smithson (1993), Mian (1996), Géczy,


Minton and Schrand (1997), Fok, Carroll and Chiou (1997) and Allayannis and Ofek (2001) use the

ratio M/B as determinant of growth opportunities. Only the study conducted by Géczy, Minton and

Schrand (1997) confirms the assumption of positive relation. In accordance with prediction, the

positive effect of research and development activity are confirmed by Nance, Smith and Smithson

(1993), Géczy, Minton and Schrand (1997), Fok, Carroll and Chiou (1997), Allayannis and Ofek

(2001) and Knopf, Nam, and Thornton (2002), Nam, Ottoo, and Thornton (2003).

Financial literature shows that firm size is an incentive for hedging. The natural logarithm of

firm value is introduced into the last five models (8 to 12). All estimated coefficients are positive and

significant in accordance with theoretical developments. Our results confirm previous studies of

Nance, Smith and Smithson (1993), Francis and Stephan (1993), Mian (1996), Tufano (1996),

Berkman and Bradbury (1996), Géczy, Minton and Schrand (1997), Fok, Carroll and Chiou (1997) and

Guay (1999).

The ratio of working capital (current liabilities/ current assets) used as a hedging substitute, is

included in five models. The results confirm the assumption of positive relation. The same results are

obtained by Tufano (1996), Nance, Smith and Smithson (1993), Berkman and Bradbury (1996) and

Géczy, Minton and Schrand (1997).

Guay and Kothari (2003) show that companies which have higher risk market should prove

more incentives to purchase derivatives. Guay (1999) justifies the use of beta by the fact that the

interest rates can affect as well numerator (cash-flows) as denominator (discount rate) in equation of

stockholders' equity evaluation, hedging of cash-flows changes to the interest rate should not

necessarily reduce the equity volatility. Model 1 regards only the value of beta as an independent

variable. The estimated coefficient well that it is positive, the statistics of this model are not significant.

This variable is introduced into the last four models; the estimated coefficients are negative and

significant. Guay (1999) find the same results but not significant.

The following table shows the comparison between our results and those of previous studies.

Table 14: Comparison between results of the present study and results of previous studies

Variables Pre NSS T M BB FCC GMS G H A0 RPS

Cost of

financial

distress

EBIT/ Interest Expense - -NS - -NS - -NS -

Long-Term Debt/ Market Value of

Firm + -NS +NS + -

Total Debt/ Ln total Assets + +

Total Debt/ Book Value of Equity + +NS + +NS

Expense Charges + NS

Ln Sales - +

Market value of Equity -

Total Assets +/- +NS +

Expected

Taxes

Simulated marginal tax rate - +

Average Tax rate +

Tax Incentives dummy (1= income

in progressive region (>34%), 0=

otherwise)

+ + - +NS

Loss Carry Forwards/ total Assets + -NS - +NS

Dummy (1= Loss Carry Forwards,

0= otherwise) + - + +

Investment Tax Credit/ total Assets + + +NS

Dummy (1= Investment Tax

Credit, 0= otherwise) + +

Investment

and

Financial

Decisions

Market Value/Book Value of the

Firm + - -

Dummy (1= listed firm, 0=

otherwise) - -NS

Dummy (1= if Debt ratio.>average

and Working Capital Ratio

<average, 0= otherwise)

+ -NS

Operational Expenses/ Market

Value of firm + +


Acquisition Activities/ Market

Value of Firm + - + +NS

Ratio of Distribution of Dividend - -NS

Share Return - - -NS

Working Capital ratio - -

Market Value of firm +/- + +NS + + + + + + +

Growth

opportunitie

s

Market-to-Book Value + -NS -NS + -

R&D/ book value of Firm + + + +

R&D/ Sales + + +

Price-Earning ratio - +

Fixed Assets/ Market Value of

Firm + + -

Dividend

distribution Dividend yield + +

Ratio of distribution + +NS

Liquidity

Working Capital ratio (Current

Liabilities/Current Assets) + + +

Current Ratio - -

Current Assets/ Market Value of

Firm - NS

Substitutes

for Hedging

Convertible Debt/ Total Assets +/- -NS

Preferred Stock / Total Assets +/- -NS

(Convertible Debt + Preferred

Stock)/ Total Assets +/- NS

Risk Coefficient beta + +NS -

-NS: negative coefficient but not significant, +NS: positive coefficient but not significant, (+): positive coefficient and

significant and (-): negative coefficient and significant. NSS: Nance, Smith and Smithson (1993). T: Tufano

(1996). M: Mian (1996). BB: Berkman and Bradbury (1996). FCC: Fok, Carrol and Chiou (1997). GMS: Géczy,

Minton and Schrand (1997). G: Guay (1999). H: Haushalter (2000). AO: Allayannis and Ofeck (2001). RPS:

Results of present study.

5. Conclusion In this paper, we have given a theoretical and empirical overview of corporate hedging strategies. Data

on hedging activities of 403 non-financial US firms are obtained from annual reports published in

1999. Initially, we have determined firm exposures to risks in 1999 (currency exchange rates, interest

rate, and oil prices). Using time series models, we have regress equity return rate to risk exposure

variables. The results show that the companies are more exposed to currency exchange risk compared

to oil risk. These results are confronted with the hedging information collected from annual report.

This confrontation confirms the regression results at a level of 85.11%.

We focus on the hedging determination variables. The literature of risk management suggests

several variables as relevant indicators for hedging. Nevertheless, the choice of the variables remains

problematic. Our empirical methodology consists in regressing, first stage, return on assets (ROA) as

performance measure on hedging variables for two sub-samples (hedgers and non-hedgers). Seven

variables explain performance differences between hedgers and non-hedgers. These variables are used

in logit regressions to determine hedging strategies of non-financial US firms.

Consistent with previous studies, the results show that the probability of hedging is positively

related to tax loss carry forwards. We find that firms with important research and development

activities but less fixed assets and low current assets are most likely to engage in hedging programs.

The results indicate that larger firms are more likely to use derivatives to hedge risk exposures.


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Corporate Hedging Strategy and Firm Value

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