On the Determinants of Derivatives Usage - BI the Determinants of Derivatives Usage ......

BI Norwegian Business School - Thesis

On the Determinants of Derivatives Usage - Survey on Medium-Sized, Private, Non-F inancial F irms in Norway -

Supervisor: Paul Ehling

Exam code: G R A 19003

Written by:

Jannicke Mansika Olsen Louise E. E. Samuelsson

Hand-in date: 03.09.2012

Campus: BI Oslo

Program: Master of Science in Business and Economics – Major in Finance

This thesis is part of the MSc program at BI Norwegian Business School. The school takes no

responsibility for the methods used, results found and conclusions drawn.

Thesis in GRA 19003 03.09.2012

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A BST R A C T .................................................................................................................................... I I!

I . IN T R O DU C T I O N ................................................................................................................. 1!

I I . PRI O R L I T E R A T UR E A ND H YPO T H ESIS D E V E L O PM E N T .................................... 3!

A .! Prior empirical findings ................................................................................................ 3!

B .! Focus area ..................................................................................................................... 5!

B .1. ! C E O compensation ................................................................................................... 5!

B .2.! F inancial distress ..................................................................................................... 6!

B .3.! Foreign exchange exposure ..................................................................................... 7!

B .4.! F irm size ................................................................................................................... 7!

B .5.! Industry ..................................................................................................................... 8!

I I I . D A T A ................................................................................................................................... 11!

A .! Data collection ............................................................................................................ 11!

B .! Dependent variable ..................................................................................................... 12!

C .! Independent variables ................................................................................................. 12!

D .! Descriptive statistics .................................................................................................... 14!

E .! Correlations ................................................................................................................. 14!

I V . G E N E R A L R ESU L TS ........................................................................................................ 16!

A .! Regression results ....................................................................................................... 16!

V . M A IN A N A L YSIS ............................................................................................................... 19!

A .! U tility theory and managerial compensation ............................................................. 19!

B .! Convexity versus concavity ......................................................................................... 20!

C .! Main results ................................................................................................................. 22!

C .1.! Robustness test: Subsample analysis ..................................................................... 24!

V I . C O N C L USI O N .................................................................................................................... 26!

BIB L I O G R APH Y ......................................................................................................................... 27!

APPE NDI C ES ............................................................................................................................... 30!

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!"#$%&'$

This article investigates the determinants of derivatives usage as part of risk

management in Norwegian private firms of medium size. The data is based on a

survey conducted in 2011 and financial data for fiscal year 2010. !"#$%&'&("#)&*+,-#

net income as a proxy variable to define whether CEO cash bonus is a convex or

concave function of firm value. When incorporating this variable with other firm

characteristics, we find that firms awarding their CEOs cash bonuses that have a

concave function are significantly more pronounced derivatives users than both

(1) firms awarding their CEOs cash bonuses that have a convex function and (2)

firms with no CEO cash bonus policy. These findings are further supported by a

subsample analysis and we conclude %./%#/#)&*+-, derivatives usage is positively

affected when the firm has a concave cash bonus structure as part of their CEO

compensation. Further, we find that firms with revenues in foreign currencies are

more likely to use derivatives than firms with revenues in local currency, and

larger firms are more pronounced derivatives users than smaller firms.

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Firms are facing various types of risks such as interest rate volatility, equity risk,

commodity risk and foreign exchange exposure. To be able to monitor and

mitigate these risks, it is important for firms to identify them. In this aspect, risk

management is an important activity of any firm and is an interesting research

field. Motivations as to why firms have incentives to hedge against risk include

liquidity issues, economies of scale arguments, financial distress and ownership

structures. Extensive studies have been conducted on the use of derivatives in risk

management, the majority of which focus on large, public firms. The main

purpose is often to identify what motivates the use of such financial instruments.

Several surveys have also been conducted on the use of derivatives, contributing

to different databases for academic research in several nations. For Norwegian

firms, research on the use of derivatives is limited.

This study therefore examines risk management activities of medium-sized,

private, non-financial firms in Norway in 2010. Specifically, we investigate

whether the use of derivatives in risk management is related to managerial

incentives, financial distress, foreign exchange exposure, firm size and industry.

Our focus is on medium-sized firms and due to the limited amount of information

regarding hedging activities in such companies, we have collected data from 173

firms through questionnaires. We have also retrieved financial information from

the Proff Forvalt database. To test what determines the use of derivatives we have

applied a logistic regression.

The results we document support several of the motivations for risk management.

We find a significant positive relation between the use of derivatives and

performance based compensation in the form of cash bonuses. The results imply

that firms where the CEO is compensated through cash bonuses are more likely to

use derivatives than firms without this type of compensation.!Due to this finding

we investigate cash bonus compensation further. Bonus plans are often

characterized by having a convex and concave region1, and these two regions

induce different managerial incentives for hedging. Smith and Stulz (1985) argue

1 See Kim et al. (2008).

Thesis in GRA 19003 03.09.2012

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that managers faced with a convex compensation structure are more likely to be

risk seeking and are thus not expected to hedge. Conversely, when faced with a

concave compensation structure, managers become more risk averse and should in

theory be more likely to hedge against risks. In order to determine whether !"#$%&

compensation plans are in the concave or convex region, we applied common

features of executive bonus plans to construct proxies. We used net income as a

proxy for the earnings measure. By comparing net income figures in 2010 to

2009, we defined a firm&s cash bonus structure as concave if earnings increased

during the period. If earnings '()#(*%('+,*,!"#$&%,)*%-,./01%,%2#1)21#(,"%,'(!"0(',

as convex. When constructing an interaction term for net income and cash bonus,

and treating it as a separate variable in the regressions, we find support for Smith

and Stulz (1985). Specifically, the results show that firms in the concave region

with cash bonus policies are significantly more pronounced derivatives users than

both (1) firms in the concave region but with no cash bonus policies and (2) firms

in the convex region, regardless of cash bonus policies. Furthermore, the results

from a subsample analysis that only consists of firms with cash bonuses as part of

their compensation policy make our findings more robust.

We further find that companies with revenues in foreign currencies are more

likely to use derivatives. We are not able to find a significant association between

derivatives use and firms with costs in foreign currencies. We also document that

firm size has a positive and significant impact on the use of derivatives, which

may be supportive of the economies of scale argument. The results also confirm

that sector has a significant effect on derivatives usage. The analysis show that

firms operating in the primary products sector are more pronounced derivatives

users than both firms in the manufacturing sector and the service sector. Finally,

we find no significant relationship between firms´ debt-to-equity ratios or

liquidity ratios and the use of derivatives. Prior studies regarding this topic deliver

mixed results.

The remainder of the paper is structured as follows. In section II we discuss prior

literature and develop hypotheses. Section III describes the data and examines the

descriptive statistics. Section IV covers general results while we in section V

present and discuss the main results for this study. Section VI concludes the study.

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Financial theory provides several descriptions of incentives for derivatives usage,

and there are numerous international studies on the use of derivatives.

A . Prior empirical findings

The Wharton surveys raise important questions regarding risk management

practices suitable for academic research.2 One of the main conclusions across all

three surveys is that the use of derivatives is heavily tilted towards large non-

financial firms and is significantly less amongst smaller firms. From their four

broad classifications of risk; interest rate, foreign exchange, commodity, and

equity risk, they find that the most commonly managed risk is the foreign

exchange risk. Finally, they conclude that the use of derivatives was mainly for

risk management purposes with the main objective of reducing cash flow

volatility.

A more recent study conducted by Bartram et al. (2009) investigates 7,319 firms

across 50 countries, focusing on the underlying motives for the use of derivatives.

They test several financial theories regarding the use of derivatives, but their

findings are ambiguous. They find support for the financial distress hypothesis, as

their data indicate that firms that use derivatives have higher leverage and fewer

liquid assets. However, they also find that these firms are of larger size and have

longer debt maturities, characteristics that contradict the hypothesis. Furthermore,

theory predicting that management incentives are correlated with derivatives

usage is tested. Their expectation was that senior managers who have highly

undiversified positions will use derivatives to hedge diversifiable risk. However,

their findings exhibit no support for this hypothesis. Finally, their findings

indicate, inter alia, that derivatives usage can have significant effects on other firm

decisions such as the level and maturity of debt, dividend policy, holdings of

liquid assets and the degree of operational hedging.

2 In total, the Wharton School has engaged in three consecutive questionnaire- based surveys

regarding non-financial corporations in the United States, carried out in 1994, 1995 and 1998. See

Bodnar et al. (1995), Bodnar et al. (1996) and Bodnar et al. (1998).

Thesis in GRA 19003 03.09.2012

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Research on the use of derivatives in Norwegian firms as part of their risk

management is limited. Børsum and Ødegaard (2005) investigates how

Norwegian, non-financial firms protect themselves against flutctuations in

currency markets and what measures are undertaken to hedge this risk. They find

that 91% of the responding firms use some form of currency hedging techniques,

whether it is derivatives or natural hedging. Out of these firms, 61% report using

derivatives, thereby making it the most applied technique. They also find that

larger firms use derivatives to a greater extent than smaller firms, which is

consistent with the international empirical findings. This fact is often supported by

the economies of scale argument. They further find that 86% of the firms protect

themselves against currency volatility, both for revenues and costs. 43% of the

companies say that the motive for protecting themselves against currency

fluctuations is to reduce the risk for the owners. Only 3% of the companies report

that their motive is to speculate in the currency market, which is consistent with

the finding in the Wharton surveys.

Storm (2011) investigates Norwegian, private, non-financial firms, with data

collected through a survey where 82 firms out of 309 respondents report using

derivatives. He finds that 45% of the users are large firms, which may be support

for the economies of scale argument. Furthermore, the study concludes that the

degree of foreign exhange exposure will impact the use of derivatives.

Our study complements the survey conducted by Storm (2011), with new

questions regarding performance based compensation of CEOs. Our study differ

from Storm (2011) in that our main focus is on the relation between manager

incentives and the potential effect on firms& risk management decisions. Smith

and Stulz (1985) argue that the structure of managerial compensation can have an

(!!()2, /0, $*0*3(#%&, #"%4, *5(#%"/06, 78()"!")*99:+, "!, 2-(, incentive structure is a

concave function of firm value, making the managers utility to be concave in firm

5*91(+, 2-(,$*0*3(#, ;"99, -*5(, "0)(02"5(%, 2/, #('1)(, #"%4, *0', "02(0%"!:, 2-(, !"#$&%,

hedging activity. However, if the structure is a convex function of firm value,

making the managers utility to be convex in firm value, they suggest that the

manager becomes less risk averse and is therefore expected to hedge less.

Empirical research regarding the predictions of Smith and Stulz (1985) provide

Thesis in GRA 19003 03.09.2012

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mixed results. Rogers (2002) and Tufano (1996) both find that firms where

managers hold options use less derivatives, consistent with Smith and Stulz

(1985). Haushalter (2000) was not able to find this relationship. Kim et al. (2008)

investigate the relation between managerial bonus plans and corporate derivatives

usage, with focus on how bonus plans often have both convex and concave

regions. Their findings support Smith and Stulz (1985) as they found a negative

relation between derivatives usage and managerial bonus plans with a convex

function and a positive relation for managerial bonus plans with a concave

function.

B . Focus area

Theory suggests that the use of derivatives in risk management can, inter alia,

increase firm value, reduce cash flow volatility and/or impact managerial

incentives. These factors may be a motivation for using derivatives. In this thesis

we investigate such motivations and analyze the determinants of derivatives

usage. We will study the five hypotheses listed below, where the main focus will

be to determine if CEO compensation has an impact on derivatives usage.

B .1. C E O compensation

Executives, like most people, usually have a highly undiversified financial

position as they receive substantial wealth from their employment by the firm. If

managers are risk averse, they will hedge diversifiable risk. With this hypothesis

we will investigate the potential relationship between the use of derivatives and a

!"#$%&,)/$8(0%*2"/0,8/9"):<

!"#$ CEOs receiving performance-based compensation are more likely to

engage in derivatives usage.

Compensation schemes are important when trying to align CEO incentives with

shareholder incentives = that is, increasing firm value in order to maximize

shareholder value. Thus, incentives should be constructed in such a way that when

the CEO maximizes own expected utility, he also maximizes shareholder value.3

Smith and Stulz (1985) argue that compensation that is similar to options will

3 See Smith and Stulz (1985) for further details.

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induce managers to hedge less as the compensation structure will be more convex.

They use bonus plans as an example of a convex structure, where the manager

receives a bonus only in the scenario where accounting earnings meet a specific

target. Prior studies produce mixed findings regarding the relationship between

derivatives usage and compensation. Wysocki (1998) find no association between

the use of derivatives and the risk profile of CEO compensation. Rogers (2002)

find a significant negative relation between CEOs stock and options holdings and

!"#$%&,'(#"5*2"5(%,1%*3(+,which is consistent with theory. This also indicates that

derivatives are most commonly used as hedging instruments and not used for

speculative purposes. Rajgopal and Shevlin (2002), investigating firms in the oil

and gas industry, also find that the use of executive stock options has a negative

impact on derivatives usage. We use options, cash bonus and restricted stock as

proxies for performance based compensation.

B .2. F inancial distress

Theory predicts that the use of leveraged financing increases the likelihood that a

firm will use derivatives. Risk management can reduce the expected costs

associated with financial distress.4 By reducing the chance of financial distress, an

optimal debt-ratio can more easily be obtained. Smith and Stulz (1985) argue that

firms with existing debt can benefit from having a reputation for hedging, as

hedging may reduce the cost of debt. Several previous studies have ambiguous

findings. Nance et al. (1993); Mian (1996); Geczy et al. (1997); and Guay (1999)

are all studies that test whether economic theories for optimal hedging can predict

derivatives usage by firms. Two of these studies find support for a positive

relation between hedging and leverage while the remaining two fail to find such a

connection. Regarding liquidity, Clark et al. (2006), find that firms using

derivatives for hedging purposes have significantly lower levels of liquidity

relative to non-hedgers. Bartram et al. (2009) document the relationship regarding

debt and liquidity in their 2003 survey. They find that derivatives users have

higher leverage as well as lower quick ratios and fewer tangible assets. Based on

these theories we test the following hypothesis:

4 See Rampini and Viswanathan (2010).

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!%#$ Derivatives users have significantly higher leverage and fewer liquid

assets.

We use debt-to-equity5 as a proxy for leverage and liquidity ratio

6 as a proxy for

liquid assets.

B .3. Foreign exchange exposure

Many firms are exposed to the risk of fluctuating exchange rates. The settlement

of contracts, cash flows, and firm valuation are affected by changes in exchange

rates. A company is exposed to exchange rate risk if its value is affected by

fluctuations in a foreign currency.7 This exposure can take a direct form through

import or export and buying or selling domestic goods/ services denominated in a

different currency. Based on this, we predict that companies exposed to different

currencies related to revenues and costs are more actively using derivatives.

Bodnar et al. (1998) find evidence of this relationship. We test the following

hypothesis:

!&#$ There is a significant relation between foreign exchange exposure and

derivatives usage.

The proxy used for foreign exchange exposure is the percentage amount of

revenues and costs denominated in a foreign currency.

B .4. F irm size

There are several reasons why the size of the firm may affect the incentive to

hedge. Financial distress can lead to situations where the firm faces direct legal

cost. For smaller firms, this cost might be a higher portion of the market value of

the firm which implies that these firms are more likely to hedge.8 In addition,

small firms are likely to have fewer natural hedging alternatives. These firms

might have a smaller product range, thereby making them more exposed to

volatility in demand. This is an additional argument as to why one can expect that

5 We define debt/equity as the debt-to-equity ratio.

6 We define total current assets/ current liabilities as the liquidity ratio.

7 See Børsum and Ødegaard (2005).

8 See Nance et al. (1993) and Warner (1977).

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smaller firms in fact should use more derivatives than larger firms. However,

several studies argue that large firms are more likely to have the resources to

warrant the use of derivatives compared to smaller firms.9 This is based on an

economy of scale argument, meaning that larger firms are more likely to employ

managers with the specialized information to set up a derivatives program.

Moreover, large firms often have more developed risk management systems than

smaller firms. Finally, the market for trading derivatives includes a portion of

transaction cost. By once again looking at economies of scale, it can be argued

that this cost is easier to bear for larger firms.10

Based on these incentives, we

study the following hypothesis:

!'#$ There is a significant relation between firm size and derivatives usage.

We use total assets11

as the proxy for firm size.

B .5. Industry

We believe that the idiosyncratic risk specific for each industry may be a

significant determinant for firms risk management policies and their use of

derivatives for this purpose. The use of derivatives is more feasible for certain

industries, as the underlying assets are widely exchanged. However, in certain

industries, hedging by derivatives is not even possible, as there is no derivative for

the product. We therefore investigate the following hypothesis:

!(#$ There is a significant relation between industry and derivatives usage.

The Wharton surveys consistently document that derivatives usage is highest

among firms in the primary products sector, followed by the manufacturing sector.

Firms in the service sector have the lowest level of derivatives usage, although the

number increases throughout the period (1995 = 1998). Two proxies are used

when testing the association between industry and derivatives usage. The first is

based on industry defined by NACE codes developed by the European

9 See Pennings and Garcia (2004), Bodnar et al. (1998), and Block and Gallagher (1986).

10 See Nance et al. (1993).

11 See Guay and Kothari (2003).

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commission.12

The second divides the sample of firms based on aggregate sector

level, which includes primary products, manufacturing and service sector.

Our predictions are summarized in Table I.

12 See http://ec.europa.eu/environment/emas/pdf/general/NACEcodes_en.pdf.

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Table I (Predictions)

Based on prior empirical research we present a table of predictions with respect to

the variables in each hypothesis. Among others, we base these predictions on the

following previous literature: Smith and Stulz (1985), the Wharton surveys, Clark

et al. (2006), Nance et al. (1993), and Pennings and Garcia (2004).

Independent variables Our prediction !

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13 With respect to prior studies and existing theory, we expect there to be a negative association

between the use of derivatives and firms compensating CEOs through options (ESOs), restricted

stock and cash bonuses. 14

We expect to find a positive association between leverage and derivative usage, while we

expect a negative relation between liquidity and derivatives usage. 15

Due to previous conflicting results we are not sure what to expect in regards to firm size. 16

Testing industry based on NACE codes will impose an extensive amount of variables in a

regression. Due to a relatively small sample size we expect that it will not give any significant

results. Dividing the sample firms based on aggregated sectors will however impose fewer

variables into the regression. As the primary and manufacturing sectors usually have more liquid

exchanges for the underlying asset, we expect there to be a positive relationship between the use of

derivatives and firms in these sectors. However, we expect a negative relation between derivatives

usage and firms in the service sector. 17

Based on previous findings we expect to find a positive relationship between foreign exchange

exposure and derivatives usage, both in regards to revenues and costs.

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((()* 8&$&

Currently, there is no available database containing information about Norwegian,

8#"5*2(, !"#$%&,1%(,/!,'(#"5*2"5(%6, >0,/#'(#, 2/, %21':,'"!!(#(02, #(9*2"/0%-"8%, "0, 2-"%,

market, data needs to be collected. We find that the best way to answer our

research question and to test the hypotheses in this study is through a survey. Our

research is based on cross-sectional data with financial data retrieved from the

fiscal year of 2010.18

A . Data collection

Financial firms seldom act as an end user of derivatives as they often deal with

such instruments on behalf of clients or for other trading purposes. Because of the

0*21#(, /!, !"0*0)"*9, !"#$%&, operations, they are not considered in this study.

Moreover, the majority of prior research on this topic investigates large, public

firms.19

The ownership structure in larger firms is often centralized, whereas it is

more concentrated in smaller firms. Smith & Stulz (1985) argue that when the

ownership structure is more concentrated, the motivation to hedge increases as the

owners are less likely to hold well-diversified portfolios. Since the manager of the

firm often handles the hedging activity, his/ her risk aversion can be an important

factor for managing risk. In order to capture this relationship, this study will focus

on medium-sized firms.

The European Commission defines firm size based on headcount, turnover or

balance sheet size. For the purpose of this analysis we use turnover to define our

sample. For medium-sized enterprises, this corresponds to approximately NOK

100 million to NOK 400 million. We exclude Norwegian subsidiaries of foreign

companies. By using Proff Forvalt, a database containing firm =and financial

information on Norwegian companies, we obtain a sample of 2,449 firms.20

We

received 173 responses to our questionnaire, which gives a response rate of 7.1%.

18 We have replicated a survey conducted by Storm (2011) with some modifications. We used the

same software to distribute the questionnaire : Questback (2012). 19

See Nguyen & Faff (2002), Nance et al. (1993), and Brunzell et al. (2011). 20

This sample has been somewhat reduced due to the lack of contact information on some firms.

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Page 12

B . Dependent variable

In the questionnaire the respondents *0%;(#(',?:(%@,/#,?0/@,2/ whether they use

derivatives for risk management. Our dependent variable is therefore categorized

as a binary v*#"*.9(, 2-*2, "%, (A1*9, 2/, ?B@, "!, 2-(, !"#$, *0%;(#(', ?:(%@, C"6(6, 1%(,

'(#"5*2"5(%D+, *0', ?E@, "!, 2-(, !"#$, *0%;(#(', ?0/@6,We do not wish to investigate

derivatives usage for speculative purposes. Further, we do not include natural

hedging as part of the dependent variable, but we do observe from the

questionnaire that there are respondents who use this as part of their risk

management. Nevertheless, we will only investigate hedging by the use of

derivatives.

C . Independent variables

The proxies discussed under the five hypotheses in section II constitute our

independent variables, and can be viewed in Table II. We have continuous,

nominal and ordinal variables which can be seen from the table. The debt-to-

equity ratio and liquidity ratio were retrieved from Proff Forvalt based on

financial statements and key figures. From the questionnaire we collected the

percentage amount of revenues and cost that the firms report having in a foreign

currency. By multiplying these 8(#)(02*3(%, ;"2-, 2-(, !"#$%&, 2/2*9, #(5(01(%, *0',

costs retrieved from Proff Forvalt, we obtained the nominal amount denominated

in a foreign currency. For firms without a percentage of foreign exchange

exposure this amount is set to zero. Firm size "%,.*%(',/0,!"#$%&,2/2*9,*%%(2%6,F-(%(

numbers were also retrieved from Proff Forvalt. The three proxies for CEO

compensation are dummy variables. They take on the value /!, ?B@, "!, 2-(,GHI,

receives /82"/0%+,)*%-,./01%(%,/#,#(%2#")2(',%2/)4%+,*0',?E@,/2-(#;"%(6,>0!/#$*2"/0,

regarding such compensation was obtained through the questionnaire. For NACE

codes we have 17 different industries, which means that they take on the value

from 1 - 17. For aggregated sector level they are divided into three, where the

primary 8#/'1)2%,%()2/#, 2*4(,/0,2-(,5*91(,?B@+, 2-(,$*01!*)21#"03 sector is set to

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Proff Forvalt. Based on industry information, we divided the firms into the three

different sectors. In total we have 10 different independent variables.

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Table I I (Description of variables)

Variable De finition Type of variable SourceDerivatives Usage Nominal Survey

Debt-to-Equity Ratio Debt/Equity Continuous Proff Forvalt

Liquidity Ratio Total current assets/Total current liabilities Continuous Proff Forvalt

Foreign Currency Revenue % of revenues in foreign currency Continuous Survey/ Proff Forvalt

Foreign Currency Cost % of costs in foreign currency Continuous Survey/ Proff Forvalt

Total Assets Total equity + Total liabilities Continuous Proff Forvalt

NACE Industry Sample of firms divided into 17 different industries Nominal Proff Forvalt

Sector Sample of firms divided into 3 aggregated sectors Nominal Own division

Options Nominal Survey

Cash Bonus Nominal Survey

Restricted Stock Nominal Survey

Dummy that takes the value 1 if a firm reports using

derivatives and 0 if they report not using derivatives

Dummy that takes the value 1 if a firm reports rewarding

CEO stock options and 0 if not


CEO cash bonus and 0 if not


restricted stocks and 0 if not

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D . Descriptive statistics

Appendix 1 presents a statistical summary of the characteristics of the variables.

Out of the 173 firms that responded to our survey, 73 state that they use

derivatives. On average, derivatives users have a higher debt-to-equity ratio than

non-users, which is in line with our expectations. However, we also observe that

derivatives users have higher liquidity ratios than non-users, which is conflicting

with our expectations. Furthermore, the descriptive statistics show that derivatives

users have higher degree of foreign exchange exposure compared to non-users.

This can be seen in Appendix 4. The average and median size of a firm within the

group of derivatives users is considerably larger than for firms within the non-user

group, measured in total assets. This indicates that our sample supports that larger

companies are more likely to use derivatives. For an overview of the industries

and their representation in our sample, we refer to Appendix 2. Due to the fact that

the sample size is relatively small, the breakdown of all respondents into different

industries based on NACE classification is most likely biased. Hence, there is a

chance that the sample cannot be generalized to all firms in each industry.

Therefore, dividing the firms into an aggregate sector level will provide a less

biased division. As can be seen in Appendix 3, derivatives usage is most dominant

in the primary product sector. With respect to performance based compensation,

40% of the firms report the use of cash bonuses for CEOs. 5% of the respondents

report the use of options as CEO performance based compensation, and only 3%

report that their CEO receives restricted stocks.

E . Correlations

We estimate the Spearman correlation coefficients in order to determine whether

there is a significant association between the dependent and the independent

variables, and if so, how they correlate with each other.21

As we have non-normal

variables, the Spearman correlation matrix is more appropriate to apply than the

Pearson correlation matrix as it does not require a linear relationship.22

We

observe that six of the variables are significantly correlated with the dependent

variable.

21 See Table III.

22 See http://datalab.morningstar.com/knowledgebase/aspx/Article.aspx?ID=550&Country=us.

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Table I I I (Spearman correlation matrix)

Derivatives Usage Debt-to-Equity Ratio Liquidity Ratio Foreign Currency Revenue Foreign Currency Cost Total Assets NACE Industry Sector Options Cash Bonus Restricted Stock

Derivatives Usage 1 -0.036 0.034 0.451** 0.290** 0.276** -0.167* -0.287** -0.042 0.188* -0.034

Debt-to-Equity Ratio 1 -0.586** -0.126 -0.095 -0.075 0.199** 0.063 -0.100 -0.114 -0.206**

Liquidity Ratio 1 0.056 0.039 -0.007 -0.119 -0.010 0.112 0.102 0.104

Foreign Currency Revenue 1 0.558** 0.291** -0.229** -0.252** 0.048 0.264** 0.156*

Foreign Currency Cost 1 0.125 -0.157* -0.131 0.091 0.301** 0.145

Total Assets 1 -0.049 -0.358 0.109 0.113 0.063

NACE Industry 1 0.627** -0.118 -0.038 -0.051

Sector 1 -0.060 0.013 -0.058

Options 1 0.128 0.098

Cash Bonus 1 0.233**

Restricted Stock 1

*. Correlation is significant

at the 0.05 level (2-tailed).

**. Correlation is significant

at the 0.01 level (2-tailed).

Thesis in GRA 19003 03.09.2012

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(9)* :2+2%&1*%2#.1$#

A . Regression results

In Table IV we report the results of seven logistic regression models.23

Each

model contains different combinations of the independent variables. The results

identify several variables as being %"30"!")*029:,#(9*2(',2/,*,!"#$%&,'(cision to use

derivatives in their risk management programs. The findings suggest that a firm is

more likely to hedge if it has revenues denominated in foreign currency

(significant at 1 = 5% level), if the firm is large (significant at a 5 = 10% level),

and if it operates in the primary products sector (significant at a 1 = 5% level). We

do not find a positive association between the use of derivatives and leverage,

neither do we observe the expected negative relation between liquidity and

derivatives usage. In the case of costs denominated in foreign currencies, the

estimated coefficient is consistently positive as expected, but only significant in

one of the models (5% level).

Furthermore, for performance based compensation, which is the main focus of this

study, the coefficient for options is negative as expected but not significant. The

estimated coefficient for restricted stock is also negative, consistent with our

expectations, but is only significant in one of the models (10% level). Only cash

bonus exhibits statistically significant results across all four models where it has

been included. In two of the models, cash bonus is significant at a 10% level and

in the remaining two at a 5% level. The fact that there is a positive relation implies

that firms awarding CEOs cash bonuses are more pronounced derivatives users

than firms without this type of compensation. This goes against our initial

expectation. Bonus plans are structured to impose different managerial incentives.

Smith and Stulz (1985)24

argue that bonus plan compensation is a convex function

of accounting earnings and theory thus predicts that the manager will be more risk

= seeking, thereby being less likely to use derivatives. However, typical bonus

characteristics introduce both a convex and a concave region in the bonus plan. In

order to explain the documented positive relation between cash bonuses and

derivatives usage we want to control for the region of the compensation structure.

23 See Appendix 5 for further information on the logit regression model.

24 See page 403 in their article.

Thesis in GRA 19003 03.09.2012

Page 17

More specifically, we will investigate whether the CEOs in our sample who

receive cash bonuses have an end-of-period wealth which is a concave function of

the end-of-period firm value.

Thesis in GRA 19003 03.09.2012

Page 18

Table I V (General regression results)

This table represents the regression results from 7 logit models consisting of different combinations of the independent variables. The significance is given at a

1%(*), 5%(**) or a 10%(***) level. For the sector variable, the primary sector acts as the reference category, meaning that the impact that manufacturing and

service has on derivatives usage is compared to the reference. This gives us no coefficient for the primary sector. None of the 17 NACE industries are significant.

N=173 Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig.

Debt-to-Equity Ratio -0.001 (0.323) 0.000 (0.789) -0.001 (0.190)

Liquidity Ratio 0.051 (0.567) 0.049 (0.438)

Foreign Currency Revenue 0.094** (0.03) 0.123* (0.000) 0.117* (0.000) 0.093* (0.002)

Foreign Currency Cost 0.014 (0.678) 0.010 (0.755) 0.067** (0.017)

Total Assets 0.006** (0.097) 0.009** (0.016) 0.007*** (0.095)

NACE Industry - - - -

Sector

Primary (0.03) (0.000) 0.012

Manufacturing -0.838 (0.129) -1.185** (0.018) -1.002** (0.056)

Service -1.323* (0.009) -1.792 (0.000) -1.408* (0.003)

Options -1.146 (0.118) -1.013 (0.265) -0.737 (0.336)

Cash Bonus 0.624*** (0.097) 1.321* (0.001) 0.997* (0.004) 0.642*** (0.063)

Restricted Stock -1.842*** (0.07) -1.175 (0.217) -0.912 (0.221)

Model 7Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Thesis in GRA 19003 03.09.2012

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9)* ;&/+*&+&14#/#

A . Utility theory and managerial compensation

Smith and Stulz (1985) state that managers& expected utility depends on the

distribution of the firm&s payoff. They further assume that managers are risk

averse, and that the indirect utility function of wealth is strictly concave. Hedging

)-*03(%, 2-(, '"%2#".12"/0, /!, *, !"#$&%, 8*:/!!, which further changes managers&

expected utility. They also find that managers& hedging decisions have the

following properties:

(1) I!,2-(,$*0*3(#&% end-of-period wealth is a concave function of the end-of

period firm value, the optimal hedging strategy is to hedge the firm

completely, given that this is feasible. If the firm is completely hedged,

the manager&s expected income is maximized. They explain this through

M(0%(0&%, >nequality which states that the expected value of a concave

function of a random variable is smaller than the value of the function

evaluated at the expected value of the random variable. Hence, the

manager will only want to bear the risk if he is rewarded with a higher

payoff. This is not the case if the firm is fully hedged as the expected

income will be maximized, i.e. the manager does not want to take on risk.

(2) I!,2-(,$*0*3(#&% end of period wealth is a convex function of the end-of

period firm value, but the manager&s expected utility is still a concave

function of the end-of-period value of the firm, the optimal strategy will

generally be to eliminate some uncertainty through hedging. Here, the

manager&s expected income is higher if the firm does not hedge, since

his/her income is a convex function of firm value. However, since the

manager is risk averse he will therefore wish to give up some expected

income to reduce risk. Despite this trade off, the manager will generally

not choose a strategy that results in a riskless income.

(3) If the $*0*3(#&% end-of-period utility is a convex function of the end-of-

period firm value, Jensen&s Inequality implies that if the firm is not

hedged at all, the $*0*3(#&% end-of-period utility has a higher expected

Thesis in GRA 19003 03.09.2012

Page 20

value. Managers will thus be risk-seeking when the expected utility is a

convex function of the value of the firm.

Based on this, Smith and Stulz (1985) find that managers, whose compensation is

a concave function of firm value, have the incentive to reduce the risk of volatility

in cash flow, i.e. engage in derivatives for risk management. In our analysis we

found that firms with CEOs who receive cash bonuses are more pronounced

derivatives users than firms without this type of compensation. We therefore

proceed to investigate whether these CEOs have compensation structures that

make their end-of-period utility a concave function of the end-of-period value of

the firm.

B . Convexity versus concavity

Kim et al. (2008) investigate the effect of risk management incentives resulting

!#/$, $*0*3(#"*9, ./01%, 89*0%, /0, !"#$%&, '(#"5*2"5(%, 1%*3(6, F-(:, !/)1%, /0, -/;,

typical bonus plan payoffs have both a convex and a concave region which means

that managers can have an incentive to either increase or decrease a firm&s risk in

order to maximize their expected bonus payments. Their paper is based on Smith

and Stulz (1985) theories on concavity and convexity in compensation structures.

In order to determine whether the bonus plans are in the concave or the convex

region, Kim et al. (2008) look at the following features of bonus plans: First,

executives need to exceed a certain threshold level in order to receive a payoff.

Below this threshold, no bonus is received. Above this threshold, however, the

8*:/!!, "0)#(*%(%,;"2-,8(#!/#$*0)(,18,2/,*,)(#2*"0,?)*8@6 Most bonus plans have

this structure even though the details differ. Also, in a typical bonus plan, a target

payoff is established based on the achievement of a performance target. Murphy

(1998, 2000) concludes that the target levels that are set for these measures are

1%1*99:,.*%(',/0,8#(5"/1%,:(*#&%,8(#!/#$*0)(,/#,)1##(02,:(*#&%,.udget. In order to

understand how a bonus plan can affect the incentives to either take on risk or

reduce risk, consider the following two scenarios. In the first case, a CEO expects

the performance in the current year to be above the target that is already known.

The CEO is likely to believe that he/she is facing a concave function as he/she is

far from the threshold level but most likely closer to the cap. In this case, it is

Thesis in GRA 19003 03.09.2012

Page 21

desirable to lock in high bonus payments. This is because when the expected level

of performance is high, the expected payoff is also high as payoff is determined

based on performance. By locking in at this performance level the high bonus

payoff is thereby also secured. In the second case, the CEO expects their

performance to be below the target. In this scenario, the performance is much

closer to the threshold than the cap. This makes the incentive to reduce risk quite

small since locking in at this level might result in little or no bonus payment. The

CEO is therefore facing a convex payoff function.

In order to investigate this relationship it is desirable to know the detail of each

$*0*3(#&%,89*06,71)-,"0!/#$*2"/0,"%,0/2,3(0(#*99:,'"%)9/%(',*0',;(,'/,0/2,-*5(,

this information at hand. Neither did Kim et al. (2008) when constructing their

analysis. However, due to the common features of executive bonus plans it is

possible to construct proxies in order 2/, '(2(#$"0(, ;-(2-(#, 2-(, !"#$&%

compensation plan is in the concave or convex region. Kim et al. (2008) took

advantage of these common features and used net income as an earning measure.

By doing this, they constructed the convexity or concavity of the bonus payoff

function. They used net income as an earnings measure and found a ratio by

dividing net income for the current year by net income in the previous year. In this

scenario, firm performance in the current year is compared to the fi#$&%

performance in the previous year, making the previous year the target. They

defined the convex payoff group as those firms with a ratio less than or equal to

1.0, and the concave payoff group as those firms with a ratio greater than 1.0.

Based on this they hypothesize that CEOs in firms with a ratio below 1.0 are

likely to be closer to the threshold level than the cap (i.e. performance below the

target), which is defined as the convex payoff zone. They further argue that firms

with a ratio above 1.0 (i.e. performance above the target) are closer to the payment

cap than the threshold, which is defined as the concave payoff zone.

Kim et al. (2008) found a negative relation between derivatives usage and CEO

compensation for firms whose managers face a convex payoff function, and a

positive relation for firms whose managers face a concave payoff function. These

results support the prediction of Smith and Stulz (1985).

Thesis in GRA 19003 03.09.2012

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C . Main results

We have replicated the procedure used by Kim et al. (2008) in order to determine

whether the firms in our sample that compensate CEOs with cash bonuses also lie

within the concave payoff region. We use net income as a proxy for the earnings

measure. We construct a dummy for net income which is give0, 2-(,5*91(,?B@, "!,

net income is higher in 2010 than in 2009, i.e. the relevant firm is in the concave

region and above the target. Further, it "%, 3"5(0, 2-(, 5*91(, ?E@, "!, 0(2, "0)/$(, is

lower in 2010 than in 2009, i.e. the relevant firm is in the convex region and is

below the target. In order to investigate whether firms in the concave region,

combined with having a cash bonus compensation policy, are more pronounced

derivatives users than firms without both these characteristics, we constructed

further regressions. The results can be seen in Table V.

In Model 1 we have constructed a multiplicative regression where net income is

multiplied with cash bonus, i.e. the variable explains whether firms that use cash

bonuses as a compensation policy, and are in the concave region, have a

significant impact on derivatives usage. As seen from the results we find no

support for such a relation in this model. Theory suggests that one should always

include the two components in the model when having an interaction term.

However, there may be certain circumstances where it is preferable to only

include the interaction term if it has some particular substantive meaning. In this

instance, we are not interested in the effect that net income has on derivatives

usage alone and we have already documented the effect of having cash bonuses.

Instead, we want to test the effect cash bonus has on derivatives usage when net

income show a convex or a concave structure. Hence, we are only interested in the

interaction between the components and not the components themselves. In

addition, when the interaction term is highly correlated with the components, it

may cause problems with multicollinearity.25

We have therefore constructed

models where we consider the interaction term as a separate variable and include

some of our other previous proxies as control variables. The results are shown in

model 2, 3, and 4 in Table V.

25 See Appendix 6 for correlation matrix.

Thesis in GRA 19003 03.09.2012

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Table V (Main results)

This table presents 4 different models, *99, )/0%"%2"03, /!, 2-(, N0(2, "0)/$(, 2"$(%, )*%-, ./01%@, 5*#"*.9(, C2-(, "02(#*)2"/0, 2(#$D, "0, /#'(#, 2/, 9//4, *2, "2%, "$8*)2, /0,

derivatives usage. In the first model we look at the interaction term combined with its components while in model 2-4 we have excluded the components from the

regression. The significance is given at a 1%(*), 5%(**) or a 10%(***) level.

N=173 Coeff. Sig. N=173 Coeff. Sig. Coeff. Sig. Coeff. Sig.

Net Income times Cash Bonus 0.846 (0.204) Net Income times Cash Bonus 1.142* (0.006) 1.182* (0.003) 1.314* (0.003)

Net Income 0.164 (0.691) Debt-to-Equity Ratio 0.000 (0.506) -0.001 (0.155)

Cash Bonus 0.213 (0.682) Liquidity Ratio 0.149 (0.361) 0.052 (0.332)

Foreign Currency Revenue 0.105* (0.000)

Foreign Currency Cost 0.050 (0.110)

Total Assets 0.011** (0.018)

NACE Industry - -

Sector

Primary (0.004)

Manufacturing -1.136** (0.037)

Service -1.692* (0.001)

Options -1.139 (0.196)

Restricted Stock -1.240 (0.188)

Model 1 Model 2 Model 3 Model 4

Thesis in GRA 19003 03.09.2012

Page 24

As can be seen, net income times cash bonus is positive and significant at a 1%

level in all three models. The results show that firms in the concave region with

cash bonus policies are significantly more pronounced derivatives users than both

(1) firms in the concave region but with no cash bonus policy and (2) firms in the

convex region, regardless of cash bonus policy.

C .1. Robustness test: Subsample analysis

When removing the two components in the previous analysis, there is a risk that

our inferences about the results are not valid. Therefore, in order to find further

support for our findings, we construct an additional regression. In this sample,

only firms with cash bonuses as part of their compensation policy for CEOs are

included. This results in a weaker sample of 69, but we no longer have an issue

with the interaction term. In this case, we investigate the effect that the net income

dummy has on derivatives usage. More specifically, we examine whether firms

that use cash bonuses as part of the compensation for their CEOs and are in the

concave region have a higher use of derivatives than firms who also use cash

bonuses but are in the convex region. In this instance, we are able to only

investigate this relationship without having to include firms with no cash bonuses.

The results are provided in Table VI. The net income dummy is positive and

significant at a 5% level in two of the models and at a 10% level in the remaining

model. This means (despite having reduced the sample with 60%) that we still

manage to find a positive and significant relation between firms in the concave

region and derivatives usage. Moreover, the results show that firms with CEO

cash bonuses that have a concave structure are more pronounced derivatives users

than firms with CEO cash bonuses that have a convex structure.

The results from this subsample analysis make our findings more robust and we

conclude 2-*2,*,!"#$&%,'()"%"/0,2/,1%(,'(#"5*2"5(%,"%,8/sitively affected when the

firm is in the concave payoff zone and use cash bonuses as part of their CEO

compensation. Furthermore, we are able to support the prediction of Smith and

Stulz (1985) and the findings made by Kim et al. (2008).

Thesis in GRA 19003 03.09.2012

Page 25

Table V I (Subsample test)

This table includes 69 observations representing the firms in our sample where CEOs

receive cash bonuses. It investigates the effect net income has on derivatives usage. The

variable is a dummy, w-(#(,?B@,"%,'(!"0(',*%,2-(,)/0)*5(,#(3"/0,*0',?E@,"%,'(!"0(',*%,2-(,

convex region. The significance is given at a 1%(*), 5%(**) or a 10%(***) level.

N=69 Coeff. Sig. Coeff. Sig. Coeff. Sig.

Net Income 1.060*** (0.056) 1.172** (0.040) 1.255** (0.046)

Debt-to-Equity Ratio 0.014 (0.697)

Liquidity Ratio 0.028 (0.693)

Foreign Currency Revenue 0.029 (0.449)

Foreign Currency Cost 0.040 (0.316) 0.028 (0.525) 0.005 (0.517)

Total Assets 0.009 (0.123)

NACE Industry - -

Sector (0.170)

Primary -1.101 (0.242)

Manufacturing -1.670*** (0.068)

Service

Options

Restricted Stock

Model 1 Model 2 Model 3

Thesis in GRA 19003 03.09.2012

Page 26

9()* <,+'1.#/,+

F-"%, %21':, "05(%2"3*2(%, 2-(, '(2(#$"0*02%, /!, !"#$%&, #"%4, $*0*3($(02, 8#*)2")(%,

using data from 2010 on 173 Norwegian, private, non-financial firms that are of

medium size. One must be conservative when interpreting the results of a multi-

industry study of a few dozen observations. Keeping this in mind, our study offers

support for several findings in prior studies regarding certain firm characteristics,

and their effect on the use of derivatives.

First, we do not find significant results for the financial distress hypothesis. We

do, however, document a positive association between the extent of derivatives

usage and the amount of revenues denominated in foreign currencies. Although

the same results cannot be found for costs, we cannot reject the foreign exchange

exposure hypothesis. Furthermore, the evidence shows that both size and industry

affects hedging activity. Our results show that large firms are more actively using

derivatives. This might be because large firms often enjoy economies of scale

benefits. It could also be that larger companies have more competent management

and stronger focus on risk management, as well as typically having a more

developed risk management system, than smaller companies. In terms of industry,

we find that firms in the primary sector are more pronounced derivatives users

than firms in both the manufacturing and service sector. Finally, although we find

no significant results for CEO options or restricted stock, the evidence show that

firms where CEOs are rewarded cash bonuses are more pronounced derivative

users than firms with no cash bonuses. Bonus schemes often have thresholds that

must be met in order to trigger bonus payments+,*0',*9%/,*,?)*8@,2-*2, 9"$"2%, 2-(,

*$/102,2-*2,)*0,.(,#()("5('6,F-(,2-#(%-/9',9(5(9,*0',2-(,?)*8@,"02#/'1)(,*,)/05(L,

and a concave region to the compensation, inducing different managerial risk

behaviors. Based on Smith and Stulz (1985), we expected that cash bonuses with

a convex structure would induce CEOs to take more risk, and hence hedge less.

Conversely, cash bonuses with a concave structure would make CEOs more risk-

averse and therefore hedge more. Our results show that firms with a concave

payoff structure indeed use more derivatives than firms both in the convex payoff

zone (regardless of cash bonus) and firms that are in the concave region without

the use of cash bonuses. Based on this analysis we conclude that manager

incentives appear to affect choices made in the firms risk management.

Thesis in GRA 19003 03.09.2012

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!552+-/'2#

Appendix 1 (Descriptive statistics)

The first window of this table display simple characteristics of all firms in our sample. The two subsequent windows breaks down the characteristics

into derivative users and non-users.

Min. Max. Mean Median STD Min. Max. Mean Median STD Min. Max. Mean Median STD

Derivatives Usage 0,00 1,00 0,42 0,00 0,50 1,00 1,00 1,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00

Debt-to-Equity Ratio -8,61 4 559,85 47,34 2,21 369,35 -9,00 1 161 26,00 2,00 143,48 0,00 4 560 63,00 2,00 468,03

Liquidity Ratio 0,14 72,42 2,07 1,37 5,47 0,00 72,00 3,00 1,00 8,29 1,00 7,00 2,00 1,00 1,00

Foreign Currency Revenue 0,00 1 532 131 62 719 0,00 158 813 0,00 1 532 131 115 327 73 957 225 304 0,00 278 990 24 315 0,00 55 515

Foreign Currency Cost 0,00 1 426 022 52 409 0,00 121 078 0,00 1 426 022 78 196 45 221 170 043 0,00 234 403 33 584 0,00 58 432

Total Assets 4 882 19 917 752 578 513 117 211 2 117 813 4 882 19 917 752 1 090 391 176 685 161 830 23 144 2 818 608 204 842 87 284 361 165

NACE Industry 1,00 16,00 6,95 7,00 3,29 1,00 13,00 6,00 7,00 3,26 1,00 16,00 7,00 7,00 3,19

Sector 1,00 3,00 2,35 3,00 0,78 1,00 3,00 2,00 2,00 0,84 1,00 3,00 3,00 3,00 0,67

Options 0,00 1,00 0,05 0,00 0,22 0,00 1,00 0,00 0,00 0,20 0,00 1,00 0,00 0,00 0,24

Cash Bonus 0,00 1,00 0,40 0,00 0,49 0,00 1,00 1,00 1,00 0,50 0,00 1,00 0,00 0,00 0,47

Restricted Stock 0,00 1,00 0,03 0,00 0,18 0,00 1,00 0,00 0,00 0,16 0,00 1,00 0,00 0,00 0,20

A ll F irms Firms with De rivative s Usage Firms without De rivative s Usage

Thesis in GRA 19003 03.09.2012

Page 31

Appendix 2 (Representation by industry)

Appendix 3

(Representation by aggregate sector level)

NACE Classification

Arts & Entertainment 1 0 0 %

Human Health & Social Work 2 0 0 %

Administrative & Support Activities 6 2 33 %

Scientific & Technical Activities 14 5 36 %

Real Estate 8 5 63 %

Information & Communication 9 2 22 %

Accommodation & Food Service 1 1 100 %

Transporting & Storage 12 8 67 %

Wholesale & Retail Trade 48 14 29 %

Construction 18 3 17 %

Water Supply 4 0 0 %

Electricity & Gas 14 11 79 %

Manufacturing 28 18 64 %

Mining & Quarrying 3 2 67 %

Agriculture 4 2 50 %

Other 1 0 0 %

Number of

Respondents

Number of

Derivatives Users

% Derivatives Use

by Industry

Sector

Primary Product 33 23 69,7 %

Manufacturing 46 21 45,7 %

Service 94 29 30,9 %

Number of

Respondents

Number of

Derivatives Users

% Derivatives Use

by Sector

Thesis in GRA 19003 03.09.2012

Page 32

Appendix 4 (Representation by foreign currency exposure)

This table shows the percentage of derivatives users and non-users that are subject

to a foreign currency with regards to revenues and costs. It also shows the degree

of exposure; whether it is non-existing (0%) or as high as having all denominated

in a foreign currency (100%).

Revenues Costs

0% exposure 29 % 32 %

25% exposure 25 % 34 %

50% exposure 11 % 21 %

75% exposure 27 % 13 %

100% exposure 8 % 0 %

Total 100 % 100 %

Revenues Costs

0% exposure 75 % 60 %

25% exposure 13 % 24 %

50% exposure 5 % 7 %

75% exposure 6 % 9 %

100% exposure 1 % 0 %

Total 100 % 100 %

De rivative s Us e rs

Non-us e rs

Thesis in GRA 19003 03.09.2012

Page 33

Appendix 5 (Logit regression model)

When dealing with a binary dependent variable, an effective model to employ is

the logit model and is expressed as follows:

)*+ ,- . , / 0

Y is a function of the explanatory variables, so this is equivalent to expressing the

model as

)*+ ,- . , / $12 3415675

8

59"

Solving for P gives

,7 /-

- 3 :;<=>?@ =ABCADAEF G

This way, we will not incur probabilities that are either negative or greater than 1.

This model uses a cumulative logistic function to transform the model so that the

predicted probabilities lie within the correct range of 0 to 1. Another model, called

the probit model, applies a cumulative normal distribution to do the same. The

probit model can be expressed as follows:

H<I7G /-

JKLM:;

"%NOCPQ R

However, both the logit and the probit model will in most instances give

approximately the same results. The only situation where the two models will give

significantly different estimation outputs is when the distribution of the binary

dependent variable is heavily skewed towards either 1 or 026

. For our dependent

variable, the distribution is fairly balanced with 42% of the firms reporting the use

of derivatives and 58% reporting no use. Therefore, we will apply the logit model

in our estimation.

26 See Brooks (2008).

Thesis in GRA 19003 03.09.2012

Page 34

Appendix 6 (Correlation matrix)

Appendix 7

(Distributed questionnaire)

Survey on the use of derivatives in Norwegian private firms !This survey is conducted for our final thesis for the Master of Science degree at BI Norwegian

Business School. It seeks to identify the use of derivatives in Norwegian non-financial private

firms. You will anonymously be answering a questionnaire which will require only 5-15 minutes,

depending on your answers.!

!We are very grateful for your contribution and for the time spent to complete the survey.!

!Best of regards!

!Louise Samuelsson and Jannicke Olsen!

!

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Cash Bonus -0.781 0.431 1.000

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ID - number: 0855435 ID - number: 0857807

BI Norwegian Business School

- Preliminary Thesis Report !

Supervisor: Paul Ehling

Exam code and name:

G R A 19002 ! Preliminary Thesis in F inance

Hand-in date:

15.01.2012

Campus:

BI Oslo

Program:

Master of Science in Business and Economics ! Major in Finance

MSc Preliminary Thesis Report 15.01.2012

Page i

!"#$%&'(&)'*+%*+,&

SU M M A R Y .................................................................................................................................... I I!

IN T R O DU C T I O N ........................................................................................................................... 1!

R ESE A R C H PR O B L E M A ND M O T I V A T I O N .......................................................................... 1!

RESEARCH PROBLEM ..................................................................................................................... 1!

BRIEF LITERATURE REVIEW .......................................................................................................... 3!

M E T H O D O L O G Y ......................................................................................................................... 4!

QUESTIONNAIRE ........................................................................................................................... 5!

BIB L I O G R APH Y ........................................................................................................................... 6!

APPE NDI X ...................................................................................................................................... 8!

PROGRESS PLAN ............................................................................................................................ 8!

QUESTIONNAIRE ........................................................................................................................... 9!


Page ii

Summary

In our master thesis we will study private, non-financial Norwegian firms, with

emphasis on their usage of financial derivatives in risk management programs.

These criterions leaves us with a population of 4 237 firms that will be

investigated based on a questionnaire. We will replicate a survey conducted by

Storm (2010) with certain modifications. In addition, we will focus on the

following two topics:

1) How different sources of compensation to the CEO and CFO will affect

hedging, and also what types of compensations have a strong influence on

the use of derivatives.

2) How higher leverage and fewer liquid assets in the company may affect

hedging.


Page 1

Introduction

Firms are facing various types of risks, such as interest rate volatility, equity risk,

commodity risk and foreign currency exposure (Bartram, Brown and Fehle 2009)

(Bodnar, Hayt and Marston 1998). These risks are important for firms to be aware

of in order to monitor and handle them. Therefore, risk management is an

important part of the firm and an interesting focus area. There have been

conducted several surveys on the use of derivatives, contributing to different

databases for academic research in several nations. Even though Norway is also

exposed to the various types of risks, research on this area is very limited. In our

survey, we therefore wish to examine financial derivatives practice in risk

management programs for private, non-financial firms in Norway.

Research problem and motivation

Research problem

Theory suggest that the use of derivatives in risk management can increase firm

value, reduce cash flow volatility or handle managerial incentives, among other.

These may be motivations for using financial derivatives. However, because of

the complex nature of such instruments they are often used in the wrong way,

which can lead to firm destruction. The main research problem in this survey is:

How is the use of financial derivatives in risk management programs in

Norwegian, private, non-financial firms that are of medium size?

We will study the six hypotheses listed below:

Hypothesis 1: CEOs and C F Os receiving performance-based compensation are

more likely to engage in financial derivatives usage.

Most executives have a highly undiversified financial position as they receive

substantial wealth from their employment by the firm. This may lead to risk

aversion, which again leads to hedging of diversifiable risk. With this hypothesis

we will investigate if there exists a positive relationship between the use of


Page 2

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plans or bonus schemes are often linked to firm performance. If there is a

substantial part of non-controllable risk for the CEO or CFO inherent in his/her

compensation, they might be more willing to use derivatives in order to hedge this

risk. This will be one of the main focus areas of our thesis. Wysocki (1998) find

no relationship between the use of derivatives and CEO/CFO compensation that

contains risk Wysocki (1998).

Hypothesis 2: Derivatives users have significantly higher leverage and fewer

liquid assets.

Companies that are in financial distress and have a high debt-ratio are more likely

to use derivatives for hedging purposes. Financial risk management can reduce the

expected value of cost associated with financial distress. Also, by reducing the

chance of financial distress, an optimal debt-ratio can more easily be obtained.

Previous studies also find this relation (Bartram, Brown and Fehle 2006).

Hypothesis 3: The main motivation for using derivatives is to reduce and manage

the volatility in cash flow.

Previous studies support this (Wharton Survey 1995, 1996 and 1998).

Hypothesis 4: There is a significant relationship between firm size and derivatives

usage.

Large firms are more likely to have the resources to warrant the use of derivatives

(Pennings and Garcia 2001). Previous studies confirm that derivatives usage is

heaviest among large firms (Wharton survey 1998). Also, other studies (Mian

1996 and Carter and Sinkey 1998), find evidence for a positive relationship

between a firms derivatives-practice and its size.

Hypothesis 5: There is a significant relationship between industry and derivatives

usage.

In the Wharton surveys, (Bodnar, Hayt and Martson 1996) (Bodnar, Hayt and

Marston 1998), there is evidence of a higher degree of derivatives usage in some

industries than others.


Page 3

Hypothesis 6: There is a significant relationship between foreign exposure and

derivatives usage.

We assume that companies exposed to different currencies related to cost and

income or assets and liabilities, are more actively using derivates. We also assume

that companies that are naturally hedged practice less derivative usage than

companies that are not. Bodnar and Martson (1998) find evidence of this.

Brief literature review

There exists an extensive amount of surveys conducted internationally on the use

of financial derivatives. Of international studies, the Wharton surveys raise

important questions regarding risk management practices suitable for academic

research. In 1994, the Wharton School undertook its first survey that was sent to

non-financial corporations in the United States (Bodnar, Hayt and Martson 1996).

They concluded from the survey that derivatives were mostly used to reduce cash

flow volatility, and not commonly used for speculation (Bodnar, Hayt, Martson

and Smithson 1995). In total, the Wharton School has engaged in three

consecutive questionnaire-surveys. Based on their second survey published in

1996, they conclude that less than half of all non-financial firms use derivatives

and that the usage is tilted heavily towards larger firms in the commodity and

manufacturing sectors (Bodnar, Hayt and Martson 1996). In their last survey from

1998, they found no evidence that the number of firms using derivatives had

declined over time (Bodnar and Martson 1998). They also concluded that the use

of derivatives in the service industry increased significantly faster than for other

industries. New questions were raised in this survey, especially related to foreign

currency.

Bartam, Brown and Fehle provide international evidence on financial derivatives

usage from 2006. The findings indicate, inter alia, that derivative usage can have

significant effects on other firm decisions such as the level and maturity of debt,

dividend policy, holdings of liquid assets and the degree of operating hedging.

Their analysis also examines the question of what motivates the use of financial

derivatives by corporations (Bartram, Brown and Fehle 2009).


Page 4

!"#"$%&'()*(+'"(,#"()-(-.*$*&.$/(0"%.1$+.1"#(.*(2)%3"4.$*(-.%5#6(%.#7(5$*$4"5"*+(

practice is limited. One significant contribution was conducted by Norges Bank in

the summer of 2004 and the results were summarized in 2005 by Børsum and

Ødegaard (2005). Their survey investigated how Norwegian firms can protect

themselves against flutctuations in currency markets and what measures were

undertaken. They find that 91 % of the responding firms use some form of

currency hedging technique, whether it is financial derivatives, natural hedging or

operational hedging etc. However, the most used technique is financial derivatives

which 61 %, of the firms reported using. They also find that larger firms use

financial derivatives to a greater extent than smaller firms, which is consistent

with international empirical findings. This fact is most often supported by the

argument of economies of scale. Larger firms have more resources available to

provide knowledge about derivatives and afford the use of them. Also, firms with

economies of scale in applying and continuing risk management operations are

found to use more derivatives (Adams 1999) (Pennings and Garcia 2004). They

further find that 86 % of the firms protect themselves against currency volatility in

income and cost. 43 % of the companies say that the motive for protecting against

currency fluctuations is to reduce the risk for the owners. Only 3 % of the

companies report that their motive is to speculate in the currency market.

Storm (2011) was the first to investigate Norwegian, private, non-financial firms

that are of medium-size. His findings support that firm size matters for the use of

derivatives. He also finds that different types of industries use derivatives on

different levels. Furthermore, the study concludes that the degree of foreign

exposure will impact the use of derivatives.

Methodology

Currently, there exist no database containing information about Norwegian,

8%.1$+"( -.%5#6(,#"()-(0"%.1$+.1"#9( :*()%0"%( +)( #+,0;(0.--"%"*+( %"/$+.)*#'.8#( .*( +'.#(

market, data needs to be collected. We find that the optimal way to answer our

research question and the hypotheses inherent therein will be through a survey.

Last year, the first survey regarding this topic was conducted by Storm (2011).

We will replicate the survey this year with some modifications.


Page 5

Questionnaire

The data will be collected through a similar questionnaire that will be based on

Storm´s survey, but with some modifications. Modifications of certain questions

are mainly due to poor response to the questions last year. We also modify some

questions to increase the degree of information that can be retrieved, and add new

questions for additional hypotheses testing. We will use the same software

(Questback 2012) as Storm (2010) to build and distribute the questionnaire.

In order to define our population we use the European Commissions definition of

firm size. They define firm size depending on headcount, turnover or balance

sheet total. Based on turnover, medium-sized enterprises are defined as having a

turnover between EUR10 million and EUR 50 million. This corresponds to

approximately NOK 100 million and NOK 400 million. By using Proff Forvalt, a

web site containing firm !and accounting information on Norwegian companies,

we can obtain the relevant population for our survey. By setting the relevant

"#$%&#$'()* +,#$-'%&* .$#/)0* 1$%2* '334'5* %4#36-&#* 7&%1&&3* 89:* ;<<* /$55$63* '3=*

NOK 400 million, excluding firms defined by NACE codes as financial firms), we

obtain a population of 4 237 firms (Proff Forvalt 2010).

The greatest concern regarding the questionnaire is the response rate. It is

expected that there will be non-responses, for example due to a refusal to

participate in the research, a lack of e-mail addresses or other reasons (Saunders,

Lewis and Thornhill 2009). In their survey, Eriksen and Wedøe (2010) made a

comparison of survey responses, and found that the average response rate of a

total of 10 surveys was 31.3 %. The lowest response rate was 20.7 % while the

highest was 76.6 %. Storm (2010) achieved a response rate of 25.7%. He used a

probability sample approach where 2000 firms were randomly selected out of a

total population of 5000 (Storm 2011; 7). In this survey we will include the total

population of 4 237 with the goal of increasing the number of firms responding.


Page 6

Bibliography

Adams, Don. "Why Corporations Should Hedge." ASX Perspective (Macquarie

University), 4th quarter 1999: 29-32.

Bartram, Söhnke M., Gregory W. Brown, and Frank R. Fehle. "International

Evidence on Financial Derivatives Usage." F inancial Management Vol.38, no. 1

(Spring 2009): 185-206.

Bodnar, Gordon M., Gregory S. Hayt, and Richard C. Marston. "1998 Wharton

Survey of Financial Risk Management by US Non-Financial Firms." F inancial

Management Vol.27, no. 4 (Winter 1998): 70-91.

Børsum, Øystein G., and Bernt Arne Ødegaard. "Valutasikring i norske

selskaper." Penger og Kreditt, January 2005: 29-40.

Carter, David A., and Joseph F Sinkey Jr. "The Use of Interest Rate Derivatives

by End-users: The case of Large Community Banks." Journal of F inancial

Services Research Vol.14, no. 1 (July 1998): 17-34.

circa.europa.eu. CIRCA.

http://circa.europa.eu/irc/dsis/nfaccount/info/data/esa95/en/een00070.htm

(accessed January 5, 2012).

Colquitt, L.Lee, and Robert E. Hoyt. "Determinants of Corporate Hedging

Behavior: Evidence from the Life Insurance Industry." Journal of Risk &

Insurance Vol.64, no. 4 (December 1997): 649-671.

Eriksen, Krister, and Ola Wedøe. "Foreign exchange risk management: How are

the largets non-financial companies in Norway managing their foreign exchange

rate exposure?" June 2010: 1-114.

Euromoney. Euromoneycountryrisk. June 3, 2011.

http://www.euromoneycountryrisk.com/ (accessed January 5, 2012).


Page 7

Géczy, Christopher, Bernadette A. Minton, and Catherine Schrand. "Why Firms

Use Currency Derivatives." Journal of F inance Vol.52, no. 4 (September 1997):

1323-1354.

Mian, Shehzad L. "Evidence on Corporate Hedging Policy." Journal of F inancial

& Quantitative Analysis Vol.31, no. 3 (September 1996): 419-439.

Nance, Deana R., Clifford W. Smith, and Charles W. Smithson. "On the

Determinants of Corporate Hedging." Journal of F inance Vol.48, no. 1 (March

1993): 267-284.

Pennings, Joost M.E., and Philip Garcia. "Hedging behavior in small and medium-

sized enterprises: The role of unobserved heterogeneity." Journal of Banking &

F inance Vol.28, no. 5 (May 2004): 951-978.

Proff Forvalt. PRO F F F ORVALT - utvidet firma- og regnskapsinformasjon. 2010.

http://www.forvalt.no/foretaksindex2/Default.aspx?show_advanced=1&search_ty

pe=segmented&search_result_type=stacked#searchresult (accessed January 9,

2012).

Questback. Questback Norge. 2012. http://www.questback.com/ (accessed

January 9, 2012).

Saunders, Mark, Philip Lewis, and Adrian Thornhill. Research methods for

business students. Fifth edition. Essex: Pearson Education Limited, 2009.

Storm, Johan Herman. "Survey on Financial Risk Management - Evidence on

Derivatives Usage by Norwegian Non-financial Firms." December 2011: 1-45.

Stulz, René M. Risk Management & Derivatives. 1. South-Western College/West,

2002.

Wysocki, !"#"$%&'%(Managerial Motives and Corporate Use of Derivatives: Some

)*+,"-."/%0123%45567%4-35.


Page 8

Appendix

Progress plan

Below is a schedule on our intended work progression on the master thesis.

January

- Produce the questionnaire in questback

- Obtain a list of all the 4 237 firms and belonging e-mail addresses

- Provide a list of firms where we could not obtain the belonging e-mail address

- Send out the questionnaire

F ebruary

- Send a reminder to firms that have not yet responded

- Start to structure current available data

- Initiate writing on parts of the thesis which does not require data

March

- Close survey and structure data

- Start hypothesis testing

April ! May

- Start to write results

June

- Finish first draft by June 20th

July

- Finish our thesis by the end of July


Page 9

Questionnaire

1) Does your firm use derivatives for financial risk management (futures,

swaps, options etc.)?

a. Yes

b. No

2) Please indicate the most important reason for not using derivatives:

a. Inefficient exposure to financial risk.

b. Exposure more effectively managed by other means

c. Derivatives are too complex for our business

d. Accounting matters

e. Concern to investors

f. Cost of managing the derivatives

g. Others, please specify

3) If your firm uses operational hedging, how is the operational hedging done

by your firm? (If you do not use operational hedging, please skip question)

a. Change in price strategy

b. !"#$%&'($')*+,-./0'1(23

c. Adjust to different markets and market segments

d. Order goods in different currencies

e. Change in suppliers

f. Charge customers more in NOK

g. Moving the firm or part of the firm abroad

h. Borrow or buy foreign currency

i. Other, please specify:

4) Approx. how many countries does your firm have subsidiaries in?

a. 0

b. 1-5

c. 6-10

d. 11-15

e. 16-20

f. More than 20

g. 4,$50'6$,7

5) How many subsidiaries of your firm are based abroad?

a. 0

b. 1-5

c. 6-10

d. 11-15

e. 16-20

f. More than 20

g. 4,$50'6$,7


Page 10

6) What share of your firms! revenue, cost, equity and liabilities are in

foreign currency?

None 25% 50% 75% 100% Not applicable

Revenue

Cost

Equity

Liabilities

7) How is the use of derivatives in your firm compared to before the financial

crisis of 2008?

a. Higher

b. Lower

c. Approx. the same

d. Do"!#$%"&'

8) Do you feel that your firm is financially constrained?

a. Substantially

b. Somewhat

c. A little

d. Not at all

e. (&"!#$%"&'

9) When approaching the following risks, how would you describe the

derivatives strategy when managing these risks? Which of the following

strategies best describes how your firm approaches the use of derivatives

to manage the following risk?

Interest Rate r isk Foreign E xchange r isk Commodity r isk Equity r isk

Exposure not managed

with derivatives

The firm has a formal

predefined strategy

of handling this type of

risk

The firm deals with this

type of risk on a

)*+,-to-*+,-$.+/0/

Other strategy

Not applicable


Page 11

10) Which of the following risks does your firm use derivatives to hedge?

a. Interest rate risk

b. Exchange-rate risk

c. Commodity risk

d. Equity risk

e. Other, please specify

11) For interest rate risk, what type of contracts does your firm use?

Not Applicable Never used Sometimes Used Regularly used Frequently used

a. Forward/Futures

b. Swaps

c. Options

d. Other, please specify:

12) For exchange-rate risk, what type of contracts does your firm use? Not Applicable Never used Sometimes Used Regularly used Frequently used

a. Forward/Futures

b. Swaps

c. Options

d. Other, please specify:

13) For commodity risk, what type of contract does your firm use? Not Applicable Never used Sometimes Used Regularly used Frequently used

a. Forward/Future

b. Swaps

c. Options

d. Other, please specify

14) How often does your firm review their a hedge? (approx.)

a. Once per day

b. Once per week

c. Once per month

d. Once every 2 months

e. Once every 6 months

f. Once every year

g. Less

h. Depends on the derivative, no formal strategy

i. Not until maturity

j. !"#$%&'#"(


Page 12

15) What percent of your derivatives have the following maturities? (All

derivatives position). 0% 1-25% 26-50% 51-76% 76-100% Not !"#$%&'#"(

applicable

90 days or less

91 to 180 days

181 days to one year

One to three years

More that three yrs.

16) How often does your firm transact in the interest rate derivatives market

to:

Not Applicable Never Sometimes F requently

Swap from fixed rate to floating rate

Debt

Swap from floating to fixed rate debt

Fix in advance the rate on new debt

Reduce cost or lock-in rates

based on market view

17) Please indicate which of the following contracts your firm has used in the

last year for the following exposures: Foreign exchange risk Interest rate r isk Commodity r isk Other risk

Standard European

Style

Standard American

Style

Average Rate

(price) Options

Basket Options

Barrier Options

Option combinations

!"#$%&'#"(


Page 13

18) Does your firm have a documented firm policy with respect to derivatives?

a. Yes

b. No

c. Other, please specify

d. !"#$%&'#"(

19) Is speculation with derivatives allowed in your firm? (actively taking

derivatives positions for profit)

a. Yes

b. No, not allowed

c. Other, please specify:

d. !"#$%&'#"(

20) How frequently is derivatives activity reported to the Board of Directors?

a. Never

b. Monthly

c. Quarterly

d. Annually

e. As needed

f. Not applicable

g. Other, please specify

21) How frequently does your firm value your derivatives portfolio?

a. Daily

b. Weekly

c. Monthly

d. Quarterly

e. Annually

f. As needed

g. Other, please specify:

h. !"#$%&'#"(

22) Please provide an estimate for the cost of managing the use of derivatives

within your firm (in NOK per year)

a. Unsure

b. Prefer not to answer

c. 0-1M NOK per year

d. 1M-2,5M NOK per year

e. 2,5M-5M NOK per year

f. 5M-10M NOK per year

g. 10M-25M NOK per year

h. 25M-50M NOK per year

i. More than 50M NOK per year


Page 14

23) Do you estimate the gain from using derivatives to be larger than the cost?

a. To a large degree, yes

b. To some extent, yes

c. It more or less balances

d. The costs exceed the gain to some extent

e. The cost clearly exceed the gain

f. !"#$%&'#"(

24) What is the most important reason for your firm to use derivatives? (Please

range from most to less important).

a. Reduce volatility in income/cost

b. Reduce volatility in cash flow

c. Reduce risk of financial problems

d. Reduce risk for owners

e. Make budgeting/accounting easier

f. Reduce liquidity risk

g. Other, please specify

h. !"#$%&'#"(

25) Does your CEO and CFO receive performance based compensation?

CEO CFO

a. Yes

b. No

c. Other, please specify

26) What kind of performance based compensation does your CEO and CFO

receive?

CEO CFO

a. Stock options

b. Cash Bonus

c. (Restricted) stock

d. Options

e. Other, please specify

27) What is the composition of compensation?

CEO CFO

a. Salary _____%

b. Cash bonus _____%

c. Stock options ____%

d. (Restricted) stock _____%

e. Options _____%

f. Other (please specify) _____%

g. Other (please specify)_____%

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