risk management in the gold mining industry

Created by Mbaakoh Longinu, Coventry University

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Dedication

To my dear father and mother Mr & Mrs Longinu Fombi


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Acknowledgement

I am extremely grateful to Mr Robert Evans of the Coventry Business School for

inspiring and guiding me through this dissertation. His perspective and guidance

have been invaluable. Thank you to Mr Azoh-mbi Solomon and Mr Akonde John

Best for their words of encouragement and support. This study has also

benefited immeasurably from the comments and suggestions of Ijeoma, Karen

and Agnese. Finally special thanks to Amanpreet for reading through the first

draft. Her suggestions were extremely helpful and insightful.


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Table of Contents

Dedication.............................................................................................................1 Acknowledgement.................................................................................................1 Table of Contents..................................................................................................3 Abstract.................................................................................................................5 List of Tables ........................................................................................................7 List of Figures .......................................................................................................8 1.0 Introduction .....................................................................................................9

1.1 Research Question .................................................................................................. 12

1.2 Research Objectives................................................................................................ 14

2.0 Literature Review..........................................................................................15 2.1 Risk Management Theories .................................................................................... 16

2.11 Financial Distress Model .................................................................................. 16

2.12 Tax Incentives and Hedging ............................................................................. 17

2.13 Management Incentives and Risk Aversion ..................................................... 19

2.14 The Underinvestment Problem ......................................................................... 20

2.15 Financial Sophistication Hypothesis................................................................. 21

2.2 Empirical Evidence on the Determinants of Corporate Risk Management............ 21

2.21 Empirical Evidence from Surveys ........................................................................ 22

2.22 Empirical Evidence from Cross Sectional Studies ........................................... 22

2.3 Empirical Evidence in the Gold Mining Industry................................................... 25

3.0 Research Methodology .................................................................................26 3.1 Research Perspective .............................................................................................. 27

3.2 Research Approach ................................................................................................. 28

3.3 Research Design...................................................................................................... 29

3.4 Data Collection Methods ........................................................................................ 31

3.5 Sample Description................................................................................................. 32

3.61 Measuring Financial distress............................................................................. 34

3.62 Measuring Investment Opportunity .................................................................. 34

3.63 Measuring Tax Convexity................................................................................. 35

3.64 Alternatives to Risk Management..................................................................... 35

3.7 Construction of the dependent variable .................................................................. 37

4.0 Data Analysis ................................................................................................41 4.1 Univariate Analysis................................................................................................. 42

4.2 Regression Analysis................................................................................................ 44

4.3 Presenting Regression Results ................................................................................ 46

4.31 Results of Financial Distress Variables ............................................................ 47

4.32 Results of Investment Opportunity Variables................................................... 49

4.33 Results of Firm Size.......................................................................................... 49

4.34 Results of Taxation ........................................................................................... 50

5.0 Conclusions ..................................................................................................51 Appendixes 1 Global Positions in OTC derivative market...................................55 Appendix 2 Calculations of Independent Variables.............................................55 Appendix 3 Company year end derivative positions ...........................................57 Appendix 4 Determination of Portfolio delta........................................................58


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Appendix 5 Summary of Pooled Data .................................................................61 List of References and Links...............................................................................62


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Abstract

Purpose – In the last two decades a number of studies have examined the risk

management practices within the gold mining industry. For instance some

studies report on the use of derivatives in the North American Gold mining

companies. Yet, another group of researchers have investigated the

determinants of corporate hedging policies. This and other studies of similar

focus have made important contributions to the literature. This dissertation uses

four mining companies including one based in South African to shed light on

some determinants of corporate hedging. The determinants examined include

financial distress hypothesis, the Underinvestment Problem and the Tax

Incentives to hedging. Furthermore the report investigates the existence of

alternatives to risk management.

Design/methodology/Approach- This dissertation presents the results of case

study of four companies: Barrick Gold, AngloGold Ashanti, Kinross Gold

Corporation and Agnico-Eagle Ltd. Using the linear regression model the work

focuses on testing for statistical significance some of the theoretical determinants

of corporate hedging decisions. Furthermore, it investigates the extent to which

the results are consistent or inconsistent with previous empirical works.

Findings- The results indicate that companies with high leverage are more likely

to hedge consistent with the financial distress model. However the results also

indicate an inverse significant relationship between cash costs and hedging. That

is over the period examined companies’ reduced hedging activity despite

increases in production contrary to popular theory. The results also show that

larger firms are likely to hedge than smaller firms. Large firms benefit from scale

economies and that information and transaction considerations have more

influence on hedging activities than the cost of raising capital. Other effects

measured such taxes, investment opportunity and cash balance found little

evidence supporting the theoretical models underpinning them.

Research Limitations - As with any case study, the small sample size severely

limits the power of generalisation. Furthermore, the researcher could not verify if

the linear regression model was the most appropriate for data analysis. Further


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research could improve the power of the tests by including more detailed

variables, different time spans and larger sample size.

Originality/Value – To highlight the determinants of corporate risk management

in the gold mining industry using four cases in environment of rising gold prices.

Paper type - Dissertation


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List of Tables

Table 1 10 Year gold price history in US $ per ounce……………………………11

Table 2 Firm yearly gold production……………………………………………….32

Table 2 Summary of variables……………………………………………………..35

Table 4 Sample data on Barrick Gold risk management activity……………….37

Table 5 Portfolio delta calculation of Barrick Gold……………………………….38

Table 6 Descriptive Statistics of Pooled data…………………………………….45

Table 7 Determinant of degree to which gold mining firms engage in price risk

management using financial derivatives……………………………….46


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List of Figures

Figure 1 Firms facing concave and convex tax schedule………………………17

Figure 2 Firm yearly gold productions…………………………………………….32

Figure 3 Percentage gold production hedged by firms………………………….41

Figure 4 Firm sizes as measured by total assets………………………………..42

Figure 5 Relationship between Leverage, Cash balance and Hedging Factor.43


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1.0 Introduction

The corporate use of derivative products for risk management has grown rapidly

over the last two decades. In 2004, the notional value of all over the counter

(OTC) derivatives traded in domestic and international markets exceeded US

$221 trillion, an increase of more than 1100% on the 1996 figure of US$20

trillion. Corporate risk management is thought to be an important element of the


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overall business strategy both in financial and non financial institutions (El-Masry

2006). However, despite serious managerial and public policy implications, the

rationales behind firm hedging decisions have remained unconvincing and

mixed. Firms facing the same exposure have adopted different approaches to

financial risk management through the use of derivatives.

Derivatives have generally been used to manage three financial risks

♦ Commodity Price risks

♦ Interest rate risks

♦ Foreign exchange risk

Commodity price risk forms part of business risk. It can be readily defined as risk

faced by a business due the possibility of adverse changes in the price of

commodities (Stephens 2001). Commodities are divided into three broad

categories. The first category is agricultural products, the second category is

metals and the third category is energy.

Interest rate risk represents the companies’ exposure to fluctuations in interest

rates. The debt structure of firms will possess different maturities of debt,

different interest structures (such as fixed versus floating) and different

currencies of denominations. Interest rates are currency –specific. Hence the

multi currency dimension of interest rate risk is of serious concern to firms.

Similarly foreign exchange risk is the risk faced by a business due variability in

exchange rates. These risks could severely impact a firm’s financial stability.

Several financial instruments have been developed over the years to manage

these exposures. Hedging is the ‘taking of position, acquiring a cash flow, an

asset or a contract that will rise (fall) in value and offset a fall (rise) in the value of

an existing position (Moffett, Stonehill & Eiteman 2004:199)’. Hedging therefore

protects the owner of an existing asset from loss. However it also eliminates any

gain from an increase in value of the asset depending on the instrument

employed. A brief review of the more widely instruments are presented below.


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Forward market (contract)

The forward market is an over the counter trade involving contracting today for

the future purchase or sale of a commodity or exchange rate. It is an agreement

between a buyer and a seller for delivery of specified quantity and quality product

at an agreed upon place and date in the future, in return for payment of an

agreed upon price. They are not exchange traded and can be tailor-made to suit

both parties. This feature distinguishes it from futures contracts, which are

standardized contracts traded on an exchange. However commodity forward

market does have some disadvantages such as credit risk to both parties. The

use of forwards is associated with linear strategy of risk management as this

eliminates all exposures the pay off is certain.

Options

An option is contract giving the owner the right, but not the obligation, to buy or

sell a given quantity of an asset against a premium at a specified price (strike

price) at some time in the future. There are two basic types of options namely

calls and puts. An option to buy the underlying asset is a call, and an option to

sell the underlying asset is a put. Because the option holder does not have to

exercise the option if it is to his disadvantage, the option has a price, or premium.

Swaps

A swap is an agreement between two parties to exchange a periodic stream of

benefits payment over a prearranged period. The payments could be based on

the market value of an underlying asset. The two parties to the contract are

called the counterparties. Swaps are mostly used to manage interest rate

exposure. Other derivative products commonly employed in financial risk

management include futures, spot deferred contracts and synthetic products

such as collars and floors. These products are used differently depending on the

industry and type of risk faced. This research will seek to examine some of the


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theoretical rational for hedging in the gold mining industry in the context of an

increasing gold price trend.

1.1 Research Question

There are several reasons to examine this industry:

♦ There is only one major source of risk- the risk of a fall in the price of gold.

Table 1 illustrates the gold price movement over the last decade.

Table 1

10 Year gold price history in US $ per ounce

www.goldprice.org

Table 1 shows the movement of gold price over the last decade. The 1990s saw

gold prices averaging US$300. Prices picked up in early 2002 and have

maintained the upward trend. A second reason for studying the gold mining

industry is that


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♦ There exist liquid markets for derivatives based on gold and so there are

hedging vehicles available to hedge the risks.

♦ Gold mining firms provide detail information on their hedging activities

(more so than most other industries, as they provide details of their

hedging activities in their quarterly reports).

♦ Even though many gold producers hedge the price risk, some do not,

leading to strongly opposing views among mining firms on the desirability

of hedging.

♦ Gold is a unique commodity, and the factors that influence its price make

for an interesting analysis of the advantages and disadvantages of

hedging

A substantial amount of research has been carried to test the various corporate

theories on risk management in the gold industry with mixed results. Theorists

continue to advance new rationales for corporate risk management while

researchers seeking to test these theories have been held back by the lack of

reliable data (Tufano 1996). Furthermore the studies carried so far do not cut one

way or the other. Several theoretical rationales have been advanced for why

companies hedge. They include the financial distress theory which states

businesses with high debt levels tend to hedge more. Secondly the

underinvestment theory posits that businesses with investment opportunity

would hedge more so as to secure financing while the tax convexity explanation

suggest businesses facing a convex tax shield tend to hedge more to lower the

average tax bill. The three hypotheses constitute the shareholder maximisation

rational for hedging. Dionne and Garand (2002) and Allayannis and Weston

(2001) found evidence shareholder maximization hypothesis. On the contrary,

Tufano (1996) found little evidence in the gold mining industry. The second

rational for hedging developed by Smith and Stulz (1985) is concerned with

managerial incentives and risk aversion. Once again there is empirical evidence

is mixed. This dissertation will highlight the aspects of the shareholder


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maximization rational by seeking the answer the following questions using four

gold mining firms.

1. What is the extent of hedging in the gold mining firms?

2. What firm characteristics significantly impact on hedging decision?

3. Why do some firms’ hedge and others do not?

4. Are there alternatives to hedging?

1.2 Research Objectives

The questions discussed above constitute the basis on which the following

objectives will be studied.

♦ Examine for statistical significance the financial distress theory

♦ Testing of the Underinvestment theory in the pooled data

♦ Investigate the significance tax shield on the hedging variable

♦ Examine the use of alternative strategies to risk management

This research is divided into five chapters. Chapter 1 introduces the concepts

corporate risk management by identifying the different types of exposures

faced by firms and the nature of the instruments used for its management.

Additionally, discussion on the research questions and objective is examined.

Chapter 2 highlights the dominant theories of why companies hedge including

an evaluation key empirical studies and literature on hedging. This is followed

by examination of the evidence in the gold mining industry. In the light of the

discussion in chapter 2, chapter 3 describes the methodology to be applied.

This is carried out by reviewing the firm characteristics (variables) that theory

would use to explain the cross sectional disparity in risk management

choices. Chapter 4 examines using poled data variation financial risk

management practices. This is undertaken by testing for significance the


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several firm characteristics when regressed against the extent of hedging as

measured by firm delta. The results are evaluated in evaluated in the context

of other studies. The last chapter concludes the research with a discussion on

the implications of the findings for current theory and subsequent research on

risk management. In addition, a detailed appendix depicting on the

computations used in this research is provided.

2.0 Literature Review

Hedging with financial derivatives is an integral part of most risk management

structures. However the debate about its merit has been the subject of numerous

academic discussion with both proponents and detractors coming to separate


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conclusions. Two main groups of financial theory on hedging theory exist, most

of which arrive at optimal hedging policies by introducing some friction to the

classic Modigliani and Miller model. The first group assumes that managers

hedge to maximise firm value while the second group predicts managers hedge

for personal diversification purposes, or to maximise their personal utility (Stulz

1984 & Tufano 1996). According to Miller and Modigliani paradigm, risk

management is irrelevant to the firm. Shareholders can do it on their own, for

example, by holding well diversified portfolios. An extension of the shareholder

maximisation theory is examined by Bartraun, Brown and Fehle (2004) who

suggest that firm’s hedge after acquiring a certain level of financial sophistication.

This section looks the theoretical motivations of hedging including the factors that

might lead to more or less hedging. This followed by a review of some seminal

works on hedging. The chapter ends with a discussion on the empirical evidence

on hedging in the gold mining industry.

2.1 Risk Management Theories

Corporate risk management is underlined by number theoretical underpinnings

such as the financial distress models, Underinvestment theory, Tax incentives,

financial sophistication and managerial incentive and risk aversion. These

theories have been the subject extensive research in both financial and non

financial firms.

2.11 Financial Distress Model

Volatilities in cash flows can lead firms into situations where available liquidity is

insufficient to meet fixed payment objectives such as wages, and interest rate

payments especially for firms with huge amount debt. High leverage firms are

most likely to face difficulties servicing debt in a falling gold price environment

because of the debt covenants (Smith and Stulz 1985). Financial risk

management can reduce the probability of such occurrences and thus lower the

expected value of costs associated with expected financial distress by lowering

cash flow variability (Smith & Stulz 1985). These costs include bankruptcy,


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reorganisation or liquidation and situations in which the firm faces direct legal

costs. Warner (1997) finds that these direct costs of financial distress are less

proportional to firm size, implying that small firms are more likely to hedge. On

the hand Block and Gallagher (1986) and Booth, Smith and Stulz (1984) argue

that hedging programs exhibit informational economies of scale and that larger

firms are likely to employ managers with specialized information to manage a

hedging program. Therefore the relation between firm size and hedging remains

an empirical question.

2.12 Tax Incentives and Hedging

The structure of the tax code can make it beneficial for companies to hedge and

therefore maintain some level of cash flow predictability. If a firm faces convex

tax function, then hedging that reduces the volatility of taxable income reduces

the firm’s expected tax liability (Smith and Stulz 1985). Graham and Smith (1998)

and Mayers and Smith (1982) argue that for firm facing some form of tax

progressivity, when taxable income is low, its effective marginal tax rate will be

low. But when income is high, its tax rate will be high. If such a firm hedged, the

tax increase in circumstances where income would have been low is smaller than

the tax reduction in circumstances where income would have been high thus

lowering expected taxes. From their analysis of 80000 firm observations, they

found in approximately 50% of the case, corporations face convex effective tax

functions and thus an incentive to hedge. In approximately 25% of the cases,

firms face linear tax functions. The remaining firms face concave tax functions.

Firms are most likely to face convex tax functions when:

1. Their expected taxable income is near zero

2. Their income are volatile

3. Their income exhibit negative serial correlation (hence the firm is likely to

shift between profits and losses).


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Much of the convexity is induced by the asymmetric treatment of profits and

losses in the tax code. That is a zero tax rate on negative income, moderate

progressivity and constant rate thereafter. The convex region is extended by tax

preference items like investment tax credits and deferred taxation. Figure 1 and

1a illustrates the tax liability for firm facing either a convex or concave tax shield

Figure 1

Firm facing concave and convex tax schedule

Adapted from (Smith & Stulz 1985: 293)

Figure 1a illustrates firm facing concave tax shield and figure 1b depicts the firm

facing convex tax shield. This simplistic illustration highlights the benefits of

hedging when firm is facing a convex tax schedule. The tax liability T1 for convex


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schedule is less than for concave schedule leading tax savings. In fact Graham

and Smith found that among firms facing convex functions, average tax savings

from five percent reduction in volatility of taxable income were about 5.4 percent

of expected tax liabilities. In extreme cases these savings exceeded 40 percent.

2.13 Management Incentives and Risk Aversion

Managerial attitude to risk has been found in some studies to be a significant

factor in determining the extent of hedging in some firms (Merton 1973 and

Tufano 1996). Stulz (1984) and Smith and Stulz (1985) argue that managers are

often unable to diversify firm specific risks. Most senior managers derive

substantial wealth from the firm and consequently their financial position is highly

undiversified. Consequently risk aversion may cause some managers to deviate

from acting in the best interests of shareholders by allocating resources to hedge

diversifiable risk (Stulz, Mayers & Smith 1985). They argue that unless managers

are faced with proper incentives they will not maximise shareholder wealth.

When a risk adverse manager owns a large number of the firm shares, his

expected wealth is significantly affected by variations in the firms expected

profits. Hedging changes the distribution of the firm’s payoffs locking in an

expected cash flow, and therefore changes the managers expected utility. These

arguments imply that, all else been equal, managers with more wealth in firm’s

equity will have a greater incentive to hedge the firm’s risks.(Christopher, Minton

& Catherine 1997: 1326).Thus firms that are closely held will be more likely to

use derivatives. Consequently Smith and Stulz (1984) predict a positive relation

between managerial wealth invested in the firm and the use of derivatives as the

managers’ end of period wealth is more a linear function of the value of the firm.

In support of the managerial ownership hypothesis Tufano (1996) contends that

not only the level of management’s equity ownership, but also the form by which

that equity stake is held, is related to firm’s risk management choices. Firms

whose managers own more options tend to hedge less than those with equity

ownership. This is so because as long as managers hold options, they are

sheltered from downside risks. Therefore firms whose managers hold large

number of shares of stock may be willing to hedge than those holding options on


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the shares. Hence Smith and Stulz (1985) predict negative correlation between

option holdings and derivative usage.

This has serious implications for managerial compensation scheme because by

increasing equity component of managerial compensation, firms can align

managers’ incentives more closely with those of other shareholders. This

alignment enables optimal investment decisions to be taken.

2.14 The Underinvestment Problem

The investment and financing policies of a firm can be harmonized and

integrated by risk management to increase shareholder value (Froot, Scharfstein,

and Stein, 1993). They argue without risk management, firms will be forced

reject potential investment project; projects with positive Net Present Value.

Firms may underinvest because of expensive external cost of capital. When the

firms’ cash flow is low, obtaining additional financing is very costly inducing firms

to make suboptimal investment decisions. In this case derivatives can be used to

lower the cost of capital through the financing and investing decisions (Bartaum,

Brown & Fehle 2004). When leverage is high underinvestment problem can

occur. Establishing sound risk management policy can limit the underinvestment

costs by reducing the volatility of firm cash flow and firm value (Allayannis and

Weston 2001). Admittedly firms facing significant growth and investment

opportunities are likely to be plagued by the underinvestment problem (Bartraum,

Brown & Fehle 2004). Hence Froot, Scharfstein, and Stein’s (1993) theory

suggest that firms with key planned investment programs and costly external

financing would be inclined to use risk management to avert the need to access

costly external financing to continue these programs. They also argue that

smaller firms are likely to hedge more to avoid the expensive costs of external

financing. Various measures such as market to book ratio, research and

development expenses to sales ratio, capital expenditure to sales, net assets

from acquisitions to size are used for testing the underinvestment hypothesis.


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2.15 Financial Sophistication Hypothesis

Bartraun, Brown and Fehle (2004) propose this alternative theory after

examining empirical evidence on the theoretical motivations of firm use of

derivatives. They suggest that because of ambiguous results on theoretical

motivation for hedging, firms that hedge do so because of their ability to do so,

regardless of other firm characteristics. Financial sophisticated companies

described as firms with multiple industry segments, mature treasury and foreign

equity listings. It follows then larger firms are more likely to fulfil these

characteristics and are expected to hedge more than smaller firms. Their findings

are in contrast to Warner (1997) who suggested that from a financing perspective

small firms are expected to hedge more.

2.2 Empirical Evidence on the Determinants of Corporate Risk Management

Early research on the use of financial derivatives as risk management tool has

been inconclusive. The motivations and instruments used vary across industry

and geographic presence. This led to hypothesis put forward by Bartraum, Brown

and Fehle that firms simply hedge once a certain level of financial sophistication

is reached (2004).

Most empirical studies have followed the neoclassical work of Modigliani and

Miller (1958) where financial risk management at the firm level create

shareholder value when in inefficiencies in the capital market give rise to

deadweight costs born by the shareholders. In addition early studies test hedging

motives of firms on the basis of survey data. For instance, Bodnar et al. (1995);

Bodnar et al. (1996); Javlilvand et al (2002) surveyed derivative usage among

non financial firms.


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2.21 Empirical Evidence from Surveys Bodnar et al (1996). Survey 530 US non financial firms about the use of financial

derivatives. Their findings indicate that large firms tend to use over the counter

(OTC) products, while small firms tend to use a mixture of OTC and exchange

traded-products. They also find that 80 percent of firms use derivatives to hedge

firm commitments, and 44 percent of firms use derivatives to hedge the balance

sheet. One of the key goals of hedging with derivatives is to minimise cash flow

fluctuations. Similar survey evidence undertaken by Alkebaeck and Hagelin

(1999) on Swedish non financial firms found the use of derivatives to be more

common among larger than smaller firms and that the principal use of derivatives

is for hedging purposes consistent with Bodnar et al (1996). However Bodnar

and Gebhardt (1999) found distinctive differences between German and US non

financial firms including the primary goal of hedging firms, firms’ choices of

hedging instruments and the influence of market view when taking derivative

positions. The choice of instruments varies across industries. Based on

evidence for a global sample, non financial firms mostly use forwards (36

percent) to manage foreign exchange risk, while swaps (11 percent) and options

(10 percent) are less popular (Bartraum, et al, 2003). For interest rate

management, swaps are more frequently (29 percent); interest rate options are

used as well, but less often (7 percent). Commodity price derivatives are

generally used less frequently, and there are few differences across different

instruments (3 percent for futures; 2 percent for options) with some variation

across industry.

2.22 Empirical Evidence from Cross Sectional Studies

The majority of theoretical models of corporate risk management indicate that

derivatives use increases with leverage, the existence of tax losses, the

proportion of shares held by directors, and the pay out ratio. On the other hand,

the extent of hedging decreases with the interest coverage and liquidity (Smith &

Stulz, 1985; Froot et al, 1993; Nance et al., 1993).


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However, empirical studies find only weak and at times ambiguous evidence

consistent with theory. Mian (1996) finds that there is empirical evidence on the

determinants of corporate hedging decisions. Based on non survey data for a

sample of 3002 firms the study provides evidence, which emphasize that hedging

is desirable because it lowers contracting costs, financial distress costs (Mayers

& Smith 1982; Smith & Stulz 1987) and external financing costs associated with

capital market imperfections (Froot, Scharfstein & Stein 1993). The evidence is

strong with respect to financial distress theory but weak in respect of

underinvestment and tax models. However evidence is supportive of the

hypothesis that hedging activities exhibit economics of scale.

Grezy, Minton and Schrand (1997) analysed a sample of 372 Fortune 500 non

financial firms in the United States. They find that firms with greater growth

opportunities and tighter financial constraints are more likely to use derivatives to

reduce the variation in cash flows or earnings that might otherwise preclude firms

from investing in valuable growth opportunities. The evidence is in line with

underinvestment theory (Shapiro & Titman, 1986; Froot, Scharfstein and Stein

1993). The underinvestment cost explanation for optimal hedging suggests

without hedging firms are likely to pursue suboptimal investment projects. Hence

derivatives may provide a valuable benefit to firms that use them rationally

(Allayannis and Weston 2001).

Graham and Smith (1999) and Graham and Rogers (2002) using simulation

model investigate the tax incentive to hedge that a firm facing a convex tax

function, hedging that reduces the volatility of the taxable income reduces the

firm’s expected tax liability

Among firms facing convex tax functions, average tax savings from a five percent

reduction in volatility are about 5.4 percent of expected tax liabilities; in extreme

cases, these savings exceed 40 percent. However they point out any such

program must be compared to the cost of hedging. In addition for firms with

convex effective tax functions, the tax savings of hedging are not mutually


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exclusive from hedging the benefits of controlling the underinvestment problems,

increased debt capacity. In contrast Haushalter (2004) in a study of oil and gas

producers using the Tobit regression model found no conclusive evidence

between firm’s risk management policy and tax function. These results highlight

the difficulty in correctly capturing tax save due to hedging. Both the

measurement and analysis of the variable involve substantial statistical

challenges. Furthermore the complexities of certain tax code present an added

dimension to the problem.

Some studies focused on specific industries or individual firms benefit from the

availability of detailed data on exposure and corporate hedging activities.

Admittedly these data ensure the calculation of more precise measures of the

extent of hedging. In a study of a sample of 100 oil and producers in the US

Haushalter (2000) finds evidence of a positive correlation between the extent of

hedging and financial leverage supporting the theory that corporate risk

management is used to alleviate financing costs. Secondly a positive correlation

was observed between the decision to hedge and the total asset. This is

consistent with the notion that companies can face significant economies of scale

in hedging, particularly in setting up a hedging program and therefore increases

firm value. Contrasting these findings Jin and Jorion (2006) found no relationship

between derivative activities and firm value in the US oil and gas industry.

Similarly, Brown (2001) undertakes a clinical study of a US based manufacturers’

use of FX derivatives and finds little support for the financial distress or other

primary theories of risk management and instead proposes that earnings

management, competitive factors in the product market, or contracting efficiency

gains motivate hedging.

Clearly statistical support for popular theories of derivative use is mixed.

However Bartraum, Brown and Fehle found evidence supporting a ‘naïve’

hypothesis that firms simply hedge once a certain level of financial sophistication

is reached. Their study examines the use of derivatives by 7319 firms in 50


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countries that together comprise 80 percent of the global market capitalisation on

non financial companies. Their results are supportive of the theory that derivative

use increases firm value especially for firms using interest rate derivatives.

2.3 Empirical Evidence in the Gold Mining Industry

In this industry commodity price exposure is transparent and easy to hedge by

investors. Theory might predict that no firms manage gold price risk since

investors can diversify the way the risks. On the contrary risk management is

practised by over 85 percent of the firm in the industry (Tufano, 1996). Though

faced with identical price exposure gold mining firms have adopted very different

approaches to risk management. Hedging policy has been extensive studied in

the gold mining industry but the results have been at best weak and inconclusive.

Tufano, (1996) examined 48 North American firms and finds risk management

practices are consistent with some extant theory. He finds virtually no

relationship between risk management firm characteristics that value maximising

risk management theories would predict. In contrast managerial risk aversion

seems particularly relevant bearing out Smith and Stulz (1985) prediction that

firms whose managers own more stock options manage less gold price risk, and

those whose managers have wealth invested in common stock manage more

gold price risk. Another study of 44 North American gold firms from 1991 to 2000

Jin and Jorion (2006) found no relationship between hedging activities and firm

values as measured by Tobin’s Q. The Tobin Q is defined as the ratio of the

market value of the firm to the replacement cost of the assets, evaluated at the

end of the fiscal year. However, the findings of Dionne and Garand show that

seven variables (deferred taxation, tax save, production cost, dividend pay out

ratio, preferred shares, and firm size) related to maximizing the firm’s value

significantly affect the decision to hedge the price gold (2000). They considered

hedging decisions based on quarterly data and extended analysis over longer

period.


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Arguably empirical tests on the determinants of corporate hedging policy have

yield mixed results. The results are significantly influenced in some cases by the

sample size, complexity of variables measured and analytical model applied. In

the light of the mixed results it is important for further research to be conducted

especially in environment of higher gold prices. This research will be limited to

testing the financial distress, underinvestment and tax incentive theories.

3.0 Research Methodology

Research methodology involves a description of the process, variables to be

measured and analytical tools. It examines the relevance and appropriateness


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research design to objective. This section examines the perspective, approach

and research design adopted. Issues about sampling and data collection

methods are also explored. The latter part looks at different firm characteristics

and their relevance to the objective. Firm characteristics will be measured by the

construction of six explanatory variables and one dependent variable. These

explanatory variables will then be pooled and regressed against the extent of

hedging given by hedge ratio.

3.1 Research Perspective

Two research perspective; positivist and interpretivist are widely associated with

management research (Collis and Hussey 2005). The positivist approach seeks

the facts or causes of social phenomena, with little regard to the subjective state

of the individual. Furthermore the researcher assumes the role of an objective

analyst, making interpretations about data that have been collected in justifiable

manner (Saunders, Lewis & Thornhill 2003). As a result the positivist perspective

emphasises on a highly structured methodology to facilitate replication and

quantifiable observations that lend themselves to statistical analysis.

Critics of positivism argue that the social world of business and management is

far too complex to be defined by laws in the same way as the physical sciences

(Saunders et al 2003). They are argue that important insights into this complex

world is lost if such complexity is reduced to a series of law-like generalisations.

Interpretivism stresses the importance of complexity and uniqueness of business

situations (Bryman and Bell 2003). The approach emphasises the importance of

making sense of the world through our own experiences. It argues that if

businesses are unique and the business environment is always changing then

there is little value in law-like generalisations (Saunders et al 2003).

The positivist perspective will dominate the research as much of the variables

under investigation are easily quantifiable such as leverage ratio, quick ratio, and


28

portfolio deltas. However the use of simple linear regression model to evaluate

the relation severely limits the generaliasability of the results. On the other hand

the research will involve four companies in a particular context involving variables

influenced by people, economic and social factors. This can be seen as

interpretivist perspective. Hence both perspectives will guide the research

approach.

3.2 Research Approach

Research projects also involve the use of theory and the extent to which theory is

explicit in the design of the project raises important questions about the approach

being adopted. Two approaches have identified in literature: deductive and

inductive. The deductive approach entails development of a theory or hypothesis,

and designing a research strategy to test the hypothesis. On the other in

inductive approach theory is developed out of the data analysis.

The deductive approach involves the development of a theory that is subjected to

rigorous test. It owes much to the thinking of scientific research and positivism

(Saunders et al 2003). There are several important characteristics of deductive

approach. First, there is the search to explain the causal relationships between

variables. These variables must be quantifiable and hence quantitative data is

paramount to any analysis. In order to ascertain causality controls are introduced

to allow testing of hypothesis (Bryman and Bell). The controls would ensure the

direction of causality is ascertained. The final characteristic of deductive

approach is generalisation. However in order to generalise observations it is

necessary to select sample of sufficient numerical size.

Induction or theory building is the alternative approach to deduction. This

involves the data collection, analysis and as end result the formulation of theory.

One of the criticisms of deductive approach is that it enabled cause-effect link to

make between particular variable without an understanding of the way in which

humans interpret their social world (Saunders et al 2003). Developing such an


29

understanding is one of the strengths of deductive research. Research using

deductive approach would be particularly concerned with the context in which

events are taking place and involves the collection of qualitative data. This

approach owes much to interpretivism.

The deductive approach has been the dominant approach in research on the

corporate risk management. This can justified in the sense that the data is readily

available and empirical evidence on cause-link between variables are easier to

measure (Bryman and Bell 2003). However the mixed nature of evidence

suggests lack of detail understanding of the context and interaction among

variables which an inductive approach might shed light on.

For the purpose of this dissertation, the deductive approach will be applied for

number of reasons. First, time constraints does not permit elaborate data

collection needed to conduct inductive research. Secondly the data to be used is

readily available for analysis making it less risky than otherwise would be with

questionnaires and interviews associated with inductive approach.

3.3 Research Design

Research design can take several forms (Saunders et al 2003)

• Experimental design

• Cross sectional

• Longitudinal design

• Case study design

• Comparative design

• Survey

Early studies on hedging in the gold industry were based on survey literature

(Bodnar et al 1995; Alkebaeck and Hagelin 1999 Bailey, N. 1985). Tufano 1996

works on the practices of risk management in the gold industry focused on cross

sectional data among 48 North American Gold mining firms. Other cross


30

sectional studies includes Adam, Fernando & Salas 2007; Dionne, & Garand

2000).

This research will bear elements of case study design and cross sectional

design. Collis and Hussey (2003) define a case study as ‘an extensive

examination of a single instance of a phenomenon of interest.’ The importance of

context is essential as it focuses on understanding the dynamics present within a

particular setting. Yin (1994) identifies the following characteristics of case study

research:

� The research aims not only to explore certain phenomena, but to

understand them within a particular context

� The research does not commence with a set a of questions and notions

about the limits within which the case study will take place

� The research uses multiple methods for collecting data which may be

quantitative and qualitative.

However these characteristics are open to debate (Collis and Hussey 2005).

They argue that if one is taking a more positivist approach one might wish to

commence with strong theoretical foundation and specific research questions as

is the case with this dissertation. Saunders contends that case study design often

uses multi- cases to explore phenomena and is for a particular purpose.

Some elements of cross sectional design will be introduced into this research.

Cross sectional design ‘entails the collection of data on more than one case at a

point in time in order to collect a body of quantitative or quantifiable data in

connection with two or more variables, which are then examined to detect

patterns of association’ (Bryman & Bell 2003: 48). Cross sectional design permits

the examination of the relation between variables. Though establishing the

directional of causality is problematic, useful inferences can deduce using

appropriate statistical package.


31

One major criticism of the case study and cross sectional design is that

generalisation is difficult. This research seeks to test some of the theories on

hedging within among four selected companies. Clearly the results might not

generaliasable but important conclusions could be drawn.

3.4 Data Collection Methods

There are two main approaches to data collection: quantitative and qualitative

and each present a mixture of advantages and disadvantages. One of the main

advantages of quantitative approach to data collection is the relative ease and

speed with which collection can occur. However the analytical and predictive

power which can be gained from statistical analysis must be set against the

issues of sample representativeness, errors in measurement and quantification

Collins and Hussey (2005).

Qualitative data collection methods can be extensive and time consuming

although it can be argued that qualitative data in business research provides a

more ‘real’ basis for analysis and interpretation (Bryman and Bell 2003).

Moreover qualitative approach presents problems relating to rigour and

subjectivity. Data collected for this dissertation is mostly quantitative. There exist

several ways to collect data for research purposes. These include:

� Using secondary data

� Through observation

� Using interviews

� Using questionnaires (Saunders, M 2003)

This research involves analysis of pooled data over a five year period for four

companies. Consequently secondary data has been extensive used. The main

sources of data are company annual reports Form 10k disclosures and Form


32

20F. Other sources include yahoo finance, financial times and Edgar-online

database.

3.5 Sample Description

Selecting a sample is a fundamental element of a positivist study. The sample

size can determine the extent to which results are representative of the

population. Several sampling methods exist some of which include:

� Random sampling

� Systematic sampling

� Stratified sampling

� Quota sampling

� Cluster sampling etc (Collis & Hussey 2005)

Among the methods quota sampling will be employed in this research. The aim

of quota sampling is to produce a sample that reflects the industry in terms size.

Most of theories on hedging in the gold industry have an element of firm size.

Consequently size will be the key factor in the selection of the four companies.

Size will measured in terms of average production per year over the last five

years as well as the total assets. Large firms are considered as producing more

than two million ounces per year. Average production per year has been used as

close substitute for market capitalisation; the industry measure of firm size

(Tufano1996). Table 2 and figure 2 illustrate the company’s gold production in

million of ounces over five years.

Table 2

Firm yearly gold production

2002/m

ounces

2003/m

ounces

2004/m

ounces

2005/m

ounces

2006/m

ounces


33

Barrick

Gold

5.695 5.51 4.958 5.46 8.643

AngloGold

Ashanti

6.516 6.161 6.639

6.765 6.182

Kinross 0.888634

1.62041 1.653784 1.608805 1.476329

Agnico-

Eagles ltd

0.26 0.236653 0.271567 0.241807 0.245826

Data collected from annual reports Figure 2

0

2

4

6

8

10

Production

(million/Oz)

Barrick Kinross Agnico-

Eagles

AngloGold

Anshanti

Gold Production

2002

2003

2004

2005

2006

From Table 2 and figure 2 Barrick gold and AngloGold Anshanti are considered

large firms while Kinross and Agnico- Eagles are termed small firms. All firms

considered in the sample use derivatives as part of risk management strategy.

3.6 Construction of independent variables

This research is concerned with examining the significance of three of the major

determinants often cited to justify risk management activities all of which were

reviewed in chapter 2. It also seeks to test the existence of alternatives to risk

management. The determinants include

♦ Reduction in expected costs of financial distress;


34

♦ Increase investment opportunities or the underinvestment problem

♦ Reduction in expected tax payments

These outcomes are measured by constructing variables to capture their

likelihood. A summary description of the variables is shown in Table 3.

3.61 Measuring Financial distress

Gold mining firms face financial distress if the price of gold falls below their costs

of production often termed cash-costs. Cash costs is used on the basis that firms

with high production costs are less efficient and more prone to financial failure.

Additionally they are more likely to pay higher premiums to their partners. To

measure the relative likelihood of financial distress data is collected on the firm

cash costs. Another variable used to measure costs of financial distress is long

term debt weighted according to market value (Tufano 1996; Dionne & Garand

2000). Measuring cash costs is more closely related to the probability of financial

distress, whereas leverage has more to do with costs resulting from financial

distress, supposing such costs are proportional to the face value (Dionne &

Garand 2002). In this research long term debt is weighted according to total

assets because this value was readily available. Theory predicts positive

relationship between delta percentage and both cash costs and leverage. The

data on cash cost and leverage for the companies is presented in Appendix 2.

3.62 Measuring Investment Opportunity

Scharfstein and Stein (1987) theory predicts that firms with key planned

investment programs and costly external financing would be inclined to use risk

management to mitigate the need to access costly external financing to continue

these programs. A decline in the price of gold could severely obstruct the

investment programs of mining firms: exploration and acquisition. To measure

the significance and importance of these activities, information is collected on the


35

firms’ annual exploration expenditure and net cash acquisition activities from both

income and cash flow statements. Here again it scaled by total assets instead of

market value. If risk management is used to protect the continued funding of

these programs, theory predicts a positive relationship between these measures

and the delta percentages.

Similarly it is reasonable to suggest that transaction costs and information

asymmetries for smaller firms are greater than for larger firms; at least for

financing activities (Tufano 1996). Hence theory suggests from a financing

perspective an inverse relationship between firm size and delta percentage. That

is smaller firms might actively adopt risk management so as to avoid to seek

costly external financing. For this research firm size is measured by total assets

as shown in appendix 2. However reserves are common measure of firm size in

the gold industry.

3.63 Measuring Tax Convexity

Firms facing convex tax structure may lower average taxes through reducing

fluctuations in earnings. The complex nature of tax structure has meant that no

obvious variables have been agreed by researchers as appropriate for

measuring the convexity of the tax structure (Dionne & Garand 2000). Graham

and Smith (1999) formulated an equation allowing the computation of taxes

saved as a result of risk management. This variable should have a positive effect

on hedging. However for this research that used by Dionne and Garand (deferred

income tax) will be employed. Tax credits for losses reduce deferred income thus

the ratio measures the inverse of the tax function’s convexity. So a negative sign

is predicted. The data is shown in Appendix 2

3.64 Alternatives to Risk Management

Some firms pursue alternative activities that substitute for financial risk

management strategies. Diversification could be undertaken instead of hedging


36

or they could adopt conservative financial policies such as carrying a large cash

balance or maintaining a low leverage. Consequently firms that pursue this

strategy should be associated with less financial risk management and lower

delta percentage. However these do not represent explanations for financial risk

management, but rather controls for substitute forms of risk management. To

measure the existence of these alternatives information is collected on firm cash

balances and firm leverage as shown in appendix 2. The quick ratio represents

the degree of available cash balance in excess of current needs. This ratio is

given determined by (cash and cash equivalents + receivable) dividend by

current liabilities (McKenzie 2003).

Table 3

Summary of Variables

Delta %

The delta is the variation in the value of the portfolio of the derivative products for

every $1 variation in the price of gold. The aggregate value of the portfolio,

calculated yearly is then divided by the firm’s gold production over the same

period. The delta measures the level of derivatives used, that is the degree of

risk management

Cash Cost

Average Cost to produce an ounce of gold. The cash cost is used to capture the

likelihood of financial distress. When the price of gold decreases less efficient

firms will be may be unable to pay current expenses. A positive sign is predicted.

The annual value is used in this research.

Total Assets

Book Value of total assets is used as substitute for market value

Long term Debt/ Total assets

The book value of long term debt divided by the book value of the firm’s total

assets. Debt generates obligatory interest payments. If the firm is unable to make

its interests payments, it will get into financial hardship. This ratio therefore

captures financial distress factor. A positive sign is predicted


37

Exploration Expenditures/ Total Assets

The net cash payments for acquisitions divided total assets. This variable is

collected on an annual basis. This variable captures the growth opportunities

factor to the extent that exploration efforts are profitable. A non significant

relationship is expected, since the correlation between investment opportunities

and cash flows is positive in the gold mining industry. That is gold mining

companies benefit from natural hedging.

Acquisitions/ Total Assets

The net cash payments due to acquisition activities divided by total assets. A non

significant relationship is presumed, since correlation between investment

opportunities and cash flow is positive in the gold industry.

Deferred Income Tax/Total Assets

The deferred Income item of the statement divided by total assets.

Cash balance (Quick Ratio)

Cash plus cash equivalent divided by current liabilities (McKenzie 2003). Liquidity

can act as a cushion for financial disasters. They are thus substitute for risk

management and a negative sign is predicted

3.7 Construction of the dependent variable

The extent of risk management for each firm is determined by calculating the

effective amount of ounces of gold that each firm has hedged, or sold forward

denoted by delta. Rather than analyse each financial contract separately, the

portfolio delta is calculated. Portfolio delta gives a measure of reported financial

risk management activity and is regarded as the industry measure of investment

portfolio. The delta represents the change in the price of the portfolio with respect

to a small change in the price of the underlying asset (Hull 2003). Table 4

illustrates the risk management activities of Barrick Gold as reported in the

annual report. As of 2002, the firm had committed to sell 365000 ounces of gold

under forward sales commitments at an average price of $365. It had purchased

put options expiring before the end of 2002 with an average strike price of

$297/ounce on 160,000 ounces. Finally, it wrote call options on gold at a price of


38

$330/ounce on1330, 000 ounce, expiring before the end of 1991.The data for the

three others is shown in appendix 3. Table 5 displays the determination of

portfolio delta for Barrick Gold. For forward sales or spot deferred contracts, the

delta is equal to -1 because there is no uncertainty that the transaction will occur;

but for firms that hold options an effective portfolio delta must be calculated using

the Black and Scholes formula (see Table 5) which takes into the account that

the option will be exercised. Finally the firm’s total is calculated by dividing the

sum of ounces whose price is covered over the same period by the total

production for that year. Hence delta expresses the equivalent number of

ounces (3564880 in 2002) that the firm would need to hold in a replicating

portfolio to their hedged positions. In other words the firm had a gross short

position in gold equal to 3564880 ounces of gold sold. In aggregate for 2002, for

a $1 drop in the gold price, the market value of the firm’s gold portfolio should

rise by $3564880. Although quarterly data for instruments would be more

appropriate yearly data is collected because of time constraint. The portfolio delta

calculations for rest of the companies are shown in Appendix 4.

Table 4

Sample Data on Barrick Gold Risk Management Activity

2002 2003 2004 2005 2006

Ounces Price/US$ Ounces Price Ounces Price Ounces Price Ounces Price

Forward 2800 365 2800 340 1350 345 1550 335 1540 338

Put sold 1600 297 250 344 300 310 300 317 250 332

Call options sold 1330 303 425 363 570 328 550 336 1460 362

Table 4 shows the risk management activity of Barrick gold. All prices are in US$

and contracts are in thousands of ounces.

Table 5

Delta of Barrick Gold Derivative Portfolio


39

Year

Calculation

of Portfolio

Delta

2002 Ounces/ Delta Equivalent Ounces

Forward 2800000 -1 -2800000

Put sold 160000 -0.325 -52000

call sold 1330000 -0.536 -712880

Equivalent ounces 3564880

production 5695000

Delta percentage 62.59666

2003 Forward 2800000 -1 -2800000

Put sold 250000 -0.309 -77250

call sold 425000 -0.622 -264350


production 5510000


2004 Forward 1350000 -1 -1350000

Put sold 300000 -0.108 -32400

call sold 570000 -0.849 -483930


production 4958000


2005 Forward 1550000 -1 -1550000

Put sold 300000 -0.076 -22800

call sold 550000 -.889 488950


production 5460000


2006 Forward 1540000 -1 -1540000

Put sold 250000 -0.11 -27500

call sold 1460000 -0.974 -1422040


production 8643000


40


Black Scholes formula

Call delta = ∆c = TeN d

δ−)(1

where T

TrKSd

σ

σδ )2/()/ln( 2

1

+−+=

Put delta =∆p = - )( 1dN −

S = Stock price

K = Strike price

T = time to maturity assumed to be a year

r = risk free rate ( average 10 year US Treasury note rate 5.1%

=σ Volatility of gold (average return standard deviation of annual gold returns

over the past 30 years; 30.13%

=δ Annual Gold lease rate of 0.39%

(Nitzsche & Cuthberston 2003: 270)

Table 5 is a sample illustration of the portfolio delta of Barrick Gold at the end of

year. The calculations assume that the options expire at the end of the year. The

average spot prices for 2002, 2003, 2004, 2005 and 2006 are taken to be 310,

364, 410, 445 and 604 US$ respectively. The same methodology is used for all

four firms to enable consistent results. The consistency of approach ensures the

data collected is reliable and valid conclusions can be drawn. The variables

measured are recognised industry benchmarks and have been used by Tufano

(1996). However it might difficult to generalise conclusions from a case study

especially given the fact that only four companies are studied. To attempt to

overcome this factor the data will be pooled to obtain 20 observations as shown

in Appendix 5.

The calculated variables will be used in the next section to undertake univariate

analysis with further examination using the linear regression model. Regression

analysis will allow for the study of relation among variables including their

strength and significance.


41

4.0 Data Analysis This section involves the analysis and presentation of data. The first part

presents a summary of the key findings univariate results including descriptive

statistics. Any observable trend will be highlighted and further examined using


42

the simple linear regression model. The linear regression model allows

statistically significant observations to identify through the use of P value test.

The results of the regression will then be evaluated against the theoretical

models and previous empirical studies in the Gold industry.

4.1 Univariate Analysis

The univariate analysis uses five year averages of firm characteristics for all four

companies. Figure 3 below displays the extent of hedging among the companies

as measured by the percentage of production hedged.

Figure 3

Figure 3 illustrates the five year averages of percentage of total production

hedged as measured by the hedging factor delta. The Y axis shows the values of

the delta values while the companies are shown on X axis. The results show that

Barrick Gold and AngloGold Ashanti are more active users of derivative as

corporate risk management strategy while the remaining two companies have

been less reliant on derivatives with Agnico- Eagles selling almost all of its entire

production on the spot market. Secondly it could be argued from larger firms’

hedge more than smaller firms. Figure 4 displays the firm size values as

measured by total assets.

42.37

28.05

14.54

1.07 0.00

10.00

20.00

30.00

40.00

50.00

Barrick AngloGold Kinross Agnico

Percentage production Hedged

Delta Percentage


43

Figure 4

A quick look at figure 2 and 3 appear to show larger firms end to hedge more

than

It appears to show larger firms hedge more than smaller firms. This observation

will need further examination using the simple linear regression model.

Another variable that merits discussion from univariate analysis is the apparent

relationship between hedging and the company long debt. Leverage has been

used to measure the company debt position over the years. Figure 5 presents

five yearly averages for leverage and cash balances measured against the

hedging factor given by the delta percentage.

Figure 5

0

2000

4000

6000

8000

10000

U$ thousands

Barrick AngloGold Kinross Agnico

Firm Size as measured by Total Assets

Total Assets


44

The figure above show that firms employing little risk management are barely

distinguishable from those employing moderate to high levels of risk

management, apart from carrying higher cash balances as predicted by theory.

Analyses of the other variables do not yield substantial variations.

Given the correlations among the different firm characteristics, these univariate

tests do not reveal significant differences in firm traits, holding other attributes

firm attributes constant. Thus multivariate tests would be appropriate. However

this work the linear regression model will be employed.

4.2 Regression Analysis

Regression analysis is used primarily for the purpose of prediction. The goal in

regression analysis is to develop a statistical model that can be used to predict

the values of dependent variable or response variable based on the values of at

least on explanatory variable or independent variable (Berenson, Livine &

Krehbiel 2002). The nature of the relationship between variables can take many

forms ranging from simple to extremely complicated mathematical functions. In

this research the linear model will be applied. The relationship between the

variables could be positive linear in which case as the independent variable

increases the dependent also increases while a negative linear relationship will

Relationship between Leverage, Cash balance

And hedging factor

0.00 10.00 20.00 30.00 40.00 50.00

Barrick

AngloGold

Kinross

Agnico

Percentage

Quick Ratio

Leverage

Delta Percentage


45

involve one variable increasing while the other decreases. The sign coefficient of

regression given by the slope of the regression line is indicative of the nature of

the relationship. A positive coefficient relates to positive linear relationship and

vice versa. To examine the ability of the independent variable to predict the

dependent variable in the statistical model, several measures of variation have

been developed. One of the measures is the coefficient of determination. It

measures the proportion of variation of the dependent variable that is explained

by the independent variable in the regression model. For example of 91% implies

91% of the dependent variable can be explained by the variability independent

variable. This is an example of a strong positive linear between the two variables.

To test for the significance of the relationship the t Test for the slope is employed.

By setting a level of significance of 0.05, any p value < 0.05 is regarded as

significant. However for regression analysis to hold three assumptions have to be

satisfied

♦ Normality of Error

♦ Homoscedasticity

♦ Independence of Errors

The first assumption, normality, requires that the error around the line of

regression be normally distributed at each value of the independent variable (X).

The second assumption homoscedasticity requires that the variation around the

line of regression be constant for all values of X. This means that the errors vary

the same amount when X is a low value as when X is a high value. The third

assumption, independence of error, requires that the errors around the

regression line be independent of each value of X. This assumption is particularly

important when data is collected over a period of time. In such situations the

errors for a specific time period are often correlated with those of the previous

time period.

With these assumptions in mind the hedging factor is regressed against the firm

characteristics in order to create a regression equation that can used to generate


46

the strength of relationships among variables. The signs of coefficient indicate

nature of the relationship while the p value determines the significance. For this

research p = 0.05.This implies that all p < 0.05 is regarded as significant.

4.3 Presenting Regression Results In order to regress the variables the data five year observations for each

company was pooled to produce a sample of 20 observations.

Table6

Descriptive Statistics of Pooled Data

N Minimum

Maximum Mean Std. Deviation

Delta percentage 20 .0 62.6 21.401 18.9741 Cash cost ($US/oz 20 177 690 274.05 112.659 Leverage % 20 9.17 43.89 31.1283 10.89632 Exploration activities( %

20 .45 2.56 1.2549 .69873

Acquisition activities (%)

20 .02 222.85 24.8427 48.47892

Deferred Taxation (%)

20 .55 14.77 6.2420 4.34859

Quick Ratio 20 .53 8.51 2.7238 2.25439 Total assets

20 593.81 21373.00

4763.3965

4993.28998

Valid N (listwise ) 20

Table 6 illustrates the descriptive statistics of the pooled data. Average the firms

hedged 21.4% of production over the period under observation. The standard

deviation of 18.97% is indicative of high degree of dispersion among the firms

into active and moderate hedgers.

The results of regressing annual percentage delta against the firm characteristics

described in table 3 are shown in the table below.


47

Table 7

Determinants of the degree to which Gold Mining Firms Engage in Price

Risk Management Using Financial Derivatives.

INSTRUMENT VARIABLE

COEFFICIENT 2R P VALUE

Cash Costs ($/Oz) -0.077 21.2% 0.0406 Leverage (%) 0.8334 22.9% 0.03278 Exploration Activities

7.520 7.6% 0.2371

Acquisition Activities

-.0.03125 0.6% 0.7379

Deferred Taxation 0.3025 0.48% 0.7714 Quick Ratio -2.855 11.5% 0.1433 Firm Size 0.0016 19.9% 0.0481

The dependent variable for each firm year observation is the delta percentage,

the percentage of estimated production that has effectively been sold short

through financial contracts. The independent variables are defined in Table 3.

The second column gives the regression coefficient while R 2 represents the

coefficient of determination. The P value indicates the desired level of

significance. P values less than 0.05 are shown in bold face type.

4.31 Results of Financial Distress Variables

Table four suggests that the notions of corporate risk management on tendency

of financial distress have some predictive power among firms in the gold mining

industry. There negative sign for coefficient for cash suggest an inverse

relationship between cost of production as measured using cash costs and

hedging factor. This is contrary dominant theory dominant theory of a positive

relationship. A p-value of 0.0406 suggests this relationship is statistically

significant. In other words increases in production cost over the period of

observation have been followed by decrease in hedging activity not increase.

This result is contrary to Tufano (1996) who found no significant relationship


48

between cash costs and Dionne and Garand (2000) who found significant

positive relationship. Many factors can be attributed for the inverse relationship

ship among which is the consolidation in the gold mining industry. Previous

research was carried over periods when the gold mining industry was fragmented

and the disparity between efficient and non efficient firms was great. Cash costs

as measure probability of financial distress is based on the premise that less

efficient firms are more likely to face financial difficulties. But the spate of

mergers and acquisitions activities in the industry coupled with increasing gold

price trend could account for the inverse relationship. That is the companies have

tended to less hedge less though cash costs have been increasing. Similarly it

could be argued that because gold prices have been rising over the last five

years less efficient mines have been brought on board raising the average cost

of production sustained by the increased revenue from sale on the spot market.

In terms the of likelihood of financial distress as measured by the leverage

scaled by total assets the results as shown in figure 5 are consistent with theory .

A positive relationship is predicted and obtained in the results. This result is

statistically significant with a p value of 0.03278. This result is in line Dionne &

Garand (2000) and Haushalter (2000) who found a significant positive

relationship between hedging and leverage. That is higher levered firms tend to

hedge more than low levered firms. On the hand the results are contrary (Tufano

1996) Jin & Jorion (2006). Tufano (1996) argues that financial distress may be

less of a rational of risk management in the gold mining industry because

deadweight costs of bankruptcy may be small. As opposed to many other

companies gold mines own tangible assets the produce an ‘unbranded’

commodity product with no after market price, leading to little loss of franchise

value in terms of financial distress (Tufano 1996).


49

4.32 Results of Investment Opportunity Variables

The investment opportunities as measured by exploration and acquisition

activities show no statistical significant observation. This result is consistent with

Dionne and Garand 2002 but contrary to Allayannis and Weston (2001). Theory

predicts positive sign for both exploration and investment activities. A positive

relationship emerges for exploration activities and negative for acquisition

activities. The non significance of the results is therefore contrary to the notion to

that firms set up risk management programs to protect large on going investment

programs. However these results might have been influenced the values of the

acquisition figures used which represented the net cash figure shown in the cash

flow statements. Tufano (1996) used the dollar value of attempted acquisitions

from the acquisition and mergers database but found no significant relation

between hedging and acquisition activities.

4.33 Results of Firm Size

Total assets has been used a close substitute for firm size for the research.

Theory predicts an inverse relationship between firm size and hedging at least

from a financing perspective. As a rule, the largest firms have greater negotiation

power and thus low financing costs, which reduces the need to hedge. However,

most empirical research shows that larger firms tend to hedge more than smaller

firms in support of the financial sophistication hypothesis. From Table 6 a positive

significant relationship exists between firm size and use of financial derivatives.

This result strengthens the univariate observation that Barrick tended to hedge

more than Agnico- Eagle ltd. This positive association between firm size and

hedging suggests that the relationship between size and hedging is more

strongly influenced by economies of scale in risk management activities rather

than financial distress models or costs associated with raising capital (Allayannis

and Weston 2001)


50

4.34 Results of Taxation

The complex nature of the tax structure implies no consistent variables and

results have been maintained for the extent of taxes on corporate hedging

activities. Generally theory predicts an inverse relationship between firm’s

deferred taxes and hedging. Deferred taxes measure the inverse of convexity.

That is firms facing convex tax structure may lower average taxes by reducing

fluctuations in earnings. The results from table 5 show no significant relationship

between amount of deferred taxes and the extent of hedging. This implies the

value taxation cannot be used to predict the extent of hedging by firms. Previous

empirical research has found no consistent relationship between measures of tax

– schedules and degree of derivative use. Nance, Smith, and Smithson (1993)

find a positive relationship, but Grezy, Minton and Schrand (1995) do not.

Generally the predictive power of the tax save function has been difficult to

quantify with accuracy because of the complex nature of the tax system across

countries. This has serious limited the ability of research work into tax incentive

hypothesis to hedging.

4.35 Results of Cash balances (Quick Ratio)

Some firms pursue alternative activities as a substitute for financial risk

management. High cash balance can be used as buffer against adverse

movement in prices. Univariate analysis showed firms with higher cash balances

engaged less in risk management. This results appears be borne out after

regressing the quick ratio against the hedging variable. A negative sign emerges

as predicted that as firms accumulated high cash reserves they tend to hedge

less. This appears to be the case for Barrick Gold which has substantial reduced

its level of hedging with an increasingly high cash balance. That notwithstanding

a p value > 0.05 makes the not statistically significant.


51

5.0 Conclusions This paper studies the hedging activities of 4 gold mining companies between

2002 and 2006 and examines the relationship between gold hedging and three of

shareholder maximization theories which include the financial distress models,

underinvestment problem and tax save incentive. Pooling the data over the

period generated 20 observations on which regression was done.

The unique aspects of the study are:

♦ Use of financial statement footnotes to derive information on corporate

hedging decisions, instead of survey data as is typical of most previous

work on hedging

♦ Use of case study approach focuses on just four companies and unlike

previous works focusing on mostly North American Mining firms this paper

includes AngloGold Ashanti a South African based gold mining firm.

♦ This dissertation is among the few studies that have been carried out in

environment of rising gold prices and should shed considerable light on

the light on the validity of theoretical underpinnings of hedging in the gold

mining industry.

Out of the four companies two are classified as large firm and the remaining two

small firms based on their totals assets and yearly gold production. As far as the

empirical tests of the determinants of hedging are concern, the relevant question

is whether there is any statistical significance between firm characteristics (cash

costs, leverage, investment activities, exploration activities, taxes, size, and cash

balance) defined in this research as independent variable and the extent of

hedging as defined by the percentage of yearly production that has effectively

been shorted, delta.

The evidence is mixed is with respect to models of hedging emphasising the

likelihood of financial distress and determinant of hedging. Financial distress as


52

measured by the cash costs has a significant inverse relationship with extent of

hedging. Cash cost is used as measure of likelihood of financial distress with the

assumption that less efficient firms (high production cost) are more likely to

encounter financial difficulties and hence hedge more. However on the evidence

of this work as cash costs are rising hedging has reducing. This evidence is

contrary most works done in the gold mining industry including those of (Tufano

1996) who found positive but insignificant relations between cash cost and

hedging among North American firms and Dionne and Garand who revealed a

positive significant relationship between the two variables. Possible explanations

for this result could be the correlation between the price of gold and hedging. In

this sample the price of gold is inversely proportional to changes in delta

hedging. In other words as the companies have tended to hedge less as prices

have increased. Another explanation still related to price of gold could be as

prices of gold have risen less efficient mines have been brought on stream

leading to high average production cost of gold to rise.

The second variable used to measure financial distress leverage with a p value

of was found to be significantly positively correlated with hedging variable. The

result show firms with high debt are more likely to hedge consistent with the

works Stulz and Smith (1985) but at variance with Tufano who found no

significant relation. The mixed nature of the findings highlight the issues

associated with measuring an effect such financial distress.

Another important observation is the strong positive correlation between firm size

and hedging. This report supports the theory that larger firms are more likely to

hedge than smaller firms. Large firms benefit from scale economies and that

information and transaction considerations have more influence on hedging

activities than the cost of raising capital. The result contradicts Warner (1997)

external finance hypothesis that smaller firms are likely to hedge to avoid costly

external finance.


53

The underinvestment hypothesis is not supported in this dissertation as the

report found little evidence in its favour. These findings could have been

influenced however by the fact the value of acquisitions and exploration activities

were net cash positions report on the cash flow statements and not the gross

values.

This report also finds little evidence that hedging strategies are motivated by tax

saving strategies. Deferred Taxation which measures the inverse of tax convexity

yielded no significant result. However as discussed earlier the documented

problems associated with selecting a variable and different tax structures among

the companies impacted the results. The evidence in this study suggest that not

all aspects of the shareholder maximisation theory are valid hence the mixed

results.

Clearly the dissertation being a case study limits the generalization of results but

presents a fresh perspective on the debate on the merits and rational for risk

management. One major shortcoming of the project was the linear regression

method used. A more appropriate would have been multivariate analysis which

permits the interaction between variables to be isolated.

There exist correlations between the variables and some of were strong enough

to have influenced the results. Formal interpretations of these correlations require

specifications of a simultaneous equations framework. This report did not

examine the managerial aversion incentive to hedging which was found by

Tufano (1996) to be more significant influence on hedging decisions than the

concept of shareholder maximization. Further investigation of these issues is

suggested as a line for future research. Additionally most studies in the gold

mining industry have concentrated on north American mining firms more

research should carried including mines from other parts of the globe to improve

generalization of the results. This report makes that attempt by including South


54

African based mining company. Subsequent work might increase the power of

the tests used in this dissertation:

♦ Use of more data

♦ Use of continuous measure of hedging activity

♦ More effective separation and description of variables.


55

Appendixes 1 Global Positions in OTC derivative market

Appendix 1

http://www.bis.org/triennial.htm

Appendix 2 Calculations of Independent Variables

Barrick Gold 2002 2003 2004 2005 2006

Production/million (m) 5.695 5.51 4.958 5.46 8.643

Financial Distress

Cash costs/US $ 177 189 214 227 282

long-term debt/ ‘000’ 1927 1864 2711 3012 7173

Total assets/’000’ 5261 5358 6274 6862 21373

Leverage 36.628017 34.7891 43.21007332 43.89391 33.56103495

Investment opportunity

Exploration/US $ million 104 137 141 141 171

Exploration/total asset 1.9768105 2.556924 2.247370099 2.054795 0.800074861

Acquisition/US$ million 58 334 821 1180 1593


56

Acquisition/total asset 1.102452 6.233669 13.08575072 17.19615 7.453328966

Taxation Deferred Income tax/US$m 155 230 139 114 798

Deferred/Tax 2.9462079 4.292647 2.215492509 1.661323 3.733682684

Quick Ratio 2.373 3.308 4.18 2.403 2.087

AngloGold Ashanti

2002 2003 2004 2005 2006

Financial Distress

Cash costs/US$ 213 225 268 277 308

long-term debt/’000’ 1886 2009 3516 3815 3397

Leverage 43.35632184 37.60059891 37.42018 41.86327225 35.70903 Investment opportunity

Exploration/$ 28 40 44 45 61


Acquisition/US$ million 305 11907 4640 4355 80


Taxation Deferred Income tax/m$ 505 789 1158 1152 1275

Total assets/’000’ 4350 5343 9396 9113 9513

Deferred/total asset 11.6091954 14.76698484 12.32439 12.64128169 13.40271

Production/kg 184722 174668 188223 191783 175263

Quick Ratio 1.738 0.951 0.802 0.639 0.544

Kinross 2002 2003 2004 2005 2006

Financial Distress

Cash costs/US$ 201 222 243 275 319

long-term debt/m 92.4 164.5 533.4 607.6 570.6

Total assets/m 598 1794.5 1834.2 1698.1 2053.5

Leverage 15.45151 9.166899 29.0808 35.78117 27.78670562


Exploration/m 2.7 24.3 25.8 26.6 39.4


Acquisition/m 0.1 81.9 442.3 257 257


Production/m ounce 0.888634 1.62041 1.653784 1.608805 1.476329

Taxation Deferred Income tax/million 3.3 54.1 119.9 129.6 114.4



57

Quick Ratio 2.81 1.954 0.526 0.712 0.932

Agnico-Eagle Mines Ltd

2002 2003 2004 2005 2006

Financial Distress

Cash costs/US$ 182 269 56 43 690

long-term debt/’000’ 175.02 199.975 213.446 267.45 197.148

Total Assets/’000’ 593.807 637.101 718.164 976.069 1521.488

Leverage 29.47422 31.38827 29.72107 27.40073 12.95758

Production/ounce 260000 236653 271567 241807 245826

0.26 0.236653 0.271567 0.241807 0.245826


Exploration/m 3.766 5.975 3.584 16.581 30.414

Acquisition/million 66.609 105.907 94.832 66.539 299.723



Deferred Income tax 20.899 29.378 17.684 52.5 90.793


Quick Ratio 8.511 4.19 4.8 3.455 7.56

Appendix 3 Company year end derivative positions

Barrick Gold

2002 2003 2004 2005 2006

Ounces/’000’ Price/$ Ounces Price Ounces Price Ounces Price Ounces Price

Forward 2800 365 2800 340 1350 345 1550 335 1540 338

Put sold 1600 297 250 344 300 310 300 317 250 332

call options sold 1330 303 425 363 570 328 550 336 1460 362

AngloGold Ashanti Derivative Position

2002 2003 2004 2005 2006

kg price ounces price ounces price ounces price ounces price

Forward 61.727 299 15.289 307 18.056 313 34.021 315 30.428 333

Put purchased 10.238 312 5.808 352 796 291 757 291 563 291

put sold 3.732 273 12.752 307 7.466 317 6.21 397 4.354 339

call purchased 24.535 338 4.555 351 0.572 360 9.88 340 3.03 351

call sold 24.584 340 18.83 332 5.829 330 29.49 322 18.017 329


58

Kinross Gold Corporation

2002 2003 2004 2005 2006

Ounces price Ounces price Ounces price Ounces price Ounces Price

Forward 113 271 137.5 278 137.5 278 200 452

Calls 50 340 100 320 50 340 225 522 Put options bought 150 250 150 250 150 250

Agnico Eagle

2002 2003 2004 2005 2006

ounces price ounces price ounces price ounces price ounces price

Put options bought 136.644 260 190.2 260 152 260

Appendix 4 Determination of Portfolio delta

AngloGold Ashanti

2002 kg Delta Equivalent Ounces

Forward 61727 -1 -61727

Put purchased 10238 -0.386 -3951.868

Put sold 3732 -0.232 -865.824

Call purchased 24532 -0.506 -12413.192

Call sold 24584 -0.499 -12267.416

Equivalent ounces -91225.3

Production 184722


2003 Forward 15289 -1 -15289

Put purchased 5808 -0.337 -1957.296

Put sold 12752 -0.19 -2422.88

Call purchased 4555 -0.663 -3019.965

Call sold 18830 -0.727 -13689.41


Production 174668


2004 Forward 18056 -1 -18056

Put purchased 796 -0.074 -58.904

Put sold 7466 -0.122 -910.852

Call purchased 572 -0.874 -499.928

Call sold 5829 -0.845 -4925.505


59


Production 188223


Kinross Gold Corporation

Calculation of Portfolio Delta Kinross Ounces Delta Equivalent Ounces

2002 Forward 113000 -1 -113000

calls 50000 -0.498 -24900

Aggregate equivalent portfolio (ounces) -137900

Production 2002 (ounces) 888634


2003 Forward 273000 -1 -273000

Calls 100000 0.766 -76600



delta percentage 21.57478663

2004 Forward 137500 -1 -137500

Calls 50000 0.82 -41000

Put bought 150000 -0.0257 -3855




2005 Forward 200000 -1 -200000

Put bought 150000 0.01314 -1971

Production 1608805



2006 calls 225000 0.78257 -176078.25

puts bought 150000 0.010876803 -1631.52044

Aggregate equivalent portfolio (ounces) -177709.7704

Production 1476329


Calculation of Portfolio Delta Agnico-Eagles Mines Ltd Ounces Delta Equivalent Ounces

2004 puts 136644 0.0344 -4700.5536 Aggregate equivalent portfolio (ounces) -4700.5536 production 271567 Delta 1.7309

2005 puts 190200 0.0182 -3461.64


60

Aggregate equivalent portfolio (ounces) -3461.64

production 241807

Delta 1.431571

2006 puts 152340 0.00094 -143.1996 Aggregate equivalent portfolio (ounces) -143.1996 production 245826 Delta 0.058252

Black Scholes formula

Call delta = ∆c = TeN d

δ−)(1

where T

TrKSd

σ

σδ )2/()/ln( 2

1

+−+=

Put delta =∆p = - )( 1dN −

S = Stock price

K = Strike price

T = time to maturity assumed to be a year

r = risk free rate ( average 10 year US Treasury note rate 5.1%

=σ Volatility of gold (average return standard deviation of annual gold returns

over the past 30 years; 30.13%

=δ Annual Gold lease rate of 0.39%


61

Appendix 5 Summary of Pooled Data

Stock Ticker Year

Delta percentage

Cash cost ($US/oz

Leverage %

Exploration activities( %

Acquisition activities (%)

Deferred Taxation (%)

Quick Ratio

Total assets

ABX 2002 62.60 177.00 36.63 1.98 1.10 2.95 2.37 5,261.00

ABX 2003 57.20 189.00 34.79 2.56 6.23 4.29 3.31 5,358.00

ABX 2004 37.64 214.00 43.21 2.25 13.09 2.22 4.18 6,274.00

ABX 2005 19.85 227.00 43.89 2.05 17.20 1.66 2.40 6,862.00

ABX 2006 34.57 282.00 33.56 0.80 7.45 3.73 2.09 21,373.00

AU 2002 49.39 213.00 43.36 0.64 7.01 11.61 1.74 4,350.00

AU 2003 20.83 225.00 37.60 0.75 222.85 14.77 0.95 5,343.00

AU 2004 12.99 268.00 37.42 0.47 49.38 12.32 0.80 9,396.00

AU 2005 27.79 277.00 41.86 0.49 47.79 12.64 0.64 9,113.00

AU 2006 29.24 308.00 35.71 0.64 0.84 13.40 0.54 9,513.00

KGC 2002 15.52 201.00 15.45 0.45 0.02 0.55 2.81 598.00

KGC 2003 21.57 222.00 9.17 1.35 4.56 3.01 1.95 1,794.50

KGC 2004 11.03 243.00 29.08 1.41 24.11 6.54 0.53 1,834.20

KGC 2005 12.55 275.00 35.78 1.57 15.13 7.63 0.71 1,698.10

KGC 2006 12.04 319.00 27.79 1.92 12.52 5.57 0.93 2,053.50

AEM 2002 0.00 182.00 15.45 0.63 11.22 3.52 8.51 593.81

AEM 2003 0.00 269.00 9.17 0.94 16.62 4.61 4.19 637.10

AEM 2004 1.73 290.00 29.08 0.50 13.20 2.46 4.80 718.16

AEM 2005 1.43 410.00 35.78 1.70 6.82 5.38 3.46 976.07

AEM 2006 0.06 690.00 27.79 2.00 19.70 5.97 7.56 1,521.49


62

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45-47 http://www.anglogold.com http://www.barrick.com/ http://www.digitallook.com/ http://www.edgar-online.com http://www.gold.org/ http://www.kinross.com/ http://www.ft.com

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