Does Hedging Increase Firm Value? Evidence from the Gold Mining Industry

Does Hedging Increase Firm Value?
                    Evidence from the Gold Mining Industry

                                        YANBO JIN*
                                     PHILIPPE JORION**

                                     This version: July 2007

* Corresponding author, Department of Finance, Real Estate and Insurance, California State
University, Northridge
** Paul Merage School of Business, University of California at Irvine

Philippe Jorion                                     Yanbo Jin
Paul Merage School of Business                      Department of Finance, Real Estate and Insurance
University of California at Irvine                  California State University, Northridge
Irvine, CA 92697-3125                               Northridge, CA 91330-8379
Phone: (949) 824-5245                               Phone: (949) 285-4166
Fax: (949) 824-8469                                 Fax: (818) 677-6079
E-mail:                             E-mail:
Does Hedging Increase Firm Value?
               Evidence from the Gold Mining Industry


This paper studies the relationship between risk management practices and firm value for a
sample of 44 North American gold mining firms from 1991 to 2000. We first show that
hedging activities are recognized by the market, as hedging variables do have an impact on
stock price exposure to gold prices. Controlling for other variables, however, we cannot find
a positive relationship between hedging activities and firm values, as measured by Tobin’s Q
ratio. If anything, the relationship is negative. This result is inconsistent with theories
implying that hedging increases firm value. In this industry, commodity price exposure is
transparent and easy to hedge by investors, so there is no reason to expect that gold mining
firms hedging their gold price risk should have higher market values.

North American gold mining companies have vastly different hedging practices. At one

end of the hedging spectrum, companies like American Barrick have been hedging extensively

their gold production. At the other end, firms like Homestake Mining choose not to hedge their

gold production at all.

           This raises a number of questions. First, what could justify such different hedging

practices given that all of these companies have similar exposure to gold prices? Second, how

do these hedging policies affect company valuations, if at all?

           Financial theories attempt to answer the first question by following one of two groups of

explanations. The first group assumes that managers hedge to maximize firm value. In this

context, hedging can achieve this goal by reducing the cost of financial distress, by reducing

expected taxes, or by relieving the under-investment problem.1 The second group assumes that

managers hedge for personal diversification purposes, or to maximize their personal utility.2

These two classes of explanation have very different implications for the effect of hedging on

firm value. In one case, hedging should be associated with higher firm value, unlike in the other.

           Earlier empirical studies have focused on the first question, with mixed results. For

example, Tufano (1996) studies the derivatives hedging activities of the gold mining industry in

1990-1993 and finds little support for the firm value maximization theory. On the contrary, his

evidence is consistent with manager utility maximization. Managers who hold more stocks tend

to hedge more, while managers who hold more options tend to hedge less. Focusing on a broad

sample of firms exposed to interest rate and exchange rate risk, Graham and Rogers (2002) also

find that derivatives use is related to managers’ equity positions. They also report that firms

    See, for example, Smith and Stulz (1985) and Froot, Sharfstein and Stein (1993).
    See, for example, Stulz (1984).

hedge to increase debt capacity, which is consistent with the firm value maximization theory;

they report tax benefits that amount to an average of 1.1 percent of firm value.

       Recently, more attention has been focused on the second question, testing directly

whether hedging is related to firm value. The first evidence is provided by Allayannis and

Weston (2001). Using a sample of 720 large U.S. firms over a six-year time period, they claim

that firms using foreign currency derivatives enjoy a 5 percent hedging premium relative to

others. Given that the median firm has a market value of $4 billion, this corresponds to a

premium of $200 million, which is significant. More recently, Carter et al. (2005) report that US

airlines enjoy a 14 percent premium from hedging fuel cost, which is an even larger effect.

       These results have stimulated more research along this line. In particular, Guay and

Kothari (2003) claim that the potential gains from typical derivatives positions are small

compared to economic exposures. Their interpretation is that the observed increase in market

values is driven by other risk management activities, such as operational hedges, that are value-

enhancing and are positively correlated with derivatives positions, or is spurious.

        To shed more light on these issues, the natural resource industry provides a set of ideal

controlled experiments. Gold mining is a very homogeneous industry group, with high exposure

to gold prices. In addition, it does not offer much scope for vertical integration and

diversification, unlike the oil and gas industry. Gold price risk can be easily hedged by investors

if they so choose, using for instance exchange-listed futures. This raises the classic Modigliani

and Miller (M&M) question of why hedging with derivatives should add any value. Perhaps

value added is created because derivatives carry an unrecognized risk premium. Alternatively,

the firm may have expertise such that active trading activities create a profit.

In the U.S. oil and gas industry, Jin and Jorion (2006) found no relationship between

derivatives activities and firm value. The gold mining industry, however, provides a rich sector

for risk management studies. Petersen and Thiagarajan (2000) provide a detailed comparison of

the risk management practices of two gold mining firms. American Barrick hedges most of its

price risk using derivatives; Homestake Mining does not use derivatives. The authors, however,

also argue that risk management can take other forms than using derivatives. Homestake Mining

manages its risk using a combination of operational and accounting decisions. The firm manages

its extraction costs in line with the price of gold and reduces the volatility of its accounting

income through discretionary choices. The authors indicate that the equity exposure to gold

prices is almost identical for the two firms. Whether this result extends to the entire industry,

however, is an open question.

         Adam and Fernando (2006) show that firms with gold hedging programs have

consistently realized economically significant cash flow gains over the period 1990 to 2000.

This is because the term structure of gold forward or futures prices is typically in contango,

meaning that forward prices are systematically higher than spot prices. Thus gold producers are

selling at a forward price that is on average higher than the spot price, locking in a typical profit

of 3%.3 They report that their typical firm realized an average gain of $11 million, or $24 per

ounce of gold hedged per year, as compared to an annual net income of $3.5 million only.

         Whether this translates into higher market value has not been tested. This is not obvious,

however. If a risk premium exists in this market, it can be captured easily by buying gold

futures, which are now trading on the New York mercantile exchange and have been offered

since 1974.

 This is based on 1-year forward contract. This number implies is that spot prices did not rise, on average, by the
amount embedded in the forward premium over this period. In other words, there was a bias in the forward rate.
Whether this bias is a risk premium is another issue.

In this paper, we study the hedging activities of 44 North American gold mining firms

from 1991 to 2000, and evaluate their impact on equity exposure and firm value. We show that,

although hedging with derivatives reduces gold price exposure of most firms, hedging does not

seem to increase firm value. If anything, hedging seems to be associated with lower firm value.

       Our result offers further evidence that hedging commodity price does not automatically

increase firm value. This is consistent with Jin and Jorion (2006). In the gold mining industry,

similar to the oil and gas industry, gold price risk is an operating risk. Investors most likely

choose to invest in this industry simply to gain exposure to gold prices, which implies that firms

hedging this operating risk will not be valued more by its investors.

       As an intermediate step, we also test if hedging reduces gold mining firms’ stock price

sensitivity to gold prices. We offer evidence that hedging effectively dampens the gold price

exposure of gold mining firms, consistent with Tufano (1998). This shows that financial markets

do recognize the gold hedging activities taken by the firms. This result is important, as it

establishes a necessary condition to test the relation between hedging and firm value.

       The remainder of the paper proceeds as follows. Section I summarizes risk management

theories and related empirical evidence. In Section II, we describe the sample and explain

measures of hedging and firm value. Section III examines the effect of hedging on gold mining

stock exposure. Section IV examines the relation between firm value and hedging. Finally,

Section V provides some conclusions.

I. Risk Management Theories and Empirical Evidence

       Two strands of theory attempt to explain the motives of risk management. One is based

on firm value maximization theories. The other is based on managers’ utility maximization.

A. Firm Value Maximization Theories

       Firm value maximization theories states that firms can hedge to reduce certain costs or

capital market imperfections related to volatile cash flows. There are typically three lines of

explanations. First, hedging can reduce deadweight costs of financial distress (Mayers and

Smith (1982), Smith and Stulz (1985)). Second, hedging may also be motivated by tax

incentives. When firms face a convex tax function, hedging should help reduce expected taxes

(Mayers and Smith (1982), Smith and Stulz (1985)). Hedging can also increase a firms’s debt

capacity, therefore generating greater tax advantages from greater leverage (Leland (1998)).

       These two explanations imply that corporate hedging can add value when firms face

convex costs such as progressive taxation and bankruptcy costs. Similarly, MacKay and Moeller

(2007) argue that hedging can add value if revenues are concave in product prices.

       The third line of argument is that hedging may also help relieve the problem of

underinvestment, that is, when firms have many growth opportunities and external financing is

more expensive than internally generated funds (Froot, Scharfstein, and Stein (1993)). This

underinvestment problem arises when investment opportunities are negatively correlated with

cash flows. For instance, airlines suffer from underinvestment when opportunities to buy

distressed assets at a good price occur during a down cycle for the industry. The present value of

these saved costs should be reflected in a higher market valuation.

B. Manager Utility Maximization Theory

        Another strand of theory claims that hedging stems from the incentive of managers to

maximize their personal utility functions. Risk-averse managers may engage in hedging if their

wealth and human capital are concentrated in the firm they manage and if they find the cost of

hedging on their own account is higher than the cost of hedging at the firm level (Stulz (1984),

Smith and Stulz (1985)). According to this second group of theories, hedging should not affect

market values.

C. Empirical Evidence

        Earlier empirical literature focused on the relation between firm characteristics and

hedging, trying to identify which theory best explains actual hedging activities. Results have

been mixed. For instance, risk management activities are found to be more prevalent in large

firms. One would expect to find that small firms, which are more likely to experience financial

distress, would be more likely to hedge. Instead, hedging seems to be driven by economies of

scale, reflecting the high fixed costs of establishing risk management programs.4

        On the other hand, Dolde (1995) and Haushalter (2000) report a positive and significant

relation between hedging and leverage, consistent with the theory that hedging helps reduce

financial distress. Graham and Rogers (2002) provide evidence that tax convexity does not seem

to be a factor in the hedging decision but do find that firms hedge to increase debt capacity. This

evidence is in line with the second explanation above. Finally, both Nance, Smith, and Smithson

(1993) and Geczy, Minton, and Schrand (1997) find that hedging firms have greater growth

  These costs include hiring risk management professionals and purchasing computer equipment and software for
risk management. See, for example, Nance et al. (1993), Mian (1996), Geczy et al. (1997), Haushalter (2000), and
Graham and Rogers (2002). Brown (2001) estimates annual costs at about $4 million for a large multinational with
$3 billion in derivatives positions.

opportunities, which is consistent with the argument that hedging mitigates the potential

underinvestment problems.

       On the whole, however, there is mixed support for value maximization theories. Mian

(1996) surveys their implications and reports that the only reliable observation is that hedging

firms tend to be larger. Similarly, Tufano (1996) examines the hedging activities of gold mining

firms and finds no empirical support for the value maximization theory. Instead, he finds strong

evidence that supports the managerial risk-aversion theory, according to which managers who

hold more stock tend to undertake more hedging activities.

       In recent years, researchers have started to examine the direct relation between firm value

and hedging. Allayannis and Weston (2001) report that the market value of firms using foreign

currency derivatives is 5% higher than for nonusers, on average. This result is economically

important, but puzzling in view of the mixed empirical evidence on hedging theories. Graham

and Rogers (2002) argue that derivatives-induced debt capacity should increase firm value by

1.1% on average. However, as mentioned previously, the validity of these results is questioned

by Guay and Kothari (2003). More recently, Bartram, Brown, and Conrad (2007) examine a

large sample of 6,888 firms from 47 countries and find hardly any relationship between

derivatives hedging and firm value.

       More recently, Jin and Jorion (2006) examine a sample of U.S. oil and gas producers, and

document no association between hedging and firm value. In this industry, commodity

exposures are disclosed and easy to hedge by individual investors, so it is not clear why hedging

should be related to firm value. Likewise, commodity exposure in the gold mining industry is

fairly transparent and easy to hedge. The advantage of focusing on one industry is that this

automatically controls for endogeneity, or differences in the hedging propensity of firms across

industries. The question is whether hedging should be associated with differences in firm value

for gold mining companies.

        Callahan (2002) also looks at the effect of hedging but in a time series framework. He

first computes the alpha in a regression of mining firms stock returns on a market index. Second,

he regresses the alpha on a hedging variable and does not find much relationship. Such setup has

little statistical power, however, and does not directly addresses the relationship between the

level of firm value and hedging activities. With constant hedging, a firm could be worth a fixed

proportion more than a non-hedger, which implies that the relative rate of change in the price, or

alpha, would be no different. Instead, our paper looks directly at the price level embodied in the

Q ratio, which is a better measure of value added.

                                           II. Sample Description

        Our analysis is based on a sample of 44 gold mining firms in the United States and

Canada, over the time period of 1991 to 2000. This consists of the majority of gold mining firms

in North America over this period.

A. Hedging Variables

        Our measure of the extent of hedging activities comes from two sources. The hedging

variable from 1991 to 1998 is computed from quarterly surveys of hedging activities of North

American gold mining firms.5 These quarterly surveys document all the hedging activities that

gold mining firms undertake at the end of each quarter. They are summarized into a measure

called delta. The hedging activities include not only outstanding derivatives positions such as

 We would like to thank Georges Dionne for providing us with the hedging data. For detailed description of the
data, please see Dionne and Garand (2003).

forward sales of gold, put and call options, but also other hedges such as the gold loans with

reimbursement in gold over a number of years.

         A delta is calculated for each position at the end of each quarter. Delta equals −1 for

linear contracts such as forward sales or gold loans. For non-linear contracts such as put and call

options, delta is calculated using the Black-Scholes formula. The sum of deltas is then divided

by the estimated production for the rest of the year and the next two calendar years.6 This gives

us ∆, which measures the extent of hedging of gold price risk. As in Tufano (1996), we calculate

the annual ∆ by averaging the quarterly ∆ over a year, as most of the firm data are available only

on an annual basis.

         The hedging variable for year 1999 and 2000 comes from the data in Callahan (2002),

which is derived from annual reports. Similar to the delta documented above, this represents the

total gold hedging positions for each firm. As before, delta equals −1 for linear contracts such as

forward sales or gold loans, and is calculated by the Black-Scholes formula for options and

collars. Here, however, delta is computed on an annual basis, at fiscal year-end., dividing by the

estimated next three-year production.

         Table 1 displays the distribution of annual ∆. Out of 257 firm-year observations, 30 firm-

year observations (or 11.7% of the sample) have no hedging activity. On the other hand, 54

firm-years (or 21% of the sample) hedge more than 40% of the next three years’ production.7

On average, each firm appears 5.8 times (or 257 observations divided by 44 firms) in the sample.

B. Tobin’s Q Ratio

 This is because most of the hedging activities are designed to cover the production for the same time period.
 These firm-year observations are not independent, however, because firms typically adopt similar hedging
programs over time.

We measure firm value by Tobin’s Q ratio, defined as the market value (MV) of assets

over the book value (BV) of assets. The market value of assets is measured as the market value

of common equity plus the book value of other assets. Hence, the Q ratio is defined as:

             BV total assets - BV common equity + MV common equity
        Q=                                                                                    (1)
                                   BV total assets

       Table 2 provides summary statistics on firm size and Tobin’s Q ratios. Panel A shows

summary statistics for the entire sample. The average gold mining firm has $676 million in book

value of total assets, $991 million in market value of common equity, and 9.29 million ounces of

proven and probable gold reserves. The average Tobin’s Q ratio is 1.72. Panel B and C display

summary statistics for subsamples of firms with and without gold hedging activities. Out of 257

observations, 30 have no hedging activities and 227 have some hedging activity. Hedging firms

tend to be larger (average BV assets is $739m, MV equity is $1080m), compared to firms with

no hedging (average BV assets is $200m, MV equity is $322m). This matches evidence in other

markets that hedging is concentrated in larger firms. Because larger firms have lower default

risk, this contradicts the bankruptcy cost explanation of hedging. Instead, hedging programs are

probably explained by their fixed costs, which are more easily absorbed by larger firms.

       In terms of Tobin’s Q, hedging firms tend to have lower Q ratios (mean=1.69,

median=1.52), compared to non-hedging firms (mean=1.96, median=1.74). This observation is

not consistent with firm maximization theories of hedging.

       Before we proceed, we need to confirm that financial markets recognize firms’ hedging

activities. This can be tested by examining the effect of hedging on the firm’s stock price

exposure to gold price movements. Normally, firms with more extensive hedging should

experience lower sensitivity of their stock prices to gold price swings. The following section

tests this hypothesis.

III. Stock Return Sensitivity and Hedging

         Stock returns of gold mining firms are positively related to gold price changes. For

example, Tufano (1998) shows that for each 1% change in gold prices, gold mining stocks move

by 2% on average. We would expect that firms hedging with derivatives should experience

dampened exposure to gold prices. Similarly, a gold mining firm’s exposure to gold price should

be positively related to the amount of its gold reserves, scaled by its market value of equity.

         During the sample period of 1991 to 2000, gold price moved between $250 and to over

$400. In the first half of the 1990s, gold price was relatively stable, moving around $350 to

$400. However, the second half of 1990s saw big drop in gold prices, from $350 to $250 in less

than two years. Figure 1 shows the daily spot gold price between 1991 and 2002. Thus, this

sample period experienced substantial variations in prices, which is required for meaningful


         In this section, we first describe gold mining firm’s exposure to gold price movement,

and then test whether hedging reduces such exposure.

A. Exposure of Gold Mining Firms

         We estimate gold price exposures from a two-factor time-series model:

         Ri , t = α i + β m ,i * Rmkt , t + β gold ,i * Rgold , t + ε i , t                  (2)


     Ri ,t is the total stock rate of return for firm i in month t

     Rmkt ,t is the monthly rate of change in the stock market index, taken as the CRSP

         NYSE/AMEX/NASDAQ value-weighted monthly return

     Rgold , t is the monthly rate of change in the spot price of gold

Table 3 displays the cross-sectional distribution of estimated betas, using firms with

complete monthly data over the entire sample period of 1991-2000 (Panel C), and the two sub-

sample periods of 1991-1995 (Panel A) and 1996-2000 (Panel B). Gold beta is almost always

positive and significant across all sample periods, confirming that gold mining firms have

significant exposure to gold price movements. For example, for 16 firms with complete monthly

data between 1991 and 2000, the average mining stock moves by 2.67% for each 1% change in

gold price. Between 1991 and 1995, the average mining stock moves by 2.40% for each 1%

change in gold price. In the second half of 1990s, the average mining stock moves by 2.79% for

each 1% change in gold price. These numbers are remarkably consistent across subperiods.

B. Effect of Hedging on Gold Exposure

        Next, we test whether hedging reduces gold beta. The following equation is used for the


                                                                         gold reservei,t-1
         Ri ,t = α1 + β m * Rmkt ,t + (γ 1 + γ 2 ∆ gold ,i ,t −1 + γ 3                     ) Rgold ,t + ε i ,t   (3)


     ∆ gold ,i ,t −1 is the annual ∆ for firm i, representing the percentage of next three year’s gold

        production effectively hedged at the end of each year

    gold reserve i,t-1/MVEi,t-1 is the dollar value of reserves divided by the total market value of


  For increased precision, both the numerator and denominator are updated each month using changes in gold and
stock prices. The ratio is reset to the number reported at the end of each year.

Our main hypothesis is that hedging reduces gold beta. Therefore, we expect a negative

sign for γ2. In addition, the amount of gold reserves should increase a firm’s gold beta.

Therefore, we expect a positive sign for γ3. This equation is estimated for firm-years with

hedging activities only. Data were available for 24 firms for a total of 110 firm-years after

excluding certain outliers.9

         Table 4 displays the results of the estimation. Model A uses pooled cross-section time-

series regression, with standard errors corrected for correlation at the firm level and for

heteroscedasticity with the Huber-White-Sandwich estimator. Model B reports results using

fixed-effect regression. The results confirm our hypothesis. First, γ2 is negative and significant,

consistent with our conjecture that gold mining firms’ stock exposure to gold prices is effectively

reduced by hedging. Tufano (1998) also found that hedging reduces gold beta. Second, γ3 is

positive and significant, showing that a firm with larger gold reserves has greater exposure to

gold prices.

         These results confirm that markets do recognize the effect of hedging activities on the

stock exposure to gold prices. The results do not generalize the claim by Petersen and

Thiagarajan (2000) that gold price risk can be managed as effectively by other means than

derivatives contracts. In the next section, we test whether hedging firms are valued differently

from non-hedging firms.

  A firm-year observation is included if we have at least 3 consecutive monthly stock returns for the year. We
excluded firms with gold reserves of less than 1 million ounces. These are smaller firms with less frequent trading,
which unduly reduces the gold price exposure of the stocks. We also excluded outlier observations where the annual
gold beta on monthly returns is less than 0.5 (3 observations), or the gold beta is greater than 9 (2 observations), or
the gold reserve/MVE ratio greater than 30 (1 observation).

IV. Firm Value and Hedging

A. Univariate Analysis

       In this section, we test whether hedging firms have higher Tobin’s Q ratios than non-

hedging firms, using univariate analysis. Panel A in Table 5 presents the results of this

comparison. We find that hedging firms actually have lower Q ratios than non-hedging firms.

The difference between the median Tobin’s Q of hedging firms and non-hedging firms is −0.22,

with a p-value of 0.03 using Wilcoxon’s rank-sum Z-test. In addition, we find that hedging firms

are much larger than non-hedging firms. The median value of assets for hedging firms is twice

that of non-hedging firms.

       Table 1 reports the distributions of hedging activities. There seems to be a natural

grouping in terms of extent of hedging in the gold mining industry. At the low end of the

spectrum, there are 30 firm-years (out of 257 observations, or 11.7%) with no hedging

whatsoever. At the high end of the spectrum, there are 54 firm-years (or 21%) that hedge more

than 40% of their next three year projected production. Following Tufano (1996), we group the

observations into three categories: “no hedging” are firm-years with ∆=0; “modest hedging” are

firm-years with 040%.

       Panel B and Panel C of Table 5 compare firm size and Tobin’s Q ratios for different

groups partitioned by the extent of hedging. Panel B compares firms with “modest” hedging

activities to firms with no hedging activities. Panel C compares firms with “extensive” hedging

activities to firms with “modest” hedging activities. Across the two panels, firm size seems to be

monotonically increasing with the extent of hedging, while Tobin’s Q ratio seems to be

monotonically decreasing with the extent of hedging. For example, extensive hedgers, modest

hedgers, and non-hedgers have average asset value of $1,140 million, $614 million, and $200

million, respectively. On the other hand, the median Tobin’s Q ratio is 1.33, 1.54, and 1.74 for

extensive hedgers, moderate hedgers, and non-hedgers, respectively. The difference in Tobin’s

median Q ratios is statistically significant at 10% level for both panels.

        Since hedging firms tend to be larger, the difference in Tobin’s Q between hedging and

non-hedging firms may simply be a size effect. Specifically, the lower Q for hedging firms

might be reflecting a potentially negative correlation between Q and firm size. However, in our

sample, the correlation between Tobin’s Q and firm size as measured by total assets is 0.12, with

p-value of 0.05, which is positive. In addition, Panel D compares non-hedgers with hedgers best

matched in terms of firm size. Tobin’s Q is still lower for hedging firms, although not

statistically significant so. So, there is no reason to believe that the higher Q ratio for non-

hedgers is a size effect.

        In addition to firm size, other firm characteristics may potentially impact Q ratios as well.

In section B, we use multivariate regressions to control for other effects.

B. Multivariate Analysis

        We estimate two model specifications:

        Q = α + β × Dummy (=1 if hedging) + Σi γ i × Control_variablei + ε                              (4)

        Q = α + β × Delta_production           + Σi γi × Control_variablei + ε                          (5)

        Delta_production is the annual percentage of the next three year’s gold production


        Using the hedging dummy variable is a very simple, binary, measure of any hedging

activities. In contrast, Delta_production is a more continuous variable. It takes values of zero

for firms not engaged in any hedging whatsoever, but otherwise varies from zero up to 123

percent. According to firm value maximization theories, if hedging has a positive effect on firm

value, we should observe that firms derive more benefits from greater hedging, unless, of course

hedging is irrelevant.

       We use the natural log of Tobin’s Q ratio as the dependent variable, as the raw Q’s are

skewed to the right. We include the following control variables following Allayannis and

Weston (2001):

    1) Firm size: Previous empirical evidence on the relationship between firm size and firm

value is ambiguous. However, it is important to control for size because large firms are more

likely to hedge than small firms. The proxy is the log of total assets.

    2) Profitability: Profitable firms are more likely to have higher Q’s than less profitable ones.

The variable is taken as the ROA, defined as the ratio of net income to total assets. We expect a

positive coefficient on this variable.

    3) Investment growth: Firm value may also depend on future investment opportunities. We

use capital expenditure over total assets as a proxy. We expect a positive coefficient on this


    4) Access to financial markets: If hedgers have limited access to financial markets, their Q

ratios may be high because they are constrained to take only the projects with the highest net

present values. To proxy for a firm’s ability to access financial markets, we use a dividend

dummy that equals one if the firm paid dividend on common equity in the current year. In this

interpretation, the coefficient should be negative. On the other hand, dividends can be viewed as

a positive signal coming from management for growth prospects, which should imply a positive


5) Leverage: A firm’s capital structure may be related to its value. If the benefits of debt tax

shields outweigh the expected costs of financial distress, then leverage can increase the firm

value. On the other hand, if the costs of financial distress are perceived to be higher than the

potential tax benefit coming from debt, firm value can become lower with leverage. We use a

leverage variable defined as the book value of long-term debt over the book value of equity.

     Next, we add a new variable that is specific to this industry:

     6) Cash costs: Gold mining firms’ profitability is closely related to the cost of producing

gold. Cash cost refers to the dollar cost of producing one ounce of gold. This includes all direct

and indirect costs of mining, crushing, processing and general and administrative expenses of the

mine, including royalties and mining taxes.10 Cash costs vary with the quality of ore deposits

and operating efficiencies. Therefore, we expect that firms with lower average cash cost would

enjoy higher firm value. Thus, we expect a negative coefficient on this variable.11 However,

because of significant numbers of missing observations for this variable, Table 6 reports results

with and without this control variable.

         Table 6 reports the results of the regressions. It displays the results for all firm-year

observations, which include 43 firms. Similar to the results in univariate analysis, we see that

hedging is still negatively related to firm value, even after controlling for other firm

characteristics. All the coefficients on the hedging dummy and delta are negative. The

coefficient on the hedging dummy is significant at the 10% level in Model 1.12

   Cash costs exclude noncash items, such as depreciation, depleting and amortization, as well as interest expense,
corporate SG&A, exploration, and extraordinary costs.
   Gold mining firms report cash cost either at a per mine bases or for the company as a whole. If the figures are for
each mine, we compute a weighted-average cash cost for that year. However, because of significant numbers of
missing observations on this data, we report in Table 6 the regression results with and without this control variable.
   Apparently, American Barrick is an outlier, with 100 percent of its 3-year production fully hedged. As in Tufano
(1996), we also estimate the regressions without this firm. The coefficients on delta are still negative and are now
significant at the 1% level.

Several control variables also show a significant relationship to Tobin’s Q ratio. As

expected, firm’s profitability (ROA) and investment growth are positively related to the Q ratio,

indicating that firms with higher profitability and higher growth potentials are rewarded with

higher Q ratios. In addition, Q ratios seem to be positively related to firm size among gold

mining firms. We also see that cash cost is negatively related to the Q ratio, as expected,

although the relationship is not significant.

                                          V. Conclusions

       This paper studies the hedging activities of 44 gold mining firms between 1991 and 2000,

and examines the relationship between gold hedging and firm value. We first show that gold

hedging reduces mining firm’s stock exposures to gold prices. However, contrary to the

argument that hedging increases firm value, we do not find a positive association between

hedging and firm value, as measured by Tobin’s Q ratio. In fact, the relationship appears


       Our study is in line with the findings in Jin and Jorion (2006), who find no association

between derivatives hedging and firm value for a sample of oil and gas producers. Within the

gold industry, these results support the conclusions in Tufano (1996), who finds little empirical

support for theories claiming that hedging stems from firm value maximization motives. Instead,

he shows that hedging appears to be driven primarily by managerial risk aversion. If so, there

should be no association between hedging and firm value, which is confirmed by our empirical

analysis over an extended sample period.

       As in the oil and gas industry, the commodity price risk of gold mining firms is easy to

identify and hedge. Hedging at the firm level does not confer special advantages. Even if there

was a risk premium in gold forward contracts, such premium can be captured easily by investors.

The firm environment is closer to that described by Modigliani and Miller irrelevance conditions.

Under such conditions, it is hard to understand how hedging commodity price risk could increase

firm value. This is confirmed by the empirical analysis in this paper.


Adam, Tim and Chitru S. Fernando. 2006. “Hedging, Speculation and Shareholder Value.” Journal of
   Financial Economics 81, pp. 283-309.
Allayannis, George and James P. Weston. 2001. “The Use of Foreign Currency Derivatives and Firm
   Market Value.” Review of Financial Studies 14:1, pp. 243-76.
Bartram, Sohnke, Gregory Brown, and Jennifer Conrad. 2007. “The Effects of Derivatives on Firm
   Risk and Value.” Working Paper, Lancaster University.
Callahan, Matthew. 2002. “To Hedge or Not to Hedge...That Is the Question: Empirical Evidence from
   the North American Gold Mining Industry 1996–2000.” Financial Markets, Institutions &
   Instruments 11:4, pp. 271-88.
Carter, David A., Daniel A. Rogers, and Betty J. Simkins. 2006. “Does Hedging Affect Firm Value?
   Evidence from the US Airline Industry.” Financial Management 35:1, pp. 53-86.
DeMarzo, Peter, and Darrell Duffie. 1995. “Corporate incentives for hedging and hedge accounting.”
   Review of Financial Studies 8, pp. 743-771.
Dolde, Walter, 1995. “Hedging, leverage, and primitive risk.” Journal of Financial Engineering 4, pp.
Dionne, Georges and Martin Garand, 2003. “Risk management determinants affecting firms’ values in
   the gold mining industry: new empirical results.” Economic Letters 79, pp. 43-52.
Froot, Kenneth A., David S. Scharfstein, and Jeremy C. Stein. 1993. “Risk Management: Coordinating
   Corporate Investment and Financing Policies.” Journal of Finance 48:5, pp. 1629-58.
Geczy, Christopher, Bernadette A. Minton, and Catherine Schrand. 1997. “Why Firms Use Currency
   Derivatives?” Journal of Finance 52:4, pp. 1323-54.
Graham, John R. and Daniel A. Rogers. 2002. “Do Firms Hedge in Response to Tax Incentives?”
   Journal of Finance 57:2, pp. 815-39.
Guay, W. and S.P. Kothari. 2003. “How much do firms hedge with derivatives?” Journal of Financial
   Economics 80:3, pp. 423-461.
Haushalter, David. 2000. “Financing policy, basis risk, and corporate hedging: Evidence from oil and
   gas producers.” Journal of Finance 55, pp. 107-52.
Jin, Yanbo and Philippe Jorion. 2006. “Firm Value and Hedging: Evidence from the U.S. Oil and Gas
   Producers.” Journal of Finance 61:2, pp. 893-919.

Leland, Hayne. 1998. “Agency cost, risk management, and capital structure.” Journal of Finance 53, pp.
MacKay, Peter and Sara B. Moeller. 2007. “The Value of Corporate Risk Management.” Journal of
   Finance, Forthcoming.
Mayers, David, and Clifford Smith. 1982. “On the corporate demand for insurance.” Journal of
   Business 55, 281-296.
Mian, Shehzad. 1996, “Evidence on corporate hedging policy.” Journal of Financial and Quantitative
   Analysis 31, pp. 419-39.
Nance, Deana, Clifford Smith, and Charles Smithson, 1993. “On the determinants of corporate
   hedging.” Journal of Finance 48, pp. 267-84.
Petersen, Mitchell A. and S. Ramu Thiagarajan. 2000. “Risk Measurement and Hedging: With and
   Without Derivatives.” Financial Management 29:4, pp. 5-30.
Rajgopal, Shivaram. 1999. “Early evidence on the in formativeness of the SEC's market risk disclosures:
   The case of commodity price risk exposure of oil and gas producers.” Accounting Review 74:3, pp.
Simkins, Betty J. and Charles W. Smithson. 2005. “Does Risk Management Add Value? A Survey of
   the Evidence.” Journal of Applied Corporate Finance 17:3, pp. 8-17.
Smith, Clifford W. and Rene M. Stulz. 1985. “The Determinants of Firm's Hedging Policies.” Journal
   of Financial and Quantitative Analysis 20:4, pp. 391-405.
Stulz, Rene M. 1984. “Optimal Hedging Policies.” Journal of Financial and Quantitative Analysis,
   19:2, pp. 127-40.
Tufano, Peter. 1996. “Who Manages Risk? An Empirical Examination of Risk Management Practices in
   the Gold Mining Industry.” Journal of Finance 51:4, pp. 1097-137.
Tufano, Peter. 1998. “The Determinants of Stock Price Exposure: Financial Engineering and the Gold
   Mining Industry.” Journal of Finance 53:3, pp. 1015-52.

Table 1: Distribution of Hedging Activity in the Gold Mining Industry

This table displays the distribution of hedging activity measured by the delta-percentage. Delta-percentage
measures the fraction of gold production hedged by each firm for the next three years. From 1991 to 1998, data are
taken from quarterly surveys, averaging over the year. For the years 1999 and 2000, data are taken from annual

          Delta-Percentage                     Number of firm-year                  Percentage of
          (Firm-year observations)                observations                    total observations
          Exactly 0                                    30                               11.7%
          0.1-10                                       59                               23.0%
          10-20                                        52                               20.2%
          20-30                                        39                               15.2%
          30-40                                        23                                8.9%
          40-50                                        15                                5.8%
          50-60                                        11                                4.3%
          60-70                                         9                                3.5%
          70-80                                         3                                1.2%
          80-90                                         3                                1.2%
          90-100                                        8                                3.1%
          >100                                          5                                1.9%
          Total                                       257                                100%

          Mean                                          25.0
          Median                                        16.4
          Standard Deviation                            25.9
          Minimum                                        0.0
          Maximum                                      122.7

Table 2: Summary Statistics for Firm Characteristics

Panel A describes the sample of 44 gold mining firms from 1991 to 2000, with a total of 257 firm-year observations.
A subsample of firm-years with gold hedging activities is reported in Panel B. Firm-years without gold hedging
activities are displayed in Panel C. Total assets are measured as the book value (BV) of assets. Also shown is the
market value (MV) of equity. Gold reserves are reported in millions of ounces. Gold production hedged is the
annual average of quarterly delta-percentage from 1991 to 1998, and the end-of-year delta percentage for year 1999
and 2000. Tobin’s Q ratio is measured as the BV of total assets – BV of common equity + MV of common equity
divided by the BV of total assets.

                                          Panel A: All Firm-Years
                                  Obs.        Mean          Median        Std. Dev.     10th Perc.     90th Perc.
Total Assets ($m)                 257          676           280            950            49           1801
Market Value of equity ($m)       257          991           331           1734            36           3073
Gold Reserves (million oz)        174          9.29          3.34          14.03          0.60          26.52
Tobin’s Q                         257          1.72          1.54           0.89          0.81           2.82

                         Panel B: Firm-Years with Gold Hedging Activities
                                  Obs.        Mean          Median        Std. Dev.     10th Perc.     90th Perc.
Total Assets ($m)                 227          739            332           991            52           2081
Market Value of equity ($m)       227         1080            340          1822            35           3395
Gold Reserves (million oz)        156         10.07          14.03         14.56          0.60          26.52
Gold production hedged (%)        227           28             21            26             3             63
Tobin’s Q                         227          1.69           1.52          0.89          0.81           2.81

                       Panel C: Firm-Years without Gold Hedging Activities
                                  Obs.        Mean          Median        Std. Dev.     10th Perc.     90th Perc.
Total Assets ($m)                  30          200           157            188            41            393
Market Value of equity($m)         30          322           240            385            51            531
Gold Reserves (million oz)         18          2.57          1.04           4.19          0.33           4.30
Tobin’s Q                          30          1.96          1.74           0.88          0.89           3.35

Table 3: Statistical Properties of Stock Price Exposures

This table presents the statistical properties of the cross-sectional exposure coefficients from the time-series model
Ri ,t = α i + β m ,i * Rmkt , t + β gold ,i * Rgold , t + ε i ,t

Monthly stock returns from 1991 to 1995, 1996 to 2000 and 1991 to 2000 are used for firms with complete monthly data
during the period. Rmkt ,t and Rgold , t are the CRSP NYSE/AMEX/NASDAQ value-weighted monthly return and the
monthly change in the spot price of gold. Statistical significance is assessed for a one-sided hypothesis.

                                                 Panel A: 1991-1995 (25 firms)
                                                                   Beta_mkt    Beta_gold
                    Mean                                                0.46        2.40
                    Median                                              0.42        2.28
                    Standard deviation                                  0.40        0.67
                    Minimum                                            -0.43        1.26
                    Maximum                                             1.29        4.15

                    %>0                                                88%        100%
                    %>0 and significant at p
Table 4: Effect of Hedging on Gold Beta

This table displays the regressions of monthly stock returns on the market and gold price changes, with coefficients adjusted
for the effect of hedging and reserves. The model setup is
                                                                                 gold reservei,t -1
                       Ri ,t = α1 + β m * Rmkt ,t + (γ 1 + γ 2 ∆ gold ,i + γ 3                      ) Rgold ,t + ε i ,t
                                                                                    MVE i,t -1
Data includes 24 firms (110 firm-year observations) over the calendar year 1992 to 2001. Model A uses pooled cross-section
time-series regression, with standard errors corrected for correlation at the firm level and for heteroscedasticity with Huber-
White-Sandwich estimator; model B reports results using fixed-effect regression. T-statistics are reported in parentheses. *, **
and *** denote significance at the 10%, 5% and 1% level respectively.

                Independent variable                                  A: Pooled                      B: Fixed-effect
                R_mkt                                                     0.446                            0.456
                                                                         (4.72)***                       (5.59)***
                R_gold                                                    3.081                            3.100
                                                                         (9.06)***                      (16.61)***
                Delta*R_gold                                             -1.094                           -1.145
                                                                         (-2.26)**                       (-3.27)***
                [Reserve_(gold)/MVE]*R_gold                                0.057                           0.054
                                                                         (1.41)                           (2.71)***
                R-square                                                 38.89%                         40.67%
                Number of observations                                    1312                           1312

Table 5: Comparison of Gold Hedgers and Non-Hedgers
This table compares the means and medians of Tobin’s Q, total assets, and MV of equity for different groups of
firm-years in terms of their gold hedging activities. Panel A compares firms with and without gold hedging
activities. Panel B compares firms with modest hedging activities (defined as delta-percentage 
40%) to firms with modest hedging activities. Panel D compares hedging firms with non-hedging firms, with two
groups best matched in terms of firm size (total assets). Comparison of means is constructed using a t-test assuming
unequal variances; comparison of medians is constructed using Wilcoxon rank-sum Z-test. Two-sided p-values are

                     Panel A: Gold Hedging vs. Non-Gold Hedging Firm-Years
                                       Hedgers       Nonhedgers                     t-stat (mean)
 Variable                             (227obs.)       (30 obs.)      Difference   Z-score (median)    p-value
 Tobin’s Q (mean)                       1.69             1.96          -0.27            -1.55          0.13
 Tobin’s Q (median)                     1.52             1.74          -0.22            -2.11          0.03
 Total assets ($m, mean)                 739             200            539             7.26           0.00
 Total assets ($m, median)               332             157            175             3.25           0.00
 MV of equity ($m, mean)                1080             322            758             5.42           0.00
 MV of equity ($m, median)               340             240            100             1.62           0.11

                Panel B: Modest Gold Hedging vs. Non-Gold Hedging Firm-Years
 Variable                             Hedgers        Nonhedgers                     t-stat (mean)
                                     (173 obs.)       (30 obs.)      Difference   Z-score (median)    p-value
 Tobin’s Q (mean)                       1.73            1.96           -0.23            -1.30          0.20
 Tobin’s Q (median)                     1.54            1.74           -0.20            -1.83          0.07
 Total assets ($m, mean)                614             200             414             6.20           0.00
 Total assets ($m, median)              303             157             146             2.80           0.00
 MV of equity ($m, mean)                827             322             505             4.40           0.00
 MV of equity ($m, median)              332             240              92             1.56           0.12

            Panel C: Extensive Gold Hedging vs. Modest Gold Hedging Firm-Years
                                     Extensive         Modest
 Variable                            Hedgers           Hedgers                      t-stat (mean)
                                     (54 obs.)        (173 obs.)     Difference   Z-score (median)    p-value
 Tobin’s Q (mean)                      1.57             1.73           -0.16            -1.17          0.25
 Tobin’s Q (median)                    1.33             1.54           -0.21            -1.69          0.09
 Total assets ($m, mean)               1140             614             526             2.54           0.01
 Total assets ($m, median)              474             303             171             2.16           0.03
 MV of equity ($m, mean)               1891             827            1064             2.59           0.01
 MV of equity ($m, median)              371             332              39             0.61           0.54

Panel D: Gold Hedging vs. Non-Gold Hedging Firm-Years
                            Best Matched with Firm Size (Total Assets)
                                  Hedgers    Nonhedgers                  t-stat (mean)
Variable                          (30obs.)    (30 obs.)   Difference   Z-score (median)   p-value
Tobin’s Q (mean)                   1.78         1.96        -0.18           -0.68          0.50
Tobin’s Q (median)                 1.45         1.74        -0.29           -1.44          0.15
Total assets ($m, mean)            200          200           0             0.00           0.99
Total assets ($m, median)          156          157           -1            0.01           0.99
Delta_production (mean)            31%            0
Delta_production (median)          24%            0

Table 6: Hedging and Firm Value

This table presents pooled time-series cross-sectional regressions of firm value on hedging variables. The models used are:
                             Q ratio = α + β * Dummy (=1 if hedging) + Σi γi * control variables
                                Q ratio = α + β * Delta_production + Σi γi * control variables

Q ratios are measured by the natural log of Tobin’s Q, defined as the market value of assets divided by the book value of
assets. The sample includes 43 firms over calendar years of 1991 to 2000, or a total of 254 firm-years. Delta is the annual
average of quarterly delta from survey for 1991 to 1998, or the end-of-year delta from annual report for year 1999 to 2000.
Log(asset) is the natural log of BV of total assets. ROA is defined as the ratio of net income over the previous year to total
assets. Inv_growth is measured by capital expenditure over total assets. Leverage is defined as the BV of long-term debt over
the BV of equity. Dividend dummy equals one if the firm paid dividend on its common equity in the current year. Cash cost
is the dollar cost of producing one ounce of gold. Year dummies are included in the regressions but are not reported here.
Standard errors are corrected for correlation on a firm level and for heteroskedasticity using the Huber-White-Sandwich
estimator. T-statistics are reported in the parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level

    Model                  1                      2                     3                      4
    Observations           254                    254                   205                    205
    R2                     0.409                  0.414                 0.427                  0.430
    Hedging dummy          -0.210                                       -0.148
                           (-1.82)*                                     (-1.09)
    Delta                                         -0.292                                       -0.207
                                                  (-1.27)                                      (-0.95)
    Log(asset)             0.057                  0.060                 0.049                  0.055
                           (1.69)*                (1.92)*               (1.34)                 (1.49)
    ROA                    0.446                  0.411                 0.355                  0.327
                           (3.33)***              (3.40)***             (2.15)**               (2.13)**
    Inv_growth             2.014                  2.119                 1.927                  1.954
                           (4.18)***              (4.42)***             (3.00)***              (3.08)***
    Leverage               -0.004                 -0.007                -0.066                 -0.079
                           (-0.15)                (-0.28)               (-0.95)                (-1.11)
    Dividend dummy         0.110                  0.138                 0.057                  0.077
                           (1.12)                 (1.53)*               (0.46)                 (0.69)
    Cash cost                                                           -0.002                 -0.002
                                                                        (-1.45)                (-1.39)

Figure 1: Daily Gold Spot Price between 1991 and 2002





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