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

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

Does Hedging Increase Firm Value? Evidence from the Gold Mining Industry YANBO JIN* and 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:

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

2 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 1 See, for example, Smith and Stulz (1985) and Froot, Sharfstein and Stein (1993).

2 See, for example, Stulz (1984).

3 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.

4 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.

3 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.

5 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.

6 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.

7 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 4 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.

8 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

9 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 5 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).

10 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 6 This is because most of the hedging activities are designed to cover the production for the same time period. 7 These firm-year observations are not independent, however, because firms typically adopt similar hedging programs over time.

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

12 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 tests.

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: t i t gold i gold t mkt i m i t i R R R , , , , , , * * ε β β α + + + = (2) where t i R , is the total stock rate of return for firm i in month t , mkt t R is the monthly rate of change in the stock market index, taken as the CRSP NYSE/AMEX/NASDAQ value-weighted monthly return t gold R , is the monthly rate of change in the spot price of gold

13 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 estimation t i t gold t i gold t mkt m t i R R R , , 1 - t i, 1 - t i, 3 1 , , 2 1 , 1 , ) MVE reserve gold ( * ε γ γ γ β α + + ∆ + + + = − (3) where 1 , , − ∆ t i gold 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 equity8 8 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.

14 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. 9 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).

15 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

16 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 hedged.

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

17 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 variable. 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 coefficient.

18 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 10 Cash costs exclude noncash items, such as depreciation, depleting and amortization, as well as interest expense, corporate SG&A, exploration, and extraordinary costs.

11 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. 12 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.

19 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 negative. 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

20 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.

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23 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 reports.

Delta-Percentage (Firm-year observations) Number of firm-year observations Percentage of 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

24 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

25 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 t i t gold i gold t mkt i m i t i R R R , , , , , , * * ε β β α + + + = 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.

t mkt R , and t gold R , 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

26 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 t i t gold i gold t mkt m t i R R R , , 1 - t i, 1 - t i, 3 , 2 1 , 1 , ) MVE reserve gold ( * ε γ γ γ β α + + ∆ + + + = 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 (4.72)*** 0.456 (5.59)*** R_gold 3.081 (9.06)*** 3.100 (16.61)*** Delta*R_gold -1.094 (-2.26)** -1.145 (-3.27)*** [Reserve_(gold)/MVE]*R_gold 0.057 (1.41) 0.054 (2.71)*** R-square 38.89% 40.67% Number of observations 1312 1312

27 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 reported.

Panel A: Gold Hedging vs. Non-Gold Hedging Firm-Years Variable Hedgers (227obs.) Nonhedgers (30 obs.) Difference t-stat (mean) 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 Modest Hedgers (173 obs.) Nonhedgers (30 obs.) Difference t-stat (mean) 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 Variable Extensive Hedgers (54 obs.) Modest Hedgers (173 obs.) Difference t-stat (mean) 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

28 Panel D: Gold Hedging vs. Non-Gold Hedging Firm-Years Best Matched with Firm Size (Total Assets) Variable Hedgers (30obs.) Nonhedgers (30 obs.) Difference t-stat (mean) 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

29 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 respectively.

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

30 Figure 1: Daily Gold Spot Price between 1991 and 2002 200 250 300 350 400 450 Dec-90 Dec-91 Dec-92 Dec-93 Dec-94 Dec-95 Dec-96 Dec-97 Dec-98 Dec-99 Dec-00 Dec-01 Dec-02 date Price_gold

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