Why do firms speculate? Evidence from the gold mining industry∗ Tim R. Adam MIT Sloan School of Management 50 Memorial Drive, E52-403A Cambridge, MA 02142 Tel.: (617) 253-5123 Fax: (617) 258-6855 E-mail:
[email protected] Chitru S. Fernando Michael F. Price College of Business University of Oklahoma 307 West Brooks, Room 205 Norman, OK 73019, USA Tel.: (405) 325-2906 Fax: (405) 325-7688 E-mail:
[email protected] Jesus M. Salas Michael F. Price College of Business University of Oklahoma 307 West Brooks, Room 205 Norman, OK 73019, USA Tel.: (405) 325-1775 Fax: (405) 325-7688 E-mail:
[email protected]
May 2007 JEL Classification: G11; G14; G32; G39 Keywords: Corporate risk management; selective hedging; speculation; managerial compensation. ∗
We thank Louis Ederington, Gary Emery, Dirk Jenter, Leonid Kogan, Scott Linn, Gustavo Manso, Bill Megginson, Jun Pan, Roberto Rigobon, Antoinette Schoar, Pradeep Yadav and seminar participants at MIT and the University of Oklahoma for their comments. We also thank Ted Reeve for providing us with his derivative surveys of gold mining firms, and Sridhar Gogineni and Leung Kam Ming for excellent research assistance. We are responsible for any remaining errors.
Why do firms speculate? Evidence from the gold mining industry Abstract We study the selective hedging puzzle, using data on the speculative activity of a sample of 92 North American gold mining firms, a setting that seems likely to satisfy the conditions stipulated by Stulz (1996) for shareholder value-maximizing selective hedging. Contrary to our predictions, we find that smaller firms speculate more than larger firms, although they are less likely than larger firms to possess the information and financial advantages necessary to outperform the market through speculation. We also find that a higher probability of financial distress is linked to a higher likelihood of corporate speculation. Nonetheless, this finding fails to explain the speculation undertaken by the bulk of the firms in our sample, which are financially sound and far from bankruptcy. Our findings on the relationship between speculation and managerial incentives provide no support for (and indeed, contradicts) the possibility that managers may be speculating in their own self interest. We find that rewarding managers through stock and options, and also insider ownership of the firm’s shares, actually work to reduce managerial incentives to speculate. Since neither shareholders nor managers seem to benefit from selective hedging, our findings are consistent with the remaining possibility for selective hedging identified by Stulz (1996) – that managers hedge selectively because they erroneously believe they can outperform the market – which raises many new questions for future research.
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1. Introduction Recent studies of corporate risk management have documented a considerable divergence between the theory and practice of hedging. The existing theory of corporate risk management assumes that firms use derivatives only to hedge risk exposures brought about during their normal course of business, which adds value by reducing transaction and contracting costs, taxes, and other market imperfections.1 In contrast, there is growing evidence that firms incorporate market views into their hedging programs and thus hedge selectively.2 Firms that hedge selectively vary the size and timing of their derivatives transactions based on their expectations about future market prices. For selective hedging to be value increasing, however, firms would need to have information that is not available to the rest of the market, and the ability to act on this information without jeopardizing their core business. Stulz (1996) argues that in the course of their regular business activity, some firms may accumulate privileged information about their market, citing as an example the case of a large copper consumer whose significant market presence provides a unique ability to aggregate demand and supply information about the copper market and forecast prices more accurately than the market.3 In such cases, firms may be tempted to hedge selectively using their proprietary information. However, firms may be misunderstanding the source of their comparative advantage and thus believe they possess valuable information when in fact they
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See, for example, Stulz (1984), Smith and Stulz (1985), Stulz (1990), Froot, Scharfstein and Stein (1993), DeMarzo and Duffie (1995), and Mello and Parsons (2000) for further discussion on the theoretical motives for hedging. 2 See, for example, Dolde (1993), Bodnar, Hayt and Marston (1998), Glaum (2002), Faulkender (2005), Adam and Fernando (2006), Brown, Crabb and Haushalter (2006), Geczy, Minton and Schrand (2006), Beber and Fabbri (2006), and Faulkender and Cherchenko (2006). 3 See, for example, Grossman (1976), Diamond and Verrecchia (1981) and Huberman and Schwert (1985) for development of the notion of information aggregation in a securities market context.
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do not. Moreover, notwithstanding any private information a firm might have, selective hedging exposes it to considerably more risk relative to the case in which it engages in pure hedging. Stulz (1996) concludes that a firm that seeks to add shareholder value by selective hedging must not only have a genuine information advantage but also have the financial strength to support the extra risk that selective hedging entails. In a recent study, Adam and Fernando (2006) find considerable evidence of selective hedging in a sample of 92 North American gold mining firms. In contrast to the shareholder value-maximizing outcome that the aforementioned notion of rational selective hedging might predict, they find no evidence of economically significant cash flow gains arising from selective hedging for their sample of firms.4 This observed lack of success raises important questions about the motivations for firms to speculate in this way. Stulz (1996) advances three other lines of reasoning that could explain selective hedging. First, he notes that from an agency-theoretic standpoint, taking gambles on the market could be a rational strategy for managers of firms that are in financial distress.5 Second, he argues that some incentive compensation schemes could induce managers to engage in selective hedging even if it does not benefit shareholders. Finally, he raises the possibility that some managers may engage in selective hedging because they erroneously believe that they have a comparative advantage in hedging selectively, which in turn might lead them to overestimate their ability to beat the market.
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The evidence in Adam and Fernando (2006) is confirmed by Brown, Crabb and Haushalter (2006), who also find little evidence of successful selective hedging in their sample of 44 gold producers. 5 In a somewhat related argument, Campbell and Kracaw (1999) show that speculation may sometimes be optimal for firms that have minimum-scale projects and meager internal resources, when external financing is costly.
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Using as our frame of reference the Stulz (1996) criteria for a shareholder valuemaximizing selective hedging strategy and employing the data from Adam and Fernando (2006), we examine cross sectional differences across gold mining firms that hedge selectively and analyze how these differences relate to the cash flows they earn from selective hedging. We also examine how managerial compensation and the presence of inside owners might influence selective hedging activity. Despite the fact that the conditions for selective hedging stipulated by Stulz (1996) are more likely to be satisfied in the gold mining industry than in many other industry settings, we find no evidence of selective hedging practice that seems consistent with the Stulz (1996) criteria for rational shareholder value-maximizing speculation. When we examine the relationship between selective hedging and firm size, we find a negative relationship, which is the opposite of what we would expect to find if larger firms have a true comparative advantage in speculation due to information access and creditworthiness. It is possible that smaller firms are more likely to erroneously believe that they have information the market does not have, perhaps reflecting their lower financial sophistication relative to larger firms (Graham and Harvey (2001)). It is also possible that smaller firms may be more constrained in external capital raising due to asymmetric information and engage in selective hedging to supplement their smaller internal resources, as predicted by Campbell and Kracaw (1999). We also find support for the notion that the probability of financial distress might affect the likelihood of corporate speculation. We find that the firms in our sample that have the highest probability of bankruptcy also speculate more, which seems to support the agencytheoretic notion articulated by Stulz (1996) that shareholders of firms close to bankruptcy may have incentives to speculate at the cost of bondholders. Nonetheless, as observed by Glaum
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(2002), while this argument might explain some of the relative differences in the intensity of speculation across our sample, it fails to explain the speculation undertaken by the bulk of the firms in our sample, which are financially sound and far from bankruptcy. Our findings on the relationship between speculation and managerial incentives provide no support for (and indeed, contradicts) the possibility raised by Stulz (1996) that managers may be speculating in their own self interest.
Our finding that speculation
decreases with the vega of option holdings by both CEOs and CFOs directly contradicts the view that stock option ownership may induce executives to speculate to increase volatility. Our finding that speculation decreases with the delta of managerial stock and option ownership suggests that rewarding managers through stock and options actually works to reduce their incentives to speculate. This conclusion is supported by our finding for insider ownership. Given that selective hedging adds no value, it is not rational for managers to speculate even their own self interest, and this is what our insider ownership findings seem to suggest, that the presence of insiders deters firms from engaging in speculation and reduces the losses that result from such speculation. Overall, it would seem that neither shareholders nor managers benefit from selective hedging in our sample of firms. Notwithstanding the possibility that the likelihood of bankruptcy might partially explain some of our findings, our collective results are consistent with the remaining possibility for selective hedging highlighted by Stulz (1996) that managers hedge selectively because they erroneously believe that they can outperform the market. Two recent papers have examined the question of what firm and managerial characteristics distinguish firms that hedge selectively. Géczy, Minton and Schrand (2006) base their study on the firms in the Wharton survey (Bodnar, Hayt and Marston (1998)) and
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find that CFO stock price sensitivity (delta) is positively associated with the probability of actively taking positions, while CEO stock price sensitivity is negatively related to speculation. They argue that these findings are consistent with the CFO being the active executive in speculation decisions, whose actions are not impeded by the CEO. For a sample of large U.S. non-financial firms with currency exposure, Beber and Fabbri (2006) examine the link between CEO compensation and selective hedging. They find no statistically significant relation between CEO delta and selective hedging, although they find a decrease in hedging when the sensitivity of CEO compensation to stock price volatility (vega) increases. They also find that CEO characteristics such as gender, age, tenure and holding the MBA degree can explain speculative behavior. Neither of these studies utilize data sets that permit the authors to determine whether or not the selective hedging that they document is successful and thereby provide a context for what the motives for selective hedging might be in their respective samples. In contrast, the evidence from the gold industry documented by Adam and Fernando (2006) and Brown, Crabb and Haushalter (2006) clearly show that selective hedging gains in the gold industry are not economically significant on average. Additionally, both these papers examine selective hedging in the currency market, an arena in which it is unlikely that any individual firm would have a comparative advantage in selective hedging as defined by Stulz (1996). In contrast, the gold mining industry is a more specialized commodity industry and it is easier to conceive of the possibility that some firms in this industry have a comparative advantage in selective hedging. Additionally, our data set from the gold mining industry permits us to undertake a more in-depth examination of selective hedging at the level of individual transactions and provide new insights by looking at hedging across different maturities.
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The rest of our paper is organized as follows. In Section 2 we briefly review the existing evidence on selective hedging and formulate our empirical hypotheses. Section 3 describes our data set. Section 4 examines the relationships between selective hedging, firm size and other firm characteristics. Section 5 analyzes firms’ cash flows from selective hedging, differentiated by hedge maturities and in relation to firm characteristics. Section 6 examines the relationship between selective hedging, and managerial compensation and insider ownership. Section 7 concludes.
2. Corporate speculation The existing theory of corporate risk management assumes that firms use derivatives purely for hedging purposes, and that the benefits of derivatives usage accrue solely from the alleviation of market imperfections. In contrast, there is considerable survey evidence that managers’ market views affect the risk management programs of many firms. In a survey of 244 Fortune 500 firms, Dolde (1993) reports that almost 90% of the firms surveyed at least sometimes based the size of their hedges on their views of future market movements. Bodnar, Hayt and Marston (1998) survey derivatives usage by 399 U.S. non-financial firms and find that about 50% of their sample firms admit to sometimes (and 10% frequently) altering the size and or the timing of a hedge based on their market views. Glaum (2002) surveys the risk management practices of the major non-financial firms in Germany. He finds that the majority follows forecast-based, profit-oriented risk management strategies.
Faulkender (2005)
examines whether firms are hedging or timing the market when selecting the interest rate exposure of their new debt issuances and shows that the interest rate risk management practices of the firms in his sample are primarily driven by speculation or myopia instead of
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hedging considerations. In a follow-on paper, Faulkender and Chernenko (2006) further explore the reasons for the interest rate timing behavior documented by Faulkender (2005). Their empirical findings suggest that swap usage and the choice of interest rate exposure are primarily driven by a desire to meet consensus earnings forecasts and to raise managerial pay. Adam and Fernando (2006) find considerable evidence of selective hedging in their sample of gold mining firms but find no economically significant cash flow gains on average from selective hedging. Brown, Crabb, and Haushalter (2006) also study selective hedging in the gold mining industry and arrive at a similar conclusion. Beber and Fabbri (2006) analyze the time-series variation of foreign currency derivatives in a sample of large U.S. non-financial firms and document a substantial time-series variation in currency derivatives holdings in excess of what can be explained by changes in currency exposure, which they attribute to selective hedging. Collectively, this body of evidence suggests that the practice of selective hedging is widespread in the corporate world. Since it does not appear to generate significant profits, one wonders why managers devote resources to trying to beat the market. We next develop a series of hypotheses to guide the empirical analysis of selective hedging in our sample.
2.1 Empirical hypotheses In contrast to the theories of corporate hedging, Stulz (1996) has argued that some firms may have an incentive to speculate if they possess inside information and have a comparative advantage in risk bearing due to their financial strength. In his example of a large copper consumer, the firm obtains access to specialized information about the copper market as a result of its copper purchasing activity. In a more general context, Diamond and
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Verrecchia (1981) model information aggregation in securities markets, and show that prices can aggregate diverse sources of information possessed by individual traders.6 In a corporate setting where individual firms can aggregate information from diverse business transactions, it is conceivable that they may obtain an advantage in doing so over others in the market. It is reasonable to assume that the potential for a firm to access specialized information or aggregate information will increase with its presence in the market and the resources it can expend on gathering such information, both of which can be proxied by the size of the firm. A comparative advantage in bearing risk is a function of a firm’s size and the strength and quality of its balance sheet, which can be measured by the firm’s leverage, liquidity, and other attributes of creditworthiness. Therefore, we expect to find that large firms are more likely to engage in selective hedging than small firms. We also expect to find more creditworthy firms to display a higher propensity to engage in selective hedging than less creditworthy firms. Additionally, to the extent selective hedging creates shareholder value, we expect to find the value created by selective hedging to increase with the size of the firm. In our sample, firms use derivatives with maturities of up to five years. Since markets are less efficient at forecasting prices further out into the future, we would expect any relative informational advantage of larger firms to increase as the time to maturity of derivatives instruments increases. Additionally, from a creditworthiness standpoint, larger firms are more likely to have access to long-term derivatives positions than smaller firms. Therefore, we expect a positive relationship between firm size and the average hedging duration. Additionally, to the extent that these transactions produce non-zero cash flows, we expect to
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See also Grossman (1976) and Huberman and Schwert (1985).
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see larger firms being relatively more successful than smaller firms as the duration of hedging increases. Stulz (1996) also notes that financially distressed firms could have an incentive to speculate regardless of whether or not they believe they can beat the market. In this case, stockholders would benefit if the speculation is successful but only bondholders would lose if it is not. If this holds true for our sample, we expect to find an increase in speculation as the probability of bankruptcy increases. In a somewhat related line of reasoning to Stulz (1996), Campbell and Kracaw (1999) argue that financially constrained firms may also have incentives to speculate. If this were true, we expect to find an increase in speculation as financial constraints increase. If selective hedging is driven by managerial incentives, we expect to find an increase in selective hedging with various incentive compensation features that could make managers better off if they speculate. Since speculative activity increases stock return volatility, we would expect to find a monotone relationship between selective hedging and stock option compensation. Conditional on selective hedging being successful, we expect to find a positive relationship between selective hedging and stock ownership. And conditional on selective hedging being unsuccessful, we expect to find a negative relationship between selective hedging and stock compensation. Similarly, conditional on selective hedging being successful, we expect to find an increase in selective hedging with insider ownership. In contrast, if selective hedging is unsuccessful, we expect to find selective hedging activity decreasing with insider ownership.
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3. Data The sample of firms and their derivatives transactions that we study in this paper is identical to the sample used by Adam and Fernando (2006). This sample consists of 92 gold mining firms in North America, encompassing the majority of firms in the gold mining industry. These firms are included in the Gold and Silver Hedge Outlook, a quarterly survey conducted by Ted Reeve, an analyst at Scotia McLeod, from 1989 to 1999. Firms not included in the survey tend to be small or privately held corporations. The survey contains information on all outstanding gold derivatives positions, their size and direction, maturities, and the respective delivery prices for each instrument. The derivatives portfolios consist of forward instruments (forwards, spot-deferred contracts, and gold loans) and options (put and call). There are a total of 2,541 firm-quarter observations of which 1,450 firm-quarters represent nonzero hedging portfolios. Tufano (1996) and Adam and Fernando (2006) provide further details about this data set. Using this data set, together with market data on average gold spot and futures prices, interest rates, and the gold lease rate, Adam and Fernando (2006) calculate the quarterly net cash flows associated with each derivatives transaction for each firm. They also separate hedge ratios and derivatives cash flows into (1) a component attributable to pure hedging and (2) a component attributable to speculation in the form of selective hedging. Appendix A provides a summary of this decomposition of hedge ratios and cash flows. A more detailed description of how these calculations are performed is provided in Adam and Fernando (2006). We perform our analysis using the hedge ratio components and derivatives cash flows attributed by Adam and Fernando (2006) to selective hedging.
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We obtain financial data from Compustat, or from a manual search of firms’ financial statements if a firm is not covered by Compustat. Stock market return data comes from the CRSP database and Datastream. We hand collect operational data, e.g., gold production figures, production costs per ounce of gold, etc., from firms’ financial statements. We gather compensation data through ExecuComp or through manual collection if the firm is not covered by ExecuComp. Specifically, we gather proxy statements for firm-year observations not available on ExecuComp. We then calculate the stock and option holdings for CEOs and CFOs and aggregate stock holdings for all insiders (executives and members of the board of directors). In addition, we estimate the sensitivities of stock and option holdings to changes in stock price level and volatility following Core and Guay (1999). A detailed description of the compensation variable estimation is provided in Appendix B. Table 1 provides summary statistics of our sample. Figure 1 plots the median selective hedging levels in our sample (median differentials between actual and predicted hedge ratios, i.e., hedge ratio residuals) over time for one-year hedge maturities. Similar patterns exist for longer hedge maturities. As in previous studies, we observe considerable volatility. [Place Table 1 and Figure 1 about here]
4. Hedging, speculation and firm size In this section, we examine how selective hedging is related to firm size and other characteristics. Since selective hedging is undertaken only by firms that use derivatives, we begin by examining how the likelihood of derivatives use (i.e., a firm being a “hedger”) is related to a firm’s size and other characteristics. Thereafter we analyze selective hedging for
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the firms that are identified as being derivatives users. We also examine how the use of derivatives and selective hedging varies by hedge maturity.
4.1 The likelihood of being a hedger We first analyze the question of the likelihood that a firm is a hedger as a function of firm size by using a logit model where the dependent variable is equal to one if a firm is hedging and zero if it is not. As we can see from Table 2, there is a strong positive relationship between the likelihood of hedging and size. Furthermore, the relationship between hedging likelihood and size becomes stronger for longer hedging maturities. We also compute predicted values from the model, which confirm that the predicted likelihood of hedging drops sharply as the maturity becomes longer. The overall results are consistent with the general notion in the literature that larger firms are more likely to hedge than smaller firms, possibly due to their higher levels of expertise and resources, even though the need to hedge may be greater for smaller firms. [Place Table 2 about here] The regression results in Table 3 show that the magnitude of hedging, as measured by hedge ratios, is also increasing in firm size. The coefficient on firm size is significant and positive across all hedging maturities. Hedge ratios decline with hedge maturity, indicating that the percentage of near-term production that firms hedge is higher than the corresponding percentage of longer-term production. [Place Table 3 about here]
4.2 Speculation and firm size
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Having identified the firms in our sample that use derivatives, we explore the question of which of these firms are more likely to speculate. In Table 4, we use the standard deviation of actual hedge ratios as a measure of speculation to examine how selective hedging is related to firm size for the sample of firms that hedge. Our regression results show that larger firms speculate less than smaller firms for all hedging maturities, a finding that stands in stark contrast to our previous findings reported in Tables 2 and 3, that larger firms are more likely to hedge and hedge a greater percentage of their production than smaller firms. The relationship between speculation and size remains robust when we control for the level of hedging using hedge ratios. [Place Table 4 about here] The argument from hedging theory that hedging decreases expected bankruptcy costs (e.g., Smith and Stulz (1985)) suggests that firms are more likely to hedge when they are closer to bankruptcy. As noted by Stulz (1996), it is also the case from agency theory that stockholders will have more incentive to speculate when firms are closer to bankruptcy. In Table 5, we present results from our analysis in this regard. Specifically, Panel A of Table 5 presents our findings on the relationship between hedging and the likelihood of bankruptcy while Panel B presents our findings on the relationship between speculation and the likelihood of bankruptcy. We use Altman’s z-score as a measure of the firm’s likelihood of bankruptcy, where higher z-scores are associated with a lower chance of bankruptcy. We also allow for the relationship between hedging and z-scores and between speculation and z-scores to be non-linear by including the square of z-scores. We find a convex relationship between zscores and the likelihood of hedging across all hedging maturities, i.e., firms hedge more both when they are close to bankruptcy and when they are far from bankruptcy, relative to
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intermediately-placed firms. Interestingly, we find a similar convex relationship between zscores and selective hedging, i.e., firms also speculate more both when they are close to bankruptcy and when they are far from bankruptcy, relative to intermediately-placed firms. We should also note that the previously observed relationships between hedging and size and between speculation and size remain largely robust when we control for the likelihood of bankruptcy. [Place Table 5 about here] In Table 6, we test the robustness of the speculation-size relationship by re-running the speculation-size regressions while controlling for other firm characteristics: market-to-book (M/B) ratio, Herfindahl Index (measuring each firm’s concentration in gold mining as determined by books assets allocated to gold mining versus other business segments the firm may be involved in), dividend dummy (equal to one if the firm pays dividends in year t), quick ratio, leverage ratio (based on book values) and free cash flow (based on market values). [Place Table 6 about here] As would be observed from Table 6, the previously identified relationship between speculation and firm size remains broadly robust, although weaker (perhaps due to the smaller sample size). The significantly negative relationship between speculation and size persists for one and two-year maturities, but loses significance thereafter. Looking at the regression results that link selective hedging to other firm characteristics, we see several interesting patterns. First, speculation is negatively related (when statistically significant) to the market-to-book ratio regardless of maturity, suggesting that firms with higher growth options speculate less. The Herfindahl index is weakly
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negatively related to speculation for the five-year-ahead hedging maturity. Since the Herfindahl index that we use measures the extent to which a firm specializes in gold mining, it provides another measure of the firm’s potential to have access to specialized information. In this context, however, the result for the five-year maturity is the opposite of what we would expect if firms were speculating to exploit an information advantage. The quick ratio is negatively related to speculation for the three-, four-, and five-year maturities. Perhaps surprisingly, we found no relationship between speculation and whether or not the firm pays dividends, between speculation and leverage (with book values)7 and between speculation and free cash flow. Overall, these results suggest that more liquid and less financially constrained firms speculate less, which is consistent with the idea in Campbell and Kracaw (1999).
4.3 Discussion We find that larger firms are more likely to hedge and that they hedge a higher percentage of their production when they do hedge, relative to smaller firms. These findings are not surprising and are consistent with the view that larger firms hedge more than smaller firms because of their higher financial strength, and higher levels of expertise and other resources that they can commit to hedging. In contrast, smaller firms speculate more than larger firms. This finding is puzzling and is contrary to the notion from Stulz (1996) that to the extent firms speculate based on an actual or perceived information advantage, larger firms would have a higher advantage than smaller firms. It is possible that smaller firms are more likely to erroneously believe that they have information the market does not have. It is also possible that smaller firms may be more constrained in external capital raising due to
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We also explore using leverage using market values with similar results. However, the number of observations was much more limited.
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asymmetric information and engage in selective hedging to supplement their smaller internal resources, as predicted by Campbell and Kracaw (1999). Our finding of non-linear relationships between both hedging and speculation with the likelihood of bankruptcy seems interesting and provides new insights. On the one hand, our finding that the firms in our sample that have the lowest probability of bankruptcy hedge and speculate more is not surprising. Both these findings can be explained by the firms in question having the deep pockets to engage in hedging or speculation, despite the fact that in the latter case the merits of what they are doing may be questionable. Our finding that the firms in our sample that have the highest probability of bankruptcy also hedge more provides further support for the theoretical argument that hedging reduces bankruptcy costs (Smith and Stulz, 1985). Our finding that the firms in our sample that have the highest probability of bankruptcy also speculate more seems to support the agency-theoretic notion articulated by Stulz (1996), that shareholders of firms close to bankruptcy may have incentives to speculate at the cost of bondholders. Nonetheless, as observed by Glaum (2002), while this argument might explain some of the relative differences in the intensity of speculation across our sample, it fails to explain the speculation undertaken by the bulk of the firms in our sample, which are financially sound and far from bankruptcy.
5. Cash Flow Effects of Speculation In this section, we examine the cash flows from selective hedging activities and how these cash flows vary across firms. Our cash flow measure is the difference between the total derivatives cash flows and the predicted hedge cash flows, i.e., the selective hedging or speculative cash flows. Adam and Fernando (2006) find no conclusive evidence based on
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their analysis of selective hedge cash flows that firms are systematically successful at speculating on changes in their market views. We explore this question further by examining how selective hedging cash flows vary with firm size and hedge maturities.
5.1 Selective hedging cash flows and firm size In this section we analyze how selective hedging cash flows are related to hedge maturity and firm size. Table 7 summarizes the results of the analysis of cash flow gains from speculation, using quarterly observations. [Place Table 7 about here] We test whether cash flows from selective hedging are related to size, which would be the case if larger firms have a true comparative advantage in speculation due to information access and creditworthiness. Surprisingly, we find a significant negative relationship between selective hedging cash flows and size for the aggregate selective hedging cash flows and (weakly) for the one-year maturity. The negative coefficient on the aggregate cash flows is quite surprising and contrary to what we would expect based on a rational theory of speculation as in Stulz (1996). Our results provide no evidence that the conditions for successful speculation stipulated by Stulz (1996) exist in the gold market or if they do, that firms are able to successfully exploit them. The extent of hedging (hedge ratio) and speculation (volatility of hedge ratio) are not significantly related to selective hedging cash flows.
6. Managerial compensation, insider ownership, and speculation
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We now turn to the second alternative hypothesis proposed by Stulz (1996), that managers speculate as a rational response to the way in which they are incentivised through compensation and ownership of the firm. As noted previously, managers are more likely to speculate when their wealth increases with stock volatility, which would be the case when they own stock options. A commonly used measure of the sensitivity of executive stock option holdings to changes in volatility is the aggregate vega of the option holdings. In this section we examine whether corporate speculation is positively related to the aggregate vega of option holdings for both the CEO and the CFO. On the other hand, especially given the findings in the literature that selective hedging does not have a beneficial impact on shareholder wealth, it is possible that the ownership of shares in the firm might attenuate any incentive for managers to speculate. Thus, we also examine the relation between selective hedging and the delta of stock and option holdings by the CEO and CFO. Along similar lines, we also examine the relation between selective hedging and insider stock holdings. We present results from our analysis of speculation and executive compensation in Table 8 (for CEOs) and Table 9 (for CFOs). After controlling for firm size, we find that the relationship between speculation and vega is consistently negative for both CEOs and CFOs, which directly contradicts the view that stock option ownership may induce executives to speculate to increase volatility. In contrast, the relationship between speculation and delta is also consistently negative for both CEOs and CFOs, which suggests that the ownership of stock does reduce the incentive for these executives to engage in speculation. [Place Tables 8 & 9 about here] We present our findings of the relationship between speculation and insider ownership in Table 10. We find a significant negative relationship between insider ownership and
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speculation for one-year hedge maturities, which persists when we control for firm size and hedge ratio. Given that selective hedging adds no value, this finding is consistent with the notion that insider ownership attenuates the incentive for speculation. A similar but weaker result holds for the two-year maturity. In contrast, we find a weakly positive relation between speculation and insider ownership for four-year hedge maturities. [Place Table 10 about here] While speculative cash flows are not a perfect measure of speculation (since they arise from the product of speculation and price), they have the benefit of allowing us to obtain an aggregate measure across the five hedging maturities. Using selective hedging cash flows as a measure of aggregate speculation by our sample firms, we repeat our analysis of the relationship between speculation and compensation, and speculation and insider ownership. The results are reported in Table 11. [Place Table 11 about here] We find no significant relationship between selective hedging cash flows and managerial compensation measures. In contrast, we find a positive relationship between selective hedging cash flows and insider ownership after controlling for size. This finding is interesting, especially when viewed in conjunction with our finding in Table 10 of a significant negative relationship between insider ownership and speculation for one- and twoyear hedge maturities. Noting that the bulk of speculation occurs at the one-year maturity, this finding suggests that the presence of insiders deters firms from engaging in speculation and reduces the losses that result from such speculation.
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6.1 Discussion Overall, our findings on the relationship between speculation and managerial incentives provide no support for (and indeed, contradicts) the notion that managers may be speculating in their own self interest. Our finding that speculation decreases with the vega of option holdings by both CEOs and CFOs directly contradicts the view that stock option ownership may induce executives to speculate to increase volatility. Our finding that speculation decreases with the delta of managerial stock and option ownership actually suggests that rewarding managers through stock and options actually work to reduce their incentives to speculate. This conclusion is supported by our finding for insider ownership. Given that selective hedging adds no value, it is not rational for managers to speculate even in their own self interest, and this is what our findings seem to suggest, that the presence of insiders deters firms from engaging in speculation and reduces the losses that result from such speculation.
7. Conclusions There is considerable evidence that firms use derivatives to speculate, but the gains from speculation appear to be small at best. This raises the puzzle of why managers commit time and resources to an activity that does not appear to increase shareholder value. We examine this puzzle by studying the North American gold mining industry. This industry is likely to satisfy the conditions stipulated by Stulz (1996) for rational selective hedging that maximizes shareholder value. However, we find that small firms are more active speculators than large firms. This is surprising because small firms are less likely than large firms to have an information advantage and thus be able to beat the market. Furthermore, small firms are
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less able to bear the additional risks of selective hedging than large firms. We find a positive relation between the probability of financial distress and the likelihood of speculation, consistent with an agency-theoretic explanation of corporate speculation. Nonetheless, this finding fails to explain the speculation undertaken by the bulk of the firms in our sample, which are financially sound and far from bankruptcy. Our findings on the relationship between speculation and managerial incentives provide no support for (and indeed, contradicts) the possibility that managers may be speculating in their own self interest. We find that rewarding managers through stock and options, and also insider ownership of the firm’s shares actually work to reduce managerial incentives to speculate. Since our findings show that neither shareholders nor managers benefit from selective hedging, our study renews the challenge of explaining this behavior from a rational valuemaximizing standpoint. Our overall results point to the remaining possibility for selective hedging highlighted by Stulz (1996) that managers hedge selectively because they erroneously believe that they can outperform the market. Our findings in support of this possibility also raise many new questions that have relevance for both academics and practitioners, especially from the standpoint of corporate governance and behavioral finance.
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Appendix A: Identifying hedge ratio components and cash flows attributable selective hedging8 Consider a commodity producing firm that sells a fraction (the “hedge ratio”) of its expected future production forward at the forward price of F(t,T). The hedge ratio for production sold x years forward can be expressed as:
x − year hedge ratio =
− Portfolio delta ( x − year contracts) , Expected production ( x years ahead )
where portfolio delta is the weighted sum of the deltas of the individual derivatives positions.9 In our gold sample, x = 1, 2, 3, 4, 5. The firm will be considered a “pure hedger” if it changes its hedge ratios only in response to changes in fundamental drivers of hedging, such as leverage or liquidity. Alternatively or in addition to hedging due to fundamental reasons, it is also possible for a firm to hedge “selectively” by varying its hedge ratios over time due to changes in its expectation of the future spot price relative to the forward price that is currently observed in the market. Such a firm would reduce its hedge ratio if it increases its estimate of the future spot price relative to the forward price and vice versa. Hedge ratios and cash flows attributable to a firm’s derivatives positions can be separated into two components: i.
a component attributable to pure hedging.
ii.
a component attributable to selective hedging.
Adam and Fernando (2006) utilize a Cragg (1971) two-stage regression model to identify the hedge ratio component that is attributable to a pure hedging rationale. The
8 9
Based on Adam and Fernando (2006). See Tufano (1996) and Adam and Fernando (2006) for further details.
24
predicted values from these regressions provide estimates of hedge ratios that firms would maintain under a pure hedging strategy. The differentials between the actual hedge ratios maintained by the firm and the predicted hedge ratios from the regression model provide an estimate of the hedge ratio “residual” that can be attributed to selective hedging. The predicted hedge cash flow, i.e., the cash flow that the would have accrued to the firm had it maintain the hedge ratios predicted by the regression model, is calculated by using a firm’s actual derivatives portfolio but adjusting the number of contracts outstanding for each instrument using N predicted = N actual ×
predicted hedge ratio , actual hedge ratio
where N actual equals the actual number of contracts outstanding for each contract type while
N predicted is the number of predicted contracts for the corresponding contract type. The predicted hedge cash flow is calculated using exactly the same procedure used to calculate the total derivatives cash flow based on the actual number of contracts, described in Allen and Fernando (2006). The selective hedge cash flow is the difference between the total derivatives cash flow and the predicted hedge cash flow.
25
Appendix B: Estimation of compensation measures10
We compute the delta - sensitivity of the option value to a one percent change in the stock price and the vega - sensitivity of the option value to a one percent change in the stock return volatility - using the Black-Scholes option pricing model, as modified by Merton to account for dividend payouts:
Call = Se-dT N(Z) – Ke-dT N(Z – σT 0.5 )
(A.1)
Delta = 0.01e-dT N(Z) S
(A.2)
Vega = 0.01e-dT N’ (Z) ST 0.5
(A.3)
where:
Z = (ln (S/K) + T (r - d + 0.5))/( σT 0.5 ) S = price of the underlying stock. K = exercise price of the option. T = time to maturity of the option (number of years). d = dividend yield on the underlying stock. r = risk-free interest rate. σ = expected stock return volatility over the life of the option (annualized). N( ) = standard normal cumulative density function. N( )’ = standard normal probability density function.
We hand collect option holdings and in the money value of options from proxy statements for gold producing firms for each year between 1989 and 1999. We compute the delta and vega 10
Based on Core and Guay (1999)
26
measures for option holdings by CEOs and CFOs. We obtain S from Compustat as the end of year stock price. We follow the methodology of Core and Guay (1999) to obtain K and T. More specifically, we compute K in two steps: first, we divide the potential realizable value of options by the number of options held at year-end, obtaining an average difference between the stock price and the strike price. Then, we subtract this ratio from the stock price to get an average strike price. We set T to 7.5 as suggested by Core and Guay (1999) for cases in which option maturity is unavailable. We obtain dividends paid by the firm through Compustat. r is the Treasury bond yield to maturity, from CRSP as quoted at the firm's fiscal year end. Since we set
T to 7.5, the closest available bond maturity is the seven-year bond. We compute stock return volatility with weekly returns from Compustat and Datastream adjusted for distributions and stock splits. We compute delta and vega for the average option grant using equations (A.2) and (A.3) and then multiply them by the number of options held by the executive. We compute the delta of the executive shareholdings as the number of owned shares multiplied by one percent of the stock price at the end of the fiscal year. The vega of the shareholdings is assumed immaterial, consistent with Coles et al. (2003). We obtain the delta of the executive compensation as the sum of the delta of the options and the delta of the shareholdings. We obtain the vega of the executive compensation as the sum of the vega of the option holdings of the executive. For speculation in year t we use compensation data from proxies issued in year t, which correspond to compensation data on t-1.
27
References
Adam, T.R. and C.S. Fernando, 2006, “Hedging, Speculation and Shareholder Value,” Journal of Financial Economics 81, 283-309. Beber, Alessandro and Daniela Fabbri, 2006, Who times the Foreign Exchange Market? Corporate Speculation and CEO Characteristics. University of Lausanne and FAME Working Paper. Bodnar, G.M., G.S. Hayt and R.C. Marston, 1998 Wharton Survey of Derivatives Usage by U.S. Non-Financial Firms, Financial Management 27(4), 70-91. Brown, G.W., P.R. Crabb, and D. Haushalter, 2006, Are Firms Successful at Selective Hedging? Journal of Business, forthcoming. Campbell, T. and W. A. Kracaw, 1999, Optimal Speculation in the Presence of Costly External Financing. In “Corporate Risk: Strategies and Management,” G. Brown and D. Chew, editors, Risk Publications, London. Core, John and Wayne Guay, 1999, The use of Equity Grants to Manage Optimal Equity Incentive Levels, Journal of Accounting and Economics 28, 151-184. Cragg, J., 1971, Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods, Econometrica 39, 829-844. DeMarzo, P. and D. Duffie, 1995, Corporate Incentives for Hedging and Hedge Accounting, Review of Financial Studies 8, 743-772. Diamond, D.W. and R.E. Verrecchia, 1981, Information Aggregation in a Noisy Rational Expectations Economy, Journal of Financial Economics 9, 221-235. Dolde, W., 1993, The Trajectory of Corporate Financial Risk Management, Journal of Applied Corporate Finance 6, 33-41. Faulkender, Michael W., 2005, Hedging or Market Timing? Selecting the Interest Rate Exposure of Corporate Debt, Journal of Finance 60, 931-962. Faulkender, Michael W. and S. Chernenko, 2006, Why are Firms using Interest Rate Swaps to Time the Yield Curve? Washington University at St. Louis Working Paper. Froot, K.A., D.S. Scharfstein and J.C. Stein, 1993, Risk Management: Coordinating Corporate Investment and Financing Policies, Journal of Finance 48, 1629-1658. Géczy, Christopher, Bernadette A. Minton, and Catherine M. Schrand, 2006, Taking a View: Corporate Speculation, Governance, and Compensation, Journal of Finance, forthcoming.
28
Glaum, M., 2002, The Determinants of Selective Exchange Risk Management – Evidence from German Non-Financial Corporations, Journal of Applied Corporate Finance, 14, 108-121. Graham, J.R. and C.R. Harvey, 2001, “The theory and practice of corporate finance: Evidence from the field,” Journal of Financial Economics 60, 187-243. Grossman, S., 1976, On the Efficiency of Competitive Stock Markets When Traders have Diverse Information, Journal of Finance, 31, 573-585. Huberman, G. and G. W. Schwert, 1985, Information Aggregation, Inflation and the Pricing of Indexed Bonds, Journal of Political Economy, 93, 92-114. Mello, A. and J. Parsons, 2000, Hedging and Liquidity, Review of Financial Studies 13, 127-153. Smith, C., and R.M. Stulz, 1985, The Determinants of Firms’ Hedging Policies, Journal of Financial and Quantitative Analysis 20, 391-405. Stulz, R.M., 1984, Optimal Hedging Policies, Journal of Financial and Quantitative Analysis 19, 127-140. Stulz, R.M., 1990, Managerial Discretion and Optimal Financing Policies, Journal of Financial Economics 26, 3-27. Stulz, R.M., 1996, Rethinking Risk Management, Journal of Applied Corporate Finance 9, 8-24. Tufano, P., 1996, Who Manages Risk? An Empirical Examination of Risk Management Practices in the Gold Mining Industry, Journal of Finance 51, 1097-1137.
29
Figure 1: Median Differentials between Actual and Predicted Hedge Ratios, 1989-1999 (One-year hedge maturity)
0.2
0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 19 89 12 19 90 06 19 90 12 19 91 06 19 91 12 19 92 06 19 92 12 19 93 06 19 93 12 19 94 06 19 94 12 19 95 06 19 95 12 19 96 06 19 96 12 19 97 06 19 97 12 19 98 06 19 98 12 19 99 06 19 99 12
Hedge Ratio Residuals
0.15
Time
30
Table 1. Summary Statistics of Firm, Hedging, and Executive Compensation Characteristics. This table presents summary statistics for firm, hedging, and executive compensation characteristics. Panel A presents statistics for firm characteristics. Log (firm size) is the log of the market value of assets. Herfindahl Index measures each firm’s concentration in gold mining as determined by books assets allocated to gold mining versus other business segments the firm may be involved in. Dividend dummy is a dummy variable that is equal to one if the firm paid dividends in year t and zero otherwise. Leverage is the leverage of a firm using book values. Panel B presents statistics for hedging characteristics. Hedge ratio is the percentage of production hedged into the future. Speculation is measured by the standard deviation of hedge ratios. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. Panel C presents statistics for measures of executive compensation. Log (Delta of compensation of CEO/CFO) is the log of aggregate delta of option and stock holdings for CEOs/ CFOs. Vega CEO (CFO) is the aggregate vega of option holdings for CEOs (CFOs). Log (insider) is the log of the value of stock holdings of insiders as a group, i.e., executives and board directors. Panel A: Firm Characteristics log (firm size) Market to Book Ratio Herfindahl Index Dividend dummy Quick Ratio Leverage (based on book value) Free Cash Flow Z-score
No. Obs. 534 534 578 539 531 534 513 419
Mean 5.5867 1.8586 0.9486 0.4323 3.6598 0.4039 -0.0571 4.9981
Median 5.4581 1.5641 1.0000 0.0000 1.6178 0.1671 -0.0270 2.471
Std. Dev 1.7537 1.1234 0.1594 0.4959 8.5898 3.4906 0.1343 13.348
No. Obs. 2000 2002 2020 2058 2077 383 322 232 159 95
Mean 0.3474 0.1875 0.0924 0.0423 0.0349 0.2186 0.1403 0.1009 0.1027 0.1494
Median 0.2295 0.0300 0.0000 0.0000 0.0000 0.1689 0.1081 0.0648 0.0670 0.0938
Std. Dev 0.4406 0.2859 0.1901 0.1221 0.1432 0.2800 0.1526 0.1047 0.1105 0.1683
No. Obs. 274 139 293 202 205
Mean 10.2445 9.1782 8.4939 7.2826 15.7774
Median 10.0302 9.5058 8.9066 8.0123 15.6146
Std. Dev 1.9721 3.0420 2.9067 2.8142 2.0597
Panel B: Hedging Characteristics Hedge ratio of production 1 year ahead Hedge ratio of production 2 years ahead Hedge ratio of production 3 years ahead Hedge ratio of production 4 years ahead Hedge ratio of production 5 years ahead Speculation 1 year ahead Speculation 2 years ahead Speculation 3 years ahead Speculation 4 years ahead Speculation 5 years ahead Panel C: Compensation Characteristics Log (Delta of compensation of CEO) Log (Delta of compensation of CFO) Log (Vega of compensation of CEO) Log (Vega of compensation of CFO) Log (dollar value of insider ownership )
31
Table 2: Likelihood of Hedging and Firm Size
Panel A presents the logit regression results of the likelihood that a firm is a hedger. The dependent variable is a dummy variable that is equal to one if the firm hedges at time t. Figures in parentheses denote t-statistics. In Panel B, the predicted likelihood that a firm is a hedger is estimated and summary statistics are presented. Panel A: Logit regression results
Log (firm size) Pseudo R2 Chi sq.-value N
1-year
2-year
3-year
4-year
5-year
Hedging Dummy
Hedging Dummy
Hedging Dummy
Hedging Dummy
Hedging Dummy
0.3218*** (5.06) 0.0475 27.91 498
0.4163*** (6.99) 0.0821 56.24 502
0.3974*** (6.89) 0.0778 53.43 504
0.4557*** (7.20) 0.095 59.39 515
0.4402*** (6.20) 0.0843 43.07 518
*** denotes significance at the 1% level. Panel B: Predicted likelihood of hedging
Mean Median Std. deviation N
1-year
2-year
3-year
4-year
5-year
Hedge Dummy
Hedge Dummy
Hedge Dummy
Hedge Dummy
Hedge Dummy
0.72 0.73 0.11 498
0.57 0.57 0.16 502
0.42 0.4 0.16 504
0.3 0.26 0.15 515
0.19 0.16 0.12 518
32
Table 3: Magnitude of Hedging and Firm Size This table presents the regression results of hedging activity as a function of firm size, conditional on the firm being a hedger. We estimate the following regressions: hedge ratio = a + b*X + e. The dependent variable is the percentage of production that a firm hedges at time t. Figures in parentheses denote t-statistics.
Log (firm size) R-squared N
1-year
2-year
3-year
4-year
5-year
Hedge ratio
Hedge ratio
Hedge ratio
Hedge ratio
Hedge ratio
0.024** (2.11) 0.0116 382
0.036*** (3.71) 0.0355 375
0.022*** (3.68) 0.0351 375
0.010*** (2.62) 0.0176 384
0.019*** (4.42) 0.0485 385
*** denotes significance at the 1% level. Panel B: Predicted hedge ratios
Mean Median Std. deviation N
1-year
2-year
3-year
4-year
5-year
Hedge ratio
Hedge ratio
Hedge ratio
Hedge ratio
Hedge ratio
0.296 0.292 0.063 498
0.155 0.151 0.063 502
0.078 0.076 0.036 504
0.034 0.033 0.016 515
0.029 0.027 0.026 518
33
Table 4: Speculation and Firm Size This table presents the OLS regression results of speculative activity as a function of firm characteristics. We estimate the following regressions: Speculation = a + b*X + e if firm is a hedger in period t where speculation is measured by the standard deviation of actual hedge ratios. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. The dependent variable is the percentage of production that a firm speculates at time t. Figures in parentheses denote t-statistics.
Log (firm size)
1-year 2-year 3-year 4-year Speculation Speculation Speculation Speculation -0.03*** -0.035*** -0.027*** -0.035*** -0.01*** -0.02*** -0.006 -0.01*** (-3.35) (-4.94) (-5.30) (-7.86) (-3.25) (-5.03) (-1.08) (-2.88)
4-quarter average Hedge ratio R2 F-value N
0.474*** (14.96) 0.0311 11.20 351
0.4104 121.10 351
0.281*** (10.67) 0.0858 28.04 301
0.3385 76.26 301
0.23*** (8.54) 0.0472 10.59 216
*** and ** denote significance at the 1% and 5% level respectively.
34
0.2903 0.0079 43.56 1.17 216 150
5-year Speculation -0.006 -0.009 (0.52) (-1.28)
0.564*** (12.35) 0.5130 77.41 150
0.626*** (12.36) 0.0029 0.27 94
0.6277 76.72 94
Table 5. Hedging, selective hedging, and the likelihood of bankruptcy This table presents results from the analysis of hedging, selective hedging, and the likelihood of bankruptcy. We use Altman's z-scores as a measure of the likelihood that the firm will go bankrupt. Panel A presents the results of the analysis of hedging and the likelihood of bankruptcy. Specifically, we run the regressions (hedging dummy) = a + b ( z-score) + c (z-score ^2) and (hedging dummy ) = a + b ( z-score) + c (z-score ^2) + d * (firm size) where hedging dummy is equal to one if the firm hedges at time t and zero otherwise. We run this regression separately for each maturity. Panel B presents the results for the selective hedging and the likelihood of bankruptcy. Specifically, we run the regression (Speculation) = a + b ( z-score) + c (z-score ^2) and (Speculation) = a + b ( z-score) + c (z-score ^2) + d ( firm size) conditional on firms being hedgers. The extent of speculation is measured by the standard deviation of hedge ratios. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. We present t-statistics in parentheses below each coefficient. Panel A. Hedging and likelihood of bankruptcy 1 year ahead 2 years ahead I II III IV Log 0.051*** 0.086*** (firm size) (3.94) (5.91) -0.007* -0.010*** -0.007* -0.014*** (Z-score) (-1.82) (-2.73) (-1.87) (-3.36) 0.000002 0.00005 0.0001 0.0001** (Z-score)^2 (0.72) (1.47) (1.04) (2.27) R squared 0.0211 0.061 0.0152 0.1007 # Obs. 389 379 389 379
3 years ahead V VI 0.096*** (6.53) -0.009** -0.015*** (-2.18) (-3.77) 0.00003 0.0001** (0.84) (2.12) 0.0299 0.1309 389 379
4 years ahead VII VIII 0.104*** (7.51) -0.006* -0.013*** (-1.72) (-3.43) 0.00002 0.0001** (0.68) (2.04) 0.0175 0.1442 399 388
5 years ahead IX X 0.094*** (7.47) -0.008** -0.013*** (-2.25) (-3.99) 0.00004 0.0001 (1.34) (2.75) 0.0187 0.1436 402 391
Panel B. Selective hedging and likelihood of bankruptcy I
1 year ahead II
Log (firm size)
III
IV
-0.040*** -0.033***
2 years ahead V
VI
VII
3 years ahead VIII IX
-0.036*** -0.040***
(-3.18) (-4.93) (-6.37) (-1.74) 0.517*** 0.274*** 0.283*** 0.228*** (12.08) (8.66) (8.38) (7.11) -0.012* -0.009* -0.005** -0.010** -0.009*** -0.012*** -0.014** (-1.80) 0.0001* (1.69)
R squared # Obs.
0.0648 211
0.3882 297
4 years ahead XI XII
-0.011* -0.019***
(-2.95)
Average 0.488*** Hedge Ratio (13.34) -0.007** (Z-score) (-2.41) 0.00006** (Z-score)^2 (2.33)
X
0.006
XIII
-0.008
5 years ahead XIV XV 0.031**
-0.001
(-3.48) (0.76) (-1.34) (2.12) 0.236*** 0.520*** 0.523*** 0.601*** (6.77) (11.05) (9.62) (12.22) -0.008 -0.007*** -0.021*** -0.011** -0.008** -0.030**
(-0.06) 0.597*** (10.39) -0.016**
(-1.78) (-2.35) (-2.57) (-2.77) (-4.47) (-2.55) 0.0001* 0.00003** 0.0001** 0.0001*** 0.001*** 0.001* (1.92) (2.03) (2.36) (2.72) (3.01) (1.78)
(-1.53) (-2.90) (-3.19) (-2.36) (-2.32) (-2.48) 0.0003 0.001*** 0.001*** 0.001** 0.001*** 0.002 (0.71) (2.70) (2.75) (2.42) (2.86) (1.47)
(-2.04) 0.002** (2.25)
0.4526 211
0.3375 141
0.6529 73
0.2418 262
0.1757 185
0.4069 185
0.2936 192
***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.
35
0.1143 141
0.5179 141
0.0930 110
0.5179 110
0.6679 91
0.1020 73
Table 6. Speculation and firm characteristics
This table presents the regression results of speculating activity as a function of firm characteristics. We estimate the following regressions: Speculation = a + b*X + e if firm is a hedger in period t. Speculation is measured as the standard deviation of hedge ratios. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. Log (firm size) is the natural log of market value of assets. Herfindahl Index measures each firm’s concentration in gold mining as determined by books assets allocated to gold mining versus other business segments the firm may be involved in. Dividend dummy is a dummy variable that is equal to 1 if the firm paid dividends on year t and 0 otherwise. Leverage is the leverage of the firm based on book values. Figures in parentheses denote t-statistics.
Log (firm size) Market to book Herfindahl index Dividend payer Quick ratio
1 year ahead
2 years ahead
3 years ahead
4 years ahead
5 years ahead
Speculation
speculation
speculation
speculation
speculation
-0.040*
-0.017***
-0.002
-0.001
0.028
(-1.88)
(-2.62)
(-0.25)
(-0.13)
(1.41)
-0.015
-0.026***
-0.021*
0.003
-0.043
(-0.49)
(-2.89)
(-1.77)
(0.20)
(-1.46)
0.031
0.027
0.003
0.075
-0.222*
(0.25)
(0.72)
(0.07)
(1.06)
(-1.95)
0.004
-0.006
-0.032
0.017
0.007
(0.06)
(-0.31)
(-1.47)
(0.53)
(0.14)
-0.001
0.000
-0.009**
-0.008*
-0.015**
(-0.17)
(0.17)
(-2.58)
(-1.70)
(-2.03)
0.017
0.005
0.002
-0.029
-0.017
(0.53)
(0.57)
(0.15)
(-1.14)
(0.46)
-0.029
-0.019
0.047
0.019
-0.076
(-0.13)
(-0.31)
(0.64)
(0.20)
(-0.51)
Intercept
0.435** (2.59)
0.237*** (4.72)
0.188*** (2.83)
0.061 (0.65)
0.277* (1.72)
Observations
211
185
139
104
68
R-squared
0.0481
0.2147
0.1213
0.0561
0.2063
Debt to equity Free cash flow
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
36
Table 7: Cash Flow Gains from Speculation and Firm Characteristics This table presents the regression results of selective hedging cash flows (in $/hedged ounce) as a function of firm size for the sample of hedgers. Specifically, we regress quarterly residual cash flows (actual cash flows minus predicted cash flows) based on the Cragg model on firm size, using hedge ratio and standard deviation of hedge ratio as additional control variables. Figures in parentheses denote t-statistics. Aggregate 1-year 1-year 1-year 2-year 2-year 2-year 3-year 3-year 3-year 4-year 4-year 4-year 5-year 5-year 5-year residual residual residual residual residual residual residual residual residual residual residual residual residual residual residual residual cash cash cash cash cash cash cash cash cash cash cash cash cash cash cash flows flows flows flows flows flows flows flows flows flows flows flows flows flows cash flows flows Log -1.999*** -1.850* -1.858* -1.854* -0.335 -0.432 0.333 0.079 0.024 0.122 0.408 0.351 0.438 0.129 0.181 0.329 (firm size) (-3.06) (-1.87) (-1.87) (-1.75) (-0.45) (-0.57) (0.37) (0.19) (0.06) (0.27) (1.13) (0.94) (1.16) (0.20) (0.27) (0.48) Average -0.889 4.937 2.375 2.640 -1.350 Hedge ratio (-0.21) (1.21) (0.84) (0.70) (-0.33) 0.030 24.666 7.599 1.770 -5.537 Std dev. Hedge ratio (0.01) (1.61) (1.05) (0.36) (0.99) # Obs. 190 207 207 197 188 188 175 144 144 132 107 107 100 53 53 51 2 R 0.0473 0.0168 0.017 0.0163 0.0011 0.0089 0.0159 0.0002 0.0052 0.0085 0.012 0.0167 0.0149 0.0008 0.003 0.0218 ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
37
Table 8: Speculation and CEO Compensation We present regression results of speculative activity as a function of executive compensation. The dependent variable is the extent of speculation at time t, as measured by the standard deviation of the hedge ratio. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. In Panel A, the independent variables are aggregate vega of option holdings for CEOs and log of firm size. In Panel B, the independent variables are aggregate delta of option and stock holdings of CEOs and firm size. Figures in parentheses denote t-statistics. Panel A: Speculation, vega and log of firm size
Log (Vega)
-0.008** (-2.05)
Average Hedge Ratio Log(Firm Size)
1-year
2-year
Speculation
Speculation
-0.007 (-1.45)
-0.005 -0.009** -0.011** (-1.22) (-2.23) (-2.06) 0.212*** (7.81) -0.018** -0.021*** -0.013 (-2.52)
(-3.39)
3-year
4-year
Speculation -0.010** (-2.18) 0.326*** (10.52) -0.024***
(-1.64)
(-3.63)
-0.002 (-0.79)
-0.006 (-1.64)
5-year
Speculation
0.004
-0.008** (-2.41) 0.221*** (7.57) -0.001
(0.68)
(-0.16)
0.000 (0.01)
-0.001 (-0.28)
-0.006
Speculation
-0.003 -0.002 -0.018* (-0.93) (-0.31) (-1.76) 0.532*** (11.69) -0.010* 0.018
-0.014 (-1.42) 0.115** (2.58) 0.021*
(-0.73)
(-1.82)
(1.40)
(1.68)
0.0011 0.0368
0.1058
R2
0.0177
0.0631
0.2642
0.0226
0.0598
0.3905
0.0039
0.0173
0.2851
0.0000
0.0089
0.5381
N
236
227
227
217
208
208
161
157
157
123
120
123
92
89
90
Panel B: Speculation, delta and log of firm size
Log (Delta)
2-year
3-year
4-year
5-year
Speculation
Speculation
Speculation
Speculation
-0.014*** -0.006 (-2.67) (-0.97)
-0.005 -0.018*** -0.013* (-0.89) (-3.14) (-1.96) 0.204*** (7.39) -0.020*** -0.023*** -0.014*
Average Hedge Ratio Log(Firm Size) 2
1-year Speculation
(-2.84)
(-3.57)
-0.013** (-2.51) 0.326*** (10.50) -0.023***
(-1.70)
(-3.47)
-0.001 (-0.21)
-0.000 (-0.04)
-0.001
-0.003 (-0.70) 0.217*** (7.14) -0.004
(-0.16)
(-0.88)
-0.001 (-0.26)
0.001 (0.19)
-0.007
0.001 -0.008 -0.012 (0.18) (-0.95) (-1.27) 0.534*** (11.61) -0.012** 0.011
(-0.87)
(-2.29)
(0.89)
(0.01)
0.0104 0.0195
0.6247
R
0.0315
0.0654
0.2539
0.0466
0.0609
0.3967
0.0003
0.0003
0.2562
0.0006
0.0070
0.5407
N
222
220
220
204
202
202
153
152
152
120
120
120
***, **, and * denote significance at the 1%, 5%, and 10% level respectively.
38
-0.010* (-1.70) 0.592*** (11.64) 0.000
88
88
88
Table 9: Speculation and CFO Compensation We present regression results of speculative activity as a function of executive compensation. The dependent variable is the extent of speculation at time t, as measured by the standard deviation of the hedge ratio. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios.In Panel A, the independent variables are aggregate vega of option holdings for CFOs and log of firm size. In Panel B, the independent variables are aggregate delta of option and stock holdings of CFOs and firm size. Figures in parentheses denote t-statistics.
Panel A: Speculation, vega and log of firm size
Log(Vega)
1-year
2-year
3-year
4-year
5-year
Speculation
Speculation
Speculation
Speculation
Speculation
-0.02*** -0.02*** (-3.65) (-2.73)
-0.014*** (-2.90)
2
-0.006 (-0.96)
-0.010** (-2.06)
-0.007* (-1.71)
-0.004 (-0.91)
-0.008** (-2.07)
-0.007 -0.002 (-1.49) (-0.30)
-0.004 (-1.15)
-0.014 -0.013 (-1.40) (-1.15)
0.426***
0.222***
-0.013
(7.40) -0.009
-0.024**
(11.00) -0.021***
-0.011*
(6.31) -0.009*
(9.72) -0.02** -0.015***
-0.001
(12.94) -0.005
(-1.43)
(-1.24)
(-2.22)
(-2.69)
(-1.81)
(-1.73)
(-2.41)
(-2.68)
(-0.05)
(-0.63)
0.0235 0.0820
0.5522
0.0285 0.0285
0.7282
R
0.0775
0.0874
0.3257
0.0273
0.0580
0.4880
0.0251
0.0509
0.3012
N
161
159
159
150
148
148
116
115
115
0.502***
-0.007 (1.09)
0.248***
Average Hedge Ratio Log(Firm Size)
-0.012** (-2.04)
94
94
94
0.762***
69
69
69
Panel B: Speculation, delta and log of firm size 1-year
2-year
Speculation Log(Delta)
-0.02*** -0.02*** (-3.65) (-2.95)
2
Speculation
4-year
Speculation 0.001 (0.13)
0.000 (0.10)
5-year
Speculation -0.004 (-1.15)
-0.009*** (-3.23)
0.275***
0.119***
0.238***
0.619***
-0.015
(6.27) -0.022**
-0.002
(4.11) -0.007
0.005
(6.04) -0.000
-0.003
(11.75) -0.004
0.004
(11.43) -0.001
(-1.44)
(-2.41)
(-0.33)
(-1.43)
(0.62)
(-0.04)
(-0.39)
(-0.77)
(0.25)
(-0.04)
0.0106 0.0131
0.7116
0.0074 0.0090
0.7768
R
0.1097
0.1241
0.3627
0.0962
0.0922
0.2255
0.0002
0.0058
0.3367
N
110
109
109
103
102
102
78
77
77
***, **, and * denote significance at the 1%, 5%, and 10% level respectively.
39
0.004 (0.79)
61
0.004 (0.87)
Speculation
-0.016*** -0.010*** -0.01*** (-2.85) (-3.28) (-2.89)
Average Hedge Ratio Log(Firm Size)
3-year
61
0.001 (0.35)
61
-0.006 -0.007 (-0.55) (-0.59)
-0.005 (-0.80) 0.666***
42
42
42
Table 10: Speculation and Insider Ownership This table presents the regression results of speculation as a function of insider ownership. The dependent variable is the extent of speculation at time t, as measured by the standard deviation of the hedge ratio. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. Log (insider) is the natural log of the cash value of insider stock holdings. Figures in parentheses denote t-statistics.
Log(Insider)
1-year
2-year
3-year
4-year
5-year
Speculation
Speculation
Speculation
Speculation
Speculation
-0.021*** -0.019*** -0.016*** -0.016** -0.006 (-3.63)
(-2.72)
-0.008
(-2.62) 0.208*** (6.11) -0.014*
(-0.94)
(-1.71)
Average Hedge Ratio Log(Firm Size) 2
(-2.37)
-0.012*
0.003
0.007
0.001
0.006
0.011*
0.008**
0.003
-0.006
(-1.86) (0.84) 0.358*** (8.99) -0.029** -0.037***
(1.33)
(1.12)
(1.77)
(-0.60)
-0.015
(2.15) 0.597*** (12.11) -0.022***
(0.37)
-0.009
(0.31) 0.199*** (5.39) -0.011
(-1.48)
(-3.51)
(1.24)
(0.15)
0.0022 0.0266
0.5749
(-0.69)
(-2.59)
(-4.06)
(-1.11)
(-1.64)
0.0067 0.0183
0.2360
0.0138
0.0377
0.6497
106
91
88
88
R
0.0756
0.0849
0.2682
0.0377
0.0828
0.4296
N
163
153
153
145
137
137
108
***, **, and * denote significance at the 1%, 5%, and 10% level respectively.
40
106
64
-0.006
(-0.88) 0.500*** (8.80) 0.020 0.002
64
64
Table 11: Selective hedging cash flows and executive compensation/insider ownership In this table, we present results of regressions of selective hedging cash flows on executive compensation and insider ownership. Panel A summarizes results of regressions of selective hedging cash flows on CEO compensation. Panel B summarizes results of regressions of selective hedging cash flows on CFO compensation. Panel C summarizes results of regressions of selective hedging cash flows on insider ownership. We measure speculation as the standard deviation of hedge ratios. Specifically, for each year and company, we calculate the standard deviation of the four quarterly hedge ratios. t-statistics for coefficients are in parentheses. Panel A. Cash flows and CEO compensation Delta of CEO
Model I -0.51 (-0.56)
Model II -0.26 (-0.24)
Vega of CEO -0.53 (-0.45) 139 0.004
Firm Size # of observations R-squared
139 0.002
Model III
Model IV
0.24 (0.35)
147 0.001
0.00 (0.29) -1.48 (-1.27) 144 0.013
Model VII
Model VIII
0.17 (0.29)
0.47 (0.73) -1.25 (-1.30) 99 0.018
Panel B. Cash flows and CFO compensation Delta of CFO
Model V -0.015 (-0.03)
Model VI 0.05 (0.08)
Vega of CFO -0.39 (-0.37) 66 0.002
Firm Size # of observations R-squared
66 0.004
Panel C. Cash flows and insider ownership Model IX 1.06 (1.19)
Model X 2.48** Log (Insider Ownership) (2.10) -2.91* Firm Size (-1.81) # of observations 97 97 R-squared 0.015 0.048 ** and * denote significance at the 5% and 1% level respectively.
41
99 0.001
