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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]
February 2007
JEL Classification: G11; G14; G32; G39
Keywords: Corporate risk management; selective hedging; speculation; managerial
compensation.

We 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.
2
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
1
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.
3
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.
4
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.
4
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. 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
(2002), while this argument might explain some of the relative differences in the intensity of
5
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
find that CFO stock price sensitivity (delta) is positively associated with the probability of
6
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.
7
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
8
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
9
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
6
See also Grossman (1976) and Huberman and Schwert (1985).
10
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.
11
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.
12
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. Figure 2
plots the percentage of selective hedgers in our sample over time for each of the five
maturities contained in our sample, where selective hedgers are defined as firms whose
selective hedging exceeds a threshold level. We observe a general increase in the incidence of
selective hedging over time.
[Place Table 1 and Figs 1 & 2 about here]
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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
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
14
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
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
15
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
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
16
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
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
7
We also explore using leverage using market values with similar results. However, the number of observations
was much more limited.
17
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
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.
18
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
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
19
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
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
20
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
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 two-
21
year 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.
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
22
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
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.
23
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
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28
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Smith, C., and R.M. Stulz, 1985, The Determinants of Firms’ Hedging Policies, Journal of
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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
Figure 2: Percentage of Selective Hedgers, 1989-1999
60%
40%
30%
20%
10%
Year
Maturity 1
Maturity 2
Maturity 3
31
Maturity 4
Maturity 5
1999
1999
1998
1998
1997
1997
1996
1996
1995
1995
1994
1994
1993
1993
1992
1992
1991
1991
1990
1990
0%
1989
Percentage of Selective Hedgers
50%
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 the absolute value of the actual hedge ratio minus the predicted value
(using the Cragg model). 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
No. Obs.
534
534
578
539
531
534
513
Mean
5.5867
1.8586
0.9486
0.4323
3.6598
0.4039
-0.0571
Median
5.4581
1.5641
1.0000
0.0000
1.6178
0.1671
-0.0270
Std. Dev
1.7537
1.1234
0.1594
0.4959
8.5898
3.4906
0.1343
No. Obs.
2000
2002
2020
2058
2077
1121
1270
1521
1741
1890
Mean
0.3474
0.1875
0.0924
0.0423
0.0349
0.1018
0.0557
0.0331
0.0196
0.0185
Median
0.2295
0.0300
0.0000
0.0000
0.0000
0.0076
0.0000
0.0000
0.0000
0.0000
Std. Dev
0.4406
0.2859
0.1901
0.1221
0.1432
0.2373
0.1332
0.0865
0.0674
0.0904
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 )
32
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
33
Table 3: Magnitude of Hedging and Firm Size
Panel A presents the regression results of hedging activity as a function of firm size. 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. In Panel B, the predicted percentage of gold
hedged is estimated and summary statistics are presented.
Panel A: OLS regression results
Log (firm size)
R2
F-value
N
1-year
2-year
3-year
4-year
5-year
Hedge ratio
Hedge ratio
Hedge ratio
Hedge ratio
Hedge ratio
0.0365***
(4.00)
0.0313
16.03
498
0.0359***
(4.95)
0.0448
24.47
502
0.0205***
(4.77)
0.0433
22.73
504
0.0092***
(3.48)
0.023
12.14
515
0.015***
(4.93)
0.045
24.29
518
*** 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
34
Table 4: Speculation and Firm Size
This table presents the OLS regression results of speculatve 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. The dependent variable is the percentage of production that a firm speculates at
time t. Figures in parentheses denote t-statistics.
1-year
Speculation
Log (firm size)
3-year
Speculation
4-year
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
2-year
Speculation
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.
35
0.2903
43.56
216
5-year
Speculation
-0.006
(0.52)
0.564***
(12.35)
0.0079
1.17
150
0.5130
77.41
150
-0.009
(-1.28)
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 (selective hedging ) = a + b ( z-score)
+ c (z-score ^2) and (Selective hedging ) = a + b ( z-score) + c (z-score ^2) + d ( firm size) conditional on firms being hedgers. The extent of selective
hedging is measured by the standard deviation of 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
2 years ahead
V
-0.040*** -0.033***
(-2.95)
VI
VII
3 years ahead
VIII
IX
-0.036*** -0.040***
(-3.18)
0.517*** 0.274***
(-4.93)
0.006
(-6.37)
(-1.74) (-3.48)
0.283*** 0.228***
0.236*** 0.520***
(-1.80)
(-1.78)
(-2.35)
(-2.57)
(-2.77)
(-4.47) (-2.55)
0.0001* 0.0001* 0.00003** 0.0001** 0.0001*** 0.001*** 0.001*
(1.69)
(1.92)
(2.03)
(2.36)
(2.72)
(3.01)
(1.78)
(-1.53)
0.0003
(0.71)
R squared
# Obs.
0.0648
211
0.3375
141
0.3882
297
4 years ahead
XI
XII
-0.011* -0.019***
Average
0.488***
Hedge Ratio (13.34)
-0.007**
(Z-score)
(-2.41)
0.00006**
(Z-score)^2
(2.33)
-0.012*
X
(0.76)
XIII
-0.008
5 years ahead
XIV
XV
0.031**
(-1.34)
(2.12)
0.523*** 0.601***
-0.001
(-0.06)
0.597***
(12.08)
(8.66)
(8.38)
(7.11)
(6.77)
(11.05)
(9.62) (12.22)
(10.39)
-0.009* -0.005** -0.010** -0.009*** -0.012*** -0.014** -0.008 -0.007*** -0.021*** -0.011** -0.008** -0.030** -0.016**
0.4526
211
0.2418
262
0.1757
185
0.4069
185
0.2936
192
***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.
36
0.1143
141
(-2.90) (-3.19) (-2.36) (-2.32) (-2.48)
0.001*** 0.001*** 0.001** 0.001*** 0.002
(2.70)
(2.75)
(2.42)
(2.86) (1.47)
0.5179
141
0.0930
110
0.5179
110
0.6679
91
0.1020
73
(-2.04)
0.002**
(2.25)
0.6529
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: Standard deviation of hedge ratios = a + b*X + e if firm is a hedger in
period t. The dependent variable is the percentage of production that a firm speculates at time t. 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.
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
Log (firm size)
Market to book
Herfindahl index
Dividend payer
Quick ratio
Debt to equity
Free cash flow
*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
37
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 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
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
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
(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)
Average
-0.889
4.937
2.375
2.640
Hedge ratio
(-0.21)
(1.21)
(0.84)
(0.70)
0.030
24.666
7.599
1.770
Std dev.
Hedge ratio
(0.01)
(1.61)
(1.05)
(0.36)
# Obs.
190
207
207
197
188
188
175
144
144
132
107
107
100
53
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
*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
38
5-year
residual
cash
flows
0.181
(0.27)
-1.350
(-0.33)
53
0.003
5-year
residual
cash
flows
0.329
(0.48)
-5.537
(0.99)
51
0.0218
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. 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.
39
-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. 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)
-0.006
(-0.96)
0.248***
(7.40)
Average
Hedge Ratio
Log(Firm
Size)
2
-0.012**
(-2.04)
-0.010**
(-2.06)
-0.007*
(-1.71)
-0.004
(-0.91)
0.426***
(11.00)
-0.008**
(-2.07)
-0.007 -0.002
(-1.49) (-0.30)
0.222***
(6.31)
-0.004
(-1.15)
0.502***
(9.72)
-0.013
-0.009
-0.024**
-0.021***
-0.011*
-0.009*
-0.02** -0.015***
(-1.43)
(-1.24)
(-2.22)
(-2.69)
(-1.81)
(-1.73)
(-2.41)
0.0235 0.0820
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
-0.014 -0.013
(-1.40) (-1.15)
94
-0.007
(1.09)
0.762***
(12.94)
-0.001
-0.005
(-2.68)
(-0.05)
(-0.63)
0.5522
0.0285 0.0285
0.7282
94
94
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)
Average
Hedge Ratio
Log(Firm
Size)
2
3-year
Speculation
4-year
Speculation
-0.016*** -0.010*** -0.01***
(-2.85)
(-3.28)
(-2.89)
-0.009***
(-3.23)
0.275***
(6.27)
0.119***
(4.11)
0.001
(0.13)
0.000
(0.10)
5-year
Speculation
-0.004
(-1.15)
0.004
(0.79)
0.004
(0.87)
0.238***
(6.04)
Speculation
0.001
(0.35)
-0.006 -0.007
(-0.55) (-0.59)
0.619***
(11.75)
-0.005
(-0.80)
0.666***
(11.43)
-0.015
-0.022**
-0.002
-0.007
0.005
-0.000
-0.003
-0.004
0.004
-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.
40
61
61
61
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. 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)
Average Hedge
Ratio
Log(Firm Size)
2
(-2.62)
0.208***
-0.008
(6.11)
-0.014*
(-0.94)
(-1.71)
(-2.37)
(-0.69)
-0.012*
0.003
0.007
0.001
0.006
0.011*
0.008**
0.003
-0.006
-0.006
(-1.86)
0.358***
(0.84)
(1.33)
(0.31)
0.199***
(1.12)
(1.77)
(2.15)
0.597***
(0.37)
(-0.60)
(-0.88)
0.500***
(8.99)
-0.029** -0.037***
-0.009
(5.39)
-0.011
-0.015
(12.11)
-0.022***
0.020
(8.80)
0.002
(-1.48)
(-3.51)
(1.24)
(0.15)
0.0022 0.0266
0.5749
(-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.
41
106
64
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. 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.
42
99
0.001