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Why do firms speculate? Does speculating lead to outperforming of the market?
Evidence from AEX listed companies.
Eddy Jordaan
5619173
1
Index
Introduction
4-5
Section I
1. Literature Review
5-7
Section II
2.1 Research Methodology
7-8
2.2 Empirical Results
9
Section III
3.1 Limitations
9-10
3.2 Recommendations
11
3.3 Conclusion
11-12
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Abstract
This research addresses the relationship between firms who are using financial derivatives
products for speculative purpose and compare these results with firms who are using financial
derivatives products for hedging purposes, within AEX listed companies. Based on existing
literature a model will be used which classifies AEX listed firms in the category “using
derivatives for speculative purposes” versus the category “using derivatives for hedging
purposes”. Data is manually obtained from annual reports for a period of five year and the
empirical research explains the relationship between firm return and the following parameters:
book asset value, variable part of remuneration package of the CEO, net derivatives on the
end of the year and the dummy variable “firm uses derivatives for speculative purposes”. In
this particular research speculating by firms leads to outperformance of the market.
Keywords: Risk Management, hedging, speculating, interest swaps, derivatives
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Introduction
Dutch housing corporation Vestia became in big financial problems in 2012 because of their
big derivative portfolio. The government rescued Vestia at the cost of 2 billion euros. They
became in financial distress because Vestia was holding a derivative portfolio which was
acquired to anticipate on an increasing interest rate. Unfortunately, against Vestia’s
management expectations, the interest rate declined. The derivatives used where mainly
derivatives which needed to protect for the upside interest risk. The portfolio consisted
mainly of interest swaps. When the interest rate started to decline banks started to ask for
extra liquidity. This doesn’t necessarily has to result in liquidity problems but the relatively
large derivative portfolio compared to their liabilities exacerbated their liquidity problems in
fast paces. This contradiction phenomenon seized my interest: financial products which are
supposed to minimize risk are used in a way that they make the corporate risk bigger that it
used to be.
Another case of a derivative scandal is the 1992 collapse of Barring Bank. Because of
unauthorized and fraudulent deals, by Nick Leeson, the Baring Bank lost approximately 1.4
billion dollars, twice the available trading capital of the bank. The Baring Bank was declared
insolvent on 26-02-1995.
According to Geczy, Minton and Schrand (1997) firms are using derivatives for hedging
purposes in order to minimize interest risk. When firms are speculating on the contrary they
increase the interest risk. Most of the time the term speculating is used to indicate that the
firm is obtaining a trading profit on the underlying asset. But when can we speak from
speculating at a firm and when can we speak from hedging?
This leads to the following research questions:

Why do firms speculate?

Does speculating lead to outperforming the market?
In order to answer the research question, additional (sub-) research questions must be
answered:

How can one make a difference between speculating and hedging?
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This research will point out that the answer on this question is less straightforward than one
might initial think and there is need to develop a more enhanced model, which can tell if a
firm is using derivatives for speculative or hedging purposes. Section I covers a literature
review, section II the methodology and section III covers the limitations, recommendations
en the conclusion.
1. Literature review
Rene Stulz (1984) uses the following definition for hedging. “A firm can hedge by trading in
a particular futures, forward or option market even though it has no identifiable cash position
in the underlying commodity. Furthermore, a firm can hedge by altering real operating
decisions; for instance, a merger can produce effects similar to those of hedging in terms of
the market value of the firm”.
There are different reasons for using hedging.
According to Chava and Purnanandam (2007) a risk of hedging by firms is that high-powered
executive compensation contracts stimulate the use of more hedging because a part of the
surplus can be obtained through hedging. Because of the highly convex CEO remuneration
packages contracts, managers can benefit from the upside from speculation but they don’t pay
for the downside of speculation.
Second hedging can reduce the expected corporate tax liability, when the after-tax value of
the firm is a concave function of its pre-tax value, which increases the expected post-tax firm
value. This is only the case if the costs for the hedge is not too large (Stulz ,1984).
Hedging can also effect the transaction cost bankruptcy: “if transactions cost of bankruptcy
are a decreasing function of firm value, expected after-tax firm value net of bankruptcy costs
is higher if the firm can costless hedge”(Stulz ,1984). Graham and Smith (2002) support this.
They haven’t found any evidence that firms hedge to reduce tax liability, when their tax
functions are convex. They found evidence that firms are hedging in order to increase debt
capacity and interest deductions.
Third, there is a relationship between managerial risk aversion and hedging argues Stulz
(1984). Managerial compensation contracts are supposed to be designed the way that when
firm value increases their expected utility increases as well. Smith and Watts (1982) showed
that firm value and managerial compensation positive correlated. Through hedging managers’
pay utility can be influenced, because hedging can influence a firm’s pay-offs. The firm’s
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pay-offs are again positive correlated to managers expected utility. Stulz (1984) argues that if
the wealth function of the manager is concave at the end-of-period firm value, the optimal
hedging strategy is to hedge completely because this maximizes his/her income. If the
manager’s end-of-period wealth however is a convex function of the end-of-period firm value,
the optimal strategy is to hedge some of the involved risk. The expected pay-out is higher if
the manager decides not to hedge but the manager would be willing to give up some income
in order to eliminate some risk, because of risk aversion. “If expected returns to financial
assets vary, the manager faces a trade-off between expected income and risk of income. In
such cases, he will hedge less if hedging involves going short in a portfolio with a high
expected return. If transaction costs increase, the firm will hedge less, as hedging decreases
the manager’s expected end-of-period wealth. We also must assume that the firm has a
comparative advantages in hedging over the manager. In other words, it should not pay for
the manager to hedge his end-of-period wealth on his personal account. The combination of
transactions costs, economies of scale, and the large number of managers within any firm
make this comparative advantage likely”.
Last Stulz argues that “with costly hedging, shareholders have incentives to devise a
compensation plan that discourages managers from devoting excessive resources to hedging.
This can be accomplished when computing the manager’s compensation by filtering out those
changes in firm value that are not under the manager’s control and by making the manager’s
compensation a more convex function of firm value. However, it will generally not be
efficient to eliminate all incentives to hedge.” (Stulz, 1984).
The Modigliani and Miller (1958) and Miller and Modigliani (1961) irrelevance propositions
essence is that, if there are no market imperfections, hedging wouldn’t change firm value. If
there’s perfect market information investors are able to choose their required risk profile and
there would be no need for firms to hedge (Graham and Rogers, 2002).
Berkman and Bradbury (1996) argue that recent finance theories suggested that hedging can
increase firm value by reducing agency cost, uncertainty of expected taxable income and the
expected cost of financial distress. However, in their research they only find support for the
hypothesis that derivative use is positively related to the value of firm’s growth options when
using fair value measurement of hedging activity. Besides this they find that short-term asset
growth, foreign assets as a proportion of total assets, and the use of alternative equity
instruments are not related to the use of derivatives.
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According to Demarzo and Duffie (1995) accounting standards where firms report in can
have a significant influence on the accounting profit of a firm. Demarzo and Duffie argue that
financial hedging can be motivated by career concerns because Early in a managers career the
impact of reported profits are relative more important on their career then from their older
colleagues. Report profits can indirect influence managers ‘future payments so are relatively
important for younger managers.
What follows is that managers who have good news would be more willing to hedge in order
to increase the likelihood of a good outcome. On the contrary, managers with bad news,
would be more willing to increase risk and don’t hedge (Demarzo and Duffi, 1995). These
managers could also increase risk through speculation instead of hedging.
It’s difficult to tell if the use of derivatives has a speculative or hedging purpose. Most of the
time it’s hard to draw a line between speculating and hedging, but if firms hedge it to large
amounts this becomes speculative. Chernenko and Faulkender (2011) argue that if the
relationship between derivative usage and a given firm characteristic (for example total
liabilities outstanding or total executive compensation) varies, then it’s more likely that this
characteristic is speculative rather than for hedging purpose. Further Chernenko and
Faulkender argue that unless the underlying firm liability structure is similarly changing over
time with total amount of derivatives used, the derivatives used have a speculative purpose.
The overall findings from Chernenko and Faulkender point out that firms use derivatives for
hedging and speculating purposes. Both Gezcy et al. (2007) and Chernenko and Faulkender
concluded that nonfinancial firms whose managers have high-powered incentives are more
likely to use derivatives for speculative purposes.
2.1 Research methodology
There isn’t a model in existing literature which tells if derivatives are used for speculative or
hedging purpose as already mentioned before. However based on the article by Chernenko
and Faulkender (2011), I decided to use the ratio derivatives divided by total liabilities as a
measure for the degree of speculation by a firm. They argue in their article that chances, from
year to year, in the derivatives outstanding divided by a fundamental firm characteristic could
be an indication for speculation. Dividing by the total liabilities outstanding seems a pretty
straightforward choice for fundamental firm characteristic, taken into account that the CEO’s
using derivatives in the best interest of the firm and not for maximizing their own personal
payoff, because the reason for using derivatives is hedging against their liabilities outstanding.
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From this ratio the standard deviation has been taken. The firms with the top 28% most
extreme values from these standard deviations are assumed to be firms who are using
financial derivative products for speculating purposes instead of hedging purposes. Aabo,
Hansen and Pantzalis (2012) found in their article that 28% of a sample of 186 firms actively
speculate. The assumption made in this paper is that this 28% is also applicable for our
sample. Cross sectional data analysis is used for explaining the difference in the firm based
returns from the firms, which use derivatives for speculative purpose, and compare these
returns with the returns from the firms which use derivatives for non-speculative purposes
(hedging).
Figure 1 shows the histogram of the standard deviations of the ratios, derivatives divided by
total liabilities outstanding. As can be seen in the histogram 20% of the histogram gives an
extreme value which stands clearly apart from the others. 20% from 15 is three and besides
that 20% falls within the used limit of 28% by Aabo, Hansen and Pantzalis. All of this taken
into account, resulted in the assumption that the top 20%, instead of 28%, are firms from our
sample who speculate and the others are hedging. A dummy variable will be used, firms who
are considered speculating will be awarded the value one for this dummy variable.
Correlations between the parameters will be analysed in order to tell which parameters should
be included in the regression. Data includes Equity, Total Assets, Book Asset Value (Total
Assets-(depreciation+amortization), Revenues, Variable Remuneration CEO and Net
derivatives. The model is formulated below
The correlations between the different parameters are summarized in table 3 and based on
these correlations the model is formulated. Using parameters which are correlated with each
other can cause multicollinearity. In order to avoid this parameters used in the model should
not be correlated too much. This results in the following model. In the model y is the return
of the firm, β the coefficient of the dummy variable dumSpec, this is called a interaction term,
γ the coefficient of the logarithm of the Variable Remuneration from the CEO,
Besides this model an T-test is conducted which tests whether the return on firms who
speculate is higher compared to those of firms who are hedging. The T-test looks like this.
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2.2 Empirical Results
As Figure 1 points out, three firms, 20% of the data set, have the most extreme values of the
standard deviation from the derivatives divided by total liabilities. These three firms are
Corio, SBM Offshore and Wolters Kluwer.
The correlations of the different parameters formed the basis for deciding which parameters
were added to the regression. Based on table 3 there can be concluded that Equity is highly
correlated to all parameters, so Equity isn’t used in the model. The same is the case for Total
Assets and Revenues. Which leaves only dumSpec, VarRem, BAV, NetDer in the model. As
mentioned before it’s not possible to take logarithm from NetDer because these values can
also be negative. The regression yields some very interesting results. Table 4A shows that
dumSpec, lnBAV, NetDer are significant respectively on a 5%, 1% and 1% level. Which
makes them all significant parameters. LnVarRem however, isn’t significant.
Worth mentioning is the correlation between NetDer and VarRem, 0,2807. Based in our data
set we can conclude that there is an relationship between these two parameters, even though
this relationship is not very strong. The
of the regression 0,387, which is very high for a
regression with only 75 observations. The F-value of the regression is 11.04 which is also
very high. The coefficient of dumSpec is 0.046,which tells us that if a firm is speculating,
rather than hedging, the return of the firm will yield on average a 4.6% higher return.
The T-test conducted is comparing the means of the returns of speculating firms versus the
means of the returns of hedging firms. This T-test yields some significant results. The null
hypothesis is that the means are equal and this is tested against three different alternative
hypothesis’. The results are summarized in Table 4. As can been seen the alternative
hypothesis diff<0, which means that the returns on speculative firms exceeds the returns of
those of non-speculative firms, is significant on a 1% level. This outcome is very significant.
The second alternative hypothesis, diff=0 is also significant, but less as the first alternative
hypothesis. The second alternative hypothesis is significant on a 2% significance level which
still is very high. The last alternative is hypothesis is clearly not significant, the outcome is
0.9920. So there is clearly no evidence for rejecting the null hypothesis in favour of diff>0.
3.1 Limitations
Firms have different kind of accounting systems. Some of the firms in the data set for
example use cost price hedge accounting while others don’t. This can give a some serious
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distortion of information. Besides this the data set used is relatively small, it exist of fifteen
companies which all were observed for five years, which results in 75 observations in total.
The AEX exist at this moment of 22 companies, but not all of them have been listed already
for five years in the AEX. On top of this the financial firms (Aegon and ING) are not used in
the data set because their liabilities (and assets too) exist for large parts, compared to nonfinancial AEX listed firms, of financial products which require relatively large amounts of
derivatives to hedge these liabilities. Using these financials in the data set could lead to
serious distortion of information.
Besides this, the model which is used to make a difference between firms who speculate and
firms who hedge is a very simple one which isn´t tested. However, it was the only one there
was literature about.
Also, the amount of derivatives used can become negative because derivatives can have a
negative value as well, which makes it impossible to make outliers in the data set more
unlikely through the use of a logarithmic function because it´s impossible the use logarithms
for negative values.
Last, the data about the derivatives is collected by hand through taking it out of the annual
reports of all the firms. The problem about this is that the amount of derivatives can vary day
by day. The amount of derivatives which is in the reported in the annual report doesn´t
necessarily tells us if a firm traded a lot of derivatives. It only tells us something about the
value of the derivative portfolio which the firm owns on that specific moment. It could be the
case that even though a firm, from the data set, might have the biggest derivative portfolio
on a specific moment at that time, another firm could have traded far more in derivatives
during the year but ended up with a lower end of year derivative portfolio value. The amount
derivatives traded during a year is the best indicator for derivative usage within a firm.
Unfortunately, data about this is not publicly available (neither through WRDS or Datastream)
and the best data which is available is the ending of year derivative portfolio value. This
could lead however to serious distortion of information. Within the limitations stated above
we have some very strong results. The limitations however are numerous which causes the
reliability of the outcome of the results to decrease.
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3.2 Recommendations
Some serious improvements to the research could be made through enlarging the data set,
both through observing more years and making the intervals smaller. So for example through
collecting data about the value of the portfolio value from month to month (or preferably
even more often). This could be realized through firm inquiry. Information about the amount
derivatives traded from firm to firm would increase the reliability of the research also. It´s
however the question if firms would be willing to share this firm specific confidential
corporate information.
During this research it a appeared to me that there is great gap in the field of risk
management/corporate derivative usage in the area of determining whether derivatives are
used for speculative or hedging purposes. Developing a reliable model, which could test
whether firms are speculating or hedging would be very useful. Specifically for financial
firms, because using derivatives can come with big risks, when not used on the right way (we
have seen this during 2012 with Vestia). In times were banks are “too big to fail”, which one
on one means that they will be rescued, with money from the tax payer, in case of default the
public has the right to know about risks the financial firms take. In my opinion developing a
model which could tell something about derivative speculating is could help informing the
public and besides this it could be very useful to the firm itself as well.
3.3 Conclusion
In the introduction we have stated the sub question as “How can one make a difference
between speculating and hedging?”. Chernenko and Faulkender (2011) pointed out that one
could divide the derivatives by the a fundamental firm characteristic. We did this and picked
the total liabilities as a fundamental firm characteristic. This resulted in the conclusion that
from the AEX listed firms Corio, SBM Offshore and Wolters Kluwer are the firms who
speculate most.
We found numerous reasons in literature why firms are speculating. These reasons include
the convex CEO remuneration package, reducing expected corporate tax liability, between
managerial risk aversion and hedging, expected cost of financial distress. These reasons for
speculating are supported by some significant results in the empirical part of the research.
The variable dumSpec is significant, taken into account a 1% significance level. This is
supported by the strong outcome of the T-test, which is conducted. We found very strong
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evidence, at 2% significance level, that the null hypothesis, that the means of the standard
deviations of the ratio from the derivatives divided by the total liabilities outstanding of the
firms who are hedging versus those who are speculating can be rejected in favour of the
alternative hypothesis’ that the means of the standard deviations of the ratio from the
derivatives divided by the total liabilities outstanding of the firms who are hedging are
smaller or equal than those from firms who are speculating.
The coefficient of dumSpec is 0.046,which tells us that if a firm is speculating, rather than
hedging, the return of the firm will yield on average a 4.6% higher return. This tells us
directly that the firms who are speculating, based on our assumptions for speculating, Corio ,
SBM Offshore and Wolters Kluwer are outperforming the market (the non-speculating AEX
listed firms). Outperforming the market is a good reason for preferring speculating over
hedging.
The results gave some pretty solid evidence, given the limitations given, that firms who
speculate have a higher firm return firms who are hedging. However, the limitations are
numerous as already mentioned before, mainly because of the relatively small data set.
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References
Berkman, Henk, and Michael E. Bradbury. “Empirical evidence on the corporate use of
Derivatives.” Financial Management 25, (1996), 5–13.
Chava, S., and A. Purnanandam. “Determinants of the Floating-to-Fixed Rate Debt Structure
of Firms.” Journal of Financial Economics, 85 (2007), 755–786.
Chernenko, S., and M. Faulkender. “The two sides of derivatives usage: hedging and
speculating with interest rate swaps.”, Journal of financial and quantitative analysis 46,
(2011). 1727-1754.
DeMarzo, P., Duffie, D. “Corporate incentives for hedging and hedge accounting.” Review of
Financial Studies 8 (1995), 743–772
Geczy, C.; B.A. Minton; and C. Schrand. “Why firms use currency derivatives.” Journal of
Finance, 52 (1997), 1323-1354.
Graham, J. R., and D. A. Rogers. “Do Firms Hedge in Response to Tax Incentives?” Journal
of Finance, 57 (2002), 815–839.
Miller, Merton H., and Franco Modigliani “Dividend policy, growth, and the valuation of
shares." Journal of Business, 34 (1961), 411–433.
Modigliani, Franco, and Merton H. Miller. “The cost of capital, corporation finance and the
theory of investment.” American Economic Review 48 (1958), 261–297.
Stulz, R. “Optimal Hedging Policies.” Journal of Financial and Quantitative Analysis, 19
(1984), 127-140.
Smith, Clifford and Ross Watts. “Incentive and Tax Effects of U.S. Executive Compensation
Plans. “Australian Journal of Management, 7 (1982), 139-157.
Fortcoming
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Aabo, T., Hansen, M.A., and Pantzalis, C. “Corporate foreign exchange speculation and
integrated risk management.” Managerial Finance (forthcoming).
Figure 1
14
80
60
Percent
40
20
0
0
.01
.02
STDEV
.03
.04
Table 1. Summarize statistics with log variables
variable
N
mean
sd
min
max
p50
skewness
kurtosis
lnEquity
75
8.58
1.10
6.55
11.10
8.45
0.51
2.78
lnTA
75
9.47
1.04
6.90
11.80
9.52
0.26
2.88
lnBAV
75
9.13
0.98
6.65
11.04
9.10
0.10
2.35
lnREV
75
8.87
1.46
5.22
11.45
9.43
-0.43
2.41
lnVarREM
75
0.18
0.91
-2.09
2.26
0.41
-0.74
3.44
NetDer
75
5.36
185.88
-913.00
511.00
0.00
-1.33
9.93
Table 2. Summarize statistics without log of the variables
15
variable
N
mean
sd
min
max
p50
Equity
75
10304.02
15330.22
699.99
66100.00
4687.00
2.72
9.54
TA
75
22824.11
30803.63
991.29
133625.00
13603.00
2.77
9.99
BAV
75
14683.20
15018.79
774.17
62187.00
8931.00
1.63
4.81
REV
75
15945.95
18921.33
185.09
93936.84
12399.90
1.92
6.91
VarREM
75
1.69
1.45
0.12
9.59
1.50
2.68
13.92
NetDer
75
5.36
185.88
-913.00
511.00
0.00
-1.33
9.93
Table 3. Correlations
Equity
TA
BAV
REV
Equity
1.0000
TA
0.9668*
1.0000
BAV
0.8773*
0.9179*
1.0000
REV
0.7734*
0.8490*
0.8404*
1.0000
VarREM
0.1993
0.3054*
0.3108*
0.3561*
NetDer
-0.2136
VarREM
1.0000
0.2807*
Table 4A. Regression
16
NetDer
1.0000
skewness
kurtosis
DivRend
Coef.
Std Err.
t-value
p-value
Intercept
-0.129**
0.056
-2.28
0.026
dumSpec
0.046**
0.018
2.59
0.012
lnVarREM
0.001
0.008
0.11
0.91
lnBAV
0.022***
0.006
3.6
0.001
NetDer
0.0001***
0.000
-4.4
0
Nobs.
75
Rsq
0.387
adj. Sq.
0.352
F-value
11.04
P(F)
0.000
Significant at *p<10%, **p<5%, ***p<1%
Table 4B. T-Test
17
Two-sample t-test
Group
Obs
Mean
0
60
0.071
0.007
0.057
1
15
0.110
0.012
0.046
combined
75
0.078
0.007
0.057
-0.039
0.016
diff
Std.Err
Std.Dev
diff=mean(0)-mean(1)
t=-2.47
Ho:diff=0 diff
degrees of freedom = 73
Ha:
diff<0
Ha: diff!=0
Ha:diff>0
Pr(T<t)=0.0080
Pr(|T|>|t|)=0.0160
Pr(T>t)=0.9920
18