in exchange for accepting lower return. In this sense, risk and return are
entirely interchangeable through leverage (that is, through varying exposure).
table 20.1 a Comparison of two Managers
return risk (Standard Deviation) return/risk ratio
Manager A 10% 5 2:1
Manager B 25% 25 1:1
table 20.2 a Comparison of two Managers revisited
return risk (Standard Deviation) return/risk ratio
Manager A 10% 5 2:1
Manager B 25% 25 1:1
Manager A 3× 30% 15 2:1
2 For strategies that use margin (e.g., futures, foreign exchange, options), managers need only a small percent-
age of the nominal investment to meet margin requirements. In these instances, investors can often use notional
funding—that is, funding an account with a smaller amount of cash than the nominal level. For example, an
investor might notionally fund an account with $300,000 cash to be traded as a $900,000 investment, implicitly
leveraging the cash investment 300 percent vis-à-vis an investment that is not notionally funded. T echnically
speaking, although notional funding increases the exposure per dollar invested, it does not actually imply leverage,
since there is no borrowing involved. Our example assumes notional funding. Nevertheless, in the ensuing discus-
sion, we use the term leverage to indicate increased exposure (even if there is no borrowing involved). For strate-
gies that must be fully funded, the leveraged portion of returns would have to be reduced by borrowing costs.
1 Although this chapter is written from the perspective of an investor comparing investments with two different
managers, exactly analogous comments would apply to a trader comparing two different systems or two differ-
ent trading strategies.

321
HOW TO EVALUATE PAST PERFORMANCE
 As a practical example to illustrate this concept, in Figure 20.1 we compare two actual manag-
ers. Assuming we consider past performance indicative of potential future performance—at least in a
relative sense—which manager provides a better investment? It would appear the answer is indetermi-
nate: Manager C clearly achieves a superior return, but Manager D displays considerably lower risk, as
evidenced by much smaller equity drawdowns throughout the track record. The seeming inability to
determine which manager exhibits better performance is true only in a superfi cial sense, however. In
Figure 20.2 , we again compare Managers C and D, but this time we assume the exposure to Manager
D is doubled.
3 Now it is clear that Manager D is superior in terms of both return and risk, achieving a
signifi cantly higher ending net asset value (NAV) and still doing so with visibly lower equity drawdowns
(despite the doubling of exposure). Even though Manager C ended up with a higher return in Figure
 20.1 , investors could have achieved an even higher return with a 2× investment in Manager D while still
maintaining less risk. The lesson is that return is a faulty gauge; it is the return/risk ratio that matters.
 3 Managers C and D are commodity trading advisors (CTAs) who trade futures, so increased exposure could
have been achieved through notional funding (i.e., without leverage through borrowing). The returns depicted
in Figure 20.1 were adjusted to remove interest income, so that doubling exposure (whether through notional
funding or through borrowed leverage) would multiply all the returns by a near-exact factor of 2.0. (If returns
included interest income, then doubling the exposure would not fully double the returns because there would be
no interest income on the additional exposure.)
 FIGURE  20.1 Two Paths to Return
April
January
4,000
2,000
3,000
1,000
July
October
April
January
July
October
April
January
July
October
April
January
July
October
April
January
January
July
October
April
January
July
October
April
January
July
October
Manager C
Manager D
April
January
July
October
322A COMPLETE GUIDE TO THE FUTURES MARKET
 What if leverage is not available as a tool? For example, what if investors have a choice between
Managers C and D in Figure 20.1 but there are practical impediments to increasing the exposure of
Manager D? Now return and risk are inextricably bundled, and investors must choose between the
higher-return/higher-risk profi le of Manager C and the lower-return/lower-risk profi le of Manager
D. It might seem that risk-tolerant investors would always be better off with Manager C. Such inves-
tors might say, “I don’t care if Manager C is riskier, as long as the end return is higher.” The fl aw in this
premise is that investors who start with Manager C at the wrong time—and that is easy to do—may
actually experience signifi cant losses rather than gains, even if they maintain the investment, and espe-
cially if they don’t. The more volatile the path of returns, the more likely investors will abandon the
investment during one of the equity plunges and, as a result, never realize the higher return. After all,
investors in real time do not know the investment will eventually recover. Thus, even though Manager
C ends up ahead of Manager D, many investors will never survive the ride to see the eventual suc-
cessful outcome (and even those who do may have initiated their investment on an upside excursion,
reducing or even eliminating their net return). The greater the volatility, the larger the percentage of
investors who will close out their investments at a loss.
 Clearly, there is a need to use risk-adjusted returns rather than returns alone to make valid perfor-
mance comparisons. In the next section we consider some alternative risk-adjusted return measures.
April
January
4,000
5,000
6,000
7,000
8,000
2,000
3,000
1,000
July
October
April
January
July
October
April
January
July
October
April
January
July
October
April
January
January
July
October
April
January
July
October
April
January
July
October
Manager C
Manager D 2×
April
January
July
October
 FIGURE  20.2 Doubling the Exposure of the Lower-Risk Manager
323
How to EvaluatE Past


return measures.
April
January
4,000
5,000
6,000
7,000
8,000
2,000
3,000
1,000
July
October
April
January
July
October
April
January
July
October
April
January
July
October
April
January
January
July
October
April
January
July
October
April
January
July
October
Manager C
Manager D 2×
April
January
July
October
 FIGURE  20.2 Doubling the Exposure of the Lower-Risk Manager
323
How to EvaluatE Past PErformancE
 ■ Risk-Adjusted Return Measures
Sharpe ratio
The Sharpe ratio is the most widely used risk-adjusted return measure. The Sharpe ratio is defined
as the average excess return divided by the standard deviation. Excess return is the return above the
risk-free return (e.g., the Treasury bill rate). For example, if the average return is 8 percent per year
and the T -bill rate is 3 percent, the excess return would be 5 percent. (It should be noted that during
certain periods, such as the years following the 2008 financial crisis, zero, or near-zero, interest rates
can effectively eliminate the expectation of a meaningful “risk-free” return. For reference, the
 average
three-month T -bill rate from 2009 through 2015 was only 0.08 percent. In contrast, from 2002 to 2008
the average three-month T -bill rate was 2.58 percent, and during 1995–2001 it was 5.03 percent.) The
standard deviation is a measure of the variability of return. In essence, the Sharpe ratio is the average
excess return normalized by the volatility of returns:
SR AR RF
SD= −
where SR = Sharpe ratio
AR = average return (used as proxy for expected return)
RF = risk-free interest rate (e.g., Treasury bill return)
SD = standard deviation
The standard deviation is calculated as follows:
SD
XX
N
iI
N
=
−
−
∑ () 2
1
where X = mean
Xi = individual returns
N = number of returns
Assuming monthly data is used to calculate the Sharpe ratio, as is most common, the Sharpe ratio
would be annualized by multiplying by the square root of 12. Note that the return is an arithmetic
average return, not the compounded return.
There are two basic problems with the Sharpe ratio:
 1. the return measure is based on average rather than compounded return. The
return an investor realizes is the compounded return, not the average return. The more volatile
the return series, the more the average return will deviate from the actual (i.e., compounded)
return. For example, a two-year period with a 50 percent gain in one year and a 50 percent
loss in the other would represent a zero percent average return, but the investor would actually
realize a 25 percent loss (150% × 50% = 75%). The average annual compounded return of
–13.4 percent, however, would reflect the reality (86.6% × 86.6% = 75%).
324A COMPLETE GUIDE TO THE FUTURES MARKET
 2. the Sharpe ratio does not distinguish between upside and downside volatility.
The risk measure inherent in the Sharpe ratio—the standard deviation—does not refl ect the
way most investors perceive risk. Investors care about loss, not volatility. They are averse to
downside volatility, but actually like upside volatility. I have yet to meet any investors who
complained because their managers made too much money in a month. The standard deviation,
and by inference the Sharpe ratio, however, makes no distinction between upside and downside
volatility. This characteristic of the Sharpe ratio can result in rankings that would contradict
most investors’ perceptions and preferences.
4
 Figure 20.3 compares two hypothetical managers that have identical returns over the period
depicted, but very diff erent return profi les. Which manager appears riskier? Decide on an answer
before reading on.
 4 T o be fair, in some cases, high upside volatility can be indicative of a greater potential for downside volatility, and
in these instances the Sharpe ratio will be an appropriate measure. The Sharpe ratio, however, will be particularly
misleading in evaluating strategies that are designed to achieve sporadic large gains while strictly controlling
downside risk (that is, “right-skewed” strategies).
 FIGURE  20.3 Which Manager Is Riskier?
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
December
February
April
June
August
October
December
2,000
1,800
1,600
1,400
1,200
2,400
2,200
1,000
800
Manager A
Manager B
325
How to EvaluatE Past PErformancE
Most likely you chose Manager A as being riskier. Manager A has three drawdown episodes in
excess of 20 percent, with the largest being 28 percent. In contrast, Manager B’s worst peak-to-valley
decline is a rather moderate 11 percent. Y et the standard deviation—the risk component of the Sharpe
ratio—is 30 percent higher for Manager B. As a result, even though both Managers A and B have equal
cumulative returns and Manager A has much larger equity retracements, Manager A also has a signifi-
cantly higher Sharpe ratio: 0.71 versus 0.58 (assuming a 2 percent risk-free rate). Why does this occur?
Because Manager B has a number of very large gain months, and it is these months that strongly push
up Manager B’s standard deviation, thereby reducing the Sharpe ratio. Although most investors would
clearly prefer the return profile of Manager B, the Sharpe ratio decisively indicates the reverse ranking.
The potential for a mismatch between Sharpe ratio rankings and investor preferences has led to
the creation of other return/risk measures that seek to address the flaws of the Sharpe ratio. Before
we review some of these alternative measures, we first consider the question: What are the implica-
tions of a negative Sharpe ratio?
Although it is commonplace to see negative Sharpe ratios reported for managers whose returns
are less than the risk-free return, negative Sharpe ratios are absolutely meaningless. When the Sharpe
ratio is positive, greater volatility (as measured by the standard deviation), a

eBooks\A Complete Guide to the Futures Market Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads\A Complete Guide to the Futures Market Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads.pdf


me of these alternative measures, we first consider the question: What are the implica-
tions of a negative Sharpe ratio?
Although it is commonplace to see negative Sharpe ratios reported for managers whose returns
are less than the risk-free return, negative Sharpe ratios are absolutely meaningless. When the Sharpe
ratio is positive, greater volatility (as measured by the standard deviation), a negative characteristic,
will reduce the Sharpe ratio, as it logically should. When the Sharpe ratio is negative, however, greater
volatility will actually increase its value—that is, the division of a negative return by a larger number
will make it less negative. Comparisons involving negative Sharpe ratios can lead to absurd results.
Table 20.3 provides an example. Manager B has a negative excess return twice the size of Manager
A’s (–10 percent versus –5 percent) and four times the volatility of Manager A. Even though Manager
B is much worse than Manager A in terms of both return and volatility, Manager B has a higher (less
negative) Sharpe ratio. This preposterous result is a direct consequence of higher volatility resulting
in higher (less negative) Sharpe ratios when the Sharpe ratio is in negative territory. What should be
done with negative Sharpe ratios? Ignore them.
5 They are always worthless and frequently misleading.
Sortino ratio
The Sortino ratio addresses both of the Sharpe ratio’s previously cited problems. First, it uses the
compounded return, which is representative of the actual realized return over any period of time, 5 What if some value must be used, as in an application such as ranking a list of managers based on the ratio? In
this case, a dual rank criterion makes much more sense: ranking managers based on the Sharpe ratio when excess
returns are positive and on excess returns when Sharpe ratios are negative.
table 20.3 a Comparison of two Managers with Negative Sharpe ratios
average annual
return
risk-Free
return excess return
annualized Standard
Deviation Sharpe ratio
Manager A –3% 2% –5% 5 –1.0
Manager B –8% 2% –10% 20 –0.5
326
A Complete Guide to the Futures mArket
instead of the arithmetic return. Second, and most important, the Sortino ratio focuses on defining
risk in terms of downside deviation, considering only deviations below a specified minimum accept-
able return (MAR) instead of a standard deviation (used in the Sharpe ratio), which includes all devia-
tions, upside as well as downside. Specifically, the Sortino ratio is defined as the compounded return
in excess of the MAR divided by the downside deviation, as follows:
SR ACRM AR
DD= −
where SR = Sortino ratio
ACR = annual compounded return
MAR = minimum acceptable return (e.g., zero, risk-free, average)
DD = downside deviation
where DD is defined as:
DD
XM AR
N
ii
N
=
−∑ (min (, ))0 2
where Xi = individual returns
MAR = minimum acceptable return (e.g., zero, risk-free, average)
N = number of data values
For example, if we define MAR = 0, then DD calculations will include only deviations for months
with negative returns (the other months will equal zero).
The MAR in the Sortino ratio can be set to any level, but one of the following three definitions is
normally used for the MAR:
 1. Zero. Deviations are calculated for all negative returns.
 2. risk-free return. Deviations are calculated for all returns below the risk-free return.
 3. average return. Deviations are calculated for all returns below the average of the series being
analyzed. This formulation is closest to the standard deviation, but considers deviations for only
the lower half of returns.
Frequently, the fact that a manager has a higher Sortino ratio than Sharpe ratio is cited as evidence
that returns are positively skewed—that is, there is a tendency for larger deviations on the upside
than on the downside. This type of comparison is incorrect. The Sortino and Sharpe ratios cannot be
compared, and as formulated, the Sortino ratio will invariably be higher, even for managers whose
worst losses tend to be larger than their best gains. The reason for the upward bias in the Sortino
ratio is that it calculates deviations for only a portion of returns—those returns below the MAR—
but uses a divisor based on the number of all returns to calculate the downside deviation. Because
it distinguishes between upside and downside deviations, the Sortino ratio probably comes closer to
reflecting investor preferences than does the Sharpe ratio and, in this sense, may be a better tool for
327
How to EvaluatE Past PErformancE
comparing managers. But the Sortino ratio should be compared only with other Sortino ratios and
never with Sharpe ratios.
Symmetric Downside-risk Sharpe ratio
The symmetric downside-risk (SDR) Sharpe ratio, which was introduced by William T . Ziemba,6 is
similar in intent and construction to the Sortino ratio, but makes a critical adjustment to remove the
inherent upward bias in the Sortino ratio vis-à-vis the Sharpe ratio. The SDR Sharpe ratio is defined
as the compounded return minus the risk-free return divided by the downside deviation. The down-
side deviation is calculated similarly to the downside deviation in the Sortino ratio with one critical
exception: a multiplier of 2.0 is used to compensate for the fact that only returns below a specified
benchmark contribute to the deviation calculation.
7 The benchmark used for calculating the down-
side deviation can be set to any level, but the same three choices listed for the MAR in the Sortino
ratio would apply here as well: zero, risk-free return, and average return. (In his article, Ziemba uses
zero as the benchmark value.) Unlike the Sortino ratio, the SDR Sharpe ratio (with the benchmark
set to the average) can be directly compared with the Sharpe ratio.
8
SDRSR ACRR F
DD
= −
×2
where SDRSR = symmetric downside-risk Sharpe ratio
ACR = annual compounded return
RF = risk-free interest rate (e.g., T -bill return)
DD = downside deviation
6 Willia


rage return. (In his article, Ziemba uses
zero as the benchmark value.) Unlike the Sortino ratio, the SDR Sharpe ratio (with the benchmark
set to the average) can be directly compared with the Sharpe ratio.
8
SDRSR ACRR F
DD
= −
×2
where SDRSR = symmetric downside-risk Sharpe ratio
ACR = annual compounded return
RF = risk-free interest rate (e.g., T -bill return)
DD = downside deviation
6 William T . Ziemba, “The Symmetric Downside-Risk Sharpe Ratio,” Journal of Portfolio Management (Fall 2005):
108–121.
7 Ziemba used the term benchmark instead of MAR in deﬁning downside deviation. If the median were used as
the benchmark, only half the returns would be used to calculate the downside deviation, and a multiplier of 2.0
would then provide an exact compensating adjustment. For other choices for the benchmark (e.g., zero, risk-
free return, average), the number of points below the benchmark would not necessarily be exactly half, and a
multiplier of 2.0 would provide an approximate adjustment.
8 T o be perfectly precise, there would be a tendency for the SDR Sharpe ratio to be slightly lower for a symmetric
distribution of returns because the SDR Sharpe ratio uses the compounded return rather than the arithmetic
return used in the Sharpe ratio, and the arithmetic return will always be equal to or higher than the compounded
return. If, however, zero or the risk-free return is used as the benchmark in the downside deviation calculation,
assuming the manager’s average return is greater than the risk-free return, there would be a tendency for the
SDR Sharpe ratio to be higher than the Sharpe ratio for a symmetric distribution of returns for two reasons:
1.
 There will be fewer than half the returns below the benchmark, so the multiplication by 2.0 will not fully
compensate.
2.
 Downside deviations from the risk-free return (and especially zero) would be smaller than deviations from
the average.
These two factors would cause the downside deviation to be smaller than the standard deviation, implying a
higher SDR Sharpe ratio than Sharpe ratio.
328
A Complete Guide to the Futures mArket
where DD is defined as:
DD
XX
N
ii
N
=
−
−
∑ (min (, ))0
1
2
where Xi = individual returns
X = benchmark return (e.g., mean, zero, risk-free)
Since the SDR Sharpe ratio includes only the downside deviation, multiplying by the square root
of 2 (a consequence of doubling the squared deviations) is equivalent to assuming the upside deviation
is equal (i.e., symmetric) to the downside deviation. This proxy replacement of the upside deviation is
what makes it possible to compare SDR Sharpe ratio values with Sharpe ratio values.
The SDR Sharpe ratio (with any of the standard choices for a benchmark value) is preferable to
the Sharpe ratio because it accounts for the very significant difference between the risk implications
of downside deviations versus upside deviations as viewed from the perspective of the investor. The
SDR Sharpe ratio is also preferable to the Sortino ratio because it is an almost identical calculation,
9
but with the important advantage of being directly comparable with the widely used Sharpe ratio.
Also, by comparing a manager’s SDR Sharpe ratio versus the Sharpe ratio, an investor can get a sense
of whether the manager’s returns are positively or negatively skewed.
Gain-to-pain ratio
The gain-to-pain ratio (GPR) is the sum of all monthly returns divided by the absolute value of the
sum of all monthly losses.
10 This performance measure indicates the ratio of cumulative net gain to
the cumulative loss realized to achieve that gain. For example, a GPR of 1.0 would imply that, on
average, an investor has to experience an amount of monthly losses equal to the net amount gained.
The GPR penalizes all losses in proportion to their size, and upside volatility is beneficial since it
impacts only the return portion of the ratio.
9 Besides the essential introduction of the 2.0 multiplier term, which allows unbiased comparisons between the
SDR Sharpe ratio and the Sharpe ratio, the only difference between the SDR Sharpe ratio and the Sortino ratio
is that it subtracts the risk-free return from the compounded return instead of the MAR (which may or may not
be the risk-free return).
10 The gain-to-pain ratio (GPR) is a performance statistic I have been using for many years. I am not aware of any
prior use of this statistic, although the term is sometimes used as a generic reference for return/risk measures
or a return/drawdown measure. The GPR is similar to the proﬁt factor, which is a commonly used statistic in
evaluating trading systems. The proﬁt factor is deﬁned as the sum of all proﬁtable trades divided by the absolute
value of the sum of all losing trades. The proﬁt factor is applied to trades, whereas the GPR is applied to interval
(e.g., monthly) returns. Algebraically, it can easily be shown that if the proﬁt factor calculation were applied to
monthly returns, the proﬁt factor would equal GPR + 1 and would provide the same performance ordering as
the GPR. For quantitatively oriented readers familiar with the omega function, note that the omega function
evaluated at zero is also equal to GPR + 1.
329
How to EvaluatE Past PErformancE
GPR
X
X
ii
N
ii
N= =∑
∑
1
0|m in(, )|
where Xi = individual returns
A key difference between the GPR and measures such as the Sharpe ratio, the SDR Sharpe ratio,
and the Sortino ratio is that the GPR will be indifferent between five 2 percent losses and one
10 percent loss, whereas the other ratios discussed so far will be impacted far more by the single
larger loss. This difference results because the standard deviation and downside deviation calcula-
tions used for the other ratios involve squaring the deviation between the reference return level
(e.g., average, zero, risk-free) and the loss. For example, if the reference return is zero percent,
the squared deviation for one 10 percent loss would be five times greater than the squ


the single
larger loss. This difference results because the standard deviation and downside deviation calcula-
tions used for the other ratios involve squaring the deviation between the reference return level
(e.g., average, zero, risk-free) and the loss. For example, if the reference return is zero percent,
the squared deviation for one 10 percent loss would be five times greater than the squared devia-
tion for five 2 percent losses (10
2 = 100; 5 × 2 2 = 20). In the GPR calculation, by contrast, both
cases will add 10 percent to the denominator. If an investor is indifferent as to whether a given
magnitude of loss is experienced over multiple months or in a single month, then the GPR would
be a more appropriate measure than the SDR Sharpe ratio and Sortino ratio. However, an investor
who considers a single larger loss worse than multiple losses totaling the same amount would have
the opposite preference.
Although the GPR would typically be applied to monthly data, it can also be calculated for other
time intervals. If daily data are available, the GPR can provide a statistically very significant measure
because of the large amount of sample data. The longer the time frame, the higher the GPR because
many of the losses visible on a shorter time interval will be smoothed out over a longer period. In my
experience, on average, daily GPR values tend to be about one-sixth as large as the monthly GPR for the
same manager, although the ratio between daily and monthly GPR values can range widely. For monthly
data, roughly speaking, GPRs greater than 1.0 are good and those above 1.5 are very good. For daily
data, the corresponding numbers would be approximately 0.17 and 0.25.
One advantage of the GPR over the other ratios is that rankings remain consistent even for nega-
tive returns—that is, a smaller negative GPR is always better than a larger negative GPR (a relation-
ship that is not necessarily true for the other ratios). A GPR of zero means that the sum of all wins is
equal to the sum of all losses. The theoretical minimum GPR value is –1.0 and would occur if there
were no winning months. The closer the GPR is to –1.0, the smaller the ratio of the sum of all wins
to the sum of all losses.
11
tail ratio
An important question for the investor is whether a manager’s extreme returns tend to be larger
on the upside or the downside. Managers with frequent small gains and occasional large losses
(negatively skewed managers) are more risky and less desirable than managers with frequent small
11 The ratio of the sum of wins to the sum of losses is equal to GPR + 1. So, for example, a GPR of −0.25 would
imply that the ratio of the sum of wins to the sum of losses is 0.75.
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A Complete Guide to the Futures mArket
losses and occasional large gains (positively skewed managers). Although there is a statistic that mea-
sures skewness—the degree to which a return distribution has longer tails (extreme events) on the
right (positive) or left (negative) side than the symmetric normal distribution—it is difficult to attach
intuitive meaning to specific values (beyond the value of the sign).
The tail ratio measures the tendency for extreme returns to be skewed to the positive or negative
side in a statistic whose value is intuitively clear.
TR
X
N
X
N
pp
pT
pT
ppT
p
pT
=
=
=
<
=−
=
>−∑
∑
0
100
100
100
where Xp = return at percentile p
 T = threshold percentile to calculate numerator of tail ratio (Implicit assumption: Lower
percentile rankings represent higher return. For example, the top 10% of returns
would be all returns less than T, where T = 10.)
 Np<T = number of returns below percentile
 Np>100−T = number of returns above percentile 100−T
The tail ratio requires one parameter input: the upper and lower percentile threshold used to
calculate the statistic. If the threshold is set to 10, for example, the tail ratio would be equal to the
average of all returns in the top decile of returns divided by the absolute value of the average of all
returns in the bottom decile of returns. (Note: If the average of bottom decile returns is positive, the
tail ratio would have no meaning and cannot be calculated.) If returns were normally distributed, the
tail ratio would equal 1.0. A ratio significantly less than 1.0 would indicate a tendency for the largest
losses to be of greater magnitude than the largest gains, while a ratio significantly greater than 1.0
would indicate the reverse tendency. For example, if the tail ratio was equal to 0.5, it would imply
that the magnitude of the average loss in the bottom decile was twice as large as the average gain in
the top decile—a reading indicative of a potentially very risky manager.
Mar and Calmar ratios
The MAR ratio is the annualized compounded return divided by the maximum drawdown.
MAR ACR
NAV
NAV
j
i
=
− 




1m in
where ACR = annual compounded return (expressed in decimal form)
NAV = net asset value
j > i
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How to EvaluatE Past PErformancE
The Calmar ratio is exactly the same except the calculation is specifically restricted to the past
three years of data. Although these ratios are useful in that they are based on a past worst-case situa-
tion, the fact that the risk measure divisor is based on only a single event impedes their statistical sig-
nificance. Also, if applied over entire track records, the MAR will be strongly biased against managers
with longer records, because the longer the record, the greater the potential maximum drawdown.
(This bias does not exist in the Calmar ratio because, by definition, it is based on only the past three
years of data.) Manager comparisons should be limited to common time periods, a restriction that is
especially critical when using the MAR ratio.
return retracement ratio
The return retracement ratio (RRR) is similar to the MAR and Calmar ratios in that it is a measure
of the average annual compounded return divided by a retracement measure. The key difference,
however


d on only the past three
years of data.) Manager comparisons should be limited to common time periods, a restriction that is
especially critical when using the MAR ratio.
return retracement ratio
The return retracement ratio (RRR) is similar to the MAR and Calmar ratios in that it is a measure
of the average annual compounded return divided by a retracement measure. The key difference,
however, is that instead of being based on a single retracement (the maximum retracement), the RRR
divides return by the average maximum retracement (AMR), which is based on a maximum retrace-
ment calculation for each month. The maximum retracement for each month is equal to the greater
of the following two numbers:
 1. The largest possible cumulative loss that could have been experienced by any existing investor in
that month (the percentage decline from the prior peak NAV to the current month-end NAV).
 2. The largest loss that could have been experienced by any new investor starting at the end of that
month (the percentage decline from the current month-end NAV to the subsequent lowest NAV).
RRR ACRR F
AMR= −
where ACR = annual compounded return
RF = risk-free return
AMR = average maximum retracement = MRi/N
where N = number of months
MRi = max(MRPNHi, MRSNLi)
where MRPNHi is the maximum retracement from prior NAV high, and is defined as:
MRPN HP NH NA VP NHii ii=−() /
where PNHi = prior NAV high (prior to month i)
NAVi = NAV at end of month i
MRSNLi is the maximum retracement to a subsequent NAV low , and is defined as:
MRSN LN AV SN LN AVii ii=−() /
where SNLi is the subsequent NAV low (subsequent to month i).
332
A Complete Guide to the Futures mArket
The reason for using both metrics to determine a maximum retracement for each month is
that each of the two conditions would be biased to show small retracement levels during a seg-
ment of the track record. The first condition would invariably show small retracements for the
early months in the track record because there would not have been an opportunity for any large
retracements to develop. Similarly, the second condition would inevitably show small retrace-
ments during the latter months of the track record for analogous reasons. By using the maximum
of both conditions, we assure a true worst-case number for each month. The average maximum
retracement is the average of all these monthly maximum retracements. The return retracement
ratio is statistically far more meaningful than the MAR and Calmar ratios because it is based on
multiple data points (one for each month) as opposed to a single statistic (the maximum drawdown
in the entire record).
Comparing the risk-adjusted return performance Measures
Table 20.4 compares Managers A and B shown in Figure 20.3 in terms of each of the risk-adjusted
return performance measures we discussed. Interestingly, the Sharpe ratio, which is by far the
most widely used return/risk measure, leads to exactly the opposite conclusion indicated by all
the other measures. Whereas the Sharpe ratio implies that Manager A is significantly superior in
return/risk terms, all the other performance measures rank Manager B higher—many by wide
margins. Recall that both Managers A and B had identical cumulative returns, so the only differ-
ence between the two was the riskiness implied by their return paths. The Sharpe ratio, which uses
the standard deviation as its risk metric, judged Manager B as being riskier because of higher vola-
tility, as measured across all months. Most of Manager B’s volatility, however, was on the upside—a
table 20.4 a Comparison of risk-adjusted return Measures
Manager a Manager b b as percent of a
Sharpe ratio 0.71 0.58 82%
Sortino ratio (zero) 1.27 1.44 113%
Sortino ratio (risk-free) 1.03 1.15 112%
Sortino ratio (average) 0.87 0.94 107%
SDR Sharpe ratio (zero) 0.75 0.85 113%
SDR Sharpe ratio (risk-free) 0.73 0.81 112%
SDR Sharpe ratio (average) 0.62 0.66 107%
Gain-to-pain ratio (GPR) 0.70 0.71 101%
Tail ratio (10%) 1.13 2.86 253%
Tail ratio (5%) 1.10 2.72 247%
MAR ratio 0.41 1.09 265%
Calmar ratio 0.33 1.70 515%
Return retracement ratio (RRR) 0.77 1.67 218%
333
How to EvaluatE Past PErformancE
characteristic most investors would consider an attribute, not a fault. Although Manager A had
lower volatility overall, the downside volatility was significantly greater than Manager B’s—a char-
acteristic that is consistent with most investors’ intuitive sense of greater risk. The Sharpe ratio
does not distinguish between downside and upside volatility, while the other risk-adjusted return
measures do.
Although all the risk-adjusted return measures besides the Sharpe ratio penalize only downside
volatility, they do so in different ways that have different implications:
 ■ Sortino ratio and SD r Sharpe ratio. These ratios penalize returns below a specified level
(e.g., zero) with the weight assigned to downside deviations increasing more than proportionately
as their magnitude increases. Thus, one larger downside deviation will reduce the ratio more than
multiple smaller deviations that sum to the same amount. These ratios are unaffected by the order
of losing months. Two widely separated losses of 10 percent will have the same effect as two con-
secutive 10 percent losses, even though the latter results in a larger equity retracement.
 ■ Gpr. The GPR penalizes downside deviations in direct proportion to their magnitude. In contrast
to the Sortino and SDR Sharpe ratios, one large deviation will have exactly the same effect as
multiple smaller deviations that sum to the same amount. This difference explains why Managers
A and B are nearly equivalent based on the GPR, but Manager A is significantly worse based on
the Sortino and SDR Sharpe ratios: Manager A has both larger and fewer losses, but the sum of
the losses is nearly the same for both managers. The GPR is similar to the Sortino and SDR Sharpe
ratios in terms of being indifferent to the order of losses; that is, it d


is difference explains why Managers
A and B are nearly equivalent based on the GPR, but Manager A is significantly worse based on
the Sortino and SDR Sharpe ratios: Manager A has both larger and fewer losses, but the sum of
the losses is nearly the same for both managers. The GPR is similar to the Sortino and SDR Sharpe
ratios in terms of being indifferent to the order of losses; that is, it does not penalize for consecu-
tive or proximate losses.
 ■ tail ratio. The tail ratio focuses specifically on the most extreme gains and losses. The tail ratio
will be very effective in highlighting managers whose worst losses tend to be larger than their best
gains. In terms of the tail ratio, Manager B, who achieves occasional very large gains but whose
worst losses are only moderate, is dramatically better than Manager A, who exhibits the reverse
pattern.
 ■ Mar and Calmar ratios. In contrast to all the foregoing performance measures, these ratios
are heavily influenced by the order of returns. A concentration of losses will have a much greater
impact than the same losses dispersed throughout the track record. Both of these measures, how-
ever, focus on only the single worst equity drawdown. Therefore losses that occur outside the
interim defined by the largest peak-to-valley equity drawdown will not have any impact on these
ratios. Because the maximum drawdown for Manager A is much greater than for Manager B, these
ratios show a dramatic difference between the two managers.
 ■ return retracement ratio (rrr). The RRR is the only return/risk measure that both penal-
izes all downside deviations and also penalizes consecutive or proximate losses. In contrast to the
MAR and Calmar ratios, which reflect only those losses that define the maximum drawdown, the
RRR calculation incorporates all losses.
Table 20.5 summarizes and compares the properties of the different risk-adjusted return measures.
334
A Complete Guide to the Futures mArket
Which return/risk Measure Is best?
T o some extent, the choice of which return/risk measures to use depends on the performance mea-
sure properties favored by the individual investor. The major advantages and disadvantages of these
performance measures can be summarized as follows:
 ■ Sharpe ratio. Although the Sharpe ratio is the most widely used risk-adjusted metric, it provides
rankings that are least consistent with most people’s intuitive sense of risk because it penalizes
upside gains.
 ■ Sortino ratio. This ratio corrects the main deficiency of the Sharpe ratio by focusing on down-
side risk instead of total volatility as the measure of risk. In addition, the Sortino ratio uses a com-
pounded return, which matches actual return over the entire period, whereas the Sharpe ratio
uses an arithmetic average return, which does not. One disadvantage of the Sortino ratio is that
it is not directly comparable with the Sharpe ratio because its calculation is biased to delivering
higher values.
 ■ SDr Sharpe ratio. This ratio provides the same fix as the Sortino ratio, and it has the advan-
tage of an additional adjustment that allows for direct comparisons of its values with Sharpe
ratio values. Similar to the Sortino ratio, the SDR Sharpe ratio also uses the compounded return
instead of the arithmetic average return. Since the SDR Sharpe ratio will provide nearly identical
rankings as the Sortino ratio and has the advantage of allowing for comparisons with the Sharpe
ratio for the same manager, it seems the better choice for any investor. Using both ratios would
be redundant.
 ■ Gain-to-pain ratio (Gpr). Similar to the Sortino and SDR Sharpe ratios, the GPR penalizes a
manager only for losses (zero percent is also a common choice for minimum acceptable return or
benchmark in the Sortino and SDR Sharpe ratios). The GPR weights losses proportionately to their
table 20.5 properties of risk-adjusted performance Measures
property Sharpe ratio
SDr Sharpe
ratio
Sortino
ratio G pr tail ratio
Mar and
Calmar rrr
Is impacted by upside volatility X
Is impacted only by downside
volatility
X X X X X X
Reﬂects all downside volatility X X X X X
Gives more than proportionate
weight to large losses
X X X X
Is impacted by proximity of losses X X
Focuses on extreme returns only X
Rankings remain consistent for net
negative returns
X X
335
How to EvaluatE Past PErformancE
magnitude, whereas the Sortino and SDR Sharpe ratios magnify the weight of larger losses.
Investors who view one 10 percent monthly loss the same as five 2 percent losses might prefer
the GPR, whereas investors who consider the single 10 percent monthly loss to be worse might
prefer the SDR Sharpe ratio.
 ■ tail ratio. Since, by definition, the tail ratio considers only a small percentage of all returns
(20 percent or less), it is not intended as a stand-alone risk-adjusted return measure. Its focus
on extreme returns, however, makes it a very useful supplemental metric to one of the other
measures.
 ■ Mar and Calmar ratios. These ratios will penalize for losses that occur with sufficient proximity
to be part of the same drawdown. The other ratios (with the exception of the RRR) are unaffected
by the sequence of returns. The drawback of these ratios is that the risk is defined by only a single
event (the maximum drawdown), impeding their statistical significance and representativeness.
 ■ return retracement ratio ( rrr). This ratio is both based on downside deviations and im-
pacted by proximate losses. Its big advantage vis-à-vis the MAR and Calmar ratios is that it reflects
all retracements, with the risk number based on all monthly numbers, rather than just a single
event and single statistic: the maximum drawdown. Although the MAR and Calmar ratios might
still be consulted as supplemental measures reflecting a worst-case situation, the RRR is prefer-
able as a return/drawdown ratio.
 ■ Visual Performance Evaluation
Many people will find that the performance charts in this


ements, with the risk number based on all monthly numbers, rather than just a single
event and single statistic: the maximum drawdown. Although the MAR and Calmar ratios might
still be consulted as supplemental measures reflecting a worst-case situation, the RRR is prefer-
able as a return/drawdown ratio.
 ■ Visual Performance Evaluation
Many people will find that the performance charts in this section provide a better intuitive sense of
relative performance (in both return and risk terms) than do performance statistics.
Net asset Value (NaV) Charts
An NAV chart, such as was illustrated in Figure 20.3, provides an extremely useful way of evaluating
a track record. The NAV chart depicts the compounded growth of $1,000 over time. For example,
an NAV of 2,000 implies that the original investment has doubled from its starting level as of the
indicated time. The NAV chart can offer a good intuitive sense of past performance in terms of both
return and risk. In fact, if an investor were to examine only a single performance gauge, the NAV
chart would probably be the most informative.
The way we visually perceive conventionally scaled NAV charts that depict longer-term periods,
however, may result in misleading inferences. Consider Figure 20.4, and answer the following three
questions before reading on:
 1. Was return higher in the first half of the track record or the second?
 2. Was the manager riskier during the first half of the track record or the second?
 3. Was the return/risk performance better during the first half of the track record or the second?
336A COMPLETE GUIDE TO THE FUTURES MARKET
 If you picked the fi rst half as the answer to any of these three questions, you are wrong. If you
picked the second half for any answer, you are also wrong. The two halves are exactly the same. In fact,
all four quarters of the track record are the same. Figure 20.4 was created by copying the returns of
Manager A in Figure 20.3 and pasting the sequence three times to the end to create an extended NAV
that repeats the same return pattern, displaying it four times in all. Looking at Figure 20.4 , however, it
seems as if both the return and the volatility are increasing sharply over time. They are not. The illusion
is an artifact of depicting NAV charts on a conventional arithmetic scale. On an arithmetic scale, an
NAV decline of 1,000 when the NAV is at 16,000 looks the same as an NAV decline of 1,000 when the
NAV is 2,000. The two declines, however, are radically diff erent: a modest 6 percent decline in the fi rst
instance and a huge 50 percent drop in the second. The distortion on an arithmetic scale chart will get
magnifi ed when the NAV range is wide, which is frequently a serious problem for long-term charts.
 The ideal way to depict an NAV chart is on a logarithmic scale. On a log scale chart, the increments
for a fi xed amount of movement (e.g., 1,000) become proportionately smaller as the level increases,
and as a result, equal percentage price moves will appear as equal size moves on the vertical scale.
Figure 20.5 depicts the same NAV as Figure 20.4 , but on a log scale. The self-replicating nature of the
chart is now evident as equal percentage changes now look identical wherever they appear. The moral is
that a log scale is always the correct way to represent an NAV chart and is especially critical when there
is a wide NAV range (more likely on long-term charts). A log scale was used for Figures 20.1 and 20.2
earlier in this chapter to allow for an accurate representation of relative volatility across time.
 FIGURE  20.4 How Has Performance Changed over Time?
20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
December
June
0
337
HOW TO EVALUATE PAST PERFORMANCE
 rolling Window return Charts
 The rolling window return chart shows the return for the specifi ed time length ending in each month.
For example, a 12-month rolling window return chart would show the 12-month return ending in
each month (beginning with the 12th month of the track record). The rolling window return chart
provides a clear visual summary of the results of investing with a manager for a specifi ed length of
time and answers such questions as: What would have been the range of outcomes with a manager
for investments held for 12 months? 24 months? What was the worst loss for investments held for
12 months? 24 months?
 For any December, the rolling 12-month return would be the same as the annual return. The
important diff erence is that the rolling window return chart would show the analogous returns for
all the other months as well. There is only a one-out-of-12 chance that December will be the worst
12-month return for the year. By showing all 12-month returns ending in any month, the rolling win-
dow chart will encompass worst-case events likely to be missed by annual returns and will provide
a much more representative performance picture for one-year holding periods. The rolling window
return chart can be calculated for other time intervals as well (e.g., 24 months, 36 months).
 T o illustrate the use of the rolling window return chart as a graphic analysis tool, we compare
the two managers shown in Figure 20.6 , who diff er only moderately in terms of return (Manager
E’s annual compounded return is 1.3 percent higher), but diff er widely in terms of the stability of
returns. As shown in Figure 20.7 , Manager E’s 12-month returns range enormously from a severe
 FIGURE  20.5 Log Scale: Equal Percentage Price Moves Appear Equal
20,000
10,000
5,000
4,000
3,000
2,000
1,000
500
December
June
December
June
December
June
December
June
D


in terms of return (Manager
E’s annual compounded return is 1.3 percent higher), but diff er widely in terms of the stability of
returns. As shown in Figure 20.7 , Manager E’s 12-month returns range enormously from a severe
 FIGURE  20.5 Log Scale: Equal Percentage Price Moves Appear Equal
20,000
10,000
5,000
4,000
3,000
2,000
1,000
500
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
June
December
December
June
338A COMPLETE GUIDE TO THE FUTURES MARKET
 FIGURE  20.6 Small Diff erence in Return; Wide Diff erence in Stability of Return
March
December
2,000
3,000
4,000
1,000
500
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
September
December
March
June
Manager F
Manager E
 FIGURE  20.7 12-Month Rolling Return: Manager E
March
December
–60%
–40%
–20%
20%
40%
60%
80%
100%
120%
140%
160%
0%
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
September
December
March
June
339
HOW TO EVALUATE PAST PERFORMANCE
loss of 49 percent to a spectacular gain of 142 percent. In contrast, manager F’s 12-month returns
are contained in a far more moderate range of –10 percent to +29 percent (see Figure 20.8 ).
Investors who were patient enough to stay with Manager F for at least 12 months would have
experienced only a handful of investment initiation months that would have resulted in a net loss.
Such patience, however, would not have provided any solace to investors with Manager E, who
would have witnessed more than one-quarter of all 12-month holding periods resulting in net
losses exceeding 15 percent, with several in excess of 40 percent. Even investors who committed
to a 24-month holding period with Manager E would still have been subject to nearly one-fi fth
of all intervals with losses in excess of 15 percent (see Figure 20.9 ). In contrast, the worst-case
outcome for investors with Manager F for a 24-month holding period would have been a positive
return of 4 percent (see Figure 20.10 ).
 Investors can use the rolling window return chart to assess the potential frequency and magnitude
of worst-case outcomes as an aid in selecting investments consistent with their holding period toler-
ance for a losing investment. For example, an investor who is unwilling to maintain a losing invest-
ment for more than 12 months should avoid managers who have a meaningful percentage of negative
12-month returns, regardless of how favorable all the other performance statistics may be.
 Rolling charts can also be used to depict other statistics besides return. For example, a rolling
chart of annualized volatility (using daily data and a window of several months) can be used as a tool
to monitor both managers and portfolios for early evidence of a possible increase in risk.
 FIGURE  20.8 12-Month Rolling Return: Manager F
March
December
40%
–20%
–10%
20%
30%
10%
0%
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
March
June
September
December
September
December
March
June
340A COMPLETE GUIDE TO THE FUTURES MARKET
 FIGURE  20.9 24-Month Rolling Return: Manager E
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
300%
280%
260%
240%
220%
200%
180%
160%
140%
120%
100%
80%
60%
40%
20%
–20%
–40%
–60%
0%
December
 FIGURE  20.10 24-Month Rolling Return: Manager F
35%
20%
25%
30%
10%
5%
0%
15%
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
February
April
June
August
October
December
341
HOW TO EVALUATE PAST PERFORMANCE
 Underwater Curve and 2DUC Charts
 The underwater chart shows the worst possible cumulative percentage loss any investor could have
experienced as of the end of each month—an assumption that implies an investment started at the
prior NAV peak. The low point in the NAV chart is the maximum retracement (the risk measure
used in the MAR and Calmar ratios). The underwater chart, however, provides far more informa-
tion because it shows not only the worst possible loss for the entire track record (the maximum
retracement), but the worst possible loss as of the end of every other month in the track record as
well. Figure 20.11 illustrates the underwater chart for the same two managers with widely disparate
stability of returns depicted in Figure 20.6 . The diff erence between the two could hardly be starker.
Manager F’s retracements are very shallow and relatively short-lived (a rise to the 0 percent level
indicates a new NAV high); Manager E’s retracements are both deep and protracted. The underwater
chart provides an excellent visual representation of an investment’s relative risk in a way that is very
consistent with the way most investors perceive risk.
 One shortcoming of the underwater curve is that it will understate risk for months in the
early portion of the track record because there is an insuffi cient look-back period for a prior NAV
peak. For these earlier months, there is no way of assessing a true worst-case loss representa-
tion, because a prior track record of suffi cient length simply does not exist. Also, the underwater
curve is constructed


ming of the underwater curve is that it will understate risk for months in the
early portion of the track record because there is an insuffi cient look-back period for a prior NAV
peak. For these earlier months, there is no way of assessing a true worst-case loss representa-
tion, because a prior track record of suffi cient length simply does not exist. Also, the underwater
curve is constructed from the perspective of the worst cumulative loss that could have been
 FIGURE  20.11 Underwater Curve: Manager E vs. Manager F
0%
−10%
−20%
−30%
−40%
Manager E
Manager F
−50%
−60%
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
342A COMPLETE GUIDE TO THE FUTURES MARKET
experienced by an existing investor. Arguably, the worst loss suff ered by new investors may be an
even more relevant measure. One solution to these inadequacies in the underwater curve calcula-
tion is to also consider the worst loss that could have been experienced by any investor starting
in each month, assuming the investment was exited at the subsequent lowest NAV point. W e can
then create a two-direction underwater curve (2DUC) that for each month would show the maxi-
mum of the following two losses:
 1. The cumulative loss of an existing investor starting at the prior NAV peak.
 2. The cumulative loss of an investor starting that month-end and liquidating at the subsequent
NAV low.
 The average of all the points in the 2DUC chart would, in fact, be the risk measure used in the
return retracement ratio (the average maximum retracement). The underwater excursions for Man-
ager E become signifi cantly more extreme in the 2DUC chart (Figure 20.12 ), widening from an
average monthly value of 21 percent to 30 percent (the AMR). The underwater curve for Manager F
remains subdued in the 2DUC chart with a still very low average value of 3 percent. The 2DUC chart
implies that the average worst-case scenario for investors with Manager E is 10 times worse than with
Manager F; that is a lot of extra risk for a 1.3 percent diff erence in the average annual compounded
return. Based on performance, it would be diffi cult to justify choosing Manager E over Manager F,
even for the most risk-tolerant investor.
 FIGURE  20.12 2DUC: Manager E vs. Manager F
 2DUC: Manager E vs. Manager F
0%
−10%
−20%
−30%
−40%
Manager E
Manager F
−50%
−60%
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
January
April
July
October
343
How to EvaluatE Past PErformancE
 ■ Investment Insights
Many investors place too much emphasis on return. Since return can always be improved by increas-
ing exposure (i.e., taking on greater risk), the return/risk ratio is a far more meaningful performance
measure. An investment with higher return/risk and lower return than an alternative investment with
the reverse characteristics can be brought up to the same higher return level with lower risk by using
leverage.
The Sharpe ratio is by far the most widely used return/risk metric. The Sharpe ratio, however,
penalizes upside volatility the same as downside volatility, which is not consistent with the way most
investors view risk. Other return/risk measures detailed in this chapter, which focus on losses as
the proxy for risk, more closely reflect the way most investors perceive risk. Investors can use Table
20.5, which summarizes the properties of different return/risk measures, to select the performance
measures that best fit their criteria.
Return/risk statistics can be supplemented with the performance charts detailed in this chapter,
which provide a tremendous amount of information in an intuitive and accessible format and should
be at the core of any performance analysis. I recommend using the following performance charts in
any manager or fund evaluation:
 ■ An NAV chart
 ■ Both 12-month and 24-month rolling window return charts
 ■ A 2DUC chart
■ ■ ■
Note: Some of the statistics and chart analytics described in this chapter are my own invention and
hence not yet available on any existing software. Many of these statistics and analytical charts can be
accessed for free on FundSeeder.com.

FuNdaMeNtal aNalysis
Part V

Fourteen Popular
Fallacies, or What
Not to
do Wrong
Cha P ter 21
347
The fault, dear trader, is not in the fundamentals, but in ourselves.
(With apologies to shakespeare)
 ■ Five Short Scenes
Scene 1
the u.s. treasury announces a new plan to sell stockpiled gold. Not surprisingly, the market opens
with near-limit losses the following day. you reason that the new gold sales will sharply increase sup-
ply and that, therefore, the market still offers a good selling opportunity, even given the decline. you
are somewhat concerned about expectations for continued increasing inflation and dollar weakness
but decide the gold sale will dominate market action over the near term.
after going short, the market hovers for two days and then, as you expected, breaks sharply. One
week later, your trade is substantially in the plus column and convinced that you have caught a new
bear market in its infancy, you resolve to hold the position as a long-term trade.
the next week,
however, the market begins to rally inexplicably, and your profits evaporate. Paradoxically, despite
an absence of any meaningful bullish news, the rally continues and prices even surpass the levels they
were at before the
u.s. treasury announcement. your losses continue to grow , and finally you bail out,
promising yourself, “that’s the last time i trade on fundamentals.”
348
A Complete Guide to the Futures mArket
Scene 2
you’ve done your homework and feel confident the u.s. department of agriculture’s 50-state Hogs
and Pigs report, which will be released in


rices even surpass the levels they
were at before the
u.s. treasury announcement. your losses continue to grow , and finally you bail out,
promising yourself, “that’s the last time i trade on fundamentals.”
348
A Complete Guide to the Futures mArket
Scene 2
you’ve done your homework and feel confident the u.s. department of agriculture’s 50-state Hogs
and Pigs report, which will be released in the afternoon, will reflect a large expansion in hog produc-
tion. you anticipate that hog numbers will be up at least 7 percent over the year-ago level. Hog prices
have already sold off sharply in recent weeks, but you reason that a report in line with your expecta-
tions will push prices still lower.
although you are quite familiar with the dangers of riding a position into a major report, this is
one time you cannot resist. at the report release time, eyes glued to your computer screen and your
heart pounding, you read the critical figure. a smile crosses your face as you see the number. “i knew
it!” you shout triumphantly. the report shows hog numbers up 8 percent.
the next day the market opens limit down, and you begin to calculate what your profits will be
after three limit-down days—a conservative assumption. But before you can even finish calculating
your profits, a strange thing happens:
the market begins to rally. By the end of the session, hog prices
are actually 100 points higher! the uptrend continues in subsequent trading sessions, and one week
later you liquidate your position with a sizable loss. you feel cheated. you were right in your expecta-
tions: the report was bearish, wasn’t it?
Scene 3
you’ve been long corn for three weeks and it’s been one of your best trades ever. the market has
moved steadily and sharply higher with export rumors flying in all directions. that evening on the
news, the lead story is the official announcement of an additional large grain sale to Japan. daydream-
ing, you wonder if this is the trade you will retire on.
Next morning you call your broker. “Corn is due 8 to 10 cents a bushel higher,” he says. Not as
good as you thought, but it will do. However, by the time corn is ready to open, the call has dropped
to unchanged, and the market actually opens 2 cents lower.
several days later, corn has fallen more
than 40 cents, and your profits have virtually evaporated.
Scene 4
Cattle futures have rallied to near all-time record highs. you are well aware that cattle supplies are
down and expected to remain low , but upon closer examination, you have discovered that supplies
were lower in a number of other past situations when prices were lower.
you reason the current rally
is overdone and go short.
When cattle rallies an additional 10¢/lb, you figure the market is an even better short, and add to
your position. Prices are still moving higher when you finally throw in the towel on the trade.
Scene 5
you have read that sugar prices are below costs of production, a factor that seems to suggest that
prices have overdone the down side. you go long. Not only does the price fail to rise, but it actually
349
FOurteeN POPular FallaCies, Or WHat NOt tO dO WrONg
continues to slide steadily lower. you can’t understand why producers continue to sell sugar at a loss.
you are both confused and frustrated at the seemingly logic-defying market price action as your losses
continue to mount.
■ ■ ■
these five scenes appear to provide proof that fundamental analysis just does not work. at least, that
is the conclusion a great many futures traders have drawn from such experiences.
the simple truth, however, is that much that passes for fundamental analysis is either incomplete
or incorrect—and frequently both. the trader who ignores fundamentals completely is almost cer-
tainly better off than the trader who uses fundamentals incorrectly. However, this in no way alters the
fact that good fundamental analysis is a useful, and even powerful, tool.
Before turning to how to do things right, it is essential to first cover what not to do wrong. W e
begin by exploring 14 common fallacies in fundamental analysis. incidentally, these fallacies do not
represent mistakes made solely by the novice trader. in fact, virtually all these errors have been
repeated numerous times in the most respected financial news outlets and in myriad commodity
research publications.
there is no significance to the order of the list.
 ■ The Fourteen Fallacies
1. Viewing Fundamentals in a Vacuum
“the fundamentals are bearish” is often thought to be synonymous with an abundant supply situation.
such an interpretation might seem plausible, but it can lead to inaccurate conclusions.
For example, assume the sugar market is trading at 30¢/lb. and in transition from tightness to
surplus. given this scenario, the fundamentals can indeed be termed “bearish,” and an expectation of
lower prices would be reasonable. assume that prices begin to move lower. are the fundamentals still
bearish at 25¢? V ery likely. at 20¢? Maybe. at 15¢? at 10¢? at 5¢? the point is that at some price
level, the fundamentals are no longer bearish, no matter how large the projected supply.
in fact, it is entirely possible that the fundamentals could be bullish in a surplus situation if prices
have overdone the downside—a situation that is far from infrequent. thus, fundamentals are not
bullish or bearish in themselves; they are only bullish or bearish relative to price. the failure of many
analysts to realize or acknowledge this fact is the reason why the fundamentals are so often termed
bullish at market tops and bearish at market bottoms.
2. Viewing Old Information as New
Financial news outlets frequently report old information and new information in much the same
manner. For example, a story with the headline “W orld Cotton Production Projected to
rise 10
Percent” may sound very bearish. However, what the story is not likely to indicate is that this may be
the fourth or fifth such estimate rele


s and bearish at market bottoms.
2. Viewing Old Information as New
Financial news outlets frequently report old information and new information in much the same
manner. For example, a story with the headline “W orld Cotton Production Projected to
rise 10
Percent” may sound very bearish. However, what the story is not likely to indicate is that this may be
the fourth or fifth such estimate released. V ery likely, the previous month’s estimate also projected
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A Complete Guide to the Futures mArket
an approximate 10 percent increase in world production. For that matter, the previous month’s esti-
mate might have forecast a 12 percent increase, and the current estimate actually represents a price-
constructive development.
the main point to keep in mind is that much information that sounds new
is actually old news, long discounted by the market.
3. One-Y ear Comparisons
the use of one-year comparisons is fairly widespread, probably because it offers a simple means of
instant analysis. this approach is overly simplistic, however, and should be avoided. For example,
consider the following market commentary: “the december Hogs & Pigs report indicates that large
pork supplies are around the corner. Market hogs on all farms are up 10 percent. the projected 10
percent increase in hog slaughter should push prices lower. . . .” although this type of analysis could
be right on target in some situations, it will be susceptible to error if used consistently.
sharp-eyed readers may already be citing fallacy number 1—that is, large supplies do not neces-
sarily imply lower prices, since the market may already be discounting such a development. How-
ever, some additional potential errors pertain specifically to the one-year comparison. First, just
because the
december report indicated a 10 percent increase in hog numbers does not mean it
implies large supplies. Perhaps hog numbers were extremely low the previous year. second, the rela-
tionship between hog slaughter and market hogs can vary significantly. it is possible that the preceding
year the ratio of slaughter to market hogs was abnormally high. in this case, a 10 percent increase in
market hogs would imply a smaller increase in slaughter. although one-year comparisons can be used
sparingly for illustrative purposes, they should never represent the sole basis of fundamental analysis.
4. Using Fundamentals for timing
if this list of fallacies were ordered on the basis of frequency of occurrence, this item would be a
strong contender for the number 1 spot. Fundamental analysis is a method for gauging what price
is right under given statistical conditions and can be used in constructing annual, quarterly, and in
some instances monthly price projections. However, it is ludicrous to attempt to boil supply-demand
statistics down to the point at which they provide an instantaneous price signal, which is exactly what
some traders do when they rely on fundamentals for timing.
trading on the basis of market websites, newspaper articles, and newswire stories, falls into this
category. it is no surprise that speculators who base their trades on such items are usually spectacu-
larly unsuccessful. the only major exception are those traders who use this type of information in a
contrary way, such as viewing the failure of the market to rally after the release of a bullish newswire
story as a signal to go short.
the fundamental researcher must also guard against the natural instinct of wanting to take a
market position right after completing an analysis that indicates either an underpriced or overpriced
situation.
the market is not aware of the timing of a researcher’s personal price discovery. even if the
analysis is correct, the right time may be three weeks or even three months off. in short, for purposes
of timing, even the fundamental analyst should use some form of technical input.
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FOurteeN POPular FallaCies, Or WHat NOt tO dO WrONg
5. Lack of Perspective
assume the following scenario: scanning the financial pages one day, you notice the following
headline: “government Officials estimate 10,000 Head of Cattle Killed in recent Midwest Winter
storm.” does such a large production loss suggest a major buying opportunity? Wait a minute. What
large production loss? ten thousand cattle might sound like a very big number if you were to picture
them on your front lawn, but viewed in terms of a total u.s. cattle population of about 90 million
head (and many times that globally), the loss does not even equal the proverbial drop in the bucket.
this example is based on supply, but cases involving domestic consumption or exports could be
illustrated just as easily. in each instance, the same question should be asked: How important is the
event (e.g., production loss, new export sales) in terms of the total picture?
6. Ignoring relevant time Considerations
true or false: Higher grain prices imply higher meat prices. No cheating—think before reading on.
actually, this is not a fair question, because the answer depends on the time frame. Most peo-
ple would probably answer true, since rising grain prices do suggest increased costs of production
for feedlot operators, a development that would lead to reduced meat production and higher meat
prices. (Cost of production is itself a primary source of misconception and is discussed separately.)
However, this reasoning is true only for the very long run (2½ years plus).
Over the short to intermediate term—the time frame that is really of primary concern to futures
 traders—the effect might be exactly the opposite. if high grain prices are effective in influencing cattle
feeders to reduce production, the preliminary impact will be increased marketings and lower prices as a
result of breeding herd liquidation. Higher grain prices might reduce the weight to which cattle are fed, but
this effect is relatively minor.
increased feeding costs would only imply a shift in the flow of supp


e exactly the opposite. if high grain prices are effective in influencing cattle
feeders to reduce production, the preliminary impact will be increased marketings and lower prices as a
result of breeding herd liquidation. Higher grain prices might reduce the weight to which cattle are fed, but
this effect is relatively minor.
increased feeding costs would only imply a shift in the flow of supply (since
cattle gain weight more slowly on grass) rather than a change in total actual supplies over the longer run.
in the world of economics, the cause-and-effect relationship is not necessarily instantaneous. in
some cases, an event will trigger a very quick price response; in other instances, such as the cattle
situation, the effect will not occur for many years.
7. assuming that Prices Cannot Decline Significantly
Below the Cost of Production
No matter how many times this old saw is disproved by actual events, it never seems to be laid to rest. the
cost of production is not—repeat, not—a price-supporting factor, especially for nonstorable commodities.
Once a commodity is produced, the market does not care about the cost of production. Prices
will be determined on the basis of existing supply and demand. if prices fall to the cost of production
and there is still a surplus, prices will continue to decline until an equilibrium price level is reached.
Why should producers sell a commodity below the cost of production? the fact is they don’t have
much choice. agricultural markets are highly competitive, with literally thousands of sellers. Conse-
quently, any individual is powerless to pass on production costs to the marketplace. instead, individu-
als must accept the price that the market will bear. after all, a low price is better than no price.
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A Complete Guide to the Futures mArket
Of course, an unprofitable situation will lead to production cutbacks, but this will not happen
overnight. the minimum time lag might be one year, but in many instances, it will take several years
before prices below the cost of production actually result in reduced output. in this sense, fallacy
number 7 is a corollary of fallacy number 6—ignoring relevant time considerations. Many commod-
ity markets have witnessed periods in which prices have fallen and stayed below cost of production
for years at a time. Keep this empirical reality in mind the next time you read a recommendation to
purchase a commodity because it is at or below the cost of production.
8. Improper Inferences
Fallacy number 8 might be best explained by citing some examples. First, cattle-on-feed numbers
do not necessarily provide an indication of potential future slaughter.
reason: cattle on feed do not
include grass-fed cattle. as long as grass-fed cattle account for a stable percentage of total slaughter,
there is no problem. But if the percentage varies widely over time (as has tended to be the case), the
straightforward use of cattle-on-feed numbers to predict slaughter can lead to a totally erroneous con-
clusion.
if, for instance, high feed prices influence a shift toward increased grass feeding of cattle, the
total number of cattle could be higher, even if the cattle-on-feed figure shows a significant reduction.
Much market analysis and commentary naively ignores the preceding complication in projecting
cattle slaughter. How bad is this error? table 21.1 shows the relationship between percentage changes
in cattle on feed and total slaughter. there is a great deal of variability between the two sets of figures.
in fact, from 1995 through 2014 there were 34 quarters when the deviation in percentage changes
between cattle on feed and total slaughter exceeded 5 percent and seven quarters with deviations
greater than 10 percent!
it is not an overstatement to say that one can achieve far more accurate
slaughter projections using the naive assumption that slaughter in any given quarter will equal the
corresponding previous year’s level.
this is a clear example of no information being far preferable to
incorrectly used information.
taBLe 21.1 Percentage Changes in Cattle on Feed Numbers V ersus Percentage Changes in Slaughter
Quarter
Cattle on Feed as Percentage
of Previous Y ear
Cattle Slaughter as Percentage
of Previous Y ear
Discrepancy between two
Percentagesa
Jan-2015 94.48% 100.98% 6.50%
Oct-2014 91.17% 99.49% 8.31%
Jul-2014 91.72% 97.61% 5.89%
apr-2014 94.14% 99.53% 5.39%
Jan-2014 94.77% 94.79% 0.02%
Oct-2013 97.01% 92.31% –4.70%
Jul-2013 99.85% 96.81% –3.05%
apr-2013 100.19% 95.01% –5.18%
Jan-2013 96.95% 94.37% –2.58%
Oct-2012 98.66% 97.40% –1.26%
Jul-2012 95.37% 102.66% 7.28%
apr-2012 96.18% 102.00% 5.82%
353
FOurteeN POPular FallaCies, Or WHat NOt tO dO WrONg
Quarter
Cattle on Feed as Percentage
of Previous Y ear
Cattle Slaughter as Percentage
of Previous Y ear
Discrepancy between two
Percentagesa
Jan-2012 96.53% 103.02% 6.49%
Oct-2011 97.00% 104.86% 7.85%
Jul-2011 99.84% 103.74% 3.90%
apr-2011 99.54% 104.92% 5.39%
Jan-2011 101.85% 104.83% 2.99%
Oct-2010 105.02% 102.91% –2.10%
Jul-2010 102.74% 103.26% 0.52%
apr-2010 100.89% 96.46% –4.42%
Jan-2010 102.36% 97.99% –4.37%
Oct-2009 100.78% 100.57% –0.22%
Jul-2009 96.14% 94.73% –1.41%
apr-2009 94.99% 95.53% 0.54%
Jan-2009 96.44% 92.90% –3.53%
Oct-2008 95.30% 94.97% –0.33%
Jul-2008 101.84% 95.88% –5.96%
apr-2008 102.58% 100.34% –2.24%
Jan-2008 101.42% 101.03% –0.39%
Oct-2007 103.22% 96.33% –6.89%
Jul-2007 99.60% 98.76% –0.84%
apr-2007 100.25% 98.58% –1.67%
Jan-2007 103.97% 101.44% –2.53%
Oct-2006 103.73% 108.61% 4.89%
Jul-2006 102.94% 104.60% 1.66%
apr-2006 106.22% 108.64% 2.41%
Jan-2006 103.25% 104.47% 1.22%
Oct-2005 100.45% 99.81% –0.64%
Jul-2005 101.69% 102.57% 0.88%
apr-2005 97.22% 100.99% 3.77%
Jan-2005 96.43% 100.41% 3.98%
Oct-2004 98.28% 102.73% 4.45%
Jul-2004 87.31% 101.96% 14.65%
apr-2004 90.09% 100.33% 10.24%
Jan-2004 94.33% 105.58% 11.26%
Oct-2003 91.22% 98.05% 6.83%
Jul-2003 103.14% 94.62% –8.51%
apr-2003 103.37% 92.45% –10.92%
Jan-2003 99.


6.22% 108.64% 2.41%
Jan-2006 103.25% 104.47% 1.22%
Oct-2005 100.45% 99.81% –0.64%
Jul-2005 101.69% 102.57% 0.88%
apr-2005 97.22% 100.99% 3.77%
Jan-2005 96.43% 100.41% 3.98%
Oct-2004 98.28% 102.73% 4.45%
Jul-2004 87.31% 101.96% 14.65%
apr-2004 90.09% 100.33% 10.24%
Jan-2004 94.33% 105.58% 11.26%
Oct-2003 91.22% 98.05% 6.83%
Jul-2003 103.14% 94.62% –8.51%
apr-2003 103.37% 92.45% –10.92%
Jan-2003 99.29% 91.60% –7.70%
Oct-2002 100.62% 93.63% –6.99%
taBLe 21.1 (Continued)
(Continued)
354
A Complete Guide to the Futures mArket
Quarter
Cattle on Feed as Percentage
of Previous Y ear
Cattle Slaughter as Percentage
of Previous Y ear
Discrepancy between two
Percentagesa
Jul-2002 103.08% 95.24% –7.84%
apr-2002 101.37% 100.47% –0.90%
Jan-2002 98.93% 98.03% –0.91%
Oct-2001 100.65% 100.99% 0.34%
Jul-2001 97.13% 105.89% 8.76%
apr-2001 98.22% 102.87% 4.65%
Jan-2001 94.41% 102.81% 8.40%
Oct-2000 98.64% 107.20% 8.56%
Jul-2000 99.18% 108.61% 9.43%
apr-2000 100.26% 107.58% 7.33%
Jan-2000 103.09% 107.57% 4.48%
Oct-1999 102.15% 104.96% 2.81%
Jul-1999 102.89% 104.30% 1.41%
apr-1999 102.03% 102.63% 0.60%
Jan-1999 100.62% 95.63% –4.99%
Oct-1998 98.42% 97.83% –0.59%
Jul-1998 97.99% 102.27% 4.28%
apr-1998 96.69% 97.27% 0.58%
Jan-1998 97.54% 105.65% 8.11%
Oct-1997 99.57% 112.69% 13.12%
Jul-1997 101.46% 114.26% 12.80%
apr-1997 97.00% 105.90% 8.90%
Jan-1997 99.19% 102.05% 2.86%
Oct-1996 100.12% 96.94% –3.17%
Jul-1996 98.32% 85.05% –13.27%
apr-1996 105.91% 99.50% –6.42%
Jan-1996 106.58% 107.92% 1.34%
Oct-1995 103.04% 101.63% –1.41%
Jul-1995 105.14% 106.00% 0.85%
apr-1995 105.48% 100.17% –5.31%
Jan-1995 103.14% 94.61% –8.53%
avg. (abs) 4.73%
Med. (abs) 4.40%
Max. (abs) 14.65%
Min. (abs) 0.02%
Qtrs. w/ abs discrepancy ≥ 5% 34
Qtrs. w/ abs discrepancy ≥ 10% 7
aColumn 2 minus column 3 percentages.
taBLe 21.1 (Continued)
355
FOurteeN POPular FallaCies, Or WHat NOt tO dO WrONg
another example of an improper inference is provided by the projection of production from
acreage figures. a given percentage change in acreage does not necessarily imply a similar change
in production (even assuming equivalent yields). For most crops, the distribution of production is a
critically important variable. For example, average cotton yields in some states, such as California, are
approximately three times as high as average yields in other states, such as
texas. although consider-
ably more time consuming, production projections should be based on a breakdown of acreage by
area (region or state) rather than on a total acreage figure.
9. Comparing Nominal Price Levels
it is inaccurate to compare current prices with the actual recorded prices of previous years. in draw-
ing comparisons with past seasons, it is necessary to adjust historical prices for inflation. even though
u.s. inflation has been subdued since the mid-1980s, over broad periods of time, even low inflation
can have a significant cumulative effect. Moreover, in the future, higher inflation levels could recur,
making this factor a critical consideration.
as an example, assume that an exhaustive survey of the statistical data for commodity x in past
years indicates that 1997 and 2003 were very similar to the current season in terms of overall funda-
mentals.
does this observation imply that current-season prices will be about in line with the price
levels of 1997 and 2003? Of course not. in real dollar terms, the prices may be roughly equivalent,
but because of the impact of inflation, current nominal prices are likely to be higher.
inflation cannot be considered in a vacuum, however. For example, a protracted downshift in
demand for most physical commodities (resulting from reduced inventory requirements) begin-
ning around 1980 provided a counterbalancing force to inflation. Because demand is very difficult
to quantify—as will be discussed in detail in Chapter 22—the net effect is that inflation-adjusted
forecasts can be biased to the high side.
in other words, ironically, in some cases it is possible that a
naive analyst who ignores both demand shifts and inflation adjustments may derive a more accurate
forecast than the analyst who adjusts for inflation.
such accidental accuracy is likely to be a temporary
phenomenon. the correct procedure would be to incorporate inflation adjustments in the model and
then infer and include demand shifts in the model as well.
10. Ignoring expectations
Markets often place greater emphasis on expectations for the following year (or season) than on pre-
vailing fundamentals.
this pattern is especially true in transitional periods when the supply situation
is moving from surplus to tightness, or vice versa.
the 1990 wheat market provided an excellent example. in the 1989–1990 season, the winter
wheat crop proved very disappointing because of below-average yields. as a result, carryover stocks
(measured as a percent of utilization) fell to their lowest level in 15 years. Moreover, winter wheat
seedings for the 1990 crop increased only slightly, thereby seeming to suggest the extension of a tight
supply situation into the new season.
despite the apparent bullish scenario, wheat prices moved steadily and sharply lower from the
very beginning of 1990. this price slide cannot be explained in terms of prevailing fundamentals,
but only in terms of expectations. as the year progressed, it became increasingly evident that the
356
A Complete Guide to the Futures mArket
1990–1991 hard red winter wheat crop would result in extremely good yields. as it turned out, the
yield of the 1990–1991 winter wheat crop increased by an imposing 16 percent over the previous
season’s level, and the percentage of planted acreage harvested rose from 75 percent to 88 percent.
as a result of excellent yields and sharply lower acreage abandonment, 1990–1991 winter wheat
production increased by a huge 39 percent, despite the fact that planted acreage was only marginally
higher, and carryover stocks returned to comfortable levels.
although t


16 percent over the previous
season’s level, and the percentage of planted acreage harvested rose from 75 percent to 88 percent.
as a result of excellent yields and sharply lower acreage abandonment, 1990–1991 winter wheat
production increased by a huge 39 percent, despite the fact that planted acreage was only marginally
higher, and carryover stocks returned to comfortable levels.
although the fundamental transition just described was reflected by data available after mid-spring
1990, during early 1990 such changes would have fallen into the category of expectations. thus, price
action in the wheat market during the first half of 1990 provided a classic example of expectations
dominating prevailing fundamentals.
11. Ignoring Seasonal Considerations
almost every commodity exhibits one or more seasonal patterns. ignoring seasonal factors can
easily lead to the misinterpretation of fundamental data. For example, a 5 percent increase in hog
slaughter during the fourth quarter relative to the third-quarter level would actually be indicative
of a trend toward reduced production—not expanded production.
the explanation behind this
apparent paradoxical statement lies in the fact that hog production is highly seasonal. Producers
breed hogs so that the largest pig crop is born during the spring and the smallest in winter. Because
it takes approximately six months for hogs to reach market weight, slaughter tends to be heaviest
during the fall and lightest during the summer.
thus, it is essential to adjust for the seasonal pro-
duction pattern in drawing slaughter comparisons between the current period and the preceding
month or quarter.
Comparisons of production and consumption figures with the corresponding figures of previous
years obviously do not require any consideration of seasonal factors. However, if comparisons of fun-
damental data involve different time periods during the year, it is essential to examine historical data
carefully for possible seasonal behavior and to make any necessary adjustments.
12. expecting Prices to Conform to target Levels in
World trade agreements
the history of commodities is replete with examples of world trade agreements that totally failed to
achieve their stated goals. trade agreements typically attempt to support prices through export controls
and stockpiling plans. although these provisions provide some underlying support to the market and occa-
sionally even spark temporary rallies, they are usually not sufficiently restrictive to maintain prices signifi-
cantly above equilibrium levels for any extended period of time.
the international sugar agreement and
the international Cocoa agreement are two examples of world trade agreements that ultimately failed to
support prices above the lower end of their respective stated target ranges (in the years when these agree-
ments attempted to support prices; they no longer even attempt to do so). Perhaps the most effective
price-supporting organization has been the Organization of the Petroleum
exporting Countries (OPeC),
but even the oil cartel has frequently seen prices fall below their target level—often by a wide margin.
it should be noted that world trade agreements are even more impotent in terms of restraining a
price advance. in the case in which prices approach the upper end of a target range, the most powerful
357
FOurteeN POPular FallaCies, Or WHat NOt tO dO WrONg
action that any agreement could take would be the elimination of all restrictions—in other words, a
return to a free market.
13. Drawing Conclusions on the Basis of Insufficient Data
sometimes it is virtually impossible to construct a fundamental forecasting model for a market
because of a lack of sufficient comparative historical data. a perfect case in point was provided in the
august 1972 issue of Commodities magazine (now called Modern T rader), which ran a detailed study of
fundamentals in the cotton market. the article ultimately came to the valid conclusion that only two
seasons since 1953 could truly be termed free markets. as the article explained, during the 1950s and
1960s, government programs had maintained cotton prices above the levels that would have been
realized had prices been determined by the interaction of supply and demand.
so far, so good.
the proper and very worthwhile conclusion would have been that existing data were insufficient to
permit the use of fundamentals in forecasting prices. after all, how can you interpret the price impli-
cations of a projected statistical balance if there are only two previous years to use as a comparison?
unfortunately, the author went on to sketch an entire set of price forecasting conclusions on the
basis of admittedly very limited relevant information. Quoting the first item, “Final stock levels under
3½ million bales imply a very tight supply situation and suggest a likelihood of a price rise well above
30¢ in such seasons.”
although this statement certainly proved true, by implication it severely understated the upside
potential in the cotton market. Only a little more than one year after the article was published, cotton
prices reached an all-time peak of 99¢/lb.
incidentally, i was the author of that article.
14. Confusing the Concepts of Demand and Consumption
demand is probably one of the two most misused words in futures literature and analysis (parameter
being the other; see Chapter 19). the confusion between demand and consumption is not a matter of
semantics; the two terms represent very different concepts, and their frequent interchangeable use
leads to many major analytical errors.
an adequate explanation of this statement requires a diversion
into a short review of basic supply-demand theory, which is the subject of the next chapter.
at this point, it might be instructive to return to the five scenes depicted at the beginning of this
chapter to try to determine which of the 14 fallacies were responsible for the incorrect


geable use
leads to many major analytical errors.
an adequate explanation of this statement requires a diversion
into a short review of basic supply-demand theory, which is the subject of the next chapter.
at this point, it might be instructive to return to the five scenes depicted at the beginning of this
chapter to try to determine which of the 14 fallacies were responsible for the incorrect trading con-
clusions. Note that each scene reflects two or more fallacies.
the answers can be found in table 21.2.
taBLe 21.2 Fallacies Committed in the Five Scenes
Scene Fallaciesa
1 4, 5, 10
2 1, 3, 4
3 2, 4
4 9, 10
5 7
athe inclusion of additional items is not necessarily incorrect. Other fallacies might
also be applicable (e.g., fallacy number 1 in any of the scenes), but are not listed
because the text provides insufficient information to make such a determination.

359There are in the fields of economics no consistent relations, and consequently, no measurement
is possible.
—Ludwig Edler von Mises
 ■ Supply and Demand Defined
Supply curves slope upward, meaning more is offered to the market at higher prices (Figure 22.1). 1
Assuming that the time unit shown on the horizontal axis in Figure 22.1 equals one season, the sup-
ply that can be offered to the market will be limited to total production plus stocks, regardless of the
price. At high prices, however, producers will be willing to hold smaller inventories and therefore
offer greater quantities to the market. Conversely, at lower prices, producers will prefer to store
Supply-Demand
Analysis: Basic
Economic Theory
Chapter 22
1 The supply and demand curves in this section are drawn as straight lines for simplicity of exposition. It also
seemed desirable to avoid the unnecessary digression of discussing the factors that determine the precise shapes
of these curves. Although the straight-line assumption may often be adequate within normal boundaries, supply
and demand curves will not be linear over the entire price range. For example, as prices rise and the quantity
consumed declines, it will usually take greater and greater increases in price to induce a given further reduction
in the amount consumed. As another example, over the short run, at some point the supply curve must begin to
rise asymptotically, since the supply offered to the market cannot exceed the existing total supply (i.e., stocks
plus current production).
360A COMPLETE GUIDE TO THE FUTURES MARKET
 FIGURE  22.1 Supply Curve
Quantity (per unit time)
Price
larger quantities rather than marketing their goods at prevailing depressed levels. The slope of the
supply curve will refl ect this tradeoff between the options of sale and storage. 2
 For perishable (e.g., eggs, potatoes 3 ) or nonstorable (e.g., cattle, hogs) commodities, supply is
approximately fi xed and can be represented by a vertical line (Figure 22.2 ). For example, if a supply
curve is drawn for the hog market for a time unit of one-half year, the amount off ered to the market
during that period will be relatively independent of market prices. Low prices will not reduce the
quantity supplied, because once hogs reach market weight, with the exception of temporary delays,
producers have little choice but to bring those hogs to market, regardless of the price. However, since
there is a lag of nearly one year between producers’ breeding decisions and the time that resulting
 2 For longer time units (e.g., 10 years), the supply curve will also refl ect the potential for an expansion in pro-
duction beyond current levels. For example, high prices may encourage shifts in acreage to the high-priced com-
modity and increased usage of fertilizer in new crops. From the vantage point of futures trading, however, it is
most useful to limit the discussion of supply and demand to short time units (i.e., season or fraction of a season).
 3 These commodities are no longer traded as futures markets, but provide perfect illustrations of perishable goods.
 FIGURE  22.2 Fixed Supply
Quantity (per unit time)
Price
361
SUPPLY -DEMAND ANALYSIS: BASIC ECONOMIC THEORY
off spring reach market weight, high prices cannot induce an increase in the quantity supplied. In fact,
if anything, the supply curve in such a market exhibits a perverse behavior; that is, high prices will
reduce the quantity supplied. The reason is that high prices will infl uence producers to withhold hogs
from the market for breeding, thereby reducing current supplies. However, for simplicity’s sake, we
will assume a vertical supply curve in the case of perishable or nonstorable commodities.
Demand can be defi ned as a schedule of the various quantities of a commodity that will be con-
sumed at each price level. In a sense, demand is a barometer of consumer buying pressure. Demand
curves slope downward, meaning more will be demanded at lower prices (Figure 22.3 ).
Elasticity of demand can be defi ned as the percentage increase in the amount demanded divided by
the percentage decrease in price. If the demand for a commodity is inelastic, it means that a relatively
large percentage change in price will only induce a small percentage change in the amount demanded.
Figure 22.4 presents illustrations of elastic and inelastic demand curves.
4
 The elasticity of demand is primarily determined by two basic factors:
 1. availability of substitutes. The elasticity of demand will vary directly with the availability
of substitutes. For example, the demand for salt is highly inelastic, but the demand for a given
brand of salt is very elastic.
 2. percentage of total income spent on the good. The elasticity of demand will vary directly
with the percentage of expenditures allocated to a good. For example, the demand for automobiles
is far more elastic than the demand for salt, even though there are no close substitutes for either item.
 Generally speaking, the demand curves for most commodities tend to be inelastic; that is, a
given pe