. 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 percentage change in price will induce a smaller opposite percentage change in the amount
demanded. This is a signifi cant consideration, since prices of goods with inelastic demand curves are
more subject to wide price swings in times of shortage.
 FIGURE  22.3 Demand Curve
Quantity (per unit time)
Price
 4 Elasticity is not constant along each demand curve. Elasticity is a concept that relates to a given point, not to
the entire curve. As we move rightward along a line or demand curve (in both the elastic and inelastic cases), the
elasticity of demand will decrease, since any given change in price will represent a larger percentage change, and
will infl uence the same absolute, but smaller percentage, change in the quantity demanded. In other words, as
can be verifi ed in Figure 22.4 , rightward movement along the demand curve will increase the denominator and
decrease the numerator of the elasticity of demand.
362A COMPLETE GUIDE TO THE FUTURES MARKET
 FIGURE  22.4 Elasticity of Demand
Quantity (per unit time)
Price
ELASTIC
INELASTIC
A
B
OC D
Quantity (per unit time)
CD= OD
AB
OB
Price
A
B
OC D
 ■ The Problem of Quantifying Demand
 As all students of Economics 101 know , price is determined by the intersection of the supply and
demand curves (Figure 22.5 ). However, there is one major problem in using supply-demand analysis
to project prices: Demand is not readily quantifi able—that is, there is no way of determining how
much will be consumed at any given price level. Whereas in most cases supply can be either approxi-
mately fi xed as in the case of perishable and nonstorable commodities, or at least roughly estimated
using production and stock statistics,
5 demand is entirely intangible. It is hardly feasible to query all
 5 The precious metal markets provide an important exception. See the section, Why Traditional Fundamental
Analysis Doesn’t W ork in the Gold Market, at the end of this chapter.
363
SUPPLY -DEMAND ANALYSIS: BASIC ECONOMIC THEORY
 FIGURE  22.5 Equilibrium
Quantity (per unit time)
Price
Supply
Demand
P
OQ
potential consumers as to the amount of a good they would purchase at various price levels. Even if a
sampling procedure were used—presumably an impractical and prohibitively expensive approach for
the analyst—there is no reason to assume that consumers could even describe their demand curves.
 The only theoretically acceptable means of quantifying demand is to infer demand curves through
a detailed analysis of historical consumption and price data. Although this is an easy task if demand
is relatively stable, unfortunately, it is either diffi cult or impossible if demand is subject to frequent
wide shifts.
 ■ Understanding the Difference between Consumption
and Demand
 Perhaps the most commonly employed solution to the problem of quantifying demand is the use of
consumption as a proxy for demand. This approach, however, has one major drawback: It is totally
incorrect. The synonymous use of consumption and demand represents a confusion of two entirely
diff erent concepts. Consumption is the amount of a good used and is determined by price, which in
turn is determined by supply and demand factors. Demand refers to the amount of a good that will be
used at any given price level and, along with supply, determines price.
 An increase in demand means that more will be consumed at any given price level (Figure 22.6 ).
Factors that might aff ect demand include disposable income, consumer tastes, and the price of substi-
tute goods but, by defi nition, not price. For most commodities, a rise in disposable income will result
in an increase in demand; that is, at each given price, more will be consumed than before. A price
decline will lead to increased consumption, showing movement along the same demand curve, but it
does not imply anything about demand. In other words, all else being equal, the same amount will be
consumed at each given price level unless there is a change in demand.
364A COMPLETE GUIDE TO THE FUTURES MARKET
 FIGURE  22.6 Increase in Demand
Quantity (per unit time)
Price
 Figure 22.7 summarizes the relationship between demand and consumption. Consumption (i.e.,
the amount consumed) is directly dependent on price, where price is determined by the interaction
of supply and demand. The key point to keep in mind: Consumption is a consequence of price, not a
determinant of price. Thus the concept that consumption mirrors demand is totally erroneous; con-
sumption is determined by both supply and demand.
 FIGURE  22.7 Supply-Demand Interaction
Production
Stock levels
Marketing policy
(the percentage
of total supply
which will be
offered at each
given price)*
Cunsumer tastes
Level of
disposable income
Inflation
*For perishable of nonstorable commodities, such as hogs
and cattle, the entire amount will be offered during the
season, regardless of price level. In this case, supply for the
season is fixed, that is, equal to total production.
Population size
Price of substitute
goods
Means influences
Supply
Demand
Price Consumption
365
SUPPLY -DEMAND ANALYSIS: BASIC ECONOMIC THEORY
 In fact, for perishable and nonstorable commodities, consumption primarily refl ects supply, not
demand. For example, assume that pork consumption has increased sharply. Does this mean that pork
demand has suddenly improved dramatically? Absolutely not. The consumption increase is merely the
result of increased hog slaughter. Recalling that the supply curve for hogs (and therefore pork) can be
approximated by a vertical line, Figure 22.8 demonstrates


ption primarily refl ects supply, not
demand. For example, assume that pork consumption has increased sharply. Does this mean that pork
demand has suddenly improved dramatically? Absolutely not. The consumption increase is merely the
result of increased hog slaughter. Recalling that the supply curve for hogs (and therefore pork) can be
approximated by a vertical line, Figure 22.8 demonstrates the consumption level will be determined
by supply and will be the same, no matter which demand curve prevails. Thus, an increase in con-
sumption would merely refl ect an increase, or rightward shift, in supply—a bearish development—
and not an increase in demand, which would be a bullish development.
 It is entirely possible for a demand increase and a consumption decrease to occur simultaneously.
Figure 22.9 illustrates how this is possible for both the variable supply and fi xed supply cases. At the
 FIGURE  22.8 Consumption Refl ects Supply (in Fixed Supply Case)
Quantity (per unit time)
OQ
Supply
Demand
curves
Price
 FIGURE  22.9 Higher Demand, Lower Consumption: Variable Supply (a) and Fixed Supply (b)
Quantity (per unit time)
(a)
Quantity (per unit time)
(b)
OO BA BA
1
1 1
1
2
2
2
2
Price
Price
366
A Complete Guide to the Futures mArket
start, in period 1, the equilibrium consumption level is at point A. Although demand increases in
period 2, the equilibrium consumption level declines to B as a result of the decline in supply.
Even the U.S. Department of Agriculture (USDA), one of the nation’s leading employers of econ-
omists, has misused the term demand. The popularly termed supply-demand reports are in reality sup-
ply-disappearance reports (with disappearance defined as total domestic consumption plus exports).
Quite frequently, when the USDA changes its estimates for items that are sometimes discussed
under the label of “demand” (domestic consumption, exports),
6 the revision reflects a change in sup-
ply, not demand. For example, if the projected carryover for a commodity is already at estimated
minimum pipeline requirements, a reduced production forecast will mean that the USDA has to
lower either the domestic consumption estimate, the export estimate, or both. Otherwise, the USDA
might find itself in the absurd position of projecting a near-zero or even negative carryover. However,
the key point is that such revisions do not imply that demand has been reduced—a bearish conclu-
sion—but rather that high prices will ration scarce supplies, thereby resulting in reduced usage.
 ■ The Need to Incorporate Demand
Because of the difficulties involved in quantifying demand, there is often a temptation to concentrate
solely on supply factors in constructing a fundamental price-forecasting model. This can be a grave
error, because a demand shift can often be the dominant force in a major price move. The 1980–1982
copper market provided a classic example of the dangers of ignoring demand. T o focus in on this
example, we will examine a 21-year segment of the copper market (1973–1993) that contains three
major bull-bear price cycles, with the bear phase of the middle cycle containing the 1980–1982 mar-
ket that is the center of our attention.
Consider the following copper price-forecasting model:
Pf S
C= 
 

where P = average deflated copper price during the period
 S = copper stock level (U.S. plus foreign refined copper stocks)
 C = copper consumption level during the period (annualized refined copper deliveries,
United States plus foreign)
f( ) is read as “is a function of,” which basically means “is dependent on.”
At surface glance, this model seems reasonably plausible. In essence, the model implies that prices
will be low when copper stocks are large relative to the usage level and high in the reverse case. This
model certainly seems logical enough. Figure 22.10, which illustrates the relationship between cop-
per prices and the stock/consumption ratio during a 21-year segment (1973–1993) that is centered
near the 1980–1982 bear market that we wish to focus on, appears to confirm this expected market
 behavior. The strong inverse correlation between the stock/consumption ratio and copper prices is
6 USDA report tables, however, correctly label these items as components of “disappearance.”
367
SUPPLY -DEMAND ANALYSIS: BASIC ECONOMIC THEORY
 FIGURE  22.10 Average Monthly Copper Nearest Futures Price vs. Copper Stock/Consumption Ratio
Jan-73
20
40
60
80
¢/Lb.
Ratio
100
120
140
160
180
0
4
10
15
20
25
30
35
40
Jul-73
Jan-74
Jul-74
Jan-75
Jul-75
Jan-76
Jul-76
Jan-77
Jul-77
Jan-78
Jul-78
Jan-79
Jul-79
Jan-80
Jul-80
Jan-81
Jul-81
Jan-82
Jul-82
Jan-83
Jul-83
Jan-84
Jul-84
Jan-85
Jul-85
Jan-86
Jul-86
Jan-87
Jul-87
Jan-88
Jul-88
Jan-89
Jul-89
Jan-90
Jul-90
Jan-91
Jul-91
Jan-92
Jul-92
Jan-93
Jul-93
Price
broadly evident across the entire period shown. However, note the seemingly puzzling 1980 to mid-
1982 price behavior. During this period, prices plunged dramatically despite a slide in the stock/
consumption ratio to a major low . How can this counter-to-expected price action be explained?
 There is no mystery. Although the stock/consumption ratio is an important price-infl uencing
factor, it only refl ects supply. The apparent paradoxical behavior from 1980 to mid-1982 is explained
by the fact that the model does not incorporate demand. During this period, the anticipation and
ultimate realization of a severe recession combined with high real interest rates (interest rate minus
infl ation rate) drastically reduced the inventories users wished to hold at each given price level. In
other words, there was a sharp downward shift in the demand curve. This crucial fundamental devel-
opment simply could not be refl ected by the model just described.
 The moral is that it is always necessary to take demand into account. The next section discusses
several methods for incorporating demand in the price-forecasting model. But even when this type of
analysis is not poss

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


e level. In
other words, there was a sharp downward shift in the demand curve. This crucial fundamental devel-
opment simply could not be refl ected by the model just described.
 The moral is that it is always necessary to take demand into account. The next section discusses
several methods for incorporating demand in the price-forecasting model. But even when this type of
analysis is not possible, demand must still be considered. If demand is not part of the model because
of the inherent diffi culties in quantifying demand, then the analysis should be divided into two steps:
 1. Model projection
 2. Informal evaluation of the potential impact of demand factors
368
A Complete Guide to the Futures mArket
 ■ Possible Methods for Incorporating Demand
How can the problem of nonquantifiable demand be circumvented? The answer depends on the mar-
ket. The following types of markets permit various solutions to the problem of quantifying demand:
Stable Demand
For some markets, the supposition that demand is stable is a reasonable simplifying assumption. In
effect, in this type of market, fundamental price forecasts can be based strictly on supply statistics.
Growth pattern in Demand Change
For other markets, although demand changes from year to year, the pattern of change can be described
by a simplified assumption (e.g., demand increases by 3 percent annually). For markets of this type,
demand can be represented by an index that changes in a manner consistent with the assumed growth
pattern for demand.
Identification of Demand-Influencing Variables
For some markets, although changes in demand cannot be described by any consistent growth pat-
tern, the factors that affect demand can be identified. For example, beef demand increases in some
years and decreases in others. Nevertheless, it can easily be demonstrated that these shifts are depen-
dent on other identifiable factors, such as availability of competitive meat supplies. In such cases, one
can bypass the problem of precisely specifying the demand curve by directly formulating a price-

forecasting model that uses supply statistics and the factors determining demand as inputs. An exam-
ple of such a model is given by the following equation:
QCPf CS HS BS T= (, ,, )
where QCP = average quarterly cattle price
 CS = quarterly cattle slaughter
 HS = quarterly hog slaughter
 BS = quarterly broiler slaughter
 T = time trend
In the preceding example, CS represents a supply variable, while HS, BS, and T represent variables
that affect demand. Trend affects demand through inflation (more will be demanded at each nominal
price level because of inflation) and possible other factors that have a trending characteristic.
As another example, in attempting to forecast copper prices, one of the ways we could incorpo-
rate the demand effect would be by focusing on the level of activity in the key copper-using indus-
tries.
 Figure 22.11 illustrates the relationship between copper prices and an index of new housing
for the same 21-year period that was surveyed in Figure 22.10. Figure 22.12 illustrates the rela-
tionship between copper prices and domestic auto sales during the same period. Note how the
369
SUPPLY -DEMAND ANALYSIS: BASIC ECONOMIC THEORY
 FIGURE  22.11 Average Monthly Copper Nearest Futures Price vs. Index of New
Private Housing
Jan-73
20
40
60
80
¢/Lb.
Index
100
120
140
160
180
40
60
80
100
120
140
160
180
200
Jul-73
Jan-74
Jul-74
Jan-75
Jul-75
Jan-76
Jul-76
Jan-77
Jul-77
Jan-78
Jul-78
Jan-79
Jul-79
Jan-80
Jul-80
Jan-81
Jul-81
Jan-82
Jul-82
Jan-83
Jul-83
Jan-84
Jul-84
Jan-85
Jul-85
Jan-86
Jul-86
Jan-87
Jul-87
Jan-88
Jul-88
Jan-89
Jul-89
Jan-90
Jul-90
Jan-91
Jul-91
Jan-92
Jul-92
Jan-93
Jul-93
Price
Index
 FIGURE  22.12 Average Monthly Copper Nearest Futures Price vs. Annualized Seasonally
Adjusted Auto Sales
Jan-73
Jul-73
Jan-74
Jul-74
Jan-75
Jul-75
Jan-76
Jul-76
Jan-77
Jul-77
Jan-78
Jul-78
Jan-79
Jul-79
Jan-80
Jul-80
Jan-81
Jul-81
Jan-82
Jul-82
Jan-83
Jul-83
Jan-84
Jul-84
Jan-85
Jul-85
Jan-86
Jul-86
Jan-87
Jul-87
Jan-88
Jul-88
Jan-89
Jul-89
Jan-90
Jul-90
Jan-91
Jul-91
Jan-92
Jul-92
Jan-93
4
5
6
7
8
9
10
Price
11
12
20
40
60
80
100
¢/Ld.
120
140
160
180
Million
Sales
370A COMPLETE GUIDE TO THE FUTURES MARKET
 FIGURE  22.13 Consumption as Proxy for Inelastic Demand
Quantity (per unit time)
Price
S1
S1
S2
S2
D1
D2
D3
D4
D1
D2
D3
D4
declines in housing starts and automobile sales preceded downturns in copper prices, including the
1980 to mid-1982 decline. Recall from the previous section that the imposing 1980 to mid-1982
bear market seemed somewhat puzzling when viewed solely relative to the stock/consumption ratio.
Figure 22.11 and 22.12 illustrate how this seeming paradox can be resolved once demand factors are
considered. Of course, the specifi c demand factors included would change over time. For example,
our copper illustration focused on the 1973–1993 time segment. In a current copper price model,
indicators of emerging market demand would be far more critical than they were then.
 highly Inelastic Demand (and Supply elastic relative to Demand)
 Although conceptually incorrect, practically speaking, for markets of this type it is possible to use
consumption as a proxy for demand. Since by defi nition in these markets consumption in a given
year will not vary widely, regardless of price level, one can assume the prevailing consumption level
roughly refl ects the demand level. For example, Figure 22.13 illustrates a series of inelastic demand
curves and two diff erent supply curves. Note how the quantity consumed at the equilibrium price
level is primarily dependent on the prevailing demand curve. Hence, consumption can serve as a
proxy indicator for the unknown demand curve.
371
Supply -DemanD analySiS: BaSic economic Theory
An example of this approach is provided by the following model:
DASP f IS P
C= +
 

where DASP = deflated average annual sugar price
 IS = initial stocks
 P = production
 C = c


the equilibrium price
level is primarily dependent on the prevailing demand curve. Hence, consumption can serve as a
proxy indicator for the unknown demand curve.
371
Supply -DemanD analySiS: BaSic economic Theory
An example of this approach is provided by the following model:
DASP f IS P
C= +
 

where DASP = deflated average annual sugar price
 IS = initial stocks
 P = production
 C = consumption
Note that initial stocks plus production is a proxy for supply, and consumption is a proxy for demand.
 ■ Why Traditional Fundamental Analysis Doesn’t
Work in the Gold Market
Unfortunately, the approaches we have enumerated for dealing with the elusiveness of demand do
not encompass all cases. For some markets, not only is demand highly erratic, it is also difficult or
nearly impossible to define a stable relationship that describes the precise dependence of demand on
other variables.
Gold is a perfect example of such a market. Gold demand is basically dependent on the market’s
psychological perception of the value of gold, which in turn is dependent on a myriad of interrelated
variables, including relative inflation rates, world interest rates, currency fluctuations, trade balance
figures, OPEC actions, and political turmoil. The problem of specifying gold demand is further com-
plicated by the fact that the relative importance of any of these factors in influencing gold demand
is subject to considerable variation. For example, during some periods, currency fluctuations may
become the pivotal price-influencing factor, while at other times developments in this area exert only
a minor price impact.
In the case of gold, even the supply side of the equation cannot be readily approximated. Similar
to demand, supply is subject to wide, erratic shifts that are also dependent on market psychology. This
instability of the supply curve is primarily attributable to shifts in dishoarding rather than to changes
in commercial supply.
The combination of highly erratic, intangible supply and demand curves makes the gold market a
fundamental analyst’s nightmare. Some analysts attempt to construct a fundamental model for gold
by focusing on such statistics as mine production and industrial usage. This approach represents true
folly, since these figures are equal to only a minuscule fraction of total gold supply. Gold prices are
dependent on the psychological considerations detailed earlier, and there is no way to avoid this fact.
In effect, for a market such as gold, the traditional fundamental approach just does not work.
Constructing an econometric model to predict gold prices is like trying to write a computer program
that will predict a photographer’s next picture on the basis of her past shots—the answer may be
somewhat better than a blind guess but is hardly worth the effort.

373
Chapter 23
Types of
Fundamental Analysis
When you can measure what you are speaking about, and express it in numbers, you know
something about it; but when you cannot measure it, when you cannot express it in numbers,
your knowledge is of a meager and unsatisfactory kind: it may be the beginnings of knowledge,
but you have scarcely, in your thoughts, advanced to the stage of science.
—William Thomson, Lord Kelvin
 ■ The “Old Hand” Approach
The “old hand” approach refers to the analytical method used by analysts whose familiarity with
the market is so finely honed that they have developed a virtual sixth sense with respect to its price
fluctuations. By talking to a variety of commercial participants, they get a feel for market tone. They
are also well tuned in to the flow of market news and are constantly assessing the market’s behavior
in response to this information. This is strictly a nonscientific approach, with the individual acting
as the computer. It is not intrinsically inferior to more sophisticated approaches; its value is strictly
dependent on the skills and intuition of the practitioner. In fact, it is hardly unusual for some analysts
of this school to consistently outperform their econometrically oriented counterparts. This approach
is strictly individualistic, however, and by definition can only be acquired by personal experience.
 ■ The Balance Table
The balance table summarizes the key components of current-season supply and disappearance, along
with prior-season comparisons. The balance between supply and disappearance will indicate a season-
ending carryover; it is the relative magnitude of this figure that is considered the primary price-
determining statistic. Table 23.1 illustrates a U.S. Department of Agriculture (USDA) balance table
374
A Complete Guide to the Futures mArket
table 23.1 U.S. Wheat Supply/Disappearance balance, June–May Crop Y ear (million bushels)
1988–1989 1989–1990 1990–1991 1991–1992 1992–1993 1993–1994 a
Beginning stocks 1,261 702 536 366 472 529
Imports 23 23 37 41 70 75
Production 1,812 2,037 2,736 1,981 2,459 2,493
total supply 3,096 2,762 3,309 2,888 3,001 3,097
Food use 726 749 786 789 829 845
Seed use 103 100 90 94 93 94
Feed/Residual 146 143 500 254 196 325
T otal domestic use 975 992 1,376 1,137 1,118 1,264
Exports 1,419 1,233 1,068 1,280 1,354 1,125
total disappearance 2,394 2,225 2,444 2,416 2,472 2,389
ending stocks 702 536 866 472 529 708
Ending stocks as % of
total use 29 24 35 20 21 30
aProjected.
Source: USDA.
for the wheat market. The analyst who relies heavily on the balance table will focus on possible shifts
in the various components of supply and disappearance in an effort to anticipate the probable direc-
tion of price change.
The balance table is a valuable aid that succinctly summarizes the key market statistics. By itself,
however, the balance table is insufficient in answering the critical question of what price is right
under the given conditions. In fact, the analyst who uses only the balance-table approach to forecast
prices will be guilty of fallacy number 1 detailed in Chapter 21 (i.e., viewing fundamentals in


e balance table is a valuable aid that succinctly summarizes the key market statistics. By itself,
however, the balance table is insufficient in answering the critical question of what price is right
under the given conditions. In fact, the analyst who uses only the balance-table approach to forecast
prices will be guilty of fallacy number 1 detailed in Chapter 21 (i.e., viewing fundamentals in a
vacuum).
 ■ The Analogous Season Method
In the analogous season method, the analyst finds past seasons that shared the same fundamental charac-
teristics of a current season and then uses the price profiles of those analogous seasons as a “road map”
in projecting current season price swings. For example, if in the current season production is up,
usage is down, and the ending stocks/usage ratio is down, the analyst might find all past seasons that
also exhibited these conditions. Next, the analyst would identify key price turning points in the analo-
gous seasons (e.g., harvest low , postharvest high, winter low , crop-scare high). The timing and relative
magnitude of price swings between these key turning points would then be calculated for each past
analogous season. Finally, price swing ranges and turning point time windows would be projected for
the current season, based on the assumption that the current season price patterns would be at least
roughly similar to the price action of prior analogous seasons.
375
TyPES oF FUNDAMENTAL ANALySIS
 ■ Regression Analysis
How can you determine which fundamental factors are most important in determining price levels?
Even assuming you can make a reasonable conjecture as to what are the key fundamental factors
influencing prices, how can you translate the current levels of these factors into a price forecast?
Say, for example, you are trying to forecast hog futures prices.
you assume that hog prices will be
inversely correlated with hog slaughter levels and also inversely correlated with competitive meat
supplies (e.g., broiler slaughter, cattle slaughter).
you also assume that for any combination of supply
levels for these various meats, prices will be higher in the current year than in past years because of
the influence of inflation. Even if all these assumptions are correct, how can you determine the price
implications for any given combination of the various meat supplies?
Simply comparing current supply levels to past year levels will not yield any price forecast. For
example, what are the price implications of hog slaughter being 3 percent lower than in some prior
year while broiler slaughter and cattle slaughter are each 2 percent higher? How does one reconcile
the multiple comparisons of the current year to each of the past years examined? How much differ-
ence does a given time separation make when drawing comparisons between different years? All of
these questions seem impossible to answer by simply comparing current and past data.
Regression analysis provides a statistical procedure that can be used to translate fundamental data
into price projections. The assumptions we just made regarding the plausible key influences on hog
prices could be formalized into the following equation:
Pa bH bB bC bT=+ ++ +12 34
where P = average price
 H = hog slaughter
 B = broiler slaughter
 C = cattle slaughter
 T = time trend
The values of a, b1, b2, b3, and b4 are determined by the regression analysis procedure (explained
in the appendices). Given projections for hog slaughter, broiler slaughter, and cattle slaughter, we can
plug those values and the time trend value for the current year into the preceding equation and obtain
a precise price forecast.
Even if you are not mathematically inclined, think twice before dismissing the regression-analysis
approach. Regression analysis embeds a number of important attributes:
 1. Regression analysis makes it possible to combine multiple fundamental inputs, compared across
multiple years, to derive a price forecast.
 2. Regression analysis can be used to test the relative significance of each of the price-influencing
variables (called independent variables) as well as the forecasting equation as a whole.
 3. Regression analysis provides an efficient learning tool for understanding the interrelationships
between various fundamental factors and price.
376
A Complete Guide to the Futures mArket
Regression analysis is probably the single most useful analytical tool in fundamental analysis.
Appendices A through F provide an in-depth discussion of regression analysis.
 ■ Index Models
Sometimes we may wish to construct a fundamental model that uses scores of explanatory variables as
indicators of a market’s price. For example, we might postulate that bond prices are inversely related
to a variety of inflation indicators (e.g., gold prices, S&P Goldman Sachs Commodity Index, con-
sumer price index), economic indicators (e.g., employment, industrial production, housing starts),
and monetary indicators (e.g., yield spread). Given the wide range of such indicators, and allowing
that each indicator can be used with multiple time lags (as the relationship between bond rates and
an indicator will frequently not be contemporaneous), it is easy to see how the number of possible
explanatory variables could reach 50 or even higher.
Regression analysis cannot handle situations that involve large numbers of explanatory (inde-
pendent) variables. Typically, a regression equation will employ five or fewer independent variables.
There are two primary reasons why regression analysis cannot be applied to cases involving a multi-
tude of variables:
 1. If large numbers of independent variables are used, there is a great danger of overfitting (i.e.,
deriving a model that is tailored to fit past data but will be useless as a tool for projecting future
prices or price trends).
 2. When large numbers of independent variables are employed it is virtually inevitable that a num-
ber of such variables wil


ases involving a multi-
tude of variables:
 1. If large numbers of independent variables are used, there is a great danger of overfitting (i.e.,
deriving a model that is tailored to fit past data but will be useless as a tool for projecting future
prices or price trends).
 2. When large numbers of independent variables are employed it is virtually inevitable that a num-
ber of such variables will be closely related to each other. High correlations among independent
variables in a regression model will result in a statistical problem called multicollinearity, which
destroys the reliability of the derived forecasting equation. (This problem is discussed in greater
detail in Appendix E.)
one method of handling a large number of explanatory variables is to combine them all in an
index model. The following step-by-step approach illustrates one possible procedure:
 1. Assign each indicator a value of +1 if its current status is considered bullish for the price of the
given market and a value of −1 if it is considered bearish. (How such a determination is made
will be discussed momentarily.)
 2. Add all the assigned indicator values to obtain an index value.
 3. Normalize the index by multiplying by 100 divided by number of indicators. This step will yield
an index with a theoretical range of −100 (if all the indicators are bearish) to +100 (if all the
indicators are bullish). For example, if there are 50 indicators, and 30 are bullish and 20 bearish,
the preceding procedure would yield a normalized index value of +20.
of course, an equal split
between bullish and bearish indicators would yield an index value of 0, as is intuitively desirable.
This procedure sounds simple enough. The key question, however, is: how does one determine if
a current indicator value is bullish or bearish? Deciding on some value as the division line between
377
TyPES oF FUNDAMENTAL ANALySIS
 bullish and bearish values is highly undesirable for two key reasons: (1) many variables trend over
time; and (2) due to structural changes over time, the definition of “high” and “low” will tend to shift
for many, if not most, variables. Hence, it is far more practical to categorize a variable as bullish or
bearish based on its direction of movement (i.e., trend) rather than its level. Trend categorization,
however, falls more within the realm of technical analysis than fundamental analysis. Indeed, some of
the very basic tools of technical analysis (e.g., crossover moving average) can be applied to defining
the assigned indicator values in an index model of the type described in this section. For example, if
using a crossover moving average to define the trend direction of the indicators, an indicator would
be assigned a value of +1 if the short-term moving average was greater than the long-term moving
average, and a value of −1 in the reverse case.

379
Chapter 24
The Role
of Expectations
What we anticipate seldom occurs; what we least expect generally happens.
—Benjamin Disraeli
 ■ Using Prior-Year Estimates Rather Than Revised Statistics
Historical data are based on final revised estimates rather than the estimates that were available at the
time. For example, the historical levels of U.S. corn production are revised throughout the season with
the final revision occurring after the end of the season. These final revised estimates for each season
(the actual levels) can differ substantially from the crop estimates that prevailed during each season (the
expected levels). Similarly, historical corn consumption and export levels (the actual levels based on final
revised estimates) can be very different from the expected levels that prevailed during each season.
Typically, fundamental models would use actual historical data as inputs. But is this default approach
the best procedure? A strong argument can be made that the data levels expected at the time are more
relevant to explaining price behavior than actual data levels that only became known after the price
forecast period in question. Thus, it may be possible to build a more accurate model using past estimates
rather than actual statistics as the price-explanatory variables. For example, if we are trying to construct
a model to explain and predict September–November corn prices, we might well find that the past
production and usage estimates released during the September–November period are more helpful than
the actual supply statistics in explaining the year-to-year historical variation in September–November
prices. Such price behavior would merely reflect that what the market thought was true in the past
was more important in determining prices than what was actually true (as defined by the final revised
estimates)—a reasonable outcome given that market participants have no way of determining actual
statistics and must rely on prevailing estimates for their marketing, purchasing, and trading decisions.
The key point is that using past expected data rather than actual statistics might be theoretically
sounder and may well yield better price-forecasting models. Of course, using past expected statistics in
380
A Complete Guide to the Futures mArket
a model will require considerably more work in terms of data gathering, which may explain why such
data are far less frequently used than the final revised numbers. As is true for most of life’s endeavors,
creating a better product (price-forecasting model in this case) requires more effort. There are no
shortcuts in doing things right.
 ■ Adding Expectations as a Variable
in the Price-Forecasting Model
Thus far, we have discussed the choice between using anticipated versus actual data for explaining
past price variation. Regardless of which is used, one can also consider adding a variable to represent
expectations for a key statistic in a following season. T o clarify this distinction, we list the four possible
variations in the extent to which expectations are incorporated in a mo


ting Model
Thus far, we have discussed the choice between using anticipated versus actual data for explaining
past price variation. Regardless of which is used, one can also consider adding a variable to represent
expectations for a key statistic in a following season. T o clarify this distinction, we list the four possible
variations in the extent to which expectations are incorporated in a model:
 1. Price is a function of concurrent-season actual statistics (no use of expectations).
 2. Price is a function of estimates for concurrent-season statistics (expectations used for concurrent-
season data).
 3. Price is a function of concurrent-season actual statistics and expectations for the following sea-
son (expectations used for following-season data).
 4. Price is a function of concurrent-season estimates and expectations for the following season
(expectations used for both concurrent- and following-season data).
The expectations for a coming season can often exert a more pronounced price impact than
do prevailing fundamentals. This observation is especially true during the latter half of a season—a
time at which the fundamentals for the given season are usually well defined and not subject to large
variation. In fact, frequently, when there is a dichotomy between the implications of old-crop funda-
mentals and new-crop expectations, the latter tend to dominate the price picture.
Why should expectations for a coming season affect current-season prices? Expectations influence
current selling and buying psychology. For example, if supplies are burdensome and a shift toward
supply tightness is anticipated, sellers will have an incentive to hold back the commodity and will
offer less to the market at each given price level (the supply curve will shift upward in response to
reduced supply). At the same time, buyers will attempt to build inventories and therefore will pur-
chase increased quantities at any given price level (the demand curve will shift upward). These two
effects reinforce each other, and the net result will be higher prices in the current season.
 ■ The Influence of Expectations on Actual Statistics
Ironically, bullish new-crop expectations can actually cause current-season fundamentals to appear
more bearish. The following cause-and-effect diagram illustrates this point:
Bullish expectations forn ew crop pr iced uringo ld-c rops eason
o
→↑ →
lld-crop consumpt iona nd export so ld-c rops tock s↓→ ↑
381
THE ROlE OF ExPEcTATIONS
As a result of this string of events, seasons that experience bullish new-crop expectations are likely
to appear inexplicably overpriced based on old-crop fundamentals—another reason why new-crop
expectations should be incorporated in the price-forecasting model wherever possible.
 ■ Defining New-Crop Expectations
On the supply side, new-crop expectations can be based on planting intentions and subsequently on
acreage estimates. In using these estimates to define expectations, one usually assumes a trend yield
(the yield implied by a regression-derived best-fit line of past yields) or an average yield (e.g., five-
year average for each state or region) if there is no pronounced trend. Such neutral projections would
then be adjusted upward in the case of very favorable growing weather, or downward if conditions
were adverse.
On the usage side, expectations are defined by the historical behavior pattern. For example, if
in recent years consumption changes for a given commodity have tended to range from −2 percent
to +4 percent, as a function of the direction and magnitude of price change, in the absence of any
additional information, one might use a 1 percent consumption increase as a representative figure for
expected new-crop consumption.
Historical expectation statistics can be generated in a similar manner or by surveying past com-
mentaries in U.S. Department of Agriculture situation reports, trade reports, and industry market
reports. Unfortunately, there is some unavoidable arbitrariness in the latter approach, since the expec-
tation figures depend on the sources chosen and on the weights assigned to each source. However, this
ambiguity is not a critical drawback, since at any given time, new-crop projections by various sources
tend to cluster in the same general area.

383Nothing so weakens government as persistent inflation.
—John Kenneth Galbraith
I
n designing price-forecasting models, it is essential to keep in mind that the measure of prices—the
dollar—is itself a variable. Thus, using nominal prices to compare widely separated years makes as
much sense as comparing the dollar price of a commodity in one season to the euro price in another
season. It is safe to say that any model that does not adjust for inflation is critically flawed.
Figures 25.1 through 25.4 illustrate the difference between nominal and inflation-adjusted prices
in different futures markets from 1995 to 2015. These charts illustrate that adjusting for the effect of
inflation can alter the relationship between past highs and lows, as well as the relative magnitudes of
prior past moves. For example, in Figure 25.1, the 2004 highs in lumber nearest futures were above
the 1996 and 1999 highs in nominal terms (solid line), but lower than these previous peaks on an
inflation-adjusted basis (dashed line). In Figure 25.2, in March 2004 soybean nearest futures eclipsed
their March 1997 high on a nominal basis, but the inflation-adjusted series made a lower high in
March 2004.
Figure 25.3 compares nominal and inflation-adjusted copper nearest futures. Successively higher
highs in 2007 and 2008 in the nominal series were slightly lower highs in the deflated series. Also
note that although nominal prices were substantially higher at the end of the period than at the start,
inflation-adjusted prices were near unchanged for period as a whole.
Finally, in Figure 25.4, the nominal price graph reflects a strong uptrend in live cattle prices dur-
in


Successively higher
highs in 2007 and 2008 in the nominal series were slightly lower highs in the deflated series. Also
note that although nominal prices were substantially higher at the end of the period than at the start,
inflation-adjusted prices were near unchanged for period as a whole.
Finally, in Figure 25.4, the nominal price graph reflects a strong uptrend in live cattle prices dur-
ing 1995 through 2011 (the sharp correction in 2008 notwithstanding), while the inflation-adjusted
Incorporating
Inflation
Chapter 25
384A CoMPlETE GUIDE To THE FUTUrES MArKET
 FIGURE /uni00A025.1 lumber: Nearest Futures, Nominal vs. Defl ated by PPI*
 *Monthly closing prices defl ated by PPI indexed to June 2005 = 100.
 FIGURE /uni00A025.2 Soybeans: Nearest Futures, Nominal vs. Defl ated by PPI*
 *Monthly closing prices defl ated by PPI indexed to June 2005 = 100.
385
INCorPorATING INFlATIoN
 FIGURE /uni00A025.3 Copper: Nearest Futures, Nominal vs. Defl ated by PPI*
*Monthly closing prices defl ated by PPI indexed to June 2005 = 100.
 FIGURE /uni00A025.4 live Cattle: Monthly Nearest Futures, Nominal V ersus Defl ated by PPI*
*Monthly closing prices defl ated by PPI indexed to June 2005 = 100.
386
A Complete Guide to the Futures mArket
price series moved essentially sideways during the same period, with little net change for the period
as a whole. In other words, the entire rise in nominal prices during this 17-year period was nothing
more than the inflation effect.
Some of the ways inflation can be incorporated into the price-forecasting model include the fol-
lowing:
 1. A representative inflation index is chosen, such as the producer price index (PPI), consumer
price index (CPI), or the gross domestic product (GDP) deflator, and each historical price is
divided by the contemporaneous index value, yielding a deflated price series. (Actually, the
reported index value is divided by 100, since the index figures are quoted as a percent of a
base of 100.) The inflation-adjusted price series in Figure 25.1 through 25.4 were deflated in
this manner, using monthly PPI data with June 2005 as the base month (i.e., June 2005 PPI =
100). Table 25.1 applies this method to corn futures price data. A price forecast derived using
this approach would be translated into current dollar terms by multiplying the projection by an
estimate of the inflation index for the forecast period.
 2. Alternately, all historical prices can be transformed into current dollar equivalents by mul-
tiplying each past price by the ratio of the estimated inflation index for the forecast period
to the index value during the given past period. Table 25.2 illustrates this approach for a
September–November 2015 forecast period, based on the assumption that PPI numbers are
available only through August 2015. The table estimates the average September–November
2015 PPI by assuming the year-to-year percentage PPI change for this period will be the
same as the known year-to-year percentage change in the June–August 2015 average PPI.
(The estimated 7.5 percent decrease in PPI using this approach compared with an actual
decrease of 7.3 percent.)
Note that even when the PPI estimate used for the forecast period proves somewhat out of
line, the distortion to the price analysis will be limited for two reasons. First, any reasonable
inflation estimate will almost invariably be within a few percentage points of the actual figure
and usually much closer. Second, all past prices will be overstated or understated equivalently
(in percentage terms), thereby maintaining their relative relationship and leaving any price-
explanatory model virtually unaffected. In any case, the forecast error attributable to an inac-
curate inflation projection would be minuscule compared with the distortion that would result
from the use of nominal rather than inflation-adjusted prices.
 3. The inflation influence can be incorporated through its impact on the demand curve. Inflation
implies an upward shift in the demand curve. All else being equal, the amount consumed at each
given price level will increase over time, since each nominal price level represents a lower real
price. However, because of the previously discussed problems in quantifying demand curves,
this method represents more of a theoretical concept than a practical approach.
The actual method used to adjust for inflation is of secondary importance. The key point is
that inflation is a critical input that should be incorporated in any fundamental price-forecasting
model.
387
INCorPorATING INFlATIoN
table 25.1 Corn Monthly Nearest Futures prices: Nominal and Deflated
Y ear avg. Dec Futures price, Sep–Nov avg. Sep–Nov ppIa Inflation-adjusted avg. price
1995 325.00 81.21 400.20
1996 277.83 83.04 334.57
1997 269.67 82.78 325.77
1998 215.58 80.23 268.70
1999 198.42 82.96 239.18
2000 204.17 87.51 233.31
2001 209.42 84.99 246.41
2002 246.42 86.11 286.17
2003 237.50 90.02 263.83
2004 200.17 97.02 206.32
2005 196.42 106.31 184.76
2006 320.08 106.33 301.03
2007 377.67 113.89 331.61
2008 412.83 121.00 341.19
2009 370.92 113.78 325.99
2010 535.92 120.80 443.63
2011 613.58 130.96 468.54
2012 753.33 131.71 571.95
2013 428.33 131.26 326.33
2014 357.75 131.93 271.17
areported index values would be divided by 100.0, since reported figures are quoted as a percentage of June 2005 base = 100.0.
Ironically, in the post-1979 period there were some instances when naïve price-forecasting mod-
els that totally ignored the effect of inflation may actually have provided more accurate projections
than models incorporating this important factor. This apparent paradox can be explained by the
extraordinarily high real interest rates (nominal rates minus inflation) witnessed in 1979–1980,
which triggered a permanent change in inventory psychology. The high cost of holding commod -
ity inventories provided a strong incentive to reduce inventories all along the pipeline (from raw
product to retail). In ef


ncorporating this important factor. This apparent paradox can be explained by the
extraordinarily high real interest rates (nominal rates minus inflation) witnessed in 1979–1980,
which triggered a permanent change in inventory psychology. The high cost of holding commod -
ity inventories provided a strong incentive to reduce inventories all along the pipeline (from raw
product to retail). In effect, this widespread decision to hold lower inventories represented a classic
example of a downshift in the demand curve.
once set in motion by the shock of the high inflation/
high interest rate environment of 1979–1980, and abetted by technological advances and new inven-
tory theories (e.g., “just-in-time”), inventory demand continued to contract even when inflation and
interest rates fell sharply. The resulting sustained downshift in demand tended to counterbalance the
influence of inflation.
The preceding discussion certainly is not intended to imply that inflation can be safely ignored,
but rather that major shifts in the demand for commodities, which can run counter to the inflation
388
A Complete Guide to the Futures mArket
effect, as was the case for the pronounced downward shift in demand evident in the 1980s and early
1990s, must also be incorporated. Some examples of ways the latter factor can be included (assuming
a regression model) are the addition of a trend variable
1 and the use of a dummy variable to segment
the data by different periods. (Dummy variables are discussed in Appendix E.)
1 Note that a trend variable need not increase for the entire period used in the analysis, but can be assumed to
level off if the trending variable (the downward shift in demand in our example) is assumed to dissipate at some
point.
table 25.2 average September–November price of December Corn Futures: Nominal and estimated
2015 Dollar equivalent terms
Y ear
avg. Dec Futures
price, Sep–Nov
avg. Sep–
Nov ppIa
estimated avg.
Sep–Nov 2015 ppI
Multiplier to Convert
past Season prices into
2015 terms
Dec Futures avg.
Sep–Nov price in
2015 $ terms
1995 325.00 81.31 120.16 1.478 480.35
1996 277.83 83.24 120.16 1.444 401.19
1997 269.67 82.63 120.16 1.454 392.10
1998 215.58 80.02 120.16 1.502 323.80
1999 198.42 82.91 120.16 1.449 287.51
2000 204.17 87.84 120.16 1.368 279.30
2001 209.42 83.86 120.16 1.433 300.10
2002 246.42 86.24 120.16 1.393 343.26
2003 237.50 90.24 120.16 1.332 316.35
2004 200.17 97.56 120.16 1.232 246.61
2005 196.42 106.48 120.16 1.128 221.56
2006 320.08 106.37 120.16 1.130 361.69
2007 377.67 114.99 120.16 1.045 394.67
2008 412.83 115.38 120.16 1.041 429.76
2009 370.92 114.65 120.16 1.048 388.72
2010 535.92 121.84 120.16 0.986 528.42
2011 613.58 130.11 120.16 0.923 566.33
2012 753.33 131.09 120.16 0.917 690.80
2013 428.33 130.85 120.16 0.918 393.21
2014 357.75 129.90 120.16 0.925 330.92
aPPI indexed to June 2005 = 100.
389
Chapter 26
The freeze may come in winter, but the seasonal rally comes in fall.
—Jack D. Schwager
 ■ The Concept of Seasonal Trading
Various markets exhibit seasonal tendencies. Sometimes these seasonal patterns can be attributed
to obvious fundamental causes, such as harvest selling or buying in front of potential freeze danger
periods for some agricultural markets. Financial markets can also exhibit seasonal patterns tied to
fundamental causes (e.g., Treasury refundings, year-end book squaring). Sometimes, however, sea-
sonal patterns will not be associated with any apparent fundamental factors.
The concept of utilizing seasonal patterns in making trading decisions is based on the assumption
that seasonal influences will cause biases in the movements of market prices. Of course, such correla-
tions will be far from perfect. It is hardly uncommon for markets to move opposite to their normal
seasonal trends. The key question is whether, on balance, there is enough positive correlation between
future price movements and past seasonal patterns for such information to be useful. Because (as will
be detailed later) apparent seasonal patterns would be expected to appear even in random series, it is
difficult to determine to what extent seasonal price patterns reflect true biases as opposed to random
occurrences. Hence, there is an unavoidable degree of subjectivity in deciding how much weight to
give past seasonal patterns. A reasonable approach is to use seasonal analysis as a supplement to fun-
damental and technical analysis in making trading decisions, but never as a sole input.
 ■ Cash versus Futures Price Seasonality
It is important to understand that seasonal patterns in futures and cash prices may not be equivalent.
For example, even if cash prices move lower for a given crop during harvest time with great con-
sistency, it doesn’t mean this pattern will provide a trading opportunity. It is entirely possible the
Seasonal Analysis
390
A Complete Guide to the Futures mArket
futures market will discount harvest-time weakness in the cash market, thereby eliminating any profit
opportunity. Because we are concerned with trading futures, not the cash commodities or financial
instruments, the key question is whether a seasonal pattern exists in futures. Therefore, futures data
should be used for all seasonality calculations.
 ■ The Role of Expectations
Because markets tend to discount expected events, such as changes in the seasons, true seasonal
patterns often differ radically from conventional beliefs regarding such patterns. For example, it is
widely believed that markets that are vulnerable to severe cold weather, such as heating oil, frozen
concentrated orange juice, and coffee, exhibit strength during the winter. (For coffee, the relevant
winter period is June through August.) However, these markets often exhibit seasonal strength prior
to the advent of winter and tend to decline with the onset of winter.
 ■ Is It Real or Is It Probability?
Even if a market exhibits a seemingly pronounced seasonal pattern, this does not mean a t


concentrated orange juice, and coffee, exhibit strength during the winter. (For coffee, the relevant
winter period is June through August.) However, these markets often exhibit seasonal strength prior
to the advent of winter and tend to decline with the onset of winter.
 ■ Is It Real or Is It Probability?
Even if a market exhibits a seemingly pronounced seasonal pattern, this does not mean a true seasonal
pattern exists. If enough markets are examined for enough periods of time, the emergence of some
apparent seasonal patterns will be a virtual certainty even if all the examined price series are random. In
other words, past seasonal patterns could simply be due to normal probability and not suggest any
potential bias for future price behavior.
T o illustrate how patterns can occur—in fact, are likely to occur—even if the distribution of price
movements is random, we can use coin tosses to represent up or down price changes: heads repre-
sents a week with a net price gain; tails a week with a net price loss. Assume we flip a coin 10 times
to represent the price movement in a given market in each of the past 10 years. W e then repeat this
10-flip trial for a total of 52 times (one corresponding to each week in the year).
Although an equal number of heads and tails (i.e., an equal number of years of up and down price
movement) will be the most common event, more than 75 percent of the trials will yield an unequal num-
ber of heads and tails (an unequal number of up and down years). In fact, some of these trials will result in
a highly unbalanced number of heads and tails. It can be shown through probability theory that in 52 trials
(weeks) there is a better than 75 percent chance of getting one or more 10-flip trials with at least 9 out
of 10 heads or tails (one or more weeks with at least 9 out of 10 years of up or down price movements).
If the preceding process of 52 ten-flip trials is repeated a total of 25 times (to represent 25 dif-
ferent markets), then the probability of getting one or more 10-flip trials with at least 9 of 10 heads
or tails is a virtual certainty (99.999999998 percent). In fact, there is a better than 99 percent prob-
ability there will be more than 15 ten-flip trials with at least 9 out of 10 heads or tails. T o state this
in market equivalent terms, even if the distribution of up and down price movements is random in
all markets, in a group of 25 markets, there is a better than 99 percent chance of finding more than
15 instances in which a market moved higher (or lower) in at least 9 out of the past 10 years during a
391
SEASONAl ANAlySIS
given week. Thus, it is important to understand that a certain number of apparent seasonal patterns
are inevitable even if the distribution of price movements is random.
 ■ Calculating a Seasonal Index
There are many methods for calculating a seasonal index. This section examines two basic approaches.
average percentage Method
The average percentage method is by far the simplest way to calculate a seasonal index. This method
involves the following steps:
 1. Calculate an annual average for each year or season.
 2. Express each data item (daily, weekly, or monthly value) as a percentage of the corresponding
annual average. Either daily, weekly, or monthly data can be used in constructing seasonal indi-
ces. A daily or weekly seasonal index is obviously preferable to a monthly index, particularly
for trading purposes, but it also requires far more data manipulation. This section uses monthly
indices solely for simplicity of illustration.
 3. Average the percentage values for each period (month, week, or day). The resulting figures are
the seasonal index.
T o illustrate this method, we will calculate the seasonal index for heating oil. Table 26.1 lists the
average monthly prices for the 1996–2015 December heating oil contracts (which expire in Novem-
ber). Note the first column of data (November) is listed for later use and is not included in calculating
the annual average. The final column in Table 26.1 indicates the 12-month average for each contract.
Table 26.2 expresses each monthly price as a percentage of the annual average. These percentage
 figures are then averaged for each month to yield a seasonal index at the bottom of the table.
In calculating a seasonal index, it is wise to check for any extreme years that might distort the
results. The question of what constitutes an extreme year can only be answered subjectively. With
regard to the heating oil market from 1996 through 2015, one year stands out: 2008. As Table 26.1
shows, the December 2008 heating oil contract traversed an extraordinarily wide range. It is usually
best to exclude such uncharacteristic years when calculating a seasonal index, unless some adjustment
scheme is used to modify their exaggerated influence. However, there are no concrete rules, and the
ultimate decision must depend on the judgment of the researcher.
Although a sense of the seasonal pattern can be gained by examining the seasonal index at the bot-
tom of Table 26.2, a graphic presentation is far more convenient and informative. Figure 26.1 shows the
seasonal index, both with and without the inclusion of 2008. In this case, the extreme year does not have
a significant impact on the basic seasonal pattern. As is readily apparent, there is a seasonal tendency for
prices to reach relative highs around September
 –October and to bottom around December–January.
It is important to note the average percentage method does not remove any trend from the data.
Thus, what appears to be a seasonal pattern might partially reflect a long-term trend in prices. In fact,
392
table 26.1 December heating Oil Contract: average Monthly prices
Y ear of Contract
expiration Nova Dec Jan Feb Mar apr May Jun Jul aug Sep Oct Nov Dec–Nov avg
1996 0.507 0.515 0.510 0.509 0.527 0.543 0.538 0.543 0.572 0.601 0.672 0.711 0.701 0.579
1997 0.587 0.591 0.610 0.586 0.584 0.574 0.591 0.574


ppears to be a seasonal pattern might partially reflect a long-term trend in prices. In fact,
392
table 26.1 December heating Oil Contract: average Monthly prices
Y ear of Contract
expiration Nova Dec Jan Feb Mar apr May Jun Jul aug Sep Oct Nov Dec–Nov avg
1996 0.507 0.515 0.510 0.509 0.527 0.543 0.538 0.543 0.572 0.601 0.672 0.711 0.701 0.579
1997 0.587 0.591 0.610 0.586 0.584 0.574 0.591 0.574 0.567 0.573 0.575 0.600 0.564 0.582
1998 0.572 0.553 0.528 0.514 0.495 0.500 0.489 0.458 0.435 0.398 0.417 0.416 0.358 0.463
1999 0.444 0.418 0.411 0.386 0.427 0.469 0.466 0.475 0.535 0.570 0.609 0.589 0.641 0.500
2000 0.563 0.570 0.601 0.651 0.686 0.647 0.720 0.794 0.794 0.877 0.977 1.016 1.004 0.778
2001 0.771 0.725 0.722 0.750 0.734 0.754 0.803 0.789 0.733 0.760 0.739 0.643 0.568 0.727
2002 0.600 0.596 0.597 0.596 0.665 0.702 0.707 0.685 0.709 0.735 0.787 0.778 0.720 0.690
2003 0.669 0.702 0.748 0.805 0.779 0.722 0.738 0.781 0.808 0.841 0.780 0.842 0.841 0.782
2004 0.760 0.785 0.828 0.841 0.899 0.916 1.018 1.036 1.120 1.219 1.272 1.491 1.402 1.069
2005 1.282 1.189 1.225 1.324 1.533 1.622 1.498 1.655 1.789 1.956 2.047 1.992 1.716 1.629
2006 1.807 1.857 1.934 1.959 1.949 2.128 2.135 2.171 2.239 2.177 1.916 1.729 1.725 1.993
2007 1.940 1.943 1.788 1.861 1.947 2.004 2.011 2.072 2.156 2.065 2.192 2.352 2.604 2.083
2008 2.445 2.477 2.531 2.624 2.897 3.115 3.634 3.902 3.917 3.383 3.001 2.438 1.926 2.987
2009 2.140 1.695 1.710 1.481 1.528 1.575 1.685 1.961 1.810 1.951 1.818 1.963 1.985 1.763
2010 2.261 2.213 2.209 2.118 2.231 2.378 2.234 2.162 2.090 2.140 2.163 2.279 2.343 2.213
2011 2.441 2.521 2.700 2.984 3.115 3.299 3.123 3.027 3.076 2.987 2.937 2.904 3.059 2.978
2012 2.971 2.919 3.000 3.160 3.276 3.214 2.988 2.657 2.811 3.019 3.139 3.122 3.014 3.027
2013 2.966 2.974 3.015 3.065 2.977 2.912 2.875 2.911 2.983 3.073 3.047 2.982 2.950 2.980
2014 2.904 2.963 2.915 2.921 2.937 2.917 2.921 2.986 2.952 2.879 2.763 2.558 2.374 2.840
2015 2.375 2.073 1.813 1.903 1.897 1.922 2.015 1.959 1.791 1.583 1.613 1.547 1.452 1.797
aNovember of year preceding the contract year. This column is needed to calculate Table 26.3.
393
table 26.2 December heating Oil Contract: Monthly price as a percentage of the December–November average
Y ear of Contract
expiration Dec Jan Feb Mar apr May Jun Jul aug Sep Oct Nov
1996 88.98 88.23 88.03 91.14 93.87 92.90 93.92 98.87 103.79 116.20 122.85 121.21
1997 101.58 104.71 100.63 100.21 98.54 101.46 98.50 97.29 98.46 98.71 103.09 96.82
1998 119.36 113.98 111.00 106.75 107.83 105.46 98.76 93.94 85.97 89.97 89.80 77.18
1999 83.77 82.32 77.32 85.38 93.79 93.27 95.04 107.05 114.11 121.92 117.81 128.22
2000 73.26 77.23 83.67 88.15 83.09 92.53 102.08 102.05 112.71 125.60 130.58 129.07
2001 99.76 99.44 103.16 101.02 103.82 110.46 108.63 100.83 104.54 101.72 88.50 78.11
2002 86.40 86.59 86.45 96.42 101.80 102.44 99.28 102.74 106.53 114.13 112.80 104.43
2003 89.80 95.63 102.86 99.60 92.25 94.41 99.82 103.24 107.49 99.76 107.59 107.56
2004 73.41 77.43 78.72 84.08 85.72 95.26 96.92 104.80 114.03 119.00 139.49 131.12
2005 72.99 75.22 81.29 94.12 99.56 91.94 101.64 109.82 120.06 125.69 122.28 105.39
2006 93.16 97.01 98.28 97.76 106.75 107.12 108.92 112.35 109.23 96.14 86.73 86.54
2007 93.28 85.82 89.37 93.47 96.21 96.54 99.48 103.50 99.13 105.23 112.94 125.01
2008 82.92 84.73 87.84 96.98 104.28 121.65 130.65 131.13 113.26 100.47 81.63 64.47
2009 96.14 96.95 83.98 86.64 89.31 95.56 111.19 102.64 110.63 103.11 111.31 112.57
2010 100.00 99.80 95.71 100.82 107.42 100.93 97.69 94.44 96.68 97.71 102.96 105.84
2011 84.67 90.67 100.21 104.60 110.78 104.88 101.66 103.31 100.33 98.62 97.54 102.73
2012 96.44 99.13 104.41 108.23 106.17 98.74 87.79 92.87 99.76 103.71 103.16 99.58
2013 99.77 101.17 102.83 99.89 97.72 96.47 97.68 100.08 103.11 102.25 100.05 98.98
2014 104.33 102.61 102.82 103.38 102.69 102.83 105.12 103.91 101.36 97.29 90.06 83.59
2015 115.31 100.86 105.88 105.53 106.95 112.14 108.97 99.66 88.07 89.77 86.08 80.77
averages:
all Y ears 92.77 92.98 94.22 97.21 99.43 100.85 102.19 103.23 104.46 105.35 105.36 101.96
excl. 2008 93.28 93.41 94.56 97.22 99.17 99.75 100.69 101.76 104.00 105.61 106.61 103.93
394A COMPlETE GUIDE TO THE FUTURES MARKET
for data exhibiting a strong trend, the eff ect of the trend will often totally swamp any true seasonal
pattern. (By this we mean the seasonal pattern that remains after the trend has been removed from
the data.) An unadjusted seasonal index, such as the average percentage method, is relevant because
it more directly refl ects the past results of implementing a position on a given date and exiting it on
another given date. However, because secular trends may change, it can be argued that the detrended
seasonal index might be more relevant in refl ecting seasonal patterns. The next section describes one
method for deriving a detrended seasonal index.
 link relative Method
 The link relative method involves the following steps:
 1. Express each data value as a percentage of the previous month’s value.
 2. Average these percentage values for each month.
 3. Set the fi rst month’s value at 100.0 and reexpress all the other monthly averages as relative
percentages of the fi rst month’s value.
 4. Adjust the resulting values for trend.
 5. Multiply each of these values by the appropriate common factor so that the average monthly
seasonal index value equals 100.0.
 These steps will be clearer if we work through an example. Table 26.3 indicates each month’s
price as a percentage of the previous month’s price. (These fi gures are derived from Table 26.1 .) The
monthly averages for all these percentages are listed at the bottom of the table.
 FIGURE  26.1 December Heating Oil Contract Seasonal Index: Average Percentage
Method
 December Heating Oil Contract Seasonal Index: Average Percentage

395
table 26.3 December heating Oil Contract: Monthly average price as a percentage of the previous Month’s price
Y ear of Contract
expiration Dec Jan Feb Ma


e
monthly averages for all these percentages are listed at the bottom of the table.
 FIGURE  26.1 December Heating Oil Contract Seasonal Index: Average Percentage
Method
 December Heating Oil Contract Seasonal Index: Average Percentage

395
table 26.3 December heating Oil Contract: Monthly average price as a percentage of the previous Month’s price
Y ear of Contract
expiration Dec Jan Feb Mar apr May Jun Jul aug Sep Oct Nov
1996 101.51 99.16 99.77 103.53 103.00 98.97 101.10 105.26 104.98 111.95 105.73 98.66
1997 100.70 103.09 96.10 99.58 98.33 102.96 97.08 98.78 101.20 100.25 104.44 93.92
1998 96.74 95.50 97.39 96.17 101.01 97.80 93.65 95.11 91.52 104.65 99.82 85.94
1999 94.23 98.27 93.93 110.42 109.85 99.45 101.90 112.64 106.59 106.84 96.63 108.84
2000 101.21 105.43 108.33 105.35 94.26 111.37 110.31 99.97 110.45 111.43 103.97 98.84
2001 93.95 99.68 103.74 97.93 102.77 106.39 98.35 92.82 103.68 97.30 87.01 88.26
2002 99.38 100.23 99.83 111.53 105.59 100.63 96.91 103.49 103.68 107.13 98.84 92.58
2003 104.99 106.50 107.56 96.82 92.62 102.34 105.74 103.43 104.12 92.80 107.85 99.97
2004 103.24 105.47 101.67 106.81 101.95 111.13 101.74 108.14 108.81 104.36 117.22 94.00
2005 92.72 103.06 108.07 115.79 105.77 92.35 110.54 108.05 109.33 104.69 97.29 86.18
2006 102.77 104.13 101.31 99.48 109.20 100.34 101.69 103.15 97.22 88.02 90.21 99.78
2007 100.16 92.01 104.13 104.58 102.94 100.34 103.05 104.04 95.78 106.15 107.33 110.69
2008 101.31 102.18 103.67 110.41 107.52 116.65 107.40 100.37 86.38 88.71 81.24 78.98
2009 79.23 100.84 86.63 103.16 103.08 106.99 116.36 92.31 107.78 93.21 107.95 101.13
2010 97.90 99.80 95.91 105.33 106.55 93.96 96.79 96.67 102.38 101.06 105.38 102.79
2011 103.30 107.09 110.52 104.38 105.91 94.67 96.93 101.62 97.11 98.29 98.91 105.32
2012 98.24 102.79 105.32 103.66 98.10 92.99 88.92 105.79 107.41 103.97 99.47 96.52
2013 100.26 101.40 101.64 97.14 97.84 98.72 101.25 102.46 103.03 99.17 97.85 98.93
2014 102.05 98.35 100.21 100.54 99.33 100.13 102.23 98.85 97.55 95.98 92.57 92.82
2015 87.27 87.46 104.98 99.67 101.34 104.85 97.18 91.46 88.37 101.93 95.88 93.84
average 98.06 100.62 101.54 103.61 102.35 101.65 101.46 101.22 101.37 100.89 99.78 96.40
396
A Complete Guide to the Futures mArket
As the next step, in Table 26.4 we express each month’s value relative to the first month
(December), which is set at 100.0. Thus, since the average ratio of January to December prices
is 100.62 percent from Table 26.3, its value is set to 100.62 (i.e., 100.62 percent of 100.0).
Similarly, since the average ratio of February to January prices is 101.54 percent, the February
value would be set to 101.54 percent of 100.62 or 102.17. The value for March would be
103.61 percent of 102.17, or 105.86, and so on. Note that the entry for the second value of
December is equal to 98.06 percent of the November value (98.06 is the average December
value from Table 26.3).
The higher value for the second December entry reflects the trend in the data. T o remove this
trend, we must find the constant factor that will increase to 109.10 (the ratio of the second Decem-
ber value to the first) when multiplied by itself 12 times. In other words, we want to find a constant
monthly growth factor X. This can be expressed as X
12 = 109.10. The derivation of this value requires
the use of logarithms (readers unfamiliar with logarithms can skip to the immediately following
description of an alternative approach for detrending the data):
X
X
X
X
12 10 91
12 1 091
11 21 091
00 031
=
=
=
=
(. )
logl og(. )
logl og(. )
log.
/
552
00 03152 10 07284antilogo
fr ounded to1.0073.. ,=
In other words, (1.0073)12 = 1.091.
W e assume a constant growth trend. The first month’s (December) value will still equal 100.0; the
second month’s value will be divided by 1.0073; the third month’s value will be divided by (1.0073)2;
the fourth by (1.0073) 3, and so on. These calculations are illustrated in Table 26.5. The final month
(the second December entry) will be divided by (1.0073)12, thereby transforming its value to 100.0.
Since both December values are equal after the adjustment of the data by the constant growth factor,
the trend has been removed from the data.
alternative approach
The following steps, which do not require the use of logarithms, can be used to derive a reasonably
good approximation of the last column in Table 26.5.
table 26.4 December heating Oil Contract 1996–2015: Monthly average price as a percentage of the
prior December average price
Dec Jan Feb Mar apr May Jun Jul aug Sep Oct Nov Dec
100 100.62 102.17 105.86 108.35 110.14 111.74 113.10 114.65 115.67 115.42 111.26 109.10
397
SEASONAl ANAlySIS
 1. Find the difference between the two December values in Table 26.4 (9.10).
 2. Multiply this difference by 1/12 and subtract the product from the second month’s (January)
value (100.62 − 0.76 = 99.86).
 3. Multiply the difference found in step 1 by 2/12 and subtract the product from the third month’s
value. Multiply the difference found in step 1 by 3/12 and subtract the product from the fourth
month’s value. Continue this progression for the remaining months. Using this method, the
adjusted values would be:
Dec Jan Feb Mar apr May Jun Jul aug Sep Oct Nov
100 99.86 100.65 103.59 105.32 106.35 107.19 107.79 108.58 108.85 107.84 102.92
These approximated figures are very close to the precise adjusted values shown in Table 26.5.
For the sake of uniformity, it is desirable that the average of the monthly seasonal index values
equal 100, or equivalently, that the sum of the monthly index values equal 1200. Table 26.5 shows the
sum of the index values in this case is more than 1200, which makes it necessary to adjust the figures
by a multiplier:
Multiplier == 1200
1256 71 09 549. .
Dividing each of the values in Table 26.5 by 0.9549 produces the seasonal index values in Table 26.6,
which are plotted in Figure 26.2. The average percentage method and link relative method indices are
compared in Fi


Table 26.5 shows the
sum of the index values in this case is more than 1200, which makes it necessary to adjust the figures
by a multiplier:
Multiplier == 1200
1256 71 09 549. .
Dividing each of the values in Table 26.5 by 0.9549 produces the seasonal index values in Table 26.6,
which are plotted in Figure 26.2. The average percentage method and link relative method indices are
compared in Figure 26.3. Note there is a great deal of similarity between the two methods. The basic
table 26.5 trend adjustment for Monthly Index Values
Month Values from table 26.4 trend-adjustment Divisor
trend-adj. Divisor
Numerical equivalent adjusted Value
Dec 100 100
Jan 100.62 (1.007284)1 1.007284 99.89
Feb 102.17 (1.007284)2 1.014621 100.69
Mar 105.86 (1.007284)3 1.022012 103.58
Apr 108.35 (1.007284)4 1.029456 105.25
May 110.14 (1.007284)5 1.036954 106.21
Jun 111.74 (1.007284)6 1.044508 106.98
Jul 113.10 (1.007284)7 1.052116 107.50
Aug 114.65 (1.007284)8 1.059779 108.18
Sep 115.67 (1.007284)9 1.067499 108.36
Oct 115.42 (1.007284)10 1.075275 107.34
Nov 111.26 (1.007284)11 1.083107 102.73
total 1256.71
398A COMPlETE GUIDE TO THE FUTURES MARKET
 table 26.6 Seasonal Index for December heating Oil Contract Using the link relative Method
 Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
95.49 95.38 96.15 98.91 100.50 101.42 102.15 102.65 103.30 103.47 102.50 98.09
 FIGURE  26.2 December Heating Oil Contract Seasonal Index: link Relative Method
 December Heating Oil Contract Seasonal Index: link Relative Method
 FIGURE  26.3 December Heating Oil Seasonal Index: Comparison of Average Percentage
Method and link Relative Method
 December Heating Oil Seasonal Index: Comparison of Average Percentage

399
SEASONAl ANAlySIS
diff erence is that the average percentage method index refl ects the long-term trend, whereas the link
relative method does not. Both approaches indicate a relative low in the December–January period
and a relative high in September.
 Figures 26.4 through 26.9 Illustrate the seasonal graphs for specifi c contract months in several
futures markets, both unadjusted (average percentage method) and detrended (link relative method),
 FIGURE  26.4 December WTI Crude Oil Seasonal Index: Comparison of Average
Percentage Method and link Relative Method
 FIGURE  26.5 December E-Mini S&P 500 Seasonal Index: Comparison of Average
Percentage Method and link Relative Method
 December E-Mini S&P 500 Seasonal Index: Comparison of Average

400A COMPlETE GUIDE TO THE FUTURES MARKET
 FIGURE  26.6 December Gold Seasonal Index: Comparison of Average Percentage
Method and link Relative Method
 FIGURE  26.7 September Coff ee Seasonal Index: Comparison of Average Percentage
Method and link Relative Method

401
SEASONAl ANAlySIS
 FIGURE  26.8 November Frozen Concentrated Orange Juice Seasonal Index:
Comparison of Average Percentage Method and link Relative Method
 November Frozen Concentrated Orange Juice Seasonal Index:
 FIGURE  26.9 December Corn Seasonal Index: Comparison of Average Percentage
Method and link Relative Method
 December Corn Seasonal Index: Comparison of Average Percentage
based on data from 1996 through 2015 (with the exception of the E-mini S&P 500 in Figure 26.6 ,
which used data from 1998 through 2015).
 It should be stressed that seasonal patterns should never be used as the sole basis for making trad-
ing decisions, as they are only one infl uence and can easily be swamped by fundamental and technical
forces impacting the market.

403
Chapter 27
Markets are never wrong—opinions often are.
—Jesse Livermore
 ■ Evaluating Market Response for Repetitive Events
A market’s response to key fundamental developments can provide important clues about the prob-
able future price direction. When these developments are repetitive, such as the release of key eco-
nomic numbers or U.S. Department of Agriculture (USDA) reports, a systematic approach can be
used to analyze the implications of market response. The general analytic procedure would involve
the following steps:
 1. Identify the event to be studied (e.g., the Treasury market’s response to the monthly employ-
ment report).
 2. Construct a table comparing the market’s immediate reaction to a report’s release to subse-
quent price trends.
 3. Search for consistent patterns.
There is no single correct format for analyzing market response. The objective of this chapter is to
illustrate the analysis process rather than to provide specific market-response models for trading the
markets. The observed responses in the following examples are moderate and there is not enough data
to rule out that the results could simply be due to chance. The reader can apply a similar methodology
for analyzing market reaction for other situations that may be of interest.
Analyzing Market
Response
404
A Complete Guide to the Futures mArket
example a: t-Note Futures response to Monthly
U.S. employment report
The U.S. employment situation report released by the Bureau of Labor Statistics is the most closely
watched monthly economic release, capable of triggering volatile moves in a wide range of markets.
Let’s say our goal is to check whether the direction (and magnitude) of the price move in U.S. Trea-
sury futures on the day the monthly employment report is released is indicative of the price action
in subsequent weeks. In other words, we want to check the hypothesis that a “bullish” or “bearish”
market response to the employment report is an indicator of probable near-term price direction. W e
might proceed as follows:
 1. Determine the threshold to determine a bullish or bearish initial response to the employment report.
 2. Measure the market’s price action in the N days following employment report days that satisfy
this criterion.
Table 27.1 shows how the U.S. 10-year- T -note futures traded in the first 10 trading days (two
weeks) after the monthly employment report between 2006 and 2015 based on whether the move
from the cl


reshold to determine a bullish or bearish initial response to the employment report.
 2. Measure the market’s price action in the N days following employment report days that satisfy
this criterion.
Table 27.1 shows how the U.S. 10-year- T -note futures traded in the first 10 trading days (two
weeks) after the monthly employment report between 2006 and 2015 based on whether the move
from the close of the day prior to the report to the report day’s close was bullish or bearish. In this
case, a bullish initial response was defined as a closing gain (measured from the previous day’s close)
of 0.50 points (16/32) or more on the day of the employment report, while a bearish initial response
was defined as a 0.50-point or larger decline. (This nominal amount was selected solely for illustra-
tion purposes and has no special significance.)
Twenty-six employment report days fulfilled the bullish criteria from 2006 through 2015 (top half
of table), while 33 fulfilled the bearish criteria (bottom half of table). Table 27.1 shows the cumula-
tive average and median gains from the close of the employment report day to the closes of the next
10 days. For comparison, the table also includes the average price changes for all 1- to 10-day periods
during the 10-year analysis window . The table also shows the percentage of times the T -note futures
contract closed higher than the employment report day close after bullish and bearish initial responses
to the report, along with the percentage of higher closes for all 1- to 10-day periods. For example,
on the first day after bullish initial reactions to the employment report, 10-year T -note futures closed,
on average, –0.054 points lower (–0.039 points median), compared to an average 0.021-point one-
day gain for all days. The market closed higher 42.31 percent of the time one day after initial bullish
responses, compared to 51.93 percent for all days.
Because it is often easier to digest such data visually, Figure 27.1 graphs the results for the bullish
initial responses, while Figure 27.2 graphs the results for the bearish initial responses. Surprisingly,
the analysis suggests that, if anything, there was a tendency for T -note price action in the near-term
period following employment reports to move in the opposite direction of the market’s initial
response. Specifically, there seems to be a notable tendency for contrarian price action in the two
days following a bullish initial response to the unemployment report (after which trading was mixed),
while the price action was more consistently bullish after an initial bearish response. Figure 27.1
shows that after two days T -notes closed below the close of the employment report day 73 percent of
the time, with an average decline of 0.205 points (around 6/32nds). This observation suggests that
those seeking to enter a position in the direction of the market’s bullish response to the report might
405
AnALyzIng MARkeT ReSPonSe
 taBLe 27.1 10-Y ear t -Note Futures response to Monthly employment report: Cumulative Change as of
Indicated Day (2006–2015)
Bullish
26 instances Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10
Median post-report
Change
-0.039 -0.172 0.016 0.164 0.211 0.063 0.117 0.344 -0.055 0.117
average post-report
Change
-0.054 -0.205 -0.035 -0.030 0.119 0.214 0.093 0.189 0.082 0.111
average Change
all Days
0.021 0.042 0.063 0.084 0.105 0.126 0.147 0.168 0.188 0.208
higher Close than
report Day (% times)
42.31% 26.92% 50.00% 53.85% 65.38% 53.85% 53.85% 53.85% 46.15% 57.69%
all Days higher Close
(%times)
51.93% 53.85% 55.09% 55.24% 54.94% 55.04% 56.09% 57.14% 56.79% 56.79%
Bearish
33 instances Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10
Median post-report
Change
0.031 0.219 0.141 0.328 0.484 0.422 0.406 0.609 0.313 0.484
average post-report
Change
0.081 0.285 0.306 0.401 0.474 0.605 0.707 0.649 0.622 0.603
average Change
all Days
0.021 0.042 0.063 0.084 0.105 0.126 0.147 0.168 0.188 0.208
higher Close than
report Day (% times)
51.52% 57.58% 57.58% 63.64% 63.64% 72.73% 69.70% 66.67% 72.73% 63.64%
all Days higher Close
(%times)
51.93% 53.85% 55.09% 55.24% 54.94% 55.04% 56.09% 57.14% 56.79% 56.79%
 FIGURE  27.1
 10- y ear T -note after Bullish Initial Response to Jobs Report (Cumulative)
0.35
75%
50%
25%
0%
Post-report average
Post-report higher close
12 34 56 78 91 0
All days higher close
All days average
Post-report median
0.15
−0.05Gain/loss from report day close
% higher closes
Day
−0.25
406A CoMPLeTe gUIDe To THe FUTUReS MARkeT
be better oﬀ waiting a couple of days before entering a position. In contrast, Figure 27.2 highlights the
market’s tendency to move higher after bearish initial responses. It should be noted that the period
surveyed was one that witnessed a long-term uptrend. Therefore, the appropriate comparison is to
the corresponding changes for all days.
 Figures 27.3 and 27.4 present a slightly diﬀ erent perspective of the performance of 10-year T -note
futures after bullish and bearish initial responses to the monthly employment report. Instead of show-
ing the cumulative performance from the close of the employment report day, these charts show each
day’s gain or loss. Figure 27.3 highlights the negative average returns in the fi rst two days after bull-
ish initial responses, while Figure 27.4 shows a tendency for higher prices in the fi rst two days after
bearish report responses.
 In examining historical patterns (e.g., market response, seasonal tendencies), it is usually impos-
sible to say whether apparent proclivities refl ect true market biases (or ineﬃ ciencies) or whether
such results are strictly a function of chance. even clearly random events with a 50 percent expected
outcome will sometimes deviate signifi cantly from 50 percent simply by chance. For example, if you
fl ipped 10 coins 1,000 times, approximately 17 percent of the time you would get seven or more
heads. getting seve


pparent proclivities refl ect true market biases (or ineﬃ ciencies) or whether
such results are strictly a function of chance. even clearly random events with a 50 percent expected
outcome will sometimes deviate signifi cantly from 50 percent simply by chance. For example, if you
fl ipped 10 coins 1,000 times, approximately 17 percent of the time you would get seven or more
heads. getting seven or more heads on any individual toss of 10 coins certainly wouldn’t imply the
coins have a tendency to land on heads. Two factors should be considered in trying to assess whether
a past pattern might be meaningful rather than due to chance:
 1. Number of observations. The greater the number of observations, the more likely a past
pattern might be signifi cant.
 2. theoretical explanation. If there is a logical reason why a past pattern might have occurred,
it enhances the potential signifi cance of the observed tendency.
0.85
0.65
0.45
0.25
0.05
75%
50%
25%
0%
Post-report average
Post-report higher close
12 34 56 78 91 0
All days higher close
All days average
Post-report median
Gain/loss from report day close
% higher closes
Day
 FIGURE  27.2 10- y ear T -note after Bearish Initial Response to Jobs Report (Cumulative)
407
AnALyzIng MARkeT ReSPonSe
 FIGURE  27.3 10- y ear T -note after Bullish Initial Response to Jobs Report (Daily)
0.20
75%
50%
25%
0%
Post-report average
Post-report higher close
12 34 56 78 91 0
All days higher close
Post-report median
0.10
0.00
Gain/loss from prev. day
% higher closes
Day
−0.10
 FIGURE  27.4 10- y ear T -note after Bearish Initial Response to Jobs Report (Daily)
0.25
0.20
50%
25%
0%
Post-report average
Post-report higher close
12 34 56 78 91 0
All days higher close
Post-report median
0.15
0.10
0.05
0.00
−0.05Gain/loss from prev. day
% higher closes
Day
408A CoMPLeTe gUIDe To THe FUTUReS MARkeT
 FIGURE  27.5 e-Mini S&P Change 500 after Bullish Initial Response to Jobs Report
(Cumulative)
20
50%
25%
0%
Post-report average
Post-report higher close
1 23456789 10
All days higher close
Post-report median
All days average
15
10
5
0
−5 Gain/loss from report day close
% higher closes
Day
 example B: Stock Index Futures response to employment reports
 Stock index futures also are prone to volatile moves in response to monthly employment reports.
Figures 27.5 and 27.6 show the results of an analysis of the e-mini S&P 500 futures contract’s
performance in the fi rst 10 trading days following bullish and bearish initial responses to employment
reports from 2006 through 2015. In this case, however, bullish and bearish are defi ned not by the price
change on the report day, but rather the location of the closing price within the report day’s range:
 1. A close within the upper 25 percent of the day’s range is defi ned as a bullish initial response.
 2. A close in the bottom 25 percent of the day’s range is defi ned as a bearish initial response.
 of the 120 employment reports from 2006 through 2015, 42 satisfi ed the bullish response criteria,
while 26 satisfi ed the bearish response criteria. Figure 27.5 shows the e-mini S&P’s average and
median cumulative gain/loss from the close of bullish response report days to the closes of the next
10 consecutive days, while Figure 27.6 provides an analogous chart for bearish response days. The
most noticeable pattern is the tendency for follow-through weakness in the week following bearish
response days (Figure 27.6 ). The chart for bullish response days (Figure 27.5 ) is fairly inconclusive.
 Table 27.2 shows the results of a related analysis. In this case, an initial bullish reaction was defi ned
as a close 1.35 percent or more above the previous day’s close and a bearish reaction as a close
1.35 percent or more below the previous day’s close. Initial responses between these thresholds were
classifi ed as neutral. These initial responses were then compared to the subsequent moves from the
report day’s close to the close of the day immediately preceding the next month’s employment report
(approximately 20 days, but ranging from 18 to 25 days for any given month). In this example, the
cumulative price changes after bullish and neutral initial reactions were similar (and positive), while the
409
AnALyzIng MARkeT ReSPonSe
 FIGURE  27.6 e-Mini S&P 500 Change after Bearish Initial Response to Jobs Report
(Cumulative)
50%
75%
25%
0%
Post-report average
Post-report higher close
123456789 10
All days higher close
Post-report median
All days average
10
5
0
−5
−10Gain/loss from report day close
% higher closes
Day
 taBLe 27.2 e-Mini S&p 500 Cumulative Change in Month Following Intitial
response to employment report, 2006–2015
 Bullish Initial
response
 Bearish Initial
response
 Neutral Initial
response
 average 5.54 –16.05 7.88
 Median 18.13 –8.75 14.25
 % higher Close 64.29% 35.71% 65.91%
performance after bearish initial responses tended to be negative. Because there was a decisive uptrend
in place during the survey period, there is a bias toward getting bullish price behavior following any
defi ned event—a tendency refl ected by the bullish result following neutral response days. Therefore,
the bullish price action following bullish response days may be more a matter of refl ecting the prevailing
long-term trend than any meaningful pattern, particularly since the price responses following bullish
and neutral response days were similar. The negative price action following bearish response days, how-
ever, seems potentially more signifi cant since it runs counter to the prevailing long-term trend. Still,
even here, there is the caveat that the results are based on a small number of observations.
 Isolated events
 expectations are the key to evaluating market response for any single event. In other words, the failure
of a market to respond to a fundamental development as decisively as might have been anticipated
could provide an important signal regarding the market’s inherent strength or


ll,
even here, there is the caveat that the results are based on a small number of observations.
 Isolated events
 expectations are the key to evaluating market response for any single event. In other words, the failure
of a market to respond to a fundamental development as decisively as might have been anticipated
could provide an important signal regarding the market’s inherent strength or weakness.
410A CoMPLeTe gUIDe To THe FUTUReS MARkeT
 A classic example of this principle was the counter-to-anticipated response of the gold market dur-
ing the 1991 gulf War. As the United States’ January 17 deadline for starting air strikes approached
without any concessions from Iraq, gold prices fi rmed. The start of the air war during nighttime
hours in the United States saw gold surge to a three-month high of $410/oz. in the overnight mar-
ket. But this rally abruptly fi zzled, and gold prices began to sink rapidly. By the time the gold market
opened in the United States the next morning, prices were actually $28/oz. lower than the previ-
ous evening’s close. This extremely weak price action in response to an event that could have been
expected to rally prices—even allowing for what proved to be the market’s correct anticipation for a
quick U.S. victory—suggested that gold prices were vulnerable to further erosion. As can be seen in
Figure 27.7 , prices did indeed continue to slide in the ensuing months, falling to new contract lows.
 The basic principle is that a price response to an important event that is radically diﬀ erent from
what might normally have been anticipated may provide an important clue as to the market’s probable
near-term direction.
 Limitations of Market response analysis
 The following are some of the ambiguities that arise in conducting market response analysis:
 1. In any type of market response analysis, the answers we obtain are dependent on the param-
eters used in the analysis. For example, the various thresholds used to defi ne bullish and bearish
response days in this chapter were representative values selected to illustrate the analysis
process; they were not the result of any attempt to fi nd optimal defi nitions of what constitutes
strong (bullish) or weak (bearish) reactions. The choice of analysis parameters, which will often
 FIGURE  27.7 April 1991 gold
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.

411
AnALyzIng MARkeT ReSPonSe
be subjective, can have a large impact on the results—a reality that provides a strong argument
for analyzing a range of parameters.
 2. When dealing with market events that occur relatively infrequently, there is the problem of
determining the significance of results based on samples that might be too small (or spread out
across too long and varied an analysis period) to be considered statistically valid. For example,
some of the examples in this chapter were based on only 13 or 14 observations.
 3. Response patterns may shift over time. A market’s response to a particular report or event
might be consistent for an extended period during one type of economic environment or mar-
ket regime, but that tendency could diminish or disappear if conditions change—for example,
in the change from a rising interest rate environment to a declining rate environment.
In view of the foregoing limitations, market response patterns should be viewed as one potential
indicator of near-term market direction, which could be combined with other analysis to support a
trading opinion, as opposed to being used as a stand-alone market signal.

413
Chapter 28
Building a Forecasting
Model: A Step-by-
Step Approach
Economics as a positive science is a body of tentatively accepted generalizations about economic
phenomena that can be used to predict the consequences of changes in circumstances.
—Milton Friedman
B
ecause of the heterogeneous nature of commodity markets, there is no such thing as a standard
fundamental model. Among the key substantive characteristics that differentiate markets are de-
gree of storability, availability of substitutes, importance of imports and exports, types of government
intervention, and sensitivity to general economic conditions. Consequently, in contrast to technical
analysis, in which a specific system or methodology can often be applied to a broad spectrum of mar-
kets, the fundamental approach requires a separate analysis for each market.
The time-consuming nature of fundamental analysis makes it virtually impossible to cover a large
number of markets adequately using this approach. Thus, as a practical matter, a trader wishing to
employ fundamental inputs in trading decisions must resort to one of the following alternatives:
 1. Restrict fundamental analysis to a superficial examination of the key statistics in a broad range
of markets.
 2. Employ in-depth fundamental analysis for only a few markets and trade all other markets based
on technical input only.
 3. Rely on published fundamental analysis.
The first alternative is usually a poor compromise. Market knowledge based on a cursory exami-
nation of fundamentals is often worse than total ignorance. In fact, next to poor money management,
414
A Complete Guide to the Futures mArket
perhaps the most common reason that nonprofessional traders lose money in commodities is that
they base their trading decisions on superficial fundamental information (e.g., market blogs, online
forums, brokers' two-sentence market summaries). The analytic approach outlined in this chapter
implicitly assumes alternative 2. It is a good idea to start by analyzing and following only one or two
markets fundamentally, expanding this list only after all research ideas on previously chosen markets
have been investigated. The third alternative is a reasonable supplement to individual research, as long
as one is selective. Unfortunately, a significant portion of published research is analytically unsound.
However,