nd variable, then the use of nominal prices implicitly assumes that equivalent fundamental
conditions in two widely spaced years should result in approximately equal price levels. Obviously, this
assumption is wrong. All else being equal, inflation will result in considerably higher prices in the more
recent year. The subject of adjusting prices for inflation is covered in greater detail in Chapter 25.
The answers to questions 2 and 3 depend primarily on the particular price you wish to forecast.
Although this consideration is also a factor in answering question 4, the choice of the time period
should depend more heavily on the fundamental characteristics of the market. Of course, if the initial
choice is inappropriate, the misjudgment will become apparent in analyzing the regression results.
However, by giving some thought to the intrinsic market fundamentals before selecting the forecast
period, it is possible to minimize unnecessary trial and error in the regression-analysis process.
For example, in most agricultural markets, the statistical balance for a given season will have a far
greater impact on price levels during the first half of the season than on price levels during the second half.
This typical market behavioral pattern reflects the fact that by the second half of the season, the prevail-
ing fundamental situation is well-defined and frequently largely discounted. More often than not, major
price shifts during the latter part of a season reflect developments affecting new crop expectations (e.g.,
drought and freeze). Consequently, for a fundamental model that does not include new crop expecta-
tions as an explanatory variable, it would generally make more sense to select a price forecast period that
approximates the first half of the season rather than the full season. This approach does not mean that we
ignore the other six months. Rather, the implication is that it will be necessary to develop other models
to forecast prices in those months. For example, the latter months of a season might be grouped with
the early months of the following season in a model that employed new-crop statistics to forecast prices.
In some markets, intrinsic fundamental considerations will not dictate a specific observation
period. In such cases, the choice will involve only the time frame of the individual observations (e.g.,
annual, semiannual, quarterly, monthly).
1 Here a general rule might apply: start with the longest
period (i.e., annual or semiannual), and if the regression model is satisfactory, work toward a shorter
period. Although the shortest time frame projection is most useful for trading purposes, the difficulty
of forecasting is inversely proportional to the length of the time period. Also, the shorter the time
frame, the more likely the problem of autocorrelation. For example, in a monthly model there is a
high probability that a high positive residual in one month will be followed by a positive residual in
the next month. Thus, for monthly and even quarterly models, transformations to remove autocor-
relation may be necessary (e.g., first differences).
1 The choice of the length of the period must also be made for markets in which the structure of the model
depends on the forecast period. For example, a model based solely on old-crop statistics (i.e., a model that does
not incorporate new-crop expectations) could use a six-month forecast period (coinciding with the first half of
the season) or it could be applied to two separate three-month periods.
675
PRACTICAL CONSIDERATIONS IN APPLYING REGRESSION ANALYSIS
 ■ Selecting the Independent Variables
General Considerations
There is more to selecting the independent variables than choosing the factors that intuitively appear
to be good candidates for explanatory variables. Perhaps the pivotal question to be considered is
whether the regression equation is intended for explaining or forecasting the dependent variable.
Sometimes, a regression equation is only intended as an explanatory model. For example, a wheat
producer might be interested in determining the relationship between yield and the quantity of fertil-
izer applied. In this case, his goal is not to forecast yield, a projection that will also depend heavily on
other factors, such as weather conditions, but to understand the implications of various management
choices. Furthermore, he need not worry about estimating the independent variable (quantity of
fertilizer), since it is entirely under his control.
In contrast, most applications of regression analysis in the futures markets will be concerned with
forecasting. If an equation is intended primarily for prediction, it is critical to choose explanatory
variables that can be determined with relative reliability. For example, if we were to construct a cop-
per price-forecasting model in which the concurrent gross domestic product (GDP) was an impor-
tant input, the equation would be useless if GDP levels were no more predictable than copper prices,
even if R
2 = 1.00. Thus, in selecting independent variables, the researcher should keep in mind the
precision with which these variables can be estimated before the forecast period.
If they prove statistically significant, lagged variables are the ideal choice for explanatory variables.
A lagged variable is one whose value is determined during a period before the period for the corre-
sponding dependent variable. For example, the average GDP during the prior six months would be a
lagged variable. Thus, even if the lagged value of GDP were substantially less significant in explaining
copper prices than the concurrent value, it might still be a preferable choice.
Unfortunately, the analyst will rarely be lucky enough to construct a regression equation that uses
only lagged variables. Concurrent variables that can be forecast with reasonable accuracy provide an
acceptable alternative. In fact, some variables, such as population, can be f


ntially less significant in explaining
copper prices than the concurrent value, it might still be a preferable choice.
Unfortunately, the analyst will rarely be lucky enough to construct a regression equation that uses
only lagged variables. Concurrent variables that can be forecast with reasonable accuracy provide an
acceptable alternative. In fact, some variables, such as population, can be forecast with such accuracy
that they are similar to lagged variables. Other variables can be projected within a reasonable range. For
example, in the hog model, hog slaughter is much easier to project than hog prices, since it depends on
lagged variables (e.g., prior pig crop, market hogs at start of period). In short, the essential question to
consider is whether the values for a potential explanatory variable are known before the forecast period
or are at least substantially easier to project than the values for the dependent variable.
Another criterion in selecting the independent variables is that they should not be correlated,
in order to avoid the problem of multicollinearity. If several correlated variables seem to be good
choices for explanatory variables, they should be tested individually.
 ■ Should the Preforecast Period Price Be Included?
An important question to be decided in a price-forecasting equation is whether to include the pre-
forecast period (PFP) price as an explanatory variable. One reason for including the PFP price is that
676
Appendix F
it is usually an important factor. For example, consider the following two situations in which the PFP
price was not taken into account by the model:
Situation A Projected average price for forecast period = 60¢;
price on day before forecast period = 40¢.
Situation B Projected average price for forecast period = 60¢;
price on day before forecast period = 80¢.
W ould it be reasonable to expect the same price level in both cases? Definitely not! Some textbook
theories notwithstanding, in the real world, prices do not adjust instantaneously to changing funda-
mentals. In situation A, a major uptrend would be required for prices merely to reach the forecasted
equilibrium level. Such an advance will not occur overnight. Furthermore, it is not sufficient for
prices to reach 60¢ in order to achieve the projected 60¢ average. Prices would have to go far beyond
.60¢ in order to make up for all the days of sub-60¢ prices during the early part of the period. Simi-
larly, in situation B, prices would have to go far below 60¢ to achieve a 60¢ average. In practice, prices
may well reach 60¢ in both situations A and B, but the average price is likely to be well below 60¢ in
situation A and well above 60¢ in situation B.
The preceding example illustrates that the PFP price may often be an important explanatory
variable. Then why not always include it in the model? Ironically, the answer is that it may sometimes
be too good in explaining price behavior. In other words, if the PFP price swamps the effect of the
other independent variables, the price projection will primarily reflect current price levels. Thus, if
the PFP price accounts for a large percentage of the R
2, the model may be good at explaining prices,
but will be ineffective at predicting price changes, which after all is the primary goal in price projec-
tion. However, in some cases, the other independent variables may explain a major portion of total
variation, even when the PFP price is included. In these situations, including the PFP price may help
eliminate a significant portion of the unexplained variation that would exist if it were omitted, while
still yielding a model that is capable of predicting price changes.
The decision of whether to include the PFP price as an independent variable must be made empir-
ically on a case-by-case basis. A reasonable procedure would be to use a stepwise regression approach
(see the section titled “Stepwise Regression” in this appendix) both with and without including the
PFP price on the list of independent variables. Although the model that includes the PFP price will
always exhibit better summary statistics, it should only be chosen if the effect of the PFP price is sig-
nificant without being overwhelming.
 ■ Choosing the Length of the Survey Period
Ideally, it is desirable to use the longest feasible survey period, since more data points will increase the
accuracy of the regression statistics. However, in the real world, there is a tradeoff between the length
of the survey period and the relevance of the earliest data points to current conditions. For example,
it would be ludicrous to include data before 1973 in a fundamental forecasting model for currency
rates, since exchange rates were fixed before that point.
677
PRACTICAL CONSIDERATIONS IN APPLYING REGRESSION ANALYSIS
As the preceding example illustrates, fundamental considerations will often limit the number of
observations that can be included without distorting the model. Basically, the longest survey period
consistent with current market conditions should be used. Scatter diagrams for the dependent vari-
able plotted against each of the explanatory variables may be helpful in making this decision. It will
often be necessary to run several regressions for periods of different lengths in order to decide on the
optimum number of observations to be included. Occasionally, it may be possible to include earlier
nonrepresentative years through the use of dummy variables.
 ■ Sources of Forecast Error
In order to build the best model as well as understand its potential limitations, it is important to be
aware of the potential sources of forecast error. These include:
1. Random errors for true population regression. Any regression equation is only a simpli-
fication that cannot include all possible influences on the dependent variable. Thus, even if we knew the
true population regression equation, which we never do, and the explanatory variables were precisely

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limitations, it is important to be
aware of the potential sources of forecast error. These include:
1. Random errors for true population regression. Any regression equation is only a simpli-
fication that cannot include all possible influences on the dependent variable. Thus, even if we knew the
true population regression equation, which we never do, and the explanatory variables were precisely
determined, this source of error would still exist. In other words, this type of error can never be avoided.
2. Random errors in the estimated regression coefficients. Since the data used to run a
regression represents only a sample from the population, the estimated regression coefficients will
deviate from the true population values.
3. Regression equation may be misspecified. The regression model may not represent the
true underlying model because of the following reasons:
a. Omission of significant variables;
b. True model is nonlinear or wrong functional form is assumed in a linear transformation;
c. Error terms are autocorrelated
2.
4. errors in independent variable values. Often, the independent variables must themselves
be projected, thereby introducing another tier of potential forecasting error. Sometimes, unexpected
events (e.g., droughts, freezes, export embargoes) can result in the actual values of the explanatory
variables deviating sharply from the estimated levels. In these situations, the regression projections
can prove wide of the mark, even when the model would have provided an accurate forecast if the
input had been correct.
5.
data errors. Lagged variable data and the data used to forecast the independent variables may
be inaccurate because of sampling or compilation errors.
6. Structural changes. Structural change accounts for perhaps the most serious vulnerability
of the regression forecast. Regression analysis is a static approach to a dynamic process; that is, the
structure and behavior of a market are constantly changing. Thus, even if a model offers a good rep-
resentation of the past, it may fail to describe a market adequately in the future. Any major structural
change in a market can lead to large forecast errors.
2 Of course, conditions 3(a) and 3(b) could also result in autocorrelation; the implication here is autocorrelation
that exists without the presence of 3(a) and 3(b).
678
Appendix F
As an example, consider the plight of the unfortunate fundamental analyst using historical regres-
sions to forecast prices for the 1981–1982 period, when the unprecedented combination of severe
recession and high interest rates resulted in a dramatic downward shift in demand for many com-
modities. As a result, prices in a broad spectrum of markets declined to well below the levels that
might have been anticipated on the basis of fundamental models that worked well in prior years, but
did not include these effects. As a more recent example, the late 2008 financial crisis had such a huge
depressant impact on commodity prices across the board that virtually any viable fundamental model
for any commodity market would have been likely to yield price forecasts for the late 2008, early
2009 period that were far too high.
The preceding examples illustrated structural changes simultaneously affecting a broad range of
markets. A structural change can also be confined to a single market. One example of such a change
was the dramatic shift in corn usage for ethanol production. Corn use for fuel went from one-tenth
the feed-use level before 2000 to greater than feed usage by 2010.
It is important to realize the standard error measures in regression analysis only account for the
first two sources of error just listed. Perhaps even more sobering is the fact that with the exception of
a misspecified equation (3), all these sources or error are beyond the control of the analyst. However,
the potential variability attributable to errors in estimating the independent variables (4) can at least
be defined by allowing for a range of possibilities. For example, in addition to generating a price fore-
cast based on a set of best estimates for the explanatory variables, projections can also be derived for
sets of bearish and bullish assumptions. In this way, it is at least possible to gauge the potential impact
of inaccurate estimates for the independent variables. Furthermore, some solace can be drawn from
the fact that the various types of errors listed here are not necessarily cumulative; that is, they may
partly offset each other.
As a final word, it should be emphasized that this list of potential errors is not intended to discour-
age the potential user of regression analysis, but rather to instill a sense of realism in interpreting
regression results.
 ■ Simulation
As the previous section demonstrated, comparisons between the fitted values of the regression equation
and actual observations may severely understate potential forecasting errors. The process of determining
how forecasts based on the given model would have compared with reality is called simulation, which is an
extremely useful technique for testing a model under near real-life conditions. Simulation should only be
undertaken once the choice of a model has been finalized, or at least reduced to a small number of pos-
sibilities. Ideally, the simulation period should be long enough to include a variety of conditions (e.g., at
least one bull, one bear, and one neutral market in a price-forecasting equation).
For example, assume it is 2015 and we have decided the past 20 years of data are relevant to the
current market structure. Given the constraint that each forecast must be based on at least 10 years of
data, a 10-year simulation of a calendar-year forecast could be constructed as follows:
 1. Using only data available on January 1, 2005, derive a regression equation for the same model
for 1995 through 2004.
679
PRACTICAL CONSIDERATIONS IN APPLYING REGRESSION ANALYSIS
 2. Using only data a


e
current market structure. Given the constraint that each forecast must be based on at least 10 years of
data, a 10-year simulation of a calendar-year forecast could be constructed as follows:
 1. Using only data available on January 1, 2005, derive a regression equation for the same model
for 1995 through 2004.
679
PRACTICAL CONSIDERATIONS IN APPLYING REGRESSION ANALYSIS
 2. Using only data available on January 1, 2005, estimate the independent variables.
 3. Plug these values into the 1995–2004 regression equation to obtain a forecast for 2005.
 4. Repeat an analogous procedure for each subsequent year (2006–2014).
 5. Compare simulations to actual values and calculate the root mean square (defined later in this
section).
For a quarterly model, the simulation procedure would be analogous. However, with a quarterly
model, very little would be lost by revising the regression equation only once every four times (each
year) in order to reduce the amount of computation.
It may be instructive to compare the differences between the simulation forecasts and actual values
with the residuals of the current regression equation. Of course, the former will almost invariably
be higher, since simulation results are based on forecasts, while the regression equation is a best fit
of past values.
A measure that may be useful in comparing the simulation results of different models is the root
mean square (RMS):
rms =
−()
=
∑ YY
N
t
F
t
A
t
N 2
1
where
Yt
F = forecasted value of Y for period t
Yt
A = actual value of Y for period t
N = number of simulated observations
Note that the RMS calculation is analogous to the formula for the standard error of the regres-
sion (SER) (except for the number of degrees of freedom) and reflects the same underlying
meaning.
 ■ Stepwise Regression
Ideally, having selected a list of explanatory variables, regression equations would be generated for
each possible equation form. For example, given a dependent variable Y and three independent vari-
ables X
1, X2, and X3 there would be eight possible equations:
 1. Y vs. X1, X2, and X3 (all independent variables included)
 2. Y vs. X1, X2
 3. Y vs. X1, X3
 4. Y vs. X2, X3
 5. Y vs. X1
 6. Y vs. X2
 7. Y vs. X3
 8. YY= (no independent variables included)
680
Appendix F
Such a procedure, however, is not very efficient. The total number of possible equations doubles
with the addition of each independent variable (e.g., 16 for four variables, 32 for five).
Stepwise regression is a highly useful and efficient procedure for isolating and providing summary
results for the most statistically interesting equations. There are two basic types of stepwise regression:
 1. Forward selection. The program selects the single independent variable that provides the
highest r2 value to form the first equation. Explanatory variables are then added one at a time
to form subsequent equations, with the choice depending on which variable will result in the
highest R
2 equation. The program terminates with an equation that includes all of the specified
explanatory variables.
 2. Backward elimination. The program begins by listing the equation that includes all the speci-
fied independent variables. The program then deletes the variable with the lowest t value to form
the second equation. Subsequent equations are formed by continuing to delete variables, one at a
time, with the elimination decision dependent on which remaining variable has the lowest t value.
The two methods will not necessarily yield the same subset of equations. Overall, the backward
elimination process is preferable, particularly if the PFP price is one of the explanatory variables. In
the forward selection process, the PFP price will usually be chosen first, since it is likely to explain
more variation in the dependent variable than any other single variable. However, once more explana-
tory variables are added, the significance of the PFP price may drop sharply, as other variables in
combination explain a portion of the variation originally attributed to the PFP price. Thus, in the
backward elimination process, at some stage the PFP price might have a lower t value than any of the
remaining variables.
Although the PFP price is effective as an explanatory variable, its inclusion may yield equations
that are less useful for forecasting purposes. With the forward selection process, there is a higher
probability that all of the chosen equations will include the PFP price, since the first variable chosen
remains in all subsequent equations.
Once the stepwise regression results have been analyzed, detail should be generated for the equa-
tions that appear to be the most promising.
3 Minimum detail would include a listing of actual observa-
tions, predicted values, and residuals. Residual plots should also be constructed for these equations
and modification implemented as suggested by these plots.
 ■ Sample Step-by-Step Regression Procedure
There is no single right order in which to perform the various elements of regression analysis. The
following order merely represents one suggested sequence:
 1. Determine the dependent variable.
 2. List all possible choices for explanatory variables.
3 The summary statistics would not be the only criteria for making this choice. For example, an equation that did
not include the PFP price as a dependent variable might be preferable to one that did if the summary statistics
were only modestly less favorable.
681
PRACTICAL CONSIDERATIONS IN APPLYING REGRESSION ANALYSIS
 3. Choose a subset of these (usually no more than five), taking care to avoid selecting correlated
independent variables. Scatter diagrams can be used as an aid in this selection process.
 4. Choose the length of the survey period. Scatter diagrams can also be used as an aid in this step.
 5. Apply a stepwise regression program to the selected variables.
 6. Analyze the results by examining the various key statistics: t values, SER, CR2, F, and DW. If t


aking care to avoid selecting correlated
independent variables. Scatter diagrams can be used as an aid in this selection process.
 4. Choose the length of the survey period. Scatter diagrams can also be used as an aid in this step.
 5. Apply a stepwise regression program to the selected variables.
 6. Analyze the results by examining the various key statistics: t values, SER, CR2, F, and DW. If there
is any evidence of multicollinearity, check out this possibility and rerun stepwise regression
with a different set of variables if necessary.
 7. Generate detail and construct residual plots for the most promising equations in the stepwise
regression run.
 8. Check residual plots for outliers. Decide whether outliers should be deleted.
 9. Check residual plots for autocorrelation.
 10. If outliers or autocorrelation exist, try to correct through the addition of variables or transfor-
mations to achieve linearity.
 11. If autocorrelation is still a problem, try a transformation to eliminate autocorrelation (e.g., first
differences).
 12. Check the correlation matrix or R 2 values for various combinations of equations based on the
explanatory variables in order to verify that multicollinearity is not a problem.
 13. Repeat steps 3–12 for other selections of explanatory variables.
 14. Optional: After narrowing the number of possible models to three or less, generate simulations.
 ■ Summary
Regression analysis is an extremely efficient and powerful tool; it is a virtual necessity for fundamen-
tal analysis. The foregoing appendices were intended to provide the necessary background to inter-
pret and analyze the results available on standard regression software packages. Regression analysis
provides the means for precisely answering the question: What is the approximate equilibrium level,
given the specified conditions and assumptions? The italicized qualification is essential. There is a danger
of viewing regression results with great rigidity because of the scientific manner in which they are
derived. This would be a mistake. As explained in the section “Sources of Forecast Error,” a variety of
factors are capable of causing the regression projection to be inaccurate. Therefore, the trader must
always be open to the possibility the regression forecast might be wrong. However, given such a sense
of realistic awareness, fundamental regression models can provide valuable insight into a market’s
current state and its potential future direction.

683
REFERENCES AND RECOMMENDED READINGS
W onnacott, R. J., and T . H. W onnacott. Econometrics (New Y ork, NY: John Wiley & Sons, 1980). This is an
extraordinarily lucid treatment of an abstruse subject and is an excellent choice for readers interested in
a more in-depth understanding of regression analysis. One of the outstanding features of this book is that
it is divided into two separate parts, which cover essentially the same material but on different levels of
difficulty. As a result, Part I, which provides a comprehensive and insightful overview of the key concepts of
regression analysis, is fully accessible to a reader with only limited mathematical background.
Chatterjee, Samprit, and Ali S. Hadi. Regression Analysis by Example, 5th edition (New Delhi: Wiley India, 2012).
This may be the best book available on the practical application of regression analysis. As promised in the
title, the essential concepts are demonstrated by example. Perhaps the book’s best feature is its thorough
exposition of the use and interpretation of residual plots, an extremely effective yet easy-to-apply method
for analyzing regression results.
Pindyck, R. S., and D. L. Rubinfeld. Econometric Models and Econometric Forecasts, 4th edition (New Y ork, NY:
McGraw-Hill/Irwin, 1997). The first of the three sections in this book covers single-equation regression
analysis. (The other two sections are Multi-Equation Simulation Models and Time Series Models.) This book
offers a clear exposition of theoretical concepts, as well as many useful insights into the practical application
of regression analysis. Readers with limited mathematical background will find the presentation more
difficult than Part I of W onnacott and W onnacott.
Makridakis, S., and S. C. Wheelwright. Forecasting Methods and Applications, 3rd edition (New Y ork, NY: John
Wiley & Sons, 1997). This text provides a broad overview of forecasting techniques, with regression
analysis accounting for one of six sections. The presentation is aimed at an audience interested in practical
applications rather than theory. This book is clearly written, covers a wide range of topics, and provides a
plethora of examples to illustrate the discussion.
Freund, J. E., and F. J. Williams. Elementary Business Statistics: The Modern Approach, 6th sub edition (Upper Saddle
River, NJ: Prentice Hall College Div ., 1992). This book provides a good general overview of elementary
statistics for the nonmathematical reader. The text is clearly written and replete with examples.
Kimble, G. A. How to Use (and Misuse) Statistics (Englewood Cliffs, NJ: Prentice-Hall, 1978). This introduction
to elementary statistics is written with style and a sense of humor. Although it may be hard to believe, this is
one statistics book that can actually be read for entertainment value alone.

685
Achievement, elements of, 586–587
Acreage figures, 355
Action, taking, 583
Actual contract series, 279–280
Adjusted R
2, 642
Adjusted rate mortgages (ARMs), 423, 424
Advice, seeking, 580
Agricultural markets. See also U.S. Department of
Agriculture (USDA)
acreage figures, 355
cattle (see Cattle)
corn (see Corn)
cotton (see Cotton)
grain prices and, 351
hogs (see Hog production)
production costs and, 351
seasonal considerations and, 356
wheat (see Wheat market)
AMR. See Average maximum retracement (AMR)
Analogous season method, 374
Analysis of regression equation:
autocorrelation and (see Autocorrelation)
dummy variables and, 659–


Agriculture (USDA)
acreage figures, 355
cattle (see Cattle)
corn (see Corn)
cotton (see Cotton)
grain prices and, 351
hogs (see Hog production)
production costs and, 351
seasonal considerations and, 356
wheat (see Wheat market)
AMR. See Average maximum retracement (AMR)
Analogous season method, 374
Analysis of regression equation:
autocorrelation and (see Autocorrelation)
dummy variables and, 659–663
Durbin-Watson statistic, as measure of
autocorrelation, 652–654
heteroscedasticity and, 672–673
missing variables, time trend and, 655–658
multicollinearity and, 663–666
outliers and, 649–673
residual plot, 650–651
topics, advanced, 666–671
a priori restriction, 660
Arbitrage, pure, 530
ARMs. See Adjusted rate mortgages (ARMs)
At-of-the-money call, buying, 555
At-of-the-money options
definition of, 480
delta values and, 485
ATR. See Average true range (ATR)
Autocorrelation:
definition of, 651
Durbin-Watson statistic as measure of, 652–654
implications of, 654–655
transformations to remove, 670–671
Availability of substitutes, 361
Average maximum retracement (AMR), 331
Average parameter set performance, 311
Average percentage method, seasonal index,
391–394
Average return, 323, 326
Average true range (ATR), 262, 463
Backward elimination, stepwise regression, 682
Bad luck insurance, 257
Balanced spread, 455
Balance table, 373–374
Bar charts, 35–39
Bear call money spread, 535–538
case 1: short call with lower strike price/long call
with higher strike price, 535–536
case 2: short call with lower strike price/long call
with higher strike price, 537–538
Index
686
Index
Bullishness:
bullish put trade, 477
fundamentals and, 349
market response analysis and, 404, 405
Bullish T exas option hedge, 517–519
Bull market:
flags, pennants and, 131–132
intramarket spreads and, 460
run days in, 118
spread trades and, 443
thrust days and, 116, 117
Bull put money spread:
case 1: long put with lower strike price/short put
with higher strike price, 538–539
case 2: long put with lower strike price/short put
with higher strike
price, 540
“Bull trap”:
about, 205–211
confirmation conditions, 208, 209
Butterfly spread, 542
Buy and sell signals, trend-following systems and,
252
Buy hedge, cotton mill, 12–13
Call options, 477
Calmar ratio, 331
Calmness, 585
Cancel if close order. See CIC (cancel if close) order.
Candlestick charts, 43–44
“real body,” 43, 44
“shadows,” 43
Carrying-charge markets, 282
Carrying charges, limited-risk spread and, 446,
447–448
Carryover stocks, 355, 432–433
Case-Shiller Home Price Index, 423, 424
Cash settlement process, 4
Cash versus futures price seasonality, 389–390
Cattle:
cattle-on-feed numbers, 352–354
futures, 348, 385
inflation and, 385
production loss, 351
spread trades and, 444–445
Central limit theorem, 609–612
Change of market opinion, 204
Bearishness:
bearish put trade, 477
fundamentals and, 349
market response analysis and, 404, 406
Bearish T exas option hedge, 519–520
Bear market:
of 1980-1982, 366–367
flags, pennants and, 133–134
run days in, 118, 119
spread trades and, 443
thrust days and, 116
“Bear trap”:
about, 205–211
confirmation conditions, 208, 210
Beat the Dealer, 587
Bell-shaped curve, 601
Benchmark, 327
Bernanke, Ben, 431–432
Best fit, regression analysis and, 591–593
deviations, 591–592
least-squares approach, 592–593, 594
“Best linear unbiased estimators” (BLUE), 621
Bet size, variation in, 581
“Black box” system, 576
Blind simulation approach, system optimization, 311
BLASH approach, 27–28
BLUE. See “Best linear unbiased estimators” (BLUE)
Bottom formations. See T op and bottom formations
Bowe, James, 482–483
Box size, 42
Breakout(s), 86–89
confirmation of, 86
continuation patterns and, 180–181
counter-to-anticipated, flag or pennant, 219–222
definition of, 33
downside, 87, 88
false signals for, 151, 153
false trend-line, 211–213
flags, pennants and, 128
opposite direction breakout of flag or pennant
following normal, 222–225
upside, 87, 89
winning signals for, 152, 153
Breakout systems, 243–244
British pound (BP), intercurrency spreads and,
472–473
Bull call money spread, 534–535
687
Index
Comfortable choices, trading principles
and, 584
Comfort zone, trading within, 577
Commissions, 19
Commodities:
bearing little or no relationship to general rule,
444–445
conforming to inverse of general rule, 444
demand curves and, 361
general spread trade rule and, 443–444
intercommodity spread and, 441–442
nonstorable, 351, 360, 444
perishable, 360, 364
Commodities, 357
Commodity T raders Consumers Report, 434
Commodity trading advisors (CTAs), 23, 578
Comparing indicators, 157–165
difference indicators, 158, 159
indicator correlations, 161–162, 163
popular comparisons, 164
Comparisons:
nominal price levels, 355
one-year, 350
two managers, 320–322
Compounded return, 323
Computer testing of trading systems. See T esting/
optimizing trading systems
Confidence, 579–580
Confidence interval(s), 612–614
for an individual forecast, 627–629
multiple regression model and, 642
Confirmation conditions, 247–250
bull or bear trap, 208
pattern, 249, 250
penetration as, 248
time delay and, 248–249
Confirmation myth, 170
Congestion phases. See Continuation patterns
Consistency, 582
Constant-forward (“perpetual”) series, 281–282
Consumer price index (CPI), 383
Consumption:
definition of, 363
demand and, 357, 363–366
price and, 364
as proxy for inelastic demand, 370
Contingent order, 18
Chart(s):
BLASH approach, 27–28
equity, 566
linked-contract (see Linked-contract charts)
Random Walkers and, 29–34
types of (see Chart types)
Chart analysis, 149–154
confirmation conditions and, 150
false breakout signals, 151, 153
long-term chart, 152, 154
most important rules in, 205–231
spread trades and, 449
trading range and, 150
winning breakout signals, 152, 153
Chart-based objectives, 189
Chart patterns, 109–147
continuation patterns, 123–134
flags and pennants (see Flags and pennants)
head and shoulders, 138–141
one-day patterns (see One-day patterns)
reversal days, 113–116, 147
rounding


breakout signals, 151, 153
long-term chart, 152, 154
most important rules in, 205–231
spread trades and, 449
trading range and, 150
winning breakout signals, 152, 153
Chart-based objectives, 189
Chart patterns, 109–147
continuation patterns, 123–134
flags and pennants (see Flags and pennants)
head and shoulders, 138–141
one-day patterns (see One-day patterns)
reversal days, 113–116, 147
rounding tops and bottoms, 141–143
run days, 116, 118–119
spikes (see Spikes)
thrust days, 116, 117
T op and bottom formations (see T op and bottom
formations)
Triangles (see Triangles)
wedge, 146–147
wide-ranging days (see Wide-ranging days)
Chart types, 35–44
bar charts, 35–39
candlestick charts, 43–44
close-only (“line”) charts, 40–42
linked contract series: nearest futures versus
continuous futures, 39–40
point-and-figure charts, 42–43
CIC (cancel if close) order, 188
Close-only (“line”) charts, 40–42
CME/COMEX contract, 459
Cochrane-Orcutt procedure, 671
Code parameter, 293
Coefficient of determination (r
2), 630–633
Coffee:
intercommodity spreads and, 453–454, 456
seasonal index and, 400
spread trade example, 445–446
688
Index
Countertrend systems, 254–256
contrary opinion, 256
definition of, 236
fading minimum move, 255
fading minimum move with confirmation delay, 255
general considerations, 254–255
oscillators, 255
types of, 255–256
Countertrend trade entry signals, 182
Covered call write, 526–527
Covered put write, 527–528
CPI. See Consumer price index (CPI)
CR
2 (corrected R2), 642–643
CRB Commodity Yearbook, 414
Credit spread, 535
Crop reports, 434
Crop years, intercrop spread and, 441
Crossover points, moving averages and, 182
Crude oil market. See also WTI crude oil
money stop and, 185
poor timing and, 425
seasonal index and, 399
CTAs. See Commodity trading advisors (CTAs)
Currency futures, 471–476
intercurrency spreads, 471–473
intracurrency spreads, 473–476
Curvature, breaking of, 229, 230
Daily price limit, 8–9
Data errors, 679
Data insufficiency, conclusions and, 357
Data vendors, futures price series selection and, 287
Day versus good-till-canceled (GTC) order, 16
Degrees of freedom (df), 615, 640, 644
Deliverable grade, 9
Delivery, 4
Delta (neutral hedge ratio), 484–485
Demand:
consumption and, 357, 363–366
definition of, 359–362, 363
elasticity of, 361–362
highly inelastic, 370–371
incorporation of (see Incorporation of demand)
increase in, 364
inflation and, 355
price and, 362–363
quantifying, 362–363
stable, 368
Continuation patterns, 123–134
flags and pennants (see Flags and pennants)
trading range breakouts and, 180–181
triangles (see Triangles)
Continuous (spread-adjusted) price series,
282–285
Continuous distribution, 600–601
Continuous futures. See also Nearest vs. continuous
futures
price gaps and, 282
rule of seven and, 194–196
Continuous futures charts:
creation of, 47
measured moves and, 190–193
nearest futures vs., 39–40
Continuous parameter, 292–293
Contract months, 5, 8
Contract rollovers. See Rollover dates
Contract size, 5
Contract specifications:
about, 5–9
sample, 6–7
Contrary opinion, 203–204, 256
Conversion, 530
Copper:
inflation and, 385
price-forecasting model, 366–367
price moves and, 428, 429
Corn:
ethanol production and, 680
intercommodity spreads and, 457–459
major resistance area and, 427
price movements and, 430
production, 348
seasonal index and, 401
unexpected developments and, 419,
420, 421
Corrected R
2 (CR2), 642–643
Correlation coefficient (r), independent variables
and, 665–666
Correlation matrix, 666
Costs. See also Carrying charges
production, price declines and, 351–352
transaction, 295–296, 313
Cotton:
carryover and, 432–433
unexpected developments and, 418
yields, 355
689
Index
Dummy variables, 659–663
Durbin-Watson statistic, as measure of
autocorrelation, 652–654
Eckardt, Bill, 578, 584
Edge, having an, 576
Efficient market hypothesis, 428, 431
Elasticity of demand, 361–362
Elementary statistics, 597–618
central limit theorem, 609–612
confidence intervals, 612–614
measures of dispersion, 597–599
normal curve (Z) table, reading, 604–606
population mean, 607
populations and samples, 606
probability distributions, 599–604
sampling distribution, 608–609
standard deviation, 599, 607
t-test, 614–618
E-Mini Dow:
descending triangle, 127
futures, uptrend line, 60
intermarket stock index spreads, 461–470
E-Mini Nasdaq 100:
double bottom, 135, 137
downtrend lines and, 67
flags and pennants, 130
intermarket stock index spreads, 461–470
uptrend lines and, 59, 61
wide-ranging down bar, 123
E-Mini S&P 500:
intermarket stock index spreads, 461–470
market response analysis and, 408, 409
options on futures and, 482, 484
price envelope bands and, 107, 108
seasonal index and, 399
trend lines and, 74
upthrust/downthrust days and, 117
Employment report:
stock index futures response to, 408–409
T -Note futures response to monthly, 404–407
ENPPT . See Expected net profit per trade (ENPPT)
Equal-dollar-spread ratio, 472
Equal-dollar-value spread, 455–460
Equally weighted term, 453
Equilibrium, 363, 365
Equity change, intercurrency spreads and, 472–473
Equity chart, 566
Demand curve, 359, 361
Demand-influencing variables, 368–370
DeMark, Thomas, 66, 69, 199
DeMark sequential, 199–203
Dependent variable, determining, 675–676
Detrended seasonal index, 394
Deviation:
definition of, 623
total, 630–631
Diagonal spread, 542
Diary, maintaining trader’s, 565
Difference indicators:
Close – Close vs. Close – MA, 158
ratio versions, 159
Discipline, 578
Discrete parameter, 293
Discrete variable, 600
Discretionary traders, losing period adjustments,
562–563
Disloyalty/loyalty, 583–584
Dispersion, measures of, 597–599
Disturbance, definition of, 623
Diversification:
planned trading approach and, 560, 561–562
trend-following systems and,
256–258
Dividends, 462
Dollar:
equal-dollar-value spread, 455–460
intercurrency spreads and, 471
price, 383
Dollar value, option premiums and,
477–478
Double top, penetration of, 227–229
curvature, breaking of, 229
Double tops and bottoms:


–584
Dispersion, measures of, 597–599
Disturbance, definition of, 623
Diversification:
planned trading approach and, 560, 561–562
trend-following systems and,
256–258
Dividends, 462
Dollar:
equal-dollar-value spread, 455–460
intercurrency spreads and, 471
price, 383
Dollar value, option premiums and,
477–478
Double top, penetration of, 227–229
curvature, breaking of, 229
Double tops and bottoms:
double bottom, 137–138
double top, 136
triple top, 129
Down run day, 118, 268
Downthrust day, 116
Downtrend channels, 62, 63
Downtrend lines:
definition of, 57
examples of, 59, 61, 65
false breakout signals, 211, 213
Driehaus, Richard, 578, 585
Druckenmiller, Stanley, 581, 584
690
Index
expectations, ignoring, 355–356
improper influences, 352–355
lack of perspective, 351
nominal price levels, comparing, 355
one-year comparisons, 350
price declines/cost of production, 351–352
prices, target levels and, 356–357
relative time considerations, ignoring, 351
seasonal considerations, ignoring, 356
short scenes and, 347–349
using fundamentals for timing, 350
viewing fundamentals in a vacuum, 349
viewing old information as new , 349–350
False trend-line breakouts, 211–213
Faulkner, Charles, 585, 586
Fill-or-kill (FOK) order, 18
Filter, trend-following systems and, 250–251
Financial crisis of 2008, 323, 423, 680
First notice day, 9
Fixed or nonoptimized parameter, 293
Flags and pennants:
bearish signal, 133–134
breakout from, 128
bullish signal, 131–132
counter-to-anticipated breakout of, 219–222
E-mini Nasdaq 100, 130
Euro Schatz continuous futures, 130
main trend and, 131
natural gas continuous futures, 128
opposite direction breakout of, following normal
breakout, 222–225
soybean continuous futures, 129
stop-loss points and, 184–185
wheat, 129
FCOJ. See Frozen concentrated orange juice (FCOJ)
FOK. See Fill-or-kill (FOK) order
Forecast error, regression analysis and,
679–680
Forecasting model, building, 413–415
Forward selection, stepwise regression, 682
Fourteen fallacies. See Fallacies
Free markets, 357
Frozen concentrated orange juice (FCOJ):
crop reports and, 434
seasonal index and, 401
unexpected developments and, 418, 419
F-test, 643–644
Full carry, 446–447, 448
Equity retracements, dampened, 257
Error, definition of, 623
Error of the mean, 628
Error of the slope, 628
Ethanol production, 680
Euphoria, 585
Eurocurrency rates, 473
Events, pivotal, 422
Excess return, definition of, 323
Exchange, 5
Exercise price, 477
Exit points, planning time routine and, 563
Expectations:
ignoring, 355–356
new-crop, 381
role of (see Role of expectations)
seasonal analysis and, 390
Expected gain, 550, 551
Expected net profit per trade (ENPPT), 560
Expiration date, 477
Explained variation, 630
Exponentially weighted moving average (EWMA),
239–240
Exponential moving average (EMA), 165–167
Exposure, leverage and, 322
Extrapolation, 630
Fabrication, 313
Fading minimum move, 255
with confirmation delay, 255
Failed signals:
about, 205, 206
bull and bear traps, 205–211
curvature, breaking of, 229
false trend-line breakouts, 211–213
flag or pennant, counter to anticipated breakout,
219–222
flag or pennant, opposite direction breakout
following normal breakout, 222–225
future reliability of, 229–231
spike extremes, return to, 213–216
top and bottom formations, penetration
of, 225–229
wide-ranging day extremes, return
to, 216–218
Fallacies, 347–357
data insufficiency, conclusions and, 357
demand/consumption, confusion of concepts, 357
691
Index
Generic trading systems:
breakouts (see Breakout systems)
moving averages and, 237–243
Gold:
fundamental analysis and, 347, 371
futures (see Gold futures)
intramarket stock index spreads and, 461
market response analysis and, 410
prices, 284
seasonal index and, 400
spot, buying, 555
Gold futures:
buying, 555
volume shift in, 10
Gold/silver spread, 454
Good-till-canceled (GTC) orders. See GTC orders
Government regulations, potential impact
of, 415
Government reports, unexpected developments
and, 420
GPR (gain-to-pain ratio), 328–329
Grain prices, 351
Great Recession, 423
Gresham’s law of money, 312
Grinder, John, 586
Gross domestic product (GDP):
deflator, 383
independent variables and, 677
GTC orders:
about, 16
order placement and, 568
stop-loss points and, 183, 188
trade exit and, 569
Hard work, skill versus, 576–577
Head and shoulders:
about, 138–141
failed top pattern, 227–229
Heating oil:
alternative approach, 396–397
average percentage method, 391, 392–394,
398
link relative method, 394–396, 398
Hedge, definition of, 517
Hedge ratio, neutral (delta), 484–485
Hedging, 11–13
applications, 554–555
buy hedge, 12–13
Fundamental analysis:
about, 16
analogous season method, 374
balance table, 373–374
danger in using, 417
discounting and, 428–430
expectations, role of, 379–381
fallacies. see Fallacies
forecasting model, building, 413–415
gold market and, 371
index models, 376–377
inflation, incorporation of, 383–388
long-term implications versus short-term
response, 432–435
market response analysis (see Market response
analysis)
money management and, 426–427
“old hand” approach, 373
pitfalls in, 418–426
reasons to use, 427–428
regression analysis, 374–375
seasonal analysis and, 389–401
spread trades and, 449
supply-demand analysis, 359–371
technical analysis and, 21–24, 417–418, 426–427
trading and, 417–435
types of, 373–377
FundSeeder.com, 343
Futures markets, nature of, 3–4
Futures price series selection, 279–288
actual contract series, 279–280
comparing the series, 285–287
constant-forward (“perpetual”) series, 281–282
continuous (spread-adjusted) price series, 282–285
nearest futures, 280
Gain(s):
expected, 550, 551
maximization of, 583
Gain-to-pain ratio (GPR), 328–329
Gardner, John, 314
GDP . See Gross domestic product (GDP)
General rule, spreads, 443–445
about, 443
applicability and nonapplicability, 443–444
commodities bearing little or no relationship to,
444–445
commodities conforming to inverse of, 444
692
Index
Intercurrency spreads, 471–473
equity change and, 47


ures, 280
Gain(s):
expected, 550, 551
maximization of, 583
Gain-to-pain ratio (GPR), 328–329
Gardner, John, 314
GDP . See Gross domestic product (GDP)
General rule, spreads, 443–445
about, 443
applicability and nonapplicability, 443–444
commodities bearing little or no relationship to,
444–445
commodities conforming to inverse of, 444
692
Index
Intercurrency spreads, 471–473
equity change and, 472–473
reasons for implementing, 471–472
Interest rate differentials, intracurrency spreads and,
473, 476
Interest rate parity theorem, 475
Interest rate ratios, intracurrency spreads
and, 475
Interest rates:
option premiums and, 482–483
recession and, 367
Intermarket spreads, 442, 453, 462–470
Internal trend lines, 73–78
alternate, 75
versus conventional, 74, 76–77
support and resistance and, 106
International Cocoa Agreement, 356
International Sugar Agreement, 356
In-the-money options:
definition of, 480
delta values and, 485
Intracurrency spreads, 473–476
interest rate differentials and, 473, 476
interest rate ratios and, 475
Intramarket (or interdelivery) spread, 441
Intramarket stock index spreads, 461–462
Intrinsic value, of options, 489
Intuition, 586
Investment insights, 343
Japanese stock market, 22
Japanese yen (JY), intercurrency spreads and, 471
Jobs report. See Employment report
Kitchen sink approach, 312
Kuwait, 1990 invasion of, 420
Last notice day, 9
Last trading day, 9
Leading Indicator myth, 171–172
Least-squares approach, 592–593, 594
Lefèvre, Edwin, 178, 570, 580–581
Lessons, trader’s diary and, 565
Leverage:
negative, 320
risk and, 320
through borrowing. see Notional funding
Limit days, automatic trading systems and, 296
financial markets and, 13–14
general observations on, 13–15
sell hedge, 11–12
Heteroscedasticity, 672–673
Hidden risk, 320
Hildreth-Lu procedure, 671
Hite, Larry, 585
Hog production:
fundamentals and, 348, 350, 356
regression analysis and, 374, 589–591
regression equation and, 633
supply-demand analysis and, 360, 365
Hope, as four-letter word, 584
Housing market:
Case-Shiller Home Price Index, 423
housing bubble, 2003-2006, 423, 425
Implied volatility, 483–484
Incorporation of demand:
demand change, growth pattern in, 368
demand-influencing variables, identification of,
368–370
highly inelastic demand (and supply elastic
relative to demand), 370–371
methods for, 367–371
need for, 366–367
stable demand, 368
Independence, 579
Independent variables:
forecasting model, building, 415
multicollinearity and, 665
regression analysis and, 677, 679
Index models, 376–377
Individual contract series, 279–280
Inflation:
adjustments, 355
price data for, 414
price-forecasting models and, 383–388
Inflationary boom, 422
Inflation indexes, 383
Information, viewing old as new , 349–350
Intelligence, 582–583
Intercommodity spreads, 441–442. See also Limited-
risk spread
about, 441–442
contract ratios and, 453–460
Intercrop spreads, 441, 460
Hedging (continued)
693
Index
Market(s):
agricultural, 351
bear (see Bear market)
bull (see Bull market)
correlated, leverage reduction and, 562
excitement and, 585
exiting position and, 584–585
free, 357
housing (see Housing market)
nonrandom prices and, 587
planned trading approach and, 560
trading results and, 317
Market characteristic adjustments, trend-following
systems and, 251–252
Market direction, 449
Market hysteria, 585
Market-if-touched (MIT) order, 18
Market observations. See Rules, trading
Market opinion:
appearances and, 582–583
change of, 204
Market order, 16
Market patterns, trading rules and, 572–573
Market Profile trading technique, 585
Market psychology, shift in, 429
Market response analysis, 403–411
isolated events and, 409–410
limitations of, 410–411
repetitive events and, 403–410
stock index futures response to employment
reports, 408–409
T -Note futures response to monthly U.S.
employment report, 404–407
Market Sense and Nonsense: How the Markets Really Work,
319
Market statistics, balance table and, 373–374
Market wizard lessons, 575–587
Market Wizards books, 575, 579, 580, 581, 585, 586
MAR ratio, 330, 335
MBSs. See Mortgage-backed securities (MBSs)
McKay, Randy, 576, 581, 583
Measured moves (MM), 190–193
Measures of dispersion, 597–599
Mechanical systems. See T echnical trading systems
Metals. See Copper; Gold market
Method:
determination of, 576
development of, 576
Limited-risk spread, 446–448
Limit order, 17
“Line” (close-only) charts, 40–42
Linearity, transformations to achieve, 666–669
Linearly weighted moving average (LWMA),
239–240
Linked-contract charts, 45–56
comparing the series, 48
continuous (spread-adjusted) price series, 47
creation of, methods for, 46–48
nearest futures, 46–47
nearest vs. continuous futures, 39–40, 48–51,
52–56
necessity of, 45–46
Linked contract series: nearest futures versus
continuous futures, 39–40
Link relative method, seasonal index, 394–396
Liquidation information, 564
Live cattle. See Cattle
Livestock markets, 287. See also Cattle; Hog
production
Long call (at-the-money) trading strategy, 491–492
Long call (out-of-the-money) trading strategy,
493–494
Long futures trading strategy, 489–490
Long put (at-the-money), 503–504
Long put (in-the-money), 506–508
Long put (out-of-the-money), 504–506
Long straddle, 515–516
Long-term implications versus short-term response,
432–435
Long-term moving average, reaction to, 181–182
Look-back period, 173
Losing period adjustments, planned trading
approach and, 562–563
Losing trades, overlooking, 313
Losses:
partial, taking, 583
temporary large, 245
Loyalty/disloyalty, 583–584
Lumber, inflation and, 384
“Magic number” myth, 170
Managers:
comparison of two, 320–322
negative Sharpe ratios and, 325
MAR. See Minimum acceptable return (MAR)
Margins, 19
694
Index
trending market and, 79, 80
types of, 165–167
Multicollinearity, 663–666
independent variables and, 665
multiple regression and, 639
regression coefficients and, 665
Multimarket system testing, 313–314
Multiple regression equation, 637
Multiple regression model, 637–647
basics o


omparison of two, 320–322
negative Sharpe ratios and, 325
MAR. See Minimum acceptable return (MAR)
Margins, 19
694
Index
trending market and, 79, 80
types of, 165–167
Multicollinearity, 663–666
independent variables and, 665
multiple regression and, 639
regression coefficients and, 665
Multimarket system testing, 313–314
Multiple regression equation, 637
Multiple regression model, 637–647
basics of, 637–639
confidence intervals for individual forecast, 642
definition of, 625
F-test, 643–644
regression run, analyzing, 644–647
R
2 and corrected R2, 642–643
standard error of the regression and, 641–642
t-test application in, 640–641
Multiple-unit option strategies, 543
Mutually exclusive, 600
Natural disasters, 418
N AV. See Net asset value (NAV)
NAV charts, 335–336
Nearest futures chart, 39–40
Nearest vs. continuous futures:
chart analysis and, 48–51
futures price series selection and, 280
linked contract series, 39–40
measured moves and, 190
support and resistance, 91
Negative leverage, 320
Net asset value (NAV), 321, 331
Net asset value (NAV) charts, 335–336
Neuro-linguistic programming (NLP), 585
New-crop expectations, 381
New Science of T echnical Analysis, The, 199
Nikkei index futures, unexpected developments
and, 418, 420
NLP . See Neuro-linguistic programming (NLP)
Nominal price levels, comparing, 355
Nonoptimized parameters, 293
Nonsensitive (slow) systems, 245, 246
Nonstorable commodities, 351, 360
Normal curve (Z) table, reading, 604–606
Normal distribution, 599
Normal distribution curve, 601
Notional funding, 320, 321
Midtrend entry, 177–182
continuation pattern and trading range breakouts,
180–181
goals and, 178–179
percent retracement, 178
reaction to long-term moving average, 181–182
Minimum acceptable return (MAR), 326
Minimum price fluctuation, 5
Minor reaction, reversal of, 179–180
Mint Management, 585
Missing variable, 421
Mission, 586
Mistakes, trading, 584
MIT (market if touched) order, 18
MM (measured moves), 190–193
Models, equations and, 593
Modern T rader, 357
Modifications, trend-following systems, 247–254
buy and sell signals, differentiation between, 252
confirmation conditions, 247–250
filter, 250–251
market characteristic adjustments, 251–252
pyramiding, 252–253, 254
trade exit, 253, 254
Momentum, 163
Momentum indicator, 167
Money management:
fundamental analysis, technical analysis and,
426–427
risk control and, 560, 577–578
rules, other, 570
trade exit and, 569
Money stop, stop loss points and, 185, 187
Mortgage-backed securities (MBSs), 423, 425
Mortgage lending, subprime, 423
Moving average convergence-divergence (MACD), 199
Moving averages, 78–81, 157–165
calculation of, 238
crossovers of, 182
definition of, 78, 238
exponentially weighted moving average
(EWMA), 239–240
linearly weighted moving average (LWMA),
239–240
long-term, reaction to, 181–182
sideways market and, 79, 81
technical trading systems for, 237–243
695
Index
Option trading strategies, 487–555
comparing, 487–489
hedging applications and, 554–555
multiunit strategies, 543–544
optimal, choosing, 544–554
profit/loss profiles (see Profit/loss profile)
spread strategies, other, 542–543
Orders, types of, 16–19
Ordinary least squares (OLS), 654
Organization of the Petroleum Exporting Countries
(OPEC), 356, 425
Original trading systems, 261–278
run-day breakout system, 268–273
run-day consecutive count system, 273–278
wide-ranging-day system (see Wide-ranging-day
system)
Oscillators, 167–170, 255
Out-of-the-money call, buying, 555
Out-of-the-money options:
definition of, 480
delta values and, 485
Outright positions, spread tables and, 440, 441
Overbought/oversold indicators, 198–199
Parabolic price moves, 585
Parameter(s):
definition of, 291, 606
types of, 292–293
Parameter set:
average performance, 311
definition of, 291
Parameter shift, trend-following systems
and, 247
Parameter stability, optimizing systems
and, 297
Past performance, evaluation, 319–341
investment insights, 343
return alone, 319–322
risk-adjusted return measures, 323–335
visual (see Visual performance evaluation)
Patience, virtue of, 580–581
Pattern(s). See also Chart patterns; Continuation
patterns; One-day patterns
market, 572–573
seasonal, 415
Pattern recognition systems, definition of, 237
Penetration of top and bottom formations, 225–229
Observations, market. See Rules, trading
OCO (one-cancels-other) order, 18
Oil. See Crude oil market; Heating oil; WTI
crude oil
“Old hand” approach, 373
OLS (Ordinary least squares), 654
One-cancels-other (OCO) order, 18
One-day patterns:
about, 109–123
spikes, 109–113
One-tailed test, 614, 617
One-year comparisons, 350
OPEC. See Organization of the Petroleum Exporting
Countries’ (OPEC)
Open interest, volume and, 9–10
Open-mindedness, 585
Optimization:
definition of, 297
past performance and, 313
Optimization myth, 298–310
Optimizing systems, 297–298
Option(s):
fair value of, theoretical, 483
qualities of, 489
Option premium curve, theoretical, 481
Option premiums, 480–483
components of, 480
interest rates and, 482–483
intrinsic value and, 480
strike price and current futures price, 480–481
theoretical versus actual, 483–484
time remaining until expiration,
481–482
time value and, 480–483
volatility and, 482
Option-protected long futures:
long futures + long at-the-money put, 520–521
long futures + long out-of-the-money put,
522–523
Option-protected short futures:
short futures + long at-the-money call, 523–524
short futures + long out-of-the-money call,
524–525
Options on futures, 477–485
about, 477–479
delta and (neutral hedge ratio), 484–485
option premiums and (see Option premiums)
696
Index
Preforecast period (PFP) price, 677–678
Premium(s):
definition of, 477
dollar value of option, 477–478
Price(s):
consumption and, 364
dollar price, 383
grain, 351
nonrandom, 587
preforecast period (PFP) price, 677–678
strike or exercise, 477
supply, demand and
swings (see Price swings)
target levels and, 356–357
Price changes, price series and, 285
Price envelope bands, 107–108
Price


ms)
696
Index
Preforecast period (PFP) price, 677–678
Premium(s):
definition of, 477
dollar value of option, 477–478
Price(s):
consumption and, 364
dollar price, 383
grain, 351
nonrandom, 587
preforecast period (PFP) price, 677–678
strike or exercise, 477
supply, demand and
swings (see Price swings)
target levels and, 356–357
Price changes, price series and, 285
Price envelope bands, 107–108
Price-forecasting models:
adding expectations as variable in, 380
demand and, 366–367
inflation and, 383
Price-indicator divergences, 171–172
Price levels:
nearest futures and, 91, 101
nearest futures price series and, 48
nominal, comparing, 355
price series and, 285
Price movements:
dramatic, 428
fitting news to, 431–432
linked series and, 286
parabolic, 585
trend-following systems and, 245, 246
Price oscillator, 163
Price quoted in, 5
Price reversals, 229
Price seasonality, cash versus futures, 389–390
Price-supporting organizations, 356
Price swings:
nearest futures and, 101
nearest futures price series and, 48
Price trigger range (PTR), 262
Probability:
distributions, 599–604
heads and tails coin tosses, 390
real versus, 390–391
Probability-weighted profit/loss ratio (PWPLR),
550–551
Producer price index (PPI), 383
Pennants. See Flags and pennants
People’s Republic of China (PRC), 418
Percent retracement, reversal of minor reaction,
179–180
Percent return, optimizing systems and, 297
Performance evaluation, visual. See Visual
performance evaluation
Perishable commodities, 360
“Perpetual” (constant-forward) series, 281–282
Personality, trading method and, 576
Personal trading, analysis of, 565–566
Perspective:
keeping, 587
lack of, 351
Petroleum. See Crude oil market; Heating oil;
Organization of the Petroleum Exporting
Countries’ (OPEC); WTI crude oil
Philosophy, trading, 559, 578
Pivotal events, 422
Planned trading approach, 559–566
markets to be traded, 560
personal trading, analysis of, 565–566
planning time routine and, 563
risk control plan (see Risk control plan)
trader’s diary, maintaining, 565
trader’s spreadsheet, maintaining, 563–564
trading philosophy and, 559
Point-and-figure charts, 42–43
Population, definition of, 598
Population mean, estimation of, 607
Population regression line, 619–620
Populations and samples, 606
Position, trading around, 581–582
Position exit criteria, 189–204
change of market opinion, 204
chart-based objectives, 189
contrary opinion, 203–204
DeMark sequential, 199–203
measured moves, 190–193
overbought/oversold indicators, 198–199
rule of seven, 194–196
support and resistance levels, 196–197
trailing stops, 204
PPI. See Producer price index (PPI)
PRC. See People’s Republic of China (PRC)
Precious metals market. See also Gold market
carrying charges and, 446
demand and, 362
697
Index
Prudence, 583
PTR. See Price trigger range (PTR)
Pure arbitrage, 530
PWPLR. See Probability-weighted profit/loss ratio
(PWPLR)
Pyramiding:
midtrend entry and, 182
rejected signals and, 251
trend-following systems and, 252–253
Quantum Fund, 22
Random error, 628, 679
Random sample, definition of, 608
Random variable, 599
Random Walkers, 29–34
Rate of change, 163
Ratio call write, 532–534
Reaction count, 179–180
Recession, severe, 367. See also Great Recession
Regression analysis, 589–595, 675–683
about, 374–375, 589–591
assumptions of, basic, 620
best fit, meaning of, 591–593
dependent variable, determining, 675–676
example, practical, 593
forecast error and, 679–680
independent variables, selecting, 677
least-squares approach, 592–593, 594
practical considerations in applying, 675–683
preforecast period (PFP) price and, 677–678
regression forecast, reliability of, 593–595
simulation, 680–681
step-by-step procedure, sample, 682–683
stepwise regression, 681–682
survey period length and, 678–679
Regression coefficients:
computing t-value for, 626
multicollinearity and, 665
sampling distribution of, 621
testing significance of, 620–626
Regression equation, 619–635
analyzing (see Analysis of regression equation)
coefficient of determination R
2, 630–633
confidence interval for an individual forecast,
627–629
extrapolation, 630
misspecification and, 679
Production costs, price declines and, 351–352
Profit(s):
partial, pulling out, 584
slow systems and, 245, 246
winning trades and, 570–571
Profit/loss matrix, short puts with different strike
prices, 514
Profit/loss profile:
alternative bearish strategies, three, 548
alternative bullish strategies, three, 547
alternative neutral strategies, two, 549
bear call money spread, 536, 538
bearish “T exas option hedge,” 520
bear put money spread, 539, 542
bull call money spread, 535
bullish “T exas option hedge,” 518
bull put money spread, 541
covered call write, 527
covered put write, 528
definition of, 488
key option trading strategies and, 489–542
key trading strategies and, 489–542
long call (at-the-money), 492, 495
long call (out-of-the-money), 494
long futures, 490
long futures and long call comparisons, 497
long futures and short put comparisons, 514
long put (at-the-money), 504
long put (in-the-money), 507
long put (out-of-the-money), 505
long straddle, 516
option-protected long futures, 521, 522
option-protected short futures, 524, 525
ratio call write, 533
short call (at-the-money), 498
short call (in-the-money), 501
short call (out-of-the-money), 500
short futures, 491
short futures and long put comparisons, 509
short futures and short call comparisons, 503
short put (at-the-money), 510
short put (in-the-money), 513
short put (out-of-the-money), 511
short straddle, 517
synthetic long futures, 529
synthetic short futures, 532
trading strategies and, 488–489
two long calls vs. long futures, 544
698
Index
Risk-adjusted return measures, 323–335
advantages/disadvantages of, 334–335
Calmar ratio, 331
comparison of, 332–334
gain-to-pain ratio (GPR), 328–329,
334–335
MAR ratio, 330, 335
properties of, 334
return retracement ratio (RRR), 331–332, 335
SDR Sharpe ratio, 334
Sharpe ratio, 323–325, 332, 334, 343
Sortino ratio, 325–327, 334
s


tegies and, 488–489
two long calls vs. long futures, 544
698
Index
Risk-adjusted return measures, 323–335
advantages/disadvantages of, 334–335
Calmar ratio, 331
comparison of, 332–334
gain-to-pain ratio (GPR), 328–329,
334–335
MAR ratio, 330, 335
properties of, 334
return retracement ratio (RRR), 331–332, 335
SDR Sharpe ratio, 334
Sharpe ratio, 323–325, 332, 334, 343
Sortino ratio, 325–327, 334
symmetric downside-risk (SDR) Sharpe ratio,
327–328
tail ratio, 329–330, 335
Risk control:
money management rules, other, 570
trade exit and, 569–570
Risk control plan, 560–562
correlated markets, leverage reduction
and, 562
equity size, position changes and, 562
losing period adjustments, 562–563
market volatility adjustments, 562
risk per trade, maximum, 560–561
stop-loss strategy, 561
“Risk-free” return, 323, 326
RMS. See Root mean square (RMS)
Rogers, Jim, 22
Role of expectations, 379–381
actual statistics, influence on, 381
adding expectations as variable, 380
prior year estimates and, 379–380
Rolling window return charts, 337–340
12-month returns, 337, 338–339
24-month returns, 339, 340
Rollover, 15
Rollover dates:
continuous series and, 48, 51
nearest futures charts and, 47
Root mean square (RMS), 681
Rounding tops and bottoms:
breaking of curvature and, 229, 230
chart patterns and, 141–143
RSI, 199
R
2 (coefficient of determination), 630–633
R2 and corrected R2, 642–643
Rule of seven, 193–196
population regression line, 619–620
regression analysis and, 620
regression coefficients, testing significance of,
620–626
spurious (“nonsense”) correlations, 634–635
standard error of the regression (SER), 627
Relative highs and relative lows:
definitions of, 66
stop-loss points and, 185, 186
Relative strength index (RSI), 198
Reminiscences of a Stock Operator, 178, 570,
580–581
Residual, definition of, 623
Residual plot, 650–651
Residuals, 592
Resistance. See Support and resistance
Responsibility, 578–579
Results, negative, 314–315
Retracement criterion, percent retracement, 178
Return(s). See also Minimum acceptable return
(MAR)
average, 323
compounded, 323
return alone, meaninglessness of, 319–322
risk-adjusted return measures, 319–322
“risk-free,” 323
rolling, 337–340
small difference in, 338
stability of, 338
Return retracement ratio (RRR), 335
Return/risk statistics, performance charts
and, 343
Return to spike extremes, 213–216
Return to wide-ranging day extremes, 216–218
Reversal days, 113–116
definition of, 114–115
spike reversal days, 115–116
spikes and, 147
Reversal of minor reaction, 179–180
Reversal size, 42
Reverse conversion, 530
Risk:
hidden, 320
ignoring, 312–313
leverage and, 320
low-risk idea, 581
scared money, 584
Regression equation (continued)
699
Index
detrended, 394
link relative method, 394–396, 398
Seasonal patterns, forecasting model, 415
Securitizations, 423
SEE. See Standard error of the estimate (SEE)
Segmented trades, analysis of, 565–566
Sell hedge, cotton producer, 11–12
Sell signals, trend-following systems and, 252
SER. See Standard error of the regression (SER)
Series selection. See Futures price series selection
Settlement type, 9
Sharpe ratio, 323–325, 334, 343. See also Symmetric
downside-risk (SDR) Sharpe ratio
Short call (at-the-money) trading strategy,
498–499
Short call (in-the-money) trading strategy, 500–502
Short call (out-of-the-money) trading strategy,
499–500
Short futures trading strategy, 490–491
Short put (at-the-money), 509–510
Short put (in-the-money), 512–513
Short put (out-of-the-money), 510–512
Short straddle, 516–517
Short-term response versus long-term implications,
432–435
Sideways market, moving averages and, 79, 81
Signal price, limit days and, 296
Signals, failed, 205, 206
Simple moving average (SMA), 165–167
Simple regression, 625
Simulated results, 312–313
fabrication, 313
kitchen sink approach, 312
losing trades, overlooking, 313
optimization and, 317
risk, ignoring, 312–313
terminology and, 311
transaction costs, 313
well-chosen example, 312
Simulation, blind, 311
Single market system variation (SMSV), 256–257
Skill, hard work versus, 576–577
Sklarew , Arthur, 194
Slippage:
automatic trading systems and, 295
sampling distribution and, 608
transaction costs and, 291
trend-following systems and, 247
Rules, trading, 567–574
analysis and review of, 573–574
market patterns and, 572–573
miscellaneous, 571–572
risk control (money management), 569–570
trade entry, 568–569
trade exit, 569–570
winning trades, holding/exiting, 570–571
Run-day breakout system, 268–273
basic concept, 268
daily checklist, 269
illustrated example, 270–273
parameters, 269
parameter set list, 270
trading signals, 269
Run-day consecutive count system, 273–278
basic concept, 273
daily checklist, 274
definitions, 273
illustrated example, 275–278
parameters, 274
parameter set list, 274
trading signals, 273–274
Run days, 116, 118–119, 268
Russell 2000 Mini, intermarket stock index spreads,
461–470
Samples, populations and, 598, 606
Sampling distribution, 608–609
Sands, Russell, 434
Saucers. See Rounding tops and bottoms
Saudi Arabia, 425
Scale order, 18
Schwager, Jack, 319
Schwartz, Marty, 22, 585
SDR Sharpe ratio, 327–328, 334
SE. See Standard error (SE)
Seasonal analysis, 389–401
cash versus futures price seasonality,
389–390
expectations, role of, 390
real or probability, 390–391
seasonal index (see Seasonal index)
seasonal trading, 389
Seasonal considerations, ignoring, 356
Seasonal index, 391–401
alternative approach, 396–401
average percentage method, 391–394
700
Index
stock index futures and (see Stock index futures)
time, 542
types of, 441–442
Spread seasonality, 449
Spreadsheet, maintaining traders, ’ 563–564
Stability:
of return, 338
time (see Time stability)
Standard deviation:
calculation of, 323, 599
estimation of, 607
Standard error (SE), 612, 627
Standard error of the estimate (SEE), 627
Standard error of the mean, 612
Standard error of the regression (SER):
about, 627
multiple regression model and, 641–642
simulation and, 681
Standardized residu


y, 449
Spreadsheet, maintaining traders, ’ 563–564
Stability:
of return, 338
time (see Time stability)
Standard deviation:
calculation of, 323, 599
estimation of, 607
Standard error (SE), 612, 627
Standard error of the estimate (SEE), 627
Standard error of the mean, 612
Standard error of the regression (SER):
about, 627
multiple regression model and, 641–642
simulation and, 681
Standardized residuals, regression run analysis and,
647
Statistic, definition of, 606
Statistics:
elementary (see Elementary statistics)
forecasting model, building, 414
influence of expectations on actual, 381
using prior-year estimates rather than revised,
379–380
Steidlmayer, Peter, 585
Stepwise regression, 681–682
Stochastic indicator, 199
Stock index futures:
dividends and, 462
intermarket stock index spreads, 462–470
intramarket stock index spreads, 461–462
most actively traded contracts, 463
response to employment reports, 408–409
spread pairs, 463
spread trading in, 461–470
Stock market collapse, 425
Stop, trailing. See Trailing stop
Stop close only, 18
Stop-limit order, 17
Stop-loss points, 183–188
flags and pennants and, 184–185
money stop and, 185, 187
relative highs and relative lows, 185, 186
relative lows and, 185, 186
selecting, 183–188
SMSV . See Single market system variation (SMSV)
Soros, George, 22
Source/product spread, 442
Soybeans, inflation and, 384
Spike(s), 109–113
definition of, 112–113
reversal days and, 147
“spike days,” 237
Spike days. See Spike(s)
Spike extremes, return to, 213–216
Spike highs:
penetration of, 214–215
qualifying conditions and, 110–111
significance of, 109
spike extremes and, 213–216
Spike lows:
penetration of, 215
price declines, 109
significance of, 110
Spike penetration signals negated, 216
Spike reversal days, 115–116
Spot gold, 555
Spread-adjusted (continuous) price series, 282–285
Spread order, 15, 19
Spreads:
about, 439–440
analysis and approach, 448–449
balanced, 455
butterfly, 542
chart analysis and, 449
credit, 535
currency futures and (see Currency futures)
definition of, 440
diagonal, 542
equal-dollar-value spread, 455–460
fundamentals and, 449
general rule (see General rule, spreads)
historical comparison and, 448
intercommodity (see Intercommodity spreads)
intercrop, 441, 460
intermarket, 442, 453
intramarket (or interdelivery), 441
limited-risk, 446–448
pitfalls and points of caution, 449–451
rather than outright - example, 445–446
reason for trading, 440–441
seasonality and, 449
similar periods, isolation of, 449
701
Index
relative low , 66
TD downtrend line, 66, 67, 68–69, 71–72
TD uptrend line, 67–68, 69–70
true high and true low , 72–73
T echnical analysis:
about, 16
fundamental analysis and, 21–24, 417–418,
426–427
money management and, 426–427
T echnical indicators, 155–171
about, 155–156
calculations, basic, 157
comparing indicators (see Comparing indicators)
moving average types, 165–167
myths about, 170–172
oscillators, 167–170
trading signals, 167–170
“types,” 173
T echnical trading systems, 233–259
benefits of, 236
countertrend systems (see Countertrend systems)
pattern recognition systems (see Pattern
recognition systems)
trend-following systems (see Trend-following
systems)
types of, overview , 236–237
T echniques of a Professional Commodity Chart Analyst,
194
T esting/optimizing trading systems, 289–318,
291–293
assumptions, realistic, 295–296
concepts and definitions, 291
continuous futures and, 51
example, well-chosen, 289–291, 314–315
multimarket system testing, 313–314
negative results, 314–315
optimizing myth, 298–310
optimizing systems, 297–298
price series and, 287
price series selection, 293
simulated results, truth about, 312–313
steps in constructing/testing system, 315–316
testing versus fitting, 310–311
time period selection, 293–295
trading systems, observations, 316–318
T exas option hedge:
bearish, 519–520
bullish, 517–519
trading ranges and, 184
trailing stop and, 187–188
trend lines and, 183–184
wide-ranging days and, 185, 186
Stop-loss strategy, planned trading approach and,
561
Stop order, 17
Strategies. See Option trading strategies
Stress, 585–586
Strike price, 477
Subprime mortgage lending, 423–425
Substitutes, availability of, 361
Sugar prices, 348–349
Supply:
consumption and, 365
definition of, 359–362
elastic relative to demand (highly inelastic
demand and), 370–371
fixed, 360
price and, 362–363
Supply curve, 359, 360–361
Supply-demand interaction, 364
Supply-demand reports, 366
Support and resistance, 91–108
levels, 196–197
nearest or continuous futures, 91
price envelope bands and, 107–108
prior major highs and lows, 94–100
resistance zone, 104, 105
support zone, 101–104, 105–106
trading ranges, 92–94
trend lines/channels and, 106
Swiss franc, unexpected developments and, 420,
421
Symmetric downside-risk (SDR) Sharpe ratio,
327–328
Synthetic long futures, 528–531
Synthetic short futures, 531–532
System-testing platforms, price series and, 287
System variation, 291
Tail ratio, 329–330, 335
Tautological relationship, 631
Tax considerations, 19–20
T -bill rates, 323
TD lines, 66–73
definitions and, 66–67, 72
relative high, 66
702
Index
Trading method, personality and, 576
Trading philosophy, 559
Trading plan, 578
Trading range(s), 83–89, 92–94
breakouts from, 86–89, 180–181
definition of, 33
intraday, 86
multi-year, 83–85
stop-loss points and, 184
trading considerations, 83–86
trend-following systems and, 245
Trading rules, 567–574
Trading signals, 167–170
Trading system(s):
definition of, 291
price series and, 287
updating, 563
Trailing stop, 187–188
profitable trades and, 571
stop-loss points and, 187–188
trade exit point and, 204
Transaction costs, 295–296, 313
Trend(s), 57–81
downtrend lines, 65
internal trend lines (see Internal
trend lines)
middle portion of, 583
moving averages (see Moving averages)
news coverage and, 579
participation in major, 257
TD lines (see TD lines)
uptrend lines, 63–65
“whipsawing” signals, 80
Trend channels:
definition of, 62
rules applied to, 63
support and resistance a


e exit point and, 204
Transaction costs, 295–296, 313
Trend(s), 57–81
downtrend lines, 65
internal trend lines (see Internal
trend lines)
middle portion of, 583
moving averages (see Moving averages)
news coverage and, 579
participation in major, 257
TD lines (see TD lines)
uptrend lines, 63–65
“whipsawing” signals, 80
Trend channels:
definition of, 62
rules applied to, 63
support and resistance and, 106
Trend-following systems, 237–244
breakout systems, 243–244
common problems with, 244–247, 259
countertrend systems, 254–256
definition of, 236
diversification, 256–258
modifications for (see Modifications, trend-
following systems)
moving average systems, 237–243
Thorp, Edward, 587
Thrust count, 180
Thrust days, 89, 116, 117
Ticker symbol, 5
“Tick” size and value, 5
Time considerations, ignoring relative, 351
Time-outs, 580
Time spread, 542
Time stability:
automatic trading systems and, 295
optimizing systems and, 297
Time value, of options, 489
Time value decay, 481
Timing:
poor, 422
using fundamentals for, 350, 425
T -Note:
futures response to monthly U.S. employment
report, 404–407
market response analysis and, 406, 407
T op and bottom formations:
double tops and bottoms, 134, 136–138
penetration of, 225–229
V tops and bottoms, 134
T otal variation, 630
Trade(s):
entering, 568–569
exiting, risk control and, 569–570
new , planning, 563
reason for, 565
scaling in and out of, 581
segmented, analysis of, 565–566
winning (see Winning trades)
Trade entry:
poor timing and, 422–425
timing of, 415
Trade exit comments, 565
Trade opportunity, forecasting model and, 415
Trader’s diary, maintaining, 565
Trader’s spreadsheet, maintaining, 563–564
Trading:
about, 15–16
around a position, 581–582
fundamental analysis and, 417–435
seasonal, 389
Trading hours, 8
703
Index
Variable(s):
dependent, determining, 675–676
discrete, 600
dummy, 659–663
independent, 415, 665, 677
lagged, 677
missing, 421, 655–658
random, 599
Variance, 607
Variation:
degrees of freedom and, 642
R
2 and, 642–643
total, 630
Visual performance evaluation, 335–342
2DUC charts, 341–342
net asset value (NAV) charts, 335–336
rolling window return charts, 337–340
underwater curve, 341–342
V olatility:
implied, 483–484
market adjustments, 562
option premiums and, 482, 483
planned trading approach and, 560
Sharpe ratio and, 324
spread trades and, 440
V olatility ratio (VR):
trend-following systems and, 247
wide-ranging days and, 119
V olume, open interest and, 9–10
V tops and bottoms, 134, 135
Wallet Street Week, 29–32
W edge, 146–147
W eighted least squares (WLS), 672
W eighted moving average (WMA), 165–167
W einstein, Mark, 580
Wheat market:
balance table, 373–374
crop expectations, 355–356
intercommodity spreads and, 457–459
Whipsaws:
trend-following systems and, 244
trend signals, 80
Wide-ranging days:
definition, 262
stop-loss points and, 185, 186
Trending phase, price sample and, 294
Trend lines, 106, 183–184. See also Internal trend
lines
breakouts, false, 211–213
rules applied to, 63
stop-loss points and, 183–184
Triangles, 123–127, 143–146
ascending, 125–126
descending, 126–127
nonsymmetrical, 123
symmetrical, 123, 124–125
triangle bottom, 145
triangle top, 144
Triple top, 129
True range:
definitions, 262
wide-ranging day and, 261
t-test:
about, 614–618
multiple regression model and, 640–641
2DUC charts, 341–342
Two-tailed test, 614, 617
Type 1 error, 614
Type 2 error, 614
Underwater curve, 341–342
Unexpected developments, 418
Unexplained variation, 630
Up run day, 118, 268
Upthrust day, 116
Uptrend:
definition of, 57
examples of, 58
Uptrend channel, 62
Uptrend lines, 63–65
examples of, 59, 60, 62
false breakout signals, 211, 212
U.S. Department of Agriculture (USDA):
balance table, 373–374
consumption, demand and, 366
forecasting model, building, 414
unexpected developments and, 420
U.S. dollar (USD). See Dollar
U.S. employment report, T -Note futures response to
monthly, 404–407
U.S. Treasury, fundamentals and, 347
704
Index
gains and, 583
holding/exiting, 570–571
losing and, concepts of, 578, 580
needing to win, 584
Wizard lessons, market, 575–587
WLS. See W eighted least squares (WLS)
W orld trade agreements, 356–357
WRDs. See Wide-ranging days (WRDs)
WTI crude oil. See also Crude oil market
poor timing and, 426
unexpected developments and,
420, 421
Zero return, 326
Ziemba, William T ., 327
Z-test, 614, 615
Wide-ranging days (WRDs), 119–121
down bar, 123
down days, 120
extremes, return to, 216–218
stop-loss points and, 185, 186
up and down days, 121–122
up days, 120
up weeks, 122
Wide-ranging-day system, 261–268
basic concept, 261–262
daily checklist, 262–263
illustrated example, 264–268
parameter set list, 263
system parameters, 263
trading signals, 262
Winning trades:
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The
InTellI genT
OpTIOn
Inves TOr
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The
InTellI genT
OpTIOn
Inves TOr
Applying Value Investing to the
World of Options
erik Kobayashi-solomon
new Y ork Chicago s an Francisco Athens l ondon Madrid Mexico City
Milan n ew Delhi s ingapore s ydney Toronto
Copyright © 2015 by Erik Kobayashi-Solomon. All rights reserved. Except as permitted under the
United States Copyright Act of 1976, no part of this publication may be reproduced or distributed
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To Fred Solomon
(1930–2013)
To my family and my “tribe”
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vii
Contents
Acknowledgments xi
Introduction xiii
Part I: options for the Intelligent Investor 1
Chapter 1: Option Fundamentals 3
Characteristics and history 4
Directionality 9
Flexibility 20
Chapter 2: The Black-scholes-Merton Model 29
The BsM’s Main Job is to predict stock prices 30
The BsM is lousy at Its Main Job 39
Chapter 3: The Intelligent Investor’s guide to Option pricing 49
how Option prices are Determined 50
Time value versus Intrinsic value 56
how Changing Market Conditions Affect Option prices 59
Part II: A sound Intellectual Framework for Assessing Value 75
Chapter 4: The golden rule of valuation 77
The value of an Asset 78
Cash Flows generated on Behalf of Owners 80
The Company’s economic life 82
Time value of Money: summing Up Cash Flows Over Time 87
Chapter 5: The Four Drivers of value 91
Bird’s eye view of the valuation process 91
A Detailed look at the Drivers of value 97
viii  •   Contents
Chapter 6: Understanding and Overcoming Investing pitfalls 113
Behavioral Biases 114
structural Impediments 131
Part III: Intelligent option Investing 141
Chapter 7: Finding Mispriced Options 143
Making sense of Option Quotes 144
Delta: The Most Useful of the greeks 151
Comparing an Intelligent valuation range with a BsM range 155
Chapter 8: Understanding and Managing leverage 163
Investment leverage 164
simple Ways of Measuring Option Investment leverage 169
Understanding leverage’s effects on a portfolio 174
Managing leverage 183
Chapter 9: gaining exposure 187
long Call 189
long put 201
strangle 205
straddle 208
Chapter 10: Accepting exposure 211
short put 212
short Call (Call spread) 220
short straddle/short strangle 230
Chapter 11: Mixing exposure 233
long Diagonal 235
short Diagonal 238
Covered Call 240
protective puts 248
Collar 258
Chapter 12: risk and the Intelligent Option Investor 263
Market risk 263
valuation risk 265
Intelligent Option Investing 267
Appendix A: Choose Y our Battles Wisely 269
Where the BsM Works Best 269
Where the BsM Works Worst 273
Appendix B: The Many Faces of leverage 282
Operational leverage 282
Financial leverage 285
Appendix C: p ut-Call parity 287
Dividend Arbitrage and put-Call parity 288
Notes 295
Index 305
Contents    • ix
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xi
ACknowledgments
Many thanks to all the people who have been part of the process du

eBooks\Erik Kobayashi-Solomon - The Intelligent Option Investor- Applying Value Investing to the World of Options\Erik Kobayashi-Solomon - The Intelligent Option Investor- Applying Value Investing to the World of Options.pdf


tles Wisely 269
Where the BsM Works Best 269
Where the BsM Works Worst 273
Appendix B: The Many Faces of leverage 282
Operational leverage 282
Financial leverage 285
Appendix C: p ut-Call parity 287
Dividend Arbitrage and put-Call parity 288
Notes 295
Index 305
Contents    • ix
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xi
ACknowledgments
Many thanks to all the people who have been part of the process during
the writing of this book. I am indebted to three people in particular,
Mr. Brent Farler, Mr. Ben louviere, and Mr. neil Kozarsky, who have gra-
ciously offered their time, help, and business expertise in bringing this pro-
ject to fruition. Certainly this book would be much different and of not
nearly the quality without Brent’s guidance, thorough reading, and insight-
ful, helpful suggestions, starting with the very first draft in late 2012.
In the literary world, I cannot say enough good things about Mr. sam
Fleishman, of l iterary Arts r epresentatives, and Mr. Knox h uston and
Ms. Daina penikas, my editors at Mcgraw-hill, all of whom have allowed
this work to move from conception to completion and whose advice and
support have made all the hard work worthwhile.
In the investment-management world, I am indebted to Mr. steve
silverman, owner and portfolio manager of Ironbound Capital Manage-
ment, who taught me important lessons about the business of investing and
about how to critically assess the value of a company, and to Mr. Deepinder
Bhatia, Founding partner of Bayard Asset Management llC, a true expert
in the art and science of equity research and analysis.
In addition, I thank Mr. rafael garcia, of the International Financial
Corporation; Mr. Joe Miramonti, of Fedora Investment p artners;
Mr. Franco Dal pont, of Batalha Capital Management; and Mr. paul neff,
of the Federal reserve Bank of Chicago, for the excellent discussions about
valuation, option theory, and bringing the touchstone of valuation into the
realm of option investments.
When I began work on this book, I did not realize just what an
enormous process it would be. Truly, without the help and support of
all the people mentioned here and all my friends and family around
the world, I would have had a much more difficult time completing
this work.
xii  •   Acknowledgments
xiii
IntroduCtIon
You have a tremendous advantage over algorithmic trading models,
investment bank trading desks, hedge funds, and anyone who appears on or
pays attention to cable business news shows. This book is written to show
where that advantage lies and how to exploit it to make confident and suc-
cessful investment choices. In doing so, it explains how options work and
what they can tell you about the market’s estimation of the value of stocks.
even if, after reading it, you decide to stick with straight stock in-
vesting and never make an option transaction, understanding how options
work will give you a tremendous advantage as an investor. The reason for
this is simple: by understanding options, you can understand what the rest
of the market is expecting the future price of a stock to be. Understanding
what future stock prices are implied by the market is like playing cards with
an opponent who always leaves his or her hand face up on the table. Y ou
can look at the cards you are dealt, compare them with your opponent’s,
and play the round only when you are sure that you have the winning hand.
By incorporating options into your portfolio, you will enjoy an even
greater advantage because of a peculiarity about how option prices are
determined. Option prices are set by market participants making trans-
actions, but those market participants all base their sale and purchase
decisions on the same statistical models. These models are like sausage
grinders. They contain no intelligence or insight but rather take in a few
simple inputs, grind them up in a mechanical way, and spit out an option
price of a specific form.
An option model does not, for instance, care about the operational
details of a company. This oversight can lead to situations that seem to be
too good to be true. For instance, I have seen a case in which an investor
could commit to buy a strong, profitable company for less than the amount
of cash it held—in effect, allowing the investor to pay $0.90 to receive a
dollar plus a share of the company’s future profits! Although it is true that
these kinds of opportunities do not come along every day, they do indeed
come along for patient, insightful investors.
This example lies at the heart of intelligent option investing, the es-
sence of which can be expressed as a three-step process:
1. Understanding the value of a stock
2. Comparing that intelligently estimated value with the mechani-
cally derived one implied by the option market
3. Tilting the risk-reward balance in one’s favor by investing in the
best opportunities using a combination of stocks and options
The goal of this book is to provide you with the knowledge you need to be
an intelligent option investor from the standpoint of these three steps.
There is a lot of information contained within this book but also a lot
of information left out. This is not meant to be an encyclopedia of option
equations, a handbook of colorfully named option strategies, or a treatise on
financial statement analysis. Unlike academic books covering options, such
as hull’s excellent book,
1 not a single integration symbol or mathematical
proof is found between this book’s covers. Understanding how options are
priced is an important step in being an intelligent option investor; doing dif-
ferential partial equations or working out mathematical proofs is not.
Unlike option books written for professional practitioners, such as
natenberg’s book,2 you will not find explanations about complex strategies
or graphs about how “the greeks”3 vary under different conditions. Floor
traders need to know these things, but intelligent option investors—those
m