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1. SECTION II
Asset Relationships

2. CHAPTER 10
Intermarket
Indicator

3. CHAPTER AGENDA
▪ Analyze and interpret relative strength of different
assets
▪ Analyze and interpret Bollinger Band Divergence
signals
▪ Interpret data from multiple regression divergence
signals to predict a target market
▪ Prepare a recommendation or other response based
on asset correlation data
3

4. Intermarket Indicator
4
Relative
Strength
Bollinger
band
divergence
Intermarket
Disparity
Index
Intermarket
LRS
Divergence
Intermarket
Regression
Divergence
Intermarket
Momentum
Oscillator
Z-Score
Divergence
Multiple
Intermarket
Divergence
Multiple
Regression
Divergence
Intermarket
Moving
Average
Congestion
Index

5. Relative Strength
▪ Relative strength (RS) is a popular indicator which compares one
security with another or with a benchmark index, for example a
specific US stock with the S&P 500.
▪ The relative strength is calculated by dividing the price of
one security by the benchmark. The ratio is further smoothed by
a moving average in order to eliminate the effect of erratic price
movements
▪ The relative strength between a stock and the appropriate
index can also be used to evaluate one‘s stock portfolio.
▪ If the relative strength drops below its moving average
while the price of the security is still rising, then it is time to
consider heading for the exit while the stock is still strong.
5

6. Relative Strength
▪ Composite Chart of the Gold
ETF (GLD) and the Dollar Index
(DXY) for the 2-Year Period from
July 2005 to July 2007. The 3-day
moving average of the gold/dollar
relative strength is depicted in the
middle window and the 2000-day
intermarket momentum oscillator
(see Section 14.6) in the top
window.
▪ Up and down arrows indicate sell
and buy signals triggered by the
oscillator crossing its 4-day
moving average at overbought
(over 80) and oversold (less than
50) levels respectively.
6

7. Bollinger Band Divergence
▪ BB in order to calculate the divergence between price and money flow.
It can also be used to calculate the divergence between a security and a
related market.
▪ First the relative position of both securities in the Bollinger Bands is
calculated and the divergence is derived by subtracting the relative
position of the intermarket security from the base security.
▪ Indicator values vary from −100 to +100, values less than zero indicate
negative divergence and over zero positive divergence.
▪ Buy signals are generated when the indicator reaches a peak above a
certain level (usually 10 to 30) and subsequently declines.
▪ Similarly, sell signals are triggered when the indicator reaches a bottom
below a certain level (usually −10 to −30) and rises.
7

8. Bollinger Band Divergence
▪ BB have some limitations with the underlying Bollinger
Band theory that should be understood before applying it
to trading:
▪ The formula involves the calculation of the standard
deviation which makes the assumption of normality and it
is therefore subject to error when the price distribution
deviates significantly from normality.
▪ Because of scaling factors the formula doesn’t work so
well with negatively correlated markets.
8

9. Intermarket Disparity
▪ The disparity index was first introduced by Steve Nison in his book Beyond
Candlesticks . It is defined as the percentage difference or disparity of the
latest close to a chosen moving average, and the formula is:
9
Positive and negative
values indicate positive
and negative divergence
respectively. A sell signal
is generated when the
divergence reaches a
bottom and reverses below
a certain negative level
and a buy signal is
generated when the
indicator reaches a top
and falls above a certain
positive level.

10. Intermarket LRS Divergence
▪ Divergence money flow divergence between price and the volume flow
indicator.
▪ First the linear regression slope of both securities is calculated and the
intermarket slope is then adjusted to take into account the difference in
volatilities between the two securities.
▪ The divergence is then derived by subtracting the base security slope from
the intermarket. Values below zero indicate negative divergence and values
over the zero line positive divergence.
▪ Buy signals are generated when the indicator reaches a peak above a certain
level (usually between 10 and 40) and subsequently declines.
▪ Similarly, sell signals are triggered when the indicator reaches a bottom
below a certain level (usually from −10 to −40) and rises. Divergence levels
vary according to the intermarket securities chosen for comparison.
10

11. Intermarket LRS Divergence
11
Composite Chart of the Gold
ETF (GLD) and the CRB Index
(CRB). The 15-day intermarket
divergence LRS indicator is
depicted in the top window.
Buy signals are triggered by
the intermarket LRS
divergence reaching a top and
turning down on positive
divergence levels of 15 or
greater. Similarly, sell signals
are generated by the
intermarket LRS divergence
reaching a bottom and turning
up, but only for divergence
values below the −30 level
(negative divergence).

12. Intermarket Regression Divergence
▪ The linear regression equation derived is used to make a prediction of likely
values of the dependent variable or the security to be predicted (gold in our
examples), based on values of a correlated market. This method can only be
used with price differences or yields as raw prices violate the two basic
assumptions of regression: linearity and normality.
12
Composite Chart of the Gold ETF (GLD) and the
XAU. The 300-day intermarket regression
divergence indicator of the 15-day Gold-XAU
yields is depicted in the top window. Buy signals
are triggered by the intermarket regression
divergence reaching a top and turning down on
positive divergence levels of 3 or greater.
Similarly, sell signals are generated by the
intermarket regression divergence reaching a
bottom and turning up but only for divergence
values below the −4 level (negative divergence).

13. Intermarket Momentum Oscillator
▪ A serious disadvantage in using all the above indicators is that extreme
divergence levels vary according to the markets being analyzed. This is mainly
because of price scaling differences between the base and intermarket
security.
▪ To automate the tedious work of identifying indicator extreme values, I use a
simple momentum oscillator which I call the intermarket momentum
oscillator. I use the following formula to calculate the new oscillator:
▪ where MA,3=3-day moving average; Highest and Lowest are the highest and
lowest values of the indicator over the previous 200-day period.
13

14. Intermarket Momentum Oscillator
▪ The formula above will normalize the divergence on a scale from 1
to 100. Trading signal interpretation is similar to that of the
stochastic oscillator:
▪ Buy when the oscillator falls below a specific level (e.g. 30) and then
rises above that level, and sell when the oscillator rises above a
specific level (e.g. 80) and then falls below that level.
▪ Buy when the oscillator falls below a specific level (e.g. 30) and then
rises above a higher level (e.g. 40), and sell when the oscillator rises
above a specific level (e.g. 90) and then falls below a lower level (e.g.
80).
▪ Buy when the oscillator rises above its signal line (moving average)
and sell when it falls below the signal line . A combination of either
one of the first two methods with the third.
14

15. Intermarket Moving Average
▪ Crossovers over the conventional (actual) moving average indicate that gold
is undervalued and crossovers under the conventional moving average
indicate that gold is overvalued relative to its related markets. Crossovers,
however, cannot be used to generate buy and sell signals as gold can remain
undervalued or overvalued for some time before following its peers.
15
The intermarket (predicted) 5-day
moving average (thick line)
is plotted together with the classic 5-
day moving average in the bottom
window and the multiple divergence
indicator in the top window. Notice
that when the intermarket moving
average crosses above the classic
moving average the divergence (top
window) turns positive.

16. Multiple Regression Divergence
▪ In multiple regression, the goal is to predict the value of the dependent
variable (the market to be predicted) from a set of k independent variables
(related markets).
▪ Multiple regression finds a set of partial regression coefficients bk such that
the dependent variable could be approximated as well as possible by a linear
combination of the independent variables
▪ The coefficients of the multiple regression equation represent the amount by
which the dependent variable (to be predicted) increases when one
independent (predictor) variable is increased by one unit and all the other
independent variables are held constant.
▪ Gold has the stronger correlation with the XAU and the dollar index when
taking 9-day yields. This time segment was therefore used to construct the
correlation matrix
16

17. Multiple Regression Divergence
17
Correlation Matrix of 9-Day Yields. The TNX is the
CBOE 10- year Treasury yield index. Data for the most
recent 15-year period were used to calculate the
correlations. Only 10 years of data were available for
the euro and therefore it was not included in the
Pearson’s 10-Year Correlation
Matrix of 9-Day Percentage
Yields. Data for the most recent
10-year period were used to
calculate the correlations.

18. Multiple Regression Divergence
18
Model Summary. The first model includes only the XAU. Silver,
the dollar, the CRB and the yen are added one at a time in
models 2, 3, 4 and 5 respectively. Adding the CRB and the yen
make very little difference to the coefficient of determination
R2.
Unstandardized Regression Coefficients

19. Multiple Regression Divergence
19
Standardized Regression
Coefficients. The yen produces the
smallest regression coefficient
(0.034).
Percentage Weight of Each Variable in the Model

20. Multiple Regression Divergence
20
The multiple regression
divergence between gold, the
XAU, silver and the dollar index
using 9-day percentage yields
is depicted in the top window.
Extreme divergences are
indicated by the horizontal
lines at +/−20 levels. Buy
signals are triggered by the
indicator forming a top above
the extreme level and declining
below 20, and sell signals are
triggered by the indicator
declining below −20, forming a
bottom and rising above −20.

21. Multiple Intermarket Divergence
▪ This concept can be extended to multiple markets where a prediction of
the future direction of a security is extended to include multiple
predictor markets.
▪ This can be achieved by adding the divergences for each market after
taking into account the redundant cross correlation between the
predictor (independent) variables.
▪ This can be achieved by weighing each predictor variable according to its
part (or semi-partial) correlation coefficient with the dependent variable
(the security to be predicted).
▪ The linear regression slopes were compared over a 20-day interval. The
time interval was used as a switch for increasing or decreasing the
amount of trading signals. Decreasing the time segment produced more
signals while increasing it reduced them.
21

22. Multiple Intermarket Divergence
22
The 20-day double
divergence between
gold, the dollar index
and the silver ETF is
depicted in the top
window. Extreme
values of the
divergence are
indicated by the
horizontal lines at
+15 and −20.

23. Z-Score Divergence
▪ A comparison of two securities on the same chart is sometimes difficult and
misleading because of price scaling differences. A convenient method of
normalizing both prices on an equal scale is by converting the prices to their
Zscores. These are computed by subtracting the mean and dividing by the
standard deviation according to the formula:
▪ Z-scores use the standard deviation as a unit of measure and indicate how many
standard deviations the price falls above or below the mean. The Z-score
divergence is computed by subtracting the Z-score of the base security from the
intermarket and multiplying the result by their correlation coefficient.
▪ The Z-score will normalize prices regardless of statistical merit which is only
absolutely correct in the case of perfectly normally distributed data.
23

24. Z-Score Divergence
24
Composite Chart of the Gold
ETF (GLD) (thick line with the
scale on the right Y-axis) and
the Euro (thin line on the left
Y-axis). The 250- day (1 year)
Z-scores of gold (thick line)
and the Euro (thin line) are
depicted in the upper window
on the same scale. The Z-
score illustrates more clearly
the relation between gold and
the Euro, especially in periods
where both
charts are very close in the
bottom window.

25. Congestion Index
▪ Market movements can be characterized by two distinct types or
phases.
▪ first, the market shows trending movements which have a directional
bias over a period of time.
▪ The second type of market behavior is periodic or cyclic motion, where
the market shows no consistent directional bias and trades between
two levels.
▪ Trending markets need trend-following indicators such as moving
averages, moving average convergence/divergence (MACD), and so on.
▪ Trading range markets need oscillators such as the relative strength
index (RSI) and stochastics, which use overbought and oversold levels.
25

26. Congestion Index
▪ The congestion index attempts to identify the market‘s character by dividing
the actual percentage that the market has changed in the past x days by the
extreme range
▪ To minimize daily noise the CI is further smoothed by a 3-day exponential moving
average.
▪ The congestion index fluctuates between 100 and −100. The larger the absolute
value, the less congested the current market. Readings between +20 and −20
indicate congestion or oscillating mode.
▪ Crossing over the 20 line from below indicates the start of a rising trend.
Conversely, the start of a down turn is indicated by crossing under −20 from
above.
▪ The CI can also be used as an overbought/oversold oscillator. The indicator
signals an exhaustion of the prevailing price trend and warns of an impending
price reversal when it reaches an extreme reading either above 85 or below −85
and then reverses direction.
26

27. Congestion Index
▪ Vertical horizontal filter (VHF) or Wilder‘s ADX, the congestion index is
directional for the following reasons:
- It is self-contained in the sense that it eliminates the need for a
second indicator to identify the trend direction.
- It is better in identifying trend reversals.
- It provides more accurate readings in cases of temporary pullbacks.
- The chart space is less congested.
▪ CI was the first to identify a rising trend which started at the beginning
of April 2007 and lasted until the end of May. On 5 April, the CI crossed
over 20, decisively followed eight days later by the VHF crossing over
0.35 (the indicator‘s default value for a trend) on 16 April.
27

28. Congestion Index
28
Chart of the S&P 500 from 1
February 2007 to 12 October
2007. The 3-day moving
average of the 28-day CI is
plotted in the top window,
the 28-day vertical horizontal
filter below and the 14-day
ADX in the second from
bottom window. CI detected
both up trends (in gray) with
virtually no
lag.

29. Congestion Index
▪ The VHF is calculated using the formula:
▪
▪ where Highest(C,28) is the highest close in the last 28 days;
Lowest(C,28) is the lowest close in the last 28 days; and the
denominator is the sum of the absolute value of the difference
between consecutive daily closes for the last 28 days.
▪
29

30. Points to be Remember
 Relative strength (RS) is a popular indicator which
compares one security with another or with a benchmark
index
 Bollinger Band Divergence in order to calculate the
divergence between price and money flow.
 Indicator values vary from −100 to +100, values less than zero
indicate negative divergence and over zero positive divergence.
 Buy signals are generated when the indicator reaches a peak
above a certain level (usually 10 to 30) and subsequently
declines.
 Similarly, sell signals are triggered when the indicator reaches a
bottom below a certain level (usually −10 to −30) and rises.

31. Points to be Remember
 Disparity Index indicator is a technical momentum
indicator that compares the current market price with the
moving average of price over a particular time period.
 The Disparity Index indicator was developed by Steve
Nison.
 This indicator calculates the percentage change from a specific moving
average. Suppose a stock is quoting at Rs. 105. The 14-day simple moving
average is at 100. So the Disparity Index will show 105-100 = 5%.
 Traders can create buy/ sell positions by identifying short term overbought or
oversold positions.
 Intermarket Momentum Oscillator - The formula above
will normalize the divergence on a scale from 1 to 100.
Trading signal interpretation is similar to that of the
stochastic oscillator

32. Points to be Remember
 Intermarket LRS Divergence money flow divergence
between price and the volume flow indicator.
 First the linear regression slope of both securities is
calculated and the intermarket slope is then adjusted to
take into account the difference in volatilities between the
two securities
 Values below zero indicate negative divergence and
values over the zero line positive divergence.
 Buy signals are generated when the indicator reaches a peak above a certain
level (usually between 10 and 40) and subsequently declines.
 Similarly, sell signals are triggered when the indicator reaches a bottom
below a certain level (usually from −10 to −40) and rises

33. Points to be Remember
 Intermarket Regression Divergence - linear regression
equation derived is used to make a prediction of likely
values of the dependent variable or the security to be
predicted (gold in our examples), based on values of a
correlated market.
 The congestion index attempts to identify the market‘s
character by dividing the actual percentage that the
market has changed in the past x days by the extreme
range
 Multiple regression, the goal is to predict the value of
the dependent variable (the market to be predicted) from
a set of k independent variables (related markets).

34. Points to be Remember
 Z Scores Divergence - A convenient method of
normalizing both prices on an equal scale is by converting
the prices to their Z scores.
 Z-scores use the standard deviation as a unit of measure
and indicate how many standard deviations the price falls
above or below the mean.
 The intermarket Moving Average - 5-day moving
average is plotted together with the classic 5-day moving
average in the bottom window and the multiple
divergence indicator in the top window.
 Notice that when the intermarket moving average crosses
above the classic moving average the divergence (top
window) turns positive.

35. Thanks