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Intermarket Analysis:

Preliminary Quantification

In the Introduction to Intermarket Analysis we reviewed the work of John Murphy on market relationships. Here we look at Murray Ruggiero's work.

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Index of Page Topics
Sample Relations	Gold
Table of Relations	Oil
Pearson's Correlation	T-bonds
Stocks	Money
Statistics	Trends
Logic	Correlation

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Sample Relations

Here is a sample of relations presented by Ruggiero:

T-bonds turned down about five months before the S&P 500 (1987).

Costs increase, profits decrease.

T-bonds bottomed when the S&P 500 bottomed (1987).

Rates hit highs, prospects improve.

The CRB turned up about a month before T-bonds turned down (1987).

Commodity costs trigger need to raise interest rates.

Gold turned up about three months before Eurodollars turned down (1987).

Gold anticipates drop in value of money.

T-bonds turned down about three months before the S&P 500 (1987).

Sensitive (smart?) money first to move out of equities.

T-bonds turned up about a month before the S&P 500 (1994).

Sensitive money first to move into equities.

Eurodollars topped out just before the S&P 500 (1994).

Money more sensitive to interest rates than stocks.

DJ Utilities topped out about five months before the S&P 500 (1993)

Utilities more sensitive to money than ordinary stocks.

DJ Utilities and S&P 500 bottomed together (1994)

Utilities about as sensitive to money as ordinary stocks.

DJ Utilities peaked a few weeks before T-bonds (1993).

Utilities slightly more sensitive to interest rates than bonds

Electric Utilities turned up about two months before T-bonds (1994).

Utilities anticipate bond prices.

Copper bottomed as T-bonds topped (1993).

Commodities and bonds anticipate improving economy.

The (German) D-Mark and Comex Gold turned up together ((1989)

D-Mark up, dollar down.

The dollar turned down as crude oil began to bottom (1993)

Dollar under pressure late in bull market.

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Table of Relations

The sample relations presented by Ruggiero can be tabulated somewhat as follows, but for details you should consult his book.

Trade	Compare	Corr.	General Comments
S&P 500	T-Bonds	Positive	T-Bonds peak earlier.
	Utilities	Positive	Utilities lead S&P
T-Bonds	CRB	Negative	Reflected in CPI within few months
	Copper	Negative	Copper bottoms as bonds top
	Lumber	Negative	Not as reliable as copper
	Crude Oil	Negative	Less reliable than lumber
	Eurodollar	Positive	Euro usually leads at turning points
Gold	Eurodollar	Negative	Gold anticipates inflation (CRB)
	XAU	Positive	XAU leads gold
US Dollar	Crude Oil	Negative	Crude and dollar travel together
	Gold	Negative	Gold leads dollar
	D-Mark	Negative	D-Mark goes with gold
	T-Bonds	Negative	T-Bonds go with non-US currencies
Crude Oil	US Dollar	Negative	Tend to go together
	XOI	Positive	XOI is even or takes slight lead

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Pearson's Correlation

As an introduction to the correlation of sets of market data, consider the general relationship between two linear price functions of time. Any two linear functions differ either by the value of their slope or by their crossing point of the time axis. So if one function is:

p₁ = at₁

then the other is:

p₂ = bt₂ = b(t₁ + c) = b(p₁/a + c),

after substituting for t₁. The angle of the slope in each function is defined by the parameters, a and b.

If you now think of p1 and p2 not as functions but as clusters of price data, you can see a correlation between them.

The general formula for the correlation coefficient of two data clusters, as I presented it here, is:

r = å(x_i - X)(y_i - Y)/Öå (x_i - X)²Öå(y_i - Y)²

where the variances are given explicitly and X and Y are the respective means of the samples x and y.

Converting x and y to two time sets, separated by c (the lead or lag between them), the price group correlation coefficient is:

r_p = å(p₁(t)_i - P₁)(p₂(t+c)_i - P₂)/Öå (p₁(t)i - P₁)²Ö åp₂(t+c)_i - P₂)²

For this coefficient, p₁ and p₂ are the price groups and P₁ and P₂ are their respective means. The groups are separated by the time c and have different "slopes."

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