Intermarket Analysis

Intermarket analysis looks at more than one related asset class or financial market to determine the strength or weakness of the financial markets or asset classes being considered.

Instead of looking at financial markets or asset classes on an individual basis, intermarket analysis looks at several strongly correlated markets or asset classes, such as stocks, bonds and commodities. This type of analysis expands on simply looking at each individual market or asset in isolation by also looking at other markets or assets that have a strong relationship to the market or asset being considered. For example, when studying the U.S. market it is worthwhile to look at the U.S. bond market, commodity prices and the U.S. dollar. The changes in the related markets, such as commodity prices, have an impact on the U.S. stock market and need to be understood to obtain a greater understanding of the future direction of the U.S. stock market.

Intermarket analysis should be considered fundamental analysis, not really meant to work on a tick by tick basis. It gives you a general sense and direction. There are different approaches to intermarket analysis, including mechanical and rule-based.

Performing an analysis of intermarket relationships is usually possible with widely available, free data and a simple spreadsheet or charting program. A simple correlation study is the easiest type of intermarket analysis to perform. This type of analysis is when one variable is compared with a second variable in a separate data set.

A positive correlation can go as high as +1.0, which represents a perfect and positive correlation between the two data sets. A perfect inverse (negative) correlation depicts a value as low as -1.0. Readings near the zero line would indicate that there is no discernible correlation between the two samples.

Perfect correlation between any two markets for a very long period of time is rare, but most analysts would probably agree that any reading sustained over the +0.7 or under the –0.7 level (which would equate to approximately a 70 percent correlation) is statistically significant. Also, if a correlation moves from positive to negative, the relationship would most likely be unstable, and probably useless for trading.

The most widely accepted correlation is the inverse correlation between stock prices and interest rates, which postulates that as interest rates go up, stock prices go lower, and conversely, as interest rates go down, stock prices go up.