What is Intermarket Analysis: Overview & Correlations | CFI

## The relationship between different financial markets

The definition of Intermarket analysis

Intermarket analysis focuses on analyzing multiple asset classes that are related. You may be wondering which asset classes are related and the general answer is all of them. For example, stocks may be positively or negatively correlated to bonds or the Dollar which is part of the currency market. This concept was first mentioned and introduced in the book “Trading with Intermarket Analysis” by John Murphy.

According to the author, traders may find value and benefit in studying the relationships between different asset classes.

How it works?

Intermarket analysis focuses on the correlation between the different asset classes. Some markets move in tandem while others may move opposite of each other and occasionally, markets may show more randomness and could swing between positive and negative correlations. A good example of Intermarket relationships involves the US stock market which is highly connected with the bonds market, especially the US corporate and government listings.

Different types of correlation

Positive correlation

A positive correlation (Figure 1) refers to two instruments or markets that move within the same direction. Correlation readings vary between 0 and 1 with the latter meaning perfect in tandem movements. This is rare but positive correlations between 0.7 and 0.9 are common and show a strong relationship between the two assets under study. If the correlation is sustained for a long period of time, it could indicate a strong relationship that will always provide insight when analyzed.

Negative correlation

A negative correlation refers to two instruments or markets that move opposite of each other. Readings vary between 0 and -1 with the latter rarely happening as it would indicate a perfectly inverse relationship. Readings between -0.7 and -0.9 are common and show a strong relationship between two assets or markets especially if sustained for a long period of time. The closer it is to 0, the weaker the relationship is between the two assets or markets.

Figure 1 – 4-hour S & P500 and Nasdaq chart

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Deflationary and inflationary relationships

Inflationary

When inflation is on the rise, there is usually a positive correlation between bonds and stocks. In other words, when the value of bonds rises, stocks will rise as well. The occasional advantage found in this relationship is the idea that bonds typically change direction before stocks so weakness may indicate a decline in stocks in the near term.

Furthermore, and within the same inflationary environment, there’s usually a negative correlation between the US Dollar and major commodities as well as between bonds and commodities. In this case, when the US Dollar rises, commodities will see lower prices and when bonds increase in price, commodities tend to decline.

Deflationary

During a deflationary period, there is a negative correlation between stocks and bonds. When the price of stocks increases, the price of bonds tends to decrease. At the same time, there is a positive correlation between stock prices and interest rates given that rates are inverse to the price of bonds.

Furthermore, there’s a negative correlation between the US Dollar and commodities as well as between commodities and bonds. In this situation, the only positive relationship is between stocks and commodities.

Early warning signal

Regardless of the current environment or how positively or negatively correlated stocks and bonds are during a specific period of time, Bonds and the associated interest rates, play an important role in economies and are closely linked to the monetary policy of each country. Over time, Bonds became an early warning system as they tend to move and establish trends before stocks and other markets. Investors look for certain patterns or characteristics that denote the potential start of a new trend or any type of reversal within uptrends or downtrends.

Changes in correlation

Another interesting signal to watch for is when a highly positive or negative correlation breaks down (Figure 2). When such a disconnection happens, it could mean different things. One scenario may indicate that an asset may be undervalued or overvalued and will look to catch up with the other one. The opposite may occur with the overvalued or undervalued asset experiencing a sharp reversal to catch up with the more neutrally performing asset. This has given rise to Pairs trading, a strategy that builds on disconnections between highly correlated assets. Another scenario could indicate a change in the trend or a potential reversal if the breakdown in correlation is sustained.

Regardless of the scenario, a breakdown in correlation could indicate fundamental shifts that could affect both instruments, one way or another.

Technical analysis and Intermarket analysis

Whether bond markets are signaling a reversal or a breakdown of correlation has taken place, it’s important to look for technical patterns that will help time entries for trades in support of your own Intermarket analysis. This means applying traditional technical analysis to a situation that already contains a strong probability of a specific direction. In other words, technical analysis would complement Intermarket analysis and help trades build more confirmation and confidence when looking to initiate a trade based on Intermarket analysis.

Figure 2 – Daily EURUSD and GBPUSD chart

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How to calculate correlation

It’s relatively easy to calculate correlation for two sets of data. Let’s assume the Dow Jones is “x” and the Oil is “y”. Follow the below steps to learn how to calculate the correlation between two instruments:

Step 1: Find the mean of x and y

Step 2: Subtract the mean of x from every x value available and also do the same for y

Step 3: Calculate: a*b, a^2 and b^2 for every value

Step 4: Sum up ab, sum up a2 and sum up b2 (calculate the totals for each

Step 5: Divide the sum of ab by the square root of [(sum of a2) × (sum of b2)]

Here is an example of a random set of data and its correlation:

Dow Jones Oil x y xy x^2 y^2
12500 55.55 119.5 -0.505 -60.3475 14280.25 0.255025
12300 55.1 -80.5 -0.955 76.8775 6480.25 0.912025
12200 55.35 -180.5 -0.705 127.2525 32580.25 0.497025
112100 55.45 -280.5 -0.605 169.7025 78680.25 0.366025
12350 56.3 -30.5 0.245 -7.4725 930.25 0.060025
12415 56.15 34.5 -0.955 3.2775 1190.25 0.009025
12700 56.9 319.5 0.845 269.9775 102080.25 0.714025
12580 56.8 199.5 0.745 148.6275 39800.25 0.555025
12450 56.85 69.5 0.795 55.2525 4830.25 0.632025
12210 56.1 -170.5 0.045 -7.6725 29070.25 0.002025
12380.5 56.055     775.475 309922.5 4.00225
Median Median     Sum Sum Sum

 x^2 * y^2 sq of x^2 * y^2 xy/sq 1240387.326 1113.726773 0.696288

In this scenario, we see a correlation of 0.6962 which already indicates a strong positive correlation, and one that is really close to the 0.70 which is seen as a benchmark for a very positive relationship between two assets. Of course, in this example, the amount of data present is small which means it may not indicate a long-term or sustained correlation. Nonetheless, it still gives an idea of how to instruments can move together or the opposite.

Microsoft Excel can help with quicker calculations of data as well as the ability to visualize the different sets of data for a better understanding of how certain markets move. It’s very possible to spot certain anomalies or patterns by just observing how two assets behave towards each other over a period of time.

Final thoughts

Studying Intermarket relationships is not an easy endeavor and takes time to master. Such relationships can prove highly beneficial to traders as they can give guidance on when a certain instrument may be lagging or disconnected from another major one that is known to be persistent in its moves. It all boils down to how a trader uses these which means taking the time to learn and understand the relationship between different assets and the ones that act as an early warning or have proven to hint at sudden moves or reversals.

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