Correlation

Correlation is nothing more than taking two different things, and proving they are related in one way or another. Sales of ice cream and high tempratures are obviously co-related in the fact that as tempratures go up, people are more than likely to purchase ice cream.

Francis Galton, who created the primace of a mathematical formula for co-relation, first found a relationship between forearm length and a person’s height. Shortly thereafter, “Pearson’s Correlation Coefficient” was born, and can be applied to a wide variety of numbers to mathematically prove what is closely related:

1. Years of education and salary have been proven to be correlated — on average, the longer your education, the higher the salary. These two sets of numbers – education and salary – are correlated.

2. Graph (wife-husband.jpg) shows a wife’s age has a strong correlation to a husband’s age. The higher the husband’s age, on average, the higher the wife’s age as well. These two sets of numbers – a wife’s age and a husband’s age – are correlated.

3. The hotter the temprature outside, the less amount of clothes people will wear to counteract the heat. These two sets of numbers – temprature and amount of clothing worn – are correlated.

It is easy to pick out what can be correlated, and if you think about it, it is ALL over the place. Amount of hours worked and paycheck size is correlated, amount of time spent in a gym vs muscle size is correlated, and even amount of carbohydrates eaten and the resulting energy level is correlated as well.

Every single one of these examples can be mathematically “proven” using the “Pearson’s Correlation Coefficient” mentioned above. Once you run the numbers through the calculation for the two different things you wish to compare, you get a number that is between -1 and +1.

The closer to +1, the stronger they are POSITIVELY correlated (meaning one set of numbers goes up, the other goes up as well — like the example of a wife’s age and a husband’s age — they both go up at the same time, therefore they are POSITIVELY correlated).

The closer you get to -1, the stronger they are NEGATIVELY correlated (meaning one set of numbers goes down and the other goes up — like the example of the temprature going up, and the amount of clothing worn goes down — these two are NEGATIVELY correlated).

The closer you get to 0, the weaker the correlation.

So literally — 0.98 on this scale is 98% positive correlation. That means, roughly 98% of the time, they move the same direction as eachother.

Inversely, -0.87 would be an 87% negative correlation. This means that roughly 87% of the time, they move in the OPPOSITE direction of eachother.

But how does this apply to currency trading?

In the same way that we can determine the similarities between ice cream sales and hot summer months, we can determine similarities between two different countries’ currencies. One currency can be heavily dependant on other currency, meaning when one goes up, the other will go up (or down) to follow. Lets take the EUR/USD and the GBP/USD for example. They both are based on the US Dollar, and both are European currencies. That, coupled with other fundamental factors means they are strongly correlated. In this case, one goes up, there is a high probability that the other will as well.

For example, take a look at the chart. The charts look extremely similar, though one is the GBP/USD chart, and the other is the EUR/USD chart.

Remember, the Pearson’s Correlation Coefficient is a measure, from -1 to +1, of the correlation strength..+1 being a strong positive correlation (one will follow the other), -1 being a strong negative

If you were to run the two currencies through the Pearson’s Correlation Coefficient stated above, the EUR/USD and the GBP/USD have over a 0.9 correlation, meaning over 90% of the time, these currencies will follow eachother!

As another example, take a look at the second chart. These charts look like mirror images of eachother. They are in fact the EUR/USD and the USD/CHF. These two currencies have a super negative correlation of about -0.98. This means that 98% of the time, if the EUR/USD goes up, the USD/CHF will go down!

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