This post is an introduction and background into a mini-series of research and strategies focused on the gold market. The primary aim will be to investigate the impact of correlated markets on gold prices. The exact scope and direction we take during the mini-series will be flexible, open and honest. This is not a guru’s guide to trading gold but rather an open exploration of some common assumptions we make. If we find something interesting, our direction may pivot. If a reader has a good suggestion, we may also pivot. Some ideas will be bad and that is a good thing! You will have the benefit of not wasting time on those ideas yourself. Ultimately though, the hope is that we do find some useful tips or at least spark some creative ideas that you can take away and explore further yourself.
Who is this for?Regular readers of this site will note that each article usually targets a specific platform. However, in order to be as flexible as possible in our direction, this series will not target a specific platform. Instead, we will work within the platform that is best suited to the task at hand. For example, we might explore quick strategy ideas in Tradingview. Alternatively, we might use QuantConnect’s excellent research tools to perform some statistical analysis. Or maybe we will head over to Backtrader when we want to use some funky python packages. Finally, we might even just play around in Pandas. You get the idea… This mini-series is for everyone.
What is Correlation?This is not the first article on the site that covers correlation. However, for some beginners, it might be their first encounter with the term. As such a brief introduction follows taken from the original article. The term correlation is used to describe a relationship between two variables, items, objects or things. Correlated things move in a proportional manner to one and another and as such, we infer/assume that they have some sort of a relationship. This relationship could be a “cause and effect” type relationship where one variable is driving the other or it could be that they are both affected by the same external factor. When talking about correlation, the following terms are used to describe the relationship between the two items/data points we are looking at:
- Positive Correlation: Is used when
variable Aincreases and
variable Balso increases at the same (or similar time). For example, one could say that there is a correlation between the number of Big Mac’s a person eats per week and their body fat.
- Negative Correlation: Also known as an inverse correlation, describes the relationship where
variable Aincreases whilst
variable Bdecreases. Taking the Big Mac example a step further, one might say that there is an inverse correlation between Big Mac’s eaten and life expectancy.
- Spurious Correlation: This is the dangerous one. It is a term given to two items or datasets that appear to have a high correlation but are not actually related. This can easily happen with data sets which are both rising or falling by a proportional amount over a given measurement period. There is a great website that lists some amusing spurious correlations and will allow you to quickly realize that not all correlated items have a valid relationship with each other. You can find that here: http://www.tylervigen.com/spurious-correlations. I particularly enjoyed the correlation between the US cheese consumption per capita and the number of people who died by becoming tangled in their bed-sheets. Stay away from cheese supper folks!
Correlation CoefficientNow we know how to describe correlations, we need some way to measure it. This is where a correlation coefficient comes into play. It is a mathematical formula that results in a numerical measure of the correlation between
- Exactly –1. A perfect downhill (negative) linear relationship
- –0.70. A strong downhill (negative) linear relationship
- –0.50. A moderate downhill (negative) relationship
- –0.30. A weak downhill (negative) linear relationship
- 0. No linear relationship
- +0.30. A weak uphill (positive) linear relationship
- +0.50. A moderate uphill (positive) relationship
- +0.70. A strong uphill (positive) linear relationship
- Exactly +1. A perfect uphill (positive) linear relationship
Gold CorrelationsReturning to gold, you may sometimes hear that the price of gold is primarily driven by (correlated with) two key factors. First, is the strength of the US dollar and secondly, the US central bank interest rate. The reasons for this are:
- Gold is priced in USD. As such, when the dollar strengthens, gold becomes more expensive to the rest of the world where buyers must convert their local currency into USD before they can pick up the yellow metal.
- Opportunity cost. Gold is considered a safe haven asset. It is something we gravitate to when times are tough as it holds its value over the long term. However, US Government bonds are also considered a safe-haven, risk-free asset (although the risk-free part is debatable!) and they have one important advantage… They pay interest. So as the interests rate rises, the opportunity cost increases. In other words, you are missing out on the opportunity of receiving interest.
Preliminary AnalysisWithout going into too much detail for an introductory post, let’s at least take a little look at the general correlation between Gold, the US Dollar and Interest Rates. This will form the starting point in our journey and is not too out of place for an introduction. The first chart we compare Gold, the US Dollar Index and the effective federal reserve interest rate over the past 5 year period. (The lines on the chart appear in that order) We can see that the dollar index and gold charts appear to be almost a mirror image of each other. On the other side of the table, it is hard to see any excess declines in gold prices as the federal fund rate begins to increase. So what about if we actually look at a US 10 year bond yield instead? Now it becomes much easier to eyeball a more negative correlation. Finally, let’s remove the prices for the US Dollar index / 10-year bond yield and replace them with correlation coefficient indicators.
More to comeTo avoid this introduction becoming too long and unwieldy, we will leave it here. In the next article will dive deeper into the topic and provide some actual code to tinker with. Promise!
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