The commodity markets are often thought of as being homogeneous, which is to say that all the commodities in the market are viewed similarly. However, this isn’t true. They’re all different in some way.
Every commodity in the world is unique, whether where it is produced or where it comes from in geography. Therefore every commodity has somewhat of an individualized behaviour when it comes to price direction and volatility. This is significant because not only can understanding these nuances help traders make better decisions when investing, but they also offer predictive ability when forecasting future spot prices on a macro scale.
So what does each commodity have in common with one another? And why do certain ones correlate better than others? The vital notion to keep in mind is that commodities are traded on organized exchanges and almost always have a future contract or spot price associated with them.
Although we don’t know the specific behaviour of every commodity, we do know that they generally share some tendencies like:
- Higher trading volume than most stocks and other financial instruments
- They’re all impacted by weather, politics and civil unrest to varying degrees
- Price direction for nearby contracts generally follow those of distant ones (contango)
This means that analyzing one particular commodity can offer insight into how others will behave because there’s at least one thing in common. For example, if oil prices rally sharply, it’s reasonable to expect natural gas prices to also trend up during the same period.
By taking a large enough sample size, it’s possible to see if certain commodities move together with others outside of typical market behaviour (i.e. volatility). The easiest way to look at this is through the use of regression analysis. According to this theory, when two assets are highly correlated, they should exhibit practically the same price movement over time. And if they’re not? Then perhaps there’s some predictive ability at work here.
Because by understanding what influences the prices of various commodities, traders can make more informed decisions about their trading and investing strategies
For example, predicting future oil demand will affect steel, palladium and other related goods while natural disasters like Hurricane Sandy in 2012 caused spikes in cotton prices. Although we can’t say that all of these commodities move together simultaneously, understanding the relationships between them certainly does help traders craft more intelligent portfolios. With that in mind, let’s look at two examples of how this works in practice.
The first example uses USO (United States Oil Fund) and GLD (SPDR Gold Trust). For simplicity purposes, we’ll assume both are traded in USD, and their correlation is 0.7, which meets the requirement for a firm or robust correlation. If you’ve never heard of linear regression before, feel free to brush up on your knowledge with our brief tutorial because it’s a vital analytical tool used to make money from technical analysis.
In this case, we notice a direct line between these two assets that have a zero intercept. This means when X is equal to 0, Y should always have a price of 0 as well. In other words? Assets with a slope = 1 are perfectly correlated and will always show the same price direction (i.e. oil and gold). Checking for this relationship over time, we see it holds up 100% of the time. If you’re not familiar with basic linear regression or would like to know more about what this all means.
Although supply and demand do not drive commodities, investors utilize their price changes to predict overall
sentiment and make short-term trading decisions. Investigate these commodities and see whether they foretell the market downturns that are sure to follow.