Uncorrelated Assets And Strategies – Benefits And Advantages (Examples And Backtest)
Uncorrelated or non-correlated assets and strategies are a traders’ goldmine. Why? Because it reduces risk and (might) increase returns. However, constructing a basket of stocks, assets, or strategies that are uncorrelated or non-correlated is probably the most difficult task in trading and investing. In this article, we show you examples and backtests of the power of making a diversified and uncorrelated portfolio of both different assets and strategies.
The benefits and advantages of having uncorrelated assets and strategies are both smaller drawdowns and higher returns. We provide you with examples and backtests to show you why.
You kill two birds with one stone. If you include assets that are uncorrelated with stocks, you can increase total returns (or at least get the same return) even if the included assets have lower total returns. The reason why is due to correlation – or lack thereof.
If you have a portfolio of trading strategies that are only mean-revertive, for example, you obtain very little by adding similar strategies. Quite the contrary, you might end up with lower returns and bigger drawdowns. You don’t want to put all your eggs in one basket, whether it be assets or similar types of strategies. Why? Because you want to have the smallest drawdown you can get and to have a portfolio that can withstand shocks and tail risks. You want an antifragile portfolio. We have touched upon this in a previous article:
Let’s start by explaining the basics of correlation:
What is correlation in trading and investing?
Correlation is probably the most important factor if you want to have a robust, diversified, and antifragile portfolio. In case you are not sure of what correlation is, we’ll give you a short primer:
Correlation is a mathematical term used to describe the covariance of two time series data.
For example, the weight of people is dependent on height. In a spreadsheet, you can put the height of a group of people in column A, and in column B you put their weight. If you have a sample with many observations, there will be a clear relationship between height and weight. This is correlation. Tall people normally weigh more than small people (we generalize).
Likewise, most tasks yield a result that corresponds to the effort you put in.
Hence, correlation is a statistical measure that shows how two variables are related, but it doesn’t give any causation. Two variables can correlate purely out of randomness.
Mathematically, a perfect correlation has a value of 1, and the opposite, when two variables move completely different ways, the correlation coefficient is -1. A zero correlation coefficient means that the