Positional correlations represent a power and misleading tool for investors to use for examining the implicit risks of their portfolio. These implicit risks are demonstrative of how it is vague market psychological patterns, as traders group specific securities together into batches that they will trade in unison.
As these positions are traded in unison, their positions will fluctuate in sync with each other, resulting in a correlation in their movements. While not useful as a predictive tool, correlations do provide us with a means of searching for general trends in movements that might indicate redundant risk exposures, or interlocking exposure to the same general underlying opportunities.
Correlations themselves are a fairly simple thing to track. If two positions move in similar percentage amounts at similar times, they are arguable correlated. From there, we can calculate the percentage degree to which they are correlated, and then assess the probability that there is a tangible underlying trend that is directing both of these positions in the same direction.
For example, if two gold companies are correlated to being within 80% of each other, it might stand to reason that they are both being driven by gold prices by as much as 80%, and then the individual performance of the companies themselves is only priced in at 20%. While further investigation into such a situation would certainly be required, it does demonstrate the effectiveness of percentage correlation analysis.
Upon having determined a performance correlation between two asset classes, we need to then look deeper into the securities to understand whether this relationship demonstrates a risk to our personal investment portfolios. For example, if we are seeing two financial securities, we might see the correlation as being a representation of how it is that both securities are exposed to the same underlying market risks, such as inflation and interest rates. Alternatively, if we are comparing two mutual funds, we might see upon deeper investigation that the two funds are carrying similar portfolios, to the point at which they are going to be generating a great deal of their yearly returns from the same sources.
The final step to examining performance correlations between securities is to begin expanding the equation to take into account a few benchmark assets as well. For example, we might want to compare the correlation of two commodity-equities to the performance of the commodity price itself, to see how much of the correlation comes from their product’s price fluctuations.
Otherwise, we can then compare the fluctuations against the movements of an overall index, to see if maybe the positions simply tend to respond to over-arching market trends in similar a similar manner. From there, we can ideally use this information to isolate distinct portfolio positions with discrete risk aspects, which we can then use to diversify our investment portfolios.