We see Correlation throughout our Statistical Analysis and all of our tools that do predictions use Correlations. In our Seasonal Charts, you can see Correlation in numbers. We’ll review the Seasonal Charts tool again and show where and why the Correlation is important.
Seasonal Charts are graphs depicting the historical performance of each Stock to help forecast future performance. They show how a stock has performed historically and will likely fare under different conditions. If a stock or ETF is correlated to historical price movements, it can give you what is the likelihood that the stock will follow that Correlation. It can also show approximately where Tops and Bottoms are likely to occur so that you can set Buy and Sell Stops. It also helps determine which price ‘blips’ are indicative of \trend changes.
Correlation can be defined in the world of finance as a statistical measure of how two securities move in relation to each other. In order to predict stocks, you need to correlate them to some index or baseline. Correlation is computed into what is known as the Correlation Coefficient which ranges between -1 and +1. A Perfect Positive Correlation has a Correlation Co-efficient of +1 and implies that as one security moves either up or down, the other security will move in lockstep, in the same direction.
Alternatively, Perfect Negative Correlation has a Correlation Co-efficient of -1 and means that if one security moves in either direction, the security that is perfectly negatively correlated will move in the opposite direction.
A perfect example would be any Retail Stock like Home Depot and Lowe’s, which are simultaneously announcing their earnings. Since both are in the same sector, have the same business model, and follow the same seasonal patterns, it can be said that they are highly-correlated.
Another example is Morgan Stanley and J.P. Morgan. It is very important to study these Seasonal Correlations, since certain stocks or banks will follow each other’s pattern. A binary event can break the correlation for a short period of time, but from the historic perspective it will revert to its mean.
Seasonality can provide a retail trader with an insight about whether current prices are correlated to historical price movements. In Figure 47, Correlation is 49% for SPY. You can see that the current prices of the stock market are correlated to its historic performance. We also know that the stock market usually sells off or trades in the range during the summer. This is why we see in June, July, August and September, that we are in the range bound trading. But, starting with November and going into December, we are making new higher highs. Even though we roughly know this by experience, it would be nice to type in any ticker, find this seasonality, and identify correlation of how the stock behaves today versus how it behaved in the past.