So the original question I should have worded like this.
Say your looking at a stock, Stock A, and want to find one other stock that has as close as possible linear relationship to that stock. So you look at the relationship between Stock A and another stock from the same sector, Stock B. You find that they have strong covariance because when stock A goes up 6% Stock B goes up 5%, then Stock A 8% and Stock B 8.5% etc, so they vary strongly together and have a high covariance.
Now you want to test Stock A against another Stock to see if it is perhaps better than the Stock B. So you look at Stock A compared to another stock in the same sector, Stock C. Now when Stock A rises 6% Stock C only rises 3%, so they don't vary as strongly together as Stock A and Stock B, so the covariance is not as high. But you notice if stock A rises 4% Stock C will rise 2% and will always maintain that predictable pattern. So although Stock A and Stock B vary at a higher amount together, Stock A and Stock C are more predictable in nature.
Now to answer this question I have concluded that to find the better linear relationship we need to normalise the distributions. Stock A and Stock B have a very similar distribution, so when Stock A goes up 8% say 2 standard deviations away from the mean, then Stock B will also rise by around 8% about 2 standard deviations away from mean.
Stock A and Stock B have a more widely distributed frequency of returns, so the distributions of Stock A and Stock B are almost identical. Stock C does not have a similar looking distribution, it is not as volatile as Stock A, so it will not vary as much as Stock A does in percentage growth e.g. 8% gain. But it will always be half of what Stock A return is.
So by normalising the distribution of returns to fit the same units I would find that Stock A and Stock C actually have a more linear and predictable relationship. The behaviour of stock A and Stock B is almost the same but not as predictable, the behaviour of Stock A and Stock C is not obvious straight away from covariance but is infact more linear.
By normalising the covariance of the two Stocks through the correlation coefficient, I change both stocks to similar units so I do not have the error of covariance which may give a false impression of which Stock has a stronger relationship due to face value of similar distributions.
Sorry for the stupid question in the first place.. I was not using the term Covariance correctly. Please confirm or add anything of value to this answer!?!?