Quote from Fraze14:
Return correlation seems to be a more accurate measure because it doesn't treat the moves of a $5 stock and a $20 stock the same, like price correlation.
If there are 2 functions (in our case 2 stocks) then the best way to find out if the functions related to each other is to find correlation between them (thatâs mathematics). You of course may try to find correlation between secondary functions and in some cases the result will be close.
For example, if there are 2 stocks (A and B) and during 3 days period they move like this:
(A): 3,6,9 and (B): 2,4,6 then both correlations (price and return) would give you 100%.
However you donât trade secondary functions (in your case you trying to find correlations between 2 differentials divided by functions itself). You trade the stocks â primary functions. Even if the secondary functions will be highly correlated, stocks themselves might behave quite different.
Here are some examples:
Let say stock A goes like this: 10,1,2; And stock B goes like this: 10,9,18
I would not say these are highly correlated stocks. Return Correlation however would give you 100%; but Price correlation would give negative (!) 10%
Some say that the most important that Difference between stocks should be in some range. If it goes on edge, then, based on historical data, we might go into trade. Yes, we might, and in some cases we could make money. However, good thing about correlated stocks that you are protected (kinda) against market moves. If you trade correlated stocks you are market neutral (and then - range may go into account). Letâs take another example:
(A): 10,11,10,9,10â¦; (B): 10,9,10,11,10â¦
Difference would be between â2 and 2. However, these stocks correlated with negative 100%. Based on historic data we may trade this pair. But, in that case we as well might trade these stocks just by themselves. They donât need a pair.
Can we make money here? Yes. Are we going to be protected against market moves â not in this âpairâ.