Covariance: How does it matter while evaluating stocks?
Covariance is considered to provide important insights into the stocks to the investors traditionally. It helps in predicting the potential price movements after analyzing the movement of stocks in the past.
What is Covariance and how is it calculated?
Covariance is a parameter that helps in calculating the movement of nature of movement of the two variables (stocks) when put together. An investor can evaluate looking the covariance value if the two stocks being considered share positive covariance (movement in same direction) or negative covariance (movement in opposite direction).
A portfolio having stocks with positive covariance is considered the best from investor’s point of view.
While calculating covariance, an investor looks into past prices of the stocks, in which he holds an interest. Generally closing price for each day is used to find the return from one day to the next. A list can be prepared to compare the stocks.
|Day||ABC Returns (%)||XYZ Returns (%)|
|Table 1: Daily returns for two stocks using the closing prices|
Calculating average return for every stock
For ABC (1.0 + 1.8 + 2.1 + 1.6 + 0.2) / 5 = 1.30
For XYZ (3 + 4.0 + 4.9 + 4.2 + 2.5) / 5 = 3.74
= [(1.0- 1.30) x (3 – 3.74)] + [(1.8 – 1.30) x (4.0 – 3.74)] + [(2.1 – 1.30) x (4.9 – 3.74)] + …
Covariance between the two stock returns is 0.665.
Here positive number means the stocks move in the same direction. When ABC had a high return, XYZ also had a high return.
Uses of Covariance
Covariance tells how the stocks move together, but to conclude the strength of the relationship, we need use ‘correlation’. The correlation should be used in combination with the covariance.
Cov (X,Y) = covariance between X and Y
σX = standard deviation of X
σY = standard deviation of Y
Correlation always carries a measurement value between -1 and 1.
If the correlation is 1, both stocks move perfectly together, and if the correlation is -1, then they move in opposite directions.
If the correlation is 0, then they move in random directions from each other.
(With inputs from Investopedia)