Correlation Between CanSino Biologics and Seadrill
Can any of the company-specific risk be diversified away by investing in both CanSino Biologics and Seadrill at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining CanSino Biologics and Seadrill into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CanSino Biologics and Seadrill Limited, you can compare the effects of market volatilities on CanSino Biologics and Seadrill and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in CanSino Biologics with a short position of Seadrill. Check out your portfolio center. Please also check ongoing floating volatility patterns of CanSino Biologics and Seadrill.
Diversification Opportunities for CanSino Biologics and Seadrill
-0.56 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between CanSino and Seadrill is -0.56. Overlapping area represents the amount of risk that can be diversified away by holding CanSino Biologics and Seadrill Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Seadrill Limited and CanSino Biologics is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on CanSino Biologics are associated (or correlated) with Seadrill. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Seadrill Limited has no effect on the direction of CanSino Biologics i.e., CanSino Biologics and Seadrill go up and down completely randomly.
Pair Corralation between CanSino Biologics and Seadrill
Assuming the 90 days horizon CanSino Biologics is expected to generate 2.39 times more return on investment than Seadrill. However, CanSino Biologics is 2.39 times more volatile than Seadrill Limited. It trades about 0.05 of its potential returns per unit of risk. Seadrill Limited is currently generating about -0.22 per unit of risk. If you would invest 413.00 in CanSino Biologics on December 20, 2024 and sell it today you would earn a total of 47.00 from holding CanSino Biologics or generate 11.38% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 98.33% |
Values | Daily Returns |
CanSino Biologics vs. Seadrill Limited
Performance |
Timeline |
CanSino Biologics |
Seadrill Limited |
CanSino Biologics and Seadrill Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with CanSino Biologics and Seadrill
The main advantage of trading using opposite CanSino Biologics and Seadrill positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CanSino Biologics position performs unexpectedly, Seadrill can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Seadrill will offset losses from the drop in Seadrill's long position.CanSino Biologics vs. Canopy Growth Corp | CanSino Biologics vs. Aurora Cannabis | CanSino Biologics vs. SNDL Inc | CanSino Biologics vs. Cronos Group |
Seadrill vs. Nabors Industries | Seadrill vs. Borr Drilling | Seadrill vs. Patterson UTI Energy | Seadrill vs. Noble plc |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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