Correlation Between CanSino Biologics and Food Life
Can any of the company-specific risk be diversified away by investing in both CanSino Biologics and Food Life 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 Food Life into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CanSino Biologics and Food Life Companies, you can compare the effects of market volatilities on CanSino Biologics and Food Life 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 Food Life. Check out your portfolio center. Please also check ongoing floating volatility patterns of CanSino Biologics and Food Life.
Diversification Opportunities for CanSino Biologics and Food Life
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between CanSino and Food is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding CanSino Biologics and Food Life Companies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Food Life Companies 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 Food Life. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Food Life Companies has no effect on the direction of CanSino Biologics i.e., CanSino Biologics and Food Life go up and down completely randomly.
Pair Corralation between CanSino Biologics and Food Life
Assuming the 90 days trading horizon CanSino Biologics is expected to generate 2.26 times more return on investment than Food Life. However, CanSino Biologics is 2.26 times more volatile than Food Life Companies. It trades about 0.09 of its potential returns per unit of risk. Food Life Companies is currently generating about 0.03 per unit of risk. If you would invest 292.00 in CanSino Biologics on October 20, 2024 and sell it today you would earn a total of 56.00 from holding CanSino Biologics or generate 19.18% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
CanSino Biologics vs. Food Life Companies
Performance |
Timeline |
CanSino Biologics |
Food Life Companies |
CanSino Biologics and Food Life Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with CanSino Biologics and Food Life
The main advantage of trading using opposite CanSino Biologics and Food Life positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CanSino Biologics position performs unexpectedly, Food Life 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 Food Life will offset losses from the drop in Food Life's long position.CanSino Biologics vs. USU Software AG | CanSino Biologics vs. VITEC SOFTWARE GROUP | CanSino Biologics vs. Sumitomo Rubber Industries | CanSino Biologics vs. Applied Materials |
Food Life vs. United Rentals | Food Life vs. Air Lease | Food Life vs. GigaMedia | Food Life vs. Canadian Utilities Limited |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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