Correlation Between Canlan Ice and Opus One
Can any of the company-specific risk be diversified away by investing in both Canlan Ice and Opus One 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 Canlan Ice and Opus One into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Canlan Ice Sports and Opus One Resources, you can compare the effects of market volatilities on Canlan Ice and Opus One 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 Canlan Ice with a short position of Opus One. Check out your portfolio center. Please also check ongoing floating volatility patterns of Canlan Ice and Opus One.
Diversification Opportunities for Canlan Ice and Opus One
-0.14 | Correlation Coefficient |
Good diversification
The 3 months correlation between Canlan and Opus is -0.14. Overlapping area represents the amount of risk that can be diversified away by holding Canlan Ice Sports and Opus One Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Opus One Resources and Canlan Ice 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 Canlan Ice Sports are associated (or correlated) with Opus One. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Opus One Resources has no effect on the direction of Canlan Ice i.e., Canlan Ice and Opus One go up and down completely randomly.
Pair Corralation between Canlan Ice and Opus One
Assuming the 90 days trading horizon Canlan Ice is expected to generate 43.27 times less return on investment than Opus One. But when comparing it to its historical volatility, Canlan Ice Sports is 10.3 times less risky than Opus One. It trades about 0.02 of its potential returns per unit of risk. Opus One Resources is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest 2.00 in Opus One Resources on September 24, 2024 and sell it today you would earn a total of 2.50 from holding Opus One Resources or generate 125.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Canlan Ice Sports vs. Opus One Resources
Performance |
Timeline |
Canlan Ice Sports |
Opus One Resources |
Canlan Ice and Opus One Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Canlan Ice and Opus One
The main advantage of trading using opposite Canlan Ice and Opus One positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Canlan Ice position performs unexpectedly, Opus One 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 Opus One will offset losses from the drop in Opus One's long position.Canlan Ice vs. BMTC Group | Canlan Ice vs. Caldwell Partners International | Canlan Ice vs. TWC Enterprises | Canlan Ice vs. Madison Pacific Properties |
Opus One vs. Canlan Ice Sports | Opus One vs. VIP Entertainment Technologies | Opus One vs. Brookfield Investments | Opus One vs. Thunderbird Entertainment Group |
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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