Correlation Between Juggernaut Exploration and BCM Resources
Can any of the company-specific risk be diversified away by investing in both Juggernaut Exploration and BCM Resources 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 Juggernaut Exploration and BCM Resources into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Juggernaut Exploration and BCM Resources, you can compare the effects of market volatilities on Juggernaut Exploration and BCM Resources 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 Juggernaut Exploration with a short position of BCM Resources. Check out your portfolio center. Please also check ongoing floating volatility patterns of Juggernaut Exploration and BCM Resources.
Diversification Opportunities for Juggernaut Exploration and BCM Resources
0.46 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Juggernaut and BCM is 0.46. Overlapping area represents the amount of risk that can be diversified away by holding Juggernaut Exploration and BCM Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BCM Resources and Juggernaut Exploration 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 Juggernaut Exploration are associated (or correlated) with BCM Resources. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BCM Resources has no effect on the direction of Juggernaut Exploration i.e., Juggernaut Exploration and BCM Resources go up and down completely randomly.
Pair Corralation between Juggernaut Exploration and BCM Resources
Assuming the 90 days horizon Juggernaut Exploration is expected to generate 4.12 times more return on investment than BCM Resources. However, Juggernaut Exploration is 4.12 times more volatile than BCM Resources. It trades about 0.2 of its potential returns per unit of risk. BCM Resources is currently generating about 0.27 per unit of risk. If you would invest 4.67 in Juggernaut Exploration on November 22, 2024 and sell it today you would earn a total of 1.59 from holding Juggernaut Exploration or generate 34.05% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 95.45% |
Values | Daily Returns |
Juggernaut Exploration vs. BCM Resources
Performance |
Timeline |
Juggernaut Exploration |
BCM Resources |
Juggernaut Exploration and BCM Resources Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Juggernaut Exploration and BCM Resources
The main advantage of trading using opposite Juggernaut Exploration and BCM Resources positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Juggernaut Exploration position performs unexpectedly, BCM Resources 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 BCM Resources will offset losses from the drop in BCM Resources' long position.Juggernaut Exploration vs. BCM Resources | Juggernaut Exploration vs. Eskay Mining Corp | Juggernaut Exploration vs. Nevada King Gold | Juggernaut Exploration vs. Skeena Resources |
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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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