Correlation Between Sierra Madre and CMC Metals
Can any of the company-specific risk be diversified away by investing in both Sierra Madre and CMC Metals 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 Sierra Madre and CMC Metals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sierra Madre Gold and CMC Metals, you can compare the effects of market volatilities on Sierra Madre and CMC Metals 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 Sierra Madre with a short position of CMC Metals. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sierra Madre and CMC Metals.
Diversification Opportunities for Sierra Madre and CMC Metals
0.08 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Sierra and CMC is 0.08. Overlapping area represents the amount of risk that can be diversified away by holding Sierra Madre Gold and CMC Metals in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CMC Metals and Sierra Madre 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 Sierra Madre Gold are associated (or correlated) with CMC Metals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CMC Metals has no effect on the direction of Sierra Madre i.e., Sierra Madre and CMC Metals go up and down completely randomly.
Pair Corralation between Sierra Madre and CMC Metals
Assuming the 90 days horizon Sierra Madre is expected to generate 3.13 times less return on investment than CMC Metals. But when comparing it to its historical volatility, Sierra Madre Gold is 2.14 times less risky than CMC Metals. It trades about 0.02 of its potential returns per unit of risk. CMC Metals is currently generating about 0.04 of returns per unit of risk over similar time horizon. If you would invest 12.00 in CMC Metals on January 10, 2025 and sell it today you would lose (10.82) from holding CMC Metals or give up 90.17% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 96.16% |
| Values | Daily Returns |
Sierra Madre Gold vs. CMC Metals
Performance |
| Timeline |
| Sierra Madre Gold |
| CMC Metals |
Sierra Madre and CMC Metals Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Sierra Madre and CMC Metals
The main advantage of trading using opposite Sierra Madre and CMC Metals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sierra Madre position performs unexpectedly, CMC Metals 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 CMC Metals will offset losses from the drop in CMC Metals' long position.| Sierra Madre vs. Equity Metals | Sierra Madre vs. Silver Wolf Exploration | Sierra Madre vs. Western Alaska Minerals | Sierra Madre vs. Summa Silver Corp |
| CMC Metals vs. Piedmont Lithium Ltd | CMC Metals vs. Sigma Lithium Resources | CMC Metals vs. Standard Lithium | CMC Metals vs. MP Materials Corp |
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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