Correlation Between Commodities Strategy and Gmo Resources
Can any of the company-specific risk be diversified away by investing in both Commodities Strategy and Gmo 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 Commodities Strategy and Gmo Resources into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Commodities Strategy Fund and Gmo Resources, you can compare the effects of market volatilities on Commodities Strategy and Gmo 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 Commodities Strategy with a short position of Gmo Resources. Check out your portfolio center. Please also check ongoing floating volatility patterns of Commodities Strategy and Gmo Resources.
Diversification Opportunities for Commodities Strategy and Gmo Resources
0.35 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Commodities and Gmo is 0.35. Overlapping area represents the amount of risk that can be diversified away by holding Commodities Strategy Fund and Gmo Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Gmo Resources and Commodities Strategy 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 Commodities Strategy Fund are associated (or correlated) with Gmo Resources. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Gmo Resources has no effect on the direction of Commodities Strategy i.e., Commodities Strategy and Gmo Resources go up and down completely randomly.
Pair Corralation between Commodities Strategy and Gmo Resources
Assuming the 90 days horizon Commodities Strategy Fund is expected to generate 0.78 times more return on investment than Gmo Resources. However, Commodities Strategy Fund is 1.28 times less risky than Gmo Resources. It trades about 0.03 of its potential returns per unit of risk. Gmo Resources is currently generating about -0.16 per unit of risk. If you would invest 2,907 in Commodities Strategy Fund on September 28, 2024 and sell it today you would earn a total of 57.00 from holding Commodities Strategy Fund or generate 1.96% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.41% |
Values | Daily Returns |
Commodities Strategy Fund vs. Gmo Resources
Performance |
Timeline |
Commodities Strategy |
Gmo Resources |
Commodities Strategy and Gmo Resources Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Commodities Strategy and Gmo Resources
The main advantage of trading using opposite Commodities Strategy and Gmo Resources positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Commodities Strategy position performs unexpectedly, Gmo 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 Gmo Resources will offset losses from the drop in Gmo Resources' long position.Commodities Strategy vs. Basic Materials Fund | Commodities Strategy vs. Energy Services Fund | Commodities Strategy vs. Energy Fund Investor | Commodities Strategy vs. Real Estate Fund |
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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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