Correlation Between GreenX Metals and Future Metals
Can any of the company-specific risk be diversified away by investing in both GreenX Metals and Future 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 GreenX Metals and Future Metals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GreenX Metals and Future Metals NL, you can compare the effects of market volatilities on GreenX Metals and Future 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 GreenX Metals with a short position of Future Metals. Check out your portfolio center. Please also check ongoing floating volatility patterns of GreenX Metals and Future Metals.
Diversification Opportunities for GreenX Metals and Future Metals
0.54 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between GreenX and Future is 0.54. Overlapping area represents the amount of risk that can be diversified away by holding GreenX Metals and Future Metals NL in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Future Metals NL and GreenX Metals 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 GreenX Metals are associated (or correlated) with Future Metals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Future Metals NL has no effect on the direction of GreenX Metals i.e., GreenX Metals and Future Metals go up and down completely randomly.
Pair Corralation between GreenX Metals and Future Metals
Assuming the 90 days trading horizon GreenX Metals is expected to generate 0.86 times more return on investment than Future Metals. However, GreenX Metals is 1.16 times less risky than Future Metals. It trades about -0.29 of its potential returns per unit of risk. Future Metals NL is currently generating about -0.36 per unit of risk. If you would invest 4,150 in GreenX Metals on August 30, 2024 and sell it today you would lose (650.00) from holding GreenX Metals or give up 15.66% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 95.65% |
| Values | Daily Returns |
GreenX Metals vs. Future Metals NL
Performance |
| Timeline |
| GreenX Metals |
| Future Metals NL |
GreenX Metals and Future Metals Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GreenX Metals and Future Metals
The main advantage of trading using opposite GreenX Metals and Future Metals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GreenX Metals position performs unexpectedly, Future 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 Future Metals will offset losses from the drop in Future Metals' long position.| GreenX Metals vs. Givaudan SA | GreenX Metals vs. Antofagasta PLC | GreenX Metals vs. Centamin PLC | GreenX Metals vs. Atalaya Mining |
| Future Metals vs. Givaudan SA | Future Metals vs. Antofagasta PLC | Future Metals vs. Centamin PLC | Future Metals vs. Atalaya Mining |
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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