Correlation Between Green Star and First Graphene
Can any of the company-specific risk be diversified away by investing in both Green Star and First Graphene 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 Green Star and First Graphene into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Green Star Products and First Graphene, you can compare the effects of market volatilities on Green Star and First Graphene 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 Green Star with a short position of First Graphene. Check out your portfolio center. Please also check ongoing floating volatility patterns of Green Star and First Graphene.
Diversification Opportunities for Green Star and First Graphene
-0.45 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Green and First is -0.45. Overlapping area represents the amount of risk that can be diversified away by holding Green Star Products and First Graphene in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on First Graphene and Green Star 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 Green Star Products are associated (or correlated) with First Graphene. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of First Graphene has no effect on the direction of Green Star i.e., Green Star and First Graphene go up and down completely randomly.
Pair Corralation between Green Star and First Graphene
Given the investment horizon of 90 days Green Star Products is expected to generate 2.23 times more return on investment than First Graphene. However, Green Star is 2.23 times more volatile than First Graphene. It trades about 0.08 of its potential returns per unit of risk. First Graphene is currently generating about 0.05 per unit of risk. If you would invest 0.10 in Green Star Products on August 30, 2024 and sell it today you would earn a total of 0.01 from holding Green Star Products or generate 10.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Green Star Products vs. First Graphene
Performance |
| Timeline |
| Green Star Products |
| First Graphene |
Green Star and First Graphene Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Green Star and First Graphene
The main advantage of trading using opposite Green Star and First Graphene positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Green Star position performs unexpectedly, First Graphene 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 First Graphene will offset losses from the drop in First Graphene's long position.| Green Star vs. Akzo Nobel NV | Green Star vs. Avoca LLC | Green Star vs. Arkema SA ADR | Green Star vs. HUMANA INC |
| First Graphene vs. Haydale Graphene Industries | First Graphene vs. Versarien plc | First Graphene vs. NanoXplore | First Graphene vs. G6 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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