Correlation Between Gmo High and Power Floating
Can any of the company-specific risk be diversified away by investing in both Gmo High and Power Floating 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 Gmo High and Power Floating into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Gmo High Yield and Power Floating Rate, you can compare the effects of market volatilities on Gmo High and Power Floating 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 Gmo High with a short position of Power Floating. Check out your portfolio center. Please also check ongoing floating volatility patterns of Gmo High and Power Floating.
Diversification Opportunities for Gmo High and Power Floating
0.48 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Gmo and Power is 0.48. Overlapping area represents the amount of risk that can be diversified away by holding Gmo High Yield and Power Floating Rate in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Power Floating Rate and Gmo High 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 Gmo High Yield are associated (or correlated) with Power Floating. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Power Floating Rate has no effect on the direction of Gmo High i.e., Gmo High and Power Floating go up and down completely randomly.
Pair Corralation between Gmo High and Power Floating
Assuming the 90 days horizon Gmo High is expected to generate 1.08 times less return on investment than Power Floating. In addition to that, Gmo High is 2.62 times more volatile than Power Floating Rate. It trades about 0.17 of its total potential returns per unit of risk. Power Floating Rate is currently generating about 0.49 per unit of volatility. If you would invest 984.00 in Power Floating Rate on September 12, 2024 and sell it today you would earn a total of 20.00 from holding Power Floating Rate or generate 2.03% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 98.44% |
| Values | Daily Returns |
Gmo High Yield vs. Power Floating Rate
Performance |
| Timeline |
| Gmo High Yield |
| Power Floating Rate |
Gmo High and Power Floating Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Gmo High and Power Floating
The main advantage of trading using opposite Gmo High and Power Floating positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Gmo High position performs unexpectedly, Power Floating 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 Power Floating will offset losses from the drop in Power Floating's long position.| Gmo High vs. General Money Market | Gmo High vs. Edward Jones Money | Gmo High vs. The Gabelli Money | Gmo High vs. Prudential Government Money |
| Power Floating vs. John Hancock Money | Power Floating vs. Blackrock Exchange Portfolio | Power Floating vs. Putnam Money Market | Power Floating vs. Money Market Obligations |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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