Correlation Between Gabelli Global and Poplar Forest
Can any of the company-specific risk be diversified away by investing in both Gabelli Global and Poplar Forest 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 Gabelli Global and Poplar Forest into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Gabelli Global Financial and Poplar Forest Nerstone, you can compare the effects of market volatilities on Gabelli Global and Poplar Forest 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 Gabelli Global with a short position of Poplar Forest. Check out your portfolio center. Please also check ongoing floating volatility patterns of Gabelli Global and Poplar Forest.
Diversification Opportunities for Gabelli Global and Poplar Forest
0.93 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Gabelli and Poplar is 0.93. Overlapping area represents the amount of risk that can be diversified away by holding Gabelli Global Financial and Poplar Forest Nerstone in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Poplar Forest Nerstone and Gabelli Global 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 Gabelli Global Financial are associated (or correlated) with Poplar Forest. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Poplar Forest Nerstone has no effect on the direction of Gabelli Global i.e., Gabelli Global and Poplar Forest go up and down completely randomly.
Pair Corralation between Gabelli Global and Poplar Forest
Assuming the 90 days horizon Gabelli Global Financial is expected to generate 1.83 times more return on investment than Poplar Forest. However, Gabelli Global is 1.83 times more volatile than Poplar Forest Nerstone. It trades about 0.11 of its potential returns per unit of risk. Poplar Forest Nerstone is currently generating about 0.12 per unit of risk. If you would invest 1,570 in Gabelli Global Financial on December 29, 2024 and sell it today you would earn a total of 105.00 from holding Gabelli Global Financial or generate 6.69% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 98.39% |
Values | Daily Returns |
Gabelli Global Financial vs. Poplar Forest Nerstone
Performance |
Timeline |
Gabelli Global Financial |
Poplar Forest Nerstone |
Gabelli Global and Poplar Forest Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Gabelli Global and Poplar Forest
The main advantage of trading using opposite Gabelli Global and Poplar Forest positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Gabelli Global position performs unexpectedly, Poplar Forest 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 Poplar Forest will offset losses from the drop in Poplar Forest's long position.Gabelli Global vs. Global Resources Fund | Gabelli Global vs. Invesco Energy Fund | Gabelli Global vs. Energy Basic Materials | Gabelli Global vs. Alpsalerian Energy Infrastructure |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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