Correlation Between Angel Oak and Gabelli Multimedia
Can any of the company-specific risk be diversified away by investing in both Angel Oak and Gabelli Multimedia 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 Angel Oak and Gabelli Multimedia into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Angel Oak Financial and The Gabelli Multimedia, you can compare the effects of market volatilities on Angel Oak and Gabelli Multimedia 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 Angel Oak with a short position of Gabelli Multimedia. Check out your portfolio center. Please also check ongoing floating volatility patterns of Angel Oak and Gabelli Multimedia.
Diversification Opportunities for Angel Oak and Gabelli Multimedia
0.06 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Angel and Gabelli is 0.06. Overlapping area represents the amount of risk that can be diversified away by holding Angel Oak Financial and The Gabelli Multimedia in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on The Gabelli Multimedia and Angel Oak 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 Angel Oak Financial are associated (or correlated) with Gabelli Multimedia. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of The Gabelli Multimedia has no effect on the direction of Angel Oak i.e., Angel Oak and Gabelli Multimedia go up and down completely randomly.
Pair Corralation between Angel Oak and Gabelli Multimedia
Given the investment horizon of 90 days Angel Oak Financial is expected to generate 1.18 times more return on investment than Gabelli Multimedia. However, Angel Oak is 1.18 times more volatile than The Gabelli Multimedia. It trades about 0.16 of its potential returns per unit of risk. The Gabelli Multimedia is currently generating about 0.01 per unit of risk. If you would invest 1,267 in Angel Oak Financial on December 20, 2024 and sell it today you would earn a total of 70.00 from holding Angel Oak Financial or generate 5.52% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Angel Oak Financial vs. The Gabelli Multimedia
Performance |
Timeline |
Angel Oak Financial |
The Gabelli Multimedia |
Angel Oak and Gabelli Multimedia Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Angel Oak and Gabelli Multimedia
The main advantage of trading using opposite Angel Oak and Gabelli Multimedia positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Angel Oak position performs unexpectedly, Gabelli Multimedia 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 Gabelli Multimedia will offset losses from the drop in Gabelli Multimedia's long position.Angel Oak vs. Eaton Vance National | Angel Oak vs. Blackrock Muniholdings Ny | Angel Oak vs. Nuveen California Select | Angel Oak vs. MFS Investment Grade |
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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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