Correlation Between Juniper Networks and Federated Hermes
Can any of the company-specific risk be diversified away by investing in both Juniper Networks and Federated Hermes 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 Juniper Networks and Federated Hermes into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Juniper Networks and Federated Hermes ETF, you can compare the effects of market volatilities on Juniper Networks and Federated Hermes 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 Juniper Networks with a short position of Federated Hermes. Check out your portfolio center. Please also check ongoing floating volatility patterns of Juniper Networks and Federated Hermes.
Diversification Opportunities for Juniper Networks and Federated Hermes
-0.58 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Juniper and Federated is -0.58. Overlapping area represents the amount of risk that can be diversified away by holding Juniper Networks and Federated Hermes ETF in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Federated Hermes ETF and Juniper Networks 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 Juniper Networks are associated (or correlated) with Federated Hermes. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Federated Hermes ETF has no effect on the direction of Juniper Networks i.e., Juniper Networks and Federated Hermes go up and down completely randomly.
Pair Corralation between Juniper Networks and Federated Hermes
Given the investment horizon of 90 days Juniper Networks is expected to under-perform the Federated Hermes. In addition to that, Juniper Networks is 1.22 times more volatile than Federated Hermes ETF. It trades about -0.15 of its total potential returns per unit of risk. Federated Hermes ETF is currently generating about 0.25 per unit of volatility. If you would invest 2,618 in Federated Hermes ETF on September 4, 2024 and sell it today you would earn a total of 291.00 from holding Federated Hermes ETF or generate 11.12% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 98.44% |
Values | Daily Returns |
Juniper Networks vs. Federated Hermes ETF
Performance |
Timeline |
Juniper Networks |
Federated Hermes ETF |
Juniper Networks and Federated Hermes Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Juniper Networks and Federated Hermes
The main advantage of trading using opposite Juniper Networks and Federated Hermes positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Juniper Networks position performs unexpectedly, Federated Hermes 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 Federated Hermes will offset losses from the drop in Federated Hermes' long position.Juniper Networks vs. Infinera | Juniper Networks vs. Lumentum Holdings | Juniper Networks vs. Extreme Networks | Juniper Networks vs. Clearfield |
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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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