Correlation Between Ceragon Networks and Fidelity Advisor
Can any of the company-specific risk be diversified away by investing in both Ceragon Networks and Fidelity Advisor 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 Ceragon Networks and Fidelity Advisor into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ceragon Networks and Fidelity Advisor Value, you can compare the effects of market volatilities on Ceragon Networks and Fidelity Advisor 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 Ceragon Networks with a short position of Fidelity Advisor. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ceragon Networks and Fidelity Advisor.
Diversification Opportunities for Ceragon Networks and Fidelity Advisor
0.57 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Ceragon and Fidelity is 0.57. Overlapping area represents the amount of risk that can be diversified away by holding Ceragon Networks and Fidelity Advisor Value in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fidelity Advisor Value and Ceragon 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 Ceragon Networks are associated (or correlated) with Fidelity Advisor. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fidelity Advisor Value has no effect on the direction of Ceragon Networks i.e., Ceragon Networks and Fidelity Advisor go up and down completely randomly.
Pair Corralation between Ceragon Networks and Fidelity Advisor
Given the investment horizon of 90 days Ceragon Networks is expected to generate 4.24 times more return on investment than Fidelity Advisor. However, Ceragon Networks is 4.24 times more volatile than Fidelity Advisor Value. It trades about 0.15 of its potential returns per unit of risk. Fidelity Advisor Value is currently generating about 0.17 per unit of risk. If you would invest 294.00 in Ceragon Networks on September 4, 2024 and sell it today you would earn a total of 122.00 from holding Ceragon Networks or generate 41.5% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Ceragon Networks vs. Fidelity Advisor Value
Performance |
Timeline |
Ceragon Networks |
Fidelity Advisor Value |
Ceragon Networks and Fidelity Advisor Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ceragon Networks and Fidelity Advisor
The main advantage of trading using opposite Ceragon Networks and Fidelity Advisor positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ceragon Networks position performs unexpectedly, Fidelity Advisor 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 Fidelity Advisor will offset losses from the drop in Fidelity Advisor's long position.Ceragon Networks vs. Cambium Networks Corp | Ceragon Networks vs. KVH Industries | Ceragon Networks vs. Knowles Cor | Ceragon Networks vs. AudioCodes |
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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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