Correlation Between Blrc Sgy and Federated Hermes
Can any of the company-specific risk be diversified away by investing in both Blrc Sgy 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 Blrc Sgy and Federated Hermes into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Blrc Sgy Mnp and Federated Hermes Emerging, you can compare the effects of market volatilities on Blrc Sgy 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 Blrc Sgy with a short position of Federated Hermes. Check out your portfolio center. Please also check ongoing floating volatility patterns of Blrc Sgy and Federated Hermes.
Diversification Opportunities for Blrc Sgy and Federated Hermes
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Blrc and Federated is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding Blrc Sgy Mnp and Federated Hermes Emerging in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Federated Hermes Emerging and Blrc Sgy 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 Blrc Sgy Mnp 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 Emerging has no effect on the direction of Blrc Sgy i.e., Blrc Sgy and Federated Hermes go up and down completely randomly.
Pair Corralation between Blrc Sgy and Federated Hermes
Assuming the 90 days horizon Blrc Sgy Mnp is expected to under-perform the Federated Hermes. But the mutual fund apears to be less risky and, when comparing its historical volatility, Blrc Sgy Mnp is 5.01 times less risky than Federated Hermes. The mutual fund trades about -0.03 of its potential returns per unit of risk. The Federated Hermes Emerging is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 1,524 in Federated Hermes Emerging on December 30, 2024 and sell it today you would earn a total of 86.00 from holding Federated Hermes Emerging or generate 5.64% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Blrc Sgy Mnp vs. Federated Hermes Emerging
Performance |
Timeline |
Blrc Sgy Mnp |
Federated Hermes Emerging |
Blrc Sgy and Federated Hermes Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Blrc Sgy and Federated Hermes
The main advantage of trading using opposite Blrc Sgy and Federated Hermes positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Blrc Sgy 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.Blrc Sgy vs. Advent Claymore Convertible | Blrc Sgy vs. Absolute Convertible Arbitrage | Blrc Sgy vs. Columbia Convertible Securities | Blrc Sgy vs. Calamos Dynamic Convertible |
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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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