Correlation Between Federated Global and Tax-managed
Can any of the company-specific risk be diversified away by investing in both Federated Global and Tax-managed 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 Federated Global and Tax-managed into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Federated Global Allocation and Tax Managed Large Cap, you can compare the effects of market volatilities on Federated Global and Tax-managed 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 Federated Global with a short position of Tax-managed. Check out your portfolio center. Please also check ongoing floating volatility patterns of Federated Global and Tax-managed.
Diversification Opportunities for Federated Global and Tax-managed
0.91 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Federated and Tax-managed is 0.91. Overlapping area represents the amount of risk that can be diversified away by holding Federated Global Allocation and Tax Managed Large Cap in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tax Managed Large and Federated 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 Federated Global Allocation are associated (or correlated) with Tax-managed. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tax Managed Large has no effect on the direction of Federated Global i.e., Federated Global and Tax-managed go up and down completely randomly.
Pair Corralation between Federated Global and Tax-managed
Assuming the 90 days horizon Federated Global Allocation is expected to generate 0.73 times more return on investment than Tax-managed. However, Federated Global Allocation is 1.37 times less risky than Tax-managed. It trades about 0.15 of its potential returns per unit of risk. Tax Managed Large Cap is currently generating about 0.08 per unit of risk. If you would invest 1,959 in Federated Global Allocation on November 7, 2024 and sell it today you would earn a total of 33.00 from holding Federated Global Allocation or generate 1.68% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Federated Global Allocation vs. Tax Managed Large Cap
Performance |
| Timeline |
| Federated Global All |
| Tax Managed Large |
Federated Global and Tax-managed Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Federated Global and Tax-managed
The main advantage of trading using opposite Federated Global and Tax-managed positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Federated Global position performs unexpectedly, Tax-managed 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 Tax-managed will offset losses from the drop in Tax-managed's long position.| Federated Global vs. Federated Max Cap Index | Federated Global vs. Federated Kaufmann Fund | Federated Global vs. Federated Strategic Income | Federated Global vs. Federated Bond Fund |
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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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