Correlation Between Federated Max-cap and Mirova Global
Can any of the company-specific risk be diversified away by investing in both Federated Max-cap and Mirova Global 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 Max-cap and Mirova Global into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Federated Max Cap Index and Mirova Global Green, you can compare the effects of market volatilities on Federated Max-cap and Mirova Global 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 Max-cap with a short position of Mirova Global. Check out your portfolio center. Please also check ongoing floating volatility patterns of Federated Max-cap and Mirova Global.
Diversification Opportunities for Federated Max-cap and Mirova Global
0.45 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Federated and Mirova is 0.45. Overlapping area represents the amount of risk that can be diversified away by holding Federated Max Cap Index and Mirova Global Green in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mirova Global Green and Federated Max-cap 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 Max Cap Index are associated (or correlated) with Mirova Global. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mirova Global Green has no effect on the direction of Federated Max-cap i.e., Federated Max-cap and Mirova Global go up and down completely randomly.
Pair Corralation between Federated Max-cap and Mirova Global
Assuming the 90 days horizon Federated Max Cap Index is expected to generate 3.52 times more return on investment than Mirova Global. However, Federated Max-cap is 3.52 times more volatile than Mirova Global Green. It trades about 0.05 of its potential returns per unit of risk. Mirova Global Green is currently generating about -0.12 per unit of risk. If you would invest 766.00 in Federated Max Cap Index on October 25, 2024 and sell it today you would earn a total of 6.00 from holding Federated Max Cap Index or generate 0.78% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Federated Max Cap Index vs. Mirova Global Green
Performance |
Timeline |
Federated Max Cap |
Mirova Global Green |
Federated Max-cap and Mirova Global Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Federated Max-cap and Mirova Global
The main advantage of trading using opposite Federated Max-cap and Mirova Global positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Federated Max-cap position performs unexpectedly, Mirova Global 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 Mirova Global will offset losses from the drop in Mirova Global's long position.Federated Max-cap vs. Vanguard Total Stock | Federated Max-cap vs. Vanguard 500 Index | Federated Max-cap vs. Vanguard Total Stock | Federated Max-cap vs. Vanguard Total Stock |
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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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