Correlation Between Mfs Blended and Mfs Lifetime
Can any of the company-specific risk be diversified away by investing in both Mfs Blended and Mfs Lifetime 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 Mfs Blended and Mfs Lifetime into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mfs Blended Research and Mfs Lifetime 2065, you can compare the effects of market volatilities on Mfs Blended and Mfs Lifetime 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 Mfs Blended with a short position of Mfs Lifetime. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mfs Blended and Mfs Lifetime.
Diversification Opportunities for Mfs Blended and Mfs Lifetime
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Mfs and Mfs is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding Mfs Blended Research and Mfs Lifetime 2065 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mfs Lifetime 2065 and Mfs Blended 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 Mfs Blended Research are associated (or correlated) with Mfs Lifetime. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mfs Lifetime 2065 has no effect on the direction of Mfs Blended i.e., Mfs Blended and Mfs Lifetime go up and down completely randomly.
Pair Corralation between Mfs Blended and Mfs Lifetime
Assuming the 90 days horizon Mfs Blended Research is expected to under-perform the Mfs Lifetime. In addition to that, Mfs Blended is 1.3 times more volatile than Mfs Lifetime 2065. It trades about -0.16 of its total potential returns per unit of risk. Mfs Lifetime 2065 is currently generating about 0.19 per unit of volatility. If you would invest 1,030 in Mfs Lifetime 2065 on August 31, 2024 and sell it today you would earn a total of 26.00 from holding Mfs Lifetime 2065 or generate 2.52% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Mfs Blended Research vs. Mfs Lifetime 2065
Performance |
| Timeline |
| Mfs Blended Research |
| Mfs Lifetime 2065 |
Mfs Blended and Mfs Lifetime Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Mfs Blended and Mfs Lifetime
The main advantage of trading using opposite Mfs Blended and Mfs Lifetime positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mfs Blended position performs unexpectedly, Mfs Lifetime 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 Mfs Lifetime will offset losses from the drop in Mfs Lifetime's long position.| Mfs Blended vs. Cref Money Market | Mfs Blended vs. John Hancock Money | Mfs Blended vs. Blackrock Exchange Portfolio | Mfs Blended vs. American Century Investment |
| Mfs Lifetime vs. Mfs Aggressive Growth | Mfs Lifetime vs. Mfs Research Fund | Mfs Lifetime vs. Mfs Series Trust | Mfs Lifetime vs. Mfs California Municipal |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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