Correlation Between Mfs Lifetime and Mfs Commodity
Can any of the company-specific risk be diversified away by investing in both Mfs Lifetime and Mfs Commodity 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 Lifetime and Mfs Commodity into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mfs Lifetime 2065 and Mfs Commodity Strategy, you can compare the effects of market volatilities on Mfs Lifetime and Mfs Commodity 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 Lifetime with a short position of Mfs Commodity. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mfs Lifetime and Mfs Commodity.
Diversification Opportunities for Mfs Lifetime and Mfs Commodity
0.36 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Mfs and Mfs is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding Mfs Lifetime 2065 and Mfs Commodity Strategy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Mfs Commodity Strategy and Mfs Lifetime 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 Lifetime 2065 are associated (or correlated) with Mfs Commodity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Mfs Commodity Strategy has no effect on the direction of Mfs Lifetime i.e., Mfs Lifetime and Mfs Commodity go up and down completely randomly.
Pair Corralation between Mfs Lifetime and Mfs Commodity
Assuming the 90 days horizon Mfs Lifetime 2065 is expected to generate 0.78 times more return on investment than Mfs Commodity. However, Mfs Lifetime 2065 is 1.28 times less risky than Mfs Commodity. It trades about 0.16 of its potential returns per unit of risk. Mfs Commodity Strategy is currently generating about 0.04 per unit of risk. If you would invest 1,042 in Mfs Lifetime 2065 on May 17, 2025 and sell it today you would earn a total of 58.00 from holding Mfs Lifetime 2065 or generate 5.57% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 98.39% |
Values | Daily Returns |
Mfs Lifetime 2065 vs. Mfs Commodity Strategy
Performance |
Timeline |
Mfs Lifetime 2065 |
Mfs Commodity Strategy |
Mfs Lifetime and Mfs Commodity Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mfs Lifetime and Mfs Commodity
The main advantage of trading using opposite Mfs Lifetime and Mfs Commodity positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mfs Lifetime position performs unexpectedly, Mfs Commodity 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 Commodity will offset losses from the drop in Mfs Commodity's long position.Mfs Lifetime vs. Tweedy Browne Global | Mfs Lifetime vs. Matson Money Equity | Mfs Lifetime vs. Franklin Government Money | Mfs Lifetime vs. Transamerica Funds |
Mfs Commodity vs. Guidemark Large Cap | Mfs Commodity vs. Lord Abbett Affiliated | Mfs Commodity vs. Guidemark Large Cap | Mfs Commodity vs. Aqr Large Cap |
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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