Correlation Between Mfs Intermediate and BNY Mellon
Can any of the company-specific risk be diversified away by investing in both Mfs Intermediate and BNY Mellon 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 Intermediate and BNY Mellon into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Mfs Intermediate High and BNY Mellon High, you can compare the effects of market volatilities on Mfs Intermediate and BNY Mellon 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 Intermediate with a short position of BNY Mellon. Check out your portfolio center. Please also check ongoing floating volatility patterns of Mfs Intermediate and BNY Mellon.
Diversification Opportunities for Mfs Intermediate and BNY Mellon
0.21 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Mfs and BNY is 0.21. Overlapping area represents the amount of risk that can be diversified away by holding Mfs Intermediate High and BNY Mellon High in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BNY Mellon High and Mfs Intermediate 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 Intermediate High are associated (or correlated) with BNY Mellon. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BNY Mellon High has no effect on the direction of Mfs Intermediate i.e., Mfs Intermediate and BNY Mellon go up and down completely randomly.
Pair Corralation between Mfs Intermediate and BNY Mellon
Considering the 90-day investment horizon Mfs Intermediate is expected to generate 119.57 times less return on investment than BNY Mellon. But when comparing it to its historical volatility, Mfs Intermediate High is 1.38 times less risky than BNY Mellon. It trades about 0.0 of its potential returns per unit of risk. BNY Mellon High is currently generating about 0.22 of returns per unit of risk over similar time horizon. If you would invest 256.00 in BNY Mellon High on August 27, 2024 and sell it today you would earn a total of 9.00 from holding BNY Mellon High or generate 3.52% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Mfs Intermediate High vs. BNY Mellon High
Performance |
Timeline |
Mfs Intermediate High |
BNY Mellon High |
Mfs Intermediate and BNY Mellon Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Mfs Intermediate and BNY Mellon
The main advantage of trading using opposite Mfs Intermediate and BNY Mellon positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Mfs Intermediate position performs unexpectedly, BNY Mellon 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 BNY Mellon will offset losses from the drop in BNY Mellon's long position.Mfs Intermediate vs. Credit Suisse High | Mfs Intermediate vs. Western Asset High | Mfs Intermediate vs. Western Asset Global | Mfs Intermediate vs. Allspring Income Opportunities |
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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 Volatility Analysis module to get historical volatility and risk analysis based on latest market data.
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