Correlation Between Loomis Sayles and Jpmorgan Hedged
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Jpmorgan Hedged 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 Loomis Sayles and Jpmorgan Hedged into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loomis Sayles Global and Jpmorgan Hedged Equity, you can compare the effects of market volatilities on Loomis Sayles and Jpmorgan Hedged 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 Loomis Sayles with a short position of Jpmorgan Hedged. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Jpmorgan Hedged.
Diversification Opportunities for Loomis Sayles and Jpmorgan Hedged
0.12 | Correlation Coefficient |
Average diversification
The 3 months correlation between Loomis and Jpmorgan is 0.12. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Global and Jpmorgan Hedged Equity in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jpmorgan Hedged Equity and Loomis Sayles 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 Loomis Sayles Global are associated (or correlated) with Jpmorgan Hedged. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jpmorgan Hedged Equity has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Jpmorgan Hedged go up and down completely randomly.
Pair Corralation between Loomis Sayles and Jpmorgan Hedged
Assuming the 90 days horizon Loomis Sayles Global is expected to under-perform the Jpmorgan Hedged. In addition to that, Loomis Sayles is 2.32 times more volatile than Jpmorgan Hedged Equity. It trades about -0.1 of its total potential returns per unit of risk. Jpmorgan Hedged Equity is currently generating about 0.08 per unit of volatility. If you would invest 3,273 in Jpmorgan Hedged Equity on September 30, 2024 and sell it today you would earn a total of 87.00 from holding Jpmorgan Hedged Equity or generate 2.66% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Loomis Sayles Global vs. Jpmorgan Hedged Equity
Performance |
Timeline |
Loomis Sayles Global |
Jpmorgan Hedged Equity |
Loomis Sayles and Jpmorgan Hedged Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loomis Sayles and Jpmorgan Hedged
The main advantage of trading using opposite Loomis Sayles and Jpmorgan Hedged positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Jpmorgan Hedged 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 Jpmorgan Hedged will offset losses from the drop in Jpmorgan Hedged's long position.Loomis Sayles vs. Loomis Sayles Global | Loomis Sayles vs. Natixis Equity Opportunities | Loomis Sayles vs. Loomis Sayles Growth | Loomis Sayles vs. Columbia Balanced 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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