Correlation Between Jp Morgan and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both Jp Morgan and Loomis Sayles 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 Jp Morgan and Loomis Sayles into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Jp Morgan Smartretirement and Loomis Sayles Bond, you can compare the effects of market volatilities on Jp Morgan and Loomis Sayles 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 Jp Morgan with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of Jp Morgan and Loomis Sayles.
Diversification Opportunities for Jp Morgan and Loomis Sayles
0.26 | Correlation Coefficient |
Modest diversification
The 3 months correlation between JTSQX and Loomis is 0.26. Overlapping area represents the amount of risk that can be diversified away by holding Jp Morgan Smartretirement and Loomis Sayles Bond in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loomis Sayles Bond and Jp Morgan 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 Jp Morgan Smartretirement are associated (or correlated) with Loomis Sayles. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Loomis Sayles Bond has no effect on the direction of Jp Morgan i.e., Jp Morgan and Loomis Sayles go up and down completely randomly.
Pair Corralation between Jp Morgan and Loomis Sayles
Assuming the 90 days horizon Jp Morgan Smartretirement is expected to generate 2.48 times more return on investment than Loomis Sayles. However, Jp Morgan is 2.48 times more volatile than Loomis Sayles Bond. It trades about 0.03 of its potential returns per unit of risk. Loomis Sayles Bond is currently generating about 0.04 per unit of risk. If you would invest 2,374 in Jp Morgan Smartretirement on September 13, 2024 and sell it today you would earn a total of 14.00 from holding Jp Morgan Smartretirement or generate 0.59% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Jp Morgan Smartretirement vs. Loomis Sayles Bond
Performance |
| Timeline |
| Jp Morgan Smartretirement |
| Loomis Sayles Bond |
Jp Morgan and Loomis Sayles Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Jp Morgan and Loomis Sayles
The main advantage of trading using opposite Jp Morgan and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Jp Morgan position performs unexpectedly, Loomis Sayles 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 Loomis Sayles will offset losses from the drop in Loomis Sayles' long position.| Jp Morgan vs. Franklin High Yield | Jp Morgan vs. Artisan High Income | Jp Morgan vs. Ab Global Bond | Jp Morgan vs. Blrc Sgy Mnp |
| Loomis Sayles vs. Franklin Lifesmart Retirement | Loomis Sayles vs. Dimensional Retirement Income | Loomis Sayles vs. Sa Worldwide Moderate | Loomis Sayles vs. Jp Morgan Smartretirement |
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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