Correlation Between Capital Income and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both Capital Income 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 Capital Income and Loomis Sayles into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Capital Income Builder and Loomis Sayles Global, you can compare the effects of market volatilities on Capital Income 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 Capital Income with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of Capital Income and Loomis Sayles.
Diversification Opportunities for Capital Income and Loomis Sayles
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Capital and Loomis is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Capital Income Builder and Loomis Sayles Global in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loomis Sayles Global and Capital Income 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 Capital Income Builder 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 Global has no effect on the direction of Capital Income i.e., Capital Income and Loomis Sayles go up and down completely randomly.
Pair Corralation between Capital Income and Loomis Sayles
Assuming the 90 days horizon Capital Income is expected to generate 1.11 times less return on investment than Loomis Sayles. But when comparing it to its historical volatility, Capital Income Builder is 1.37 times less risky than Loomis Sayles. It trades about 0.14 of its potential returns per unit of risk. Loomis Sayles Global is currently generating about 0.12 of returns per unit of risk over similar time horizon. If you would invest 2,486 in Loomis Sayles Global on September 1, 2024 and sell it today you would earn a total of 239.00 from holding Loomis Sayles Global or generate 9.61% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Capital Income Builder vs. Loomis Sayles Global
Performance |
Timeline |
Capital Income Builder |
Loomis Sayles Global |
Capital Income and Loomis Sayles Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Capital Income and Loomis Sayles
The main advantage of trading using opposite Capital Income and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Capital Income 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.Capital Income vs. Income Fund Of | Capital Income vs. New World Fund | Capital Income vs. American Mutual Fund | Capital Income vs. American Mutual 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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