Correlation Between NYSE Composite and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both NYSE Composite 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 NYSE Composite and Loomis Sayles into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and Loomis Sayles Investment, you can compare the effects of market volatilities on NYSE Composite 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 NYSE Composite with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Loomis Sayles.
Diversification Opportunities for NYSE Composite and Loomis Sayles
-0.58 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between NYSE and Loomis is -0.58. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Loomis Sayles Investment in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loomis Sayles Investment and NYSE Composite 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 NYSE Composite 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 Investment has no effect on the direction of NYSE Composite i.e., NYSE Composite and Loomis Sayles go up and down completely randomly.
Pair Corralation between NYSE Composite and Loomis Sayles
Assuming the 90 days trading horizon NYSE Composite is expected to generate 1.94 times more return on investment than Loomis Sayles. However, NYSE Composite is 1.94 times more volatile than Loomis Sayles Investment. It trades about 0.41 of its potential returns per unit of risk. Loomis Sayles Investment is currently generating about 0.17 per unit of risk. If you would invest 1,925,354 in NYSE Composite on September 2, 2024 and sell it today you would earn a total of 101,850 from holding NYSE Composite or generate 5.29% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
NYSE Composite vs. Loomis Sayles Investment
Performance |
Timeline |
NYSE Composite and Loomis Sayles Volatility Contrast
Predicted Return Density |
Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
Loomis Sayles Investment
Pair trading matchups for Loomis Sayles
Pair Trading with NYSE Composite and Loomis Sayles
The main advantage of trading using opposite NYSE Composite and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite 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.NYSE Composite vs. Simon Property Group | NYSE Composite vs. Merit Medical Systems | NYSE Composite vs. Catalent | NYSE Composite vs. Titan Machinery |
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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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