Correlation Between Loomis Sayles and Asg Managed
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Asg Managed 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 Asg Managed into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loomis Sayles Growth and Asg Managed Futures, you can compare the effects of market volatilities on Loomis Sayles and Asg Managed 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 Asg Managed. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Asg Managed.
Diversification Opportunities for Loomis Sayles and Asg Managed
-0.46 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Loomis and Asg is -0.46. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Growth and Asg Managed Futures in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Asg Managed Futures 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 Growth are associated (or correlated) with Asg Managed. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Asg Managed Futures has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Asg Managed go up and down completely randomly.
Pair Corralation between Loomis Sayles and Asg Managed
Assuming the 90 days horizon Loomis Sayles Growth is expected to generate 1.81 times more return on investment than Asg Managed. However, Loomis Sayles is 1.81 times more volatile than Asg Managed Futures. It trades about 0.24 of its potential returns per unit of risk. Asg Managed Futures is currently generating about -0.03 per unit of risk. If you would invest 2,399 in Loomis Sayles Growth on September 3, 2024 and sell it today you would earn a total of 384.00 from holding Loomis Sayles Growth or generate 16.01% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Loomis Sayles Growth vs. Asg Managed Futures
Performance |
Timeline |
Loomis Sayles Growth |
Asg Managed Futures |
Loomis Sayles and Asg Managed Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loomis Sayles and Asg Managed
The main advantage of trading using opposite Loomis Sayles and Asg Managed positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Asg Managed 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 Asg Managed will offset losses from the drop in Asg Managed's long position.Loomis Sayles vs. Diamond Hill Large | Loomis Sayles vs. Loomis Sayles Growth | Loomis Sayles vs. Loomis Sayles Growth | Loomis Sayles vs. Natixis Equity Opportunities |
Asg Managed vs. Aqr Managed Futures | Asg Managed vs. Pimco Trends Managed | Asg Managed vs. Pimco Trends Managed | Asg Managed vs. American Beacon Ahl |
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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