Correlation Between Loomis Sayles and Vaughan Nelson
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Vaughan Nelson 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 Vaughan Nelson into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loomis Sayles Investment and Vaughan Nelson Value, you can compare the effects of market volatilities on Loomis Sayles and Vaughan Nelson 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 Vaughan Nelson. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Vaughan Nelson.
Diversification Opportunities for Loomis Sayles and Vaughan Nelson
-0.65 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Loomis and Vaughan is -0.65. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Investment and Vaughan Nelson Value in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Vaughan Nelson Value 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 Investment are associated (or correlated) with Vaughan Nelson. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Vaughan Nelson Value has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Vaughan Nelson go up and down completely randomly.
Pair Corralation between Loomis Sayles and Vaughan Nelson
Assuming the 90 days horizon Loomis Sayles is expected to generate 10.37 times less return on investment than Vaughan Nelson. But when comparing it to its historical volatility, Loomis Sayles Investment is 3.77 times less risky than Vaughan Nelson. It trades about 0.05 of its potential returns per unit of risk. Vaughan Nelson Value is currently generating about 0.13 of returns per unit of risk over similar time horizon. If you would invest 2,437 in Vaughan Nelson Value on September 15, 2024 and sell it today you would earn a total of 488.00 from holding Vaughan Nelson Value or generate 20.02% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 99.21% |
Values | Daily Returns |
Loomis Sayles Investment vs. Vaughan Nelson Value
Performance |
Timeline |
Loomis Sayles Investment |
Vaughan Nelson Value |
Loomis Sayles and Vaughan Nelson Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loomis Sayles and Vaughan Nelson
The main advantage of trading using opposite Loomis Sayles and Vaughan Nelson positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Vaughan Nelson 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 Vaughan Nelson will offset losses from the drop in Vaughan Nelson's long position.Loomis Sayles vs. Asg Managed Futures | Loomis Sayles vs. Asg Managed Futures | Loomis Sayles vs. Natixis Oakmark | Loomis Sayles vs. Natixis Oakmark International |
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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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