Correlation Between Loomis Sayles and Deutsche Global
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Deutsche Global 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 Deutsche Global into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loomis Sayles Smallmid and Deutsche Global Inflation, you can compare the effects of market volatilities on Loomis Sayles and Deutsche Global 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 Deutsche Global. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Deutsche Global.
Diversification Opportunities for Loomis Sayles and Deutsche Global
-0.31 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Loomis and Deutsche is -0.31. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Smallmid and Deutsche Global Inflation in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Deutsche Global Inflation 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 Smallmid are associated (or correlated) with Deutsche Global. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Deutsche Global Inflation has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Deutsche Global go up and down completely randomly.
Pair Corralation between Loomis Sayles and Deutsche Global
Assuming the 90 days horizon Loomis Sayles Smallmid is expected to generate 4.14 times more return on investment than Deutsche Global. However, Loomis Sayles is 4.14 times more volatile than Deutsche Global Inflation. It trades about 0.07 of its potential returns per unit of risk. Deutsche Global Inflation is currently generating about 0.02 per unit of risk. If you would invest 1,267 in Loomis Sayles Smallmid on September 29, 2024 and sell it today you would earn a total of 128.00 from holding Loomis Sayles Smallmid or generate 10.1% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Loomis Sayles Smallmid vs. Deutsche Global Inflation
Performance |
Timeline |
Loomis Sayles Smallmid |
Deutsche Global Inflation |
Loomis Sayles and Deutsche Global Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loomis Sayles and Deutsche Global
The main advantage of trading using opposite Loomis Sayles and Deutsche Global positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Deutsche Global 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 Deutsche Global will offset losses from the drop in Deutsche Global's long position.Loomis Sayles vs. Deutsche Global Inflation | Loomis Sayles vs. Guidepath Managed Futures | Loomis Sayles vs. Ab Bond Inflation | Loomis Sayles vs. Altegris Futures Evolution |
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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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