Correlation Between Loomis Sayles and Champlain Mid
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Champlain Mid 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 Champlain Mid 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 Champlain Mid Cap, you can compare the effects of market volatilities on Loomis Sayles and Champlain Mid 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 Champlain Mid. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Champlain Mid.
Diversification Opportunities for Loomis Sayles and Champlain Mid
0.96 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Loomis and Champlain is 0.96. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Growth and Champlain Mid Cap in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Champlain Mid Cap 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 Champlain Mid. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Champlain Mid Cap has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Champlain Mid go up and down completely randomly.
Pair Corralation between Loomis Sayles and Champlain Mid
Assuming the 90 days horizon Loomis Sayles Growth is expected to under-perform the Champlain Mid. In addition to that, Loomis Sayles is 1.43 times more volatile than Champlain Mid Cap. It trades about -0.1 of its total potential returns per unit of risk. Champlain Mid Cap is currently generating about -0.09 per unit of volatility. If you would invest 2,294 in Champlain Mid Cap on December 30, 2024 and sell it today you would lose (133.00) from holding Champlain Mid Cap or give up 5.8% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Loomis Sayles Growth vs. Champlain Mid Cap
Performance |
Timeline |
Loomis Sayles Growth |
Champlain Mid Cap |
Loomis Sayles and Champlain Mid Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loomis Sayles and Champlain Mid
The main advantage of trading using opposite Loomis Sayles and Champlain Mid positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Champlain Mid 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 Champlain Mid will offset losses from the drop in Champlain Mid's long position.Loomis Sayles vs. American Mutual Fund | Loomis Sayles vs. Metropolitan West Total | Loomis Sayles vs. John Hancock Disciplined | Loomis Sayles vs. Edgewood Growth Fund |
Champlain Mid vs. Champlain Small Pany | Champlain Mid vs. T Rowe Price | Champlain Mid vs. American Mutual Fund | Champlain Mid vs. Loomis Sayles Growth |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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