Correlation Between Pear Tree and Loomis Sayles
Can any of the company-specific risk be diversified away by investing in both Pear Tree 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 Pear Tree and Loomis Sayles into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pear Tree Polaris and Loomis Sayles Growth, you can compare the effects of market volatilities on Pear Tree 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 Pear Tree with a short position of Loomis Sayles. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pear Tree and Loomis Sayles.
Diversification Opportunities for Pear Tree and Loomis Sayles
-0.7 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Pear and Loomis is -0.7. Overlapping area represents the amount of risk that can be diversified away by holding Pear Tree Polaris and Loomis Sayles Growth in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Loomis Sayles Growth and Pear Tree 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 Pear Tree Polaris 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 Growth has no effect on the direction of Pear Tree i.e., Pear Tree and Loomis Sayles go up and down completely randomly.
Pair Corralation between Pear Tree and Loomis Sayles
Assuming the 90 days horizon Pear Tree Polaris is expected to under-perform the Loomis Sayles. But the mutual fund apears to be less risky and, when comparing its historical volatility, Pear Tree Polaris is 1.37 times less risky than Loomis Sayles. The mutual fund trades about -0.1 of its potential returns per unit of risk. The Loomis Sayles Growth is currently generating about 0.24 of returns per unit of risk over similar time horizon. If you would invest 2,671 in Loomis Sayles Growth on September 2, 2024 and sell it today you would earn a total of 430.00 from holding Loomis Sayles Growth or generate 16.1% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Pear Tree Polaris vs. Loomis Sayles Growth
Performance |
Timeline |
Pear Tree Polaris |
Loomis Sayles Growth |
Pear Tree and Loomis Sayles Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Pear Tree and Loomis Sayles
The main advantage of trading using opposite Pear Tree and Loomis Sayles positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pear Tree 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.Pear Tree vs. Loomis Sayles Growth | Pear Tree vs. Edgewood Growth Fund | Pear Tree vs. Nuance Mid Cap | Pear Tree vs. Parnassus Mid Cap |
Loomis Sayles vs. Loomis Sayles Growth | Loomis Sayles vs. Diamond Hill Large | Loomis Sayles vs. Diamond Hill Large |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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