Correlation Between Loomis Sayles and Pax High
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Pax High 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 Pax High into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loomis Sayles Inflation and Pax High Yield, you can compare the effects of market volatilities on Loomis Sayles and Pax High 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 Pax High. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Pax High.
Diversification Opportunities for Loomis Sayles and Pax High
0.19 | Correlation Coefficient |
Average diversification
The 3 months correlation between Loomis and Pax is 0.19. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Inflation and Pax High Yield in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Pax High Yield 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 Inflation are associated (or correlated) with Pax High. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Pax High Yield has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Pax High go up and down completely randomly.
Pair Corralation between Loomis Sayles and Pax High
Assuming the 90 days horizon Loomis Sayles is expected to generate 2.59 times less return on investment than Pax High. In addition to that, Loomis Sayles is 1.96 times more volatile than Pax High Yield. It trades about 0.05 of its total potential returns per unit of risk. Pax High Yield is currently generating about 0.26 per unit of volatility. If you would invest 605.00 in Pax High Yield on August 30, 2024 and sell it today you would earn a total of 5.00 from holding Pax High Yield or generate 0.83% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Loomis Sayles Inflation vs. Pax High Yield
Performance |
| Timeline |
| Loomis Sayles Inflation |
| Pax High Yield |
Loomis Sayles and Pax High Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Loomis Sayles and Pax High
The main advantage of trading using opposite Loomis Sayles and Pax High positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Pax High 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 Pax High will offset losses from the drop in Pax High's long position.| Loomis Sayles vs. Gmo Resources | Loomis Sayles vs. Energy Services Fund | Loomis Sayles vs. Firsthand Alternative Energy | Loomis Sayles vs. Icon Natural Resources |
| Pax High vs. Hartford Moderate Allocation | Pax High vs. Qs Moderate Growth | Pax High vs. Pro Blend Moderate Term | Pax High vs. Dimensional Retirement Income |
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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