Correlation Between Pear Tree and High-yield Municipal
Can any of the company-specific risk be diversified away by investing in both Pear Tree and High-yield Municipal 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 High-yield Municipal 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 High Yield Municipal Fund, you can compare the effects of market volatilities on Pear Tree and High-yield Municipal 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 High-yield Municipal. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pear Tree and High-yield Municipal.
Diversification Opportunities for Pear Tree and High-yield Municipal
0.17 | Correlation Coefficient |
Average diversification
The 3 months correlation between Pear and High-yield is 0.17. Overlapping area represents the amount of risk that can be diversified away by holding Pear Tree Polaris and High Yield Municipal Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on High Yield Municipal 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 High-yield Municipal. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of High Yield Municipal has no effect on the direction of Pear Tree i.e., Pear Tree and High-yield Municipal go up and down completely randomly.
Pair Corralation between Pear Tree and High-yield Municipal
Assuming the 90 days horizon Pear Tree Polaris is expected to under-perform the High-yield Municipal. In addition to that, Pear Tree is 2.53 times more volatile than High Yield Municipal Fund. It trades about -0.17 of its total potential returns per unit of risk. High Yield Municipal Fund is currently generating about 0.07 per unit of volatility. If you would invest 890.00 in High Yield Municipal Fund on August 31, 2024 and sell it today you would earn a total of 11.00 from holding High Yield Municipal Fund or generate 1.24% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Pear Tree Polaris vs. High Yield Municipal Fund
Performance |
Timeline |
Pear Tree Polaris |
High Yield Municipal |
Pear Tree and High-yield Municipal Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Pear Tree and High-yield Municipal
The main advantage of trading using opposite Pear Tree and High-yield Municipal positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pear Tree position performs unexpectedly, High-yield Municipal 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 High-yield Municipal will offset losses from the drop in High-yield Municipal's long position.Pear Tree vs. Pear Tree Polaris | Pear Tree vs. Pear Tree Polaris | Pear Tree vs. Artisan International Value | Pear Tree vs. Rondure New World |
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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 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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