Correlation Between American Funds and Calamos Convertible
Can any of the company-specific risk be diversified away by investing in both American Funds and Calamos Convertible 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 American Funds and Calamos Convertible into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between American Funds 2015 and Calamos Vertible Fund, you can compare the effects of market volatilities on American Funds and Calamos Convertible 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 American Funds with a short position of Calamos Convertible. Check out your portfolio center. Please also check ongoing floating volatility patterns of American Funds and Calamos Convertible.
Diversification Opportunities for American Funds and Calamos Convertible
0.27 | Correlation Coefficient |
Modest diversification
The 3 months correlation between American and Calamos is 0.27. Overlapping area represents the amount of risk that can be diversified away by holding American Funds 2015 and Calamos Vertible Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Calamos Convertible and American Funds 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 American Funds 2015 are associated (or correlated) with Calamos Convertible. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Calamos Convertible has no effect on the direction of American Funds i.e., American Funds and Calamos Convertible go up and down completely randomly.
Pair Corralation between American Funds and Calamos Convertible
Assuming the 90 days horizon American Funds 2015 is expected to under-perform the Calamos Convertible. In addition to that, American Funds is 1.23 times more volatile than Calamos Vertible Fund. It trades about -0.29 of its total potential returns per unit of risk. Calamos Vertible Fund is currently generating about -0.27 per unit of volatility. If you would invest 1,938 in Calamos Vertible Fund on October 12, 2024 and sell it today you would lose (85.00) from holding Calamos Vertible Fund or give up 4.39% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
American Funds 2015 vs. Calamos Vertible Fund
Performance |
Timeline |
American Funds 2015 |
Calamos Convertible |
American Funds and Calamos Convertible Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with American Funds and Calamos Convertible
The main advantage of trading using opposite American Funds and Calamos Convertible positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if American Funds position performs unexpectedly, Calamos Convertible 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 Calamos Convertible will offset losses from the drop in Calamos Convertible's long position.American Funds vs. Calamos Vertible Fund | American Funds vs. Invesco Vertible Securities | American Funds vs. Victory Incore Investment | American Funds vs. Allianzgi Convertible Income |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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