Correlation Between Franklin Lifesmart and Calamos Dynamic
Can any of the company-specific risk be diversified away by investing in both Franklin Lifesmart and Calamos Dynamic 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 Franklin Lifesmart and Calamos Dynamic into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Franklin Lifesmart 2040 and Calamos Dynamic Convertible, you can compare the effects of market volatilities on Franklin Lifesmart and Calamos Dynamic 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 Franklin Lifesmart with a short position of Calamos Dynamic. Check out your portfolio center. Please also check ongoing floating volatility patterns of Franklin Lifesmart and Calamos Dynamic.
Diversification Opportunities for Franklin Lifesmart and Calamos Dynamic
0.0 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between FRANKLIN and Calamos is 0.0. Overlapping area represents the amount of risk that can be diversified away by holding Franklin Lifesmart 2040 and Calamos Dynamic Convertible in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Calamos Dynamic Conv and Franklin Lifesmart 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 Franklin Lifesmart 2040 are associated (or correlated) with Calamos Dynamic. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Calamos Dynamic Conv has no effect on the direction of Franklin Lifesmart i.e., Franklin Lifesmart and Calamos Dynamic go up and down completely randomly.
Pair Corralation between Franklin Lifesmart and Calamos Dynamic
If you would invest 1,747 in Calamos Dynamic Convertible on September 4, 2024 and sell it today you would earn a total of 630.00 from holding Calamos Dynamic Convertible or generate 36.06% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Flat |
| Strength | Insignificant |
| Accuracy | 0.0% |
| Values | Daily Returns |
Franklin Lifesmart 2040 vs. Calamos Dynamic Convertible
Performance |
| Timeline |
| Franklin Lifesmart 2040 |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
OK
| Calamos Dynamic Conv |
Franklin Lifesmart and Calamos Dynamic Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Franklin Lifesmart and Calamos Dynamic
The main advantage of trading using opposite Franklin Lifesmart and Calamos Dynamic positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Franklin Lifesmart position performs unexpectedly, Calamos Dynamic 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 Dynamic will offset losses from the drop in Calamos Dynamic's long position.| Franklin Lifesmart vs. Ultrasmall Cap Profund Ultrasmall Cap | Franklin Lifesmart vs. Royce Opportunity Fund | Franklin Lifesmart vs. Fpa Queens Road | Franklin Lifesmart vs. Columbia Small Cap |
| Calamos Dynamic vs. Calamos Convertible Opportunities | Calamos Dynamic vs. Calamos Global Dynamic | Calamos Dynamic vs. Calamos Strategic Total | Calamos Dynamic vs. Calamos LongShort Equity |
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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