Correlation Between NYSE Composite and Calamos Growth

Can any of the company-specific risk be diversified away by investing in both NYSE Composite and Calamos Growth 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 NYSE Composite and Calamos Growth into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between NYSE Composite and Calamos Growth Income, you can compare the effects of market volatilities on NYSE Composite and Calamos Growth 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 NYSE Composite with a short position of Calamos Growth. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Calamos Growth.

Diversification Opportunities for NYSE Composite and Calamos Growth

0.41

Correlation Coefficient

NYSE Composite  Calamos Growth

Very weak diversification

The 3 months correlation between NYSE and Calamos is 0.41. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Calamos Growth Income in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Calamos Growth Income and NYSE Composite 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 NYSE Composite are associated (or correlated) with Calamos Growth. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Calamos Growth Income has no effect on the direction of NYSE Composite i.e., NYSE Composite and Calamos Growth go up and down completely randomly.

Pair Corralation between NYSE Composite and Calamos Growth

Assuming the 90 days trading horizon NYSE Composite is expected to generate 0.91 times more return on investment than Calamos Growth. However, NYSE Composite is 1.09 times less risky than Calamos Growth. It trades about 0.04 of its potential returns per unit of risk. Calamos Growth Income is currently generating about -0.06 per unit of risk. If you would invest  1,923,848  in NYSE Composite on December 27, 2024 and sell it today you would earn a total of  34,735  from holding NYSE Composite or generate 1.81% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Weak
Accuracy	100.0%
Values	Daily Returns

NYSE Composite  vs.  Calamos Growth Income

Pair Trading with NYSE Composite and Calamos Growth

The main advantage of trading using opposite NYSE Composite and Calamos Growth positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Calamos Growth 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 Growth will offset losses from the drop in Calamos Growth's long position.

The idea behind NYSE Composite and Calamos Growth Income pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.

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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.