Correlation Between NYSE Composite and Cal Maine

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

By analyzing existing cross correlation between NYSE Composite and Cal Maine Foods, you can compare the effects of market volatilities on NYSE Composite and Cal Maine 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 Cal Maine. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Cal Maine.

Diversification Opportunities for NYSE Composite and Cal Maine

0.8

Correlation Coefficient

NYSE Composite  Cal Maine

Very poor diversification

The 3 months correlation between NYSE and Cal is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Cal Maine Foods in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cal Maine Foods 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 Cal Maine. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cal Maine Foods has no effect on the direction of NYSE Composite i.e., NYSE Composite and Cal Maine go up and down completely randomly.

Pair Corralation between NYSE Composite and Cal Maine

Assuming the 90 days trading horizon NYSE Composite is expected to generate 3.52 times less return on investment than Cal Maine. But when comparing it to its historical volatility, NYSE Composite is 4.38 times less risky than Cal Maine. It trades about 0.12 of its potential returns per unit of risk. Cal Maine Foods is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest  9,280  in Cal Maine Foods on May 5, 2025 and sell it today you would earn a total of  1,508  from holding Cal Maine Foods or generate 16.25% return on investment over 90 days.

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

NYSE Composite  vs.  Cal Maine Foods

Pair Trading with NYSE Composite and Cal Maine

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

The idea behind NYSE Composite and Cal Maine Foods 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 Latest Portfolios module to quick portfolio dashboard that showcases your latest portfolios.