Correlation Between NYSE Composite and ConAgra Foods

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

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

Diversification Opportunities for NYSE Composite and ConAgra Foods

-0.69

Correlation Coefficient

NYSE Composite  ConAgra Foods

Excellent diversification

The 3 months correlation between NYSE and ConAgra is -0.69. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and ConAgra Foods in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on ConAgra 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 ConAgra Foods. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of ConAgra Foods has no effect on the direction of NYSE Composite i.e., NYSE Composite and ConAgra Foods go up and down completely randomly.

Pair Corralation between NYSE Composite and ConAgra Foods

Assuming the 90 days trading horizon NYSE Composite is expected to generate 0.45 times more return on investment than ConAgra Foods. However, NYSE Composite is 2.24 times less risky than ConAgra Foods. It trades about 0.02 of its potential returns per unit of risk. ConAgra Foods is currently generating about -0.01 per unit of risk. If you would invest  1,907,793  in NYSE Composite on December 29, 2024 and sell it today you would earn a total of  19,237  from holding NYSE Composite or generate 1.01% return on investment over 90 days.

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

NYSE Composite  vs.  ConAgra Foods

Pair Trading with NYSE Composite and ConAgra Foods

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

The idea behind NYSE Composite and ConAgra 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 Piotroski F Score module to get Piotroski F Score based on the binary analysis strategy of nine different fundamentals.