Correlation Between NYSE Composite and Kraft Heinz
Can any of the company-specific risk be diversified away by investing in both NYSE Composite and Kraft Heinz 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 Kraft Heinz into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NYSE Composite and Kraft Heinz Co, you can compare the effects of market volatilities on NYSE Composite and Kraft Heinz 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 Kraft Heinz. Check out your portfolio center. Please also check ongoing floating volatility patterns of NYSE Composite and Kraft Heinz.
Diversification Opportunities for NYSE Composite and Kraft Heinz
-0.33 | Correlation Coefficient |
Very good diversification
The 3 months correlation between NYSE and Kraft is -0.33. Overlapping area represents the amount of risk that can be diversified away by holding NYSE Composite and Kraft Heinz Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kraft Heinz 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 Kraft Heinz. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kraft Heinz has no effect on the direction of NYSE Composite i.e., NYSE Composite and Kraft Heinz go up and down completely randomly.
Pair Corralation between NYSE Composite and Kraft Heinz
Assuming the 90 days trading horizon NYSE Composite is expected to under-perform the Kraft Heinz. But the index apears to be less risky and, when comparing its historical volatility, NYSE Composite is 1.34 times less risky than Kraft Heinz. The index trades about -0.18 of its potential returns per unit of risk. The Kraft Heinz Co is currently generating about 0.03 of returns per unit of risk over similar time horizon. If you would invest 3,059 in Kraft Heinz Co on January 4, 2025 and sell it today you would earn a total of 22.00 from holding Kraft Heinz Co or generate 0.72% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
NYSE Composite vs. Kraft Heinz Co
Performance |
Timeline |
NYSE Composite and Kraft Heinz Volatility Contrast
Predicted Return Density |
Returns |
NYSE Composite
Pair trading matchups for NYSE Composite
Kraft Heinz Co
Pair trading matchups for Kraft Heinz
Pair Trading with NYSE Composite and Kraft Heinz
The main advantage of trading using opposite NYSE Composite and Kraft Heinz positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NYSE Composite position performs unexpectedly, Kraft Heinz 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 Kraft Heinz will offset losses from the drop in Kraft Heinz's long position.NYSE Composite vs. AKITA Drilling | NYSE Composite vs. Eltek | NYSE Composite vs. Major Drilling Group | NYSE Composite vs. Tigo Energy |
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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 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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