Correlation Between ConAgra Foods and BranchOut Food
Can any of the company-specific risk be diversified away by investing in both ConAgra Foods and BranchOut Food 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 ConAgra Foods and BranchOut Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between ConAgra Foods and BranchOut Food Common, you can compare the effects of market volatilities on ConAgra Foods and BranchOut Food 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 ConAgra Foods with a short position of BranchOut Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of ConAgra Foods and BranchOut Food.
Diversification Opportunities for ConAgra Foods and BranchOut Food
-0.41 | Correlation Coefficient |
Very good diversification
The 3 months correlation between ConAgra and BranchOut is -0.41. Overlapping area represents the amount of risk that can be diversified away by holding ConAgra Foods and BranchOut Food Common in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BranchOut Food Common and ConAgra Foods 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 ConAgra Foods are associated (or correlated) with BranchOut Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BranchOut Food Common has no effect on the direction of ConAgra Foods i.e., ConAgra Foods and BranchOut Food go up and down completely randomly.
Pair Corralation between ConAgra Foods and BranchOut Food
Considering the 90-day investment horizon ConAgra Foods is expected to under-perform the BranchOut Food. But the stock apears to be less risky and, when comparing its historical volatility, ConAgra Foods is 3.54 times less risky than BranchOut Food. The stock trades about -0.39 of its potential returns per unit of risk. The BranchOut Food Common is currently generating about 0.26 of returns per unit of risk over similar time horizon. If you would invest 165.00 in BranchOut Food Common on October 27, 2024 and sell it today you would earn a total of 32.00 from holding BranchOut Food Common or generate 19.39% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
ConAgra Foods vs. BranchOut Food Common
Performance |
Timeline |
ConAgra Foods |
BranchOut Food Common |
ConAgra Foods and BranchOut Food Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with ConAgra Foods and BranchOut Food
The main advantage of trading using opposite ConAgra Foods and BranchOut Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ConAgra Foods position performs unexpectedly, BranchOut Food 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 BranchOut Food will offset losses from the drop in BranchOut Food's long position.ConAgra Foods vs. Kellanova | ConAgra Foods vs. General Mills | ConAgra Foods vs. JM Smucker | ConAgra Foods vs. Hormel Foods |
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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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