Correlation Between COFCO Joycome and Cal Maine
Can any of the company-specific risk be diversified away by investing in both COFCO Joycome 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 COFCO Joycome and Cal Maine into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between COFCO Joycome Foods and Cal Maine Foods, you can compare the effects of market volatilities on COFCO Joycome 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 COFCO Joycome with a short position of Cal Maine. Check out your portfolio center. Please also check ongoing floating volatility patterns of COFCO Joycome and Cal Maine.
Diversification Opportunities for COFCO Joycome and Cal Maine
0.49 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between COFCO and Cal is 0.49. Overlapping area represents the amount of risk that can be diversified away by holding COFCO Joycome Foods 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 COFCO Joycome 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 COFCO Joycome Foods 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 COFCO Joycome i.e., COFCO Joycome and Cal Maine go up and down completely randomly.
Pair Corralation between COFCO Joycome and Cal Maine
Assuming the 90 days horizon COFCO Joycome is expected to generate 7.59 times less return on investment than Cal Maine. In addition to that, COFCO Joycome is 1.93 times more volatile than Cal Maine Foods. It trades about 0.02 of its total potential returns per unit of risk. Cal Maine Foods is currently generating about 0.29 per unit of volatility. If you would invest 6,328 in Cal Maine Foods on September 3, 2024 and sell it today you would earn a total of 2,812 from holding Cal Maine Foods or generate 44.44% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
COFCO Joycome Foods vs. Cal Maine Foods
Performance |
Timeline |
COFCO Joycome Foods |
Cal Maine Foods |
COFCO Joycome and Cal Maine Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with COFCO Joycome and Cal Maine
The main advantage of trading using opposite COFCO Joycome and Cal Maine positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if COFCO Joycome 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.COFCO Joycome vs. Nestl SA | COFCO Joycome vs. Kraft Heinz Co | COFCO Joycome vs. General Mills | COFCO Joycome vs. Kellogg Company |
Cal Maine vs. ADRIATIC METALS LS 013355 | Cal Maine vs. Datang International Power | Cal Maine vs. Science Applications International | Cal Maine vs. Harmony Gold Mining |
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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