Correlation Between INDOFOOD AGRI and Cal Maine
Can any of the company-specific risk be diversified away by investing in both INDOFOOD AGRI 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 INDOFOOD AGRI and Cal Maine into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between INDOFOOD AGRI RES and Cal Maine Foods, you can compare the effects of market volatilities on INDOFOOD AGRI 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 INDOFOOD AGRI with a short position of Cal Maine. Check out your portfolio center. Please also check ongoing floating volatility patterns of INDOFOOD AGRI and Cal Maine.
Diversification Opportunities for INDOFOOD AGRI and Cal Maine
0.67 | Correlation Coefficient |
Poor diversification
The 3 months correlation between INDOFOOD and Cal is 0.67. Overlapping area represents the amount of risk that can be diversified away by holding INDOFOOD AGRI RES 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 INDOFOOD AGRI 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 INDOFOOD AGRI RES 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 INDOFOOD AGRI i.e., INDOFOOD AGRI and Cal Maine go up and down completely randomly.
Pair Corralation between INDOFOOD AGRI and Cal Maine
Assuming the 90 days trading horizon INDOFOOD AGRI is expected to generate 6.32 times less return on investment than Cal Maine. In addition to that, INDOFOOD AGRI is 1.02 times more volatile than Cal Maine Foods. It trades about 0.04 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 | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
INDOFOOD AGRI RES vs. Cal Maine Foods
Performance |
Timeline |
INDOFOOD AGRI RES |
Cal Maine Foods |
INDOFOOD AGRI and Cal Maine Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with INDOFOOD AGRI and Cal Maine
The main advantage of trading using opposite INDOFOOD AGRI and Cal Maine positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if INDOFOOD AGRI 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.INDOFOOD AGRI vs. TOTAL GABON | INDOFOOD AGRI vs. Walgreens Boots Alliance | INDOFOOD AGRI vs. Banco Santander SA | INDOFOOD AGRI vs. Peak Resources Limited |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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