Correlation Between Cal Maine and Alibaba Group
Can any of the company-specific risk be diversified away by investing in both Cal Maine and Alibaba Group 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 Cal Maine and Alibaba Group into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Cal Maine Foods and Alibaba Group Holding, you can compare the effects of market volatilities on Cal Maine and Alibaba Group 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 Cal Maine with a short position of Alibaba Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of Cal Maine and Alibaba Group.
Diversification Opportunities for Cal Maine and Alibaba Group
-0.06 | Correlation Coefficient |
Good diversification
The 3 months correlation between Cal and Alibaba is -0.06. Overlapping area represents the amount of risk that can be diversified away by holding Cal Maine Foods and Alibaba Group Holding in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Alibaba Group Holding and Cal Maine 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 Cal Maine Foods are associated (or correlated) with Alibaba Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Alibaba Group Holding has no effect on the direction of Cal Maine i.e., Cal Maine and Alibaba Group go up and down completely randomly.
Pair Corralation between Cal Maine and Alibaba Group
Assuming the 90 days trading horizon Cal Maine Foods is expected to generate 0.74 times more return on investment than Alibaba Group. However, Cal Maine Foods is 1.35 times less risky than Alibaba Group. It trades about 0.16 of its potential returns per unit of risk. Alibaba Group Holding is currently generating about 0.05 per unit of risk. If you would invest 4,824 in Cal Maine Foods on September 14, 2024 and sell it today you would earn a total of 5,236 from holding Cal Maine Foods or generate 108.54% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Cal Maine Foods vs. Alibaba Group Holding
Performance |
| Timeline |
| Cal Maine Foods |
| Alibaba Group Holding |
Cal Maine and Alibaba Group Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Cal Maine and Alibaba Group
The main advantage of trading using opposite Cal Maine and Alibaba Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Cal Maine position performs unexpectedly, Alibaba Group 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 Alibaba Group will offset losses from the drop in Alibaba Group's long position.| Cal Maine vs. Zijin Mining Group | Cal Maine vs. Harmony Gold Mining | Cal Maine vs. Nufarm Limited | Cal Maine vs. HYDROFARM HLD GRP |
| Alibaba Group vs. Siamgas And Petrochemicals | Alibaba Group vs. China BlueChemical | Alibaba Group vs. Sanyo Chemical Industries | Alibaba Group vs. LANDSEA GREEN MANAGEMENT |
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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