Correlation Between Golden Agri-Resources and AppHarvest
Can any of the company-specific risk be diversified away by investing in both Golden Agri-Resources and AppHarvest 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 Golden Agri-Resources and AppHarvest into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Golden Agri Resources and AppHarvest, you can compare the effects of market volatilities on Golden Agri-Resources and AppHarvest 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 Golden Agri-Resources with a short position of AppHarvest. Check out your portfolio center. Please also check ongoing floating volatility patterns of Golden Agri-Resources and AppHarvest.
Diversification Opportunities for Golden Agri-Resources and AppHarvest
0.0 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Golden and AppHarvest is 0.0. Overlapping area represents the amount of risk that can be diversified away by holding Golden Agri Resources and AppHarvest in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AppHarvest and Golden Agri-Resources 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 Golden Agri Resources are associated (or correlated) with AppHarvest. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AppHarvest has no effect on the direction of Golden Agri-Resources i.e., Golden Agri-Resources and AppHarvest go up and down completely randomly.
Pair Corralation between Golden Agri-Resources and AppHarvest
Assuming the 90 days horizon Golden Agri Resources is expected to generate 0.44 times more return on investment than AppHarvest. However, Golden Agri Resources is 2.3 times less risky than AppHarvest. It trades about 0.04 of its potential returns per unit of risk. AppHarvest is currently generating about -0.08 per unit of risk. If you would invest 19.00 in Golden Agri Resources on November 6, 2024 and sell it today you would earn a total of 4.00 from holding Golden Agri Resources or generate 21.05% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Flat |
| Strength | Insignificant |
| Accuracy | 34.38% |
| Values | Daily Returns |
Golden Agri Resources vs. AppHarvest
Performance |
| Timeline |
| Golden Agri Resources |
| AppHarvest |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Golden Agri-Resources and AppHarvest Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Golden Agri-Resources and AppHarvest
The main advantage of trading using opposite Golden Agri-Resources and AppHarvest positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Golden Agri-Resources position performs unexpectedly, AppHarvest 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 AppHarvest will offset losses from the drop in AppHarvest's long position.| Golden Agri-Resources vs. Wilmar International | Golden Agri-Resources vs. SLC Agricola SA | Golden Agri-Resources vs. Brasilagro Adr | Golden Agri-Resources vs. Alico Inc |
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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