Correlation Between Astral Foods and Fortress Income
Can any of the company-specific risk be diversified away by investing in both Astral Foods and Fortress Income 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 Astral Foods and Fortress Income into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Astral Foods and Fortress Income, you can compare the effects of market volatilities on Astral Foods and Fortress Income 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 Astral Foods with a short position of Fortress Income. Check out your portfolio center. Please also check ongoing floating volatility patterns of Astral Foods and Fortress Income.
Diversification Opportunities for Astral Foods and Fortress Income
0.59 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Astral and Fortress is 0.59. Overlapping area represents the amount of risk that can be diversified away by holding Astral Foods and Fortress Income in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fortress Income and Astral 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 Astral Foods are associated (or correlated) with Fortress Income. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fortress Income has no effect on the direction of Astral Foods i.e., Astral Foods and Fortress Income go up and down completely randomly.
Pair Corralation between Astral Foods and Fortress Income
Assuming the 90 days trading horizon Astral Foods is expected to under-perform the Fortress Income. In addition to that, Astral Foods is 1.47 times more volatile than Fortress Income. It trades about -0.04 of its total potential returns per unit of risk. Fortress Income is currently generating about 0.09 per unit of volatility. If you would invest 199,000 in Fortress Income on October 1, 2024 and sell it today you would earn a total of 2,400 from holding Fortress Income or generate 1.21% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Astral Foods vs. Fortress Income
Performance |
Timeline |
Astral Foods |
Fortress Income |
Astral Foods and Fortress Income Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Astral Foods and Fortress Income
The main advantage of trading using opposite Astral Foods and Fortress Income positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Astral Foods position performs unexpectedly, Fortress Income 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 Fortress Income will offset losses from the drop in Fortress Income's long position.Astral Foods vs. Standard Bank Group | Astral Foods vs. eMedia Holdings Limited | Astral Foods vs. Frontier Transport Holdings | Astral Foods vs. Life Healthcare |
Fortress Income vs. Master Drilling Group | Fortress Income vs. Zeder Investments | Fortress Income vs. Astoria Investments | Fortress Income vs. Life Healthcare |
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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