Correlation Between Ardagh Metal and Karat Packaging
Can any of the company-specific risk be diversified away by investing in both Ardagh Metal and Karat Packaging 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 Ardagh Metal and Karat Packaging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ardagh Metal Packaging and Karat Packaging, you can compare the effects of market volatilities on Ardagh Metal and Karat Packaging 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 Ardagh Metal with a short position of Karat Packaging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ardagh Metal and Karat Packaging.
Diversification Opportunities for Ardagh Metal and Karat Packaging
-0.23 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Ardagh and Karat is -0.23. Overlapping area represents the amount of risk that can be diversified away by holding Ardagh Metal Packaging and Karat Packaging in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Karat Packaging and Ardagh Metal 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 Ardagh Metal Packaging are associated (or correlated) with Karat Packaging. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Karat Packaging has no effect on the direction of Ardagh Metal i.e., Ardagh Metal and Karat Packaging go up and down completely randomly.
Pair Corralation between Ardagh Metal and Karat Packaging
Given the investment horizon of 90 days Ardagh Metal Packaging is expected to under-perform the Karat Packaging. In addition to that, Ardagh Metal is 1.39 times more volatile than Karat Packaging. It trades about -0.23 of its total potential returns per unit of risk. Karat Packaging is currently generating about 0.19 per unit of volatility. If you would invest 3,021 in Karat Packaging on September 14, 2024 and sell it today you would earn a total of 187.00 from holding Karat Packaging or generate 6.19% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Ardagh Metal Packaging vs. Karat Packaging
Performance |
Timeline |
Ardagh Metal Packaging |
Karat Packaging |
Ardagh Metal and Karat Packaging Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ardagh Metal and Karat Packaging
The main advantage of trading using opposite Ardagh Metal and Karat Packaging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ardagh Metal position performs unexpectedly, Karat Packaging 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 Karat Packaging will offset losses from the drop in Karat Packaging's long position.Ardagh Metal vs. Crown Holdings | Ardagh Metal vs. Amcor PLC | Ardagh Metal vs. Avery Dennison Corp | Ardagh Metal vs. Packaging Corp of |
Karat Packaging vs. Greif Bros | Karat Packaging vs. Reynolds Consumer Products | Karat Packaging vs. Silgan Holdings | Karat Packaging vs. O I Glass |
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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