Correlation Between Ainsworth Game and Antipa Minerals
Can any of the company-specific risk be diversified away by investing in both Ainsworth Game and Antipa Minerals 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 Ainsworth Game and Antipa Minerals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ainsworth Game Technology and Antipa Minerals, you can compare the effects of market volatilities on Ainsworth Game and Antipa Minerals 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 Ainsworth Game with a short position of Antipa Minerals. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ainsworth Game and Antipa Minerals.
Diversification Opportunities for Ainsworth Game and Antipa Minerals
0.16 | Correlation Coefficient |
Average diversification
The 3 months correlation between Ainsworth and Antipa is 0.16. Overlapping area represents the amount of risk that can be diversified away by holding Ainsworth Game Technology and Antipa Minerals in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Antipa Minerals and Ainsworth Game 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 Ainsworth Game Technology are associated (or correlated) with Antipa Minerals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Antipa Minerals has no effect on the direction of Ainsworth Game i.e., Ainsworth Game and Antipa Minerals go up and down completely randomly.
Pair Corralation between Ainsworth Game and Antipa Minerals
Assuming the 90 days trading horizon Ainsworth Game is expected to generate 108.13 times less return on investment than Antipa Minerals. But when comparing it to its historical volatility, Ainsworth Game Technology is 1.98 times less risky than Antipa Minerals. It trades about 0.0 of its potential returns per unit of risk. Antipa Minerals is currently generating about 0.04 of returns per unit of risk over similar time horizon. If you would invest 2.10 in Antipa Minerals on October 5, 2024 and sell it today you would earn a total of 0.60 from holding Antipa Minerals or generate 28.57% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 99.79% |
Values | Daily Returns |
Ainsworth Game Technology vs. Antipa Minerals
Performance |
Timeline |
Ainsworth Game Technology |
Antipa Minerals |
Ainsworth Game and Antipa Minerals Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ainsworth Game and Antipa Minerals
The main advantage of trading using opposite Ainsworth Game and Antipa Minerals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ainsworth Game position performs unexpectedly, Antipa Minerals 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 Antipa Minerals will offset losses from the drop in Antipa Minerals' long position.Ainsworth Game vs. Jupiter Energy | Ainsworth Game vs. WA1 Resources | Ainsworth Game vs. OD6 Metals | Ainsworth Game vs. Zip Co Limited |
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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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