Correlation Between Ainsworth Game and K2 Asset
Can any of the company-specific risk be diversified away by investing in both Ainsworth Game and K2 Asset 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 K2 Asset 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 K2 Asset Management, you can compare the effects of market volatilities on Ainsworth Game and K2 Asset 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 K2 Asset. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ainsworth Game and K2 Asset.
Diversification Opportunities for Ainsworth Game and K2 Asset
0.07 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Ainsworth and KAM is 0.07. Overlapping area represents the amount of risk that can be diversified away by holding Ainsworth Game Technology and K2 Asset Management in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on K2 Asset Management 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 K2 Asset. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of K2 Asset Management has no effect on the direction of Ainsworth Game i.e., Ainsworth Game and K2 Asset go up and down completely randomly.
Pair Corralation between Ainsworth Game and K2 Asset
Assuming the 90 days trading horizon Ainsworth Game is expected to generate 29.24 times less return on investment than K2 Asset. But when comparing it to its historical volatility, Ainsworth Game Technology is 1.57 times less risky than K2 Asset. It trades about 0.0 of its potential returns per unit of risk. K2 Asset Management is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 3.80 in K2 Asset Management on September 14, 2024 and sell it today you would earn a total of 3.90 from holding K2 Asset Management or generate 102.63% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Ainsworth Game Technology vs. K2 Asset Management
Performance |
Timeline |
Ainsworth Game Technology |
K2 Asset Management |
Ainsworth Game and K2 Asset Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ainsworth Game and K2 Asset
The main advantage of trading using opposite Ainsworth Game and K2 Asset positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ainsworth Game position performs unexpectedly, K2 Asset 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 K2 Asset will offset losses from the drop in K2 Asset's long position.Ainsworth Game vs. The Environmental Group | Ainsworth Game vs. Infomedia | Ainsworth Game vs. Super Retail Group | Ainsworth Game vs. COAST ENTERTAINMENT HOLDINGS |
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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