Correlation Between Cyclone Metals and Kkr Credit
Can any of the company-specific risk be diversified away by investing in both Cyclone Metals and Kkr Credit 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 Cyclone Metals and Kkr Credit into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Cyclone Metals Limited and Kkr Credit Income, you can compare the effects of market volatilities on Cyclone Metals and Kkr Credit 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 Cyclone Metals with a short position of Kkr Credit. Check out your portfolio center. Please also check ongoing floating volatility patterns of Cyclone Metals and Kkr Credit.
Diversification Opportunities for Cyclone Metals and Kkr Credit
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Cyclone and Kkr is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding Cyclone Metals Limited and Kkr Credit Income in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kkr Credit Income and Cyclone Metals 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 Cyclone Metals Limited are associated (or correlated) with Kkr Credit. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kkr Credit Income has no effect on the direction of Cyclone Metals i.e., Cyclone Metals and Kkr Credit go up and down completely randomly.
Pair Corralation between Cyclone Metals and Kkr Credit
Assuming the 90 days trading horizon Cyclone Metals Limited is expected to generate 17.91 times more return on investment than Kkr Credit. However, Cyclone Metals is 17.91 times more volatile than Kkr Credit Income. It trades about 0.27 of its potential returns per unit of risk. Kkr Credit Income is currently generating about 0.2 per unit of risk. If you would invest 2.33 in Cyclone Metals Limited on September 20, 2024 and sell it today you would earn a total of 1.87 from holding Cyclone Metals Limited or generate 80.26% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Cyclone Metals Limited vs. Kkr Credit Income
Performance |
Timeline |
Cyclone Metals |
Kkr Credit Income |
Cyclone Metals and Kkr Credit Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Cyclone Metals and Kkr Credit
The main advantage of trading using opposite Cyclone Metals and Kkr Credit positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Cyclone Metals position performs unexpectedly, Kkr Credit 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 Kkr Credit will offset losses from the drop in Kkr Credit's long position.Cyclone Metals vs. Kkr Credit Income | Cyclone Metals vs. Advanced Braking Technology | Cyclone Metals vs. Ras Technology Holdings | Cyclone Metals vs. Air New Zealand |
Kkr Credit vs. Westpac Banking | Kkr Credit vs. ABACUS STORAGE KING | Kkr Credit vs. Odyssey Energy | Kkr Credit vs. Sims |
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 Idea Optimizer module to use advanced portfolio builder with pre-computed micro ideas to build optimal portfolio .
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