Correlation Between Freeport McMoRan and Check Point
Can any of the company-specific risk be diversified away by investing in both Freeport McMoRan and Check Point 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 Freeport McMoRan and Check Point into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Freeport McMoRan and Check Point Software, you can compare the effects of market volatilities on Freeport McMoRan and Check Point 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 Freeport McMoRan with a short position of Check Point. Check out your portfolio center. Please also check ongoing floating volatility patterns of Freeport McMoRan and Check Point.
Diversification Opportunities for Freeport McMoRan and Check Point
0.45 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Freeport and Check is 0.45. Overlapping area represents the amount of risk that can be diversified away by holding Freeport McMoRan and Check Point Software in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Check Point Software and Freeport McMoRan 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 Freeport McMoRan are associated (or correlated) with Check Point. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Check Point Software has no effect on the direction of Freeport McMoRan i.e., Freeport McMoRan and Check Point go up and down completely randomly.
Pair Corralation between Freeport McMoRan and Check Point
Assuming the 90 days trading horizon Freeport McMoRan is expected to under-perform the Check Point. In addition to that, Freeport McMoRan is 1.06 times more volatile than Check Point Software. It trades about -0.09 of its total potential returns per unit of risk. Check Point Software is currently generating about -0.02 per unit of volatility. If you would invest 19,521 in Check Point Software on September 23, 2024 and sell it today you would lose (828.00) from holding Check Point Software or give up 4.24% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 98.48% |
Values | Daily Returns |
Freeport McMoRan vs. Check Point Software
Performance |
Timeline |
Freeport McMoRan |
Check Point Software |
Freeport McMoRan and Check Point Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Freeport McMoRan and Check Point
The main advantage of trading using opposite Freeport McMoRan and Check Point positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Freeport McMoRan position performs unexpectedly, Check Point 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 Check Point will offset losses from the drop in Check Point's long position.Freeport McMoRan vs. Uniper SE | Freeport McMoRan vs. Mulberry Group PLC | Freeport McMoRan vs. London Security Plc | Freeport McMoRan vs. Triad Group PLC |
Check Point vs. Toyota Motor Corp | Check Point vs. SoftBank Group Corp | Check Point vs. OTP Bank Nyrt | Check Point vs. Freeport McMoRan |
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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