Correlation Between CyberArk Software and Sumitomo Rubber
Can any of the company-specific risk be diversified away by investing in both CyberArk Software and Sumitomo Rubber 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 CyberArk Software and Sumitomo Rubber into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CyberArk Software and Sumitomo Rubber Industries, you can compare the effects of market volatilities on CyberArk Software and Sumitomo Rubber 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 CyberArk Software with a short position of Sumitomo Rubber. Check out your portfolio center. Please also check ongoing floating volatility patterns of CyberArk Software and Sumitomo Rubber.
Diversification Opportunities for CyberArk Software and Sumitomo Rubber
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between CyberArk and Sumitomo is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding CyberArk Software and Sumitomo Rubber Industries in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sumitomo Rubber Indu and CyberArk Software 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 CyberArk Software are associated (or correlated) with Sumitomo Rubber. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sumitomo Rubber Indu has no effect on the direction of CyberArk Software i.e., CyberArk Software and Sumitomo Rubber go up and down completely randomly.
Pair Corralation between CyberArk Software and Sumitomo Rubber
Assuming the 90 days trading horizon CyberArk Software is expected to generate 2.22 times more return on investment than Sumitomo Rubber. However, CyberArk Software is 2.22 times more volatile than Sumitomo Rubber Industries. It trades about 0.14 of its potential returns per unit of risk. Sumitomo Rubber Industries is currently generating about 0.14 per unit of risk. If you would invest 30,790 in CyberArk Software on October 8, 2024 and sell it today you would earn a total of 1,940 from holding CyberArk Software or generate 6.3% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
CyberArk Software vs. Sumitomo Rubber Industries
Performance |
Timeline |
CyberArk Software |
Sumitomo Rubber Indu |
CyberArk Software and Sumitomo Rubber Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with CyberArk Software and Sumitomo Rubber
The main advantage of trading using opposite CyberArk Software and Sumitomo Rubber positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CyberArk Software position performs unexpectedly, Sumitomo Rubber 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 Sumitomo Rubber will offset losses from the drop in Sumitomo Rubber's long position.CyberArk Software vs. CARSALESCOM | CyberArk Software vs. ONWARD MEDICAL BV | CyberArk Software vs. OBSERVE MEDICAL ASA | CyberArk Software vs. Advanced Medical Solutions |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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