Correlation Between Ormat Technologies and Kenon Holdings
Can any of the company-specific risk be diversified away by investing in both Ormat Technologies and Kenon Holdings 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 Ormat Technologies and Kenon Holdings into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ormat Technologies and Kenon Holdings, you can compare the effects of market volatilities on Ormat Technologies and Kenon Holdings 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 Ormat Technologies with a short position of Kenon Holdings. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ormat Technologies and Kenon Holdings.
Diversification Opportunities for Ormat Technologies and Kenon Holdings
0.77 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Ormat and Kenon is 0.77. Overlapping area represents the amount of risk that can be diversified away by holding Ormat Technologies and Kenon Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kenon Holdings and Ormat Technologies 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 Ormat Technologies are associated (or correlated) with Kenon Holdings. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kenon Holdings has no effect on the direction of Ormat Technologies i.e., Ormat Technologies and Kenon Holdings go up and down completely randomly.
Pair Corralation between Ormat Technologies and Kenon Holdings
Assuming the 90 days trading horizon Ormat Technologies is expected to under-perform the Kenon Holdings. But the stock apears to be less risky and, when comparing its historical volatility, Ormat Technologies is 1.53 times less risky than Kenon Holdings. The stock trades about -0.37 of its potential returns per unit of risk. The Kenon Holdings is currently generating about 0.0 of returns per unit of risk over similar time horizon. If you would invest 1,090,000 in Kenon Holdings on September 16, 2024 and sell it today you would lose (5,000) from holding Kenon Holdings or give up 0.46% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Ormat Technologies vs. Kenon Holdings
Performance |
Timeline |
Ormat Technologies |
Kenon Holdings |
Ormat Technologies and Kenon Holdings Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ormat Technologies and Kenon Holdings
The main advantage of trading using opposite Ormat Technologies and Kenon Holdings positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ormat Technologies position performs unexpectedly, Kenon Holdings 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 Kenon Holdings will offset losses from the drop in Kenon Holdings' long position.Ormat Technologies vs. Elbit Systems | Ormat Technologies vs. Nice | Ormat Technologies vs. Tower Semiconductor | Ormat Technologies vs. Nova |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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