Correlation Between REVO INSURANCE and CLEAN ENERGY
Can any of the company-specific risk be diversified away by investing in both REVO INSURANCE and CLEAN ENERGY 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 REVO INSURANCE and CLEAN ENERGY into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between REVO INSURANCE SPA and CLEAN ENERGY FUELS, you can compare the effects of market volatilities on REVO INSURANCE and CLEAN ENERGY 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 REVO INSURANCE with a short position of CLEAN ENERGY. Check out your portfolio center. Please also check ongoing floating volatility patterns of REVO INSURANCE and CLEAN ENERGY.
Diversification Opportunities for REVO INSURANCE and CLEAN ENERGY
-0.41 | Correlation Coefficient |
Very good diversification
The 3 months correlation between REVO and CLEAN is -0.41. Overlapping area represents the amount of risk that can be diversified away by holding REVO INSURANCE SPA and CLEAN ENERGY FUELS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on CLEAN ENERGY FUELS and REVO INSURANCE 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 REVO INSURANCE SPA are associated (or correlated) with CLEAN ENERGY. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of CLEAN ENERGY FUELS has no effect on the direction of REVO INSURANCE i.e., REVO INSURANCE and CLEAN ENERGY go up and down completely randomly.
Pair Corralation between REVO INSURANCE and CLEAN ENERGY
Assuming the 90 days horizon REVO INSURANCE SPA is expected to generate 0.74 times more return on investment than CLEAN ENERGY. However, REVO INSURANCE SPA is 1.35 times less risky than CLEAN ENERGY. It trades about 0.17 of its potential returns per unit of risk. CLEAN ENERGY FUELS is currently generating about 0.08 per unit of risk. If you would invest 912.00 in REVO INSURANCE SPA on October 11, 2024 and sell it today you would earn a total of 228.00 from holding REVO INSURANCE SPA or generate 25.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
REVO INSURANCE SPA vs. CLEAN ENERGY FUELS
Performance |
Timeline |
REVO INSURANCE SPA |
CLEAN ENERGY FUELS |
REVO INSURANCE and CLEAN ENERGY Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with REVO INSURANCE and CLEAN ENERGY
The main advantage of trading using opposite REVO INSURANCE and CLEAN ENERGY positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if REVO INSURANCE position performs unexpectedly, CLEAN ENERGY 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 CLEAN ENERGY will offset losses from the drop in CLEAN ENERGY's long position.REVO INSURANCE vs. SOUTHWEST AIRLINES | REVO INSURANCE vs. American Airlines Group | REVO INSURANCE vs. United Airlines Holdings | REVO INSURANCE vs. Waste Management |
CLEAN ENERGY vs. Reinsurance Group of | CLEAN ENERGY vs. CN MODERN DAIRY | CLEAN ENERGY vs. Direct Line Insurance | CLEAN ENERGY vs. Tyson Foods |
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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