Correlation Between Clean Energy and Comba Telecom
Can any of the company-specific risk be diversified away by investing in both Clean Energy and Comba Telecom 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 Clean Energy and Comba Telecom into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Clean Energy Fuels and Comba Telecom Systems, you can compare the effects of market volatilities on Clean Energy and Comba Telecom 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 Clean Energy with a short position of Comba Telecom. Check out your portfolio center. Please also check ongoing floating volatility patterns of Clean Energy and Comba Telecom.
Diversification Opportunities for Clean Energy and Comba Telecom
0.63 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Clean and Comba is 0.63. Overlapping area represents the amount of risk that can be diversified away by holding Clean Energy Fuels and Comba Telecom Systems in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Comba Telecom Systems and Clean Energy 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 Clean Energy Fuels are associated (or correlated) with Comba Telecom. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Comba Telecom Systems has no effect on the direction of Clean Energy i.e., Clean Energy and Comba Telecom go up and down completely randomly.
Pair Corralation between Clean Energy and Comba Telecom
Assuming the 90 days horizon Clean Energy Fuels is expected to generate 1.43 times more return on investment than Comba Telecom. However, Clean Energy is 1.43 times more volatile than Comba Telecom Systems. It trades about 0.15 of its potential returns per unit of risk. Comba Telecom Systems is currently generating about 0.13 per unit of risk. If you would invest 123.00 in Clean Energy Fuels on April 21, 2025 and sell it today you would earn a total of 53.00 from holding Clean Energy Fuels or generate 43.09% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Clean Energy Fuels vs. Comba Telecom Systems
Performance |
Timeline |
Clean Energy Fuels |
Comba Telecom Systems |
Clean Energy and Comba Telecom Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Clean Energy and Comba Telecom
The main advantage of trading using opposite Clean Energy and Comba Telecom positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Clean Energy position performs unexpectedly, Comba Telecom 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 Comba Telecom will offset losses from the drop in Comba Telecom's long position.Clean Energy vs. Compagnie Plastic Omnium | Clean Energy vs. SANOK RUBBER ZY | Clean Energy vs. BANKINTER ADR 2007 | Clean Energy vs. Ameriprise Financial |
Comba Telecom vs. CVW CLEANTECH INC | Comba Telecom vs. ALERION CLEANPOWER | Comba Telecom vs. Clean Energy Fuels | Comba Telecom vs. TV BROADCAST |
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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