Correlation Between Carnegie Clean and Clean Energy
Can any of the company-specific risk be diversified away by investing in both Carnegie Clean 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 Carnegie Clean and Clean Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Carnegie Clean Energy and Clean Energy Fuels, you can compare the effects of market volatilities on Carnegie Clean 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 Carnegie Clean with a short position of Clean Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Carnegie Clean and Clean Energy.
Diversification Opportunities for Carnegie Clean and Clean Energy
0.63 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Carnegie and Clean is 0.63. Overlapping area represents the amount of risk that can be diversified away by holding Carnegie Clean Energy 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 Carnegie Clean 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 Carnegie Clean Energy 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 Carnegie Clean i.e., Carnegie Clean and Clean Energy go up and down completely randomly.
Pair Corralation between Carnegie Clean and Clean Energy
Assuming the 90 days trading horizon Carnegie Clean Energy is expected to generate 1.12 times more return on investment than Clean Energy. However, Carnegie Clean is 1.12 times more volatile than Clean Energy Fuels. It trades about 0.17 of its potential returns per unit of risk. Clean Energy Fuels is currently generating about 0.15 per unit of risk. If you would invest 1.70 in Carnegie Clean Energy on April 21, 2025 and sell it today you would earn a total of 1.02 from holding Carnegie Clean Energy or generate 60.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Carnegie Clean Energy vs. Clean Energy Fuels
Performance |
Timeline |
Carnegie Clean Energy |
Clean Energy Fuels |
Carnegie Clean and Clean Energy Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Carnegie Clean and Clean Energy
The main advantage of trading using opposite Carnegie Clean and Clean Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Carnegie Clean 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.Carnegie Clean vs. Materialise NV | Carnegie Clean vs. Applied Materials | Carnegie Clean vs. The Yokohama Rubber | Carnegie Clean vs. Vulcan Materials |
Clean Energy vs. Compagnie Plastic Omnium | Clean Energy vs. SANOK RUBBER ZY | Clean Energy vs. BANKINTER ADR 2007 | Clean Energy vs. Ameriprise Financial |
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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