Correlation Between Clean Energy and Treasury Wine
Can any of the company-specific risk be diversified away by investing in both Clean Energy and Treasury Wine 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 Treasury Wine 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 Treasury Wine Estates, you can compare the effects of market volatilities on Clean Energy and Treasury Wine 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 Treasury Wine. Check out your portfolio center. Please also check ongoing floating volatility patterns of Clean Energy and Treasury Wine.
Diversification Opportunities for Clean Energy and Treasury Wine
-0.4 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Clean and Treasury is -0.4. Overlapping area represents the amount of risk that can be diversified away by holding Clean Energy Fuels and Treasury Wine Estates in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Treasury Wine Estates 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 Treasury Wine. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Treasury Wine Estates has no effect on the direction of Clean Energy i.e., Clean Energy and Treasury Wine go up and down completely randomly.
Pair Corralation between Clean Energy and Treasury Wine
Assuming the 90 days horizon Clean Energy Fuels is expected to generate 3.05 times more return on investment than Treasury Wine. However, Clean Energy is 3.05 times more volatile than Treasury Wine Estates. It trades about 0.15 of its potential returns per unit of risk. Treasury Wine Estates is currently generating about -0.09 per unit of risk. If you would invest 123.00 in Clean Energy Fuels on April 23, 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 Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Clean Energy Fuels vs. Treasury Wine Estates
Performance |
Timeline |
Clean Energy Fuels |
Treasury Wine Estates |
Clean Energy and Treasury Wine Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Clean Energy and Treasury Wine
The main advantage of trading using opposite Clean Energy and Treasury Wine positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Clean Energy position performs unexpectedly, Treasury Wine 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 Treasury Wine will offset losses from the drop in Treasury Wine's long position.Clean Energy vs. Hope Education Group | Clean Energy vs. Grand Canyon Education | Clean Energy vs. Geratherm Medical AG | Clean Energy vs. XTANT MEDICAL HLDGS |
Treasury Wine vs. Clean Energy Fuels | Treasury Wine vs. Corporate Office Properties | Treasury Wine vs. Microbot Medical | Treasury Wine vs. China Yongda Automobiles |
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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