Correlation Between Clean Energy and INSURANCE AUST
Can any of the company-specific risk be diversified away by investing in both Clean Energy and INSURANCE AUST 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 INSURANCE AUST 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 INSURANCE AUST GRP, you can compare the effects of market volatilities on Clean Energy and INSURANCE AUST 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 INSURANCE AUST. Check out your portfolio center. Please also check ongoing floating volatility patterns of Clean Energy and INSURANCE AUST.
Diversification Opportunities for Clean Energy and INSURANCE AUST
0.68 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Clean and INSURANCE is 0.68. Overlapping area represents the amount of risk that can be diversified away by holding Clean Energy Fuels and INSURANCE AUST GRP in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on INSURANCE AUST GRP 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 INSURANCE AUST. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of INSURANCE AUST GRP has no effect on the direction of Clean Energy i.e., Clean Energy and INSURANCE AUST go up and down completely randomly.
Pair Corralation between Clean Energy and INSURANCE AUST
Assuming the 90 days horizon Clean Energy Fuels is expected to under-perform the INSURANCE AUST. In addition to that, Clean Energy is 2.16 times more volatile than INSURANCE AUST GRP. It trades about -0.1 of its total potential returns per unit of risk. INSURANCE AUST GRP is currently generating about -0.07 per unit of volatility. If you would invest 486.00 in INSURANCE AUST GRP on December 21, 2024 and sell it today you would lose (48.00) from holding INSURANCE AUST GRP or give up 9.88% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Clean Energy Fuels vs. INSURANCE AUST GRP
Performance |
Timeline |
Clean Energy Fuels |
INSURANCE AUST GRP |
Clean Energy and INSURANCE AUST Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Clean Energy and INSURANCE AUST
The main advantage of trading using opposite Clean Energy and INSURANCE AUST positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Clean Energy position performs unexpectedly, INSURANCE AUST 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 INSURANCE AUST will offset losses from the drop in INSURANCE AUST's long position.Clean Energy vs. TAL Education Group | Clean Energy vs. National Retail Properties | Clean Energy vs. Japan Tobacco | Clean Energy vs. CARSALESCOM |
INSURANCE AUST vs. Playa Hotels Resorts | INSURANCE AUST vs. COMMERCIAL VEHICLE | INSURANCE AUST vs. InPlay Oil Corp | INSURANCE AUST vs. TRAVEL LEISURE DL 01 |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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