Correlation Between Waga Energy and Charwood Energy
Can any of the company-specific risk be diversified away by investing in both Waga Energy and Charwood 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 Waga Energy and Charwood Energy into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Waga Energy SA and Charwood Energy SA, you can compare the effects of market volatilities on Waga Energy and Charwood 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 Waga Energy with a short position of Charwood Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Waga Energy and Charwood Energy.
Diversification Opportunities for Waga Energy and Charwood Energy
0.29 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Waga and Charwood is 0.29. Overlapping area represents the amount of risk that can be diversified away by holding Waga Energy SA and Charwood Energy SA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Charwood Energy SA and Waga 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 Waga Energy SA are associated (or correlated) with Charwood Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Charwood Energy SA has no effect on the direction of Waga Energy i.e., Waga Energy and Charwood Energy go up and down completely randomly.
Pair Corralation between Waga Energy and Charwood Energy
Assuming the 90 days trading horizon Waga Energy SA is expected to generate 0.61 times more return on investment than Charwood Energy. However, Waga Energy SA is 1.63 times less risky than Charwood Energy. It trades about 0.08 of its potential returns per unit of risk. Charwood Energy SA is currently generating about -0.14 per unit of risk. If you would invest 1,516 in Waga Energy SA on October 20, 2024 and sell it today you would earn a total of 40.00 from holding Waga Energy SA or generate 2.64% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Waga Energy SA vs. Charwood Energy SA
Performance |
| Timeline |
| Waga Energy SA |
| Charwood Energy SA |
Waga Energy and Charwood Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Waga Energy and Charwood Energy
The main advantage of trading using opposite Waga Energy and Charwood Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Waga Energy position performs unexpectedly, Charwood 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 Charwood Energy will offset losses from the drop in Charwood Energy's long position.| Waga Energy vs. OVH Groupe SAS | Waga Energy vs. Hydrogene De France | Waga Energy vs. Neoen SA | Waga Energy vs. Haffner Energy SA |
| Charwood Energy vs. Hydrogene De France | Charwood Energy vs. Hydrogen Refueling Solutions | Charwood Energy vs. Neoen SA | Charwood Energy vs. Hopium SAS |
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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