Correlation Between Fusion Fuel and Dow Jones
Can any of the company-specific risk be diversified away by investing in both Fusion Fuel and Dow Jones 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 Fusion Fuel and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Fusion Fuel Green and Dow Jones Industrial, you can compare the effects of market volatilities on Fusion Fuel and Dow Jones 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 Fusion Fuel with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Fusion Fuel and Dow Jones.
Diversification Opportunities for Fusion Fuel and Dow Jones
-0.46 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Fusion and Dow is -0.46. Overlapping area represents the amount of risk that can be diversified away by holding Fusion Fuel Green and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Fusion Fuel 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 Fusion Fuel Green are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Fusion Fuel i.e., Fusion Fuel and Dow Jones go up and down completely randomly.
Pair Corralation between Fusion Fuel and Dow Jones
Assuming the 90 days horizon Fusion Fuel Green is expected to generate 29.17 times more return on investment than Dow Jones. However, Fusion Fuel is 29.17 times more volatile than Dow Jones Industrial. It trades about 0.04 of its potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.08 per unit of risk. If you would invest 78.00 in Fusion Fuel Green on November 5, 2024 and sell it today you would lose (76.30) from holding Fusion Fuel Green or give up 97.82% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 99.8% |
| Values | Daily Returns |
Fusion Fuel Green vs. Dow Jones Industrial
Performance |
| Timeline |
Fusion Fuel and Dow Jones Volatility Contrast
Predicted Return Density |
| Returns |
Fusion Fuel Green
Pair trading matchups for Fusion Fuel
Dow Jones Industrial
Pair trading matchups for Dow Jones
Pair Trading with Fusion Fuel and Dow Jones
The main advantage of trading using opposite Fusion Fuel and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Fusion Fuel position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.| Fusion Fuel vs. Fusion Fuel Green | Fusion Fuel vs. Advent Technologies Holdings | Fusion Fuel vs. Eos Energy Enterprises | Fusion Fuel vs. CuriosityStream |
| Dow Jones vs. Fernhill Beverage | Dow Jones vs. Fomento Economico Mexicano | Dow Jones vs. Loud Beverage Group | Dow Jones vs. Diageo PLC ADR |
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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