Correlation Between Vestas Wind and FOM Technologies
Can any of the company-specific risk be diversified away by investing in both Vestas Wind and FOM Technologies 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 Vestas Wind and FOM Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Vestas Wind Systems and FOM Technologies AS, you can compare the effects of market volatilities on Vestas Wind and FOM Technologies 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 Vestas Wind with a short position of FOM Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of Vestas Wind and FOM Technologies.
Diversification Opportunities for Vestas Wind and FOM Technologies
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Vestas and FOM is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Vestas Wind Systems and FOM Technologies AS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FOM Technologies and Vestas Wind 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 Vestas Wind Systems are associated (or correlated) with FOM Technologies. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FOM Technologies has no effect on the direction of Vestas Wind i.e., Vestas Wind and FOM Technologies go up and down completely randomly.
Pair Corralation between Vestas Wind and FOM Technologies
Assuming the 90 days trading horizon Vestas Wind Systems is expected to generate 0.74 times more return on investment than FOM Technologies. However, Vestas Wind Systems is 1.35 times less risky than FOM Technologies. It trades about -0.07 of its potential returns per unit of risk. FOM Technologies AS is currently generating about -0.25 per unit of risk. If you would invest 10,470 in Vestas Wind Systems on November 3, 2024 and sell it today you would lose (576.00) from holding Vestas Wind Systems or give up 5.5% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Vestas Wind Systems vs. FOM Technologies AS
Performance |
| Timeline |
| Vestas Wind Systems |
| FOM Technologies |
Vestas Wind and FOM Technologies Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Vestas Wind and FOM Technologies
The main advantage of trading using opposite Vestas Wind and FOM Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Vestas Wind position performs unexpectedly, FOM Technologies 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 FOM Technologies will offset losses from the drop in FOM Technologies' long position.| Vestas Wind vs. Orsted AS | Vestas Wind vs. Danske Bank AS | Vestas Wind vs. Bavarian Nordic | Vestas Wind vs. DSV Panalpina AS |
| FOM Technologies vs. cBrain AS | FOM Technologies vs. Penneo AS | FOM Technologies vs. Shape Robotics AS | FOM Technologies vs. ALK Abell AS |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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