Correlation Between DBV Technologies and Valneva SE
Can any of the company-specific risk be diversified away by investing in both DBV Technologies and Valneva SE 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 DBV Technologies and Valneva SE into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DBV Technologies SA and Valneva SE, you can compare the effects of market volatilities on DBV Technologies and Valneva SE 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 DBV Technologies with a short position of Valneva SE. Check out your portfolio center. Please also check ongoing floating volatility patterns of DBV Technologies and Valneva SE.
Diversification Opportunities for DBV Technologies and Valneva SE
0.18 | Correlation Coefficient |
Average diversification
The 3 months correlation between DBV and Valneva is 0.18. Overlapping area represents the amount of risk that can be diversified away by holding DBV Technologies SA and Valneva SE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Valneva SE and DBV Technologies 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 DBV Technologies SA are associated (or correlated) with Valneva SE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Valneva SE has no effect on the direction of DBV Technologies i.e., DBV Technologies and Valneva SE go up and down completely randomly.
Pair Corralation between DBV Technologies and Valneva SE
Assuming the 90 days trading horizon DBV Technologies is expected to generate 1.36 times less return on investment than Valneva SE. In addition to that, DBV Technologies is 1.83 times more volatile than Valneva SE. It trades about 0.07 of its total potential returns per unit of risk. Valneva SE is currently generating about 0.17 per unit of volatility. If you would invest 214.00 in Valneva SE on December 26, 2024 and sell it today you would earn a total of 118.00 from holding Valneva SE or generate 55.14% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 98.41% |
Values | Daily Returns |
DBV Technologies SA vs. Valneva SE
Performance |
Timeline |
DBV Technologies |
Valneva SE |
DBV Technologies and Valneva SE Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DBV Technologies and Valneva SE
The main advantage of trading using opposite DBV Technologies and Valneva SE positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DBV Technologies position performs unexpectedly, Valneva SE 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 Valneva SE will offset losses from the drop in Valneva SE's long position.DBV Technologies vs. Genfit | DBV Technologies vs. Innate Pharma | DBV Technologies vs. Cellectis | DBV Technologies vs. Nanobiotix SA |
Valneva SE vs. Innate Pharma | Valneva SE vs. DBV Technologies SA | Valneva SE vs. Genfit | Valneva SE vs. Valneva SE 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 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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