Correlation Between Molecular Partners and NYSE Composite
Can any of the company-specific risk be diversified away by investing in both Molecular Partners and NYSE Composite 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 Molecular Partners and NYSE Composite into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Molecular Partners AG and NYSE Composite, you can compare the effects of market volatilities on Molecular Partners and NYSE Composite 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 Molecular Partners with a short position of NYSE Composite. Check out your portfolio center. Please also check ongoing floating volatility patterns of Molecular Partners and NYSE Composite.
Diversification Opportunities for Molecular Partners and NYSE Composite
-0.6 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Molecular and NYSE is -0.6. Overlapping area represents the amount of risk that can be diversified away by holding Molecular Partners AG and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Molecular Partners 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 Molecular Partners AG are associated (or correlated) with NYSE Composite. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NYSE Composite has no effect on the direction of Molecular Partners i.e., Molecular Partners and NYSE Composite go up and down completely randomly.
Pair Corralation between Molecular Partners and NYSE Composite
Assuming the 90 days trading horizon Molecular Partners AG is expected to generate 4.58 times more return on investment than NYSE Composite. However, Molecular Partners is 4.58 times more volatile than NYSE Composite. It trades about -0.03 of its potential returns per unit of risk. NYSE Composite is currently generating about -0.22 per unit of risk. If you would invest 338.00 in Molecular Partners AG on February 1, 2024 and sell it today you would lose (9.00) from holding Molecular Partners AG or give up 2.66% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 95.45% |
Values | Daily Returns |
Molecular Partners AG vs. NYSE Composite
Performance |
Timeline |
Molecular Partners and NYSE Composite Volatility Contrast
Predicted Return Density |
Returns |
Molecular Partners AG
Pair trading matchups for Molecular Partners
NYSE Composite
Pair trading matchups for NYSE Composite
Pair Trading with Molecular Partners and NYSE Composite
The main advantage of trading using opposite Molecular Partners and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Molecular Partners position performs unexpectedly, NYSE Composite 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 NYSE Composite will offset losses from the drop in NYSE Composite's long position.Molecular Partners vs. Relief Therapeutics Holding | Molecular Partners vs. Meyer Burger Tech | Molecular Partners vs. NRX Pharmaceuticals |
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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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