Correlation Between Constellation Brands and Meta Platforms
Can any of the company-specific risk be diversified away by investing in both Constellation Brands and Meta Platforms 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 Constellation Brands and Meta Platforms into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Constellation Brands Class and Meta Platforms, you can compare the effects of market volatilities on Constellation Brands and Meta Platforms 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 Constellation Brands with a short position of Meta Platforms. Check out your portfolio center. Please also check ongoing floating volatility patterns of Constellation Brands and Meta Platforms.
Diversification Opportunities for Constellation Brands and Meta Platforms
-0.24 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Constellation and Meta is -0.24. Overlapping area represents the amount of risk that can be diversified away by holding Constellation Brands Class and Meta Platforms in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Meta Platforms and Constellation Brands 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 Constellation Brands Class are associated (or correlated) with Meta Platforms. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Meta Platforms has no effect on the direction of Constellation Brands i.e., Constellation Brands and Meta Platforms go up and down completely randomly.
Pair Corralation between Constellation Brands and Meta Platforms
If you would invest 24,652 in Constellation Brands Class on January 24, 2024 and sell it today you would earn a total of 1,322 from holding Constellation Brands Class or generate 5.36% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 1.61% |
| Values | Daily Returns |
Constellation Brands Class vs. Meta Platforms
Performance |
| Timeline |
| Constellation Brands |
| Meta Platforms |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Constellation Brands and Meta Platforms Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Constellation Brands and Meta Platforms
The main advantage of trading using opposite Constellation Brands and Meta Platforms positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Constellation Brands position performs unexpectedly, Meta Platforms 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 Meta Platforms will offset losses from the drop in Meta Platforms' long position.| Constellation Brands vs. Naked Wines plc | Constellation Brands vs. Andrew Peller Limited | Constellation Brands vs. Iconic Brands | Constellation Brands vs. Naked Wines plc |
| Meta Platforms vs. Meta Platforms | Meta Platforms vs. Alphabet Inc Class A | Meta Platforms vs. Twilio Inc | Meta Platforms vs. Snap Inc |
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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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