Correlation Between CAVA Group, and Turning Point
Can any of the company-specific risk be diversified away by investing in both CAVA Group, and Turning Point 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 CAVA Group, and Turning Point into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CAVA Group, and Turning Point Brands, you can compare the effects of market volatilities on CAVA Group, and Turning Point 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 CAVA Group, with a short position of Turning Point. Check out your portfolio center. Please also check ongoing floating volatility patterns of CAVA Group, and Turning Point.
Diversification Opportunities for CAVA Group, and Turning Point
0.79 | Correlation Coefficient |
Poor diversification
The 3 months correlation between CAVA and Turning is 0.79. Overlapping area represents the amount of risk that can be diversified away by holding CAVA Group, and Turning Point Brands in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Turning Point Brands and CAVA Group, 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 CAVA Group, are associated (or correlated) with Turning Point. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Turning Point Brands has no effect on the direction of CAVA Group, i.e., CAVA Group, and Turning Point go up and down completely randomly.
Pair Corralation between CAVA Group, and Turning Point
Given the investment horizon of 90 days CAVA Group, is expected to generate 3.57 times less return on investment than Turning Point. In addition to that, CAVA Group, is 1.04 times more volatile than Turning Point Brands. It trades about 0.13 of its total potential returns per unit of risk. Turning Point Brands is currently generating about 0.5 per unit of volatility. If you would invest 4,769 in Turning Point Brands on September 4, 2024 and sell it today you would earn a total of 1,320 from holding Turning Point Brands or generate 27.68% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
CAVA Group, vs. Turning Point Brands
Performance |
| Timeline |
| CAVA Group, |
| Turning Point Brands |
CAVA Group, and Turning Point Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CAVA Group, and Turning Point
The main advantage of trading using opposite CAVA Group, and Turning Point positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CAVA Group, position performs unexpectedly, Turning Point 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 Turning Point will offset losses from the drop in Turning Point's long position.| CAVA Group, vs. Tencent Music Entertainment | CAVA Group, vs. Asbury Automotive Group | CAVA Group, vs. Stagwell | CAVA Group, vs. Fluent Inc |
| Turning Point vs. Universal | Turning Point vs. Imperial Brands PLC | Turning Point vs. British American Tobacco | Turning Point vs. Philip Morris International |
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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