Correlation Between Driven Brands and Asbury Automotive
Can any of the company-specific risk be diversified away by investing in both Driven Brands and Asbury Automotive 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 Driven Brands and Asbury Automotive into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Driven Brands Holdings and Asbury Automotive Group, you can compare the effects of market volatilities on Driven Brands and Asbury Automotive 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 Driven Brands with a short position of Asbury Automotive. Check out your portfolio center. Please also check ongoing floating volatility patterns of Driven Brands and Asbury Automotive.
Diversification Opportunities for Driven Brands and Asbury Automotive
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Driven and Asbury is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding Driven Brands Holdings and Asbury Automotive Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Asbury Automotive and Driven 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 Driven Brands Holdings are associated (or correlated) with Asbury Automotive. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Asbury Automotive has no effect on the direction of Driven Brands i.e., Driven Brands and Asbury Automotive go up and down completely randomly.
Pair Corralation between Driven Brands and Asbury Automotive
Given the investment horizon of 90 days Driven Brands Holdings is expected to under-perform the Asbury Automotive. In addition to that, Driven Brands is 1.27 times more volatile than Asbury Automotive Group. It trades about -0.02 of its total potential returns per unit of risk. Asbury Automotive Group is currently generating about 0.05 per unit of volatility. If you would invest 17,655 in Asbury Automotive Group on August 24, 2024 and sell it today you would earn a total of 8,475 from holding Asbury Automotive Group or generate 48.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Driven Brands Holdings vs. Asbury Automotive Group
Performance |
| Timeline |
| Driven Brands Holdings |
| Asbury Automotive |
Driven Brands and Asbury Automotive Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Driven Brands and Asbury Automotive
The main advantage of trading using opposite Driven Brands and Asbury Automotive positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Driven Brands position performs unexpectedly, Asbury Automotive 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 Asbury Automotive will offset losses from the drop in Asbury Automotive's long position.| Driven Brands vs. CarGurus | Driven Brands vs. KAR Auction Services | Driven Brands vs. Kingsway Financial Services | Driven Brands vs. Group 1 Automotive |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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