Correlation Between Driven Brands and Wilhelmina
Can any of the company-specific risk be diversified away by investing in both Driven Brands and Wilhelmina 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 Wilhelmina 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 Wilhelmina, you can compare the effects of market volatilities on Driven Brands and Wilhelmina 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 Wilhelmina. Check out your portfolio center. Please also check ongoing floating volatility patterns of Driven Brands and Wilhelmina.
Diversification Opportunities for Driven Brands and Wilhelmina
-0.52 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Driven and Wilhelmina is -0.52. Overlapping area represents the amount of risk that can be diversified away by holding Driven Brands Holdings and Wilhelmina in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wilhelmina 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 Wilhelmina. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wilhelmina has no effect on the direction of Driven Brands i.e., Driven Brands and Wilhelmina go up and down completely randomly.
Pair Corralation between Driven Brands and Wilhelmina
Given the investment horizon of 90 days Driven Brands is expected to generate 1.02 times less return on investment than Wilhelmina. But when comparing it to its historical volatility, Driven Brands Holdings is 1.27 times less risky than Wilhelmina. It trades about 0.23 of its potential returns per unit of risk. Wilhelmina is currently generating about 0.18 of returns per unit of risk over similar time horizon. If you would invest 347.00 in Wilhelmina on August 24, 2024 and sell it today you would earn a total of 42.00 from holding Wilhelmina or generate 12.1% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Driven Brands Holdings vs. Wilhelmina
Performance |
| Timeline |
| Driven Brands Holdings |
| Wilhelmina |
Driven Brands and Wilhelmina Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Driven Brands and Wilhelmina
The main advantage of trading using opposite Driven Brands and Wilhelmina positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Driven Brands position performs unexpectedly, Wilhelmina 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 Wilhelmina will offset losses from the drop in Wilhelmina'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 |
| Wilhelmina vs. First Advantage Corp | Wilhelmina vs. Civeo Corp | Wilhelmina vs. Performant Financial | Wilhelmina vs. Network 1 Technologies |
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 Competition Analyzer module to analyze and compare many basic indicators for a group of related or unrelated entities.
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