Correlation Between Driven Brands and BloomZ Ordinary
Can any of the company-specific risk be diversified away by investing in both Driven Brands and BloomZ Ordinary 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 BloomZ Ordinary 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 BloomZ Ordinary Shares, you can compare the effects of market volatilities on Driven Brands and BloomZ Ordinary 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 BloomZ Ordinary. Check out your portfolio center. Please also check ongoing floating volatility patterns of Driven Brands and BloomZ Ordinary.
Diversification Opportunities for Driven Brands and BloomZ Ordinary
-0.55 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Driven and BloomZ is -0.55. Overlapping area represents the amount of risk that can be diversified away by holding Driven Brands Holdings and BloomZ Ordinary Shares in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BloomZ Ordinary Shares 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 BloomZ Ordinary. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BloomZ Ordinary Shares has no effect on the direction of Driven Brands i.e., Driven Brands and BloomZ Ordinary go up and down completely randomly.
Pair Corralation between Driven Brands and BloomZ Ordinary
Given the investment horizon of 90 days Driven Brands Holdings is expected to generate 0.12 times more return on investment than BloomZ Ordinary. However, Driven Brands Holdings is 8.19 times less risky than BloomZ Ordinary. It trades about 0.15 of its potential returns per unit of risk. BloomZ Ordinary Shares is currently generating about -0.01 per unit of risk. If you would invest 1,134 in Driven Brands Holdings on September 1, 2024 and sell it today you would earn a total of 551.00 from holding Driven Brands Holdings or generate 48.59% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 72.22% |
Values | Daily Returns |
Driven Brands Holdings vs. BloomZ Ordinary Shares
Performance |
Timeline |
Driven Brands Holdings |
BloomZ Ordinary Shares |
Driven Brands and BloomZ Ordinary Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Driven Brands and BloomZ Ordinary
The main advantage of trading using opposite Driven Brands and BloomZ Ordinary positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Driven Brands position performs unexpectedly, BloomZ Ordinary 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 BloomZ Ordinary will offset losses from the drop in BloomZ Ordinary'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 |
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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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