Correlation Between Brinker International and FAT Brands
Can any of the company-specific risk be diversified away by investing in both Brinker International and FAT Brands 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 Brinker International and FAT Brands into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Brinker International and FAT Brands, you can compare the effects of market volatilities on Brinker International and FAT Brands 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 Brinker International with a short position of FAT Brands. Check out your portfolio center. Please also check ongoing floating volatility patterns of Brinker International and FAT Brands.
Diversification Opportunities for Brinker International and FAT Brands
-0.71 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Brinker and FAT is -0.71. Overlapping area represents the amount of risk that can be diversified away by holding Brinker International and FAT Brands in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FAT Brands and Brinker International 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 Brinker International are associated (or correlated) with FAT Brands. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FAT Brands has no effect on the direction of Brinker International i.e., Brinker International and FAT Brands go up and down completely randomly.
Pair Corralation between Brinker International and FAT Brands
Considering the 90-day investment horizon Brinker International is expected to generate 2.69 times more return on investment than FAT Brands. However, Brinker International is 2.69 times more volatile than FAT Brands. It trades about 0.52 of its potential returns per unit of risk. FAT Brands is currently generating about 0.11 per unit of risk. If you would invest 9,773 in Brinker International on August 28, 2024 and sell it today you would earn a total of 3,325 from holding Brinker International or generate 34.02% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Brinker International vs. FAT Brands
Performance |
| Timeline |
| Brinker International |
| FAT Brands |
Brinker International and FAT Brands Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Brinker International and FAT Brands
The main advantage of trading using opposite Brinker International and FAT Brands positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Brinker International position performs unexpectedly, FAT Brands 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 FAT Brands will offset losses from the drop in FAT Brands' long position.| Brinker International vs. Dennys Corp | Brinker International vs. Bloomin Brands | Brinker International vs. Jack In The | Brinker International vs. Dine Brands Global |
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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