Correlation Between FAT Brands and Bloomin Brands
Can any of the company-specific risk be diversified away by investing in both FAT Brands and Bloomin 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 FAT Brands and Bloomin Brands into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FAT Brands and Bloomin Brands, you can compare the effects of market volatilities on FAT Brands and Bloomin 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 FAT Brands with a short position of Bloomin Brands. Check out your portfolio center. Please also check ongoing floating volatility patterns of FAT Brands and Bloomin Brands.
Diversification Opportunities for FAT Brands and Bloomin Brands
0.21 | Correlation Coefficient |
Modest diversification
The 3 months correlation between FAT and Bloomin is 0.21. Overlapping area represents the amount of risk that can be diversified away by holding FAT Brands and Bloomin Brands in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bloomin Brands and FAT 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 FAT Brands are associated (or correlated) with Bloomin Brands. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bloomin Brands has no effect on the direction of FAT Brands i.e., FAT Brands and Bloomin Brands go up and down completely randomly.
Pair Corralation between FAT Brands and Bloomin Brands
Assuming the 90 days horizon FAT Brands is expected to under-perform the Bloomin Brands. But the stock apears to be less risky and, when comparing its historical volatility, FAT Brands is 1.01 times less risky than Bloomin Brands. The stock trades about -0.03 of its potential returns per unit of risk. The Bloomin Brands is currently generating about 0.07 of returns per unit of risk over similar time horizon. If you would invest 795.00 in Bloomin Brands on March 20, 2025 and sell it today you would earn a total of 139.00 from holding Bloomin Brands or generate 17.48% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
FAT Brands vs. Bloomin Brands
Performance |
| Timeline |
| FAT Brands |
| Bloomin Brands |
FAT Brands and Bloomin Brands Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with FAT Brands and Bloomin Brands
The main advantage of trading using opposite FAT Brands and Bloomin Brands positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FAT Brands position performs unexpectedly, Bloomin 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 Bloomin Brands will offset losses from the drop in Bloomin Brands' long position.| FAT Brands vs. FAT Brands | FAT Brands vs. Brinker International | FAT Brands vs. Jack In The | FAT Brands vs. Potbelly Co |
| Bloomin Brands vs. Dine Brands Global | Bloomin Brands vs. BJs Restaurants | Bloomin Brands vs. The Cheesecake Factory | Bloomin Brands vs. Brinker 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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