Correlation Between FAT Brands and MasterBeef Group
Can any of the company-specific risk be diversified away by investing in both FAT Brands and MasterBeef Group 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 MasterBeef Group into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FAT Brands and MasterBeef Group Ordinary, you can compare the effects of market volatilities on FAT Brands and MasterBeef Group 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 MasterBeef Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of FAT Brands and MasterBeef Group.
Diversification Opportunities for FAT Brands and MasterBeef Group
-0.47 | Correlation Coefficient |
Very good diversification
The 3 months correlation between FAT and MasterBeef is -0.47. Overlapping area represents the amount of risk that can be diversified away by holding FAT Brands and MasterBeef Group Ordinary in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on MasterBeef Group Ordinary 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 MasterBeef Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of MasterBeef Group Ordinary has no effect on the direction of FAT Brands i.e., FAT Brands and MasterBeef Group go up and down completely randomly.
Pair Corralation between FAT Brands and MasterBeef Group
Assuming the 90 days horizon FAT Brands is expected to generate 5.76 times less return on investment than MasterBeef Group. But when comparing it to its historical volatility, FAT Brands is 1.54 times less risky than MasterBeef Group. It trades about 0.09 of its potential returns per unit of risk. MasterBeef Group Ordinary is currently generating about 0.33 of returns per unit of risk over similar time horizon. If you would invest 894.00 in MasterBeef Group Ordinary on May 2, 2025 and sell it today you would earn a total of 581.00 from holding MasterBeef Group Ordinary or generate 64.99% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
FAT Brands vs. MasterBeef Group Ordinary
Performance |
Timeline |
FAT Brands |
MasterBeef Group Ordinary |
FAT Brands and MasterBeef Group Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with FAT Brands and MasterBeef Group
The main advantage of trading using opposite FAT Brands and MasterBeef Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FAT Brands position performs unexpectedly, MasterBeef Group 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 MasterBeef Group will offset losses from the drop in MasterBeef Group's long position.FAT Brands vs. FAT Brands | FAT Brands vs. FAT Brands | FAT Brands vs. Good Times Restaurants | FAT Brands vs. Nathans Famous |
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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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