Correlation Between Ford and FAT Brands
Can any of the company-specific risk be diversified away by investing in both Ford 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 Ford and FAT Brands into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ford Motor and FAT Brands, you can compare the effects of market volatilities on Ford 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 Ford with a short position of FAT Brands. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ford and FAT Brands.
Diversification Opportunities for Ford and FAT Brands
-0.47 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Ford and FAT is -0.47. Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor and FAT Brands in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FAT Brands and Ford 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 Ford Motor 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 Ford i.e., Ford and FAT Brands go up and down completely randomly.
Pair Corralation between Ford and FAT Brands
Given the investment horizon of 90 days Ford Motor is expected to under-perform the FAT Brands. But the preferred stock apears to be less risky and, when comparing its historical volatility, Ford Motor is 2.57 times less risky than FAT Brands. The preferred stock trades about -0.4 of its potential returns per unit of risk. The FAT Brands is currently generating about 0.15 of returns per unit of risk over similar time horizon. If you would invest 921.00 in FAT Brands on September 14, 2024 and sell it today you would earn a total of 39.00 from holding FAT Brands or generate 4.23% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Ford Motor vs. FAT Brands
Performance |
Timeline |
Ford Motor |
FAT Brands |
Ford and FAT Brands Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Ford and FAT Brands
The main advantage of trading using opposite Ford and FAT Brands positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ford 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.The idea behind Ford Motor and FAT Brands pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.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 Center module to all portfolio management and optimization tools to improve performance of your portfolios.
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