Correlation Between Flowers Foods and Martin Marietta
Can any of the company-specific risk be diversified away by investing in both Flowers Foods and Martin Marietta 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 Flowers Foods and Martin Marietta into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Flowers Foods and Martin Marietta Materials, you can compare the effects of market volatilities on Flowers Foods and Martin Marietta 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 Flowers Foods with a short position of Martin Marietta. Check out your portfolio center. Please also check ongoing floating volatility patterns of Flowers Foods and Martin Marietta.
Diversification Opportunities for Flowers Foods and Martin Marietta
0.37 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Flowers and Martin is 0.37. Overlapping area represents the amount of risk that can be diversified away by holding Flowers Foods and Martin Marietta Materials in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Martin Marietta Materials and Flowers Foods 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 Flowers Foods are associated (or correlated) with Martin Marietta. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Martin Marietta Materials has no effect on the direction of Flowers Foods i.e., Flowers Foods and Martin Marietta go up and down completely randomly.
Pair Corralation between Flowers Foods and Martin Marietta
Assuming the 90 days horizon Flowers Foods is expected to under-perform the Martin Marietta. But the stock apears to be less risky and, when comparing its historical volatility, Flowers Foods is 1.1 times less risky than Martin Marietta. The stock trades about -0.05 of its potential returns per unit of risk. The Martin Marietta Materials is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 48,681 in Martin Marietta Materials on September 22, 2024 and sell it today you would earn a total of 1,919 from holding Martin Marietta Materials or generate 3.94% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Flowers Foods vs. Martin Marietta Materials
Performance |
Timeline |
Flowers Foods |
Martin Marietta Materials |
Flowers Foods and Martin Marietta Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Flowers Foods and Martin Marietta
The main advantage of trading using opposite Flowers Foods and Martin Marietta positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Flowers Foods position performs unexpectedly, Martin Marietta 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 Martin Marietta will offset losses from the drop in Martin Marietta's long position.Flowers Foods vs. Mitsui Chemicals | Flowers Foods vs. Datang International Power | Flowers Foods vs. Siamgas And Petrochemicals | Flowers Foods vs. Data3 Limited |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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