Correlation Between Flowers Foods and Realty Income
Can any of the company-specific risk be diversified away by investing in both Flowers Foods and Realty Income 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 Realty Income into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Flowers Foods and Realty Income, you can compare the effects of market volatilities on Flowers Foods and Realty Income 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 Realty Income. Check out your portfolio center. Please also check ongoing floating volatility patterns of Flowers Foods and Realty Income.
Diversification Opportunities for Flowers Foods and Realty Income
0.31 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Flowers and Realty is 0.31. Overlapping area represents the amount of risk that can be diversified away by holding Flowers Foods and Realty Income in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Realty Income 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 Realty Income. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Realty Income has no effect on the direction of Flowers Foods i.e., Flowers Foods and Realty Income go up and down completely randomly.
Pair Corralation between Flowers Foods and Realty Income
Assuming the 90 days horizon Flowers Foods is expected to generate 1.14 times more return on investment than Realty Income. However, Flowers Foods is 1.14 times more volatile than Realty Income. It trades about 0.02 of its potential returns per unit of risk. Realty Income is currently generating about -0.09 per unit of risk. If you would invest 1,998 in Flowers Foods on October 8, 2024 and sell it today you would earn a total of 22.00 from holding Flowers Foods or generate 1.1% 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. Realty Income
Performance |
Timeline |
Flowers Foods |
Realty Income |
Flowers Foods and Realty Income Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Flowers Foods and Realty Income
The main advantage of trading using opposite Flowers Foods and Realty Income positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Flowers Foods position performs unexpectedly, Realty Income 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 Realty Income will offset losses from the drop in Realty Income's long position.Flowers Foods vs. Superior Plus Corp | Flowers Foods vs. NMI Holdings | Flowers Foods vs. SIVERS SEMICONDUCTORS AB | Flowers Foods vs. Talanx AG |
Realty Income vs. UPDATE SOFTWARE | Realty Income vs. Calibre Mining Corp | Realty Income vs. Micron Technology | Realty Income vs. PKSHA TECHNOLOGY INC |
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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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