Correlation Between Flowers Foods and Treehouse Foods
Can any of the company-specific risk be diversified away by investing in both Flowers Foods and Treehouse Foods 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 Treehouse Foods into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Flowers Foods and Treehouse Foods, you can compare the effects of market volatilities on Flowers Foods and Treehouse Foods 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 Treehouse Foods. Check out your portfolio center. Please also check ongoing floating volatility patterns of Flowers Foods and Treehouse Foods.
Diversification Opportunities for Flowers Foods and Treehouse Foods
0.86 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Flowers and Treehouse is 0.86. Overlapping area represents the amount of risk that can be diversified away by holding Flowers Foods and Treehouse Foods in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Treehouse Foods 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 Treehouse Foods. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Treehouse Foods has no effect on the direction of Flowers Foods i.e., Flowers Foods and Treehouse Foods go up and down completely randomly.
Pair Corralation between Flowers Foods and Treehouse Foods
Considering the 90-day investment horizon Flowers Foods is expected to generate 0.43 times more return on investment than Treehouse Foods. However, Flowers Foods is 2.35 times less risky than Treehouse Foods. It trades about 0.0 of its potential returns per unit of risk. Treehouse Foods is currently generating about -0.08 per unit of risk. If you would invest 2,282 in Flowers Foods on August 28, 2024 and sell it today you would lose (7.00) from holding Flowers Foods or give up 0.31% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Flowers Foods vs. Treehouse Foods
Performance |
| Timeline |
| Flowers Foods |
| Treehouse Foods |
Flowers Foods and Treehouse Foods Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Flowers Foods and Treehouse Foods
The main advantage of trading using opposite Flowers Foods and Treehouse Foods positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Flowers Foods position performs unexpectedly, Treehouse Foods 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 Treehouse Foods will offset losses from the drop in Treehouse Foods' long position.| Flowers Foods vs. ConAgra Foods | Flowers Foods vs. McCormick Company Incorporated | Flowers Foods vs. Campbell Soup | Flowers Foods vs. Kellanova |
| Treehouse Foods vs. Lancaster Colony | Treehouse Foods vs. John B Sanfilippo | Treehouse Foods vs. Seneca Foods Corp | Treehouse Foods vs. Seneca Foods Corp |
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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