Correlation Between Alliance Data and US Foods
Can any of the company-specific risk be diversified away by investing in both Alliance Data and US 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 Alliance Data and US Foods into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Alliance Data Systems and US Foods Holding, you can compare the effects of market volatilities on Alliance Data and US 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 Alliance Data with a short position of US Foods. Check out your portfolio center. Please also check ongoing floating volatility patterns of Alliance Data and US Foods.
Diversification Opportunities for Alliance Data and US Foods
0.36 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Alliance and UFH is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding Alliance Data Systems and US Foods Holding in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on US Foods Holding and Alliance Data 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 Alliance Data Systems are associated (or correlated) with US Foods. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of US Foods Holding has no effect on the direction of Alliance Data i.e., Alliance Data and US Foods go up and down completely randomly.
Pair Corralation between Alliance Data and US Foods
Assuming the 90 days trading horizon Alliance Data is expected to generate 1.89 times less return on investment than US Foods. In addition to that, Alliance Data is 1.57 times more volatile than US Foods Holding. It trades about 0.06 of its total potential returns per unit of risk. US Foods Holding is currently generating about 0.19 per unit of volatility. If you would invest 6,550 in US Foods Holding on November 4, 2024 and sell it today you would earn a total of 350.00 from holding US Foods Holding or generate 5.34% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Alliance Data Systems vs. US Foods Holding
Performance |
| Timeline |
| Alliance Data Systems |
| US Foods Holding |
Alliance Data and US Foods Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Alliance Data and US Foods
The main advantage of trading using opposite Alliance Data and US Foods positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Alliance Data position performs unexpectedly, US 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 US Foods will offset losses from the drop in US Foods' long position.| Alliance Data vs. ETFS Coffee ETC | Alliance Data vs. MagnaChip Semiconductor Corp | Alliance Data vs. UMC Electronics Co | Alliance Data vs. AOI Electronics Co |
| US Foods vs. JIAHUA STORES | US Foods vs. GOME Retail Holdings | US Foods vs. Caseys General Stores | US Foods vs. Costco Wholesale 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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