Correlation Between BURLINGTON STORES and Coca Cola
Can any of the company-specific risk be diversified away by investing in both BURLINGTON STORES and Coca Cola 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 BURLINGTON STORES and Coca Cola into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BURLINGTON STORES and Coca Cola HBC, you can compare the effects of market volatilities on BURLINGTON STORES and Coca Cola 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 BURLINGTON STORES with a short position of Coca Cola. Check out your portfolio center. Please also check ongoing floating volatility patterns of BURLINGTON STORES and Coca Cola.
Diversification Opportunities for BURLINGTON STORES and Coca Cola
0.33 | Correlation Coefficient |
Weak diversification
The 3 months correlation between BURLINGTON and Coca is 0.33. Overlapping area represents the amount of risk that can be diversified away by holding BURLINGTON STORES and Coca Cola HBC in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Coca Cola HBC and BURLINGTON STORES 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 BURLINGTON STORES are associated (or correlated) with Coca Cola. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Coca Cola HBC has no effect on the direction of BURLINGTON STORES i.e., BURLINGTON STORES and Coca Cola go up and down completely randomly.
Pair Corralation between BURLINGTON STORES and Coca Cola
Assuming the 90 days trading horizon BURLINGTON STORES is expected to generate 1.68 times more return on investment than Coca Cola. However, BURLINGTON STORES is 1.68 times more volatile than Coca Cola HBC. It trades about 0.04 of its potential returns per unit of risk. Coca Cola HBC is currently generating about 0.07 per unit of risk. If you would invest 18,400 in BURLINGTON STORES on September 3, 2024 and sell it today you would earn a total of 8,800 from holding BURLINGTON STORES or generate 47.83% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
BURLINGTON STORES vs. Coca Cola HBC
Performance |
| Timeline |
| BURLINGTON STORES |
| Coca Cola HBC |
BURLINGTON STORES and Coca Cola Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with BURLINGTON STORES and Coca Cola
The main advantage of trading using opposite BURLINGTON STORES and Coca Cola positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BURLINGTON STORES position performs unexpectedly, Coca Cola 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 Coca Cola will offset losses from the drop in Coca Cola's long position.| BURLINGTON STORES vs. ULTRA CLEAN HLDGS | BURLINGTON STORES vs. Clean Energy Fuels | BURLINGTON STORES vs. COSTCO WHOLESALE CDR | BURLINGTON STORES vs. PICKN PAY STORES |
| Coca Cola vs. AEON STORES | Coca Cola vs. Darden Restaurants | Coca Cola vs. COSTCO WHOLESALE CDR | Coca Cola vs. BURLINGTON STORES |
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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