Correlation Between Hyster-Yale Materials and Coca Cola
Can any of the company-specific risk be diversified away by investing in both Hyster-Yale Materials 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 Hyster-Yale Materials and Coca Cola into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hyster Yale Materials Handling and Coca Cola HBC, you can compare the effects of market volatilities on Hyster-Yale Materials 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 Hyster-Yale Materials with a short position of Coca Cola. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hyster-Yale Materials and Coca Cola.
Diversification Opportunities for Hyster-Yale Materials and Coca Cola
-0.64 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Hyster-Yale and Coca is -0.64. Overlapping area represents the amount of risk that can be diversified away by holding Hyster Yale Materials Handling 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 Hyster-Yale Materials 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 Hyster Yale Materials Handling 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 Hyster-Yale Materials i.e., Hyster-Yale Materials and Coca Cola go up and down completely randomly.
Pair Corralation between Hyster-Yale Materials and Coca Cola
Assuming the 90 days trading horizon Hyster Yale Materials Handling is expected to under-perform the Coca Cola. In addition to that, Hyster-Yale Materials is 1.3 times more volatile than Coca Cola HBC. It trades about -0.09 of its total potential returns per unit of risk. Coca Cola HBC is currently generating about 0.23 per unit of volatility. If you would invest 3,294 in Coca Cola HBC on December 30, 2024 and sell it today you would earn a total of 946.00 from holding Coca Cola HBC or generate 28.72% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Hyster Yale Materials Handling vs. Coca Cola HBC
Performance |
Timeline |
Hyster Yale Materials |
Coca Cola HBC |
Hyster-Yale Materials and Coca Cola Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hyster-Yale Materials and Coca Cola
The main advantage of trading using opposite Hyster-Yale Materials and Coca Cola positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hyster-Yale Materials 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.Hyster-Yale Materials vs. RYANAIR HLDGS ADR | Hyster-Yale Materials vs. CHINA SOUTHN AIR H | Hyster-Yale Materials vs. EITZEN CHEMICALS | Hyster-Yale Materials vs. Wizz Air Holdings |
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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 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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