Correlation Between Hyster-Yale Materials and BHP Group
Can any of the company-specific risk be diversified away by investing in both Hyster-Yale Materials and BHP Group 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 BHP Group 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 BHP Group Limited, you can compare the effects of market volatilities on Hyster-Yale Materials and BHP Group 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 BHP Group. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hyster-Yale Materials and BHP Group.
Diversification Opportunities for Hyster-Yale Materials and BHP Group
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Hyster-Yale and BHP is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Hyster Yale Materials Handling and BHP Group Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BHP Group Limited 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 BHP Group. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BHP Group Limited has no effect on the direction of Hyster-Yale Materials i.e., Hyster-Yale Materials and BHP Group go up and down completely randomly.
Pair Corralation between Hyster-Yale Materials and BHP Group
Assuming the 90 days trading horizon Hyster Yale Materials Handling is expected to under-perform the BHP Group. In addition to that, Hyster-Yale Materials is 1.54 times more volatile than BHP Group Limited. It trades about -0.27 of its total potential returns per unit of risk. BHP Group Limited is currently generating about -0.27 per unit of volatility. If you would invest 2,530 in BHP Group Limited on October 4, 2024 and sell it today you would lose (175.00) from holding BHP Group Limited or give up 6.92% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Hyster Yale Materials Handling vs. BHP Group Limited
Performance |
Timeline |
Hyster Yale Materials |
BHP Group Limited |
Hyster-Yale Materials and BHP Group Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hyster-Yale Materials and BHP Group
The main advantage of trading using opposite Hyster-Yale Materials and BHP Group positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hyster-Yale Materials position performs unexpectedly, BHP Group 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 BHP Group will offset losses from the drop in BHP Group's long position.Hyster-Yale Materials vs. Superior Plus Corp | Hyster-Yale Materials vs. NMI Holdings | Hyster-Yale Materials vs. Origin Agritech | Hyster-Yale Materials vs. SIVERS SEMICONDUCTORS AB |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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