Correlation Between GreenPower and Hyster Yale
Can any of the company-specific risk be diversified away by investing in both GreenPower and Hyster Yale 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 GreenPower and Hyster Yale into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GreenPower Motor and Hyster Yale Materials Handling, you can compare the effects of market volatilities on GreenPower and Hyster Yale 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 GreenPower with a short position of Hyster Yale. Check out your portfolio center. Please also check ongoing floating volatility patterns of GreenPower and Hyster Yale.
Diversification Opportunities for GreenPower and Hyster Yale
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between GreenPower and Hyster is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding GreenPower Motor and Hyster Yale Materials Handling in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hyster Yale Materials and GreenPower 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 GreenPower Motor are associated (or correlated) with Hyster Yale. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hyster Yale Materials has no effect on the direction of GreenPower i.e., GreenPower and Hyster Yale go up and down completely randomly.
Pair Corralation between GreenPower and Hyster Yale
Allowing for the 90-day total investment horizon GreenPower Motor is expected to generate 1.16 times more return on investment than Hyster Yale. However, GreenPower is 1.16 times more volatile than Hyster Yale Materials Handling. It trades about 0.02 of its potential returns per unit of risk. Hyster Yale Materials Handling is currently generating about -0.14 per unit of risk. If you would invest 101.00 in GreenPower Motor on August 30, 2024 and sell it today you would earn a total of 0.00 from holding GreenPower Motor or generate 0.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 95.65% |
Values | Daily Returns |
GreenPower Motor vs. Hyster Yale Materials Handling
Performance |
Timeline |
GreenPower Motor |
Hyster Yale Materials |
GreenPower and Hyster Yale Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GreenPower and Hyster Yale
The main advantage of trading using opposite GreenPower and Hyster Yale positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GreenPower position performs unexpectedly, Hyster Yale 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 Hyster Yale will offset losses from the drop in Hyster Yale's long position.GreenPower vs. Hyster Yale Materials Handling | GreenPower vs. Columbus McKinnon | GreenPower vs. AGCO Corporation | GreenPower vs. Titan International |
Hyster Yale vs. Alamo Group | Hyster Yale vs. Titan International | Hyster Yale vs. Terex | Hyster Yale vs. Oshkosh |
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 Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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