Correlation Between Daimler Truck and Columbus McKinnon
Can any of the company-specific risk be diversified away by investing in both Daimler Truck and Columbus McKinnon 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 Daimler Truck and Columbus McKinnon into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Daimler Truck Holding and Columbus McKinnon, you can compare the effects of market volatilities on Daimler Truck and Columbus McKinnon 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 Daimler Truck with a short position of Columbus McKinnon. Check out your portfolio center. Please also check ongoing floating volatility patterns of Daimler Truck and Columbus McKinnon.
Diversification Opportunities for Daimler Truck and Columbus McKinnon
-0.76 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Daimler and Columbus is -0.76. Overlapping area represents the amount of risk that can be diversified away by holding Daimler Truck Holding and Columbus McKinnon in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Columbus McKinnon and Daimler Truck 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 Daimler Truck Holding are associated (or correlated) with Columbus McKinnon. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Columbus McKinnon has no effect on the direction of Daimler Truck i.e., Daimler Truck and Columbus McKinnon go up and down completely randomly.
Pair Corralation between Daimler Truck and Columbus McKinnon
Assuming the 90 days horizon Daimler Truck Holding is expected to generate 0.37 times more return on investment than Columbus McKinnon. However, Daimler Truck Holding is 2.7 times less risky than Columbus McKinnon. It trades about 0.11 of its potential returns per unit of risk. Columbus McKinnon is currently generating about -0.22 per unit of risk. If you would invest 3,801 in Daimler Truck Holding on December 4, 2024 and sell it today you would earn a total of 519.00 from holding Daimler Truck Holding or generate 13.65% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Daimler Truck Holding vs. Columbus McKinnon
Performance |
Timeline |
Daimler Truck Holding |
Columbus McKinnon |
Daimler Truck and Columbus McKinnon Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Daimler Truck and Columbus McKinnon
The main advantage of trading using opposite Daimler Truck and Columbus McKinnon positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Daimler Truck position performs unexpectedly, Columbus McKinnon 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 Columbus McKinnon will offset losses from the drop in Columbus McKinnon's long position.Daimler Truck vs. Hino Motors Ltd | Daimler Truck vs. Volvo AB ADR | Daimler Truck vs. Columbus McKinnon | Daimler Truck vs. Hyster Yale Materials Handling |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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