Correlation Between Deutsche Post and DSV Panalpina
Can any of the company-specific risk be diversified away by investing in both Deutsche Post and DSV Panalpina 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 Deutsche Post and DSV Panalpina into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Deutsche Post AG and DSV Panalpina AS, you can compare the effects of market volatilities on Deutsche Post and DSV Panalpina 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 Deutsche Post with a short position of DSV Panalpina. Check out your portfolio center. Please also check ongoing floating volatility patterns of Deutsche Post and DSV Panalpina.
Diversification Opportunities for Deutsche Post and DSV Panalpina
-0.39 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Deutsche and DSV is -0.39. Overlapping area represents the amount of risk that can be diversified away by holding Deutsche Post AG and DSV Panalpina AS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DSV Panalpina AS and Deutsche Post 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 Deutsche Post AG are associated (or correlated) with DSV Panalpina. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DSV Panalpina AS has no effect on the direction of Deutsche Post i.e., Deutsche Post and DSV Panalpina go up and down completely randomly.
Pair Corralation between Deutsche Post and DSV Panalpina
Assuming the 90 days horizon Deutsche Post is expected to generate 2.42 times less return on investment than DSV Panalpina. In addition to that, Deutsche Post is 1.42 times more volatile than DSV Panalpina AS. It trades about 0.01 of its total potential returns per unit of risk. DSV Panalpina AS is currently generating about 0.05 per unit of volatility. If you would invest 7,509 in DSV Panalpina AS on August 28, 2024 and sell it today you would earn a total of 2,923 from holding DSV Panalpina AS or generate 38.93% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 94.96% |
| Values | Daily Returns |
Deutsche Post AG vs. DSV Panalpina AS
Performance |
| Timeline |
| Deutsche Post AG |
| DSV Panalpina AS |
Deutsche Post and DSV Panalpina Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Deutsche Post and DSV Panalpina
The main advantage of trading using opposite Deutsche Post and DSV Panalpina positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Deutsche Post position performs unexpectedly, DSV Panalpina 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 DSV Panalpina will offset losses from the drop in DSV Panalpina's long position.| Deutsche Post vs. Kuehne Nagel International | Deutsche Post vs. United Parcel Service | Deutsche Post vs. FedEx | Deutsche Post vs. GXO Logistics |
| DSV Panalpina vs. Kuehne Nagel International | DSV Panalpina vs. United Parcel Service | DSV Panalpina vs. FedEx | DSV Panalpina vs. GXO Logistics |
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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