Correlation Between Pitney Bowes and Deutsche Post
Can any of the company-specific risk be diversified away by investing in both Pitney Bowes and Deutsche Post 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 Pitney Bowes and Deutsche Post into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Pitney Bowes and Deutsche Post AG, you can compare the effects of market volatilities on Pitney Bowes and Deutsche Post 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 Pitney Bowes with a short position of Deutsche Post. Check out your portfolio center. Please also check ongoing floating volatility patterns of Pitney Bowes and Deutsche Post.
Diversification Opportunities for Pitney Bowes and Deutsche Post
0.08 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Pitney and Deutsche is 0.08. Overlapping area represents the amount of risk that can be diversified away by holding Pitney Bowes and Deutsche Post AG in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Deutsche Post AG and Pitney Bowes 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 Pitney Bowes are associated (or correlated) with Deutsche Post. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Deutsche Post AG has no effect on the direction of Pitney Bowes i.e., Pitney Bowes and Deutsche Post go up and down completely randomly.
Pair Corralation between Pitney Bowes and Deutsche Post
Considering the 90-day investment horizon Pitney Bowes is expected to generate 1.61 times more return on investment than Deutsche Post. However, Pitney Bowes is 1.61 times more volatile than Deutsche Post AG. It trades about 0.24 of its potential returns per unit of risk. Deutsche Post AG is currently generating about 0.04 per unit of risk. If you would invest 753.00 in Pitney Bowes on November 5, 2024 and sell it today you would earn a total of 138.00 from holding Pitney Bowes or generate 18.33% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Pitney Bowes vs. Deutsche Post AG
Performance |
| Timeline |
| Pitney Bowes |
| Deutsche Post AG |
Pitney Bowes and Deutsche Post Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Pitney Bowes and Deutsche Post
The main advantage of trading using opposite Pitney Bowes and Deutsche Post positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Pitney Bowes position performs unexpectedly, Deutsche Post 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 Deutsche Post will offset losses from the drop in Deutsche Post's long position.| Pitney Bowes vs. Expeditors International of | Pitney Bowes vs. Landstar System | Pitney Bowes vs. JB Hunt Transport | Pitney Bowes vs. Hub Group |
| Deutsche Post vs. Kuehne Nagel International | Deutsche Post vs. Kuehne Nagel International | Deutsche Post vs. DSV Panalpina AS | Deutsche Post vs. DSV Panalpina AS |
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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