Correlation Between Dataproces Group and Shape Robotics
Can any of the company-specific risk be diversified away by investing in both Dataproces Group and Shape Robotics 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 Dataproces Group and Shape Robotics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dataproces Group AS and Shape Robotics AS, you can compare the effects of market volatilities on Dataproces Group and Shape Robotics 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 Dataproces Group with a short position of Shape Robotics. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dataproces Group and Shape Robotics.
Diversification Opportunities for Dataproces Group and Shape Robotics
-0.36 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Dataproces and Shape is -0.36. Overlapping area represents the amount of risk that can be diversified away by holding Dataproces Group AS and Shape Robotics AS in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Shape Robotics AS and Dataproces Group 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 Dataproces Group AS are associated (or correlated) with Shape Robotics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Shape Robotics AS has no effect on the direction of Dataproces Group i.e., Dataproces Group and Shape Robotics go up and down completely randomly.
Pair Corralation between Dataproces Group and Shape Robotics
Assuming the 90 days trading horizon Dataproces Group AS is expected to under-perform the Shape Robotics. But the stock apears to be less risky and, when comparing its historical volatility, Dataproces Group AS is 2.54 times less risky than Shape Robotics. The stock trades about -0.04 of its potential returns per unit of risk. The Shape Robotics AS is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest 1,800 in Shape Robotics AS on August 29, 2024 and sell it today you would earn a total of 200.00 from holding Shape Robotics AS or generate 11.11% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Dataproces Group AS vs. Shape Robotics AS
Performance |
Timeline |
Dataproces Group |
Shape Robotics AS |
Dataproces Group and Shape Robotics Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Dataproces Group and Shape Robotics
The main advantage of trading using opposite Dataproces Group and Shape Robotics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dataproces Group position performs unexpectedly, Shape Robotics 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 Shape Robotics will offset losses from the drop in Shape Robotics' long position.Dataproces Group vs. Penneo AS | Dataproces Group vs. Bactiquant AS | Dataproces Group vs. cBrain AS | Dataproces Group vs. FOM Technologies 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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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