Correlation Between Scandic Hotels and Datagroup
Can any of the company-specific risk be diversified away by investing in both Scandic Hotels and Datagroup 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 Scandic Hotels and Datagroup into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Scandic Hotels Group and Datagroup SE, you can compare the effects of market volatilities on Scandic Hotels and Datagroup 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 Scandic Hotels with a short position of Datagroup. Check out your portfolio center. Please also check ongoing floating volatility patterns of Scandic Hotels and Datagroup.
Diversification Opportunities for Scandic Hotels and Datagroup
-0.17 | Correlation Coefficient |
Good diversification
The 3 months correlation between Scandic and Datagroup is -0.17. Overlapping area represents the amount of risk that can be diversified away by holding Scandic Hotels Group and Datagroup SE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Datagroup SE and Scandic Hotels 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 Scandic Hotels Group are associated (or correlated) with Datagroup. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Datagroup SE has no effect on the direction of Scandic Hotels i.e., Scandic Hotels and Datagroup go up and down completely randomly.
Pair Corralation between Scandic Hotels and Datagroup
Assuming the 90 days trading horizon Scandic Hotels is expected to generate 8.13 times less return on investment than Datagroup. But when comparing it to its historical volatility, Scandic Hotels Group is 1.58 times less risky than Datagroup. It trades about 0.02 of its potential returns per unit of risk. Datagroup SE is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest 3,965 in Datagroup SE on September 13, 2024 and sell it today you would earn a total of 635.00 from holding Datagroup SE or generate 16.02% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.46% |
Values | Daily Returns |
Scandic Hotels Group vs. Datagroup SE
Performance |
Timeline |
Scandic Hotels Group |
Datagroup SE |
Scandic Hotels and Datagroup Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Scandic Hotels and Datagroup
The main advantage of trading using opposite Scandic Hotels and Datagroup positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Scandic Hotels position performs unexpectedly, Datagroup 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 Datagroup will offset losses from the drop in Datagroup's long position.Scandic Hotels vs. National Bank of | Scandic Hotels vs. Royal Bank of | Scandic Hotels vs. Games Workshop Group | Scandic Hotels vs. Komercni Banka |
Datagroup vs. Samsung Electronics Co | Datagroup vs. Samsung Electronics Co | Datagroup vs. Hyundai Motor | Datagroup vs. Reliance Industries Ltd |
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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