Correlation Between Uber Technologies and SBM Offshore
Can any of the company-specific risk be diversified away by investing in both Uber Technologies and SBM Offshore 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 Uber Technologies and SBM Offshore into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Uber Technologies and SBM Offshore NV, you can compare the effects of market volatilities on Uber Technologies and SBM Offshore 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 Uber Technologies with a short position of SBM Offshore. Check out your portfolio center. Please also check ongoing floating volatility patterns of Uber Technologies and SBM Offshore.
Diversification Opportunities for Uber Technologies and SBM Offshore
-0.01 | Correlation Coefficient |
Good diversification
The 3 months correlation between Uber and SBM is -0.01. Overlapping area represents the amount of risk that can be diversified away by holding Uber Technologies and SBM Offshore NV in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SBM Offshore NV and Uber Technologies 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 Uber Technologies are associated (or correlated) with SBM Offshore. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SBM Offshore NV has no effect on the direction of Uber Technologies i.e., Uber Technologies and SBM Offshore go up and down completely randomly.
Pair Corralation between Uber Technologies and SBM Offshore
Assuming the 90 days trading horizon Uber Technologies is expected to under-perform the SBM Offshore. In addition to that, Uber Technologies is 1.03 times more volatile than SBM Offshore NV. It trades about -0.21 of its total potential returns per unit of risk. SBM Offshore NV is currently generating about -0.11 per unit of volatility. If you would invest 1,795 in SBM Offshore NV on September 12, 2024 and sell it today you would lose (98.00) from holding SBM Offshore NV or give up 5.46% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 95.65% |
| Values | Daily Returns |
Uber Technologies vs. SBM Offshore NV
Performance |
| Timeline |
| Uber Technologies |
| SBM Offshore NV |
Uber Technologies and SBM Offshore Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Uber Technologies and SBM Offshore
The main advantage of trading using opposite Uber Technologies and SBM Offshore positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Uber Technologies position performs unexpectedly, SBM Offshore 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 SBM Offshore will offset losses from the drop in SBM Offshore's long position.| Uber Technologies vs. Home Depot | Uber Technologies vs. Chrysalis Investments | Uber Technologies vs. Neometals | Uber Technologies vs. Coor Service Management |
| SBM Offshore vs. Zoom Video Communications | SBM Offshore vs. Neometals | SBM Offshore vs. Coor Service Management | SBM Offshore vs. Fidelity Sustainable USD |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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