Correlation Between Uber Technologies and Sumitomo Mitsui
Can any of the company-specific risk be diversified away by investing in both Uber Technologies and Sumitomo Mitsui 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 Sumitomo Mitsui into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Uber Technologies and Sumitomo Mitsui Financial, you can compare the effects of market volatilities on Uber Technologies and Sumitomo Mitsui 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 Sumitomo Mitsui. Check out your portfolio center. Please also check ongoing floating volatility patterns of Uber Technologies and Sumitomo Mitsui.
Diversification Opportunities for Uber Technologies and Sumitomo Mitsui
-0.25 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Uber and Sumitomo is -0.25. Overlapping area represents the amount of risk that can be diversified away by holding Uber Technologies and Sumitomo Mitsui Financial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sumitomo Mitsui Financial 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 Sumitomo Mitsui. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sumitomo Mitsui Financial has no effect on the direction of Uber Technologies i.e., Uber Technologies and Sumitomo Mitsui go up and down completely randomly.
Pair Corralation between Uber Technologies and Sumitomo Mitsui
Assuming the 90 days trading horizon Uber Technologies is expected to under-perform the Sumitomo Mitsui. In addition to that, Uber Technologies is 1.43 times more volatile than Sumitomo Mitsui Financial. It trades about -0.01 of its total potential returns per unit of risk. Sumitomo Mitsui Financial is currently generating about 0.2 per unit of volatility. If you would invest 7,069 in Sumitomo Mitsui Financial on September 16, 2024 and sell it today you would earn a total of 1,958 from holding Sumitomo Mitsui Financial or generate 27.7% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 95.31% |
Values | Daily Returns |
Uber Technologies vs. Sumitomo Mitsui Financial
Performance |
Timeline |
Uber Technologies |
Sumitomo Mitsui Financial |
Uber Technologies and Sumitomo Mitsui Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Uber Technologies and Sumitomo Mitsui
The main advantage of trading using opposite Uber Technologies and Sumitomo Mitsui positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Uber Technologies position performs unexpectedly, Sumitomo Mitsui 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 Sumitomo Mitsui will offset losses from the drop in Sumitomo Mitsui's long position.Uber Technologies vs. ServiceNow | Uber Technologies vs. Shopify | Uber Technologies vs. Autodesk | Uber Technologies vs. The Trade Desk |
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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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